esa-sceva/satcom-chunk-collection huggingface dataset:
# Tabular Data Models for Predicting Art Auction Results
## 3 Results
### sMAPE Score Analysis Across Datasets
Models such as DeepGBM and VIME also exhibit subpar performance, particularly in the auction datasets where higher-dimensional transformations  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AuctionResultsNolmg** & **AuctionResultsColor** & **AuctionResultsSVD** & **AuctionResultsColorSVD** & **CaliforniaHousing** \\ \hline LinearModel & 101.33 \(\pm\) 0.78 & 101.01 \(\pm\) 0.84 & 102.75 \(\pm\) 1.16 & 101.13 \(\pm\) 0.93 & 28.70 \(\pm\) 0.41 \\ \hline KNN & 61.38 \(\pm\) 0.35 & 62.68 \(\pm\) 0.32 & 60.18 \(\pm\) 0.65 & 60.93 \(\pm\) 0.53 & 22.75 \(\pm\) 0.51 \\ \hline DecisionTree & 58.92 \(\pm\) 0.62 & 58.39 \(\pm\) 0.69 & 56.70 \(\pm\) 0.78 & 59.21 \(\pm\) 0.69 & 21.70 \(\pm\) 0.56 \\ \hline RandomForest & 61.20 \(\pm\) 0.60 & **55.95 \(\pm\) 0.29** & 56.94 \(\pm\) 0.30 & **55.51 \(\pm\) 0.80** & 17.50 \(\pm\) 0.36 \\ \hline XGBoost & **55.11 \(\pm\) 0.60** & 57.50 \(\pm\) 0.61 & **54.83 \(\pm\) 0.59** & 58.76 \(\pm\) 1.07 & 14.84 \(\pm\) 0.25 \\ \hline CatBoost & 58.91 \(\pm\) 0.79 & 60.45 \(\pm\) 1.02 & 58.20 \(\pm\) 0.64 & 58.25 \(\pm\) 0.48 & 14.92 \(\pm\) 0.46 \\ \hline LightGBM & 57.53 \(\pm\) 2.32 & 58.29 \(\pm\) 2.34 & 58.04 \(\pm\) 0.59 & 57.52 \(\pm\) 1.22 & **14.71 \(\pm\) 0.31** \\ \hline MLP & 62.98 \(\pm\) 0.89 & 61.86 \(\pm\) 1.14 & 63.68 \(\pm\) 0.32 & 65.02 \(\pm\) 0.75 & 17.52 \(\pm\) 0.63 \\ \hline VIME & 75.29 \(\pm\) 3.37 & 66.29 \(\pm\) 1.59 & 86.44 \(\pm\) 2.28 & 74.09 \(\pm\) 2.91 & 19.40 \(\pm\) 1.69 \\ \hline ModelTree & 68.50 \(\pm\) 0.37 & 66.59 \(\pm\) 0.66 & 71.68 \(\pm\) 3.62 & 68.70 \(\pm\) 1.35 & 23.86 \(\pm\) 0.33 \\ \hline DeepGBM & 76.92 \(\pm\) 7.66 & 77.14 \(\pm\) 7.40 & 87.96 \(\pm\) 10.62 & 76.18 \(\pm\) 4.23 & 35.13 \(\pm\) 2.11 \\ \hline DeepFM & 63.10 \(\pm\) 0.76 & 63.45 \(\pm\) 0.87 & 63.28 \(\pm\) 0.25 & 63.06 \(\pm\) 0.81 & 17.75 \(\pm\) 0.36 \\ \(e.g., SVD) are applied. In contrast, for the CaliforniaHousing dataset, more models demonstrate good generalization abilities, with LightGBM and XGBoost achieving the best results. Tree-based ensemble methods, particularly gradient-boosting techniques like XGBoost and RandomForest, consistently deliver the best performance across various datasets, especially in handling the complexity of auction data with nonlinearity and high-dimensional features. While these models achieve relatively lower sMAPE scores compared to others, indicating their robustness and adaptability to tabular data, the overall prediction errors remain above what would be considered optimal, underscoring the challenge of achieving precise predictions in this domain.
# Circulating Tumor Cells in Metastatic Breast Cancer: Clinical Applications and Future Possibilities
## 2 Prognostic Relevance of CTC Counts  
While most studies conducted in early breast cancer setting stratified patients based on the presence of at least one CTC (i.e., into a CTC-negative and a CTC-positive subgroup), this approach is less feasible in metastatic disease. In general, tumor cell counts are significantly higher in patients with advanced cancer. Therefore, the most widely used cut-off value in metastatic breast cancer is 5 CTCs per 7.5 mL peripheral blood using the only FDA approved detection system (CellSearch) [4].  
The first report on the prognostic value of CellSearch-based CTC detection in metastatic breast cancer was published in 2004 by [PERSON] et al. from the M.D. Anderson Cancer Center [5]. These results have been since confirmed in a large meta-analysis of data from 18 centers [6]. In this study, blood samples from 2,436 patients were analyzed using CellSearch at time of the first diagnosis of metastatic disease or progression. Elevated CTC counts (\(\geq\)5 CTCs) were found in 47% of patients. After a median follow up of 14.9 months, CTC numbers were associated with overall survival (OS; 16 vs. 36.3 months in CTC-high and CTC-low groups, respectively). This observation was independent of the tumor subtype and the localization of metastatic disease (OS in patients with visceral metastasis: 13.2 vs. 29.9 months and in patients with bone-only disease: 23.8 vs. 46.9 months, respectively).
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 3 Path Planning Problem  
Path planning is to find one or more available paths in the workspace. In this article, statically known environments are considered. That is, all obstacles within the workspace are static, and their location information is entirely known to the robot. The starting location and target of the robot are signified as \(S\) and \(T\), respectively. Then, path planning is devised to find one or more paths from \(S\) to \(T\) that do not collide with obstacles. Next, the environment modeling, path representation and the three optimization objectives are defined. In this work, two-dimensional space is adopted. Any obstacle is supposed to be an arbitrary polygonal shape. Moreover, a polygon robot can be converted into a single point by using the Minkowski sums in the field of computational geometry [56]. A path is signified with \(p=[S=p_{0},p_{1},p_{2},...,p_{n},p_{n+1}=T]\). \(p_{i}\) is the \(i\)-th rotation points (RPs). \(p_{i}\) and \(p_{i+1}\) are connected by straight segments. The representation of a path is shown in Figure 1. The coordinate of \(p_{i}\) is denoted as \((X_{i},Y_{i})\).
## 1 Introduction
In the light of the above considerations, the main aim of this research is to develop for the first time a framework on SWOT analysis for investigating the strengths, weaknesses, opportunities and threat of remote sensing (RS) techniques to measure potential power from OW installations. In particular, the present study aimed at elaborating a SWOT analysis for assessing the wind energy potential with RS techniques in the biggest islands of the Mediterranean Sea: Sicily and Sardinia. This study raises a better understanding of how to use RS technology to replace traditional wind measurement tools. The results of the SWOT analysis are expected: (i) to highlight satellites' ability to measure marine renewables; (ii) to identify pros and cons of using these techniques.
## 8 Conclusions

Table 20: Comparison of the accuracy of the BiLSTM + CNN and bare BERT with other similar models (using Intel300, fastText300, GloVe300, word2 vec300 embeddings) from paper [66]. the deep and transformer models training which practically prevents the ultimate comparison of all models, with all pre-processing settings, hyper-parameter fine-tuning using cross-validation technique. In our experiments, we decided to evaluate the models' performance using standard, commonly used settings, then fine-tune the hyper-parameters of the best-performing architecture using grid search on sampled data. Similarly, we assumed the learning variance during the initial experiment using 10-fold cross-validation and did not perform the cross-validation of an entire set of pre-processing experiments, which would result in extreme time and resource-consuming setup. Conceptualization, M.S. and V.M.-K.; methodology, M.S. ; software, V.M.-K.; validation, V.M.-K.; formal analysis, P.B. ; investigation, V.M.-K. and K.M. ; resources, M.S. ; writing--original draft preparation, V.M.-K. and M.S. ; writing--review and editing, V.M.-K., M.S. and P.B. ; supervision, P.B. ; All authors have read and agreed to the published version of the manuscript. This work was partially supported by the Slovak Research and Development Agency under the contracts No. APVV-16-0213 and No. APVV-17-0267. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
BERT Bidirectional Encoder Representations from Transformers  
CNN Convolutional Neural Network  
GRU Gated Recurrent Unit  
LSTM Long Short-Term Memory  
FFNN FeedForward Neural Network  
NLP Natural Language Processing  
CBOW Continuous Bag of Words  
BCE Binary Cross Entropy  
ReLU Rectified Linear Unit  
AUC Area Under Curve  
ROC Receiver Operating Characteristic
## 4 Results and Discussion
### Landscape Heterogeneity Impact on USTARFM Performance
In Figure 6g, the higher reflectance values for red and green bands seem to be underestimated in some scatter points (residential region, a MODIS pixel scale), which may be because the pure coarse pixel was affected by the surrounding pixels when it is decomposed in Equation (1), and the error of the unmixing data causes accumulated error in the USTARFM (Figure 6a). However, for the NIR band, the accuracy of the unmixing data is slightly better than directly resampled data in the homogeneous area (\(\gamma\): 0.99 _vs_. 0.98; RMSE: 0.0487 _vs_. 0.0492), which makes the RMSE of the USTARFM lowest compared to the STARFM and unmixing methods (0.0198 _vs_. 0.0235 _vs_. 0.0487) (Figure 5e) and the scatterplot more close to 1:1 (Figure 6c). This is mainly because, relative to the green and red, NIR is the long-wave bands, which are not sensitive to weather related factors such as haze and have a high signal-to-noise when they are decomposed linearly [37]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(h\) & \multicolumn{4}{c}{**Unmixing Data**} & \multicolumn{4}{c}{**Resampled Data**} \\ \cline{3-8}  & & **Green** & **Red** & **NIR** & **Green** & **Red** & **NIR** \\ \hline \multirow{8}{*}{\(\gamma\)} & 1 & 0.77 & 0.83 & 0.99 & 0.89 & 0.90 & 0.98 \\  & 2 & 0.75 & 0.80 & 0.98 & 0.72 & 0.75 & 0.96 \\  & 3 & 0.82 & 0.
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration
### Antenna Dimensions  
The azimuth length of the antenna determines the width of the swath coverage. A longer azimuth dimension allows for a wider coverage area. The azimuth length affects the azimuth resolution. A longer length enables a finer azimuth resolution, resulting in improved target separation and detail. The elevation width of the antenna affects the range resolution. A wider elevation dimension allows for a finer range resolution, resulting in improved target discrimination along the range direction. The width of the antenna affects the antenna gain. A larger width generally leads to a higher gain, which enhances the received signal strength and improves the system performance. The width of the antenna can impact on the presence and characteristics of side lobes in the antenna radiation pattern [33; 37; 48].
## 2 Materials and Methods
### Assessment Metrics  
A comparative analysis was conducted to evaluate the accuracy of model predictions using two metrics: the mean average error (MAE) and the mean squared error (MSE).  
The MSE and MAE are commonly employed metrics for evaluating the error of a model. Lower values of MSE and MAE indicate a higher level of prediction accuracy. The MSE and MAE formulas are as follows:  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}{\left|{y_{i}-{y^{\prime}}_{i}}\right|}} \tag{4}\]  
\[MSE=\frac{1}{N}{\sum_{i=1}^{N}{\left({y_{i}-{y^{\prime}}_{i}}\right)^{2}}} \tag{5}\]  
where \(N\) represents the total number of samples in the test set, while \(y\prime\) and \(y\) denote the true and predicted values, respectively.
## 3 Proposed CPS-HBM
### Data Management Domain  
The data management domain comprises three parts: semantic data extraction, knowledge-based storage, and cloud servers. Semantic data extraction may be a framework that can look, analyze, and conclude. A robot makes choices based on data that sensors distinguish, at which point these choices are transmitted for semantic data extraction. Semantic data extraction analyzes these choices and makes a judgment of which choices can be put away in knowledge-based storage. For illustration, the robot makes the choices agreeing with the patient's movement, at which point semantic data extraction investigates the choice to make a conclusion, so the specialist can effortlessly grant feedback.  
This model can visually allow clients to induce crucial data rapidly and effectively. The data of choice (extracted by semantic data extraction) and the start activity (from the SOA) are stored in the knowledge-based repository, whose structure makes the framework intelligent. Information in the knowledge-based storage is various leveled. Information within the lowest level is \"fact\"; that within the middle level is \"rules and processes\" (activity); that within the most noteworthy level is \"strategy\" (choice). The structure is chosen by the characteristic of the information itself. The initial information can be both organized and unstructured, and knowledge-based storage can make them modular. In expansion, completely different information layers are all stamped with validity, which suggests that questionable data do not exist. At that point, a knowledge-based repository transfers information to cloud servers. Cloud servers are where information is stored and shared. Cloud servers can have either physical or virtual frameworks, and it ensures beyond any doubt that information-accessing dependable personnel (such as specialists) can access information remotely through the web. Cloud servers provide quick and continuous communication; in the meantime, they are successful because users have to pay for what they require, helping to avoid additional costs. At that point, the framework achieves human-to-machine interaction. This is actualized within the genuine world, where it can be used to prevent illnesses, such as heart disease, cancer, and hypertension. The utilized SOA does not guarantee security, whereas performed errands are organized from the choice. It progresses in QoS and vitality. Our model also provides secure strategies that avoid secrecy issues.
## 4 Experiments
### Sub-Concepts Formulation  
For our experiments, we considered both sub-concepts that are expected to be relevant for the detection of the considered vulnerabilities (e.g., the presence of a cast to integer), and generic sub-concepts that can be identified in the source code, but that are supposed not to be related to the vulnerabilities (e.g., the cyclomatic complexity). In details, we considered the following sub-concepts:  
**Cast to integer**: The presence (or absence) of a cast-to-integer operation. This sub-concept can be significant when dealing with both the divide-by-zero and uncontrolled memory-allocation vulnerabilities.
**Square brackets**: The presence of an high number (i.e., \(>\)=12) of square brackets. This concept can be relevant in general since it is strictly related to the presence of an high number of accesses to array elements.
**Cyclomatic complexity**: This sub-concept [31] addresses the structural complexity of a program, and it is a classical software engineering metric. It is defined as the number of linearly independent paths in the control flow graph of a program and, dealing with Java programs, can be easily computed by counting 1 point for the beginning of the method, 1 point for each conditional construct and for each case or default block in a switch-case statement, 1 point for each iterative structure and 1 point for each Boolean condition. We consider, as a sub-concept, a cyclomatic complexity higher than 10.
**I/O relationship**: This sub-concept considers the _semantic_ of a method in terms of the relation between what is passed as argument (i.e., the input) and the returned object (i.e., the output). We only consider a subset of all the possible I/O relations, namely, the presence or the absence of an array among the input arguments and whether the returned object is an array or a single element. In particular, in our experiments, we consider as a sub-concept the many-to-many relation, namely the methods that contain (at least) an array among their arguments, and that return an array.
**Random**: This concept is defined by simply assigning random labels to the methods in the dataset. The obtained partition is obviously meaningless, and the experiments on this concept are used as a baseline to assess the validity of the other results.  
Excluding the baseline random concept, the first two sub-concepts resemble some traits that a human expert takes into account when attempting to identify the presence of vulnerabilities, while the others represent elements that are relevant and recognizable, but that are not directly related to the considered vulnerabilities. As an analogy for the image-processing domain, for a model trained in recognizing zebras, we can think of concepts such as the presence of stripes or having four paws as sub-concepts related to the task, and the prevalence of a given color as an unrelated concept.
# Non-Destructive Porosity Measurements of 3D Printed Polymer by Terahertz Time-Domain Spectroscopy
## 2 Materials and Methods
### THz Time-Domain Spectroscopy  
The terahertz time-domain spectroscopy (THz TDS) measurements were carried out using a TeraFlash Pro spectrometer from Toptica Photonics (Toptica Photonics, Munich, Germany) set up in a standard optical configuration with four F/2 parabolic mirrors [47; 48]. The THz beam path was purged with dry air to eliminate absorption from atmospheric water vapor. The samples were placed in the collimated section of the beam, and laser alignment was used to ensure that they were positioned normal to the THz beam. The frequency resolution was 10 GHz.  
The frequency-dependent (\(\
u\)) refractive index (\(n(\
u)\)) and absorption coefficient (\(\alpha(\
u)\)) of each sample were calculated by the standard method of applying Fourier transform to the time-domain data to obtain the frequency-dependent field amplitude (\(E(\
u)\)) and phase (\(\phi(\
u)\)), and using the equations [47]:  
\[n(\
u)=1+\frac{\left(\phi_{s}(\
u)-\phi_{ref}(\
u)\right)c}{2\pi vd} \tag{1}\]  
\[\alpha(\
u)=-\frac{2}{d}\ \ln\left[\frac{\left(n+1\right)^{2}}{4n}\ \frac{E_{s}(\
u)}{E_{ref}(\
u)}\right] \tag{2}\]  
where \(c\) is the speed of light, \(d\) is the sample thickness, and the subscripts \(s\) and \(ref\) refer to the sample and reference data, respectively. THz TDS has an important advantage in the measurement of the THz optical properties of materials, in that it enables unambiguous, straightforward determination of the refractive index and absorption coefficient [47; 48].  
The measurement uncertainty had two main components: uncertainty in sample thickness; and measurement repeatability. Since 3D printed samples deviated from being optically flat and plane-parallel by as much as 100 \(\upmu\)m, both of these factors were significant and are responsible for the large uncertainties in the figures below.
# Radiochromic Films for the Two-Dimensional Dose Distribution Assessment
## 5 Conclusions and Outlook  
This review paper summarizes the main aspects on radiochromic films for two-dimensional dose distribution measurements. The structure, dynamics and spatial resolution of selected RCF types is reported in order to shed a light on their high dynamic range and spatial resolution. This definitely results in their widespread use, particularly in medical applications. Features less than 25 micrometers can be resolved, although specific measurements of the RCF's spatial resolution would add important information. The use of flatbed scanner is advantageous and widespread in medical applications. However, their use is not trivial, as it is demonstrated by the high number of works on this topic, and it demands strict adherence to RCF protocols, as well as deep knowledge of the subject. Main sources of uncertainty are the lateral scanning effects and the warm-up of the scanner, as well as the dependence on the orientation of the film on the flatbed scanner. Changes of environmental conditions, such as the temperature, humidity, exposure to UV light, etc., affect the RCF reading and should be considered, as well. The available technical options for flatbed-scanner-based RCF dosimetry allow the achievement of a predefined degree of precision, with overall measurement uncertainty up to about 2%. Densitometers are easy-to-use and provide direct measurement of the optical density at a fixed wavelength or in a fixed wavelength range. The spectral response of RCFs introduces an important feature in the RCF analysis. Indeed, a careful wavelength study allows to select parts of the spectrum of different sensitivity to radiation-induced changes on the film. This has been proved helpful in order to use some RCF types in non-standard conditions of dynamics and sensitivity. RCF's dependence on the energy is overall weak. However, care should be taken in particular when dealing with charged particle beams, where RCFs have proved to have a significant under-response, also referred to as quenching effect, which should not be ignored for measurements in the Bragg peak region. RCFs have found wide applicability as beam profile detectors in charged particle accelerators and have been found to be particularly suited for applications in high dose rate particle beams, such as the laser-driven accelerators. This study pointed out these key aspects in RCF measurements in order to provide the reader with a concise updated summary on the topic.  
This work was partially funded by the Swiss National Science Foundation (SNSF) (Grant CRSII5_180352).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
I would like to sincerely thank [PERSON] for supporting me in this work, as well as for useful discussions. I also would like to thank my colleagues of the Group of Medical Applications of Particle Physics at LHEP; in particular, I would like to thank [PERSON] for the help provided in reviewing the manuscript.  
The author declare no conflict of interest.
## 3 Materials and Methods  
In this article, a new IMD-EACBR method has been designed to maximise energy efficiency and lifetimes of WSNs. At the primary level, the IoT nodes in the network are randomly placed and communicate with one another for the information collection process. Then, the presented IMD-EACBR technique primarily selects CHs and organizes clusters using the IAOAC technique. This is followed by the optimal selection of routes using the TLBO-MHR technique. Once the optimal routes are identified, the CHs use optimal routes for transmitting data to a BS. Figure 1 demonstrates the general procedure of the proposed IMD-EACBR method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference No.** & **Published Year** & **Approach** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{[11]} & \multirow{2}{*}{2020} & Simulated annealing and genetic & \multirow{2}{*}{Self-organization} & High communication \\  & & algorithm-based hybrid (SAGA-H) & & cost \\ \hline
[12] & 2018 & Clustering and Sailfish Optimizer (SFO) & Low communication cost & High latency \\ \hline
[13] & 2021 & Novel hybrid NN based energy-effective & \multirow{2}{*}{Load complementary} & High latency, low \\  & & routing & & scalability \\ \hline
[14] & 2021 & Cluster based routing (CBR) & Fast convergence, low & \\  & & overhead & Low coverage \\ \hline
[15] & 2022 & Robust cluster-based routing protocol & Minimum overhead, low & Only uniform node \\  & & (RCBRP) & latency & distribution \\ \hline
[16] & 2021 & Stability-aware cluster-based routing (SACR) & Support node heterogeneity & Low scalability \\ \hline
[17] & 2022 & Metaheuristic artificial intelligence approach & \multirow{2}{*}{Prolongs network lifetime} & Needs parameter \\  & & based on social behavior of grey wolves & & adjustments \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing approaches.  
[PERSON], [PERSON] et al. [21] introduced AOA, inspired by the work of [PERSON], that can be determined from rules of physics. The presented method focused on the object that was completely or partially immersed in the fluid. The initialisation procedure of each object can be implemented by Equation (1):  
\[0_{i}=l_{i}+rand\times(u_{i}-l_{i}),i=1,2,\ldots,N \tag{1}\]  
where \(l_{i}\) and \(u_{i}\) indicate the lower and upper bounds of \(i\)th object, respectively, and \(N\) denotes the number of objects. Rand are numbers that are produced at random and are spread out evenly across the range [0, 1]; rand can refer to a scale factor in the range [0, 1]. The density and volume of all the objects are initialised by the following equation:  
\[den_{i}=rand,vol_{i}=rand \tag{2}\]