esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Recommendation of Discussion Threads
Indeed, this is a limitation that extends, in general, to the entire evaluation: there are not enough posts for an important number of topics. In particular, we found that the number of queries without relevant recommendations is higher for the word embeddings approach (8 queries) than for the bags of words and bags of key phrases approaches (3 queries). We next analyze the results for the \"relaxed\" evaluation (i.e., we consider as relevant to a query all the post that address either the same problem or the same algorithmic scheme), which are represented by the solid line in Figures 2-4. We can see that, for the bags of words representation, 14 posts obtain 5 relevant recommendations. In the case of the key phrases representation, this number raises to 45, while in the case of the word embeddings, the number reaches 38 posts. If we consider the number of posts for which we obtain more than 80% of relevant recommendations (i.e., 4 out of the 5 posts recommended are relevant), we find that, for the bags of words representation, posts receive 23 relevant recommendations. In the case of the key phrases representation, this number raises to 73, while in the case of the word embeddings, the number reaches 56 posts. This seems to indicate that, especially for the case of the key phrases and word embedding approaches, most of the recommendations made to the students are being of help. However, in the case of the word embedding approach, we find that the number of recommendations where precision is under 0,4 is higher than expected (13%), while in the case of the key phrases approach it is 0%. A problem that we have found and that affects to all three textual representations is the presence of posts and queries in which neither the name of the problem nor the name of the scheme is mentioned, only the page of the book in which the problem the student refers to appears. This makes it difficult for the algorithm to infer what the topic of the problem is, and to recommend relevant threads. This may be seen, for example, in the following query:  
Exercise 7.3: I do not quite understand what is being asked for in this exercise. Shouldn't it always be the same area that is trimmed from the board? i.e., the sum of the different products x*y. We have also found a poor performance for the queries that ask about problems that can be solved using different schemes, which means that, frequently, when a student asks about the solution to a problem using a given scheme, the algorithm recommends posts about the same problem but solved using a different scheme, which is not exactly what the student is asking for. This is the case, for example, of the _packing_ problem, that may be solved using various schemes studied in the course, such as _greedy_ and _dynamic programming_. Finally, orthographic errors and typos, which are quite frequent in the course forum, also pose a problem for our recommendation algorithm. Figure 4: Evaluation results for the word embeddings representation.
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 4 Healthcare Applications
### Tissue Engineering
#### 4.1.1 3D Models and Organoids
Through bioprinting, they were able to create a model consisting of embryonic stem-cell-derived thyroid spheroids and epithelial spheroids, in close proximity to each other, that allowed for invasion and vascularization of the thyroid spheroids by the epithelial cells [95]. They also confirmed the functionality and neovascularization of the organoid by achieving thyroid homeostasis after implanting the bioprinted organoid in hypothyroid mice [95]. Bioprinting has emerged over the past few years as a capable method for creating highly complex organoids and lab models. To date, many labs have utilized this technique for creating biologically functioning models made up of a wide variety of tissues such as liver [87; 94; 96], mammary epithelium [97], myocardium [98], skeletal muscle [53], kidney [61], skin [14; 93], neurons [99] and malignant tumors [100]. These models are becoming useful drug screening tools within both research labs and the pharmaceutical industry [98; 101; 102; 103]. There are, however, a few limitations associated with bioprinting. The properties of currently used bioinks result in a trade-off between creating a viable growth environment for cells, while providing structural support to the printed model. More elastic biomaterials can allow for creation of complex models that can hold their shape but at the same time limit cellular interactions and movement [104]. More viscous biomaterials create a fluid environment for cells, while having less capability to create large models without collapsing under their own weight [105]. As the field of bioprinting advances, so does the need for discovering new biomaterials that provide structural support without hindering cell viability and extracellular matrix deposition.
###### Abstract  
Motor Imagery (MI) classification using electroencephalography (EEG) has been extensively applied in healthcare scenarios for rehabilitation aims. EEG signal decoding is a difficult process due to its complexity and poor signal-to-noise ratio. Convolutional neural networks (CNN) have demonstrated their ability to extract time-space characteristics from EEG signals for better classification results. However, to discover dynamic correlations in these signals, CNN models must be improved. Hyperparameter choice strongly affects the robustness of CNNs. It is still challenging since the manual tuning performed by domain experts lacks the high performance needed for real-life applications. To overcome these limitations, we presented a fusion of three optimum CNN models using the Average Ensemble strategy, a method that is utilized for the first time for MI movement classification. Moreover, we adopted the Bayesian Optimization (BO) algorithm to reach the optimal hyperparameters' values. The experimental results demonstrate that without data augmentation, our approach reached 92% accuracy, whereas Linear Discriminate Analysis, Support Vector Machine, Random Forest, Multi-Layer Perceptron, and Gaussian Naive Bayes achieved 68%, 70%, 58%, 64%, and 40% accuracy, respectively. Further, we surpassed state-of-the-art strategies on the BCI competition IV-2a multiclass MI database by a wide margin, proving the benefit of combining the output of CNN models with automated hyperparameter tuning.  
hyperparameters; ensemble learning (EL); brain-computer interface (BCI); MI-EEG; multiclassification +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Aroma Profiling  
As presented in Table 4, overall. 25 VOCs were found in the headspace of dried sweet potato, while 22 were successfully identified. For the fresh sweet potato, the highest contribution in VOC composition had phenylacetaldehyde, (\(E\))-2-octen-1-al, and benzaldehyde, with shares of 44.90%, 9.43%, and 8.18%, respectively. Further, of interest was the influence of kale addition to the raw material, which introduced into the dried material VOC profile compounds, such as hexanal, (\(E\))-2-hexenal, (\(Z\))-2-hexenal, (\(Z\))-3-hexen-1-ol, and hexan-1-ol. Unfortunately, the influence of onion addition to the raw material was not so clearly presented. This result was strongly confusing, especially in reference to the intensive onion aroma. Nevertheless, it is possible that the material (sweet potato) structure had strongly influenced the onion-originated VOCs, the contribution of which was totally decreased during drying procedures.  
The most important was the influence of various drying techniques on the total VOC concentration. It was clearly presented that, in comparison to the fresh sample, all drying procedures had reduced the VOC concentration by at least 28.8%. The optimal method for preserving the VOCs content was FD with sodium chloride addition and FD with kale addition. The type of impregnating solution (\(p=0.735\)) did not have a significant influence on the tested parameter. Volatile compounds were only positively correlated with water activity (r = 0.73) and negatively correlated hardness (r = \(-0.68\)) and method (r = \(-61\)). Overall, the most destructive for VOC content in dried products were all the samples that were treated with VM drying. Those results correspond to numerous studies referring to drying technology, where FD is very often treated as the reference drying method [1; 46]. In terms of the destructive influence of particular drying methods, it has been proven before that it is impossible to point out undoubtedly which drying method is the less optimal one [47; 48]; it has always been dependent on the object of the study. In the case of sweet potato with kale, onion, or sodium chloride addition, it may be concluded that FD or VD should be applied; however, it has to be highlighted that onion addition has some unexpected influence on the VOC retention. This may be due to the difference in concentration between the impregnating solutions and the solids content of the onion, which makes it difficult to transport water. In dried sweet potatoes with the addition of onions, the lowest value of dry matter was observed, which affected the above studies.
### Related Work  
To ensure transmission integrity, a large volume of literature exists where different schemes were presented for mitigating the FWM effect. Some advanced contributions and their limitations are presented as follow: fiber optical parametric amplification (FOPA) [28], electronic absorption modulation process with different optical filters such as infinite impulse response (IIR), Butterworth, and Bessel [29] are the advance procedure for controlling the impact of FWM. In [30] fiber bragg grating (FBG) technique is used for long haul networks to reduce FWM effect and a method for minimizing FWM impact using channel spacing technique was reported in [31]. Digital signal processing (DSP) techniques for compensating inter-and intra-channel fiber non-linearities have been investigated in [32] by manipulating input parameters such as launch power and fiber length. Self phase modulation (SPM) non-linear issue has been investigated in [33] where conversion between different formats, such as 8-quadrature amplitude modulation (8-QAM) to quadrature phase shift keying (QPSK) and to on-off-keying (OOK) has been performed through optical methods to adapt the channel conditions. Different values of optical signal-to-noise ratio (OSNR), optical power, and chromatic dispersion were used to conduct analysis on the performance of the proposed system; however, the format conversion to a lower order results in decrease of data rates. In [34], the authors discuss inter-model nonlinear issues in few-mode fiber using 16-QAM up to 36 km transmission and report mitigation of the XPM effect. High cost components have been used in [35] such as indium phosphate and silicon photonics to achieve high bandwidth in the presence of fiber non-linearities. The voltra assisted method was explained in [36] in which the authors overcome the limitations of optical phase conjugation (OPC) technique. All above mentioned techniques have higher complexity and are not cost efficient. Hence, FWM effect still need to be investigated in depth for LHOCSs. Moreover, modern LHOCSs require multiplexing of several channels for which different parameters of a WDM-based system need to be analyzed. Therefore, we present a detailed analysis of FWM by varying input power, fiber length, channel spacing, and multichannel. To evaluate the executions of the presented LHOCS, an in depth investigation is carried out theoretically and experimentally.
# Multi-Parametric Imaging of Etruscan Chamber Toms:
## 2 Materials and Methods
### GPR Data Acquisition  
GPR is an electromagnetic technique that detects the location of objects or interfaces that are buried beneath the shallow portion of the subsoil and characterized by their dielectric properties. In some soil types, such as those characterized by a high content of mineralized water (i.e., clay), or those derived from volcanic rocks or otherwise high in iron or metallic content, conduction and dielectric effects can cause the absorption of EM radiation, reducing the effectiveness of the GPR method [39; 40; 41].  
Herein, the outcropped volcanic unit is mainly characterized by stratified and friable volcanic tuff and is thus suitable for the GPR method. The survey was conducted during the summer to reduce the impact of moisture content. Moreover, void detection is also one of the most suitable targets for this kind of investigation, and the Etruscan burial roofs were expected to occur at a relatively shallow depth (approximately 2 m). Based on these considerations, we recorded three GPR profiles (labeled GPR1, GPR2, and GPR3 in Figure 3) using a GSSI Sir3000 instrument equipped with a 400 MHz central-frequency antenna. The main acquisition settings were as follows: range, 70 ns; vertical sampling, 512; horizontal sampling, 100 scan/m (120 scan/s); 16 bit dynamic. Data post-processing included the application of vertical and horizontal band-pass filters, deconvolution, gain equalization and migration. Reflection arrival times were converted to depth using an averaged EM wave speed of 0.11 m/ns, which corresponds to a dielectric constant of \(\varepsilon\)= 7.5. The \"\(\varepsilon\)\"starting value was calculated by measuring the two-way travel time (TWT) of the reflections generated from the top of the EB1 excavated burial (Figure 3). This value was integrated using velocity analyses on diffraction hyperbolas occurring along the profiles and was found to be consistent with those proposed in the literature for the same soil type [42; 43].  
The measured GPR data were georeferenced by linking a geodetic GPS receiver (Topcon GB1000) and using a survey wheel to obtain a more rigorous determination of scan spacing.
## 3 Results and Discussion  
### Accuracy and Processing Time of Proposed Method  
In order to calculate the accuracy of the proposed method, the clustering accuracy was first obtained using the perceptron artificial neural network with 10 neurons and a learning rate of 0.2 for each view of the apples. In some apples that had a particular feature and were only identified in one view, such as worminess, the accuracy of one view was very low, but in the second phase, two views were selected from each apple to be used in the training of the neural network. Finally, all three views were used to learn the pattern, which significantly improved the accuracy of this technique, relative to one view and two views.  
The total processing time for building the dictionary was about 220 s. After creating the dictionary, pest and disease detection was about 0.175 s.
## 5 Numerical Results  
To demonstrate the effectiveness of the proposed IALO method, four main study cases are implemented as follows:  
1. Consider MFs for each unit of a 10-unit system; Case 1: Consider four load cases of 2400, 2500, 2600, and 2700 MW without VPEs; Case 2: Consider one load case of 2700 MW with VPEs; Case 3: Consider one load case of 2700 MW with VPEs and constraints such as SR, POZ, and RRL;
2. Consider SF for each unit of a 20-unit system supplying a 2500 MW load considering power loss;
3. Consider MFs and VPEs for the 80-unit system with a 21,600 MW load;
4. Consider SF for each unit for the 15, 30, 60, and 90-unit systems with POZ constraints.  
The proposed method together with the ALO were programmed in the Matlab platform and run on a PC (Processor with 2.7 GHz, RAM with 4.0 GB). In order to investigate the real performance of the IALO method, another comparison criterion was also considered to be the number of fitness evaluations, \(N_{f\text{es}}\), which is shown in the following equation:  
\[N_{f\text{es}}=\omega*N_{p}*G_{\max} \tag{46}\]  
In the equation above, \(\omega\) is the number of new solution generation times in each iteration. The IALO method has only one generation time for each iteration, so \(\omega\) is 1 for IALO while \(\omega\) is 2 for CSA [27] and RCSA [29]. For other methods such as FOA and IFOA in [37], \(N_{f\text{es}}\) had another model as follows:  
\[N_{f\text{es}}=\frac{N_{p}*\big{(}N_{p}+1\big{)}*G_{\max}}{2} \tag{47}\]Furthermore, we also computed the improvement of the proposed method over other compared methods by using the best cost of the compared methods and our proposed method. The improvement in % (_ic_) can be obtained by the following model:  
\[ic\ (\%)=\frac{\text{Best cost of another method }-\text{ Best cost of IALO}}{\text{Best cost of another method}}\times 100\% \tag{48}\]  
In Equation (48), values of _ic_ can be classified into three cases in which Case 1 corresponds to positive values, Case 2 corresponds to zero values, and Case 3 corresponds to negative values. If Case 1 occurs, the suggested method is more effective than the compared method. In the case of an _ic_ with zero value, the suggested method is as good as other ones. In contrast, Case 3 indicates the suggested method is less effective than the compared methods if the compared methods have reported valid solutions and used smaller \(N_{\textit{fs}}\) values in Equation (47).
# Regulation of the _Pseudomonas syringae_ Type III Secretion System by Host Environment Signals
### Transcriptional Control of P. syringae T3 SS Genes by hrp/hrc Regulators
tomato DC3000, indicating that a tradeoff between the activation of the T3 SS cascade and central cellular metabolism may occur in _P. syringae_[60]. Due to the central role of HrpL and HrpR/HrpS in orchestrating T3 SS deployment, the majority of characterized mechanisms of T3 SS regulation described in more detail below converge on the activity of these key upstream regulators. HrpA1, the pilus protein that forms the extracellular filament of the T3 SS, also functions as an auto-regulator of T3 SS gene expression [14; 62]. Early studies of T3 SS expression in _P. syringae_ pv. tomato DC3000, a pathogen of tomato and the model plant Arabidopsis, noted that HrpA1 was required for the extracellular secretion of T3 SS substrates, including harpins and T3 SS effectors, as well as the intracellular accumulation of T3 SS-encoding mRNA transcripts including upstream regulators _hrpL_ and _hrpRS_[62]. HrpA1 was also required for the accumulation of _hrpL_ and _hrpS_ transcripts by _P. syringae_ pv. _phaseicola_ 1448a in the leaves of host bean plants [57]. Interestingly, HrpA1 did not influence _hrpR_ transcript abundance in this study, despite the fact that _hrpR and hrpS_ are typically co-transcribed in _P. syringae_[63; 56]. This effect may be due to the hypothesized role of HrpR as a transcriptional activator of _hrpS_ expression in some _P. syringae_ pathovars, such as _P. syringae_ pv. _phaseicola_ NPS3121 [58]. The mechanism by which HrpA1 positively auto-regulates the expression of the T3 SS remains unknown. In addition to autoactivation by HrpA1, the T3 SS cascade is also subject to negative self-regulation. HrpL negatively regulates its own promoter activity through binding to a _hrp_-box element within the promoter of _hrpL_, a regulatory gene in the HrpL regulon whose promoter is adjacent to that of _hrpL_[54]. Subtle changes in the level of _hrpL_ transcripts in _P. syringae_ conditioned large-scale shifts in the expression level of downstream T3 SS-encoding genes, suggesting that substantial signal amplification occurs through the HrpL-T3 SS signaling cascade [54]. As such, negative autogenous regulation of the T3 SS cascade may be a way to fine-tune HrpL levels in order to prevent excessive accumulation of T3 SS substrates.
# Framework for Malware Triggering Using Steganography
## 3 Methodology
### Geolocation Techniques
#### 3.1.2 IP Geolocation Services  
IP geolocation is the process of determining the geographical location of a specific IP address. For this study, we investigated the accuracy of eight IP geolocation services: DB-IP, Maxmind GeoIP2, IP2 Location, Ipinfo, Skyhook Hyperlocal IP, Google Geolocation API, Ipregistry, and Whois XML API. These services usually provide country, region, city, zipcode, latitude/longitude, ISP, domain or company name, time zone, and even connection type.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Scientific and Technological Challenges
#### 3.2.4 Precipitation over the Ocean  
Precipitation retrievals over the ocean exploit more emission-based MW channels, since the emissivity of the water surface is rather homogeneous. A general qualitative agreement can be observed between the climatologies from the available products, which reproduce the major characteristics of the precipitation patterns over oceans [204]. However, quantitative disagreements occur globally, but also in terms of regional patterns, especially in regions of high precipitation; a better agreement is generally found in the northern hemisphere. There is an obvious lack of dedicated observations over the oceans to ensure a sufficient validation standard; limited observations are available on islands and on board ships. OceanRAIN [108] provided in situ, along-track, shipboard data of precipitation, evaporation, and the resulting freshwater flux at 1-min resolution over global oceans from June 2010 to April 2017 and is the first observation dataset of its kind. Studies on the frequency of precipitation occurrence over global oceans [205] are now being conducted to capture the seasonal cycle and investigate shortcomings in previous climatologies, such as the amount of trade cumulus precipitation, which appears to be higher than expected [206]. Moreover, it remains widely unknown how precipitation scales with sea surface temperature (SST), particularly at sub-daily resolution, though the oceans cover more than 70% of the Earth's surface and receive 77% of precipitation [207]. Studies on the sensitivity of oceanic precipitation to SST are thus very important [208]. Finally, salinity changes resulting from long-term alternations between surface evaporation and precipitation are evident and need to be studied in depth [209]. Recent studies explore the freshening of sea water in the first centimeter caused by rainfall using SMOS and SMAP data, finding that, in the general, case salinity is restored within a few hours, while occasionally, the freshening may persist for 24 h or more [210].
# Sex Estimation from Computed Tomography of Os
###### Abstract  
This study aimed to evaluate the DSP method's applicability to Romania's contemporary population and to assess the accuracy and reliability of variables derived from CT images. A total of 80 pelvic CT scans were analyzed. Participants ranged from 22 to 93 years, with a mean age of 59.51 \(\pm\) 22.7 years. All variables measured from the CT scans were analyzed using DSP software. The study found that sex estimation was possible in 71.25% of cases overall, with varying rates between males (57.50%) and females (85%). Despite encountering undetermined specimens comprising 42.5% males and 15% females, only one misclassification occurred. Regarding accuracy, the overall rate remained notably high at 98.24%. All female specimens that could be estimated were correctly classified (100% accuracy), while for males, the accuracy rate was 95.65%. Undetermined cases were noted to potentially impact the accuracy of sex classification, underscoring the critical role of precision in forensic contexts. In conclusion, the study highlights the importance of accuracy in forensic sex estimation. It emphasizes the confidence with which DSP software can be utilized, if not the only method, at least as a preliminary or adjuvantly accurate technique for sex estimation in forensic anthropology.  
forensic anthropology; sex estimation; Probabilistic Sex Diagnosis (DSP); pelvic CT scans; os coxae +
Footnote †: journal: Applied Sciences