esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Workflow Technology for Big Data Analysis Service
## 2 Big Data Analysis Oriented Cloud Workflow System
### Workflow Parsing Engine  
The workflow parsing engine includes workflow definition, XML description, parsing process, and workflow persistence. XML description is employed for two purposes: to record the process information of workflows and as an intermediate form to convert workflows to DAGs. XML description is a markup language to record the process information of workflow, configuration, input data, and output information. XML provides a uniform way to describe and exchange structured data between applications and providers. After the workflow construction, the workflow parsing engine parses the workflow into XML description documents which employ Java Business Process Model Process Definition Language (JPDL) [14] to describe workflow. XML description files are stored in the workflow instance database. Then, the description file is converted into DAG, which is used to guide the workflow implementation. DAG is a directed acyclic graph to recode the execution process of workflows. XML description is an intermediate form which makes it easy to convert workflows to DAGs.  
During the workflow parsing process (shown in Figure 4), each node of the workflow represents a component. Nodes can be divided into control nodes and function nodes according to their functions (as shown in Figure 5). Control nodes support many workflow conditions, including And, Or, and SwitchCase. Function nodes implement analysis tasks which include batch offline and real-time online analyses. Offline analysis components use a MapReduce calculation model, whereas real-time online components use a Storm streaming calculation model. Each component should mark the  
Figure 3: Work procedure. ETL: extract transform load.  
types, resources, parameters, and result outputs. The workflows of big data analyses are data-driven operation processes which automatically run tasks according to the rules of processes or the context state of runtime.  
In the workflow parsing process, the parsing engine verifies the configurations of components according to the configuration requirements and sends the verification results to users. Workflow persistence is an important function of workflow engines, while traditional workflow systems reparse the workflow documents and run workflows at each time. In the traditional condition, the workflow statuses, intermediate results, and crucial points are not traceable. Consequently, the workflow persistence stores parsed workflow description files in a workflow description database. Every time workflow engines run workflows, they create workflow instances from the workflow instances in the workflow instance database. The workflow instances record the intermediate statuses, which can assist in operating workflows (e.g., stopping, continuing, workflow monitoring, history result query, and timer execution of the entire workflow executable process).
## 3 LoRa and Localization Algorithms
### Overview of LoRa
#### 3.1.1 Spreading Factor  
The initial frequency of the chirp is recognized as a symbol. The encoded bits in a symbol are configured by a unique parameter known as the spreading factor (\(SF\)). This indicates that a chirp with the spreading factor \(SF\) represents \(2^{SF}\) bits per symbol, which implies that one symbol is described by multiple chips that are spread spectrum code pulses. \(SF\) is expressed as:  
\[SF=\frac{\mathit{Chirp\ rate}}{\mathit{symbol\ rate}} \tag{1}\]
## 4 Results
### Feature Significance and Co-Linearity  
We calculated the significance for each feature for time intervals starting from \(t_{i}=-48\) h until \(t_{i}=-6\) h, at 6 h increments, using a Mann\(-\)Whitney U. In Figure 2, a heatmap of the significance of each HRV feature is highlighted and Figure 16 in the Appendix A presents a complete overview of the significance of the features from all of the vital parameters. Interestingly, the results indicate that the significance of each feature differed over time. For instance, Figure 2 shows that the median (median_nni) of the periods \(t_{i}=-48\) till \(t_{i}=-30\) are non-significant. Moreover, the plot shows that the entropy (entropy_nn) feature was not significant around \(t_{i}=-42\) and \(t_{i}=-36\). For the other vital parameters, see Figure 16, we also saw that some features were non-significant. In both PFI and SpO2, it was shown that the mean and median of these parameters were non-significant. Moreover, we saw that in respiratory rate (RF), the skewness (RF_skew) was non-significant. We opted to discard all features with segments that were entirely or partly non-significant.  
Figure 2: Significance of all HRV features over time from \(t_{i}=-48\) h until \(t_{i}=-6\) h, with 6 h increments.  
After we selected the significant features, we performed a correlation analysis between the selected features for the entire time window (i.e., t = \(-48\) h; see Figure 3). Interestingly, except for the PFI_mean and PFI_median features, only the HRV features were strongly correlated (i.e., correlation higher than 0.9). In order to avoid co-linearity, which would have inhibited us from drawing conclusions about the feature importance of the model, the co-linearity was removed. This is achieved by randomly selecting either one of each co-linear feature pair.
# An Improved V-Net Model for Thyroid Nodule Segmentation
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline
**Author** & **Model** & **Dataset** & **Performance Results** \\ \hline [PERSON] et al. [18] & MGU-Net & Non-public & Dice: 95.76\% IoU: 91.46\% \\ [PERSON] et al. [5] & TNSNet & Non-public & Dice: 85.33\% Acc: 95.81\% \\  & & & Dice: 60.2\% (benign) \\ [PERSON] et al. [19] & SSE-WSSN & Non-public & Jaccard (IoU): 46.2\% (benign) \\  & & & Dice: 71.3\% (malignant) \\  & & & Jaccard (IoU): 56.1\% (malignant) \\  & & & Dice: 90.26\% Precision: 90.68\% \\ [PERSON] et al. [20] & LCA-Net & TN-SCUI2020 & Jaccard (IoU): 82.65\% \\  & & & Recall: 91.84\% \\  & & & Dice: 82.08\% Precision: 80.55\% \\  & & & TN3K & Jaccard (IoU): 71.18\% \\  & & & Recall: 85.34\% \\ [PERSON] et al. [21] & Method Based on Suggestion & DDTI & Dice: 61.2\% \\  & and Enhancement Networks & 3 DThyroid & Dice: 83.7\% \\ [PERSON] et al. [22] & FDE-Net & Non-public & Dice: 83.54\% \\  & & MNTS & Dice: 82.65\% IoU: 74.38\% \\ [PERSON] et al. [23] & TCNet & TN-SCUI2020 & Dice: 86.42\% IoU: 78.56\% \\  & & GLAS (Colon histology) & Dice: 84.89\% IoU: 80.02\% \\ [PERSON] et al. [8] & FCG-Net & DDTI & Dice: 80.42\% Acc: 95.40\% \\  & & DDTI + Hospital dataset & Dice: 86.70\% Acc: 94.88\% \\ [PERSON] et al. [24] & Ska-Net & DDTI & Dice: 84.98\% Acc: 98.06\% \\  & & & IoU: 73.88\% \\  & & & Dice: 86.01\% Acc: 94.61\% \\  & & & No-public & IoU: 75.45\% \\  & & & UCLA (Image-wide) \\ [PERSON] et al. & Anomaly Detection (AD) & SResUnet-AD & Dice: 80.8\% F1 Score: 83.9\% \\
[16] & Module & UCLA (Average Across \\  & & Positive Images-AAPI) & Dice: 65.5\% IoU: 56.5\% \\  & & & MSUNet-AD \\  & & DDTI (AAPI) & Dice: 40.2\% IoU: 33.3\% \\  & & & Stenford CINE (AAPI) \\  & & & SResUnet-AD & Dice: 59.2\% IoU: 51\% \\  & & & DDTI & Dice: 74.93\% Hd95: 24.51 \\ [PERSON] et al. [25] & TNSeg & TN3K & Dice: 85.71\% Hd95: 12.93 \\  & & & Jaccard (IoU): 73.18\% \\  & & Non-public (TNUD) & Dice: 92.06\% Hd95: 13.35 \\ [PERSON] et al. [26] & MAUNet & D1 (Non-public) & Dice: 90.8\% \\  & & D2 (Non-public) & Dice: 91.2\% \\  & & D3 (Non-public) & Dice: 88.7\% \\ [PERSON] et al. [27] & YOLO-Thyroid & DDTI & mAP0.5: 43.6\% Recall: 58.2\% \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of the literature review encompassing DL-based studies for thyroid nodule segmentation. indistinct boundaries, irregular tissues, and issues related to multiple nodules. This study proposes a novel DL-based method to enhance thyroid nodule segmentation performance for two-dimensional datasets by strengthening V-Net's encoder layers through a triple fusion structure. An SE mechanism was integrated into this structure to improve its performance further.
###### Abstract  
Accurate and reliable prediction of dam deformation (DD) is of great significance to the safe and stable operation of dams. In order to deal with the fluctuation characteristics in DD for more accurate prediction results, a new hybrid model based on a decomposition-ensemble model named VMD-SE-ER-PACF-ELM is proposed. First, the time series data are decomposed into subsequences with different frequencies and an error sequence (ER) by variational mode decomposition (VMD), and then the secondary decomposition method is introduced into the prediction of ER. In these two decomposition processes, the sample entropy (SE) method is innovatively utilized to determine the decomposition modulus. Then, the input variables of the subsequences are selected by partial autocorrelation analysis (PACF). Finally, the parameter-optimization-based extreme learning machine (ELM) models are used to predict the subsequences, and the outputs are reconstructed to obtain the final prediction results. The case analysis shows that the VMD-SE-ER-PACF-ELM model has strong prediction ability for DD. The model is then compared with other nonlinear and time series models, and its performance under different prediction periods is also analyzed. The results show that the proposed model is able to adequately describe the original DD. It performs well in both training and testing stages. It is a preferred data-driven model for DD prediction and can provide a priori knowledge for health monitoring of dams.  
dam deformation prediction; VMD; decomposition modulus selection; secondary decomposition; extreme learning machine +
Footnote †: journal: Medical Imaging
# Automatic Tumor Identification from Scans of Histopathological Tissues
## 2 Related Work  
### Medical Imaging  
The field of computer-aided medical image analysis, particularly the recognition of cancer in histopathological images, has received extensive attention from researchers [42; 43; 44]. It has been heavily focused on the area of computer-aided medical image analysis, new and improved applications in bioinformatics [45], particularly the identification of cancer in histopathological images as it enhances the benefits of quality diagnostics for patients [46]. One of the medical disciplines that has seen a sizable number of deep learning applications is digital pathology and microscopy, especially as advancements in technology have given us an opportunity in using histopathological scans-digital images [47; 48; 49].  
Bright prospects have been demonstrated for the detection, segmentation, and classification of diseases, especially finding cancer cells using newly developed techniques that provide pathologists with decision support and most likely are guiding to more accurate results. A new cross-domain field of graph-based deep learning, which seeks to learn informative representations of medical images in an end-to-end manner [50], has emerged because of the increased attention given to the adaptation of deep learning from images. Convolutional neural networks, a deep learning method that excels at tasks like histology segmentation, are being used more frequently in pathology [51; 52].  
Thus, artificial intelligence tools based on machine learning techniques could fundamentally alter pathologists' and medical professionals' workflow in the future, whether through computer-aided diagnosis or simply by accelerating laborious manual tasks. The current rigid classification and analysis system that enables difficult visual recognition tasks, like separating tumors from normal tissue in slides and classifying different types of tumors [53] may be replaced by a more analytical and flexible model that incorporates radiological, biological, and clinical variables based on deep learning [54; 55].
## 4 Discussion
### Comparison of the Steady-State Method and the Discharge Method
Inversely, if the chamber volume is too large, the measured pressure change is almost equal to the sensor resolution, and this also deteriorates the measuring accuracy. In the experiments, it is considered that at least 20 kPa/s is needed. Therefore, the upper limit volumes for 7-11 mm samples (group 2) are 0.85 L, 1.04 L, 1.27 L, 1.46 L, and 1.88 L, respectively. In Figure 12, careful observation shows that the 11-mm sample exhibits more apparent deviation from the steady-state data points than others. This is because the selected volume of the isothermal chamber is very close  
Figure 12: Comparison of the theoretical and experimental flow rate characteristics for the five porous media of group 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{
\begin{tabular}{c} **Porous** \\ **Medium** \\ \end{tabular} } & \multicolumn{2}{c}{\(E\) (Group 1)} & \multicolumn{2}{c}{\(E\) (Group 2)} & \multicolumn{2}{c}{\(E\) (Group 3)} \\ \cline{2-7}  & **Steady-State** & **Discharge** & **Steady-State** & **Discharge** & **Steady-State** & **Discharge** \\  & **Method** & **Method** & **Method** & **Method** & **Method** & **Method** \\ \hline
7 mm & 0.97\% & 2.16\% & 0.88\% & 2.39\% & 1.03\% & 2.55\% \\
8 mm & 0.59\% & 0.80\% & 1.09\% & 1.64\% & 1.21\% & 1.78\% \\
9 mm & 1.94\% & 1.18\% & 0.51\% & 2.34\% & 0.77\% & 1.28\% \\
10 mm & 1.60\% & 2.26\% & 0.93\% & 1.91\% & 0.62\% & 1.25\% \\
11 mm & 2.08\% & 2.74\% & 1.02\% & 2.31\% & 0.58\% & 0.76\% \\ \hline \end{tabular}
\end{table} 
Table 5: Deviations from the experimental data by the steady-state method and the discharge method. to the lower limit, and probably the isothermal condition cannot be strictly maintained during the discharge process. The discharge method greatly reduces the test time and air consumption. In the steady-state case, flow measurement is maintained for a while to reduce the measuring error. Taking the sample of 11 mm (group 2) as an example, the maximum flow is maintained for at least 30 s to ascertain the inertial coefficient, and thus the air consumption is about 39 L (ANR). Noted that ANR (Atmosphere Normale de Reference) after the unit depicts air volume at conditions 101.325 kPa absolute, 20 \({}^{\circ}\)C, and 65% relative humidity. For the discharge method, the testing procedure is comparatively simple because it can be finished in a few seconds, and the consumed air is the amount of compressible air initially stored in the chamber, which is about 4 L (ANR).
# TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection
## 4 Results
### Ablation Study
#### 4.4.1 TIG-DETR  
To analyze the significance of each component in TIG-DETR, we systematically incorporated TE-FPN and IAM into the model to assess the influence of each component on the model's performance. The results of all the experiments are presented in Table 2.  
According to Table 2, the inclusion of the Instance-Based Advanced Guidance Module enhances the model's accuracy by 2.8 APs. Additionally, the introduction of multiscale fusion leads to improved detection performance for instances of various sizes, particularly for large object instances with a notable improvement of 3.1 APs.  
The inclusion of the Instance-Based Advanced Guidance Module, which excludes multiscale fusion, solely enhances the detection performance of large object instances by 1.7 APs. The overall improvement for the model is 0.4 APs.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**IAM** & **S-IAM** & **TE-FPN** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{S}}\)** & **AP\({}_{\mathbf{M}}\)** & **AP\({}_{\mathbf{L}}\)** \\ \hline  & & & 40.3 & 60.5 & 42.9 & 22.2 & 44.5 & 57.4 \\ \(\surd\) & & & 43.1 & 62.1 & 46.2 & 24.7 & 46.8 & 60.5 \\  & \(\surd\) & & 40.7 & 60.6 & 44.1 & 22.1 & 44.4 & 59.1 \\  & \(\surd\) & & 43.7 & 62.4 & 47.6 & 26.7 & 47.6 & 60.7 \\  & \(\surd\) & \(\surd\) & **44.1** & **62.8** & **48.4** & **26.6** & **47.9** & **62.4** \\ \hline \end{tabular} IAM denotes Instance-Based Advanced Guidance Module, S-IAM denotes Instance-Based Advanced Guidance Module after removal of multiscale fusion, TE-FPN denotes Texture-Enhanced FPN. The bolded part of the font indicates the largest indicator in the comparison experiment.  
\end{table}
Table 2: TIG-DETR ablation experiments on COCO.  
Figure 7: TE-FPN vs FPN. red bounding box shows the detection result of FPN, while blue bounding box shows the detection result of TE-FPN.  
The introduction of Texture-Enhanced FPN results in a remarkable improvement of 3.4 APs in the model, highlighting the substantial performance enhancement brought by Texture-Enhanced FPN to TIG-DETR.
## 5 Attacks and Countermeasures
### P2P Countermeasures
#### 5.2.1 Countermeasures for P2P Routing Attacks
These steps are repeated until the first request reaches the destination. Due to the combination of _Redundant_ and _Iterative_ routing, the packet delivery rate is increased strongly. Yet, this is paid for by a high overhead and delay as multiple request messages have to be sent iteratively. Besides increasing the redundancy of routing algorithms directly, distributing replicas can also increase the reliability of the lookup process. As every countermeasure discussed so far has to rely on the benign behavior of at least the destination node, the success probability of the lookup process can not be improved beyond the fraction of nodes behaving benign on average. Using replicas can overcome this drawback. While [PERSON] _et al._[23] proposed to spread the replicas in the neighborhood of the node that is responsible for the original object according to the DHT specification, [PERSON] and [PERSON] [27] proposed a replica set evenly distributed in the P2P overlay. As a result of equally distributed replicas, DHTs as Pastry can provide guaranteed disjunct multi-path routing. Due to the tree based structure of the primary routing table, storing a replica in each prefix domain of the highest level of the tree will ensure disjunct paths as different parts of the lookup table are used during the lookup for each replica. Replicas can further reduce the impact of _Storage and Retrieval Attacks_ on the network, to deny access to a specific object, all nodes that are responsible for this service (_i.e._, all nodes holding replicas) would have to behave maliciously. As an intermediate node could pretend to be the destination of a request and respond with an incorrect result, the source has to validate each responding node that delivers an unexpected result. For this, [PERSON] _et al._ proposed a _Routing Failure Test_. The source node has to check the virtual distance to the neighbor nodes and estimates an average node density. As most DHTs distribute the node-IDs equally in the ID space, the node density at the destination is equal to the node density at the source on average. Whenever object-ID and node-ID differ strongly regarding the node density estimated in the neighborhood of the source node, the responding node might not be the legitimate node that is responsible for the object.
## 5 Conclusions  
To match the technological improvement of the low-cost GNSS positioning and navigation solutions regarding hardware development and advanced processing methods, this study proposed the low-cost positioning accuracy assessment method. Besides its affordability for farmers worldwide, its flexibility and straightforwardness allow for the regular examination of GNSS receivers mounted on agricultural machinery without its detachment and in actual field conditions. Present ISO standards for GNSS RTK accuracy assessment cannot be fulfilled adequately and are not suitable for the receiver mounted on agricultural machinery nor do they represent actual field conditions during agrotechnical operations. While the proposed method still has a slight disadvantage of the operator's subjective impact, various approaches are possible to further reduce this issue. The most suitable ones are an increased number of repetitions and longer trajectory to retain the low-cost property of the method, while the implementation of autosteering would completely remove this impact but requires additional investments.  
As the positioning accuracy assessment results based on the four of the most commonly used GNSS corrections in Croatia indicated, the GNSS observations under actual field conditions during agrotechnical operations can produce lower accuracy compared to their declared positioning accuracy. Additionally, some site-specific conditions, such as the availability of mobile Internet signal and the distance to the base station, could produce heterogenous GNSS positioning accuracy, even within a county or a municipality. Therefore, greater emphasis should be put on the local properties and multiple study areas for the assessment of GNSS positioning accuracy in both professional farming activities and future scientific work. Resolving these issues will be a subject of future studies to provide more reliable insight into the current solutions for GNSS positioning and navigation for agricultural machinery under various field conditions.  
Conceptualization, D.R. and I.P.; methodology, D.R.; software, D.R.; validation, D.R.; formal analysis, D.R., I.P., G.H. and M.J.; investigation, D.R. and I.P.; resources, D.R. and I.P.; data curation, D.R.; writing--original draft preparation, D.R.; writing--review and editing, D.R., I.P., G.H. and M.J.; visualization, D.R.; supervision, I.P., G.H. and M.J.; project administration, M.J.; funding acquisition, D.R. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 3 Results
### The Characteristics of the Canopy Red-Edge Spectra  
Figure 5 shows the first derivative for the red-edge reflectance spectra averaged over each of the treatments. There was a clear platform region at 700 nm-715 nm and a double peak, with existing multiple small peaks on the second peak, at 716 nm-740 nm. As the days after heading increased, the phenomenon of double peak became more significant, and the first derivative reflectance decreased gradually. The largest difference region of the first derivative reflectance spectra between T1 and T2 was in the double peak region, where the first derivative reflectance averaged over all the treatments of T2 was 16% higher than that of T1 (2016).  
More detailed information about the red edge can be gained from Table 5, which lists the red-edge parameters (\(\lambda_{0}\), \(D\lambda\), \(\alpha_{0}\), and \(\sigma\)) under different treatments. Compared with CK, it shows that the \(\lambda_{0}\) of T1 and T2, with a value in the range of 685-690 nm, shifted toward the long-wavelength region of the spectra. However, there was no significant difference of \(\lambda_{0}\) between T1 and T2. The \(D\lambda\) of T1 and T2 were generally higher than that of CK. The \(D\lambda\) value of T1 and T2 were in the range of 0.0054-0.01 nm\({}^{-1}\), and compared with T1, \(D\lambda\) of T2 increased by 8.70%, 17.19%, and 7.41% at DAH 14d, DAH 21d, and DAH 28d (2016). The \(\alpha_{0}\) of T1 and T2, with a value in the range of 0.25-0.43, was also higher than that of CK. Compared with T1, the \(\alpha_{0}\) of T2 increased by 13.89%, 13.33%, and 12.00% at DAH 14d, DAH 21d, and DAH 28d (2016). The \(\sigma\) has no obvious change among all treatments. The distribution of red-edge parameters in 2016 and 2017 was also analyzed, which showed that the distribution was consistent. The results show that an increase in diffuse radiation fraction led to an increase in parameters \(D\lambda\) and \(\alpha_{0}\).  
Figure 5: The first derivative spectra curve of the red edge with different treatments: (**a–c**) are DAH, 14d, DAH 21d, and DAH 28d in 2016; (**d–f**) are DAH 14d, DAH 21d, and DAH 28d in 2017.