esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Outage and Error Probability
## 1 Introduction
This process continues till either of the two termination criteria are reached; the destination receives a signal with an acceptable quality or all available relays are expired. 2. We proposed two routing protocols for UWSN; ACE [10] and E-ACE. In ACE protocol, retransmission mechanism is incorporated in a cooperative manner to enhance reliability of an existing routing protocol; Energy Efficient Depth Based Routing (EEDBR) [3]. ACE allows only two retransmissions in case of erroneous data reception at destination. However, in noisy underwater environment, two retransmissions may not be sufficient. Therefore, enhanced version of ACE, E-ACE is proposed. In E-ACE, all nodes present in common region of source and destination's transmission range perform retransmission. Increased number of retransmissions improves reliability and throughput. 3. In our work, the relays perform retransmissions in a cooperative manner. The idea of cooperative retransmissions is taken from CARQ scheme [11]. CARQ scheme is proposed for underwater channel. In this scheme, for a given source-destination pair, neighbor nodes make a cooperative nodes set. The cooperative nodes are selected to retransmit the erroneous packet in a closest one first manner. However, we introduced channel affects and different relay nodes selection criteria in our proposed scheme. For channel affects, BER is calculated at each destination to evaluate the signal quality. Additionally, we presented the cooperative nodes selection method, i.e., minimum depth and highest residual energy among the cooperative nodes. Furthermore, signal combining technique is incorporated in the proposed protocols to reduce Bit Error Rate. Rest of the paper is organized as follows. Section 2 describes related work, Section 3 presents the proposed schemes and performance analysis in terms of outage and error probability, simulation results and discussions are given in Section 4, and Section 5 concludes the paper.
# Efficient Data Exchange between WebAssembly Modules
## 3 Related Work
### Inter-Module Communication  
Currently, WASM Interface Type (WIT) [15] is the most popular technology for defining types and interfaces, as used by WASI Preview 1 [16; 17]. Alternatives such as waPC use MessagePack for serialization to enable host-WASM communication, with ease of implementation and support across multiple languages [18]. Language-specific options, including Go-Plugin, use Google Protobuf for communication between the host and TinyGo modules [19].  
The component model extends WASM, using WIT for description and establishing a specific ABI termed \"Canonical ABI\" [20], which features a sophisticated type conversion system, \"Lifting and Lowering Values\", for interoperability across different languages and binary representations. However, it requires a specialized code generator and new WASM instructions and, thus, demands adaptability from languages, compilers, and runtimes to support the proposed extensions. Currently, this extension is still under development and is not fully implemented or defined [21].  
Other projects like Extism [22] simplify data transfer for simple data types such as text, and JSON is suggested as one means of sharing more complex data [23], but this may be inefficient for data-conversion needs. The rise of WASM in cloud and edge computing services has led to companies creating their own communication protocols and definingtheir own foreign function (FFI) and ABI interfaces. Projects like Envoy and WasmCloud are examples of such solutions [3; 24].  
All alternatives follow the \"share-nothing\" philosophy, which prevents data sharing by reference and requires data to be copied. Communication from guest to host can be zero-copy, but communication from host to guest or guest to another guest requires memory copies, and, in some cases, serializations such as JSON, Google Protobuf, or MessagePack are still necessary, which potentially impacts performance.
# An Exploit Traffic Detection Method Based on Reverse Shell
## 4 Evaluation
### Experimental Design Scheme
#### 4.2.1 Comparison of Detection Results of Mixed Traffic in Three Proportions
Figure 3: Comparison of the number distribution of delay packets in the reverse flow. The following experiments compare the results of the decision tree model with three proportions of reverse shell traffic. Table 2 shows that the _Accuracy_, _Recall_, _F1-score_, and _F2-score_ of the model fluctuate slightly with the proportion of benign traffic increasing. As the ratio of reverse shell traffic to benign traffic reach 1:300, the _Accuracy_ remains above 75%, and the _Recall_ is 72%. The values of the _F1-score_ and _F2-score_ are still over 0.7, indicating that the fusion information feature vector not only can accurately detect reverse shell traffic but also has a relatively stable detection effect. Figure 4: Comparison of K-S test results of bi-direction packet length sequences. Figure 5: Comparison of the Hamming test results between the delay packet direction sequence and the change sequence of packet direction. In addition, with the increase in flow scale, the time required for model training and detection of original features increased significantly. On the contrary, the fusion information feature remains stable, as shown in Table 3 for details. When the proportion is 1:300, the average required time of the fusion information feature group is only 1.379 s. The best group \"packet length + packet direction + packet delay\" sequence needs 13.992 s, 10.1 times longer, but its _F1-score_ and _F2-score_ are only  
Figure 8: Comparison of detection results before and after feature fusion with the ratio 1:300. Figure 7: Comparison of detection results before and after feature fusion with the ratio 1:100. 0.06 and 0.04 higher than the former, respectively. The fusion information feature is more practical for reverse shell traffic detection in large-scale traffic scenarios.
## 1 Introduction
### The State of the Art and Analyses
This antenna utilized a 2 \(\times\) 14 \(\times\) 14 array with PIN diodes for mode switching and employed genetic algorithms for phase optimization. It offered 1-bit phase quantization and a limited beam resolution and had a complex multilayer structure with numerous active components, which increased its size, power consumption, and thermal management challenges. A dual-port, dual-beam, pattern-reconfigurable antenna operating at 3.6 GHz with a bandwidth over 400 MHz, achieving 360\({}^{\circ}\) azimuth and 60\({}^{\circ}\) elevation scanning, was developed by [PERSON]. [27]. This design employed reconfigurable metal lines (RMLs) with PIN diodes, offering independent 2D beam control per port, and delivered an average gain of 8 dBi, up to 80% efficiency, and port isolation of more than 20 dB. However, the complexity of the systems increased, as well as their weight, power consumption, and costs. Thus, while effective for terrestrial applications, this design is not optimal for CubeSat communications. Ref. [28] introduced another reconfigurable electromechanical reflectarray (REMR) antenna operating at 5.5 GHz, which employed cam-actuated ground plane displacements to achieve continuous phase shifts of up to 200\({}^{\circ}\), enabling beamsteering across incidence angles ranging from 0\({}^{\circ}\) to 70\({}^{\circ}\). The design eliminated the need for active electronics, offering low-loss performance and mechanical stability, even when powered off. However, the mechanical actuators introduced some problems in the harsh space environment due to latency, and the potential reliability may not be ideal for CubeSat platforms. Lastly, Ref. [29] developed a passive multibeam antenna for CubeSat applications, operating at 39 GHz within the Ka-band. This design integrated a 2D microstrip antenna array with a 3D-printed half-ellipsoid lens, enabling beam selection across 16 discrete directions. The antenna achieved gains between 14 and 16 dBi with half-power beam widths ranging from 14\({}^{\circ}\) to 18\({}^{\circ}\), and the lens weighs approximately 15.2 g. While 3D printing offers a cost-effective and lightweight solution, the reliance on discrete beam switching limits real-time adaptability, and the physical size of the lens poses integration challenges that may not be optimal for CubeSat communications, despite this innovative approach. A detailed comparison of research presented in this state-of-the-art section is presented in Table 1.
## 2 Materials and Methods
### Part II: Case Study  
As an illustration of this sensor fusion used to assess performance, two jumps by the same athlete and during the same training session were compared in a case study. The jumps were chosen on the basis of similarities in wind conditions, as wind is an important performance-determining parameter in ski jumping [40]. By using the same athlete for both jumps, the effect of body weight can be neglected, which is, together with potential equipment variations between athletes, also an important performance variable in ski jumping [19; 51].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
**Phase** & **Dist.** & **Method** & **Tr.** & \(v\) & \(v_{x}\) & \(v_{y}\) & \(F_{b}\) & \(F_{p}\) & \(F_{D}\) & \(F_{L}\) & \(F_{L}\)/\(F_{D}\) & \(\psi\) & \(\theta\) & \(\gamma\) & \(v_{\psi}\) & \(v_{\theta}\) & \(v_{\gamma}\) \\  & **[m]** & & & **[m s\({}^{-1}\)]** & & & & **[N]** & & & **[I]** & & & **[rad]** & & & **[rad s\({}^{-1}\)]** \\ \hline in-run & \(-\)65–0 & dGNSS & X & X & X & X & X & X & & & & & & & & & & & & \\ Take-off & \(-\)5–20 & PosEst & X & X & X & X & & & & X & X & X & X & X & X & X & X & X \\ Flight & 0–90 & dGNSS & X & X & X & X & & & X & X & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the performance parameters measured in the different phases of the ski jump and the method used for the analysis. Dist. is the horizontal distance from the jump edge and Tr. the trajectory of the head for the dGNSS and CoM for the PosEst.  
The analyses are presented in chronological order, following the two jumps through the in-run, take-off and flight phase to highlight the differences and similarities that lead to a performance difference. The data from dGNSS were used to analyse the in-run and flight phase. During the take-off section the CoM data calculated from the PosEst system was used since the head position (dGNSS) is assumed to be an inaccurate representation of CoM in the section where the ski jumper moves from the in-run position to the constant flight position. Table 1 shows an overview over the performance parameters measured in the different phases and the methods that were used for the analysis. No statistical analysis was performed as this part only consisted of two jumps.
# Models to Identify Small Brain White Matter Hyperintensity Lesions
## 2 Related Work
### Deep Learning in Brain Lesion Segmentation  
UNet is a fully convolutional network (FCN) architecture that consists of a contracting path (encoder) and an expansive path (decoder), which gives it a U-shaped structure that has been widely used in biomedical image segmentation [33] due to the network's encoder-decoder structure enables precise boundary detection.  
In this sense, [PERSON] et al. [22] proposed an enhanced combination of U-Net with fractal networks, which reported an accuracy of 0.9908 and a DSC value of 0.8993 for the stroke penumbra estimation (SPEC) and an accuracy of 0.9914 and a DSC of 0.883 for sub acute stroke lesions (SISS), using public databases ISLES 2015 and ISLES 2017. Similarly, [PERSON] et al. [34], using the same database (ISLES), a U-Net-based 3D CNN architecture, and 32 filters, obtained values of DSC as 0.59 for SISS and 0.84 for SPEC.  
Other studies have explored alternative architectures, such as a u-shaped residual network (uResNet) for WMH differentiation proposed by [PERSON] et al. [35], yielding 69.5% DSC for WMHs and 40.0% DSC for ischemic stroke. [PERSON] et al. [36] reported a DSC of 0.60 for WMH lesions using the classical Random Forest (RF) classifier, highlighting the need for deep learning-based solutions. Likewise, [PERSON] et al. [37] determined a sensitivity of 0.73 using a combination of AdaBoost and RF algorithms for the WMHs concerning cerebral small-vessel disease (SVD).  
With the goal of emphasizing relevant features and suppressing irrelevant ones to enhance segmentation accuracy, especially in regions with complex boundaries, [38] proposed a segmentation model of small demyelination lesions based on the implementation of Context Information Weighting Fusion (CIWF) and Modified Channel Attention (MCA) components into the classic UNet. The results showed a DSC: 73.81, precision: 75.28, recall: 74.84, and AUC: 87.28. The dataset used was a private dataset with FLAIR modality, including data on 204 pediatric patients.  
Along similar lines, [PERSON] et al. [39] showed a U-Net segmentation for pixel-level structure information and deep convolutional networks to classify demyelinating diseases accurately. The reported results for precision, recall, accuracy, and AUC score values were 96.96%, 96.96%, 99.19%, and 96.66%, respectively. Also, a DSC of 71.1% was achieved for segmentation.
# Cyclic Elasto-Plastic Behaviour of 51 CrV4 Steel for Railway Parabolic Leaf Spring Design
## 3 Material and Analysis Procedure
### Chemical Composition and Microstructure  
When used in parabolic leaf springs, 51 CrV4 steel (with its chemical composition presented in Table 1) is thermally treated following the ISO 683-14 standard [22]. According to the ISO 683-14 standard, parabolic leaf springs are quenched at 850 \({}^{\circ}\)C in an oil bath and then tempered at 450 \({}^{\circ}\)C, approximately, to obtain the required mechanical characteristics.  
As a result of this heat treatment, 51 CrV4 steel (as received) presents a microstructure of tempered martensite, as shown in Figure 2[24].  
Concerning mechanical strength, 51 CrV4 following the ISO 6892-1 [90] standard, with a modulus of elasticity, \(E\), of 200.54 MPa exhibits a high yield strength of \(\sigma_{y}\) = 1271.48 MPa and a tensile strength of \(\sigma_{uts}\) = 1438.5 MPa but has low ductility (percentage of elongation at fracture of \(\varepsilon_{f}\) = 7.53% and percentage reduction of area, \(R_{A_{f}}\), of 34.69%). The monotonic mechanical properties for 51 CrV4 steel are summarised in Table 2.
## 2 Data
### Ground-Based CWV Data  
The aerosol robotic network (AERONET) is a global ground-based aerosol observation network, which has been in operation for nearly 25 years [24]. Although it is specially established for observing aerosols rather than water vapor, it can also provide high-quality CWV data. The uncertainty of AERONET CWV data is about 10% [25; 26]. Due to the continuity and high accuracy of AERONET CWV data, it is widely used for quality assessment of remote sensing CWV data [27]. In this paper, the last version (Version 3.0) of Level 1.5 AERONET data after cloud mask and quality control were used for the validation of remote sensing CWV data [28]. A total of 350 sites were used in this work, and their spatial distribution is shown in Figure 2. The AERONET CWV data mentioned above are all available from the Internet ([[https://aeronet.gsfc.nasa.gov/](https://aeronet.gsfc.nasa.gov/)]([https://aeronet.gsfc.nasa.gov/](https://aeronet.gsfc.nasa.gov/)), last accessed on 23 January 2022).
## 2 Related Work
In [59], the authors present a fuzzy-based data fusion approach for increasing the QoS whilst improving the data collection rate and reducing the energy consumption of the sensor network. The topology control mechanism is utilized widely for reducing energy consumption to prolong the network lifetime by predicting the topology control messages in WSNs [60]. For coping with the data collection with delay-constraint, a load-aware power-increased topology control algorithm is presented to heuristically solve the problem, which reaches the O(1)-approximation ratio for the linear networks [61]. A new topology control mechanism, called hierarchical neighbor graphs, is proposed to build a structure for data collection depending on a single parameter, which requires only local knowledge at each node to be formed and repaired [62]. In [63], the authors propose approaches to maximize the topological network lifetime of the WSNs by considering a two-tiered Wireless Sensor Network deployed around strategic locations and base-stations. These techniques with topology controlling schemes are utilized for saving the energy and prolonging the network lifetime. Very few papers in the literature focus on the maximum data collection rate with data aggregation and topology control simultaneously in R-WSNs. In contrast to earlier works, which either focus on static battery-powered network or maximum data generation rate without considering data fusion and topology control, in this work, we present a maximum data collection rate based on data aggregation and topology control for R-WSNs. In summary, on observing the lack of data aggregation and topology control techniques for improving data flow in existing routing protocols, we introduce the first generic routing protocol algorithm with the data aggregation and topology scheme in R-WSNs. To the best of our knowledge, there is no prior work studying the problem of maximum data generation rate routing protocol with data aggregation and topology control for R-WSNs.
## 4 Experimental Outcomes and Evaluations
### Heatmap  
Heat maps are valuable tools for visually representing patterns, correlations, and trends throughout extensive datasets. The process of creating a heat map involves matching the dataset's values to a color gradient, where each color represents a different value or level of data intensity. The color spectrum normally spans from cold hues, such as blue, for lower values, to warm hues, such as red, for higher values. The heat map is shown in Figure 6. The heat map visually emphasizes regions within the dataset that exhibit greater or lower values, facilitating the identification of discernible patterns and trends. Typically, brighter or darker colors are used to depict high values, whereas brighter or fainter colors are used to represent low values. The use of color encoding enables the easy identification of different values or data intensities. Heat maps are often used for analyzing extensive information to detect and ascertain patterns or relationships. In this study, we used heat maps to examine the projected values of liver disorders.  
\begin{table}
\begin{tabular}{l l} \hline
**Types of Hyperparameters** & **Their Corresponding Values** \\ \hline Optimizer & “ADAM” \\ Rate of Learning & 0.001 \\ Size of each Batch & 128 \\ Total Epochs & 50 \\ CNN Architecture & (Cov2D-ReLU-MaxPool2D) \(\times\) 4 \\ LSTM Units & 128 \\ Dropout Rate (CNN) & 0.25 \\ Dropout Rate (LSTM) & 0.5 \\ Function of Loss applied & Binary Cross-entropy \\ Stopping Patience Early & 5 \\ \hline \end{tabular}
\end{table}
Table 1: Model training and Hyperparameter tuning values.  
Figure 5: Testing and training accuracy graph of epochs and loss.
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 1 Introduction
The topic of PtInPy has been discussed in many fields in the past, especially in the GIS field. Some methods can obtain good results with area preprocessing if these target areas are fixed. However, targets with areas are a lot in epidemic tracking and may continuously change. Therefore, when considering many targets and regions and evolving rapidly, this research needs to include those skills. In light of the above discussion, we propose an efficient GNSS coordinate recognition algorithm for epidemic management. This research is applicable to the positioning of two-dimensional coordinates, not only GNSS coordinates. However, for epidemic management, the coordinates of GNSS latitude and longitude need to be known in order to map this to the location in the real world. The main novelty of this algorithm is the dynamic blocking and real-time setting of range. This algorithm can be applied in order to recognize targets within a targeted area, even if the targeted area's range is very large and there are numerous targets. Furthermore, for most targets, it is not necessary to use a PtInPy function to confirm whether these targets are inside or outside of a targeted area in order to save computation time. In addition, this method also considers the capacities of database access and computation to achieve efficient recognition. The planning of a simulation requires areas with an extensive range as the targeted areas and many geographical points with latitude and longitude coordinates as the targets. For our samples, we used the check-in locations on a well-known social networking site as targets--corresponding to about one million geographical points--and three administrative cities as the targeted areas. These check-in places were locations where people had socialized in the past. These conditions made our simulations more realistic. In addition, these check-in places had their names, textual descriptions, and check-in counts, i.e., a quantitative measure of their popularity, as well as their latitude and longitude values, on the website. Many valuable applications have also been based on these check-in places [22; 23; 24; 25; 26]. The rest of this paper is organized as follows. In Section 2, we introduce the related work. Section 3 introduces the system architecture and describes the PtInPy problem. Section 4 describes the recognition algorithm where we use a city as the range to show the steps of the algorithm. Section 5 provides the simulated environment and performs a demonstration. This demonstration offers targets within three selected fields of hundreds to thousands of square kilometers calculated from more than one million targets. In Section 6, we offer the simulation results. Finally, in Section 7, we give a conclusion.