esa-sceva/satcom-chunk-collection huggingface dataset:
# Three Rapid Methods for Averaging GPS Segments
## 3 Three Rapid Methods
### Curvy Model C: Divide-and-Conquer  
As the third alternative, we consider a simple divide-and-conquer approach as follows. If the descriptors indicate that the curvy model is applied, we divide the trajectories into two sub-segments at the median points (M) of each trajectory. Both sub-segments are then recursively processed by the same overall algorithm until the sub-segment reduces to the linear model, see Algorithm 5 and Figure 9.  
```
1:Input: GPS trajectories: X; threshold value: T
2:Output: representative: Y
3:\(M\leftarrow\) calculate the mean of the set of median points from each trajectory in X;
4:IF \(\cos(\text{$\varepsilon$}\text{SDM})\leq\text{T}\parallel\cos(\text{$\varepsilon$} \text{DSM})\leq\text{T}\);
5:\(\text{X}^{1}\), \(\text{X}^{2}\leftarrow\) cut each trajectory in X into two parts by the point at the median location;
6:ADD **CurveSegment** (\(\text{X}^{1}\), S, M, T) TO Y;
7:ADD **CurveSegment** (\(\text{X}^{2}\), M, D, T) TO Y;
8:RETURN Y
9:ELSE:
10:RETURN M;
```  
**Algorithm 5** (RapSeg-C): **CurveSegment** (X, S, D, T):  
An example of this curvy model is shown in Figure 9. Figure 10 shows selected visual examples of all three variants. We can see that the variant C uses much fewer points than the other two variants. The number of points is also adapted to the situation; more points are allocated for the curvy parts of the segment, whereas the linear parts use only a few points to represent them.
# A 920-MHz Dual-Mode Receiver with Energy Harvesting for UHF RFID Tag and IoT +
## 2 Proposed UHF RFID System
### Architecture of RFID Tag  
The architecture of the proposed self-powered RFID tag is shown in Figure 2. The dual-mode ED-Rx, RF energy harvester, backscatter circuit and transmitter are connected to the RF port and the input impedance is conjugate matched with a dipole antenna. The fully on-chip DC-DC boost converter is connected to the off-chip PV and TE cell and harvests energy from the environment or the human body.  
Depending on the power-supply mode, the ED-Rx can operate in high-sensitivity and low-power-receiving modes, and the maximum sensitivity is over \(-40\) dBm. It operates in high-sensitivity receiving and active-transmitting mode in default when powered by PV and TE harvester. Considering the energy is not sufficient during the night and for indoor environment, the RF energy-harvesting topology is also adopted [14]. It powers the chip when the input RF signal is larger than \(-20\) dBm and the tag could switch to backscatter mode and low-power-receiving mode by detecting the strength of received RF carrier. The low-power-receiving mode is fully compatible with conventional passive RFID tags.  
Due to the small amount of transmitting data, the operating time of active transmitter is within 1 ms. The microwatt energy harvester charges the off-chip storage capacitor and enables the milliwatt transmitter in duty-cycle [16; 17; 18]. The power management circuits such as current source and voltage references are designed in the subthreshold region and the power consumption is several nanowatts [18; 19; 20].
## 2 Methodology
### Numerical Experiments
#### 2.1.2 Finite Element Models  
Finite element (FE) modeling was carried out with a commercial software ABAQUS to obtain the thermal and thermomechanical responses of the steel ladle considering elastic material behavior. The simplified two-dimensional numerical model representing a horizontal cut through the slag-line position in the upper part of the steel ladle is depicted in Figure 1. The model was composed of five layers, namely, a two-half brick working and permanent lining, an insulation lining, a fiberboard, and a steel shell. There was 0.4 mm circumferential expansion allowance between bricks. The modeling considered the first process cycle of the steel ladle, which included preheating the hot face of the working lining to 1100 \({}^{\circ}\)C over 20 h, tapping the steel melt of 1600 \({}^{\circ}\)C into the ladle, transport and refining for 95 min, and a 50 min idle period. The radial displacement of linings was free and the circumferential one was constrained by a symmetry condition. The temperature-dependent surface film condition function in ABAQUS was applied to define the heat transfer between both the melt and the hot face of the working lining and the steel shell and the ambient atmosphere. A heat flux crossed the interfaces between linings, and radiation and convection were allowed by applying a heat transfer coefficient. From 160 numerical experiments, the end temperature and the maximum tensile stress at the cold end of the steel shell and the maximum compressive stress at the hot face of the working lining were obtained from FE modeling.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Variables** & **Range of Variable Values** & **Label of Variables** \\ \hline \multirow{4}{*}{Thickness (m)} & Working lining & 0.03–0.27 & A \\  & Permanent lining & 0.05–0.14 & B \\  & Insulation & 0.003–0.042 & C \\  & Steel shell & 0.015–0.035 & J \\ \hline \multirow{2}{*}{Thermal conductivity (Wm\({}^{-1}\)K\({}^{-1}\))} & Working lining & 1.5–10.5 & D \\  & Permanent lining & 1.0–10.0 & E \\  & Insulation & 0.05–1.55 & F \\ \hline \multirow{2}{*}{Young’s modulus (GPa)} & Working lining & 25–115 & G \\  & Permanent lining & 5–110 & H \\ \cline{1-1}  & Insulation & 0.1–39.1 & I \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometrical and material property variables of steel ladle [26].  
Figure 1: Two-dimensional model representing steel ladle slag zone linings [26].
## 5 Conclusions  
This study took the Xilingol grassland as a case study and used ground survey data and MODIS productivity data for the growing seasons of the years 2005-2012 to build a unitary linear regression model to retrieve the aboveground biomass of the Xilingol grassland, and then analyze the spatial and temporal distribution of aboveground biomass. The grassland aboveground biomass averaged 14.35 Tg in the Xilingol grassland during the years 2005-2012, and showed a gradually decreasing trend from east to the west. There were large interannual variations in the aboveground biomass, ranging from 10.56-17.54 Tg, and the aboveground biomass showed differences among different grassland types.  
The study made up for the inadequacy of vegetation index to estimate the grassland biomass, and provided an improvement for grassland biomass estimation. Although the study showed promising results for remote sensing based grassland biomass estimation, there are limitations to the accuracy of biomass inversion using MODIS productivity data, therefore, further work is needed to improve the estimation accuracy. In addition, spatio-temporal patterns of aboveground biomass and their relationships with climate factors still need further research in the Xilingol grassland. [PERSON], _et al._[23] have already studied the relationship between GSP (growing season total precipitation) with grassland biomass and the relationship between GST (growing season average temperature) with grassland biomass. However, the climate factors are much more than these. For example, how the growing season maximum and minimum temperatures affect grassland biomass and whether the temperature in winter affects grassland biomass need in-depth research. Therefore, the grassland response to climate change is complex, and deserves more detailed and deeper inquiry in future research.
# An SSI-Based Solution to Support Lawful Interception
## 2 Background
### Self-Sovereign Identity
#### 2.2.6 Verifiable Data Registry  
A Verifiable Data Registry is used in SSI to obtain the property of decentralization to create an identity system that supports privacy without a central authority. The verifiable data registry is typically a blockchain used to manage the DIDs and the public keys. The blockchain must be public [25] to enable access to everyone and can be permissioned or permissionless. In our proposal, the blockchain used is Sovrin, which is public and permissioned. It is important to store the DID of the issuers on the blockchain to facilitate the verification process carried out by the verifiers.
## 4 Results and Discussion
### VR Shopping Model Improvement  
#### 4.2.1 Improvement Program  
To enhance consumers' sensory experience with the improved system, it is crucial to analyze the factors affecting their experience and contributing to low ratings before making improvements. The evaluation results indicate that interaction design, haptics, and the accuracy of packaging function realization require improvement. In the VR shopping system, interaction design encompasses three main aspects: basic walking, packaging interaction, and checkout. Haptic feedback primarily involves the opening and deformation of packaging, while the accuracy of packaging function realization pertains to the animation design of the packaging model. Analysis of the three areas for improvement reveals that their common realization path involves the interaction between consumers and product packaging. In the virtual environment, packaging exists in only two states: open and closed. When the packaging is opened by pressing a button to trigger the function, the animation plays directly without simulating the force application process observed in reality. This discrepancy from real-world interactions results in a suboptimal sensory experience for consumers.  
In the virtual environment of the VR shopping system, the states of package opening and closing are controlled by animation. To allow consumers to experience the process of applying force when opening the package, analysis indicates that the virtual hand gradually increases force as the participant pulls the package open over time. The distance the hands pull outward approximately correlates with the magnitude of the applied force. As the numerical relationship between the hand-pulling distance and the applied force is not predefined, self-defined hand distances for opening the package (MaxDis) were established for potato chips and cola, as shown in Figure 13, to simulate the force application process in the virtual space. After iterative adjustments to MaxDis, the optimal hand movement distances were determined as follows: 20 cm for opening the cola bottle cap and 30 cm for opening the potato chip package.  
\begin{table}
\begin{tabular}{c c c} \hline
**Criteria Layer** & **Sub-Criteria Layer** & **Score** \\ \hline \multirow{3}{*}{Scene experience (B4)} & Adaptation of scene and packaging (C12) & 9 \\  & Scene immersion (C13) & 8 \\  & Scene entertainment (C14) & 7 \\ \hline \multirow{3}{*}{Packaging function (B5)} & Accuracy of packaging function realization (C15) & 5 \\  & Matching degree of packaging function (C16) & 7 \\  & Packaging model and workmanship (C17) & 7 \\ \hline \multicolumn{3}{c}{Total score} & 6.68 \\ \hline \end{tabular}
\end{table}
Table 12: _Cont._
## 4 Conclusions  
6G networks will enhance worldwide communication solutions by introducing novel application cases, offering innovative technology knowledge, and fostering growth in the economy. We have rigorously evaluated the most recent approaches presented in a substantial corpus of pertinent literature, emphasizing the related advancements and obstacles. We have deliberated on the corresponding concept for 6G. The fundamental difficulties confronting 6G research and the relevant research paths have been examined via the lenses of basic research, sustainable networks, and the essential technologies produced to promote the envisioned 6G framework.  
In summary, 6G studies and regulation continue to encounter several unresolved problems. This study has elucidated the anticipated attractive characteristics of 6G, and it is anticipated that it has offered renewed inspiration and encouragement for the community's 6G development efforts. The discussion and analysis that were presented in this study is expected to benefit the community in the near future especially the developers and researchers.  
The following contributions have been achieved in this study:  
1. Overview of the 6G networks, its features, characteristics, goals and objectives;
2. Recent studies and insights of the supporting technologies including AI, digital twin, hybrid and integrated networks, and cloud;
3. Review and discussion of the challenges of the supporting technologies;
4. Challenges and recommendations for the challenges have been presented and discussed;
5. Future research challenges have been presented and summarized.  
Future directions for the 6G networks are expected to strengthen the technology and provide new solutions, including integrating 6G network with the supporting technologies in various environments and applications. Another promising avenue for future research in 6G networks lies in AI-driven studies, encompassing the development of advanced models and innovative applications tailored to enhance network intelligence, efficiency, and adaptability. AI algorithm performance comparison is also another future direction that will provide further insights.  
Conceptualization, S.S.M. and R.B.; methodology, H.J.A. and S.A.M.; validation, P.C., R.A.A. and M.D.S.; formal analysis, S.S.M. and S.A.M.; investigation, M.D.S.; resources, H.J.A. and R.A.A.; data curation, P.C.; writing--original draft preparation, S.S.M. and S.A.M.; writing--review and editing, H.J.A., P.C. and R.B.; visualization, R.B. and P.C.; supervision, M.D.S.; project administration, S.A.M. and S.S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Conflicts of Interest:** Author [PERSON] is employed by the company BEAMBRIDCE LIMITED, and author [PERSON] is employed by the company ESWIN. The remaining authors declare that their research was conducted independently and without any commercial or financial relationships that could be construed as potential conflicts of interest.
## 2 Materials and Methods
### Instrumentation  
A high-resolution Rayleigh backscattering photonic sensor is used (Figure 5) providing a continuous (in this case 2 m max length) strain map with a gauge length of 5.2 mm for a total number of 468 sensing points, with a resolution \(<\)1 \(\mu\epsilon\) and accuracy of \(\pm\)30 \(\mu\epsilon\)[31].  
The NDI is instead performed by phased array ultrasonic testing (PAUT) tests with an Olympus OmniScan SX flaw detector with a 16:64 PR phased array unit equipped also with a conventional UT channel for pulse-echo, pitch-catch or TOFD (Time of Flight Diffraction) inspection [32]. Phased array ultrasonic instruments utilize high frequency sound waves to check for the internal structure of a test piece, or measure its thickness, and rely on the same basic laws of physics that govern sound wave propagation. The probe is moved along one axis while the beam electronically scans along the other one, according to the focal law sequence (Figure 6).  
Tests are carried out by exploiting an encoded 5 MHz, 64 elements linear array probe with a straight wedge and by employing a specific gel as coupling medium. No calibration blocks are used, the measurement of the specimen thickness being taken as reference [33], and the nondestructive control is performed by sliding the probe along the longitudinal axis of each stringer (Figure 6).  
Figure 4: Impact position indicated by a star: PNL1 panel in the center of the skin (**a**); PNL2 panel under a stringer (**b**).  
Figure 5: Fiber optic sensor interrogation unit.
# Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints
## 3 Game Design
### Learnability of \(C_{k_{j}}(a)\)  
When considering only spatial coupled constraints, the function \(C_{k_{j}}(a)\) in Equation (3) is well-designed if it is learnable [1; 28; 29]. However, when considering games with both spatial and temporal constraints, additional restrictions on function \(C_{k_{j}}(a)\) need to be imposed.  
**Example 1**.: _Consider that agent \(i\) selects a path \(a_{i}=\{k_{1},k_{2}\}\) such that some spatial constraints are violated by \(k_{1}\). Agent \(i\)'s local utility can be easily computed as_  
\[u_{i}(\{k_{1},k_{2}\},a_{-i}^{*})=-C_{k_{1}}(a)+v_{k_{2}}+c \tag{8}\]  
_where \(v_{k_{2}}\) and \(c\) are some positive rewards that agent \(i\) gained from attending \(k_{2}\). Now, assume that there exists a task \(k_{3}\in a_{-i}^{*}\) that \(k_{2}\) is spatially dependent on. Furthermore, \(k_{3}\) is also temporally dependent on \(k_{2}\) such that if agent \(i\) considers \(a_{i}=k_{2}\), the temporal constraints for \(k_{3}\) cannot be satisfied and \(k_{3}\) will be unassigned. In this way, agent \(i\)'s local utility for \(a_{i}=k_{2}\) is_  
\[u_{i}(k_{2},a_{-i}^{*})=-C_{k_{2}}(a). \tag{9}\]  
_The (possibly) only feasible solution is \(a_{i}=\mathbf{0}\) which leads to zero local utility for \(i\). However, for some game settings (e.g., \(C_{k_{1}}(a)\) is the number of spatial constraint violations for task \(k_{1}\) as a result of collective action, \(a\), and reward \(v_{k_{2}}\) is significantly large), it is possible that_  
\[u_{i}(\{k_{1},k_{2}\},a_{-i}^{*})\geq u_{i}(\mathbf{0},a_{-i}^{*})\geq u_{i}(k _{2},a_{-i}^{*}) \tag{10}\]  
_and thus, path \(a_{i}=\{k_{1},k_{2}\}\), which is infeasible, is the preferred action. For the game to converge to a feasible solution, it is imperative that_  
\[u_{i}(\{k_{1},k_{2}\},a_{-i}^{*})<u_{i}(\mathbf{0},a_{-i}^{*}) \tag{11}\]  
\[-C_{k_{1}}(a)+v_{k_{2}}+c<0 \tag{12}\]  
_or rather,_  
\[-\sum_{k_{j}\in\mathcal{K}_{\text{unconstrained}}}C_{k_{j}}(a)+\sum_{k_{i}\in \mathcal{K}_{\text{unconstrained}}}v_{k_{i}}+c<0\quad\forall k_{i},k_{j}\in a _{i}, \tag{13}\]  
_where \(\mathcal{K}_{\text{unconstrained}}\) are tasks assigned to agent \(i\) that do not violate any spatial constraints, and \(\mathcal{K}_{\text{constrained}}\) are tasks assigned to agent \(i\) that violate some spatial constraints._  
In other words, the magnitude of the penalty needs to be sufficiently large to ensure that agents will never prefer a path that is infeasible. A possible design for \(C_{k_{j}}(a)\) which satisfies Equation (13) and is learnable can then be  
\[C_{k_{j}}(a)=G\times N_{k_{j}}(a) \tag{14}\]  
where \(G\) is a gain, and \(N_{k_{j}}(a)\) is the number of agents attending to task \(k_{j}\) based on the collective action \(a\). Any sufficiently large gain should satisfy the constraint.
## 5 FPGA Prototyping
### Demapper FPGA Prototype and Validation  
Using the same methodology adopted for equalizer architecture, the NISC-based demapper architecture has been prototyped and validated. Figure 15 shows the on-chip prototype diagram of the designed demapper. The contents of input memory blocks are generated from the fixed-point reference software module. The output results are captured using ChipScope and are compared to reference software output results.  
Figure 14: ChipScope Pro Analyzer software waveform window.  
Figure 15: NISC-based demapper architecture on-chip prototype.  
Among all _CWs_ generated by the NISC tool set, only those composed of bits controlling the demapper resources are chosen to be loaded into _CMem_. The depth of _CMem_ relies on the number of constellation symbols involved in determining the LLRs associated with one input symbol. This number depends on the adopted system configuration (modulation type, mapping style and constellation sub-partitioning). The contents of the _YMem_, _oMem_, _oMem_ and _AprMem_ memory blocks are generated automatically from the fixed-point software reference model along with a reference result file containing the output of the demapper. _Constellation LUT_ is composed of three memory blocks. The first block is proposed to store the binary mapping of constellation symbols, whereas the other two blocks are proposed to store \(I\) and \(Q\) components of these symbols (\(x^{I}\) and \(x^{Q}\)). The depth of _YMem_, _oMem_, _oMem_, _AprMem_ and _Constellation LUT_ depends on the number of input modulated symbols in each data block. Similar to \(\frac{1}{x}\) LUT presented in previous subsection, the contents of \(\frac{1}{2x}\) LUT are generated by the software model. The content values represent the halves of reciprocals corresponding to all positive numbers.
# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
## 3 Proposed System Model
### Dataset Description
#### 3.2.1 IoT-23 Dataset  
The first dataset utilized in this study is the IoT-23 dataset [27], which consists of a significant 1,048,576 records. This dataset is particularly suitable for investigating intrusion detection. This dataset is specially tailored to identify intrusions in Internet of Things (IoT) settings, where ensuring network security is of utmost importance owing to the rapid increase in linked devices. The dataset contains 24 characteristics and one target feature, representing network traffic examples. This facilitates the creation and assessment of intrusion detection algorithms. Every entry in the dataset has several properties, including duration, original bytes, response bytes, missing bytes, original packets, and other details, which provide comprehensive information on network flow cases. The \"label\" feature, the target variable, is used in classification jobs to accurately differentiate between benign traffic and other sorts of attacks. The dataset sample is shown in Table 2.
## 2 Background on Technologies for RSBD Processing and Related Works
### Hadoop for Distributed Big Data Integration: Hortonwork  
Hadoop has become the pioneer platform for BD storage and processing [6]. Hadoop is a group of IT tools for distributed storage and processing of BD. Hadoop is fault-tolerant, scalable, and very simple to expand. Hadoop can handle massive amounts of data sets that are incapable of being distributed or formerly needing expensive super-computers. Hadoop can currently schedule and administer thousands of computers, storage, and cumbersome processes at a petabyte (PB) level [7]. One of Hadoop MR's critical advantages is that it lets non-expert users run analytical operations over BD. Hadoop MR gives users complete control over how input datasets are processed. Users code their queries using Java rather than SQL. This makes Hadoop MR informal to use for a more substantial number of developers: no skills in databases are obligatory, and only basic knowledge in Java is essential [8].  
Many milestone works have been conducted to empower RSBD processing in the Hadoop platform. However, Hadoop principally plans to process large-scale web data [8]; it does not, by default, support the RS data formats, such as HDF, NetCDF, and BUFR. Two methods threaten RSBD in the Hadoop platform. One conceivable way is to convert the RS data to Hadoop-friendly data formats, such as CSV databases. The second approach is to develop complementary plugins, allowing Hadoop to support the scientific RS data formats.  
Designing a BD architecture is an excellent method to split the problems of BD processing. We must create and make all BD's essential components well, where each layer has a specific function [9]. More than a few distributions manipulate and manage BD: HortonWorks, Cloudera, MR, IBM Infosphere BigInsights, Pivotal, Microsoft HDInsight, etc. Table 1 details a technical comparison of the five Hadoop distributions based on 19 criteria [10].  
We notice that most providers offer distributions based on Apache Hadoop and project open sources related to the comparative table. They also deliver a software solution installable on the organization, infrastructure in a private or public cloud. The frameworks that build Hadoop are open source. A subscription is paid to the benefit of technical support. Additionally, functions that are not available in the community version and the training can be used. Presently, there is no absolute winner on the market because each supplier focuses on the main features, such as security, integration, performance, and governance.