esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 V2X Testbed Setup and Algorithm Implementation
### Algorithm Implementation
#### 6.2.2 C-ITS Long-Range Message Exchange via Uu Using Geocasting  
Contrary to the direct short-range message exchange between V2X nodes that focuses on broadcasting messages via C-V2X PC5 and DSRC/ITS-G5, long-range message exchange requires additional mechanisms and configurations due to its unicast nature. In [24], a broker-centric architecture is introduced for C-V2X communication, characterized by its openness and scalability. The messaging transport protocol employed is MQTT, which follows a Client-Server model utilizing a publish/subscribe pattern. This approach enables V2X nodes to exclusively receive messages relevant to their interests. The MQTT protocol operates over Transmission Control Protocol/Internet Protocol (TCP/IP) or other reliable network protocols, ensuring ordered, lossless, and bidirectional connections. In summary, MQTT's adaptable features render it a dependable and efficient choice for various communication requirements, particularly in resource-constrained environments where efficiency is crucial. The content of MQTT messages adheres to SAE standards [25], which delineate various message types, encompassing CAM, DENM, and IVIM, contingent upon the specific service provided. Similarly, an Advanced Message Queuing Protocol (AMQP)-based alternative broker-centric architecture called the Interchange has been developed in the NordicWay project [26], and later on adopted in the C-Roads specifications on hybrid C-ITS [27], and in the 5G Automotive Association (5 GAA) V2X Application Layer Reference Architecture [28]. In this work, an open source MQTT broker, named 'Mosquito' is set up in the portable O-RAN-based 5G standalone (SA) setup (Figure 7) that is used for long-range V2X communication. The architecture of the geocasting solution is intricately tied to the topic structure within the central broker. Intrinsic geocasting occurs when data is disseminated through short-range wireless communication, usually within a reception range spanning 300 to 1000 m, contingent upon local conditions. Conversely, when employing internet-based communication through cellular networks, a dedicated geocasting solution becomes imperative to uphold scalability. Multiple geocasting solutions exist, with one such approach being the tiling concept presented in [29]. The objective of geocasting is to guarantee that only messages pertinent to the recipient's location are transmitted.
## 4 Numerical Examples
### Example 4--An Optimal Bridge
It is important to point out that \(\beta_{i}\) produces geometry effects on the structure because dimensions and volume are random variables \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distribution Parameter** & **Distribution Type** & **Mean (\(\mu\))** &
\begin{tabular}{c} **Standard Deviation** \\ **(\(\sigma\))** \\ \end{tabular} \\ \hline \hline \(\mathrm{nelx}\) (mm) & Normal & 140 & 0.1 \\ \hline \(\mathrm{nely}\) (mm) & Normal & 20 & 0.1 \\ \hline \(\mathrm{nelz}\) (mm) & Normal & 10 & 0.1 \\ \hline \(\mathrm{E}\)(GPa) & Normal & 210 & 0.1 \\ \hline \(\
u\) & Constant & 0.30 & 0 \\ \hline \(\mathrm{P}\) (\(100\,\mathrm{N}/\mathrm{m}^{2}\)) & Normal & 100 & 0.1 \\ \hline Volume (mm\({}^{3}\)) & Normal & 0.25 & 0.1 \\ \hline Compliance (N\(\cdot\)mm) & Normal & 21.2080 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters related to the design domain. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reliability Index** & **Number of Iterations** & **Number of Iterations** & **Number of Iterations** \\ **(\(\beta_{i}\))** & **(Reliability)** & **(Optimization Procedure)** & **RBTO Method** \\ \hline \(1.0\) & 2 & 52 & 104 \\ \hline \(2.0\) & 5 & 53 & 265 \\ \hline \(3.0\) & 8 & 53 & 424 \\ \hline \(4.0\) & 10 & 52 & 520 \\ \hline \(5.0\) & 13 & 53 & 689 \\ \hline \(6.0\) & 16 & 53 & 848 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Efficiency of the proposed approach for different reliability indices. are considered as uncertainties [52]. The second analysis proposes to incorporate the uncertainties regarding material property and structure compliance to make the analysis more realistic. In this analysis, compared to the DTO design, a decrease in the structural mass, volume, strength and compliance is noted (Figure 17). In this sense, the proposed RBTO algorithm defines a more rigid design than that proposed by the DTO analysis.
# A Novel Multi-Task Performance Prediction Model for Spark
## 4 Experimentation
### Ablation Experiments  
In order to test the usefulness of each part of the proposed model, ablation experiments of the MHAC model are presented in this section. We analyze the MHAC model by removing the customized multi-head attention network, the CNN, the residual connection, and the PCA algorithms, respectively. We obtain four new models: MHAC-Att, MHAC-CNN, MHAC-Residual, and MHAC-PCA. Finally, we compare with the original model MHAC in four benchmark applications. The experimental results are shown in Figure 5.  
The experimental comparison results are shown in Figure 5. In the application of WordCount, the MAPE of the MHAC model is 0.148, and that of MHAC-CNN is 0.147, which is 0.001 different from that of MHAC. In addition, our model outperforms MHAC-Residual, MHAC-CNN, MHAC-PCA, and MHAC-Att in the MAPE of four benchmark  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Task Name** & **Model Name** & **MAPE** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline \multirow{6}{*}{PageRank–WordCount} & KNN & 2.772 & 7.952 & 5.860 & \(-\)0.722 \\  & LR & 0.377 & 3.444 & 2.575 & \(-\)0.417 \\  & SVC & 0.992 & 8.759 & 6.325 & \(-\)1.089 \\  & RF & 2.793 & 8.298 & 6.162 & \(-\)0.875 \\  & GBDT & 2.052 & 5.930 & 4.014 & 0.043 \\  & XGB & 1.689 & 5.785 & 4.058 & 0.089 \\  & MHAC & 0.333 & 2.494 & 1.958 & 0.257 \\ \hline \multirow{6}{*}{PageRank–WordCount-LR} & KNN & 2.772 & 7.952 & 5.860 & \(-\)0.722 \\  & LR & 0.417 & 6.903 & 4.064 & \(-\)0.306 \\  & SVC & 0.993 & 8.761 & 6.326 & \(-\)1.089 \\  & RF & 2.796 & 8.362 & 6.162 & \(-\)0.904 \\  & GBDT & 2.052 & 5.930 & 4.014 & 0.043 \\  & XGB & 1.689 & 5.785 & 4.058 & 0.089 \\  & MHAC & 0.477 & 5.213 & 3.515 & 0.255 \\ \hline \multirow{6}{*}{Pi–PageRank–WordCount–LR} & KNN & 2.348 & 6.675 & 4.814 & \(-\)0.213 \\  & LR & 1.917 & 6.516 & 4.363 & 0.115 \\  & SVC & 14.596 & 31.133 & 29.386 & \(-\)25.389 \\  & RF & 2.056 & 5.765 & 4.082 & 0.095 \\  & GBDT & 2.023 & 5.673 & 4.033 & 0.124 \\  & XGB & 1.989 & 5.785 & 4.058 & 0.089 \\  & MHAC & 1.863 & 5.625 & 4.021 & 0.138 \\ \hline \end{tabular}
\end{table}
Table 4: This is a table of experimental results for the PageRank–WordCount, PageRank–WordCount-LR, and Pi–PageRank–WordCount–LR datasets.  
Figure 5: This is the MAPE result for the benchmark dataset of ablation experiments.  
applications. For example, in LR, the MHAC-Residual is 0.109, the MHAC-CNN is 0.114, the MHAC-Att is 0.126, the MHAC-PCA is 0.112, and the MAPE of MHAC is 0.098. The metric of MHAC is lower than that of other models. In addition, the MAPE metric of MHAC is 10.53% lower than MHAC-Att in PageRank.
# Study and Simulation of an Under-Actuated Smart Surface for Material Flow Handling
## 3 Analytical Model
### Friction Forces Model
Now all the elements to describe the two forces in the plane of the surface (\(F_{par}\), \(F_{perp}\)) have been introduced and the result, according to Figure 4a, is summarized in Equation (2a,b). \[F_{par_{i}}=\mu_{max}N*\tanh(k|V_{p}|)*sgn(\cos(\alpha)) \tag{2a}\] \[F_{perp_{i}}=\mu_{min}N*\tanh(k|V_{p}|)*sgn(\sin(\alpha)) \tag{2b}\]  
where the two friction coefficients are, respectively:  
* \(\mu_{max}\) is the friction coefficient between the object and the rotor;
* \(\mu_{min}\) is the friction coefficient that represents the rolling resistance of the rotor. The \(sgn\) function models the direction of the forces, according with the orientation of the object velocity in the contact point, whereas \(tanh\) and the coefficient \(k\) (selection of this parameter explained in Section 6) smooth the discontinuities ([41]). The perpendicular force would be ideally 0, however, it is generally small as inertia forces are neglected and only the rotational friction of the rotor around its axis is considered. The parallel friction force can reach significantly higher values and is opposed (in the axis direction) to the object velocity. Therefore, it is the dominant driver to manipulate the object.
# Energy Efficiency Perspectives of PMR Networks +
## 5 Technical and Economic Considerations on Energy Efficiency of Next Generation PMR Systems
### Business Models for Mobile Broadband PMR
#### 5.1.2 Obtain Service from a Government Mobile Virtual Network Operator  
The MVNO approach has become prevalent in the commercial sector, where branded operators resell bulk-purchased wireless services to consumers while providing their own usage plans, billing and customer support. The MVNO approach can be extended to the support of public safety users. In that case, the MVNO, called a G-MVNO (Government MVNO) provides added-value services (such as user and device management, customer care, end-to-end security, billing and so on) to the public safety users that, in turn, get access to secure broadband data services when the G-MVNO leverages the 4G access network from the MNO. The G-MVNO model offers more control over services and security than the MNO approach, providing a ready-made network for basic public safety needs. It keeps CAPEX moderate (mainly terminals and a few LTE core network nodes). A G-MVNO can manage services and management over a mix of 4G, 3G and PMR platforms for the best possible availability in routine situations and major crisis. It can be configured to combine security, availability, ease of use and economics tailored for public safety, while keeping the effective management of end-to-end data service a first priority. About the cons of this approach, as with the MNO model, a G-MVNO provides no absolute control over coverage (especially in rural areas), availability and assured resilience. In addition, depending on the MNO, it may offer limited support of some critical public safety features, such as direct mode or group calling.
## 4 Conclusions  
In this paper, the performance of a transcritical CO\({}_{2}\) air conditioning system for an electric bus has been investigated from an energy and exergy point of view. The effects of the normalized refrigerant charge, ambient temperature and EEV opening degree on the cycle performance have been analyzed in detail. The crucial conclusions are summarized below:  
1. In order to ensure the maximum COP, the optimal range of the NRC is between 0.248 and 0.336 for transcritical CO\({}_{2}\) electric bus air conditioning units.
2. The effect of an insufficient charge on cooling capacity degradation is more penalizing than that of a slight refrigerant overcharge. This result is more evident with rise in ambient temperature.
3. The maximum value of the exergy efficiency is equal to 30.74% in the sufficient charging condition. However, the maximum value of the exergy efficiency and the optimal value of COP do not coincide.
4. Similarly to the optimal COP conditions, the exergy efficiency is not sensitive to slight insufficient charge and refrigerant overcharge is even beneficial to the exergy efficiency. In addition, insufficient refrigerant charge affected the irreversibilities in the indoor and outdoor HXs significantly, whereas slight refrigerant overcharge has little effect on the thermodynamic performance of each component. Finally, the work has revealed the need to enhance the efficiency of the compressor.  
Conceptualization, H.W. and Y.S.; methodology, H.W. and S.L.; software, H.W.; validation, Y.S. and S.L.; formal analysis, Y.S. and X.Y.; investigation, H.W. and S.L.; resources, Y.S. and F.C.; data curation, H.W.; writing--original draft preparation, H.W. and P.G.; writing--review and editing, All authors; visualization, H.W.; supervision, Y.S., X.Y. and F.C.; project administration, Y.S. and F.C.; funding acquisition, F.C. and Y.S. All authors have read and agreed to the published version of the manuscript.  
H.W., S.L., Y.S., X.Y. and F.C. gratefully acknowledge the financial supports from the National Natural Science Foundation of China (No. 51976153 and No. 52006161).  
Figure 8: Variation of exergy destruction percentage of each component corresponding to the optimal COP with NRC at ambient temperature of 35 \({}^{\circ}\)C.
## 3 Experiment Dataset and Evaluation
### Evaluation Metrics  
In this paper, we experimentally test the validity and accuracy of each model and evaluate the model performance based on several metrics: the 'overall accuracy' (OA), 'precision','recall', 'F1-score', and intersection over union ('IoU'). The five metrics are presented as follows:  
1. OA refers to the proportion of correctly predicted building and background pixels to all pixels in the image: \[\text{OA}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}\] (4) where TP represents the number of buildings extracted as buildings and the actual number of buildings; FP represents the number of buildings extracted as buildings and the actual number of backgrounds; TN represents the number of buildings extracted as backgrounds and the actual number of buildings.
2. Recall refers to the proportion of correctly predicted building pixels in the image to the true value pixels in the building area: \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (5)
3. Precision refers to proportion of correctly predicted building pixels to all predicted building pixels in the image: \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (6)
4. F1-score represents the weighted average of OA and Precision: \[\text{F1}=\frac{2}{\left(\frac{1}{\text{Precision}}\right)+\left(\frac{1}{ \text{Recall}}\right)}\] (7)
5. IoU, which can describe segment-level accuracy: \[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}}\] (8)
# Intelligent TCP Congestion Control Policy Optimization
## 4 Experiment
### Bandwidth Sensitivity Comparison  
The amount of data that may be transmitted per unit of time is referred to as network bandwidth (generally 1 s). The greater the bandwidth, the greater its traffic capacity. Figure 4 shows the bandwidth sensitivity comparison, and the link bandwidth in our Mininet is set to between 1 Mbps and 1000 Mbps, with 0 ms latency, 1000 packet queues, and 0% random loss. TCP-PPO\({}_{2}\) works well in this bandwidth range; when the link bandwidth is less than 18 Mbps, both traditional and deep reinforcement learning-based congestion control protocols produce large delays, mainly due to the narrow bandwidth and too long queue length causing queuing problems, but when the bandwidth is higher than 50 Mbps, TCP-PPO\({}_{2}\) can achieve extremely low latency.
# Beat the Heat: Syscall Attack Detection via Thermal Side Channel
## 2 Materials and Methods  
### Background  
#### Linux Commands and System Calls  
Linux terminal commands are text-based instructions used to interact with the operating system through the command line interface (CLI). These commands allow users to perform a wide range of tasks such as managing files and directories, installing software, configuring the system, and monitoring performance. For our experiments, we have selected _ls_ and _chmod_ commands based on the research performed in [2], and the augmentation was achieved with the _icctl_ syscall.  
A syscall (short for system call) is an interface between the operating system and a process, containing a mechanism that allows the program to request a service from the kernel of the operating system [24].  
Computer systems have two operation modes, due to security reasons: kernel mode and user mode [25]. The kernel space is a highly privileged area of memory where the core components of the operating system (OS) operate. Access to this space is restricted and requires specific privileges because it contains critical system code and data structures that manage the overall functionality and resources of the computer. In contrast, user space is where user applications run. These applications are generally unprivileged, meaning that they do not have direct access to the hardware or critical system functions. Instead, they operate within a controlled environment that limits their capabilities to prevent them from inadvertently or maliciously affecting the system's stability or security [26].  
The syscalls are important because they bridge the gap between the low-level functions handled by the kernel and the higher-level operations performed by user applications [27]. They can be accessed via assembly functions, direct call, or C libraries [28]. The first two are less secure because there is a lot of context switching going on, e.g., while printing words there will be a context switch for printing every letter, whereas calling syscalls from libraries use a buffer and fewer context switching [19]. However, the multiple context switching can lead to security breaches.
# Hydrakon, a Framework for Measuring Indicators of Deception in Emulated Monitoring Systems
## 6 Proposed Method
### Discussion  
Some of the key observations from the experiment are:  
1. Windows environments show the highest consistency across all comparisons, except for the Flarevm sandbox.
2. Services remain relatively consistent across different machine types.
3. Processes, devices, Windows files and applications show the most variation between real and virtualized environments.
4. Virtual machines and sandboxes consistently show lower similarity to real machines in devices, Windows files and applications.
5. The Flarevm sandbox shows the most divergence from a real machine, particularly in applications and Windows files categories.  
These results suggest that while virtualization technologies can closely mimic real machines in terms of Windows environments and services, they show noticeable differences in processes, devices, and especially applications. This information is important for malware analysis, where understanding the differences between real and virtualized environments is critical in the area of evading analysis by malware and threat actors. The cosine similarity analysis of these environments reveals some interesting points in relation to cybersecurity. The high similarity in Windows environments and services across most comparisons indicates that virtualization technologies have achieved a high degree of fidelity in these areas. The consistently lower similarity scores in the devices category highlight the challenges in perfectly emulating hardware in virtual environments. This discrepancy could be crucial for malware analysis as some malware may detect virtualization through device discrepancies, potentially altering their behavior in virtual environments. The variations in processes and applications similarities suggest that the software ecosystem in virtual environments differs noticeably from physical machines. The markedly low similarity scores for the Flarevm sandbox, especially in applications and Windows files categories, indicate that it may be more easily detectable as a non-standard environment. This is particularly relevant for the analysis of advanced malware with sandbox detection capabilities, which might alter its behavior in this environment.  
This analysis reveals interesting patterns in how different virtualized environments compare to real machines and to each other. It highlights areas where virtual machines and sandboxes closely mimic real environments (like Windows files and services) and areas where they differ significantly (like devices and applications). Security analysts in these fields should be aware of these similarities and differences, choosing the appropriate environment based on their specific needs and compensating for known discrepancies when necessary.
# Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
## 2 Materials and Methods
### Equipment  
The TLS equipment was a FARO Focus3D X 330 (FARO Technologies, Inc., Lake Mary, Florida) (Figure 4a), which utilizes phase shift technology to read the distance to an object. This reader was mounted on a Manfrotto Super Pro Mk2B tripod and Manfrotto 3D Super Pro head (Manfrotto, Cassola, Italy) (Figure 4a) for each single scan station. This equipment's field of view is almost complete, with a 360\({}^{\circ}\) view on a horizontal plane and 300\({}^{\circ}\) on a vertical plane because of its gyratory base and rotation mirror. The scan resolution was configured to 6 mm to 10 m, with a beam divergence of 0.19 mrad (1 cm to 25 m) and a ranging error of \(\pm\)2 mm (10 to 25 m). It also contains an RGB camera, GPS receiver, electronic compass, clinometer, and altimeter (electronic barometer) to approximately correlate the individual scans in postprocessing. For the accurate joining of different point cloud-calibrated white spheres, an ATS SRS Medium (ATS Advanced Technical Solutions AB, Molndal, Sweden) was used (Figure 4a). The information captured with this equipment was processed by the software SCENE 6.2 (FARO Technologies Inc., Lake Mary, United States), resulting in a unique georeferenced and colored 3D point cloud.  
MMS is a Topcon IP-S2 Compact+ (Topcon Corporation, Tokyo, Japan) (Figure 4b). This is a system that integrates five laser scanners, a 360\({}^{\circ}\) spherical digital camera with six optics, an IMU (Inertial Measurement Unit), a dual frequency GNSS receiver, and a wheel encoder. The laser scanners are all SICK LMS511-10100S01 (SICK AG, Waldkirch, Germany), and the spherical camera is a FLIR LadyBug 5+ (FLIR Integrated Imaging Solutions Inc., Richmond, Canada). The system was mounted in a regular \(4\times 4\) car, with additional batteries and a control system (a high performance rack system, with an i7 processor, 32 GB RAM, and redundant SSD with industrial USB 3.0). The capture software was Topcon Spatial Collect 4.2.0 (Topcon Corporation, Tokyo, Japan). The postprocessing software for the data collected by this equipment was the Topcon Geoclean Workstation 4.1.4.1 (Topcon Corporation, Tokyo, Japan). The resulting product was a georeferenced and colored 3D point cloud. Considering  
Figure 3: Situation map (EPSG:3857).  
the mobile condition of the equipment and the integration of the sensors, the five possible returns for each laser scanner were filtered to the highest intensity to ensure false observations (noise points).  
For the acquisition of geolocation data, we used a GPS-RTK (global positioning system--real time kinematic) with Topcon HiPer V (Topcon Corporation, Tokyo, Japan) receptors and postprocessing software MAGNET Tools 5.1.0 (Topcon Corporation, Tokyo, Japan) in the reference point measurement, using the MMS trajectory solution with centimetric precision.  
The main characteristics of both laser scanners are reviewed and compared in Table 1.