esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 3 The LoRa Physical Layer
### Overview of the Physical Layer  
LoRa is a chirp spread spectrum modulation [18], which uses frequency chirps with a linear variation of frequency over time in order to encode information. Because of the linearity of the chirp pulses, frequency offsets between the receiver and the transmitter are equivalent to timing offsets, easily eliminated in the decoder. This also makes this modulation immune to the Doppler effect, equivalent to a frequency offset. The frequency offset between the transmitter and the receiver can reach 20% of the bandwidth without impacting decoding performance [19]. This helps with reducing the price of LoRa transmitters, as the crystals embedded in the transmitters do not need to be manufactured to extreme accuracy. LoRa receivers are able to lock on to the frequency chirps received, offering a sensitivity of the order of \(-130\) dBm [19; 20].  
As the LoRa symbol duration is longer than the typical bursts of AMinterference generated by Frequency Hopping Spread Spectrum (FHSS) systems, errors generated by such interference are easily corrected through Forward Error-correction Codes (FECs). The typical out-of-channel selectivity (the maximum ratio of power between an interferer in a neighboring band and the LoRa signal) and co-channel rejection (the maximal ratio of power between an interferer in the same channel and the LoRa signal) of LoRa receivers is respectively 90 dB and 20 dB [19; 20]. This outperforms traditional modulation schemes, such as Frequency-Shift Keying (FSK), and makes LoRa well suited to low-power and long-range transmissions.
## 3 Results and Discussion
### Fatigue Life Analysis of Pavement Under Coupling  
In order to ensure that the asphalt pavement structure has good serviceability and durability, the asphalt pavement design methods in many countries take the fatigue charac  
\begin{table}
\begin{tabular}{c} **Percentage** \\ **Stress** \\ \end{tabular} \\ \hline
0 & 1.028 & 1.120 & 0.92 & 0.422 & 0.654 & 0.65 & 0.532 & 0.743 & 0.72 \\
0.04 & 0.816 & 1.186 & 0.69 & 0.212 & 0.655 & 0.32 & 0.250 & 0.690 & 0.36 \\
0.09 & 0.500 & 0.880 & 0.57 & 0.073 & 0.468 & 0.16 & 0.081 & 0.468 & 0.17 \\
0.15 & 0.178 & 0.455 & 0.39 & 0.060 & 0.312 & 0.19 & 0.066 & 0 teristics of the pavement as the basic design principle [30; 31]. Therefore, it is important to study the fatigue performance of asphalt pavement structures under natural environmental conditions and traffic loads. In this study, the fatigue life equation from the American Ground Asphalt Association's pavement design methodology is used [32; 33], which is modeled as:  
\[N_{f}=0.2659\times 10^{4.84\times(VFA-0.69)}\times\left(4.325\times 10^{-3} \times\varepsilon^{-3.291}\times\left|E^{*}\right|^{-0.854}\right)\]  
where \(N_{f}\) represents the fatigue life of asphalt mixtures; \(\varepsilon\) represents bending and tensile strains; \(E^{*}\) represents the dynamic modulus; VFA is asphalt saturation; and 0.65 is taken here.  
Combined with the above finite-element model, the representative wheel load pressures of different axle types are 0.8221 MPa, 0.6216 MPa, and 0.7520 MPa, respectively. The longitudinal tensile strains at the bottom of the asphalt layer for different seasons were calculated and shown in Figure 15.  
As can be seen from Figure 15, the longitudinal tensile strain at the bottom of the asphalt layer under different temperature-axial load couplings is shown to be the largest in the summer and smaller in the other three seasons. The winter strain was slightly higher  
Figure 15: Longitudinal tensile strain at the bottom of asphalt layer with different axial patterns.  
than the spring and fall seasons. The fatigue life of asphalt pavements was calculated using representative strains under different temperature-axial load couplings, respectively. The results are shown in Figure 16.  
From the fatigue life of Figure 16, it can be seen that from the coupling effect of different axle types, single and triple axles have a greater impact on the fatigue effect of the pavement, and double axles have a smaller impact. The reason for this is mainly related to the axle loads in the axle load statistics section. In terms of different seasonal variations, coupling has the greatest effect on pavement fatigue life in summer, followed by winter, and the least effect in spring and fall.
# Protection of Personal Data in the Context of E-Commerce
## 6 Research Objectives and Methodology
### Results Analysis
#### 6.3.4 Payment Methods and Economic Trust  
The survey results provide valuable insights into consumer payment preferences and their relationship to economic trust in online transactions. A considerable number of respondents, approximately 61%, exhibited an apparent inclination toward utilizing mobile payment methods for their online transactions. The increasing popularity of mobile payments can be attributed to the convenience and accessibility they offer, especially among younger consumers who are well-versed in using smartphones for their everyday activities.  
Nevertheless, the survey brings attention to a significant worry among consumers regarding the security of various payment methods. Although mobile payments enjoy widespread popularity, 42% of respondents expressed complete confidence in their security, highlighting a noticeable disparity between convenience and trust. There is a suggestion that although mobile payments are preferred for convenience, there is still a significant concern regarding their security. Older age groups exhibit more caution when embracing new payment technologies and may have less trust in them.  
According to the survey, most respondents (58%) consider credit card payments more secure than mobile payments, despite the latter being more popular. The preference for credit cards may be attributed to their added protection, including fraud detection and the ability to dispute charges. These features contribute to fostering consumer trust in economic transactions. It is worth mentioning that the security of storing credit card information online raised concerns for 22% of respondents, suggesting that complete trust in this payment method is not universal.  
Despite their secure nature, only 9% of respondents opted for bank transfers, making them the least popular payment method. Possible reasons for the low usage may stem from bank transfers being more cumbersome compared to the convenience offered by mobile and credit card payments. In addition, consumers who are used to the instant gratification provided by other payment methods may be discouraged by the lack of immediacy in bank transfers.  
A crucial finding from the survey is that the level of trust that consumers have in online transactions is significantly impacted by the transparency and security of the payment methods provided. Clear information about the security measures in place to protect payment data increased the likelihood of respondents completing a purchase in online stores. The significance of providing secure payment options and effectively communicating these security features to establish consumer trust cannot be overstated.  
Based on these findings, it is suggested that older consumers be educated about the security and benefits of newer payment methods, such as mobile payments. This could help bridge the trust gap and promote wider adoption. Proactively addressing these concerns can significantly enhance consumer economic trust, increasing customer satisfaction and loyalty.
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
## 1 Introduction
Finally, an external torque was applied to the rotating shaft while the strain sensors were interrogated with the interrogation unit. As a general conclusion, it can be stated that commercial sensors can be used to measure torque and temperature of a rotating shaft. However, the sensors' response is hampered by some particular characteristics of COTS components that have a negative impact on the accuracy of the complete system. These characteristics have been identified and corrective measurements proposed.
## 4 Experimentation and Results
### Model Evaluation Metrics
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **True Positives (TP)** & **False Negatives (FN)** & **Recall** \\ \hline Training Set & 550 & 103 & 84.23\% \\ Validation Set & 170 & 30 & 85.00\% \\ Test Set & 127 & 23 & 84.65\% \\ \hline \end{tabular}
\end{table} 
Table 7: Overview of the recall. Figure 13 depicts the F1 scores for the different datasets, offering a comprehensive view of the model's overall performance by balancing precision and recall. The scores are closely aligned across the datasets, indicating reliable model behavior. Table 9 provides an overview of computing requirements and latency for the YOLOv5 model used in real-time item detection in retail settings, and cost offers crucial performance measures. The model's effectiveness in processing input data is demonstrated by its average detection time of 0.050 s per frame. It is essential for applications that need an immediate response, such as tracking consumer interactions. Furthermore, the model's reasonable resource requirement--an average memory usage of 200 MB per inference--allows it to function well on standard hardware without taxing system resources. Finally, the CPU/GPU load is characterized as \"manageable for deployment\", emphasizing that the computational requirements of the model are appropriate for standard retail configurations. Figure 12: Graph of the recall. Figure 13: Graph of the F1-score calculation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Precision** & **Recall** & **F1-Score** \\ \hline Training Set & 91.67\% & 84.23\% & 87.79\% \\ Validation Set & 89.47\% & 85.00\% & 87.18\% \\ Test Set & 89.44\% & 84.65\% & 86.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Overview of the of F1-score calculation. Combined, these measures emphasize how practicable and efficient utilizing the YOLOv5 model in real-time scenarios is. A bar chart comparing three metrics--average detection time (0.050 s), average memory consumption (200), and CPU/GPU load (1)--is shown in Figure 14. The system has low latency, moderate memory consumption, and little resource burden, as indicated by the color-coded bars that reflect each statistic. The labeled bars make evaluating various performance factors easier, demonstrating the system's effectiveness.
## 5 Opportunities, Challenges, and Future Developments of VPL-Based Design
### VPL-Based Design--Opportunities
#### 5.1.4 BIM Applications, Interoperability  
As described in the Introduction, visual programming is one of the fundamental tools of BIM technology. It is gradually being introduced into successive modeling environments and related programs (detailing, calculations, etc.) and is beginning to become one of their basic add-ons and components. In this respect, VPL programs offer basic functionality that is fully compliant with the BIM standard. With the ability to create scripts independently within the available nodes and packages, VPL programs allow the creation and manage ment of BIM models in multiple dimensions, 4D, 5D, and beyond. Today, two main areas of VPL-based design programs in function and integration with BIM are:  
* Modeling the geometry of objects.
* Information management.  
These two areas are more or less integrated, depending on the specific nature of the object being designed. For example, in the case of a building, this integration may be greater because of the ability to shape the geometry and manage the key parameters of the object. In the case of an isolated structure, such as a support structure, visual programming will provide greater support for shaping the geometry.  
As mentioned above, Dynamo is integrated into the Revit environment as a plug-in. It largely supports the core BIM functionality of this environment. The Dynamo node library includes the following categories: Analysis, application, elements, filters, geometry references, schedules, steel connections, transactions, and views. As you can see, Dynamo's level of integration and functionality allows you to do more than just create and manage the geometry of designed objects. For example, it is possible to create schedules, provide BIM 5D-level functionality, and detail steel connections. Importing data into Revit and then exporting to other parts of the system is also essential. These functionalities go much further [30] as energy and sensitivity analyses are performed [31]. Specialized applications of VPL in BIM are also of interest, such as bird collisions on building facades [32] or the Parametric Adaptive Skin System (PASS), which consists of kinetic facade components [33].  
The capabilities of the VPL tools described here are nothing more than one of the core functionalities of BIM technology, i.e., interoperability. When linked together and meeting the requirements of certain standards, these tools can operate at the highest level of BIM technology, Level 3, known as iBIM.
## 3 Results and Discussion
### Algorithm Performance for LST Forecasts
#### 3.1.2 The Second Case-Study: The Linate Airport  
The second case-study shows the obtained results at the Milano Linate Airport airstrip. The study is carried out with a dataset of four years from 1 April, 2015 to 31 March, 2019 where the algorithm performance in estimating LST values is analyzed with the same strategy of the previous case-study with the exception of no post-processing corrections for the observed dataset, since the Infra-Red Temperature Sensor (IRTS) directly measures the LST. Regarding the solar radiation input, the 3-h moving average values slightly improves the performance, as in the first case-study, hence, the comparison with hourly data are here neglected. The following Tables 9 and 10 summarize the obtained results.  
For the Linate airport case-study, there is a good performance both for simulated and forecasted data with good skill scores especially during clear sky days with the NSE index between 0.90 and 0.91 and a determination coefficient between 0.93 and 0.94. However, even in this case study a slight lower performance is found in forecasting LST values during cloudy days with the NSE equal to 0.71 and R\({}^{2}\) to 0.82.  
Figures 5 and 6 show the scatterplots for observed vs simulated data,  
As for the Pedemontana case-study, a general overestimation can be found during clear sky days for simulated and forecasted LST values (Figure 5) when temperatures are close and below 0 \({}^{\circ}\)C. Conversely, a slight tendency to underestimate is generally present during cloudy days, even with the lowest temperature values (Figure 6).
# Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling
## 3 DS-FedIDS
### Experimental Setup  
The experiments were conducted in a simulated FL environment comprising 5 clients, each representing a distinct network type: CDN, IoT, Database, Office, and Industrial. The setup utilized a dual-GPU system with 2 NVIDIA GPUs, leveraging PyTorch with CUDA support for distributed computation across both GPUs. Each client processed its local dataset independently, with model aggregation performed on a central server implemented in Python. Communication between clients and the server was simulated with a bandwidth constraint of 100 Mbps to mimic realistic network conditions.  
The dataset was synthetically generated by augmenting traffic data from the UNSW-NB15 dataset [34], a widely recognized benchmark for intrusion detection. UNSW-NB15 was originally collected using the IXIA PerfectStorm tool in a controlled testbed at the University of New South Wales, capturing real normal traffic and synthetic attack traffic across various devices (e.g., servers, workstations). Labeling was performed using software tools--tcpdump for capture and Argus/Bro-IDS for categorization into normal and attack classes (e.g., Exploits, DoS)--with ground truth verified against known attack signatures. In our study, we processed this dataset to create non-IID distributions tailored to each client, clustering normal traffic with Gaussian Mixture Models (GMM) to simulate client-specific patterns and filtering attack traffic to reflect network-specific threat profiles. The resulting synthetic dataset varied in size from tens of thousands to millions of flow records per client (e.g., IoT with 2 million samples, Database with 1 million) but was not separately hosted due to its derivation from the original UNSW-NB15 data.  
Training occurred over 10 federated rounds, with local updates aggregated via FedAvg. The simulation did not involve real-time attack generation or physical networks; instead, detection relied on the pre-labeled attack data processed by DS-FedIDS's classification of flow features (e.g., protocol, byte counts). Table 1 summarizes the key hyperparameters, selected through preliminary tuning to optimize convergence and detection performance across the heterogeneous datasets.
# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
## 3 Proposed Approach
### Procedures of the Proposed Approach  
Here, we propose the TCIBCF approach. In the proposed approach, the time-related correlation and covering degree are applied to compute item-item similarity and rating prediction, respectively. Here, \(\theta\) is set as the rating score threshold and items with \(r_{u,x}\geq\theta\) are defined as relevant items for user \(u\). The detailed procedures are described in the following.  
Step 1: Time-aware similarity computation. According to user-item rating matrix RM, we insert the time-related correlation degree (Equation (3)) into the Pearson correlation coefficient to compute item-item similarity. Here, we obtain the following:  
\[sim(x,y)=\frac{\Sigma_{u\in U_{x}\cap U_{y}}(r_{u,x}-\overline{r_{x}})*\left( r_{u,y}-\overline{r_{y}}\right)*f(u,x,y)}{\sqrt{\Sigma_{u\in U_{x}\cap U_{y}}(r_{u,x}- \overline{r_{x}})^{2}}\sqrt{\Sigma_{u\in U_{x}\cap U_{y}}(r_{u,y}-\overline{ r_{y}})^{2}}} \tag{7}\]  
Step 2: Neighborhood selection. When obtaining the item-item similarity, for each target item \(ti\) that has not been rated by target user \(tu\), after sorting all items in descending order of similarity, we select the top \(k\) items as the neighborhood \(N_{ti}(k)\) of target item \(ti\). Furthermore, for each similar item \(p\in N_{ti}(k)\), we select the top \(n\) items from the similarity list of item \(p\), which comprises the item set \(N_{p}(n)\).  
Step 3: Covering-based rating prediction. In domain \(I\), relevant items for each target user \(tu\) comprise the relevant set \(R_{tu}\), where  
\[R_{tu}=\{x\in I\big{|}r_{tu,x}\geq\theta\} \tag{8}\]  
Then, on the basis of the covering degree function (Equation (6)), we compute the covering degree \(CD(p,R_{tu})\) between each item \(p\) and \(R_{tu}\) and apply it to the weighted sum approach to predict the rating score of item \(ti\) from target user \(tu\):  
\[p_{tu,ti}=\frac{\Sigma_{p\in N_{ti}(k)\cap I_{tu}}sim(t_{i,p})*r_{tu,p}*CD(p,R _{tu})}{\Sigma_{p\in N_{ti}(k)\cap I_{tu}}sim(t_{i,p})*CD(p,R_{tu})\big{|}} \tag{9}\]  
Step 4: Item recommendations. When all predictions for target items are complete, the proposed approach sorts all target items in descending order of predicted rating scores and the top \(N\) items are selected as the recommended items for target user \(tu\).
## 2 Related Works
The fundamental disadvantage of LEACH is that every node is assumed to be communicate with sufficient ability to arrive at the BS, if necessary, and that every node has computational ability to facilitate distinctive MAC conventions. Even though the LEACH-E and LEACH-I have advantages over LEACH, in terms of prolonging the network's lifetime, they have energy hole and long distance problems, respectively. The calculation of \(d_{ij}\) in LEACH-E only involves nodal distance and residual energy, which can lead to energy hole issues when subjected to a greater round of information exchange. On the other hand, the calculation of \(d_{ij}\) in I-LEACH and its derived algorithm [30], when evaluated for randomly deployed sensor nodes and a BS located far from the network system, and with a high numerical value of \(r\), degrades the network performance. Two-level CH selection for LEACH is introduced [18]. Novel level CHs were selected from two-level CH based on the highest ratio of remaining energy to distance to the base station. Data was aggregated and transmitted from nodes to two-level CHs, from two-level CHs to level CHs, and ultimately to the BS. However, modern LEACHs are still having difficulty handling outside nodes while clustering due to inefficient boundary conditions. A distance threshold in first-level clustering and angular distance estimation in second-level clustering are very useful for selecting CHs and exploring boundary conditions. Hence, in this paper, we propose a novel dynamic routing TLC algorithm that uses angular distance estimation and reliable boundary conditions to ensure a higher energy efficiency among the sensor nodes in WSN.
## 1 Introduction
### Research Gaps  
Upon conducting a thorough evaluation of prior research endeavors, it becomes evident that none of the preceding studies have employed coordinated control of both the PSS and UPFC with the primary objective of facilitating real-time protection of the power system against cyber-physical attacks. Furthermore, these studies have overlooked the simultaneous consideration of the economic and technical facets of the power system. For instance, in references [21; 32], there was a proposal for a coordinated design involving a power system stabilizer and a unified power flow controller. However, this study did not incorporate a controller for the UPFC, and it did not consider the economic aspects of the power system. Similarly, reference [3] focused on enhancing a power system stabilizer and unified power flow controller within a power system. This was achieved by utilizing statistical and testing datasets to assess system efficiency under varying load conditions. However, this research did not address other technical aspects of the system, such as voltage deviations and operational costs.