esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Percentage Distributions of the Likert-Scale Items
#### 3.3.3 Activity Involving Digital News  
This subsection presents the percentage results (Table 4) related to the perceived impact and educational value of the digital news activity. The six items explored whether the activity contributed new knowledge or skills, its relevance to future teaching practice, its influence on the use of media--particularly digital newspapers--the extent to which it met students' expectations, their willingness to recommend it to others, and their overall evaluation of the experience.  
For this dimension, the percentage of participants selecting \"Satisfactory\" or \"Very satisfactory\" (responses 4 and 5) ranged from 77.9% to 94.8%. Neutral responses (score 3) varied between 3.9% and 19.5%, while dissatisfaction (responses 1 and 2) remained low, ranging from 1.3% to 3.9%. Broken down by group, the following observations were made:  
* Group A reported satisfaction levels ranging from 67.9% to 92.9%, with neutral responses between 7.1% and 28.6%, and dissatisfaction ranging from 0% to 3.6%.
* Group B showed satisfaction rates from 78.6% up to 100%, neutral responses between 0% and 21.4%, and no reported dissatisfaction (0%).
* Group C demonstrated satisfaction levels from 85.7% to 90.5%, neutral responses ranging from 0% to 9.5%, and dissatisfaction between 4.8% and 9.5%.  
Overall, satisfaction with the impact and applicability of the digital news activity was high across all groups, although Group A exhibited a somewhat broader spread of neutral responses and Group C showed slightly higher levels of dissatisfaction compared to the others.
## 3 Results
### Sentinel-1 SAR-based Flood Mapping
#### 3.1.2 Tropical Storm Imelda Flood Event  
The reliability of the developed model, based on temporal coherence change assessments of Sentinel-1 imagery as a measure of surface changes for mapping the recurrence of flood impact on the Greater Houston area, was tested on the two earlier flood events: Tax Day storm and Hurricane Harvey. The flooding that arose as a result of the recent (2019) severe weather event, Tropical Storm Imelda, was used as a validation event to test the robustness of the proposed approach to map areas that are at greater risk of flooding following possible severe weather events. Prior to the assessment of the impact following Tropical Storm Imelda (co-event analysis), the consistency of the pre-event coherence pixels identified in the earlier two severe weather events was tested using the approach outlined in Section 2. The results of the analysis of SAR data to detect flood recurrence following Tropical Storm Imelda are shown in Figure 4. As outlined earlier, the intensity and coverage of the flooding event as a result of the heavy rains from Tropical Storm Imelda are much smaller compared to the earlier flood events, and hence, the potential risk of flooding in parts of the study area identified through the datasets from the earlier two severe weather events could not be fully corroborated. Our analyses showed that the impact of the storm, and hence the extent of flooding, was severe in the southwestern (Fort Bend county; Figure 4), south (Brazoria and Galveston counties; Figure 4), and northeastern (Harris and Liberty counties; Figure 4) parts of the study area. Similar to the earlier severe weather-induced flooding events, the areas proximal to the two major rivers (San Jacinto and Brazos rivers) also experienced large-scale flooding following the recent event (Figure 4). In general, the pattern and extent of flooding for the areas affected by Tropical Storm Imelda highly resemble the pattern and extent of floodwater distribution observed in the earlier events (Figures 3 and 4). Figure 5 shows a demonstration of the flood recurrence in the George Bush Intercontinental Airport area (red box in Figure 4) in all three of the severe weather events using the temporal change in coherence values. As revealed in Figure 5, the three events induced a significant decline in the coherence of the pixels/surfaces located in the airport area. All in all, our analysis shows that more than 71% of the delineated pixels that exhibited a significant decline in coherence values following Tropical Storm Imelda lie at (same position) or within a 500 m radius of the pixels mapped using the same approach for the earlier events. One possible reason for this slight offset between pixels might be due to possible co-registration errors among the Sentinel-1 pairs during data analysis; nonetheless, the assessment demonstrated the reliability of the proposed approach to map areas that are at risk of being inundated by flooding from probable future severe weather events using Sentinel-1 datasets acquired before and after Tropical Storm Imelda.
# Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
## 2 Technical Background
### Security Approach in SafeCOP  
SafeCOP aims to extend the current wireless protocols for both safe and secure cooperation. SafeCOP propose an application-level \"safety layer\" on top of existing protocols to ensure safe and secure cooperation such that Co-CPS can be certified.  
The wireless communication channels considered in SafeCOP and addressed by Use Cases span from 802.11p (for automotive domain), 802.11 for generic communication support, 802.15x for short range communication, as well as mobile LTE. In general, such technologies have been designed to meet communication requirements and significant progress has been done to secure the channels, but they cannot satisfy the safety requirements imposed by the selected use cases. Therefore, SafeCOP is working to enhance current wireless communication protocols to ensure that safety requirements are preserved, together with security, in the highly dynamic scenarios envisaged for Co-CPSs, where traditional safety assurance methods may not be sufficient. Details on communication technologies adopted by each use case and the protocol enhancements proposed within SafeCOP project are presented in Sections 3 and 4.  
However, attention is taken to the technology evolution of mobile networks since the future 5G technologies will address most of the challenging network issues, e.g., providing higher bandwidth, very low latency, specific priority schemes, device-to-device (D2D) communications, as well as more mobile-specific capabilities. Network virtualization increases the flexibility of 5G networks, and improves their adaptability to the specific communication requirements of Co-CPSs. While in the multi-provider environments of current mobile networks communication services are conveyed by different providers, the 5G ecosystem allows a further degree of flexibility through virtualization: application functions and services fall in the paradigm of \"Everything as a Service\" of cloud computing. Moreover, the storage capability of the cloud allows to collect a wide range of (sensors) data and to support the application logic with data analytics. A deeper discussion on 5G is available in Section 5.
# On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
## 5 Test Results
### Measurements of the Network Links  
As presented above, the measurements of the network at the beginning of the connection (1 min-duration) should provide information about the functioning of the network during the whole connection between UAV and control station (20-min duration). In short, the input data of the machine learning algorithms are information of the links during 1 min. This information is used by the resource-usage prediction models of the Machine Learning for predicting the throughput available for the connection during the next 20 min (estimated flight duration). In order to train the ML, the total throughput of the links is also measured in the links 1 (UAV to antenna) and 2 (control station and antenna).  
The link 1 (between the UAV and the antenna) has been measured by monitoring at the UAV the data throughput (Data Throughput Info Message CMD TYPE: 0 \(\times\) 60) and the round-trip delay of signaling messages. For this scope, we use the abovementioned X-CAL application with the option of sending LTE_DATA_THROUGHPUT_INFO (0 \(\times\) 60) messages and, on the other hand, the sending of ICMP messages (ping) for measuring round-trip delays on the link.  
After that, the output data are measured by monitoring the network and measuring the mean throughput during 20 min. This is available thanks to the data registered in the chipset (reading operation provided by X-CAL) even if the whole available throughput has not been used by the connection during all the time.  
Figure 4 presents one sample of the input and output reading data in link 1 for one operator's network. The chipset is read each 10 s. The input data are read during the first minute and the output data during 20 min.  
The parameters that measure the quality of the connection between control station and antenna (link 2) are the round-trip delay (defined as before), the throughput and TCP packet losses (measured during 1 min). Also in this case, the throughput of the network is measured during the next 20 min in order train properly ML\({}_{2}\).  
In both links, three different LTE operators are measured, so that ML algorithms will estimate the throughput available in each one of the operator's networks.
###### Abstract  
It may be helpful to integrate multiple aircraft communication and navigation functions into a single software-defined radio (SDR) platform. To transmit these multiple signals, the SDR would first sum the baseband version of the signals. This outgoing composite signal would be passed through a digital-to-analog converter (DAC) before being up-converted and passed through a radio frequency (RF) amplifier. To prevent non-linear distortion in the RF amplifier, it is important to know the peak voltage of the composite. While this is reasonably straightforward when a single modulation is used, it is more challenging when working with composite signals. This paper describes a machine learning solution to this problem. We demonstrate that a generalized gamma distribution (GGD) is a good fit for the distribution of the instantaneous voltage of the composite waveform. A deep neural network was trained to estimate the GGD parameters based on the parameters of the modulators. This allows the SDR to accurately estimate the peak of the composite voltage and set the gain of the DAC and RF amplifier, without having to generate or directly observe the composite signal.  
Keywords:gain estimation; digital-to-analog converter; software-defined radio; aircraft communication; signal processing; deep learning +
Footnote †: journal:
## 3 Results and Discussion
### Rheological Characteristics Results (Ambient Temperature)  
The performance of various asphalt binder formulations was evaluated using G*sin8 (kPa), and their statistical significance at 25 \({}^{\circ}\)C after undergoing RTFO + PAV aging is shown below in Figure 4 and Table 6. The analysis involved blends of PG 64-22 with TLA at 10% and 20%, and further modifications with SBS in varying proportions (5%, 10%, and 15%). The parameter G*sin8 is critical for assessing rutting resistance, while statistical analysis identifies significant performance improvements. The baseline PG 64-22 binder exhibited a G*sin8 value of 3403 kPa, well below the Superpave limit of 5000 kPa, indicating moderate stiffness suitable for standard pavements. However, the addition of 10% TLA increased the G*sin8 value to 5692 kPa, and 20% TLA further increased it to 6551 kPa, significantly exceeding the permissible limit. These results demonstrate the stiffening effect of TLA, which improves rutting resistance but may lead to potential cracking due to reduced flexibility. To address this, the inclusion of SBS modifiers effectively reduced the stiffness imparted by TLA. For instance, PG 64-22 + 10% TLA + 5% SBS achieved a G*sin8 value of 3865.12 kPa, within the acceptable range. Similarly, for 20% TLA, adding 5% SBS reduced G*sin8 to 5494.83 kPa, showing notable improvement but still slightly above the limit. Increasing the SBS content to 10% and 15% further optimized the performance, with values dropping to 3448.98 kPa and 4179.00 kPa for 10% and 20% TLA, respectively. The most significant results were observed at 15% SBS, where G*sin8 values fell drastically to 765.47 kPa and 1182.15 kPa for 10% and 20% TLA, respectively. These results indicate that SBS modification effectively balances stiffness and flexibility, enhancing binder performance. The statistical analysis results further validate these findings. For the original condition, the baseline PG 64-22 showed significant differences (S) when compared with TLA and SBS-modified combinations, highlighting the influence of additives on binder properties. Notably, combinations involving 15% SBS consistently showed significant improvements (S) under both original and RTFO conditions, underscoring the effectiveness of SBS in mitigating the stiffness caused by TLA. The statistical analysis also showed significant differences across the majority of formulations, confirming that SBS-modified binders maintain their performance benefits even after long-term aging. In summary, the addition of SBS modifiers to TLA-blended PG 64-22 binders significantly improves the balance between stiffness and flexibility, ensuring compliance with Superpave performance limits. The statistical analysis confirms the robustness of these improvements, particularly at higher SBS dosages. These findings emphasize the potential of SBS-modified binders for producing high-performance pavements capable of withstanding heavy traffic and environmental stress.
# Failure Mode and Effect Analysis for Cyber-Physical Systems
## 5 Communications-Based Train Control
#### CBTC Risk Prevention Analysis
Moreover, preliminary results showed that RRIDS was able to detect replay and message corruption attacks with a 100% success rate, and close to 0% CPU usage, throughout the experience. [PERSON] et al. [31] proposed an innovative version of the ARP protocol for ARP poisoning prevention. This MitM prevention strategy compensates for the ARP lack of authentication and security consideration, with Internet Protocol (IP)/Media Access Control Address (MAC) mapping retaining until the machine goes offline. The proposed MitM-Resistant Address Resolution Protocol (MR-ARP) uses a long-term IP/MAC mapping table with three fields, IP, MAC, and a timer for checking if the machine is still running. Moreover, uses a Voting-based Conflict Resolution (VbCR) for cases where a node with an empty ARP cache and long-term table may receive first ARP malicious responses. Its solution consists of allowing neighbor hosts to send the correct MAC of the host to the first node, which is trying to communicate with it. [PERSON] et al. [32] designed a message dropping attack detecting scheme based on node querying after messages exchange. Each sampled node must reply with an authenticated acknowledgment (ACK) where if each bit is 1 means that the node received the message and 0 means it missed the message. The authors ranked all the overlay network nodes with a static balanced tree-structured topology. While this scheme was defined mainly for systems with packet broadcasting and overlay networks, it is possible to adapt its attack detection method of querying every node upon receiving a message to the CBTC urgent concerns. Next, we proceed to our CBTC system risk analysis through our version of the FMEA's RPN calculation.
# Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
## 2 Methodology
### Performance of Variable-Power Charging and Discharging Control Strategy
More importantly, the peak-to-valley coefficient has risen to 0.4122, whereas the peak-to-valley difference rate has decreased to 0.5878. This indicates a higher degree of curve flattening and a smaller range of load fluctuations, thereby better meeting the requirements of the evaluation criteria. The multi-BESS coordinated variable-power strategy generates equivalent continuous adjustable power at the grid level through the combination of start/stop sequences of discrete constant power units, achieving precise load tracking. As shown in Figure 8, the load curve is strictly constrained within the target band: during the midday off-peak period, three-stage charging continuously raises the valley value from 44.92 MW to 47.2 MW; during the evening peak period, time-of-use discharge precisely suppresses peak fluctuations. The performance of the constant and variable-power control strategies is inherently dependent on the configured BESS parameters, primarily the rated power and total capacity. It is critical that these core BESS parameters are kept constant in order to provide a fair comparative analysis of the two fundamentally different control strategies under identical conditions. The results in Tables 3 and 5 and Figures 6-9 show that the proposed coordinated variable-power strategy utilizing multiple BESS units achieves significantly superior peaking performance compared to a single BESS constant power strategy, even when using the same underlying BESS resources. This highlights the inherent advantages of the coordination mechanism and dynamic scheduling achieved by the variable-power strategy.
## 3 The Proposed Method
### Multi-Scale Feature Fusion
Weighted Bidirectional Feature Fusion Process: To integrate features from different levels in an adaptable manner, weighted feature fusion is employed by the BiFPN layers, wherein the significance of every input feature map is determined by dynamically learned weights. In order to achieve efficient multi-scale feature integration, the feature maps are first pushed to normalized weights. The following is a representation of the adaptive multi-scale feature fusion process:  
\[\text{P}_{\text{fused}}=\text{Conv}_{\text{fusion}}\Bigg{(}\text{Swish} \Bigg{(}\sum_{i}w_{i}\cdot\text{P}_{i}\Bigg{)}\Bigg{)} \tag{9}\]  
While swish is an attention mechanism used to improve feature representation and dynamically weight the contributions of various feature levels, \(w_{i}\) represents the learned weight for each feature map \(\text{p}_{i}\) and \(\text{Conv}_{\text{fusion}}\) is a convolution operation applied to the combined feature map. Several essential elements are involved in this process: while swish activation is performed on the weighted sum of features to add non-linearity and improve feature representation, ReLU activation is applied to the learnable weights before normalization. Figure 3: The structure of a bi-directional feature pyramid network layer. The swish attention mechanism can be represented mathematically as follows:  
\[P_{i,\text{up}}=\text{Swish}\Big{(}W_{i}^{T}\cdot P_{i}+W_{i}^{\text{up}}\cdot \text{Upsample}(P_{i+1})\Big{)} \tag{10}\]  
\[P_{i,\text{down}}=\text{Swish}\Big{(}W_{i}^{T}\cdot P_{i}+W_{i}^{\text{down}} \cdot\text{Downsample}(P_{i-1})\Big{)} \tag{11}\]  
For each feature map \(P_{i}\), \(W_{i}^{\text{up}}\) and \(W_{i}^{\text{down}}\) indicate the weights applied after the up-sampling and downsampling procedures, respectively. \(W_{i}^{T}\) denotes the weight matrix applied to the feature map. The procedures that modify the feature maps' resolution are \(\text{Upsample}(P_{i+1})\) and \(\text{Downsample}(P_{i-1})\). For each layer of the six cascaded BIFPN layers, this fusion procedure is performed for each of the five feature maps \([P_{i}]\). A set of refined feature maps at different scales is output at each BiFPN layer \(\{P_{3}^{\text{fused}},P_{4}^{\text{fused}},P_{5}^{\text{fused}},P_{6}^{ \text{fused}},P_{7}^{\text{fused}}\}\). The final output of the BiFPN is a concatenation of all fused feature maps across the levels, which can be summarized as  
\[F_{\text{output}}=\text{Concat}(P_{3}^{\text{fused}},P_{4}^{\text{fused}},P_{ 5}^{\text{fused}},P_{6}^{\text{fused}},P_{7}^{\text{fused}})\]  
Finally, we send \(\{F_{\text{output}}\}\) to the refine module for further image refining at the final BiFPN layer to enhance the quality of the output by leveraging the most representative features captured during multi-scale feature fusion.
# Performance Modeling and Analysis of LTE/Wi-Fi Coexistence
## 3 Modeling
### Mathematical Formulation of the Model
Next, we calculate the maximum capacity in each zone: The maximum capacity of zone 1: \(Zone1_{Cap}=\left(Max_{Cell_{Cap}}/Min_{bitrate}\right)\) \(Zone2_{Cap}=\left(Zone1_{Cap}/\left(Min\_bit\_rate*BR_{2}/BR_{1}\right)\right)+1\) The average bit rate of zone 1 is given as: \[ABR_{1}=\left(\frac{BR_{1}}{BR_{2}}\right)*\left(\frac{BR_{2}}{N_{TOTAL}+1}\right)\] (14) The average bit rate of zone 2 is given as: \[ABR_{2}=\left(\frac{BR_{2}}{N_{TOTAL}+1}\right)\] (15) First, we calculate the total bit rate per zone: \[TBR_{z}=\left((zone_{z}Cap*WB_{ratio})+(zone_{z}Cap*\ STR_{ratio})+(zone_{z} Cap*\ VOLTE_{ratio})\right)*ABR_{z}\] (16) where \(z\) is the zone number: \(z=1,\ldots,\)\(k\) and \(k\) is the number of zones. \(ABR_{z}\) is the average bit rate per zone. Then, the mean total bit rate in the whole cell is: \[TMBR_{cell}=\sum_{z=1}^{k}TBR_{z}\] (17)
5. Utilization (\(U\)): \[U=\frac{TMBR_{cell}}{MCC}\] (18) \(TMBR_{cell}=\) Mean total bit rate in the cell. \(MCC=\) Maximum cell capacity. Average bit rate in the whole cell: \[ABR_{cell}=\frac{TMBR_{cell}}{MEAN_{Total}}\] (19)
7. Delay: \[Delay=\frac{ABR_{Cell}}{MCC}\] (20)
8. Throughput: \[Throughput=TMBR_{cell}\] (21)
9. Aggregate average bit rate per zone: \[P_{loss}=\sum_{i=0}^{N}P_{i}*L(P_{i}) \tag{13}\]0 (22)
10. Aggregate average bit rate per service in each zone: \[P_{loss}=\sum_{i=0}^{N}P_{i}*L(P_{i}) \tag{13}\]1 (23)
## 3 Methodologies
### CMGraph for Efficient Congestion Monitoring
#### 3.3.1 Graph Representation for Spatiotemporal Data  
In this section, we describe the modeling of crowd mobility data as a sequence of crowd mobility graphs (CMGraphs). Crowd volume data recorded over a discrete time span \(t=1,\ldots,T_{obs}\) are formulated as a set of undirected and unweighted dynamic graphs \(\{G(t)=(V(t),\ E)\}\). Here, \(V(t)=\{v_{1},\ldots,v_{N}\}\) denotes the set of \(N\) nodes where each node corresponds to an egress region. The node feature matrix, \(X^{N\times D}\) stores the collective crowd flow data, where \(D\) is the number of node features. \(E\) denotes the set of time-invariant edges, where an edge \(e_{jk}\in\{0,1\}\) connecting node \(v_{j}\) and node \(v_{k}\) is 1 if two egress regions are adjacent to each other and 0 otherwise. Two egress regions are adjacent if a pedestrian is able to walk from one region to another without entering a third egress region. Figure 4 shows an example of how the egress regions of the Grand Central Station in New York are modeled as the CMGraph.  
CMGraph allows our framework to integrate space-time information into a graphical data structure. Since edges connect egress regions in the vicinity, the topological structure of the CMGraph naturally models the flow of pedestrians from one region to another. For example, a graph neural network (GNN) model can pass node information along the edges of a CMGraph such that the crowd flow information in an egress region relates to the flow of nearby regions. This allows us to account for the effect of the physical space on crowd dynamics. On the other hand, the temporal data stored in the nodes \(V(t)\) allow us to use sequence-generating neural networks, such as recurrent neural networks (RNN) to perform time series forecasting.