esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Vulnerability Detection
### Detailed Comparative Analysis on Vulnerability Detection Methods
#### 5.2.6 Comprehensive Surveys and Domain-Specific Enhancements  
Numerous surveys synthesize existing research, highlight trends, and suggest promising directions. For example, works such as [77; 79; 93] provide broad overviews of blockchain vulnerabilities, ranging from protocol-level to DApp-level weaknesses. Other studies [63; 98] focus on the evolution of analysis tools and the rise of deep learning in detection. Additional surveys [81; 95; 96] concentrate on Ethereum-specific issues, while others [65; 68; 99] highlight modern challenges such as zero-day vulnerabilities and advanced ML approaches. The consensus emerging from these surveys is that no single method can cover the extensive, evolving threat landscape; instead, hybrid strategies are necessary.  
Domain-specific enhancements are also crucial. In the context of blockchain and IoT security, research [100; 101] adapts ML-based intrusion detection and consensus strategies to accommodate IoT constraints, such as limited computational power. Similarly, automotive security research [103] addresses vulnerabilities in automotive blockchain networks, focusing on real-time and safety-critical requirements. Other works [64; 69; 104] propose domain-focused modifications to block structure, consensus, or communication patterns, aiming to balance security and efficiency. Future research should focus on optimizing these approaches to minimize resource consumption without compromising security, and on developing robust data collection and preprocessing techniques to ensure high-quality training data.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 7 Conclusions  
In smart cities, maintaining a resilient network is crucial. The proposed MPResiSDN system improved network resilience by exploiting SDN capabilities to implement multipath routing in case of challenges. The system was evaluated under normal operation and under two different types of natural challenges. The evaluation results demonstrate that MPResiSDN routing improved data delivery under challenges by up to 100% compared to Spanning Tree when a suitable value for \(k\) diverse paths was selected. In addition, the results show that fewer paths are required to confront a relatively trivial challenge, such as a storm challenge, while more paths are needed to confront a more severe challenge, such as a fire challenge. However, increasing the number of paths increases overhead;thus, to avoid unnecessary overhead, the number of paths needed to achieve the required resilience should be decided thoughtfully when implementing the system. To clarify, the value of \(k\) should be determined based on the required level of resilience and the severity level of the challenge. The more severe the challenge, the more alternative paths should be used, so larger \(k\) values are recommended. This emphasizes the importance of the challenge detecting phase. In future, further studies could be conducted to investigate the best ways to determine the optimal value of \(k\) based on the type of natural challenge. In addition, other types of natural challenge, such as floods, earthquakes, and volcanoes, could be considered.  
[PERSON] performed the experiments, analyzed the data, and wrote the paper. [PERSON] supervised the research and critically revised the paper. Both authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No (RG-1441-512).  
Not applicable.  
The authors thank the Deanship of Scientific Research and RSSU at King Saud University for their technical support.  
The authors declare no conflict of interest.
## 5 Discussion
### Infrastructure Assessment and User Experience  
The factor analysis of the infrastructure evaluation enhances our comprehension by pinpointing two main dimensions. The primary dimension, referred to as \"Technical and Operational Quality\", includes factors concerning the state and upkeep of bicycles, the quality of bike lane surfaces, and the overall reliability of the system. The second dimension, \"Service Availability and User Convenience\", encompasses factors related to the usability of mobile apps, immediate data on bike availability, affordability, and the connection of bike-sharing with larger public transport systems. These dimensions reflect the current performance of the service and highlight possible areas for enhancement. Consistent with the insights of [PERSON] et al. (2014) and [PERSON] et al. (2015), improving these infrastructural components may greatly increase the perceived advantages and total usefulness of bike-sharing systems.  
The regression analyses provide a detailed assessment of the relationships between infrastructural features and perceived benefits. Notably, the usability of the mobile application (I1) shows positive associations with key outcomes, such as time savings (B4, coefficient = 0.185) and increased physical activity (B5, coefficient = 0.166), underscoring the role of user-friendly digital interfaces in enhancing service efficiency. Similarly, access to real-time information on bike availability (I4) correlates positively with practical benefits, including reduced traffic congestion (B2, coefficient = 0.279) and improved cost-effectiveness (B3, coefficient = 0.226). These findings align with current smart mobility research, which emphasises the value of real-time data and accessible technologies in overcoming operational barriers ([PERSON], 2016; [PERSON] et al., 2016). On the other hand, adverse connections are noted for specific infrastructural shortcomings. The technical state of the bicycles (I9) exhibits an inverse relationship with advantages tied to environmental friendliness, traffic evasion, and cost-effectiveness, whereas the visual state of the bicycles (I10) negatively impacts views on time efficiency and compatibility with public transportation. These findings indicate that neglecting maintenance not only diminishes the visual attractiveness of the bikes but also detracts from their functionality, potentially diminishing user confidence--a conclusion aligned with previous research emphasising the vital role of maintenance and operational dependability in shared mobility systems ([PERSON] et al., 2016; [PERSON] et al., 2016).
## 2 Bluetooth Low Energy Protocol Stack
### Physical Layer  
BLE operates in the 2.4 GHz Industrial Scientific Medical (ISM) band and defines 40 Radio Frequency (RF) channels with 2 MHz channel spacing. There are two types of BLE RF channels: advertising channels and data channels. Advertising channels are used for device discovery, connection establishment and broadcast transmission, whereas data channels are used for bidirectional communication between connected devices.  
Three channels are defined as advertising channels. These channels have been assigned center frequencies that minimize overlapping with IEEE 802.11 channels 1, 6 and 11, which are commonly used in several countries.  
An adaptive frequency hopping mechanism is used on top of the data channels in order to face interference and wireless propagation issues, such as fading and multipath. This mechanism selects one of the 37 available data channels for communication during a given time interval.  
All physical channels use a Gaussian Frequency Shift Keying (GFSK) modulation, which is simple to implement. The modulation index is in the range between 0.45 and 0.55, which allows reduced peak power consumption. The physical layer data rate is 1 Mbps.  
Figure 1: (**a**) BLE protocol stack; (**b**) structure of a BLE data unit (note: the size of each field is expressed in bytes).  
The receiver sensitivity is defined in BLE as the signal level at the receiver for which a Bit Error Rate (BER) of \(10^{-3}\) is achieved. The BLE specification mandates a sensitivity better than or equal to \(-70\) dBm. The coverage range is typically over various tens of meters.
# Dynamic Nonprehensile Manipulation of a Moving Object Using a Batting Primitive
## 3 Method
### Ball Trajectory Estimation
#### 3.2.1 Least Square Regression  
The \(x\) and \(y\) planes of the robot coordinate system are horizontal planes and the \(z\) direction is perpendicular to the x-y plane. A linear function was used to estimate the horizontal trajectory of the ball, and a quadratic function was used to estimate the vertical trajectory. Because the ball moves in a parabolic trajectory, the \(z\)-axis trajectory of the ball is fitted as a quadratic function, unlike other axes. The trajectory function for each of the \(x\), \(y\), and \(z\) axes of the ball is given by  
\[\left\{\begin{array}{rl}&x=a_{1}t+b_{1}\\ &y=a_{2}t+b_{2}\\ &z=a_{3}t^{2}+b_{3}t+c\end{array}\right. \tag{5}\]  
Whenever three-dimensional position data are newly measured in the stereo vision sensor, the coefficient of the trajectory function (5) is newly calculated through least square regression [28]. The position data sampling interval of the ball is 20 ms, which is the image acquisition period of the stereo vision sensor. Since the flight time of the ball is 0.5 s on average, approximately 15 to 20 ball position data points are measured. Figure 7 represents the estimated trajectory of the ball according to the number of data points using least squares regression. As the number of data points increases, it can be seen that the estimated trajectory converges to one trajectory.  
Figure 6: Histogram showing the calibration error for 15 marker positions.
## 3 Principles of Operation
### Wind Energy Conversion  
Conventionally, a wind energy subsystem is illustrated in Figure 2. The physical subsystem consists of a wind turbine, a gear box, a generator, a DC/AC converter, and a transformer linking to the power grid. In this study, energy loss over the power conversion processes is ignored. Wind power can be expressed by Equations (1) and (2).  
Figure 1: Schematic diagram of the proposed system.  
Figure 2: Conversional transformation of a power system from wind energy.  
Equation (1) presents the relationship between the wind speed and the mechanical power.  
\[p_{wt}=\left\{\begin{array}{ll}0,&if\ v<v_{cut\_in}\ or\ v>v_{cut\_out}\\ \frac{v-p_{cut\_in}}{v_{v}-v_{cut\_in}}.P_{r}\,&if\ v_{cut\_in}\leq v\ \leq v_{r}\\ \frac{P_{r}}{P_{r}}\,&if\ v_{r}<v\leq v_{cut\_out}\end{array}\right. \tag{1}\]  
where \(p_{wt}\), \(P_{r}\), \(v_{cut\_in}\), \(v_{cut\_out}\), \(v_{r}\) and \(v\) represent the output power, nominal power, input wind speed, output wind speed, nominal wind speed, wind speed at the turbine blade tip.  
\[p_{wind}=\frac{1}{2}.\rho A.v^{3} \tag{2}\]  
Equation (2) is another calculation method of electrical power output. \(p_{wind}\), \(\rho\), \(A\) and \(v\) stand for the wind power, air density, equivalent area, and average wind speed, respectively.
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
## 3 Results
### Measurement
#### 3.2.2 Radiation Characteristics of the GAA Prototypes  
The measurement set-up in anechoic chamber (see Figure 10) includes a 3D-printed, polylactic acid (PLA)-based piece for fixing the prototypes. Both Phi = 0\({}^{\circ}\) and Phi = 90\({}^{\circ}\) pattern cuts (see Figure 11) exhibit HPBW and SLL almost identical to simulations [36], with slightly rising XP levels, but still good for the envisioned application. Intercomparison [37] with a Flann Microwave Standard Horn 20240-25 was used to measure the gain, and the results are indicated in Table 4.  
Figure 9: Measurement results of the reflection coefficient, S\({}_{11}\) (dB), for the fabricated GAA prototypes vs. simulation results.  
Regarding radiation efficiency, the following results have been obtained: 82% for the GAA and MTR-GAA, and 64% for the GAA+radome. This indicates that the radome significantly impairs the antenna's performance in terms of radiation efficiency.  
The S\({}_{11}\) of the GAA is shifted down in frequency, and so is the band for agreement in terms of gain (G). The discrepancies between simulation and measurement results in terms of 1S\({}_{11}\), gain, pattern deformation and XP level rise are mainly attributable to manufacturing tolerances and connectorization (slight manual shifting of optimal position and the crucial curing of the conductive paste). The effect of a drop of silver-loaded epoxy adhesive and hardener for connector fixing reduces by 0.4 dB the gain [34] compared to an ideal simulation. Also, the cable, reflections in the set-up and misalignment with the horn can disturb the G results [37].  
Figure 11: Measured CP and XP components for Phi = 0\({}^{\circ}\) and Phi = 90\({}^{\circ}\) cuts at 24.15 GHz for the GAA, the GAA+radome and MTR-GAA.  
Figure 10: Measurement set-up in an anechoic chamber. Antenna under test (AUT) arrangement and GAA prototype.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 2 Study Area and Data
### Data
#### 2.2.2 Sentinel-1 Imagery  
This study utilized Sentinel-1 data as a supplement to optical imagery for mapping tea gardens in Anji. Sentinel-1 is a C-band synthetic aperture radar (SAR) satellite constellation, comprising two polar-orbiting satellites, Sentinel-1A and Sentinel-1B, which are 180 degrees apart. The revisit period for a single satellite is 12 days, and with the constellation of two satellites, the revisit period is reduced to 6 days [31], allowing for all-weather, day and night imaging of the Earth's surface. Sentinel-1 offers four imaging modes, and this study utilizes the IW (interferometric wide swath) mode, which acquires imagery with a resolution of 5 m by 20 m, a swath width of 250 km, and includes both VV and VH polarization modes. The Sentinel-1 satellite has the characteristic of penetrating clouds and rain showers, enabling all-weather imaging, which gives it a significant advantage in remote sensing identification of crops in areas severely affected by cloud and rain. The study downloaded full-year 2019 Sentinel-1 imagery covering the entire study area. Considering issues such as incomplete coverage, image overlap, and missing images in the original single-scene imagery, the raw data was synthesized with a mean composite every ten days. Ultimately, 33 images were obtained (Figure 3c). Figure 4 displays false-color composite images of the Sentinel-1 VV imagery for the study area on 1 May 2019, 3 June 2019, and 28 September 2019. All Sentinel-1 data were acquired and preprocessed using Google Earth Engine, with the resolution resampled to 10 m.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectral Band-Name** & **Central Wavelength (nm)** & **Spatial Resolution (m)** \\ \hline B2-Blue & 496 & 10 \\ B3-Green & 560 & 10 \\ B4-Red & 664 & 10 \\ B5-Red Edge 1 & 704 & 20 \\ B6-Red Edge 2 & 740 & 20 \\ B7-Red Edge 3 & 782 & 20 \\ B8-NIR & 835 & 10 \\ B8A-Red Edge 4 & 864 & 20 \\ B11-SWIR1 & 1613 & 20 \\ B12-SWIR2 & 2202 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The bands, wavelengths, and spatial resolutions of the Sentinel-2 images utilized in the study.  
Figure 4: Anji Sentinel-1 satellite imagery (**a**) and its zoomed-in section (**b**). The image was synthesized through false-color composite using Sentinel-1 VV imagery from 1 May, 1 June, and 1 October 2019, as red, green, and blue channels, respectively.
# The Effect of Space Objects on Ionospheric Observations: Perspective of SYISR
## 3 Preliminary Attempts to Eliminate Space Object Pollution
At an altitude with a strong ionospheric background echo, such as the F2 peak region, the detection of space objects with low power is easily missed. Figure 5 shows an example of this case. The space object with low power appears at the altitude region of the ionosphere peak. The increase in power is approximately 1 dB. In the ACF and power spectrum profiles, its effect is weak and difficult to determine. After the  
Figure 4: An example of power profile ((**a**,**b**) unit: dB) and autocorrelation function ((**c**,**d**) ACF) of SYISR ionospheric measurement observed on 14 April 2022 at 09:38:44 using an alternating code (AC) signal. Panels (**e**,**f**) show the retrieved electron density profiles during the entire day of 14 April 2022. The left (right) column shows the results before (after) eliminating the space object pollution. inversion, the space object pollution rises to an abnormal increase in peak electron density in the results. This type of space object pollution remains in the observations and results in unrealistic ionospheric results.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 3 New Mathematical Model  
In this section, we propose a new mathematical model based on a semi-Markov process theory, to analyze the accurate performance of the 3Z system from the viewpoint of the total signaling cost on radio channels.
## 5 Future Directions and Limitations  
The accuracy and application of the WBW technique has been well established as the gold standard for sweat electrolyte measurement. However, the use of this method has been less investigated in correspondence with HA protocols because of the limitations of field setting functionality. Therefore, future research should investigate sweat electrolyte concentration adaptations following HA comparing the WBW method to regional sweat patches. In addition, sex and age differences between WBW and regional sweat patches need to be explored due to the underrepresentation of females in exercise thermoregulation research and differences in thermoregulation between young and older individuals. Lastly, other electrolyte assessment modalities have been gaining attention due to emerging technology, such as microfluidic systems, and commercially available wearables [71; 72; 73; 74]. Third-party reliability and validity studies should be conducted to compare these modalities to the WBW technique.  
This review has summarized the current literature on sweat electrolyte adaptations following heat training utilizing either the WBW technique or regional sweat patches. However, this study was not without limitations. Narrative reviews inherently contain limitations regarding the objectivity of study selection and the interpretation of results [75]. Furthermore, no statistical analysis was executed to compare findings between studies that used the WBW technique to those that used regional sweat patches. We considered performing a meta-analysis but deemed it not appropriate as there is a large disparity in the number of studies between methodologies. However, this does result in a bias in the studies' interpretation. Even so, we chose to include all studies from our search strategy and aimed to present this review in the most objective way possible.
## 3 Recording of Atmospheric Conditions Using a Weather Monitoring Station to Predict RSSI Value
### Measurement of the RSSI Parameter  
The values of the received RSSI optical power on both sides of the FSO line can be visualized using software from the manufacturer Flight Manager PC in the form of a graph. The Flight Manager PC software records RSSI in specially organized files with the extension *.dat (application/octet-stream; charset = binary) from which it is possible to visualize the recorded RSSI later.  
In the case of the FSO of the FlightStrata 155E line and its control software, the received RSSI optical power is only informative (a form of visualization). However, this parameter plays an important role in the analysis of availability and reliability. By applying reverse software engineering, the *.data files (output of the Flight Manager PC software) were subjected to a detailed analysis with the aim of continuously recording the value of the received optical power with the corresponding time record.  
The recorded data was updated and overwritten in a file every time it was turned on, and after the measurement was finished, it was sent to the Raspberry Pi 3 microcomputer, where the weather conditions were also recorded. Subsequently, they were entered into the common MySQL database.  
Since the values of the RSSI parameter were recorded using software compatible with our FlightStrata 155E head, we can say that the RSSI parameter has the same parameters (in terms of wavelength, etc.) as the FSO head after correct setting.
# CF-CNN: Coarse-to-Fine Convolutional Neural Network
## 2 Coarse-to-Fine Convolutional Neural Network
### Loss Function for CF-CNN  
Figure 1d shows the architecture of the proposed CF-CNN. Given the group labels for the hierarchical structure, the labels in each hierarchical level are simultaneously trained by using the main and subnetworks. All feature maps of the last convolution layer in each subnetwork are used to predict fine classes via the refine layer. To obtain hierarchically structured group labels, we adopt disjoint grouping regularization proposed by [PERSON] et al. [22]. Let \(\mathbf{x}_{i}\in\mathbb{R}^{d}\) represent the input data instance, \(\mathbf{y}_{i}\in\{1,....,C\}\) the class label, and \(C\) the total number of classes. Given \(M\) training samples, \(D=\left\{\mathbf{x}_{i},\mathbf{y}_{i}\right\}_{i=1}^{M}\), and corresponding class scores, \(S=\left\{\mathbf{x}_{i},\mathbf{s}_{i}\right\}_{i=1}^{M}\), obtained by the pretrained deep CNN, the goal of the disjoint grouping method is to obtain hierarchical multilevel labels \(Q^{l}=\left\{\mathbf{x}_{i},\mathbf{y}_{i}^{l}\right\}_{i=1}^{M}\) for coarse classification at subnetworks.  
The class score vector, denoted as \(\mathbf{s}_{i}\in\mathbb{R}^{C}\), is obtained by applying the softmax function to the deep CNN result, where \(C\) can be considered as the dimension of the class score. Let \(\mathbf{y}_{i}^{l}\in\left\{1,...,G^{l}\right\}\) and \(G^{l}\) represent the label and number of groups at the \(l\)-th hierarchy level, respectively. The label of the first level, that is \(\mathbf{y}_{i}^{1}\), is equal to the original class label \(\mathbf{y}_{i}\). To train the CF-CNN, we define the loss function as  
Figure 2: Comparison with the CIFAR-100 dataset.  
\[\min_{W}\sum_{l=1}^{L}\mathcal{L}\left(\mathbf{W},\mathbf{x},\mathbf{y}^{l}\right), \tag{1}\]  
where \(\mathcal{L}\left(\mathbf{W},\mathbf{x},\mathbf{y}^{l}\right)\) represents the cross-entropy loss of hierarchically structured group labels at hierarchy level \(l\in\{1,...,L\}\) on the training data, \(L\) is the total number of hierarchy level, and \(\mathbf{W}\) is the weight parameters of a network.