esa-sceva/satcom-chunk-collection huggingface dataset:
# HISEA-1: The First C-Band SAR Miniaturized Satellite for Ocean and Coastal Observation
## 2 Typical Data and Preliminary Results  
### Sea Ice  
The sea ice image of the Bohai Sea in China was obtained from the striping imaging mode (Figure 1a). The shape and area of sea ice could be identified clearly. Considering some subsets of this image, the sea ice in different states could be observed. The backscatter is determined by many factors, including the dielectric properties of the constituents, the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **Incident Angle (\({}^{\circ}\))** & **Polarization** & **Resolution (m)** & **Width (km)** \\ \hline Striping & 20–35 & VV & 3 & 20 \\ Spotlight & 20–35 & VV &
\begin{tabular}{c} up to 1 \\ (Azimuth) \\ \end{tabular} & \(5\times 5\) \\ Scanning & 20–35 & VV & 10 & 50 \\ (Wide) & 20–35 & VV & 20 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main Parameters of HISEA-1.  
volume fraction of each constituent, the geometry (shape, size, and orientation) of brine pockets in ice, the surface conditions, as well as the property of snow cover [13; 14], which makes the sea ice show bright and dark values in the SAR images.  
In Figure 1b, the bright area shows the distribution of the sea ice. This sea ice was rougher due to snowfall, and the edge of sea ice appears darker. The small striped bright pixels marked by red circles in Figure 1b indicate the ships trapped in the sea ice. The sea ice in Figure 1c appears darker because the ice was smoother and younger and contained some leads. The leads were narrow irregular cracks consisting of thinner ice or open water, which are useful for the guidance of icebreaker routes.  
Some interesting phenomena are also reflected in Figure 1b. There were a series of stripes extending from the sea surface to the sea ice. Those stripes that appear in the sea ice should be further investigated.
## 2 Image Processing in Remote Sensing
### Feature Extraction
The main reason for using the angular features is that certain structures such as buildings can be easily differentiated from lands. But the main drawback faced is the underestimation of high structures because of certain image disparities. In high resolution images, they exhibit ambiguous features. Here some matching errors are observed as part of shadows in the images.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 4 Proposed Methods
### Calculation of Threshold
#### 4.3.1 Hamming Distance  
The Hamming distance [49] is employed to quantify the dissimilarity between two strings of equal length. It denotes the count of differing characters in corresponding positions between the two strings. The formula for calculating the Hamming distance is typically represented as follows in Equation (1), where \(x_{i}\) and \(y_{i}\) represent the binary values at the current position \(i\). \(H_{d}(x,y)\) represents the Hamming distance between the current binary strings \(x\) and \(y\).  
\[H_{d}(x,y)=\sum_{i=1}^{k}|x_{i}-y_{i}| \tag{1}\]  
In this paper, we propose a modification to the Hamming distance by transforming it into a normalized value between 0 and 1 using Equation (2). Numerator \(H_{d,j}(x,y)\) corresponds to the Hamming distance calculated using Equation (1). In normal conditions, \(H_{d,j}(x,y)\) represents the Hamming distance of the \(j\)th data frame within the window. Variable \(sum\) represents the total number of bits in the current window. \(K\) represents the sum of frames in windows. From this, we calculate one of the Hamming distances within a window, denoted as \(p\).  
\[p=\frac{\sum_{j=1}^{K}H_{d,j}(x,y)}{sum} \tag{2}\]
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 2 Methods
### Fusion Model of R-IoU and Optical Flow Features
#### 2.1.2 Optical Flow Feature Matching  
In optical flow vector similarity matching, we use the Euclidean distance between optical flow vectors to compare the similarity of the average optical flow vectors of targets in consecutive frames. If the Euclidean distance between two average optical flow vectors falls within a certain set threshold, the targets within the detection and prediction regions are considered likely to be the same target. This is shown in Equation (6).  
\[d=\left\|\vec{M}_{1}-\vec{M}_{2}\right\| \tag{6}\]  
where \(\vec{M}_{1}\) and \(\vec{M}_{2}\) are the average optical flow vectors of two target boxes in consecutive frames. By setting a threshold \(\tau\), if \(d>\tau\), it indicates that the optical flow information of the targets does not match. Conversely, if \(d<\tau\), it indicates that the optical flow information of the targets matches.
# A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms
## 5 Framework  
### General Framework  
Fault diagnosis is related to traffic safety, so two perspectives of the intelligent algorithm and human supervision were combined in the framework in the Figure 4: (1) carrying out detailed and micro-analysis on a large amount of communication code data in the train with rule mining and classification algorithms, and (2) performing a macro-analysis on the text data of the engineer's diagnosis daily work report by applying topic modeling to obtain the judgement rules that can be used for manual detection.  
The framework has four main modules. First, it preprocesses the different communication codes returned in the work log of urban rail trains and then uses rule mining and feature intersection to perform feature engineering. Second, it evaluates the performance of different classification algorithms and analyzes the importance of different features. Third, Chinese text in the daily work report is cleaned by deleting punctuation marks and numbers, changing capitalization, word segmentation, and deleting stop words. Fourth, the topic probability distribution of the text data is calculated, and the characteristics of turn-back with domain knowledge are analyzed. The framework that we used in this study is Spark, and the tool is Spark ML.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Punctuation** & **Char** & **Word** & **Word Density** & **Uppercase Word** \\ \hline Std & 29.84 & 300.68 & 142.65 & 0.06 & 17.44 \\ Min & 15 & 191 & 96 & 1.97 & 7 \\
25\% & 41.75 & 351.75 & 164.25 & 2.06 & 17.75 \\
50\% & 65 & 628 & 296 & 2.09 & 31.50 \\
75\% & 90 & 800.75 & 388 & 2.13 & 46.25 \\ Max & 102 & 1067 & 518 & 2.20 & 58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of text data.  
Figure 4: General framework.  
The specific data processing, feature extraction, and topic analysis in this framework are described in detail below.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 5 Discussion
### Network Performance Discussion
We found that SRPL-RP in the random topology, SRPL-RP (Rank Attack) in the random topology, and SRPL-RP (Version Number Attack) in the random topology had the lowest and the best performancefor the average energy consumption compared with other topologies and with Standard RPL with Attacks, SBIDS [13], and RPL+Shield [14]. On the other hand, the effects of attacks in Standard RPL with Attacks were higher in the random topology for the average energy consumption. These were also higher in SBIDS [13] in the grid-center topology. At the same time, the effects were higher in both the grid-random topology and the random topology compared with the grid-center topology of RPL+Shield [14] even after applying the mitigation mechanism. This shows that the best average results for the average energy consumption were extracted in the random topology. This is because there will be fewer paths among nodes owing to the impact of SRPL-RP, which results in fewer packets lost and generates fewer control messages. The average energy consumption was greater in the random topology compared with the other topologies and this was because of both attacks and because there existed longer paths among nodes. Thus, the majority of packets lost at the routing layer was due to routing errors caused by both attacks that made most of the nodes drop their packets, which consumed a great deal of energy.
## 3 Method
### Measurement  
This study employed validated scales adapted from prior research to measure privacy concerns and the key constructs of the TAM. To assess privacy concerns, measurement items were adapted from [PERSON] and [PERSON] [43]. Items measuring perceived ease of use, perceived usefulness, attitude toward using, and behavioral intention to use were adapted and modified from [PERSON] and [PERSON] [44]. All items were revised to reflect the context of Augmented Reality Smart Glasses (ARSGs) with recording and sharing capabilities. All items were measured using a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree) and were pre-tested for clarity and contextual relevance before formal data collection. Measures were originally developed in English and then translated into Korean by two native speakers. To enhance the transparency of the results and facilitate reproducibility of our findings, the analysis guidelines and scripts are provided in the Supplementary Materials. Specifically, Supplementary Table S1 lists every measurement item along with its corresponding Likert scale, while Supplementary Table S2 defines the variable abbreviations used in the scripts.
## 2 Materials and Methods
### Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE)  
In order to determine protein fractions, an SDS-PAGE was performed. A 6.0% pooling gel and a 12% separating gel were prepared. The lyophilized mycelial samples of _Ganoderma lucidum_, _Ganoderma neo-japonicum_, _Cordyceps sinensis_, and _Cordyceps millitaris_ were rehydrated in deionized water at a concentration of the resulting solution of 200 mg/mL and centrifuged for 10 min at 4 \({}^{\circ}\)C and at speed of 8000 rpm on a Beckman centrifuge. For final sample preparation, 1 part of the supernatant was mixed with 1 part of sample buffer containing 0.2 M Tris HCL (pH 6.8). A protein electrophoresis system, Pharmacia LCB MultiDrive XL equipped with OmniPAGE WAVE Electrophoresis System, was used. Electrophoresis was performed at a running condition of 30 mA and an initial voltage of 105 V for 200 min. The gel was stained with a Coomassieie brilliant blue G--250 stain solution for 30 min. The protein purity was calculated densitometrically on an \"ERI-10\". Gel reading and molecular mass calculations were performed using Gel Analyzer-19 software. The following markers were used: Sigma marker, Mix 5, STD (BSA, Lc, Lg, La), and BSA.
# Adaptive, Observer-Based Synchronization of Different Chaotic Systems
## 6 Closed-Loop System Stability  
If the matrix \(\mathbf{Q}\) is such that \(\mathbf{Q}-0.5\mathbf{c}\mathbf{c}^{T}\) is positive definite and \(K_{2}>0.5\), then according to (56), the Lyapunov function derivative becomes negative outside a certain compact set \(\Delta\) in the state space of the aggregated state vector  
\[\xi=\Big{[}\epsilon,\;v_{1},v_{2},v_{3f},\;\widetilde{\mathbf{\theta}},\widetilde {\mathbf{\Theta}}\Big{]}. \tag{57}\]  
According to the well-known extension of the Lyapunov theorem [37], all signals in (57) are bounded and uniformly ultimately bounded to \(\Delta\). A designer is able to decrease the volume of \(\Delta\) increasing the design parameters \(K_{1},\;K_{2}\),and \(\;K_{3}\) and, therefore, to shape trajectories of \(v_{1},v_{2},\text{and}\;v_{3f}\). Practical or numerical problems may be the only limit for the growth of \(K_{1},\;K_{2}\), and \(K_{3}\). As the actual parameters \(\mathbf{\theta}\) and \(\mathbf{\Theta}\) are bounded, boundedness of adaptive parameters \(\hat{\mathbf{\theta}},\;\hat{\mathbf{\Theta}}\) follows from the boundedness of adaptation errors \(\widetilde{\mathbf{\theta}},\;\widetilde{\mathbf{\Theta}}\). The matrix \(\mathbf{P}\) is generated as an output of a stable linear system (10), subject to the bounded excitation (as \(y\) is bounded); therefore, it is bounded itself. Hence, both \(\hat{\mathbf{\theta}}\) and \(\hat{\mathbf{\theta}}=\mathbf{\Gamma}P(\epsilon e_{1}+\mathbf{\alpha}v_{1}+K_{1}\mathbf{ \alpha}v_{2})-\sigma\mathbf{\Gamma}\hat{\mathbf{\theta}}\) are bounded. Consequently, the observer (4) may be considered as the stable linear system (\(A_{0}\) is stable) with the bonded input; hence, \(\hat{\mathbf{z}}\) is bounded. Finally, both stabilizing functions \(w_{2d},\;w_{3d}\) are bounded, and therefore, boundedness of \(w_{1},\;w_{2}\), and \(\omega\) follows from the fact that \(v_{1},v_{2},\;\text{and}\;v_{3f}\) are bounded. Inspection of (51) assures that the control is also bounded as a sum of bounded components.  
To summarize, the slave state variable \(w_{1}\) follows the master output \(y\), the tracking accuracy may be arbitrarily improved, and all signals in the closed-loop system remain bounded.
## 2 Data and Methods
### Methods  
#### 2.3.1 Calculation of Soil Water Use Efficiency (SWUE)  
SWUE describes the rate of carbon assimilation per unit of soil water loss, which links the photosynthetic productivity of ecosystems to soil water. In this study, the eight-day SWUE of each pixel in northeast China was calculated by soil moisture (SM) and gross primary productivity (GPP), expressed by the following formula:  
\[\text{SWUE }=\frac{\text{GPP}}{\text{SM}}. \tag{1}\]  
The units of GPP, SM, and SWUE were \(\text{gC/m}^{2}\), \(\text{m}^{3}\cdot\text{m}^{-3}\), and \(\text{gC/kg}\)\(\text{H}_{2}\text{O}\), respectively.
# Wideband Millimeter-Wave Perforated Hemispherical Dielectric Resonator Antenna
## 2 Antenna Configuration
### A Single-Layer HDRA  
As a reference, a single-layer hemispherical DRA was initially examined, employing dielectric constants of \(\varepsilon_{r2}=1\) and \(\varepsilon_{r1}=10\), with a radius of \(a_{1}\)=3.8 mm.  
The proposed antenna was placed on a 45 \(\times\) 45 mm\({}^{2}\) ground plane printed on a thin Rogers RO4350 dielectric substrate, featuring a thickness and dielectric constant of 0.42 mm and 3.48, respectively.  
A feeding microstrip line was positioned on the lower side of the dielectric substrate, measuring 22.5 mm in length and 0.88 mm in width, tailored for optimal impedance matching.  
Resonance modes of the single-layer DRA were determined by solving Equations (1) and (2) utilizing MATLAB (2021) code. For the specified dielectric constants, radius, feeding slot, and frequency range, the single-layer HDRA supported dual resonance modes, namely TE\({}_{112}\) and TE\({}_{511}\), at 25 GHz and 30.8 GHz, respectively. Figure 3 illustrates the performance, showcasing the achievement of a dual-band with impedance bandwidths of 19.29% and 9.37%, spanning frequencies from 24.3 to 29.6 GHz and 30.5 to 33.5 GHz, respectively. The simulated axial ratio bandwidth was obtained over a frequency range of 28.3-29.5 GHz, corresponding to 4.2%. These bandwidths were attained with a broadside gain of 5.7 dBic.
## 3 Proposed Techniques
### Initial Phase
#### 3.1.1 Number of Member Nodes of the Cluster (\(N_{o}\))  
The proposed protocol supposes that each cluster will have a limited number of member nodes, which means that each CH can relate to a specific number of member nodes and cannot surpass it. The main target for this assumption is to make a balance between the clusters. Thus, the expected number of the CHs for a network owns \(J\) sensor nodes will be [12]:  
\[\text{Number of the CH}_{\text{s}}=\sum_{n=1}^{J}p\times 1=J\times p \tag{6}\]  
Based on the above, the limited number of sensor nodes that can be joined to each CH will be [12]:  
\[N_{O}=\frac{J}{\text{Number of the CH}_{\text{S}}} \tag{7}\]  
For creating the network clusters, all the CHs announce themselves to other nodes of the network via broadcast messages. Each announcement message includes the CH identification (CH ID) and the coordinates of its position. On the other side, the normal sensor node, who receives this message, calculates the distance to the CH. Afterward, it inserts this value in the selection table (ST), which is later used to choose the closest CH. Moreover, the proposed approach assumes that the sensor node is only able to join the CHs, whose distances are less than \(d_{o}\). Hence, the distances that are equal or greater than \(d_{o}\) will be neglected from the ST. Usually, the sensor node transmits a JOIN-REQ message toward the CH that owns the lowest distance value in the ST. The CH, in turn, checks the availability in the TDMA schedule. Once the CH finds a free slot, it allocates and sends back a response to the node. Else, the CH transmits a refusal message. In the previous case, the node goes to the second lowest value in the ST and transmits another JOIN-REQ message to the corresponding CH and so on [12].
# Spoofing Traction Strategy Based on the Generation of Traction Code
## 4 Discussion
### Limitations and Prospects  
The proposed strategy has certain limitations. First, the proposed traction strategy is only for BPSK modulation and is suitable for the BeiDou Navigation Satellite System B1I and Global Positioning System LIC/A. Second, the proposed traction strategy is actually a variant of conventional Strategy 1. If the code-carrier coherence is detected, the proposed strategy may be detected when the traction speed is high. Finally, the scope of this study is limited to the generation of spoofing signals and the spoofing effect for a single satellite; multi-satellite positioning and timing spoofing have not been analyzed.  
However, the traction concept of the proposed strategy is worth investigating. According to this idea, the traction code can be derived provided the nominal correlation function is given. Therefore, the applicability of the proposed strategy to other modulated signals should be investigated further. In the case of a high traction rate, necessary improvements can be made to maintain the code-carrier coherence and avoid detection. After the traction is completed, the influence of the proposed strategy on the subsequent message demodulation and positioning also needs to be analyzed.
## 5 Discussion
### RQ 2. Which Modality Combinations Are Used in MI Systems with IoT and AR?  
According to Table 8, the most commonly used input modality combination was vision and touch. This result is consistent with the fact that a mobile device was the most commonly used interaction device, which is primarily used by touch-based interaction and it has a built-in camera for AR content. Proprioception is another input modality that was used with either vision or touch on mobile devices. Other input modalities, such as audition and kinematics, were combined with either vision or proprioception on other interaction devices (i.e., HoloLens and stationary camera connected to a PC/TV monitor). Notably, kinematics was only utilized when the interaction device was not a mobile device. This observation reveals unused input modality combinations on mobile devices. The findings also indicate the possibility of expanding the diversity of input modality combinations in future research. Other input modalities, such as neural oscillation (e.g., brainwave) [55] and galvanism (e.g., electrical activity of human body parts) [56; 57], which were not found in the reviewed studies, can be used to explore new input modality combinations.  
In addition, there also appears to be room for expanding output modality combinations, as we found that only the combination of vision and audition modalities was used as output. Technological developments can allow MI system developers to use various modalities that were not observed in the reviewed MI systems, such as olfaction [58], gestation [59], haptics [60], and vibration reception [61].
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 3 Methods
### Visualization
#### 3.3.2 Model Deployment and Visualization  
The website server integrates the most effective model for the classification of disaster-related social media posts. The mapping interface is dynamically updated, periodically refreshing to display markers stored in JSON format. A backend Python script emulates a streaming environment by retrieving 10 new social media data points every 20 s. This update cycle is optimized for system performance, with each batch of 10 posts requiring approximately 14 s for model reasoning, followed by an additional 2 s for data loading, preprocessing, and output storage. Once processed, the classification results for each post are stored in a JSON marker, which includes relevant details such as text, geographic location, and classification labels. These markers are then visualized on the interactive map, allowing users to monitor the spatial distribution of disaster-related information.  
Figure 3: Markers with corresponding classifications.  
Figure 4 illustrates the detailed architecture and workflow of our proposed disaster response visualization system. Initially, social media data are continuously streamed into the server, simulating incoming information from platforms such as Twitter/X. Each incoming social media post undergoes preprocessing, where irrelevant textual elements, such as URLs, special characters, and stop words, are removed to ensure clean and standardized input data for the classification model. The cleaned data is then processed by our optimized text classification model, which categorizes each post according to predefined disaster-related classifications. Following classification, geographic information associated with the post is combined with the classification results and original textual content into structured JSON markers. These JSON markers are stored temporarily on the server, ready for visualization. The dynamic mapping interface, built with Leaflet and JavaScript, periodically retrieves these markers, updating the interactive map every 20 s to display the latest disaster-related events. Each category of disaster event is visually differentiated using distinct color-coded markers, enabling emergency responders to quickly interpret severity and prioritize actions. Users can interact with these map markers to view detailed information about each classified social media post, enhancing situational awareness and facilitating informed, timely decision-making during crisis management scenarios. By integrating automated classification with geospatial visualization, the system provides a practical and data-driven solution for improving decision-making in disaster management scenarios.