esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Discussion and Limitations  
The above experimental results show that the secure communication method and distributed identity authentication proposed not only solve the single point of failure caused by centralized management, but also have good scalability, and the identity authentication time will not increase with the increase of blockchain nodes. As can be seen from Figure 6, compared with the existing PKI system, the identity authentication time of the proposed scheme is slightly higher. However, it has better fault tolerance and security, and the normal operation of the system can still be ensured in the presence of malicious nodes. More importantly, the scheme proposed in this paper has a wide range of application scenarios and it can also be applied to some Internet of Things devices. The research of [PERSON] et al. [49] indicates that the vehicular ad hoc networks also face some security problems, including malicious vehicle attacks. The identity authentication, and secure communication scheme proposed can also be applied to vehicular ad hoc networks to resist attacks from malicious vehicles and ensure secure communication between vehicles. To  
Figure 8: Identity authentication time according to the number of blockchain nodes.  
Figure 7: Public key request time according to the number of blockchain nodes.  
complete the secure communication between UAVs, this paper uses smart contracts to realize the functions of identity registration, identity authentication and public key request. By referring to the k + 1 Symmetric Test Pattern proposed by [PERSON] [50], the functional test is carried out, which reduces the number of test cases on the premise of ensuring the accuracy of the test. Although the scheme proposed has better security, applicability, and scalability, the practical Byzantine fault tolerance (PBFT) algorithm adopted by the blockchain will cause communication bottlenecks when the number of consensus nodes increases, which is a major factor limiting the performance of the scheme. Therefore, the lightweightness of the blockchain consensus algorithm will be the next research content.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 3 Proposed Method
### Workflow for Semantic Segmentation of Aerial Imagery  
The proposed method as shown in Figure 3 leverages a comprehensive approach to the semantic segmentation of aerial imagery of Dubai, obtained through the use of satellites from the Mohammed Bin Rashid Space Centre (MBRSC). The dataset, consisting of 72 images grouped into 8 larger tiles, has been meticulously annotated for pixel-wise semantic segmentation across six classes: Building, Land, Road, Vegetation, Water, and Unlabeled. The task at hand involves effectively segmenting these diverse land cover features for applications such as urban planning, environmental monitoring, and infrastructure management.  
To preprocess the dataset, a patch-based augmentation technique is employed. Large images are divided into smaller, manageable patches, each serving as a distinct section of the original image. These patches, with a specified size, are obtained through the Patchify library. The augmentation process includes loading the image, dividing it into patches, and determining whether these patches overlap or not. The resulting patches are instrumental in simplifying the handling of large datasets, enabling more efficient analysis and manipulation.  
Figure 2: The process of depth-wise separable convolution. This process includes depth-wise convolution and \(1\times 1\) convolution.  
Following dataset preprocessing, the method proceeds to model development. The architecture chosen for this task is the U-Net with self-attention and separable convolutions, a deep learning model known for its effectiveness in semantic segmentation tasks. The model is compiled with a custom loss function, combining Dice Loss and Focal Loss, aiming to enhance the training process and improve the model's ability to handle imbalanced classes. The model is trained on the prepared dataset, and its performance is evaluated on a separate test set. Training history is visualized through plots to assess the model's learning progress.  
Ultimately, the trained model is applied to predict land cover classes in new, unseen images, facilitating the generation of valuable insights for further analysis. The proposed method concludes with a comprehensive analysis of results and their implications, providing a robust framework for semantic segmentation in aerial imagery and contributing to advancements in remote sensing applications for urban landscapes.
# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 2 Related Work
### Genetic Algorithm
The LNCA is a new clustering method in which the clustering process is based on the similarity of mobile nodes. The Evolutionary-based Clustered Routing Protocol (ERP) [32] overcomes the limitations of GA-based clustering algorithms by combining the clustering aspects of cohesion and separation error, and it implements a new fitness function based on these two factors. The dynamic clustering of heterogeneous wireless sensor networks based on a GA (DCHGA) [33] improved the ERP. It considered many heterogeneous factors, e.g., the data processing capability of different nodes, and the ability to serve as a cluster head. The GA is also used to obtain an optimal clustering result and extended the lifetime of the network. In this approach, information is dynamically updated after each transmission round. Additionally, several factors are integrated based on the framework. However, the DCHGA method has no clear route for assessing these heterogeneous factors. In this paper, both the DCMST and GA are introduced to assess the relevant heterogeneous factors. The initial clustering process is based on this information.
# High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
## 3 Rotor Drag Force Model Implementation
### Experiments
#### 3.2.1. Thrust Experiments  
To calculate the drag force in our model, the thrust value of the rotor is needed. Therefore, experiments have been conducted to determine the relationship between the PWM signals sent from Pixhawk to the ESC and the thrust produced by the rotor. The experimental setup can be seen in Figure 14. We used a Sunnysky V3508 700 Kv BLDC motor (Shenzhen, China) with an \(11\times 4.7\) inch propeller, the same as what was used in our drone. This motor was attached to a metal base fixed to a scale using tape. A 14.8 V battery powered the ESC. Throttle commands to adjust the rotor speed were sent from a remote control (RC) transmitter to an RC receiver, which then transmitted corresponding PWM signals to the electronic speed controller (ESC) responsible for regulating the rotor's speed. We monitored the PWM values on a notebook through a ROS topic, received directly from the Pixhawk controller via USB connection. During experiments, we manually adjusted the remote controller to vary the PWM values from 1000 to 1950 in steps of approximately 20. We recorded thrust values displayed on the scale's screen with a 1 g resolution. For each PWM value, we recorded five thrust values over 5 s, with measurements taken every 1 s. The test results are presented in Figure 15.
## 4 Methodology
### User Clustering  
To address the research questions that require identifying distinct user groups, we draw on prior research indicating that superfans are heavy users of digital random platforms and are deeply engaged in various platform activities. Many previous studies identify \"heavy users\" by applying a specific threshold (e.g., the top \(X\%\)). For example, [PERSON] et al. [23] defined the top 1% of chatbot users as superusers, while Wikipedia researchers commonly regard individuals with at least 5000 or 10,000 edits as heavy contributors [33]. In OpenStreetMap, the 20:80 Pareto principle underpins the notion of power users, where a small subset of contributors produces the majority of edits [34].  
In our dataset, however, some users predominantly posted, while others focused more on commenting. A simple binary classification (e.g., general users vs. superfans) would not capture these nuanced patterns. Therefore, we applied \(k\)-means clustering with various \(k\) parameters using scikit-learn (v1.3.0) ([[https://scikit-learn.org/](https://scikit-learn.org/)]([https://scikit-learn.org/](https://scikit-learn.org/)), accessed on 27 February 2025), an open-source Python (v3.11.5) machine learning library. The input features were each user's total number of posts and comments, both standardized through scikit-learn's _StandardScaler_ to account for wide variability.  
To identify the most suitable \(k\) value, we performed additional analyses using the elbow method and silhouette coefficients to validate our choice of the number of clusters (see Table 3). Based on the inertia values from the elbow method, we observed that the rate  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Language** & **Posts (\# / \%)** & **Comments (\# / \%)** \\ \hline English & 70,921 (42.4\%) & 173,188 (35.8\%) \\ \hline Korean & 30,380 (18.1\%) & 138,666 (28.6\%) \\ \hline Japanese & 10,595 (6.3\%) & 51,837 (10.7\%) \\ \hline Russian & 9129 (3.3\%) & 18,268 (3.8\%) \\ \hline Tagalog & 15,467 (9.2\%) & 13,630 (2.8\%) \\ \hline Arabic & 994 (0.6\%) & 12,557 (2.6\%) \\ \hline Spanish & 3475 (2.1\%) & 2348 (0.5\%) \\ \hline Other & 26,495 (18.0\%) & 73,943 (15.3\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of languages in posts and comments.  
of decrease in inertia began to level off around \(k=3\). Furthermore, our silhouette analysis showed that \(k=3\) yielded the highest silhouette coefficient (0.905), indicating well-defined cluster separation. As a result of applying \(k\)-means clustering with \(k=3\), 3301 users (96.8%) were classified as _general_, 71 (2.1%) as _comment-heavy_, and 38 (1.1%) as _post-heavy_, thereby revealing two distinct types of superfans.
## 1 Introduction
For example, the routing protocol must deal with the constraints imposed by the high mobility of vehicles. Furthermore, constrains such as mobility patterns, fading wireless channel, density of vehicles and the availability of infrastructure are closely related to the specific deployment scenario, _i.e._, highway or urban [26, 37]. For example, the speeds achievable on highways are significantly higher than in urban scenarios. Thus, a protocol designed to work in urban scenarios may not be able to cope with the higher vehicle speeds present in highway scenarios. Therefore, the specific application scenario should be considered when designing a routing strategy in order to adequately address the particular constraints imposed by a given scenario. In this context, it is important to note that highways account for a significant amount of the road infrastructure deployed throughout several countries. For example, highways represent about 75% of the total statute miles in the U.S. [1]. Deploying roadside infrastructure to provide coverage for the entire network of roads and highways would take a long time and require a huge investment. Hence, enabling vehicle communications in highways through the use of VANETs is an important scenario that should be further studied, as recently research have clearly pointed out [6, 21, 38, 39, 40, 41, 42]. This paper introduces an efficient multi-hop routing mechanism to enable V2V communications in highway scenarios without employing underlying infrastructure. The routing algorithm was developed considering that the vehicles speed can be as high as 60 m/s under regular traffic conditions. Furthermore, other important constraints pertaining to this application scenario are considered within the routing algorithm design including: dynamic transmission ranges, vehicle acceleration, and high vehicle mobility. Our proposed routing strategy addresses these issues by using a broadcast approach with a light retransmission mechanism in the location service, altogether coupled with a reliable position-based routing algorithm. It is important to note that the routing strategy considers realistic propagation conditions following the results reported in [37]. Our main contributions include:  
1. A new location service based on a broadcast approach coupled with a new broadcast suppression technique. The location service addresses the hard conditions imposed by the wireless channel, providing a high success rate for route discoveries. 2. A novel light routing protocol that considers the constraints imposed by a realistic channel propagation model in its design. The remainder of this paper is organized as follows: Section 2 provides a literature review. Section 3 introduces the Freestanding Position Based Routing (FPBR) protocol proposed in this paper. Section 4 then presents several performance metrics obtained when using FPBR for V2V communications in highway scenarios. The performance evaluation setup is also described in this section. Finally, Section 5 presents the conclusions of this work.
# Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
## 3 Analytical Time-Constrained Reentry Guidance
### Determination of the Bank Angle Symbol Based on the Heading Angle Deviation Corridor  
The bank angle profile designed in this paper is divided into two parts, the initial reentry section and the glide section, which can meet the flight time constraint from the initial reentry time. Here, the heading angle error corridor is used to confirm the roll angle symbol. The heading angle error corridor piecewise function is expressed as follows:  
\[\left|\Delta\psi_{th}\right|=\left\{\begin{array}{ll}10,&6000<V\leq V_{0}\\ 15,&3000<V\leq 6000\\ \frac{7}{1200}(V-3000)+15,&1800<V\leq 3000\\ 8,&V\leq V_{f}\end{array}\right. \tag{30}\]  
where \(\left|\Delta\psi_{th}\right|\) is the boundary constraint of the heading angle error corridor in units of degrees (\({}^{\circ}\)), and \(V_{0}\) and \(V_{f}\) represent the reentry time and the expected terminal velocities, respectively. When the deviation between the heading angle and the line-of-sight angle \(\Delta\psi\) exceeds the boundary of the heading angle error corridor during the flight of the HGV, the bank angle is reversed. The reverse strategy is as follows:  
\[sign(\sigma^{i})=\left\{\begin{array}{ll}-1,&\Delta\psi\geq\left|\Delta\psi_ {th}(V)\right|\\ sign(\sigma^{i-1}),\Delta\psi\in(-\left|\Delta\psi_{th}(V)\right|,\left| \Delta\psi_{th}(V)\right|)\\ 1,&\Delta\psi\leq-\left|\Delta\psi_{th}(V)\right|\end{array}\right. \tag{31}\]  
where \(\Delta\psi=\psi-\psi_{los}\) is the deviation value of the heading angle and line-of-sight angle.  
In summary, the size and symbol of the bank angle can be obtained, that is, the bank angle profile can be calculated.
# Study on New Bolted T-Stub Connection with Inserted Plates under Axial and Cyclic Loads
###### Abstract  
The bolted T-stub connection joining beam with column is being widely applied. To enhance the energy dissipation capacity of conventional T-stub connections, two rectangular plates are proposed to be inserted between the T-stub and column, so that the T-stub flange can yield both under tensile and compressive loads. This study put forward a mechanical model of a new T-stub connection with inserted plates and investigated important factors that could affect its mechanical behavior through experimental tests. Thirty specimens were designed with different configurations that differed according to the existence or absence of inserted plates, the fabrication method and the width of inserted plates. These configurations were tested under axial and cyclic loading conditions, and results showed that the proposal aiming to improve the energy dissipation capacity was feasible. The mechanical model presented coincided with the test observation and data. The advent of two inserted plates elevated the load bearing capacity, stiffness and ductility of connections under compression, whereas in tension the properties were not substantially enhanced. The welded T-stub connections outperformed those cut from standard section steel. The energy dissipated by connections with inserted plates was about 150% of that by traditional connections without inserted plates. Only within a reasonable range can the increment of plate width promote the energy dissipation capacity of T-stub connections. The optimum width of plates in terms of energy consumption accounted for around 31% of the overall width of connections.  
T-stub connection; inserted plates; mechanical model; experimental research; failure mode; energy dissipation capacity; hysteretic behavior; optimum width of plates +
Footnote †: journal: Applied Physics Letters
# Study on the Evolution Law of Overlying Rock Collapse Induced by Mining Based on BOTDR
## 2 The Principle of Distributed Optical Fiber Monitoring  
Brillouin optical time-domain reflectometry (BOTDR) is a single-ended optical fiber sensing technology that does not require the formation of a closed-loop circuit for measurement. As illustrated in Figure 1, The frequency shift has a linear relationship with temperature or axial strain changes in the optical fiber. The frequency shift is obtained, and the change of temperature and strain along the whole distributed sensing is measured from the spontaneous Brillouin scattering light [27]. Thus, in this experiment, variations in the Brillouin frequency shift of the optical fiber sensor were monitored to assess changes in strain and temperature within the rock strata.  
The relationship between the Brillouin frequency shift, strain, and temperature can be expressed as follows:  
\[v_{\text{B}}(\varepsilon,T)=v_{\text{B}}(0)+\frac{\text{d}v_{\text{B}}( \varepsilon)}{\text{d}\varepsilon}\varepsilon+\frac{\text{d}v_{\text{B}}(T)}{ \text{d}T}(T-T_{0}) \tag{1}\]  
where \(v_{\text{B}}(\varepsilon\),\(T)\) represents the Brillouin frequency shift after changes in strain and temperature, \(v_{\text{B}}(0)\) is the initial Brillouin frequency shift of the optical fiber, \(\varepsilon\) is the axial strain of the sensing fiber, and \(T_{0}\) and \(T\) denote the initial and changed temperatures, respectively. Given that the experiment was conducted under controlled indoor conditions, the temperature effect on the test can be considered negligible.  
In this study, an AV6419 optical time-domain strain measurement instrument was used. The device operates at a wavelength of 1550 \(\pm\) 5 nm, with a maximum sampling interval of 0.05 m and a spatial resolution of 1 m. The strain measurement resolution of this device is 1 \(\mu\varepsilon\), enabling highly sensitive detection of subtle deformations. The strain measurement accuracy is (\(\pm\)0.2 + 2\({}^{\circ}\)sampling interval + 2\({}^{\circ}\)10\({}^{-5}\)-distance). It supports self-initiated, single-ended measurements and offers several advantages, including a long measurement range (up to 80 km), strong environmental adaptability, wide sensing coverage, and a high dynamic range.  
A high-transmission tight-sheath strain sensing cable (NZS-DSS-C07) was employed in the experiment. This cable incorporates an elastomeric sheath that enhances its coupling with the rock strata without compromising optical fiber sensitivity, thereby improving data reliability.
## 2 Deep Learning Models
### State-of-the-Art Models  
The first major success of CNNs in the task of pattern recognition dates back to LeNet (LeNet5) [14]. It was designed for handwritten and machine-printed character recognition. According to current trends, LeNet5 is a very simple model. The network architecture encompasses two sets of convolutional and average pooling layers. Those layers are followed by a flattening layer, then two fully connected layers and finally a softmax classifier.  
AlexNet [15] follows the ideas of LeNet5, incorporating several new concepts and more depth. It consists of eight layers: five convolutional layers and three fully connected (FC) layers. The great success of AlexNet is due to several advantages such as the ReLU activation function, the dropout, the data augmentation, as well as the use of GPUs to speed up computation.  
Later VGGNet [16] based on AlexNet has been developed as an enhancement that replaces large convolutional filters of size \(11\times 11\) and \(5\times 5\), respectively, in the first and second convolutional layer in AlexNet with multiple small filters of size \(3\times 3\). With a given receptive field (the effective area size of input image on which output depends), several smaller filters stacked together are better than one larger filter because several nonlinear layers increase the depth of the network, allowing it to learn more complex features at a lower cost.  
Another important model is ResNet [17]. The fundamental breakthrough of ResNet was to enable the training of extremely deep neural networks with more than 150 layers with no loss in gradient. Before training with residual layers, very deep neural networks were difficult to train because of vanishing gradients problem. ResNet introduced the concept of jump connection (residual connection) which allows the model to learn an identity function that ensures that upper layers will work at least as well as lower layers.  
In the trend of developing DL models, Google has proposed Inception GoogleNet or Inception [18]. It is essentially a CNN that has 27 layers depth. In CNNs, much of the work consists of choosing the right layer to apply with the most common options (filters sizes, types of pooling, etc.). It is sufficient to find the optimal local construction and repeat it spatially. GoogleNet uses all these layers in parallel and then combines their results.  
These models are very often reused for different tasks in agriculture. Table 1 shows some important works that use state-of-the-art models in agriculture.