esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Extraction
#### 2.4.1 Local Binary Pattern (LBP) Filter  
The Local Binary Pattern (LBP) filter is a well-known techniques used for image representation and classification in machine learning models. It is a simple but very efficient texture operator that labels the pixels of an image by thresholding the neighborhood of each pixel and assigning the result as a binary value. Because of its inequitable strength and computational efficient, it has become a suitable approach for different applications. Due to this, it is seen as a uniting approach and has replaced the conventional structural and divergent statistical texture analysis. Therefore, this method is more suitable for a wide range of applications, such as facial expression recognition, texture classification, and object detection. This filter applies a sliding window of size \(3\times 3\) and extracts the LBP code.  
This LBP code is obtained by thresholding the neighborhood pixels with the center pixel of that window. The LBP code is given by the equation:  
\[LBP=\sum_{n=0}^{7}s(i_{n}-i_{c})2^{n} \tag{8}\]  
where \(i_{n}\) is the neighborhood pixel value and \(i_{c}\) is the center pixel value  
\[s(z)=\begin{cases}1,&z\geq 0\\ 0,&z<0\end{cases}\]  
Thus, considering the binary pattern code as a number and replacing the center pixel with this number gives the output. The neighboring pixels are replaced with ones and zeros by comparing with the center pixel value.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 2 Related Works
### VOS Network with Spatio-Temporal Constraint  
There have been many previous attempts [8; 9; 10; 11; 18; 26] to mitigate the distractor objects problem. Kernelized memory networks [9] alleviate the issue of misalignment by introducing Gaussian kernels to reduce the non-locality of STM [1]. KMMN [18] introduces a top-k guided and a kernel-guided memory matching module to achieve efficient memory matching. SCM [26] introduces a spatially constrained module that utilizes spatial prior values from previous predictions. This helps to eliminate appearance confusion and false predictions. RMNet [8] introduces a pixel-level motion estimation method to reduce match consumption and distractor objects. Previous methods often use pixel-level motion estimation to guide the matching for addressing distractor objects. However, this brings heavy computation costs and struggles against occlusion or fast/blurry motion. Unlike pixel-level motion prediction, the object-level motion prediction proposed in this paper can effectively deal with fast/blurry motion and occlusion scene challenges.
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 3 Datasets, Labelling, and Augmentation
### Synthetic Data  
Synthetic datasets are widely used in computer vision [92; 93] and LiDAR perception [94; 95] for autonomous driving. Experiments [96] show the networks trained with synthetic data can generalise well in the real-world. By using synthetic radar data, the labelling cost can be completely avoided. Moreover, simulation can be used to generate the safety-critical long-tail scenarios [97]. Physics-based simulation methods, such as ray tracing [98; 99], are widely applied to generate synthetic radar point clouds. Experiments [99] show that ray tracing can successfully model the multi-path propagation and separability issue of close objects. However, it is difficult to capture the RCS variation in azimuth with current methods. Another type of simulation is to build a probabilistic model of radar detections, also known as model-based augmentation. The spatial distribution of radar detections over the vehicle can be approximated by the surface-volume model, including the volcanormal measurement model [100], variational Gaussian mixture model (GMM) [100], and hierarchical truncated Gaussian (HTG) [101]. Model parameters can be learned from data. Using this model, we can augment new synthetic radar detections to real point clouds. It is arguable that what level of fidelity is necessary for downstream tasks. In [102], model-based and ray-tracing methods are compared with respect to multiple target tracking. Experiments indicate that the ray-tracing-based model achieves the lowest simulation-to-reality gap.  
There are some seminal works utilising learning-based generative models for radar simulation. For example, the deep stochastic radar model [103] adopts a conditional-VAE architecture. The encoder consists of two heads, one for the RAD tensor and one for the object list. The extracted features are concatenated and further processed with an MLP. The decoder generates a radar intensity map in the polar grid conditioned on the encoded feature and random noise. Generative models can also be used in cross-modality data generation, for example GAN-based LiDAR-to-radar generation [104], GAN-based radar-to-image generation [105], and VAE-based radar-to-image generation [106].
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 2 Methodology
### Extreme Learning Machine  
The Extreme Learning Machine (ELM) is a learning algorithm developed for Single-Hidden-Layer Feedforward Neural Networks (SLFNs) [14; 15], known for its fast and lightweight nature. The main advantage of this learning algorithm is that if the activation function is infinitely differentiable in any interval, the input weights and biases can be sampled from any random distribution and kept fixed through the training procedure, still ensuring the convergence, as shown in [14]. In this way, the only parameters to be tuned are the output weights; therefore, the training process does not need an iterative tuning of the input weights and biases. The ideal number of hidden neurons, however, can be only determined empirically, as well as the amplitude of the distributions that the input weights and biases are sampled from. The absence of an iterative process drastically reduces the computational burden of the training phase, making the ELM an ideal solution when dealing with strict requirements in terms of hardware, as happens in the case of onboard applications. The architecture of an SLFN employing the ELM algorithm is shown in Figure 1.  
Given an ELM-trained SLFN composed of \(M\) input neurons, \(L\) hidden neurons, \(J\) output neurons, an activation function, \(g(\cdot)\), and \(N\) training samples (\(\mathbf{T}\), \(\mathbf{Y}_{true}\)), with \(\mathbf{T}\in\mathbb{R}^{N\times M}\) and \(\mathbf{Y}_{true}\in\mathbb{R}^{N\times J}\), the vector of the weights connecting the input neurons to the \(l\)-th hidden neuron can written as \(\mathbf{w}_{l}=[w_{1l},w_{2l},\ldots,w_{Ml}]\), and the weight connecting the \(l\)-th hidden neuron to the \(j\)-th output neuron is the quantity \(\beta_{lj}\). Finally, let \(b_{l}\) be the bias of the \(l\)-th hidden neuron. With these definitions, the \(j\)-th output with respect to the \(m\)-th input vector is expressed as  
\[y_{mj}=\sum_{l=1}^{L}\beta_{lj}\delta(\mathbf{w}_{l}\cdot\mathbf{t}_{m}+b_{l}), \tag{16}\]  
and, therefore, in matrix form, as  
\[\mathbf{y}=\mathbf{H}\boldsymbol{\beta} \tag{17}\]  
where \(\mathbf{H}\) is the \(N\times L\) matrix of the activation functions.  
Let \(N\) be the number of training samples with target matrix \(\mathbf{Y}_{true}\); the training process aims at finding the output weight matrix \(\boldsymbol{\beta}\) which minimizes the quantity \(||\mathbf{H}\boldsymbol{\beta}-\mathbf{Y}_{true}||\). Such an optimal matrix is given by the following smallest-norm least-square solution:  
\[\boldsymbol{\hat{\beta}}=\mathbf{H}^{\dagger}\mathbf{Y}_{true}, \tag{18}\]  
where \(\mathbf{H}^{\dagger}\) is the Moore-Penrose generalized inverse matrix of \(\mathbf{H}\).
## 3 Results and Discussion
According to the literature, during the long-term heat treatment of garlic, during its aging, the content of allicin and allicin metabolites, such as DAS, DADS, DTS or SAC, may increase several times (6-7 times), which increases the antioxidant activity of black garlic [13, 48]. The temperature-dependent \(\gamma\)-glutamyl transpeptidase (\(\gamma\)-GTP) from \(\gamma\)-glutamyl-\(S\)-allycysteine (GSAC) in raw unprocessed garlic produces \(S\)-allyl-cysteine [11, 12, 52, 59], a substance with strong antioxidant properties. According to the recently published studies by [PERSON] et al. (2022) [50], the thermal stability of \(\gamma\)-glutamyl-\(S\)-allycysteine (GSAC) is much higher than that of \(S\)-allyl-l-cysteine (SAC), and its content during the fermentation processes of black garlic (in conditions of high temperature and humidity) can increase by over 20% [50]. In addition, the antioxidant activity of black garlic significantly increases during the aging process as a result of the Maillard reactions taking place at that time (with different intensities depending on temperature and humidity conditions) [18, 19, 20, 27]. In turn, the share, profile and mutual proportions of the Maillard reaction products determine not only the antioxidant properties of black garlic, but also its specific taste and smell [60, 61, 62]. Therefore, in this study, we focus on the identification of volatile compounds contained in fresh, unprocessed white garlic (WG) (responsible for its specific, pungent taste and aroma) and in black garlic (BG) and its processed products, in which the unpleasant taste and the aroma are lowered and improved as a result of the metabolites appearing during the long-term heat treatment of garlic, which in turn determine the specific, delicate sweet and sour taste and pleasant aroma [60, 61]. Table 2 shows the main volatile compounds detected in all garlic samples, while Figure 4 shows the relative areas of peaks of the main volatile compounds indemnified in the headspace of the garlic samples. Based on chromatograms, the 43 compounds were detected in WG, 25 in BG-C, 24 in BG-P and 23 in BG-S. Fresh garlic contain 13 sulfur compounds, while in black garlic 10 compounds were found, and in paste and powder, 8 compounds were detected. Similarly, [PERSON] et al. (2021) [63] showed that the total sulfide content tended to decrease during the aging process, possibly due to the strong volatility of sulfur compounds. The dominant compounds in fresh garlic does were sulfur compounds and ketones followed by aldehydes. According to the literature, similar compounds were detected in fresh garlic samples in several studies [63, 64, 65]. With aging, the number of sulfur compounds decreased; thus, the relative area (%) of detected peaks decreased. Some of the sulfur volatiles in fresh garlic, such as 1-pentanehiol, dimethyl sulfoxide, 3-mercapto-3-methylbutyl formate and 1-decanethiol, were not detected in black garlic.
## 4 Performance Analysis  
### Theoretical Analysis  
#### 4.1.1 The Delivery Probability of Intra-Cluster Routing  
According to the routing mechanism, nodes are divided into different clusters, and different forwarding methods are used for intra-cluster and inter-cluster routing. Assume that \(T_{N}\) is the maximum delivery delay, then \(P_{intra-cluster}^{ij}(\Delta t\leq T_{N})\) is the delivery probability between any two nodes (for example, node \(i\) and node \(j\)) and \(\Delta t\) is the time difference from the moment of the generation of a message until now. First, it is assumed that a message is delivered to node \(j\) in \(T_{M}(T_{M}\leq T_{N})\), and node \(k\) in \(T_{M}-1\), the probability \(P_{intra-cluster}^{ij}(\Delta t=T_{M})\) can be computed as:  
\[P_{intra-cluster}^{ij}(\Delta t=T_{M})=(1-P_{intra-cluster}^{ik}(\Delta t=T _{M}-1))\times\varepsilon_{kj} \tag{5}\]  
\(P_{intra-cluster}^{ij}(\Delta t\leq T_{N})\) can be computed from the following relationship:  
\[P_{intra-cluster}^{ij}(\Delta t\leq T_{N})=\sum_{1\leq T_{k}\leq T_{N}}P_{ intra-cluster}^{ij}(\Delta t=T_{k}) \tag{6}\]  
Since the contact probability between any two nodes in a same cluster should be greater than the threshold \(\gamma\), to simplify the computation, we assume the contact probability is \(\gamma+\Delta\gamma(0\leq\Delta\gamma,\)\(\gamma+\Delta\gamma\leq 1)\) for any two nodes in the same cluster. We can get the minimum delivery probability \(P_{intra-cluster}^{ij}(\Delta t\leq T_{N})^{\prime}\) as follows:  
\[\begin{split} P_{intra-cluster}^{ij}(\Delta t\leq T_{N})=\sum_ {1\leq k\leq T_{N}}P_{intra-cluster}^{ij}(\Delta t=k)\\ =(\gamma+\Delta\gamma)+\left(1-(\gamma+\Delta\gamma)\right) \times(\gamma+\Delta\gamma)+\cdots+\left(1-(\gamma+\Delta\gamma)\right)^{T_{N} -1}\\ \times(\gamma+\Delta\gamma)=1-(1-(\gamma+\Delta\gamma))^{T_{N}} \geq 1-(1-\gamma)^{T_{N}}\\ =P_{intra-cluster}^{ij}(\Delta t\leq T_{N})^{\prime}\end{split} \tag{7}\]  
As a result, for the intra-cluster routing, when the threshold \(\gamma\) is determined, \(P_{intra-cluster}^{ij}(\Delta t\leq T_{N})^{\prime}\) can be computed.
## 2 Analytical Framework
### Topological Parameters and Their Characteristics
#### 2.1.4 Eigenvector Centrality (EC)  
Eigenvector centrality (EC) assesses the connectivity of a particular segment to the other surrounding segments on the network in their local and global group [40]. It captures the importance of a connected segment as it captures the direct influence and influence over the segment in global terms by considering the entire topological network [41]. The EC of segment \(i\), \(EC_{i}\), can be calculated as follows [37]:  
\[EC_{i}=\frac{1}{\lambda}\sum_{j=1}^{N}A_{ij}EC_{j} \tag{4}\]  
where \(\lambda\) is the largest eigenvalue of adjacency matrix \(A\), and \(EC_{j}\) is the eigenvector centrality score for segment \(j\).  
The above chosen topological parameters provide a comprehensive framework for assessing the vulnerability of transportation networks when critical links in the network are impacted by disruptive events in each topological parameter perspective. For instance, SC captures the direct connections of road segments, offering a straightforward metric for understanding the local scale-free properties of the network. BC, in contrast, highlights the criticality of segments that act as intermediaries in traffic flows, making it a robust indicator of the network's vulnerability to disruptions in high-utilization links, (i.e., passed by road segments). CC provides insight into the overall accessibility of road segments, which is crucial for evaluating the network's efficiency and responsiveness to failures. Finally, EC assesses the global influence of segments, capturing the interdependence of key nodes in maintaining network cohesion. Together, these parameters address various dimensions of vulnerability, from local connectivity to global network influence, enabling a holistic evaluation.
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
###### Abstract  
Quasi-Synchronous Frequency hopping (FH) Multiple Access (QS-FHMA) systems feature high communication efficiency, strong flexibility, and low operational costs, and they have been widely used in various FH communication scenarios such as satellite communication, military communication, and radio measurement. The low-hit zone (LHZ) FH sequences set (LHZ FHS set) plays a critical role in QS-FHMA systems, enabling user access with permissible time-delay offsets while maintaining superior performance. In this paper, three new methods to construct LHZ FHS sets based on _m_-sequences are proposed. The newly constructed sequence sets achieve optimality with respect to the Peng-Fan bound. Compared with existing LHZ FHS sets constructed from _m_-sequences, these new sequence sets offer more flexible parameters. Furthermore, due to the simple structure of _m_-sequences and their extensive adoption in engineering applications, the proposed new sequence sets possess significant practical value for engineering implementation.  
**Keywords:** frequency-hopping communication; frequency-hopping sequence set; quasi-synchronous frequency-hopping; low-hit zone; maximum periodic Hamming correlation  
**MSC**: 94A55
## Abbreviations  
BETs Bare Electrodynamic Tethers BLE Ballistic Limit Equation CADETS A Calorimetric Detection System for Hypervelocity Impacts CFRP Carbon-Fiber-Reinforced Panels CISAS Center for Space Activities \"[PERSON]\" CSTS Collision Simulation Tool Solver ENVINAT ENVInronmental SATellite EMR Energy-to-Mass Ratio ESA European Space Agency E.T.Pack Electrodynamic Tether technology for PAssive Consumable-less deorbit Kit FASTT Fragmentation Algorithms for Strategic and Theater Targets GEO Geosynchronous Equatorial Orbit IMBEMUS IMpact BEHavior of MUltifunctional materialS JAXA Japan Aerospace Exploration Agency LEO Low Earth Orbit LGG Light-Gas Gun LOFT Large Observatory For X-ray Timing ME Macroscopic Element NASA National Aeronautics and Space Administration PEEK PolyEther Ether Ketone ReDSHIFT Revolutionary Design of Spacecraft through Holistic Integration of Future Technologies SBM Standard Breakup Model TFHFG Thin-Film Heat-Flux Gauges
## 4 Elevator People-Counting System
### System Architecture and Usage Scenarios  
The system's architecture diagram is illustrated in Figure 2. On various floors of the building, we installed NVIDIA Jetson Nano embedded development boards equipped with cameras. Our developed people flow counting system was integrated into these development boards. When the development board captures the current waiting and  
Figure 1: User interface display.  
in-elevator passenger numbers using the camera and completes the counting process on the board, the data are transmitted back to the backend server through Wi-Fi. Based on the returned IP address, the server identifies the floor information and updates the relevant data. Finally, the organized data are presented on both the mobile interface and the electronic screen interface.  
In the envisioned application scenario, within a four-story building, there is an elevator system designed to transport people. During peak hours, a substantial number of individuals require elevator usage. However, due to the absence of available information regarding the current waiting queues on each floor and the occupancy status within the elevator, users often experience anxiety and are unable to determine whether to continue waiting, resulting in inefficient time utilization. To address this issue, we implemented embedded development boards equipped with cameras and Wi-Fi capabilities at elevator entrances, exits, and corridors on each floor. Each embedded board captures real-time images of waiting queues, elevator occupancy, and pedestrian flow in the corridor through the embedded camera. The system analyzes these data locally and transmits them to a backend database via Wi-Fi, with distinct IPs assigned to differentiate data originating from different floors. The collected data are then presented on large screens and web pages for each floor. Users can instantly access this information upon entering the building by scanning QR codes. This provides insights into the current status of waiting queues on various floors, the elevator occupancy, and the corridor pedestrian density.  
Figure 2: System architecture.  
Beyond smartphone access, users can also acquire this information from large screens placed on each floor. Armed with insights into elevator wait times and occupancy, users can make informed decisions regarding whether to continue waiting for the elevator or explore alternative transportation options. These data can also be utilized to optimize elevator scheduling algorithms. By incorporating this information into scheduling algorithms, the system can devise the most efficient route for users to reach their destinations within the building, facilitating prompt elevator usage or alternative modes of transportation for users, thus minimizing travel time.
# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 4 Challenges and Future Directions
### Hybrid Computing and Learning Modes  
With various data collection sources, different computing modes have been used to collect and process different types of data, e.g., cloud computing, mobile computing, edge computing, fog computing, etc. Different computing modes have different computation and communication capacities and how to integrate and utilize these computing modes with existing big data infrastructures remains challenging. Some studies are exploring in this direction. For example, based on IoT devices and fog computing, a low-cost vehicular traffic monitoring system was developed in [107], which collects vehicle GPS traces and uses fog devices to process the collected data and extract traffic information.  
Traffic estimation and prediction problems are usually formulated as supervised problems from the perspective of machine learning. However, other learning modes can be leveraged for solving the challenges in these problems, e.g., transfer learning [108] and generative adversarial learning [109]. Transfer learning is a potential solution for the data sparsity problem in different locations. A POI-embedding mechanism was proposed in [110] to fuse human mobility data and city POI data. Furthermore, CNN and LSTM were combined to capture both spatiotemporal and geographical information, and mobility knowledge was transferred from one city to another, which was proven effective for improving the prediction performance for the target city with only limited data available.  
Existing traffic estimation and prediction methods are developed with the assumption that the traffic infrastructures remain the same. This may not hold in some cases with new urban planning. A novel off-deployment traffic estimation problem was proposed and defined in [111], and a traffic generative adversarial network approach named TrafficGAN was further proposed to solve this problem, which was able to describe how the traffic patterns change with the travel demand and underlying road network structures.