esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Problem Formulation  
The decision phase is the frame for the formulation of the optimization problem. The choice of a network to perform VH is a very important issue when considering QoS parameters. In this choice the key is the weight tuning, because the fitness function is very sensitive to the values of the weights. In this sense, our objective is to find an optimal solution, where each solution is a sequence of weights determining the QoS. Some techniques have been developed to adjust the weights in order to find the minimum fitness, _i.e._, Analytic Hierarchy Process [11]. Another way is to assign directly the weights by the user when a call is initialized and after that they are dynamically tuned according to the observed QoS; but this is a subjective technique and waste the user's time. Other ways are based on automatic procedures or policy-enabled mechanisms [12]. For example, when the mobile sensor is reaching a low level of battery, then the weight of the power consumption can be increased. Other proven algorithms for the decision phase have been of the type Multiple Criteria Decision-Making (MCDM), such as Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [8,13]. Nevertheless, we think that a good way to tackle this problem is to cover all the space of possible solutions computing as many of them as be possible in regular intervals, being this characteristic the advantage against other algorithms and the basis of our work.
## 4 Experiments and Results
### Experimental Setup  
The investigations utilized in this research were performed on the Google Colab Pro+ platform [53]. In order to streamline the process of training deep learning models, a Tesla V100-SXM2-16 GB GPU module was utilized. A range of foundational deep learning models, including the proposed Att-ResBiGRU model, were executed utilizing the Python library in conjunction with the Tensorflow backend (version 3.9.1) and CUDA (version 8.0.6). The GPU was utilized to accelerate the training and evaluation of deep learning models. The examinations were conducted utilizing the following Python libraries:  
* In order to read, manipulate, and comprehend the sensor data, the data management tools Numpy and Pandas were implemented.
* In order to visually represent and illustrate the results of data exploration and model evaluation, Matplotlib and Seaborn were employed.
* The Scikit-learn (Sklearn) library was utilized in sampling and data generation studies.
* TensorFlow, Keras, and TensorBoard were utilized to implement and train the deep learning models for model construction.
## 6 Simulations and Discussion
### Discussion  
The results of Simulation 1 are shown in Table 3. The results demonstrated that the DSP algorithm provided fault-tolerant communication for WSNs.  
The results of Simulation 2 showed that the traffic performance of the DSP-based approach was much better than that of the standard HSR protocol and other techniques. By forwarding unicast frames through redundant paths from the source to the destination, the DSP-based approach provides fault-tolerant communication with improved traffic performance. Unlike the standard HSR protocol, which duplicates and floods unicast frames to all rings of HSR networks, the DSP-based approach forwards the unicast frames through two paths that are pre-established between the source and the destination. Therefore, the DSP-based approach significantly reduces redundant unicast traffic in HSR network compared with the standard HSR protocol. Numerically, for the simulation networks used in this paper, the DSP-based approach reduces network traffic by 80% to 88% compared with the standard HSR protocol, by 66% to 80% compared with the QR technique, and by 59% to 76% compared with to the PL technique, thus improving network traffic performance. Additionally, the DSP-based approach has the same network traffic performance as the DVP technique. Unlike DVP, which establishes node-based dual paths for each connection pair of HSR terminal nodes, the proposed approach sets up dual paths for each connection pair of QuadBox pairs. In addition, unlike the DVP, under which each terminal node broadcasts an additional frame to learn MAC addresses and build neighbor tables, the DSP-based approach learns and builds MAC tables based on the existing supervision frames of the standard HSR protocol sent periodically by HSR terminal nodes. Therefore, the proposed DSP-based approach significantly reduces the control overhead for establishing dual paths as well as the memory space required to store these dual paths in networks compared to the DVP technique.
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 6 Mimblewimble Implementations
### Grin
#### 6.1.2 Privacy and Security Properties  
The cut-through process, as explained in Section 4.1, provides scalability and further anonymity. \(\mathtt{Grin}\) performs this process in the transaction pool, which we formalized as _mempool_ (Definition 8). Outputs that have already been spent as new inputs are removed from the mempool, using the fact that every transaction in a block should sum to zero.  
CoinJoin, as we mentioned in Section 4.2, combines inputs and outputs from multiple transactions into a single transaction in order to obfuscate them. In \(\mathtt{Grin}\), every block is a CoinJoin of all other transactions in the block.  
In addition, \(\mathtt{Grin}\) supports a _pruning process_. This process could be applied to past blocks. Outputs that have been spent in a previous block are removed from the block. Block validity (Property 2) should be invariant w.r.t. the pruning process. Each node maintains a local state with a local copy of the chain. The pruning process can be applied recursively to the chain and can keep it as compact as possible. Pruning is useful to free space. As a consequence, when a new node wants to join the network, it can receive just a pruned (i.e., partial) chain and the node needs to validate it, which makes the synchronization process faster. In Section 3.5, _validChain_ should be modified to guarantee the validity of a partial chain.  
As we have mentioned in Section 4.2, Switch commitments provide perfect hiddenness and statistical bindingness. \(\mathtt{Grin}\) implements a switch commitment [55] as part of a transaction output in order to provide more security than computational bindingness (Definition 13), which is crucial for the age of quantum adversaries.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 5 Conclusions  
Researching ADAS security faces numerous challenges, including a huge code base, varying code quality of different components, and strict confidentiality of related codes and data. To address these challenges, this work proposes a method for mining BOF vulnerabilities in ADAS devices using a communication-traffic-assisted approach. This method involves capturing communication traffic, extracting firmware, analyzing and locating operating systems and applications, identifying risk points based on the communication traffic, tracking data flow, and performing a static analysis. We utilized this method to identify BOF vulnerabilities in commercial ADAS devices and employed ROP to achieve remote command execution. Additionally, we developed a remote vehicle control procedure through a reverse engineering analysis of CAN bus traffic and BOF vulnerabilities and proved its feasibility on actual vehicles.  
It should be noted that current research often assumes accessibility to the ADAS's local area network, which may not be feasible in real attack scenarios. Merely relying on BOF vulnerabilities for attacking vehicles is insufficient. It is essential to explore wireless access methods to enhance the effectiveness of attacks. Future efforts will involve exploring more feasible remote attack paths, such as integrating security research on T-Box or in-vehicle entertainment systems, to establish a comprehensive attack chain. This approach aims to enable wireless access to a vehicle's internal network and gain control over the vehicle.  
Conceptualization, M.L. In addition, Y.L.; methodology, M.L. In addition, Y.L.; validation, C.C. In addition, J.L.; formal analysis, J.L. In addition, Y.L.; investigation, C.C.; resources, Y.L. In addition, J.L.; data curation, M.L. In addition, J.L.; writing--original draft preparation, M.L., Y.L. In addition, J.L.; writing--review and editing, C.C.; visualization, J.L.; supervision, C.C.; project administration, J.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Henan Science and Technology Major Project (No. 221100240100), Shanghai Automotive Industry Science and Technology Development Foundation, SongShan Laboratory Pre-Research Project (No. YYJC042022016), Shanghai Science and Technology Innovation Action Plan (No. 21511102500), National Science Foundation of China (No. 62002213) and Shanghai Sailing Program (No. 21 YF1413800 and No.20 YF1413700).  
The data that support the findings of this paper are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Fast and Efficient Lunar Finite Element Gravity Model
## 1 Introduction
### Finite Element Method  
This section presents an overview on the research attempts based on finite element methods corresponding to gravitational modeling. There have been a variety of approximation techniques and basis functions implemented for gravity representation, which includes weighted functions [12; 23; 24], wavelets [25], splines [26], octrees [27], pseudocenters [28] and 3D digital modeling [29]. FEM can be used in a wide variety of applications that are both interesting and important in the fields of mathematical modeling, engineering, and computational analysis. Some examples include using FEM to analyze electric sail static structures [30], determination of the gravity potential and tidal stresses of asteroids [31], and using FEM to analyze a lunar lander during a soft landing [32]. Similar to our approach, each method balances accuracy with efforts to encourage computational speed. FEM was first proposed by [PERSON] [17] as a way to interpolate the geopotential of Earth-based gravitational modeling. The work in Bani Younes [21] provided the computational framework to implement FEM to model the geopotential, acceleration due to gravity, and radial adaption about the Earth's surface. With this descent, the purpose of this paper is to interpolate the lunar gravitational acceleration about the surface and above the surface to help aid future lunar-based spacecraft missions that require precise orbital propagation.  
Using the lunar FEM model, truncating the classical expansion at \(n=2\) allows for local gravity representation at the higher-order perturbations. The FEM modeling approach is applicable to any spherical model and even to irregularly shaped astronomical bodies, while SH is purely for spherical bodies. Furthermore, this method increases computation by trading computer memory for run-time speed.  
For our purposes, FEM is used on the lunar surface, where the method subdivides the entire lunar surface as a large system and subdivides it into smaller finite elements. The gravitational differential equations will then be solved in each shell and will then be connected by nodes. However, determining the correct shell iteration for these higher-order approximations is key with this method.
# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
## 3 The Construction Process of the Synthesis Health Index for Metro Vehicle Wheelsets
### Dimension Reduction of Metro Vehicle Wheelset Data
#### 3.1.3 Feature Dimensionality Reduction of Metro Vehicle Wheelsets  
The wheelset profile data of metro vehicles exhibit nonlinear relationships, making traditional linear dimensionality reduction methods less effective. KPCA employs kernel methods that are effective in addressing high-dimensional datasets with nonlinear relationships [21]. Therefore, this paper utilizes KPCA for the dimensionality reduction of the wheelset profile data.  
The relative size of the eigenvalues \(\lambda_{i}\) can explain the variance in the kernel space. In this study, the contribution rate of each principal component is calculated based on the relative magnitudes of the eigenvalues:  
\[Contri_{k}=\frac{\lambda_{k}}{\sum_{i=1}^{m}\lambda_{i}}\times 100\% \tag{7}\]  
The cumulative contribution proportion (CCP) of multiple principal components can be calculated. Once it exceeds the specified threshold, the number of principal components can be determined:  
\[CCP=\sum_{i=1}^{No.of.PC}Contri_{i} \tag{8}\]While KPCA is well-suited for handling complex, nonlinear relationships, it is important to compare it with other methods such as t-SNE and autoencoders. t-SNE excels in visualizing high-dimensional data, but it is computationally expensive and less suitable for large datasets. Autoencoders, though powerful for nonlinear feature extraction, require large amounts of training data and lack the interpretability offered by KPCA.  
In this study, KPCA was chosen due to its ability to effectively capture nonlinearities while maintaining computational efficiency and interpretability, making it the most appropriate method for dimensionality reduction in the wheelset profile data.
## 4 Design of MIM-QSMS Decoder for RC-QC-LDPC Codes
### Proposed SMIM-DE
#### 4.1.3 VN Update  
For the VN update, the SMIM-DE calculates the conditional pmf \(P_{R|X}^{(t,j)}\) for layer \(j\) at the \(t\)th iteration following the reconstruction, calculation, and quantization steps. Based on the conditional pmfs \(P_{S|X}^{(t,j)}\) and \(P_{L|X}\), we denote \(h(l)=\log(P_{L|X}(l|0)/P_{L|X}(l|1))\) for \(l\in\mathcal{L}\), and \(h^{(t,j)}(s)=\log(P_{S|X}^{(t,j)}(s|0)/P_{S|X}^{(t,j)}(s|1))\) for \(s\in\mathcal{S}\), respectively. Inspired by [15], the reconstruction LUTs associated to a degree-\(d\) (\(d\in\mathcal{D}_{v}\)) VN at layer \(j\) and iteration \(t\) can be generated by  
\[\left\{\begin{array}{l}\phi_{ch}^{(t,j)}(l)=\text{round}(\eta\cdot|h(l)|),\\ \phi_{v}^{(t,j)}(s)=\text{round}(\eta\cdot|h^{(t,j)}(s)|),\end{array}\right. \tag{15}\]  
where \(\text{round}(x)\) returns the closest integer to \(x\), and \(\eta\) is the scaling factor, i.e.,  
\[\eta=\frac{2^{q_{v}-1}-1}{d+1}\cdot\max\Bigl{(}\{|h^{(t,j)}(s)|:s\in\mathcal{ S}\}\cup\{|h(l)|:l\in\mathcal{L}\}\Bigr{)}, \tag{16}\]  
to allow the maximum number of \(q_{v}\) bit width for the VN update. Assume that the C2V messages sent to the VNs in the same layer are i.i.d. We denote the vector of the C2V messages and the channel output received at a degree-\(d\) VN by \((L,\mathbf{S})\in\mathcal{L}\times\mathcal{S}^{d-1}\), where \(\mathbf{s}=(s_{1},s_{2},\ldots,s_{d-1})\in\mathcal{S}^{d-1}\) and \(l\in\mathcal{L}\) is a realization of \(\mathbf{S}\) and \(L\), respectively. The joint pmf at layer \(j\) and iteration \(t\) can be computed by  
\[P_{L,\mathbf{S}|X}^{(t,j)}(l,\mathbf{s}|x)=P_{L|X}(l|x)\prod_{k=1}^{d-1}P_{S|X} ^{(t,j)}(s_{k}|x). \tag{17}\]  
Define \(\mathcal{B}^{(t,j)}\) as the alphabet set of the V2C computational message calculated at layer \(j\) and iteration \(t\) for the VN update, where \(B\in\mathcal{B}\) is the random variable for the V2C computational message. After reconstruction, the VN update function \(\Phi_{v}\) computes the realization \(b\) of \(B\) based on each input \((l,\mathbf{s})\in\mathcal{L}\times\mathcal{S}^{d-1}\) to form the alphabet set \(\mathcal{B}^{(t,j)}\). According to the fraction \(\theta_{d}\) given by (8), the conditional pmf of the V2C computational message is represented by  
\[P_{B|X}^{(t,j)}(b|x)=\sum_{d\in\mathcal{D}_{v}}\theta_{d}\cdot\sum_{(k)\in \mathcal{L}\times\mathcal{S}^{d-1}\atop\Phi_{v}(l,\mathbf{s})\to b}P_{L, \mathbf{S}|X}^{(t,j)}(l,\mathbf{s}|x). \tag{18}\]  
Similar to (14), we can compute (18) in a recursive manner for a low computational complexity. With \(P_{B|X}^{(t,j)}\) and \(\mathcal{B}^{(t,j)}\), we perform DP to design the quantization LUT \(\Gamma_{v}^{(t,j)}\) as an optimal SDQ [21] and obtain the conditional pmf \(P_{R|X}^{(t,j)}\) such that  
\[[P_{R|X}^{(t,j)},\Gamma_{v}^{(t,j)}]=\mathbf{DP}(\mathcal{B}^{(t,j)},P_{B|X}^{ (t,j)}), \tag{19}\]  
where \(\Gamma_{v}^{(t,j)}\) operates the same as \(\Gamma_{ch}\) in (9).
## 3 Integration of Sensors and Geometric Processing
### Georeferencing of Point Spectrometer Data  
While point spectrometers offer high spectral resolution, their data contain no spatial reference. Thus, precise positioning and orientation information as well as an accurate digital surface model are necessary to project the measurement points on the surface. One approach is to use precise GNSS/IMU equipment, which is still expensive. An alternative is to align and capture data simultaneously with a 2D imager, such as for example with an either monochrome or RGB machine vision camera (e.g., [25]). In a next step, computer vision algorithms such as SfM can then be used to derive the orientation and position of the images and the associated point spectrometer measurements, assuming that the images contain sufficient features and are captured with enough overlap [99; 100]. While the second approach is cheaper than the first, both add additional payload to be carried by the sensing system. For both approaches, accurate time synchronization between the spectroradiometer and the GNSS/IMU or camera is required. Furthermore, each spectroradiometer has a certain FOV determined by the slit or foreoptic, which can be constrained with additional accessories, such as a Gershun tube or collimating lens. Finally, the integration time of the spectroradiometer will also have an impact on the size of the footprint. For a spectroradiometer with a relatively long integration time (e.g., 1 s) on a moving UAV platform, the circular footprint will be 'dragged out' into an elongated shape. Additionally, in off-nadir measurements, the circular footprint also elongates to an elliptical shape [24]. The combined effects of the position, orientation, FOV, integration time of the spectroradiometer, flying height and speed of the UAV, and the surface topography will determine the location and size/shape of the spectral footprint. Finally, one should also consider that measurements of a field spectrometer are centerweighted within their FOV, and the configuration of the fiber and fore optic might influence the measured signal [101].
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 3 Materials and Methods
### Linear Analysis of LST Variation in Pakistan  
Monthly, seasonal, and yearly data were processed from the daily data to identify LST fluctuations in Pakistan. The following equation was used to calculate the mean temperatures at various temporal scales:  
\[LST_{m}=\frac{\sum_{i=1}^{N}(\text{T}_{i})}{N} \tag{18}\]  
where \(LST_{m}\) represents the mean of the month, season, or year T\({}_{i}\) satellite overpass times, and \(N\) is the number of times. We comprehensively analyzed the spatial variation of LST through the annual, seasonal, and monthly variation rates of LST, and focused on the regions with an obvious variation trend from the 2018 to 2021 years' data. In this study, the spring (March to May), summer (June to August), autumn (September to November), and winter (December to February) seasons were utilized to classify Pakistan's seasons into four categories. The linear monthly, seasonally, and yearly changes of temperature were calculated as:  
\[Change\text{ {in}}\ LST=LST_{2018}-LST_{2021} \tag{19}\]
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 3 LoRa Technology Overview  
Long Range (LoRa) is a wireless technology in which a low power transmitter forwards small packets of data (between 0.3 kbps and 5.5 kbps) to a receiver, usually over a long distance. These features make LoRa fall into the category of LPWAN networks [20]. Although LoRa's coverage range is wide, it is highly dependent on the environment and the building materials that make it up. The range for rural surroundings is approximately 20 km. However, in urban surroundings, it is reduced to 5 km. Nonetheless, some tests confirm that data was received from a distance of hundreds of kilometers with adequate and unobstructed direct vision in the Fresnel area between the devices [18; 21].  
LoRa Wireless technology was developed by the French company Cycleo. In 2012 the American company Semtech acquired Cycleo, who now owns the patent for the radio part and the LoRa modulation and whose codes are closed. Semtech provides intellectual property licenses to other companies, especially to component manufacturers such as HopeRF, Microchip, etc.  
On the other hand, LoRaWAN protocols are open and defined by the LoRa Alliance, a non-profit organization founded in 2015 with more than 500 partner companies (IBM, Microchip, Orange, Cisco, etc.) created with the commitment to allow and encourage large-scale deployments of LPWAN IoT devices that implement their standards.  
This section presents an overview of this technology and the main characteristics that make it the most suitable to develop our proposal.