esa-sceva/satcom-chunk-collection huggingface dataset:
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 3 Materials and Methods
### Our Approach
#### 3.3.1 Time-Series Data of Travel Time on Road Segments  
To create the time-series data of travel time on road segments as input data, the GPS vehicle and road data were processed as follows.  
First, the GPS vehicle data were the set of relations expressed by  
\[\text{(vid, ts, lat, lon)}\in\text{GPSTX}, \tag{1}\]  
where GPSTX is a set of the GPS transactions of the vehicles. The elements were the vehicle identifier (vid), timestamp (ts), latitude (lat), and longitude (lon). The format of the timestamp was \"YYYY-MM-DD hh:mm:ss\", where YYYY, MM, DD, hh, mm, and ss  
Figure 4: Line chart of the average hourly travel times from Table 1, where the \(x\)-axis shows intervals of hours in chronological order, and the \(y\)-axis is average travel times of each hour in seconds.  
Figure 5: Our overall approach. The time-series data, which are created from Global Positioning System (GPS) data and road data, were passed through a filter to be used as an input of the Self-Attention Long Short-Term Memory (LSTM). The predicted travel time was the output.  
indicate the year, month, day, hour, minute, and second, respectively. A typical example of data corresponding to this expression is displayed in Figure 1.  
Second, the road data including the polyline of coordinates of each road were formulated as  
\[\text{(rid, RPL)}\ \epsilon\ \text{ROADS and} \tag{2}\]  
\[\text{(lat, lon)}\ \epsilon\ \text{RPL}, \tag{3}\]  
where ROADS is a set of road shapes, which are relations including a road identifier (rid) and a road polyline (RPL). The RPL is an ordered set of pairs of latitude (lat) and longitude (lon) points that form the line of a road. An example visualization of some roads in Thailand is presented in Figure 3.  
Next, the locations (lat and lon) of each member of GPSTX were mapped to the polyline of each road, so any vehicles passing through every point on each road were documented. After this, the road segments that had high occurrences of vehicles passing through them were selected.  
Finally, the timestamps (ts) of the vehicles between the endpoints of each road segment were calculated and became travel time entries. Then, these values were averaged according to hours and presented as time-series data of the travel time on each road segment, which is described by the following expression:  
\[\text{(rid, dh, tt)}\ \epsilon\ \text{TTR}, \tag{4}\]  
where TTR is a set of travel time on road segments. The equation elements are road id (rid), date-hour (dh), and average travel time (tt) of a road in that hour. An example of the data is shown in Table 1, where the rid is presented by the road name instead of the road identifier. It should be noted that long-duration parked vehicles were eliminated. This was calculated by finding the duration that a vehicle had stayed in the same latitude and longitude and that exceeded a pre-defined threshold, such as parking for longer than 20 min. The results of this step, which were a time-series dataset of travel time on road segments, became the input for the analytics in the next modules.
# An FPGA Design with High Memory Efficiency and Decoding Performance for 5G LDPC Decoder
## 3 The Proposed Decoder Architecture
### Decompress Block  
It should be noted that the CTV message is stored in CN-MBs as a compressed \(\beta-\)message. The Decompress block (DECOM) converts the CTV messages from a compressed to an uncompressed format (Figure 3). Moreover, this block is responsible for reducing the input value \(min1\) by a negative offset (\(\beta\)). In the case that the difference value (\(min1-\beta\)) is less than 0, the output of this block is set to 0. The detailed architecture of the DECOM block is depicted in Figure 7. The \(d_{\text{cmax}}\) outputs of the DECOM represent \(d_{\text{cmax}}-1=18\) replications of \((\max\{(\text{min1}-\beta),0\})\) magnitudes and only one \(min1\) magnitude (its position equals the \(index\_min1\)). The FCU (Format Conversion Unit) converts a sign-magnitude binary number in \(q\)-bits (i.e., 1-bit sign and \(q-1\) magnitude) to \(q\)-bits in two's complement format.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 4 Use of USVs in Environmental Disasters with Contaminants
### Chemical Hazards
#### 4.1.4 Oil Spill Simulation  
As in situ experiments are costly and time-consuming tasks, environments which simulate the real world can be an asset for oil spill DM. On the other hand, accurate and computationally efficient simulations of ocean pollution are critical to the continued development of new techniques for autonomous environmental monitoring [117]. Therefore, research involving the simulation of environment and methods to deal with oil spills are attracting a significant number of researchers.  
[PERSON] et al. [115] present a simulation of a single robot that performs oil plume tracking. This simulation aims to perform the validation of the controller as well as test the robustness of the control using complex probabilistic environmental models. Later field experiments are performed using a dying marker to collect further data to improve future simulations [117]. [PERSON] et al. [141] expand this idea and simulate plume tracking algorithms using multi-robot. In contrast to existing work purely relying on gradient measurement, the transport model of pollution source is explicitly considered in tracking control design.  
[PERSON] et al. [116] propose a decentralized coordination method that allows multiple robots to efficiently sample and predict the behavior of environmental boundaries, such as the ones generated by oil spills. In their method, the robots first identify the boundary of the substance. When the robots reach the boundary, their primary task is to accurately follow a static or time-varying boundary by maintaining the robots equidistantly distributed along the curve. The method can estimate the shape of the boundary using the collected point-wise measurements. They test their method in a small-scale experiment using multiple USVs in a pool.
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 5 Evaluation
### Detection Time Cost  
In the second experiment, we investigated the time cost of each method spent on vulnerability detection. Note that the preparation time for detection, including pattern extraction in _SmartCheck_, _Slither_, and _Securify_, model training in _DR-GCN_ and our approaches, was not considered. We only took into account the detection time spent on each type of vulnerability. The average values for our two approaches are illustrated in Figures 6 and 7, respectively.  
In Figure 6, _SmartCheck_ spends more than 10 seconds on vulnerability detection, most of which is consumed by its XPath pattern matching. In contrast, _Slither_ has more stable and efficient detection time, i.e., six seconds on average in the four cases. This is contributed by its specific intermediate representation. As two methods based on machine learning technique, _DR-GCN_ and _ASGVulDetector_ have a low detection time, since most work is accomplished during the model training period. However, the time cost of our approach is slightly higher than _DR-GCN_, which is mainly caused by the extra matching step in GMN. Theoretically, the cost of the GNN embedding model used in _DR-GCN_ is in the order of \(O(|V|+|E|)\), while the cost of that in GMN is \(O(|V|\times|V|)\), where \(|V|\) and \(|E|\) are the numbers of nodes and edges, respectively. Nevertheless, the average detection time of _ASGVulDetector_ is only around three seconds, which can be acceptable in most applications.  
Figure 6: Comparison of detection time for _ASGVulDetector_.  
In Figure 7, _ContractFuzzer_ takes nearly 350 seconds on average to find a bug since it needs to actually run the EVM code for certain rounds. _Oyente_ spends much less time than _ContractFuzzer_, around 33 seconds per each possible vulnerability detection, which is mainly consumed by constraint solvers. _Securify_ is regarded as the most time-expensive one among the four methods, as it needs to transform the bytecode to a specific language and traverse all patterns to detect violations. _BASGVulDetector_ has the least detection time, only about six seconds on average. However, compared to _ASGVulDetector_, it costs more time since generating and embedding the contract graph for the bytecode is more complex than those for the source code.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Based Localization Techniques
#### 2.2.4 Multidimensional Scaling (MDS)  
The multidimensional scaling (MDS) localization technique is a dimensional reduction technique that converts the multidimensional data into a lower dimensional space, while keeping the essential information [29]. In more detail, the distance between all pairs of nodes is first obtained by utilizing any ranging technique for creating the MDS distance matrix. Then, the shortest path algorithm is applied, such as [PERSON]'s or [PERSON]'s algorithms. Next, the matrix of the squared distances of proximity is computed and double centering is applied. Singular value decomposition (SVD) follows, which is a matrix decomposition method for reducing a matrix, and the relative matrix coordinates are obtained and transformed to an absolute map by using the known transmitting nodes. Finally, the matrix is rotated, translated, and shifted for achieving the same orientation as the original one [45].  
The aim is to find a low dimensional representation of a group of objects, such that the various distances between the objects fit, and a given set of measured dissimilarities indicating the dissimilar objects. For this, a central processing unit for collecting all available dissimilarities and performing the function minimization is required. In [46], the MDS technique was used for the CSI fingerprinting dimension reduction, in combination with the triangular centroid algorithm, aiming for real-time positioning. Alternatively, the MDS can be combined with the refinement and trilateration (MDS-RT) by adjustment [45], with the aim of improving the accuracy of the localization. In this context, the trilateration is used as an optimization tool for eliminating the MDS poor accuracy. In addition, distributed weighted multidimensional scaling [47], as well as combining MDS with the multilateral algorithm [48], have accurate range measurements. It was also employed in [49] for improving the distance estimation for three-dimensional (3D) nodes localization.
These services make possible to compute \"within the network\" common operators like _average_, _min_, _max_, or _count_, thereby greatly decreasing the amount of data to be transmitted. Services typically rely on synchronized routing trees along which data is processed and aggregated along the way from the leaves to the root [10; 11]. Recently, we have shown that a data aggregation service can be used to represent sensor measurements in a different space [14]. We suggested that the space defined by the principal component basis, which makes data samples uncorrelated, is of particular interest for sensor networks. This basis is returned by the Principal Component Analysis (PCA) [15], a well-known technique in multivariate data analysis. The design of an aggregation scheme which distributes the computation of the principal component scores (i.e., the transformed data in the PCA space) has three major benefits. First, the PCA provides varying levels of compression accuracies, ranging from constant approximations to full recovery of original data. Second, simple adaptive protocols can leverage this flexibility by trading network resources for data accuracy. Third, principal component scores contain sufficient information for a variety of WSN applications like approximate monitoring [16], feature prediction [17; 18] and event detection [19; 20]. The approach we proposed in [14] exclusively addresses the distribution of the computation of the principal component scores and requires the component basis to be computed beforehand in a centralized manner. This limits the applicability of the PCA to small networks, as the centralized computation of the principal component basis does not scale with the network size. The main contribution of this article is to provide a distributed implementation of the principal component basis computation. The algorithm is based on the Power Iteration Method [21, 22], an iterative technique for computing the principal component basis, which can be implemented in a distributed manner by means of an aggregation service. In particular we show that this algorithm can properly compute the principal component basis under the mild hypothesis that the sensor measurements collected by distant sensors are not significantly correlated. We also extend the previous work of [14] by an in-depth discussion of the network tradeoffs and scalability issues. The article is structured as follows. Section 2 reviews previous work in the domain by describing the principles of data aggregation services in WSN and detailing how principal component scores can be computed by an aggregation service. This section also provides a brief overview of PCA together with potential WSN applications and related tradeoffs. Section 3 presents the main issues in the distribution of the PC basis and proposes an implementation based on a data aggregation service. Experimental results illustrating the tradeoffs between accuracy and communication costs are discussed in Section 4. Additional related work and future research tracks are addressed in Section 5.
## Appendix A Derivation of the e2e SNR's CDF Expression
\right)dc+\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_ {D}}\sum_{n=0}^{\infty}\frac{\Phi_{2}\phi_{2R}}{\mu_{0}^{c_{2}}}\int_{0}^{1}c^ {\varrho_{2}-1}G_{7,3}^{1,6}\left(\frac{2^{4}\mu_{R}c}{\psi_{1R}^{2}z} \begin{vmatrix} 1-\kappa_{2R,1}\\ 0,1-\kappa_{1R}\end{vmatrix} \right)dc\] \[+\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\sum_{n=0}^{\infty} \frac{\Phi_{3}\phi_{2R}}{\mu_{0}^{c_{3}}}\int_{0}^{1}c^{\varrho_{3}-1}C_{7,3}^ {1,6}\left(\frac{2^{4}\mu_{R}c}{\psi_{1R}^{2}z} \begin{vmatrix}1-\kappa_{2R,1} \\ 0,1-\kappa_{1R}\end{vmatrix} \right)dc, \tag{100}\]  
where \(\varrho_{1}=\frac{\xi_{1}^{2}}{2}\), \(\varrho_{2}=\frac{n+\kappa_{D}}{2}\), and \(\varrho_{3}=\frac{n+l}{2}\). The closed-form solution for \(\mathcal{I}_{A1}\) can be obtained with the aid of [40] [Equation (07.34.21.0084.01)], and after some simplification by using [40] [Equation (07.34.17.0012.011)], \(\mathcal{I}_{A1}\) in (100) is given as  
\[\mathcal{I}_{A1} =\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\frac{\Phi_{1}\phi_{ 2R}}{\mu_{0}^{c_{1}}}G_{4,8}^{7,1}\left(\frac{\psi_{1R}^{2}z}{2^{4}\mu_{R}} \begin{vmatrix}1,\kappa_{1R},1+\varrho_{1}\\ 0,1,\kappa_{2R},0\end{vmatrix} \right)+\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{ \beta_{D}}\sum_{n=0}^{\infty}\frac{\Phi_{2}\phi_{2R}}{\mu_{0}^{c_{2}}}G_{4,8 }^{7,1}\left(\frac{\psi_{1R}^{2}z}{2^{4}\mu_{R}} \begin{vmatrix}1,\kappa_{1R}, 1+\varrho_{2}\\ \varrho_{2},\kappa_{2R},0\end{vmatrix} \right)\] \[+\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\sum_{n=0}^{\infty} \frac{\Phi_{3}\phi_{2R}}{\mu_{0}^{c_{3}}}G_{4,8}^{7,1}\left(\frac{\psi_{1R}^{2 }z}{2^{4}\mu_{R}} \begin{vmatrix}1,\kappa_{1R},1+\varrho_{3}\\ 0,\kappa_{2R},0\end{vmatrix} \right). \tag{101}\]  
Furthermore, \(\mathcal{I}_{A2}\) in (101) can be solved as  
\[\mathcal{I}_{A2}=F_{\tau_{R}}(z)\int_{1}^{\infty}f_{\gamma_{D}^{\prime}}(c)dc= \left(1-F_{\gamma_{D}^{\prime}}(1)\right)F_{\tau_{R}}(z), \tag{102}\]  
where \(F_{\gamma_{D}^{\prime}}(1)\) is given in (100) and \(F_{\tau_{R}}(z)\) can be obtained from (12). Finally, the CDF of e2e instantaneous SNR is derived by plugging (101) and (102) into (101) as given in (102). Please note that, when performing numerical calculations, the upper-limit of the two infinite summations in (101) are truncated to finite \(N\) terms in a way that is minimizing the convergence error. One can choose a suitable value of \(N\) that yields a negligible convergence error. We consider \(N=15\) for all the analytical curves presented in Section 4.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### Dissemination Process  
The message forwarding process is performed by choosing the most suitable vehicle to forward (relay) the message to the destination node. FOXS-GSC has two wireless communicating components, namely, cloudlets and vehicles. Cloudlets receive messages directed to them that originate from a vehicle. Cloudlets process these messages, and respond to the originating vehicle when the message is a route request. In addition, cloudlets exchange messages with other cloudlets to update traffic conditions. Vehicles send periodic messages containing information about road conditions, and when necessary send a route request to the cloudlets. Moreover, vehicles can work as relay nodes to retransmit messages according to the proposed protocol rules.  
The proposed protocol has two routing methodologies. These methodologies are related to the destination of the message sent. If the message is directed to a cloudlet (e.g., requests for routes or road conditions), the only information about the destination is the cloudlet's geographical position. As cloudlets in FOXS-GSC only send messages to specific vehicles when requested, the response message (e.g., new route) to the vehicle contains the list of all relay nodes used to forward the message to the destination.  
The proposed protocol adds the following information to the original message presented in Table 2.
## 2 Materials and Methods
### System Modeling and Membership Functions
#### 2.1.2 Membership Functions for \(SoC_{LI}(i)\)  
The variable \(SoC_{LI}(i)\), representing the battery state-of-charge, ranges from 0 to 100. Its membership functions define five linguistic categories: Very Low (VL), Low (L), Medium (M), High (H), and Very High (VH). The precise formulation of these functions ensures accurate classification of the input data.  
* Membership functions for \(SoC_{LI}(i)\): \[\mu_{Very\ Low}(SoC_{LI}(i))=\left\{\begin{aligned} 0&if \ SoC_{LI}(i)\leq 0\\ \frac{SoC_{LI}(i)}{20}&if 0<SoC_{LI}(i)\leq 20\\ \frac{40-SoC_{LI}(i)}{20}&if 20<SoC_{LI}(i)\leq 40\\ 1&ifSoC_{LI}(i)>40\end{aligned}\right.\] (1) \[\mu_{Medium}(SoC_{LI}(i))=\left\{\begin{aligned} 0&if \ SoC_{LI}(i)\leq 20\\ \frac{SoC_{LI}(i)-20}{20}&if 20<SoC_{LI}(i)\leq 40\\ \frac{60-SoC_{LI}(i)}{20}&if40<SoC_{LI}(i)\leq 60\\ 1&ifSoC_{LI}(i)>60\end{aligned}\right.\] (2)\[\mu_{\text{{High}}}(SoC_{LI}(i))=\begin{cases}\begin{array}{cc}0&if\ SoC_{LI}(i) \leq 40\\ \frac{SoC_{LI}(i)-40}{20}&if\ 40<SoC_{LI}(i)\leq 60\\ \frac{80-SoC_{LI}(i)}{20}&if\ 60<SoC_{LI}(i)\leq 80\\ 1&if\ SoC_{LI}(i)>80\end{array}\end{cases} \tag{4}\]  
\[\mu_{\text{{Very High}}}(SoC_{LI}(i))=\begin{cases}\begin{array}{cc}60&if\ P_{PV}(i) \leq 9\\ \frac{SoC_{LI}(i)-9}{6}&if\ 9<P_{PV}(i)\leq 12\\ 1&if\ P_{PV}(i)>12\end{array}\end{cases} \tag{5}\]  
The output \(\alpha\), representing the smoothing factor, is also modeled using triangular membership functions. In this case, \(\alpha\) is defined over the range 0 and 10 and categorized into five linguistic labels: Very Low (VL), Low (L), Medium (M), High (H), and Very High (VH). These categories allow the fuzzy controller to generate appropriate smoothing adjustments for power fluctuations.  
* The membership functions for \(\alpha\): \[\mu_{\text{Very Low}}(\alpha)=\left\{\begin{array}{rl}1&if\ \alpha\leq 2\\ \frac{8-\alpha}{6}&if\ 2<\alpha\leq 4\\ 0&if\ \alpha>4\end{array}\right.\] (11) \[\mu_{\text{Low}}(\alpha)=\left\{\begin{array}{rl}1&if\ \alpha\leq 4\\ \frac{6-\alpha}{2}&if\ 4<\alpha\leq 6\\ 0&if\ v>6\end{array}\right.\] (12) \[\mu_{\text{Medium}}(\alpha)=\left\{\begin{array}{rl}0&if\ \alpha\leq 4\\ \frac{\alpha-4}{3}&if\ 4<y\leq 6\\ \frac{8-y}{2}&if\ 6<\alpha\leq 8\\ 1&if\ \alpha>8\end{array}\right.\] (13) \[\mu_{\text{High}}(\alpha)=\left\{\begin{array}{rl}0&if\ \alpha\leq 6\\ \frac{\alpha-6}{2}&if\ 6<\alpha\leq 8\\ \frac{10-\alpha}{2}&if\ 8<\alpha\leq 10\\ 1&if\ \alpha>10\end{array}\right.\] (14) \[\mu_{\text{Very High}}(\alpha)=\left\{\begin{array}{rl}0&if\ \alpha\leq 8\\ \frac{\alpha-8}{2}&if\ 8<\alpha\leq 10\\ 1&if\ \alpha>10\end{array}\right.\] (15)
# Using Graph Neural Networks for Social Recommendations
## 2 Related Work
Comparedto other existing social recommendation models, the Diffnet architecture leverages both user-item interaction data and social network information to enhance the recommendation accuracy. In subsequent research work, [PERSON] et al. [13] introduced an enhanced version of the Diffnet model, called Diffnet++. This enhanced model builds upon the neural influence diffusion framework for social recommendations. In addition to learning user embeddings through influence diffusion from their social network, Diffnet++ incorporates user interest embeddings acquired through interest diffusion from user-item interactions. As each user is connected to their social connections, they form a user-user graph used to learn the user influence embeddings. Similarly, the user is connected to items, enabling the learning of user interest embeddings from item interactions, represented as user-consumed items graphs. Diffnet++ incorporates a neural architecture consisting of four essential components: the embedding layer, the fusion layer, the layer-wise influence diffusion layers, and the prediction layer. To ensure the efficacy of the user embeddings in both influence and interest diffusion graphs, a node attention layer is employed to selectively emphasize the most relevant information from the surrounding connections. Subsequently, after training the user-influence embeddings and user-interest embeddings separately, they are aggregated in a graph attention layer to generate the final user embeddings from the influence and interest perspectives. The model predicts the final preference for a user-item pair once the influence and interest diffusion processes have reached a stable state. This comprehensive architecture empowers the model to effectively capture both influence and interest dynamics. By incorporating item relations, the RelationalNet algorithm emphasizes both user and item influences and interests by adding the layer-wise item diffusion layer. This extension enables the algorithm to capture and leverage the complex relationships between users and items. Consequently, the algorithm aims to provide enhanced user recommendations by considering the individual interests of users and the intricate interplay between users and items. The inclusion of the item-item graph and the item-consumed users graph in RelationalNet further enriches the modeling capabilities, allowing for a more comprehensive representation of the user-item ecosystem. Through the integration of these additional relational graphs, the RelationalNet model aspires to deliver more accurate and contextually relevant recommendations to users, considering a broader spectrum of influences and connections.
## 6 Discussion
### Scalability and Network Dynamics  
The system was designed with edge computing in the RSUs to offload processing and reduce the load on the blockchain. This approach proved successful in the simulations, but in the real world, the availability and distribution of RSUs may vary, and edge resources may be limited. As vehicle density increases, the ability of the system to scale seamlessly needs to be thoroughly tested.  
In addition, the decentralized nature of the blockchain ensures that there is no single point of failure, which is crucial for defending against attacks such as DoS. However, decentralized systems are also susceptible to higher communication overhead and a slower completion of transactions on large networks. This scalability issue could be solved by exploring sharding [30] techniques or reducing the frequency of communication between vehicles and the blockchain.