esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Explanatory Application of the Method
### Data Preprocessing and Training  
Preprocessing is needed to allow the network to handle the timeseries. In general, the monitoring process should be carried out by choosing time windows consistent with the time constants that characterize the considered phenomena. Since the duration of all recorded sequences is usually much longer than the time windows, many instances are typically obtained from each sequence. Furthermore, a suitable limited number of instances must be extracted from the significant sequences of the whole database. Sequences are assumed to be significant when behaviors typical of the investigated phenomena occur within them.  
The preprocessing procedure was here applied through the following steps:  
Figure 5: Some instances of In SAR displacement timeseries at geographical points in the urban area of Naples.  
Since the MT-InSAR timeseries are sampled with variable time steps, a preliminary linear spline was used to resample all the series, by adopting a 6 day timestep, which is also roughly referred to as a \"week\" hereafter. Overall, the duration of each timeseries was 305 samples, corresponding to about 61 months.
2. On the basis of a ranking criterion, a training subset was extracted from the whole database. The displacement rate along the sequence was used as the ranking criterion. Only the timeseries overcoming a preset level of displacement rate were selected to train the SOM network. In the present example, 50,000 timeseries constituted the training set. In Figure 6, examples of a high-ranking and a low-ranking series are shown.
3. Time windows of preset duration were extracted from each timeseries. Considering L = 305 as the duration of a timeseries and N = 10 as the preset duration of the time window, (L \(-\) N + 1) = 296 time windows were extracted from each timeseries. In total, 7,400,000 time windows constituted the training set, while 121,827,976 time windows formed the whole dataset.
4. A feature extraction was performed to reduce the sensitivity of the patterns to noise. The fast Fourier transform (FFT) was calculated for each time window, obtaining 10 complex frequencies and finally a pattern of 19 real components. Principal component analysis (PCA) was performed to reduce the size of the patterns. The final dimension of the patterns was 3.  
An SOM was trained on the set of patterns by means of the hierarchical clustering procedure illustrated in Section 2.2.1. This procedure yielded a global SOM which could cluster the entire dataset of patterns. In total, we found 10,171 clusters. A histogram plotting the incidence of the occurrences in the clusters is shown in Figure 7.  
Figure 6: High-ranking and low-ranking examples.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 4 Experiments
### Experiment Setup  
We benchmarked the performance of the proposed MCDRC on four different datasets of sensor signals and compared it with existing general-purpose compressors, namely, Gzip, BSC [30], PAQ [14], CMIX, and tensorflow-compress with different recurrent units. All compressors were set to give priority to the compression ratio. DZip is not considered for comparison since it requires pre-training and saving model weights. Table 3 lists the versions and methods used by these compressors.  
In realization of all dynamic DRCs, we carefully avoided the use of non-deterministic operations [31]. This ensures the predictors are deterministic, which means that with a fixed random seed and same input data, the compressors will yield exactly the same results. In this way, the compressed data can be decompressed successfully. Each dynamic DRC was tested repeatedly for 10 times with different random seeds.  
All dynamic DRCs including tensorflow-compress and the proposed approach were evaluated on an NVIDIA 2080 TI GPU with 12 GB GRAM. Other compressors, such as Gzip and CMIX, were evaluated on an Intel I9-10900K CPU (3.70 GHz) with 32 GB memory and 20 cores.
## 2 Experimental Section
### Characterization  
\({}^{1}\)H NMR spectra were obtained in deuterated chloroform (CDCl\({}_{3}\)) or trifluoroacetic acid (CF\({}_{3}\)COOD) using Avance III 400 MHz spectrometer (Bruker, Billerica, MA, USA). Fourier transform infrared (FTIR) spectra were collected on a Spectrum 100 FTIR spectrometer (PerkinElmer, Waltham, MA, USA). Differential scanning calorimetry (DSC) analysis was performed on a DSC 8000 instrument (PerkinElmer, Waltham, MA, USA) at a rate of 20 \({}^{\circ}\)C min\({}^{-1}\) under a N\({}_{2}\) atmosphere from \(-\)30 \({}^{\circ}\)C to 200 \({}^{\circ}\)C. Thermogravimetric analysis (TGA) was carried out on a thermal analyzer (METTLER TOLEDO, Columbus, OH, USA) at a heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\) under a N\({}_{2}\) atmosphere from 50 \({}^{\circ}\)C to 600 \({}^{\circ}\)C. Tensile test was conducted on an Instron 5566 instrument (Instron, Norwood, MA, USA) with a speed of 10 mm/min at 25 \({}^{\circ}\)C.  
The bending test was employed to quantitatively evaluate the shape memory behavior of the PBLG-PPG-PBLG film. Firstly, the straight specimen was heated at 100 \({}^{\circ}\)C for 5 min, and then bent to an angle (\(\theta_{0}\)) close to 180 \({}^{\circ}\). Subsequently, it was cooled down to room temperature with an external force to maintain the deformation (\(\theta_{i}\)). Then the deformed specimen was heated to 100 \({}^{\circ}\)C, and recording the change of the angle (\(\theta_{j}\)) with time. The shape fixity ratio (\(R_{f}\)) and shape recovery ratio (\(R_{r}\)) could be calculated according to the following formulas:  
\[R_{f}=\frac{\theta_{i}}{\theta_{0}}\times 100\%\]  
\[R_{r}=\frac{\theta_{i}-\theta_{f}}{\theta_{i}}\times 100\%\]
# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 6 Conclusions  
We present a new 3D LiDAR SLAM method that is composed of a new feature voxel map and a new scan-to-model matching framework. We build a novel feature voxel map with voxels including salient shape characteristics. In order to adapt to the proposed map, we implement scan-to-model matching using POU implicit surface representation to blend the correspondence voxels in map together. Our method achieves an average translational error of 0.61% compared to 0.84% translational error of LOAM on KITTI odometry benchmark. The mapping accuracy is improved due to the application of the POU surface model. However, to implement feature matching based on the POU model burdens our system with more computation cost. Our method runs at 2 s per scan with one thread. For comparison, LOAM runs at 1 s per scan on the same KITTI dataset under the scan to model matching framework. As is shown in the the experimental results, the proposed method yields accurate results that are on par with the state-of-the-art. Future work will proceed in two directions. From the research perspective, a specific and efficient octree will be designed to get a 3D grid. Meanwhile, we will deploy the method to fulfill real time application with the aid of multiple threads or GPU to accelerate data processing.  
[PERSON], [PERSON] and [PERSON] developed the method, carried out the experiments and wrote the manuscript. [PERSON] gave the research direction and ideas, and put forward amendments and improvements to the manuscript. This research was funded by the National Natural Science Fund of China grant number 91848111, 61703387. This work is supported by the National Natural Science Fund of China (Grant No. 91848111, 61703387). The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter \(n_{p}\)** & **The Same Weight** & **POU Weight** \\ \hline \(n_{p}=3\) & 1.84\% & 0.90\% \\ \(n_{p}=4\) & 1.91\% & 0.92\% \\ \(n_{p}=5\) & 1.94\% & 0.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of drift KITTI training dataset between POU Weight and the Same Weight.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 4 Proposed Approach
### POI Extraction
#### 4.5.2 Building Our Bayesian Network
By applying the property of conditional probability \(\mathbb{P}(A,B)=\mathbb{P}(A)\mathbb{P}(B\mid A)\) and considering the conditional independence between the variables, we obtain the following assumptions:  
* The day of the week \(D\) and the time \(T\) are independent of each other: \(\mathbb{P}(T\mid D)=\mathbb{P}(T)\). * The visit frequency \(V\) depends only on the category of place \(C\): \(\mathbb{P}(V\mid D,T,C)=\mathbb{P}(V\mid C)\). * The stay duration \(S\), given the category of place, is independent of \(D,T\), and \(V\): \(\mathbb{P}(S\mid D,T,C,V)=\mathbb{P}(S\mid C)\). Thus, the joint probability for this model, given the conditional independence assumptions, is as follows:  
\[\mathbb{P}(D,T,C,V,S,P)=\mathbb{P}(D)\mathbb{P}(T)\mathbb{P}(C\mid D,T)\mathbb{ P}(V\mid C)\mathbb{P}(S\mid C) \tag{2}\]  
Algorithm 4 describes the construction of a Bayesian network from historical data. It takes as input a user's mobility graph \(G\) and aims to produce a Bayesian network \(bn\) with the constructed CPTs. In the first step, it calculates the basic probabilities for the day \(D\) and time \(T\) indices. It initializes counts for these indices and iterates through the nodes and edges of the graph \(G\). For each edge connected to a source node, it retrieves the movement data \(\mathcal{W}\) and increments the counts for the day and time indices based on the extracted values. Next, the algorithm calculates the conditional probabilities for each movement category \(C\) given the day and time indices. This involves aggregating the occurrences of each category and normalizing them to obtain the required probabilities. It also computes the conditional probabilities of variables \(V\) and \(S\) given \(C\). These probabilities are essential for understanding the relationships between different variables within the network. In the third step, the algorithm constructs the CPTs for each node using the previously calculated probabilities: \(\mathbb{P}(D),\mathbb{P}(T),\mathbb{P}(C\mid D,T),\mathbb{P}(V\mid C)\), and \(\mathbb{P}(S\mid C)\). This construction involves organizing the probabilities into a tabular format that represents the conditional dependencies between variables. These tables are crucial for the accurate functioning of the Bayesian network. Finally, it integrates the calculated CPTs into the corresponding nodes of the Bayesian network \(bn\) and returns the completed network. This integration ensures that each node in the network is equipped with the necessary probabilistic information to perform inference.
## 2 Materials and Methods
### Testing Procedures
#### 2.4.1 Landing Kinematics  
Before data collection, an eight-camera motion capture system (Qualisys, Goteborg, Sweden) was fully calibrated. The CAST marker set was used with 38 reflective markers placed on participants as follows: bilateral markers on the anterior superior iliac spines, posterior superior iliac spines, greater trochanter, medial and lateral femoral epicondyles, medial and lateral malleoli, calcaneus, as well as on the first, second and fifth metatarasal heads. Marker clusters consisting of four markers were used for the thigh and shin segments [22; 23; 24]. Markers on the femoral condyles, malleoli and greater trochanters were retained only for the static calibration trials. Whereafter, they were removed for the dynamic trials. All motion capture data were sampled at a rate of 200 Hz, whereas kinetic data were captured from an AMTI digital force plate at a rate of 2000 Hz (AMTI, Watertown, MA, USA) [25]. More specifically, ankle ROM was recorded throughout the trial as this was of specific importance during the landing phase (i.e., how much range is present in the ankle during landing and weight acceptance).
## 1 Introduction  
Research methodology has changed from traditional observational methods to a data-driven research paradigm, which is called the fourth generation research paradigm [1]. According to the paradigm of existing scientific research, there has been a change from the first generation paradigm that describes natural phenomena through observation, the second generation through modeling and generalization, and the third generation research paradigm using computer simulation technology [2]. Recently, a data-driven research paradigm that analyzes and makes discoveries using vast amounts of data from large research equipment has emerged [3, 4]. Efficiently analyzing this vast amount of data requires massive amounts of computing power [5, 6]. In general, computer clustering technologies are utilized in computing resources of groups of various sizes, from small lab units to data centers. This is called high throughput computing (HTC) [7]. A job management program is required for batching jobs and managing queues to use the HTC system more efficiently, such as HTCondor [8, 9, 10], PBSPro [11, 12], Slurm [13, 14], and Torque [15, 16]. HTCondor is an open source program that was released in 1988 (the name was Condor at the time of release [10]) and has an active open source community.  
We currently operate a data center to support researchers in basic science [17]. Not only do new researchers continue to ask for support, but the demand for resources annually submitted by existing researchers continues to grow [18]. Figure 1 shows that resource demand steadily increases by about 2000-3000 cores each year [19]. The total amount of demand will increase from 3900 cores in 2019 to 13,700 cores in four years. This trend is not confined to domestic, but also to international research groups [20]. However, due to the limited size of equipment that can be introduced on a limited budget, it is impossible to meet the requirements of all researchers. However, the user's request is to meet peak demand at a specific point in time, and since the utilization rate of the cluster is not always high, we integrated the dedicated clusters of each research group into one in order to satisfy this as much as possible.  
First, we chose to change the existing system without deploying a new system as a method for management of integrated resources [21]. Accordingly, we chose to share worker nodes using existing HTCondors but limit quotas of individual groups. In this case, it is difficult to continuously match the information of newly added or deleted users and groups. To solve this problem, we separated the job submission node. Users' access and defined user groups are based on the submission node. We also considered how the temporary resources that occur occasionally can be effectively utilized while maintaining the minimum allocated resources for each group [22].  
In this paper, we discuss how shared resources can be used simultaneously and how the minimum allocated resources can be maintained for each group. We also analyzed the user's job histories to examine changes in job processing patterns. We also analyzed TimeLoss according to job characteristics.
# Area-Efficient Early-Termination Technique for Belief-Propagation Polar Decoders
###### Abstract  
Early-termination techniques for a belief-propagation (BP) decoder of polar codes can improve the decoding throughput by finishing a decoding iteration when an early-termination condition is satisfied. In the BP decoders, the early-termination condition plays an important role, as it affects decoding iteration savings. In this letter, an area-efficient early-termination criterion is proposed, which simplifies the previous threshold-based termination condition by completely eliminating redundant computations. According to the experimental results, the proposed structure for (1024, 512) polar codes can reduce 72.7%, 66.5%, and 59.7% of hardware resources without any degradation in the error-correction performance and decoding throughput compared to the previous threshold-based, information-BER (bit error rate)-based, and frozen-BER-based early-termination techniques, respectively.  
a 2019 8 1001 4 August 2019; Accepted: 4 September 2019; Published: 6 September 2019
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 2 Related Work
### Attention Mechanism  
The attention mechanism is a method inspired by the human visual and cognitive systems, enabling models to learn automatically and selectively focus on the most crucial information in the input data for the current task, thereby enhancing the model's performance and generalisation ability. In multitask processing, attention mechanisms exhibit excellent capability in capturing long-range dependencies and have found widespread applications in natural language processing (NLP), speech recognition (ASR), machine translation (MT), computer vision (CV), and other domains. Among recent notable attention models, GMAN [29] employs a multi-level attention mechanism to dynamically adjust the correlations between different geographical sensors in temporal prediction tasks. However, this approach requires setting up a separate model at each time step, resulting in high computational complexity. GAT [52] utilises a self-attention mechanism to compute attention weights between the central node and its adjacent nodes, capturing local spatial dependencies of the central node. Transformer [53] employs a self-attention mechanism to facilitate the propagation of temporal information, addressing the challenge of capturing long-range dependencies in multitask processing that traditional Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) struggle with. However, Transformer is computationally expensive and is not suitable for large-scale long sequence prediction tasks. Subsequently, Informer [54], based on the Transformer architecture, replaces the dot-product attention mechanism with the ProbSparse self-attention mechanism, significantly alleviating the computational cost issue in long sequence prediction tasks.
# Network Performance Optimization for Low-Voltage Power Line Communications
###### Abstract  
Low-voltage power line communication (LVPLC) medium access control protocols significantly affect home area networks performance. This study addresses poor network performance issues caused by asymmetric channels and noise interference by proposing the following: (i) an improved \(Q\) learning method for optimizing the improved artificial LVPLC cobweb, wherein the learning-based hybrid time-division-multiple-access (TDMA)/carrier-sense-multiple-access (CSMA) protocol, the asymmetrical network system, is modeled as a discrete Markov decision process, associates the station information using online trial-and-error learning, builds a routing table, periodically studies stations to choose a better forward path, and optimizes the shortest backbone cluster tree between the central coordinator and the stations, guaranteeing network stability; and (ii) an improved adaptive \(p\)-persistent CSMA game optimization method is proposed to optimize the improved artificial cobweb saturation throughput and access delay performance. The current state of the game (e.g., the number of competitive stations) for each station is estimated by the hidden Markov model. The station changes its equilibrium strategy based on the estimated number of active stations, which reduces the collision probability of data packets, optimizes channel transmission status, and increases performance by dynamically adjusting the probability \(p\). An optimal saturation performance is achieved by finitely repeating the game. We present numerical results to validate our proposed approach.  
s  
mart grid; power line communication; artificial intelligence; performance optimization; access control
## 3 Methodology
### Classifier  
In order to better fuse information from different modalities, we used the late fusion method in the classifier. We first concatenate the text features \(T_{en}\) and image features \(V_{en}\) into the fully connected layer with only two neurons to obtain the weights \(W_{I}\) and \(W_{T}\). At the same time, we input the text features \(T_{en}\) and image features \(V_{en}\) into different fully connected layers to obtain the classification results \(V_{R}\) (image classification result) and \(T_{R}\) (text classification result). Then we use weighted summation with \(W_{I}\) and \(W_{T}\) as weights for \(V_{R}\) and \(T_{R}\) to obtain the final classification results. The formula is expressed as follows:  
\[F =Concatenate(T_{en},V_{en}) \tag{11}\] \[(W_{I},W_{T}) =Weight-Generation(F)\] (12) \[V_{R} =Img-Class(V_{en})\] (13) \[T_{R} =Text-Class(T_{en})\] (14) \[Result =W_{I}*V_{R}+W_{T}*T_{R} \tag{15}\]where \(F\in R^{n*1024}\) is the joint feature after concatenating text and image features, and \(n\) is the batch size for one input. \(W_{I}\) and \(W_{T}\) are the weights that can be obtained by training. Weight-Generation(), Img-Class(), and Text-Class() are all fully connected layers with a Softmax activation function. \(Result\in R^{m}\) is the classification result, and \(m\) is the number of labels in the dataset. We use the cross-entropy loss function to train our model. The formula expression is as follows:  
\[L=-\sum_{i=1}^{m}[ylogy^{\prime}+(1-y)log(1-y^{\prime})] \tag{16}\]  
where \(y\) is the real label value. \(y^{\prime}\) is the probability value of the predicted.
## 5 Results and Analysis
### Tight Coupling Solutions
#### 5.2.2 Segment 2  
The trajectories obtained with the different TC approaches for this scenario are shown in Figure 14. The GPS/INS solution does not present large errors during the GPS outages as in the corresponding LC  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multicolumn{6}{c}{**Position Error (m)**} \\ \cline{2-7}  & \multicolumn{3}{c}{_RMS_} & \multicolumn{3}{c}{_Maximum_} \\ \cline{2-7}  & _East_ & _North_ & _Up_ & _East_ & _North_ & _Up_ \\ \hline TC GPS/INS & 14.1 & 20.4 & 13.3 & 61.4 & 70.1 & 55.7 \\ TC GG/INS & 8.2 & 12.9 & 13.0 & 61.1 & 69.7 & 46.4 \\ TC GG/INS YVH & 4.8 & 9.5 & 4.0 & 18.4 & 30.5 & 6.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Position error obtained with TC configurations.  
Figure 13: Trajectories obtained with the tight coupling approach (Segment 1).  
case, owing to the ability to compute an integrated position in case of partial GNSS outages. A slightly better trajectory is obtained by including GLONASS measurements and the considered constraints.
## 3 Research Methodology
### Testing (Phase D)  
In this testing phase, there were three main objectives to be fulfilled. It began with evaluating the execution time of our proposed method on selecting the services from a large-scale repository. Existing methods from other authors and our proposed method were simulated by using the simulation tool Netlogo. Retrieval of the execution time on selecting the services was tested on the same environment and dataset. The results were compared by using a data visualization method.  
The second objective was to evaluate the correctness of the selected service of the proposed method. Figure 6 shows a flowchart detailing how the test was carried out. The functional requirement was tested in this phase, where the proposed method would collect the parameter from the end-user, which was the method name. To test the correctness of the service, the selected services were intentionally removed from the repository, and the outputs were re-validated.  
The dynamic selection was tested in this phase as well by using a substitution method [19; 20], as shown in Figure 7. According to [20], the substitution was one of the self-healing techniques, which was capable of diagnosing the failure and applying a suitable repair in the composition of WS. This had ensured the composition to be able to service the end-user in DWSC, as illustrated in Figure 7; Algorithm 1 describes how the substitution worked.  
Figure 6: Graphical view for the time taken to complete the iteration.