esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulations
### Simulation Results
** & \\ \hline - & 1 & 100 & 99.9 & 100.17 & 0.06 & 38.78 & - & - & - & - & - \\ \hline \multirow{4}{*}{\(B\)-} & 2 & 99.93 & 99.82 & 100.08 & 0.05 & 33.48 & - & - & - & - & - \\ \cline{2-11}  & 4 & 99.89 & 99.75 & 100.05 & 0.05 & 28.48 & 99.89 & 99.8 & 100 & 0.05 & 27.94 \\ \cline{2-11}  & 8 & 99.82 & 99.72 & 99.88 & 0.04 & 19.14 & 99.81 & 99.72 & 99.95 & 0.05 & 18.78 \\ \cline{2-11}  & 16 & 99.71 & 99.61 & 99.85 & 0.06 & 8.2 & 99.71 & 99.61 & 99.79 & 0.04 & 7.88 \\ \hline \multirow{4}{*}{\(W\)-W} & 2 & \(6.54\times 10^{10}\) & \(1.17\times 10^{9}\) & \(1.89\times 10^{11}\) & \(5.06\times 10^{10}\) & 48.12 & - & - & - & - & - \\ \cline{2-11}  & 4 & \(7.35\times 10^{10}\) & \(4.41\times 10^{8}\) & \(3.25\times 10^{11}\) & \(5.42\times 10^{10}\) & 48.82 & \(8.56\times 10^{10}\) & \(1.62\times 10^{9}\) & \(2.28\times 10^{11}\) & \(5.49\times 10^{10}\) & 49.46 \\ \cline{2-11}  & 8 & \(1.13\times 10^{11}\) & \(8.82\times 10^{8}\) & \(3.29\times 10^{11}\) & \(7.45\times 10^{10}\) & 51.2 & \(9.55\times 10^{10}\) & \(1.11\times 10^{10}\) & \(2.48\times 10^{11}\) & \(6.09\times 10^{10}\) & 50.32 \\ \cline{2-11}  & 16 & \(1.27\times 10^{11}\) & \(101.55\) & \(2.80\times 10^{11}\) & \(8.34\times 10^{10}\) & 52.24 & \(1.36\times 10^{11}\) & 102.12 & \(4.13\times 10^{11}\) & \(8.45\times 10^{10}\) & 52.52 \\ \hline \multirow{4}{*}{\(W\)-B} & 2 & 99.86 & 99.78 & 99.97 & 0.04 & 25.6 & - & - & - & - & \\ \cline{2-11}  & 4 & 99.78 & 99.69 & 99.89 & 0.05 & 14.96 & 99.79 & 99.71 & 99.87 & 0.04 & 16.62 \\ \cline{2-11}  & 8 & 99.72 & 99.59 & 99.82 & 0.06 & 8.98 & 99.73 & 99.6 & 99.89 & 0.06 & 10.42 \\ \cline{2-11}  & **16** & **99.63** & **99.45** & **99.82** & 0.09 & **4.54** & 99.68 & 99.52 & 99.91 & 0.08 & 7.54 \\ \hline \multirow{4}{*}{\(B\)-W} & 2 & \(1.59\times 10^{11}\) & \(4.60\times 10^{10}\) & \(3.63\times 10^{11}\) & \(7.17\times 10^{10}\) & 54.6 & - & - & - & - & - \\ \cline{2-11}  & 4 & \(1.95\times 10^{11}\) & \(3.75\times 10^{10}\) & \(3.39\times 10^{11}\) & \(7.09\times 10^{10}\) & 56.8 & \(2.00\times 10^{11}\) & \(3.67\times 10^{10}\) & \(3.74\times 10^{11}\) & \(8.06\times 10^{10}\) & 56.98 \\ \cline{2-11}  & 8 & \(2.64\times 10^{11}\) & \(5.38\times 10^{10}\) & \(4.89\times 10^{11}\) & \(9.61\times 10^{10}\) & 59.36 & \(2.12\times 10^{11}\) & \(4.96\times 10^{10}\) & \(4.06\times 10^{11}\) & \(8.03\times 10^{10}\) & 57.22 \\ \cline{2-11}  & 16 & \(3.58\times 10^{11}\) & \(6.11\times 10^{10}\) & \(6.25\times 10^{11}\) & \(1.49\times 10^{11}\) & 61.16 & \(3.18\times 10^{11}\) & \(2.26\times 10^
## 4 Experiments and Results
### Experimental Results for Different Charging Angles  
This experiment compares the relationship between the size of the charging angle and the average number of chargers. The experimental angle ranges from 45\({}^{\circ}\) to 360\({}^{\circ}\). The RPRO, PBAD, and SMRU algorithms were tested separately. The charging radius is 16 units, and the number of sensors is 40. The experiment found that the larger the charging angle, the fewer the number of chargers required. The different charger requirements for different algorithms are as follows: SMRU has the smallest, followed by PBAD, and the worst is RPRO. Additionally, for the same parameter settings of the same algorithm, there seems to be a lower demand limit on the number of charger requirements. As shown in Figure 9, it can be observed from the experimental results that there is no significant difference in the average number of chargers after 270\({}^{\circ}\) between PBAD and SMRU. However, the difference is significant when the angle is smaller than 270\({}^{\circ}\).  
Figure 8: The average execution time.
## 4 Testing the Performance of Measurement Matrix Using Simulated Underwater Echo
### Simulation of Underwater Echo and Its Compression and Reconstruction
The output signal is then passed to the signal modulator and the LDS multi-channel data acquisition stage, with a sample frequency of 1 MHz. Finally, the signal is stored in the computer: this is the signal used in this paper. There are two rotation stages, one for the transmitting/receiving transducer manufactured by the 726 institute in Shanghai, China (working at 40 kHz-80 kHz) and one for the rig holding the model \begin{table}
\begin{tabular}{c c c c c c} \hline
**Compression Ratio** & **Gaussian Matrix** & **Partial Hadamard Matrix** & **Matrix Based on Cyclic Direct Product and QR Decomposition** & **Bernoulli Matrix** & **Toeplitz Matrix** \\ \hline
0.1 & 0.094 & 0.076 & 0.0374 & 0.083 & 0.038 \\
0.2 & 0.116 & 0.106 & 0.0387 & 0.199 & 0.055 \\
0.3 & 0.190 & 0.147 & 0.0398 & 0.293 & 0.071 \\
0.4 & 0.218 & 0.152 & 0.0399 & 0.308 & 0.080 \\
0.5 & 0.237 & 0.198 & 0.0414 & 0.517 & 0.096 \\
0.6 & 0.317 & 0.204 & 0.0421 & 0.636 & 0.131 \\ \hline \end{tabular}
\end{table} 
Table 1: Construction time for various types of measurement matrix at different compression ratios (t/s). Figure 6: SNR enhancement comparison by different types of measurement matrices for different SNR (**a**) SNR = 4 dB; (**b**) SNR = 0 dB; (**c**) SNR = \(-3\) dB. submarine. They are 20 m apart, and both are 10 m underwater. In order to obtain echoes at different incident angles, the submarine model is rotated counterclockwise at uniform velocity. Two typical angles, 30\({}^{\circ}\) and 45\({}^{\circ}\), are selected for presentation of the measurements in this study. Figure 8 is the original echo signal obtained at incident angles of 30\({}^{\circ}\) and 45\({}^{\circ}\). These signals are compressed and reconstructed by CS with Gaussian matrix, Bernoulli matrix, partial Hadamard matrix and Toeplitz matrix as the respective measurement matrix. The performance of the five types of measurement matrices is compared. Figure 9 plots the changes in the SNR with the compression ratio. The results processed from the experimentally measured data confirm the superior performance of the measurement matrix based on the cyclic direct product and QR decomposition in the processing of underwater echo, as compared to Gaussian matrix, Bernoulli matrix, partial Hadamard matrix and Toeplitz matrix. It provides greater enhancement in the SNR with increasing compression ratio. The partial Hadamard matrix is a deterministic measurement matrix, and is more effective for signal processing. However, it falls short on the length of the signal that it can process. This is overcome in the measurement matrix based on the cyclic direct product and QR decomposition, which also yields a  
Figure 8: Original echo signal. Figure 7: Deployment method in the test [28]. better processing outcome than the other measurement matrices. Furthermore, the amount of data is also reduced, and the SNR is improved as anticipated.
# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Sequential Recommendation
#### 3.2.2 Long- and Short-Term Sequence Recommendation
Another modified RNN model adjusts for batch generation by considering dwell time to more precisely capture user behaviors, discovering the potential impact of dwell time on the results [89]. However, a significant challenge is that when all candidate items rank above popular items based on a popularity-driven sampling method, the learning speed of the developed model is constrained, particularly when recommending long-tailed items. Addressing this issue, an RNN model with top-k gains [90] introduced a novel sampling strategy for GRU4 Rec, blending uniform sampling with popularity-based sampling to markedly improve performance. To enhance the modeling of item information, not only was the item's ID considered but other features such as text descriptions and images were also incorporated. This was effectively realized using a parallel RNN (p-RNN) ar chitecture for efficient recommendation [91]. In terms of training strategies, the authors innovatively introduced methods such as alternating training, residual training, and inter-laced training to optimize the performance of the p-RNN. Furthermore, [PERSON] explored multiple approaches to combine a session-based KNN classifier with GRU4 Rec, such as switching, cascading, and weighted blending, further enhancing the accuracy of the output recommendations [92]. The RUM model [93] incorporates a user memory module to preserve such interaction information. Utilizing a dual-layer memory mechanism, the model employs an external memory mechanism to simulate the user's memory process, thereby capturing both the long-term and short-term preferences of the user. Recurrent structures are employed to capture the user's recent activities, whereas an external memory unit stores the user's historical behaviors. Attention mechanisms are applied to select the most relevant historical information. A multilayer network architecture is finally used to capture deep interaction information. By dynamically updating the memory unit, the model reflects user preference changes, as indicated in the following Equation (11). \[add_{i}=\tan\,\hh(A^{T}q_{i}+b_{a}),m_{k}^{u}\gets m_{k}^{u}+z_{ik}\cdot add _{i} \tag{11}\]  
The above equation is utilized to update the user's preference memory. A and \(\bb_{a}\) act as trainable additive parameters with the aim of erasing existing information prior to the incorporation of new data. Transformers have achieved significant breakthroughs in NLP tasks, with substantial pretrained models such as BERT leading the way. Bert4 Rec [94] adapts this architecture for recommender systems. To achieve optimal recall results, it is essential for the employed system to account for both the long- and short-term interests of the target user. In practical applications, recommendation models can discern these two preferences from the user's historical sequence and ultimately merge them using gated unit modules.
## 3 Workflow and System Architecture  
This study aims to provide a symptom-based screening system. The heart of the proposed system is the classification model, generated based on collected patient data. Each data example representing one patient has a label stating that this patient is infected or not infected with a given pathogen--in the case of the presented analysis, this is the SARS-CoV-2 virus and the COVID-19 disease.  
The system is based on data collected from patients who are able to provide results of a test for the presence of the pathogen. A very good source of reliable data seems to be a hospital, where patients can be examined and tested. In order to collect the necessary information, each patient is asked to fill the questionnaire in. The surveys are collected by medics and supplemented with a label resulting from the laboratory test (e.g., PCR test in case of SARS-CoV-2) indicating the disease or lack thereof. Moreover, the medics should anonymise the surveys and pass them on to analysts for digitalisation. The data (in digital form) are then processed and cleaned. Classification model generation can be twofold. A simpler approach that does not require the involvement of an experienced data scientist is to use an automated machine learning (AutoML) solution and automatically generate the classifier. A more sophisticated approach may be required if the data are complex and AutoML does not provide results of sufficient quality. In this situation, AutoML can still indicate interesting directions that can be extended by data scientists. The finally generated decision model is deployed in an application that performs screening on the basis of end-user input, visualises the results of the analysis, and explains the premises of the model's decisions. The newly collected data can be used for validation of the classifiers, as users can add labels to the submitted symptoms after receiving the test result. The presented workflow is illustrated in Figure 1. The steps for data processing, automatic model generation, and model generation by experienced data scientists are discussed in the following sections as a case study for symptom-based screening for COVID-19.  
The application proposed in this work, which performs the screening of patients on the basis of symptoms, consists of three main components related to the frontend, backend, and a database. The backend consists of the analytical server executing classification models as a web service. The frontend is a web application that can be used by users of various types, e.g., end users, medics, and data scientists. The architecture of the created solution is presented in Figure 2.  
Figure 1: System workflow presenting the steps leading to the generation of the model within the DECODE engine, as well as the actors and environments involved in this process.  
Figure 2: System architecture consisting of three main components.
## 4 Results and Discussion
### Analysis of Differential Interferometric Effects
Quantitative analysis reveals that the \"Fucheng-1\"/\"Shenqi\" constellation demonstrates a superior interferogram quality, showing 21.47% lower mean SPD values with 70.99% reduced standard deviation compared to Sentinel-1A. When using the mean PSD with a 19 \(\times\) 19 sliding window, the dual-star constellation exhibits 35.47% lower mean PSD values accompanied by 70.36% lower standard deviation, conclusively demonstrating its enhanced phase quality in differential interferograms relative to Sentinel-1A.
# Temporal Convolutional Network-Based Axle Load Estimation from pavement Vibration Data
###### Abstract  
Measuring the axle loads of vehicles with more accuracy is a crucial step in weight enforcement and pavement condition assessment. This paper proposed a vibration-based method, which has an extended sensing range, high temporal sampling rate, and dense spatial sampling rate, to estimate axle loads in concrete pavement using distributed optical vibration sensing (DOVS) technology. Temporal convolutional networks (TCN), which consist of non-causal convolutional layers and a concatenate layer, were proposed and trained by over 6000 samples of vibration data and ground truth of axle loads. Moreover, the TCN could learn the complex inverse mapping between pavement structure inputs and outputs. The performance of the proposed method was calibrated in two field tests with various conditions. The results demonstrate that the proposed method obtained estimated axle loads within 11.5% error, under diverse circumstances that consisted of different pavement types and loads moving at speeds ranging from 0-35 m/s. The proposed method demonstrates significant promise in the field of axle load reconstruction and estimation. Its error, closely approaching the 10% threshold specified by LTP, underscores its efficacy. Additionally, the method aligns with the standards set by Cost-323, with an error level-up to category C. This indicates its capability to provide valuable support in the assessment and decision-making processes related to pavement structure conditions.  
Keywords:weigh in motion; axle load estimation; pavement vibration; temporal convolutional networks; distributed optical vibration sensing +
Footnote †: journal:
# A 245 GHz Real-Time Wideband Wireless Communication Link with 30 Gbps Data Rate
###### Abstract  
This paper presents a 245 GHz wireless communications system with a data rate of 30 Giga bits per second (Gbps) at a 1.2 m distance, which proves the potential for future high-speed communications beyond 5G technology. The system consists of low-complexity and real-time base-band modules to provide the high-speed wideband signal processing capability. Multi-channel base-band signals are combined and converted to 15.65 \(\pm\) 6.25 GHz wideband intermediate frequency (IF) signals. A novel 245 GHz waveguide bandpass filter (BPF) with low loss and high selectivity is designed and applied to a terahertz (THz) front-end for image rejection and noise suppression. Configuration of the base-band, IF, and THz front-end modules is also given in detail. The 245 GHz wireless communication link is demonstrated over a distance of 1.2 m.  
terahertz communication; real-time; low complexity; FPGA implementation; front-end 20229, 9, 683. [[https://doi.org/10.3390/photonics9100683](https://doi.org/10.3390/photonics9100683)]([https://doi.org/10.3390/photonics9100683](https://doi.org/10.3390/photonics9100683))
###### Abstract  
Space-based Earth Observation (EO) systems have undergone a continuous evolution in the twenty-first century. With the help of space-based Maritime Domain Awareness (MDA), specially Automatic Identification Systems (AIS), their applicability across the world's waterways, among others, has grown substantially. This research work explores the potential applicability of Synthetic Aperture Radar (SAR) and Distributed Satellite Systems (DSS) for the MDA operation. A robust multi-baseline Along-Track Interferometric Synthetic Aperture Radar (AT-InSAR) Formation Flying concept is proposed to combine several along-track baseline observations effectively for single-pass interferometry. Simulation results are presented to support the feasibility of implementing this acquisition mode with autonomous orbit control, using low-thrust actuation suitable for electric propulsion. To improve repeatability, a constellation of this formation concept is also proposed to combine the benefits of the DSS. An MDA application is considered as a hypothetical mission to be solved by this combined approach.  
astrionics; autonomous orbit control; autonomous systems; constellation of formations; control systems; distributed satellite system; electric propulsion; Formation Flying; intelligence surveillance and reconnaissance (ISR); maritime domain awareness; multi-baseline AT-InSAR; Trusted Autonomous Satellite Operations (TASO)
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 2 Background
### Commutative Cipher Based En-Route Filtering  
The verification process of CCEF based on query-response model is illustrated in Figure 2. In this model, a session is established by sending a query message (\(Q\)) in an area of interest from where the sensors transmits a message containing response (\(R\)) to the cluster head (\(CH\)) which routes it towards the base station (\(BS\)). The \(BS\) transmits a query message \(Q=\left[Q_{id},CH_{id},\left\{k_{s}\right\}_{K_{HCH}}\right]\) to the respective \(CH\) in that area. This message is composed of the Query ID (\(Q_{id}\)), \(CH\) ID (\(CH_{id}\)) and session key (\(k_{s}\)) encoded with the \(CH^{\prime}s\) node key (\(k_{n}\)), i.e., \(\left\{k_{s}\right\}_{K_{HCH}}\).  
A session is established by dropping a copy of witness key (\(k_{w}\)) on all en-route nodes. By using (\(p=1/\alpha h\))--a probabilistic method--a fixed number of nodes are selected as verification nodes. Events including false reports are being generated randomly in a \(WSN\). The sensors encountering the event information form consensus about identity of the event choose a \(CH\) and play their part in report generation.  
The neighbours, receiving the event's report information, endorse and forward it to the respective \(CH\). As the query arrives at the \(CH\), it uses the \(k_{n}\) key to decode the \(k_{s}\) key to find if it was generated by the originating \(BS\). The \(CH\) compresses the session key (\(MAC_{s}\)), created by the event sensing nodes; \(E\), \(F\), \(G\), and \(H\) with a simple \(XOR\) operation and transmits a response including the \(MAC_{s}\) and the \(IDs\) of report endorsing nodes. In turn, the \(CH\) transmits a response message \(\left[Q_{id},\,R,\left\{E_{id},F_{id},G_{id}\right\},\,MAC_{s},\,MAC_{n}\right]\) and, as a result, a session is created. Consequently, reports created by the event sensing nodes are transmitted to the \(BS\) by the respective \(CH\).  
After a session is established, reports are transmitted to the \(BS\) by the \(CH\). The sensors used \(k_{w}\) keys to determine \(Q_{id}\) to check the query validation. Consequently, \(k_{w}\) is used to validate the \(MAC_{s}\) without having \(k_{s}\) with the help of the commutative cipher property. The \(BS\), after receiving the report in the response message, produces the \(MAC_{n}\) and validates it along with \(MAC_{s}\). In case both conditions are met, \(CH\) and all of the report-endorsing nodes are validated. However, if the any of conditions are not validated, either the \(CH\) or at least one of the endorsing nodes is taken over by an adversary.  
Figure 2: Query-response model for en-route filtering schemes.
## 2 Theoretical Foundation
### Radar Tracking Process  
According to [9], a tracking system is used to observe moving objects and provide an ordered sequence of position data. This process involves estimating the state of a moving object from remote, indirect, and imprecise measurements. The measurements can originate from one or several sensors, including image and georeferenced tracking. In addition, uncertainties in the source of the measurement can also occur when multiple targets are closed, and even when the observed detections can be separated, they cannot be reliably associated with the targets.  
A similar situation occurs in the track formation problem when there are multiple targets but their quantity is unknown and some of the measurements could be spurious; moreover, [10] indicates that the application related to the radar display translates the position of the echoes on the screen into a metric coordinate system based on the time and spatial position of the echoes and the software automatically calculates multiple flight parameters, such as the speed with respect to the ground, and the length and duration of the trajectory.
# QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks
## 3 Proposed Architecture
### OF-NB Logic and Traffic Detection
This traffic detection table consists of a group of flow entries responsible for detecting traffic, which requires a specific QoS treatment in accordance with its user profile. When traffic matches with a flow entry to service detection, the appropriate MPLS labels are added, and the packets are sent to the SDD. On the other hand, if there are no matches with a specific detection flow entry, the traffic is dropped without action because the packet was already routed as traffic without the QoS requirement, as was indicated in Step 1. Continuing Pipeline Processing 2, if the packet is sent to SDD, it processes the packet in search of service characteristics such as the multimedia type, source IP address, destination IP address, protocols, ports used, encoder type, QoS requirement, etc. Next, the SDD sends the service information to the PCRF or PCF mobile element, and the procedure to create the specific EPS-Bearer with QoS requirement is executed. It is important to note that the OF-Bearer with QoS and the Radio Bearer with  
Figure 5: Flow diagram of traffic detection logic at OF-NB. QoS are linked using the specific service flow entry creating the EPS-Bearer with QoS, as is indicated in Figure 4.