esa-sceva/satcom-chunk-collection huggingface dataset:
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 5 Conclusions  
The energy consumption levels in Figure 7 show that the proposed scheme yields very low energy requirements compared to other schemes. The energy efficiency here is attributed to the reduced time using the RF chains. For example, 4.9 millijoulles are required to achieve initial access for the deep learning scheme. Meanwhile, the grating lobes, sidelobes, metaheuristics, iterative search, GPS and subarray, and DL-UL schemes consume 16, 31.25, 47, 62.5, 84 and 166 microjoules, respectively. Overall, the proposed scheme achieves 75% higher energy efficiency and faster access times compared to the closest scheme, i.e., grating lobes approach.  
In this paper, a novel initial access scheme is proposed for standalone millimeter wave communications using deep learning models. The network operates in learning  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Variable** & **Value** \\ \hline Operation & \(f\) (GHz), BW(MHz), \(d\) & 28, 500, 200 \\ \hline Power consumption (mWatt) & \(q_{n}\), \(q_{\text{PS}}\), \(q_{\text{ULNA}}\), \(q_{\text{ADC}}\), \(q_{\text{BB}}\), \(q_{\text{MIX}}\), & 5.4, 78, 20, 20, 20, 19, 5, 14, 5 \\  & \(Q_{\text{LO}}\), \(q_{\text{LIF}}\), \(q_{\text{AMP}}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: System Parameters.  
Figure 6: Initial access times for various access schemes, downlink (DL)–uplink (UL).  
Figure 7: Energy consumption levels for various access schemes.  
and training modes, where the best beam index in the subsequent time step is predicted without the requirement for beam scanning. Hence, the scheme features ultralow access times, along with efficient power and energy consumption levels versus existing schemes. Future efforts will investigate the learning model while taking into account outage effects caused by blockage and mobility.  
Conceptualization, A.A., F.S., and M.J.; methodology, M.J.; software, F.S.; validation, A.A., F.S. and M.J.; formal analysis, M.J.; investigation, A.A.; resources, A.A.; data curation, F.S.; writing--original draft preparation, M.J.; writing--review and editing, A.A.; visualization, F.S.; supervision, A.A.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by DEANSHIP OF SCIENFOT RESAARCH at KING FAISAL UNIVERSITY, grant number 1811025. The authors acknowledge the Deanship of Scientific Research at King Faisal University for the financial support under the Research Group support track (Grant No: 1811025).  
The authors declare no conflict of interest.
# Optical Lensless-Camera Communications Aided by Neural Network
## 2 Proposed Scheme
### Image Decoding Algorithm  
Our image decoding algorithm is based on the BP neural network, which includes two stages. One is image preprocessing, the other is neural network training.  
Figure 2 shows four images of the same sender captured by two different receivers. Figure 2a is the \"on\" image (communication light source is on) captured by a camera with lens; Figure 2b is the \"off\" (communication light source is off) image captured by a camera with lens. The image quality is fine.  
Figure 1: Lensless camera communication system: (**a**) Lensless camera communication model; (**b**) proposed system block diagram. AP: access point; LED: light-emitting diode; OOK: on-off keying.  
The white LED is used as the communication light source. The blue LED is used as an interference light source. To decode the data for these images, the communication light source should be identified first; accordingly a light source positioning procedure should be carried on. As a result of the interference and the relative movements between light source and receiver, the positioning algorithm is complex. Figure 2c is the \"on\" image captured by a lensless camera, and Figure 2d is the \"off\" image captured by a lensless camera. No real image of object could be captured, only the blurred, shape-less light spot surrounding the object. In fact, the essential component of OCC is the use of the camera as a receiver to identify an on/off state of a light source; detail of the image is not important at this stage of OCC. From this angle, our lensless receiver is suitable for OCC and could realize decoding easily. Without a positioning process, our algorithm realizes the decoding with the help of the BP neural network, which could identify the difference between two images, the image with \"on\" communication light and the image with \"off\" communication light. The proposed algorithm can work under the interference as long as the interference light does not change quickly. Additionally, the algorithm has robust movements because the position of the LED on the image is not important.
# Environmental-Based Speed Recommendation for Future Smart Cars +
## 2 Related Work
Authors in [33] take advantage of the advanced wireless capabilities of modern cars and they analyze real-time traffic data in order to estimate local traffic density, so as to provide a more efficient and comfortable driving travel. Authors in [34] are able to increase intelligent vehicle's perception of the surrounding environment. They detect and classify moving objects on the street with high accuracy, by performing combinational processing of data from heterogeneous visual sensors. Other researchers [35] utilize wireless networking, e.g., vehicle-to-vehicle and vehicle-to-infrastructure communication, in order to alleviate traffic congestion and enhance comfort during a vehicle trip. Authors in [36] study a new coordinated system that would facilitate vehicle traffic in critical conditions, i.e., crossing a specific intersection, in order to achieve the highest traffic efficiency. Regarding traffic management at crossroads with Vehilce-to-Vehicle (V2V) communication, the authors in [37] assume that vehicles are connected and adapt their speed according to other vehicles (in a cooperative way), while the authors in [38] present a multi-agent model to manage the vehicles in platoon. As described in [39; 40], variable speed limit control is an alternative method to mitigate congestion and improve traffic operations (e.g., freeway bottlenecks). However, these approaches focus on improving the quality of the driving experience by reducing the travel time. The proposed work utilizes the connectivity of modern vehicles to gather data about weather, on-route and vehicular information in order to create a better understanding about the physical characteristics of the environment and how the vehicle should adapt. Additionally, since such sophisticated services require flexible computing architectures, the proposed methodology was developed as a service layer on top of distributed computing framework (Hydra [20]) for smart cars.
## 3 Implementation and Results
In addition, the achieved results exceed initial expectations when compared to the simulation results presented in Figure 6.  
Finally, a comprehensive analysis comparing our proposed FSS isolating structure with related research is presented in Table 3, considering factors such as array size, isolation, operating frequency bands and other noteworthy features. Our design, despite its lower attenuation, exhibits numerous benefits, including compact size, straightforward construction, dual-band operation, cross-polarization capabilities in both horizontal and vertical planes and robust response across various inclinations. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Unit Cell Design** & **FS5 Size** & **Isolation** & **Bandwidth** & **Remarks** & **Ref. ** \\ \hline \hline \multirow{3}{*}{Interdigitalted} & \multirow{3}{*}{4.1\(\lambda\times\) 4.1\(\lambda\)} & \multirow{3}{*}{12 dB} & \multirow{3}{*}{3.2–3.7 GHz} & single band operation, narrow bandwidth, composite low-loss substrate, complex design & \multirow{3}{*}{[21]} \\ \cline{3-3} \cline{5-6} \cline{
# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 3 Materials and Methods
### Domain Adaptation Methods  
To assess the performance of domain adaptation models on Arabic language, we considered DANN [10] and mSDA [11] from the models reviewed in Section 2 due to their excellence and common usage by other researchers. In addition, DARL [6] and AraBERT-ALDA [7] were considered, since they are current models that have so far been proposed and tested for Arabic sentiment classification. These methods were briefly described in Section 2, and more details can be found in the original papers. To fairly assess AraBERT-ALDA, we reported the results of DANN on top of AraBERT (AraBERT-DANN) in addition to the straightforward adaptation by simply finetuning AraBERT.
# Mechanism and Application of Soilbags Filled with Excavated Soil in Soft Soil Subgrade Treatment
## 2 Principle of Soilbag Treatment for Soft Soil Subgrade
### Unconfined Compression Tests and Test Results
Upon reaching a compression strain of approximately 40%, the soilbag's maximum compression strength was recorded, measuring 733 kPa. This strength surpasses that of 5% lime soil, which is around 600 kPa [30]. \begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline Liquid limit, LL (\%) & 63.7 \\ Plastic limit, PL (\%) & 34.4 \\ Specific gravity, \(G_{\text{S}}\) (g/cm\({}^{3}\)) & 2.647 \\ Density, \(\gamma\) (g/cm\({}^{3}\)) & 1.65 \\ Hydraulic conductivity, \(k_{\text{v}}\) (10\({}^{-7}\) cm/s) & 2.85 \\ Cohesion, \(c\) (kPa) & 12.2 \\ Friction angle, \(\varphi\) (\({}^{\circ}\)) & 18.2 \\ \hline \end{tabular}
\end{table} 
Table 1: Physical and mechanical properties of air-dried clayey soil. Figure 2: Results of unconfined compressive tests on soilbags: (**a**) stress–strain curve: (**b**) deformation modulus. In applied engineering scenarios, it is recommended that soilbags undergo compaction using a vibrating roller. The ground pressure exerted by this roller typically ranges between 400 kPa and 600 kPa [31,32]. As illustrated in Figure 2b, the deformation modulus curve of the soilbags under unconfined compression indicates that, following rolling, the deformation modulus of the soilbags can reach up to 6.5 MPa. This value is comparable to the modulus typically observed in hard plastic clay. From the data of the unconfined tests, it can be inferred that the compression strength and deformation modulus of soilbags filled with clayey soil meet the criteria required for subgrade construction materials.
## 2 Derivation of the Azimuth and the Elevation Error under Frequency Mismatch
### Partial Derivative of the Cost Function with Respect to the Azimuth
\tag{10}\]  
Due to \(\sum\limits_{m=0}^{M-1}\sin(2a_{m})=0\) and \(\sum\limits_{m=0}^{M-1}\cos(2a_{m})=0\), which are proven in Appendix B, (10) is simplified to:  
\[\sum\limits_{m=0}^{M-1}\left(\cos(a_{m})\cos(\psi^{(0)})\cdot\left(\sin(a_{m}+ \Delta\theta)\cos(\psi^{(0)}+\Delta\psi)\right)\right)=0. \tag{11}\]  
With angle addition and subtraction theorems, (11) is written as:  
\[\cos(\psi^{(0)})\cos\left(\Delta\theta\right)\cos(\psi^{(0)}+ \Delta\psi)\sum\limits_{m=0}^{M-1}\cos(a_{m})\sin\left(a_{m}\right)\] \[+\cos(\psi^{(0)})\sin\left(\Delta\theta\right)\cos(\psi^{(0)}+ \Delta\psi)\sum\limits_{m=0}^{M-1}\cos(a_{m})\cdot\cos\left(a_{m}\right)=0. \tag{12}\]  
From (A9) and (A12) in Appendix B, (12) can be rewritten as:  
\[\frac{M}{2}\cos(\psi^{(0)})\sin\left(\Delta\theta\right)\cos(\psi^{(0)}+ \Delta\psi)=0. \tag{13}\]  
From (13), we get:  
\[\sin\left(\Delta\theta\right)\cos(\psi^{(0)}+\Delta\psi)=0. \tag{14}\]  
From (14), the partial derivative of the cost function with respect to the azimuth is identically zero if either of the following conditions is true:  
\[\sin(\Delta\theta)=0 \tag{15}\] \[\cos(\psi^{(0)}+\Delta\psi)=0. \tag{16}\]
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 2 Mathematical Model
### Objective Function  
The function \(f_{1}\) represents the revenue of tasks, expressed as:  
\[f_{1}=\sum_{k=1}^{NS_{z}}\psi_{k}^{c}priority_{k}^{c} \tag{21}\]  
where \(priority_{k}^{c}\) represents the priority of task \(vp_{k}^{c}\), and \(\psi_{k}^{c}\in[0,1]\) represents the probability of \(vp_{k}^{c}\) being executed. The calculation formula of \(\psi_{k}^{c}\) is as follows:  
\[\psi_{k}^{c}=\prod_{i,j}\left(1-\delta_{(i,j)}^{pc_{k}^{c}}b_{ij}^{c}\right) \tag{22}\]  
Function \(f_{2}\) represents the number of all new links in the graph \(\mathcal{G}(c)\), expressed as:  
\[f_{2}=\sum_{i=1}^{n}\sum_{j=1}^{n}ns_{ij}^{c} \tag{23}\]  
where \(ns_{ij}^{c}\) is an element in \(\mathcal{NS}_{c}\).  
Function \(f_{3}\) represents the total cost of the graph \(\mathcal{G}(c)\), expressed as:  
\[f_{3}=\sum_{k=1}^{NS_{c}}\sum_{i,j}\delta_{(i,j)}^{pc_{k}^{c}}d_{i,j}^{c} \tag{24}\]  
Taking into account the different magnitudes and importance of \(f_{1}\), \(f_{2}\) and \(f_{3}\), the non-dimensional parameter \(\mathbf{q}=[q_{1},q_{2},q_{3}]^{\top}\) and weight \(\mathbf{w}=[w_{1},w_{2},w_{3}]^{\top}\) are introduced into the objective function \(f\). The optimization objective is defined as:  
\[\max f=(w_{1}\cdot p_{1}\cdot f_{1}-w_{2}\cdot p_{2}\cdot f_{2}-w_{3}\cdot p_{ 3}\cdot f_{3}) \tag{25}\]  
where \(w_{1},w_{2},w_{3}\in[0,1]\) represents the weight coefficient, set according to the actual situation.
# A Novel Wideband Splitter for a Four-Element Antenna Array
## 2 Related Work
The authors concluded that the advantages of the proposed design are its compact size, wide operating bandwidth, good input-output impedance matching, low insertion loss, good amplitude balance at the output ports, and reasonable isolation among the output ports. Therefore, the solution should be very competitive in real-world applications in 5G networks. [PERSON] et al. proposed a meander line-based Wilkinson power divider for the 433 MHz frequency band in [21]. The application in mind was communication with UAV. The proposed design was based on the implementation of a meander line design on the quarter-wavelength transformer. Based on the presented results, the return loss was 19.05 dB, the insertion loss was 3.25 dB, and the port isolation was 26.53 dB. Moreover, the authors claimed that the proposed power divider has a linear phase, and that therefore there are no distortions to the phase nonlinearity. An extension of the operation bandwidth of the Wilkinson power divider by the replacement of the quarter-wave transmission line with an impedance transformer was demonstrated by [PERSON] in [22]. The author provided details on the design of the power divider with the structure of three segments. Based on the measurement results, a \(-20\) dB fractional operation bandwidth of 101% was achieved, which is better than for traditional Wilkinson power dividers with a total length of \(\lambda/4\). [PERSON] et al. described a single-stage two-way Wilkinson power divider for Internet of Things applications in [23]. The design was based on a single-section meandered line Wilkinson power divider with an open stub network and a defected ground structure. Based on the presented results, the proposed power divider achieved a fractional bandwidth of 107% with a central frequency of 600 MHz.
# Kinematic Modelling and Experimental Validation of a Foldable Pneumatic Soft Manipulator
## 3 Numerical Calculation and Analysis
### Shape Deformation of the Pneumatic Module  
In this calculation, the internal pressure of the straight arm is fixed at 0.025 MPa. The loading air pressure of the pneumatic actuator is taken from 0.02 MPa to 0.1 MPa, with an interval of 0.02 MPa. Firstly, we inflate one bellows; the other two bellows are not inflated. Then we inflate two bellows with the same pressure, while the other one is not inflated. The numerical results are shown in Figures 3 and 4.  
It can be seen in Figures 3(a) and 4(a) that the shape deformation increases with the increase of the internal pressure with both one bellows and two bellows inflated. The line of the straight arm is just along the tangent direction of the end point of the pneumatic actuator. It can be seen in Figures 3(b) and 4(b) that the projection of the numerical results in the X-Y plane is a straight line, and the projections in these two situations are almost symmetric about the Y-axis. This phenomenon is consistent with the physical law, indicating that there is no singularity and disturbance occurring during the deformation process predicted by the theoretical model. From the analysis above, it is ver  
Figure 4: Space distribution of the soft manipulator with two bellows inflated.  
Figure 3: Space distribution of the soft manipulator with one bellows inflated.  
results of the theoretical model are reasonable. However, the accuracy of the shape deformation predicted by the proposed model still needs to be validated through experiment.
# Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
## 2 Research Background
### Personalized Path Planning Based on Log Data  
One of the main studies in online learning is how to provide personalized learning paths according to learners' log data [21]. During online learning, these \"footprints\" can be used as the basic parameters for constructing a learning path [22]. Collecting learning behavior data provides opportunities for researchers or teachers to understand the learners' learning process and to predict their academic achievements [22,23]. For example, Xia et al. proposed a system that can provide suitable questions based on the history data of other students in parallel learning scenarios. The system helped learners to obtain customized and adaptable quiz sequences from a massive question bank [24]. Liu et al. proposed a learning path combination recommendation method based on log data from learners [25].  
The log data-based recommendation system made use of students' historical behavior data to grasp their features and recommend the learning objects they need. However, this kind of path generation methods ignored the knowledge structure and learners' unique features. The personalized learning path derived from group data may not be appropriate for all learners. Moreover, it will be challenging to encounter the cold-start problem when data are not sufficient.
## 4 Experiments
### Training Data  
In this section, we describe the datasets used in this paper, along with their division into training, validation, and test sets. For the segmentation task, we used the Trans10K-v2 dataset, which is based on the original Trans10K dataset. It consists of 5000 images for training, 1000 images for validation, and 4428 images for testing. For the dehazing task, we utilized the I-Haze dataset, which contains 25 indoor hazy images (size \(2833\times 4657\) pixels) for training. It includes 5 hazy images for validation, each paired with its corresponding ground-truth image. We used the validation data as the test set in our experiments. For the optical flow task, we used the MPI Sintel benchmark. The MPI (Max Planck Institute) Sintel dataset is designed for optical flow evaluation and contains 1064 synthesized stereo images with ground-truth disparity data. Sintel is derived from the open-source 3D animated short film Sintel. The dataset includes 23 different scenes. The stereo images are RGB, while the disparity maps are grayscale. Both types of images have a resolution of \(1024\times 436\) pixels and 8 bits per channel. The test data are hosted on a server, and the ground truth is not publicly available. To obtain final metrics, we had to submit our results to the server for evaluation.
# Simulated Annealing with Exploratory Sensing for Global Optimization
## 4 Numerical Simulation
### Parameter Setting and Configuration  
The parameters values used in SAES algorithm are set based on the typical setting in the literature, or determined through our preliminary numerical experiments, as shown in Table 1. Based on the configuration of the SAES control parameters, there are three SA-based versions:  
* The SAES without the diversification phase and final intensification, which is the standard annealing algorithm and denoted by the SA method.
* The SAES without the final intensification, which is denoted by SAES\({}_{w}\).
* The complete SAES method.  
In Table 1, there are two groups of parameters. The first one in the common parameter setting, which is used in all versions of the proposed method. The other group of parameters is used in the SAES\({}_{w}\) and SAES versions, except the final intensification budget is only used in the later version.  
Based on the parameter values shown in Table 1, the cost of the objective function evaluations used in the SAES method can be computed as:  
* Function evaluations in the initialization: 100 function evaluations.
* Function evaluations in the diversification phase: \(M_{D}\times(40n+1)\), where \(M_{D}\) is the number of Markov chains in the diversification phase, which is at maximum 18.
* Function evaluations in the diversification phase: \(40n\times(60-M_{D})\).
* Function evaluations in the final intensification: at maximum \(500n\).  
Therefore, the total number of function evaluations used by the SAES method is bounded by \((2900n+118)\) functions evaluations. Likewise, it can be concluded that these numbers in the SA and SAES\({}_{w}\) versions are bounded by \((2400n+100)\) and \((2400n+118)\), respectively.  
The local search method used in the final intensification is consists of applying the MATLAB functions \"fminsearch.m\" and \"fminunc.m\" starting from the best solutions obtained in the previous search stages. Specifically, the function \"fminsearch.m\" is first called with the half of the local search budget, then the other MATLAB function is called to improve the output of the first one with the same budget as recommended in [74; 75].
## 2 Background
### Concept-Drift Handling  
[PERSON] et al. in [18] proved and showed that a well-trained defect-prediction model may output inaccurate results if the data distribution changes over time. They also proved that training a prediction model becomes more difficult in such scenarios. This points out that concept drift must be handled if data distributions change over time. In our current study, we present an approach to detect and handle concept drift in SDP.  
While predicting defects in software, drift could happen when the trained predictor does not perform well due to the presence of a changing concept. Therefore, these changing concepts must be discovered to update the predictor. Our proposed approach, as described in the following sections, efficiently discovers the concept-drift points in software-defect data.