esa-sceva/satcom-chunk-collection huggingface dataset:
# NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing
## 2 NC-OFDM Satellite Communication System Model Based on Spectrum Sensing  
The NC-OFDM satellite communication system based on cognitive radio is a new type of communication system. In addition to the non-continuous subcarrier functions and the up-and-downlink data transmission functions of satellite communication systems, a spectrum-sensing module is specifically designed. This module can panoramically perceive the spectrum usage of communication satellites and detect spectrum holes. It reasonably allocates non-continuous subcarriers according to the number and frequency band range of the spectrum holes, and sends the dynamic subcarrier allocation scheme to the subcarrier control module in real time for spectrum forming. The basic model of the transmission and reception system is shown in Figure 1.  
As shown in Figure 1, the transmit source undergoes channel coding and PSK/QAM modulation, and then it is converted to a set of parallel data \(d_{0},d_{1},\ldots,d_{M-1}\), where \(M\) represents the available number of subcarriers in the satellite channel perceived by the spectrum-sensing module. Just like the implementation principle of the OFDM system, the modulation of NC-OFDM can be equivalently realized by Inverse Discrete Fourier Transform (IDFT). The frequencies of the NC-OFDM subcarriers are not continuously distributed because they are determined by the number of spectrum holes and the subcarrier allocation algorithm.  
By denoting the number of all subcarriers to be allocated in the satellite channel as \(N\), the NC-OFDM sampling signal can be obtained by \(N\)-point IFFT:  
\[s_{k}=\sum_{i\in\{0,1,2,\ldots,N-1\}}d_{i}\exp\left(j\frac{2\pi ik}{N}\right). \tag{1}\]  
Figure 1: NC-OFDM satellite communication system model based on spectrum sensing.  
Similarly, at the receiving terminal, \(s_{k}\) can be inversely transformed to restore the original data \(d_{i}\):  
\[d_{i}=\sum_{k=0}^{N-1}s_{k}\exp\biggl{(}-j\frac{2\pi ik}{N}\biggr{)}. \tag{2}\]
###### Abstract  
Topology optimization has evidenced its capacity to provide new optimal designs in many different disciplines. However, most novel methods are difficult to apply in commercial software, limiting their use in the academic field and hindering their application in the industry. This article presents a new open methodology for solving geometrically complex non-self-adjoint topology optimization problems, including stress-constrained and stress minimization formulations, using validated FEM commercial software. The methodology was validated by comparing the sensitivity analysis with the results obtained through finite differences and solving two benchmark problems with the following optimizers: Optimality Criteria, Method of Moving Asymptotes, Sequential Least-Squares Quadratic Programming (SLSQP), and Trust-constr optimization algorithms. The SLSQP and Trust-constr optimization algorithms obtained better results in stress-minimization problem statements than the methodology available in ABAQUS[9]. A Python implementation of this methodology is proposed, working in conjunction with the commercial software ABAQUS[9] 2023 to allow a straightforward application to new problems while benefiting from a graphic user interface and validated finite element solver.  
topology optimization; stress constraints; Python; ABAQUS; educational +
Footnote †: journal: Applied sciences
## 4 Experiments and Results
### Datasets  
The Smartphone Image Denoising Dataset (SIDD) [70] is a benchmarked dataset with real-world noisy images under diverse lighting and ISO conditions, photographed with smartphones. It contains a variety of noise levels in the images. We use a total of 1600 pairs, of which 320 image pairs are used for training and 1280 are used for validation. The datasets are prepared following a series of processing steps to handle camera shift alignment, exposure time adjustment, and intensity scaling. Some sample images of DND and SIDD dataset are shown in Figure 3.  
Figure 3: Sample images from SIDD [70] and DND [71] datasets.  
The Darmstadt Noise Dataset (DND) [71] is a benchmark dataset including 50 pairs, each consisting of real noisy and ground-truth data captured with various consumer-grade cameras. Taking into consideration the ISO, noisy images and reference images are taken with higher and base levels, respectively. As high-resolution images are used, it contains 20 extracted crops of size 512 \(\times\) 512 per image, resulting in a total of 1000 patches. All of these are used for testing (DND lacks training or validation sets). The ground-truth noise-free images are not publicly released, so an online server is used to provide images for quantitative measures.
# Nonlinear Optimal Control Law of Autonomous Unmanned Surface Vessels
## 4 Discussion
### Parameterizations of Controlled AUSV and Ocean-Environment Disturbances  
Parameters of the dynamics of the controlled AUSV were given by [PERSON] (1998). Parameters of AUSV in Figure 2 and hydrodynamic parameters are exhibited in Tables 1 and 2.  
By applying the values of Tables 1 and 2, the dynamics-parameter matrices of the controlled AUSV are given below:  
\[\mathbf{M}=\left[\begin{array}{ccc}4.5096&0&0\\ 0&7.5608&-22.68\\ 0&-22.68&2968.3\\ \end{array}\right]\times 10^{6}\] \[\mathbf{D}(\beta)=\left[\begin{array}{ccc}0.05138&0&0\\ 0&0.1698&-1.5081\\ 0&-1.5081&253\\ \end{array}\right]\times 10^{6}\] \[\mathbf{C}(\beta)=\left[\begin{array}{ccc}0&0&-7.5608v-22.68r\\ 0&0&4.5096\\ 7.5608v+22.68r&-4.5096&0\\ \end{array}\right]\times 10^{6}.\]  
The perturbed uncertainties of the parameters of the AUSV were a random 5-10% of inertial matrix \(\mathbf{M}_{\eta}\) and Coriolis matrix \(\mathbf{C}_{\eta}\). The parameters of external disturbances--wind, wave and ocean current-induced forces and torques--are shown in Table 3, which are valid when the wind velocity is in the range of 2-3.96 m/s, and when encounter angles range from \(-180^{\circ}\) to \(180^{\circ}\). Detailed values for simulating the environmental disturbances, including wind, wave and current, are listed as Table 3[13].  
In Table 3, \(V_{\omega}\) (m/s) is the average wind speed above the ocean surface; \(C_{X\omega}(\gamma_{R})\), \(C_{Y\omega}(\gamma_{R})\), and \(C_{N\omega}(\gamma_{R})\) are nondimensional forces and moments induced by wind; \(\rho_{air}\) (kg/m\({}^{3}\)) is air density; \(\rho_{w}\) (kg/m\({}^{3}\)) is water density; \(g\) (m/s\({}^{2}\)) is gravity acceleration; \(N\) is the order of the adopted wave form; \(\beta\) is the angle between the heading and the direction of the incoming wave; \(A_{i}\) is the wave amplitude of the \(i\)th wave component; \(\phi_{i}\) is the random phase of the \(i\)th wave component angle; and \(\lambda_{i}\) is the \(i\)th wave length.
# Wake-Up Receiver with Equal-Gain Antenna Diversity +
## 4 Experimental Results  
To verify the design and to test the performance of the wake-up diversity node, several experiments were performed. First, a static experiment was used to verify expected input signal gain of about 3 dB. Then, the performance of the wake-up diversity system was investigated in a multipath environment. Throughout the tests, the wake-up diversity node was equipped with two identical monopol antennas with omnidirectional radiation patterns and an antenna gain of maximal 2 dBi and 50 \(\Omega\) impedance. The antennas were mounted at an angle of 90\({}^{\circ}\) to each other to reduce the correlation coefficient between the two antennas and to achieve a polarization diversity system.
# Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites
## 7 Results and Discussion
### Evaluation of Wave Propagation Characteristics Among the Tested Beams  
The wave propagation characteristics across all beams were best illustrated by variations in signal amplitude, which were influenced by the sensor placements at different locations (top and bottom) relative to the repair area (Figure 1).  
Figure 4 depicts the signal amplitudes at the time of the first crack detection in all the beams. It can be observed that beams repaired under compression or tension experienced variations due to wave propagation. These variations were caused by differences in the density of the ECC and LWSCC, which affected the signal amplitude depending on the sensor location. Specifically, the signal amplitude at sensor 1 differed significantly from those at sensors 2 and 3, indicating signal attenuation due to the differing material densities (l  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Beam**} & \multirow{2}{*}{**Sensor**} & \multicolumn{2}{c}{**CSS \(\times\) 10\({}^{7}\)**} & \multicolumn{2}{c}{**Number of Hits**} \\ \cline{3-6}  & & **First Crack** & **Ultimate Load** & **First Crack** & **Ultimate Load** \\ \hline \multirow{3}{*}{ECCP} & CH-1 & 2.42 & 35.1 & 2705 & 25162 \\  & CH-2 & 3.59 & 48.7 & 2964 & 27902 \\  & CH-3 & 2.75 & 38.6 & 2910 & 27140 \\ \hline \multirow{3}{*}{ECCS} & CH-1 & 2.03 & 40.2 & 2245 & 25902 \\  & CH-2 & 3.05 & 45.6 & 2492 & 28690 \\  & CH-3 & 2.30 & 43.9 & 2417 & 27910 \\ \hline \multirow{3}{*}{ECCP-C} & CH-1 & 1.36 & 12.8 & 1514 & 21925 \\  & CH-2 & 2.03 & 33.7 & 1669 & 24305 \\  & CH-3 & 1.58 & 31.4 & 1621 & 23650 \\ \hline \multirow{3}{*}{ECCS-C} & CH-1 & 0.33 & 22.7 & 1617 & 22290 \\  & CH-2 & 1.12 & 5.64 & 1789 & 24705 \\  & CH-3 & 0.45 & 19.9 & 1750 & 24050 \\ \hline \multirow{3}{*}{ECCP-T} & CH-1 & 2.55 & 30.3 & 2816 & 22644 \\  & CH-2 & 3.77 & 34.8 & 3120 & 25102 \\ \cline{1-1}  & CH-3 & 2.89 & 32.4 & 3035 & 24420 \\ \hline \multirow{3}{*}{ECCS-T} & CH-1 & 2.94 & 32.5 & 2453 & 24090 \\ \cline{1-1}  & CH-2 & 3.12 & 37 & 2810 & 26695 \\ \cline{1-1}  & CH-3 & 2.99 & 34.5 & 2736 & 25971 \\ \hline \end{tabular}
\end{table}
Table 6: CSS and number of hits at first crack and ultimate load.  
For the ECCP-T beam, sensor 1 showed a signal amplitude of 40 dB, while sensors 2 and 3 displayed 47 dB and 46 dB, respectively (Figure 4). Conversely, beams entirely cast in ECCP or ECCS showed negligible signal propagation, resulting in very similar amplitude values (Figure 4). For instance, in ECCP, the signal amplitudes at sensors 1, 2, and 3 were 56 dB, 56 dB, and 55 dB, respectively at the first crack detection.
# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 4 Challenges and Future Directions
### Heterogeneous Data Fusion  
A single data source may not be enough for traffic tasks, when different transportation modes are entangled together. Data from multiple sources can be combined to make a better estimation and prediction for traffic situations. Heterogeneous data fusion is driven by this observation, which often uses deep learning for urban big data fusion [100]. There are already some relevant studies. For example, cellular data and loop detectors were integrated for freeway traffic speed estimation in [101]. Sparse stationary traffic sensor data were combined with GPS trace data to estimate the traffic flow in the entire road network in [102].  
License plate recognition data and cellular signaling data were combined for traffic pattern and population distribution correlation analysis in [103]. License plate recognition data and GPS trajectory data are combined for traffic flow estimation in [104]. Geomagnetic detector data, floating car data, and license plate recognition data were combined for average link travel time extraction [105]. Bus transit schedule data, real-time bus location data, and cell phone data from geographical mapping software were combined to predict bus delays, and a mean absolute percentage error (MAPE) of approximately 6% was achieved [106]. The success of these attempts demonstrates both the necessity and the prospect of heterogeneous data fusion techniques.
## 5 Conclusions and Future Work
This study identifies these critical road segments as those responsible for substantial declines in traffic volume, where no alternative paths are available after their disruption, resulting in a significant drain in traffic volume. By incorporating topological parameters into the evaluation, we provide valuable insights into how these parameters can be effectively utilized to assess network vulnerability, emphasizing the severe consequences that disruptions to certain parameters can have on overall network performance and traffic flow. Vulnerability analyses are essential for developing preventive measures that mitigate both the occurrence and consequences of network disruptions. This study demonstrates that different disruption stages affect networks in distinct ways, each requiring tailored policy interventions. This highlights the importance of evaluating disruption impacts at each specific stage before integrating findings in later assessments--ensuring more precise and targeted policy responses. This research provides a practical approach to assessing multi-stage network disruptions by analyzing critical-link attacks and their cascading effects on network performance and traffic flow. For instance, early-stage disruptions (5-10%) require dynamic flow management, such as rerouting traffic from high-betweenness segments (e.g., segment 10), while mid-stage disruptions (10-20%) call for accessibility buffers like temporary transit corridors to prevent fragmentation. In late-stage disruptions (20-30%), modular infrastructure (e.g., pop-up lanes) can mitigate irreversible connectivity losses. Restoration should prioritize betweenness-critical (BC) links to re-establish efficiency, followed by closeness (CC) and eigenvector centrality (EC) links to restore accessibility and influence. The case study highlights that unsatisfied demand--trips rendered impossible due to disrupted or unavailable alternative segments--constitutes the most severe impact on travel during major disruptions. Attacks on critical road segments cause significant traffic volume loss when no alternative paths exist, leading to cascading failures that fragment the network and degrade performance. These impacts intensify with disruption duration and segment importance, underscoring the need for rapid restoration. The findings also show that as disruptions escalate, flow is concentrated on remaining roads, necessitating demand management strategies like congestion pricing to alleviate bottlenecks. To enhance resilience, transportation authorities should integrate vulnerability analysis into road design, prioritize infrastructure upgrades (e.g., road widening, improved drainage), and focus investments on prevention and quick restoration rather than costly redundancy-based expansions. In aligning policies with topological insights, networks can better withstand disruptions and maintain system performance.
# Two-Terminal Electronic Circuits with Controllable Linear NDR Region and Their Applications
## 2 Review
[34] presented five electronic circuits of NDR VCOs based on a GaAs transistor and single-output BJT CM. [PERSON] et al. [35] considered an NDR circuit comprising a FET and a simple BJT CM with multiple outputs that control the slope of the current-voltage curve by changing the number of CM outputs. [PERSON] [36] investigated a resonant tunneling electronic circuit with reactance elements having high and multiple peak-to-valley current density ratios displayed in the NDR curve. [PERSON] [37] considered a monolayer structure based on vanadium phosphide with a current-voltage characteristic having the NDR region. [PERSON] et al. [38] observed an NDR region in the current-voltage curve in graphene oxide two-terminal device with precise control of carbon-oxygen ratio. The fabricated novel electronic device can find application in switches and oscillators. [PERSON] et al. [39] synthesized graphene oxide quantum dots based on graphene oxide, cysteine, and H\({}_{2}\)O\({}_{2}\) having N-type current-voltage characteristics with PVCR of 4.7. [PERSON] et al. [40] demonstrated an NDR device on the base of a phosphorene/rhenium disulfide (BP/ReS\({}_{2}\)) heterojunction. It has a high PVCR of 4.2 at room temperature. The peak and valley currents are 3 and 0.7 nA, respectively. We can draw the following conclusions from the review of published studies:  
1. Considerable attention is paid to developing new NDR devices [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 33; 36; 37; 38; 39], indicating the research topic's relevance. 2. Most NDR devices and circuits use one, two, or even three power supplies. Such devices have two [14; 25; 26; 27; 28; 29; 34; 35; 38; 39], three [29; 31; 32; 33; 33], or four [27] terminals. 3. In most of the published studies, there is no possibility of controlling the PVCR. The PVCR control is available in the NDR devices considered in the studies [26; 27; 31; 39], but the maximum current levels are in the nA and \(\upmu\)A ranges. In the NDR circuit [17], the control of PVCR is possible in the mA range by changing the value of one of the resistors.
## 2 Methodology
### Dataset Formulation  
Data samples are collected every 100 microseconds duration over 5 cycles. The complete dataset preparation mechanism is presented in Figure 1.  
Sinusoidal Signals Used for Data Preparation  
The data of the various sinusoidal voltage signals with noise (5 to 50%) is extracted by generating the signals in MATLAB as shown in Figure 2. In this paper, distorted sinusoidal signals are generated with white gaussian noise. Similarly, the data of the various sinusoidal voltage signals with total harmonic distortion from 10% to 50% is extracted by generating the signals in MATLAB as shown in Figure 3. Distorted sinusoidal signals are generated by adding unity amplitude fundamental signal with 5 th order harmonic signals with varying amplitude from 0.1 to 0.5. THD of these distorted signals is estimated using FFT analysis based on equation  
\[THD=\frac{\sqrt{\sum_{h=2}^{\infty}V_{h}^{2}}}{V_{f}} \tag{1}\]  
where \(V_{h}\) is RMS value of harmonic voltage signal and \(V_{f}\) is RMS value of fundamental voltage signal.  
In the same way, a few more sinusoidal signals were generated in MATLAB with a combination of both noise and harmonics as shown in Figure 4.
# An Empirical Recommendation Framework to Support Location-Based Services +
## 1 Introduction
### Motivating Example  
Presently, collaborative filtering (CF) is a widely used method to generate a recommendation system. The existing user-based collaborative filtering method in the recommender system intends to find a similar user based on their previous activity. However, the important thing is that most of these suggestions do not concern about the user's interest profile and thus there can be a lot of suggestions on irrelevant places. Considering this problem, it is necessary to generate the user's profile to build a more effective recommender system. Figure 1 shows a pictorial representation of some check-in places of four users _user a_, _user b_, _user c_, and _user d_. Each user's visiting spots are defined with a pin of different shapes. There are nine different places 'Museum', 'Airport', 'Park', 'Shopping Mall', 'Movie Theatre', 'Drama Theatre', 'Restaurant', 'Library', and 'University', where the four users checked in. Table 1 gives detailed information about their visiting places shown in the figure.  
The CF method usually generates a similarity score between different users and based on the highest score it makes recommendations. Let us make a recommendation for _user a_. We can see that the activity of _user a_ has a higher similarity with _user b_ as both of them checked into 'Movie Theatre', 'Drama Theatre', and 'Restaurant'. Now, the CF method makes the recommendation 'Library' for _user a_ based on the activity of _user b_. However, it is not the best suited for _user a_ because the visiting information of _user a_ demonstrates the interest in 'Recreation' purposes, such as 'Museum'. To overcome this limitation, we are going to create an interest profile for each user so that recommendations can happen accordingly.  
One of the limitations of the existing RS is that it does not recommend a common place for a group of users based on their preferred choices. Thus, we are going to propose a framework that can suggest appropriate places for some users.  
The rest of the paper is arranged as follows. Section 2 summarizes the related work. Section 3 includes the underlying ideas about RS system. Section 4 describes the recommendation generation method. Section 5 demonstrates the experimental and evaluating process. Section 6 includes the discussion and lessons learned. Finally, Section 7 concludes the paper and provides future research directions.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 4 Experiments and Evaluation
### Dataset Description  
We use the optical remote sensing image dataset DIOR created by [PERSON] et al. of Northwestern Polytechnical University to evaluate the performance of the algorithm model [52]. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DPU** & \multirow{2}{*}{**DSP**} & \multirow{2}{*}{**LUT**} & **Peak** & **Channel** & \multirow{2}{*}{**Pixel**} \\
**Architectur** & & & & **Operand** & **Parallelism** & \\ \hline B512 & 118 & 27,893 & 512 & 8 & 4 \\ B800 & 166 & 30,468 & 800 & 10 & 4 \\ B1024 & 230 & 34,471 & 1024 & 8 & 8 \\ B1152 & 222 & 33,238 & 1152 & 12 & 4 \\  
DIOR dataset is a large-scale optical remote sensing image dataset, including 23,463 images with a size of 800 \(\times\) 800 and a resolution ranging from 0.5 m to 30 m. There are 192,472 instances in total, covering 20 object categories.The images in the DIOR dataset cover a span of several years, capturing a wide range of imaging conditions, weather variations, and seasons. This diversity ensures that the dataset provides a representative collection of remote sensing data. In our experiments, 80% of the images in the DIOR dataset are selected as the training set, along with 10% for the validation set and 10% for the test set. Figure 9 shows some examples of images from the DIOR dataset. Sample images of golffield, vehicle, transitation, chimney, groundtrackfield, airplane, stadium, tenniscourt, storage tank, ship, windmill and airport are shown in (a) to (l), respectively.