esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 4 Analysis of Key Parameter Dependencies, Limitations, and Impacts
### Impact of Key Parameters on Voltage Notch Waveforms
#### 4.2.6 Scenario 5: Background Voltage Unbalance and Distortion  
This scenario examines the impact of background voltage unbalance and distortion on voltage and current waveforms with all components connected, including the rectifier, linear loads, and capacitor bank.  
* Background unbalance is increased up to 2%, based on the IEC 61000-2-2 standard.
- Both non-detuned (Figure 13a) and detuned (Figure 13b) capacitor banks are analyzed. Fixed parameters from previous simulations are used, with \(\alpha=60^{\circ}\).
- Voltage waveforms show slight changes with background unbalance levels of 0%, 0.5%, 1%, and 2% for both capacitor banks.
- Line currents exhibit no observable changes.
* The background distortion level is varied according to Table 4.
- Simulations use fixed parameters from previous sets. Fixed phase angles for 5 th, 7 th, 11 th, and 13 th harmonic voltages are set to 185\({}^{\circ}\), 30\({}^{\circ}\), 5\({}^{\circ}\), and 95\({}^{\circ}\), respectively.
- Voltage waveforms significantly change with increased background distortion, resulting in deeper notches and flat-top distortion at higher distortion levels.
- When using a detuned capacitor bank, these effects are more pronounced.
- Input currents in the rectifier are not significantly impacted.
- Total line currents exhibit higher oscillations as distortion levels increase, likely due to more distorted currents drawn by the capacitor bank.  
The results indicate that while background voltage unbalance has minimal impact, background voltage distortion significantly affects voltage waveforms and total line currents, particularly when a detuned capacitor bank is used.  
Figure 13: Impact of background unbalance on voltage notch waveforms (**top**), input line currents of the rectifier (**middle**), and total line currents of the LV installation (**bottom**). (**a**) Non-detuned capacitor bank and (**b**) detuned capacitor bank.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 2 Related Works
It is clear from the previously documented works that there is an excellent opportunity to predict the path loss with the best accuracy and less time consumed by adopting ML and deep learning algorithms. To the best of our knowledge, there is a severe gap in organizing and concluding the previously conducted efforts for path loss prediction based on ML methods. As it is well-known, all ML-based models mainly depend on the datasets for the models' training. These datasets are brought from different environments and communication scenarios at various frequency bands for several applications. Almost all of the existing related works have proposed specific ML-based (or deep-learning-based) models and compared their performance with the traditional empirical and/or a few other ML-based path loss models to show better prediction accuracy. However, until now, there is no way to guess which ML method is the best for a specific radio propagation environment since the real measurement data will be similar in such environments. This goal can be achieved by comparing several ML-based path loss prediction models with the same datasets and input features and running away from the thinking of comparing with the traditional linear models since ML-based models already perform better according to open the literature due to the ability to create a complex non-linear relationship between their inputs and outputs. Motivated by that, this work attempts to fill this gap for a typical enclosed indoor corridor environment by providing a comparative analysis of several relevant techniques used for path loss prediction. This study will give other researchers an insight into the best ML-based model for enclosed indoor small-cell communications in the SHF and mmWave frequency regimes.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
###### Abstract  
This paper presents a proposal for an architecture in FPGA for the implementation of a low complexity near maximum likelihood (Near-ML) detection algorithm for a multiple input-multiple output (MIMO) quadrature spatial modulation (QSM) transmission system. The proposed low complexity detection algorithm is based on a tree search and a spherical detection strategy. Our proposal was verified in the context of a MIMO receiver. The effects of the finite length arithmetic and limited precision were evaluated in terms of their impact on the receiver bit error rate (BER). We defined the minimum fixed point word size required not to impact performance adversely for \(n_{T}\) transmit antennas and \(n_{R}\) receive antennas. The results showed that the proposal performed very near to optimal with the advantage of a meaningful reduction in the complexity of the receiver. The performance analysis of the proposed detector of the MIMO receiver under these conditions showed a strong robustness on the numerical precision, which allowed having a receiver performance very close to that obtained with floating point arithmetic in terms of BER; therefore, we believe this architecture can be an attractive candidate for its implementation in current communications standards.  
FPGA; QSM; MIMO; Near-ML detection Article
## 1 Introduction
Therefore, the main goal of the article is to emphasize the importance of the data understanding, data gathering phase, stress the limitations of the available datasets of educational data, compare the performance of several machine learning classifiers, and show that also a limited set of features, which are available for teachers in the e-learning course, can predict student's dropout with sufficient accuracy if the performance metrics are thoroughly considered. Even though the main topic of the paper is quite frequent, the paper describes a case study, which emphasizes the necessity to follow well-known knowledge discovery methodology even in the case of commonly used classifiers and consider not only a limited set of performance metrics. The applied approach has the potential to predict the student's dropout at the course level if the course structure has not significantly changed and the course has been re-opened for several years. The research paper is organized as follows. At first, the summary of related works focused on effective predictions of students' success using machine learning techniques in the learning analytics domain is presented. The individual phases of the CRISP-DM methodology, which manages the knowledge discovery process, are gradually introduced, starting Section 3. This section provides information about the main characteristics of the researched dataset. Subsequently, data understanding and pre-processing phases are described in detail. Section 4 deals with the models of prediction of the student's dropout, in which different machine learning classifiers had been applied. Section 5 concerns the obtained results and evaluation of the models based on different performance measures. Moreover, the performance of the machine learning classifiers is compared using [PERSON]'s test. Finally, Section 6 summarizes the research results, discusses the research limitation, and proposes its possible future direction.
## 2 Feature Extraction Method--Dual Time-Frequency Images Fusion
### Data Preprocessing
#### 2.1.1 Continuous Wavelet Transforms  
The continuous wavelet transform (CWT) has higher time resolution and lower frequency resolution at high frequencies and has lower time resolution and higher frequency resolution at low frequencies, which exhibits multiresolution analysis characteristics. The CWT implements the wavelet transform, which is used to analyze nonperiodic signals and transient signals at different scales or resolutions. The CWT of the signal \(x(t)\) is defined as:  
\[W_{\varphi}(b,a)=\frac{1}{\sqrt{|a|}}\int x(t)\varphi^{*}(\frac{t-b}{a})dt \tag{1}\]  
where \(\varphi^{*}\) represents the conjugate transpose of the mother wavelet function \(\varphi\), \(a\) is a scale factor, \(b\) is the translation factor and \(|a|^{1/2}\) represents the energy normalization across the different scales.
# Performance Evaluation of a Full-Duplex Relaying-Enabled Satellite Sensor Network
## 2 System Model  
As shown in Figure 1, a sensor node is denoted as earth station (ES)-1 sends signal \(x\left[i\right]\) to a remote data center or exchange center denoted as ES-2 with the help of a satellite relaying (The satellite is considered to be of transparent manner, which the bent-pipe type satellite amplifies the received signals from ES by a gain factor, and then retransmit to intended terminal [8; 9; 10]). Particularly, the considered scenario involves the FD mode on the uplink (ES-1 to satellite) and downlink (satellite to ES-2). The return path from user terminal (UT), i.e., ES-\(i\), to gateway (GW) can also be implemented in a similar FD manner (Although some mechanisms (credited as Full Duplex) have been proposed to increase the spectrum reuse with different frequencies for the transmission and reception, the full Duplex concept in this paper always refer to in-band full duplex, that is, the ability to transmit and receive simultaneously on the same frequency band [14; 15]. Under the consideration of residual interference on-board the satellite, the performance degradation requires further investigation to be quantitatively determined for the guidance of system design and performance evaluation, which will be the topic of our future research.) The Geostationary satellite (GEO) system is considered in this work, while the Doppler effect is not within the scope of this research in this work. When the relay operates in FD mode, it concurrently receives a desired signal \(x\left[i\right]\) along with loop interference \(t\left[i\right]\) as (The on-board residual loop interference is taken into account due to imperfect cancellation [17]. A simple extrapolation of the radio astronomy mask shows that the admissible emission in the satellite uplink RX band would be way too high with respect to the received signal magnitude. This out-of-band leakage coming from the transmitter reduces the sensitivity of the receiver [14]. The self-interference between the on-board satellite antennas for transmitting and receiving antennas can be accordingly alleviated with directional antenna while both the complementary analog and digital active cancellation units are still required to maintain the SI under acceptable level [14; 15].) \[r\left[i\right]=\sqrt{P_{1}}h_{1}x\left[i\right]+\sqrt{P_{2}}h_{1}t\left[i \right]+n_{1}\left[i\right], \tag{1}\]  
where \(P_{1}\) and \(P_{2}\) denotes the transmit power at the ES-1 and satellite, respectively. \(h_{1}\) is the channel coefficient between ES-1 and satellite, \(h_{1}\) is the residual loop interference channel coefficient due to imperfect SI cancellation, and \(n_{1}\left[i\right]\) is the noise term satisfying \(\mathrm{E}\left\{\left|n_{1}\left[i\right]\right|^{2}\right\}=\sigma_{1}^{2}\). Then, the relay amplifies the received signal by a gain factor \(\beta\), which introduces a delay of \(\tau\) as  
\[\sqrt{P_{2}}t\left[i\right]=\beta r\left[i-\tau\right]. \tag{2}\]  
Figure 1: Illustration of the system model.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
Footnote †: Abstract  
Underwater Wireless Sensor Networks (UWSNs) using electromagnetic (EM) technology in marine shallow waters are examined, not just for environmental monitoring but for further interesting applications. Particularly, the use of EM waves is reconsidered in shallow waters due to the benefits offered in this context, where acoustic and optical technologies have serious disadvantages. Sea water scenario is a harsh environment for radiocommunications, and there is no standard model for the underwater EM channel. The high conductivity of sea water, the effect of seabed and the surface make the behaviour of the channel hard to predict. This justifies the need of link characterization as the first step to approach the development of EM underwater sensor networks. To obtain a reliable link model, measurements and simulations are required. The measuring setup for this purpose is explained and described, as well as the procedures used. Several antennas have been designed and tested in low frequency bands. Agreement between attenuation measurements and simulations at different distances was analysed and made possible the validation of simulation setups and the design of different communications layers of the system. This leads to the second step of this work, where data and routing protocols for the sensor network are examined.  
underwater wireless sensor networks (UWSNs); electromagnetic; antennas; sea water; measurements; simulations; testbed +
Footnote †: footnote]Note: [
# Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
## 2 Materials and Methods
### Temporal Trend Analysis
#### 2.3.1 Mann-Kendall Test (MK)  
The Mann-Kendall is a rank-based non-parametric test, widely employed to detect monotonic trends (increasing or decreasing) in time series of hydro-climatic variables [38]. Mann-Kendall test does not require the data to be normally distributed, and has low sensitivity to abrupt changes in time series [39]. For the null hypothesis H0 for MK test, the deseasonalized data (x\({}_{1},\ldots,\)x\({}_{n}\)) comprise a sample of N independent and identically distributed random variables. For hypothesis H1, the distributions of x\({}_{k}\) and x\({}_{j}\) are not identical for all k, j \(\leq\) N with k \(\
eq\) j. The Mann-Kendall test is based on the statistic S:  
\[\begin{split}\text{S}=\sum\limits_{Z=1}^{\text{N}-1}\sum \limits_{j=Z+1}^{\text{N}}\text{sign}(\text{y})\\ \text{where sign}(\text{y})=\text{sign}\left(\text{x}_{j}\ -\text{x}_{z}\right)=\left\{ \begin{array}{ll}+1&\text{if }y>0\\ 0&\text{if }y=0\\ -1&\text{if }y<0\end{array}\right.\end{split} \tag{6}\]  
where x\({}_{j}\) and x\({}_{z}\) are the annual values (j \(>\) z).  
A very high positive value of S indicates an upward trend, and vice versa. For N \(>\) 10, the test Z approximately follows a normal distribution. It is estimated as follows:  
\[\begin{split} Z=\left\{\begin{array}{ll}\frac{\text{S}-1}{ \sqrt{\text{Var }(\text{S})}}&\text{if }S>0\\ 0&\text{if }S=0\\ \frac{\text{S}+1}{\sqrt{\text{Var }(\text{S})}}&\text{if }S<0\end{array}\right.\end{split} \tag{7}\]  
In this test, the null hypothesis H0 is that there is no monotonic trend in the series. If Z \(>\) 0, it indicates an increasing trend, while if Z \(<\) 0, it indicates a downward trend in the series. When testing either positive or negative monotonic trends at the \(\alpha\) significance level, the null hypothesis is rejected for an absolute value of Z greater than Z1-\(\alpha\)/2 [5].
# Deep Learning of Diffuse Optical Tomography Based on Time-Domain Radiative Transfer Equation
## 4 Applications to Multiple Absorbers
### Multi-Beam Injection Model  
Thus far, we have considered the case of only one incident laser beam from S2 shown in Figure 1. Here, we analyze the cases for laser beams injected at multiple positions, that is, S1 to S8 in Figure 1. We can use eight datasets for different incident beams. Applying the data subtraction method, it is found that the detection probability is higher for an absorber located near an incident beam.  
Figure 10 shows distributions of detection probabilities. In yellow domains near the beam position, the detection probabilities are high, while probabilities are low in blue domains. If we apply the present method to datasets for three incident beams simultaneously from S1, S2, and S4, the accuracy rate is 99.9% to detect one absorber. In addition, we tested the multi-beam model for three absorbers, using the data subtraction method. The results are shown in Figure 11, where each symbol corresponds to predicted positions for each beam. In this example, the blue absorber is correctly detected, while the orange absorber is correctly detected for one position by detector S1. The total score is 7, 4 for S2, 2 for S1 and 1 for S4. The maximum score is 12. Thus, the accuracy rate is 7/12 = 58%. Additional datasets for different beams will improve the detection accuracy rate.  
Figure 8: The left panel shows a point at the position for our scoring. If one detects the absorber position, one obtains two points. The right panel shows one example of scoring for two absorbers that leads to three total points, which means the detection probability of 75%.  
Figure 9: Example results for a big absorber. The left panel shows the classification for four domains, while the right panels shows eight domains. We apply a subtraction method for the case using four domains, but not for the case using eight domains. This figure shows the comparison between the results using a subtraction method and those for small domains without using subtraction. The red circle is the absorber, and predicted positions are indicated by blue squares.
## 2 Related Work
### Image Manipulation Localization  
Early related works primarily focused on localizing specific types of operations. For example, some studies [6; 7; 8; 9; 10] concentrated on detecting splicing, while others [11; 12; 13] focused on copy-move manipulation, and [14; 15] dealt with removal operations. But in reality, the type of manipulation is not always known, which has motivated recent research interest in general manipulation detection. Consequently, recent works have attempted to develop models capable of handling multiple types of forgeries within a single framework. RGB-N [16], for instance, adopts a two-stream Faster R-CNN-based network that includes an RGB stream and a noise stream generated by the SRM filter [25]. However, when images are taken by the same type of camera, their noise patterns tend to be consistent, leading to the failure of the noise branch in distinguishing manipulations. Another approach, SPAN [19], introduces a spatial pyramidal attention network which enables efficient as well as explicit comparison of patches at multiple scales through local self-attention blocks. While SPAN leverages local area correlations, it does not fully exploit spatial correlation and thus has limited generality. PSCC-Net [20] takes a stepwise method to enhance multiscale feature representation and incorporates the Spatial and Channel Correlation Module (SCCM) to better explore the correlation between space and channel. However, the structure of PSCC-Net becomes complex in order to capture more spatial information. ObjectFormer [22] introduces the Transformer to image tampering detection, but it simply concatenates CNN and Transformer in a sequential manner without effectively leveraging the strengths of both approaches. In this paper, we leverage the modeling capability of Transformers to improve the identification of tampered areas, aiming to benefit from its advantages in capturing global relationships and context.
# DSP-Based 40 GB/s Lane Rate Next-Generation Access Networks +
## 3 Simulation Results  
### Optimization of Transceiver Bandwidth  
Figure 3 shows the optimization of 40 Gb/s PAM-4 transceiver bandwidths, and the dependence of PAM-4 receiver sensitivity at a BER of 1 \(\times\) 10\({}^{-3}\) on chromatic dispersion subject to different transceiver bandwidths is shown in the figure. It clearly indicates that there exists an optimum transmitter/receiver bandwidth, corresponding to which PAM-4 has the best dispersion tolerance, and/or the maximum receiver optical power sensitivity. The optimum transmitter bandwidth is identified to be 20 GHz, which gives rise to the best trade-off between dispersion tolerance and receiver sensitivity. This is attributed to the fact that a larger transmitter bandwidth allows less ISI but experiences a stronger impact of chromatic dispersion. The optimum receiver bandwidth is 12.5 GHz, which is mainly determined by two factors: the receiver noise and the receiver-caused ISI. The optimization of transceiver bandwidths for 40-Gb/s electrical Duobinary and optical Duobinary is similar, which has been detailed in [18] and [11], respectively. The optimum transmitter/receiver bandwidth for electrical Duobinary (optical Duobinary) is 25 GHz/12.5 GHz (17 GHz/17.5 GHz) [11, 18]. Note that the 50 G-ITU grid is adopted for all cases. It is worth mentioning that the system performance not only depends on transceiver 3-dB bandwidth, but can also be affected by the spectral profile beyond the 3 dB bandwidth frequency [5].
###### Abstract  
This contribution deals with new geology, petrography, and bulk-rock/mineral chemistry of the poorly studied ultramafics of Wadi Ibib-Wadi Shani (WI-WS) district, South Eastern Desert, Egypt. These ultramafics are dismembered ophiolitic rocks that can be subdivided into serpentinites and serpentinized peridotites. Primary minerals such as olivine and pyroxene are absent in serpentinites, but relics of them occur in serpentinized peridotites. Pseudomorph after olivine is indicated by common hourglass textures with less mesh, whilst schistose bastites reflect a pyroxene pseudomorph. Chromite can be subdivided into Cr-spinel and Al-spinel. Cr-spinel ranges from chromite to magnesochromic in composition, whereas Al-spinel belongs to the spinel field. Cr-spinel includes YCr (Cr/(Cr+Al+Fe+3), YAl (Al/(Al+Cr+Fe+3), and YFe+3 (Fe+3)/(Fe+3+Al+Cr), similar to forearcal peridotite, whilst Al-spinel is more similar to abysmal peridoite, and may be formed during inanition of subduction processes in proto forearc environments. The main secondary minerals are tremolite, talc, and chlorite--which is subdivided into pycnochlorite and diabantine--and their temperature ranges from 174 to 224 \({}^{\circ}\)C. The examined rocks had undergone high partial melting degrees (>25%), as indicated by the Cr# of their unaltered cores (Cr-spinel, >0.6), whole rocks (Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), CaO, and MgO), trace and REEs, depleted Na\({}_{2}\)O, Al\({}_{2}\)O\({}_{3}\), and Cr\({}_{2}\)O\({}_{3}\) of clinopyroxene, and high forsterite content ((Fo = 100 Mg/Mg + Fe), av. 95.23 mol%), consistent with forearc settings.  
s 20 serpentinites, talc-carbonates, and listuareinte series (carbonate-rich, silica-carbonate, and birbirites) rocks are the main alteration products of the ophiolitic harzburgite/dunite fragments, due to interaction with CO\({}_{2}\)- and SiO\({}_{2}\)-rich fluids [13]. These processes are widely observed along faults and shear zones. Ultramafic rocks are enriched with magnesium and iron silicate minerals; therefore, carbonation processes take place via the hydrolysis of these minerals, through eviction of Si and combination of these cations with carbonates [3; 14]. The tectonic setting of Egyptian ophiolitic mantel rocks (ultramafics) is still a subject of debate; some authors suggest mid-ocean ridge (MOR), while others suggest supra-subduction zone (SSZ) [4; 15].  
MORs form abyssal mantle rocks of anhydrous melting at low degrees (5-15%), whilst more than 20% represents a high degree of melting, forming refractory or depleted rocks in SSZs [16]. The melting degree of the first type depends on the interaction between mantle rocks and melts, as well as the temperature and pressure at melting depth. Conversely, the melting degree of the latter represents a complex process, and depends on the interaction between melts and fluids generated from the subducted slab [17]. In this paper, new geological, mineralogical, and geochemical data are discussed, with a view to detecting the hydrothermal effects, origins and tectonic setting of the studied ultramafic rocks.