esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The 3 rd Generation Partnership Project (3 GPP) is in the process of developing 5 th generation (5G) radio access technology, the so-called new radio (NR). The aim is to achieve the performance requirements forIMT-2020 radio interface technology. In this paper, we focus on the analysis of the transmission of 5G NR uplink physical channels, such as physical uplink shared channel (PUSCH) and physical uplink control channel (PUCCH), dedicated for data and control channels, respectively, as specified in the 3 GPP standard, using digital signal processing (DSP)-assisted frequency division multiple access (FDMA) and time division multiple access (TDMA) channel aggregation techniques on an analogue radio-over-fiber (A-RoF) architecture. We verified that there is \(\sim\)34% spectral efficiency gain and lower error vector magnitude (EVM) achieved using the TDMA technique.  
A-RoF; DSP; fronthauling; uplink; NR Article
## 3 Results and Discussion
### Photocatalytic Efficiency of Pure and Palladium-Modified ZnO Films
Further degradation steps lead to the formation of lower-molecular-weight products (V), which can eventually be mineralized into CO\({}_{2}\), H\({}_{2}\)O, and other inorganic compounds. A general trend observed across all degradation pathways is that the photodegradation products are less environmentally harmful than the parent doxycycline molecule, with the notable exception of intermediate II, as depicted in Figure 12 [47]. While the complete mineralization of doxycycline cannot be confirmed for the ZnO-based materials presented in this work, their overall removal efficiency, as determined from the UV-vis spectral data, is comparable to--or even superior to--other examples reported in the literature. A comparison of these results is presented in Figure 8:2 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Photocatalyst** & **Experimental Conditions** & **Removal Rate (Time)** & **Ref.** \\ \hline Pristine ZnO & 14.6 cm\({}^{2}\) geometric area & 8.3\% (240 min) & \\ Pd/ZnO 10 J cm\({}^{-2}\) & 150 mL 15 ppm DC & & \\  & (in distilled water) & 31.5\% (240 min) & \\ Pristine ZnO & 14.6 cm\({}^{2}\) geometric area & 16.4\% (240 min) & This work \\ Pd/ZnO 10 J cm\({}^{-2}\) & 150 mL 15 ppm DC & & \\  & (in water) & 69.1\% (240 min) & \\ Electrodeposited ZnO rods & 2.5 cm\({}^{2}\) geometric area & & \\  & 20 mL 25 ppm DC & 85\% (240 min) & [51] \\ Spray-py hydrolyzed ZnO & 200 mL 25 ppm DC & 5\% (120 min) & [52] \\ TiO\({}_{2}\)-ZnO & 0.2 mg/mL suspension & 97\% (60 min) & [53] \\ heterojunction & 50 mL 20 ppm DC & 97\% (60 min) & [53] \\ CDots/Ag/ZnO & 0.3 mg/mL suspension & 96\% (60 min) & [54] \\  & 50 mL 30 ppm DC & 96\% (60 min) & [54] \\ AgIn\({}_{5}\)S\({}_{8}\)/ZnO & 0.5 mg/mL suspension & 88\% (60 min) & [55] \\  & 100 mL 10 ppm DC & 96\% (60 min) & [54] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the activity of pristine ZnO and the best-case Pd/ZnO photocatalytic thin films with the literature-reported ZnO-based photocatalysts for doxycycline removal. Figure 12: Plausible mechanism and main intermediates of doxycycline photocatalytic removal, following the literature-established demethylation and ring-opening pathway. However, a notable research gap exists regarding the use of supported ZnO-based photocatalysts for doxycycline removal. Only two relevant examples were identified in the literature, involving electrodeposited ZnO ([PERSON] et al. [51]) and spray-pyrolysis-deposited ZnO ([PERSON] et al. [52]). In the former study, 85% doxycycline removal was achieved within 240 min, albeit for a much smaller reaction volume of 20 mL. In the latter case, only 5% removal was observed over 120 min in a comparable reaction volume of 200 mL. Most studies in the literature focus on dispersed ZnO-based photocatalysts [53; 54; 55], which generally exhibit higher activity due to their larger effective surface area and reduced mass-transfer limitations.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 6 Performance Indicators
### Adversarial Attack Performance Indicators  
To compare the adversarial attack methods in this section, the adversarial performance is measured by the misclassification rate, the average confidence of the wrong class prediction, the average confidence of the correct class prediction, the perturbation-to-signal ratio and the \(L_{2}\) norm.  
The misclassification ratio (MR) is the most important attribute in adversarial attacks. In untargeted attacks, MR is defined as the proportion of all samples that are successfully misclassified into any class, and therefore can also be called the attack success rate. Specifically as follows:  
\[MR=\frac{1}{N}\sum\limits_{i=1}^{N}{{{{count(f(x_{i}^{a})\
eq y_{i})}}}} \tag{47}\]where \(f(\cdot)\) represents the classification model, there are \(f(x)=y\), \(x\) represents the normal sample, and \(x^{a}\) represents the adversarial sample. In the experiment, all the adversarial samples were from the samples that were correctly classified by the target model.  
The average confidence of adversarial class (ACAC) represents the model's confidence in predicting the class of the sample after perturbation as follows:  
\[ACAC=\frac{1}{n}\sum_{i=1}^{n}Q(x_{i}^{a})_{f(x_{i}^{a})} \tag{48}\]  
where \(Q\) is the softmax layer output of the classifier \(f\), there exists \(f(x)=\arg\max_{j}Q(x)_{j}\), and \(Q(x)_{j}\) represents the probability of the \(j\)th class.  
The average confidence of true class (ACTC) represents the model's confidence in the correct class of the sample after the attack. ACAC is used to evaluate the degree to which the adversarial sample deviates from the true class as follows:  
\[ACTC=\frac{1}{n}\sum_{i=1}^{n}Q(x_{i}^{a})_{y_{i}} \tag{49}\]  
The \(L_{2}\) norm is the most frequently used norm for calculating Euclidean distances and is also often employed as the regularization term for optimizing the objective function.  
The perturbation-to-noise ratio is the ratio of perturbation power to noise power, which can be calculated through perturbation-to-signal ratio (PSR) and signal-to-noise ratio (SNR), as follows:  
\[PNR=\frac{p_{per}}{p_{n}}=PSR\times SNR \tag{50}\]  
where \(p_{per}\) represents the disturbance power and \(p_{n}\) represents the noise power.
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 6 ANN Identification Results for Healthy Conditions  
The continuous lines in the Figures 6-10 are the estimated SDM parameters provided by ANN; they refer to the healthy conditions and put into evidence the intrinsic relationships among SDM parameters with the environmental conditions for the PV panel under test. It is worth noting that, apart from \(I_{ph}\), which is almost linear with G and practically insensitive with respect to the temperature, the behavior of the remaining parameters is completely different from the cases analyzed in [26; 30]. This result is not very surprising, given that the relationships among parameters and the environmental conditions change significantly from panel to panel.  
Figure 6: ANN identification of the photoinduced current in healthy condition.  
Figure 7: ANN identification of the saturation current in healthy condition.  
Figure 8: ANN identification of the ideality factor in healthy condition.  
Figure 10: ANN identification of the shunt resistance in healthy condition.  
Figure 9: ANN identification of the series resistance in healthy condition.  
To demonstrate the goodness of the ANN parameters estimation, in Figures 11 and 12, the plots of experimental data, selected randomly from the NREL database, in comparison with the reconstructed _I-V_ curves obtained with the estimated parameters at low and high irradiance conditions are shown.  
The error area, defined as the difference in the area below the reconstructed _I-V_ curves and the area below the corresponding experimental _I-V_ curve, is calculated for the tested cases; a 5% maximum error is found for a few cases at low irradiance conditions. This corresponds to the plots shown in Figure 11; however, the error can be considered acceptable since, as is already remarked in [32], the single-diode model is less precise for low irradiance conditions.
## 3 Problem Formulation  
### Objective Functions  
* First Objective Function  
As discussed, the MCMG system's objectives incorporated with coupled P2G and CAES are to reduce operation cost and CO2 pollution. First, the first objective function pertained to the total operation cost is modeled in Equation (3). The objective function consists of several terms. The cost of utility (purchased power and gas) from the power and gas markets are represented by two first terms, respectively. The CAES costs during multiple modes are given in the third, fourth, and fifth terms. The sixth term models the P2G operation cost during the gas-producing mode. The DRP cost is given in the seventh term. The wind power spillage cost is given in the last term of the objective function.  
\[\begin{array}{l}OF_{1}=\sum\limits_{s=1}^{NS}\pi_{s}\left(\sum\limits_{t=1} ^{NH}\left(\lambda_{t}^{e}P_{t,s}^{buy}+\lambda_{h}^{g}G_{t,s}^{buy}\right)+ \right.\\ \left.\sum\limits_{t=1}^{NT}\left.\left(\left[P_{t,s}^{dis}\times\left(HR^{dis} \times\lambda_{t}^{g}+VOM^{\text{exp}}\right)\right]\right.\right.\right.\\ \left.\left.+\left[P_{t,s}^{ch}\times VOM^{\text{c}}\right]\right)\\ +\left.C^{\text{P2G}}G_{t,s}^{dis}+C^{dr}\times dr_{t,s}+C^{w,sp}\times P_{t,s }^{\text{p},sp}\right)\end{array} \tag{3}\]  
* Second objective function  
The second objective function aims to minimize the proposed integrated system's emission pollution modeled in Equation (4). The first and second terms of Equation (4) determine the emission tax resulted from purchasing power and gas energy carriers from the markets. It is worth noting that the P2G with capturing CO2 has a positive impact on the whole system from environmental perspectives. Thus, this characteristic reduces the pollutant emission of gas consumption given in the second term Equation (4). The last two terms of Equation (4) investigate the produced pollutant by CAES associated with discharge and simple-cycle modes.  
\[OF_{2}=\sum\limits_{s=1}^{NS}\pi_{s}\left(\sum\limits_{h=1}^{NT}\left(\gamma^{e }P_{t,s}^{buy}+\gamma^{g}(G_{t,s}^{buy}-G_{t,s}^{dis})+\gamma^{dis}P_{t,s}^{dis }+\gamma^{si}P_{t,s}^{si}\right)\right) \tag{4}\]  
Figure 2: Schematic of the proposed MCMG system.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## Average resource utilization
#### 4.2.3 Simulation Summary
In this case, if the traffic pattern of the UBSs is changed, the probability that backhaul capacity for each UBS is adjusted to reflect the updated traffic condition becomes high. Thus, the average message overhead mainly due to this adjustment can be large when \(\beta\) is large. * For \(7\leq\lambda\leq 10\), there is a close relationship (or a tradeoff) among the average resource utilization, the average message overhead, and the average reserved capacity of the UBSC, which is different from the relationship for \(\lambda\leq 7\). * Specifically, as \(\beta\) becomes higher, the average resource utilization becomes higher, the average message overhead also gets higher, and the reserved capacity of the UBSC also becomes lower. * Note that for this case as \(\beta\) becomes higher, the average resource utilization becomes higher. This is because, for \(7\leq\lambda\leq 10\), some UBSs can have higher backhaul capacity (given by \(\frac{\mu_{UBSC}}{N}+\beta\)), and this capacity (greater than \(\frac{\mu_{UBSC}}{N}\)) can handle burst traffic as well as instantaneous high traffic due to large \(\lambda\). Consequently, the backhaul resource can be almost fully utilized, producing high average resource utilization. * Therefore, when we determine the operational value of \(\beta\) in the real UCWN, we first need to consider the average packet arrival rate \(\lambda\) of UBSs.
# 3D U-Net for Skull Stripping in Brain MRI
## 2 Materials and Methods
### Training  
Adam optimizer of the TensorFlow framework was employed for network training [50]. A variant of popular nonparametric non-uniform intensity normalization (N3) algorithm [51] was used for bias field correction. The algorithm works quite well when the brain mask is available. Therefore, we apply the method to the input sample (see Table 1 for details). The network output was generated by using the softmax and cross-entropy that are both standard in machine learning. The output of SoftMax function is compared with the ground truth labels using weighted cross-entropy loss. First, we assign \(k\) pairs of the training images (\(X\)) and labels (\(\gamma\)) as \(\left\{X^{j},Y^{j}\right\}_{j=1,2,...,k}\). Then, we flatten \(X^{j}=\left(x_{i}^{j}\right)_{i=1,2,...,n}\) into a one-dimensional (1D) vector. Similarly, \(Y^{j}=\left(y_{i}^{j}\right)_{i=1,2,...,n}\) are also flattened into 1D vector., where \(i,j\) are the voxel and the image order, respectively. For each image, the posterior probability of voxel \(i\) with label \(l\) computed by the SoftMax classifier can be written:  
\[p(y_{i}=l|X(M_{i}))=\frac{e^{f_{y_{l}}(M_{i})}}{\sum_{k^{\prime}=1}^{K}e^{f_{y_ {l^{\prime}}}(M_{i})}} \tag{1}\]  
where \(f_{y_{i}}(.)\) is the CNN computation function, \(M_{i}\) is the patch of the voxel \(i\) and \(k^{\prime}\) is the class number.  
The output value of the SoftMax function is a real number between 0 and 1, and the sum of the all the values is 1. The weighted cross-entropy loss function is written as follows:  
\[Loss=-\sum_{i}log(p(y_{i}=groundtruth|X(M_{i}))) \tag{2}\]  
Since the cross-entropy between the actual distribution \(q\) and the estimated distribution \(p\) is \(-\sum_{i}q(y_{i})log(p(y_{i}|X(M_{i})))\) and the actual distribution \(q(y_{i})\) is 1 for ground truth, and 0 otherwise.  
Weights are initialized using truncated normal distribution. The standard deviation of the random values was set to 0.1. Out of 125 scans, 105 scans were randomly selected for training the network and the remaining 20 scans for testing. We ran about 420,000 training iterations on two NVIDIA 1080 Ti GPUs (NVIDIA Corp., Santa Clara, CA, USA, 2018), which took approximately two days.  
Figure 1: 3D U-Net architecture. Blue blocks are output from convolutional neural network (CNN), batch normalization (BN), and ReLu. Green arrows are concatenation with cropped feature map from encoder part. The number above each blue box is the number of channels. The transparent box with blue outlines (except the input/output) on the decoder is the concatenated box from the encoder.
# Analysis of a Bistatic Ground-Based Synthetic Aperture Radar System and Indoor Experiments
## Materials and Methods  
### Bistatic GB-SAR System  
A bistatic GB-SAR system was constructed inside a common lecture room, 8 m wide and 10 m long, with multiple desks, chairs and three trihedral corner reflectors (CR) as shown in Figure 1. A vector network analyzer (VNA, Anritsu MS2028B) was used to transmit and receive the microwave signal. Tx and Rx antennas were physically separated by a baseline \(B\) for the bistatic GB-SAR configuration. Two square horn antennas in Kuband were mounted on top of a platform running the rail system. The position and velocity of the platform were controlled by a stepping motor and a gear system connected to a conveyor belt. The motion of the platform was calibrated after the tension of the belt was adjusted. The whole system was controlled by a notebook computer with an in-house data acquisition software.  
Figure 1: A bistatic ground-based synthetic aperture radar (GB-SAR) experiment inside a lecture room with (**a**) a schematic geometry, (**b**) a diagram of the indoor targets and system, (**c**) a photo of the system and the targets, and (**d**) a photo of the lecture room seen from the system with chairs and three trihedral corner reflectors (CR).  
The center frequency used for this experiment was 15.95 GHz. The bandwidth was 1.6 GHz so that the range resolution was 9.4 cm nominally. VV polarization (vertically polarized wave both in Tx and Rx) is used to avoid mutual coupling between antennas aligned horizontally [8]. The operable maximum length of the rail system is 6 m. The azimuth stepping distance was set to 3 cm so that the number of azimuth pulses was 201. The azimuth resolution of the real-aperture radar (RAR) would be 11.97\({}^{\circ}\) with 9 cm aperture length of the antenna, while that of SAR in strip-map mode would be a half of the real aperture, i.e., 4.5 cm for a conventional monostatic GB-SAR configuration [10]. Table 1 shows the system parameters and variables used for the experiment.
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 3 Results and Analysis
### Results Analysis
Examples of such noise sources include engine sounds and mechanical interference. Figures 4b and 5b illustrate the speech enhancement results obtained from the DEMUCS model, which demonstrate distortion in several segments of the time-domain waveform. Although high-frequency noise is reduced in the spectrogram, a substantial amount of background noise remains, and the model's effectiveness in addressing mid- to low-frequency noise is inadequate, resulting in significant residual background noise. In contrast, Figures 4c and 5c present the speech enhancement results achieved with the MetricGAN+ model, showing improved performance in processing mid- to low-frequency bands. However, notable energy attenuation is observed in the high-frequency bands, likely due to the over-suppression of high-frequency noise. This over-suppression results in visible distortion and excessive attenuation in the time-domain waveform, leading to an overall processing effect that remains suboptimal. Figures 4d and 5d illustrate the results from the PHASEN model, demonstrating strong performance in the mid- to high-frequency bands. However, there is residual energy present in the low-frequency bands, resulting in a mechanical, noise-like sound that affects perceptual quality. While some distortion is evident in the time-domain waveform, the overall performance surpasses that of the first two models. In contrast, Figures 4e and 5e present the results from the baseline CMGAN model, showing effective denoising across all frequency bands. Although a small amount of noise residue remains  
Figure 5: Spectrograms of enhanced speech signals with the different models: (**a**) noisy; (**b**) DEMUCS; (**c**) MetricGAN+; (**d**) PHASEN; (**e**) CMGAN; (**f**) clean; (**g**) DeCGAN. Brighter colors indicate stronger signal presence at a given frequency and time, while darker colors indicate weaker signal presence. in the low-frequency band, the overall enhancement closely resembles clean speech, and the time-domain waveform exhibits fewer distortions and instances of over-suppression compared to the earlier models. Finally, Figures 4g and 5g present the results from the DeCGAN model proposed in this paper. The time-domain waveform clearly illustrates that the model effectively suppresses distortion while preserving the details, bringing it closer to clean speech. The model successfully removes noise across all frequency bands and can effectively eliminate irrelevant phonemes during periods of silence. The overall denoising effect, in terms of both detail enhancement and noise reduction, is significantly superior. Through comparative experiments, it is evident that the proposed model outperforms others in speech enhancement, delivering superior denoising effects and improved speech quality and intelligibility.
## 5 Application of Generative AI Models for Context-Aware Vulnerability Scoring  
### Dataset Characteristics  
The dataset utilized for this study comprises 117 records, each detailing various security vulnerability metrics. A significant majority of vulnerabilities exhibit a Network-based Attack Vector (104 instances), with fewer Adjacent (2) and Local (11) vectors. Attack Complexity is predominantly low (108 cases) compared to high (9). Most vulnerabilities maintain an Unchanged Scope (100 instances) rather than Changed (17), and User Interaction is largely unnecessary (97 instances) versus required (20). In terms of Privileges Required, 60 entries demand None, 48 require Low, and 9 need High privileges. The impact metrics show varying distributions: Confidentiality Impact is most frequently Low (57), followed by High (37) and None (23). Integrity Impact leans towards Low (63), with High (33) and None (21) being less common. The Availability Impact presents a near-equal split between Low (35), High (34), and None (48). The Base Score metrics indicate a broad severity range (Minimum: 2.4, Maximum: 10), with a mean of 6.6 and a median of 6.4, suggesting a prevalence of moderate to critical vulnerabilities within the dataset.
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 5 Experimental Results
### Road Passenger Point Statistics  
Resident travel demand varies over time, leading to changes in passenger hotspot areas. Therefore, by comprehensively considering the travel demand within each area and the supply of taxis, we establish a regional passenger probability model to determine the road passenger probability, which serves as a crucial indicator for taxi drivers' route choices [38]. The probability of carrying passengers in different areas is modeled as follows:  
\[P(Li)=\frac{NLi}{N} \tag{14}\]  
Figure 10: Specific points recommended for midday peak passenger-seeking on August 23 (non-weekday).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Recommended Area** & **Supply/Demand Ratio** & **Road Density** & **Number of Passengers at Hot Spots** \\ \hline
1 & Chengdu North & 1.166 & 14.868 & 319 \\
2 & Sichuan University & 0.766 & 23.28 & 244 \\
3 & Shuangliu Airport & 3.231 & 15.44 & 243 \\
4 & Evergrande Plaza & 1.916 & 13.096 & 218 \\
5 & Provincial Hospital & 1.987 & 11.204 & 189 \\
6 & RT Mart & 0.6 & 13.752 & 156 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic information on recommended areas for taxi passenger-seeking in the morning peak on August 18 th.  
where \(P(Li)\) represents the probability of carrying passengers in the \(Li\) road area, \(NLi\) represents the number of pick-up points in the order data in the \(Li\) area, \(N\) represents the sum of the number of all orders in the study area, and \(i\) denotes the number of hotspots for carrying passengers. The larger the value of \(P(Li)\), the greater the attractiveness of the area to taxi drivers.  
After determining the number of passengers for each road segment, the probability distribution can be directly calculated based on the current scheme. Similarly, the passenger probability for different time periods can be obtained. As the scheme and time change, the road passenger probability also varies. For instance, during the evening peak period from 18:55 to 19:00 on August 18, the passenger probability distribution is as follows: Second Ring Elevated Road (0.181), East Third Section of First Ring Road (0.03), Jindong Section of East Street (0.2), East Third Section of Second Ring Road (0.045), Second Ring Road (0.076), North Remmin Road (0.076), North Fourth Section of Second Ring Road (0.045), Second Section of Fuqing Road (0.045), Shuanglin Road of Xinhua Avenue (0.045), North Fourth Section of First Ring Road (0.091), East Second Section of First Ring Road (0.045), and Huaxing Road (0.121).