esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
### Data Collection and Preprocessing  
To train and evaluate our proposed model, we used low-Earth-orbit (LEO) communication satellites as the subjects for individual identification research. By collecting and preprocessing the RF signals emitted by LEO communication satellites, we have established our own RF signal dataset for experiments. The whole process of collecting RF signals and building a dataset is shown in Figure 4.  
To achieve stable collection of signals and control the quality of signals, we have built a signal acquisition system. The hardware components of the system consisted of a parabolic antenna, a downconverter, and a software-defined radio (SDR) device. The software components of the system comprise antenna control software and SDRPlusPlus. The detailed process of signal acquisition is as follows:  
* Firstly, the RF signals were received and amplified by a 60 cm parabolic antenna, then fed into the downconverter via RF feed lines;  
Figure 4: The process of collecting RF signals and building a dataset.  
Subsequently, the RF signals were converted into intermediate frequency (IF) signals by the downconverter;
* Finally, the IF signals were converted to baseband signals by an SDR device and the data were stored in IQ format.  
After obtaining the raw signals, we performed the following preprocessing operations: High-quality signal data files for 42 LEO communications satellites over different days were selected. The high signal-to-noise ratio segment (10 dB) from each satellite signal data was utilized as the reference signals \(x_{reference}\), while the remaining signal segments (0-10 dB) were considered as dynamic noisy signals \(x_{dynamic}\). Due to the potential presence of the RFF features at any position within the signal, it was necessary to perform signal segmentation on long signal data to facilitate feature extraction by deep learning models [33]. The specific process of signal segmentation is illustrated in Figure 5. Each sample in IQ format was sampled into a complex-valued signal sample of length 1024 after the segmentation.  
After processing the signal data files from 42 LEO communication satellites, the final dataset we have built is described as follows:  
* Each satellite has a reference signal set and a dynamic noisy signal set, both of which contain 4096 complex-valued signal samples;
* Each complex-valued signal sample corresponds to a label of the specific satellite;
* The SNR of the reference signal set is maintained at 10 dB, while the SNR of the dynamic noisy signal set varies between 0 and 10 dB;
* The signal dataset of LEO communication satellites comprises a total of 344,064 complex-valued signal samples labeled from 42 satellites.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 4 Alginate Modification and Coupling Strategies
### Hydroxyl Groups
#### 4.2.5 Graft Copolymerization  
Grafting can be performed through the living-free radical method. A single electron transfer living radical polymerization (SET-LRP) is performed on TBA alginate salt that is to be grafted with methylmethacrylate (MMA) [98]. The TBA salt is soluble in an organic solvent, which allows anhydrous esterification. Thus, it can be reacted with the initiator for the SET-LRP procedure. The SET-LRP resulted in a self-assembled amphiphilic alginate derivative. Free radical grafting polymerization has been performed through atom transfer radical polymerization (ATRP) [99]. The method is utilized to synthesize a macrophage-targeted carrier from sodium alginate to be grafted with allylamine with the presence of ammonium persulfate (APS). The resulting polymer is then grafted with mannose as the targeted ligand.
## 4 Experiments and Results
### Hyper-Parameters Setup  
The experiments were conducted on a computer with an Ubuntu 20.04.6 LTS operating system, an Intel(R) i5-13600 KF central processing unit (2.60 GHz), and a NVIDIA GeForce GTX 4090 (24 G) GPU. All models were implemented by PyTorch 1.9.1, which is a machine learning toolkit released by Facebook that can run on the graphic processing units (GPUs) for acceleration. All complex-valued components were implemented by Complex-Torch 1.0.0, which is a toolkit built on PyTorch with the intention of implementing lightweight interfaces for common complex-valued neural network operations and architectures [34].  
Setting hyper-parameters is essential for the training and optimization of the models. After several experiments to adjust the parameters, we selected the following hyper-parameter values to train the CC-GAN:  
* The number of training iterations was set to 100 and the batch size was set to 128;
* The Adam optimizer was chosen for minimizing the loss function, featuring a learning rate of \(2\times 10^{-4}\), a mean exponential decay rate \(\beta_{1}\) of 0.5, and a uncentered variance exponential decay rate \(\beta_{2}\) of 0.999;  
Figure 5: The segmentation process of the original signal sample.  
* Binary cross-entropy (BCE) loss was used to calculate the loss value for the discrimination of authenticity and categorical cross-entropy loss was used to calculate the loss value for classification.
# Internet Video Delivery Improved by Super-Resolution with GAN
## 5 Datasets  
Two datasets are considered for training and testing the proposed models in this work. The first is a high-definition image dataset used to train the proposed models. The second is a video dataset used to test the models. Due to its diversity of scenarios, the first dataset demonstrated excellent fitting for training the models. It includes 1.8 million images from 365 categories in the training set; and 36,500 images, 100 per category, in the validation set. In [60], the authors presented and named this dataset as Places365-Standard.  
The dataset of videos has 220 five-second video clips, each with four resolutions: 1080p, 720p, 540p, and 360p. This range of video resolutions targets the prevalence of 1080p and 720p in video-streaming applications for widescreens, e.g., smart IVs and laptops, and the prevalence of 540p and 360p in video-streaming applications for small screens, e.g., smartphones and tablets. This dataset was presented in [48] and is called VideoSet.  
H. 264/AVC encoded all clips in the color space YCbCr4:2:0, and QP in [0; 51]. \(QP=0\) indicates that the videos are losslessly encoded, and \(QP=51\) means that the videos have the highest compression ratio, which shows the highest (lowest) bitrate and best (worst) image quality per frame, respectively.  
In [48], 30 subjects evaluated the video quality of all pieces of the VideoSet. Each subject watched encoded videos and identified three JND points, splitting the encoded videos into four sets, with \(Q_{1}\) presenting the best-perceived quality and \(Q_{4}\) the worst quality. The QP values range in \([7;47]\); hence, encoded videos with QP in \([0,6]\) have unperceived quality changes, and those with QP in \([48;51]\) have unacceptable quality.  
\[Q_{i}=\begin{cases}Q_{1}&\text{if }QP_{V_{i}}<QP_{1^{st}JND\in V_{i}}\\ Q_{2}&\text{if }QP_{1^{st}JND}\leqslant QP_{V_{i}}<QP_{2^{nd}JND\in V_{i}}\\ Q_{3}&\text{if }QP_{2^{nd}JND}\leqslant QP_{V_{i}}<QP_{3^{rd}JND\in V_{i}} \\ Q_{4}&\text{if }QP_{V_{i}}\geqslant QP_{3^{rd}JND\in V_{i}}\end{cases}\]  
The VideoSet comprises five-second clips sampled from videos of various subjects. Table 2 shows the video titles, the number of samples, and the number of FPS of each sample.  
The general conclusion is that VideoSet's contents and qualities are representative samples of the available content in today's video service. For instance, the title Tears of Steel is a cartoon-like action movie, El Fuente is a TV-like drama series, and Unspoken Friend is a 90-min movie. All of this content is encoded to target multiscreen audiences.
# Pathophysiologyiology and Treatment of Soriasis: From Clinical Practice to Basic Research
## 5 Challenges and Innovations in Transdermal Drug Delivery System for Poriasis Treatment
### Dressing-Based Transdermal Delivery for Soriasis Treatment
#### 5.3.1 Polymeric Gels for Soriasis Treatment  
Gel formulations offer significant advantages in transdermal delivery systems, primarily due to their soft texture, breathability, and high viscoelasticity. These properties not only provide a comfortable user experience but also enable controlled drug release for prolonged therapeutic effects. Common materials used in gel formulations include carbomers, cellulose derivatives, propylene glycol, and glycerin. These materials combine with solvents like water or glycerin to form a uniform, smooth gel matrix that supports stable drug release. Additionally, gel formulations allow for customization based on the specific characteristics and therapeutic needs of the drug, enabling personalized treatment. For instance, incorporating environmentally responsive materials can create smart gels sensitive to temperature, pH, or other factors, further optimizing drug delivery and therapeutic efficacy.  
Figure 5: A brief introduction to microneedle and how it is released. Created with BioRender.com.  
Kaempfer, a flavonoid found in vegetables, fruits, and spices, is renowned for its antioxidant and anti-inflammatory properties. However, its poor water solubility and bioavailability limit its clinical application. [PERSON] [154] et al. developed a hydrogel (DK-pGEL) using Pluronic F127 combined with a deep eutectic solvent (DES) containing varying concentrations of kaempfer, which inhibited the proliferation of HaCaT cells without significant cytotoxicity, exhibited antioxidant effects, reduced ROS production, and provided hydration to damaged skin, making it a promising candidate for clinical treatment. [PERSON]'s team [155] reported an eco-friendly silver nanoparticle (AgNP) gel formulation using Pongamia pinnata seed extract to load AgNPs. The gel matrix, based on carbopol 980P and Cremophor A25, enhanced the retention, accumulation, and penetration of AgNPs into psoriatic skin. The AgNPs were formed through a mechanism involving recognition, reduction, and nucleation, with the optimized formulation showing improved retention and accumulation in psoriatic lesions, offering a potential therapeutic option for psoriasis.
# Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model
## 2 Related Works  
### Correlation Analysis  
In the process of actual production, life and scientific research, it is often found that multiple factors have an impact on a certain object or phenomenon at the same time. Since there is generally a certain correlation, either strong or weak, between multiple variables, information overlaps to some extent, hindering the in-depth analysis of objects or phenomena. Correlation analysis is a statistical method to analyze whether two or more objects or phenomena are correlated and the strength of their correlation [3].  
PM2.5 in the atmosphere is produced by both primary and secondary sources of pollution. Environmental monitoring stations for PM2.5 detect mainly secondary particulate matter, mainly contributed by ammonium, sulfate and nitrate, which are substances produced by SO\({}_{\text{x}}\) and NO\({}_{\text{x}}\) emissions [4]. Therefore, NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), etc. are key factors influencing the atmospheric PM2.5 concentration [5]. The generation and flow of PM2.5 are significantly related to the local climate environment [6]. Different meteorological conditions also have a large impact on the diffusion and transport of pollutants. [PERSON] [7] discussed the correlation between air pollutants and meteorological conditions in Zhumadian City and found that different seasons and meteorological conditions have a certain influence on air pollution concentrations, with a negative correlation between air pollutants and temperature and precipitation, and a positive correlation with wind speed. Co The main factors affecting the change of PM2.5 concentrations include not only the mutual transformation between various atmospheric pollutants caused by chemical interactions, but also the influence of different meteorological factors and geographical characteristics on the atmospheric environment. Since the regional geographical characteristics are relatively stable, this paper focuses on the correlation among atmospheric pollutants and between atmospheric pollutants and meteorological factors.  
The measurement of correlation was proposed and studied in the early stage of statistics, and the most widely used measurement is the Pearson Correlation Coefficient (PCC). [PERSON] al. [8] used PCC for correlation analysis to obtain the correlation matrix of six major AQI indicators in a city regarding available monitoring data. [PERSON] et al. [9] used PCC to analyze the correlation between PM2.5 concentration in summer and autumn in Beijing and six meteorological factors, including air temperature, relative humidity, wind speed, water vapor pressure, atmospheric pressure and wind direction. However, PCC is limited to linear Gaussian cases and is often inadequate for complex nonlinear natural phenomena; therefore, its application is very limited. If PCC is applied without considering the preconditions, the conclusions drawn are unreliable.
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 3 Corpus and Annotation
### Token-Level Labeled Corpus  
Most of the work that focuses on grammatical error detection using Neural Networks treat this task as a sequence labeling problem where the model receives a sentence and takes context into consideration to produce the probability of each word in the sentence being correct or incorrect [1]. This kind of task requires training on a token-level labeled corpus such that every word in a sentence is labeled as correct or incorrect. However, to the best of our knowledge, there is no such corpus that has been created for Arabic sentences.  
Therefore, for the purpose of this work, we manually transformed the parallel corpus in [33] to accommodate a token-level error detection task similar to the work in [1]. We did not consider punctuation marks or punctuation errors in the experiments presented here. We extracted all the incorrect sentences and labeled each word as being correct (c) or incorrect (i). We used the corrected versions of the sentences and compared them to their erroneous counterparts to indicate which word(s) are those that should be labeled as incorrect.  
In case there was an extra word in the incorrect sentence, it was labeled as \"i\". Furthermore, in case there was a missing word, the word after the incorrect gap was labeled as \"i\". Table 2 shows statistics related to the original corpus.
## 4 Experiment
### Experimental Results  
**Anomaly detection. ** Deep learning methods are known to be generally superior to machine learning methods. Still, machine learning methods may be better on some specific datasets, so, to show the overall performance of CAT-IADEF, we compare it with four classical methods and four deep learning methods. In the comparison experiments, to ensure the fairness of the comparison, we add dynamic threshold adjustment to all methods, and each group of experiments is carried out 5 times to obtain the average value. Table 2 shows the accuracy, precision, recall, and F1 scores of CAT-IADEF and the other compared models on seven datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & \begin{tabular}{c} **Number of** \\ **Instances** \\ \end{tabular} & \begin{tabular}{c} **Train Ratio** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Test Ratio** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Features** \\ \end{tabular} &
\begin{tabular}{c} **Anomaly** \\ **Proportion (\%)** \\ \end{tabular} \\ \hline SMAP & 562,800 & 24.0 & 76.0 & 25 & 13.13 \\ MSL & 132,046 & 44.2 & 55.8 & 55 & 10.72 \\ SWaT & 946,719 & 52.5 & 47.5 & 51 & 11.98 \\ SKAB & 46,806 & 20.1 & 79.9 & 8 & 35.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset data characteristics. It can be seen from Table 2 that CAT-IADEF achieved the best F1 values on the data subsets T4 of MSL, Valve1-4 and Valve1-14 of SKAB, and D15 of MSL; these values were, respectively, 0.747, 0.929, 0.987, and 0.993. On the P1 subset of SMAP, OmniAnomaly's method achieved the best F1 value of 0.924, and CAT-IADEF's F1 value of 0.923 ranked second. On the data subset of SWaT, the TranAD method achieved the best F1 value of 0.814, and CAT-IADEF's F1 value of 0.811 ranked fourth. On the data subset T1 of SMAP, the LOF method performed the best, with an F1 value of 0.971, and the F1 value of CAT-IADEF was 0.960, ranking second. We rank the F1 of the models comprehensively on all datasets, as shown in Figure 2. CAT-IADEF has a combined ranking of about 1.7, ranking first among all the compared models. To analyze the different models, we first analyze four machine learning methods. The number of dimensionality reductions finally obtained by the PCA, that is, the number of latent variables, cannot be well estimated; therefore, its performance is not outstanding on the seven datasets. COPOD does not need to adjust parameters nor calculate the distance between samples, and the overhead is negligible. COPOD and OCSVM perform well on the T1 dataset and poorly on the rest of the datasets. This is due to the following problem: the scene changes in the face of non-stationary, unbalanced time series. The performance of LOF on other datasets is not outstanding. Still, the optimal F1 value is obtained on the  
\begin{table}
# Estimation of SMPS' Conducted Emission According to CISPR Standards
## 4 Discussion
This approach follows the idea of dividing the big analysis into smaller pieces and prevents from running out of resources and the need for extremely high computational time, when analysis is being performed. Maximum frequency of the analysis and the equivalent circuit: authors of this work used two solvers, quasi-static and full wave, and eventually results were compared. This decision was based on commercial information about software capabilities:  
* Quasi-static solver is capable to reproduce phenomena in structures up to \(\frac{\lambda}{\mathrm{I}0}\), where wavelength (\(\lambda\)) for 100 MHz signal is 3 m,
* Full wave solver is suitable for phenomena related to structures of size \(\frac{\lambda}{2}\) or even smaller. The results obtained in this work show that both solvers are accurate enough in order to extract accurate equivalent circuits for conducted emission estimation purposes. One of the most important factors that can have significant impact on the result is the submodels. During creation of them it should be taken care that they reflect the behavior \begin{table}
\begin{tabular}{c c c} \hline \hline
**Area** & **Simplification Level** & **Resources Required** \\ \hline Layout & Low & High amount of memory, CPU, comp. time \\ Layout & High & Low amount of memory, CPU, comp. time \\ Electrical simulation & Low & High amount of comp. time \\ Electrical simulation & High & Low amount of comp. time \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Areas of the model simplification and its effects. of the layout in frequency range according to the time domain simulation with low error. As a general rule, the submodels, when created, should contain as much as possible both low- and high-frequency range margins. This will allow one to exclude any issues related to this part of the work. In the following cases, the submodels frequency ranges should be restricted: when analysis using the submodel in time-frequency domain analysis takes more time than is acceptable; or when time or memory resources required for extracting the submodel parameters from the 3D model (both full wave or quasi-static) are not acceptable. According to knowledge of the authors, it is the only workflow presented in literature verified according to CISPR 16-2-1 standard. The other results presented in the literature could be based on other time-domain parameters, which are not always known and presented, and could affect the results. Because of this fact, it is impossible to compare obtained results in this work with results from other authors.
## 5 Experimental Results
### Performance Evaluation of Employed IoT devices
On the other hand, Raspberry Pi 2 Model B computes our prepared scenarios several times faster (see Table 4 for a detailed comparison) and therefore becomes a preferred embedded (low-power) device from the performance perspective. If we take a closer look at the difference between the RPi  
Figure 11: Performance comparison. Figure 10: Selected embedded devices. (**a**) Intel® Edison; (**b**) Intel® Galileo Board Gen2; (**c**) Raspberry Pi 1 Model B+; (**d**) Raspberry Pi 2 Model B. Model B+ and the RPi 2, one could think that the main reason why RPi 2 is almost 7 times faster (for our scenarios) is in the number of CPU cores (single-core vs. quad-core). However, we have implemented our environment as a single thread and hence these performance differences may only be caused by (i) the CPU clock speed (700 MHz vs. 900 MHz) and especially (ii) the change of the SoC architecture (ARM11 vs. Cortex-A7), see Table 3.  
Aiming to provide a comprehensive and balanced view, we additionally compared the performance of our tested embedded devices with the laptop Lenovo ThinkPad W540 equipped with the Core i7 4700 MQ CPU (Haswell gen.) and having 16 GB of memory. The obtained execution times for the NTB were ten times shorter than those for Rapsberry Pi 2. Noteworthy, Rapsberry Pi 2 is the only embedded device in our experiments having the performance comparable to that of the NTB.
## 3 The Proposed NeXtFusion 3D Object Detection Network
### Bounding-Box Regression  
The object detection task can be streamlined by dividing it into two simpler subtasks: identifying objects and pinpointing their locations. Finding these objects relies on a technique called bounding-box regression (BBR). This method essentially draws a rectangular box around the predicted object location within the image, maximizing the overlap with the actual object. The extent of this overlap is measured by mean squared error (MSE) or the intersection-over-union (IoU) losses. This popular metric evaluates the similarities and differences between two arbitrary shapes. Mathematically, it represents the ratio between the area shared by the predicted bounding box (denoted as \(A\)) and the ground-truth bounding box (\(B\)) as follows:  
\[\text{IoU}(\text{A},\text{B})=\frac{\text{A}\cap\text{B}}{\text{A}\cup\text{B}} \tag{1}\]  
Figure 5: A visual depiction of the spatial pyramid pooling operation.  
While IoU loss is widely used in BBR, it encounters a challenge when the predicted bounding box and the actual object's ground-truth box do not intersect, i.e., when IoU of \(A\) and \(B\) equals zero, it fails to provide an overlap ratio. To overcome this limitation, the NextDet object detector has integrated a more robust approach called generalized IoU (GIoU) [33]. GIoU addresses this issue by actively encouraging a greater overlap between the predicted and ground-truth boxes, effectively steering the prediction closer toward the target. Mathematically, GIoU loss (\(\mathcal{L}_{\text{GIoU}}\)) is expressed as follows:  
\[\mathcal{L}_{\text{GIoU}}=1-\text{IoU}+\frac{|\text{C}-\text{A}\cup\text{B}|}{ |\text{C}|} \tag{2}\]  
Here, \(C\) represents the smallest enclosing box that incorporates both the predicted bounding box (\(A\)) and the ground-truth box (\(B\)). As defined in [33], experiments reveal that GIoU loss delivers superior performance compared to both mean squared error (MSE) and standard IoU losses. Notably, it also demonstrates effectiveness in tackling vanishing gradients when the predicted and ground-truth boxes fail to intersect. Figures 6 and 7 provide a visual overview of the difference between IoU and GIoU.