esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 1 Introduction
The far-field solution of a loop of any size is deduced in [14], and the far-field of a square loop is also discussed, finally proving that when the circular and square loops are the same in area and small in size with respect to wavelength, their far-field solutions are identical, but they are different when the size is large in terms of wavelength. From [15], it can be seen that the far-field approximation solution of the electromagnetic field cannot be used in near-field systems because of the large relative percent error. The exact integration of vector potentials of thin circular loop antennas is then introduced to solve this problem. In this paper, using [PERSON]'s equations, the analytical solution for the synthesized electric and magnetic field, both by the primary and secondary circular coil in an IPT system, are deduced in this paper, and the electric field is analyzed in detail. The numerical integration method is used to find the induced voltage in the secondary coil. Finite element software is utilized to compare with the analytical results, and an IPT system is set up to validate the analytical results experimentally.
## 3 Results  
### Analysis of LUCC from 2015 to 2020  
In this paper, the land use/cover data in the study area are divided into 3,839,397 cells, each of which is spatially displayed as a grid of 100 m \(\times\) 100 m. The land use data we obtained from 2000 to 2020 are processed into the same format and geographic coordinate system, so the number and location of these cells remain unchanged over time.  
The actual situation of mutual conversion between various types of land use/cover during the period from 2015 to 2020 is shown in Table 3. There are 3,792,559 cells with no change in land use/cover type, and 46,838 cells with type conversion, accounting for about 1.24% of the number of changed cells. In 2015, grass, shrub, and impervious surfaces were not converted to other land use/cover types, indicating that these three land use/cover types were relatively stable or not easily converted to other types in the study area. The conversion of cropland to impervious land type accounted for 63.86% of the total change. It shows that the metropolitan area of Chongqing is in a period of rapid development from 2015 to 2020, and the expansion of urban land mainly occurs in the cropland area.  
\begin{table}
\begin{tabular}{c c c c c c c c}
**2015** & **2020** & **Cropland** & **Grass** & **Forest** & **Shrub** & **Impervious Surfaces** & **Water Area** & **Total** \\ \hline Cropland & 2,312,150 & 382 & 9258 & 610 & 29,910 & 852 & 2,353,162 \\ Grass & 0 & 13,271 & 0 & 0 & 0 & 0 & 13,271 \\ Forest & 4249 & 190 & 1,191,481 & 182 & 858 & 88 & 1,197,048 \\ Shrub & 0 & 0 & 0 & 30,426 & 0 & 0 & 30,426 \\ Impervious surfaces & 0 & 0 & 0 & 0 & 184,467 & 0 & 184,467 \\ Water area & 128 & 24 & 43 & 0 & 64 & 60,764 & 61,023 \\ total & 2,316,527 & 13,867 & 1,200,782 & 31,218 & 215,299 & 61,704 & 3,839,397 \\ \end{tabular}
\end{table}
Table 3: Confusion matrix/error or land conversion from 2015 to 2020.
## 3 Results
### Comparison with Other Approaches  
In this study, our MLR model is compared with other approaches from the literature in the task of predicting DAT inhibitors. The results obtained by using the MLR-based model are shown in Table 4, where the models obtained with other approaches are also shown.  
A comparative analysis of the presented models reveals key differences in performance, dataset composition, and validation rigor. As shown in Table 4, the previously developed studies to identify novel compounds with potential radiopharmaceutical applications were all based on congeneric series, which limits their applicability to chemical structures that differ from those used during model training. In contrast, our model was developed using a dataset with greater structural diversity, thereby enhancing its ability to predict compounds outside the original chemical space.  
We would like to highlight that the model targeting the adenosine A\({}_{\text{2A}}\) receptor inhibitors [28] achieved the highest internal coefficient of determination (R\({}^{2}\) = 0.901) among all the models; however, it also exhibited the lowest external R\({}^{2}\) value (0.540), indicating limited predictive performance on the test set. Similarly, the PLS model for dopamine D\({}_{2}\) receptor [27] showed acceptable internal performance (R\({}^{2}\) = 0.731; Q\({}^{2}\)\({}_{\text{LOO}}\) = 0.623) as well as external performance (R\({}^{2}\)\({}_{\text{ext}}\) = 0.742), but also relied on a small, structurally homogeneous dataset.  
Among the models targeting the dopamine transporter (DAT), the PLS model reported by [PERSON] et al. [30] achieved the highest external predictivity (R\({}^{2}\)\({}_{\text{ext}}\) = 0.997), along with strong internal validation metrics (Q\({}^{2}\)\({}_{\text{LOO}}\) = 0.761; Q\({}^{2}\)\({}_{\text{LMO}}\) = 0.746). However, such a pronounced discrepancy between internal fit values and external predictability is uncommon in computational modeling and is likely attributable to the specific selection of the external test set. The models reported by [PERSON] et al. [29] were built using the largest dataset (69 compounds), which is advantageous in terms of statistical power; however, the absence of appropriate cross-validation and external validation metrics weakens confidence in their robustness and predictive reliability. By contrast, the model proposed in the present work stands out because of its use of a structurally diverse dataset and its comprehensive validation strategy. In spite of a slightly lower adjusted R\({}^{2}\) (0.755), it demonstrated consistent performance across internal (Q\({}^{2}\)\({}_{\text{LOO}}\) = 0.680; Q\({}^{2}\)\({}_{\text{LMO}}\) = 0.648; RMSEcv = 0.409) and external metrics (R\({}^{2}\)\({}_{\text{ext}}\) = 0.709), suggesting a better balance between robustness, generalizability, and practical utility for identifying novel radiopharmaceutical candidates targeting DAT.
## 4 Results
### Study Design  
The studies under investigation analyzed different sets of activities and tasks. These can be classified into 11 different categories: activities of daily living, balance task, finger tapping, MDS-UPDRS task, postural tremor, pronation-supination movements of the hands, reaction time, rest tremor, timed up and go (TUG) test, gait task, and others.  
Due to the diversity of exercises performed within a category, similar activities were grouped in a more general category. Specifically, the finger tapping activity includes four types of sub-activities: index and middle finger tapping, alternate index finger tapping, thumb-index finger tapping, and two-target finger tapping test. Similarly, the gait includes: 2-min walk at convenient speed, 20-m straight walk at convenient speed, 20-m straight walk at fast speed, 10-m at convenient speed, 20-m circular walk at fast speed, 20-steps straight walk, normal walking, rhythmic auditory cued walking, treadmill walking, and 2-min circular walk at convenient speed.  
Figure 4 shows the distribution of the different activities evaluated in the selected studies.  
A total of 52 different activities were investigated in the 22 articles analyzed. The most frequent activity is gait (N = 16, 31%), followed by finger tapping (N = 8, 15%), balance task (N = 5, 10%), and TUG test (N = 4, 8%). Other activities that appear less frequently include postural tremor, pronation-supination, rest tremor, MDS-UPDRS tasks and reaction time.  
Less than one third of the studies (N = 6, 27%) used public datasets. In more detail, 3 studies [(68; 79; 84)] used the mPower dataset [(86)] and 3 studies [(65; 69; 70)] used data from the Physionet vertical ground reaction force (VGRF) dataset [(87)]. The Physionet database contains measures of gait from 93 PwPD and 73 HC. The database includes the VGRF records of subjects as they walked at their usual, self-selected pace for approximately 2 min on level ground. The mPower database includes data from more than 1000 PwPD and 4000 HC. Different activities (i.e., memory, tapping, voice and walking) were recorded with the built-in smartphone sensors (i.e., triaxial accelerometer, microphone and touchscreen) in unsupervised environments.
# Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models
## 2 Literature Review
### The Available Datasets for Automatic Abusive Content Detection in the Arabic Language  
Table 1 summarizes the available datasets for abusive content detection in the Arabic language. The table lists seven different datasets (DS), each with their own unique characteristics such as number of tweets/posts, language, classification type, and source. The DS1 was created by [PERSON] et al. [19] and contains 6100 tweets in DA collected from Twitter. The tweets are classified as binary type. DS2 was created by [PERSON] and [PERSON] [8] and contains 9300 tweets in DA collected from Twitter. The tweets are classified as binary type and are specific to Saudi Arabia dialects. DS3 is called OSACT 4 [24] it was constructed in the 4 th Workshop on OSACT and contains 10,000 tweets in DA/MSA collected from Twitter. The tweets are classified as binary type. The DS4 is called OSACT 5 [33], it was constructed in the 5 th Workshop on OSACT and contains 12,600 tweets DA/MSA collected from Twitter. The tweets are classified as binary type and multi-class (based on factors such as gender, race, religion, etc.). DS5 was created by [PERSON] et al. [21] and contains 5800 tweets in Levantine Arabic dialect and the tweets are classified as ternary type. DS6 was created by [PERSON] et al. [34] and contains 6020 posts and comments from Facebook and YouTube in DA specifically focused on Tunisia dialect. The posts and comments are classified as ternary type. DS7 was created by [PERSON] et al. [9] and contains 3300 tweets in DA collected from Twitter. The tweets are classified according to multiple aspects including directness, hostility, target, group, and annotator.  
Noticeably, the majority of relevant works on abusive Arabic content detection used binary and ternary classification task, except the recently released OSACT 5 [33] dataset and the selected dataset used in this study [9]. Furthermore, the main dataset collected from Twitter and annotated using different strategies was the dataset with its annotation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
* List of acronyms used in this summary: HS (hate speech), OFF (offensive language), MSA (modern standard Arabic), DA (dialetcal Arabic), L-HSAB (Levantine hate speech and abusive), T-HSAB (Iunisian hate speech and abusive dataset), OSACT (Open-Source Arabic Corpora and Processing Tool), DS (dataset).  
\end{table}
Table 1: Summary of the available datasets for automatic abusive content detection in Arabic *.  
uploaded in GitHub. In addition, the available datasets had some limitations, such as the number of instances in small size, most of which did not exceed 13,000 instances.
# The Application of Microplasma in the Terahertz Field: A Review
## 3 Challenges
### The Amplification of THz Radiation by Microplasma  
Regarding the amplification mechanism of THz radiation by microplasma, [PERSON] et al. believed that four-wave-mixing parametric processes are the main reasons for amplification of THz radiation by laser-induced plasma. This is different from the explanation from [PERSON] et al. that the negative absolute conductivity of laser-induced plasma should be responsible for the amplification. Moreover, [PERSON] et al. proposed that the mechanism based on electric dipoles is attributed to amplifying THz wave inside a meandering waveguide by the microplasma in the capillary, where the microplasma is generated by electrical discharge instead of femtosecond laser energy pulses. Reference [44] reported that the relationship between the gain of terahertz wave amplification and the electron density is nonlinear. The amplification mechanism of THz radiation by microplasma is unclear yet, which will be solved in the near future.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 4 AI-Assisted Sensing and Survivability Techniques  
Recently, AI has become increasingly important and many countries have taken it as the future research focus for the next few years. AI has been applied in many aspects of optical networks.  
Figure 9: The RSA for end-to-sensing paths: (**a**) end-to-sensing paths; (**b**) the transmission distance and capacity of each kind of modulation level; (**c**) the lowest modulation level allocation; (**d**) the distance-adaptive modulation level allocation.  
In [38], the authors gave an overview of the application of machine learning in optical communications and networking. The current research involves failure localization and anomaly detection, routing and resource allocation, QoT estimation, etc. Moreover, AI has been applied in vehicular networks, and vehicular edge and vehicular fog computing [137; 138; 139; 140; 141]. In all AI-assisted applications, the QoT estimation is an important one. In the field of optical sensing, AI also has important applications, including learning, reasoning, and self-correction.
# Present and Future Marine Microwave Satellite Missions in China
## 5 Development Trends of Marine Microwave Remote Sensing Satellites in China
### Development Trends of Surveillance Satellites in China  
Similar to the marine dynamic satellite constellation, China is also building a monitoring satellite constellation, which is expected to be completed in 2022 [86]. The main payload of that constellation is a 1 m resolution synthetic aperture radar. The constellation will be composed of two satellites. After completion, it will provide high-effectiveness and high-resolution observational data for global marine disaster prevention and mitigation, as well as maritime search and rescue.  
China's surveillance satellite constellation will not only play an important role in ocean applications, but also provide quasi real-time monitoring for such aspects as agriculture, forest coverage, and changes to grassland regions.
Traditional rubber dampers perform excellently under low-frequency and small-amplitude vibrations but prove ineffective under high-frequency and large-amplitude conditions. In contrast, SMAs exhibit strong energy dissipation capabilities under high-frequency and large-amplitude vibrations; therefore, the alignment between their operating frequencies and amplitudes requires further investigation [32; 33; 34; 35]. This study explores the influence of structural parameters on the stiffness and damping characteristics of existing rubber-based SMA composite dampers. A reliable Adams multi-body dynamic model was developed and validated. Subsequent optimization of the suspension system parameters aimed to achieve superior vibration reduction performance under harmonic excitation with varying loading rates and amplitudes. This study targeted a 45% increase in stiffness and a 64.5% increase in damping, with a focus on minimizing \(z\)-axis vibration acceleration at the driver's seat. This was achieved by optimizing the \(z\)-axis stiffness and damping parameters of the suspension system under different operating conditions. Response surface methodology was employed to design the composite shock absorber structure, followed by a comparative analysis of the damping performance of the optimized front and rear suspension systems. Through the optimization of the structural parameters of rubber-based SMA composite shock absorbers and the stiffness and damping parameters of the suspension system, the vibration acceleration of the driver's seat in the \(z\)-axis direction has been mitigated. Particularly at low loading rates, the root mean square value of seat \(z\)-axis acceleration undergoes a significant reduction. At a loading rate of 120 mm/s, it drops from 3.307 m/s\({}^{2}\) to 1.991 m/s\({}^{2}\), representing a decrease of 39.79%. This implies that during the driving process, drivers and passengers will experience fewer bumps and vibrations, thereby enhancing the comfort of the ride. Simultaneously optimized suspension systems can more effectively adapt to different road conditions and load rates. As the load rate increases, the stiffness and damping values of the suspension system correspondingly increase, enabling the vehicle to maintain excellent stability when driving at high speeds or encountering complex road conditions. Under high load rates, the z-direction displacement amplitude of the seat decreases. Although it takes longer to reach a stable state, the acceleration amplitude in the z-direction is higher, which helps the vehicle recover stability faster and improve driving safety.
## 2 Background and Related Work
### Dynamic Process Data  
If the rows in the input data matrix, \(\mathbf{X}\), are uniformly sampled from the underlying manifold, methods such as Isomap are generally able to learn the manifold, in the presence of sufficient data. However, here, we consider the scenario where \(\mathbf{X}\) represents a dynamic process, i.e., instances in \(\mathbf{X}\) are partitioned into \(T\) trajectories, \(\Gamma_{1},\Gamma_{2},\ldots,\Gamma_{T}\). \(\Gamma_{I}\) represents one trajectory which is specified by a \(\tau\)-parameterized sequence of \(m_{I}\) data points. Thus, the sum of the lengths of the \(T\) trajectories is equal to \(n\), i.e., \(\sum_{T}m_{I}=n\). In other words, \(\Gamma_{I}=(x_{I}(\tau_{1})\), \(x_{I}(\tau_{2})\),..., \(x_{I}(\tau_{m_{I}}))\), where \(\tau_{i}<\tau_{j}\) when \(i<j\). Parameter \(\tau\) usually denotes time, and trajectory \(\Gamma_{I}\) can be a function of one or more additional parameters.  
The target application for this study is the study of material morphology evolution during the fabrication of organic thin film [18]. In particular, we use process data produced by the numerical simulation of the morphology evolution. Each input trajectory (denoted as \(\Gamma(\phi,\chi)\), from here on)corresponds to a simulation which is parameterized by two fabrication process variables, viz.: (i) \(\phi\), or the blend ratio of polymers making organic film; and (ii) \(\chi\), or the strength of interaction between these polymers. Each trajectory consists of a series of \"images\", generated over time, where each image is actually a 2D morphology snapshot that is produced by the numerical simulation for a given time step. A simple preprocessing step transforms the image to a high-dimensional vector, \(\mathbb{R}^{D}\). See Figure 1 for some examples of the images that are generated for a selected combination of \(\phi\) and \(\chi\). A detailed description of the data and the data generation process is provided in Section 5.  
Ideally, to understand the impact of the input parameters, \(\phi\) and \(\chi\), on the final properties of the material, a large number of simulations need to be run that can uniformly span the domains of \(\phi\) and \(\chi\). However, the computational cost associated with generating one trajectory, corresponding to a single combination of \(\phi\) and \(\chi\), means that the sampling is sparse. On the other hand, sampling in the time dimension is relatively dense. Instances belonging to the same trajectory tend to be strongly correlated, which is reflective of how the morphologies evolve. These factors strongly influence the connectivity of the neighborhood graph, \(G\), which strongly impacts the approximation of the manifold distances by the Isomap algorithm.
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 4 Results and Discussion
### Evaluation Results  
#### 4.2.1 Quantitative Evaluation  
Methods for monocular depth estimation have been evaluated using different metrics. We quantitatively compare our method against state-of-the-art methods using the six standard metrics. These error metrics are defined as:  
**1. Average relative error**  
\[Rel=\frac{1}{n}\sum_{p}^{n}\frac{\left|y_{p}-{y_{p}}^{\prime}\right|}{y_{p}} \tag{11}\]  
**2. Root mean square error**  
\[RMS=\sqrt{\frac{1}{n}\frac{n}{p}{\left(y_{p}-{y_{p}}^{\prime}\right)}^{2}} \tag{12}\]  
**3. Average error**  
\[Log10=\frac{1}{n}\sum_{p}^{n}\lvert log_{10}{\left(y_{p}\right)}-log_{10}{ \left(y_{p}{}^{\prime}\right)}\rvert \tag{13}\]  
**4. Threshold accuracy**  
\[(\delta_{i})=\%\text{of}y_{p}\text{suchthat},max\left(\frac{y_{p}}{{y_{p}}^{ \prime}},\frac{{y_{p}}^{\prime}}{y_{p}}\right)<1.25^{i}(i=1,2,3) \tag{14}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Type** & **Image Size** \\ \hline
**NYU-depth v2** & **Indoor and outdoor scenes** & **640 \(\times\) 480** \\ KITTI & Road environment & 1280 \(\times\) 384 \\ Cityscapes & Urban streets & 1024 \(\times\) 2048 \\
**DIODE** & **Indoor and outdoor scenes** & **1024 \(\times\) 768** \\ Make3D & Outdoor scenes & 1704 \(\times\) 2272 \\ Middlebury & Static indoor scenes & 3000 \(\times\) 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets used in deep learning for depth estimation.  
In above metrices, \(y_{p}\) is the pixel in depth image \(y\), \({y_{p}}^{\prime}\) is a pixel in predicted image, and \(n\) is the total number of pixels for each depth image. The results of the NYU-depth v2 dataset compared with state-of-the-art methods are listed in Table 3.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 2 Related Knowledge
### Rhombus Pattern Based PEE
#### 2.3.1 The Embedding Procedure  
The predicted value \(p^{\prime}_{i,j}\) is the average of four adjacent pixel values of \(p_{i,i}\) and is calculated by Equation (8).  
\[p^{\prime}_{i,j}=\left\lfloor\frac{p_{i,j-1}+p_{i,i+1}+p_{i-1,j}+p_{i+1,j}}{4}\right\rfloor \tag{8}\]  
The prediction error \(e_{i,j}\) is the difference between the original pixel \(p_{i,j}\) and the predicted value \(p^{\prime}_{i,j}\) and is calculated by Equation (9).  
\[e_{i,j}=p_{i,j}-p^{\prime}_{i,j} \tag{9}\]  
The difference expansion method [26] is employed to expand the prediction error \(e_{i,j}\) for data embedding. Equation (10) is used for PEE.  
\[e^{\prime}_{i,j}=2\times e_{i,j}+b \tag{10}\]  
where \(e^{\prime}_{i,j}\) is the modified prediction error after data embedding and \(b\) is one bit of the message to be embedded. Thus, each group can embed one bit of information. The modified pixel value \(P_{i,j}\) after data embedding is calculated by Equation (11).  
\[P_{i,j}=e^{\prime}_{i,j}+p^{\prime}_{i,j} \tag{11}\]