esa-sceva/satcom-chunk-collection huggingface dataset:
# Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models
## 5 Results and Discussion
### Results of the MTL Models  
To boost the performance of automatic fine-grained abusive content detection in Arabic, the MTL approach with the MARBERT language model was applied on a multi-aspect annotation dataset. A benefit of MTL is that multiple tasks can be learned simultaneously, which allows for the transfer of information and the generalization of skills from one task to another. We performed four experiments (MTL with MARBERT, MTL with LSTM + MARBERT, MTL with LSTM + CNN + MARBERT, and MTL with BiLSTM + CNN + MARBERT). The results achieved for the four experiments are presented in Table 7, and we compare it with single task single language (STSL), which were used in previous work [9] by utilizing a BiLSTM model. Notably, using MTL with MARBERT in different architectures shows better results in four out of the five attributes compared to the previous study [9]. The macro-F1 performance results in the four attributes were 0.71 for the target attribute, 0.91 for the group attribute, 0.34 for the hostility attribute, and 0.23 for the annotator attribute. The precision and recall scores for each experiment are shown in Figure 5. However, the weak performance in the hostility and annotator attributes was due to the highly imbalanced and low number of samples in each label of those attributes in the dataset. Therefore, some of the limitations and directions for future are addressed in this paper. Overall, it is important to highlight that dealing with a multi-aspect annotation dataset is a very challenging task, especially given some of limitations that have been discussed in this study. The study's findings have implications for social media platforms to adopt more effective methods for detecting and addressing abusive content in Arabic especially by using pretrained Arabic LMs. Moreover, we believe that the abusive content detection in tweets is more reliable using the multi-aspect annotation dataset.
# Focused Ultrasound in the Rehabilitation Setting:
###### Abstract  
Focused ultrasound (FUS) is an emerging noninvasive technology with significant therapeutic potential across various clinical domains. FUS enables precise targeting of tissues using mechanisms like thermoablation, mechanical disruption, and neuromodulation, minimizing damage to surrounding areas. In movement disorders such as essential tremor and Parkinson's disease, MR-guided FUS thalamotomy has demonstrated substantial tremor reduction and improved quality of life. Psychiatric applications include anterior capsulotomy for treatment-resistant obsessive-compulsive disorder and major depressive disorder, with promising symptom relief and minimal cognitive side effects. FUS also facilitates blood-brain barrier opening for drug delivery in neurological conditions like Alzheimer's disease. Musculoskeletal applications highlight its efficacy in managing chronic pain from knee osteoarthritis and lumbar facet joint syndrome through precise thermal ablation. Additionally, FUS has shown potential in neuropathic pain management and peripheral nerve stimulation, offering innovative approaches for amputees and cancer survivors. Cognitive and neuromodulatory research underscores its ability to enhance motor function and interhemispheric cortical balance, benefiting stroke and traumatic brain injury rehabilitation. Despite these conditions frequently leading to various kinds of disabilities, no direct exploration of the possible FUS application in rehabilitation is yet available in the literature. All this considered, this review aims to discuss how FUS could be applied in rehabilitation, exploring the current status of knowledge and highlighting future directions.  
focused ultrasound; rehabilitation; movement disorders; pain management; cognitive rehabilitation 2025  
154743
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 4 NB-IoT Downlink Channel Estimation
### LMMSE Estimation with SVD and Banded Technique
\right) \tag{24}\]  
According to the above concept, we subdivide the channel matrix into several smaller matrices as shown in Figure 5. The \(r^{th}\) subvector \(\textbf{\Psi}_{r,LMMSE}\) of order \(S\times 1\) can be found from Equation (24) as  
\[\textbf{\Psi}_{r,LMMSE}=\textbf{U}_{r}diag(\Delta_{r},\ldots,0)\textbf{U}_{r} ^{H}\textbf{\Psi}_{r,LS} \tag{25}\]  
where \(r\) is defined by the floor function as given below:  
\[r=floor[(p-1/S)]+1,\text{ for }1\leq p\leq L\]  
and \(\textbf{\Psi}_{r,LS}\) is the LS estimation of \(r^{th}\) subvector, which is  
\[\textbf{\Psi}_{r,LS}=\left[\frac{\textbf{B}_{S(r-1)+1}}{\textbf{A}_{S(r-1)+1 }},\ldots,\frac{\textbf{B}_{S(r-1)+Q}}{\textbf{A}_{S(r-1)+Q}}\right]^{T}\]  
Therefore, the final LMMSE estimation vector by applying the approximation banded approach becomes  
\[\widetilde{\textbf{\Psi}}_{banded,LMMSE}=\left[\textbf{\Psi}_{1,LMMSE}^{T}, \textbf{\Psi}_{2,LMMSE}^{T},\ldots,\textbf{\Psi}_{R,LMMSE}^{T}\right]^{T} \tag{26}\]
# Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition
###### Abstract  
Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.  
three-component microseismic data; denoising method; re-constrain VMD; constrained optimization; polarization analysis 2021
## 1 Introduction
[PERSON] et al. [27] examined red shale in deep mines and found that compressive strength decreased significantly with increasing in situ stress and unfavorable bedding angles, indicating the importance of structural anisotropy in roadway support design. [PERSON] et al. [28] employed numerical simulations to evaluate how abrupt changes in coal properties impact gas outbursts. They found that stress is transferred non-uniformly between soft and hard coal seams, potentially increasing the risk of dynamic disasters in transition zones. [PERSON] et al. [29] proposed an \"external anchor-internal unloading\" control technology for gob-side entries in ultra-deep mines. Their results demonstrated that this technique effectively redistributed stress and limited surrounding rock deformation, particularly during excavation near goafs. [PERSON] et al. [30] studied coal pillar failure induced by fault-induced stress concentration. They identified zones of high elastic energy accumulation within coal seams and revealed that the coupling between coal seam, roof, and floor plays a key role in inducing bursts. While these studies offer important insights, most focus on specific geological conditions or individual stress orientations. Few systematically investigate how variations in the angle between the roadway axis and the principal horizontal tectonic stress affect surrounding rock behavior. Further research is needed to understand the mechanical response of surrounding rock when the roadway is oriented at different angles to deep tectonic stress, particularly in the extreme case of perpendicular (90\({}^{\circ}\)) alignment, which is associated with the most severe deformation and stress concentration. This study addresses this gap through numerical simulations and field validation under varying angular conditions. This paper examines the 1232(3) working face at Dingji Coal Mine. The mine is situated near Huainan, China. We analyze a soft rock roadway subjected to significant tectonic stress. Our investigation focuses on the stability of roof rock. We evaluate mechanical reactions at various stress-roadway angles. The redistribution of stress is contingent upon this angle. We also examine the impact of excavation under tectonic loading. The findings will inform pragmatic engineering solutions.
## 5 Performance Evaluation and Analysis
### GRU Performance
#### 5.2.2 A2C CNN-GRU Model  
The Actor-Critic Central Neural Network (A2C CNN-GRU) model, as detailed in Table 6, integrates convolutional, GRU, and dense layers to optimize radio resource management in V2X networks. The model's critic component evaluates the value of state-action pairs, which helps to allocate resources like frequency and bandwidth efficiently. The consistent decrease in critic loss values reflects the improving accuracy in these evaluations.  
The actor component maintains stable policy decisions, as indicated by the low actor loss values in Figure 9, ensuring effective decisions on channel allocation and data prioritization. The GRU layers capture temporal dependencies, allowing the model to predict future network states and to adjust resource allocations accordingly. This capability helps manage dynamic changes in traffic and demand, ensuring optimal use of the radio spectrum and enhancing communication reliability and efficiency.  
The two provided graphs illustrate the model's predictions for the distance to neighboring vehicles, which are normalized and compared across actual, training, and testing datasets. The \(x\)-axis represents time steps, while the \(y\)-axis denotes the distance in meters. In Figure 8a,b, both graphs present the normalized distance to neighboring vehicles in meters over 500 time steps for three datasets: actual, training, and testing. The blue line represents the actual dataset, which shows a rapid increase in distance that stabilizes at around  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Parameters** \\ \hline GRU-1 & \([-1,1,150]\) & 0 \\ GRU-2 & \([-1,2,300]\) & 0 \\ Flatten-3 & \([-1,300]\) & 0 \\ Linear-4 & \([-1,150]\) & 45,150 \\ Linear-5 & \([-1,75]\) & 11,325 \\ Linear-6 & \([-1,1]\) & 76 \\ Sigmoid-7 & \([-1,1]\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: GRU model architecture.  
60 m after approximately 100 time steps. The red line, indicating the training dataset, closely follows the actual dataset, signifying that the model has been trained effectively and captures the underlying trend accurately. Similarly, the green line for the testing dataset closely follows the actual dataset, demonstrating that the model generalizes well to unseen data, maintaining a consistent prediction trend.  
These results suggest that the model is robust, effectively trained, and capable of generalizing well to new data, making it reliable for predicting the normalized distance to neighboring vehicles over time. The alignment of the training and testing datasets with the actual measurements indicates that the model accurately captures the movement patterns of vehicles. This accuracy is crucial for anticipating changes in traffic flow and managing resource allocation.
# Temporal and Spatial Diversity of Renewable Groundwater Resources in the River Valley
## 4 Results  
### Groundwater Depth (GD)  
The model studies provided data on the average depths to groundwater throughout the study area and in the individual geomorphological units. The average depths to the groundwater table in the Vistula valley area in the years 1999-2013 varied from 1.41 to 6.26 m b.g.l (below ground level) in different parts of the valley (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Geomorphological Unit**} & \multicolumn{3}{c}{**Groundwater Depth in Model Variant–Average Value**} \\ \cline{2-4}  & **Variant 1 (Figure 7)** & **Variant 2 (Figure 8)** & **Variant 3 (Figure 8)** \\ \hline Lower flood plain (IVb) & 2.01 & 0.00 & 1.47 \\ Higher flood plain (IVa) & 1.44 & 0.43 & 1.80 \\ Praga Terrace (III) & 1.59 & 0.56 & 1.98 \\ Falenica Terrace (II) & 1.41 & 0.53 & 1.96 \\ Otwock Terrace (I) & 3.22 & 2.42 & 3.85 \\ Warsaw plain & 6.26 & 6.66 & 7.49 \\ Average value & 2.15 & 1.16 & 2.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average groundwater depth in geomorphological units.  
Figure 7: Groundwater depth in variant 1.  
The shallowest groundwater level, ranging from 1.41 to 1.59 m b.g.l, was recorded in the Falenica (II) and Praga (III) terraces, classified as GDE areas (Table 3; Figures 7 and 8). In the uppermost terrace (I), the groundwater table was at the greatest depth attaining the average of 3.22 m b.g.l. In the lowest part of the valley, in the floodplain (IVb), the GD was at the average of 2.01 m b.g.l.  
The GD also has been changed over time. The variations in depth for the individual variants are significant. During high levels (variant 2), the level rose in the valley by an average of 0.99 m compared to the average value in variant 1. In all the geomorphological units, the groundwater table was at a shallower depth ranging from 0.8 m (I) to 2.01 (IVb), except in the Warsaw Plain. In variant 3 of the lowest groundwater levels, identified with the period of possible hydrogeological drought, the depth of groundwater table was 0.43 m lower than an average depth. In all geomorphological units of the valley, the groundwater table was deeper compared to the mean values, i.e., at depths ranging from 0.36 (IVa) to 1.23 m (I) (Figure 7).  
Figure 8: Groundwater depth in: (**A**): variant 2; (**B**): variant 3.
## 2 Signal Model  
In the receiver system, the sampled signal, denoted as \(y_{i}\), is represented as follows:  
\[y_{i}=\alpha e^{j2\pi f_{i}t_{s}}s_{i}+w_{i}, \tag{1}\]  
where \(s_{i}\) denotes the baseband signal, shaped using a root-raised cosine pulse with a random roll-off factor. The sampling duration is represented by the variable \(t_{s}\), while the complex-valued channel fading gain, capturing the flat fading experienced by the signal, is represented by \(\alpha\). The methodology that we have developed is specifically designed to be effective for narrowband signals, which have a bandwidth smaller than the coherence bandwidth of the channel. By narrowband, we refer to signals whose frequency span falls within a range where the channel exhibits consistent characteristics and a limited range of frequency-related variations. In this scenario, each frequency component undergoes the same block-fading coefficient, allowing for simplified representation using a single-tap channel filter \(\alpha\). The term \(f_{o}\) accounts for the carrier frequency offset, arising from a discrepancy between the RF signal frequency and local oscillator frequency. The term \(w_{i}\) denotes zero mean Gaussian noise. The carrier frequency offset (CFO) in the received signal \(y_{i}\) is estimated using an eighth-order non-linearity and corrected using the method proposed in [11]. After that, the signal that has been corrected for the CFO is resampled to an integer multiple of the estimated symbol rate for matched filtering. The system accommodates for the presence of additive white Gaussian noise (AWGN) and factors in slow and flat fading channels. The received symbol \(x_{i}\) is expressed as  
\[x_{i}=\alpha h_{i}+w_{i}^{\prime}, \tag{2}\]  
where \(h_{i}\) represents the extracted symbol from one of the considered modulation schemes, and \(w_{i}^{\prime}\) is Gaussian noise. The signal-to-noise ratio (SNR) for symbols with unit power can be defined as:  
\[SNR=10\log_{10}\frac{P_{s}}{\sigma_{w}^{2}}, \tag{3}\]  
where \(P_{s}=\frac{1}{N}\sum_{i=0}^{N-1}|x_{i}|^{2}\).
###### Abstract  
In order to solve the problems of tuber infection and nutrient loss caused by potato germination during storage, this paper conducted a systematic study using low-temperature plasma jet technology to inhibit potato germination and determine its optimal treatment conditions. This study focused on optimizing the plasma treatment parameters, including voltage, gas flow rate, and treatment time, to effectively control potato germination, reduce weight loss during storage, and determine the quality indexes such as hardness, crispness, and antioxidant enzyme activity of potatoes in storage. The study showed that the optimal conditions for plasma treatment of potatoes were voltage 18.05 kV, treatment time 20.21 s, and gas flow rate 12.79 L/min. Under these conditions, the germination rate of potatoes was significantly reduced to 31.42%, and the weight loss rate was reduced to 2.15%. For the convenience of operation, the parameters of the validation experiment were determined as a treatment voltage of 18 kV, treatment time of 20 s, and gas flow rate of 13 L/min. The resultant potato germination rate was 31.26%, and the weight loss rate was 2.29%. Compared with the blank control group, the plasma-treated group significantly increased the activities of potato antioxidant enzymes (CAT, SOD, etc.). After 16 days of storage, SOD (superoxide dismutase) activity and CAT (catalase) activity of the plasma-treated group increased by 52.63% and 29.27%, respectively, compared with the control; POD (peroxidase) and PPO (polyphenylool oxidase) activities of the treated group increased by 8.69% and 18.58%, respectively, compared with the control. Compared with the blank control, the plasma treatment group increased the hardness and brittleness of potatoes. Specifically, the hardness of the treated group increased by 6.06% compared with the control, and the brittleness of the treated group decreased by only 24% within 16 days, compared with a 37.19% decrease in the control. In addition, plasma treatment also reduced the accumulation of reduced sugar and dry matter consumption, thus maintaining the storage quality of potatoes, in which reducing sugar in the treated group was reduced by 32.56% compared with the control group, and dry matter in the treated group was increased by 7.66% compared with the control group. Therefore, the reasonable use of plasma treatment can effectively inhibit and slow down the sprouting process of potatoes, which lays a foundation for revealing the mechanism of plasma technology in inhibiting potato sprouting and improving its quality.  
potato; plasma jet; inhibit germination; response surface +
Footnote †: journal: Applied Sciences
## 4 Experimental Setup
### Trace Collection  
As shown in the setup of Figure 7, we use the monitor to play a series of driving video clips at 10 min each from driving action movies. Each video was captured by a PTS (Pan Tilt Zoom) camera taken from a vehicle. The video is encrypted and highly compressed at a ratio of 250:1 at the transmitter block. Using one processor for each section of the screen, the 96-array processors in the transmitter block can achieve fast independent image processing. The transmitter sends the highly compressed encrypted video images to the control center via a wireless link using the non-licensed 900 MHz FHSS radio-band. Please note that 900 MHz is more affordable to us presently than WiMax and DSRC. Since they are all wireless links, they will all be subjected to the same noise sources, such as EM interference. We only need to obtain similar type of interference and their statistics first to verify our platform.  
Figure 6: Video calling party flow diagram with traffic controller implementation.  
The receiver block decodes and decompresses the received video frames and plays the image at the control center at about 20 fps. The control-center laptop and the receiver are connected using a RS232 serial port. The control center is then connected through a router to a computer hosting the packet trace-capturing tool--Wireshark(tm) [54]. Once the system is turned on, the computer with the Wireshark(tm) software is set to access the \"capture\" folder in the control center which would then stream the video via a router for trace capture.  
A video trace (a record sequence of time and size of each arriving packet) captured by Wireshark is shown in Figure 8. Due to the loss and duplication that will change its original characteristics, this type of traffic trace is closer to those from our application in the real environments. We want to characterize such a life driving video trace using proven stochastic processes and integrate it into a model that has as few parameters as possible but which can produce traces to match the real measurements.  
In order to model video traces of different correlation/fractal characteristics arising from different activities, we collected traces playing three different video clips. The video clips were chosen based on their activity rates. Actions like highway chasing movies (with a lot of movement on the edge  
Figure 8: Live driving video trace.  
Figure 7: Setup for taking video traces.  
of the screen/camera and hence unique variation in frame sizes), stories in a parking lot (with less and slower movement) and dramas on the street (on the average driving speed) were chosen. We combine these clips to form a two-hour video trace (consisting of the time and size of each arriving frame), which is sufficient to characterize the fractal nature of the driving video traffic as well as the wireless channel effect.