esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 9 BCI Applications for Rehabilitation
### Virtual Rehabilitation
To achieve this, they designed a VRy environment system using Unity3D for the modeling of six different scenarios that provide three different clinical rehabilitation actions, including grasping, forearm rotation, and daily life exercises (drinking, eating fruit, solving a jigsaw puzzle, catching a butterfly, playing volleyball, and pulling a radish). An actiCHamp recorded this activity for eight channels. This information then enabled the multisensory-feedback system--visual, auditory, and haptic--that helps patients constantly adjust their state to motor feedback/imagery. Another study on the use of gamification could be found in [89]. This study showed the application of a Hololens system and a Kinect-developed VRy system based on gamification that could provide rehabilitation for patients post-stroke. This system comprises the therapist module that asses the exercises and the patient module that provides the exercises. This feedback accomplished 36.2% deviation vs. the movement. The group with no VRy feedback obtained 68.4% deviation. Another application of gamification can be seen in [PERSON] et al. [90], where a VRh system using Kinect to rehabilitate left-handed people working in industrial environments is proposed. Their applications determine the level of risk of injury that a left-handed employee has when using machinery designed for right-handed people. They used a laterality scale score from 30 to 50 points. If the score was above 35, the person was considered left-handed and at risk of injury. Today, there are devices that provide rehabilitation. An example of a device that could implement a rehabilitation service can be seen in [91]. This study designed a device based on sensors that could deliver rehabilitation for upper limbs and hands. Another device can be seen in [87]. This work involved designing a video game in Unity software. The participants were divided into groups, and at the end of the intervention, the authors measured their FMA scores to assess the advantages of VRh and then adjusted the parameters of a robot device. Others can be found in the study of [PERSON] et al. [92], which reviews nine studies that talk about the use of telerehabilitation in wheelchair assessment. Table 5 are referring the mentioned studies about the use of VRy for rehabilitation.
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
## 4 Numerical Simulation, Experimental Results and Discussions
### Numerical Simulation and Discussions
#### 4.2.1 Scenario 1: Tracking Step Altitude References  
This scenario consists of three stages: (i) initialization; (ii) take-off; and (iii) descent. During the first five seconds the vehicle is initialized, i.e., it is armed and prepares for take-off. The altitude, vertical velocity, and acceleration are all zero (Figures 7-10) and the control signal, \(U_{1}\), increases from the initial value of zero to the hovering value of about 19.62 N (Figure 11).  
During the second stage (from \(t=5\) to \(t=30\) s), the quadcopter takes off and ascends to the desired altitude of 15 m. The altitude responses are nearly identical for both controllers during the early stage of the flight (\(5\leq t<9\) s in Figure 7). Initially, the conventional PID controller responds slightly faster than our new controller (see Inset 1 in Figure 7). This is reversed during the later stages of the flight when the tracking error becomes smaller than \(d_{l}\) (\(e_{z}\leq d_{l}\approx 5.1\) m) and the controller smoothly switches to the linear mode. For \(9\leq t<30\) s our new controller exhibits a significantly superior performance compared to the conventional PID. With our algorithm, the tracking error converges to a small range closed to zero (\(e_{z}\) is less than 0.5%) at \(t\approx 21\) s while the conventional PID only reaches this range around \(t\approx 25\) s (Inset 1 in Figure 8). The velocity, acceleration, and control signal of the conventional PID controller exhibit large amplitudes and slow converging speeds (Figures 9-11).  
In order to evaluate the performance of our controller when the step amplitude is smaller than \(d_{l}\), during the third stage (\(30\leq t\leq 50\) s), the quadcopter is commanded to descend from the current altitude of 15 m to the desired altitude of 12 m (corresponding to a step of 3 m). If we look at the insets 2 in Figures 7 and 8, it is clearly seen that our controller aggressively responds and quickly reaches the altitude reference at \(t\approx 45\) s while the conventional PID controller only reaches this reference at \(t\approx 49\) s.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value and Unit** \\ \hline \(a_{\max}\) & 2.5 m/s\({}^{2}\) \\ \(k\) & 0.7 \\ \(k_{p}\) & 1.1 \\ \(k_{i}\) & 0.002 \\ \(k_{d}\) & 0.2 \\ \(z_{d}\) & 0, 15, 12, and 10 sin (0.1\(t\)) m \\ \hline \end{tabular}
\end{table}
Table 2: Controller parameters and the altitude references.  
Figure 6: The system signal flow block diagram.  
Figure 8: Comparison of altitude tracking error convergence between our new controller and a conventional PID controller for Scenario 1.  
Figure 7: Comparison of altitude performance between our new controller and a conventional PID controller for Scenario 1 (tracking a step altitude reference).  
Figure 9: Comparison of the vertical velocity performance between our new controller and a conventional PID controller for Scenario 1.
# Moisture Sorption Behavior of Deproteinized Sunflower Meal and Patterned Food Extrudate
## 3 Results and Discussion
### Models Evaluation  
The coefficients and the fitting criteria for the five three-parameter models are presented in Table 2 for DSM and Table 3 for PFE. For the DSM, the residual plots of modified Henderson and modified Halsey models were evaluated as patterned, which makes them unsuitable. The residual plots established for the modified models of Oswin, Chung-Pfost, and GAB showed a random distribution. The lowest values of _ARE_ and _SEE_ were obtained for the modified model of Oswin, which demonstrated the best fit. On the other hand, for PFE, the modified Chung-Pfost and the modified Halsey models were not found to be appropriate due to patterned residual plots. The residual plots determined for the modified models of Oswin, Henderson, and GAB showed a random distribution. The lowest values of _ARE_ and _SEE_ showed that the modified model of Oswin presented the best fit, which determined its further application for a description of sorption isotherms of DSM and PFE. Figure 2 shows the random residual plots of the modified Oswin model only.  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Coefficients**} & \multicolumn{1}{c}{\(R\)} & \multicolumn{1}{c}{**SEE**} & \multicolumn{1}{c}{**ARE, \%**} & \multicolumn{1}{c}{**Plot of Residuals**} \\ \hline \multirow{4}{*}{Chung–[PERSON]} & \(a=32.76313\) & \multirow{4}{*}{0.98} & \multirow{4}{*}{1.03} & \multirow{4}{*}{6.18} & \multirow{4}{*}{Random} \\  & \(b=5.88479\) & & & & \\  & \(c=12.07121\) & & & & \\  & \(a=5.475196\) & & & & \\ \cline{1-1} \cline{2-6} \multirow{2}{*}{Halsey} & \(b=-0.033334\) & \multirow{2}{*}{0.98} & \multirow{2}{*}{2.30} & \multirow{2}{*}{8.03} & \multirow{2}{*}{Patterned} \\  & \(c=1.916773\) & & & & \\ \hline \multirow{4}{*}{[PERSON]} & \(a=16.52953\) & \multirow{4}{*}{0.99} & \multirow{4}{*}{0.89} & \multirow{4}{*}{5.81} & \multirow{4}{*}{Random} \\  & \(b=-0.12708\) & & & & \\ \cline{1-1} \cline{2-6} \multirow{2}{*}{[PERSON]} & \(c=3.10444\) & & & & \\ \cline{1-1} \cline{2-6}  & \(a=0.00013\) & & & & & \\ \cline{1-1} \cline{2-6} \multirow{2}{*}{[PERSON]} & \(b=34.86715\) & \multirow{2}{*}{0.97} & \multirow{2}{*}{1.60} & \multirow{2}{*}{10.85} & \multirow{2}{*}{Patterned} \\  & \(c=1.74088\) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modified model coefficients (\(a\), \(b\), \(c\)) fitted to EMC values of DSM, correlation coefficients (\(R\)), standard error of estimation (_SEE_), average relative error (_ARE_), and plot of residuals obtained by adsorption.  
Figure 1: Hysteresis effect of DSM and PFE at 20 \({}^{\circ}\)C. Different letters (a and b) mean statistically significant difference.
## 3 Results and Discussion
### Field Data Example
\[Q=10\text{log}_{10}\Bigg{(}\frac{\big{\|}d_{input}\big{\|}_{2}}{\big{\|}d_{ input}-d_{clear}\big{\|}_{2}}\Bigg{)} \tag{5}\]  
Here, \(d_{input}\) represents the raw data, \(d_{clear}\) represents the denoised data, and \(\big{\|}d\big{\|}_{2}\) denotes the L2 norm of vector d. We calculated the Q values of the results after denoising with WPI-SD, the curvelet transform, and F-K filtering, as shown in Table 2. The results are consistent with qualitative understanding, indicating that WPI-SD exhibits a better suppression of ground roll waves. Figure 11: _Cont._  
Figure 11: Demonstrates the effectiveness of ground roll suppression in actual seismic data: (**a**) original seismic record; (**b**) F-K spectrum of the original seismic record; (**c**) seismic record after suppressing ground roll using F-K transform; (**d**) ground roll noise suppressed by F-K transform; (**e**) F-K spectrum corresponding to the seismic record after suppressing ground roll using F-K transform; (**f**) seismic record after suppressing ground roll using curvelet transform; (**g**) ground roll noise suppressed by curvelet transform; (**h**) F-K spectrum corresponding to the seismic record after suppressing ground roll using curvelet transform; (**i**) seismic record after suppressing ground roll using WPI-SD transform; (**j**) ground roll noise suppressed by WPI-SD transform; (**k**) F-K spectrum corresponding to the seismic record after suppressing ground roll using WPI-SD transform. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **F-K** & **Curvelet** & **WPI-SD** \\ \hline Q & \(-0.771\) & \(-0.696\) & \(-0.801\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Q values of different denoised methods.
## 2 System Model
The transmission rate of two NOMA users on the subchannel \(n\) of BS \(b\) is given by:  
\[R_{1,b}^{n}=\frac{B}{N_{\text{SC}}^{b}}\log_{2}\left(1+\frac{\left|h_{1,b}^{n} \right|^{2}\alpha_{b}^{n}p_{b}^{n}}{\left|h_{1,b}^{n}\right|^{2}\left(1-\alpha_ {b}^{n}\right)p_{b}^{n}+\sum\limits_{b^{\prime}=1,b^{\prime}\
eq b}^{N_{\text {BS}}}\left|h_{1,b^{\prime}}^{n}\right|^{2}p_{b^{\prime}}^{n}+\frac{\sigma^{2 }B}{N_{\text{SC}}^{b}}}\right), \tag{3}\]  
\[R_{2,b}^{n}=\frac{B}{N_{\text{SC}}^{b}}\log_{2}\left(1+\frac{\left|h_{2,b}^{n} \right|^{2}\left(1-a_{b}^{n}\right)p_{b}^{n}}{\sum\limits_{b^{\prime}=1,b^{ \prime}\
eq b}^{N_{\text{BS}}}\left|h_{2,b^{\prime}}^{n}\right|^{2}p_{b^{ \prime}}^{n}+\frac{\sigma^{2}B}{N_{\text{SC}}^{b}}}\right). \tag{4}\]  
If there is only one user \(u\) on the subchannel \(n\) of BS \(b\), the user will transmit in full power mode, and the transmission rate is:  
\[R_{u,b}^{n}=\frac{B}{N_{\text{SC}}^{b}}\log_{2}\left(1+\frac{\left|h_{u,b}^{n} \right|^{2}p_{b}^{n}}{\sum\limits_{b^{\prime}=1,b^{\prime}\
eq b}^{N_{\text{BS} }}\left|h_{u,b^{\prime}}^{n}\right|^{2}p_{b^{\prime}}^{n}+\frac{\sigma^{2}B}{ N_{\text{SC}}^{b}}}\right). \tag{5}\]
## 3 Digital Twins for Health Systems
### Elements of Healthcare-Based Digital Twins  
Healthcare-based digital twins comprise several key elements, as illustrated in Figure 8, which enable their functionality and interaction within the virtual world. These include the following [24; 25; 26]:  
1. Physical Asset/System: The physical asset or system serves as the real-world counterpart that the digital twin replicates. It may represent a patient, a medical device, or another medical entity.
2. Sensors and IoT Devices: These devices are embedded within the physical asset/system and are responsible for collecting data on its operations, condition, and performance. The sensors capture various parameters and transmit the data to the DT for analysis and visualization.
3. Data Processing Layer: The components that manage the handling, analysis, and archiving of the information gathered from an actual asset or system form this layer. It covers methods for ingesting, storing, processing, and analyzing data, including artificial intelligence algorithms and machine learning.
4. Digital Twin Model: This model identifies the virtual representation of the real asset/system within the medical system. It establishes the relationships and interconnections between the different components of the DT and the physical component.
5. Visualization and Interaction Interfaces: These interfaces enable users to view and interact with the DT. They may include immersive environments, user interfaces, and interactive controls that allow users to explore, manipulate, and engage with this technology.
6. Real-Time Simulation Engine: The simulation engine is responsible for modeling the behavior and responses of the digital twin based on the real-time data received from the physical component/system. It ensures that the DT closely mirrors the actions and characteristics of the physical counterpart.
7. Social Interaction Engine: This component facilitates social interactions and collaboration within the biomedical process. It enables users to connect, communicate, and collaborate while interacting with the virtual components.  
Figure 7: The architecture of healthcare-based digital twins.  
These elements work together to create a healthcare-based digital twin that offers an immersive, interactive, and dynamically simulated experience of the physical asset/system. They enable data-driven analysis, visualization, real-time monitoring, and collaborative exploration within the virtual environment.
## 3 Tapped Delay Line Model
### A2G TDL Model
#### 3.2.1 Average Power Delay Profile, Delay and Power Value  
Due to the system bandwidth, the observable delay resolution of MPCs is limited. The average power delay profile (APDP) can be calculated as [29]  
\[P(n\Delta\tau)=\frac{1}{Q}\sum_{q=0}^{Q-1}||h^{*}(n\Delta\tau,q\Delta t)||^{2},0\leq n\leq N-1, \tag{4}\]  
where \(q=0,\ldots,Q-1\) is the time index of measured CIR in a local area. To calculate the APDP for communication application, the CIR shall be normalized in both delay and power domains. The term \(h^{*}\) is the CIR normalized in power by the total received power, and in delay by the LoS [25]. \(q\Delta t\) is the observation instant of the \(q\)th CIR. \(\Delta\tau\) is delay resolution, which equals to \(\Delta\tau=1/W\), where \(W\) is the measurement bandwidth. \(n\) is delay tap index, and \(N\) is the maximum number of CIR samples from the measurement. In the channel model, the number of taps \(M\) is determined with the tap index \(m=1,\ldots,M\), which depends on the bandwidth of wireless radio system and threshold \(\beta_{m}\), and the channel tap samples with power values below the threshold is set to zero [30]. In the following, the average delay and power of the \(m\)th taps are given by  
\[\tau_{m}=\frac{\sum_{n=i_{m}}^{i_{m}+b-1}n\Delta\tau\cdot P(n\Delta\tau)}{\sum _{n=i_{m}}^{i_{m}+b-1}P(n\Delta\tau)}, \tag{5}\]  
\[a_{m}=\frac{1}{b}\sum_{n=i_{m}}^{i_{m}+b-1}|h^{*}(n\Delta\tau,q\Delta t)|^{2}, \tag{6}\]  
where \(i_{m}\) is the sample number start of each group, where \(i_{1}\) is the first nonzero sample after applying the threshold [31]. The variable \(b\) represents samples which are calculated by the obtained \(L\) samples divided into \(M\) groups.  
Figure 4 visualizes an example of the APDP normalized both in delay and in power. It is worth noting that the CIR samples are discarded if the corresponding normalized power  
Figure 3: Representation of a TDL model.  
is 25 dB below relative to the strongest path. The \(x\)- and \(y\)-coordinates are corresponding to the normalized delay and power of individual tap, respectively.
# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 2 Related Work
### Evidence Classification  
In the design of evidence classification algorithms based on belief function frameworks, the evidential \(K\)-nearest neighbor classification (EK-NN) plays a significant role. In the EK-NN, each neighbor of the sample is considered as evidence, which is used to provide decision support for the class label of the sample. The final decision is presented by combining these \(K\)-nearest neighbor pieces of evidence.  
Consider a data classification problem where the object \(o\) will be classified into a certain class that belongs to the class set \(\Omega=\{\omega_{1},\cdots,\omega_{c}\}\). If the nearest neighbor \(o_{j}\) with a feature distance of \(d_{j}\) from the target \(o\) belongs to the group \(\omega_{k(j)}\), then the evidence of each neighbor can be represented by the following formula.  
\[\begin{cases}m_{j}(\left\{\omega_{k(j)}\right\})=\varphi(d_{j})\\ m_{j}(\Omega)=1-\varphi(d_{j})\end{cases} \tag{6}\]  
where \(\varphi\) is a non-increasing mapping from \([0,+\infty)\) to \([0,1]\), and it was proposed to choose \(\varphi\) as:  
\[\varphi(d_{j})=\alpha_{0}\exp(-\gamma_{q}d_{j}) \tag{7}\]  
The set of these nearest neighbors is represented by \(N_{K}\); therefore, the combination of the mass function of the nearest neighbors becomes:  
\[m=\mathop{\oplus}\limits_{j\in N_{k}}m_{j}. \tag{8}\]  
At this point, the final decision can be made that object is assigned to the class \(\omega_{q}\) with the highest confidence level. The EK-NN provides a good idea for data classification with uncertain information.  
In order to present the characteristics of evidence classification more intuitively and clearly, the flowchart of evidence classification is shown below in Figure 1:
# The Clinical Role of SRSF1 Expression in Cancer: A Review of the Current Literature
## 3 Results
### Review  
#### 3.2.1 Prostate  
The increasing interest in SRSF1 led to studies about the most frequent cancers, obviously prostate cancer as the first cancer in men has been investigated in recent years. There is not a large number of studies yet, however, some literature findings are interesting and indicate that SRSF1 could also play a role in this tumor. Some studies have demonstrated the higher expression of VEGF in PCa cells [15; 16], furthermore, a study published in 2016 found a higher expression of SRSF1 and SRPK1 (Serine Arginine Protein Kinase-1) in malignant Pca cells, as well as demonstrating that SRPK1 plays a role in the alternative splicing of VEGF to a more angiogenic isoform (VEGF165b) leading to faster cancer growth [17]. As is well known, VEGF expression is regulated by the alternative splicing factor SRSF1, which is, in turn, regulated by SRPK phosphorylation [18; 19]. In 2020 [PERSON] et al. published a paper that investigated the role of HSD17B2 (7B-Hydroxy dehydrogenase 2) in PCa. In their in vitro study, performed on culture and in vivo PCa cells using a xenograft model in mice, they found that HSD17B2 expression was inversely proportional to PCa development and progression; in fact, it was expressed more in benign tissue than in adjacent tumor tissue. The authors suspected that higher levels of HSD17B2 could suppress the synthesis of DHT (dihydrotestosterone) from androstenedione, which is well known as a PCa stimulating hormone, thus they knocked down HSD17B2 from a cell line and proved that the cell line without HSD17B2 had higher DHT synthesis. Furthermore, some authors found a higher expression of SRSF1 in PCa cells that expressed lower levels of HSD17B2 and, moreover, found a correlated presence of two isoforms of HSD17B2, the middle length (M) isoform and the short length (S) isoform. These isoforms were unable to interact with androgens and the authors demonstrated their capacity of degradation of HSD17B2 wild type (L, long, isoform). In conclusion, these authors affirmed that SRSF1 could modulate an alternative splicing event on PCa cells leading to
# A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
## 3 Treatment Techniques for Short-Chain PFASs
### Other Techniques
#### 3.3.4 Bioremediation Techniques  
[PERSON] et al. degraded PFOS (1400-1800 ug/L) by cultivating _P. aeruginosa_ microorganism with the removal rate of 67% [65]. However, short-chain PFASs were difficult to degrade by common microbes. For example, the concentration of PFBS in the effluent of sewage treatment plants remained unchanged or increased after conventional activated sludge or biofilm bioreactors. That is, short-chain PFASs showed high resistance to various activated sludge systems [5]. While in plant tissues, both short and long-chain PFASs all have a tendency to accumulate. Recent studies have shown that the biological accumulation of PFASs followed a U-type trend. The lowest hydrophobicity (e.g., PFBA and PFPeA) and the maximum hydrophobic species (e.g., PFNA and PFDA) presented the greatest absorb efficiency [66]. Another study on the uptake and distribution of PFASs in maize showed that plant adsorption and distribution of PFASs were dependent on chain length, functional groups, and plant tissue. Generally, short-chain PFASs were transferred to the overground portion of plants, while long-chain PFASs were mainly transported to the root [67].
## 4 Results and Discussion
### Phase Image Banarization by Otsu Algorithm  
Binarization was performed for low frequencies that showed sufficient phase contrast: 0.01 Hz, 0.02 Hz, and 0.03 Hz. Binarization processing was performed according to Equations (2)-(7). Figure 10 shows the binarization result of Otsu algorithm processing on the phase image for each modulation frequency. As shown in Figure 10, good detection potential was shown for thinning rates of 40% to 90%. However, as the frequency decreases from 0.01 Hz to 0.03 Hz, the shape becomes more blurred. Comprehensively, the modulation frequency of 0.01 Hz is the most advantageous.