esa-sceva/satcom-chunk-collection huggingface dataset:
# DFA-UNet: Efficient Railroad Image Segmentation
## 1 Introduction  
With the continuous development of intelligent technology, driverless technology has been widely used in the field of transportation. In the future, high-speed rails will be a very critical mode of transportation. Cameras on the front of the vehicle help visualize obstacles on the track for driverlessness. To achieve driverlessness, the first step is to process the relevant images. Image segmentation is a complex and critical step in the field of image processing and analysis, the purpose of which is to segment the parts of an image that have some special meaning and extract the relevant features. Splitting out image targets is a difficult task. Some common methods of traditional image segmentation are threshold-based segmentation, region-based segmentation, model-based deformation, etc. However, due to the complex background and changing scenes of railroad images, they often contain a large amount of noise, which is challenging to be accurately segmented by traditional image segmentation algorithms. Therefore, it is urgently needed to develop a fast and accurate segmentation method to improve the accuracy of segmentation in complex scenes and achieve real-time railroad segmentation.  
CNN-based encoder-decoder architecture is a commonly used image processing method. CNN has excellent feature extraction capability that overcomes the limitations of manual feature extraction. However, many feature extractions can only extract high-level semantic information while ignoring the underlying semantic information, thus not having a good segmentation effect.  
In this paper, we propose an efficient segmentation network, DFA-UNet. The module can focus on the image's main features at the encoder stage and further process them by downsampling. We present the GAMP module, which improves the network feature extraction capability by global average pooling as well as maximum global pooling for the channels.  
The network model proposed in this paper consists of the following two main works:  
1. A new convolution module, GAMPIn this paper, we propose a new convolutional module, GAMP, which is able to increase the perceptual field of the network without increasing the number of network parameters compared to other convolutional blocks. It enables the network to extract richer semantic information from the images effectively. Compared with other modules, the GAMP module can better capture the features and detailed areas of the input image in the results of image segmentation, and the segmentation is perfect.  
2. Depthwise fusion-attention block  
A fused attention mechanism is added to our model. By fusing the globally averaged pooling and the globally maximal pooling feature maps, it is possible to output feature maps that include both the spatial information of the input channels and capture the salient semantic information in the inputs.
## 3 Method and Materials
### Preprocessing
#### 3.2.1 Cleaning of Metadata  
The first step involved the removal of any NaN values present in the metadata to maintain consistency and reliability in the dataset. This cleaning process ensured that each image in the dataset had complete and accurate metadata for analysis. Specifically, the removal of NaN values is imperative for the effective application of Principal Component Analysis (PCA), which requires complete datasets to function correctly. Moreover, ensuring that each image's metadata matches perfectly is essential for the integrity of the data linkage, enabling accurate correlation between the image features and their corresponding clinical information.
## 2 Methods
### Classical Online Trajectory Control  
Typically, the commercially available manipulators have few available protocols for controlling the movement of the robot. Usually, there is a simplified interface, where simple point-to-point (PTP) motions can be executed. In that case, the responsibility for the trajectory execution is delegated to the proprietary robot controller. To implement a custom controller, the availability of a lower-level interface to the robot control box is necessary.  
The robot driver communicates with the robot control box and sends encoded desired velocity commands over a TCP/IP connection. In UR robots, the online control of robot movement can be achieved with _servoq_ and _speedq_ URScript instructions [25], which allow for the online position and velocity control, respectively, of the robot joints. These commands are sent to the robot with a control loop frequency of 125 Hz. In our work, we use the _speedj_ interface to implement a custom position-velocity control loop. The diagram of the robot control system is shown in Figure 3.  
Figure 2: The designed trajectory in the \(x\)-\(y\) plane.  
The trajectory generator provides desired joint configuration and joint velocity vectors \(q_{des}\) and \(q_{.des}\). The controller input signals are calculated as \(e_{pos}=q_{des}-q_{act}\) and \(e_{vel}=q_{.des}-q_{.act}\), where \(q_{act}\) and \(q_{.act}\), are the actual joint positions and velocities, respectively. The control system presented above is implemented in Matlab, using a custom ROS (Robot Operating System) node to send the velocity commands to the robot and to read the joint states.  
As a baseline, the following system is implemented as the controller. The velocity command signal is generated according to the following rule:  
\[\dot{q}_{cmd}=k_{p}\cdot e_{pos}+k_{v}\cdot e_{vel} \tag{3}\]  
where \(\dot{q}_{cmd}\) is the controller output, \(e_{pos}\) and \(e_{vel}\) are the position and velocity errors, respectively, and \(k_{p}\) and \(k_{v}\) are the controller coefficients. In our work, these control law coefficients are set to \(k_{p}=4.0\) and \(k_{v}=0.2\).
## 3 Results and Discussion  
### Results of Multiphysics Simulation  
In previous studies, the induction heating depth, current, and frequency were usually analyzed using spiral-shaped coils, without specifying the effects of special-shaped ones on conclusions. Therefore, these factors are researched and compared in this section to verify whether related conclusions are followed. To explore the influence of different parameters on results, the depth was set to be 5 mm, 7 mm, and 9 mm, respectively; the current was set to 800 A, 1000 A, and 1200 A, respectively; and the frequency was set to 20 KHz, 30 KHz, and 40 KHz, respectively. As shown in Figure 2a, while the mold surface temperature rise  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **A** & **B** & **C** \\ \hline
1 & 5 & 800 & 20 \\
2 & 5 & 1000 & 30 \\
3 & 5 & 1200 & 40 \\
4 & 7 & 800 & 30 \\
5 & 7 & 1000 & 40 \\
6 & 7 & 1200 & 20 \\
7 & 9 & 800 & 40 \\
8 & 9 & 1000 & 20 \\
9 & 9 & 1200 & 30 \\ \hline \end{tabular}
\end{table}
Table 4: Design of the L9 orthogonal experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Level of Factors**} & \multicolumn{3}{c}{**Experimental Factors**} \\ \cline{2-4}  & **(A) Depth/mm** & **(B) Current/A** & **(C) Frequency/KHz** \\ \hline
1 & 5 & 800 & 20 \\
2 & 7 & 1000 & 30 \\
3 & 9 & 1200 & 40 \\ \hline \end{tabular}
\end{table}
Table 3: Levels of experimental factors.  
rate was 2.3 \({}^{\circ}\)C/s when the coil is 5 mm from the surface, it gradually decreased to 2.1 \({}^{\circ}\)C/s and to 1.9 \({}^{\circ}\)C/s when the depth expanded to 7 mm and to 9 mm. The small depth gradient means the temperature rise rate decreases slightly. According to the temperature rise curves at different currents in Figure 2b, the temperature rise rate accelerates significantly at higher currents. When the current increases from 800 A to 1200 A, this rate rises from 1.2 \({}^{\circ}\)C/s to 2.6 \({}^{\circ}\)C/s. The temperature rise curves at varying frequencies are presented in Figure 2c. It can be seen that there is little difference among these curves due to the small gradient of this parameter. However, the partial enlarged view shows that the temperature rise rate increases with the frequency. In summary, the temperature rise rate of induction heating increases with increased currents and frequencies but decreases with the increase of heating depth, which is consistent with correlating studies [22] and is in accordance with the principles of heat transfer and Joule heating. As the current increases, the heating power of the power supply increases and the heating rate of the mold increases significantly. As the coil frequency increases, the skin effect of the current becomes stronger and the mold heats up faster. However, as the heating depth increases, the heat transfer distance of the mold also increases and the heat loss becomes more and more serious, so the heating rate of the mold surface decreases as the heating depth increases. The temperature distribution nephogram of the mold surface after being heated for 30 s under different conditions is shown in Figure 3.
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 5 Experiment Methodology
### Hardware and Software Platform  
We use an NVIDIA Tesla V100 GPU to evaluate the performance of GPU tensor sensing. The GPU incorporates 5120 CUDA cores @ 1.53 GHz and 32 GB DDR5 memory. It is installed on a server with 128 GB DDR memory and an Intel i7-7820x CPU with 8 cores @ 3.60 GHz supporting 16 hardware threads with hyper-threading technology. The server is running Ubuntu 18.04 LTS with Kernel Version 4.15.0. The CPU and GPU tensor sensing is running with MATLAB R2017b and CUDA 10.0, respectively.
## 3 Results
### Muscle Activity (EMG)
#### 3.1.3 Interaction Effects between Working Height and Passive Exoskeleton on EMG  
The interaction between working height and intervention was significant in five muscles, including two upper-limb muscles, the upper trapezius (UT) and middle deltoid (MD), and three lower-limb muscles, the rectus femoris (RF), biceps femoris (BF), and tibialis anterior (TA). In the case of the shoulder muscle, the muscle activity of UT significantly decreased for W\({}_{\text{CEX}}\) and W\({}_{\text{CC}}\) (CEX: 58.1%, CC: 77.7%) compared to WO at the lowest working height of 60 cm, while the UT activity significantly increased when wearing the exoskeletons at 85 and 110 cm working heights (85 cm--CEX: 92.5% and CC: 110.0%; 110 cm--CEX: 513.6% and CC: 563.6%). The MD also showed a similar trend to the UT. The muscle activity of MD significantly decreased when wearing the exoskeletons (W\({}_{\text{CEX}}\) and W\({}_{\text{CC}}\)) at the lowest working height of 60 cm (CEX: 32.5%, CC: 40.0%), and it significantly increased at heights above 85 cm (85 cm--CEX: 13.8%, CC: 27.6%; 110 cm--CEX: 68.2%, CC: 90.9%) (Figure 3). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Muscle**} & \multicolumn{4}{c}{**Intervention [Unit: \%MVC]**} \\ \cline{2-5}  & **WO** & **W\({}_{\text{CC}}\)** & **W\({}_{\text{CEX}}\)** & **\(p\)-Value** \\ \hline Upper trapezius (UT) & 9.2 (1.8) & 9.3 (1.3) & 10.1 (1.4) & 0.854 \\ \hline Middle deltoid (MD) & 3.0 (0.2) & 3.4 (0.4) & 3.2 (0.3) & 0.622 \\ \hline Triceps brachii (TB) & 3.8 (0.6) & 4.6 (1.2) & 4.1 (1.1) & 0.860 \\ \hline Biceps brachii (BB) & 9.1 (1.0) & 10.1 (1.2) & 10.3 (1.2) & 0.669 \\ \hline Erector spinae (ES) & 12.8 (1.1) & 11.6 (1.1) & 14.1 (1.1) & 0.226 \\ \hline Rectus femoris (RF) & 6.4 (1.3) A [FOOTNOTE:A]Footnote : A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A: A:: A: A: A: A:: A: A: A: A: A:: A: A: A: A: A: A: A: A:: A: A: A: A: A:: A: A: A: A: A:: A: A: A:: A: A: A:: A: A:: A: A:: A: A: A: A:: AThe RF muscle activity showed a significantly decreasing trend for W\({}_{\text{CEX}}\) and W\({}_{\text{CC}}\) at the working height of 60 cm (CEX: 92.6%, CC: 83.7%), but the muscle activity of RF was significantly increased in the case of W\({}_{\text{CEX}}\) at 85 and 110 cm working heights (85 cm: 229.6%; 110 cm: 141.9%). In the case of the BF, the muscle activity decreased when wearing the exoskeletons (W\({}_{\text{CEX}}\) and W\({}_{\text{CC}}\)) at all working heights, and this tendency was particularly pronounced at the 85 cm working height (CEX: 82.9%; CC: 85.0%). In the case of the TA, there were no significant differences in muscle activity regardless of whether the exoskeletons were worn at 85 and 110 cm, but there was a significant decrease when the device was worn (W\({}_{\text{CEX}}\) and W\({}_{\text{CC}}\)) at the 60 cm (CEX: 74.4%; CC: 92.6%) working height (Figure 4).
# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 2 Related Work
### Sentimental Analysis Using Arabic Language  
Sentiment analysis using Arabic text has been a topic of interest in recent years. This is due to the increasing use of social media platforms and the growing amount of Arabic text data available. Sentiment analysis in Arabic text has been applied to a variety of domains such as politics [12], e-commerce [13], and customer reviews [14].  
One of the main challenges in sentiment analysis of Arabic text is the lack of resources and lexicons for the Arabic language. Additionally, the complexity of the Arabic language and its use of diacritics and different forms of writing (e.g., classical, modern standard) make it difficult to accurately process and analyze the text.  
Several studies have attempted to address these challenges by developing methods for preprocessing and normalizing Arabic text [15]. Other studies have proposed the use of machine learning techniques such as support vector machines and decision trees to classify the sentiment of Arabic text [16].  
Despite the progress made in the field, there are still limitations in these studies. The methods used in these studies may not be able to accurately capture the full range of sentiments and emotions expressed in Arabic text. Additionally, the lack of resources and lexicons for the Arabic language is still a major challenge. Therefore, further research is needed to improve the accuracy of sentiment analysis of Arabic text and to develop resources for the Arabic
## 5 Development of an Evaluation System
### Implementation of the Underwater Propagation Attenuation Model in the Low-Frequency Band  
#### 5.2.1 Propagation Attenuation Equation  
An attenuation equation that can model underwater communication has been developed. It can be expressed as a sum of diffusion loss and absorption loss [13,14]. As a sound wave propagates  
Figure 9: Example transition model of Group Based Random Way Point: (**a**) At the beginning of the simulation; (**b**) determination of the next points; (**c**) movement after the previous movement.  
through seawater, its acoustic energy is converted into other types of energy, resulting in the sound wave being absorbed in water. This is called absorption loss. It is necessary to define an absorption coefficient to calculate absorption loss. As an empirical equation for absorption coefficient, Ainslie and [PERSON] equation created based on [PERSON] and [PERSON]'s equation has been used. This equation is simpler than that of [PERSON] and [PERSON] and yet is said to give values closer to empirical values. Absorption loss (AL) is expressed by:  
\[\mathrm{AL}=d\ \log_{10}a(f) \tag{1}\]  
where \(d\) is an inter-node distance, and \(a(f)\) is an absorption coefficient. Since absorption loss is expressed in dB, the relation between absorption loss and an absorption coefficient is expressed by:  
\[10\log a(f)=\alpha \tag{2}\]  
where \(\alpha\) is an absorption coefficient. Using Ainslie and [PERSON] equation [15], it is expressed by:  
\[\alpha=0.106\frac{f_{1}f^{2}}{f_{1}^{2}+f^{2}}e^{\frac{pH-8}{0\%}}+0.52\bigg{(} 1+\frac{\mathrm{T}}{43}\bigg{)}\bigg{(}\frac{\mathrm{S}}{35}\bigg{)}\frac{f_{2 }f^{2}}{f_{2}^{2}+f^{2}}e^{-\frac{\mathrm{D}}{6}}+\ 0.00049f^{2}e^{-(\frac{\mathrm{T}}{2}+\frac{ \mathrm{D}}{12})}\ \ \ \ (\mathrm{dB/km}) \tag{3}\]  
where T is water temperature (\({}^{\circ}\)C), D is water depth (m), S is salt concentration (\(\%\)c), and \(f\) is frequency (Hz). Further details of the equation are given in reference [15].  
Next, diffusion loss is the surface pressure of a spherical space. Therefore, it can be calculated in the same way as the spatial loss of a terrestrial free space is calculated. Diffusion loss is expressed by:  
\[\mathrm{TL}=k\ \log_{10}l \tag{4}\]  
where \(k\) is a diffusion factor (\(1\leq k\leq 2\)), and \(a(f)\) is an absorption coefficient. In shallow water, a sound wave does not spread in a spherical shape but in a cylindrical shape. Therefore, \(k=1\). In deep water where there is no obstacle, a sound wave spreads in a spherical shape. In that case, \(k=2\). In Equation (4), \(l\) is an inter-node distance (km). As mentioned earlier, underwater propagation attenuation is a sum of diffusion loss and absorption loss. Thus, it is expressed by:  
\[\mathrm{P}(f,\mathrm{d})=d^{k}a(f)^{l} \tag{5}\]  
Underwater propagation attenuation varies depending on the frequency and inter-node distance.
## 5 Dynamic Firefighter Guiding  
The objective is to determine the safe paths to desired locations, such as escape paths, and make them available to the firefighters. Various emergency path computation methods have been reported in the literature. In the simplest methods, when the firefighter wants to exit from a building, it broadcasts a packet requesting the escape path. The packet is sent to a decision center that computes the escape path and sends it to the mobile node. Delays and packet loss in multi-hop channels are the main disadvantages. As already discussed, the distributed guiding methods require having local information of the building and cannot be applied in our problem. The proposed guiding method adopts a hybrid approach. The computation of escape paths is carried out at the Base Station, which keeps updated information of fire/smoke status. On the other hand, the guiding information is distributed within the WSN. Each static node only keeps the information to guide the firefighter to the next adjacent room in the safest escape path.  
The operation of the proposed method is as follows. When a firefighter wants to escape, it triggers the Escape Guiding Functionality in its mobile node. Then, the mobile node starts periodically broadcasting _Escape Request_ packets at a high rate, 500 ms in the experiments. All static nodes that listen the packet immediately transmit a _EscapeResp_ packet in response to their local guiding information. A count field is included in both _EscapeRequest_ and _EscapeResp_ packets so that mobile nodes can discard potential old _EscapeResp_ packets. The mobile node receives the packets and extracts the local guiding information. If the mobile node receives packets with contradictory escape path information, a simple method is applied to determine which is more reliable. The mobile node keeps broadcasting until the Escape Guiding Functionality is deactivated. The guiding information reaches the mobile node in only one hop, leaving the rest of the network unperturbed: the method can be used simultaneously by many firefighters. The guiding information stored in each node requires few bytes and is independent of the size of the WSN.  
Figure 5: Evaluation of the effect of \(p\) and the number of anchor nodes in the WCL method.
## 4 Discussion
Similarly, for Class C terrain features, the measured values are close to the standard values at lower heights. This is partly because the atmosphere is more stable at lower altitudes, and wind speed variations are more uniform. Additionally, a wind profile exponent closer to the value used in the standards (0.22) indicates that the measured data at this height are more likely to approximate the long-term data based on Class C terrain features, as specified in the standards. However, extreme wind speeds measured at higher altitudes are accompanied by stronger turbulence and wind speed fluctuations, leading to relatively larger wind pressure height variation coefficients at higher altitudes compared to the Class C values specified in the standards. The potential limitations of this article stem from the introduction of time-varying functions to the parameters, which means that conventional methods may amplify the impact of trends when calculating values for larger return periods, making it necessary to use effective return levels for interpretation. Furthermore, this paper introduces linear functions and periodic functions into the parameters to fit annual trends and seasonal trends. Subsequent research could consider using the variations of sine functions (sine waves with attenuation terms) to reconcile these two types of trends, or directly introduce other functions such as quadratic functions or piecewise functions to fit the more realistic and complex variations in actual data. Building on this, it could also explore calculating non-fixed air densities based on the joint distribution of wind speed and air density, or quantifying surface roughness to derive more accurate wind pressure height variation coefficients. Additionally, although the data span a large timeframe (fully experiencing urbanization), the years of the data are not continuous, with only representative years selected for analysis. Future research could use more covariates to more accurately estimate the changes in and the range of parameters, and they could also employ a more comprehensive data sample for more precise simulations.
# Development Status and Service Performance Preliminary Analysis for BDSBAS
## 3 Message User Algorithms and Service Performance Evaluation Methods
### Service Availability Evaluation Method  
Service availability is an indication of the ability of the system to provide usable service within the specified coverage area. It can be expressed as the percentage of time that the position accuracy and integrity of the SBAS service remain within the specified threshold. The availability determined with the GNSS and SBAS observations is described below:  
1. During the testing period, the starting and ending epochs are indicated as \(t_{start}\) and \(t_{endt}\), respectively, and the observation sampling interval is expressed as T.
2. The time series of receiver HPE and VPE are calculated with SBAS corrections and the corresponding HPL and VPL are determined with the SBAS integrity information.
3. At observation epoch \(i\), the receiver HPE and VPE are compared with the service horizontal accuracy threshold (\(HPOS_{lim}\)) and vertical accuracy threshold (\(VPOS_{lim}\)), respectively, to determine the accuracy availability indicator at epoch \(i\). \[Pos\_flag(i)=\begin{cases}1,&\mathit{HPE}<HPOS_{lim}\&VPE<VPOS_{lim}\\ 0,&\mathit{other}\end{cases}\] (6)where \(Pos\_flag(i)\) is the accuracy availability indicator at epoch \(i\), \"1\" indicates service accuracy is available, and \"0\" indicates service accuracy is not available.  
1. The service integrity indicator is calculated as Equation (5). Considering both the service integrity indicator and accuracy availability indicator, the service availability is given by Equation (7): \[A_{wall}=\frac{\sum\limits_{t=t_{start}\_mc=Top}^{t_{end}-Top}\left\{\prod \limits_{i=t,inc=T}^{t+Top}[Integrity\_Flag(i)\cdot Pos\_flag(i)]\right\}}{\frac{t_{ end}-t_{start}}{Top}}\] (7)  
where \(A_{wall}\) means the service availability, \(Top\) is the service sample interval and usually is set to 150 s for APV I approach.  
Based on the service integrity and availability evaluation methods, the BDSBAS service performance is preliminarily evaluated with real observation data.