esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Implementation Details  
We perform all experiments on a workstation with one INTEL XEON CPU and four TESLA V100 GPUs (32 GB memory), CUDA 11.4, and CUDNN 8.0.5 on an ubuntu system. A single GPU is used for both training and testing. All codes are developed based on the deep learning framework pytorch 1.10.0. We uniformly scale the image size to 224 \(\times\) 224 and use the stochastic gradient descent (SGD) optimizer to optimize the parameters during the training process. The weight decay is fixed to 0.0001 and the momentum is 0.9. The batch size for all experiments is eight. The initial learning rate is 0.001 and the maximum number of iterations is 17,000. The learning rate is reduced to 10% at 11,500 and 15,000 iter. The backbone is initialized with the pre-trained weight parameters in ImageNet to speed up the convergence of the model, and the rest is initialized with 'Xavier' [39]. In this study, the backbone of all models is ResNet50, unless we specifically mark. For the generation of anchors, we adopt the same settings as Faster R-CNN. The base anchor ratios of features in each level are (0.5, 1, 2), and the base anchor sizes are (32 \(\times\) 32, 64 \(\times\) 64, 128 \(\times\) 128, 256 \(\times\) 256, 512 \(\times\) 512). The anchor boxes with different scales are used at different levels of feature maps for prediction.  
The fc-head and conv-head play complementary roles to perform category classification and bounding box regression. We follow the setting in [36], classification tasks and regression tasks are assigned in fc-head and conv-head, respectively, and \(\lambda^{fc}\) is set to 0.7 and \(\lambda^{conv}\) is 0.8. In the total loss, \(\omega^{fc}\) and \(\omega^{conv}\) are set to 2.0 and 2.5, respectively, which means that it is more inclined to obtain accurate regression positions. In addition, the number of ROIs is reduced from 1000 to 500 to further improve the inference speed of the model.
# Path Planning for Yarn Changing Robots Based on NRBO and Dynamic Obstacle Avoidance Strategy
## 3 NRBO-DWA Fusion Algorithm
### NRBO-DWA Fusion Algorithm
#### 3.3.2 Fusion Algorithm  
The DWA algorithm excels in dynamic obstacle avoidance but struggles with finding optimal paths in complex environments and is susceptible to local optima. To overcome this limitation, this paper introduces a novel fusion strategy that integrates the global path planning capabilities of the NRBO algorithm with the local dynamic obstacle avoidance strength of the DWA algorithm, resulting in more efficient path planning. Key nodes from the NRBO-generated global path are utilized as local target points for the DWA algorithm [21]. This approach not only improves the DWA's performance in local obstacle avoidance but also ensures global optimality in path planning by leveraging guidance from these key nodes.  
The proposed fusion algorithm employs global path key nodes as local targets within the DWA framework, effectively guiding the spool-changing robot in both path tracking and dynamic obstacle avoidance. However, during the actual path search, the robot may deviate from its planned trajectory when avoiding local obstacles, leading to longer paths and extended travel time. To address this, a path correction mechanism is introduced. As shown in Figure 5, the strategy involves evaluating the robot's relative position to the local target point during obstacle avoidance and subsequent realignment. After dynamic obstacle avoidance, the angle between the robot's current position and the local target point, relative to the global path, is calculated to assess whether the robot has passed the target point. If the angle is less than 90\({}^{\circ}\), the robot continues to use the target point as a reference; if the angle exceeds 90\({}^{\circ}\), it indicates a deviation from the target point, prompting the correction strategy to realign the robot's trajectory and minimize unnecessary detours.  
The flowchart of the enhanced fusion algorithm is shown in Figure 6. This algorithm not only optimizes dynamic obstacle avoidance but also reduces the overall path length and time associated with the process.  
Figure 5: Schematic diagram of path correction.  
Figure 6: Flowchart of the NRBO-DWA fusion algorithm.
## 3 Construction of Simulation Model
### Kinematics Model Level  
Setting the dynamic models of the two sides can change the maneuverability of the two sides, realize the confrontation tests with different speed ratios and overload ratios, and simulate the multi-domain manned or unmanned platforms such as air, sea, and land by setting different types of dynamic kinematics models.  
A three-dimensional global coordinate system is established with the ground in the center of the simulation area as the origin, and the position of each UAV in the UAV swarm in space is represented by (\(X\), \(Y\), \(Z\)). In the simulation, in order to truly reflect the UAV motion state, according to the real state of the pilot operating the aircraft, the pilot's throttle lever and control lever operation mode are simulated, and a throttle channel and two heading angle channels are set up to establish a second-order three-degrees-of-freedom control model. Compared with the three-dimensional six-degrees-of-freedom equation, using the second-order three-degrees-of-freedom control model can reduce the calculation dimension of three degrees of freedom, save the computing consumption in the emergence of swarm intelligence, and pay more attention to the research problem itself.  
\[\begin{array}{llll}x_{t+1}=x_{t}+udt&u=V\cos\gamma\cos\psi&V_{t+1}=V_{t}+ adt\\ y_{t+1}=y_{t}+vdt&v=V\cos\gamma\sin\psi&\tau_{t+1}=\tau_{t}+\dot{\gamma}dt\\ z_{t+1}=z_{t}+wdt&w=V\sin\gamma&\psi_{t+1}=\psi_{t}+\dot{\psi}dt\end{array} \tag{1}\]  
The throttle channel controls the flight speed \(V\) to change the running speed of the aircraft by changing the acceleration \(a\). The two angle channels control the pitching angle \(\gamma\) and yaw angle \(\Psi\), respectively, and change the running direction of the aircraft by changing the pitching angular acceleration \(\dot{\gamma}\) and yaw angular acceleration \(\dot{\psi}\). In order to limit aircraft overload, boundary constraints are set for three control quantities.  
The action space of the mission UAV is three-dimensional, and the output control quantities are \(\left[\begin{array}{c}a\\ \dot{\gamma}\\ \dot{\psi}\end{array}\right]\), \(a\in[a_{min}a_{max}]\), \(\dot{\gamma}\in[\dot{\gamma}_{min}\dot{\gamma}_{max}]\), \(\dot{\psi}\in[\dot{\psi}_{min}\dot{\psi}_{max}]\). The attributes of the relevant parameters in the kinematic model are given in Table 1.
## 2 Materials and Methods
### Characterization Techniques
#### 2.3.3 Surface Area and Pore Volume Distribution  
The surface area, pore volumes, and pore size distribution were determined from the N\({}_{2}\) adsorption/desorption isotherm using a Micromeritics ASAP 2020 machine (National Scientific Company Ltd., Jeddah, Saudi Arabia). Firstly, 100 mg of a sample was outgassed at 300 \({}^{\circ}\)C for twelve h, and then the analysis was conducted at \(-\)195.86 \({}^{\circ}\)C in a liquid nitrogen bath. The surface area was calculated by the Brunauer-Emmet-Teller (BET) method, while the pore size distribution was determined using the Horvath-Kawazoe method.
###### Abstract  
Many people with MS (pwMS) use unregulated cannabis or cannabis products to treat the symptoms associated with the disease. In line with this, Sativex, a synthetic combination of cannabidiol (CBD) and \(\Delta^{9}\)-tetrahydroocannabinol (\(\Delta^{9}\)-THC) has been approved to treat symptoms of spasticity. In animals, CBD is effective in reducing the amounts of T-cell infiltrates in the spinal cord, suggesting CBD has anti-inflammatory properties. By doing this, CBD has shown to delay symptom onset in animal models of multiple sclerosis and slow disease progression. Importantly, combinations of CBD and \(\Delta^{9}\)-THC appear more effective in treating animal models of multiple sclerosis. While CBD reduces the amounts of cell infiltrates in the spinal cord, \(\Delta^{9}\)-THC reduces scores of spasticity. In human studies, the results are less encouraging and conflict with the findings in animals. Drugs which deliver a combination of \(\Delta^{9}\)-THC and CBD in a 1:1 ratio appear to be only moderately effective in reducing spasticity scores, but appear to be almost as effective as current front-line treatments and cause less severe side effects than other treatments, such as baclofen (a GABA-B receptor agonist) and tizanidine (an \(\alpha\)2 adrenergic receptor agonist). The findings of the studies reviewed suggest that cannabinoids may help treat neuropathic pain in pwMS as an add-on therapy to already established pain treatments. It is important to note that treatment with cannabinoid compounds may cause significant cognitive dysfunction. Long term double-blind placebo studies are greatly needed to further our understanding of the role of cannabinoids in multiple sclerosis treatment.  
multiple sclerosis; cannabinoid; \(\Delta^{9}\)-tetrahydroocannabinol; cannabidiol; spasticity; neuropathic pain; inflammation; experimental autoimmune encephalomyelitisitis; neuroprotection; cognition; animal models +
Footnote †: journal: Medical  
Currently approximately 124 million people use cannabis annually, with the most popular method of administration being smoking and inhaling the flower in a joint [3]. Despite its potential for both physical and psychological dependence being relatively low [4; 5; 6], cannabis is a class C drug in the UK and Ireland and has varying degrees of legality in the United States and further afield. Despite being legalized in some countries and regions, cannabis use is still marginalized and considered a vice [7]. A shift in this attitude can be seen between 1980 and 1990 where cannabis started being prescribed as an anti-emetic medication and an appetite increase in HIV and cancer patients, as well as for the reduction of intraocular pressure in glaucoma patients [5; 6].  
For the medical use of cannabis in multiple sclerosis (MS), there are four main routes of administration, these being: oral administration, mucosal administration, subcutaneous administration (SC), and transdermal administration [8]. There is evidence to suggest that up to 4% of pwMS currently have access to, or have previously illegally accessed, unregulated cannabis products to treat their disease symptomatology [6; 7].
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 5 Ongoing R&D
### The IoT-SATBACK and 5G-SENSOR@SEA Projects  
An example of studies of upper layer enhancements and optimizations is represented by the ESA project IOT-SATBACK [27] (MBI S.r.L. Pisa, Italy and Software Radio Systems Ltd., Cork, UK), whose primary objective is to design, develop, test and demonstrate a testbed capable of providing satellite backhauling services for future NB-IoT. The targeted improvement is to enable new satellite communication services for backhauling M2M and Internet of Things communications, and this is achieved by means of a component named the IP Optimizer which implements optimizations of F-SIM layer 2 and upper layers. The optimization includes techniques for the reduction in the overheads and the IP payload compression which contributes to increasing the backhauling spectral efficiency.  
The outcomes of the IOT SATBACK studies have a natural follow-up in the ARTES C&G project named 5G SENSOR@SEA, where a complete end-to-end system including a satellite part based on enhancements of the optimizations mentioned above is going to be tested in a real operational scenario to transmit sensor data from cargo ships in open or near sea to an IoT platform. The final target is a complete platform which can be sold as a commercial product to be provided to cargo ship companies and other companies interested in container, goods or fleet monitoring in the logistic and transport area.
## 2 Mechanisms for Generating Shaft Frequency Signals of Vessels
### Method for Intrinsic Mode Function Extraction of Shaft Frequency Electric Field Signals  
Signal decomposition algorithms break down the original signal into several Intrinsic Mode Functions (IMFs), a crucial step in preprocessing for signal feature extraction techniques. Empirical Mode Decomposition (EMD) is an adaptive sequential data decomposition algorithm primarily suitable for non-stationary and nonlinear sequences. It extracts information from the original signal itself, ensuring the maximum preservation of the original signal's integrity. The decomposed IMF contains the local feature information of the original signal at different time scales, and the signal decomposition flowchart is shown in Figure 5.  
The specific algorithm steps are as follows:  
1. Input original time series signal \(S(t)=\{s(t_{1}),s(t_{2}),\cdots,s(t_{n})\}\)\(n=1,2,\cdots N\) and employ cubic spline interpolation to obtain local maxima and minima, forming the upper and lower envelopes of the signal \(Y(t)\);
2. From the upper envelope \(Y_{\max}(t)=\{y_{\max}(t_{1}),y_{\max}(t_{2}),\cdots,y_{\max}(t_{i})\}\), fitted to the sequence of locally very large points, and the lower envelope \(Y_{\min}(t)=\{y_{\min}(t_{1}),y_{\min}(t_{2}),\cdots,y_{\min}(t_{i})\}\), fitted to the sequence of locally very small points, find the average value \(M(t)=\{m(t_{1}),m(t_{2}),\cdots,m(t_{i})\}\) of each extreme point in the sequence, and subsequently demean the raw signal through mean value subtraction, yielding the centered signal \(h(t)\).
3. Check whether \(h(t)\) satisfies the IMF conditions. There are two main conditions: firstly, the number of local extremum points and zero-crossings must be equal or differ by one; secondly, at any given moment, the average of the envelope of local maxima and the envelope of local minima must be zero. If the conditions are not met, use signal \(h(t)\) as a basis and return to step (2) for further screening until the decomposed signal after \(k\) iterations satisfies the IMF conditions. The first IMF component \(h_{k}(t)\) of the original signal is represented as \(c_{1}(t)\);  
Figure 4: Design scheme for sensor and simulated source: (**a**) installation method of Ag/AgCl electric field sensors and (**b**) design scheme for simulated sources.  
(4) Subtract the first IMF component \(r_{1}(t)\) from the original signal \(S(t)\) leaving the residual signal \(r_{n}(t)\) and repeat step (2) for decomposition. Continue this process until the residual signal \(r_{n}(t)\) becomes a monotonic function that cannot be further decomposed. The sum of all IMF components and the residual component is the original signal \(S(t)\), i.e.,  
\[S(t)=\sum_{j=1}^{N}c_{j}(t)+r_{n}(t) \tag{1}\]
## 4 Simulation and Analysis
### The Impact of Spot Radius on Position Detection Accuracy  
Assume that the SNR is 31.74 dB and \(N\) is 1024. The spot radius \(\omega\) is controlled to increase from 1 mm to 3.6 mm in steps of 0.6 mm. Figure 7a shows the relationship between the position detection accuracy and the spot radius when three spots are located at the same positions. Figure 7b is the relative error curve of Figure 7a. Figure 7c is the relationship between the position detection accuracy and the spot radius when the three spots are located at different positions. Its relative error curve is Figure 7d. It can be seen from Figure 7a,c that the spot position detection can receive better accuracy with the decrease in the spot radius. The reason for this is that increasing the radius of the Gaussian spot will cause its energy to not be able to be less concentrated, which leads to a decrease in the position detection accuracy. Therefore, in practical systems, the position detection accuracy can be improved by reducing the spot radius within the detection range [27].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Gaussian Spot** & \(\mathbf{e_{meanN\_x1}}\) & \(\mathbf{e_{meanN\_x2}}\) & \(\mathbf{e_{meanN\_x3}}\) \\ \hline Same Position & 5.31\% & 5.03\% & 4.49\% \\ Different Positions & 4.48\% & 4.30\% & 5.79\% \\ \hline \end{tabular}
\end{table}
Table 1: The average relative error between position detection accuracy value by simulation and value calculated by formula when changing the \(N\) value.  
Figure 6: (**a**) The influence of the number of FFT points on position detection accuracy when the spots are at the same position. (**b**) The relative error curve when the spots are at the same position. (**c**) The influence of the number of FFT points on position detection accuracy when the spots are at different positions. (**d**) The relative error curve when the spots are at different positions.  
When the spot radius is changed, the average relative error of the value by simulation relative to the value calculated by formula is shown in Table 2. As can be seen from Table 2, when the spots are at the same position, the AREs are 4.01%, 3.61%, and 3.29%, respectively. When the spots are at different positions, the AREs are 3.91%, 2.33%, and 4.51%. This indicates that the simulation value of the position detection accuracy is close to the mathematical model.
## 5 Open Research Issues and Challenges
### Reducing Interference  
Accurate detection of the physical locations of biomedical devices can help reduce mutual interference. A radio-frequency identification (RFID)-based transceiver was introduced in [6] to reduce interference in biomedical devices by detecting their physical location. RFID transceivers may be suitable for CRBANs because of their low power transmission, but they can cause interference in biomedical devices [31]. Without the accurate physical location offered by RFID, reducing mutual interference between primary and secondary users becomes a significant challenge. CRBANs can be designed using physical location information or other approaches to mitigate interference.
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 7 Internet Access
### Design and Implementation  
The remote access method proposed in this paper is based on a two-step translation process relying on the collaboration between the controller card and the IMS, enabling them to share the workload. Even more importantly, the method has been designed so that at least part of the required computations can be performed at compile time. More specifically, the translation proceeds as follows:  
1. The controller card and the IMS identify shared variables by means of unique values of an enumerated data type.
2. When necessary, the IMS resolves symbolic names represented as variable-length strings of characters into the corresponding enumerated values.  
An additional upside of this approach is that when multiple controller cards are present, the IMS can act as a centralized mapping point for all of them. Furthermore, shifting the workload towards the Internet node on which the IMS runs can be considered an advantage as well, because these nodes typically have more processing power than controller cards.
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 6 Results
### GARCH Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Seizure Channels**} & \multicolumn{2}{c}{**Non-seizure Channels**} \\ \hline  & **RAST3** & **RAST4** & **RMST4** & **Half Life** \\ \hline \multirow{4}{*}{**Segment**} & **HL** & **HL** & **HL** & \\  & **95\% CI** & **95\% CI** & **95\% CI** & **95\% CI** & **Median** \\  & **Overlapping** & **Overlapping** & **Overlapping** & **Min** \\  & **Interval Count** & **Interval Count** & **Interval Count** & **Max** \\ \hline \multirow{4}{*}{**Segment**} & 1.13 & 1.47 & 6.49 & 7.64 \\  & (0.86,1.41) & (1.18,1.75) & (2.73,10.24) & 1.16 \\  & 2 & 2 & 42 & 186.05 \\  & 1.72 & 1.88 & 10.94 \\
2 & (1.20,2.23) & (1.47,2.29) & 52 & 4.89 \\  & 2 & 2 & 67.61 \\  & 1.75 & 1.71 & 13.65 & 15.25 \\
3 & (1.06,2.44) & (1.31,2.11) & (3.83,23.47) & 3.06 \\  & 7 & 5 & 53 & 115.7 \\  & 1.84 & 1.35 & 39.8 & 12.83 \\
4 & (1.35,2.34) & (1.14,1.58) & (6.99,72.6) & 6.13 \\  & 4 & 1 & 50 & 74.15 \\  & 5.61 & 6.18 & 16.56 & 6.61 \\
5 & (4.30,6.95) & (4.94,7.43) & (7.42,25.69) & 2.58 \\  & 31 & 31 & 48 & 54.82 \\  & 1.67 & 1.55 & 8.98 & 10.68 \\
6 & (1.26,2.09) & (1.29,1.80) & (3.1,14.86) & 4.67 \\  & 3 & 3 & 45 & 49.25 \\  & 5.50 & 1.71 & & 6.84 \\
7 & (4.66,6.33) & (1.47,1.95) & N/A & 2.65 \\  & 30 & 4 & & 187.9 \\  & 1.47 & 1.55 & 6.46 & 6.45 \\
8 & (1.17,1.78) & (1.32,1.79) & (3.07,9.85) & 5.68 \\  & 3 & 3 & 44 & 73.61 \\  & 1.41 & 1.34 & 6.49 & 13.08 \\
9 & (1.12,1.70) & (1.15,1.54) & (3.28,9.7) & 4.59 \\  & 2 & 2 & 32 & 92.17 \\ \hline \multicolumn{4}{c}{**(B)** Awake data.} \\ \hline \multicolumn{4}{c}{**Seizure Channels**} & \multicolumn{2}{c}{**Non-seizure Channels**} \\ \hline  & **RAST3** & **RAST4** & **RMST4** & **Half Life** \\ \hline \multirow{4}{*}{**Segment**} & **HL** & **HL** & **HL** & \\  & **95\% CI** & **95\% CI** & **95\% CI** & **Median** \\  & **Overlapping** & **Overlapping** & **Overlapping** & **Min** \\  & **Interval Count** & **Interval Count** & **Interval Count** & **Max** \\ \hline \multirow{4}{*}{**2**} & 8.43 & 14.86 & 6.26 & 36.04 \\  & (6.39,10.47) & (9.97,19.75) & (4.89,7.64) & 12.69 \\  & 3 & 23 & 0 & 96 \\  & 7.93 & 13.28 & 14.38 & 41.09 \\  & (5.61,10.25) & (7.10,19.47) & (7.94,20.82) & 12.95 \\  & 2 & 18 & 21 & 85.08 \\  & 7.38 & 15.51 & 7.84 & 39.87 \\
3 & (5.82,8.93) & (10.79,20.24) & (6.35,9.32) & 7.64 \\  & 2 & 16 & 3 & 90.62 \\  & 10.13 & 6.44 & 13.05 & 34.97 \\
4 & (7.92,12.33) & (4.98,7.91) & (9.92,16.17) & 13.22 \\  & 7 & 0 & 9 & 65.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._