esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Design
### Two-Element MIMO Antenna Design
#### 2.2.2 Analysis of Orientation  
In a MIMO system, the orientation of antennas can significantly influence the isolation between them. The isolation between antennas is the degree to which their signals interfere with one another. Several decoupling mechanisms can result in varying degrees of isolation at various orientations. The proposed antenna employs the spatial diversity decoupling technique. Basically, spatial diversity is accomplished by using antennas that are physically separated. When these antennas are oriented differently, they sample distinct spatial signal paths, reducing the likelihood that fading or interference will simultaneously affect both antennas. In the case of orientation I, the two elements of the antenna are situated in close proximity to one another, with a specific separation distance of 4 mm, as depicted in Figure 7a. During this configuration, the antenna offers improved bandwidth and enhanced isolation. According to Figure 8a-c, the antenna resonates at frequencies of 9.7, 16.3, 18.8, and 21.4 GHz, with three bands encompassing the frequency ranges of 8 to 11.1 GHz, 16 to 16.5 GHz, and 17.9 to 28 GHz, with a maximum isolation of \(-\)40 dB.  
As depicted in Figure 7b, the design of orientation II involves the inversion of antenna II position, while the position of antenna I remains intact. The antenna resonant frequencies and bandwidths in this particular orientation can be deduced from the data presented in Figure 8a-c. It is evident that the antenna resonates at frequencies of 10.3 GHz,  
Figure 6: Distance variation, scattering parameters (in dB): (**a**) S11; (**b**) S22; (**c**) S12 and S21.  
Figure 7: MIMO antenna with different orientations: (**a**) I; (**b**) II; and (**c**) III.  
16.4 GHz, 18.8 GHz, and 21.5 GHz, with corresponding bandwidths of 3000 MHz, 600 MHz, and 9900 MHz. The maximum level of isolation attained is \(-\)38.74 decibels. As indicated in Figure 7c, both antennas are turned 90 degrees for orientation III. The antenna exhibits resonance at two specific frequencies, namely 8.2 and 19.2 GHz, with a maximum attainable isolation of \(-\)37.21 dB. Upon doing a comparative analysis of the outcomes from all three orientations, it is evident that orientation I exhibits superior performance in terms of bandwidth and isolation when compared to the other two orientations. Summarized results of all three orientations are presented in Table 5. Orientation I is commonly regarded as the most suitable design for a MIMO antenna.  
Figure 8: _Cont._
# Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
## 1 Introduction  
With the evolution of cellular mobile communication technology, 5G ultra-reliable low latency communication (uRLLC) is gradually being applied to factory automation fields because of its excellent QoS guarantee [1]. Factory automation information is usually time-sensitive and safety-critical, and is essential for the performance of automation systems, so it requires carrying networks qualified with capabilities of low latency, high reliability and deterministic guarantee. Therefore, the integration of 5G and factory automation applications is creating new technical solutions for Industrial 4.0 while also introducing more challenges to 5G.  
Deterministic communication is usually fulfilled by assuring bounded latency and low jitter for time-critical applications [2]. TSN is an Ethernet-based networking technology and is standardized in the IEEE 802.1 task group. With time synchronization among bridges and stations, TSN proposes several traffic-shaping schemes based on a Gate Control List (GCL) to send time-critical applications at a precise point in time, and this time-triggered scheduling paradigm can provide a bounded latency guarantee for time-critical applications regardless of other ongoing flows competing for the same networkresources simultaneously [3]. In order to promote the deterministic transmission capability of 5G, 3 GPP has introduced TSN to integrate with 5G systems (5 GSs) in Release 16 [4]. According to 3 GPP specifications, a set of new functionalities has been incorporated into 5 GS architecture to connect TSN with 5G in both control and data planes. There are two main scenarios of integration between 5 GS and TSN: one is 5 GSs as a TSN bridge entity and the other is using TSN in 5G Xhaul networks (e.g., fronthaul, midhaul and backhaul).  
The 3 GPP Release 16 proposes an integration framework for 5G-TSN as well as new 5G function entities in both control and data planes to support TSN protocols. However, 5G and TSN are completely different networking methodologies in terms of physical layer techniques, MAC schemes and upper layer protocols. Therefore, how to guarantee time-critical communication services transmission across 5G and TSN has become a hot topic [5]. It requires making a resource allocation decision in 5G gNB dynamically based on GCL status. However, to the best of the authors' knowledge, limited literature addresses the resource scheduling of 5G radio access networks taking into consideration TSN scheduling.  
The remainder of this article is organized as follows. In Section 2, we first overview the relevant TSN scheduling scheme, and give a detailed description of architecture and related research works about resource scheduling schemes in 5G-TSN integration networks. In Sections 3 and 4, we explain the system model and formulate and analyze the optimal problem. Then a joint resource scheduling algorithm based on DDPG and corresponding simulation results are described in Sections 5 and 6. Finally, this article is concluded in Section 7.
###### Abstract  
We propose a source separation architecture using dilated time-frequency DenseNet for background music identification of broadcast content. We apply source separation techniques to the mixed signals of music and speech. For the source separation purpose, we propose a new architecture to add a time-frequency dilated convolution to the conventional DenseNet in order to effectively increase the receptive field in the source separation scheme. In addition, we apply different convolutions to each frequency band of the spectrogram in order to reflect the different frequency characteristics of the low- and high-frequency bands. To verify the performance of the proposed architecture, we perform singing-voice separation and music-identification experiments. As a result, we confirm that the proposed architecture produces the best performance in both experiments because it uses the dilated convolution to reflect wide contextual information.  
source separation; DenseNet; broadcast contents; music identification; dilated convolution +
Footnote †: journal: Applied Sciences
## 4 Numerical Work
### Material Calibration  
#### 4.2.1 Aluminium Skins  
Following the same procedure as [PERSON] et al. [36], the strain hardening parameters for the aluminium skins were calibrated against DIC-results obtained from the quasi-static tension tests described in Section 2.1. As the strain at necking is significantly lower than the failure strain, an inverse modelling approach using the optimisation tool LS-OPT (Version 5.2.1, Livermore Software Technology (LST), Livermore, CA, USA, 2018) [42] was applied, where sequential simulations of the same model were run with different choices of hardening parameters. A model of the test specimen was created using the same shell-element size and formulation as used in the simulations of the shock tube tests (see Section 4.3), i.e., quadrilateral four-node Belytschko-Tsai shell elements measuring 1 mm \(\times\) 1 mm with reduced integration and five integration points across the thickness. An initial value for the hardening parameters \(\sigma_{0}\), \(Q_{i}\) and \(C_{i}\) was found by a least-square fit of the Cauchy stress--logarithmic plastic strain curve to necking for the representative test. These initial parameters were then used as starting values in the optimisation routine. The engineering stress-strain curve from one representative test in the rolling direction (\(0^{\circ}\)) of the plate was chosen as the target curve for the calibration. A generic optimisation routine in LS-OPT was applied in the optimisation, where 100 equidistant regression points were used in the calculation of the mean square error to ensure a good fit in all parts of the engineering stress-strain curve. The hardening parameters were found by minimising the mean square error and are given in Table 4. The density, elastic constants and strain-rate constants are taken from [PERSON] et al. [34] and are listed in Table 5. A comparison between numerical and experimental results is shown in Figure 1, and one can see that the numerical model captures the overall material response well.
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 4 Design Improvement of XY Stage  
The results in the previous section have shown that the yaw angle of the stage decreases as the _l/w_ ratio increases and as the parallelism of the LM Guides improves and as the platform's centers of stiffness and mass come closer to the driveline of the linear actuator. The position repeatability of the stage with respect to the reciprocating motion becomes better with a reduced yaw angle.  
The PCB electrical test machine has an _XY_ stage with offset-driven moving platforms. In particular, it has disadvantageous spatial constraints against precision driving with the aspect ratio _l/w_ of 0.41 and an _FO_ of 80%.  
To overcome this challenge, the following design improvements are proposed. First, maximize \(k_{1}\) and \(k_{2}\) while keeping \(k_{3}=k_{4}=0\) with simple support of the blocks on rail number two. For this, THK's HSR35L and HSR20L were selected for rail number one and rail number two, respectively.  
Furthermore, to best match the center of mass with the driveline, a balancing weight was added to the base of the moving platform assembly, as shown in Figure 24. Since the platform moves along the y-axis with a stroke of 300 mm, the _CM_ fluctuates between 60-71%. The linear drive actuator system was composed of a 20 mm lead ball screw, a 750 W servo motor, and a linear encoder.  
Figure 23: Yaw angles according to change of _CM_ and _CS_.  
Table 11 shows the results of the yaw error calculated using Equation (10) for the improvements.  
Since the predicted yaw error is below the target repeatability of \(\pm 1.0\)\(\upmu\)m, a detailed design of the x-axis was performed. The improved design of the XY stage is shown in Figure 24. Figure 25 shows the details of the simply supported blocks in Figure 24. In Figure 25, the platform was mounted onto the support plate, and a pin was assembled in the platform hole. A urethane ring was inserted between the pin and the platform hole to absorb the parallelism error of the LM Guide to prevent any constraint on the platform. A pin top block was utilized to constrain the z-direction motion of the platform. The pin length was processed and assembled approximately 10 \(\upmu\)m longer than the platform thickness.
## 4 Implementation and Results
### Selection of Feature Extraction Methods and Classification Algorithms
#### 4.1.1 Feature Extraction Methods
according to the International 10-20 system, a standardized method to locate the sensors on the scalp in a specific pattern based on anatomical landmarks of the head. We used the algorithms DASM and RASM, described in Table 1, to extract these spatial features.
# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 2 Related Work
### Filtering Window and Tag Window  
For handling our proposed method, a filtering window and a tag window were defined as follows. A matrix \(X\) with the size (R \(\times\)C) was built for the input image. The filtering window was a square matrix. It had odd \((2W+1)\times(2W+1)\) dimensions. It is represented as follows:  
\[X_{i,j}=\{x_{s,t}|i-W\leq s\leq i+W,\ j-W\leq t\leq j+W\}, \tag{1}\]  
where \(1\leq i\leq R,1\leq j\leq C,W\geq 1\).  
The corresponding binary tag matrix \(F\) is the same size as \(X\). The sub matrix of \(F\) is called tag window \(F_{ij}\); its central pixel is at location \((i,j)\), the same as \(X_{i,j}\) on the image, and it is formatted as:  
\[F_{i,j}=\{f_{s,t}|i-W\leq s\leq i+W,\;j-W\leq t\leq j+W\}, \tag{2}\]  
When \(x_{s,t}\) in Equation (1) was detected as a noisy pixel, \(f_{s,t}\) in Equation (2) was set to one; when \(x_{s,t}\) was not detected as a noisy pixel, \(f_{s,t}\) was set to zero.
## 4 Field Experiments
### Prototype and Field Testing Platform Display  
To evaluate the real-time communication capability of the ultraviolet communication system under tailing effects and duty cycle variations, we set up a testing platform outdoors and conducted experiments on the BER at different distances. Figure 11a illustrates the relative positions of the transmitter and receiver, while Figure 11b depicts the physical prototype and the constructed outdoor testing platform.  
During the testing, we varied the communication distance from 500 m to 700 m and recorded the performance of BER under different duty cycle settings and tailing effect influences at various distances. The transmitter and receiver were both set at a 10-degree offset angle.
## 3 Materials and Methods
### Gas Chromatography-Olfactometry/Mass Spectrometry (GC-O/MS) Analysis  
Gas chromatography was performed using an Agilent 6890N GC equipped with an Agilent 5975 mass selective detector (Agilent Technologies, Santa Clara, CA) and olfactometer (ODP 2, Gerstel GmbH and Co. KG, Mulheim, Germany). Samples were separated and evaluated using a polar DB-FFAP column (60 m \(\times\) 0.25 mm i.d, 0.25 \(\upmu\)m film thickness, Agilent Technologies, Inc., Santa Clara, CA). The oven temperature was programmed to 40 \({}^{\circ}\)C for 2 min, then increased at a rate of 4 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, and maintained there for 10 min. The carrier gas used was helium, at a flow rate of 2.0 mL/min. A split ratio of 1:1 was used for injection.  
Half of the carrier flow was diverted to the olfactory port, and mixed with warm, humid air for sniffing. The sniffing port temperature was set to 250 \({}^{\circ}\)C. Three trained panelists (two females and one male) received two months of training on GC-O involving, at a minimum, 30 aroma-active reference compounds at concentrations that were 10 times their odor thresholds in air. During GC operation, each panelist placed his/her nose near the olfactory port to record the retention times, intensity values, and aroma descriptions of the samples. Odor intensities were evaluated using a 6-point intensity scale from 0 to 5, with \"0\" = no, \"3\" = moderate, and \"5\" = extreme intensity. Each panelist conducted the GC-O evaluation four times. The final intensity value of each aroma was an average of the aroma intensities obtained from all of the panelists.  
The other half of the carrier flow was diverted to the mass spectrometer. The ion source temperature was set to 230 \({}^{\circ}\)C, electron impact ionization was applied in positive ion mode (70 eV), and the mass range scanned was m/z = 25\(\sim\)300.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 9 Challenges  
The implementation and maintenance of a monitoring system in precision agriculture faces several challenges. The greatest challenge in the sensor layer is for sensor nodes to achieve efficient and continuous operation for a long time in a natural environment, while taking into account the climate change and wildlife interventions. The battery life of sensor nodes is not considered satisfactory, and it is necessary to design and implement energy-saving protocols with the highest possible system performance among other precautions. In addition, depending on the type of application, the supported agricultural work and the implementation technologies, the problems that arise can be differentiated. For instance, the use of sensors and controllers from different manufacturers prevents communication between them and makes it more difficult to interconnect with other agricultural components. Also, the sensor inertia phenomenon was observed in a high-speed WSN due to non-steep changes in humidity and soil temperature.  
In the network layer, the basic challenges regarding the operation of a crop monitoring system with WSN and IoT technologies include the limited computational capabilities of sensor nodes. The restricted memory of the nodes disables them to handle large amounts of communication data and cluster-based interconnection procedures. Due to this fact, long data queues are created in each node, leading to greater delay in transmissions. The same outcome can be triggered by the long communication distance of sensor nodes. One major issue that routing algorithms must deal with in such cases is the high level of energy consumption, which leads to a reduction in the overall viability of the network. In precision agriculture monitoring systems routing protocols should offer minimum delay, be able to provide efficient services in many sensor nodes, while taking into account the limited resources. They should also be capable of accepting all sorts of environments including severe and loss environments, while providing information security and privacy. Most routing protocols use some localization technique to obtain knowledge concerning their locations. The performance of the routing protocol is a function of network size and transmission media. Therefore, transmission media of good quality enhances the network performance directly.  
However, in many cases the failure of such advanced monitoring system may be due to the geographic, cultural or socio-economic distance between system designers and the intended user community. Cost is an important limiting factor in the implementation of such systems. The cost depends to a great extent on the quality of the materials and the topology of the network.
# A Study on the Pantograph Slide Wear Model Based on Energy Dissipation
## 2 Calculation of Current-Carrying Frictional Energy Dissipation in the Pantograph-Catenary System  
For the pantograph-catenary current-carrying friction pair, the tribological behavior of the pantograph slide is influenced by the combined effects of electrical and mechanical factors. Specifically, the Joule heating effect and frictional heating effect primarily influence the wear of the pantograph slide through the work done by the current and the frictional force [12]. During the wear process, these two effects counterbalance each other and are interrelated. Therefore, the occurrence of pantograph slide wear is inevitably accompanied by the consumption of system energy. By relating the current-carrying friction in the pantograph-catenary system to wear through energy, the wear process can be viewed as an energy dissipation process. This process consumes energy in the form of heat generation at the contact surface, electromagnetic noise, the wear of the pantograph slide surface, and the extrusion of abrasive particles, among other forms. The sum of the work done by the current and the work done by the frictional force is defined as the total dissipated energy, referred to as the pantograph-catenary current-carrying frictional energy dissipation.
## 3 Field Data Acquisition
### Major Agricultural Field Operations  
Experiments were conducted across four agricultural operations--moldboard plow tillage, subsoiling, rotary tillage, and baler operation--at the four locations in Korea, as shown in Figure 6. Moldboard plow tillage, subsoiler tillage, rotary tillage, and baler operations were performed at sites 1, 2, 3, and 4. The major specifications of each attached implement are shown in Table 5. The moldboard plow tilling operation was performed at a tillage depth of about 15-20 cm under the gear stage M3 Low (7.09 km/h) [4]. Subsoiler and rotary tillage were performed at a tillage depth of about 35-40 and 15-20 cm, respectively, under the gear stage L3 Low (2.38 km/h). The baler operation was performed under the gear stage M1 Low (3.78 km/h).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sites**} & **Soil** & **Soil Moisture** & \multicolumn{5}{c}{**Cone Index (kPa) by Depth**} \\ \cline{3-8}  & **Texture** & **Content (\%)** & **0 cm** & **5 cm** & **10 cm** & **15 cm** & **20 cm** & **25 cm** \\ \hline Site 1 & Loam & 27 & 199 & 474 & 828 & 1484 & 2467 & - \\ Site 2 & Loam & 32 & 270 & 875 & 1146 & 1670 & 3549 & 3743 \\ Site 3 & Loam & 42 & 114 & 389 & 720 & 1552 & 2794 & 2750 \\ Site 4 & Clay loam & 37 & 191 & 423 & 476 & 742 & 2241 & 3052 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical properties of soil for each field site.  
Figure 6: Field experiments of major agricultural tractors: (**a**) moldboard plow tillage, (**b**) subsoiler tillage, (**c**) rotary tillage, and (**d**) baler operation.  
During operations, the engine speed was set as full throttle (2510 rpm). The moldboard plow, subsoiler, and rotary tillage operations were performed by a skilled expert of TYM, while the baler operation was performed by a skilled expert of the LIVEMAC company.
## 3 Methods
### Data Processing
#### 3.3.2 Feature Extraction  
In this study, the window size for feature extraction was adjusted to match the length of the RMS of EMG as the ground truth. Statistical features are commonly employed to extract important information from non-periodic type signals [42,43]. In this study, the following four statistical features were employed in the feature extraction process: the mean, standard deviation (std), kurtosis, and skewness. The mean is a common feature that is usually utilized as a smoothing method. The std was employed to extract information correlated with the deflection in the value of a signal over time. Skewness and kurtosis are features that extract distribution information from data. Skewness measures the asymmetry of a variable within a distribution. Zero skewness signifies a symmetric distribution, while positive skewness indicates a right-skewed distribution and vice versa. Meanwhile, kurtosis is used to quantify the tail characteristic of a distribution. A positive kurtosis means super-Gaussian distribution, while a negative kurtosis means sub-Gaussian.