esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Review of Microgrid Control and Design
### Secondary Control Level  
While primary control provides a rapid response, it may not be sufficient to fully restore voltage and frequency stability, especially in large or complex MGs. Secondary control, operating on a slower timescale, addresses the limitations of primary control. It uses communication and coordination among DERs and central controllers to achieve precise voltage and frequency regulation. For this purpose, several methods have been suggested in research publications, as [PERSON] et al. [95] propose a two-layer control architecture for effective secondary control in microgrids. The top layer, a communication-based layer, facilitates the exchange of information between the central controller and DER agents. The bottom layer comprises the DERs themselves, which respond to control signals from the top layer. The authors in [96; 97] have pointed out that communication plays a pivotal role in secondary control. DERs can exchange information about their local voltage and frequency measurements with their neighbors and the central controller. This information is essential for coordinated control actions that restore and maintain voltage and frequency stability across the microgrid. Also, some researchers have investigated the optimization techniques for secondary control that can be implemented using various optimization techniques. [PERSON] et al. [98] focus on optimization methods driven by economic considerations, while [PERSON] et al. [99] emphasize power quality optimization. Another important aspect to consider in secondary control is voltage and reactive power sharing, where maintaining voltage stability and ensuring equitable reactive power sharing among DERs are key challenges in secondary control. [PERSON] et al. [100] introduced a method to get voltage and frequency back to their nominal settings without appealing to power sharing between DG systems. Practical MG implementations must consider the possibility of communication failures. Effective secondary control schemes should be capable of maintaining stability and power quality even under communication disruptions [99]. There are two possible implementation modes for secondary control: distributed and centralized control. A central controller that manages all DERs is required for centralized control, while distributed control utilizes decentralized algorithms that enable self-coordination among DERs. A typical hierarchical control technique used to regulate frequency and voltage for a voltage source inverter is shown in Figure 22.  
Figure 22: Typical hierarchical control strategy of an island microgrid [77].
## 1 Introduction
### Objectives of the Study  
Due to the interest of analyzing the behavior of a DLSC when it is charged or discharged through a voltage source (or discharged through a constant resistance) and the importance of having analytical expressions, the following section will develop the mathematical formulae needed to calculate all the electrical quantities, using the variable capacitance model. This will fill a small gap in the scientific literature on SCs.  
So far, it has been most common to use the constant capacitance RC series model, due to its great simplicity. In this study, the analysis has been extended to a more accurate and complex case, by using the variable capacitance model, which, in addition, is transformed into the constant capacitance model by simply modifying only one parameter. It can be said that this study is an extension of those carried out to date. In addition, a comparison of the results produced by both models will be shown, so that researchers working in this field can select the option that most interests them, depending on what they intend to analyze and the precision they need.  
The rest of this paper is arranged as follows: Section 2 will show the mathematical study to obtain the electrical variables of a DLSC connected to a voltage source. The equations obtained will be valid for both charging and discharging. In Section 3, several examples will be presented, where the DLSC will be charged by a voltage source and the solutions obtained will be compared with those of the RC series model and the variable capacitance model. Finally, Section 4 will summarize the conclusions of the whole study.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 3 Materials and Methods
### Optimization Procedures of Multi-Objective Problems
Multi-Objective Optimization (MOO) problems can be solved using various algorithms, with Evolutionary Algorithms (MOEAs) and Multi-Objective Particle Swarm Optimization (MOPSO) being among the most widely used. An MOEA is based on natural selection and genetic principles, employing crossover and mutation to evolve a population of solutions. MOPSO, an extension of Particle Swarm Optimization (PSO), mimics the social behavior of birds or fish, guiding solutions through the search space based on individual and collective experiences. Among MOEAs, NSGA-II is the most commonly used in energy system planning [37]. Studies comparing NSGA-II and MOPSO in optimizing hybrid renewable energy systems have found that NSGA-II provides more diverse solutions along the Pareto front, whereas MOPSO converges faster but with less diversity. Other algorithms, such as the cuckoo search algorithm, have also been applied to long-term energy system planning. NSGA-II can be coupled with EnergyPLAN through EPLANopt, a Python package that integrates optimization frameworks with energy system modeling. EPLANopt, built on the DEAP evolutionary algorithm library, permits users to identify optimization problems and constraints to achieve cost-optimal or environmentally optimal arrangements of renewable technologies and storage systems [31; 55]. This approach has been applied at both national and island scales. The optimization process presents solutions on a Pareto front, where no single solution is best for every target [31]. Decision-makers select from this set built on predefined trade-offs and/or criteria. Some research works employ the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to order results according to policymakers' choice order, adjusting subjective weights for different scenarios 79. Another approach, the Weighted Sum Method (WSM) [54], transforms a multi-objective problem into a single-objective one by assigning relative importance to each factor. These methods help navigate complex decision-making in energy system planning by balancing cost, environmental impact, and reliability. The general form of WSM is:  
\[minF(x_{i})=w_{1}\cdot f_{1}(x_{i})+w_{2}\cdot f_{2}(x_{i})+\cdot\cdot\cdot+w_{n }\cdot f_{n}(x_{i}) \tag{3}\]  
where \(f_{i}(x)\) denotes the single objective functions, \(w_{i}\) corresponds to the weights given to each respective objective function, and \(F(x)\) is the combined objective function to be minimized. In our issue, optimization implies two competing objectives: the total annual cost and GHG emissions. The conversion of CO\({}_{2}\) emissions into economic terminology effectively leads to a weighted sum of these objectives, permitting us to pose a simplified optimization problem from two objectives to a single one. The EU Emissions Trading Scheme (EU ETS) sets a price for each GHG equivalent emitted ton [56].
## 2 Theoretical Considerations
### Additive Selection
#### 2.2.3. EVA (Ethylene Vinyl Acetate)  
It is a copolymer resin of ethylene and vinyl acetate. Unlike general PVC, it is light, soft, easy to process, and easily deformed when heated, making it one of the most used materials in various fields. In particular, the density, flexibility, etc., increase according to the content of vinyl acetate, and the elasticity, heat bonding temperature, durability,  
\begin{table}
\begin{tabular}{c c c} \hline
**Chemical Name** & **Minimum Area (m2/g)** & **CAS No.** \\ \hline Vinyl Trichloro Silane & 480 & 75-94-5 \\ Vinyl Trimethoxy Silane & 515 & 2768-02-7 \\ Vinyl Ris(2-Methoxy Ethoxy) Silane & 410 & 1067-53-4 \\
2-(3,4 Epoxycyclohexyl) Ethlytrimethoxy Silane & 278 & 3388-04-3 \\
3-Glycidyloxy Propyl Trimethoxy Silane & 317 & 2530-83-8 \\
3-Glycidyloxy Propyl Methyl Diethoxy Silane & 330 & 2897-60-1 \\
3-Glycidyloxy Propyl Triethoxy Silane & 356 & 2602-34-8 \\
3-Methacryloxy Propyl Methyl Dimethoxy Silane & 280 & 14513-34-9 \\
3-Methacryloxy Propyl Trimethoxy Silane & 314 & 2530-85-0 \\
3-Methacryloxy Propyl Methyl Diethoxy Silane & 300 & 65100-04-1 \\ N-2(Amino Ethyl)-2-Amino Propyl methyl dimethoxy Silane & 380 & 3069-29-2 \\ N-2(Amino Ethyl)2-Amino Propyl Trimethoxy Silane & 351 & 1760-24-3 \\ N-2(Amino Ethyl)2-Amino Propyl Triethoxy Silane & 295 & 5089-72-5 \\
3-Amino Propyl Trimethoxy Silane & 436 & 13822-56-5 \\
3-Amino Propyl Triethoxy Silane & 353 & 919-30-2 \\ N-Phenyl-3-Amino Propyl Trimethoxy Silane & 307 & 3068-76-6 \\
3-Chloro Propyl Trimethoxy Silane & 393 & 2530-87-2 \\
3-Mercapto Propyl Trimethoxy Silane & 398 & 4420-74-0 \\ \hline \end{tabular}
\end{table}
Table 3: Types of silane coupling agents.  
Figure 6: Types of fillers. (**a**) Fine powdered filler; (**b**) fiber-type filler.  
and penetrating power are different. In addition, it has greater transparency and adhesion compared to LDPE, excellent elasticity, low-temperature heat-sealing properties, and is economically superior to other polar polymers, such as polyurethane.  
The molecular structure of EVA is shown in Figure 7.  
EVA forms a polymer main chain by randomly mixing LDPE (low-density polyethylene) with vinyl acetate. When the content of vinyl acetate is increased, the strength, crystallinity, melting point, and chemical durability decrease, and impact strength, transparency, and friction coefficient increase. In the synthesis process for EVA, ethylene and vinyl acetate are produced in a continuous process through free-radical addition polymerization in a high-pressure reactor. This is a chain-growth polymerization method in which the initiator is broken to form free radicals and continuously reacts with excessively added double-bond monomers (vinyl monomers). Therefore, in this study, it was used as an additive to improve the elongation of the waterproofing material in the final formulation of the material.
# Imitating Human Go Players via Vision Transformer
## 2 Background and Related Work
### Vision Transformer (ViT)  
In 2017, the Transformer architecture was introduced by [PERSON] et al. (2016) to address challenges in natural language processing. Building on the success of Transformers and the self-attention mechanism, [PERSON] et al. (2016) presented the Vision Transformer (ViT) for computer vision (Figure 3). ViT processes input images by first splitting them into patches. These patches are flattened, linearly projected into embedding vectors, and combined with learnable positional embeddings to encode spatial information. An additional class embedding is included, and these embeddings serve as inputs for the Transformer blocks. The final output is passed to an MLP head, which produces the classification result. This figure is adapted from Figure 1 of ([PERSON] et al., 2016).  
The innovation of the ViT lies in its ability to effectively capture global context and long-range dependencies, outperforming CNNs that focus on local receptive fields. ViT treats image patches as tokens, akin to words in a sentence, enabling it to process the entire image simultaneously. After encoding spatial information through positional embeddings, ViT applies Transformer encoder blocks to perform self-attention operations on the patches. The MLP head at the last encoder block generates the final classification. A detailed explanation of the ViT architecture is provided in Section 3.  
ViT has demonstrated state-of-the-art performance on multiple image classification benchmarks, often surpassing traditional CNNs. The success of ViT and its derivatives, such as the Swin Transformer ([PERSON] et al., 2016), has inspired this study to explore the application of ViT in analyzing the game of Go.  
Figure 3: Model overview of the ViT.  
The closest work to this study is that of [PERSON] et al. [18], in which EfficientFormer was used to play Go. However, their goal focused on maximizing the win rate rather than imitating human experts. Although their work applied Transformers to Go, the task itself differs from our approach. To the best of our knowledge, this is the first study to specifically focus on ViT in the context of Go with the goal of imitating human expert moves. We believe the results of this study can significantly contribute to the development of applications that mimic human Go experts, ultimately serving as valuable training tools.
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
## 3 System Framework
### Software Framework
Disaster messages can be sent by ground disaster warning agencies, and also can be identified automatically. In 5 GsatEC, the IP addresses of services change dynamically. In the process of system service expansion, failure recovery, and online upgrading, the running instances number of services is also dynamic. The user needs to obtain the available services and their invocation style through the service discovery mechanism. SatEC uses a highly reliable database to establish a service registry center at the master node to maintain the services list. The service registry provides service registration and query services through the REST API. POST\DELETE\GET are operations of REST API. The newly deployed services register their IP addresses and ports to the service registry through the POST operation. Services can be cancelled through a DELETE request. Through the GET operation, users can obtain information relating to available services.
## 1 Introduction  
Fine-grained temporal action segmentation/alignment [1, 2, 3] is important in many applications, such as daily activity understanding, human motion analysis, and surgical robotics, to name a few. Given a video of length \(T\), \(\mathbf{x}=(x_{1},\cdots,x_{T})\), the goal of temporal segmentation/alignment is to localize each occurrence of a given action \(a_{n}\in\mathcal{A}\) in the time domain. A frame-to-action segmentation/alignment can be mathematically defined densely, as a sequence of occurring action labels at every frame in a video \(\mathbf{\pi}=(\tau_{1},\cdots,\tau_{T})\), \(\pi_{t}\in\mathcal{A}\); or sparsely, as a set of temporally segmented clips \(\mathbf{\ell}=(\ell_{1},\cdots,\ell_{N}^{\text{}})\), \(\ell_{n}\in\mathcal{A}\), with each segment associated with a start time, finish time, and label. \(N=|\mathbf{\ell}|\) is the length of the transcript sequence; note that usually \(T\gg N\) since the sampling frequency of the machine at the encoding end is orders of magnitude higher than the granularity of the manual labeling at the decoding end.  
The difference between the tasks of segmentation/alignment is that during training, only the ordered sequence of video-level actions \(\ell\) (defined as transcript [4, 5, 6]) is available for alignment, while the classes of each frame are given for segmentation. Figure 1 shows the training/testing settings in the task of temporal action alignment learning, as compared to temporal action segmentation, where accurate dense labels of each frame are also available during training.  
We also restrict our focus on modeling instructional videos with relatively stable background, usually composed of dozens of actions lasting minutes, recorded in a kitchen or surveillance setup. Under this setting, research focus can be saved from variances in extrinsic shooting conditions, and concentrates upon the major challenges of the task.
# Double Unloading Gas Control Technology for Fracturing Soft Coal Seams in Overlying Key Strata
## 3 Analysis of Simulation Results
### Coal seam Permeability Change Rule
#### 3.2.3. Change Rule of Double Unloading Permeability  
The method of hydraulic fracturing was employed to reduce the overburden rock stress from 13 MPa to 9.65 MPa. The unloading pressure was approximately 3.35 MPa. The permeability of the coal seams was monitored for different borehole spacing (4 m, 5 m, 6 m, and 7 m), and the comprehensive permeability of the coal seams is illustrated in Figure 9. Initially, the permeability of the coal seam was \(3.95\times 10^{-18}\) m\({}^{2}\) when it was not fractured or subjected to large-diameter burrowing. However, by combining the method of fracturing the key strata with large-diameter burrowing, the permeability increased to a range of 5.59 \(\times\) 10\({}^{-17}\) m\({}^{2}\) to 7.46 \(\times\) 10\({}^{-17}\) m\({}^{2}\). When the spacing of drilling holes was 4 m, the permeability of coal seam was the largest, reaching 7.46 \(\times\) 10\({}^{-17}\) m\({}^{2}\), but its anti-reflection range was also the smallest. As the spacing of drilling holes increases to 5 m and 6 m, the permeability of coal seam gradually decreases. When the spacing of drilling holes was 7 m, the average permeability of coal seam was 6.45 \(\times\) 10\({}^{-17}\) m\({}^{2}\) and the anti-reflection range was up to 30 m. For the soft coal seam boring process, the use of key layer hydraulic fracturing and the coal seam large-diameter hole-making method for field tests can overall improve the level of gas management in the working face.
## 5 Conclusions  
We demonstrated the generation and measurement of the stable phase squeezed vacuum states at audio-band frequencies. A homemade cw single-frequency 532 nm and 1064 nm dual-wavelength laser was employed in the experiment. The OPO was pumped by 532 nm output of the laser and the threshold of the OPO was 350 mW. The 1064 nm output of the laser was used as the LO beam of the BHD system; the auxiliary beam to lock the length of the subthreshold OPO cavity; and the CCF beam to control the relative phase between the squeezed vacuum states and LO and the relative phase between the pump and CCF. When 532 nm pump power was 100 mW, the relative phase between the squeezed vacuum states and LO was locked to \(\pi/2\) and the relative phase between the pump and CCF was fixed to 0, phase squeezed vacuum states were generated and recorded continuously. To detect vacuum squeezed states at audio-band frequencies, the effect of the LO noise, the CMRR of detectors and the phase jitter between the LO and squeezed vacuum field were considered. The power of the LO of the BHD system was optimized to 1 mW. The CMRR of photodetectors in the BHD was improved to be more than 60 dB at audio-band frequencies by employing the differential fine-tuning circuit and adjustable BV. The phase jitter between the LO and squeezed vacuum field was reduced to 1.7\({}^{\circ}\) with the help of the coherent locking scheme. Based on the above techniques, 6.1 \(\pm\) 0.3 dB phase squeezed vacuum states at audio-band frequencies from 5 kHz to 20 kHz were generated, and a 3.0 \(\pm\) 0.3 dB phase squeezed vacuum state was obtained at the audio frequency of 3.5 kHz.  
Conceptualization, Y.G., Y.L., J.F. and K.Z.; methodology, Y.G., J.F. and K.Z.; software, Y.L.; validation, Y.G. and Y.L.; formal analysis, J.F.; investigation, Y.G., Y.L. and J.F.; resources, Y.G., Y.L. and J.F.; data curation, J.F. and K.Z.; writing--original draft preparation, Y.G. and J.F.; writing--review and editing, J.F. and K.Z.; visualization, Y.L. and K.Z.; supervision, K.Z.; project administration, J.F. and K.Z.; and funding acquisition, K.Z.  
This research was funded by National Key R&D Program of China (No. 2016 YFA0301401), Fund for Shanxi \"1331 Project\" Key Subjects Construction (No. 1331 KS).  
The author declares no conflict of interest.
# Correlation of Optical Constants and Morphologies with Mueller Matrix for Micro-Rough Surfaces
## 2 Theoretical Background  
### Gaussian-Distributed Randomly Micro-Rough Surface Model  
In practical applications, rough surfaces demonstrate spatially heterogeneous profiles that statistically conform to the classification of Gaussian-distributed randomly rough surfaces. These surfaces are quantitatively defined by two fundamental parameters: the root mean square roughness, \(\sigma\), representing the standard deviation of the height distribution, and the correlation length, \(\tau\), describing the distance above which the heights of two points are statistically uncorrelated. For Gaussian-distributed randomly rough surfaces, the corresponding autocorrelation function is as follows [26]:  
\[\left\langle H(\overrightarrow{p})H(\overrightarrow{p}+\overrightarrow{q}) \right\rangle=\sigma^{2}\exp\left[-\left(q^{2}/\tau^{2}\right)\right] \tag{1}\]  
where \(H(\overrightarrow{p})\) represents the height at position \(\overrightarrow{p}\), \(\overrightarrow{q}\) is a spatial vector, and \(q\) denotes its magnitude.  
In ellipsometry characterization, the term \"rough surface\" operationally refers to micro-rough surfaces satisfying the Rayleigh criterion for optical smoothness [27]:  
\[4\pi\sigma cos\theta/\lambda<\pi/2 \tag{2}\]  
where \(\theta\) is the incident angle and \(\lambda\) denotes the wavelength of incident light. Consequently, this work focuses exclusively on micro-rough surfaces with relative roughness \(\sigma/\lambda<1/8\).
## 2 Disturbance Torque Model of the Sun-Tracking Solar Array  
### Electromagnetic Torque Model of Stepping Motors  
Stepper motors convert electrical pulse signals into rotor angular displacement. Due to the advantages of having a simple structure, high positioning accuracy, no accumulating error, and an easy-to-use open-loop control, it is widely used in SADA.  
For a bi-phase hybrid step motor with a sine-cosine subdivision drive strategy, the winding currents of the two phases are respectively characterized by sine and cosine patterns. However, due to the limit of the current subdivision number, the reference currents approximate to sine or cosine with the step function form. Reference currents for the positive drive are [22]:  
\[\left\{\begin{array}{l}\tilde{i}_{A}=I\cos[2\pi\cdot\mathrm{round}(z\theta_{ m}\ /\ \Delta\theta)/n)]\\ i_{B}=I\sin[2\pi\cdot\mathrm{round}(z\theta_{m}\ /\ \Delta\theta)/n)]\end{array}\right. \tag{1}\]  
where \(\tilde{i}_{A}\) and \(\tilde{i}_{B}\) respectively signify the currents of phase-A and phase-B stator winding, \(I\) is the motor winding rated current, \(z\) is the rotor teeth number, \(\theta_{m}\) is the angular displacement of the stepping motor rotor, \(\Delta\theta\) is the step angle of the stepping motor, \(n\) is the subdivision number.  
The driving voltages are obtained by a winding current loop PID controller, based on errors between practical currents and reference currents. The current loop control equation is as follows:  
\[\left\{\begin{array}{l}u_{A}=K_{p}\big{(}\tilde{i}_{A}-i_{A}\big{)}+K_{I} \!\int\!\big{(}\tilde{i}_{A}-i_{A}\big{)}dt+K_{D}\frac{d\big{(}\tilde{i}_{A}- i_{A}\big{)}}{dt}\\ u_{B}=K_{p}\big{(}\tilde{i}_{B}-i_{B}\big{)}+K_{I}\big{(}\tilde{i}_{B}-i_{B} \big{)}dt+K_{D}\frac{d\big{(}\tilde{i}_{B}-i_{B}\big{)}}{dt}\end{array}\right. \tag{2}\]  
where \(K_{p}\), \(K_{I}\), and \(K_{D}\) are the proportional coefficient, integral coefficient, and differential coefficient, respectively.  
The equilibrium equation of voltage describes the relationship between drive voltages and winding currents. Taking winding resistance, self-inductance effect, and cogging distribution into account, the voltage equilibrium equation for the hybrid stepper motor is as follows:  
\[\left\{\begin{array}{l}i_{A}=\frac{1}{K}\left[L\frac{d\tilde{i}_{A}}{dt}-u_{ A}+K_{m}\omega_{m}\sin(z\theta_{m})\right]\\ i_{B}=\frac{1}{K}\left[L\frac{d\tilde{i}_{B}}{dt}-u_{B}+K_{m}\omega_{m}\cos(z \theta_{m})\right]\end{array}\right. \tag{3}\]  
where \(R\) is the winding resistance, \(L\) is the winding self-inductance coefficient, \(\omega_{m}\) is the motor output shaft speed, \(K_{m}\) is the motor torque constant.  
The motor torque of the hybrid step motor is [23]:  
\[T_{e}=K_{m}[i_{B}\cos(z\theta_{m})-i_{A}\sin(z\theta_{m})]-K_{d}\sin(4z\theta _{m}) \tag{4}\]  
where \(T_{e}\) is the motor electromagnetic torque, \(K_{d}\) is the detent torque constant.