esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Algorithms for Calculation
### Partial Tensioning of Strands over Multiple Cycles
#### 3.2.2 Subsequent (Higher) Cycles (c > 1)  
At the beginning of the c-th cycle (c > 1), all strands are anchored and tensioned with forces \(\text{Z}_{i}{}^{(\text{c}-1)}\) from the previous cycle, with shortened axes \(\xi_{i}{}^{(\text{c}-1)}\). The calculation procedure involves tensioning the strands successively, starting from i = 1 to i = n, after which the strand to which the force \(\text{Z}_{\text{u}}\) is applied is anchored. For the tensioned strand i = j, calculations are conducted for the forces and shortening of axes of all strands (i = 1 \(\div\) n). Tensioning the last (n-th) strand concludes the c-th cycle. For i = j, the forces in the strands are \(\text{Z}_{\text{ij}}{}^{(\text{c})}\), and in the cable \(\text{Z}_{\text{kj}}{}^{(\text{c})}\), with shortened axes \(\xi_{\text{ij}}{}^{(\text{c})}=\xi_{\text{kj}}{}^{(\text{c})}\), where \"i\" refers to the strand for which the tension force and cable deformation are determined. The outlined calculation procedure is depicted by the algorithm (Figure 8). Given the defined characteristics of the cable and known \(\text{Z}_{\text{k}}\) and \(\xi_{\text{k}}\), the number of cycles (m) depends on the realization factor \(\gamma^{(\text{c})}\) according to relation (5), which also expresses the compatibility of stress and deformation states of the cable for each cycle:  
\[y_{\text{k}}^{(\text{c})}=\frac{\text{Z}_{\text{k}}^{(\text{c})}}{\text{Z}_{ \text{k}}}=\frac{\xi_{\text{k}}^{(\text{c})}}{\xi_{\text{k}}} \tag{5}\]  
In the final cycle, it is required that \(\gamma^{(\text{c})}\) = 1, meaning that the applied forces are fully realized, or 100%.  
Figure 8: Flowchart of the algorithm for subsequent cycles of multi-cycle strand tensioning.
## 3 Proposed Framework
### Data Pre-Processing
#### 3.2.3 Annotation and Classification of Situational Information  
For annotation purposes, we randomly selected 3000 tweets as a sample set from the collected dataset and labeled them according to the seven categories of situational information that we defined earlier. The annotation of data took 4 weeks to label all the 3000 samples of data tweets. The data were labelled by three research assistant post graduate students majoring in computer sciences with research areas in natural language processing, and then we calculated the Cohen's Kappa value of annotators, which was 0.84, which indicates the satisfactory nature of the labeling results. Table 1 presents the labeling results and definitions of each type of situational information. In the table, we can see that type 2 information notifications and measures have the highest number of posts when we label the 3000 random posts manually, followed by posts criticizing the government. The category count can be analyzed graphically in Table 1. We defined and described the situational information categories by following the definitions in prior studies [8; 15].  
After manually labeling the posts, machine learning classifiers were performed on labelled data by using five-fold cross validation as a train-test split to check the accuracy of each classifier. Machine learning classifiers only process numbers and the nature of our data is purely textual, so to apply and train the algorithms we had to transform the textual data into feature vectors and transform the data into numbers; in machine learning, there are different ways to do this, such as bag of words [50], term frequency(TF) and TF-IDF [51] to transform the data from text into numbers, and ultimately into vectors. We used the TF-IDF feature extraction method in this paper. The TF-IDF value increases with an increase in the frequency of a particular word the corpus of text. To balance the high frequency of the most common words, the term frequency is cut down by the overall frequency in the corpus. Term frequency measures how important a word is in a given text [52].
## Appendix A Propagation Distance in Clean Air and Fog  
The calculation of all distances is determined by the position of the RIS center at the \(n\)th time slots (Note: The time index \(n\) is ignored below unless necessary for notational convenience). We define the clean air propagation distance as  
\[d_{c}=d_{c_{1}}+d_{c_{2}}, \tag{1}\]  
where \(d_{c_{1}}\) and \(d_{c_{2}}\) denote the clean air propagation distance from LS to RIS and from RIS to PD, respectively. Then, the propagation distance in fog can be denoted as  
\[d_{f}=d_{OG}+d_{GS}-d_{c}, \tag{2}\]  
where \(d_{OG}\) and \(d_{GS}\) denote the beamline from LS to RIS and from the RIS to the PD, respectively, which are shown in Figure 11. The distance from the fog center to the center of the RIS is written as \(d_{FG}\). Then we denote distances between the fog center and beamlines as \(d_{FI}\) and \(d_{FI}\). Furthermore, the points \(M_{u}\), \(N_{u}\), \(N_{f}\) and \(M_{f}\) are the intersection points of beamlines \(d_{OG}\) and \(d_{GS}\) with fog surface sphere.  
The different situations for the propagation distances depend on the related position between the UAV and the fog. The judgement conditions and situations analysis of propagation distances are given in (A3) and (A4) on the top of the next page.  
\[d_{c_{1}}=\begin{cases}d_{OG},&d_{FI}\geqslant r_{f}\\ d_{OG},&(d_{FI}<r_{f})\cap(d_{OG}\leqslant d_{OM})\\ d_{OG}-d_{OM_{u}},&(d_{FI}<r_{f})\cap(d_{OM_{u}}<d_{OG}\leqslant d_{OM_{f}})\\ d_{OG}-d_{M_{u}M_{f}},&(d_{FI}<r_{f})\cap(d_{OM_{f}}<d_{OG}),\end{cases}\] (A3)  
\[d_{c_{2}}=\begin{cases}d_{GS},&d_{FI}\geqslant r_{f}\\ d_{GS},&(d_{FI}<r_{f})\cap(d_{GS}\leqslant d_{GN})\\ d_{GS}-d_{GN_{u}},&(d_{FI}<r_{f})\cap(d_{GN_{u}}<d_{OG}\leqslant d_{GN_{f}})\\ d_{GS}-d_{M_{u}M_{f}},&(d_{FI}<r_{f})\cap(d_{GN_{f}}<d_{GS}),\end{cases}\] (A4)  
\[W_{y}[n] =\frac{\sqrt{(x_{O^{\prime}}[n]-x_{S^{\prime}}[n])^{2}+(y_{O^{ \prime}}[n]-y_{S^{\prime}}[n])^{2}+z_{O^{\prime}}[n]^{2}}}{\sin{(\pi-\angle x -\frac{\delta}{2})}}\] (A5) \[+\frac{\sqrt{(x_{O^{\prime}}[n]-x_{S^{\prime}}[n])^{2}+(y_{O^{ \prime}}[n]-y_{S^{\prime}}[n])^{2}+z_{O^{\prime}}[n]^{2}}}{\sin{(\angle x- \frac{\delta}{2})}},\]
# Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities
## 2 Virtual Power Plants for Smart Energy Communities  
A VPP for an SEC consists of three parts: the VPP, the SEC and the utility grid, as shown in Figure 1. A VPP is configured and operated by a VPP service provider (SP). The VPP's SP recruits generation resources for the VPP's service. Generation resources for the VPP include individual power generators such as PVs, wind turbines and electric vehicles. Aggregated resources are utilized to supply power to VPP service participants such as SECs. An SEC consists of community members and the smart energy service provider (SESP) [29]. The community members are residential or commercial units. The SESP is a demand aggregator similar to the VPP SP. The SESP manages the energy transactions between members and energy producers such as between the VPP SP and the utility grid. Through the contract between the VPP SP and the SESP, the community members participate in power service from the VPP. The power generated by the resources in the VPP is transmitted to the SEC members through the utility grid. For energy balance, the SEC is also connected to the utility grid.  
The main problem in a VPP for an SEC is how to effectively serve the power consumption of the SEC using the resources in the VPP. The VPP serves according to the capacity limit of resources. The capacity limit is determined according to what kind and size of resource the VPP has implemented. If the capacity is insufficient, it will not be able to provide adequate service, and if there is too much capacity, the burden of operating cost is increased. Therefore, VPP implementation is a prerequisite for effective VPP operation.
###### Abstract  
This study identified the Pleistocene depositional succession of the group (A) (marine, estuarine, and fluvial depositional systems) of the Melor and Inas fields in the central Malay Basin from the seafloor to approximately \(-\)507 ms (522 m). During the last few years, hydrocarbon exploration in Malay Basin has moved to focus on stratigraphic traps, specifically those that existed with channel sands. These traps motivate carrying out this research to image and locate these kinds of traps. It can be difficult to determine if closely spaced-out channels and channel belts exist within several seismic sequences in map-view with proper seismic sequence geomorphic elements and stratigraphic surfaces seismic cross lines, or probably reinforce the auto-cyclic aggregational stacking of the avulsing rivers precisely. This analysis overcomes this challenge by combining well-log with three-dimensional (3D) seismic data to resolve the deposition stratigraphic discontinuities' considerable resolution. Three-dimensional (3D) seismic volume and high-resolution two-dimensional (2D) seismic sections with several wells were utilized. A high-resolution seismic sequence stratigraphy framework of three main seismic sequences (3\({}^{\text{rd}}\) order), four Parasequences sets (4\({}^{\text{th}}\) order), and seven Parasequences (5\({}^{\text{th}}\) order) have been established. The time slice images at consecutive two-way times display single meandering channels ranging in width from 170 to 900 m. Moreover, other geomorphological elements have been perfectly imaged, elements such as interflaves, incised valleys, chute cutoff, point bars, and extinction surfaces, providing proof of rapid growth and transformation of deposits. The high-resolution 2D sections with Cosine of Phase seismic attributes have facilitated identifying the reflection terminations against the stratigraphic amplitude. Several continuous and discontinuous channels, fluvial point bars, and marine sediments through the sequence stratigraphic framework have been addressed. The whole series reveals that almost all fluvial systems lay in the valleys at each depositional sequence's bottom bars. The degradational stacking patterns are characterized by the fluvial channels with no evidence of fluvial aggradation. Moreover, the aggradation stage is restricted to marine sedimentation incursions. The 3D description of these deposits permits distinguishing seismic facies of the abandoned mud channel and the sand point bar deposits. The continuous meandering channel, which is filled by muddy deposits, may function as horizontal muddy barriers or baffles that might isolate the reservoir body into separate storage containers. The 3\({}^{\text{rd}}\), 4\({}^{\text{th}}\), and 5\({}^{\text{th}}\) orders of the seismic sequences were established for the studied succession. The essential geomorphological elements have been imaged utilizing several seismic attributes.  
Keywords:seismic sequence stratigraphy; geomorphic geometries; fluvial deltaic; seismic facies; Malay Basin +
Footnote †: journal: Applied Sci.
## 5 Concluding Remarks  
In this work, we have presented analytical and numerical solutions for three different characteristic response (softening, perfect plastic and hardening) for an infinite gliding plate, to demonstrate the unique plastic fields generated by the implementation of micromorphic approach for gradient plasticity.  
For the softening case, our analytical effort clearly presents the characteristic solution which controls the size of the localization band where an intrinsic length scale is defined, and our numerical findings also verify the band formation which is controlled by the wavelength of the sinusoidal function.  
Considering the perfect plastic response, our findings are well in line with similar approaches in the literature where the profile of the plastic strain parabolic and the formation of boundary layer is not possible without an internal variable hardening.  
With hard boundary conditions and considering linear hardening response, our analytical predictions show that the response of the gliding plate is defined by a hyperbolic function with unique properties. The plastic strain profile shows a boundary layer for small values of \(\omega\) with sharp strain plastic gradients around top and bottom layer which mimics a hard phase material at the boundaries. It is also observed that the analytical solution yields a parabolic response for \(\omega>1.0\) similar to perfect plastic case.  
Our study shows that the micromorphic approach for gradient plasticity has a great potential in representing the size-dependent behavior for both softening and hardening response of crystalline solids which also provides unique possibilities to apply higher order boundary conditions. The approach makes it possible to control the size-dependent behavior with a single length scale parameter and our numerical findings are encouraging to use the presented model for more complex 3D problems.  
Conceptualization, O.A. and E.B.; methodology, O.A. and E.B.; software, O.A.; validation, O.A.; writing--original draft preparation, O.A. and E.B.; writing--review and editing, O.A. and E.B.; visualization, O.A.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 5: Comparison between numerical and analytical solution for the hardening plate. Parameters: B = 10.0 GPa, \(\beta\) = 300.0 MPa mm\({}^{2}\) and 2000.0 MPa mm\({}^{2}\), H = 500.0 MPa, h = 10 mm.
###### Abstract  
Composite materials of conjugated polymers/cellulose were fabricated by incorporating different polythiophene-derivative polymers: Poly(3,4-ethylenedioxythiophene) (PEDOT) and an alkylated derivative of poly(3,4-propylenedioxythiophene) (PProDOT). These conjugated polythiophenes were deposited by casting or spray coating methodologies onto three different cellulose substrates: Conventional filters papers as cellulose acetate, cellulose grade 40 Whatman(r) and cellulose membranes prepared from cellulose microfibers. The preparation of composite materials was carried out by two methodologies: (i) by employing in-situ polymerization of 3,4-ethylenedioxithiophene (EDOT) or (ii) by depositing solutions of poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) or lab-synthesized PProDOT. Composite materials were studied in terms of electrical conductivity and surface morphology assessed by impedance spectroscopy, surface conductivity, SEM, and 3D optical profilometry. In-situ composite materials prepared by spray coating using iron trifluoromethane sulfonate as oxidizing agent can be handled and folded as the original cellulose membranes displaying a surface conductivity around 1 S-cm\({}^{-1}\). This versatile procedure to prepare conductive composite materials has the potential to be implemented in flexible electrodes for energy storage applications.  
Keywords:conjugated polymers; in-situ composite materials; conductivity; 3D optical profilometry; flexible electrodes; energy storage +
Footnote †: journal: Applied Physics Letters
## 2 RPC Model and Generative Adversarial Network
### RPC Model  
For reasons of technical secrecy and extended utility, commercial satellite companies replaced physical sensor models with generic RPC models. The generic RPC model is a mathematical model that represents the mapping relationship between image coordinates and spatial 3D coordinates. Equation (1) shows the projection of the generic RPC model.  
\[(x,y)=P(X,Y,Z)=\left(\frac{NumL(X,Y,Z)}{DenL(X,Y,Z)},\frac{NumS(X,Y,Z)}{DenS(X, Y,Z)}\right), \tag{1}\]  
where \((x,y)\) and \((X,Y,Z)\) represent the normalized coordinates of the image point and the 3D point, respectively, \(P(\cdot)\) denotes satellite camera projection. As shown in Equation (2), the coordinates \((x,y)\) and \((X,Y,Z)\) are all normalized to the range of \([-1,1]\). The original coordinates of the satellite image point and the 3D point are presented as \(\left(\overset{\sim}{x},\overset{\sim}{y}\right)\) and \(\left(\overset{\sim}{X},\overset{\sim}{Y},\overset{\sim}{Z}\right)\), respectively. There are usually 10 normalization parameters in the RPC model, which contains 5 offset parameters and 5 scale parameters, as shown in Equation (2).  
\[\left\{\begin{array}{l}X=\frac{\overset{\sim}{X}-LAT\text{ OFF}}{LAT\_SCALE}\\ Y=\frac{\overset{\sim}{Y}-LONG\text{ OFF}}{LONG\_SCALE}\\ Z=\frac{\overset{\sim}{Z}-HEIGHT\text{ OFF}}{HEIGHT\_SCALE}\\ x=\frac{\overset{\sim}{Z}-LINE\text{ OFF}}{LINE\_SCALE}\\ y=\frac{\overset{\sim}{y}-SAMP\text{ OFF}}{SAMP\_SCALE}\end{array}\right. \tag{2}\]  
In Equation (1), \(NumL(\cdot)\), \(DenL(\cdot)\), \(NumS(\cdot)\), and \(DenS(\cdot)\) stand for the rational polynomials in RPC models, which have the following form:  
\[\begin{array}{ll}NumL(X,Y,Z)&=a_{0}+a_{1}Y+a_{2}X+a_{3}Z+a_{4}YX+a_{5}YZ+a_{6 }XZ+a_{7}Y^{2}+a_{8}X^{2}\\ &+a_{9}Z^{2}+a_{10}XYZ+a_{11}\ Y^{3}+a_{12}YX^{2}+a_{13}YZ^{2}+a_{14}Y^{2}X\\ &+a_{15}X^{3}+a_{16}XZ^{2}+a_{17}\ Y^{2}Z+a_{18}X^{2}Z+a_{19}Z^{3}.\end{array} \tag{3}\]  
The forms of \(DenL(\cdot)\), \(NumS(\cdot)\), and \(DenS(\cdot)\) are the same as \(NumL(\cdot)\), so that the RPC model has 80 rational polynomial coefficients. The RPC model, which has the advantages of no oscillation on the coefficients and independence from sensors, approximates the satellite imaging model in the form of rational functions.
# Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction
## 6 Performance Evaluation
### MaxCSPR with Workload Prediction Evaluation
**Implementation Challenges and Engineering Analysis:**  
Current cloud service providers have implemented various approaches to predictive elastic resource scheduling, each facing distinct technical challenges in practical deployments: (1) AWS: limited workload prediction accuracy in predictive scaling mechanisms due to reliance on conventional machine learning models. (2) Rackspace: fragmented resource orchestration in hybrid cloud architectures, which hinders the efficiency of multi-cloud deployments. (3) Aliyun: suboptimal cost-performance trade-offs in intelligent scheduling, particularly under dynamic workload conditions. Our methodology systematically addresses these challenges by enhancing workload prediction accuracy through adaptive multimodal prediction techniques, improving resource orchestration via unified abstraction frameworks, and optimizing cost-performance trade-offs using dynamic adaptation strategies. This comprehensive approach not only resolves existing implementation limitations but also provides a foundation for more efficient elastic resource management in cloud environments.
# JFET Integration Using a Foundry SOI Photonics Platform
###### Abstract  
We explore the monolithic integration of conventional electronics with SOI photonics using the commercial silicon photonics foundry technology offered by A*STAR's Institute of Microelectronics (IME). This process offers optical waveguide modulators and photodetectors, but was not intended to support transistors. We present the implementation of junction field effect transistors (JFETs) integrated with optical waveguides and photodetectors. A simple SPICE model is developed for the JFETs based on the available ion implant parameters, and the geometry feature size allowed by the technology's layout rules. We have demonstrated the monolithic integration of photonics and electronics circuits. This work could be useful for application in waveguide sensors and optical telecommunications.  
silicon photonics; junction field-effect transistor (JFET); optoelectronics integration +
Footnote †: journal: Applied Sciences  
1
Footnote 1: _Applied Sciences_
## 4 Propagation Characteristics and Phenomena of LMS Communication
### Tropospheric Effects  
The tropospheric effects are important for the signal propagation and they are more significant at higher frequencies. The signal is affected by different phenomena due to hydrometers, such as rain or clouds, and atmospheric gases, such as oxygen, water vapor, etc. [24].
## 4 Experiments
### Performance Analysis
#### 4.4.4 Performance for Dog Emotion Recognition in Surveillance Videos  
The models used in the dog detection, tracking, and emotion recognition experiments were categorized into different types, listed in Table 1. Type_1, Type_2, and Type_3 use the LDFDMN model with backgrounds for emotion recognition; Type_4, Type_5, and Type_6 use the LDFDMN model without backgrounds for emotion recognition. Because the number of dog reidentification data-sets used in this study was insufficient, the use of the LDFMN model produced unsatisfactory dog emotion recognition results. Therefore, the CNN model uses weights pretrained on the ImageNet data-set.  
The results of the experiments conducted using the IMG_0033 and AngryDogs datasets are presented in Tables 17 and 18, respectively. In the experiment conducted using the IMG_0033 data-set, the identification accuracy of the Type_2 (YOLOv3 + DeepSORT_retrained + LDFDMN with background) and Type_3 (YOLOv3 + DeepDogTrack + LDFDMN with background) models was the highest (76.36%), indicating that the models that removed the image backgrounds did not effectively recognize the dogs' emotions. Furthermore, the characteristics of happiness and neutrality in dogs are similar (Figure 22), which can result in emotion recognition errors.  
\begin{table}
\begin{tabular}{c c c} \  
In the experiment conducted using the AngryDogs data-set, the Type_1, Type_2, and Type_3 models achieved the highest identification accuracy (76.36%), and Type_4 and Type_5 achieved the lowest (53.24%). This indicates that, as with the IMG_0033 data-set, the models that removed the image backgrounds did not effectively recognize the dogs' emotions. Because the dogs in this data-set remain mostly still over the course of the video, the tracking results and identification accuracy values of the Type_1, Type_2, and Type_3 models were the same.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 4 Adaptability of the Formulation
### Conservation of Total Probability  
Also, the modified Liouville equation must preserve the normalization of \(\rho\), i.e.,  
\[\frac{d}{dt}\int\rho\,d\Omega=0. \tag{62}\]  
To verify this, integrate both sides over phase space:  
\[\frac{d}{dt}\int\rho\,d\Omega=\int\frac{\partial\rho}{\partial t}\,d\Omega=- \int[F,\rho]\,d\Omega. \tag{63}\]  
Using the antisymmetry of the Poisson bracket, \([F,\rho]=\
abla F\cdot\mathbf{J}\), where \(\mathbf{J}=(\partial_{\mathbf{p}},-\partial_{\mathbf{q}})\) is the symplectic gradient, we apply [PERSON]'s theorem in phase space:  
\[\int[F,\rho]\,d\Omega=\oint_{\partial\Gamma}\rho\
abla F\cdot d\mathbf{S}- \int\rho\
abla\cdot(\
abla F)\,d\Omega. \tag{64}\]  
Assuming that \(\rho\) and \(\
abla F\) vanish at infinity (or periodic boundary conditions), the boundary term vanishes. For Hamiltonian systems, \(\
abla\cdot(\
abla F)=0\) due to the symplectic structure, ensuring that:  
\[\frac{d}{dt}\int\rho\,d\Omega=0. \tag{65}\]  
Thus, the total probability is conserved under the modified dynamics, even when phase-space volume compression occurs.  
Example: Harmonic Oscillator with Entropy Gradient  
We considered above a damped oscillator (Section 3.8.1) with \(\overline{S}=-\frac{\zeta\tau}{2m\tau}p^{2}\) and numerical integration of \(\int\rho\,d\Omega\) over time (Figure 2) confirms normalization invariance.