esa-sceva/satcom-chunk-collection huggingface dataset:
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 4 Problem Conversion and Solution
### Optimization of RIS Phase Shift Matrix
#### 4.2.2. RIS Phase Shift Matrix for URLLC \(\Phi_{u}^{*}\)  
However, the \(\Phi_{e}^{*}\) obtained in question \(P1.2.2\) does not consider the arrival of URLLC business. The \(\Phi_{u}^{*}\) is designed to improve the network's channel gain for all URLLC users.  
Therefore, the objective of the RIS phase-shift matrix optimization problem is to maximize the minimum URLLC channel gain. The expression is as follows:  
\[\begin{array}{l}P3:\max\limits_{\Phi_{u}}\min\limits_{1\leq u\leq U}\left|C \right|^{2}\\ s.t.C=G_{RIS,u}\Phi_{u}H_{gNB,RIS}+D_{gNB,u}\\ \left|\Phi_{u}\right|\leq 1,0\leq\theta_{u}\leq 2\pi,\forall n\end{array} \tag{43}\]  
Let \(v^{H}=\left[v_{1},\cdots,v_{N}\right]^{H}\), where \(v_{n}=e^{i\phi_{n}}\). It follows that  
\(\left|G_{RIS,u}\Phi_{u}H_{gNB,RIS}+D_{gNB,u}\right|^{2}=\left|v^{H}\Theta+D_{ gNB,u}\right|^{2}\), where \(\Theta=diag(G_{RIS,u})H_{gNB,RIS}\).  
\[\begin{array}{l}P3:\max\limits_{\Phi_{u}}\min\limits_{1\leq u\leq U}\left|C \right|^{2}\\ s.t.C=G_{RIS,u}\Phi_{u}H_{gNB,RIS}+D_{gNB,u}\\ \left|\Phi_{n}\right|\leq 1,0\leq\theta_{u}\leq 2\pi,\forall n\end{array} \tag{44}\]  
Thus, problem \(P3\) is equivalent to:  
\[\begin{array}{l}P3.1:\max\limits_{v}\min\limits_{1\leq u\leq U}v^{H}\Theta \Theta^{H}v+D_{gNB,u}\Theta^{H}v\\ +v^{H}\Theta D_{gNB,u}+\left|D_{gNB,u}\right|^{2}\\ s.t.|v_{n}|^{2}=1,\forall n\end{array} \tag{45}\]  
Problem \(P3.1\) is a nonconvex quadratic constrained quadratic programming (QCQP) problem, which can be transformed into a homogeneous QCQP. Specifically, by introducing an auxiliary variable \(\delta\), problem \(P3.1\) is equivalently written as:  
\[\begin{array}{l}P3.2\max\limits_{\overline{v}}\min\limits_{1\leq u\leq U} \overline{v}^{H}\Lambda_{u}\overline{v}+\left|D_{gNB,u}\right|^{2}\\ s.t.|\overline{v}_{n}|^{2}=1,n=1,\cdots,N+1\end{array} \tag{46}\]  
where \(\Lambda_{u}=\begin{bmatrix}\Theta\Theta^{H}&\Theta D_{gNB,u}\\ D_{gNB,u}\Theta^{H}&0\end{bmatrix},\overline{v}=\begin{bmatrix}v\\ \delta\end{bmatrix}\), let \(\overline{v}^{H}\Lambda_{u}\overline{v}=tr(\Lambda_{u}\overline{v}\overline {v}^{H})\) and \(V=\overline{v}\overline{v}^{H}\), and it needs to meet \(V\succ 0,rank(V)=1\). Since the rank-1 constraint is nonconvex, the constraint can be slackened using the semi-definite relaxation (SDR) method. Thus, problem \(P3.2\) can be simplified as follows:  
\[\begin{array}{l}P3.3:\max\limits_{V}\min\limits_{1\leq u\leq U}tr(\Lambda_ {u}V)+\left|D_{gNB,u}\right|^{2}\\ s.t.V_{n,u}=1,n=1,\cdots,N+1\\ V\succ 0\end{array} \tag{47}\]  
Obviously, we can also use CVX to solve problem \(P3.3\). Given that it is a semi-definite programming (SDP) problem, usually, the optimal \(\overline{v}\) obtained by solving this problem does not satisfy the rank-1 constraint and the rank-1 solution can be obtained by applying Gaussian randomization techniques [14].
###### Abstract  
Although the immunomodulatory effects of Astragali Radix extract (AR) have been documented, its anti-mutagenic activity, a problem arising from chemotherapeutic agents, is rarely reported. Therefore, the anti-mutagenic and immunomodulatory effects of AR were investigated using a cyclophosphamide (CPA)-induced immunosuppressioned mouse model to develop an alternative immunomodulatory agent. The fluid-bed-dried aqueous extract of AR containing 37.5% dextrin and exopolymers purified from _Auroubsidium pullulans_ SM-2001 (EAP) were used in this study. The therapeutic potentials of AR at doses ranging from 100 mg/kg to 400 mg/kg was estimated by a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) based cytotoxicity and splenocyte proliferation assay, body weight and lymphatic organ weight measurements, hematological measurements, serum and spleen cytokine level measurements, natural killer (NK) cell activity measurements, real-time RT-PCR expressions of splenic mRNA, a micronucleus test, histopathological observations, and immunohistochemical measurements. In CPA-treated mice, a clear immunosuppressive effect was observed for all tested parameters. However, the oral administration of AR (100, 200, and 400 mg/kg) showed dose-dependent and favorable inhibitory activities on CPA-induced immunoprecipitation and mutagenicity as compared to 200 mg/kg EAP. Furthermore, AR (100-400 mg/kg) up-regulated the nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-kB) which are related to NK-, T-, and B-cell activation, with no critical cytotoxicity. The results of this study clearly demonstrate that AR at an appropriate oral dose could act as a potential alternative agent with significant anti-mutagenicity and immunomodulatory properties.  
Keywords:anti-mutagenicity effect; Astragali Radix, _Astragalus membranous_ Bunge; cyclophosphamide; immunomodulatory effect; mouse model +
Footnote †: journal: Applied Sciences
## 4 Limitations of AIDMs When Applied with the Presence of GSCI
### Passive Method Type  
A droop control strategy is usually used in inverters (GSCI case) due to its ease of parallelization [4; 5; 9; 10]. The droop control manages the active and reactive power, modifying the module and phase of the AC capacitor voltage \(U_{C}\)[9; 10] with respect to the PCC voltage \(U_{PCC}\). Nevertheless, for its proper operation, the droop control is based on grid-connected operation and needs the grid to hold the voltage of the PCC (module and phase). Due to this, the droop control requires great effort to change the injected power, active or reactive, under the absence of the mains. In other words, when the voltage loop's control action attempts to correct \(U_{C}\), so that it varies the power flow exchange with \(U_{PCC}\), the \(U_{PCC}\) follows \(U_{C}\), which results in displacement. This phenomenon is qualitatively represented in Figure 4 by a mechanical analogy. In grid-connected mode, the grid is the point of support, i.e., acts as a wall/ground surface. Thus, the phase and amplitude between \(U_{C}\) and \(U_{PCC}\) are the \"forces\" connected to the point of support by rotational and linear springs, respectively. These springs are related to the impedance between \(U_{C}\) and \(U_{PCC}\). The more \"elongation\" there is or the more \"angle rotated\" it is, the more the \"force\" results (active or reactive power managed by means of droop phase or amplitude action). In the case of grid absence, the point of support is lost, and any effort in gaining elongation or angle by \(U_{C}\) will consequently drag \(U_{PCC}\).  
Figure 4: Vectorial representation of droop control under a grid disconnection.  
Although droop's control actions are affected by the sudden power flow interruption of grid-connected to grid-disconnected transitions, the effects result in being excessively smooth. This is due to the implicit slow dynamic behavior when emulating synchronous generator inertias [5]. Thus, due to a reduced time response, as well as limited droop control action's swing (in terms of voltage amplitude and phase), PM are not adequate because several standards require a detection in less than 160 ms, as will be detailed in Section 4.2.4. Thus, AI-PM shows an implicit lack of effectiveness in the conventional concept of resonant load and NZPF and ZPF.
## 5 Semi-Active Control Analysis of the Multi-DOF Seat Damping Suspension
### Design of the Semi-Active Controller  
#### 5.1.1 Semi-Active Control Algorithm  
Since the seat suspension platform was based on the cubic Stewart structure, it could achieve dynamic decoupling between the legs of the structure. This meant that the multi-dimensional vibration-reduction problem could be converted into one-way vibration damping for each leg of the vibration-damping platform problem. The one-way vibration model is shown in Figure 20 and in Equation (13).  
\[m\ddot{x}_{2}(t)+k(x_{2}(t)-x_{1}(t))+c(\dot{x}_{2}(t)-\dot{x}_{1}(t))=0 \tag{13}\]  
In Formula (13), \(x_{1}\) is the load displacement, \(x_{2}\) is the excitation displacement, \(m\) is the load mass one-way vibration model, \(k\) is the spring stiffness of the model, and \(c\) is the damping coefficient. For the legs of a seat suspension platform, the damping element in the model is an MRF damper. Therefore, a semi-active control method can be designed for a single leg. The dynamic formula of a single leg is simplified in the following:  
\[\left\{\begin{array}{l}m\ddot{x}_{2}(t)+k(x_{2}(t)-x_{1}(t))+f_{MRF}=0\\ f_{MRF}=f_{passive}+f_{control}\end{array}\right. \tag{14}\]  
where \(m\) is the load mass acting on each leg of the seat suspension platform, \(k\) is the spring stiffness of the leg, and \(f_{MRF}\) is the output damping force of the MRF damper. In semi-active control, this force includes two parts: the passive damping force \(f_{passive}\) when the input current is 0A, and the semi-active control force \(f_{control}\) realized by the input control current, calculated by the control algorithm.
# OpticalTrust: A Sensor-to-Blockchain Framework Using Free-Space Optical Communication
## 3 Methodology
### FSOs Model Design  
This study aims to design and analyze an optical transmission system utilizing both Fiber Optic and Free-Space Optical channels. The analysis will be conducted using OptiSystem, a widely adopted simulation program in optical communications. OptiSystem provides an intuitive interface commonly used in various electrical engineering applications, particularly in optics. The FSOs communication system considered for this study [37] incorporates a CW laser source with a Mach-Zehnder modulator driven by a pseudo-random bit generator through an NRZ pulse generator at the transmitter section, followed by an FSO channel split between an optical spectrum analyzer and EDF amplifier. The receiver section comprises a PIN photodiode and Bessel filter for signal detection and conditioning. The system will be evaluated at data rates of 8, 12, 16, and 20 Gbps over distances ranging from 5 to 25 km, specifically at 5, 10, 15, 20, and 25 km intervals. The design incorporates a Bessel filter and an Erbium-Doped Fiber (EDF) amplifier to enhance signal quality. Performance metrics for the FSOs channel will include Bit Error Rate (BER) and Q-Factor assessments. In this study, the system simulation utilizes a wavelength of 1552.52 nm with an attenuation coefficient of 0.2 dB/km, as specified in Table 1. This attenuation parameter was selected to represent typical FSOs channel losses, including atmospheric absorption and scattering under clear weather conditions, as well as baseline propagation losses in the FSOs channel. Table 1 outlines the key design parameters and their corresponding values chosen for the proposed FSOs communication system simulation in OptiSystem.
## 5 Implications of the Study
### Opportunities for Improving and Expanding Applications of NAS-GANs  
* Current evaluation metrics, such as IS and FID, may not capture all aspects of GAN performance. Developing new metrics to assess diversity, fidelity, and realism can provide a more holistic evaluation framework.
* There is a pressing need to improve the interpretability and explainability of generated GAN architectures. Understanding why specific architectures perform better can guide future designs and enable practical applications.
* Integrating NAS with AI paradigms, such as self-supervised learning, could enhance the robustness and generalizability of GANs.
* Addressing the environmental and computational costs of NAS processes is crucial. Developing energy-efficient and sustainable AI models will make NAS-GAN methods more accessible and scalable.
* Beyond image generation, NAS-GAN techniques have potential in other domains, such as natural language processing and time-series data. Researching these applications could unlock new opportunities for GANs.
* The application of NAS techniques to transformer-based GAN architectures remains unexplored. Given the success of transformers in various machine learning tasks, incorporating NAS into transformer-based GANs could significantly advance the field.
## 4 Characterization of the Discharge of Insulation Defects in Switchgear
### Characterization of Characteristic Gases Generated by Metal Protrusion Discharge  
#### 4.1.1 Variable Properties of Characteristic Gases in Metal Protrusion Discharge  
(1) Characterization of the change in volume fraction of CO  
The characteristic curves of the volume fraction of CO with time after the voltage of 11 kV, 13 kV, 15 kV, and 17 kV are applied to the metal protrusion defects are shown in Figure 7. The pathway for CO production under metal protrusion defects is that the air contains a small amount of CO\({}_{2}\), which is ionized and converted to CO when the discharge occurs.  
(2) Volume fraction change characteristics of NO\({}_{2}\)  
The volume fraction of NO\({}_{2}\) in the 11 kV, 13 kV, 15 kV, and 17 kV voltages to the metal protrusion defects, change with the time characteristic curve shown in Figure 8.  
Figure 8: NO\({}_{2}\) gas volume fraction of air gap defects.  
Figure 7: CO gas volume fraction variation curve.  
The generation of NO2 under the defect of metal protrusion includes two pathways. First, N2 collides with O particles and generates NO2 by chemical reaction. Second, the high-energy particles composing the plasma are generated by a collision breaking the N-N bond of the N element, and NO2 is generated by the compounding of N and O. The strong oxidizing gases oxidize NO to substances with higher nitrogen content ratios, such as NO2, NO3, and N2O5. Once the reaction attains dynamic equilibrium, the volume fraction of different gas components becomes locally saturated and stable.  
(3) O3 volume fraction change characteristics  
The O3 volume fraction in the 11 kV, 13 kV, 15 kV, and 17 kV voltages to the metal protrusion defects, and the characteristic curve with the time change, are shown in Figure 9.  
The O element in NO2 under metal protrusion defects originates from the decomposition of O2 into O element under the action of energetic particles. From Figure 9, it can be seen that the O3 gas volume fraction experiences rapid growth before 40 min and tends to stabilize after 40 min. The reason for this is that reducing gases in the air (e.g., CO) limit the growth of O3.
## 5 Long-Term Evolution
### Kessler Syndrome and Mega-Constellations  
One of the most important problems currently being investigated is the possible beginning of the collisional cascading effect, the so-called \"Kessler Syndrome\" [32]. This effect has to be taken into account in long-term simulations, since it cannot be ruled out that it will become the dominant effect in the generation of space debris in the distant future. Although today catastrophic collisions in space are still relatively rare, a gradual transition from the linear increase in the space debris population by entering new objects to the exponential increase by the onset of Kessler syndrome is possible. Several studies are currently investigating the instability, especially of the LEO population [33; 34]. Due to the high spatial debris density at 800 km altitude, it is expected that the Kessler syndrome will occur at these altitudes. Deviating from this expectation, however, the mega-constellations must also be examined separately [35; 36]. If such constellations will be established, the problem of space debris may also occur on different orbits. Mega-constellations are characterized by an extraordinarily high number of operational satellites. Although these constellations should preferably be installed at altitudes at which there is a low debris density today, it is to be expected that this can lead to inter-constellation collisions, if the control over the satellites is lost. In particular, the question of the disposal of these satellites from their orbits at the end of their operation life time is of crucial importance for the stable operation of such a constellation. These questions are currently being studied intensively. The Institute has already been involved in associated work [37]. The most important statement is that constellations can perhaps be operated stably if a reliable post-mission disposal of satellites can be guaranteed. However, the requirements for such a maneuver are extremely high and the technical reliability must be guaranteed.
## 2 Selection of Baseline Model  
YOLOX [27] is an anchor-free version of YOLO; the first version was officially released in July last year, and its performance exceeds that of all previous YOLO series. It draws on many advantages of YOLO series networks; free from a priori frame constraints, its detection speed is faster, detection accuracy is higher, and end-to-end deployment is more flexible.  
The following are the four most important components of the network model:  
1. Input YOLOX uses two data enhancement technologies, Mosaic and Mixup, which were used in previous YOLO versions.
2. Backbone YOLOX uses the Darket-53 network. It has a total of 53 convolutional layers, and the last layer is the full connection layer. The remaining 52 convolutions are used as the main network.
3. Neck The structure of FPN is used for fusion. FPN allows the features of the different layers of the pyramid to be displayed, such as high semantic features and high-resolution features. the presence of FPN allows the network model to focus on small objects, further improving the performance of the algorithm.
4. Prediction YOLOX uses decoupled head, anchor-free detector, label assignment strategy SimOTA (Simplified Optimal Transport Assignment), and loss calculation.  
For YOLOX, the basic structure of all four models is the same; for all models, X-s, X-m, X-l, or X-x, the network model part is the same. The only difference is that the depth and width of the models are set differently, controlled by two parameters: depth and width. The width and depth of the four models are 0.33 \(\times\) 0.50, 0.67 \(\times\) 0.75, 1.0 \(\times\) 1.0, and 1.33 \(\times\) 1.25, respectively, from small to large.  
YOLOX applies some of the industry's latest and most advanced technologies to the development of detectors, yielding the best results available on most models. Compared to the previous YOLO series, YOLOX offers faster detection, higher detection accuracy, and more flexibility in end-to-end deployment. The superior performance and flexibility were decisive factors for choosing YOLOX as the base network in this paper.  
YOLOX has been updated and improved several times within six months after its release. In version 0.1.1, image caching was supported for faster training, preprocessing was optimized for faster training, and the old distortion enhancement was replaced with the new HSV aug for faster training and better performance. In this work, the baseline of this paper is YOLOX version 0.2.0, updated in January 2022.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 2 Basic Theory of Transient Stability Analysis  
Transient stability is the capability of a power system to regain a stable equilibrium operation point when subjected to a large disturbance such as a three-phase ground fault, and the dynamic behaviours of multiple complex dynamic components should be considered.
## Appendix A  
**Definition A1**.: _Three noncollinear points, \(V_{1}(V_{1x},V_{1y},V_{1z})\), \(V_{2}(V_{2x},V_{2y},V_{2z})\) and \(V_{3}(V_{3x},V_{3y},V_{3z})\) in \(R^{3}\), determine a plane where the nonzero vectors, \(\overrightarrow{V_{2}V_{1}}\) and \(\overrightarrow{V_{2}V_{3}}\), lie on that plane. The cross product of these vectors produces the normal vector \(\overrightarrow{n}\) following the right-hand rule, as indicated in Figure A1._  
**Definition A2**.: _Given a plane \(\mathbf{Q}\) in \(R^{3}\), a point \(P_{0}(x_{0},y_{0},z_{0})\) is located, given a nonzero vector \(\overrightarrow{n}\)\((n_{x},n_{y},n_{z})\) perpendicular to \(\mathbf{Q}\). Then, \(\mathbf{Q}\) consists of the points \(P_{l}(x,y,z)\) satisfying the vector equation called a point-normal form of the plane (Figure A2):_  
\[n_{x}(x-x_{0})+n_{y}(y-y_{0})+n_{z}(z-z_{0})=0\] (A1)  
\[ax+by+cz+d=0\] (A2)  
_where_  
\[a=n_{x};b=n_{y};c=n_{z};d=-(n_{x}x_{0}+n_{y}y_{0}+n_{z}z_{0})\] (A3)  
_and in general,_  
\[\overrightarrow{n}P_{l}+d=0\] (A4)  
**Definition A3**.: _An infinite line in space can be described through the parametric vector equation, using a point \(P_{0}\) on the line and the direction of the line represented by a vector \(\overrightarrow{v}\) in such a way that the following holds:_  
\[P=P_{0}+t\overrightarrow{v}\] (A5)  
**Definition A4**.: _Given a triangle defined by the vertices \(V_{1},V_{2},V_{3}\) (Figure A3), then any point inside the triangle can be defined as an addition of weighted vectors, such as the following:_  
\[P=V_{2}+u(V_{1}-V_{2})+w(V_{3}-V_{2})\] (A6)  
\[u(V_{1x}-V_{2x})+w(V_{3x}-V_{2x})=P_{x}-V_{2x}\] (A7)  
\[u(V_{1y}-V_{2y})+w(V_{3y}-V_{2y})=P_{x}-V_{2y}\] (A8)  
solving for \(u\) and \(w\)  
\[u=\frac{(P_{x}-V_{2x})(V_{3y}-V_{2y})-(V_{3x}-V_{2x})(P_{y}-V_{2y})}{(V_{1x}-V_ {2x})(V_{3y}-V_{2y})-(V_{3x}-V_{2x})(V_{1y}-V_{2y})}\] (A9)\[w=\frac{(P_{y}-V_{2y})(V_{1x}-V_{2x})-(V_{1y}-V_{2y})(P_{x}-V_{2x})}{(V_{1x}-V_{2x} )(V_{3y}-V_{2y})-(V_{3x}-V_{2x})(V_{1y}-V_{2y})} \tag{10}\]  
The point is inside the triangle if and only if \(u\geq 0\) and \(w\geq 0\) and \((u+w)\leq 1\).
# Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER
## 4 Experimental Setup
### Hyperparameter Setup  
As preliminary experiments, we explored the optimal values of several major hyper-parameters such as dropout, optimizer with learning rate, and the number of bidirectional LSTM layers and also tested several versions of the popular pre-trained word embeddings including GloVe and FastText (More detailed information is given in the Appendix A). The other hyper-parameter values were  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Named Entity** & **Train Set** & **Valid Set** & **Test Set** \\ \hline Location & 8297 & 2094 & 1925 \\ Organization & 10,025 & 2092 & 2496 \\ Person & 11,128 & 3149 & 2773 \\ Malicious & 4593 & 1268 & 918 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of named entities in CoNLL 2003 found in the training, development, and test. The highest proportion in CoNLL 2003 is location, followed by person, organization, and miscellaneous in that order.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Named Entity** & **Train Set** & **Valid Set** & **Test Set** \\ \hline Person & 37,393 & 5354 & 3646 \\ Norp & 9956 & 13.45 & 1152 \\ Facility & 3089 & 363 & 392 \\ Organization & 56,954 & 8964 & 4705 \\ Product & 1812 & 471 & 160 \\ Event & 3096 & 504 & 250 \\ Work of art & 4513 & 639 & 516 \\ Law & 1657 & 239 & 162 \\ Language & 372 & 36 & 22 \\ Location & 4143 & 596 & 417 \\ GPE & 27,354 & 4555 & 3263 \\ \hline Money & 15,130 & 2287 & 1103 \\ Percentage & 8989 & 1504 & 992 \\ Ordinal & 2151 & 333 & 204 \\ Cardinal & 13,813 & 2141 & 1318 \\ Quantity & 3123 & 522 & 415 \\ Date & 40,077 & 6527 & 3793 \\ Time & 3505 & 731 & 451 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of named entities in OntoNotes 5.0 found in the training, development, and test sets. In OntoNotes 5.0, the highest proportion is organization, followed by person and Geopolitical Entity (GPE).  
borrowed from the earlier works [2; 33; 4; 5; 2]. Table 3 shows the hyper-parameter values used in our experiments. The hyper-parameter values are nearly identical between CoNLL 2003 and OntoNotes 5.0, except for the maximum word length and character embedding dimension, which were borrowed from the earlier studies [2; 5], respectively.