esa-sceva/satcom-chunk-collection huggingface dataset:
# Pullout Response of Ultra-High-Performance Concrete with Twisted Steel Fibers
## 1 Introduction
Several studies have presented the typical pullout force response of HPFRC with twisted fibers [1; 13], while a theoretical or empirical formula of pullout force has not been established, particularly for UHPFRC with the ultra-high compressive strength of more than 150 MPa. The objective of this study is to develop a pullout force formula and thus to enhance the understanding of the damage and fracture mechanisms of UHPFRC with twisted fibers through a pull-out test and numerical modeling. The pull-out response of twisted fibers through the cementitious matrix was first reviewed and then analytically modelled considering elastic-plastic mechanics. The analytical model empirically assumes the tri-linear relationship of pullout force and pullout deformation. A microscale in-situ X-ray computed tomography (uXCT) was used to provide visual evidence for the failure pattern. The formula was verified by the pullout test results. The untwisting mechanism of the single twisted fiber during the pullout process was also investigated by conducting an image analysis and a finite element analysis (FEA). The developed pullout force formula and the understanding of damage mechanisms are expected to optimize the design of the UHPFRC with twisted fibers.
## 3 Test Results and Discussion  
### Damaged Process and Mechanism  
Different failure modes were observed, such as flexure-dominant failure mode for walls with a large aspect ratio of 2.8 and 2.15 bending-shear failure mode for walls with a small aspect ratio of 1.75. As similar behaviors of the other walls were observed, typical specimens of HPLW500-1 (flexure-dominant failure mode) and HPLW 800-1(bending-shear failure mode) were taken to illustrate the failure process and failure mechanism.
## 2 Results
### Expression Patterns of DoTPS Genes under Abiotic Stress  
To better understand the role of _DoTPS_ genes in response to cold and osmotic stresses, transcriptome data combined with the RT-qPCR assay were employed to investigate the expression levels of _DoTPS_ genes in _D. officinal_ under cold (0 \({}^{\circ}\)C) or osmotic (mannitol) treatment. Results showed that _DoTPS_ genes exhibited distinct expression patterns under osmotic treatment, showing two trends, an upward trend and a downward trend (Figure 7A). Half of the _DoTPS_ genes, including _DoTPS1_, -3_, -6_, -8_, -11_, -16_, -18_, -19_, -21_, -22_, -23_, -24_, -25_, -26_, -27_, -31_, and -32, were obviously suppressed (1.2-48.7-fold) by mannitol-induced osmotic stress. The other half of _DoTPS_ genes exhibited an increasing trend, but the highest expression level was either at 12 h (_DoTPS9_, -10_, -14_, and -28), 24 h (_DoTPS2_, -4_, -7_, -12_, -13_, -15_, -20_, -29, and -30), or 48 h (_DoTPS5_, -17_, -33, and -34) in response to mannitol treatment (Figure 7A).  
After cold acclimation (0 \({}^{\circ}\)C) for 20 h, 26 _DoTPS_ genes were upregulated, more than the number of suppressed genes (8; Figure 7B). Compared to the non-acclimated controls, the transcription levels of _DoTPS4_, -8_, -13_, -15_, -23_, -26, and -33 were downregulated between 1.6- and 14.3-fold. In contrast,  
Figure 6: Tissue-specific expression profiles of _DoTPS_ genes in different _D. officinal_ organs. (**A**) The transcription levels of _DoTPS_ genes in different tissues. The different tissues were sepals (Se), labellum (la), green root tips (Rt), roots (Ro), white part of roots (Rw), pollinia (Po), flower buds (Fb), gynostemium (Gs), stems (St), and leaves (Le) in two-year-old _D. officinal_ adult plants. Heatmap was generated using the TBtools server ([[https://github.com/CJ-Chen/TBtools](https://github.com/CJ-Chen/TBtools)]([https://github.com/CJ-Chen/TBtools](https://github.com/CJ-Chen/TBtools))), and gradient color from green to red was expressed as the log2-transformed expression levels of each _DoTPS_ gene that was normalized to the internal reference gene _DoEF-1a_, GenBank accession no. JF825419. (**B**) _D. officinal_ “Zhongke 5” used in this study. All fragments per kilobase of transcript per million fragments mapped (FPKM) values that were used were downloaded from NCBI under BioProject PRJNA262478 [25], and are listed in Table S7.  
the expression levels of most other _DoTPS_ genes (27/34) were clearly upregulated between 1.9- and 103.1-fold, except for the tiny variation of _DoTPS20_ and _DoTPS32_. These results suggested that _DoTPS_ genes might be involved in cold and osmotic stress responses in _D. officinal._
## 5 Discussion  
Table 6 compares the presented method against other works reported in the literature to highlight its advantages. First, it is worth noting that the proposed method uses only 100 samples to obtain a 97.55% general accuracy, which is a near value compared with the presented works from 90% to 100% of accuracy. The proposed method increases its general accuracy when the sample number is increased, but it stays above 99% from 8000 samples. Similar accuracies are obtained in [12, 22]. The previous studies utilized vibration signals [12, 22] and electrical signals [23, 24] as input parameters. In contrast, our proposed approach combines both vibration and current signals to extract two statistical features. Unlike the previous research that relied on a single statistical index [23, 24], this method achieves high accuracy without the need for spatial transformations The maximum number of samples processed in our work is less than the number of samples used in the presented works, so our work is efficient in processing time, since it only requires 100 samples to obtain a comparable percentage of precision. Additionally, the presented method is developed in the time domain, which avoids spatial transforms (e.g., wavelet transform), proposing a more efficient processing tool.  
In summary, the QSA method is efficient in SCT classification using statistical values such as variance and standard deviation, which are low-processing calculations. The general accuracies are from 99% to 100% among different classes and window samples, except in 5 SCTs, which vary from 87% to 96%. The method obtains good classification in a short time having a low number of samples and amount of processing in the time domain. In addition, low resources in the KNN algorithm allow the implementation of the complete method in a portable processor for real analysis in future works adding other fault classes.
## 7 Topology Control
### Random Key Predistribution Scheme
#### 7.1.1 Eschenauer-Gligor Random Key Predistribution Scheme (EG Scheme)  
In EG scheme, there are \(n\) number of sensors in a keying network. This scheme uses an offline pool of keys containing \(Pn\) keys. Before deployment, each sensor is assigned \(Kn\) number of discrete keys selected from the pool of keys. \(Pn\) and \(Kn\) both are the functions of n for generality motives. \(Kn\) keys in each sensor establish sensor's key ring. After deployment, two sensors can establish secured communication link only if they have at least one key(\(s\)) in common i.e., \(1\leq s\leq Kn\). Confidentiality and authenticity are achieved with symmetric key encryption mode [94; 95]. Figure 7 shows the EG scheme.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 3 Technical Overview
### Network Infrastructure  
Similar to several time-critical protocols in the IEEE 802.15 family, the IEEE 802.15.7 is also supported by a superframe structure synchronized by periodic beacons. The format of the superframe is set by the coordinator based on the network topology. The superframe structure consists of a beacon period in which beacons are sent for synchronization. It consists of a Contention Access Period (CAP) in which communication is carried out using CSMA/CA, and a Contention Free Period (CFP) in which communication is carried out using Guaranteed Time Slots (GTS). In what follows we present a set of superframe structures used for various topologies:
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
###### Abstract  
Large penetration of wind energy systems into electric-grids results in many power quality problems. This paper presents a classification of power quality issues, namely harmonics and short-duration voltage variation observed due to the integration of wind power. Additionally, different techniques and technologies to mitigate the effect of such issues are discussed. The paper highlights the current trends and future scopes in the improvement of the interconnection of wind energy conversion systems (WECSs) into the grid. As the voltage variation is the most severe power quality issue, case studies have been presented to investigate this problem using steady-state time-series simulations. The standard IEEE test system namely IEEE 123-node test feeder and IEEE 30-node grid are solved under different operating conditions with wind power penetration. Typical daily load profiles of a substation in Riyadh, Saudi Arabia, and an intermittent wind power generation profile are used in all case studies. Mitigation of voltage variations due to wind intermittency is achieved using reactive power compensation of the interface inverter. The results show the effectiveness of these approaches to avoid voltage variation and excessive tap setting movements of regulators and keep the voltage within the desired operating conditions.  
power quality issues; Saudi electric grid; wind penetration; harmonic analysis; voltage fluctuation; reactive power compensation; FACTs; demand-side management; energy storage; voltage regulation; voltage control; linear approximation; power-flow analysis; time-series simulation +
Footnote †: journal: Applied  
_Article_
# A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems
## 2 VLC System Model Applied to Underground Mining Environments  
VLC systems applied to underground mines have marked differences compared to VLC systems applied to non-mining indoor environments. Among the features included in the modeling for UM-VLC systems are the angular positioning of LEDs and PDs, non-regular walls, and scattering and shadowing phenomena. Therefore, based on the work developed in the mining channel quote, in this section, we briefly describe the components of the VLC system applied to the underground mine, its components and characteristics.
# Valkyrie--Design and Development of Gaits for Quadruped Robot Using Particle Swarm Optimization
## 3 Stability Analysis of the Quadruped Robot
### Moment of Stability (\(M_{stab}\))  
The moment is defined as the product of force acting on each link or each leg and the distance between the link or leg to the ground and is calculated as,  
\[moment=force\times distance \tag{6}\]  
The conditions to achieve \(M_{stab}\) are,  
Figure 2: Support polygon of the quadruped robot.  
\(M_{stab}>0\)  
2. \(M_{stab}=Min[M_{L1}\ M_{L2}\ M_{L3}\ M_{L4}]\)  
where, \(M_{stab}\) is the moment at which stability is achieved; \(M_{L1},M_{L2},M_{L3},M_{L4}\) are the moments of each leg.  
\[\text{Moment acting at a leg is calculated as, }\ M_{L1}\ =M_{l1}+M_{l2} \tag{7}\]  
where, \(M_{l1}\) and \(M_{l2}\) are the individual moments of the two links that form a single leg. As torque values are known, force is calculated using the below equation;  
\[force=\frac{ torque}{ length+\sin\left(angle\right)}=\frac{11}{7+sin(20)}=1.497\ \text{kg} \tag{8}\]  
The moment for link 1(\(M_{l1}\)) = \(force\ x\ distance\) = 8.832 kg - cm. In the same manner, the moment for link 2 (\(M_{l2}\)) is found to be 14.0718 kg - cm. Thus, the total moment acting on the leg (\(M_{L1}\)) is 22.90 kg - cm. Since the legs are symmetric, the moments for the other legs will also be within the range of the moment of the calculated leg. The conditions for \(M_{stab}\) are satisfied as to the value of \(M_{stab}\) is greater than zero and the same moment at stability is the minimum of the moment of the other legs.  
\[M_{stab}=Min[22.90\ M_{L2}\ M_{L3}\ M_{L4}]\ \text{kg}-\text{cm} \tag{9}\]  
Therefore, the moment at which stability is achieved should have a value that is greater than or equal to 23 kg - cm.
## 4 Simulation and Results
### Simulation Results
As a result, executing the same number of tracking tasks with the improved method requires less time, and then the saved time and resources can be used to perform other types of tasks such as search tasks. Overall, compared to traditional methods, the improved approach achieves more efficient resource allocation and enhances overall radar performance.
# Progress in THz Rectifier Technology: Research and Perspectives
### THz Diode Technology: Open Questions
In addition, the large contact area and short length induce negative effects on the resistance. The contact area effect is proportional to the capacitance and is inversely proportional to the resistance. In other words, there is a trade-off so that the resistance and the capacitance cannot decrease simultaneously. Coupling efficiency is obtained by taking into account some considerations during the MIM design. The conductivity and resistivity of the metals of the electrodes should have a high value to achieve high current density and a negligible value, respectively. Moreover, a great difference in work function is required to guarantee asymmetry. For insulators, great electron-affinity values that are quite equal to the metal work function ones are preferred since they produce a small band-offset at the interface needed to guarantee nonlinear characteristics. Furthermore, the dielectric constant should have a low value in order to cope with the reduced tunneling resistance. The narrow bandgap allows a sharp turn-on voltage. Finally, to increase the asymmetric barrier of the tunnel, it is possible to use stacking of insulators (MI\({}^{\text{TM}}\)M devices), which have different band gaps and electron affinities (\(\chi\)) [24; 28; 29; 36; 37; 38; 39].
## 5 Performance Evaluation
### Experimental Environment Settings  
We deploy the MFLBS algorithm to the following two network models, and their detailed configuration is as follows:  
* FPN model. It consists of two core switches, four aggregation layer switches, and four access layer switches. This network is fully populated, and the access layer switch connects with the client host.
* Partial mesh network model. It is mainly composed of nine core switches, and its specific topology is shown in Figure 2. Each switch is connected to its access layer switch, and the access layer switch connects with the client. We will focus on the mesh topology, so the access layer switches are not interconnected.  
We conducted simulations in Visual Studio Code (Version 1.61) on the Windows 10 system. Additionally, to simulate the real workload, we also built a cluster group of Hadoop 2.7.3 and HBase 1.2.4 to test the data volume of tasks, such as range queries in KD-Trees and ping. We set the network link bandwidth as 15 MBfor 500 s, generating 10 flows per second in the network. In addition, we send a 100 Mb ping command per second to simulate small flows. The host on the sending end and the host on the receiving end are equally possible and random. For the reliability of the results, we repeated the simulation experiment 20 times and averaged it. Most of the experimental parameters are listed in the Table 4 below.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 1 Introduction
### Event Reconstruction  
Cherenkov event reconstruction is a procedure used in IACTs to reconstruct the properties of VHE cosmic rays and gamma rays that have interacted with the Earth's atmosphere and refers to the process of analyzing the signals collected by the detectors in order to infer the properties of the incident particles. This typically involves several steps, including image cleaning, image parameterization, and stereo reconstruction.  
Image cleaning is the process of removing unwanted or spurious signals from the detector image. This includes removing noise (both instrumental noise and night sky diffused background light), correcting for detector inefficiencies, and identifying and removing signals that are likely not related to Cherenkov radiation. Image parameterization consists of describing the cleaned image using a set of morphological indicators and allows us to quantify the properties of the particle interaction, such as the position, size, and orientation of the Cherenkov cone produced by the particle. Stereo reconstruction refers to the process of combining the information from multiple detectors in order to reconstruct the full 3D trajectory of the primary incident. This may involve triangulating the image of the shower as recorded by multiple detectors, or using other techniques to reconstruct the primary  
Figure 1: Cherenkov shower images simulated as observed by the ASTRI MiniArray telescopes, illustrating the morphological differences between different types of events (the contour matches that of the ASTRI telescope camera). The images include two gamma ray events (**Top**), a hadronic event with similarities to gammas (**Bottom Right**), and a distinct hadronic event (**Bottom Left**). These images showcase the often faint but discernible variations in the Cherenkov shower morphology.  
trajectory. Finally, this whole set of information is used to reconstruct the energy, type, and direction of the primary particle (particle reconstruction). This can be achieved using Machine Learning methods, and the final output is the most probable characterization of the primary as either a photon or hadron (in the form of a global parameter, usually named _gammaness_), and an estimation of its energy.  
The objective of this study is to identify the best reconstruction method for gamma/hadron discrimination and energy estimation using Machine Learning techniques, primarily those based on trees. We are not focusing on the reconstruction of the incoming particle direction, since the analytic stereo reconstruction method employed in ASTRI Mini-Array already produces optimal results in this regard.  
While Machine Learning methods based on trees have been shown to be effective for gamma/hadron discrimination and energy estimation, it is possible that other Machine Learning techniques not considered in this study could perform better in certain scenarios. Examples of such techniques include Deep Learning algorithms (see [6] and references therein).
## 5 Algorithm
### DRL Problem Setup
As for each flow request, \(k\) candidate paths denoted as \(a_{t}^{k}\) are considered. **Transition:** The adopted transition function defines a probability of moving from state \(s_{t}\) to \(s_{t+1}\) given the action \(a_{t}\), \(T(s_{t+1}|s_{t},a_{t})\). **Reward:** The reward is given to DGA for each computed path sent to the SDNC for allocation. The reward functions for local and overlay agents are shown in Equation (13) and Equation (14), respectively. \[r_{t}= \begin{cases}1-std(u)+std\Big{(}\frac{b^{GFRR}}{b^{GFRR}}\Big{)}+RC+QF;&f_{d}=1\\ -QF;&f_{d}=0\end{cases}  \tag{13}\]\[r_{t}^{\Psi}= \begin{cases}1-std(u^{\Psi})+RC+QF;&f_{d}=1\\ -QF;&f_{d}=0\end{cases}  \tag{14}\]  
While the primary goal of 3 DQR is to provide QoS paths, the algorithm is expected to consider load balancing (at both the domain and overlay levels), QoS class priorities, and SDN CP overhead. To this end, the proposed reward formula includes multiple terms that guide the agents to carry out the following:  
* Distribute path allocations across links to maximise overall throughput--standard deviation of the utilisation of links \(std(u)\), and overlay links \(std(u^{\Psi})\);
* Punish frequent rerouting to conserve SDN CP resources as each flow rerouting requires modification of forwarding rules in switches; this is achieved by using the heuristic Rerouting Cost (RC): \[RC= \begin{cases}\xi;\,\tau\in(-1,0)&\text{if flow is rerouted}\\ 0&\text{otherwise}\end{cases} \] (15)
* Prioritise traffic and scale the punishments for allocation failures; therefore, the QoS Factor (QF) heuristic is introduced, which defines the value of each allocated flow based on the QoS identifier \(q_{id}\): \[QF:q_{id}\mapsto QF(q_{id})\in(0,1)\] (16)
* Maximise throughput that can be consumed by flows--if there is remaining capacity in the link, high values of GFBR/MFBR indicate the substantial portion of excess bandwidth that can be consumed by flows; it also encourages the agent to allocate flows with different GFBR/MFBR ratios (commonly associated with different traffic classes) to maximise aggregate the GFBR and decrease the MFBR, allowing the excess bandwidth to be shared across active flows.
# Man-in-the-Middle Attacks in Mobile Ad Hoc Networks (MANETs): Analysis and Evaluation
## 5 Results and Discussion
### Simulation Results  
In this section, we present the results of MITM assaults in MANET by simulating MITM assailants and evaluating the system performance according to the metric of evaluation explained above. Moreover, each experiment was carried out ten times with a random seed amount and in four repetitions to satisfy an individual initial node assignment within the system.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 2 Temporal Convolutional Neural Networks
### Temporal Convolutions  
Convolutional layers were proposed to limit the number of weights that a network needs to learn while trying to make the most of the structuring dimensions in the data--e.g., spatial, temporal or spectral--[55]. They apply a convolution filter to the output of the previous layer. As compared to dense layers (i.e., the fully-connected layer presented in Section 2.1) where the output of a neuron is a single number reflecting the activation, the output of a convolution filter is an activation map. For example, if the input is a univariate time series, then the output will be a time series where each point in the series is the result of the convolution filter.  
Figure 3 shows the application of a gradient extraction filter \([-1\ -1\ 0\ 1\ 1]\) onto the time series depicted in blue. The output is depicted in red. It takes high positive values where an increase in the signal is detected, and low negative values where a decrease in the signal occurs. Note that the so-called convolution is technically a cross-correlation.  
Figure 3: Convolution of a time series (blue) with the positive gradient filter \([-1\ -1\ 0\ 1\ 1]\) (black). The result (red) takes high positive values when the signal sharply increases, and conversely.  
Convolutional layers have for specificity to share their parameters across different locations: the same linear combination is applied by sliding it over the entire input. This drastically reduces the number of weights in the layer, by assuming that the same convolution might be useful in different parts of the time series. This is why the number of trainable parameters only depends on the filter size of the convolution \(f\) and on the number of units \(n\), but not on the size of the input. Conversely, the size of the output will depend on the size of the input, and also on two other hyper-parameters--the stride and the padding. The stride represents the interval between two convolution centers. Padding controls the addition of values (usually zeros) at the start and end of the input series, before the calculation of the convolution. It makes it possible, for instance, to ensure that the output has the same size as the input.