esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion for Geiger Domes
### Design Solutions of Geiger Domes  
The geometry of a Geiger dome consists of uniformly distributed, flat, load-bearing girders. Domes with six load-bearing girders were analyzed. Two variants of the geometry of the load-bearing girders were considered, i.e., with a closed upper section, type A (Figure 6a), and an open upper section, type B (Figure 6b). Additionally, two designs--regular (Figure 7a,b) and modified (Figure 7c,d)--were taken into account. This means that four different small-scale steel Geiger dome design solutions were compared, i.e., regular type A dome [6; 10], regular type B dome [4; 5; 7; 8; 9; 17; 30; 31], modified type A dome [15; 16; 31], and modified type B dome [32]. Their comparison in terms of the natural frequency range was the subject of our previous study [18].  
The load-bearing girders (Figure 6) consisted of tensioned cables (elements: 1, 2, 3, 4) and compressed struts (elements: S1, S2, S3), which were connected by circumferential cables (elements: C1, C2, C3, C4). In the case of modified solutions, additional circumferential cables C5 and C6 were added to connect the upper nodes. The node coordinates of the load-bearing girders are presented in Table 1. The small-scale steel domes were analyzed. The domes' diameter was 12 m, and their height was 3.25 m. The domes were supported at each external node of the lower section. Three translational degrees of freedom are taken away. This type of support corresponds to a circumferential clamping ring. It was assumed that the cables were made of S460N steel. Type A cables with a Young modulus \(E_{e}=210\) GPa [33] were used. The struts were made of S355J2 steel with a Young modulus \(E_{e}=210\) GPa. The density of steel is equal to \(\rho_{e}=7860\) kg/m\({}^{3}\). The material and geometrical characteristics are presented in Table 2. The load-bearing capacity was calculated by  
Figure 6: Load-bearing girders of a Geiger dome: (a) type A; (b) type B.  
Figure 7: Geiger domes: (a) regular type A; (b) regular type B; (c) modified type A; (d) modified type B.  
taking into account a partial factor for structural resistance. In the case of struts (made with a cold-finished circular hollow section), the load-bearing capacity depends on length \(l\).  
The calculations were performed with the use of a non-linear model implemented in a proprietary program written in the Mathematica environment.
## 2 Route Generation Methodology
### Higher-Level Path Generation: Genetic Algorithm (GA)
#### 2.3.3 Crossover Operation  
Since the crossover operation mixes the genes of two solutions, any index in the chromosome cannot be selected as the crossover point. This is because crossover should change the order in which the sub-areas are serviced. Therefore, the vehicle-area assignment and starting point should be preserved.  
Figure 4: The GA algorithm process visualized in a flowchart.  
Figure 5: The chromosome structure for the proposed research.  
To achieve this, the chromosome can be encoded into \"chromosome blocks\" that group every three genes together (sub-area, starting point, and vehicle assignment). The crossover point can be selected such that the chromosome blocks are preserved by selecting indexes that are divisible by three. The chromosome blocks and possible crossover points can be seen in Figure 6.
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
###### Abstract  
Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. Existing methods often fail to consider how to effectively combine their complementary advantages and fail to fully capture the internal information in the load sequence, leading to a decrease in accuracy. To achieve accurate and efficient short-term load forecasting, this study proposes a novel power grid load forecasting model that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Multi-Head Self-Attention Mechanism (MHSA), Global Attention Mechanism (GAM), and Channel Attention Mechanism (CAM) to achieve efficient and precise short-term load forecasting. This model aims to address the issue in traditional methods where complex temporal features and important information in power grid load data are not fully captured. Firstly, the CNN module is used to extract high-dimensional spatial features from the load data, and a pooling layer is applied to reduce dimensionality while retaining key information. Then, the Multi-Head Self-Attention mechanism is employed to model the long-range dependencies of the sequence data, enhancing the ability to extract temporal features. Next, the LSTM layer further captures the time dependencies in the load sequence. Subsequently, the Global Attention mechanism helps the model focus more on the most relevant parts of the input sequence, improving the model's performance and generalization ability. The Channel Attention module is then applied to weight different feature channels, highlighting important information and reducing redundancy. Finally, the flattened output layer produces the forecast results. Experimental validation shows that the proposed CNN-MHSA-LSTM-GAM-CAM model outperforms existing mainstream methods in terms of load forecasting accuracy, providing effective support for the optimized scheduling of smart grids.  
power grid; energy forecasting; convolutional neural network; long short-term memory network; attention mechanism 20252435  
2025152435
# Review of Structural Modal Tracking in Operational Modal Analysis: Methods and Applications
## 2 Method of Automatic Modal Parameter Estimation
Auxiliary algorithms might be employed to carry out parameter selection during the clustering process [88], despite their generalizability across diverse structural typologies remaining unverified. Consequently, in the stages of cleaning and interpreting stabilization diagrams, developing adaptive threshold determination mechanisms capable of identifying disparate structures and data quality is a pivotal challenge that must be overcome to achieve robust AMPE in engineering applications. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Criterion** & **Symbol** & **Reference** & \begin{tabular}{c} **Ideal** \\ **Physical Mode** \\ \end{tabular}  & \ \begin{tabular}{c} **Ideal** \\ **Spurious Mode** \\ \end{tabular}  & **AMPE** & \begin{tabular}{c} **Modal** \\ **Tracking** \\ \end{tabular}  \\ \hline Relative frequency difference & \(d\big{(}f_{i},f_{j}\big{)}\) & [17] & 0 & 1 & \(\surd\) & \(\surd\) \\ Relative damping ratio difference & \(d\big{(}\hat{x}_{i},\hat{x}_{j}\big{)}\) & [17] & 0 & 1 & \(\surd\) & \(\surd\) \\ Modal distance & \(d\big{(}\lambda_{i},\lambda_{i}\big{)}\) & [17] & 0 & 1 & \(\surd\) & - \\ Modal phase collinearity & \(\text{MPC}(\hat{\phi}_{i})\) & [75] & 1 & 0 & \(\surd\) & - \\ Mean phase deviation & \(\text{MPD}(\hat{\phi}_{i})\) & [76] & 0 & 1 & \(\surd\) & - \\ Modal assurance criterion & \(\text{MAC}(\hat{\phi}_{i},\hat{\phi}_{j})\) & [77] & 1 & 0 & \(\surd\) & \(\surd\) \\ Modal coherence indicator & \(\text{MC}L_{i}\) & [78] & 1 & 0 & \(\surd\) & - \\ Modal transfer norm & \(\text{MTN}_{\text{soft}}\) & [79] & Large & 0 & \(\surd\) & - \\ Relative \(\text{MTN}_{\text{soft}}\) difference & \(d
## 2 Materials and Methods  
### General Information  
GC analysis was carried out on an Agilent Technologies 6890N instrument (Varian, Agilent Technologies, Santa Clara, CA, USA) using a DB-17 column (cross-linked methyl silicone gum, 30 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m), (J & W Scientific, Folsom, CA, USA), retention times were as follows: 3.7 min for chlorolactone (SI, Figure S1) 4.1 min for bromolactone (SI, Figure S2), 4.7 min for iodolactone (SI, Figure S3) and 4.0 min for hydroxlactone (SI, Figure S4). The enantiomeric excesses of the products obtained during biotransformation were determined by GC analysis using the chiral column Supelco \(\beta\)-DEX 325 (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) (Supelco Bellefonte, PA, USA) under the following conditions: injector 200 \({}^{\circ}\)C, detector (FID) 250 \({}^{\circ}\)C, column temperature: 140 \({}^{\circ}\)C (hold 45 min), 140-200 \({}^{\circ}\)C (rate 20 \({}^{\circ}\)C/min), and 200 \({}^{\circ}\)C (hold 1 min), retention times of hydroxlactone were as follows: R\({}_{\text{f}}\)(+)-**5**: 12.78 min and R\({}_{\text{f}}\)(\(-\))-**5**: 13.29 min, R\({}_{\text{S}}\) = 2.89 (SI, Figures S5-S8). Enantiomeric excess was calculated:  
\[ee=\ \frac{E1-E2}{E1+E2}\ 100\% \tag{1}\]  
\(E1\)--peak area of compound one; E2--peak area of compound two.  
The molar masses of the new products were confirmed by high resolution mass spectrometry analysis using a Waters LCT Premier XE instrument (ESI ionisation) (Waters Division, Milford, MA, USA).  
\({}^{1}\)H NMR spectra were recorded in a CDCl\({}_{3}\) solution on a Bruker Avance DRX 300 MHz spectrometer (Bruker, Billerica, MA, USA) or on a Bruker AvanceTM 600 MHz spectrometer (Bruker, Billerica, MA, USA). IR spectra were recorded on a Thermo-Nicolet IR300 FT-IR spectrometer (Waltham, MA, USA). Optical rotations were determined on a P-2000 polarimeter (Jasco, Easton, PA, USA) in chloroform solutions. The melting points were determined on a Boetius apparatus. The refractive index was measured on Carl Zeiss Abbe and Pulfrich refractometer (Jena, Germany).
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## 4 Materials and Methods  
We devised a number of tests to assess the proposed despeckle method. These we use to compare performance with state-of-art procedures, while covering a number of illustrative cases. In particular, the proposed experiences focus on three different questions: (i) axial and lateral resolution, (ii) correlation between data contrast obtained with the proposed method and the expected standard phantom values and (iii) reliability of soft tissue interfaces in complex structures.  
Depending on the experiences and the specific setup, different arrays were chosen as listed in Table 2.  
For real measures, acquisition equipment was a multichannel generation-acquisition system [42], developed by DASEL S.L. company (www.daselsistemas.es). A bandwidth of up to 68% has been achieved on all acquisitions. A sampling frequency of 40 MHz was set for all acquisitions using Array A, and 80 MHz otherwise--Array B.  
For simulations (Array B) we used a 68% Gaussian pulsed signal and a white noise equivalent to 1 bit in a 12 bits sample depth. Array elements are 0.1386\(\times\)1.386 mm in size and interleaved 0.1540 mm. For RiB current implementation, we chose \(N_{z}=10^{4}\) virtual arrays.  
Hereafter, we describe the devised tests, and compare both the proposed method (RiB) and DAS beamformer under different conditions.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 4 Architecture of VLC Transceiver Circuits
### Receiver in VLC System  
On the receiver side, the optical signal was received by the PD and converted into the photo-electronic current, and then this current was transformed into voltage and amplified by the TIA. The following ADC sampled the TIA output and saved it as.csv-formatted data. In the MATLAB offline baseband, this received signal was synchronized first, and then an NN-based post-equalizer compensated for this signal, followed by PAM-8 demodulation.  
For the AFE in the VLC receiver shown in Figure 10, the TIA was the core circuit, and its schematic is presented in Figure 12. The TIA unit converted the photoelectronic current generated by the PD to a voltage output through feedback resistor R1. The output current of the PD was as small, from several to dozens of uA, to guarantee the subsequent signal process, and it required a conversion of the signal from current to voltage with large  
Figure 11: Schematic of CML LED driver.  
amplification, which could be satisfied by the TIA unit. Since the signal conversion in the TIA was processed by the feedback resistor following equation \(\rm V_{out}\)= \(\rm R_{1}\times\rm I_{in}\), the gain of the TIA was determined by the value of the feedback resistor when the stability of the core amplifier was ensured. Due to the requirement of SNR in receiving the signal, the noise of the TIA should be as low as possible to avoid bringing large noise into the receiver [34]. The noise in the TIA unit consisted of the thermal noise of the TIA and the equivalent input thermal noise of the MOSFET. These two types of noises were inversely proportional to the transimpedance of the TIA. Taking the requirement of both high gain and small noise into consideration, the realization of the high-sensitivity receiver required a large feedback resistor. Meanwhile, the first-stage amplifier should own a large transimpedance to further increase the performance of the TIA. Moreover, the three-stage structure adopted in this TIA provided a stable high gain due to the alleviation of bandwidth limitation brought by the low noise requirement.
## 5 Results and Discussion
### Forward Flight Validation
In summary, the nonlinear model has high accuracy over a large speed range, which guarantees the effectiveness of later analysis. In this validation, it is proved that the 2-mode controlled wing can work effectively, and the wing model based on the linear interference assumption is credible below 27 m/s. The ducted fan is efficient in providing forward thrust and unloading the main rotor. The nonlinear model with modification is successful in predicting the whole-helicopter dynamics.
## 5 Results and Discussion
### Energy Consumption  
Energy Consumption is the total amount of energy consumed by a network node. It reduces with a Black Hole and Gray Hole attack because packets sent between the source and destination are discarded, leading to reduced transmission between nodes. Figure 12 compares the energy consumed by the BAODV, idsAODV, EAODV and OEAODV schemes as node speed increases. The goal of this experiment is to compare the overhead imposed in the OEAODV system owing to processing and communication costs to the EAODV and other schemes, which ultimately consume more energy. Packet transmitting and receiving utilize the majority of the node's energy. Our OEAODV system does not increase the quantity of messages exchanged; instead, it makes use of existing routing packets (which are currently required by routing protocol requirements) to share data such as queue and path status. The only additional energy needed by our OEAODV system is for the computation of unique information, such as MAC layer details, queue data, and link shift rates, which are minimal [21]. As could be observed in the diagrammatic representation, the energy expended in both the EAODV and BAODV and other schemes is high, implying that our solution considerably increases network security while consuming very little energy. Figure 13 shows the total energy consumption.
###### Abstract  
Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.  
discrete particle swarm optimization; wireless sensor network with mobile sinks; routing; energy efficiency +
Footnote †: journal: Sensors
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 5 Evaluation
### Evaluation of the Attack Effectiveness
#### 5.4.2 Effectiveness against the Direct Forwarding Method  
In this scenario, the controller forwards packets by packet-out messages instead of installing flow rules. We count the number of packet-in messages and measure how many packets are forwarded by the controller.  
Figure 13 shows the number of packet-in requests with and without the attacks, respectively. Before the flow table is full, the numbers of packet-in messages are close in both scenarios. When the flow table is overflowed, the of packet-in rate increases significantly. After the flow table suffers saturation, the packet-in rate is 551 pps, while the average rate under the normal scenario is 350 pps.  
We also measure the ratio of packet-in messages caused by background flows and attack flows after the table saturation. As shown in Figure 14, 98% of the forwarded packet-ins are caused by benign traffic. The reason is that we limit the attack flow generation  
Figure 12: The number of benign flow rules and malicious flow rules under the saturation attack.  
Figure 13: The number of packet-in messages with and without the attack when the controller handles packet-in by sending packet-out messages after the flow table is overflowed.  
rate to prevent the rapid growth of malicious flow rules. Thus, defensive approaches against saturation attacks towards the controller cannot throttle the attack traffic, but may affect the benign traffic. Note that we allow up to 80 attack flows to trigger the rule installation simultaneously. The packet-in rates can be further reduced by configuring fewer attack flows.  
To demonstrate the impact on the network performance, we measure the RTT of service requests sent to Server-1. We send 10 packets for each round of requests to calculate the RTT and repeat 10 rounds. The results are presented in Figure 15. When no attack occurs, except for the first packet, which must be delivered by the controller, the average latency of subsequent packets is 1.16 ms. When the storage space is exhausted by attackers, the delay of all packets of new flows is about 7.2 ms, which is a significant increase.
# Test Center Location Problem: A Bi-Objective Model and Algorithms
## 3 Test Center Location Problem
### Test Center Location Problem Formulation  
To establish a comprehensive model test center location problem, we introduce the concept of assigning weights to each demand point, with each weight corresponding to the population count residing at that particular demand point. This weighted approach significantly enhances the problem's applicability to real-world scenarios. For instance, all individuals residing in an apartment complex can be represented as a single demand point, with its weight equal to the number of residents within it. In larger-scale instances of the problem, such as those involving extensive urban areas, it becomes feasible to preprocess the data by clustering residents who are in proximity. The center of each cluster is then assigned a weight equivalent to the size of that cluster. This preprocessing step leads to a substantial reduction in the problem's dimensionality, ultimately facilitating the proposal of an efficient solution. We now define the notation and problem formulation precisely.  
Given a weighted set of demand points, denoted as \(P=\{(p_{1},w_{1}),(p_{2},w_{2}),\ldots,(p_{n},w_{n})\}\), a set of potential facility centers \(Q=\{q_{1},q_{2},\ldots,q_{m}\}\), the travel distance \(d_{ij}\) for any pair \((p_{i},q_{j})\), and an integer \(k\), the goal is to select (or open) \(k\) centers from the available \(m\) potential locations such that achieves the following objectives:  
The first objective function, workload balance, is defined as follows  
\[\text{\emph{Workload}}\ \ \text{balance}:\ \ \text{\emph{Minimize}}\ \ F_{1}(C)=u-l, \tag{1}\]  
where \(u\) and \(l\) are the maximum and minimum number of individuals (demand points) allocated to any opened center, respectively. The second objective function is \(k\)-median objective, that is  
\[\text{\emph{Weighted}}\ min\ sum:\ \ \text{\emph{Minimize}}\ \ F_{2}(C)=\frac{1}{ \Sigma_{i=1}^{n}w_{i}}\Sigma_{i=1}^{n}w_{i}d_{i\delta(p_{i})}\, \tag{2}\]  
where \(d_{i\delta(p_{i})}\) denotes the traveling distance between demand point \(p_{i}\) and its closest opened center, \(\delta(p_{i})\). We assume \(\delta(p_{i})\) is unique. Therefore, this objective aims to minimize the average (weighted) travel distance for all individuals. It's important to note that there are no stringent constraints other than the requirement to precisely open \(k\) centers from the initial set of \(m\) potential centers, a choice usually influenced by financial or expertise limitations, such as constraints on available nurses or doctors.
# Yeast-Based Magnetic Biocomposite for Efficient Sorption of Organic Pollutants
###### Abstract  
The study aimed to prepare a biocomposite containing _Yarrowia lipolytica_ yeast cells with magnetic properties. The work proposes the use of this biocomposite as a sorbent for the removal of organic pollutants like methylene blue from liquids. The sorption process was conducted in a periodic process through which different parameters were analyzed such as initial concentration (50-250 mg/dm\({}^{3}\)), time of the process (0.167-24 h), and temperature (25-40 \({}^{\circ}\)C). To fit the experimental data to theoretical models, the Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich models were used. In addition, pseudo-first-rate, pseudo-second-rate order, Weber-Morris and Elovich kinetic models were evaluated. The highest fit was the Freundlich isotherm model (R\({}^{2}\) = 0.9959 and ARE = 3.41%) and the pseudo-second-rate order model (R\({}^{2}\) = 0.9989 and ARE = 1.14%). It has been shown that the process of removing methylene blue using a biocomposite is exothermic and its usefulness decreases with an increase in temperature (from 32.10 mg/g to 23.64 mg/g). To acquire information about the material characteristics, different instrumental methods were applied: FTIR, SEM-EDX, TGA, and VSM. This study provides new information on the possibility of using composites made of biochar, yeast, and magnetic particles in the process of removing dyes from an aqueous environment. The obtained biocomposite is effective in removing contaminants and is easily separated after the performed process.  
dye; adsorption; biosorption; equilibrium; kinetic; thermodynamic; biomass; methylene blue +
Footnote †: journal: Applied Sciences