esa-sceva/satcom-chunk-collection huggingface dataset:
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 6 Grid Constraints and Network Visibility
### Network Visibility  
The DSO needs clear visibility of assets and their operation within the grid to help address issues such as congestion. The prosumers, on the other hand, require guarantees that transport capacity is always available as they trade with one another or offer energy in support of grid reliability. In such cases, the DSO needs to know the electrical and geographical location of all DER and DR assets in its domain [104]. With such visibility, the DSO may rely on the grid-edge capabilities to address local flow constraints and voltage violations [104]. To achieve this, one of the hurdles to cross is the development of new tools. Traditional analytic tools such as state estimation and power flow requiredetailed electrical modelling of the grid using the network topology information. Since most of the DER and DR devices are on the customer side of the meter, sometimes connected through secondary transformers, the traditional method must give way to data-driven modelling techniques. Apart from the electrical characteristics of the network, other crucial information such as DR program, contractual constraints and prosumer preference may not be readily available. Therefore, a structured platform is required to harness these pieces of data from various sources and provide consolidated information to the DSO from which the system state can be evaluated. This implies that all TE actors and assets must be capable of exchanging information with the TE platform.
# Analytical Approach in the Development of RF MEMS Switches
## 2 Analytical Approach to the Development of RF MEMS Switches of Electrostatic Type
### Development and Modeling of Equivalent Circuit  
For this integrated RF MEMS switch, the electrical impedance is determined by Equation (30):  
\[Z=R_{s}+jwL+\frac{1}{jwC} \tag{30}\]  
For a given LC circuit the resonance frequency is determined by Equation (31):  
\[f_{0}=\frac{1}{2\pi}\frac{1}{\sqrt{LC}} \tag{31}\]  
Thus, the resistance of the switch can be represented by Equation (32):  
\[\begin{cases}\frac{1}{jwC},f<<f_{0}\\ R_{s},f=f_{0}\\ jwL,f>>f_{0}\end{cases} \tag{32}\]  
The elastic fastening of the membrane plays an important role for the RF parameters of the switch, since they have inductance and resistance, affecting the passage of the RF signal.  
To calculate the RF characteristics of the proposed design of an integrated RF MEMS switch LC isolation elements (\(C_{1},C_{2},C_{3},C_{4},C_{5},C_{6}\)) for the control voltage from the RF signal were introduced into the existing electrical circuit which is implemented directly on the topology--capacitances located on the signal line (\(C_{1},C_{2}\)) and membrane anchors (\(C_{3},C_{4},C_{5},C_{6}\)) separating the constant control voltage from the RF signal. Inductance preventing the passage of the RF signal in power supply is located outside the circuit.  
To calculate the inductance of the elastic fastening element (\(L_{1},L_{2},L_{3},L_{4}\)) were used Equation (33) [76].  
\[L=0.002l(\ln(\frac{2l}{w+t})+0.50049+(\frac{w+t}{3l})) \tag{33}\]  
where \(l,w,t\)--the length, width, thickness of the conductor.  
When calculating the switch, the inductance resistance was calculated by Equation (34):  
\[R=\rho\frac{L}{S} \tag{34}\]  
where \(\rho\)--the resistivity of multilayer structure; \(L\)--the length of the conductor; \(S\)--the area of the conductor.  
As a result of the calculations, the parameters of the electrical circuit of the proposed integrated RF MEMS switch were obtained, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(N\) & \(k\), \(N\)/m & \(V_{pull-down}\), \(V\) & \(V_{hold-down}\), \(V\) & \(V_{return}\), \(V\) & \(t_{pull-down}\), \(\mu\)s & \(t_{pull-up}\), \(\mu\)s \\ \hline
1 & 65.65 & 5 & 3.8 & 0.89 & 9.3 & 2.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculated values of the coefficient of stiffness, pull- and hold-down voltage, return voltage, switching time.  
A electrical circuit of a integrated RF MEMS switch with LC isolation control signal is shown in Figure 16. The results of simulation of RF signal transmission are shown in Figure 17. Optimization and calculation of RF parameters of integrated RF MEMS switch is carried into effect in the Advanced Design System (ADS) CAD.
# CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification
## 4 Experiment
### Implementation Details  
Since active learning focuses on sampling strategies, we did not modify the model or evaluation methods to our advantage. We used the BERTforSentenceClassification model from the HuggingFace library [32]. This model applies BERT-base [13] with an added linear layer for classification. We train the model for 3 epochs in each iteration, evaluating it 5 times per epoch [33]. We keep the model that records the lowest validation loss among these evaluations and use it for evaluation on the Test Set, OOD (Out-Of-Domain), and the unlabeled data pool. This method not only improves performance but also prevents the model from fitting the training data too strongly. If the model fits too strongly to the current labeled data pool, the features of most labeled data will be strongly crowded, and the uncertainty of model predictions will be extremely low. A visualization of the process of evaluating and sampling data in the unlabeled data pool based on the labeled data pool  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **Task** & **Domain** & **Ood Dataset** & **Train** & **Val** & **Test** & **Classes** \\ \hline IMDB & Sentiment Analysis & Movie Reviews & SST-2 & 22.5 K & 2.5 K & 25 K & 2 \\ SST-2 & Sentiment Analysis & Movie Reviews & IMDB & 60.6 K & 6.7 K & 871 & 2 \\ QNLI & Natural Language & Wikipedia & - & 99.5 K & 5.2 K & 5.5 K & 2 \\ QQP & Paraphrase Detection & Social QA Questions & TWITtTERPPDB & 327 K & 36.4 K & 80.8 K & 2 \\ PUBMED & Topic Classification & Medical & - & 180 K & 30.2 K & 30.1 K & 5 \\ AGNEWS & Topic Classification & News & - & 114 K & 6 K & 7.6 K & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset analysis. All datasets use reference settings, and all classes are balanced: 1% of each train set is used as a labeled data pool, and 99% is used as an unlabeled data pool. After that, 2% is sampled from the train set.  
is shown in Figure 4. We conduct these experiments using five different random seeds from the range [1, 9999].
# Analysis of a Landslide on a Railway Track Using Laser Scanning and FEM Numerical Modelling
###### Abstract  
In this study, we present an analysis of the causes of a landslide along a railway track in the Polish Lowlands. The landslide damaged the railway track and caused significant material losses. Digital models of the terrain surface before and after the landslide were elaborated. Remote sensing using LIDAR aerial technique and a terrestrial laser scanner was performed to determine the morphology. Soil mass behaviour was analysed by 3D numerical simulation. A numerical model was created based on geotechnical tests. Taking into account the behaviour of the dry and wet models, the numerical simulation showed the most probable scenario of mass movement. The main reasons for the landslide were rainwater infiltration in the track basement and the unfavourable morphology of the area on which the railway embankment was located. The study demonstrates that combined methods--laser scanning, geotechnical testing of the soil material, and 3-dimensional numerical simulation--enabled the assessment of the causes of the analysed landslide.  
railway embankment; stability analysis; laser scanning; numerical modelling +
Footnote †: journal: Applied  
**Category**  
+++ ==WARNING: Truncated because of repetitions==  
++  
of great importance when a landslide is activated. Water infiltration significantly affects the level of groundwater. Frequent fluctuations in the groundwater level (alternating saturation and drying) contribute to a reduction in soil strength, which facilitates ground movement [13].  
Ground surface monitoring is quite frequently used to reduce the risk of landslides. One monitoring method is the remote sensing technique. A terrestrial laser scanner gives good results for monitoring earth structures, as well as for railway and road embankments [14; 15; 16; 17; 18; 19; 20; 21].  
In this study, we present an analysis of the causes of a landslide along a railway track in the Polish Lowlands. This type of area is not considered to be prone to the formation of land movements. In the north of Poland, landslides are rare and usually occur on slopes next to rivers. In this case, specific conditions for the activation of mass movements of soil occurred. We performed an analysis of a landslide along a railway track using morphology derived from laser scanning, geotechnical analysis of the constituent soil material, and 3-dimensional finite element analysis. The analysis is common practice nowadays; however, as the study is on a railway track, it has significant social importance.
## 3 Results
### Metabolism Alterations in Response to Day/Night Cycle and Temperature Fluctuations  
An assessment of the effects on the biochemical composition of the \(N\). sp. QU130 (total sugar, protein and total lipids content) was carried out under the four different cultivation conditions (Figure 8) and at four different times during each cycle (Figure 9).  
In constant light and temperature conditions, the culture accumulated 30%, 10%, and 60% DW of total sugar, lipid, and protein content respectively. Conversely, a decrease in sugar content to approximately 20% DW was observed in the results obtained under day/night, temperature, and combined cycles, and a significant increase in total lipids to 20%, 40%, and 50% DW respectively. The explanation for these phenomena might be the accumulation of lipids as a resistance mechanism under varying temperature and light conditions [37]. However, the protein content was stable during the day/night cycle at 60% DW, but decreases to 40% and 30% DW were recorded for protein concentrations under the temperature fluctuation and combined cycles. This again emphasizes the marked effect of stressful conditions on cells physiology. According to our results, this could be mainly attributed to the effect of temperature here, which resulted in an increase in lipids with a parallel decrease in proteins.  
In addition, total sugar, protein, and total lipids contents were analyzed for each cultivation regime over 24 h (Figure 9, lipids were not measured because the volume needed for analysis was too great). The results from day/night cycles did not reveal any significant change in either total sugar or protein concentration with increasing light intensity (Figure 9A), although these findings are clearly contrary to the trend described and confirmed by several researchers--that increasing light intensity is always associated with a decrease in protein content and an increase in the lipid fraction and polysaccharides [38; 39; 40]. Additionally, an assessment of the biochemical composition evolution under the temperature cycle also revealed stability in the sugar and protein concentrations under temperature fluctuation stress (Figure 9B). On the other hand, a different observation was recorded under combined light and temperature cycles (Figure 9C). The results showed that total sugar concentration increased up to 60% at midday, when PFD and T were at high levels, but decreased again by the same percentage at night. This decrease in carbohydrate concentration occurs when microalgae cells convert carbohydrates into lipids when it is dark [41], and can also be explained by the fact that sugar is consumed for respiration in the absence of light [42].  
Figure 8: Total sugar, protein and total lipids contents of \(N\). sp QU130 after the implementation of fluctuation stress of three different cultivation regimes: Cultivation under constant light and temperature, day/night cycles, temperature fluctuation and combined light and temperature cycles.
# Unraveling Convolution Neural Networks: A Topological Exploration of Kernel Evolution
## 3 Experiments
### Comparative Analysis of Betti and ROC Curves Across Iterations  
To provide deeper insight into the relationship between the topological changes in the convolutional kernels and the performance of the CNN models, we conducted an experiment to track the development of the ROC curve--a reliable performance metric for classification models--over the duration of network training. The ROC curve serves as a graphical representation of a model's diagnostic ability, plotting the true positive rate against the false positive rate at various threshold settings. By examining the trajectory of the ROC curve along with the concurrent topological shifts in the convolutional kernels, encapsulated by the Betti-1 curves, our goal was to discern the interplay between a CNN's learning process and the evolution of its internal structural complexity.  
The experimental results are presented in Figure 6, which includes a comparative analysis of Betti and ROC curves across different training iterations. The ROC curve (Figure 6a) shows the trade-off between the true positive rate and the false positive rate. To enhance the clarity of our ROC analysis, we have included a zoomed-in view of the ROC curve (Figure 6b), which allows for a detailed examination of the model's performance in the critical threshold range. The progression toward the upper left corner with increasing iterations indicates an improvement in model accuracy.  
Figure 5: Representation of Betti curves corresponding to the third-layer convolutional kernels of Network II across two distinct datasets: one with randomly generated, three-channel noisy images and another comprising solely images with pure black or white pixels.  
Figure 6: Comparative analysis of Betti and ROC curves across iterations.  
The Betti-1 curve (Figure 6c) illustrates the evolution of the topological features within the convolutional layers of the CNN. Throughout the course of training iterations, the Betti-1 curve undergoes significant changes, suggesting a refinement in the topological complexity of the kernel space. Notably, the peak of the Betti-1 curve becomes less pronounced with more iterations, implying that the network is optimizing its internal representations. A correlation analysis between the shifts in the Betti-1 curve and the ROC curve demonstrates that as the Betti-1 curve stabilizes (indicating a mature kernel topology), the area under the ROC curve (AUC) increases. This suggests that the topological changes in the convolutional kernels are reflective of the CNN's learning progress.
## 1 Introduction
### Active Distribution Network Management
#### 1.1.3 InteGrid's Approach  
The European Union's Horizon 2020 project InteGrid [22] aims at bridging the gap between citizens and technology/solution providers such as utilities, aggregators, manufacturers and other agents providing energy services. Hence, it expands from DSO's distribution and access services to active market facilitation and system optimization services, while ensuring sustainability, security and quality of the energy supply. In the InteGrid project, a new tool for voltage control designed specifically for LV networks is currently being developed--the LVC module. In the context of InteGrid, it is assumed that the presence of DER can be exploited by the DSO for grid control and management purposes [23]. These DER can be either owned by the DSO, such as storage devices, or licensed by DSOs to participate in grid operation under a contractual agreement. The proposed tool is intended to be a decision support tool, to assist the DSO in the management of the LV grid in an active manner. Its aim is to identify a set of preventive control actions that can be implemented by the DSO in order to avoid foreseeable technical problems in the LV grid, such as voltage violations or branch/transformer overloads. Preliminary results, with a test LV network and typical load and generation profiles are presented in Reference [24]. In this work, the proposed LVC algorithm is described, and extensive results from simulations performed on real LV networks were obtained and partially presented in this paper.
## 3 Silicon Germanium Waveguides and Nonlinear Applications
### Low Birefringence SiGe Waveguides  
The impact of Ge concentration on a host of optical properties means an additional flexibility in creating nonlinear photonic devices with specific phase matching and/or modal characteristics. One specific example that this versatility facilitates is the ease of realizing low-birefringence nonlinear waveguides.  
Low birefringence behavior, characterized by the presence of a significant Transverse Electric (TE)/Transverse Magnetic (TM) modal symmetry, is a crucial requirement in many signal processing applications that utilize vector parametric processes such as polarization-insensitive wavelength conversion. It was shown, that a strong TE/TM mode similarity across a broad wavelength range as well as a large nonlinear coefficient can be achieved in SiGe devices through multiple device designs and structural geometries [27; 30; 54; 55]. In particular, it was found that a buried device structure (i.e., encapsulating the waveguide in a Si layer) allows a simple and effective route towards combining low loss, high nonlinearity and low birefringence behavior.  
Figure 11 shows the calculated dispersion curves as well as the nonlinear coefficient of the two polarization axes of a 1 \(\upmu\)m Si\({}_{0.8}\)Ge\({}_{0.2}\) buried waveguide around the 1.55 \(\upmu\)m region where the two polarization modes were predicted to exhibit very similar dispersion and nonlinearity across the 1.40-1.70 \(\upmu\)m band [30; 55]. There properties were exploited to demonstrate the first single-pass polarization-insensitive wavelength converter in a silicon-based device [27], achieving a polarization dependent loss (PDL) of 0.42 dB only. Furthermore, these SiGe devices were utilized to demonstrate the first passive and CW-pumped polarization-assisted (PA) phase sensitive amplifier (PSA) in a silicon-based waveguide exhibiting a record phase sensitive extinction ratio (PSER) of 28.6 dB for a low input pump power [29; 30]. These demonstrations highlight the versatility of the SiGe platform and its emergence as a competitive technology and a promising candidate for a wide range of all-optical signal processing applications. Please refer to Ref. [27] for a more detailed description of the device shown in Figure 11 and the capabilities of such designed devices.
# A Channel Allocation Mechanism for Cellular Networks
###### Abstract  
In cellular networks, call blocking causes lower customer satisfaction and economic loss. Therefore, the channel allocation for call block avoidance is an important issue. This study proposes a mechanism that considers the real-time traffic information (e.g., traffic flow and vehicle speed) and the user behavior (e.g., call inter-arrival time and call holding time) to analyze the adaptable number of communication calls in the specific cell for channel allocation. In experiments about call block probabilities (CBP), this study simulated two cases that are the situations of the whole day and traffic accident. The simulation results show that all CBPs in the scenario of whole day are less than 21.5% by using the proposed mechanism, which is better than using the static channel allocation (SCA) mechanism. Moreover, all CBPs in the scenario of traffic accidents are less than 16.5% by using the proposed mechanism, which is better than using the SCA mechanism. Therefore, the proposed mechanism can decrease the number of CBPs effectively.  
cellular network; call drop; call block; channel allocation +
Footnote †: journal: International Journal of Information Management and Finance  
_Article_  
A Channel Allocation Mechanism for Cellular Networks
# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
###### Abstract
The first case in the UAE was reported on 23 January 2020 and case numbers have been rising ever since, with an average of 1000 cases daily during the pandemic days [6; 7]. To combat COVID-19, the UAE has taken many initiatives, which are: surveillance and contact tracing, by introduction of apps such as Al Hons; containment of spread, by limiting the gathering of people; online schooling and remote work; closure of public places; sanitation drives; more frequent testing and more accessibility to testing, by adding more government testing centers and adding extra PCR-test requirements, such as the need to have a negative test result 6 h before flight when traveling from high-risk countries; and fast and organized vaccine drives for all people living in the UAE. Despite the effectiveness of these intensive precautions measures, the country was far from having zero cases per day. To reduce the spread of the disease, the available data must be analyzed in order to be used in forming more accurate future policies and interventions. The UAE government provided such data through the Federal Competitiveness and Statistics Centre (FCSC) which reports daily the cumulative number of cases, deaths, recovered cases, tests, and vaccinations that were recorded on that specific day. Hence, the goal of this work is to build a model that can forecast and analyze the trend of the spread of a pandemic or contagious disease. COVID-19 in the UAE was considered as a case study. Thus, the main objectives of this paper are defined as follows:  
1. Determine whether there is a relationship between the vaccinations and the trend in number of positive cases. 2. Forecast the number of cases per day based on previous history of the number of cases, deaths, and recoveries. 3. Forecast the number of cases per day using the added feature regarding events that can have a major impact on the spread of the disease, such as national holidays, airport closures, etc., to see the event's effect on trends and compare with the actual case trend. 4. Apply the objectives (1-3) considering the COVID-19 pandemic outbreak in the UAE as a case study. The contribution of this work lies in it being the only work found in the literature that looks into vaccination and events as suitable attributes for finding the best model for forecasting COVID-19. The rest of the paper is organized as follows. Section 2 presents the literature review. Section 3 discusses the materials and methods utilized in this work. The experimental results are presented in Section 4 and these results are discussed and interpreted in Section 5. Section 6 concludes the paper.
# Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment
## 3 HGSA Algorithm
### Initialization  
The key parameters of the HGSA algorithm are listed in Table 2.  
We do not set the maximum iteration of the GA because an automatic switching mechanism from the GA to SA is designed. When the optimization ability of the GA shows a decay trend, the algorithm automatically switches to SA to avoid falling into a local optimum.