esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Drying Kinetics  
Figure 1a shows the drying kinetics of _P. nodiflora_ leaves dried with CD and CPD-VMFD, while Figure 1b shows the drying kinetics of _P. nodiflora_ dried using VMD. Drying kinetics of all drying methods were best described by an exponential function, namely, the modified Page model (Equation (4)). Table 1 shows the values of both statistical parameters and mathematical constants of the [PERSON], modified Page and Henderson and Pabis model. Selection of the best model was based on the highest Coefficient of determination (\(R^{2}\)) and the lowest root-mean-square error (RMSE), thereby suggesting goodness of fit. The values, \(R^{2}\) and RMSE, obtained suggest a good agreement between the experimental values obtained and the modified Page model. Figure 1: (**a**) Drying kinetics of _Phyla nodiffora_ leaves dried using convective drying (CD) method at 40, 50, and 60 \({}^{\circ}\)C and vacuum microwave finish drying (VMFD) at 9 W/g preceded by convective predrying (CPD) at 50 \({}^{\circ}\)C. (**b**) Drying kinetics of _P. nodiffora_ leaves dried using vacuum microwave drying (VMD) at 6, 9, and 12 W/g. Drying duration of CD was the longest compared with VMD and CPD-VMFD. At the initial stage of CD, moisture loss was fast; however, successive moisture loss reduced with time. This phenomenon occurs because CD uses surface heating; the evaporation of moisture from the surface of plant material was efficient but moisture diffusion from internal region to the surface is slow and rate-limiting [19]. However, when VMFD at 9 W/g was applied to convective pre-dried leaves, drying time was 105 min, as opposed to 240 min of CD, thereby achieving 2.3 times shorter drying duration. The relationship between drying rate and moisture ratio is shown in Figure 2. The drying rate of VMD was the highest, which was attributed to the volumetric heating of microwave drying. Microwaves penetrate inner regions of leaf tissue, thereby producing heat by the absorption of microwave energy by water molecules. Increased positive steam pressure in leaf structure pushes vapor to the surface to be removed [20]. CD recorded the lowest drying rates, but these values increased with increased temperatures. An increase from 40 to 50 \({}^{\circ}\)C showed an average increase of 0.003 min\({}^{-1}\), whereas in the case of VMD, an increase from 6 to 9 W/g showed a higher average drying rate of 0.012 min\({}^{-1}\). The low drying rate of CD at 50 \({}^{\circ}\)C can be compensated for by the introduction of VMFD, which showed a considerable improvement in drying rate at low moisture ratios.
## 4 Results and Discussion
### Inference Results
Secondly, the economic development stage, industrial structure, and population distribution will impact land use classification, so the application of data sets should be combined with local socio-economic conditions. In the future, we can conduct case studies in specific regions or countries, verify and adjust the development methods of data sets, and gradually extend them to wider regions. Through practice and continuous improvement, the portability and universality of this method can be enhanced. On a larger data set, the model's accuracy may remain stable or decrease, depending on the quality and diversity of the data. In different cities, the model's accuracy may vary due to changes in land use patterns. The model's generalizability to new cities or larger data sets is critical. If the model overfits the original data set, its generalizability may be compromised. Consequently, it is essential to refine the model and establish a standardized data processing and model training protocol to facilitate rapid deployment across diverse data sets and enhance its capability to handle large data sets.
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
## 5 Parameter Analysis  
To quantitatively analyze the impact of three factors--the distance between the pipeline and the foundation pit, the depth of pipeline burial, and the depth of pit excavation on pipeline deformation--a comparative study was conducted using the relative sensitivity analysis method [22]. The sensitivity coefficient \(\eta_{\text{SR}}\) is defined as the ratio of the change rate in the output results to the change rate in the input variables. The calculation formula for sensitivity \(\eta_{\text{SR}}\) is as follows:  
\[\eta_{\text{SR}}=\frac{\left[\frac{f(x_{\text{IR}})-f(x)}{f(x)}\right]}{ \left[\frac{x_{\text{IR}}-x}{x}\right]} \tag{1}\]  
In this formula, \(x\) and \(x_{\text{LR}}\) represent the reference and various values of the input variable, respectively;\(f(x)\) and \(f(x_{\text{LR}})\) correspond to the output results for \(x\) and \(x_{\text{LR}}\).  
The calculation formula for sensitivity \(\eta_{\text{SS}}\) is as follows:  
\[\eta_{\text{SS}}=\eta_{\text{SR}}\frac{\max(x_{\text{R}})-\min(x_{\text{R}})} {x} \tag{2}\]  
where \(\max(x_{\text{R}})\) and \(\min(x_{\text{R}})\) represent the maximum and minimum values of the parameter's variation. By normalizing the magnitude of the changes, different sensitivity ratios can be dimensionless-weighed. Table 6 presents the sensitivities of each factor to pipeline displacement. The sensitivities to both vertical and horizontal displacements of the pipeline,  
Figure 10: Comparison of pipeline displacements for different pipeline burial depths. (**a**) Vertical displacement; (**b**) horizontal displacement.  
in descending order, are the distance between the pipeline and pit, the depth of pipeline burial, and the depth of pit excavation. The impact of the distance between the pipeline and pit on vertical displacement is less than that on horizontal displacement. The influence of pipeline burial depth on vertical displacement is greater than on horizontal displacement. The effect of pit excavation depth is almost equal on both.  
Therefore, it is evident that during excavation in composite soil-rock strata, the distance between the pipeline and the pit has the greatest impact on pipeline deformation, followed by pipeline burial depth, with the depth of pit excavation having a relatively weaker impact. In the pit design process, priority should be given to the horizontal distance between the pit and the pipeline, and the pipeline should be buried as deep as possible, considering construction costs.
# Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning
## 4 VM Migration Strategy Based on MADRL  
For the above optimization objectives, taking into account the mobility of users and the dynamic changes of the server, as well as the sensitive delay requirements of tasks, it is difficult for traditional methods to solve them, so this paper uses the method of deep reinforcement learning to solve the migration strategy. In the existing DRL-based VM migration strategy research, it is often assumed that the global information of mobile devices and servers in the system can be obtained, but in practical applications, due to the delay of network transmission, it is difficult to achieve global information synchronization. Therefore, this paper uses MADRL to generate the VM migration strategy in the mobile edge environment. Each MEC server, as an independent agent, learns the migration strategy by interacting with the environment to minimize the migration cost.  
In Reference [22], the system considered by the authors consists of a central control unit. The authors first assume that the tasks of User Equipment (UE) are successfully received to the central control unit of MEC as a UE request. The central control unit then detects the incoming UE request, selects the best Mobile Edge Server (MES) for the request and allocates resources, during which the authors assume the UE continuous transmission location. Finally, the central control unit can issue a command to migrate its VM to another MES and migrate the VM to that MES. Referring to the previous work related to virtual machine migration, we also assume that the VM migration mechanism between server hosts uses live VM migration technology. According to the concept of virtualization, VM is hosted by a virtual machine monitor (VMM), which runs on the server host. In modeling, we assume that the operational state of the VMM can be integrated into the operational state of the server. When the server makes a decision that the VM needs to be migrated, it triggers the live migration of the VM. The monitor identifies the destination server host to which this VM is to be migrated [23]. The live migration process is triggered by loadingthe VM instance and related user data from Network Area Storage (NAS) [24; 25] to the destination server host by switching the current connection. After this process is completed, the VM can restart and continue to perform subsequent computing tasks on the server. In order to simplify the model migration process, we do not consider the unexpected failures in the process of data transfer between servers. These failures may be sudden network outages, memory errors at the source or destination, data packet corruption or loss in transmission [26].
# Climatic Indices over the Mediterranean Sea: A Review
## 3 Large-Scale Atmospheric Forcing: Climatic Indices
### The East Atlantic (EA) Pattern  
The EA pattern is defined as the SLP anomaly in the region between the two centers of the NAO dipole (approximately between Greenland and the British Islands-Baltic Sea area, Figure 3). When the EA is in a positive phase, this region is characterized by a negative SLP anomaly, enhancing strong cyclonic winds in the North Atlantic (Figure 3a). Conversely, a negative EA phase results in positive SLP anomalies (Figure 3b) that induce northeasterly flow of cold dry air, increasing the air-sea temperature and humidity gradients and leading to strong positive anomalies in the heat loss over the Mediterranean basin.  
In a recent research [54], it is estimated that, on an annual basis, the climatological effect of the EA and NAO are of the same magnitude (\(\sim\)5.5 Wm\({}^{-2}\) in 46% of the basin). One study [28] found a stronger effect of the EA pattern on winter heat losses and a minor impact of NAO. Since they analyzed a similar time period (1958-2006) of the same dataset, differences are probably attributable to the seasonal variability of the EA pattern that is reinforced in wintertime and weakens in spring and summer [44; 56]. The enhanced EA high (in its negative state) and a slight drift towards the east during winter may lead to a more important influence over the whole basin in this season and a prominent role in the heat loss represented by intermediate and deep water formation processes [28; 29; 32].  
Figure 6: Time series of the yearly (dashed lines) and decadal (5-year running means, solid lines) variability of the main climatic indices over the Mediterranean region. Notice that the interannual variability is of more interest than the value itself.
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 3 Results
### SIF and Climate: Linear Mixed Model Analysis  
The largest fixed continuous effect at all Linear Mixed Models (LMMs) was that of \"Soil Moisture\" (Table 2, Estimate and Chisquare values). The categorical factor \"Ecoregion\" was the most significant variable for all unmodified SIF variables as well as for the \"Prod\" and \"SZA\" modifications but, the \"Yield\" modification reduced its relative importance and increased that of \"Soil Moisture\" (as encapsulated in Chisquare values). While SIF\({}_{FR}\) and SIF\({}_{R}\) models responded in a similar manner, concerning the proportional effects of each factor, SIF\({}_{R/FR}\) was not significantly affected by any factor (Table 2). Figure 5: Data trends of SIF\({}_{FR}\), SIF\({}_{R}\), Temperature and Precipitation, from February 2007 to December 2017. Trends obtained from time-series decomposition with Locally Estimated Scatterplot Smoothing (LOESS). SIF\({}_{FR}\) and SIF\({}_{R}\) are Sun-Induced Fluorescence from GOME-2 at the far-red and red peaks, respectively. Temperature is MODIS daytime Land Surface Temperature. Precipitation is TRMM Precipitation rate. All graphs have the same axis ranges for all variables. The random factor Month explained a similar amount of variance as was ascribed to residuals in all models except those involving \(\text{SIF}_{R}\), capturing the seasonality effect as expected (Appendix A Table A3). The nested Month:Year random factor was not so significant to the models but was maintained to constrain the error structure as it was shown to improve model quality during model selection (results not shown). Model analysis showed that \(\text{SIF}_{FR}\)-Prod had the best fit among the tested variables (Table 3, R\({}^{2}\) values) however, \(\text{SIF}_{FR}\)-SZA, \(d\text{SIF}_{FR}\) and \(\text{SIF}_{FR}\) were very close by comparison. Nevertheless, \(\text{SIF}_{FR}\)-SZA was the tested model of highest quality as can be observed by its low RMSE, RMEL and BIC results (Table 3). The model using \(\text{SIF}_{FR}\)-Yield as its response variable performed worst than unmodified variables, including \(\text{SIF}_{R}\). The model using \(\text{SIF}_{R}\) had worse fit than most other models and \(\text{SIF}_{R/FR}\) was not significantly affected by any fixed or random variable and the model employing it as the response variable had the worst results in all model quality and goodness-of-fit metrics (Tables 2 and 3).
## 4 Architecture Distribution Characteristics
### Quantitative Analysis of Lobe Dimensions  
The individual lobe is the most developed and widely distributed architectural element in the study area. Based on the single-sand-body architectural dissection results for each sublayer, a statistical analysis of lobe dimensions was conducted. The results indicate that, overall, lobe thickness and width exhibit a linear correlation, with width-to-thickness ratios predominantly ranging from 70 to 170, averaging around 128 (Figure 6).  
At the sublayer level: During the X52-X51 period, lobe development initially intensified and then weakened. In the early stages, lobes had relatively small width-to-thickness ratios. By the middle to late depositional stages, channels became shallower, lobe connectivity increased, and width-to-thickness ratios grew, primarily clustering around 130. During the X42 period, width-to-thickness ratios exhibited a broader range, with varying intensities of channels and lobes. By the X41 period, channel-lobe preservation stabilized, with width-to-thickness ratios predominantly between 120 and 140.  
Comprehensive analysis reveals a positive correlation between lobe thickness and width: larger lobes tend to be both thicker and wider. In proximal areas, lobes exhibit smaller width-to-thickness ratios, whereas in distal areas, increased lobe connectivity leads to progressively larger width-to-thickness ratios. This relationship provides a basis for estimating lobe development scale and depositional positioning.
# Meta-Heuristics Optimization of Mirrors for Gravitational Wave Detectors: Cryogenic Case
## 4 The Optimization Methods
### Optimization Heuristic NSGA2 and NSGA3  
The NSGA2 algorithm (see references [34; 35]) implements two main strategies: a fast non-dominated sorting solution, and the preservation of the solution's diversity. The initial population is generated at random according to the number of defined chromosomes. The population sorting is made by non-dominated solutions. This step follows the same procedure used in [35]; the crowding distance is also applied. The selection (i.e., the natural selection) is made by the tournament method, where the confrontation of two individuals of the initial population (chose at random) is made to stock the individuals with the best Pareto front. If the two confronted members are of the same front, the decision is made according to the crowding distance criteria. The crossover algorithm generates two cut-points selected at random in one parent, so the chromosome is divided into three sub-vectors (segments); then, the first segment is exchanged with the last one.  
The last step is a population update. After applying the genetic operators and the population sorting criterion (fronts and crowding distance), we must determine the individuals that will be kept for the next generation. We begin with an initial population; we create the children in order to have a final population that will be classified in terms of fronts and the crowding distance to keep a constant number of individuals (i.e., the population size remains unchanged).  
The NSGA3 algorithm is a modification of NSGA2 specifically designed to handle optimizations with more than two objectives. This enhanced algorithm was proposed in [36] but with some selection mechanisms changed.
# Flexible Antennas: A Review
## 10 Flexible Antenna for Future Wireless Solutions
A parametric study has also been conducted on how changing the angle of the arms of the Y shaped antenna affects the resonant frequency and the bandwidth. The study shows that changing the Y shaped structure to T shaped structure gradually lowers the resonant frequency from 27.71 to 24.60 GHz. It has been claimed as a reconfigurable antenna, that is not justified in the article it how it can be tuned dynamically. In another study [278], a dual-band wearable antenna for smartwatch is proposed based on new flexible material \"ULTRALAM(r)850 HT\", and operates at 38 and 60 GHz. It is a rectangular antenna with six U shaped slots in the radiating patch. The response of the antenna is verified using two commercial software HFSS and CST. No experimental validation has been provided in this research. 5G technology is aiming at tackling the vastly growing and developing IoT domain. Many novel and future solutions, such as integrated wearable devices, household appliances, industry solutions, robotics, self-driving cars, and other solutions, are expected to benefit from the 5G network. Another equally important aspect of the forthcoming 5G is the ability to manage vast amounts of 'always-connected' IoT devices. Flexible antennas are a key component for realizing future wireless solution leveraging the 5G technology.
# Establishing Reference Data for Electromyographic Activity in Gait: Age and Gender Variations
## 2 Materials and Methods
### Signal Processing  
Raw EMG signals were processed as follows: band-pass filtering (Butterworth filter, cutoff frequency: 20-350 Hz), rectification, smoothing (Butterworth low-pass filter, cutoff frequency: 9 Hz), amplitude normalization to the mean signal, and time normalization to one gait cycle (101 data points). Since most lab visitors are patients with motor control deficits and anatomical deformities, and no software robustly determines gait events, these events (for patients and healthy individuals) were manually identified under the supervision of experienced operators. A gait cycle was defined as the interval between two consecutive heel strikes of the same foot [23]. The processed data were then averaged across valid strides using MATLAB (2018b, The MathWorks, Inc., Natick, MA, USA) [21]. The peak value of the time series and its occurrence within the gait cycle, along with the mean values over the entire cycle, were calculated. Additionally, these features were analyzed in relation to time by computing them for the full stride, as well as separately for the stance and swing phases [23].  
To support the findings from EMG envelopes, additional kinematic, kinetic, and spatiotemporal (ST) parameters, namely cadence, walking speed, stride time, stride length, and foot-off time, were calculated from the gait data to examine differences between age and gender groups. Additionally, the gait data included the peak angles, net joint moments, and powers (normalized to body mass), at the ankle, knee, and hip joints in the sagittal plane (flexion-extension/dorsiflexion-plantarflexion).
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 4 G2V/V2G Operations
### Grid-Connected Three-Phase SMR/Inverter  
#### 4.2.1 Power Circuit  
The schematic of the established grid-connected SMR/inverter is shown in Figure 16, with the following specifications and system components:  
* Three-phase AC line voltage: \(V_{LL}=220\) V/60 Hz.
* DC-link voltage: \(V_{dc}=400\) V.
* PWM switching frequency: \(f_{s}=30\) kHz.
* DC-link capacitor: \(C_{dc}=1100\)\(\mathrm{\SIUnitSymbolMicro F}/900\) V(DC).
* Energy storage inductors: the three inductors are \(L_{A}\approx\ L_{B}\approx\ L_{\mathrm{C}}=2.5\)\(\mathrm{mH}/30\) kHz.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 4 The PingBasedDown Algorithm
### Link Evaluation Model
#### 4.1.2 Jitter Accumulating Module  
Calculating the variance of data is a commonly used approach to measure the jitter. However, this method is inaccurate in some cases. For example, the two vectors \(d_{1}=(1,1,1,100,100)\) and \(d_{2}=(1,100,1,100,1,100)\) have the same variance, but what we want is that the jitter of \(d_{2}\) is higher than that of \(d_{1}\). Therefore, in this module, we quantify the jitter of \(\widehat{I_{n}}(v_{i},v_{j})\) by accumulating the quantified variation rate. Let \(\mathcal{A}\) denote the quantification result. We quantify the jitter by the following steps:  
Giving a vector of numbers \(X\in\mathbb{R}^{N_{w}}\), we use first-order difference of this vector \(\Delta X\) to extract the rate of variation, i.e., jitter. Then we obtain the quantified jitter by accumulating the absolute value of \(\Delta X\). The output of this module \(\mathcal{A}\) can then be represented as follows:  
\[\mathcal{A}:\mathbb{R}^{N_{w}}\rightarrow\mathbb{R},\ \mathcal{A}(X)=\sum| \Delta X| \tag{5}\]  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Notation** & **Parameter Name** & **Description** \\ \hline \(N_{w}\) & History size & How many groups of rtt history we use as the basis. \\ \hline \(F_{S}\) & Filter strength & Indicates the strength of filtering, e.g., the proportion of noise in the records. \\ \hline \(L_{pos}\) & Latency positioning factor & Indicates rate of pure latency participate in calculating among history of RTTs. \\ \hline \(T_{alert}\) & Alert threshold & If the normalized accumulated value is higher than this threshold, mark the link as Unhealthy \\ \hline \(T_{safe}\) & Safe threshold & If the normalized accumulated value is lower than this threshold, mark the link as Healthy \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of Link Evaluation Model.