esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 3 Methodology and Data Processing
### Inversion of Dispersion Curves  
The inversion of the obtained dispersion curves allows retrieving of the Vp, Vs, and density models at each of the recording sites. The Rayleigh wave phase velocity depends on these soil properties to a depth of one wavelength [43]. However, the layout and maximum diameter of the sensor array are critical to the success of the inversion process. To better constrain the results, prior knowledge of sediment layer velocities and thicknesses is also required. This information can be obtained from various sources, such as boreholes, geological sections, and seismic refraction techniques.  
There are different inversion methods, a review of which can be found at [44; 45]. In any case, the inversion process does not generate a unique solution. It generates multiple models with varying degrees of misfit between the calculated dispersion curve and the theoretical dispersion curve of the Rayleigh waves. The velocity model with the lowest misfit is contemplated.  
In the present work, the inversion process was performed using the Dinver software from the Sesarray package, which uses the neighborhood algorithm [46]. As the dispersion curves obtained from the f-k and SPAC analyses are almost similar, the dispersion curves obtained from the f-k analysis were used for the inversion process due to their simplicity of calculation compared to the SPAC method, which requires a dispersion curve to be manually picked. The input parameters required for the inversion, including Vp, Vs,and density ranges, as well as the number of layers, have been extracted from the nine boreholes (Figure 2) (Table 1). The borehole data have been obtained from the Laboratoire des Travaux Publics de l'Ouest (LTPO) and the Laboratoire National de l'Habitat et de la Construction (LNHC). A Poisson's ratio ranging between 0.2 and 0.5 (typical values for soils) was selected. The maximum number of iterations was set to 300, and for each iteration, 100 models have been generated. The experimental mean dispersion curve has been compared to the theoretical curve using a misfit value. The Vs model corresponding to the model with the minimum misfit has been then selected. The misfit is calculated using Equation (1) [47]:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(v_{i}^{\prime}-v_{i}\right)^{2}}{N}} \tag{1}\]  
where \(v_{i}^{\prime}\) is the \(i\)-th phase velocity of the obtained curve for each dispersion range, and \(v_{i}\) is the \(i\)-th phase velocity of the dispersion curve obtained with the raw signal. \(N\) represents the number of frequency points considered for the dispersion curve calculation.
# Physics-Guided Neural Network-Based Feedforward Control for Seamless Pipe Manufacturing Process
## 2 Materials and Methods
### Mathematical Models
#### 2.1.3 Linearized Reduced-Order Actuator Model with External Force
In a typical feedforward-feedback control structure, the feedforward component must generate the dominant part of the control signal to improve the system's responsiveness. Since the feedforward signal in this study is composed of two parts--one derived from the reduced-order physical model and the other from a neural network correction term--the overall control signal accounts for discrepancies in the analytical model. The neural network component compensates for the reduced-order model's limitations, particularly in regions where the approximation error is larger. Additionally, feedback control further refines the control signal, ensuring robustness even in operating conditions that deviate from the training set. Thus, despite the observed error levels in force prediction, the reduced-order model remains a suitable foundation for the proposed control architecture, as it provides a sufficiently accurate approximation of the system dynamics while enabling efficient PGNN-based correction. Figure 5: Comparison of pushing force step responses between fully nonlinear (\(F_{p}\)) and reduced-order linearized actuator model (\(F_{p,lin}\)). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\hat{a}\) & 14 [\%] \\ \(\hat{p}_{L}\) & 20 [bar] \\ \(\hat{Q}_{L}\) & 3.37 [L/min] \\ \(C_{q}\) & 0.286634 [L/min/\%] \\ \(C_{qp}\) & 0.019195 [L/min/bar] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Nominal values for the operating point.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 2 Background
### Bluetooth Mesh Stack  
Bluetooth Mesh network stack consists of the following layers:  
* **Bluetooth Low Energy Core Specification:** Bluetooth Mesh stack is built on top of the BLE specification.
* **Bearer Layer:** This layer defines exchange messages among nodes using ADV and GATT bearers, where ADV carries most of the traffic in the network.
* **Network Layer:** This layer defines addressing of transport messages, rules to relay, accept or reject messages, network message format, and encryption or authentication.
* **Lower Transport Layer:** This layer defines how to handle message segmentation and reassembly of transport PDUs.
* **Upper Transport Layer:** This layer defines how to handle encryption, decryption, and authentication of application data.
* **Access Layer:** This layer defines the format of application data, controls data encryption and decryption, maintains network context and application keys.
* **Foundation Model Layer:** This layer defines models to configure and manage a mesh network.
* **Model Layer:** This layer defines standardized application models such as lightning and sensor.
# Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting
## 3 Modeling of the PV Generation Scheduling Problem
### PV Scenario Generation  
In this paper, PV scenarios are generated using a normal distribution. The forecasted PV generation is used as the mean, and the energy deviation for each generation forecast level is used as the standard deviation.  
\[PV_{t}^{s}\ \sim\ \left(PV_{t}^{f},\left(\sigma_{t}\right)^{2}\right) \tag{24}\]  
where \(PV_{t}^{f}\) is a forecasted PV generation at time \(t\), \(\sigma_{t}\) is a standard deviation of the energy deviation of PV generation at time \(t\), which represents the standard deviation of the energy deviation for each level of forecasted power generation. The \(\sigma_{t}\) can be expressed as follows:  
\[\sigma_{t}=\left\{\begin{array}{c}\sigma_{(1)},\ b_{(1)}\leq PV_{t}^{f}<b_{( 2)}\\ \sigma_{(2)},\ b_{(2)}\leq PV_{t}^{f}<b_{(3)}\\ \vdots\ \ \ \ \vdots\\ \sigma_{(n)},\ b_{(n)}\leq PV_{t}^{f}<b_{(n+1)}\\ \vdots\ \ \ \ \vdots\\ \sigma_{(N)},\ b_{(N)}\leq PV_{t}^{f}<b_{(N+1)}\end{array}\right. \tag{25}\]  
where \(b_{(n)}\) is the PV generation boundary in the \(n\)-th interval, and \(\sigma_{(n)}\) is the standard deviation of the energy deviation in the set of conditions. PV generation scenarios can be generated by separately calculating the standard deviations of the energy deviation for different forecasted output ranges.  
Assuming that the PV scenarios follow a normal distribution, the probability of occurrence in each scenario can be calculated as follows:  
\[p^{s}=\frac{1}{S} \tag{26}\]  
where \(s\) is the number of scenarios. The randomly generated scenarios from the probability distribution in Equation (24) are applied to the \(PV_{t}^{s}\) in Equations (4), (5) and (10).
## 2 Study Area and Data
### Data Description and Processing  
#### 2.2.1 Landsat7 ETM+ Image and Pre-Processing  
The spatial resolution of Landsat7 ETM+ ([[https://glovis.usgs.gov](https://glovis.usgs.gov)]([https://glovis.usgs.gov](https://glovis.usgs.gov))) is 30 m, enabling high-precision extraction of crop area on a small scale [40; 41]. According to the weather conditions, image quality, and phonological characteristics of summer maize, the ETM+ images on June 9 th (sowing), September 13 th (milk-ripe), and October 31 st (harvested stage), 2012, were selected to obtain the summer maize cultivated area in the reference area. Striping, radiation calibration, atmospheric correction based on the FLAASH model, and clipping were performed for 3 images before classification.
###### Abstract  
Among the adaptive solutions, phase change material (PCM) technology is one of the most developed, thanks to its capability to mitigate the effects of air temperature fluctuations using thermal energy storage (TES). PCMs belong to the category of passive systems that operate on heat modulation, thanks to latent heat storage (LHS) that can lead to a reduction of heating ventilation air conditioning (HVAC) consumption in traditional buildings and to an improvement of indoor thermal comfort in buildings devoid of HVAC systems. The aim of this work is to numerically analyze and compare the benefits of the implementation of PCMs on the building envelope in both active and passive strategies. To generalize the results, two different EnergyPlus calibrated reference models--the small office and the midrise apartment--were considered, and 25 different European cities in different climatic zones were selected. For these analyses, a PCM plasterboard with a 23 \({}^{\circ}\)C melting point was considered in four different thicknesses--12.5, 25, 37.5, and 50 mm. The results obtained highlighted a strong logarithmic correlation between PCM thickness and energy reduction in all the climatic zones, with higher benefits in office buildings and in warmer climates for both strategies.  
phase change material; thermal energy storage; energy efficiency; passive strategies; active strategies; adaptive envelopes +
Footnote †: journal: Applied Sciences  
Article
## 2 Materials and Methods
### Numerical Method
#### 2.2.3 Mathematical Model  
The mathematical model that governs the studied phenomenon has already been presented in [PERSON] et al. [23] and will be briefly examined herein. The concept of interpenetrating continua relies on the fact that each phase occupies the whole domain, and thus, a separate set of conservation equations is solved for each phase. In the present case, written for \(q\)-th phase, which could be either vapor or liquid, we have  
* conservation of mass: \[\frac{\partial}{\partial t}\big{(}\alpha_{q}\rho_{q}\big{)}+\
abla\cdot\Big{(} \alpha_{q}\rho_{q}\overset{\rightarrow}{v}_{q}\Big{)}=\dot{m}_{pq}\] (3)
* conservation of momentum: \[\frac{\partial}{\partial t}\Big{(}\alpha_{q}\rho_{q}\overset{\rightarrow}{v} _{q}\Big{)}+\
abla\cdot\Big{(}\alpha_{q}\rho_{q}\overset{\rightarrow}{v}_{q }\overset{\rightarrow}{v}_{q}\Big{)}=-\alpha_{q}\
abla p+\
abla\cdot \overset{\rightarrow}{\tau}_{q}+\overset{\rightarrow}{R}_{pq}+\dot{m}_{pq} \overset{\rightarrow}{v}_{pq}\] (4)
* conservation of energy: \[\frac{\partial}{\partial t}\big{(}\alpha_{q}\rho_{q}h_{q}\big{)}+\
abla\cdot \Big{(}\alpha_{q}\rho_{q}\overset{\rightarrow}{u}_{q}h_{q}\Big{)}=\alpha_{q} \frac{dp_{q}}{dt}+\overset{\rightarrow}{\tau}_{q}:\
abla\overset{ \rightarrow}{u}_{q}+\Phi_{pq}+\dot{m}_{pq}r_{0}\] (5)  
where \(\alpha_{q}\) denotes the volume fraction of \(q\)-th phase, \(\rho_{q}\) is \(q\)-th phase's density, \(\overset{\rightarrow}{v}_{q}\) is the \(q\)-th phase velocity, \(\dot{m}_{pq}\) is the mass transfer rate per unit volume for a phase pair, \(p\) is the system pressure, \(\overset{\rightarrow}{\tau}_{q}\) is the \(q\)-th phase stress-strain tensor, \(\overset{\rightarrow}{R}_{pq}\) is the general representative of the interphase drag, \(\overset{\rightarrow}{v}_{pq}\) is the interphase velocity, \(h_{q}\) is \(q\)-th phase specific enthalpy, \(\Phi_{pq}\) is the interphase heat transfer term, and \(h_{pq}\) is the specific heat of vaporization. The derivation of the mass transfer model applied herein is presented in the Appendix A.  
Additionally, as noted by [PERSON] et al. [35], the two-fluid model stems from [PERSON]'s Nobel theory for the superfluidity of Helium. The author, furthermore, brings out the concise historical development of the two-fluid model, thereby addressing its stability problem. Apart from its classical application within nuclear and chemical engineering, it is also a promising approach to resolving turbulent motion [36], which, to the author's knowledge, is a last and, until now, unresolved problem of classical physics.
## 2 Data and Methods
### Methods
#### 2.3.3 Downscaling of Pleiades Spatial Resolution  
The usefulness of the very high spatial resolution of Pleiades for turbidity monitoring in the GE was examined by resampling the imagery. Original resolution products were compared to 20 m (e.g., SPOT or Sentinel-2/MSI) and 250 m (e.g., MODIS) products simulated by mean averaging.  
Comparisons were made around the Pauillac autonomous station, along transects shown as red line in Figure 1b for test site 1 and as blue line in Figure 1c for test site 2 (see Section 3.4). Scatterplots comparing original and downscaled Pleiades data and comparative maps are shown in Section 3.4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor** & \multicolumn{2}{c}{**OLI to Pleiades**} & \multicolumn{4}{c}{**MODIS to Pleiades**} \\ \hline
**Band** & **Relationships** & **RMSE** & **MAPE** & **Relationships** & **RMSE** & **MAPE** \\ \hline Green & y = 0.94\(\times\)\(-\) 0.0005 & 0.0094 & 7\% & y = 0.97\(\times\)\(+\) 0.0007 & 0.0040 & 3\% \\ Red & y = 1.00\(\times\)\(-\) 0.0048 & 0.0027 & 1.5\% & y = 1.00\(\times\)\(-\) 0.0080 & 0.0031 & 1.6\% \\ NIR & y = 1.00\(\times\)\(+\) 0.0110 & 0.0123 & 18\% & y = 0.99\(\times\)\(+\) 0.0092 & 0.0087 & 12\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relationships between water reflectance in the green, red and NIR spectral bands of Pleiades-1B and OLI and Pleiades-1B and MODIS sensors, where x is the water reflectance of OLI or MODIS as a function of y (water reflectance of Pleiades-1B).  
Figure 3: Relative Spectral Responses of OLI (**a**), MODIS (same for Terra and Aqua) (**b**), Pleiades-1A (**c**) and Pleiades-1B (**d**) at Blue, Green, Red and NIR wavebands.
## 5 Numerical Results
For example, when average SNR of the FSO channel is 12 dB and RF link eavesdropping, the SOP of the hybrid system with \(\xi\) = 3 and \(\xi\) = 4 is \(2.6~{}\times~{}10^{-3}\) and \(2.55~{}\times~{}10^{-3}\), respectively; when average SNR of the FSO channel is 12 dB, \(\xi^{E}\) = 1 and FSO link eavesdropping, the SOP of the hybrid system with \(\xi\) = 3 and \(\xi\) = 4 is \(6.057~{}\times~{}10^{-4}\) and \(5.329~{}\times~{}10^{-4}\), respectively. It can also be seen from Figure 9 that the larger \(\xi^{E}\), the larger the SOP of the hybrid system with FSO eavesdropping. For example, when the average SNR of the FSO channel is 12 dB, \(\xi\) = 4 and FSO link eavesdropping, the SOP of the hybrid system with \(\xi_{E}\) = 1 and \(\xi_{E}\) = 2 is \(5.329~{}\times~{}10^{-4}\) and \(21.146~{}\times~{}10^{-3}\), respectively. Assuming that \(\alpha_{m}=2\), \(\mu_{m}=1\), \(\overline{\gamma}^{RF}\) = 10 dB, \(\overline{\gamma}^{E}\) = 0 dB, \(\xi\) = 2, and \(\xi^{E}\) = 1, Figure 10 describes the relationship between the SPSC of the hybrid system with FSO link eavesdropping and RF link eavesdropping and the average SNR of the FSO channel under different turbulence intensity. When the average SNR of the FSO channel is lower than 10 dB, the SPSC of the hybrid system with the RF link eavesdropping is lower than that of the FSO link eavesdropping; whether the RF link eavesdropping or the FSO link eavesdropping, the SPSC decreases with the increase of atmospheric turbulence intensity. It can be noted that analytical results are perfectly matched with MC simulations presented in all figures. Figure 8: Under different \(R_{s}\) conditions, the relationship between the SOP of the hybrid system with RF and FSO link eavesdropping and the average SNR of the FSO channel.
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 3 System Model and Problem Description
### Load Balancing Model  
The network system can more readily adjust to the dynamic fluctuations in network traffic because of network load balancing [3]. Load balancing is implemented for time-varying network traffic in order to improve network resilience following VNF migration and encourage long-term resource optimization.  
The system's efficiency in terms of load balancing is assessed using the change in resources. The average resource usage rate of physical nodes is directly correlated with the variance of system resources [12]. The load on physical node m may change as a result of VNF j being moved from physical node n to physical node m during the VNF migration process.  
At this time, the load on physical node m can be represented as follows:  
\[L_{m}^{\rm{cpu}}(t)=\sum_{j\in N_{i}^{n}}x_{m}^{i,j}\text{-C}_{i,j}^{p}(t) \tag{17}\]\[L_{m}^{\text{mem}}(t)=\sum_{j\in N_{i}^{p}}x_{i,j}^{ij}\cdot M_{i,j}^{p}(t) \tag{18}\]  
where the CPU and memory resource loads of physical node m are denoted by \(L_{m}^{\text{cpu}}(t)\) and \(L_{m}^{\text{mem}}(t)\), respectively. To determine overall load balancing, the variation in resources on a single physical node m is insufficient. Based on variations in system resources, the load-balancing capability of the system can be assessed. Consequently, the mean CPU and memory load values at time \(t\) are:  
\[L_{\text{mean}}^{\text{cpu}}(t)=\frac{\sum_{m\in NP}L_{m}^{\text{cpu}}(t)/C_{m }}{|N^{P}|} \tag{19}\]  
\[L_{\text{mean}}^{\text{mem}}(t)=\frac{\sum_{m\in NP}L_{m}^{\text{mem}}(t)/M_{ m}}{|N^{P}|} \tag{20}\]  
where \(C_{m}\) and \(M_{m}\) represent the physical node \(m\)'s CPU and memory resource capacity, respectively. The network's variations in CPU and memory resources are stated as follows:  
\[L_{\text{var}}^{\text{cpu}}(t)=\frac{\sum_{m\in NP}\left[L_{m}^{\text{cpu}}( t)-L_{\text{mean}}^{\text{cpu}}(t)\right]^{2}}{|N^{P}|} \tag{21}\]  
\[L_{\text{var}}^{\text{mem}}(t)=\frac{\sum_{m\in NP}\left[L_{m}^{\text{mem}}(t )-L_{\text{mean}}^{\text{mem}}(t)\right]^{2}}{|N^{P}|} \tag{22}\]  
We employ the percentage of resource consumption as the measurement unit when assessing a network system's load balancing to make sure that storage and processing resources are quantified in the same unit. Consequently, the weighted sum of the variations of computing and storage resources can be used to determine the load-balancing capability \(L_{\text{total}}(t)\) of the network system, which is expressed as follows:  
\[L_{\text{total}}(t)=\omega_{1}L_{\text{var}}^{\text{cpu}}(t)+\omega_{2}L_{ \text{var}}^{\text{mem}}(t), \tag{23}\]  
where the weight factors for the impact of storage and computing resources on network load balancing are denoted by \(\omega_{1}\) and \(\omega_{2}\), respectively. Both resources are assumed to have the same effect on the network in this paper, with \(\omega_{1}=\omega_{2}=0.5\).  
Thus, minimizing the variation in computing and storage resources is the system's optimal goal, as indicated by  
\[minL_{\text{total}}(t) \tag{24}\]
## 4 Results
### Land Surface Classification Results  
#### 4.2.1 Result with the Mono-Spectral VIIRS/DNB Moonlight Image  
The moonlight image of VIIRS/DNB was classified to systematically explore the potential of moonlight remote sensing. The overall accuracy (OA) and kappa coefficient are 79.80% and 0.45, respectively. From the confusion matrix table, although there are some misclassifications of snowy mountains and rivers, the results (Figure 9) still demonstrate a good performance of the mono-spectral moonlight remote sensing to characterize land surface in low-light areas under full moon illumination.
## 1 Introduction  
### Flood Hazard in the City of Elblag  
The city of Elblag, situated in northeastern Poland, faces significant flood risks due to its complex hydrological setting. The city lies along the Elblag River, which flows from Druzno Lake to the Vistula Lagoon (Figure 1), creating a unique hydrological system with specific challenges [1, 2, 3, 4]. The primary flood hazards in this region stem from two main sources. First, the phenomenon of 'backflow' occurs when strong northeastern winds cause water from the Vistula Lagoon to flow upstream in the Elblag River [5, 6, 3]. This process leads to elevated water levels throughout the river's course and causes water accumulation in Druzno Lake, significantly increasing flood risk for the city. The second risk factor relates to the geographical location within Zuawy Wislane, a vast delta plain of the Vistula River. The area known as Zuawy Elblaskie (Figure 1) forms part of this region, with substantial portions lying below sea level in depressive areas. Zuawy Elblaskie belongs to the catchment area of the estuary of the right bank of the Nogat and the Elblag River. The landscape is characterized by extensive polder systems along the western bank of the Elblag River, bordered by the Elblag Upland to the east. The flood vulnerability of this region is further complicated by potential storm surges from the Vistula Lagoon [5], the formation of ice phenomena on the surface of the reservoir [7] and also the increasingly frequent heavy rains due to climate change [8]. Additionally, the low-lying polder areas face flood risks from the Nogat and Szkarpawa rivers, making the entire Zuawy Wislane region particularly susceptible to flooding [9]. This complex interaction of geographical, hydrological, and meteorological factors creates a challenging environment for flood management and risk mitigation [10].  
The flood hazard level for the city of Elblag is assessed based on warning and alarm water levels at two representative gauging stations--Nowakowo, located in the Elblag Bay of the Vistula Lagoon, and Elblag on the Elblag River (Figure 2). According to the Polish Institute of Meteorology and Water Management (IMGW-PIB), these warning and alarm levels are 590 and 630 for Nowakowo, and 590 and 610 for Elblag, respectively. When converted to elevation ordinates, these correspond to water stages 0.82 and 1.22 m above sea level (m asl) in Nowakowo, and 0.79 and 0.99 m asl in Elblag. Flood threats in this region, caused by wind surges in the Baltic Sea and the Vistula Lagoon, occur frequently, as evidenced by recorded water levels at the Nowakowo gauging station--approximately 1.0 m asl in 1986 [6], 1.6 m asl in 2019 [11], 1.3 m asl in 2025 and the absolute maximum of nearly 2.0 m asl in 2009.  
Figure 1: Vistula Lagoon (blue), Zuławy low-lands (green) and city of Elblag (based on [11]).