esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### GNSS and Seismicity Data
This approach enabled a comprehensive analysis of tectonic activity and strain distribution in the Eastern Java back---arc thrust along the Kendeng Fault area, encompassing various seismic events. After acquiring the daily solutions with respect to the Sunda block, we estimated each GNSS station's velocity by utilizing combinations of mathematical expressions designed to account for the geophysical signal. The final stage of the process entailed the decomposition of the GNSS daily solutions, which was achieved through the utilization of the linear least squares method [29]. Therefore, the equation can be written as follows:  
\[[y_{a}]=m[x]+b \tag{1}\]  
Figure 3: (**a**) Visual representation of the campaign GNSS time series consisting of the N, E, and U components. (**b**) Visual representation of the continuous GNSS time series consisting of the N, E, and U components. Red dots denote the daily solution, while blue lines indicate the least square solution. The matrix \(\left[y_{a}\right]\) represents the displacement observed at a designated location \(a\) encompassing three spatial components: East, North, and Up. In this context, \(m\) signifies the gradient line, which characterizes the relationship between observed displacements and their respective spatial displacements, providing a framework for analyzing variations in movement. The variable \(x\) denotes the epoch observation matrix at station \(a\), reflecting the temporal dimension of the measurements taken over successive epochs. Additionally, \(b\) represents a constant associated with the ordinate (y--axis) during the observation period, serving as a baseline reference for interpreting displacement changes. The generated velocity data were subjected to a reduction process aimed at eliminating outliers (Figure 4). As shown in Figure 4, the EW component had no relationship with NS component velocities, while the uncertainties were directly proportional between the EW and NS components. This process is essential, as outliers can distort statistical calculations and lead to erroneous conclusions, affecting the overall interpretation of the investigated geological and geodynamic phenomena.
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 1 Introduction
[PERSON] proposed an in situ calibration method, which used the short range positional accuracy of the machine as a reference, employed least squares best fit of a low order polynomial, and verified the accuracy in a practical example [26]. [PERSON] adopted the sine function to record the relationship of every position and its output value within 3 mm range, and the calibration error of the sensor reached less than 0.6% [27; 28]. However, whether the linear methods ignore the nonlinearity problem, or the nonlinear methods cause an overfitting problem in the calibration process will restrict the improvement of the accuracy. In this paper, a high accuracy calibration method based on linearity adjustment has been proposed, which combines the weighted support vector machine and the cyclical linearity adjustments of the output characteristic curve, consist of mid-scale adjustments, zero-scale adjustments and full-scale adjustments. Meanwhile, both accuracy and precision of ECDs are also discussed. The rest of this paper is organized as follows: Section 2 briefly describes the displacement measurement principle of the ECDS, and the original sensitivity and linearity are tested. Section 3 details the proposed calibration method, which is based on the linearity adjustment models, and gives the final adjustment function. Section 4 verifies the calibration accuracy and precision, and conclusions are summarized in Section 5.
## 3 Results Analysis
### AvB Estimation Results
#### 3.3.2 Experimental Results in 1 Gbps Network  
From the previous experiment, estimations in the QEMU environment showed the most precise results. Test results of AvB estimation by _Yaz_ on a QEMU VM with 1 Gbps link with approximately 500 Mbps cross traffic are shown in Figure 17.  
Figure 16: AvB estimation results by _Yaz_ in Virtual Environment: (**a**) medians; and (**b**) variances.  
Figure 17: _Cont._  
It should be noted that such data rate cannot be achieved with the initial algorithm of _Yaz_ due to the limitations based on the precision of the standard time acquisition and needed additional tuning of inter-packet spacing in the source code. Experiments show that MADs of estimations with _HPT_ are less than half those with system timing operations. The rate of outliers is also noticeably higher for the standard way of time acquisition. The median in the case of _HPT_ is equal to 587 Mbps and closer to the actual AvB in comparison to 620 Mbps with system timers.
###### Abstract
In recent years, significant advancements have been made in the field of geophysical exploration, driven by the development of fundamental theoretical principles and the introduction of updated exploration equipment. Both field data collection and analysis of indoor data have witnessed remarkable progress [19; 20; 21]. The rapid evolution of these techniques has greatly expanded the scope of application for geophysical exploration methods [22; 23]. Among them, an increasing number of scholars have introduced geophysical exploration technology in the investigation and management of landslides [24; 25; 26; 27; 28; 29; 30]. This study employs the ERT method to conduct field testing on site. To obtain an electrical resistivity tomography, the apparent resistivity values must be inverted by using inversion routines. Currently, there are numerous methods for inverting resistivity data, yet Res2 dinv remains the most widely utilized [31; 32; 33; 34; 35; 36]. This study uses the software, Res2 Dinv (version 3.54.44), and employs the smooth constrained least squares method, enabling the acquisition of two-dimensional cross sections through finite difference or finite element calculations with terrain corrections factored in [37; 38]. ERT offers advantages such as flexible deployment and relatively low requirements for terrain, making it the preferred method for exploration in mountainous regions [39]. This method is a type of direct current (DC) electrical exploration technique that utilizes the differences in electrical resistivity of subsurface materials to extract and analyze the response characteristics of a stable underground electric field established artificially [40]. It aims to actively explore the geological structure and formation of underground layers to achieve the purpose of determining the subsurface structure and geological features [41]. The measurement results yield a two-dimensional cross-section of apparent resistivity, which, upon qualitative analysis, allow for the simultaneous study of the electrical characteristics in both the horizontal and vertical directions within a certain depth range underground [42]. The electrical resistivity is primarily influenced by the mineral composition and groundwater in the subsurface media, which can be utilized to analyze the variations in subsurface media caused by differences in grain size, water content, and rock mineralization level [43; 44]. This paper selects the Lijiazzu landslide in Hunan Province as a case study. With the assistance of ERT surveying and field investigation data, the paper analyzes the parameters of the geological resistivity, determines the sliding interface, depth, and range of the landslide, and explores its deformation and failure mechanism. The aim was to provide a scientific basis for landslide control. At the same time, this research is also of great significance for the study of typical landslides in Hunan Province.
## 2 Measuring Principle  
### Measurement Principle of the Coaxial Capacitance Sensor  
The coaxial capacitance sensor consists of a center electrode and an outer electrode, that is, the metal shell. The center electrode on the central line of the pipe is coated with an insulating layer as shown in Figure 1. The outer electrode is in contact with the fluid and forms a capacitance with the center electrode. When oil and water phases flow between the two electrodes, the capacitance values are different due to the different dielectric constant of oil and water, so the change of capacitance value can indicate the change of the water cut.  
Figure 1: Structure diagram of the coaxial capacitance sensor.  
When the oil cut is high, the oil is used as the continuous phase in the fluid, and the dielectric property of the fluid is weak. At this time, the entire flow runner is basically insulated, and the sensor has the greatest capacitance value. When the water cut is high, the capacitance of the coaxial capacitance sensor is mainly determined by the size and quantity of the oil bubble near the central electrode. However, the reaction is not obvious for the oil bubble farther away from the central electrode, that is, when the water cut is high, the size, quantity, and distribution of the oil bubble will affect the measurement effect of the sensor.
## 1 Introduction
### Motivation
IoT end devices have short range transmission capabilities and energy constraints [8]. Thus, centralized estimation by direct information transmission between IoT end devices and internet back bone connected devices are not feasible [21]. Therefore, multi-hop transmissions are required. The chain-based and tree-based topological structures are more realistic to use for multi-hop transmissions in SG environments. For example, the chain structure is well suited to deploy on overhead/underground cables and thermal conductors, whereas the tree structure is well suited to be deployed in larger areas such as wind or solar farms. Furthermore, these structures are simple to implement and easier to scale. Transmitting all the information collected in end devices is the simplest multi-hop information gathering approach. However, this introduces an unnecessary redundant information transmissions that require excessive resources in terms of energy compared to serial distributed detection [22]. Therefore, effective information fusion technique of distributed estimation is needed to combine the information at intermediate devices [23; 24].
## 6 Non-Stationary Object FoR Estimation
The mean \(\mu^{t}(G)\) and standard deviation \(\sigma^{t}(G)\) are formed from the previous model estimate \(M(t-1)\), which is denoted as follows. \[ \begin{split}\mu^{t}(G)&=(1-\alpha^{t-1}M(t-1)\mu^ {t-1}M(t-1)+\alpha^{t-1}m^{t}M(t)\\ \sigma^{t}(G)&=(1-\alpha^{t-1}M(t-1)\sigma^{t-1}M(t-1 )+\alpha^{t-1}\upsilon^{t}M(t)\end{split}  \tag{17}\]  
where \(m(t)\) denotes the average mean of all previous \((0,1,2,\ldots,M(t-1)\) models, and \(\upsilon(t)\) denotes the statistical variance of previous models. Once the bounding box is accurately estimated, we check the threshold values (distance computation in case of social distance and temperature in case of thermal inspection), which are denoted as \(d\). If the obtained values are higher than \(d\), it indicates a 3 FFs violation, and alarms are raised by the UAV swarm network. Vaccine Forecasting and Blockchain-Based Vaccine Distribution  
\(E_{\text{MC}}\) based on the real-time data obtained by 3 FFs UAVs would form an estimated model for the prediction of COVID-19 patients in the future. Based on the estimate, \(E_{\text{MC}}\) would be notified about the requirement of vaccine supplies. Authors in [39] have proposed a scheme, _ABV-CoViD_, which forms a hybrid time series estimation of the availability of COVID-19 resources (based on occupied beds and ventilators at \(E_{HC}\)). The prediction model allows an effective supply of vaccines and other resources to nearby hospitals by \(E_{\text{MC}}\). Similarly, in vaccine distribution by UAVs, authors in [19] proposed a vaccine distribution scheme, named _SanjeeVni_, where the authors proposed registration of users based on priority (front-line workers, hospital staff, senior citizens, and others) for vaccination. The users have to register themselves to the Solana blockchain network, owing to its high rate of adding transactions (\(\approx\)65 K TPS and efficient consensus protocol Proof-of-History. The entire distribution logic from the vaccine warehouse to municipal centers is carried by UAVs at the backdrop of 6G-ERLLC communication links.
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
### Motivation  
Several approaches have been discussed about how to monitor [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], control [14; 15; 29; 30; 31], and forecast [32; 33; 34; 35; 36] the main loads in a smart home, mainly focusing on Data Analytics and Communication Network. However, this analysis has been mostly performed independently. In order to fit the requirements of \"beyond 2030\" communication (5G, 6G) and Industry 4.0 technologies, a more holistic approach is necessary.  
In this paper, we propose a cyber-physical approach for Home Energy Management System (HEMS). The proposed architecture consists of three layers: Data Acquisition, Communication Network, and Data Analytics. We review state-of-the-art solutions for each layer highlighting and identifying the main challenges. In the Acquisition stage, we define a metering device based on the standard IEC TS 63297:2021. For Communication Network, we summarize different technologies deployed inside the house, and discuss the best candidates for communication in the smart city. In addition, we define the Analytics stage as the cyber part of a HEMS. We present a real case study in which a cyber-physical architecture is implemented and validated for monitoring purposes using real data collected in a house located in Valparaiso, Chile. To the best of our knowledge, no previous work has analyzed the requirements of HEMS to be deployed in the advent of beyond 2030 communication and CPS era.
## 2 Hybrid Desiccant Cooling System and Air-Conditioned Space  
The desiccant cooling system consists of a regenerative heat source, a desiccant device, and an evaporative cooler. Herein, district heating supplied to apartment houses was treated as the heat source. The temperature of the hot water supplied from district heating was 70 \({}^{\circ}\)C. A solid desiccant manufactured in the form of a rotor was used [6] in the present system. With the desiccant rotor, the hybrid desiccant cooling system [8] shown in Figure 1a was applied to high-humidity climates and residential environments. Its operation process is shown on the psychrometric diagram in Figure 1b.  
In Figure 1a, the upper part is the regeneration unit and the lower part is the desiccant unit. In the regeneration unit, the air heated through a heat pump and the district heating (heating coil) regenerates the desiccant rotor. In the desiccant section, a mixture consisting of 70% of the return air (A in Figure 1b) and 30% of the outdoor air (C in Figure 1b) is supplied to the desiccant rotor. The air that has passed through the desiccant rotor (D in Figure 1b) is cooled primarily through the evaporative cooler (E in Figure 1b). Then, it is additionally cooled while passing through the evaporator of the heat pump, and supplied as the cooling air to the room (F in Figure 1b). The design COPs of the evaporative cooler and heat pump are 0.64 and 3, respectively.  
The hybrid desiccant cooling system used herein was developed in Korea. Its prototypes were installed in 10 apartment houses located in Yongin-si, Gyeonggi-do, and the performance of each was verified through field tests during the summer of 2013 [9]. The system was designed to have a cooling capacity of 7 kW at the dry and wet bulb temperatures of 35 \({}^{\circ}\)C and 23.9 \({}^{\circ}\)C, respectively,  
Figure 1: Hybrid desiccant cooling system: (**a**) system schematics; (**b**) thermodynamic process on the psychometric chart.  
which are the standard experimental conditions of AHRI [27]. In Korea's summer outdoor conditions (dry-bulb temperature, 24-34 \({}^{\circ}\)C; absolute humidity, 0.012-0.022 kg/kg), the cooling capacity is typically 5.5-7 kW [9]. The heat pumps that make up the hybrid system have a nominal cooling capacity of 1.9 kW.  
The test site was one of the apartment houses in Yongin-si, Gyeonggi-do, the floor plan and interior photos from which are shown in Figure 2. Here, the air-conditioning space covers the living room, dining room, and kitchen. The desiccant cooling unit was installed in one corner of the living room, and the cooling air was supplied from the top of the equipment. The total area of the cooling space was 126 m\({}^{2}\). The air after cooling was recovered through a slot in the ceiling. Furthermore, there were windows on the front and side of the living room. Thermal comfort was evaluated without curtains on the windows.
# Monitoring of Evolving Laser Induced Periodic Surface Structures
## 4 Results  
Figure 4 shows the simulated two-dimensional near field distribution of the light backreflected from a structured titanium sample. The structure is applied only in the central region with a diameter of 30 \(\upmu\)m.  
The near field distribution shows modulations in the central region of the reflected beam (where the incident beam interacted with the structured region) (Figure 4a) but not in the Airy rings (cf. Figure 4b).  
In Figure 5 we show the simulated far-field distribution of the backreflected light from (a) an unstructured and (b) a structured titanium surface at 50 \(\upmu\)m distance from the sample surface. In this case, the structure was applied to the entire sample, the period of the structure was 600 nm and its height was 300 nm.  
Figure 4: Simulated near field distribution of the reflected light at 500 nm distance from the surface. Two different color scales are used to show that fringes are visible only in the central part (**a**) and not in the Airy ring (**b**).  
Figure 5: Simulated far-field distribution of the reflected light at 50 \(\upmu\)m distance from the surface. (**a**) Unstructured titanium. (**b**) Sinusoidally structured titanium surface (600 nm period, 300 nm height).  
The color scale was chosen such that modifications in the Airy rings are clearly visible, that is, the central spot is saturated.  
The intensity of the light backscattered from the structured sample is strongly reduced. This is because the sub-wavelength nanostructure strongly reduces the reflectivity of the sample. In addition, we clearly observe the appearance of vertical fringes in the presence of the nanostructure. In order to elucidate the origin of the observed fringe pattern, we will numerically investigate the role of relevant parameters, in the following.
## 3 A Construction Based on Digital Signatures
Let \(Q\) be the set of pairs \((m_{h},\sigma_{h})\), where \(m_{h}=h\) and \(\sigma_{h}\) are signatures corresponding to \(m_{h}\), for \(h=1,\ldots,i-1\). The adversary \(A\), on inputs \(pk\) and \(Q\), works as follows:  
* First, \(A\) sets \(pub=pk\);
* Then, \(A\) runs ACT-STAT\({}_{1}\), which, taking as inputs the security parameter \(1^{\tau}\) and the maximum number of sessions \(\ell\), outputs the indexes \(i\) and \(j\) such that \(1\leq j<i\leq\ell\);
* Moreover, \(A\) constructs the sequence \(hist_{i-1}=(a_{1},a_{2},\ldots,a_{i-1})\), by setting \(a_{h}=(h,\sigma_{h})\), for \(h=1,\ldots,i-1\). Notice that, for each \(h=1,\ldots,i-1\), the pair \((h,\sigma_{h})\) belongs to the set \(Q\), which is an input to \(A\);
* Afterwards, \(A\) runs ACT-STAT\({}_{2}\) on inputs \((1^{\tau},pub,i,j,hist_{i-1})\), in order to obtain the fake authentication messages \((a_{j}^{\prime},\ldots,a_{j}^{\prime})\);  
Figure 1: A Pictorial Representation of the DS-EAC. Finally, \(A\) outputs the pair \((m,\sigma)\), where \(m=i\) and \((i,\sigma)=a_{i}^{\prime}\). Since ACT-STAT is able to break the OM-UEA scheme \(\Pi=(Gen,Auth,Ver)\) obtained by the DS-EAC, then \(Ver(1^{\tau},i,a_{i}^{\prime},pub,hist_{i-1}^{\prime})=1\), where \(hist_{i-1}^{\prime}=(a_{1},a_{j-1},a_{j^{\prime}}^{\prime},\ldots,a_{i-1}^{ \prime})\). Thus, it also holds that \(Vrfy(pk,i,a_{i}^{\prime})=1\), i.e., the adversary \(A\) is able to build a forgery for the message \(i\) which does not belong to \(Q\). The view of adversary ACT-STAT, when executed as a subroutine by \(A\), is the same as that in the impersonation experiment \(\mathbf{Imp}_{\texttt{ACT-STAT},\Pi}(1^{\tau})\). Thus, since the advantage of ACT-STAT in the experiment \(\mathbf{Imp}_{\texttt{ACT-STAT},\Pi}(1^{\tau})\) is non-negligible, the advantage of \(A\) in the signature-forgery experiment \(\mathbf{Sig-Forge}_{A,\Sigma}(1^{\tau})\) is also non-negligible. Contradiction. The DS-EAC construction, although simple, has the merit of showing that a weak form of unforgeability is sufficient to achieve security against active adversaries for OM-UEA schemes. Therefore, it is possible to instantiate it with existing concrete signature schemes to be chosen according to the characteristics of the application domain. In the next session, we show a lightweight instance suitable for applications in the IoT scenario.
## 5 Simulation Research
### Speed Segmented Flow Control Simulation
#### 5.1.1 Flow Control Simulation of Single Actuator  
The handle 1 acts alone. Set the maximum flow of the main oil circuit to 160 L/min. Set the pressure of the load proportional overflow valve to 1 MPa. The ratio of target flow to maximum flow is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Speed of Electric Motor** & **Displacement of Pump** & **Flow of System** & **Load Sensing Pressure Difference** \\ \hline
500–2000 rpm & 8–80 cc/r & 4–160 L/min & 1.8 MPa \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of key components.  
Figure 8: Variable constant power module in the simulation model.  
Figure 7: Simulation model in AMESim.  
During 0-5 s, the handle is in the middle position, and ratio of target flow to maximum flow is 0.  
During 5-7 s, the ratio of target flow to maximum flow of the handle increases linearly from 0 to 0.25.  
During 7-17 s, the ratio of target flow to maximum flow increases linearly from 0.25 to 1.  
During 17-22 s, the ratio of target flow to maximum flow remains 1.  
The displacement of variable displacement pump, electric motor speed, and hydraulic system flow can be obtained in Figure 10.  
As can be seen, during 0-5 s, the ratio of target flow to the maximum flow of the pilot handle is 0. The target flow of the system is 0 L/min. The electric motor is in the idle stage, and the speed is 500 rpm. The pump works at the minimum displacement of 8 mL/r, so the actual hydraulic system overflows with the minimum flow.  
During 5-7 s, the ratio of target flow to the maximum flow of the handle increases from 0 to 0.25. At this time, the target flow of the system increases from 0 to 40 L/min. This stage is in the variable displacement pump stage. The pump displacement increases from 8 to 80 mL/r, which can meet the system flow demand. Therefore, the electric motor keeps running at 500 rpm, the actual flow of the system is 40 L/min, and all flow into the actuator oil circuit.  
During 7 to 17 s, the ratio of target flow to maximum flow increases from 0.25 to 1, and the pump displacement remains near the maximum displacement of 80 mL/r. The estimated flow of the system increases with the increase in handle pilot pressure. The  
Figure 10: Pump displacement, motor speed, and system flow curve. (**a**) Curve of pump displacement; (**b**) curve of electric motor speed; (**c**) curve of system flow.  
Figure 9: Pilot signal curve.  
electric motor enters the variable speed stage. The system adjusts the electric motor speed step by step according to the speed segmented control strategy. The electric motor speed runs from 500 to 1000 rpm and 1500 to 2000 rpm with the increase in the pilot pressure of the handle. The actual flow of the system also starts from 40 L/min and then at 80 L/min, 120 L/min, and 160 L/min.  
During 17 to 22 s, the ratio of target flow to the maximum flow of the handle is maintained at 1. The estimated flow of the system is maintained at 160 L/min. The electric motor is maintained at 2000 rpm. The hydraulic system is maintained at the maximum flow stage.