esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The study was previously submitted and approved by the local Animal Care and Use Committee of the Universite de Bordeaux. Six male C57/BL6 mice (16 weeks old, 24.6 \(\pm\) 1.9 g) were used. The number of subjects was defined following our previous experience with these types of studies [6; 7], taking into account the interindividual variability and also keeping in mind the guidelines from the Directive 2010/63/EU of the European Parliament on the protection of animals for scientific purposes. The animals were housed at the INSERM U1034 animal facility with controlled temperature and humidity conditions (21 \(\pm\) 1 \({}^{\circ}\)C, 40-60%), exposed to regular 12 h light/12 h darkness cycles with food and water ad libitum. Animals were anesthetized with an intraperitoneal administration of a mixture of ketamine (137.5 mg/kg) and xylazine (11 mg/kg). Ten minutes after induction, animals were placed on a heated pad and allowed to stabilize for another 10 min while measurement probes were being attached. The baseline record was used as a reference, meaning that each animal served as its own control. Atenol (Tenormine IV, 5 mg/10 mL, AstraZeneca Ltd., Luton, UK) was then administered intraperitoneally (5.0 mg/kg), subdivided into two doses of 2.5 mg/kg, with measurements recorded in three phases: 10 min baseline, 10 min after the first dose, and 10 min after the second dose. Blood pressure was monitored on the tail artery with a CODA High Throughput system (Kent Scientific, Torrington, CT, USA). Perfusion was assessed on the plantar surface of a randomly chosen hindlimb using LDF (407 probes, Perimed, Jarfalla, Sweden) attached to a Periflux 5000 system (0.03 time constant, 32 Hz sampling rate). Heart rate was assessed by electrocardiography (ECG, lead I) using needle electrodes subcutaneously attached to the fore and hindlimbs (Power Lab/4 SP, AD Instruments, Bella Vista, New South Wales, Australia). Respiratory rate was assessed by spirometry (Spirometer, AD Instruments, New South Wales, Australia). Raw LDF signals were decomposed into their spectral components (cardiac, respiratory, myogenic, sympathetic, endothelial NO-dependent, and endothelial NO-independent) with the Morlet wavelet transform (Matlab R2015, Mathworks, Natick, MA, USA). After confirmation that the samples did not follow a normal statistical distribution using the Shapiro-Wilk test, nonparametric statistics were applied for phase comparisons (Wilcoxon signed rank test) and a \(p<0.05\) adopted.
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### Assessment Strategies
#### 3.3.4 Modelling and Simulations  
A high number of studies investigated the RF-EMF exposure by modelling and simulations methods. The most accurate method to perform computational estimation is directly solving the Maxwell's equations: the EMF induced in human tissues are directly calculated by using full wave techniques such as Finite Difference Time Domain, or equivalent methods. However, as the large requirements in terms of memory use and the high computational cost make these methods inappropriate for large area calculations, most of the previous studies based on computational methods focused on the analysis of cases in which the sources are placed close to the human body (see, e.g., [37]), and only few of them specifically addressed the specificity of the exposure when considering indoor environments.  
An alternative approach based on modeling and simulations to assess exposure in large spaces as indoor environments is represented by using methods traditionally used for indoor propagation prediction models, such as ray tracing and ray launching methods. These approaches offer a good approximation with a lower computational cost. Ray tracing and launching methods consist in launching rays from an antenna to a scenario or to a part of it, choosing radiation pattern; every ray will result in a reflected, transmitted or diffracted ray, depending on the stage element to impact and how the impact occurs. The rays on the impact surface will contribute to the total received field at that point, depending on the electromagnetic parameters of the materials interacting with the propagating waves. Implementation based on a combination of geometric optics and physical optic allowed using ray tracing in order to characterize wireless propagation and consequently RF-EMF exposure in very complex indoor scenarios [38; 39].  
A further method combining heuristic predictions and ray tracing methods, based on the calculation of the dominant path between transmitter and receiver, was used in a large number of studies dealing prediction of downlink exposure, i.e., exposure due to the power received by a user device, and uplink exposure i.e., exposure due to the power emitted by a user device, for indoor wireless networks planning, thus giving an indirect estimation of the RF-EMF exposure in different indoor environments, such as offices [40] and trains [41].  
A step ahead in the assessment of the exposure in indoor environments considering realistic and uncertain scenarios is represented by stochastic dosimetry. Stochastic dosimetry is an innovative approach based on statistics that, starting from a set of observations obtained either by deterministic methods such as FDTD or ray tracing or by measurements, builds surrogate models able to replace the heavy computational problem with analytical problems. This approach was recently used to assess the exposure to RF-EMF sources in indoor environments considering highly uncertain scenarios [42].
## 2 Materials and Methods
### Data Sources and Reprocessing
#### 2.2.3 Meteorological Data  
Meteorological data from 2001 to 2020 were used to analyze the effects of climate factors on vegetation phenology in Northeastern China, including temperature (Temp), precipitation (Prcp), and relative humidity (RH). The temperature and precipitation data for the monthly precipitation dataset and mean air temperature datasets are from the monthly precipitation dataset and the average temperature dataset provided by the National Tibetan Plateau Scientific Data Center [23; 24; 25] ([[http://data.tpdc.ac.cn/](http://data.tpdc.ac.cn/)]([http://data.tpdc.ac.cn/](http://data.tpdc.ac.cn/)), accessed on 1 September 2022). This dataset [26] is generated by the Delta spatial downscaling scheme in China, based on the global 0.5\({}^{\circ}\) climate dataset published by Climatic Research Unit (CRU) and the global high-resolution climate dataset published by WorldClim. Additionally, it uses 496 independent weather observation point data for validation, whose results are credible. The relative humidity data were obtained from the monthly average relative humidity of 1 km provided by the National Science Center ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), accessed on 1 September 2022), a national platform for basic conditions of science and technology.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Causal and Temporal Dependency Tests
#### 6.4.2 Datasets  
In this section, as is visible from Table 14, there are only three datasets of interest; this is mainly due to the fact that only three papers belong to this class. However, there is one dataset that is used the most. In fact, the 2017 BPI Challenge, a process-mining benchmark, serves as the primary validation ground for two studies, reflecting its usefulness in modeling business workflows with inherent time dependencies. Its reuse highlights its suitability for detecting drift in structured, event-log-driven systems, but also points to a potential over-reliance on a single dataset for generalizing the method.
## 3 Problem Analysis
### Model of the Cylinder Control System
In such case, the parameters of the second-order dynamic hydraulic system depend on the following variables:  
\[k=k_{h}=f(V_{c},\ E_{c},E_{l}) \tag{1}\]  
\(k\)--stiffness parameter of the dynamic system,  
\(k_{h}\)--stiffness parameter of the hydraulic system,  
\(E_{c}\)--bulk modulus of the liquid,  
\(V_{c}\)--volume of the confined liquid,  
\(E_{l}\)--modulus of elasticity of the power supply line. \[c=c_{h}=f\Big{(}F_{f},F_{c}\Big{)} \tag{2}\]  
\(c\)--damping parameter of the dynamic system,  
\(c_{h}\)--damping parameter of the hydraulic system,  
\(F_{f}\)--mechanical friction in the cylinder,  
\(F_{c}\)--viscous friction value in the liquid. The stiffness and damping parameters of the hydraulic and dynamic systems can be identified empirically, by analyzing responses of the dynamic system. However, such analysis should be based on the responses recorded over a time period in which the control  
Figure 1: Basic physical model of the analogy of a hydraulic cylinder and a second-order dynamic system. valve is certainly and completely closed. The moment when the valve closes completely is not identical to the moment in which the valve control signal changes, and the period between the signal change and the actual complete closure of the valve may be sufficiently long to affect the accuracy of the cylinder movement. It is therefore justified to extend the dynamic model of a hydraulic system with a function related to the valve characteristics (Figure 2). Momentary flow (\(Q_{v}\)) is here understood as a short-lasting flow of liquid through the valve reconfigured into the closed position. This flow is probably caused by the momentary opening of the valve due to variable pressure in the chamber and in the power supply line. This pressure value is referred to as the momentary value of the pressure \(\Delta p(t)\). In the model shown in Figure 2, the value of momentary flow rate in the control valve can be described as a function of the following parameters:  
\[Q_{v}(t)=f(\Delta p(t),I_{s}(t)) \tag{3}\]  
\(I_{s}(t)\)--valve control signal value in time. Importantly, the momentary flow rate value recorded on the valve generates hydrodynamic forces which also influence the characteristics of the valve response to the control signal change. An experiment which would allow a precise identification of momentary flow rates seems problematic. However, the total volume of the liquid flowing through the valve to the cylinder (\(Q_{vac}\)) is directly proportional to the cylinder extension value (\(\Delta x_{ac}\)) recorded after the valve is switched to the closed position. \[Q_{vac}=f(\Delta x_{ac}) \tag{4}\]  
The above relationship, with assumed identical dimensions and within the range of internal resistances in the investigated cylinder, allows an empirical comparative analysis of the systems on the basis of the measured values of piston rod displacements in the cylinder after the stopping process has been initiated.
## 4 Test System  
This section presents the test systems employed to validate the proposed optimization model for the optimal integration of FACTS in the IEEE 33-, 69-, and 85-bus systems. Figure 2 depicts the topologies of three test systems, whose main features are as follows:  
1. The IEEE 33-bus test system has 33 buses and 32 transmission lines in its radial configuration, as shown in Figure 2a. It has a substation at node 1 that works with 12.66 kV, as well as peak active and reactive demands of \(3715+j2300\) kVA, respectively. These operating conditions generate active and reactive power losses of 210.9876 kW and 143.1283 kvar. The test system's peak demand, resistance, and reactance values are listed in Table 1. These values were taken from [39].
2. The IEEE 69-bus test system is fitted with 69 buses and 68 transmission lines, as depicted in Figure 2b. Its substation is located at node 1, which works at 12.66 kV, and its peak active and reactive demand is \(3890.7+j2693.6\) kVA. Under these operating conditions, the active and reactive power losses generated are 210.9876 kW and 143.1283 kvar, respectively. Table 2 lists this system's demand, resistance, and reactance values. These values were taken from [39].
3. The IEEE 85-bus test system has 85 buses and 84 transmission lines, as illustrated in Figure 2c. It has a substation at node 1 that works with 11 kV, as well as a peak active and reactive demand of \(2570.28+j2622.20\) kVA. The test system's peak demand, resistance, and reactance values are listed in Table 3. These values were taken from [39].  
Figure 2: _Cont._  
Typically, electrical distribution systems exhibit a daily load variation. The demand curves for the active and reactive power are considered as illustrated in Figure 3. These curves represent the typical behavior of an electrical distribution system in Colombia [40].  
The parameter values of the objective function \(z\) (1) are shown in Table 4. The costs associated with the FACTS were taken from [39].
## 1 Introduction  
Biomedical implantable devices have been available for more than sixty years. The first transistorized biomedical implanted device was designed and developed by [PERSON] in 1957 for the cardiac pacemaker [1]. Investigations on implanted devices have been focused of the most important issues of biomedical implants, which are patient safety and comfort [2]. This can be achieved by reducing the power consumption and ensuring efficient energy transfer to the implanted devices [3]. Therefore, wireless energy transfer is an important issue for implanted devices [4].  
Implanted medical devices are electronic devices that monitor and diagnose the electromyography (EMG), electrocardiogram (ECG), electroretiuogram (ERG) and electrooculography (EOG) of the patient and send current to various parts of a patient body. In general, the implanted device consists of two parts: the internal part located underneath the body skin and an external part. _i.e._, controller [5,6]. The external part is used for powering the combination and sending data to the outside world. Implantable devices are self-operating devices which adjust their operation depending upon the patient's condition. These devices do not rely on external sources of power. Thus, low power consumption and high data rate are the main requirements for medical implant devices [7]. In order to minimize cost, patient trauma and risk associated with the repeated surgeries, it is necessary to increase the lifetime of implanted batteries by conserving energy at every stage of a device's operation. Thus, in order to consume the power during monitoring and diagnosis, implanted devices powered by RF signals through inductive coupling links are used to reduce the complexity of the system [8].  
There are many methods to conserve power and reduce the interference on nearby electronics by choosing the suitable modulation and suitable design of the modulator and demodulator. Details of the modulation and demodulation of wireless telemetry and implanted devices such as amplitude shift keying (ASK), frequency shift keying (FSK) and phase shift keying (PSK) have been discussed in many reviews. However, still there are many drawbacks with the communication systems of the existing designed and developed devices. There are a lot of papers in the field of wireless low power electronics, such as optical biomedical sensing [9], cochlear implants [10] biomedical wireless sensor networks [11], intelligent biomedical devices [12] and body sensors [13]. This paper provides a detailed review of the literature concerning the design of low power transmitters for different forms of digital bandpass modulations used in implanted devices covering the period from 2000 to 2011. The purpose of this review is to discuss and classify all the types of digital modulation used in wireless telemetry bio-devices and biomedical implanted devices to provide a good background on the challenges and problems that are being faced and to develop appropriate solutions.
## 4 Construction Monitoring  
In order to verify the deformation and permanent deformation of the cableway gondola structure, various structure monitoring systems can be used during the fatigue tests. In the presented studies, two independent monitoring systems were chosen.  
The first discussed method of controlling displacements of the structure was laser sensors HG-C1030-P from Panasonic. It is an optical laser sensor designed for precise measurements. Sensor response time up to 1.5 ms. The applicability range is 30 mm and the accuracy is 10 \(\upmu\)m. Based on the use of a system of two such laser sensors, it was possible to measure the actual distance of the bottom frame from the ground during the test and verify the uniformity of deformation of this structure.  
Due to the assumptions of the EN 13796-3 standard, the gondola had to be inspected every \(500,000\) cycles. In addition to the visual inspection assumed by the standard, tests were conducted based on Digital Image Correlation (DIC) tools. On the gondola structure, a \"cloud of reference points\" should be marked, which have been verified geometrically using an optical 3D coordinate measuring device--TRITOP by GOM. Then, in each test break, the geometrical coordinates of these points were re-measured, based on which it was possible to assess their mutual displacement, angles of rotation, and permanent deformation of the entire structure. The accuracy of the aforementioned measuring system, in the case of a structure with the dimensions of the tested gondola, is about 30 \(\upmu\)m.  
As part of the research, deformations of the gondola structure were measured in order to check whether the structure is not permanently deformed as a result of the applied load. A total of 11 measurements were made. The first one was made before the structure was loaded and it was a reference measurement for subsequent measurements. These were performed in the intervals between successive series of load cycles. A single run contained 500,000 cycles.  
The GOM TRITOP measurement system was used in the research. The system consists of the following elements:  
* A camera with a modified lens,
* wi-fi data transmission module,
* Computer with dedicated software for digital image correlation,
* A set of coded markers constituting reference points in measurements,
* A set of self-adhesive markers constituting measuring points,
* A set of precise crossbars constituting the dimensional basis for measurements.  
Figure 9: Scheme of the spacing of C-beams simulating the load on the gondola floor.  
The research of DIC consisted of three stages.
## 3 Results  
### Experimental Setting  
The size of the experimentally captured original image was about 100 \(\times\) 100, and in order to evaluate the performance of the proposed method, existing image segmentation techniques and texture feature extraction methods were compared.  
First, the weighting constant \(\alpha\) of the gray-domain weighting coefficients used in the SSR luminance decomposition process is based on adaptive bilateral filtering, which controlled the adjustment range of the filtering intensity. During the experiment, the range of \(\frac{mean_{all}}{mean_{bf}}\) was between 0.5 and 2.3. During the transition from the smooth area to the edge area, the process of filter intensity reduction was too fast. When the filter size became larger, the range of \(\frac{mean_{all}}{mean_{bf}}\) became smaller accordingly, which made the process of filter intensity change more reasonable but caused the loss of detailed information on the reflection layer. This experiment used a filter size of 40 \(\times\) 40, in which the weighting constant \(\alpha=0.85\) was taken, the adjustment process of filter intensity was more reasonable, and the reflection layer separation effect was better.  
The weighting constant \(\beta\) of the gray-domain weighting coefficients was used in the SSR luminance decomposition process of adaptive bilateral filtering by which the degree of filtering compensation at the edges was adjusted. This degree of compensation could not be too large. Otherwise, it would be in the reflection layer at the edge of the image directly producing shadow interference. After experimental testing and verification, from the center of the image to the edge, the filter strength was reduced to two-thirds of the original, which was more reasonable. So, by taking \(\beta=\frac{1}{3}\), the filtering could be compensated at the edges of the image without shadow interference from the reflective layer.  
Regarding the parameter determination of the support vector machine, among the kernel functions (linear kernel, polynomial kernel, Gaussian kernel), the penalty factor size (0.01, 0.1, 1, 10), and the gamma value (0.01, 0.1, 1, 10), the optimal combination of parameters determined by means of grid search was to use the linear kernel function with the penalty factor size of 1 and no gamma value (the linear kernel function had no gamma value).
## 1 Introduction
Recent studies have illustrated that GPR can be useful to characterize a wide range of glacial and frozen conditions [26, 27, 28], such as monitoring the spatial-temporal distribution of frozen ground, mapping thaw fronts or assessing freeze-thaw dynamics, the thickness of the active layer above the permafrost and the depth to the permafrost table [29, 30]. Furthermore, rapid deployment of GPR along series of parallel transects can be used to estimate frozen depth over a broad area. [PERSON] _et al._[31] reported that through the use of the GPR system, the depth of thaw within the active layer could be mapped with a precision on the order of 10 cm vertically, while [PERSON] _et al._[32] proposed an empirical equation for common offset GPR measurements to estimate the density of frozen media. A high frequency GPR direct ground wave has also been tested to monitor the dynamics of the seasonal development of the thin soil frozen layer [33]. Recently, [PERSON] _et al._[34] measured soil frost depth in a forest ecosystem using GPR and reported that deep snow and shallow frost can make frost detection with GPR difficult, due to signal attenuation. Since commercially-available GPR systems are mostly ground coupled, the structure of snow cover or frozen soil can be significantly disturbed by dragging the GPR antenna over the surface. As a result, precise information on shallow freeze-thaw dynamics is difficult to monitor via time-lapse on-ground GPR measurements, without disturbing the snow cover substantially. In this research, we tested an off-ground GPR system to monitor the seasonal freeze-thaw dynamics of soil over a snow-covered surface. A horn antenna was used in the frequency range of 200-800 MHz and fixed at a height of about 110 cm above the ground surface in the field. Within the footprint of the horn antenna, the surface of the bare soil was exposed to the natural environmental conditions, such as precipitation, snowfall and evaporation. Near the GPR setup, sensors were installed in snow and at different depths in a trench to monitor the vertical and lateral dynamics of soil temperature and dielectric permittivity. Time-lapse GPR, soil temperature and dielectric permittivity measurements were recorded for nine days, with five freeze-thaw cycles occurring during that period. GPR-derived permittivity was compared to the permittivity obtained by the sensors.