esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Active Control of a Quasi-Zero-Stiffness Isolator  
### Description of a Quasi-Zero-Stiffness Isolator  
The active force controller described above was applied to a non-linear stiffness vibration isolator with the quasi-zero-stiffness (QZS) mechanism. Such a simple hybrid isolator that combines the passive and active components consisted of some linear springs with one vertical spring connected with several inclined springs, and a linear viscous damper, as shown in Figure 2. This isolator was used to isolate the base excitation \(x_{b}\) with the auxiliary control force \(f_{c}\). The isolated body with the mass \(m\) was supported above the base by the vertical spring with stiffness \(k_{v}\) and its static equilibrium position was set-up while the oblique springs with stiffness \(k_{n}\) were compressed in the horizontal position. The damper with linear damping coefficient \(c\) was placed in parallel with the vertical spring. The force-deflection relationship of the stiffness elements for the isolator is given by [12; 13]:  
\[f=k_{v}(x-x_{b})+nk_{n}\bigg{(}1-\frac{a_{0}}{(x^{2}+a^{2})^{1/2}}\bigg{)}(x- x_{b}) \tag{15}\]  
where \(a\) is the free length of the oblique springs and \(a_{0}\) is their length when they are in the horizontal position. The equation of motion of the hybrid isolator can be described by:  
\[m\ddot{x}+c\Big{(}\dot{x}-\dot{x}_{b}\Big{)}+k_{v}(x-x_{b})+nk_{n}\bigg{(}1- \frac{a_{0}}{(x^{2}+a^{2})^{1/2}}\bigg{)}(x-x_{b})=f_{c} \tag{16}\]  
where \(n\) is the number of oblique springs.  
It is the usual case in general that the base excitation \(x_{b}\) and \(\dot{x}_{b}\) are unable to be known accurately beforehand and there are some uncertainties for the physical parameters of the passive isolation components. Now let \(\psi_{1}(\cdot)\) in Equation (1) be:  
\[\psi_{1}=c\Big{(}\dot{x}-\dot{x}_{b}\Big{)}+k_{v}(x-x_{b})+nk_{n}\bigg{(}1- \frac{a_{0}}{(x^{2}+a^{2})^{1/2}}\bigg{)}(x-x_{b}) \tag{17}\]  
and \(\psi(\cdot)=-(m-\hat{n})\ddot{x}-\psi_{1}\), then Equation (16) is rewritten as the same formulation as Equation (3), where \(\psi\) is also considered to be unknown here. The mass estimation \(\hat{m}\) is supposed to be known beforehand, and its velocity \(\dot{x}\) is the measured variable required only for the design of the active control force \(f_{c}\). The displacement \(x\) can be obtained from the numerical integration of the velocity \(\dot{x}\).  
Figure 2: A non-linear stiffness vibration isolator with quasi-zerostiffness (QZS).  
For the purpose of numerical simulation, an example of the isolator was utilized to validate the control design proposed in the preceding section. The nominal values of physical parameters of the isolator were \(m=300\) kg, \(k_{v}=15000\) N m\({}^{-1}\), \(k_{n}=k_{v}\)/2.5, \(c=85\) N s m\({}^{-1}\), \(a=0.6\) m, \(a_{0}=1.0\) m, and \(n=4\). The base excitation \(x_{b}\) was a zero-mean Gaussian white noise of standard deviation 0.01 m, which was filtered using a low-pass filter with 15 Hz cut-off frequency. This setup was used to produce state variables of the mass. The simulation was conducted using the ode45 function of MATLAB with a time step of 0.01 s.
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 1 Introduction  
Biodiversity and natural habitat conservation is one of the challenges of the 2020 EU Environment Action Program. Water quality is, in fact, one of the most outstanding fields of action. The EU Water Framework Directive demands the member states check the quality of their waters (e.g., rivers, lakes) and to inform the strategies that will be adopted to clean them [1].  
In limmology (the study of continental aquatic ecosystems), water quality is monitored through analysis and classification of different biophysical parameters. For environmental sampling, conventional techniques are used such as in situ manual measurements [2], on-board aircraft sensor systems [3] and satellite images [4].  
In situ or local measurements are performed by people and involve meticulous processes that cause fatigue and result tedious. As a result, an incorrect practice may be generated with significant high costs. Satellite images offer a limited spatial resolution in comparison with the on-board aircraft sensor systems, which improve the spatial resolution of the sampling. However, the services are still quite expensive, they must be reserved, and the flights have strict rules driven by weather conditions. In addition, the environment to analyze might be covered by vast vegetation as closed canopy trees on the banks of a riverbed. Thereby, neither satellites nor aircraft would be able to obtain samples,representing a problem to certain temporal windows in more closed environments, such as rivers or narrow streams.  
In recent years, remote sensing has hugely assisted in some scientific areas (e.g., precision agriculture [5]), due to the use of unmanned aerial vehicles (UAVs) low cost of acquisition and ease of handling. In limnology, the interest in the use of UAVs is increasing [6; 7].  
On the other hand, UAV data acquisition in external environments is conditioned to limitations (e.g., autonomy in flight time, operation maneuvers, climatic adversities, and emergency landing). In aquatic environments, the UAV platform could fall into the water and be permanently damaged if the operator fails the landing. A possible solution to this problem is the use of an unmanned surface vehicle (USV) to support UAV operations. Finally, the combination of both platforms is an added value point for environment monitoring [8].  
This work presents the design and construction of a maritime robotic system named Strider V1.0 that serves as a platform for the landing of UAVs in fluvial environments. For its design, several aspects were taken in consideration, such as portability, low-cost, and the physical characteristics of the most common UAVs in the market (e.g., DJI Phantom, AR. Drone). Finally, a set of analytical and real experiments verifies the correct functioning of the system with suitable results.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 2 Technological Background  
The LoRaWAN network infrastructure consists of three major elements, namely, end nodes, gateways (GW), and a central network server connected in a star-of-star topology.  
EDs are battery-powered endpoints such as sensors or actuators that transmit to and receive data from the network using the LoRa PHY layer technology. LoRaWAN is classified as the media access control (MAC) layer. The EDs transmit data packets via the GW for onward transmission to the network server (uplink packets) and the NS transmits data through the GW to the EDs (downlink packets). There is no peer-to-peer communication between the EDs. The GW is an intermediary between the EDs and the NS connected through an IP-based backhaul connection. The network server provides for the network management and controlling access permissions to it. The NS handles the authentication of EDs, manages the scheduling of transmissions, and coordinates the distribution of network transmission parameters. The NS then connects to numerous application servers to be accessed by users. LoRaWAN provides the Internet of Things with three distinct Classes A, B, and C to accommodate various application protocols. The downlink latency varies according to the device class and, therefore, there is a trade-off between latency and energy utilisation in LoRaWAN [15]. There are two reasons why a sent packet is not correctly received in LoRaWAN: (i) in cases where the signal falls below the required SNR threshold and (ii) in situations where a collision occurs and the signal strength is insufficient in comparison to the noise. Table 1 presents the sensitivity and signal-to-noise ratio (SNR) thresholds linked with various spreading factors within the 868 MHz band.
## 2 Data and Methods
### Evaluation Metrics  
The performance of the neural network is evaluated using the mean average error (MAE) (Formula (2)) and the root mean square error (RMSE) (Formula (3)) for TC precipitation on the test set. In Formulas (2) and (3), \(y_{i}\) represents the GPM precipitation observation and \(\dot{y}_{i}\) represents the model precipitation forecast.  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-\dot{y}_{i}\right)^{2}} \tag{2}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-\dot{y}_{i}\right) \tag{3}\]  
However, it is important to note that reducing the RMSE and MAE does not always result in an increase in the threat score (TS) [58], which is another commonly used metric for evaluating precipitation forecast skills. The TS objectively determines whether the intensity of precipitation will exceed a certain threshold and is calculated using the following formula:  
\[\text{TS}=\text{A}/(\text{A}+\text{B}+\text{C}) \tag{4}\]  
where A represents the number of correct occurrence forecasts (hits), B represents the number of wrong occurrence forecasts (false alarms), and C represents the number of incorrect non-occurrence forecasts (misses). Different precipitation forecast thresholds correspond to different TS scores. In this study, we primarily evaluate the TS scores using precipitation thresholds of 10 mm/day, 25 mm/day, 50 mm/day, and 100 mm/day.  
Furthermore, we calculate the spatial correlation between the precipitation forecasts and observations to identify the precipitation forecast model that best matches the spatial distribution of precipitation forecasts to the observations.
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 2 Related Work  
### Bound Gesture Recognition  
At present, bound gesture recognition usually uses wearable sensors deployed in gloves, smart watches, etc. The commonly used sensors include accelerometer sensors, gyroscopes, inertial sensors, etc. [PERSON] et al. [16] use inertial sensors in gloves to obtain hand and arm data, then use the designed convolutional neural network structure SLRNet to extract the features of gesture data and classify them, with an accuracy rate of 99.2%. [PERSON] et al. [5] use accelerometers, linear accelerometers and gyroscope sensors in a smartwatch to collect gesture data, adopting the proposed gesture detection and segmentation algorithm to find out the start and finish points of the gesture and then converting the raw sensor reading data into Spectral features. Finally, a Bidirectional Long Short-Term Memory (BiLSTM) network is used for feature extraction and softmax classification. The gesture recognition rate is as high as 96%. [PERSON] et al. [17] use accelerometers and gyroscopes in ordinary smartwatches to collect gesture data and combine a One-Dimensional Convolutional Neural Network (1D-CNN) with a BiLSTM network to analyze, learn and represent the sensory signals from the sensor signal features, reaching 90% accuracy. In addition, [PERSON] et al. [18] use the somatosensory sensor to obtain the person's depth information and bone information, decompose the gesture into a gesture sequence composed of micro gestures, and then match the gesture sequence with the gesture template to obtain experimental results. The recognition rate reaches 92%. [PERSON] et al. [19] proposed a method for muscle activity recognition, which detected muscle activity by measuring the air pressure change in the air sac contacting the muscle of interest, processed the data with high-pass and second-order low-pass filters and finally used the fuzzy logic method to classify gestures. [PERSON] et al. [20] proposed a gesture recognition method using an inertial sensor and ElectroMyoGraphy (EMG) signal fusion and used fourth-order Butterworth high-pass filter to filter the signal for both EMG and Inertial Measurement Unit (IMU). The EMG is segmented by adopting the double-threshold technique. Each active segment of the EMG and IMU data is divided into 250 m overlapping windows and temporal features are obtained from the windows. Finally, an adaptive Least Squares Support Vector Machine (LS-SVM), a bilinear model-based classification method and a MultiLayer Perceptron (MLP) network for classification were utilized, with an average accuracy of 67.5-84.6%. Although bound gesture recognition has excellent advantages in recognition accuracy, it will cause an additional physical burden on users, affect people's lives and cause high deployment and maintenance costs.
## 3 EMF-Aware Routing Framework for WMNs
### Theoretical Approach of EMF Exposure on WMNs  
We first introduce a mathematical function that could be used to characterize the EMF exposure in a WMN. This will be afterwards used to identify performance indicators to be used in the EMF-aware routing protocol for this type of networks. In a nutshell, we consider the EMF in wireless networks as a cumulative effect that is induced in an area surrounding a transmitter. This way, the higher the transmission power and number of times a node transmits, the higher the exposure. In this work we will not take into account other effects such as antenna directionality, which may have a strong impact on the induced exposure, but we focus on the routing networking techniques.
## 3 Results and Discussion
### Study of the Photo-Oxidation Kinetics of AHMPD with Polymers P2, P3, and P4 as Photosensitizers

Table 2: Effectiveness of polymers P1–P4 in the photo-oxidation of AHMPD (pH = 7 and pH = 11) and singlet oxygen generation was measured with the model reaction of ADPA oxidation. is considered that the divalent ionic compound product of the oxidation is susceptible to being separated from the recovered water via electrodialysis or chemical precipitation. On the other hand, it is known that in O\({}_{2}\)(\({}^{1}\Delta_{\text{g}}\))-mediated photooxidations of compounds possessing aromatic OH groups, the overall O\({}_{2}\)(\({}^{1}\Delta_{\text{g}}\)) quenching increases in higher polarity of the solvent and is favored with the OH group in the ionized form [44]. Additionally, [PERSON], who was the first to study the photo-oxidation reaction of AHMPD, also proposes a mechanism involving an intermediate complex with partial charge transfer between singlet oxygen and AHMPD during the photo-oxidation process [36] (Figure 6). In the presence of alkali, the OH-ionized form predominates, and the reaction with O\({}_{2}\)(\({}^{1}\Delta_{\text{g}}\)) is favored. This fact explains the increase in efficiency of the photo-oxidation process at pH = 11. Nevertheless, the reaction also occurs, albeit less favorably, at pH = 7; this is due to the presence of 2-amino and, especially, 6-methyl groups, which increases the electron donor ability of the substrate and, consequently, favors the generation of the charge transfer-mediated encounter complex. Regarding the effect of the supported photosensitizers, it is worth noting that RB was one of the first photosensitizers used in the heterogeneous phase, with pioneers [PERSON] and [PERSON] being the first to covalently anchor it to low-crosslinked chloromethylated polystyrene resins [39; 45; 46]. The photo-oxidation mechanism of supported RB is the same as that of free RB, as it has only been immobilized on support to achieve heterogeneity in photocatalysis, ensuring its recovery and reuse. Previous studies have already demonstrated that for polymers P2-P4 (which contain RB immobilized in their structure), the mechanism of action is the same as that of molecular RB [19; 20]. On the other hand, novel strategies can be pursued in future work with these materials to increase the reaction kinetics. For example, recent studies describe improved mechanisms to enhance the efficiency in the rate of dye in the presence of semiconductor nanostructures with dielectric Mie resonances [47], displaying links between the scattering and carrier dynamics [48]. Moreover, the molecular control of the charge-transfer process using an interface and local field engineering strategies [49] may also be of interest and evaluated in future work. Figure 5: Illustration of the possible primary photo-oxidation of AHMPD [36; 43] for the degradation of the pollutant AHMPD using singlet oxygen (**b**) generated from a photosensitizer excited with visible light (hv) (**a**). Figure 6: Quenching of singlet molecular oxygen O\({}_{2}\)(\({}^{1}\Delta_{\text{g}}\)) by AHMPD.
## 3 Results and Discussion
### Aerosols and Climate Change  
Earlier, a similar high aerosol presence over the Baltic Sea due to wild forest fire activity in eastern Europe was observed in 2002 [(41; 34)]. That situation was also a result of a warm and dry spring and summer in Europe. However, the 2019 episode is very prominent when bearing climate change consequences in mind. The extreme weather situation was present across Europe, leading to extreme wild forest fire activity, which caused many fatalities and posed health risks to thousands of Europeans, especially with the wind capacity to transport the particles.  
Unfortunately examples also include the very recent pan-European problem with the extreme wildfires in Greece and other regions of Europe in the summer of 2023. In the following months, Ww must analyze the regional consequences and the level of atmo  
Figure 14: An overview of the major convective events in 2019 [(40)].  
spheric aerosols related to the wildfires. Atmospheric aerosols produced in wildfires cause a significant impact on climate through the radiative effect, owing to their various optical and chemical properties [12; 27; 41]. Biomass burning events produce large amounts of trace gases and aerosols, which enter the troposphere, causing adverse air quality conditions locally but also in distant regions located downwind. Extreme wildfires may emit aerosol particles into the stratosphere, allowing for global transport [12; 27]. Fire-induced aerosols cause the reduction of global \"green areas\", leading to the reduction of carbon uptake by ecosystems.  
These fire-related modifications in radiative balance further induce modifications in meteorological and hydrologic factors. These, in turn, affect the frequency of fire occurrence through the modification of moisture levels, hence local weather conditions. These effects are visible in Figure 15, which presents the radiation budget for selected days between May and August 2019 for the Baltic Sea. Clearly, the radiative budget shows very high values, indicating the warming of the atmosphere, which is very likely connected with the presence of biomass burning aerosol particles, which absorb solar radiation.  
With this impact on the radiation budget and solar radiation, the nucleation process to create new aerosol particles can be affected. In fact, this process is highly related to solar radiation (for more details about nucleation, see [9]). Thus, we can assume that aerosols from wildfires can impact other aerosol particle formation, like nucleation processes, by influencing the radiation budget over the Baltic Sea.  
Moreover, on top of environmental disturbances due to the presence of biomass burning aerosols in the atmosphere, wildfire air pollution also poses hazards to global public health through the increase of risks and threats of a variety of respiratory and cardiovascular diseases, thereby increasing mortality. The global estimates show that fire-related aerosol particles can be responsible for as many as 33,000 deaths per annum [3].
# Inductive Thermography as Non-Destructive Testing for Railway Rails
## 6 Measurements of Head Checks from Rails in Service
### Characterization of Head Checks  
To automatically compute \(\alpha\), the surface angle of head checks, an edge detection algorithm was developed in such a way that the head checks in a phase image are recognized and marked by red lines (see Figure 15). These red lines correspond to the head check lines at the rail surface. The surface angle \(\alpha\) is computed as the angle between a horizontal line along the gauge corner and an approximating straight line along the head check, see green line in Figure 15. The mean angle is calculated by averaging for all head checks in the image. The phase image in Figure 15 shows the same part of the rail as the photo in Figure 13a.  
The head checks show an approximately 3 mm wide low phase value range at the right side. This is in good agreement with the simulated images of Figures 7c and 11d, therefore the head checks have approximately a 30\({}^{\circ}\) inclination angle and a depth around 1 mm.  
In Figure 16 two ways of evaluation of the number of head checks as well as their distances on a phase image are shown:  
* In Figure 16a the phase profile along a line over the head checks is evaluated, and the maximum values of the profiles are counted. With the image resolution, given as pixels per mm, the average distance between two head checks is calculated. For the case in Figure 16a it gives 13 head checks for 25 mm, resulting an average distance of 1.9 mm.
* Alternatively, the same edge detection algorithm, as in Figure 15, is used to compute the number and the distances of the head checks. Evaluating the results of the edge detection along a straight line produces a plot with peaks where the head checks are located, see Figure 16b. In this case 14 head checks were recognized in the 25 mm distance. As the number of the head checks found along a single line slightly depends on the position of the line, the method was improved in such a way that several lines were taken and an average number of head checks was counted from all of them.  
Figure 14: (**a**) A photo of the head checks on RP04; (**b**) a micrograph of head checks on RP04.  
Figure 15: Phase image of RP03 with edge detection of head checks (heating pulse duration: 0.1 s).
## 2 Regional Geological Background  
The Conaxue Co landslide dam is located in Lieyi Township, Batang County of the Garze Tibetan Autonomous Prefecture, Sichuan Province, China. Its geographic coordinates are 99\({}^{\circ}\)31\({}^{\prime}\)56.6\({}^{\prime\prime}\) E and 30\({}^{\circ}\)11\({}^{\prime}\)54.6\({}^{\prime\prime}\) N. The study area is located within the QTP and is a crucial region for studying the uplift mechanism of the QTP (Figure 1). The Batang area is positioned at the junction of two primary fault blocks: the Qiangtang fault block (Qiangtang F.B.) and the Chuan-Dian fault block (Chuan-Dian F.B.), as illustrated in Figure 1. This junction constitutes a structurally active zone where intense tectonic movements can readily trigger strong earthquakes (Figure 1). For example, both the 1973 Ms 7.6 Luhuo earthquake and the 2010 Ms7.1 Yushu earthquake occurred at the junction zone between these two blocks [18,19].  
Figure 1: Regional geological background of the landslide dam. **(A)–**Nationwide base map; **(B)–**geological background of the landslide dam. I–**Qinghai–Tibet block; II–**Yangzi block; III–**North China block.  
The Conaxue Co landslide dam is structurally situated at the convergent junction between the Sanjiang Orogenic Belt and the Songpan-Garze orogenic belt. Regional crustal deformation in this area is dominantly governed by three active faults: a NE-striking dextral strike-slip fault (Batang Fault; slip rate of 2.8 \(\pm\) 0.3 mm/yr), a NNW-striking thrust fault (Changgai-Namakuo fault; dip angle of 45\({}^{\circ}\) SE), and a N-striking normal fault (Chalong-Ranbu fault; vertical displacement of \(>\)150 m since Late Pleistocene; Figure 2A). The exposed strata in the study area, from oldest to youngest, include late Yanshanian (\(\gamma\beta_{5}^{3}\)), Triassic (T), and Quaternary (Q) formations. Among them, the Late Yanshanian (\(\gamma\beta_{5}^{3}\)) strata are predominantly composed of granite, while the Triassic (T) strata primarily consist of metamorphic sandstone and slate. The strata that formed the source area of the Conaxue Co landslide dam slide are primarily late Yanshanian granite (Figure 2B).