esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones
## 2 Drone Charging Station Design and Development
### Mechanical Clamp Design  
A clamping mechanism is proposed to ensure alignment and a minimum gap between the ferromagnetic cores. Figure 5a shows the two parts of the clamp holding the cable, while Figure 5b shows the clamp-closing mechanism located at the rear. This mechanical design maximizes the flux concentration in an effective and low-reluctance magnetic path, improving the energy-harvesting capability. In order to meet the requirements of drone integration, effector manipulation and weather hazards, the mechanical design was optimized to provide:  
* Easy vertical docking of the charging station and the power cable;
* Low weight \(\approx 5\) kg;
* Strong clamping for harsh environments;
* Easy installation and robotic manipulation.  
During the installation process, the UAV brings the open clamp close to the cable. Figure 6a illustrates how the shape of the claw guides the cable for accurate positioning. As shown in Figure 6b, the ferromagnetic core achieves a low-reluctance magnetic path to harvest energy from the magnetic field around the cable when the clamp is closed. A set of interchangeable jaw pads ensures an efficient grip between the cable and the clamp, while allowing the charger to be used on cables with different diameters. These grooved pads are  
Figure 4: Power stage diagram for energy conversion and management.  
Figure 5: Drone charging station for overhead power lines designed in the Aerial-Core project: (**a**) front view; (**b**) back view.  
designed to increase roughness and therefore friction, preventing the charger from rotating on the cable.  
A two-part clamp design was defined to perform the opening and closing movements of the clamp during the installation and removal maneuvers (Figure 7a). While the lower half of the clamp remains fixed, the upper half can rotate around a fixed axis (Figure 7b). A worm gear mechanism produces the rotation to move the clamp (Figure 8a). This mechanism is designed to be operated by a single actuator. The servo rotary motion is converted into linear motion via a screw-and-nut system mounted on the motor shaft. Each rotation of the threaded rod produces a rotation in this threaded coupling, which, since the axial movement of the rod is limited, results in movement along the axis of the clamp guide (Figure 8b). The movement along the guide allows the clamp to open and close.  
Figure 6: Ferromagnetic core located inside the clamp to achieve a low-reluctance magnetic path to harvest energy from the magnetic field around the cable and interchangeable jaw pads: (**a**) clamp opened; (**b**) clamp closed.  
Figure 7: Two-piece clamp with a hinged half that can pivot around a fixed axis to execute the opening and closing movements: (**a**) clamp closed; (**b**) clamp opened.
## 3 Results
### Shear-Wave Splitting Parameters
Colours correspond to the respective normalized time-delay, \(t_{\text{n}}\). The total number of measurements (N) and average \(\varphi\) and \(t_{\text{n}}\) are presented at the bottom-left of each panel. The red arrows indicate the average \(\varphi\) direction. See Figure S16 for rose diagrams of the other stations. The distribution of \(\varphi\) values for null measurements, with \(\varphi\) being within \(\pm 10^{\circ}\) from \(p\) or \(p+90^{\circ}\), i.e., sub-parallel or perpendicular to the polarization direction of the corrected (for anisotropy) shear-wave, \(p\), is presented in Figure S17. The distribution for all stations indicates a \(\overline{\varphi}_{\rm null}\) = 5.4\({}^{\circ}\)\(\pm\) 6.1\({}^{\circ}\), i.e., in an approximate N-S direction, that is perpendicular to the respective \(\overline{\varphi}\) of the valid measurements. This suggests that the majority of null \(\varphi\) values likely correspond to the polarization direction of the S\({}_{\rm slow}\) wave. However, there are (fewer) cases where \(\varphi_{\rm null}\) measurements of certain stations are similar with their \(\overline{\varphi}\), e.g., SERG (\(\overline{\varphi}_{\rm null}\) = 64.9\({}^{\circ}\)\(\pm\) 12.1\({}^{\circ}\)). In most stations, the average \(t_{\rm n}\) for null measurements is larger than the respective for valid ones. Regarding the temporal variations of the SWS parameters, we take advantage of the large number of measurements from the combination of the manual and automatic catalogues to investigate patterns of change with time. The time-delay between the S\({}_{\rm fast}\) and S\({}_{\rm slow}\) waves, normalized by the ray-path length, can be used as an indicator for the degree of anisotropy in the medium. However, factors besides the traveled distance also affect \(t_{\rm d}\). These include the difficulties in the precise measurement of \(\varphi\) or \(t_{\rm d}\) related to the radiation pattern (focal mechanism), the distortion of polarization due to non-perpendicularity of the incident seismic rays to the surface, variations in the frequency content between S\({}_{\rm fast}\) and S\({}_{\rm slow}\) and larger attenuation of the S\({}_{\rm slow}\)[44; 63], effectively contributing to a significant degree of scatter. To exclude measurements that are less affected by changes in the geometry of the fluid-filled microcracks, we select event-station pairs whose ray-paths belong to \"Band-1\". This is defined as the double-leafed solid angle between 15\({}^{\circ}\) and 45\({}^{\circ}\) on either side of the vertical crack-plane (whose azimuth is defined by \(\overline{\varphi}\)) passing through the station (see Figure S21 for a schematic view of Band-1). Ray-paths of event-station pairs
## 3 Results and Discussion
### Isotherm Study of Adsorption  
Evaluation of the transfer of dye molecules from a solution to adsorbent particles at equilibrium is commonly conducted through an analysis of adsorption isotherms. Among various approaches, adsorption isotherm analysis is widely recognized as the most efficient and valuable method. In Figure 11, the adsorption of MB on NGC is visually depicted, while the parameters for each isotherm are presented in Table 2. These parameters are derived from the slopes and intercepts of the plots. To determine the most appropriate isotherm for the experimental data, the isotherm exhibiting the highest correlation coefficient (\(R^{2}\)) was selected, indicating its strong correlation with the data [60].  
The results obtained indicated that the Langmuir model was followed by the adsorption of MB due to the strong coefficient correlation (\(R^{2}\)). Consequently, a monolayer of MB is expected to be formed on the surface of the NGC as the potential adsorption mechanism. Additionally, the (\(R^{2}\)) values also demonstrated that MB adsorption on NGC did not adhere to the Freundlich, Temkin, or Dubinin-Radosevich isotherms, as shown in Table 2. Generally, if \(n>1\), it suggests that the adsorbate is favorably adsorbed on the adsorbent. The fact that in our study \(n\) is significantly greater than unity at all investigated temperatures indicates that NGC is a suitable adsorbent for the adsorption of MB from an aqueous solution [61].  
The Temkin model is not suitable for representing the equilibrium isotherms of MB on NGC due to the low \(R^{2}\) values. In contrast, the Dubinin-Radushkevich (D-R) model was employed to determine the apparent free energy of adsorption (E), which is commonly used to differentiate between physical and chemical adsorption. The adsorption energy calculated from the D-R isotherms for MB on NGC indicated physical adsorption [62; 63; 64], with \(Q_{m}\) values decreasing as temperature increased, suggesting an exothermic adsorption process [65].  
Table 3 presents a comparison of the adsorption capacities \(Q_{m}\) achieved in this investigation with various types of MB adsorbents in previous research studies.  
The adsorption of dyes involves various mechanisms such as ion exchange, hydrogen bonding, Van der Waals forces, electrostatics, and dipole-ion interactions. The results from the kinetic and isotherm studies suggest that physical attraction is the main adsorption mechanism for MB onto NGC. Therefore, it can be inferred that the primary mechanism of adsorption is electrostatic interactions [68].
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 3 System Model and Problem Formulation
### Energy Consumption and Harvesting Model  
In RSNs, the energy management of user satellites consists of two parts: energy consumption and energy harvesting. The three factors that affect energy consumption are the satellite's orbital movement, data acquisition, and transmission. Denote \(E_{i}^{c}\) as energy consumption of user satellite \(i\) in slot \(t\), which is given by  
\[E_{i}^{c}(t)=\tau\times\bigg{(}P_{n}+P_{t}\cdot\frac{\gamma_{i}(t)}{\xi_{ \text{max}}}\cdot\delta_{ij}(t)+P_{r}\cdot\frac{\varphi_{i}(t)}{\varphi_{\text {max}}}\bigg{)}. \tag{8}\]  
Wherein, \(P_{n}\), \(P_{t}\), and \(P_{r}\) are the constant power of regular operation, data transmission, and data acquisition, respectively. Since the data collection rate \(\varphi_{i}(t)\) has a maximum value of \(\varphi_{\text{max}}\) and the inter-satellite transmission rate \(\gamma_{i}(t)\) is with the maximum value of \(\xi_{\text{max}}\). We derive the upper bound of total energy consumption as \(E_{\text{max}}^{c}=\tau\cdot(P_{n}+P_{t}+P_{r})\).  
Since a satellite moves around the earth, it harvests energy when the satellite runs to the sunny side. Denote \(E_{i}^{h}\) as the amount of energy harvested by satellite \(i\) (\(i\in U\)) during slot \(t\) as follows  
\[E_{i}^{h}(t)=\rho_{i}(t)\times\text{max}\{0,\tau-\omega_{i}(t)\}, \tag{9}\]  
where \(\omega_{i}(t)\) is the remaining time of user satellite \(i\) in the shadow during time slot \(t\), and \(\omega_{i}(t)=0\) implies satellite \(i\) is in eclipse. The energy harvesting rate \(\rho_{i}(t)\) is bounded by \(\rho_{\text{max}}\).  
In the dynamic process of energy, buffered energy \(E_{i}(t+1)\) at user satellite \(i\) is related to \(E_{i}(t)\), consumed power, and harvested energy in slot \(t\). We build the time-varying energy queue model as follows  
\[E_{i}(t+1)=E_{i}(t)-E_{i}^{c}(t)+E_{i}^{h}(t). \tag{10}\]  
Normally, the remaining energy in the battery at the end of time slot \(t\) is bound by its maximum capacity. In addition, the battery needs to retain a certain amount of energy to ensure the regular operation of the satellite. Thus, we have the following constraint  
\[\mathcal{B}(1-\mu)\leq E_{i}(t)\leq\mathcal{B},\qquad\forall i\in U, \tag{11}\]  
where \(\mathcal{B}\) denotes the capacity of the satellite battery, and \(\mu\) represents the maximum energy consumption proportion of the battery.
## 4 Design and Electrical Performance Characterization  
This section outlines the design procedure and intermediate analyses required to accurately characterize the dielectric resonator, the input and output coupling (including quality factor and bandwidth), and the inter-resonator coupling, which arise when studying the behavior of both direct and cross-coupled configurations for a given resonant mode.
## 3 Results and Discussion
### Linear Expansion Test (Swelling) (LP) Test
#### 3.2.3 Effects of M-GGBS-RHA on Swelling  
Figures 15-17 reveal that the use of the M-GGBS-RHA stabilizer (10 and 20 wt. %) modified the behavior of the volumetric change and resulted in a significant decrease in the swelling magnitude of the gypseous soil. After 28 days of observation, the swelling magnitudes for 5M-2.5 GGBS-2.5 RHA, 5M-5 GGBS-10 RHA, 5M-10 GGBS-5 RHA, and 5M-12.5 GGBS-2.5 RHA were 0.04%, 0.089%, 0.044%, and 0.001%, respectively, with swelling values suppressed to 99.78%, 98.98%, 99.5%, and 99.98% respectively. Nevertheless, the longer curing period (90 days) produced further declines in the swelling rate, thereby achieving minimum swellings of 0.01%, 0.058%, 0.017%, and 0.001%, where the swelling was roughly inhibited to 99.67%, 97.38%, 99.23%, and 99.95% respectively.  
Figure 15: Vertical swelling strain of soils stabilized with 10% and 20% of L, M, and M-GGBS-RHA after 7 days of curing: (**a**) results presented as a line curve, (**b**) results presented as columns.  
Results from the linear expansion test indicated that swelling behavior disappeared in the high-sulfate soil, which was treated with the M-GGBS-RHA-stabilizer. The volume changes of the treated soils were less than those of the lime-treated soils, indicating that the swelling characteristics were enhanced in the treated high-sulfate soils.  
These results indicate that the M-GGBS-RHA-stabilized soil resisted sulfate attacks better than the lime-stabilized soil. Moreover, the results showed that the swelling of the lime-stabilized soil was higher than that of the M-GGBS-RHA-stabilized soil with an increase in the soaking time. The phenomenon can be explained by two factors: (1) a larger amount of calcium in the lime-stabilized soil contributed to ettrinigite formation in the presence of sulfate and (2) the consumption of calcium by sulfate, which led to a reduction in the formation of cementitious material such as CSH, CAH, and CSAH. [PERSON] et al. [4] reported that there are various levels of risk in relation to swelling, as  
Figure 16: Vertical swelling strain of soils stabilized with 10% and 20% of L, M, and M-GGBS-RHA after 28 days of curing: (a) results presented as a line curve, (b) results presented as columns.  
Figure 17: Vertical swelling strain of soils stabilized with 10% and 20% of L, M, and M-GGBS-RHA after 90 days of curing: (a) results presented as a line curve, (b) results presented as columns.  
shown in Table 6. The risk level was \"very strong\" in comparison with the maximum expansions of 25.226% and 18.313% obtained in the current study from the mixing of K-10L-10G and K-20L-10G, respectively.
# On the Spectral Efficiency for Distributed Massive MIMO Systems
## 5 D-MIMO System on a Semi-Anechoic and Semi-Reverberation Chamber in the mmWave Band  
### Measurement Scenario  
This last section is focused on the analysis of a D-MIMO system in the mmWave band acquired in a controlled scenario, such as a semi-anechoic and semi-reverberation chamber. For that purpose, two distinct scenarios are taken into account. On the one hand, the semi-anechoic part is commonly used for the characterization of radiating elements due to the absorption of any reflection in the scenario. In this case, it is used as a communication channel where the lack of reflections decreases the diversity of the scenario. Only one direct beam reaches the receiver through the LoS. Therefore, it is expected to notice a very high correlation between channel measurements. On the other hand, the semi-reverberation part implies a rich scattering environment since the metallic walls generate several propagation paths for the electromagnetic waves. In this case, any movement on the BS/UE generates a totally different propagation path, which makes the correlation between channels low. A complete and detailed description of the chamber is available in [27].
# Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP
## 2 Low-Rank Representation and Local Binary Pattern
### Local Binary Pattern  
There are many LBP operators and LBP feature extraction methods are reported in image recognition and image security fields, among which the RiuLBP operator is one of most popular texture operators due to its rotation invariance and low dimension [37]. The basic LBP is a gray-scale invariant which transforms the neighborhood pixels into a set of binary codes by taking the center pixel as a threshold representing the center pixel, and it is defined as follows:  
\[LBP_{P,R}=\sum_{p=0}^{P-1}s\big{(}g_{p}-g_{c}\big{)}2^{p}, \tag{5}\]  
where \(R\) denotes the scale of the radius of neighborhoods, \(P\) denotes the number of sampling points, \(g_{c}\) is the gray value of the center pixel, \(g_{p}\) is the circularly symmetric neighbor, and \(s\)(*) is the sign function that returns the sign of the specified number.  
When there are at most two bitwise 0/1 transitions, the pattern is called a uniform one. The number of uniform patterns is \(P(P-1)+2\), which is less than the number \(2^{P}\) of the basic LBP feature. A function \(U(\Delta)\) is defined to return the number of spatial transitions (bitwise 0/1 transitions) in the pattern \(\Delta\), and it can be written as:  
\[U(LBP_{P,R})=\sum_{p=0}^{P-1}\Big{|}s\big{(}g_{\mathrm{Mod}(p+1,P)}-g_{c} \big{)}-s\big{(}g_{p}-g_{c}\big{)}\Big{|}, \tag{6}\]where the function \(Mod(x,y)\) returns the remainder after a number \(x\) is divided by a divisor \(y\).  
In order to achieve good discriminability and robustness of the perceptual image hash, the RiuLBP feature descriptor is utilized to extract stable image features. Instead of the ordered binary coding of the basic LBP, the center pixel of RiuLBP is denoted by simply counting ones in the basic LBP coding for uniform patterns [38], denoted by \(LBP_{P,R}^{tri}\), as shown below:  
\[LBP_{P,R}^{tri}=\left\{\begin{array}{cc}\sum\limits_{p=0}^{P-1}s(g_{p}-g_{c })&\quad\text{if }\text{U}(LBP_{P,R})\leq 2,\\ P+1&\quad\text{otherwise}.\end{array}\right. \tag{7}\]  
From Equation (7), we can see that the RiuLBP feature only has \(P+2\) distinct patterns.
## 2 Background
### Wearable Devices
Arguably, the most critical components of wearable devices are sensors. In the field of human activity recognition, the accelerometer, gyroscope, magnetometer, and barometer are the most commonly used [42]. These sensors measure and collect data on motion, orientation, magnetic fields, and atmospheric pressure. The comprehensive framework for wearable health monitoring systems is shown in Figure 1. The data collected from the sensors of wearable devices can be processed in two primary ways: Edge processing and Offline processing. In edge processing, data is analyzed in real-time directly on the device, enabling immediate responses, albeit with limited computational resources. In contrast, offline processing involves transmitting the data to external systems, such as servers or computers, where more extensive and resource-intensive analysis can be performed. Regardless of the processing method, the overall process follows a similar structure. It begins with a pre-processing stage, where raw sensor data is cleaned and transformed to ensure it is suitable for subsequent steps. Next, feature extraction is performed to identify key features or patterns within the data. These extracted features could then input into machine learning (ML) or deep learning (DL) models, which generate predictions, providing insights or diagnoses based on the analyzed data. In PD, wearable devices have been used to assess various symptoms. In some cases, symptom assessment has been done by time and frequency analysis, in other cases by extracting relevant features from the measured data, another strategy is the development of classifiers of disease severity [43; 44], resting tremor [45], bradykinesia [46] or freezing of gait [47]. Several studies have presented devices specifically designed for PD patients such as STAT-ON(r) [48], KinetiGraphTM(r) [49] or PD Monitor(r) [50]. Figure 2 presents examples of wearable devices employed in the detection and monitoring of PD. These devices could  
Figure 1: Comprehensive framework for wearable health monitoring systems. be smartwatches, inertial measurement units (IMUs), pressure insoles, sensor belts, or smartphones with body adaptors, among others. In [51], commercial devices used for PD management were evaluated, considering their validation process and clinical applications along with the strengths and weaknesses of each. In [52] the authors explored the wearable devices used for PD in hospitals, concluding that the most common types of wearable devices include IMUs and smartwatches. In [53] a comprehensive review was performed on how wearable sensors can support tasks such as early diagnosis, human motion analysis, motor fluctuations, and home and long-term monitoring for PwPD. However, although wearable devices hold promise for improving PD management, their adoption in the clinical setting is limited by problems such as inadequate technical and clinical validation [54].
## 3 ICN Architecture
### Smart Flooding (SF)  
Smart Flooding (SF) is a forwarding strategy for ICN architectures described in detail in [24]. In SF, interfaces are given one of three states, green if it can bring back information, yellow if it is unknown whether the interface may bring back information and red if it is clear that the interface cannot bring back information. When an ingress Interest PDU gets passed to the FIB, the PDU is forwarded through the highest ranked green interface available. After a predetermined amount of time, if the PIT entry for the related ICN name continues to be unsatisfied, lower ranked green interfaces are tested and the initially highest ranked interface is demoted. If the PIT entry is unsatisfied, the process continues, in descending order, through all green, yellow and red interfaces available. When a PIT entry exhausts the FIB entries available for the ICN name, then a special Interest PDU, called a Non-Acknowledgement (NACK) PDU, is transmitted in a downstream manner. Neighboring nodes whose ingress interface receive this NACK PDU are automatically set to red state for the related ICN name. Should the NACK PDU set all FIB entries for the ICN name to the red state, then the NACK PDU is again forwarded downstream. On the other hand, when the ICN named information is found and sent downstream in a Data PDU, the downstream nodes automatically promote the related ingress interface to the green state.  
Figure 2: Forwarding process for Data PDUs at an ICN node.
###### Abstract  
Flexible wearable wireless devices have found practical uses as their cost has fallen and Internet of Things applications have gained further acceptance. These devices are gaining further use and acceptance in the consumer and wearable space for applications such as logistical tracking and maintaining sensor information, including temperature, humidity, and location. In such applications, antennas are exposed to bending and crumbling. Therefore, flexible substrate antennas for use with polymer-based flexible devices are an important area of research that needs to be addressed. In this study, the bending capabilities of flexible polymer substrate antennas for general IoT applications were practically analyzed by fabricating flexible antennas on Polyethylene Terephthalate (PET), Polytetrafluoroethylene (PTFE) Teflon, and Polyvinylchloride (PVC) substrates operating at 2.45, 4.45, and 7.25 GHz frequencies. The basic premise was to investigate the flexibility and bending ability of polymer materials, and their tendency to withstand deformation. In the current paper, we start by providing an equivalent model for the flexible microstrip patch antenna under bent conditions, followed by outlining the process of designing flexible antennas on polymer substrates. Finally, the fabricated flexible antennas were tested in an anechoic chamber for various radiation characteristics such as reflection coefficients, operating frequency shifts, and impedance mismatch with the transmission line, under bending conditions up to 7 mm. The practical outcomes were then compared with our recent investigation on flexible polymer substrate antennas for wearable applications. This study provides a means to select a suitable polymer substrate for future wearable sensors and antennas with high bendability.  
Keywords:polymers substrates; wearable sensors; flexible antennas; bending analysis; wearable applications +
Footnote †: journal: Applied Sciences