esa-sceva/satcom-chunk-collection huggingface dataset:
# Studies regarding the Use of Pneumatic Muscles in Precise Positioning Systems
## 2 Materials and Methods
### Method  
The aim of this study was to identify the occurrence of hysteresis in the operation of a small pneumatic muscle in different conditions. The experiment to find the hysteresis of the pneumatic muscle was performed as follows:  
1. An air pressure is applied to the actuator from 0 to 6 bar by 0.5 bar steps. There is no load applied to the free end of the muscle (\(M=0\) kg);
2. At each step, the length of the PMA is measured;
3. A 2.55 kg load is attached to the free end of the muscle;
4. Steps 1 and 2 are repeated;
5. A 4.55 kg load is attached to the free end of the muscle;
6. Steps 1 and 2 are repeated;
7. The obtained data is processed.  
After each pressure level was reached, and prior to performing the measurement, a minute of rest was allowed in order for the muscle length to stabilize. The measurements were repeated five times, and five cycles of inflation/deflation with air of the pneumatic muscle were conducted. The contraction was measured using a linear potentiometer, 200 mm stroke code 167090 (produced by Festo AG & Co., Esslingen, Germany).  
The axial contraction of the pneumatic muscle decreases while the applied pressure is raised. Even if the contraction ratio (\(\epsilon\)) is specified as being 25% for the considered muscle, it is not fixed for all actuators and it depends on the type of inner rubber tube, the diameter of the PMA, and the maximum diameter of the braided sleeve [37].  
The study in [37] also confirms that the contraction ratio of a pneumatic muscle depends on the stiffness and diameter of the actuator and is not fixed.  
In [38], the hysteresis curves that describe the radial and axial dimensional modifications of a small pneumatic muscle, as well as the variation of the developed forces for different feed pressures, were presented.
## 1 Introduction  
The Xihu Sag of the East China Sea Shelf Basin has undergone more than 40 years of exploration [1], and several large- and medium-scale oil and gas fields have been discovered. It is the most important exploration zone and main oil and gas production area in the offshore basins of China. In recent years, a series of important exploration breakthroughs have been made in the Xihu Sag, especially in the lithologic and stratigraphic exploration fields on the flanks of the Central Anticline Belt and the deep-buried low-permeability to ultra-low-permeability fields, where large-scale natural gas exploration discoveries have been made, showing great potential for oil and gas exploration.  
Series of research have been conducted on the gas migration and accumulation in the central anticline belt of the Xihu Sag, covering aspects such as source rocks, reservoirs, petrophysical properties, preservation conditions, and fault activities [2; 3; 4; 5; 6; 7; 8]. Source rocks serve as the basis for gas formation, and the gas exhibits the typical characteristic of \"near-source accumulation\". The closer to the effective source rock, the more favorable for hydrocarbon migration and accumulation [9; 10; 11]. The distribution and petrophysical properties of reservoirs govern the location and saturation of hydrocarbons [12; 13; 14]. Faults are generally regarded as the main migration pathways for hydrocarbons. Different fault types under various stresses determine the effectiveness of traps and thereby control the scale of hydrocarbon enrichment [15; 16; 17; 18; 19; 20; 21; 22]. A series of international approaches toward the fault types, activities, and models have been used in characterizing the fault belt evolution and understanding processes related to climate change in the basin, which could serve as useful methods for further analysis [8; 17; 19].  
However, there are few studies that focus on the systematic oil and gas accumulation and enrichment patterns of the Central Anticline Belt in the Xihu Sag, and there is a lack of detailed comparative studies between different structural units. The studies on the hydrocarbon accumulation process of the Central Anticline Belt are relatively weak. The main controlling factors of differential accumulation remain to be further investigated. Thus, the major aims of this article are (1) selecting typical structures in the Xihu Sag to systematically summarize the formation of gas fields, their distributions, and hydrocarbon enrichment patterns, (2) combining various information such as structural evolution, fault conductivity, and geochemical characteristics to comparatively analyze the gas accumulation process in different structural units and conclude the main factors controlling the differential accumulation, and (3) discussing and establishing the gas accumulation models of different structural units of the Central Anticline Belt in the Xihu Sag, East China Sea Shelf Basin.
## 4 Discussion
### Impact of Human Activities on the MODEI
#### 4.2.2 Changes in Land Use  
Severe human disturbances (such as higher population densities and the expansion of construction land) can put enormous pressure on ecosystems, resulting in a decline in ecological quality ([PERSON] et al., 2018; [PERSON] et al., 2018).  
The areas where the MODEI had significantly decreased were concentrated on both sides of the Irtysh River and the Ulungur River. Since 2006, the government has set up new settlement villages with unified housing standards along the banks of the Ulungur River to encourage the herders to settle down, improve their living conditions and relieve the pressure on the grasslands. The resettlement project for herders began on a large scale in 2009 ([PERSON] et al., 2018). Due to the implementation of the herdsmen settlement project, the area of construction land increased. The alluvial plain of the Ulungur River has relatively flat terrain and superior natural conditions, so the construction of newly settled villages was mainly concentrated on Ulungur River banks. According to statistics of land use change data, the area of land for industrial and mining residents was 16.89 km\({}^{2}\) and 71.94 km\({}^{2}\) in 2008 and 2018, respectively, increasing by 3.26 times. Therefore, due to the expansion of construction land, some areas on both sides of the river have shown a downward trend in the MODEI in the past ten years.  
In addition, the MODEI improved significantly in the areas that were also distributed on both sides of the Irtysh and Ulungur rivers, which was possibly caused by the settlement project of herdsmen. The families of herdsmen who moved to the newly settled village were given an average of 3.3 hectares of farmland to plant artificial grassland ([PERSON] et al., 2018); as a result, the  
Figure 12: Changes in theoretical carrying capacity (**a**), actual carrying capacity (**b**), and grazing pressure index (**c**) in Fuyun County from 2001 to 2018.  
area of artificial grassland has significantly increased. In addition to artificial grassland, the area of cropland also increased. The cropland area in Fuyun County was 378.39 km\({}^{2}\) and 474.38 km\({}^{2}\) in 2008 and 2018, respectively, with an increase of 25.37% (Figure 13). Therefore, due to the planting of artificial grassland and the increase in cropland, the MODEL of some areas on both sides of the river improved.
# Laser Synthesized Graphene and Its Applications
## 3 Applications of Laser Synthesized Graphene
### Gas and Liquid Sensing  
The general working principle of gas sensors based on LSG is the same as that outlined in Section 3.1 for RH sensors. In this case, to the author's knowledge, only resistive sensing was investigated. It is clear that the selectivity challenge associated with sensing systems based on nanomaterials equally applies to LSG systems. One gas sensing study using LRG was reported by [PERSON] et al. for NO\({}_{2}\) sensing on flexible substrates [35]. However, the sensor response here was limited to \(\Delta\)R/R \(<\) 1% under exposure to 20 ppm NO\({}_{2}\). Multiple gas sensing was reported by [PERSON] et al., who investigated H\({}_{2}\)S, H\({}_{2}\), and ethanol as analytes [33]. The authors reported sensitivity in the region of 0.2% upon exposure to 20 ppm gas, and used principal component analysis to achieve selectivity from the gas response measurements [33].  
LIG was used for flexible gas sensors by [PERSON] et al., who investigated a range of gases such as CO\({}_{2}\), O\({}_{2}\), N\({}_{2}\), H\({}_{2}\), argon, helium, with sensitivities reported up to 5% and detection limits estimated in the 100-1000 ppm range [31]. The authors of this study did not speculate on the selectivity of their system, although they showed the different response and recovery times for the gases, which through appropriate data analytics might provide species information in a field setting.  
Liquid sensing has been proposed by [PERSON] et al. [58], who characterized the \(\Delta\)R/R response of LRG to a range of different liquids. It can be observed that the response is made of three stages, as shown in Figure 10: the first stage sees the \(\Delta\)R/R increase due to the absorption of a drop of liquid within the porous structure of rGO; in the second stage \(\Delta\)R/R maintains constant for a short time, then in the third stage the liquid evaporates and the \(\Delta\)R/R goes back to zero. This is repeated over 50 cycles showing robustness and repeatability [58].  
Figure 9: (**a**) Optical micrograph of the LIG electrode; (**b**) photographic image of the sensing device; (**c**) sensing response of the obtained device to human breath. Reproduced with permission from Ref. [31]. Copyright 2020, American Chemical Society.
## 3 Materials and Methods
### Coaxial Alignment Correction Algorithm Based on Optical Path  
#### 3.3.1 Coaxial Alignment of Optical Path  
To align the projected image with the actual object, this paper proposes a method that combines coaxial correction and a homography matrix. Through the adjustment of the optical path to mitigate the influence of the curved surface projection and employing the homography matrix to refine the projected image, the resulting image is then perfectly aligned with the actual object.  
The process of capturing images with a camera involves transforming the object from a world coordinate system to a pixel coordinate system, while the process of projection involves moving the image on a pixel coordinate system to a world coordinate system. Therefore, a projector can be viewed as an inverse camera [35]. To ensure that the projected image aligns perfectly with the object, the image in the inverse camera should be the same  
Figure 4: Differences in the two blocks. (**a**) The structure of an inverted residual block and (**b**) residual block.  
as the image captured by camera. According to [PERSON]'s camera calibration method, the image captured by the camera is essentially a projection of a three-dimensional object onto a two-dimensional plane.  
When a camera and an inverse camera observe the same surface, the smaller the angle between their optical paths, the smaller the discrepancy in the acquired surface information. When the optical paths of the two devices coincide, it is equivalent to the projector and the camera being in the same position. The image projected onto the surface is precisely the image captured by the camera, and the impact caused by the projection on the curved surface is also reduced. Then, a string of letters is printed on an A4 piece of paper and attached to a bucket-shaped surface, and the images captured by the camera from angles A and B are shown in Figure 5.  
From the above figure, it can be observed that, due to the difference in position, the camera captures less information on the left side at angle B. Therefore, to reduce the impact of surface projection and minimize information loss, it is necessary to align the optical paths of both the camera and the projector. By using the properties of the hot mirror, the near-infrared image can be reflected into the camera. Subsequently, the image is transmitted to the projector and projected as visible light, shining onto the dorsal hand through the hot mirror, as illustrated in Figure 6.  
Figure 5: The surface information acquired by the camera at different angles. (**a**) Image captured from angle A perspective and (**b**) image captured from angle B perspective.  
Figure 6: Schematic diagram of the light path.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### The SAC Architecture: System and Software  
The SAC model is based on a system architecture where the STN nodes are assumed to be ultra high frequency (UHF) RFID passive tags, which are outfitted with sensors. These nodes allow tags to be interrogated at a range of up to 10 meters [15], but cannot communicate with each other, even while being energized by the reader due to their passive characteristics. An HSN node integrates both sensor capabilities for sensing what is happening in the environment and identification capabilities to identify numbers from tagged objects or persons. They are endowed with communication interfaces to enable communication with each other and dissemination of the sensor readings to the gateway. The HSN nodes follow a communication model borrowed from [6; 16], where two separate channels are used by the hybrid nodes to avoid interference between transmission on the sensor and reader channels. Thus, a 2.4-GHz channel is used by the sensor side of the HSN, while the RFID side uses the 915-MHz channel. The 915-MHz reader channel is assigned to reader-tag communication, while the 2.4-GHz sensor channel is used for communication between HSN and HSN nodes or between an HSN node and its cluster head. By assuming that the micro-controller can differentiate between the different types of data and take appropriate actions that involve decisions on where to send the received data, this results in a multi-layer network where the 2.4-GHz backbone network is layered above 915 MHz, used to carry the STN-to-HSN data. Conventional sensor nodes are also important players of the SAC model, as they can act as relays while providing additional information about their environment.  
Figure 1: The hybrid Internet-of-Things model. HSN, hybrid sensor node.  
The service-aware clustering (SAC) model proposed in this paper is based on a software architecture that maps the sensor nodes' service delivery into energy awareness and network roles defined by Table 1. The different services delivered by the sensor nodes include sensing by the CSN, sensing + tagging by the STN and sensing + reading by the HSN and aggregation, analysis and processing by the BSN. The different routing roles played by these nodes include cluster head (CH), cluster slave (CS), reader slave (RS), reader master (RM) and base station (BS).  
Depending on their service delivery, the different types of nodes and roles may be assigned different energy consumption profiles and levels used to determine the role to be assigned to these nodes during the traffic engineering process. They may be tasked to perform the high-energy consuming roles of the network, for instance, being a cluster head, or the least-energy consuming, such as cluster slave, depending on these profiles and associated energy levels in order to balance the energy dissipation in the network.
## 3 Results and Discussion
### Surface Analysis: XPS  
X ray photoelectron spectroscopy was carried out to further evaluate the surface elemental composition of the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C fibers. Figure 7 shows the general survey as well as the spectra for C (1s), O (1s), Ti (2p), and Fe (2p). Two sharp peaks with binding energies of 710 and 725 eV are observed, which correspond to Fe\({}^{3+}\)2p\({}_{2/3}\) and Fe\({}^{3+}\)2p\({}_{1/2}\), respectively. Large and broader satellite peaks at 720 and 735 eV are also observed, which are characteristic of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\). For the Ti (2p) spectrum, the peaks at 458 and 464 eV correspond to Ti\({}^{4+}\)2p\({}_{3/2}\) and Ti\({}^{4+}\)2p\({}_{1/2}\) of TiO\({}_{2}\), respectively. These distinct peaks revealed the presence of the Ti\({}^{4+}\) valence state in the prepared fibers. The high carbon content and oxygen component of the metal oxides are also confirmed by XPS analysis.  
Figure 6: (**A**) HAADF-STEM image for a selected area with corresponding EDS maps for O, C, Fe, and Ti elements. (**B**) EDS spectrum of Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C composite fibers.
## 2 Materials and Methods
### Performance Evaluation by Simulation of the UAV Network
#### 2.3.1 UAV Characteristics and Experimental Flight Tests
Both components, the WiFi board, and micro SD card module, are very lightweight, weighing 8 and 5 g, respectively. The Wi-Fi boards were programmed in Arduino code. The Arduino software allows the writing of programs and uploading them to the Wi-Fi board. The range of the Wi-Fi boards was tested. The connection between them and file transfer was done at a distance of up to 85 m with no obstacles in between. In our model, when transferring files, the UAV was hovering over the station at a height of 30 m which is much less than the maximum distance obtained in range tests. The transfer speed, including writing and reading on/from an SD card, was also tested at a distance of 30 m. An average of 5.81 Mbps was obtained, which makes possible the transfer of a file of 10 MB in 13.77 s.
## 5 Experiments
### Experimental Results and Comparisons on the QAPLIB  
QAPLIB is the most popular collection of instances for QAP to evaluate performances of a new algorithm by comparing with other methods. In this part of experiments, we compare our proposed HGA with TLBO-RTS and ABC-QAP in terms of averaged percentage deviation (APD) from the best-known solution (BKS) value of the problem instances given by QAPLIB and averaged execution time (denoted by \"min.\" in Table 8) to reach the reported result. We also take into account the number of reaching an optimal or BKS over 10 repeated executions. Since small and easy instances from the collection are easily and fast solved with no substantial differences between state-of-the-art algorithms, we present the results of challenging instances. Table 8 shows comparative results on the selected 19 instances. In this comparison, 255 number of processors were used in parallel for ABC-QAP algorithm. The proposed HGA outperformed the tested methods in the 15 problems, and it was both the most accurate and the fastest in terms of the average time in the 7 problems.  
From Table 8, we observe that HGA shows competitive and better performance on all instance except only a few, finding the best-known solution for 16 out of 19 instances. Especially, for harder instances, tai40a, tai50a, tai60a, tai80a, and tai100a, it consistently outperforms the other algorithms.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 5 Measured and Predicted Results
### Radiation Performance  
The measured efficiency and gain, together with the predicted results of the wideband antenna (Ant#3), are shown in Figure 18. A gain comparison method was used for the antenna gain measurements. Again, a good agreement between the measured and predicted results is observed. The measured maximum efficiencies of this antenna in the mid and high bands are 96% and 94%, respectively, and the minimum efficiencies are 66% and 79%, respectively. The measured maximum gains within the reflection coefficient bandwidth (VSWR \(<\) 3) of this antenna in the mid and high bands are 2.9 and 5.6 dBi, respectively, and the minimum gains are 1.6 and 4.2 dBi, respectively.  
Figure 17: Measured and predicted reflection coefficients of the wideband antenna (Ant#3).  
Figure 18: Measured and predicted gains and efficiencies of the wideband antenna (Ant#3).  
The measured \(E_{\theta}\) and \(E_{\phi}\) radiation patterns in the three principal planes at 1.8, 2.6, and 3.6 GHz are shown in Figure 19 and compared with the predicted results. The radiation patterns are normalized with the maximum value of the corresponding plane. A good agreement between the predicted and measured results is found. At 1.8 and 2.6 GHz, strong \(E_{\phi}\) radiation is observed in the x-y and y-z planes compared to \(E_{\theta}\) radiation, and at 3.6 GHz, strong \(E_{\phi}\) radiation is observed in the x-z and x-y planes compared to \(E_{\theta}\) radiation. The radiation is nearly omnidirectional in the y-z and x-y planes in the mid band and in the x-z and z-y planes in the high band. In addition, there are no nulls in the radiation patterns of the mid and high bands. In addition, the \(E_{\theta}\) and \(E_{\phi}\) levels are comparable in both the x-z plane at the mid-band frequencies and the y-z plane at the high-band frequencies, which makes the antenna suitable for practical applications with complex propagation environments [18; 24; 26].
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 3 Methods
### Geographically Weighted Regression (GWR) Analysis  
Building upon the assessment of spatial autocorrelation (detailed in the previous section) and acknowledging the inherent spatial clustering introduced by our stratified sampling design across LULC classes, we implemented Geographically Weighted Regression (GWR) as our primary spatial analytical technique [51]. GWR is particularly appropriate for this dataset, as it explicitly models spatial non-stationarity, allowing regression parameters to vary continuously over geographic space. This approach enables the identification of localized relationships between proximity to specific LULC types and temperature variations while accounting for the inherent spatial dependence in thermal patterns [52].  
To explore the spatial heterogeneity of temperature influences, GWR was implemented. For each temperature measurement point, Euclidean distances to the nearest instances of each LULC type were calculated using a k-d Tree nearest neighbor approach [53]. These distances were chosen as predictor variables, based on the hypothesis that the thermal properties of surrounding land cover significantly influence the land surface temperature at a given location. The GWR model was specified in Equation (2) as  
\[T(u_{i},v_{i})=\beta_{0}(u_{i},v_{i})+\sum_{k}\beta_{k}(u_{i},v_{i})X_{k}+ \varepsilon_{i} \tag{2}\]  
where \(T(u_{i},v_{i})\) represents the LST at location \((u_{i},v_{i})\), \(\beta_{k}(u_{i},v_{i})\) are location-specific regression coefficients for each predictor variable \(X_{k}\) (representing the Euclidean distance to the nearest instances of each LULC type), and \(\varepsilon_{i}\) is the error term.  
The optimal bandwidth for the GWR model, which determines the spatial extent over which data points influence local parameter estimation, was determined using a cross-validation procedure. Specifically, the Golden Section Search method was employed to optimize the bandwidth by minimizing the cross-validation score [54], thereby ensuring the best possible local model fit. The performance of each GWR model was evaluated using \(R^{2}\) and adjusted \(R^{2}\) metrics, which quantified the proportion of variance in LST explained by the predictor variables at local scales, thus assessing the spatially varying influence of LULC features on surface temperatures.