esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Channel Models
### Energy Harvesting at the Relay  
The received optical signal is converted into an electrical signal at the P-D. Then, this electrical signal is separated into AC and DC components. We consider collecting the DC component from (3), \(\eta_{R}\mathcal{R}_{R}A_{R}h_{R}\sqrt{\mathcal{P}_{S}}\mathcal{B}\), which is generally filtered out, as the transmit power for the relay. Meanwhile, the AC component, which contains the information sig  
Figure 1: An illustration of a SLIPT transceiver architecture [13].  
nal, is modulated and re-transmitted to the destination. By applying the time-splitting technique, as illustrated in Figure 2, a portion of the available block transmission time is dedicated for energy harvesting at the relay, and the other portion is utilized for information transmission.  
Let \(T\) (in seconds) denote the block transmission time and \(\tau\) be the portion of the time that is solely allocated for the phase in which only energy is harvested at the relay. During this phase of EH, the transmitter eliminates the AC component and maximizes the DC bias, i.e., \(\zeta=0\) and \(\mathcal{B}=\mathcal{B}_{\text{max}}\) in (1). The amount of energy harvested during this first phase is given as  
\[E_{h}^{(1)}=\frac{0.75V_{t}\big{(}\eta_{R}\mathcal{R}_{R}A_{R}h_{R}\sqrt{ \mathcal{P}_{S}}\mathcal{B}_{\text{max}}\big{)}^{2}}{I_{0}}(\tau T), \tag{7}\]  
where \(V_{t}\) and \(I_{0}\) are the thermal voltage and the dark saturation current of the P-D, respectively [34]. In the second phase, the objective is to maximize the received SNR at the relay. In this second phase, both the AC component for information decoding and the inherent DC component of the optical signal are transmitted. To maximize the SNR, we set \(\zeta=\frac{\mathcal{B}_{\text{max}}-\mathcal{B}_{\text{min}}}{2\tau}\) and \(\mathcal{B}=\frac{\mathcal{B}_{\text{max}}+\mathcal{B}_{\text{min}}}{2}\) in (1). Thus, the energy harvested in this second phase is given as  
\[E_{h}^{(2)}=\frac{0.75V_{t}\big{(}\eta_{R}\mathcal{R}_{R}A_{R}h_{R}\sqrt{ \mathcal{P}_{S}}\big{(}\frac{\mathcal{B}_{\text{max}}+\mathcal{B}_{\text{min} }}{2}\big{)}\big{)}^{2}}{I_{0}}\frac{(1-\tau)T}{2}. \tag{8}\]  
The total harvested energy for one block transmission time is, therefore, given as  
\[E_{h}=E_{h}^{(1)}+E_{h}^{(2)}, \tag{9}\]  
and the harvested power available at the relay is given as  
\[\mathcal{P}_{R}=\frac{E_{h}}{(1-\tau)(T/2)}=\frac{0.75V_{t}\big{(}\eta_{R} \mathcal{R}_{R}A_{R}h_{R}\sqrt{\mathcal{P}_{S}}\big{)}^{2}}{I_{0}}\Bigg{(} \frac{2\tau\mathcal{B}_{\text{max}}^{2}}{(1-\tau)}+\bigg{(}\frac{\mathcal{B}_ {\text{max}}+\mathcal{B}_{\text{min}}}{2}\bigg{)}^{2}\Bigg{)}. \tag{10}\]
###### Abstract  
A goniometer is currently the gold standard for range of motion (ROM) measurements. However, trained staff are required for accurate measurements. The objective of this study is to assess an agreement between the proposed standalone inertial measurement unit glove, smartphone device, and a standard goniometer for the measurement of wrist range of motion. Twenty participants performed wrist flexion, wrist extension, pronation, supination, ulnar deviation, and radial deviation movements with three operators measuring the movements with three devices. Average measurements from the three approaches had within 1.5 degrees of difference from each other for all of the movements. Both the proposed IMU glove and smartphone showed a strong correlation to the goniometer in most of the movements, with an intraclass correlation coefficient (ICC) between 0.914 and 0.961, and between 0.929 and 0.951, respectively. Only wrist supination using the smartphone has an ICC of 0.828. In comparison with a standard goniometer, a smartphone device is a more convenient method and readily available. The proposed IMU glove requires additional hardware but is easier to use and is more suitable for measuring and monitoring dynamic motion than a smartphone or a goniometer. These patient-friendly approaches could be used by the patients at home and provide remote quantitative monitoring during the wrist rehabilitation process.  
wrist rehabilitation; range of motion; goniometer; inertial measurement unit glove; smartphone +
Footnote †: journal: Applied Sciences
# Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
## 4 Experiment Details
### Comparison Models  
In the performed experiments, the following models were compared:  
* SVD model: The latent vectors represent the user and items, and their inner product is used to generate the estimated ratings.
* SVD++ model: An extension of SVD, which considers the implicit feedback.
* NGCF model: A state-of-the-art graph-based collaborative filtering model, which explores higher-order connectivity in the user-item graph by using deep learning techniques.
* Subtract-trust (S-Trust) model: As discussed in Section 3.4, in this model, the latent item vectors and latent user vectors are added to the trust latent vectors. The predictions are made in a manner similar to that used in the SVD++.
* Weighted-trust (W-Trust) model: This method is similar to the S-trust, except that the latent vectors are multiplied instead of added.
* Mean-trust concatenation (M-Trust) model: This method is also similar to the S-Trust and W-Trust models; however, instead of adding or multiplying the trust vectors, the mean values of the latent trust vectors are added to the item and user latent vectors.
## 2 Literature Review  
### Supplier Selection  
Suppliers play crucial roles in supply chain management, producing components, ensuring product quality, and indirectly managing and assisting with the operational costs of their partners [15]. The quality level of their products determines the degree to which the quality of the final product can be guaranteed and the ability of all the members of the supply chain to control costs. For this reason, the selection of the right suppliers is vital for companies. Supplier selection is a decision-making process that involves a number of steps and several criteria--both quantitative and qualitative. Table 1 summarizes important criteria associated with supplier selection. These criteria are as follows:  
* Cost/Price: the procurement costs, labor costs, material costs, and transportation costs of a company;
* Quality: the quality of supplier products;
* Delivery period: the time to delivery of the suppliers;
* Technology: the implementation of modern technology;
* Relations: the duration of collaboration and the closeness of relationships with the suppliers;
* Service: the ability of the suppliers to support and coordinate with products or technologies;
* Communication: the ability of the suppliers to respond regarding products or services and communicate with internal and external partners;
* Green: whether the products or packaging of the suppliers have green marks and follow the 3 Rs (reduce, reuse and recycle);
* Sustainability: the impact of the suppliers (or their products) on society, the economy, and the environment. In addition to conventional quantitative indices such as the quality, delivery period, and cost/price, this also includes qualitative indices such as supplier service, relations, communication, green, and sustainability. The four criteria of cost/price, quality, delivery and service are the most commonly used factors for the supplier evaluation and selection problem. Researchers have applied a number of evaluation standards to this problem, including the multiple-criteria decision-making (MCDM) model [16], the analytic hierarchy process (AHP) [17], the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [18], and grey analysis [19]. However, most of these methods use crisp numbers to process decision-making information and cannot process uncertain or imprecise information. Furthermore, the imprecision of the information source, including unquantifiable information, incomplete information, and some unknown information, makes it even more difficult to efficiently select suppliers. Thus, these approaches have been ineffective for supplier selection problems due to the high degree of uncertainty in supplier selection. For this, a key issue is how to evaluate and select the most suitable supplier for companies.
## 3 Results and Discussion  
The evaluation of the results in this study is divided into two parts. In the first part, experimental results for current and voltage parameters were evaluated for different ambient temperatures (\(-10\)\({}^{\circ}\)C, \(25\)\({}^{\circ}\)C, \(40\)\({}^{\circ}\)C), and a statistical analysis (ANOVA) was performed to evaluate the results. The accuracy of the selected parameters in predicting the output points of the battery at different SOC points (SOC 90, SOC 50, SOC 30) was examined and shown statistically by taking RMSE values into consideration. In the second part, the model with the highest accuracy was determined by ANOVA analysis, and then the most suitable model was determined by the genetic algorithm obtaining equivalent circuit models (ECMs) for diverse combinations of these parameters. After the parameters  
Figure 3: Optimisation process block diagram for lithium-ion battery cell.  
were determined based on the model that minimizes the RMSE value, the EKF algorithm was utilized for SoC estimation; then, this model was tested in real time, and comparisons were made by making SOC predictions.
## 3 Finite Element Analysis of the Process
### Model Verification  
To obtain temperature versus time and SOC versus time curves, we selected eleven points (Nodes 2, 5, and 7-11) for our numerical validation as shown in Figure 7. In the experiments, the temperatures of these points were measured with a thermocouple embedded in the tire. Nodes 2, 8, and 10 were the critical points, which were used to monitor whether the press time was adequate to meet cure criteria and to verify the accuracy of the FEA model. The temperature of Nodes 1, 3, 4, and 6 on the surface of the tire also needed to be measured by the thermocouple since they were taken as the boundary conditions of the FE model. Since the thermocouples embedded in the inner positions of the tire are difficult to be pinpointed and it is inevitable to introduce artifacts. However, such artifacts should be ignorable.  
Figure 6: Finite element (FE) models: (a) 2D smooth model, (b) half pitch of pattern, and (c) 3D patterned tire.  
As shown in Figure 8, the simulated data was in good agreement with the test data, and the maximum error in the initial stage of vulcanization was caused by the simulated temperature boundary error. Conventionally, SOC at the critical point required as high as 50% when the tire was removed from being pressed and this period was called t\({}_{50}\) of the critical point or the curing time in mold. Table 2 compares the simulated and test values of Node 2, 8 and 10 in terms of temperature and t\({}_{50}\) differences. Results show that the maximum errors of the three points were all less than 5%, indicating the reliability of the simulation model. The effectiveness of this method was proved not only on the passenger car tire but also on the off-the-road-tire (OTR) 27R49_TB526.
## 3 Case Study: Purse Seiner in the Adriatic Sea
### LCCA Models for Different Power Systems
#### 3.3.2 The LCCA of an LNG-Powered Ship  
The investment cost of an LNG-powered ship is EUR 1290/kW, and it refers to the retrofitting of an existing ship with a dual-fuel engine for LNG and pilot fuel (diesel), all additional equipment, and an LNG storage tank [42]. The maintenance cost of the considered power system is equal to EUR 0.015/kWh [107]. Fuel cost is calculated by multiplying fuel consumption by fuel prices from Table 5, while the carbon tax is calculated as described in Section 2.2.
###### Abstract  
The drawdown outside of a deep foundation pit has to be controlled during excavation. However, the vertical curtain cannot cutoff a deep and thick confined aquifer during deep excavation. In this study, a microbial-induced carbonate precipitation (MICP) horizontal seepage reducing body (HSRB) was proposed to control drawdown combined with a partially penetrating curtain. MICP HSRB is formed by using the seepage field generated by the recharge wells to drive the migration of a _Sporosarcinna pasteuri_ solution, stationary solution, and cementation solution into the deep confined aquifer. The migration of each solution was numerically simulated to study the HSRB formation process. The influence of different factors on solute migration was studied. The results show that the solutes in the fixed fluid and cementation fluid can reach the area under the driving of the seepage field, which proves that MICP HSRB can be formed. The calcium ions and urea in the cementation solution are more likely to migrate to the designated area than the bacterial solution. Increasing the injection rate of bacterial solution and adding recharge wells both made the bacterial solution migrate more quickly to the designated area. In the case of multiple grouting, the solute migration in the later stage will be hindered by the plugging of pores caused by calcium carbonate generated in the earlier stage. Therefore, different grouting methods need to be designed to drive the seepage field so that the solute injected in the later stage can continue to migrate. The MICP HSRB grouting technology can be used in foundation pit dewatering, providing reference for similar engineering.  
horizontal seepage reducing body (HSRB); microbially induced calcite precipitation (MICP); confined aquifer; deep excavation; numerical simulation
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 6 NRA Crossing and Sense for Circular Arcs  
For circular arcs, a few changes occur relative to linear segments. What is the most apparent is the fact that circular arcs can cross the NRA more than once or may even be tangent (Figure 13). Moreover, the parametric definition of arcs is a little more elaborate than for straight lines, as described next.
# A Review of Unmanned Aerial Vehicle Based Antenna and Propagation Measurements
## 3 UAV-Based Propagation Measurements
### UAV-Based Small-Scale Propagation Measurements
The UAV flew in a vertical path with an altitude of 0-25 m over two buildings of 15 m and 25 m in height. Although there were other obstacles, such as trees and cars, in addition to the two main buildings, the diffraction losses owing to other obstacles were minimal at high altitudes. A comparison was made between UAV-based propagation measurements and theoretical modeling, such as the KED model. In general, the measurements by UAVs and the theoretical calculations are in good agreement at lower frequencies; however, at higher frequencies, the diffraction losses are more significant, and the theoretical calculations underestimate these losses.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 4 Results and Discussion
### Discussion and Major Findings
#### 4.5.1 Synthesis of Essential Aspects in This Study
* Challenges for system over-dimensioning: excess renewable capacity can result in energy excesses, economic ineffectiveness, and environmental impacts, especially when storage options are restricted. * Importance of large-scale storage: sufficient storage solutions (e.g., pumped hydro, CAES, large BESS) can help manage energy surpluses, but their implementation is often constrained by geographical, technical, and economic factors. * Fossil fuel backup systems: while reliable and cost-effective, fossil fuels are unsustainable due to their high GHG emissions. * Renewable backup systems: options such as geothermal and biomass have reduced GHG emissions but face challenges related to resource availability, elevated costs and operational complexities.
# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 2 Materials and Methods
### Remote Sensing Data Collection
#### 2.2.2 UAVimage Data  
The UAVimage acquisition was conducted on 19 March 2020, at the end of the leaf-off season, with the presence of some young leaves on hornbeams and also low vegetation. The UAV data were collected using a DJI Matrice 600 Pro (SZ DJI Technology Co. Ltd., Shenzhen, China) equipped with a stabilized Sony A7 RII camera (Sony Corporation, Tokyo, Japan) and a Samyang 24 mm AF lens (Samyang Optics, Masan, South Korea). The timestamp of each exposure moment was recorded in a Receiver Independent Exchange Format (RINEX) file generated using an onboard Emlid Reach M+ GNSS receiver (Emlid, St. Petersburg, Russia), which was further used to calculate the exterior orientation (EO) position of images in the post-processing.  
The flight was planned with 90% endlap and 80% sidelap, with a flying speed of 5 m\(\cdot\)s\({}^{-1}\) at an altitude of 120 m, resulting in an approximately 2 cm ground sampling distance. During a single 10 min flight, a total of 269 images with 7952 \(\times\) 5304 pixels resolution were taken. Five ground control points (GCPs) were stabilized using wooden stakes (5 \(\times\) 5 \(\times\) 40 cm) and signalized using A4 format paper checkerboard targets. The coordinates of the GCPs were determined in the second total station survey.  
The EO positions of each exposure station were calculated in post-processing with RTKLib QT open-source software, relative to virtual reference station (VRS) data obtained via Geodetic Precise Positioning Service (GPPS) of Croatian Positioning System (CROPOS). Photogrammetric processing was carried out in Agisoft Metashape Version 1.6.5 (Agisoft LLC, St. Petersburg, Russia). Images were aligned using ground (GCPs) and aerial (EO positions) control and image tie points. The image tie points were generated at full  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Source** & **Date of Data Collection** & **Sensor** & **Data Collection Duration** \\ \hline PLShh & 8 February 2019 & Geoslam Zeb Horizon & 45 min \\ TLS & 20 March 2020 & Faro S70 & 6 h 30 min \\ UAVimage & 19 March 2019 & Sony A7 RII & 40 min \\ ULS & 26 April 2019 & LidarSwiss & 25 min \\ LS Nano M8 & & & \\
1 st total station survey & February 2019 & Topcon e58 & 16 h \\
2 nd total station survey & January 2021 & Topcon e58 & 5 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of used datasets.  
resolution (parameter accuracy set to high), and the camera interior parameters were optimized within the bundle adjustment. Images were downsized by a factor of two (parameter quality set to high) and used to generate the dense point cloud, which resulted in point clouds with an average of \(\sim\)2700 points\(\cdot\)m\({}^{-2}\), and \(\sim\)4 million points per plot.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Spatial Distribution and Land Subsidence Rate
#### 3.1.10 Sidoardjo  
Sidoardjo (7.4474\({}^{\circ}\) S, 112.7025\({}^{\circ}\) E) is located in East Java Province, approximately 40 km south of Surabaya, and has a population of approximately 2 million, according to the 2020 census. It is located on an alluvial plain area and renowned for its oil and gas production and industrial zones. Significant LS in this region is caused by the Lumpur Sidoardjo (LUSI) mud volcano eruption in 2006 [49; 56; 57].  
Figure 12 illustrates the LS phenomena in Sidoarjo, with an LS rate up to 140 mm/year. Figure 12a shows the spatial distribution of the LS rate, while Figure 12b,c, respectively, present the LS time series from 2017 to 2023 and field conditions based on satellite imagery. The results show that LOS deformation in the LUSI mud volcano continued into 2024, although at a lower rate compared to the 2006-2014 period [56]. It is worth noting that the spatial extent of deformation in LUSI remains consistent with previous observations. Additionally, other affected areas have been identified, including a well site, rice field, and residential zones located west and east of the LUSI mud volcano, respectively.  
Figure 11: LS in Surabaya: (**a**) LS rate in mm/year in Surabaya area; (**b.1**) LS time series retrieved from InSAR from 2017 to 2023; and (**b.2**) land use of the region highlighted in the black box (**b**).
# A Novel Signal Design and Performance Analysis in NavCom Based on LEO Constellation
## 3 Model Design  
In this section, the proposed model will be more comprehensively introduced. Specifically, we include not only the signal scheme design but also the signal generation system design. In addition, basic attributes such as time domain and frequency domain will be analyzed.
# An Electric Load Simulator for Engine
## 4 Development and Validation of the System Model  
### Mathematical Model of the System  
Assuming that eddy currents and magnetic saturation are ignored, there was no gas force acting on the actuator, according to the differential equation of motion, the voltage balance equation, and the electromagnetic force equation of the electromagnetic actuator, the differential equations of the system can be deduced as Equation (9) [25]:  
\[\left\{\begin{array}{l}I=-\frac{R}{m}I-\frac{k_{m}}{m}v+\frac{\mu}{L}\\ \dot{v}=\frac{k_{m}}{m}I-\frac{c}{m}v\\ \dot{S}=v\end{array}\right. \tag{9}\]  
where \(k_{m}\) and \(m\) are the force constant and mass of the electromagnetic actuator; \(c\) is the damping coefficient; \(S\) and \(v\) represent the displacement and velocity; \(I\) is the current in the coil; \(R\) and \(L\) are the coil resistance and coil inductance.  
Equation (9) shows that when the system inputs voltage \(v\), coils generate current \(I\). Then, coils with current suffer the electromagnetic force \(F_{mag}\) in the air gap magnetic field. The electromagnetic force drives the electromagnetic actuator. The movement of the electromagnetic actuator generates displacement \(S\) and velocity \(v\), which change the value of the electromagnetic force in the form of back-EMF (Electromotive Force). The force constant and the velocity of the electromagnetic actuator will affect the value of current in turn. Therefore, the electromagnetic actuator needs to consider the relationship among various subsystems fully to build an accurate mathematical model.  
The PID control algorithm is widely used in industrial control due to its advantages of being a simple algorithm, good robustness, high tracking accuracy, and reliability. Furthermore, it is suitable for a system that has uncertain parameters. Considering that the electric load simulator needs to follow  
Figure 9: Gas force acting on the exhaust valve.  
the exhaust valve passively, the PID controller was selected as the control algorithm of the electric load simulator [5; 25], which can be described as Equation (10).  
\[U(s)=K_{p}(1+\frac{1}{T_{\mathit{S}}}+T_{\mathit{d}}s)E(s) \tag{10}\]