esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Horizontal Mechanical Properties Tests of CBRBs
### Ultimate Horizontal Shear Test
#### 4.3.2 Mechanical Properties Analyses  
The hysteretic curve, the effective horizontal stiffness and damping ratio of the R1 bearing under a constant vertical pressure of 6 MPa are shown in Figures 24-26.  
Figure 23: Bearing R1 under limit horizontal shear test tests at different shear strains. (**a**) 50%; (**b**) 100%; (**c**) 150%; (**d**) 200%.  
Figure 24: Hysteretic curve of R1 in the ultimate compression-shear test.  
Figure 25: The effective horizontal stiffness of R1 in the ultimate compression-shear test.  
It can be seen from Figure 24 that the ultimate shear strain of this bearing is in the range from 150% to 200%. Figure 25 shows that, when shear strain \(\gamma\) is lower than 125%, the effective horizontal stiffness of the bearing decreases continuously, due to the rollover deformation; when the shear strain lies in the range from 125% to 150%, the side face of the bearing comes into contact with the upper and lower plates, the reduction in the stiffness slows down, and a hardening behavior is observed; unfortunately, as the shear strain is greater than 150%, the bonding strength of the cohesive gradually fails, which leads to a rapid decrease in the stiffness of the bearing, and failed to observe the further hardening behavior of the CBRB. Therefore, improving the bonding quality is an important target in the follow-up researches of this study. Figure 26 shows that the effective horizontal damping ratio of the bearing firstly decreases slowly with the increase of the horizontal shear strain, when the shear strain is greater than 150%, the bonding strength of the cohesive failed, causing a rapid growth in the damping ratio.
# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 2 Artificial Intelligence and Machine Learning in the Biomaterial Field
### Inorganic Materials
\begin{table}
\begin{tabular}{p{56.9 pt} p{284.5 pt}} \hline \hline
**Categories** & **Applications** \\ \hline \multirow{3}{*}{Electronic properties} & Band gap prediction: Using ML to predict band gaps of various inorganic compounds, aiding in the design of semiconductors. \\  & Electrical conductivity: Forecasting electrical conductivity for materials used in electronic devices and sensors. \\ \hline \multirow{3}{*}{Thermal management} & Thermal conductivity: Predicting thermal conductivity to optimize materials for heat dissipation in electronics and other high-temperature applications. \\  & Thermal expansion: Modeling thermal expansion coefficients to prevent material failure due to temperature changes. \\ \hline \multirow{3}{*}{Material discovery} & Crystal structure prediction: Identifying new stable crystal structures with desired electronic and mechanical properties. \\  & Doping optimization: Using AI to determine optimal doping levels and types to enhance material properties like conductivity and magnetism. \\ \hline \multirow{3}{*}{Synthesis optimization} & Chemical vapor deposition (CVD): Optimizing CVD processes for high-quality thin films and coatings. \\  & Sol–gel processes: Enhancing sol–gel synthesis parameters to produce materials with improved purity and properties. \\ \hline \multirow{3}{*}{Mechanical enhancement} & Hardness and strength: Developing materials with superior hardness and tensile strength for industrial applications. \\  & Fracture toughness: Improving fracture toughness to create more durable materials. \\ \hline \multirow{3}{*}{Environment applications applications} & Catalytic converters: Designing materials for efficient catalytic converters and pollution control systems. \\  & Photocatalysis: Developing materials for photocatalytic applications like water splitting and CO\({}_{2}\) reduction. \\ \hline \multirow{3}{*}{Energy applications} & Battery components: Enhancing materials used in batteries, such as anodes, cathodes, and electrolytes, for better performance and longevity. \\  & Photovoltaic materials: Optimizing materials for solar cell applications, including perovskites and other semiconductors. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AI and ML applications in inorganic materials. Figure 15: Diagram of the bio-hybrid method for detecting cancer biomarkers in exhaled human air. The figure is reproduced from [88].
## 4 Spatio-Temporal Big Data Visualization Framework
### Multidimensional Query for Interactive Visualization  
Generally, HBase supports two methods of retrieving data: Get and Scan. The Get operation is performed with a given _rowkey_ to fetch a single row in the HBase table, while the Scan operation filters the entire table with a _rowkey_ range defined by the start-key and end-key. Our multidimensional query is implemented with HBase scan operations. The pseudocode of the multidimensional query is illustrated in Algorithm 2, while the details are explained below:  
1. The interactive visualization operations will produce a collection of requests, which will later be sent to the server separately in an asynchronous mode. When the server receives the query request, the middleware parses the request parameters into the space/ time/attribute filters.
2. For the spatial filters, the query geometry will be recursively divided into multi-level SFC cells, while the translated spatial ranges are calculated using our recursive approximation algorithm, published in [35];
3. The temporal and attribute filters will be discretized and appended to the translated spatial ranges and derive the final scan ranges according to the _rowkey_ schema;
4. The scan ranges are sent to HBase for scan operations, filtering out of false positive data from the initial scan results and aggregating final query results for client rendering.  
```
1:Qtime(Tstart,Tend)
2:Qhome(minLat,minLon,maxLat,maxLon)
3:AttributeList
4:ResultSet
5:SpatialRanges = getSpatialRangeswithSFC(Qbbox);
6:QtimeBins = getTimeBins(Qtime,timeCoarseBin);
7:AttributeFilter = getQualitizerFilter(AttributeList);
8:for each timeBin in QtimeBins do:
9:for each range in SpatialRanges do:
10:startRowkey = Tstart + range.rangeStart;
11:endRowkey = Tend + range.rangeEnd;
12:rowkeyRange = (startrowkey, endrowkey);
13:Scan = scan(rowkeyRange, AttributeFilter);
14:ResultScanner = table.getScanner(Scan);
15:for each Result in ResultScanner do:
16:if Result in Qrange and Result in Qtime:
17:Resultset.add(Result);
18:endif
19:endfor
20:endfor
21:return ResultSet
```  
**Algorithm 2** The pseudocode of the multidimensional query in MAP-Vis  
In order to avoid excessive I/O communication between servers and clients, the mechanism of the HBase CoProcessor is used to implement the above-mentioned multidimensional query algorithm, as illustrated in Figure 9. HBase's coprocessors, which are modeled after Google's BigTable, resemble the stored procedures in the relational databases and can be invoked as a service by the client at any time. When invoked, the coprocessor program is executed remotely at the target HBase regions; the execution results can be processed before being returned to the client. Here, HBase CoProcessor mainly accelerates the exclusion of false positive points and the post-processing before visualization in the former Step 4.
## 4 Comparative Analysis Convolutional Codes with LDPC Codes, BCH Codes, and Turbo Codes
### Convolutional Codes vs. Turbo Codes
Convolutional codes can detect and correct errors in real-time, while block codes require the entire block to be received before errors can be corrected. Overall, while block codes can offer more robust error correction, they require more complex algorithms and larger block sizes, which can make them slower than convolutional codes in some applications like GPS systems. Therefore, BCH, LDPC, and turbo codes are not suitable for GPS systems. Also, difficulties in LDPC, BCH, and Turbo codes make them require the following:  
* More memory to store the parity check matrix used for decoding;
* Significant processing power, which can be problematic for low-power and low-cost GPS devices that are commonly used in commercial applications;
* Slow encoding and decoding operation. On the other hand, Convolutional codes and the Viterbi algorithm can offer a favorable balance of computational efficiency and error-correction performance specifically suited for Global Positioning System (GPS) receivers.
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 3 Methodology
### RL Controller Architecture
#### 3.3.2 Feature Difference-Based Temporal Feature Encoder  
For better adaptation to the target's different velocities, not only the spatial features, but also the temporal features of the dynamic target must be determined. A small optical flow network is used to incorporate explicit temporal information of the dynamic target in [28]. However, computing optical flow is still expensive, and we can encode the temporal information more efficiently. Unlike the latent flow method in [29], which fuses raw images and their differences directly, we focus on our target of interest and first extract the  
Figure 3: Architecture of the end-to-end perception and high-level decision-making RL controller.  
spatial features of three sequential raw images by the shared convolutional block attention module and then concatenate the spatial features and their differences. A 2D convolution layer processes these fused features further to extract the temporal information of the dynamic target.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 5 Performance Analysis
### Throughput  
Throughput, considering \(RTT\) of LEO satellites, is calculated as follows:  
\[Throughput(bps)=\frac{TBS}{RTT+TTI}, \tag{13}\]  
where \(RTT\) is the time taken for the signal sent by the UE to arrive at the gateway via the satellite, assuming 25.77 ms [21]. The transmission time interval (\(TTI\)) depends on the number of RUs, a unit for NPUSCH scheduling. \(TBS\) is the total number of transmitted bits and is calculated as follows:  
\[TBS(bits)=(T_{RE}-DMRS_{RE})\times N_{BIT}, \tag{14}\]  
where \(T_{RE}\) is the total number of resource elements (REs), \(DMRS_{RE}\) is the total number of REs in the \(DMRS\) symbols, and \(N_{BIT}\) is the number of bits per RE.
## 4 Case Study and Results
### Experimental Setup
#### 4.1.1 Public Transport Network Modeling  
A digital twin of the Belfort PTS was constructed using data provided by the Regie des Transports du Territoire de Belfort (RTTB). The network includes the topology of the bus lines, GPS coordinates of each stop, and scheduled inter-stop travel times.  
Each bus line was modeled as a directed graph, where:  
Nodes represent bus stops and store 2D GPS coordinates and altitude (retrieved via Google Maps Elevation API).  
Edges represent road segments between stops and include the following:  
* The real travel distance (from OpenStreetMap via OSRM API);
* Bus travel time (from RTTB data);
* Straight-line UAV distance (computed via WGS4 ellipsoid).  
This graph structure enables accurate computation of drone cruise distances and synchronization with bus movement.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Flight Condition** & **Average Thrust Variation** \\ \hline Hovering above static surface (0.1 m) & +9.25\% \\ Hovering above static surface (0.2 m) & +1.05\% \\ Hovering over moving bus (15 m/s) & \(-\)10.3\% \\ Hovering over moving bus (20 m/s) & \(-\)14.8\% \\ Free-flight cruise (10 m/s) & \(-\)7.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thrust Variation Under Different Environmental Conditions
## 4 UVSQ-SAT First Observations
### UVSQ-SAT Time Series  
From the measurements made by the different sensors of UVSQ-SAT and using the instrumental equations described in Sections 3.1.1 and 3.1.2, we obtain the evolution of the albedo and the OLR. Figure 12a shows the evolution of the signal measured by the six photodiodes over time (example given for about 2 observation orbits). The eclipse periods are clearly identifiable by the absence of signal. Figure 12b shows a good agreement between the calculated albedo and thus modeled from the ERA 5 data using different FOV (referred as \(\sigma\) values). On Figure 12c, the satellite temperatures evolve in accordance with the UVSQ-SAT thermal qualification (Figure 4). These temperature and the signal measured by the ERS sensors allow to retrieve the OLR (Figure 12d), which is directly observed during eclipse periods. Once again, the measured OLR fluxes show a good agreement with the modeled ones from the ERA 5 data.  
Figure 12: (**a**) Evolution of the measured flux by the six photodiodes during a given period. (**b**) Evolution of the albedo modeled from the ERA 5 data and for UVSQ-SAT observations. (**c**) Evolution of the satellite temperatures. (**d**) Evolution of the OLR.  
The accumulation of OLR measurement points over time for the Earth's location points will provide averages to characterize the evolution of the OLR over time. It is important to have new record of the OLR to bring new lights and to continue historical records (Figure 6). A long term continuous record is possible using a new disruptive space observational system based on UVSQ-SAT pathfinder.
## 5 Conclusions  
In conclusion, the occupancy rate of photogenerated carriers for different energy valleys is measured by comparing the photocurrent of terahertz field integrating with respect to time with the photocurrent measured by oscilloscope. The results indicate that 93.06% of all the photogenerated carriers are in the \(\Gamma\) valley when the electric field bias is 3.33 kV/cm, and 68.6% of all photogenerated carriers transfer to the satellite valley when the electric field bias is 20.00 kV/cm. With increasing electric field bias, the carrier occupancy rate for the satellite valley saturates at 72.16% when the \(\Gamma\) valley mobility is 8000 cm\({}^{2}\)/V - s, and the satellite valley (L valley) mobility is 920 cm\({}^{2}\)/V - s. Due to the impact of ionization and recombination radiation during the formation of the PACD, the available electric energy is not sufficient to make more carriers generate valley scattering; it provides a theoretical basis for generating high-power terahertz radiation using GaAs PCA's nonlinear working mode.  
Conceptualization, W.S. and C.D.; methodology, W.S., F.X. and Y.H.; software, W.S.; validation and investigation, W.S., F.X., Y.H. and C.D.; writing--original draft preparation, C.D.; writing--review and editing, W.S., F.X. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 61427814, in part by the National Key Research and Development Program of China under Grant 2017 YFA0701005, in part by the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect of China under Grant SKLIPR1812, in part by the Foundation Research Project of Jiangsu Province (The Natural Science Fund NO. BK20190187).  
The authors declare no conflict of interest.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
### Purpose and Objectives  
This study aims to evaluate how different types of radio signal features--received signal strength indicator (RSSI), angle of arrival (AOA), elevation of arrival (EOA), and their integrated use as a hybrid method--contribute to localization accuracy. Through comparative analysis, we seek to clarify the effectiveness of each signal type.  
The specific objectives of this study are as follows:  
* To improve localization accuracy beyond prior work by incorporating a hybrid approach that integrates RSSI, AOA, and EOA with appropriate weighting.
* To construct a high-efficiency and high-accuracy localization system by leveraging UAV mobility for LoS (line-of-sight) acquisition and path optimization.
* To assess the impact of factors such as UAV orbit radius and sequential estimation on localization performance.
## 2 Class-D PA for Polar-Mode Architectures
### Envelope Nonlinearities in Polar-Mode RF PAs  
Regarding the RF input port (\(v_{DR}\)), the drive feedthrough effect has been widely covered [10]. However, the literature content is less focused on the distortion affecting the envelope input port because this issue is only present in high-fractional-bandwidth transmitters.  
The class-D PA (Figure 3) is a good candidate for this application since it combines high-efficiency and wideband operation with design flexibility, which allows the user to choose the best structure to prevent nonlinearities related to the envelope path. The basic idea of the class-D operation is to use a square signal \(s_{C}(t)\), as follows:  
\[s_{C}(t)=\frac{4}{\pi}\sum_{n=1}^{\infty}\frac{1}{2n-1}\text{sin}[(2n-1) \omega t] \tag{1}\]  
to drive a switchmode power stage, its amplified output is applied to a resonator that removes its harmonic components. This resonator may consist of a low-Q LC circuit that acts as a wide bandpass filter to allow for the use of wideband modulations.  
Figure 3: Linearity behavior of the envelope for different switchmode RF PAs operating in polar mode: (**a**) Class-D CVS, (**b**) Class-D TCCS, and (**c**) Class-D TCVS.  
In support of this idea, the following sections analyze, in a simplified and graphical form, the nonlinearities that concern the envelope (\(v_{E}\)) path for the three class-D PA topologies described in the literature [11; 12; 13; 14]. Based on [12], Equations (1)-(9) help to describe the behavior of these circuits.
## 4 Analysis of Factors Affecting the Impact Factor
### Effect of Pavement Roughness Dimensions  
As shown in Figure 14, the graph illustrates the relationship between the impact factor and vehicle speed on two-dimensional and three-dimensional pavement roughness under different pavement grades. It can be observed that within the speed range of 20 km/h to 100 km/h, the impact factor on three-dimensional pavement roughness is smaller than that on two-dimensional pavement roughness for pavement grades A, B, and C. At a vehicle speed of 120 km/h, the impact factor on three-dimensional pavement roughness is greater than that on two-dimensional pavement roughness for pavement grades A and B. The occurrence of these individual opposite phenomena can be attributed to the randomness of pavement roughness samples. For grade C pavement, the impact factor on three-dimensional pavement roughness is smaller than that on two-dimensional pavement roughness. Calculating the average impact factor for each pavement grade reveals that, on average, the impact factor on three-dimensional rough pavements is reduced by 2.4%, 7.3%, and 13.5%. These reductions are observed when compared to two-dimensional rough pavements for grades A, B, and C, respectively. Therefore, overall, the impact factor on three-dimensional pavement roughness tends to be generally smaller than on two-dimensional pavement roughness. This can be explained by the frequency of the coupling force between the vehicle and the bridge. When the pavement roughness is two-dimensional, the coupling force frequency of the left and right wheels is more consistent [20]. Therefore, it is more likely to increase the dynamic response of the bridge.  
Figure 14: Comparison of impact factors between two- and three-dimensional rough pavements. (**a**) Grade A; (**b**) grade B; (**c**) grade C.  
Figure 13: Impact factor variation with pavement grade chart. (**a**) Two-dimensional roughness; (**b**) three-dimensional roughness.
## 4 Results  
### Statistics Analysis  
The poor correlation of the observed AGB and the predictors was acquired from the low value of \(r\) (\(-\)0.288-0.263). Among predictors, VV_MAX (\(r=-\)0.288), VV_ENE (\(r=-\)0.284), VV_HOM (\(r=-\)0.277), VV_ASM (\(r=-\)0.276), and VV_ENT (\(r=0.263\)) were significantly correlated (\(p\)-values \(<\) 0.05) with AGB. Predictors from the texture characteristics of Sentinel-1 VV polarization were significantly associated with AGB, as similarly found by [PERSON] and [PERSON] (2018) [58]. Those \(r\) values represent an average global correlation; thus, it also indicated that the global linear regression was inappropriate for AGB modeling in this study.  
Among the 44 explanatory variables, VV_ASM (\(r_{vv\_ASM\_MAX}\) = 0.99, VIFs = 41.3), VV_ENE (\(r_{vv\_ENE\_MAX}\) = 0.995, VIFs = 106.8), VV_ENT (\(r_{vv\_ENT\_MAX}\) = \(-\)0.97, VIFs = 18.2), and VV_HOM (\(r_{vv\_HOM\_MAX}\) = 0.98, VIFs = 20.1) were excluded from model building because their VIFs exceeded the above-mentioned threshold (VIFs \(\geq\) 10), and this reduced the number of explanatory variables from 44 to 40.
# Noise Cancellation Method for Mud Pulse Telemetry Based on Discrete Fourier Transform
## 2 Methods
### Pump Noise Cancellation Based on DFT
Let  
\[\alpha_{i}=A_{1}cos(2\pi f_{i}t),\;\beta_{i}=A_{1}sin(2\pi f_{i}t),\;i=0,\;1, \;2,\ldots,P \tag{6}\]  
The interference can be rewritten as  
\[p(t)=\sum_{i=1}^{p}\alpha_{i}sin(2\pi f_{i}t)+\beta_{i}cos(2\pi f_{i}t) \tag{7}\]The problem becomes a linear estimation problem. The signal can be sampled at equal time intervals to obtain  
\[r[k]=\sum_{i=1}^{p}\alpha_{i}sin(2\pi f_{i}[k])+\beta_{i}cos(2\pi f_{i}[k])+ \omega[k],\ k=0,\ 1,\ 2,\ldots,\ n \tag{8}\]  
(6) Perform discrete Fourier Inverse Transform (IDFT) to recover the pump noise signal after phase shifting. (7) Implement pump noise subtraction on the original pump noise signal in the time domain to obtain the estimation value of the real signal. (8) Judge whether the de-noising length of the signal reaches the data length. If not, repeat step (5) to step (7); if yes, output the de-noised signal. The performance of this method will be verified in the following sections.
# Design and Application of Daylight-Based Lighting Controller on LED Luminaire
## 1 Introduction
An application of fuzzy logic in LED lighting control has been presented in [28]. The methodology in this paper integrated fuzzy logic that considered two primary factors--lighting comfort and daylight contribution--that allowed a user to set the desired illuminance level. In addition, an actual prototype was installed, and the results showed satisfactory performance. A lighting control proposed by [PERSON] and [PERSON] [29] integrated presence detection capability with daylight control in order to provide a satisfactory illuminance for occupancy while minimizing power consumption. From the literature review discussed above, it can be seen that daylight control in a lighting system with LED luminaire can further reduce energy consumption and improve energy efficiency to the highest degree. This research proposed a lighting control circuit in a lighting system integrated in LED luminaire based on a daylight control. The aim is to reduce energy consumption in lighting system while maintain the lighting quality on the working plane on the level that does not impact health and working efficiency of occupant. This research was divided into two parts: simulation and experiment on a prototype. The simulation with DIALux software aimed to show the advantages of the LED luminaire as compared to conventional T5 fluorescent that widely used in Thailand's building. The experiment on a prototype was an actual physical test for evaluating the daylight control circuit performance in LED luminaire in terms of electrical parameters and lighting quality. Electrical parameters--voltage, current and power--were measured with a power meter. Illuminance on a working plane was measured with a lux meter. This paper is organized as follows. Section 2 presents the DIALux software simulation of both LED and T5 fluorescent luminaire. The lighting control circuit, its operation sequence and experimental setup are discussed in Section 3. Results from the actual field test are presented in Section 4. Finally, the conclusion of this research is provided in Section 5.