esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Distributed Image Codec
### The Decoder  
The decoder has to reconstruct the full image from the CS measurements of the individual image blocks. A block diagram of the proposed decoder is shown in Figure 2b. The key issue that affects the quality of the reconstructed image is the CS reconstruction strategy. This will be discussed in Section 4.3.1. The theory of distributed coding tells us that side information is needed to reconstruct these blocks effectively. So, for a distributed codec, another issue to be addressed is the generation of appropriate side information. This will be addressed in Section 4.3.2.
## 4 Experimental Studies
### Case 2: Five-Level PUC MLI  
The PUC5 MLI is realized when the voltage value of the auxiliary capacitor is half of the input voltage of the PUC. Two different loads are selected as RL\({}_{1}\) and RL\({}_{2}\) for the experimental studies. While the input voltage of the PUC MLI is 45 V, the voltage of the auxiliary capacitor is 22.5 V with the closed loop system. Figure 29a,b represent the output  
Figure 27: PUC7 MLI for RL\({}_{1}\).  
Figure 28: PUC7 MLI for RL\({}_{2}\).  
voltage, output current, input voltage, and auxiliary capacitor voltage of the PUC5 MLI for RL\({}_{1}\). When the RL\({}_{1}\) load is used, the voltage and current THD of the PUC5 MLI are 28.8% and 5.49%, respectively.  
Figure 30a,b represents the output voltage, output current, input voltage, and auxiliary capacitor voltage of the PUC5 MLI for RL\({}_{2}\). When the RL\({}_{2}\) load is used, the voltage and current THD of the PUC5 MLI are 24.96% and 5.09%, respectively.  
In the comparison of the RL\({}_{1}\) load and RL\({}_{2}\) load, the output current is lower for RL\({}_{2}\) since the L\({}_{2}\) value is larger than the L\({}_{1}\) value. Furthermore, in terms of harmonic values, it is seen that the THD values of the RL\({}_{2}\) load are lower than the THD values of the RL\({}_{1}\) load.
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 4 Deviation Correction Control Method of Tracked Robot
### Robot Measurement and Control System  
To achieve obstacle avoidance control for inspection robots in a roadway environment, it is essential to establish a measurement and control system using LiDAR. This system enables the robot to compute its turning radius based on the obstacle distance information gathered by LiDAR. The inspection robot adopts a distributed and modular approach to build the inspection robot measurement and control system. The system is divided into three levels, namely, a sensing and signal processing layer, a decision control layer, and a motion execution control layer. Figure 8 shows the overall structure of the inspection robot measurement and control system.  
The sensor and signal processing layer uses multiple sensors to monitor the internal state and external environment information of the robot, and uploads the collected data to the IPC (Industrial Personal Computer) of the decision control layer through their respective conditioning circuits and communication buses. The main function of the decision control  
Figure 8: Overall framework of inspection robot measurement and control system.  
Figure 7: Detection principle of LiDAR: (**a**) the detection range of LiDAR; (**b**) detection interface of LiDAR.  
layer is to fuse the data of each sensor and then convert the processing result into the command of the motion execution control layer. The motion executive control layer is mainly composed of a driver and a motor. By controlling the movement of two main track motors and four swing arm motors, the combination of various actions of multiple motors can realize the robot's forward, backward, turning, obstacle avoidance, and other behaviors. Through the three levels of the measurement and control subsystems, the coordination of the inspection robot is achieved to complete various tasks.
# Energy Regeneration Hydraulic System via a Relief Valve with Energy Regeneration Unit
## 3 Control System
### Control Flow  
In an energy regeneration system for the energy loss of a PRV, the decision whether to regenerate the overflow energy depends on the relationships of the inlet/outlet pressure of the PRV and the maximum working pressure of the HA. Figure 3 shows the principle diagram of the tested PRV with a HERU connected to its outlet. The overload protection unit is used to protect the pump 4 when the PRV does not work. When the valve 1 is powered off, the regulating pressure of safety valve 2 is 31.5 MPa to ensure that no oil flows through valve 2 when PRV is working. In comparison, when the PRV does not work, valve 1 is powered to allow oil to flow from the pump to the tank with a low energy loss.  
The flow meters 8, 12, and 13 are used to detect the flow rate in and out of the PRV. Pressure sensors 10, 11, 19, and 20 are used to gain the pressures of the pump, the inlet and outlet of the PRV and the HA. These pressures are used to determine the energy recovery process. In Figure 3, \(p_{\mathrm{s}}\) is the outlet pressure of the pump and pa is the pressure of the HA. The detailed control process is shown in Figure 4. The control strategy is based on following logic:  
* Energy recovery mode When the pressures satisfy \(p_{\mathrm{a}}<p_{\mathrm{a}2}\), the PRV works in the energy recovery mode. In this condition, the valve 1 is powered off and the lower electromagnet of the valve 14 is powered on to make the valve 14 work at the bottom position. The overflow energy through the PRV will be stored in the HA until it reaches the maximum working pressure.
* Traditional mode When the pressures satisfy \(p_{\mathrm{a}}\geq p_{\mathrm{a}2}\), the PRV works in the traditional mode. In this condition, valve 1 is powered on and both the electromagnets of valve 14 are powered off. The pump is unloaded through the safety valve 2, which is unloaded by valve 1. The valve 14 works at top position, while the HA is disconnected to the outside and the pressure is kept constant.  
Figure 3: Principle diagram of the test for the PRV with HERU (1—2/2 Solenoid directional valve; 2—Safety valve; 3—Filter; 4—Fixed displacement pump; 5—Electric variable frequency motor; 6 and 15—Check valve; 7 and 18—Pressure gauge; 8, 12, and 13—Flow meter; 9—PRV; 10, 11, 19, and 20—Pressure sensor; 14—3/4 Solenoid directional valve; 16—Safety shutoff valve; 17—HA (HERU)).
## 3 Results
### Areas of Research Focus  
It was discovered in literature study that the main concern of academia was how to promote the application practice of BCG/SCG technology, with the focuses on such aspects as signal model establishment [43; 44; 45], signal acquisition and measurement methods [46; 47; 48], signal processing approaches [49; 50; 51; 52; 53], and signal standardization [54]. At the same time, the limitations were also exposed in the study, which included great variability in the waveform itself [55; 56; 57; 58], difficulty in signal standardization system [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24], and complexity of independent measurement and analysis of signals [59; 60].
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.19 The Indicator to Measure the Efficiency of Water Consumption  
This indicator promotes using water conservation practices and reduces water consumption in public spaces, by simultaneously reducing the production of effluents and pressure in the drainage systems. This indicator is promoted by SNTool, BREEAM C, and LEED ND, and addressed by Levels, ISO 37120, and SDGs. Therefore, this indicator is recognized as a prioritized environmental concern. In the SBToolPT Urban Guide, the calculation of this indicator is made by using a verification checklist. Most of the list's criteria have the possibility of being calculated using a CIM model. However, the criterion for establishing an education program cannot be calculated through the CIM. Thus, the indicator is considered \"PARTIALLY\" applicable in the CIM model.
# Effect of Topography Truncation on Experimental Simulation of Flow over Complex Terrain
## 2 Experimental Method
### Transition Sections  
The transition section connecting the wind tunnel floor or the bottom of the numerical computational domain was proposed to handle the \"artificial cliff\" generated by topographic truncation, as shown in Figure 2a. This idea was inspired by the contraction curve of the wind tunnel, which can achieve good flow quality and avoid flow separation.  
Four cases conducted in this study are shown in Figure 3: original topography (OT), without transition section (WTS), Witozinsky transition curve (WTC), and ramp transition curve (RTC). According to previous studies (2018; 2019; 2020; 2020), the performance of the transition curve was evaluated by comparing the characteristics of the flow after the transition section with incoming flow, and the less differences observed, the better. However, the studies of [PERSON] et al. (2018) and [PERSON] et al. (2019) highlighted the significant effect of the leading edge geometry of the terrain model on the flow behavior over the Bolund Hill. The implementation of a transition section to smooth the altitude difference between the model edge and the wind tunnel floor may change the leading-edge geometry, but the degree to which the flow is influenced is not completely understood. Furthermore, when transition sections were applied in the complex terrain, flow characteristics observed at measurement locations were affected not only by transition sections but also by complex terrain. Hence, it is not appropriate to evaluate the performance of transition sections by comparing flow characteristics with incoming flow. For this purpose, the test case with original topography was carried out as criterion instead of incoming flow, and the case without transition sections and the one with two forms of transition sections were also conducted in this study for comparison. Figure 2b shows a sketch of these two transition curves. The first one is the WTC, which is widely applied in low-speed wind tunnels. The formula of WTC can be described as follows:  
\[y=H\{1-\frac{\left[1-\left(x/L\right)^{2}\right]^{2}}{\left[1+\alpha\left(x/L \right)^{2}\right]^{3}}\} \tag{1}\]  
where \(\alpha=50\), \(H\) is the altitude difference between the model edge and the wind tunnel floor, and \(L\) is the length of the transition curve at certain points corresponding to \(H\) to maintain the equivalent slope \(H\)/\(L=0.5774\). The other is RTC with the same slope but a straight line for the transition curve.  
Figure 2: Transition section: (a) sketch of transition section; (b) sketch of transition curve.
## 6 Conclusions  
This paper proposed a hybrid method for flood mapping using medium resolution optical remote sensing data based on multiple endmember spectral mixture analysis and Random Forest classifier. A spectral library was first established using four image endmembers, including water, vegetation, impervious surface, and soil. A total of 35 optimal endmembers were selected and 3111 SMA models were constructed for each pixel to derive the fractional information. In order to increase the separability between different land cover types, the original reflectance bands, NDWI, DEM, slope, and aspect together with the fraction maps derived through MESMA were combined to construct the multi-dimensional feature space. A Random Forest classifier consisting of 200 decision trees was utilized to extract the inundated areas of Yuyao City, China. Experimental results indicated that the proposed hybrid method showed good performance with an overall accuracy of 94% and a Kappa index of 0.88. The inclusion of fractions from MESMA can improve the classification accuracy with an increase of 2.5%. Comparison experiments with other methods including maximum likelihood classifier and NDWI thresholding verified the effectiveness of the proposed method.  
Above all, the hybrid method of this paper can extract inundated areas accurately using medium resolution multispectral optical data. Meanwhile, the proposed method can be expanded to the field of hyperspectral image analysis. Future studies should include more study cases to further verify the role of MESMA in the improvement of classification accuracy. A statistical sampling method should also be considered to further increase the reliability of the results of the accuracy assessment. Additionally, multi-sensor and high resolution remote sensing data are required to increase flood mapping accuracy.
# Optimizing Foreign Language Learning in Virtual Reality:
## 4 VR-CCL Framework for Optimized Language Learning
### Interactive Engagement (Based on Constructivist Learning Theory)
#### 4.1.4 Feedback and Assessment Mechanisms in Interaction  
Feedback, an integral pillar of efficacious learning, assumes an augmented stature within the purview of VR linguistic tutelage. Feedback metamorphoses within this enveloping VR expanse, becoming instantaneous, situationally apt, and markedly interactive. Distancing from archaic paradigms, where feedback could be temporally staggered or unduly broad, VR proffers punctual rectifications and mentorship, ameliorating the trajectory of comprehension.  
Envision, for a moment, a disciple engrossed in a virtual tableau. Herein, auditory discernment apparatuses could identify lapses in articulation, furnishing immediate reformals. Optical indicators, perhaps through chromatic delineations, might spotlight syntactic pleimbishes or lexemic deviations, empowering learners to amend transgressions post-haste. Such brisk interventions buttress apt lingual practices and elevate the learner's self-assuredness.  
VR architectures can seamlessly incorporate mutable evaluation instruments. The matrix can modulate the intricacy of assignments, safeguarding that those conundrums consistently strike the optimal balance of profundity. This fluid evaluative approach assures incessant ascension without inundating or marginalizing the aspirant.  
In augmentation, post-engagement dissections can furnish granular examinations into a scholar's virtuosities and facets necessitating refinement. By gauging parameters such as lexemic expansiveness, syntactic intricacy, and engagement fruition coefficients, pedagogues and scholars can discern explicit domains ripe for concerted cultivation.  
The evaluative and feedback apparatuses inherent to VR linguistic pedagogy are avant-garde, bequeathing punctual, mutable, and nuanced perceptions that conventional methodologies scarcely rival.
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 4 Application of Modulation Spectroscopy for Methane Detection
### Frequency Modulation Spectroscopy Applied for Methane Detection  
#### 4.2.1 Principle of Frequency Modulation Spectroscopy  
FMS is an offshoot of WMS, and the difference between WMS and FMS lies in the magnitude of the modulation frequency. When modulation frequency is larger than the width of the absorption line of interest, the technique is named by frequency modulation (FM). Compared with that of WMS in the 1/\(f\) noise dominated region (10 kHz), the modulation frequency of FMS is the 100 kHz range, which is in a shot noise limited domain [29]. When detector quantum noise becomes the limiting factor for the sensitivity of the detection system, the detection limit can be described by Equation (2).  
\[SNR=\frac{P_{signal}}{\left(P_{1/f}^{2}+P_{sn}^{2}+P_{tn}^{2}\right)^{1/2}} \tag{2}\]  
wherein, \(P_{1/f}\) represents the detector excess noise, \(P_{sn}\) represents the shot noise of the detector, \(P_{tn}\) represents the detector thermal noise, and \(P_{signal}\) represents the signal proportional to the incident laser power on the detector.
# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
## 5 Instance Validation
### Feature Extraction
#### 5.1.3 Autocorrelation Coefficient Characteristics  
In time series forecasting, the autocorrelation and partial autocorrelation coefficients can be used to measure the correlation between current and past series values and to indicate the most useful past series values for predicting future values.  
Autocorrelation is the correlation of a signal with itself at different points in time, and it can identify repetitive signals (such as periodic signals masked by noise), as well as fundamental frequencies that disappear implicitly in the harmonic frequencies of the signal. The partial autocorrelation is a summary of the relationship between the time series and the previous time series after the interference is removed.  
The autocorrelation coefficients and partial autocorrelation coefficients of each attribute are extracted to form the timing characteristics. The autocorrelation coefficients and partial autocorrelation coefficients of the discharge voltage attributes in the first cycle are shown in Figure 9.  
In Figure 9, The upper and lower blue horizontal lines indicate the upper and lower limits of the autocorrelation coefficient and the partial autocorrelation coefficient, respectively., and the part beyond the bounds indicates the existence of a correlation. As can be seen from Figure 9, The red line represents the magnitude of the value at different lags the absolute value of the autocorrelation coefficient maintains a large value for a long time, and there is a gradually decreasing trend, which indicates the phenomenon of \"trailing\". The partial autocorrelation plot, on the other hand, fluctuates around the value of zero after the second order, i.e., the \"truncated tail\" phenomenon, which indicates that the time series is a smooth series. In this paper, the autocorrelation coefficient and partial autocorrelation coefficient of each attribute are selected as the third type of characteristics.
###### Abstract  
Managing cellular networks in the Beyond 5G (B5G) era is a complex and challenging task requiring advanced deep learning approaches. Traditional models focusing on internet traffic (INT) analysis often fail to capture the rich temporal and spatial contexts essential for accurate INT predictions. Furthermore, these models do not account for the influence of external factors such as weather, news, and social trends. This study proposes a multi-source CNN-RNN (MSCR) model that leverages a rich dataset, including periodic, weather, news, and social data to address these limitations. This model enables the capture and fusion of diverse data sources for improved INT prediction accuracy. An advanced deep learning model, the transformer-enhanced CNN-RNN (TE-CNN-RNN), has been introduced. This model is specifically designed to predict INT data only. This model demonstrates the effectiveness of transformers in extracting detailed temporal-spatial features, outperforming conventional CNN-RNN models. The experimental results demonstrate that the proposed MSCR and TE-CNN-RNN models outperform existing state-of-the-art models for traffic forecasting. These findings underscore the transformative power of transformers for capturing intricate temporal-spatial features and the importance of multi-source data and deep learning techniques for optimizing cell site management in the B5G era.  
deep learning; data fusion; transformer models; mobile traffic forecasting; recurrent neural networks; convolution neural networks; self-organizing networks +
Footnote †: journal:
## 3 Methodology  
Since the viable solution to the positioning problem is in continuous space, directly dividing the feasible space and calculating the probability wastes much computational power. To lower the computation complexity and communication requirements, NBP uses particles to represent potential functions. Nevertheless, the amount of data to transmit is still huge Figure 1.  
This paper proposes a simple belt propagation and variation expectation-maximization (SBPVEM) method to realize the cooperative localization of nodes. The algorithm consists mainly of Simplify Belief Propagation (SBP), high-aggregation sampling, and variation expectation-maximization (VEM). The concrete operation of the algorithm on the node is shown in Figure 2.  
Figure 1: Localized anchor nodes are represented by the red five-pointed star, while unlocalized target nodes are represented by the blue circle. Each node sends its opinion of the distribution of the neighborhood node on the basis of its belief and measured distance.
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
## 2 System Model  
The system model of secure query of the routing table in anonymous networks is shown in Figure 2. The workflow of the SQRT scheme proposed in this paper includes three steps. Firstly, a user equipment \(UE_{k}\) generates a routing node query request. Secondly, the directory server stores and updates the status information of the routing nodes. Lastly, the directory server queries the status information of the routing nodes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Schemes** & **Advantages** & **Disadvantages** \\ \hline
[4] & The Onion Routing & Anonymous Routing & Sniffer Attack \\
[7] & The Galicia Routing & Anonymous Routing & Sniffer Attack \\
[10] & BLAZE & Consistency Protection & High communication overhead \\
[11] & TRIDENT & Consistency Protection & High communication overhead \\
[12] & CDT & Anti-malicious routing nodes & Sniffer Attack \\
[13] & DID & Anti-DoS attack & Transmission Route Leakage \\ Our Paper & SQRT & Bilateral Anonymity & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: The technical advantages and disadvantages of existing schemes.
# In-Situ Observation of Adhesion Behavior During Ultrasonic Al Ribbon Bonding
###### Abstract  
In-situ observation was performed on a transparent silica substrate during ultrasonic Al ribbon bonding, using a high-speed video camera with differing frame rates, \(10^{4}\) fps and \(10^{3}\) fps, to clarify the adhesion behavior. The bonding process was observed as follows. Initially, friction slip occurred, producing multiple island streaks in the direction parallel to the ultrasonic vibration. The island streaks were formed as a scratch, due to surface waviness of the Al ribbon. Momentarily, a belt-shaped bond zone was formed at the center, normally due to the ultrasonic vibration. The island streaks could be clearly observed at \(10^{4}\) fps. However, the central belt zone was unclear and appeared translucent at \(10^{4}\) fps; although it was clear when observed at \(10^{3}\) fps. The island streaks were unclear at \(10^{3}\) fps. The positional relation of the island streaks and the central belt zone was confirmed from in-situ observation results of a twist and peel test of Al ribbon bonded to silica substrate. The central belt zone was between the island streaks and the silica substrate.  
ultrasonic bonding; in-situ observation; adhesion; friction slip; bonding mechanism; aluminum ribbon; silica glass; high speed video camera; laser Doppler measurement Article