esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Hybrid Vibration Sensor for Measuring Downhole Drilling Tool Vibrational Frequencies
## 3 Experiments
### Power Generation Performance Experiments  
As shown in Figure 10, the relationship between the output voltage, output current, and load of TENG, PENG, and EMG is explored. The formula for calculating the output power of each part of the sensor is as follows:  
In the equation, U represents the output voltage after rectification, and I represents the output current after rectification.  
The voltage of the TENG unit and PENG unit Increases with the increase in load, and the current decreases with the increase in load. The TENG works at the frequency of 10 Hz, and the peak power reaches the maximum when the load is \(10^{8}\)\(\Omega\), which is 447.8 nW. At this point, the voltage is 14.4 V and the current is 31.1 nA. The output of TENG is alternating current, which is rectified by a three-phase rectification current, as shown in the figure, and the alternating current is converted into direct current output. When PENG works at a frequency of 1k Hz, the load is \(10^{8}\)\(\Omega\), and the peak power reaches the maximum, which is 5347 nW. At this time, the voltage is 37 V and the current is 144.5 nA. When EMG works at a frequency of 1k Hz and the load is 100 \(\Omega\), the peak power is the largest, which is 3.04 mW, the voltage is 0.78 V, and the current is 3.9 mA. It can be observed that among  
Figure 10: Experimental results of power generation performance. (**a**) The voltage and current output of TENG under different loads (vibration frequency is 10 Hz); (**b**) the peak power of TENG under different loads; (**c**) the voltage and current output of PENG under different loads (vibration frequency 1k Hz); (**d**) peak power of PENG under different loads; (**e**) voltage and current output of EMG under different loads (vibration frequency is 1k Hz); (**f**) peak power of EMG under different loads.  
the three working parts of the sensor, the output power of the EMG is much higher than that of the TENG and PENG. This also indicates that the EMG can significantly increase the output power of the sensor, making it possible for the sensor to provide power to other low-power devices underground in emergency situations.  
Furthermore, since the sensor's output is alternating current, it needs to be rectified in order to power other devices, as shown in Figure 11a. To validate the possibility of the sensor powering other low-power devices underground, we rectified the EMG section of the sensor to power an array of LED lights. The results indicate that the output power of the sensor can illuminate multiple LED lights.
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 1 Introduction
In fact, the OSTST (Ocean Surface Topography Science Team) strongly recommended (in its 2012 meeting) that Sentinel-6 altimeter shall deliver LRM (low resolution mode, i.e., conventional altimetry) and SAR mode over all oceans, seamlessly and simultaneously, by implementing a sort of \"interleaved mode\". This continuous transmission modality is advantageous also for small specular targets, thanks to the lobing features of their radar returns, which will be demonstrated further in this paper. Thus, the archive of IEs collected by RA-2 offers today a unique opportunity to experiment the potentialities of the coherent processing of radar altimetry data in the observation of surface water bodies. In particular, this work deals with inland water applications, by discussing the main properties of specular targets, the potentialities of the Doppler processing of the returned echoes, and finally introducing selected case studies, in order to show the possibility to focus and range relatively small water bodies. The paper is organized as follows: Section 2 is focused on the modeling of specular echoes in water targets from a theoretical point of view. Section 3 describes the IE data sets and the processing of specular echoes. Section 4 illustrates the selected case studies of Rio Nahuapa (Peru), River Semliki (Congo) and Prek Toal (Cambodia) flood area. Section 5 discusses the results and provides the main conclusions and future perspectives.
# Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model
## 2 Methods
### The Artificial Neural Network  
The different factors considered in sediment variability, as well as various assumptions in the transport process, lead to distinct sediment prediction. As the coefficients in the empirical formula of sediment transport are mostly carried out under different laboratory or experimentally measured conditions, the results of the formula calculation are inconsistent, and some even differ greatly. It is difficult for theoretical formulas to reflect the interaction between these factors, while the artificial neural network can link each factor.  
Neurons in artificial networks are connected in diverse ways to form models of artificial neural networks. [PERSON] et al. viewed artificial intelligence as belonging to the realm of statistical applications, focusing as much on how to estimate complex functions statistically with computers and less on providing confidence intervals for those functions [23]. In this article, computation using neural networks is divided into two main steps. Firstly, the network is trained, that is the learning of neural networks; then, the trained network is used to solve the problem, thus the network is verified.  
As seen in Figure 1, the input layer is used to represent the number of inputs for the network, where the wavelet decomposition value \(X_{b}\) is used as the number of processing units. The output layer is the objective function \(h_{(\theta)}(x)\). The implicit layer between the input layer and the output layer is used to denote the interaction effects between the input processing units. To reduce the impact of data partitioning on the model, the multiple linear regression model is used for training and prediction [24].  
\[h_{(\theta)}(x)=\left[\theta_{0}\;\theta_{1}\;\cdots\;\theta_{b}\right]\left[ \begin{array}{c}X_{0}\\ X_{1}\\ \ldots\\ X_{b}\end{array}\right] \tag{9}\]  
\[P(\theta)=\frac{1}{2a}\sum_{i=1}^{a}\left(h_{\theta}\left(x^{(i)}\right)-y^{( i)}\right)^{2} \tag{10}\]  
where \(a\) means the number of samples and b represents the number of input variables; \(\theta\) is harmonic parameter to make the model work best, which is searched to minimize the cost function. The most appropriate parameter \(\theta\) was defined when the cost function \(P(\theta)\) was minimized. \(x^{(i)}\) and \(y^{(i)}\) represent respectively the value of all input variables and measured value for samples \(i\).
## 3 Numerical Simulation Study  
### Establishment of Three-Dimensional Geological Model  
Based on the latest contour data of the subsidence area and rock strata interfaces, a coupled modeling approach using the Rhinoceros 7.0, Gridle 2.0, and FLAC3D 6.0 software was employed to establish a three-dimensional geological model of the subsidence area. The model consists of several components, including the overburden layer (glacial deposits), the subsidence area, and the rock strata. The model's spatial coordinates in the Y direction represent true north, with a length of 1045 m in the north-south direction  
Figure 3: Extent of mining and location of surface collapse.  
Figure 2: Aerial view of the study area.  
and 676 m in the east-west direction. The model's horizontal bottom is situated at an elevation of 3690 m. A fully hexahedral grid with grid sizes of 4 m was utilized, comprising 1,725,316 grid cells and 1,345,258 nodes. The 3D model mesh is shown in Figure 4.  
The 3D numerical model is shown in Figure 5. It is important to note that a Quaternary glacial deposit cover exists above the ore body, with an average thickness of 16.84 m, reaching a maximum of 85.73 m in certain areas. Since the commencement of mining operations, a significant amount of glacial deposits have entered the subsidence crater as the ore body sank. Additionally, related entities have back-filled some of the Quaternary glacial deposits around the subsidence crater into the mining pit, with some of these glacial deposits infiltrating underground through rock fractures. Consequently, the exact thickness of the glacial deposit layer within the subsidence crater is currently unclear. Based on the analysis of available data, our model establishes the boundary elevation between the upper glacial deposits and the rock strata as 3820 m. Furthermore, in conjunction with information related to underground mining, the model clearly defines the scope of the simulated mining, as illustrated in the cross section of the model in Figure 5b.
## 4 Discussion
### Limitations and Future Directions  
Although this study provided insight into pitch discrimination abilities in children, the small number of total participants means that findings should be interpreted with caution. Furthermore, while the CwTV group did not have vocal complaints, they were not endoscopically screened for the absence of a voice disorder, and some participant's voices were rated as dysphonic; thus we cannot definitively rule out the presence of a voice disorder. Future work is needed with a larger number of children in the CwVN and CwTV groups to confirm the absence of differences in auditory discrimination abilities. Additionally, hearing screenings were not conducted on the majority of the CwVN group, as well as some participants in the CwTV group. Therefore, we cannot rule out potential auditory threshold differences between the groups in the current study. Although there is some emerging evidence of hearing differences between CwVN and children with voice disorders [59; 60], these studies examine a relatively small sample size of children with multiple voice disorders. The hearing differences outlined included both temporary conductive hearing loss and sensory neural hearing loss; therefore, further work in a larger group is needed to elucidate whether CwVN have a higher incidence of hearing difficulties than age-matched peers. Some participants in the current study (in both the CwVN and the CwTV groups) had speech or language deficits. Although including these participants makes this group more representative as a sample of the population, it resulted in less homogenous participant groups. Without directly examining speech, language, and cognition, we cannot say whether these groups were distinct in these areas.  
Additionally, participants were recorded in different locations; although all participants were recorded in a quiet area, there may have been an unwanted effect based on participant comfort. Specifically, many CwVN were recorded before a medical examination, potentially increasing their anxiety or distractibility. Future work should control these external and participant factors when examining children with and without vocal fold nodules. Due to the nature of the task, it is possible that younger participants could become fatigued towards the end of a run, which could impact their JND scores. This was unlikely to be an issue given the average run times for both children (5.16 min) and adults (4.17 min); however, future work should evaluate JND values for this groups on a shorter paradigm. Finally, the current work focused on discrimination abilities of vocal pitch, and therefore does not provide information on the perception of other vocal percepts common in CwVN, such as breathiness and strain. Vocal pitch was selected, as it is a salient feature of voice that would not require extensive training to learn, thereby reducing the complexity of the task, and making it appropriate for a range of children. Future work should examine whether discrimination abilities for additional vocal percepts differ between CwVN and CwTV.
## 4 Congestion-Avoidance and Vehicle-Detection Modules of ACO-PT for Vehicles
### Automatic-Vehicle-Detection Module
#### 4.2.1 Image Preprocessing  
The first stage consists of preparing the image for further processing. It typically includes a series of operations, such as image enhancement and noise extraction. These operations are applied to improve image quality and extract useful information. The goal of image preprocessing is to make the image more suitable and useful for the recognition and detection of vehicles. First, the camera receives the street-image frame provided by the matrix \(src_{i}\). To remove uninteresting pixels from the image, a binary-threshold transformation is applied so that only pixels that are above or below a certain value remain in the final image (\(dst_{i}\)).  
\[dst(x,y)=\begin{cases}1,\;\;if\begin{cases}|I_{t}(x,y)-I_{i+1}(x,\;y)|>T\\ M\\ 0,\;\;else\end{cases} \tag{16}\]  
where \(I_{t}\) is an initial street image with the \(i\)-th pixel; \(dst(x,\;y)\) is an image after thresholding; \(T\) is the threshold value, chosen for sufficient detection of contours in the original image; and \(M\) is the maximum pixel value of the threshold transforms.  
**Definition 2**.: _The gradient of the image determines the direction of the steepest increase in a certain value._  
\[|grad\;g(x,\;y)|=\sqrt{\left(\frac{\partial f}{\partial x}\right)^{2}+\left( \frac{\partial f}{\partial y}\right)^{2}} \tag{17}\]  
**Property 1**.: _Edges are normal to the direction of the gradient and can be classified by a light profile in the direction of the gradient at a given pixel \(\omega\), which can be determined as:_  
\[\omega=arg\left(\frac{\partial f}{\partial x^{\prime}},\;\frac{\partial f}{ \partial y}\right) \tag{18}\]  
Figure 7: Automatic-vehicle-detection process.  
**Definition 3**.: _To detect horizontal edges, calculate the difference between a value of one pixel and the next pixel value, up or down from the first, starting from the top-left origin:_  
\[H_{c}=diff_{y}(x,y)=value(x,y)-value(x,y+1)\]  
_Likewise, equivalent row detections can be illustrated as follows:_  
\[H_{R}=diff_{x}(x,\ y)=value(x,y)-value(x-1,\ y) \tag{19}\]  
_where \(H_{c},\ H_{R}\) are column and row detectors, respectively._  
**Definition 4**.: _Sobel edge detection selects all edges of an image regardless of direction._  
\[M=\sqrt{(h_{1}^{2}+h_{2}^{2})} \tag{20}\]  
**Property 2**.: _The approximate amplitude of the Sobel detector is a summation of the edge module:_  
\[M=|h_{1}|+|h_{2}| \tag{21}\]  
_where \(M\) is a Sobel edge detector and \(h_{1}\) and \(h_{2}\) are the edges._
###### Abstract  
Unmanned aerial vehicles (UAVs) are becoming more and more widely used in battlefield reconnaissance and target strikes because of their high cost-effectiveness, but task planning for large-scale UAV swarms is a problem that needs to be solved. To solve the high-risk problem caused by incomplete information for the combat area and the potential coordination between targets when a heterogeneous UAV swarm performs reconnaissance and strike missions, this paper proposes a distributed task-allocation algorithm. The method prioritizes tasks by evaluating the swarm's capability superiority to tasks to reduce the search space, uses the time coordination mechanism and deterrent maneuver strategy to reduce the risk of reconnaissance missions, and uses the distributed negotiation mechanism to allocate reconnaissance tasks and coordinated strike tasks. The simulation results under the distributed framework verify the effectiveness of the distributed negotiation mechanism, and the comparative experiments under different strategies show that the time coordination mechanism and the deterrent maneuver strategy can effectively reduce the mission risk when the target is unknown. The comparison with the centralized global optimization algorithm verifies the efficiency and effectiveness of the proposed method when applied to large-scale UAV swarms. Since the distributed negotiation task-allocation architecture avoids dependence on the highly reliable network and the central node, it can further improve the reliability and scalability of the swarm, and make it applicable to more complex combat environments.  
heterogeneous UAV swarm; reconnaissance and strike; distributed negotiate; time coordination; deterrent maneuver
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 4 Evaluation  
### Evaluation Scenario  
The scenario for the evaluation of the traffic detection system is shown in Figure 11, which includes important streets in the city of Colima, Mexico that presents significant traffic congestion during peak hours, mainly at street intersections.  
The scenario shown in Figure 11 was previously studied in order to establish monitoring points along the avenues and at the main intersections of the study area. Each monitoring point was geographically referenced with its specific monitoring diameter and information concerning the average speed of its area of coverage, data which was required by the traffic detection algorithm. It is  
Figure 11: Evaluation scenario with the coverage points in place.  
Figure 12: Detailed evaluation scenario.  
important to note that it was necessary to add more monitoring points at the main street intersections in order to avoid false alerts in areas where stoplights exist, as shown in Figure 12.
## 3 Results and Discussion
### Global Performance Labels  
In order to define the boundaries for each global performance label, combinatory techniques (i.e., combinations with repetitions) were applied to determine all possible situations and establish the limits for each label class (see Figure 24). These limits are the same for each category.  
The geometric mean will penalize the global value if one of the properties does not meet the technical requirements, because it will be rated as zero, and it will only take into account the number of available properties.  
As already mentioned in Section 2.2, the environmental performance category includes some non-quantifiable properties. In this case, if the result is positive, a class label will be directly added to the obtained environmental label from the geometric mean.  
Again, it should be noted that the attainment of a D grade in terms of mechanical and functional performance means that the mixture does not comply with the technical standard; thus, its use must be refused. However, in the case of environmental performance, this label has merely informative character and it only pretends to encourage the use of more sustainable technologies.
###### Abstract  
Storage and discharge conditions of primary lithium batteries are studied and the capacity estimation models during the whole life cycle of underwater vehicles is developed based on temperature. The storage experiments for 90 days at different temperatures and discharge experiments at different temperatures and current rates are conducted. At low temperatures, experimental results reveal that there is no significant capacity decay during storage. At high temperatures, the charge storage capacity attenuates with the increase of storage, and the attenuation rate is directly related to the temperature. In discharge experiments, maximum available capacity increases with temperature during 0 \({}^{\circ}\)C to 25 \({}^{\circ}\)C, whereas the given phenomenon is not significant at other temperatures. Moreover, the current rate is less important for the maximum available capacity. To establish capacity estimation models during the storage stage and working stage of underwater vehicles, we have developed a capacity decay model and a temperature calibration model. Moreover, the model accuracy is evaluated, and the errors in capacity decay model and temperature calibration model are less than 2% and 0.7%, respectively. Capacity estimation models provided in this research are not only accurate, but also relatively simple, which have high value for underwater vehicles in engineering applications. primary lithium batteries; capacity decay model; capacity calibration model; underwater vehicles +
Footnote †: journal: Applied Sciences  
to the fuel gauge in the electric car, the accurate monitoring of the battery charge capacity can reduce the range anxiety of users. At present, the rapid development of the new energy vehicle industry drives the progress of lithium-ion batteries, and its state estimation of charge and health has been more mature [2]. The common methods, advantages, and disadvantages of lithium-ion battery capacity estimation are summarized here and listed in Table 1, including the test method, parameter-based method, ampere-hour integral method (Ah), model-based method and data-driven method. However, compared with the lithium-ion battery, research on primary lithium batteries is quite lacking. Meanwhile, a reaction mechanism between lithium-ion batteries and primary lithium battery is completely different, meaning that some capacity estimation approaches of the lithium-ion battery may not be applicable to lithium primary batteries. Currently, the most accurate method to determine battery capacity is the test method. It performs a complete discharge to obtain the available charge capacity of battery, which is one way based on the definition of charge capacity. However, the test method has obvious shortcomings, such as non-real-time, long-time-consuming, and high-cost. Moreover, it is
# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
###### Abstract  
Multi-focal image and multi-modal image fusion technology can fully take advantage of different sensors or different times, retaining the image feature information and improving the image quality. A multi-source image fusion algorithm based on bidimensional empirical mode decomposition (BEMD) and a region sharpness-guided region overlapping algorithm are studied in this article. Firstly, source images are decomposed into multi-layer bidimensional intrinsic mode functions (BIMFs) and residuals from high-frequency layer to low-frequency layer by BEMD. Gaussian bidimensional intrinsic mode functions (GBIMFs) are obtained by applying Gaussian filtering operated on BIMF and calculating the sharpness value of segmented regions using an improved weighted operator based on the Tenengrad function, which is the key to comparison selection and fusion. Then, the GBIMFs and residuals selected by sharpness comparison strategy are fused by the region overlapping method, and the stacked layers are weighted to construct the final fusion image. Finally, based on qualitative evaluation and quantitative evaluation indicators, the proposed algorithm is compared with six typical image fusion algorithms. The comparison results show that the proposed algorithm can effectively capture the feature information of images in different states and reduce the redundant information.  
image fusion; bidimensional empirical mode decomposition; Gaussian filter; region sharpness; region overlapping
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 6 Performance Evaluation and Benchmarking
### Metrics for Evaluating VLC-Based IPS
#### 6.1.5 Hardware Cost  
As compared to conventional approaches in wireless technologies such as Wi-Fi and Bluetooth, VLC-based IPSs have several advantages, including high accuracy, low latency, and immunity to electromagnetic interference. However, one of the critical factors that can limit the wide adoption of VLC-based IPSs is the hardware cost. Deploying a high density of light transmitters in an indoor environment may become expensive, making it challenging to scale these systems to large deployments. Therefore, hardware cost is an important metric when evaluating the feasibility and commercial viability of VLC-based IPS solutions.  
Hardware costs can vary significantly depending on the components and design choices made during the system development phase. For instance, the cost per node can increase if high-resolution cameras or complex signal processing algorithms are used. Additionally, designing customized hardware components can also add to the total cost of the system. Therefore, it is essential to consider the hardware cost metric while designing and developing VLC-based IPSs, particularly when aiming for large-scale deployment.