esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design and Result Analysis of the Battery Cycle Life Test
### Experimental Results and Analysis
Hence, the nominal capacity of the battery is always used instead of \(Q_{all}\). However, the real \(Q_{all}\) is restricted by the working conditions at that time; on the other hand, with the degradation of the battery, even if the working conditions are the same, the \(Q_{all}\) is different. By using Equation (5), this experiment is imprecise in estimating the SOC. Another way to get the SOC of the battery is based on the OCV-SOC curve, that is, the measured value of OCV, the corresponding SOC value is calculated. However, LIB has the hysteresis effect. The charging OCV-SOC curve is not consistent with the discharging OCV-SOC curve; in other words, the charging OCV value is different from the discharging OCV value, while the corresponding charging SOC value and the discharging SOC value are the same. The OCV-SOC curve is affected by the history of operating conditions. For example, for two OCV values corresponding to the 50% SOC of the charging state, the first 50% SOC is obtained by charging the battery from a 20% SOC to the 50% SOC and the second 50% SOC is obtained by charging the battery from a 30% SOC to the 50% SOC. Obviously, the two SOC values are the same; however, even though the two OCV values are both for in the charging state, they are different. The OCV-SOC curve changes with the degradation of the battery showed in Figure 4. The SOC value is obtained through the OCV-SOC curve, the precision is affected by the hysteresis effect and the degradation of the battery. (2) At present, the SOH prediction methods use equal discharging durations or equal voltage drops as health factors. Although such health factors can characterize the change in the available capacity of the battery, for estimating the SOH, extra discharging tests are needed and the health factor must be obtained under predefined operating conditions. 1. Compared to charging/discharging the battery to a certain SOC, charging/discharging the battery to a certain voltage is simpler and more precise. 2. Terminal voltage drop during battery rest can reflect the battery degradation. 3. Using the terminal voltage drop during battery rest to estimate the available capacity is not affected by the operating conditions that the battery is under. This study proposed a novel SOH estimation method that adopts the terminal voltage drop during the battery rest as a health factor and the aforementioned certain voltage is the charging cutoff voltage.
## Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Department of Languages and Computer Science, University of Malaga, Boulevar Louis Pasteur 35,  
Malaga 29071, Spain; E-Mails: hfc(@lcc.uma.es (J.C.); [EMAIL_ADDRESS] (M.D.);  
[EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (A.R.); [EMAIL_ADDRESS] (B.R.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-95-2132753; Fax: +34-95-2131397.  
Academic Editor: [PERSON]  
_Received: 22 May 2015 / Accepted: 23 June 2015 / Published: 26 June 2015_  
**Abstract:** Recently, slab track systems have arisen as a safer and more sustainable option for high speed railway infrastructures, compared to traditional ballasted tracks. Integrating Wireless Sensor Networks within these infrastructures can provide structural health related data that can be used to evaluate their degradation and to not only detect failures but also to predict them. The design of such systems has to deal with a scenario of large areas with inaccessible zones, where neither Internet coverage nor electricity supply is guaranteed. In this paper we propose a monitoring system for slab track systems that measures vibrations and displacements in the track. Collected data is transmitted to passing trains, which are used as data mules to upload the information to a remote control center. On arrival at the station, the data is stored in a database, which is queried by an application in order to detect and predict failures. In this paper, different communication architectures are designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. In addition, to ensure communication between the sensing devices and the train, the communication system must take into account parameters such as train speed, antenna coverage, band and frequency.  
**Keywords:** wireless sensor networks; slab track infrastructure protection; communication architecture; structural health monitoring
# A Novel 3D Analytical Scattering Model
###### Abstract  
A geometry-based three-dimensional (3D) novel stochastic channel model for air-to-ground (A2G) and ground-to-air (G2A) radio propagation environments is proposed. The vicinity of a ground station (GS) is modelled as surrounded by effective scattering points; whereas the elevated air station's (AS) vicinity is modelled as a scattering-free region. Characterization of the Doppler spectrum, dispersion in the angular domain and second order fading statistics of the A2G/G2A radio communication channels is presented. Closed-form analytical expressions for joint and marginal probability density functions (PDFs) of Doppler shift, power and angle of arrival (AoA) are derived. Next, the paper presents a comprehensive analysis on the characteristics of angular spread on the basis of shape factors (SFs) for A2G/G2A radio propagation environments independently in both the azimuth and elevation planes. The analysis is further extended to second order statistics of the fading channel; where the behaviour of the level crossing rate (LCR), average fade duration (AFD), auto-covariance and coherence distance for the A2G/G2A radio propagation environment is studied. Finally, the impact of physical channel parameters, such as the mobility of AS, the height of AS, the height of GS and the delay of the longest propagation path, on the distribution characteristics of Doppler shift, angular spread and second order statistics is thoroughly studied.  
Doppler spectrum; multipath channels; air-to-ground; geometric channel model; shape factors; second order statistics; angular spread Article
# Automated Destination Renewal Process for Location-Based Robot Erands
## 3 Methods
### Data Collection of Indoor Space
#### 3.1.2 Obstacle Update  
To incorporate information about obstacles in the real environment occupied by furniture, exploration is performed using distance-measuring sensors based on the grid map obtained through drawing-based map building. LiDAR, capable of real-time long-range distance extraction, has the advantage of easily acquiring and avoiding surrounding obstacles during robot navigation. However, due to the characteristics of LiDAR sensors, only 2D distances in the form of line scanning can be obtained, and there is a mechanical limitation that prevents the detection of obstacles around the robot depending on the mounting position.  
To address this limitation, a depth camera is utilized to obtain proximity information from the robot up to a certain distance. To process this information as obstacles within the grid map, the laserscan_kinect module is employed to project it in 2D [34]. Ground plane information extracted from depth data is processed using a conversion algorithm to prevent it from being treated as an obstacle. Subsequently, the distances measured by the two sensors are matched to the cells of the grid map, and the corresponding cells are assigned weights. If a cell receiving weight surpasses a threshold, the cell is changed to occupied space, and conversely, if it falls below the threshold, it is changed to free space (see Figure 2).  
Figure 2: Example of obstacle update when above threshold: (**a**) before the laser scan, (**b**) after the laser scan.
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Brief Introduction tokineros 2
#### 3.4.3 Study Basin Parameterization  
In order to conduct the calculations correctly, each element must be characterized with its geometry, flow length, vegetation cover and soil properties. KINEROS 2 parameterizes all components, giving them hydraulic and hydrology properties. This information cannot be extracted from the DEM. The Automated Geospatial Watershed Assessment (AGWA) uses a practical geometric relationship to calculate channel geometry as a function of the surface and background areas for each channel section.  
Next, the soil and vegetation cover values must be parameterized, using the surface coverage map extracted from SIGMA and the soil sample analysis made. The hydrological properties of each type of cover and soil are included in the '_LookUp_' table that we must attach so that KINEROS assigns each element the characteristic properties of its composition and coverage.
## 4 Materials and Methods
### LSTM/RNN/FFN Input Sequence Preparation  
The input sequence preparation for the algorithm described in this paper is mainly implemented through the following steps, with the core logic centered around the time window and sequence construction:  
1. Time Window Setting: Window length (n_steps): Defined by the function parameter n_steps, with a default value of 20 in the code. This parameter indicates that each input sequence contains historical data from the previous 20 time steps, which is used to predict the target value of the next time step. Function: Converts time-series data into a supervised learning format, i.e., using input features within the historical window to predict the output value at the next moment after the window ends.
2. Sequence Construction Logic (create_sequences function) This function is the core of input sequence preparation, with the specific process as follows: Input: Raw data (data) and window length (n_steps). Output: Input sequences (X) and corresponding target values (y). Specific operations: Iterate through each time step in the data. For the i-th time step: The input sequence X[i] contains input features from i to i + n_steps \(-\) 1, totaling n_steps time steps (i.e., data [i: i + n_steps, :-1] in the code, which takes all columns except the last one as features). The target value y[i] is the output variable at the I + n_steps-th time step (i.e., data [i + n_steps, -1] in the code, which takes the last column as the target value).
3. Data Preprocessing and Adaptation Merging features and targets: Input features (input_columns) and output variables (output_column) are merged into a 2D array combined_data to ensure temporal alignment. Standardization: Data is scaled to the range [0, 1] using MinMaxScaler to avoid the impact of magnitude differences between features on training stability. Splitting into training and test sets: Sequential data (X_train, X_test, y_train, y_test) is split at an 8:2 ratio to ensure the model's generalization ability is validated on unseen data.
4. Sequence Shape Adaptation to Algorithm Input Requirements  
Figure 8: Results of LSTM prediction for solar arrays using load current and MEA as feature factors.  
The model requires input shapes in the format (number of samples, time steps, number of features), where  
Number of samples: Total number of generated sequences (len(data) - n_steps).  
Time steps: Equal to n_steps (window length).  
Number of features: Dimensionality of input features (defined by input_columns in the code; for example, if input_columns = [\"load\"], the number of features is 1).  
The shape of X generated in the code is (number of samples, n_steps, len(input_columns)), which fully meets the input format requirements of the algorithm.  
Input sequences are constructed by sliding a fixed-length time window. The core parameter n_steps defines the window length (i.e., how many historical time steps of features are used for prediction). The create_sequences function converts time-series data into a supervised learning format, ultimately generating input sequences suitable for the model.
## 3 Experiments
### Used Instrumentation and Recording Arrangement
#### 3.2.1. Phonation Signal Recording
In all cases, the SPL values were measured by the sound level meter Lafayette DT 8820 mounted on the holder at the same height from the floor as the recording microphone (75 cm). For purpose of this study, we are not interested in MR images that are automatically generated by the MRI control system after finishing the currently running scan sequence [36]. To prevent their creation and storage, it is possible to manually interrupt passing of the running scan sequence from the operator console. This approach was practically applied in all our experiments, so no MR images of the tested persons were collected or stored. The phonation/sound signal was analyzed by a pitch-asynchronous method with a frame length of 24 ms and a half-frame overlap. For calculation of spectral properties, the number of fast Fourier transform (FFT) points was \(N_{\text{FFT}}\) = 1024; for estimation of the formant frequencies and their bandwidths, the complex roots of the 18 th order LPC  
Figure 7: An arrangement of the phonation and PPG signal recording in the MRI Opera (for measurement phases \(MF_{2}\) and \(MF_{3}\)): (1) pick-up microphone Mic1, (2) noise SPL meter, (3) recording devices outside the shielding cage, (4) electronic part of the wearable PPG sensor, (5) reflection optical pulse sensor on the forefinger of the left hand, (6) door of the shielding cage. polynomial were used. In contrast with our first-step work [26] and with the aim to obtain results with higher precision, computation of the full covariance matrix [19] and 512 mixtures were finally applied. The length of the input feature vector for GMM creation, training, and classification was set experimentally to \(N_{\text{FEAT}}\) = 32, and \(N_{\text{ITER}}\) = 1500 iterations were used. The phonation signal processing as well as implementation of basic functions for the GMM classifier was currently realized in the Matlab environment (ver. 2019a).
## 4 Discussions
### Evaluations of the Proposed Downscaling Method  
Serving as an effective tool in the estimation of GPP, the carbon cycle model is typically executed at coarse resolutions due to the paucity of the data and computing complexities. Landscape complexities are assumed to be simplified in a carbon cycle model by ignoring spatial heterogeneities within each modeling grid. It has been illustrated that the oversimplification of landscape complexities may inevitably cause model simulations to become considerably biased [8; 9; 10]. In this approach, massive forcing data were required to run the carbon cycle model, including the spatial distributions of temperature, radiation, vapor pressure deficits and so on. In mountainous areas, the regional estimation of the meteorological element was a challenge, especially at fine resolution [12; 13; 45].  
In this study, a downscaling algorithm of GPP was developed to obtain the GPP distribution at 500 m resolution. The proposed downscaling method contains two phases: a regression process and an ATPK process. The first phase was used to reduce uncertainties caused by topographic effects and vegetation heterogeneities. A regression analysis was first conducted between the GPP estimation and spatial heterogeneity factors; then, the ATPK was performed to downscale residuals from the regression process.  
This study tested the thesis that the 500 m GPP could be obtained from 1 km GPP and spatial heterogeneity information, which avoided complex calculation and the limit of forcing data through the carbon cycle models. It was found that the proposed method could be effectively used to obtain the GPP at 500 m resolution, showing significant advantages over the kriging method, spline method and IDW method. Our results also illustrated that both endogenous factors (i.e., LAI) and exogenous factors (i.e., altitude, slope and aspect) were useful for GPP downscaling. Exogenous factors have significant effects on the various instantaneous land surface fluxes due to the redistribution of water and their interactions with atmospheric elements [43; 46; 47]. The endogenous factors can be regarded as the accumulated outcomes of the surrounding environmental conditions [48], such as topography, climate and soil property. Therefore, both exogenous and endogenous factors should be considered in the downscaling scheme to remove biases within the coarse modeling grid.
## 4 Experimental Principles
### GA-DQN Anti-Jamming Decision-Making Method  
#### 4.2.1 DQN Algorithm  
In the MDP, the goal of the intelligent anti-jamming communication system is to find an optimal policy \(\pi^{*}\), such that in any given state the selected action can maximize the long-term cumulative reward. To achieve this goal, a neural network is used to approximate  
Figure 2: The network architectures of the generator and discriminator in a LACGAN (the red border is the improvement).  
the state-action value function \(Q(\mathbf{s},\mathbf{a};\theta)\), where \(\theta\) represents the network parameters. Thus, it can be expressed using the optimal Q-function as  
\[Q^{*}(s,\mathbf{a})=\mathrm{E}^{\pi}\Bigg{[}\sum_{\tau=0}^{\infty}\gamma^{\tau}r (s_{t+\tau},\mathbf{a}_{t+\tau})|s_{t}=s,\mathbf{a}_{t}=\mathbf{a}\Bigg{]}, \tag{11}\]  
where \(\mathrm{E}^{\pi}[]\) is the mathematical expectation operator and \(\tau\) denotes the subsequent steps starting from time slot \(t\).  
To enhance training stability, an independent target Q-network is employed to compute the target Q-value. The parameters \(\theta^{-}\) of the target network are periodically copied from the main network, with the update frequency controlled by hyperparameters. The loss function of the DQN is defined as the Mean Squared Error (MSE):  
\[L(\theta)=\mathrm{E}\Big{[}(r_{t}+\gamma\max Q(\mathbf{s}_{t+1},\mathbf{a}^{ \prime}\beta^{-})-Q(\mathbf{s}_{t},\mathbf{a}_{t}\beta))^{2}\Big{]}. \tag{12}\]  
During the algorithm training phase, the \(\epsilon\)-greedy policy is adopted for training updates. The optimal policy \(\pi^{*}(a|\mathbf{s})\) can then be inferred as follows:  
\[\pi^{*}(a|\mathbf{s})=\left\{\begin{array}{l}\operatorname*{argmax}_{a\in A }Q^{*}(\mathbf{s},\mathbf{a}),0\leq x\leq 1-\epsilon\\ random,1-\epsilon\leq x\leq 1\end{array}\right.. \tag{13}\]
## Highlights:
Utilizing software COMSOL 5.6, the proposed model integrates the flow field, concentration field, temperature field, and deformation field, facilitating the simultaneous resolution of multiple physical fields. Adaptive meshing and automatic meshing methods are used to describe cavity boundary expansion. The feasibility and accuracy of the model are verified by comparisons with the field data of the Volgograd horizontal salt cavern. Analysis encompasses both the mechanism and the influence of heat transfer on the flow field, brine concentration, and cavity expansion, as well as the characteristics of the turbulent flow field, concentration field, and cavity expansion. Several groups of simulations have been executed for the optimization of the technological parameters, with a discussion on how these parameters influence the cavern volume and the outlet brine concentration.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
###### Abstract  
Forests are traditionally characterized by stand-level descriptors, such as basal area, mean diameter, and stem density. In recent years, there has been a growing interest in enhancing the resolution of forest inventory to examine the spatial structure and patterns of trees across landscapes. The spatial arrangement of individual trees is closely linked to various non-monetary forest aspects, including water quality, wildlife habitat, and aesthetics. Additionally, associating individual tree positions with dendrometric variables like diameter, taper, and species can provide data for highly optimized, site-specific silvicultural prescriptions designed to achieve diverse management objectives. Aerial photogrammetry has proven effective for mapping individual trees; however, its utility is limited due to the inability to directly estimate many dendrometric variables. In contrast, terrestrial mapping methods can directly observe essential individual tree characteristics, such as diameter, but their mapping accuracy is governed by the accuracy of the global satellite navigation system (GNSS) receiver and the density of the canopy obstructions between the receiver and the satellite constellation. In this paper, we introduce an integrated approach that combines a camera-based motion and tree detection system with GNSS positioning, yielding a stem map with twice the accuracy of using a consumer-grade GNSS receiver alone. We demonstrate that large-scale stem maps can be generated in real time, achieving a root mean squared position error of 2.16 m. We offer an in-depth explanation of a visual egomotion estimation algorithm designed to enhance the local consistency of GNSS-based positioning. Additionally, we present a least squares minimization technique for concurrently optimizing the pose track and the positions of individual tree stem[s].  
direct visual odometry; simultaneous localization and mapping: non-linear least squares; computer vision; GNSS/GPS 20232337043  
2023
## 2 Handcrafted Representation-Based Approach
### Appearance-Based Approaches
#### 2.2.1 Shape-Based Approaches  
The shape-based approaches capture the local shape features from the human image/silhouette [85]. These methods first obtained the foreground silhouette from an image frame using foreground segmentation techniques. Then, they extract the features from the silhouette itself (positive space) or from the surrounding regions of the silhouette (negative space) between canvas and the human body [86]. Some of the important features that can be extracted from the silhouette are contour points, region-based features, and geometric features. The region-based human action recognition method was proposed in [87]. This method divides the human silhouette into a fixed number of grids and cells for action representation and used a hybrid classifier Support Vector Machine and Nearest Neighbour (SVM-NN) for action recognition. For practical applications,the human action recognition method should be computationally lean. In this direction, an action recognition method was proposed using Symbolic Aggregate approximation (SAX) shapes [88]. In this method, a silhouette was transformed into time-series and these time-series were converted into a SAX vector for action representation followed by a random forest algorithm for action recognition.  
In [89], a pose-based view invariant human action recognition method was proposed based on the contour points with sequence of the multi-view key poses for action representation. An extension of this method was proposed in [90]. This method uses the contour points of the human silhouette and radial scheme for action representation and support vector machine as a classifier. In [86], and [91] a region-based descriptor for human action representation was developed by extracting features from the surrounding regions (negative space) of the human silhouette. Another method used pose information for action recognition [92]. In this method, first, the scale invariant features were extracted from the silhouette, and then these features were clustered to build the key poses. Finally, the classification was performed using a weighted voting scheme.