esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Environmental Data  
We modeled current and future potential distributions of IAPS in protected areas using eight bioclimatic variables, namely the annual mean temperature, annual precipitation, temperature seasonality, mean diurnal range, mean temperature of the wettest quarter, mean temperature of the warmest quarter, precipitation of the driest month, and precipitation seasonality (averages from 1950 to 2000; www.worldclim.org (accessed on 1 August 2014)), which were downloaded from the WorldClim database. We selected two greenhouse gas concentration scenarios (mohc_hadgem2_es RCP 4.5 and 8.5) from global climate models to simulate the future potential distributions of IAPS in the 2080s (2071-2099; [[http://www.ccfs-climate.org](http://www.ccfs-climate.org)]([http://www.ccfs-climate.org](http://www.ccfs-climate.org)) (accessed on 1 August 2014)). RCP 4.5 differs from RCP 8.5 in that RCP 8.5 has higher cumulative concentrations of carbon dioxide than RCP 4.5. Thus, it predicts different climates caused by various anthropogenic concentrations of greenhouse gases and other pollutants. RCP 8.5 and RCP 4.5 were used as the high- and low-concentration scenarios, respectively (www.ccfs-climate.org). Here, we used four resolution scales of bioclimatic variables (2.5, 5.0, 10.0, and 30.0 arcminutes) as the gradient spatial scales because these resolutions are typically used in SDMs.  
The distribution of IAPS is determined not only by climatic factors but also by the presence of suitable patches for colonization and establishment. Habitat invisibility can depend on topography, soil characteristics, land cover, and the disturbance regime. Hence, we downloaded four soil variables with a spatial resolution of 5 min (\(\sim\)10 km), including soil pH in H\({}_{2}\)O and KCl solutions, bulk density (kg m\({}^{-3}\)), cation exchange capacity (cmol+/kg),  
Figure 1: The distribution map of the protected areas analyzed in this study.  
and soil clay (wt%) of fine soil fraction (<2 mm) from a study by [PERSON] et al. [25]. Agricultural land (cropland and pasture area) data for the year 2000, as an indicator of land cover and land use, was downloaded from EarthStat ([[http://www.earthstat.org/](http://www.earthstat.org/)]([http://www.earthstat.org/](http://www.earthstat.org/)) (accessed on 10 July 2022)) with a spatial resolution of 5 min (~10 km). These data show the proportion of farmland and pasture area (i.e., a fraction of the 5-arcminute grid cell area) quantifying the satellite-derived land cover data and agricultural inventory data [8]. Elevation data (2.5, 5.0, 10.0, and 30.0 arcminutes) were downloaded from the WorldClim database (www.worldclim.org). The data on agricultural lands and the soil variables were resampled to 2.5, 5.0, 10.0, and 30.0 arcminutes, the same as the climate and elevation variables.
## 3 Results and Analysis
### The Influence of Different Altitudes on Vegetation Evaluation Indicators  
Figure 10 illustrates the statistical data for EQI and KEQI across various elevations for each country within the Lancang-Mekong forest ecosystem region. The elevations range from 250 m to 2000 m with contour intervals of 250 m, and from 2000 m to 3000 m with contour intervals of 500 m. In Figure 10a, the findings indicate a general trend of increasing EQI and FCI as elevation rises, with the exception of Cambodia, where forest coverage is absent above 2000 m. Among the five countries, Cambodia, Laos, and Thailand exhibit forest coverage indices ranging from 50% to 99% at elevations exceeding 500 m. Additionally, their corresponding EQI values surpass 80 at elevations of 500 m, 750 m, and 1250 m, respectively. Conversely, both Myanmar and Vietnam show forest coverage indices below 70% at 1750 m elevation, and the EQI values remain below 81 across all elevations in these two countries. In Figure 10b, the findings indicate negative KEQI values for Cambodia and Laos, implying a decreasing trend as elevation rises. The most significant absolute value, reaching 0.66, was observed at an elevation of 250 m in Cambodia. These results align with those of Figure 9. However, for Myanmar, Thailand, and Vietnam, the KEQI exhibited a decline from 2010 to 2020. Initially displaying positive values, reaching a maximum of 0.46 at an elevation of 250 m, the KEQI gradually transitioned to negative values, reaching a minimum of \(-\)0.59 at an elevation of 3000 m. Notably, the KEQI approached zero at elevations of 1500 m or 1750 m.  
Figure 10: _Cont._  
Figure 9: Statistics for the FCI in 2020 and the differences in annual average LAI, annual maximum FVC, annual cumulative GPP, and KEQI from 2010 to 2020 at different FCIs for each country over the forest ecosystem in the Lancang–Mekong region. (**a**) Area percentage of FCI for each country; (**b**) difference in FCI between 2020 and 2015; (**c**) difference in annual average LAI; (**d**) difference in annual maximum FVC, (**e**) difference in annual cumulative GPP (gC/m\({}^{2}\)/yr), and (**f**) KEQI.
# Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation
## 5 Parameter Optimization of the Controller Based on PSO  
Since the sliding surface coefficient c and the parameters of the reaching law \(\varepsilon\) and k can affect the tracking effect of the supplied material device when designing the slider controller, it is necessary to optimize the design of the above three parameters. The efficient search capability of PSO helps find better solutions, and its global search feature can find the best solution in a larger range and avoid falling into the local best. Therefore, this paper uses particle swarm optimization, seeking the optimal combination of the above three parameters to improve the robustness of the control system. PSO is a method to search for the best result through a large number of parameters to optimize an optimal result, with structural simplicity and rapid convergence [19]. The optimal combination of parameters can be found through crowd search, and track tracking control of the supplied material device can be achieved based on the assumption that the target function J obtains the minimum value. The specific design steps are as follows [20]:
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 1 Introduction
Our method has no limitation on the position of the camera installation, and it does not needs to know the parameters of the camera, nor to detect or track the vehicles. Given enough scenes covered by the constructed training set, we can estimate the vehicle average speed in any scene. Therefore, our method is simple to operate, with high maneuverability and wide application range. As with video action recognition, vehicle speed estimation task is also a problem about video understanding. Inspired by the successful application of 3D ConvNets in video action recognition, we use 3D ConvNets as the base model. The base model yields promising results on the test set with mean absolute error (MAE) as 2.73 km/h and mean squared error (MSE) as 14.62. Although numerical experiments suggest an inferior accuracy level of our method to that of hardware-based methods. Yet our method is sufficiently accurate for most practical purposes such as vehicle flow computation and traffic congestion identification. Our work can be summarized as follows:  
Figure 1: Flow Chart of Our Method. * We propose 3D ConvNets to estimate average vehicle speed based on video footage. In contrast to camera calibration-based methods, our method is an end-to-end method, independent of external factors. In particular, our method does not entail detecting or tracking vehicles. * We propose to include non-local blocks so as to capture spatial-temporal information with a 'global' perspective. * We propose to add optical flow from video as an input to the model. Optical flow contains useful information on the vehicle motion, which can be used to improve the model accuracy. * We propose to employ the multi-scale convolution to extract the information of vehicles with different scales contours. This design resolves the problem of vehicle size varying due to their different distances from the camera.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Demodulation  
The digitized signal \(y_{\mathrm{rx}}\) is translated into the baseband for each of the \(N_{\mathrm{lo}}\) channels in the Digital Down-Converter (DDC) block, as shown in Figure 6. The Fast Hilbert Cross-Correlator (FHX) block compresses the signal further into arrays \(y_{\mathrm{flax}}\) for additional dimensions for each of the reference chirps of both slope signs. The block join & Downsample (JDS) attempts coherent addition and subtraction of the 2 signal arrays for each channel. The resulting sum and difference signals in in \(y_{\mathrm{flas}}\) are analyzed by the Frame Detect & Downsample (FDDS) block and the input signal divided into separate frames \(y_{\mathrm{sym}}\), now at symbol rate. The final decision block translates the symbols into binary values \(d\) and estimates the demodulation performance. Each block is described below in detail.  
Figure 6: Comparison of the conventional and proposed demodulation as a block diagram in detail. (**a**) In the former case, the received sequence \(y_{\mathrm{rx}}\) is processed by Digital Down-Coverter (DDC), compressed through a Fast Hilbert Cross-Correlator (FHX), converted into symbol space through Frame Detect & Downsample (FDDS), which is interpreted by a binary decision (Decide) block, and finally assembled into the estimated data output \(d_{\mathrm{est}}\) through a reverting multiplexer (De-MUX). (**b**) We propose the insertion of a superposition in the compressed sample space through the Join & Downsample (JDS) block that creates a sum signal for symbol timing and a difference signal for symbol extraction.
## 2 System Model
### RFID Tracking of Goods Delivery  
When performing multihop delivery as in the scenario of Figure 1, in the absence of real-time internet connectivity, measures need to be put in place to make sure that the goods are delivered to the correct destination and collected by the right people since the UAV stops at multiple locations. This can be accomplished by tagging the goods with RFID tags and having the UAV carry an RFID reader. When the UAV reaches one of the destinations, the local person (to whom the delivery is addressed) can open the box and then collect the relevant goods (also tracked through RFID), as illustrated in Figure 2. In case they make a mistake by collecting items destined for other locations, the UAV's RFID reader detects that the wrong object was removed from the UAV's carried package. In this case, a message can be displayed to the local personnel (e.g., communicated to a suitable mobile app by the UAV, or simply an alarm message/signal issued by the UAV) to warn them of the error. In case the item is still offloaded at the wrong location, this can be reported in the logs (ID of the item, actual intended destination, location where the incident occurred, time, etc.). A picture could also be taken (by the UAV) of the user to confirm the correct delivery and include it in the logs of any incident. Preliminary investigations for using RFID for ensuring correct data delivery were presented in [23], where a testbed/prototype showcasing the implementation of this approach was designed by a student team supervised by the first author. The prototype of [23] included an RFID reader and tags, a GPS for tracking the locations, a database for storing the logs, and a mobile application. However, it proved the concept using a robot instead of a UAV, without involving encryption. The same approach can be easily extended to UAV delivery, while the present work proposes efficient encryption techniques that can be used in this scenario.
## 4 Experiment
### Discussion and Analysis
#### 4.3.3 Position Uncertainty Analysis  
To verify the effect of array performance when the array element position is varied, Gaussian perturbation is added to the element position of the on-grid array designed using GEWOA in Section 4.2 to compare the effect of the PSLL performance. The outer  
Figure 19: Curve of the radiation impedance changing with the array element spacing.  
layer of most fiber optic towing arrays is the polyurethane tubing, which is filled with oil. This connection limits the offset of the array element position. Assume that the position perturbation with Gaussian distribution \(\mathcal{N}\left(0,(\frac{\lambda}{12})^{2}\right)\), which can guarantee 99.73% probability within a quarter-wavelength error range.  
The beampattern of the sparse array designed using GEWOA before and after adding the position perturbation is shown in Figure 21. It can be seen that the PSLL of the designed array is \(-17.58\) dB, while the PSLL of the array is \(-15.87\) dB in the case of perturbation of all array elements. We performed 100 experiments using Monte Carlo simulations. The average value of PSLL for the position perturbation case is 16.23 dB. A quarter-wavelength Gaussian position perturbation causes the PSLL of the array to degrade by about 1.35 dB.
# Deep Feature Learning with Manifold Embedding for Robust Image Retrieval
## 6 Experiments
### Experimental Setup
#### 6.1.2 Implementation Details  
During the selection of training image pairs, we adopt RDP to refine the similarity for its tremendous effectiveness. It should be mentioned that other similarity learning algorithms can also be used here. Similarity refinement is performed with publicly released code by the author of [52]. All parameters about RDP are set following the original paper [52].  
Network fine-tuning is conducted with the open-source deep learning platform TensorFlow [53]. Convolutional layers of VGGNet-16 (VGGNet-16 denotes 16-layer VGGNet [44] model) and ResNet-50 (ResNet-50 denotes 50-layer ResNet [45] model) and their corresponding ReLU, pooling, and/or batch normalization layers are selected to be the backbone of the Siamese network. Considering limited computational resources, we only fine-tune three conv5 layers of VGGNet-16, while all convolutional layers of ResNet-50 are fine-tuned. In the training stage, images are resized to \(352\times 352\) and then fed into the network. We adopt a Stochastic Gradient Descent (SGD) optimizer with a momentum of \(\mu=0.9\) and a weight decay of \(\lambda=5\times 10^{-4}\) to tune network parameters. The learning rate is kept to be 0.001 and 0.01 for VGGNet-16 and ResNet-50, respectively, which are one-tenth of the initial learning rate of the pre-trained models. Learning rate decay is not adopted in this work because no evident performance gain is observed. The batch size is set to 8 for both VGGNet-16 and ResNet-50 due to the limitation of GPU memory. Previous works have demonstrated that properly using large images and maintaining the aspect ratio yield better performance in image retrieval tasks [54]. For feature extraction, we directly feed images into the network without resizing (For the Holidays dataset, all images are resized in advance so that the long side is 1024 and the aspect ratio maintained). All of our models are trained on a single Nvidia K40 GPU with 12 GB memory.  
With different dataset sizes and backbones, the convergence speed varies. For VGGNet-16, the training is done for at most 20 epochs, which takes approximately 10, 2, and 20 h for UKBench, Holidays, and INSTRE, respectively. For ResNet-50, it has a faster convergence speed with at most 15 epochs of training, and the training time is 4, 1, and, 8 h, respectively. It is notable that, due to the variant capability of manifold learning algorithm on different datasets, we perform two iterations of training pairs mining and network fine-tuning for both UKBench and INSTRE and only one iteration on Holidays. Since output features are \(l_{2}\) normalized, we use the cosine distance to measure image similarity for online retrieval.
# Numerical Modeling of Wave Disturbances in the Process of Ship Movement Control
## 2 Wave Disturbances Generated by Wind
\begin{array}{l}\beta=cutf(polyval([0.000156-0.0058\;0.0859-0.5842\;2.1039 \;],h_{3\%}),0,9)\\ x_{r}=cutf\Big{(}polyval\Big{(}[0.0242\;-0.1725\;0.0483\;1],\frac{\pi\cdot L}{ \lambda}|\cos(\xi)|\Big{)},0,3.7\Big{)}\\ x_{T}=cutf(polyval([-4.2\;1],T/\lambda),0,0.23)\end{array}  \tag{6}\]  
where  
\(polyval(p,x_{i})\)--a function returning the value of the polynomial with coefficients written in a table \(p\), in a set point \(x_{i}\);  
\(cutf(f(x),a,b)\)--function returning the value \(f(x)\) for \(x\in\langle a,b\rangle\), value \(f(a)\) for \(x<a\) and value \(f(b)\) for \(x>b\). ```
% x = [x0, x1,..., xN], y = [y0, y1,..., yN] % p--coefficients of Lagrange polynomial
```  
function p = lagranp(x,y)  N = length(x) - 1;  p = 0; for m = 1:N + 1  P = 1; for k = 1:N + 1  if k \(\sim\) = m  P = conv(P,[1 - x(k)])/(x(m) - x(k));  End  End  p = p + y(m) * P;  end if  end for ```  
**Algorithm 1** Using Lagrange interpolation  
In relation to Equation (6), input parameters of the numerical model of sea wave generation acting on the ship hull are:  
* Wave angle \(\xi\);
- wave height \(h_{3\%}\);
- wave length \(\lambda\);
* Maximum draft \(T\);
- length \(L\);
- speed \(V\). In addition, taking into account the data from Table 1, we can refer to the model input to wind force (Beaufort wind scale) or to sea state (Douglas scale).
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 2 Proposed Generalized Fault Location Equations
### Line-To-Line (LL) Faults  
Consider the generalized model of a fault illustrated in Figure 2. By modifying the states of the switches \(s1,~{}s2\), and \(s3\), and maintaining switch \(S4\) open, any line-to-line (LL) fault type can be obtained. According to this model, voltages at the faulted location are estimated according to Equation (9).  
\[I_{F_{h}}=-I_{F_{a}} \tag{8}\]  
\[V_{F_{a}}=V_{k_{a}}+x^{2}\cdot 0.5\cdot M_{a}-x\cdot N_{a}=V_{k_{b}}+x^{2} \cdot 0.5\cdot M_{b}-x\cdot N_{b}+Z_{F}\cdot I_{F_{a}} \tag{9}\]  
Making the same procedure as for phase-to-ground faults, a generalized expression for line-to-line (LL) faults is achieved, where \(u\) and \(v\) are faulted phases.  
\[x^{2}\cdot 0.5\cdot\Im\big{\{}(M_{u}-M_{v})\cdot I_{F_{u}}^{*}\big{\}}-x\cdot \Im\big{\{}(N_{u}-N_{v})\cdot I_{F_{u}}^{*}\big{\}}+\Im\big{\{}(V_{k_{u}}-V_{k _{v}})\cdot I_{F_{u}}^{*}\big{\}}=0 \tag{10}\]  
Equations (5) and (10) are second-order polynomials in \(x\); consequently, two solutions are estimated. Thus, the fault distance \(x\), which represents the physically correct solution, must be estimated as shown in [11].  
The previous analysis allows the fault distance estimation in a line section. However, it is necessary to develop an algorithm which allows its application in each line section of the power distribution system. An algorithm, such as the one presented previously [14], can be used. The limitation of such algorithm is that it does not consider the presence of DER.  
Consider now for INIDER, the approximate model of the synchronous machine given by an ideal voltage source that represents the internal voltage of the generator, and the reactance of direct axis during the subtransient period [29; 30]. The choice of the subtransient period ensures that the concatenated flows in the rotor remain constant, as well as the internal voltage of the machine between the period of prefault and the time taken for analysis in the fault [17; 29; 30]. Therefore, the machine's prefault period internal voltage is estimated. In the case that the reactance is not known, typical values reported in the technical literature can be used [31]. The knowledge of the internal voltage in the DG model allows modeling its behavior in the fault period. This enables the estimation of the current contribution to the fault point by the application of circuit reduction theorems such as the Thevenin's theorem [16].  
Similarly, IIDER are considered in this method by including the fault response model of the inverter operating in current limiting mode. In this case, DER are represented as a voltage source and an impedance, which are defined in function of the saturation current of the inverter and the filter parameters [27].
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 2 Establishment of Fractional-Order Model and Parameter Identification
### Establishment of Fractional-Order Model  
According to the detailed analysis of EIS in [32], this study established the fractional-order equivalent circuit model of a lithium-ion battery, as shown in Figure 1. \(R_{0}\) represents the Ohmic internal resistance of the battery, \(R_{1}\) and \(R_{2}\) are the pure resistive impedance, \(CPE\) stands for the constant phase element, and \(W\) represents the Warburg element. The terminal voltage and OCV of the battery are represented by \(U_{d}\) and \(U_{\alpha\alpha}\), respectively, and \(I\) is the current value of the circuit.  
Figure 1: Schematic diagram of fractional-order equivalent circuit model.  
In this circuit, the impedance of the fractional-order element can be described by the following equation:  
\[\left\{\begin{array}{l}Z_{\text{{C}PE_{1}}}=\frac{1}{C_{1}s^{\alpha}}\\ Z_{\text{{C}PE_{2}}}=\frac{1}{C_{2}s^{\beta}}\\ Z_{W}=\frac{1}{WS^{\gamma}}\end{array}\right. \tag{1}\]  
where, \(C_{1}\), \(C_{2}\), and \(W\) indicate the parameters of fractional-order elements; \(\alpha\) and \(\beta\) represent the fractional order of \(\text{{C}PE_{1}}\) and \(\text{{C}PE_{2}}\) components. \(\gamma\) is the fractional order of the Warburg components.  
The fractional differential equation of the parallel circuit of CPE and resistance can be expressed as:  
\[D^{\alpha}U_{\text{{C}PE_{1}}}(t)=-\frac{1}{R_{1}C_{1}}U_{\text{{C}PE_{1}}}(t) +\frac{1}{C_{1}}I(t) \tag{2}\]  
\[D^{\beta}U_{\text{{C}PE_{2}}}(t)=-\frac{1}{R_{2}C_{2}}U_{\text{{C}PE_{2}}}(t) +\frac{1}{C_{2}}I(t) \tag{3}\]  
The fractional differential equation of the Warburg element is shown in Equation (4):  
\[D^{\gamma}U_{\text{{W}}}(t)=-\frac{1}{W}I(t) \tag{4}\]  
According to the definition, the system state of the SOC as a fractional-order model is presented in Equation (5) as follows:  
\[D^{1}SOC(t)=-\frac{\eta}{C_{\text{n}}}I(t) \tag{5}\]  
where \(C_{\text{n}}\) is the battery rated capacity, \(\eta\) is the coulombic efficiency, and \(\eta\) is desirable 1 for lithium-ion batteries.  
According to [PERSON]'s law of current and voltage, we obtain  
\[U_{d}=U_{\text{{o}cv}}-I(t)R_{0}-U_{\text{{C}PE_{1}}}-U_{\text{{C}PE_{2}}}-U_ {W} \tag{6}\]  
where \(U_{\text{{o}cv}}\) is the OCV of the battery, which is a monotonic function of the SOC.  
In summary, the fractional-order equivalent circuit model of a lithium-ion battery can be established by Equations (1)-(6). A complex lithium-ion battery system can be described by the simple structure and limited parameters of the model. The parameters to be identified in the fractional model are shown in Equation (7), below:  
\[\theta=[R_{0}\;\;R_{1}\;\;C_{1}\;\;R_{2}\;\;C_{2}\;\;W\;\;\alpha\;\;\beta\;\;\gamma] \tag{7}\]