esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermodynamics of Fluoride Adsorption on WA  
The experimental results for fluoride removal by WA at different reaction temperatures are presented in Figure S1, along with a plot of \(\ln(K_{e})\) and \(1/T\), using Equation (S10) to evaluate the thermodynamic parameters. The calculated parameters are presented in Table S3. The positive \(\Delta H^{0}\) value suggests that the fluoride adsorption reaction is endothermic [3], indicating that external heat can be supplied to accelerate fluoride removal by WA [46]. The value of \(\Delta S^{0}\) indicates the degree of randomness, with a positive value indicating an increase in randomness [12]. The value of \(\Delta G^{0}\) indicates whether the adsorption reaction is spontaneous or non-spontaneous, with a negative value indicating a spontaneous reaction [47]. The results demonstrated that the reaction for fluoride removal by WA exhibits increasing randomness (\(\Delta S^{0}\) > 0) and non-spontaneous adsorption characteristics (\(\Delta G^{0}\) > 0) at low reaction temperatures (15 \({}^{\circ}\)C, 25 \({}^{\circ}\)C). However, at a high reaction temperature (35 \({}^{\circ}\)C), the process exhibits spontaneous adsorption characteristics (\(\Delta G^{0}\) < 0).
## 3 Data Coverage and Method
### Modified Stacking Method  
The stacking technique can mitigate atmospheric delays and measure uniform surface deformation by averaging data with multiple interferograms [24]. However, there are very limited images for every frame (\(\leq\)11 images) in this study (see Table S1-S3). What is worse, lots of SAR images have significant atmospheric delays due to strong evaporation in the LZP, especially in summer. For example, four of the 10 observations have serious atmospheric effects for track466/frame400 of ALOS1 acquisitions (see Figure S1 and Table S1). In order to make full use of all available images in the stacking process, we firstly obtained unwrapped differential interferogram pairs frame by frame. Secondly, we manually identified the images with serious atmospheric delays and then used pair wise logic instead of the traditional stacking method which combine pairs in a chronological order. It means every identified image should be treated as master and slave in turn for the final differential pairs used, see Figure S2. This process can not only reduce the atmospheric effect but also strengthen the effective value decided by mean coherence in the combined interferogram. Figure 3 shows the differential interferogram of ALOS1 before and after using this strategy and the coherence histograms of Figure 3c,d are also calculated in Figure 3e. We can find that the mean coherence of stacking in Figure 3c is obviously better than that of Figure 2d obtained by direct interferometry (see Figure 3e). The atmosphere effects of image 20090209 represented by black dashed lines in Figure 3a,b are also mitigated in Figure 3c by the combination of Figure 3a,b. Moreover, the cumulative perpendicular baseline of each frame shouldbe close to zero to reduce topography error in the final stacking results. Finally, we calculated the linear displacement rate with the ratio between the cumulative unwrapped phase and cumulative time. It should be noted that the method above is different from the one proposed by [PERSON] [25], in which every SAR acquisition can be used only twice through the chain stacking. We used SAR images with atmosphere contaminated twice only if the frequency of master and slave were the same. In addition, to analyze the development of subsidence in urban areas more scientifically, we also used a traditional SBAS-InSAR to calculate the deformation time-series in the capital city of LZP, Zhanjiang city [12].
# Groundwater Prediction Using Machine-Learning Tools
## 4 Groundwater Prediction Methodology
### Feature Selection  
Since the dataset in this research is small, it was particularly important to choose a set of features of limited size (to avoid overfitting) but which still captures essential information that can be used for prediction. In our model we used both local and global spatiotemporal features, and additionally performed a rescaling, as described in the following subsections.
# Optical Analysis of the Impact Transmission in Steel Sheet Arrays with Bolted-Type Points
## 2 Materials and Methods
### Optical Metrology and Processing  
A laser as a source to evaluate the displacements was used. Coherence, unidirectionality, and Gaussian distribution are some of its properties [31]. The last one is relevant since the beam hits a rough material; thus, the reflection can be studied with the probability density function of intensity \(P_{(I)}\):  
\[P_{(I)}=\frac{1}{4\pi\sigma^{2}}exp\left(\frac{-I}{\alpha^{2}}\right) \tag{1}\]  
where \(\sigma\) is the average intensity \(I\) of the speckles in the field. The speckles have a very large number of random variables; thus, the central limit theorem is followed [32], as real and imaginary parts of the field are asymptotically Gaussian.  
The specklegram is recorded in slow-motion with the camera (C). Then, 2D images are brought out employing free software. Those images are then correlated by code into Octave(r), comparing \((A,B)\) in the Fourier domain iteratively, expressing them as the reference and deformed, respectively [3; 4; 5]. The displacement field can be calculated by:  
\[D_{f}=\frac{Re(C_{c})}{(s_{1}*s_{2})^{2}M} \tag{2}\]  
where \(C_{c}\) is the mathematical correlation. Normalization is performed, stating \(M\) as the image size and \(s_{1}\) and \(s_{2}\) as the standard deviation of the images corresponding to [8; 9].  
Equation (2) gives a matrix-type result; thus, the maximum amplitude point was logically defined. It is part of the intensity field \(I\) in (1), and it is related to the real and  
Figure 4: Experimental set-up: (**a**) 2D illustration; (**b**) used devices.  
imaginary part of the complex amplitude of each speckle [32]. As this is an iterative process, from the second to the \(n\) image, those peak amplitudes are plotted as scalar values measured in millimeters according to [8; 9].  
Before the response was obtained, signal processing by performing a low-pass filter was followed. The obtained result has high-frequency values, and after filtering the signal, the main impact displacement was observed. Also, a numerical first derivative of the signal was proposed. From it, an analytical approach by means of damped and harmonic oscillations was performed. It obtained more than 98% of accuracy by fitting. Finally, an analytical displacement was introduced by means of analytical integrating the function. From this, equivalent movement was observed compared with the experimental results.
###### Abstract  
Dynamic human activity recognition (HAR) is a domain of study that is currently receiving considerable attention within the fields of computer vision and pattern recognition. The growing need for artificial-intelligence (AI)-driven systems to evaluate human behaviour and bolster security underscores the timeliness of this research. Despite the strides made by numerous researchers in developing dynamic HAR frameworks utilizing diverse pre-trained architectures for feature extraction and classification, persisting challenges include suboptimal performance accuracy and the computational intricies inherent in existing systems. These challenges arise due to the vast video-based datasets and the inherent similarity in the data. To address these challenges, we propose an innovative, dynamic HAR technique employing a deep-learning-based, deep bidirectional long short-term memory (Deep BiLSTM) model facilitated by a pre-trained transfer-learning-based feature-extraction approach. Our approach begins with the utilization of Convolutional Neural Network (CNN) models, specifically MobileNetV2, for extracting deep-level features from video frames. Subsequently, these features are fed into an optimized deep bidirectional long short-term memory (Deep BiLSTM) network to discern dependencies and process data, enabling optimal predictions. During the testing phase, an iterative fine-tuning procedure is introduced to update the high parameters of the trained model, ensuring adaptability to varying scenarios. The proposed model's efficacy was rigorously evaluated using three benchmark datasets, namely UCF11, UCF Sport, and JHMDB, achieving notable accuracies of 99.20%, 93.3%, and 76.30%, respectively. This high-performance accuracy substantiates the superiority of our proposed model, signaling a promising advancement in the domain of activity recognition.  
pre-trained neural networks; HAR; MobileNetv2; deep bidirectional LSTM +
Footnote †: journal: Applied  
Acticle  
Dynamic Human Activity Recognition
# Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
###### Abstract  
In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required.  
extended power-divergence measure; computed tomography; iterative reconstruction; expectation-maximization algorithm; optimization
# Fast Type-II Hartley Transform Algorithms for Short-Length Input Sequences
## 3 Fast Algorithms for Small-Size DHT-II
### Algorithm for 5-Point DHT-II
.\\ \\ \text{We define}\ \ s_{0}^{(5)}=(a\ -\ b)/2;\ s_{1}^{(5)}\ =\ \ \ \ -\ (a+b)/2;\ s_{2}^{(5)}=b/2;\ s_{3}^{(5)}=(c+d)/4;\\ s_{4}^{(5)}=(c\ -\ d)/4.\ \text{Then}\\ \mathbf{C}_{4}^{(d)}\bar{\mathbb{I}}_{2}\otimes\mathbf{I}_{2})(\mathbf{H}_{2} \otimes\mathbf{I}_{2})(\mathbf{T}_{2\times 3}^{(4)}\oplus(\bar{\mathbb{I}}_{2} \mathbf{H}_{2}))\times diag\Big{(}s_{0}^{(5)},\ s_{1}^{(5)},\ s_{2}^{(5)},\ s_{3}^{(5)},\ s_{4}^{(5)}\ )\ (\mathbf{T}_{3 \times 2}^{(3)}\oplus\mathbf{H}_{2})(\mathbf{H}_{2}\otimes\mathbf{I}_{2} \Big{)}. \tag{17}\]  
Taking into account the properties of structural matrices [5,32], the computational procedure for the five-point DHT-II is represented by the expression  
\[\mathbf{Y}_{5\times 1}=\mathbf{P}_{5}^{(0)}\mathbf{W}_{5\times 7}\mathbf{W}_{7}^{ (0)}\mathbf{W}_{7\times 8}\mathbf{D}_{8}\mathbf{W}_{8\times 7}\mathbf{W}_{7}^{(1)} \mathbf{W}_{7\times 10}\mathbf{W}_{10\times 5}\mathbf{P}_{5}^{(1)}\mathbf{X}_{5\times 1} \tag{18}\]  
where  
\[\mathbf{P}_{5}^{(0)}\ =\ \begin{bmatrix}1&&\\ &1&&\\ 1&&&\\ &&&1\end{bmatrix} ,\ \mathbf{P}_{5}^{(1)}\ =\ \begin{bmatrix}1&&\\ &1&&\\ &&1&\\ &&&1\end{bmatrix} \]  
\[\mathbf{W}_{8\times 7}=1\oplus\mathbf{T}_{3\times 2}^{(3)}\oplus\mathbf{H}_{2} \oplus\mathbf{I}_{2},\ \mathbf{W}_{7}^{(1)}\ =\ 1\oplus(\mathbf{H}_{2}\oplus\mathbf{I}_{2})\oplus\mathbf{I}_{2},\ \mathbf{W}_{7\times 10}=\mathbf{I}_{5}\oplus \mathbf{P}_{2\times 5}^{(2)},\ \mathbf{W}_{10\times 5}=\mathbf{1}_{2\times 1}\otimes\mathbf{I}_{5},\]  
\[\mathbf{W}_{7\times 8}=1\oplus\mathbf{T}_{2\times 3}^{(4)}\oplus(\bar{\mathbb{I}}_{2} \mathbf{H}_{2})\oplus\mathbf{I}_{2},\ \mathbf{D}_{8}=diag(1,\ s_{0}^{(5)},\ s_{1}^{(5)},\ s_{2}^{(5)},\ s_{3}^{(5)},\ s_{4}^{(5)},\ 1,\ 1),\]  
\[\mathbf{P}_{2\times 5}^{(2)}\ =\ \begin{bmatrix}-1&1&1&-1\\ 1&1&\\ &-1&&1&\\ &&&1\end{bmatrix} ,\ \mathbf{W}_{5\times 7}\ =\ \begin{bmatrix}1&&1\\ 1&&&1\\ &1&&&1\\ &&1&&1\\ &&&1&1\end{bmatrix} \]  
A data flow graph of the proposed algorithm of the five-point DHT-II is presented in Figure 4. In particular, the number of multiplication operations may be reduced from 16 to 5, but the number of addition operations is increased from 20 to 23. Figure 4: Data flow graph of the algorithm for the computation of five-point DHT-II.
## 4 Results
### Unit Root Test, Box-Jenkins Model, and ARCH Test  
The ARCH-GARCH model can be built after going through several stages of estimation. The first is to test the stationarity of the data to be analyzed. Second, we identify the Box-Jenkins model (AR, MA, ARMA, and ARIMA) by observing the stationarity test results. Third, we test the ARCH effect of the selected Box-Jenkins model. The ARCH effect test will determine whether the selected model can be further analyzed using the ARCH-GARCH model. Fourth, we estimate the ARCH-GARCH model by choosing the best model. Finally, we use the normality test to evaluate the model and perform the ARCH-LM test to determine whether the selected ARCH-GARCH model is free from the ARCH effect. If desired, forecasting can be done on the ARCH-GARCH. The data on rice prices for estimation of volatility value were tested for stationarity. The stationarity test can be done by looking at graphs or correlograms, or doing the unit root test. The unit root test can use many test tools, one of which is the augmented Dickey-Fuller (ADF) test. The stationarity test is essential to do on time series data so that the resulting data is predictable and unbiased, and it can be carried out at the level, first difference, and second difference. Research on rice prices in Indonesia shows that the data are stationary in the first difference.  
The Box-Jenkins model was determined after the stationarity test was carried out, including auto-regressive (AR), moving average (MA), auto-regressive moving average (ARMA), and auto-regressive integrated moving average (ARIMA) models. If the data are stationary at the level, then the estimation model uses ARMA; if the information is stationary in the first difference, it uses ARIMA. The model was made for all 33 provinces, showing that the variable is stationary at the first difference level, so the Box-Jenkins model used is ARIMA (Table 2). The best model is chosen after doing several ARIMA model simulations. The criteria for selecting ARIMA are based on significant estimation coefficients, with the largest R-squared and adjusted R-squared, the smallest AIC and SIC values, and relatively small values of standard error of regression and sum square residual.  
The ARCH-GARCH model can calculate volatility if there is an ARCH effect on the selected ARIMA model. The ARCH effect test is intended to determine whether there is a heteroscedasticity model to determine the actions to take for the next step.  
There is a problem in the three rice price variables of all provinces. It can be seen that the probability value of each food price is less than 5% of the real level, so it can be concluded that there is a heteroscedasticity problem in the rice price variables. The heteroscedasticity problem can be solved using the ARCH-GARCH model. A probabilistic value above 0.05 indicates homoscedasticity. Testing the ARCH effect (Table 2) shows that there are models with a heteroscedasticity problem for six provinces, because the ARCH effect is found. The ARCH effect found in each of the best ARIMA models indicates that the calculated volatility varies over time.
## 4 Efficiency and Fairness Signal Optimization Model
### Feasibility Analysis
#### 4.1.2 Feasibility Discussion  
Calculation method  
The first step involves calculating the average delay per vehicle \(D^{\prime}\) and the corresponding fairness evaluation index \(H^{\prime}\) based on the intersection data using the Webster model.  
In the second step, building upon the classical signal timing optimization model, the objective function is replaced with the fairness evaluation index \(H\). Additionally, to further explore delay sacrifice, a constraint on delay sacrifice is added to the model, as shown in Constraint (19):  
The feasibility condition for optimizing the intersection signal timing scheme of the model is to increase the fairness of the intersection to varying degrees. It is necessary to appropriately relax the total delay value of the intersection output by the model to represent the applicable conditions of the comprehensive model.  
\[(w+5\%)\cdot D^{\prime}\geq D\geq w\cdot D^{\prime} \tag{19}\]  
Results  
After conducting the calculations six times for the 362 sets of data, the results indicate that the overall conversion rate is not favorable, with a mean value of only 0.69. As the sacrifice level increases, the average conversion rate decreases at an average rate of 29%. However, at a delay sacrifice level of [100%, 105%], the average conversion rate is 1.78, exceeding 100%. For other sacrifice levels, the average conversion rate is only 0.48, with [125%, 130%] being as low as 0.28. This suggests that sacrificing delay for fairness is feasible when the delay sacrifice is small, indicating that this approach has minimal impact on delay, with an average impact of only 2.5%.  
Regarding saturation, as saturation increases, the average conversion rate gradually decreases. The saturation range of [0, 0.2] has the highest average conversion rate of 1.25, which remains the highest across all sacrifice levels. At a sacrifice level of [100%, 105%], it even reaches 3.08. In contrast, the saturation range [0.6, 0.8] has an average conversion rate of only 0.46. This indicates that sacrificing delay for fairness is more effective in low-saturation scenarios. The figure below illustrates the conversion rate curves for different saturation and sacrifice levels.  
In conclusion, sacrificing delay for fairness is feasible and has minimal impact on delay, making it more suitable for low-saturation intersections. The purpose is to demonstrate the fair conversion rate achieved through sacrificing delay. Initially, when the total delay time sacrificed is relatively short, the conversion rate is high, indicating a significant fairness effect. However, as the total delay time sacrificed increases, the fairness effect obtained becomes less apparent, as shown in Figure 5.
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 4 Load-Balancing Routing Optimization Scheme Design
### Load-Balancing Optimization Model
The calculation is formulated in Equations (22)-(24), where \(C_{b}^{\varphi}\) represents the standard deviation of the node load balance, and \(\overline{C_{b}}\) denotes the average node load balance. \[C_{b}^{\varphi}=\frac{C_{b}^{\varphi}}{\overline{C_{b}}} \tag{22}\]  
\[C_{b}^{\varphi}=\sqrt{\frac{\sum_{i=1}^{N_{t}}\left(C_{S_{i}}-\overline{C_{b}} \right)^{2}}{N_{t}}} \tag{23}\]  
\[R_{M_{i}}\geq\text{Success}_{M_{i}} \tag{15}\]0  
7. Optimization Objectives  
Through the analysis of the load-balancing optimization model, the routing load-balancing problem is transformed into minimizing the node load balance while satisfying constraints on communication delay, link reliability, node occupancy rate, and node load rate. The optimization objectives are shown in Equations (25) and (26), which aim to minimize the node load balance and average node load rate. \[\text{s.t. }\left\{ \begin{array}{c}\text{min}(C_{b}^{\varphi})\\ \text{$T_{delay}\leq T_{\text{maxdelay}}$}\\ \text{$R_{M_{i}}\geq\text{Success}_{M_{i}}$}\\ \text{$R_{S_{i}}\leq 1$}\\ \text{$C_{S_{i}}\leq 1$}\end{array} \right. \tag{25}\]  
If the node load balance is equal, the set of parameters that minimizes the average node load rate is considered. \[\text{s.t. }\left\{ \begin{array}{c}\text{min}\left(\overline{C_{S_{i}}} \right)\\ \text{$T_{delay}\leq T_{\text{maxDelay}}$}\\ \text{$R_{M_{i}}\geq\text{Success}_{M_{i}}$}\\ \text{$R_{S_{i}}\leq 1$}\\ \text{$C_{S_{i}}\leq 1$}\end{array} \right. \tag{26}\]
## 4 Results and Discussion
### LHTS in Multi-Energy Systems  
This section reports the results achieved in the analysis of the Multi-Energy System. The technologies activated for supplying the electricity load are the micro-cogeneration (CHP) system and the photovoltaics (PV). The extra demand is supplied purchasing the electricity from the grid; the adoption of the batteries makes the production more flexible. The electricity consumption is due to both the building load and the electricity absorbed by the electric heat pump and the electric storage. Concerning the cooling load, this is supplied through an electric heat pump (EHP). As far as heating is concerned, Figure 10 includes the power evolution of the technologies selected by the optimizer. On the left of the figure, the production evolutions are reported; on the right, consumptions are reported. The production consists in the sum of the power due to the production/conversion technologies operated, the energy purchased from the grid, and the storage discharging phase. The consumption consists in the sum of the building load, the energy sold to the grid, the storage charging phase, and the consumption of the other technologies such as the heat consumed by the absorption heat pump. The sum of the production and consumption must be equal. As a result, the heat load is mainly provided by the micro-cogeneration system, the heat-only boiler, and the thermal solar. The LHTS allows storing energy in the valley to supply it during the peak loads.  
Figure 10: Production/consumption evolution for the energy vectors: (**a**) hot production; (**b**) hot consumption.  
Figure 9: Building heat demand (15-min average thermal power) with and without LHTS.