esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
Table 2 presents the classification results for the two voting methods of the proposed architecture and the results of the B-mode images for comparison purposes. The proposed ensemble CNN architecture exhibited a better classification performance for all backbone networks than the traditional architecture with B-mode images only. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backbone** & **Method** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** & **AUC** \\ \hline \multirow{4}{*}{VGG-16} & B-mode & 75.5 & 87.43 & 73.96 & 80.13 & 0.8021 \\  & Uniform & **80.0** & 88.43 & 77.43 & 82.57 & 0.8590 \\  & Weighted & 79.0 & **90.38** & **78.70** & **84.14** & **0.8670** \\ \hline \multirow{4}{*}{ResNet-50} & B-mode & 76.5 & 73.00 & **82.06** & 77.27 & 0.8058 \\  & Uniform & 80.0 & 79.67 & 78.63 & 79.15 & **0.8910** \\  & Weighted & **82.0** & **84.57** & 81.73 & **83.13** & 0.8900 \\ \hline \multirow{4}{*}{DenseNet-201} & B-mode & 79.0 & 82.43 & **81.60** & 82.01 & 0.8677 \\  & Uniform & **83.0** & **91.33** & 79.57 & **85.05** & **0.9161** \\ \cline{1-1}  & Weighted & 81.0 & 88.48 & 79.17 & 83.56 & 0.9143 \\ \hline \multirow{4}{*}{EfficientNetV2-L} & B-mode & 74.5 & 84.62 & 72.61 & 78.15 & 0.7665 \\ \cline{1-1}  & Uniform & 76.0 & 90.33 & 72.17 & 80.24 & 0.8627 \\ \cline{1-1}  & Weighted & **80.0** & **92.24** & **75.72** & **83.17** & **0.8735** \\ \hline \hline \end{tabular} Note that the best scores of each performance metric are marked in bold for comparison.  
\end{table} 
Table 2: Classification performances of the proposed ensemble CNN architecture. Figure 6: Average classification results of 5-fold experiments using the VGG-16 backbone network for 40 test images. Because other parametric images, such as entropy, phase, and attenuation images, except for the B-mode image, could compensate for a false prediction of the B-mode image alone, the ensemble framework with multiple images generally improved the classification performance in terms of all metrics. The proposed ensemble learning method showed up to 5.5% higher accuracy and 11.6% higher recall scores than the traditional B-mode only method. While the precision scores of the traditional network were slightly better than those of the proposed network, the overall classification performances were improved for both voting algorithms. Regarding the performance differences between the two voting algorithms, both showed similar and comparable prediction results because of the small size of the dataset used. However, the weighted voting method performed better than the uniform voting method because the weights were determined to maximize the prediction results using the Bayesian optimization technique if a larger dataset was available.
## Methods
### Land Surface Phenology Modeling
#### 3.1.3. Convex Quadratic Model  
We fitted the EVI2 time series as a quadratic function of _AgDD_ (Equation (4)) using the process described in [18]:  
\[\text{EVI2}=\alpha+\beta\times\text{AGDD}-\gamma\times\text{AGDD}^{2}, \tag{4}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) (alpha, beta, and gamma, respectively) are the parameter coefficients to be fitted. Alpha--a constant component--directly regulates the peak EVI2 value over the growing season, as a changing value of alpha solely would move the fitted curve up or down along the EVI2-axis. Beta--a linear component--affects the position of the peak on the thermal time axis (timing of peak growth), as a changing value of beta solely would move the fitted curve in an upward quadratic pattern. Changing the value of gamma--a quadratic component--would make the fitted quadratic curve become thinner or fatter (how fast values on the two sides depart from the peak). The negative sign on gamma in Equation (4) indicates that we accepted only a fitted curve that is downward arching, since the EVI2 values will rise, peak, and then decrease over the growing season. From each fitted model, we derived a suite of 17 variables to be used in the LULC classification, including fitted parameter coefficients, derived phenological metrics, and model fit statistics (Table 1).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Meaning** \\ \hline \(\alpha\), \(\beta\), \(\gamma\) & Fitted parameter coefficients of CxQ model (Equation (4)) \\ \(\text{TTP}_{\text{CxQ}}\) & Thermal time to peak (AGDD at the max fitted EVI2) (TTP = \(-\beta\)/2\(\times\gamma\)) \\ \(\text{PH}_{\text{CxQ}}\) & Peak height EVI2 (max fitted EVI2) (PH = \(\alpha-\beta^{2}\)/4\(\times\gamma\)) \\ HTV & Half-Time Value is value of EVI2 at half-TTP (HTV = \(\alpha+\beta\times\)TTP/2 + \(\gamma\times\)TTP\({}^{2}\)/4) \\ ymax & Highest observed EVI2 value \\ \(\text{R}^{2}\) & Coefficient of determination of the fitted model \\ lpos, rpos & Observation index of start and end of the fitting window \\ o\_all & The total number of “good” observations \\ o\_fit & Number of observations used to fit the CxQ model \\ o\_per & Ratio between “o\_fit” and “o\_all” \\ minx, maxx & _AGDD_ at left and right ends of the fitted curve in the first quadrant \\ peaks & Number of high EVI2 values (\(\geq\)0.8*ymax) outside the fitting window \\ jumps & Number of times that \(\Delta\text{EVI2}\geq 0.2\) \\ \hline \end{tabular}
\end{table}
Table 1: Variables derived from the Convex Quadratic Model.
## 3 Future Directions of MES Development
### Overview of MES  
#### 3.2.1 MES Concept and Definition  
The MES concept was born from the demand on the manufacturing enterprise to fulfil the requirements of markets from a point of view of reactivity, quality, respect of standards, reduction in cost, and deadlines [34]. MESs are software packages used to manage factory floor material control and labor and machine capacity, and to track and trace components and orders, manage inventory, optimize production activities from order launch to finished goods, among other tasks [31].  
The International MES Association, MESA, defined MES as follows: MES delivers information that enables the optimization of production activities from order launch to finished goods. Using current and accurate data, MES guides, initiates, responds to, and reports on plant activities as they occur. The resulting rapid response to changing conditions, coupled with a focus on reducing non-value-added activities, drives effective plant operations and processes [35].  
MES provides critical key performance indicators (KPIs) to the company for making commercial decisions and improving production performance [22]. Through the use of KPIs, (1) companies improve the return on operational assets as well as on-time delivery, inventory turns, gross margin, and cash-flow performance [34]; (2) MES is expected to provide further real-time information to the operational departments by giving an \"allround view\" of all the resources involved in the production and will act as a \"manufacturing cockpit\" [36].
# Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
## 4 Discussion
### Future Work  
Some future developments could be carried out to improve the results of this work:  
* A multi-FPGA design could sustain the acceleration of more threads of Minimap2 with more kernels. For this, the implementation has to be updated to the Vitis environment since SDAccel instances have been deprecated during the length of this work, and a new multi-FPGA AWS instance has to be created with an additional cost. All of these must be evaluated since, according to the findings in [32], the best performance/cost instance is the single FPGA one, and, for memory and communication intensive applications, the increase in the number of FPGAs may not bring a proportional performance increase, unless careful management of the virtual machine is executed.
* Trace-back support could be added to the design to increase the acceleration, mainly by reducing the size of the transferred data (from trace-back pointers to CIGAR strings). This implies changing the Verilog RTL description with a probable increase in the hardware area in the programmable logic, which has to be evaluated with the current design.
* A second affine function could be incorporated into the GACT-X's hardware design to properly mirror Minimap2's alignment scores and improve the compared accuracy, especially for datasets with many long gaps.
# Effect of Surface Mass Loading on Geodetic GPS Observations
## 2 Materials and Methods
### Wavelet Coherence  
Firstly, we will briefly outline the continuous wavelet transform (CWT). The CWT of a time series (\(x_{n}\), \(n=1\), 2,..., \(N\)) is defined as follows [21]:  
\[W_{n}^{x}(s)=\sqrt{\delta t/s}\sum_{n^{\prime}=1}^{N}x_{n^{\prime}}\varphi_{0} \big{[}(n^{\prime}-n)\frac{\delta t}{s}\big{]} \tag{1}\]  
where \(W_{n}^{x}(s)\) is the wavelet coefficient, \(\delta t\) is the time step, \(s\) and \(\varphi_{0}\) are the wavelet scale and the mother function, respectively, and \(n^{\prime}\) and \(\sqrt{\delta t/s}\) represent the reversed time and the normalization factor, respectively. The idea behind the wavelet transform is to apply the wavelet as a band-pass filter to the time series where the wavelet scale is linearly related to the characteristic period of the filter. Based on the _CWT_, the _WTC_ of the two-time series \(X_{n}\) and \(Y_{n}\) (\(n=1\), 2,..., \(N\)) is defined as follows [21]:  
\[R_{n}^{2}(s)=\frac{\big{|}S\big{(}s^{-1}W_{n}^{XY}(s)\big{)}\big{|}^{2}}{S(s^ {-1}|W_{n}^{X}(s)|_{2})\cdot S(s^{-1}|W_{n}^{Y}(s)|_{2})} \tag{2}\]  
where \(S\) is the smoothing operator, \(W_{n}^{X}(s)\) is the wavelet coefficient of the time series \(X_{n}\), \(W_{n}^{Y}(s)\) is the wavelet coefficient of the time series \(Y_{n}\), \(W_{n}^{XY}(s)\) is the cross wavelet transform of the two time series \(X_{n}\) and \(Y_{n}\), the complex argument arg(\(W_{n}^{XY}(s)\)) is the local relative phase between \(X_{n}\) and \(Y_{n}\) in time frequency space, \(s\) is the wavelet scale, \(R_{n}(s)\) is the localized correlation coefficient and \(n\) is the number of stations. The advantage of WTC is its ability to locate regions in the time-frequency domain where the two time series are significantly correlated, and further reveal the relative phase between the two time series in the time-frequency space. In our analysis, the relative phasing of the two time series can be quantified by using the circular mean of the phase or the WTC-based semblance. The definition of the circular mean of a set of angles (\(\alpha_{i}\), \(i=1\), 2,..., \(N\)) is expressed as follows [21]:  
\[a_{m}=a\tan 2(X,Y)=a\tan 2(\frac{1}{n}\sum_{i=1}^{n}\cos{(\alpha_{i})},\frac{1} {n}\sum_{i=1}^{n}\sin{(\alpha_{i})}) \tag{3}\]  
Meanwhile, the WTC-based semblance is defined as follows [22]:  
\[\rho=\cos(\alpha_{i}) \tag{4}\]The value of \(\rho\) ranges from \(-1\) (inversely correlated) to 0 (uncorrelated) to 1 (fully correlated). In this analysis, we used the WTC-based semblance as the key parameter to compare the two time series.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 5 Simulated Experiments and Analysis
### Experiment Settings  
We used the dataset from Shanghai Telecom [23; 24] to obtain the location data of edge servers. The coverage radius of each server was set within a range of 450 m-750 m [7]. According to [11], the data transmission rate between users and edge servers was set within a range of 100 kb/s-800 kb/s, and the data transmission rate between users and remote cloud server was 100 kb/s. In addition, for the ease of calculation, we only considered one resource type (i.e., CPU) in this paper, and more complicated scenario (i.e., more than one resource type) can be easily generalized. The CPU capacity of each server was set as a random number, and the range was from 2-6. We assumed that the CPU capacity of the remote cloud server was enough. The user's speed was set within 1 m/s-7 m/s. To get all users' moving paths, we randomly selected two points on the _BaiduMapand_ obtained a path between two points by the navigation function. Unless stated otherwise, we assumed that the round-trip latency of remote cloud servers was 200 ms [9], and the downtime generated by the service migration was 2.0 s [15].
## 4 Discussion
### Limitations/Trade-Offs with the Proposed Reconfiguration Method
#### 4.2.1 Limitations  
_Scale of the Solution Space_: As shown in Table 1, the solution space grows exponentially with the number of rows in the PV array. For instance, for a seven-row configuration, the solution space reaches a magnitude of 30 digits. Scanning this space using traditional methods is computationally impractical. However, metaheuristic methods like genetic algorithms provide an effective tool for managing such large spaces.  
_Hardware Complexity_: The switching and control circuits required to connect adaptive panels to fixed panels involve relatively simple topologies. These circuits are also an essential part of any reconfiguration approach, making their inclusion inevitable.  
_Dynamic Reconfiguration Time_: When shading scenarios change rapidly (e.g., due to fast-moving clouds), the switching time required for reconfiguring physical connections depends on the type of semiconductor electronic switching element used (e.g., LGBT). Since this time is on the order of milliseconds (ms) and microseconds (\(\upmu\)s), short-term energy production losses are negligible.
# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 1 Introduction
Taking the height error as an example, [PERSON] [63; 64] has established the target positioning algorithm based on the digital elevation model, which solved the problem of excessive target height error caused by the positioning algorithm based on the earth ellipsoid model; [PERSON] [72] used the feature points in the image to obtain height information and locate the target without terrain data, but this method needs to control the imaging distance and the identifiable feature points must be ensured to exist in the graph. On account of the situation that there is no ground control point in the target area, this paper classifies the above positioning algorithms according to the different mathematical models. In the second section, the positioning principles with the pros and cons of diverse algorithms are comprehensively compared. In the third section, two simulation analysis models of positioning error are established to analyze and sort out the error factors that cause the accuracy of the algorithm to change. In the fourth section, this paper concludes the research status and development direction of aerial camera positioning algorithms.
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 4 Results
### Evaluation Results for the Case of Interpolation  
Six different missing patterns were involved in the evaluations in order to investigate the influence of the numbers of missing frames on the models. The first 5 patterns miss 1, 2, 4, 6, 8 successive intermediate frames, respectively. The last missing pattern misses 4 discrete intermediate frames. The chosen missing patterns were representative because the number of successive missing frames was usually not too high in real radar data sets. Figure 4: Scheme chart for the masking procedure and the deep neuron network (DNN) model. Table 1 presents the MAE, CSI-40 scores and HSS scores for the CNN-BiConvLSTM model and baseline models with respect to several specific missing patterns. It was found that the performances of the CNN-BiConvLSTM model were the best among all the models and for all evaluation metrics and missing patterns involved. Apart from the CNN-BiConvLSTM model, the 3 DCNN model generally performed better than the other models. Whereas for the CNN-ConvLSTM, the performances were unstable and are highly dependent on evaluation metrics and missing patterns. Specifically, the MAE and HSS scores for the CNN-ConvLSTM model were generally better than those of the optical flow model, but the CSI-40 scores were poorer than those of the optical flow model, especially under conditions of multiple successive missing frames (e.g., '1000000001'). Considering identical network structures between CNN-BiConvLSTM and CNN-ConvLSTM except for the bidirectional structure, it can be inferred that the spatial-temporal information in both directions is quite important. This was also the reason why the 3 DCNN model achieved a better performance. The output radar images in the 3 DCNN model used encoded features from all input radar images (Figure 3), while the output radar images in the CNN-ConvLSTM model used only encoded features from previous radar images. When features from subsequent radar images were taken into account in the CNN-ConvLSTM model (i.e., CNN-BiConvLSTM), the performances were superior to those of the 3 DCNN model. This result also implied the higher effectiveness of RNNs in extracting spatial-temporal features compared to CNNs. In addition to higher average scores, the scores of each sample for the CNN-BiConvLSTM model were found mostly higher than that of the baseline models. Figure 5 shows histograms for the missing pattern of '1110000111' as a representative case. The differences of scores between the CNN-BiConvLSTM model and baseline models for test samples were found mostly less than zero for the MAE and larger than zero for the CSI scores and HSS scores. The results further highlighted the effectiveness of the CNN-BiConvLSTM model.
# Catalytic Advanced Oxidation Processes for Sulfamethoxazole Degradation
## 1 Introduction
Therefore, the following catalytic advanced processes were studied to enhance the degradation of SMX: photocatalysis (L), catalytic ozonation (O\({}_{3}\)), and catalytic oxidation with hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)). The combination of two or more processes--photocatalytic ozonation (L/O\({}_{3}\)), photocatalysis with hydrogen peroxide (L/H\({}_{2}\)O\({}_{2}\)), catalytic ozonation with hydrogen peroxide (O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\)), and photocatalytic ozonation with hydrogen peroxide (I/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\))--for the degradation of SMX were also carried out. The processes cited above were also studied in detail in the absence of catalysts [33]. The main subject of this research is to study the degradation of sulfamethoxazole by different oxidation processes and in the presence of various catalysts. For that propose, four solid-state materials with optical semiconductor properties were used as catalysts and they were extensively characterized to better understand their performance. Carbon nanotubes (CNT) were chosen due to their strong catalytic activity with O\({}_{3}\), especially for promoting the O\({}_{3}\) decomposition and surface reactions. Titanium dioxide (TiO\({}_{2}\)) was used due to its stability and photocatalytic activity which promote the formation of oxidative radicals in the presence of light. A composite of carbon nanotubes and titanium dioxide (TiO\({}_{2}\)/CNT) was chosen because it can improve the reaction in the presence of light, once it avoids the recombination of the pair e\({}^{-}\)/h\({}^{+}\) and enhances the activity of TiO\({}_{2}\). Finally, iron supported on carbon nanotubes (Fe/CNT) were used since H\({}_{2}\)O\({}_{2}\) can be decomposed in the presence of Fe, generating radicals. Since this work does not consider only photo-based processes and it is not a net photochemistry study, detailed photophysical information was not evaluated. The efficiency of various processes was critically compared in terms of SMX concentration, mineralization level, and formation of intermediate compounds (3-amino-5-methylisoxazole--AMI, p-benzoquinone--BZQ, oxamic acid--OXM, and oxalic acid--OXL) and ions (sulphate--SO\({}_{4}\)\({}^{2-}\), nitrate--NO\({}_{3}\)\({}^{-}\), and ammonium--NH\({}_{4}\)\({}^{+}\)).
## 4 Simulation Result  
The proposed LVRT control method was validated using MATLAB/Simulink, SimPowerSystems. The WT and ESS are modelled by detailed circuit model and the simulation time step is 50 microseconds. Since the simulation time is about 3 s, the data size is 60,000 for respective values of voltage and current. The fixed step solver with the sampling time as 50 microseconds is used. The capacity of a PMSG WT is 1.6 MW while the ESS energy capacity is 1 kWh. We considered an 80% voltage sag condition for a PCC duration of 0.625 s in cases with the strictest regulation, such as those of Ireland. The overall system parameters are described in Table 1. We considered the performance of typical LVRT methods with or without additional devices such as an ESS. Without this additional device, WT LVRT operation is handled only by pitch and converter controls whereas with additional devices, a chopper can be used. Even though a chopper can successfully limit dc link voltage variation, it just dissipates the produced WT power. Moreover, it experiences dc link voltage fluctuations owing to its operational characteristics.
# Airborne Electromagnetics to Improve Landslide Knowledge in Tropical Volcanic Environments
###### Abstract
AEM is therefore useful to analyze the internal structure of large landslides [48; 49; 50; 51]. In 2013, an AEM survey was conducted over Martinique Island [52]. The SkyTEM system was designed for mapping geological structures and for hydrogeological and environmental investigations [53]. AEM data, acquired along flight lines, provide information to a depth of 150 m and allow continuous and homogeneous imagery of resistivity variations [30]. If the subsurface resistivity has an indirect relationship with the soil characteristics [31], it can also provide relevant information on both formation thicknesses and their spatialization [32]. To be fully relevant, correlation with direct observations or independent geological datasets (outcrop, borehole geological log, etc. ; [31; 32]) remains mandatory. Recently, in the framework of future development [15], there has been demonstrated interest in AEM to delineate landslide-prone areas and to improve landslide hazard assessment. New information brought by AEM has allowed a landslide hazard map to be modified by combining a 3D geological model derived from a joint analysis of field observations and AEM results and a PBM. This study, for regulatory purposes, focused on only one type of island environment and on shallow (failure < 2 m) and moderately deep-seated landslides (failure from 2 m to 10 m). Therefore, deeper landslides (failure > 10 m) and associated formations inducing recurrent and very costly damage were not assessed. Considering these results and despite an exhaustive inventory and landslide hazard maps available for the entire island, there is still a lack of knowledge about (i) the nature of certain landslide-prone materials and (ii) the failure mechanisms and triggering conditions for landslides with different failure depths. This work suggests a landslide analysis methodology that combines information about regolith and bedrock derived from AEM results with a spatialized PBM (SPBM) adapted for different types and depths of failures. Two different areas that are well known for different recurrent slope instabilities, are typical of the island and have caused significant damage for more than 20 years, were chosen to challenge the methodology. The latter is divided into 3 main steps:Definition of the internal structure of grounds by coupling field observations, geological data and AEM results;  
Building of a geological model;  
Integration of the topography and geological model in a SPBM and analysis of slope stability following a specific protocol taking field observations into account. The test sites have benefited from previous field, geophysical and geotechnical studies [54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65]. These studies enable (i) a criticism of the AEM results and their interpretation and (ii) the development of realistic failure scenarios based on historical observations and measurements.