esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Quick Reduct for Feature Drift Detection
## 3 Results
### Tests
#### 3.3.3 Test 3. Shift with Partially Dependent Features  
The dataset used in the third test has a larger number of features. The specificity of this dataset with respect to QR is that the degree of dependence of the complete set of features never reaches unity and in many cases is less than 0.8 (Figure 3).  
Furthermore, the low fraction of selected features indicates that many of them are redundant, non-discriminative and actually worsen performances. In this test, features 9 and 13 were swapped from observation 300 to the end of the dataset. The window is fixed to 100 observations and the overlap is of 95 observations, so the drift occurs at the 40 th iteration. Figure 4 shows the plots of the selected features at each iteration without drift (a) and with drift highlighted in red (b).  
As for feature 9, the detection occurs at iteration 60 with a delay of 20 iterations (100 observations) while the drift detection on feature 23 occurs at iteration 76 with a delay of 36 iterations (180 observations). From this test it is evident that the detection accuracy is strongly related to the discriminating power of the features.  
Figure 3: Dependency degree of the full set of features (red) and the selected features (blue). \(y\) axis represents the dependency degree; \(x\) axis the number of the iteration.  
3.3.4. Test 4. Shift with Highly Dependent Features  
To further investigate this aspect, the fourth test was conducted on a dataset with a larger number of features and observations, where the degree of feature dependency is very high. Features 26 and 35 were swapped from observation 3000 to the end of the dataset. The window is fixed at 200 observations and the overlap is 190 observations, so the drift occurs at the 280 th iteration. Figure 5 shows the plots of the selected features at each iteration without drift (a) and with drift highlighted in red (b).  
Figure 4: Test 3. Selected features at each iteration: (**a**) without drift (**b**) with drift. \(y\) axis represents the iteration number; \(x\) axis the number of the feature.  
As for feature 26, the detection occurs at iteration 296 with a delay of 16 iterations (80 observations) while the drift detection on feature 35 occurs at iteration 297 with a delay of 17 iterations (85 observations). In this case the algorithm was able to adapt to the drift in less than half of the window.
## 2 Methodology  
For this study, data from 1216 multi-stage hydraulically fractured horizontal wells from five shale formations in the United States (US) were collected. All the wells were active between 2010 and 2018, recently abandoned. All the data used were from publicly reported sources [16] and included both completion and production data of horizontal and multi-stage hydraulically fractured wells. The type of production data collected was the monthly production data of horizontal and multi-stage hydraulically fractured wells that were producing for at least three years. The estimated ultimate recovery (EUR) values used in this study were the cumulative oil and gas quantities produced by a well until it reached its economic limit assigned by the operator, which was estimated as the last reported flow rate when the well was reported to be in a \"shut-in\" status and abandoned. To capture the entire production decline of a well, only wells that were shut-in after reaching low flow rates (i.e., economic limit) and abandoned were used. The average shut-in production rate for all the wells used in this study was around 800 barrel of oil equivalent (BOE)/month (26 BOE/day). To capture a wider variety of different types of wells and formations, five formations were selected to obtain production and completion data. The five formations were all in the US and varied in terms of the geology and the type of produced fluids. Two of the formations, the Barnett and Haynesville, mainly had gas wells. Another two formations, the Eagle Ford and Bakken, mainly had oil wells. The remaining formation, the Niobrara, was known to have liquid-rich gas wells. Table 1 shows the number of wells collected and used from each formation. Performing data analysis on a large number of datasets required automation. Therefore, all the steps in this work were performed using a computer program developed using a MATLAB code.  
After testing several DCA models against the production data available, it was observed that the SEPD model and the Arps hyperbolic decline model showed the most promising results in matching actual production data of 1216 hydraulically fractured horizontal wells from five different formations. Therefore, these two DCA methods were used perform production history matching and forecasting.
# Machine Reading Comprehension Model Based on Fusion of Mixed Attention
## 2 Problem Formulation and Research Motivation
### Background and Investigation of Machine Reading Comprehension
#### 2.2.5 Future Research Directions  
The model proposed in the article, based on BERT and hybrid attention mechanisms, exhibited impressive performance on the DuReader dataset. Future research endeavors may contemplate the integration of external knowledge bases, such as knowledge graphs, and the incorporation of inference algorithms to enhance the model's performance across diverse datasets.
## 3 Results and Discussion
### Spectral Characteristic Analysis and Pretreatment
Data acquisition instruments and dimensions are inconsistent, so dimensionality reduction is adopted according to Equation (7) to normalize data after second derivative pretreatment, in order to improve the model recognition accuracy. \[X=\frac{x-\overline{u}}{\sigma} \tag{7}\]  
\(X\) is new spectral vector after normalization, \(\sigma\) is the second derivative matrix of spectral, \(u\) is the second derivative spectra mean value of sample, and \(\sigma\) is the standard deviation of the second derivative spectral variables. The representative spectrum of normalized data is shown in Figure 4, in which Figure 4 is within the range of 500-1000 nm, and Figure 4 is within the range of 1000-2500 nm. It is shown in Figure 4 that the trend of the normalized spectra of the five types of representative blades in the two spectral ranges is basically similar. In Figure 4, in the range of 650-750 nm, the reflectance decreases from the maximum value to the minimum value. Compared with normal leaves, the reflectance spectrum of other leaves shows a right-shift tendency, which may be related to the absence of chlorophyll. The main reason why the reflectance of the five representative leaves in Figure 4 decreased from the maximum value to the minimum value near 1900 nm may be the change of water. Figure 3: Representative spectra of 5 kinds of leaves after 2 derivative.
# Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System
## 4 Experiment and Analysis
### Accuracy Analysis of Regional Atmosphere Modeling and Interpolation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**B1C+B2a (ION)**} & \multicolumn{3}{c}{**B1C+B2a (TRP)**} & \multicolumn{3}{c}{**B11+B3I (ION)**} & \multicolumn{3}{c}{**B11+B3I (TRP)**} \\ \hline
**PRN** & **CZ** & **JX** & **DIFF** & **CZ** & **JX** & **DIFF** & **CZ** & **JX** & **DIFF** & **CZ** & **JX** & **DIFF** \\ \hline B24 & 0.006 & 0.008 & 0.005 & 0.005 & 0.011 & 0.007 & 0.01 & 0.02 & 0.01 & 0.006 & 0.017 & 0.012 \\ B25 & 0.006 & 0.01 & 0.006 & 0.011 & 0.009 & 0.007 & 0.013 & 0.008 & 0.014 & 0.025 & 0.012 \\ B26 & 0.008 & 0.012 & 0.006 & 0.016 & 0.022 & 0.01 & 0.006 & 0.013 & 0.01 & 0.022 & 0.034 & 0.015 \\ B33 & 0.007 & 0.01 & 0.005 & 0.029 & 0.022 & 0.011 & 0.009 & 0.016 & 0.009 & 0.028 & 0.02 & 0.017 \\ B34 & 0.009 & 0.01 & 0.007 & 0.01 & 0.009 & 0.006 & 0.012 & 0.014 & 0.006 & 0.017 & 0.022 & 0.011 \\ B35 & 0.006 & 0.014 & 0.01 & 0.028 & 0.027 & 0.012 & 0.008 & 0.015 & 0.012 & 0.014 & 0.019 & 0.016 \\ B39 & 0.004 & 0.006 & 0.003 & 0.004 & 0.007 & 0.004 & 0.005 & 0.01 & 0.006 & 0.005 & 0.012 & 0.008 \\ B41 & 0.01 & 0.018 & 0.011 & 0.015 & 0.016 & 0.013 & 0.013 & 0.022 & 0.015 & 0.017 & 0.024 & 0.013 \\ B42 & 0.008 & 0.013 & 0.008 & 0.037 & 0.035 & 0.01 & 0.012 & 0.021 & 0.011 & 0.033 & 0.032 & 0.014 \\ B44 & 0.005 & 0.011 & 0.008 & 0.005 & 0.015 & 0.013 & 0.007 & 0.015 & 0.01 & 0.006 & 0.018 & 0.014 \\ AVG & 0.007 & 0.011 & 0.007 & 0.016 & 0.018 & 0.010 & 0.009 & 0.016 & 0.010 & 0.016 & 0.022 & 0.013 \\ \hline \hline \end{tabular} Note: B40 is the reference satellite of this period; AVG represents the mean value of the corresponding index RMS of each satellite; CZ modeling value /JX reference value/DIFF mutual difference of two values.  
\end{table} 
Table 5: RMS Statistics of atmospheric modeling values and reference values at Station SC17 under different frequency signal combinations (m). baselines around the main reference station, which is essentially equivalent to smoothing the atmospheric information of each baseline, from the time series of the B33 satellite given in Figure 7, it can also be seen that the modeling value should be smoother. Secondly, the statistical results of all indicators in the BIC+B2a combination are better than those in the B1I+B3I combination, which is consistent with the analysis results of atmospheric correction in Section 4.1. The final RMS values of model value (CZ), reference value (JX), and mutual difference of two values (DIFF) of ionosphere and troposphere were B1C+B2a (0.007 m, 0.011 m, 0.007 m, 0.016 m, 0.018 m, 0.01 m), B1I+B3I (0.009 m, 0.016 m, 0.01 m, 0.016 m, 0.022 m, 0.013 m).
# Pathophysiologyiology and Treatment of Soriasis: From Clinical Practice to Basic Research
## 4 Diagnosis and Clinical Treatment of Psoriasis
### Clinical Treatment of Psoriasis
#### 4.2.3 Topical Therapies for Psoriasis  
Topical therapy remains an essential treatment approach for psoriasis, especially for patients with mild to moderate disease. These medications can be applied directly to affected skin areas, circumventing the liver's first-pass effect, offering rapid efficacy, enhancing patient adherence, reducing adverse effects, and enabling controlled, flexible dosing. Common topical agents include emollients, moisturizers, vitamin D3 derivatives, corticosteroids, calcineurin inhibitors, and tar preparations [107]. Combination formulations, such as calcipotriol with betamethasone, clotasol propionate with retinoids, and taxarotene with betamethasone, have been developed to enhance therapeutic efficacy, reduce adverse effects, and improve patient convenience. However, long-term use of topical medicine may lead to local adverse reactions, such as skin atrophy and capillary dilation. Calcipotriol, a vitamin D analog, binds to the vitamin D receptor in keratinocyte nuclei to inhibit their proliferation. However, its long-term use is associated with local skin reactions, which often compromise patient adherence. Additionally, approximately 20% of patients fail to respond to calcipotriol treatment, highlighting its limited efficacy in certain cases [91]. Coal tar exhibits anti-inflammatory, antimicrobial, antipruritic, and cytostatic properties, making it effective for treating psoriasis. Long-term exposure to coal tar can cause allergic dermatitis, follicilitis, occupational acne, epidermal atrophy, and hyperpigmentation. Skinirritation, phototoxic reactions, and burns are the common adverse effects [92]. Topical corticosteroids, such as fluticasone propionate and montasone furoate, are widely used in the treatment of psoriasis due to their effectiveness in relieving symptoms. However, their use may lead to adverse effects. Clinical trials involving fluticasone propionate reported only local cutaneous reactions, which affected up to 19% of patients. These included pruritus, burning, dryness, stinging, folliculitis, irritation, or exacerbation of the dermatological condition [93; 94; 95]. Topical tacrolimus serves as an alternative to corticosteroids by inhibiting cytokine production by blocking the activation of the nuclear factor of activated T cells (NF-AT). Tacrolimus does not impair collagen synthesis, avoiding the risk of skin atrophy. However, its efficacy in treating plaque-type psoriasis is limited, likely due to poor penetration through psoriatic plaques [96]. For patients with widespread lesions, topical application may not be practical [108].
# Energy Optimisation of Industrial Limestone Grinding Using ANN
## 5 Evaluation Indicators and Process Parameters
### Measured Process Parameters  
The key process variables recorded by the SCADA system were used to develop and validate the proposed energy indicators. Table 1 summarizes the selected parameters and their respective notations and units.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**No.** & **Description** & **Symbol** & **Unit** \\ \hline
1 & Pressure before the mill & \(P_{\text{in}}\) & mbar \\ \hline
2 & Hydraulic system pressure (lifting) & \(P_{\text{hyd}}\) & bar \\ \hline
3 & Burner load & \(L_{\text{burner}}\) & \% \\ \hline
4 & External separator speed & \(n_{\text{sep,ext}}\) & rpm \\ \hline
5 & Internal separator speed & \(n_{\text{sep,int}}\) & rpm \\ \hline
6 & Screw conveyor motor speed & \(n_{\text{conv}}\) & rpm \\ \hline
7 & Pressure before the main fan & \(p_{\text{fan,in}}\) & mbar \\ \hline
8 & Main fan motor power & \(P_{\text{fan}}\) & kW \\ \hline
9 & Mill motor power & \(P_{\text{mill}}\) & kW \\ \hline
10 & Main fan vibration (right side) & \(v_{\text{fan}}\) & mm/s \\ \hline
11 & Gas damper 1 position & \(a_{g1}\) & \% \\ \hline
12 & Gas damper 2 position & \(a_{g2}\) & \% \\ \hline
13 & Filter damper position & \(a_{\text{fit}}\) & \% \\ \hline
14 & Limestone mass flow rate to the mill & \(\dot{m}\) & Mg/h \\ \hline
15 & Air flow before the main fan & \(Q_{\text{air}}\) & m\({}^{3}\)/h \\ \hline
16 & Differential pressure across the mill & \(\Delta p\) & mbar \\ \hline
17 & Average cylinder position & \(x_{\text{cyt}}\) & mm \\ \hline
18 & Temperature before the mill & \(T_{\text{in}}\) & \({}^{\circ}\)C \\ \hline
19 & Temperature after the mill & \(T_{\text{out}}\) & \({}^{\circ}\)C \\ \hline
20 & Mill shaft vibration & \(v_{\text{mill}}\) & mm/s \\ \hline
21 & Moisture content of ground limestone (product) & \(w\) & \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of selected measured process parameters.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 4 Experiment
### Qualitative Evaluation  
The experimental results on various datasets are compared to the state-of-the-art low-light image enhancement methods to demonstrate the better results of the SGPC, as shown in Figures 3 and 4. According to experimental findings, our method produces visually appealing results in terms of brightness, chromaticity, and naturalness. When there are under- and over-exposure regions in an image, our method can effectively restore dark areas while preserving the visual quality from bright parts. This is ensured by a luminance correction guide that employs low-light images as enhancement models. While RetinexNet [35] successfully recovers texture information in dark areas, it tends to amplify noise, yielding severely degrading local details. By contrast, as illustrated in the sky region of Figure 4, our proposed method, supported by PGF, smooths noise while preserving texture details. Although DRBN [36] performs well under supervised evaluation metrics, their training processes are sensitive to data distribution, resulting in lower generalization capabilities for such models.  
Figure 3: Visual comparisons on a testing example from MEF [37] dataset among various LLIE methods, including traditional methods like LECARM [31], SDD [32], and GCP [33], supervised methods like RetinexDIP [34], RetinexNet [35], DRBN [36], and unsupervised methods like Zero-DCE [20], RUAS [18], SCI [19], and PairLIE [17], as well as our proposed method.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 5 Details of Prototype System
### Basic Components
#### 5.3.3 Dispatch Service  
The dispatch service matches passenger requests available taxis based on proximity. It subscribes to MQTT messages containing passenger requests and, upon receiving one, spawns lightweight Elixir processes to concurrently calculate proximity for all idle taxis. This concurrent approach significantly reduces matching computation time. Additionally, we plan to implement load-balancing algorithms in the future.  
The dispatch module publishes the assignment result via MQTT to the assigned driver's topic. If no drivers are available, it sends a notification to the passenger about the failed assignment. Leveraging Elixir's efficient concurrency and distributed communication primitives, this module enables fast and reliable order dispatching.
## 3 Imaging Attitude Control
### Coordinate Frames
#### 3.1.3 Target (T) Frame  
This frame defines the target imaging attitude and has its origin \(O_{T}\) coincident with \(O_{N}\). \(\sigma\) is the direction of the aircraft's velocity. Rotating the N frame about \(Z_{N}\) by \(\sigma\) will result in the target frame, while the target pitch angle and target roll are zero degrees.
## 1 Introduction  
As a forward error correction (FEC) code, low-density parity-check (LDPC) code [1] has an excellent performance close to the Shannon limit. Due to its exploitable parallelism, LDPC decoder was easily implemented on field-programmable gate array (FPGA) devices [2].  
Among various LDPC codes, a quasi-cyclic (QC) LDPC code exhibits high facility in structural routing and memory addressing. QC-LDPC code has a parity-check matrix (PCM) that is composed of all-zero submatrices and circularly shifted identity submatrices. PCM can be simply represented by a base graph matrix. Owing to these advantages, QC-LDPC is now widely applied in many communication standards (ITU [3], DVB-S2X [4], WiMAX [5], and 5G NR [6]).  
With the increase in communication rate, LDPC decoder, as a key component, needs to increase the throughput. The methods involve reducing the iteration number together with the decoding cycles per iteration and increasing the operating frequency [7].  
In order to decrease the number of iterations, the layered decoding schedule [8] is widely applied since it shows twice decoding convergence for the same decoding performance compared with the flooding decoding schedule [9].  
The decoding cycle per iteration can be reduced by improving the processing parallelism. Considering the tradeoff between the throughput and hardware utilization, the common approach is to use the partially parallel architecture [10]. The parallelism is usually equal to the lifting size (Z), which is the size of submatrix in PCM.  
Higher operating frequency can be achieved by inserting more pipeline stages. Nevertheless, this will increase the probability and number of pipeline conflicts. Pipeline conflictswill impact the update of log-likelihood ratios (LLRs), result in the loss of decoding performance, and increase extra iteration numbers for the same decoding performance.
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 7 Results and Discussion
### Unbalanced Load Case Study  
For more validation of the proposed configuration, the system has been tested under the case of unbalanced load. In this case of study, the load on phase C has been assumed to be changed at 1 s by 3 s, and the current at the grid side with imbalanced load is shown in Figure 21. The effectiveness of the proposed control scheme based on the optimization technique of HHO is examined. The voltages and currents at the load side are shown in Figure 22. Figure 23 shows the grid side voltages and currents, which indicate a balance in the grid currents and voltages. Moreover, Figure 24 shows the DC link voltage of the MMC for this case of study. As observed from the figures, in the case of imbalanced load, the proposed MMC STATCOM with the optimal design based on the optimization algorithms was able to maintain the voltage and currents as balanced as possible. Moreover, the maintenance of the voltage in the DC-link, constant without fluctuations, was also desired, and it has been achieved as observed in Figure 24.  
Figure 19: The reactive and active power with connection to the MMC STATCOM. (**a**) Reactive power; (**b**) Active power.  
Figure 20: Phase voltage converters.  
Figure 21: The current at the grid side with an imbalanced load.  
Figure 22: The magnitude voltage and current load with an imbalanced load.
# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 7 Limitations and Future Work  
EdgeSAM operates with an input size of 1024 \(\times\) 1024, resulting in considerable computational requirements. While the use of RepViT facilitates re-parameterization during inference to mitigate computational overhead, the overall model complexity remains high. Furthermore, the heterogeneity in imaging equipment, such as variations in microscopes, cameras, and staining protocols across different hospitals, leads to inconsistencies in the acquired sperm images. These discrepancies present challenges for achieving consistent image standardization and may impact the generalizability of the model across diverse clinical settings.  
In the future, we plan to reduce the model's parameter size and input image resolution to decrease computational complexity, thereby enabling deployment on resource-constrained devices. Additionally, we will investigate approaches for standardizing the color style of sperm images or refining the training methodology to improve the robustness and generalization capability of the classification model.
## 4 Heat Transport Pipeline Damage Probability Model
### ML Model
#### 4.3.3 LightGBM (Light Gradient Boosting Machine)  
LightGBM (LGBM) employs a boosting technique similar to XGB. It is a tree-based, high-performance algorithm commonly used to determine feature importance in regression and classification tasks. By utilizing a subset of the data for rapid computation and feature reduction, the model achieves improved computational efficiency and demonstrates effectiveness in handling imbalanced datasets [34; 35]. It is efficient in handling large datasets.  
The model calculates the loss function using the cross entropy. The cross entropy is calculated using Equation (7). N is the number of samples, K is the number of classes, \(y_{ij}\) is the binary variable that indicates whether the i-th sample belongs to the j-th class, and \(P_{ij}\) is the probability that the i-th sample will belong to the j-th class. LGBM derives results through learning by updating the model while minimizing the cross entropy received from the previous model [36].  
\[\text{CE}=\frac{1}{N}{\sum}_{i=1}^{N}{\sum}_{j=1}^{N}{y}_{ij}\text{log}\left( P_{ij}\right) \tag{7}\]