esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.2.5 Supervised Classification and Accuracy Assessment  
LULC mapping was conducted for 1984 and 2018 using a support vector machine classifier in a supervised classification approach and 70% of ground truth data that was collected in a field survey as well as from Google Earth. Support Vector Machine (SVM) is a classification system derived from statistical learning theory that provides good classification results from complex and noisy data. It performs supervised classification on images using a support vector machine to identify the class associated with each pixel. It separates the classes with a decision surface that maximizes the margin between the classes. The radial basis function (RBF) kernel was selected for classification since it produces the best accuracy [24]. The non-linear and complex nature of the data makes RBF suitable for classification. The mathematical representation of the RBF is shown in Equation (1):  
\[\mathrm{K(x_{i},x_{j})}=\mathrm{exp}\left(-\gamma\big{|}\big{|}\mathrm{x_{i}- x_{j}\big{|}}\big{|}2\right),\ \gamma\ >0 \tag{1}\]  
where \(\gamma\) is the gamma term representing the width of the kernel function, \(\mathrm{x_{i}}\) belongs to Rn, which is an n-dimensional vector, and \(\mathrm{x_{j}}\) belongs to {1, +1}1 representing the label of each class i = 1,...., k. \(\gamma\) is a user-controlled parameter, and its correct definition significantly increases the accuracy of the SVM solution. Several authors have demonstrated the effectiveness of the SVM algorithm in mapping urban areas, especially in areas where training data is limited, as is the case in Harare [34]. Post-classification accuracy assessment was conducted using an independent set of ground truth data (30% of collected samples/GPS points).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Date** & **Path/Row** \\ \hline
**Landsat 5 TM** & 22 June 1984 & 170/072 \\
**Landsat 8** & 20 June 2018 & 170/072 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Landsat images used in the study.
# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Year** & **Data Type** & **Main Contribution** & **Detection Type** &
\begin{tabular}{c} **Nature of** \\ **Abnormal Data** \\ \end{tabular} \\ \hline
[20] & 2019 & \begin{tabular}{c} Mixed (images, \\ time-series, text) \\ \end{tabular} & \begin{tabular}{c} Survey on deep learning for \\ general anomaly detection \\ \end{tabular} & \
\begin{tabular}{c} Anomaly \\ detection \\ \end{tabular} &
\begin{tabular}{c} Anomalies \\ within data or \\ sequence \\ \end{tabular} \\ \hline
[21] & 2020 & \begin{tabular}{c} Images (CIFAR, \\ TinyImageNet) \\ \end{tabular} & \begin{tabular}{c} OOD detection without using \\ any OOD data via \\ confidence-based methods \\ \end{tabular} & \
\begin{tabular}{c} OOD \\ classification \\ \end{tabular} &
\begin{tabular}{c} Unseen semantic \\ categories \\ \end{tabular} \\ \hline
[22] & 2021 & \begin{tabular}{c} Images (ImageNet, \\ SVHN) \\ \end{tabular} & \begin{tabular}{c} Evaluation and analysis of the \\ limits of OOD detection \\ methods \\ \end{tabular} & \
\begin{tabular}{c} OOD \\ classification \\ \end{tabular} &
\begin{tabular}{c} Unseen semantic \\ categories \\ \end{tabular} \\ \hline
[23] & 2021 & \begin{tabular}{c} Time-series (sensors, \\ finance) \\ \end{tabular} & \begin{tabular}{c} Survey of DL techniques for \\ time-series anomaly detection \\ \end{tabular} & \
\begin{tabular}{c} Anomaly \\ detection \\ \end{tabular} &
\begin{tabular}{c} Anomalies \\ within sequences \\ \end{tabular} \\ \hline
[24] & 2021 & \begin{tabular}{c} Time-series (sensors) \\ \end{tabular} & \begin{tabular}{c} Survey of anomaly detection \\ in multivariate time-series \\ \end{tabular} & \
\begin{tabular}{c} Anomaly \\ detection \\ \end{tabular} &
\begin{tabular}{c} Anomalies \\ within sequences \\ \end{tabular} \\ \hline
[25] & 2023 & \begin{tabular}{c} Time-series (ECG, \\ UCR, etc.) \\ \end{tabular} & \begin{tabular}{c} Survey of deep learning \\ approaches for time-series \\ anomaly detection \\ \end{tabular} & \
\begin{tabular}{c} Anomaly \\ detection \\ \end{tabular} &
\begin{tabular}{c} Anomalies \\ within sequences \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related studies on out-of-distribution (OOD) detection. Building on this, the present study explores a more challenging setting in which OOD classes exhibit patterns that closely resemble those of the target classes, thereby increasing the difficulty of discrimination. OOD classes were designed to have similar patterns to the target classes and were tested while they were untrained. The proposed approach focuses on evaluating various training strategies to achieve reliable recognition of non-target activities. The remainder of this paper is organized as follows. The collected data and proposed method are described in Section 2, followed by the presentation of experimental results and related discussions in Section 3. Finally, the study concludes with Section 4.
## 4 Results and Analysis
### Trend Prediction of Dual-System Coupling Coordination Degree  
#### 4.3.1. GM (1, 1) Model Testing  
The accuracy of the GM (1, 1) model constructed in Table 7 was tested, and Matlab software was used to calculate the predicted value, posterior ratio C, and small error probability \(p\). The results are shown in Table 5. The C value and \(p\) value are combined to judge each model. The accuracy of the models reached the qualified level, and a gray prediction can be made.
# Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives
## 4 Discussion
### Spectroscopic Imaging  
In their in vivo study, [PERSON] et al. [108] acquired PA images at 756 nm and 797 nm and using these PA images they evaluated the oxygen saturation of hemoglobin (SO\({}_{2}\)) and total hemoglobin concentration (HbT) of the lesions. [PERSON] et al. [47] developed a dual-mode PA and US imaging system which acquired PA images at 756 nm and 797 nm. [PERSON] et al. [39] acquired in vivo PA images of 22 malignant cases at 756 nm and 797 nm. [PERSON] et al. [110] evaluated the feasibility of functional PAT at 733 nm, 775 nm, and 808 nm for high-resolution detection and characterization of breast cancer on six lesions. [PERSON] et al. [122] performed a clinical study on 6 cases of healthy breasts, five cases of IBCs and two cases of DCIS with their handheld dual mode PA-US imaging system and acquired PA images at five different wavelengths 700 nm, 730 nm, 760 nm, 800 nm, and 850 nm. Using multiwavelength PA data, they estimated the concentration of deoxygenated hemoglobin (Hb), oxygenated hemoglobin (HbO\({}_{2}\)), HbT, and SO\({}_{2}\). They observed that for IBC patients, the concentration of Hb, HbO\({}_{2}\), HbT corresponding to diseased tissue increased compared to healthy breast tissue while, for DCIS cases, these parameters corresponding to diseased tissue varied slightly relative to healthy patients. [PERSON] et al. [123] performed a pilot clinical study on three cases of healthy breasts, two cases of IBCs and eight malignant cases of non-specific breast cancers with their PAT system and acquired PA images at 28 different wavelengths. Using multiwavelength PA data, they estimated the concentration of Hb, HbO\({}_{2}\), HbT, lipid, and water. For a wide range of tumor sizes, they found that HbT concentrations at the tumor periphery are higher than that of HbT concentrations within tumors. [PERSON] et al. [124] performed a spectroscopic PA imaging at three wavelengths 700 nm, 800 nm, and 850 nm using their integrated PA-US imaging system to detect physiological changes in the breast during the menstrual cycle. They included 22 pre-menopausal and eight post-menopausal volunteers in their study and found that PA-US intensity values in the secretory phase were significantly higher than that of the proliferative phase.
## 3 Controller Design
### Proof of Stability  
**Theorem 1. **_Consider the closed loop pneumatic servo system consisting of the plant Equation (6), the nonlinear control law Equation (25), the pressure observers Equation (7), and the parameter and disturbance estimation algorithm Equations (16)-(18) under the Assumption 1-2. If there exists a set of the feedback gains and the filter constants satisfying \(\gamma=\min\biggl{\{}k_{1}-1,k_{2}-\frac{1}{2}-\overline{A}_{\rm a},k_{3}- \frac{\overline{A}_{\rm a}}{2},\frac{1}{\tau_{1}}-1,\frac{1}{\tau_{2}}-\frac{ 1}{2}-\frac{\overline{A}_{\rm a}}{2}\biggr{\}}>0\), then the closed loop system is uniformly and ultimately bounded._  
**Proof. ** Consider the following Lyapunov function candidate:  
\[ \begin{array}{l}V_{\rm c}=V_{\rm cs}+V_{\rm cz}+V_{\rm\Theta},\ V_{\rm cs}= \frac{1}{2}\varepsilon_{1}^{2}+\frac{1}{2}\varepsilon_{1}^{2}+\frac{1}{2} \varepsilon_{2}^{2}+\frac{1}{2}\varepsilon_{3}^{2},\ V_{\rm cz}=\frac{1}{2} \varepsilon_{1}^{2}+\frac{1}{2}\varepsilon_{2}^{2}+\frac{1}{2}\varepsilon_{3} ^{2},\\ V_{\rm\Theta}=\frac{1}{2}\gamma_{1}^{-1}\overline{b}^{2}+\frac{1}{2}\gamma_{2 }^{-1}\overline{F}_{\rm f}^{2}+\frac{1}{2}\gamma_{3}^{-1}\overline{d}_{\rm f }^{2}\end{array}  \tag{27}\]  
where \(z_{1}=x_{2\rm d}-\overline{x}_{2\rm d}\), \(z_{2}=\zeta_{\rm d}-\overline{\zeta}_{\rm d}\), \(z_{3}=\zeta_{\rm d}-\overline{\zeta}_{\rm d}\). Substituting Equation (12) into Equation (11) gives:  
\[\dot{e}_{1}=s_{1}+z_{1}-k_{1}e_{1}. \tag{28}\]  
Substituting Equation (15) into Equation (14) and noting \(\dot{s}_{3}=s_{2}+\zeta_{\rm d}=s_{2}+z_{2}+\overline{\zeta}_{\rm d}\) yields:  
\[\dot{s}_{1}=\overline{A}_{\rm a}(s_{2}+z_{2})-\frac{1}{m}\Big{[}\overline{b}x_ {2}+\overline{F}_{\rm f}S_{\rm f}(\dot{x})+\overline{d}_{\rm f}\Big{]}+ \overline{A}_{\rm a}\overline{x}_{3}-k_{2}s_{1}-\frac{h_{1}^{2}}{4\eta_{1}}s_{1}, \tag{29}\]  
where \(\overline{b}=b-\hat{b}\), \(\overline{F}_{\rm f}=F_{\rm f}-\hat{F}_{\rm f}\), \(\overline{d}_{\rm f}=d_{\rm f}-\hat{d}_{\rm f}\). Figure 3: Block diagram of the control system. Substituting Equation (22) into Equation (21) and noting \(\hat{x}_{4}=s_{3}+\zeta_{\rm d}=s_{3}+z_{3}+\overline{\zeta}_{\rm d}\) yields:  
\[\dot{s}_{2}=\frac{RT_{s}a}{V_{\rm a}}(s_{3}+z_{3})-k_{3}s_{2}. \tag{30}\]  
Substituting Equation (25) into Equation (24) gives:  
\[\dot{s}_{3}=-k_{4}s_{3}. \tag{31}\]  
Therefore, the time derivative of \(V_{1}=V_{\rm cs}+V_{\rm\theta}\) is:  
\[ \begin{array}{rl}\dot{V}_{1}=&-k_{1}e_{1}^{2}-k_{2}s_{1}^{2}-k_{3}s_{2}^{2}- k_{4}s_{3}^{2}+e_{1}s_{1}+e_{1}z_{1}+\overline{A}_{\rm a}(s_{1}s_{2}+s_{1}z_{2})+ \frac{RT_{s}a}{V_{\rm a}}(s_{3}+z_{3})\\ &+\gamma_{1}^{-1}\overline{\dot{b}}\left[\dot{b}-(-\frac{\gamma_{1}}{m}x_{2}s_ {1})\right]+\gamma_{2}^{-1}\overline{F}\left[\dot{F}_{\rm f}-(-\frac{\gamma_{ 2}}{m}S_{\rm f}(x_{2})s_{1})\right]+\gamma_{3}^{-1}\overline{\dot{d}}\left[ \dot{\tilde{d}}_{\rm f}-(-\frac{\gamma_{3}}{m}s_{1})\right]\\ &+s_{1}\left(-\frac{\tilde{h}_{1}^{2}}{4\gamma_{1}}s_{1}-\frac{1}{m}\left[ \overline{\dot{w}}x_{2}+\overline{F}_{\rm f}S_{\rm f}(\dot{x})+\overline{ \dot{d}}_{\rm f}\right]+\overline{A}_{\rm a}\overline{x}_{3}\right\}\end{array}
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
###### Abstract  
This paper presents an innovative approach to wind energy forecasting through the implementation of an extended long short-term memory (xLSTM) model. This research addresses fundamental limitations in time-sequence forecasting for wind energy by introducing architectural enhancements to traditional LSTM networks. The xLSTM model incorporates two key innovations: exponential gating with memory mixing and a novel matrix memory structure. These improvements are realized through two variants, i.e., scalar LSTM and matrix LSTM, which are integrated into residual blocks to form comprehensive architectures. The xLSTM model was validated using SCADA data from wind turbines, with rigorous preprocessing to remove anomalous measurements. Performance evaluation across different wind speed regimes demonstrated robust predictive capabilities, with the xLSTM model achieving an overall coefficient of determination value of 0.923 and a mean absolute percentage error of 8.47%. Seasonal analysis revealed consistent prediction accuracy across varied meteorological patterns. The xLSTM model maintains linear computational complexity with respect to sequence length while offering enhanced capabilities in memory retention, state tracking, and long-range dependency modeling. These results demonstrate the potential of xLSTM for improving wind power forecasting accuracy, which is crucial for optimizing turbine operations and grid integration of renewable energy resources.  
A Article  
1  
Michael W. Hall School of Mechanical Engineering, Mississippi State University, Starkville, MS 39762, USA; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
wind energy forecasting; time series prediction; recurrent neural networks; long short-term memory; extended long short-term memory
## 4 Discussion
Our results indicated that the bacterial diversity in OMW is larger than that of OMP. Furthermore, the most abundant phyla existing in OMW were Bacteroidetes, Tenericutes, Actinobacteria and Firmicutes; and in OMP Proteobacteria phylum was dominating. Previous studies have reported that the main phyla revealed using high-throughput sequencing technologies in different organic wastes including OMW are members of Bacteroidetes, Actinobacteria, Proteobacteriaand Firmicutes [4; 26; 47; 48; 49]. OMW's dominating families were those of Prevotellaceae, Spiroplasmataceae, Atopobiaceae, Lactobacillusaee and Erysipelotrichaceae. Whereas, OMP's most abundant families were Acetobacteriaceae and Holosporaceae. Interestingly, [PERSON] and [PERSON] [50] have found that the Prevotellaceae family existed in the OMW because of the anaerobic condition created in case of harvested olive accumulation (before olive oil extraction). Additionally, the Spiroplasmataceae family was mainly represented by the _Spiroplasma_ sp., which was reported to contribute to the degradation of multiple categories of organic wastes as mentioned by [PERSON] and [PERSON] [51]. The non-H\({}_{2}\) producing bacteria _Olsenella_ sp. was abundantly present in the OMW, and it was identified to turn hexose into propionic acid and acetic acid in food wastes [52]. Other than that, previous works proved the major role of indigenous microbiota in the degradation of OMW hazardous components during the composting process [18; 53; 54; 55]. Lastly, PICRUST was used to identify gene function abundance based on the sequence similarity with the input marker gene. The major bacterial functions were dedicated to crucial functions such as amino acid, carbohydrate and energy metabolisms. Similar findings indicated that bacterial genetic composition was mainly devoted to the vital bacterial functions [28; 56]. Surprisingly, bacterial functions of OMW and OMP involved human diseases (cardiovascular, infectious, and metabolic diseases). Similarly, [PERSON] et al. [57] cited that intense OMW discharge near urban areas affects the water surface leading to human health problems. Concerning the fungal microbiota, we demonstrated that the fungal communities of OMW and OMP were very similar to each other. They were mainly composed of Ascomycota, Basidiomycota and unclassified fungi. These findings accorded with the fungal sequences deposited in GenBank, which indicated that Ascomycota, Basidiomycota and unclassified fungi were mainly abundant in the OMW environments [25]. The predominant species in our study were _Nakazawa molendit-olei, Saccharomyces cerevisiae, Lachancea fermentati, Candida diddensiae, Candida adriatica_ and _Alternaria_ sp. Many of them were widespread in olive fruits, olive oil, or olive mill solid and liquid wastes derived from several countries including Morocco and Italy [58; 59; 60]. Researchers proved that yeasts such as _Saccharomyces_ and _Candida_, and fungi such as _Lachancea_, _Alternaria_ and _Pichia_ were able to reduce multiple recalcitrant compounds, thus contributing to the bioremediation of the olive mill discharges [61; 62; 63].
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 5 Experiments and Evaluation Settings
### Evaluation Settings  
Evaluation Measures  
To evaluate the classification performance of our model, we utilized validation accuracy, precision, F1-score, and recall, as these metrics are suitable for evaluating models on a balanced dataset.  
Accuracy: measures the percentage of correct predictions made by the model. It is calculated by dividing the number of correct predictions by the total number of predictions (Equation (2)). We selected accuracy as the primary metric for identifying the best model, as it reflects the overall performance of the model.  
Accuracy = Total Number of Predictions/Number of Correct Predictions (2)  
Precision: represents the ratio of correctly classified positive samples to the total number of positive predictions, which includes both true positives (TP) and false positives (FP) (Equation (3)).  
Precision = TP/(TP + FP)  
Recall: It measures how effectively the model identifies all positive instances in the dataset. It is the ratio of true positive predictions to the total number of actual positives, which includes both true positives (TP) and false negatives (FN) (Equation (4)).  
Recall = TP/(TP + FN)  
F1 Score: it combines the precision and recall of a model, providing a balanced measure that accounts for both metrics (Equation (5)).  
F1 Score = 2 \(\times\) (Precision \(\times\) Recall)/(Precision + Recall)  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **Hyperparameters** \\ \hline SVM & C: 1.0, kernel: ‘rbf’, degree: 3 (used only for ‘poly’ kernel), gamma: ‘scale’, coef0: 0.0, shrinking: True, probability: False, tol: 1 \(\times\) 10\({}^{-3}\), max_iter: \(-1\), class\_weight: None, random_state: None \\ \hline LR & penalty: \(\Omega\)2, dual: False, tol: 1 \(\times\) 10\({}^{-4}\), C: 1.0, fit_intercept: True, intercept_scaling: 1, class\_weight: None, solver: ‘lbf’s’, max_iter: 100, multi_class: ‘auto’, n_jobs: None, random_state: None, verbose: 0, warm_start: False, l1_ratio: None \\ \hline DT & criterion: ‘gini’, splitter: ‘best’, max_depth: None, min_samples_split: 2, min_samples_leaf: 1, min_weight_fraction_leaf: 0.0, max_features: None, random_state: None, max_leaf_nodes: None, min_impurity_decrease: 0.0, class\_weight: None, ccp_alpha: 0.0 \\ \hline NB & priors: None, var_smoothing: 1 \(\times\) 10\({}^{-9}\) \\ \hline \end{tabular}
\end{table}
Table 2: The default hyperparameters for SVM, LR, DT, and NB algorithms.
## 2 Methods  
We predominantly used DWI and T2 image sequences from mp-MRI images in this study. Our main goal was to classify patients with prostate cancer as clinically severe and clinically less severe. Figure 1 illustrates the main framework of our proposed method, which has 3 main parts. First, we rigidly aligned [29; 30] the T2 images with the DWI images in the planar spatial domain to correct the misalignment of the prostate region due to image sequences and biases with different MRI contrasts in the acquisition process. We then cropped out each T2 image with the DWI image containing the entire prostate region using an automatic method used for prostate region boundary detection, and then the cropped images were pixel-normalized. Third, we used the aligned and cropped T2 and DWI images to create a new 3D image sequence of the prostate, and we fed the new 3D image sequence into the 3D-CNN and obtained two outputs. The details of each step are presented in the following sections.  
Figure 1: The framework of the proposed method consists of four key steps: (1) rigid multiparameter (DWI, T2) image alignment, (2) prostate region cropping, and (3) building a new 3D image sequence for input into a 3D-CNN.
# I-BIM Applied in Railway Geometric Inspection Activity: Diagnostic and Alert
## 5 Dynamo Visual Programming
### Development of the Dynamo Script  
Dynamo programming presents a friendly interface environment. Dynamo's interactive interface allows users to handle the selection and connection of action nodes, the application of a wide variety of algorithms responsible for managing a distinct type of functions and the creation of facility add-ins. The programming process is intuitive and visual, but the script algorithm generation can become complex with a larger volume of operations, requiring the application of text-based programming languages like Python to introduce additional flexibility. Dynamo uses the IronPython command, enabling Python scripts to interact with the Revit plug-in.  
Specific Python nodes were created and applied in the elaboration of the script. These Python nodes supported the data input action in the BIM model and the data handling in order to perform the geometric inspection, diagnostic and alert. For this process, several stages were considered:  
* As the available inspection data were organized in an Excel format, the Python node used to import the listed values into the model was the \"Data.Import.Excel\";
* Next, the selection of the railway elements was realized, using the node \"Family Types\" related to the rail components included in the model;
* The imported data were then correctly associated with the respective parameter of each selected element, through the application of the \"Element.SetParameterByName\" and \"List.GetItemAtIndex\" nodes, allowing data to be connected by index;
* An important function required for the script was the inspection of the value evaluation requiring the analyses of the parametric values, confronted by the tolerance standard values, and the attribution of a category concerning the alert level. Figure 10 shows the routine program related to the value evaluation of a parameter and the identification of the respective alert level (unchanged, alert and intervention).  
* The final step concerned the association of a color to each alert level, allowing users to visualize it over the rail I-BIM model. In Dynamo, the colors were assigned using Alpha, Red, Green and Blue (ARGB) inputs, where Alpha represented the color transparency and the others the basic colors. In the routine listed in Figure 11, each color was assigned to its respective alert level. The green color denoted no action  
Figure 10: Function related to the alert level for the track gauge parameter.  
required, yellow was the alert level, orange was the intervention level and red was an immediate action level.