esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation Objectives
### Domain-Driven Evaluation Studies  
The third category, domain-driven, focuses on applying explainability in specific domains or application areas. Studies in this category [34; 41; 51; 59; 60] address how to deploy explainable systems in real-world scenarios and practical applications, such as news recommendation systems [47], Facebook's news feed algorithm [55], deceptive review detection [31], diagnostic decision support systems for both laypeople [49] and experts [77], explainable learning platforms [56], recommendation of scientific publications [58], and even applications concerning autonomous vehicles [67], music, and product recommendation systems [26].  
In contrast to concept-oriented research, domain-oriented studies address the specific application context and emphasize the relevance of the evaluated factors within the particular application domain. By considering the intricacies and specifics of the application context, domain-based research aims to provide insights that are not only theoretically valuable but also practically applicable and useful in real-world scenarios. For this purpose, domain-independent explanation concepts such as \"what-if\" explanations or feature attribution are adapted for the respective use case, or new concepts are designed and implemented for the specific application [26]. Frequently, different explanatory approaches are also combined within a single application. They also tend to be more holistic than concept-driven studies. In domain-driven studies, users typically should not evaluate isolated explanation concepts; they should instead use applications where explanatory elements are embedded.  
There are also a few ethnographically oriented studies, which focus less on what explainable systems do to users, and more on what users do with explainable systems. For example, [PERSON] et al. [30] investigated the usage and comprehension of interpretability tools (such as the Interpret ML implementation of GAMs and the SHAP Python package) by data scientists, identifying potential issues and misconceptions. Another instance is the study conducted by [PERSON] et al. [29], which examined the experiences of Instagram users affected by the platform's algorithmic block and their need for explanations regarding the decision-making process. Such studies are often less rigorous but possess the highest grade of ecological validity as they do not assess the use of explainable systems under pre-defined tasks in artificial lab conditions.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(I\)} & \multicolumn{3}{c}{**PGA [\(g\)]**} & \multicolumn{3}{c}{**VHpga**} \\ \cline{2-7}  & \(-\mathbf{1\sigma}\) & **Median** & \(+\mathbf{1\sigma}\) & \(-\mathbf{1\sigma}\) & **Median** & \(+\mathbf{1\sigma}\) \\ \hline V \({}^{\circ}\)MCS & 0.0113 & 0.0118 & 0.0122 & 0.530 & 0.491 & 0.455 \\ VI \({}^{\circ}\)MCS & 0.0243 & 0.0252 & 0.0261 & 0.582 & 0.540 & 0.500 \\ VII \({}^{\circ}\)MCS & 0.0521 & 0.0540 & 0.0560 & 0.640 & 0.593 & 0.550 \\ VIII \({}^{\circ}\)MCS & 0.1115 & 0.1157 & 0.1201 & 0.703 & 0.652 & 0.604 \\ IX \({}^{\circ}\)MCS & 0.2390 & 0.2480 & 0.2573 & 0.773 & 0.716 & 0.664 \\ X \({}^{\circ}\)MCS & 0.5122 & 0.5315 & 0.5514 & 0.849 & 0.787 & 0.729 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Vertical empirical estimates of PGA and empirical VHpga computed by Equation (1) for different macroseismic intensities. We can see from Table 2 that the VHpga values differ significantly from the ones suggested by Eurocode 8. However, they are in good agreement with the ratios suggested by the 1981 former Yugoslav regulations [39], shown in Table 3, as well as the ratios suggested by the empirical GMPEs for the former Yugoslavia [22] and California [19].
## 6 Industrial Application of the Developed Approach
### Application of Step 1: Solution Space Structuring  
A summary of the results from applying _step 1_ is shown in Figure 7. First, substep 1.1 derives fixed degrees of freedom from the input strategic network scenario data shown in Figure 6 and the system constraints (_SC01_ to _SC09_). Next, substep 1.2 deals with the clustering of the remaining variable degrees of freedom by simulation input data types according to VDI3633-1:2014-12 [43].  
All necessary input data types (organizational, technical, and system load data) are addressed. Subsequently, substep 1.3 derives a set of sub-solution spaces from grouping variable degrees of freedom into sub-decision problems. Based on the application results, the following five sub-solution spaces were derived.  
* Capacity framework: This sub-solution space sets capacity-related degrees of freedom, such as the _number of production lines_, the _production line shift model_, the _max. output of the production line_, and the _production line flexibility_. For this sub-solution space, _SC01_ and _SC02_ are applied in terms of standard shift models and standard working hours.
* Production line behavior: This sub-solution space deals with the _production line failures_ degree of freedom and is subject to _SC04_ and _SC05_ because only the worst and most realistic cases are considered for the production line behavior.
* Logistics information: This sub-solution space deals with the _number of warehouses_, the _warehouse inventory_, and the _number of customer-supplier-relations_ degrees of freedom and is subject to _SC08_ and _SC07_, as the inventory is only varied between two categories (low and high).
* Network structure: This sub-solution space addresses the _number of production lines_, the _number of warehouses_, and the _customer-supplier-relations_ degrees of freedom and is subject to _SC03_, _SC05_, and _SC07_.
* Product Mix: This sub-solution space addresses the _number of component and product variants_, the _demand for each variant_, and the _BOM_ degrees of freedom and is subject to _SC09_, as only two different product mixes are considered.  
In summary, the sub-solution spaces specifically address operational decisions within the strategic network design, such as inventory dimensioning, the number of production lines, and shift models.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 5 Data Fusion for Site-Specific Crop Parameter Monitoring
### Lai  
LAI is a measure of the photosynthetically active region and is subjected to transpiration at the same time. Estimating LAI with the help of remote sensing systems has been a key objective in precision agriculture [206]. The most common method for the estimation of LAI is the fusion of empirical relationships between vegetation indices (Vis) and LAI data (Table 1) [207; 208; 209; 210; 211; 212]. The fusion of empirical models has been widely adopted for LAI monitoring; however, these models do not show efficient results in all environmental conditions, as their parameters are limited to particular regions [213; 214; 215].  
Although the use of remote sensing systems to provide LAI data has been widely adopted, it shows discontinuous patterns in its spatial and temporal results due to clouds, snow, and technological problems. This has restricted the collection of LAI in crop monitoring, process simulations, and agriculture research [216]. The method involving the fusion of images from multiple sensors provides spatial, spectral, and temporal information of LAI (Table 2) [217]. This discontinuity in spatial and temporal data is caused by a gap in remote sensing data. This gap is addressed by the fusion of high-spatial- and temporal-multisource remote sensing data, which generates high-spatiotemporal-resolution imagery. They are classified into two categories: enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) [218] and unmixing method. ESTARFM was tested by [PERSON] et al. [219], [PERSON] et al. [220] and [PERSON] et al. [221], who all reported the enhancement of the fusion method for complex heterogeneous regions. [PERSON] et al. [219] tested the fusion of spatial and temporal adaptive reflectance fusion models (STARFM) with the ESTARFM with different spatiotemporal features.  
The unmixing method is the process in which the coarse resolution images are disaggregated. However, the disaggregation of coarse images cannot be processed in all conditions due to the spatial variability of surface reflectance. Several studies have reported that the solution to this is to analyze the spectral properties of landcover that shows little similarities [222].
## 7 Conclusions  
In order to assess the seismic hazard in different areas, it is suggested to observe the spatial and temporal correlation of seismic events and monitored AEs. It emerges that AEs behave as earthquake precursors. A statistical method of analysis is proposed, based on the Grassberger-Proaccia integral. It gives the cumulative probability of the events' occurrence in a specific area, considering the AE records and the seismic events during the same period of time. Two important constructions of Italian cultural heritage were considered: the \"Sacred Mountain of Varallo\" and \"Asinelli Tower\". Given the interesting premises that emerged from this study, the Grassberger-Proaccia integral could also be applied more extensively in several monitoring sites belonging to different seismic regions, not only for defining the seismic hazard, but also to handle it as an effective tool for earthquake risk mitigation.  
However, the authors are aware that a rigorous investigation to ascertain the existence of crustal stress crises crossing areas of a few hundred kilometers' radius during the preparation of a seismic event would require simultaneous and numerous operations of AE monitoring sites, adequately placed in the territory.
## 3 IEEE 802.15.4  
To create a standardized protocol stack for constrained networks and devices, the IETF builds further upon the IEEE 802.15.4 standard. The IEEE 802.15.4 standard is maintained by the IEEE 802.15 working group [8] and defines low-data-rate, low-power, and short-range radio frequency transmissions for _wireless personal area networks_ (WPANs). The working group aims to keep the complexity of the standard and the cost of the necessary hardware low, making it suitable for wireless communication among constrained devices such as sensors and actuators. The standard describes a Physical (PHY) layer and a _Medium Access Control_ (MAC) sublayer. These will be discussed in the following two subsections. Furthermore, 802.15.4 has proven to be a popular technology for wireless communication in WPANs and a number of examples that have adopted 802.15.4 are listed in the last subsection.
## 4 Modulation Schemes
### Data Rate  
Data-rate enhancement is a major issue in OCC performance improvements. The majority of existing modulation techniques do not support high-data-rate communication. OOK, the most conventional scheme for OCC, was utilized in [10] achieving a data rate of 896 bps. However, several studies have already demonstrated healthy data rates by introducing new modulation techniques [12; 22]. A data rate of 10 kbps was achieved using a multilevel intensity-modulation (m-IM) technique [30]. The DC-biased optical orthogonal frequency-division multiplexing technique employed by [PERSON] et al. attained a data rate of 55 Mbps [22]. The proposed color intensity modulation combining CSK and multilevel pulse amplitude modulation (PAM) in Reference [12] was able to achieve a data rate of 95 kbps.
## 2 Conceptual Model for Coordinated Automatic Generation Control in a Multimachine Smart System
### Coordinated Automatic Generation Control Strategy  
The proposed strategy emphasizes the coordination of both technical and economic goals:  
* Technical Objectives: The model is designed to manage various technical parameters, including the control of power system oscillations (both local and inter-area), the management of voltage fluctuations, and the minimization of system losses. These technical aspects are critical for maintaining system stability and ensuring reliable power delivery.
* Economic Objectives: In parallel with technical management, the model seeks to optimize generation costs and ensure the efficient use of all available resources within the network. This economic optimization is crucial for reducing operational costs while maintaining high system performance.
* Physical Contingencies: The model includes strategies to address and mitigate physical disruptions within the system, ensuring that both economic and technical objectives are maintained even during unforeseen events.
## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Statistical Methods
#### 3.1.1 Regression Methods  
This set of techniques tries to find the association between a dependent variable (GHI, DNI, DHI solar generated power, etc.) and independent variables (predictors), based on statistical analysis. Depending on a time series treatment, different techniques can be applied: stationary or non-stationary, linear or non-linear. The most relevant techniques studied by researchers are the following:  
Stationary linear models. Studies based on these models usually propose approaches using autoregressive models AR (regress the dependent variable on lags of itself in time period t), or derived more generic models, such as the ARMA autoregressive moving average model (historical values are considered as errors) [37; 112], ARX models (AR with exogenous inputs) [17] or its adaptation VARX (vector ARX) [113], and the ARMAX model (ARMA with exogenous variables) [114].  
Non-stationary linear models. In the analysis of a non-stationary time series, the main ones are as follows: the autoregressive integrated moving average model ARIMA (using data variances and regressions) and SARIMA (Seasonal ARIMA) [115], in which a seasonal component is introduced [116].  
Stationary non-linear models. These can be obtained mainly by means of the Non-linear Autoregressive Models with Moving Average and Exogenous Input (NARMAX) model, which achieves unbiased estimates in the presence of non-linear noise [117].
## 3 Robustness and Reliability: New Challenges in CMR Segmentation
### Challenges to Robust Segmentation
#### 3.3.1 Domain Shift  
Domain shift, or distribution shift, refers to a change in the data distribution between the one observed at training dataset, and the one the model encounters at inference, i.e., when deployed. Domain shift represents a critical risk for supervised deployed models because it has been shown that the inference error increases proportionally to the difference between samples from the two distributions [68]. In a strict sense, domain shifted data do not constitute an invalid input because it is still representative of the phenomenon under study. In this work, we follow a computer system approach where we consider domain shifted data as deviated from the \"specifications\" in which the model is developed or trained. As such, it does not affect reliability. However, the model is expected to perform well even in the presence of the domain shifted data, i.e., they should be robust. In CMR segmentation, this drift can be caused by numerous factors, such as changes in demographics, modalities, acquisition protocols and scanner vendors or simply anatomical variability or, even, an adversarial attack that may alter the statistical properties of the input [69]. The M&Ms challenge [15] was designed to assess the capacity of existing methods to cope with CMR domain shift. The result was an overall drop in performance showing a lack of robustness in existing methods.
# A Nonlinear Method for Characterizing Discrete Defects in Thick Multilayer Composites
###### Abstract  
Discrete defects in thick composites are difficult to detect for the small size and the structure noise that appears in multilayer composites. In this paper, a nonlinear method, called recurrence analysis, has been used for characterizing discrete defects in thick section Carbon Fiber Reinforced Polymer (CFRP) with complex lay-up. A 10 mm thick CFRP specimen with nearly zero porosity was selected, and blind holes with different diameters were artificially constructed in the specimen. The second half of the backscattered signal was analyzed by recurrence analysis for areas with or without a defect. The recurrence plot (RP) visualized the chaotic behavior of the ultrasonic pulse, and the statistical results of recurrence quantification analysis (RQA) characterized the instability of the signal and the effect of defects. The results show that the RQA variable differences are related to the size of blind holes, which give a probable detection of discrete geometric changes in thick multilayer composites.  
thick multilayer composites; discrete defects; ultrasonic pulse echo; nondestructive testing (NDT); recurrence plot (RP); recurrence quantification analysis (RQA); statistical results; chaotic behavior +
Footnote †: journal: Applied Physics Letters
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 3 Materials and Methods
### Classifier Models
#### 3.4.1 Convolutional Neural Networks (CNN)  
Convolutional Neural Networks have impressive results in computer vision and image processing areas [39; 40; 41]. It is a model that has come to be increasingly used in NLP research. The use of of CNNs for texts first started with [PERSON] and [PERSON]'s research [42]. They used a look-up table to transform words into a vector representation. Firstly, the word tokenization process takes place, whereby these words are transformed into a word embedding matrix with of selected or determined dimension. After this step, the convolution process is applied to the embedding matrix with selected kernels to create a feature map. The max-pooling operation follows the convolution step to reduce the dimension of output and obtain the fixed-length output [11; 43]. Figure 4 shows CNN modeling for text.  
Figure 4: CNN modelling for text [11] (© 2018 IEEE. Reprinted, with permission, from IEEE Computational Intelligence Magazine).
# Multi-Modal 3D Shape Clustering with Dual Contrastive Learning
## 4 Experimental Results  
### Experimental Setup  
The proposed DCL-Net was implemented on the PyTorch platform using a GeForce GTX 1080 Ti GPU and an Intel i7-8700K processor at 3.70 GHz. During the network training phase, Adam [52] was adopted as the optimizer and the learning rate was set to 1.0 \(\times\) 10\({}^{-4}\). The batch size was set to 128 for all of the experiments and the trade-off parameters \(\lambda_{1}\) and \(\lambda_{2}\) were fixed to 1 and 5, respectively. After the training phase, Equation (20) was utilized to calculate the final clustering results.  
To evaluate the clustering performance of the proposed DCL-Net, experiments were conducted on two widely used 3D shape benchmark datasets: ModelNet10 [53] and ModelNet40 [53]. The ModelNet10 dataset consists of 4,899 3D CAD models from 10 classes, while the ModelNet40 dataset includes 12,311 3D CAD models from 40 classes. Following the experimental settings of [12], 1,024 points from the surface of each CAD model were sampled to form the point clouds and twelve 2D views of each CAD model were rendered to obtain multiple views. Note that this paper focused on unsupervised multi-modal 3D shape clustering, thus the class labels were not provided in all of the experiments.  
Following the previous clustering work [54], four commonly used evaluation metrics, i.e., accuracy (ACC), normalized mutual information (NMI), adjusted rand index (ARI), and F-score, were employed to evaluate the clustering performance of the proposed DCL-Net and comparison methods. Different metrics were used to measure the consistency between the predicted labels and the ground truth labels from different perspectives. Specifically, the ACC represented the proportion of correctly predicted samples in the total samples. The NMI was used as a normalized measure of the correlations between the distributions of the predicted labels and the ground truth labels. The ARI was a modified version of RI [55] and indicated the distribution correlations between the predicted labels and the ground truth labels. Finally, the F-score was the harmonic mean of the precision and recall, where precision and recall represented the fraction of correctly predicted samples in the total positive predictions and the actual positives, respectively. For all of these metrics, higher values indicated a better clustering performance.
# ECC: Passenger Counting in the Elevator Using Commodity WiFi
## 3 Materials and Methods
### Multi-Dimensional Feature Extraction
#### 3.5.3 Metric Feature  
The variance operator is applied to both sides of (3), then we obtain \(D(\hat{H}_{r})\). \(D(\hat{H}_{r})\) increases with a growing number of moving people in the room due to more reflection paths and strong signal fluctuations being introduced [29]. However, the people are in a static state in the elevator, and the block of the signal propagation path is more serious as the number of people grows, which causes the number of receive paths \(p\) to reduce. This variation is opposed to the indoor environment of moving people. Based on this, the value of the PEM can be calculated as follows: (a) Transform CSI matrix: Transform the CSI amplitude values in the matrix composed of all PCs into a two-dimensional matrix; the CSI amplitude data are converted into integers \(I_{nt}\), which is expressed as:  
\[I_{nt}=\frac{B[i][j]-B_{\min}}{B_{\max}-B_{\min}}\cdot(R-1)+1 \tag{14}\]  
where \(B[i][j]\) is the CSI amplitude value after noise filtering, \(B_{\max}\) and \(B_{\min}\) are the maximum and minimum values, and \(R\) is the number of rows of the CSI matrix. The elements in row \(I_{nt}\) and column \(j\) in the matrix are set to \"\(1\)\". There is a \"\(1\)\" in each column, and the rest are \"\(0\)\"s, and a new CSI matrix \(C\) is formed. (b) Dilate CSI matrix: The dilate matrix \(C_{0}\) is calculated by setting the elements around \"\(1\)\" to \"\(1\)\"s with the dilatation coefficient \(d\). The experiments prove that when \(d=15\), PEM can implement the best effect in ECC. (c) Calculate PEM value: Calculate the percentage of the non-zero elements in \(C_{0}\), which is the PEM value. (d) Integrating CSI fingerprint: Gather the CSI fingerprint to find the relationship between PEM and the people.  
Figure 8 shows the variation of the PEM value in the elevator and the room. The values of PEM have a monotone decreasing relationship with the growing people number in the elevator, while the change in the room is the opposite. However, the performance of PEM is poor when people keep relatively static in the room due to weak signal fluctuations.  
Figure 8: Comparison of the variation of PEM in two scenes. (**a**) In the elevator. (**b**) In the room.