esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The fault warning is mainly studied from four aspects: sensitive feature [19, 20, 21], probability model [22, 23], state classification [24, 25, 26, 27, 28, 29], and time series prediction [30]. For example, [PERSON] et al. [31] realized early detection and prediction of fluidized bed boiler leakage with the use of the artificial neural network (ANN) method. [PERSON] et al. [16] conducted an early warning study of power plant failure based on the evidential k-nearest neighbor (EKNN) rule. [PERSON] et al. [32] proposed an early warning method for transmission line galloping based on the support vector machine (SVM) and AdaBoost bi-level classifiers. [PERSON] et al. [33] recommended an improved autoregressive integrated moving average (ARIMA) model, which can identify abnormal events earlier in the light curve obtained from ground-based wide-angle camera arrays (GWACs). [PERSON] et al. [26] applied the improved multi-state estimation technology of the process memory matrix to provide an early warning of failure of auxiliary equipment in power plants. However, although these methods study the setting of the warning value from the perspective of the algorithm, they are still essentially dichotomy-based methods with a fixed threshold and still possess the above disadvantages. In actual factory applications, in addition to identifying whether the equipment is operating abnormally, its state also needs to be determined, and different dynamic warning values should be set for its different states. To develop a more effective equipment alarm technology, an adaptive warning method for equipment based on the similar proportion and probability model is proposed to solve the fixed and single-alarm threshold. First, the equipment state is divided into four categories in accordance with closely related sensitive features, and the alarm threshold in a specific state is then obtained by inputting historical equipment data into the kernel density estimation model. Second, a similar ratio function is defined, and the initial threshold for online operation of the device is accordingly determined. Third, the warning value of the multi-state equipment is instantly updated by combining the probabilistic neural network and the dynamic-\(\sigma\) method. Finally, the proposed method is verified by means of the lifetime data of a rolling bearing. The rest of this paper is arranged as follows: In Section 2, the self-adaptive early warning method and dynamic early warning line drawing method are described in detail. In Section 3, the proposed method is verified by specific experimental data. Section 4 summarizes the article and outlines future research directions.
## 4 Discussion
(2023) [21] investigated the utility of \(\text{RSI}_{\text{hor-DIST}}\) during 3-Hop assessments, finding small to moderate negative correlations with 505 change-of-direction test performance (\(r=-0.15\) to \(-0.45\)) and 10 m sprint performance (\(r=-0.03\) to \(-0.16\)) in male volleyball players. They reported \(\text{RSI}_{\text{hor-DIST}}\) values ranging from 5.24 to 7.57 m\(\cdot\)s\({}^{-1}\), whereas in this study, 3-Hop assessments yielded values between 7.26 and 10.71 m\(\cdot\)s\({}^{-1}\), with stronger correlations with sprint performance (\(r=-0.49\) to \(-0.69\)). This stronger relationship may be attributed tothe higher reactive strength capacity of the subjects in this study. The mean hop distance in our sample was 6.43 m (\(\pm\)0.67), compared to 5.91 m (\(\pm\)0.51) in the [PERSON] et al. [21] study, with our participants also demonstrating shorter ground contact times, which also amplifies the resulting RSI. Another focus of the research was to determine whether the 5-Hop/3-Hop ratio or RSI\({}_{\text{hor}}\) could differentiate between sprinters of differing ability. According to [PERSON], sprinters with a capacity to express strength whilst sprinting should be able to hop approximately 70% further in a 5-Hop compared to a 3-Hop. However, our findings indicated that the hop ratio could not (\(p<0.05\)) differentiate between fast and slow sprinters, as both group 5-Hop to 3-Hop differences were 71 to 72.5% (\(p>0.05\)). While it might seem intuitive that athletes capable of achieving higher stretch-loads in a 5-Hop would also demonstrate faster sprinting speeds, our study, conducted with a heterogeneous sample, did not provide any evidence to support this hypothesis. Rather, it seems that RSI\({}_{\text{hor}}\) is a better variable to differentiate sprinting ability, and, therefore, most likely a better variable to measure and monitor. It also appears that RSI\({}_{\text{hor}}\) values from the 3-Hop assessments were just as strongly correlated with sprint distance as those from the 5-Hop test, suggesting that the additional injury risk associated with the 5-Hop may not be justified in a training-assessment context. Nevertheless, in a training environment, the 5-Hop might still be valuable for eliciting the higher stretch-load stimuli for those athletes ready for such incremental loading.
## 3 Results
### Process Turnaround Time (TAT) Analysis
The generation of AI algorithms that could assist in the self-verification of routine tests according to the patients' medical records have been recently established, and their impact need to be assessed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**HIV**} & \multicolumn{2}{c}{**COVT–COVIDG**} \\ \cline{3-6}
**[Time in Hours]** & **KPI** & **Pre-TLA** & **Post-TLA** & **Pre-TLA** & **Post-TLA** \\
**(VSD)** & **(VSD)** & **(VSM)** & **(VSD)** & **(VSD)** \\ \hline Total Equipment Time & TET & 27:57:14 & 0:39:39 & 1:21:20 & 1:18:20 \\ Total Direct Labour Time & TDLT & 0:07:14 & 0:06:16 & 0:07:08 & 0:06:18 \\ Total Waiting Time & TWT & 5:17:07 & 3:24:08 & 8:55:15 & 6:58:58 \\ \hline Equipment & WT\({}_{\text{E}}\) & * & 0:07:05 & * & 0:16:43 \\ Technician & WT\({}_{\text{T}}\) & 0:46:48 & 1:28:49 & 0:32:21 & 1:31:15 \\ Doctor & WT\({}_{\text{D}}\) & 4:30:19 & 1:48:14 & 8:22:54 & 5:11:00 \\ \hline
**Turnaround Time** & **TAT** & **33:21:36** & **4:10:03** & **10:23:42** & **8:23:36** \\ \hline \hline \end{tabular}
* No data available. The total waiting time (TWT) is divided into waiting time for equipment, technicians and doctor. Turnaround Time (TAT) is marked in bold as process lead-time KPI.  
\end{table} 
Table 3: Turnaround Time (TAT) results comparison between pre-TLA and post-TLA and its contributors by analysis.
# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
## 2 Methodology
### Initial Physical Layer Network Planning
#### 2.4.1 Coverage and Throughput  
The heuristic is based on the statistical \(PL\) model from (1), which is validated for industrial environments [28]. In (1), \(d\) is the distance in m, \(PL_{0}\) is the reference \(PL\) at distance \(d_{0}\) of 1 m, \(n\) is the dimensionless \(PL\) exponent, \(L_{R}\) is the rack attenuation in dB, and \(\chi_{\sigma}\) is a fading term in dB based on a normal distribution with zero mean and standard deviation \(\sigma\). For the considered environments, we use a \(PL_{0}\) value of 46.91 dB and \(PL\) exponent value \(n\) of 1.96 [28]. The standard deviation is 2.39 dB and the rack attenuation \(L_{R}\) is assumed to be 4.6 dB. As a PL model depends on the specific environment, a one  
Figure 2: Different phases, from initial network-planning to the monitoring loop.  
day measurement campaign is proposed in [29] to obtain a specific \(PL\) model for the environment where network-planning is needed.  
\[PL(d)=PL_{0}+10~{}n\log_{10}(d/d_{0})+\sum_{i}L_{R_{i}}+\chi_{\sigma} \tag{1}\]  
The maximum theoretic throughput depends on \(PL\) via the used modulation and coding scheme (MCS). In general, a more complex MCS can be used for a higher received power, which results in a higher data rate. The received power depends on the TX power and \(PL\), and as the maximum TX power is limited, \(PL\) defines the coverage and throughput. The network-planning tool can also be used to calculate theoretic throughputs based on a floor plan geometry and AP configurations, by calculating received power at every grid point.
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 5 Network Performance Comparison
### Methods for Controlling Network Performance  
One method to control latency and reduce traffic delay was to deploy Quality of Service (QoS) on networks. This approach has been demonstrated to improve network performance by prioritizing and allocating resources based on specific criteria [48].  
There are various ways to reduce delay of a network:  
* **Subnetting:** Grouping together endpoints that communicate most frequently can reduce latency across the network.
* **Traffic shaping:** By utilizing the QoS, traffic shaping, and bandwidth allocation, it is possible to improve latency for specific network segments.
* **Load balancing:** Load balancing distributes traffic to areas of the network with the capacity to handle the additional activity.  
Applying the QoS enables control over network bandwidth by priority, which can significantly reduce latency on the network in terms of delay time (ms) [49]. To achieve this, the following actions can be taken to control network traffic [50]:  
* **Class-map:** Categorize traffic into groups.
* **Policy-map:** Allocate the quantity of bandwidth and priority to the traffic from the class-map.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 4 Discussion
### Legal and Epistemological Considerations on 3D Cheloscopy
#### 4.6.2 Specific Roles and Skills Among Dentists and Medical Examiners  
The use of 3D helioscopy in the forensic field requires highly specialized skills, where dentists and forensic doctors make complementary contributions [32]. Dentists, thanks to their in-depth training on orofacial morphology and the management of advanced technologies such as the 3D scanner, can precisely detect and analyze lip impressions, identifying unique details [29; 33]. Forensic doctors, on the other hand, contextualize this data in the context of judicial investigations, ensuring its validity and integrity as forensic evidence [2]. This interdisciplinary collaboration strengthens the reliability and effectiveness of identification through lip prints [34].
## 1 Introduction
However, a great part of these investigations is focused on valorizing biomass coming from conventional crops (instead of residues) and/or these are directly focused on the environmental impacts of the final products, paying little attention to the production and purification of the intermediate sugar monosaccharides. Other authors, such as [PERSON] et al. (2008) [19], [PERSON] et al. (2013) [20], [PERSON] el al. (2018) [21], [PERSON] et al. (2019) [22], and [PERSON] et al. (2020) [2], reported LCA results focused on the production of sugar monosaccharides and their refining, but starting from non-residual raw materials (mainly maize and wheat grains). Thus, there are still few studies in the literature devoted to ascertaining the environmental profile of glucose coming from lignocellulosic biomass wastes. One of the most relevant pieces of research in this field is the work of [PERSON] et al. (2017) [23], who evaluated different technologies for glucose production using highly recalcitrant softwood residues. LCA results were compared with those achieved in the conventional process for glucose production, based on sugarbeet as starting feedstock. Results suggested that, for all the evaluated scenarios, softwood biomass involves higher environmental impacts than sugarbeet when used as starting raw materials. This is an expected conclusion because of the complexity and recalcitrant nature of the residual woody biomass used in this research. Nevertheless, when the agricultural stages of conventional crops, such as sugarbeet, are included in the LCA study, results are not too unfavorable for the woody waste alternative. This work aims to compare the environmental impacts associated with the production of glucose through the treatment of both maize starch and woody residual biomass, using an LCA cradle-to-gate approach. In both cases, a similar enzymatic process was used for cellulose depolymerization but, according to the diverse nature of each raw material, very different pretreatment and conditioning steps were considered, leading to interesting differences. Likewise, the influence of the agricultural phases necessary for starch production is confronted with the residual origin of the woody biomass.
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 3 Materials and Methods
### Graphs and Graph Neural Networks
#### 3.3.2.2 Processor  
The embeddings obtained from the encoders are used for iterative message-passing and node updates. In each layer of the processor, the embeddings of a node are aggregated with the embeddings of its neighbors and connecting edges, collecting knowledge from the node's neighborhood for updating its own embeddings. The processor comprises three convolutional layers, with a central layer named CrystalConv nestled between the two additional layers known as ECCConv. This selection of layer types was made due to their remarkable ability to incorporate edge features--a critical component in the training process. Both layer types are described below:  
* **ECCConv Layer** is an Edge-Conditioned Convolution (ECC) layer that not only captures the features of individual nodes, but also ties the weights, \(\mathbf{W}^{l}\), to the edge features in each layer \(l\). The output of ECCConv layers in this study takes the form of a vector with 64 distinct components. The ECC convolution layer is mathematically formulated as a function of both the node and edge features in the following manner [51]: \[\mathbf{X}_{i}^{l}=\mathbf{X}_{i}^{l-1}\,\mathbf{W}_{root}+\sum_{j\in N(i)} \mathbf{X}_{j}^{l-1}\,\mathrm{MLP}\Big{(}\mathbf{e}_{j\to i}^{l} \Big{)}+\mathbf{b}^{l},\] (4) where \(\mathbf{X}^{l}\) and \(\mathbf{e}_{j\to i}^{l}\) are the node and edge features of layer \(l\), respectively. \(\mathbf{W}_{root}\) is the weight for the node features of the previous layer. A neighborhood \(N(i)=\{j,(j,i)\in\mathbf{E}\}\) of vertex \(i\) includes all adjacent vertices \(j\). MLP is a multilayer perceptron that outputs an edge-specific weight, \(\mathbf{W}^{l}\), as a function of the edge features. \(\mathbf{b}^{l}\) is the bias vector as a model parameter being updated and optimized during the training process.
* **CrystalConv** is a Crystal Convolution layer introduced by [PERSON] and [PERSON] [52]. This layer computes: \[\mathbf{X}^{l}(i)=\mathbf{X}^{l-1}(i)+\sum_{j\in N(i)}\sigma\Big{(}\mathbf{Z} _{ij}^{l-1}\,\mathbf{W}_{f}^{l}+\mathbf{b}_{f}^{l-1}\Big{)}\odot g\Big{(} \mathbf{Z}_{ij}^{l-1}\,\mathbf{W}_{s}^{l}+\mathbf{b}_{s}^{l-1}\Big{)},\] (5) where \(\mathbf{Z}_{ij}^{l-1}=\mathbf{X}_{i}^{l-1}\oplus\mathbf{X}_{j}^{l-1}\oplus \mathbf{e}_{j\to i}^{l-1}\) concatenates neighbor vectors with the features of the connecting edge. Meanwhile, \(\odot\) indicates an element-wise multiplication; \(\sigma(\cdot)\) denotes a sigmoid function; and \(g(\cdot)\) is an activation function defined by the user. In this work, summation pooling and ReLU are used as the pooling and activation functions, respectively.
# Machine Learning Reveals Migmatic Fertility of Skarn-Type Tungsten Deposits
## 5 Discussion
### Geological Implications of the Nanling Case  
The independent validation performed on the Nanling Range demonstrated that the RF model achieved a high predictive accuracy (90%) for 10 rock samples encompassing222 data entries. This confirms the robust applicability of the model within the Nanjing Range, carrying significant regional geological implications. Firstly, Nanling represents one of the largest skarn-type tungsten metallogenic provinces worldwide, with tungsten mineralization closely associated with Yanshanian granitic intrusions [6; 39; 82]. SHAP analysis revealed linear relationships of trace elements such as Sr, Gd, and La, which strongly correspond with the highly differentiated and volatile-rich characteristics of Nanjing granites. Specifically, relatively low concentrations of Gd and Tb, coupled with elevated La contents, likely indicate crustal contamination or partial melting processes. This geochemical signature aligns well with multi-stage magmatic activities under an intracontinental extensional regime in the Nanling Range [82].  
Secondly, the model's accuracy of 90% in predicting the dominant classification of rock samples indicates a systematic spatial distribution of fertile magmas in the Nanling Range, potentially concentrating near the contact zones between granite intrusions and carbonate rocks. This interpretation is further supported by the known locations of significant tungsten deposits, such as the Dayao Mountain and Huangshaping deposits [83]. After threshold adjustment, recall for fertile apatite increased to 0.98 (Figure 5f), underscoring the model's effectiveness in identifying potential ore-bearing areas, thus significantly reducing the risk of missing exploration targets. This is especially crucial for exploration activities targeting concealed deposits located at the periphery or greater depths within the Nanjing Range.  
However, the reliability of apatite as an indicator of mineralization may be influenced by fluid metastatic processes. Within the Nanling skarn mineralization systems, strong hydrothermal alteration could significantly modify apatite chemistry, thereby obscuring primary magmatic signals and reducing its efficacy as a standalone indicator [6]. Additionally, the misclassification of certain fertile samples as barren observed during independent validation may result from spatial heterogeneity of apatite chemistry. Samples proximal to mineralized zones commonly exhibit distinct geochemical enrichment signatures, whereas distal samples may lack such clear indicators. This heterogeneity highlights the necessity of incorporating comprehensive regional geological context and multi-indicator analysis to further enhance predictive accuracy when applying the model.
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 3 Experimental Results and Discussion
### Experimental Results  
Evaluations and experiments are conducted on three pre-trained models: DenseNet121, Inception-v3, and ResNet-50. Assessment metrics encompass F1-score, recall, accuracy, and precision. The training process involves fifty epochs for each of the Inception-v3, DenseNet121, and ResNet-50 models. Upon completion of the training, the testing accuracies for the three models are as follows: 95% for Inception-v3, 94% for DenseNet121, and 93% for ResNet-50. Additionally, the validation accuracies are reported as 92%, 91%, and 91% for Inception-v3, DenseNet121, and ResNet-50, respectively. Table 3 shows the precision, recall and F1-Score for each model. These results provide insights into the model performance, indicating their ability to generalize well to unseen data. Furthermore, Figure 6 displays the model's static visual results. Actual labels are displayed in the first row, followed by the predicted labels of the Inception-v3 model in the second row, the DenseNet121 model predicted labels in the third row, and the ResNet-50 model predicted labels in the last row. The correct predicted labels are displayed as black, while the incorrect predicted labels are given as red as shown in Figure 7. We can see from Table 2 that Inception-V3 performs better than DenseNet121 and ResNet-50. Additionally, it demonstrates how the DenseNet121 and ResNet-50 models misunderstand certain labels, such as \"river\", \"runway\", and \"airplane\". Additionally, the confusion matrices and heatmaps for the models and classes are displayed in Figures 8 and 9, respectively.  
Figure 7: Visual results of the three models. Red color are used to represent the incorrect prediction where models are confused but inception model is not confused and give us correct prediction of all classes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No** & **Model** & _Precision \%_ & _Recall \%_ & _F1-Score_ \\ \hline
1 & Inception-V3 & 93 & 92 & 92 \\ \hline
2 & DensNet201 & 91 & 90 & 89 \\ \hline
3 & ResNet-50 & 91 & 90 & 88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average _precision, recall_, and F1-_score_ of the three models.  
Finally, Table 2 displays the \(F1\)-_score_, _precision_, and _recall_ for all Inception-v3, DensNet121, and ResNet-50 models in the land areas. This illustrates that DenseNet121 and ResNet-50 are not as effective as Inception-V3 because some classes' accuracies are lower comparatively.  
Figure 8: Confusion matrices of all three models: Inception-V3, DenseNet121, and ResNet-50.  
Figure 9: Heatmap analysis of all classes.
# Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
## 4 Experimental Results
### Evaluation of Performance for the Hyperspectral Image Application  
In this section, we present a comparison of the performance between the different power supplies when used in light sources for hyperspectral imaging. Initially, we implemented data preprocessing techniques designed to extract information from the HS cubes automatically. This automation is crucial for eliminating the need for manual intervention and selective feature extraction. Following the extraction of valuable information from the HS data, we quantified various light parameters for the different power supplies to characterize their performance.