esa-sceva/satcom-chunk-collection huggingface dataset:
# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 4 Results and Discussion
### Adaptive Frame Threshold  
Video data played a crucial role in this study as it allowed for adjusting the frame threshold according to the duration of continuous driving. Tables 4-7 display the confusion matrix for the four videos utilized to test the adaptive frame threshold. A total of 500 recorded frames were analyzed empirically for each video. In Table 4, there are five accurate predictions for drowsiness and three instances that were incorrectly predicted as not drowsy. In Table 5, nine predictions for drowsiness are correct while five are incorrect. For Table 6, there are 12 correct predictions for drowsiness and 7 incorrect ones. In Table 7, 8 predictions for drowsiness are accurate while 10 are incorrect. Various factors influenced each video; three different short videos, each lasting 10 min, were merged to create a longer video, and each short video had different camera positions. This resulted in some videos showing simulated driving persons zoomed in (with their faces close to the camera) while others showed persons zoomed out (with their faces far away from the camera). This variation affected the effectiveness of the Dlib shape predictor in accurately mapping the 68 landmark points on each captured frame. However, considering real-time videos with the correct camera position would significantly increase the number of accurate predictions for drowsiness.  
From Table 8, the accuracies of each video of 30 min were high with the highest values being 98.2% and the lowest being 94%. Sensitivity is affected by the number of True Positives and False Negatives. In each of the videos, we see that the False Negatives increased in some of the videos causing the computed sensitivity of each video to hover around 64% with the lowest around 44%. This is expected due to the small duration of each video (30 min); a smaller number of frames can be processed before the duration is elapsed. Also, the selection of merged short videos has played a significant effect as different individuals with varying camera orientations contribute to how the shape predictor can evaluate the ECR and MAR of each frame. Given a longer duration and symmetric merged short videos, the sensitivity will be greatly improved.  
Figure 16 illustrates the results obtained from applying the adaptive frame threshold algorithm during instances of closed eyes or yawning by the driver.  
The proposed algorithms are direct and offer extensive applicability within smart cities. Furthermore, the incorporation of machine learning techniques could enhance both the sensitivity and accuracy of the adaptive threshold method [1]. [PERSON] et al. in the papers [36; 37] give a recommendable suggestion for employing cost-optimized resources for video streaming.
## 5 Performance Evaluation of Multi-Hop Wired Network  
For the evaluation, we provide the numerical delay and PDV bounds for \(n\) servers with cross traffic in series, as shown in Figure 3. Each server has a capacity of \(C\), and the leftover service is determined according to the general scheduling model.  
A variety of arrival processes, such as voice audio sources, have been widely described using Markov models. We utilized these models to describe random processes \((\sigma_{c}(\theta),\rho_{c}(\theta))\), which represent the arrival process for cross traffic, and we were able to derive the corresponding MGFs based on these models. A typical irreducible and homogeneous Markov chain includes \(n\) states and a stationary state distribution \(D\). During state \(i\), the traffic workload is processed at \(h_{i}\). The discrete-time model assumes \(H(\theta)\) as the diagonal matrix \(diag(e^{\theta h_{1}},e^{\theta h_{2}},\ldots,e^{\theta h_{n}})\) and \(Q\) as the transition probability matrix, where \(q_{ij}\) is the transition probability from state \(i\) to state \(j\)[20].  
It is possible to find closed-form solutions to Markov models with two states. The closed-form bounds for two-state, continuous-time models have been published for all \(t\geq 0\).  
Let us consider the continuous-time model, as shown in Figure 4, with two states, on and off, and represent the elements of \(Q\) by \(q_{ij}\), where \(q_{11}=1-\mu,q_{12}=\mu,q_{21}=\lambda\), \(q_{22}=1-\lambda\) and \(\lambda,\mu>0\). During the off state, no workload is processed. The traffic workload is processed only in the on state with the rate \(h\) and \(\hat{r}\) being the average rate. We have \(\overline{M}_{S}(\theta,t)\leq e^{-\theta(\rho_{c}(\theta))t}\) for all \(t\geq 0\) and all \(\theta\)[21], such that  
\[h=\hat{r}(1+\frac{\lambda}{\mu}), \tag{36}\]  
\[\rho_{c}(\theta))=\frac{1}{2\theta}(-g+\sqrt{g^{2}+4\mu\theta h}), \tag{37}\]  
where, \(g=\lambda+\mu-\theta h\).  
For the analysis, we simulated the network using various parameters, shown in Table 1, and computed the delay and PDV bounds in the MATLAB simulator. We used a two-state Markov model to describe cross traffic with \(\rho_{c}(\theta)\) computed using (37). We completed the analysis for single- and multiple-stage networks.
## 5 Conclusions  
In this paper, a rate-compatible family of source codes is proposed based on an achievable system rate analysis and the PEXIT algorithm. Simulation results demonstrate that the proposed source codes exhibit good source decoding thresholds as well as high shaping gains for the JSCCM system across a wide range of source probabilities, making them well suited for practical applications with changing source probabilities.  
Conceptualization, C.C. and Z.X.; methodology, C.C.and S.L.; software, C.C.; validation, S.L. and Z.X.; formal analysis, C.C. and S.L.; investigation, C.C.; resources, S.L.; data curation, C.C.; writing--original draft preparation, C.C.; writing--review and editing, Z.X. and Q.C.; visualization, C.C.; supervision, Z.X.; project administration, C.C. and Q.C.; funding acquisition, C.C. and Z.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grants 61901182 and 62101195, the Science Foundation of the Fujian Province, China (Grant No. 2020J05056), the Fujian Province Young and Middle-aged Teacher Education Research Project (No. JAT220182), the Fundamental Research Funds for the Central Universities (ZQN-1008), the Scientific Research Funds of Huaqiao University (19 BS206, 20 BS105, and 21 BS118), the Jimei University Startup Research Project (No. ZQ202039), and the Scientific Research Foundation of Jimei University (No. XJ2022000201).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: BER comparison of different source codes (\(R_{s}=1/3\)) with different source probabilities.
## 4 Application Results
### Accuracy, Sensitivity, Specificity and Predictive Value  
In order to evaluate the efficiency of the clinical decision support system, some measures are obtained. A confusion matrix is used to obtain classification accuracy, sensitivity, specificity, positive predictive value and negative predictive value. A confusion matrix [30,31] contains information about actual and predicted classifications done by a classification system.  
Table 1 shows the confusion matrix for the clinical decision support system. Classification accuracy, sensitivity, specificity, positive predictive value and negative predictive value can be defined by using the elements of the confusion matrix as:  
\[\text{Classif.accuracy}(\%)=\frac{TP+TN}{TP+FP+FN+TN}\times 100=\frac{33}{44}=75.0\% \tag{3}\] \[\text{Sensitivity}\left(\%\right)=\frac{TP}{TP+FN}\times 100=\frac{29} {35}=82.86\%\] (4) \[\text{Specificity}\left(\%\right)=\frac{TN}{FP+TN}\times 100=\frac{4} {9}=44.4\%\] (5) \[\text{Positive predictive value}\left(\%\right)=\frac{TP}{TP+FP} \times 100=\frac{29}{34}=85.29\%\] (6) \[\text{Negative predictive value}\left(\%\right)=\frac{TN}{FN+TN} \times 100=\frac{4}{10}=40.0\% \tag{7}\]  
Figure 9: Rule set Excel spreadsheet (Spanish).  
Our system reaches an accuracy of 75% in a set of 44 clinical cases. Sensitivity has a high value, denoting the ability of the system to detect diseases. On the contrary, specificity has a lower value. This can be explained taking into account that our system proposes several diagnoses in a first approach in order to help the primary care clinician, so it is not unusual to propose several diseases that could not affect the patient. An important measure for our system is the true predictive value. With a value of 85, 29% is a very promising result. Thus, in the presence of a disease, our system proposes the right diagnosis in most of the cases, helping the clinician appropriately in the decision step.  
Most current clinical diagnosis systems focus on a disease or family of diseases. This allows their systems to classify between a reduced group of diseases or the absence of disease [15, 20, 21, 32]. The main difference between our system and these other diagnosis systems in the literature is that our system is intended to give a set of proposals in the first approach. Our system is intended to help the clinician, who determines the diagnosis regarding his/her knowledge, experience and results of physical examination together with our system output. In this way, our system helps to reduce the uncertainty that arises from the diagnosis step.  
A comparison between our system and others is not possible, due to the different datasets used, so that our system uses a not open dataset to measure accuracy. It should also be noted that our system is not intended to make a very specific diagnosis among a family of diagnoses but to propose several diseases in a very broad range in order to guide the clinician on diagnosing the patient and determining necessity of further tests.
## 3 The Methods
### Paired Dataset Synthesis  
Although glare removal has been explored in general image enhancement, it remains under-investigated in nighttime road scenarios for autonomous driving. This is largely due to the lack of annotated datasets and the complexity of nighttime lighting--such as multiple light sources, reflections, and severe localized overexposure. These factors make it impractical to manually annotate large-scale glare removal datasets, as pixel-level annotation using software tools for glare artifacts is extremely labor-intensive and costly.  
To address these challenges, synthetic paired datasets have become a practical and effective alternative for training and evaluation in glare removal tasks. Inspired by previous work [34], we employ the same algorithms to synthesize datasets. Specifically, we use a large collection of natural-scene background images as the base and composite them with glare-only images. A previously released dataset [37] provides the glare images used in our synthesis. The details of the procedure are described below.  
First, the background image \(I_{b}\) and the glare image \(I_{f}\) are gamma-corrected, where the gamma value \(\gamma\) is randomly sampled from a Gaussian distribution \(\gamma\sim\mu(1.8,2.2)\). The gamma correction is defined as follows:  
\[I_{b}^{\gamma}=(I_{b})^{\gamma} \tag{1}\]  
\[I_{f}^{\gamma}=\left(I_{f}\right)^{\gamma} \tag{2}\]  
Next, a mixing coefficient \(\alpha\) is randomly sampled from a Gaussian distribution \(\alpha\sim\mu(0.5,1.0)\) and used as the blending weight for \(I_{b}^{\gamma}\) and \(I_{f}^{\gamma}\). The synthetic image is generated as follows:  
\[I_{b-f}=I_{b}^{\gamma}\times\left(1-\alpha\cdot M_{f}\right)+I_{f}^{\gamma} \times\left(\alpha\cdot M_{f}\right) \tag{3}\]  
where \(M_{f}\) denotes the glare mask.  
Afterward, the synthesized image undergoes inverse gamma correction, expressed as follows:  
\[I_{b}^{f}=\left(I_{b-f}\right)^{\frac{1}{\gamma}} \tag{4}\]  
During the synthesis process, the weighted blending before and after gamma correction may result in pixel values exceeding the range of [0, 1]. Finally, we clip \(I_{b}^{f}\) to [0, 1] to ensure valid pixel values.
# Satellite Network Task Deployment Method Based on SDN and ICN
## 4 Task Deployment Method Based on Genetic Algorithm  
The complete process of task deployment is shown in Figure 3.  
Task deployment of a satellite network based on the SDN involves resource allocation among multiple satellites. The deployment path of a single task has a variety of ways, so the task deployment strategy cannot find the optimal solution within the polynomial-time complexity. Therefore, a heuristic algorithm is considered to achieve an approximate solution within the allowable time. A genetic algorithm imitates the principle of biological evolution and has advantages in solving search problems. The steps of a task deployment strategy include coding, fitness calculation, selection, crossover, and mutation.  
The satellite deployment strategy mentioned above refers to the problem of transferring user-initiated computing tasks to multiple satellite nodes. This is according to the path under the centralized decision of the SDN controller under the condition of limited resources of a single satellite. The task deployment strategy mentioned in this paper needs to meet the following prerequisites: 1. The design of satellite nodes needs to be based on the idea of the ICN, such that the nodes can perceive the content of local resources; 2. under the SDN satellite network architecture, the LEO satellite can quantify its resources for calculation, caching, and communication, and can upload the available resource information to the SDN controller in real-time.
## 3 Actuator Fault Detection and Diagnosis Scheme  
### Neural Network Approximation  
Radial basis function (RBF) neural networks have good capability in function approximation which is unknown and continuous. In this section, the following RBF neural networks are used to reconstruct the generalized perturbation \(\rho(t)\) in Equation (11), which can be expressed as  
\[\rho(t)=W^{T}\varphi(t)+\varepsilon_{0} \tag{16}\]  
where \(W\in\mathbb{R}^{p\times 3}\) is an ideal but unknown weight matrix and \(p\) is the number of the implicit layer. \(\varepsilon_{0}\) is the extremely small and unknown bounded approximation error. \(\varphi(t)=[\varphi(t),\ \ldots,\ \varphi_{p}(t)]^{T}\) represents the known basis function, which can be expressed by a Gaussian function as  
\[\varphi_{i}(t)=\exp(-\frac{\|\omega-\
u_{i}\|^{2}}{\eta_{i}^{2}}),i=1,\ldots,p \tag{17}\]  
where \(\
u_{i}\) and \(\eta_{i}\) are the center and width of neural cell of the \(i\)th neurons, respectively. The uncertain parameters are unknown, but their distribution area can be obtained through experiments. Based on this, the basis functions with the centers in the distribution area and the widths superimposed to cover this area can construct \(\varphi(t)\).  
The optimal weight value \(W\) of RBF neural network is given by  
\[W=\arg\ \min_{\dot{W}\in\mathbb{R}^{p\times 3}}\Bigl{\{}\sup_{\omega\in \Gamma}\Bigl{|}\rho(t)-\dot{W}^{T}\varphi(t)\Bigr{|}\Bigr{\}} \tag{18}\]  
Since the optimal weight value \(W\) and approximation error \(\varepsilon_{0}\) are unknown, we have  
\[\dot{\rho}(t)=\dot{W}^{T}\varphi(t)+\varepsilon_{0} \tag{19}\]where \(\hat{\rho}(t)\) is the estimated value of \(\rho(t)\).
## 3 Proposed ATMALS-GSK Algorithm
### Atmals-Gsk
#### 3.2.4 Adaptive \(P\)  
The parameter \(P\), ranging within [0, 1], determines the probability of selecting individuals to participate in knowledge sharing during both the junior and senior learning phases. In practice, \(P\) is typically set to values less than 0.5, as lower values promote more selective and targeted interactions. This cautious approach helps the algorithm avoid excessive imitation, maintain diversity, and support broader exploration of the search space. In the proposed ATMALS-GSK algorithm, \(P\) is adaptively adjusted during the execution of the algorithm, typically within a refined range of 0.01 to 0.5, depending on the evolving needs of the search. Keeping \(P\) relatively small ensures that only a selective subset of individuals engages in knowledge exchange at each generation, preventing premature convergence and preserving healthy exploratory behavior, especially in early iterations.  
To adaptively control \(P\), the same Gaussian-based probability function used for tuning \(K\) in Equations (3)-(7) is applied, enabling the algorithm to learn from historical success and gradually fine-tune \(P\) as optimization proceeds, considering a range of variation for \(P\) as [0.05, 0.15] with a discretized level of 0.01. By restricting knowledge diffusion to high-quality individuals early on and allowing more sharing in later stages, the algorithm achieves a smooth transition from diverse exploration to focused exploitation, resulting in improved convergence reliability and solution quality across diverse problem domains.
# Impact of 50 Hz Electromagnetic Field on the Growth of _Chlorella vulgaris_
## 3 Results and Discussions
By analyzing Figure 9, it can be noticed that by stimulating the algal mass growth with a 50 Hz ELF in the investigated culture medium, the nitrogen content obtained after 96 h of culture increases linearly depending on the applied EMF intensity from 8.47% (reference--without electric field (ELF)) to 12.12% (ELF stimulation 50 Hz--at 15 V/m). According to the data in Table 2, this increase of about 3% can be explained by the fact that the exposure to 15 V/m EMF of 50 Hz determines an increase in the ability of _Chlorella vulgaris_ to metabolize nitrogen from air. Also, Figure 9 shows that by stimulating the algal mass growth with 50 Hz in the investigated culture medium, the phosphorus content obtained after 96 h of culture decreases depending on the applied EMF intensity from 0.77% (reference--without electric field) to 0.57% (ELF stimulation 50 Hz--at 15 V/m). This finding, correlated with data from Figure 7, suggests that the decrease in phosphorus content of the algal mass grown in 15 V/m EMF 50 Hz is due to the rapid depletion of the phosphorus resource in the culture medium. Figure 8: Comparative evolution of pH during the 96 h of growth at various intensities of the applied electromagnetic field.
# Using Enhanced Representations to Predict Medical Procedures from Clinician Notes
## 4 Implementation Design and Evaluation
### Vectorized Annotations  
Converting text into numeric vectors is essential in any ML task using unstructured text as input. There are multiple strategies for generating vectorized representations out of raw text, and in our approach, we cover two different methods: Term Frequency--Inverse Document Frequency (TF-IDF) features as a representation based on word importance and contextualized embeddings from transformers.  
TF-IDF features encode the importance of a set of words in a specific document, which can be helpful in text classification [35]. We established a vocabulary size of 10,000 words, and fitted the implementation with the training set.  
When using TF-IDF features, we extend the preprocessing outlined in Section 4.1. This process involves removing the stop words using the default English stop words from Natural Language Toolkit (NLTK) [36], then converting all words to lowercase, and finally removing words containing no alphabetic characters. This pre-processing is applied during training, and the resulting model is used to generate the vectorized representations.  
Contextual embeddings from transformers are dense vector representations of words that are dynamic and influenced by the surrounding words [37]. Other works have previously used contextual embeddings in text classification tasks as a way to generate powerful representations to use in ML and DL algorithms [38; 39; 40], with some works reporting better results compared to traditional word embeddings, as shown in the study of [PERSON] et al. [41]. In our specific use case, we considered two different BERT-based models to extract contextualized embeddings, including DistilBERT [42] as a reference model trained on a non-specialized English corpus and ClinicalBERT [43]. This model was explicitly trained on notes from the MIMIC-III database.  
We performed full fine-tuning of both models for the task of ICD procedure prediction using the texts in the training dataset, which, in our use case, showed significantly better results compared to only adjusting the top layers. We used a sequence length of 512 tokens, the maximum supported by both models, with padding and right-side truncation. Finally, we extracted the embedding from the last hidden state of the \"[CLS]\" token. The output of  
Figure 3: Word count distribution in MIMIC-III discharge summaries.  
the \"[CLS]\" is inferred by the rest of the words in the sentence, which is considered to be a good representation of the contents of the whole text. However, active research is still trying to find the optimal representation from contextual embeddings [44].  
The vectorized representations obtained in this step are combined with the diagnosis codes to train a downstream classifier for the procedure prediction task.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Evaluation of Weighted Fusion Methodology
#### 3.3.4 Winter Condition Analysis (December 2024)  
The December 2024 evaluation presents a distinct operational scenario with winter heating loads significantly impacting consumption patterns. Figure 12 portrays that direct aggregation consistently overestimated load during this period, particularly during early morning hours (3:00-7:00). This overestimation is quantified in Figure 13, which shows MAPE values for direct aggregation exceeding 7% during these hours.  
The weighted fusion approach substantially mitigated this overestimation problem, maintaining MAPE values below 2.7% even during challenging early morning periods. As depicted in Figure 14, weighted fusion achieved exceptional performance during December, with \(R^{2}\) values of 0.9819 for peak hours and 0.9726 for non-peak hours. This represents the best performance among all three evaluation periods, confirming the adaptability of our methodology across diverse seasonal conditions.  
Figure 11: August 2024 scatter comparison.  
Figure 12: December 2024 forecast comparison (The yellow shadow indicates the peak area).
## 2 CNN-LSTM Based Automatic Multi-Class Classification Network
### CNN-LSTM Network Structure
#### 2.2.2 One-vs-Rest Combinational Classifier  
To achieve multi-class classification of various charged particles, we utilize a one-vs-rest (OVR) classifier to classify the feature vectors obtained from the feature extractor. The basic principle of the one-vs-rest strategy is that, for N-class classification, N-independent classifiers are used to determine whether a sample belongs to the N-th class, generating a corresponding score for each class. After obtaining the scores for all classes, the classwith the highest score is selected as the final classification decision. Figure 3b illustrates the structure of the one-vs-rest classifier. Compared with the one-vs-one strategy, which requires a classifier for each pair of classes (i.e., \(\text{C}_{N}^{2}\) classifiers), the one-vs-rest strategy only requires N classifiers, making it more adaptable and efficient in a wider range of scenarios.