esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Tuning of Hyperparameters  
Hyperparameter selection for _Scenario1_ is described in Table 11, where FNN method outperformed the other methods. Thus, the following parameters were varied for the FNN method to find the best parameter values.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{5}{c}{**Parameters**} & \multicolumn{3}{c}{**FNN Results**} & \multicolumn{3}{c}{**GRU Results**} & \multicolumn{3}{c}{**LSTM Results**} \\ \hline
**nnodes** & **nlayers** & **Look Back** & **dropout** & **MAE** & **epochs** & **MAE** & **epochs** & **MAE** & **epochs** \\ \hline
**32** & **1** & **5** & **0** & 226.09 & 319 & **195.31** & **69** & 234.16 & 39 \\
32 & 1 & 5 & 0.05 & 179.33 & 362 & 204.72 & 72 & 251.48 & 30 \\
32 & 1 & 5 & 0.1 & 184.27 & 384 & 217.90 & 50 & 223.01 & 50 \\
32 & 1 & 5 & 0.15 & 205.2 In Table A1, the minimum loss of 170.41 MWatt is obtained when nnodes = 32, nlayers = 2, lookback = 10 days, dropout = 0, and epochs = 351 from FNN model. Minimum loss of 200.75 MWatt results when nnodes = 32, nlayers = 2, lookback = 5 days, dropout = 0, and epochs = 51 on LSTM model, and minimum loss of 195.31 MWatt is obtained by nnodes = 32, nlayers = 1, lookback = 10 days, dropout = 0, and epochs = 69 from GRU model.  
Similarly, from Table A2 minimum loss of 234.22 MWatt is obtained when nnodes = 64, nlayers = 2, lookback = 8 days, dropout = 0, and epochs = 99 from GRU model. Minimum loss of 242.12 MWatt results when nnodes = 64, nlayers = 2, lookback = 8 days, dropout = 0, and epochs = 99 from GRU model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Parameters**} & \multicolumn{3}{c}{**FNN Results**} & \multicolumn{3}{c}{**GRU Results**} & \multicolumn{3}{c}{**LSTM Results**} \\ \hline
**nnodes** & **nlayers** & **dropout** & **MAE** & **epoch** & **MAE** & **epoch** & **MAE** & **epoch** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table A1: _Cont._and epochs = 56 on LSTM model, and minimum loss of 242.12 MWatt is obtained with nnodes = 64, nlayers = 3, lookback = 8 days, dropout = 0, and epochs = 180 from FNN model.  
Figure A1: Validating the results on hyperparameter testing for _Scenario_1.
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
## 6 Discussion
### Sensitivity to Hand Movement  
Practical VLC systems require lenses to focus beams of light on the small photo-diode. If the hand is placed off-center, the Rx beam will be slightly off-focus and the accuracy may be reduced. This issue can be solved using an automatic lens or by employing multiple spatially separated PDs. An interesting alternative solution would be to employ the neural network to learn and predict gestures in cases where the beam is defocused. A study on the performance as a function of hand-offset position is considered as part of the future work.  
Figure 10: Normalized figure-of-merit versus sensor sample-rate.
# Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
## 3 Results
### Evaluation Index  
The PM2.5 prediction theory adopted in this paper can be regarded as a regression modeling problem. Therefore, statistically-based regression analysis metrics can effectively evaluate the performance of all models. This paper uses MAE (mean absolute error) and RMSE as classic statistical indicators. It is defined as follows:  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{N}(y\_ture_{i}-y\_pred_{i})^{2}}{N}} \tag{7}\]  
\[MAE=\frac{\sum\limits_{i=1}^{N}|y\_ture_{i}-y\_pred_{i}|}{N} \tag{8}\]  
where y-pred is the predicted value, y-true is the true value, and N is the number of samples.
## 2 Literature Review
### Machine Learning  
Text classification is common in NLP. Numerous studies have focused on sentiment analysis, spam classification, and topic classification. The traditional text classification method uses features generated using a vector space model, such as term frequency-inverse document frequency (TFIDF), to represent a text vector, which is typically a high-dimensional and sparse feature vector that often ignores contextual semantic and word order information [23]. ML algorithms can detect cyberbullying or malicious speech based on the aforementioned features. Most methods use labeled training data for supervised learning. [PERSON] et al. [24] collected and labeled data from a website and trained a C4.5 decision tree ML model to achieve an accuracy of 78.5%. [PERSON] et al. [25] employed four ML approaches to detect comments related to three sensitive categories (i.e., racial discrimination, sexual bullying, and personal attacks) on YouTube. [PERSON] et al. [19] revealed that research on cyberbullying detection has mainly focused on the content of comments and has ignored user background information. Therefore, to mark the bullying type of a comment, a user's age and comment history should be included. Additional features, such as the frequency of attack words, have been used to improve cyberbullying detection in YouTube comment data when using a support vector machine (SVM) model. [PERSON] et al. [26] used keywords as features to detect cyberbullying content automatically in a Twitter dataset. They improved the detection accuracy by considering user relationships and conducting social network analysis. [PERSON] et al. [27] used the word embeddingmethod to predefine attack vocabulary and assign weights to cyber features for detecting cyberbullying incidents by using an SVM classifier, which exhibited an excellent performance when using Twitter data. [PERSON] et al. [21] distinguished general, offensive, and cyberbullying users on the basis of cyberbullying repetitiveness and used a random forest classifier to detect cyberbullying incidents whose areas under the curve exceeded 90%. [PERSON] et al. [28] proposed the use of an OneR model to classify situations of cyber aggression on social networks among Spanish-language users in Mexico. Their results indicated that the OneR model classified such situations with greater than 90% accuracy. [PERSON] et al. [29] considered social networks to include multiple modes of information, such as images, self-introductions, times, and locations. Such information is relevant for obtaining effective cyberbullying detection models constructed using a heterogeneous network of multimodal information.
## 2 Graphene and Graphene-Related Materials
### Graphene and Related Materials, Characterization
#### 2.2.4 Thermogravimetric Analysis (TGA)  
Thermogravimetric analysis or thermal gravimetric analysis is an analytical technique used to determine the thermal stability of a material and its fraction of volatile components by monitoring the change in weight that occurs as a specimen is heated [76; 77]. The measurement is normally conducted in air or in inert atmosphere, such as helium or argon, and the weight is recorded as a function of increasing temperature. This technique can be coupled to a mass or infrared spectrophotometer to analyse the gases released during the thermal decomposition. TGA is mainly used in the study of the thermal stability of graphene and differences in the thermal degradation between GO and rGO can be distinguished.
# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 2 Methods
### Processing of Measurement
#### 2.3.3 Gaze on Dashboard  
In order to improve driver attention monitoring and increase the accuracy of detecting instances when eyes are off the road, it is important to develop a method for examining interactions with the central console controls on the dashboard. In our study, the climate control button array and the integrated touchscreen located on the central console of the passenger car, as UI elements, play a pivotal role. The analysis of the driver's visual distraction during the operation of these controls (NDRTs) is a key focus. To facilitate this, the identification of the central console as an AOI is essential. In our case, the console's slight orientation towards the driver, the perpendicular angle of incidence between the driver's gaze and the plane of the central console, and the positioning of the physical button array and touchscreen on the same plane made it feasible to easily mark the corner points with ID tags. However, typically one ID tag becomes obscured due to the position of the steering wheel or driver's hand. Therefore, the dashboard boundaries were demarcated using only the data from ID tag 1 and ID tag 2. To compensate for the missing left-hand corner points, predefined offsets were applied. The upper offset should be 300 pixels and the lower offset should be 170 pixels, both parallel to the line connecting ID tag 4 and the marked corner points of ID tag 5. Figure 7 provides an example of this approach.  
The method can be applied universally, but the specific predefined plane figure and geometric rules used to calculate the position of missing or obscured UI boundary ID tags are determined by the measurement environment. Since the analysis and processing of these points occur post-experiment, the method can be refined based on the specific environmental conditions present. This approach allows for a tailored analysis that accommodates varying dashboard layouts and driver interaction dynamics, ensuring a more accurate assessment of driver attention and UI interaction in diverse driving scenarios.  
Figure 6: Gaze position (yellow circle with green fill), detected tags (blue squares), compensated tag positions (dots in the corners) and “gaze-on-road” indication (dots are red not green).
## 3 Proposed Method
### Teacher Model Using a Multi-View Integrated Ensemble to Generate Pseudo-Labels
In the teacher's overall loss \(\mathcal{L}_{T}\) introduced in Equation (3), the first loss is the supervised segmentation loss \(\mathcal{L}_{sup}^{t}\), defined based on the cross-entropy (CE) loss as follows:  
\[\mathcal{L}_{sup}^{t}=\frac{1}{|D_{L}|}\sum_{(x_{i}^{l},y_{i}^{l(n)})\in D^{L} }l_{ce}(g_{g}^{\omega}\circ h_{\theta_{h}^{t}}(x_{i}^{l}),y_{i}^{l(n)}) \tag{4}\]  
where \(l_{ce}\) is the cross-entropy loss function, and \(x_{i}^{l}\) and \(y_{i}^{l(n)}\) represent the \(i\)-th labeled image and corresponding \(n\)-th teacher's label, respectively. \(h_{\theta_{h}^{t}}\) and \(g_{\theta_{g}^{t}}\) represent the teacher's encoder and decoder, respectively. \(g\circ h\) is the composition function of \(h\) and \(g\). The second term in Equation (3) is the unsupervised segmentation loss \(\mathcal{L}_{unsup}^{t}\) for the pseudo-labels made using the new ensemble technique MVIE. We use MVIE to filter out only reliable pixel-level pseudo-labels and ignore unreliable ones. Therefore, unreliable pseudo-labels are not subject to supervision, and we define the pseudo-labels of the \(n\)-th teacher made via MVIE for the \(i\)-th unlabeled image at pixel \(j\), as follows:  
\[\hat{y}_{ij}^{(n)}= \begin{cases}0,&\text{if }\hat{y}_{ij}^{t(k)}=0,\ \forall k \
eq n,\\ &\quad\quad k\in\{1,2,\ldots,C-1\},\\ 1,&\text{if }\hat{y}_{ij}^{t(k)}=2,\ \forall k\
eq n,\\ &\quad\quad k\in\{1,2,\ldots,C-1\},\\ 2,&\text{if }\prod_{k=1,k\
eq n}^{C-1}\hat{y}_{ij}^{t(k)}=1,\\ \text{ignore,}&\text{otherwise},\end{cases}  \tag{5}\]  
where \(C\) represents the number of classes. The unsupervised segmentation loss \(\mathcal{L}_{unsup}^{t}\) is defined as  
\[\mathcal{L}_{unsup}^{t}=\frac{1}{|D_{u}|}\sum_{x_{i}^{n}\in D^{u}}l_{ce}(g_{ \theta_{g}^{t}}\circ h_{\theta_{h}^{t}}(A_{uv}(x_{ij}^{u})),\hat{y}_{ij}^{(n)}) \tag{6}\]  
where \(x_{ij}^{u}\) and \(\hat{y}_{ij}^{(n)}\) represent the \(i\)-th unlabeled image and corresponding pseudo-labels at pixel \(j\), respectively, and \(A_{uv}(\cdot)\) represents a weak augmentation function, such as image flipping, cropping, or scaling. Finally, each teacher is trained with a loss function, which is the weighted sum of Equation (4), \(\mathcal{L}_{sup}^{t}\), based on ground truth labels and Equation (6), \(\mathcal{L}_{unsup}^{t}\), based on pseudo-labels made through MVIE in Equation (5).
## 4 Receive Diversity
In the presence of channel\(h_{j}\), the instantaneous SNR,\(\gamma_{j}\), at \(j^{th}\) receive antenna is given by:  
\[\gamma_{j}=\frac{\left|h_{j}\right|^{2}E_{b}}{N_{0}} \tag{14}\]  
where \(\frac{E_{b}}{N_{0}}\) is the ratio of the bit energy to noise power spectral density. But since we are equalizing the channel with \(h^{H}\), with \(N\) receive antennas, the effective SNR is given by:  
\[\gamma_{b_{j}} = \sum_{j=1}^{N_{r}}\left|h_{j}\right|^{2}\frac{E_{b}}{N_{0}} \tag{15}\] \[\gamma_{b_{j}} = N_{r}Y_{j} \tag{16}\]  
The received symbols are then passed through a maximum-likelihood detector to produce the estimate of transmitted signal \(\tilde{\mathfrak{s}}(t)\).
# Globally Scheduling Volunteer Computing
## 1 Introduction  
Volunteer computing (VC) uses consumer devices, such as desktop and laptop computers, tablets, smartphones, appliances and cars, to do high-throughput scientific computing. People can participate in VC by installing a program that downloads and executes jobs from servers operated by science projects. About 900,000 devices are currently participating in VC. These devices have 5 million CPU cores and 560,000 GPUs, and provide an average throughput of over 100 PetaFLOPS.  
There are currently about 30 VC projects in many scientific areas and based at many institutions. The research enabled by VC has resulted in numerous papers in Nature, Science, PNAS, Physical Review, Proteins, PloS Biology, Bioinformatics, J. of Mol. Biol., J. Chem. Phys, and other top journals [1].  
Most VC projects use BOINC, an open-source middleware system [2] (one major project, Folding@home, does not use BOINC [3]). BOINC is a client/server system. Each project operates a server, which distributes jobs. Volunteers run the client program on their computing devices. A client can be \"attached\" to any set of projects. It periodically communicates with the project servers to report completed jobs and request new ones.  
Originally, BOINC volunteers had to browse the set of projects and decide which ones to attach their clients to. More recently, BOINC has moved to a \"coordinated model\" in which volunteers select science areas rather than projects. A central coordinator dynamically assigns devices to projects, based on volunteer preferences and other factors. This model has several advantages. Most notably, projects no longer must recruit volunteers, and new projects can be assured a certain level of computing power. This reduces the barriers to entry for new projects.  
The coordinated model is implemented in a system called Science United [4]. Science United is designed to act as a \"global scheduler\" in a literal sense: dividing the world's computing resources among the world's computational scientists. The way it does this--its \"scheduling policy\"--must satisfy certain constraints: for example, jobs with particular RAM or GPU requirements can only be run on devices with those resources. Furthermore, it must try to balance several possibly conflicting goals: to maximize computationalthroughput, to honor volunteer preferences, and to allocate computing power to projects in proportion to per-project \"shares\".  
This paper describes global scheduling in Science United. Section 2 outlines the architecture of BOINC. Section 3 describes volunteer preferences. Section 4 describes the scheduling policy. Section 5 discusses the implementation and results, and Section 6 proposes future work.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 2 Materials and Methods
### Measuring Device
#### 2.2.6 Operation Instructions  
Figure 5 shows the flowchart of the portable instrument used to detect the main milk components. First, 5 mL of a milk sample is measured and placed in a cuvette. The cuvette is then placed in the measuring device. After the instrument is powered on and the Read button is clicked, the LED source is turned on continuously for five seconds. Infrared light is then transmitted through the milk sample, and the multi-spectral sensor acquires six channels of wavelength data as the input features of the model. Finally, the Measure button is clicked, and the model outputs the protein and fat content data.
## 3 Results and Discussion
### Accumulation Characteristics in the Coking Chamber after Coal Charging
#### 3.4.6 The Establishment of the Prediction Model of Accumulation Characteristics  
After determining that the four factors had a significant influence on coal accumulation characteristics, a series of tests were designed to deduce the prediction model of coal accumulation.  
\[\gamma^{4}+2^{P}\gamma^{2}-2^{P-1}(P+0.5M_{0})=0 \tag{2}\]  
\(M_{0}\)-Number of basic tests, \(P\)-Number of variables, \(\gamma\)-Unknown variable. For this experiment, \(M_{0}=3\), \(P=4\), then \(\gamma=1.55\). The number of trials was \(2^{P}+2P+M_{0}=27\)[40]. By designing 27 experimental schemes and adopting polynomial stepwise regression method according to the results of experimental schemes, the prediction model of bulk density and repose angle was obtained, as shown in the Table 6.  
\[\text{BD}=a-b\cdot A-c\cdot B-d\cdot C+e\cdot D+f\cdot A\cdot B+g\cdot A\cdot C +h\cdot B^{2}-i\cdot D^{2}+g\cdot C^{2} \tag{3}\]  
\[\text{RA}=a-b\cdot A-c\cdot B+d\cdot C+e\cdot D+f\cdot A\cdot B+g\cdot B\cdot C -h\cdot A\cdot D+i\cdot B^{2}+g\cdot D^{2}-g\cdot C^{2} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Accumulation Parameter** & **Equation** & \(\mathbf{R^{2}}\) \\ \hline Repose angle & y = 0.565x + 20.919 & 0.9923 \\ Bulk density & y = 14.333x + 698.44 & 0.9971 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The result of linear fitting.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Factor** & **Dependnt Variable** & **SUM of Square** & **DOF** & **MMSE** & **F** & **P** \\ \hline A & \(\rho\) & 10,912.27 & 4 & 2728.07 & 6.037 & 0.015 \\ A & \(\alpha\) & 74.40 & 4 & 18.60 & 41.129 & 0.000 \\ B & \(\rho\) & 18,998.18 & 4 & 4749.55 & 10.51 & 0.003 \\ A & 66.40 & 4 & 16.60 & 36.71 & 0.000 \\ C & \(\rho\) & 11,145.45 & 4 & 2786.36 & 6.17 & 0.014 \\ A & 56.11 & 4 & 14.03 & 31.02 & 0.000 \\ D & \(\rho\) & 7738.62 & 4 & 1934.66 & 4.28 & 0.038 \\ a & 2.50 & 4 & 0.63 & 1.38 & 0.322 \\ Error E & \(\rho\) & 3615.10 & 8 & 451.89 & & \\ a & 3.62 & 8 & 0.452 & & \\ \(\rho\) & 14,065,018.95 & 25 & & & \\ a & 17,213.98 & 25 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of variance.  
BD-Bulk density, kg\(\cdot\)m\({}^{-3}\); RA-Repose angle; a\(-\)g are equation coefficients. The experimental results were substituted into (3) and (4) for comparison between the real value and the predicted value. The comparison results are shown in Figure 15. Through comparison between real accumulation and predicted accumulation, the prediction model had a high accuracy.
## 4 Results and Discussion
### Mathematical Modeling
#### 4.2.2 Dimensional Analysis
Therefore, we choose the results of the linear regression method, and the final dimensionless correlation to predict \(U_{\text{0 mf}}\) and \(\Delta P_{\text{mf}}\) is as follows:  
\[U_{\text{0 mf}}=(\text{Ar})^{0.504}\cdot\left(\frac{\varepsilon_{0}}{\phi} \right)^{-1.355}\cdot\left(\frac{H_{0}}{D_{0}}\right)^{0.326}\cdot\left(\frac{d _{\text{p}}}{D_{0}}\right)^{0.236}\cdot\left(\frac{\beta}{R}\right)^{-0.635} \cdot\frac{\mu}{d_{\text{p}}\rho_{\text{g}}} \tag{31}\]  
\[\Delta P_{\text{mf}}=(\text{Ar})^{-0.003}\cdot\left(\frac{\varepsilon_{0}}{ \phi}\right)^{-0.642}\cdot\left(\frac{H_{0}}{D_{0}}\right)^{0.020}\cdot\left( \frac{d_{\text{p}}}{D_{0}}\right)^{0.010}\cdot\left(\frac{\beta}{R}\right)^{-0.354}\cdot\rho_{\text{g}}gH_{0} \tag{32}\]
# An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning
## 3 Materials and Methods
### Deep Learning Models
#### 3.3.1 Long Short-Term Memory (LSTM)  
The Artificial Neural Networks (ANNs) can handle the multifaceted associations but cannot detect long-term dependences in time-service data [32], and the prediction performance primarily depends on the dataset features. On the other hand, the Recurrent Neural Network (RNN) model is the one type of ANN that can resolve the handle temporal dependencies using the previously hidden state of data and better predict the short-term time-series data, unable to predict the long-term dependencies. The LSTM [33] model resolves the RNN with sophisticated gated memory in the network for effective sequence prediction. The LSTM is a sophisticated gated memory created to address the vanishing gradient issues that limit a simple RNN. Desirable features of the LSTM include their prolonged ability to detect long-term dependences and to process time-series data. Its hidden units in each layer in the memory block allow the information flow between the input and output ports. The sigmoid function determines which information should be forgotten, saved, and passed the next hidden state within the input and output gates. This model performance is highly reliant on tuning the hyper-parameters for better performance [34]. We implemented the LSTM model using TensorFlow 2.0 of the python package. We used only a set of parameters (units, activation function, return sequence, and dropout) of LSTM from the list of 23 parameters available in the LSTM layer and kept the rest of the parameters with default values. We used 200 epochs with batch size of 64 and verbose of 2, with validation split as 0.2.
# \"Network Sentiment\" Framework to Improve Security and Privacy for Smart Home
## 4 Network Sentiment
### Security Considerations  
Like every networked system, also SHIELD is sensible to threats. As a matter of fact, an attacker could take advantage of the SHIELD system to fake an ongoing attack, causing (for example) a DoS. For this reason, it is important that the SHIELD system is protected and considered as a primary asset of the ISP. If we define a security zone as a network area with a well-defined perimeter and a strict boundary protection, we have that:  
* The SLU and the SCN are in the same security zone,
* The SLU and the SH are in different security zones,
* The SCN and the SH are in different security zones.  
In other terms, we can safely assume that the SLU and the SCN are inside the administrative domain of the ISP and their communication security is automatically guaranteed. On the contrary, a (MiM) attack between the SH and the ISP network is considered possible because an attacker can disguise itself as a legitimate user. As a consequence, the communications between the IDS probes (or the distributed IDS system) and the SH must be adequately secured.  
It is out of the scope of this paper to describe the security algorithms that can be used to properly secure the above mentioned communication channels but the system should, at minimum, provide a strong authentication between the SH and the other two SHIELD entities, possibly by using certificates and/or smart cards. Moreover, the SH device should pass severe vulnerability assessment tests and be tamper-proof. If this is not the case, the SHIELD system must consider all the attacks reports from the users as simple warnings (low confidence reports), and use consensus algorithms to reject spurious data.