esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Instance Feature Extraction Step
#### 3.1.2 Feature Metrics  
Three sets of features will be used to numerically characterize CVRPTW problem instances: Spatial, Clustering, and Time Window features.  
Spatial Features  
Mainly taken from [13], this set of features describes the client distribution and the level of _vehicle burden_ of each problem instance. We define vehicle burden as the amount of work each vehicle must undertake for all clients to be served. Naturally, vehicle burden is inversely proportional to the number of vehicles and their capacity, while increasing with client demands and the number of clients. Here, we consider the following features:  
1. Number of clients: Measures the instance size.
2. Distance from centroid to depot: Measures client distribution bias with regards to the depot.
3. Average distance between depot and clients: Measures the degree of clustering around the depot.
4. Average distance between centroid and clients: Measures client distribution density.
5. Coefficient of Variation (CV) of distance between centroid and clients: Measures client distribution heterogeneity. The coefficient of variation was used over standard deviation as it tended to be correlated with the average.
6. Ratio of average client demand to capacity: Measures vehicle burden.
7. Ratio of the standard deviation of client demands to capacity: Measures vehicle burden heterogeneity.
8. Ratio of clients to vehicles: Measures vehicle burden.
9. Average of distances to the nearest neighbor: Measures client distribution density.
10. CV of distances to the nearest neighbor: Measures uniformity of client distribution density.  
\begin{table}
\begin{tabular}{c c c} \hline
**Set** & **Client Distribution** & **Capacity** \\ \hline C1 & Densely Clustered & 200 \\ C2 & Sparsely Clustered & 700 \\ R1 & Random & 200 \\ R2 & Random & 1000 \\ RC1 & Semi Clustered & 200 \\ RC2 & Semi Clustered & 1000 \\ \hline \end{tabular}
\end{table}
Table 1: Grouping subsets considering the client distribution and capacity of vehicles.  
All the previously distance-related features defined have been normalized by the longest possible distance in the corresponding problem instance. This value was computed as the diagonal of the instance's bounding box. This is to prevent the instance size from influencing the values of other features.  
For nearest neighbor based features, i.e., _Average of distances to the nearest neighbor_, and _CV of distances to the nearest neighbor_, a value of \(k=2\) was chosen as presented in [13]. The authors initially decided to use average, standard deviation, skewness, kurtosis, and CV for all spatial features. They also applied a principal analysis component and reduced the initial set of 386 features to seven components, containing 71% of the data set's variance. Here, we use only average and CV metrics to reduce the _curse of dimensionality_. We also avoid using the principal analysis component algorithm to maintain the interpretability of our results.
## 3 Materials and Methods
### Data Preparation for Algorithm Validation  
#### 3.3.1 Simulated Students' Data  
Before applying the proposed in this paper algorithm to assess real students, a number of simulations were performed. The number of synthetic students in each experiment was set to 120, to be close to an expected number of real students. A large number would give EBA an advantage, as it would mean that each question item would receive more answers from students simplifying its difficulty estimation procedure. Each simulated assessment had items on multiple topics. Each topic was represented by a vector of weights equal in length to the number of items within the quiz. Each element of the topic vector denoted the item's relevancy to the topic. The number of items on each topic was set to be equal when possible. When the number of items was indivisible by the number of topics, the remaining items were assigned to separate topics.  
The difficulty of simulated items, seed Elo, was generated using a normal distribution with mean, \(\mu=1000\), and standard deviation, \(\sigma=300\). Each synthetic student was assigned a general level of knowledge \(sElo\) from a normal distribution, \(\mu=1000\) and \(\sigma=400\). Then that value was used as a basis for topic knowledge \(sElo_{t}\), \(\mu=\) SElo and \(\sigma=200\). Finally, the student's capacity to answer an item was calculated by estimating relevant to the item Elo rating using, item weights, \(w_{ti}\), and topic knowledge:  
\[aElo_{si}=\sum_{t=1}^{T}sElo_{t}*w_{ti} \tag{4}\]  
Then the correct answer was chosen as a Bernoulli random variable with the probability calculated using Equation (2), guess factor \(P_{g}=0.25\).
## 2 Experimental Setup
### Reference Pulse Characterization  
Using one of the aluminum-coated D-shaped mirrors (Figure 1, BS3), the reference pulse was split into two parts. One passed through an optical delay line controlled with a closed-loop piezoelectric actuator. These two laser pulses were non-collinearly focused into an SD medium (cover glass, thickness 170 \(\upmu\)m) by an aluminum-coated concave mirror (_f_ = 200 mm). The generated SD signal was focused into an optical fiber (QP200-2 VIS-BX, core diameter 200 \(\upmu\)m, length 2 m, Ocean Optics, Dunedin, FL, USA) equipped with a multi-channel spectrometer (Maya2000-Pro, Ocean Optics) by a fused-silica lens (_f_ = 50 mm). By scanning the time delay using the piezoelectric actuator, the SD FROG trace was obtained for the reference pulse. A pinhole was used to extract the SD signal. The mirror in front of the SD medium (RM in Figure 1) was then removed to guide the two split pulses into a 5-\(\upmu\)m-thick type-I beta-barium borate crystal (BBO, cutting angle \(\phi\) = 90\({}^{\circ}\) and \(\theta\) = 45\({}^{\circ}\)) to generate the second harmonic of the reference pulse. The distance from RM to the BBO crystal is designed to the same value to that from RM to the SD medium. The silver mirror (RM) has a flat curve for the reflection ratio between 750 nm and 850 nm. For these reasons, the difference in the optical pass is negligible. By focusing the SHG beam into the optical fiber-coupled multi-channel spectrometer using a concave mirror (_f_ = 50 mm) and scanning the time delay using the piezoelectric actuator, the SHG FROG trace was acquired for the reference pulse. A pinhole was used to extract the SHG signal. All the phase  
Figure 1: Experimental setup. WP: half-wave plate; DM: dichroic mirror; CM1,2: concave mirror; SF: sum-frequency signal; SH: second harmonic signal; SD: self-diffraction signal; BBO: BBO crystal; RM: removable mirror; MS: multi-channel spectrometer; BS1–4: beam splitter; CG: cover glass.  
retrievals in the present research were performed using commercial software (FROG3.1.2, FemtoSoft Technologies, 2004, Burlington, MA, USA), and random noise signals were used as initial guesses.
# On Beamforming with the Single-Sideband Transform
## 4 Comparisons and Simulations
### General Simulation Results  
For all simulations, the ideal SSBT-A results are similar (although mostly slightly worse) to the STFT-A results. Given that the SSBT beamformer is a strictly real-valued filter, this could be a worthwhile tradeoff of a slight performance loss for a faster beamforming algorithm, leading to a cheaper implementation. However, for a useful implementation of the SSBT in beamforming, these results should be followed in the estimated RFR case. However, in this scenario, the SSBT results were drastically worse when compared to those through the STFT, except for the specific case of a multiplicative filter, with a large number of samples per frame and a low iSER. This suggests that employing the SSBT with an inherently more robust beamformer with the MTF and high \(N\) could lead to viable applications.
# Skin Lesion Segmentation Using Image Bit-Plane Multilayer Approach
## 6 Experimental Results  
### Evaluation Metrics  
The performance of the implemented procedure was assessed by comparing the CADe outputs with the ground truths indicated by expert clinicians, which are collected inside the used database.  
The metrics suggested in the International Skin Imaging Collaboration (ISIC) challenges are evaluated for benchmarking [44; 45; 46]. The sensitivity represents the method ability to correctly identify the lesion pixels in the image, while the specificity is the method ability to correctly identify the  
Figure 3: Image segmentation into color coherent areas by the proposed approach. The outer circular area is the selected ROI.  
non-lesion pixels. The Jaccard index and the Dice coefficient measure the similarity between the obtained segmentation result and the ground-truth mask. Finally, the accuracy was defined as the observed agreement between the procedure results and the physician opinion about the lesion under test and the balanced accuracy was the arithmetic mean of class-specific accuracies.  
The aforementioned metrics are computed adopting the following expressions:  
\[Sensitivity\;(Se)=\frac{TP}{TP+FN} \tag{2}\]  
\[Specificity\;(Sp)=\frac{TN}{TN+FP} \tag{3}\]  
\[Accuracy\;(AC)=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]  
\[Dice\;coefficient(DI)=\frac{2 TP}{2 TP+FN+FP} \tag{5}\]  
\[ Jaccard\;index\;(JA)=\frac{TP}{TP+FN+FP} \tag{6}\]  
where:  
* TP (number of true positives) is the number of lesion pixels correctly classified as lesion inside the image under test.
* FN (the number of false negatives) is the number of lesion pixels incorrectly identified as non-lesion.
* FP (the number of false positives) is the number of non-lesion pixels incorrectly identified as lesion.
* TN (the number of true negatives) is the number of non-lesion pixels correctly identified as non-lesion.  
Usually, metrics with the highest values are recommended for computer-aided systems but a trade-off shall be achieved according to their relative importance [47; 48].
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 3 Theoretical Background
### RNN-Based Long Short-Term Memory  
Despite extensive research, the non-stationary demand data and the long-term forecasting horizon of the STLF make it difficult to obtain an accurate estimate. The capacity of LSTM to identify long-term dependencies in a time series is its key benefit. The LSTM representation was chosen as one input because it contains methods that account for the sequential character of time series. Therefore, we utilized the LSTM, a specialized form of RNN architecture, to tackle the short-term load forecasting challenge [47; 48].  
The memory cell, which acts as an accumulation of the state information, is the primary innovation of the LSTM. Three functions of gate controllers are demonstrated as follows [49]:  
* Forget gate \(f_{(t)}\) decides which part of the long-term state \(c_{(t)}\) should be omitted.
* Input gate \(i_{(t)}\) controls which part of \(\widetilde{c}_{t}\) should be added to the long-term state \(c_{(t)}\).
* Output gate \(o_{(t)}\) determines which part of \(c_{t}\) should be read and outputs to \(h_{(t)}\) and \(y_{(t)}\).  
The forget gate is used to determine which information to remove from the cell state, as seen in Figure 3.  
\[f_{(t)}=\sigma\left(W_{f^{\prime}}\left[h_{(t-1)},x_{(t)}\right]+b_{f}\right) \tag{6}\]  
\[i_{(t)}= \sigma\left(W_{i^{\prime}}\!\left[h_{(t-1)},x_{(t)}\right]+b_{i}\right) \tag{7}\]  
\[\widetilde{c}_{(t)}=\tanh\!\left(W_{c^{\prime}}\!\left[h_{(t-1)},x_{(t)} \right]\right)+b_{c} \tag{8}\]  
\[c_{(t)}=f_{(t)}*c_{(t-1)}+i_{(t)}*\widetilde{c}_{(t)} \tag{9}\]  
In conclusion, it is necessary to decide on the output, which involves two steps. First, a sigmoid layer is utilized as an output gate to selectively filter the cell state. Subsequently, the cell state is passed through a _tanh_ function and multiplied by the output \(o_{t}\) to obtain the desired information.  
\[o_{(t)}=\sigma\left(W_{o^{\prime}}\!\left[h_{(t-1)},x_{(t)}\right]\right)+b_{o} \tag{10}\]  
\[h_{(t)}=o_{(t)}*\tanh\!\left(c_{(t)}\right) \tag{11}\]  
\(W_{i}\), \(W_{f}\), \(W_{c}\), and \(W_{o}\) represent the appropriate weight matrices. The vectors \(b_{i},\ b_{f},b_{c}\), and \(b_{o}\) denote the corresponding bias vectors. The most significant difficulty associated with the LSTM recurrent neural network lies in the process of adjusting its hyperparameters, such as determining the appropriate number of hidden layers, the nodes within each layer, the batch size, the number of epochs, and the learning rate, and optimizing the connection weights, biases of the network, etc. [51].  
Figure 3: The architecture of LSTM memory block [50].
## 4 Chemical Methods for Synthesis of Magnetic Ferrite Nanoparticles
### Chemical Coprecipitation Method  
The chemical coprecipitation synthesis method is widely used to produce multifunctional magnetic ferrite nanoparticles. As shown in Figure 5, this method has advantages over other methods due to its simplicity, high degree of control over particle size and distribution, highly pure and homogeneous nanoparticles, and cost-effectiveness [131; 132; 133; 134]. By adjusting parameters such as pH (typically between 8 and 14), temperature (ranging from 25 \({}^{\circ}\)C to 100 \({}^{\circ}\)C), salt concentrations (normally 0.1 M to 1.0 M), duration (generally  
Figure 4: Schematic representation of the magnetic ferrite nanoparticles’ application in water treatment.  
Figure 3: The schematic representation of chemical synthesis methods used to prepare magnetic ferrite nanoparticles with various shapes and morphologies.  
from 30 min to several hours), and the concentration of growth stabilizers (such as citric acid, oleic acid, and polyethylene glycol in concentrations ranging from 0.01 M to 0.1 M), one can precisely control the size, distribution, morphology, and magnetic properties of magnetite ferrite nanoparticles [70; 131; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144].  
The principle of chemical coprecipitation synthesis in aqueous solutions is the growth of iron hydroxide nuclei in monophasic aqueous solutions in media. During synthesis, a precipitate of Me (II) and Fe (III) appears as jelly iron oxide deposits when an alkali, such as NH\({}_{4}\)OH or NaOH, is introduced to a solution containing Fe (III), Fe (II), or Me (II) salts, where Me (II) represents another metallic element salt [146; 147; 148]. This reaction leads to the formation of ferrite (MeFe\({}_{2}\)O\({}_{4}\)) and water to achieve the desired magnetic properties; the resulting ferrite can then be processed further [149]. The following general equation can represent the coprecipitation reaction for synthesizing ferrites [150].  
\[\mathrm{Me}^{2+}+2\mathrm{Fe}^{3+}+8\mathrm{OH}^{-}\mathrm{\rightarrow}\mathrm{ MeFe}_{2}\mathrm{O}_{4}+4\mathrm{H}_{2}\mathrm{O}\]  
Chemical coprecipitation synthesis in aqueous solutions is most likely a way to synthesize superparamagnetic ferrite nanoparticles (SPIONs) [136; 151; 152; 153; 154]. Additional surfactants are introduced to attain electrostatic stabilization during the coprecipitation synthesis of magnetic ferrite nanoparticles. Stabilizers such as citric and oleic acid, and polyethylene glycol, among others, are added to the reaction solution, resulting in the formation of a charged layer around each magnetic ferrite particle [135; 155].
## 2 Introduction to Taipower Auxiliary Services  
The Taipower power trading platform [25] operates as a mechanism for power transactions, with a focus on auxiliary services [26] and reserve capacity. Established for the auxiliary services market, the platform incorporates a greater number of rapidly responsive, flexible, and diverse distributed resources into the power system. Taipower's auxiliary service products encompass a series of services aimed at ensuring the safe operation of the power system, restoring system security, and ensuring a stable power supply. Figure 1 illustrates the current types of auxiliary services promoted and implemented by Taipower. Currently, there are three auxiliary service transaction projects in the market: regulation reserve, spinning reserve, and supplemental reserve.  
Due to the variability in the power grid during the power generation process, significant fluctuations can lead to overall instability in grid frequency. Therefore, frequency regulation services are necessary to assist in stabilizing the grid frequency. Frequency regulation reserve services can be further divided into static regulation reserve (sReg) and dynamic regulation reserve (dReg). Within the dReg category, there are three technical specifications: dReg0.25, dReg0.5, and E-dReg. The technical specifications for E-dReg are introduced as follows.
# A Survey of Internet of Things (IoT) Authentication Schemes +
## 6 Related Works  
Several research works surveyed the IoT authentication solutions. The authors of [154] presented a survey of the authentication protocols deployed in Internet of Things (IoT) use cases by categorizing them into four categories: Internet of Sensors (IoS), Internet of Energy (IoE), Machine-to-Machine (M2M) communication, and Internet of Vehicles (IoV). A taxonomy and comparison of authentication protocols are provided in terms of: network model, goals, main processes, computation complexity, and communication overhead. In [155], the authors provided a classification and comparison of different authentication protocols for IoT. The authors created two different classifications: the first one based on whether the authentication technique is being distributed or centralized, flat or hierarchical, and the second one based on the characteristics of the authentication process. In [156], the authors provided an analytical survey of the existing literature. They outlined the potential issues and challenges of authentication in IoT for further research but without providing a comparison of the existing schemes. The authors of [157] provided different authentication methods used within the IoT context with emphasis on lightweight and mutual authentication methods but without classification or comparison. In [158], a review of the prominent and \"recent\" authentication techniques for IoT systems is provided with an indication of their limitations and future research trends, but without providing a classification. The authors of [159] provided a classification of selected research works addressing security concerns and authentication methods, in addition to their evaluation in terms of advantages and disadvantages. In [160], the authors mapped the current state of the art of the authentication in an IoT environment, featuring the difficulties and the primary methods utilized in authentication scheme but without performing any comparison. The authors of [161] provided a brief overview of authentication mechanisms with an evaluation of the proposed schemes in the literature. They also evaluated each method based on its resource consumption (e.g., energy, memory, computation and communication).
# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 3 Methods and Techniques
### Neighborhood Component Analysis (NCA) Feature Selection  
After the extraction of features with the DCNN segment of our proposed model, it is necessary to select relevant features in order to prevent misclassification and increase the accuracy of the entire model. Feature selection [34] occupies a significant position in this work, as it determines the level of performance. Many models other than the proposed model could have yielded an optimal result in speech emotion classification, but the absence of an appropriate feature selection algorithm incapacitated those models. It is very essential to know that huge features from speech data can be extracted, but not all these features are relevant [35] for emotion classification. The more irrelevant the features in the classifier, the greater the likelihood of misclassification and reduction in the accuracy and efficiency of the SEC model. However, even though feature selection is important, a careful choice of which feature selection techniques perform best and under which condition is another dicey question. The choice must be made depending on the model architectural framework. In this work, NCA, a feature selection method based on distance, has been chosen for selecting discriminative features and its performance in maximizing classification accuracy. The normalized equation for computing the NCA feature selection for a multi-class SEC problem is given in Equation (3).  
\[S=\{(x_{i},y_{i}),i=1,2,\ldots,n\} \tag{3}\]  
where \(n\) is the number of observations, \(x_{i}\in\mathbb{R}^{p}\) denotes the feature vectors, \(y_{i}\in\{1,2,\ldots,c\}\) denotes class labels of emotional features, and c is the number of emotional class labels. To simplify, the goal is to provide relevant features (from the feature extraction segment of the model) for the classifier f: \(\mathbb{R}^{p}\rightarrow\{1,2,\ldots,c\}\), which takes a feature vector and makes classification \(f(x)\) for the ground truth (label) \(y\) of \(x\). The NCA algorithmic [36] procedure is defined in Algorithm 1.  
```
1:procedureNCAFS(T,\(\alpha\), \(\sigma\), \(\lambda\), \(\eta\) ) \(\rhd\) T: set of training,\(\alpha\): initial step length, \(\sigma\): kernel width, \(\lambda\): parameter for regularization, \(\eta\): small positive integer constant;
2: Initialization: \(w^{0}=(1,1,\ldots,1)\), \(e^{0}=-\infty\), \(t=0\)
3:while\([e^{t}-e^{t-1}]<\eta\)
4:for\(i=1,\ \ldots,N\)do
5: Compute \(p_{ij}\) and \(p_{i}\) using \(\mathbf{w}^{t}\) with respect to (2) and (3)
6:for\(l=1,\ \ldots,d\)do
7:\(\Delta l=2\Big{(}\frac{i}{\pi}\Sigma_{i}\Big{(}p_{i}\Sigma_{i}\
eq jp_{ij}|x_{il }-x_{jl}|-\Sigma_{j}y_{ij}p_{ij}|x_{il}-y_{il}|\Big{)}-\lambda\Big{)}w_{l}^{t}\)
8: t = t + 1
9:\(w^{t}=w^{t-1}+\alpha\Delta\)
10:\(\epsilon^{t}=\xi(w^{t-1})\)
11:if\(e^{t}>e^{t-1}\)then
12:\(\alpha=1.01\alpha\)
13:else
14:\(\alpha=0.4\alpha\)
15:wend
16:\(w=w^{t}\)
17:return
```  
**Algorithm 1** NCA Feature Selection Procedure
# Bearing Behavior of Cold-Formed Thick-Walled Steel Plates with a Single Bolt
## 2 Experimental Investigation
### Mechanical Properties of Cold-Formed Thick-Walled Steel  
Steel plates used in the tensile coupon test were taken from the same batch and same location as that of lap shear test. For each thickness of cold-formed thick-walled steel plates, three tensile coupon tests were conducted in accordance with GB/T 228.1-2010 [18]. Mechanical properties were also tested for 10 mm virgin steel plates. Stress-strain relationships of all steel plates are presented in Figure 2, and the measured material properties are summarized in Table 2 in which \(E\) is the elasticity modulus, \(f_{\rm y}\) is the yield strength, \(f_{\rm u}\) is the tensile strength, \(\varepsilon_{\rm u}\) is the strain corresponding to the tensile strength, and \(\Delta\) is the elongation ratio at fracture.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen ID** & **f (mm)** & **\(e_{1}\)/\(d_{0}\)** & **\(e_{2}\)/\(d_{0}\)** & **\(d_{0}\) (mm)** \\ \hline D6.0-1.0-3.0 & 6  
Being different from cold-formed thin-walled steels, which are generally characterized by a rounded stress-strain response with no yield plateau, curves of stress-strain relationship of cold-formed thick-walled steel are similar to those of hot-rolled steels, which can be characterized by four ranges: being linearly elastic, yielding, hardening, and necking. However, cold-formed thick-walled steels have higher strength and smaller ductility than those of hot-rolled steels. For example, the tensile strength of 10 mm cold-formed steel increased by 9.6% compared to the corresponding value of 10 mm virgin steel.