esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Evaluation Metrics  
Our experiment evaluated the impact of different feature extraction techniques on a selected set of ML, DL, and transformer models that are well known for their performance in text classification problems. The performance of each classifier was evaluated based on average precision (P), average recall (R), and average F1-score (F1-score). These were computed using the confusion matrix, which was created by applying the classification model. The precision, recall, and F1-score measures were computed using Equations (2), (3), and (4), respectively:  
\[\text{Precision }(\text{P})=\frac{\text{TruePositive}}{\text{TruePositive}+ \text{TrueNegative}} \tag{2}\]  
\[\text{Recall }(\text{R})=\frac{\text{TruePositive}}{\text{TruePositive}+ \text{FalseNegative}} \tag{3}\]  
\[\text{F1-Measure }(\text{F1})=\frac{2.\text{P.R}}{\text{P}+\text{R}} \tag{4}\]  
We chose to calculate the results in the F1-score, as it is used when false negatives and false positives are more significant than true positives and true negatives, as in accuracy. In the majority of real-world classification problems, class distribution is unbalanced [90]; therefore, the F1-score is a more appropriate metric for evaluating our model.  
Models were developed using Python version 3.7 using the Tensorflow Keras sequential API. All experiments were carried out on a system with a dual-core Intel Core i7 processor and 11 GB of RAM, running on a Macintosh operating system with a 64-bit processor and access to an NVidia K80 GPU kernel. The dataset included a total of 1536 combined satirical and non-satirical articles, specifically, 768 satirical and 768 non-satirical articles. Furthermore, 70% training and 30% testing ratios were conducted.
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 2 Materials and Methods
### Signal Processing
#### 2.2.2 Other Biosignals  
The Savitzky-Golay (SG) filter was used for denoising the ACC [54], ST [55], HR [11], and GSR [55] measurements, whereas the BVP signal was processed to remove artifacts and noise. The process of  
Figure 5: Noise cancellation and artifact removal with nonlinear recursive least-squares (NRLS) adaptive filter.  
Figure 6: Blood-volume pulse (BVP) signal processing improvements after signal processing implementations (During treadmill (TR) exercise).  
tuning the filtering parameters involves iterative trial-and-error while maximizing the classification accuracy. Different values for the order and frame length were evaluated to find the best filter parameters based on the classification outcomes. After tuning the parameters of the SG filter, the order and frame length parameters were determined for the measurements as: ACC (order = 7 and frame length = 15), ST (5 and 9), HR (5 and 9), and GSR (5 and 11). Signal-to-noise ratio (SNR) is a measure that compares the level of a desired signal to the level of background noise. SNR is defined as the ratio of signal power to the noise power, often expressed in decibels. Higher numbers generally mean a better specification, since there is more useful information (the signal) than unwanted data (the noise). The SNR is calculated as:  
\[SNR=P_{\text{signal}}/P_{\text{noise}}, \tag{6}\]  
where \(P_{\text{signal}}\) denotes the average power of the signal and \(P_{\text{noise}}\) denotes the average power of the noise. We calculated the SNR values for each signal before and after the specified signal processing algorithms. A randomly selected data set was analyzed through the filtering algorithms and the improvements were assessed. The findings showed that SNR of the processed signals were consistently higher than raw measurements (overall SNR values improved 57% (absolute percentage changes)), which showed that the signal cleaning algorithms improve the signal quality by reducing the noise in the signals.
# A Domain-Independent Classification Model for Sentiment Analysis Using Neural Models
## 3 Approach
### Evaluation Metrics and Validation Concept  
To evaluate the overall performance of the classifiers, we consider several performance metrics. In particular, we use precision, recall, f-measure, and accuracy, as in [32].  
Equations (1)-(4) show mathematical expressions of the metrics accuracy, precision, recall, and f-measure, respectively, where TP, TN, FP, and FN refer respectively to \"True Positives\", \"True Negatives\", \"False Positives\", and \"False Negatives\", respectively:  
\[Accuracy=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+\text{TN}} \tag{1}\]  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[F1=\frac{2\cdot precision\cdot recall}{precision+recall} \tag{4}\]  
Regarding the evaluation scenarios, we consider two cases: the domain-dependent and domain-independent cases. Domain-dependent means training and testing have been performed for each dataset. Domain-independent means the training has been performed on a IMDb datasets of subjects and testing has been performed on a totally new datasets. The reason for training on IMDb is due to its large size and thus can support a better generalized model if the training has been preformed and regularized properly.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Parameters** \\ \hline SVM (poly) & Degree of the polynomial kernel function = 3, \(\gamma=\frac{1}{numberfeatures}\) \\ SVM (rbf) & \(\gamma=\frac{1}{numberfeatures}\) \\ Random Forest & Number of estimators estimators =10 trees, criterion = Gini impurity, \\  & The minimum number of samples required to split an internal node = 2 \\ Naive Bayes & Prior = probabilities of the classes \\ Proposed (CNN) & Loss = Softmax, optimizer = Adamax, \(batch\_size=\)1000, epochs = 30 \\ Proposed (SNN) & Loss = cross entropy, optimizer = Adam, \(batch\_size=\)128, epochs = 40, \\  & lr = 0.001, \(beta_{1}=\) 0.9, \(beta_{2}=\) 0.999, epsilon = 0.01, decay = 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of parameters of proposed CNN, SNN, and other classifiers.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Learning-Based Methods  
Unlike traditional methods, learning-based methods [67] can improve the accuracy of the evaluation results by learning the training image features and achieving the mapping of quality scores. The general framework of learning-based methods is shown in Figure 6. The methods can be divided into SVM-based, deep learning-based and dictionary-based methods.
# Macro-Modelling Approach for the In-Plane Cyclic Response of Cold-Formed Steel Partition Walls
## 3 Development of the Proposed Analytical Model  
### In-Plane Displacements of CFS Partition Walls  
When a CFS partition wall exposed is to earthquakes, its in-plane horizontal displacements are mainly dependent on the racking of GBs. To simplify the in-plane displacement of CFS partition walls, the racking behavior can be divided into vertical and horizontal displacements at the corner of GBs. As shown in Figure 3, it is assumed that the GB rotates around its centroid which agrees with that of a connected stud, and the that studs have no initial imperfection. Vertical displacement at the centroid of a GB does not occur when a horizontal displacement, \(\delta_{h}\) is imposed at the top of the partition wall. The vertical displacement, \(\delta_{v}\) at the corner of a GB can be derived using the triangle proportionality theorem. A value of rotation angle, \(\theta\) of the GB is defined using similarities among triangles denoted as 1, 2, and 3 in the figure. From triangle 1 in the figure, the value of \(\theta\) is first derived:  
\[\theta=\arctan(\delta_{h}/H) \tag{1}\]  
where \(H\) is the height of the CFS partition wall.  
Values of \(y_{1}\) and \(y_{2}\) shown in triangles 2 and 3 can be expressed as:  
\[y_{1}=\frac{H}{2}\cos\theta=\frac{H}{2}\times\frac{H}{\sqrt{H^{2}+\delta_{h}^{ 2}}} \tag{2a}\] \[y_{2}=\frac{W}{2}\sin\theta=\frac{W}{2}\times\frac{\delta_{h}}{\sqrt{H^{2}+ \delta_{h}^{2}}} \tag{2b}\]  
where \(W\) is the width of the GB. The vertical displacement, \(\delta_{v}\) at the corner of the GB is a relative displacement from the original position \(A\) to the deformed position \(A^{\prime}\). An ordinate of \(A\) from the  
Figure 2: Damage mechanism of a typical CFS partition wall.  
bottom of the partition wall is \(H\) while that of \(A\)' is \(0.5H+y_{1}+y_{2}\) shown in triangles 2 and 3. Finally, the value of \(\delta_{v}\) due to the racking behavior of a GB can be calculated from:  
\[\delta_{v}=\left(\frac{H}{2}+y_{1}+y_{2}\right)-H=-\frac{H}{2}+\frac{(H^{2}+W \delta_{h})}{2\sqrt{H^{2}+\delta_{h}^{2}}} \tag{3}\]  
In which the racking behavior is represented by the displacement, \(\delta_{h}\) and the dimensions of the partition wall.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 3 Advantages and Challenges
### Advantages  
The advantages of massive MIMO are listed below [7]:  
* _High spectral efficiency_: In massive MIMO, using large number of BS antennas and having aggressive spatial multiplexing gives the ability to the users to simultaneously use the same frequency band without interference. This increases the spectral efficiency of the system.
* _High power efficiency_: Since in massive MIMO we have large number of BS antennas, the energy can be concentrated in a small area and this increases the power efficiency. In these systems the users can reduce their transmit power as we increase the number of BS antennas, while being able to have a performance similar to the SISO case.  
Figure 2: Block diagram of massive MIMO system in downlink.  
* _High degree of freedom_: Massive MIMO systems have high degree of freedom. For example if we have 200 antennas at the BS and 20 single antenna users, then we have 180 degree of freedom which can be used to help reduce the interference. In other words, we can design the transmitted signals from the BS antennas in a way that all these signals are added constructively at the users and destructively almost anywhere else.
* _Enabling reduction of latency_: Fading limits the performance of wireless communications systems and makes building low latency links hard. When a user experiences a fading dip it has to wait for suitable change of the channel before receiving the data. However, massive MIMO systems avoid fading dip by having large number of BS antennas and, therefore, enable reduction of latency.
# Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference
## 2 Controller Design
### Lateral Control  
As shown in Figure 5, the lateral controller is composed of PPC-FAR and a fuzzy logic inference machine. PPC-FAR calculates the desired steering angle to track the desired path. Meanwhile, the fuzzy logic inference machine is provided to turn the suboptimal parameters of PPC-FAR according to the desired path and the current velocity of the driverless bus.
# Research on Sentiment Classification of Online Travel Review Text
## 2 Materials and Methods
### Text Representation  
(1) Text representation of comments based on Word2 vec  
Representing text as structured data that is able to be handled by machine learning classification algorithms is a highly important part of the text classification process. In 2013, Google released the software tool Word2 vec for training word vectors [62]. Word2 vec's high-dimensional vector model solves the multi-dimensional semantic problem, because it can quickly and effectively express words in high-dimensional vector form through the optimized training model according to a given corpus,thereby providing a new tool for the application research in the field of natural language processing [63]. Academic research [64; 65] demonstrates that Word2 vec has achieved excellent performance in the fields of text similarity calculation and text classification. In light of the above analysis, this study opted to construct Word2 vec vectors for the pre-processed and semantically expanded comment text.  
(2) Data normalization  
Normalized data exhibits enhance stability for attributes with very small variances, while maintaining 0 entries in the sparse matrix [62]. Therefore, this study used the normalization method to scale the text vector represented by Word2 vec to between 1 and 0. The formula utilized is as follows:  
\[x_{i}\,^{\prime}\,=\,\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
In Equation (1), \(x_{i}\,^{\prime}\,\) represents the result of normalization, while \(x_{i}\,\) represents the data that needs to be normalized. Moreover, \(x_{max}\) and \(x_{min}\,\) represent the maximum and minimum values in the dataset, respectively.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 3 Request Expectation Index Based Cache Replacement Algorithm
### Request Expectation Index \(Rxi(i,j)\)
#### 3.1.1 File-Level Request Probability \(P_{f}(i)\)  
File-oriented request probability has been well established in extensive studies. For example, the request probability for web objects follows the Zipf distribution and Mandelbrot-Zipf distribution for objects delivered by P2P networks [38]. However, the file-level probability \(P_{f}(i)\) at an ICN router varies by time and location. Therefore, at each ICN router, the cache replacement should be adjusted based on the dynamic variation of the local file-level probability.  
To achieve this goal, each router monitors the local real-time file-level access frequency to obtain request ratio \(REQ_{f}(i)\), as shown in Figure 3, and the calculation of \(REQ_{f}(i)\) is given bellow, as shown in Equation (2):  
\[REQ_{f}(i)=\frac{Rec_{i}}{Rec}. \tag{2}\]  
\begin{table}
\begin{tabular}{c l} \hline \hline \(P_{f}(i)\) & File-level request probability of file \(f_{i}\) among different files at an Information-Centric Networking (ICN) router. \\ \(REQ_{f}(i)\) & Local request ratio of file \(f_{i}\) measured at each ICN router. \\ \(\delta t\) & The time unit of the \(REQ_{f}(i)\) measurement, e.g., \(\delta t=1s\) denotes that \(REQ_{f}(i)\) is recalculated \\  & each second. \\ \(P_{f_{i}}(j)\) & Chunk-level internal request probability for chunk \(C_{i,j}\), which refers to the request probability \\  & for chunk \(C_{i,j}\) among all other chunks within streaming file \(f_{i}\). \\ \(IRXI_{f_{i}}(j)\) & Internal request expectation index (IRXI) of chunk \(C_{i,j}\). \\ \(RXI(i,j)\) & Request expectation index (RXI) of chunk \(C_{i,j}\). \\ \(Q(x)\) & Request duration distribution. \\ \(q(x)\) & Probability density function of request duration distribution \(Q(x)\). \\ \(\Delta T_{i}\) & Average request interval of consecutive chunks in file \(f_{i}\). \\ \(M_{i}\) & Total chunk number of file \(f_{i}\), e.g., for chunk \(C_{i,j},j\in\{1,...,M_{i}\}\). \\ \(n\) & Total number of chunks cached in a router. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation. REQ: Request ratio.  
\(Rec_{i}\) refers to the number of requests for file \(f_{i}\) in time period \((t,t+\delta t)\), and \(Rec\) indicates the total number of request received by this node in the same duration. Additionally, the calculation time unit \(\delta t\) affects the ability of \(REQ_{f}(i)\) to accurately reflect the variation of the local file-level request probability. \(REQ_{f}(i)\) may be changed by the arrival of new requesters; therefore, we stipulate that the time unit for these calculations is equal to the average time interval for the arrival of a new requester. In summary, the file-level request probability \(P_{f}(i)\) can be reasonably approximated by the measured request ratio \(REQ_{f}(i)\), as shown in Equation (3):  
\[P_{f}(i)\approx REQ_{f}(i). \tag{3}\]
# Advances in Deep Space Probe Navigation
## 2 An Overview of Published Articles
### Interstellar Voyage Propulsion System  
The periodic a1 nd active comet 29P/Schwassmann-Wachmann 1 is an interesting but unexplored small celestial body that has been regarded as an object of the Centaur group. Ref. [1] analyzes the optimal transfer towards the periodic comet of a solar sail-based spacecraft. A classical (heliocentric) orbit-to-orbit transfer is studied from an optimal viewpoint, by finding the spacecraft trajectories that minimize the flight time for a given value of the solar sail characteristic acceleration, that is, the typical performance parameter of a photonic sail. Research has found that high-performance solar sails can fly for over 10 years, while high-performance sails can fly for less than 10 years. The calculation results of the two-dimensional simplification method are similar to those of the three-dimensional model, but the calculation time is significantly reduced.  
An Electric Solar Wind Sail (E-sail) is a propellantless propulsion concept that extracts momentum from the high-speed solar wind stream to generate thrust. Ref. [2] analyzes the performance of an E-sail in obtaining the transition from prograde to retrograde motion. A mechanism for flipping a circular heliocentric orbit through a two-dimensional propulsion trajectory is proposed, and the optimal trajectory and flight time under different propulsionscenarios is studied. The results indicate that the trajectory can be divided into two symmetrical parts, with the spacecraft reaching a stationary state at the aphelion. Direct transfer and single solar wind assisted transfer each have their own characteristics, with the latter being able to shorten flight time, but considering the limitation of perihelion distance.  
Ref. [3] studies the optimal control law of a spacecraft equipped with a Solar Wind Ion Focusing Thruster (SWIFT) in three-dimensional (3D) heliocentric orbit transfer. A thrust vector model suitable for 3D scenes is proposed. The optimal guidance law is obtained by solving optimization problems, and it is verified in simplified Earth-Venus and Earth-Mars transfer missions. The results show that the flight time of 3D transfer has increased compared to two-dimensional cases, and the transfer performance is closely related to the design parameter K.
## 7 Conclusions  
The iron pyrite-epoxy composite, formulated in a 3:2 weight ratio, uncovers significant insights into its electromagnetic wave properties. The composite has a controllable band gap of 1.3 eV, enabling selective absorption and emission of electromagnetic radiation, which is very beneficial for UV spectroscopy applications. The transmission coefficient recorded at a remarkable 90% throughout a frequency range of 1 GHz to 15 GHz, demonstrating exceptional transparency to electromagnetic waves and underscoring its potential for high signal integrity in sensing devices and communication systems. The composite has a low reflection coefficient of 10%, substantially reducing reflecting interference, which is essential for precise measurements in electromagnetic wave systems. The UV-Vis spectroscopy findings provide an absorption value of 0.875% at a band gap of 1.3 eV, highlighting the composite's effective interaction with UV radiation. The S21 transmission coefficient varied from \(-10\) dB to \(-80\) dB, reaching a maximum of \(-40\) dB at 6 GHz, signifying robust energy transfer characteristics appropriate for antenna applications. The S11 reflection coefficient ranged from \(-5\) dB to \(-55\) dB, exhibiting significant reductions between 4 GHz and 14 GHz, where it reduced to \(-45\) dB, highlighting the composite's efficacy in lowering signal reflection at essential frequencies. The advantages of iron pyrite include its natural abundance and inexpensive cost, making it an economically feasible choice for composite materials. The disadvantages pertain to its relatively poor stability under environmental conditions, potentially impacting its long-term application performance. These results highlight the iron pyrite-epoxy composite's remarkable appropriateness for applications necessitating high transmission efficiency, little reflection, and proficient interaction with electromagnetic waves, especially as an antenna substrate.  
Methodology, B.M.; Software, M.P. and N.R.; Validation, M.P., B.M. and D.K.P.; Formal analysis, N.R., D.K.P. and A.E.; Investigation, N.R.; Resources, B.M., D.K.P. and A.E.; Writing--original draft, M.P.; Supervision, A.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
### Objectives and Structure  
This study bridged these gaps by focusing on wearable ECG signals under real-world, dynamic scenarios and leveraging activity-annotated datasets to improve robustness. The main aim was to contribute to the identification of features that can effectively assess usable segments within an ECG signal, particularly under the dynamic conditions typicalof wearable devices. This area remains underdeveloped, yet it holds significant importance in today's world. A supervised model was designed to classify whether an ECG segment is acceptable (free from noise) or unacceptable (contains noise).  
The paper is structured as follows: Section 1.2 addresses the challenges of wearable technology for firefighters and the limitations of smart undergarments. Section 1.3 offers a review of the existing literature concerning ECG quality assessment algorithms, with a focus on wearable ECG signal assessment. Section 2 details the Materials and Methodology, including the characterization of the ScientISST MOVE dataset [12] (Section 2.1) and the use of the Python BioSPPy library [19] for processing ECG signals and generating ground truth labels (Section 2.2); feature extraction for model training (Section 2.4) and parameter optimization for classification models are also described (Section 2.5). Additionally, Section 2.6 presents a case study with firefighters under real conditions, demonstrating the effectiveness of the algorithm in practical scenarios. The results are presented in Section 3, followed by the Discussion in Section 4. Section 4.1 addresses the performance of the ECG assessment model, Section 4.2 examines the features used, and Section 4.3 discusses the analysis of ECG signals collected from firefighters in real-life conditions, emphasizing the importance of an ECG assessment algorithm that prioritizes quality. Lastly, Section 5 outlines the main conclusions.