esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of Mission Analysis
### Volume, Mass, and Power Analysis  
A strong limiting factor that threatened the possibility of integrating the satellite into a 3U mission is the volume available for the components and subsystems. The internal system layout of and component arrangement of MeznSat is shown in Figure 16, with all subsystems fitting into the available 3U volume. A fit check was conducted using a 3D printed structure (while the main structure was under construction), and all components were found to fit as planned (Figure 17).  
Figure 16: MeznSat internal system layout.  
Figure 15: Spectrometer footprint coverage analysis for 36-month.  
MeznSat mass budget is shown in Table 2. The results show that the satellite mass was found to be 2966 g with a mass margin of 26% from the maximum mass of 4 kg identified by the CubeSat Standard [27].  
Figure 18 shows the simulated power profile of the satellite for one orbit. The figure shows a healthy power profile with a maximum expected battery Depth of Discharge of 5.5%. The figure shows the generated power, the consumed power (based on the mission's power budget) and the battery profile.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Subsystem** & **Component** & **Mass (g)** \\ \hline
1 & On-board computer (OBC) & Main + daughter board & 105 \\ \hline
2 & Communication & Mainboard & 89 \\  & & Antenna & 89 \\ \hline  & & EPS Board & 105 \\
3 & Electrical power subsystem (EPS) & CSS & 16 \\  & & SP & 578 \\  & & Battery Pack & 284 \\ \hline  & & ADCS & 530 \\
4 & ADCS & RW & 173 \\  & & STS & 69 \\  & & MAG & 16 \\ \hline
5 & Payload & Spectrometer & 315 \\  & & RGB Camera & 84 \\ \hline
6 & Structural & Main Structure & 320 \\  & & Screws, washers, and spacers & 55 \\ \hline
7 & Harness & Cables & 105 \\  & & Connectors & 33 \\ \hline  & Total mass with uncertainty & & 2966 \\ \hline  & Maximum mass allowable & & 4000 \\ \hline  & Mass Margin & & 26\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: MeznSat mass budget.  
Figure 17: Fit check using 3D printed structures. Bottom 2U fitcheck (**left**) and top 1U fitcheck (**right**).
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 8 Conclusions  
This paper studied a simple nonlinear dynamical system based on a jerk equation with a discrete-time map. The system can show periodic as well as aperiodic behavior based on the value of the control parameter. MATLAB simulations were used to verify the theoretical aspects of the proposed system. An electronic circuit of the nonlinear system was made in NI Multisim using basic electrical engineering knowledge. Further, it was implemented in real-time on a breadboard to verify the system's chaotic nature. A fair consensus was seen between the simulation & experimental results. High frequency range, quasi-periodicity, autonomous chaotic existence, and low power consumption are some of the important aspects of the design, which can find applications in cryptography, secure communication, compressed sensing, biology, weather forecasting, mechanical engineering etc.  
**Author Contributions:** Conceptualization, M.H.A. and M.K.; methodology, M.H.A.; software, M.H.A.; validation, M.H.A. and M.K.; formal analysis, M.H.A.; investigation, M.H.A., M.K. and M.A.A.; resources, A.E.H. and M.K.; writing--original draft preparation, M.H.A.; writing--review and editing, A.E.H., M.K. and M.A.A.; supervision, M.K.; project administration, M.K.; funding acquisition, A.E.H., M.K. and M.A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to acknowledge the support provided by King Fahd University of Petroleum & Minerals through the Research Group funded project #DF191004. The authors also acknowledge the funding support by King Abdullah City for Atomic and Renewable Energy (K.A. CARE), Energy Research & Innovation Center (ERIC) at KFUPM.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available within the article.  
**Acknowledgments:** The authors would like to acknowledge the support provided by King Fahd University of Petroleum & Minerals through the Research Group funded project #DF191004. The authors also acknowledge the funding support by King Abdullah City for Atomic and Renewable Energy (K.A. CARE), Energy Research & Innovation Center (ERIC) at KFUPM. We would like to acknowledge [PERSON] for his assistance with the experimental verification.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MLE & Maximum Lyapunov Exponent \\ ODE & Ordinary differential equation \\ SNFS & Saturated Nonlinear Function Series \\ DOF & Degree of freedom \\ H.O.T. & Higher-order terms \\ DSR & Deanship of Scientific Research \\ K. A. CARE & King Abdullah City of Atomic \& Renewable Energy \\ \end{tabular}
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.2 Impact of Dropout Rate on Experiments  
As shown in Figure 6, multiple dropout rates of 0.1, 0.2, 0.3, 0.4, and 0.5 were selected for the experiments to evaluate their effects on training loss, validation loss, and test loss, as well as to measure the model's performance using the Hits@1 and Hits@10 metrics.  
As the dropout rate increased, the training loss gradually decreased, indicating an enhanced fitting ability of the model on the training set. However, when the dropout rate exceeded 0.3, the rate of decrease in training loss slowed, suggesting that the model might begin to lose important features. On the validation set, a dropout rate of 0.3 yielded the lowest validation loss, demonstrating that this rate effectively balanced the model's complexity and generalization capability. Lower dropout rates, such as 0.1 and 0.2, failed to effectively prevent overfitting, while higher dropout rates, such as 0.4 and 0.5, led to an increase in validation loss, indicating a decline in the model's performance on the validation set. On the test set, the model with a dropout rate of 0.3 also performed exceptionally well, achieving the lowest test loss, which further validated the effectiveness of this dropout rate. In terms of practical performance evaluation, the model with a dropout rate of 0.3 achieved the best performance on the Hits@1 and Hits@10 metrics, with values of 0.342 and 0.633, respectively. This result indicates that selecting a dropout rate of 0.3 not only improved the model's accuracy but also enhanced its effectiveness in practical applications.  
In summary, the experimental results demonstrate that choosing a dropout rate of 0.3 is based on a comprehensive consideration of model performance, effectively preventing overfitting, improving the model's generalization ability, and achieving optimal results on both the validation and test sets.  
Figure 6: The impact of different dropout rates on the experiments: (**a**) Comparison of training loss. (**b**) Comparison of validation loss. (**c**) Comparison of test loss. (**d**) Comparison of experimental results.
## 2 Material and Method
### TOPSIS Methods  
The classification diagram of the TOPSIS method was given in Figure 5.  
For a multi-attribute decision-making (MADM) issue, theorize each alternative is measured with particular to the n attributes, whose values institute a decision matrix [32].  
\[\mathrm{X}=\left(x_{uv}\right)_{mxn}=\left[\begin{array}{cccc}x_{11}&x_{12}& \ldots&x_{1n}\\ x_{21}&x_{22}&\ldots&x_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ x_{m1}&x_{m2}&\ldots&x_{mn}\end{array}\right] \tag{10}\]  
The technique for order of preference by similarity to an ideal process was shown as follows [32]: Step 1: present the decision matrix.  
In general, the attribute consists of benefits attribute and cost attribute. In order to meet all characteristics in dimensionless units and allow for easier comparisons between each inter-attribute, the following formulas are used to normalize each attribute value \(x_{uv}\) in decision matrix \(\mathrm{X}=(x_{uv})_{mxn}\) into a correlate factor \(g_{uv}\) in a normalized decision matrix given by:  
\[\mathrm{G}=\left(g_{uv}\right)_{mxn}=\left[\begin{array}{cccc}g_{11}&g_{12}& \ldots&g_{1n}\\ g_{21}&g_{22}&\ldots&g_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ g_{m1}&g_{m2}&\ldots&g_{mn}\end{array}\right] \tag{11}\]  
where  
\[g_{uv}=\frac{x_{uv}}{\sqrt{\sum_{u=1}^{m}\left(g_{uv}\right)^{2}}},\text{ for benefit attribute }x_{uv},\ u\in\{1,2\ldots m\},\ v\in\{1,2\ldots n\} \tag{12}\]  
and  
\[g_{uv}=1-\frac{x_{uv}}{\sqrt{\sum_{u=1}^{m}\left(g_{uv}\right)^{2}}},\text{ for cost attribute }x_{uv},\ u\in\{1,2\ldots m\},\ v\in\{1,2\ldots n\} \tag{13}\]  
Step 2: compute the weighted normalized decision matrix.  
The entropy method is one of the advanced methods used to determine uncertain information formulated using probability theory [39]. To determine the entropy weights, the entropy value, denoted as \(E_{v}\), is first computed according to the following equations:  
\[E_{v}=-\frac{\sum_{u=1}^{m}p_{uv}\ln(p_{uv})}{\ln(m)},\ u\in\{1,2\ldots m\}, \ v\in\{1,2\ldots n\} \tag{14}\]  
Figure 5: The classify diagram of the technique for order of preference by similarity to an ideal solution (TOPSIS) method.  
where  
\[p_{uv}=\frac{g_{uv}}{\sum_{u=1}^{m}g_{uv}},\text{ and }m\text{ is the quantity of alternatives} \tag{15}\]  
Therefore, the degree of deviation (\(d_{v}\)) of the mean native information given by the correlates, with assignment ratings on point of reference \(p_{v}\) can be defined as:  
\[d_{v}=1-E_{v},\text{ \ }v\in\{1,2\ldots n\} \tag{16}\]  
The value of \(d_{v}\) deputize the natural contrast intensity of \(p_{v}\), and the entropy weight of \(v\)th is:  
\[w_
# Steel Surface Defect Classification Based on Small Sample Learning
## 3 Experiments
### Results and Analysis  
During the training, 1000 1-shot tasks were collected every two epochs to calculate the average accuracy of different classification networks. Figure 5 shows that the performance of the feature extraction network using only four convolutional layer stacks was the worst. The reason is that it cannot learn the deep abstract features of the image. In the first half of the training phase, the classification accuracy of Residual Net10 and Dense Net fluctuated greatly. As a lightweight network, MobileNet had the smoothest accuracy curve and was relatively stable. From the perspective of training, the classification algorithm using MobileNet as the feature extraction network had the best performance in the six types of steel surface defect classification tasks.  
Figure 4: Categories of defects: (**a**) Cr, (**b**) In, (**c**) PA, (**d**) PS, (**e**) RS, (**f**) Sc.  
the eigenvectors normalized by L2-normalization are more discrete under the Euclidean metric. In the 1-shot setting, these two feature transformations were the most effective for convolution 4, which can improve the classification accuracy by 5.87% and 5.6%, respectively. In the Residual Net series network, Residual Net10 had the best performance. The MobileNet + L2 normalization method had the highest accuracy, reaching 87.30%. In the 5-shot setting, mean subtraction was the most effective for Residual Net50, which was increased by 5.33%. L2 normalization was the most effective for Residual Net10, which was increased by 4.55%. In the Residual Net series network, Residual Net10 had the best performance. The Dense Net + L2 normalization method had the highest accuracy, reaching 92.33%. The performance of the Residual Net series network shows that in the case of small sample, the deepening of the network depth may not improve the accuracy. Comparing Residual Net50 and DenseNet121, it was found that the classification performance of DenseNet was better although it was deeper. This shows that in the case of a small sample, dense block can better solve the degradation problem caused by the deepening of the network than the residual block.
# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 2 Materials and Methods
### ExerPlayer Module  
ExerPlayer runs as a desktop application, installed on the patient's computer. The central component of the ExerPlayer module is the OpenPose library, which allows the  
Figure 1: General architecture of ExerCam.  
Figure 2: Architecture of the multistage CNN from the 2018 version of OpenPose [37].  
real-time human pose estimation to be obtained through the patient's webcam. ExerPlayer makes use of the REST API to transmit and receive data regarding the tasks and exercises of patients. This module allows the patient to perform exercises from home thanks to the application developed as an augmented reality mirror type, where the patient is reflected on the screen, along with other virtual objects. In this way, it provides the means to stimulate physical activity, by inducing movement, or gross motor learning, as it is able to provide the three key principles in motor learning: repetition, feedback and motivation [39]. ExerPlayer has three types of virtual objects (see Figure 3a):  
* Real-time superposition of a virtual skeleton on the user's body, where the joints are highlighted. A human pose skeleton represents the orientation of a person in a graphical format (that is, it is a set of coordinates that can be connected to describe the pose of the person). Each coordinate in the skeleton is known as a joint or a key point (or part). A valid connection between two joints is known as a pair (or a limb). The parts of the body that can be used correspond to those provided by the COCO model [40]. This model provides an image data set with 17 key points for each person. They can be seen in Figure 3b.
* Target objects, represented by several concentric circles, are targets that the players must reach with their joints. When the targets are hit, a sound is played that provides positive feedback and increases the feeling of immersion and motivation in the game [41]. The targets can be assigned to a specific joint, in which case the target will be shown in the same color as the one assigned to the joint, and the player will have to use that joint to reach the target. The targets can be fixed or mobile (with predefined trajectories). In addition, they can be configured to appear or disappear at a certain time or in response to events such as the triggering of timers or the impact of previous objectives.
* Visual aid, decoration and instructions. They can be: \(\bigcirc\) & Image decoration; \(\bigcirc\) & Text with instructions, score, time elapsed, etc.; \(\bigcirc\) & Progress bar-like buttons to start an exercise; \(\bigcirc\) & Geometric shapes to help place the user and calibrate the system.  
Figure 3: Visual aid, decoration and instructions. (**a**) Virtual objects in ExerCam 2D. (**b**) COCO body key points.  
To access the ExerPlayer module, the patient must be registered in the system and enter their credentials. Once the application is accessed, the patient has several modes of use: (1) calibration mode, (2) task mode and (3) game mode.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 1 Introduction
### Literature Review
Most existing research has focused on specific building types, regions, or limited prediction horizons, often achieving accuracy levels ranging from approximately 10% to 20%, which may not be sufficient for precise energy planning and demand management. Additionally, many studies primarily concentrate on single factors like weather, insulation, or lighting without integrating the complex interplay of multiple variables influencing energy demand simultaneously. Furthermore, while some research has compared the performance of ANNs and RFs, there is limited exploration of advanced ensemble techniques or hybrid models that could potentially enhance prediction accuracy and stability across different building types and operational conditions. There is also a lack of comprehensive analysis on the models' robustness under varying socioeconomic and environmental scenarios, especially in different geographical contexts. Therefore, future research should aim to develop more integrated, scalable, and adaptive prediction frameworks that combine multiple data sources, incorporate advanced machine learning algorithms, and evaluate their performance over extended periods and diverse building typologies to support more effective energy efficiency strategies. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Mathematical** & **Variables** & **Proposed** & **Time Period** & **Accuracy** \\ \hline \multirow{6}{*}{
\begin{tabular}{c} [PERSON] et al. \\ [20] \\ \end{tabular} } & \multirow{3}{*}{ANN} & -Weather conditions & & & \\  & & -Building structure and & & & \\  & & characteristics & & & \\  & & -Energy consumption of & & & \\  & & components (like lighting & & & \\  & & and HVAC systems) & & & \\  & & -Level of occupancy & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summarizing the literature review.
## 2 Related Works
### Topic Models and RNNs  
To capture the semantic features of the text, topic models play a vital role. The semantic features are extracted as latent topics. There have been numerous studies and applications of topic models since this idea was first introduced by [PERSON] et al. [6]. These studies [30; 31; 32] have covered many research areas ranging from scientific studies to mathematical equations. Recently, many studies are using deep learning methods in combination with topic models. In [32], RNN is used along with topic models to enhance the performance of topic modeling for scientific texts. In [33; 34], a neural topic model has been presented. Some recent studies have paid more attention and focused on neural variational inferences in order to train the topic models [35; 36]. Moreover, for sequences that have long term dependencies, RNN proved to be an effective solution [37; 38]. Other studies that have used RNNs to model different language-related problems include handwriting recognition [39], LATEX modeling [40], and semantic parsing [41], etc.  
Our model is driven from a hybrid of the approaches which are based on a joint topic language model. The motivation behind these architectures is to extract the latent topics through topic models, e.g., LDA and these pre-trained models are used for Deep neural networks, such as RNNs or CNNs, modeling. In [42], a pre-trained LDA model was incorporated into an RNN model. In other studies [43; 44], both topic models and neural networks are trained together, which we also aim to perform. A recent work [45] has proposed a Sentence Level Recurrent Topic Model (SLRTM), where for each sentence, a topic is decided based on a non-sequential Dirichlet structure similar to LDA. Therefore, it is not much useful for capturing long-range temporal dependencies, also as it uses the whole vocabulary, which enriches it with lots of features. In our case, we are using only topics, not the entire dictionary. There are other models introduced in [46; 47], which are based on recurrent latent variables; in these models, RNN is enabled with latent variables in order to cater to the inconsistenciesin the input data. Though, these models focus on images and speech data; while we use a discrete input data space and uses text data and numeric data.  
In the previous studies, LDA and deep learning models are combined to either improve the language modeling or on the same word text. In our case, we are building a recommendation model on top of LDA-LSTM hybrid model. User preferences are added as an extra layer of input. One key difference of our model is that the latent topics are used in the LSTM layer while other models, such as [39] and [43], integrate them in the output layer of LSTM.
## 5 Simulation Analysis
### Simulation Results 2--Effect of the Additional BESS Assisting  
The effect of the BESS assisting the ASG control explained in Section 4 is evaluated in this section. The BESS controls its power to suppress the power system frequency fluctuation directly. Simulation results for the case of Wind No.6 are presented here because its fluctuation level is highest, as shown in Figure 12. The left part of Figure 15 shows the standard deviation of the power system frequency. As shown in Figure 15, power system frequency fluctuations can be suppressed more effectively than the case with only the ASG. Maximum battery power is also shown in the center part of Figure 15. Even when all of the diesel generators are conventional FS-SG, the BESS exhibits good performance. However, a larger BESS capacity will be needed. As the ASG capacity increases, the maximum output of the BESS decreases, and thus the power capacity of the BESS can be reduced. The right part of Figure 15 shows the standard deviation of the varying rotor speed of the ASG. It becomes smaller in the case where BESS is installed. It can also be said that the mechanical stress of the ASG can be reduced. Therefore, it can be concluded that the proposed combined control of the ASG and the BESS is very effective for stabilizing power system frequency fluctuations.  
Figure 14: Standard deviation of the rotor speed of the ASG.
## 3 Development of Reliability Assessment Program
### DB and Formula for Reliability Assessment of Gear  
The efficiency DB of the gear, which is the main mechanical equipment of the secondary energy conversion system, was built, and the energy conversion formula for reliability assessment was built using this DB. In the gear system, power loss does not occur in the shaft and oil, while power loss due to gear friction is the most significant part. Therefore, assuming that there is no loss due to friction of the gear system, efficiency can be calculated by considering only the loss of the gear itself depending on the shape, size, and material of the gear. In this study, the transmission efficiency DB of the gear system was built based on the commercial products of KHK Gears and is shown in Table 2. It is classified according to category and type [34]. The efficiency value can be calculated through the DB of the table. For reference, in general, as the gear stage increases, more energy loss occurs, and thus efficiency decreases. In addition, the formula for applying the efficiency of the gear system is as shown in the below Equation (1).  
\[V_{2}=V_{1}\frac{N_{1}}{N_{2}}\eta\ (\text{rpm}) \tag{1}\]  
where, \(V_{1}\) is the revolution per minute (RPM) of the rotor shaft (rpm); \(V_{2}\) is the RPM of the output shaft (rpm); \(N_{1}\) is the number of teeth in the gear 1; \(N_{2}\) is the number of teeth in gear 2; \(\eta\) is the efficiency of the gear (%).  
Figure 6: PFD of secondary energy conversion system started with the hydraulic system.  
The ratio of the number of teeth of the gear becomes the ratio of the rotational speed. Accordingly, the performance formula by secondary energy conversion using gears, theoretically without loss, is derived as in Equation (1).
## 3 Background Knowledge
### DNS-over-HTTPS  
As one of the new methods to secure DNS communications, DNS-over-HTTPS [13] (or DoH) uses the widely-used HTTPS protocol between DNS clients and DoH resolvers. To use DoH, both the client and the resolver should be able to interpret this protocol. Figure 2 demonstrates how a browser executes DoH. The local DoH proxy wraps a DNS query in an HTTPS request and sends it to the DoH server. The DoH server, which acts as a DNS resolver, receives this request and responds after performing standard DNS resolving activity (such as checking the DNS cache and performing DNS resolution). The response would be back in the HTTPS protocol. Once the browser obtains the IP address, it can access the web server. Using the HTTPS protocol also retains the advantages of the HTTP protocol, such as caching, redirection, and compression. Since all the request and response actions relative to the browser are secure, no user's information is exposed.