esa-sceva/satcom-chunk-collection huggingface dataset:
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 7 The RINA Domain
### HD Video Streaming Demonstration  
For this demo, we have injected the same synthetic application flows in the network between PE3 and HR2, measured the carried average traffic between IR1 and IR2, and limited the capacity of this link reproducing a 90% load scenario.  
In this congested network scenario, we have established a HD video streaming session over UDP from server node S to the client node C, transmitting a 1080p HD video file using VLC VideoLAN v3.0.1. Setting up an IP tunnel interface using iporinad, we have been able to transmit the IP traffic of the video streaming session over the HD Video Streaming Application DIF.  
In full RINA scenario, we have been able watch it with perfect QoE, highlighting the adequacy of the RINA QoS support. In the TCP/IP-like case, QoE of the received client has been significantly worse, observing that it starts and stops constantly (video stuttering), also skipping a substantial number of frames each time it starts playing again. Both effects seem caused by the high congestion existing in the bottleneck link between IR1 and IR2 nodes in the 10 node RINA scenario. In fact, the video streaming session increases previous congestion even more, as this traffic around 10 Mbps was not considered when limiting link capacity to reproduce the 90% offered load scenario.
# Fuzzy Model Predictive Control for Unmanned Helicopter
## 3 Control Methodology
### Model Predictive Control Principles
In the MPC problem, an analytical expression for the Hessian matrix of the cost function \(J(\Delta\mathbf{U})\) is given in Equation (16). \[\frac{\partial^{2}J}{\partial\Delta\mathbf{U}^{2}}=\bar{\mathbf{R}}+\mathbf{Z}^{T}\bar\mathbf{{Q}}\mathbf{Z} \tag{16}\]  
Provided that the block-diagonal matrix \(\bar{\mathbf{R}}\) is positive definite and \(\bar{\mathbf{Q}}\) is at least positive semi-definite, and assuming that the matrix \(\mathbf{Z}\) has full rank, the Hessian matrix is guaranteed to be positive definite. This ensures that the optimization problem is convex and has a unique global minimum. Knowing that control systems in engineering applications are constrained by the limitations of the actuators onboard, it is necessary to include these limitations in the control law. An alternative to analytically solving the MPC problem is a numerical approach, which, in this case, reduces to a constrained quadratic programming problem, whose general form is given by Equation (17) below:  
\[\min_{\Delta\mathbf{U}}\quad\frac{1}{2}\Delta\mathbf{U}^{T}\  
\[ \begin{bmatrix}-1&1&0&\cdots&0\\ 1&-1&0&\cdots&0\\ 0&-1&1&\cdots&0\\ 0&1&-1&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&\cdots&0&-1&1\\ 0&\cdots&0&1&-1\\ \end{bmatrix}\begin{bmatrix}\Delta u(k)\\ \Delta u(k+1)\\ \Delta u(k+2)\\ \vdots\\ \Delta u(k+N_{u}-1)\\ \end{bmatrix} \leq \begin{bmatrix}\delta u_{\max}\\ -\delta u_{\min}\\ \delta u_{\max}\\ -\delta u_{\min}\\ \vdots\\ -\delta u_{\max}\\ -\delta u_{\min}\\ \end{bmatrix}  \tag{19}\]  
The numerical solution of the formulated problem ensures the correct projection of the cost function minimum onto the constraint space. However, its practical application comes at a higher computational cost than using the analytical solution. In the presented approach, due to limited computational resources of the control system, the analytical solution of the MPC problem was used.
## 2 Dataset and Feature Extraction  
### Collection PPG Signals  
Our methodology uses two public biometric datasets as shown in Figure 1. We first compare the ML algorithm performance using the RRSYNTH dataset ([[http://peterhcharlton.github.io/RRest/syntheticdataset.html](http://peterhcharlton.github.io/RRest/syntheticdataset.html)]([http://peterhcharlton.github.io/RRest/syntheticdataset.html](http://peterhcharlton.github.io/RRest/syntheticdataset.html)) (accessed on 3 May 2021) [37]) with simulated PPG and ECG signals. The dataset consists of 192 wave signals, every 210 s in length, using a sampling frequency (Fs) of 500 Hz. There are three types of modulation: frequency modulation (FM), baseline wander (BW), and amplitude modulation (AM) [37]. Only AM data was used in this study because heart failure was associated with the pulse amplitude of the PPG signal. The AM of the PPG signal reduces the stroke volume during suction due to changes in the pressure in the chest, resulting in reduced pulse amplitude [37]. The decrease in stroke volume was known to be heart failure closely [38]. Therefore, RR was predicted from PPG signals using the AM-type signal modulation method. Upon closer inspection, 192 AM records were used to develop the proposed GBA; however, 64 AM records were not used because of their untampered AM properties.  
Second, we use the BIDMC dataset ([[http://peterhcharlton.github.io/RRest/bidmcdataset.html](http://peterhcharlton.github.io/RRest/bidmcdataset.html)]([http://peterhcharlton.github.io/RRest/bidmcdataset.html](http://peterhcharlton.github.io/RRest/bidmcdataset.html)) (accessed on 10 June 2022) [37]), which is extracted from the MIMIC-II resource [39]. The BIDMC dataset consists of ECG, PPG, and impedance pneumography (IP) respiratory signals acquired from intensive care patients. The dataset consists of 53 recordings of PPG, ECG, and IP signals (Fs = 125 Hz) obtained from adult patients aged 19-90 years for 8 min. Patients in the dataset were randomly selected among a cohort of patients admitted to Beth Israel Deaconess Medical Center (BIDMC) in Boston, USA. Reference RR values were derived using two sets of annotations of individual breaths of IP signals.
# Biogas Production Prediction Based on Feature Selection and Ensemble Learning
## 2 Materials and Methods
### Biogas Prediction Based on Heterogeneous Ensemble Learning
#### 2.2.4 Evaluation Metrics  
In order to verify the prediction effect of heterogeneous ensemble learning, the two-dimensional vector Pearson correlation coefficient is used as an analytical evaluation index to comprehensively analyze the distribution of prediction errors among the different primary learners. By calculating the Pearson correlation coefficient of the errors generated by the prediction between each primary learner, the learners with larger differences are then filtered as the primary learners of the stacking model.  
The prediction accuracy and performance of the prediction models are mainly evaluated by the error between the predicted value and the true value, and the main evaluation indexes are the MSE (mean squared error), the MAE (mean absolute error), and the MAPE (mean absolute percentage error). The MSE is used to detect the deviation of the predicted value from the true value and has a better interpretation, and the larger the value is, the larger the prediction error is. The MAE reflects the mean of the absolute error, which can better reflect the actual situation of the error of the predicted value. The formulas for calculating the correlation error are as follows:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right)^{2} \tag{3}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|\hat{y}_{i}-y_{i}\right| \tag{4}\]  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{5}\]  
where \(\hat{y}_{i}\) is the predicted biogas yield, \(y_{i}\) is the actual biogas yield, and \(n\) is the length of the predicted biogas yield series.  
Figure 4: Biogas prediction based on stacking heterogeneous ensemble learning model.
# Optimal Design of a Residential Photovoltaic Renewable System in South Korea
## 5 Conclusions  
This study proposes a design optimization model for the residential PV systems in South Korea, where the objective function to be minimized consists of three costs, such as the monthly electric bill, the PV-related construction costs, and the PV-related maintenance cost. Here, the monthly electric bill has six ranges in the form of a stepped piecewise linear function. The PV-related construction costs also include the government's subsidy and the building-material cost savings. The initial construction costs, and the annually occurring maintenance costs are fairly compared by introducing the capital recovery factor.  
Regarding residential electrical consumption, four consumption types, such as year-round electric appliances, seasonal electric appliances, lighting appliances, and stand-by power, were considered. Also, regarding residential PV generation, the monthly generation amount was calculated by considering different solar altitude angles.  
While local optimal solutions, this model could find the global optimal solution by using a genetic algorithm. We hope that this optimization model will be practically used in residential PV system designs in South Korea.  
For future study, we plan to construct more detailed PV design optimization models by considering discrete PV size variables [12; 13; 14], ESS (energy storage systems) [15; 16], AC-DC conversion [17], and more energy-efficient lighting devices (light-emitting diodes). Normally, the size of PV is discrete, because a PV system consists of an integer number of panels. Thus, we would like to consider this discrete nature of the PV size after gathering sufficient data in the future. In order to efficiently utilize surplus energy from the PV system, we may install an ESS and optimally schedule it [15].  
The climate change cast over in Korea has made its summers hotter than before, which has led to more energy consumption in the summer months, and higher energy bills. Thus, the Korean government is about to reform the multi-stage progressive electric rate, in order for lower-income groups to be able to afford to pay it. Once all-new data, including billing, panel capacity and costs,ESS capacity & costs, etc., are obtained, we will correspondingly construct a more detailed and up-to-date model design.  
[PERSON] and [PERSON] wrote the paper, and [PERSON] supervised the submission.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP), and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20163010140690).  
The authors declare no conflict of interest.
# Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
## 6 Conclusions and Outlook  
1. This study proposes an innovative CNN-LSTM deep-learning model that effectively predicts the future driving trajectories of target vehicles by integrating the characteristics of the CNN and LSTM. The model not only considers the dynamic interactions between vehicles but also incorporates multiple risk factors, such as the collision probability and severity, forming a comprehensive system for risk quantification and classification. By precisely categorizing different levels of risk, this model offers more accurate guidance for traffic management and safety interventions, contributing to a safer environment for autonomous driving.
2. The study utilizes the High-D dataset, which provides extensive real-world traffic scene data. Leveraging this dataset, the model accurately derives the target vehicle's speed and acceleration based on the displacement and time changes between points along the predicted trajectory. This dynamic predictive capability enhances the model's sensitivity to potential risks, achieving prediction accuracies exceeding 86% for moderate- to high-risk scenarios. Such a high accuracy and effectiveness offer crucial support for the safe operation of connected autonomous vehicles in complex traffic environments, while also improving the scientific rigor and reliability of overall traffic safety management.
3. There are still some limitations in the current study. In the future, the model will be expanded to incorporate references to hybrid integrated models in autonomous driving environments. Additionally, the effectiveness of the model in more complex traffic conditions, its applicability to other datasets, and its performance in environments such as highways will be explored to further enhance the driving safety of connected autonomous vehicles.  
Software, L.W.; investigation, D.S.; data curation, Z.Z.; writing--original draft, K.W.; writing--review and editing, K.W.; funding acquisition, D.Q. and K.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [grant number: 52272311]. \"Modeling of driving safety potential field and resolving longitudinal& lateral dimensions steady-state response mechanism of vehicle clusters based on molecular dynamics in connected environment\" and Qingdao West Coast New District College President's Fund: [grant number: GXXZJ]202301]. \"Carbon Fiber Fully Wound with Plastic Lining for Automotive Use High Pressure Gas Cylinder (Type IV Cylinder) R&D\".  
Not applicable.  
The data are available upon request owing to restrictions such as privacy.  
The authors declare no conflict of interest.  
Figure 9: Illustration of driving risk variation.
## 4 Multiple Objective Motion Optimization Problem
### Combining the Acceleration Constraints
#### 4.2.1 Discretize the Feasible Acceleration  
Due to the constraints of the maximum acceleration and turning radius, the feasible acceleration will be in a two-dimensional region, as shown in Figure 3. The constraint in (10) restricts the accelerations to be in a circle with radius \(ACC_{max}\). The constraint in (11) further excludes a set of accelerations that violate (11). The shaded area excludes the accelerations that may cause the MASS to turn over.  
In order to optimize the acceleration selection, a reasonable approach is to discretize the continuous feasible region of accelerations to a set of discrete accelerations. The discretized points work as possible candidates for acceleration selection at time \(t\). An example of the set of discretized accelerations is show in Figure 3. The discretized acceleration set is denoted by \(\mathbf{A}_{t}\). Then, from the set of possible accelerations and the velocity update function in (6), a set of possible velocity vectors at time \(t\) can be obtained, as shown in Figure 4. Let us denote the set of achievable velocity vectors by \(\mathbf{V}_{t}\). The goal of motion optimization is to select the best velocity update at \(t\) so that the multiple optimization objectives can be achieved as best as possible.
## 2 Nonlinear-Trajectory SAR Deception Jamming Algorithm Based on HDE
### Deceptive Jamming Based on HDE
#### 2.3.1 Decomposition of JFR  
Considering the complex double integration operation in JFR, Equation (10) can be decomposed as follows:  
\[H_{jammer,P}\big{(}x^{\prime},\eta\big{)}=\frac{H_{trans,P}(x^{\prime},\eta)}{H _{jamfilter}(x^{\prime},\eta)}, \tag{12}\]  
where \(H_{jamfilter}(x^{\prime},\eta)\) is the jammer-related filter:  
\[H_{jamfilter}\big{(}x^{\prime},\eta\big{)}=\exp\bigg{(}j\frac{\eta^{2}}{4b} \bigg{)}w^{2}\bigg{(}\frac{x^{\prime}-x_{J}}{L_{a}}\bigg{)}\exp\bigg{[}-j\bigg{(} \eta+\frac{4\pi}{\lambda}\bigg{)}R^{\prime}_{J}\big{(}x^{\prime},x_{J},r_{J} \big{)}\bigg{]}. \tag{13}\]  
In addition, \(H_{trans,P}(x^{\prime},\eta)\) is the SAR system-related filter:  
\[H_{trans,P}\big{(}x^{\prime},\eta\big{)}=\text{rect}\bigg{(}\frac{\eta}{bcT_{r }}\bigg{)}\iint\sigma(x_{P},r_{P})w^{2}\bigg{(}\frac{x^{\prime}-x_{P}}{L_{a}} \bigg{)}\exp\bigg{[}-j\bigg{(}\eta+\frac{4\pi}{\lambda}\bigg{)}R^{\prime}_{P} \big{(}x^{\prime},x_{P},r_{P}\big{)}\bigg{]}dxdr. \tag{14}\]  
Comparing Equations (13) and (14), we can find that the main computation of SAR JFR comes from Equation (14) and is independent of the relevant parameters of the jammer. To design an efficient algorithm for calculating the JFR, the derivation of Equation (14) with a double integration calculation needs to be performed next.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### System Accuracy Test Experiment
#### 4.6.3 System Resolution  
The micro motion platform moved slowly with the minimum displacement, that is, at an interval of 1 \(\upmu\)m. In the system accuracy test experiment, when the micro motion platform moved 10 \(\upmu\)m, the coordinates of the laser spot from the system software started to change, indicating that the system resolution was 10 \(\upmu\)m.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 4 Proposed Solution
Afterwards, it generates an alarm and notifies to response team and this data is transmitted in its original form to the fog server so that medical staff can use this emergency data for future analysis. If the data are normal, it is simply transmitted further for record-keeping. When aggregated data is reached at Fog Server, it is checked for integrity by calculating its hash. In case the data is not corrupted, it is saved and further transmitted to the cloud server. Otherwise, it is rejected. The mechanism is also explained in Algorithm 3. ```
1. Input: Receive R\({}_{\text{data}}\)
2. Output: Data Storage at fog server
3. R\({}_{\text{data}}\) = AggregateMessage (N[i].Readings[])
4. For i = 1 to k
5. Extract R\({}_{\text{data}}\)
6. Calculate Hash\({}_{\text{cal}}\)
7. If Hash\({}_{\text{cal}}\) equals Hash\({}_{\text{org}}\)
8. Store readings into Fog server and send to cloud
9. else
10. Data is corrupted and not saved to Fog server
11. End If
12. End For
```  
**Algorithm 3** Data Received at Fog Server
## 5 Real World Application  
In this section, we present a small case study centered on meerkat population territories within the Kalahari Desert. Meerkats (_Suricata suricatta_), belonging to the mongoose family, are well-known for their social behavior. They live in small social groups known as clans, typically consisting of an average of 20 members [46; 47]. Competition among these clans plays a pivotal role in the social structure of meerkats. This competition arises due to the scarcity of resources within the desert environment, with each clan needing to safeguard its territory. These territories encompass burrow systems, foraging areas, and essential water sources. Meerkats employ mobbing calls as a means of intra-clan communication regarding the presence of rival clans [48]. Meerkats exhibit a preference for landscapes with sparse vegetation, as it enhances their ability to defend against potential predators. Furthermore, their inclination toward specific sandy environments is a focal point of our exploration here.
## 4 Results and Discussion
### Comparisons with Available Test Data
It is evident from Figure 6 that the initial impact kinetic energy of 300 J from the bullet is continuously dissipated, with the crack's initial propagation reaching a point at 0.15 ms, where its kinetic energy approaches zero. At this stage, the kinetic energy of the projectile is converted into the internal energy of the target plate, primarily comprising elastic and plastic strain energies, stored due to the material's deformation. Subsequently, the projectile rebounds, resulting in a slight increase in its kinetic energy and a corresponding decrease in the internal energy. Figure 5: Stress triaxiality–time history of the center point of the target plate at the rear surface. Figure 6: Energy–time histories at an impact energy of 300 J. Figure 7 presents a comparison between the numerically predicted results and the experimental observations for 5.73 mm thick FMLs. The FMLs, composed of a polypropylene (PP) fiber/PP matrix composite, were subjected to transverse impact by a 46.7 g hemispherical impactor with a diameter of 12.7 mm, delivering an impact energy of 353 J (impact velocity of 113 m/s) [23]. The material parameter values employed in the numerical simulations are detailed in Tables 1 and 2. It is evident from Figure 7 that the damage modes of the impact surface as well as the rear surface and the deformation of the cross-section predicted by the present model agrees well with the experimental observations. It is also evident from Figure 7 that the failure pattern predicted by the present model exhibits superior accuracy compared to the predictions obtained from the JC + BW model and the approach by [PERSON] et al. [14]. It is also clear from Figure 7 that the present model successfully reproduces the main feature of the petal-shaped crack on the rear surface, whereas the JC + BW constitutive model fails to reproduce this damage pattern. The present model accurately predicts the evolution of the rear surface crack from an initial unidirectional main crack to a petal-shaped crack with increasing impact energy, primarily due to the failure criteria, which incorporate the effects of stress triaxiality, Lode angle, and strain rate. It is noteworthy that the appearance of the initial unidirectional crack on the rear surface of the target plate is a common phenomenon associated with the ballistic limit, as similarly observed by [PERSON] et al. [26] in their impact experiments on FMLs.
# Build Orientation Optimization Based on Weighted Analysis of Local Surface Region Curvature
## 5 Case Study
The same local region is measured three times and the average surface roughness \(Ra\) is calculated and marked in parentheses. As we can see, the surface quality in the region of free-form surface and edges is higher and the staircase effect is decreased than the other two methods. It should be noted that the surface roughness model [33; 34; 10; 35] is established to theoretically calculate and verify the surface quality of the model printing, the design of the theoretical model still uses the triangular facet normal and layer height to calculate. The final result is essentially the same as the volume error, so their method is not used in the article, but directly verified by instrument measurement. From the case study above, curvature shift strategy can effectively determine the surface regions with higher curvature, which help specify a larger weight factor for complex surfaces, especially those with obvious curvature changes. Those regions will be given higher weights to participate in the construction of the volume error function. The error function is actually a vector function with the shortest projection distance along the build orientation. The higher the weight of the triangle facets, the more contribution to minimize the volume error function, The optimal build orientation is the vector to leads to minimum volume error, which means surface roughness of those regions get higher accuracy. In other words, the final build orientation can help improve the printing accuracy of high-weight regions. As can be seen from the printing example (Figure 14) and error data (Tables 1 and 2), the method proposed can improve printing accuracy and shorten printing time too. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Orientation** & **Size (mm)** & **Length (m)** & **Weight (g)** & **Cost (g)** & **Time (h)** \\ \hline Default & 73.68 \(\times\) 72.01 \(\times\) 82.71 & 18.29 & 55.98 & 2.58 & 6.38 \\ EWO & 78.04 \(\times\) 91.13\(\times\)103.95 & 17.88 & 54.07 & 2.49 & 6.12 \\ Proposed & 85.65 \(\times\) 92.06 \(\times\) 99.76 & 15.33 & 46.08 & 2.12 & 5.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Volume error comparison in different methods. Figure 13: Printing results by H2000A Printer.