esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper a vehicle dynamics control system is designed to compensate the change in vehicle handling dynamics of lightweight vehicles due to variation in loading conditions and the effectiveness of the proposed design is verified by simulations and an experimental study using a fixed-base driving simulator. Considering the electrification of future mobility, the target vehicle of this research is a lightweight vehicle equipped with in-wheel motors that can generate an additional direct yaw moment by transverse distribution of traction forces to control vehicle yawing as well as side slip motions. Previously, the change in vehicle handling dynamics for various loading conditions have been analyzed by using a linear two-wheel vehicle model in planar motion and a control law of the DYC system based on feed-forward of front steering angular velocity and feedback of vehicle yaw rate. The feed-forward controller is derived based on the model following control with approximation of the vehicle dynamics to 1 st-order transfer function. To make the determination of the yaw rate feedback gain model-based and adaptable to various vehicle velocity conditions, this paper selects a method where the yaw rate feedback gain in the DYC system is determined in a way that the steady-state yaw rate gain of the controlled loaded vehicle matches the gain of the unloaded vehicle. The DYC system is simulated in a single lane change maneuver to confirm the improved responsiveness of the vehicle while simulations of a double-lane change maneuver with a driver steering model confirms the effectiveness of the DYC system to support tracking control. Finally, the effectiveness of the proposed DYC system is also verified in an experimental study with ten human drivers using a fix-based driving simulator.  
vehicle dynamics control; lightweight design; direct yaw moment control (DYC) +
Footnote †: journal: Applied Sciences
## 4 Future Research Trends for ML-Based IRS-Assisted Wireless Communication
### Data Collection and Model Training  
Data collection is a crucial part of training ML models and the accuracy of the model depends on the data [107]. As biased data can result in imperfect models, data collection is a challenge for the deployment of ML-based techniques in IRS-based communication. The main estimation factors are signal detection, CE, and the beamforming design for the receiver. For the successful implementation of ML-based techniques, data collection algorithms can be a future research topic.
# Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
## 7 Conclusions  
In response to the challenges associated with traditional single TCN networks, such as difficulty in controlling convergence speed, susceptibility to local optima, and less-than-ideal accuracy in predicting air conditioning loads, this paper enhances the predictive system's accuracy by incorporating BiGRU and attention mechanisms. To address the hyperparameters within the prediction model, such as learning rate, convolution kernel size, and the number of recurrent units, this study utilizes the global optimization and local search capabilities of the Golden Jackal Optimization (GJO) Algorithm. It proposes the application of the GJO-TCN-BiGRU-Attention intelligent algorithm to forecast the energy consumption of buildings. The GJO-TCN-BiGRU-Attention is compared with the TCN-BiGRU-Attention network in terms of load forecasting for a building over the course of a year. The comparative analysis of the prediction outcomes indicates that the GJO-TCN-BiGRU-Attention not only improves the accuracy of air conditioning load forecasting but also achieves more stable results during abrupt changes in air conditioning loads. This demonstrates that the use of the GJO Algorithm to optimize the network and its application in forecasting can more effectively address the issue of dynamic building energy consumption prediction accuracy.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### The Optimization-Based Method  
As discussed in Section 2, the cross-layer design methods can be abstracted as a series of optimization issues. The parameters that are considered in cross-layer design methods are energy consumption, propagation delay, throughput, transmission rate, and so on. Due to the fact that we cannot get optimal solutions for all factors at the same time, the traditional cross-layer methods cannot deal with the NP-hard problems. Unfortunately, many optimizations of the cross-layer parameters are NP-hard issues, such as the minimum energy consumption and the maximum throughput, the minimum energy consumption and the highest transmission rate, _etc._ However, there are still many cross-layer optimization issues that can be solved by cross-layer methods.  
Since the cross-layer routing protocols have the capability to handle more than one cross-layer parameter, we cannot distinguish the cross-layer routing protocols based on the energy consumption, the propagation delay, or the transmission rate, _etc._ In this part, we classify the traditional cross-layer design routing protocols based on whether the location information is needed.
## 6 Conclusions  
In this study, the 6-DOF manoeuvring mathematical model of the SFRM with the autopilot system was developed to conduct a preliminary evaluation of motion performance for optimal control gains. In order to realise the turning and diving performances of the SFRM, a series of manoeuvring tests, including turning circle, horizontal zigzag, vertical zigzag, meander, and spiral tests, would be implemented based on three sailing speeds with empty ballast tanks. Furthermore, the PID tuner was used to acquire the optimal control gains for each manoeuvring test by considering characteristics parameters. Therefore, two types of control gains by combining different horizontal plane motion tests with vertical plane motion tests were adopted for subsequent autopilot simulation. The post-processor based on the Cinema 4D modelling is capable of allowing the animated model to match the simulated trajectories and course angles graphically. The concluding remarks can be summarised as below:  
1. There were only linear terms of hydrodynamic coefficients considered in the present simulation regardless of the scale effect. The sailing speed and the operation of control surface were considered as the influential parameters in the manoeuvring tests with empty ballast tanks.
2. Through a series of manoeuvring tests, the operation performances by adopting different combinations of rudder commands and sailing speeds were well analysed.
3. After evaluating the characteristics parameters of the PD controller for manoeuvring tests in the horizontal and vertical plane, both the A-type (horizontal zigzag plus vertical zigzag) and the B-type (horizontal zigzag plus meander) controllers were compared in the autopilot simulation.  
Figure 27: Time series of velocity components. (**a**) Surge rate: \(u\) [m/s], (**b**) sway rate: \(v\) [m/s], (**c**) heave rate: \(w\) [m/s], (**d**) roll rate: \(p\) [rad/s], (**e**) pitch rate: \(q\) [rad/s], and (**f**) yaw rate: \(r\) [rad/s] for the A-type and B-type controllers, respectively.  
When conducting the autopilot simulation in the 3D map, the A-type PD controller with the LOS guidance algorithm was proved to be more efficient than the B-type in the average power, total sailing distance, and consuming time.
* 5. In case of low-speed conditions, the SFRM can not reach the desired pitch angle with empty ballast tanks. In the future study, the PD control on the ballast tanks and the attitude adjusting system will be considered in the autopilot system of the SFRM for improving its stability and manoeuvrability.  
[PERSON] is the principal investigator and [PERSON] and [PERSON] are the research assistants in this project. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology for a grant under Contract No. MOST 109-2221-E-006-100-MY2.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Reflector Antenna
#### 3.6.1 Corner Reflector Antenna  
The corner reflector type of antenna is very effective yet very simple. The antenna shown in Figure 7 has plane reflectors in the form of a panel and a dipole element [60].  
The antenna arrangement prohibits radiation in backwards and sideways direction and, as such, they are more directional [61; 62]. A prototype of reconfigurable corner reflector using PIN-diode-switched frequency selective surfaces (FSS) has been developed [63]. A reflector aperture angle of 45\({}^{\circ}\) is prototyped for application at 4 GHz, and its pattern characteristics are altered by on-off state biasing of the PIN diodes. Readers interested in the theory of corner reflectors with tilted dipole can consult [64].  
Figure 6: Helical antenna [59].  
Figure 7: Corner reflector antenna [60].
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 3 GSFedSec: GS-Based SA for Privacy Preservation in FL
### System Setup
#### 3.1.1 Key Generation  
We adopt the short GS described in [25] for key generation. The admin is the trusted entity, and it is responsible for two types of key generation: signature key and secret key. A signature key is generated for all members of the group. We have one isomorphic bilinear group pair \((G_{1},G_{2})\) with the pair member generators \(g_{1}\) and \(g_{2}\), respectively, and the property \(g_{1}\leftarrow\psi(g_{2})\). The admin selects \(h\gets G_{1}/\{1_{G_{1}}\}\), \(\xi_{1},\xi_{2}\gets Z_{p}^{*}\), and \(\gamma\gets Z*_{p}\); thus, it computes \(u^{\xi_{1}}=v^{\xi_{2}}=h\) and \(A_{pk}=g_{2}^{T}\). Here, \(A_{pk}\) is the public key of the admin, and \((\xi_{1},\xi_{2})\) is the private key tuple, which is used to trace the signer in the case of any dispute. \(\gamma\) is the private key of the admin. Using this \(\gamma\), a tuple \((A_{i},x_{i})\) for the \(i\)-th device in the group can be computed as follows: \(A_{i}\gets g^{1/\left(\gamma+x_{i}\right)}\in G_{1}\), where \(x_{i}\) is selected randomly from \(Z_{p}^{*}\). \((A_{i},x_{i})\) is the tuple for corresponding \(i\)th device. Then, the admin publishes the group public key as \(g_{pk}=(g_{1},g_{2},h,u,v,A_{pk})\).  
After the server sends a list of clients selected for FL to the admin, the admin generates a secret key and divides it into \(n\) parts, where \(n\) is the number of clients selected for FL. This division is such that when the parts are combined, everything is canceled out. Then, the admin sends one part of the secret key \(S_{i}\) and \(A_{i}\) to each respective client \(i\).  
Figure 3: Original secure aggregation scheme [12].
## 2 Materials and Methods
### Performance Metric Evaluation  
The model will produce an ECG signal that has been denoised and classified into four categories: P-waves, QRS-complexes waves, T-waves, and isoelectric lines. Its performance will be evaluated using both regression and classification metrics to provide a comprehensive assessment of accuracy and effectiveness [38]. This dual approach ensures the model's predictions are accurate, distinguishes between different classes reliably, and effectively removes noise from ECG signals. Regression metrics include maximum absolute distance (MAD) to measure the largest absolute difference between the predicted values and the actual values,  
\[MAD(s_{1},s_{2})=\max\lvert s_{1}(n)-s_{2}(n)\rvert\ \ 1\leq n\leq N \tag{1}\]  
The sum of squared distances (SSD) between the predicted values and the actual values is  
\[SSD(s_{1},s_{2})=\sum_{n=1}^{N}(s_{2}(n)-s_{1}(n))^{2}\ \ \ 1\leq n\leq N \tag{2}\]  
Percentage root-mean-square difference (PRD) represents the root-mean-square difference between the predicted values and the actual values as a percentage,  
\[PRD(s_{1},s_{2})=100\%.\sqrt{\frac{\sum_{n=1}^{N}(s_{2}(n)-s_{1}(n))^{2}}{ \sum_{n=1}^{N}\left(s_{2}(n)-\overline{s_{1}(n)}\right)^{2}}}\ 1\leq n\leq N \tag{3}\]  
Cosine similarity (CosSim) measures the cosine of the angle between two non-zero vectors,  
\[CosSim(s_{1},s_{2})=\frac{\sum_{n=1}^{N}(s_{1}(n)\cdot s_{2}(n))}{\sqrt{\sum_{ n=1}^{N}s_{1}^{2}(n)}\cdot\sqrt{\sum_{n=1}^{N}s_{2}^{2}(n)}}=\frac{\langle s _{1},s_{2}\rangle}{\lvert\lvert s_{1}\rvert\lvert\lvert s_{2}\rvert\rvert} \ 1\leq n\leq N \tag{4}\]  
Signal-to-noise ratio (SNR) measures the ratio of the power of the signal to the power of the noise,  
\[SNR(s_{1},s_{2})=10 log_{10}\left[\frac{\sum_{n=1}^{N}s_{2}^{2}(n)}{\sum_{n=1} ^{N}\left(s_{2}(n)-s_{1}(n)\right)^{2}}\right]1\leq n\leq N \tag{5}\]  
where \(s_{1}\) is the original signal, \(s_{2}\) are the filtered signals, \(n\) is the index of the current sample, and \(N\) is the length of the signals.  
The metrics provide various perspectives on the accuracy and reliability of regression models, helping to identify both average performance and specific areas of strength or weakness in the predictions. The classification metrics are assessed using a confusion matrix, including accuracy, sensitivity, precision, and F1 score [39]. However, in this study, we highlight the F1 score. In the ECG signal delineation study, the F1 score allows us to evaluate how well our model distinguishes between the P-wave, QRS-complex, T-wave, and isoelectric line classes across unseen data. By using the F1 score, we ensure that the proposed model maintains a good balance between identifying true positives and minimizing false positives and false negatives, leading to more reliable and accurate ECG signal classification.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 4 Modular Training Strategy and Loss Functions
### Feature Filtering Stage Loss Functions  
To make the homogeneous and isolated features be successfully extracted, the feature-guided loss is proposed as follows:  
\[L_{\text{FG}}=\left(CCC\left(I_{i}^{x},I_{i}^{y}\right)\right)^{2}+\left(1-CCC \left(I_{h}^{x},I_{h}^{y}\right)\right)^{2}, \tag{13}\]  
where \(I_{i}^{n}\) and \(I_{h}^{n}\) are decoded from \(f_{i}^{n}\) and \(f_{h}^{n}\), respectively; \(n\) refers to the corresponding image. During training, \(CCC(I_{i}^{x},I_{i}^{y})\) tends toward \(0\) to minimize \(L_{\text{FG}}\), indicating that the correlation between \(I_{i}^{x}\) and \(I_{i}^{y}\) decreases. However, \(CCC(I_{h}^{x},I_{h}^{y})\) should approach \(1\), signifying that the correlation between \(I_{h}^{x}\) and \(I_{h}^{y}\) is positively increased. The consistency correlation coefficient \(CCC(X,Y)\) is an operator that returns the correlation between input information with a range of \([-1,1]\). The formula for \(CCC(X,Y)\) is given as follows:  
\[\rho_{c}=\frac{2\rho\sigma_{x}\sigma_{y}}{\sigma_{x}^{2}+\sigma_{y}^{2}+(\mu_ {x}-\mu_{y})^{2}}. \tag{14}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the variables; \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the respective variances, and \(\rho\) is the Pearson correlation coefficient between the two variables.  
Figure 5: Implementation process of modular training strategy.  
This entropy loss is implemented to maximize the similarity and amount of information in homogeneous and isolated features. This approach helps to prevent the feature filter from producing meaningless solid-color images. The entropy loss is defined as follows:  
\[L_{EN}=\frac{(EN(I_{i}^{x})-EN(I_{h}^{x}))^{2}+(EN(I_{i}^{y})-EN(I_{h}^{y}))^{2} }{EN(I_{i}^{x})+EN(I_{h}^{x})+EN(I_{i}^{y})+EN(I_{h}^{y})+\epsilon}, \tag{15}\]  
where \(\epsilon=0.01\) is added to prevent division by zero, and \(EN(\cdot)\) is the entropy operator. The number \(n\) represents the possible pixel values, and \(p(i)\) is the probability of pixel value \(i\). The entropy operates defined as follows:  
\[H=-\sum_{i=0}^{n}p(i)\log_{2}p(i). \tag{16}\]  
The total loss function during training for the feature filtering stage, which combines the feature-guided loss and entropy loss, is defined as follows:  
\[L_{FF}=L_{FG}+\mu_{2}L_{EN}, \tag{17}\]  
where \(\mu_{2}\) is a tuning parameter.
# Flocculation Harvesting Techniques for Microalgae: A Review
## 1 Introduction
As the flocculant plays a major role in the flocculation harvesting process, the discovery of a highly efficient and cost-effective flocculant has forever remained a challenge in most studies. Nowadays, the use of conventional inorganic metal salts such as aluminum sulfate and ferric sulfate has been reduced due to high dosage and biomass contamination [9]. Various natural and synthetic organic flocculants have been designed and developed to improve flocculation efficiency. However, the former have high production cost and a short shelf-life while the latter have adverse effects  
Figure 1: Schematic diagram of the flocculation harvesting process of algal cells with a recyclable flocculant. on harvested biomass and non-biodegradability, due to their petroleum origins [24]. Metal cations released from the electrode under direct electric current condition are able to electrostatically attract almost all types of algal cells, resulting in efficient flocculation. Significant efforts are being directed to prevent electrode/biomass fouling and to reduce systemic/electric cost for large-scale algae harvesting. Nanoparticles in either single or hybrid forms decorated with various cationic chemicals have been employed for rapid algae separation and/or multi-functionalities such as cell disruption and lipid extraction [25]. This approach although highly efficient, is expensive and is mostly limited to laboratory-scale studies. Spontaneous aggregation of algal cells under specific conditions and the use of a self-flocculating microorganism can be considered as sustainable and environment-friendly [26; 22]. However, species-specific reactivity, availability of low-cost bio-flocculant-microorganisms, and process scale-up should be properly considered for practical applications. Ideally, in addition to excellent harvesting efficiency and promising scalability, the industrial flocculant should satisfy the demands for recyclability, low toxicity, low-cost material, and massive production process. Many review articles are available on the use and mechanism of specific flocculants/ techniques [9; 27; 28; 29; 30; 19], but a comprehensive comparison of various flocculation-based methods including classical inorganic chemicals and newly engineered nanoparticles, is limited. We herein classify the flocculation techniques that have been studied and applied in the algal biorefinement process, into five categories which are auto-flocculation, bio-flocculation, chemical flocculation, particle-based flocculation, and electrochemical flocculation. The main objective of this review is to introduce the readers to these flocculation techniques and the key issues in their commercialization. The review thus provides an updated overview of the recent technical developments and progress in this field. Further research directions have also been proposed to direct the development of flocculation harvesting processes suitable for large-scale algae applications.
# Photoelasticity as a Tool for Stress Analysis of Re-Entrant Auxetic Structures
###### Abstract  
The presented study illustrates the use of photoelasticity as an effective tool for validating the results of finite element method (FEM) simulations of auxetic structures. This research focuses on comparing stress distributions in planar auxetic models under symmetrical and asymmetrical loading conditions. Experimental measurements, conducted using an optically sensitive material (PSM-1), were found to align closely with FEM predictions, with deviations within 5%. This agreement highlights the accuracy of both methods, though discrepancies were noted in areas with lower stress levels due to fringe order reading precision. The experimental process makes it possible to take into account real conditions and inaccuracies in production, while numerical modelling is based on ideal conditions. The findings affirm the value of photoelasticity for stress field analysis in complex geometries, particularly for auxetic structures, and underscore its role in verifying and refining computational models. The study concludes that photoelasticity can be a valuable tool for designers and engineers in verifying FEM simulations, even without the use of digital processing and the evaluation of measured data.  
Keywords:auxetic metamaterials; re-entrant; photoelasticity; FEM analysis +
Footnote †: journal: Applied sciences
## 6 A New Era of Structural Study of Bacterial Transcription Using _E. coli_ RNA Polymerase  
Crystal structures of bacterial RNAPs have been determined using the _Thermus_ genus. Due to a high degree of RNAP sequence conservation among all bacterial species, mechanistic insight derived from the _Thermus_ RNAP structure has been generalized to represent the transcription apparatus in all bacteria. Nevertheless, understanding the structure of _E. coli_ RNAP is essential to fully interpret the enormous amount of biochemical, biophysical, and genetic data that has been collected on _E. coli_ RNAP. In 2013, the first crystal structure of the _E. coli_ RNAP \(\upsigma\)[70] holoenzyme was determined (PDB: 4 YG2) [35]. _E. coli_ RNAP can be readily prepared using an overexpression system, which allows RNAP structural study to move in a new direction using RNAP mutants.  
Characterization of _E. coli_ RNAP has created new possibilities for the structural study of bacterial RNAP. Sixteen structures of _E. coli_ RNAP with transcription factors or small molecules have been determined already, including the long-awaited structure of _E. coli_ RNAP in complex with (p)ppGpp, a master regulator of stress response in bacteria (PDB: 4 JK1, 4 JK2, 4 JKR) [74, 75] (Table 3 and Figure 3). The ppGpp binding site of _T. thermophilus_ RNAP was also determined from the crystal structure of the _T. thermophilus_ RNAP-ppGpp complex (PDB: 1 SMY) [76], but it failed to verify its relevance to _E. coli_ transcription regulation by ppGpp [77].
# Feasible Energy Density Pushes of Li-Metal vs. Li-Ion Cells
## 1 Introduction
CE or Faraday efficiency can be simply described as the proportion of the total charge extracted from the cell (Q\({}_{\text{dc}}\)) to the total charge put into the cell (Q\({}_{\text{c}}\)):  
\[\text{CE}=\text{Q}_{\text{dc}}/\text{Q}_{\text{c}} \tag{1}\]  
For conventional LIBs, it was observed that the CE increases with the first 15-20 cycling, after which a CE-value of more than 99.9% [15; 16] can be reached. Here the question is what type of Li-metal battery cell design is required in order to achieve at least the same performance values of the current LIB technology? Within the scope of this article, we will discuss different Li-ion excesses, with different anode and cathode thicknesses to understand the effect of different numbers and parameters on the columbic efficiencies and the energy density. This paper aims to present various realistic scenarios in order to answer how much energy density can be gained by the technology transition from LIBs to Li-metal batteries (LMBs). For this aim, the paper is organized with the following sections:  
* Understanding the need for a Li-metal anode: Why do we need Li-metal instead of conventional graphite or silicon/graphite for anode active materials? * Excess lithium and energy density: How much lithium do we need in the cell in order to gain higher energy density and higher CE? * Thickness of the Li-metal anode: Does a thick Li-metal anode provide the best performance? What is the limit? * Achieving long cycle life and long driving distance range with a reasonable excess lithium: Different EV battery capacities and different energy consumption scenarios. What do these scenarios tell us? * Liquid electrolytes vs solid electrolytes for LMBs: As an option, the feasibility of liquid electrolytes will also be discussed if the increase of the energy density is considered as the main goal. At the end the cost aspect will also be reflected on.