esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
## 1 Introduction
Then, the main problem of the paper is formulated. In Section 3, we propose a temporal logic planning approach by designing a product automaton where some key nodes are labeled by particle filters. In Section 4, we propose an RHC with temporal logic planning, where the corresponding movement trajectory meets a given LTL formula and guides the robot to locate all signal sources while avoiding obstacles. In Sections 5 and 6, we use Matlab simulation and real experiments to illustrate the effectiveness of the proposed control method. Finally, we give the conclusions in Section 7.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 5 Conclusions  
In this paper, an aero-engine remaining useful life (RUL) prediction model is put forward, which is based on the combination of a multichannel 1D convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and a self-attention mechanism. Taking into account the data characteristics and analysis requirements, only the FD001 and FD003 subsets of the C-MAPSS dataset were utilized to construct and validate the proposed model in this research.  
Regarding the model construction, a separate channel was established for each sensor to construct a model for feature extraction. A two-layer CNN was employed to process the monitoring data of the sensors, which enabled the effective extraction of spatial features. Subsequently, BiLSTM was adopted to ensure the extraction of time-series feature information. Meanwhile, with the assistance of the self-attention mechanism, the focus of the feature information was concentrated, endowing the model with a robust capability of capturing and analyzing features.  
Nevertheless, it should be noted that the FD001 and FD003 subsets possess certain limitations. They represent specific operating conditions and are unable to cover all the  
Figure 16: Comparison of RMSE results for different methods.  
Figure 17: Comparison of Score results of different methods.  
operating scenarios of aero-engines. This may impact the generalizability of the model. When the model is applied to other scenarios, its performance may deviate from the current validation results. Hence, to improve the model, the validation scope needs to be expanded.  
Despite the aforementioned limitations, the exploration conducted in this study based on specific subsets still offers valuable ideological and methodological references for the field of aero-engine RUL prediction and lays a certain foundation for the subsequent development of a more comprehensive and universal model.  
Conceptualization, Y.H. and C.W.; methodology, Y.H., C.W. and W.X.; software, Y.H.; validation, Y.H., C.W. and W.X.; formal analysis, Y.H.; investigation, Y.H.; resources, Y.H.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, Y.H., C.W. and W.X.; visualization, W.X.; supervision, Y.H.; project administration, Y.H. and W.X.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guiding Project of Science and Technology Research Programmer of Hubei Provincial Department of Education, grant number B2022047.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 6 Various Criteria for Assessing the Power Quality and Efficiency of the Studied System
### Power Quality  
Electrical power quality has become a critical factor in the design and operation of power systems and electrical machines. This subject has garnered considerable interest from both academic researchers and industrial practitioners, leading to the publication of numerous works in this dynamic and relatively recent field [132].  
Power quality encompasses several key issues, such as voltage dips, outages, harmonics (notably THD), over-voltages, unbalances, and voltage fluctuations, among others.  
With the introduction of new converter configurations, two additional quality concerns arise: common-mode voltage and zero-sequence voltage.  
This study addresses a range of quality-related challenges, including harmonic distortion, common-mode voltage, zero-sequence voltage, DC bus overcharging, and the regulation of floating capacitor voltages.
## 3 Results and Discussion
### Mechanical Properties of LCMs under Sodium Sulphate Corrosion
The strength of LCMs is affected by the decrease in the cement content. The contribution of fly ash to the strength is lower than that of cement. The compressive strength of the specimens decreases with the increasing fly ash content, consistent with the published data [45; 46]. Figure 5 shows the changes in specimen strength with the concentration of sodium sulphate: under the erosive action of sodium sulphate, the strength first increases, then decreases with the increasing concentration of sodium sulphate. The peak strength was found when cured in a 5% sodium sulphate solution. The effect of sodium sulphate on LCMs is mainly attributed to the chemical action of sulphate ions on the hydration products of materials. When sulphate ions penetrate the matrix, they will react with the hydration products and form ettrin96, which swells and absorbs water during its growth; this produces hoop tension on the pore wall, and leads to structural changes. When the amount of corrosion products is small, the corrosion products will increase the strength of the material while filling the original pores. In Figure 5, when the concentration of sodium sulphate is 5%, the strength of the specimens will be increased (albeit only slightly). On the one hand, the effects of ettrin96 and other corrosion products on the strength of LCMs are proved; on the other, the increase in strength also indicates that the artificial cellular pores in LCMs have a certain tolerance for the generation of corrosion products. For LCMs, containing many bubbles, AFt crystal occupied the volume of pores in its growth process due to its porous nature, resulting in limited expansion stress during the growth of the crystalline phase. Moreover, the presence of numerous pores in the LCMs also played a buffer  
Figure 5: Effect of the sodium sulphate concentration on the compressive strength of the specimens with different high-sulphur fly ash contents. role mitigating the swelling of the crystals; therefore, when the sodium sulphate concentration is low, LCMs can reduce the damage caused by ettrinigite, and AFt crystals can fill pores as an effective structural component of LCMs, promoting its strength to a certain extent. This is why the strength increases when the sodium sulphate concentration is 5%. As the concentration of sodium sulphate was increased, the rate of growth of the ettrinigite was accelerated. The original pore volume can no longer contain the growth of the ettrinigite, which eventually leads to structural damage and a reduction in strength. The test results show that LCMs with high-sulphur fly ash can resist the corrosion of sodium sulphate with a concentration not exceeding 5%.
## 5 Non-Iterative BER-Driven DDM Synthesis
### Discussion  
As expected, the evaluation of BER using (12) overlaps with the results obtained from transmitting \(10^{6}\) symbols, although the injected artificial orthogonal noise follows a zero-mean non-Gaussian distribution, as shown in Figure 10b.  
In Figure 10a, we can observe that the injected artificial noise was adjusted precisely in the direction of side lobes, while reduced interference was introduced in the nulls of the conventional pattern (where no information was transmitted). The proposed specifications were achieved for \(PE_{DDM}\) = 45% without the need to follow an iterative procedure where BER needs to be evaluated many times. Surprisingly, the BER curve is almost symmetric, although the restrictions were not applied symmetrically. Another interesting feature of this synthesis procedure is that we have been able to adjust the BER performance for side lobes and the information beam width independently.
###### Abstract  
In this study, a cryogenic turbo-based refrigerator, with high-speed foil gas bearing turboexpander and plate-fin heat exchanger, was presented. For the cooling process of a cryogenic refrigerator is long and complex, the efficient simulation of the dynamic performance is vital for its evaluation and operation. It is difficult to achieve this purpose. Because the cryogenic refrigerator will go through great temperature changes, resulting in dynamic performances of components going to complexity. In this article, the turboexpander was simulated using CFX, and the matching models with expansion works of consumption and utilization were unified and modified. With numerical calculation of heat exchanger, the dynamic simulation model of the refrigerator was improved via MATLAB code. The calculation method of the required input energy of the refrigerator in cooling processes was proposed based on the simulation and integration. The energy efficiency and economy cost of the refrigerator were evaluated under different operation modes. The refrigerator with a motor was more stable than the one with a blower. The refrigerator was tested under different conditions, the results of which agreed well with simulation data. The simulation method is efficient to serve practical applications, improve energy efficiency and reduce costs.  
Keywords:turbo-based refrigerator; dynamic simulation; evaluation; turboexpander; energy recycle; energy efficiency; economy cost
# ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security
## 6 Experiments
### Evaluation Setup  
We trained ResNet-20 on the CIFAR-10 [58] dataset. The mini-batch size was 32. In this section, our experiments were conducted in real distributed environments with CPU workers. We used \(\eta=0.2\) in the synchronous mode and \(\eta=0.1\) in the asynchronous mode. In the epochs 100 and 150, the learning rate decayed by 0.1 in the synchronous mode and 0.2 in the asynchronous mode. We used a constant \(\alpha=1\) in the synchronous mode. In the asynchronous mode, we used an initial value \(\alpha=0.4\), which decayed by 0.5 in the epochs 100 and 150.  
The communication overhead was reduced by both message compression and infrequent synchronization. We used random block-wise sparsification to compress the communication. Whenever a worker sent an update of a block of parameters to the server, with the probability of 0.2, it ignored the communication and put the update into the local residual error. Furthermore, the number of local steps \(H\) was 8.  
For the validators, we set \(b=3\) for both the trimmed mean and Phocas. We set \(\gamma=0.6\), \(\rho=-0.001\), \(\epsilon=0\) for Zeno validation in the synchronous mode. In the asynchronous mode, we set \(\gamma=0\) (with clipping), \(\rho=-0.02\), \(\epsilon=0\) for Zeno validation.  
We randomly and evenly partitioned the training data into the number of workers plus one parts and assigned the additional part to every validator.  
We evaluated ZenoPS in both the synchronous mode and the asynchronous mode. In the synchronous mode, we used Mean, Trimmed mean, Phocas [17], and Zeno (the score based validation defined in Definition 6) as the validators. In the asynchronous mode, we used FedAsync (no validation) and Zeno as the validators.  
In the experiments, we used the following two types of attacks:  
* **Sign-flipping attack:** The worker multiplies the original updates by \(-\zeta\), i.e., flips the sign and rescales the updates by \(\zeta\). We call this type of attack a \"sign-flipping attack rescaled by \(\zeta^{\prime\prime}\). The same type of attacks have also been used in previous work [20; 23].
* **Random attack:** The worker uses Gaussian random values with a 0 mean to replace the original values. If we use Gaussian random values with variance \(\zeta\), then we call this type of attack a \"random attack rescaled to \(\zeta^{\prime\prime}\). On the other hand, if we use Gaussian random values and rescale the Byzantine vector, so that the \(\ell_{2}\) norm of the Byzantine vector is \(\zeta\) times the original one, then we call this type of attack a \"random attack rescaled by \(\zeta^{\prime\prime}\). The same type of attacks have also been used in previous work [47].  
We simulated the Byzantine attacks by randomly replacing the vectors sent from the workers to the servers with a probability of 0.2.
# Dynamic Modeling and Characteristic Analysis of Articulated Steering Vehicles
## 2 Dynamic Model of ASVs
### Model of Steering System
#### 2.2.2 Kinematic Model  
According to the geometric relationship of the steering system in Figure 3b, the forces of the cylinders act on the front and rear frames, and, thus, different angles are formed when the articulated angle \(\varphi\) changes, causing these angles to affect the action arm of the cylinders. The different angles between the cylinders and the frames can be expressed as  
\[\left\{\begin{array}{l}\gamma_{1}=\arcsin(\sin(\varphi+\pi/2)Y_{L}/X_{L})\\ \beta_{1}=\arcsin(\sin(\pi/2-\varphi)Y_{R}/X_{R})\end{array}\right. \tag{14}\]  
\[\left\{\begin{array}{l}\gamma_{1}+\varphi=\gamma_{2}\\ \beta_{1}-\varphi=\beta_{2}\end{array}\right. \tag{15}\]  
where \(\gamma_{i}\) and \(\beta_{i}\) (\(i=1\), \(2\)) denote the angles between the left and right cylinders and the front and rear frames, respectively. Additionally, the length of the cylinders \(X_{L}\) and \(X_{R}\) and the intermediate variables \(Y_{L}\) and \(Y_{R}\) can be expressed as  
\[\left\{\begin{array}{l}X_{L}=\sqrt{OP_{1}^{2}+OR_{1}^{2}-2 OP_{1}OR_{1}\cos( \varphi_{0}+\varphi)}\\ X_{R}=\sqrt{OP_{2}^{2}+OR_{2}^{2}-2 OP_{2}OR_{2}\cos(\varphi_{0}-\varphi)} \end{array}\right. \tag{16}\]  
\[\left\{\begin{array}{l}Y_{L}=b-(a/\cos\varphi-c\tan\varphi)\\ Y_{R}=b-(a/\cos\varphi+c\tan\varphi)\end{array}\right. \tag{17}\]  
According to Equations (12)-(17), the force and moment acting on the front and rear frames can be obtained, which can be expressed as follows:  
\[\left\{\begin{array}{l}M_{2f}=(F_{L}\cos\gamma_{1}+F_{R}\cos\beta_{1})B-\\ (F_{L}\sin\gamma_{1}+F_{R}\sin\beta_{1})L_{f1}\\ M_{2r}=(-F_{L}\cos\gamma_{2}+F_{R}\cos\beta_{2})B+\\ (-F_{L}\sin\gamma_{2}+F_{R}\sin\beta_{2})L_{r1}\\ F_{sxf}=(F_{L}\cos\gamma_{1}+F_{R}\cos\beta_{1})\\ F_{syf}=(F_{L}\sin\gamma_{1}-F_{R}\sin\beta_{1})\\ F_{sxr}=(-F_{L}\cos\gamma_{2}-F_{R}\cos\beta_{2})\\ F_{syr}=(-F_{L}\sin\gamma_{2}+F_{R}\sin\beta_{2})\end{array}\right. \tag{18}\]  
Figure 3: The ASV steering system. (**a**) The hydraulic circuit of the steering system; (**b**) the geometric relationship of the system.  
Then, by substituting Equations (3)-(11) and (18) into Equations (1) and (2), the dynamic model of ASVs in the XY plane can be obtained. The tire force in the lateral and longitudinal directions originates from the steering maneuver and the power train system. The model of the power train is given in the next subsection.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 3 Results
### Classification Results
(**k**) Cross polarized ratio of the L-band. (**l**) The changing trend of the cross polarized ratio. As shown in Figure 10, the proposed method can generate competitive visual effects with good connectivity and smooth borders, compared with the ground truth. Thus, lithology can be classified in a good way with this method. Although favorable results of lithology classification can be obtained with the proposed method, several different classes of lithology are confused. The confusion matrix is shown in Table 4.  
The Diluvium is mostly confused with Alluvium-Diluvium, leading to a low classification accuracy of these classes. This is primarily due to the fact that both Diluvium and Alluvium-Diluvium have a low back-scattering power.
# Exploring the Architectural Composition of Cyber Ranges: A Systematic Review
## 4 Discussion
### General Interpretation of the Literature Findings
#### 4.1.5 Geolocation  
CRs can be deployed and accessed in one of the following three locations.  
_On-premises:_ In this case, the architectural composition of the platform is simplest, using virtualization resources provided by developers with full awareness of the operating nodes [16; 54].  
_Cloud-based:_ The architecture is based on appropriate web-based APIs. ECSO [16] reports three types: (i) private, (ii) public, and (iii) federated. Popular examples are AWS Elastic Compute Cloud (EC2) [81], IBM Cloud Computing [82], and Microsoft Azure [83].  
_Federated CRs:_ The authors in [16; 84] refer to a unified, interoperable combination of cloud CRs integrating different types of DBs, applications, and systems. This interoperability allows for easier data sharing and collaboration across elements.
# Conductive Textiles for Signal Sensing and Technical Applications
###### Abstract  
Conductive textiles have found notable applications as electrodes and sensors capable of detecting biosignals like the electrocardiogram (ECG), electrogastrogram (ECG), electrocapphalogram (EEG), and electromyogram (EMG), etc; other applications include electromagnetic shielding, supercapacitors, and soft robotics. There are several classes of materials that impart conductivity, including polymers, metals, and non-metals. The most significant materials are Polypyrrole (PPy), Polyaniline (PANI), Poly(3,4-ethylenedioxythiophene) (PEDOT), carbon, and metallic nanoparticles. The processes of making conductive textiles include various deposition methods, polymerization, coating, and printing. The parameters, such as conductivity and electromagnetic shielding, are prerequisites that set the benchmark for the performance of conductive textile materials. This review paper focuses on the raw materials that are used for conductive textiles, various approaches that impart conductivity, the fabrication of conductive materials, testing methods of electrical parameters, and key technical applications, challenges, and future potential.  
e-textiles; smart textiles; conductive polymers; conductive textile testing 2022  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]; et al. Conductive Textiles for Signal Sensing and Technical Applications Applications  
31 August 2022  
R.S.R.S.
## 2 Fundamental Theory and General Assumptions  
The limit state is the condition in which there is a failure mechanism in the structure interior, and it is the critical state of the structure at the boundary with and without destruction. According to the lower bound theorem, a load related to a statically admissible stress field will not exceed the true collapse load of the structure. A statically admissible stress field should meet the following three conditions: (a) equilibrium condition; (b) yield condition; and (c) boundary condition. The ultimate aim of the lower bound limit analysis is to determine the maximum of the collapse load.  
To apply the lower bound theorem and simplify the calculation, the following assumptions must be made in this study: (a) The rock block cut by fully persistent joints is a rigid body, and it does not produce any deformation or destruction. Failure only occurs in the joint surfaces between the adjacent rock blocks. Furthermore, the rock bridges are isotropic and only subject to shear failure. (b) The material of the joints and rock bridges optimizes the plasticity and obeys the Mohr-Coulomb yield condition based on plane strain theory. (c) All deformations or changes in the geometry of the rock mass at the limit load are small and negligible; and (d) only the sliding failure of the rock mass is considered, whereas rotational failure is not considered.
# Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller
## 2 Materials and Methods
### Vector Control  
The application of vector control for the IM drive in dynamic situations offers closed-loop speed control with robust stability. The use of IM for general purpose and industrial applications requires composite efficiency. In speed control applications, the major task of IM is to achieve speed closely matched with reference speed irrespective of the parameter, load, or environmental uncertainties. Direct torque control governed by speed vector modulation (SVM) helps in the reduction in higher torque demand and fluctuations in flux [21]. The indirect torque control particle swarm algorithm (PSO)-tuned P-I controller offers lower ripples and a faster response time for speed and torque variations [22]. Swarm optimization highlights intermittent faults to isolate faulty components and dual gradation is used to predict the life span of faulty components [23].  
The driving pattern for EV was designed in MATLAB (Simulink) to analyze vehicle speed control following a fixed route [24]. The P-I controller and fuzzy logic controller-based hybrid control of the EV model provides improvements in torque response [25]. The P-I controller mathematical form for torque generation and an error signal is shown in Equations (1) and (2) as:  
\[T_{e}^{*}=K_{p}e\big{(}t\big{)}+K_{i}\int e\big{(}t\big{)}dt \tag{1}\]  
\[e\big{(}t\big{)}:\omega^{*}-\omega \tag{2}\]  
\(\omega^{*}\): measured speed;  
\(\omega\): reference speed;  
\(e\big{(}t\big{)}\): error signal;  
\(K_{p}\): proportional gain;  
\(K_{i}\): integral gain;  
\(T_{e}^{*}\): generated torque.  
The P-I-based speed controller in \"Figure 2\" lacks the rejection of load disturbance and overshoot during the tracking of the reference speed. The complex nature of the system does not permit the controller to surpass minimum permissible limits [26; 27].
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 3 Methods
### Hyperparameter Optimization  
Hyperparameter optimization is performed using grid search, which exhaustively explores all possible combinations of hyperparameters to enhance model performance. A parameter grid is first constructed by defining the hyperparameters to be optimized and their range of possible values. Then, the model is trained and evaluated for each parameter combination to find the combination that yielded the best performance in terms of the negative mean squared error. Finally, the parameter combination with the best performance is selected for the final training and testing of the model. Through systematic search, the model's performance can be effectively enhanced.  
Figure 4: LSTM unit structure diagram.  
Figure 5: BiLSTM unit structure diagram.