esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 4 Modeling the Query Flow Within the Mini-Datacenter
### Fuzzy Modeling of Granular Selection in Query Forwarding  
We have utilized fuzzy modeling to select the appropriate query forwarding strategy for the mini-datacenter, which utilizes multi-modal query forwarding. Fuzzy logic can be computationally simple to implement in the resource-constrained WSN environment, and does not require heavy calculations like machine learning models; moreover, fuzzy rules can be easily designed to reflect the behavior of the system. The main objective is to determine when to use fine-grain forwarding (redundant) vs. coarse-grain forwarding (direct) based on the dynamic states of the number of available gateways and the localized congestion in the selected gateway buffers. Fuzzy logic enables a gradual transition between the coarse-grain and fine-grain strategies, allowing for decisions based on degrees of certainty rather than pre-defined thresholds.  
We have considered two fuzzy input parameters: the proportion of available gateways (\(G_{available}\)) relative to the deployed, which reflects the degree of possible redundancy for query forwarding at time t, and the proportion of overloaded gateways (\(G_{overload}\)) against the available, which indicates the probable level of congestion. These parameters help to ensure a smooth query forwarding under varying load while minimizing the risk of data loss.  
An output parameter is defined to select the forwarding strategy, which is either fine-grain or coarse-grain, based on the input conditions. The fuzzy system adapts in real-time to fluctuating conditions, making decisions that maximize the system's reliability. Fine-grain forwarding is further subdivided into 'low','medium', and 'high', where redundancy is varied based on the mini-datacenters' load; it maintains a minimum redundancy to enhance reliability. Instead, coarse-grain forwarding is preferred when gateways are heavily loaded or redundancy is not feasible, ensuring a seamless data flow.
## 4 Discussion
### Best Tilting Plane  
In the literature, asymmetrical arm movements in the frontal plane have been presented as the best way to create aerial twists in straight somersaults [1]. Arm motions near the sagittal plane were even disapproved of [3], which is refuted by our optimal solutions. At the beginning and at the end of the skill, optimal 3D solutions consist of arm motions in the frontal plane, but at some points they clearly include arm movements in the sagittal plane. Escaping from the frontal plane enabled 3D-PHP techniques (III, IV and V) to outperform 2D-UHP technique (II) in terms of the number of twists, complexity and effort, reinforcing the superiority of 3D motion over 2D. Of all 3D-PHP techniques, technique IV (composed of two lowering/raising per arm, the last raising being synchronous and in the sagittal plane) has the best potential for teachability due to the least effort being involved, and the \(\Delta_{perf}\) and the \(\Delta_{kin}\). A mechanical explanation of this gain in performance is further provided thanks to the BTP, introduced in this paper (Section 2.6).  
The angular momentum is coincident with the athlete's mediolateral axis at take-off and is constant throughout the skill. Aerial twist strategies aim to decrease (resp. increase) the tilt angle between the angular momentum and the twist axes to increase (resp. decrease) the twist velocity [1]. According to the equation of motion, the most direct way to transfer angular momentum from the somersault axis to the twist axis is to incline the body strictly around the tilting axis by rotating a segment in a plane parallel to the BTP in the opposite direction. Since the BTP rotates with the somersault rotation, most efficient arm trajectories must be 3D and sometimes get closer to the sagittal plane of the body (see Appendix A for more details). The concept of moving the arms in 3D to twist efficiently is a generalization of the usual advice for 2D arm strategies. Indeed, coaches are already aware of the timing importance in the execution of 2D techniques in the frontal plane [21]; it turns out that these particular instants correspond to the alignment of the BTP and of the frontal plane. As a consequence, 2D strategies usually include portions without motion between these precise instants. Our mechanical analysis thus corroborates usual 2D techniques, but 3D arm motions offer new possibilities as the BTP is always reachable, aside from the unphysiological range of arm motions. Moving segments (arms or other body segments) in the BTP is an efficient motor strategy to generate twists.
## 4 Experimental Results
### Prototype Performance Evaluation
Regarding the calibration errors obtained with the calibrator prototype, the maximum absolute error is a little bit higher but still lower than 3.6 mmHg, which corresponds to a maximum relative error almost equal to 2.6% relative to the same pressure measurement range. Moreover, it is also important to underline that the Pearson correlation coefficient values that were obtained with the commercial and certified BP calibrator and with the calibrator prototype were equal to 0.999 and 0.997, respectively. In both cases, the calibration results that were obtained verify the pass clinical validation criteria that, according to the British Hypertension Society (BHS), require mean and standard deviation errors, against the gold standard, to be lower than \(\pm\)5 mmHg and 8 mmHg, respectively. Figure 10: Test setup used to calibrate the commercial home blood pressure monitor: (**a**) equipment interconnections (1—commercial and certified BP calibrator, 2—home blood pressure monitor, 3—dummy arm used to fix the ABPMD cuff); (**b**) front view of the home blood pressure monitor with an example of displayed measurement results. Figure 11: Calibration results of the home BP monitor (VEROVAL) when the calibration is performed with a commercial and certified BP calibrator (DATATREND). Figure 12: Calibration results of the home BP monitor (VEROVAL) when the calibration is performed with the proposed BP calibrator prototype.
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 5 Results and Discussion
### Average Delay  
The TARS protocol shows less average delay as compared to the DBSR and POVRP. TARS uses a traffic congestion avoidance algorithm which reduces the unnecessary messages caused by vehicular congested traffic. The RSU only transmits warning messages and vehicles exchange safety messages according to the present traffic condition (i.e., vehicle velocity and the distance between each vehicle to the other vehicle). TARS overcomes the condition in which vehicles move at high speed and broadcast lots of messages, where there are a large number of vehicles causing congestion due to message dissemination. Moreover, after detecting congestion, the RSU transmits warning messages at the exact time for the upcoming vehicle to select an alternate route in order to prevent congestion and avoid accidents, the upcoming vehicle makes more intelligent decisions and selects a less congested route while applying a basic safety message strategy (vehicles share their velocity and vehicle-ID with other vehicles) to follow the specified path. In this way, the TARS protocol provides less delay in order to transmit warning messages, as a result, driver safety is increased, as shown in Figure 12.  
Figure 11: Basic Safety Message in the highway scenario.  
Figure 12: Average Delay in the highway scenario.
## 5 Simulation Results
### Discussions  
Simulations are conducted for three cases, such as nominal system, perturbed system without disturbance observers, and perturbed system with disturbance observers. Simulation results of the nominal system for the linear position command trajectory show that the performance of the PTSBSC system exceeds that of the other BSC and YTBSC systems. This achievement is obtained, owing to the improved performance of the proposed tracking differentiator. The proposed tracking differentiator shows the best performance over the conventional tracking differentiators, including the recently developed ones.  
The second simulation under the condition of the perturbation with wind gust and 200% increase in the mass is conducted to the control system without the disturbance observer. In this case, the control performance is only dependent on the property of the controller itself. The proposed PTSBSC system maintains the least performance variation compared with other two BSC and YTBSC systems, where a large variation of the output tracking results appears.  
Finally, the disturbance observers are utilized to estimate uncertainties, such as the wind gust and 200% and 300% variations in the mass. Simulations are conducted for the linear position and 8-shaped command trajectories to evaluate various aspects of the control performance. The output performances are determined by the superiority of the adopted observer, where the proposed disturbance observer shows the outperforming estimation results. Therefore, the proposed control system with the novel disturbance observer is ascertained to provide the outperforming results compared to those of the traditional similar controller and disturbance observer systems.  
In addition, the phenomenon of pilot-induced oscillations (PIOs) that represent a particular and specific aspect of the framework of human-machine interactions was and still is a dynamic behavior of great interest in the design of aircraft [51]. PIOs consists of sustained or uncontrollable oscillations, resulting from efforts of the pilot to control the aircraft. The disturbance problem of PIOs can occur also in controlling UAVs. However, the possibility to exploit strategies from nonlinear dynamics for controlling PIOs took fewer attention in the UAV control system. In this study, this type of disturbance was not considered yet. As a further study including an experimental examination, it is worth to explore this disturbance by the proposed nonlinear controller and disturbance observer to seek a more feasible control system under various aspects of real flight conditions of UAVs.
# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 4 Results
### Comparison with State-of-the-Art  
Table 3 shows comparison of the results achieved by the proposed model against others. In addition, it also contain the results from a traditional ML trained by other researchers in research articles and on the Kaggle competition webpage. The '' represents the models (both traditional ML) we trained and tested ourselves with similar data. Many of the results were not based on 5-fold cross validation. To get a fair results comparison, we retrained same techniques using 5-fold cross validation. One point to highlight is that the published state-of-the-art is only available for the actual original model classification performance i.e., We have no state-of-the-art results for the correctness of features highlighted by LRP.  
The results showed that our results from 1D-CNN (M-1D-CNN-1-28*) in the case of using all 28 features is lower than XG-Boost [56] by 0.0003. However, for the same XG Boost-28* when we trained and calculated a performance after 5-cross validation, it showed 0.0615 fewer performance than our best model (M-1D-CNN-1-28*) with 28 features as input.  
In terms of precision, Random Forest-28* achieved the highest precision of 0.7172, which is 0.0478 and 0.0161 higher than Random Forest results reported in [62] and our M-1D-CNN-1-28* model, respectively. However, in terms of F1-score, our model showed better result than that of Random Forest-28* and reported by [62] by 0.2514 and 0.088, respectively.  
The state-of-the-art results are achieved when we use our proposed model for selecting a subset of features and then using those selected features as input to the same networks (1D-CNN and traditional ML techniques) to train and test. Our model M-1D-CNN-5-16* achieved a 0.8554 accuracy, 0.7399 precision, and 0.7260 F1-score, which are higher than all other models at a good margin. This shows that XAI as an approach for subset selection of discriminative features can give us almost equal or better results with both the proposed 1D-CNN model and traditional ML techniques. This can be used as a strategy of first using DL, and then when we have the reduced feature set, using those with a simple classifier which paves the way to investigating this approach for use on embedded or edge devices where there are limitations on memory.
# A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference
## 3 Methodology
### Step1: The Framework of CCL: C-YOLOv4  
#### 3.2.1 C-YOLOv4  
YOLO is a typical single-stage target detection algorithm with high accuracy and speed. The complex and sizable architecture of YOLOv4 necessitates a highly configured hardware platform to operate effectively, achieving the requisite speed and accuracy. This requirement for substantial computational resources constrains the algorithm's application within this study. Researchers have improved the original model using lightweight networks. For example, [PERSON] et al. [29] proposed a lightweight model, Latent Graph Attention, which achieves efficient image transformation with lower computational costs. [PERSON] et al. [28] enhanced the YOLOv5 algorithm using MobileNetV3, which achieved good results with lower complexity and a smaller model size. Additionally, [PERSON] et al. [25] utilized MobileNetV3 to improve deep learning models for plant disease detection. It is not proper to deploy at the port gate. This paper proposes an improved lightweight YOLOv4  
Figure 1: The structure of proposed ACCR framework.  
algorithm (C-YOLOv4), and its structure is shown in Figure 2. Specific improvements are as follows:  
1. The algorithm uses a lightweight network MobileNetV3 as the backbone network and removes the Spatial Pyramid Pooling (SPP) network to lighten the network. And the Feature Pyramid Network (FPN) is adopted as the neck network to increase the speed of network;
2. The algorithm cancels the 13 \(\times\) 13 scale feature map and reduces the scale of the feature-extraction network. Then, a K-means++ algorithm was used to recalculate the six more appropriate prior boxes to improve the detection accuracy;
3. The algorithm uses Enhanced Intersection Over Union (EIOU) to improve the loss function of the yolo4 algorithm, which enhances the training effect.  
The output prediction network still uses Yolo Head, while predicting and regressing the class and location of the object at the same time.
# Bounded-Error Parameter Estimation Using Integro-Differential Equations for Hindmarsh-Rose Model
## 3 Hindmarsh-Rose Model  
The model of Hindmarsh-Rose (HR) results from a simplification and a generalization of the Hodgkin-Huxley model [8, 9]. From this slow-fast model, rich dynamics of a neuron can be reproduced, such as spiking, bursting and chaotic behaviors. The HR model [9] reads as follows ( \(\theta=(a,b,d,\varepsilon)^{T}\)):  
\[\left\{\begin{array}{ll}\dot{x}_{1}(t,\theta)=&x_{2}(t,\theta)+ax_{1}(t, \theta)^{2}-x_{1}(t,\theta)^{3}-x_{3}(t,\theta)+u(t),\\ \dot{x}_{2}(t,\theta)=&1-dx_{1}(t,\theta)^{2}-x_{2}(t,\theta),\\ \dot{x}_{3}(t,\theta)=&\varepsilon(b(x_{1}(t,\theta)-c_{x_{1}})-x_{3}(t, \theta))\end{array}\right. \tag{8}\]  
where  
* \(x_{1}\) describes the membrane potential;
* \(x_{2}\) is the recovery variable, associated with the fast current, due to the passage of the Na\({}^{+}\) or K\({}^{+}\) ions;
* \(x_{3}\) is the adaptation current, associated with the slow current, due to the passage of the Ca\({}^{+}\) ions.  
The variable supposed to be observed is the membrane potential, and we denote \(y=x_{1}\). \(u\) corresponds to the applied current (in amperes), and, afterwards, it is supposed constant. \(u\) generates the opening or closing of ion channels at one point in the membrane, which produces a local change in the membrane potential. Notice that the experimental data can be obtained in vivo, by using the current stimulus to generate a potential difference (see [22, 23], for more details). Parameters \(a,b\) and \(d\) are, experimentally, determined from measurements of membrane potentials, while \(c_{x_{1}}\) is the \(x_{1}\)-coordinate of the leftmost equilibrium of the two-dimensional system, given by the first two equations of (8), when \(u(t)=0\) and \(x_{3}(t,\theta)=0\), thus, \(\theta=[a,\ b,\ d,\ \varepsilon]^{T}\). Finally, parameter \(\varepsilon\) represents the ratio of time scales between fast and slow fluxes, across the membrane of a neuron, and controls the speed of variation of the slow variable \(x_{3}\).  
It has been proven in [5] that the proposed model is globally identifiable (in a stochastic framework), thus, the identifiability property and, consequently, uniqueness of the parameters is maintained in a set-membership framework [24]. Relying on global identifiability, we know that this interval can be as small as possible (due to the threshold chosen in the bisection algorithm: SIVIA).  
This model permits to obtain different dynamics, with respect to the parameter values. For example, for \(a=3\), \(b=4\), \(d=5\), \(u(t)=3.25\)A, the authors of [13] prove that the parameter \(\varepsilon\) presents a Hopf bifurcation. When a Hopf bifurcation occurs, a local periodic solution near an equilibrium point appears or disappears, with the change of one parameter value. The time series of system (8), for the two parameter values \(\varepsilon=0.12\) and \(\varepsilon=0.13\), are represented in Figure 1. The chosen case is the second one, which is \(\varepsilon=0.20\).
# Dispersion Curve Interpolation Based on Kriging Method
###### Abstract  
Volcanic eruptions significantly impact human life. However, real-time high-precision imaging in this context still has limitations. Spatial-temporal interpolation can replace real-time data imaging, in order to obtain the state of a given volcano at any moment. The dispersion curve is interpolated in space as a foreshadowing for subsequent temporal interpolation. In this paper, kriging is applied for the interpolation of dispersion curves, and the feasibility of the process is verified through several tests. Through cross-validation, the \"spherical\" variogram model and universal kriging were determined. The mean relative error of the predicted dispersion curve is less than 10%, and the mean root mean square error of each predicted dispersion curve is less than 0.1. The results show that the interpolation of dispersion curves based on the kriging method is feasible. In addition, the application of kriging interpolation in ambient noise tomography can expand the imaging area, as well as complement the low ray density area. Taking the ambient noise tomography of the Changbai volcano as an example, in the deep area, the expansion multiple can reach 2.4.  
kriging; ambient noise tomography; dispersion curves; interpolation +
Footnote †: journal: Applied Sciences
## 4 Parameter Effects on VFA-SF and VFA-SF-OPT
### Effect of Node Equilibrium Distance  
In most WSN applications, sensor communication and the corresponding routing protocol affect system performance, so they are very important topics in theoretical modelling and field tests. In this paper, we do not consider any communication and mechanical parts since our VFA-SF and VFA-SF-OPT are a concept of a node-deployment solution. If a designer wants to reduce the errors from the node position or neighbor location not updating instantaneously, it is better to require each sensor node to wait for a while to receive the updating information from neighbor nodes after it approaches the estimated position. In such a case, in order to reduce the influence of the location error, it can be solved by controlling the node equilibrium distance.  
Figure 1 shows the final node distributions by the VFA-SF for the indicated node equilibrium distance \(D_{m}=1.732\) (theoretical value), 1.5, and 1.3, respectively. The corresponding coverage area by these 500 nodes is 1299, 974.1, and 732 from the area of total hexagon cells. PCD values are 0.03, 0.06, and 0.09. It is obvious that a small \(D_{m}\) causes system redundancy and reduces the whole coverage area. Most importantly, a small \(D_{m}\) compresses node distance so that the final network may become worse. In such a case, for a fixed target area, a small \(D_{m}\) can dramatically avoid location errors but increase the number of sensor nodes and amount of system consumption.
# Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm
## 2 Literature Review
### Solution Algorithm
#### 2.3.3 Meta-Heuristic Algorithm  
Meta-heuristic algorithm is based on heuristic algorithm, uses sampling algorithm to generate initial population, and optimizes based on the search mechanism of the evolutionary algorithm itself. It can quickly obtain the (approximate) optimal solution of the problem, which can make up for the shortcomings of the exact algorithm and heuristic algorithm. This type of algorithm is a research hotspot [53]. The meta-heuristic algorithms include particle swarm optimization algorithm [54], tabu search algorithm [55], GA [56], and ant colony algorithm [57]. Among the many complex meta-heuristic algorithms, GA has the advantages of good convergence, short simulation calculation time, high calculation accuracy, and is not restricted by the search space. From the perspective of design ability and application effect, GA is considered to be the best solution among many solutions [58,59]. Since the introduction of GA by [PERSON] [60] in 1975, GA has been widely used to solve engineering planning problems [60], such as flow operation [61], construction projects [62], and highway construction [63]. The proposal of GA provides an effective solution for complex project scheduling optimization problems, and its good search and computing capabilities are generally recognized by researchers. However, the traditional GA is prone to premature convergence or fall into local optimization [58]. Therefore, the following research will improve the design of the traditional GA to meet the needs of model solving.
## 3 Discussion of Results
### Noisy Signals  
As a last step, noise was added to the simulated sensors data in order to evaluate the noise effects on the fitting accuracy of the 2 DOF method. Figure 13 shows the sensors data contaminated by noise. This noise was assumed as an additive white Gaussian noise from the analysis of the typical noise patterns of the BTT sensors signals in literature [16; 35; 36; 37]. The typical noise pattern adds dynamic disturbances to the signal, without changing the overall behavior of the signal. The influence of noise on the residual is plotted in Figure 14. As expected, by increasing the noise level, that is by decreasing the Signal-to-Noise Ratio (SNR), the residual from the curve fitting increases, that is the quality of fitting becomes worst. The errors in the estimated damping values are depicted versus the SNR in Figure 14. It can be seen that beyond an SNR level of 20 dB (see Figure 13), the error on the damping value estimation is below 5%, which is an acceptable value from an engineering point of view. The errors in the estimated natural frequencies are depicted in Figure 14 against the SNR. In this case the error is always much lower than 1% which is almost negligible.  
Figure 12: Comparison of non-dimensional amplitudes obtained by different fitting methods for SD = 0.10, (**a**) EO = 10, \(\zeta_{1,2}=0.003\), (**b**) EO = 10, \(\zeta_{1,2}=0.006\).  
Figure 13: Typical sensor data contaminated by noise for EO = 5 and SD = 0.10, (**a**) Signal-to-Noise Ratio (SNR) = 20 dB, (**b**) SNR = 25 dB.