esa-sceva/satcom-chunk-collection huggingface dataset:
# Ride Comfort Prediction on Urban Road Using Discrete
## 2 Roughness Indexes and Ride Comfort Standard
### Discrete Roughness Index (DRI)  
The DRI is also calculated based on the standard quarter-car model. As shown in Figure 2[22], assuming that the vehicle response at the location point \(j\) is related to all the previous road profile excitation (location points \(0\)-\(j-1\)), then we define the fraction  
Figure 1: Quater-car model.  
response at the point \(j\) of the excitation at the point \(i\) as the fractional response coefficient, \(f_{ij}\), as shown in Equation (5),  
\[f_{ij}=\begin{cases}0&i>j\\ \frac{|\Delta z_{j}h(t_{j}-t_{i})\Delta t_{i}|}{\sum_{k=1}^{j}|\Delta z_{k}h(t_ {j}-t_{k})\Delta t_{k}|}&i\leq j\end{cases} \tag{5}\]  
where \(\Delta z_{i}\) is the elevation difference between the point \(i\) and the \(i-1\); \(\Delta t_{i}\) denotes the corresponding time interval. The response function \(h\big{(}t_{j}-t_{i}\big{)}\) denotes the response of the suspension travel at the time \(t_{j}\) due to a unit impulse at the previous time \(t_{i}\). Note that the sum of the coefficients of all the previous excitation points to the response the point \(j\) is equal to 1, as expressed in Equation (6).  
\[\sum_{i=1}^{N}f_{ij}=1 \tag{6}\]  
The fractional response coefficient can be regarded as the contribution of the excitation at the point \(i\) to the response at the point \(j\). Then, the DRI is defined as roughness attributable to a particular excitation, for a particular location point \(i\), where the DRI is defined by Equation (7),  
\[\text{DRI}_{i}=\sum_{j=1}^{N}\frac{1}{u_{j}}f_{ij}|\hat{z}_{j}|\Delta t_{j} \tag{7}\]  
where \(\hat{z}_{j}=\hat{z_{s}}-\hat{z_{u}}\) denotes the suspension responses at point \(j\).  
Compared with the IRI, the DRI is a series of discrete indicators characterizing the roughness of particular points. Moreover, it can be easily proved that the mean value of the DRI (DRIavg) converges to the IRI as the distance between the sampled points becomes smaller and the whole length of the road becomes larger, which is expressed by Equation (8),  
\[\lim_{\Delta u_{j}\to 0}DRI_{\text{avg}}= IRI \tag{8}\]  
Therefore, the DRI can not only provide more details about the road profiles but also demonstrates the compatibility with the standard IRI measure. It is worth noting that the distance between the sampling points is an important factor in the calculation. Smaller distance provides more precise pavement roughness information but requires higher computational cost, while a longer distance has a lower computational cost but may neglect the local feature within a short distance.
# Inverse Kinematics: An Alternative Solution Approach Applying Metaheuristics
## 3 Experiments and Results
### Obtaining the IK for a Point in the Space
#### 3.1.1 First Experiment  
As previously explained, the first experiment consisted in solving the IKP as a single optimization problem. Ten executions of the algorithm were carried out taking as a stop criterion a limit of 100,000 generations or to obtain a solution whose error function is lower than 0.001 = \(10\times 10^{-3}\) cm. The obtained results are shown in Table 2, including the mean and the standard deviation of the results, where the best results are in bold type. In addition to the objective function, the similarity between the final and the initial configurations was calculated by using the norm of the difference of the initial solution and the final posture, where the similarity is inversely proportional to the distance. Since there was no an explicit mechanism that minimized the difference, the general similarity was low.  
For this experiment, no solution was obtained in any of the 10 executions that met the error criterion before reaching the stop criteria. The result with the smallest distance from the initial pose was generated in the 8 th run, whose angular values of the kinematic chain were 1.2055, \(-\)1.8763, \(-\)0.3180, 0.1339, \(-\)1.3818, 0.1600, \(-\)0.8093, \(-\)0.4621, 0.7072, \(-\)0.7136, while the run that best positioned the end effector was the 10 th, whose values were 2.2508, \(-\)1.5886, \(-\)1.3051, \(-\)0.4985, \(-\)0.1481, \(-\)2.0907, 0.0696, 0.7274, 0.0150, 0.8789. The kinematic chain adopting the angular values of the solutions and the initial pose is shown in Figure 3. All the figures showing the results of the different experiments were generated using MATLAB R2020a.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Run** & **Objective Function (cm)** &
\begin{tabular}{c} **Distance to the Original** \\ **Pose (rad)** \\ \end{tabular} \\ \hline
1 & 0.0741 & 5.8277 \\
2 & 0.0915 & 5.6552 \\
3 & 0.1670 & 9.5290 \\
4 & 0.0997 & 4.5117 \\
5 & 0.0626 & 10.0037 \\
6 & 0.0480 & 4.8227 \\
7 & 0.0948 & 23.2495 \\
**8** & **0.1025** & **3.1664** \\
9 & 0.1248 & 9.1732 \\
**10** & **0.0232** & **3.4065** \\ \hline Mean & 0.0888 & 7.9345 \\ \hline Standard deviation & 0.0403 & 5.6318 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the first experiment.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 6 Performance Evaluation and Discussion
### Effective Distance  
Figure 9 shows the effective distance of two different dummy location selection algorithms, the proposed RR-DLS algorithm and E-DLS algorithm in [18]. For E-DLS algorithm, the anonymous area is maximized considering the query probability. From Figure 9a, the means of effective distance of two algorithms are close. Moreover, from Figure 9b, we observe that the variance of effective distance of proposed RR-DLS algorithm is much smaller than that of E-DLS algorithm. The proposed RR-DLS algorithm can guarantee the distributed and uniform distribution of dummy locations to ensure the validity of dummy locations.
# Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
## 2 Methodology
### Performance of Constant Power Charging and Discharging Control Strategy  
The BESS charges and discharges at a constant power of 10 MW. After analyzing the data, the load peak-shaving and valley-filling variation curve under the constant power strategy can be obtained, as shown in Figure 6. The output of the BESS is shown in Figure 7, and the corresponding indicator changes are shown in Table 2.  
Figure 6 provides a visual comparison of the effectiveness of the BESS in peak shaving for power grids before and after the adoption of a constant power charge-discharge control strategy. The figure shows the changes in the original load curve and the adjusted load curve. The original load curve exhibits the significant fluctuations characteristic of the natural load on a typical day. After being regulated by the BESS's constant power charge-discharge control, the load curve undergoes changes: during peak periods, the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Time Period Type** & **Start Time (h)** & **End Time (h)** & **Duration (h)** & **Power (MW)** \\ \hline Charge & 4.25 & 4.75 & 0.50 & \(-\)10.0 \\ Discharge & 10.75 & 11.75 & 1.25 & 10.0 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of energy storage peak-shaving plans.  
Figure 6: Changes in load curves under constant power strategy.  
Figure 7: Schematic diagram of BESS output under constant power strategy.  
BESS discharges at a fixed power, effectively reducing peak load; during off-peak periods, the BESS charges, thereby increasing the off-peak load. Figure 6 demonstrates the effectiveness of the constant-power control strategy in achieving peak-shaving and valley-filling functionality.  
Figure 7 shows the operating status of the BESS under a constant power strategy, corresponding to the scheduling plan in Table 2: two actions throughout the day and the BESS charging and discharging at rated power but exposing certain defects: first, the separation of charging and discharging periods leads to relatively low equipment utilization; in addition, the output curve reflects the poor flexibility of a single fixed-power strategy.  
The constant power strategy performs charging and discharging operations at a fixed-power level during preset time periods, which achieves basic peak-shaving and valley-filling functions but has significant limitations. The peak shaving effect of BESS using a constant power strategy is shown in Table 3. Its adjustment strategy is crude, equipment utilization is low, and the strategy relies on a single-energy storage device, which is relatively inflexible when dealing with continuous load fluctuations.
## 3 Simulation Experiments  
In this section, we undertake a series of simulation experiments to substantiate the inter-satellite SFST technique for GP determination over the TS spheres and to establish an Earth Gravitational Model (EGM). Presently, the stability of an onboard satellite atomic clock approximates \(10^{-13}\tau^{-1/2}\) (\(\tau\) in seconds) [17; 39]. Nevertheless, the most advanced optical atomic clock currently available on the ground achieves the stability of \(9.7\times 10^{-18}\tau^{-1/2}\)[15]. Therefore, our experiments implement clock stability levels ranging from \(10^{-13}\tau^{-1/2}\) to \(10^{-17}\tau^{-1/2}\), in anticipation of the eventual integration of superior clocks onboard satellites. We will juxtapose observed GP distributions with their true values at the TS's orbit to assess our approach. Additionally, we will compare the derived EGM and the EGM2008 model [40]. The experiment's framework is depicted in Figure 4. To assess the efficacy of our method, we conduct a comparative study of the observed and true values of the TS's gravitational potential. Additionally, we compare the EGM2008 model with the recovered Earth Gravitational Models up to degree/order 200.  
Figure 3: The intervisibility between a GEO satellite and a LEO satellite (the target satellite). The two satellites are intervisible for more than half of the orbit period of the target satellite.
## 3 Electrocardiogram
### The ECG Waveform  
The ECG is recorded as a reading of voltage versus time measured in millivolts (mV) and seconds. A representation of a traditional lead II ECG waveform is presented in Figure 3. It shows a sequence of peaks and waves that gives information about the depolarization and repolarization process of the ventricles or atria.  
The depolarization process of the cardiac cycle is initiated by the firing of the sinoatrial node in the right atrium of the heart. The atria then depolarizes, causing the P wave to be produced. The P wave occurs in the interval of 80-100 ms. As the P wave terminates, this indicates the complete depolarization of the atria, and following which, there is a contraction. The contraction then causes action potentials to spread across the atrioventricular node. Then, the left and right ventricles start to depolarize, leading to the production of the QRS complex. This represents the commencement of ventricular contraction and the end of the atrial contraction. After the contraction the signal falls to the baseline, and the ventricles begin to repolarize after contraction, leading to the emergence of the T wave. This process is continuously repeated in the next cycle of the heartbeat.  
Notable in the ECG waveform is several other regions that are of tremendous clinical importance. They include the P-R interval, the S-T segment and the Q-T interval. The P-R interval is measured as the space in between the starting point of the P wave to the starting point of the QRS complex. The S-T segment is measured as the interval from the S wave component of the QRS complex to the starting point of the T wave. The Q-T interval is measured from the starting point of the QRS complex to the end point of the T wave.
## 2 Materials and Methods
### Preprocessing in GEE
ConvLSTM uses the three-gate control structure of LSTM [43] and uses convolutional operations to extract spatial features. The ConvLSTM model used in this paper was set up with 3 ConvLSTM2D layers and a convolutional kernel size of 1 \(\times\) 2. 3. CNN-LSTM. CNN can learn relevant features from images. The LSTM network performs well on data processing of long time sequences. The CNN-LSTM model used in this study consists mainly of a two-dimensional convolutional neural network and an LSTM network. The CNN first extracts spatial features and then passes the extracted spatial features to the LSTM network. The input to the model is based on the 32 \(\times\) 5 \(\times\) 14 feature variables generated by GEE preprocessing. Figure 3 shows the CNN-LSTM model architecture we used. The output value is the predicted rice yield value. The Conv2D layer of CNN is set with 64 filters and the convolutional kernel size is 1 \(\times\) 2. The convolution layer connects the BatchNormalization layer. Then there is a MaxPooling2D layer with a kernel of 1 \(\times\) 2. The batch normalization process allows the CNN to resist vanishing gradients during training, which can reduce training time and lead to better performance [44]. The Conv2D layer is applied to every temporal slice of the inputs for feature extraction via the TimeDistributed wrapper. Then the output is passed to the LSTM layer. The LSTM layer is set with 256 neurons. The Dense layer is set with 64 neurons. After that, the Flatten layer is connected and some of the neurons during training are randomly turned off with 0.5 dropout probability. The dropout strategy can prevent overdependence between neurons. Finally, the predicted yield values are output using a one neuron Dense layer. The activation functions of the model are chosen as linear rectifier function (Relu) and hyperbolic tangent function (Tanh). The optimization function of the learning rate uses [PERSON]'s algorithm to provide an adaptive learning rate.
# An Experimental Strain-Based Study on the Working State of Husk Mortar Wallboards with Openings
## 1 Introduction
The available data regarding full-scale wallboard's load capacity remains limited. Investigations on the strain data for analyzing structural behavior are particularly scarce. Most of these researches focus on the load and displacement data. No experimental program has yet, to the authors' knowledge, been undertaken with strain analysis method on husk mortar energy-saving (HMES) wallboards. The tests and numerical analyses mentioned above obviously reflect two issues: (1) A reasonable numerical model would not have been established due to the configuration complexity of HMES wallboards. As a result, an accurate prediction for the ultimate strength of HMES wallboards has not been achieved by the existing analytical theories and methods. (2) The displacement data and force data were fully utilized, but the strain data were not fully applied. The structural working features embodied in the strain data have not been explored. Both problems implied that new theories and methods should be developed to solve them [23]. [PERSON]'s structural stressing state theory explored a new way to deeply understand the structural working behavior features from the experimental strain data and provided a proper method to definite structural failure load [24; 25]. In view of [PERSON]'s theory, this paper investigated the structural working state of full-scale HMES wallboards with different openings. It was considered that the structural working state related to the individual working states of the local zones within the wallboard. So the strain values with the load increase were measured at the points uniformly distributed on both sides of the wallboard. The study revealed the inherent leap characteristics implied in the working states of wallboards and redefined their failure loads. An empirical relationship among the experimental failure loads of the wallboards was derived based on the failure characteristics of the wallboards. which could provide the reference to the accurate prediction of the wallboards' load-bearing capacity. In addition, a new way was explored to apply the enormous amount of experimental strain data to structural analysis.
## 3 Threat Model  
In the black-box attack setting, queries to the network are permitted but access to internal states is prohibited (e.g., executing backpropagation). Hence, our threat model, or scenario, is as follows:  
* The attacker is unaware of the network's design, settings, or training data.
* The attacker has no access to intermediate values in the target model.
* The attacker can only use a black-box function to query the target model.  
Note that the above threat model determines the comparisons we perform, which focus on attacks that are:  
1. Black-box,
2. Untargeted,
3. \(l_{\infty}\)-norm bounded.  
We can consider a network model to be a function:  
\[f:[0,1]^{d}\rightarrow\mathbb{R}^{C}, \tag{1}\]  
where \(d\) is the number of input features and \(C\) is the number of classes. The \(c\)-th value \(f_{c}(x)\in\mathbb{R}\) specifies the predicted score of classifying input image \(x\) as class \(c\). The classifier assigns class \(y=\operatorname*{argmax}_{c=1,\dots,C}f_{c}(x)\) to the input \(x\).  
A _targeted_ attack aims to create an image that will be incorrectly classified into a given (incorrect) class. An _untargeted_ attack aims to create an image that will be incorrectly classified into _any_ class except the correct one. An image \(\hat{x}\) is termed an adversarial example, with an \(l_{p}\)-norm bound of \(\epsilon\) for \(x\), if:  
\[\operatorname*{argmax}_{c=1,\dots,C}f_{c}(\hat{x})\
eq y,\] \[s.t. \|\hat{x}-x\|_{p}\leq\epsilon\text{ and }\hat{x}\in[0,1]^{d}. \tag{2}\]  
To wit, the model should classify \(\hat{x}\) incorrectly, while preserving similarity between \(x\) and \(\hat{x}\) under an \(l_{p}\) distance metric.  
We focus on a black-box, score-based attack, wherein the only information of the threat model is the raw output (logits).  
Our suggested black-box approach may theoretically be used in conjunction with classic machine-learning models, with the same input-output relationship. Because DNNs have reached state-of-the-art performance in a variety of image tasks, we focus on them in this paper.
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 5 Experimental Analysis
### Detection Latency  
In order to evaluate the detection latency after GNSS attacks of ACNA, EKF and RIO, we define the detection latency \(\Delta\delta\). The detection latency \(\Delta\delta\) represents the time used for the algorithm to detect a GNSS attack. The larger \(\Delta\delta\) becomes, the worse of the algorithm in terms of detecting the GNSS attacks, and vice versa.  
Table 7 concludes the detection latency of ACNA, EKF and RIO after GNSS attacks on data set \(\widetilde{\Psi}_{4}\). It can be seen that the EKF algorithm fails to detect GNSS attacks, and the detection latency is null. The RIO algorithm detects GNSS jump attacks at a latency of 1s and detects GNSS slow-change attacks at a latency of 27 s. Our proposed ACNA detects GNSS jump attacks at a latency of 2 s and detects GNSS slow-change attacks at a latency of 5 s, which is consistent with the parameter analysis for the credible verification window set in Section 4.3. For the detection latency in GNSS jump attacks, our proposed algorithm is one second later than RIO because of the setting of the jump verification window, which is consistent with the analysis to prevent false alarms in Section 3.1. For the detection latency in GNSS slow-change attacks, our proposed algorithm is 22 s before RIO. Therefore, ACNA can reduce the detection latency better when GNSS attacks occur.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 5 Algorithmic Design
### Processing  
The features are passed to the main processing components after the data are pre-processed. The main processing components consist of detection and classification mechanisms. Both the detection and classification tasks are formulated as supervised learning problems, with the detection problem further defined as a binary classification task.
###### Abstract  
The ability to rapidly conduct in-situ chemical analysis of multiple samples of soil and other geological materials in the field offers many advantages over a traditional approach that involves collecting samples for subsequent examination in the laboratory. This study explores the application of complementary spectroscopic analyzers and a data fusion methodology for the classification/discrimination of \(>\)100 soil samples from sites across the United States. Commercially available, handheld analyzers for X-ray fluorescence spectroscopy (XRFS), Raman spectroscopy (RS), and laser-induced breakdown spectroscopy (LIBS) were used to collect data both in the laboratory and in the field. Following a common data pre-processing protocol, principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to build classification models. The features generated by PLSDA were then used in a hierarchical classification approach to assess the relative advantage of information fusion, which increased classification accuracy over any of the individual sensors from 80-91% to 94% and 64-93% to 98% for the two largest sample suites. The results show that additional testing with data sets for which classification with individual analyzers is modest might provide greater insight into the limits of data fusion for improving classification accuracy.  
laser-induced breakdown spectroscopy; LIBS; Raman spectroscopy; RS; X-ray fluorescence spectroscopy; XRFS; chemometrics; machine learning; multianalyzer data fusion
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
###### Abstract  
For the past several decades, research dealing with hypersonic flight regimes has been restricted mainly to military applications. Hypersonic transportation could be a possible and affordable solution to travel in the medium term and there is renewed interest from several private organisations for commercial exploitation in this direction. Various combined cycle propulsion configurations have been proposed and the present paper deals with implications for the nozzle component of a ramjet configuration as part of one such combined cycle propulsion configuration. An investigation was undertaken for a method of turbine-based propulsion which enables the hypersonic vehicle to take off under its own power and propel the aircraft under different mission profiles into ramjet operational Mach regimes. The present study details an optimal method of ramjet exhaust expansion to produce sufficient thrust to propel the vehicle into altitudes and Mach regimes where scramjet operation can be initiated. This aspect includes a Computational Fluid Dynamics (CFD)-based geometric study to determine the optimal configuration to provide the best thrust values. The CFD parametric analysis investigated three candidate nozzles and indicated that the dual bell nozzle design produced the highest thrust values when compared to other nozzle geometries. The altitude adaptation study also validated the effectiveness of the nozzle thrust at various altitudes without compromising its thrust-producing capabilities. Computational data were validated against published experimental data, which indicated that the computed values correlated well with the experimental data.  
hypersonic; propulsion; ramjet; nozzle; convergent-divergent; expansion; shock wave Article