esa-sceva/satcom-chunk-collection huggingface dataset:
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
## 4 Simulation
### Compare DUTs with the Same Existing Indexes  
Figure 6 are the averaged spectrum of two DUTs, which have exactly the same SNR, THD, and SINAD indexes. The difference is the harmonic power distribution. Although DUT3 seems to have a clearer spectrum than DUT4, but there is a much higher second-order harmonic around the fundamental signal. Thus, it is difficult to distinguish the performance difference from the same existing indexes.  
Figure 5: Signal flow of ASR verification experiment. Hanning window; FFT points: 2048; Frame overlap: 50%.  
The ADDR plots corresponding to the above-mentioned DUT3 and DUT4 are shown in Figure 7. The SNR and SINAD index of these DUTs are the same, which makes the ADDR plots have the same start and end positions. However, it is obvious that the middle parts of these ADDR plots are not the same, where the ADDR plot of DUT3 is higher than that of DUT4. This difference of the middle part results in the area of the specified region of DUT4 is smaller than that of DUT3. Based on the area comparison method and the judgement rules stated in Section II, Subsection B, the performance of DUT3 is better than DUT4 because the area 'S3' is smaller than 'S4'.  
Generally, under other conditions unchanged, the cleaner the speech through the ASR system is, the lower the WER is. Thus, the DUT with lower overall distortion will achieve a lower averaged WER. The averaged WERs of the test utterances distorted by the above-mentioned simulated DUT3 and DUT4 are shown in Table 2, respectively.  
The '\(\Delta\) (%)' column represents the WER differences of the latter DUT and the former DUT. In the 'Match?' column, the symbol '\(\surd\)' indicates that the comparison result of the ASR experiment is consistent with the comparison result of the ADDR plot, and the symbol '\(\times\)' indicates that the comparison result of the ASR and the comparison result of the ADDR are inconsistent, and the symbol '\(\cdot\)' indicates that the DUTs have the same WER. It can be seen that for different categories of noises, as the SNR setup gradually increases (larger attenuator setup), the WERs changes from a relatively poor level to relatively good level.  
Figure 6: Output spectrum of DUT in situation A. (**a**) DUT3. (**b**) DUT4. Test signal: 996.1 Hz, 2 V\({}_{\text{PP}}\); Band: DC-20 kHz; Sampling frequency: 40 kHz; Sampling points: 2048; Spectrum averaged times: 100; Kaiser window (\(\beta=0\)).  
Figure 7: ADDR plots of DUT3 and DUT4 in situation A.  
During the process, the averaged WER of the DUT3 and DUT4 is about 42.57%, and the WERs of DUT2 are on average 5.11% higher than that of DUT3. This difference accounts for 5.11/42.57\(\times\)100% = 11.29%. Such a big difference is indistinguishable by existing indexes. However, a majority of '\(\surd\)' symbols verify the conclusion of the ADDR plots.  
In summary, the ADDR index can distinguish the performance difference of DUTs when they have the same SNR, SINAD, THD and SFDR indexes, which is an effective characteristic in real applications.
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 3 Results
### Comparative Results  
The gearbox model previously developed based on ELM is now compared with other normality models that predict exactly the same target from the same input variables but using PLS, SVM and DANN instead. All of these alternative models have already been previously explored in the field of wind turbines [17; 18; 20; 21]. The comparison between models is made on the basis of their ability to correctly predict the target variable for each of the different turbines, as can be seen in Figure 15. In all these cases a model of the park is obtained and the comparison is performed by calculating the average quadratic error between the prediction and the real value. The models have been trained under the same conditions, using the data of the whole park. The first half of the data is used for the training and the second half is used for the test. The resulting park model is tested on each of the turbines individually.  
The optimisation of the ELM scheme follows the methodology previously described, exploring the size of the network for optimising the performance. In this case, 10 training sessions are carried out for each measurement, keeping the one with the best results. In the PLS-based scheme, the number of components \(N_{comp}\) is determined by the optimal CV MSE knee curvature method [18]. The parameter to be optimised is the minimum of the logarithm of cross-validation loss, which is \(log(1+cv_{loss})\). Finally, for the optimisation of the DANN, after several empirical tests to determine its architecture, we ended up choosing an architecture that presents an input layer of 6 neurons, a hidden layer of 20 neurons with ReLu the as activation function and a dropout percentage of 20%, a second hidden layer of 10 neurons with ReLu as the activation function and a dropout percentage of 10%, a third hidden layer of 5 neurons with ReLu as the activation function and an output layer of 1 neuron. For the training of this architecture the Adam optimizer algorithm was used with this configuration: _max epochs_ = 50 and _minibatch size_ = 128.  
The RMSE obtained for each of the models on each individual turbine of the wind park is shown in Figure 15, together with the mean of all the turbines for each type of model. In all cases (individual turbines RMSE or mean RMSE) the ELM-based model obtains the best accurate prediction, suggesting that the ELM technique seems optimal when used in regression models. PLS works quite well, close to ELM in performance but always with a major error, while SVM and DANN are the worst out of all the models for all turbines. Note that each turbine behaves differently, with WT#84 being the most difficult to predict (higher RMSE). In this case, even the PLS behaves badly, with results similar to those of the SVM and DANN, while the ELM is able to maintain a low RMSE.  
Figure 15: _Cont._
# Geophysical Study of a Large Landslide Affecting the Urban Area of Albuuuelas (S Spain)
## 5 Results
### S-Wave Velocity Profiles  
Once this first _Vs_ profile is obtained, situated purposely in the central part of the landslide, the analysis of the other sites was performed going from the nearest points to the furthest ones, situated on the top of the hill and the proximity of the Saleres River. In this way, the connection between the adjacent sites has been taken into consideration, obtaining coherent _Vs_ profile results along the studied landslide.  
All the analyzed H/V curves exhibit one or more evident peaks due to the resonance effect associated with the impedance contrast between the different layers or stratums. Thus, the principal interfaces have been clearly identified after the inversion process. Moreover, the presence of resonance peaks in the H/V curves also indicates possible amplification phenomena, which constitutes an important parameter for site response studies and for future seismic hazard studies in this area.  
Figure 5: Examples of S-wave profiles obtained by inversion of H/V curves at six different sites of the landslide: (**a,b**) Sites N27 and N12 (east cross-section); (**c,d**) sites N9 and N4 (west cross-section); (**e,f**) sites N21 and N16 (central cross-section). On the left panel, the minimum misfit model (black) is shown together with the models comprising inside the minimum misfit + 10% (dark gray) and all the tested models (light gray). On the right panel, the experimental H/V curve (mean value plus standard deviation bars in black) is shown together with the ellipticity curve associated with the best-fitting model (darkest gray), minimum misfit + 10% (dark gray) and all the other tested models (light gray). The minimum misfit value is shown above the H/V plot.  
Figure 6: \(Cont.\)  
In most of the obtained dispersion curves, it is observed that the Rayleigh wave-phase velocity decreases with the increasing frequency, which is typically associated with simple stratigraphic conditions where shear-wave velocity consistently increases with depth. In some sites, especially in the sites where conglomerates emerge, the observation of a kink or flat zone in the experimental dispersion curve could be indicative of the presence of a soft layer below a stiffer one [55], which is subsequently confirmed by the estimated _Vs_ profiles.
## 3 Data Acquisition  
Since 2017, a monitoring plan to the rock-wall of the lakeside reservoir has been commissioned by the Infrastructures Area of the Diputacio de Valencia. This change detection operation is performed yearly. After a first geodetic survey to detect fixed targets and select some check points, the data acquisition was carried out with a reflex camera for a CRP from different points of view to cover the whole area of interest. In 2019, in addition to the two techniques mentioned, a third survey was added: the MMS was tested to make this type of survey more complete, and verified through a comparison operation between point clouds. The combination of the two surveys had the objective of completing the 3D mapping of the site, needed to support the monitoring plan of the rock-wall.
## 2 Methodology
### Classification  
Computation of features for each segment in the training set allows for creation of the feature space. As the data may not follow the classical form of two, well separated, hyper-Gaussian distributions, a non-parametric classification method is applied. We use the k-Nearest Neighbors (k-NN) algorithm [26] because of its simplicity and efficiency. The k-NN model evaluates an input sample against its neighborhood in the training data. Following the extraction of the \(k\) nearest neighbors from the training data, a voting procedure is performed. If more than \(h\) neighbors belong to either of the classes, the sample is classified accordingly.  
The k-NN model is greatly affected by the different metrics of the individual cues, particularly because of their different units and scale. Great differences are expected in scale and distribution, motivating the need to normalize the data. For normalization, the whitening (Mahalanobis) transformation [26] is used to transform the data into the same scale and variance in all dimensions. If \(\mathbf{X}\) is a training set of size N\(\times\)3, with N, the number of segments distributed with \(\sim\)\(\{\mu\), \(\mathbf{\Sigma}\}\); using the SVD, \(\mathbf{\Sigma}\) can be factorized into \(\mathbf{\Sigma}=\mathbf{U}\mathbf{D}\mathbf{V}^{\mathrm{T}}\), where \(\mathbf{U}\) is orthonormal, \(\mathbf  
where \(\mathbf{X^{\prime}}\) is the transformed set. Following the whitening transformation, the data is distributed with zero mean and unit variance in all dimensions of the feature space. Distance measures in this space become uniform in all directions.  
The _k_-NN depends on the number of evaluated neighbors (_k_), and on the cardinality parameter (_h_). A bigger \(k\) will lead to a more general model (using more samples to decide means that more information is weighed in) but less distinct (the extreme is when all samples are used as neighbors). The choice of \(h\) affects the sensitivity of the classification model. Assigning \(h\) too small of a value, the model may become error prone, whereas setting \(h\) too strictly the number of false positives decreases at the expense of a large number of false negatives (type _II_ error). Our choice of \(h\) is based on finding a value that provides the highest level of accuracy (ACC) as defined by  
\[ACC=\frac{\text{True-Positive}+\text{True-Negative}}{\text{Positive}+\text{ Negative}} \tag{5}\]  
Such values can be derived by experimenting with different values for \(k\) and \(h\). For each such trial a confusion matrix, C, is recorded  
\[\text{C}\equiv\left[\begin{array}{cc}true\ positive&false\ negative\\ false\ positive&true\ negative\end{array}\right] \tag{6}\]  
and the one with the highest accuracy value (Equation (5)) determines both the \(h\) and \(k\) parameters.
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 3 Methodology
### Literature Review  
An extensive review of existing literature was conducted in the initial phase to collect data on conventional and mechanized systems, press-in methods, and the construction of ultra-deep assembled shafts. Key sources included peer-reviewed research articles, industry reports, conference proceedings, and technical notes. A systematic search was performed across key databases to categorize related studies, including Google Scholar, Scopus, CNKI, and Web of Science. The literature review focused on studies discussing the following:  
* Conventional and press-in techniques in soft soil conditions;
* Development of mechanized systems;
* Technological advancement in design principles for ultra-deep shafts;
* Case studies of shaft applications in UUS;
* Advancements and challenges in PIAUS construction technology.  
Through this literature review, methodologies related to mechanized systems, as well as the design and construction of ultra-deep assembled shafts, are explained, and limitations in the research are identified.
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 1 Introduction
The model also has an encryption solution using AES and Blowfish to the hidden message to protect the content of the secret message from being exposed and used by unauthorized individuals. Disinformation represents intentionally inaccurate or false information that is deliberately spread. This research mainly addresses the problem of detecting an attacker's alteration of a concealed confidential message. It also addresses the issue of improving the unauthorized individual's steganography of a personal protection technique by attaching an unauthorized individual to verify integrity. This layer assists in recognizing possible amendments to a confidential message that is not a constituent of the original message. The main contributions of the research paper are summarized as follows:  
1. Development of an effective and efficient data hiding technique using steganography: The research paper proposes a method that combines steganography and encryption algorithms to ensure the confidentiality and integrity of the transmitted data. The goal is to hide the message inside a cover medium to remain undetectable to attackers. 2. Enhancement of steno image security: The proposed approach aims to increase the security of the steno image by minimizing any visual differences between the cover image and the steno image. This helps to prevent visual attacks that may reveal the presence of hidden data. 3. Integration of encryption algorithms: The research paper utilizes advanced encryption algorithms such as AES (Advanced Encryption Standard) and Blowfish to protect the content of the hidden message. Encrypting the message prevents unauthorized individuals from accessing and using confidential information. The remainder of the paper is organized as follows. Section 2 discusses the related work, and Section 3 presents the methods. Section 4 describes the result and discussion, and, finally, we conclude the paper with future research directions in Section 5.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 2 Application Scenario  
Imagine a futuristic scenario in which tens or even hundreds of autonomous vehicles are working together on Mars. In this work, the terms _autonomous vehicles_ and _swarm_ encompass the various types of robotic and remote sensing devices used for exploration and research purposes, including rovers on the surface, aerial drones, and satellites orbiting Mars. As we illustrated in Figure 2, this swarm of vehicles facilitates the collection of in situ resources for geological surveys and/or building infrastructure [11]. High-level tasks could include the following:  
* Explore, monitor, and map remote Martian areas to find useful geological resources.
* Retrieve useful resources and samples, e.g., by drilling holes in the Martian surface.
* Transport resources from measurement locations to a central collection point.
* Construct and maintain habitats, e.g., by 3D printing using collected resources.
* Repair and maintain other vehicles in an autonomous fashion, i.e., search and rescue of a royer.  
Figure 1: Overview of key concepts required for collaborative intelligent systems for sustainable Mars exploration in this work.  
Cooperation between the robotic explorers currently on Mars is limited. However, as the number of autonomous vehicles increases, there will be a need for more cooperation. As the vehicles are assigned different tasks requiring varying amounts of resources, they will benefit from communication and resource sharing. More specifically, a swarm of vehicles will be able to better deal with resource constraints and energy limitations compared to a scenario in which vehicles need to perform all tasks individually. To illustrate the benefits of vehicle cooperation in the use case of geological surveys (i.e., collecting resources and analyzing samples), the remainder of this section provides some concrete examples.
# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 4 Experimental Results and Analysis  
### Case Description and Parameter Settings  
This study assumes that the tourist attraction's shuttle fleet operates for 8 h daily, with departures every 6 min. Each departure is recorded as a time step. For the parameter setup, the study duration is set to one week (7 days), resulting in a total of 560 time steps, so the length of set \(T\) equals 560. The training set \(T_{1}\) = 560 \(\times\) 0.8 = 448, and the testing set \(T_{2}\) = 560 \(\times\) 0.2 = 112. For M2, the number of future scenarios is set as \(F\) = 3.  
To account for significant fluctuations in passenger numbers, we model the number of passengers as a random variable following a normal distribution, with a mean of [5, 6, \(\ldots\), 50] and a variance of [3, 4, \(\ldots\), 30]. The waiting penalty is fixed at \(p\) = USD 5. The load capacity of each vehicle is \(c\) = 6 [50]. Based on previous research [8], the cost of the first vehicle in each fleet per unit of time is calculated as c1 = driver cost + energy cost + power loss cost = USD4 + USD10 + USD10 = USD24. The unit time cost for additional vehicles in each fleet is \((1-20\%)\times(\text{energy cost + power loss cost})\) = \((1-20\%)\times 20\) = USD 16.  
The simulation will repeat 30 times to avoid randomness in the experiment. The experiment was conducted on a laptop with an i5-8250U CPU and 8.0 GB of RAM, using the GUROBI solver in Python 3.8.
## 3 Methodology
### Loss Function  
Box regression loss for YOLOv5 calculated by CIoU loss function [38]. Although CIoU improves the convergence speed and regression accuracy of the network, the \(v\) in the formula only reflects the difference in aspect ratio and does not show the true difference between the aspect ratio and its confidence level. Moreover, as inferred from the prediction frame and the prediction formula, \(w\) and \(h\) cannot be increased and decreased at the same time, so there may be a situation that prevents the model from optimizing the similarity effectively. To address the above problem, EIoU solves theoretically the problem that \(w\) and \(h\) cannot be scaled up or down simultaneously in CIoU Loss by directly penalizing the loss function of the prediction results of \(w\) and \(h\), as shown in Equation (7).  
\[\begin{split}&\text{EIoULoss }=L_{IoU}+L_{dis}+L_{asp}\\ &=1-IoU+\frac{\rho^{2}\big{(}b,bs^{t}\big{)}}{c^{2}}+\frac{\rho^{ 2}\big{(}w,ws^{t}\big{)}}{C_{w}^{2}}+\frac{\rho^{2}\big{(}h,bs^{t}\big{)}}{C_{ h}^{2}}\end{split} \tag{7}\]  
It can be seen that the EIoU loss function consists of the intersection ratio loss of the predicted frame and the real frame, the center distance loss of the predicted frame and the real frame, and the width and height loss of the predicted frame and the real frame, where \(C_{w}\) and \(C_{h}\) are the width and height of the two rectangular closures, respectively. Bounding box regression also suffers from the problem of unbalanced training samples, that is, the number of low-quality anchor boxes with large regression errors in a single image is much larger than the number of low-quality samples with small errors, and such samples can cause excessive gradients to affect the training effect. To solve this problem, Focal Loss is introduced to solve the problem of sample imbalance based on the original one, which makes the EIoU Loss loss function more consistent with the detection situation in this study.
## 1 Introduction
proposed an enhanced Kurtogram method for the fault diagnosis of bearings, where kurtosis values were calculated based on the power spectrum of the envelope of the signals extracted from wavelet packet nodes at different depths; their method was proven to be effective for the detection of various bearing faults [21]. Above all, the traditional envelope analysis and fast Kurtogram methods have been effectively applied to the fault diagnosis of bearings and gearboxes. However, most of these approaches try only to select one sensitive sub-band scale signal that contains the most fault-related components for further analysis, and a large number of fault-related components that exist in other sub-bands are ignored, which may lead to a degradation of the fault diagnosis result, especially at the early stage of a fault. Therefore, a robust multi-scale demodulation method based on the weighted-EMD de-noising technique and time-frequency impulse envelope analysis is proposed; the fault-related components in the entire frequency range scale are extracted for further analysis. A planetary gearbox with a sun gear spalling fault and a fixed shaft gearbox with a crack fault are used to demonstrate the effectiveness of the proposed method. The rest of the paper is organized as follows. Section 2 discusses the physical mechanisms of the modulation phenomenon of a gearbox based on the dynamic simulation method. Section 3 introduces the detailed procedure of the proposed robust multi-scale demodulation method. Then, in Section 4 and Section 5, experimental studies on a planetary gearbox with a seeded sun gear spalling fault and a fixed shaft gearbox with a crack fault are conducted to confirm the effectiveness of the proposed method. Finally, concluding remarks are provided in Section 6.