esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of Slurry Fracturing during Shield Tunneling under a Reservoir
## 3 Verification of Numerical Solution
### Analysis of Calculation Results and Comparative Validation
The slurry fracturing of the clay blind-hole samples under the condition of slurry with eight viscosities was simulated. The simulation results are compared with the test data, as illustrated in Figure 12. It can be observed that the simulation of slurry fracturing pressure under different slurry viscosities is similar to the test value. The simulated value is always slightly larger than the test value due to the error caused by the disturbance to the samples during the test. The comparison demonstrates that the effect of slurry viscosity on the fracturing pressure is the same. The slurry pressure increases with the increase of slurry viscosity. When the slurry viscosity reaches a certain level (about 25 s), the slurry fracturing pressure will no longer be significantly affected by the slurry viscosity. When the viscosity of the slurry is lower, it is easier for the slurry to reach the tip of the  
Figure 11: Comparison of simulated values and tests of slurry fracturing pressure under different unconfined compressive strengths [17]. fracturing crack and transmit the pressure there. With the increase of slurry viscosity, the ability of slurry to transmit pressure to the fracturing tip decreases, leading to an increase in the slurry fracturing pressure of clay samples. When the viscosity of slurry increases to a certain value, it is difficult for slurry to enter the fracturing crack, and the ability to transmit pressure to the fracturing tip does not change significantly. Therefore, the range of controlling the fracturing pressure of clay samples by the viscosity of slurry is limited. Based on the comparison between the above simulation results and the test, the XFEM numerical simulation method proposed in this paper can effectively simulate the failure mode and slurry fracturing pressure of the clay samples. The good correspondence between the numerical and test results verifies that the XFEM is a valid tool for the simulation of shield slurry fracturing. In the following sections, the XFEM method will be used to simulate slurry fracturing during the construction of the shield tunnel crossing the reservoir.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n
## 3 Results  
### Image Dataset  
The NEU-DET [32] dataset was utilized in this study, consisting of a steel surface dataset from Northeastern University on which training and validation were performed. This dataset contains 1800 images in total, consisting of six categories of classified defects, and each type of defect is exemplified by 300 images which contain rolled-in scale (RS), patches (Pa), crazing (Cr), scratches (Sc), pitting surfaces (Ps), and inclusions (In), as shown in Figure 5. The dataset images contain 200 \(\times\) 200 pixels and the total of 1800 images were randomly divided into an 8:1:1 ratio to create NEU-DET training, test, and validation sets, i.e., 1440 training samples, 180 test samples, and 180 validation samples.
# GANBA: Generative Adversarial Network for
## 2 Background
### Voice Integration Systems: Joint ASV and PAD  
In the integration approach, each utterance has two attributes: (i) an indicator of the target speaker (\(\mathcal{S}\)), and (ii) an indicator of the _bonafide_ speech (\(\mathcal{N}\)). Therefore, the null hypothesis \(\mathcal{H}_{(\mathcal{S},\mathcal{N})}\) is that the test utterance is _bonafide_ speech uttered by the target speaker. On the other hand, the complementary hypotheses is a union of the other three hypotheses:  
\[\mathcal{H}_{(\overline{\mathcal{S},\mathcal{N}})}=\mathcal{H}_{(\mathcal{S},\overline{\mathcal{N}})}\cup\mathcal{H}_{(\overline{\mathcal{S},\mathcal{N} })}\cup\mathcal{H}_{(\overline{\mathcal{S}}\overline{\mathcal{N}})}, \tag{2}\]  
where \((\mathcal{S},\overline{\mathcal{N}})\) denotes a _spoofing_ attack, \((\overline{\mathcal{S}},\mathcal{N})\) represents _bonafide_ speech uttered from a non-target speaker (i.e., it is _zero-effort_ impostor), and \((\overline{\mathcal{S}},\overline{\mathcal{N}})\) represents _spoofing_ speech from a non-target speaker. The latter case, commonly referred to as naive attack, does not make much sense in an authentication context and it is usually discarded. Then, there are three types of utterances that PAD and ASV systems may encounter: (i) _bonafide_ or _genuine target_, (ii) _zero-effort impostor_ or _genuine non-target_, and (iii) _spoofing target_ attacks.  
The integration of PAD and ASV systems can be achieved at the score level [37] or at the feature level [11; 38]. Most existing integration methods perform the integration at the score level, where dedicated classifiers are developed for both PAD and ASV separately, and the scores computed by each separate system are combined. In this work, we focus at the score-level integration. Specifically, we use the cascaded integration system depicted in Figure 1 which has been used in the last three ASVspoof challenges [8; 9; 10].  
The integration systems are typically evaluated using the \(\text{EER}_{\text{joint}}\) which can be measured, for example, on a test dataset that contains a combination of _bonafide_ utterances, _zero-effort_ attacks and _spoofing_ attacks. However, the \(\text{EER}_{\text{joint}}\) does not account for the costs of falsely accepting _spoofing_ attacks and _zero-effort_ impostors or missing target genuine users, nor the prior probabilities of each. In order to take these costs and priors into account, the min-tDCF metric [36; 39] was recently proposed for evaluating complete voice integration systems based on score-level decisions.
# A Survey of Scenario Generation for Automated Vehicle Testing and Validation
## 3 Scenario Generation Methods
### Non-Adaptive Test Scenario Generation Methods  
Non-adaptive test scenarios involve testing autonomous vehicles in environments where the movements and behaviors of movable objects are simulated using predefined rules or scripts. They follow fixed actions that are unaffected by external influences during the testing process [14].  
Non-adaptive scenarios have several notable disadvantages. As highlighted in [14], traffic participants in non-adaptive test scenarios lack interaction awareness, failing to capture the complexity of the human-like social behavior observed in real-world driving environments. Furthermore, since the behaviors of traffic participants are predefined based on the autonomous vehicle (AV) being tested, hard-coded driving behaviors result in simulation environments with limited interaction categories, reducing the efficiency of testing. Additionally, predefined scenarios often focus on specific test objectives, such as assessing the comfort of the AV under test, and are rarely designed for multi-objective testing. Given that AV intelligence involves multiple performance metrics, non-adaptive scenarios struggle to address such diverse evaluation requirements. Moreover, non-adaptive scenarios are typically reconstructed from driving data collected by sensor-equipped vehicles over time. As a result, if certain risky scenario patterns are absent from the dataset, they are overlooked in the evaluation, making the results highly dependent on the dataset's completeness [15]. The following subsections present a review of the literature on non-adaptive scenarios.
## 3 Results and Discussion  
### Solubility Index, Water Absorption, and Water Activity  
Starch-based foams are known to be susceptible to moisture. Water molecules affect the hydrogen bonds of starch, weakening them and reducing their functional properties. In this manner, water resistance is a parameter that needs to be considered in research in order to improve the applications of these foams.  
Solubility index and water absorption analyses can be performed to determine the resistance of starch foams to moisture. With the addition of different concentrations of plasticizer and natural fiber (C10, C15, and C20 xanthan gum/oats), the solubility of foams varied between 20% and 25%. As demonstrated in Table 1, the C10 package offers greater solubility as compared to other concentrations, and the samples do not differ statistically (\(p<0.05\)) owing to the high standard deviation between the samples.  
Although both solubility and water absorption were influenced by the interaction of the xanthan gum/oat concentration, the water absorption analysis revealed a statistically significant difference between the samples (\(p<0.05\)). The C20 packaging presented the lowest water absorption index, which, in turn, is desirable for packaging. On the other hand, the C10 packaging showed the highest absorption after 60 min of immersion. [PERSON] and [PERSON] (2015), [PERSON] et al. (2014), and [PERSON] and [PERSON] (2016) reported similar observations in their studies. According to these authors, the addition of cellulosic fibers can reduce the moisture sensitivity of starch foams by acting as a hydrophobic agent, thus decreasing the foams' water absorption.  
When water activity is observed as a function of xanthan gum/oat content (Table 1), all formulations were found to exhibit similar behavior and without any statistical difference (\(p<0.05\)).
###### Abstract  
This paper presents a study of vertical land movements conducted in northwestern Poland, covering the city of Szczecin. The land of Szczecin is made up of organic soils, covered in many places by water (lagoons, rivers, and lakes), which are susceptible to displacement; therefore, benchmarks in this area are susceptible to changes in height. The analysis of the geological structure of the near-surface layer of the Szczecin area, such as organic soils, sands, and clays, was carried out. The detailed recognition of geological structure in connection with height changes of benchmarks made it possible to locate stable benchmarks in the researched area. In the first step, the authors chose benchmarks in a stable geological structure based on archive data from the Polish Geodesy Documentation Center. In the second step, precise GNSS 3D measurements were performed in the direct vicinity of these benchmarks in order to verify their stability. Verified stations provided a reference for the reliable monitoring of the deformation of other surfaces resulting, for example, from changes in organic sediments. Existing and new engineering structures erected in the area of the Oder River Szczecin islands were selected for the final research of vertical land motions. Static GNSS observations were conducted in November 2020 and May 2021.  
geological structure; precision levelling; vertical land movements; GNSS measurements in Northwestern Poland.
## 3 Attitude Controller Design
### Model Predictive Control Performance  
For a given control objective \(\mathbf{\kappa}_{d[k+i]}\), the performance index for the control within the prediction horizon \(N_{\mathrm{p}}\) at step \(k\) is defined as:  
\[J=\left\|\mathbf{\kappa}_{[k+N_{\mathrm{p}}[k]}-\mathbf{\kappa}_{d[k+N_{\mathrm{p}}]} \right\|_{\mathsf{S}}^{2}+\sum\limits_{i=1}^{N_{\mathrm{p}}-1}\left\|\mathbf{ \kappa}_{[k+i]}-\mathbf{\kappa}_{d[k+i]}\right\|_{\mathbf{Q}}^{2}+\sum\limits_{i=0}^ {N_{\mathrm{c}}-1}\left\|\delta\mathbf{v}_{[k+i]}\right\|_{\mathbf{R}^{\prime}}^{2} \tag{24}\]  
where \(\mathsf{S}\), \(\mathbf{Q}\) and \(\mathbf{R}\) are weighting matrices for the terminal state, process state and input variables, respectively, and all are positive definite matrices. Defining the target vector \(\mathbf{Z}_{d[k]}=\left(\mathbf{\kappa}_{d[k+1]}\quad\mathbf{\kappa}_{d[k+2]}\quad\cdots \quad\mathbf{\kappa}_{d[k+N_{\mathrm{p}}]}\right)^{\mathrm{T}}\) and the equation above can then be rewritten in matrix vector form as shown in (24):  
\[J=\left\|\mathbf{Z}_{[k]}-\mathbf{Z}_{d[k]}\right\|_{\overline{\mathbf{Q}}}^{2}+\left\| \delta\mathbf{V}_{[k]}\right\|_{\overline{\mathbf{R}}^{\prime}}^{2} \tag{25}\]  
where \(\overline{\mathbf{Q}}=\text{diag}\Big{(}\mathbf{Q}\quad\cdots\quad\mathbf{Q}\quad\mathsf{S} \Big{)}\) and \(\overline{\mathbf{R}}=\text{diag}\Big{(}\mathbf{R}\quad\cdots\quad\mathbf{R}\Big{)}\).  
By substituting (22) into (24) and defining:  
\[\mathbf{E}_{[k]}=\mathbf{Z}_{d[k]}-\mathbf{P}\delta\mathbf{\varkappa}_{[k]k}-\mathbf{I}\mathbf{\varkappa} _{[k]k}, \tag{25}\]  
a standard quadratic performance index in terms of \(\delta\mathbf{V}_{[k]}\) is obtained:  
\[J=\frac{1}{2}\delta\mathbf{V}_{[k]}^{\mathsf{T}}(\mathbf{H}^{\mathsf{T}}\overline{ \mathsf{Q}}\mathbf{H}+\overline{\mathbf{R}})\delta\mathbf{V}_{[k]}-(\mathbf{H}^{\mathsf{T}} \overline{\mathsf{Q}}\mathbf{E}_{[k]})^{\mathsf{T}}\delta\mathbf{V}_{[k]}. \tag{26}\]
## 2 Materials and Methods
### Concepts for Discriminating the Oil-Slick Category
#### 2.3.5 Concept 5: Linear Discriminant Analysis (LDA)  
We have been exploring LDA-based algorithms to find a linear combination of predictors (i.e., attributes--e.g., size information and SAR basic statistics) to best separate targets (i.e., oil slicks) [64]. We deal with a classification problem of discriminating seeps from spills into mutually exclusive groups with this parametric method. The LDA is a simple, standard statistical binary classifier that produces a model whose effectiveness can be as good as more complex non-parametric regression algorithms [71; 72] (i.e., machine learning techniques)--for instance, artificial neural network (ANN) or support vector machine (SVM) [28; 63]. The use of such non-linear mappings to discriminate seeps from spills should be further explored.  
The LDA specifies the maximum probability of an incorrect discrimination to be minimized [64]. It uses predetermined information (i.e., explored attributes) along with the a priori category membership (i.e., seep or spills). To this matter, the dependent variable (i.e., discriminant function: DF(\(x\))), is given by the summation of all independent variables (\(x_{n}\)) multiplied by their weight (\(w_{n}\)), minus a constant offset (_off_), such that: DF(\(x\)) = (\(w_{1}x_{1}+w_{2}x_{2}+w_{n}x_{n}\)) - _off_. The independent variables are represented by the values of the explored attributes, whereas \(w_{n}\) and _off_ are calculated by the best fit of the model [64; 71; 72].  
The dependent variable is compared to the category membership to estimate the LDA power. To access the discrimination accuracy in our investigations, we used a two-by-two table (i.e., confusion matrix--Table 1[73; 74]) and its associated standard statistical metrics--Table 2[75; 76; 77]. At present, best overall accuracies are about 70%, obtained from the analyses of several dataset combinations: 44 (initial analysis [29; 30]) and 32 (first study [31; 32]). The LDA-based algorithms were trained with all 4562 oil-slick samples.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **LDA Oil Seeps** & **LDA Oil Spills** & **Known Oil Slicks** \\ \hline
**Known oil seeps** & A & B & A + B \\ \hline
**Known oil spills** & C & D & C + D \\ \hline
**LDA oil slicks** & A + C & B + D & A + B + C + D \\ \hline \end{tabular}
\end{table}
Table 1: Confusion matrix [73; 74]. In our current research, our linear discriminant analyses (LDAs) explore a condensed two-by-two table format—refer to Table 3.
## 3 Construction of a Model based on the ViP Principle and the AHP
### Analytic Hierarchy Process (AHP)
If the forward and reciprocal matrices satisfy a consistent matrix, the principle of consistency testing is to check whether there is much difference between the judgment matrix we constructed and the consistency matrix. The steps of consistency testing include calculating the consistency index, calculating the random consistency index, RI, calculating the consistency ratio, CR, etc. 4. Weight vector and consistency index: The judgment matrix A obtained through pairwise comparison does not necessarily meet the reciprocity condition of the judgment matrix. The AHP uses a quantitative standard to measure the degree of inconsistency of the judgment matrix A. The formula of the consistency test is the average random consistency index * order RI. In various applications, the analytic hierarchy process helps decision makers with program selection, resource allocation, prioritization, and risk assessment. For example, in business management, companies can use the analytic hierarchy process to determine the strategic goals of the company, as well as the measures and resource allocation required to achieve these goals. In project evaluation, the AHP can be used to prioritize projects for more effective allocation of resources and time. In addition, the AHP can also be applied to prediction and decision support systems. For example, in environmental engineering, the AHP can be used to evaluate environmental quality, predict environmental change trends, and formulate environmental protection strategies. In military command, the analytic hierarchy process can be used to evaluate the risks and benefits of military operations and provide support for military decision making. The application scope of the analytic hierarchy process is typically extensive. It can be applied to any decision-making scenario that requires the combination of qualitative and quantitative analysis and the comparison and weight analysis of multiple factors. This approach can assist decision makers in understanding and dealing with complex problems and improve the feasibility and effectiveness of decision making.
# Ce- and B-Doped Silica Fibers for Monitoring Low-Energy Proton Beams on a Medical Cyclotron
## 3 Results and Discussion  
### Beam-Current Effect  
Figure 3 shows the results from run 1A through D with the Ce-600 fiber, mounted on the Tl target (round/red points), the water target (square/black points) and the gas target (triangle/green points). The signal height tracks with the distance to the irradiated target and the target length and could be due to a geometric effect. It should also be noted that the prompt radiation from the different target materials is not the same due to the different nuclear reactions taking place.  
Figure 3 shows the results from the Ce-200 fiber (inverted triangle/blue points). While the signal is lower than in the larger Ce-doped fiber when the \({}^{18}\)O gas target is irradiated (triangle/green points), it does not scale with the cross-sectional area of the fiber by a factor of nine but only drops by a factor of four. It should be noted that the two Ce-doped fibers were of different lengths, with the larger fiber being 2.6 times longer, which may account for the difference in signal response.  
The diamond/cyan points in Figure 3 show the result from the B-200 fiber with a signal height about 20% lower than with the Ce-200 fiber of the same diameter (inverted triangle/blue points). This could be due to a different light-output efficiency, especially as the B-doped fiber is 2.5 times longer than the Ce-doped fiber of the same diameter.  
For all irradiations a correlation between the beam current and the fiber signal is observed, independent of the fiber diameter and dopant. The only deviation from linearity was observed for high currents in experiment 1C (triangle/green points), when the Ce-600 fiber was used at a current above 12 \(\upmu\)A. Here, the correlation was anti-linear, decreasing with increasing beam current. This effect was reversible: Once the beam current was decreased, linearity was restored. One possible explanation could be the result of the high temperature due to the power deposition of the beam. In fact, previous research has indicated that excessive internal fiber temperatures lead to lower signal outputs [16]. Another hypothesis is that the MPPC at high light levels reacts with decreasing digital output. This is not observed with either 200 \(\upmu\)m fiber, likely due to their reduced volume.  
Based on the results, both the B-200 and Ce-200 fibers are valid candidates for measuring changes in beam current. A sufficiently strong signal is induced with a linear relationship even at higher currents, in addition to offering better spatial resolution than with the Ce-600 fiber.
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 8 Control Methods Used in the Active Seating Suspension System
### Robust \(H_{\infty}\) Controller
The Linear Matrix Inequality (LMI) was applied to obtain the desired controller. The active vehicle and seat suspension model were mainly used to minimize the vertical acceleration of the driver to ensure the ride comfort. The seat and human model were considered in the vehicle model. Three types of road profiles were applied to excite the active vehicle and seat suspension system namely: bump road profile, type C road profile and combination of the bump and type C road profile. The simulation results showed that the proposed controller can provide a good vibration attenuation under the road disturbances. However, the effect of the non-linear factors and input delay were not included in the study. Takagi-Sugeno fuzzy control based on a disturbance observer was applied to improve the ride comfort of the driver [45]. Disturbance observer was used to estimate all possible disturbances caused by model simplification, friction force and actuator saturation. The driver mass variation was estimated using T-S fuzzy control. The \(H_{\infty}\) feedback controller was designed based on the acceleration and suspension deflection which could be measured during the real application. The bump and random road profiles have used to validate the proposed controller with different driver masses of 55 kg and 70 kg. The RMS accelerations using the \(H_{\infty}\) feedback controller was significantly decreased by 45.5% when compared with those using a passive seat suspension under the bump road test and reduced by 49.5% under the random road profile. However, the driver mass was assumed to be known in the experiment. The estimation parameter algorithm of the real driver mass was not illustrated in the paper. A robust \(H_{\infty}\) sampled data control based on input saturation is proposed in [78]. The passenger dynamic model and the seating suspension system model were considered in the active suspension system model to ensure a high precise dynamic model, which contains all uncertainties related to the system. The robust controller was designed to meet some criteria such that the body acceleration must be small to ensure a high level of ride comfort under road disturbances. The seat deflection must not exceed the maximum allowable stroke of the suspension to ensure the safety of the seat, and finally, the dynamic tire load must be smaller than the static load [79]. The proposed controller was validated through a practical design example. A \(H_{\infty}\) controller for active seating suspension system within a chosen finite frequency range was reported in [80]. The chosen frequency was considered as 4 Hz at which the severest vibration is transmitted into the human body. The results in the entire low frequency range and in a finite frequency range were compared. A better performance of the proposed controller in a finite frequency range was guaranteed in terms of the disturbance attenuation.