esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
## 2 Materials and Methods
### Hydrodynamic Analysis
#### 3.3.2 Experimental B  
The distributions of surface residual currents in the surrounding sea areas before and after the change in the coastline in Area 2 are shown in Figure 10a,b. The main changed area was near Dasong, where the direction of residual currents changed from south-north to southwest-northeast, with a slightly increasing tendency in velocity. However, the impact on the distribution of residual currents in distant areas is significant.  
Figure 10: Current changes in Area 2. (**a**) Residual currents distribution before the change in coastline; (**b**) Residual currents distribution after the change in coastline; (**c**) Changes in the velocity of the peak flood tidal currents; (**d**) Changes in the velocity of the peak ebb tidal currents.  
Figure 9: Velocity changes in the peak flood tidal currents along Section A.  
The changes in flood and ebb surface current velocities before and after the change in the coastline in Area 2 are shown in Figure 10c,d. After the coastline changes, similar to Area 1, the velocity during flood tide and ebb tide in this area also showed an alternating effect (increasing and decreasing). However, the eastern side showed a decreasing trend while the western side showed an increasing trend. In this study, station B (29.74\({}^{\circ}\) N, 121.91\({}^{\circ}\) E) located at the center of the deceleration water area on the east side was selected to compare the changes in flood and ebb current velocities before and after the coastline change. Before the coastline change, the maximum current velocities for flood tide and ebb tide at Station B were 1.41 m/s and 1.27 m/s, with the average velocities being 0.87 m/s and 0.75 m/s, respectively. After the coastline change, the maximum current velocities for flood tide and ebb tide were 0.99 m/s and 0.90 m/s, with the average velocities being 0.61 m/s and 0.54 m/s, respectively. Compared to currents before the coastline change, the maximum flood and ebb current velocities at this point decreased by 0.42 m/s and 0.26 m/s, and the average velocities decreased by 19% and 28%, respectively.
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## 4 Results
### Settlements' Prediction and Comparison to Monitored Data  
Monitored LOS displacements from Figure 11a are converted into vertical settlements according to Equation (8) and projected on cross sections (A to I of Figure 10, Figure 11a or Figure 11b orthogonal to the first excavated tunnel axis), to be compared to predictions.  
On the other hand, settlement profiles are predicted using Equation (15), which considers the influence of horizontal displacements. When using Equation (15), values of K = 0.45; \(z_{0}\) = 45 m; and volume loss V\({}_{\text{L}}\) = 0.25, 1.0, and 1.75% are assumed. The local incidence angle in the AoI is roughly equal to \(\vartheta\) = 50\({}^{\circ}\) and the average angle \(\gamma\) formed by the cross sections with the LOS direction is 55\({}^{\circ}\). Thus, the expected increment in maximum settlement is equal to \(\vartheta\) \(\mid\) max = 1.04. From Equation (14), the shift of the maximum settlement toward the satellite is \(\Delta\)x\({}_{\text{v}}\) = -5.7 m.  
Figure 12 shows the comparison between monitored data projected on the vertical plane (black dots) and the expected settlement profiles using Equation (15). In most cases, monitored data fall within the expected range, as reported in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & \\ \hline A & B & C & D & E & F & G & H & I \\
64\% & 53\% & 67\% & 59\% & 78\% & 55\% & 64\% & 79\% & 54\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage of monitored scatters that fall within the expected range.  
Figure 12: Comparison between monitored data projected on the vertical plane (black dots) and predicted settlement profiles dv using Equation (15) (dashed-solid-dashed lines for VL = 0.25, 1, and 1.75%, respectively). Sections A to I refer respectively to those in Figures 10 and 11a,b.
# An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders
## 2 Related Work  
### Hand-Crafted Features Based Method  
Generally, three modules can be extracted from the hand-crafted features based anomaly detection method: (i) extracting features from the normal patterns; (ii) modeling to characterize the distribution of the extracted features; (iii) identifying the outliers as anomalies based on the model. For the feature extraction module, various feature descriptions are designed. In some works, low-level trajectory features from a sequence of images were utilized to describe normal motion patterns [23; 24; 25]. However, these methods focused on the anomaly caused by a crowd instead of a single object as a fundamental unit. These trajectory features were mainly based on crowd tracking so that these methods were unable to handle single object anomaly detection. In addition to these trajectory features, some other low-level spatiaotemporal features were widely used, such as the histogram of oriented flows (HOF) [26] and the histogram of oriented gradients (HOG) [27]. [PERSON] et al. [28] used the distribution of spatiotemporal gradients to represent the rich motion information in local spatiotemporal motion patterns. In the work of [29], a motion feature represented by the histogram of the optical flow was used as a low-level feature for the motion-pattern description. To model the extracted features, [PERSON] et al. [30] utilized an exponential distribution to characterize the flow probability matrix. [PERSON] and [PERSON] [31] applied the mixture of probabilistic principal component analyzers (MPPCA) algorithm to model the local activity patterns with the optical flow as a low-level measure. [PERSON] et al. [32] learned a model for normal crowd features based on mixtures of dynamic textures (MDT) and [PERSON] et al. The authors of [33] used a conditional random field (CRF) to integrate the outputs of the model on this basis. In order to model the appearance and motion features from principal component analyzers, [PERSON] et al. [34] constructed a deep Gaussian mixture model (GMM). Besides the literatures above, some sparse coding or dictionary learning based methods were used to encode the normal patterns. In the work of [35], a normal dictionary was learned from an over-complete normal basis set, then the sparse reconstruction cost was used to measure the normalness of the testing sample. In order to accelerate both the training and testing process, [PERSON] et al. [36] learned multiple dictionaries to encode normal size-invariant patches from multiscale frames. [PERSON] et al. [37] captured the low-rank property of the bases in dictionary learning phase, then a weighted sparse reconstruction method was used to measure the abnormality of testing samples.
## 2 Materials and Methods
### Experimental Setting
#### 2.4.2 Outdoor Test  
Using the same configuration of monitoring devices, we also measured the ambient PM\({}_{2.5}\) concentration at the outdoor Roof-top of one of the national PM\({}_{2.5}\) Supersites located in Daejeon, South Korea, operating BAM (MetOne, Grants Pass, OR, USA). Main body of BAM was installed at inside of an experiment laboratory of the Supersite while the inlet of BAM was located at the Roof-top. The outdoor temperature and RH during were measured by the sensors in ESCORTAIR, PA and PDR-1500 and the values were crosschecked. Measurements from IRMs were collected every minute, except for PA, which provided a response every 80 s. To compare the hourly concentration values provided by the Supersite, we calculated the hourly mean values using measured values acquired at hourly intervals from each IRM device. Final sample size for the outdoor data was 240 (2 sets of each device \(\times\) 24 data points/day \(\times\) 5 days) for PM\({}_{2.5}\), as well as temperature and relative humidity.
# Neurorotrophic Factors in Glaucoma and Innovative Delivery Systems
## 2 Neuroprotection: Insights on Biochemical Pathways and Treatment Opportunities
### Neurotrophic Factors Rationale for Use in Glaucoma Treatment and Preliminary Results
#### 2.1.1 Nerve Growth Factor
Recently, a recombinant human nerve growth factor (rhNGF) molecule named Cenegermin, has been developed for clinical use and it is currently available for topical treatment of patients with neurotrophic keratopathy (NK) [52; 53; 54; 55; 56; 57]. This compound has been tested in preclinical setting in animal models of retinitis pigmentosa and of optic nerve crush showing efficacy in improvement of retinal cells survival [58; 59; 57]. As possible further applications, the efficacy and safety of Cenegermin eye drops solution are currently being tested in a large, controlled, randomized phase I clinical trial in patients with primary open-angle glaucoma despite IOP control, but results are not yet available [59].
# Experimental Study on Damage Constitutive Model of Rock under Thermo-Confining Pressure Coupling
## 3 Results and Discussion
### Model Analysis of Elastic Modulus of Rock  
#### 3.3.1 Modeling the Elastic Modulus of Rock under the Influence of Temperature  
Clearly, at low temperatures microcracks within the rock tend to close, while at high temperatures these microcracks may expand or new cracks may form, leading to a nonlinear variation in the rock's elastic modulus. Additionally, the differing thermal expansion coefficients of various minerals cause extra internal stress and strain within the rock, thereby affecting its mechanical properties. Under the influence of temperature, the tangent elastic modulus of some rock masses may also exhibit a decreasing trend. Considering these factors, the relationship between the rock's elastic modulus and temperature can be well-fitted using a polynomial function:  
\[E(T)=AT^{3}+BT^{2}+CT+E_{0} \tag{3}\]  
where \(E_{0}\) represents the elastic modulus of the rock under uniaxial compression at room temperature (with room temperature taken as \(T=20\)\({}^{\circ}\)C), and \(A\), \(B\), and \(C\) are the fitting parameters to be determined.  
Figure 12 shows the fitting results for different types of rocks. By integrating the data from the four rock types, the overall goodness of fit (R\({}^{2}\)) for the polynomial model is above 0.90, indicating a good fit. It is observed that the gray sandstone has a lower goodness of fit for the temperature model at a confining pressure of 5 MPa. This is likely due to the higher porosity of gray sandstone causing the internal pores to close during the initial stages of confining pressure changes, which leads to abrupt changes in the tangent elastic modulus. This behavior is challenging for the model to accurately describe. However, in other developmental stages, the model effectively fits the trend of the tangent elastic modulus with temperature changes for all four rock types. This demonstrates that the polynomial model can accurately describe the nonlinear changes in the tangent elastic modulus of rocks under varying temperatures.
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation
### Consumer Interface Generation System  
The system in charge of code generation is the consumer interface generation system, an SOA-based software system that bridges service exchanges between non-interoperable systems. The system has been implemented in the frame of the Eclipse Arrowhead Framework [28]. The Eclipse Arrowhead is an open-source framework designed to provide automation capabilities for SoS, such as real-time, security, safety, and engineering of automation functionalities. The framework established the local cloud concept to meet the aforementioned automation system requirements.  
The generator generates and dynamically instantiates the code for the interface that serves as an adaptor between the consumer and provider. The input to the generator consists of the service contract descriptions of both the consumer and provider systems. The output of the generator is executable code designed to communicate with the core systems of the local cloud and to enable consumption of the service offered by the provider.  
The purpose of this system is as follows:  
* Solve interoperability problems at the service interface level between heterogeneous systems.
* Generate new autonomous and runtime consumer interface codes.
* Translate service interfaces.  
Figure 2 represents the interaction between the consumer interface generator and other Arrowhead core systems. The generator communicates with the authorization system, the service and system registry and the orchestrator. The service provider and consumer are able to achieve successful communication via the new interface generated by the generator.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 2 Basic Principles of Sound Mixing and Speech Separation  
### Sound Mixing of Microphone Arrays  
Compared to a linear microphone array, a circular microphone array has similar performance in all directions on the horizontal plane, and there is no obvious blind spot. Therefore, circular microphone arrays are most widely used in speech applications [13; 14]. Figure 1 depicts a circular microphone array with \(M\) microphones, where \(S\) denotes a speech source.  
The center microphone (\(i=0\)) is defined as the reference point and the angle between the source and the vertical axis is \(\theta\). The time-delay of microphone \(i\) (\(i\
eq 0\)) relative to the center microphone can be denoted by  
\[t_{i}(\theta)=\frac{d\cdot\cos\left(-\frac{\alpha}{2}+i\alpha-\theta\right)}{c} \tag{1}\]  
where \(d\) denotes the distance between the \(i\)th microphone and the reference point, \(\alpha\) denotes the angle between two adjacent microphones, and \(c\) denotes the sound speed.  
The phase transfer at each frequency is given by  
\[\tau_{i}(\theta)=2\pi ft_{i}(\theta) \tag{2}\]  
where \(f\) denotes frequency.  
The observed mixture signal in the frequency-domain is given by  
\[X(f,t)=H(f)\cdot S(f,t) \tag{3}\]  
where \(t\) denotes the time index, \(X(f,t)\) denotes the observed signal vector, \(S(f,t)\) denotes the source signal vector, and \(H(f)\) denotes the mixing matrix. They are given by  
\[S(f,t)=\left[S_{1}(f,t),\ldots,S_{N}(f,t)\right]^{\mathrm{T}} \tag{4}\]  
\[X(f,t)=\left[X_{1}(f,t),\ldots,X_{M}(f,t)\right]^{\mathrm{T}} \tag{5}\]  
\[H(f)=\left[\begin{array}{ccc}H_{11}(f)&\cdots&H_{1N}(f)\\ &\vdots\\ H_{M1}(f)&\cdots&H_{MN}(f)\end{array}\right] \tag{6}\]  
where \({}^{\mathrm{T}}\) denotes the transpose, \(M\) denotes the number of microphones, and \(N\) denotes the number of sound sources.  
Figure 1: Circular microphone array configuration.  
Finally, the mixing matrix in Equation (6) is transformed into Equation (7) without considering echo and reverberation interference, as well as amplitude difference between two channels.  
\[H(f)=\left[\begin{array}{ccc}1&\cdots&1\\ \dot{\mathrm{e}}^{\mathrm{j}\tau_{1}(\theta_{1})}&\cdots&\dot{\mathrm{e}}^{ \mathrm{j}\tau_{1}(\theta_{N})}\\ &\vdots\\ \dot{\mathrm{e}}^{\mathrm{j}\tau_{M-1}(\theta_{1})}&\cdots&\dot{\mathrm{e}}^{ \mathrm{j}\tau_{M-1}(\theta_{N})}\end{array}\right] \tag{7}\]  
In a real speech environment, due to noise interference and reverberation, there may be some distortions in the mixing matrix.
## 5 Discussion and Conclusions  
According to the experiments proposed in this study, the LSTM model showed relatively lower performance when learning non-timeline data than timeline data. In this experiment, the Feature Importance and SHAP force plot confirmed more than 96% of both the accuracy score and the F1-score performance for intrusion in the drone network. These results are due to the fact that the Explainer model has the ability to select the intrusion pattern in the payload of the single model.  
This study developed an ensemble model that can simultaneously detect multiple types of intrusion. In preprocessing, the patterns within the payload using a measure of Feature Importance were distinguished from attack and normal data, and as a result, improved the accuracy of the ensemble model. Through the experiment, both the accuracy score and F1-score were verified for practical utility through 97% detection performance measurement. However, it is also necessary to reconsider the limitation of three types of intrusion in this study. In future work, we plan to continuously evaluate the potential of these problems to increase the practical applicability of the model.  
Conceptualization, methodology, software, validation, writing--original draft preparation, formal analysis, investigation, visualization: Y.-W.H.; validation, writing--review and editing, supervision, project administration, funding acquisition, project administration, resources: D.-Y.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by Hongik University's new faculty research support fund.  
Not applicable.  
Data Availability StatementData available in a publicly accessible repository. The UAVCAN Attack dataset utilized in our experiments originates from the HCRL (Hacking and Countermeasure Research Lab). The dataset can be acquired at [[https://ocslab.hksecurity.net/Datasets/uavcan-attack-dataset](https://ocslab.hksecurity.net/Datasets/uavcan-attack-dataset)]([https://ocslab.hksecurity.net/Datasets/uavcan-attack-dataset](https://ocslab.hksecurity.net/Datasets/uavcan-attack-dataset)) (accessed on 19 November 2023).  
The authors declare no conflict of interest.
# A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector
###### Abstract
For example, a rating-review pair <1, \"This movie is excellent\"\(>\) might be conceptually inconsistent since this user indicates low satisfaction with the item through a poor rating \"1\", but he or she also shows high satisfaction with the same item through his or her textual opinion \"This movie is excellent\". Similarly, a rating-review record <5, \"I think it was a little overhyped. To me, it wasn't as good as everyone raves about.\"\(>\) presents another contradictory case. Unsurprisingly, if the raw ratings are the unique input for a product or service recommender, the low-quality ratings would also be entered, increasing the probability to generate biased results, bad recommendations, bad experiences for users, and a decrease in revenue of e-companies. Therefore, using textual reviews to inspect the quality of numeric ratings, separating the high-quality data from the noise, is a possible scenario worth to be analyzed. To fulfill the need, this paper proposes a two-phase deep learning-based approach: (1) use of textual reviews to create a sentiment predictor that serves as a raw data quality inspector, preprocessing and cleaning the input data for a (2) collaborative filtering recommender system, which consequently, boost its recommendation accuracy. The main goal of our research is to create an input data quality inspector for the overall performance enhancement of collaborative filtering engines. The proposed inspector should be capable of differentiating between high-quality and noisy rating-review records well. The realization of this goal has a significant impact since current research has given little attention to discuss the effect of the rating quality as the input data for recommenders. Moreover, the inspector described in this article can be directly and automatically applied in collaborative filtering recommenders to boost their performance. This research allows perceiving the importance of the data quality inspection process and data cleaning in this field, guiding future research on directions to reduce bias. The contributions of this study are the following:  
1. Develop a sentiment predictor of textual reviews that acts as a quality inspector of the ratings. Determine if a quality inspector based on textual comments truly enhances the performance of a recommender system. 3. Apply state-of-the-art deep learning algorithms for constructing effective sentiment predictors and collaborative filtering recommenders. The remainder of this paper is organized as follows. Section 2 reviews the relevant research. Section 3 introduces the proposed two-phase deep learning-based recommender system: enhanced by the data quality inspector. Section 4 describes the evaluation process to show the performance of the proposed system. Section 5 discusses the obtained results and mentions future work suggestions. Finally, Section 6 presents the conclusions.
# Development of a New Eye Movement Measurement Device Using Eye-Tracking Analysis Technology
## 3 Results  
### Simultaneous Measurement of Visual Target and Eye Positions  
No participants were excluded, and all 16 subjects were recorded. Using the EyeScore, the positions of the target and both eyes were recorded simultaneously. As shown in Figure 3, these three components were in good agreement, except for a small bilateral deviation in horizontal eye position (\"Horizontal\") and transient detection errors at downward gaze (\"Vertical\"). As the visual target moved in a front-parallel plane, the recorded traces of both eyes should theoretically be the same. To confirm the similarity between the movements of both eyes, cross-correlations were calculated for each participant. The mean maximum values (mean \(\pm\) standard deviation) of the cross-correlation coefficients between the right and left eyes were 0.995 \(\pm\) 0.008 and 0.967 \(\pm\) 0.062 for horizontal and vertical eye movements, respectively, indicating appropriate recording of fronto-parallel eye movements eliciting visual target movements on an LCD (Figure 4).  
Figure 3: Eye positions for horizontal and vertical eye movements. Examples of (**left**) horizontal and (**right**) vertical eye movements elicited by horizontal and vertical target motions, respectively. The target was moved at a rate of 20 deg/s. The black line indicates the trajectory of the visual target, and the red and blue lines represent the right and left eye positions, respectively. Positive values indicate a rightward gaze for horizontal eye movements and upward gaze for vertical eye movements. The target position was indicated by an angle centered on the root of the nose, and the gaze of each eye was indicated by an angle centered on the pupil center. Therefore, slight deviations were observed between the left and right eyes during horizontal eye movements.