esa-sceva/satcom-chunk-collection huggingface dataset:
# BioShare: An Open Framework for Trusted Biometric Authentication under User Control
## 3 BioShare Design
### Service Application (SA)  
BioShare achieves the goal of providing open services via the Service Application running on the server, and Algorithm 2 shows the process flow in detail. SA listens for biometric authentication requests from remote network applications, verifies the request data, and checks the user in the user-registration database. If the user has registered the biometric service, SA will process the request with either of the following modes:  
**Forwarding Mode:** If the user does not authorize the server to conduct biometric authentication, then SA will forward the request to the user terminals, and on the terminal side, UA coordinates with TA to conduct biometric authentication and returns the result to the server, as described in step \"Service registration\" of Section 3.2.  
**Authorized Mode:** If the user has authorized the server, SA will call Enclave Module to conduct biometric authentication and return the result, as described in step \"Authorizing the server\" of Section 3.2.  
The BioShare Service Application is also designed to handle command requests from the users, including service-registration requests and authorizing-the-server requests, which are described in Section 3.2. The server maintains a user-registration database that records the registration and authorization information for each user.  
```
1:The ID of The Target User, \(userId\)
2:Biometric Modality Value, \(biometricType\)
3:Biometric Feature/Raw Data, \(biometricData\)
4:Authentication Result, \(returnResult\)
5:(0-false/1-true/-1-failed)
6:Initialize \(returnResult\) with -1
7:Receive service requests from Network Applications
8:Verify input data
9:Search database for UserRecord where UserRecord.UserId=\(userId\)
10:ifUserRecord IS NOT NULL then // user registered
11:ifUserRecord.ServerAuthorized = false then
12://forwarding mode
13:Connect to the terminal with UserRecord.TerminalId
14:Call SCM to build a secure channel to TA through UA
15:Call TA to conduct biometric authentication
16:\(returnResult\) = authentication result of TA
17:else// authorized mode
18:Call EM to conduct biometric authentication
19:\(returnResult\) = authentication result of EM
20:endif
21:endif
22:returnResult
```  
**Algorithm 2** Process for Handling Service Requests from Network Applications
## 2 Materials and Methods
### Hazelnut Groves Characterization  
After delineating the hazelnut groves within the AOI, the spectral-temporal signature for each pixel falling within the HZ polygons was extracted from the 10 m/pixel reference dataset. The hazelnut-specific data were then normalized, and to address dimensionality and inter-band correlation, a Principal Component Analysis (PCA) was applied, selecting the minimum number of principal components that collectively accounted for at least 90% of the data variability. Once the principal components were obtained, the scores matrix underwent unsupervised clustering using K-means, in a framework similar to [84]. The appropriate number of clusters was identified using the elbow method [85] based on SSE (Sum of Squared Errors or Inertia), which computes the sum of squared distances of data points to their assigned cluster centroids. The method aims to identify the optimal number of clusters by examining the rate of change of SSE; a sharp change in the slope of the SSE curve indicates the elbow point, thus the optimal number of clusters. The resulting clusters were then projected back onto the AOI in QGIS, where the cluster-based classification was subjected to a 20-pixel sieve with 4-connectivity.  
To provide interpretability to the clustering results (Figure 7), two approaches were followed:  
* Ground validation and historical imagery.
* Interpretation through indices: A total of 1700 random sampling points were placed within the polygons of each cluster, resulting in a total of 5100 sampling points. Spectral-temporal data were extracted from the reference dataset at these points, and three specific indices were applied to analyze and interpret the data. These indicesThe VH/VV ratio in decibels is given by:  
\[\text{VH/VV ratio (dB)}=\text{VH (dB)}-\text{VV (dB)}\]  
This index indicates the presence of structures capable of strengthening the VH signal in the presence of high entropy surfaces, such as canopies. Moreover, VV is influenced by leaf or soil water content, making this index indicative of biomass and water content [65].  
The NDVI (Normalized Difference Vegetation Index), widely utilized for assessing vegetation health, is highly correlated with various biophysical parameters of vegetation [88]. It represents the normalized ratio between the NIR (Near-Infrared) and Red bands. In the case of Sentinel-2, it is given by:  
\[\text{NDVI}=\frac{\text{B8}-\text{B4}}{\text{B8}+\text{B4}}\]  
The third index is the NDMI (Normalized Difference Moisture Index), used to estimate leaf water content. It utilizes the NIR and SWIR (Short-Wave Infrared) bands [89]. In the case of Sentinel-2, it is given by:  
\[\text{NDMI}=\frac{\text{B8}-\text{B11}}{\text{B8}+\text{B11}}\]  
Moreover, an additional analysis was conducted on the DEM data for each random point within the different clusters. This analysis aimed to assess potential differences arising from a temporal shift in phenological phases associated with varying altitude bands.  
Figure 6: Workflow for Hierarchical Random Forest training via Euclidean distance analysis of spectral signatures.
# Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control
## 3 An Active Noise Control System with Dynamically Adapted Reconstruction and Anti-Aliasing Filters
### Dynamic Matching of Data-Driven Anti-Aliasing Filters  
During the control process, the power spectrum of the output signal of each individual filter can be estimated based on the amplitude-frequency response of the filter.  
\[\hat{S}_{xx,k,p}(m)=\left|T_{xd,p}(k)\right|^{2}\hat{S}_{xx,k}(m) \tag{18}\] \[\hat{S}_{\alpha\kappa,k,p}(m)=\left|T_{cd,p}(k)\right|^{2}\hat{ S}_{\alpha\kappa,k}(m)\]  
where \(p\) is the selected filter number in the filter library. Assuming that the extraction ratio of the high sampling rate over the low sampling rate is Q in the digital extraction process, the reference signal and error signal introduce the following aliasing noise in the frequency coefficient range of \(0<k<\frac{k_{1}}{2}\):  
\[N_{xx,p}(m)=\sum\limits_{i=1}^{Q-1}\left|T_{xd,p}(ik_{L}+k) \right|^{2}\hat{S}_{xx,ik_{L}+k,p}(m)+\sum\limits_{i=1}^{Q}\left|T_{xd,p}(ik_ {L}-k)\right|^{2}\hat{S}_{xx,ik_{L}-k,p}(m) \tag{19}\] \[N_{ee,k,p}(m)=\sum\limits_{i=1}^{Q}\left|T_{cd,p}(ik_{L}+k) \right|^{2}\hat{S}_{ee,ik_{L}+k,p}(m)+\sum\limits_{i=1}^{Q}\left|T_{cd,p}(ik_ {L}-k)\right|^{2}\hat{S}_{ee,ik_{L}-k,p}(m)\]  
For the reference microphone and error microphone, the coherence between the aliasing noise and original low-frequency noise has different impacts on the control system [1]. When the aliasing noise is uncorrelated with the original low-frequency noise, the control system often suppresses the aliasing of the reference microphone more strictly. According to Equations (18) and (19), once the filter is selected, the dynamic ranges of the reference microphone and the error microphone in the control frequency band can be calculated in advance, satisfying the following conditions:  
\[D_{\lambda,k}(m)=10\text{log}_{10}\frac{\hat{S}_{xx,k,p}(m)}{N_{ xx,k}(m)} \tag{20}\] \[D_{\epsilon,k}(m)=10\text{log}_{10}\frac{\hat{S}_{\alpha\kappa,k,p}(m)}{N_{\alpha\kappa,k,p}(m)}\]  
According to the preset constraints over the dynamic ranges of \(D_{\lambda,k}(m)\geq D_{\lambda,k,\min}\) and \(D_{\epsilon,k}(m)\geq D_{\epsilon,k,\min}\), the digital anti-aliasing filter groups that meet these requirements can be selected. Finally, comparing the group delays of the selected filter groups in the digital filter library, the filter group with the smallest average group delay in the control frequency band is selected as the digital anti-aliasing filter employed in this study.
## 5 Conclusions and Future Work  
This article focused on the four DOFs' mathematical model development for an AUV using parameter estimation procedures based on field test data, along with the design and validation of an LOS-based, path-following algorithm in a simulation and an open environment at sea using the Girona500 AUV platform. The four DOFs mathematical model of the AUV was developed to verify the proposed GNC architecture. The model validation was based on parameter estimation procedures using field test data for surge, sway, heave, and yaw motions. After validating the AUV mathematical model, this paper developed the GNC system with an LOS-based, path-following algorithm with separate position PID controllers. This path-following algorithm used waypoints from a predefined coverage planar area. Finally, the experimental results showed two control scenarios in simulation and field tests, validating the designed GNC architecture and demonstrating its correct implementation.  
Future work will include a comparison of this architecture with more advanced non-linear controllers and incorporate, comprehensive obstacle-avoidance abilities. These controllers will incorporate the environmental forces' compensation to reduce the cross-track error of the LOS-based, path-following algorithm in the system... Moreover, future work will improve the mathematical model of the AUV to study all six DOFs of the vehicle and all the cross-terms of the complete dynamic model.  
Conceptualization, J.V.; methodology, J.V.; software, J.V. and G.V.; validation, J.V. and G.V.; formal analysis, J.V.; investigation, J.V. and G.V.; resources, G.V.; data curation, J.V.; writing--original draft preparation, J.V.; writing--review and editing, G.V., J.A., P.R. and K.T.K.; visualization, J.V.; supervision, J.A. and K.T.K.; project administration, J.A., P.R. and K.T.K.; funding acquisition, P.R. and K.T.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the project MOCAV-G500 from EUMarineRobots that has received funding from the European Union's Horizon 2020 (grant agreement No 731103), with the collaboration of the aColor project, which is funded by Technology Industries of Finland Centennial and Jane & Aatos Erkko Foundations. The APC was funded by the Faculty of Engineering and Natural Sciences (ENS) at Tampere University (TAU). The authors declare no conflict of interest.
## 5 Experiments  
### Datasets and Preprocessing  
We evaluate our models on WMT17 Chinese-English and English-German datasets. English-German and Chinese-English translation tasks are high-resource cases. The parallel corpus of Chinese-English and English-German on WMT17 is shown in Table 1. To simulate the low-resource situation, we only use the News Commentary v12 parallel corpus as our training set in the translation stage. The specific data details are as follows:  
**English-German** We extract the monolingual data from WMT17 monolingual corpus News Vrawl 2016, and use the first 4.5 million English and German parts of the datasets as the training data in the pretraining stage. At the same time, the News Commentary v12 English-German dataset is used as the parallel corpus, which contains 270,769 parallel sentence pairs. We use newstest2016 and newstest2017 as validation and test sets.  
**Chinese-English** We use the Chinese part of all parallel corpus in CWMT corpus as the Chinese monolingual corpus, which contains 9 million sentences in total. The whole monolingual corpus News Crawl 2016 is used as the English monolingual corpus, which contains 20.6 million sentences. We only use News Commentary v12 Chinese-English dataset as the parallel corpus for Chinese-English translation, which contains 227,330 parallel sentence pairs. We use newsdev2017 and newstest2017 as validation and test sets.  
All training data used in our experiments are shown in Table 2. We use [PERSON] script ([[https://github.com/moses-smt/mosesdecoder](https://github.com/moses-smt/mosesdecoder)]([https://github.com/moses-smt/mosesdecoder](https://github.com/moses-smt/mosesdecoder))) to process English and German data, and use Jieba word segmentation tool ([[https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba)]([https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba))) for Chinese word segmentation. We use BPE [31] with 60,000 merge operations in the same way as XLM [18].
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 1 Introduction
First, the method develops the ST-GAT model to capture spatial relationships between monitoring points installed around multiple steel struts, assigning different weights to consider their importance. By visualizing the weight distribution between nodes in the GAT network and generating a feature attribution map across different features, the interpretability of model can be significantly improved. Second, a novel proactive control method for wall deflections is proposed based on the predicted values from multiple steel struts using the ST-GAT model.
## 1 Introduction
[PERSON] and [PERSON] [17] proposed an empirical AAF model and spanwise coherence of the aerodynamic forces applied on a series of streamlined bridge deck cross sections through measurement of the unsteady surface pressures on a closed box girder under turbulence. [PERSON] et al. [18] identified six-component AAFs of a closed box based on a common force and pressure measurement test in passive grid-generated turbulent flow. For truss girders, studies focused on the AAF are relatively few and have no universality due to the various geometric details. [PERSON] [19] directly measured the admittance of a truss girder in a wind tunnel test. [PERSON] [20] gave the admittance of a stiffening truss girder of the Akashi Kaikyo Bridge. [PERSON] (2018) directly identified a truss girder's aerodynamic lift admittance using buffeting force measurements based on the assumption that the truss girder is equivalent to a continuous cross-section girder. However, all of the studied truss sections had a single deck and were only used in long-span road bridges. To date, research on the AAFs for DDTGs applied in long-span cable-stayed road-rail bridges has not been carried out. To overcome this deficiency, a series of wind tunnel tests were performed to identify the AAFs of a DDTG segment model based on a force measurement test in a passive spire-generated turbulent flow. In Section 2, the AAF identification approach is proposed theoretically. Section 3 gives a detailed introduction on the wind tunnel test. Section 3.1 describes the construction and installation of the test model. The test conditions are shown in Section 3.2. The turbulent wind flow characteristic in the wind tunnel and comparison with the target spectrum are studied in Section 3.3. In Section 4, the test results of AAFs are provided and fitted based on a combination of the common formula and an extremum expression. The identified AAFs are compared with the Sears function in terms of each component under the service and construction stages in Section 4.2. Finally, in Section 5, the buffeting responses for three AAF types (AAF = 1.0, the Sears function, and the identified AAFs) are discussed. All symbols used are listed in Table 1.
## 3 Verification by Comparison with Previous Experimental Results
### Scope of the Application of the RC Beam Plane Conservation Assumption
#### 3.2.2 Investigation of the Estimation Accuracy Based on the Deflection Ratio \(R_{\rm D}\)  
In this section, the accuracy of the estimation method is assessed using the deflection ratio without considering the influence of the shape and dimensions of the beams. The value obtained by dividing the maximum deflection \(\delta_{\rm u}\) by the clear span length \(L\) was defined as the deflection ratio \(R_{\rm D}\).  
\[R_{\rm D}=\delta_{\rm u}/L \tag{18}\]  
Figure 9 shows the relationship between the experimental and estimated deflection ratios. The figure shows that the estimated value of \(R_{\rm D}\) overestimates the experimental value \(R_{\rm D,exp}\) when \(2\%<R_{\rm D}<9\%\). In contrast, when \(R_{\rm D}\) is larger than \(9\%\), the estimated \(R_{\rm D}\) value corresponds relatively well to the experimental value \(R_{\rm D,exp}\). Therefore, the accuracy and the applicable range of the estimated values are difficult to examine based on the deflection ratio \(R_{\rm D}\).
## 1 Introduction
Meanwhile, some numerical methods have been developed based on the theory of stress wave propagation in jointed rock masses, such as finite difference, finite element and boundary element methods, etc. [PERSON] et al. [12] derived a general Green's function solution to express the elastic wave propagation in rock blasting and a coupled finite difference-boundary element method cracks had been used to calculate the initiation and propagation due to an explosion in the rock [13]. Some researchers also pointed out two kinds of models: a thin-layer interface model and a zero-thickness model. According to the thin-layer inter-face model, the displacement at each side of the rock joints was continuous. However, there is a distinct jump in the displacement at the zero-thickness interface. Based on the above theory, many scholars have done a lot of research [14; 15]. Fracture characteristics, loading conditions and material types are main factors influencing the seismic response of artificial filled joints under high strain rate impact. Under the action of a stress wave, both sand and clay show obvious viscoelastic behavior. The Kelvin model [16] was often used to describe the propagation of stress waves in clay. In natural rock joints, sand acted as a particle skeleton, and clay filled the space between the skeletons, which would reduce deformation of the skeleton under high-amplitude stress waves. Particularly, the mixing of clay, sand, water and air would significantly alter the joint characteristics. [PERSON] et al. [17] made 'natural soil' by mixing dry clay and dry sand in different mass ratios. Then, the dynamic responses of different natural soils under dry and saturated conditions were compared. [PERSON] et al. [18] conducted low-velocity impact tests on consolidated sand-clay mixtures with different clay weight fractions. Then, it was found that when the weight fraction of clay was 30%, the clay would completely fill the space between sand particles. While the joint stiffness reached the maximum, the transmission of seismic wave reached the maximum. Through the above research, it can be found that the compressibility of clay, sand and water is obviously different. Different mixing ratio and saturation will significantly affect the wave transmission across the filled fracture. Meanwhile, the impact process is accompanied by obvious particle crushing and energy dissipation, but there are few reports related to these two perspectives. In this paper, the SHPB test system was used to carry out high amplitude impact tests on artificial filled joints with different clay contents and moisture contents. The propagation law of the high amplitude stress wave was explored from the perspectives of energy distribution and particle crushing. The propagation law and stress wave attenuation characteristics in the different rock joints were studied, which enrich the research on the interaction between stress wave and filled fracture.
## 3 Results and Discussion
### Limitations of the Study  
In the conducted study, the focus was on the production of wheat bread enriched with CP and PP at three different concentrations: 10%, 20%, and 30%. Although the mineral content was assessed, an important limitation of this study is the lack of a bioavailability analysis of these ingredients, which determines the actual uptake of the minerals by the body. Future studies are recommended to include methods to assess the bioavailability of minerals from bread enriched with vegetable powders, which would allow a more precise determination of the extent of mineral absorption by the human body.  
Furthermore, the present study was limited to basic antioxidant tests, such as TPC and TFC. In future studies, it would be important to apply methods for the determination of individual phenolic compounds, carotenoids, and other bioactive substances, which could provide important information on the mechanisms of antioxidant activity and potential health benefits associated with regular consumption of enriched bread.  
Another limitation is the lack of sensory evaluation of the bread in the present study. The authors plan to conduct a detailed sensory analysis in future studies to assess the consumer acceptability of the different bread variants. Determining the optimum concentration of CP and PP in terms of taste, aroma, and textural qualities will allow the products to be adapted to consumer expectations.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 6 Proposed Algorithm: Communication Optimisation Integration
### Algorithm Evaluation for \(Lb=92\) dB  
In this subsection, we present the algorithm evaluation for the technical solution in [58] (Wi-Fi, \(LB=92\) dB) for three scenarios, with three different values of the distance between the gateway and the served WSN (\(d=2.393\) km, \(d=7.2209\) km, \(d=9.3362\) km). We assumed that the investigated propagation scenario is Rural Macro-Cell, for which the maximum radius is 10 km. Therefore, the maximum distance between the served WSN and GW for which we performed the simulations did not exceed this value.
## 4 Experiment
### Experimental Setup  
The 64-channel Scan-Genie II data acquisition integration system developed by Acellent Technologies, Inc. is used for signals excitation and reception. At each crack length measurement, the relevant ultrasonic guided wave signal responses are recorded. Considering that the signal energy of the guided waves is mostly concentrated in the vicinity of the narrowband probing frequency domain and the dispersion characteristics can also be reduced accordingly, the narrowband sinusoidal tone burst modulated by a Hanning window is calculated by Equation (10) as an excitation frequency. The excitation signal is applied to A\({}_{1}\)-A\({}_{3}\) of PZT-5 As, respectively, and the peak-to-peak voltage of the output signal is 50 V, it is amplified by the gain parameter setting before it is sent to A\({}_{1}\)-A\({}_{3}\). The remaining S\({}_{4}\)-S\({}_{6}\) of PZT-5 As are used to measure the guided waves signals. In the meantime, the sampling rate is 24 MHz, the sampling point is 6000 and the quality of measurement is improved by averaging the signals with multiple acquisitions. Therefore, the output signal responses are obtained six times and averaged to improve the signal-to-noise ratio.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & **D (mm)** & **\(\rho\)(kg/m\({}^{3}\))** & **f (kHz)** & **E (Gpa)** & **G (Gpa)** & \(\upsilon\) \\ \hline SMA490 BW & 3 & 7850 & 270 & 210 & 80.7 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of the SMA490 BW steel plate.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Material** & **C** & **Si** & **Mn** & **P** & **S** & **Cr** & **Ni** & **Cu** & **Other** \\ \hline SMA490 BW & 0.18 & 0.65 & 1.4 & 0.035 & 0.75 & 0.75 & 0.30 & 0.5 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the SMA490 BW steel plate.  
Figure 2: Experimental setup of an SMA490 BW steel plate (**a**) The experimental equipment and specimen; (**b**) Schematic diagram of the specimen.  
\[\mathrm{h}(\mathrm{n})=\frac{1}{2}\Big{[}1-\cos\!\left(\frac{2\mathrm{m}}{\mathrm{N} -1}\right)\Big{]}\left(\mathrm{n}\ =\ 0,1,\ldots,\mathrm{N}-1\right) \tag{10}\]