esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy
## 4 Smoothing Algorithms  
Algorithms to locate peaks can be applied to raw or smoothed data, and among the smoothing options considered (including wavelet-based [32], spline-based, local-linear smoothing) the iterative bias reduction (IBR) algorithm has been shown to be effective [23; 24] so it will be used here. The basis for assessing IBR effectiveness is the extensive testing results reported in [23] in which separate root mean squared error (RMSE) results were reported for on-peak and off-peak smoothing. The IBR algorithm is quite successful in adaptively smoothing to reduce bias in peak regions by smoothing less, and smooth more heavily in off-peak regions without explicitly identifying the peak regions.  
Smoothing data over neighboring energy bins can reduce noise in the raw counts, at the cost of introducing a bias that de-emphasizes the peaks and valleys of a spectrum. Reference [23] described a new two-stage smoothing procedure that uses a multiplicative bias correction (working with the ratio data/smooth) for adjusting initial smoothed spectra. Applying the multiplicative bias correction to an initial smoother reduces the bias of the initial smoother in the peaks and valleys with no or a negligible increase in its variance. As an example, Figure 4 is the real data (from an item enriched in U-235, for which the major peak is located at 185.7 keV) and two smooth fits using IBR. Smooth fit 2 uses fewer degrees of freedom (df) than smooth fit 1 and so smooth fit 2 oversmooths in the peak. In practice, the df is chosen using cross validation. The vertical black (red, green) lines are the local maxima in the raw data and smooth fit 1 and fit 2, respectively. The obvious peak near 185.7 is found using all three (raw, smooth 1 and smooth 2).  
All smoothers exhibit bias to varying degrees in regions of rapid change such as near a peak. An ideal smoother smooths peak regions very little and off-peak regions much more [23]. The IBR algorithm evaluates the ratio data/smooth to detect regions of oversmoothing using iteration.  
Figure 3: Raw data and example fits to one peak or to two peaks.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 7 Performance Evaluation  
In this section, we evaluated the performance of the BSPGH scheme in terms of computational cost and communication cost during the first handover authentication and group handover authentication phases and compare its performance with those of three other protocols, namely, 5G standard specified by 3 GPP TS 23.501 R16 [21], a privacy-preserving handover authentication protocol for a group of MTC devices in 5G networks (PPHAP) [1], and a novel authentication scheme supporting multiple user access for 5G and beyond (NASS) [7]. In the analysis, it is assumed that all symmetric encryption keys are 256 bits, _MACs_ are 160 bits, and elements in the Hash functions, \(TID\), \(GID\), _Sig_, \(n\cdot P\), and \(Z_{q}^{*}\) are 128 bits. The timestamp is represented by 32 bits, and elements in group \(G\) have sizes of 320 bits.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 3 Results
### Data Acquisition and Preprocessing  
The data for this study were obtained from multiple sources: material source testing data, real-time compaction monitoring system reports, meteorological data from an intelligent weather monitoring system, and test pit experimental data. These data provide the material parameters, compaction parameters, meteorological parameters, and dry density values. Following the data collection and preprocessing methods discussed in the previous section, the collected data were integrated into a single dataset, which includes 106 sets of data. The partial statistical summary of the sample set is shown in Table 2.
# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain
## 3 Quality Control Using In-Situ Methods
### In-Line Quality Control
Here, a change in the signal recorded by the eddy current sensor triggers the camera, and a defect can be identified on the basis of the images. This system is likewise trained, as already discussed, with stored defect images and ML algorithms to detect defects autonomously [67]. The use of infrared (IR) cameras has increased in recent years due to their cheaper design. The advantage over visible-light imaging is that IR cameras are insensitive to smoke and can be used in dark environments. In addition, every object emits infrared radiation, and the intensity increases, the warmer the object is. In industry, these cameras are preferably used for temperature control as well as for defect detection, since a crack becomes detectable through a change in temperature. Especially for quality control in the field of welding and additive manufacturing, IR cameras are increasingly encountered. Another advantage is the image analysis, which can be adopted from the visible-light imaging [68; 69]. In practice, some inspection methods based on X-rays are also employed. In particular, radioscopy and computed tomography (CT) have become widely established. The main difference lies in the two-dimensional image produced by radioscopy, while CT is used for more precise 3D analyses. The advantage of radioscopy is that image reconstruction is faster than CT, although it is not possible to determine the exact location or size of the defect, which is the case with CT. However, there are efforts to improve the use of CT in inline quality control by means of high speed area array detectors and a shorter exposure time [70; 71].
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 1 Introduction
We present existing techniques that address tamper-proof recording of data. In addition, blockchain-based approaches and concepts from the automotive and maritime sectors will also be considered. In Section 4, our approach to decentralized tamper-proof documentation of maritime traffic situations is presented in detail. Afterwards, in Section 5, the approach is tested and evaluated on a mooring maneuver in practice. Finally, Section 6 summarizes the obtained results and provides insight into limitations and future work.
# Structural and Biochemical Characterization of Thioredoxin-2 from _Deinococcus radiotrans_
## Materials and Methods
### Construction of Mutant Strains  
Detailed information pertaining to the bacterial strains and plasmids involved in this study are presented in Table S1. \(\Delta\)_dr0944_ (\(\Delta\)_drtrx1_) and \(\Delta\)_drA0164_ (\(\Delta\)_drtrx2_) disruption mutants were constructed by targeted mutagenesis using the double crossover recombination method, as previously described [(43)]. Briefly, PCR amplified 1 kb fragments from the up-stream and down-stream regions of _dr0944_ and _drA0164_ genes were amplified by polymerase chain reaction (PCR) and subsequently digested using appropriate restriction enzymes and separately ligated to the corresponding sites of pKatAPH3 plasmid. The recombinant plasmids were transformed into _D. radiodurans_ cells from exponentially grown cultures. The positive mutant strains harboring a kanamycin resistance marker were selected on TGY agar plates supplemented with 8 \(\mu\)g/mL kanamycin. The disruption of target genes was verified via diagnostic PCR and DNA sequencing. The primers used in this study are listed in Table S2.  
Expression plasmids for _drtrx1_ and _drtrx2_ were constructed using the pRADZ4 vector, which contains the _dr1124_ promoter for constitutive gene expression _D. radiodurans_[(44)]. Complete _drtrx1_ and _drtrx2_ coding sequences were PCR-amplified from the genomic DNA of _D. radiodurans_ using p4_dr0944F/p4_dr0944R and p4_dra0164F/p4_dra0164R primers, respectively, which carry a Spel restriction site for forward primers and a Notl restriction site for reverse primers (Table S2). The transformed _D. radiodurans_ cells were selected using chloramphenicol (3 \(\mu\)g/mL) supplementation.
## 3 Performed Full-Scale/Model Test Comparisons
### Low-Rise Buildings
#### 3.2.2 Full-Scale Measurement Campaigns Undertaken by [PERSON]
Thus, it was reasonable for him to compare data measured on the prototype to those obtained from rigid models, with regard to the fact that the self-excited forces measured on those full-scale buildings were limited. Other researchers also conducted full-scale/model test comparisons similar to [PERSON]'s works. In 2008, [PERSON] et al. [36] measured the ABL wind characteristics and the wind-induced responses of two prototype tall buildings. Comparing the full-scale rms values of the acceleration samples with the corresponding wind tunnel data, it was found that the force balance model tests were basically conservative when considered for use with the full-scale/model test relative differences in rms values of the acceleration samples, which were in the range of 12.4-16.5%. In 2010, [PERSON] et al. [37] conducted field measurements for the wind effects on the Guangzhou West Tower from Typhoon Megi. Meteorological winds, structural accelerations, and surface pressures were simultaneously measured on that building. After full-scale/model test comparisons, it was found that the structural accelerations obtained from the force balance model tests were basically in good agreement with those obtained from field measurements. Slight differences were observed in the mean wind pressures between the two experiments. In 2015, [PERSON] and [PERSON] [38] presented the results of full-scale measurements and force balance model tests to ascertain the wind-induced structural responses of a tall building with a height of 420 m in Hong Kong. After comparison, it was shown that the model test could provide reasonable predictions of the realistic resonant responses of the building, but it could not predict the realistic background responses very well.
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 5 Discussion  
In this paper, we propose a path tracker for a low-speed automated driving system based on 4 WS and verify its performance through simulations and experiments. The path tracker was designed using geometric relationships suitable for low-speed driving with low computational requirements and based on the Stanley method. By incorporating feedforward terms of the curvature of the reference path and the turning radius of the vehicle into the control input, the response characteristics were improved. The lateral distance error and heading angle error, which are performance indicators of the path tracker, were reduced by more than 30% compared to conventional 2 WS and conventional 4 WS path trackers. Additionally, the reduction in the side-slip angle demonstrated an improvement in vehicle stability. However, in the 5 KPH experiment, it was observed that the side slip angle and yaw rate increased in the existing conventional 4 WS path tracker, which is presumed to be due to excessive gain values.  
In the future, it is considered necessary to study a lookup table-based gain setting that adjusts the path tracker gain values according to driving speed, as well as adaptive control techniques, and a dynamic model-based path tracker suitable for high-speed driving.  
Conceptualization, H.-S.P. and M.-S.K.; methodology, H.-S.P.; software, H.-S.P.; validation, H.-S.P. and M.-S.K.; formal analysis, H.-S.P.; investigation, H.-S.P.; resources, H.-S.P.; data curation, M.-S.K.; writing--original draft preparation, H.-S.P.; writing--review and  
Figure 13: Real-car experiment results. The left figures show a driving speed of 5 KPH, while the right figures show a driving speed of 10 KPH: (**a**) driving path; (**b**) lateral distance error; (**c**) heading angle error; (**d**) side-slip angle; (**e**) yaw rate.  
editing, M.-S.K.; visualization, H.-S.P.; supervision, M.-S.K.; project administration, M.-S.K.; funding acquisition, M.-S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Planning & Evaluation Technology (KEIT) grant funded by the Ministry of Trade Industry & Energy (MOTIE, Republic of Korea). (No. 20018448, Development of Demand-responsive Automated Valet Parking and Service Technology).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 3 Generic Algorithm to Determine Maximum Bottleneck Node Weight-Based Data Gathering Trees
### System Model  
In this subsection, we present the graph model considered for the sensor networks, the channel availability model for the nodes and an overview of the data aggregation process. We assume a wireless sensor network (WSN) to be modeled as a weighted undirected graph \(G=(V,E,W_{V},W_{E})\) of a set of vertices (\(V\)) and edges (\(E\)); the link weights (\(W_{E}\)) may or may not be related to node weights (\(W_{V}\)). The link weights and node weights are positive real numbers. We assume each node in the graph has a weight; if two or more nodes have the same weight during the execution of the algorithms (described in Sections 3.2-3.5), ties are broken arbitrarily. We do not anticipate frequently encountering ties, as the node weights (as well as link weights) are real numbers (and not integers). We use higher precisions for the real numbers (of length allowed by the programming environment) in the simulations. In addition, in the simulations, since we run multiple iterations of the algorithms for the same operating condition and average the results, our conjecture is that the arbitrary breaking of the ties is the best way to avoid any bias towards excessive usage of any particular node. For example, if the ties are broken in favor of the node with a smaller ID or larger ID, then such nodes get overused all the time in situations of a tie and the results could be biased.  
Each vertex in the graph corresponds to a node in the WSN. There exists an undirected edge between two vertices if the Euclidean distance between the corresponding nodes in the network is within the transmission range of the nodes. We assume the transmission range of the nodes to be identical. A data-gathering tree (DG tree) of a WSN graph is rooted at a particular node (referred to as the leader node) and spans all the vertices of the graph and aggregation of data proceeds from the leaf nodes towards the leader node. An intermediate node waits for aggregated data or individual data (depending on the case) coming from its immediate child nodes, aggregates its own data with it and forwards the aggregated data to its own upstream node in the tree. We assume the energy level at the sensor nodes to be sufficient enough throughout the network session and that there would be no failure of nodes due to energy exhaustion (this way, we could extract the best possible and/or expected performance from the benchmarking algorithms with respect to the metrics that they are designed to optimize). We assume the sensor nodes to be both TDMA (Time Division Multiple Access) and CDMA (Code Division Multiple Access) enabled [14]. An intermediate node assigns TDMA time slots to its immediate child nodes so that they can send the aggregated data to their parent node, one at a time, without any collision. However, two intermediate nodes at the same level or different levels of the DG tree could simultaneously gather data from their respective child nodes by using different CDMA codes.
# Domestic Use Simulation and Secondary Shelf Life Assessment of Industrial _Pesto alla genovese_
## 2 Materials and Methods
### Sensory Evaluation  
The sensory test was performed on day 5, 11, 16, and 20 after the first opening. A panel of 12 judges (six males and six females, aged 23 to 63) was selected among the staff of the Department. The panelists were familiar with sensory evaluation and had previous experience in testing food through the triangular test. The sensory analysis aimed to understand the acceptance of samples during household storage. Triangular tests were performed on pesto samples from each of the five domestic conditions, presented together with a reference sample, which belonged to the same production batch (same formulation, processing, and storage conditions) but freshly opened. The reference jar, even if not opened, was also stored in the refrigerator before the sensory evaluation. Each assessor was asked to evaluate five triplets of randomly coded samples. Each set contained two identical samples and one different sample, which was randomly selected between the stored pesto (from home environment A, B, C, D or E) and the control one. The objective of the sensory evaluation was to assess: (i) whether judges were able to discriminate the stored samples from the control (just opened) one, and (ii) which was the degree of acceptance of samples throughout the time after the first opening.  
Randomization was applied in the presentation order for all the panelists. All the samples were served at the same temperature to avoid the \"stimulus error\" due to the preparation of the samples. The participants were provided with unsalted crackers and water to be used during the tasting session. The judges were asked to select the different sample within each triplet, and to express its overall acceptability on a scale from 0 (extremely dislike) to 10 (extremely like). Figure S1, found in the Supplementary Material, shows the pesto samples prepared for the sensory analysis.  
A significance level of 5% was chosen (\(\alpha\) = 0.05), which means accepting a 5% risk of finding a difference when there is none. Using an \(\alpha\) value of 0.05 and a 12-member panel, the minimum number of correct answers to reject the assumption of no difference is 8 [16].
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 5 Mission Process Control Strategy
### Control Strategy Solution
#### 5.2.2 Controllable Mission Validity
: _The current moment is the controllable moment \(t_{c}\), for the controllable mission \(m_{i}\), if \(m_{i}\) belongs to the critical path, i.e., \(m_{i}\in\hat{\mathcal{K}}^{*}(t_{c})\), then \(m_{i}\) must be a valid controllable mission, and if \(m_{i}\) is not part of the critical path, i.e., \(m_{i}\
otin\hat{\mathcal{K}}^{*}(t_{c})\), then \(m_{i}\) is an invalid controllable mission._  
Proof of Theorem 5.: According to the equation for calculating the plan start time of a mission shown in (10) and (14), the plan start time \(T_{PS}^{m_{i}}(t)\) of mission \(m_{i}\) is the latest plan end time of all prior-missions of \(m_{i}\), i.e.,  
\[T_{PS}^{m_{i}}(t)=\max_{m_{j}\in\mathcal{K},M_{m_{i}}}\Bigl{\{}T_{PF}^{m_{j}} (t)\Bigr{\}} \tag{51}\]  
We call (51) the \"Long Plate Principle\". According to the definition of the critical path, if \(m_{i}\) belongs to the critical path, the mission with the latest plan end time among all prior-missions of \(m_{i}\) must be the prior-mission \(m_{j}^{*}\) on its critical path. \[ \begin{split} m_{j}^{*}&=\text{args}\biggl{(}\max_{ m_{j}\in\mathcal{K},M_{m_{i}}}\Bigl{\{}T_{PF}^{m_{i}}(t)\Bigr{\}}\biggr{)}\\ m_{j}^{*}&\in\hat{\mathcal{K}}^{*}(t_{c})\end{split}  \tag{52}\]  
As illustrated in Figure 11(a), in order to facilitate the visualization of the critical path length, we utilize a dual coding system to represent the critical paths. In this system,  
Figure 11: Diagrams of core critical path and critical path (single code representation). critical path missions are represented by red circles, while non-critical path missions are represented by blue rectangles. In Figure 12a, both mission \(x_{(5,6)}\) and mission \(x_{(9,10)}\) are controllable tasks. Mission \(x_{(5,6)}\) belongs to the critical path, while mission \(x_{(9,10)}\) is not a critical path. In the event of a delay disturbance, it is necessary to reduce the plan end time of the unstarted controllable mission in accordance with the existing plan. This results in a compression of the duration of the unstarted mission, thereby eliminating the delay disturbance. As demonstrated by (50), the output \(y(t)\) is contingent upon the length of the critical path. As illustrated in Figure 12b, if the plan end time of the non-critical path mission \(x_{(9,10)}\) is reduced, the length of the critical path remains unchanged, i.e., \(y(t)\) remains unaltered. Consequently, the non-critical path mission is deemed an invalid controllable mission. Conversely, as illustrated in Figure 12c, by reducing the plan end time of the critical path mission \(x_{(5,6)}\), the length of the critical path can be effectively compressed, thereby achieving the objective of reducing \(y(t)\), and thus the critical path mission must be a valid controllable mission. This completes the Proof of Theorem 5.
## 4 Developed Testing Methodology and Test Results
### Testing Methodology in the Planned Experiment  
To create a digital shadow, in accordance with the terminology used in the analyzed literature, it is necessary to create a digital model of the real machine component and ensure a continuous one-way flow of information from the real object to the digital model. The research work presents an illustration of the digital shadow operation, where the real object was replaced by the created 3D model (Figure 7) and the angular displacements of each  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**i** & & & \(\text{c}_{\text{i}}\) [Nm/rad] & & \\ \hline
1 & 44.311 & 47.100 & 89.152 & 113.457 & 281.482 & 290.282 \\
2 & 906.273 & 344.402 & 176.354 & 906.689 & 345.517 & 176.508 \\
3 & 200.305 & 495.875 & 511.619 & 78.194 & 83.189 & 156.993 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dynamic parameters of the searched elastic elements.  
sensor placed on the model were considered to be equivalent to the values that would occur during operation of the real system.  
For this purpose, a 3D model of the analyzed real machine component was developed in the form of a system prepared for simulation of motion using the Motion module of PLM Siemens NX 2306 software. The digital model was built in the form of a discrete vibrating system in Matlab/Simulink R2021a software. Ensuring continuous, one-way data exchange was achieved by using co-simulation between the Motion module of the PLM Siemens NX 2306 software and the Matlab/Simulink R2021a program.  
The developed monitoring method using DS was first used to determine the reference characteristics. The reference characteristics (Figure 8) are created as follows:  
* The angular displacements are measured and recorded in the time domain using the adopted sensor system (sensors S1, S2, S3 and S4);
* The recorded angular displacements are transferred via co-simulation to the discrete model in digital form;
* Solving the task of determining the waveform of variability of the angular displacement of the inertial elements of the discrete model allows for the determination of the reference characteristics (S1w, S2w, S3w, S4w). It was assumed that the obtained waveforms constitute the \"0 line\" of reference in relation to the characteristics of the monitored machine component recorded in the same way.  
Figure 8: Determination of reference characteristics using the developed methodology.  
Figure 7: Graphical representation of the formulated monitoring method using DS.  
The monitoring and diagnostics of the state of the tested machine component using the developed testing methodology consist of recording the current characteristics of the system in use (S1u, S2u, S3u, S4u) and comparing them with the previously developed reference characteristics (Figure 9).  
Based on the deviation of the current characteristic from the reference one and the value of this deviation, inference rules can be defined to identify the area of possible damage to the monitored machine component.