esa-sceva/satcom-chunk-collection huggingface dataset:
# Automotive Seat Comfort and Vibration Performance Evaluation in Dynamic Settings
## 2 Materials and Methods
### Field Evaluation
#### 2.3.3 Measures  
WBV  
Vibration data were collected at a sampling rate of 2048 Hz with two tri-axial accelerometers (Model 356A44; PCB Piezotronics; Depew, NY, USA) instrumented in each seat (per vehicle): one in the cushion foam under right Ischial Tuberosity (IT); and one at the rear end of the front passenger seat track (Figure 4). The two accelerometers were aligned to provide the most accurate measurement of vertical vibration transmissibility.  
Vibration data were processed in HEAD Actoustics Artemis (HEAD acoustics GmbH, Hersogenrath, Germany) software with following parameters: septrum size: 8192; windowing: Hanning; overlapping: 90%; FRF estimator: H\({}_{1}\). The transmissibility was defined as the transfer function of the acceleration ratio between the cushion and seat track. The attenuation frequency here was defined as the frequency that was higher than resonance frequency (where the peak transmissibility occurred), and at the frequency where the transmissibility value fell below 1 (Figure 5). As can be seen from Figure 4, 4.x Hz was the resonant frequency and 7.x Hz the attenuation frequency.  
Figure 4: Accelerometers instrumentation for field test. Accelerometer in the cushion (**left**); accelerometer on the floor-mounted seat track (**right**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean \(\pm\) SD** & **Range** \\ \hline Height (cm) & 171.7 \(\pm\) 8.8 & 152.4–185.4 \\ Weight (kg) & 76.4 \(\pm\) 23.9 & 49.8–135.0 \\ BMI (kg/m\({}^{2}\)) & 25.7 \(\pm\) 7.0 & 17.7–43.9 \\ Daily Commute Time 1 (mins) & 27.5 \(\pm\) 11.0 & 15.0–50.0 \\ Personal Vehicle Age (years) & 5.4 \(\pm\) 7.3 & 0.0–20.0 \\ \hline \hline \end{tabular} \({}^{1}\) Daily commute time defined as one way between home and work.  
\end{table}
Table 3: Field vibration study participants’ demographic summary (\(n=12\)).  
The weighted average vibration (A\({}_{w}\)) values were calculated per ISO 2631-1. Seat Effective Amplitude Transmissibility (SEAT) values [35] were then calculated as the vibration ratio for A\({}_{w}\) between the cushion and seat track (i.e., truck floor) in order to compare the vibration performance between the two seats.  
Before and after each ride, self-reported seating comfort was evaluated using a custom-designed comfort questionnaire [26]. The participants were asked to rate the seat firmness and support on a 5-point scale (i.e., too little support being \(-2\), and too much support being 2, with ideal being 0) and the overall comfort of the complete seat on a 10 point scale (with 1 being very uncomfortable and 10 being very comfortable). The participants were also asked about their perception of the vibration for each of the 5 road segments at 7 different body regions (Feet/Lower legs, Thigh, Buttocks/Hips, Lower back, Middle Back, Upeer back/Shoulder, and Neck/Head). The participant's perception for vibration on the 7 body regions was recorded as Y/N. If they felt the vibration on one body region, it was counted as one vibration felt on that body region.
## 3 Results  
Through the developed search strategy, 1374 titles were initially retrieved (Cochrane Library: 65 abstracts; Pubmed: 590 abstracts; ISI web of science: 719 abstracts) and seven papers were further included by manual search. After the screening phase, 63 abstracts and, subsequently, 21 full texts were selected for review (18 from electronic search; 3 from manual search). A total of 21 articles were included in the review (Figure 2). All the included studies were designed as case report, case series or single-cohort clinical investigations; no randomized controlled trials were identified. To simulate a virtual patient three different 3D datasets were used:  
* Facial skeleton (FS): acquired by CBCT or conventional CT scans
* Facial soft tissues (ST): acquired by stereophotogrammetry, laser scanner or 3D photography with a smartphone app. * Dentition (DENT): acquired by intraoral scanners (IOS) or digitization of plaster casts or dental impression through dental laboratory scanners (DLS). The 3D data most frequently reported from the selected papers were FS (n = 19), followed by dentition (n = 16) and ST (n = 8). The 3D data were superimposed according to four different combinations (Figure 3):  
* 13 papers merged skeleton with dentition (FS + DENT = 13)
* 5 papers merged skeleton with facial soft tissues (FS + ST = 5)
* 2 paper merged facial soft tissues with dentition (ST + DENT = 2)
* 1 paper integrated all the 3D datasets (FS + ST + DENT = 1)  
Figure 2: Flowchart of the review process. The included studies performed different superimposition protocols:  
* 11 studies used the surface-based method
* 6 studies used the point-based method
* 2 studies compared the surface-based method and point-based method
* 1 article used the voxel-based method
* 1 article compared the voxel-based and surface-based method  
Most of the studies retrieved declared as a primary outcome the assessment of the accuracy of the chosen superimposition procedure (n = 15); moreover, the feasibility to simulate a digital patient was analyzed (n = 10) and the accuracy of two different superimposition methods (n = 3) were also analyzed. The results of the research are summarized in Table 1.  
The included studies which declared to analyze of the accuracy of a superimposition procedure or the agreement between the results of different methods were further analyzed [Table 2]. Figure 3: Summary of the included studies.
## 2 Study Area and Datasets
### Datasets  
Based on the previous monitoring [8], as the reclaimed land surface is still undergoing substantial change, long time span images will cause SAR image decorrelation, causing the newly reclaimed area to be impossible to monitor. Therefore, in this study, we employed 36 ascending Sentinel-1A TOPS mode Interferometric Wide Swath (IWS) images, acquired from 3 January 2018 to 16 April 2019, to ensure high correlation and estimate land reclamation evolution and time series subsidence.  
Figure 3: The land reclamation history of Dadeng Island. (**a**) The temporal and spatial evolution of land reclamation on Dadeng Island; (**b**) the land area change for each period.  
The Sentinel-1 satellites developed by the Europe Space Agency (ESA) of Copernicus Program conduct C-band SAR imaging with a wavelength of 5.6 cm. Compared with other SAR missions, Sentinel-1 has the characteristics of short revisit time (twelve days for one satellite, and six days for two satellites constellation) and short baselines (i.e., orbital separations). Detailed information and main parameters regarding to the Sentinel-1 data are shown in Table 1.  
We set the image 20180328 as master image for co-registration with other images. With a threshold of temporal baseline below 60 days and spatial perpendicular baseline below 105 m, a total of 137 interferograms were generated. Detailed spatial and temporal baselines of these Sentinel-1 datasets are shown in Figure 4. ALOS World 3D 30 m Digital Elevation Model (DEM) was used to remove the topography-related phase and assist the geocode processing.
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 3 Design of Framework
### Data Preprocessing  
The flow-image transformation method [42], which was used in our previous study for raw traffic features, is utilized in the data preprocessing stage. Data preprocessing converts raw traffic data from PCAP packages to IDX file format through three steps: traffic split, traffic clean, and image conversion. We split traffic packets containing the same five-tuple information as a flow using SplitCap so that each small PCAP file contains a single TCP or UDP session. In order to lessen the subsequent workload, the traffic cleaning step removes packets that are not useful for traffic classification, such as short sessions with insufficient payload size and some auxiliary packets, such a Domain Name Service (DNS) packets for host name resolution. As MAC and IP addresses cannot be used as training features for traffic classification and can obstruct the feature extraction process of the neural network, which can result in model overfitting, traffic anonymization is also carried out in this step. Each data-cleaned PCAP file is cropped into a group of 1024 (\(32\times 32\)) bytes, each of which is represented as a pixel, and is then converted into a single-channel grayscale image of size \(32\times 32\). These pictures show the natural characteristics of the traffic. It is evident that the various traffic image types can be easily distinguished from one another. To provide data to the deep learning model, the file containing all pixel sequences is transformed into an IDX format file. Through the preprocessing process, we convert the raw traffic data into images, facilitating the application of advanced techniques in image processing to traffic processing.
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
## 3 Materials and Methods
### GC-MS-Olfactometry  
GC-MS-O was performed by trained panelists on a sniffing port (Sniffer 9000, Brechbuhler, Schlieren, Switzerland). The modified method described earlier by [PERSON] et al. [18] was used in this study and the conditions of SPME and GC-MS were the same to the volatile analysis described above.  
Four trained panelists take part in the detection frequency analysis (DFA) combined with GC-MS-O for identification aroma-active compounds. The panel consisted of an age from 20 to 35 years (mixed of male and female). The panelists were trained by solutions of artificial odorants and different cubeb berries samples to be familiar with the odor descriptions. In total, eight runs by GC-MS-O were conducted by four assessors (two runs for one person). The judges sniffed the effluent from the mask and recorded the time and odor characteristic of the aroma-active compounds of different cubeb berries samples. When the total detection frequencies were more than twice for the odorants perceived by two different assessors at the sniffing port, the odorants were considered potential aroma-active compounds [18; 35].
## 3 Test Data and Evaluation Methodology
### Evaluation Methodology  
The described research focuses on a fully automated process for optical image orthorectification. Its main part focuses on the extraction of GCPs in various steps to improve the accuracy of geometric correction for VHR panchromatic images. The acquired test images are processed in three different ways to better understand the contribution of the proposed extraction algorithm to the whole procedure: without GCPs, with GCPs that are extracted with the previous road mask detection algorithm, and with GCPs extracted with the proposed detection algorithm.  
The test images from two AOIs exhibit different acquisition dates and off-nadir viewing angles, which range from 2.2\({}^{\circ}\) to 23.6\({}^{\circ}\) (Figure 7). The images are acquired from all sides, which practically makes a lot of scenarios to test the road mask detection, as, for each angle, the buildings occlude different parts of the road network. Moreover, due to the varying acquisition dates, the images exhibit different shadow shapes that significantly influence the road extraction procedure. Section 4.2 presents the relationship between the experimental results and the acquisition angle and time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Date** & **Samples** & **Lines** & **Area (km\({}^{2}\))** & **Off-nadir ang. (\({}^{*}\))** & **AOI** & **Mode** \\ \hline
1 & 22.7.2013 & 13,981 & 16,976 & 68 & 19.5 & LJ & PAN \\
2 & 3.8.2013 & 14,070 & 15,119 & 68 & 23.6 & LJ & PAN \\
3 & 3.8.2013 & 15,483 & 17,448 & 68 & 6.8 & LJ & PAN \\
4 & 3.8.2013 & 15,597 & 17,648 & 68 & 2.2 & LJ & PAN \\
5 & 3.8.2013 & 15,594 & 17,628 & 68 & 2.7 & LJ & PAN \\
6 & 9.9.2014 & 14,807 & 16,928 & 68 & 16.2 & LJ & PAN \\
7 & 8.7.2013 & 11,477 & 12,494 & 38 & 12.5 & KR & PAN \\
8 & 8.7.2013 & 11,405 & 12,350 & 38 & 12.2 & KR & PAN \\
9 & 27.7.2013 & 11,433 & 12,403 & 38 & 11.2 & KR & PAN \\
10 & 27.7.2013 & 11,382 & 12,597 & 38 & 13.2 & KR & PAN \\
11 & 28.7.2013 & 11,600 & 12,760 & 38 & 10.8 & KR & PAN \\
12 & 28.7.2013 & 11,606 & 12,068 & 38 & 13.6 & KR & PAN \\
13 & 29.7.2013 & 10,178 & 12,325 & 38 & 21.7 & KR & PAN \\
14 & 29.7.2013 & 10,615 & 12,142 & 38 & 20.8 & KR & PAN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected characteristics and metadata parameters for the 14 Pleiades images (primary level of processing).  
To only explore the impact of the densely built city area on the accuracy of the geometric corrections, the non-urban GCPs are removed. In this way, the results of the geometric model only reflect the accuracy of the extracted GCPs in those areas where the detection of points is very challenging. Section 4.3 shows the results with only the urban GCPs.  
The efficiency of the proposed algorithm is also tested with different number of GCPs and with two RPC refinement (correction) methods: affine and offset.  
As the resulting orthoimages mostly reflect the accuracy of the geometric model results, they are not evaluated and presented in this paper.
# Predicting the [PERSON]'s Modulus of Concrete Using a Particle-Based Movable Cellular Automata Method
## 4 Comparison the MCA Results with Experiments  
To test the effectiveness of the MCA method in modelling the mechanical properties of concrete, the results of numerical simulations were compared with experimental data available from the literature. The studies selected for comparison were those in which the authors provided detailed information on the materials composing the concrete as well as precise numerical data regarding the compressive strength and Young's modulus of concrete samples at day 28, as we applied the cement paste properties from day 28 in our simulations. Simulations were performed for w/c ratios between 0.3 and 0.6 due to the availability of modulus of elasticity values in [52]. The tested ratios are provided in Table 1.  
The mechanical properties of coarse aggregates, which usually consist of crushed stone, are sometimes provided in the studies concerning concrete, as rocks can be examined relatively easily. Fine aggregate usually consists of sand. Sand is a granular material; thus, establishing its mechanical properties is a complex problem, since it exhibits behaviour that depends on such various factors as moisture, compaction, and grain structure. These data are generally not provided in the literature. The same problem arises when modelling assemblies of soil (sand, silt, clay) with the Discrete Element Method (DEM). The DEM method simulates the dynamic behaviour of a system built from individual interacting particles. The mechanical properties of sand are estimated numerically [61; 62; 63; 64]. Therefore, in these studies, the Young's moduli of aggregates were also determined through numerical simulations.  
In the numerical simulations, the Poisson's ratios for cement paste and sand were assumed to be 0.2. The dry friction coefficient of the sand was set to 0.5, which is the same value typically used in numerical simulations using the DEM method [62; 64]. The coefficient of viscous friction of the sand was assumed to be 0.2.  
The static Young's modulus \(E_{s}\) is commonly obtained as the tangent of the stress-strain curve, while the stress applied on specimens during mechanical loading-unloading cycles must not exceed 30-40% of the maximum strength:  
\[E=\frac{\sigma_{2}-\sigma_{1}}{\epsilon_{2}-\epsilon_{1}} \tag{14}\]  
where \(\sigma_{1}=0.5\) MPa and \(\epsilon_{1}\) is the corresponding strain for this value, while \(\sigma_{2}\) and \(\epsilon_{2}\) are the stress and strain corresponding to 40% of the maximum stress according to [65] or 30% of the maximum stress according to [66]. The same approach was applied in this study, and the specimens' compressive strength was taken from the experimental results provided in the literature. In the numerical simulations, specimens were subjected to uniaxial compression at a constant strain rate of \(2\times 10^{-5}s^{-1}\). All simulations in this study were conducted for cylindrical specimens with a diameter-to-length ratio of 1:2.  
The first comparison of simulation results with the experimental data was conducted for mortars.
## 3 Results
### The Use of Ozone for Dye Removal
Color reduction can be attributed to the reaction of ozone with the unsaturated bonds of Indigo Carmine, leading to the cleavage of bonds and dissociation of rings, according to the Criegee mechanism. Ozone attacks the double bonds and aromatic rings in the Indigo molecule, leading to their dissociation and the formation of fragments. Degradation products, such as aldehydes, acids, and sometimes fragments of the molecules, which are less colored than the original dye, are produced [19; 34]. Ozonation of Indigo Carmine may lead to the formation of compounds such as isatin-5-sulfonic acid, which is a product of partial degradation of the dye. This compound may potentially be toxic to the aquatic environment [35; 36]. In contrast, Tartrazine contains sulfonic groups (-SO\({}_{3}\)H) and double bonds within its azo structure (-N=N-). Sulfonic groups can slow down the reaction as they are stable and increase the dye's hydrophilicity, which directly affects the removal efficiency. Ozonation may lead to the formation of nitrogenous compounds, such as nitrites and nitrates, as well as simple organic acids. There is also the possibility of aromatic amines being formed as a result of the degradation of this dye, which may be harmful [37; 38]. Figure 8: The effect of the ozonation process with the addition of heterogeneous catalyst and hydrogen peroxide on the removal of dyes; (**a**) pH = 5.5; (**b**) pH = 3. The third analyzed dye, Allura Red AC, is characterized by azo groups (-N=N-), aromatic rings, and sulfonic groups in its structure. Ozone can also break down aromatic groups, but sulfonic groups can hinder the complete destruction of the molecule by stabilizing its structure. Similar to Tartrazine, ozone will initially break the azo bonds and aromatic rings. Indigo Carmine lacks as many stabilizing groups (e.g., sulfonic groups) as Tartrazine and Allura Red AC, making it less resistant to degradation. Dyes with azo (-N=N-) groups and aromatic rings are more prone to reactions with ozone. Sulfonic groups (-SO\({}_{3}\)H) are stable and do not easily undergo oxidation, but they can affect the accessibility of ozone to active sites in the dye molecule. Both Tartrazine and Allura Red AC contain sulfonic groups (-SO\({}_{3}\)H), which increase the stability of the molecule and hinder ozone reaction. These groups reduce the reactivity of key areas of the molecule with ozone. Azo bonds are more resistant to ozonation compared to the carbonyl-Indigo system in Indigo Carmine. Ozone attacks azo bonds more slowly, especially when they are stabilized by other functional groups, such as sulfonic groups.
# XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework
## 2 Preliminaries
### Predictive Process Monitoring
#### 2.1.1 PPM Workflow  
According to the previously reported survey results [2; 3], a PPM task follows two stages, _offline_ and _online_. Each stage has steps, and the two stages together with their corresponding steps constitute _a PPM workflow_.  
1. **PPM Offline Stage.** This stage starts with _constructing a prefix log_ from the input event log. A prefix log is needed to provide a predictive model with incomplete process instances (i.e., a partial trace) for the training phase of an ML model. Therefore, prefixes can be generated by truncating process instances in an event log up to a predefined number of events. Truncating a process instance can be done up to the first k events of its trace, or up to k events with a gap step (g) separating each two events, where k and g are user-defined. The latter prefixing approach denoted as _gap-based prefixing_. _Prefix preprocessing_ sub-steps include bucketing and encoding. Prefix bucketing groups the prefixes according to certain criteria (e.g., number of activities or reaching a certain state during process execution). The former criteria are defined by the bucketing technique [3]. Single, state-based, prefix length-based, clustering, and domain knowledge-based are examples of prefix bucketing techniques. Encoding is the second sub-step of prefix preprocessing. Prefix encoding involves transforming a prefix to a numerical feature vector that serves as input to the predictive model, either for training or making predictions. Encoding techniques include static, aggregation, index-based, and last state techniques [2; 3]. In the following step, _a predictive model is constructed_. Depending on the PPM task, an appropriate predictive model is chosen. The prediction task type may be classification or regression. Next, the predictive model is trained on encoded prefixes representing completed process instances. For each bucket, a dedicated predictive model needs to be trained, i.e., the number of predictive models depend on the chosen bucketing technique. Finally, the performance of the predictive model needs to be evaluated.
2. **PPM Online Stage.** This stage starts with a running process instance that has not completed yet. Buckets formed in the offline stage are recalled to determine the suitable bucket for the running process instance, based on the similarity between the running process instance and the prefixes in a bucket. The running process instance is then encoded according to the encoding method chosen for the PPM task. The encoded form of the running process instance constitutes the input for the prediction method after having determined the relevant predictive model from the models created in the offline stage. Finally, the predictive model generates a prediction for the running process instance according to the predefined goal of the PPM task.
## 5 Challenges and Open Research Directions
### Regulatory and Privacy Considerations  
The use of decentralized technologies like Holochain raises privacy and regulatory challenges, especially as IoT devices collect sensitive data. Ensuring compliance with privacy laws like GDPR and CCPA is essential. For example, GDPR's right to be forgotten conflicts with the immutability of decentralized systems. Research should explore how Holochain can balance regulatory compliance with privacy and data integrity. Key research directions include:  
* Developing mechanisms to support the right to be forgotten while preserving decentralization principles.
* Implementing privacy-preserving techniques such as zero-knowledge proofs, differential privacy, and secure multi-party computation.
* Designing regulatory-compliant data governance models for decentralized IoT ecosystems.
* Creating auditing and compliance monitoring frameworks tailored for decentralized architectures.
* Investigating policy-driven access control mechanisms to ensure legal and ethical data usage.
## 4 Performance
### Total Packets  
Figure 11 shows the total number of packets received by the sink node and the total number of broadcast packets sent by sensor nodes in the failure affected area. ARF showed the highest packet reception with the lowest broadcast packets. ARF recovered the destroyed routing tree as soon as the failure occurred and thus most of the packets were received, showing the highest packet reception. At the initial stage, a short transmission interval for routing control messages was only used for 20 s and for 50 s for routing recovery, while it was set at the longer interval during other periods. Therefore, the number of broadcast packets was very low. In contrast, both Proc and MintRoute with 5,000 ms (short interval) showed high packet reception levels but the number of broadcast packets was also very high. Both Proc and MintRoute with 10,000 ms (long interval) showed low packet reception levels with a small number of broadcast packets. As a consequence, ARF can collect more data with less message broadcast overhead than other protocols.