esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Analysis of \"in Vivo\" VOCs
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Control**} & \multicolumn{4}{c}{_O. spheodesub. spheodes_} & \multicolumn{4}{c}{_O. bertolonil subsp. benacensis_} & \multicolumn{4}{c}{_N. tridentau_} \\ \hline
**Compounds** & **RT \({}^{*}\)** & **Mean (\(n=3\)) \({}^{b}\)** & **SD \({}^{*}\)** & **Mean (\(n=3\)) \({}^{a}\)** & **SD \({}^{*}\)** & **Mean (\(n=3\)) \({}^{a}\)** & **SD \({}^{*}\)** & **Mean (\(n=3\)) \({}^{a}\)** & **SD \({}^{*}\)** \\ \hline sculpture & 21.65 & ND & - & 0.42 & 0.16 & ND & - & ND & - \\ isoptrochulane & 22.66 & ND & - & 0.98 & 0.12 & 0.19 & 0.00 & 1.03 & 0.66 \\ _trans_-linalool oxide & 22.88 & 0.05 & 0.00 & 0.34 & 0.00 & 0.94 & 0.66 & 0.06 & 0.18 \\ cyclostivene & 23.11 & 0.30 & 0.00 & 2.47 & 1.75 & 0.28 & 0.21 & ND & - \\ copene & 23.40 & ND & - & ND & - & 1.11 & 0.46 & 0.81 & 0.00 \\ isolongifolene & 23.88 & ND & - & 1.46 & 0.12 & ND & - & ND & - \\
4-hujanol & 24.57 & ND & - & ND & - & 1.09 & 1.14 & ND & - \\ linalool & 24.71 & ND & - & 0.59 & 0.60 & 0.16 & 0.23 & ND & - \\ o-zinghirene & 24.93 & 0.07 & 0.01 & 9.98\({}^{3}\) & 7.15 & 0.26 & 0.11 & 0.26 & 0.90 \\ sequiterpene & 25.09 & ND & - & 0.90 & 0.31 & ND & - & 0.46 & 0.12 \\ a-bergamottene & 25.49 & 0.22 & 0.00 & ND & - & 0.55 & 0.00 & ND & - \\ cryophilene & 25.60 & ND & - & ND & - & 6.73\({}^{3}\) & 1.84 & 0.69 & 0.47 \\
4-terpineol & 25.79 & 1.19 & 0.41 & 2.06 & 0.43 & 1.28 & 1.18 & 0.65 & 0.01 \\ menthol & 26.53 & 0.20 & 2.54 & 2.02 & 1.06 & 0.90 & 1.14 & 0.14 & 0.03 \\ verbenone & 27.66 & 0.24 & 0.00 & 0.55 & 0.45 & 0.60 & 0.47 & 0.25 & 0.01 \\ caevarone & 27.77 & 0.20 & 0.01 & 1.44 & 1.27 & 0.19 & 0.19 & ND & - \\ naphtalene & 28.91 & 0.14 & 0.00 & 1.12 & 1.04 & 0.20 & 0.09 & 0.50 & 0.04 \\ sesquibellandrene & 29.09 & ND & - & ND & - & 2.44 & 0.38 & ND & - \\ estragol & 30.07 & 0.28 & 0.35 & ND & - & 0.37 & 0.89 & 0.35 & 0.11 \\ piperitone oxide & 31.39 & ND & - & 0.63 & 0.00 & 0.77 & 0.41 & ND & - \\ asexaridide & 31.78 & ND & - & 0.37 & 0.07 & ND & - & ND & - \\ e-santolene & 33.74 & ND & - & 0.47 & 0.00 & ND & - & ND & - \\ \hline
**Total** & & **5.51** & & **61.92** & & **21.58** & & **12.72** & \\ \hline _Free fatty acids_ & & & & & & & & & \\ acetic acid & 22.26 & 0.62 & 0.00 & 0.31 & 0.35 & 0.71 & 0.00 & 1.33 & 0.02 \\ formic acid & 23.44 & 0.69 & 0.08 & 11.7\({}^{2}\) & 0.18 & ND & - & ND & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Exposure Time to Work-Related Hazards and Factors Affecting Musculoskeletal Pain in Nurses
## 3 Results
### Musculoskeletal Plains and Exposure Times to Hazards
#### 3.2.3 Comparison of Exposure Time to Hazards by Lower Limb Pain  
Table 5 presents the results of the mean test for exposure times of physical, chemical, or ergonomic hazards between nurses with and without complaints of lower limb pain.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Hazard**} & \multicolumn{2}{c}{**No Pain**} & \multicolumn{2}{c}{**Back Pain**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{3-3} \cline{5-6}  & & **Mean** & & & **SD** \\ \hline \multirow{4}{*}{Physical hazard} & Vibration & 0.9078 & 0.6142 & 1.0758 & 0.9141 & 0.028 * \\  & Noise & 0.8470 & 0.3782 & 1.0472 & 0.9289 & 0.001 * \\  & High temperature & 0.8175 & 0.2613 & 0.9108 & 0.6499 & 0.029 * \\  & Low temperature & 0.8050 & 0.1880 & 0.8829 & 0.5900 & 0.032 * \\  & Fumes/dust & 0.7959 & 0.0792 & 0.8150 & 0.1281 & 0.062 \\  & Vapor & 0.7993 & 0.1019 & 0.8352 & 0.3215 & 0.069 \\ Biological hazard & Chemical contact & 0.8997 & 0.6271 & 0.9472 & 0.7790 & 0.516 \\  & Tobacco smoke & 0.7988 & 0.0916 & 0.8536 & 0.5072 & 0.054 \\  & Infection & 1.1628 & 1.1929 & 1.4455 & 1.5396 & 0.045 * \\  & Awkward posture & 1.8537 & 1.6819 & 3.1650 & 2.4229 & \textless{0.001 }* \\  & Patient lifting & 1.2282 & 0.9596 & 1.7541 & 1.5579 & \textless{0.001 }* \\ Ergonomic hazard & Load handling & 1.0098 & 0.07079 & 1.4948 & 1.4635 & \textless{0.001 }* \\  & Standing posture & 4.1645 & 1.8571 & 4.6368 & 2.0008 & 0.023 * \\  & Repetitive motion & 2.8401 & 2.2422 & 3.6489 & 2.4711 & 0.001 * \\ \hline \hline \end{tabular}
* significant at 0.05, SD = standard deviation.  
\end{table}
Table 3: Results of mean comparison test for exposure times of hazards between nurses with and without back pain complaints.  
Regarding physical hazards, there were differences in the average exposure times for vibration, noise, and high temperature. Nurses with lower limb pain complaints had longer average exposure times for these physical risk factors compared to those without complaints. Nurses with lower limb pain complaints had higher average exposure times for ergonomic risk factors such as awkward postures, patient lifting/carrying, handling heavy objects, standing postures, and repetitive motion than those without complaints. In terms of biochemical hazards, nurses who complained of lower limb pain had higher average exposure times for fumes/dust and infection compared to those without complaints.
## 2 Materials and Methods
### Methodology
#### 2.4.2 Monthly Composite Imagery  
We then compiled monthly composite data from Sentinel-1 (24 composites) and Sentinel-2 (24 Composites). Sentinel-1 SAR data were not affected by clouds; however, the data had a high noise-to-signal ratio. We used the Median Value Composite (MedVC) of the monthly time series to reduce the noise in the VH polarization data, especially in the area with overlapping data scenes [22; 54].  
We calculated the NDVI from the radiance on the red (band 4) and near-infrared (NIR; band 8) measured by Sentinel-2 as follows:  
\[\mathrm{NDVI}\,=\frac{\mathrm{NIR}\,\,(\mathrm{band}\,\,8)-\mathrm{Red}\,\,( \mathrm{band}\,\,4)}{\mathrm{NIR}\,\,(\mathrm{band}\,\,8)+\mathrm{Red}\,\,( \mathrm{band}\,\,8)} \tag{1}\]  
Figure 2: Workflow of mapping rice extent and cropping calendar using fusion Sentinel-1 and 2 time-series data.  
However, several factors can cause a considerable change to NDVI values. The most important factors are changing illumination and viewing conditions within a single image, and also between images of different days; the presence of cloud cover; and variation in atmospheric constituents--in particular, the variation in water vapour and aerosol concentrations [55]. Maximum Value Composites (MaxVCs) are generally accepted as an effective way to reduce these undesirable factors [55] and have been successfully used in studies using MODIS and SPOT data [56; 57; 58; 59]. Thus, the MaxVCs of NDVI were used to generate the monthly composite imagery from Sentinel-2.  
The monthly composite for both Sentinel-1 and 2 also addressed the issue of time alignment as the two satellites acquired images at different times. Using monthly values also allowed alignment with the rice growth stages described in Section 2.2.
## 4 Results and Discussions
### Comparisons of Numerical and Experimental Results  
The comparisons of the numerical results and the experimental results are presented in this section. Figures 11 and 12 are plotted to show the numerical and experimental results  
Before discussing the results presented in Figures 11-13 and Table 2, it should be declared that the numerical analysis and the experimental tests have the similar trends for all cases. Hence, a good agreement is achieved between numerical and experimental analyses.  
According to Figures 11 and 12, the average difference between the experimental and numerical velocities for the C1-, C2-, and C3-type collectors are, respectively, 7.26%, 6.25%, and 8.57%. The average differences between the experimental and numerical temperatures for the C1-, C2- and C3-type collectors are, respectively, 2.99%, 2.27%, and 2.07%.  
As a final result, the efficiencies were calculated using both the measured values and the predicted numerical values. The efficiencies are shown in a bar graph in Figure 13. It is evident from the graph that the most efficient case is the C1 case, and the least efficient case is the C3 case. The agreement between the numerical and experimental efficiency values is within the acceptable range. The difference can be explained by the uncertainties.  
Figure 11: Comparison of experimental and numerical air velocities; (a) C1, (b) C2, (c) C3.  
Figure 12: Comparison of experimental and numerical temperatures; (a) C1, (b) C2, (c) C3.
# A Method to Estimate Dynamic Pore Water Pressure Growth of Saturated Sand-Gravel Materials
## 3 Analysis of Dynamic Triaxial Test Results of Saturated Sand-Gravel Material
### Effective Stress Path  
Studying the stress paths of sand-gravel material liquefaction allows for an understanding of the developmental process of saturated sand-gravel materials. The parameters in Figure 8 are as follows:  
\[p^{\prime}=(\sigma_{1}^{\prime}+2\sigma_{3\varsigma})/3;\;q=(\sigma_{1}^{\prime}- \sigma_{3\varsigma})/2 \tag{2}\]  
where \(p^{\prime}\) represents the mean effective stress, \(q\) represents the deviator stress, \(\sigma_{1}^{\prime}\) is the effective axial stress, and \(\sigma_{3\varsigma}\) is the initial effective confining pressure.  
Figure 8 shows the effective stress path of sand-gravel material as the number of loading cycles increases. The arrows illustrate the direction of development for the mean effective stress path of the sand-gravel materials, while the red line shows the final 10 cycles of the \(p^{\prime}\)-\(q\) curve as it approaches the critical state. Cyclic loading causes a dense and undrained soil sample, which in turn causes an increase in the pore water pressure and a decrease in mean effective stress. There is, however, a stage during which the mean effective stress increases during each stress cycle. This is due to the volume shrinkage and expansion of the dense sand-gravel material that occurs during the cyclic loading process. The pore water pressure tends to be stable during the later stages of dynamic loading. The effective stress path of the next loading cycle is close to the previous cycle path, while the effective stress paths tend to overlap.  
Figure 8: Mean effective stress paths of sand-gravel. (**a**) S1 mean effective stress path; (**b**) S2 mean effective stress path; (**c**) S4 mean effective stress path; (**d**) S7 mean effective stress path.
## 3 Background
### Attestation Tokens  
An attestation token is a message containing a claim set about an entity, which can be either a hardware component (e.g., IoT device, network router) or a software component (e.g., TEE, process). It is defined by the RATS Working Group [18], which specifies common claims that an EAT token can include, with potential broad applicability. Additionally, they describe EAT as a framework for defining attestation tokens tailored to specific use cases, facilitating further standardization of proprietary claims.  
The EAT specification builds upon CBOR Web Token (CWT) [30] or JSON Web Token (JWT) [31], with an emphasis on claims specifically designed for attestation purposes. For devices with limited resources, CBOR (Concise Binary Object Representation) [32] is the preferred format because it provides a more compact and efficient encoding, reducing both bandwidth usage and processing overhead compared to JSON. This makes CBOR particularly suitable for IoT devices and other constrained environments where minimizing resource consumption is critical.  
A claim is represented as a key-value pair, where the key identifies the claim and the value holds its content. Claims can be application specific or standardized by recognized bodies to ensure interoperability. IANA maintains a global registry of defined claims [33], which can be used in application-specific EATs. If a required claim is not listed, organizations or developers can create their own, deciding whether to keep them private, document them publicly, or pursue standardization. For instance, the IETF defines standardized claims like iss (issuer), sub (subject), and exp (expiration time) in CWT, which are widely used in secure communication protocols.  
In the context of PSA Attestation, claims such as psa_instance_id (device identifier), psa_security_lifecycle (security state), or psa_implementation_id (RoT version) are documented and standardized as outlined in the PSA Attestation Token specification [34].  
The EAT is created on the device and securely transmitted to the relying party using a COSE-based container [19]. COSE is an open standard designed for signing, encryption, and ensuring data integrity, leveraging CBOR for efficient data encoding. Compared to JSON-based schemes such as JOSE [35], COSE-signed tokens are significantly smaller in size, making them particularly suitable for IoT devices that operate in low-bandwidth environments or where data transmission rates are constrained.
## 4 Results and Discussion
### Evaluation of System Overstrength Factor  
The FEMA P695 methodology stipulates that the system overstrength factor must not be less than the highest average overstrength value calculated among the archetypes. Referring back to the values presented in Section 4.2, the overstrength factor for the considered system was determined to be \(\Omega_{0}=6.5\). This value was calculated as the greater of the average overstrength values in the x- and y-directions (6.47 and 6.23, respectively) and then rounded to the nearest multiple of 0.5.  
This selected value satisfied the assumptions established by the methodology: it was a multiple of 0.5 and did not exceed by more than 50% of the value of the response reduction factor (\(R\)) used in the structural design (see Tables 8 and 9), which was validated in the preceding steps.
## 4 Discussion  
### Key Factors Shaping Dispatchers' Trust in ATS Systems  
The transparency of the ATS interface significantly enhances dispatchers' trust in the system, supporting hypothesis 1. A transparent interface provides operators with clear and accessible information, which is critical for building trust, especially under high time pressure [29]. This aligns with traditional research showing that transparent interfaces improve operator trust and subjective acceptance of automation systems, provided operators are proficient in their use [61]. Additionally, the capability redundancy of the ATS system directly and positively affects dispatchers' trust, supporting Hypothesis 3. This finding is consistent with studies highlighting the importance of system reliability redundancy in fostering automation trust [43; 62]. Furthermore, dispatchers' knowledge of the ATS system significantly influences their perception of interface transparency, supporting Hypothesis 2b. This suggests that a deeper understanding of the system enhances operators' ability to interpret and utilize interface information effectively [27].  
However, Hypothesis 2a, which posited that dispatchers' knowledge directly affects their trust in the ATS system, was not supported. This contrasts with [PERSON]'s study, which identified understanding of automation as the strongest dimension for constructing trust [39]. The discrepancy may stem from differences in the methodology, as follows: while [PERSON]'s study used observational methods with a small sample, this study examined the impact of long-term accumulated knowledge on trust in a larger sample. Additionally, subway dispatchers' work is highly complex and information-intensive, which may lead to a disconnect between knowledge and trust [63]. This phenomenon is also observed in other domains, such as self-driving vehicles, where operators may express difficulty in fully grasping automation-related knowledge but may still trust and rely on the system [64; 65].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**95\% Confidence**} & & & & & \\  & & & \multicolumn{4}{c}{**Intervals**} & & & & & & **Hypothesis** & **Validation** \\ \hline
**Dep** & **Pred** & **Estimate** & **SE** & **Lower** & **Upper** & \(\beta\) & **z** & \(p\)** & **Code** & **Status** \\ \hline DTA & TAI & 0.251 & 0.097 & 0.060 & 0.442 & 0.225 & 2.575 & 0.010 & H1 & Supported \\ TAI & ASK & 0.665 & 0.103 & 0.464 & 0.866 & 0.549 & 6.482 & \textless{}0.001 & H2b & Supported \\ DTA & ACR & 0.224 & 0.088 & 0.053 & 0.396 & 0.227 & 2.560 & 0.010 & H3 & Supported \\ RBR & DTA & 0.170 & 0.076 & 0.021 & 0.319 & 0.197 & 2.238 & 0.025 & H4a & Supported \\ RBE & RBR & 0.267 & 0.082 & 0.106 & 0.429 & 0.276 & 3.245 & 0.001 & H4c & Supported \\ RBR & ACR & 0.169 & 0.075 & 0.022 & 0.317 & 0.199 & 2.253 & 0.024 & H5a & Supported \\ RBE & ACR & 0.174 & 0.071 & 0.035 & 0.314 & 0.211 & 2.451 & 0.014 & H5b & Supported \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fine-tuned model: standardized path coefficients between latent variables and their significance levels.
# Operation Margin of the ITER Central Solenoid During the Plasma Scenario
## 3 Model Description
### Model of the CICC in Thea
#### 3.2.2 Strain  
The critical current density of the superconducting strands is a crucial parameter describing its electromagnetic characteristics. In Nb\({}_{3}\)Sn CICCs, the critical current density is a function of the temperature, magnetic field and strain. The concept of the effective strain, defined as the value of uniform strain that determines a given value of the current-sharing temperature of the conductor, is adopted here.  
The effective strain in the conductors is modelled according to relation (3). The impact of the transverse force (I \(\times\) B-dependent component) on the effective strain was assessed from tests of straight samples in the SULTAN facility (SPC, Villigen, Switzerland) in cycled conditions [47; 48]. In Equation (3), a fraction of the hoop strain--equal to 80%, as established in the test campaign of the CS insert [49]--is considered:  
\[\varepsilon_{eff}=\varepsilon_{cd}+\varepsilon_{crush}+0.8\cdot\varepsilon_{ hoop}=\varepsilon_{cd}-k\cdot I\left[\mathrm{A}\right]\cdot B[\mathrm{T}]+0.8\cdot \varepsilon_{hoop} \tag{3}\]  
where \(\varepsilon_{cd}\) is the cooldown strain, which is usually compressive; \(\varepsilon_{crush}\) represents the lateral compressive force that crushes the cable against the jacket (proportional to the product of I \(\times\) B with the current values in Amps). The parameters \(\varepsilon_{cd}\), \(k\) and \(\varepsilon_{hoop}\) are reported in Table 3 for the JAS, FUR and KAT conductors. Since the hoop strain map is not available for the plasma scenario, it is not considered (\(\varepsilon_{hoop}\) = 0%). The current-sharing temperature and temperature margin are thus computed under the conservative assumption of no improvement in the conductor's performance due to the tensile hoop strain.  
Figure 5: Friction factor as a function of the flow regime in the bundle and spiral of the CICCs manufactured with KAT, JAS and FUR strands.
## 2 Materials and Methods  
Overview of Procedure for Simulating Impact and Mitigation Effects of O\({}_{3}\) on Photosynthetic CO\({}_{2}\) Uptake  
In the present study, the impact and mitigation effects of O\({}_{3}\) on the annual photosynthetic CO\({}_{2}\) uptake per area of sunlit first-flush or early leaves of four Japanese deciduous broad-leaved trees--_Fagus crenta_, _Quercus serrata_, _Quercus mongolica_ var. _crispula_, and _Betula platyphylla_ var. _japonica_--were simulated in 2011-2015. First, environmental data and phenological data, such as leaf emergence date and leaf senescence date, for estimating stomatal O\({}_{3}\) uptake were collected from the databases of Japan Meteorological Agency ([[https://www.data.jma.go.jp/obd/stats/etrn/index.php](https://www.data.jma.go.jp/obd/stats/etrn/index.php)]([https://www.data.jma.go.jp/obd/stats/etrn/index.php](https://www.data.jma.go.jp/obd/stats/etrn/index.php))), National Institute for Environmental Studies ([[https://www.nies.go.jp/igreen/](https://www.nies.go.jp/igreen/)]([https://www.nies.go.jp/igreen/](https://www.nies.go.jp/igreen/))), and Moderate Resolution Imaging Spectroradiometer (MODIS) product ([[https://doi.org/10.5067/MODIS/MCD12Q2.006](https://doi.org/10.5067/MODIS/MCD12Q2.006)]([https://doi.org/10.5067/MODIS/MCD12Q2.006](https://doi.org/10.5067/MODIS/MCD12Q2.006))). Second, COU at the O\({}_{3}\) monitoring stations was estimated for the four tree species using species-specific parameters of the stomatal O\({}_{3}\) uptake model developed by [PERSON] et al. [19]. The meteorological and phenological data to calculate the COU were obtained from nearby meteorological stations and nearby areas where the trees were distributed, respectively. Third, COU in the tree distribution areas (Ministry of the Environment, [[http://www.biodic.go.jp/index.html](http://www.biodic.go.jp/index.html)]([http://www.biodic.go.jp/index.html](http://www.biodic.go.jp/index.html))) was estimated by interpolating the COU at the monitoring stations of O\({}_{3}\) using ordinary kriging. Fourth, according to species-specific responses of annual CO\({}_{2}\) uptake to COU reported by [PERSON] et al. (2018), O\({}_{3}\)-induced reductions in CO\({}_{2}\) uptake for the four trees were calculated. Finally, the effects of O\({}_{3}\) mitigation, i.e., impact of reducing the surface O\({}_{3}\) concentration, on CO\({}_{2}\) uptake were calculated for the four tree species by simulating the input O\({}_{3}\) concentrations. A summary of information on simulation settings, required data, and data sources is provided in Table 1.
# Transfer Convolutional Neural Network for Cross-Project Defect Prediction
###### Abstract  
Cross-project defect prediction (CPDP) is a practical solution that allows software defect prediction (SDP) to be used earlier in the software lifecycle. With the CPDP technique, the software defect predictor trained by labeled data of mature projects can be applied for the prediction task of a new project. Most previous CPDP approaches ignored the semantic information in the source code, and existing semantic-feature-based SDP methods do not take into account the data distribution divergence between projects. These limitations may weaken defect prediction performance. To solve these problems, we propose a novel approach, the transfer convolutional neural network (TCNN), to mine the transferable semantic (deep-learning (DL)-generated) features for CPDP tasks. Specifically, our approach first parses the source file into integer vectors as the network inputs. Next, to obtain the TCNN model, a matching layer is added into convolutional neural network where the hidden representations of the source and target project-specific data are embedded into a reproducing kernel Hilbert space for distribution matching. By simultaneously minimizing classification error and distribution divergence between projects, the constructed TCNN could extract the transferable DL-generated features. Finally, without losing the information contained in handcrafted features, we combine them with transferable DL-generated features to form the joint features for CPDP performing. Experiments based on 10 benchmark projects (with 90 pairs of CPDP tasks) showed that the proposed TCNN method is superior to the reference methods.  
Keywords:cross-project defect prediction; semantic feature learning; transfer learning; maximum mean discrepancy +
Footnote †: journal: Applied Sciences