esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Food Safety
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Formula** & **Compounds** & **Types** & **RI** & **Odor** & **I\%** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{C\({}_{13}\)H\({}_{20}\)O} & \multirow{2}{*}{\(\beta\)-Ionone} & \multirow{2}{*}{Terpenoids} & \multirow{2}{*}{1491} & floral, woody, sweet, fruity, berry, tropical, beeswax & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline
2 & C\({}_{12}\)H\({}_{26}\) & 2,2,4,6,6-Pentamethylheptane & Hydrocarbons & 990 & & 3.2 \\ \hline
3 & C\({}_{10}\)H\({}_{14}\)O & 3-(4-Methyl-3-pentenyl) furan & Terpenoids & 1101 & woody & 2.37 \\ \hline
4 & C\({}_{10}\)H\({}_{18}\)O & \multirow{2}{*}{Linalool} & Terpenoids & 1101 & floral, green & 1.68 \\ \hline
5 & C\({}_{10}\)H\({}_{14}\)O & 2,6,6-Trimethylbicyclo[3.2.0]hept-2-en-7-one & Ketone & 1108 & & 1.68 \\ \hline
6 & C\({}_{10}\)H\({}_{14}\) & (3\(\!E\),5\(\!E\))-2,6-Dimethyl-1,3,5,7-octateraene & Hydrocarbons & 1131 & & 1.66 \\ \hline
7 & C\({}_{11}\)H\({}_{16}\)O\({}_{2}\) & \multirow{2}{*}{Dihydroactinidiolide} & Heterocyclic compound & 1532 & sulfury, meaty, fishy, metallic & \\ \hline
8 & C\({}_{10}\)H\({}_{14}\)O & (1-Methoxpropyl)benzene & Aromatics & 1104 & & 1.52 \\ \hline
9 & C\({}_{11}\)H\({}_{15}\)Br & (5-Bromopent)benzene & Aromatics & 1487 & & 1.43 \\ \hline
10 & C\({}_{5}\)H\({}_{6}\)OS & 2-Methyl-3-furanthiol & Heterocyclic compound & 870 & sulfury, meaty, fishy, metallic & 1.35 \\ \hline
11 & C\({}_{6}\)H\({}_{10}\)N\({}_{4}\) & 3,5-Dimethyl-1\(\!H\)-pyrazole-1-carboximidamide & Heterocyclic compound & 1096 & & 1.28 \\ \hline
12 & C\({}_{6}\)H\({}_{14}\)N\({}_{2}\) & 1-Methylpyrrolidine-2-methylamine & Heterocyclic compound & 1104 & & 1.23 \\ \hline
13 & C\({}_{14}\)H\({}_{22}\) & 1-Nonen-4-ol & Alcohol & 1485 & & 1.17 \\ \hline
14 & C\({}_{9}\)H\({}_{18}\)O & 8,8-Dimethyl-9-methylene-1,5-cycloundecatiene & Hydrocarbons & 1103 & & 1.17 \\ \hline
15 & C\({}_{8}\)H\({}_{16}\)O & 3-Cyclopentyl-1-propanol & Alcohol & 1102 & & 1.06 \\ \hline
16 & C\({}_{8}\)H\({}_{7}\)NO & 1,3-Dihydro-2\(\!H\)-indol-2-one & Heterocyclic compound & 1478 & & 1.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main volatile components of _V. kulingiana_ leaves analyzed via HS-SPME-GC-MS.  
may have varying tolerances to the same food components, potentially leading to allergies or unexpected reactions. (4) Besides components, factors such as processing techniques and storage conditions can also affect food safety. Therefore, if there are plans for future development and utilization, such as the development of new foods or feeds, toxicity studies are indispensable.
###### Abstract  
This study explores the combined use of convective and infrared drying (CIRD) for bread slices, utilizing advanced MEMS sensors to monitor temperature, moisture, and drying rates in real time, optimizing efficiency, and energy use. The dehydration kinetics of 1 cm thick bread slices under a controlled CIRD method was used. This analyzes drying rate (water loss speed, WLS) and energy efficiency (EE) using sensor technology. IR drying used 150 W lamps at 7 cm and 15 cm, while convective drying involved 60 \({}^{\circ}\)C hot air at 3 m/s. Sensor data aligned with gravimetric measurements. The most energy-efficient model used a 150 W IR lamp at 7 cm (0.645 kWh, 21.572 kWh/kg water removed) but had the longest drying time (220 min at 15 cm). The least efficient model used a 250 W IR lamp at 15 cm (EE = 32.734 kWh/kg). These results of CIRD in bread drying are statistically significant and can be applied to industrial bakery drying processes, helping manufacturers to reduce energy costs, and adopt sensor-driven process control for enhanced sustainability. The CIRD model, which uses a 150 W IR lamp placed 15 cm above the bread slices being dried, represents the most effective optimization strategy.  
convective drying; IR drying; sensors; bread Article
## 6 Results
### Generation of Optimized Routes with the Clark and Wright Savings Algorithm  
The Clark and Wright savings algorithm for home gas distribution has been evaluated considering 30 customers, who need to refill their tanks. These are located at different points in an area of the city, as shown in Figure 13 and Table 7. Each customer has a specific request, and these vary depending on the needs of each customer and the capacity of the domestic stationary tank.  
The filling plant of the distribution gas company is located at the coordinates (\(19^{\circ}41^{\prime}95^{\prime\prime}\), \(-98^{\circ}14^{\prime}32^{\prime\prime}\)). Since distribution tanker trucks are supplied here, this is the point where the journey of the different routes begins and ends. It is assumed that each distribution tanker truck has a maximum capacity of 5000 L of LPG. Thus, two tanker trucks are assigned to serve the 30 requests, respecting capacity restrictions, direction of circulation, and a maximum travel distance of 15 km.  
In real life, it is common to make route changes due to vehicular traffic density, accidents, and so on. However, suggesting route changes in real time due to incidents is outside the scope of this work.  
The actual distances between the locations are obtained using the Geopy library in Python considering geographic coordinates (longitude and latitude). The heuristic algorithm was coded in Python 3.11 and the numpy, itertools, geopy, and folium.m libraries were used with the data from Table 7.  
Figure 13: Location of the filling plant and 30 customers in a specific area of the city.  
Figure 14 shows the route generated for distribution tanker 1 (route 1), which delivers LPG to 16 of the 30 customers, in the following order:  
* Route 1: Filling plant \(\to 12\to 11\to 30\to 9\to 27\to 10\to 22\to 6\to 21\to 23\to 26\to 24\to 25\to 28\to 29\to 7\to\) Filling plant.  
Figure 15 shows the second route generated for distribution tanker 2 (route 2), which delivers LPG to the other 14 customers, in the following order:  
* Route 2: Filling plant \(\to 18\to 2\to 14\to 5\to 1\to 8\to 3\to 17\to 15\to 20\to 16\to 19\)\(\to 4\to 13\to\) Filling plant.  
In this way, it is demonstrated that the Clark and Wright algorithm is very useful for optimizing LPG distribution routes for tanker trucks. Linking delivery points through optimized routes can reduce operating costs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task ID** & **Coordinates (Lat. and Lon.)** & **Request (L)** & **Tank ID** & **Coordinates (Lat. and Lon.)** & **Request (L)** \\ \hline
1 & \(19^{\circ}41^{\prime}36^{\prime\prime}\), \(-98^{\circ}14^{\prime}46^{\prime\prime}\) & 300 & 16 & \(19^{\circ}41^{\prime}41^{\prime\prime}\), \(-98^
# Ferroelectric Transition in Sr- and W-Doped BaTiO3 Solid Solutions
## 3 Results and Discussion
As the Sr content x increased, the intensity of the DSC peaks around T\({}_{\rm C}\) decreased, and the enthalpy (marked with colors)  
Figure 3: (a) Changes in the lattice constants (a and c) and volume (V) as functions of x and y; (b) the ratios of a and c.  
decreased to almost zero. With x = 0.1, the transition seemed to nearly disappear in the DSC curves. The T\({}_{\rm C}\) also decreased as the Sr content increased. Considering the significant decrease in the ratio of the lattice constants in the doped BTO, as mentioned above, we concluded that the substitutional Sr atoms at the A sites play a certain role in suppressing the distinct transition between the ferroelectric and paraelectric phases in BTO. This is because of the modified cube-like crystal structure, which has lattice constants (a and c) that are similar. Figure 4c shows the DSC curves of BaTi\({}_{1-y}\)W\({}_{y}\)O\({}_{3}\) (y = 0.02, 0.04, and 0.06), which were obtained under the same experimental conditions as in the case of Ba\({}_{1-x}\)Sr\({}_{x}\)TiO\({}_{3}\). Considering the strong dependency of the Sr content on the T\({}_{\rm C}\) and the enthalpy in Sr-doped BTO, the T\({}_{\rm C}\) of the W-doped BTO seemed to be invariant with all doping contents (i.e., 123.1 \({}^{\circ}\)C with heating and 114.7 \({}^{\circ}\)C with cooling). The enthalpy of W-doped BTO gradually decreased, providing a similar change to that in the Sr-doped BTO case. The subsequent decreases in the lattice constants in the Sr-doped BTO indicated that smaller Sr\({}^{2+}\) atoms occupied the spaces that were previously occupied by larger Ba\({}^{2+}\) atoms. Therefore, a significant decrease in volume was observed, together with a decrease in T and the latent heat in the Sr-doped BTO. On the other hand, in the W-doped BTO, the lattice constants changed, but the volume and T\({}_{\rm C}\) seemed to be invariant, while the latent heat in the W-doped BTO decreased with the increase in the dopant content of W. The A-site doping with Sr atoms effectively contributed to the change in the volume of the system, which indirectly influenced the c/a ratio. However, the B-site doping with W atoms contributed to a change in the lattice constants in the tetragonal structure, which directly influenced the c/a ratio. Considering that the W atoms were located at the center of an oxygen octahedron in the W-doped BTO, the ferroelectricity originating from the off-center O\({}^{2-}\) could be degraded through B-site doping by using a precisely modulated crystal field for the oxygen octahedron without changing the volume. Therefore, we insist that the doping of both the A and B sites in BTO can prevent the transition between the tetragonal and cubic structures by reducing the c/a ratio. The amount of latent heat seemed to be influenced by the c/a ratio, while the values of T\({}_{\rm C}\) seemed to be influenced by the volume.
# Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols +
## 4 Measurement
### Statistical Anomalies in Selfish Mining  
Li [24,25] proposes a method for detecting selfish mining behaviour in empirical networks. Their central idea is to exploit the increased probability for selfish miners to publish multiple blocks in a row. They assume that selfish miners always use the same address for the coinbase transaction (block reward) so that they can be identified as the same miner. To validate this methodology, we implement their _Miner Sequence Bootstraping model_ (\(MSB\)).  
Following the \(MSB\) notation, we describe our statistical approach. First, we count the number of times that in period \(T\) miner \(i\) continuously discovers two blocks, \(C_{i}^{T}\). Next, we shuffle the chain repeatedly and after each shuffle \(t\) we count the number of times that miner \(i\) continuously discovers two blocks in period \(T\), which we call \(S_{i}^{T}(t)\). Based on the repeated shuffle simulation, we define \(\langle S_{i}^{T}\rangle\) as the expected number of times a miner \(i\) should continuously discover two blocks in period \(T\). Then, the \(MSB\) index for miner \(i\) is defined as  
\[MSB_{i}^{T}=\frac{C_{i}^{T}-\langle S_{i}^{T}\rangle}{\sigma[S_{i}^{T}]} \tag{9}\]  
where \(\sigma[S_{i}^{T}]\) is the standard deviation of all observations \(S_{i}^{T}(t)\). This being a z-score, the \(MSB\) value is statistically significant at the 95% level for \(MSB_{i}>2\). The larger \(MSB_{i}\) index means that for consecutively discovering two blocks during period \(T\), miner \(i\) succeeded more times than expected (i.e., based on her revenue share). Therefore, we compute an average \(MSB\) score for the selfish miner set in our ABM, in order to test whether the selfish mining behavior would divulge such identifiable statistical anomaly.
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 3 Neural Network Architectures
### ResNet-50  
ResNet is an architecture composed of residual layers, with its main components being residual blocks and skip connections between layers that allow information to bypass one or more layers [32]. This architecture effectively addresses the issue of vanishing gradients as the depth of the neural network increases, unlike other models, where the performance tends to degrade with deeper networks. In addition, it reduces the computational complexity and enhances the network's training capacity. ResNet-50, which is a variant of ResNet, comprises 50 layers and more than 25.6 million parameters.  
In this study, the ResNet-50 architecture was trained on a high-quality Korean-speech dataset instead of a pre-trained model, in order to build a model specialized for age classification. The structure of the model is illustrated in Figure 4. The architecture begins with a \(7\times 7\) convolutional layer with stride 2, followed by five sequential sets of residual blocks: three blocks with 64 filters (\(\times 3\)), four blocks with 128 filters (\(\times 4\)), six blocks with 256 filters (\(\times 6\)), and three blocks with 512 filters (\(\times 3\)). Each residual block incorporates \(1\times 1\) and \(3\times 3\) convolutions, complemented by skip connections that preserve feature information and improve gradient flow. These skip connections are particularly crucial for capturing subtle differences in age-related voice characteristics. The network concludes with an average pooling layer, followed by a fully connected layer with softmax activation, which performs the final classification into ten age groups.  
Figure 4: ResNet-50 architecture.
# RF Exposure Assessment by Drone-Based Technology
###### Abstract
In order to provide a more comprehensive view of the spatial distribution of EMF levels, measurements were conducted both at ground level and at 20 m height using a PEM attached to a drone. The collected data were analyzed using geostatistical methods, including kriging, which allows for the creation of continuous spatial distributions from discrete measurement points [14]. By comparing the electric-field levels measured at different heights and analyzing their spatial distribution, this study seeks to provide valuable insights into the factors that influence EMF exposure in urban environments.
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 4 Performance Assessment and Validation
### Experiment Results
#### 4.2.3 Integration Validation Using Xiaomi Data  
The 2D trajectories shown in Figure 9 overall fluctuate more significantly away from truth than the U-Blox results, and an extremely large (over 1000 m) 2D error from GNSS and KF-based integration methods can be found in some epochs in Figure 10. Compared with the U-Blox receiver, the results of Xiaomi in Table 6 also exhibit larger values in both the RMSE and STD for each of the integration methods. All of those indicate a larger positioning error and less smoothness and coincides with the expectation that Xiaomi has a larger solution uncertainty than U-Blox.  
Figure 10: 2D positioning errors of different positioning solutions using Xiaomi data throughout 920 epochs, with the periods at junctions A, B, and C shaded in gray. The upper subplot depicts the full range of 2D errors, while the lower one shows the zoom-in view from 0 to 100 m.  
Figure 9: Trajectories of different positioning solutions using Xiaomi data, with the large drift at junction C being pointed out by the magenta arrow.  
The results also depict that the 2D error at all junctions are larger than those of U-Blox. It is noted that at junction C, significant drift in Figure 9 from the truth trajectory and an extremely large (over 1500 m) 2D positioning error in Figure 10 occurs. Similar to U-Blox, this large drift can be caused by the propagation with INS data only when GNSS becomes unavailable. Other reasons behind it may involve the longest waiting time (i.e., around 100 s) without GNSS information among the three junctions as well as the large uncertainty characteristics of the Xiaomi GNSS receiver and the poor-quality built-in IMU. Nevertheless, due to the robustness given by more historical information, the FGO-based methods still eliminate the large drift (corresponding to the shaded area C in Figure 10) occurring in KF and AKF solutions. AFGO outperforms the original GNSS positioning and still provides the best positioning solutions in terms of both RMSE and STD among the GNSS/INS integration methods. Moreover, the GNSS information of Xiaomi becomes less available compared with the U-Blox receiver, and this may result in shorter computational time for all integration methods, as demonstrated in Table 6.
## 3 Existing Space Systems
Leaving aside the cost of procuring such commercial data (indicative costs for these products range from $5 to $50/km\({}^{2}\)[25]), this volume of imagery is clearly insufficient as a primary source for the routine archaeological survey of the region specified above. Through the European Space Agency Third Party Mission programme, access was granted to data obtained by the SkySat constellation of spacecraft (operated by Planet) to evaluate its use in detection and monitoring of archaeological sites. Through this, a request was made to Planet for one image to be collected of the study area (Figure 3) each week in July 2020 using the SkySat spacecraft. In order to satisfy this request Planet collected multiple images on almost every day in July. During the month of July, 150 images were collected, but only 26 of these have \(<\)50% cloud cover; this corresponds to just 17% of images. Furthermore, if the images are to be used for the detection of time varying copmarking, multiple images collected on the same day are of limited value. While this data volume could be sufficient for the detection of crop proxies in summer, it has required dedicated daily collections from a total of 12 different spacecraft.
# Performance Analysis of Continuous-Variable Quantum Key Distribution with Multi-Core Fiber
###### Abstract  
Performance analysis of continuous-variable quantum key distribution (CVQKD) has been one of the focuses of quantum communications. In this paper, we propose an approach to enhancing the secret rate of CVQKD with the multi-core fiber (MCF) system that transmits multiple spatial modes simultaneously. The excess noise contributed by the inter-core crosstalk between cores can be effectively suppressed by quantum channel wavelength management, leading to the performance improvement of the MCF-based CVQKD system. In the security analysis, we perform numerical simulations for the Gaussian-modulated coherent state CVQKD protocol, considering simultaneously the extra insert loss of fan-in/fan-out (FIFO), which is the extra optical device that should be used at the input and the output of the fiber. Simulation results show that the performance of the one-way and two-way protocols for each core are slightly degraded because of the insert loss of the FIFO, but the total secret key rate can be increased, whereas the performance of the measurement-device-independent CVQKD protocol will be degraded due to the effect of the insert loss of the FIFO. These results may provide theoretical foundation for the space-division multiplexing CVQKD system.  
Keywords:quantum cryptography; continuous-variable quantum key distribution; multi-core fiber +
Footnote †: journal: Applied Sciences
# Failure Trends of High-Voltage Porcelain Insulators Depending on the Constituents of the Porcelain
###### Abstract  
The aging of porcelain insulators is responsible for the failure of power utilities. Porcelain insulators from different places in South Korea, possessing various aging times and years of installation, have been investigated to carry out lifetime statistics. These samples have a mass of 36,000 lbs and are operated at 154 kV. X-ray fluorescence (XRF) and scanning electron microscopy (SEM) were performed on the porcelain bodies of the samples collected. XRF and SEM revealed trends in the weight percentage of SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\) in the porcelain bodies of the collected samples. The SiO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) weight percentage reduced to 8.93% and 73.17%, respectively, in sample C compared to A. However, the Al\({}_{2}\)O\({}_{3}\) weight percentage increased to 16.23% in sample C compared to sample A. This change in weight percentage of SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), and Al\({}_{2}\)O\({}_{3}\) contributed toward enhancing the mechanical and electrical properties of the insulators. Mechanical load tests with electrical voltage (M&E), thermal and mechanical load tests (T&M), and hardness tests (HRB) were performed to evaluate these characteristics. Experiments revealed an increase of 90.9% in the inequality factor (K) in sample C compared to A. The impact of constituents of porcelain on the lifetime expectancy of a porcelain insulator was reported by implementing statistical strategies, such as the Weibull distribution.  
Keywords:XRF; SEM; Weibull distribution; normal distribution; porcelain insulator; failure probability +
Footnote †: journal: Applied Sciences
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 4 The Design Procedure of Circuit Component Parameters
### Step 1: Determining the Specification of Converter  
Based on the circuit topology, an isolated type bidirectional symmetric resonant converter with 1 kW output power was implemented to test the validity of the conception. The specification of the converter is listed in Table 1.
## 2 Materials and Methods
### Methods
#### 2.2.4. APU Metrics  
The deviations between two data sets are evaluated through an assessment of the Accuracy, Precision and Uncertainty (_APU_) metrics [28]. The Accuracy (_A_) or mean bias measures the average actual difference between two data sets, X and Y:  
\[A=\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}}(X_{i}-Y_{i})= \overline{X}-\overline{Y} \tag{1}\]  
As such, \(A\) retains the sign of the difference between the data sets and is a measure for systematic bias. Precision (_P_) or repeatability represents the dispersion of product retrievals around their expected value and is estimated by the standard deviation of the bias between retrieved satellite products:  
\[P=\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}}(X_{i}- Y_{i}-A)^{2}} \tag{2}\]  
Finally, Uncertainty (_U_) is defined as the overall difference, including random and systematic differences, and is measured through the root mean squared difference:  
\[U=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}}\Big{(} \frac{X_{i}-Y_{i}}{\underset{i}{\sum}}\Big{)}^{2}} \tag{3}\]  
High _APU_ values reflect discrepancies between two compared data sets, whereas low values indicate high consistency [29]. Accuracy is mainly influenced by differences in absolute radiometric calibration and spectral response. Precision is influenced by temporal, spatial and angular sampling; spectral response and the performance of the spectral resampling algorithm applied for SYN VGT; and the atmospheric effects and performance of pixel classification algorithms to exclude satellite observations perturbed by clouds, cloud shadows or snow. Uncertainty provides an overall measure of differences between two datasets.