esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Geology and Development Conditions of the Research Area  
The Yexian salt mine is in Pingdingshan City, Henan Province, China. Geologically, it is located in the Wuyang sag. The sag is a narrow and long faulted Salt Lake basin with an area of approximately 1800 km\({}^{2}\). From the Cretaceous to the Quaternary, the Hugang  
Figure 1: Different cavern construction methods. (**a**) Mining by single well; (**b**) mining by two vertical wells with small spacing; (**c**) mining by two wells with a horizontal well and a vertical well. The arrows indicate the direction of fluid flow.  
Figure 2: Brine mining with directional butted wells in salt production companies.  
Formation (K\({}_{2}\)h), Yuhuangding Formation (E\({}_{2}\)y), Daengfang Formation (E\({}_{2}\)d), Hetaoyuan Formation (E\({}_{2}\)h), Liaozhuang Formation (E\({}_{3}\)l), Shangsi Formation (Nsh), and Pingyuan Formation (Qp) were deposited successively. The salt-bearing strata are the Hetaoyuan Formation of the Paleogene Oligocene. After years of exploration and research, it has been determined that the sag presents a salt bearing area of 400 km\({}^{2}\) and a salt resource of 236.3 billion tons, belonging to a super-large salt rock deposit. There are 21 salt groups developed from top to bottom in the deposit, with a salt layer thickness range of 120-550 m and a top burial depth range of 700-1200 m. Since 1988, six salt production companies have successively mined and produced salt in this area, and more than 80 production wells have been formed so far. In 2020, the Huabei Oilfield Company planned to build an underground salt cavern gas storage in this region and reached a co-operation intention with the Pingmei United Salt Mine Company, intending to construct the facility through a combination of new cavity construction and old cavity transformation. The Pingmei Company's old cavities are mainly produced by a directional butted well with interactive injection-production mode. Since the mining operations began in 2008, a total of approximately 59 million cubic meters of brine have been extracted, which is estimated to form about 6 million cubic meters of effective underground space. Among these mining wells, 13 pairs of old wells have been selected for storage facility renovation, as in Figure 3. In addition, a total of 14.28 km\({}^{2}\) of 3D seismic data acquisition, processing, and interpretation, as well as the drilling of two data wells (D1, D2), the cleaning of six old wells, and the sonar inspection of old cavities (as shown in Figure 4), have been successively completed, providing fundamental data for this study.  
Figure 3: Distribution map of 13 pairs of old butted wells and two new drilled wells in Yexian mining area.
## 4 Computer Simulation Results
### Frequency Conversion to Radiation with the Wavelength 4 \(\mu\)m
#### 4.1.1 Incident IFW's Intensity Is Equal to Zero
Thus, the HFW pulse possesses a local minimum near the pulse center in all cases (Figure 3a-d) despite the LFW pulse being smooth, with a small duration in comparison with the HFW pulse duration. The conversion efficiency is larger in the first three cases (18%, 34%, 38% in Figure 3i,j,k, correspondingly), but lower for the incident intensity of the LFW equal to \(|A_{10}|^{2}=0.1\) than the one occurring for the positive phase mismatching: \(\Delta_{21}k=20\) (only 43%).
## 5 Simulation Scenarios
### Case Study 2: Interactions in a Narrow Area  
In this section we present the results of the simulations done using the same settings as in the previous section, but in this case the nodes were deployed in an area 30 \(\times\)3,000 m. The main goal to repeat the simulations was to try understanding the influence that the shape of the working area has on both, the node energy consumption and the energy saving produced by the predictor. Next we present the obtained results considering the studied mobility patterns.
# Monitoring Perishable Commodities Using Cellular IoT:
## 2 Materials and Methods
### Cloud and Database  
The data measured by the datalogger were stored at the 1 NCE operator (see Figure 1). These data were retrieved using the API provided by 1 NCE using flows and a Javascript code programmed using the Node-RED tool (version 3.1), the operation of which is shown in the flowchart in Figure 7. 1 NCE was chosen among the different operators because of its cost, global coverage and ease of integration in the cloud.  
To communicate with the operator, it is first necessary to request a token giving permission, and, when sending the data, it is automatically encoded in Base64, so it must then be decoded. The flow is automatically executed every 45 min. After obtaining the date of the last value sent using the telemetry data, it requests the geolocation from the operator and the data history, updating the dates and decoding the result from Base64 and CayenneLPP to send it to the database created with InfluxDB in JSON format. After decoding the data, the system sends an alarm to the mail or to the cell phone via Telegram in case preset maximum and minimum thresholds have been exceeded, which are listed in Table 5.  
It should be pointed out that the operator provides a geolocation update every 24 h. Also, to obtain locations more frequently, the datalogger records the codes of the cell towers  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Maximum Threshold** & **Minimum Threshold** \\ \hline Luminosity & 10,000 lux & - \\ \hline CO\({}_{2}\) concentration & 1000 ppm & 100 ppm \\ \hline Temperature & 30 \({}^{\circ}\)C & \(-\)10 \({}^{\circ}\)C \\ \hline Relative humidity & 80\% & 10\% \\ \hline Acceleration & 1.5 g (abs) & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thresholds to be exceeded in order to send an alarm.  
Figure 7: Flowchart of the Node-RED operation to access the operator API.  
it has connected to and sends them to the server, so that the location is obtained using a cell tower location database.  
InfluxDB is a time series database management system that allows the handling of large amounts of time-stamped data and uses a query language based on SQL. The database created has the structure shown in Figure 8. It contains the table or measurement where the submitted data are recorded, which contains the timestamp of when the data were recorded, the fields, which are the packet counter, timestamp, luminosity, CO\({}_{2}\) concentration, relative humidity, temperature, maximum acceleration, average acceleration, number of vibrations and battery voltage, as well as the geolocation, and the tags, which indicate the device or node that recorded the data.  
Finally, to monitor and analyze the data stored in InfluxDB in real time, the Grafana platform was used to create sophisticated graphical dashboards for data monitoring. As mentioned, the Node-RED interface, the InfluxDB database and the Grafana visualization dashboards were hosted on the server. Node-RED, InfluxDB and Grafana were used because they are open-source tools, free of charge and are used in many applications for NB-IoT.
# Air-Polishing Powders' Effect on the Color of CAD/CAM Restorative Materials
## 3 Results  
The results of the colorimetric analysis revealed that applying APPs affects the final color of CAD/CAM restorative material (\(p\) < 0.05, \(p\) < 0.001). In accordance with the two-way ANOVA test results in Table 3, the type of CAD/CAM restorative material had a significant effect on the \(\Delta\)L* (F: 21.121; \(p\) < 0.001), \(\Delta\)a* (F: 18.063; \(p\) < 0.001), \(\Delta\)b* (F: 49.923; \(p\) < 0.001), \(\Delta\)C\({}^{\prime}\) (F: 43.939; \(p\) < 0.001), \(\Delta\)H\({}^{\prime}\) (F: 19.243; \(p\) < 0.001), and \(\Delta\)E\({}_{00}\) (F: 6.205; \(p\) < 0.001) values. In addition, the type of APP had a significant effect on the \(\Delta\)a* (F: 28.589; \(p\) < 0.001), \(\Delta\)H (F: 27.578; \(p\) < 0.001), and \(\Delta\)E\({}_{00}\) (F: 5.505; \(p\) < 0.001) values, but did not have a significant effect on \(\Delta\)L* (F: 2.399; \(p\): 0.051), \(\Delta\)b* (F: 1.805; \(p\): 0.129), or \(\Delta\)C\({}^{\prime}\) (F: 2.062; \(p\): 0.087) values. The interaction terms were not significant for the \(\Delta\)L* (F: 1.095; \(p\): 0.361), \(\Delta\)a* (F: 1.552; \(p\): 0.084), \(\Delta\)b* (F: 0.854; \(p\): 0.623), \(\Delta\)C\({}^{\prime}\) (F: 0.948; \(p\): 0.515), \(\Delta\)H\({}^{\prime}\) (F: 1.669; \(p\): 0.054), or \(\Delta\)E\({}_{00}\) (F: 0.996; \(p\): 0.462) values. As illustrated in Tables 4-8, in the MZ groups, the samples' \(\Delta\)L*, \(\Delta\)a*, \(\Delta\)b*, and \(\Delta\)C\({}^{\prime}\) values decreased, while the \(\Delta\)H\({}^{\prime}\) values increased, regardless of the APP used. The APP of aluminum trihydroxide increased the \(\Delta\)b* and \(\Delta\)C\({}^{\prime}\) values of LU significantly compared with the APP of glycine. In addition, calcium carbonate increased the \(\Delta\)a* and \(\Delta\)H\({}^{\prime}\) values of LU significantly, compared with the other APPs. Furthermore, aluminum trihydroxide
## 3 Results and Discussion
### Fresh Consistency Results  
The change in the amount of fly ash in the binder mass did not affect the consistency results when untreated fly ash or fly ash sieved through 125 \(\upmu\)m were used in the mix (Figure 5). An interaction of the two studied factors was observed in the case of fly ash sieved with the 63 \(\upmu\)m sieve. With the increase in the amount of fly ash sieved with the 63 \(\upmu\)m sieve, the consistency significantly decreased from 135.8 mm to 112.5 mm. The mean consistency of the control specimens was 140.8 mm.  
All specimens with the addition of fly ash had consistency results smaller than the control specimens. When comparing specimens with a 5% amount of fly ash in the binder, the value of consistency was significantly higher in specimens with the addition of FA(B)63 fly ash than with the addition of FA(B)125 fly ash, which might be explained by the elimination of the biggest particles of silica visible in the SEM photographs in Figure 3, which resulted in the increased water demand of the material. Consequently, the observed impact of FA(B)63 fly ash on the consistency results can be explained by the increase in the specific surface of fly ash. The influence of particle size distribution on consistency is in line with the results of the study of particle size distribution on the water demand of biomass fly ash and tests conducted on commercial scre  
Figure 5: Fresh consistency test results of mortars with addition of sieved fly ash FA(B)63 and FA(B)125 compared with the results of mortars with untreated fly ash FA(B)N (ANOVA 95% CI in the whiskers).  
implications for the future use of activated fly ash can be given from the achieved results. Sieving through a 125 \(\upmu\)m sieve might not require additional consistency control with the correction of water content in the mix or the use of a plasticizer.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Satellite Design
#### 2.3.5 Software Architecture  
The software that enables the satellite to perform its scientific mission is developed alongside hardware and embedded software development.  
During the development of ESTCube-1, it became clear that working with the STM hardware abstraction layer (HAL) created more problems than it solved, as described in [18]. A custom HAL was created by the team called ECHAL. ECHAL supports STM32 MCUs from F7, L4, and H7 families, making the software functions above this layer portable between them. The memory footprint of ECHAL is far smaller than STM HAL, and it fits into a bootloader.  
Currently, the algorithm development is ongoing. The aim is to use the bootloader to deploy the final software onto the satellite after the launch. Before the launch, the main focus is sensor validation software. It focuses on the preprocessing of the sensor data and the relevant drivers that enable taking measurements from the sensors. As the satellite's payload requires advanced attitude determination and control, many algorithms are under development to enable a successful mission.  
The main focus on algorithm development has been on a robust Unscented Kalman Filter (UKF), satellite attitude and orbit control methods, and the ST algorithms. These algorithms are developed in a simulation environment and with more computing than is available on the satellite. Although this helps validate the algorithms, porting for use with ECHAL and optimizing them for use in orbit is still necessary. This will be finalized after receiving the first measurements from space and testing the algorithms using the sensor logs.  
The firmware on all the satellite's avionics stack systems is separated into logical layers, as summarized in Figure 13.  
The ICP is used for the avionics stack system's internal communication. A detailed description of this can be found in [18].
## 3 Material and Method
### Experimental Design
#### 3.2.5 Data Preprocessing  
Before making the predictions, the first crucial step was data preprocessing. In the context of this model diagram, as shown in Figure 2, we applied an exponential smoothing technique to pre-process the gathered data. Exponential smoothing serves as a data-smoothing method designed to mitigate noise and random fluctuations. This is particularly beneficial when dealing with data exhibiting high levels of volatility and erratic variation, as shown in Figure 7a. This approach contributes to the generation of more refined data, which can be observed by comparing the original and smoothed data, as shown in Figure 7b, where smoothing makes the fluctuation level appear sharper. The implementation of the exponential smoother formula on the time measurement and voltage datasets involves the following steps:  
* The initial value of smoothed data \(S_{1}\) was determined from the first observation, \(Y_{1}\) in the voltage dataset.
* We then chose the smoothing constant \(\alpha\) to control the desired level of smoothing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Observation** & **Datetime** & **Voltage** & **Temperature** & **Degradation Value** \\ \hline
1 & 26 June 2023 12:44:12.403 & 13.43 & 29.88 & 0.22 \\
2 & 26 June 2023 12:44:15.033 & 13.45 & 29.88 & 0.2 \\
3 & 26 June 2023 12:44:17.663 & 13.5 & 29.88 & 0.15 \\
4 & 26 June 2023 12:44:20.293 & 13.43 & 29.81 & 0.22 \\
5 & 26 June 2023 12:44:22.923 & 13.43 & 29.88 & 0.22 \\
6 & 26 June 2023 12:44:25.553 & 13.4 & 29.88 & 0.25 \\
7 & 26 June 2023 12:44:28.183 & 13.35 & 29.88 & 0.3 \\
8 & 26 June 2023 12:44:30.813 & 13.5 & 29.88 & 0.15 \\
9 & 26 June 2023 12:44:33.443 & 13.38 & 29.81 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample of dataset.  
* The formula \(S_{t}=\alpha\) x \(Y_{1}\)+ (1 \(-\) \(\alpha\)) x \(S_{t-1}\) was used to calculate the smoothed value at each time t, where \(Y_{t}\) is the voltage value at time t and \(S_{t-1}\) is the previous smoothed value.
* This step was applied to each voltage observation, resulting in a new dataset with a smoothed voltage value.
###### Abstract  
Among the existing insulator defect detection methods, the automatic detection of inspection robots based on the instance segmentation algorithm is relatively more efficient, but the problem of the limited accuracy of the segmentation algorithm is still a bottleneck for increasing inspection efficiency. Therefore, we propose a single-stage insulator instance defect segmentation method based on both an attention mechanism and improved feature fusion network. YOLACT is selected as the basic instance segmentation model. Firstly, to improve the segmentation speed, MobileNetV2 embedded with an scSE attention mechanism is introduced as the backbone network. Secondly, a new feature map that combines semantic and positional information is obtained by improving the FPN module and fusing the feature maps of each layer, during which, an attention mechanism is introduced to further improve the quality of the feature map. Thirdly, in view of the problems that affect the insulator segmentation, a Restrained-IoU (RIoU) bounding box loss function which covers the area deviation, center deviation, and shape deviation is designed for object detection. Finally, for the validity evaluation of the proposed method, experiments are performed on the insulator defect data set. It is shown in the results that the improved algorithm achieves a mask accuracy improvement of 5.82% and a detection speed of 37.4 FPS, which better complete the instance segmentation of insulator defect images.  
insulator defect; instance segmentation; YOLACT; attention mechanism; feature fusion; loss function +
Footnote †: journal: Applied sciences
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 5 Experiment and Evaluation
### Evaluation
#### 5.4.3 Robustness of Prediction  
To evaluate the robustness of DimAug-TimesFM, we conducted comparative experiments in resource pools where TimesFM exhibited significant performance degradation. The experimental results demonstrate that DimAug-TimesFM effectively mitigates 78% of the performance decline observed in TimesFM, significantly enhancing the model's prediction stability in complex scenarios.  
Taking two representative resource pools as examples, Table 3 presents the detailed performance of TimesFM and our model on 64-day and 128-day forecasting tasks. In the 64-day forecasting task for resource pool 190, TimesFM's MAE, MAPE, and RMSE reached 69.2020, 20030.6820, and 83.1930, respectively. In contrast, our model optimized these metrics to 0.1347, 39.0636, and 0.1514, achieving reductions of over 99% in MAE and RMSE, and a 99.8% reduction in MAPE. Similarly, in the 128-day forecasting task for resource pool 229, TimesFM's MAE, MAPE, and RMSE were 121.0135, 24054.3601, and 149.4365, respectively, whereas our model improved them to 0.6453, 128.2051, and 0.7477, reducing MAE and RMSE by over 99% and MAPE by 99.5%.  
These results indicate that DimAug-TimesFM maintains outstanding predictive performance even in complex scenarios. Our data augmentation technique further enhances the model's adaptability to anomalous data, significantly improving its overall robustness.
## 4 The Considered Nodes' Synchronization and Data Transmission Protocol
### Inter-Node Synchronization Time Statistics  
We are now in the position of deriving the statistics of the inter-node synchronization time \(\delta_{s}\), which represents the time needed for completing the SYNCH phase at node \(s+1\) starting from the time \(T_{S}\) of correct SYNCH packet delivery at node \(s\). Note that according to the above notations, we have \(T_{S}=t_{s+1}+T_{O}\), and \(\delta_{s}=t_{s+2}-T_{S}+T_{O}=t_{s+2}-t_{s+1}\). Hence, \(\delta_{s}\) is an integer multiple of the packet duration \(T_{P}\), with \(P_{\delta_{s}}(n,t)=\text{Prob}\left\{\delta_{s}=nT_{P}\left|t_{s+1}=t\right.\right\}\), with \(n\geq 1\), given by:  
\[P_{\delta_{s}}(n,t)=\left\{\begin{array}{ll}\text{Prob}\left\{r_{s+2}\leq t \right\}=\int\limits_{-\infty}^{t}f_{t_{s+2}}\left(y|R_{s+2}\right)dy&\text{if $n=1$}\\ \text{Prob}\left\{(n-2)T_{P}<r_{s+2}-t\leq(n-1)T_{P}\right\}=\int\limits_{t+(n -2)T_{P}}^{t_{+}(n-1)T_{P}}f_{t_{s+2}}\left(y|R_{s+2}\right)dy&\text{for $n\geq 2$} \end{array}\right. \tag{14}\]  
Hence, the unconditioned distribution \(P_{\delta_{s}}(n)\) can be evaluated as:  
\[P_{\delta_{s}}(n)=\int\limits_{-\infty}^{\infty}P_{\delta_{s}}(n,t)f_{t_{s+1} }\left(t\right)dt \tag{15}\]  
To further elaborate, the minimum waiting time interval \(\Delta_{S}=mT_{P}\) at node \(s\) to guarantee that node \(s+1\) has terminated the SYNCH procedure can be evaluated by imposing a predetermined target success probability \(P_{S}\), i.e., \(m\) is the minimum integer such as \(\sum\limits_{k=1}^{m}P_{\delta_{s}}(k)\geq P_{S}\). Of course, owing to the random nature of the synchronization errors, it is always possibile that node \(s+1\) cannot terminate the SYNCH phase before node \(s\) starts transmitting its data packets. In this case, node \(s+1\) completes the SYNCH procedure and soon after starts listening to the channel to receive the remaining data packets. Then, the missed packets can be replaced with dummy packets in order to keep the structure of the protocol intact.
## 6 Numerical Results and Analysis
### Simulation Results and Analysis
It can be observed that the useful throughput ratio performance of NCFM is much better than that of the 1:N and 1 + 1 protection mechanisms. From the comparison results in Figure 14, it can be concluded that NCFM can proactively offer fault-tolerant functions for native packets only if the network coding condition is satisfied. Compared with the traditional 1:N and 1 + 1 fault-tolerant mechanisms, we observe that NCFM has higher transmission efficiency, lower network expenses, and a more reliable fault-tolerant capacity.
# Effect of Genetic and Environmental Factors on the Impaction of Lower Third Molars
## 3 Results  
The study group consisted of 212 twins and was divided into the dizygotic and monozygotic twins groups. The dizygotic twins group included 80 twins with the mean age 18 \(\pm\) 0.6, 42 (52%) males and 38 (48%) females with CVM5 stage 42% and CVM6 stage 58%. The monozygotic twins group consisted of 132 subjects with the mean age 19 \(\pm\) 0.7, 78 (59%) males and 54 (41%) females with CVM5 stage 40% and CVM6 stage 60%. The differences between the groups of age, sex and CVM were not statistically significant; \(p\) values ranged from 0.47 to 0.89. The ICC for all the measurements fluctuated from 0.79 to 0.89 and showed almost perfect agreement. The method error by [PERSON] for repeatability of the angular measurements ranged from 0.56 to 1.07\({}^{\circ}\) and for the linear measurements varied from 0.25 to 0.57 mm with no significant differences. The Kolmogorov-Smirnov tests proved the normal distribution for all the studied variables; thus, the use of the parametric tests was established. The angulations of the lower third, second and first molar did not have statistically significant differences in dizygotic twins. The correlations between siblings varied from 0.28 to 0.41 and were the highest in the region of the first and second molar. Whereas monozygotic twins had higher correlations which ranged from 0.69 to 0.98. The highest correlation values were for the first and second molars (Table 1). The eruption space analysis for the lower third molar did not show significant differences between siblings in dizygotic and monozygotic twins. However, the correlations fluctuated from 0.53 to 0.91 and again, were higher in the monozygotic twins (Table 2). The eruption level of the evaluated teeth in the dizygotic twins showed almost the same tendency among siblings. The correlations fluctuated from 0.13 to 0.50 and were the highest for the first and second molar eruption levels. Furthermore, the eruption values in the monozygotic twins among the siblings did not differ statistically significantly. The correlations ranged from 0.76 to 0.94 and were the highest for the first and second molars. Finally, the genetic analysis showed that the most parsimonious fitting model for the angulation of the molars and their eruption space was AE ( additive genetic and specific environmental effect), although for the teeth eruption level the model ACE (additive genetic, common and specific environmental effect) showed higher significance. The genetic determinants were moderate to high, with a minimum of 60% reaching 88%. A specific environment affected up to 28%, and the common environment reached 32% (Table 3).
## 4 Performance of Waste-Plastic-Modified Bitumen Binder and Mixture
### Characteristics of Plastic-Modified Bituminous Mixtures  
Several literature reviews highlight the benefits of using plastic waste such as PET, HDPE, and PP in asphalt mixtures [9; 10; 18; 48; 49]. These reviews combine findings from more than twenty years of global research, illustrating that plastic may improve mechanical characteristics, decrease resource dependency, and boost sustainability metrics of asphalt mixtures.