esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Color Kinetics of the NR Sheets  
Figure 7 presents the \(YI\) kinetics of the NR sheet change during the drying process for each type of coagulating material, airspeed, and temperature. When the test started, the \(YI\) values of the experimental NR sheets and reference were between 35.1 and 35.7. Figure 7 presents that the increase in \(YI\) value related to the type of coagulating material and the increase in the airspeed and drying temperature. Figure 6: The NR sheet moisture content change during the drying process (HA = hot air, PR = para rubber, Bb = bamboo, and CS = coconut shell). Figure 7: The yellowness kinetics of the NR sheet change during the drying process (HA = hot air, PR = para rubber, Bb = bamboo, and CS = coconut shell). Table 2 shows the experimental and predicted average _YI_ value of the NR sheet samples at the final moisture content and total drying time. The results showed that the average _YI_ values of the dried NR sheet by hot air were between 51.2 and 67.6, while that of the reference NR sheet was 59.8. For the type of coagulating materials, the dried sheet samples using para rubber wood vinegar as the coagulating material had a color value at the same level as the acetic acid and the referent. However, the bamboo and coconut shell wood vinegar were at lower levels. At the lower drying temperature, the airspeed affected the _YI_ value, but for the higher temperature it was not affected. The results showed that the increase in temperature had a prime influence on increasing the average _YI_ values. For the NR sheet samples dried at 70 \({}^{\circ}\)C, the average _YI_ value of all the samples was higher than that of the reference, which presented that their color was falling below the level 1 standard of the ADS rubber.
# Fretting Fatigue Experiment and Finite Element Analysis for Dovetail Specim at High Temperature
## 5 Discussion  
### Contact Pressure and Stress Distribution  
The distribution of von Mises stress on the 3D model of the specimen is shown in Figure 11a. It is evident that the von Mises stress is tremendous due to the stress concentration at the upper edge of the contact area. The maximum von Mises stress is 1924.2 MPa which is larger than the material yield limit. The distribution of maximum shear stress shown in Figure 11b is similar to the von Mises stress field and there is also a stress concentration at the exact location as the von Mises stress. The von Mises stress distributes along the direction of the blue dotted line which is almost perpendicular to the contact surface; hence, the fretting fatigue crack is prone to initiate in this direction, which is in conformity with the experimental results in Section 3.2. The same result can be obtained for the analysis of the maximum shear stress in Figure 11b.  
Figure 11: Stress distribution of dovetail specimen (13,500 N). (**a**) Von Mises stress; (**b**) maximum shear stress.  
Figure 10: Observation of the cracked specimen. (**a**) Fractured dovetail specimen; (**b**) SEM of the fracture surface.  
It can be roughly seen that the stress distribution in Figure 11 is almost the same along the thickness direction of the dovetail specimen, based on which many studies use 2D models instead of 3D models to analyze the stress distribution [19; 22]. However, the stress distribution along the thickness direction is not entirely uniform. The contact pressure values for the element nodes on the contact surface of the dovetail specimen are presented in Figure 12, from which the distribution can be described more clearly. The two figures in Figure 12 are based on the local coordinate system, i.e., x'-y'-z' coordinate in Figure 5.  
The theoretical values of contact pressure are marked as a blue plane in Figure 12. The plane for the theoretical value intersects the numerical contact pressure distribution curves at two end areas. The pressure distribution at the right end is considerably higher than the theoretical value due to the stress concentration. The left end is only slightly higher than the theoretical value, as shown by \(\delta\) in Figure 12. Moreover, in the middle of the distribution curve, the theoretical value is greater than the numerical results for the contact pressure, as shown by \(\Delta\). This phenomenon is due to the larger contact pressure in the upper contact area resulting in a larger strain, which destroyed the originally uniformly distributed contact pressure, leading to the numerical result of the middle area being smaller than the theoretical value. As a result, more severe fr
# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
## 3 Detailed Description of the Method
### Feature Detection and Stereo Matching  
Many feature detectors have appeared in SVO research, such as Harris [20], Shi-Tomasi [16], FAST [21], SIFT [22], and SURF [23]. These detectors have their own advantages and disadvantages. Note that both SIFT and SURF have been proven to be invariant to certain changes in perspective. The latter builds upon the former but uses box filters to border on the Gaussian, contributing to a faster computation [3]. Therefore, in this work, a SURF detector was employed to search for interest points in a first stereo frame and compute the SURF descriptors, where the stereo frame represents the left and right images taken at the same time.  
Figure 1: Flowchart of the proposed approach.  
After feature detection, stereo matching was performed on the basis of the similarity of the SURF descriptors, and an epipolar constraint was imposed. Specifically, with respect to the similarity measurement, the Euclidean distance between each descriptor in the left image and all the descriptors in the right image was calculated. Two feature points are considered to have correspondence only if their descriptors satisfy both conditions. First, the distance between two candidate points is less than a predefined threshold. In this case, for the normalized SURF descriptor, the distance threshold was set to 0.35. In addition, the distance from all other candidate points is larger than a certain threshold. This is implemented by checking whether the ratio between the closest and the second closest match is small enough. Typically, the ratio threshold for determining whether the correspondence is still live is set to 0.6. In addition, the epipolar constraint means that, for a well-calibrated stereo rig, the row coordinates of the correspondence feature points are approximately equal to the noise tolerance of one pixel.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion  
### Applications of Flexibility Quantification  
Flexibility quantification can be used to inform the integration of renewable energy resources, enhance power grid stability and security, support DR program development, and guide policy development.
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 4 Conclusions  
A SnO\({}_{2}\)-based MOS sensor is used to investigate the detection of VOCs in fermented beverages such as beer, wine and distillates. Synthesized and characterized SnO\({}_{2}\) is used to discriminate the concentration of diacetyl vapors in different operating atmospheres, as to replicate a typical alcoholic fermentation scenario.  
Under air, N\({}_{2}\) and CO\({}_{2}\) gases, SnO\({}_{2}\) shows different behavior and baseline resistances due to different interactions with its surface. In particular, measurements carried out under air and N\({}_{2}\) atmospheres show the n-type behavior of the SnO\({}_{2}\) sensor, whereas measurements carried out in CO\({}_{2}\) ambient prove that SnO\({}_{2}\) acted as a p-type sensor.  
Tests performed in aqueous and alcoholic diacetyl solutions show good response in terms of diacetyl detection in the range of concentration 0.2-3.2 mg/L at 200 \({}^{\circ}\)C in CO\({}_{2}\) atmosphere, with LDL of 0.1 mg/L and 0.01 mg/L, respectively.  
Response and recovery times trends reveal the diacetyl concentration dependance, with response and recovery times constant less than 50 s.  
Figure 6: Time parameters for sensor exposed at different concentration of diacetyl in alcoholic solution and a CO\({}_{2}\) environment; (**a**) sensor response and recovery times at different diacetyl concentration; (**b**) plot of time constants as a function of diacetyl concentration. Error bars are calculated on three runs.  
**Author Contributions:** Conceptualization, A.G., A.F., L.B. and A.D.; methodology, A.G., A.F. and L.B.; formal analysis, A.G. and A.F.; investigation, A.G. and A.F.; data curation, A.G.; writing--original draft preparation, A.G. and A.F.; writing--review and editing, A.G. and L.B.; supervision L.B. and A.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was co-financed with the support of the European Commission, the European Social Fund and the Calabria Region. The author is solely responsible for this research and the European Commission and the Calabria Region decline any responsibility for the use that may be made of the information contained therein.  
**Institutional Review Board Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion
### Policy Implications  
In recent years, the combination of remote sensing and big data has provided a new approach to housing vacancy research. As a dynamic and variable phenomenon, it is impossible to evaluate housing vacancy through a single piece of data. The advent of big data fills this gap. The real-time nature of big data allows us to monitor the state of housing and understand the status of housing use at any time. In terms of research methods, big data can be combined with remote sensing to develop a real-time housing vacancy monitoring method to keep track of the state of housing in cities. Transforming scientific research into practical applications.  
The conclusions of this study provide a reference for government departments to formulate relevant policies to solve the problem of vacant housing and alleviate housing pressure.  
1. In areas with serious housing vacancy problems, such as the suburbs, it is recommended to limit large-scale real estate development, strictly control the number of houses, and avoid oversupply. However, the vacancy rate in the city center was low, which indicates that there is a high demand for housing in these areas. Authorities should provide more housing for home buyers/renters, ensure the housing supply, and alleviate housing pressure. For example, housing needs can be met by increasing the proportion of plots in urban areas.
2. We suggest guiding developers to develop rationally and avoid blindly developing large and high-priced houses to pursue profits, resulting in a mismatch between the supply and demand structure.
3. For areas with high altitudes, due to the fact of remoteness and other reasons, it is recommended to further strengthen the construction of housing infrastructure, provide residents with convenient transportation, mature and perfect facilities, etc., to meet the convenience needs of residents in work and daily life.
4. Finally, we also suggest curbing speculation by some developers (e.g., overselling) that lead to a large number of vacant homes.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 3 LoRa Technology
### LoRa Bit Rate and Sensitivity
The lower bound of the signal-to-noise ratio (SNR) at the receiver, at which it is still possible to demodulate the LoRa signal, depends on the SF, according to the values reported in Table 3; the greater the SF, the lower the signal-to-noise ratio that must be guaranteed for reliable communications. From the lower bound of the SNR and from the BW, the receiver sensitivity (S), expressed in dBm, can be estimated as:  
\[\text{S}=-174+10\log_{10}(\text{BW})+\text{NF}+\text{SNR} \tag{8}\]  
where \(-174\) is the noise floor, BW is the bandwidth, NF is the noise figure and is equal to 6 dB, and SNR is the signal-to-noise ratio limit for the specified SF. As an example, some values of sensitivity based on the values of BW and SF, with CR = 1, are reported in Table 4.  
The larger the distance between the transmitter and the receiver, the smaller the signal level will be at the receiver due to the degradation of the signal in the channel; if the signal level is lower than the sensitivity S reported in Table 3, it cannot be detected by the receiver. Equation (8) shows that also the bandwidth influences the sensitivity. Therefore, as the distance between the transmitter and the receiver increases, the SF must be increased and the BW reduced to ensure suitable receiver \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**SF**} \\ \hline
**BW** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
125 & 5468 & 3125 & 1757 & 977 & 537 & 292 \\
250 & 10,937 & 6250 & 3515 & 1953 & 1074 & 585 \\
500 & 21,875 & 12,500 & 7031 & 3906 & 2148 & 1171 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bit rate values depending on bandwidth (BW) and spreading factor (SF), with code rate (CR) = 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**SF** & **SNR Limit** \\ \hline
7 & \(-7.5\) \\
8 & \(-10\) \\
9 & \(-12.5\) \\
10 & \(-15\) \\
11 & \(-17.5\) \\
12 & \(-20\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Minimum signal-to-noise ratio (SNR) to be guaranteed for signal demodulation based on SF values. sensitivity to obtain a reliable communication. Therefore, it can be stated that with low values of SF and maximum BW values, greater values of the bit rate, lower sensitivities, and, thus, lower achievable ranges are obtained. As an example, a sensitivity as low as \(-137\) dBm and an output power, \(TX_{POWER}\), of \(+17\) dBm results in a link budget of more than 154 dB, since the link budget (L), expressed in dB, is:  
\[\text{L(dB)}=Tx_{POWER}(\text{dBm})+\text{S(dBm)} \tag{9}\]  
LoRa modulation is a physical layer implementation that provides significant improvement to the link budget compared to conventional narrowband modulation. In general, the technology penalizes the throughput to obtain an advantage in terms of robustness and presents an optimal trade-off between transmission speed and communication distance.
## 2 Engineering Overview
### On-Site Observation of the Surrounding Rock Characteristics in the II5 Rail Rise  
As can be observed from the photos of roadway damage in the test section given in Figure 2, the soft and broken surrounding rock of the roadway displays serious asymmetric  
Figure 1: Basic information of the roadway. (**a**) Layout plan of the II5 Rail Rise; (**b**) Initial support of the roadway; (**c**) Geological histogram.  
deformation and support failure phenomena under the long-term action of deep high-ground stress, including floor heave, side-beam and top-beam bending, twisting, kneeling leg, pulp burst, rib and roof bulge, etc. The monitored data of the roadway surface displacement show that the rib-to-rib displacement is 1000-1500 mm and the floor heave is 800-1200 mm, which threatens the safety of coal mining.  
The boundary of the loose circle was measured using the single-borehole method with the aid of the CT2 ultrasonic surrounding rock crack detector (Figure 3a). Considering the serious overall roadway damage, test boreholes were arranged in the full section of the tested roadway (Figure 3b). Boreholes 1#-5# were located at the left rib, the left 45\({}^{\circ}\) shoulder corner, the midpoint of the roof, the right 45\({}^{\circ}\) shoulder corner, and the right rib of the roadway, respectively. Since sound waves propagate more slowly in the fracture zone than in the original rock mass, the wave velocity fluctuated obviously at the periphery of the loose circle. Hence, the abrupt change point of the sound velocity was taken as the boundary of the loose circle. Figure 3c shows the relationship between the wave velocity and the borehole depth. It can be seen that with the increase in the borehole depth, the wave velocity curves of the five boreholes rose gradually, then reached their peaks at the critical points, and finally tended to stabilize. The test results show that the average sizes of the loose circle corresponding to boreholes 1#-5# were 3.0 m, 2.2 m, 1.8 m, 2.5 m, and 2.9 m, respectively. The large sizes of the loose circle at the roadway left rib (borehole 1#) and the right shoulder corner (borehole 4#) are indicative of serious breakage and internal crack development in the surrounding rock at these two positions. Therefore, the real range of the loose circle obtained in this test was about 3.0 m, and it was difficult to effectively control the deformation of the roadway surrounding rock with merely the shed support.  
Figure 2: Roadway damage in test section. (**a**) Floor heave and side-beam and top-beam bending; (**b**) Pulp burst; (**c**) Rib and roof bulging.
# Spare Parts Made by Additive Manufacturing to Improve Preventive Maintenance
## 4 Discussion  
The selection of parts was not efficient or productive, requiring a lot of time to select the parts. The lists obtained are a small sample with an average of 15-20 parts per list compared to the total number of parts in the inventory. The part classifications found in the database are not adequate, as the choice of candidate parts for AM requires technical criteria. Moreover, the criteria chosen in the above listings are economic criteria, for which price and consumption have been considered as being more important than any other parameter. In the database, the parts have been ordered according to two variables: decreasing consumption (from the most consumable part to the non-consumable part) and decreasing price (from the most expensive part to the cheapest part). Thus, the verification of each part was necessary to study its possibility of being 3D printed, which complicated the selection. There is a need to improve the databases so that they can be linked with technical information data that can help to study 3D printability. It is worth mentioning that the material, dimensions and documentation of the numerical definition (NDF), i.e., 3D files and drawings, are not available for some parts.  
An update of existing databases and the addition of missing data should be undertaken for efficient use of the inventory in the future. The printed spare parts should be tested in the company to study an example of use in preventive maintenance and they should be put in the same conditions as the regular ones to compare their operating parameters. In order to solve these problems, one suggestion would be to implement a control model for the spare parts inventory. [PERSON] et al. [37] have proposed models that use predictions of the Remaining Useful Life (RUL) of the monitored components, obtained through a Health Monitoring and Prognosis (PHM) system. This is intended to predict future demands for spare parts used in preventive maintenance [38].
## 3 Results and Discussion
### Analysis of Typical Performances
#### 3.2.1 Crystallization Characteristics
In general, doping microcapsules can reduce the crystal grain size, increase the grain number, and concentrate the distribution of grain size, thus affecting the crystallization characteristics of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & \(\mathbf{T_{p}}\) (\({}^{\circ}\)**C**) & \(\mathbf{\Delta T_{p}}\) (\({}^{\circ}\)**C**) & \(\mathbf{\Delta H_{m}}\) (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}\) **(\%)** \\ \hline Pure LDPE with original process & 124.71 & 7.77 & 123.1 & 41.93 \\ Pure LDPE with modified process & 124.57 & 5.75 & 119.6 & 40.74 \\ Microcapsule/LDPE composite & 124.82 & 5.46 & 129.1 & 44.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Crystallinity and melting peak of samples. Figure 6: The changes in crystallinity in the polyethylene material. polyethylene. On the one hand, the reduction in preparation pressure can reduce the efficiency of induction nucleation, and the prolongation of preparation time can destroy the grain structure, leading to the homogeneous nucleation of polyethylene. On the other hand, the interface between the microcapsule and matrix can enhance heterogeneous nucleation, increasing the number of spherulites in the polyethylene matrix.
## 5 Experiment
### Experimental Setup
#### 5.1.2 Evaluation Metrics  
In this paper, exact set match accuracy and execution accuracy were adopted as the evaluation metrics for Spider, while only execution accuracy was adopted for Spider-Syn. For exact set match accuracy, instead of simply conducting a string comparison between the predicted and gold SQL queries, each SQL query was decomposed into several clauses, where a set comparison was performed. Exact set match accuracy is given by the following formula:  
\[\text{EM}=\frac{\text{TF}}{\text{TF}+\text{NF}}, \tag{12}\]  
where EM represents the obtained exact set match accuracy, TF denotes the number of results that are the same as the gold SQL, and NF represents the number of results that are generated incorrectly. Execution accuracy is given by the following formula:  
\[\text{EX}=\frac{\text{TQ}}{\text{TQ}+\text{TQ}} \tag{13}\]  
where EX represents the obtained execution accuracy, TQ denotes the queries that produce the same execution results as the gold SQL, and NQ represents the queries with different results.  
Exact set match accuracy and execution accuracy provide a comprehensive evaluation of the effectiveness of SQL generation at both structural and semantic levels, ensuring that the model is both precise and practical in the task of SQL query generation.
###### Abstract  
Hydrogen (H\({}_{2}\)) production from biomass has emerged as a promising alternative to fossil-based pathways, addressing the global demand for low-carbon energy solutions. This study compares the environmental impacts of two biomass-based H\({}_{2}\) production processes, biogas reforming and agricultural residue gasification, through a life cycle assessment (LCA). Using real-world data from the literature, the analysis considered key system boundaries for each process, including biogas production, reforming, and infrastructure, for the former, and biomass cultivation, syngas generation, and offgas management, for the latter. Environmental impacts were evaluated using SimaPro software (Version 9.4) and the ReCiPe midpoint (H) method. The results revealed that biogas reforming emits approximately 5.047 kg CO\({}_{2}\)-eq per kg of H\({}_{2}\), which is 4.89 times higher than the emissions from agricultural residue gasification (1.30 kg CO\({}_{2}\)-eq/ kg H\({}_{2}\)), demonstrating the latter's superior environmental performance. Gasification consumes fewer fossil resources (3.20 vs. 10.42 kg oil-eq) and poses significantly lower risks to human health (1.51 vs. 23.28 kg 1,4-DCB-eq). Gasification water consumption is markedly higher (5.37 compared to biogas reforming (0.041 m\({}^{3}\)/ kg H\({}_{2}\))), which is an important factor to consider for sustainability. These findings highlight gasification as a more sustainable H\({}_{2}\) production method and emphasize its potential as an eco-friendly solution. To advance sustainability in energy systems, integrating socio-economic studies with LCA is recommended, alongside prioritizing agricultural residue gasification for hydrogen production.  
biogas reforming; hydrogen; gasification; LCA; low carbon; energy; GHG emission; socio-economic; eco-friendly Article