esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
From this research, we found that the OBIA labels can help the models produce satisfying predicted labels with IoU values of target built-up structures greater than 0.5 even though the Manual labels still outperform the OBIA labels for each camp during each season. Intermixing the data from two camps and two seasons (TCTS) can slightly improve the performance of models trained with the OBIA labels. However, the other two strategies (OCTS and TCOS) cannot bring stable improvement and even sometimes harm the models. In addition, we found that both types of labels cannot properly detect brownish facility buildings, brownish dwellings partially occluded by trees, nor densely distributed large dwellings covered by brown cloth. The falsely predicted pixels around the boundary of target built-up structures may be the main source of prediction errors compared with other sources. Additionally, the reason that the Manual labels outperform the OBIA labels is mainly because the models trained with the Manual labels can produce fewer errors around the boundaries of structures. Additionally, it can be observed that the IoU values are higher during the wet season than during the dry season. This is likely because of the higher similarity between built-up structures and the surrounding environment during the dry season. Furthermore, we found fine-tuning models pretrained with the OBIA labels from Scenario I with smaller shares (10%, 20%, 30%, 40%, and 50%) of the Manual labels can enable the models to produce comparable or even better IoU values than the models trained with purely Manual labels.  
These outcomes prove the remarkable potential of the OBIA labels to replace the Manual labels in future research. More research is required to make better use of the produced OBIA labels for future humanitarian operations.  
Conceptualization, Y.G., S.L. and D.T.; methodology and formal analysis, Y.G.; investigation, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, Y.G., S.L., D.T., G.W.G. and L.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, the Christian Doppler Research Association (CDG), and Medecins Sans Frontieres (MSF) Austria.  
Not applicable.  
Note applicable.  
**Data Availability Statement:** Those who are interested in the data can send data requests to [EMAIL_ADDRESS]. The data will be provided pending approval of the request by Medecins Sans Frontieres (MSF) Austria.  
The authors declare no conflict of interest.
## 5 LEFM-Based Analytical Model
### Assessment of Analytical Predictions  
The proposed analytical model is further assessed by taking advantage of the available test results and of nominal material properties earlier discussed. Parametric calculations were carried out on the grouped specimens (average estimates) in terms of shear force and stress, by changing the adhesive type, arrangement and environment condition.  
In this regard, the analytical model proved to offer reliable estimates for both the parameters of force and stress agreeing with the general trends of Figure 14. Comparative examples are proposed in Figure 15, in terms of force or stress, as obtained for grouped specimens as a function of their service class. It should be noted that the error ratio R is found both to overestimate or underestimate the expected parameter, for all the types of BiR specimens. Most importantly, however, is that the collected \(R\) values confirm the rather small scatter for all the analytical predictions, thus confirming the validity and accuracy of the approach.  
Figure 14: Analytical prediction of shear (**a**) force, (**b**) stress and (**c**) strain in BiR connections with epoxy or polyurethane adhesives (examples for 9% moisture and parallel rod-to-glue arrangement).
## 4 Experimental Validation and Result Analysis
### RUL Prediction Process and Result Analysis
#### 4.2.3 Validation of the Generalization Ability of the Prediction Model
By applying the proposed RUL prediction model to realize the prediction of bearing RUL, preventive maintenance of machinery can be achieved, which provides staff with time to formulate maintenance strategies and reduces economic losses caused by faults.
# A New Algorithm for Computing the Distance and the Diameter in Circulant Graphs
## 4 Diameter Formulas for Circulant Graphs
### Diameter of \(C_{n}(1,s)\) When \(\lambda\leq\gamma\)  
Theorem 7 ([4]): Let \(s=a\gamma+b\), \(a=\lfloor\frac{s}{\gamma}\rfloor\), and \(s\equiv b\pmod{\gamma}\). Define \(p_{0}=\lfloor\frac{\lambda+\gamma}{2}\rfloor\), \(p_{1}=\lfloor\frac{\gamma-b+(a+1)\lambda+1}{2}\rfloor\), \(p_{2}=\lfloor\frac{\gamma+b+(a-1)\lambda+1}{2}\rfloor\), \(p_{3}=\lfloor\frac{b+a\lambda+1}{2}\rfloor\), and \(p_{4}=(\gamma+b)(a\lambda-\lambda+1)\). Let \(e=\min\{\max\{p_{1},p_{3}\},\max\{p_{0},p_{2}\}\}\). If \(\lambda\leq\gamma\) and \(b\leq a\lambda+1\), then  
\[diam(C_{n}(1,s))=\begin{cases}p_{1}-1&\text{if }p_{1}=p_{2}\text{ and }p_{4}\equiv 1 \pmod{2},\\ e&\text{otherwise.}\end{cases}\]  
For the remaining values of \(n\) and \(s\), although our algorithm yields an exact value for the diameter of \(C_{n}(1,s)\), it does not provide a general explicit formula. To address this limitation, we proceed to present upper bounds on the diameter of \(C_{n}(1,s)\) for all possible values of \(n\) and \(s\) in the upcoming section.
## 3 Results
### Mechanical Characterization
#### 3.3.4 Repeated Load Axial Test  
The durability of the bituminous mixtures was also evaluated in terms of rutting resistance. In fact, this represents a common pavement disease for high bitumen content mixtures for surface layers. According to the [45] standard, method A, a bituminous sample is placed between two parallel plates. The upper plate applies a cyclic compression load and has a dimension smaller than the bituminous sample in order to achieve a certain confinement. The standard specifies a load frequency of 0.5 Hz and a pressure of 100 \(\pm\) 2 kPa. The specimen's accumulated axial deformation is measured after 3600 loading cycles at 40 \({}^{\circ}\)C. Three SGS samples (100 gyrations, [40]) were tested for each mixture and the average results are presented in Table 10 and Figure 5.  
Overall, in terms of accumulated strain, there is not a remarkable difference between the two mixtures. The reference SMA, confirming the higher performance highlighted in the previous mechanical characterization, recorded the lowest accumulated strain and, therefore, the highest creep modulus. In this case, the reference technical specifications do not specify any limit for the RLAT but the obtained results were in line with values recorded in previous studies on SMAs with polymer-modified bitumen and modified fibers [38].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mixture** & **Accumulated Strain (\%)** & **Creep Modulus (MPa)** \\ \hline
**SMA0** & 0.36 (\(\pm\)0.1) & 29.6 \\
**SMAF** & 0.38 (\(\pm\)0.1) & 26.5 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Average RLAT results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mixture** & **Thickness (mm)** & **Max Load (N)** &
\begin{tabular}{c} **Displacement** \\ **(mm)** \\ \end{tabular} & **ITS\({}_{\textbf{wet}}\) (MPa)** \\ \hline
**SMA0\_1** & 60.4 & 10,540 & 2.40 & 1.11 \\
**SMA0\_1** & 60.0 & 10,420 & 2.32 & 1.11 \\
**SMA0\_1** & 60.2 & 11,250 & 2.40 & 1.19 \\
**avg, SMA0** & - & - & - & 1.13 \\
**SMAF\_1** & 61.8 & 7280 & 4.06 & 0.75 \\
**SMAF\_1** & 60.9 & 10,290 & 2.76 & 1.08 \\
**SMAF\_1** & 61.2 & 9020 & 3.50 & 0.94 \\
**avg, SMAF** & - & - & - & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Indirect tensile strength results after saturation in water at 40 \({}^{\circ}\)C for 72 h.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mixture** & **ITS (MPa)** & **ITS\({}_{\textbf{wet}}\) (MPa)** & **ITSR (\%)** \\ \hline
**SMA0** & 1.29 (\(\pm\)0.04) & 1.13 (\(\pm\)0.02) & 88 \\
**SMAF** & 0.99 (\(\pm\)0.03) & 0.92 (\(\pm\)0.03) & 93 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average ITSR results.  
The lower performance of the experimental SMA might be related to the higher porosity of the mixture and therefore to the lower stiffness recorded for the SMAF mixture during the ITSM characterization at high temperatures.
## 2 Methods
Since \(D\) is ignored, \(E\) solved by Equation (6) is not accurate enough and can be represented as \(E_{near}\), as mentioned above. Then, the \(E_{near}\) is subtracted from the original measurement data to obtain the data processed first. 2. The data processed first are plugged into Equation (5) to solve for an approximate wavefront. With this approximate wavefront, the approximate \(D_{near}\) is calculated, which represents the sub-aperture aberration signal. The \(D_{near}\) is subtracted from the original measurement data to obtain the data processed second. 3. The data processed second are plugged into Equation (6) to obtain a more accurate \(E_{near}\). By iterating through these steps, continually updating \(E\) and \(D\), the process is repeated until the relative change in \(D\) is less than a threshold, which is set for simulations in this paper as 0.0055 \(\upmu\)m.
# New Trends in Separation Techniques of Lithium Isotopes: A Review of Chemical Separation Methods
## 1 Introduction
### Challenges in Development of Separation Methods for Lithium Isotopes Using Electrochemical
#### 2.2.1 Challenges in Development of Separation Methods for Lithium Isotopes with Electromigration
It was found that the \({}^{6}\)Li isotope separation factor with electrodialysis using highly porous Teflon film of 3 mm thickness was larger than that using highly porous Teflon film of 1-2 mm thickness [27]. In particular, \({}^{6}\)Li isotope separation factor using the Teflon of 3 mm thickness was about 1.15 at 0.01% of Li movement rat; on the other hand, using the Teflon of 1-2 mm thickness it was about 1.05 [27]. Besides the use of ionic liquids, the separation of Li isotopes with electrodialysis also can be achieved using lithium solid ceramics. [PERSON] et al. developed a method for separation of lithium isotopes using La\({}_{0.57}\)Li\({}_{0.29}\)TiO\({}_{3}\) as a solid ceramic electrolyte in the temperature range 298.15-323.15 K [28]. With the respect to the rate of transfer of \({}^{6}\)Li and  
Figure 2: Separation of lithium isotopes with electrodialysis using an impregnated organic membrane with PP13-TFSI ionic liquid. The formula of ionic liquid was generated using InChI (Computed by InChI 1.0.6-PubChem release 7 May 2021) within the software Freeware ACD/ChemSketch 2021 2.0 version (Advanced Chemistry Development, Inc., Toronto, ON, Canada). Adapted from [27]. \({}^{7}\)Li into water with electrodialysis, an Arrhenius-type dependence of temperature was concluded [28]. Overall, this study showed that the apparent activation energy of \({}^{7}\)Li is approximately 5% higher than that of \({}^{6}\)Li, and this is attributable to the quantum effect [28]. Nevertheless, \({}^{6}\)Li isotope separation factor was not calculated in the above-mentioned paper and further studies are needed. Furthermore, the lithium solid ceramics can be used to separate lithium ions from sodium and potassium, and [PERSON] et al. used lithium composite membrane to separate lithium from sodium ions [29] with electrodialysis, but the selectivity coefficient of Li\({}^{+}\) over Na\({}^{+}\) was recorded to 112.3, a value which is higher than the selectivity coefficients previously reported in other electrodialysis methodologies [30]. Nevertheless, cation-exchange resins methods are usually preferred as preconcentration steps employed for the separation of lithium from other alkaline ions. Challenges in Development of Separation Methods for Lithium Isotopes Using Displacement Chromatography Methods
# Simulation of the Cooling Effect of Porous Asphalt
## 5 Finite Element Simulation of the Pavement Temperature Field
### Model Establishment
#### 5.1.1 Boundary Conditions  
High-temperature instability damage to pavement mostly occurs in asphalt pavement when exposed to high-intensity light exposure and drying conditions. Therefore, in evaluating the influence of the air voids on the cooling effect of PAC pavement, the pavement was considered in dry conditions and with no water in the voids. Environmental factors that affect the temperature field of the pavement include solar radiation, air temperature, and wind speed. The convective heat transfer coefficient of each PAC specimen was studied as mentioned above. In addition, it was necessary to determine the daily variation of solar radiation and air temperature.  
(1) The Variation of Daily Solar Radiation  
The daily variation of solar radiation \(q(t)\) used in the models is shown in Equation (5):  
\[q(t)=\left\{\begin{array}{ll}0&0\leq t\leq 12-\frac{\zeta}{2}\\ q_{0}\cos m\omega(t-12)&12+\frac{\zeta}{2}\leq t\leq 12+\frac{\zeta}{2}\\ 0&12+\frac{\zeta}{2}\leq t\leq 24\end{array}\right. \tag{5}\]  
where \(q_{0}=0.131m\)Q is the maximum radiation intensity at noon (J/m\({}^{2}\)), \(m=12/c\), Q is the total amount of solar radiation in a day (J/m\({}^{2}\)), \(c\) is the effective sunshine hours (h), and \(\omega=2\pi/24\) is the angular frequency (rad).  
(2) The Variation of Daily Temperature  
Two sinusoidal functions were used to express the variation of daily temperature, as shown in Equation (6):  
\[T_{a}=\overline{T}_{a}+T_{m}[0.96\sin\omega(t-t_{0})+0.14\sin 2\omega(t-t_{0})] \tag{6}\]  
where \(T_{a}\) is the air temperature (\({}^{\circ}\)C); \(\overline{T}_{a}\) = (\(T_{max}\) + \(T_{min}\))/2 is the average daily temperature (\({}^{\circ}\)C), \(T_{m}\) = (\(T_{max}-T_{min}\))/2 is the amplitude of daily temperature (\({}^{\circ}\)C), and \(t_{0}\) is the initial phase.  
In this study, \(T_{max}\) = 40 \({}^{\circ}\)C, \(T_{min}\) = 26 \({}^{\circ}\)C, and \(t_{0}\) = 3.
## 4 Analysis and Results
### Signal Lane Usage While Entering and Exiting Junction Control
The observed significant association between the factors of junction control and turn signal usage while entering and exiting suggests that traffic signals have a substantial impact on driver behavior in terms of signaling intentions at roundabouts. Signalized roundabouts provide drivers with clear visual cues and regulatory guidance, creating a higher level of compliance with turn signal usage while entering and exiting. The presence of traffic signals acts as a constant reminder for drivers to signal their intentions both when entering and exiting the intersection, leading to a significantly better observed rate of turn signal usage in signalized roundabouts compared to non-signalized roundabouts. Figure 5 presents a graphical illustration showcasing a summary of the results for all cases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Junction** \\ **Control Type** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Number of Vehicles** \\ **That Used Turn** \\ **Signals While Exiting** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Number of Vehicles** \\ **That Did Not Use** \\ **Turn Signals** \\ **While Exiting** \\ \end{tabular} } & \multicolumn{2}{c}{**Rate of Turn**} & \multirow{2}{*}{\(\chi^{2}\)**} & \multirow{2}{*}{\(p\)-Value**} & \multirow{2}{*}{**Cramer’s V**} \\  & & & & & & \\ \hline Signalized & 72 & 28 & 72\% & \multirow{2}{*}{40.504} & \multirow{2}{*}{0.001} & \multirow{2}{*}{0.450} \\ Non-signalized & 27 & 73 & 27\% & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Chi-square test and Cramer’s V of turn signal usage while exiting signalized and non-signalized roundabouts. Figure 5: A graphical illustration of the results.
# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 2 Material and Methods
### Weather Station  
Because of their quality and relevance, it is important to use data from the nearest meteorological station that provides the most high-resolution time data possible. At the same time, this station must provide good quality measurements and sensor placements. Both mentioned issues have a decisive influence on the results of the experiment.  
The meteorological station is also capable of maintaining a history, inspecting the sensors and validating the data. With daily monitoring of the station's performance, we can obtain metadata to help us when we detect certain extreme values that may indicate a failure of the station or a particular sensor.  
These stations need to be designed as closely as possible according to the World Meteorological Organization (WMO) standards [12], which ensure comparability between other measurement data recorded on different meteorological instruments worldwide. There are two main characteristics of the station, as follows:  
* The location;
* The architecture of the meteorological station.  
The first characteristic refers to a particular micro-location, while the architecture determines the type and quality of the weather data acquired by the control unit.  
The meteorological station used in our study is located at an altitude of 291 m above sea level. The coordinates of the station are 46,6002\({}^{\circ}\) north latitude and 15,8770\({}^{\circ}\) east longitude. Thus, the red square in Figure 1a indicates the location of the meteorological station. As seen in the satellite image Figure 1a, our weather station is located in an area with a garden nearby, no buildings, and no forest in a 50 m radius. The terrain is a bit hilly, so the station is on a small hill. For the wind parameter, only the forest blocks the east side; other directions are open.  
As the station is located in a small village, there is no urban area within a few kilometers of the station. There is a lot of vegetation around. The location was also carefully chosen, taking into consideration WMO standards. Few trees are located near the station; moreover, there is a clear view, and the effects of the urban area should be minimal.  
In Figure 1b, the architecture is described by the position of the sensors with which the meteorological station is equipped. The sensors are either embedded into or connected to the central unit. All the components of the station are designated with numbers surrounded by circles in the figure, denoting the following:  
1. The central unit with an embedded air pressure sensor and integrated solar panels;
2. The rain gauge;
3. The anemometer;
4. The Stevenson screen containing temperature and relative humidity;A webcam that can record the whole setup and presents the real weather situation at the micro-location online.  
The mentioned components are discussed in detail in the remainder of the paper.
## 4 A Routing Protocol for LLNs (RPL)
### RPL Objective Functions for Non-Malicious LLNs
#### 4.2.3 Fuzzy Logic Method
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimisation Method** & **RPL Objective Functions** & **Routing Metrics** & **Metrics Category** \\ \hline \multirow{8}{*}{Single Metric Method (Group 1)} & OF0 [81] & Hop Count & Node Metric \\  & ENTOT [90] & Energy Consumption & Node Metric \\  & Energy-OF [91] & Node’s Remaining Energy & Node Metric \\  & MRMFO [85] & ETX & Link Metric \\  & AVG-DEL [92] & Average Delay & Link Metric \\  & RSSI-based IPv6 [93] & RSSI & Link Metric \\  & ELITE [94] & SPR & Link Metric \\ \hline \multirow{8}{*}{Combined Metrics Method (Group 2)} & WRF-RPL [98] & Remaining Energy and Parent No. & Node Metric \\  & CA-OF [99] & ETX and Buffer Occupancy & Link Metrics \\  & QU-RPL [100] & ETX, Hop Count, and Queue Utilisation & Both \\  & CAQL [101] & Congestion Level and ETX & Link Metrics \\  & PER-HOP ETX [54] & ETX and Hop Count & Both \\  & ETX, Forwarding Traffic, Energy & Both \\  & L2 AM [103] & ETX and ELC & Both \\  & I-RPL [104] & Child No., Parent No., Hop Count, ETX, & Both \\  & CB-RPL [105] & ETX, Hop Count, and & \\  & & Remaining Energy & \\  & CMOF [108] & ETX and Latency & Link Metrics \\  & FMOF [109] & ETX, Hop Count, and RSSI & Both \\  & FAHP-OF [110] & ETX, Hop Count, and RSSI & Both \\  & OPT-FLQE [111] & SPRR, SRNP, ASNR, and ASL & Link Metrics \\  & OF-Fuzzy [112] & ETX, Delay, and Node’s Remaining & Both \\  & OF-FL [113] & ETX, Delay, Hop Count, and Node’s & Both \\  & OF-EC [114] & RETA and Energy Consumption & Both \\  & FLEOF [115] & ETX, Energy Consumption, and & Both \\  & Hop Count & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Categories of RPL objective functions for non-malicious LLNs and the used metrics. required to update or specify new rules. For example, in OF-FL [113], the if-then rules have been set based on the following network scenario: an area of 600 \(\times\) 600 m with 100 nodes. Thus, OF-FL considers a destination node with ten hop counts away from the source node as average hops compared to the area size and node density. However, in a different network scenario, e.g., 100 \(\times\) 100 area with 20 nodes, ten hop counts are considered very far, while the average hop count may lie between three and five hops. Therefore, human intervention is needed to update the fuzzy rules and membership functions to obtain more accurate results for OF-FL in a smaller network area with lower node density. It should be emphasised that, to the best of our knowledge, none of the existing RPL solutions mentioned in Section 4.2 take into consideration the reliability issues caused by malicious nodes.
## 4 Multi-Objective Solution  
### Non-Preemptive Solution Approach  
The proposed optimization model in this study considers multiple objectives. Thus, the single-objective, weighted-sum method known as a non-preemptive approach is presented to find a solution from a model with multiple objectives. That is, the ideal (\(I\)) and anti-ideal (\(AI\)) solutions are initially computed, representing the best and worst solution obtained on solving a particular objective function. Then, the linear normalization technique shown in (58) is further used to obtain comparable objectives regardless of their initial units by converting each objective function with different units of measurement to be in the range [0,1]. Here, whereas \(Z_{i}^{I}\) indicates the ideal solution based on the max \(Z_{i}\) for the benefit criterion and min \(Z_{i}\) for the cost criterion, \(Z_{i}^{AI}\) indicates the anti-ideal solution based on min \(Z_{i}\) for the benefit criterion and max \(Z_{i}\) for the cost criterion. Then, all the linearly normalized objective functions are transformed to a maximization problem and can be reformulated as the weighted objective model, asc presented in (59). Weight \(\alpha_{i}\) emphasizes the importance of objective function \(i\), which can be obtained using the AHP method presented in the next section.  
\[0\leq\frac{Z_{i}-Z_{i}^{AI}}{Z_{i}^{I}-Z_{i}^{AI}}\leq 1\text{ for benefit criterion; }0\leq\frac{Z_{i}^{AI}-Z_{i}}{Z_{i}^{AI}-Z_{i}^{I}}\leq 1 \text{ for }\cos\text{t criterion} \tag{58}\]  
\[\text{Maximize }\alpha_{1}\left(\frac{Z_{1}^{AI}-Z_{1}}{Z_{1}^{AI}-Z_{1}^{I}} \right)+\alpha_{2}\left(\frac{Z_{2}-Z_{2}^{AI}}{Z_{2}^{I}-Z_{2}^{AI}}\right)+ \alpha_{3}\left(\frac{Z_{3}^{AI}-Z_{3}}{Z_{3}^{AI}-Z_{3}^{I}}\right)+\alpha_{4 }\left(\frac{Z_{4}^{AI}-Z_{4}}{Z_{4}^{AI}-Z_{4}^{I}}\right) \tag{59}\]