esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### S3 Analysis
The instrument was also provided with the auto-sampler head space system HT2010H, supporting a 42-loading-sites carousel and a shaking oven to equilibrate the sample head space. The vials were placed in a randomized mode into the carousel. Each vial was incubated at 50 \({}^{\circ}\)C for 5 min in the auto-sampler oven and shaken for 1 min during the incubation. The sample head space was then extracted from the vial in the dynamic head space path and released into the carried flow (80 sccm). Figure 1C shows the experimental setup (S3 and auto-sampler with its carousel filled with vials). The analysis timeline can be divided into three different steps for a duration of 420 s (7 min) per sample, which are preceded by a warm-up step that allows for the achievement of the base line for the entire system:  
* _Injection_: the sample HS is flowed in the sensor chamber for 60 s (actual analysis time); then, for 30 s, environmental air flows through the same tube to clean it from any residual VOCs;
* _Restore_: when the _injection_ period is finished, the filtered air is flowed into the sensors camber. During this time (330 s), the sensors restore the original condition of the base line. Thanks to the processor integrated in the S3 instrument, the frequency at which the equipment works is equal to 1 Hz. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Materials** & & & & & \\
**(Type)** & **Composition** & **Morphology** & **Operating** & **Response to 5** & **Selectivity** & **Limit of Detection** \\  & & & **Temperature (\({}^{\circ}\)C)** & **ppm of Ethanol** & & **(LOD) of Ethanol (ppm)** \\ \hline SnO\({}_{2}\)Au (n) & SnO\({}_{2}\) functionalized & RGTO & 400 \({}^{\circ}\)C & 6.5 & 3 & 0.5 \\  & with Au clusters & & & & & \\ \hline SnO\({}_{2}\) (n) & SnO\({}_{2}\) & RGTO & 300 \({}^{\circ}\)C & 3.5 & 2.5 & 1 \\ \hline SnO\({}_{2}\) (n) & SnO\({}_{2}\) & RGTO & 400 \({}^{\circ}\)C & 4 & 2 & 0.8 \\ \hline SnO\({}_{2}\)A+Au (n) & SnO\({}_{2}\) grown with Au & & & & & \\ SnO\({}_{2}\)A+Au (n) & and functionalized with gold clusters & Nanowire & 350 \({}^{\circ}\)C & 7 & 2.5 & 0.5 \\ \hline SnO\({}_{2}\)A+Au (n) & SnO\({}_{2}\) grown with Au & Nanowire & 350 \({}^{\circ}\)C & 5 & 2.1 & 1 \\ \hline CuO (p) & CuO & Nanowire & 400 \({}^{\circ}\)C & 1.5 & 1.5 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Type, composition, morphology, operating temperature, response (\(\Delta\)R/R), selectivity (response ethanol/response carbon monoxide) and limit of detection (LOD) of ethanol for S3 sensors made at the SENSOR Laboratory.
## 3 Results
### Evaluation of MODIS GPP Products and MOD17 Algorithm in the Arid Region
#### 3.1.2 Biome-Specific Evaluation of MODIS GPP Product and MOD17 Algorithm  
The MOD17A2H GPP products and the other three model estimated GPP based on MOD17 algorithm were compared with the flux-derived eight-day time scale of GPP values for various biome types (Figure 5). We divided the original grassland into two types, grasslands, and desert grasslands, because of the large diversities in species and climate conditions in these sites. As shown in Figure 5, the original MOD17A2H GPP products were significantly underestimated in grassland, cropland and forest ecosystems, but not in the desert ecosystems. A good correlation between MOD17A2H GPP products and EC-observed GPP is illustrated in grassland ecosystems (R\({}^{2}\) = 0.82), followed by the cropland ecosystems (R\({}^{2}\) = 0.80) and forest ecosystems (R\({}^{2}\) = 0.53), while the weakest was in desert ecosystems (R\({}^{2}\) = 0.42). In addition, the slope of the linear regression for the scatter plot can also revealed the biases between MOD17A2H GPP and tower-observed GPP. We can see the slope of linear regression at the forest ecosystems is far from the 1:1 line, which demonstrates the largest biases between MOD17A2H GPP and the tower based GPP, followed by those of the cropland ecosystems, then the grassland and desert ecosystems. Therefore, it indicates that larger biases existed in most ecosystems in the arid regions of China, especially for the forest and cropland ecosystems. As we used the in situ forcing data, we did not find significant improvement for all biome types, and the simulations of GPP forced with in situ data in most ecosystems were still underestimated. However, as we optimized the parameters of the MOD17 model, the GPP simulation results were improved significantly in most ecosystems. The scatter points of modeled GPP and EC-measured GPP were distributed closely around the 1:1 line, indicating that the GPP simulation results can be improved after the parameter optimization of LUE\({}_{\text{max}}\) and other parameters in most ecosystems in  
Figure 4: Comparisons of annual GPP of MOD17A2H products and GPP simulations by MOD17 model with the flux tower GPP for all sites: (**a**) Original MOD17A2H products; (**b**) in situ meteorology forcing data; (**c**) only LUE optimized results; and (**d**) all parameters optimized results. The units of RMSE and rRMSE are gC/m\({}^{2}\)/y and %, respectively.  
the arid region. However, a larger bias still existed even after parameter optimization. The impacts of parameter optimization on GPP simulation of desert ecosystems were less, indicating that it is difficult to effectively simulate the GPP in desert ecosystems in the current MOD17 model.
## 1 Introduction
Besides, the capacity depends on the partial pressure of CO\({}_{2}\), temperature, and humidity [24; 25]. Absorption proved to not be economical for treating flue gas streams with CO\({}_{2}\) partial pressures lower than 15 vol% [26]. Chemical absorption has relatively high selectivity, but also high energy consumption for regeneration, chemicals' make-up, and high environmental impact [27; 28]. Taking into account the potential role of porous carbon materials for CO\({}_{2}\) capture, the authors propose the production of these materials starting from renewable sources, mainly residual biomasses. Hydrothermal carbonization (HTC) is an alternative synthesis method to produce precursors for high value-added renewable carbon materials from residual lignocellulosic biomasses [29]. This thermochemical conversion occurs in hot compressed water, at a relatively low temperature under autogenous pressure [30]. HTC optimization was studied considering the re-use of the liquid phases in the process itself [31; 32], the online monitoring of the carbonization time-course [33; 34], and the recovery of valuable platform chemicals from the liquid [35; 36]. Hydrochar attracts the attention for several potential applications, mainly in support of the energy production chain [37; 38]. Nevertheless, owing to its coal-like structure, hydrocarbon could be favorably transformed into porous activated carbon with tunable morphologies and porosities, which is hard to achieve using traditional pyrolytic methods, with a surface area up to 3000 m\({}^{2}\)g\({}^{-1}\) and pore volume in the range of 0.5-1.5 cm\({}^{3}\)g\({}^{-1}\)[39; 40]. The focus of this paper is on the separation of CO\({}_{2}\) for the upgrading of biogas to bio-methane by PSA onto HTC from silver fir sawdust, a waste product available in Central Europe and _Abies_ species available worldwide [41].
## 4 Optimal \(\alpha\) for Minimizing Power Consumption  
In Equations (8) and (22), the power allocated to each user's signal determines its point-to-point BER performance. Of cause, using a lot of power may guarantee the BER requirements of the users. However, the available energy of the BS is limited in general, and thus it is necessary to use the power efficiently. This can be accomplished by transmitting the signal with the minimum power at the BS while satisfying the required BER performance of each user. For given BER requirements for users, there exists the optimal power allocation coefficient, \(\alpha\), to minimize the required energy, \(\mathcal{E}\), at the BS. In general, the BER performance of the MUST-enhanced UE becomes improved as \(\alpha\) increases for a given \(\mathcal{E}\), while that of the MUST-based UE becomes improved as \(\alpha\) decreases for a given \(\mathcal{E}\). Thus, there exists a trade-off between the BER performances of the MUST-based UE and the MUST-enhanced UE according to \(\alpha\).  
In this section, we formulate the optimization problem to minimize the power consumption at the BS for given BER requirements for users. \(\mathcal{E}_{B}\left(\alpha,P_{b.B}^{req}\right)\) is defined as the required power at the BS to satisfy the BER requirement of the MUST-based UE \(P_{b.B}^{req}\) for a given \(\alpha\). Similarly, \(\mathcal{E}_{E}\left(\alpha,P_{b.E}^{req}\right)\) is defined as the required power at the BS to satisfy the BER requirement of the MUST-enhanced UE \(P_{b.E}^{req}\) for a given \(\alpha\). Then, the optimal power allocation coefficient to minimize the power consumption of the BS is given as:  
\[\begin{split}\alpha^{*}=\operatorname*{arg\,min}_{\alpha}\left[ \max\left(\mathcal{E}_{B}\left(\alpha,P_{b.B}^{req}\right),\mathcal{E}_{E} \left(\alpha,P_{b.E}^{req}\right)\right)\right]\\ s.t.\quad 0<\alpha<0.5.\end{split} \tag{23}\]  
The optimization problem shown in Equation (23) can be solved easily as follows. For given BER requirements \(P_{b.B}^{req}\) and \(P_{b.E}^{req}\), \(\mathcal{E}_{B}\left(\alpha,P_{b.B}^{req}\right)\) is a monotonically increasing function \(\alpha\), while \(\mathcal{E}_{E}\left(\alpha,P_{b.E}^{req}\right)\) is a monotonically decreasing function of \(\alpha\) since \(\alpha\) denotes the power portion allocated to the MUST-enhanced UE. Thus, the optimal power allocation coefficient \(\alpha^{*}\) can be obtained by solving the following equation:  
\[\mathcal{E}_{B}\left(\alpha^{*},P_{b.B}^{req}\right)=\mathcal{E}_{E}\left( \alpha^{*},P_{b.E}^{req}\right). \tag{24}\]
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 4 Results
### Simulation Results
#### 4.1.3 No Energy-Saving Mode in an Eclipse  
The previous cases considered that the CubeSat entered an energy-saving mode during the eclipse (that is, \(L=1\)). This subsection illustrates the same satellite behavior when no energy-saving actions are taken during the eclipse. For instance, Figures 18 and 19 present, respectively, the state of charge and energy levels for scenarios without energy savings. Note that in this way, the variations in the state of charge of the battery tend to be greater, decreasing its useful life. Another consequence is that the higher expenditure during the eclipse state does not allow the CubeSat to return to the energy level \(L=5\).  
Figure 16: State of Charge (\(SoC\)) for \(T_{set}=283\) K and \(T_{set}=\) variant.  
Figure 17: Battery temperature (\(T_{B}\)) for \(T_{set}=283\) K and \(T_{set}=\) variant.  
From the presented results, it is possible to perceive that the framework can be used to better understand the behavior of the temperature and energy of the satellite during the orbit. This allows designers to adapt their control algorithms according to the needs and power available in the target CubeSat.
## 4 Experiments and Results Analysis
### Criteria for Evaluation  
In this experiment, the precision, recall and \(\mathrm{F_{1}}\) value were used to evaluate the performance of the model:  
\[\mathrm{precision=correct}_{\mathrm{num}}/\mathrm{predict}_{\mathrm{num}} \tag{22}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Learning rate & \(2\times 10^{-5}\) \\ Batch size & 3 \\ Epoch & 20 \\ lstm\_embedding\_size & 1024 \\ Hidden size & 768 \\ Bert model & RoBERTa-wurm-ext \\ Embedding size & 512 \\ Optimizer & AdamW \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter setting.  
\[\text{recall}=\text{ correct}_{\text{num}}/\text{ golden}_{\text{num}} \tag{23}\]  
\[\text{F}_{1}=\frac{2*\text{precision}*\text{recall}}{\text{precision}+\text{ recall}} \tag{24}\]  
Here, precision denoted the precision rate, recall signified the recall rate, and correct \({}_{num}\) indicated the count of accurate predictions whereas predict\({}_{num}\) represented the total number of predictions, golden\({}_{num}\) represented the number of labeled entities, and the F1 value was the average of the precision rate and recall rate. This was capable of balancing the influence of the precision rate and the recall rate, and reflecting the performance of the model in a more comprehensive way.
# Crack Detection of Concrete Images Using Dilatation and Crack Detection Algorithms
###### Abstract  
Crack detection in structures is an important and time-consuming element of monitoring the health of structures and ensuring structural safety. The traditional visual inspection of structures can be unsafe and may produce inconsistent results. Thus, there is a need for a method to easily and accurately identify and analyze cracks. In this study, algorithms for automatically detecting the size and location of cracks in concrete images were developed. Cracks were automatically detected in a total of 10 steps. In steps 5 and 9, two user algorithms were added to increase crack detection accuracy, where 1000 crack images and 1000 non-crack images were used, respectively. In the crack image, 100% of the cracks were detected, but 95.3% of the results were very good, even if the results that were not bad in terms of quality were excluded. In addition, the accuracy of detecting non-crack images was also very good (96.9%). Thus, it is expected that the crack detection algorithm presented in this study will be able to detect the location and size of cracks in concrete. Moreover, these algorithms will help in observing the soundness of structures and ensuring their safety.  
image processing; crack detection; concrete cracks; user algorithms; dilatation +
Footnote †: journal:  
A crack image can be segmented into binary image using various white box methods. Algorithms are generally divided into edge-based (Canny edge detector, Wavelet transformation, etc.) and threshold-based (Otsu thresholding, etc.) [13]. Although these methods are effective, many false positives in the form of noise reduce the accuracy of crack detection results.  
In this study, an algorithm for dilating the image and an algorithm for determining crack size were developed to improve the accuracy of crack detection in concrete. A total of 40,000 images of METU campus buildings were used [14; 15], consisting of 20,000 crack images and 20,000 non-crack images. In this study, 1000 images from each category, a total of 2000 images, were selected and analyzed. The flow chart used to automatically detect the location and size of cracks was developed using a total of 10 steps. In steps 5 and 9, two user algorithms were added to increase crack detection accuracy. As a result of the analysis, an accuracy of over 95% was found in both the crack images and the non-crack images, which is considered very excellent.
## 4 Experiments and Results
### Results
#### 4.4.2 Comparison SNN in DFU-Helper framework  
As discussed in previous sections, the DFU-Helper framework employs a Siamese Neural Network (SNN) for implementing similarity learning. It is crucial to assess the SNN's reliability when dealing with DFU images. Since there is no existing work specifically focusing on SNN for DFU, we conducted a classification test using our trained SNN for similarity learning on a dataset of 5734 test samples. The predictions were then uploaded to the online platform provided by the organizers of the DFU2021 challenge. The results, presented in Table 2, demonstrate the exceptional performance of our model across all evaluated metrics. This outcome signifies that our model effectively distinguishes between similar and dissimilar images.  
In Table 2, we present the application of the Siamese Neural Network (SNN) within the DFU-Helper Framework for the classification task. This comparison encompasses an evaluation against leading research endeavors that have leveraged DFU images for reference. Our SNN, trained using similarity learning, demonstrate superior performance compared to the results achieved by other researchers, with the exception of the infection F1-Score.
## 5 Damage Detection in Wavefield Images Based on NCA-RPCA Algorithm
### Establishment of Lamb Wave Damage Model and Parameter Selection
#### 5.1.2 Simulation Parameter Configuration  
Since the focus of this experiment is on transient analysis, it is necessary to set a reasonable time increment to ensure the stability and convergence of the transient analysis. If the time increment is too small, it will increase computation time and cost. On the other hand, if the time increment is too large, it may lead to numerical instability and reduced accuracy. The time increment for the finite element model needs to satisfy the Newmark time integration scheme [44], which is given by  
\[T\leq\frac{1}{20f_{\text{max}}} \tag{36}\]  
Since the selected \(f_{\text{max}}\) is 300 KHz, which corresponds to \(T\leq 0.16\upmu\)s, in order to achieve a higher temporal resolution, the actual simulation calculations use \(T=0.1\upmu\)s.  
Similarly, the grid size plays a crucial role in the stability and convergence of finite element analysis simulations. The relationship between the grid size and the wavelength of Lamb waves needs to satisfy the following condition [45].  
\[L\leq\frac{\lambda_{\text{min}}}{10} \tag{37}\]  
where \(\lambda_{\text{min}}\) represents the minimum wavelength of Lamb waves under the selected excitation. This minimum wavelength typically corresponds to the highest-frequency mode of Lamb waves, which is the shortest periodic vibration mode within the structure. Larger grid sizes lead to increased errors in accuracy, as shown in Figure 11. When the grid size \(L>\frac{\lambda_{\text{min}}}{10}\), it can result in distorted wave characteristics, causing deformation or distortion in the waveform. This misestimation of the waveform shape and amplitude leads to inaccurate estimation of the propagation path of Lamb waves, consequently affecting the accuracy of simulation results and compromising their validity. Therefore, while ensuring result accuracy, reducing the grid size is crucial to decreasing computational load and enhancing computational efficiency. Hence, the minimum wavelength can be deduced as \(\lambda_{\text{min}}=c_{\text{pmn}}/f_{\text{max}}=21.33\) mm, leading to \(L\leq 2.133\) mm, with the actual selection being \(L=1.2\) mm.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 3 System Model and Problem Statement
### Cognitive Metrics  
To select an optimal parent node according to changes in network conditions, each node needs to recognize the current network state using multiple cognitive metrics. Recall that the main goal of this study is to jointly achieve the high reliability, low latency, and energy efficiency. However, there are many considerations to select the best parent node, for example, in a scenario where multiple sensor nodes choose the same parent, a queue is building at the parent node and thus the queueing delays increase or packets are lost. In addition, the link quality between the parent node and the child node depends on their geographical positions; hence, obstacles can make the transmission unreliable. To address diverse parent selection problems, we define the following cognitive metrics.
# Numerical Study of Cone Penetration Tests in Lunar Regolith for Strength Index
## 1 Introduction
[PERSON] and [PERSON] (2012), using North American soil data, proposed a formula for calculating relative density (\(D_{\mathrm{r}}\)) using cone resistance (\(q_{\mathrm{c}}\)) and effective overburden stress (\(\sigma_{\mathrm{v}}^{{}^{\prime}}\)), as shown in Equation (6). \[D_{\mathrm{r}}=\arctan(0.29+\frac{1}{2.68}\log(\frac{q_{\mathrm{c}}}{\sigma_{ \mathrm{v}}^{{}^{\prime}}})) \tag{6}\][PERSON] et al. [13], building on [PERSON]'s method, developed a formula for calculating the relative density of sand using a substantial amount of laboratory test data, which is shown in Equation (7). \[D_{\rm r}=26.8\ln\left[\left(\frac{q_{\rm c}/P_{\rm a}}{\left(\sigma_{\rm v}^{ \prime}/P_{\rm a}\right)^{0.5}}\right)\right]-b_{\rm x} \tag{7}\]  
Here, \(P_{\rm a}\) represents atmospheric pressure. For highly compressible soils, \(b_{\rm x}=52.5\); for moderately compressible soils, \(b_{\rm x}=67.5\); for slightly compressible soils, \(b_{\rm x}=82.5\). This equation is applicable to normally consolidated fine to medium sands. Building on [PERSON]'s formula, [PERSON] [14] constructed a formula for calculating the relative density of moderately compressible soil, which is presented in Equation (8). \[D_{\rm r}=100\left[0.268\ln(\frac{q_{\rm t}/P_{\rm a}}{\sqrt{\sigma_{\rm v}^{ \prime}/P_{\rm a}}})-0.675\right] \tag{8}\]  
In this equation, \(q_{\rm t}\) represents the corrected cone tip resistance, accounting for boundary effects. The in situ study of lunar regolith shear strength index is very important for lunar base construction and other missions. However, the empirical formulas for calculating soil parameters based on CPT results in terrestrial environments are highly specific, with each formula tailored to a particular soil type or region. Directly applying empirical CPT formulas from terrestrial environments to lunar regolith is unreasonable given the unique lunar surface environment and the physical and mechanical properties of lunar regolith. Therefore, CPT-related formulas specific to the lunar environment must be developed for accurately calculating the in situ soil properties. Addressing this research gap, we employed discrete element method (DEM) software PFC3D 6.0 to perform triaxial and CPT simulations in a lunar setting. We identified a relationship for calculating the shear strength index of lunar regolith based on the normalized cone tip resistance in a lunar environment by referencing the empirical CPT formula for calculating the shear strength index of sand in terrestrial environments.
# Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s
## 6 Conclusions  
In this paper, the influence of training parameters on real-time object detection has been investigated. The newly collated construction details dataset of similar objects has been collected and analyzed. The main problem of such an analysis is that the construction details are similar in their features (similar shape, color, size, etc.), so it is important to choose the right training parameters that allow the higher accuracy of the model to be obtained in the training and testing phases. The hyperparameters of the YOLOv5s algorithms have not been taken into account. However, the best hyperparameters are used based on the analysis of related works.  
The experimental results have shown that the highest influence on the accuracy of the model has been the selected iteration number. The small iteration number is not suitable for this kind of object detection because the models are not trained well at all. The color images allow for slightly better results compared to the greyscale images. The higher resolution of the images does not increase the accuracy of similar object detection, and for such a kind of analysis, a huge environment and lots of resources need to be implemented. The optimal resolution of the images should be 320 or 640 and the batch size equal to 16 or 32. In future work, an experimental investigation should be improved by trying different data-augmentation methods and focusing on YOLOv5s hyperparameters.  
Conceptualization, T.K. and P.S.; methodology, P.S.; validation, T.K.; formal analysis, T.K. and P.S.; data curation, T.K. and P.S.; writing--original draft preparation, T.K. and P.S.; writing--review and editing P.S.; visualization, P.S.; supervision, P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: Example of a test dataset in real-time object detection.