esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Temporal Variation of CO\({}_{2}\) and CH\({}_{4}\) Mole Fractions
Furthermore, solar thermal power plants with molten salt in the south (~2 km) (Figure 1) likely release emissions such as CO\({}_{2}\) and CH\({}_{4}\) during their operation and maintenance. Although the technical details of the solar power plants nearby were not known, it was well documented \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Unit** & **Mean** & **Min** & **Max** \\ \hline \multicolumn{5}{c}{Surface GHGs mole fraction a  } \\ CO\({}_{2}\) & ppm & 412.62 \(\pm\) 2.06 & 408.30 & 416.44 \\ CH\({}_{4}\) & ppb & 1959 \(\pm\) 13 & 1945 & 1998 \\ CO\({}_{2}\)/CH\({}_{4}\) & / & 211 \(\pm\) 2 & 207 & 213 \\ \hline \multicolumn{5}{c}{Column GHGs and the corresponding surface GHGs mole fraction a  } \\ XCO\({}_{2}\) & ppm & 413.00 \(\pm\) 1.09 & 410.55 & 416.51 \\ XCH\({}_{4}\) & ppb & 1876 \(\pm\) 6 & 1861 & 1886 \\ XCO & ppb & 84 \(\pm\) 3 & 78 & 91 \\ XH\({}_{2}\)O & ppm & 3426 \(\pm\) 749 & 1999 & 5175 \\ XCO\({}_{2}\)/XCH\({}_{4}\) & / & 220 \(\pm\) 1 & 218 & 223 \\ XCO\({}_{2}\)/XCO & \(\times 10^{-3}\) & 4.9 \(\pm\) 0.2 & 4.6 & 5.3 \\ XCH\({}_{4}\)/XCO & / & 22.5 \(\pm\) 0.9 & 20.8 & 24.3 \\ CO\({}_{2}\) b  & ppm & 413.90 \(\pm\) 1.80 & 409.99 & 419.97 \\ CH\({}_{4}\) b  & ppm & 1947 \(\pm\) 11 & 1937 & 2015 \\ CO\({}_{2}\)/CH\({}_{4}\) b  & / & 213 \(\pm\) 1 & 208 & 215 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of surface and column mole fractions of greenhouse gases observed from 26 June to 19 July 2021, at a desert site in Dunhuang, west China. that the solar power plants (e.g., the technology of power tower, parabolic trough, and linear Fresnel lens) use some natural gas or diesel for their operation and maintenance or back-up [38; 39; 40; 41; 42]. The CH\({}_{4}\) mole fraction peaked at 9:00 in the daytime and then decreased to a valley at 17:00 because of the increase in PBL height and chemical removal of CH\({}_{4}\) with OH radical [31; 43]. The CH\({}_{4}\) mole fraction increased again in the late afternoon (after 18:00 CST) due to a decrease in the PBL height and photochemical reaction rate (Figure 2D). Dust storms have different effects on the mole fraction of GHGs in the Gobi Desert. A dust storm occurred in the late afternoon on July 18 and lasted for several days. The ambient CO\({}_{2}\) mole fraction suddenly decreased by 1.2 ppm from 413.7 ppm to 412.5 ppm when the dust storm arrived, while the CH\({}_{4}\) mole fraction increased by 5 ppb from 1947 to 1952 ppb (Figure S3). After the dust storm occurred, the hourly CO\({}_{2}\) mole fraction decreased continuously until 6:00 CST the next day. However, the CH\({}_{4}\) mole fraction decreased slightly and remained at 1950 ppb over the next 24 h (Figure S3B).
## 3 Application to Cultural Heritage Science  
In this section, we will review some of the latest work regarding cultural heritage applications. The first application of the negative muons for the characterization of material is dated back to the 1980s [30; 31; 32], but only in the last ten years, applications for cultural heritage have been developed, especially for elemental characterization and depth profile studies. An initial study on the characterization of archaeological artefacts was carried on by analyzing a Chinese bronze mirror and a bronze horse model in Japan [20]. As for elemental characterization, depth profiling was also carried out at ISIS, where a multilayered sample was studied by varying the beam energy, thus obtaining information about all the layers [7; 23] (Figure 5).  
Figure 6: The experimental setup at the ISIS muon facility. Thanks to the flexibility of the experimental setup (i.e., the detectors being easily moved), samples of different shapes and sizes can be measured. The figure was taken and adapted with permission from Ref. [7] © 2016, Elsevier Ltd.
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 2 Method
### Loss Function Design  
In the linear classifier, the features output from the Swin Transformer are first fed into the average pooling layer, then passed through the normalization layer, and then, finally, the prediction results are obtained using the fully connected layer. The loss function is used to evaluate the extent to which the predicted results of the model are different from the true results. Therefore, the loss function is of great importance in the optimization process of the model. The commonly used loss function in the field of image classification is the cross-entropy loss function, which can be expressed by Equation (8):  
\[L_{cro}=-\frac{1}{M}\sum_{i=1}^{M}\sum_{j=1}^{N}p\left(x_{j}\right)\log\left(q \left(x_{j}\right)\right) \tag{8}\]  
where the size of the mini-batch and the number of classification classes is M and N, respectively, \(p\left(x_{j}\right)\in\left\{0,\,1\right\}\) is the ground truth of sample \(x\) belonging to class \(j\), and \(q\left(x_{j}\right)\in\left[0,\,1\right]\) represents the probability of sample \(x\) being predicted as class \(j\).  
The GCI classification task can be regarded as a fine-grained classification. The feature difference between cloud regions in the image is small, so the simple cross-entropy loss is not sufficient to adequately supervise the learning of such fine-grained features. Therefore, we introduce the center loss [53] to improve the supervisory ability of the model. The center loss function is defined in Equation (9).  
\[L_{cen}=\frac{1}{2}\sum_{i=1}^{M}\left|\left|x_{i}-c_{y_{i}}\right|\right|_{2}^ {2} \tag{9}\]  
where M is the size of the mini-batch and \(x_{i}\) denotes the feature of the \(i\)-th sample being extracted. \(c_{yi_{i}}\in\mathrm{R}^{\mathrm{d}}\) denotes the \(yi\)-th class center of deep features, where d is the feature dimension, and the \(c_{yi_{i}}\) should be updated as the deep features change. This function can minimize the intra-class distance while ensuring the separability of inter-class features, thus improving the discriminability between features. In conclusion, the final loss function of the whole model can be expressed as:  
\[Loss=L_{cro}+\lambda L_{cen} \tag{10}\]  
where \(\lambda\) is the hyperparameter to balance the two loss functions.
# A Review of Research on Secure Aggregation for Federated Learning
## 3 Privacy-Preserving Mechanisms
### Blockchain
The practical Byzantine fault tolerance algorithm can resist Byzantine attacks and is suitable for scenarios involving a small number of trusted nodes. However, it incurs high communication overhead, which limits its scalability.
## 8 Implementation and Results
### Brute-Force Attack Results  
The brute-force attack scenario is implemented to monitor famous video contents in the NDNtube simulation. In this attack scenario, the performance of the attacks was analyzed by different CS policies (LRU, LFU, and FIFO) on the gateway (edge) routers on AT&T topology.  
Figure 13 illustrates average CHR values (or metrics) that were obtained globally from all edge routers (gw-): \(\approx\)16.4% in nfd:LRU, \(\approx\)15.9% in nfd:FIFO, and \(\approx\)18.0% in nfd:LFU during the attack period (\(\approx\)21-35 s). In these scenarios, the attack results showed that an adversary can succeed more in the least frequently used policy because it keeps the famous contents in CS compared to other cache policies. Note that the attack success may be variously determined by the quantity of adversary, targets, network topology, and CS policy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Link Type**} & \multicolumn{2}{c}{**Delay**} & \multicolumn{2}{c}{**Bandwidth**} \\ \cline{2-5}  & **Min.** & **Max.** & **Min.** & **Max.** \\ \hline Client–Gateway & 10 ms & 70 ms & 1 Mbps & 3 Mbps \\ Gateway–Backbone. & 5 ms & 10 ms & 10 Mbps & 20 Mbps \\ Gateway–Gateway & 5 ms & 10 ms & 40 Mbps & 100 Mbps \\ Backbone–Backbone & 5 ms & 10 ms & 40 Mbps & 100 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bandwidth and delays of AT&T topology.  
Figure 12: Real network topology conversion for NDNtube: (**a**) Raw AT&T network topology, (**b**) rocket-fuel-converted AT&T for ndnSIM.  
The hops between the NDN routers are identified by \"hop counts\". During an attack, the hop count can be decreased and used to identify the adversary's faces. Figure 14 illustrates the global hop counts for the adversary and legitimate node faces on NDNtube in LRU, FIFO, and LFU scenarios during the simulation time (0-60 s). In these results, the average, minimum, and maximum of hop counts were illustrated for the adversary and the legitimate faces, respectively. The following average hop counts were obtained: _i._ adversary faces, 1.40 in rfd:LRU, 1.95 in rfd:FIFO, and 1.52 in rfd:LFU. _ii._ legitimate faces, 3.72 in rfd:LRU, 4.15 in rfd:FIFO, and 4.15 in rfd:LFU.  
Figure 15 illustrates the attack findings with target locations. There are evaluated by the adversaries as the following clusters: _i._ 30.4% for edge clusters, _ii._ 18.2 % for neighbor clusters, and _iii._ 51.4% for away clusters. These results were obtained by rfd:LRU (default) scenario without any countermeasures applied.  
Figure 14: NDNtube global hop counts.  
Figure 13: NDNtube brute-force attack performance on CS scenarios.
## 3 Results
### Rectangular Gap Reconstruction  
For the second case, a rectangular gap was made in the dataset, as shown in the top rows of Figure 5c,d. The size of the gap was \(30\times 10\) with the bottom-left and top-right corners at \((-1.5,0)\) and \((1.5,1)\), respectively. The gap covers almost 18% of the area of the region. The first row of Figure 5a shows the reference without the gap, which is what both methods were aimed at recovering by filling the gap. The second row shows the noise-free and noisy samples taken by using the first random sampling mask. The third row shows the reconstruction of csDMD method with corresponding RMSE values of 0.334 for the noise-free samples and 0.181 for the noisy samples. The bottom row shows the reconstruction of DMDct method, where the RMSE values were found as 0.154 and 0.138 for the noise-free and noisy samples, respectively. The respective PVNR values of csDMD and DMDct for the noisy case were 21.8 dB and 24.2 dB. A summary of the error metrics of reconstruction based on the first random sampling mask is presented in Table 1 for comparison.  
Figure 5: Results of Duffing dataset reconstruction using the first random sampling mask with both DMDct and csDMD. The snapshot numbers are shown at the top of each column. The error metrics are listed in Table 1. (**a**) The noise-free case without gap. (**b**) The noisy case without gap. (**c**) The noise-free case with the rectangular gap (the white hollow). (**d**) The noisy case with the rectangular gap (the white hollow).
## 5 Results
### Outcomes of BIM2 BEM-Flow
#### 5.2.1 Database Structure  
In addition to the web UI for creating libraries and parameters, etc., libraries can also be imported entirely via a CSV structure. This capability enables existing data records, often maintained in Excel format, to be easily transferred to the database. The database features a modular architecture and a RESTful API, facilitating seamless integration with external tools and access to database data. To use the platform, a company must register an account. Once registered, the company can grant administrative access to its employees. Library managers bear significant responsibility, as all downstream processes, including simulation, rely on the accuracy of the data stored on the server (see Figure 9; YAPS Manager or Property Manager). Users can create libraries, such as a comprehensive company library or one specifically for a project. These libraries can be shared with other companies involved in the same project. The coordination of libraries among different project participants or companies takes place within the Project Workflow Manager (PWM), developed in BIM2 BEM-Flow and mentioned here for context.  
A library consists of component classes (building elements), which can be either virtual (e.g., room and project) or physical (e.g., window and wall), and can be linked hierarchically. This hierarchical structure is crucial for inheritance, allowing parameters (e.g., name) to be passed from parent to child components (Figure 10).  
Figure 9: Database structure (YAPS = BIM2 BEM-Flow project database name).  
A library element includes attributes such as name, IFC class, optional type assignment, description, and associations with parameter groups (see Figure 11). Linking to an IFC class is essential, as Revit's import process depends on this classification (e.g., Building \(\rightarrow\) IfcBuilding). For example, if the IFC import settings in Revit map Building \(\rightarrow\) IfcBuilding, the component class \"Building\" is translated to the Revit _BuildInCategory_ (Revit-API specific element) \"Building Model\". By working with the open-standard IFC entities, the database structure remains manufacturer-independent.
# Tracking Sensor Location by Video Analysis in Double-Shell Tank Inspections
###### Abstract  
Double-shell tanks (DSTs) are a critical part of the infrastructure for nuclear waste management at the U.S. Department of Energy's Hanford site. They are expected to be used for the interim storage of partially liquid nuclear waste until 2050, which is the target date for completing the immobilization process for all Hanford nuclear waste. At that time, DSTs will have been used about 15 years beyond their original projected lifetime. Consequently, for the next approximately 30 years, Hanford DSTs will undergo periodic nondestructive evaluation (NDE) to ensure their integrity. One approach to perform NDE is to use ultrasonic data from a robot moving through air slots, originally designed for cooling, in the confined space between primary and secondary tanks. Interpreting ultrasonic sensor output requires knowing where measurements were taken with a precision of approximately one inch. Analyzing video acquired during inspection is one approach to tracking sensor location. The top edge of an air slot is easily detected due to the difference in color and texture between the primary tank bottom and the air slot walls. A line fit to this edge is used in a model to calculate the apparent width of the air slot in pixels at targets near the top edge that can be recognized in video images. The apparent width of the air slot at the chosen target in a later video frame determines how far the robot has moved between those frames. Algorithms have been developed that automate target selection and matching in later frames. Tests in a laboratory mockup demonstrated that the method tracks the location of the ultrasonic sensor with the required precision.  
n _Applied_  
* 2023
# Development of a Maintenance and Spare Parts Distribution Model for Increasing Aircraft Efficiency
## 4 Simulation Model Results
### Managerial Implications  
Based on the conceptual model and the simulation study performed, the steps presented in Figure 11 can be derived as a methodology to be used by managers when deciding how to design, manage, improve, and implement distribution networks for spare parts management:  
\begin{table}
\begin{tabular}{c l c c c c} \hline
**No.** & **Key Indicator** & **Model PF-1** & **Model PF-2** & **Model PV-1** & **Model PV-2** \\ \hline
1 & \(\mathcal{O}\) spare parts demand (units) & 28.6 & 28.6 & 22.0 & 22.0 \\
2 & On-time delivery (OTD) (\%) & 100 & 87.8 & 100 & 85.9 \\
3 & Service level (\%) & 100 & 88.3 & 100 & 86.9 \\
4 & \(\mathcal{O}\) spare parts backlog (units) & 0 & 27.1 & 0 & 29.4 \\
5 & \(\mathcal{O}\) stock in network (units) & 3111 & 3145 & 5530 & 5576 \\
6 & \(\mathcal{O}\) stock in CW (units) & 64 & 1731 & 67 & 3994 \\
7 & \(\mathcal{O}\) stock in RW (units) & 3047 & 1414 & 5463 & 1581 \\
8 & \(\mathcal{O}\) production (units) & 31 & 31 & 27 & 27 \\
9 & \(\mathcal{O}\) WIP (Work-In-Progress) (units) & 3168 & 1528 & 5567 & 1669 \\
10 & \(\mathcal{O}\) aircraft availability (\%) & 79.7 & 77.0 & 83.7 & 80.8 \\
11 & \(\mathcal{O}\) aircraft on ground (\%) & 20.3 & 23.0 & 16.3 & 19.2 \\
12 & \(\mathcal{O}\) interval for planned & 706 & 706 & 1567 & 1567 \\  & maintenance (operating hours) & & & & \\
13 & \(\mathcal{O}\) interval for unplanned & 1974 & 1974 & 1577 & 1577 \\  & maintenance (operating hours) & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Simulation results for the second demand scenario—low demand for spare parts.
# Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries
## 3 Results  
### Simulations Performed  
The first objective was to reproduce the original results reported in the papers containing the models. This was accomplished for calendar degradation in Figures 2a-7a, using Equations (25), (29), (35), (41), (44), and (46), respectively. For cyclic degradation, the results are in Figures 2b-7b, using Equations (26), (30), (36), (42), (45), and (47), respectively.  
In order to test electrical and thermal models, a simulation of combined charge, discharge, and rest time was carried out using Equations (48)-(51). With the current profile and ambient temperature as inputs, the outputs were cell voltage and cell temperature. The simulation involved consecutive 30 min periods of rest, charge, rest, discharge, rest, charge, and rest, with charging and discharging at CC, \(I=1.5\) A, \(\delta_{t}=15\) s., and \(T_{a}=22\)\({}^{\circ}\)C. Figure 8 shows the results of the simulation.  
Further, the charging of an LFP cell from 0.1 to 0.9 of the \(SoC\) was simulated to verify the temperature model at low \(CR\) with \(T_{a}=22\)\({}^{\circ}\)C, and \(CR=0.1\) C. Figure 9 shows the temperature variation with respect to \(SoC\).  
In order to evaluate individual differences between versions #1, #2, and #3, the root mean square error (RMSE) was applied using version #3 as a reference. Version #3 is used as a reference in order to compare values due to the presumption that version #3 provides results close to real values. Three values of \(CF\) were taken into account with version #3: 0.9, 0.5, and 0.1. RMSE was calculated for several values of \(SoC\) from 0.0 to 1.0 for calendar degradation and for several values of \(DoD\) from 0.0 to 1.0 for cyclic degradation. Parameters used in the RMSE for calendar degradation are shown in Tables 3 and 4 for cyclic degradation. Tables 5 and 6 show the resultant RMSE values.  
The primary applicability of this study is in optimization problems where the computational time is significant. All calculations in this study were performed on an i5-3340M CPU machine with 4 cores at 2.70 GHz. In order to evaluate computational time for calculation, a single cell charge was simulated. Simulations used to measure the execution time consist of 100 cycles of half an hour rest time, then discharge from 0.9 \(SoC\) to 0.2 \(SoC\) at 1 \(CR\), then another rest time and then a charge from 0.2 \(SoC\) to 0.9 \(SoC\) at 1 \(CR\). Table 5 shows, in detail, the computational times for the simulations.
## 2 Materials and Methods
### Dry to Wet Reflectance Ratio  
Note that the wavelength dependence of the backscattering coefficient is a direct consequence of the wavelength dependence of the relative index of refraction. One important consequence of this dependence on the relative index of refraction is the reduction in \(b_{b}(\lambda)\) when the interfaces between the grains are filled with water instead of air. This effect is the source of the lowering of the irradiance reflectance of materials and vegetation immersed in water. Because the interstitial gaps are small the grain structure and spacing \(<\)\(d\)\(>\) is the same in both cases, we can estimate the water to air ratio directly.  
\[\frac{b_{bw}(\lambda)}{b_{b}(\lambda)}=\frac{\omega_{t}(n/n_{w})}{\omega_{t}(n)}\, \tag{13}\]  
Since the absorption does not change, we can directly estimate the ratio of backscattering albedo.  
\[\frac{x_{bw}(\lambda)}{x_{ba}(\lambda)}=\frac{\frac{a(\lambda)}{b_{b}(\lambda) }+1}{\left[\frac{\omega_{t}(n)}{\omega_{t}(n/n_{w})}\right]\frac{a(\lambda)}{ b_{b}(\lambda)}+1}\, \tag{14}\]  
\(x_{bw}(\lambda)\) is the backscattering albedo in water while \(x_{ba}(\lambda)\) is the corresponding backscattering albedo in air.  
We can at this time estimate the wet to dry reflectivity factors for three of the most important and frequently found components of materials and vegetation, crystalline quartz, calcite and cellulose.  
These are shown in Figure 2. The detailed formulas as a function of wavelength for these important indices are given in Appendix A. These indices can be found in references [11; 12; 13; 14; 15].
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 4 Experiments and Discussion
### Ablation Experiments on Gradient Operator  
As described in Section 3.3, the gradient operator is tailored to the distribution characteristics of spherical pixels in the QUVS image for computing the gradient of the target pixel in the SCP image. In this experiment, we studied how the SCP gradient operator influences the odometry accuracy compared to the conventional LK optical flow gradient operator. Table 6 presents the odometry accuracy outcomes obtained from using LK and SCP gradient operators in seven distinct scenarios from both datasets. The proposed SCP gradient operator shows a better overall performance, with lower RMSE and standard deviation values in both RPEt and RPEr.  
The LK gradient operator, designed for planar image structures, considers nine surrounding pixels for each target pixel. Although this configuration involves more pixels than the SCP operator, it does not align with the actual distribution characteristics of hexagonal  
Figure 12: The comparison of translation and rotation errors among the proposed SCP-based VIO, LF-VIO, and VINS-MONO in the ID01 scenario of the PALVIO dataset. The experimental results indicate that our SCP-based VIO exhibits the most stable performance under situations of glare, overexposure, and motion blur.  
pixels in the QUVS image, resulting in inaccuracies in gradient computation. Experimental results indicate that differences in gradient accuracy directly impact the tracking precision of visual odometry.  
In addition to the comparison between the SCP and conventional LK optical flow gradient operators, we further conducted an ablation study to investigate the effect of different weighting configurations within the SCP operator. The results are presented in Table 7, where parameters \(a\) and \(b\) have a fixed ratio with respect to each other, and their changes basically control the optical flow ratio. In particular, the configuration \((a~{}=~{}5,b~{}=~{}10)\) and \((a~{}=~{}6,b~{}=~{}12)\) achieved the lowest RPEt and ATE, suggesting an optimal tradeoff between spatial scale support and flow consistency on the spherical domain.