esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model
### Bending Stiffness  
Analogous to the equilibrium of forces in Equation (18), the equilibrium of moments is  
\[\sum_{k=1}^{2j+1}(F_{k}-F_{m,k})\cdot\cos(\alpha-\varphi_{k})\cdot r_{k}=0. \tag{24}\]  
Inserting Equations (16), (17) and (20), the curvature of the actuator is  
\[\kappa=\frac{\sum_{k=1}^{2j+1}F_{k}\cdot\cos(\alpha-\varphi_{k})r_{k}}{\sum_ {k=1}^{2j+1}E_{k}\cdot\cos^{2}(\alpha-\varphi_{k})r_{k}^{2}\frac{L}{L_{0}}}. \tag{25}\]  
The bending stiffness \(B\) relates the moment \(M\) acting on the actuator to its curvature \(\kappa\). The moment in Equation (25) is  
\[M=\sum_{k=1}^{2j+1}F_{k}\cdot\cos(\alpha-\varphi_{k})r_{k}, \tag{26}\]and thus the bending stiffness is  
\[B=\frac{M}{\kappa}=\sum_{k=1}^{2j+1}E_{k}\cdot\cos^{2}(\alpha-\varphi_{k})\cdot r _{k}^{2}\cdot\frac{L}{L_{0}}. \tag{27}\]  
Simplification is again possible because of \(r_{2j+1}=0\) (Equation (15)) and because of the regular arrangement of the sections in a circle  
\[\sum_{k=1}^{2j}\cos^{2}(\alpha-\varphi_{k})\cdot(k\mod 2)=\sum_{k=1}^{2j}\cos^{ 2}(\alpha-\varphi_{k})\cdot(k+1\mod 2)=\frac{j}{2},\text{if }j\geq 3, \tag{28}\]  
where the modulo operation distinguishes between sections with and without chamber. Hence, the bending stiffness becomes  
\[B=\frac{j}{2}\cdot\left(E^{I}(r^{I})^{2}+E^{II}(r^{II})^{2}\right)\cdot\frac{L }{L_{0}}. \tag{29}\]  
Similar to the extensional stiffness \(E\), the bending stiffness \(B\) takes the number of sections \(j\) into account, and additionally their arrangement by \(r^{I}\) and \(r^{II}\). The central section has no influence on the bending stiffness, since its length is independent from the curvature and its individual bending stiffness is neglected. In our approach, the bending stiffness results from a difference in length of the individual sections of the actuator. The more the actuator stretches due to pressurization or external loads, the larger the difference and, hence, the bending moment needs to become zero in order to remain the same curvature. Consequently, the bending stiffness increases for a higher stretch ratio \(\frac{L}{L_{0}}\). Although the actuator is partitioned into discrete sections, the bending stiffness is independent from the direction of bending \(\alpha\) because of Equation (28).
# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 2 Materials and Methods
### Frameworks and Tools  
In this section we enumerate and briefly describe tools and hardware used in order to conduct the preprocessing and analysis steps addressed in Section 2.2 and the experiments described in the following paragraphs of this manuscript. As data were provided in CSV format, every preliminary phase from acquisition to aggregation and cleaning was carried out using a scientific Python 3.6 environment through a combination of _pandas_[26] and _numpy_[27] libraries, with the support of the _statsmodels_ package [28] for time series analysis and _matplotlib_ for data visualization. For the data imputation phase and the following experiments, we leveraged the popular _scikit-learn_ library [29], which offers rich functionalities in many different machine learning domains, from data preprocessing to the metrics computation, and provides out-of-the-box robust implementations for many popular models and algorithms. Specifically, together with the previously mentioned packages, we used this library for data normalization, definition of the cross validation, other testing setups described in the next section and implementation of the Bayesian Ridge linear regressor. As deep learning counterpart, we employed the PyTorch framework [30], another extensive library providing a wide variety of features. In particular, the latter was leveraged for the implementation and training of the LSTM regressor. A detailed description of software libraries and respective versions is provided in Table 4. In order to carry out the experiments with the BRNN model, we also leveraged an R environment considering the lack of equivalent Python implementations. In this case, we maintained the same processing pipeline using the libraries listed in the right section of Table 4 to reproduce _pandas_ and _numpy_ functionalities, then we trained the model provided by the namesake package _brnn_. Once trained, the results were stored and evaluated in the Python pipeline, in an identical manner to the other solutions.  
All the procedures and experiments described in this paper were performed on a Linux workstation equipped with an Intel Core i9-7940X processor with a base frequency of 3.10 GHz and a total of 14 cores, 128 GB of RAM and 4\(\times\) Nvidia GTX 1080 Ti video cards, with CUDA 10.1 capabilities.
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 4 Performance Evaluation
### Comparative Evaluation of UDP in Terms of the Received Signal Strength (RSS)
The achievable UE average throughput using the PDPP and PDPRR technique under LOS conditions is ranging from 1.2 to 5 Mbps and 0.9 to 1.4 Mbps, respectively. As discussed in Section 4.1, UDP performance is superior to that of KDP in terms of offloading percentage. This means that the UAV-BSs in the case of the UDP method will be more loaded as compared to the case where UAV-BSs operate under the KDP method. Hence, UAV-BSs will distribute the available spectrum to more users in the case of the UDP method contrary to the KDP method where the available spectrum will be allocated to fewer users. It is important to note that UDP exceeds KDP in terms of system sum-rate and average UE throughput, while the KDP method allocates the available bandwidth to fewer users than the UDP method. This happens because UDP gives a higher average downlink RSS compared to the KDP, as discussed in Section 4.2. Thereby, UDP is twice as profitable as KDP, since it significantly increases the offloading percentage of the terrestrial  
Figure 6: Downlink system sum rate for different number of UAV-BSs/PBSs. network, as compared to KDP and at the same time, provides better sum-rate and average UE throughput performance than KDP. This can be proved through Figure 8, which shows the spectral efficiency of the UDP and KDP methods, for different number of UAV-BSs. It is observed that the UDP method outperforms the KDP method for all configurations. As explained above, since the average downlink received power provided to users using the UDP method is much better than in the case of KDP, the utilization of the spectrum is much higher even for a smaller part of it. Figure 8: Average UE spectral efficiency for different number of UAV-BSs. Figure 7: Average UE throughput for different number of UAV-BSs/PBSs.
## 3 Results and Analysis
### Results and Analysis of Superpave Evaluation Index of Modified Bitumen  
#### 3.2.1 Brookfield Rotational Viscosity Test  
The results are shown in Figure 5.  
Figure 5 shows that the Brookfield rotational viscosity of the Sasobit\(-\)modified, XT-W3\(-\)modified, and Evotherm\(-\)modified bitumens decreased compared to that of the virgin bitumen at the same test temperature, and all of them decreased with the increase in admixture. Compared with the viscosity of the virgin bitumen, the viscosities of the Sasobit\(-\)modified bitumen decreased by 21.4%, 28.5%, and 32.3%, respectively; those of XT-W3\(-\)modified bitumen decreased by 9.1%, 12.3%, and 13.7%, respectively; and those of Evotherm\(-\)modified bitumen decreased by 15.8%, 25.5%, and 30.0%, respectively. The test results show that incorporating modifiers can effectively reduce the high\(-\)temperature viscosity of virgin bitumen and improve its construction and ease. Three modifiers were compared to the Sasobit\(-\)modified bitumen to improve the viscosity of the most pronounced effect. According to the test results, SBS\(-\)modified bitumen has the largest rotational viscosity among several types of bitumen, indicating that it produces less defor  
Figure 4: Penetration ratio of bitumen before and after aging.  
mation under an external load, is more closely combined with bitumen, and shows stronger rutting resistance; however, its construction is also more challenging by comparison.
## 3 Results  
For demonstration purposes (and without loss of generality), PERFORM incorporates two different autonomous path planning techniques for evaluation and direct comparison: (1) a multilayered Potential Field Method/A-Star (PFM/A*) approach [36] and (2) a Probabilistic Roadmap (PRM) method [37]. Details on both algorithms may be found in Appendix A. Both the PFM/A* and PRM techniques are evaluated and compared in the case of three separate mission scenarios:  
* Case I: waypoint navigation with single obstacle;
* Case II: waypoint navigation with multiple obstacles;
* Case III: area survey (i.e., lawnmower path survey)  
For the given test scenario, an analytical binary occupancy grid is provided to the path planners to differentiate between free and occupied space. It should be noted that the path planners are deliberately left untuned to generate non-optimal paths to better simulate experimental test data and provide higher-integrity data for observing the efficacy of the proof-of-concept IT2-FL autonomy testing and evaluation framework. Routes given are not intended to represent planner capabilities, but to give a reasonable representation of the actual path a vehicle might take given commands generated from the path planners.  
For the proposed FL-based evaluation framework and given mission performance criteria, the IT2-FL based autonomy evaluation procedure is summarized as follows:  
1. Determine user-specified testbed parameters (e.g., the size of the test area and its location, choosing between a two or three-dimensional environment);
2. Select the input performance parameters (total distance, time, etc.) of interest;
3. Determine appropriate performance measurement tools/criteria (i.e., sensors) and corresponding uncertainty levels (e.g., GPS accuracy limits for measuring navigational coordinates);
4. (Optional) use simulations (performed a priori) to provide further context to choosing suitable MF intervals and insight into expected parameter values;
5. Generate appropriate MFs using insight gathered from (3) and (4);
6. Perform autonomy test missions;
7. Gather and post process data from (6) to use as inputs to the IT2-FL Fuzzy Inference System (FIS);  
Figure 6: T2-FL System.  
Obtain overall performance score(s) from IT2-FL evaluation method for final evaluation/comparison of autonomous platform(s)/engine(s).  
It is of note that each parameter (performance criteria) may be individually analyzed, in addition to the overall autonomy performance score represented by the final FIS crisp output. The autonomy performance testing method proposed in this paper is intentionally modular and scaleable in design, allowing for testing as simple or complex as the test objectives warrant.
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 2 Materials and Methods
### Materials
#### 2.2.5 Land Surface Temperature Datasets  
Land surface temperature datasets were collected through MODIS sensors aboard the Terra and Aqua satellites. MODIS can provide the global land surface temperature records of day and night with an error-controlled between \(-\)1 K and 1 K. MODIS1A2 products with a spatial resolution of 1 km and an eight-day temporal resolution were obtained at ([[https://lpaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006](https://lpaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006)]([https://lpaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006](https://lpaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006))). In 2015, there were forty-five 8-day periods, we calculated the averaged day and night land surface temperatures over the TP to determine the year-round land surface temperature for day (LST-d) and night (LST-n).
## 6 Summary of Experimental Investigation  
As discussed earlier, most studies in the past were focused on the OOPB end connection brace frame, and very limited studies were conducted on the newly developed IPB end connections braced frame. As traced by the authors, the first full-scale test of the IPB braced system was conducted by [PERSON] [31] on the reusable three-story frame with a composite concrete slab at each story. The design limit state criteria to prevent out-of-plane buckling were not considered, and undesirable failure mode, including out-of-plane buckling of braces, was observed in the third story of the braced frame at a drift of 0.52%. Some tests were also conducted at the component level without considering the actual end connection and biased moment of inertia along the two perpendicular buckling axes [32]. However, these tests did not depict the actual end connection arrangement. Recently, [PERSON] et al. [10,27] conducted four tests on the IPB braced system, out of which three failed through out-of-buckling of the brace member. One specimen did not fail through the aforementioned  
Figure 4: Failure modes observed in past experimental studies [10,31,32].  
mode because the rectangular brace member was used to prevent out-of-plane buckling by orienting the lower moment of inertia of the brace member along the in-plane direction. Out of the four tests, three were conducted on the one-story frame and one was conducted on the two-story frame with an IPB connection at the ground story only. The authors [24] also conducted three tests on IPB connection with sub-assemblag test configuration on the hollow circular section as the brace member. Table 1 shows the details of the experimental program along with modes of buckling and failure modes observed in the studies.  
The brace member used in the studies has a uniform moment of inertia about both the buckling axes except the hollow rectangular section that was used for specimen S-1A and S-1B in the experimental studies of [PERSON] et al. [10] (Table 1). It is worth noting that despite the lower radius of gyration along the in-plane direction, the S-1B specimen which shows in-plane buckling at a lower drift range of 0.6% but fails due to out-of-plane buckling at 1.5% drift range. The undesirable buckling mode also attracts premature failure at the interface of the gusset plate and reduces the seismic performance of the braced frame system.
## 4 Security Analysis and Simulation Verification
### Performance Analysis and Security Comparison
#### 4.4.2 Security Comparison  
This section compares the Chaudhry protocol [26], Ren protocol [27], and Tsobdiou protocol [28] and conducts a comprehensive evaluation of the security of the protocols described in this study. The security analysis and verification results of the protocols are presented in Sections 4.2 and 4.3, respectively, and the security flaw analysis of each protocol are described in Section 2. IAR represents an anti-impersonation attack, RAR represents an anti-replay attack, UA represents user anonymity, MA represents mutual authentication, PFS represents perfect forward security, KKS represents known key security, and KSTR represents an anti-known session-specific temporary information attack. Combined with the security-flaw analysis results of the representative protocols in Section 2, a security comparison of each protocol is shown in Table 5.  
Table 5 shows that the proposed protocol can meet all security properties described in Section 3; that is, the protocol can resist common attack methods such as imitation attacks, replay attacks, and known session-specific temporary information attacks. In addition, it has user anonymity, complete forward security, known key security, and two-way authentication.
###### Abstract  
Asymmetric rotor systems widely exist in commercial plants. In the previous studies about asymmetric rotor systems, parameters such as material properties and boundary conditions are deterministic. To obtain a deep understanding of the dynamics of asymmetric rotor systems, a generator rotor system considering uncertain factors is studied in this paper. The equations of motion of the three-dimensional finite element model are solved in the rotating frame. The component mode synthesis is used to reduce the degrees of freedom. By employing the Chebyshev interval method (CIM), the uncertain gravity responses of the generator rotor system are investigated. The influences of the uncertainties in the bearing's properties and the rotor's material properties on the gravity response are studied in cases with a single uncertainty and double uncertainties. The accuracy and the efficiency of CIM are validated by comparing with the results of the scanning method. The results show that uncertainties have remarkable influences on the gravity response, and that these influences are different from each other. The proposed method and the results can provide guidance to the design and optimization of the rotary machinery.  
asymmetric rotor; scanning method; CIM; double frequency vibration; uncertainty; interval analysis +
Footnote †: journal: Applied Sciences
# Intelligent Monitoring of Data Center Physical Infrastructure
## 3 Results
### An Overview of Alternative Literature on Big Data Reducing Methods
In particular, a distributed implementation of the optimal Kalman filter was recently developed. A disadvantage of this algorithm is that the fusion center needs access to each node so as to compute a consistent state estimate, which requires full communication each time an estimate is requested.
# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 3 Materials and Methods
### Metrics  
Accuracy  
The accuracy, which in this case is the classification accuracy, is the ratio of correct predictions over the total predictions.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
where \(TP\) (True Positive) is the sum of the predictions in which a specific class was predicted and this prediction was successful, \(TN\) (True Negative) the sum of the predictions in which another class was predicted and it was indeed another class, \(FN\) (False Negative) the sum of the predictions in which another class was predicted, but, in fact, it was this class, and finally, \(FP\) (False Positive) is the sum of the predictions in which the specific class was predicted while the real label was another class.  
The precision metric computes the rate of correct predictions of a class over the total prediction of this class. It is defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
The recall measures the fraction of correct predictions of a class to the total real data points of the class. It is defined as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Kernel Size** & **Stride** & **Hidden Units** \\ \hline
1D conv ReLU 1 & \(12\times 3\) & 1 & - \\
1D Max Pooling 1 & \(3\times 1\) & 3 & - \\
1D conv ReLU 2 & \(12\times 192\) & 1 & - \\
1D Max Pooling 1 & \(3\times 1\) & 3 & - \\ Flatten & - & - & - \\ Concat & - & - & - \\ FC ReLU & - & - & 512 \\ Softmax & - & - & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detailed layer description.  
F1-score is a combination of the precision and the recall metrics and is defined by the following equation:  
\[\text{F1-score}=\frac{2\cdot Recall\cdot Precision}{Recall+Precision} \tag{11}\]
# Rainfall Similarity Search Based on Deep Learning by Using Precipitation Images
## 3 Rainfall Similarity Search Based on NDCG-IPSO
### Feature Extraction  
#### 3.1.1 Regional Precipitation  
Regional precipitation usually represents the total amount of precipitation within a given area at a specific time. In the precipitation image, each color corresponds to a range of precipitation amount. Hence, the regional precipitation feature can be obtained by weighting the color histogram, which can record the frequency of each color in the precipitation image.  
Figure 2: The process flow of precipitation image searching.  
Let the color histogram corresponding to the precipitation image contain \(K\) colors, namely \(C_{1}\), \(C_{2}\),..., \(C_{K}\), the occurrence number of each color in the image is \(num(C_{i})\) (\(1\leq i\leq K\)), and the precipitation amount corresponding to each color is \(pm(C_{i})\), then the regional precipitation within a given area can be calculated as follows:  
\[P=\sum\limits_{i=1}^{K}num(C_{i})pm(C_{i}) \tag{4}\]  
Moreover, let \(P_{1}\) and \(P_{2}\) be the regional precipitation feature of the two precipitation images. The defined Manhattan distance \(D_{p}\) to measure the similarity between the regional precipitation features of the two images can be calculated as follows:  
\[D_{P}=|P_{1}-P_{2}| \tag{5}\]
## 3 Results
### Geomechanical Considerations
#### 3.2.4 Environmental and Safety Concerns  
Underground hydrogen storage holds great promise for large-scale energy storage, yet it is not without its environmental and safety risks. These risks arise primarily from hydrogen's unique physical and chemical properties, as well as the complex interactions between hydrogen and the storage environment. This section discusses these risks, referring to key insights from Sections 3.2.2 and 3.2.3 for microbial, chemical, and geomechanical considerations, and exploring additional safety concerns identified in the literature.  
One major concern is the environmental impact arising from potential contamination and ecosystem disruption from leaks or chemical byproducts. As discussed in detail in previous sections, hydrogen's high diffusivity and buoyancy, leakage pathways through fractures, faults, or poorly sealed wells, and stress and deformation caused by hydrogen injection and withdrawal cycles leading to microcracking, caprock failure, subsidence, and fault reactivation are all significant contributors (e.g., [112; 113]). The possible geochemical reactions and microbial activity further increase this risk by altering the physicochemical properties of the reservoir and seal rocks and producing potentially harmful by-products (e.g., [114]). Leaked hydrogen can alter the geochemistry of surrounding aquifers, affecting water quality and microbial ecosystems [115].  
In addition to environmental concerns, UHS presents significant safety challenges. Hydrogen's flammability and wide explosive range (4-75% in air) pose significant risks during storage and transport, and the near-invisibility of hydrogen flames complicates detection [116]. Hydrogen embrittlement affects infrastructure integrity by reducing the ductility and strength of materials, particularly in high-pressure environments, both during field operation and laboratory experimental work [113]. The hydrogen sulphide--a possible by-product of sulphate reducing bacterial activity--is highly corrosive and can weaken metal well casings and pipelines. [PERSON] et al. [115] provide a detailed analysis of storage integrity in depleted gas reservoirs, while [PERSON] and [PERSON] have published a review of barriers affecting UHS [116].  
Addressing these challenges requires a multifaceted approach. Effective site selection, real-time monitoring systems, material innovations to resist hydrogen-induced damage, and operational optimization are essential to mitigate these risks [112; 113; 114; 115; 116].