esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Data Collection Method  
To improve heat dissipation and stress distribution in the dam body, arch dams typically employ a block-segmentation construction technique, dividing the dam along its axis into blocks with lengths of 20-25 m and thicknesses of 1.5-4.5 m. These blocks, as the smallest control units for temperature regulation and crack prevention in concrete dams, are cast in a staggered sequence, resulting in a jagged surface profile between adjacent blocks during dam construction. Firstly, such a construction method leads to varying heat dissipation conditions for the blocks at various stages. Secondly, the interior of the blocks generally contains cooling water pipes, allowing engineers to regulate internal temperatures by adjusting the cooling water's temperature and flow rate. Additionally, the exothermic process of cement hydration in concrete is also influenced by environmental conditions and construction practices. These time-varying factors result in significant temporal characteristics in the temperature evolution patterns of the pouring blocks. To obtain the temperature of the pouring blocks, engineers typically embed temperature sensors between two layers of cooling water pipes within them to conduct dynamic temperature monitoring. Figure 2 provides a schematic diagram of the arrangement of cooling water pipes and temperature sensors within a block. Several temperature sensors, such as thermocouple temperature sensors, fiber optics, etc., are usually installed in a block, and the frequency of temperature data collection is set according to the size and construction requirements of the dam. Moreover, data such as air temperature and cooling parameters of the water pipes, including water temperature and flow rate, can be obtained according to on-site monitoring and construction records.
## 1 Introduction
As an extension, we analytically model the optimal cache deployment for a given network topology and a given distribution of popularity of a VoD-content catalog, and then derive a closed-form formula which serves as a tool to optimally distribute storage capacity among caches of the various levels of a hierarchical metro-area network. The analytical model takes as an input the number of caches deployed at each network level, the amount of storage capacity available, the number of VoD items in the content catalog and the average VoD content size, and provides as an output the number of items to be stored in caches of each hierarchical level; the overall amount of network resources occupied for VoD content request provisioning is minimized. We verify the correctness of the analytical model using the dynamic VoD provisioning simulator, and then, having validated the analytical model, we provide a framework to identify the most effective cache deployments along the various levels of the metro network by varying factors which are later proven decisive, such as the location of the metro cache, the popularity distribution and the available storage capacity.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 2 Background
### Computational Infrastructure for Resilience
#### 2.2.1 Software Containers: Docker  
The purpose of using containers is to provide the ability to run multiple processes and applications separately, to make better use of infrastructure while maintaining the security of separate systems [28]. Docker technology uses the Linux kernel and its features, such as cgroups and namespaces, to segregate processes to run independently and safely. Containers such as Docker offer an image-based deployment model, which allows an application or a set of services with their corresponding dependencies to be shared across multiple environments. This approach also automates the implementation of the application (or combined sets of processes that constitute an application) in the container environment, leaving improvement features such as portability, lightness, and self-sufficiency in each application involved.
## 4 Results and Discussion
### Prediction of Bond Strength  
To predict bond strength between concrete and steel rebars, existing models proposed by past researchers for NAC, described below, have been employed in this study. Similarly, the model proposed by [PERSON] et al. [45] for brick aggregate concrete has also been used.  
[PERSON] et al. [45] studied bond behavior between brick aggregate concrete (BAC) and reinforcing rebars. For this purpose, they tested cylindrical as well as cubic specimens having design strengths of 20, 25, and 30 MPa. The several varying test parameters were also studied to assess their influence on the bond strength, which included bond length, bar diameter, concrete confinement, concrete compressive strength, and specimen shape. Finally, they proposed Equation (4) based on experimental results for the estimation of bond strength between BAC and rebar. In Equation (5), \(f_{c}^{\prime}\) = concrete compressive strength, \(c\) = concrete cover, and \(d_{b}\) = diameter of the embedded bar.  
\[\tau=0.525\sqrt{f_{c}^{\prime}}\bigg{(}\frac{c}{d_{b}}\bigg{)}^{0.42} \tag{4}\]
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 5 Simulation Results  
Simulations were performed in a MathWorks MATLAB(tm) environment for each standard separately according to their recommendations. A description of the triggering processes of the individual standards that constitute the simulations is in Section 4.  
In the first instance, the user set some of the parameters influencing the simulation. At first, the standard to be simulated was chosen. The available choices were the following: GPON, XG-PON, NG-PON2, EPON and the optimized GPON. Furthermore, the number of ONUs to be connected to the OLT at the specified interval (the maximum number of ONUs that can connect was determined by the selection of the standard mentioned previously) and the length of the distribution network also need to be chosen at the specified interval needs. Then, the total activation time was calculated. The output was visualized as a graph that shows how long the activation of the ONUs that were connected to the OLT occurred.
## 1 Introduction
An altimeter error due to wind speed is indicated by the International Civil Aviation Organization (ICAO) [19]. Furthermore, certain articles analyse how turbulence also affects radio altimeter height measurements and errors in pressure altitudes [20; 21]. The turbulence effect on an aircraft is more complicated to determine, either because of the ambiguity of turbulence and the lack of knowledge about it or because of the difficulty measuring such effects. However, several parameters can be useful when studying the effects of turbulence in an aircraft. [PERSON] et al. [22] establish a relationship between vertical wind and induced lift. [PERSON] et al. [23] show in their paper a method for estimating an aircraft's vertical acceleration and eddy dissipation rate in turbulent flights. [PERSON] et al. [24] evaluate low-altitude atmospheric turbulence patterns for monitoring aircraft aeroelasticity. [PERSON] et al. [25] study the influence of turbulence on the aircraft's aerodynamic properties. Meanwhile, [PERSON] et al. [26] analyse the effects of atmospheric turbulence on aircraft dynamics, and [PERSON] et al. [27] estimate the severity of vertical displacements of aircrafts in turbulent flights. Moreover, a large number of projects attempt to perform flight and turbulence simulations for the observation of their effects using collected data and estimations [28; 29; 30; 31; 32]. In addition, articles have been found with instructions on what procedures should be followed in the presence of severe turbulence [33; 34; 35]. However, no project, article, or study has been found about the correlation between the turbulence intensity and vertical displacement in aircraft altitude, which normally occur in these conditions, and its calculation is necessary to safely and efficiently set the additional clearance for mountainous areas. Therefore, this paper is mainly based on research into the effects of turbulence on an aircraft's altitude. From all the information gathered and the collection of parameters affecting an aircraft's attitude, a methodology was developed which provides a numerical result of this vertical displacement depending on initial conditions that must be defined. Finally, an aircraft's loss of altitude due to turbulence was obtained as a function of the wind speed, which combined with the altimeter error determines the additional clearance in mountainous terrains that should be added according to the flight segment.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Opportunities for Future Research
#### 9.2.6 Overcoming Communication Limitations in Cooperative SLAM  
Collaborative SLAM systems are transforming multi-robot operations, enhancing scalability and flexibility in underwater exploration. However, communication constraints pose a significant challenge [183]. Systems such as Swarm-SLAM enable multiple robots to work together, sharing mapping information to improve the overall environmental understanding. Future research should focus on the following aspects:  
* **Alternative Communication Technologies:** exploring optical or electromagnetic communication methods to complement traditional acoustic channels, improving bandwidth and reliability;
* **Machine Learning-Based Predictive Communication:** designing predictive communication models to optimize data exchange strategies and adjust bandwidth allocation dynamically, enhancing the resilience of cooperative SLAM systems;
* **Distributed SLAM Frameworks:** developing robust distributed SLAM frameworks that enable efficient information exchange and collaborative mapping among multiple robots [162; 184].
## 4 Benchmark Databases and Performance Evaluation Metrics
### Dataset Used
#### 4.1.1 DARPA'98/99  
This is one of the most widely used for intrusion detection studies. It was generated in a simulated network environment at the MIT Lincoln Lab. It includes packet-based records of network traffic for seven weeks and five days. The dataset is available for download at [186] and is open to the public. Despite its widespread use, investigations in [187] discovered numerous redundancies that prevent it from being used for more realistic and real-world attack detection.
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
### Variables in 3D Printing
#### 1.3.3 Post-Processing  
The objects printed with an SLA printer require washing, drying, and post-curing. These post-processing steps may result in dimensional changes and affect the overall accuracy and precision of the printed objects. The examples of the sources of error include: surface material loss during washing in propan-2-ol, more commonly known as isopropyl alcohol (IPA) [22; 23]; over-drying leading to the formation of cracks; and curing time and method, which affects the degree of polymerisation shrinkage during the post-curing stage. Whilst some of these can be easy to account for during design and print preparation, others can vary from print to print.
## 2 Antenna Structure  
Figure 1 shows the geometry of the portable wideband directional antenna scheme for digital terrestrial television reception, and Figure 2 illustrates the proposed antenna scheme of 75 \(\Omega\) input impedance with radome. The proposed antenna scheme was an adaptation of the Yagi-Uda antenna, consisting of the folded bowtie radiator, semicircular corrugated reflector, and V-shaped director.  
Figure 1: Geometry of the portable wideband directional antenna: (**a**) top view, (**b**) perspective view, (**c**) side view, and (**d**) folded bowtie radiator.  
Figure 2: Geometry of the portable wideband directional antenna with radome: (**a**) top view, (**b**) side view.  
The folded bowtie radiator and the corrugated reflector were made of a thin stainless-steel sheet (0.2 mm in thickness). The semicircular reflector was corrugated to reduce the overall physical size of the antenna while increasing its electrical size. In addition, the V-shaped director was made of a hollow aluminum tube 8 mm in diameter. The director was incorporated to improve the directivity and gain of the antenna.  
The three elements (i.e., the folded bowtie radiator, corrugated reflector, and V-shaped director) were mounted on a circular plastic supporting plate 165 mm in diameter. The antenna was fed by a coaxial cable connected to a 75 \(\Omega\) F-type connector, and a balun (\(\lambda\)/4 coaxial balun) was utilized to convert unbalanced to balanced output signal. The portable wideband directional antenna was also covered with a 0.5 mm-thick cylindrical acrylonitrile butadiene styrene (ABS) radome 175 and 209 mm in diameter and height.  
Simulations were carried out to optimize the antenna parameters using CST Microwave Studio Suite [31]. Table 1 tabulates the optimal physical dimensions of the portable wideband directional antenna scheme for the 470-890 MHz frequency band. (Note: The experimental results revealed that the ABS radome had a negligible effect on the antenna performance, including the impedance bandwidth, gain, and radiation pattern).
## 4 Experimental Results and Discussion  
### Selection of Classification Model  
We used 10-fold cross-validation to train and validate the classification model. The 10-fold cross validation method separates the whole dataset into ten sub-sets. Selecting one of the ten sub-sets iteratively as a validation set, and other nine as the training sets. The average accuracy of the 10 rounds, for each algorithm is used as a guide to select between different models.  
Two feature sets were investigated for training the classification model. In the first approach considered only the location data, and the second approach used both movement vectors and the distance to be basket. Several machine learning models were utilised to compare the two approaches for classification as described in previous section. The results are summarized in Table 1.  
As illustrated in Table 1, the second approach seems to be more suitable for most machine learning algorithms. The features of the first approach are not informative enough for the classifiers to learn the difference between the two classes.  
After the 10-fold cross validation, the confusion matrix and receiver operating characteristic curve (ROC) of each algorithm can be computed. This study uses ROC to analyse the validation result. Area Under the Curve (AUC) means the probability that a randomly chosen positive example is ranked higher than a randomly chosen negative example, that represents the performance of this classification experiment. The model of the higher AUC value has the stronger classification capability. According to the comparison of the following three pictures, the KNN algorithm has the largest AUC value with  
\begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Approach 1: Location Data** & **Approach 2: Movement Vectors and Distance** \\ \hline _Quadratic SVM_ & 49.7\% & 68.9\% \\ _K-NN Classifier_ & 51.8\% & 71.5\% \\ _Decision Trees_ & 46.2\% & 65.6\% \\ \hline \end{tabular}
\end{table}
Table 1: Accuracy of Different Classifiers on two approaches of defensive strategy classification.  
Figure 6: Different feature vectors used for classification machine learning algorithms.  
0.8. The classification capability of SVM is 0.02 stronger than Decision Tree. The confusion matrices and the ROC curves for the different classification algorithms are illustrated in Figure 7.
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 3 Results and Discussion
### In Situ DRIFTS Measurements  
To ascertain the types of acidic sites on the surface of the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst and the paths of the NH\({}_{3}\)-SCR denitration reaction, samples of the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst were measured and analyzed by in situ DRIFTS measurements.
## 4 Experiment
### Experimental Results  
#### 4.2.1 Experiment 1: Comparison with Baseline Algorithms  
To verify the effectiveness and superiority of the two CTS algorithms proposed in this paper across various binary classification tasks, we tested all eight datasets with the CTS algorithms and other baseline algorithms. We recorded the accuracy and F1 scores for each dataset, with Table 4 showing the accuracy of each ensemble algorithm on different datasets and Table 5 displaying their F1 scores. In both tables, bolded values represent the best result on each dataset. It can be seen that our proposed CTS algorithm achieved the highest accuracy on five datasets, while the CTS-SR algorithm achieved the highest accuracy on two datasets. Across both algorithms, they achieved the highest F1 score on six datasets. The results significantly outperform traditional ensemble learning strategies and HMTS, clearly demonstrating the applicability and superiority of our proposed ensemble strategy in various binary classification tasks.  
The traditional majority voting and random forest ensemble algorithms heavily rely on the majority results within the group, meaning that the answers affirmed by most base learners significantly influence the final decision. Our proposed CTS algorithm, to some extent, breaks through the limitations of traditional classification, especially by considering and correctly recovering the correct results provided by minority classifiers even when the majority of classifiers agree. As shown in Table 4, CTS improves accuracy by more than 4% compared to majority voting on the musk and Hilly datasets, and by 6% and 9% compared to AdaBoost on the magazine and musk datasets, respectively. This indicates that our method can correctly classify more instances. The data in Table 5 further demonstrate that CTS improves the F1 score by 2-7% compared to traditional random forest and AdaBoost ensemble algorithms. This suggests that our proposed method not only accurately classifies more instances but also maintains a good balance between precision and recall, reflecting the completeness and balance of the approach. This performance improvement is attributed to our introduction of classifier confidence as a crucial factor in the decision-making process, allowing the correction of answers by a few highly confident expert classifiers when the majority provides incorrect results.
## 3 Results
### Climate Factor Response Analysis
#### 3.4.3 Intra-Annual Variations in Climate Factor Correlations  
Using the 16-day scale GPP data and climatic factor data, correlation calculations were performed for each time point (16-day time scale) within a year, resulting in 23 correlation data points. These results are then arranged along the elevation intervals. To better illustrate the interannual variations, a 3D plot is employed for visualization.  
Figure 9(a) shows the distribution of precipitation correlation variations within a year with elevation. Firstly, from an overall perspective, the trend of precipitation correlation across the different time points within the year is generally similar with respect to elevation changes. Specifically, as elevation increases, the correlation stabilizes at a relatively constant level, with a slight decline around 1300 m. This pattern is consistent with the previously calculated precipitation correlation at a 16-day time scale. Secondly, in terms of the intra-annual variation, the precipitation correlation exhibits a trend of initially increasing and then decreasing over time. The peak occurs between days 193 and 257, corresponding to the summer period.  
Figure 10: Intra-annual variation in the correlation between GPP and climatic variables in northwestern Hubei (where the \(y\)-axis represents the days, ranging from 1 to 353 (16-day resolution); the \(x\)-axis represents the elevation; and the \(z\)-axis represents the mean correlation). (**a**) Correlation with precipitation. (**b**) Correlation with temperature.  
Figure 10b illustrates the distribution of temperature correlation variations within a year with elevation. Firstly, from an overall perspective, the trend of temperature correlation across the different time points within the year is generally similar with respect to elevation changes. Specifically, as elevation increases, the correlation remains relatively stable, with a slight decline around 2800 m, followed by a second peak near the maximum elevation. This pattern aligns with the previously calculated temperature correlation at a 16-day time scale. Secondly, in terms of the interannual variation, the temperature correlation exhibits a pattern of rising, falling, rising again, and then falling over time. Peaks occur between days 129 and 193, as well as between days 257 and 321, corresponding to the periods before and after summer.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 5 Description of the Considered Signaling Schemes
### Laco-Ofdm
Assuming that different signal constellation sizes are used in different layers (to adjust the overall spectral efficiency), i.e., \(M_{\ell}\)-QAM signal constellation in the \(\ell^{\text{th}}\) layer, the spectral efficiency of LACO-OFDM is  
\[\gamma_{\text{LACO}}=\frac{N}{2(N+N_{\text{CP}})}\ \sum_{\ell=1}^{L}\frac{1}{2^{ \ell}}\log_{2}M_{\ell}\quad(\text{bps/Hz}). \tag{10}\]
## 5 Results and Discussion
### Year-Long Results
#### 5.1.2 Energy Bill Reductions Due to Storage  
Interestingly, despite the decrease in SC, the bill reductions per kWh in the Greedy with pre-charge heuristic are superior to the ones obtained in greedy strategy.  
UPAC A and UPAC C are the most paradigmatic examples of this situation, more than doubling the energy bill's reductions when using the smallest storage combination (2.4 kWh, 1 kW). However, this happens at the expense of 90% pre-charge, which leads to a considerable drop in the SC. Considering this, UPAC C is a clear case in which the installed solar PV capacity is under-sized.  
For example, even with a 7.2 kWh (B3 combo) nominal storage capacity, the %PC is set to the maximum SOC allowed (90%).  
Concerning the single-rate tariff with greedy control in the larger UPACs, the battery combinations selected by the Equation (2) achieve the highest SC levels but are also the less profitable concerning the initial investment. Interestingly, in this control strategy, the second smallest combinations (A2 in UPAC D and UPAC E and B3 in UPAC F) are the ones that offer the best trade-offs between SC and the costs of acquiring the storage device.  
Another observation is that larger inverters do not add much to the profits independently of the adopted control strategy. This effect is visible in UPAC C (A2 vs. B2), UPAC D (A2 vs. B2, A3 vs. B3), UPAC E (A2 vs. B2, A3 vs. B3) and UPAC F (B4 vs. C4) where storage combos A, B and C have 1, 2.4 and 4 kW inverters, respectively. The reason for this is the relatively low instantaneous power values observed in all UPACs, which are mostly below 1 kW (see Figure 5). Furthermore, as seen in Figure 3, larger inverters are less efficient at lower power levels. Therefore, smaller inverters will cover the majority of the situations, making them more profitable.