esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain and Smart Contracts for Insurance: Is the Technology Nature Enough?
## 4 A SWOT Analysis  
The above discussion should have provided the reader with a broad overview of potential applications of blockchain technology in the insurance sector. As seen, advantages appear to be numerous. Nonetheless, only a few prototypes exist so far, and it has been estimated that blockchain-based applications will be available to the wider public only in 10-15 years [5]. Starting from the considerations drawn in Section 3, in the following a SWOT analysis summarizing advantages and disadvantages of this technology is provided (Table 1). The objective here is to abstract from the specific domain considered, i.e., insurance, and to perform an analysis, which could potentially be helpful in a variety of contexts/sectors. The strengths of blockchain technology are mainly related to the technological aspects presented in Section 2. By removing intermediaries, the cost of money transfers can be lowered (e.g., bank commissions cease to exist). Transfers can also be made faster, as cryptocurrencies are directly moved from a wallet's address to another without intermediate steps (as it usually occurs, e.g., in overseas bank transfers). Smart contracts provide a high degree of automation. Transparency is guaranteed as well, as the blockchain could be accessed worldwide. In addition, since everyone could potentially write on the ledger, the blockchain could become the repository of a huge amount of information, which could be used for data analytics in different sectors (not necessarily related to insurance and finance, such as medicine, education, etc.). The underlying cryptographic mechanism guarantees that data are not modified and that transactions could not be repudiated. Finally, the replication of the blockchain on each network node ensures that the blockchain would survive to unexpected events. The most relevant weaknesses are related to scalability, energy consumption and performance. In fact, at present, the number of transactions that could be handled per second is extremely low when compared to traditional systems (mainly because of the computational power required to validate new blocks). If, at the present time, blockchain-based transactions are quicker than traditional bank transfers (on average they require few seconds to several minutes, instead of 1-2 days), for instant payments and for other kinds of applications, performance should not be adequate to needs. In this respect, it is worth outlining that some blockchain platforms are changing the process of validating blocks, reducing the complexity of the mathematical problem to be solved and restricting the possibility to perform mining only to a subset of trusted nodes. Apart from time, space is also an issue, sincedata are replicated on each network node. To make an example, the Bitcoin blockchain requires more than 170 GB of storage on each network node [57].
# Cloud PricingOps: A Decision Support Framework to Explore Pricing Policies of Cloud Services
## 3 Introducing CloudPricingOps Framework
### Stakeholders and Use Cases  
The CloudPricingOps framework is tailored to meet the needs of a diverse set of stakeholders, each with unique objectives when it comes to navigating cloud services.  
The framework's stakeholders include engineering, finance, and leadership roles, such as cloud architects, financial managers, and IT teams. Engineering roles rely on the framework for insights into resource allocation and performance trade-offs. They use the framework's analytical capabilities to better understand the impact of various service configurations on costs, allowing them to optimize their designs for both performance and budgetary requirements.  
Furthermore, leaders in small- and medium-sized enterprises (SMEs) frequently struggle to understand complex cloud pricing models due to a lack of in-house expertise. For these users, the framework simplifies the decision-making process, offering a clear comparison of service options without requiring deep technical knowledge. This enables SMEs to focus on selecting services that meet their operational needs, reducing uncertainty and facilitating more effective cloud adoption.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs.** & **Capabilities** & **DM Methods** & **Cloud Service Type** \\ \hline (20) & Factor Prioritization & AHP, TOPSIS & MLaaS \\ (18) & Theoretical Framework Factor Prioritization & AHP & IaaS \\ (19) & Factor Prioritization & AHP, FAHP & IaaS \\ \hline (21) & Cost Analysis & AHP & IaaS \\ (23) & Cost Framework & Technology Suitability Analysis, Financial Analysis & IaaS \\ Implementation of DSS & & AHP & IaaS \\ (24) & VM Selection for Cloud Migration & AHP & IaaS \\ (25) & Risk Analysis & Critical Analysis of Existing DSS & IaaS \\ (26) & SLA Prioritization & Top-K Nearest Neighbor Algorithm & IaaS \\ \hline Current Work & Pricing Analysis, Cost Comparison, Price Prediction & Hedonic Method, Clustering, ML Techniques & IaaS/CaaS/PaaS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the literature review.  
Finally, finance roles are primarily concerned with controlling costs and ensuring that cloud expenditures are in line with the organization's financial goals. The framework's forecasting capabilities provide these users with insights into potential price trends, allowing them to plan budgets with greater accuracy and anticipate future financial requirements. By aligning projected costs with expected market developments, financial managers can better manage risks associated with price volatility.
## Materials and Methods
### Physicochemical Parameter Analyses
#### 2.4.7 Colour-Metrics
The calibration curve of absorbance (\(y\)) versus concentration (\(x\)) of [DPPH\({}^{\bullet}\)] was expressed by the following equation:  
\[y=0.0174x-0.0048;\,R^{2}=0.9689 \tag{3}\]  
Determination of In Vitro Antioxidant Activity. All honey samples were dissolved in distilled water to obtain a concentration of 0.12 g/mL, that is a solution simulating the daily consumption of 30 g of honey dissolved in a glass of water (250 mL) (mother solution, \(w/v\)) [28]. Thereafter, volumes of 1.9 mL of methanol solution of [DPPH\({}^{\bullet}\)] (0.044 mg/mL, 1.12 \(\times\) 10\({}^{-4}\) mol/L) and 1 mL of acetate buffer 100 mM (pH = 7.10) were placed in a cuvette and the absorbance of the [DPPH\({}^{\bullet}\)] radical was measured at \(t\) = 0 (A\({}_{0}\) = 0.8048). Subsequently, 0.1 mL solution of each honey type was added to the above medium (final [DPPH\({}^{\bullet}\)] concentration of 70.93 umol/L) and the absorbance was measured every 30 min (regular time periods) until the value reached a plateau (steady state, At). The reaction was completed in 4 h. The absorbance of the reaction mixture was measured at 517 nm. The [DPPH\({}^{\bullet}\)] radical scavenging activity was calculated using the following equation:  
\[\%\mathrm{AA}=(\mathrm{A}_{0}-\mathrm{A}_{\mathrm{t}}/\mathrm{A}_{0})\times 100 \tag{4}\]  
where A\({}_{0}\) is the initial absorbance of the DPPH free radical standard solution and At is the absorbance of remaining [DPPH\({}^{\bullet}\)] free radical after reaction with honey antioxidants, at steady state (t, plateau). The effective concentration of aqueous honey solutions that is the specific concentration that could decrease the [DPPH\({}^{\bullet}\)] concentration by 50% was estimated based on the following formula:  
\[\mathrm{EC}_{50}\,(\mathrm{g}/\mathrm{mL})=(\mathrm{Caq}\times 50\%)/(\% \mathrm{AA}) \tag{5}\]  
where Caq: aqueous solution of honey added in the reaction medium (0.12 g/mL) and AA% the percentage inhibition of [DPPH\({}^{\bullet}\)] after the addition of honey water soluble antioxidants. Finally, methanol and acetate buffer (2:1, \(v/v\)) were used as the blank sample. Reported results are the average \(\pm\) standard deviation values of three replicates (\(n\) = 3).
## 8 Implementation and Performance Evaluation
### Comparative Analysis with Other Protocols  
This section provides a comparative study between the enhanced GN-AK protocol and several well-known ECC-based authenticated key agreement protocols. We evaluate security robustness, computational efficiency, and suitability for resource-constrained applications.  
The following protocols are used as benchmarks:  
* **HMQV:** A hash-based MQV variant was proposed by [PERSON] [33].
* **ECKAS-DH1:** An ECC-based Diffie-Hellman authenticated key exchange scheme [34].
* **NAXOS:** A secure key exchange scheme combining long-term and ephemeral secrets [35].
* **Original GN-AK:** As described in [1].
## Abbreviations  
\begin{tabular}{l l} BER & Bit error rate \\ Bi-IFDTE & Bidirectional IFDTE \\ CSI & Channel status information \\ CIR & Channel impulse response \\ CE & Channel estimation \\ CS & Compress sensing \\ DIPNLMS & Differential IPNLMS \\ EP & Expectation propagation \\ EXIT & Extrinsic information \\ FDTE & Frequency domain turbo equalization \\ FDDF & Frequency domain decision feedback \\ ICE & Iterative channel estimation \\ IFDTE & Improved FDTE \\ ISI & Inter-symbol interference \\ IPNLMS & Improved proportionate NLMS \\ LE & Linear equalization \\ MAP & Maximum a posteriori probability \\ MMSE & Minimum mean square error \\ MSE & Mean square error \\ NLMS & Normal least mean square \\ Prob. & Probability \\ SM & Set-membership \\ SZA & Selective zero-attracting \\ SIMO & Single-input multiple-output \\ TDTE & Time-domain turbo equalization \\ UWACs & Underwater acoustic communications \\ \end{tabular}
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 4 Experiments and Evaluations
### Results of Performance Verifications
#### 4.3.2 Different Packet Sizes  
As shown in Figures 11 and 13, the throughputs of OVS are 0.16 Gbps and 2.8 Gbps when the packet sizes are 64 and 1518. In the OVS network, the throughput in packet size 1518 is 16.4 times as high as the throughput in packet size 64. In addition, the throughput of OVS-DPDK in packet size 64 is only 1.4 Gbps. When the packet size is 1518, the throughput of OVS-DPDK is 9.93 Gbps. In the OVS-DPDK network, the throughput in packet size 1518 is 7 times as high as the throughput in packet size 64. Regarding SR-IOV, the throughputs are 9.5 Gbps, 9.96 Gbps, and 9.96 Gbps when the packet sizes are 64, 512, and 1518. Therefore, the impact of packet size is less significant when packet processing capacity is higher.  
Because the VM accesses the physical NIC directly, the performance of SR-IOV is near the line rate in all cases of different packet sizes. On the contrary, the throughputs of OVS and OVS-DPDK dramatically decrease when the packet size is small. That is because the sender generates more packets when the packet size is small. A large workload of packet processing is not affordable for virtual switches because the capacity of each virtual switch for processing packets is limited. The more packets per second the sender generates, the more packets are dropped. Therefore, the throughput of small packet size is lower than that of a large one.
# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
## 2 Materials and Methods
### Econometric Methods  
#### 2.3.1 Spatial Correlation Analysis  
The global Moran's I index and local Moran's I index describe the global and local cluster characteristics of the FEF, respectively. They are expressed as follows [39]:  
\[Moran^{\prime}sI=\frac{n\sum_{i=1}^{m}\sum_{j=1}^{n}\left(x_{i}-\overline{x} \right)\left(x_{j}-\overline{x}\right)}{\sum_{i=1}^{n}\sum_{j=1}^{n}W_{ij} \sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)} \tag{2}\]  
\[Local\,Moran^{\prime}sI=(\frac{x_{i}-\overline{x}}{s^{2}})\sum_{i=1}^{n}W_{ ij}\left(x_{j}-\overline{x}\right) \tag{3}\]  
where \(x\) represents the FEFI; \(n\) is the number of counties; \(\overline{x}\) is the average value of all counties; \(W_{ij}\) is the spatial weight matrix; and \(s^{2}\) is the sample variance.  
[PERSON]'s I index ranges from \(-1\) to \(1\). The index approaching 1 signifies a positive autocorrelation and FEF in adjacent areas are similar, while a [PERSON]'s I value nearing \(-1\) denotes negative spatial autocorrelation. If the spatial distribution of FEF is absolutely random, the Moran's I index approximates 0. As for the local Moran's I index, a positive value indicates that counties with high (low) FEF are also surrounded by adjacent counties with high (low) values. On the contrary, a negative value indicates that counties with high (low) FEF are encircled by neighboring counties with low (high) values. Based on the local Moran's I indices, the Local Indicators of Spatial Association (LISA) map is constructed and shows four clustering patterns: high-high (H-H), low-low (L-L), high-low (H-L), and low-high (L-H) [40]. In this study, a significant spatial correlation of FEF is the prerequisite for applying the geographically weighted regression (GWR) model.
## 2 Calibration of Rock Materials  
The mechanical parameters of red sandstone were obtained through uniaxial compression and Brazilian splitting experiments, which are summarized in Table 1. In this paper, the rock-breaking process of an arcing-blade cutter is simulated using the finite element method, and the Riedel-Hiermaier-Thoma (RHT) material intrinsic model is utilized to describe the red sandstone material. The RHT material model divides the stress development of rock materials under dynamic loading into three stages: the elastic stage, linear strengthening stage, and damage softening stage, and correspondingly introduces two limit surfaces: the elastic yield surface and failure surface. The residual strength surface is used to describe the change patterns of material failure strength, initial yield strength, and residual strength, which can better simulate the rock failure process and crack propagation [24]. In order to accurately simulate red sandstone, it is necessary to calibrate this rock sample.  
Taking uniaxial compression calibration as an example, a cylindrical rock sample with a size of \(\mathcal{O}50\) mm \(\times\) 100 mm corresponding to the uniaxial compression experiment was generated and compressed in the simulation. Then, the calculated curves of the axial compressive stress and axial strain in the simulation results were extracted. Following this step, the simulation parameters were adjusted to fit the simulation curve to the experimental curve. Figure 2 shows the results of the uniaxial compression experiments and simulations. Among them, the rock in Figure 2b has the same size as the rock in Figure 2a. In the simulation, a certain displacement was applied to the upper surface of the rock, causing the rock to break. The rock-breaking process in Figure 2a was simulated, and the output stress-strain curve was compared with the experimental data. Figure 2 indicates that the crack extension and material failure in the simulation of the rock sample was in good agreement with the experimental phenomenon.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rock Type** & \begin{tabular}{c} **Compressive** \\ **Strength/MPa** \\ \end{tabular} & \begin{tabular}{c} **Tensile** \\ **Strength/MPa** \\ \end{tabular} & \
\begin{tabular}{c} **Density/kg m\({}^{-3}\)** \\ \end{tabular} &
\begin{tabular}{c} **Elastic** \\ **Modulus/GPa** \\ \end{tabular} \\ \hline Red sandstone & 71.8 & 4.7 & 2370.6 & 6.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock mechanics parameters of experiment.  
Figure 2: Experimental and simulated phenomena in uniaxial compression test: (**a**) uniaxial compression experiment and (**b**) uniaxial compression simulation.  
Figure 3 illustrates a comparison of the extracted axial compressive stress and axial strain curves. It can be seen that the simulation result exhibits a high consistency with the experimental one. This outcome serves as a foundation for the subsequent numerical modeling of cutting rock with arcing-blade cutters.
## 8 Discussion  
The repeatability of all test objects increased with the increase in verification points, and the maximum repeatability of the indication errors of the 5 m arc ruler, 20 m measuring-depth steel tape, and 30 m steel tape were 0.025 mm, 0.038 mm, and 0.030 mm, respectively. According to the uncertainty evaluation method from the literature [20,21], the uncertainty evaluation of the indication error of each steel tape is shown in Table 2. The extended uncertainty \(U\) of the indication error of each test object was not more than one third of the maximum permissible error value (MPEV). The measuring principle, method, and procedure of the standard device are scientific and feasible.  
The SMTCD is an image measurement system composed of a million-level analog camera with a resolution of 1944 \(\times\) 2592, an image acquisition card, and image-processing software to achieve subpixel accuracy. At the same time, human error is reduced to achieve accurate and reliable measurement results. The SMTCD, based on the visual array image measurement technology, ensures the timely measurement of steel tape, and the verification efficiency is increased by more than 100% (the verification time of the same steel tape is reduced to more than half of the time required by manual verification in the past). Meanwhile, the SMTCD overcomes the previous need for a manual hanging weight, the need to move the reading microscope at each line for reading, the manual recording of data, and other tedious steps. Now, according to the operating procedures, the verification personnel are only required to prompt the SMTCD to automatically complete the verification work, greatly reducing the labor intensity of the verification personnel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Steel Measuring Tape** & \(\mathbf{s}\) & \(\mathbf{u_{c}}\) & \(\mathbf{U}\) & **MPEV** \\ \hline
5 m arc ruler & 0.025 & 0.08 & 0.16 & 1.3 \\ \hline
20 m measuring-depth steel tape & 0.038 & 0.27 & 0.54 & 2.0 \\ \hline
30 m swing-type steel measuring tape & 0.030 & 0.39 & 0.78 & 6.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Each steel measuring tape’s \(s\), \(u_{c}\), \(U\) (\(k\) = 2), and MPEV (unit: mm).  
Figure 15: Indication error test of 30 m swing-type steel measuring tape.  
The device has a high degree of automation, fast detection link, high accuracy, and saves labor time and reduces labor intensity. However, there are certain disadvantages, involving the time required to install and unload the fixture before and after the verification of the steel tape. In addition, only one steel measuring tape can be calibrated at a time. Therefore, in the near future, the steel-tape automatic verification device's performance can be further improved by the simultaneous verification of multiple stations and convenient loading and unloading.
## 2 Materials and Methods
### Statistical Analysis  
Based on the peak area normalization method, the VOC content was obtained by comparing the peak area with that of the internal standard (cyclohexanone), and the content of VOCs (mg/kg FW) = (peak area of VOCs/peak area of the internal standard) \(\times\) content of the internal standard. Statistical analyses and bar graphs were performed for the number and content of volatile components using Excel version 2016 (Microsoft, Redmond, WA, USA). Origin 2021 software (OriginLab Corporation, Northampton, MA, USA) was used to plot correlation heat maps. Heat maps were plotted by TBtools version 1.09876 (Guangzhou, China). SIMCA version 14.1 (Umetrics, Umea, Sweden) was used to perform the principal component analysis (PCA), HCA, OPLS-DA and VIP. And one-way analysis of variance (ANOVA) was performed with SPSS version 26.0 (International Business Machines Corporation, Armonk, NY, USA); \(p<0.05\) was considered significant.
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 4 Results of the Tilt Estimation Method
Table 4. Given the small yet non-negligible impact of tilt on the azimuth offset, the final azimuth corrections were recalculated using a bullseye model after undergoing tilt and elevation offset correction. These tilt corrections were then applied to the data within each time period such that 10 different corrections were applied during the whole duration of the project. In almost all cases, the tilt model approach was employed: however, notable exceptions are as follows. Firstly, Periods 2 and 3 were combined, using a single correction for both due to the closeness of their original results in Table 3. During this combined period, the fitted tilt model was also discounted as more general radar observations (individual reflectivity PPIs and monthly echo occurrence plots, for example) showed no evidence of a pointing offset in excess of \(1.0^{\circ}\), with this result highlighting the difficulty of applying a tilt correction at high latitudes in winter (see discussion). The other period for which the tilt model was discounted was Period 5, which was the second-shortest period, with a low number of solar hits per day. In this case, the bullseye fit was taken as a final correction based on analysis of polar volume radar data and processed data products (see verification). Given all but one of the corrections is based on a time interval significantly shorter than the 16 weeks recommended by [PERSON] et al. (2016), additional verification of the correction has been considered prior to acceptance of the corrections for application to future work. The following section describes that approach and its results.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 3 Materials and Methods  
### YOLOv5 Network Model  
The YOLO model has always been widely used. There have been five updated versions, from YOLOv1 to YOLOv5. With continuous improvement and innovation, it has been used by deep learning enthusiasts as one of the preferred frameworks for object detection [24; 25]. The official code of YOLOv5 [26] provides a total of five versions of the object detection network: YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, and YOLOv5n. YOLOv5n is mainly designed for mobile and CPU environments; it is fast, but not accurate. Among the other four versions, YOLOv5s is the network with the narrowest feature map width and the shallowest depth. The following three models continue to widen and deepen these aspects, respectively. The YOLO network model is mainly composed of the backbone, neck, and prediction layers. The backbone is a convolutional neural network that aggregates different image granularities and simultaneously forms image features [27; 28]. The neck is a series of network layers that mix and combine image features. Its main function is to transfer image features to the prediction layer. The prediction layer predicts the features of the image, generates the bounding box of the detection target, and predicts the type of the target object [29; 30].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object Detection Networks** & **Precision** & **Recall** & **mAP** &
\begin{tabular}{c} **Average Detection** \\ **Speed (s/pic)** \\ \end{tabular} & **Reference** \\ \hline O-YOLOv2 & 0.94 & 0.94 & 0.94 & 0.17 & [12] \\ YOLOv2 & 0.91 & 0.88 & 0.87 & 0.17 & [12] \\ YOLOv3 & – & 0.64 & 0.825 & 0.25 & [13] \\ Tiny-YOLOv3 & – & 0.49 & 0.6241 & 0.068 & [13] \\ YOLOv4-uw & – & – & 0.7534 & 0.023 & [14] \\ Faster R-CNN & 0.82 & 0.88 & – & 0.32 & [15] \\ Modified Faster R-CNN & 0.92 & 0.96 & – & 0.55 & [15] \\ RetinaNet & 0.81 & 0.97 & – & 0.11 & [15] \\ CenterNet & 94.3 & 94.9 & 0.8924 & 0.032 & [16] \\ improved CenterNet & 96.8 & 95.5 & 0.9361 & 0.027 & [16] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different models for wildlife detection.