esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fatigue Life Model Based on G-Value  
Regardless of the origin of the parameters affecting the G-value, the logarithm of its absolute value shows a good correlation with the logarithm of the different stress levels (Log (S)), with R\({}^{2}\)adj. = 0.89 (see plot presented in Figure 11).  
This correlation is quite valuable because it can correlate the damage levels in concrete sensors with the fatigue life of the concrete under dynamic loading, provided that the composition and the overall characteristics of the concrete are comparable with those considered in the present research. Therefore, the damage occurring inside the material can be monitored in real-time by measuring the change in electrical resistance or voltage of the sensor during loading and unloading. This finding rolls the way to use this type of concrete sensor for structural health monitoring in concrete structures. Estimation of the life span in a concrete structure is a key point for the management system. Based on this finding, a log (G)-log (N) description can be used instead of traditional S-log (N) fatigue curves (also known as Wohler curves), as depicted in Figure 12.  
Figure 11: Correlation between the slopes of the sensors’ response baselines (G-value) and the corresponding stress levels (S) in fatigue tests.  
Figure 12: Relationship between the slope of the sensors’ response baseline and fatigue life for different stress levels.  
In fact, Figure 12 indicates a good correlation between the concrete sensors' electrical output responses (i.e., resistance or voltage) under fatigue loading versus the life of the concrete structure (i.e., time of loading or number of load cycles). In Table 4, the parameters for two different fatigue models are reported. The model in the form of Log (G) = a \(-\) b \(\times\) log (N) was selected as the most reliable one.  
This finding helps to define a continuous and online monitoring system and it could be useful in estimating the damage propagation in time. Besides, different loading conditions and any other destructive phenomena have a cumulative effect on the G-value in time. Therefore, by monitoring the G-value, all destructive effects are considered and the total life of concrete members under these effects can be estimated.
## 3 Results
### NASSS Framework
#### 3.2.3 Value Proposition  
Three-dimensional printing technology can provide highly precise and accurate surgical guides, implants, and models that fit each patient's unique anatomy. This can lead to improved surgical outcomes, faster recovery times, and increased communication and collaboration between healthcare professionals. Patients can benefit from improved surgical outcomes and personalized solutions tailored to their specific needs and anatomy. In general, this technology can create a better experience for the patient. In addition, the use of 3D printing can lead to long-term cost savings for patients and healthcare providers. While traditional surgical models and techniques may still be used, 3D printing can provide a more efficient approach, as well as providing more accurate and personalized planning and implementation of surgery [15].  
Evidence has shown that 3D printing improves surgical outcomes and reduces complications in various medical fields, including oral and maxillofacial surgery. Additionally, the use of 3D-printed surgical guides has been shown to reduce the need for multiple surgeries and revisions, ultimately resulting in cost savings for patients and healthcare providers [18]. Overall, the value proposition of 3D printing in oral and maxillofacial surgery is that it can provide a precise, personalized, and cost-effective approach to surgical planning and execution that leads to better outcomes and better patient experiences. However, some points should also be paid attention to and efforts should be made to resolve such issues, such as the high cost of 3D printing, the limited materials available for medical applications, the time-consuming process [41], the possibility of errors in precision and accuracy, and regulatory obstacles that must be resolved. There is also a lack of standardization among different manufacturers and models, and the need for specialized knowledge and expertise are among the things that should be improved. By highlighting these challenges, the goal is to protect medical professionals and decision makers from possible disadvantages (Figures 7 and 8) [29].  
Figure 7: Survey responses of maxillofacial surgeons regarding advantages of 3D printing.
# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 2 Basic of Cache Based on Popularity and Class  
There are two types of packages in NDN, namely interest packet and data packet. The interest packet contains a request for content (data) and consumers send it to the network. The data packet includes contents that are requested by consumers. The interest packet is sent by the consumer, while the producer or the router sends the data packet. The terms 'interest packet' and 'data packet' are used often in this paper. The name 'data' means 'content' and can be interchangeable. In NDN, the router or producer node sends the data packet to consumers only when consumers first send the interest packet to inform what content they need. This process is efficient and fast when the data location is close to them because it only involves less node or router in the network. There are lots of content in the network, therefore, the caching mechanism is needed to decide where the node is to cache the copy of data and in the amount saved on the router's content store. It is important to keep the data close as much to the consumer and provide the optimal network performance.
## 1 Introduction
### Related Work  
The scheme of refinement can be tracked to the origin of two-stage detectors, where R-CNN [2] uses a region proposal algorithm that generates a fixed number of regions. The regions are classified and they are refined by bounding box regressor head. Faster R-CNN [3] replaces the region proposal algorithm with a region proposal network. The same bounding box regressor head can be used iteratively to obtain a more precise detection [4; 5]. The effect of iterative refinement may be increased by involving the LSTM module [6]. The aim of refinement can also be anchors; RefineDet [7; 8] refines them to obtain customized anchors for each cell. Currently, the top refinement scheme, Cascade R-CNN [9] uses a sequence of bounding box regressor heads to create an \(n\)-staged object detector. In Cascade R-CNN, head \(h_{0}\) takes proposals from the region proposal network and feeds the regressed bounding boxes to network head \(h_{1}\) and so on. All heads work on the same features extracted from a backbone network. The cascade scheme shows that \(h_{1}\) depends on the quality of the predictions produced by the \(h_{0}\) head. If \(h_{0}\) includes some bias, \(h_{1}\) balances it. Therefore, all heads have to be trained together (part by part), and if \(h_{0}\) is retrained, \(h_{1}\) should be retrained as well.  
The difference between BBRefinement and the existing refinement schemes is that BBRefinement is trained on the original data standalone, so that it is not integrated into any existing architecture during the training stage; it is independent. That makes BBRefinement universal and able to be applied to various existing image detectors without retraining a detector or BBRefinement. A two-stage object detector like Faster R-CNN refines areas from the first stage using the features extracted from the backbone that are shared across the two stages. Thus, the two stages are directly connected and trained together. The second stage of a particular model cannot be applied to the first stage of a different model. By contrast, BBRefinement is trained standalone, and the input is not the extracted features but the images themselves. This makes it universal and capable of being linked to an arbitrary object detector (even a two-stage detector) to improve its ability (Figure 1).  
The novelty lies in such standalone training with the usage of mixture data and the combination with existing models during inference. To our knowledge, it is original and has not been described in the literature so far. According to the results presented later, BBRefinement leads to universal usage and a nice boost of mAP without significantly decreasing the processing speed.
# A Blockchain-Based Digital Asset Platform with Multi-Party Certification
## 4 Technical Basis of BDAP
### Verifiable Byzantine Fault Tolerant Consensus Mechanism  
BDAP adopts the innovative VBFT consensus mechanism to make sure of the integrity of asset information, which is a new consensus algorithm combining POS, Verifiable Random Function (VRF) and Byzantine Fault Tolerance (BFT). The VBFT mechanism is based on the BFT algorithm, but it introduces a \"verifiable random function\" (VRF). VRF is used to calculate and randomly select the verification nodes and confirmation nodes, and then the selected nodes are used to complete the consensus by the BFT algorithm. Due to the randomness introduced by VRF, the alternative proposal nodes/verification nodes/confirmation nodes are different for each round of blocks, which is difficult to predict in advance, thus greatly improving the safety of nodes.  
In each proof cycle, the storage node will automatically generate a storage proof and broadcast it to the shared network; after the verification node receives the storage proof, it will cooperate with the VRF function to complete the validity verification of the current round of proof in the self-proving process.  
The self-proving process is shown in Figure 8. First, the storage node randomly selects c block indexes from n block indexes and selects a corresponding random number for each block index to form a challenge request; then, based on the challenge request value, it returns {\(\sigma\), \(\mu\)} as evidence to the provers; finally, after the consensus node (challenge contract) receives the evidence {\(\sigma\), \(\mu\)}, it determines whether the data are complete.  
BDAP uses the Balanced-Merkle-DAG sharding method shown in Figure 9, which retains the original characteristics of Merkle-DAG while solving the problem of its data traversal. In a Merkle tree, the bottom mass of leaf nodes contains the base data; each intermediate node is a hash of its two leaf nodes. The hash value changes when the transaction of any of the leaf nodes is modified, so the hash value can be used as a unique summary of the digital asset transaction. In addition, BDAP strictly controls the multiple backups through the frequent PDP proof to ensure the integrity of the file; once the file is missing, the storage protocol will automatically replicate the backup to ensure that the data of assets has always existed in the entire network.  
Figure 8: Self-Proving process of VBFT.
## 2 Related Work
### Energy-Aware Routing Protocols  
As one of the most important performance metrics, the energy is highlighted for most real-world deployments of wireless sensor networks (WSNs). [PERSON] et al. [16] proposed a number of criteria commonly used in the design of WSN protocols, where the energy-related issue is of great attention. Unfortunately, attempts to optimize energy efficiency are often in conflict with the network performance demand, e.g., the real-time observation and high throughput requirements. To increase network lifetime, [PERSON] et al. [17] proposed an adaptive routing algorithm to minimize the number of nodes involved in routing and balance their forwarding load. [PERSON] et al. [18] proposed a reactive data acquisition scheme, which is built on the synergies arising from a combination of the data reduction methods and energy-efficient data compression schemes. By combining compressed sensing, data prediction and adaptive sampling strategies, the proposed scheme dramatically reduces the amount of unnecessary data transmission in the deployment for environmental monitoring and surveillance networks. [PERSON] et al. [19] also proposed a clustering-based routing protocol suitable for deserts in a VANET scenario. However, the above-mentioned protocols might not be easily applied to vehicular sensor networks. As one distinct difference with the general sensor network, the vehicle as a sensor will almost be unaffected by the energy. Differently, the dynamically-changing vehicular scenario is one of the most essential issues to be considered on routing protocol design.
# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 4 Materials
### Data Analysis
#### 4.3.4 Voice Quality  
The mean values for all voice quality measures in Korean and English are shown in Table 5. In general, English speakers with dysarthria are observed to have higher values for all measures except shimmer, while Korean dysarthric speakers have higher values for all measures except jitter and shimmer. Results from the two-way mixed ANOVA suggest no significant two-way interaction for the English TORGO dataset, but a significant interaction for the Korean QoLT dataset, where F (5.06, 55.67) = 20.01, \(p\) < 0.05. Furthermore, the main effect of the severity group and voice quality measure is significant for all measures (\(p\) < 0.05). Pairwise comparisons tests further reveal that significant differences depend on the specific measure and severity group. A significant difference is seen between healthy vs. severe and healthy vs. moderate utterances for the degree of voice break and the number of voice breaks (\(p\) < 0.05), between healthy and mild utterances for jitter, shimmer, and mean HNR (\(p\) < 0.05). Significant differences are also apparent between mild and moderate utterances for all measures except the number of voice breaks (\(p\) < 0.05), between mild and severe for all measures (\(p\) < 0.05), and between moderate and severe for mean HNR (\(p\) < 0.05).
## 2 Main Lacase Types and Their Structure
Standard lacases contain four copper atoms, which mediate the redox process and are present at three different sites (T1, T2 and T3, defined according to their spectroscopic properties, described in the next section), as shown in Figure 2. The T1 site has a trigonal conformation, with histidine and cysteine acting as equatorial ligands and leucine or phenylalanine acting as axial ligands [40; 41]. At the T2 site, copper coordinates with two histidines and a water molecule, while at the T3 site, three histidines and a hydroxyl bridge between the copper pair are present as ligands. The T2 copper center and the two T3 copper atoms form a trinuclear center whose function is to catalyze the reduction of oxygen to water. Laccases can be grouped considering the redox potential (low, medium, and high) of the T1 site, which influences the efficiency of the enzyme's catalytic activity [42]. As a rule, a low redox potential (up to 460 mV) is characteristic of bacterial lacases, a medium redox potential (460-710 mV) comprises the lacases of the fungi Ascomycetes and Basisidomycetes, and a high redox potential (more than 710 mV) is typical of the lacases of wood-degrading white rot fungi [43]. Figure 2: Representation of the lacase structure of _Trametes versicolor_; copper atoms (T1, T2 and T3) are highlighted in brown (reprinted from Ref. [44] under open access conditions).
## 7 Result and Discussion
### The Energy Consumption in Dynamic and Adaptive Cases  
For the dynamic case, we compared the proposed semantic-based parallel NSGA-II on three different multi-agent strategies (Ring, Master/Slave, and Hybrid) in terms of energy consumption and QoS. Figure 14 shows the results obtained by the two objective functions \(f_{energy}\)_and \(f_{QoS}\)_using the three agents' strategies. From the obtained results, we notice that the proposed dynamic agent-based NSGAII with hybrid strategy is better than other strategies in terms of optimized energy consumption and QoS scores.  
Figure 14: Pareto Optimal for QoS score and energy consumption in different strategies agents (**a**) Ring; (**b**) Master/Slave; (**c**) Hybrid.  
Figure 13: Execution time of different strategy agents (Ring, Master/Slave, and Hybrid).  
For the adaptive case, we use three objective functions \(f_{energy}\)\(f_{QoS}\)_and_\(f_{cost}\) on incremental changes of workload, energy device capacity, and changes in customer needs/preferences. The strategy adopted is hybrid because the results in Figures 12-14 show that it is the best strategy compared with Ring and Master/Slave strategies. From the obtained results of Table 6, we can observe that the adaptation cost is correlated with the number of rejected services (infeasible services). This is due to the number of deleted services from the current CCS that should be replaced with other services having enough energy capacity. On the other hand, when the needs/preferences of the customer change, the new CCS replaces the majority or the totality of its services, so the adaptation cost increment changes considerably.  
Table 6 shows the different adaptations of CCS with context changes. The CCSt+1 is the composite service when the global workload of nodes changes. The CCSt+2 is the composite service adapted to new device's capacity after a lapse of execution time. CCSt+3 is the adapted composite service to the new needs of the customer. CCSt+4 is the new composite service when the needs of the customer change and the capacity of many devices are incapable of executing their elementary service (infeasible services). In the case of CCSt+3 and CCSt+4, \(f_{cost}\) equal to 1 because all services of the current composite service are replaced.
## 2 Introduction of pH Value Regulation System and Analysis of Regulation Process  
### pH Control System Structure Composition  
Figure 1 is the structural block diagram of the pH value regulation system of liquid fertilizer. The regulation system includes a water storage tank, solenoid valve, fertilizer tank, regulating liquid tank, flowmeter, pH value sensor, hose pump, mixing tank, and other main devices. The liquid in the water storage tank, fertilizer tank, and regulating liquid tank finally flows into the mixing tank and is stirred inside. A pH sensor is installed inside the mixing tank to monitor the pH value. The outlet of the mixing tank is connected with the field drip irrigation belt to transport the adjusted fertilizer to the field. The dilution ratio of liquid fertilizer is set to 1:8, the pH value of fertilizer before dilution is 7.5, and dilute hydrochloric acid with a concentration of 0.2 mol/L is used as the regulating liquid. Flowmeter and pressure gauge are installed at the inlet and outlet of the mixing tank. The hose pump is used as the conveying device of the regulating system. The three-phase asynchronous motor is connected with the pump body of the hose pump. The pressure formed by squeezing the hose by the roller is used to transport the materials. The system adjusts the outlet flow of the hose pump by changing the frequency of the frequency converter connected with the hose pump, to accurately adjust the pH value.  
In this paper, the STM32F103 ZET6 microcontroller is used as the control core, and the BP-PID-Smith prediction compensation algorithm is written in it. The set pH value is used as the input value, and the pH value of the liquid fertilizer collected by the pH sensor is used as the actual feedback value for calculation. The required regulating liquid flow is calculated, and the flow is converted into the working frequency of the hose pump, to achieve the purpose of efficiently regulating the pH value of liquid fertilizer.  
When the pH value regulation system of liquid fertilizer is working, the monitoring end will input the set pH value of liquid fertilizer and fertilization flow into the system, the hose pump at the fertilizer tank and the hose pump at the water storage tank will pump liquid fertilizer and water to the mixing tank in corresponding proportion for mixing. When the regulation system monitors the pH value in the mixing tank and the set value,  
Figure 1: Structural block diagram of pH value regulation system of liquid fertilizer: 1, regulating liquid tank; 2, fertilizer tank; 3, water storage tank; 4, check valve; 5, solenoid valve; 6, hose pump; 7, flowmeter; 8, pressure gauge; 9, mixing tank; 10, liquid level gauge; 11, Y-type filter; 12, pressure holding valve; 13, drip irrigation belt; 14, pH sensor; 15, mixing pump.  
the solenoid valve opens and the hose pump at the regulating liquid tank starts to run to extract the regulating liquid into the mixing tank, and the stirring pump stirs the liquid in the mixing tank, and when the regulation system monitors the pH value of liquid fertilizer in the mixing tank reaches the set value, the system maintains a stable state.
## 2 Data and Methodology
### Description and Preprocessing of Data  
For this study, the 16-day composite MODIS NDVI and EVI products at 500-m resolution (MOD13A1 V004) were obtained for the seven-year period between February 2000 to December 2006 from the EOS Data Gateway [30]. For the coverage of Turkey, the following four tiles of the MODIS data are required: h20v04, h20v05, h21v04 and h21v05, where h and v denote the horizontal and vertical tile number, respectively. The MODIS VI datasets provided in Hierarchical Data Format (HDF) were imported to GeoTIFF format by MODIS Reprojection Tool (MRT) 3.0a [31] and reprojected from the Integerized Sinusoidal (ISIN) projection to a geographic projection (lat/lon, World Geodetic System 1984-WGS84). The MODIS VI datasets in GeoTIFF format were imported to ERDAS Imagine 8.7 [Leica Geosystems, Norcross, GA] and converted to a float data type. The entire  
Figure 1: Spatial distribution pattern, location, and extent of potential natural terrestrial ecosystems [24].  
time series of the 16-day MODIS intervals acquired for the seven-year period consisted of 161 16-day composite images (365 days yr\({}^{-1}\) / 16-day composite \(\equiv\) 23 composite images yr\({}^{-1}\)).  
The MODIS VI values equal to or below zero were assumed to be typically caused by water bodies, and thus, excluded from both seasonal and interannual curve fits by extracting pixels with negative VI values in ArcGIS 9.2 [32]. The 16-day MODIS VI datasets for the entire Turkey were spatially divided on the basis of the potential NTE distribution estimated by DECP model [24] in order for groups of biogeoclimatically similar pixels to be analyzed simultaneously in ArcGIS 9.2 [32]. The 16-day MODIS VI datasets were seasonally averaged for each of both the years and the 16 NTE types in ArcGIS 9.2 [32]. For seasonal interpretation of the results, the seasons were defined as days 65-144 (Spring = March 6 to May 24), days 145-240 (Summer = May 25 to August 28), days 241-336 (Autumn = August 29 to December 2), and days 337-64 (Winter = December 3 to March 5). The seasonally-averaged MODIS VI data were plotted in the following recurrent seasonal order of (1) Winter, (2) Spring, (3) Summer, and (4) Autumn. Seasonal and multi-year averaging helps to overcome local errors, create a baseline from which future changes can be assessed, identify vegetation characteristics and classify land cover types. Spatial disaggregating assisted in the separate analyses of time series to detect possible differences in vegetation dynamics (e.g. greenness amplitude and phase) among the NTE types with different fractional tree cover as well as to develop a generalized model to predict the spatio-tempral variation of observed differences for each of the 16 NTE classes. Spatial disaggregating has the advantage of detecting local-to-regional changes in vegetation dynamics that may hold important information about ecosystem structure and function.
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 3 Analytical Method  
The study aims at proposing convolutional neural networks (CNN) models to estimate the seismic response of the surface based on the seismic motion measured at bedrock. The software, SHAKE2000, is utilized to compare the performance of proposed models with its performance.