esa-sceva/satcom-chunk-collection huggingface dataset:
# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 3 Proposed Method
### Construction and Correction Fusion of Belief Functions
#### 3.2.1 Initial Classification of Radar Plots  
For the radar plots dataset \(O=\{o_{1},\cdots,o_{n}\}\), based on the constructed deep network model classifier under the confidence recognition framework \(\Omega=\{C_{o},C_{n},\Theta\}\), the category membership of target \(O_{i}\) (\(i=1,\cdots,n\)) can be obtained as \(\mu_{i}=(\mu_{i1},\cdots,\mu_{ic})\), which satisfies the following equations:  
\[\mu_{ij}\in[0,1],\ \sum\limits_{j=1}^{c}\mu_{ij}=1 \tag{9}\]  
Due to the limitations of the framework \(\Omega\), the value of category \(C\) is taken as 3, representing the target, clutter, and uncertainty, respectively. The number of radar plots belonging to classes \(C_{o}\), \(C_{n}\), and \(\Theta\) is separately counted as \(N_{1}\), \(N_{2}\), and \(N_{3}\). The total belief of each category is defined as:  
\[\left\{\begin{array}{c}Bel(C_{o})=\sum\limits_{i=1}^{N_{1}}\mu_{i}(C_{o})\\ Bel(C_{n})=\sum\limits_{i=1}^{N_{2}}\mu_{i}(C_{n})\\ Bel(\Theta)=\sum\limits_{i=1}^{N_{3}}\mu_{i}(\Theta)\end{array}\right. \tag{10}\]  
Here, the total belief can be seen as a description of the confidence density of radar plots in various categories. The sample data around the target can be used as auxiliary evidence for category decision-making, among which samples belonging to the same category can be used for confidence enhancement, while heterogeneous samples can be used for confidence correction. Therefore, a certain number of nearest neighbor samples can be selected from the three types of target plots formed by the above initial classification to construct decision evidence.
## 1 Introduction
In these regions and under water stress conditions, _ET_ is very sensitive to soil moisture. In MOD16, canopy resistance to water vapor relies on vegetation-related RS inputs (e.g., _LAI_) [38] and stomatal conductance while soil resistance to water vapor relies on the vapor pressure deficit (_VPD_) and relative humidity (_RH_), which are indicators of water stress [33; 34]. The Priestley-Taylor equation [39] bridges the limitation of estimating these resistances by using an empirical multiplier \(\alpha_{PT}\)[40]. The Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model combines the PT-approach with the reduction of potential _ET_ based on eco-physiological constraints to land-atmosphere water fluxes [41]. With the aim to minimize the need for _RH_ climatic reanalysis data and to improve the performancein semiarid regions, [PERSON] et al. [42] proposed a modification of the PT-JPL model (PT-JPL-thermal herein) by introducing the _LST_ with a thermal inertia approach as a proxy to the soil moisture status. This approach has proven to perform well in the drylands under Mediterranean and monsoonal conditions and to perform better than other RS models as the PM model adapted by [PERSON] et al. [15]. Although the PT-JPL-thermal model has been benchmarked at EC flux sites [42], it has not been tested with modelled net radiation or at regional levels. The main objectives of this work were: (i) to assess whether the RS PT-JPL-thermal model that uses the thermal inertia approach for soil evaporation can perform similarly to a hydrological model calibrated with in-situ data that requires soil moisture-associated parameters to retrieve _ET_, (ii) to compare and assess the _ET_ derived from the PT-JPL-thermal against the global _ET_ product MOD16, which estimates soil evaporation as a function of _VPD_ and _RH_ climatic reanalysis, in the Mediterranean region of Donana including natural ecosystems (wetland, shrubland, and coniferous forest) and irrigated areas (mixed-irrigated areas and rice fields).
## 3 Data Source
### Data Collection
Among them, the highway passenger volume is the only dependent variable, and the five influencing factors are the independent variables for predicting the highway passenger volume. Figure 1 shows that the highway passenger volume peaked in 2012 and then declined. There are four reasons for this phenomenon:  
1. Between 2008 and 2012, with the rapid development of information technology, people had a greater curiosity about the outside world. The logistics industry was also growing exponentially. However, since 2013, the popularity of motor vehicles and aircraft has gradually increased, and people's curiosity about the outside world has declined. 2. Motor vehicles and aircraft have become the first choice for long-distance travel, reducing the number of transfers, and thus affecting the highway passenger traffic. 3. Relatively capable people were more willing to choose travel modes that take them further, such as high-speed rail and aircraft. 4. In 2008, the statistical standard of highway passenger traffic volume was changed to be measured annually, while the statistical caliber of highway passenger traffic in 2013 was more refined, taking into account more factors such as passenger vehicle type, passenger flow density, etc. The intelligent technology in the transportation industry could also count and analyze road passenger traffic data more accurately. \begin{table}
\begin{tabular}{c c c c c c c} \hline Year & GDP & Population & \begin{tabular}{c} **Private Car** \\ **Ownership** \\ \end{tabular}  & \begin{tabular}{c} **Railway Passenger** \\ **Volume** \\ \end{tabular}  & \ \begin{tabular}{c} **Air Passenger** \\ **Volume** \\ \end{tabular}  & \begin{tabular}{c} **Highway** \\ **Passenger Traffic** \\ \end{tabular}  \\ \hline
1995 & 58,478.1 & 121,121 & 249.96 & 102,745 & 5117 & 1040810 \\
1996 & 67,884.6 & 122,389 & 289.67 & 94,797 & 5555 & 1,122,110 \\
1997 & 74,46
# Food Processing with UHP Waterjets
## 5 Non-Through Cutting Applications
### Peeling and Skinning  
Peeling is another application that waterjets have been used for. In 1982, the Ross Pickles company of the Newcastle upon Tyne, UK installed onion preparation machines which remove the onion skin using high-pressure water jets [35; 36]. They claimed that the waterjet method of skinning is much gentler than any other method of machine peeling. It also cleans off any blemishes and ensures that the onions are transferred to the brine for maturing in scrupulously clean conditions, which greatly reduces any risk of the fermentation process going wrong.  
The use of ice powder as an abrasive was attempted by [PERSON] and [PERSON] [37] for peeling some food products such as potatoes and carrots. Instead of a waterjet they used air. While results were qualitatively acceptable, the rates of peeling did not warrant commercial implementation. Perhaps the use of fan waterjets will be more productive and thus some research is recommended in this area.  
Tests on the peeling of lemons, as shown in Figure 27, to extract skin oils were conducted to evaluate the feasibility of the waterjet peeling process. Similarly to a lathe process, lemons were held and rotated while a jet traversed. A fan-type jet was used to travel on top of a rotating lemon and along the axis of rotation. The middle picture shows the result when a traverse rate of 0.5 m/sec was used under a 245 MPa fanjet with an about 75-100 mm standoff distance. Slower speeds will result in more peeling.  
For processing squid and the removal of skin, [PERSON] and [PERSON] [38] patented a process where the squid is treated by being severed into three longitudinally separate tentacle, eye or head, and body portions. The body portion is impaled on a peg and is rotated and subjected to external waterjets that remove the skin, which is discarded, and to internal waterjets that remove the viscera, which are also discarded. This leaves the squid body clean on the outside and inside.
## 4 Link Budget
### Link Budget under Realistic Atmospheric Conditions
#### 4.2.4 Atmospheric Distributions  
The sampling frequency of the onboard lidar is chosen to be 10 Hz by taking into account the spatial horizontal resolutions of the considered satellite data (no significant statistical differences are observed for higher PRF). For each lidar shot, the corresponding _AOT_ and _COT_ were derived from the nearest (in space and time) MODIS data. For one revisit cycle, we calculated the distribution (histogram) and then the cumulative distribution function **F**(\(\tau\)) of the total atmospheric optical thickness \(\tau\) for the lidar shots inside the two selected areas (Figure 14).  
Figure 14: _Cont._
# On the Robustness of No-Feedback Interdependent Networks
## Simulation and Analysis  
In this section, the robustness of the no-feedback interdependent networks is analyzed based on the following two considerations:  
* Fully coupled interdependent systems as shown in [1] are rare in reality. In most circumstances, only part of the nodes have dependency partners, such systems are known as partially interdependent networks [2; 46].
* Not all nodes will fail after their dependency nodes failing, i.e., failure of the dependency link does not absolutely lead to dependency node failure. Given the huge loss caused by cascading failure, a real-world coupled system usually has protection measures on key nodes to ensure these nodes can maintain a working state with a certain probability after the failing of their dependency nodes [25; 46], such a system is called weakly interdependent networks.  
The parameter _coupling strength_ (\(CS\)) is utilized to describe the fraction of nodes in one subnetwork that has dependency partners in another subnetwork [2]. Specifically, \(CS_{A}(CS_{B})\) indicates the fraction of nodes in subnetwork A(B) that depend on the nodes in subnetwork B(A). In addition, in the weakly interdependent networks model, the failure probability of the node after losing its dependency node is defined as _dependency strength_ (\(DS\)). In order to avoid the mutual influence between \(CS\) and \(DS\) during simulation, they were investigated separately in this work. For example, if one parameter is studied, the other parameter will be fixed with value 1. In order to balance the computation cost of the simulation and characteristics of the real coupled system, the target network employed for simulation is with a scale of \(N_{A}=N_{B}=5000\), and the average degree is \(<\)\(k_{A}>\) = \(<\)\(k_{B}>\) = 4.
## 3 Encapsulation Protocol
### Https
#### 3.2.1 Traditional Detection  
**Fingerprint**  
For \(|A\circ B|\), an SSL/TLS handshake fingerprint (including supported cipher suites, TLS extensions, etc.) is mostly focused on detecting tunnel traffic in HTTP header (as shown in Table 4). According to [PERSON] et al. [15], certificate authority-based detection can be improved by examining the trust relationships between root/intermediate authorities and leaf certificates extracted by the Web server.  
**Statistic Feature**  
Besides identifying tunnel traffic, classifying the applications in tunnel based on features such as the length of certificate packets, IAT, and an SSL/TLS handshake session is popular. In [PERSON]'s [52] model, traffic features were only determined by the normalized frequency distribution of IP packet sizes in the stream using Multinomial Naive Bayes (MNB). As a result of TF-IDF transformation, HTTPS tunnel traffic can be identified with 97% accuracy in private datasets. [PERSON] [25] labeled SSL/TLS handshake state, established first-order homogeneous Markov fingerprints and judged 12 applications within HTTPS tunnel traffic based on their convergence toward the normal traffic state. In private datasets, the accuracy rate is above 97%, but two disadvantages occur with this method. First, IP can only be resolved into a domain name model through DNS, and second, the efficiency and accuracy of this method will decrease as packet numbers increase.  
In order to increase the diversity and multi-attribute quality of traffic characteristics, [PERSON] [17] used a hybrid method in the first byte of handshake-based messages to identify HTTPS tunnel traffic. With fine granularity, [PERSON] [38] proposed using Markov Probability Fingerprints (MaMPF) to identify tunnel traffic applications. MaMPF can calculate the power law distribution and relative probability of all 18 applications modeling for each application in order to avoid the overfitting caused by excessive packet length. MaMPF achieves 96.4% TPR and 0.2% FPR in private datasets.
## 3 Experiments and Validation
### Estimation of Required Memory  
The exact determination of the memory requirement is complicated; for this reason, it is to be estimated over the elements to be processed. Here, we again assume that a point cloud \(\mathcal{P}\in\mathbb{R}^{n\times 3}\) is given. For the application of RooTri(), therefore, \(3n\) elements must be processed. In the case of contour(), in addition to the \(3n\) elements, \(3\sqrt{n}\sqrt{n}\) elements must be considered due to the matrices required for the griddata() function. Thus, we obtain the ratio  
\[\frac{q_{\mathrm{c}}}{q_{\mathrm{r}}}=\frac{3n+3\sqrt{n}\sqrt{n}}{3n}=2 \tag{11}\]  
to estimate the required memory. On this basis, we can formulate the statement that the contour() function takes up twice the memory used by the Rootri() function for the same task.
## 4 CKC-Based Taxonomy on Security Threats to SF and PA
### The Taxonomy
#### 4.2.1 Threats Related to the Reconnaissance Stage  
Our review demonstrates that many state-of-the-art cyber-attacks on SF focus on several layers of the multi-layered architecture to collect information in the reconnaissance stage [37]. Moreover, IoT hardware is of great interest in the reconnaissance stage for many attackers targeting SF [1,2] or PA [2,3] systems. Thus, most cyber-threats to SF and PA fall into one of the following two categories.  
* **Threats to the IoT hardware:** Although IoT devices are commonly protected by software applications, they may be vulnerable to some hardware attacks such as default password attacks [2,87].
* **Threats to the multi-layer architecture:*
* These threats target different layers of the multi-layered architecture of Figure 1 as explained below.
* **Physical Layer**[2,88,89]: where sensors and actuators spread over farms and greenhouses gather environmental data and transmit it through gateways or receive command and control messages via the gateways. This layer is threatened by a variety of attacks during the reconnaissance stage.
* **Edge Layer:*
* SF and PA environments rely on the edge layer devices for their real-time or near real-time computations and services. In some scenarios, attackers may be able to shutdown the function of these devices, causing disruption, delay, customer dissatisfaction, and financial loss [2].
* **Network Layer:*
* This layer resolves the heterogeneity of devices spread over farms and greenhouses. Moreover, this layer provides connectivity, which makes devices available to SF and PA. DoS attacks and botnets are commonly conducted in this layer [2,70,71].
* **Cloud Layer:*
* This layer provides the virtualization for cloud services. Web attacks and application blocking attacks are examples of attacks on this layer [2,88].
# Blockchain-Based Information Management for Supply Chain Data-Platforms
## 4 Findings
### Accounting and Administration  
Interviewees highlighted how both the shared ledgers' functionality and the peer-to-peer payments will boost the blockchain adoption for accounting and administration. Blockchain indicates advanced stages in payments and platforms for administrative tools, facilitating transactions in a global context. Furthermore, blockchain deployment impacts all recordkeeping processes, which also include the approaches used to initiate transactions as well as processing and authorization, including recording and reporting duties. Therefore, all these changes may impact business models and processes affecting the administrative activities, financial statements, and tax liability. Interviewees remarked that blockchain considerably fosters digitalization and a high degree of transparency for tax preparation and audits.  
Therefore, in public administration, revenue and customs divisions may represent a key asset on which to deploy blockchain. In this application, companies and governments need to cooperate in designing a mutually beneficial toolkit platform for taxation monitoring and control. Governments will receive the higher benefit from a blockchain deployment. Additionally, tax collection agencies incur costs of intermediation and bureaucracy, which can be drastically reduced by blockchain by applying new digital tools and duties' automation, albeit initial investments are necessary.  
Interviewees explained how a blockchain-based accounting system guarantees no-double spending, dispute resolution, immutability of records, lean management, accuracy, and a clear advantage in both international finance and trade.  
The interviewees emphasized that for international trade, blockchain platforms assure innovative cost-effective methods for invoicing, accounting, purchases, and sales. These cost-effective methods will impact the financial business units of companies, changing the way of operation and communication with the systems involved, including public administration.  
Finally, artificial intelligence (AI) plays an important role in bookkeeping. Blockchain and AI are cutting-edge technologies on their own, although they can become especially revolutionary if merged. Each of them may improve the capabilities of the other, increasing the potential for better oversight and accountability.  
Contrary to what has been discussed so far in this section, a different point of view from interviewee ICT_4 is provided hereunder. Concerns about the nature of blockchain, in terms of openness and collaborative approaches, is in contrast with federated systems of public administration. There may be many risks and federal governments must not collude with lower powers regarding centralized artefacts. However, before designing strategies and action plans for blockchain in public administration, regulatory control is necessary, as governments are required to adhere by the welfare function.
# Study on Shielding Effect of the Pile Group in a Soft-Soil Foundation
## 3 Results
### Stress Analysis
When the distance exceeds 20 m, the incremental X-direction stress induced by the piles tends toward zero, and the X-direction stress reaches approximately 90 kPa; this indicates that the piles no longer disturb the soil foundation. As each row of piles is installed, the X-direction stress of soil on both sides of the pile group experiences a superimposed effect, with the stress increase on the right side of the first row of piles being more pronounced than that on the left side. The result verifies that the existing piles provide a certain shielding effect on the left-side soil foundation. The X-direction stress of soil in the B-B' profile at a depth of 5 m was also extracted (Figure 13). Unlike the A-A' profile, the soil stress on both sides of B-B' profile is less affected by the shielding effect of existing piles, and has a similar change trend. The soil stress between piles is much smaller than that near the pile body. As the distance from P2 center increases, the soil stress on both sides of the pile group exhibits a decreasing-then-increasing trend, and stabilizes at around 90 kPa from HD = \(\pm\)15 m. With the penetration of three pile rows, the soil stress between piles significantly increases; it contributes to the shielding effect as well. Figure 11: Variation of soil stress-XX with depth at (**a**) X = \(-\)5 m and (**b**) X = 5 m along the \(x\)-axis. Figure 12: Variation in soil stress-XX along the A-A’ profile at a depth of 5 m.
## 3 Results
### The Average Threshold Analysis of Each Parameter Type  
In order to obtain generic impact thresholds of these parameters on perceptual reverberation, appropriate average thresholds are necessary. The error bars of the average thresholds of each parameter with different reverberation time are presented in Figure 12. The error bars indicate the standard error of test data without outliers of each parameter type. As shown in Figure 12a, the average impact thresholds of RE removal are 15.81 ms, 17.49 ms and 18.18 ms corresponding to 0.31 s, 0.91 s and 1.51 s reverberation time, respectively. For FER removal, as shown in Figure 12b, the corresponding average thresholds for these three reverberation time are 27.68 ms, 34.33 ms and 34.6 ms. Its average thresholds are higher than the average threshold of RE removal, so its effect on perceptual reverberation is less than RE removal. When ITDG is extended, as shown in Figure 12c, the average thresholds are 18.37 ms, 25.21 ms and 30.75 ms, respectively. As shown in Figure 12d,  
Figure 11: Impact thresholds of LR removal on perceptual reverberation shown as a percentage of threshold ranges for BRIRs with three different reverberation times.  
Figure 10: Impact thresholds of ITDG removal on perceptual reverberation shown as a percentage of threshold ranges for BRIRs with three different reverberation times.  
the average impact thresholds of LR removal on perceptual reverberation are 435.52 ms, 771.16 ms and 1276.9 ms corresponding to 0.31 s, 0.91 s and 1.51 s reverberation time.
## 4 Results and Discussion
### Energy Consumption of the Fish Farm  
Based on the energy diagnosis, a monthly consumption of 3275 kW/hour was estimated, structured in energy for water pumping (24%), energy for the aeration system (57%), energy for lighting (12%), and energy for electrical outlets (7%). Thus, the consumption of the aeration system is clearly observed as a key factor in the use of electrical energy; in aquaculture farms, about 60% of the energy used is for the aeration systems and up to 40% for water pumping [78], and these data coincide with those found by this study and highlight the importance of working more efficiently in the energy consumption of aquaculture production units. The relationship between energy consumption and Tilapia production per month is very important, since a minimum production of 143 kg per month is needed to reach energy payments. If production increases then energy consumption increases, being a directly proportional relationship. In general, with the current low voltage tariff there is an annual consumption of 39,300 kw/hour at a total cost of MXN 120,161.56, (USD 6056.14). According to this, a sale of 1716 kg of Tilapia is required to obtain economic resources to pay for electricity, with a sale price per kilogram of live Tilapia of MXN 70.02 (USD 3.52) in the local market.
## 3 Results and Discussion
### Baseline Case Dynamics  
3.1.1. Glottal Flow Rate Waveform  
The simulation was successfully carried out for eight vocal fold vibration cycles. Self-sustained vibration was established after the second cycle. Figure 3 shows the phase-averaged volumetric flow rate at the glottic exit based on the steady cycles. Phase-averaging is done by dividing each steady cycle into 1000 phases and calculating the average value at each phase over all the steady cycles. The flow rate shows a typical glottal flow rate waveform of human phonation which is characterized by a slower rising, faster dropping, and a lasting period of glottic closure. Notice that the flow rate did not drop to zero during glottic closure due to the artificial minimum glottic gap enforced in the simulation. Several typical voice quality related quantities were computed based on the waveform, including the \(f_{0}\); the skewness quotient (\(\tau_{\text{s}}\)), defined as the ratio of the duration of flow acceleration to flow deceleration; the peak glottal flow rate; the mean glottal flow rate; and the MFDR. The values are found within the typical physiological ranges of human phonation [31] (Table 2).