esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Findings
### IoT Device Gateway  
A blockchain-based connected gateway design is presented in [111] for BLE-based devices to address privacy preferences in IoT networks. Each user has to consent to data access by any third party using the gateway to prevent privacy leakage. Furthermore, a robust digital signature technique is presented to facilitate the secure management and authentication of privacy preferences. The proposed blockchain gateway consists of gateway administrators, end-users, and administrators or owners. The administrator stores the information of all devices in the network and their privacy policies: for example, device features, manufacturer information, unique device name, and other attributes. The architecture of the blockchain gateway consists of the user interface and administrator interface and the internal components that can be managed from these interfaces. In the proposed gateway design, device binding refers to the administrator's registering or addinga new device. Later, a new Proposed Digital Signature Scheme (PDSS) is proposed based on robustness and intractability using bilinear pairing and ECDLP. PDSS is realized using six phases. Furthermore, this study discussed the privacy preference preserving concept and intelligent access control on IoT devices. Detailed evaluation scenarios are implemented in this study for PDSS, blockchain gateway, and smart contract management.  
Another approach addressed the authentication and decentralization of the IoT device gateway by implementing a basic interface using blockchain technology [112]. In addition, this architecture supports IoT infrastructure with lacking versatility and anonymity within its design. In addition to the interface, IP mapping for network nodes is included. The design environment consists of a customized hub, wired connections, and distributed ledger, preventing direct communication with the internet (only through the home server) and allowing the server to run on any device using a programming language (Node.js). The home server conducts the process of obtaining data (collection) and monitoring devices. The proposed design consists of four parts: smart device, home router, home server, and remote service. The process starts when data are generated from a smart device and passed to the home router for port forwarding. The data are transmitted to the home server; in this step, the data are parsed, and the request is appropriately encrypted. Unused data by remote services are removed, and the home router receives the request. It allows data to be sent to the remote service, and the remote service parses the incoming data and decides the proper action. The home router receives the data using port forwarding from the allowed service and transfers the action to the smart device to be performed. Further considerations can be made to improve the security by providing a flexible interface from the manufacturers, and a list of IP addresses must be included to identify legitimate access requests. Further experiments can be conducted to determine how robust this design is against possible IoT infrastructure attacks.
# Thermal Exchange and Skid Resistance of Chip Seal with Various Aggregate Types and Morphologies
## 4 Conclusions  
This study explored the characteristics of steel slag and the effect of steel slag on the thermal exchange and aggregate retention performance of chip seal. Furthermore, the surface features, including texture depth and skid resistance, of chip seal were also examined. The following conclusions can be summarized:  
1. The relative ranking of aggregate types according to thermal conductivity places BOF slag as the lowest and FER slag as the highest, with the basalt falling in between. The differences in thermal behavior can be attributed to the various chemical compositions.
2. The morphologies of FER slag are more rounded and circular than those of the BS and BOF slag due to the air-granulated treatment methods, and BOF slag has the most abundant morphological features among the aggregates.
3. The thermal exchange performances of chip seal made of FER slag are better than those of BOF slag and basalt. The recycling of FER slag as aggregate in chip seal can allow the chip seal to resume traffic about ten minutes earlier than original samples.  
Figure 11: Aggregate loss rate of different chip seal samples.  
The BPN and MTD values of chip seal made of FER slag are the worst among the three kinds of aggregate. However, they still meet the requirements of the specification, although they are less than those of ordinary aggregates.
5. FER slag offers the best aggregate retention performance due to its spherical particles and alkaline surface. It can reduce the friction force and strengthen the interface bond between aggregate and asphalt.  
Conceptualization, T.Y. and Y.L.; methodology, P.C.; validation, T.Y. and Y.L.; formal analysis, Y.L.; investigation, P.C.; data curation, M.W.; writing--original draft preparation, M.W.; writing--review and editing, P.C.; supervision, S.W.; project administration, S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51708437 and 7181101180. The APC was funded by National Natural Science Foundation of China.  
The authors gratefully acknowledge Wuhan University of Technology for materials and experimental instrument support.  
The authors declare no conflict of interest.
## 3 System Model, Trust Model and Design Goals  
### System Model  
A typical ITS scenario with a central authority, server, RSUs, and vehicles equipped with digital computers and on-board sensors is under consideration as shown in Figure 2. In particular, we consider a vehicular network topology, in which an individual user vehicle wants to assess trust of all other users vehicle within its transmission range. It is assumed that a centralized architecture is adopted to initialize the network and maintain a brief history of the vehicles, i.e., an opinion table and trust score. After the initial setup, the vehicles can authenticate each other by using the proposed scheme to reduce extra routing overhead, bandwidth, and time. The role of each network entity is as follows:  
**TA**: A TA has a very significant role regarding registration of all RSUs, servers, and vehicles. Also, it has sufficient storage space for data servers, so it can maintain all evaluations records and a brief history of vehicles.  
**RSUs**: RUSs communicate with vehicles through wireless channels such as base stations in cellular networks. The RSUs can be the first step to the network initialization and baseline for the first step towards security.  
**Vehicles**: Vehicles on the road can be considered to be mobile nodes equipped with the on board unit (OBU). The vehicles within the specified transmission range can use the proposed TER scheme to search the routes among neighbor vehicles in VIoT. Among these vehicles, there are requesting and target vehicles.
## 5 Performance Comparison
We compared four different methods, namely, HRM_CS, one_hop method [3], traditional LEACH [25] and GM_CS[13]. Here GM_CS means that we use the Gaussian random matrix as the routing matrix jointly with CS. As described in Table 1, \(L1\)-minimization is more unstable than BCS in this real WSN scenario. Therefore, here we use the BCS as CS recovery \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Threshold Value**} & **Number of Significant** & **Number of** \\  & **Components** \(K\) & **Clusters** \(M\) \\ \hline
20 & 35 & 140 \\
23 & 30 & 120 \\
25 & 25 & 100 \\
29 & 20 & 80 \\
39 & 15 & 60 \\
51 & 10 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationship of \(M\), \(K\) and threshold value. Figure 11: Experimental results of the original and recovered signals with HRM_CS1 and GM_BCS. (**a**) HRM_CS1 mean error = 0.0101; (**b**) GM_BCS mean error = 0.0084.  
algorithm in our experiment. As shown in Figure 13, our solution HRM_CS, is superior to the other methods since the HRM_CS adopts compressive sampling, which decreases the number of communication packets and prolongs the lifetime as a consequence.
# Effect of Ultrasonic Treatment on the Quality of Pumpkin Juice Fermented by Yeast
## 3 Results
### Influence of Ultrasonic Treatment on the Total Acid Content of Pumpkin Juice  
The total acid content in pumpkin juice was a major factor affecting the sensory quality of pumpkin juice. When ultrasonic waves propagated in a liquid, phenomena such as cavitation, mechanical vibration, and thermal effects occurred. These physical effects might have changed the structure or distribution of acid molecules in the food, thereby influencing the total acid content [16]. As shown in Figure 2A, compared with the untreated group, the total acid content in the pumpkin juice decreased after treatment with ultrasonic waves of different powers. This result aligned with the findings of [PERSON] et al. [17], who reported that ultrasonic treatment could reduce the total acid content in fruit juices. The relatively high total acid content in the non-ultrasonic-assisted fermented pumpkin juice might have negatively impacted the sensory properties of yeast-fermented pumpkin juice. Initially, ultrasonic treatment caused a significant decrease in the total acid content of pumpkin juice. However, as the ultrasonic power increased, the enhanced cavitation and mechanical effects led to complex changes [18]. On the one hand, the increased energy from higher-power ultrasonic waves might have disrupted certain chemical bonds or molecular interactions that kept acidic substances in a bound state within the food matrix, releasing some of the originally bound acidic substances and increasing the number of free acids. But on the other hand, other concurrent physical and chemical processes might have counteracted or modified this increase, resulting in the total acid content of the ultrasonic-treated pumpkin juice showing a slow upward trend after the initial decrease. Overall, this indicated that
# Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter
## 6 Experimental Evaluation
### Algorithms for Comparison  
Our data assimilation scheme based on the EnKF algorithm was compared to the EKF method, which is commonly used for hydraulic predictions for river networks. The corresponding observed noise level and filtering time period were consistent with those calculated using the ensemble Kalman filter for both testing cases. As both algorithms contain an observation noise level parameter, this parameter was varied, at 0.0005, 0.005, and 0.05 m, to examine whether it affected the difference in performance between the EnKF algorithm and the EKF algorithm. Note that the observation noise value of 0.05 m was so high that it was impractical in real river networks; thus, this setting was only used for research purposes.
## 2 Literature Review
### The Inland Container Transportation Problem with Limited Empty Containers  
Although there are abundant studies pertaining to the inland container transportation problem, it is worth noting that the consideration of depot capacity has been somewhat scarce. In the existing literature, only a handful of papers have examined the situation in which the depot has a restricted supply of available empty containers and it never experiences a stockout. [PERSON] et al. [1] take the initiative to investigate such an extension. To mathematically model this problem, a determinate-activities-on-node graph is introduced to represent this problem. On the basis of this graphical representation, a mixed-integer non-linear programming model is formulated. The main drawback of the graph modelling approach is that the proposed model is non-linear and makes the resolution of the problem considerably more challenging. Therefore, in order to solve the problem in an effective manner, a meta-heuristic that is based on reactive tabu search is proposed to obtain near-optimal solutions. An extension to [PERSON] et al. [1] is presented by [PERSON] et al. [2], which takes into account four distinct categories of container requests. The problem is graphically modeled through a directed network and mathematically formulated as a non-linear programming model with dual objectives. Encouragingly, [PERSON] et al. [2] transform the non-linear programming model to a linear version by introducing auxiliary variables, which forms the primary distinction between their work and that of [PERSON] et al. [1]. Due to the intrinsic difficulty of the problem, [PERSON] et al. [2] also resort to a heuristic algorithm to address large-sized problems. A similar problem is discussed in the recent work of [PERSON] et al. [3]. In their problem setting, an empty container's demands can be covered by the limited inventory within the hinterland terminal, and the reuse of empty containers returned by customers or retrieval of empty containers from the depot. The challenge of the problem is to synchronize truck journeys to guarantee that the limited quantity of empty containers should not be excessively depleted. A mixed-integer linear programming formulation is presented to minimize the route costs and then resolved by implementing an exact column-and-row generation approach. To enhance clarity and accessibility, we provide a summarization table (Table 1) that highlights and compares the key features, models, and methodologies of the most relevant studies in the field. In the table, a checkmark indicates that the corresponding literature has considered the relevant feature.
## 3 Results and Discussion
### The Effect of Incidence Angle on SEY  
Incident primary electrons can collide on the inner surface of a multistage depressed collector with different incident angles. Thus, the effect of incident angle on the SEYs of laser processed Mo metals is assessed and discussed for the first time.  
The SEY of samples #2 (orange curve) and #6 (dark blue curve) at different incidence angles, e.g., 90\({}^{\circ}\), 80\({}^{\circ}\) and 60\({}^{\circ}\), respectively, as shown in Figure 4. With the decrease in the incident angle, the SEYs of sample #2 by the cross hatched pattern and sample #6 by the line hatched pattern increased at various degrees. For sample #2, the SEY curves at the incidence angles of 90\({}^{\circ}\) and 80\({}^{\circ}\) were basically the same at 100 eV \(\leq E_{\mathrm{P}}\leq 3000\) eV. For sample #6, the SEY difference for the incident angle of 90\({}^{\circ}\) and 80\({}^{\circ}\) increased with the increase of PE energy. By comparing it with the SEY at an incident angle of 90\({}^{\circ}\), the ones with a 60\({}^{\circ}\) angle increased by about 0.03-0.14. As the SEY theory predicted, the normalized yield (\(\delta\)/\(\delta_{\mathrm{max}}\)) varied as 1/cos(90\({}^{\circ}\)-\(\theta\)), where \(\delta_{\mathrm{max}}\) denotes the maximum SEY at a normal incidence [21]. Therefore, the SEY of laser processed Mo increased with the decrease of the incident angle, which is consistent with reported references [24; 25; 26; 27].  
Compared to the previous studies [6; 7], the \(\delta_{\mathrm{max}}\) of molybdenum masked ion-textured OFHC and ion modified OFHC varied between 0.30 and 1.00. In this study, the minimum \(\delta_{\mathrm{max}}\) of laser processed molybdenum ranging between 0.80 and 0.93 was obtained at different incident angles. The results indicated that a laser processed molybdenum surface is an effective way for the SE mitigation in MDCs, which should be considered for the application of the efficiency improvement of MDCs.  
Figure 4: The SEY curves of (**a**) sample #2 and (**b**) sample #6 at different incidence angles (90\({}^{\circ}\), 80\({}^{\circ}\) and 60\({}^{\circ}\), respectively) of primary electrons (Pes).
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 2 Instantaneous Frequency Estimation
### Hilbert Transform Approach  
The first method employed for real-time instantaneous frequency estimation is the Causal Hilbert Transform (CHT). In the discrete-time domain, the analytic representation of a real signal \(x[n]\) can be constructed using the Fast Fourier Transform (FFT):  
\[x_{a}[n]=x[n]+j\hat{x}[n] \tag{3}\]  
where \(\hat{x}[n]\) is the Hilbert transform of \(x[n]\). The instantaneous phase \(\theta[n]\) is then obtained by computing the phase angle of the analytic signal:  
\[\theta[n]=\angle x_{a}[n]=\arctan{\frac{\hat{x}[n]}{x[n]}}. \tag{4}\]  
The instantaneous frequency \(\hat{\theta}[n]\) is determined by differentiating the instantaneous phase with respect to time:  
\[\hat{\theta}[n]=\frac{\theta[n]-\theta[n-1]}{\Delta t}. \tag{5}\]  
To ensure practical implementation in real-time simulation, the method restricts access to only past and current water elevation data. A short-time Hilbert transform via FFT is performed within a predefined window, whose size is determined by multiplying the dominant wave peak period by a factor of 10 [14]. For example, if the dominant wave period is 9 s, then a window size of 90 s is used to compute the instantaneous frequency at each time step. The procedure is illustrated in Figure 1.  
A notable challenge arises when applying FFT at the end of a data sequence, where abrupt frequency spikes may occur due to an end effect of FFT. To mitigate this issue, a median filter is introduced. The median filter processes a window of past signal values and computes the median, rather than directly using noisy endpoint data.  
\[\dot{\theta}[n]:=median(\dot{\theta}[n-\delta],\;\dot{\theta}[n-\delta+1],...,\; \dot{\theta}[n-1],\;\dot{\theta}[n]) \tag{6}\]  
where the \(\delta\) is the integer of the median window size. This effectively smooths instantaneous frequency estimation, reducing fluctuations caused by outliers. However, it introduces a slight delay in response, as the estimated value relies on past data. Despite this trade-off, the filter significantly enhances the stability and reliability of the estimated frequency.
# Reliable Visual Exploration System with Fault Tolerance Structure
## 8 Conclusions  
A fault tolerance design for robust visual tracking and mapping is proposed in this paper. A visual localization and mapping module is used for environment modeling, where parallel design and a submap-based back-end are introduced for maintaining mapping and tracking. In addition, an active controller detached from global localization is developed. Due to the reactive strategy, our system is able to keep exploration and mapping even in the case of tracking failure. In addition, we propose a termination condition based on mapping completeness evaluation is proposed, which is verified to strengthen the re-localization performance of the built map.  
Through experiments, the effectiveness of our system has been verified. For reliable visual exploration, we provide a feasible solution to fault tolerance through fault prevention and fault recovery.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/4/662/s1](http://www.mdpi.com/2076-3417/9/4/662/s1)]([http://www.mdpi.com/2076-3417/9/4/662/s1](http://www.mdpi.com/2076-3417/9/4/662/s1)), Video S1: Demonstration of Active Controller, Video S2: Autonomous Visual Exploration.  
**Author Contributions:** [PERSON] and [PERSON] co-organized the work, conceived and designed the structure and performed the experiments. [PERSON] wrote the manuscript. [PERSON] and [PERSON] co-worked to prepare the final manuscript. [PERSON] and [PERSON] co-supervised the research.  
**Funding:** The work in this paper is supported in part by the National Natural Science Foundation of China (Grant No. 61673125, 61703115), the Frontier and Key Technology Innovation Special Funds of Guangdong Province (Grant No. 2014B090919002, 2016B090910003), and the Program of Foshan Innovation Team of Science and Technology (Grant No. 2015 IT100072).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion  
This paper uses AHP as its core method and completes the following work of scheme optimization of the entire working process of UAV tilt photogrammetry and obtains useful results.  
1. Through the expert investigation method, the analytic hierarchy process model for the evaluation of tilt photogrammetry monomer modeling scheme was constructed and the comprehensive weight of the evaluation index was calculated.
2. The analytic hierarchy process model was substituted into the specific experiment for calculation and evaluation analysis.
3. The optimal shooting scheme was calculated by building a mathematical model, and the reliability of this method was verified by an experimental case.
4. The system evaluation and scheme optimization method proposed in this paper was applied to production practice, and the work efficiency was increased by 32.4%.  
In this field, future research can add multi-dimensional and multi-level evaluation indicators, such as the complexity of building structure, building volume, surface area, and color, so as to build a more refined analytic hierarchy process model in order to realize systematic and scientific analyses, obtain control of the entire process of tilt photography 3D modeling, and finally, achieve scientific decision-making and optimization of the scheme.  
Data curation, Z.Z.; formal analysis, Z.Z.; funding acquisition, J.W.; resources, X.L.; software, Q.C.; supervision, J.W.; writing--original draft, Z.Z.; writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Natural Science Foundation of China (NNSFC, 61966010).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Photos/x**} & \multirow{2}{*}{**Truth Value/y**} & \multicolumn{6}{c}{**Linear Fitting Function Model**} & \multicolumn{6}{c}{**Quadratic Fitting Function Model**} \\ \cline{3-13}  & & \multicolumn{2}{c}{**Estimated/y**} & \multicolumn{2}{c}{**Deviation**} & \multirow{2}{*}{_MSE_} & \multirow{2}{*}{_RMSE_} & \multirow{2}{*}{_R_\({}^{2}\)**} & \multirow{2}{*}{**Estimate/y**} & \multirow{2}{*}{_Deviation_} & \multirow{2}{*}{_MSE_} & \multirow{2}{*}{_RMSE_} & \multirow{2}{*}{_R_\({}^{2}\)} \\  & & & & & & & & & & & & \\
10 & 2.3 & 3.09 & 0.79 & & & & 2.43 & 0.14 & & & \\
30 & 3.63 & 3.32 & \(-\)0.31 & & & & 3.44 & \(-\)0.19 & & & \\
50 & 4.2 & 3.54 & \(-\)0.66 & & & 4.04 & \(-\)0.16 & & & \\
70 & 4.16 & 3.76 & \(-\)0.40 & 0.26 & 0.51 & 0.35 & 4.23 & 0.07 & 0.03 & 0.17 & 0.94 \\
90 & 3.9 & 3.98 & 0.08 & & & 4.03 & 0.13 & & & \\
110 & 3.7 & 4.20 & 0.50 & & & 3.43 & \(-\)0.27 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistics of deviation value of function model.