esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Pollinator Monitoring  
Insect visits to flowers were assessed by analysing recordings obtained with Bushnell HD cameras. Observations took place in August, once per week, on days with weather stability; they were conducted between 06:00 and 20:00 UTC in branches with 20 flowers from 3 plants for each monitoring location. Nocturnal observations were not performed because these flowers remain closed at night. During weekly sampling, five quantificationswere performed for each plant for 10 min every 3 (\(-\)2) h (at 05:00, 08:00, 11:00, 14:00, and 17:00 UTC), for a total of 900 min of observation (150 min in each location). The floral visitors were considered pollinators if they clearly contacted the reproductive parts of the flowers and had pollen grains of _Asparagus_ fixed on their bodies based on the field observations and pollen control in brightfield microscope specimens [80]. To confirm the pollen-carrying capacity of the visiting insects, the following procedure was implemented. Initially, the insects were captured with a net and carefully transferred into a vial equipped with a plastic grid at one end. By gently guiding the insects with a small stick, they were directed into the plastic net. To extract the pollen, a small piece of glycerol-gelatine held in place with tweezers was employed. The gelatinous globules containing pollen were subsequently placed onto a microscope slide, which was heated and covered with a glass cover slip. These samples were preserved until further examination. After successfully extracting the pollen, the insects were released back into their natural habitat. Optical microscopy was used to identify the pollen samples by comparing them with pollen from our own collections and with pollen described in specific references [81; 82]. Samples of each pollinator species were captured for identification by local specialists.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 4 Security Analysis
### Resist Known Attacks
#### 4.5.2. Main-in-the-Middle Attack (MITM)  
The MITM is an attack where an attacker will be in the middle of the sender and receiver, listening or modifying the messages between each other. In the method, we added an encryption and decryption mechanism to the communication protocol. These encrypted and decrypted scenarios are shown in Equations (7), (8), (20), (21), (31), (32), (39), (40), (47), (48), (55), (56), (63), (64), (71), (72), (79), (80), (87), (88), (95), (96), (103), and (104). For example, in the logistics shipping phase, SHP encrypts the message \(M_{7}\) to a cipher message  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multicolumn{2}{c}{**Party**} & \multirow{2}{*}{**Signature**} \\ \cline{2-2} \cline{4-4}  & **Sender** & & **Receiver** \\ \hline \multirow{2}{*}{Authentication Phase} & A & B & \((r_{A_{1}},s_{A_{1}})=Sign(h_{A_{1}},k_{1},d_{A})\) \\  & B & A & \((r_{B_{1}},s_{B_{1}})=Sign(h_{B_{1}},k_{2},d_{B})\) \\ \hline \multirow{4}{*}{Issuing and Manufacture Phase} & MF & IDI & \((r_{MF_{1}},s_{MF_{1}})=Sign(h_{MF_{1}},k_{3},d_{MF})\) \\  & IDI & CA & \((r_{IDI_{1}},s_{IDI_{1}})=Sign(h_{IDI_{1}},k_{4},d_{IDI})\) \\  & CA & IDI & \((r_{CA_{1}},s_{CA_{1}})=Sign(h_{CA_{1}},k_{3},d_{CA})\) \\  & IDI & MF & \((r_{IDI_{2}},s_{IDI_{2}})=Sign(h_{IDI_{2}},k_{6},d_{IDI})\) \\ \hline \multirow{2}{*}{Logistics Phase (Shipping)} & SHP & LG & \((r_{SHP_{1}},s_{SHP_{1}})=Sign(h_{SHP_{1}},k_{7},d_{SHP})\) \\  & LG & SHP & \((r_{LG_{1}},s_{LG_{1}})=Sign(h_{LG_{1}},k_{8},d_{LG})\) \\ \hline \multirow{2}{*}{Logistics Phase (Receiving)} & LG & RCP & \((r_{LG_{2}},s_{LG_{2}})=Sign(h_{LG_{2}},k_{9},d_{LG})\) \\  & RCP & LG & \((r_{RCP_{1}},s_{RCP_{1}})=Sign(h_{RCP_{1}},k_{10},d_{RCP})\) \\ \hline \multirow{2}{*}{Consumption Phase} & RT & CA & \((r_{RT_{1}},s_{RT_{1}})=Sign(h_{RT_{1}},k_{11},d_{RT})\) \\  & CA & RT & \((r_{CA_{2}},s_{CA_{2}})=Sign(h_{CA_{2}},k_{12},d_{CA})\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Non-repudiation’s verification of the proposed scheme.  
\(C_{SHP_{1}}\) with LG's public key \(Puk_{LG}\). The LG decrypts the message after receiving the cipher message with their private key \(Prk_{LG}\). The related equations are shown as follows:  
\[C_{SHP_{1}}=E_{Puk_{LG}}(M_{7}) \tag{112}\]  
\[M_{7}=D_{Prk_{LG}}(C_{SHP_{1}}) \tag{113}\]  
Scenario: The attacker eavesdrops or tampers with the communication messages between the sender and receiver and analyzes the content.  
Analysis: The message is encrypted with the receiver's public key, and the receiver must have a relevant private key to decrypt the message. However, the attacker did not have the private key of the receiver, so they are unable to decrypt the message to learn the content of the transmission.
###### Abstract  
In China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of professional knowledge that represent the cutting-edge progress in fruit disease research. Traditional knowledge engineering methods for knowledge acquisition require extensive and cumbersome preparatory work, and they demand a high level of professional background and information technology skills from the handlers. This paper, from the perspective of fruit tree industry knowledge dissemination, aims at users such as fruit farmers, fruit tree experts, fruit tree knowledge communicators, and information gatherers. It proposes a fast, cost-effective, and low-technical-barrier method for extracting fruit tree disease knowledge from research paper abstracts--K-Extract, based on large language models (LLMs) and prompt engineering. Under zero-shot conditions, K-Extract utilizes conversational LLMs to automate the extraction of fruit tree disease knowledge. The K-Extract method has constructed a comprehensive classification system for fruit tree diseases and, through a series of optimized prompt questions, effectively overcomes the deficiencies of LLM models in providing factual accuracy. This paper tests multiple LLM models available in the Chinese market, and the results show that K-Extract can seamlessly integrate with any conversational LLM model, with the DeepSeek model and the Kimi model performing particularly well. The experimental results indicate that LLM models have a high accuracy rate in handling judgment tasks and simple knowledge Q&A tasks. The K-Extract method is simple, efficient, and accurate, and can serve as a convenient tool for knowledge extraction in the agricultural field.  
Keywords:research papers; knowledge extraction; large language models; prompt engineering; fruit tree diseases +
Footnote †: journal: applied sciences
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 4 C-PBFT Consensus Algorithm
### Reputation Calculation Using Multi-Weighted Subjective Logic Model  
When a vehicle and an RSU interact positively, the vehicle generates a good rating for the RSU. Positive engagement indicates that the vehicle believes the RSU provides relevant and helpful services or that the data supplied by the RSU is accurate. On the contrary, some malicious vehicles may generate malicious evaluations for RSUs to provide false information, too many malicious evaluations will harm RSU node reputation, and false evaluations will result in some malicious nodes being elected to the committee, resulting in unreliable and insecure consensus networks.  
Therefore, it is necessary to design a secure and efficient RSU node reputation value evaluation model and prevent collusion between RSUs and vehicles, and selecting suitable candidate nodes to build the consensus committee by reputation value can ensure a safe and reliable consensus process. In this section, a multi-weighted subjective logic model is proposed for the calculation of the reputation value of RSU nodes.
## 2 Materials and Methods
### Modelling Approach
#### 2.1.1 Thermal Model  
As per usual, the basis of the thermal model used in this work is the transient heat conduction equation:  
\[\rho C_{p}\frac{\partial T}{\partial t}=\frac{\partial}{\partial x}\Big{(}k \frac{\partial T}{\partial x}\Big{)}+\frac{\partial}{\partial y}\Big{(}k\frac {\partial T}{\partial y}\Big{)}+\frac{\partial}{\partial z}\Big{(}k\frac{ \partial T}{\partial z}\Big{)} \tag{1}\]  
where \(\rho\) is the density of the material, \(C_{p}\) the specific heat capacity and \(k\) the thermal conductivity. \(\rho,~{}C_{p}\) and \(k\) are all temperature dependent.  
In the model used to represent the LPBF process, the temperature is applied to the simulated part by introducing a layer of elements at an elevated temperature. Each layer of elements represents a number of real layers. This element layer is, by default, deposited at the melting temperature of the material under investigation. When cooling down, the heat from this layer is partially dissipated through the gaseous atmosphere and powder surrounding the part, and partially through underlying, earlier deposited layers, heating them up in the process. Out of these possible mechanisms, most heat will be conducted through the already completed sections of the part, since the solid material conducts more heat than the surrounding powder [18; 43]. To reduce the need for simulating the powder surrounding the part, this mechanism is ignored in the presented simulations. The assumption underlying the deposition of the molten layers is that each material point in one of these new elements will have reached the melting temperature during the real process as well. This assumption will be investigated in this study.  
After the LPBF process is completed, the part is allowed to cool down to room temperature, via the fixed temperature boundary conditions at the surfaces exposed to the furnace, i.e., the surfaces of the part, and the top of the base plate, on which the part is constructed.  
In this work, two essential post processes are included in the process chain simulation: the cutting process and a possible stress relief heat treatment at 350 \({}^{\circ}\)C. The cutting process involves element death of the base plate and will be further discussed in the section on the mechanical model. In the heat treatment, the part is heated up to the heat treatment temperature in two hours, after which it is kept at this temperature for another two hours. Afterwards, the part is cooled down slowly, to ensure homogeneous cooling and no local stress build-up, which could result in new thermal stresses.  
The temperature cycle is applied to the part via a fixed temperature boundary condition to the external faces of the part. This temperature profile is presented in the result section.
## 5 Experimental Evaluation  
Ideally, the objective speech quality measure in VoIP applications should be able to estimate the quality independent of the type of speech distortions introduced by the VoIP system, regardless of whether they are network distortions, speech encoding-decoding, environmental noise, or the speech enhancement algorithm. This is highly challenging in IP audio applications as there are many factors, and the DNN training dataset must include at least all of these factors. In [35], the impact of network degradation features was studied independently on transmitted voice quality by using their MOS_LQO values and fixing the values of the remaining features. The results showed that each feature independently has an effect and can be used as a feature in the training set. In evaluating the proposed MiniatureVQNet single-ended method, the accuracy of the MiniatureVQNet was examined on different environmental noise features and transmission network quality features. The acoustic features considered during evaluation were the noise type and signal-to-noise ratios, whereas the network factors examined were the packet loss, delay, and jitter using a G722 encoder. Two variations of the proposed MiniatureVQNet model were evaluated. First, the MiniatureVQNet model was trained on a dataset with only environmental noise, referred to as **MiniatureVQNet-Noise**. Then, the second model was trained on noise and network distortions, referred to as **MiniatureVQNet-Noise-Network**.
## 1 Introduction
For cities that are rapidly developing and changing, their timeliness, flexibility, and ability to continuously track are relatively poor. Therefore, some scholars have turned to seeking more timely indirect data to explore the issue of vacant housing in cities. For example, [PERSON] et al. [22] used night-time light data to construct a \"ghost city\" index. [PERSON], LF and others proposed the new concept of \"ghost community\" [23]. With the continuous advancement of remote sensing technology and the proliferation of multi-source big data, current research that integrates remote sensing and big data has evolved from merely identifying the general vacancy conditions of regions to assessing specific vacancy rates. For example, [PERSON] and [PERSON] et al. [24] utilized remote sensing and Weibo check-in data to conduct a pixel-based study on the vacancy rate of housing in 30 sample cities. Their findings indicated that the vacancy rate in the eastern regions of China is relatively low, while it is higher in the central and western regions, with more pronounced vacancy issues in small- and medium-sized cities. [PERSON] et al. [25] adopted a method combining point-of-interest (POI) data, high-resolution imagery, and night-time light data to conduct an in-depth study on the vacancy rate of housing in the Yuelu District of Changsha City. The results show that the overall vacancy rate in the study area was 17.88%, with significantly higher vacancy rates in non-urbanized areas compared to urbanized areas. Moreover, the vacancy rate exhibited distinct spatial clustering characteristics, where high and low vacancy rate areas aggregated separately. [PERSON] et al. [26] used Luojia-1 night-time light data along with OpenStreetMap (OSM) and high-resolution imagery to investigate the vacancy rate of housing in Guiyang City. The study found that houses located farther from the city center and newly constructed houses had higher vacancy rates. The current research predominantly focuses on a large-scale analysis of vacancy rates, primarily describing the spatial distribution patterns of housing vacancies at the regional level. However, studies that can refine the research scale to the residential community level are relatively scarce. In light of this research gap, this study proposes an innovative method for assessing housing vacancy rates by integrating multi-source big data, aiming to achieve a precise evaluation of the vacancy rates in specific residential communities. This study takes the Longquanyi District of Chengdu City as a case for estimation and analysis and validates the results through field sampling surveys. It is hoped that this approach will provide new perspectives and methodologies for the investigation of urban housing vacancy rates.
## 2 Integrated Navigation System of SINS/DVL/MCP/PS
### Error Propagation Model of SINS  
The SINS is composed of an inertial measurement unit (IMU) and a navigation solution unit. In the IMU, three gyroscopes and accelerometers are orthogonally installed to measure the vehicle's motion with six degrees of freedom. As a self-depended navigation method, SINS can provide attitude, velocity, and position with solutions for the angular rates and accelerations. The differential equations describing the solution process are as follows [4]:  
\[\dot{\mathbf{C}}_{b}^{n}=\mathbf{C}_{b}^{n}\left(\mathbf{\omega}_{nb}^{b}\times\right) \tag{1}\]  
\[\dot{\mathbf{V}}^{n}=\mathbf{C}_{b}^{n}\dot{\mathbf{p}}^{b}-\left(2\omega_{ie}^{n}+\omega_ {ie}^{n}\right)\times\mathbf{V}^{n}+\mathbf{g}^{n} \tag{2}\]  
\[\dot{L}=\frac{V_{N}^{n}}{R_{M}+h},\dot{\lambda}=\frac{V_{N}^{n}\text{Sec}\text {L}}{R_{N}+h},\dot{h}=V_{U}^{n} \tag{3}\]  
where \(\mathbf{\omega}_{nb}^{b}=\mathbf{\omega}_{ib}^{b}-\left(\mathbf{C}_{b}^{n}\right)^{\text{T }}\left(\mathbf{\omega}_{ie}^{n}+\mathbf{\omega}_{en}^{n}\right)\), \(\mathbf{\omega}_{en}^{n}=\left[ \begin{array}{cc}-V_{N}^{n}/\left(R_{M}+h\right)& V_{E}^{n}/\left(R_{N}+h\right)&V_{E}^{n}\text{tan}L/\left(R_{N}+h\right)\end{array}  \right]^{\text{T}}\). Superscripts (or subscripts) of \(n\), \(b\), \(i\) and \(e\) denote navigation, body, inertial and earth frames. The symbol \(\mathbf{A}_{BC}^{\text{D}}\) denotes the projected motion vector of \(\mathbf{A}\) from frame \(C\) to frame \(B\) in frame \(D\), _i.e._, \(\omega_{en}^{n}\) denotes the velocity rated from the navigation frame to the earth frame in the navigation frame, \(\mathbf{C}_{b}^{n}\) is the attitude matrix in the SINS describing the converting relationship from frame \(b\) to frame \(n\), and \(\mathbf{V}^{n}=\left[ \begin{array}{cc}V_{E}^{n}&V_{N}^{n}&V_{U}^{n}\end{array}  \right]^{\text{T}}\) is the velocity vector of SINS in navigation frame. \(L\), \(\lambda\), and \(h\) denote the latitude, longitude, and height of SINS. \(R_{M}\) and \(R_{N}\) denote the radius of Earth in meridian and prime,  
Figure 1: Integrated navigation scheme of Strapdown Inertial Navigation System/Doppler Velocity Log/Magnetic Compass/Press Sensor (SINS/DVL/MCP/PS). respectively, whereas the symbol \"\(\times\)\" denotes the skew symmetric matrix of the vector. In the SINS solution, Equations (1)-(3) should be discretized to update the calculation; more details can be found in [4]. Errors in attitude, velocity, and position are generated because of inherent sensor errors, including the biases of gyroscopes and accelerometers, and initial alignment errors, such as initial misalignment errors and navigation solution algorithm errors.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions
### Technical Challenges
#### 6.1.4 Understanding Performance Boundaries and Operational Contexts  
The contradictory findings in the ETC literature highlight the critical need for a better understanding of performance boundaries and operational contexts. Future research should focus on the systematic characterization of the conditions under which ETC systems maintain optimal performance, including comprehensive empirical validation across diverse urban environments. This includes establishing clear guidelines for system configuration, data quality thresholds, and integration strategies tailored to specific operational contexts.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 5 Security Analysis
### _Informal Analysis_
#### 5.2.5 Session Key Disclosure Attack  
The attacker may attempt to determine the session key using messages from the public channel and the obtained values. However, to compute the session key, the attacker must guess the values of \(N_{i}\), \(N_{j}\), and \(R_{i-j-cs}\), which are masked in a hash function using \(s\), \(xi\), \(yj\), and random values. The attacker cannot obtain these values; thus, the proposed protocol resists session key disclosure attacks.
## 3 Results and Discussion
### Enzymatic Hydrolysis Effect on Dietary Fiber Composition of SBP and SBP-CO\({}_{2}\)
These results may be explained by the partial disruption of the cell wall through the enzymatic hydrolysis process, which contributes to the release of carbohydrates from the cell matrix. Various factors such as growing conditions, crop year, cultivar, ripening, changes in the juicing process and the testing methods used may influence the carbohydrate composition of berries, their juices and pomace. [PERSON] et al. [39] demonstrated that enzymatic treatment of apple pomace with commercial cellulases and pectinases increased the yield of oligosaccharides. Preparations rich in higher-order oligosaccharides (DP \(\geq\) 7) exhibited stronger bifidogenic effects and influenced short-chain fatty acid profiles, while even lower-degree oligomers (DP 2-6) contributed to the overall prebiotic potential observed across the DP 2-10 range. [PERSON] et al. [40] reported that the breakdown of IDF promoted the generation of oligosaccharides. The authors also observed that the enzymatic cocktail acted synergistically through cellulase and hemicellulose activities, resulting in maximal increases in glucooligosaccharides and fructooligosaccharides concentrations. In another study, enzymatic hydrolysis increased oligosaccharides content in orange and apple bagasse, particularly when combined with physical pre-treatment. Although techno-functional properties and phenolic content decreased, the concentration of bioactive compounds increased, yielding materials with elevated oligosaccharides relative levels and improved techno-functional and health-related properties [41]. The TPC in modified SBP extracts varied from 4.64 to 6.15 mg GAE/g. Literature reports a wide variation in TPC values across different sea buckthorn varieties. [PERSON] et al. [42] reported that SBP extracted with 75% ethanol yielded the highest TPC, reaching 42.86 \(\pm\) 0.73 mg GAE/g, whereas [PERSON] et al. [11] observed a TPC value of 32.55 \(\pm\) 0.86 mg GAE/g d.m. Similarly, depending on the drying conditions, the TPC of SBP has been shown to vary from 19.88 \(\pm\) 0.20 to 28.56 \(\pm\) 0.24 mg GAE/g d.m., further emphasizing the influence of processing methods on phenolic compound [43]. Free TPC increased in all extracts after enzymatic hydrolysis. The differences in phenolic compounds between samples hydrolyzed with different enzymes were statistically significant (_p_ < 0.05). The highest levels of phenolic compounds were found in the extracts of SBP and SBP-CO\({}_{2}\) hydrolyzed with Celluclast(r) 1.5 L (with an increase by 7.32% and 6.03%, respectively). Some of the phenolic compounds are components of the plant cell wall and are released during the hydrolysis process when the fibers are degraded, making them easier to detect [44]. This explains the increase in TPC in the samples after hydrolysis.