esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A system for monitoring parameters of plant leaf temperature, air, and soil was developed using an IoT-based SCADA. The proposed monitoring system consists of the low-cost AMG8833 thermal camera for measuring the leaf and background temperatures, the DHT22 sensor for measuring the air temperature and humidity, the JXCT sensor for measuring the soil parameters, and the Haiwell IoT Cloud SCADA for implementing the IoT-based SCADA system. The leaf temperature is calculated from the leaf object, which is extracted using an image thresholding technique. The system was evaluated for continuous monitoring of two plants over five days and exhibited no hardware or software faults. The adopted Modbus TCP protocol functions appropriately in visualizing thermal images and other monitoring parameters. In the future, the system will be extended to monitor more plants in a larger area. Further, more environmental parameters and multi-sensor systems will be investigated for advanced applications.  
Conceptualization, A.S. and E.H.; methodology, A.S.; software, A.S.; validation, A.S.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.S. and E.H.; project administration, E.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a Research Grant from the Ministry of Education, Culture, Research, and Technology, Republic of Indonesia, Year 2023 (No.: 077/E5/PG.02.00.PL/2023).  
Not applicable.  
The authors thank the members of the Industrial Automation and Robotics Laboratory, Electrical Engineering Department, ITN Malang, for helping prepare the hardware during the data collection.  
The authors declare no conflict of interest.
# Evaluation of Mixed Traffic Flow Efficiency and Safety on Hard-Shoulder-Running Freeways
## 2 Materials and Methods
### Traffic Efficiency and Safety Metrics  
We measure traffic efficiency based on the metric (denoted as \(E\)) which was proposed by [PERSON] [30] and also recommended by the 2010 edition of the Highway Capacity Manual [31]. The definition of \(E\) is as follows:  
\[E=q\;\overline{v}\;T \tag{1}\]  
where \(E\) denotes the number of vehicle kilometers that are produced by a motorway section per unit of time; \(q\) represents the total traffic volume during the time \(T\); \(\overline{v}\) denotes the average travel velocity; \(T\) represents the duration of analysis period (usually \(T=1\) h), and \(E\) is calculated from all the vehicles completing their trips from the source to the destination after one simulation has finished.  
For the safety metrics, Time-To-Collision (\(TTC\)) is adopted, due to its proven suitability in analyzing safety conflicts on expressways [32; 33]. The \(TTC\) represents the duration of time remaining until a potential rear-end collision occurs between two vehicles, under the assumption that their speeds remain constant throughout this period. The definition of \(TTC\) is as follows.  
\[TTC(t)=\frac{x_{i-1}(t)-x_{i}(t)-l_{i-1}}{v_{i}(t)-v_{i-1}(t)},\;v_{i}(t)>v_{i- 1}(t) \tag{2}\]  
where \(v_{i}\) and \(v_{i-1}\) denote the speed of the ego vehicle \(i\) and the leading vehicle \(i-1\); \(x_{i}\) and \(x_{i-1}\) are the position of the ego vehicle \(i\) and the leading vehicle \(i-1\), respectively; and \(l_{i-1}\) is the length of the leading vehicle \(i-1\).  
A threshold value for \(TTC\) is typically chosen, to discern between relatively safe and hazardous scenarios [34]. It is generally acknowledged that an appropriate \(TTC\) threshold lies within the range of 2 to 4 s [35; 36]. Due to quicker reaction times, CAVs are capable of maintaining shorter time headways when following preceding vehicles compared to HDVs [37]. Therefore, two different \(TTC\) threshold values of 0.75 s and 2 s are chosen for CAVs and HDVs, respectively.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### SP Approaches
#### 2.2.2 Approaches  
This section describes the existing VM placement approaches in the previous work. To address limitations, we propose an SP scheme based on multi-criteria decision-making (MCDM) [8]. Figure 2 shows the classification for CDC. In other words, the metric affects the priority for selecting VM. According to a used metric in the proposed system, it can select the target machine.  
Selecting the optimal services for SP from among the increasing number of various cloud services in the cloud data centers (CDCs) is a great challenge. Many cloud services will be available in the CDCs that will further complicate the service of selecting the best or most optimal services for the user from among many different types of services. Therefore, cloud service selection will be a great challenge. To solve this challenging research problem, a decision-making method is required to assist the users in service selection [11; 12; 13; 14].  
In References [15; 16; 17], the author proposes a new model for SP in CDCs using the AHP method, which is one of the most efficient MCDM techniques. This method can find the most suitable service in the CDCs for the SP. Typically, each alternative is evaluated with the decision metric, and each metric is assigned a weight based on its importance. The alternatives are ranked according to the relative closeness of the ideal solution. The closer the alternative is, the better. VMs are sorted from the highest overloaded to the idle ones based on some metrics such as CPU, RAM, and NET, etc. Each attribute is given a weight. There are many MCDM methods, as shown in Figure 2. However, the analysis layer process (AHP) is popular since the MCDM method is used in many areas to make decisions based on weights in order to meet conflicting goals.  
However, almost all the proposed SP schemes consider one or two objectives and metrics, as shown in Table 1. In this paper, we consider multi-objectives and multi-metrics for satisfying the user's requirements.
## 4 Analysis and Discussions
### Consumer's Perspective  
The following theorem highlights consumers preference among three subsidy policies.  
**Theorem 4**.: _Irrespective of game structures, the greening level and sales volume are higher in Policy C._  
We refer to Appendix F for the details of Theorem 4. Theorem 4 demonstrates that consumers always receive product at a higher GL in Policy C, where the government can penetrate consumers directly. Therefore, direct monetary gains stimulate consumers to buy more product. If sales volumes increase, the manufacturer can compensate investment cost, and GL is also consequently increased. Recall that GL, collection rate, and retail price are identical under both games in Policy C. Therefore, Policy C outperforms others in the perspective of consumer benefit and green product consumption. Graphical representation of GLs, sales volumes, effective prices consumer needs to pay, and ratios of GLs with effective retail price in six scenarios is presented in Figure 0(a)-d. Parameter values are used for numerical examples as follows: \(a=500\), \(\beta=0.6\), \(c_{m}=50(\$/unit)\), \(c_{r}=20(\$/unit)\), \(\alpha=10(\$/unit)\), \(\delta=0.7\), \(\kappa=1500\), \(\lambda_{1}=0.3\), and \(\lambda_{2}=1\). Note that technical restrictions on parameters values are considered to ensure optimal conditions.  
Figure 0(a),0 justify the statement of Theorem 4. The effective retail price is less in Policy C compared to others, and GL is always higher in the RS game in all three policies. If the consumer perceives the retail price in their mind, then Policy C outperforms others because the ratio of GLs with retail price is maximum under Policy C. By comparing the ratios, consumers can figure out how much they need to pay to procure the product. Figure 0(c). demonstrates that the consumer needs to pay a lesser price under Policy C. One can observe that the GLs are lower in Policy RE compared to others, and the consumer needs to pay a higher price in Policy T.
## 3 Digital Star Chart Generation
### GNSS Search and Coordinate Transformation
\begin{split} D_{u}&=\text{int}\bigg{(}\frac{\delta+ \frac{\beta}{2}}{B}+\frac{N}{2}\bigg{)}\\ D_{d}&=\text{int}\bigg{(}\frac{\delta-\frac{\beta}{2}}{B}+ \frac{N}{2}\bigg{)}\end{split}  \tag{11}\]  
If \(D\) < 0, then \(D\) is taken as 0; if \(D\) > 14, then \(D\) is taken as 14. \(M\) and \(N\) are the maximum values of the numbering of the star zones according to the right ascension and declination, respectively, and \(B\) is the interval of the star zones. Let \(\theta\) be the angle between the direction of the star point and the direction of the optical axis, and FOV be the field-of-view angle of the star sensor, and the formula for its angle is shown in the following Equation (12):  
\[\text{arccos}\theta=\frac{x_{1}x_{2}+y_{1}y_{2}+z_{1}z_{2}}{\sqrt{{x_{1}}^{2} +{y_{1}}^{2}+{z_{1}}^{2}}\cdot\sqrt{{x_{2}}^{2}+{y_{2}}^{2}+{z_{2}}^{2}}} \tag{12}\]  
If \(\theta\leq FOV/2\), the point is within the star sensor's field of view and is a valid point. If \(\theta>FOV/2\), the point is invalid. After screening the valid star points, the coordinates in the celestial coordinate system need to be converted to the coordinates of the pixel pointson the LCOS plane, which can be obtained according to the resolution of the LCOS and the field of view of the star sensor as follows:  
\[\left\{ \begin{array}{l}x=f\cdot\frac{X_{1}}{Y_{1}}\\ y=f\cdot\frac{Y_{1}}{Y_{1}}\end{array} \right. \tag{13}\]  
\[f=\frac{n}{2}\cdot\frac{1}{\tan\left(\frac{R}{2}\right)} \tag{14}\]  
In Equations (13) and (14), \(X_{1}\), \(Y_{1}\), and \(Z_{1}\) are the coordinates of the star point in the star sensor coordinate system; f is the focal length; R is the field of view angle; and n is the number of pixel points in the row or column of the LCOS.
###### Abstract
Because the intricate underlying mechanisms governing the success of a cascade are oversimplified, these studies cannot fully leverage the implicit information in cascade dynamics for effective prediction. Feature-based approaches employ features from user characteristics [12; 13], temporal information [14; 15; 16; 17], content features [12; 14; 18], and the structures of propagation networks [14; 15; 19; 20; 21]. These methods heavily depend on domain knowledge and hand-crafting, making the models hard to generalize. Deep-learning-based methods can automatically capture the dynamics of information dissemination [22; 23; 24] without requiring strong prior knowledge and feature engineering. Deep-learning-based models are powerful enough to extract representative features, yet they usually work as black-box models. The prediction results derived from deep-learning-based models for cascade popularity lack interpretability and are, as a result, of little value for decision making. Furthermore, most diffusion models of the above-mentioned work have never considered exogenous factors, such as burst events. Endogenous and Exogenous Influences  
Endogenous factors are the variables we can directly extract from the social platform of the target news item, such as when a news item is shared on Twitter. Endogenous factors, such as the number of likes or the news content of the post, can be further analyzed. On the other hand, exogenous factors can be extracted outside the post's social platform. For example, from the Google Trends service, we can know how many people are searching for news stating that U.S. President [PERSON] said he has decided to run for a second term. As shown in Figure 3, both tweets come from the same person, but one has a smaller cascade size than the other. The search trend of Figure 3b peaked around the time before the source tweet was posted, which means that the news topic was quite popular at the time. In the case shown in Figure 3a, the first keyword, \"disqualified\", does not fluctuate much. In contrast, the second keyword, \"Alabama\", and the third keyword, \"Crimson Tide\", have a peak on the same day, but this was ten days before the tweet was posted, which shows that the discussion has declined. This result indicates that the search trend also plays a crucial role in predicting the influence of the tweets. Social media is inseparable from life, so information on social media is bound to be influenced by external platforms. Furthermore, the event will most likely occur outside of the social platform, implying that information will erupt on the social platform in the future. In conclusion, exogenous influences must also be considered.
# Dynamic Characteristic Master Curve and Parameters of Different Asphalt Mixtures
## 5 Conclusions  
1. The modulus master curve parameters (E\({}_{0}\), \(\alpha\), \(\gamma\)) of asphalt mixtures are closely related to the mineral voidage (VMA), mixture density (\(\gamma_{\rm t}\)), and asphalt content (Pb%) of the asphalt mixture. For the same kind of asphalt mixture, because the gradation of mineral aggregate is fixed, the volume parameters are almost the same, and the ultimate modulus of the mixture at different temperatures is unique. When the temperature changes or the asphalt changes, the shape parameter \(\beta\) of the modulus master curve changes regularly, which brings different dynamic responses, and the lower \(\beta\) will show higher modulus characteristics.
2. Asphalt is the source of the viscoelasticity of the asphalt mixture. Although the influence of particle gradation of the mixture will bring about the change of modulus, the phase angle of the mixture depends on the viscoelastic properties of the asphalt. The initial phase angle (\(\delta_{0}\)) in the main curve is positively correlated with asphalt penetration and negatively correlated with softening point and viscosity, the peak height A is negatively correlated with penetration and softening point viscosity, and the viscoelastic interval represented by FWHM (\(\omega\)) is negatively correlated with penetration, but positively correlated with the softening point and viscosity.
3. The peak position parameter \(\omega_{\rm c}\) of the phase angle master curve can evaluate the relaxation characteristics of the mixture and avoid the complex process of model calculation. In the comparison of the relaxation time of the asphalt mixture, the relaxation time of the foam cold-recycled mixture is the largest, which is significantly higher than that of other forms of asphalt mixture; emulsified asphalt cold-recycled mixture is equivalent to AC20 and LSPM30 mixtures; SBS-modified asphalt mixture  
Figure 15: Comparison of relaxation time of different asphalt mixtures.  
has the best relaxation characteristics; different relaxation times indicate different crack resistance, the longer the relaxation time, the worse the crack resistance. However, less relaxation time (less viscosity) leads to a large viscous flow in a very short time, creating a large rutting, which is one of the most serious issues in asphalt pavement on heavy-duty roadways.  
Investigation, S.M. and T.M.; Methodology, L.F. and Y.Z.; Experiment, Z.D.; Writing--original draft, S.M. and L.F.; Writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Applied Basic Research Project of the Ministry of Transport, grant number 2013319781140, and project ZR2020 QE271 supported by Shandong Provincial Natural Science.  
Not applicable.  
The authors declare no conflict of interest.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 5 Our Privacy-Preserving Swarm Learning
### Global Aggregation
#### 5.3.3 Secure Cosine Similarity Computation  
Then, we compute the cosine similarity \(cs_{t}^{k}\) between two gradients \(\bar{\textbf{g}}_{t}=[p_{1},p_{2},\ldots,p_{n}]\) and \(\bar{\textbf{g}}_{t}^{k}=[q_{1},q_{2},\ldots,q_{n}]\). And the encrypted gradients \([\bar{\textbf{g}}_{t}]_{pk_{FHE}}\) and \([\bar{\textbf{g}}_{t}^{k}]_{pk_{FHE}}\) are obtained as \([\bar{\textbf{g}}_{t}]_{pk_{FHE}}=Enc_{pk_{FHE}}([p_{1},p_{2},\ldots,p_{n}])\) and \([\bar{\textbf{g}}_{t}^{k}]_{pk_{FHE}}=Enc_{pk_{FHE}}([q_{1},q_{2},\ldots,q_{n}])\). The encrypted cosine similarity \([cs_{t}^{k}]_{pk_{FHE}}\) between \([\bar{\textbf{g}}_{t}]_{pk_{FHE}}\) and \([\bar{\textbf{g}}_{t}^{k}]_{pk_{FHE}}\) can be transformed to the inner product, which is described as follows:  
\[[cs_{t}^{k}]_{pk_{FHE}} =[\bar{\textbf{g}}_{t}]_{pk_{FHE}}\odot[\bar{\textbf{g}}_{t}^{k}] _{pk_{FHE}}\] \[=[p_{1}q_{1}+p_{2}q_{2}+\ldots+p_{n}q_{n}]_{pk_{FHE}}\]  
If \(cs_{t}^{k}<0\), it means that the angle between \(\bar{\textbf{g}}_{t}^{k}\) and \(\bar{\textbf{g}}_{t}\) is greater than \(90^{\circ}\), that is, \(\bar{\textbf{g}}_{t}^{k}\) has a negative impact on \(\bar{\textbf{g}}_{t}\). The trust score \(S_{t}^{k}\) of \(\textbf{MC}_{j}\) is defined by Equation (7) to punish the malicious gradients  
\[S_{t}^{k}=ReLU(cs_{t}^{k}) \tag{8}\]  
Equation (8) gives the definition of the \(ReLU\) function.  
\[ReLU(a)=\begin{cases}a,&\text{if }a>0\\ 0,&\text{if }a\leq 0\end{cases}\]  
According to the previous work [54], the cipher-text homomorphism operation of \(ReLU\) function is given below:  
\[[\bar{S}_{t}^{k}]_{pk_{FHE}}^{\prime}=ReLU^{\prime}([cs_{t}^{k}]_{pk _{FHE}})\] \[=\begin{cases}\frac{1}{2}([cs_{t}^{k}]_{pk_{FHE}}+[1]_{pk_{FHE}}),\\ \quad\text{if }\frac{1}{2}([cs_{t}^{k}]_{pk_{FHE}}+[1]_{pk_{FHE}})>[\frac{1}{2} ]_{pk_{FHE}}\\ \quad[\frac{1}{2}]_{pk_{FHE}},\\ \quad\text{if }\frac{1}{2}([cs_{t}^{k}]_{pk_{FHE}}+[1]_{pk_{FHE}})\leq[\frac{1}{2} ]_{pk_{FHE}}\end{cases}\]  
To compare the cipher-text of \(\frac{1}{2}([cs_{t}^{k}]_{pk_{FHE}}+[1]_{pk_{FHE}})\) and \([\frac{1}{2}]_{pk_{FHE}}\), the numerical method for the comparison of homomorphic ciphers in the range of \([0,1]\) is called [54]. The encrypted score \([\bar{S}_{t}^{k}]_{pk_{FHE}}\) is set by  
\[[\bar{S}_{t}^{k}]_{pk_{FHE}}=2\cdot[\bar{S}_{t}^{k}]_{pk_{FHE}}^{\prime}-[1]_{pk _{FHE}} \tag{9}\]
## 2 Materials and Methods
### Virtual Inertia Boundary Evaluation Based on RBF Neural Network
#### 2.1.3 Rbfnn  
The RBFNN features a compact structure and outstanding generalization abilities. Its nonlinear basis functions in the hidden layer can precisely fit the nonlinear relationship between the virtual inertia and frequency dynamic characteristics. By adaptively adjusting the network weights, it can accurately construct the mapping model between the virtual inertia parameters and system frequency deviation, thereby evaluating the boundary of virtual inertia that distributed resources within the distribution network can provide. For ESS, the two inputs correspond to the active disturbance \(\Delta P\) and the battery charge state \(Q\) of the ESS; for the PV system, the three inputs correspond to active disturbance \(\Delta P\), temperature \(T\), and light intensity \(E\); the output node corresponds to virtual inertia \(H\). To cover the input space and maintain high computational efficiency and good generalization ability, the activation function of the hidden layer in this paper adopts the Gaussian function, and the optimal number of hidden layer neurons is found through dynamic parameter adjustment.
## 2 Experiment Setup
### Test Device and Model Aerodynamic Configuration  
A 2-degree-of-freedom spring-suspended sectional model was designed to implement the flutter tests shown in Figure 1. In previous works, long steel wires were adopted to restrict the downwind vibration of the spring-suspended model. However, there was a marked drawback that the vertical vibration was also limited under a large amplitude when flutter occurred, which possibly changed the vertical stiffness of the dynamic system. To solve the problem, the air bearing devices with a low damping were used to realize the constraint of downwind vibration.  
The aerodynamic configuration of cross section plays an important role in flutter responses. To explore the influence of aerodynamic configuration, a series of different leading edges and trailing edges were fabricated at the rectangular center box with an aspect ratio of 1:10. The shapes of leading edges and trailing edges were designed as an isosceles triangle. The vertex angles varied from 30 to 180\({}^{\circ}\), including 30, 60, 90, 120, 150, and 180\({}^{\circ}\) (180\({}^{\circ}\) means a rectangular leading edge or trailing edge), as shown in Figure 2. All the test cases are listed in Table 1. To ensure adequate structural stiffness, 3D printing technology was employed to manufacture all models using high-strength photosensitive resin. Balance weights were used to make the mass and moment of inertia of all models as similar as possible even though the aerodynamic configuration was different.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Case** & **L30** & **L60** & **L90** & **L120** & **L150** & **L180/T180** & **T30** & **T60** & **T90** & **T120** & **T150** \\ \hline Leading edge & 30\({}^{\circ}\) & 60\({}^{\circ}\) & 90\({}^{\circ}\) & 120\({}^{\circ}\) & 150\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) \\ Trailing edge & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 180\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) & 90\({}^{\circ}\) & 120\({}^{\circ}\) & 150\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of test cases.  
Figure 1: A sketch of the flutter test setup.  
Figure 2: Aerodynamic configurations of the sectional model; (**a**) schematic diagram of the sectional model structure; and (**b**) configurations of the various leading edges.