esa-sceva/satcom-chunk-collection huggingface dataset:
# [PERSON]'s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm
## 4 Simulation Results and Discussion
### Quantitative Evaluation of Segmented Results  
In order to quantitatively evaluate the performance of the threshold segmentation method based on HRO, this section uses the value of the indicator to explain the effectiveness and stability of the algorithm. The evaluation methods are: fitness value, standard deviation (STD), PSNR and SSIM, the higher the fitness value, the stronger the optimization ability, the lower the STD, the more stable the algorithm, and the higher the values of PSNR and SSIM, the better the segmentation effect. Table 2 shows the average fitness values of HRO, GA, PSO, DE, ALO, WOA, and SSA in Renyi's entropy after 50 experiments. k in the table represents the threshold-levels, and the bold numbers indicate that the corresponding algorithm's average fitness value is the largest in the test image, that is, the optimization capability is the strongest. The results in Table 2 display that HRO is better than other algorithms in finding the best threshold of [PERSON]'s entropy. As is known there are 32 groups of the optimal thresholds (8 test images, 4 groups of thresholds for each image). Among the best average fitness values shown in the table, HRO-Renyi gets the optimal average solution 31 times, which takes the first place, ALO-Renyi got the optimal solution 9 times, the difference between them is 22. The number of times that other algorithms have obtained the optimal solution is less than 10 times. Figure 4 shows the proportion of the optimal average fitness values of each method, with the horizontal axis representing each method and the longitudinal axis representing the proportion. It could be seen that the number of times HRO-Renyi has found the best fitness accounts for 96.875% of the total number of experiments. In most cases, HRO-Renyi can find the optimal solution stably. In eight images, when the threshold level k = 2, all methods except GA-Renyi can find the optimal solution. When the threshold level k = 4, HRO-Renyi can get the optimal average fitness value in all images, and ALO-Renyi can get the optimal solution in some images, and the performance of other algorithms begins to decline. When the threshold-level k=6, the average fitness value obtained by HRO-Renyi in the seven images is the largest. Although WOA-Renyi performs best in \"image3\", the average fitness value of HRO-Renyi is close to WOA-Renyi, the difference is only 0.0891. When the threshold-level k = 8, the average optimal fitness value of HRO-Renyi is the maximum in the eight images.
# Distributed Uniform Streaming Framework:
## 3 DUST Core for Event Streaming
### Application Interface  
The first software module within the DUST Core is the Application interface. The design of this interface defines what can be optimized by the coordinator during runtime of the application component. The DUST framework uses the object-oriented programming paradigm to define the software structure. Every application will extend an abstract application component object. This extended object is used by the application to define the application callback functions, present DUST with the correct configuration file and allow the application to call the publish function. The structure of the events between the application components and between the application and the DUST Core is defined by the DUST-Messages. The DUST Core supports unprocessed event from a source device and events from a processor with a more complex data structure. These data-structures can be defined on the application level and are serialized to bytes. These bytes are stored in the payload field of the DUST Message. The DUST-messages data structure is defined as shown in Table 1. The DUST Core will add meta information to the DUST Message like a message id, a time stamp and source id. Because DUST is a publish/subscribe-based platform, the application can choose which application components will receive this event by selecting the topic on which the data is published.  
This interface was developed to make the middleware and the execution location transparent to the application. This is a fundamental requirement when we need to change the middleware at runtime or move the application component to a different execution location which is a basic requirement for the DUST Coordinator (See Section 4). In the future, we plan to extend this interface to add new functionalities.
# Effective IoT Congestion Control Algorithm
## 4 Results and Discussion  
According to [29; 30], Network Simulator 3 (NS3) [31] has been used due to its various advantages, such as its open source, parallel processing and faster computational time. Returning to the topology of simulation, it is essential to use dumbbell topology as a testbed for the validation process. Figure 1 outlines the dumbbell topology; it contains sending nodes, bottlenecks and receiving nodes. On the left side, there are N sending nodes numbered (S1, S2,..., Sn). They are connected to the left of router 1 (R1); router 2 (R2) forms the bottleneck. The destination nodes (D1, D2,..., Dn) are connected to R2. The parameter setting for the network is depicted in Table 1.  
In the IoT, many devices with different Transport Layer Protocols and different system capabilities are connected to the network. Within this context, algorithm performance may be investigated in wired and wireless access link channels and at different link-speeds and different background traffic levels. During the simulation, the time was fixed to 30 s, which was sufficient to explore the behavior of the algorithm.  
Different experiments were conducted and the results of these were compared with four standard internet stack protocols: TCP BBR [26], TCP NewReno, TCP Illinois and TCP Yeah.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting** \\ \hline Simulation time & 30 s \\ \hline Access Link & 10 Mbps, 10 ms \\ \hline Bottleneck & 1 Mbps and 30 ms, 2 Mbps and 30 ms \\ \hline Error Rate & \(10^{-6}\) \\ \hline TCP Variant & TCPNewReno, TCPBbr, TCPIllinois, TCPYeah \\ \hline Channel & Wire, Wireless \\ \hline \hline \end{tabular}
\end{table}
Table 1: General parameter setting.  
Figure 1: Testbed (Dumbbell Topology).
# Physicochemical and Antioxidant Properties of Selected Polish and Slovak Honeys
###### Abstract  
In this study, the physicochemical and antioxidant properties of 19 honey samples from Poland and Slovakia were assessed and models describing the relationship between antioxidant activity and the determined physicochemical features were developed. All tested honeys met the regulatory criteria of EU standards for the content of water, hydroxymethylfurfural, and diastase activity. Honey samples from Poland and Slovakia had similar glucose-to-fructose ratios, but differences were observed in diastase activity, electrical conductivity, and antioxidant potential. Polish forest honey samples showed the highest antioxidant activity, and Polish rapeseed honey showed higher antioxidant than the Slovak honey. Color analysis showed a strong correlation (R\({}^{2}\) = 0.849) between the browning index and antioxidant capacity. Cluster analysis effectively distinguished honey types based on their chemical composition, although some sample overlap was attributed to environmental influences. Regression models identified key predictors of antioxidant potential, and polyphenol content evidenced by color parameters (\(a^{\star}\), \(b^{\star}\)). This study provides valuable information on honey characteristics and demonstrates the feasibility of using statistical models to predict antioxidant properties.  
Keywords: antioxidant properties; polyphenols profile of honey; statistical models; chemometrics; HPLC +
Footnote †: journal: Applied sciences
# Quotient Network-A Network Similar to ResNet but Learning Quotients
## 3 Quotient Network
### An Example  
As an example, this section will present the simple residual networks used in the experiments on CIFAR10 and then give the corresponding quotient networks version. We use the network similar to the model the ResNet paper [10] used in the CIFAR10 experiment as the basis. That is, the first layer of the network is a 3 \(\times\) 3 convolution operation with stride one and output channels 16, and then three stages. Each stage consists of the same number of two layers' residual modules (both 3 \(\times\) 3 convolutions) stacked. The first convolution of the first residual module of stage 2 and stage 3 adopts a 3 \(\times\) 3 convolution with stride two and double channels. Therefore, the number of channels in the three stages is 16, 32, and 64, respectively, and the feature map size is 32, 16, and 8, respectively. Finally, after a global average pooling, the 10-way fully connected layer outputs prediction results. Depending on the number of modules stacked in each stage, networks of different depths can be formed, such as 20, 32, 44, 56, and 110 (the number of modules stacked in every stage is 3, 5, 7, 9, and 18). Unlike the original paper shortcuts, which add new channels  
Figure 3: The residual module (**left**) and the quotient module (**right**) when changing the number of channels.  
Figure 2: Convolution processing before stacking quotient modules.  
with all zeros when the number of channels increases, we use a 3 \(\times\) 3 convolution with stride two to increase the number of channels.  
We modify the above network to obtain the corresponding quotient network. First, all residual modules in the three stages should be replaced, as shown in Figure 1. Moreover, unlike ResNet, which uses ReLU activation functions, we replace the activation functions in the first layer of the network and the convolutions that increase the number of channels in the shortcuts, as shown in Figures 2 and 3.
_Energies_**2015**, \(8\), 11883-11915; doi:10.3390/en81011883  
_Energies_  
**ISSN 1996-1073**  
www.mdpi.com/journal/energies  
_Review_  
**State of the Art Authentication, Access Control,**  
**and Secure Integration in Smart Grid**  
**[PERSON] 1,2,* and [PERSON] 1,2,*  
* Department of Computer Science, State University of New York (SUNY) Korea, 119 Songdo, Moonhwa-ro, Yeonsu-Gu, Incheon 406840, Korea
* Department of Computer Science, Stony Brook University, New York, NY 11794, USA
* Authors to whom correspondence should be addressed; E-Mails: [PERSON]N.S.); [EMAIL_ADDRESS] (B.J.C.); Tel.: +82-32-626-1216 (N.S. & B.J.C.); Fax: +82-32-626-1559 (N.S. & B.J.C.).  
Academic Editor: [PERSON]  
_Received: 16 July 2015 / Accepted: 12 October 2015 / Published: 21 October 2015_  
**Abstract:** The smart grid (SG) is a promising platform for providing more reliable, efficient, and cost effective electricity to the consumers in a secure manner. Numerous initiatives across the globe are taken by both industry and academia in order to compile various security issues in the smart grid network. Unfortunately, there is no impactful survey paper available in the literature on authentications in the smart grid network. Therefore, this paper addresses the required objectives of an authentication protocol in the smart grid network along with the focus on mutual authentication, access control, and secure integration among different SG components. We review the existing authentication protocols, and analyze mutual authentication, privacy, trust, integrity, and confidentiality of communicating information in the smart grid network. We review authentications between the communicated entities in the smart grid, such as smart appliance, smart meter, energy provider, control center (CC), and home/building/neighborhood area network gateways (GW). We also review the existing authentication schemes for the vehicle-to-grid (V2G) communication network along with various available secure integration and access control schemes. We also discuss the importance of the mutual authentication among SG entities while providing confidentiality and privacy preservation, seamless integration, and required access control with lower overhead, cost, and delay. This paper will help to provide a better understanding of current authentication, authorization, and secure integration issues in the smart grid network and directions to create interest among researchers to further explore these promising areas.
# Modeling and Solving Method for Supporting Vehicle-to-Anything' EV Charging Mode
## 4 Simulation Cases
### Simulation Results for the Household  
The optimization goal for the EV charging of a household is to optimize the energy management so that the electricity cost of the home user can be minimized. The EV is set to plug into the power grid during the night when the EV user returns home. For comparison, a scenario where discharging of the EV is not allowed is also simulated. The simulation outcome for the household is given in Figure 10.  
Figure 10: Outcome for the EV management of the household: (**a**) load details with discharging; (**b**) load details without discharging; basic household load refers to the household load without EV load profile; the optimized load profile refers to the basic household load plus the aggregated EV charging/discharging result based on the proposed model.  
Figure 9: Basic load profiles for simulation: left axis for the building and household load; right axis for the charging station load.  
According to the results, for the case of EV charging with discharging ability permitted, in response to the price signals, the EV is arranged to perform charging for power refilling during lower price timeslots (i.e., 16:00-17:00, and 2:00-4:00) while choose to discharge for V2G when the V2G price is attractive (at 21:00) and perform V2H when the load price is high (during the time period 18:00-20:00). This demonstrates EV charging/discharging is smartly regulated based on the price tuples. In addition, the power consumption costs for the household are 101.09 p. u. and 105.71 p. u., respectively, with and without EV discharging, indicating a reduction of 4.37% in energy expense with V2A enabled. This proves that the discharging activity can help minimize the overall power consumption cost of the household.  
The energy state for the EV battery is demonstrated in Figure 11. As demonstrated, the energy desire of the home user is well achieved without SOC bound violations. The above outcome indicates that the charging model can smartly support EV charging/discharging activities for home usage.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 8 Evaluation Results
### Micro Benchmarks
#### 8.2.4 Different k  
We compared the results using different parameters \(k\) in Figure 10. Although \(k=3\) and \(k=4\) can already ensure a certain level of accuracy, \(k\) can be further increased in scenarios that require higher precision. In Sillcom \({}_{3R}\), there is almost no difference in performance between the schemes when \(k=3\) and \(k=4\). This is because the computational complexity of the shuffle then pairwise comparison design we adopted is independent of \(k\) in one-layer structure, so extending to larger \(k\) will not increase computational overhead. The time cost of performing first layer computation in Sillcom\({}_{trd}\) is also independent of \(k\), but the remaining distances in the second layer are linearly related to \(k\). Therefore, a larger \(k\) value will increase the DDCF key calculation time in the second layer. From the figure, it can be seen that the time cost difference between two Sillcom\({}_{trd}\) schemes continues to widen with  
Figure 8: Server Computation time of Sillcom\({}_{trd}\) for different Subset sizes.  
Figure 9: Server Computation time of Sillcom\({}_{3R}\) for multi-threads.  
the increase of m, which is a reflection of the increased computational complexity of DDCF keys in the second layer.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 2 Theory and Methodology
### Selection of Sensor Evaluation Indicators
#### 2.1.2 Selectivity  
Selectivity is used to measure the different response capabilities of a sensor to different types of gases, which is defined as the ratio of the response dispersions between inter-categories and intra-categories of the target gases (Equation (2)). The larger the inter-category dispersion of the gas samples and the smaller the intra-category dispersion, the better the discrimination of the sensor's response to different types of gases.  
\[F_{i}=\frac{S_{b}}{S_{w}}=\frac{\sum_{i=1}^{c}n_{i}(u_{i}-u)(u_{i}-u)^{T}}{ \sum_{i=1}^{c}\sum_{k=1}^{n}(u_{i}-x_{k})(u_{i}-x_{k})^{T}} \tag{2}\]  
where \(S_{b}\) and \(S_{w}\) are the inter-category and intra-category dispersions, respectively; \(x_{k}\) represents the detected value of the target gas by the \(k^{th}\) sensor; \(u_{i}\) is the average value of the \(i^{th}\) target gas; \(n_{i}\) is the number of \(i^{th}\) gas samples; \(u\) is the average value of the total target gases; \(n\) is the total number of gas sensors; and \(c\) is the number of categories of the target gases.
# Shape Design Optimization of a Robot Arm Using a Surrogate-Based Evolutionary Approach
## 2 Techniques for Robot Arm Analysis and Design
### Problem Description of the Robot Arm
#### 2.2.3 Design Constraints  
The above objective functions are subject to design constraints which are related to the geometry and mechanical behavior of the robot arm.  
* The first set of constraints is the limitation of the feasible design region for design variables based on Table 1. \[x_{iL}<x_{i}<x_{iU}\] (7)
* The second set of constraints considers the relationship between the radius \(R_{c}\) of the circumcircle and design variables \(H_{i}\), \(H_{m}\), and \(V_{m}\). According to the formulation of the circumcircle, the coordinates (x, y) of the circumcenter of a triangle and its radius \(R_{c}\) can be obtained as follows. \[x=\frac{\left|\begin{array}{ccc}H_{i}^{2}+155^{2}&155&1\\ H_{m}^{2}+V_{m}^{2}&V_{m}&1\\ 770^{2}+125^{2}&125&1\\ \end{array}\right|}{\rm mm}\;,\;y=\left|\begin{array}{ccc}H_{i}&H_{i}^{2}+1 55^{2}&1\\ H_{m}&H_{m}^{2}+V_{m}^{2}&1\\ 770&770^{2}+125^{2}&1\\ \end{array}\right|\] (8) \[R_{c}=\sqrt{\left(x-H_{i}\right)^{2}+\left(y-155\right)^{2}}\;\;{\rm mm}\] (9) Hence, the constraint involving y should be \[g_{1}(x)=R_{c}-y+80<0\;\;{\rm mm}.\] (10)
* One constraint is related to the weight-to-stiffness ratio (W2 SR), which is a measure of material mass efficiency used to provide unit resistance to elastic deformation. An improved W2 SR enables the realization of longer spans without incurring a significant cost penalty, which is expressed in terms of material usage and deflections due to weight [24]. The W2 SR computed from the weight and stiffness of a robot arm can be expressed as \[W2 SR=f\big{(}W,D,F_{y}\big{)}=\frac{\left(W\times D\right)}{F_{y}}\;\;(u\rm m)\] (11) where \(F_{y}\) is the applied force at the distal-end joint.
* Another constraint is related to the weight-to-moment-of-inertia ratio (W2 JR), a measure of a structural property. A higher W2 JR will push resonance and anti-resonance peaks to lower frequencies, shrinking the operating bandwidth of the machine. The measure of W2 JR in this paper is obtained as \[W2 JR=f(W,J)=\frac{W}{J}(m^{2}).\] (12)  
The above two measures of W2 SR and W2 JR are both \"the smaller the better\" when the design optimization of robot arms is targeting higher accuracy and controllability. Two additional constraints imposed on the objective functions are as follows:  
\[g_{2}(x)=W2 SR-12<0, \tag{13}\]  
\[g_{3}(x)=W2 JR-8<0. \tag{14}\]
## 4 ARP Routing Protocol Design
### Queue Management
#### 4.2.3 The Implementation of the Queue Management Scheme  
Our queue management scheme is based on both the priority and the survival time of messages. At first, messages in the queue are arranged with a decreasing order of their priority. Since we believe that the message with higher priority level is more important, it should be transmitted with a higher priority. Furthermore, for messages with the same priority level, priority should be given to those messages that have smaller survival times. A message is dropped in one of the following two occasions: once the survival time of a message in the process of updating exceeds the network's delay tolerant threshold, the message is dropped. Second, if the queue is full when a message arrives, its priority level is compared with the message at the end of the queue. If the new message has a lower priority level, it is dropped. Otherwise, the message at the end of the queue is dropped, and the new message is inserted into the queue at appropriate position according to both its priority level and survival time. This is to reduce network energy consumption, given that the message either has been delivered to the sink node with a high probability by other sensors or has been invalid in our application.
# A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems
## 5 Experimental Results
### Comparison of Meta-Heuristic Algorithms  
We compared the performance level of the HCSA with those of one GSA-based and two PSO-based algorithms, namely, MGELS [37], PSOVNS [49], and PSOMA [51], for 1000 iterations of the 12 Taillard benchmarks. The settings employed for HCSA are detailed in Table 4. MGELS is a GSA-based algorithm with an emulated local search algorithm. PSOVNS is an SPV-rule-based algorithm that combines PSO and VNS. PSOMA is an SPV-rule-based algorithm that combines PSO and NEH, and also adopts an SA-based local search.  
As shown in Table 8 and the values in bold font represent the algorithms that achieve superior performance. In terms of the BRPD values, the HCSA was superior to the MGELS, PSOVNS, and PSOMA algorithms, except for ta081 (100*20). In terms of the ARPD values, the HCSA was better than the MGELS, PSOVNS, and PSOMA algorithms, except for ta081 (100*20) and ta091 (200*10). The average ARPD value from HCSA was 0.61%, in comparison to the values of 6.00%, 2.05%, and 0.90% obtained by MGELS, PSOVNS, and PSOMA, respectively. In addition, because MGELS does not apply any initialization algorithm or local search, its performance had significant differences from other algorithms for large benchmarks. For the local search, using an SA-based search with the metaheuristic algorithm achieved better results than not using SA. In summary, from the aforementioned simulation results, it can be concluded that HCSA is more effective than the MGELS, PSOVNS, and PSOMA metaheuristic algorithms. To present the previous experimental results more clearly, the data from Table 8 are depicted visually in Figure 4. The \(x\)-axis shows the different sizes of the benchmarks, and the \(y\)-axis shows the ARPD and BRPD values for each test function.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & & **1000** & \(\mathbf{jobs\times(jobs-1)}\) \\ \hline Problem & & & \\ ta61 & BRPD & 0.00 & 0.00 \\  & ARPD & 0.02 & 0.02 \\  & \(t_{avg}\) & **6.02** & 6.22 \\ ta71 & BRPD & 0.26 & **0.23** \\  & ARPD & 0.51 & **0.50** \\  & \(t_{avg}\) & **7.32** & 7.56 \\ ta81 & BRPD & 1.72 & **1.59** \\  & ARPD & 2.11 & **2.10** \\  & \(t_{avg}\) & **10.28** & 10.32 \\ ta91 & BRPD & 0.16 & 0.16 \\  & ARPD & **0.48** & 0.56 \\  & \(t_{avg}\) & **14.70** & 15.32 \\ ta101 & BRPD & **1.08** & 1.26 \\  & ARPD & 1.41 & **1.39** \\  & \(t_{avg}\) & **19.88** & 20.79 \\ ta111 & BRPD & 1.47 & **0.89** \\  & ARPD & 1.79 & **1.06** \\  & \(t_{avg}\) & 54.14 & 55.92 \\ \hline Average & BRPD & 0.69 & 0.69 \\  & ARPD & 0.94 & 0.94 \\  & \(t_{avg}\) & **18.72** & 19.36 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of different iterations of the local search for large job sizes.