esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
Figure 6: _Cont._  
The Pareto set variation is a measure of the ability of particles in the objective search space to settle on the final optimal Pareto front. This was simulated over 100 dimensions for each case, with the results shown in Figure 7. As predicted by DER-CAM, Case 1 had the best Pareto set variation over the specified number of dimensions, and Case 4 had the worst variation. Case 2 outperformed Case 3 in being able to settle on the final PF (as seen in Figure 5). The extremely erratic response observed in Figure 7d for Case 4 is a result of all of the parameter matrices for \(f_{2}\)(\(x\),\(t\)) having been set to zero, since no DER strategy has been adopted. Therefore, there is no attempt to balance the trade-off between the two objectives. Figure 6: Solution Spread over the PF for (**a**) Case 1, (**b**) Case 2, (**c**) Case 3, (**d**) Case 4. Figure 7: Pareto Set Variation over the PF for (**a**) Case 1, (**b**) Case 2, (**c**) Case 3, (**d**) Case 4. For the analysis carried out in Figures 5-7, the best results over 10 simulation runs were selected for each case. In order to ascertain the results obtained, MIGD values were obtained over 10 runs for each of the four cases considered. The results are shown in Table 2. Each test case has 10 simulation runs, because it is observed that the MIGD values do not vary significantly beyond this number of runs. It is observed from the results that Case 1 had the best MIGD values (shown in boldface) for seven out of 10 runs for all of the cases over the two objective functions, while it was outperformed by Case 2 in two runs (Run 4 and Run 7), and by Case 3 in 1 run (Run 10). With the exception of Run 1, Case 4 had the highest MIGD value for all of the other simulation runs, which means that it had the worst performance for 90% of the simulation runs. From the results obtained, it has been established that the combination of both solar PV panels with the capability to export excess energy and a load shifting strategy has the best trade-off between the two objectives. This result also confirms the optimized output from DER-CAM for the mid-rise building, thereby confirming the capability of DER-CAM to give accurate optimization results regarding the most cost-effective DER strategies for a particular energy usage profile and location. The results obtained also raised questions regarding the economic viability of combining PV panels with and without energy export capability, and load shifting strategies for particular scenarios. While energy export to the grid may have its merits, the results obtained for cases 2 and 3 suggest that combination of energy export and load shifting may not always be the best economic strategy, given the specific energy usage profiles (particularly situations in which there are generally few peak energy usage periods).
# Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
## 4 Experimental Results and Analysis
### Comparison with One-Class Classifier Method Based on Deep Learning Features  
In the literature [39], [PERSON] et al. uses a U-Net auto-encoder (AE) for feature extraction, and OCSVM is used for classification; clearly captured pattern images are used to train the model. To apply the method, the same 80 \(\times\) 8 = 640 clear images are used to train the auto-encoder, and feature vectors of the same 60 images captured by different smartphones are used to train OCSVM.  
Due to the difficulty in training with only one normal class, there are few methods for deep one-class classification compared with binary or multi-classification. Recently, [PERSON] et al. [42] proposed using the pretrained CNN models for feature extraction, and their results show that the combination of VGG19 and OCSVM may perform better than any other combination of a classical CNN model and one-class method. The pretrained VGG19 model trained on ImageNet is used to extract features, and feature vectors of the same 60 images captured by different smartphones are used to train OCSVM.  
The identification result of the methods is shown in Table 4. As can be seen from the table, the proposed method performs better than the methods in terms of anti-counterfeiting  
Figure 12: Scatter plot and the decision boundary of classifiers based on hand-crafted features. (**a**) Scatterplot and the decision boundary of C5; (**b**) Scatterplot and the decision boundary of C6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **C51** & **C6** & **C7** & **C4 (Proposed)** \\ \hline Feature metrics & SSIM-JACCARD & HAMMING-CORR & SSIM-JACCARD-HAMMING-CORR & - \\ TP rate & 96.0 & 73.0 & 72.5 & **100** \\ FP rate & 25.5 & 50.5 & 9.0 & **0** \\ Precision & 79.0 & 59.1 & 89.0 & **100** \\ Recall & 96.0 & 73.0 & 72.5 & **100** \\ F\({}_{1}\) score & 86.7 & 65.3 & 79.9 & **100** \\ \hline \hline \end{tabular}  
* For C5, SSIM and Jaccard metrics are calculated between the captured image and digital image, and the metrics are also constructed as a 2D feature vector. For C6, a 2D feature vector is constructed by Hamming distance and Jaccard metrics. For C7, the dimension of the feature vector is 4.  
\end{table}
Table 3: Comparison with one-class classifiers based on hand-crafted features (%).  
pattern classification. For [PERSON]'s method, the feature extracted from the pretrained model can be insignificant for the pattern images. And the performance of [PERSON]'s method decreases for blurry input; a slightly blurry image may also be judged as counterfeit.
## 6 Discussion
Grad-CAM visualizes important pixels by weighting the gradient against the predicted value. The visualization of the region of interest using Grad-CAM revealed the characteristics of this region for each method. The Grad-CAM results for Methods 1 through 3 reveal the following three things about the limitations of the model. First, normal urothelial cells (Figure 3A-C) are judged to be benign by looking at areas other than urinary cells. This can be inferred from the absence of malignant cells--not by looking at the urinary cells but by the absence of malignant cells. In the light-colored stained specimens (Figure 3K,L), the cytoplasm and nucleus appear pair than other malignant cells, and it can be inferred that the cells themselves could not be recognized. This suggests that it will be essential to construct a system capable of color correction in the future, especially when using specimens with reduced stainability. Second, when recognizing cell clusters, Methods 1 and 3 focused on cells with the highest chromatin volume, whereas Method 2 focused on the edges of the clusters. These points of view seemed similar to those used in manual cytodiagnosis. Third, some degenerated urothelial cells (Figure 3E,F) tended to be misclassified as malignant cells, suggesting the need for further training with more image data. Previous studies on cytological diagnosis either used only cell images that were to be processed or used cell images to fine-tune a model that was pre-trained with natural images. This study attempted to classify urinary cells with different image characteristics using a deep learning model that was pre-trained with natural images and lung cytology images and evidently obtained satisfactory results. This suggests that rare diseases, for which only a limited number of images are available, may be processed in combination with samples of highly prevalent diseases. This might solve one of the problems of deep learning, which is the requirement of a large number of training data. A limitation of this study is that the samples were prepared at a single facility and images were collected using a single microscope. In the future, the validity of this method should be confirmed using specimens and images collected from multiple institutions. In addition, malignant cases in this study comprised high-grade patients. Future studies should include low-grade and atypical cell cases, which are more difficult to diagnose. Although the effect of fine tuning was confirmed using a limited number of urinary cells in this study, there is a close relationship between data size and fine-tuning performance. In the future, it is necessary to prepare data from a larger number of urinary cells to clarify the relationship between the number of training data and classification performance, as well as to further investigate the methods of fine-tuning.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 4 Simulation Setup and Experimental Results
### Analysis of Results  
This subsection presents an analysis of the performance of the proposed routing protocol in an IoMT network scenario. The analysis includes four key quality of service (QoS) parameters (a) packet delivery ratio (PDR%) is a quality of service (QoS) metric used to evaluate the reliability of a routing protocol. It measures the percentage of packets that are successfully delivered to their intended destination out of the total number of packets sent. In other words, it represents the ratio of the number of received data packets that reach their destination to the number of generated data packets sent. (b) Network routing load % (NRL%) measures the amount of routing overhead generated by the routing protocol, typically measured as the ratio of the number of routing packets generated to the number of data packets delivered in the network. (c) Throughput is a quality of service (QoS) metric used to evaluate the amount of data transmitted over a network in a given time period. It represents the rate at which data are successfully transmitted from the source to the destination, typically measured in kilobits per second (kbps) or megabits per second (Mbps). (d) Detection ratio (%) is a network security metric used to evaluate the effectiveness of a security system in detecting and preventing attacks. It measures the percentage of detected attacks out of the total number of attacks attempted on the network. In the following analysis, we present the results of each QoS parameter, along with their implications for the performance of the routing protocol in the IoMT network scenario.
## 2 Data and Methods
### Inversion Method
The GOF and RAE are calculated as follows:  
\[\text{GOF}=\frac{\sum\limits_{i=1}^{n}{\left(V_{fit,i}-\overline{V_{0}}\right) ^{2}}}{\sum\limits_{i=1}^{n}{\left(V_{0i}-\overline{V_{0}}\right)^{2}}}, \tag{6}\]  
\[\text{RAE}=\frac{\left|V_{0}-V_{fit}\right|}{V_{0}}, \tag{7}\]  
where \(V_{0}\) is the observed radial wind speed, \(V_{fit,i}\) is the corresponding fitted value, and \(\overline{V_{0}}\) is the average of the observed radial wind speed. If more than 10 data points are available in a full VAD at a certain range gate, then the wind direction and speed are sinusoidal fitted (the first fit) to derive the wind vectors at that range gate, otherwise the data are considered missing. In the calculation, we noticed that there are still some outliers (indicated by the red circle in Figure 2b) after using the \(\sigma_{CNR}\) filter. In order to detect these outliers, the standardized residual (\(Z_{\text{e}}\)) of the first fit is calculated. Since standard deviation (\(\sigma_{V_{0}}\)) satisfies a normal distribution, \(Z_{\text{e}}\) also satisfies a normal distribution. If the \(Z_{\text{e}}\) of an azimuthal angle falls outside the range of (\(-\)2, 2), it is discarded at the 95% confidence interval. The standardized residual \(Z_{\text{e,i}}\) for each azimuth is calculated using the radial wind speed from the first fit (\(V_{\text{fit}}\)) and the observed radial wind speed (\(V_{0}\)) as follows. \[\sigma_{V_{0}}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{\left(V_{0i}-\overline {V_{0}}\right)^{2}}}}, \tag{8}\]  
\[\overline{V_{0}}=\frac{1}{n}{\sum\
olimits_{i=1}^{n}{V_{0i}}}, \tag{9}\]  
\[Z_{e,i}=\frac{V_{fit,i}-V_{0i}}{\sigma_{V_{0}}}, \tag{10}\]  
where \(\sigma_{V_{0}}\) is the standard deviation of \(V_{0}\), \(V_{fit,i}\) is the fitted radial wind speed at the \(i\) th direction, \(V_{0i}\) is the observed radial wind speed at the \(i\) th direction, \(\overline{V_{0}}\) is the average of the observed radial wind speed at a certain range gate. Figure 3 shows the fitting result after removing outliers identified by the \(Z_{e}\) filter. It can be seen that the point with large deviation has been removed. The GOF increases from 0.948 to 0.964 and the RAE decreases from 0.205 to 0.173.
## 4 Results of Cyclic Loading Test and Discussions
### Comparison of Effective Stiffness  
The effective stiffness was determined to assess the seismic performance of each specimen [27], as shown in Figure 13. To obtain effective stiffness from the experimental results, slopes of the maximum load and displacement were estimated in both directions. The effective stiffness values at each drift ratio for each specimen calculated by this method are shown in Table 5. For the ERC specimen, which was not seismically retrofitted, the effective stiffness was initially 22.31 kN/mm, and this declined by about 24% to the drift ratio of 1%, because cracks began to form. For the NoWS specimen, the initial effective stiffness was 36.95 kN, about 1.7 times greater than that of the ERC specimen. This was subsequently reduced by about 10% to the drift ratio of 1%, where an initial crack occurred, and by 22% to a drift ratio of 2.5%. When the critical failure was observed, NoWS was twice the effective stiffness than that of ERC. This confirmed that the effective stiffness of the NoWS system increased significantly compared to the existing RC frame, and the effective stiffness decreased more gently after the crack occurred.  
Figure 12: Strain responses of specimens: (**a**) Strain of concrete; (**b**) strain of longitudinal steel; (**c**) strain of hoop; (**d**) strain of the CFST frame.
## 3 Five-Tier Architecture of Smart Grid Based on Blockchain
### Authentication and Key Establishment Phase  
The authentication and key establishment phase consists of two phases: key exchange for two adjacent intelligent gateways and group key generation.  
(1) Key Exchange for Two Adjacent Intelligent Gateways  
The intelligent gateway \(IG_{1}\) needs to exchange the key with its adjacent intelligent gateway \(IG_{2}\). From the initialization and registration phase, \(IG_{1}\) has private keys \((s_{IG_{1}},x_{IG_{1}})\) and a public key \((upk_{IG_{1}},R_{IG_{1}})\) and \(IG_{2}\) has private keys \((s_{IG_{2}},x_{IG_{2}})\) and a public key \((upk_{IG_{2}},R_{IG_{2}})\). \(IG_{1}\) sends \(Sig_{IG_{1}}(ID_{IG_{1}},(upk_{IG_{1}},R_{IG_{1}}))\) to \(IG_{2}\). Receiving this message, \(IG_{2}\) chooses a random \(k_{2}\in\mathbb{Z}_{P}^{*}\) and computes \(K_{21}=k_{2}(R_{IG_{1}}+H_{1}(ID_{IG_{1}}||R_{IG_{1}})PK_{DC})\). Then, \(IG_{2}\) sends \(Sig_{IG_{2}}(K_{21},ID_{IG_{2}},(upk_{IG_{2}},R_{IG_{2}}))\) to \(IG_{2}\). \(IG_{1}\) also needs to choose a random \(k_{1}\in\mathbb{Z}_{P}^{*}\) and computes \(K_{12}=k_{1}(R_{IG_{2}}+H_{1}(ID_{IG_{2}}||R_{IG_{2}})PK_{DC})\) after receivingthis message. Then, \(IG_{1}\) sends \(Sig_{IG_{1}}(K_{12})\) to \(IG_{2}\). Then, \(IG_{1}\) and \(IG_{2}\) can compute the shared secret key. \(IG_{1}\) computes:  
\[\begin{split} s_{IG_{1}}K_{21}&=k_{2}G\\ M_{12}^{1}&=k_{2}G+k_{1}G\\ M_{12}^{2}&=k_{1}k_{2}G\\ M_{12}^{3}&=k_{1}upk_{IG_{2}}+upk_{IG_{1}}k_{2}G \end{split} \tag{5}\]  
\(IG_{2}\) computes:  
\[\begin{split} s_{IG_{2}}K_{12}&=k_{1}G\\ M_{21}^{2}&=k_{2}G+k_{1}G\\ M_{21}^{2}&=k_{2}k_{1}G\\ M_{21}^{3}&=k_{2}upk_{IG_{1}}+upk_{IG_{2}}k_{1}G \end{split} \tag{6}\]  
The shared secret key is:  
\[M_{<1,0>}=H_{2}(ID_{IG_{1}},ID_{IG_{2}},upk_{IG_{1}},upk_{IG_{2}},R_{IG_{1}},R _{IG_{2}},K_{12},K_{21},M_{12}^{1},M_{12}^{2},M_{12}^{3}) \tag{7}\]  
(2) Group Key Generation  
The shared secret key of \(IG_{1}\) and \(IG_{2}\) is (7) from the previous phase. Refer to \(IG_{1}\) and \(IG_{2}\) for the process of sharing the secret key; the shared secret key of \(IG_{3}\) and \(IG_{4}\) is:  
\[M_{<1,1>}=H_{2}(ID_{IG_{3}},ID_{IG_{4}},upk_{IG_{3}},upk_{IG_{4}},R_{IG_{3}},R _{IG_{4}},K_{43},M_{34}^{1},M_{34}^{2},M_{34}^{3}) \tag{8}\]  
It is necessary to select \(IG_{1}\) and \(IG_{3}\) to achieve \(M_{<0,0>}\) from the logical key tree structure in Section 2. \(IG_{1}\) selects its private keys (\(s_{IG_{1}},M_{<1,0>}\)) and public keys (\(M_{<1,0>}P,R_{IG_{1}}\)), and \(IG_{3}\) selects its private keys (\(s_{IG_{3}},M_{<1,1>}\)) and public keys (\(M_{<1,1>}P,R_{IG_{3}}\)). After negotiation, \(IG_{1}\) and \(IG_{3}\) obtain their shared secret key:  
\[M_{<0,0>}=H_{2}(ID_{IG_{1}},ID_{IG_{3}},upk_{IG_{1}},upk_{IG_{3}},R_{IG_{1}},R _{IG_{3}},K_{13},M_{13}^{1},M_{13}^{2},M_{13}^{3}) \tag{9}\]  
Then, \(IG_{2}\) obtains \(M_{<0,0>}\) encrypted with \(M_{<1,0>}\) from \(IG_{1}\) and \(IG_{4}\) obtains \(M_{<0,0>}\) encrypted with \(M_{<1,1>}\) from \(IG_{3}\). Finally, the shared secret \(M_{<0,0>}\) is shared between \(IG_{2}\) with \(IG_{4}\).
# Improving Graph-Based Movie Recommender System Using Cimenatic Experience +
## 6 Conclusions  
In this paper, we propose a new, improved graph-based movie recommender system using Cinematic Experience with BERT by building a novel stacking ensemble architecture. We demonstrate that natural language information such as review data can be used in a graph-based recommender system by stacking BERT. Furthermore, through the experiment, we found that Cinematic Experience (CX) manifested via sentiment and emotion can contribute to improving the recommendation performance. As the graph structure reflects the multi-relational information between users and items, CX enhances the performance through a GNN method. Lastly, multi-relational graphs we used in the study can be replaced by other meta-information between users and items, opening up the possibility of expanding the proposed model in various domains such as shopping and social network that primarily rely on user reviews as primary information on user experience (UX).  
The limitation of the proposed model is that only information extracted directly from the review was used as features. Thus, other potentially meaningful features included in the review were not considered. In addition, the use of several graphs increased the problem of time complexity. The trade-off between the performance and efficiency aspects  
Figure 9: A visualized subgraph when the predicted score is high.  
should be addressed in a future study. The future work might consider embedding the review itself to generate vectors with richer information. Furthermore, although there exist various features that affect the user's CX, such as the viewer's individual memory, we only focused on two representative features to see if these primary aspects of CX can contribute to improving the performance of the recommender systems. In the future work, relatively personalized CX features need to be extracted and analyzed for improving the proposed recommendation model further.  
Nonetheless, this study has showed that recommendation performance could be improved by exploiting additional information from the combination of the reviews and ratings. It also suggests that, by exploiting various CX information, the recommender services can be further improved in providing more personalized experiences to their users.  
Conceptualization, C.L. and K.H.; methodology, L.C, D.H., and K.H.; software, C.L. and D.H.; validation, K.H. and M.Y.; investigation, M.Y.; resources, K.H. and M.Y.; data curation, D.H.; writing-original draft preparation, C.L. and K.H.; writing-review and editing, K.H. and M.Y.; visualization, C.L. and D.H.; supervision, K.H. and M.Y.; project administration, M.Y.; All authors have read and agreed to the published version of the manuscript.  
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Effect of CCA Extract on Insulin Resistance in LPS-Induced 3T3-L1 Adipocytes  
Similar to macrophages, LPS also activates the NF-kB and JNK pathways via Toll-like-receptors (TLR), resulting in the stimulation of cytokine production, which can impair insulin signaling in adipocytes. To evaluate the anti-insulin resistance effects of CCA, 3T3-L1 adipocytes were co-treated with the CCA extract and LPS. After that, glucose uptake and lipolysis were performed using a 2-NBDG uptake assay and glycerol content analysis, respectively.
# Secure Cooperative Routing in Wireless Sensor Networks
## 3 Results  
### Simulation Results  
The MIDS algorithm performed fuzzy c-mean (FCM) clustering which assigned cluster centers a membership function based on which they can be the part of more than one cluster. Here, the CHs are assumed as hops. The CH of each cluster receives data from its members and then looks for the shortest path for data transmission to the BS. The path which has the shortest number of hops (CHs) from a specific CH to the BS is considered as the optimal path for that particular CH.  
The deployed sensor network has \"16\" clusters. According to the above-mentioned Figure 3, consider the CH (20) whose cluster members send their sensed data towards it. The IR value computed for the CH (20). Suppose the packet received value \"Ri\" of CH (20) is \"80\", similarly the packets transmitted value \"Ti\" is \"0\". The IR value is calculated as \"IR = 80/0 = \(\infty\)\"; hence, CH (20) is a sinkhole node as indicated in Figure 4 below, and it drops all the packets.  
Figure 3: FCM and optimal path selection. Each color shows distinct cluster. Triangle in each cluster is cluster head (CH). Purple triangle in the center is base station.  
Figure 2: Anchor-based location estimation.  
The second case is for wormhole attack detection. In this case, consider that the cluster members of CH (89) delivered sensed data towards it. CH (89) after receiving data from cluster members intends to transmit it to the base station, so it broadcasts a route request and notes the time, let t1 = 5 s, when the RREQ is sent. It receives more than one route replies from the BS, which means that three routes are available to the destination, i.e., i = 3. For each route reply RREP, the sender node noted the time. Assume that it receives three replies through CH (94), CH (34), and CH (64), and the time of RREP is t2_1 = 7 s, t2_2 = 8 s, and t2_3 = 9 s, respectively. Then, the RTT for each route is calculated. T3_1 = 7 - 5 = 2, t3_2 = 8 - 5 = 3, and t3_3 = 9 - 5 = 4. Now, the threshold RTT is calculated using the previous values divided by the number of hop counts between the sender (CH (94)) and the receivers (CH (94), CH (34), and CH (64)). The hop count for the first two routes is two and the third route is one. The value of threshold RTT came out to be, ts_1 = 1, ts_2 = 1.5, and ts_3 = 4. Now, take the average of ts_i and the calculated value is tht = 2.16. Now, compare the values ts_i with tht. For the first route, ts_1 = 1 < 2.16 and the hop count is two. For the second route, the hop count value is two but the ts_2 = 1.5 is not less than the tht value.