esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 4 Protocol Tunneling Detector
### Input Sanitization  
For accurately training machine learning models, the training set should be as much \"clean\" as possible. In Section 4.1 we have already discussed how labeling based on connection metadata could be error prone either due to potential custom configurations of services, intentional misuse of specific protocols by attackers, or network protocols encapsulating compressed data. In addition, during our experimental evaluations, we have observed that in some cases the employed traffic analyzer can assign an empty label or multiple labels to a single network packet. While in the first case bit streams with empty labels can be easily discarded for the training phase, in the presence of multi-labels, it is possible to assign a unique correct label if a protocol that is monitored by the prototype itself exists among the labels. As an example, if the assigned labels are NTLM, GSSAPI, SMB, and DCE_RPC, the resulting label is SMB. For these reasons the very first step of the sanitization module is to correct the multi-labels associated to bit streams and discard the empty ones. Then, we train an ensamble of one-class SVMs, one for each protocol (see Figure 3): each different classifier is properly tuned to filter out outliers from the raw dataset. As stated in Section 4.1, HTTP and SMB requests or responses may contain either the content of (compressed) files or other types of information that are not strictly correlated with the specific protocol communication patterns. Trained models are then applied to identify these kinds of network packets, and they are removed from the raw dataset. This preprocessed dataset is used to train a supervised support vector machine, called compression/encryption detector, by applying a one-vs-all classification for detecting packets which are either compressed or encrypted. It is worth noting that all these models are still inaccurate because they are trained on a \"dirty\" dataset. Hence, to further increase the quality of the labels and obtain the final training set, the compression/encryption detector is fed with clear-text bit streams to remove possible compressed/encrypted packets from clear-text protocols, as in the case of proxied environments. The result of this sanitization process is a dataset which allows to train and validate two accurate models: an artificial neural network for clear-text protocols and an SVM for compressed and encrypted traffic.
## 2 Vehicular Networks: An Overview
### Vanets and IoT, Edge Computing
[64] considered VEC and networks with dynamic topologies, unstable connections, and unpredictable movements and proposed a near-optimal performance imitation learning-enabled online task scheduling algorithm. In their proposed algorithm, they used the terms service providing vehicles (SPV) and VEC servers interchangeably. Furthermore, the task scheduling problem was considered by minimizing the average consumed energy of offloaded computation tasks while ensuring their execution latency based on SPV clustering and imitation learning approaches. The branch-and-bound algorithm was used with a few iterations as the expert's trajectories, and the learning agent made proper task scheduling decisions by mimicking the expert's demonstrations with the help of imitation learning. They proposed an imitation learning-based task scheduling algorithm that allows the learning agent to make timely scheduling decisions instead of global searching, which is time-consuming and computationally intensive and is not suitable for online scheduling. To validate the performance of their proposed algorithm, they compared it to four other designed algorithms: the Deep Q Network (DQN)-based algorithm, DATE-V, local optimization, and FORT. As a result, the proposed algorithm's average energy consumption was much lower than that of the other algorithms, and its task-processing ratio was higher than that of the other four algorithms.
## 5 Billing Mechanism  
Multi-MG combines as a cooperative coalition via DC-link to make a deal with DNO in order to minimize the scheduling cost. After everyday's operation, the coalition needs a suitable and fair mechanism to distribute the total operation cost. Generally, each MG has its own DERs and loads, and the unit energy costs of its DERs are different. Basically, the principle is that each MG provides the own customers with the low cost DERs. That is, we should sort the DERs from the low cost to the high cost. Let \(N_{g}\) DERs in a MG indexed as \(1,2,...,N_{g}\), respectively, and to simplify the problem explanation, we omit \(t\) for the following equations (i.e., \(C\) denotes \(C(t)\), \(P\) denotes \(P(t)\) etc.).  
Firstly, the cost of DERs in a MG can be denoted as a cost set \(C\):  
\[C=\{C_{i}\},i\in[1,N_{\rm g}] \tag{34}\]  
where \(C_{i}\) is the generation cost of \(i\)th DERs.  
The output energy of DERs in the MG can be denoted as a energy set \(P\):  
\[P=\{P_{i}\},i\in[1,N_{\rm g}] \tag{35}\]  
where \(P_{i}\) is the output energy of \(i\)th DERs.  
Figure 3: Flowchart of Reconfiguration Model.  
After sorting from low to high, the cost of DERs are updated as follows:  
\[C=\{C^{j}\},j\in[1,N_{g}] \tag{36}\]  
And the corresponding energy set can be updated as well:  
\[P=\{P^{j}\},j\in[1,N_{g}] \tag{37}\]  
Moreover, the cost trading between two MGs can be expressed as:  
\[C_{pay}=C_{self}-\sum\limits_{j=1}^{N_{g}}C^{j} \tag{38}\]  
where \(C_{self}\) is the cost of power consumed by MG's own load.  
According to the principle that the low cost DERs should meet the load demand of local customers in each MG, \(C_{self}\) can be defined as a piecewise function:  
\[C_{self}=\left\{\begin{array}{cc}C^{1}(P_{l}),&P_{l}\in[0,P^{1}]\\ C^{1}+C^{2}(P_{l}-P^{1}),&P_{l}\in(P^{1},P^{2}]\\ \vdots&\\ C^{1}+C^{2}+\cdots+C^{j}(P_{l}-\sum\limits_{j=1}^{N_{g}-1}P^{j}),P_{l}\in(P^{ N_{g}-1},P^{N_{g}}]\end{array}\right. \tag{39}\]  
Take 2 MGs named \(MG1\) and \(MG2\) as an example, the cost function of \(MG1\) can be seen in Figure 4. It is assumed that there are three types of DERs within \(MG1\). The unit energy costs of its DERs combined as a cost set named \(C=\{C_{1},C_{2},C_{3}\}\), the corresponding power set is \(P=\{P_{1},P_{2},P_{3}\}\). After sorting from low to high, the cost and power set of DERs are updated as follows: \(C=\{C^{1},C^{2},C^{3}\}\), \(P=\{P^{1},P^{2},P^{3}\}\). \(P^{1}\) is the power generated by PV and WT, and \(P^{2}\) is the power generated by microturbine, and the MG exchanges power of \(P^{3}\) with DNO.Therefore, the cost which \(MG1\) ought to pay \(MG2\) can be expressed as follows:  
\[C_{pay}=C_{self}-\sum\limits_{j=1}^{3}C^{j} \tag{40}\]  
\[C^{1}=C_{d}P^{1} \tag{41}\]  
\[C^{2}=a(P^{2})^{2}+bP^{2}+c \tag{42}\]  
\[C^{3}=\eta_{1}\cdot max(P^{3},0)+\eta_{2}\cdot min(P^{3},0) \tag{43}\]  
As can be seen from Figure 4, \(C_{self}\) can be denoted by  
\[C_{self}=C_{d}P^{1}+a(P_{l}-P^{2})^{2}+b(P_{l}-P^{2})+c \tag{44}\]
## 3 Experiments and Results
### Comparing the Proposed Approach with Other Approach  
To make comparison with the proposed approach, the recent grape bunch detection method that was presented in the paper [18] was employed to test. Firstly, 14 grape clusters in the collected vineyard images were randomly chosen as the testing objects, and then all pixels region of the chosen grape clusters were extracted manually to properly evaluate the results. Secondly, the grape clusters were detected by using the two approaches, and then the intersection between the pixels region extracted by manual and the pixels region detected by the algorithms were calculated out. Finally, the pixel recognition accuracy (PRA) was computed by the previous Equation (15).  
The RPA comparison between the method [18] and the proposed method were listed in Table 4. From the table we could know that the average PRA of the proposed method was higher than with the method described in [18]. Figure 11 shows the detection process on one of the collected vineyard images by using the two methods. It is clear that the detection results of the proposed method were better those of [18], and the over-segmentation was more serious in [18] than with the proposed method, which can be seen from the right-up grape cluster. In [18], the key step of grape cluster image segmentation was mainly based on the H and V components of the HSV color space, after obtaining initial bunch areas, and the false detected bunches were eliminated by combining texture information and the use of a support vector machine. However, by simply relying on one of the color spaces, it was difficult to smoothly extract the initial bunch areas from the complex vineyard environment with changing illumination.  
Figure 11: _Cont._  
Figure 10: Detection process of the Cabernet Sauvignon grape clusters (**a**) Original image; (**b**) Initial segmentation image by the developed classifier; (**c**) The contour of the detected grape clusters; and (**d**) Detection result.
## 6 Conclusions and Future Works  
In conclusion, this study emphasizes the importance of integrating spatiotemporal and semantic aspects in metro flow prediction. To accurately predict metro flow, a novel deep learning model is proposed considering the spatial correlations, the temporal correlations from different time granularity, and the external weather factors. To model the spatial correlations, mutual information is leveraged to explore the nonlinear flow patterns between stations and determine the highly correlated stations. Then, the metro flow of recent historical time intervals, the metro flow at the same time intervals of previous days and weeks, and the temperature and precipitation data are combined and incorporated as the input data. Then, the multi-source and multi-dimensional data are fed into the transformer neural network. The self-attention mechanism of the network allows for effective handling of multidimensional data and enhances the accuracy of long-term predictions by focusing on key features. Extensive experiments with real data from the Xi'an subway validate our method's efficacy, providing insights into significant features and moments that impact metro flow. Additionally, with POI information around the selected stations, our study delves into the fluctuations in metro flow. This analysis has the potential to offer valuable insights and practical recommendations for management authorities. The proposed method and comprehensive analysis highlight the significant potential of our approach in enhancing the accuracy and applicability of metro flow predictions in urban settings.  
In the future, we will explore how to incorporate the distribution of POI features into the prediction model, investigating the influence of POI size and the distribution of geographical location on entry and exit traffic flow. Additionally, we will collect data on sudden events to study their impact on passenger flow and develop prediction models accordingly.  
Conceptualization, B.S. and Q.Y.; methodology, B.S. and Z.W.; formal analysis, Q.Y. and N.Y.; resources, J.Y.; data curation, Z.W.; writing--original draft preparation, B.S., Z.W. and J.Y.; writing--review and editing, B.S. and N.Y.; supervision, Q.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Fundamental Research Funds for the Central Universities, CHD (No. 300102210521).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data are part of an ongoing study.  
The authors declare no conflict of interest.
# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
## 2 Materials and Methods
### Loss Function  
Our network implements two tasks: segmentation and centerline extraction. We use dice loss as the loss function for the prediction results of the branch of the vessel segmentation. Dice loss is calculated as:  
\[\mathcal{Loss}_{seg}=1-\mathcal{Dice}(S,S^{\prime})=1-2\frac{|S\cap S^{\prime }|}{|S|+|S^{\prime}|} \tag{9}\]  
where \(S\) is the ground truth of vessels, and \(S^{\prime}\) is the prediction result of the vessel segmentation.  
We regard the task of centerline extraction as the classification of each pixel of the image. The categories of centerline voxels and background are not balanced; hence, we calculate the focal loss for the prediction results of the centerline extraction branch as follows:  
\[\mathcal{Loss}_{cline}=-\frac{1}{N}\sum_{i=1}^{N}(\alpha y_{i}(1-p_{i})^{7} \log p_{i}+(1-\alpha)(1-y_{i})p_{i}^{7}\log(1-p_{i})) \tag{10}\]  
where \(y_{i}\in(0,1)\) indicates a pixel in the ground truth of centerline \(C\), \(p_{i}\in(0,1)\) is the predicted probability of a pixel in the prediction result of centerline \(C^{\prime}\), \(N\) is the number of pixels in an image, and \(\alpha\) is a parameter that balances the weight of the positive and negative categories.  
Figure 3: The design of the channel and atrous spatial fusion block (CAS-FB). The dual-channel attention modules correct the features of different tasks, and the atrous spatial attention module can aggregate the blood vessel features of different scales.
# Mobility Prediction of Mobile Wireless Nodes
## 6 Results and Evaluation  
This section evaluates the accuracy of the predictions of ML classifiers using a dataset generated by different mobility models. The assessment was made using a classifier accuracy performance metric, i.e., the score function in Scikit-learn library [39]. Results were extracted using six types of classifiers (DT, LR, KNN, LDA, GNB, and SVM) and four types of mobility models (Gauss-Markov, random direction, RW, and RSSGM) in different scenarios by changing the numbers of APs in a predefined area.
# Accurate Characterization of the Properties of the Rare-Earth-Doped Crystal for Laser Cooling
## 5 Conclusions  
The LITMoS test has been adopted here to measure the laser-cooling properties of the 5% Yb\({}^{3+}\):LuLiF\({}_{4}\) crystal placed inside a special cooling-grade test chamber at room temperature. With a carefully calibrated thermal camera (temperature resolution \(<0.1\) K), the external quantum efficiency \(\eta_{\rm{ext}}\) and the background absorption coefficient \(\alpha_{b}\) of the sample were accurately measured to be \(\eta_{ext}=99.4\) (\(\pm 0.1\))% and \(\alpha_{b}=1.5\) (\(\pm 0.1\)) \(\times\) 10\({}^{-4}\) cm\({}^{-1}\), respectively. The cooling window of the sample was deduced from the LITMoS measured parameters and indicates a global MAT of \(\sim\)110 K. Our study shows that the 5% Yb\({}^{3+}\):LuLiF\({}_{4}\) crystal is of excellent laser-cooling grade for applications in cryogenic optical coolers and RBLs.  
Conceptualization, methodology, software, analysis, investigation, X.D., B.Z., Y.L., C.W., J.X., Z.Z., J.D. and J.Y.; Writing--original draft preparation, X.D.; writing--review and editing, B.Z.; visualization, supervision, B.Z. and J.Y. All authors have read and agreed to the published version of the manuscript. This work is partially supported by the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China (Grant Nos. 11604100, 11834003, 61574056, 91536218, and 11874151), the Special Financial Grant from the China Postdoctoral Science Foundation (Grant No. 2016T90346) and 111 Project (B12024). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data sets obtained in this research are available upon request. Acknowledgments: We thank [PERSON] for helpful discussion and assistance, and [PERSON] thanks [PERSON] for his generous help. Conflicts of Interest: The authors declare no conflict of interest.
## 4 Optimal Design of the Tandem Blades
### The Optimization Procedure  
#### 4.1.1 Adjustment to Reduce the Loss at the DE Point  
* FB  
Based on the flow characteristic analysis at the DE point in the previous section, it is clear that the key point to decrease the FB loss is to control the passage shock intensity. At the DE point, the passage shock intersects the pressure side at 60 percent chord length. Hence, the camber before 60 percent chord length was increased to form a stronger \"sunken profile\" pressure side. The supersonic flow near the pressure side decelerates through a series of compression waves and the shock intensity near the pressure side decreases significantly. On the other hand, the passage shock intersects the suction side near the trailing edge (Figure 6a). On the condition that the FB camber keeps constant, the flow turning angle is definite and the shock intensity near the suction side is difficult to control.  
In conclusion, at the DE point the passage shock near the pressure side can be controlled by increasing the camber before 60% chord length, and there is no effective measure to intervene the passage shock near the suction side if the FB camber stays constant.  
* AB  
At the DE point, the AB loss can be reduced by controlling the suction side normal shock intensity. Therefore, the AB was optimized in the following two aspects.  
Firstly, the AB camber line was adjusted. At the DE point, the AB works in a transonic condition with an inlet Mach number of 0.9. In basic scheme, the Mach number upstream of the shock on the AB suction side exceeds 1.3; consequently, the strong shock wave/ boundary layer interaction leads to the obvious flow separation. Therefore, the camber before the 45% chord length was reduced to control the suction side shock intensity.  
Secondly, the AB incidence was reduced. As can be seen from the Mach contour plots (Figure 6a), the AB works in a positive incidence condition. This is due to the \"nozzle effect\" decreasing the static pressure on the AB suction side, shown in Figure 8. Consequently the streamline near the leading edge deflects toward the suction side, which leads to a higher incidence. Therefore, it is necessary to decrease the AB incidence to offset the \"nozzle effect\".
## 5 Results and Analysis  
* Throughput: Throughput measures how fast data can be sent across the network.
* Packet Loss Rate: It can be measured as the percentage of number of packets dropped or lost by the number of packets sent over the link.
* End-to-End delay: It represents the responsiveness of the network. It can be measured as average over all surviving data packets from the sources to destinations.
* Packet Delivery Ratio: It is the ratio between the data packets delivered by the destination to number of packets generated by the sources.
* Jitter: It represents the variation of the packet arrival time. It can be calculated as the difference between the delay of the current packet and previous packet. The value of jitter should be less for streaming applications like audio or video applications.  
We have applied the above key strategies to TFRC and called it enhanced TFRC and we compared the performance of enhanced TFRC and classical TFRC in terms of throughput, packet loss rate, jitter and average end to end delay.  
Figure 3 depicts the throughput efficiency of enhanced TFRC and classical TFRC. With increasing data rate, enhanced TFRC produces better throughput than classical TFRC. Enhanced TFRC possesses best traffic engineering principles with effective packet loss method. When comparing the performance of enhanced TFRC and classical TFRC in terms of throughput, enhanced TFRC results in 22% more throughput than classical TFRC. Figure 4 illustrates the comparison of packet loss ratio between enhanced TFRC and classical TFRC. With increasing data rate, enhanced TFRC results in low packet loss ratio than classical TFRC due to the proper distinction of packet losses with enhanced round trip time and retransmission timeouts. When comparing the performance of enhanced TFRC and classical TFRC in terms of packet loss ratio, enhanced TFRC results in 20% less packet loss ratio than classical TFRC. Figure 5 shows a comparison of enhanced TFRC and classical TFRC in terms of End-to-End delay. Enhanced TFRC results in less average end-to-end delay over classical TFRC by 20% due to enhanced feature of retransmission of timeout and enhanced TTL. Figure 6 shows the result of packet delivery ratio. Enhanced TFRC performs better than existing TFRC by 22%. Figure 7 shows a comparison of jitter between enhanced TFRC and classical TFRC. Enhanced TFRC produces less packet variation compared with classical TFRC by 18%.  
Figure 2: Dumbbell topology.  
Figure 4: Comparison of packet loss ratio between classical TFRC and Enhanced TFRC.  
Figure 5: Comparison of End-to-End delay between classical TFRC & Enhanced TFRC.  
Figure 3: Throughput comparison of classical TFRC and enhanced TFRC.
# Practical Application of Mesh Opportunistic Networks
## 4 Results
### Videos Uploaded by the Participants  
During the session, and in the time slot from 6:00 p.m. to 9:00 p.m., a total of 46 informative audiovisual pieces were uploaded to the VSN Explorer cloud platform by the 14 participants. Thus, while the subjects were carrying out the one-to-one and group communications and establishing the location and the meeting-point pins, they were also generating audiovisual pieces. This was important to analyze the communication as though it was potentially being used in the context of professional broadcasting. For the participants who did not upload audiovisual pieces to the network, the analysis of the communication protocol lost some value because these subjects will only have focused on communication itself, whereas one goal was to study communication in the context of the creation of audiovisual content.