esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
## 7 Discussion
\begin{table}
\begin{tabular}{c l} \hline \hline
**Family Name** & **Permission List** \\ \hline \multirow{2}{*}{Adrd} & android.permission.WRITE, CALENDAR \\  & android.permission.BROADCAST\_PACKAGE\_REMOVED \\ \hline \multirow{2}{*}{DroidKunFu} & android.permission.BROADCAST\_WAP\_PUSH \\  & android.permission.CALL\_PRIVILEGED \\ \hline \multirow{2}{*}{ExplicitLinuxLotoor} & android.permission.REORDER\_TASKS \\ \hline \multirow{2}{*}{FakeInstall} & android.CCOUNT\_MANAGER \\  & android.permission.BROICK \\  & android.permission.BROADCAST\_SMS \\  & android.permission.CLEAR\_APP\_USER\_DATA \\  & android.permission.READ\_CALENDAR \\ \hline \multirow{2}{*}{Geinimi} & com.google.android,googleappers.permission.GOOCLE\_AUTH \\ \hline \multirow{2}{*}{Opfake} & android.permission.GLOBAL\_SEARCH \\  & android.permission.UPDATE\_DEVICE\_STATS \\  & com.android.alarm\_permission.SET\_ALARM \\ \hline \multirow{2}{*}{Plankton} & android.permission.ACCESS\_SURFACE\_FLINGER \\  & android.permission.BACKUP \\  & android.permission.BIND\_APPWIDGET \\  & android.permission.CHANGE\_WIFI\_MULTICAST\_STATE \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Permissions requested only by a specific family. results of [16], API calls can be a more effective feature than permissions for Android malware family classification. However, it is hard to statically extract API calls if code packing and method hiding [66], encryption, or reflection [67] techniques are applied to malware samples. As the source of the features to classify Android malware families, AndroidManifest.xml has three advantages [53]: (i) the features extracted from the code of a DEX file may bring in excessively detailed information, whereas Manifest has plentiful information about an app and its structure; (ii) the features extracted from the code may produce meaningless information due to code encryption or reflection, whereas Manifest is described in plaintext and includes many details about permissions, components, and interfaces; (iii) the Manifest file has significant information to identify malware families. According to those advantages, the classification performance would be improved if the features such as components and intents were extracted together from the Manifest file and utilized. For the metrics to evaluate a classification model, most of the existing studies for Android malware family classification adopted _accuracy_[4, 5, 8, 10, 16, 23, 37, 50, 51, 54, 55] of the _F1-score_[4, 5, 10, 16, 51, 53]. Only one previous study [15] used _balanced accuracy_ for the evaluation metric. In this paper, we used the precision, recall, F1-score at the macrolevel, accuracy, balanced accuracy, and _MCC_ for the metrics. The MCC is a good metric that evaluates classification tasks on imbalanced datasets. To the best of our knowledge, we are the first to adopt MCC for evaluating the performance of Android malware family classification. Our approach achieved a high MCC (up to 0.9484 in the case of S96 and AdaBoost) by using only the requested permissions as static features.
## 5 Results and Analysis
### Comparison of Loose and Tight Solutions
#### 5.3.3 Segment 3  
The RMS errors of LC and TC architectures for this segment are shown in Figure 18. The main remarks deduced from this figure are:  
* In this relatively benign scenario the position performance of all the considered configurations are very similar;
* Including GLONASS observations for both LC and TC architectures provides slight improvements in terms of position and velocity;
* Including GNSS-derived yaw aiding and velocity/height constraints yields the reduction of velocity and azimuth errors, but no benefits in position RMS errors.  
Figure 17: Comparison between LC and TC architectures in terms of position, velocity and attitude RMS errors (Segment 2).  
As with segment 1, these findings suggest that using a GPS/GLONASS receiver in a LC system should yield results similar to the TC case, but with a simpler system and reduced development time.  
To assess the overall performance of the considered configurations, the RMS errors for the three segments are shown in Table 7.
## 3 Methods
### VJB BRDF Method  
A novel BRDF decomposition algorithm, predicated on NDVI has been introduced for the estimation and rectification of the directional effect on satellite-derived surface  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Band** & **Archetype Number** & **AFX Range** & \(F_{iso}\) & \(F_{vol}\) & \(F_{geo}\) \\ \hline \multirow{6}{*}{Green} & 1 & (0.381, 0.714) & 0.5 & 0.0519 & 0.1384 \\  & 2 & (0.714, 0.834) & 0.5 & 0.1906 & 0.1055 \\  & 3 & (0.834, 0.920) & 0.5 & 0.2700 & 0.0806 \\  & 4 & (0.920, 1.012) & 0.5 & 0.3240 & 0.0587 \\  & 5 & (1.012, 1.160) & 0.5 & 0.4337 & 0.0329 \\  & 6 & (1.160, 1.717) & 0.5 & 0.7446 & 0.0072 \\ \hline \multirow{6}{*}{Red} & 1 & (0.382, 0.680) & 0.5 & 0.0288 & 0.1426 \\  & 2 & (0.680, 0.795) & 0.5 & 0.1282 & 0.1134 \\  & 3 & (0.795, 0.899) & 0.5 & 0.2029 & 0.0845 \\  & 4 & (0.899, 1.026) & 0.5 & 0.3082 & 0.0585 \\  & 5 & (1.026, 1.240) & 0.5 & 0.4826 & 0.0274 \\  & 6 & (1.240, 1.946) & 0.5 & 1.0859 & 0.0088 \\ \hline \multirow{6}{*}{NIR} & 1 & (0.541, 0.804) & 0.5 & 0.1218 & 0.1096 \\  & 2 & (0.804, 0.896) & 0.5 & 0.2377 & 0.0860 \\ \cline{1-1}  & 3 & (0.896, 0.966) & 0.5 & 0.3135 & 0.0679 \\ \cline{1-1}  & 4 & (0.966, 1.042) & 0.5 & 0.3521 & 0.0477 \\ \cline{1-1}  & 5 & (1.042, 1.142) & 0.5 & 0.4321 & 0.0262 \\ \cline{1-1}  & 6 & (1.142, 1.361) & 0.5 & 0.5657 & 0.0040 \\ \hline \end{tabular}
\end{table}
Table 4: AFX ranges of six BRDF archetypes in normalized (\(F_{iso}\), \(F_{vol}\), and \(F_{geo}\)) forms in the MODIS green, red, and NIR bands.  
reflectance time series [24]. This approach quantifies the BRDF profile through two distinct parameters: surface scattering (denoted as \(R\)) and volumetric scattering (denoted as \(V\)).  
\[R^{N}(45,0,0)=R(\theta_{s},\theta_{v},\varnothing)\times\frac{1+VK_{vol}(45,0,0 )+RK_{go}(45,0,0)}{1+VK_{vol}(\theta_{s},\theta_{v},\varnothing)+RK_{go}( \theta_{s},\theta_{v},\varnothing)} \tag{6}\]  
The temporal variations in the parameters V and R demonstrated a significant correlation with the fluctuations in the NDVI across vegetated regions [24]. Both V and R were delineated as linear functions of the coefficients \(V_{0}\), \(V_{1}\), \(R_{0}\), and \(R_{1}\), respectively, each in conjunction with NDV. The aforementioned coefficients can be derived from the MCD43A BRDF parameters, which were resampled to a resolution of 5 m, in conjunction with the NDVI values observed from the Jilin-1 GP01/02 PMS.
## 4 Results
### Arabic Documents Text Recognition
For testing, the required  
Figure 3: Averaged multiclass confusion matrices of Arabic documents text recognition for different datasets with corresponding classes: (**a**) BBC Arabic corpus, (**b**) CNN Arabic corpus, (**c**) OSAC, and (**d**) AIDT. time is much less than the training time because the testing sets represent only 20% of the total size of the specific dataset. For example, the proposed ArCAR system requires 1.2 s to perform the recognition process of 4550 Arabic text documents from the AlKhaleej testing set, as shown in Table 8. Each class of the AlKhaleej testing set has 650 Arabic text documents, as shown in Figure 2a. Since the same deep learning ArCAR model is used, the processing testing time for each document is \(2.64\times 10^{-4}\) s.  
Figure 4: Training-validation behavior of the proposed ArCAR system in terms of accuracy and loss functions with respect to the number of epochs using (**a**) AlKhaleej, (**b**) Akhbarona-balance, and (**c**) Alarabiya-balance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & \
\begin{tabular}{c} **Train Time/Epoch** \\ **(s)** \\ \end{tabular} & **No. of Epochs** &
\begin{tabular}{c} **Testing Time/Testing** \\ **Set (s)** \\ \end{tabular} \\ \hline AlKhaleej & 36.89 & 14 & 1.2 \\ Akhbarona & 1065.096 & 10 & 7.651 \\ unbalance & 1134.466 & 14 & 3.611 \\ Akhbarona & 307.265 & 10 & 3.689 \\ unbalance & 556.531 & 14 & 1.706 \\ BBC & 8.10 & 20 & 0.22 \\ CNN & 44.76 & 25 & 0.4 \\ OSAC & 179.251 & 10 & 0.746 \\ AIDT & 109.67 & 10 & 2.90 \\ \hline \hline \end{tabular}  
* The time is computed considering all datasets from all classes.  
\end{table} 
Table 8: Computation Measurements for the Proposed Deep Learning ArCAR System with Different Datasets: Arabic Text Documents Recognition.
## 3 Research on Sunshine Shadow Occlusion Algorithm
### Sunshine Shadow Recognition Technology Based on Ray Tracing
#### 3.2.2 Node Occlusion Detection  
The projection method of the node to be detected and the structural surface grid are shown in Figure 7. The geodetic plane was used as the projection surface, and the illumination direction of the sunlight was used as the projection direction.  
Projection point inclusion detection is a key step in the analysis of no occlusion and mutual occlusion. The primary goal was to determine the relationship between the projection node and a mesh projection. As shown in Figure 8, the projection node was outside the projection area of the surface mesh, according to the basic principle of light propagation along a straight line, which indicated that the surface mesh would not occlude the node to be detected.  
In the process of projection point inclusion detection, if the quadrilateral mesh (\(A^{\prime}B^{\prime}C^{\prime}D^{\prime}\)) obtained by the projection in Figure 8 is directly used to determine this, the calculation process is more complicated. Therefore, the quadrilateral surface mesh obtained by projection was divided into two triangles (\(A^{\prime}B^{\prime}D^{\prime}\) and \(B^{\prime}C^{\prime}D^{\prime}\)) along a diagonal to determine projection point inclusion, respectively.  
For the inclusion detection of projection points in triangular mesh elements, the triangle barycenter coordinate method (as shown in Figure 9) can be used for determination.  
Figure 8: The principle of projection point inclusion detection.  
Figure 7: The schematic diagram of the projection mode of the node to be detected and the structural surface mesh.  
As shown in Figure 9, any point \(P\) in the planecan be expressed as:  
\[P^{\prime}=D^{\prime}+u\big{(}B^{\prime}-D^{\prime}\big{)}+v\big{(}C^{\prime}-D^ {\prime}\big{)} \tag{29}\]  
The point inside the triangle \(B^{\prime}C^{\prime}D^{\prime}\) should meet the conditions in Formula (30); otherwise, the point is outside the triangle \(B^{\prime}C^{\prime}D^{\prime}\).  
\[\left\{\begin{array}{c}u\geq 0\\ v\geq 0\\ u+v\leq 1\end{array}\right. \tag{30}\]  
If the projection node is within the projection area of the mesh, the occlusion judgment needs to be made according to the projection depth of the face mesh node and the node to be detected. The projection depth is the distance between each node and its corresponding projection point, as shown in Figure 8, which is the length of \(DD^{\prime}\), \(BB^{\prime}\), \(CC^{\prime}\), and \(PP^{\prime}\).
## 4 The Smart Shipping Use Case
### Learning Outcome of the Training Module  
Shipping Company's staff have a key role in protecting IT and OT systems. Training and awareness should be tailored to the appropriate levels for:  
* On-board personnel including the master, officers and crew.
* Shore-side personnel, who support the management and operation of the ship.  
An awareness or training framework should be in place for all personnel, covering at least the following risk factors and awareness aspects:  
1. Risks related to emails and how to behave in a safe manner (examples are phishing attacks where the user clicks on a link to a malicious site);
2. Risks related to Internet usage, including social media, chat forums and cloud-based file storage where data movement is less controlled and monitored;
3. Risks related to the use of own devices (these devices may be missing security patches and controls, such as anti-virus, and may transfer the risk to the environment to which they are connected to);
4. Risks related to installing and maintaining software on company hardware using infected hardware (removable media) or software (infected package);
5. Risks related to poor software and data security practices where no anti-virus checks or authenticity verifications are performed;
6. Safeguarding user information, passwords and digital certificates;
7. Cyber risks in relation to the physical presence of non-company personnel, e.g., where third-party technicians are left to work on equipment without supervision;
8. Detecting suspicious activity or devices and how to report if a possible cyber incident is in progress (examples of this are strange connections that are not normally seen or someone plugging in an unknown device on the ship network);
9. Awareness of the consequences or impact of cyber incidents to the safety and operations of the ship.  
Applicable personnel should be able to identify the signals when a system has been compromised. For example, training scenarios should trigger and evaluate user awareness aiming at the effective and efficient identification of hidden threats between applicable signs such as:  
Figure 7: Flow of cyber threat mechanism.  
* An unresponsive or slow to respond system;
* Unexpected password changes or authorized users being locked out of a system;
* Unexpected errors in programs, including failure to run correctly or programs running; unexpected or sudden changes in available disk space or memory;
* Emails being returned unexpectedly;
* Unexpected network connectivity difficulties;
* Frequent system crashes;
* Abnormal hard drive or processor activity;
* Unexpected changes to browser, software or user settings, including permissions.
## 2 (Com)2 INet: Communication and Computation Integrated Network Architecture
### Implementing Computing Services on the (Com)\({}^{2}\)INet Platform  
In practical system applications, the observations phase is implemented through a dual monitoring approach. The system state is jointly monitored by a Network Observer module installed inside the centralized SDN controller through northbound API and an in-band network telemetry (INT) collector. For instance, multi-dimensional resource measurement quantifies computing capabilities (e.g., FLOPS, IOPS), storage metrics (e.g., disk capacity,  
Figure 4: Working principle for implementing computing services in the (Com)\({}^{2}\)INet architecture.  
bandwidth), and network parameters (bandwidth, delay), forming a standardized model for comprehensive resource characterization. Meanwhile, users submit computing tasks through the (Com)2 INet platform. The platform's service orchestration module analyzes each task's resource requirements, performance objectives, geographical constraints, and other conditions, converting them into executable data flows. Upon fetching observations, pretrained models, such as the multi-path transmission model, heterogeneous resource scheduling, and fault tolerance, make optimal decisions for the computing tasks. The orchestration policies are delivered to the orchestrator, while routing policies are delivered to the controller, ensuring that the management module and infrastructure are fully decoupled. Network configuration will affect network performance, which would in turn be reflected in their reported performance metrics. Based on the reported performance metrics, the policy model can dynamically adjust network policies to adapt to fluctuating traffic patterns and unpredictability network. To sum up, the (Com)2 INet platform facilitates a closed-loop \"measure-adapt-optimize\" process that ensures optimal computing service delivery.
# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 6 Conclusions  
In this paper, two different hypergraphs were constructed for two speaker dependencies for the first time, and a multi-hypergraph neural network--namely, ERMC-MHGNN--was designed for multi-party conversation emotion recognition to better handle speaker dependencies. The experimental results show that ERMC-MHGNN has good performance. Furthermore, through comprehensive evaluation and ablation studies, we can confirm the advantages of ERMC-MHGNN and the impacts of its modules on performance. Several conclusions can be drawn from the experimental results. First, our approach to non-speaker-specific modeling of utterances from other speakers is feasible. Second, combining average aggregation with attention aggregation can allow better hyperedge features to be obtained. Finally, although the model achieved certain results on the emotional shift issue, the ability of the model to recognize similar emotions still needs to be enhanced.  
In the future, we plan to build a hierarchical hypergraph neural network based on the existing hypergraph network to deal with interactions within a single modality and interactions among multiple modalities. We believe that hierarchical hypergraph neural networks can not only handle high-order relationships between utterances, but can also alleviate the deficiencies of single-mode features.  
Figure 6: Results of the case study on the EmoryNLP dataset, where three participants in a conversation are provided, along with their dependent historical utterances. We use green and red to highlight the right and wrong predictions.  
**Author Contributions:** Conceptualization, C.Z., X.S., H.X. and Z.Z.; methodology C.Z., X.S. and H.X.; software, H.X.; validation, H.X.; formal analysis, X.S.; investigation, C.Z.; resources, H.X.; data curation, H.X.; writing--original draft preparation, H.X. and Z.Z.; writing--review and editing, X.S. and C.Z.; visualization, H.X.; supervision, X.S. and C.Z.; project administration, H.X.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the General Programme of the National Natural Science Foundation of China (61976078) and the Major Project of Anhui Province under Grant No. 202203a05020011.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Experiments
### Ablation Study
#### 4.5.2 The Effect of DFE  
Since human parsing is a fine-grained semantic segmentation task, rich detailed semantic information is essential to identify small targets. Using the high-resolution module in CE2P can recover details, but we argue it may obtain more effective detailed features. Thus, we introduce the DFE module to grasp the detailed features. In Table 5, when there are no T and E involved, our method has a significant improvement in object recognition for small targets such as gloves and scarves compared to baseline. By employing the T, D at the same time, we can see that the categories of large-size objects, dresses and skirts, and small target socks have improved in performance. It shows that the GTM and DFE modules in our method mutually promote each other. We further conduct ablation experiments on each part of DFE, which consists of spatial attention (SA) and channel attention (CA). As shown in Table 6, when we added the CA module, the performance on the class of the skirt and scarf improve 1.02% and 0.99%, respectively. Meanwhile, the left and right symmetrical body parts, such as the left and right arms, are also enhanced. With the spatial and channel feature derived from the DFE, more details that are not available in deep layers are provided for parsing from shallow and high-resolution layers. The experimental results also demonstrate the effectiveness of our DFE module.
## 5 Results and Analyses  
### Experimental Results  
The average outdoor ambient temperature during the test was 24.5 \({}^{\circ}\)C. The temperature variation with time at each monitoring point is shown in Figure 7. The test was conducted for 62.95 h. At the beginning, due to the large temperature difference between the initial ambient environment and the cold storage plate, the temperature of the inlet, outlet, fresh-keeping area, and fresh products dropped rapidly under the work of the cold storage system, and it took 12.51 h for the naval oranges to be cooled down from the initial temperature to the target temperature of 10 \({}^{\circ}\)C. This also means that the fresh product was maintained at a lower temperature for the majority of the experiment. The temperature-controlled interval of the outlet temperature (5-10 \({}^{\circ}\)C) lasted 50.44 h. The average temperature of the fresh-keeping area within the temperature-controlled interval was 8.5 \({}^{\circ}\)C, and the average temperature of the products was 8.3 \({}^{\circ}\)C, which suggests that the retention and release rates of cold energy from the PCM allowed the temperature of the holding area to be maintained in a suitable range for a long period. The center temperature of the cold storage plates was maintained near the theoretical temperature of 0 \({}^{\circ}\)C (-2-2 \({}^{\circ}\)C interval) for 46.07 h, which accounted for 73.2% of the total test duration, reflecting the fact that the cold energy is mainly stored as the latent heat of the phase change material [37]. After 46.07 h, the temperature at the center of the plate rose faster because the PCM had ended the phase transition process, and the value of the difference between its temperature and the ambient temperature was greater than 8 \({}^{\circ}\)C, which formed a higher heat transfer rate. The temperature curves of the other monitoring points changed smoothly because the temperature difference between them and the environment was relatively small, and there was not a high rate of heat exchange. The fan was activated 88 times (9.06-45.71 h) during the test. The slight discrepancy observed between the center and surface temperatures of the cold storage plates during the test could be attributed to the uniform temperature distribution of the phase change material (PCM) during the phase transition, resulting in a surface temperature measurement that closely approximates the center temperature.
## 5 Results and Discussion
### Labeled Datasets
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Source** & **Approach** & **Labels?** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline \multirow{8}{*}{CorpusCine} & [55] & Unsupervised & ✗ & 0.6393 & 0.6274 & 0.6333 & 0.6316 \\  & [56] & Supervised & ✓ & 0.8721 & 0.8701 & 0.8710 & 0.8708 \\  & [53] & Supervised & ✓ & - & - & - & 0.7713 \\  & [57] & Supervised & ✓ & 0.8531 & 0.8959 & 0.8739 & 0.8667 \\  & [53] & Hybrid & ✓ & - & - & - & 0.8086 \\  & [54] & Hybrid & ✓ & 0.8858 & 0.8857 & 0.8856 & 0.8857 \\  & Proposal (LR) & Hybrid & ✗ & 0.6727 & 0.6632 & 0.6679 & 0.6664 \\  & Proposal (SVM) & Hybrid & ✗ & 0.6624 & 0.6509 & 0.6566 & 0.6546 \\  & Proposal (RF) & Hybrid & ✗ & 0.6484 & 0.5959 & 0.6210 & 0.6042 \\  & Proposal (NB) & Hybrid & ✗ & 0.6384 & 0.5744 & 0.6047 & 0.5840 \\ \hline \multirow{8}{*}{PaperReviews} & [58] & Supervised & ✓ & 0.4621 & 0.3596 & - & 0.3600 \\  & [59] & Supervised & ✓ & 0.7600 & 0.6600 & 0.7100 & 0.7400 \\  & [60] & Hybrid & ✓ & 0.7100 & 0.7200 & 0.7100 & 0.7100 \\ \cline{1-1}  & Proposal (LR) & Hybrid & ✗ & 0.695 & 0.5407 & 0.6082 & 0.4212 \\ \cline{1-1}  & Proposal (SVM) & Hybrid & ✗ & 0.6942 & 0.5378 & 0.6061 & 0.4176 \\ \cline{1-1}  & Proposal (RF) & Hybrid & ✗ & 0.6942 & 0.5378 & 0.6061 & 0.4176 \\ \cline{1-1}  & Proposal (NB) & Hybrid & ✗ & 0.6942 & 0.5378 & 0.6061 & 0.4176 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of our hybrid approach using four different classification algorithms and compared with state-of-the-art approaches. LR: Logistic Regression, SVM: Support Vector Machine, RF: Random Forest, NB: Naive Bayes. proposal achieves an accuracy near 0.8 for CorpusCine data and near 0.7 for PaperReview dataset, which remarkably is comparable with state-of-the-art results.