esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
In this study, the wavelet entropy decomposition and the proposed HHW feature overcame the disadvantages of the traditional methods associated with the identification of maximum energy and frequency. It promoted the potential for the use of UWB radar applications in actual postdisaster emergency rescue missions. [PERSON] et al. [17] proposed a feature of standard deviation of wavelet entropy (StdWE) to distinguish between humans and dogs through brick walls and in free space using UWB radar. The frequency band of human respiration and that of a dog's respiration intersect, similar to the situation in this study. Thus, the feature of StdWE is employed to be compared with HHW here. In the calculation of StdWE, the radar signal is divided into 128 nonoverlapping frames along slow time at each range point after wavelet entropy decompositions. Then, the average wavelet entropy (\(MWE\)) of 128 frames is illustrated by  
\[MWE(x)=\frac{1}{[N/128]}\sum_{fra=1}^{N/128}WE^{fra}(x), \tag{27}\]  
where \(WE^{fra}\) denotes the \(WE\) of the radar signal when the slow time is limited to the corresponding frame. Next, \(StdWE\) is calculated to reflect the fluctuation degree of \(WE\). It is implemented by  
\[StdWE(x)=\sqrt{\frac{\sum_{fra=1}^{[N/128]}\bigl{(}WE^{fra}(x)-WE(x)\bigr{)}}{[ N/128]}}. \tag{28}\]  
The comparison of the \(StdWE\) of the 10 groups of radar data for human and digging construction machinery positions is shown in Figure 11. There are two intersections between the two lines at the 6 th and 10 th data groups. Additionally, other \(StdWE\) values of humans and digging construction machineries are very close. This reveals that \(StdWE\) has poor distinction effects in this kind of scenario. The comparison of statistical parameters for \(StdWE\) between humans and digging construction machineries is shown in Table 4. The values of all four kinds of statistical parameters are also very close. Compared with the results in Figure 9 and Table 3 calculated from the \(HHW\) values, the distinction effectiveness of the HHW feature is verified significantly better than \(StdWE\) feature in the distinction between human and mechanical vibrations within similar frequency bands using UWB radar.  
However, there are still some improvements that need to be achieved before the method is used in practice. For example, experimental scenarios need to be more realistic. More investigation of the effect of subject number and species, clutter, and barriers between the targets and the UWB radar should be implemented. Moreover, the effect of changing the location of targets to the distinction performance needs to be researched in follow-up experiments, since survivors may be buried in different depths. In addition, the amount of radar data needs to be enlarged, and the statistical differences between the human targets and mechanical vibrations could be analyzed more accurately.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Techniques for Malware Detection  
The collection of studies on machine learning (ML) techniques for malware detection can be grouped into several thematic categories, each focusing on different methodologies and applications within cybersecurity.  
1. Ensemble Learning and Interpretability: [PERSON] and [PERSON]'s use of Shapley values with boosting (e.g., XGBoost) and bagging models enhances zero-day malware detection by improving interpretability and emphasizing critical features like file entropy [44; 45].
2. CNN and Image-Based Detection: [PERSON] et al. leverage EfficientNet to convert Android DEX bytecode into RGB images, achieving 95.7% accuracy for Android malware detection, outperforming other models [34; 46].  
Transfer Learning and Multimodal Techniques: [PERSON] et al. combine BERT for textual feature extraction and image-based techniques for visual features, resulting in over 98% accuracy in Android malware detection through ensemble classifiers [35; 45].
* Anomaly Detection and Adversarial ML: [PERSON] et al. focus on detecting network anomalies and addressing adversarial attacks using adversarial training and generative adversarial networks (GANs) to improve robustness in intrusion detection systems [47; 48].
## 3 Results
### Validation  
Our cross-validation yielded an overall kappa index of agreement (KIA) of 0.70. Eight of our categories had a KIA over 0.7, six categories had a KIA between 0.4 and 0.7, and three categories had a KIA below 0.4 (Table A5). The highest KIA corresponded to _Pennisetun_, and the lowest KIA corresponded to _Coffea_. Native land cover categories all presented high (\(>\)0.9) KIA values. Meanwhile, our ROC-AUC results are generally consistent, yielding 11 categories with an AUC above 0.7, four categories with an AUC between 0.5 and 0.7, and three categories with an AUC below 0.5 (Table A5). _Cedrela_ was the only category with an AUC value above 0.9, while Pioneer, Bare Ground, and Freshwater had AUC values below 0.5. Humid tallgrass and _Cinchona_ were not represented in our reference polygons because these land covers are usually found outside of the agricultural areas, and the most significant extensions of these vegetation covers are located in areas that are hard to reach.  
Table 12 lists validation results for each island and Table 13 details the results for each category. Only the most common land covers for the agricultural region are represented in these tables. We had an average \(R^{2}\) score of 69 for all islands, which ranged from 58 (Floreana) to 83% (Santa Cruz). Our average \(RMSE\) was 21%. We had the most problems classifying pioneer (\(R^{2}\) = 0.321, \(RMSE\) = 32.81%), _Rubus_ (\(R^{2}\) = 0.139, \(RMSE\) = 13.16), _Coffea_ (\(R^{2}\) = 0.191, \(RMSE\) = 8.84%), transitory crops (\(R^{2}\) = 0.26%, \(RMSE\) = 17.8%), and cultivated grass (\(R^{2}\) = 0.412%, \(RMSE\) = 32.81%). The classification that explained the largest amount of variation in the ground truth data were _Pennisetun_ (\(R^{2}\) = 1), _Syzggium_ (\(R^{2}\) = 1%, \(RMSE\) = 7.47%), Evergreen forest and Shubland (\(R^{2}\) = 0.938, \(RMSE\) = 15.01%), and _Cedrela_ (\(R^{2}\) = 0.907, \(RMSE\) = 14.53%).  
Heritage Site. These maps are the first attempt, using the latest satellite images available, to integrate vastly different land cover types (e.g., native ecosystems, staple food crops, invasive plant species) within the same decision-making tool, so that concerns of the agricultural and conservation sectors are both represented.
# Semi-Supervised Semantic Segmentation Network for Point Clouds Based on 3D Shape
## 2 Related Work
### Point Representation-Based Methods  
[PERSON] et al. (2019) proposed PointNet, the first model that could directly process unorganized point cloud data, generating global feature descriptors through a multilayer perceptron (MLP) to achieve semantic segmentation. Subsequently, they proposed PointNet++ ([PERSON] et al., 2019), which improved the local feature representation of data and greatly enhanced the semantic segmentation effect. [PERSON] et al. (2019) developed the EdgeConv operation to capture local geometric information while ensuring displacement invariance, thus improving the correlation features between points by introducing edge data. PointCNN ([PERSON] et al., 2019) aggregates spatial structure and local feature information on points using X-Conv but does not achieve point cloud replacement invariance. [PERSON] et al. (2019) used a series of local 3D convolutional kernels to construct kernel points that learned local geometric features by weighting the features based on the Euclidean distance from points to kernel points.  
Moreover, multiscale and shape features of point cloud data have been introduced into semantic segmentation networks to improve their spatial information representation capabilities. For example, [PERSON] et al. (2019) used point-by-point pyramidal pooling to capture local multiscale context information on point clouds in order to improve the semantic segmentation effect. [PERSON] et al. (2019) proposed a semantic segmentation network that captures fine-grained information on multiscale point cloud contexts using an attention mechanism. [PERSON] et al. (2019) used point cloud shape context information to construct an end-to-end point cloud model in order to capture aggregated point cloud features and achieve semantic segmentation. ShellNet ([PERSON] et al., 2019) uses spherical convolution for feature extraction and filtering, which can effectively retain local structural information and geometric features of point cloud data and has high feature representation accuracy.  
In this paper, we propose the SBSNet network for semantic segmentation based on point representation. Our approach extracts various pieces of geometric information from a point cloud, such as curvature and density, to encode shape features. We use an attention mechanism to fuse these features with point cloud features, enabling the network to learn and capture point cloud shape contextual information. By highlighting salient local features of point cloud data, our method improves object boundary recognition and produces promising semantic segmentation results.
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 3 Proposed Scheme
### Extraction Procedure  
The procedure of extraction is the reverse of embedding procedure. Figure 3 shows the extraction process. The detailed of watermark extraction process is outlined as follows:  
Step-1 Listless SPIHT decoding  
SPIHT decodes the image form the compressed bit string. It follows the exact reverse process that the SPIHT encoder does [36; 37].  
Step-2 Coefficient restoration algorithm  
This process restore the coefficients back into the original position, i.e., individual 8 \(\times\) 8 blocks of coefficients that has been arranged into hierarchical manner before SPIHT encoding.  
Step-3 IDCT  
Inverse DCT is applied in to all the 8 \(\times\) 8 blocks to find the image pixels and merge all 8 \(\times\) 8 block to construct a large compressed decoded watermarked image (512 \(\times\) 512 in this case).  
Figure 2: Coefficient rearrange algorithm.
# A Novel Dual Authenticated Encryption Scheme Suitable for Social Networking Services
## 1 Introduction
Three-party communication is a natural extension of conventional two-party settings when someone joins the latter. For example, the sales representatives of two enterprises might chat online using the Line messenger service. When an important procurement is going to be made, a legal representative will be asked to join the communication for ensuring the validity of this transaction. Unlike many multi-party communication environments where those participants belong to the same group sharing a common key, a three-party communication usually contains independent recipients. We hence concentrate on a specific protocol that is suitable for the case of just three independent parties. Although some existing protocols employed in social networking services also supports three-party communication, they usually utilize the techniques of group key management or symmetric key encryption. On the other hand, our scheme eliminates the cost of generating a group key and solves the problem of symmetric key encryption in which a ciphertext is bound by only a specific private key. One might further consider that the technique of (multi-party) attribute-based encryptions is applicable to the above three-party scenario. Nevertheless, the attribute issuing, verification, and management will increase the complexity of practical environments. Additionally, it would be a troublesome issue of how to prevent the attribute-collusion attack.
# Modeling Evacuees' Intended Responses to a Phased Hurricane Evacuation Order
## 3 Survey and Data Descriptions  
The analysis was based on data from a survey distributed to 2500 randomly selected households in coastal areas near Hampton Roads, Virginia. More details describing the survey distribution procedure can be found in [60]. The total sample size (n) for the survey is 415, which corresponds to a 19% response rate, a cooperation rate of 96%, a refusal rate of 1%, and a contact rate of 20%. Survey data were weighted to improve the representativeness of survey respondents in the population. Details on the weighting process can be found in [60]. In the survey questionnaire, respondents who stated they would evacuate in the described hypothetical hurricane scenario (\"a Category 4 hurricane (130-156 mph wind speed) arriving on the next Thursday morning\" and \"a mandatory evacuation order had been given\") proceeded to the question regarding their compliance behavior to a phased evacuation order. The question asked, \"If officials asked that different areas/zones start evacuating at different times, but your area/zone is asked to wait for a later slot, which of the following are you most likely to do?\" Available options included evacuate with the first group (8%), evacuate without considering the recommended start times (22%), evacuate at the recommended time for my zone (58%), evacuate later than recommended for my zone (1%), decide not to evacuate (0%; only one record), and \"I don't know\" (11%). Those who chose \"I don't know\" were removed from further analysis. Respondents who selected evacuating at, or later than the recommended time for their zone, were considered to be compliant with the phased evacuation order, while the others were considered to be noncompliant. Table 1 presents descriptive statistics of both the dependent variable and potential independent variables for evacuees. About 75% of the evacuees think that they are likely or very likely to be impacted by a hurricane in the future. If a hurricane were to impact them, 90% of the evacuees think that they will experience property damage. Only 3% of the evacuees think that the government is more responsible for their safety than themselves,  
while 57% of the evacuees think the opposite. About 79% of the evacuees think that evacuation is an effective protection action.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Test Management
#### 3.3.3 Empirical Validation of the Framework (Real-World Testing)
_Security Evaluation and Vulnerability Assessment:_ The authors conducted thorough security tests to uncover any potential weaknesses in the framework's encryption methods and assessed its resilience against common cyber threats like brute force attacks and unauthorized access attempts to guarantee robust data protection. 3. _Usability Analysis and User Experience Testing:_ The authors assessed the framework's user interface and overall user experience through usability tests. Feedback was solicited from users to gauge the framework's ease of use and effectiveness in meeting their needs, thus ensuring high adoption rates and user satisfaction. 4. _Compliance Validation and Regulatory Conformance Testing:_ The framework's compliance was verified with relevant data protection regulations such as GDPR, HIPAA, or PCI DSS. Audits were conducted to confirm adherence to legal requirements and the implementation of adequate safeguards for sensitive data stored in the cloud. 5. _Real-World Scenario Simulation and Use-Case Testing:_ The authors simulated real-world scenarios like secure file sharing and collaborative data analysis to evaluate the framework's performance in practical applications. This testing ensures that the framework effectively safeguards data privacy while supporting common tasks and workflows in real-world cloud storage environments.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
###### Abstract  
In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation information is proposed to enhance the positioning accuracy and robustness of UAV navigation systems. First, the fundamental principles of Kalman filtering and its application in navigation are introduced. Second, the basic principles of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and their applications in the navigation domain are elaborated. Subsequently, an algorithm based on a CNN and LSTM-assisted Kalman filtering fusion navigation is proposed. Finally, the feasibility and effectiveness of the proposed algorithm are validated through experiments. Experimental results demonstrate that the Kalman filtering fusion navigation algorithm assisted by a CNN and LSTM significantly improves the positioning accuracy and robustness of UAV navigation systems in highly interfered complex environments.  
convolutional neural network; long short-term memory network; Kalman filter; fusion navigation algorithm; positioning accuracy +
Footnote †: journal: Applied sciences
## 6 Applications of VSNs
### Smart House Monitoring  
A smart house is a house that has highly advanced automatic systems for lighting, temperature control, multi-media, security, window and door operations, and many other functions. A smart home appears intelligent because its computer systems can monitor so many aspects of daily living. For example, the refrigerator may be able to inventory its contents, suggest menus, recommend healthy alternatives, and order groceries. The smart home systems might even take care of cleaning the cat's litter box and watering the plants. In Figure 6, we have demonstrated a smart house designed by the Gator Tech smart housing [60].  
For the smart home automation and control system VSNs offer a wide range of services: local or remote access from the Internet to monitor the home [temperature, humidity, activation of remote video surveillance, status of the doors (locked or open) _etc._] but also for home control (activate the air conditioning/heating, door locks, sprinkler systems, _etc._). Fairly sophisticated systems can also optimize the level of energy consumption to a wide range of inputs from various sensors connected to the VSN: light sensors, presence detection sensors, temperature sensors, _etc._ in order to control electric window shades, chillers, air flow control, air that have direct interactions with the grid itself via the Internet of the grid network to report the amount of KWatts that could be load shed (home to grid) and to receive dynamic load shedding information if/ when required (grid to home). This application is also referred to as Demand-Response application. Another service known as Demand Side Management could be provided by utilities to monitor and report to the user its energy consumption with a fine granularity (on a per device basis). Other inputs such as dynamic pricing can also be received by the user from the utility that can then turn on and off some appliances according to its local policy in order to reduce energy bills. In terms of home safety and security, the VSN can have motion- and audio  
Figure 6: Gator Tech smart house (Courtesy: Gator Tech).  
sensors, sensors at doors and windows, and video cameras to which additional sensors can be added for safety (gas, water, CO, Radon, smoke detection). The VSN typically comprises a few dozen of nodes forming an _ad-hoc_ network with multi-hop routing since the nodes may not be in direct range. It is worth mentioning that the number of devices tends to grow considering the number of new applications for the home. In its most simple form, all nodes are static and communicate with a central control module but more sophisticated scenarios may also involve inter-device communication. For example, a motion/presence sensor may send a multicast message to a group of lights to be switched on, or a video camera will be activated sending a video stream to a gateway that can be received on a cell phone.
## 6 Conclusions  
The effect of damage scattering signals on the instantaneous parameter characteristics of active Lamb waves was studied in this paper, which proved the feasibility of extracting damage information from the instantaneous phase and marginal spectral changes of a single sensor signal. This paper provides a diagnostic method for active Lamb wave based monitoring based on instantaneous parameter characteristic changes. In the active Lamb wave based monitoring of plate structure health, the excitation wave and the propagation distance of the response wave are constant, and thus the instantaneous feature of sensing signal is also fixed. This is the basis of the monitoring method proposed in this paper. To prove the effectiveness of the proposed monitoring method, first, a damage monitoring signal model was established to study the relationship between the superposition position of the damage scattering signals and the instantaneous phase change of the monitoring signals. Then, the reasons for the instability of EMD decomposition of signals in the same state were studied and methods to improve the decomposition stability were proposed. Based on these studies, the relationship between damage and the change of the instantaneous phase and marginal spectral was revealed. This paper further analyzes the relationship between the marginal spectrum change and the instantaneous phase, which indicates that the marginal spectrum change of an active Lamb wave is not disordered but rather related to instantaneous phase change. The research results show that there is a causal relationship between the instantaneous parameter characteristics of active Lamb wave and the damage scattering signal. It is feasible to realize the health monitoring of plate structure based on the change of instantaneous parameter characteristics of an active Lamb wave.  
This damage monitoring method is suitable for the damage that can scatter the propagation wave, such as matrix damage and fiber fractures of composite material and crack and hole damage of metal plates whether the damage is visible or not. This diagnosis method can describe the damage information change in monitoring signals from two aspects: instantaneous phase and marginal spectral distribution. Compared with the previous diagnosis method that only depends on the difference between monitoring signals, this diagnosis method can reduce the misdiagnosis rate, does not need to be strictly consistent with each acquisition trigger threshold, improves the anti-interference ability, and reduces the hardware requirements of the monitoring system.  
Conceptualization, B.X.; methodology, B.X. and Y.S.; software, P.L.; validation, Q.C. and Y.S.; formal analysis, B.X.; writing--original draft preparation, B.X.; writing--review and editing, P.L.; supervision, M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Major Project of Natural Science Research of Jiangsu Province Universities (19 KJA410001) and Scientific Research Foundation of Nanjing Institute of Technology (CKJB201808).  
The authors declare no conflict of interest.