esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Security Analysis of Strike
### Avalanche Effect  
Our Strike cipher is inspired by the stochastic behaviour of lightning strike phenomena. This behaviour is completely random and unpredictable, as each strike starts at a random point and moves in different directions and distances (jumps). In addition, every single keystream is generated using a completely new set of scrambled parameters, which produces a new sequence of bits that differ from previously generated sequences.  
In this test, we consider the cryptographic property known as the avalanche effect. It mainly measures the impact of altering (flipping) one bit of the secret key (_SKey_) on the generated keystream (_Kstream_). The test was applied to a data set of 500 keystreams. The results are presented in Table 5. The dots visualised in Figure 8 represent the percentage of changes resulting in each keystream after flipping one bit in the secret key (_SKey_). The testing results show that Strike achieved an average avalanche percentage of about 74%.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 6 Performance Evaluation  
This section presents the performance of DRCS as obtained from tests conducted on an experimental testbed as well as from simulations. We first demonstrate that our proposed multi-channel scheme effectively reduces overhearing in comparison to CTP using an experimental testbed comprising of 18 MICAz motes. Note that in the following performance results, single channel operation refers to CTP. We also show the effectiveness of the dynamic channel selection scheme based on individual node's battery health metrics. To show the performance of our scheme in a larger network, we implement this scheme in the _Castalia_ simulator [50] on a 200-node network. Finally, we compare the performance of DRCS with a well-known channel assignment scheme TMCP [35]. Parameters pertinent to the experiments and simulations are listed in Table 1. The sensing current and duration is obtained corresponding to the onboard temperature sensor (a surface mount thermistor) of the MTS300 integrated sensor board [51].
## 3 Results
### Scanning Electron Microscope (SEM) Results  
The scanning electron microscope images show the effects caused by the interaction of the different irradiated laser parameters on the grade 4 titanium surface.  
Compared to the control image (Figure 3A), the 980 nm diode laser did not seem to negatively affect the grade 4 titanium surface (Figure 3B). However, the regular and compact texture of the control sample changed in apparently more surface roughness due to the exposure of irregular micro-crystallites of the average dimension of about 50 \(\upmu\)m. A very small increase in the dimension of the micropores and their irregular shape could also be observed after the treatment with a 980 nm diode laser.  
Figure 3: Scanning electron microscope images: (**A**) = control; (**B**) = 980 nm diode; (**C**) = 1064 nm; Q-switch Nd-YAG nano; (**D**): 1064 Nd-YAG.  
The same interpretation can be obtained from the image relating to the exposure to the 1064-nm Q-switch Nd:YAG nanosecond pulse laser, which did not seem to notably change the surface morphology of the grade 4 titanium sample examined (Figure 3C), although a moderate alteration of the roughness also occurred in this case.  
On the contrary, in the portion of the sample treated with the 1064-nm Nd-YAG laser, significant morphological alterations can be seen. Indeed, melting-like effects on the metal surfaces are visible, which can reach deformations in the range of 3 mm (Figure 3D). Rounded and overlapped plates of about 0.5 mm in diameter with very irregular boundaries but with a smooth cup-shape covered all of the surfaces analysed.
# Transfer Learning Strategies for Deep Learning-based PHM Algorithms
## 4 Case, Transfer of Rotating Machinery Fault Diagnosis Algorithm  
### Data Introduction  
The bearing data of Case Western Reserve University are used. The bearing test device is shown in Figure 4. On the left is a two-horsepower three-phase induction motor (model: 2 HP IQPreA alert Reliance Electric, Milwaukee, Wisconsin, US), on the right are a power meter and an electronic control unit (not shown), which are connected by a middle torque sensor. In the test, the acceleration sensor is installed via magnetic mount on the drive end at 12 o'clock, the fan end at 12 o'clock and the motor support chassis respectively. The test data is collected by the acceleration sensor and is a vibration signal that reflects the state of the bearing.  
The test bearings support the motor shaft, and single point faults are arranged on the inner ring, outer ring (6:00, 3:00 and 12:00 directions) and rolling element of the bearings respectively by EDM technology. The diameter of faults includes 0.007, 0.014, 0.021, 0.028 and 0.040 inches (1 inch = 2.54 cm). Vibration data were collected at 0, 1, 2 and 3 horsepower, and the frequency was 12 kHz. SKF bearings are used for 0.007, 0.014 and 0.021 fault diameters, and equivalent NTN bearings are used for 0.028 and 0.040 fault diameters.  
Drive end bearing and fan end bearing are two kinds of bearings. The drive end bearing adopts 6205-2 RS JEM, Z=9, double-sided seal deep groove ball bearings (SKF, Gothenburg, Sweden). The specific size is shown in Table 2.  
The fan end bearing adopts a SKF deep groove ball bearing 6203-2 RS JEM, Z = 8, double-sided seal. The specific size is shown in Table 3. Fan end bearing size (inch).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inside Diameter** & **Outside Diameter** & **Thickness** & **Ball Diameter** & **Pitch Diameter** \\ \hline
0.9843 & 2.0472 & 0.5906 & 0.3126 & 1.537 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Drive end bearing size (inch).  
Figure 4: Bearing test device.  
In this paper, the bearing fault diagnosis algorithm A1 is developed via using the vibration data of the bearing fault at the drive end under 0 horsepower loads. Then the obtained fault diagnosis algorithm A1 is transferred to the vibration data of the bearing fault at the drive end under 2 horsepower loads to verify the transfer effect of the algorithm under different working conditions. Finally, A1 is transferred to vibration data of fan end bearing fault under 0 horsepower loads to verify the transfer effect of algorithm between different objects.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 10 Future Research Directions
### Lightweight Cryptographic Solutions
#### 10.2.4 Privacy-Preserving Techniques  
These techniques aim to develop ML with built-in privacy-preserving mechanisms to handle sensitive data within smart grid communications. Techniques like federated learning with lightweight homomorphic cryptographic algorithms could be explored to ensure confidentiality while maintaining model performance. To protect user privacy while conducting critical analysis, smart grid privacy-preserving strategies include employing data anonymization and aggregation techniques. Furthermore, homomorphic encryption and secure multi-party computation allow for safe data processing and sharing without disclosing private information.
## 2 Research Background and Related Work  
### IFC-to-Shapefile Conversion  
In general, there are two approaches for BIM data to be converted to GIS, including IFC-to-CityGML conversion and IFC-to-Shapefile conversion. IFC-to-CityGML conversion mainly utilizes semantic-based methods to transfer semantic information between IFC and CityGML by creating intermediate data models (ontologies) between IFC and CityGML [25]. IFC-to-Shapefile conversion is mainly used by studies that are focusing on the integrated application of BIM and GIS to solve practical problems [16]. The IFC-to-CityGML conversion faces more challenges and the challenges are even more severe as compared with IFC-to-Shapefile conversion. Apart from the common geometry conversion problems, two extra problems confronting IFC-to-CityGML conversion include mapping of level of details (LoDs) and converting solid models to surface models as follows: (1) CityGML has defined five LoDs from LoD0 to LoD4 [26] and BIMForum also defined five levels of development (LOD) for BIM [27]. Unfortunately, LoDs and LODs cannot be simplified and mapped in a one-to-one manner. (2) Conversion from solid models to surface models has to be properly completed. For example, a wall represented by swept solid in IFC has to be represented by two pieces of individual surfaces in CityGML, one for interior wall and the other for exterior wall. Such a conversion is more challenging than the conversion between solid models such as IFC-to-Shapefile conversion. The difference between a surface model and a solid model is shown in Figure 2, using a house model. In addition, the degree of challenge increases dramatically when converting IFC to higher CityGML LoDs. By far, IFC models can be automatically converted to valid LoD3 models [18].  
IFC-to-Shapefile conversion is theoretically easier to realize than IFC-to-CityGML conversion, as Shapefile uses solid models. In addition, it is more practical, as shapefile is a native format of ArcGIS, which is the most frequently involved GIS platform in integrated application of BIM and GIS [28], which means shapefile datasets can be directly used by ArcGIS while CityGML datasets need to be first converted.  
Figure 2: A house model in (**a**) CityGML and (**b**) Shapefile.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 2 Types of Positioning Technologies
### Simultaneous Localization and Mapping (SLAM)
In that sense, previously derived forest map information such as stem maps derived from earlier airborne LiDAR acquisition, could also potentially be used for localization if that prior spatial information is present on the vehicle or machine's onboard computer.
# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
## 3 Materials and Methods
### NodeVector
We use four-level depth neural network (DNN) to generate node vector representations. Figure 3 shows the graphical representation of the designed depth neural network. The first level is the input level for positive or negative node pairs. We use multi-label one-hot encoding for node input representation in the neural network. That is, the input layer works with \(L_{1}\) level node representation (see Section 3.5). At this level, we use two node inputs. The first input node is called the target node and the second input node is called the context node. We use all the nodes in the network one by one as the target node. Against each target node, positive and negative sample nodes created by our sampling algorithm are used as context nodes. The second level of the DNN named EncoderLevel is a fully connected dense layer with glorot uniform initialization and the Rectified Linear Unit (ReLU) activation function. Let us denote the embedding size for each node vector representation in network \(G^{\prime}\) with \(ES_{G^{\prime}}\) and the number of nodes in network \(G^{\prime}\) with \(NS_{G^{\prime}}\). The input size of the EncoderLevel is equal to \(NS_{G^{\prime}}\), and the output size is equal to \(ES_{G^{\prime}}\). EncoderLevel basically converts the high-level node representation to the low-level node representation. We define only one EncoderLevel and use it to convert the target and the context input layers. We design the third level of the DNN to merge the low-level representation of the target and context nodes produced by the EncoderLevel. At this level of DNN, we use the maximum operator to merge node representations. The last level of the DNN is a fully connected dense layer with glorot uniform initialization and the sigmoid activation function. The input size of this layer is equal to embedding size \(ES_{G^{\prime}}\), and the output size to 1. This level converts the merged embedding to the true/false (interaction/no interaction) information. With this method that we develop, the nodes in the target network are initially represented by their \(L_{1}\) level neighborhoods. As a result, a low-dimensional representation vector of each node in the network is created with the interaction information between the nodes, which is created by analyzing the information of the entire network, the node, and its neighbors. Our algorithm can be applied to any type of network and enriched with network-specific analysis. Algorithm 2 presents the pseudocode of our network node vectorization method NodeVector.
## 3 Results
### Study Characteristics
#### 3.2.2 Geographical Distribution  
Studies were conducted across multiple regions:  
* Spain: 15.83%.
* Malaysia: 11.87%.
* USA: 11.86%.
* Italy: 9.91%.
* Japan: 9.91%.
* China: 7.91%.
* UK: 7.05%.
* Australia: 5.95%.
* Portugal: 5.89%.
* Turkey: 5.89%.
* Denmark: 3.94%.
* France: 3.94%.  
The geographical distribution analysis revealed a significant research imbalance. European countries (Spain, Italy, UK, Portugal, Denmark, France) account for approximately 46.5% of all studies, while North America (primarily USA) represents 11.86%. Together,  
Figure 2: Distribution of studies by year.  
these regions comprise 58.36% of the existing research. When including other developed regions like Japan and Australia, this figure rises to approximately 74.22%.  
Figure 3 presents the distribution of studies across different countries. This bar graph highlights the countries where the majority of studies were conducted.  
This geographical concentration presents a notable limitation in our understanding of ventilation strategies in educational facilities. Developing countries, particularly those in tropical and cold climate zones, are severely underrepresented despite facing unique challenges related to school ventilation, energy consumption, and indoor air quality. The predominant focus on universities rather than K-12 institutions further narrows the applicability of existing research.
## 6 Architectures Comparison
### Components Comparison  
The components of the two ACN architectures can be categorized into three distinct layers to facilitate a comprehensive understanding of their respective architectures.  
1. **Sensing layer**: The sensors used in each of the systems are similar. As shown by Table 4, a difference exists between the two architectures. The [PERSON] et al. [27] architecture utilizes fewer sensors but is based on a deep learning model where the user input is required to confirm the model prediction, while the second architecture [28] uses all the sensors mentioned above, and every time the values of these sensors cross the defined threshold, the user is asked to confirm the accident. The first model is based on artificial intelligence (AI) and automation prediction, while the second model is more manual and inaccurate as it will generate more alerts. In the first paper, the sensors are connected to the ECU using various communication technologies, e.g., Bluetooth, WiFi, universal serial bus (USB), CAN, ethernet, and BLE.
2. **Communication layer**: This layer is used to communicate with the vehicles and infrastructure outside the car. As reported in Table 5, the architecture proposed by [PERSON] et al. [27] uses telematics with 3G/4G and cellular services to connect to the outside V2X. This layer also includes networking protocols like BLE, Bluetooth, ethernet, WiFi, and cellular to communicate with in-vehicle sensors and other parts. Bluetooth communicates with the sensors and ECU, ethernet with the camera and ECU, and the CAN bus with the display. On the other hand, the architecture proposed  
Figure 6: [PERSON] et al. [28] architecture.  
by [PERSON] et al. [28] uses this layer to create an ad hoc network dynamically to use V2X to transfer the data to the edge. There the data are refined, filtered, and cached, and face detection takes place to recognize the number of passengers and check the injuries. The communication layer then uses TCP to send the data to the cloud. A comparison of the network protocols used in the above papers is given below.
3. **Cloud layer**: This layer in both architectures is used to contact the emergency services when an accident is detected and confirmed with the user. In the first paper [27], this layer is used to install the deep learning model. It checks if the accident has taken place by analyzing the data provided by the sensors and other mounted devices, and then contacts the emergency services with all the vehicle data and the prediction from the model. The second paper [28] uses this unit to accept the accident alert notification to contact the emergency services. It handles receiving data packets, storage, and visualization, and shares the statistics with the authorities to implement accident countermeasures.
## 2 Transmission Line Ice Cover Monitoring System
### Wide-Domain Vision Convolution Backbone Network
Assuming the input dimensions are \(H\) and \(W\), where \(H\) is the image height and \(W\) is the image width, the output feature size of the stem layer is \(\frac{H}{4}\times\frac{W}{4}\times C1\), where C1 is the number of channels after dimensionality increase. The Stage layer is composed of stacked RepLK Blocks and ConvFFNs. The RepLK Block includes normalization layers, \(1\times 1\) convolution, depthwise separable convolution, and crucial residual connections; ConvFFN replaces the fully connected layers with 1 \(\times\) 1 convolution and employs inter-layer residual connections. The Transition layer mainly utilizes PW convolution and DW convolution with a stride of 2 for image down sampling. Moreover, utilizing a large kernel backbone network can achieve a greater receptive field with fewer layers while also providing higher shape variance, as shown in the following formula:  
\[RF_{n}=RF_{n-1}\times S_{n}+(K_{n}-1) \tag{5}\]  
The above equation is the receptive field calculation formula, where \(RF_{n}\) is the receptive field size at layer \(n\), \(RF_{n-1}\) is the receptive field size at layer \(n-1\), \(S_{n}\) is the stride of layer \(n\), and \(K_{n}\) is the kernel size at layer \(n\). It is evident that there is a direct relationship between the convolution kernel size and the receptive field; larger convolution kernels can cover a broader range of the input image, allowing each neuron to perceive more extensive local information. This capability enables better capture of shape information in images, enhancing the model's representational ability to better handle complex scene detection.
###### Abstract  
The metaverse, where users interact through avatars, is evolving to closely mirror the real world, requiring realistic object responses based on users' emotions. While technologies like eye-tracking and hand-tracking transfer physical movements into virtual spaces, accurate emotion detection remains challenging. This study proposes the \"Face and Voice Recognition-based Emotion Analysis System (EAS)\" to bridge this gap, assessing emotions through both voice and facial expressions. EAS utilizes a microphone and camera to gauge emotional states, combining these inputs for a comprehensive analysis. It comprises three neural networks: the Facial Emotion Analysis Model (FEAM), which classifies emotions using facial landmarks; the Voice Sentiment Analysis Model (VSAM), which detects vocal emotions even in noisy environments using MCycleGAN; and the Metaverse Emotion Recognition Model (MERM), which integrates FEAM and VSAM outputs to infer overall emotional states. EAS's three primary modules--Facial Emotion Recognition, Voice Emotion Recognition, and User Emotion Analysis--analyze facial features and vocal tones to detect emotions, providing a holistic emotional assessment for realistic interactions in the metaverse. The system's performance is validated through dataset testing, and future directions are suggested based on simulation outcomes.  
Keywords:artificial intelligence; emotion recognition; facial expression analysis; voice analysis; facial-voice matching
# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 3 Results
### Classification of Pesticide Concentrations in Fruits with Multivariate Analysis Methods
#### 3.3.1 Plum
Likewise, in Figure 8, the data acquired by the E-tongue are projected from the organic sample to concentrations of 3 mg/kg, with a variance of 99.69% (Figure 8A) for Amistar and 98.88% (Figure 8A) in the Funlate for PC1. Figure 8A illustrates that the organic fruit samples are clearly separated from the samples treated with different concentrations of Amistar. In the case of organic plums and plums treated with Funlate and Amistar pesticides, it can be seen from the responses in Figures 7 and 8 that the E-tongue is more effective than E-Nose in discriminating between organic fruits and fruits treated with different  
Figure 8: Discrimination and classification plots of pesticide concentrations and plum using E-tongue: (**A**) PCA of Amistar, (**B**) PCA of Funlate, (**C**) LDA of Amistar, and (**D**) LDA of Funlate. Figure 7: Discrimination and classification plots of pesticide concentrations and plum using E-nose: (**A**) PCA of Amistar, (**B**) PCA of Funlate, (**C**) LDA of Amistar, and (**D**) LDA of Funlate. concentrations of pesticides. Therefore, the E-tongue provides a better separation between the groups, indicating that there is greater sensitivity and precision in detecting changes in the chemical profiles, where the E-nose shows more variability in the data, especially at higher concentrations.
## 4 DE-KELM-Based Gearbox Fault Classification
### Method Verification Experiments  
To demonstrate that the proposed DE-KELM has higher fault classification accuracy than basic KELM without parameter optimization, contrast experiments were conducted on the same experimental data. The experimental procedure is shown in Figure 12.  
The experimental data included bearing vibration signals under a normal condition and three faulty conditions, i.e., inner ring fault, outer ring fault, and roller fault. The data were measured from an experimental platform, illustrated in Figure 13, of the Case Western Reserve University Bearing Data Center [49]. The approximate motor speed range of measurement was 1730-1797 rpm, and the load of motor was from 0 hp to 3 hp. Practical details of this experimental platform can be referred to in [49; 50]. One hundred samples were taken randomly from each operating condition to form an experimental dataset of 400 samples. Among this dataset, 90% data of each condition were selected randomly for training and the remaining 10% were used for testing. Accordingly, the training set included 360 samples and the testing set had 40 samples. The 400 samples and their corresponding classification labels are shown in Figure 14.  
Figure 12: Contrast experiment of DE-KELM-based and KELM-based fault feature classifications.  
Figure 13: Experimental platform of Case Western Reserve University Bearing Data Center [49].  
From Figures 15 and 16, it can be seen that on the training set, the classification accuracy by DE-KELM was 100% and that by KELM without parameter optimization was only 88.6%. On the testing set, the classification accuracy by DE-KELM was 97.5%, higher than the 90% that was achieved by KELM. From Tables 7 and 8, misclassified sample numbers can be seen. Only one roller fault sample in the testing set was wrongly recognized by DE-KELM, while 4 in the same fault type were misclassified by the basic KELM.  
The above contrast experiments verify that KELM with parameter optimized by DE can improve classification accuracy.