esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.2 Conversion of 3D Images to 2D Images and Data Augmentation  
The final image obtained through preprocessing is a 3D image. Because the model accepts 2D images as input, each 3D image was converted to a 2D image. The 3D image was segmented into 91 pieces in the axial direction, and 28 pieces corresponding to the middle were selected. Consequently, 28 2D images were obtained for each subject. Data augmentation techniques were applied to maximize the dataset size and prevent overfitting. Specifically, we resized every image to 224 \(\times\) 224 pixels as the input for the ViT model. For data analysis for the experiment, a MW Digitbox (single processor 4 GPU) located at the Neuroscience Translational Research Solution Center (Busan, Republic of Korea) was used.  
Transfer learning can alleviate the scarcity of training samples. Although transfer-trained models are known to be less sensitive to sample size [14], the sample size still affects transmission performance. Accordingly, we applied data augmentation to increase the amount of training data [36]. Data augmentation is a technology that increases the amount of data through various algorithms using machine learning and deep learning techniques. We applied only image rotation among the affine transforms, considering that amyloid plaques were identified in the entire brain when diagnosing AD with FBB images. Rotations of \(\pm 5^{\circ}\), \(\pm 10^{\circ}\), and \(\pm 15^{\circ}\) were applied to each original image.  
Test set was selected separately as BAPL scores 1, 2, and 3 indicated in Table 2a, and train/validation set was configured in consideration of the ratio of BAPL scores 1, 2, and 3 in Table 2b. The original and augmented datasets were constructed as shown in Table 2c. For the test data, 30 subjects were selected, with 10 subjects representing AD, MCI, and HC. Based on the test data, the original dataset was allocated according to a 6:2:2 ratio. The data were randomly extracted to configure the training and validation sets, with the test set for the augmented data prepared equivalently to that for the original data. The augmented data was configured by randomly extraction by applying \(\pm 5^{\circ}\), \(\pm 10^{\circ}\), and \(\pm 15^{\circ}\) to the remaining data except the test data.  
Figure 2: Data preprocessing. (**a**) Match: match CT and PET images into one; (**b**) Spatial Norm: spatial normalization. With the matrix information obtained by matching the CT to the CT template, the shape of the PET is also matched to the CT template; (**c**) Skull stripping: Remove the skull from the matched image; (**d**) Count norm: count normalization. Divide the total pixels by the cerebellum value.
## 3 Experimental Setup and Dataset
It can be seen that the jammer attack frequently appears at the right-hand side of the sensor's active area. Moreover, when the jammer's incident angle exceeds the lens's angle of view, the jammer distribution shows a non-circular/distorted shapes as in LG\({}_{05}\), HG\({}_{02}\), and HG\({}_{21}\) at 0 dB SJR. The recorded images have been used for the training and testing of CNN-based classifier, such that 70% were used in the training phase whereas the remaining 30% are for testing the classifier. It worths noting that to predict the jammer's DoA, another dataset was built that considers the 8-ary LG modes only. This generates a dataset of 28,800 frames for the left- and right-hand jammer's DoA. In the next sections, the obtained accuracies for both M-ary modulation classification and DoA determination are discussed. Figure 4: Measured beam profiles of left-hand DoA jammer at SJRs of (**a**) \(-5\) dB, (**b**) 0 dB, and (**c**) 3 dB. Figure 3: Measured beam width diameters using D4\(\sigma\) method for (**a**) Jammer, (**b**) LG modes, (**c**) Mux-LG modes, and (**d**) HG-modes.
## 2 Literature Review  
VR is a simulation of an environment or computer-generated VE that allows participants to experience a place or event differently than where they are physically present; a flight simulator is an early example of VR technology [24]. In 1838 [PERSON] featured two mirrors positioned at a 45\({}^{\circ}\) angle to the user's eye, each reflecting an image located offsite as it was the first concept to provide VR a sensory feeling of immersion [25]. In 1950 Sensorama was the first sensory display invented by [PERSON] [26]. It was a scripted arcade-like experience, and after 11 years, he also invented the first head-mounted display (HMD) prototype that provided a stereoscopic image with stereo sound. However, there was no interactive response or motion tracking. According to previous studies, VR commercial development began in 1988, but in 1991, the first commercial VR entertainment system was unveiled called \"Virtuality\" [27]. In 1992, [PERSON] defined VR as a type of human experience enabled by the sensation of being present in a given environment [28].  
VR is used in many applications because of technological advancements in medical sciences, video gaming, cinema and entertainment, education and training, engineering, architecture, and urban planning. [PERSON] designed the prototype of the Oculus rift, which had the capability of rotational tracking [29]. In 2015, HTC and Valve corporation collaborated on developing the HTC VIVE VR headset and motion controller, and both were built on Valves' steam VR platform. Novel positional tracking technology was introduced in this release, which used infrared light and specially designed wall-mounted base stations to track the user's location. At the start of 2017, Sony developed a similar tracking system for PlayStation VR and used the same technology to create a wireless headset. In 2019, the standalone headset Oculus Quest and the Oculus Rift S were launched by Oculus. These headsets used inside-out tracking, which differed from the outside-in tracking used in earlier headsets [30]. Later in 2019, Valve introduced notable features of a 130\({}^{\circ}\) field of vision and off-ear headphones for comfort and immersion. These open-handed controllers support individual finger tracking, front-facing cameras, and a front expansion slot designed for extensibility [31]. Oculus introduced the Oculus Quest 2 in 2020 with improved performance, a lower price, and a better screen. To use this new headset, Facebook users must sign in using a Facebook account [32]. In 2021, the European Union Aviation Safety Agency (EASA) approved the first Flight Simulation Training Device based on VR. The device makes rotorcraft pilots safer by letting them practice dangerous moves in a virtual environment [33]. As COVID-19 regulations were enacted in 2020 and 2021, the virtual reality industry witnessed a rapid boom.
### The Information-Driven Approach  
PARC has developed an information-driven dynamic sensor collaboration for target tracking application [3; 25]. In order to achieve energy-efficient computing, it selects the next node which most likely improves the tracking accuracy based on both the information constraints and constraints on cost and resource consumptions. This application is built on directed diffusion as the communication medium.  
Specifically, the approach formulates the tracking problem as a sequential Bayesian estimation problem. In order to estimate an unknown, each new sensor measurement is combined with the current estimate to improve the estimation accuracy. Therefore, the problem of selecting the next sensor can be formulated as an optimization problem. The sensor selection is based on the amount of information so as to maximize the information that the selected sensor would obtain. There can be only one sensor active at any given time doing the sensing, which proves to be very energy efficient on the sensor network.  
The challenge is that in order to avoid communicating redundant or less useful information, the next node selection has to be done without explicit knowledge of the measurement. The decision has to be made solely based on the sensor characteristics of the current node and the _predicted_ contribution of other sensors.
# Conceptual Design and Preliminary Verification of Distributed Wireless System of Weigh-in-Motion
###### Abstract
With the progress of electronic component technology, the self-powered wireless sensor technology is of great concern, which is achieved through harvesting energy from the surrounding environment [23; 24]. At present, a lot of research focuses on micro/nano-devices for bioengineering [25]. With the development of artificial intelligence technology and its penetration into various fields, the concept of an intelligent expressway has been raised. [PERSON] et al. [26] proposed the concept of a self-powered wireless sensor applied to a smart pavement to monitor traffic flow. [PERSON] et al. [27] proposed a novel approach using a self-powered wireless sensor for the detection of bottom-up cracking in asphalt concrete pavements which can precisely inspect the entire process of the cracks' development. [PERSON] et al. [28], through an integrated piezoelectric sensor interface, integrated signals based on the information of dynamic physical quantities and energy harvested to supply the sensors and implemented it on a simulated weigh-in-motion application. [PERSON] et al. [29] utilized a piezoelectric transducer to convert the surrounding mechanical vibration energy into electrical energy, realizing a self-energy supply of sensors for wireless vibration monitoring. Obviously, this self-powered wireless sensor can greatly reduce the cost of battery replacements and make full use of the convenience brought by the rapid development of communication technology. It is also an important frontier technology in the field of intelligent transportation. This paper proposes a conceptual design of a self-powered WIM technology without damaging the road integrity. This phenomenon benefits from the electromechanical coupling characteristics of piezoelectric materials to collect the road strain energy caused by traffic loads. Multiple sensors could also be arranged in the driving direction of vehicles to achieve the whole dynamic wheel load of driving vehicles. By optimizing the number and layout of sensors, machine learning could be used to analyze vehicle speed and axle load inversely [30], hence achieving a new type of distributed self-powered wireless WIM system.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 4 Intrusion Detection and Prevention Systems
### Clustering Techniques for the Intrusion Detection System  
Recent academic literature [24; 38] has explored multiple clustering of intrusion detection systems. There are many different techniques we can use in this domain. The hierarchical clustering (HC) algorithm is one of the most popular approaches. A clustering algorithm in IDS intends to generate a cluster hierarchy. Hierarchical clustering can be classified into two categories: divisive and agglomerative approaches. First, the divisive approach is a top-down HC algorithm. The cluster with the significant amount of relative feature breathing space distance is selected and assorted using binary subclustering techniques. In other words, it starts in one cluster for all observations, and the divisions are recursively performed while moving down the hierarchical structure. Next, the agglomerative approach is a bottom-up HC algorithm where clusters are further broken down into sub-clusters, which are then broken down again into sub-clusters, and pairs of clusters are joined at one level above in the hierarchical structure. In fact, several bodies of work have been conducted applying unsupervised learning algorithms in the vicinity of the cyber-physical control system with malicious attack detection and mitigation. For example, a redundancy-based flexibility methodology was projected in [24], where a dyed-in-the-wool network sub-layer that can grip the circumstance was anticipated through data mining methods. This sub-layer regularly carries information from the nodes of a driver controlled in the network by itself and identifies differences in observations.
###### Abstract  
Sandy areas occupy a huge amount of land worldwide, but due to their characteristics, they are mostly low in fertility and low in organic matter. Sandy soils have coarse texture, high saturated hydraulic conductivity, low soil organic carbon, and poor aggregate stability and water retention capacity; therefore, it is necessary to add organic additives to them. The objective of this study was to assess the effect of particle size and application rate of biochar (BC) produced under different pyrolysis temperatures on the porosity \(P\), available water content for plants AWC, saturated hydraulic conductivity \(Ks\), and contact angle CA of sandy soil. The results show that an application of BC to sandy soil significantly increased AWC by 76-168%, CA by 252-489%, \(P\) by 6-11%, and significantly reduced \(Ks\) by 37-90%. Statistical analysis of the effect of three examined factors (BC application rate, particle size, and pyrolysis temperature) revealed that \(P\), AWC, and \(Ks\) were affected by all three factors, while CA was affected only by BC application rate and particle size. The statistically significant interaction between the two factors was found for \(P\) (temperature \(\times\) rate and size \(\times\) rate), AWC (temperature \(\times\) size), and \(Ks\) (size \(\times\) rate). Statistically significant interaction among the three factors was not found for any hydrophilic parameter. The application of BC to amend sandy soils can be seen as a strategy to mitigate drought conditions and to reduce the amount of irrigation, saving water. Further investigations are needed with regard to the BC application under climate conditions with long hot and dry periods, which may promote soil water repellency.  
biochar; sandy soil; water repellency; soil water retention; saturated hydraulic conductivity +
Footnote †: journal: Applied Physics Letters
###### Abstract  
Exploring the content of a video is typically inefficient due to the linear streamed nature of its media and the lack of interactivity. While different approaches have been proposed for enhancing the exploration experience of video content, the general view of video content has remained basically the same, that is, a continuous stream of images. It is our contention that such a conservative view on video limits its potential value as a content source. This paper presents An Alternative Representation of Video via feature Extraction (RAAVE), a novel approach to transform videos from a linear stream of content into an adaptive interactive multimedia document and thereby enhance the exploration potential of video content by providing a more engaging user experience. We explore the idea of viewing video as a diverse multimedia content source, opening new opportunities and applications to explore and consume video content. A modular framework and algorithm for the representation engine and template collection is described. The representation engine based approach is evaluated through development of a prototype system grounded on the design of the proposed approach, allowing users to perform multiple content exploration tasks within a video. The evaluation demonstrated RAAVE's ability to provide users with a more engaging, efficient and effective experience than a typical multimedia player while performing video exploration tasks.  
Keywords: multimedia analysis; video representation; multimodal video processing; human media interaction +
Footnote †: journal: Applied Sciences
## 5 Result Analysis
### Comparison of Different Authentication Schemes  
Compared with the single authentication or group authentication used in the literature [14; 27; 28; 29; 30], this scheme uses mixed authentication, which combines single authentication with group authentication, uses group authentication to improve authentication efficiency, and adopts single authentication to find illegal nodes in the group (Table 6). At the same time, blockchain is introduced into the scheme to realize the decentralization of the authentication scheme and effectively solve the problem of a single point of failure. By introducing the MHT algorithm, the time cost is controlled at O(logn), which improves the authentication efficiency. The main contributions of this paper are described as follows. A group authentication scheme is proposed with blockchain spatiotemporal big data. The decentralization of the blockchain is performed to tackle the single point of failure. A single authentication is adopted to accurately find illegal nodes, and group authentication is combined with individual verification.
###### Abstract  
Due to the massive multipath effects and non-line-of-sight (NLOS) signal receptions, the accuracy and reliability of GNSS positioning solution can be severely degraded in a highly urbanized area, which has a negative impact on the performance of GNSS/INS integrated navigation. Therefore, this paper proposes a multipath/NLOS detection method based on the K-means clustering algorithm for vehicle GNSS/INS integrated positioning. It comprehensively considers different feature parameters derived from GNSS raw observations, such as the satellite-elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency to effectively classify GNSS signals. In view of the influence of different GNSS signals on positioning results, the K-means clustering algorithm is exploited to divide the observation data into two main categories: direct signals and indirect signals (including multipath and NLOS signals). Then, the multipath/NLOS signal is separated from the observation data. Finally, this paper uses the measured vehicle GNSS/INS observation data, including offline dataset and online dataset, to verify the accuracy of signal classification based on double-differenced pseudorange positioning. A series of experiments conducted in typical urban scenarios demonstrate that the proposed method could ameliorate the positioning accuracy significantly compared with the conventional GNSS/INS integrated navigation. After excluding GNSS outliers, the positioning accuracy of the offline dataset is improved by 16% and 85% in the horizontal and vertical directions, respectively, and the positioning accuracy of the online dataset is improved by 21% and 41% in the two directions. This method does not rely on external geographic information data and other sensors, which has better practicability and environmental adaptability.  
GNSS/INS tightly coupled system; multipath/NLOS detection; K-means clustering algorithm; urban areas +
Footnote †: journal: 17 July 2022
## 3 Proposed Scheme  
To protect the SS data privacy, an FL-enabled SS framework is applied, where the spectrum spectrum-sensing data isdata are trained locally, and the updated model parameters are transmitted to the server for global model aggregation. Traditionally, to achieve better training performance, the complexity of the model is getting higher and higher, leading to a huge communication overhead in FL. To reduce the communication overhead during FL, model compression methods are widely applied where the local model is compressed before updating to the server. The traditional model compression method, such as principal component analysis (PCA), directly reduces the feature space and abandons the local feature, resulting in the loss of model information. Compared to the traditional model compression methods, auto-encoder achieves efficient reduction and reconstruction of high-dimensional data through the structure of encoder and decoder, can automatically learn effective representations based on data characteristics and adapt to different types of data compression requirements, and is applied in this paper to alleviate the communication overhead. In the proposed framework, the SS nodes firstlyfirst train the local CNN model, the updated model parameters are then encoded with an local auto-encoder, the encoded local model parameters are uploaded into the central server, the server decodes the received local model parameters, and a global model is aggregated finally. The overall workflow is shown in Figure 1.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 4 Case Study
### SCLM Control in Transient Condition
#### 4.4.2 Line Overload (120%) Case  
When a 1395-1415 1 CKT contingency occurs, the overload of the remaining line will be 123.42%. Like the previous case, the overload is solved through the SCLM control, and then the operating point for minimum loss is determined. The change in the operating point of the HVDC after the SCLM control and the result of the system control are shown in Tables 9 and 10 and Figures 12-15. Additionally, in this case, there are no overload lines, and the maximum/minimum voltage of the system is 1.059/0.984 at the 154 kV level, and 1.039/0.994 at the 345 kV level, which also meets the voltage criterion in Table 2.  
Figure 12: Active power operation change in the line overload case.  
Figure 13: Reactive power operation change in the line overload case at the rectifier end.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Normal Operation** & **Conventional Control** & **RAS Control** & **SCLM Control** \\ \hline AC system losses & 1564.38 MW & 1575.28 MW & 1573.28 MW & 1568.02 MW \\ \hline DC system losses & 4.72 MW & 4.72 MW & 7.20 MW & 6.62 MW \\ \hline Total losses & 1569.10 MW & 1580.00 MW & 1580.48 MW & 1574.64 MW \\ \hline \% Loading & 62.98\% &
\begin{tabular}{c} 122.27\% \\ (Security violated) \\ \end{tabular} & 116.17\% & 119.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of system losses by operating change (line overload case).