esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Flight Maneuver Recognition
### Typical Load State Recognition  
During aircraft operation, it is almost impossible to compile load spectra for all possible states due to the rapid changes in load status [25]. Therefore, this paper adopts a multi-parameter statistical combination and induction method to identify transient load states with significant impacts. After selecting 1 h takeoff and landing data, based on the classification results of typical load states, six typical load states are identified: symmetric subsonic low altitude small angle of attack (M1), symmetric subsonic low altitude large angle of attack (M2), symmetric subsonic medium altitude small angle of attack (M3), symmetric subsonic medium altitude large angle of attack (M4), symmetric subsonic high altitude small angle of attack (M5), and symmetric subsonic high altitude large angle of attack (M6). Identification is based on thresholds of several typical flight parameters, and these datasets will serve as inputs for the flight parameter neural network model in our work. Additionally, strain data measured at different positions during virtual ground calibration testing are also included as inputs. The following are the characteristic values of the selected identification results for these six load states, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rolling Angular** & **Ground Speed** & **Altitude (m)** & **Inertial Attack** & & \\
**Rate (deg/s)** & **(m/s)** & **Altitude (deg)** & **Angle (deg)** & **Strain (\(\mu\epsilon\))** & **Status Category** \\ \hline
0.52 & 199.44 & 1438 & 2.77 & 107.69 & M1 \\
0.27 & 83.44 & 1577 & 10.79 & 388.81 & M2 \\ \(-\)0.02 & 211.06 & 4197 & 4.04 & 287 & M3 \\
## 4 Results and Discussion
### Initial Simulation  
We ran the simulation model for the first time using the values listed in Table 1. According to the results, depicted in Figure 3, the mechanical failure occurs in hour 3463. Then, the process of replacing the damaged part and repairing the machine begins. If the spare part needed is in stock, the production disruption lasts 68 h, i.e., 20 h for technical inspection and machine dismantling and 48 h for part replacement. Additionally, during the disruptive event, the level of finished goods inventory decreases, yet it does not compromise scheduled deliveries, since the disruption lasts only for a short time.  
Conversely, if the spare part needed to fix the machine is not in stock (Figure 4), the spare part procurement process begins, comprising the tasks and times listed in Table 1. In this case, the production disruption lasts 716 h. The level of finished goods inventory drops to 0, but then, it gradually stabilizes again.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Values without Spare Part** & **Values with Spare Part** & **Units** \\ \hline Repair Time & 12–72 & 12–72 & Hours \\ Raw Material Inventory & 300,000 & 300,000 & Kg \\ Finished Goods Inventory & 100,000 & 100,000 & Bottles \\ Weekly Customer orders & 600,000 & 600,000 & Bottles \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
Figure 3: Time of disruption in production line with spare parts in inventory.
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## Problem Formulation
### Complexity  
The admission control with NC is divided into sub-problems and then jointly optimized together. The complexity depends upon the highest complexity in our sub-algorithms. In terms of time cost, the approximated big O of ATPSN branch-cut is \(O(log(n))\), where n is the number of users. The MANC-batch and MANC-instant complexity with the second stage as lossless is \(O(X^{2}+n)\). While the complexity of the MANC-instant with second stage lossy is \(O(X^{3}+n^{2})\). So the complexity of the MANC-instant is greater then MANC-batch as the transmission sets are calculated again after each loss of the packet. The Time Complexity of the Gale-Shapley Algorithm is \(O(n^{2})\). The overall complexity of the JAMANC algorithm for stream and batch is defined (29) and (30) respectively, as:  
\[\text{Complexity (batch)}=max[O(log(n),O(X^{2}+n),O(n^{2})] \tag{29}\]  
\[\text{Complexity (stream)}=max[O(log(n),O(X^{3}+n^{2}),O(n^{2})]. \tag{30}\]
###### Abstract  
Fundamental similarities and differences between laser-driven plasma wakefield acceleration (LWFA) and particle-driven plasma wakefield acceleration (PWFA) are discussed. The complementary features enable the conception and development of novel hybrid plasma accelerators, which allow previously not accessible compact solutions for high quality electron bunch generation and arising applications. Very high energy gains can be realized by electron beam drivers even in single stages because PWFA is practically dephasing-free and not diffraction-limited. These electron driver beams for PWFA in turn can be produced in compact LWFA stages. In various hybrid approaches, these PWFA systems can be spiked with ionizing laser pulses to realize tunable and high-quality electron sources via optical density downramp injection (also known as plasma torch) or plasma photocathodes (also known as Trojan Horse) and via wakefield-induced injection (also known as WII). These hybrids can act as beam energy, brightness and quality transformers, and partially have built-in stabilizing features. They thus offer compact pathways towards beams with unprecedented emittance and brightness, which may have transformative impact for light sources and photon science applications. Furthermore, they allow the study of PWFA-specific challenges in compact setups in addition to large linac-based facilities, such as fundamental beam-plasma interaction physics, to develop novel diagnostics, and to develop contributions such as ultralow emittance test beams or other building blocks and schemes which support future plasma-based collider concepts.  
plasma physics; accelerators; electron beams; light sources; photon science 2019
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 4 LQA-AODV Routing Protocol  
In this section, a multipath routing protocol utilizing both RSS and SINR metrics is presented. The protocol is labeled as Link Quality Aware Ad-hoc On-Demand Distance Vector (LQA-AODV). In other words, designing energy-efficient algorithms that uses SINR to derive the performance (e.g., packet error rate, PER), and RSSI can be used to make decisions about dynamic communication radius when establishing network connectivity. However, incorporating these metrics into the existing routing protocols would require some modifications as explained in the following subsections.
## 2 Materials and Methods
### Experimental Design  
During the process, at intervals of every 24 h until the end of the drying phase and every 48 h until the end of the ripening phase, a sample of both SM and SR batch was collected to monitor the pH and temperature values (each measurement was performed in triplicate, taking the mean value as the result). The change in water content throughout the ripening process of the salami was assessed by tracking weight loss. Three salamis per batch were weighted at the beginning, after the drying phase, and at the end of ripening phase. The findings are expressed as a percentage of the initial weight. During the process, a visual evaluation was performed to assess the uniformity and the potential presence of macroscopic defects, such as irregularities in color, texture, or the presence of any physical abnormalities, such as air pockets, atypical molds, or discoloration.  
After the ripening, the surface molds on the salamis were scrubbed away using hot water jets. Microbiological, physicochemical, texture, and color analysis were conducted on the edible portion of six samples (three for each salami type, SM and SR) after removing the casing aseptically. The sensory analysis was carried out on slices of three salamis per batch by a quantitative descriptive sensory panel of 12 assessors.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Step** & \multicolumn{2}{c}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Humidity (\%)**} & \multicolumn{1}{c}{**Operating**} \\ \cline{2-5}  & **Min** & **Max** & **Min** & **Max** & **Time** \\ \hline Cooling & 4 & 7 & - & - & 24 h \\ Dripping & 23 & 24 & 96 & 98 & 24 h \\ Drying 1 & 23 & 24 & 62 & 67 & 24 h \\ Drying 2 & 20 & 22 & 60 & 65 & 24 h \\ Drying 3 & 18 & 20 & 65 & 70 & 24 h \\ Drying 4 & 16 & 18 & 70 & 75 & 24 h \\ Drying 5 & 14 & 16 & 75 & 80 & 24 h \\ Ripening & 12 & 14 & 80 & 90 & 28 days \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermo-hygrometric parameters of ripening rooms.
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 2 Methods  
From Section 1, network construction for semantic segmentations involves four basic parts--the encoder, the decoder, the center part and the skip connection. First, the encoder inputs images and extracts feature by stacking convolutional and pooling layers. Second, the decoder restores the detailed features and expands feature maps with deconvolutional layers. Third, the center part can be taken as a model for expanding the receptive field, creating ensembles of features and obtaining simultaneous information. Finally, the skip connection is responsible for reusing the feature maps to recover information from the decoder path. The key factor in terms of accuracy is the design of the encoder path and the decoder path. So, in Section 2, it is necessary to introduce the design of CNNs.
## 1 Introduction
Moreover, CNNs are also deployed to extract features of distorted images to replace the conventional approach of hand-crafted features for IQA research [1]. CNN-based IQA methods should be capable of capturing two quantities, such as distortion and image visual content. This mainly arises from the fact that most CNN-based IQA methods are oriented to image-visual content, which emulates the process of image recognition [14]. The designed IQA method should be capable of handling generalization as various distortions can coexist in an image from a set of vast image distortions. Along with it, deep IQA methods required a large quantity of images to train a neural network, usually comprising more than one million images [15]. In recent times, deep neural networks have shown great improvement in different research fields such as bioinformatics [16, 17, 18], agriculture [19, 20], and medical imaging [21, 22]. The deep IQA methods utilize convolutional operations by deploying deep neural networks to extract features from distorted regions of the image [1]. These IQA methods cannot yield accurate predictions on reality-based or authentic datasets, as such methods cannot learn the patterns and feature representations that occur in these distorted images. The distortions as induced in artificial datasets range from Gaussian blur, Gaussian noise, JPEG compression, etc. [15]. While distortions in the authentic datasets are generally caused by naturally occurring conditions. This can be evidently observed by examining the gradient maps of both types of datasets. The high-frequency components evident in the gradient map are object-oriented in the case of a naturally distorted image, while such components are distributed across the whole image for artificially distorted images [23]. A no-reference IQA approach that can combine both types of distorted images, is an effective solution because it is more versatile in nature. Multiple such IQA approaches countenance this issue, but still, these methods have various issues, i.e., in order to predict the image quality, high-level semantic features are utilized. Furthermore, such approachestry to combine the effects of both types of distorted images by using a bilinear pooling operation, which is inadequate for capturing the relationship between these two types of distorted images [24].
# Trusted Data Access Control Based on Logistics Business Collaboration Semantics
## 4 Construction of Access Control Model
### Semantic Access Policy Set
#### 4.3.1 SWRL Rule Structure  
Access control rules are formally specified by SWRL. The general structure of an SWRL rule is defined as follows:  
\(Rule=B1\Lambda B2\Lambda B3\Lambda B4\Lambda....Bi.......\Lambda Bn\to A,1\leq i\leq n\).  
This structure supports the formulation of rules where a series of conditions (B1, B2,..., Bn) lead to an assertion (A). In the context of access control, these rules can specify that if certain conditions regarding the subject's attributes are met, access to a specific object is either permitted or denied.  
In particular, to grant exceptional permission to the data requester in special scenarios, a \"privilege\" attribute is automatically added after each policy result of \"deny\" when the data owner formulates access policies. This attribute acts akin to a \"master key\", allowing data access under special conditions. Benefiting from this attribute, data requesters can be granted temporary and conditional access for the data if they deduce that they are in a special situation, even if their attribute set does not meet the access requirements. To validate the need for such privileged access, requesters are required to upload appropriate documentations demonstrating the urgency of their situation, and the data owner must review and approve them. In normal situations, system users have to follow the established general access control rules.
## 1 Introduction
### Advantages of Using CR in WSNs
#### Application Specific Spectrum Band Utilization  
Currently, the number of wireless sensors deployed for different applications has increased. In WSN, data traffic is usually correlated both temporally and spatially. When any event occurs, WSNs generate packet bursts and they remain silent when there is no event. These temporal and spatial correlations introduce to the design challenge of the communication protocols for WSN. With the intelligent communication protocols in CR-WSN, it is possible that the wireless sensors deployed for the same purpose use the spectrum of different incumbents in spatially overlapping regions. This is possible with cooperative communication among SUs, which obviously mitigates interference issues.
# Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition
## 4 Experiments and Results
### Experiments with Different Resolutions, Block Sizes, and Feature Encoding Methods
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**FeatDesc**} & \multicolumn{11}{c}{**UNN-GazeEAR @320 \(\times\) 180**} \\ \cline{2-13}  & \multicolumn{3}{c}{[4 \(\times\) 4 \(\times\) 6]} & \multicolumn{3}{c}{[8 \(\times\) 8 \(\times\) 6]} & \multicolumn{3}{c}{[16 \(\times\) 16 \(\times\) 6]} & \multicolumn{3}{c}{[32 \(\times\) 32 \(\times\) 6]} \\ \cline{2-13}  & \(T^{E}\) & \(T^{V}\) & \(T^{F}\) & \(V\) & \(T^{E}\) & \(T^{V}\) & \(T^{F}\) & \(V\) & \(T^{E}\) & \(T^{V}\) & \(T^{F}\) & \(V\) & \(T^{E}\) & \(T^{V}\) & \(T^{F}\) & \(V\) & \(T^{F}\) & \(V\) & \(T^{F}\) & \(V\) & \(V\) \\ \hline GROI-HOG [5] & 22 & 83 & 78 & **96.34** & **96.48** & 18 & 34 & 22 & 94.62 & 94.56 & 17 & 6 & 6 & 95.00 & 93.96 & 16 & 2 & 2 & 94.50 & 93.80 \\ SM-HOG & 76 & 112 & 148 & **98.46** & **99.80** & 96 & 25 & 51 & 96.44 & 97.58 & 91 & 9 & 9 & 95.38 & 95.34 & 91 & 2 & 2 & 95.38 & 95.34 \\ \hline GROI-HMG [5] & 22 & 96 & 81 & 95.40 & **96.08** & 18 & 22 & 24 & 93.82 & 92.60 & 17 & 6 & 6 & 94.28 & 92.28 & 16 & 2 & **95.74** & – \\ SM-HMG & 70 & 111 & 91 & **98.32** & **98.74** & 89 & 23 & 20 & 96.66 & 97.06 & 86 & 7 & 7 & 96.22 & 95.86 & 83 & 2 & 2 & 94.50 & 95.16 \\ \hline GROI-HOF [5] & 26 & 116 & 118 & 93.94 & 91.22 & 22 & 25 & 24 & 93.90 & **92.94** & 21 & 5 & 6 & **95.62** & 91.98 & 20 & 2 & 92.88 & – \\ SM-HOF & 77 & 91 & 112 & **99.28** & **99.20** & 92 & 20 & 37 & 97.16 & 95.54 & 90 & 6 & 9 & 95.50 & 95.34 & 84 & 2 & 93.38 & 93.32 \\ \hline GROI-MBHx [5] & 41 & 96 & 89 & 90.72 & 91.80 & 33 & 31 & 30 & 93.14 & **92.48** & 32 & 7 & **95.88** & 92.12 & 30 & 2 & 94.34 & – \\ SM-MBHx & 134 & 113 & 86 & **97.22** & 96.06 & 166 & 20 & 28 & 96.58 & **97.46** & 163 & 8 & 7 & 94.82 & 96.24 & 156 & 2 & 91.78 & 95.70 \\ \hline GROI-MBHy [5] & 41 & 120 & 58 & 93.74 & 91.46 & 33 & 31 & 41 & 94.66 & 91.20 & 32 & 7 & 6 & **95.78** & **93.54** & 30 & 2 & 95.20 & – \\ SM-MBHy & 134 & 125 & 130 & **98.98** & **96.68** & 166 & 23 & 17 & 97.30 & 96.50 & 163 & 6 & 5 & 94.42 & 96.26 & 156 & 2 & 2 & 95.22 & 92.86 \\ \hline \hline \end{tabular}  
* \(T^{V}\) and \(T^{E}\): time consumption of the VLAD (vector of locally aggregated descriptors) and Fisher vector (FV) feature encoding methods (measured in seconds); GROI: gaze-region-of-interest; SM: saliency map; HOF: histogram of optical flow; HOG: histogram of oriented gradients; HMG: histogram of motion gradients; MBH: motion boundary histogram.  
\end{table} 
Table 2: Comparison of different feature descriptors under down-scaled resolution.
# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
## 2 Our Approach
### 3-Dimensional Convolutional Recurrent Architecture  
This section describes a 3D-CNN-RNN architecture for classifying earthquake magnitudes. In literature, there are numerous combinations of CNN-RNN topologies to choose from; we would want to focus on the core of 3D-CNN, which is three convolutional layers. In addition, we also incorporate RNN layers into all the kernels of the third convolutional layer. Moreover, we extract information using a fully connected (FC) last layer to identify earthquake magnitudes and perform the classification task. We process features based on log-Mel spectrograms and provide the extracted features to the 3D-CNN. After applying 3D kernels succeeded by a max-pooling operation, as shown in Figure 2, the CNN is designed using three convolutional layers as a preliminary step. With the 3D filter, we create a receptive field of \(3\times 3\times 3\) in size. We further extract the feature maps using a \(3\times 3\) kernel to fetch the features in the spatial dimension, whereas the third dimension scans it along the time axis. Each convolutional layer has a max-pooling operation of \((1\times 2\times 1)\) for the first, \((2\times 2\times 2)\) for the second, and \((3\times 2\times 2)\) for the third.  
Stride, padding, and filter size are three of the most often used hyper-parameters for creating convolutional computations, and we choose the most optimal parameters for this study. The stride parameter specifies the size of the step taken by the receptive field each time. We downsample the inputs along each dimension while gradually extending the dynamic range using the max-pooling procedure. For each convolutional layer, a rectified linear unit is used as an activation function, where \(g(.)=max(0,.)\) represents the maximum value for an activation function. Rectified linear unit's (ReLU's) back-propagation rule removes any gradient components smaller than zero. Finally, batch normalization is performed to each convolutional layer to optimize the learning rate. We prevent overfitting by using a dropout rate of 0.3 in each convolutional layer. Weights are randomly initialized for the training phase using Xavier initialization [32]. The final convolutional layer has 16 filters; therefore, GRUs are used on each filter to keep the temporal information, yielding 16 GRUs. We downsample the log-Mel spectrograms from 50 temporal features to 13, creating a recurrent layer for each feature map. Finally, recurrent layers are concatenated and linked to the FC layer using a many-to-one architecture. In addition, we obtain a flattened tensor from the previously concealed hidden layer. With the help of the sigmoid layer, it transforms them into the required output and generates a 6-dimensional vector that represents the earthquake categories.
## 5 Innovative Approaches in ITS Cybersecurity
### Blockchain  
Blockchain is an extremely dynamic technology in recent times. With regard to ITS, one of its main applications is in anonymous authentication solutions in VANET. The use of distributed storage can be very suitable for storing data of the legitimacy of nodes. The nodes decide whether to admit a new participant in the communication based on its reputation. In this way, malicious nodes are discouraged. Another option for applying a blockchain is upper architecture layers as a secure data warehouse. Although some of the described examples present MANET networks, the simulation results can be considered to be applicable to VANET as a subtype of MANET [6; 22; 23; 24; 25].  
The authors of Reference [22] introduce the concept of \"shortest, most reputed path\" using the Ad hoc On-Demand Distance Vector (AODV) routing protocol for MANETs. They create a simulation, using Matlab, dividing the network into subnets in each of which there are mining nodes that monitor of the other nodes and add transactions to the blockchain. The blockchain contains information about the reputation of the nodes. The authors claim an approximately 12% improvement in overall packet delivery in the presence of routing attacks, compared to conventional routing algorithms in MANETs.  
The authors of Reference [25] discuss the general importance of security in IoT systems, focusing on MANET. They describe a future development (similar to Reference [21])--blockchain-based OLSR (Optimized Link State Routing Protocol), taking into account not only the node's reputation but also its energy level.  
In Reference [24] an overview of significant applications of blockchain technology and possible attacks is presented. To analyze the traffic behavior on the network, five virtual clients were created. The authors conclude that the problem of ensuring data security is not completely resolved. They emphasize the possibility of identifying traffic to blockchain technology using behavioral analysis and recommend hiding traffic and preventing the interception of traffic from this technology, including by behavioral analysis.  
Reference [23] offers a different application of blockchain for IoT--SEBS (Secure Element Blockchain Stratagem). It applies blockchain in the data layer, combining it with hardware secure elements in the sensor layer. The conclusion is that the proposition can increase the performance of critical security operations by 31 times, all while reducing computational and memory overheads.  
Reference [6] introduces blockchain with floating genesis block and its contribution to resolve the issue of continuously growing blockchain within the VANET/MANET networks. The authors offer a comparative analysis with other methods that reduce the time to decide on the connection of new nodes in VANET and conclude that this modification allows resolving the blockchain growth issue completely in case blocks are downloaded from trusted nodes. They note that the modification introduces an element of centralization of the system and make a proposal to mitigate this drawback.