esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulation  
### Network Model  
In this paper, we consider a wireless sensor network composed of \(n\) nodes which are distributed uniformly and randomly in the areas of monitored regions as shown in Figure 1. Since the typical mode of communication in data aggregation involves multiple data source nodes and one sink node [17], without lost of generality we assume there are \(k\) (\(k\leq n\)) source nodes and one sink node. Node \(u\) can receive the data from node \(v\) if node \(u\) is within the communication range of node \(v\); otherwise, they have to communicate with each other through multi-hop wireless links [18, 19]. We model a WSN as an undirected graph \(G(V,E)\), where \(V\) is a finite set of sensor nodes, and \(E\) is defined as the wireless connection between nodes [20, 21, 22, 23, 24].  
Figure 1: Network model diagrams.
## 2 Prediction of Tire Pattern Noise Based on System Identification
### Estimation of Pattern Noise Using the Optimal Adaptive Filter  
We applied the developed prediction method on the prediction of tire pattern noises of four commercial tires and verified it with measured pattern noise data. The optimal filter for the prediction model was obtained by applying Equation (5) to the measured data. The filter output is the predicted pattern noise. The comparison between the predicted noises and the measured pattern noises is shown in Figure 5. There are little differences between the measured data and the estimated data. In Figure 5, the predicted noise is represented by a solid line and the measured value is represented by a dotted line. The power spectral density (PSD) [18] for those noises was calculated, and its plot is shown in Figure 6. Figure 6 shows the frequency characteristics for each pattern noise. According to previous studies [15], because the tire pattern noise occurs in the frequency band of 500-2000 Hz, the magnitude of tire pattern noise in this frequency band is represented. The predicted noise is represented by a solid blue line, and the measured value is represented by a dotted red line. According to these results, there is a slight difference between the sound waves of the measured pattern noise and those of the predicted pattern noise. However, waveform and spectrum characteristics of both pattern noises are similar. Even if there is a slight difference, in the early design stage, to estimate the sound quality of pattern noise, we need an estimation model of pattern noise associated with pattern shape. The model was validated using 10 tires which were not used for model development to check if the model can be used to estimate sound quality of pattern noise, as presented in Section 3. Our results show that although it does not provide an exact solution as an in-situation application method, it can be concluded that the proposed method can predict the tire pattern noise in the early design stage without conducting experiments. In addition, the predicted sound waves can be used for sound quality analysis of pattern noise at the early design stage using the sound quality index (SQI). The SQI of pattern noise was developed using the measured tire sounds. The index can predict the sound quality of pattern noise. It was developed based on subject evaluation and sound metrics of the measured pattern noises. The sound metrics of pattern noise should correlate with subjective evaluation results. A detailed SQI development process for pattern noise will be presented in the next section.  
Figure 4: Performance surface of a _cost function_.  
Figure 5: Time data comparison of estimated signal between measured signals. (**a**) Pattern A, (**b**) Pattern B, (**c**) Pattern C, and (**d**) Pattern D; (**y** axis unit: Pascal, _calibration factor_ = \(1.58\times 10^{3}\)).  
Figure 6: Power spectral density (PSD) comparison of estimated signal between measured signals. (**a**) Pattern A, (**b**) Pattern B, (**c**) Pattern C, and (**d**) Pattern D; (**y** axis dB_reference_ = \(2\times 10^{-5}\) Pascal).
## 4 IDS Analysis from Software Engineering Perspectives
### Shortcomings of Existing IDSs from SE Perspectives
Afterwards, the BS will compare what it has received from the CHs and SCHs and accordingly decide if an attack has been occurred or not. The attacking cases that have been considered by the authors were on the level of CHs only. For example, if the BS gets 0 data packets from the CH and gets 10 data packets from the SCH, then this CH is determined by the BS as an attacker node. Afterwards, when the BS detects an attack, it broadcasts an alarm message to all nodes to notify them about it. Each sensor node maintains a black hole table to prevent the selection of malicious nodes as CHs in the next rounds. To clarify it more, the data flow of the authors' proposed scheme (SCH-IDS) in [21] is illustrated in Figure 4.  
Figure 3: LEACH routing protocol flow. However, this proposed schema have many vague steps and requirements. Thus, after an extensive analysis, we have reached a set of questions that have no answers in their paper. Listed below are the main steps and requirements that were found missing and not adequately identified:  
* Who has selected the SCH? Was it the BS or the CH? And, if it was the CH, then who has informed the BS with the identity of the SCH? * How was BS notified as to who was the SCH? * How were the rest of the nodes notified as to who was the SCH? * How was the current energy calculated and based on what? * How was the attacker dealt with? Was the attacker excluded from the network? * How were the performance measurements calculated? In the energy consumption calculation, was the attackers' energy included in the calculations? Moreover, some of the SE drawbacks that were found in this proposed IDS are:  
* Poor System Development Planning: What is the researchers' plan to overcome collisions and node death (before packet delivery)? In other words:
* What if a certain node dies before or while sending its controls packets to the SCH? This case has a high probability of happening as control packets are sent at the end of the transmission phase so the node might be dead during the round. * What if the SCH dies before or while sending its data to the BS? * What if the CH dies before or while sending its data to the BS? * Requirements Not Adequately Validated: What is the detection approach provided if the SCH is the malicious node? * Requirements Not Well Defined: The requirement of making the associated nodes message the SCH causes too much overhead on the network and consequently causes energy loss. Figure 4: SCH-based intrusion detection scheme (SCH-IDS). Requirements Not Adequately Managed: The selection of the SCH was based on the current remaining energy which was easy to retrieve in simulation--however, in real life, how this could be known and guaranteed.
## 3 Materials and Methods
### Hyperparameters and Features  
The approach has a great number of hyperparameters, changing their values may change the output of the approach. The SOMs parameters include the following:  
1. Type of grid for cluster nodes;
2. Number of neurons;
3. Number of neighbors;
4. Number of iterations;
5. Learning rate.  
As the SOM is a clustering algorithm, in this case, a reproducibility experiment was performed to ensure that the results can be reproduced multiple times to guarantee its use outside this study.  
The one-class SVM has the hyperparameter \(\
u\), which determines the proportion of beats outside of the decision function. The kernel employs the radial basis function  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster (\(cl_{j}\))** & \# **Beats in Cluster (\(cl_{j}\))** & \# **ATB in Cluster (\(cl_{j}\))** & \# **BTB in Cluster (\(cl_{j}\))** & \# **SVBo Identified** \\ \hline
1 & 227 & 0 & 227 & 0 \\
2 & 292 & 195 & 97 & 273 \\
3 & 346 & 216 & 130 & 221 \\
4 & 478 & 316 & 162 & 385 \\
5 & 451 & 299 & 152 & 394 \\
6 & 229 & 0 & 229 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of ECG signal separated in clusters and its descriptors.  
(RBF) that uses the \(\gamma\) hyperparameter, which plays an important role in determining the decision boundary.  
Additionally, the threshold \(d_{i}\) to determine if a cluster is SVBo or VBo needs to be specified as well.  
The default hyperparameters used for SOM training have a neighbor size of 3 and a hexagonal grid topology with 200 iterations.  
Furthermore, the hyperparameters whose values are determined experimentally are as follows:  
1. The dimension of the grid of SOM;
2. The \(\
u\) in the One-Class SVM algorithm;
3. \(\gamma\) for the RBF kernel;
4. The thresholds \(d_{i}\).  
Different subsets of features were extracted and merged together to form the final features set to be experimented with to reach the highest classification performance. The first feature subset is the DWT db3 coefficients of the QRS complex; the second feature subset is DWT db6 coefficients of the whole beat; the third feature subset feature is statistical computations of the sections before, after, and within the QRS complex; the fourth feature subset is the statistical computations of the QRS complex. The statistical computations are the mean, the standard deviation, the maximum, the minimum, the skewness, and the kurtosis. The final feature sets to experiment with were as follows: feature set 1 is the PCA transformation of the first features subset; feature set 2 is the PCA transformation of the second feature subset; feature set 3 is the merged of the first and fourth feature subsets; feature set 4 is the fourth feature subset; feature set 5 is the PCA transformation of the third feature subset; feature set 6 is the merged of the first and third feature subsets; features set 7 is the merge of the second and fourth feature subsets.  
Table 2 summarizes the basic features and the set of features created with all these.
## 6 Evaluation of RPL and RPAL in a Real World Environment
### Evaluation Results
#### 6.2.3 Packet Lost Ratio  
In Figure 8a, for nodes with an average hop count from the sink node lower than two, there was almost no packet loss detected. This is the effect of configuring with four retransmissions and the existing line of sight propagation. Notice that when the hop count equals to three, the packet loss increases significantly. On the one hand, low packet delivery is caused by the bad link quality of intermediate nodes. For example, monitoring packets from node 5 and 10 have to transverse the optional parents 4, 3  
Figure 7: **(a)** Hop count evaluation results; **(b)** Number of network churns for the testbeds in the 1 st and 2 nd experiments.  
or 6, but node 4 keeps rejoining the DODAG network and this also leads to frequent disconnections when it serves as parent. This causes higher packet loss for nodes 5 and 10 in the 1 st experiment than in the 2 nd one. Besides, the statistical results also count the part of the packet loss if node 3 and 6 are dead before the end of these two tests. As the SCAOF algorithm has the ability to adapt the network topology to avoid nodes 3 and 6 as busy routers, the results indicate that these two nodes lose fewer packets in the 2 nd test. Notice that nodes 8 and 9 have more network churns in the 2 nd test and they have more lost packets, since real world network re-convergence normally requires longer time to achieve. On the other hand, the interference due to other wireless equipment (_i.e._, office Wi-Fi or a weather station in the case of the garden at IRSTEA) and the signal attenuation by passing through obstacles near the area where the testbeds are deployed will cause worse packet loss.
# Quad-Band Circular Polarized Antenna for GNSS, 5G and WIFI-6E Applications
## 1 Introduction
These systems operate at different frequency bands, GPS: L1 (1227.60 \(\pm\) 10.23 MHz) and L2 (1575.42 \(\pm\) 10.23 MHz); GALILEO: E1 (1227.60 \(\pm\) 1.023 MHz) and E5b (1575.42 \(\pm\) 1.023 MHz); BDS: B1(1561.098 \(\pm\) 2.046 MHz) and B2 (1207.14 \(\pm\) 2.046 MHz); GLONASS: L1 (1602.5625 \(\pm\) 4 MHz) and L2 (1246.4735 \(\pm\) 4 MHz). These frequency bands are so close to each other that it is best to create one single band where all four of the navigation systems are covered. In addition, to cover other wireless communications, extra frequency bands have to be created. It is, therefore, necessary to develop multiple frequency bands with circularly polarized radiation. Since the development of 5G and WIFI-6E, technology has brought about a substantial increase in data transmission rates as well as shorter network delays [29; 30]. An antenna that can match multiple frequency bands of GNSS, 5G, and WIFI 6E will see wide application in a terminal device. In this communication, a quad-band circularly polarized antenna that can cover almost all global navigation frequency bands is proposed. The antenna is formed by an L-shaped radiator, a rectangular frame ground with an L-shaped stub, and a rectangular strip at the corner. An L-shaped aperture and three rectangular slits were cut on the ground to improve current distribution, which can broaden AR bandwidth at the 5G frequency band. The detailed geometry for the proposed antenna is shown in Section 2. By optimizing parameters, impedance bandwidth and AR bandwidth are further improved. The measured results are shown in Section 5.
## 1 Introduction
Satellite data cannot directly measure EIF thickness, although it is possible to measure the freeboard (height above water surface) of ice floes [25] and icebergs in open water [26] using radar altimetry. Single, very-high-resolution (VHR) electro-optical satellite images can be used to estimate the height of ice features from shadows [27]. The usage of stereo VHR electro-optical images enable extraction of sea-ice and iceberg topography, which can potentially serve as an indicator of ice feature thickness. The advanced single-pass interferometric capability of TerraSAR-X/TanDEM-X (TDM) satellites can also be used to extract sea-ice local surface topography [28] and iceberg topography [29]. A comparison of a digital elevation model (DEM) extracted from stereo VHR electro-optical images with a DEM based on TDM InSAR [30] demonstrated also the possibility to detect icebergs in sea ice with high accuracy, which is an important practical task. The goal of this paper is to investigate and demonstrate possibilities of identification and characterization of EIFs in the Canadian Arctic using satellite data. The focus will be on hummocks and ice islands because they pose significant risks for offshore infrastructure. We will use VHR electro-optical, Landsat-8, RADARSAT-2 and TDM data to demonstrate the feasibility of achieving this goal. Portions of the Canadian Arctic are ice covered for much of the year and EIFs can persist for several years. Developing tools that automate the process of identifying EIFs can be used to develop an inventory of hazardous features that can be used for risk management for transportation or other marine operations in the area.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 6 Evaluation of the EM-REP Proposal
### Simulation Process: Algorithm Behavior
The failure represents an error of the R05 router, that is located inside of the backbone area 0. Here, the process of encapsulating and PIM flooding is repeated. When this failure occurs, R01 becomes the next source router S and begins encapsulating packets of the protected flow using the predefined M-REP multicast address. The multicast address, as we mentioned, must be configured for the protected flow. The failure  
Figure 16: Restoration of EM-REP packet to original destination address. Figure 15: Second EM-REP run. occurred in a different OSPF area than in the previous two cases. Our proposed solution reduces the flooding from one OSPF area to another, so that M-REP can use the same multicast destination address as in area 1, i.e., the multicast address 226.1.1.1. Table 5 displays the output from OMNeT++ simulation, which shows how packets are handled in the moment of the R05 router failure, and which destination addresses are used to deliver packets of the protected flow. The M-REP constructs an alternative route that leads through routers R01 \(\rightarrow\) R03 \(\rightarrow\) R04.
## 7 Practical Applications of NCS
### Autonomous Mobile Robots/Controlled Networks  
For robots, the authority to perform and act dynamically is of vital importance. By itself, they comprise of mobile systems where the changes in environment activate changes in what purposes the system should convene the quality approach to structure the system, typically stated within the artificial intelligence literature because the behavior-based artificial intelligence framework [16; 26; 27]. Most plans are to spot completely different types of controllers and responses to sensory inputs, with desired mechanism performance. This practice of structuring the system into split behaviors, dedicated to playing bound responsibilities, has gained vital momentum in AI society. In Reference [158], implementation of a cloud service for remote control of a robotic manipulator is addressed, and an optimal control model restriction and control event-triggered communication are studied to be applied to a Robotic Manipulator as a Service. One more such example of the controlled network is the autonomous vehicular system designed by Toyota company called Prius, shown in Figure 16[159]. This autonomous vehicular system consists of functions, such as automatic controlling the steering, acceleration, and brakes. This function is called Move-Box which is developed by Netherlands Organization for Applied Scientific Research. It functional system works as a line between the Peripheral Component Interface (PCI) extensions for Instrumentation (PXI) which is placed on the roof of vehicle. An eHorizon system designed by Continental company is proficient in controlling the vehicle by using Global Positioning System (GPS).
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 5 Results  
In this section we analyze the most relevant results obtained from the tests performed. We will see the influence of the ordinary and extraordinary waves on the modulation's bit energy to noise power spectral density (E\({}_{\text{b}}\)/N\({}_{0}\)) and the effectiveness of the application of polarization techniques to obtain the best demodulation results. The tags \"OR,\" \"XOR,\" \"SC,\" and \"EGC\" indicate the ordinary wave, the extraordinary wave, the selection combining, and the equal-gain combining, respectively.
## 1 Introduction  
The design of a composite connection structure is one of the difficulties in the research of composite application, and the bearing capacity of the joint directly affects the reliability of composite components. A great deal of research has been conducted on the mechanical properties of the conventional bonded [1; 2; 3; 4; 5; 6] and bolted [7; 8; 9; 10] joints of composite materials. Many adhesives undergo instantaneous brittle fracture when they fail, resulting in sudden damage to the structure. The bonded-bolted hybrid [11] connection has better security and integrity than a bonded and bolted connection. Although the hybrid connection has been used in aviation, shipbuilding, and many other industrial sectors [12; 13], studies on composite hybrid joint structure design are lacking due to its complex force transmission mechanism. Most research [14; 15; 16; 17; 18] on hybrid joints has been limited to the tensile failure process of joints with a resin-based fiber-reinforced composite plate as an adherend and metal as a fastener at normal temperature, and the failure modes are usually adhesive fracture and hole extrusion failure. Failure analysis of joints in which both the adherend and fasteners [19; 20] are high-temperature-resistant composites (C/C composites, high-temperature ceramics) under compression shear is rare. In addition to the complexity and high price of the preparation process [21] of highly temperature-resistant composites, these studies are rare due to the high brittleness, which makes the cutting and fine processing of specific shapes difficult, and damage to composites, which reduces their strength. Carbon-fiber-reinforced carbon (CFRC), or carbon-carbon [22; 23], is a unique composite material consisting of carbon fibers embedded in a carbonaceous matrix. Originally developed for aerospaceapplications, its low density, high thermal conductivity, and excellent mechanical properties at elevated temperatures make it an ideal material for the automobile, ship, and aerospace industries [24; 25; 26; 27; 28]. Moreover, to improve the mechanical properties of the structure, it is essential to reduce the bending effect of single-lap joints due to eccentric loading and improve the shear bearing capacity of the adhesive, and there are few reports [29] on this aspect. Based on the research of [30], in this study, the failure modes and force transfer mechanism of C/C composites single-lap joints at 800 \({}^{\circ}\)C were obtained by means of tests and numerical simulation. The experimental results were compared with the calculated results to verify the correctness of the numerical model. The relationship of compression shear mechanical properties between the bonded joint and bonded-bolted hybrid joint at high temperature was determined.
# Research on Mechanical Properties and Failure Mode of Conglomerate Based on Discrete Element Method
###### Abstract  
Conglomerate reservoir is an important part of unconventional oil and gas resources, which has great developmental potential. However, its sedimentary environment and structural background are complex, and its cementation types, gravel volume fraction and shape are quite different, which leads to its strong heterogeneity. When developing a conglomerate reservoir, it is extremely difficult to drill because of its strong heterogeneity. It is difficult to obtain the mechanical properties and laws of the conglomerate through physical experiments, which further restricts the development process of conglomerate reservoirs. In order to study its failure law, a three-dimensional numerical model of a conglomerate is built based on the discrete element method, and the effects of cementation strength and gravel characteristics on the mechanical properties of the conglomerate are emphatically studied. The results show that the elastic modulus and uniaxial compressive strength of the conglomerate decrease obviously with the decrease in cementation strength. With the increase in cementation strength, the normal contact force of the conglomerate model increases significantly, the distribution of normal contact force changes from cylinder to sphere, and the heterogeneity of the conglomerate decreases. There is a threshold value for the influence of cementation strength on mechanical properties of the conglomerate, and when the threshold is exceeded, the mechanical properties of the conglomerate no longer change obviously. With the increase in gravel content, the uniaxial compressive strength of the conglomerate decreases at first and then increases, the phenomenon of penetrating gravels and bypassing gravels increases, and the single diagonal crack changes into diagonal cross cracks; the cementation strength and gravel content of gravel jointly affect the mechanical properties and fracture morphology of the conglomerate, and its stress-strain relationship is the external macroscopic expression of normal contact force of internal particles.  
Keywords:conglomerate; cementitious strength; mechanical properties; crack propagation; discrete element; three-dimensional +
Footnote †: journal: Applied Sciences