esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Video Transmission Capabilities in a Simulated OFDM-Based Supplementary BPL-PLC System
## 3 Materials and Methods
### Transmission Link
In case of the de-interleaver, knowing the initial state, it was possible to undo the interleaving operation and restore the original data. In the next step, data were converted to symbols by a 256-QAM baseband modulator and then subjected to OFDM [37; 38]. OFDM preserved the orthogonality of the subcarriers, which reduced the risk of interference. It also transformed a single transmission into several data streams that were less prone to corruption. For a stream at the input of the OFDM modulator \(X(k)\), this signal was first split into \(N\) parallel streams. Then, for each symbol the inverse discrete Fourier transform (IDFT) was calculated as:  
\[x(n)=\sum_{k=0}^{N-1}X(k)e^{j\frac{2\pi}{N}nk}, \tag{11}\]  
where:  
\[\mathrm{V}_{neN}\qquad n=0,1,\ldots,\ N-1. \tag{12}\]  
Such an operation was performed in the OFDM modulator. In case of the OFDM demodulator, a reverse operation was performed, namely, the \(x(n)\) subcarrier symbol was transformed by the discrete Fourier transform (DFT) algorithm according to:  
\[X(k)=\sum_{k=0}^{N-1}x(n)e^{-j\frac{2\pi}{N}nk}, \tag{13}\]  
where:  
\[\mathrm{V}_{keN}\qquad k=0,1,\ldots,\ N-1. \tag{14}\]  
Data prepared in such a way were passed through an additive white Gaussian noise (AWGN) communication channel. This channel also inherits the sampling rate from the input signal. In order to adjust the noise level, as well as check various quality aspects, one can modify the SNR parameter as required.
## 3 TLE Parameter Determination
### Determination of the TLE Material Parameters  
Figure 6 shows the axial deflection of the TLE under axial force. For the threaded connection under axial force \(F\), as shown in Figure 6, the shear stress \(\tau\) generated by the axial force is:  
\[\tau=\frac{F}{A}=G_{t}\gamma=G_{t}\frac{u}{t}, \tag{4}\]  
where \(\gamma\) is the shear angle, \(A\) is the contact area, \(u\) is the axial displacement, and \(t\) is the thickness of the TLE.  
From Equation (4), the axial force \(F\) can be expressed as:  
\[F=\frac{G_{t}A}{t}u=K_{a}u \tag{5}\]  
Substituting Equation (1) into Equation (5), the shear modulus \(G_{s}\) of the TLE can be expressed as:  
\[G_{t}=\frac{K_{a}t}{A}=\frac{\sinh\lambda l}{\lambda(\cosh\lambda l-1)}(\frac{ 1}{E_{b}S_{b}}+\frac{1}{E_{n}S_{n}})\frac{t}{A}, \tag{6}\]  
where \(\lambda\) is calculated using Equation (2).  
The normal and tangential contact properties of the interface are independent of each other, and the two tangential contact properties are consistent (\(c_{55}=c_{66}\)). The general form of the constitutive equation for the interface element can be described by [26]:  
\[\left\{\begin{array}{l}\sigma_{n}\\ \tau_{tx}\\ \tau_{tx}\end{array}\right\}=\left[\begin{array}{ccc}c_{11}&0&0\\ 0&c_{55}&0\\ 0&0&c_{66}\end{array}\right]\left\{\begin{array}{l}\epsilon_{n}\\ \gamma_{tx}\\ \gamma_{ty}\end{array}\right\} \tag{7}\]  
Figure 5: Threaded connection (source elaborated by authors based on [24]).  
Figure 6: Axial deflection of the TLE (source elaborated by authors based on [13]).  
where the subscripts \(n\), \(x\), and y refer to the contact normal and local \(x\)- and \(y\)-tangential directions, respectively.  
The TLE can be simulated as an isotropic material [19; 27]. In this situation, parameters \(c_{11}\) and \(c_{55}=c_{66}\) are the Young's modulus (\(E_{t}\)) and shear modulus (\(G_{t}\)), respectively, which define the normal and tangential stiffness of the joint.
## 4 Results
### Model Calibration  
To calibrate GWR and GWRK, an optimal bandwidth is first found for GWR via cross-validation. The same GWR fit is then used as the trend component of GWRK, and the corresponding residual variogram is found for GWRK. To calibrate the KED models, residual variogram parameters are found via REML, which are then used to parameterize both KED forms, but where an optimal local neighbourhood is found for KED-LN via cross-validation. Variograms for KED and GWRK (Figure 6a,b), both exhibit spatial structure, indicating some value in accounting for spatial autocorrelation effects. Similarly, the cross-validation functions (Figure 6c,d) for the bandwidth in GWR and the neighbourhood in KED-LN, both reach a minimum, indicating some value in accounting for non-stationary relationship effects. As is expected, the variogram structure is much stronger for KED than for GWRK, and the bandwidth/neighbourhood function is much steeper for GWR than for KED (see discussions given in [PERSON] _et al._[37]). Interrogating model calibration functions is important, as it provides context to model implementation, as well as helping to interpret the results. Note that for a visual comparison only, we have presented the KED and GWRK variograms using the same WLS variogram fitting procedure, but KED actually used a REML variogram fit.  
Figure 6: Model calibration plots: (**a**) empirical residual variogram for KED with WLS fit, (**b**) empirical residual variogram for GWRK with WLS fit, (**c**) GWR bandwidth function with optimum at 55 nearest neighbours, (**d**) KED neighbourhood function with optimum at 135 neighbours.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 3 Communication Technologies and Protocols
### Bluetooth and Bluetooth Low Energy (BLE)  
Bluetooth (IEEE 802.15.1) is another wireless technology that was first developed by Ericsson in the mid-1990s and is used for communications over short distances (<10 m) at high data transfer rates (up to 2-24 Mbps). Bluetooth technology operates in the 2.4 GHz unlicensed ISM (Industrial, Scientific and Medical) frequency band, consists of 79 RF channels, 1 MHz channel bandwidth, a maximum of eight nodes for communications and consists of two main topologies, i.e., Piconet (formed by aWPAN and consisting of one mobile device assigned as Master and other mobile devices acting as Slaves), and Scatternet (consisting of two or more Piconets) [96; 97]. Despite advantages such as high data rates and widespread availability, the technology currently has a number of drawbacks such as a short range of operation, limited number of nodes, relatively high-power consumptions (up to 50 mA during transmission [97]), and security and interference issues (as it has no strong security layer to prevent unauthorized interference, and operates on the same frequency range (2.4 GHz) as some other wireless technologies, i.e., Zigbee, Wifi, etc.). However, Bluetooth has been a widely adopted technology within buildings for several years, although its applications are limited to low-level data communications in mobiles and other portable devices, smart home appliances, computer peripherals and smart meters.  
To overcome the power consumption limitations of the classic Bluetooth technology, a low-energy distinctive feature of the Bluetooth version v4.0 called Bluetooth Low Energy (BLE) was introduced by the Bluetooth SIG (Special Interest Group) [98]. BLE operates in the same frequency range, operates in the same Master-Slave model, and has the same number of nodes for communication as its classic counterpart. However, BLE has a larger coverage range (up to 50 m), and significantly lower energy consumptions (estimated to be up to 50% less) as it essentially operates in Sleep mode and only wakes up when a connection is initiated. These features make BLE well suited for applications such as building automation systems which only require periodic data transmissions with low-energy consumptions and fast data rates (see Table 3).
## 2 Secure Network Encryption Protocol
### Data Transmission with Weak Freshness  
Data transmission protocol with weak freshness has been presented in Figure 1. Using the only message of this protocol, any node \(A\) can send data \(D\) to another node \(B\), where \(D\) is encrypted using the shared secret \(K_{AB}\) and the counter \(C_{A}\). Moreover, the MAC of the encrypted data and the counter is calculated using the MAC key \(K^{\prime}_{AB}\). This protocol provides all of the security properties mentioned in the earlier section, except it provides weak freshness. By checking the increased counter value, the receiver can verify that the message has been sent after the previous one.  
\[A\to B:\{D\}_{\langle K_{AB},C_{A}\rangle},MAC(K^{\prime}_{AB},C_{A}||\{D\}_ {\langle K_{AB},C_{A}\rangle})\]
## 2 Methodology
### Statistical Analysis  
Once data extraction was complete, a comprehensive three-phase statistical analysis was performed using SPSS+ statistical software (v. 20.0, IBM Corp., Armonk, NY, USA): (a) A descriptive analysis was conducted, calculating the frequencies for each category of the variables studied. This step is essential for summarizing categorical data, enabling the identification of dominant patterns, trends, or potential irregularities prior to conducting inferential analyses [32]. (b) To assess the probability that certain categories occur together and to examine relationships between categories showing significant differences, a [PERSON]'s chi-square test (\(\chi^{2}\)) was performed [33,34]. This test evaluates whether the observed distribution of frequencies differs significantly from the expected distribution under the assumption of independence. The significance threshold was set at \(p<0.05\)[33]. (c) Finally, the strength and direction of associations between variables and their respective categories were assessed using adjusted residuals (AR) from the contingency tables. These standardized residuals indicate how much each cell in the table deviates from the expected frequency. The residuals provided insight into the pattern between categories: an excitatory pattern was identified when the AR value exceeded 1.96 and an inhibitory pattern when the AR value was below \(-1.96\)[35]. This methodical approach enabled a nuanced understanding of the relationships and associations within the dataset.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 1 Introduction
### Motivation  
In [27], the important role of authentication and key agreement mechanism in smart grid environment, especially in AMI system, is expounded, which can effectively protect entities from adversary attacks and threats of unauthorized illegal entities and ensure the reliable operation of the whole system. In [28], the potential risks in the AMI system are evaluated by the OCTAVE Allegro method. According to [10; 28], the key agreement scheme designed for the AMI system needs to be able to resist impersonation, man-in-the-middle, replay and other attacks in addition to mutual authentication, ensuring session key security, providing anonymity and forward confidentiality. These are the main requirements of smart grid security. At the same time, because the smart meter equipment has very limited resources, such as computing, communication and power supply, the scheme also needs to be as lightweight as possible to adapt to the increasing traffic in the AMI system.  
Through the above analysis of the existing key agreement schemes in AMI or smart grid scenarios, it can be found that most of the existing schemes have been proven to have some security problems and cannot meet the security requirements well. Secondly, the existing key agreement schemes in AMI are basically based on cryptosystems with high computational complexity, such as bilinear pairings, elliptic curves, etc., which require high energy overheads, while ultra-lightweight hash operations cannot provide good security. Last but not least, most of the above schemes do not take into account the message transmission mode in the AMI and cannot adapt well to the actual AMI system. Therefore, in order to ensure secure communication between legitimate entities in the AMI system, the designed scheme needs to establish an appropriate balance between security and efficiency. Based on the premise of satisfying the required security, the overhead should be reduced as much as possible to adapt to the equipment with limited resources, and the architecture and message transmission mode of the AMI system should be fully considered.
# A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network
###### Abstract
Additionally, incorporating a packet queuing parameter increases the route maintenance and data delivery performance. 3. Two levels of security are provided using cryptographic algorithms; the first level attains data privacy from cluster heads to fog nodes, and in the second level, a more authentic asymmetric encryption scheme is proposed between the fog nodes and cloud servers. The paper is organized into the following sections. Section 2 reviewed the existing work. Section 3 explains the discussion for the proposed secure and energy-efficient fog-based routing (SEFR) protocol along with its methodology design and components. Section 4 explains the analysis of the results of the proposed SEFR protocol with other schemes. Finally, Section 5 concludes the research work.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 4 Results and Analysis of Experiment
### Experimental Design
#### 4.1.3 Flow Characterization and Cluster Analysis  
The first aim of this project is to develop a flow characterizing mechanism based on the metrics of network performance. Unsupervised machine learning techniques were proposed to achieve this goal. K-means as unsupervised clustering can create clusters of flow without the need for predefining the classes. This algorithm isutilized to understand the differentiation of flows with respect to their network performance features. It is applied to construct three clusters, where the minimum number of clusters was investigated in this experiment. The quality of clustering is measured by using the accuracy metric. This metric is determined by two factors: inter-cluster distance and intra-cluster distance [38]. As a result, unique samples of flow are recognized for each cluster.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 3 Constructing the Framework for CNN and LSTM Assisted GNSS/INS Navigation System
### Constructing the Framework for CNN- and LSTM-Assisted GNSS/INS Navigation System
#### 3.2.3 Constructing the CNN-LSTM Network Model  
Based on the above steps, the CNN-LSTM network model is constructed. The specific architecture is illustrated in Figure 7.  
Figure 6: LSTM neural network structure.
# Bond Fatigue of TRC with Epoxy Impregnated Carbon Textiles
## 3 Results
### Development of Deformation
#### 3.3.3 Dependence on Anchorage Length  
Now, the influence of the anchorage length on the deformation is regarded. Therefore, the four different investigated anchorage lengths were compared at several load levels. Figure 9 shows an example of the cyclic creep curves at a load level of 50/85. The comparison of the creep curves at other load levels is shown in Figure A5. Again, failed specimens are marked by dashed lines.  
Figure 6: Comparison of cyclic creep curves of a failed and a non-failed specimen (here: anchorage length = 4\(a\); \(\sigma_{\min}/\sigma_{\max}\) = 30%/95%).  
Figure 7: Comparison of normalized cyclic creep curves depending on the load level (here: anchorage length = 3\(a\)): (**a**) 30% minimum stress; (**b**) 50% minimum stress.  
In Figure 9, it can clearly be seen that the deformation increases with increasing anchorage length. However, the difference between the deformation of specimens with \(1a\) and \(2a\) as anchorage lengths is larger than the difference between specimens with \(2a\) and \(3a\). Finally, the deformations of specimens with anchorage lengths of \(3a\) and \(4a\) show no differentiation anymore. The reason for this is to be found in the absolute reference strengths, increasing non-linearly with increasing anchorage length (see Section 3.1).
# Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System
## 4 Hydraulic Manipulator System Modeling and Controller Design  
### Mathematical Model of the Manipulator System  
The simplified model of the 4-DOF manipulator is shown in Figure 5, where, \(L_{1}\), \(L_{2}\), \(L_{3}\), \(L_{4}\), \(\theta_{1}\), \(\theta_{2}\), \(\theta_{3}\), \(\theta_{4}\) are the link lengths and joint angles and \(L_{12}\), \(L_{22}\), \(L_{13}\), \(L_{23}\), \(L_{14}\), \(L_{24}\) are installation distances (from the installation position to the corresponding joint) of the hydraulic cylinders. We assume that the masses of the links are concentrated at the end; they are \(m_{1}\), \(m_{2}\), \(m_{3}\), \(m_{4}\) respectively.  
Figure 5: Simplified model of the manipulator.  
According to Lagrange principle [22], the dynamic equation of the 4-DOF manipulator is as follows:  
\[M(\theta)\ddot{\theta}+C\Big{(}\theta,\dot{\theta}\Big{)}\dot{\theta}+G(\theta)+F \Big{(}\theta,\dot{\theta}\Big{)}+\tau_{d}=\tau \tag{8}\]  
where, \(\theta,\dot{\theta},\ddot{\theta}\in R_{4}\) respectively denote the position, velocity and acceleration of the joint, \(M(\theta)\in R_{4\times 4}\) is the inertial array of the manipulator, \(C\Big{(}\theta,\dot{\theta}\Big{)}\in R_{4}\) is the centrifugal and Coriolis matrix and \(G(\theta)\in R_{4}\) is the gravity matrix. \(F\in R_{4}\) denotes the multiple friction forces, \(\tau_{d}\in R_{4}\) is the external disturbance and \(\tau=[\tau_{1},\tau_{2},\tau_{3},\tau_{4}]\) denotes the torque of each link.
## 3 D-MSR Protocol Stack Architecture
### Lower Data Link Sub-Layer
Therefore, less interference occurs in these channels. A similar concept is used in the ISA100.11a standard, in which these three channels are designated as slow hopping channels for purposes such as neighbor discovery) to send the advertisements. We limit the number of channels in that period to three, in order to facilitate neighbor discovery and data sharing during joining. If a node chooses a timeslot to send the advertisement, the node will transmit an advertisement in the assigned channel most of the time. If not, it listens in a randomly selected channel (after having chosen from three advertisement channels) to receive other neighbors advertisements. However, if a node is not supposed to send the advertisement in a timeslot in the advertisement period, the node will once again listen in a randomly selected channel. The procedure of selecting a free advertisement cell will be discussed in Section 4.1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Additional Information** & **Description** \\ \hline Link table information of the advertiser & Used by receivers to construct the schedule-matrix \\  & (Discussed in Section 4.1) \\ \hline List of advertisement cells used by the & Used by receivers to select a free advertisement cell \\ advertiser and its neighbors & (Discussed in Section 4.1) \\ \hline List of free timeslots of the advertiser & Used by receivers to define initial communication links \\  & (Discussed in Section 4.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of additional information included in the advertisement payload. Figure 6: Modified Superframe. In the _data communication period_, D-MSR schedules interference-free communication links between the neighboring nodes. For example, as is shown in Figure 6, traffic 'a' and 'b' are transmitted from node A and C toward node L and P respectively. The scheduled communication for these traffic flows are shown in the slot-channel matrix in the top left of Figure 6. Each time a scheduled communication is going to occur on a link, both sides of the link calculate the radio channel of the communication by taking _\"(Absolute Slot Number + Channel offset) % Number of channels\"_. For instance, nodes I and N that select the timeslot 2 and channel offset 0, follow the frequency hopping pattern with that offset in the _data communication period_ and will use channel 20 in that timeslot, as is shown in the _data communication period_ (blue cells for traffic 'a' and red cells for traffic 'b') of the superframe. The procedure of scheduling the communication links (_i.e._, filling the link table and superframe table) in the _data communication period_ are handled by the _D-SAR_ and _neighbor connection manager_ modules in the upper data link sub-layer, which are discussed in Sections 3.2.
## 3 Experimental Results and Discussion
### Cyclic Cleaning
#### 3.3.4 Tool Repair--RJS Ageing  
Though aluminium has worse adhesion characteristics, making it the theoretical worst-case scenario, both aluminium and steel specimens were tested, in order to consider the effect of the heat cycles in the interface properties of the joint.  
The maximum load values for the aluminium specimens are shown in Figure 19, where \(1\times 150\)\({}^{\circ}\)C defines the load value after the initial cure cycle, used solely to manufacture the specimen.  
Figure 19: Maximum shear strength of RJS specimens subjected to multiple repair cycles. The red dotted line represents the threshold value for the RJS specimen’s minimum allowed joint strength (1.5 kN).  
Figure 18: Maximum shear strength of RJS specimens subjected to a jet blast of sand particles. The red dotted line represents the threshold value for the RJS specimen’s minimum allowed joint strength (1.5 kN).  
The findings show that there were no problems with the repair bonded joint milling tools, as there is no loss in shear strength. In fact, after several repair cycles the tool strength appears to have slightly improved. This phenomenon could have resulted from a post-curing effect of the adhesive.
## 4 Experiments and Results
### Analysis Results for Suburban Area
#### 4.2.2 Weekday Suburban Analysis
As can also be seen from Figure 7, the sensor hb155 position has been allocated near sensors hb156 and hb111 through the expert-base analysis, because they exhibited a more similar pattern within \(\mathbf{S}\).