esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of a MEMS-Based TRNG in a Chaotic Stream Cipher
## 2 Stream Cipher Algorithm
### Stream Cipher Algorithm  
The schematic of the proposed stream cipher is shown in Figure 3. The principle of operation is as follows: first, the accelerometer is used to generate the initial parameters of the STM, \(\gamma,\ x_{0}\) and the initial state of the LFSR, \(y_{0}\). Then, the encryption algorithm generates a pseudorandom sequence \(\{\widetilde{x}_{i}\}\) using the STM algorithm (Figure 4) and XORing the LSB of each \(x_{i}\) from the STM, \(x_{i}^{0}\), with a pseudorandom bit generated by the LFSR, \(y_{i}^{0}\). Furthermore, in order to increase the security of our system, we have only used the eight least significant bits from each \(\widetilde{x}_{i}\) (\(\widetilde{x}_{i}^{0},\ \widetilde{x}_{i}^{1},\ \ldots,\widetilde{x}_{i}^{ \gamma}\)) to encrypt the message. This way, we restrict the amount of information that an attacker could obtain, so that some attacks such as a chaotic reconstruction of the map are harder to accomplish. Therefore, the message is divided in 8-bit blocks, \(m_{i}\), and, after each iteration, these bits are combined with eight bits from \(\widetilde{x}_{i}\) with an XOR gate.  
The receiver is also composed of STM and LFSR blocks. By using the same initial parameters that were used to encrypt the message, the receiver is capable of recovering the original message by combining its sequence with the encrypted sequence with an XOR gate. In our tests we have directly introduced the same initial parameters in the transmitter and the receiver although, in a real implementation, the initial parameters should be generated in the transmitter and sent to the receiver using a secure channel. This could be easily accomplished using secure asymmetric algorithms such as Rivest, Shamir and Adleman (RSA).
# Design and Analysis of a Novel Microspeaker with Enhanced Low-Frequency SPL and Size Reduction
## 4 New Microspeaker Design
### New Coil Design  
The coil consists of three main components: copper for the current flow, an insulation layer, and a bond layer. Owing to advances in technology, a coil with a thinner bond layer has been introduced, which enables a reduction in the coil height and thickness compared with the dimensions of the conventional coil used in 1610_SPK. A comparison between the conventional and the new coil is shown in Figure 17a. Figure 17b depicts the coil winding cross section of 1410_SPK; except for the bond layer thickness, the thicknesses of the copper wires and insulation layers are similar.  
Figure 16: The prototype 1410_SPK with an air gap of 0.15 mm.  
Figure 17: Schematic of the coil structures: (**a**) conventional and new coils and (**b**) cross-sectional view of the 1410_SPK coil winding.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Air Gap** & **Flux Density** & **Force** \\ \hline
0.15 mm & 0.551 T & 0.462 N \\
0.14 mm & 0.561 T & 0.470 N \\
0.13 mm & 0.571 T & 0.478 N \\
0.12 mm & 0.580 T & 0.486 N \\
0.11 mm & 0.590 T & 0.494 N \\
0.10 mm & 0.599 T & 0.502 N \\ \hline \hline \end{tabular}
\end{table}
Table 8: Changes in the flux density and force with respect to the air gap.  
The prototype 1410_SPK has 4 horizontal layers of coil as well as 13 vertical layers. When the conventional coil is used in 1410_SPK, the bond has a thickness of approximately 0.012 mm, which results in a thickness of 0.356 mm and a height of 1.16 mm. The bond layer of the new coil is approximately 25% thinner, at 0.009 mm; in other words, for the same resistance, the newly designed coil offers reduced exterior dimensions. For a coil diameter of 0.077 mm, the thickness and height are 0.344 mm and 1.12 mm, respectively. A reduction of over 3% was achieved in terms of thickness and height. The smaller coil enables the use of larger magnets and top plates, with an air gap of 0.10 mm. This results in a 2.4% higher force than that when using the conventional coil. Another approach to improve the force is to increase the total length of the new coil using a diameter greater than 0.077. Thus, the coil length can be increased while maintaining the same exterior dimensions as those for the conventional coil diameter of 0.077 mm. A diameter of 0.080 mm for the new coil matches the overall dimensions of the conventional coil with a diameter of 0.077 mm. The specifications for 1410_SPK when using the conventional and new coils are listed in Table 9.  
The results show that using a diameter of 0.080 mm has a better force. The 1410_SPK with a diameter of 0.080 mm was chosen for the SPL experiment.
## 2 Materials and Methods
### System Design and Test
#### 2.2.2 Test Workflow
After the electrical performance test, the results of the test workflow are transferred back to the MBSE system model using set actions, that is, the parameter values of \(n_{cycle,max}\) and \(E_{EI}\). There, it is checked to see whether the specified requirements are met. For example, the permitted maximum speed is compared to the calculated quantitative value. The consumption of electrical energy is not further specified in the requirements; thus, it could be compared to benchmarks and technical variants. If requirements are not met, a new and suitable technical variant must be found.
## 4 Choosing a \(D\)-Optimal Subset of \(n\) Measurements
### Subset Selection Using the QR Factorisation with Column Pivoting
The matrix \(R_{1}\) can be used to re-parametrize the problem in terms of \(\mathbf{b}=R_{1}^{-1}\mathbf{a}\). The observation associated with parameters \(\mathbf{b}\) is \(Q_{1}\). Since D-optimal designs are independent of parametrization, it is sufficient to work with \(Q_{1}\) as follows. We use column pivoting to find the QR factorisation of \(Q_{1}^{\top}\) so that \(Q_{1}^{\top}P=UT\), where \(U\) is an \(n\times n\) orthogonal matrix and \(T\) is upper-triangular. The first \(n\) columns selected by the QR decomposition of \(Q_{1}^{\top}\) specified by \(P\) also specify the \(n\) rows of \(C\) that are a good choice for the D-optimal design. This algorithm is summarised below (Algorithm 1). Its computational complexity is \(O(mn^{2})\). ```
Data: An \(m\times n\) observation matrix \(C\). Result: Index set \(I\subset\{i=1,\ldots,m\}\) where the first \(n\) elements specify \(C_{n}\) that represent a good selection of the rows of \(C\) in terms of maximising the determinant. Steps: Determine the QR factorization of \(C=Q_{1}R_{1}\), where \(Q_{1}\) is an \(m\times n\) orthogonal matrix. Use column pivoting to find the QR factorisation of \(Q_{1}^{\top}\) so that \(Q_{1}^{\top}P=UT\), where \(U\) is an \(n\times n\) orthogonal matrix and \(T\) is upper-triangular. Assign the index \(I\) by assigning \(I(k)=i_{k}\) where \(i_{k}\) specifies the row index of the \(k\)th column of \(P\) such that \(P(i_{k},k)=1\). ```  
**Algorithm 1**SSQR: subset selection algorithm using QR with column pivoting to determine a favourable subset of an observation matrix in terms of the D-measure  
The SSQR algorithm is not targeted directly at determining a D-optimal design but is an approach that, at least heuristically, has elements that makes it likely to produce a design that is good in terms of the D-measure. The algorithm is an example of a sequential 'greedy algorithm', in that at each step, a choice is made that maximises some measure, irrespective of what previous steps have been made, and takes no account of what choices could arise in a future step. Once a row has been selected, there is no opportunity for the row to be deselected at a future iteration. The algorithm is largely independent of the ordering of the rows of the observation matrix \(C\) and, other than for reasons of rounding error effects and symmetries in the observation matrix, will arrive at the same solution for different row orderings. As has been mentioned, there is no guarantee that this solution is globally optimal. Example calculations using the algorithm are discussed in Section 5.
## 7 Simulation Results and Discussion
### Analysis of Proposed Scheme Results against Existing State of the Art
#### 7.2.6 Performance Trade-Offs  
From the simulation results, we can conclude that there is trade off between performance parameters. In GEDAR. GDGOR-IA scheme, achieves slightly better PDR is slightly high with 14% less delay in the network. This is due to the interference avoidance mechanism opted in the scheme that minimizes the delay caused due to the opportunistic routing opted in GDGOR-IA. GRMC-SM secures high PDR at low energy cost as compared with GRGOR-IA and GEDAR. While incorporating sink mobility in GDGOR-IA, energy cost associated with depth adjustment is diminished due to sink deployment in three dimensional volume for maximum coverage.
# Research on Mechanical Properties and Damage Constitutive Model of Water-Bearing Coal
## 4 Damage Constitutive Model  
### Evolution Law of Mesoscopic Damage  
#### 4.1.1 Mesoscopic Damage Caused by Water-Rock Interaction  
The water-rock interaction process is represented in Figure 8. Generally, the mesoscopic damage to coal in the water-rock interaction can be roughly divided into two categories: physical damage and chemical damage [13]. Physical damage causes colloids that hold mineral particles together to dissolve, weakening the cohesiveness between the particles and causing the particles to wash out of the coal body. Chemical damage causes soluble materials to dissolve in acidic and alkaline aqueous solutions. The coal mass's macroscopic mechanical characteristics alter as a result of the coal mass's microstructure changing due to the water-rock interaction.  
Figure 8: The schematic diagram of the water–rock interaction process.  
Figure 7: The fitting curve of mechanical parameters and moisture content of coal samples: (**a**) uniaxial compressive strength; (**b**) [PERSON]’s modulus; (**c**) peak strain.  
Considering that the Young's modulus of rock materials is easy to measure and analyze during the water-rock interaction process, the damage variable \(D_{1}\) of coal samples caused by the water-rock interaction can be expressed as:  
\[D_{1}=1-\frac{E_{\omega}}{E_{0}} \tag{4}\]  
In Equation (4): \(E_{0}\) is the Young's modulus of coal with initial natural moisture content, and \(E_{\omega}\) is the Young's modulus of coal sample with moisture content \(\omega\).  
As the moisture content of coal increases, the damage degree caused by the water-rock interaction is intensified, [PERSON]'s modulus is gradually reduced, and the damage variable \(D_{1}\) is gradually increased. Therefore, the evolution law of the damage caused by the water-rock interaction can be obtained with water content as a control variable.
## 2 Antenna Structure
### Differential-Fed 1 \(\times\) 16 Linear Slot Array  
Figure 2a shows the geometry of the different-fed 1 \(\times\) 16 linear slot array, designed to operate at 10 GHz. The SIW width \(a\) is set to 14.54 mm, which equals half of the SIW's guided wavelength \(\lambda_{g}\) at 10 GHz. Slots are spaced apart by \(\lambda_{g}/2\). Based on the Elliott formulas [7], we have developed an in-house MATLAB code to determine the slot lengths and offsets for achieving an SLL of less than \(-\)30 dB in the \(H\)-plane. The slot data for the 1 \(\times\) 16 linear slot array are displayed in the caption of Figure 2.  
The proposed 1 \(\times\) 16 linear slot array is excited using the differential feed. As seen from Figure 2a, the slot array is fed from both ends with a 180\({}^{\circ}\) out of phase signal. According to [17], the out-of-phase excitation can create a virtual short in the center of the linear slot array, virtually dividing it into two subarrays. It is well-known that the subarray technique is a widely used and most effective approach to broaden the impedance bandwidth of the WSAAs. This method exploits the advantage provided by dividing a large narrowband array into several wideband subarrays. Conventionally, these subarrays are excited by an array of coupling slots [5; 6; 7]. However, the resonant nature of coupling slots limits the impedance bandwidth performance. Meanwhile, a differential feed scheme employed in the linear slot array not only effectively divides the linear slot array into two subarrays but also eliminates the constraints on the performance of impedance bandwidth imposed by coupling slots [20]. As a result, the impedance bandwidth of the differential-fed linear slot array is broadened significantly. In our research, we have found that a differential-fed linear slot array can also maintain a low SLL in the H-plane across the entire operating band. For comparison, we also designed a conventional end-fed slot array and a center-fed slot array, as illustrated in Figure 2b,c, with the same design goal of SLL \(<\)\(-\)30 dB in \(H\)-plane. A comparison of the \(S_{11}\), SLLs, and main beam angles of three SIW linear slot arrays is conducted, as shown in Figure 2d,e. As observed in Figure 2d, the proposed differential-fed array has the widest \(-\)10 dB impedance bandwidth as compared to the conventional end-fed slot array and the center-fed slot array. Figure 2e shows that the main beam direction of the end feed design is tilted about \(\pm 1^{\circ}\) for \(\pm\)0.2 GHz frequency shift from the designed frequency of 10 GHz. For the center feed design, the main beam direction is not squinted for frequency change. However, at frequencies other than the designed frequency of 10 GHz, its SLL increases dramatically, whereas the proposed differential-fed array can achieve a low SLL of \(<\)\(-\)30 dB and zero beam squinting simultaneously over the entire operating band.  
Figure 1: 3-D exploded view of the proposed low-SLL, differential-fed, SIW-based SAA.
## 2 Related Work and Contributions
### Contributions  
In this paper, we propose the _Hydra_, a deep learning approach that consists of two components: a shared trunk and computing heads. The trunk is a customized SISR model that learns the mapping from LR to HR. The trained trunk is then appended with task-specific layers to learn multiple visual tasks in medical images. Figure 1 depicts the main difference between the _Hydra_ and existing works. As can be seen from the figure, the _Hydra_ trunk is used as a shared backbone to learn multiple visual tasks (heads). On the contrary, the majority of existing methods (traditional approach for task analysis) use the constructed HR image as input to multiple individual models. The main contributions of this paper can be summarized as follows:  
* We propose the _Hydra_ approach for enhancing medical image resolution and visual task analysis. The _Hydra_ consists of two components: a shared trunk and computing Heads.
* _Hydra_ trunk is a proposed customized SISR model that learns the mapping from LR to HR. This SISR model has a simple architecture and is trained using the entire raw image and multiple scales at once to minimize a proposed loss function. Our experimental results show that the proposed SISR model, which has a markedly lower number of training time and parameters, achieves state-of-the-art performance.
* We propose to append the customized SISR trunk with multiple computing heads to learn different visual tasks in medical images. We evaluate our approach using CXR datasets to generate HR representation followed by jointly performing lung segmentation (visual task 1) and abnormality classification (visual task 2). We focus mainly on these two tasks because classification and segmentation are the key tasks in most medical image analysis applications.
* We empirically demonstrate the superiority and efficiency of our approach, in terms of performance and computation, for SR and medical image analysis as compared to the traditional approach.  
We present next the CXR datasets used to evaluate the proposed _Hydra_ and provide detailed descriptions of _Hydra_ trunk and computing heads.
## 3 Channel Estimation  
### Noise Estimation  
The first step in estimating the channel parameters \(\mathbf{H_{d}}\) and \(\mathbf{V}\) is estimating the noise present in the channel. This is accomplished by sending the same pilot transmission signal twice, keeping the IRS configuration constant. Since we have assumed that the BS and the user is static, we can also assume that the parameters \(\mathbf{H_{d}}\) and \(\mathbf{V}\) remain the same. Writing the two equations in the form of (6), we obtain:\[\mathfrak{F}(z_{1}) =\mathbf{H_{d}}\mathfrak{F}(x)+\mathbf{\mathcal{N}}_{\mathbb{C}1} \tag{7}\] \[\mathfrak{F}(z_{2}) =\mathbf{H_{d}}\mathfrak{F}(x)+\mathbf{\mathcal{N}}_{\mathbb{C}2} \tag{8}\]  
Determining the difference between Equations (7) and (8), we see that all the terms on the RHS get cancelled, except for the noise term. We can measure this by finding the difference between the left hand side (LHS) term. Repeating this for many different IRS configurations with the same transmit pilot signal and finding the variance of all such values, we find that the new random variable will have twice the variance of the original noise random variable. We already know that the mean of a CSGN random variable is 0. Thus, with this knowledge of the mean and variance, we can estimate the noise in the future stages. The OFDM transmission employs a cyclic prefix of length \(M-1\) and generates \(K>M\) subcarriers. Therefore, a block of \(K+M-1\) time-domain signals is sent to form one OFDM block with K parallel subcarriers. In this context, M equals 20, K equals 500 subcarriers, there are 50 users, and N equals 4096 IRS cells. The received signal matrix has the dimensions \(K\times 4N=500\times\) 16,384, while the transmitted signal matrix has the dimensions \(K\times 1=500\times\) 1. Equations (7) and (8) are of the order \(K\times 4N=500\times\) 16,384.
# Anisotropy and Directivity Effects on Uniaxial Compression of Carbonaceous Slate Form Jinman Mine
## 1 Introduction
To investigate the anisotropic mechanical characteristics of carbonaceous slate considering the directivity effect, uniaxial compression tests, acoustic emission (AE) monitoring, and SEM tests were conducted with vertical loading and parallel loading on slate structures. The objectives were: (a) to obtain the characteristic stress-strain curves, (b) to investigate the characteristics of mechanical parameters, (c) to investigate AE characteristics, and (d) to determine failure patterns and fracture characteristics including macro-fracture modes and micro-cracks.
## 2 Surface Shape Measurement Principle
### Error Compensation and Calibration
#### 2.3.4 Calibration of the Detection System  
As can be seen in Figure 9, when the sensor is axially offset, it will have a great impact on the selection of the center point, especially when the point near the center is displaced. Therefore, it needs to be calibrated, and the distance from the edge of the installation equipment to the grating probe is determined by using the lever dial indicator.  
Figure 9: Axial installation error.  
An important aspect to ensure the accuracy of the detection system is to determine the relative position of the grating probe and the line laser sensor. In order to ensure the rationality and reliability of the test data, the measurement system must be calibrated first. The positioning error of the grating probe has been compensated and calibrated above, and then the position of the line laser sensor is calibrated by the detection plane determined by the laser grating probe.  
Select a standard square so that one side of it is placed parallel to the probe detection plane, and its upper plane coincides with origin \(O\) of the probe plane in Figure 11. At this time, the line laser sensor is horizontally arranged above the square, and the detected distance data should be a horizontal straight line parallel to the \(X\)-axis direction of the sensor; otherwise, the line laser sensor is not horizontally arranged. When the position at this time is zero, the detection plane of the line laser sensor and the probe are unified in \(Z\) coordinate.  
Figure 11: Calibration diagram. (**a**) \(X\)–\(O\)–\(Z\) plane; (**b**) \(X\)–\(O\)–\(Y\) plane.  
Figure 10: Schematic diagram of deviation influence. (**a**) When one measuring point is deviated; (**b**) When three consecutive measuring points are offset.  
Calibrate the \(Y\)-value of the line laser sensor in the spatial coordinate. Place a standard cylinder in space, where the axis of the cylinder is collinear with the origin \(O\) of space. The laser sensor detects the cylindrical surface, and the detected value should be displayed as a non-standard arc in the \(X\) direction of the sensor. According to the principle of space geometry, only one point on the arc is the maximum, which should be located in the \(X\)-O-\(Z\) plane in space; that is, the \(Y\)-value of the space is 0.  
The distance between the standard block and the detection plane is \(x_{c}\), but the abscissa value of the first measurement point of the standard block in the line laser sensor detection system is \(x_{Ac}\). Therefore, the abscissa of this point is recorded as \(x_{c}\), and the abscissas of other points can be calculated according to the \(X\)-value of the line laser sensor system and the included angle between the system and the \(X\)-\(O\)-\(Z\) plane of the spatial coordinate system. So far, the \(X\)-value of the measurement system has been calibrated, and the spatial position calibration of the line laser sensor is completed.
## 4 Ad Hoc Routing: Configuration, Scenarios, and Results
### Scenarios and Results  
Once everything is completed, the last step of a successful TestBed implementation is to verify its results in various scenarios. We initiated our central Server and started to listen to requests from Clients. One after another, the nodes started sending connection requests, which the Server accepted, thus creating a multi-client scenario where routing and forwarding took place automatically using the BATMAN-ADV protocol. Since the Raspberry Pies are Linux-based, we confirmed the communication through a built-in network diagnostic tool called \"traceroute\". This command shows the entire path taken by a packet from one end to another. It utilizes an IP protocol's Time To Live(TTL) field and attempts to elicit an ICMP TIME_EXCEEDED response from each node along the path to the destination.