esa-sceva/satcom-chunk-collection huggingface dataset:
# An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells
## 4 Design and Acceleration of Instruction Set
### Assembly Instruction Arrangement  
In order to improve the pipeline efficiency of the loop, we need to carry out loop unroll. By using the multisubcarrier operation, the NOP instructions inserted because of the data dependence of the microoperations used in Equations (8)-(11) can be eliminated.  
For the LS module, the assembly instructions for Equations (8)-(11) are shown in Algorithm 3.  
```
1:Repeat N/2 13 // The next 13 instructions are repeated N/2 times. Four H values of four pilot subcarriers can be obtained for each calculation. Each pilot subcarrier needs to calculate 8 H values in LS estimation. If the number of pilot subcarriers is N, then the number of cycles is N/4 * 2. //G0 stores the R1 and R2 of subcarriers 1-4, G1 and G2 stores the Z1 and Z2, Z3 and Z4 of subcarriers 1-4 respectively.
2:8:21 CM1C dst [G3:X0-X3], src1[G0:X0-X3]src0[G0:X4-X7];// Divisor for complex division by subcarriers parallelism
3:2:2 CMASC2C dst [G4:X0-X3], src1[G1:X0-X3] src0[G0:X4-X7];//dividend for complex division by data parallelism
4:1 CLUT1C dst [G5:X0], src0][G3:X0];//calculating reciprocal by complex division
5:1 CLUT1C dst [G5:X1], src0][G3:X1];
6:1 CLUT1C dst [G5:X2], src0][G3:X2];
7:1 CLUT1C dst [G5:X3], src0][G3:X3];
8:2:2 CMASC2C dst [G4:X4-X7], src1[G2:X0-X3] src0[G0:X4-X7];
9:2:2 CMASC2C dst [G4:X8-X11], src1[G2:X0-X3] src0[G0:X4-X7];
10:2:2 CMASC2C dst [G4:X12-X15], src1[G2:X0-X3] src0[G0:X4-X7];
11:8 CM1C dst [G6:X0-X3], src1[G5:X0] src0[G4:X0-X3];//data parallelism, Four H values of one subcarrier per instruction
12:8 CM1C dst [G7:X0-X3], src1[G5:X1] src0[G4:X4-X7];
13:8 CM1C dst [G8:X0-X3], src1[G5:X2] src0[G4:X8-X11];
14:8 CM1C dst [G9:X0-X3], src1[G5:X3] src0[G4:X12-X15];//Four H values of X0-X3 corresponding subcarrier 1 on G6, and subcarrier 2 on G7, and subcarrier 3 on G8, and subcarrier 4 on G9.
```  
**Algorithm 3** Assemblyinstruction code for LS estimation.  
Taking Wiener interpolation in the frequency domain as an example, the assembly instructions of Equations (3) and (4) are shown in Algorithm 4. If linear interpolation in the frequency domain is used, the instruction to calculate the Wiener interpolation tap in lines 6-12 can be replaced by a look-up table of the linear interpolation tap.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
11 & Interpolation & A group of five continuous DMRSs are multiplied by their corresponding taps to interpolate four subcarriers \\
12 & 2:4 QY4 & Double parallel instruction of finding cofactors in 2 \(\times\) 2 matrices \\
13 & 8:21 CM1C & Eight parallel instructions of multiplying a complex number \\  & & by a complex number and then by 2 \\
14 & 1 CLUT1C & Using two look-up tables for a complex data reciprocal \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dedicated acceleration command for channel estimation module.
# A Novel Low-Cost DIC-Based Residual Stress Measurement Device
###### Abstract  
Residual stress often has a significant impact on part performance and lifetime. Existing measurement techniques using strain gauges or non-destructive methods are often expensive and time-consuming. This paper presents a low-cost, novel measurement device that uses digital image correlation with the hole-drilling method to quantify the magnitude and preferred orientation of these locked-in forces. A two-axis measurement device that rapidly drills and images the surface around the hole was developed to measure residual stresses as a function of depth with sub-millimetre resolution. Validation of the device and DIC methodology was performed using a four-point bending specimen and comparison with conventional strain gauge methods. The results showed strong correlations between the two measurement techniques, as well as the theoretical estimates. The total cost of production was estimated to be approximately \(\mathcal{E}\)380, which is significantly cheaper than competitors. The device also substantially reduced the cost per measurement point (less than \(\mathcal{E}\)1 vs. \(\mathcal{E}\)50+) and shortened the experiment duration from 2 h per point to 45 min per measurement. A functional, rapid, economical device has been designed and produced, which is currently being used for residual stress analysis of industrial samples. The presented design is completely open-source, and the relevant links are provided.  
Keywords:residual stress measurements; hole-drilling method; digital image correlation; microscopic techniques; semi-destructive methods +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Performance of the Quartz Crystal Microbalance Sensor  
The QCM response was studied using palmitic acid at different pH ranges (6.0-10.7) and concentrations (0-100 mg/L). Palmitic acid aqueous solution samples were prepared by adding palmitic acid purchased from Wako Pure Chemical Industries (Osaka, Japan) into reverse osmosis-purified water at concentrations of 12.5, 25, 50, and 100 mg/L and the pH was adjusted to specified values by adding 0.1 mol/L HCl or NaOH. Subsequently the solution was heated up to 65 \({}^{\circ}\)C in a water bath and stirred by a magnetic stirrer to melt and dissociate the palmitic acid. Finally the pH was adjusted to the specified values at 25 \({}^{\circ}\)C, where solid particles of the unionized palmitic acid were present in the aqueous phase at pH 6.0, 7.5, and 9.0; however they were completely dissolved into the water at pH 10.7. To investigate responses of the QCM sensor to several chemicals, triolein (100 mg/L), acetic acid (100 mg/L), and a mixture of palmitic acid (final concentration 100 mg/L) and triolein (final concentration 100 mg/L) samples were prepared similarly. In measurements of anaerobic digestate samples, the QCM system must be operated in a batch mode after pH adjustment and removal of suspended solids. The digestate samples were prepared as follows; 1 mL of digested sludge was first diluted up to 40 times by water and the pH was adjusted to 10.7 by adding 1 mol/L NaOH.  
Figure 1: Schematic of the flow cell equipped with quartz crystal sensor. Solid-line arrows indicate travel directions of water.  
After stirring at 65 \({}^{\circ}\)C in a water bath, the pH was adjusted to 10.7 again at 25 \({}^{\circ}\)C. Finally, the solution was centrifuged at 5000 rpm for 10 min and the supernatant was used for QCM measurements.
# Study of Mixed-Mode Cracking of Dovetail Root of an Aero-Engine Blade Like Structure
## 5 Results and Discussion
### Effects of \(K_{\rm eq}\)  
A crack subjected to three-dimensional mixed-mode loading is capable of growth if \(\Delta\rm{K_{eq}}>\Delta\rm{K_{th}}\). In other words, the crack propagates if a stress intensity factor written as a function of \(\rm{K_{I}}\), \(\rm{K_{II}}\), and \(\rm{K_{III}}\) (and not \(\rm{K_{I}}\) alone) exceeds the propagation threshold. The Franc3D analysis was repeated by using \(\rm{K_{eq}}\) instead of \(\rm{K_{I}}\) for the 45\({}^{\circ}\) short flank geometry. This numerical simulation is relevant as it was observed that \(\rm{K_{II}}\) and even most importantly \(\rm{K_{III}}\) may play a relevant role in such complex cracking scenarios.  
The value of \(\rm{K_{eq}}\) used was taken from reference [14] and it is given in Equation (2).  
\[\Delta{K_{eq}}=\frac{\Delta{K_{I}}}{2}+\frac{\sqrt{\Delta{K_{I}^{2}}+5.336\ \Delta{K_{II}^{2}}+4\ \Delta{K_{III}^{2}}}}{2} \tag{2}\]  
This 3D mixed-mode criterion was compared with other criteria available in the open literature (like [PERSON] and [PERSON]) [23] and very small difference was found in terms of crack propagation (direction, kink, twist angle, and so on).  
A comparison between a propagation driven by \(\rm{K_{I}}\) alone and a propagation driven by \(\rm{K_{eq}}\) is shown in Figure 27. The geometry analysed is the 45\({}^{\circ}\) short flank.  
It could be clearly observed that the \(\rm{K_{eq}}\) is higher than considering only the \(\rm{K_{IC}}\) failure criterion when the crack is relatively short. After a certain length, however, the crack behaviour remains almost unaffected until 12 mm crack length. Compared to that size, the \(\rm{K_{I}}\) will have a higher trend than \(\rm{K_{eq}}\). The different crack shapes are shown in Figure 28. It can be seen that the long path of the crack will deviate in such a way as to be able to follow the edge of the contact.  
Figure 27: Comparison between a \(\rm{K_{I}}\) and a \(\rm{K_{eq}}\) driven crack for a 45\({}^{\circ}\) short flange geometry, 95% crack front.
# On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures
## 4 Performance Assessment
### Impact of the NPRACH Periodicity  
The impact of periodicity is investigated with Configuration 3 since the NPRACH Configurations 1 and 2 do not differ in terms of RAO periodicities. Specifically, the periodicity is doubled compared to the already considered values. The other parameters related to the third NPRACH resource configuration are summarized in Table 8.  
Figure 13: ECDF of the end-to-end delays obtained in all simulations.  
It is important to note that the only difference with respect to Configuration 1 lies in the periodicity, in order to better identifying its relationship with the number of devices accessing RAOs, as well as the success and collision probabilities. Figure 14 illustrates the number of devices accessing RAOs for both Configuration 3 and Configuration 1.  
Greater periodicities values lead to a greater number of terminals in the NPRACH since there are fewer RAOs per unit of time. The average number of sensors accessing RAOs quickly reaches extremely high values when \(M=10\),000. It is worth noting that the curves do not grow indefinitely because each device can perform the preamble transmission a maximum number of times, i.e., \(\alpha=10\).  
In parallel, Figure 15 shows the success and collision probabilities.  
The success probability is greatly reduced due to the higher density of terminals in each RAOs. However, it is worth mentioning that, when \(M=5000\), performance is almost satisfactory, however the amount of available resources is halved with respect to Configuration 1.  
This brief analysis highlights the important trade-off between the
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 4 Simulation Model
### Assumptions  
For the network setting, we consider a smart grid deployment of FBE nodes, i.e., a fully controlled industrial network operating in a greenfield environment. An example of this generic setting is a smart grid neighborhood area network [40] operating on a dedicated frequency. We are interested in determining the upper-bound performance of FBE and its variants. Therefore, we focus on network saturation conditions (i.e., a full-buffer model), although our implementation also supports a constant-rate traffic generator. Furthermore, since we assess channel access performance, we assume perfect channel conditions and no hidden nodes. Finally, we limit the study to downlink communication only and assume that acknowledgments are sent out of band (which is reasonable for NR-U operation).  
Figure 8: Operation of BITR FBE.  
Figure 7: Operation of greedy-enhanced FBE.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 4 Our Approach
### Matrix Segmentation  
The key to representing a sparse matrix in the HYB5 format is to determine how to partition the matrix. As a first step, the input matrix A has to be divided into two sub-matrices, where their number of rows and columns are equal to those of matrix A. As shown in Figure 4, the size of the sub-matrices is 16 \(\times\) 16. During matrix partitioning, we only have to save non-zero elements for both sub-matrices. The divided non-zero elements remain in their original locations for each sub-matrix. As we have mentioned, a partitioning parameter K is used to determine the position where to cut a row of the matrix. The first K non-zero elements are stored in the left sub-matrix. If there are fewer than K non-zeros, then all non-zero elements in this row are stored in the left sub-matrix. In Figure 4, K = 3 and the first three non-zero elements of each row are represented in SELL-C-\(\sigma\). The remaining non-zero elements within this row are stored in the right sub-matrix in CSR5. The resultant HYB5 structures are outlined in the upper right-hand portion of Figure 4.  
As illustrated in Algorithm 1, for each row, we use an array row_counter to record the number of non-zero elements stored in row coo_row_index[i], i.e., the row index of the \(i\)th non-zero element. When the non-zero elements of this row are accumulated to K, the rest are stored into the right matrix. In other words, the non-zero elements before K of each row will be stored as the left matrix and represented in the SELL-C-\(\sigma\) format. The right sub-matrix will be first converted into the CSR format and then to the CSR5 format. We expect that there are as few padding zeros as possible in the left sub-matrix, letting CSR5 handle the very long non-zero rows.
## 3 Methodology
### Time and Space Complexity
#### 3.4.2 Space Complexity  
The spatial complexity of the original YOLOv5 primarily consists of the parameters and activation values from the convolutional layers. The storage requirement for the weights of each convolution layer, along with the storage requirement for intermediate feature maps, is denoted as \(O\left(C_{in}\times C_{out}\times k^{2}\right)\). As the number of layers in the network increases, the spatial complexity grows linearly.  
The introduction of BiFPN adds multiple bidirectional fusion layers, each generating new feature maps, thereby requiring additional storage space. Following feature fusion, convolution operations are performed to adjust the number of channels, which also increases the parameter count of the model. However, the multi-scale feature fusion strategy improves the network's performance when handling targets of varying sizes, particularly enhancing the detection of small objects. The CBAM generates channel and spatial attention weights for each convolution layer, which necessitates additional memory for storage. While the storage demand for attention weights is relatively small, this requirement can accumulate as the network depth increases.  
In comparison to the original convolution layers, C3 Ghost has fewer parameters, thereby reducing storage demands. Overall, the spatial complexity has decreased.
## 3 Implementation Details
### Measurement  
To test the capture system, two sets of measurements were taken with the emitters positioned 0.5 m and 1 m apart from each other, with the closest sensor being 1 m from the source using the developed sensor. These captures demonstrate that the system is capable of precisely detecting small differences in time. However, it should be noted that when the time differences are very small, the overlapping of waves can occur, making it challenging to distinguish between them.  
Figure 8 presents the oscilloscope capture with emitters positioned 1000 mm and 1500 mm from the receiver. The system's precision is demonstrated by the clearly distinct arrival times of the signals and their subsequent attenuation.  
To further evaluate the system's repeatability, Figure 9 depicts the arrival and attenuation of the signals with one emitter 1000 mm and the other 2000 mm from the receiver, averaged over 100 samples. This averaging demonstrates minimal jitter, underscoring the high repeatability of the system.  
Figure 7: Emitter device used for testing, based on an STM Nucleo-32 board.  
The gain of the amplifier was set at a level adequate for direct reception, which minimizes the impact of echoes. This configuration ensures that echoes are very small compared to the direct signal, enhancing the system's accuracy in detecting the primary signals.  
These measurements demonstrate the effectiveness of the developed sensor in precisely capturing small time differences between ultrasound signals, despite the challenge of wave overlap at very short time intervals. The high directivity of the sensor effectively mitigates the problem of small intervals. Placing an array of three or four sensors per  
Figure 8: Oscilloscope capture showing the arrival of two ultrasound signals with one emitter placed 1000 mm and the other one 1500 mm from the receiver, highlighting the signal’s initial peak and attenuation over time.  
Figure 9: Oscilloscope capture depicting the arrival and attenuation of two ultrasound signals with one emitter 1000 mm and the other one 2000 mm from the receiver, averaged over 100 samples to demonstrate minimal jitter.  
robot resolves this issue and increases accuracy. The high precision of the amplifier and the system's repeatability are evident, confirming its capability for accurate and reliable time difference measurements.
## 5 System Model and Proposed Technique  
### System Architecture and Assumptions  
In this section, we define our network architecture with some assumptions.  
Generally, underwater sensor architecture consists of three important features: buoys, anchor nodes and, sensor nodes. As indicated earlier each anchor node is already synchronized and got all the information about their location, which could also work as a reference node. Whereas buoys can get their time information and location with the help of GPS, as each buoy is equipped with GPS. Lastly, we have the sensor nodes whose location and time information needs to be estimated by direct communication with anchor nodes. Here, we assume that each sensor node possesses three main features: sensing, communication, and computation. All these nodes are randomly distributed and collect information about the stochastic events as given by Figure 1. According to the proposed scenario, only one node will be synchronized and localized by using four anchor nodes. As indicated above all these anchor nodes know their positions and timings. Suppose we have \(i\)th anchor \(A_{i}\) having an initial skew \(\alpha\) and initial offset \(\beta\) which is located at \([x,y,z]\). The ordinary sensor node that needs to be synchronized is denoted by B and its location is \([x^{s},~{}y^{s},~{}z^{s}]\), which will need to know but the \(z^{s}\) is known along with the skew and offset. The initial procedure starts with range-based localization that gives high accuracy as compare to range-free localization. It initially estimates the distance and angle by using angle of arrival (AoA), time of arrival (ToA), receiver signal strength (RSS), or from the network connectivity, then it employs triangulation or multilateration for the transformation of ranges into coordinates (least square method). The anchor node sends M rounds of messages to the sensor node, during the \(m\)th round of messages anchor \(A_{i}\) sends a message at time \(\eta_{A}\) which is received by the sensor node at time \(T_{A}\) then sensor node B sends the acknowledgment message to the anchor node \(A_{i}\) at time \(T_{B}\) which is received by the anchor node at time \(\eta_{B}\). In this way, node B collects a set of timestamp information \([\eta_{A},~{}T_{A},~{}\eta_{B},~{}T_{B}]\) after M rounds of messages.
# Numerical Simulation on Dynamic Behavior of Slab-Column Connections Subjected to Blast Loads
## 4 Analysis Results
### Slab Deflection  
Figure 6 shows the deflection of the slab subjected to TNT 4 kg along the diagonal distance away from the corner of the column. The maximum deflections of the slab occurred similarly in every specimen with TNT 4 kg. The deflection of the slab increased rapidly from the point about 350 mm away from the corner of the column. This point is similar to the point where the fracture of the slab occurred. Figure 7 shows the slab deflections for TNT 12 kg. Large deflections were observed at about 150 mm away from the corner of the column, and beyond that point, fracture of the slab was observed. As the larger explosive load was applied, the fracture area was much larger than that of 4 kg TNT applied specimens.  
When comparing the effective deflection of the unbroken part of the slab, it was confirmed that the drop panel slightly reduced deflection. However, for all specimens with TNT of 4 kg and 12 kg, comparing the specimens IC, EC, and CC, the slab deflection according to the position of the column showed no significant difference.  
Figure 5: Failure shape of slab–column connections: (**a**) specimen IC4; (**b**) specimen IC-D4; (**c**) specimen EC4; (**d**) specimen CC4; (**e**) specimen IC12; (**f**) specimen IC-D12; (**g**) specimen EC12; (**h**) specimen CC12.
## 5 Multi-Objective Optimization Framework  
### Multi-Objective Problem Formulation  
The multi-objective framework aims to simultaneously maximize average output torque (\(T_{av}\)), enhance motor efficiency (\(\eta\)), and minimize torque ripple (\(\Delta T\)). The design variables include flux barrier thickness (\(b_{r}\)), PM thickness (\(b_{m}\)), and PM width (\(l_{m}\)), which were identified as highly important parameters through prior variable importance analysis in Section 3.  
Figure 11: Comparison of predicted and measured values for optimization objectives: (**a**) average output torque; (**b**) motor efficiency; and (**c**) torque ripple.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Factor** & **RMSE 1** & \(R^{2}\) **Score 2** \\ \hline \multirow{3}{*}{GRNN} & \(T_{av}\) & 0.0456 & 0.964 \\  & \(\eta\) & 0.0426 & 0.962 \\  & \(\Delta T\) & 0.0382 & 0.965 \\ \hline \multirow{3}{*}{SVR} & \(T_{av}\) & 0.0653 & 0.915 \\  & \(\eta\) & 0.0616 & 0.908 \\  & \(\Delta T\) & 0.0553 & 0.906 \\ \hline \multirow{3}{*}{MLP} & \(T_{av}\) & 0.0511 & 0.948 \\  & \(\eta\) & 0.0573 & 0.920 \\  & \(\Delta T\) & 0.0671 & 0.862 \\ \hline \multirow{3}{*}{Kriging} & \(T_{av}\) & 0.0466 & 0.957 \\  & \(\eta\) & 0.0558 & 0.925 \\ \cline{1-1}  & \(\Delta T\) & 0.0625 & 0.880 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction accuracy metrics of different surrogate models.  
Let the design variable vector be defined as \(x=[b_{r},b_{m},l_{m}]^{T}\). The relationships between the design variables and optimization objectives, established via GRNN in Section 4, are expressed as \(T_{av}=f_{1}(x),\eta=f_{2}(x),\Delta T=f_{3}(x)\).  
Consequently, we formulate the multi-objective optimization problem as a vector function mapping the design variables \(x\) to performance objectives, seeking Pareto-optimal solutions through simultaneous maximization of \(T_{av}\), \(\eta\) and minimization of \(\Delta T\):  
\[\begin{split}\max& f_{1}(x),\ f_{2}(x)\\ \min& f_{3}(x)\\ \text{s.t.}& 3\leq b_{r}\leq 5,\\ & 3\leq b_{m}\leq 5.4,\\ & 30\leq l_{m}\leq 65.\end{split} \tag{8}\]
# The Complex Relationship between Virulence and Antibiotic Resistance
### Mechanisms
#### 2.2.2 Elevated Mutation Rates  
Despite the fitness costs, antibiotic pressure selects for bacteria that have a heightened mutation rate [11; 21; 32]. _Mycobacterium_ spp. are capable of acquiring de novo resistance via mutation under inadequate treatment regimes [33; 34; 35; 36] as are _Escherichia coli/Salmonella enterica_ (fluoroquinones) [37; 38; 39] and _Pseudomonas aeruginosa_[31; 40; 41]. This increased mutation rate is typically conferred by alterations in the genes that constitute the mismatch repair (MMR) system (\(mutS\), \(mutL\), \(mutH\), \(mutT\), \(mutM\), \(mutM\), and \(uvrD\)) [42]. Mutations in the MMR system also increase the prevalence of genetic recombination, providing diversity to antibiotic resistance mechanisms [21].  
Figure 2: Horizontal gene transfer can commonly occur through conjugation and natural transformation. Additionally, it may occur through transduction, where resistance is transmitted via bacteriophage.  
Beyond heritable genetic changes to mismatch repair systems as well as mutations in gyrase and topoisomerase enzymes (quinolone resistance in _Samonella_[43]), antibiotics can also increase mutation rates via oxidative damage [44; 45; 46] and more broadly, the stress responses [44; 47].