esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
## 4 Definition of Models in FPGA-QNN
### The Accelerator System Design
From these values, we can infer that higher SIMD values are used in the W1A1 quantization type, which provides greater parallel processing power. In contrast, the lower number of PEs in W2A2 suggests fewer parallel processing units, but with each unit performing more complex operations. For the LeNet model, similarly to the MLP model, two folding levels, H and L, were established. At the L level, no folding was applied, with PE and SIMD folding parameters set to 1 (1-1-1-1). For the H folding level, convolution layers were folded at the maximum level for all quantization types, whereas the total folding parameter for fully connected layers was set to 4. From these values, it is evident that, in the LeNet model, convolution layers have higher folding ratios while folding is less in fully connected layers. This is due to the higher memory resource requirements in fully connected layers; these layers generally need to store larger amounts of weight and activation data, necessitating more memory. Therefore, the folding level is kept low to optimize memory usage. In contrast, convolution operations require less memory, allowing for higher folding levels, which increases parallel processing capacity and, consequently, performance. The selection of these parameters is critical for both efficient use of hardware resources and model performance. Table 2 provides an overview of these models in terms of PE and SIMD values:  
After the completion of FINN transformations on all models, the input image information was transformed into a form that passed through the network as a data stream to obtain the output. The models inserted into the data stream structure are subjected to FINN hardware rendering transformations to create accelerator hardware that implements the models using the resources on the FPGA. The final constructed IP model consists of input direct memory access (IDMA) and output direct memory access (ODMA), as shown in Figure 12.1 Deep learning accelerator hardware is obtained by connecting the IP blocks in this figure with the ZYNQ operating system via the AXI interface. Thus, the Linux-based operating system built on PynqZ1 subjects the image data to deep learning processing in parallel on the accelerator. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Folding** & **Precision** & **PE** & **SIMD** \\ \hline \multirow{4}{*}{MLP (FFFF) *} & L & ALL & 1-1-1 & 1-1-1 \\  & H & W1A1 & 64-32-5 & 98-64-8 \\  & H & W1A2 & 64-32-5 & 49-32-4 \\  & H & W2A2 & 32-16-5 & 49-32-2 \\ \hline LeNet & L & ALL & 1-1-1-1-1 & 1-1-1-1 \\  & H & ALL & 6-16-60-42-5 & 1-6-8-60-1 \\ \hline \hline \end{tabular}  
* F: Full Connected Layer, C: Convolutional Layer.  
\end{table} 
Table 2: Overview of the models in terms of PE and SIMD values.
## 9 Conclusions  
In this paper, several methods of fault trending detection have been trialled, including a control chart in SPC, FFT, and CWT. Vibration data sets were obtained from an automated trampoline webbing machine. The efficacy of these methods was compared by processing the experimental data sets through each of these methods. CWT has its advantage in the field of analysing signals in time and frequency domain. Fault conditions can be identified from changes in frequencies, and hence it is often used as the tool to capture a fault that has already occurred. However, frequency information alone does not imply the timing of a fault occurring, and hence is not sufficient to provide early warning of a fault occurring.  
While CWT scalogram showed promising indicative outcomes, the CWT scalograms were difficult to use for the pulse counting process proposed. Instead of analysing the whole set of data points, the experimental data is divided into several time segments. In each time segment, synchrosqueezed scalograms are generated and the dominant frequency ranges are identified. The changing number of pulses in the dominant frequency ranges, especially the presence of troughs and turning points in the time segment graphs, is a good indicator of when a fault is about to happen in that time segment.  
Based on the evaluation of efficacy, a novel method of analysing signals and predicting faults has been developed using the data-rich capability of Industry 4.0. The proposed digital signal processing method for trend prediction requires continuous performance data capture and analysis. Once the fault trend is recognised, preventive measures can be taken to stop or adjust the machine to eliminate the production of rejects and save time or re-work.  
This research shows that there are a lot of numerical variation phenomena in the signal stream leading to a fault. To understand these variations by a computational process rather than by graph observation, machine deep learning, which has emerged as a powerful signal pattern recognition tool, is a reasonable direction for future research. The application of a deep learning algorithm often requires reliable input features to train the system before it can functionally predict or identify the unhealthy condition of the machine. The proposed algorithm has its merits for extracting fault information existing in transient signals, and thus can be integrated with deep learning algorithms in future studies.  
Composition, S.F.; revision, J.P.T.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available from the corresponding author upon request. The data are not publicly available due to the further construction of the weaving machine. Conflicts of Interest: The authors declare no conflict of interest.
## 3 Geotechnical Guidelines and Practices for the Design of Integral Bridges
### USA Practices
#### 3.1.1 AASHTO LRFD Bridge Design Specification  
The AASHTO LRFD Bridge Design Specification [23] provides guidelines concerning the design of bridges including the calculations of earth pressures behind the bridge abutments. Article 11.6.1.3 of LRFD Design Specification [23] specifies that integral abutments should account for shrinkage, creep and thermal deformations of the superstructure. Lateral pressure calculations for integral abutments are generally based on the lateral earth pressure coefficients: \(K_{0}\) for the at-rest condition, \(K_{a}\) for the active condition and \(K_{p}\) for the  
Figure 1: Types of integral and semi-integral abutments [25]. Reprinted from PD 6694-1:2011+A1:2020. _Recommendations for the Design of Structures Subject to Traffic Loading to BS EN 1997-1:2004+A1:2013_; BSI (British Standard International): London, UK, 2020 (Copyright permission BSI 2020).  
passive condition. Methodologies used to calculate these lateral earth pressure coefficients are discussed in the AASHTO LRFD Specification [23]. The coefficient, \(K_{0}\), is calculated using the well-known [PERSON]'s formula [26] for normally consolidated soil and [PERSON]'s formula [27] for over-consolidated soil. The coefficient, \(K_{a}\), is calculated based on the [PERSON] wedge theory. It further recommended that, when [PERSON]'s theory is not suitable, the active earth pressure can be estimated using the trial procedures based on [PERSON]'s wedge theory [28].  
The AASHTO LRFD Specifications [23] refer to the graphs developed by the U.S. Department of Navy [29] to determine the coefficient \(K_{p}\) for the case of a sloping or vertical wall. However, for conditions that deviate from those described by the U.S. Department of Navy [29], it recommends using [PERSON]'s log spiral wedge theory [28] to determine the magnitude of \(K_{p}\). Although the mobilised earth pressure depends on the expansion/contraction of the bridge superstructure, the AASHTO LRFD Specification only provides guidance on approximate values of abutment displacement required to fully mobilise active or passive earth pressure. It does not provide explicit guidelines on the following aspects:  
* Calculations of \(K_{p}\) when the movement is more than zero but less than the movement required to fully mobilise passive and/or active pressure; however, some USA states use linear interpolation to estimate the mobilised earth pressure for such cases;
* The abutment displacement \((\Delta)\) is taken as the displacement at the top of the abutment regardless of whether the mode of movement is rotational, translational or a combination of both;
* The values of fully mobilised displacements are based on a study conducted by [PERSON] and [PERSON] [30] under monotonic loading. However, the abutment-soil interaction in integral bridges involves cyclic loading, which almost certainly differs from the monotonic loading case.
# PointSwin: Modeling Self-Attention with Shifted Window on Point Cloud
## 4 Experiments
### Efficiency of Shifted-Window with Self-Attention Mechanism
#### 4.3.1 Group 1: The MultiHead Self-Attention Mechanism  
In the Self-Attention mechanism, we use the SingleHead Self-Attention method (SSA) and the MultiHead Self-Attention method (MSA) as filter carriers, respectively, and observe the difference between the accuracy results and the time consuming of the two methods. The experimental results are shown in Table 3.  
The advantage of the MultiHead Self-Attention method is not only in parallel computing but also in the simultaneous consideration of multidimensional information, which is equivalent to promoting the interaction of features in local neighborhoods. Therefore, the MultiHead Self-Attention method combined with the shifting window mechanism is equivalent to having a party for the features in the local neighborhood, which will effectively promote the learning of local features.  
In the experiments, we observed that the SSA method is more computationally time consuming and obtains lower accuracy results than the MSA method. This result is consistent with its theoretical significance. The MSA method performs computations simultaneously across multiple attention heads, giving the model a greater possibility to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mACC** & **OA** & **SASW Module Flops \({}^{*}\)** & **Time-Consuming \({}^{**}\)** \\ \hline PointNet & 94.99 & 98.12 & - & 55 s \\ PointSwin & 95.06 & 97.94 & 26.6 M & 150 s \\ PointSwin-E & 96.04 & 98.39 & 1.8 M & 48 s \\ \hline \hline \end{tabular} \({}^{*}\)The data are the core calculation amount of the SASW module in each batch when the BatchSize is set to 64. \({}^{**}\)The data are the average time spent training each epoch under the NVIDIA TITAN RTX graphics card environment.  
\end{table}
Table 2: Comparative experimental results of PointNet [6] and the two models of PointSwin on the ShapeNetPart dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **OA** & **Time-Consuming \({}^{*}\)** \\ \hline PointNet & 89.22 & 20 s \\ SingleHead Self-Attention method & 89.46 & 142 s \\ MultiHead Self-Attention method & 89.58 & 120 s \\ \hline \hline \end{tabular} \({}^{*}\)The data are the average time spent training each epoch under the NVIDIA TITAN RTX graphics card environment.  
\end{table}
Table 3: The OA and time results of group 1.  
discover meaningful relationships between features. The SSA method is limited by the single attention head because the single learning mode leads to a decrease in accuracy.  
Consequently, in the MSA method, the setting of NumHeads is most critical. A too large NumHeads value will lead to attention distraction, and a too small NumHeads value will result in insufficient computational efficiency. In our model, we found that the value of NumHeads is more appropriate when the ratio of Channel to NumHeads is about 8.
# Dynamical Networks Modelling Applied to Low Voltage Lines with Nonlinear Filters
## 2 Electromagnetic Interference on Low Voltage Lines--Network Model  
### A short on Electromagnetic Interference  
Low-voltage metallic lines in use for communication signals are very sensitive to small voltage fluctuations. Any disturbances in these signals could result in data changes, i.e., communication errors will be spread out through the system. One of the challenging factors of variances in metallic lines is the electromagnetic interference (EMI) [1; 2], caused by electromagnetic emissions originated externally and/or internally. As they are, in general, not controllable, electromagnetic compatibility [1; 2]techniques should be applied to the recipient (the metallic line, in this example) to protect it from these undesired emissions.  
Electromagnetic compatibility (EMC) is defined as the recipient's ability to work satisfactorily in a particular electromagnetic environment. Work satisfactorily means, firstly, it should be able to function well and secondly, it should not interfere electromagnetically in its vicinity [1; 2; 3; 4; 5; 6; 7].  
Electromagnetic interference occurs when there is the involvement of a source of interference, an interfered recipient and a path coupling source and recipient (Figure 1). This structure, source/path/recipient, allows simple visualization of the interference problems and possible ways of control and compatibility, which are: (1) eliminate interference next to the source; (2) eliminate in the path; (3) or eliminate in the recipient.  
1. Eliminate EMI next to the source. If the source can be accessed, suppressing EMI could be done by, for instance, moving it away, replacement, shielding, using output filters and other suppressor devices, or by altering its internal design of circuits and components. So far, this is the best to do and the source will not be a cause of interference for the whole environment. However, in general, the source is not under control and cannot be accessed.
2. Eliminate EMI in the path. The second possibility is doing adjustments on the transmitting path. Electromagnetic waves can be transmitted through conductive means, such as cables or \"earth\", (conducted EMI) or can be radiated through the environment. The alternatives to prevent the interference are the removal of the recipient equipment, or shielding it against radiated interference, and applying various types of suppressors and filters for undesired conducted disturbances.
3. Eliminate EMI in the recipient. If it is neither possible to deal with the source nor the path, the last alternative is the elimination of EMI within the recipient. This could be a difficult task and will involve the study of the environment, the existing noise, possible transients, and surges, which will lead to a set of solutions such as physical system layout, distributed filters, suppressors and shielding.  
Before we go further with the problem of interferences and the issues they can cause, let us analyse a hypothetical communication network system, the metallic line (which will be the recipient in our following case study).
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 3 Methods
### Model Overview  
Feature Extractors  
First, the deep features of the BEV and ground-level images were extracted using a U-Net architecture [33] that was initialized using the pretrained VGG13 [34] weights with batch normalization in the encoding layers. This is a bigger and more conventional U-Net architecture than the architecture [PERSON] et al. uses, which we observed to improve results.
## 2 Methods
### Physical Tests  
1. The Sargent Jump Test is a valid and reliable test (Cronbach's alpha = 0.96, ICC = 0.96) for explosive power in the lower limbs, which measures jumping height in centimeters from a standing position [17]. The test was performed according to the protocol described by [PERSON] et al. [18].
2. The Heel-Raise Test assesses functional ankle strength by the number of heel-raise repetitions for each leg [19; 20]. The test was found reliable (ICC = 0.78-0.84) with similar reliability as in isokinetic tests [19]. Test protocol was conducted as described by [PERSON] et al. [20], with the dancers standing barefoot on the floor.
3. The Push-Up Test assesses upper limb strength by the number of push-up repetitions to fatigue, based on [PERSON] et al.'s protocol [21]. The dancers' feet were placed together, and palms were positioned at approximately shoulder width. One push-up was counted when the dancers' chests touched the five-centimeter height cone in the lower position and then raised up to the straight arms position. The test was found to be valid and highly reliable [21].
4. The Chester Step Test is a valid test for aerobic capacity in healthy adults, highly correlated to VO\({}_{2}\)max [22]. In the test we measured HR during stepping on and off a 30-centimeter step to exertion [22]. We followed [PERSON] protocol [22], using an audio file to dictate the cadence. We used a Polar H7 chest strap and the smartphone application Beat to monitor HR. The Polar H7 sensor was found to be in agreement with the electrocardiogram (r\({}_{\text{c}}\) = 0.996) for assessing HR metrics during aerobic exercises [23].
5. Hip Range of Motion (ROM) was measured in degrees using a goniometer, which was found reliable in healthy adults (ICC = 0.76-0.97) [24; 25]. Hip ROM measures are known dance injury risk factors [12]. We measured passive and functional ROM for this study: 1. Passive range for hip external rotation was measured for each leg when sitting on a firm table [25]. 2. Functional turnout was measured standing on a piece of paper in ballet's first position. The functional turnout consisted of the angle formed by the longitudinal bisection of the feet [13]. 3. Compensated turnout was calculated as the functional turnout minus the bilateral passive hip external rotation ROM [13].
# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 5 Research Challenges and Future Directions
### DigAg Cyber-Security Framework  
The digital agriculture revolution is still at an early stage. Continuous Internet connectivity, inexpensive sensors, remote deployment, non-technical end-users, and new applications and use-cases open up new vulnerabilities and security issues. Frameworks and standards are necessary to guide tradesmen, farmers, and businesses to implement security controls. Typically, a framework development takes considerable time as it involves consultation with stakeholders (business, farmers, different agriculture sectors). The framework guides all the stakeholders on implementing security at different levels for various assets (data, devices, applications, etc.). Currently, there is no security framework developed explicitly for DigAg. The National Institute of Standards and Technology (NIST) Cyber Security Framework (CSF) covers IT and operational security [30]. However, it does not capture control over all the IT assets. A closer look at the NIST framework could be a good starting point for developing a security framework specifically for DigAg.
## 3 Results and Discussion  
### Bacteria Count  
Table 4 shows the bacteria count in log CFU per gram of yogurt dry ingredient mixture influenced by different storage times and temperatures. Storage at 5 \({}^{\circ}\)C statistically did not caused any decrease in bacteria count for all investigated species (\(\pm\)0.06-00.7 log). Storage of yogurt dry ingredients at 20 \({}^{\circ}\)C caused a 0.64-0.85 log decrease in LA bacteria count. The most significant decrease in bacteria count was noted after 8 weeks of storage at 20 \({}^{\circ}\)C, decreasing from 8.2 to 7.56 log for _Streptococcus thermophiles_, from 8.54 to 7.85 log for _Lactobacillus delbrueckii_ subsp. _lactis_ and _Lactobacillus acidophilus_, and from 8.11 to 7.26 log for _Bifidobacterium lactis_ (Table 4).  
[PERSON] et al. [36] found that counts of _Streptococcus thermophilus_ and _Lactobacillus delbrueckii_ subsp. _bulgaricus_ decreased from 1.8 to 3.5 log during storage for 35 days at 5 \({}^{\circ}\)C, although the obtained yogurts maintained counts \(\geq\)10\({}^{7}\) cfu/mL for 3 weeks. [PERSON] et al. [37] also found that the average initial microbial count for each of the activated cultures was \(\sim\)10\({}^{7}\) CFU/mL. Bacteria counts 24 h after fermentation were log 8.52, 7.96 and 9.15 for LB, LA, and BL, respectively. LA counts decreased 2.34 log after 28 days of storage and maintained minimum requirements for beneficial properties for 3 weeks of storage. The _S. thermophilus_ counts were superior to those of the other strains, and they remained stable throughout the storage period. Similarly, in our research, the lowest lactic bacteria count in yogurt obtained from powdered ingredients stored at 20 \({}^{\circ}\)C for 8 weeks was still 6.9 log CFU/mL after 3 weeks of yogurt storage (data not shown). The initial count of lactic bacteria in dry yogurt ingredient mixture has a significant influence on the bacteria count in the final product.
## 3 Results and Discussion
### Turbulence Statistics
#### 3.2.3 Turbulent Dissipation Rate (\(\varepsilon\), \(\omega\))  
Turbulent dissipation rate \(\varepsilon\) and specific turbulent dissipation rate \(\omega\) represent the dissipation rate of the rke model and the sst model. The relationship between the two is as follows:  
\[\omega=\frac{\varepsilon}{C_{\mu}\cdot k}. \tag{16}\]  
As can be seen from Figure 15, the turbulent kinetic energy dissipation rate of the hydrothermal plume presents a mushroom-like spatial distribution. Its value reaches its maximum in the plume stem area near the vent orifice and decreases sharply along the radial direction. Compared with the turbulent kinetic energy, the radial attenuation of the turbulent dissipation rate has a greater degree of order of magnitude change. After the conversion of the two turbulent dissipation rates, the maximum turbulent dissipation rates calculated by rke_case2 and sst_case2 are 0.2 W kg\({}^{-1}\) and 0.18 W kg\({}^{-1}\). Further comparing with the results of [PERSON] et al., the dissipation rate distribution of the sst model is too  
Figure 14: Turbulent kinetic energy of the center line of the quasi-steady hydrothermal plume. (**a**) Normalized turbulent kinetic energy and plume elevation change; (**b**) Linear relationship between maximum turbulent kinetic energy, buoyancy flux and buoyancy frequency. Dotted lines of the same color are obtained by fitting corresponding scatter points.  
exaggerated [12]. Since turbulent dissipation rate represents the rate at which turbulent kinetic energy is converted into heat energy under the action of molecular viscosity, the outline of turbulent kinetic energy and turbulent dissipation rate cloud map is generally similar. The rke model improves the calculation accuracy of the rotating flow by improving the structure of \(\varepsilon\) transport equation. The prediction of rotational flow and free shear flow in the far field region by the sst model is not as accurate as that by the rke model, which leads to the high deviation of the sst model in predicting turbulent dissipation rate of the neutral buoyant layer.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results
### Optimum Number of Cluster Evaluation  
The C-OFSMA performs better than the OFSMA in the previous sub-section. Now, there is a query about how many clusters need to be allocated. It is not quite as though assigning more clusters improves throughput, because using more clusters demands a large antenna at the receiver end, which is prohibitively expensive. Therefore, the sensor placement for cluster optimization is evaluated in Algorithm 2. Finally, the appropriate number of clusters to provide the best reliability is evaluated in Equation (16). Using 90 subcarrier channels, with lower arrival rates, nearly 100% reliability can be achieved for 3-, 5-, and 7-packet duplication. However, this type of intra-vehicular environment performs with high data traffic; fewer data packets transmission is not our prime objective. Therefore, the reliability responsiveness scenario can only be visualized using different clusters at higher arrival conditions for different packet duplication.