esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 3 Materials and Methods
### Prediction of PQP Using LSTM
#### 3.3.3 Softmax Classification  
Generally, the Softmax function is used in the final layer of the deep learning prediction model including LSTM to classify predicted results of a final output layer. The Softmax function takes as input a vector of \(h\) real numbers and normalizes it into a probability distribution consisting of \(h\) probabilities. Here \(h\) is the number of predicted classes. Even if some vector components could be negative, or greater than one, and might not sum to 1, after applying the Softmax function, each component will be in the interval (0.0, 1.0) and the sum of the components will be 1, so it can be interpreted as a probability. We use the Softmax function to output the PQP result with the following formula:  
\[\text{PQP}_{i}=\frac{e^{a_{i}}}{\sum_{j=1}^{N}e^{a_{j}}} \tag{22}\]We used this algorithm to classify the predicted result of the LSTM output layer, where \(\text{PQP}_{i}\) is the Softmax output for the \(i\)th value in the input vector of size \(N\). The input vectors are [\(a_{1}\), \(a_{2}\), \(a_{3}\),..., \(a_{N}\)], and \(\text{PQP}_{i}\) is always positive. The standard exponential function is applied to each element \(\text{PQP}_{i}\), and the values are normalized by dividing by the sum of all the exponents. This normalization ensures that the sum of the components of the output vector \(\text{PQP}\) is 1; \(\text{PQP}_{i}\) is greater than 0 because of the exponents. Because the numerator appears in the denominator, summed up with some other positive numbers, \(\text{PQP}_{i}\) is smaller than 1. Decision criteria for \(\text{PQP}\) status were decided with many experiments by checking machine feasibility and operation status conditions according to the \(\text{PQP}\) value. Table 4 shows the criteria for production process stability. Softmax classifier results from 0.50 to 1.00 are the best results, which mean the processes might be capable of producing products without any defects. On the other hand, if the results range from 0.00 to 0.50, the quality in the criteria table is poor or terrible, which indicates that the process is so bad that it needs to be improved as soon as possible to prevent defective products.
## 4 Experimental Tests of the Polymer Liner
### FBG Sensors  
FBG sensors, the \"bare fiber\" type with an Ormocer(r) coating, were used in this research. They are characterized by the lack of an additional buffer coating, which improves the transfer of loading between a monitored object and a transducer. The optical fiber sensors were integrated with the object using X60 glue, which is a two-component adhesive for strain gauges. Before attaching the sensors, the surface was de-greased, and, locally, the paint coating enabling the DIC measurements was also removed. The sensors were attached pointwise at the two ends of the Bragg grating. Thanks to this approach, the local inhomogeneity of deformation along the sensor axis was averaged along with the distance between the attachment points. An interrogator (SI405 HBM) was used to record the signals; it allows taking measurements at a 5-Hz frequency and 1-pm accuracy (corresponding to a 2-\(\mu\)e accuracy).  
The whole liner was covered with 11 sensors in total. The placement of the sensors is shown in Figure 7. The description of the placement is provided in Table 1. Six sensors were placed on the cylindrical part of the liner (three in the circumferential direction and three in the longitudinal direction). Five sensors were placed on the dome (four concentric and one longitudinal).  
Figure 7: Placement of sensors on the liner. (**a**) Entire liner and (**b**) dome part. Colors are attributed to the different directions and locations of sensors: black—circumferential, red—longitudinal at the cylindrical part, blue—longitudinal at the dome.
# Modes Control of Lamb Wave in Plates Using Meander-Line Electromagnetic Acoustic Transducers
## 4 Verifications  
### Simulation for Modes Control of Lamb Waves  
In this section, the S0 and A0 modes controlled by using paired EMATs are verified by simulations and experiments.  
Firstly, the modes selectivity of conventional structure EMATs and paired structure EMATs are compared, and the EMAT models are set up in a commercial finite element analysis software, COMSOL Multiphysics. The electromagnetic field and the mechanics field were used. The 'AC/DC, Magnetic Fields' module is used to generate static bias magnetic and induced eddy current in the specimen. The 'Solid Mechanics (Elastic wave)' module is used to calculate body loads that are the Lorentz forces generated in the skin depth of the specimen. The parameters for symmetric and antisymmetric EMATs are selected and calculated in Table 1. A narrow bandwidth 5-peak tone burst signal is used as excitation source, because the dispersion of a Lamb wave is more obvious with the increase of bandwidth [21]. An observation point was set on the surface of the specimen to detect the wave motion in the plate. The distance between the observation point and the EMATs is 400 mm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**Phase Velocity (m/s)**} & \multicolumn{2}{c}{**Frequency Thickness**} & \multirow{2}{*}{**Center Frequency (kHz)**} & \multirow{2}{*}{**Magnet (mm)**} & \multirow{2}{*}{**Wavelength (mm)**} & \multirow{2}{*}{**Plate Thickness (mm)**} \\  & & & & & & \\  & & & & & & \\ \hline S0 & 5262 & 0.984 & 328 & \(57\times 37\times 17\) & 16 & 3 \\ A0 & 2442 & 1.1 & 366.7 & \(37\times 37\times 37\) & 6.3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of EMATs.  
Figure 5: **(a)** The paired parallel biased EMAT (PB-EMAT) configuration, and **(b)** the Lorentz forces generated by the paired PB-EMAT. Only the Lorentz forces generated by the wires in the dotted frame are plotted in **(b)** too. The meanings of symbol \(\bigotimes\) and \(\bigcirc\) are the same as in Figure 4.  
Figure 6 shows the simulation results, where the amplitude ratio of the S0 to A0 is 16.8 dB for the conventional symmetric mode EMAT (shown in Figure 6a), and 50.2 dB for the paired NB-EMAT (shown in Figure 6b). The amplitude ratio of the A0 to S0 is 21 dB for the conventional antisymmetric mode EMAT (shown in Figure 6c), and 83.8 dB for the paired PB-EMAT (shown in Figure 6d). Therefore, we can find that the paired structure EMATs have better mode selectivity compared to the conventional EMATs.  
Figure 6: Results of simulation. The observed Lamb wave harmonic displacement, S0 mode, generated by (**a**) traditional EMAT and (**b**) paired NB-EMAT; A0 mode, generated by (**c**) traditional EMAT and (**d**) paired PB-EMAT.
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 2 Numerical Model
### Sedimentation Scour Model
#### 2.1.2 Entrainment and Deposition  
In the model, entrainment and deposition are treated as two opposing micro-processes that take place simultaneously. They are combined to determine the net rate of exchange between packed and suspended sediments. For entrainment, the velocity at which the grains leave the packed bed is termed the lifting velocity [21]:  
\[\mathbf{u}_{lif,i}=\mathbf{n}_{b}\alpha_{i}d_{i,j}^{0.3}(\theta_{i}-\theta_{cr,i})^{1. 5}\sqrt{gt_{i}(s_{i}-1)} \tag{4}\]  
\[d_{i,j}=d_{i}\Big{[}\frac{\xi_{b}-1}{\
u_{f}^{2}}\Big{]}^{1/3} \tag{5}\]  
where \(\alpha_{i}\) is the entrainment coefficient of species, usually a constant set as 0.018, \(\mathbf{n}_{b}\) is the outward normal vector of the packed bed surface, \(d_{i,j}\) is the dimensionless grain size, \(s_{i}=\rho_{i}/\rho_{f}\), and \(\
u_{f}\) is the kinematic viscosity of fluid.  
Deposition takes place when suspended grains settle onto the packed bed due to the combined effect of gravity, buoyancy, and friction. The settling velocity [22] is expressed as follows:  
\[\mathbf{u}_{s settle,i}=\frac{g}{g}\Big{[}[10.36^{2}+1.049\alpha_{i,j}^{0.3}]^{1/2 }-10.36\Big{]}\frac{\
u_{f}}{2d_{i}} \tag{6}\]  
where \(\mathbf{g}\) is the gravity acceleration, and \(\mathbf{u}_{s settle,i}\) is assumed in the same direction as \(\mathbf{g}\).
## 2 Results
### Stability Analysis of the Candidate Reference Genes
#### 2.4.4 Delta Ct Analysis  
The Delta Ct analysis method involves utilizing the Ct value of a potential candidate gene to compute the average standard deviation for its stability ranking. A lower average standard deviation indicates greater gene stability. As shown in Figure 7, within the pool of the ten candidate reference genes, _Act1_ emerged as the most stable, whereas _a-TUB_ exhibited the least stability among the fruits across the various developmental periods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rank**} & \multicolumn{2}{c}{**Fruits in Different Periods**} & \multicolumn{2}{c}{**Different Tissues**} \\ \cline{2-5}  & **Gene** & \(\textbf{CV}\pm\textbf{SD}\) & **Gene** & \(\textbf{CV}\pm\textbf{SD}\) \\ \hline
1 & _UBQ_ & 1.16 \(\pm\) 0.24 & _His3_ & 1.16 \(\pm\) 0.24 \\
2 & _Act1_ & 1.7 \(\pm\) 0.35 & _Act1_ & 1.55 \(\pm\) 0.32 \\
3 & _g-TUB6_ & 1.49 \(\pm\) 0.36 & _GAPDH_ & 1.55 \(\pm\) 0.34 \\
4 & _GAPDH_ & 2.62 \(\pm\) 0.53 & _UBQ10_ & 2.68 \(\pm\) 0.51 \\
5 & _His3_ & 3.43 \(\pm\) 0.69 & _g-TUB2_ & 2.34 \(\pm\) 0.54 \\
6 & _UBQ10_ & 4.44 \(\pm\) 0.9 & _a-TUB_ & 2.57 \(\pm\) 0.55 \\
7 & _CHS1_ & 5.3 \(\pm\) 1.19 & _CHS1_ & 3.08 \(\pm\) 0.69 \\
8 & _g-TUB2_ & 4.74 \(\pm\) 1.24 & _Act100_ & 2.76 \(\pm\) 0.79 \\
9 & _Act100_ & 4.91 \(\pm\) 1.26 & _UBQ_ & 3.92 \(\pm\) 0.8 \\
10 & _a-TUB_ & 6.12 \(\pm\) 1.35 & _g-TUB6_ & 3.43 \(\pm\) 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Expression stability of the ten candidate reference genes using BestKeeper (coefficient of variation (CV) \(\pm\) standard deviation (SD)).  
Figure 7: Delta Ct stability analysis of the ten candidate reference genes. (DeltaCt can directly use the Ct value to calculate the average standard deviation to evaluate the stability of genes, and the smaller the average standard deviation of the reference gene, the more stable it is).
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model
### Uploading Acceleration  
To accelerate the uploading process of model parameters, the available bandwidth is allocated based on the distance between the clients and the edge server. When \(n\) clients are uploading simultaneously, the allocation ratio for client \(i\) is adjusted to ensure that the transmission time for all clients is nearly equal, i.e., the allocation ratio for client \(i\) is given by  
\[\min\{\frac{s_{ij}^{2}\beta_{Premain}^{(j)}}{\sum_{i=1}^{n}s_{ij}^{2}},\beta_ {\max}\}. \tag{15}\]  
If there are no other clients uploading at the same time, then the \(\min\{\beta_{remain}^{(j)},\beta_{\max}\}\) of the bandwidth is allocated to client \(i\), where \(\beta_{\max}\) is the constant, which limits the maximum ratio of bandwidth allocated by an edge server to a client.
## 3 Results
### Radiographic Evaluation  
At T1 and T2, evaluation of the CBCT images showed no cases with bone loss at the mesial or distal implant shoulder. Additionally, there was no vertical loss of the buccal lamella (Figure 4a-d). All the implants were completely covered with hard tissue. \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Study Group**} & **Sign** \\ \hline
**Baseline Data of Participants** & **Total** & **ETT** & **NETT** & \(p\)**-Value** \\ \hline Age (years) & & & & \\ Mean (SD) & 61.7 (10.5) & 62.6 (8.6) & 60.8 (12.4) & n.s. \\ Range & 28–80 & 49–80 & 28–76 & \\ Gender (male) & & & & \\ \(n\) (\%) & 15 of 34 (44) & 6 of 17 (35) & 9 of 17 (52) & n.s. \\ \hline \end{tabular}
\end{table} 
Table 1: Baseline characteristics of the participating patients at the time of augmentation procedure with autogenous dentin. \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Study Group**} & **Fisher’s Exact Test (2-Sided)** \\ \hline
**Clinical Complication** & **Total** & **ETT** & **NETT** & \(p\)**-Value** \\ \hline Total severe complications & & & & \\ \(n\) (\%) on PL & 0 of 34 (0) & 0 of 17 (0) & 0 of 17 (0) & n.s. \\ \(n\) (\%) on RL & 0 of 40 (0) & 0 of 18 (0) & 0 of 22 (0) & n.s. \\ \(n\) (\%) on IL & 0 of 45 (0) & 0 of 21 (0) & 0 of 24 (0) & n.s. \\ Wound dehiscence & & & & \\ \(n\) (\%) on PL & 2 of 34 (6) & 1 of 17 (6) & 1 of 17 (6) & 1.000 \\ \(n\) (\%) on RL & 2 of 40 (8) & 1 of 18 (6) & 1 of 22 (5) & 0.884 \\ \(n\) (\%) on IL & 2 of 45 (4) & 1 of 21 (5) & 1 of 24 (4) & 0.923 \\ Inflammation (pus) & & & & \\ \(n\) (\%) on PL & 0 of 34 (0) & 0 of 17 (0) & 0 of 17 (0) & n.s. \\ \(n\) (\%) on RL & 0 of 40 (0) & 0 of 18 (0) & 0 of 22 (0) & n.s. \\ \(n\) (\%) on IL & 0 of 45 (0) & 0 of 21 (0) & 0 of 24 (0) & n.s. \\ Total complications at all & & & & \\ \(n\) (\%) on PL & 3 of 34 (9) & 1 of 17 (6) & 2 of 17 (12) & 0.545 \\ \(n\) (\%) on RL & 3 of 40 (8) & 1 of 18 (6) & 2 of 22 (9) & 0.673 \\ \(n\) (\%) on IL & 3 of 45 (9) & 1 of 21 (5) & 2 of 24 (8) & 0.632 \\ \hline \end{tabular}
\end{table} 
Table 2: Clinical complications at the patient, region, and implant level. All the measurements of the alveolar ridge width and resorption of the buccal lamella on the implants are summarized in Tables 3 and 4 (measurement sites as shown in Figure 3a,b). The alveolar ridge width (at the patient level) at the time of bone graft was, on average, 9.3 mm for ETT and 8.7 mm for NETT at level L2. At the time of follow-up (3 months after augmentation), the alveolar ridge width was 9 mm for ETT and 8.2 mm for NETT at level L2. This means the resorption was 0.3 mm for ETT and 0.5 mm for NETT on average (Table 4). At time T2, the mean buccal lamella width at L4 was significantly lower for NETT than for ETT at the implant, region, and patient level.
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 2 System Model
### Conventional Log-MPA  
The computational load of the conventional MPA probability domain involves a great amount of exponentiation calculation and high storage burden, which limits its implementation in hardware design. To this end, the log domain MPA has been introduced to translate the exponentiation into addition, referred as Log-MPA. This Log-MPA is a bipartite factor-graph-based iterative procedure by exchanging the codeword likelihood messages between variable nodes and resource nodes to achieve a near optimal performance. By defining \(R_{j\to k}^{t}\) and \(V_{k\to j}^{t}\) as the belief propagated to the \(k\)-th resource node from the \(j\)-th variable node and the belief propagated to the \(j\)-th variable node from the \(k\)-th resource node at the \(t\)-th iteration, respectively, the procedure of standard Log-MPA can be described as follows [31].  
(1) _Initialization:_ Set \(t=1\) and the maximum iteration number \(T\). The normalized a priori log-likelihood probability of each \(j\)-th user's codeword is assumed to be equal, i.e., \(R_{j\to k}^{0}=\log 1/M\). Initialize the log-domain conditional probability of different codeword combinations for the \(j\)-th user as  
\[P_{k,j}(\mathbf{x})=-\frac{1}{2\sigma^{2}}\left|y_{k,j}-h_{k,j}\sum_{m\in \mathcal{V}_{k}}x_{k,m}\right|^{2}, \tag{6}\]  
where \(\mathbf{x}=\{\mathbf{x}_{m}\}\), \(m\in\mathcal{V}_{k}\).  
(2) _Resource Node Updating_: The belief propagated to the \(j\)-th variable node from the \(k\)-th resource node at the \(t\)-th iteration can be calculated as  
\[V_{k\to j}^{t}(\mathbf{x}_{j})=\max_{\mathbf{x}_{m}:u\in\mathcal{V}_{k} \setminus j}^{*}\Bigg{\{}P_{k,j}(\mathbf{x})+\sum_{u\in\mathcal{V}_{k} \setminus j}R_{u\to k}^{t-1}(\mathbf{x}_{u})\Bigg{\}}, \tag{7}\]  
where \(\mathcal{V}_{k}\setminus j\) represents the \(d_{c}-1\) VNs associating with the \(k\)-th RN with the \(j\)-th VN excluded, and  
\[\max^{*}(a_{1},a_{2},\cdots,a_{n})=\ln(e^{a_{1}}+e^{a_{2}}+\cdots+e^{a_{n}}). \tag{8}\]  
(3) _Variable Node Updating_: Resource nodes transmit the updated information obtained from extrinsic information to their neighboring variable nodes, given by  
\[R_{j\to k}^{t}(\mathbf{x}_{j})=\sum_{m\in\mathcal{R}_{j}\setminus k}V_{m \to j}^{t-1}(\mathbf{x}_{j}), \tag{9}\]  
where \(\mathcal{R}_{j}\setminus k\) represents the \(d_{v}-1\) RNs associating with the \(k\)-th VN with the \(j\)-th RN excluded.  
(4) _Probability Calculating and Symbol Judging_: When the maximum number of iterations is reached, the final probabilities of each codeword for each user are output. The final  
Figure 1: The SCMA factor graph for \(K=5\) and \(J=10\).  
probabilities of codeword \(\mathbf{x}_{j}\) for VN \(j\) are the sum of all the messages from its neighboring RNs, which can be computed as  
\[R_{j}(\mathbf{x}_{j})=\sum_{m\in R_{j}}V_{m\to j}^{T}(\mathbf{x}_{j}). \tag{10}\]  
The final estimated transmit codeword \(\hat{\mathbf{x}}_{j}\) of user \(j\) is the one which maximizes \(R_{j}(\mathbf{x}_{j})\).
# Multiscale Dynamic Attention and Hierarchical Spatial Aggregation for Few-Shot Object Detection
## 2 Related Work
### Attention Mechanisms in FSOD  
In recent years, incorporating attention mechanisms into FSOD has significantly advanced the field by enhancing the model's ability to generalize from limited training samples. Attention mechanisms, by selectively focusing on salient features, have been instrumental in mitigating the challenges associated with sparse data. Noteworthy advancements include the development of Attention-RPN [11], which integrates channel and spatial attention into the region proposal network to enhance feature extraction under few-shot conditions. Meta R-CNN [10] employs a meta-learning framework combined with attention mechanisms to rapidly adapt to new classes with minimal samples, utilizing high-quality class feature representations. Similarly, FSDetView [12] introduces a viewpoint-aware attention mechanism to address the issue of viewpoint variation in FSOD, thereby ensuring robust feature representation across different perspectives. Additionally, attention-based fine-tuning methods have been proposed to selectively adjust pretrained features during fine-tuning, enabling models to better adapt to few-shot scenarios. These approaches underscore the versatility and efficacy of attention mechanisms in enhancing the performance of FSOD models.
# Deep Learning-Enhanced Spectrogram Analysis for
## 4 Results
### Confusion Matrix Analysis  
A confusion matrix is created to assess the classification performance of the proposed ResNet50-based model. This matrix elucidates the model's proficiency in accurately categorizing spectrograms according to their corresponding anatomical locations.  
Figure 5 illustrates the confusion matrix, highlighting the classification outcomes across all eight regions. High diagonal values indicate strong classification accuracy, with minimal misclassification across neighboring regions.  
Figure 5: Confusion Matrix on the test set for the ResNet50-Based Classification Model. Diagonal values indicate correctly classified anatomical regions, while off-diagonal values represent misclassifications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region** & **Accuracy (\%)** & **Precision** & **Recall** & **F1-Score** \\ \hline R1 & 92.5 & 0.93 & 0.92 & 0.92 \\ R2 & 94.1 & 0.94 & 0.93 & 0.93 \\ R3 & 93.8 & 0.94 & 0.94 & 0.94 \\ R4 & 92.9 & 0.92 & 0.93 & 0.92 \\ R5 & 94.3 & 0.95 & 0.94 & 0.94 \\ R6 & 93.1 & 0.93 & 0.92 & 0.92 \\ R7 & 92.7 & 0.92 & 0.91 & 0.91 \\ R8 & 93.5 & 0.93 & 0.93 & 0.93 \\ \hline Overall & 93.37 & 0.93 & 0.93 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification Performance Across 8 Anatomical Regions.  
As shown in Figure 5, most of the test samples were correctly classified across all eight anatomical regions. However, some confusion was observed between adjacent regions, such as R3 and R4, and R5 and R6. This may be attributed to the acoustic similarity between signals captured from physically neighboring areas, where spectral patterns can overlap. These findings highlight the need for deeper feature separation or spatial priors to further improve the classification accuracy in boundary cases.
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
###### Abstract  
The recent development of gas imaging technologies has raised a growing interest for various applications. Gas imaging can significantly enhance functional safety by early detection of hazardous gas leaks. Moreover, optical gas imaging technologies can be used to identify possible gas leakages and to investigate the amount of gas emission in industrial sites, which is essential, primarily based on current efforts to decrease greenhouse gas emissions all around the world. Therefore, exploring the solutions for automating the inspection process can persuade industries for more regular tests and monitoring. One of the main challenges in gas imaging is the proximity condition required for data to be more reliable for analysis. Therefore, the use of unmanned aerial vehicles can be very advantageous as they can provide significant access due to their maneuver capabilities. Despite the advantages of using drones, their movements and sudden motions during hovering can diminish data usability. In this paper, we propose a method for gas leak detection and visually-enhancement of gas emanation involving stabilization and gas leak detection steps. In addition, a comparative analysis of candidate stabilization techniques is conducted to find the most suitable technique for the drone-based application. Moreover, the system is evaluated using three experiments respectively on an isolated environment, a real scenario, and a drone-based inspection.  
Keywords:thermography; video stabilization; gas leakage detection; unmanned aerial vehicle; aerial inspection; nondestructive testing: optical flow analysis +
Footnote †: This paper is an extended version of our paper published in Structural Health Monitoring and Nondestructive Testing 2020.
# A Performance Evaluation for Software Defined Networks with P4
## 4 Experimental Design
### Case Study 4--Content Delivery Network  
An emulation was designed to explore video traffic with live stream, using VLC-player [65] to send videos with the quality of 1080p and H.264 compression due to the demand and popularity for high-quality video streaming. For multi-path and grid topologies, a video of 600 s long was chosen, and for the Internet topology, a video of similar quality was chosen with a 1200 s duration. The length of the two video files (600 s and 1200 s) was used for the ease of emulation and for calculation purposes. As the Internet topology was larger in size in comparison to the multi-path and grid topologies, a larger video file was employed. The Internet topology emulated a higher number of hosts and switches involved. In order to establish an emulation reinforcing a live video stream, a Python script was utilised that determined transfers between multiple clients with a server.