esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Results and Discussion
#### 4.4.3 Number of Detected Paths  
Figure 4 shows the average number of detected paths as a function of PSNR. In the low-IPI case, performances of both algorithms are comparable. At low PSNR, the average number of detected paths is below the true value (\(P=4\)) because small-amplitude paths are missed. At higher PSNR values, both algorithms are able to detect the right number of paths. On the other hand, in the high-IPI case, detection capabilities are still comparable, but at high PSNR values, both algorithms overestimate a bit the number of propagation paths due to high residual interference effects. Figure 5 shows the probability distribution among all the possible values for \(P\) in the case of PSNR = 35 dB. In the low-IPI regime, both algorithms detect the right number of paths with a probability close to \(\sim\)0.98. Thus, the probability of miss detection and the probability of false alarm are close to zero (probability of miss detection \(<0.02\)). In the high-IPI regime, the probability that the algorithm finds the correct number of paths is reduced to \(\sim\)0.73 for the DDIPIC and \(\sim\)0.65 for the P-IPIC. The probability of miss detection is close to zero in both cases (\(\sim\)0.015 for the P-IPIC). In the end, the probability of false alarm is \(\sim\)0.27 for DDIPIC and \(\sim\)0.34 for P-IPIC.  
Figure 3: NRMSE of channel parameters as a function of pilot SNR.
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
###### Abstract  
Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and differences in lighting conditions. As a solution to this problem, deep learning has recently emerged, motivated by two main factors: accessibility to computing power and the rapid digitization of society, which enables the creation of large databases of labeled samples. This review paper aims to briefly summarize and analyze the current state of research on detecting defects using machine learning methods. First, deep learning-based detection of surface defects on industrial products is discussed from three perspectives: supervised, semi-supervised, and unsupervised. Secondly, the current research status of deep learning defect detection methods for X-ray images is discussed. Finally, we summarize the most common challenges and their potential solutions in surface defect detection, such as unbalanced sample identification, limited sample size, and real-time processing.  
defect detection; surface defect detection; defect detection for X-ray images; defect recognition; deep learning +
Footnote †: journal: _Algorithms_  
Autoencoders (AEs): a neural network architecture used for unsupervised learning tasks such as dimensionality reduction and anomaly detection.
* Generative Adversarial Networks (GANs): a neural network architecture used for generative tasks such as image synthesis and image-to-image translation.
* Self-Organizing Map based (SOM-based): an unsupervised learning algorithm that organizes data into a 2D grid of clusters.
* General-purpose Annotation of Photos and Replica (GAPR) datasets: created by the German Pattern Recognition Association, is a collection of images specifically designed for the detection of texture defects.
* German Association for Pattern Recognition (DAGM) datasets: a collection of images specifically designed for the detection of textured surfaces.
* Northeastern University (NEU) datasets: created by Northeastern University, a collection of images of surface defects that includes six different types of defects.
* Convolutional Denoising AutoEncoder (CDAE): a type of autoencoder designed to remove noise from images.
* Non-Destructive Testing (NDT): a method of evaluating the properties of a material, component, or system without causing damage.
* VGG: VGG is a pioneering object-recognition model that can have up to 19 layers. Created as a deep CNN, it surpasses other models on many tasks and datasets apart from ImageNet. VGG is still a widely used architecture for image recognition today.
* Mean Average Precision (mAP): a metric used to evaluate the performance of object detection models, that calculates the average precision across different classes and object instances.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Segmentation Architecture in Smooth Muscles  
CNN and U-Net can be used for smooth muscle segmentation. [PERSON] et al. (2020) and [PERSON] et al. (2020) used U-Net with a convolutional layer and ReLu with biomedical images. Both were created [60] to perform muscle thickness segmentation with a bladder wall thickness layout and to identify the best possible location [38]. This was performed alongside the use of segmented 3D ultrasound images of the ciliary muscles [39]. This study found high accuracy and was the only study with 3D images produced by muscle ultrasound imaging from 2018 to 2020. Furthermore, [PERSON] et al. (2019) used a CNN to measure the area of the urogenital hiatus and puborectalis muscle by automated segmentation [37]. The CNN was used with a self-normalizing neural network. A self-normalizing neural network is used to reduce network complexity and use a smaller mini-batch size without sacrificing the generalization performance [61]. The study showed the highest Dice result for the urogenital hiatus matrix (Table 1).  
Different from skeletal muscles, smooth muscles rarely have problems with imbalances and scarce data. Of the three cases, there is one study that used augmentation to solve image scarcity with smooth muscles. [PERSON] et al. had difficulties with the amount of image data, requiring data augmentation with a ten times random elastic deformation approach. Adding the amount of images and increasing the measurements improved the performance of deep learning for the bladder wall muscle thickness [38]. Two studies used a sufficient dataset, where [PERSON] et al. (2020) used 3D ciliary muscle images with 1000 images, and deep learning achieved high accuracy without a scarce data solution [39]. Additionally, [PERSON] et al. produced measurements with 2D images with a CNN with the urogenital hiatus and puborectalis muscle [37] with 713 images and achieved the highest Dice coefficient without any scarce data solution.
## 3 Results and Discussion
### Sensory Analysis  
Table 6 shows the sensory scores for the salami started with the different cultures. The results highlighted significant (\(p<0.05\)) differences between SM and SR salami, particularly in terms of \"color intensity\", where SR salami obtained lower scores than SM. The gustative sensorial profile revealed no significant difference in gustative sensorial profile, except for \"salt\" perception, which was more pronounced in SM than SR. Additionally, in the flavor sensory profile, SM achieved a higher score in terms of \"ripened odor\" (\(p<0.05\)). The tactile profile emphasized the hardness and the gumminess (\(p<0.01\)) of SR, confirming the instrumental texture analysis (Table 5).  
Recent studies [43; 53; 54] have demonstrated the potential of \(L\). _plantarum_ to enhance sensory properties, especially when inoculated with \(S\). _xylosus_, promoting the formation of compounds that enhance the flavor of the fermented product. This seems to be the case of SM salami, where this starter combination was used, preserving the desired organoleptic  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Descriptor** & **SM** & **SR** \\ \hline _Aspect_ & & \\ Color intensity & \(5.83\pm 0.58\) b  & \(4.00\pm 0.50\) a  \\ _Flavor sensorial profile_ & & \\ Odor intensity & \(5.83\pm 0.58\) & \(5.17\pm 0.29\) \\ Ripened odor & \(5.33\pm 0.29\) a  & \(4.33\pm 0.29\) b  \\ _Gustative sensorial profile_ & & \\ Salt & \(5.50\pm 0.50\) a  & \(4.17\pm 0.29\) b  \\ Pepper & \(3.00\pm 0.00\) & \(2.83\pm 0.29\) \\ Ripened flavor & \(4.83\pm 0.76\) & \(4.17\pm 0.29\) \\ _Tactile profile_ & & \\ Hard & \(4.50\pm 0.50\) & \(5.83\pm 0.76\) \\ Gummy & \(4.17\pm 0.29\) a  & \(6.17\pm 0.29\) b  \\ \hline \hline \end{tabular} All results expressed as (1, absence of the sensation—7, extremely intense) scale units. The A,B,B mean values differ significantly for \(p<0.05\) (lowercase) or \(p<0.01\) (capital); \(n=3\).  
\end{table}
Table 6: Sensorial and gustative descriptor mean scores of 12 assessors and standard deviations of Salame Napoli made with different starter cultures (SM, Euroferment Medium, and SR, Euroferment Rapid).  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **SM** & **SR** \\ \hline Hardness, N & \(35.60\pm 2.62\) a  & \(46.04\pm 6.53\) b  \\ Adhesiveness, N \(\times\) mm & \(-21.17\pm 4.87\) & \(-12.30\pm 7.99\) \\ Resilience & \(1.29\pm 1.65\) & \(0.45\pm 1.54\) \\ Chewiness, N & \(12.29\pm 0.54\) a  & \(19.73\pm 4.38\) b  \\ Gumminess, N & \(11.89\pm 0.71\) a  & \(19.21\pm 3.44\) b  \\ Cohesiveness, N & \(1.14\pm 5.70\) & \(-2.36\pm 2.60\) \\ \hline \hline \end{tabular} The a,b mean values differ significantly for \(p<0.05\) (lowercase); \(n=3\).  
\end{table}
Table 5: Texture profile analysis (TPA) of Salame Napoli made with different starter cultures (SM, Euroferment Medium, and SR, Euroferment Rapid).  
characteristics typical of Salame Napoli. In contrast, the rapid starter in SR salami, proven to be highly acidifying, appears to have negatively affected the eating quality of the salami [28].
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
## 2 Model Establishment and Simulation Test Design
### Design of Static Structural Mechanics Simulation Tests
#### 2.2.3 Emergency Braking Condition  
When buses are driving on urban roads, emergency braking situations frequently occur due to various road conditions. During emergency braking, the bus body frame not only bears its existing load but also experiences a significant inertial force acting forward. Under full load and emergency braking conditions, this study focuses on the impact of inertial forces on the hydrogen fuel cell city bus body frame during maximum deceleration with the maximum braking force applied. In the simulation process of the emergency braking condition, the bus body frame is subjected to a simulated acceleration of 0.8 g in the X-axis direction [33]. The boundary conditions were set as follows: the front wheels were constrained in the X-, Y-, and Z-direction translational degrees of freedom, the rear wheels were constrained in the Z-direction translational degree of freedom, and all other degrees of freedom were released.
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 5 Experiment
### Comparison with QRCSS  
Similar to VSS-QR, QRCSS also divides the secret QR code into three shares. Every share is a standard QR code and six codewords are modified in each block. Because QR code has ECC capability, every share can be decoded correctly by standard QR code. In order to reconstruct secret QR code, three shares need to do \"XOR\". The reconstructed QR code \(\mathbf{H}^{r}\) has some errors, but is lower than the ECC capability.  
The experiments randomly added the same number of codeword errors to each block in \(\mathbf{H}^{\prime}\) and \(\mathbf{H}^{r}\). Section 5.3 shows that the QR code (4-H) cannot be decoded by the standard decoder when the number of codeword errors per block exceeds 8. So, the number of codeword errors is from 1 to 9. When the number of codeword errors is 9, all of them cannot be decoded. Each experiment was performed 10,000 times. All results are shown in Table 7.  
As the number of added-codeword-error increases, some \(\mathbf{H}^{r}\) cannot be decoded by the standard decoder. When this number exceeds 4, \(\mathbf{H}^{r}\) is difficult to decoded correctly by the standard decoder. Theoretically, when this number is less than 9, a few \(\mathbf{H}^{r}\) can be decoded. All \(\mathbf{H}^{r}\) cannot be decoded correctly when this number is greater than 4. However all \(\mathbf{T}^{\prime}\) that can be decoded by the standard decoder as this number increases. Until this number exceeds 8, \(\mathbf{T}^{\prime}\) cannot be decoded correctly. Compared with QRCSS, the biggest advantage of VSS-QR is that it can completely reconstruct the secret QR code, so VSS-QR preserves the ECC capability of QR code.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Series Number** & \(\mathbf{\hat{n}(e_{m}^{1})}\) & \(\mathbf{\hat{n}(e_{m}^{2})}\) & \(\mathbf{\hat{n}(e_{m}^{3})}\) & \(\mathbf{\hat{n}(e_{m}^{4})}\) \\ \hline
1 & 19.0803 & 19.9352 & 19.4074 & 19.9332 \\
2 & 19.048 & 19.9127 & 19.4067 & 19.9441 \\
3 & 19.0621 & 19.9514 & 19.4229 & 19.9422 \\
4 & 19.0651 & 19.9321 & 19.3731 & 19.9392 \\
5 & 19.0079 & 19.9016 & 19.3957 & 19.9312 \\
6 & 19.0141 & 19.9323 & 19.4219 & 19.9458 \\
7 & 19.0258 & 19.9303 & 19.3769 & 19.903 \\
8 & 19.0909 & 19.9517 & 19.4297 & 19.9377 \\
9 & 19.0548 & 19.9258 & 19.3743 & 19.9386 \\
10 & 19.0421 & 19.9196 & 19.4264 & 19.9417 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The result of experiment about reconstruct “secret QR code” by using any two shares.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**image** & \(\mathbf{\hat{n}(1)}\) & \(\mathbf{\hat{n}(2)}\) & \(\mathbf{\hat{n}(3)}\) & \(\mathbf{\hat{n}(4)}\) & \(\mathbf{\hat{n}(5)}\) & \(\mathbf{\hat{n}(6)}\) & \(\mathbf{\hat{n}(7)}\) & \(\mathbf{\hat{n}(8)}\) & \(\mathbf{\hat{n}(9)}\) \\ \hline \(\mathbf{H}^{r}\) & 10000 & 4975 & 1084 & 273 & 128 & 0 & 0 & 0 & 0 & 0 \\ \(\mathbf{T}^{\prime}\) & 10000 & 10000 & 10000
## 3 RUL Prediction Methodology
### Bayesian RUL Prediction Based on Extended Kalman Filter
#### 3.4.2 Extended Kalman Filter
Thus, the state vector and measurement vector are rewritten as  
\[\mathbf{x}_{t}\approx\widetilde{\mathbf{x}}_{t|t-1}+\mathbf{F}_{t}\Big{(}\mathbf{x}_{t-1}- \widetilde{\mathbf{x}}_{t-1|t-1}\Big{)}+\mathbf{G}_{t}\mathbf{v}_{t-1}, \tag{14}\]  
\[\mathbf{z}_{t}\approx\widetilde{\mathbf{z}}_{t|t-1}+\mathbf{H}_{t}\Big{(}\mathbf{x}_{t}- \widetilde{\mathbf{x}}_{t|t-1}\Big{)}+\mathbf{W}_{t}\mathbf{w}_{t}, \tag{15}\]where \(\widetilde{\mathbf{x}}_{t|t-1}\) and \(\widetilde{\mathbf{z}}_{t|t-1}\) are, respectively, the noise-free a priori estimate of the state and measurement at time \(t\)[19],  
\[\widetilde{\mathbf{x}}_{t|t-1}=f\Big{(}\widetilde{\mathbf{x}}_{t-1|t-1},\mathbf{u}_{t},\mathbf{ 0}\Big{)}, \tag{16}\]  
\[\widetilde{\mathbf{z}}_{t|t-1}=h\Big{(}\widetilde{\mathbf{x}}_{t|t-1},\mathbf{0}\Big{)}. \tag{17}\]  
\(\mathbf{F}_{t}\), \(\mathbf{G}_{t}\), \(\mathbf{H}_{t}\), and \(\mathbf{W}_{t}\) are the Jacobian matrices of the partial derivatives with respect to state vector \(\mathbf{x}\), noise \(\mathbf{v}\) and \(\mathbf{w}\), respectively, defined as follows:  
\[\mathbf{F}_{t}=\
abla f_{\mathbf{x}}=\left.\frac{\partial f(\mathbf{x},\mathbf{u},\mathbf{v})}{ \partial\mathbf{x}}\right|_{(\widetilde{\mathbf{x}}_{t|t},\mathbf{u}_{t},0)}, \tag{18}\]  
\[\mathbf{z}_{t}=h(\mathbf{x}_{t},\mathbf{w}_{t}), \tag{10}\]0  
\[\mathbf{z}_{t}=h(\mathbf{x}_{t},\mathbf{w}_{t}), \tag{10}\]1  
\[\mathbf{z}_{t}=h(\mathbf{x}_{t},\mathbf{w}_{t}), \tag{10}\]2  
Based on the difference between the actual measurements \(\mathbf{z}_{t}\) and the a priori prediction of measurement \(\widetilde{\mathbf{z}}_{t}\), the prediction error of the measurement is obtained and scaled by the filter gain \(\mathbf{K}_{t}\) before adding back to correct the a priori state estimation, which gives an a posteriori estimate of state. \[\mathbf{z}_{t}=h(\mathbf{x}_{t},\mathbf{w}_{t}), \tag{10}\]3  
where the Kalman gain \(\mathbf{K}_{t}\) serves to balance the state model belief and the measurement and is chosen based on the optimality criteria regarding the stochastic nature of the state process and the measurement dynamics [19].
## 2 Materials and Methods
### Study Design  
Participants aged 18 to 50 years, who were non-smokers and had no systemic conditions potentially affecting healing, were selected for inclusion. All presented with asymptomatic bilateral mandibular third molars in symmetrical locations, diagnosed as vertical or mesioangular according to [PERSON]'s classification. Impactions further met Class II, Position B or C criteria based on Pell and Gregory's classification. Distoangular or horizontal impactions were excluded. All participants received detailed information about the surgical procedure, potential complications, and medications. All participants signed informed consent forms.  
Randomization of the surgical side (right vs. left) and treatment type (control vs. hyaluronic acid) was performed immediately before each procedure using a coin toss to ensure unbiased allocation. Both patients and outcome evaluators remained blinded to the assigned treatments throughout the study to reduce potential bias. Although the surgeon was informed of the treatment allocation to perform the procedure, they did not participate in any postoperative measurements.  
A power analysis was conducted using G*Power 3.1 software to determine the required sample size for this study. The Wilcoxon signed-rank test (matched pairs) was selected for within-group comparisons, while the Mann-Whitney U test was used for between-group comparisons. To estimate the required number of participants, mean and standard deviation values were obtained from previously published studies investigating the effects of HA after third molar surgery [20; 21; 22].  
Using these values, the effect size (d) was determined to be 0.5, assuming a two-tailed hypothesis, a significance level (\(\alpha\)) of 0.05, and a statistical power (\(1-\beta\)) of 0.80. Based on these parameters, the power analysis calculated that a minimum of 28 participants (56 extraction sites) was required for statistical validity. However, to account for potential dropouts, the final sample size was increased to 30 participants (60 extraction sites).  
The control group underwent standard impacted tooth extraction, while the HA group received standard extraction with an additional application of HA to the extraction socket. The group assignment for each tooth was determined using the closed-envelope method immediately before the first surgery.  
After three weeks, the contralateral impacted tooth in the same patient was extracted and assigned to the opposite group. During the study, three patients were excluded for not attending their second appointment, and two were excluded due to the procedural time exceeding 30 min, caused by root fracture. A final total of 30 patients (14 males 16 females) completed the study as summarized in Table 1.
## 2 Materials and Methods  
### VESFF Preparation  
In this work, hexadecyl trimethyl ammonium chloride (CTAC, C\({}_{19}\)H\({}_{2}\)CIN, cationic VES), stearyl trimethyl ammonium chloride (STAC, C\({}_{21}\)H\({}_{46}\)CIN, cationic VES), STAC and cocoamidopropyl betaine (CAB, C\({}_{19}\)H\({}_{38}\)N\({}_{2}\)O\({}_{3}\), zwitterionic VES) blends, and sodium dodecyl sulfate (SDS, C\({}_{12}\)H\({}_{25}\)SO\({}_{4}\)Na, anionic VES) and CAB blends were selected as the main reagents for the VESFFs (Table 1). Sodium salicylate (C\({}_{7}\)H\({}_{5}\)NaO\({}_{3}\), NaSal) is used as a micelle promoter for fracturing fluids containing cationic surfactants. Potassium chloride (KCI) is used as a counter ion and anti-swelling agent in VESFFs. The fluid components are designated as B, B\({}_{2}\), C, and D, respectively, while deionized water is identified as A (Table 2) [23]. The information on the test reagent, the preparation process of the FFs, and the viscosity test are detailed in reference [23].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**VES** &
\begin{tabular}{c} **Molecular** \\ **Formula** \\ \end{tabular} & **Molecular Structure** \\ \hline CTAC & C\({}_{19}\)H\({}_{42}\)CIN & \\ STAC & C\({}_{21}\)H\({}_{46}\)CIN & \\ SDS & C\({}_{12}\)H\({}_{25}\)SO\({}_{4}\)Na & \\ CAB & C\({}_{19}\)H\({}_{38}\)N\({}_{2}\)O\({}_{3}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The molecular structure diagrams of VESs.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 5 Solving a Pseudo-Static Scenario
### Checking the Fitness of Individuals: Check Function
#### 5.2.2 Quality of Service Evaluation (QoS_eval Function)
Then, \(p^{(i)}\) is the probability that a packet from station \(i\) suffers any transmission error:  
\[p^{(i)}=\left(1-P_{i}^{(i)}\right)\cup FER^{(i)}=1-P_{i}^{(i)}+P_ {i}^{(i)}\cdot FER^{(i)} \tag{26}\] \[P_{i}^{(i)}=\prod_{j=1,\ i\
eq j}^{|\mathcal{S}|}\left(1-\tau^{( j)}\right) \tag{27}\]  
where \(P_{i}^{(i)}\) represents the probability that only station \(i\) is attempting to transmit, and \(FER^{(i)}\) stands for the Frame Error Rate (in the access point case, MCS and FER are dynamically changed according to its communication partner. Our approach is to average its data bit-rate and FER) (i.e., channel noise), which can be obtained as:  
\[FER^{(i)}=1-(1-P_{\epsilon}^{(i)})^{L} \tag{28}\]  
where \(L\) is the packet size (considering preamble, headers and payload), and \(P_{\epsilon}^{(i)}\) represents the bit error rate of station \(i\), which can calculated by considering the user's \(MCS^{(i)}\) as proposed in [63; 64]. Finally, one can solve the non-linear equation system formed by Equations (21) and (26). Since the downlink performance is the dominant factor in QoS as demonstrated in [13], the packet loss and delay (the small queue assumption allows us to neglect the queuing delay and \(DEL\) can be approximated by the channel access delay) at the access point (tagged as \(i=AP\)) will represent the worst case. As such, it will be used to calculate the QoS for the set \(\mathcal{S}\). Then, the packet loss ratio and delay can be expressed as:  
\[PL=1-\frac{\left(1-FER^{(AP)}\right)\tau^{(AP)}\prod_{j=1,j\
eq AP }^{|\mathcal{S}|}\left(1-\tau^{(j)}\right)}{\lambda^{(AP)}\ E[T]} \tag{29}\] \[\eta=(1-r)+\frac{r^{2}W_{o}(W_{o}+1)}{2(1-(1-r)^{W_{o}})}+\frac{W_ {o}+1}{2(1-q)}\left(\frac{r^{2}qW_{o}}{1-(1-r)^{W_{o}}}+rp(1-q)-rq(1-p)^{2}\right)\]0  
Substituting \(PL\) and \((DEL+20)\) (recall that the delay includes the a codec packetization delay of 20 ms) in the terms \(P_{pl}\) and \(d\) from Equation (1) provides the output of the \(QoS\_Eval\) function: the \(R\) factor.