esa-sceva/satcom-chunk-collection huggingface dataset:
# Two Design Options for Compact Linear Accelerators for High Flux Neutron Source
## 2 Proton RFQ Accelerator Design
### Design Strategy of RFQ Beam Dynamics
#### 2.1.1 Vane Voltage and Kilpatrick Limit  
In general, a higher vane voltage will result in a shorter RFQ length. However, a higher voltage will increase the risk of RF breakdown during operation and result in a larger cavity power consumption. Therefore, we use two different strategies for these two RFQs and make a comparison of both designs, namely low voltage (65 kV), low Kilpatrick limit (\(K_{p}\) = 1.24) design for 162.5 MHz RFQ and high voltage (85 kV), high Kilpatrick limit (\(K_{p}\) = 1.82) design for 325 MHz RFQ.  
For normal conducting cavities, the Kilpatrick criterion for sparking as a function of frequency (MHz) is defined as following [27]:  
\[f(\mathrm{MHz})=1.64E^{2}e^{-\frac{85}{L}} \tag{2}\]  
where \(E\) in MV/m is the Kilpatrick electric field. At frequencies of 162.5 MHz and 325 MHz, the Kilpatrick electric fields are calculated to be 13.595 MV/m and 17.846 MV/m, respectively. For the highest stability, the peak surface electric field on the vane-tips is usually held below the 1.85 Kilpatrick limit.  
The averaged accelerating gradient (MeV/u/m) and Kilpatrick limit of various proton RFQs all over the world is summarized in Figure 3[28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. Thanks to cranking up the peak surface electric field to 1.82 Kilpatrick at the higher frequency of 325 MHz, a shorter RFQ can accelerate a proton beam to desired output energy. Though the 162.5 MHz RFQ has a longer cavity length, it has a lower Kilpatrick factor of 1.24 and thus less risk of electric breakdown and has more potential for future BNCT research. After comparing with the design parameters of other proton RFQs, both designs are considered to be reliable for high duty factor operations.
# A Watermarking Protocol Based on Blockchain
## 2 Related Work  
Most of the watermarking protocols documented in the literature do not exploit blockchain technology, but they are based on the well-known \"buyer and seller\" protection schemes and their variants characterized by the absence of TTPs. They are widely described and discussedin [5; 22; 23; 24]. Some of them also inspire the so-called DRM (digital rights management) systems, which are complex web platforms that adopt specific technologies and interaction schemes to enable the copyright protection of digital content on the Internet [32; 33]. More precisely, DRM systems do not actually define watermarking protocols, but they still implement mechanisms by which to prevent the unauthorized use of protected digital content without payment. To achieve such a goal, DRM systems use technologies based on encryption and key management [34]. However, such technologies cannot inhibit legitimate users from illegally sharing their purchased content on the Internet.  
To overcome the drawbacks reported above, a number of DRM systems implement protection schemes based on \"trusted computing\". They prevent the sharing of illegal keys and protected content by enabling the access to such content on the basis of the web users' biometric features [35; 36]. In fact, such systems appear to be very promising, but they lack flexibility, since they need particular hardware, such as \"trusted platform modules\" (TPMs) or fingerprint recognizers, and cannot defend against specific attacks, such as screen recording or I/O monitoring.  
The blockchain technology, in conjunction with digital watermarking, is employed in a number of DRM systems to provide some copyright management services, such as to keep track of possible and required content modifications, copyright transfers or other transaction trails related to the managed digital content [37; 38; 39]. In particular, digital watermarking is mainly used to provide content tracking by fingerprinting. However, such DRM systems do not implement protection schemes able to address the peculiar problems that affect watermarking protocols, such as the \"customer's right problem\" or the \"unbinding problem\" [4; 11; 22]. As a consequence, once content is downloaded and tampered, there is no legal way to prove the ownership of the content and to trace who should be responsible for copyright infringement. In fact, such considerations motivate the design of innovative watermarking protocols able to exploit the blockchain technology to overcome the limitations described above.
## 4 Discussion
### Practical Implications and Limitations  
Building on our findings, integrating MQ assessments into training regimens has the potential to significantly enhance athlete monitoring and strength performance evaluation. For example, climbers with similar MQ but low strength may benefit from neuromuscular training to enhance motor unit recruitment. Similarly, athletes exhibiting MQ asymmetries could potentially reduce injury risk by incorporating specific unilateral exercises into their routines. Coaches should consider adopting climbing-specific tests such as half-crimp isometric holds rather than traditional dynamometry to more accurately track progress and identify emerging talent. By leveraging these insights, coaches and athletes can design evidence-based training programs tailored to the unique demands of climbing, ultimately enhancing performance across all skill levels. Although these practical applications are promising, it is important to acknowledge several limitations.  
While our seated testing protocol standardized measurements, this protocol may fail to capture climbing-specific hanging demands, potentially underestimating the true performance capacities of climbers. Although no separate familiarization session was conducted, participants were guided on their grips during the pre-testing warm-up. Additionally, while DXA is commonly used for MQ assessment, its inability to isolate finger flexors limits the accuracy of these evaluations compared to other gold-standard methods. Moreover, future research should adopt more climbing-specific testing postures, such as hanging tests, and employ ultrasound-based muscle mass quantification to achieve greater specificity. Longitudinal studies are needed to validate the predictive value of MQ for climbing performance and to assess its responsiveness to periodized training interventions. Specifically, such studies should examine how changes in MQ over time relate to climbing progression and whether early improvements in MQ prospectively predict performance gains or reduced injury risk. Tracking MQ trajectories across different training phases (e.g., strength vs. endurance blocks) may also help determine its sensitivity to specific adaptations and identify critical thresholds linked to performance plateaus or regressions. Addressing these limitations will enhance the validity of MQ as a performance indicator and refine our understanding of climbing-specific adaptations. Overcoming these challenges will not only strengthen the role of MQ in performance evaluation but also drive forward innovative training methodologies.
# A Simple Convolutional Neural Network with Rule Extraction
## 3 Proposed Model
### CNN Architecture
#### 3.1.3 Fully Connected Layer  
In this work, a unique fully connected layer of weights follows the max-pooling layer. First, a dot product of \(s_{l}\) scalars is calculated:  
\[s_{l}=\sum_{k}(v_{kl}\cdot m_{k}). \tag{3}\]  
Symbol \(m_{k}\) represents vector components of the max-pooling layer and \(v_{kl}\) is a matrix term of weight coefficients, the bias being included in the sum. Then, a _Softmax_ activation function is applied. Specifically, for a number \(N\) of \(s_{i}\) scalars it calculates an N-dimensional vector with values between 0 and 1:  
\[o_{l}=\frac{\exp(s_{l})}{\sum_{k}\exp(s_{k})}; \tag{4}\]  
with \(o_{l}\) as the activation of a neuron in the output layer. The architecture of the CNN used in this work is summarized in Table 1. Specifically, \(I\) designates the input layer, \(C\) represents the convolutional layer with 40 kernels for each size (this value has been fixed empirically, without trying to reach the possible best predictive accuracy). \((C_{1},C_{2},C_{3})\), \(M\) is the max-pooling layer (120 neurons), and \(O\) designates the output layer including two neurons. Each word is coded in a vector of 300 components, with a maximum number of words per sample equal to 59.  
To train the network, the loss function is the cross-entropy (\(J\)); here we give its version for two classes:  
\[J(W)=-\sum_{p}\sum_{l}\left[t_{l}^{(p)}\log(o_{l}^{(p)})+(1-t_{l}^{(p)})\log( 1-o_{l}^{(p)})\right]. \tag{5}\]  
Symbol \(W\) represents all the network weights, index \(p\) is related to training samples, index \(l\) designates an index for the neurons of the output layer, \(o_{l}^{(p)}\) is the activation of an output neuron, and \(t_{l}^{(p)}\) represents a target value.  
Figure 4: The max-pool operator: The maximal value of each vector in the left is selected and concatenated in a new layer denoted as the “Max-pooling” layer (right most vector).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **Convolution** & **Max-Pooling Layer** & **Output** \\ \hline \(I\) & \(C=(C_{1},C_{2},C_{3})\) & \(M\) & \(O\) \\ \(59\times 300\) & \(1\times 300\times 40\)\(2\times 300\times 40\)\(3\times 300\times 40\) & 120 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CNN architecture. Symbols for each layer are specified in the second row and sizes in the last.
## 4 Results and Discussion
### Comparison of Different Augmentation Effects on the Performance of FC-YOLOv4  
We also investigate the effect of various image augmentation on detection outcomes. Our investigation utilized three distinct augmentations: Brightening, Mosaic, and CLAHE. We assess the influence of image enhancement on FC-YOLOv4 findings using four distinct datasets: Dataset B, Dataset C, Dataset D, and Dataset E. Figure 12a depicts the detection results following Dataset B training. Figure 12b illustrates the detection outcomes following training with Dataset C. Figure 12c depicts the detection results following Dataset D training. Figure 12d depicts the detection results following Dataset E training. The precision-recall curves for our model with different augmentation effects are depicted in Figure 13. It is clear that the combination of our proposed FC-YOLOv4 model with different augmentation effects has superior convergence and the potential to recognize clothing of diverse sizes and colors. There is a trade-off for implementing the proposed approach while we deviate from traditional practices. As depicted in Figure 14, the FC-YOLOv4 model's detection time has increased slightly after training with the image augmentation dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Before Pseudo-Labelling**} & \multicolumn{3}{c}{**After Pseudo-Labelling**} \\ \hline
**Categories** & **AP** & **TP** & **FP** & **[EMAIL_ADDRESS]** & **AP** & **TP** & **FP** & **[EMAIL_ADDRESS]** \\ \hline \multirow{2}{*}{Pants} & 94.06\% & 790 & 212 & & 97.55\% & 808 & 54 & \\ \cline{2-7}  & 46.68\% & 282 & 58 & & 96.80\% & 716 & 42 & \\ \hline \multirow{2}{*}{Hoodie} & 50.09\% & 388 & 142 & & 96.53\% & 882 & 40 & \\ \cline{2-7}  & 61.75\% & 270 & 22 & & 97.19\% & 512 & 36 & \\ \hline \multirow{2}{*}{Mid-length skirt} & \multirow{2}{*}{60.23\%} & \multirow{2}{*}{864} & \multirow{2}{*}{354} & \multirow{2}{*}{99.32\%} & \multirow{2}{*}{1526} & \multirow{2}{*}{58} & \\ \cline{2-7}  & & & & & & \\ \end{tabular}
\end{table}
Table 6: Performance metrics on YOLOv4 before pseudo-labeling and after pseudo-labeling.  
Figure 11: Detection results of before and after pseudo-labeling. (**a**) Dress Detection Result Before Pseudolabeling; (**b**) Dress Detection Result After Pseudolabeling; (**c**) Hoodie Detection Result Before Pseudolabeling; (**d**) Hoodie Detection Result After Pseudolabeling.
## 5 Results and Discussion
### Practical Engineering Validation of the Developed AOp Prediction Model Equations  
To validate the developed ANN-based equations, five datasets were randomly selected from both the datasets used in the development of the models, and the AOp was predicted using the developed equations. The results were compared with those of the MLR, USBM empirical equations, and field measurements, as presented in Table 5. In Table 5, we can observe that the developed ANN-based equation predictions are the closest to the field measurements; specifically, the optimum SMA-ANN model equation predicted the AOps with the highest accuracy. Nevertheless, both the MLR and USBM equation predictions were close to field measurements at data points 5 and 9.  
However, similar to other developed models, the application of the proposed ANN-based SMA-ANN and MVO-ANN equations could be limited by variations in the datasets employed for model generation in this study. The findings in Table 5 are supported by a graphical representation of the validation results of the different models developed against  
Figure 10: Relevance importance of model input parameters on the AOp.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data Point**} & **Field** & **SMA-ANN** & **MVO-ANN** & **MLR** & **USBM** \\  & **Measurement** & **(Equation (24))** & **(Equation (26))** & **(Equation (15))** & **(Equation (17))** \\ \hline
1 & 59.5 & 60.4 & 63.1 & 64.2 & 68.3 \\
2 & 58.5 & 58.6 & 58.9 & 63.3 & 61.2 \\
3 & 77.4 & 77.0 & 78.9 & 70.6 & 71.9 \\
4 & 67.6 & 67.3 & 63.4 & 66.1 & 69.4 \\
5 & 73.9 & 73.7 & 74.7 & 73.3 & 72.6 \\
6 & 84.6 & 84.6 & 84.6 & 56.2 & 59.0 \\
7 & 60.2 & 59.8 & 61.0 & 67.2 & 64.6 \\
8 & 58.8 & 57.1 & 56.7 & 66.1 & 63.8 \\
9 & 71.5 & 71.0 & 71.4 & 71.9 & 70.9 \\
10 & 78.9 & 78.7 & 78.0 & 67.5 & 71.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validation of the ANN-based equations for AOp prediction.  
the field measurements, as shown in Figure 11. The AOp values estimated by the optimum SMA-ANN appeared to be closest to the field measurements at all data points.  
Figure 11: Validation results of the different developed models. (**a**) column graph; (**b**) scatter plot representation.
## 4 Simulation Results and Analysis
### Analysis of Average False Detection Rate
#### 4.3.1 Analysis of Average False Detection Rate for Single-Tone and Multi-Tone Interference  
In the simulation experiments, detection is performed on background noise signals with interference signals under both single-tone and multi-tone interference scenarios. For each trial, the number of frequencies exceeding the threshold is recorded, from which the number of actual interference frequencies is subtracted. The average of the remaining samples across all trials is taken as the number of non-interference frequencies.  
As illustrated in Figures 7 and 8, the average false detection rates of all three algorithms under both single-tone and multi-tone interferences initially increase with JNR and then gradually level off. This trend occurs because, at low JNR levels, the interference signal is not prominent enough and most of the frequencies are classified as background noise, resulting in reduced algorithm sensitivity and, consequently, lower false detection rates. As the JNR increases, the algorithms begin to detect the interference more effectively; however, they also become more prone to misclassifying non-interference frequencies as interference frequencies, causing the false detection rates to rise. When the JNR becomes sufficiently high, the interference features become more distinguishable, enabling the algorithms to accurately identify interference and reduce misclassification, leading the false detection rates to stabilize. Under single-tone interference, the average false detection rates of the three algorithms are nearly identical. Under multi-tone interference, the average false detection rates of the three algorithms, in increasing order, are as follows: CME < the proposed algorithm < FCME. Overall, the average false detection rates of all three algorithms are quite similar, and nearly zero, meaning they can be considered negligible.
# Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique
## 2 Materials and Methods
### Indicators for Model Evaluation  
This research presents a comprehensive comparison of the predictive effectiveness of the KPCA-PSO-ELM model. To achieve a more accurate and comprehensive analysis, multiple error metrics are utilized, including MRE, RMSE, and R\({}^{2}\).  
The error metrics are formulated as follows:  
\[\text{MRE}=\frac{1}{n}\sum_{i=1}^{n}\frac{|y_{i}-\hat{y}_{i}|}{y_{i}}\times 100\% \tag{25}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{  
The coefficient ranges from \(-1\) to \(1\), with a larger absolute value indicating a stronger correlation. Negative values indicate an inverse relationship between the main index and the features; in contrast, a positive value indicates a direct relationship. The operational principle is illustrated in Equation (28).  
\[r=\frac{\sum_{i=1}^{n}\left(X_{i}-\overline{X}\right)\left(Y_{i}-\overline{Y} \right)}{\sqrt{\sum_{i=1}^{n}\left(X_{i}-\overline{X}\right)^{2}\left(Y_{i}- \overline{Y}\right)^{2}}} \tag{28}\]  
Figure 5: External corrosion influencing factors of buried pipelines.  
Figure 6: Histogram of corrosion rate.  
As shown in Figure 7, corrosion rate was moderately correlated with the stray current, sulphate content, chloride content, and redox potential. In order to better predict the corrosion rate, it is necessary to extract the characteristics of 10 external corrosion influences in buried pipelines. On the one hand, this can effectively reduce the data dimension and reduce the spatial complexity. On the other hand, it can simplify the model and reduce the time complexity.
# Path Loss Measurements of Indoor LTE System for the Internet of Things
###### Abstract  
The Internet of Things (IoT) provides communication service for future smart manufacturing, which is capable of independently exchanging and responding to information to manage industrial production processes. For the purpose of connecting machines, devices, sensors, and people with each other in a factory, reliable and scalable communication networks used in the cellular IoT are of great importance. This paper aims at channel parameter measurements of indoor Long Term Evolution systems in order to achieve good coverage and service reliability (SR) for the IoT. For the purpose of determining the path loss exponent and the standard deviation of the received shadow fading signal, we use software defined radio techniques to build a small cell experimental platform which contains an evolved node B and user equipment. Received power measurements were performed on this platform. Finally, based on the experimental results, the modified path loss model and the calculated fade margin (FM) for 90% SR are exploited to predict the coverage range of the small cell base station deployed in the factory. The measured path loss channel models are compared with International Telecommunication Union (ITU) path loss channel model.  
Keywords:Internet of Things; long term evolution; path loss model; fade margin; system reliability +
Footnote †: journal: Applied Sciences
## 2 Experiment Preparation  
The sandstone rock samples in this article were taken from a certain mine in China. The selected large rock samples have good homogeneity and no obvious cracks. Core sampling and grinding were carried out on the selected rock samples to prepare multiple groups of cylindrical rock samples with diameters of 50 mm and heights of 100 mm. The processing accuracy of rock samples is strictly carried out in accordance with the standard for test methods of engineering rock mass (GBT50266-2013) [21]. To ensure the uniformity of the prepared rock samples, ultrasonic tests, weight tests, height tests, and diameter tests were conducted on the rock samples. The specific test data are shown in Table 1 as follows. It can be known from Table 1 that the errors of ultrasonic waves, weight, height, and diameter of the rock samples all meet the experimental requirements. In Table 1, A1-1, A2-1 and A3-1, respectively, represent the three duplicate samples in the group with a loading rate of 1 mm/min. A1-3, A2-3, and A3-3, respectively, represent the three duplicate specimens in the group with a loading rate of 3 mm/min. A1-5, A2-5, and A3-5, respectively, represent the three duplicate specimens in the group with a loading rate of 5 mm/min. A1-7, A2-7, and A3-7, respectively, represent the three duplicate specimens in the group with a loading rate of 7 mm/min.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Rock** & **Loading Rate** & **Ultrasonic** & **Weight** & **Height** & **Diameter** \\
**Samples** & **(mm/min)** & **Wave (km/s)** & **(g)** & **(mm)** & **(mm)** \\ \hline A1-1 & & 3.02 & 504.53 & 100.32 & 49.89 \\ A2-1 & 1 & 3.05 & 506.71 & 99.83 & 49.97 \\ A3-1 & & 2.99 & 503.97 & 100.6 & 49.7 \\ \hline A1-3 & & 2.97 & 503.95 & 99.79 & 49.72 \\ A2-3 & 3 & 3.03 & 504.89 & 99.94 & 49.91 \\ A3-3 & & 2.98 & 504.24 & 99.87 & 49.95 \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of rock samples.  
Displacement loading experiments with different strain rates were carried out on the rock samples that met the experimental requirements. [PERSON] et al. [22] conducted mechanical experiments on rocks at different strain rates to study the mechanical properties and energy dissipation laws of rocks under different loading rates. The displacement loading rate of the rock samples is different, and so is the strain rate of the rock samples. Therefore, in order to study mechanical behavior characteristics of sandstone and constitutive models of energy damage under different strain rates, the displacement loading rates were 1 mm/min, 3 mm/min, 5 mm/min, and 7 mm/min, respectively. During the experiment, the stress-strain data and failure forms of the rock samples at each strain rate were recorded in real time to further compare and analyze the failure characteristics of the rock samples at different strain rates. It is declared that the high and low strain rates presented below are only within the quasi-static strain rate range of this article.
## 4 Materials and Methods
### Cell Culture  
The use of hBMSCs and HUVECs in the present study was approved by the ethical committee of Shanghai Sixth People's Hospital, with written informed consents of the donors. hBMSCs were isolated and cultured using standard protocols [44]. Briefly, bone marrow of femora midshaft was aspirated and suspended in T75 tissue culture flasks (Sarstedt, Ireland) in alpha-minimum essential medium (\(\alpha\)-MEM; Gibco, Invitrogen Pty Ltd., Thornton, Australia), supplemented with 10% fetal bovine serum (FBS; Gibco) and 50 U/mL penicillin/streptomycin (Sigma-Aldrich), and cultured at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) humidified atmosphere. The medium were changed every 3 days with non-adherent cells discarded, and the living cells were trypsinized and passaged at a ratio of 1:3 upon reaching 80% confluency. Cells of passage P3-5 were used for the following experiments. HUVECs were isolated from the human umbilical cord vein as previously described [45]. Cell pellets were resuspended in Dulbeccoco's modified Eagle's medium (DMEM; Gibco) supplemented with 10% FBS and 1% endothelial cell growth supplement (ECGS/H, Promocell, Heidelberg, Germany).