esa-sceva/satcom-chunk-collection huggingface dataset:
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 4 ICENET--Information Centric Protocol for sEnsor NETworks
### Naming Strategy  
In the Information-Centric paradigm, the element \"Name\" identifies the information provided by a certain node and, therefore, plays a key role in communication. In ICENET, naming has two elements: Node attribute (ATTR) and node region (REGION). These elements describe the physical parameter monitored by the node and location of the node, respectively. The node attribute is an alphanumeric flat name and the node region is a hierarchical name structure, composed of one or more alphanumeric components separated by \"\(\backslash\)\". The deployment region is successively divided into subregions, each represented by a different component name. The hierarchy of component names are application-dependent and must be defined before the WSN deployment.  
Figure 6 shows a tree-based network topology example, indicating the ATTR and REGION elements in each node. For example, node 2 monitors the parameter humidity, and the node region has three levels: Region A, subregion A1, and subregion Lake. A node sends data in response to an interest whenever the REGION field of the message partially matches the node region. Therefore, the field REGION of the interest message defines the scope of the queries. Considering the network shown in Figure 6, if a client asks for the parameter humidity in the subregion A1 (A\(\backslash\)A1), nodes 2 and 6 will send data in response to the received interest. Otherwise, if the client asks for humidity in the lake located at subregion A1 (A\(\backslash\)A1\(\backslash\)Lake), only node 2 processes the interest and sends data to the parent node. Figure 7 shows the name tree (hierarchy of component names) for the network topology shown in Figure 6.  
Names are not unique, because multiple sensor nodes sensing the same physical parameter can be deployed in the same region. Additionally, to fit into the IEEE 802.15.4 standard, the maximum length of a content name is 50 octets and is limited to five components with a maximum of 15 octets each. A possible solution to overcome this limitation is to translate component names into binary code.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Event** & **Action** \\ \hline \(I\) Expires & Set \(I=2\cdot I\), where \(I\leq I_{\text{max}}\) \\  & Set \(c=0\) and randomly pick \(t\in[I/2,\ I]\) \\ \hline \(t\) Expires & If \(c<k\) then transmit \\ \hline Consistency Detected & \(c=c+1\) \\ \hline Inconsistency Detected & Set \(I=I_{\text{min}}\) and randomly pick \(t\in[I/2,\ I]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operations of the Trickle Algorithm [49].
## 1 Introduction
### Objectives and Theoretical Framework of This Study  
The objective of this study is to describe the main safety culture characteristics of Spanish nuclear power plants, taking as an analysis framework the traits of the three theoretical approaches on the safety of high-risk organizations: theory of HRO, resilience engineering, and the conflicting objectives perspective. This study is conducted with secondary data obtained from independent safety culture evaluations in all Spanish nuclear power plants using the same methodology, hence providing a source of information that is reliable and consistent with the purpose of the study. The nature of the data favors the assessment of key aspects of each theoretical perspective. It is important to emphasize that this study is not aimed at engaging in theoretical discussions or comparing theoretical approaches, but to describe safety culture using available instruments and data. We adopt an eclectic theoretical perspective based on the complementarity of theoretical approaches resulting from the multidimensional nature of safety. That implies, as pointed out by [PERSON] (2017), the need to consider that concepts may overlap or complement each other, as explained in different theories. All three theories highlight how important aspects such as reliability, resilience, and decision-making are for the safety of complex organizations. There are also specific elements to each of these approaches that grant greater amplitude to the description of the subject matter. In short, our study takes note of the following statements to describe safety culture:  
* Theory of HRO: (a) the central value of safety as an aspect conditioning values and behaviors within the organization, (b) the necessary awareness of risk inherent to work activities and processes, and (c) a constructive work environment enabling discrepancies without fear of retaliation;
* Resilience engineering perspective: (a) the capacity to anticipate crisis situations and (b) the capacity to respond to unexpected events;
* Conflicting objectives perspective considers three dilemmatic organizational aspects whose resolution does impact safety: (a) safety vs. production, (b) resources vs. shortage, and (c) formalization vs. informalization.  
Lastly, our research is also based on a concept of safety culture as an inseparable part of organizational culture ([PERSON], 2017). Although the term \"safety culture\" is used, it actually serves as a conceptually useful label that links the organization's culture to its safety focus.
## 3 Results and Discussion
### Secondary Structure Analysis of GCPs and Digestive Products  
The secondary structures of collagen primarily include \(\alpha\)-helix, \(\beta\)-sheet, \(\beta\)-turn, and random coil [28]. During digestion, these structures were broken down into smaller peptide fragments and amino acids due to the action of enzymes, resulting in changes to the secondary structures. As shown in Figure 3, GCP-II exhibited a distinct positive absorption peak near a wavelength of 190 nm and notable negative absorption peaks at 208 nm and near 230 nm, indicative of a typical \(\alpha\)-helix structure [29]. Its \(\beta\)-sheet structure was present near 208 nm, manifesting as a negative absorption peak, while the positive absorption peak at 222 nm suggests that GCP-II also possesses a \(\beta\)-sheet structure [30; 31]. In contrast, the digestive products of GCP-II exhibited a significant negative absorption peak near 200 nm, indicating that the original secondary structures have been disrupted by digestion, leading to the formation of a disordered structure of random coils. Additionally, the negative absorption peaks of GCP-II digestive products in the range of 260-310 nm further confirm the generation of random coil structures.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 6 Method Comparison and Analysis  
### Model Training and Validation  
In the process of model training, the dataset is typically divided into a training set and a validation set. The model is optimized by minimizing the discrepancy between its predicted outputs and the ground truth on the training set, using a predefined loss function. This loss is then propagated backward to update the model parameters through optimization techniques. After training on the training set, the model's performance is assessed on the validation set, allowing for adjustments to the model architecture and hyperparameters to enhance the predictive accuracy.  
For deep learning models, key hyperparameters that require tuning often include the learning rate, batch size, and number of training epochs, among others. To evaluate the model performance, various metrics are commonly employed. In short-term traffic flow prediction, widely used evaluation metrics include the MAE, MAPE, and RMSE. Once training and validation are completed, the final model is tested on an independent test set to assess its generalization performance and obtain its ultimate predictive accuracy.
# Damping of Beam Vibrations Using Tuned Particles Impact Damper
## 4 Experiments
### The Influence of Container Volume and Mass of Particles
The authors of Reference [7] show that changing (increasing) the gap volume in the PID damper results in two main phenomena: initially the reduction of the system's frequency is observable, but after exceeding some specific volume threshold value the increase in the natural frequency of the system is noticed. The reason is the response of the grains movement, the occurrance contact forces and their duration in relation to the moving damped system. In the previously mentioned paper such an effect of changing the natural frequency is explained by a change of the effective mass. In the first range, the effective mass of the system increases, and after exceeding the specified value of the gap, the effective massdecreases. The structure which we proposed is evidently operating in the first described range. Such an observation shows some similarities between TPID and Tuned Mass Dampers. It can be stated that the proposed design of novel TPID device ensures all the advantages of classical PIDs [12], [41] or [3]. Figure 5: Displacement waveforms (**a**,**c**,**e**) and RMS values (**b**,**d**,**f**) for various granular grains compositions. (**a**) Displacements of the beam—\(M_{p}=0.1M_{s}\); (**b**) RMS of beam displacements—\(M_{p}=0.1M_{s}\); (**c**) Displacements of the beam—\(M_{p}=0.2M_{s}\); (**d**) RMS of beam displacements—\(M_{p}=0.2M_{s}\); (**e**) Displacements of the beam—\(M_{p}=0.05M_{s}\); (**f**) RMS of beam displacements—\(M_{p}=0.05M_{s}\).
## 2 Propagation Modeling and Key Rate Estimation
### Propagation Modeling
Considering the Kolmogorov spectrum and a quadratic approximation of the \(5/3\) power law for Rytov's phase structure function, the ensemble average term in Equation (2) can be rewritten as  
\[\left\langle\exp[\psi(\boldsymbol{\rho}_{1},\mathbf{r}_{1})+\psi^{*}( \boldsymbol{\rho}_{2},\mathbf{r}_{2})]\right\rangle_{\rm at}=\exp\{-[( \boldsymbol{\rho}_{1}-\boldsymbol{\rho}_{2})^{2}]^{5/6}/r_{0}^{5/3}\} \tag{3}\]  
where \(r_{0}\) is the spatial coherence radius of a spherical wave propagation in turbulent atmosphere channel, which can be calculated by  
\[r_{0}=\left(0.545k^{2}z\!\int_{0}^{1}C_{n}^{2}(z\cos\alpha)dz\right)^{-3/5} \tag{4}\]  
where \(\alpha\) is the zenith angle of the channel and the altitude-dependent generalized refractive-index structure parameter \(C_{n}^{2}(zcos\alpha)\) can be characterized by the ITU-R model [25]. It is also indicated that the phase distortion caused by atmospheric turbulence eventually leads to a beam decoherence [26].
# Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
## 2 Preparatory Knowledge  
### Transfer Learning  
Transfer learning is applying the knowledge or patterns learned in a certain field or task to different but related fields or problems. It is defined as follows: given a source domain \(D_{s}\) and a learning task \(T_{s}\) and a target domain \(D_{t}\) and a learning task \(T_{t}\), using the knowledge in \(D_{s}\) and \(T_{s}\) to improve the learning of the target prediction function \(f(\cdot)\) in \(D_{t}\), then \(D_{s}\
eq D_{t}\) or \(T_{s}\
eq T_{t}\). Among these values, the domain is the main body of learning, which consists of feature space and marginal probability distributions of features; the source domain refers to a domain with knowledge and a large number of labels, which is the object to be migrated; the target domain refers to the domain used to learn knowledge and apply labels; and the task is the result of learning, consisting of label space and the prediction function corresponding to the label [20; 21]. Transfer learning on convolutional neural networks refers to transferring some network parameters of pre-trained convolutional neural networks from the source domain to the target domain for training. The existing models can be trained with small samples of data to obtain training models suitable for different scenarios. In actual production, the normal data in mine filling equipment operation data that can be used for network training are far greater than the overlimit data, and the network migration effect obtained by using only normal data for training is poor. It is necessary to add a small amount of overlimit data to form a dataset on the basis of a large amount of normal data and use the maximum mean difference to measure the distribution difference between the source domain and the target domain. Then, the convolutional neural network is trained according to the data set selected by the difference value, and the completed training model is used to diagnose the overlimit condition of the target domain, which can solve the problem of insufficient overlimit data for equipment operation well.
## 3 Results
### Triangular Tracking Results  
The first contract was performed with a triangular wave due to its complexity to be followed. Figure 6 presents the error obtained with the QCSMC-ANN and PID in triangular cycles where several points can be highlighted. The PID controller had a variable performance since between 0 and 1 s, the error declined; however, in the following second, the error began to rise up until 2 s. Despite the QCSMC-ANN providing a high amplitude in the first second of this period, in the following, it was diminished.  
Certainly, at 2 s, the first critical point appeared since it is where the slope of the triangular reference changes its sign. The PID changed suddenly from 0.1 \(\upmu\)m to near \(-0.12\)\(\upmu\)m; although the controller performed an abrupt correction, the QCSMC-ANN generated a similar action but with a faster improvement in time. This is reflected after 2 s where the PID had transitory development without reaching the null value of the error. Nevertheless, the QCSMC-ANN carried with the same demeanour as previously right after the slope change.  
The fourth second of this analysis exhibits another crucial point to focus as it is the following slope change at the lower converging point. The PID unveiled a similar situation as previously at 2 s but with lower amplitude and a subsequently transitory response. On the other hand, even if the QCSMC-ANN featured a peak that has a value above \(-0.2\)\(\upmu\)m, the later reaction shows a similar trend as previously described, where the controller aims to a mean near the null value. After 4 s, since the signal is repeated, the detailed features are mirrored.  
Aside from the error development, the control signal is an important feature to analyse because it contributes to the performance of the proposed structures. Figure 7 is a contrast of the control signal generated along the analysed error of both frameworks. As main characteristics to take into account at this point, saturations or sudden changes needed focus as these can damage the PEA driver cube. Henceforth, it can be perceived that both controllers had a suitable demeanour, and any downsides were presented in the experiments.  
Figure 6: The error generated in 2 cycles of a triangular reference.
## 3 Results
### Data Analysis and Statistics: Step 2 The Effect of Initial Sensor Orientation Angle Correction  
To achieve the aim outlined in step 2, the same data were used to explore the influence of a mathematical correction algorithm. The initial orientation of the sensor was calculated using the algorithm above (ATAN2), from which an orientation quaternion (Qi) was computed. Corrected acceleration data were generated through the rotation of the existing data via the following input:  
Corrected Accelerometer Data = \(-\)Qi \(\times\) Accelerometer data,  
where Qi is the quaternion representation of the initial orientation. From these corrected acceleration data, forward bending and lateral bending were computed using the ATAN2 function as before.  
The effect of the correction on the error of the angle computation was explored using identical approaches outlined in step 1 above. Briefly, for peak ROM, estimates compared to OEM, RMSE, and 95% confidence intervals (CI95%) were calculated. The reliability of peak estimates as explored though intraclass correlation coefficients (ICC2,1). Through-range analysis for the non-static portions of the curve was used to compute RMSE between the OEM and the corrected accelerometer angles across a range of velocities up to 148\({}^{\circ}\) s\({}^{-1}\), and velocities were sub-classified into slow (average 49\({}^{\circ}\) s\({}^{-1}\)\(\pm\) 11\({}^{\circ}\) s\({}^{-1}\)) and fast (average 83\({}^{\circ}\) s\({}^{-1}\)\(\pm\) 24\({}^{\circ}\) s\({}^{-1}\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{5}{c}{**Initial Offset Tilt and Twist (\({}^{\circ}\))**} \\ \cline{3-6}  & & **0** & **5** & **10** & **15** & **20** \\ \hline \multirow{5}{*}{Initial Offset Tilt and Twist (\({}^{\circ}\))} & Accelerometer Slow RMSE (\({}^{\circ}\)) & 0.56 & 2.52 & 4.98 & 6.50 & 7.89 \\ \cline{2-6}  & Accelerometer Fast RMSE (\({}^{\circ}\)) & 0.58 & 1.85 & 4.09 & 5.57 & 6.69 \\ \cline{2-6}  & Corrected Slow RMSE (\({}^{\circ}\)) & 0.53 & 1.19 & 0.51 & 1.05 & 1.19 \\ \cline{2-6}  & Corrected Fast RMSE (\({}^{\circ}\)) & 0.53 & 0.67 & 0.98 & 1.19 & 1.18 \\ \hline \end{tabular}  
* Notes: RMSE: Root mean square error; Slow: 0.5 Hz average 49\({}^{\circ}\) s\({}^{-1}\)\(\pm\) 11\({}^{\circ}\) s\({}^{-1}\); Fast 1 Hz average 83\({}^{\circ}\) s\({}^{-1}\)\(\pm\) 24\({}^{\circ}\) s\({}^{-1}\).  
\end{table}
Table 2: Average through-movement RMSE from each data point where angular velocity is greater than 1\({}^{\circ}\) s\({}^{-1}\).  
Figure 6: Through-movement RMSE against speed for every data point where the angular velocity is greater than 1\({}^{\circ}\) s\({}^{-1}\), showing the differences in relationship between error and speed for the accelerometer data and the corrected data. RMSE—Root mean square error.
# Analysis of Acoustic Emissions for Determination of the Mechanical Effects of Scratch Tests
###### Abstract  
Acoustic Emission (AE) is a promising technique for measuring tool wear online and in real time. In this work, scratch tests were conducted to better understand the \"pre-wear\" AE response based on loading conditions that were not sufficient to generate galling. The scratch tests used the same type of indenter against two different sheet materials: aluminum and steel. The results showed that AE parameters such as the mean frequency, Centroid frequency and Shannon entropy outperformed other frequency domain techniques by discriminating between the two sheet materials in scratch tests. From the literature, the frequency region of interest was expected to be sub 300 kHz. However, in this study, activity below this threshold was found to be noise, whereas distinct frequencies were found at much higher frequencies than expected. These results are compared against single grit \"SG\" tests of both mild steel- and nickel-based superalloys to allow comparison of the two test methods and materials used. This comparison showed that the SG tests excited the acoustic emission in ways in which the scratch tests did not. Another factor when using acoustic emissions to monitor sheet metal forming is the differences obtained in energy-frequency mapping, where many report the galling phenomena between a certain amplitude and frequency range. Such results are specific to the setup and the materials/geometries used. Further work presented here compares different scratch tests where energy-frequency mapping is different for different materials/geometries.  
acoustic emission; scratch tests; mechanical inspection tests; force (load); wear mechanisms Article
## 4 Results and Discussion
### Performance Analysis
#### 4.1.6 Accuracy  
The accuracy (_Accu_) rate serves as a primary metric for attack detection, offering insight into the algorithm's performance in identifying various types of attacks. The high accuracy rate indicates the algorithm's effectiveness in promptly detecting attacks. Evaluation metrics, including false positive (\(F_{P}\)), true positive (\(T_{P}\)), false negative (\(F_{N}\)), and true negative (\(T_{N}\)), are employed to compute accuracy. This metric is defined as the percentage of appropriately classified flows relative to the total flows arriving at each controller. The mathematical representation is shown in Equation (12).  
\[Accu=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}}*100 \tag{12}\]  
Figure 7b illustrates the accuracy results of LSTM, RNN, IDS, and BSHNN. The study demonstrates that the proposed BSHNN outperforms existing methods in terms of accuracy rates, with rates of 25%, 14%, and 10.26% higher. This success is attributed to hashing flow rules before switch deployment and introducing a classifier for packet content classification.  
Figure 7: BSHNN approach simulation results. (**a**) Packet loss ratio. (**b**) Accuracy.  
The study also reveals that BSHNN outperforms LSTM, RNN, and IDS subjected to a 20% attack scenario by attaining 98.02%, 93.1%, 95%, and 95.8%.
## 3 Experiments and Results  
This section aims at the evaluation and analysis of our proposed model (SESADRN, used DRN54 pre-trained model) and other state-of-art models for butterfly classification. These comparison models are squeeze-and-excitation model based Resnet architecture (SE-Resnet50 [30]), Residual attention network (RAN56 [35], RAN92 [35]), and dual-attention dilated residual network (DADRN [32], used DRN54 pre-trained model). All models were pre-trained by the use of ImageNet. All experiments were performed on the Pytorch platform. The models were trained with 300 training epochs and the batch size was set to 16. The parameters were optimized by the use of the Adam optimizer. The initial learning rate was set to 0.0001, and the attenuation after every 50 cycles was 0.1. To avoid the problem of overfitting, we used a weight decay (weight decay = \(1\times 10^{-4}\)) and online data augmentation strategy in training. Four online data augmentation methods were used in the paper (random mirroring, random rotation, adding Gaussian noise (0, 0.01), and resizing image size). The models were trained  
Figure 5: Overall SESADRN architecture.  
on a computer with 32 GB of memory and two NVIDIA GeForce GTX 980 Ti. Since the number of each category is different, accuracy, weighted prediction, weighted recall, and weighted f1-scores were used as evaluation metric. All models employed the same parameters and configurations. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) [25] is used to visualize the classification results.
## 3 Design of Experiments
### Implementation of the SSDI Circuit  
An integrated switching circuit is designed and implemented to fulfill the switch control. The size of the integrated circuit board is only 60 mm \(\times\) 40 mm, as shown in Figure 8a. Three modules are included in the switching circuit: (1) a battery module; (2) a detection module of the displacement-extremum and (3) a switching control module. A low-pass filter is used to denoise the displacement signal and provide a proper phase delay. A comparator is implemented to finish the detection of displacement extremum by use of an ultra-low power operational amplifier setting in the circuit and by change of the displacement signal into a square signal for controlling the switch. The switch function is realized by an NMOS (N-channel metal oxide semiconductor FET) field-effect transistor and a PMOS (P-channel metal oxide semiconductor) field effect transistor (MOSFET, Metal-Oxide Semiconductor Field Effect Transistor) together with two diodes. The type of NMOS and PMOS in switching control block is NTD20N06 and NTD20P06, respectively. The comparator plays an important role in the switching circuit together with the MOSFETs, in addition it requires just a few \(\upmu\)Ws for switching operation. This low-power analog circuit can be powered by a 3V button battery (CR2032), and its battery capacity is 3 V \(\times\) 210 mAh = 630 mWh. The switching circuit board needs about 0.5 mW power in total, which means that with a 3 V button battery the whole system can work for 630/0.5 = 1200 h. A 100 mH inductor is subsequently connected to the switching circuit. Thanks to technique progress, the volume of inductor is largely reduced and small like a resistor (Figure 8b). The schematic of the electric broad is shown in Figure 8c.