esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 D-MSR Management Phases
### Reserving Management Resources (Phase-4)  
In this phase, the node reserves resources for exchanging network management messages. Once the node joins the network, in Phase-2 the initial communication links to adjacent neighbors are constructed and then in Phase-3 the routing layer constructs the \"up\" and \"down\" routes. In this phase, it is necessary to reserve the communication resources by which the routing layer control messages can be forwarded to the destination along the path. To reserve the management resources through the \"up\" path, each node runs the D-SAR protocol to allocate the required resources based on the DAO messages rate (which is defined in the routing layer). Similarly, to reserve the resources through the \"down\" path, the root runs the D-SAR protocol toward the new nodes.  
In a centralized approach, such as WirelessHART or ISA100.11a, a different procedure is defined to receive the join request from the new device, send the activation command, construct the new graphs for the new device, and reserve the management resources (e.g., management superframes and links). In these standards, the join request will be forwarded toward the network manager via the proxy device, and the network manager who has received the join request will use its centralized algorithm to allocate the management communication resources (such as graphs, superframes, and links). In the centralized approach, a join response/activation command is sent to the device after all necessary communication resources for exchanging the management messages have been configured and reserved along the path. The joining sequence of a new device in WirelessHART network is discussed in [25].  
In D-MSR we consider the node as having joined the network, after it received the activation command from the neighbor advertiser, started to send the advertisement periodically (Phase-1), defined the initial communication links with adjacent neighbors (Phase-2), constructed routes to the other nodes (Phase-3), and reserved the communication resources to exchange the management messages (Phase-4).
## 4 Simulation and Experimental Analysis
### Experimental Analysis  
The fault simulation tests were carried out on the two stage planetary gearbox in healthy and crack condition of the sun gear. The fault simulation test bench is shown in Figure 17. The planetary gearbox contains a single planetary row, the sun gear is the input, and the planet carrier is the output. The specific parameters of the test bench are shown in Table 1. The gears used in the test are all standard gears. In addition, we also assume that the gear has no manufacturing errors. In the test, the vibration sensor is install in the vertical direction of the case of the planetary gearbox. The input shaft speed of the gearbox, that is, the speed of the sun gear is 1200 r/min. The sampling frequency was 20,480 Hz. Under the same load conditions, the vibration signal data of the planetary gearbox were collected in the healthy and crack state of the sun gear. The root crack implantation method is wire cut electrical discharge machining (WEDM) in the experiments.  
The voltage signal obtained based on the sun gear acceleration sensor is shown in Figure 18. The mesh frequency of planetary gears can be easily found as well in Figure 19. Figure 20 shows the time-domain waveform of the experimental data of the crack condition of the sun gear. In the case of sun gear tooth cracks, there will be some spikes in the vibration signal. In addition, the effect of amplitude modulation is not obvious, because amplitude modulation and fault signal together cause the complexity of the vibration signal. Similar to the simulation signal verification, the envelope spectrum analysis of the acquired time-domain signal is performed, and the obtained results are shown in the following Figure 21:  
Figure 17: Experimental test rig.  
Figure 18: Test signal in the healthy condition.  
Figure 21: Test signal spectrum in the crack condition.  
Figure 19: Test signal spectrum in the healthy condition.  
Figure 20: Test signal in the crack condition.  
The corresponding gear mesh frequency \(f_{m}=437.5\) Hz and its multiplier can be found from the spectrogram of the sun gear tooth root crack\({}^{\prime}\) experimental signal. In the low-frequency band, the corresponding sun gear fault characteristic frequency 15.63 Hz can also be found clearly. Therefore, through the analysis of the experimental signal, the correctness of the TVMS solution method and the establishment of the dynamic simulation model of the planetary gear train is verified again.
# Dual-Band Single-Layered Frequency Selective Surface Filter for LTE Band with Angular Stability
## 4 Simulation Results and Discussion of C-FSS
### Angular Stability with TE and TM Mode Polarization  
Due to the uncertainty in the direction of the EM wave, an FSS can be sensitive to the oblique incident angles. Its filtering performance may vary depending on the incident angle which may exhibit significant deviations in the resonant frequency. If the deviation is too large, then the FSS may fail to perform the intended spatial filtering. Therefore, to ensure stable operation within the desired frequency band, it is essential to verify angular stability under various polarized oblique incidences.  
To verify the angular stability of the designed C-FSS, it has been simulated for oblique incidence for both TE and TM polarization. The reflection coefficient is simulated for oblique incident angles up to 80\({}^{\circ}\); it is provided in Figure 10a-d. In this context, the angle mean deviation [31] is considered to analyze the angle stability of the proposed C-FSS and calculated as follows:  
\[\Delta\theta_{m}=\frac{\left|f_{r}^{norm.}-f_{r}^{1}\right|+\left|f_{r}^{norm. }-f_{r}^{2}\right|+----+\left|f_{r}^{norm.}-f_{r}^{n}\right|}{nf_{r}^{norm.}} \times 100\% \tag{11}\]  
where \(\Delta\theta_{m}\) is the mean angle deviation; \(f_{r}^{norm.}\) is the resonant frequency at normal incidence, and \(n\) is the total number of oblique incidence angles. To get high angular stability, the value of the mean angle deviation should be low. Table 4 presents the mean angle deviation of both resonant frequencies for TE and TM modes at various incident angles. The data represent the good angular stability of C-FSS, as the mean angle deviation does not exceed 1% for both modes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Angle (Degree)** & **0\({}^{\circ}\)** & **10\({}^{\circ}\)** & **20\({}^{\circ}\)** & **30\({}^{\circ}\)** & **40\({}^{\circ}\)** & **50\({}^{\circ}\)** & **60\({}^{\circ}\)** & **70\({}^{\circ}\)** & **80\({}^{\circ}\)** & **\(\Delta\theta_{m}\)** \\ \hline \multicolumn{11}{c}{} & \multicolumn{11}{c}{} & \multicolumn{1}{c}{} & TE & & & & \\ \hline \(F_{r1}\) (GHz) & 1.92 & 1.92 & 1.92 & 1.92 & 1.91 & 1.91 & 1.91 & 1.91 & 0.32 \\ \hline \(F_{r2}\) (GHz) & 2.08 & 2.08 & 2.08 & 2.08 & 2.09 & 2.08 & 2.08 & 2.08 & 2.08 & 0.06 \\ \hline \multicolumn{11}{c}{} & \multicolumn{11}{c}{} & \multicolumn{1}{c}{} & TM & & & & \\ \hline \(F_{r1}\) (GHz) & 1.92 & 1.92 & 1.92 & 1.91 & 1.92 & 1.90 & 1.90 & 1.90 & 1.90 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Resonance frequency at different oblique incident angles for TE and TM modes.  
Figure 9: Reflection-transmission coefficients of C-FSS.
## 2 Preliminaries
### System Model  
As shown in Figure 2, based on the standard single-gateway model [13], our system model consists of three entities: physicians at different occupational levels (A, B, and C), a gateway node (GWN), and a series of body area sensor nodes (BASNs).  
In the registration phase, users and BASNs register at the gateway, which corresponds to the registration process in Figure 2. At the same time, the gateway sets the user's fine-grained authentication parameters according to the user's occupation level and type in order to prepare for subsequent fine-grained authentication between users and BASNs. At the end of registration, the physicians (BASNs) retain real-time fine-grained authentication parameters (resp. secret value), and GWN stores the identity of BASNs.  
In the login and authentication phase, if the user wants to access the data of some body area sensor nodes, they first need to initiate an authentication request in the gateway, which corresponds to process 1 in Figure 2. Next, the gateway authenticates the user. If authentication is successful, the gateway node sends the user's request to the BASN, which corresponds to process 2 in Figure 2. After the BASN receives the request, it first verifies the identity of the gateway, and if the authentication passes, it sends the authentication-related parameters to the gateway, which corresponds to process 3. Upon the gateway's receipt of data from BASN, it first authenticates the BASN and then updates relevant fine-grained authentication parameters according to the user's occupation; then, it sends the relevant authentication data to the user, which corresponds to process 4. Finally, the user negotiates a consistent session key with BASN via authentication data from the gateway node. The purpose of this phase is to ensure that only legitimate physicians can access the data resources of BASNs.
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 4 Proposed Multi-Frame Blind Super-Resolution Algorithm  
We employ the alternating minimization (AM) method for reconstructing the HR image \(\mathbf{x}\), motion matrix \(\mathbf{F}\) and the blur kernel matrix \(\mathbf{K}\) iteratively. To overcome the poor convergence of a simple alternating minimization method, we propose a method of combining the estimation of motion parameters, blur kernel estimation and parameters in image reconstruction, and finally outputting the estimated high-resolution image. In the image reconstruction stage, a coarse-to-fine strategy is used to enlarge to the required scale factor. We modify the motion matrix by combining the confidence weight in the image reconstruction stage and the Jacobian matrix of motion parameters. We use the modified motion matrix and the confidence weight of each LR image in the image reconstruction stage to modify the blur matrix.
## 5 Experimental Results
### Experiments with Intelligent Vehicle
#### 5.2.2 Experimental Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & \(\mathbf{AP^{samll}}\) & \(\mathbf{AP^{medium}}\) & \(\mathbf{AP^{large}}\) & **mAP** \\ \hline SSD & 0.682 & 0.638 & 0.658 & 0.664 \\ YOLOv2 & 0.626 & 0.690 & 0.752 & 0.694 \\ YOLOv3 & 0.696 & 0.784 & 0.712 & 0.724 \\ YOLOv5 & 0.832 & 0.906 & 0.874 & 0.876 \\ Faster-R-CNN\_ours & 0.874 & 0.918 & 0.922 & 0.906 \\ \hline \end{tabular}
\end{table} 
Table 5: Results of the comparative experiments at sunset. Figure 22: Detection results at sunset: (**a**) site 1; (**b**) site 2. Last, we can draw the following conclusions:  
1. The proposed method performs mAPs of 92.6%, 90.6%, and 86.9% at sunny daytime, sunny sunset, and on a rainy day, respectively. Although the precisions are proportional to the intensity, all the precisions are over 86%. This demonstrates that the proposed method has a high precision and robustness at different conditions. 2. Compared with SSD, YOLOv2, and YOLOv5, the proposed method exhibits better performance regardless of times or weather conditions. This proves that our method has a higher precision and is more robust than these three methods. 3. Although YOLOv5 has a higher precision at sunny daytime (mAP of 94.8% and 92.6%), when the intensity is low (sunset or rainy day), the proposed method displays a better performance than YOLOv5 (mAP of 87.6% and 90.6% at sunset, mAP of 76.6% and 86.9% on a rainy day). This proves that our method can maintain a high precision in extreme environments. This makes our method much more practical than others. Traffic sign detection is one of the core challenges for intelligent vehicles. It can help intelligent vehicles know the traffic rules of driving routes in real time. With the improvement of the automatic driving level for intelligent vehicles, the traffic sign detection accuracy, especially in low-light intensity and rainy weather, directly determines the control accuracy. The proposed method has a high precision (90.6% at sunset and 86.9% on a rainy day) in extreme environments. Therefore, the proposed method is helpful in order to improve the accuracy of environment sensing and the development of autonomous driving, as well as to improve the traffic sign detection performance in extreme environments.
## 1 Introduction
The laser irradiation conditions were varied, and the effects on the surface morphologies were clarified. The percentage of metal nanoparticles embedded in the pores and the structural change and processing damage of the zirconia substrate were also evaluated. The objective of this work is to create zirconia with nanopores embedding different material than the bulk using a laser irradiation process only. This study suggests a new approach for YSZ in various fields of industry by enhancing surface functionality. If a YSZ sample with silver nanoparticles embedded within the pores is realized, various applications can be expected based on the antibacterial properties, unique optical properties, high electrical and thermal conductivity, and nanomedicine delivery function of silver. Using this method, which uses a femtosecond pulsed laser that can minimize material damage, it is possible to obtain the effect of silver only on the surface while maintaining material strength, making it particularly suitable for YSZ, which is used as a biomaterial. Furthermore, by applying this method to other nanoparticle materials, it is expected that it will be possible to embed any material at any location on the surface and create YSZ surfaces with various desired functions. Figure 1: Surface nanopores with embedded silver nanoparticles.
## 2 Materials and Methods
### Statistical Analysis  
An a priori sample size was calculated using the G. Power 3.1.9.7. A minimum of 120 subjects (30 in each group) was required based on detecting significant medium effect size (\(p\) = 0.35) with an \(\alpha\) of 0.05 and a desired power of 0.90. The normality of residuals was verified using the Shapiro-Wilk's test. Normal data were expressed by mean and standard deviation (SD), and non-normal data were presented by the median and interquartile interval. The groups were contrasted for age and photo- and phono-phobia intensity using a one-way analysis of variance (ANOVA) with Least Significant Difference post-hoc test, and [PERSON]'s d was used to verify the effect size (ES) of the significative pairwise comparisons. According to [PERSON]'s d classification, 0.2 is considered a small effect size, 0.5 represents a medium effect size, and 0.8 a large effect size [22]. Migraine features were compared using the Kruskal-Wallis test with the Mann-Whitney test as a post-hoc for pairwise comparisons. The presence of interictal photophobia and phonophobia for each activity assessed was contrasted through groups using the Chi-square test. [PERSON]'s V was used to measure the effect size for the Chi-square test. Regarding this analysis having 3 degrees of freedom (df), the [PERSON]'s V equal to 0.06 is considered a small effect size, 0.17 is a medium effect size, and 0.29 is a large effect size [23]. [PERSON]'s rho correlation encompassed only migraine participants to verify the association between photo- and phono-phobia intensity and migraine frequency, migraine intensity, and the frequency of aura. For correlation analysis, photophobia intensity was obtained from the sum of values obtained from visual discomfort questions, and for phonophobia intensity, we consider the sum of values related to auditory discomfort. Correlation values less than 0.40 mean a weak correlation, 0.40 to 0.69 mean a moderate correlation, and more than 0.70 means a strong correlation [24]. The analysis was performed using the SPSS(r) software (IBM, New York, NY, USA, version 20.0), a two-tailed hypothesis was tested, and the significance level was set at 0.05.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Load Disaggregation Algorithm (LDA)
#### 4.3.1 Concepts and Challenges  
The aim of NILM is to partition the whole house consumption data into appliance specific consumption.  
\[P(t)=P_{1}(t)+P_{2}(t)+...+P_{n}(t)+L \tag{6}\]  
This can be seen as in Equation (6) where \(P(t)\) is the total consumption at time \(t\), \(P_{n}(t)\) is the consumption of appliance \(n\) at time \(t\), and \(L\) is the loss factor in the electricity lines. The aim is to partition \(P(t)\) back to the different appliance specific consumptions. In order to structure the problem we categorize the appliances in 4 groups [42]:  
* Type-1: Appliances that only have 2 states corresponding to ON/OFF. This could be a lamp or a water boiler.
* Type-2: These are appliances with multiple states. The appliance in this category can be modeled as a finite state machine. Many modern devices such as TV's, computers and washing machines fall in this category.
* Type-3: These appliances are referred to as \"Continuously Variable Devices\". These devices have a variable power draw and is impossible to model as a finite state machine. This could be appliances like power drills, and dimmer lights. These are by far the hardest for the NILM algorithms to detect.
* Type-4: These are a special kind of appliances that are always ON and consume energy at a constant rate. Such devices could be smoke detectors and burglary alarms.  
The appliances in Type-4 is not a particular researched area, since it does not give the user much information to calculate savings from. These appliances are often using very little energy.  
Type-1 and Type-2 are commonly used, and can cover almost any appliance. In the area of event detection is the most common approach to see all appliances as Type-1 and detect ON/OFF events. Most appliances today is Type-2 due to the growing amount of complicated electronics that are embedded in them. There are devices that are Type-2 but most of the time act like Type-1 appliances. An example of this is the vacuum cleaner. On most vacuum cleaners, the user is able to change the suction intensity, which makes it Type-2. However, most people do not change this very often, so the energy usage looks more like a Type-1 appliance.
## 1 Introduction  
The confluence of several factors is creating a nearly \"perfect storm\" requirement for the use of extensive autonomy in planetary science missions. These factors include: (a) the identification of mission targets further away from Earth, (b) an increase in communications \"round-trip\" time as the spacecraft moves further from Earth, (c) a corresponding decrease in power as the spacecraft moves further away from the sun, (d) smaller spacecraft sizes (limiting antenna size and gain levels possible), (e) the impact that b and c have on the level of communications possible and (f) the miniaturization of computers, making applications which require significant processing power possible in a suitably small form factor. Basically, this amounts to a simple decision: one can perform less mission work (due to delays), receive less data-value (due to transmission capability limitations) and have a human teleoperate the craft or hand control over to autonomous control software (which would be needed in the first instance, to some extent, to handle emergency response to situations that cannot wait for a roundtrip communication to resolve--e.g., tipping, slipping, _etc._).  
Given the high stakes of planetary missions (_i.e._, they are generally high-budget and high-profile), it is understandable that human controllers are hesitant to release control to software. Similarly, it seems reasonable that many planetary scientists may have similar misgivings regarding handing over the task of data collection, analysis and value determination to software. While the root causes of these misgivings are beyond the scope of this paper, it does attempt to determine what can be done to overcome them. This paper presents data from a survey of 230 participants at the 2013 Lunar and Planetary Science Conference in The Woodlands, Texas. Participants were asked about their views on the use of autonomous control software in directing data collection, data analysis and its prioritization for transmission.
# Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
## 3 Methods
### Data Collection and Preprocessing  
In this study, two datasets are adopted for evaluation: the first is the public dataset M-CAPTCHA on Kaggle \"[[https://www.kaggle.com/datasets/sanluo/mcaptcha](https://www.kaggle.com/datasets/sanluo/mcaptcha)]([https://www.kaggle.com/datasets/sanluo/mcaptcha](https://www.kaggle.com/datasets/sanluo/mcaptcha)) (accessed on 5 February 2024)\", and the second is a dataset generated based on the Python ImageCaptcha library, called P-CAPTCHA. M-CAPTCHA contains 25,000 images, while P-CAPTCHA has 20,000 CAPTCHAs. Every CAPTCHA image contains four characters from position  
Figure 3: The networks of Adaptive CAPTCHA.  
Figure 2: Some confusing adjacent characters in CAPTCHAs.  
one to four, and each character is taken from the 26 uppercase English letters. Figure 4a,b show some samples in the M-CAPTCHA and P-CAPTCHA datasets, respectively. As can be seen in the figures, images in P-CAPTCHA have little interference, noise, character distortion, and deformation, while the M-CAPTCHA dataset has a much stronger anti-attack mechanism, which includes more complex background interference, greater spatial deformation, and nonlinear distortion, making it very difficult to recognize.  
Figure 5a,b show that P-CAPTCHA characters present a uniform distribution, while M-CAPTCHA characters are relatively uniformly distributed, indicating that there are correlations between the characters in the M-CAPTCHA.  
Further analysis found that the M-dataset also has MTPs between characters, and the MTPs of some characters (from R to Z) in the training set are shown in Figure 6. The presence of MTPs leads to their first-order statistical distributions being closely related to the transition probabilities. Letters in the vertical coordinate represent a character, and letters in the horizontal coordinate represent the next adjacent character. The numerical values of their intersection are the MTPs of these two characters, which are zero in some grids, such as the MTP between the two consecutive letters W. The presence of MTPs indicates a strong correlation between characters, which can be better modeled by RNN compared with FC to improve recognition accuracy.  
Based on the above data analysis, it is necessary to model the inter-character correlation, so a CRNN module was adopted in this study. The images in both datasets are of the same size, with a width of 64 and a height of 192 pixels, thus requiring no scaling. However, grayscale processing of the images is necessary, which can greatly reduce the complexity of the model while maintaining the accuracy of CAPTCHA recognition. In addition, all of the images need to be normalized for better training.  
Figure 4: Samples of dataset: (**a**) M-CAPTCHA; (**b**) P-CAPTCHA.  
Figure 5: Character statistical distributions: (**a**) M-CAPTCHA; (**b**) P-CAPTCHA.