esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Statistical Analysis on Acquired Impedance Signatures  
In the previous section, the acquired impedance signatures were visually analyzed to roughly investigate how the impedance signatures varied subjected to different areas and PZT attached devices. In this section, a statistical method known as the root mean square deviation (RMSD) after averaging the impedance signatures was used to quantify the size of the difference for impedance signatures. The RMSD metric was used to analyze the EMI technique in general, as it has been found to be effective in identifying damage. Here, \(Z_{i}^{a}\) and \(Z_{i}\) represent the signatures before and after damage, respectively. The variable \(N\) in the equation represents the number of the impedance signature:  
\[\text{RMSD}=\sqrt{\sum_{N}\left[Re(Z_{i})-Re\left(Z_{i}^{o}\right)\right]^{2}/ \sum_{N}\left[Re\left(Z_{i}^{o}\right)\right]^{2}} \tag{2}\]  
Figure 5a,b shows the averaged impedance signatures for Figures 3 and 4, respectively. The 10 impedance signatures were averaged to create each line shown in the figure. For Figure 5a, the clear difference is seen between impedance signatures where the resonance peak increased from 6.5 kOhms (labeled '02_bo') to 7.6 kOhms (labeled '02_de') when moving from boned area to debonded area. Here, a RMSD of 5.75% was calculated. Next, with the thicker top plate, the resonance peak increased from 6.6 kOhms (labeled '04_bo') to 7.2 kOhms (labeled '04_de') when moving from bonded area to debonded area. Here, a RMSD of 7.51% was calculated. Next, with Figure 5b, since there was a very small difference between the impedance signatures, the graph was zoomed-in between 24.1 kHz and 24.5 kHz to visualize the difference in impedance signatures. With the figure, there is a clear difference between the bonded and debonded areas where the resonance peak increased from 12 kOhms (labeled '02_bo') to 13.5 kOhms (labeled '02_de') when moving from bonded area to debonded area. Here, a RMSD of 2.47% was calculated. Next, with the thicker top plate, the resonance peak increased with a similar height from 12 kOhms (labeled '04_bo') to 13.5 kOhms (labeled '04_de'). Here, a RMSD of 2.68% was calculated. Comparing both RMSD values, similar experimental values showed that the thickness did not significantly affect the outcome. However, with the thicker plate of 0.4 mm, we can see that the resonance frequency was higher (right shift) compared to using a top plate of 0.2 mm. This promising outcome shows the possibility of using the proposed roller device for detecting debonding of composite plates.
# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 3 Theoretical Basis  
The distribution of heat in a homogeneous and isotropic medium is described by  
\[\frac{\partial u}{\partial t}-\alpha\
abla^{2}u=0, \tag{1}\]  
in which \(u\) denotes the temperature field (in kelvin), \(\alpha\) is the thermal diffusivity (i.e., the heat transfer rate in m\({}^{2}\)/s), and \(\
abla^{2}\) is the Laplace operator (\(\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y^{2}}+\frac{ \partial^{2}}{\partial z^{2}}\) in 3D Cartesian coordinates). The response of Dirac-delta heating pulse for semi-infinite isotropic solids in 1D (z coordinate) is described by the following expression [63]  
\[T(z,t)=\frac{Q_{0}}{\sqrt{kc_{p}\rho\pi t}}\exp\left(-\frac{z^{2}}{4\alpha t} \right)\text{, for }t,z>0 \tag{2}\]  
where \(Q_{0}\) (in J/m\({}^{2}\)) is the energy density absorbed by the surface, \(k\) (W/(mK)) is thermal conductivity, \(c_{p}(J/(kgK))\) is the specific heat capacity, and \(\rho\) (kg/m\({}^{3}\)) is the mass density of the material. It is worth noting that the thermal diffusivity is expressed in terms of these three coefficients: \(\alpha=k/(c_{p}\rho)\). At the specimen surface (\(z=0\)), Equation (2) is reduced to  
\[T(0,t)=\frac{Q_{0}}{e\sqrt{\pi t}}, \tag{3}\]  
\(e=\sqrt{k\rho c_{p}}\) is the thermal effusivity, a measure indicating the ability of a material to exchange thermal energy with its surroundings. To derive the response for a long pulse stimulus of length \(t_{p}\), similar to [46; 47], we consider the thermal excitation as a sequence of thermal pulses, i.e., \(\left\{\delta(t-\tau)\ \right|\ \tau\in\left[0,t_{p}\right]\right\}\), then integrate the response \(T(0,t+\tau)\) over the time interval:  
\[T(0,t)=\int_{\tau=0}^{t_{p}}\frac{Q_{0}}{e\sqrt{\pi(t+\tau)}}\,d\tau,\text{ for }t>0. \tag{4}\]  
In this paper, we have no intention of deriving an analytical model that can precisely represent the heat distribution in a culvert with cavity. Instead, we would like to stress the relationship between the thermal effusivity and the surface temperature of a solid exposed to a long thermal pulse. Our hypothesis is that the presence of a cavity in the structure of a culvert while it is sufficiently close to (or in contact with) the exterior surface (soil side) of the pipe may lead to variations of temperature at the interior surface (inside the pipe). We think the interior surface temperatures, which are recorded by an IR camera, in the area above cavity decay at a slower rate in comparison with the other areas because the thermal effusivity of air is enormously smaller than that of soil. In other words, the rate of thermal energy exchange between the pipe and the air cavity is less than that between the pipe and the soil, thereby slowing the cooling rate in the air cavity zone. We exploit these variations for cavity detection.
## 3 Technology Overview
### Backend Services--MySQL and Grafana  
When MySQL and Grafana are combined, they create a formidable combination for monitoring performance, offering strong data management and enlightening visualization capabilities. Their integration is a matter of going through the wizard in Grafana UI for MySQL data source configuration, as described in Grafana documentation [46].  
MySQL is a well-known open-source relational database management system highly regarded for its exceptional performance, reliability, and user-friendly interface [47]. It effectively manages substantial data, making it well-suited for storing and overseeing various datasets [48]. Grafana is an open-source platform specifically created for monitoring and visualizing data. It enables users to create interactive and customizable dashboards [49] efficiently.  
MySQL employs a client-server model, in which the server oversees the database, and the clients interact with the server to solicit data. The software supports multiple storage engines, allowing users to configure them to enhance performance according to specific usage scenarios [50]. Grafana's architecture revolves around data source plugins, enabling it to connect with various databases, such as MySQL. Grafana fetches data from MySQL and displays it in a visually appealing and intuitive graphical representation [51].  
By integrating MySQL and Grafana, an organization can significantly enhance its monitoring capabilities. MySQL functions as a dependable backend database, storing essential performance metrics, while Grafana offers real-time visualization of these metrics, allowing for rapid detection of patterns and irregularities [52]. This integration is especially advantageous in situations necessitating ongoing surveillance, such as in meteorological stations or manufacturing settings [53; 54].  
Integrating MySQL's resilient data handling capabilities with Grafana's formidable visualization tools makes it a vital configuration for real-time performance monitoring. This pair assists organizations in obtaining extensive insights into their operations, encompassing monitoring environmental conditions in laboratories and analyzing human resource data [55; 56].  
To summarize, combining MySQL and Grafana provides a scalable, efficient, and adaptable solution for monitoring performance. MySQL's robust data management capabilities and Grafana's powerful visualization capabilities enable organizations to efficiently monitor, analyze, and optimize their operations.
## 3 Results  
### Vegetation Drought Monitoring in Central Asia Based on the TVDI  
In Central Asia, the temperature vegetation dryness index (TVDI) peaked in 2012 at 0.6398, followed closely by 2019 at 0.6377 (Figure 5a). In 2012, vegetation in Central Asia was primarily impacted by moderate drought conditions (56%), with drought-affected vegetation in arid regions experiencing the most severe impacts--severe and moderate droughts accounted for 37.28% and 57.55%, respectively. Approximately 77% of vegetation in semiarid areas was affected by moderate drought (Figure 5b,d). A similar pattern was observed in 2019, where moderate drought in affected regions reached 48.14%. Notably, in 2019, severe drought conditions were more pronounced in arid climate zones (42.69%), while around 75.64% of vegetation in semiarid regions experienced moderate drought (Figure 5c,e). The TVDI values for both 2012 and 2019 indicated a trend towards increasing moisture, reflecting a decrease in drought impacts on vegetation.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Indices** & **2012 Drought Attribution Formula** & **2019 Drought Attribution Formula** \\ \hline \multirow{5}{*}{EVI} & \(\Delta\)EVI \(\approx\) 0.2447 \(-\)0.0005 \(\times\) P\({}_{2012}\)\(-\) 0.007 \(\times\) T\({}_{2012}\)\(-\) 0.52 \(\times\) & \(\Delta\)EVI \(\approx\) 0.26 \(-\) 0.00083 \(\times\) P\({}_{2019}\)\(-\) 0.0089 \(\times\) T\({}_{2019}\)\(-\) 0.52 \(\times\) \\  & TVDI\({}_{2012}\)\(+\) 0.000003 \(\times\) P\({}_{2012}\)\(\times\) T\({}_{2012}\)\(+\) 0.00094 \(\times\) P\({}_{2012}\)\(\times\) & TVDI\({}_{2019}\)\(+\) 0.00005 \(\times\) P\({}_{2019}\)\(\times\) T\({}_{2019}\)\(+\) 0.0016 \(\times\) P\({}_{2019}\)\(\times\) \\  & TVDI\({}_{2012}\)\(+\) 0.018 \(\times\) T\({}_{2012}\)\(\times\) TVDI\({}_{2012}\)\(-\) 0.000013 \(\times\) P\({}_{2012}\) & TVDI\({}_{2019}\)\(+\) 0.0203 \(\times\) TVDI\({}_{2019}\)\(-\) 0.00021 \(\times\) P\({}_{2019}\)\(\times\) \\  & \(\times\) T\({}_{2012}\)\(\times\) TVDI\({}_{2012}\)\(+\) 0.01 \(\times\) EVImean2000–2020 & \(\times\) T\({}_{2019}\)\(\times\) TVDI\({}_{2019}\)\(-\) 0.03 \(\times\) EVImean2000–2020 \\ \hline \multirow{5}{*}{FVC} & \(\Delta\)FVC \(\approx\) 0.466 \(-\) 0.001 \(\times\) P\({}_{2012}\)\(-\) 0.015 \(\times\) T\({}_{2012}\)\(-\) 0.97 \(\times\) & \(\Delta\)FVC \(\approx\) 0.36 \(-\) 0.0011 \(\times\) P\({}_{2019}\)\(-\) 0.013 \(\times\) T\({}_{2019}\)\(-\) 0.7
## 5 Case Study
### Data Source Description  
The primary case study data in this section is sourced from a domestic medical infusion device manufacturing enterprise. This company is a high-tech enterprise that integrates multiple supply chain processes, including sales, procurement, and production, and specializes in manufacturing infusion sets, syringes, filters, and other related products. As shown in Table 2, for testing purposes, this section selects 15 orders from a continuous time period within the enterprise. Each order contains between 3 and 10 different product specifications, with a total of 46 unique specifications after deduplication. Additionally, each order includes key information such as required quantity and delivery time, exhibiting the characteristics of multi-variety and small-batch production. This dataset serves as the fundamental basis for subsequent order splitting and reorganization.
## 4 Experimental Evaluations
### Overview  
Using the experimental system and custom controller, the excitation parameters of each actuator could be simultaneously controlled to control the vibrotactile response in the beam. Through experimental testing, it was found that the natural frequency of the system differed depending on how many actuators were being used to excite the beam. The frequency range considered in this study was between 150 and 250 Hz, which is the range at which humans are most sensitive to vibrations [23; 24]. When exciting the system using just one actuator, the natural frequencies within the desired haptic frequency range were found to be 171 and 196 Hz. When exciting the system using both actuators at the same frequency, the natural frequencies within the desired haptic frequency range were found to be 181 and 215 Hz. Both actuators were excited starting at the same time, so no phase difference between the actuators was present. Exciting the beam at any of these four natural frequencies with the proper number of actuators resulted in a specific vibration mode shape. However, it was found that exciting the beam  
Then the system returns to being excited by Input A. This results in a change in vibration from 57.86 to 4.90 m/s\({}^{2}\) and back to 57.86 m/s\({}^{2}\). In Figure 4b, the system is first excited by Input C (the left side actuator is continuously excited at 215 Hz). After a short period, the system is excited by Input D for 2 s (both actuators are excited at 215 Hz). Then the system returns to being excited by Input C. This results in a change in vibration from 2.94 to 56.88 m/s\({}^{2}\) and back to 2.94 m/s\({}^{2}\). This exemplifies how the vibrotactile feedback can be controlled in the beam using different actuator excitation parameters.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 3 Results  
The experimental software and equipment configuration parameters are as follows: Ubuntu 18.04, 64-bit Linux operating system, Tesla T4 graphics card with 16 G memory, pytorch1.7.1, and CUDA10.1.  
In the Section 3, three sets of comparative experiments were designed. The first group analyzed the effect of the attention mechanism on the model. The second group was the ablation experiment based on the original GhostNetV2 model. The third group was to compare the improved GhostNetV2 model with the current popular lightweight network model in terms of effect and accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stage** & **Input** & **Operator** & **Stride** & **ECA** & **\#exp** & **Output** \\ \hline \multirow{8}{*}{Block2} & \(28\times 28\) & CS-bneck & 2 & - & 240 & \(14\times 14\) \\  & \(14\times 14\) & CS-bneck & 1 & - & 200 & \(14\times 14\) \\  & \(14\times 14\) & CS-bneck & 1 & - & 184 & \(14\times 14\) \\  & \(14\times 14\) & CS-bneck & 1 & - & 184 & \(14\times 14\) \\  & \(14\times 14\) & CS-bneck & 1 & 1 & 480 & \(14\times 14\) \\  & \(14\times 14\) & CS-bneck & 1 & 1 & 672 & \(14\times 14\) \\  & \(14\times 14\) & CS-bneck & 2 & 1 & 672 & \(7\times 7\) \\  & \(7\times 7\) & CS-bneck & 1 & - & 960 & \(7\times 7\) \\  & \(7\times 7\) & CS-bneck & 1 & 1 & 960 & \(7\times 7\) \\  & \(7\times 7\) & CS-bneck & 1 & - & 960 & \(7\times 7\) \\  & \(7\times 7\) & CS-bneck & 1 & 1 & 960 & \(7\times 7\) \\  & \(7\times 7\) & AvgPool & - & - & - & \(1\times 1\) \\ Head & \(1\times 1\) & Conv2d 1\(\times\)1 & 1 & - & - & \(1\times 1\) \\  & \(1\times 1\) & Fc & - & - & - & \(1\times 1000\) \\ \hline \hline \end{tabular} \#exp means expansion size.  
\end{table}
Table 1: _Cont._In this study, we used equal interval adjustment of the learning rate, which is characterized by multiplying the learning rate by a decay factor at every fixed step. It has the advantage that the learning rate can be gradually reduced according to the training process, making the network obtain the optimal solution faster. Weight decay is a method that has been often used to suppress overfitting. This method suppresses overfitting by penalizing large weights during the learning process.  
The basic training parameters were as follows: epoch is set to 50; batch size is set to 64; initial learning rate, 0.001; step_size, 25; gamma, 0.01; optimizer, Adam; weight_decay, 0.001; the training is enhanced by random flipping and rotation with a probability of 0.3 data. The change in ACC during training is shown in Figure 11. The training curve is from steep to gentle. When the epoch reaches half, the correct rate increases slowly until the model converges.
## 4 Experimental Setup
### Experiment 1: DoS Attacks in a Simulated Environment  
This subsection describes the attempted experimental setup of a DoS attack through a Tunslip interface with an IPv6 edge-node border-router network more running Contiki-NG and tunslip6. After the installation and successful setup of the subsystems needed to launch Cooja, the following steps were attempted: creation of motes, verification of correct operation, SYN packet flooding, Ping of death, and finally UDP flood. The Sky motes which were selected for this experiment were running: rpl-border-router.c, which was tasked with being both the root node and connection into the network and udp-server.c, which would provide other nodes for interaction during experimentation. However, during the experiment, a multitude of scripting issues and incompatibilities were encountered, namely with Tun interface which only worked occasionally. The original scripting toolkit (Hping3) also did not work with the network as it was IPv6-based, and a scripting toolkit was utilised instead which worked with IPv6 [45].  
The creation of motes was performed as follows:  
* First, 9/10 motes are based on default RPL-compatible motes; these are Sky motes running Contiki-NG. These motes choose their own topology as they are utilising the RPL protocol. Hence, this can become a mesh or tree topology in the form of a destination-oriented acyclic-directed graph (DODAG). This corresponds to the simulation of a typical LoRa network;
* One mote is set up as a border router (IPv6), again using the default installation from the Contiki-NG package. Default values were used within the operating system for ease of use and deployment;
* Concurrent deployment of these motes within Cooja along with verification that they work correctly;
* Creation of a tun0 interface on the host OS ready to interface with the network edge mote;
* Initial ping of the border router from the host device through tun0-; when this is verified within Cooja, the experiments can start.  
The attacks that were chosen were reply-based. This was carried out to create a clear picture of where a DoS is achieved, as the devices stop replying to packets. The attacks were made to be directly comparable on the physical scale to provide a clear vector of comparison between the two experiments. During the attempted experimentation, there were multiple variables to be measured. A packet analysis program was set up on the tunnel interface created and a.pcap file was to be recorded. Power usage was also measured as reported by the tools featured within Cooja. The attack focused on a disruption of normal service of the devices targeted. It was also draining the battery of the single point of communication within a network, which would lead to a single point of failure if the device's battery is drained. The aforementioned experiment revealed that there was no communication possible through the tun0 interface. Therefore, it was decided to proceed with the DoS attacks on physical devices, as explained in Section 4.2.
## 4 Experiments
### Comparison with Similar Methods on IXMAS Dataset  
Our method achieves a recognition rate of 89.75% with leave-one-sequence-out (LOSO) cross-validation on 11 actions. The recognition rate of individual action is presented in a confusion matrix, shown in Figure 7. The results confirm that our method outperforms the similar state-of-the-art 2D based methods such as [12; 19; 21; 24; 29; 48; 49; 50; 51; 52] recorded in Table 2. It is important to be mentioned here that the number of classes, actors, and views used in experimentations vary among these methods. For example, in [52], 89.4% accuracy has been reported but they excluded camera 4 from experimentation. Likewise, [51] also excluded the top camera and considered only the remaining 4 cameras. Moreover, most of the published methods are not appropriate for real-time application due to their high computational cost. The proposed method considers all views of the IXMAS dataset including the top view for recognition. The results indicate that the proposed method is superior to the similar 2D methods, not only in recognition accuracy but also in recognition speed as well.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Method** & **Accuracy (\%)** \\ \hline - & Proposed method & 89.75 \\
2016 & [PERSON] et al. [29] & 83.03 \\
2013 & [PERSON] et al. [24] & 85.9 \\
2013 & [PERSON] et al. [53] & 96.4 \\
2011 & [PERSON] et al. [52] & 89.4 \\
2011 & [PERSON] et al. [12] & 74 \\
2010 & [PERSON] et al. [19] & 83.4 \\
2009 & [PERSON] et al. [48] & 72.6 \\
2008 & [PERSON] and [PERSON] [21] & 82.8 \\
2008 & [PERSON] et al. [51] & 80.1 \\
2008 & [PERSON] et al. [50] & 87.0 \\
2007 & [PERSON] and [PERSON] [49] & 80.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with state-of-the-art methods on IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset.  
Figure 7: Confusion matrix of IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset with 11 actions.  
The resolution of IXMAS dataset is only 390 \(\times\) 291, which is very low resolution as compared to many other action recognition datasets. We performed experiments using MATLAB R2015b implementation on Intel(r) Core i7-4770 CPU with 8 cores @ 3.4 GHz, 8 GB RAM, and Windows 10 operating system. However, only 4 cores of the CPU were utilized during experimentation. Moreover, we did not use any optimization techniques in the code. The average testing time of our approach is 0.0288 per frame, which is almost 34 frames per second (FPS); this is much better than the existing methods as recorded in Table 3.
# Identification, Control, and Characterization of Peristaltic Pumps in Hemodialysis Machines
## 3 Analysis of Results
### Statistical Analysis Results  
The results of the statistical analysis compare the performance of the developed system with that of the commercial hemodialysis machine, validating the accuracy and stability of the estimates using the selected metrics and the paired \(t\)-test.  
The estimation of the RMSE showed that the developed system has significantly lower values than the commercial machine across all analyzed flow ranges, indicating better performance in reducing estimation errors. Additionally, the average error shows that the developed system is more aligned with the target values compared to the commercial machine, highlighting its superior overall accuracy. Finally, the standard deviation was consistently lower in the developed system, reflecting greater stability and less variability in repeated measurements.  
The paired \(t\)-test was applied to determine whether the observed differences between the mean errors of both systems were statistically significant. The results of this analysis, along with the calculated metrics, are summarized in Table 6.  
The positive \(t\)-value confirms that the errors in the developed system are lower, and the \(p\)-value of less than 0.05 establishes that these differences are statistically significant with a 95% confidence level.  
These results reinforce the superiority of the developed system in terms of accuracy and stability, supported by both the calculated metrics and the statistical analysis conducted. The lower RMSE reduced mean error, and smaller standard deviation confirm superior performance in flow estimations, while the \(t\)-test establishes that these differences are not due to chance.