esa-sceva/satcom-chunk-collection huggingface dataset:
# Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab
## 1 Introduction
The results show that this method can detect the damage. In this paper, continuous vibration testing is carried out for one year. Based on the acquired vibration testing, the simple LR model and ARX model between the first four eigenfrequencies and temperatures are established. By comparing the root mean squared error (RMSE) between the simple LR model and the ARX model, it is found that the prediction accuracy of the ARX model is better. Based on the established ARX model, two simulated cases are constructed and a hybrid PSO algorithm is adopted for the damage identification process. It can be seen that the proposed method in the paper can distinguish the normal and abnormal temperature variations. If the eigenfrequency exceeds the certain confidence interval of the ARX model, there is probably another cause that drives the eigenfrequency variations, such as structural damage. Moreover, the method can identify the damage location and severity, which is more accurate for damage identification based on temperature variation.
# Demystifying Quantum Gate Fidelity for Electronics Engineers
## Appendix C  
By extending the Gaussian integral on a one-dimensional real vector space \(\mathbb{R}^{1}\):  
\[\int_{\mathbb{R}^{1}}e^{-\frac{|x_{1}|^{2}}{2}}dx_{1}=\sqrt{2\pi}\] (A39)  
to a n-dimensional real vector space \(\mathbb{R}^{n}\), you obtain:  
\[\int_{\mathbb{R}^{n}}e^{-\frac{|x|^{2}}{2}}dx=\int_{\mathbb{R}^{n}}\prod \limits_{h=1}^{n}e^{-\frac{|x_{h}|^{2}}{2}}dx_{h}=\prod\limits_{h=1}^{n}\int _{\mathbb{R}^{1}}e^{-\frac{|x_{h}|^{2}}{2}}dx_{h}=\prod\limits_{h=1}^{n}\sqrt {2\pi}=\left(\sqrt{2\pi}\right)^{n}\] (A40)  
Following the calculation procedure adopted for Equation (A9), the integral in \(dx\) in Equation (A40) can be calculated by means of the polar coordinates as follows:  
\[\int_{\mathbb{R}^{n}}e^{-\frac{|x|^{2}}{2}}dx=\int_{S^{n-1}}\int_{0}^{\infty}e ^{-\frac{\rho^{2}}{2}}\rho^{n-1}d\rho\prod\limits_{j=1}^{n-1}d\varphi_{j}= \int_{S^{n-1}}\prod\limits_{j=1}^{n-1}d\varphi_{j}\int_{0}^{\infty}e^{-\frac{ \rho^{2}}{2}}\rho^{n-1}d\rho\] (A41)  
The integral in \(d\varphi_{j}\) is the surface \(\Omega^{n}\) of a unitary radius hypersphere in the \(\mathbb{R}^{n}\) vector space. The radius does not appear in the integral, indicating that it is unitary. Equation (A41) can be therefore rewritten as follows:  
\[\int_{\mathbb{R}^{n}}e^{-\frac{|x|^{2}}{2}}dx=\Omega^{n}\!\int_{0}^{\infty}e^{ -\frac{\rho^{2}}{2}}\rho^{n-1}d\rho\] (A42)  
The integral in \(d\rho\) can be calculated by replacing \(\rho\) by \((2t)^{1/2}\):  
\[\int_{\mathbb{R}^{n}}e^{-\frac{|x|^{2}}{2}}dx=\Omega^{n}\!\int_{0}^{\infty}e^{ -\frac{\
u^{2}}{2}}(2t)^{\frac{n-1}{2}}\frac{1}{2}\frac{2}{\sqrt{2t}}dt=\Omega ^{n}\!\int_{0}^{\infty}e^{-t}(2t)^{\frac{n}{2}-1}dt\] (A43)  
from which:  
\[\int_{\mathbb{R}^{n}}e^{-\frac{|x|^{2}}{2}}dx=\Omega^{n}\!\pi^{2}\!\int_{0}^{ \infty}e^{-t}t^{\frac{n}{2}-1}dt\] (A44)The comparison with Equation (A18) shows that the integral on the right-hand side of Equation (A44) is \(\Gamma(n/2)\), and thus Equation (A44) takes the following form:  
\[\int_{\mathbb{R}^{n}}e^{-\frac{|x|^{2}}{2}}dx=\Omega^{n}2^{\frac{n}{2}-1}\Gamma \Big{(}\frac{n}{2}\Big{)}\] (A45)  
from which:  
\[\Omega^{n}=\frac{\int_{\mathbb{R}^{n}}e^{-\frac{|x|^{2}}{2}}dx}{2^{\frac{n}{2}- 1}\Gamma\big{(}\frac{n}{2}\big{)}}\] (A46)  
The substitution of Equation (A40) into Equation (A46) yields:  
\[\Omega^{n}=\frac{\Big{(}\sqrt{2\pi}\Big{)}^{n}}{2^{\frac{n}{2}-1}\Gamma\big{(} \frac{n}{2}\big{)}}=2\frac{2^{\frac{n}{2}}\pi^{\frac{n}{2}}}{2^{\frac{n}{2}} \Gamma\big{(}\frac{n}{2}\big{)}}=2\frac{\big{(}\sqrt{\pi}\big{)}^{n}}{\Gamma \big{(}\frac{n}{2}\big{)}}\] (A47)  
which demonstrates Equation (A17).
## 4 The Proposed Hybrid Method
```
Mutation (Schedule [1... 2][1...  n], \(V\)) // \(V\) is the set of tasks NewSchedule = Schedule // NewSchedule is mutated version of Schedule For all Tasks \(t_{i}\in V\) in task graph //Count the number of parents for each task ParentsCount [\(t_{i}\)] = number of \(t_{i}\) parents in task graph ReadyTasks = {\(t_{i}\in V\) | ParentsCount [\(t_{i}\)] = 0} // Prepare the ready tasks to execute ReadyCount = Number of tasks in ReadyTasks set \(p\) = 0, \(pp\) = 0, cutpoint = RandomNumber (1, \(n\)) \(q\) = Random (1, cutpoint) // After cutpoint, the order of tasks will be changed randomly While (ReadyCount >= 0)  SelectCount = Number of tasks in ReadyTasks  SelectList = ReadyTasks If (SelectCount > 1)  pp = pp + 1  If (pp >= q) // if it is after cutpoint, the next task is selected randomly amongst ready tasks \(s\) = Random (1, SelectCount) \(t\) = SelectList (s) //choose a task from ready tasks randomly Remove \(t\) from ReadyTasks  ReadyCount = ReadyCount \(-\) 1  p = p + 1  NewSchedule [1][p] = \(t\) Else // if it is before cutpoint, the next task is selected from Schedule  p = p + 1 \(t\) = Schedule [1][p]  ReadyCount = ReadyCount \(-\) 1  For all Children \(c_{i}\in\) [Successors of \(t\)]  ParentsCount [\(c_{i}\)] = ParentsCount [\(c_{i}\)] - 1 // decrement the number of parents by one If (ParentsCount [\(c_{i}\)] == 0)  Add \(c_{i}\) to ReadyTasks set //add new ready tasks to ReadyTasks set For \(i\) = 1 to 3 //exchange the processors three times R1 = Random (1, n); R2 = Random (1, n); SWAP (NewSchedule [2] [R1] and solution [2] [R2]);
```  
**Algorithm 5** Mutation  
If the termination requirements are not met when the inner loop is terminated, the hybrid method moves on to the next session. Otherwise, the extracted archive is submitted as the best Pareto front found so far.
## 2 Related work
Receiver-initiated MAC (**RI-MAC**) is also a receiver-driven MAC protocol, and thus it is similar to RICER [14]. In RI-MAC, a sender also transmits a data packet after receiving an ID packet, however, RI-MAC uses a single channel for the transmission of packets (Figure 2(b)). In order to avoid packet collisions, RI-MAC only uses collision detection and exponential backoff. Also, in terms of the routing algorithm, the authors of this protocol used minimum hop routing. IRDT uses an adaptive intermittent  
Figure 2: Other intermittent transmission methods. interval, whereas both RICER and RI-MAC use a fixed value for the intermittent interval. Such an adaptive interval can avoid packet collisions and can attain higher performance. In this paper, we propose a simple and effective routing algorithm for IRDT which is considered for mesh networks in an effective and efficient manner. Here, an essential difference between IRDT and LPL is that nodes in intermittent operation mode transmit packets or listen to the channel, which can also be considered an essential difference between the sender-driven method and the receiver-driven method. In our previous research, we demonstrated the impact of this difference on the performance.
## 3 Results
### Influence of the Size of Stator Hub Clearance on the Evolution of Flow Topologies
#### 3.3.2 Results at the PP Condition  
The oil-flow visualization results in the PP condition are shown in Figure 8. It can be seen that at this condition the tip corner separation did not change with the variation of the stator hub clearances either. For the hub corner, the influence of hub clearance was mostly like the situation at the DE condition but with a little difference. When a small stator hub clearance (0.25\(\uptau\)) was introduced, the scale of the separation on the blade suction surface increases, and at the end of the separation line SL3 a focus point, F, appears, but after the focus point only one separation line (SL4) formed and it pointed to the upstream, which means that the flow separation originated from the separation line, SL3 did not reattach at the rear part of the blade suction surface like the situation at the DE condition, i.e., at this condition the closed separation turns into the open separation (hub corner stall). When the stator hub clearance increased to 0.50\(\uptau\), the separation lines on the hub became one separation again and an attachment line AL2 comes out on the blade suction surface, but the separation line SL1 on the blade suction surface does not vanish like that at the DE condition and is pushed to the blade rear part near the blade trailing edge and with a very small scale. When the stator hub clearance continually increased to 1.00\(\uptau\), the separation line SL1 on the blade suction surface thoroughly disappeared, and the separation line SL4 on the hub was located further away from the blade suction side.  
Combined with the five-hole probe results in Figure 7, it could be concluded that at the PP condition, the hub corner at zero clearance is still predominated by the corner separation; the introduction of a small stator hub clearance (0.25\(\uptau\)) prompts the occurrence of the hub corner stall, so at 0.25\(\uptau\) the hub corner was predominated by the hub leakage flow and the corner stall; when the stator hub clearance equalled to 0.50\(\uptau\), the hub corner separation was mostly suppressed by the leakage flow, and only little low energy flow in the end wall boundary climbed on the blade suction surface near the trailing edge, hence, at 0.50\(\uptau\) the stator hub corner is predominated by both the corner separation and the tip leakage flow; when the stator hub clearance increased to 1.00\(\uptau\), the hub leakage flow became strong enough to thoroughly suppress the migration of the end wall boundary layer, so the hub corner was completely predominated by the hub leakage flow.  
Figure 8: The evolution of flow topologies on the suction surface and hub with the variation of hub clearance at the peak static pressure rise condition (PP) condition.
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
###### Abstract  
Nowadays, the research on vehicular computing enhanced a very huge amount of services and protocols, aimed to vehicles security and comfort. The investigation of the IEEE802.11p, _Wireless Access in Vehicular Environments_ (WAVE) and _Dedicated Short Range Communication_ (DSRC) standards gave to the scientific world the chance to integrate new services, protocols, algorithms and devices inside vehicles. This opportunity attracted the attention of private/public organizations, which spent lot of resources and money to promote vehicular technologies. In this paper, the attention is focused on the design of a new approach for vehicular environments able to gather information during mobile node trips, for advising dangerous or emergency situations by exploiting on-board sensors. It is assumed that each vehicle has an integrated on-board unit composed of several sensors and _Global Position System_ (GPS) device, able to spread alerting messages around the network, regarding warning and dangerous situations/conditions. On-board units, based on the standard communication protocols, share the collected information with the surrounding road-side units, while the sensing platform is able to recognize the environment that vehicles are passing through (obstacles, accidents, emergencies, dangerous situations, etc.). Finally, through the use of the GPS receiver, the exact location of the caught event is determined and spread along the network. In this way, if an accident occurs, the arriving cars will, probably, avoid delay and danger situations.  
Car-to-car Communications; VANET; WAVE; 802.11p; road safety; routing; vehicular; data dissemination; scalable architecture +
Footnote †: journal: Sensors
## 3 Image processing and evaluation
### Image identification  
The tracking of individual ants between image frames is based on ant image identification, i.e. determination of the position and characters of each ant image. In order to complete image  
Figure 3: Sample images (256\(\times\)128 pixels). **(a)** Raw image, **(b)** Averaged, **(c)** Cleaned.  
identification, the processed digital image recording as shown in Fig. 3c is binarized according to a selected gray value threshold, so that the boundary of the ant image is fixed. The binary effect of the ant images is shown in Fig.4. In the presented case, the binarized ant images consist of teens to tens of square bright units (pixels of gray value 1) in the dark background (pixels of gray value 0). As schematically illuminated in Fig. 5, a multi-pass digital filtering is used to identify ant images. At first, as shown in the first picture from left, the bright units of the binary image are numbered one by one, whereas a great number (i.e. \(\infty\)) is given to pixels of the background. Thus, a two dimensional field of numbers \(N(x,y)\) are obtained. Then, the number  
image (\((x_{n,i}y_{n,i})\) for \(i\)=1,2,3, \(\cdots S_{n}\)) are determined, that directly enables a further determination of the position and brightness of the ant images as below.  
_Image center position:_  
\[\left(X_{n},Y_{n}\right)=\left(\frac{1}{S_{n}}\sum\limits_{i=1}^{S_{n}}x_{n,i}, \frac{1}{S_{n}}\sum\limits_{i=1}^{S_{n}}y_{n,i}\right) \tag{4}\]  
_Mean brightness:_  
\[B_{n}=\frac{1}{S_{n}}\sum\limits_{i=1}^{S_{n}}G_{k}\left(x_{n,i},y_{n,i}\right) \tag{5}\]  
Wherein \(G_{k}(x,y)\) is the gray value distribution of the original ant image recording as shown in Fig. 4a. The shape and orientation of the ant images can also be determined with the image identification results.  
As shown in Fig. 6, the roundness (\(\Theta\)) and orientation angle (\(\theta\)) are used to represent the shape characters of an ant image.The main orientation of the ant image is determined by a least square linear fit of all the pixels of the ant image, and \(\theta\) is the angle between the fitting line and the x-direction. The definition of the roundness is based on the definition of the moment of inertia for bodies with constant thickness in the axis direction and a uniform density distribution. \(\Theta\) is the ratio of the moment of inertia between the ant image and the equivalent round body, i.e.  
Figure 6: Roundness and orientation angle of ant image.  
\[\Theta_{n}=\frac{I_{s}}{I_{e}}=\frac{\rho\sum\limits_{i=1}^{S_{e}}r_{i}^{2}}{0.5 \rho\Omega_{n}^{2}/\pi}=\frac{2\pi}{S_{n}^{2}}\sum\limits_{i=1}^{S_{e}}\left[x_{i }-X_{n}\right]^{2}+\left(y_{i}-Y_{n}\right)^{2}\right]\,, \tag{6}\]  
wherein \(\rho\) is the mass of each pixel. \(\rho\) equals 1 for perfect round images, otherwise \(\rho\)\(>\)1. For square images the roundness \(\rho\) is about 1.05. There is no dominate orientation direction for round and square images, but the orientation angle \(\rho\) become an important character when \(\rho\)\(>\)1.05.
## 2 Material and Methods
### Pesticides Analysis in the Biomixtures  
The extraction of the pesticide residues from the contaminated biomixtures, at the initial and final incubation time, was carried out with some variations, using the QuEChERS method according to [PERSON] et al. [2] and [PERSON] et al. [25]. Briefly, 3 g of each biomixture were weighed into a 50-mL centrifuge tube, and 5 mL of acetonitrile was added. The mixture was vortexed for 1 min, and then, added with 1 g of QuEChERS EN Puch (Agilent Technologies, Santa Clara, CA, USA). Then, the sample was centrifuged at 3000 rpm for 5 min. Interferences were removed from the sample by transferring 1 mL of the supernatant to a tube containing 1.5 g of amine dispersive SPE, Fruit, and Veg EN (Agilent Technologies, Santa Clara, CA, USA) and 3 mL of acetonitrile. After 1 min of stirring, it was centrifuged under the conditions described above, and 0.8 mL of the supernatant were removed and re-dissolved up to 2 mL with milliQ water. The sample was passed through 0.2 micron PTFE syringe filter (Thermo Fisher Scientific Inc. Waltham, MA, USA) prior to HPLC analysis. Recoveries of the extraction method ranged from 86% to 102%, depending on the pesticide and the biomixture, with relative standard deviation never exceeding 5%.  
The pesticides were analyzed by HPLC-DAD (series 1100, Agilent Technologies, Santa Clara, CA) on a Zorbax RX-C8 column (5 mm, 2.1 \(\times\) 150 mm) (Agilent Technologies, Santa Clara, CA, USA), connected to an Eclipse XDB-C8 (5 mm, 2.1 \(\times\) 12.5 mm) precolumn (Agilent Technologies, Santa Clara, CA, USA). The mobile phase was acetonitrile and milliQ water at pH 3. A solvent gradient was used from 20% to 70% of acetonitrile. The mobile phase flow was 0.2 mL min\({}^{-1}\), the injection volume 10 \(\mu\)L and the oven temperature 40 \({}^{\circ}\)C. The wavelengths were 210 nm for metalaxyl, dimotheate,  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Diuron** & **Imidacloprid** & **Tebuconazoleol** & **Oxifluorfen** & **Dimethoate** \\ \hline CP & 0.89 \(\pm\) 0.14 & 23.2 \(\pm\) 0.5 & 24.0 \(\pm\) 1.6 & 42.9 \(\pm\) 2.4 & nd \\ CO & 0.55 \(\pm\) 0.02 & 28.2 \(\pm\) 0.5 & 42.3 \(\pm\) 3.0 & 66.8 \(\pm\) 5.4 & nd \\ \hline \end{tabular} CP: Contaminated biomixture of soil:peat:straw. CO: Contaminated biomixture of soil, vermicompost from wet olive cakeolive pruning. nd: not detected.  
\end{table}
Table 1: Concentration (mg kg\({}^{-1}\)) of pesticide residues remaining in the two biomixtures. Values (dry mass) are means \(\pm\) SD (n = 3).  
diuron, and oxyfluorfen, 215 nm for tebuconazole, and 270 nm for imidalcoprid as described in [PERSON] et al. [2]. The limit of quantification was 0.1 mg kg\({}^{-1}\).
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 4 Discussion
### Data Collection Window  
This study also investigated the impact of data collection window size on the activity in classifying activity in horse training. The research findings indicate that as the data collection window size increases, there is a corresponding improvement in the accuracy of activity classification on the test dataset. This aligns with [PERSON] et al.'s [26] study on window size's influence on classification accuracy. However, more extensive data collection windows reduced the volume of training and validation data, subsequently decreasing the classification accuracy. This is because larger data collection windows encompass multiple gaits, and the selection of a data collection window is influenced by factors such as data collection frequency and the distribution of activity data. [PERSON] et al. [27] conducted a study on the impact of data collection windows on sheep behavior classification, confirming that more oversized data collection windows are not necessarily better.  
The study identified that setting the data collection window to 26 for the analysis of activity levels resulted in optimal classification accuracy. This study investigated the close relationship between the biological characteristics of horses [28] and the size of the data collection window. Through a detailed analysis of the natural gait patterns of horses, including walking, trotting, and galloping, it was found that selecting the correct data collection window size is crucial for accurately capturing these dynamic changes. When the data collection window size is 26, this scale is sufficient to cover the range required for horses to perform a series of basic and complex behavioral transitions, ensuring a comprehensive record of horse activity patterns. Secondly, this size of the data collection window ensures data richness while avoiding the problem of excessive data processing complexity due to an overly large window, striking a balance between efficiency and accuracy.
## 3 Results and Discussion
### Physiological Characterization
#### 3.3.1 Ester Compounds  
Esters are primary contributors to the fruity and floral aromas in fruit wines, and their content is significantly influenced by yeast metabolic activity [25]. In this study, a total of 48 ester compounds were identified, with the total ester content in the JH group being significantly higher than that in the JS group, suggesting that _Pichia kudriavzevii_ GAAS-JG-1 may enhance esterification metabolism. The content of short-chain esters (e.g., ethyl acetate and ethyl propionate) increased significantly in the JH group. Specifically, ethyl acetate increased from 303.71 \(\upmu\)g/L to 4453.12 \(\upmu\)g/L, while ethyl propionate rose from 5.18 \(\upmu\)g/L to 87.75 \(\upmu\)g/L. As key aromatic components in fruit wine, short-chain esters impart distinctive fruity or tropical fruit aromas and play a crucial role in enhancing wine fragrance and sensory appeal [26; 3; 27]. These results align with previous studies, further supporting the positive effect of mixed fermentation in promoting short-chain ester accumulation and enhancing fruity characteristics. In addition, methyl acetate (11.42 \(\upmu\)g/L) and methylthioacetate (23.58 \(\upmu\)g/L), which were absent in the JS group, were detected in the JH group. This indicates that the metabolic diversity in the mixed fermentation system may result in a broader spectrum of metabolites [28]. Notably, the content of some long-chain esters (e.g., ethyl hexanoate and ethyl decanoate) decreased slightly in the JH group, dropping from 329.68 and 1148.18 \(\upmu\)g/L to 282.36 and 1006.61 \(\upmu\)g/L, respectively. Since long-chain esters are generally associated with a wine's richness and mature fruit aroma, this reduction may contribute to a fresher and more refreshing wine profile. This phenomenon could be attributed to interactions between yeast strains and changes in fermentation conditions during the mixed fermentation process. Several studies have shown that different fermentation strategies can significantly influence the accumulation patterns of volatile aroma compounds in fruit wine, thereby modulating the sensory characteristics of the final product [29; 30].  
Figure 3: Heatmap cluster analysis of volatile flavor compounds in sea buckthorn wine under different fermentation conditions. JS: Monoculture fermentation of _Saccharomyces cerevisiae_ (CICC 32168); JH: _Saccharomyces cerevisiae_ (CICC 32168) was inoculated initially, and _Pichia kudriavzevii_ GAAS-JG-1 was inoculated after 24 h.
## 9 Conclusions  
When the system is working, the system parameters usually change in a manner that reduces the model mismatching between the theoretical EPS models and actual EPS models. The identification of the EPS model parameters has also been discussed. Three identification algorithms, namely, SRIV, LSSVF and IVSVF, are applied to identify the parameters of the EPS system. Among the proposed algorithms, the SRIV algorithm is adopted for estimating the parameters of the control plant due to the highest estimation accuracy. Furthermore, in order to enhance control performance, the loop-shaping technique has been introduced to design the EPS controller system. The proposed loop-shaping controller provides the system stability and control performance of an EPS system. In addition, the effectiveness of the loop-shaping controller is confirmed, with both the classical PID and loop-shaping controllers applied to the test bench of the EPS system. The experimental results verify that the illustrated loop-shaping controller not only tracks the desired assist torque characteristic curve but also ensures the stability of the system.  
Figure 33: Actual assist characteristic curve using the proposed loop-shaping control algorithm with different velocities.  
Figure 32: Actual assist characteristic curve under the PID control algorithm with different velocities.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the proposed approach; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] verified the proposed approach; [PERSON] wrote the manuscript; [PERSON], [PERSON], [PERSON] and [PERSON] revised the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported in part by the National Natural Science Foundation of the People's Republic of China (Grant number: 51505354) and the Fundamental Research Funds for Central Universities. We would like to acknowledge the financial support for visiting scholars from the Chinese Scholarship Council in the form of grant numbers 2015 GX2070.  
**Conflicts of Interest:** The authors declare no conflict of interest.