esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Variation Characteristics of the NDVI  
Figure 3 shows the temporal evolution of the regional mean value of the NDVI (a), the spatial distribution of the multiyear mean value of the NDVI (b), the annual change rate of the NDVI in 2000-2012 (T1) (c) and 2012-2018 (T2) (e), and the \(CV\) in T1 (d) and T2 (f). The multiyear mean value of the NDVI in northern China was 0.22. In 2002, 2008, and 2012-2018, the NDVI was greater than the multiyear mean value; yet, the rest of the years showed the opposite. The maximum value for the NDVI occurred in 2016 (the regional mean value of the study area was 0.239), and the minimum value appeared in 2003 (the regional mean value of study area was only 0.202). In terms of space, the maximum value was distributed in the southeastern part of the study area, while the minimum value was concentrated in central Xinjiang Uygul Zizhiuq and other regions. The overall distribution pattern of the NDVI increased from northwest to southeast, which is consistent with the warm-humid climate pattern of the southeast and the cold-dry pattern in the northwest. During the T1 period, as a whole, the NDVI showed a downward trend in the study area. The area with significant degradation of NDVI was mostly concentrated in the northeastern  
part of the study area and in southern Qinghai Plateau, whose area accounted for 42.4% of the total. Except for the Mohe meteorological station, the vegetation coverage was relatively stable in the northeastern part of the study area, which belonged to the low-fluctuation area as a whole. The southern Qinghai Plateau mostly included areas with relatively high or moderate fluctuation, which probably indicates that the vegetation in this region is susceptible to changes in the climate, human activities, or other factors. The areas with significantly improved NDVI were mainly distributed at the junction of the Shaanxi and Shanxi provinces, which accounted for about 19.7% of the total, and most of them were relatively low-fluctuation areas. This indicates that the vegetation in this area has a strong self-repair ability after being affected by external factors. The NDVI in the rest of regions was basically unchanged. The annual change rate was mostly no more than 0.02/10a. After entering the T2 period, the NDVI in most areas improved significantly, which accounted for about 49.1% of the total. Except for the northern part of Xinjiang Uygul Zizhiqu, the rest of the regions were all low-fluctuation or relatively low-fluctuation areas, and the NDVI stability was significantly improved compared to that in the T1 period. The region where NDVI degraded significantly only covered 9.3% of the study area, and was mostly in the east of Qinghai province. Compared to the T1 period, the fluctuation degree of the NDVI in this area was reduced, and the remaining 41.6% was basically unchanged.
_applied_  
_sciences_  
_Article_  
Quantitative Monitoring Method for Conveyor Belt Deviation Status Based on Attention Guidance  
[PERSON], [PERSON], [PERSON],*(), [PERSON], [PERSON] and [PERSON]  
1 College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China; 138698727046163.com (X.Z.Y); [EMAIL_ADDRESS] (Z.Y.Y); [EMAIL_ADDRESS] (Y.Z.)
2 Libo Heavy Industries Science and Technology Co., Ltd., Taian 271000, China
* Correspondence: [EMAIL_ADDRESS]  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
**Abstract:** The efficient monitoring of the belt deviation state will help to reduce unnecessary abnormal wear and the risk of belt tear. This paper proposes a coupling characterization method involving the prediction box features of the target detection network and the linear features of the conveyor belt edge to achieve the quantitative monitoring of conveyor belt deviations. The impacts of the type, location, and number of attention mechanisms on the detection effect are fully discussed. Compared with traditional image-processing-based methods, the proposed method is more efficient, eliminating the tedious process of threshold setting and improving the detection efficiency. In detail, the improved practice and tests are carried out based on the Yolov5 network, and the Grad-CAM technique is also used to explore the effect of attention mechanisms in improving the detection accuracy. The experiments show that the detection accuracy of the proposed method can reach 99%, with a detection speed of 67.7 FTS on a self-made dataset. It is also proven to have a good anti-interference ability and can effectively resist the influence of the conveying material flow, lighting conditions, and other factors on the detection accuracy. This research is of great significance in improving the intelligent operation and maintenance level of belt conveyors and ensuring their safe operation.  
**Keywords:** belt conveyor; supervised machine learning; intelligent digital monitoring; deviation detection; feature visualization
# The Design and Investigation of Hybrid a Microfluidic Micromizer
## 2 Materials and Methods
### Micromixer Design and Performance Parameters  
Figure 1 describes the four different designs of microfluidic micromixers with geometrical features. The four geometries are differentiated by different micropillar shapes inside the microchamber. Figure 1a is a plane micromixing chamber with no micropillars. Figure 1b represents a micromixing chamber with circular-shaped micropillars that increase the acoustic streaming, leading to higher mixing performance. Similarly, Figure 1c represents the third micromixer design with hexagonal-shaped micropillars that help to attain a homogeneous mixing of two different heterogeneous fluid components at laminar flow conditions. Figure 1d represents the fourth micromixer design with blade-shaped micropillars that generate more vortices around the walls of the micropillars. The edges of blade-shaped micropillars help to increase the mixing performance of two different heterogeneous fluids.  
However, the performance of the micro-mixer is measured in terms of the mixing index and concentration of two different heterogeneous fluid components at the outlet of the micromixer. The mixing index is measured based on the statistical approach. It is  
Figure 1: Four different geometries of micromixers: **(a)** plane-mixing chamber; **(b)** mixing chamber with circular micropillars; **(c)** mixing chamber with hexagonal micropillars; and **(d)** mixing chamber with blade-type micropillars.  
determined at any cross-section of the microchannel width from the concentration of the standard deviation of the heterogeneous fluid species using the following relation [23]:  
\[M=1-\sqrt{\frac{\sigma^{2}}{\sigma^{2}\max}} \tag{1}\]  
\[\sigma=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}\left(c_{i}-\overline{c}\right)}} \tag{2}\]  
\[\sigma_{\max}=\sqrt{\overline{c}(1-\overline{c})} \tag{3}\]  
where \(\sigma\) is the standard deviation, \(c_{i}\) is the concentration species of two different heterogeneous fluids, \(\overline{c}\) is the mean concentration of two different heterogeneous fluids, and \(\sigma_{\max}\) is the value of the maximum standard deviation of the two various species of heterogeneous fluids at a specified cross-section of the micromixer channel.
## 3 Related Work  
Sensor nodes have limited battery power and can be anywhere, including areas with restricted/difficult access, making it hard to replace or recharge their batteries. Therefore, the energy consumption of each sensor node must be minimized to maximize the network lifetime. Many recent research works were proposed to investigate how to improve the wireless communication, e.g., in BLE [10,11], NB-IoT [12], and Wi-Fi [13].  
Different performance evaluations show that RPL is an effective routing protocol because of its fast network reconfiguration, relatively short delays, and the ability of nodes to recover quickly from the loss of connectivity [14; 15].  
The problem of battery consumption in the RPL can be seen in [PERSON] et al. [16], where an objective function was proposed to maximize the node's lifetime taking into account the so-called battery utility function. This OF is modeled as the remaining time interval, in seconds, that a node's battery can contain. From its maximum value, the model calculates the time spent by a node in transmitting/receiving data and subtracts it from the last residual value of the battery.  
[PERSON] et al. [17] showed the performance evaluation of the ETX, HOP, and RPL energy metrics in environments with extended transmission ranges varying the number of nodes, the topology, and the transmission/interference ranges. Some of the ideas in this article were based on the tests carried out as part of this study. Another one was conducted by [PERSON] et al. [18], which evaluated the performance of RPL in terms of two OFs, e.g., Hysteresis Objective Function (MRHOF) and OF0 in two different topologies (grid and random). To study the performance of OFs in the RPL, parameters such as PDR (Packet Delivery Ratio), energy consumption, and receive (RX) were considered.  
However, the works conducted by [PERSON] et al. and [PERSON] et al. did not consider assessing energy consumption through network resilience, as presented by our research. In our work, the evaluation method was to obtain a percentage of the total number of nodes in the network to observe its behavior and assess the variation in energy consumption. The evaluation based on statistical methods shows that it is possible to find the best topology, the best metric, and the best objective function to be used by the RPL, paving the way for optimizing the restricted sensor network and its involved routing protocols.
## 4 Simulation Results and Analyses
### EV Shedding Operation  
During the charging process of an EV, the EV enters a waiting state due to a power limit, which is called EV shedding operation. When the power is insufficient, shedding priority is according to the number from EV5 to EV1, in which situation, the \(SOC_{EV}\) is constant. In the first and third time slots, the EV connected to charger 5 is shed because of insufficient power. In the second time slot, the EVs connected to charger 5, charger 4, and charger 3 are shed in sequential order because of insufficient power.
## Appendix A
### Bessel Filter  
The transfer function of the normalized Bessel filter is written as follows [39]:  
\[\mathrm{H(s)}=\frac{1}{\hat{\mathsf{B}}_{\mathrm{n}}(\mathrm{s})} \tag{15}\]  
where \(\hat{\mathsf{B}}_{\mathrm{n}}(\mathrm{s})\) is the n-th order Bessel polynomial.  
Knowing that the Bessel polynomials for n = 1 and n = 2 are as follows:  
\[\hat{\mathsf{B}}_{1}(\mathrm{s})=\mathrm{s}+1;\quad\hat{\mathsf{B}}_{2}( \mathrm{s})=\mathrm{s}^{2}+3\cdot\mathrm{s}+3 \tag{16}\]  
The n-th order Bessel polynomial can be found using the following recurrence equation:  
\[\hat{\mathsf{B}}_{\mathrm{n}}(\mathrm{s})=(2\cdot\mathrm{n}-1)\cdot\hat{ \mathsf{B}}_{\mathrm{n}-1}(\mathrm{s})+\mathrm{s}^{2}\cdot\hat{\mathsf{B}}_{ \mathrm{n}-2}(\mathrm{s}) \tag{17}\]  
For a filter with n = 5, the Bessel polynomial is written as follows:  
\[\hat{\mathsf{B}}_{5}(\mathrm{s})=\mathrm{s}^{5}+15\cdot\mathrm{s}^{4}+105\cdot \mathrm{s}^{3}+420\cdot\mathrm{s}^{2}+945\cdot\mathrm{s}+945 \tag{18}\]  
The transfer function of the normalized Bessel filter of order n = 5 is written as follows:  
\[\mathrm{H(s)}=\frac{1}{\mathrm{s}^{5}+15\cdot\mathrm{s}^{4}+105\cdot\mathrm{s }^{3}+420\cdot\mathrm{s}^{2}+945\cdot\mathrm{s}+945} \tag{19}\]  
The filter scaling is performed by substituting Equation (A5) into Equation (A19). The expression for calculating the AFR of high-pass Bessel filter is obtained in accordance with Equation (A16). The AFR of high-pass Bessel filter is shown in Figure 14d.  
As can be seen from Figure 14d, the AFR of high-pass Bessel filter has a smooth character both in the stopband and in the passband.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 4 Results
### Anomaly Alerting
In this example, the freezer cool-down period could be set as 35 min and the fridge cool-down period could be set at 5 min. The pseudocode for the alerting system is presented in Algorithm 2. ```
FunctionAlert(\(trip\), \(temperature\), \(coolingZone\)):  coolDownPeriod = 35;  Frequency = 5;  numOfRecordings = coolDownPeriod/Frequency;  notInTrip = 0; if\(coolingZone==\"freeze\"\)then tempThreshold = \(-\)18;  alertName = \"freeze\"; else  tempThreshold = 5;  alertName = \"fridge\";  end if for\(i=1:numOfRecordings\)do if\(trip(i)==0\)then notInTrip = notInTrip+1;  end if if\(notInTrip==0\)then for\(i\)=1:size(temperature \(-\)1)do ifsize(temperature)>1 then iftemperature(\(i\)) \(\geq\)tempThreshold && temperature(\(i\)+1) \(\geq\)tempThresholdthen  Alert = alertName; else  Alert = 0;  end if  end if  end for returnAlert;
```  
**Algorithm 2**Anomaly alerting  
Alerts can be sent via email or SMS to the end-user of the system. Figure 4 shows an example of the automatic notification which is sent to personnel for timely necessary action if temperature abuse is noted. A descriptive message detailing the van model, registration, if the error has been recorded in the freeze or chill compartment, and the user-specified temperature threshold that has been abused is sent to the personnel. Additionally, the message embeds a link to the organisation-specific dashboard so that the user can quickly access the live and historical data from their mobile phone and identify if a problem has really occurred. It is worth noting that alerts are not continually sent if the vehicle remains in motion and the threshold continues to be abused. Once one alert is sent, a timeout period of \(\approx\)90 min is activated before a follow-up alert is sent. This feature is to prevent end-users' mobile phones from being spammed with text messages.
## 5 Improved DSR Using Phonological Features in Vocabulary-
### Discussion  
In our experiments, PFs have shown their advantages in enhancing vocabulary-mismatched dysarthric speech recognition without using a suitable vocabulary-informed base model. However, it is important to note that the base model [61] used for training in this section was originally pretrained with phones as the recognition targets. The speech features it outputs should contain well-trained phone knowledge. As a result, there exists the possibility that the extracted PFs are inferred from this phone information in the speech features rather than from PFs-related information directly contained in the base model. If this is the case, the trained model would lack the ability to generalize to unseen phones, as it would not have the relevant information to infer their PFs. Although we observe improved performance with the PFs method using mismatched data, where there is both word and phone vocabulary mismatch between training and test data, it is challenging to demonstrate its effectiveness in recognizing unseen phones due to the lack of reliable aligned phone transcription in the datasets used.  
Previous research [19] has demonstrated the effectiveness of using PFs for recognizing unseen phones, achieving higher accuracy compared to baseline models. This work did not involve a pretrained model with phones as the target, thereby avoiding certain concerns associated with such models. To further verify how PFs are extracted from a base model pretrained with phones as the recognition target, we plan to conduct additional experiments using reliable phone-aligned data to analyze the performance of unseen-phone recognition in future work.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 2 Virtual Inertia and Current Control Techniques
### Supplementary Adaptive Dynamic Programming Controller  
The supplementary ADP controller is an efficient method to solve continuous-time and continuous-state optimization problems iteratively. The ADP controller, based on [PERSON]'s principle of optimality [43], is a powerful neural network-based control technique that has potential for applications in power electronic systems. The ADP controller has a two-neural network-based structure consisting of an action and a critic network. Supplementary ADP controllers work alongside existing controllers to learn from, and adapt to, the original controller. The critic network is designed to evaluate the performance of the supplementary controller, while the action network produces a supplementary control signal to improve control performance [44; 45; 46; 47; 48].  
The integration of the supplementary ADP controller with the PI controller presented in Figure 2 is shown in Figure 6. In a model-free ADP-based controller design, the exact mathematical model is not a prerequisite. The controller observes the input error vector, \(X(t)\), from the system and provides the control signal, \(u(t)\), for the PWM modulator. These control signals are applied on the modulating signals to adjust the PWM gating signal. The details of ADP controller are shown in Figure 6. A reward signal (or the primary reinforcement signal), \(r(t)\), is provided based on the current system performance. The optimal cost function, \(J\), which is also the output of critic network, is used to critique the performance of the control signal.  
Figure 5: Model predictive controller. (**a**) Schematic diagram. (**b**) detailed structure.
# Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems
## 2 System Model
### Received Signal  
\[y(t)=\sum_{k=0}^{K-1}\sum_{l=0}^{L-1}\sum_{j=0}^{N_{\rm s}-1}h_{l}\psi(t-jT_{ f}-1T_{l})+n(t), \tag{3}\]where the index \(k\) represents the \(k\)-th device. The noise \(n(t)\) is modelled as an additive white Gaussian noise with mean 0 and variance \(\sigma^{2}\). Finally, the received signal of an STH-PPM system can be expressed as [26]  
\[\mathbf{y}=\mathbf{s}+\mathbf{i}+\mathbf{n}, \tag{4}\]  
where  
\[\mathbf{s} = \Big{[}s_{(0,0)},s_{(0,1)},\cdots,s_{(N_{k}-1,L-1)}\Big{]}^{T}, \tag{5}\] \[\mathbf{i} = \Big{[}i_{(0,0)},i_{(0,1)},\cdots,i_{(N_{k}-1,L-1)}\Big{]}^{T},\] (6) \[\mathbf{n} = \Big{[}n_{(0,0)},n_{(0,1)},\cdots,n_{(N_{k}-1,L-1)}\Big{]}^{T}, \tag{7}\]  
respectively. \(\mathbf{s}\) is the desired signal, \(\mathbf{i}\) denotes the effect of the present devices, \(\mathbf{n}\) is the additive white Gaussian noise (AWGN) added at the receiver, \(N_{s}\) denotes the repetition code length, \(L\) is the number of multipaths present in the system. \((j,l)\) represents the signal of the \(j\)-th frame and \(l\)-th multipath. Presence or absence of a device can be determined by the following expression  
\[i_{(j,l)}=\left\{\begin{array}{ll}0,&\text{absence of device}\\ i_{(j,l)}^{(k)},&\text{presence of device}\end{array}\right., \tag{8}\]  
where the index \(k\) represents the \(k\)-th device.
# Role of Irisin in Endocrine and Metabolic Disorders--Possible New Therapeutic Agent?
## 3 Thyroid Dysfunction  
Current data regarding the connection between serum irisin variations and thyroid function are limited. There is a wide resemblance between the functions of irisin and thyroid hormones, in terms of the homeostatic control of energy balance. The potential impacts of thyroid hormones and thyroid-stimulating hormone (TSH) on irisin levels should be considered [26].  
Variations in the serum concentrations of irisin have been observed in several thyroid pathological circumstances characterized by chronic inflammation and hypothyroidism. In primary thyroid insufficiency, an increased level of TSH induces adipogenesis, while hormones such as leptin, ghrelin, and irisin are secreted, in order to balance the fat distribution. Therefore, irisin levels have been found to be high in hypothyroidism, according to the TSH levels. Furthermore, the release of the irisin precursor FNDC5, which is found in the thyroid tissue, into the blood stream provides another explanation for the high level of irisin associated with thyroid chronic inflammation [27; 28].  
However, irisin values have been shown to negatively correlate with TSH concentrations and positively with fT4 levels in clinical trials enrolling a limited number of patients with thyroid dysfunctions. Hence, irisin concentrations may vary, according to the profile  
Figure 1: Existing data about the actions of irisin.  
of thyroid function [29]. Other non-inflammatory causes of hypothyroidism, as well the limited number of patients, may explain these differences.  
Serum irisin levels of males are higher, compared to females, and irisin levels decrease with age [30]. Irisin levels seem to vary with the duration of hypothyroidism: patients with long-lasting hypothyroidism have lower irisin levels than those with recent thyroid insufficiency [26].  
In experimental studies, short-term drug-induced hyper- and hypothyroidism were not consistent with previous clinical studies: the up-regulation of serum irisin in male rats with hyper- or hypothyroidism was associated with oxidative damage and/or myopathy observed in both conditions [31].
## 4 Conclusions  
The structural vibration of nuclear rod bundles that are induced by propagating pressure waves was simulated by using SEM. Added mass and fluid coupling of the surrounding fluid were calculated using the viscous fluid theory to investigate the fluid forces acting on the nuclear rod bundles in the viscous fluid. The external forces that were caused by the incident wave and scattering pressure fields were predicted from the solution of the acoustic wave equation. The spectral element equation for the flexural vibration of nuclear rod bundles in the fluid medium subjected to external force was derived while using the weighted-integral statement. The structural damping of nuclear rod bundles in the fluid medium increased due to viscous effects in the mass coupling. Furthermore, the natural frequency of the nuclear rod bundles was decreased by the added mass. When the number of cylinders increased, the total damping also increased, because the number of the viscous fluid coupling dissipating the vibration energy was larger. The flexural vibrations of the nuclear rod bundles that were induced by the propagating pressure wave exhibited smaller damping due to same phase responses of each cylindrical beam. When the wavelength was longer than the gaps of the nuclear rod bundles, the effective damping of the underwater structure tended to decrease. The proposed method can be utilized for designing the underwater structure of the nuclear rod bundles, water pipe systems, and vessels conveying fluid, as well as the development of damping treatment for the reduction of vibration induced by the fluid effect.  
Conceptualization, W.Y., H.K., and J.P.; Data curation, W.Y.; Formal analysis, W.Y.; Investigation, W.Y.; Methodology, W.Y.; Project administration, H.K. and J.P.; Resources, H.K.; Software, W.Y.; Supervision, J.P.; Validation, W.Y.; Writing--original draft, W.Y.; Writing--review and editing, W.Y., H.K., and J.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2019R1G1A1097252).  
Figure 11: (a) Calculated transfer functions for nuclear fuel rod bundles varying the number of cylinders subjected to pressure wave, (b) cross section, and (c) its first mode shape at \(N=25\) and \(f=1.6\) Hz.