esa-sceva/satcom-chunk-collection huggingface dataset:
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 4 Results and Analysis
### The Training of the Kiwifruit Recognition Network  
Pre-training weights and freezing part of the training layers are used to load GhostNet weights, initial training freezes part of the network layers, and after 50 generations of training starts to unfreeze the frozen part for full network training. Putting more resources on the parameter training of the later network and unfreezing this part of the network parameters afterwards can effectively guarantee the weights. In the training process, the input image size is set to 416 \(\times\) 416, the model freeze layer is set to 50 training generations, the batch sample is set to 16, the momentum factor is set to 0.9, the decay coefficient is 0.0005, and the initial learning rate is 0.001. After unfreezing, the total training generations are set to 500, the number of batch samples is eight, the momentum factor is set to 0.9, the decay coefficient is 0.0005, and the initial learning rate is 0.001. The loss value is one of the measures of the model effectiveness, and the lower the loss value is, the better the model training is theoretically. The weight file is saved once after each generation of training (epoch), and the visualization plot is recorded according to the background log information after the training is completed, and the trend of model loss value change is shown in Figure 8.  
The improved YOLOv4 network model decreases the training loss value as the number of iterations increases. As can be seen from the figure, the loss function value decreases rapidly in the first 100 iteration cycles (fast model fitting); in 100-350 iterations, the loss value slowly decreases close to the optimal solution; after 70 iterations the loss value gradually stabilizes to a small fluctuation at 0.3, then the model is considered to converge.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Parameter** \\ \hline Graphics Processing Unit (GPU) & Ge Force GTX1050 Ti \\ Operating System & Windows10 \\ Accelerated Environment & Pytorch1.8.1 CUDA11.1 \\ Development Platform & Visual Studio Code \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information about the test platform.  
The loss value of Ghostnet-YOLOv4 after stabilization is lower than that of MobilentV3-YoloV4, and the training results of Ghostnet-YOLOv4 network model are more satisfactory in terms of parameter convergence.  
From the loss function and Table 3, it can be seen that the loss function of the improved YOLOv4 model decreases rapidly. The corresponding average accuracy is stable at about 93.07%, which is because the improved YOLOv4 introduces a feature layer with a smaller perceptual field and improves the detection of model fine-grained. This indicates that the improved model is more capable of recognizing kiwifruit fruits in natural environments.
## 3 Principles of the Tuning the Temperature, Humidity, and Friction  
### Structure of the Developed Units  
#### 3.1.1 Liquid-Injection Unit  
Figure 3 shows the structure of the liquid injection unit. It consists of an airflow path with a throttled section (orifice), and a path that bifurcates from the orifice and connects to the liquid storage space within the cotton. The structure was molded using a flexible material (Dragon Skin 10). The principle of the Venturi effect is used to inject the liquid stored within the cotton onto the contact surface using the input airflow. Using the Bernoulli principle, the difference in the airflow pressure between the wide section and the orifice can be obtained as follows:  
\[p_{1}-p_{2}=\frac{\rho(v_{2}^{2}-v_{1}^{2})}{2}, \tag{1}\]  
where \(\rho\) is the density of air; \(p_{1}\), \(v_{1}\), and \(s_{1}\) are the airflow pressure, airflow velocity, and cross-sectional area of the airflow path at the wide section before flowing to the orifice, respectively; and \(p_{2}\), \(v_{2}\), and \(s_{2}\) are the airflow pressure, airflow velocity, and cross-sectional area of the airflow path at the orifice, respectively. If \(q\) is the volumetric airflow rate from the air source and is constant in the two sections, the following continuous equation is observed: \(q=v_{1}s_{1}=v_{2}s_{2}\). Considering \(v_{1}=q/s_{1}\) and \(v_{2}=q/s_{2}\), Equation (1) can be rewritten as follows.  
\[p_{1}-p_{2}=\rho q^{2}\left(\frac{1}{s_{2}^{2}}-\frac{1}{s_{1}^{2}}\right), \tag{2}\]  
where \(p_{1}>p_{2}\) is satisfied, as \(s_{2}<s_{1}\). Hence, a negative pressure is generated in the orifice. This forces the liquid to be ejected from the cotton through the orifice. The ejected liquid is sprayed onto the surface of the unit with airflow. The unit is constructed with soft materials; thus, the negative pressure around the orifice can cause undesired deformation (e.g., inflation around the orifice) in the structure, resulting in reduced performance. To avoid undesired inflation of the flow path around the orifice, the flow path area was sewn with a thread, as shown in Figure 3.  
The performance of the liquid injection unit was experimentally validated using the experimental setup illustrated in Figure 4. In the experiments, the liquid (deionized water) was injected into a paper towel ([PERSON], [PERSON], Irving, TX, USA) through inputting air at a pressure of 0.1 MPa from the air source to the liquid-injection unit. After injecting the liquid for three seconds, the weight of the paper towel was measured, and the amount of the injected liquid was calculated from the difference between the weight of the towel before and after injecting the liquid. The experiment was conducted twice, with a mean amount of injected liquid of 0.6 g between the trials.  
Figure 3: Schematic of the liquid-injection unit.
# Forced Dynamics of Elastically Connected Nano-Plates and Nano-Shells in Winkler-Type Elastic Medium
## 8 Results and Discussion
### Natural Frequency Analysis
Assuming that the nonlocal parameter is equal to zero, \(\mu=0\), and the radius of curvature of the doubly curved shallow shell tends to infinity, i.e., \(R_{1}=R_{2}=\infty\) and \(A_{11}=A_{12}=A_{22}=A_{66}=0\) in Equations (49)-(52), the equations are reduced to two-plate equations, as in the paper by [PERSON] [42; 43]. Then, the system of four coupled differential Equations (49)-(52) is reduced to a system of two coupled differential equations, which represent the transverse displacements of two elastically coupled plates, as shown in [PERSON]'s study [42; 43]. In this way, the considered nano-system is reduced to a system of two nanoplates interconnected by a Winkler-type elastic layer. The values of the natural frequencies of the observed nano-system (ECSNPS) are compared with the results from the paper by [PERSON] [42; 43]. In Equations (49)-(52), the geometric and material parameters from the paper by [PERSON] [42; 43] are used. By neglecting the nonlocal parameter and assuming that the radii of curvature of the shallow shell tend to infinity, a very good agreement between our results and the values of the frequencies obtained from the work by [PERSON] [42; 43] is obtained, as shown in Table 3.  
If we omit the upper nanoplate and the elastic medium, the validity of the derived equations is assessed by comparing them with published findings for a doubly curved shell from [22], as shown in Table 4. From Table 4, it can be seen that the natural frequency values are close to those obtained in [22]. The differences in values arise due to the application of gradient elasticity theory in [22].
## 3 Authentication Model and Authorization Model  
### Proposed Authentication Model: Blockchain-Based Certificate  
To fill the gap of the security requirements between the IoT layer and fog layer [5; 6; 7], we propose a blockchain-based certificate model of the IoT-Fog-Cloud architecture to authenticate the IoT devices to fog devices and achieve the authentication aim of this study. Figure 2 presents the operations comprising the model, which are as follows: (1) the IoT devices make a connection request to the fog devices; (2) the fog devices distribute a valid certificate to the IoT devices; (3) the handshake mechanism using the TLS cryptographic protocol is established between IoT devices and fog devices; (4) encrypted communication is established between the IoT devices and fog devices; (5) because the fog devices are expected to be limited, the blockchain technology is applied to a set of fog devices within their geographical location; and (6) each fog device inside the blockchain has a copy of transactions, such as the distributed IoT device certificates. Figure 2 shows how the blockchain-based certificate model was applied to the IoT-Fog-Cloud architecture using a real-life environment.  
Figure 1: Overview of the IoT–Fog–Cloud architecture.
## 2 KF-Based Tracking Algorithm  
### The Receiver Tracking Loop Model  
The KF-based tracking loop's block diagram is presented in Figure 1. Firstly, the digital IF (intermediate frequency) signal is separated into in-phase and the quadrature channels, which are multiplied separately by the sin and cos local replica carrier signals. These results are multiplied by the prompt local replica code. Then, we can obtain the correlated signals. The carrier NCO (numerically controlled oscillator) is applied to produce the local replica carrier signal.  
After the correlator, the coherent integration values \(I_{\text{P}}(k)\) and \(Q_{\text{P}}(k)\) can be obtained. \(I_{\text{p}}(k)\) and \(Q_{\text{P}}(k)\) are input into the discriminator to generate carrier phase error, that is the KF-based loop filter's measurement values. Finally, the carrier phase and frequency of the carrier NCO are updated by the KF loop filter's estimated results. The ultimate aim is to ensure that the local carriers can remain consistent with the received carrier signals, and then the received IF signals' carrier can be completely wiped off.
# A Compact Broadband Planar Inverted-F Antenna with Dual-Resonant Modes
## 3 Principle and Analysis
### Loading of Slots 2  
Although these two resonant modes can be combined by introducing Slots 1, the simulated \(|\)S\({}_{11}\)\(|\) is always larger than \(-10\) dB, mainly because of the relatively large resistance and reactance values within the frequency range, as shown in Figure 4a. As is known, \(|\)S\({}_{11}\)\(|\) is an important index to measure antenna performance. Generally, the port reflection of an antenna is considered acceptable when \(|\)S\({}_{11}\)\(|\) is less than \(-10\) dB. To solve this issue, Slots 2 are then loaded onto the radiating patch. This can improve the input impedance of the antenna [27,33], as shown in Figure 4b. Comparing the two figures, it is learned that, with Slots 2 loaded, the resistance within the frequency range between the two modes is smoothed from [22, 116]\(\Omega\) to [31, 80]\(\Omega\), while the reactance is reduced from [0, 62]\(\Omega\) to [0, 28]\(\Omega\). Therefore, the impedance matching of the antenna becomes much better.
## 4 Experiment and Performance Evaluation of the New Screen Sand Production  
There are currently four main types of typical sand simulation experiments: the sand arch stability experiment, the hollow cylinder experiment, the sand-filled core flow experiment and the full-size sand simulation experiment. Based on the formation of sand samples, simulated sand samples, unconsolidated or weakly consolidated reservoir sandstones as the research objects, the sand production behavior and fluid production dynamics under different conditions are observed experimentally, so as to determine the effects of the sand control and sand retention. The evaluation methods for screen flow capacity include permeability, flow area and productivity index per meter. The overflow capacity of the screen is not only related to overflow area, but also related to the structure of the screens, the characteristics of the formation of sand, and the accuracy of sand control.
# Research on Path Planning of Mobile Robot Based on Improved Theta* Algorithm
## 1 Introduction
### Related Work
Another aspect of spline curve fitting is only for the geometric smoothing of the path, and the continuity of the robot's running speed and acceleration is not taken into account. [PERSON] et al. [21] proposed a polynomial interpolation-based method to improve the continuity problem of the robot during navigation. Polynomial interpolation [22] is a simple functional method. This method is solved by finding a polynomial containing all path nodes and adding continuity constraints for node position, velocity, and acceleration. To address the problems of low computational efficiency and long convergence time of Theta* algorithm, this paper proposes a W-Theta* algorithm, which is based on the dynamic weighted improvement of Theta* algorithm to improve the path planning efficiency of Theta* in large scenarios, while using the polynomial optimization method based on minimum jerk [23; 24], so that the W-Theta* algorithm to satisfy smoothness and continuity of the planned paths. Firstly, a two-dimensional grid map is constructed to provide a simulation environment for validating the algorithm; secondly, the heuristic function of Theta* is improved, while the diagonal distance expression is used, which finally achieves the reduction of computation time while ensuring the shortest global path; finally, the generated path points are curve-fitted to make the robot's motion trajectory smoother.
## 5 Evaluation and Discussion
### Decentralized Privacy-Aware Data Storage  
In this section, we demonstrate the results of privacy-aware decentralized data storage discussed in Section 4.4.  
We used Python 3 to implement the components of our data storage solution. We evaluate the components of our framework using CPU Core i7, 1.80 GHz with 16 GB RAM.  
Figure 4: A plot of average ORTT of onion messages traveling through \(n=\{5,20,60\}\) nodes and having onion body size of \(p=\) {1 k, 2.5 k, 5 k, 10 k, 25 k, 50 k, 100 k} bytes. The average was computed on 30 onion messages.  
In our motivating scenario, we have five buildings and each building has eight sensors. We show our solution scales up by measuring the timings of our prototype with a growing number of nodes.  
For the experiments, we observed the time that is needed to write and read data on a decentralized privacy-aware data storage framework. Figure 5 presents time overhead between different numbers of sensors such as 8, 16, 24, 32, and 40. We calculated the average time consumption in seconds for data read and write operations.  
In the case of 8 sensors, the time to write data has an average of 0.420 s, and read data have an average of 0.042 s. For the case of 16 sensors, the data write time has an average of 0.451 s that does not show much difference with the case of 8 sensors. Data read time has an average of 0.058 s that is slightly higher than the average read time of 8 sensors. For the case of 24 sensors, the average data write time is 0.552 s, and the average data read time is 0.054 s that is slightly less than the read time of 16 sensors.  
In the case of 32 sensors, the average time to write data is 0.665 s, and the data read time is 0.065 s. For 40 sensors, the data write gives an average of 0.689 s, and the data read has an average of 0.082 s. The results show that a privacy-aware data storage prototype gives a reasonable time overhead. The average time to write and read data is not affected much  
\begin{table}
\begin{tabular}{c c c c c c c}
**Onion Body Size** & **Message Path Length** & **Onion Head** & **Onion Head** & **2.66** & **12.32** & **3.33** & **3** \\
**(Bytes)** & **(Number of Hops)** & **Size (Bytes)** & **Size (Bytes)** & **Size (Bytes)** & **Size (Bytes)** & **Size (Bytes)** & **Size (Bytes)** \\
**Onion Body Size** & **Message Path  
by increasing the number of sensors. We explain that this result comes from the multiple optimizations in the Python interpreter and at the operating system level (system cache).
## 4 Discussion
### Metabolic Pathways before and after Fermentation  
Seven significantly upregulated and two downregulated VOCs were found in SJ after fermentation (Figure 4B), and the metabolic pathways of fermentation were further analysed. In KEGG pathway analysis, the majority of metabolites were annotated as belonging to metabolic pathways and the biosynthesis of secondary metabolites (48 and 31 metabolites, respectively) (Figure S1A). After fermentation, 15 upregulated pathways were found (log\({}_{2}\)FC \(>\) 1). The most obviously upregulated pathway was butanoate metabolism, followed by biosynthesis of cofactors, toluene degradation, furfural degradation, etc. (Figure S1B). Fermentation significantly downregulated the fatty acid biosynthesis and lipoic acid metabolism pathways. Variable importance in projection (VIP) values were calculated for pathways, including the VOCs newly generated during fermentation (log\({}_{2}\)FC = Inf), of which five had VIP values \(>\) 1 (Figure S1C): Butanoate metabolism, microbial metabolism in diverse environments, metabolic pathways, degradation of aromatic compounds and chemical-carcinogenesis DNA adducts.  
[PERSON] et al. [52] also found that the most relevant flavour pathway in baijiu is butanoate metabolism, which is related to _Thermoactinomyces_. Butanoate metabolism involves a total of 19 flavour substances, including two acids (3-methyl-butanoic acid, butanoic acid), three alcohols (2-butanol, 3-methyl-butanol, 2,3-butanediol) and 14 esters (3-methyl-1-butanol acetate, 3-methyl-butanoic acid ethyl ester, 3-methyl-1-butanol acetate, 3-methyl-butanoic acid hexyl ester, 1-methylpropyl ester, methyl-butanoic acid 2-methyl-butanoic acid 2-methyl-butanoic acid 2-methyl-butanoic acid 2-methyl-let, etc.). The relevant differential metabolitesincluded 3-methyl-butanoic acid, 2-butanol, 2,3-butanediol, 3-methyl-butanoic acid ethyl ester, 3-methyl-butanoic acid hexyl ester. These substances are important components of wine flavour. 3-Methyl butanoic acid was identified as a key aroma compound in fermented Forastero cocoa beans, and gave them a sweaty odour [53]. The specific markers of Luzhou-flavoured fresh raw liquor distilled from Zaopei were 2-butanol [54], 2,3-butanediol [55], butanoic acid, etc., indicating that 2-butanol is a common flavour component in specific fruit wines and distilled wines. 3-Methyl butanoate presents a fruity flavour, and is the main flavour ingredient of Chinese Premium famous Liquors [56] and Qingke Liquor [57].  
Microbial metabolism in diverse environments and metabolic pathways are complex metabolic systems. Microbial metabolism in diverse environments includes carbohydrate metabolism, energy metabolism, metabolism of cofactors and vitamins and xenobiotic biodegradation. Metabolic pathways include carbohydrate metabolism, energy metabolism, lipid metabolism, nucleotide metabolism, amino acid metabolism, glycan metabolism, biosynthesis of terpenoids and polyketides, biosynthesis of other secondary metabolites and xenobiotic biodegradation.
## 4 Applications of Microextraction Techniques to the Determination of Organic Micropolultants
### Alkylphenols and Bisphenol A  
Alkylphenols (APs), their ethovylated derivatives (APEOs) and bisphenol A (BPA) are endocrine disrupting compounds (EDCs) because these compounds can alter the endocrine system of living organisms, including humans. APs are used as surfactants, whereas BPA is a monomer used during the manufacture of plastics. The main sources of these compounds in the aquatic environment are WWTPs where domestic and industrial wastewaters converge. In general, the concentrations of these substances in liquid environmental samples range from a few ng\(\cdot\)L\({}^{-1}\) in relatively clean samples, such as surface water, to hundreds of ng\(\cdot\)L\({}^{-1}\) in more complex samples, such as WWTP samples.  
During the last decade, the number of publications using liquid chromatography coupled to mass spectrometry for detection has grown significantly; the most common extraction technique is solid-phase extraction [73]. However, some microextraction techniques have been optimised for these target compounds, as shown in Table 2.  
[PERSON] _et al._ developed and validated a method that determines APs and BPA in seawater simultaneously [74]. This procedure was based on dispersive liquid-liquid microextraction (DLLME): 1-octanol and a small volume of the seawater sample were combined with liquid chromatography-electrospray ionisation tandem mass spectrometry in the negative mode (LC-ESI-MS/MS). The recoveries were satisfactory (approximately 84%-104% for all compounds). The LOQs ranged between 0.005 and 0.03 \(\mathrm{\SIUnitSymbolMicro g\cdot\mathrm{L}^{-1}}\); therefore, the levels established in Directive 2008/105/EC [14] were achieved.  
Recently, [PERSON] _et al._[75] developed a new method to extract and quantify APs in complex matrix water samples rapidly using HF-LPME with ultra-high liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). In this case, 1-octanol was used as the acceptor phase, and an enrichment factor of 800 was obtained. The quantification was carried out through isotope pattern deconvolution, which allowed the quantification of the concentrations of both compounds without a calibration graph, thereby decreasing the total analysis time. Combining HF-LPME and UHPLC-MS/MS enabled the validation of this methodology at the legislated levels, achieving LOQs of 0.1 \(\mathrm{\SIUnitSymbolMicro g\cdot\mathrm{L}^{-1}}\) and recoveries from 97% to 109%.
## 1 Introduction
Figure 1: The two systems considered in this paper: a graphene sheet (**a**) and a graphene grid of ribbons (**b**) located in between two dielectrics. A nanoparticle is located at position \(\mathbf{r}_{0}=(0,0,z_{0})\) and is characterized by a polarizability tensor \(\alpha_{0}\) in vacuum. In addition, a plane wave impinges on the nanoparticle and on graphene coming from \(z=+\infty\).