esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Postharvest UVB Irradiation on the Fruit of cv. Dottato (_Ficus carica_ L.)
## 3 Results and Discussion
### Chemical Analysis
#### 3.2.2 Total Caroenoids and Polyphenolic Content
treated samples in the first 7 days of storage (T1) and a decrease in the control ones. At T1, the values ranged from 6.89 mg/g in \(-\)UVB60 to 14.02 mg/g in +UVB60. At T2, all samples showed a decrease in total polyphenol content, however, the UVB treated fruits showed the highest values with 9.91 mg/g in +UVB10 and 12.18 mg/g in +UVB60. Comparing irradiation times (10 and 60 min), our results showed no negative effects on polyphenol content and +UVB60 showed higher total polyphenol contents than +UVB10 at both T1 and T2.
###### Abstract  
The objective of our study was to compare the effects of electron beam irradiation on the development and reproduction of _Tetranychus urticae_ strains that were susceptible or resistant to acequinacyl, bifenazate, and etocazole. Electron beam irradiation (50-400 Gy) was applied to _T. urticae_ eggs, nymphs, and adults, after which the hatching rate, the emergence rate, longevity, and fecundity were analyzed. Eggs of all the _T. urticae_ strains were irradiated with 150 Gy, and none of them hatched. When protolymphs were irradiated with 300 Gy, oviposition and thus _F\({}_{1}\)_ hatchability in newly emerged adults were completely suppressed. The _F\({}_{1}\)_ hatching rate of irradiated adults began to markedly decrease with a dose of 200 Gy, and all strains experienced 100% inhibition at a dose of 400 Gy. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (Sodium dodecyl sulfate polyacrylamide gel electrophoresis) showed substantial reductions in the levels of a 50 kDa protein in the susceptible and three resistant strains of _T. urticae_ after irradiation with 400 Gy. These results indicate that a dose of 400 Gy was required to inhibit the normal development and reproduction of _T. urticae_, and susceptible and acaricide-resistant strains of _T. urticae_ had identical radiosensitivities.  
Tetranychus urticae; acaricide resistance; electron beam +
Footnote †: journal: Applied Sciences
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
## 4 Results and Analysis
### Validation of Mechanical and Heat Transfer Performances  
As shown in Table 8, a group of optimal structural parameters of the XLS channel to develop a relevant XLS model were obtained, which had relatively excellent mechanical and heat transfer performances by the mathematical optimization model. Two validation methods were employed to verify the accuracy of this mathematical optimization model.
## 1 Introduction
Furthermore, ML techniques have recently gained a good attention among environmental modeling research community as they are advantageous in efficiently capturing the complex relationship between the environmental predictors and the response, such as flood [55; 56; 57; 58; 59; 60; 61; 62; 63], earthquake [64; 65], wildfire [66], sinkhole [67], droughtness [68], gully erosion [69; 70], groundwater [71; 72; 73; 74] and land/ground subsidence [75], and landslide in this case [54; 59; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102]. Nevertheless, investigation of new optimization algorithms and the neural fuzzy has not been carried out. The aim of this study is, therefore, to patiotically fill this gap in literature through investigating potential application of the whale optimization algorithm (WOA) and the grey wolf optimizer (GWO) algorithm and the ANFIS algorithm for spatial probability of landslide occurrence in Anyang Country, China. The WOA and GWO algorithms have been proven better than other popular meta-heuristic algorithms, such as genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) for optimization in various real world problems [103; 104]. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to evaluate the relationship between landslide and influencing factors. To our best knowledge, the hybrid framework of SWARA-ANFIS-WOA and SWARA-ANFIS-GWO has not been studied for landslide modelling. The prediction performance of the proposed methods was evaluated using several objective measures of root-mean-square error (RMSE), receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC). Finally, the effectiveness of the proposed methods was assessed by comparing the landslide susceptibility maps with the historical landslide events.
# RCM: A Remote Cache Management Framework for Spark
## 3 Problem Analysis and Modeling
### Cooperation Efficiency Model  
The network architecture of remote cache servers is shown in Figure 3. To avoid frequently querying databases, the important result will be cached in a remote cache server. When the same result is queried again, the computing node will directly request the remote cache server. However, different target servers have different efficiencies.  
At the same time, because the job of running on computing nodes and saving cache data in cache servers give dynamic load to computing resources and transmission resources, the computing capacity and transmission capacities of servers become dynamic. To place cache data to the best cache server, the measure of the cooperation efficiency of computing node and remote cache server is established.  
Let \(CNode=\{node_{1},node_{2},\ldots,node_{m}\}\) be a set of computing nodes. Let \(CServer=\{server_{1},server_{2},\ldots,server_{n}\}\) be a set of cache servers. The \(node_{i}\) represents a computing node from \(CNode\). The \(server_{j}\) represents a cache server from \(CServer\). The processing speed of the cache server represents the computing ability of \(server_{j}\). Assuming that the size of a dataset is defined as \(SD\), the execution time of the job, which loaded the same dataset, is defined as \(T_{cache\_j}\). Then, the processing speed is shown in Equation (6).  
\[\lambda_{comp\_j}=\frac{SD}{T_{cache\_j}} \tag{6}\]  
The I/O speed in the network represents transmission efficiency. The transmission time of the dataset from \(node_{i}\) to \(server_{j}\) is defined as \(T_{tran\_ji}\). Then, the I/O speed is shown in Equation (7).  
\[\lambda_{tran\_ij}=\frac{SD}{T_{tran\_ij}} \tag{7}\]  
Exactly, the processing data and transmission data are the succession of two stages, when saving the data to servers. The cooperation efficiency takes the processing speed and the I/O speed into consideration. Then, the cooperation efficiency is shown in Equation (8).  
\[\lambda_{coop\_ij}=\lambda_{comp\_j}+\lambda_{tran\_ji} \tag{8}\]where \(i\) indicates the subscript of computing nodes and \(j\) indicates the subscript of the cache servers.  
In order to make full use of cache resources, the data of computing node are assigned to the most efficient server in priority. According to the measure of cooperation efficiency, the optimization objective function can be formalized as shown in Equation (9).  
\[\begin{split} Max(\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n} \lambda_{coop\_ij})\\ s.t.&\quad i<m\\ j<n\end{split} \tag{9}\]
## 3 Design and Optimization of Pre- and Post-Equalization
Hence, in order to find the optimal coefficients of the proposed joint-PPE scheme, the following condition should be satisfied  
\[\
abla\Big{(}\theta_{\mu}(h_{\
u\mu}(k))+(\mathbf{F}\cdot\mathbf{H}\cdot\mathbf{P}-\mathbf{I} )\cdot\alpha+\left(s_{\mu,M_{\mu}-1}-\text{q}\cdot P_{\text{s},\mu}^{(\text{ max})}\right)\cdot\beta\Big{)}=0\hskip 5.690551 pt. \tag{19}\]  
To hold the strong duality between the primal and dual problems, there should be zero difference between the primal optimal solution and the dual optimal solution. The strong duality is executed using the Karush-Kuhn-Tucker (KKT) optimality conditions, which are given as  
\[(\mathbf{F}\cdot\mathbf{H}\cdot\mathbf{P}-\mathbf{I})\cdot\alpha =0\hskip 5.690551 pt, \tag{20}\] Figure 3.0 Figure 3.1 Figure 3.2 Figure 3.3 Figure 3.4  
For solving such a problem, the KKT conditions are modified, where a variable named \(t\) is introduced. This variable \(t\) is a positive number to control the degree of perturbation towards the optimal solution. The constraints, mentioned in (20), are adapted with \(t_{n}\) where \(n\) is the total number of constraints, and mentioned as  
\[ \begin{split}\alpha&=\frac{t_{1}}{(\mathbf{F}\cdot\mathbf{H} \cdot\mathbf{P}-\mathbf{I})}\enspace,\\ \beta&=\frac{t_{2}}{\left(s_{\mu,M_{\mu}-1}-\mathrm{ q}\cdot P_{\mathrm{s},\mu}^{(\max)}\right)}\enspace.\end{split}  \tag{21}\]  
The characteristics of \(t_{n}\) are that the zero-duality gap is achieved between the primal and the dual problems when \(t_{n}\to 0\), \(\forall n\). By combining (19), (20) and (21), the final optimization problem is defined as  
\[\
abla\Big{(}\theta_{\mu}(h_{\
u\mu}(k))+t_{1}\cdot\log(\mathbf{F}\cdot\mathbf{H} \cdot\mathbf{P}-\mathbf{I})+t_{2}\cdot\log\Big{(}s_{\mu,M_{\mu}-1}-\mathrm{q}\cdot P_{ \mathrm{s},\mu}^{(\max)}\Big{)}\Big{)}=0\enspace. \tag{22}\]  
Using the Newton solver, (22) is solved by choosing larger values for \(t_{n}\), which results in discovering the analytical center of the feasible region. Afterwards, the value of \(t_{n}\) decreases to locate the optimal solution of the given statement. Ultimately, the jointly designed PPE filter taps are optimized to remove the interferences, given minimal increment in the NWF. From joint-PPE, the post-equalizer will contribute to nominal noise amplification. Therefore, the hypothesis states that the joint-PPE is expected to yield better performance than ZF-based PPE and MMSE-based PE-only.
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 3 Construction of the Digital Twin Monitoring System
### Construction of the Main Modules of the Digital Twin Monitoring System
#### 3.1.1 Digital Twin Geometric Model  
The digital twin geometric model of the EBZ55 roadheader is established by using the combined modeling method of Unity3D (v2020.3.25f1c1), SolidWorks (v2019), and 3 Dsmax (v2016). A communication connection is established with the physical roadheader via the TCP protocol to obtain operational data, drive the actions of the digital twin, and send feedback data. Then, by adjusting the parent-child relationship between the components of the digital twin geometric model of the roadheader, the motion relationship between them is determined. Moreover, by writing data-driven scripts and component motion animations, the function of the data-driven digital twin of the roadheader is realized (Figure 9).
# Adaptive Satellite Navigation Anti-Interference Algorithm Based on Inverse Cosine Function
###### Abstract  
Contrasting the dilemma that the traditional time-domain least mean square (LMS) algorithm in the existing satellite navigation receiver anti-interference system cannot satisfy the convergence time is short and maintain a low level of steady-state error at the same time, an inverse cosine variable step size LMS algorithm (ICVS-LMS) is proposed. To begin with, the LMS algorithm, with a fixed step size focuses on its effectiveness in attenuating and suppressing interference signals, is analyzed, and then the proposed ICVS-LMS algorithm is analyzed. In conclusion, both the ICVS-LMS algorithm and the traditional algorithm are simulated and compared in terms of their effectiveness in suppressing interference in satellite navigation signals. The experimental results demonstrate that the improved algorithm significantly reduces convergence time while maintaining a small steady-state error. The improved algorithm demonstrates high robustness and an obvious suppression effect on interference signals. The anti-interference performance is 8.41-12.22% higher than that of the proposed algorithm.  
BeiDou navigation satellite system; time domain anti-interference; least mean square algorithm; inverse cosine function Article
## 7 Algorithm for Synthetic Control Overlapping in the Majority Class  
To check the impact of parameter hyper-tuning on the existing and synthetic overlapped dataset in this section, we proposed an algorithm to generate and add 20% synthetic samples in the majority class of each dataset:Take a multi-class dataset with an imbalanced distribution of data and overlapping samples.
2. Apply preprocessing techniques to the dataset to make it convenient for the underlying classifier. For example, all the categorical values of class labels are converted into numeric values by applying the Label Encoding scheme. Similarly, to scale the underlying data, normalization techniques are applied to scale the features to a range that is centered around zero. \[New_{value[.]}=Labelencoder.fit-transformation(existing_{value})\] The transformation method converts the existing feature value into the desired numeric value.
3. Compute the average distance \(di\) between each sample \(e_{i}\) belonging to the target class \(C\) to its \(k_{1}\) nearest neighbors \(N_{ei}\) which are not of the target class \((es)\) for each minority class sample from the majority class samples and vice versa. \[d(\overrightarrow{a},\overrightarrow{b})=\sqrt{(a_{1}-b_{1})^{2}}\text{ where } \overrightarrow{a},\overrightarrow{b}\in R^{n}\] where \(a,b\) are two vectors (sample attributes), and \(d\) is the distance between the two points. The distance for \(n\)th rows point is given below: \[d(\overrightarrow{a},\overrightarrow{b})=\sqrt{(a_{1}-b_{1})^{2}+(a_{2}-b_{2} )^{2}+\ldots\ldots+(a_{n}-b_{n})^{2}}\text{ where }\overrightarrow{a}, \overrightarrow{b}\in R^{n}\]
4. Select the majority class (MC) from the list of the class's loc set, for which synthetic samples \(S\) will be created. \[MC_{Count}=np.unique(MDS[col],return_{count}=True)\] where \(MC_{Count}\) is used to hold the samples of the majority class, \(MDS\) is the multi-class dataset, and \(col\) is the feature to be counted.
5. Choose the first nearest neighbor by selecting the value of \(k_{1}\) = 1. Distance \(d_{i}\) between the sample and its neighbor was calculated using Euclidian distance, \[N_{ei}=\text{set of }k_{1}\text{-nearest neighbors of }ei\in D\text{ of Class }\
eq MC_{Count}\] where \(ei\) is the individual sample of target class C, \(di\) is the average distance to \(kn\) sample of the other class, \(mi\) is the target class, and \(N_{ei}\) is the closest sample of \"the other\" than target class \((es)\).
6. Compute 20% of the synthetic samples to be overlapped in the majority class of the dataset, \[S=\frac{(\#T*x)}{100}\] where \(S\) is the set of synthetic samples and \(T\) is the subset of \(D\) (multiclass dataset) containing all the samples of the target class C (\(MC_{(Count)}\)).
7. Compute the synthetic samples using interpolating schemes. \[y_{new}=y+rand(0,1)*(\hat{y}-y)\] for each minority class sample \(y\), one obtains its k-nearest neighbors from other minority class samples. Secondly, one chooses one minority class sample \(\hat{y}\) among the \(k\) neighbors. Finally, one generates the synthetic sample \(y_{new}\) by interpolating between \(\hat{y}\) and \(y\).
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
### The Key Hardware Components for Physical Testbed
#### 3.1.2 Texas Instruments CC1352R Sensor Nodes  
The LaunchPad SensorTag kit CC1352R, also known as \"LPSTK-CC1352R\", is a powerful Cortex-M4F MCU with integrated environmental and motion sensors, multiband wireless connectivity, and simple software for prototyping connected applications. Environmental sensors such as humidity and temperature sensors, ambient light and inertia sensors, a Hall effect switch, and a three-axis accelerometer are among the LaunchPad modules that can be used in use cases like trap monitoring in a forest. Mobility is provided by two AAA batteries or a CR2032 coin cell installed on the module. These batteries can provide power for the transmission of their readings in a variety of settings. The LaunchPad modules include a dual-band low-power radio kit that allows for sub-1 GHz (868 MHz/915 MHz) and 2.4 GHz to run concurrently with Bluetooth Low Energy (BLE) in a single-chip solution. The 15.4 Stack protocol offers star topology networks for applications operating at sub-1 GHz or 2.4 GHz. The sub-1 GHz version has several major advantages, including longer range and improved protection against in-band interference, as well as the ability to deliver Bluetooth Low Energy (BLE) beacon packets while running in dual-band mode on a sub-1 GHz 15.4 Stack network. In this case, the dual-band functionality provides the ability to adjust the functionality of a specific SensorTag via over-the-air debugging and BLE configuration. Alternatively, an external programmer-debugger known as the \"CC1352R LaunchPad development board\" can be used to reconfigure the SensorTag. The LaunchPad SensorTag CC1352R and LaunchPad development board CC1352R are depicted in Figure 6 and Figure 7, respectively.  
Figure 5: TI CC2538 board with micro-USB.  
Code Composer Studio (CCS) software is used as an integrated development environment (IDE) that supports TI's microcontrollers to enable debugging of the targeted module. The CCS provides a package of tools for running, debugging, and altering the functionality of the LPSTK modules, including a C/C++ compiler, source code editor, project build environment, debugger, and profiler. Each LaunchPad SensorTag requires a LaunchPad development kit to be debugged using an ARM 10-pin JTAG cable and a two-wire pair for UART. When the SensorTag is connected to the development board, it allows for full debugging, programming, and UART communication via CCS. The connection between the LaunchPad SensorTag and the LaunchPad development kit to enable debugging of the LaunchPad SensorTag is shown in Figure 8.  
Figure 8: Debugging the LaunchPad SensorTag CC1352R using development board CC1352R.  
Figure 6: The LaunchPad SensorTag kit CC1352R.  
Figure 7: The LaunchPad development board CC1352R.
# ColorMedGAN: A Semantic Colorization Framework for Medical Images
## 1 Introduction
This framework utilizes a new non-adversarial cycle loss to guide the framework in minimizing texture and perceptual differences in the translated images. Other similar studies include [32; 33; 34; 35; 36]. Drawing inspiration from these works, we propose a CycleGAN-based unsupervised medical image colorization framework that pays attention to both low-level structural information and high-level semantic information. By doing so, we aim to generate high-quality colorized medical images without the need for paired data or manual alignment. In this paper, we propose a pipeline based on CycleGAN [17] and ColorCycleGAN (Single Image Colorization via Modified CycleGAN) [37] for the automatic coloring of MRI images. Our approach formulates the colorization problem as a domain transfer task in the color domain, transferring the colors from the source domain (cryo-anatomical images) to the target domain (MRI images) while preserving the semantic colors of the source domain and the structural information of the target domain. To achieve this, we pre-train a segmentation network to fuse semantic features and introduce edge loss and multi-modal discriminators to guide the model to generate rich texture details. Our approach does not require paired image sets or manual alignment of images and can be trained using cross-modal unpaired datasets. We use the Brain MRI dataset Brats2018 [38] with instance segmentation annotations as the source domain data and a subset of The Human Project [39] and The Korean Human Project [40]'s cryo-anatomical data as the target domain data. In summary, this paper:  
* Proposes a framework that can generate semantically colored images across modalities. This method can train the colorization model with an unpaired dataset and generate high-quality color images. * Introduces an instance segmentation network branch in generating phase. The generator is guided to produce highly semantic and color-consistence images based on the attention mechanism. * Introduces the edge loss and multi-modal discriminator based on edge-aware detectors which enables the generator to focus on the global edge information of the image, preserve more edge detail textures, and prevent the color bleeding problem.
## 3 Proposed Audio Watermarking Method
### Watermark Extraction Scheme  
Figure 6 shows the proposed watermark extraction procedure, where the \(|\cdot|\) symbol means the absolute function. The procedure needs to know the embedding parameters in advance: The passband frequency \([F_{lc},F_{uc}]\), the frame length \(T_{f}\), and the delay \(\tau\). The watermark extraction procedure can be regarded as a part of the embedding procedure. Assuming that the start point of watermark embedding has been found, the watermarked signal \(\overline{x}(t)\) is firstly filtered to produce the filtered watermarked signal \(\overline{s}(t)\). After then, the formula (1)-(7) is utilized to calculate the \(\overline{D}_{i}\) of each frame. If \(\overline{D}_{i}>0\), the detected bit is '1.' If \(\overline{D}_{i}<0\), the detected bit is '0.' Note that the proposed method does not need the original signal in the watermark extraction process, which indicates that we proposed a blind audio watermarking method.
# Image-Captioning Model Compression
## 4 Experiments
### Decoder Architecture Changes  
In Table 2 we evaluate models with their decoder reduced using different scale factors. A value of \(\gamma\) equal to 1 corresponds to using original models, but, as said before, with EfficientDet-D0 encoder. For the Up-Down model as well as for AoANet, decoder reduction with scale factor equal to 0.5 shows results comparable to the model without reduction, but with approximately three times less memory and number of parameters. Reducing the model further leads to greater loss in quality. Thus, for example, Up-Down model's  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Encoder Type** & **B@1** & **B@4** & **M** & **R** & **C** & **S** & **Size** & **\#Params** \\ \hline \multicolumn{10}{c}{Up-Down} \\ \hline Faster R-CNN & & & & & & & & \\ ResNet101 [9] & 79.8 & 36.3 & 27.7 & 56.9 & 120.1 & 21.4 & 468.4 MB & 122.6 M \\ Faster R-CNN & & & & & & & & \\ MobileNetV3 & 79.3 & 35.6 & 27.4 & 56.8 & 119.4 & 21 & 74.2 MB & 19.4 M \\
**EfficientDet-D0** & **79.4** & **35.8** & **27.3** & **56.7** & **119.5** & **21.1** & **15.1 MB** & **3.9 M** \\ EfficientDet-D1 & 79.7 & 36.2 & 27.5 & 56.9 & 120 & 21.3 & 25.7 MB & 6.6 M \\ \hline \multicolumn{10}{c}{AoANet} \\ \hline Faster R-CNN & 80.2 & 38.9 & 29.2 & 58.8 & 129.8 & 22.4 & 468.4 MB & 122.6 M \\ ResNet101 [10] & & & & & & & \\ Faster R-CNN & 79.4 & 38 & 29.5 & 58.2 & 127.2 & 21.9 & 74.2 MB & 19.4 M \\ MobileNetV3 & & & & & & & & \\
**EfficientDet-D0** & **79.8** & **38.3** & **29.6** & **58.3** & **129.2** & **22.1** & **15.1 MB** & **3.9 M** \\ EfficientDet-D1 & 80.1 & 38.4 & 29.8 & 58.4 & 129.5 & 22.1 & 25.7 MB & 6.6 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation results of all of tested models with different encoders. Both “Size” and “#Params” columns refer to encoder.  
CIDEr metric decreases by only 0.3 points when the scale factor of 0.5 is used, but when the scale factor of 0.25 is used it decreases by 4.9 points, which is a much bigger loss. A similar situation is seen for the CIDEr score drop for the AoANet model. So, in this case, using scale factor \(\gamma=0.5\) acts as a compromise which helps to both reduce the model size and leave quality metrics on the same level. We will use decoders reduced with a scale factor equal to 0.5 for our experiments for the rest of the paper.