esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Three Source Signals  
Let us consider a scenario when three sources placed at angles \(-5^{\circ}\), \(0^{\circ}\) and \(5^{\circ}\) emit the following signals.  
\[s_{1}(t)=e^{(0.05\pi\eta\dagger+a\eta\tau t^{3})} \tag{24}\] \[s_{2}(t)=e^{(0.3\pi\eta\dagger-a\eta\tau t^{3})}\] (25) \[s_{3}(t)=e^{(0.9\pi\eta\dagger-a\eta\tau^{3})}, \tag{26}\]  
where \(a=7.1208\times 10^{-6}\). The signals are received by uniform linear array of four sensors such that spacing between two sensors in one half of the wavelength. Let us compare the accuracy of the proposed method with the state of art methods including the Viterbi algorithm, ridge tracking algorithm use MSE as criterion for SNR ranging from \(-10\) dB to \(10\) dB by performing \(100\) simulations as shown in Figure 8.  
Let us now repeat the experiment for an under-determined scenario by assuming that we now have 2 sensors to receive the signal emitted by three sources. The MSE curves are shown in Figure 9.
## 2 Materials and Methods
### Model Selection  
Due to the large number of eggs, multiple convolutional neural network (CNN) models were trained using only one epoch. This approach was deemed sufficient to observe the preliminary performance trends of each model as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Validation Accuracy (\%)**} \\ \cline{2-5}  & **Red** & **Green** & **Blue** & **White** \\ \hline DenseNet121 & 98.50 & **93.28** & 89.96 & 90.00 \\ DenseNet169 & **99.62** & 85.82 & 92.89 & 91.87 \\ DenseNet201 & 98.87 & **93.28** & 82.01 & 91.87 \\ EfficientNetB0 & 52.63 & 54.85 & 44.35 & 45.31 \\ EfficientNetB1 & 57.14 & 45.15 & 44.35 & 45.94 \\ EfficientNetB2 & 47.37 & 45.15 & 47.28 & 54.69 \\ EfficientNetB3 & 52.63 & 55.22 & 52.72 & 60.94 \\ EfficientNetB4 & 54.51 & 54.85 & 55.23 & 55.94 \\ EfficientNetB5 & 56.39 & 56.34 & 50.63 & 46.56 \\ EfficientNetB6 & 47.37 & 45.15 & 51.05 & 53.75 \\ EfficientNetB7 & 56.02 & 54.85 & 51.05 & 47.50 \\ InceptionResNetV2 & 98.50 & 90.30 & 90.79 & 80.31 \\ InceptionV3 & 98.12 & 91.79 & 80.75 & 85.62 \\ MobileNet & **99.62** & 92.16 & 86.19 & 93.12 \\ MobileNetV2 & **99.62** & 91.04 & **94.14** & **93.75** \\ MobileNetV3 Large & 59.77 & 69.40 & 63.6 & 61.56 \\ MobileNetV3 Small & 52.63 & 54.85 & 55.65 & 49.38 \\ NASNetMobile & 98.87 & 85.45 & 87.87 & 88.44 \\ ResNet101 & 48.87 & 67.54 & 57.74 & 59.38 \\ ResNet101V2 & 98.50 & 91.79 & 90.79 & 90.00 \\ ResNet152 & 63.53 & 58.96 & 64.44 & 61.87 \\ ResNet152V2 & 96.99 & 92.16 & 87.45 & 91.25 \\ ResNet50 & 56.39 & 54.85 & 70.71 & 54.06 \\ ResNet50V2 & 99.25 & 92.91 & 93.72 & 93.44 \\ VGG16 & 92.11 & 74.25 & 67.78 & 75.94 \\ VGG19 & 92.86 & 83.58 & 69.87 & 74.37 \\ Xception & 96.99 & 87.69 & 85.36 & 90.94 \\ \hline \hline \end{tabular} Bold numbers represent the highest accuracy for each light source.  
\end{table}
Table 1: Comparison of CNN model accuracy after one epoch of training on original images.  
The results indicated that MobileNetV2 [29] achieved the highest accuracy when processing images. Thus, MobileNetV2 was selected as the base model for transfer learning. Its compact architecture includes a convolutional layer with 32 filters, 17 residual bottleneck blocks, and an inverted residual structure.  
To improve model performance in duck egg crack detection, an adaptive MobileNetV2 was developed based on the original MobileNetV2 architecture using a transfer learning approach. Blocks 0 to 13 were frozen to retain the pretrained weights from ImageNet, while only blocks 14 to 16 were unfrozen and fine-tuned using task-specific data. This adaptation enabled the model to better capture the distinctive features of egg images under various lighting conditions. The architecture also retains the bottleneck module structure, which includes pointwise and depthwise convolutions with residual connections, helping to reduce the number of parameters and increase model depth without causing gradient vanishing issues as shown in Figure 6.
## 3 Experiments and Analysis
### Results and Analysis  
#### 3.3.1. Fruit Detection Performance  
In order to validate the detection accuracy of the detection algorithms YOLOv7-tiny on the citrus dataset, four target detection methods, YOLOv5, YOLOv5-tiny, YOLOX, and YOLOv7-tiny, were tested for comparison in this paper, and the test results are showed in Table 1.  
The experiment results show that the precision, recall, and average precision of YOLOv7-tiny are 96.81%, 96.39%, and 97.23%, respectively, which are 1.97 percentage points, 3.15 percentage points, and 8.21 percentage points higher than those of YOLOX, and the precision, recall, and average precision of YOLOv7-tiny are 1.62 percentage points, 2.39 percentage points, and 3.19 percentage points higher than those of YOLOv5-tiny, respectively. Compared with YOLOv5, the precision, recall, and average precision are improved by 0.19 percentage points, 1.17 percentage points, and 3.06 percentage points, respectively. The superior detection performance of YOLOv7-tiny on the citrus dataset was demonstrated.  
YOLOv5, YOLOv5-tiny, and YOLOX have unsatisfactory citrus detection mainly due to the following reasons: (1)when two detection targets overlap each other and obscure each other, YOLOv5, YOLOv5-tiny, and YOLOX will recognize the two overlapping citrus  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods for Object Detection** & **Precision** & **Recall Rate** & **Average Detection Precision** \\ \hline YOLOv5 & 96.62\% & 95.22\% & 94.17\% \\ YOLOv5-tiny & 95.19\% & 94.00\% & 94.04\% \\ YOLOX & 94.84\% & 93.24\% & 89.02\% \\ YOLOv7-tiny & 96.81\% & 96.39\% & 97.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results of several different object detection algorithms on bagged grape dataset.  
as a single citrus, which leads to errors in tracking counts; (2) the identity ID jumping phenomenon will occur in the detection process of YOLOv5, YOLOv5-tiny, and YOLOX will occur identity ID jumping phenomenon during the detection process, counting the same citrus repeatedly; (3) in the complex environment of citrus orchards, the obscurity of leaves causes YOLOv5, YOLOv5-tiny, and YOLO to fail to detect the obscured citrus.
## 2 Classification of Electromagnetic Surfaces
### Isotropic and Anisotropic  
In three-dimensional electromagnetics, anisotropy means that the characteristic parameters of mediums are functions of applied field's direction [22], otherwise they are isotropic. For two-dimensional surfaces, since \(\hat{z}\) dimension is much smaller than \(\lambda\), discussion on isotropy and anisotropy do not concern about \(\hat{z}\) component of EM fields. Because isotropic surfaces have the same effects on \(\hat{x}\) and \(\hat{y}\) polarizations, as a result their scattering properties are not related to the azimuthal angle \(\varphi\) of wave propagation direction \(\hat{k}\), but only to the elevation angle \(\theta\). In contrast, anisotropic problems are related to not only \(\theta\) but also \(\varphi\), meanwhile cross-polarization fields may arise in reflection and transmission. An example of isotropic and anisotropic surfaces is given in Figure 6. Actually, there are no purely isotropic surfaces as frequency increases. While \(\lambda\) becomes comparable to \(p\), the so-called isotropic problems are also related to \(\varphi\). That is because even though the scatterers have the same effects on field components along different directions, the periodic square lattice of unit cells is not. As discussed before, at low frequency, \(p\) is much smaller than \(\lambda\) which makes surfaces effectively homogeneous, and further makes them isotropic.
# Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications
## 3 Intracellular Micromanipulation with Computational Image Information
### Depth Acquisition  
Intracellular manipulation tasks are tedious and time consuming, so they are difficult to be accomplished with a high successful operation rate and low operation damage even when they are performed by skillful operators. In many micromanipulations, manipulation tools should be aligned with the height plane of the targeted specimens at the beginning of experiments. However, manual manipulation becomes unreliable, especially small cells or intracellular organelles with irregular forms. Some advanced image processing strategies that can acquire depth information and automatically adjust the height of manipulators or specimens have been proposed in the literature. Template matching and autofocusing are the two main methods, which will be discussed in the following sections.
## 3 System Architecture and Design of Intelligent Reflecting Surface (IRS)
### Phase Tuning Mechanism  
The incident signals are steered toward the user by manipulating the phase of each unit element of the IRS [24]. Initially, MSs were designed for fixed tasks at specific frequencies and required a redesign and refabrication to operate at a different frequency range. However, presently MSs have evolved to a greater extent, and they are able to respond to a software-controlled stimuli. This software-controlled stimuli will be able to adjust the EM waves toward a desired direction without requiring a redesign of the tuning mechanism for different frequency or functionality [86; 87]. External stimuli such as electric, magnetic,  
Figure 4: Architecture of an IRS.  
light or thermal stimuli when applied induce a change in the MS functionality that can be referred to as the global tuning mechanism [88]. Local tuning occurs at the unit cell by integrating biased diodes into the individual unit cell elements to turn unit cells locally and enable them to reconfigure and modify functionality [85]. Moreover, electrically controlled binary-phased tuning or continuous reactant mechanisms by using diodes or varactors is a reliable tuning approach [89]. An IRS has a a wide range of tunable functions such as perfect absorption, anomalous reflection, beam shaping and beam steering [24].
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 4 Simulation and Results
### Comparison with Virtual Structure Approach (\(k=1\))
This makes it suitable for situations when formation patterns need to be reconfigured frequently.
# Synthesis of Oligomeric Silicone Surfactant and its Interfacial Properties
## 1 Introduction
However, the modification of polyether trisiloxanes via sulfonation of the epoxy group exhibits excellent chemical stability, such as acid resistance, alkali resistance, and salt resistance, but also exhibits a short siloxane chain, as well as being expensive and employing toxic chloroplatinic acid as a catalyst in the hydrosilation reaction [19]. The phosphate fluorosilicone surfactant synthesized by hydrosiylation and esterification of hydrogen-containing fluorine polysiloxane, polyethylene glycol monoallyl ether, and phosphoric acid displays excellent low surface tension, critical micelle concentration, and a good resistance to acid, alkali, and salt. When hydrosilicone oil is the raw material, the molecular weight of synthetic surfactant is very high, but it also uses the expensive and toxic chloroplatinic acid catalyst [20]. Siloxane surfactants have been rarely applied to the development of oil and gas reservoirs, despite their wide and extensive industrial usage. Current studies on silicone surfactants mainly focus on low and high molecular copolymers, such as trisiloxane and modified silicone oil surfactants. In this work, a newly designed oligomeric surfactant bearing a siloxane spacer and sulfonic group was synthesized. The surfactant properties necessary in low permeability gas-condensate reservoirs development were investigated, including surface tension, wetting ability, and foaming property. To the best of our knowledge, our study is only one of a rare few that examined the interfacial properties of oligomeric silicone surfactant for the development of gas reservoirs. Our accumulated results provide a theoretical basis for expanding the application of silicone surfactants in low-permeability condensate reservoirs.
## 2 Radio Planning Aspects
### Propagation Model  
Many propagation models, such as the Longley-Rice [16], Bullington [17], Vogler [18], Luebbers [19], and Deygout [20] models, have been used for UHF band radio planning in rural environments in addition to the recommendations of the Radiocommunication Sector of the International Telecommunication Union (ITU-R) [8; 9]. All these models estimate the mean value of the received signal in each cell of the DTM, taking into account the terrain profile between the transmitter and the receiver.  
In the radio planning tool used, propagation loss is evaluated for each terrain profile by:  
\[L\left(\mathrm{dB}\right)=L_{0}+\mathrm{L_{Terrain\; irregularities}}+L_{\mathrm{ Close\;environment}} \tag{4}\]  
where \(L_{0}\) is the free space loss, that can be calculated by:  
\[L_{0}\left(\mathrm{dB}\right)=33.44+20\;\mathrm{log}f\left(\mathrm{MHz} \right)+20\;\mathrm{log}\;d\left(\mathrm{km}\right) \tag{5}\]  
\(L_{\mathrm{Terrain\; irregularities}}\) is the diffraction loss caused by obstruction of the terrain. For rural environments, this propagation loss can be estimated using the recommendation ITU-R P.526 [8]. \(L_{\mathrm{Close\;environment}}\) is the propagation loss taking into account the multipath phenomenon. This is directly related with the morphography in the vicinity of the mobile. In the GIS, a raster was created, with a resolution of 100 \(\times\) 100 m, in which each cell has a value (in dB) representing the loss due to the type of environment (rural pine forests, suburban environment and urban environment). The Hata model [10] was used to estimate the value for suburban and urban environments. A measurement campaign, for the TETRA system (400 MHz), consisting of five routes, was carried out by the authors within a prototype--which included three base stations--to estimate the path loss for the rural (pine forest) environment. In Table 1, the obtained values for the TETRA system are shown. Moreover, in this table, the values for the LTE system (700 MHz), in which the losses for the rural environment were estimated by considering ITU-R P.833 [21], are also presented. These values were applied to the simulations performed in this study. Figure 4 shows a comparison between the propagation model and the measurements in one of the routes. Table 2 gives the mean error and standard deviation of the difference between the propagation model and the measurements for each route.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Environment** & \
\begin{tabular}{c} \(\boldsymbol{L_{\mathrm{Close\;environment}}\left(\mathrm{dB}\right)}\) \\ **(400 MHz, TETRA)** \\ \end{tabular} &
\begin{tabular}{c} \(\boldsymbol{L_{\mathrm{Close\;environment}}\left(\mathrm{dB}\right)}\) \\ **(700 MHz, LTE)** \\ \end{tabular} \\ \hline No value & 0 & 0 \\ Rural (pine forest) & 10 & 14.5 \\ Suburban & 13.75 & 13.76 \\ Urban & 25.65 & 27.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Obtained path loss for the different types of environments considered.
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 4 Evaluation Metrics and Results  
In this section, we present different scenarios with different obstacle shapes along the drone's path. To evaluate the performance of the work, we rely on the following metrics: the energy consumption, completion time, path length, and turning angles.
## 4 Experimentation and Results
### Result of Product Detection
#### 4.3.2 Tracking Customer with Deep SORT  
This section will present an algorithm that uses the DeepSORT approach to perform personalized feature tracking across image sequences. This model relies on object detection performed using the YOLOv5 model and then uses the DeepSORT algorithm for tracking.  
Figure 9 demonstrates the tracking results of the DeepSORT algorithm implemented in a store video. The video frame sequence shows how DeepSORT successfully tracked over the observed period, preserving consistent IDs for the tracked objects even as they moved throughout the store.  
Figure 8: Confusion matrix for evaluating YOLOv5 detections.  
Figure 9: Using DeepSORT algorithm in a store.
###### Abstract  
Cadmium (Cd) is one of the most toxic metals accumulated in wheat grains. Daily intake of Cd through food is posing serious health problems. There is an urgent need to reduce the uptake and accumulation of Cd in wheat and other cereal crops. In this study, we investigated the potential of moringa leaf extract (MLE) in decreasing the Cd toxic effects in wheat (_Triticum aestivum_ L.) cv. A.S. 2002. Sowing was carried out in pots under shade and natural light conditions. Two Cd concentrations (500 uM and 1000 uM) were applied with and without MLE (0 and 3%). Results revealed that plant growth parameters viz diameter of stem, number of leaves, leaf area, photosynthetic pigments were improved with MLE application under Cd stress. Moreover, biochemical attributes and osmolytes such as total soluble sugars, and soluble proteins, phenolic content and flavonoids were negatively effected by Cd stress and were improved under MLE treatment. Moreover, Cd stress enhanced phytotoxicity as higher ROS accumulation is observed under both stress conditions, whereas after MLE application MDA and H2O2 accumulation was significantly decreased. Based on current observations, MLE was effective in mitigating the biological and toxic effects of Cd by promoting the growth attributes and mineral contents in wheat. Further experiments are needed for discovering the molecular mechanisms underlying MLE and host plant interactions involved in Cd stress mitigation.  
cadmium; wheat; antioxidant; morphological; biochemical; light; oxidative stress +
Footnote †: journal: Applied Sciences
###### Abstract  
Titan yellow (TY), a triazene azo dye, was removed from contaminated wastewater samples using a green adsorbent recycled from _Aloe vera_ leaves (AV) waste. Two adsorbents were developed--air-dried _Aloe vera_ (ADAV) and thermally treated _Aloe vera_ (TTAV). Adsorption efficacy of both adsorbents was assessed in terms of percent removal (%R) of TY and adsorption capacity (\(q_{c}\)). ADAV had a better performance compared to TTAV. Plackett-Burman design (PBD) was exploited to establish the experimental pattern of the study. Four variables were studied: pH, adsorbent dose (AD), dye concentration (DC), and stirring time (ST). Analysis of variance (ANOVA) at 95.0 confidence interval (CI), control, and quality charts helped establish regression model(s). Characterization of both adsorbents was performed using FT-IR/Raman spectroscopy together with TGA/\(dT\)GA and SEM/energy dispersive X-ray spectroscopy (EDX) analyses. Textural properties were determined using nitrogen adsorption isotherms at 77 K. Results showed that the surface areas of ADAV and TTAV300 were 3.940 and 7.076 m\({}^{2}\)/g, respectively. Raman analysis showed that the TTAV had clear D- and G-bands. Equilibrium studies revealed that data were well fitted to Freundlich isotherm with a maximum adsorption capacity of 55.25 mg/g using Langmuir equation, and the adsorption was physisorption. Adsorption followed a pseudo-second order that occurred in two steps--diffusion and then adsorption.  
Aloe vera(AV) leaves; Plackett-Burman Design (PBD); Titan yellow (TY); %removal and adsorption capacity (\(q_{c}\)); characterization; equilibrium; kinetics
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 3 Proposed WMSCCN for Fault Diagnosis
### AdaBN Algorithm  
AdaBN [29] is a BN-based domain adaptive algorithm. The algorithm is based on the following assumptions: the information related to the sample category label is determined by the weight of each layer and the information related to the sample domain label is represented by the BN layer statistics. AdaBN transforms the traditional BN to make the source and target domain statistics independent at the BN layer, and the remaining network parameters are still shared between the source domain and target domain.  
The AdaBN algorithm was further introduced to enhance the adaptability of WMSCCN in the case of noise and load changes. The WMSCCN algorithm based on AdaBN first trains the WMSCCN model with training samples until the training is completed. If the field distributions of the training samples and the test samples are inconsistent, some parameters of the model need to be adjusted. Specifically, the mean and variance of all the BN layers are replaced with the mean and variance of the test set, and other network parameters remain unchanged. The fault diagnosis of the test set is carried out on the improved model. The WMSCCN algorithm based on AdaBN is shown as Algorithm 1, where \(\gamma_{i}\) and \(\beta_{i}\) are the scaling and translation parameters that have been trained for the neuron \(i\) on the BN layer in the WMSCCN model.  
```
for neuron \(i\) of BN layer in WMSCCN do  Concatenate neuron response on target domain \(k\): \(x_{i}=[\ldots,x_{i}(m),\ldots]\)  Compute the mean of the target domain \(k\): \(\mu_{i}^{k}=\mathbf{E}\big{(}\chi_{i}^{k}\big{)}\),  Compute the standard deviation of the target domain \(k\): \(\sigma_{i}^{k}=\sqrt{\mathbf{var}\big{(}\chi_{i}^{k}\big{)}}\) endfor for neuron \(i\) of BN layer in WMSCCN, testing sample \(m\) in target domain \(k\)do  Compute BN output \(y_{i}(m)=\gamma_{i}\frac{x(m)-\mu_{i}^{k}}{\sigma_{i}^{k}}+\beta_{i}\) endfor
```  
**Algorithm 1** WMSCCN algorithm based on AdaBN