esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model of the Hydrocooler
### Solution of Mathematical Model  
The weight of litchi precooling for the hydrocooler is dependent on the current situation of the orchard. According to Equation (1) and physical parameters of litchi, 1000 kg litchi precooling required thermal energy capacity \(Q_{lit}\) of 8.1 \(\times\) 10\({}^{7}\) J. Solving Equations (2)-(7), the TES capacity \(Q_{cltr}\) of the TES tank was 2.4 \(\times\) 10\({}^{8}\) J, and the ice volume reached 256 kg.  
The relationship between ice thickness and heat transfer coefficient can be obtained according to Equations (10)-(14), as shown in Figure 3. The thermal resistance increases while the heat transfer coefficient decreases with the increase in ice thickness. The total heat transfer coefficient is 17.875 W m\({}^{-2}\) K\({}^{-1}\), and the total thermal resistance is 0.056 m\({}^{2}\) K W\({}^{-1}\) when ice is not formed. The heat transfer coefficient decreases rapidly with ice thickness when the ice thickness is less than 40 mm. The heat transfer coefficient decreases significantly with ice thickness when the thickness is greater than 40 mm. The total thermal resistance is 0.143 m\({}^{2}\) K W\({}^{-1}\), and the heat transfer coefficient is 6.995 W m\({}^{-2}\) K\({}^{-1}\), which is 60.9% lower than that without ice when the ice thickness is 40 mm. Once the ice thickness exceeds 120 mm, the heat transfer coefficient reduces slowly.  
Given the thermal resistance analysis of the ice-on-coil, the best ice thickness was considered to be 40 mm. According to Equation (9), the evaporation coil length was 39.5 m. If using a single coil with 39.5 m, excessive coil length would increase the superheat, reduce the return air pressure, and cause the suction gas of the compressor to overheat and produce low refrigerant efficiency of refrigeration system. According to Equation (15), the refrigerating capacity of the compression should reach 5900 W in order to meet the TES demand in 12 h. Therefore, the cooling was divided into two refrigeration units, and the refrigerating capacity of each refrigeration system should reach 2950 W. From Equation (18), the condensation heat transfer area of the condenser is calculated to be greater than 11.3 m\({}^{2}\) for a single refrigeration unit. Solving for Equation (19) determines that the evaporation heat transfer area of the evaporator should be greater than 0.84 m\({}^{2}\) for a single refrigeration unit. The length of the evaporation coil was 19.75 m of a single refrigeration unit, so the outer surface area of the evaporation coil of a single refrigeration unit was 1 m\({}^{2}\), which met the requirements of evaporation heat transfer area. The solutions of equations and determination of ice thickness are summarised in Table 2.  
Figure 3: The variation of the total thermal resistance and heat transfer coefficient with the ice thickness.
applied sciences  
_De Novo_ Transcriptome Analysis of the Lizard Fish (_Saurida elongata_): Novel Insights into Genes Related to Sex Differentiation  
[PERSON] 1,2,3,4, [PERSON] 1,2,3, [PERSON] 1,2,3,4, [PERSON] 1,2,3,4,6, [PERSON] 1,2,3, [PERSON] 1,2,3,4,* and [PERSON] 5  
Key Laboratory of Marine Ranching, Ministry of Agriculture Rural Affairs, Guangzhou S10300, China  
Guangdong Provincial Key Laboratory of Fishery Ecology and Environment, Guangzhou 510300, China  
South China Sea Fisheries Research Institute, Chinese Academy of Fisheries Sciences, Guangzhou 510300, China  
Scientific Observation and Research Field Station of Pearl River Estuary Ecosystem, Guangzhou 510300, China  
Shenzhen Fisheries Development Research Center, Shenzhen 518067, China  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-020-8910-0850  
**Featured Application: In our study, transcriptomes of different genders of lizardfish (_Saurida elongata_) were compared. Interesting findings about sex-related genes for putative future aquaculture applications are reported here. We provide a transcriptome dataset of _S. elongata_ that will be valuable for further research into the reproductive biology of _S. elongata_ and other teleost fishes.**  
Among vertebrates, teleost fishes exhibit the largest array of sex-determining systems, resulting in many reproductive strategies. Screening these fish for sex-related genes could enhance our understanding of sexual differentiation. The lizardfish, _Saurida elongata_([PERSON], 1846), is a commercially important marine fish in tropical and subtropical seas of the northwest Pacific. However, little genomic information on _S. elongata_ is available. In this study, the transcriptomes of three female and three male _S. elongata_ were sequenced. A total of 49.19 million raw read pairs were generated. After identification and assembly, a total of 59,902 nonredundant unigenes were obtained with an N50 length of 2070 bp. Then, 38,016 unigenes (63.47% of the total) were successfully annotated through multiple public databases. A comparison of the unigenes of different sexes of _S. elongata_ revealed that 22,507 unigenes (10,419 up-regulated in a female and 12,088 up-regulated in a male) were differentially expressed between sexes. Then, numerous candidate sex-related genes were identified, including _dmrt2_, _dmrt4_, _foxl2_, _zps_ and _starts_. Furthermore, 23,941 simple sequence repeats (SSRs) were detected in SSR-containing sequences. This informative transcriptome analysis provides valuable data to increase the genomic resources of _S. elongata_.  
demersal fish; synodontidae; RNA-seq; gene ontology; differentially expressed genes
# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 4 Results and Discussion
### Experiment 1 and Results  
Experiment 1 used GIoU, DIoU and CIoU to calculate the regression loss of the prediction bounding box, and the obtained models were named YOLOv4-GIoU, YOLOv4-DIoU and YOLOv4-CIoU, respectively. The loss curves of the three models during the training process are shown in Figure 10. Due to the large loss function values in the earlier period, the figure shows the loss function value starting from the 30 th Epoch. It can be seen from the loss curve that the loss value of YOLOv4-DIoU and YOLOv4-CIoU fluctuates relatively little during the training process, and the convergence speed is relatively fast. The loss value of YOLOv4-GIoU fluctuates relatively large.  
Table 4 shows the mAP of each model on the test set and the AP value for each defect class. As shown in Table 4, YOLOv4-CIoU achieves the highest AP value for crack detection among all models, and YOLOv4-DIoU achieves the highest AP value for other defects, which means that YOLOv4-DIoU has higher performance for most classes. YOLOv4-GIoU has the worst detection performance, with lower AP values for all classes than the other models. It can be found that YOLOv4-DIoU has achieved the highest mAP of 87.9%, but the difference with YOLOv4-CIoU is very small, only 0.22%.  
The experimental results show that both YOLOv4-DIoU and YOLOv4-CIoU have excellent detection capability for sewer pipe defects. As DIoU and CIoU consider the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{4}{c}{**AP\({}_{\textbf{50}}\) (\%)**} \\ \cline{2-6}  & & **Crack** & **Deposition** & **Root** & **Stagger** \\ \hline YOLOv4-GIoU & 85.5 & 79.1 & 84.3 & 91.5 & 87.0 \\ YOLOv4-DIoU & 87.9 & 80.8 & 87.9 & 91.8 & 91.0 \\ YOLOv4-CIoU & 87.7 & 83.5 & 86.6 & 91.6 & 89.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mAP and AP of each model for each defect class.  
Figure 10: The loss curves for different models during training process.  
distance between the predicted bounding box and ground truth, they can directly minimize the distance between two boxes, which makes the model have faster convergence speed and higher detection accuracy. Due to the comparable performance of YOLOv4-DIoU and YOLOv4-CIoU, these two models are selected for further study in Experiment 2.
# A Contrastive Learning Method for the Visual Representation of 3D Point Clouds
## 3 Experiment and Results
### Part Segmentation Performance  
Part segmentation is a fine-grained, point-by-point classification task. Its goal is to predict the part category label of each point in a given shape. The data set used in this article is ShapeNetParts, which contains 16,881 objects in 16 categories, which are divided into 12,149 training objects, 2874 test objects, and 1858 verification objects. Each object consists of 2 to 6 parts, and there are a total of 50 different parts in all categories.  
This article uses mIoU as the evaluation criterion for part segmentation. For each shape of category A, the mIoU of the shape is calculated. For each component type in category A, the IoU between the real and predicted is calculated. Then, the average IoU for all component types in category A to obtain the mIoU of the shape is calculated. In order to calculate the mIoU of a category, this paper takes the average of the mIoU of all the shapes in the category.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline LatentGAN & 85.3 \\ FoldingNet & 88.1 \\ PointCapsNet & 89.3 \\ MulUnsupervised & 94.2 \\ Ours & 96.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ShapeNetParts classification accuracy of 20% of the training data.  
Figure 6: SVM classification accuracy and the number of labeled samples.  
The method in this paper is used to pre-train the network in ShapeNetCore, randomly sample 1% and 5% of ShapeNetParts' training samples, copy the digital capsule layer of the self-attention capsule network \(f(\cdot)\) 32 times to obtain 2048 point features (at each point, four layers of MLP (2048, 4096, 1024, and 50) on the features are trained), and evaluate the test data. Figure 7 shows the visualized results of part segmentation.  
In order to compare the superiority of the classification performance of the proposed method, the unsupervised methods SO-Net [19], PointCapsNet [20], and MulUnsupervised [1] are compared. The results in Table 3 show that when 1% and 5% of the training data are used for components segmentation, the model is better than the best comparison method, indicating that the model still has a very good part segmentation performance on a small amount of labeled data. Table 4 shows the comparison results of the part segmentation between this model and the supervised learning model. The results show that the mIoU achieved by this model is 2.2% smaller than the classic method, PointNet, and 6.2% smaller than the best supervised model, thus narrowing the gap with the supervised model.
# Conclusive Model-Fit Current-Voltage Characteristic Curves with Kink Effects
## 2 Preparation of as Measured Data and Fitting
### The Kink Effect
The space-time variables are adjusted with respect to the referencing frame as follows:  
\(\varphi_{e}(x,t)=f_{e}(x-vt)=f_{e}(\xi)\)  
_where_  
\(\xi\equiv x-vt\)  
Equation (4) thus reduces to the following form, with b as a dimensional adjusting parameter:  
\[\frac{\partial^{2}\varphi_{e}}{\partial\xi^{2}}-ab^{-1}(1-\frac{v^{2}}{c^{2} })^{-1}\sin b\varphi_{e}=0 \tag{5}\]  
The non-linear solution of Equation (5) is expressed as:  
\(f_{e}(\xi)=\frac{4}{b}\rm arctan\acute{e}^{\pm 7\xi}\)  
_where_  
\(\gamma=\sqrt{a}(1-\frac{v^{2}}{c^{2}})^{-1/2}\)  
If the nonlinear solution y = \(\rm arctan[exp(t/2)]\) versus t looks like \(\rm I_{DS}-V_{DS}\) characteristic curves of transistors, such as MOSFET or Bipolar Junction Transistor (BJT), the slopes of the curves result in speed variations in the carriers that would cause heat dissipation problems, because the slope maximum is equivalent to the speed maximum that causes extra heat. Referring to the \(\rm I_{DS}\) derivative to the \(\rm V_{DS}\) with the \(\rm V_{CS}\) fixed, the slope means the speed variations in carriers, which may cause a heat dissipation issue and thus a corresponding mini-scale impact on adjusting the speeds of the carriers microscopically. The peak of the derivatives is always at the center, meaning that the maximum changing pace leaves heat radiation, called Bremsstrahlung radiation, or braking radiation. Some other similar examples like synchrotron radiation may happen. These radiations causing the corresponding current corrections are intrinsically proportional to the slope (the peak, which is proven to be Gaussian-like). \[ \begin{array}{l}\frac{df(\zeta)}{d\zeta}=\frac{(4/b)\gamma\epsilon^{\gamma \zeta}}{1+e^{\gamma\zeta}}=\frac{(4/b)\gamma}{e^{\gamma\zeta}+e^{-\gamma\zeta}} \\ \rightarrow(2/b)\gamma(1-\frac{\gamma^{2}\zeta^{2}}{2})\rightarrow(2/b)\gamma \exp[-\frac{\gamma^{2}\zeta^{2}}{2}]\end{array}  \tag{7}\]  
Therefore, the variations in \(\mathrm{I_{DS}}\) in Figure 3a are easier to modify by using the above Gaussian form, followed by Figure 3b with the minimum delta reduced. The final fitting results are shown in Figure 3c. Figure 2: (**a**) \(\mathrm{I_{DS}}\)-\(\mathrm{V_{DS}}\) characteristic curves and the corresponding fitting with minimum delta (8) skill in the following Equation (3) (**b**) Nonlinear solution \(\mathrm{y}=\arctan[\exp(\mathrm{t}/2)]\) versus \(\mathrm{t}\) looks like \(\mathrm{I_{DS}}\)-\(\mathrm{V_{DS}}\) characteristic curves with slope variations that cause heat dissipation problems because the slope maximum is equivalent to the speed maximum variation (acceleration) that causes extra heat. Bremsstrahlung radiation or braking radiation is then taken into account.
# Long-Period Grating with Asymmetrical Modulation for Curvature Sensing
## 2 Sensor Fabrication and Principle
Based on this theory, electric or magnetic fields in the fiber can be expressed by a superposition of local modes and radiation field, and the core mode is partially coupled into the individual cladding modes and the radiation  
Figure 3: Evolution of the grating spectra of the LPFG during the fabrication process with offset of (**a**) 0 \(\upmu\)m, (**b**) 3 \(\upmu\)m, and (**c**) 6 \(\upmu\)m.  
modes when light propagates through the nonuniform waveguide section. The coupling behavior between the core mode and the cladding mode can be expressed by the following equations [21]:  
\[ \begin{array}{c}\frac{\mathrm{d}b_{\mathrm{co}}}{\mathrm{d}z}-i\beta_{\mathrm{ co}}b_{\mathrm{co}}=C(z)\cdot b_{\mathrm{cl}}\\ \frac{\mathrm{d}c_{\mathrm{cl}}}{\mathrm{d}z}-i\beta_{\mathrm{cl}}b_{\mathrm{cl }}=C^{*}(z)\cdot b_{\mathrm{co}}\end{array}  \tag{1}\]  
where \(b\) represents the magnetic or electric field of a mode, and \(\beta\) is the propagation constant. The resonant wavelength \(\lambda\) can be described as follows:  
\[\lambda=(n_{co}^{eff}-n_{cl}^{eff})\Lambda \tag{2}\]  
where \(n_{co}\)(\({}^{\prime}\)) and \(n_{cl}\)(\({}^{\prime}\)) are the effective indices of the core and cladding modes, respectively. The transmitted core mode is characterized by \(\cos(\kappa L)\), where \(L\) is the grating length and \(\kappa\) is the coupling coefficient, defined by [22]:  
\[\kappa=k_{0}\iint_{-\infty}^{+\infty}\delta n|F_{co}(x,y)||F_{cl}(x,y)|dxdy \tag{3}\]  
where \(\delta n\) is RI perturbation, \(F_{\mathrm{co}}(x\),\(y)\) and \(F_{\mathrm{cl}}(x\),\(y)\) are the transverse mode profiles of the core mode, and \(k_{0}\) is the wave number. The coupling coefficient is influenced by the overlap of the mode profiles of the core and the cladding mode. For the LPFG fabricated in this work, the RI modulation is asymmetrically distributed, predominately in cladding; as a result, circularly asymmetric mode coupling is induced. In the sample with smaller offset, the overlap between core mode and the asymmetric cladding mode is larger; thus, the coupling efficiency is higher, and the grating grows faster in the fabrication process. Otherwise, the coupling coefficient is lower, and grating grows slower.
## 6 Baseline Statement and AAMO Approach Results
### Baseline Statement
#### 6.3.1 Collected Data  
In order to conduct the assessment and comparison stated above, at least ten realistic software-in-the-loop simulations were executed on the _Maze_ scenario presented in Figure 10. All collected data is presented in Table 4 and are organised obeying the following scheme. The columns refer to (from left to right): figure of merits (FM); approaches, where \(Y\) and _MP_ stand for _Yamauchi_ and _MinPos_, respectively; and the fleet size \(|R|\). In each fleet size, the average _AVE_ and standard deviation _StD_ values are registered.
# Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems
## 5 Computational Complexity  
The high computational cost of data decoding is a major challenge in today's advanced MC systems, often viewed as a trade-off for performance enhancement. To address this, we analyze the decoding complexity of the proposed DL-based MC schemes by measuring the decoding runtime per sample and comparing it with baseline models included in Figure 9. The transmitter complexity of our schemes is minimal compared to the receiver since the proposed encoders require only a single linear fully connected layer. The decoding complexity of the proposed MC-AE and the baselines, as shown in Figure 9, is compared in Table 2, where the runtime is expressed in milliseconds (ms). Our proposed model achieves a runtime of 0.049 ms per sample. In comparison, the runtimes for S-OFDM and OFDM-IM are 0.121 ms and 0.107 ms per sample, respectively, which are significantly higher than our proposed model. Additionally, we have calculated the training time per sample for different training SNRs to further evaluate the system's efficiency, as presented in Table 3. From the table, it is evident that the runtime shows slight variations with changes in the training SNR value. While our model requires slightly more runtime than the OFDM and DNN-based MC-AE systems, the results clearly indicate that our scheme not only offers higher reliability but also benefits from lower computational complexity.  
During the inference (decoding) phase of the MC-AE model, computational costs are minimized through several optimizations. The channel layer, which simulates noise and fading, leverages batch operations in TensorFlow to efficiently compute the effects of the noisy channel with parallel processing. The decoder, which uses GRU layers, is less resource-intensive, helping reduce latency. Additionally, GPU acceleration further improves performance. For class prediction, the use of \(argmax\) efficiently maps probabilities to the predicted modulation classes, minimizing post-processing time.  
Figure 12: The SE and EE performance of the proposed GRU-based MC-AE system for configurations \((N,M)\) = (4, 16), (4, 32), (4, 64) across SNR values. (**a**) SE performance. (**b**) EE performance.
## 4 Experiments and Results
### Experimental Setup
#### 4.1.2 Evaluation Metrics  
The performance of our model is measured in terms of accuracy, precision, confusion matrix, and AUC (area under the receiver operating characteristic curve). Accuracy, the  
Figure 4: The FC layer for emotion prediction.  
Figure 3: The architecture of the GRU unit. It has two doors: reset door \(r_{t}\) and update door \(z_{t}\).  
most common evaluation metric in classification tasks, calculates the proportion of correctly classified samples to the total number of samples. However, it does not perform well on unbalanced data, so precision is also introduced to evaluate the model's performance. Precision is the proportion of the true-positive samples to the total number of samples predicted as positive.  
The confusion matrix is an n \(\times\) n matrix, where n is the number of categories for classification. The column labels represent the true category, and the row labels represent the predicted category. Each value in the confusion matrix denotes the probability (or the number of samples) of one category that is predicted as another specified class. It is helpful for understanding the performance of the classification models for each category of samples.  
The AUC is a performance metric for classification tasks under different thresholds. It is calculated based on the relationship between the true-positive rate (\(TPR\)) and the false-positive rate (\(FPR\)) at different classification thresholds. The calculations of the TPR and FPR are given as follows:  
\[TPR=\frac{TP}{TP+FN},FPR=\frac{FP}{FP+TN} \tag{10}\]  
where \(TP\) stands for true positive, which represents the number of positive instances correctly predicted as positive by the classification model. Similarly, \(FN\) stands for false negative, \(FP\) stands for false positive, and \(TN\) stands for true negative. The value of the AUC ranges from 0.5 to 1. The closer the value is to 1, the better the performance of the classification model. The AUC provides a comprehensive measure of its classification ability by considering the model's performance across different thresholds. Therefore, our study employed both the AUC and the confusion matrix, along with accuracy and precision, as evaluation metrics.
# Saturation-Based Airlight Color Restoration of Hazy Images
###### Abstract  
Typically, images captured in adverse weather conditions such as haze or smog exhibit light gray or white color on screen; therefore, existing hazy image restoration studies have performed dehazing under the same assumption. However, hazy images captured under actual weather conditions tend to change color because of various environmental factors such as dust, chemical substances, sea, and lighting. Color-shifted hazy images have hindered accurate color perception of the images, and due to the dark haze color, they have worsened visibility compared to conventional hazy images. Therefore, various color correction-based dehazing algorithms have recently been implemented to restore colorcast images. However, existing color restoration studies are limited in that they struggle to distinguish between haze and objects, particularly when haze veils and images have a similar color or when objects with a high saturation value occupy a significant portion of the scene, resulting in overly grayish images and distorted colors. Therefore, we propose a saturation-based dehazing method that extracts only the hue of the cast airlight and preserves the information of the object. First, the proposed color correction method uses a dominant color extraction method for the clustering of CIELAB(LAB) color images and then assigns area scores to the classified clusters. Sorting of the airlight areas is performed using the area score, and gray world-based white balance is performed by extracting the hue of the area. Finally, the saturation of the restored image is used to separate and process the distant objects and airlight, and dehazing is performed by applying a weighting value to the depth map based on the average luminance. Our color restoration method prevents excessive gray tone and color distortion. In particular, the proposed dehazing method improves upon existing issues where near-field information is lost and noise is introduced in the far field as visibility improves.  
dehazing; LAB; haze; color cast +
Footnote †: journal: Medical Image Analysis  
1
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))
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs  
This section presents a survey on routing schemes in FANETs, and a summary of the routing schemes is shown in Table 4. Based on the frameworks presented in Section 4, the routing schemes are segregated into four categories, namely adaptive, proactive, reactive, and hybrid routing schemes.