esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion
### Comparison with Deep Transfer Learning Models  
In addition to machine and deep learning models, a performance comparison was also carried out using transfer learning approaches. Visual Geometry Group (VGG-16) and AlexNet are the two deep neural networks employed in this study. The VGG-16 is based on convolution, connected, pooling, and padding layers, while AlexNet is also based on CNN and has millions of parameters. Table 9 shows a comparison of the performances of CNN, VGG-16, and AlexNet. The CNN model shows better performance, but precision, recall, and F1 scores of VGG-16 are also good.  
Table 10 shows the comparison of time needed for training the transfer learning models and the proposed CNN model to analyze their computational complexity. The training time of the proposed model is less than those of transfer learning models, which shows its efficiency in terms of time and accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & _Accuracy_ & _Precision_ & _Recall_ & _F1 Score_ \\ \hline CNN & 0.9398 & 0.95 & 0.95 & 0.95 \\ MLP & 0.9120 & 0.94 & 0.94 & 0.94 \\ RNN & 0.9100 & 0.89 & 0.91 & 0.90 \\ LSTM & 0.9691 & 0.93 & 0.93 & 0.93 \\ Random Forest (RF) without SMOTE [59] & 0.8889 & 0.89 & 0.89 & 0.89 \\ ETC with SMOTE [59] & 0.9262 & 0.93 & 0.93 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of deep learning models [59].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Accuracy** \\ \hline DT [59] & 0.8778 \\ AdaBoost [59] & 0.8852 \\ LR [59] & 0.8442 \\ SGD [59] & 0.5491 \\ RF [59] & 0.9188 \\ GBM [59] & 0.8852 \\ ETC [59] & 0.9262 \\ GNB [59] & 0.7540 \\ SVM [59] & 0.7622 \\ RNN & 0.9100 \\ LSTM & 0.9691 \\ MLP & 0.9120 \\ CNN & 0.9398 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy comparison of machine learning and deep learning models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & _Accuracy_ & _Precision_ & _Recall_ & _F1 Score_ \\ \hline VGG-16 & 0.9291 & 0.89 & 0.90 & 0.90 \\ AlexNet & 0.9170 & 0.89 & 0.89 & 0.89 \\ CNN & 0.9398 & 0.95 & 0.95 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of transfer learning models with the proposed CNN.
# Shadow Removal for Enhanced Nighttime Driving Scene Generation
## 2 Background  
### Image-to-Image Translation  
I2I translation is a technique in computer vision where an input image is transformed to produce an output image that typically belongs to a different visual domain or adopts a new style. This transformation can modify several characteristics of the image, such as its color, texture, or overall appearance, while preserving the core content or spatial arrangement of the original one.  
In the field of I2I translation, there are models based on supervised, unsupervised, and semi-supervised learning approaches. Numerous studies [11; 12; 13; 14; 15; 16] have successfully employed unpaired I2I translation methods to transform images from one domain to another. Notably, supervised approaches, such as the widely recognized pix2 pix model [17], along with various other methods [18; 19; 20], have demonstrated effectiveness in preserving semantic meaning during image translation.  
Recently, several studies [21; 22; 23; 24; 25] have leveraged I2I translation models to augment data, aiming to enhance the robustness and generalization performance of object detection and segmentation models in challenging conditions, such as nighttime or adverse weather. For instance, [PERSON]. [23; 24] improved object detection performance by transforming daytime images into various adverse weather conditions. Additionally, [PERSON]. [21] proposed a two-branch architecture that utilizes both daytime and synthesized images, leading to enhanced object recognition performance. Similarly, [PERSON]. [22; 25] improved segmentation performance in foggy scenes through data augmentation, while [PERSON]. [26] employed a two stream network to enhance semantic segmentation performance under various nighttime and adverse weather conditions.  
In addition, numerous studies have focused on synthesizing images for specific weather conditions and times of day. Research aimed at synthesizing rain streaks has employed both GAN-based methods [27; 28; 29; 30] and physics-based methods [31; 32]. There are also studies focused on fog synthesis [33; 34], snow effect generation [35; 36], and transforming daytime images into different times of day and low-light conditions [37; 38].  
Moreover, several studies [39; 40; 41; 42] have proposed models capable of transforming images across multiple weather conditions and times of day within a single architecture. TSIT (Two-Stream Image-to-Image Translation) [40] utilized a two-stream network to effectively preserve the structure of the content image, minimizing distortions and maintaining semantic meaning, while CoMoGAN [42] achieved continuous transformation across various times of day. S2 ST (Seed-to-Seed Translation) [41] employed a diffusion model for image transformation.  
However, the outputs of these methods often exhibit noticeable artifacts when compared to real-world images. In particular, the presence of distinct shadows makes it easy to identify the synthesized images as artificial.
# State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM
### Contributions  
In light of the above, this paper proposes a lithium-ion battery State of Health (SOH) estimation method based on an incremental capacity (IC) curve analysis and a TCN-RVM framework.  
The main contributions of this work are as follows:  
(1) By considering multiple health indicator types, five computational health indicators are extracted from the IC curves during the charging process. The Pearson and Spearman correlation coefficients are employed to validate the correlations between the proposed health indicators and the actual SOH.  
(2) A novel lithium-ion battery SOH estimation method based on the TCN-RVM algorithm is proposed. This approach integrates the feature extraction capability of the Temporal Convolutional Network (TCN) with the Bayesian sparse learning advantages of the Relevance Vector Machine (RVM), achieving high-precision estimates of battery capacity degradation. The results demonstrate that this method provides accurate SOH predictions.  
(3) The accuracy and robustness of the proposed method are validated using the University of Maryland's open-source lithium-ion battery aging dataset and NASA's publicly available dataset. Under diverse aging conditions of lithium-ion batteries, the proposed method achieves mean absolute error (MAE) and root mean square error (RMSE) values not exceeding 0.77% and 1%, respectively, for the University of Maryland dataset, and MAE and RMSE values not exceeding 1.5% for the NASA dataset.
## 2 Microbial Induced Carbonate Precipitation by _Sporosarcina pasteurii_
### Qualitative Analysis of Mineralized Products
#### 2.1.2 XRD Analysis  
The sample was ground into powders with a mortar and screened with a 200-mesh sieve, then a test piece with a flat surface was created. The test piece was then analyzed using an Ultima IV X-ray diffractometer. The detailed parameters of XRD are shown in Table 2. The XRD spectrogram of the sample is shown in Figure 6.  
It can be seen from Figure 6 that the sample had two crystal forms. One crystal form exhibited characteristic diffraction peaks at 2\(\theta\) = 20.909\({}^{\circ}\), 24.873\({}^{\circ}\), 27.029\({}^{\circ}\), 32.713\({}^{\circ}\), 43.804\({}^{\circ}\), 49.015\({}^{\circ}\), 49.922\({}^{\circ}\), and 55.729\({}^{\circ}\), corresponding to the crystal planes (002), (100), (101), (102), (110), (110), (112), (104) and (202), respectively, which is consistent with the standard spectrum of vaterite (Mineral Power Diffraction File Data Book ICCD No. 33-268). The other crystal form had characteristic diffraction peaks at 2\(\theta\) = 23.059\({}^{\circ}\), 29.404\({}^{\circ}\), 35.980\({}^{\circ}\), 39.419\({}^{\circ}\), 43.171\({}^{\circ}\), 47.508\({}^{\circ}\), 48.513\({}^{\circ}\), 57.416\({}^{\circ}\) and 64.681\({}^{\circ}\), corresponding to the crystal planes (012), (104), (110), (113), (202), (018), (116), (122) and (300), respectively, which is consistent with the standard spectrum of calcite (Mineral Power Diffraction File Data Book ICCD No. 05-0586). Therefore, the mineralized products were mixed with vaterite and calcite crystals.
# A Survey of Machine Learning Methods for Time Series Prediction
## 4 Deep Learning Architectures
### Recurrent Neural Networks  
Recurrent Neural Networks (RNN) distinguish themselves through their unique feedback loop architecture, where outputs from previous timesteps are fed back into the network, creating an internal memory state that enables learning from past inputs. This recursive processing allows RNN models to incorporate historical information into current computations, making them particularly well-suited for time series analysis, where current predictions often depend heavily on historical patterns.  
Among RNN variants, Long Short-Term Memory (LSTM) networks have emerged as the most popular in the literature studied for this paper, largely due to their sophisticated gating mechanisms that help mitigate the vanishing gradient problem prevalent in general RNNs. LSTM models incorporate an input, forget, and output gate to regulate the information flow and enable the network to capture both long-term dependencies and short-term patterns effectively [87]. Variants such as Bidirectional LSTMs (Bi-LSTMs), which process sequences in both forward and backward directions, enhance the model's ability to leverage contextual information from both past and future timesteps. Another notable RNN variant is the Gated Recurrent Unit (GRU), which simplifies the LSTM architecture by combining the input and forget gates into a single update gate and introducing a reset gate. GRUs are computationally more efficient than LSTMs, while retaining a comparable performance in capturing sequence dependencies.
## 1 Introduction
### Dynamic Channel Allocation
In practically implementing the proposed network, the optimum ONU launch powers and the maximum achievable transmission performance/capacity should be adaptive to the actual network status. For the proposed network, the power budget can be considerably enhanced by: (1) all the techniques capable of significantly improving IMDD PON transmission performances, as mentioned in Section 3.3; (2) high-sensitivity avalanche photodiodes (APDs) adopted in the receiver [37]; (3) relatively high ONU launch powers [37]; and (4) optical preamplifiers prior to optical-electrical conversion in the receiver [37].
# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
###### Abstract  
Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.  
condition monitoring; vibrations; rotary machines; diagnostics; predictive maintenance
## 3 Results and Discussion
### Range  
In Section 3.2 reliable linearity was found in the concentration range of 0.3-1.5 mg/L DEHP. Additionally, in Section 3.4 the accuracy of the method was studied for spike concentrations of 0.3, 0.7, 1.0, and 1.5 mg/L in a real Ouzo sample; the results showed that there is no \"proportional\" and/or \"standard\" systematic error. Furthermore, the precision studies (Section 3.5) were performed on a real Ouzo sample, with a concentration of 1.0 mg/L DEHP. The results of the precision study showed that the relative standard deviations of repeatability and intermediate precision are reliable [7]. Therefore, we can safely define the working range of the method as the range 0.3-1.5 mg/L DEHP. In this region, there is reliable linearity, precision, and accuracy.
# A Palladium-Deposited Molybdenum Disulfide-Based Hydrogen Sensor at Room Temperature
## 2 Nanocomposite Materials  
Nanocomposites are the focus of extensive research due to their unique properties, including remarkable electrical, optical, chemical, and mechanical characteristics. Nanocomposites have demonstrated applications across a multitude of industries. Among the various 2D materials, MoS\({}_{2}\) has drawn particularly significant attention from researchers. MoS\({}_{2}\) exhibits exceptional properties, such as high carrier mobility, sensitivity to toxic gases, and a substantial surface-area-to-volume ratio [9; 10]. One of the main areas of exploration related to MoS\({}_{2}\) is the application of hydrogen sensor development. However, conventional MoS\({}_{2}\)-based hydrogen sensors have posed challenges due to their high operating temperatures. In response to this limitation, noble metals have emerged as valuable catalytic materials. Among the catalysts, Pd can effectively dissociate H\({}_{2}\) gas and then adsorb H\({}_{2}\) atoms onto the surface. Pd-induced catalysis significantly reduces the activation energy required for the chemical reactions associated with hydrogen detection. Consequently, Pd not only enhances the response time but also enables operation at lower temperatures, thus improving the overall efficiency and reliability of hydrogen sensors [11].
## 2 Methodology
### Calculation
#### 2.2.1 Energy Transfer in the Evacuated-Tube Solar Collector System  
The solar radiation absorbed by evacuated tubes absorber can be calculated by using Equation (1).  
\[\dot{Q}_{rad}=IA_{ETC} \tag{1}\]  
The heat transfer rate from the tubes' tips in the manifold to the heating medium can be calculated by Equation (2).  
\[\dot{Q}_{ETC}=\dot{m}c_{P}(T_{man}-T_{St})=hA_{s}\Delta T_{lm} \tag{2}\]  
Figure 1: Principle of an evacuated-tube solar collector.  
where \(\Delta T_{lm}\) is the log mean temperature difference between the tip and the heating medium calculated from Equation (3).  
\[\Delta T_{lm}=\frac{\left(T_{s}-T_{man}\right)-\left(T_{s}-T_{St}\right)}{\ln[ \left(T_{s}-T_{man}\right)/\left(T_{s}-T_{St}\right)]} \tag{3}\]  
Depending on the arrangement of the tips (20 rows in-line) as shown in Figure 2, the outlet temperature of the heating medium is calculated by Equation (4).  
\[T_{man}=T_{tip}-\left(T_{tip}-T_{St}\right)exp\left(\frac{-A_{tip}h}{\dot{m}c_{ p}}\right) \tag{4}\]  
where \(A_{tip}=N\pi DL\) is the outer surface area of the tips contacting with heating medium.  
Heat transfer coefficient (\(h\)) can be determined through Nusselt number as shown in Equation (5). The Nusselt number correlation followed the work of [PERSON] et al. [21].  
\[Nu=\frac{hD}{k}=\ CRe^{m}Pr^{n}(Pr/Pr_{s})^{0.25} \tag{5}\]  
where C = 0.9, \(m\) = 0.40 and \(n\) = 0.36 for \(0\leq\mathrm{Re}\leq 100\).  
The energy balance of the heating medium storage tank can be expressed as Equation (6). The heating medium temperature in the storage tank (\(T_{St}\)) is assumed to be equal to the inlet temperature of heating medium to manifold.  
\[\frac{dQ_{St}}{dt}=\dot{Q}_{ETC}-\dot{Q}_{loss} \tag{6}\]  
where \(Q_{St}\) and \(\dot{Q}_{loss}\) can be expressed as Equations (7) and (8), respectively.  
\[\frac{dQ_{St}}{dt}=\frac{\rho Vc_{p}dT_{St}}{dt} \tag{7}\]  
\[\dot{Q}_{loss}=UA_{T}(T_{St}-T_{a}) \tag{8}\]  
Therefore, the change in storage temperature can be calculated by Equation (9).  
\[\frac{\rho Vc_{p}dT_{St}}{dt}=\dot{m}c_{p}(T_{man}-T_{St})-UA_{St}(T_{St}-T_{a}) \tag{9}\]  
Finally, the efficiency of ETSC can be calculated by using Equation (10).  
\[\eta=\frac{\dot{m}c_{p}(T_{man}-T_{St})}{IA_{ETC}} \tag{10}\]
## 3 Experimental Data and Evaluation Methods
### Dataset  
After data preprocessing, in order to better train and evaluate the network, 211 sub-plots 25 m \(\times\) 25 m are randomly divided into a training set and validation set according to the ratio of 8:2, resulting in 169 plots as the training set (containing 1558 trees) and 42 sub-plots as the validation set (containing 382 trees). During training, the epoch of the extreme offset deep network is 50, the training time is about 2 h, the initial learning rate is set to 0.001, the RMSprop optimizer is used, and the smoothing constant alpha is set to 0.9. The StepLR method, which dynamically adjusts the learning rate, is used, where the learning rate adjustment factor gamma is set to 0.1.  
Experimental area 1, the confer forest in Washington, USA, is selected. In experimental area 2, a mixed forest in Bretten, Germany, is selected. The test set consists of four different plots, labeled U1, U2, G1, and G2, where U1 and U2 belong to the Blue Ridge region of the Congressional National Forest in western Washington. G1 and G2 belong to the mixed forest near Bretten, Germany. The four sample plots of the test set are shown in Figure 6.  
Refer Canopy Density (RCD) is used to indicate the canopy density among different plots. RCD is obtained by dividing the number of grids containing points by the total number of grids, as shown in Equation (9):  
\[\text{RCD}=\sum_{n}^{i}\frac{o_{i}}{n}, \tag{9}\]  
where RCD is used to indicate the canopy density among different plots; \(n\) is the total number of grids; \(o_{i}\) is the marking bit, if any points exist in grid \(i\), \(o_{i}=1\), otherwise \(o_{i}=0\).  
In U1, U2, G1, and G2, the number of individual trees is 127, 186, 108, and 261; RCD is 61.58%, 69.09%, 93.25%, and 96.40%, respectively. Due to the difference in vegetation distribution and different acquisition equipment in the two areas, the point cloud density is quite different. Therefore, it is representative that the sample plots in these two areas are selected to test the method. A detailed description of the data is shown in Table 1.
## 3 Results
### Observation of Applying Carbon Paper as Electrodes in VRFB
As previously stated, higher electrode layers increase contact resistance between electrode layers because electrons cannot transfer through conductive layers (Figure 10). The high current densities of the polarization curve for the three-layer electrode, known as the concentration overpotential region associated with a limiting current, displayed an unusual feature. The current density decreased as the cell potential decreased, owing to electrolyte depletion at a very high current density, which implies that the electrolyte ion pairs can efficiently react with each other, and the redox reaction rate of the cell with the three-layer electrode was higher than the reaction rate of the cell with the ten-layer electrode [54]. The ten-layer electrode is expected to have a highly active area, but the interpretation of the results can explain that using many layers of an electrode may reduce the porosity of the electrode, resulting in a reduction in the active area [55]. Another issue that should be investigated is electrolyte flow restriction; thus, experimental activity related to flow behavior from the flow channel through the electrode was demonstrated in this section (Figures 11-14). It should be noted that the electrolyte flowed naturally in this section of the work, with no driving pressure from a pump. The flow of electrolytes from horizontal feeding channels to a flow frame and the ten-layer electrode is depicted in Figure 11. The electrolyte did not completely flow through all ten layers of the electrode, resulting in an incomplete redox reaction. Figure 12 depicts the flow of electrolytes through an electrode in a horizontal flow battery. The electrolyte completely flowed through the flow frame and diffused into the electrode layers via a laminar flow pattern; however, the bottom four layers were discovered to be dry. Even when the electrolytes were flowing under the pump's driving pressure, the ten-layer electrode was not completely wet. Figure 10: Polarization curves obtained from the discharging processes with three-layer and ten-layer electrodes. Figure 14: (**a**) the schematic of cone flow channels and (**b**) electrolyte flow into the flow frame with flow channels. Figure 12: The schematic of electrolyte diffusing into electrode layers in a horizontal flow battery. Figure 13: (**a**) Vertical electrolyte feeding channel and (**b**) a snapshot of liquid flow into the flow frame and electrode layers. However, this flow channel is too small, resulting in a capillary effect and high flow resistance (Equation (9)) [56]. The capillary effect can cause air to be trapped, resulting in an unstable flow (Figure 14). \[\mathrm{R}=\frac{8\mathrm{\eta L}}{\mathrm{\pi r^{4}}} \tag{9}\]  
where \(\mathrm{\eta s}\) fluid viscosity, \(\mathrm{L}\) is the length of the tube, \(\mathrm{r}\) is the radius, and \(\mathrm{R}\) is the resistance to flow.
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 3 Materials and Methods  
### Overview of YOLOv8  
YOLOv8 is a state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions while introducing new features and improvements to further enhance performance and flexibility. Specific changes include YOLOv8 introducing a new backbone network. The most significant change is in the head section, which replaces the coupled head of YOLOv5 with a decoupled head and transitions from YOLOv5's anchor-based approach to anchor-free. YOLOv8 utilizes a new loss function, where loss incorporates the Task-Aligned Assigner positive sample allocation strategy and introduces Distribution Focal Loss. The YOLOv8 model is capable of running on various hardware platforms, ranging from CPUs to GPUs. Furthermore, in the backbone network and neck section, YOLOv8 replaces YOLOv5's C3 structure with a C2f structure that offers a richer gradient flow. Different channel numbers are adjusted for models of various scales, and this change significantly improves model performance. When the YOLOv8 model was tested on the COCO dataset, YOLOv8 showed a significant improvement in mAP, parameter count, and FLOPs results compared to YOLOv5 and YOLOv7 [40] in terms of object detection accuracy. In order to make YOLOv8 more suitable for object detection tasks in remote sensing images, we made some modifications to YOLOv8.
# Image Mosaicing Applied on UAVs Survey
## 3 Stitching Methods
### Feature-Based: Local Hybrid Transformation
#### 3.2.1 Apap  
One mesh-based algorithm is proposed by Zaragoza [74]. Their algorithm, named As Projective As Possible (APAP), is based on the DLT used to calculate the global homography. Instead, they calculate location-dependent homography (local homography) using moving DLT (MDLT); this produces flexible warps but also maintains the global homography as much as possible. Given the estimated \(H\) to align the images and arbitrary pixel at position \(x_{*}\) in the source, image \(I\) is warped to the position \(x_{*}^{\prime}\) in the target image \(I^{\prime}\) by:  
\[\tilde{x}_{*}^{\prime}=H_{*}\tilde{x}_{*} \tag{2}\]  
The result shows an overlapped mesh, as the horizontal lines are reserved, reducing the parallax error.