esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Data Packet Delivery (DPD)-TANVEF
The priority queue that is considered is of maximum 5 that will save the source node information, the advantage of this strategy is the next time data that will be sent without calculating angle zone, but this will only be kept in a specific interval of time to maintain the priority acknowledgment, otherwise it will again compute the appropriate angle. Usually, for the medium and small-scale network, this would not happen, and once the angle has been computed, it will remain the same and be used for the rest of the network lifetime. Hence, every sensor node knows its base angle that is '\(\pi\)/2' in the upward, horizontal and diagonal direction and it is also a built-in hardware module; by the way, according to the environmental situation every node have enough capability to compute and increasing the flooding zone by changing the value of '\(K\)'. Here it is important to know that on every attempt the previously calculated area will be added into the newly defined zone to take advantage of mobility. Further, Figure 4 presents the computed angle with the value of '\(K\)', and Algorithm 2 discuss the angle-based data packet delivery, i.e. ; DPD-TANVEER, while tri-angular adjustment of horizontal '\(A\)(x)', vertical '\(A\)(y)' and diagonal '\(A\)(z)' and the scenario of DPD-TANVEER are discussed in Figures 5 and 6, respectively. Figure 4: Possibility of angle flooding zone when Node Uses \(K=1\), 2 and 3. Figure 5: Tri-angular adjustment of horizontal \(A(x)\), vertical \(A(y)\) and diagonal \(A(z)\) with reference to Figure 4.  
Figure 6: Mechanism of Data Packet Delivery (DPD)-TANVEER.
###### Abstract  
Sun-induced chlorophyll fluorescence (SIF) holds enormous potential for accurately estimating terrestrial gross primary productivity (GPP). However, current studies often overlook the spatial representativeness of satellite SIF and GPP observations. This research downscaled TROPOMI SIF (TROPOSIF) and its enhanced product (eSIF) in China's Saihanba Forest Region to obtain high-resolution SIF data. SIF was simulated using the SCOPE model, and the downscaled SIF's reliability was validated at two forest eddy covariance (EC) sites (SHB1 and SHB2) in the study area. Subsequently, the downscaled SIF data were matched to the EC footprint of the two forest sites, and the relationship between SIF and GPP was compared at various observational scales. Additionally, the ability of downscaled TROPOSIF and eSIF to track GPP was compared, along with the correlations among several vegetation indices (VIs) and GPP. The findings reveal the following: (1) Downscaled TROPOSIF and eSIF showed a strong linear relationship with SCOPE-modeled SIF (R\({}^{2}\geq\) 0.86). The eSIF closely matched the SCOPE simulation (RMSE: 0.06 mw m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)) and displayed a more consistent seasonal variation pattern with GPP. (2) Comparisons among coarse-resolution SIF, EC footprint-averaged SIF (\(\text{SIF}_{\text{ECW}}\)), and EC footprint-weighted SIF (\(\text{SIF}_{\text{ECW}}\)) demonstrated significant improvements in the linear relationship between downscaled SIF and GPP (the R\({}^{2}\) increased from the range of 0.47-0.65 to 0.78-0.85). \(\text{SIF}_{\text{ECW}}\) exhibited the strongest relationship with GPP, indicating that matching SIF to flux footprints improves their relationship. (3) As the distance from the flux tower increased, the relationship between SIF and GPP weakened, reaching its lowest point beyond 1 km from the tower. Moreover, in the highly heterogeneous landscape of the SHB2 site, the relationship between VIs and GPP was poor, with no clear pattern as distance from the flux tower increased. In conclusion, the strong spatial dependency of SIF and tower-based GPP emphasizes the importance of using high-resolution SIF to accurately quantify their relationship.  
sun-induced chlorophyll fluorescence; gross primary productivity; downscaling; eddy covariance; footprint 2024
## 2 Basic Principles
### Preprocessing
#### 2.4.4 Data Reduction  
_Principal Component Analysis_ (_PCA_) is a common method used to the reduce the dimensionality of data, which is beneficial for algorithms to learn efficiently [40]. Its main idea is to preserve the maximum variance of the data while projecting them onto a lower dimensional hyperplane using the first eigenvectors, called the _principal components_, where every predominant subset of principal components defines a plane that is orthogonal to the following principal component (see Figure 4).  
Figure 4: Data distribution in 3D space and projection onto axes of principal components.  
Due to its increased numerical stability, _Singular-Value Decomposition_ (_SVD_) is a typical method for the calculation of the principal components \(\mathbf{V}=\mathbf{v}_{1}\dots\mathbf{v}_{m}\):  
\[\mathbf{X}=\mathbf{U}\mathbf{S}\mathbf{V}^{\mathrm{T}} \tag{8}\]  
To obtain a reduced dataset, the first \(m\) principal components where the cumulated explained variance ratio exceeds a certain target threshold are selected to form a matrix, which is multiplied by the original data matrix:  
\[\mathbf{X}_{\mathrm{red}}=\mathbf{X}\mathbf{\hat{V}} \tag{9}\]
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 4 Performance Evaluation
### Effect of DT Prediction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Machine** & **Algorithm** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **ROC Area** & **PRC Area** \\ \hline  & C4.5 & 0.924 & 0.205 & 0.927 & 0.924 & 0.889 & 0.915 \\ \hline
**DT** & RepTree & 0.913 & 0.207 & 0.919 & 0.913 & 0.891 & 0.916 \\ \hline  & Random Tree & 0.913 & 0.207 & 0.919 & 0.913 & 0.891 & 0.916 \\ \hline
**Clustering** & Simple K Means & 0.891 & 0.018 & 0.939 & 0.891 & 0.937 & 0.923 \\ \hline  & Hierarchical Clustering & 0.857 & 0.870 & 0.752 & 0.857 & 0.527 & 0.771 \\ \hline
**Stacking** & Zero + Decision Table & 0.859 & 0.859 & 0.737 & 0.859 & 0.413 & 0.737 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: DT evaluation. FP: false positive; PRC Area: area under the precision–recall curve; ROC Area: area under the receiver operating characteristic curve; TP: true positive.
# Temperature Fluctuation Attenuation of Circulating Cooling Water Using Dynamic Thermal Filtering
## 1 Introduction
Temperature fluctuation attenuators using phase change material (PCM) as thermal capacity medium [16; 17; 18; 19; 20] are also proposed and attract much attention. PCM has large latent thermal capacity at its phase change temperature point, and can absorb or release great thermal energy during the process of phase change. In 2004, [PERSON] presented a numerical study to evaluate the temperature fluctuation attenuation of water based on PCM, which indicated that lower flow rate of water and larger quantity of PCM could improve the performance of temperature fluctuation attenuation [18]. In 2014, [PERSON] presented the experimental investigation to validate the stabilization of the flowing water temperature with PCM, and a kind of paraffin-based PCM is used to cover a tube as thermal capacity medium. As a result, more than 90% of temperature fluctuation of the water flowing through the tube is reduced [16; 17]. A variety of PCMs with different phase change temperature points have been investigated and used to form attenuators. However, a PCM has a fixed phase change temperature point. Therefore, one PCM based attenuator can only reduce the temperature fluctuation around the fixed temperature point of the employed PCM. Besides, new materials like nanofluids have are also attracting attention, since the enhanced conventional heat transfer performance can improve the temperature fluctuation attenuation [21; 22]. Therefore, previous work mostly concentrates on the realization of high temperature stability of CCW, while the dynamic performance of temperature control is rarely paid attention to. Therefore, a dynamic thermal filtering method (DTFM) is proposed in this paper. Some of the CCW is blocked and used as thermal capacity medium to filter the temperature fluctuation of CCW. The medium can be real time updated to dynamically follow the set value of CCW temperature, thus to achieve the quick response of CCW temperature control. The proposed method is theoretically and experimentally validated.
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 5 Modules of Hardware Acceleration Kernel
### LDPC Decoding Module  
In the accelerator, the LDPC decoder is responsible for correcting the remaining substitution and erasure errors in the codeword [35; 36]. The module is based on a partially parallel hierarchical implementation of the normalized min-sum algorithm (NMSA) [37; 38; 39; 40]. The iteration strategy of this algorithm comprises a serial iteration strategy between each group and a parallel iteration strategy within the group, which enables faster convergence of iterative decoding [41; 42]. The LDPC decoder module consists of four main components, including the check node unit, the variable node unit, the intermediate variable storage module, and the memory read/write address control module. The intermediate variable storage module contains the log-likelihood ratio (LLR) memory, the check node update RAM, and the variable node update RAM, all of which are implemented using dual-port Block RAM (Figure 10).  
Figure 10: The architecture of the LDPC decoding module.  
Figure 9: Deinterleaver module.  
Specifically, the LLR input and LLR_RAM are quantized using fixed-point fractional quantization with 1 bit for the sign, 1 bit for the integer part, and 3 decimal bits, while the data bit width transmitted between nodes is 7 bits. Therefore, the LDPC decoder module achieves rate compatibility and reduces the complexity of hardware implementation and the consumption of hardware resources while maintaining high performance.
## 3 Proposed Methodology
### Feature Extraction
#### 3.1.4 Prewitt, Sobel, and Gaussian  
Another edge detection filter that improves image edges is the Sobel filter. Prewitt and Sobel filters were applied to the reference and distorted images in this study, followed by a Gaussian filter to remove noise.
## 4 Results
### Ablation Analysis  
We performed feature analysis on multiple QRS, the result for which is shown in Table 11. Empirically, we found that the accuracy of the combined dataset increased when we increased the number of QRS due to the repetitive tendency of RNN; it is probable that the RNN model's repeating propensity helps it better understand patterns and correlations within the data, resulting in higher accuracy. However, in the case of the ECG-ID database, the consecutive QRS complex does not perform well, the small number of ECG samples in the dataset is an issue and an increasing number of QRS complexes does not result in a substantial gain in accuracy. The dataset with unbalanced samples and a higher number of classes does not reflect the improvement in biometric recognition performance. This study does not involve the analysis of arrhythmia, however, the variability among various rhythms reflected in arrhythmic patients can affect the performance of the biometric recognition system. To incorporate this, we proposed that the single QRS complex is enough for biometric recognition with less space complexity, as presented in Table 12. Similarly the time complexity analysis is presented in Table 13. Furthermore, we performed further experimentation with more than a single QRS complex, in order to analyze the empirical performance of our model. The increase in the number of consecutive QRS segments improves the recognition performance for the biometric recognition of arrhythmic patients as well as for healthy subjects. However, for the ECG-ID dataset of healthy subjects with a lesser number of segments and more classes, increasing the number of QRS complexes further reduces the emotion recognition performance. These results are summarized in Table 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1 QRS** & **2 QRS** & **3 QRS** & **4 QRS** \\ \hline ECG-ID dataset & 0.77 ms & 0.10 ms & 0.08 ms & 0.05 ms \\ MITBIH dataset & 0.53 ms & 0.76 ms & 0.74 ms & 0.75 ms \\ NSRDB dataset & 1.01 ms & 1.40 ms & 1.12 ms & 0.07 ms \\ Combined Dataset & 0.60 ms & 0.81 ms & 1.01 ms & 0.83 ms \\ \hline \hline \end{tabular}
\end{table}
Table 13: Durations of the ECG testing times on various datasets with different consecutive QRSs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1 QRS** & **2 QRS** & **3 QRS** & **4 QRS** \\ \hline ECG-ID dataset & 80.64\% & 71.05\% & 65.28\% & 63.59\% \\ MITBIH dataset & 98.81\% & 99.83\% & 99.89\% & 99.85\% \\ NSRDB dataset & 99.62\% & 99.87\% & 99.91\% & 99.89\% \\ Combined dataset & 98.24\% & 98.4\% & 98.59\% & 99.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of various datasets with various consecutive QRS complexes against learnable parameters using the proposed methodology.
## 1 Introduction  
The unique magnetic properties of CoFeB films have attracted wide attention in recent years [1; 2; 3; 4; 5; 6; 7; 8]. In spin-valve magnetic tunneling junctions (MTJs), CoFeB thin films can be sputtered into free or pinned layers, resulting in apparent tunneling magnetoresistance (TMR), perpendicular magnetic anisotropy (PMA), and soft ferromagnetic properties. The films can be applied for magnetoresistance random access memory (MRAM) and sensor components [9; 10; 11; 12; 13; 14; 15]. According to these properties, CoFeB films can be used to many kinds of spintronic devices. In 2010, [PERSON] et al. found that the PMA of the CoFeB/MgO system can combine with the perpendicular MTJ and produce a tremendous TMR effect [16]. Therefore, increasing the thickness of the PMA CoFeB layer has become a very important goal in research. In previous research by our group, the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{20}\) film has been only studied with regard to its structural and magnetic properties [17]. CoFeV is also compatible with other seed layers or semiconductor processes to improve physical performance. For example, Ru/CoFeV and Ta/CoFeV can be useful in magnetics and semiconductor applications [18]. Adding B and V elements to the CoFe material has advantages. When V is added to the CoFe alloy, the mechanical strength and magnetic properties can be improved to a high durability of materials, which could increase its ductility and decrease its coercivity (H\({}_{\text{c}}\)) and brittleness [19; 20; 21; 22; 23]. Adding B to the CoFe alloy can improve spin tunneling polarization and increase the high TMR ratio. Nevertheless, due to the low coercivity and high TMR properties of CoFeB films at room temperature (RT), most of the studies have focused on the CoFeB magnetic films [6; 7; 8]. According to above reasons, it is worthwhile investigating the specific properties of CoFeVB in this study where V was added to the CoFeB alloy to form a new Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) material. Based on the magnetic and mechanical properties, the CoFeVB has low H\({}_{\text{c}}\) and high strength, which can be suitable as a free layer of spin-value MTJ. CoFeVB is generally inserted as a seed layer, buffer layer, capping layer, and protective layer in a multilayered structure, which can be used extensively in MRAM and semiconductor applications. It is compatible with the semiconductor process and can also be used as a free or pinned layer in spin-value MTJ. Therefore, the surface energy and adhesion are relatively important in this investigation and it is a worthwhile cause to detect the structure, magnetic characteristics, and adhesive property.
## 3 Materials and Methods
### Data Processing and Result Evaluation  
All the information collected in the six experiments was stored in spreadsheets and pre-processed to verify consistency. In some cases, packets with incorrect information (unexpected characters) were received, so a column indicating whether the packet was received correctly or not (packet state) was added to the spreadsheets.  
Graphs of the collected information were made to identify the best settings in each experiment. Some contour graphs of the RSSI level were also made to identify the effect of the parameter values (BW, SF and CR). In some of the experiments, statistical analyses were performed to evaluate whether there was any linear relationship between some of the parameters and the RSSI value.  
Regarding the last three experiments, results related to distance and number of LoRa devices were analyzed.  
The analysis of experimental results featuring comparative graphs and tables to facilitate reader comprehension is considered part of the technical novelty of this work.
# Parameter Optimization of Orbital TIG Welding on Stainless Steel Pipe
## 3 Results and Discussion
### Microstructure
Analysis at larger magnification with samples S2 and S6 was conducted because these two samples are entirely penetrated. In the S2 sample, there are interdendrite structures with coarse grain size, leading to a decrease in UTS value of 385 MPa. On the contrary, the interdendrite structures in sample S6 are much finer, leading to a better UTS value of 562 MPa. This result is consistent with the report of [PERSON] et al. [32], which also revealed the impact of interdendrite structure size on the UTS of the weld joints. The HI of sample S6 was higher than that of sample S2, as shown in Table 3. Moreover, sample S6 was filled with a wire diameter of 1 mm at a Vf of 3.8 mm/s, whereas the wire diameter used in sample S2 was 0.8 mm with a Vf of 4.5 mm/s. These results indicate that although sample S6 had a higher HI, the larger diameter wire and  
slower wire feed speed compared to sample S2 caused the solidification rate of the S6 weld to be faster. Consequently, the microstructure of sample S6 was finer, and the fusion zone was smaller and more compact than that of sample S2. These results indicate that changing the filler wire diameter significantly affects HI, as it absorbs heat to melt and deposit metal into the weld pool. This adjustment can help balance HI, preventing excessive penetration, distortion, or oxidation in stainless steel. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**HI = 0.26 (kJ/mm)** & **HI = 0.29 (kJ/mm)** & **HI = 0.32 (kJ/mm)** & **HI = 0.32 (kJ/mm)** \\ \hline \hline \multirow{2}{*}{**S1**} & \(\begin{array}{c}\text{D}=1.0\\ \text{V}_{f}=3.8\\ \end{array}\) & \(\begin{array}{c}\text{W}=3.0\\ \text{UTS}=72\\ \end{array}\) & \(\begin{array}{c}\text{S2}\\ \text{V}_{f}=4.5\\ \end{array}\) & \(\begin{array}{c}\text{D}=2\\ \text{V}_{f}=4.5\\ \end{array}\) & \(\begin{array}{c}\text{W}=4.6\\ \text{UTS}=385\\ \end{array}\) & \(\begin{array}{c}\text{S3}\\ \text{V}_{f}=5.6\\ \end{array}\) & \(\begin{array}{c}\text{D}=0.76\\ \text{V}_{f}=2.8\\ \end{array}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overview of microstructures.
## 3 Results and Discussion
### Removal of Ammonium, MEA, and TMAH by Various Processes (Synthetic Wastewater)
For examples, the removal of ammonium decreased from 92 to 46% and the removal of TMAH decreased from 80 to 51%. The removal efficiencies of ammonium and TMAH were reduced to 46 and 29%, respectively, due to the presence of t-butanol. In agreement with the findings of Wang et al. [38], this result indicated  
Figure 7: Effect of salts on the removal of TMAH by the MnCe-GAC/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\) process (C\({}_{o}\) = 100 mg/L, V = 0.5 L, solid 1 g, 0.25 mmol O\({}_{3}\)/min, 0.125 mmol H\({}_{2}\)O\({}_{2}\)/min, 30 min). that the hydroxyl radical reaction might play an important role in the removal processes. Kim and Choi [39] reported that \(\bullet\)OH could initiate the degradation of TMAH by either abstracting H-atoms or adding hydrocarbon molecules. Chen et al. [40] reported that metal oxide catalysts (such as Mn-Ce-O composites) were efficient catalysts and they possessed many oxidation states to facilitate electron transfer processes in the radical-producing step of wet oxidation.
# Mobility Prediction of Mobile Wireless Nodes
## 6 Results and Evaluation
### Evaluation of the RW Mobility Model  
The second mobility model tested was the RW model. Figure 5 represents the results of the RW model's accuracy. As noted previously, accuracy values dropped as the number of access points increased in all classifier algorithms, indicating that algorithms had difficulty predicting random movement, especially when there were many transitions between APs. K-NN showed the lowest accuracy of 58%, while GNB, LR, DT, SVM, and LDA reached 59%, 61%, 65%, 68%, and 81%, respectively.  
Figure 4: Average accuracy with different number of APs using RD mobility.
# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
## 3 Methodology
### YOLOv5 Model and Performance Improvement Techniques
#### 3.2.2 Performance Improvement Techniques  
A lightweight model is an important element in a deep-learning-based system. General deep learning systems require high-performance computing capabilities. However, implementing high-performance computing using equipment and robots used on sites is difficult. Thus, a lightweight model that exhibits comparable performance is required, and numerous related studies have been conducted accordingly. A lightweight model is also required in the proposed system. Accordingly, we explain some of the typical techniques for a lightweight model, such as knowledge distillation and quantization, as well as pruning, which is used in this study.  
Knowledge distillation [21] is a technique devised to create a smaller model from ensembles of many models that are already trained. This technique reduces the storage space where the model is stored and execution memory usage by obtaining a smaller model with fewer training parameters and a smaller bit width based on previously trained models considering the performance limitation in embedded devices.  
Quantization [22] is a technique to represent 32-bit floating point (FP32)-based learning parameters with a 1K-bit size, which is a smaller-bit-width integer than that of FP32-based learning parameters. In many cases, deep learning models mainly use the FP32 type to store parameters generated after training, which affords the advantage that the model size could be theoretically reduced by \(32/\mathrm{K}\) times without discarding some parameters. However, it may cause accuracy loss because the range of numbers that could be represented is significantly limited owing to smaller-bit-width integers.  
Finally, pruning [23] (Figure 8) is a technique based on the viewpoint that \"not all parameters have the same effect on inference.\" It is a lightweight technique that removes neurons and layers that do not significantly influence the inference process while learning iteratively, thereby obtaining a model whose parameters are considerably reduced. Pruning is used in the present study to optimize the model. We attempted to lighten the model by spinning the Conv block, which occupies the most parameters in the model of YOLOv5.