esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### VGG-19 Model  
The DL networks can be applied for image classification in many fields based on large datasets with around 60 million parameters and 650,000 neurons [47]. In practice, the network architecture can have five convolutional layers and three fully connected layers with different roles. There are two first convolution layers (standard layer and max-pooling layer), the 3 rd and 4 th convolution layers (directly connected), the last convolution layer (max-pooling layer), and the output layer (softmax layer). In addition, some networks have particular architectures for unique applications. For instance, GoogleNet is a network with about 7 million parameters, 9 inception modules, 4 convolutional layers, 4 max-pooling layers, 3 average pooling layers, 5 fully connected layers, and 3 softmax layers [48]. All convolutional and dropout layers use the ReLU (activation function) with a parameter reduction ratio of 70% applied to all fully connected layers. Additionally, ResNet is similar to VGG-19 and has been adapted many times to produce ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152 [49]. Herein, we apply VGG-19 to train the tomato leaf image dataset.  
Basically, VGG has an architecture of a CNN network, and VGG-19 is one of the VGG-based architectures [50]. The VGG-19 is a deep-learning neural network with 19 connection layers, including 16 convolution layers and 3 fully connected layers. The convolution layers will extract features of the input images, and the fully connected layers will classify the leaf images for those features. In addition, the max-pooling layers will reduce the features and avoid overfitting, as described in Figure 2.  
The output of each convolutional layer is represented by the following expression:  
\[C_{j}^{l}=\varphi\left(\sum_{i=1}^{M^{l-1}}C_{i}^{l-1}\times k_{ij}^{l}+b_{j}^{ l-1}\right) \tag{4}\]  
in which \(\times\) is the convolutional function which describes the connection between the weights of the \(i\)th and \(j\)th features in the (\(l-1\))th, \(l\)th layers, \(b_{j}\) is the bias value, and \(\varphi\) is the activation function.
## 4 Experimental Results
### Evaluation Indicators  
In target detection, the average accuracy \(AP\) of each type of target and the average accuracy \(mAP\) of all targets are used to evaluate the detection effect and performance of the model. AP is the area under the recall and precision curves. The calculation formula is as follows:  
\[Pr=\frac{TP}{TP+FP} \tag{1}\]  
\[Re=\frac{TP}{TP+FN} \tag{2}\]  
\[AP=\frac{1}{101}\sum_{i=0}^{100}Pr\left(Re=\frac{i}{100}\right) \tag{3}\]  
\[mAP=\sum_{i=1}^{N}\frac{AP_{i}}{N} \tag{4}\]  
In Formulas (1)-(4), \(Pr\) is the detection accuracy, \(Re\) is the recall rate of the model, \(TP\) is the number of accurate predictions, \(FP\) is the number of false detections, \(FN\) is the number of missed detections, and \(N\) is the target category.  
The detection speed is expressed in FPS (frames per second), or the number of pictures that the model can process per second. The complexity of the model is measured by the weight of the model, the number of parameters, and the floating point number.
# A Review on Distribution System State Estimation Algorithms
###### Abstract
The rest of the paper is organized as follows: Section 2 presents the DSSE challenging attributes, Section 3 reviews the state vector options as well as the model-based, forecast aided, and data-driven DSSE algorithms, Section 4 focuses on the distribution system's observability, bad data detection, and meter placement strategies, Section 5 presents the technical requirements and applications of DSSE, Section 6 presents the future trends and challenges and, finally, Section 7 summarizes the conclusions.
# A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas
## 3 Framework of the Literature Survey
### Nature of LMD Problems  
Each delivery plan is subject to the different operation conditions given by customers, managerial options, uncertainties, and technical conditions such as real-time demand change, traffic congestion, time window, fleet accessibility, and service times at the points of delivery. The literature often considers these five aspects of the nature of problems as more important:  
Figure 2: Conventional VRP.  
Fleet capacity: Capacitated VRP (CVRP) optimizes routes considering vehicle capacity limits such as weight, volume, and number of parcels [40; 41]. Sometimes, however, these limits can be ignored for mail delivery, where the parcel volumes are so small compared with fleet capacity, and perishable item delivery, when vehicles cannot wait for the full load of items [42]. The pickup and delivery problems widely discussed in many conventional VRPs [43] and also in LMD [44; 45] are modeled as a problem with vehicle capacity limit.
2. Time window: Time windows require deliveries to be made within specified time horizons [41; 46]. LMD additionally considers the time windows of driver availability. Various types of window in a logistics network have been discussed in literature [47].
3. Fleet option: The research considers multiple types of fleet, including a heterogeneous fleet, which involves a fleet of vehicles with different capacities [48]; electric vehicles, which need additional time for charging [49]; and autonomous vehicles [50]. Various autonomous vehicles allow us diverse options for delivery network design. Autonomous vehicles for delivery are discussed in survey papers [1; 10; 51].
4. Dynamic input: Dynamic VRP (DVRP) considers the changeability of variables (e.g., a customer's change of order quantity) during delivery [41; 52].
5. Stochastic parameter: Stochastic VRP (SVRP) considers the randomness or uncertainty of the parameters [41; 53].  
In the discussion of each paper, these aspects are mentioned only when the paper explicitly considers them.
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 3 The Proposed Approach
### signal Smoothing Processing  
After threshold de-noising and wavelet reconstruction, some noises still exist in the reconstructed ECG signal. These noises are clearly visible in the low time varying components of the ECG signal i.e., the region between QRS complexes. Therefore, signal smoothing processing is applied for further enhancement of ECG signal quality. In this paper, we determine the subband information required smoothing by the subbands divided by the maximum of the minimum values and the minimum of the maximum values of reconstructed signal. We define lmax as the maximum of the minimum values of reconstructed signal, and upmin as the minimum of the maximum values of reconstructed signal. The reconstructed signal is smoothed point by point from the second point, the processing can be divided into the following situations:  
1. when the \(ith\) point of the signal, i.e., \(sig(i)\), is between lmax and upmin, consider its previous point \(sig(i-1)\) and the next point \(sig(i+1)\), and the filtering is as follows: 1. when both \(sig(i-1)\) and \(sig(i+1)\) are between lmax and upmin, the smoothing filter formula is: \[sig\left(i\right)=\frac{\left(sig\left(i-1\right)+sig\left(i\right)+sig\left( i+1\right)\right)}{3}\] (15) 2. when \(sig(i-1)\) is between lmax and upmin, and \(sig(i+1)\) is not between lmax and upmin, the smoothing filter formula is: \[sig\left(i\right)=\frac{\left(sig\left(i-1\right)+sig\left(i\right)\right)}{2}\] (16)when \(sig(i+1)\) is between lmax and upmin, and \(sig(i-1)\) is not between lmax and upmin, the smoothing filter formula is: \[sig\left(i\right)=\frac{\left(sig\left(i\right)+sig\left(i+1\right)\right)}{2}\] (17) 4. when both \(sig(i-1)\) and \(sig(i+1)\) are not between lmax and upmin, the smoothing filter formula is: \[sig\left(i\right)=sig\left(i\right)\] (18)
2. When the \(i\)th point of the signal, i.e., \(sig(i)\), is not between lmax and upmin, the smoothing filter formula is: \[sig\left(i\right)=sig\left(i\right)\] (19)
# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 5 Experiment and Analysis
### Noise Reduction Performance Analysis
Figure 7: The result of ICEEMDAN decomposition. characteristics, more significant vibration and shock, and a higher coincidence degree with the original signal compared with the signal without noise reduction. In order to verify the efficacy of the ICEEMDAN-MPE-AWT noise reduction method in improving the fault diagnosis of the model in noisy environments, the experimental dataset with a load of 7.32 Nm was selected, and noise with SNR ranging from \(-4\) dB to 6 dB was introduced to the test set. Then, the precision of the raised method before and after noise reduction was measured and compared to prove the effectiveness of the raised approach. For the sake of checking the generalization of the noise reduction method raised in this paper, the GASF+TSE-ResNeXt50 model and GASF+TSE-ResNeXt18 were selected as the test model for fault diagnosis. The recognition accuracy of the model on the test set was taken as the evaluation index, 10 repeated tests were carried out for each model, and the average value of the 10 tests was seen as the final results. The results are illustrated in Table 6. It can be found from Table 6 that after noise reduction by adopting the approach in this paper, the accuracy of GASF+TSE-ResNeXt50 can still reach 98.8% when the SNR is \(-4\) dB. Therefore, the approach introduced in this paper has better noise reduction efficacy in noisy environments and can ensure the precision of the model. Figure 9 shows the precision curves of the two models before and after noise reduction, which can more intuitively see the effectiveness of ICEEMDAN-MPE-AWT noise reduction method. Figure 10 shows the confusion matrices of GASF+TSE-ResNeXt50 before and after denoising when SNR is \(-4\) dB and the load is 7.32 Nm. The horizontal coordinate represents the true label of the failure, the vertical coordinate represents the predicted category of the failure, and the numbers on the main diagonal of the matrix represent the number of samples correctly classified for each type of failure. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **SNR (dB)** & \(\mathbf{-4}\) & \(\mathbf{-2}\) & \(\mathbf{0}\) & \(\mathbf{2}\) & \(\mathbf{4}\) & \(\mathbf{6}\) \\ \hline \multirow{2}{*}{GASF+TSE-ResNeXt50} & Before noise reduction & 95.5\% & 97.5\% & 98.5\% & 99.0\% & 99.7\% & 100\% \\  & After noise reduction & 98.8\% & 99.2\% & 99.7\% & 99.8\% & 100\% & 100\% \\  & Before noise reduction & 92.5\% & 94.2\% & 96.3\% & 97.7\% & 98.2\% & 99.2\% \\ \multirow{2}{*}{GASF+TSE-ResNeXt18} & After noise reduction & 97.5\% & 98.2\% & 98.8\% & 99\% & 99.2\% & 99.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy under different SNR. Figure 8: The comparison result between the denoised signal and the noised signal.
## 3 Results and Discussion
### Preparation of PS Microsphere Using PAMPS Microgel
The higher PDI values of P4-VP/PAMPS (Figure 15a,b) more than PAMPS (Figure 4b) confirms the polydispersity, which agree with its TEM micrograph (Figure 13b). Based on these results, it can be proposed the mechanisms for the formation of PS/PAMPS and P4-VP/PAMPS core/shell and bare spheres, respectively, as represented in Scheme 2. Figure 15: DLS data of P4-VP/PAMPS microgel at pH (**a**) 4, (**b**) 7, and (**c**) 10 in aqueous solution at room temperature. The PAMPS aggregates are broken down to nano-sizes under sonication to redisperse and self-assemble on the surface of styrene or 4-vinylpyridine monomers at the monomer-water interface. The PAMPS acts as stabilizer to form Mickering monomers/water emulsions. The protonation of 4-vinylpyridine monomers with the sulfonic group of PAMPS monomer partitioned the 4-vinylpyridine monomer droplets in emulsion or the aqueous phase. The lower solubility of styrene monomer in water facilitates the formation of micro-sized styrene droplets without formation of nano-sized secondary nuclei [48]. The presence of AIBN as hydrophobic initiator proposed two polymerization mechanisms (Scheme 2). It is expected that the AIBN is completely decomposed inside styrene droplet to initiate the crosslinking polymerization of styrene with DVB to produce PS/PAMPS microgels. The AIBN initiator diffuses into the aqueous phase in case of 4-vinylpyridine to crosslink 4-VP dissolved in aqueous phase with DVB. This type of particle is called secondary nuclei with small sizes because it is formed with the critical chain lengths due to lower solubility in aqueous phase. Accordingly, the DVB and the AIBN distribute either with secondary nuclei, water phase, or monomer droplets. This distribution is responsible for the polydispersity of P4-VP/PAMPS microgel because there is competitive polymerization either inside the secondary nuclei or emulsified droplets. The crosslinking polymerization of 4-VP inside the emulsion droplet increased until the total surface area of the secondary nuclei is much larger than that of droplet, and the crosslinking polymerization in aqueous phase of the secondary nuclei will prevail.
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 6 Results and Discussion  
The following are the findings of assessing the planned CB-UWSN network's performance characteristics in the Telnet and Superframe applications for deploying 250 nodes. Figure 9 depicts the amount of energy consumed by transmit mode routing protocols, such as STAR-LORA, with the applications of Telnet for 250 nodes. Figure 10 depicts the amount of energy consumed by receive mode routing protocols, such as STAR-LORA, with the applications of Telnet for 250 nodes. Figure 11 depicts the amount of energy consumed by idle mode routing protocols, such as STAR-LORA, with the applications of Telnet for 250 nodes.  
Figure 9: Consumption of energy when operating in the Transmit mode of STAR-LORA for Telnet deployment.  
Figure 12 depicts the amount of energy consumed by transmit mode routing protocols, such as STAR-LORA, with the applications of Superframe for 250 nodes. Figure 13 depicts the amount of energy consumed by receive mode routing protocols, such as STAR-LORA, with the applications of Superframe for 250 nodes. Figure 14 depicts the amount of energy consumed by idle mode routing protocols, such as STAR-LORA, with the applications of Superframe for 250 nodes.  
Figure 11: Consumption of energy when operating in the Idle mode of STAR-LORA for Telnet deployment.  
Figure 10: Consumption of energy when operating in the Receive mode of STAR-LORA for Telnet deployment.  
Figure 14: Consumption of energy when operating in the Idle mode of STAR-LORA for Superframe deployment.  
Figure 12: Consumption of energy when operating in the Transmit mode of STAR-LORA for Superframe deployment.  
Figure 13: Consumption of energy when operating in the Receive mode of STAR-LORA for Superframe deployment.  
The UWSN network has the following performance metrics for Telnet and Superframe applications, as shown in Table 2 for AODV, Table 3 for Fisheye, Table 4 for STAR-LORA, Table 5 for LAR1, and Table 6 for OLSR.  
amount of transmitting energy that is necessary for the AODV, LAR1, OLSR, Fisheye, and STAR-LORA routing protocols to send data at their maximum size in the proposed CB UWSN is 0.009 mWh for LAR1 in the Superframe deployment application and 0.05 mWh for STAR-LORA in the Telnet deployment application. For STAR-LORA, the value for this minimum amount of transmit energy is the goal of the CB-UWSN, which is to reduce the amount of transmit power used as much as feasible. Regarding speed and dependability, no other routing protocol can compete with STAR- LORA in the Telnet deployment application and LAR1 in the Superframe deployment application.
## 1 Introduction  
In response to ever-changing technological development and globalization, corporations continue to build capacity in organizational learning and team innovation in order to maintain sustainable operations and competitiveness. Organization innovation relies on the creativity of the employees [1] and the focus has been on the sharing of knowledge and encouraging creative thinking that seek to develop innovative solutions to address existing or future business challenges [2; 3]. While a corporation's sharing of internal knowledge through organizational learning can increase the individual employee's skill and learning effectiveness, it also helps to drive a collective innovation process that will contribute towards the corporation's overall sustainable efficacy and development [4; 5; 6]. Such an emerging trend has prompted the need to review the relationship between sustainable business development and management education that seek to facilitate effective communication of key abilities such as adaptability to face uncertainty, creativity, or detection and processing emotions, confidence, respect, dialogue, critical thinking and systematic thinking [7].  
[PERSON] (2006) states that creativity involves the attitude and action of an employee to take risks [8]. However, it is imperative that managers provide a supportive environment, and incentives to encourage their employees to think creatively and explore innovative solutions to assist in making more informed strategic decisions [9]. An individual employee's ability to be creative is directly influenced by factors such as diverse skills and autonomy in the organization [10]. [PERSON] and [PERSON] [11] indicate that feedback (e.g., audio-visual, written) also plays an important role in encouraging creativity. Computer-mediated communication (CMC) and face-to-face (Ftf) communication are two forms of feedback communication format that are used to foster creativity whereby CMC is considered having greater media richness than the traditional Ftf approach [12]. Although studies [13; 14; 15] have revealed that CMC is a more relaxing and engaging feedback communication format than Ftf, but there has been a lack of investigation into the correlation between communication formats and creativity.  
The research attempts to fill this gap by combining the media richness theory and creativity to explore the relationship between the features of media richness and creative thinking by focusing on creativity training via two different communication formats (i.e., CMC and Ftf). The following research questions are proposed:  
1. Will the use of different communication formats (i.e., CMC and Ftf) have an impact on potential creative thinking?
2. What is the correlation between media richness and potential creative thinking through the use of the above two types of communication format?
###### Abstract  
The wavefront reconstruction is a crucial step in determining the performance of wavefront detection instruments. The wavefront reconstruction algorithm is primarily evaluated in three dimensions: accuracy, speed, and noise immunity. In this paper, we propose a hybrid zonal reconstruction algorithm that introduces slope and curvature information in the diagonal, anti-diagonal, horizontal, and vertical directions by dividing the neighbor sampling points into subregions in groups of four. By canceling the same parameters in integration equations, an algorithm using multi-directional slope-curvature information is achieved with only two sets of integration equations in each subregion, reducing the processing time. Simulation experiments show that the relative root-mean-square reconstruction error of this algorithm is improved by about 4 orders of magnitude compared with existing algorithms that use multi-directional slope information or slope-curvature information alone. Compared with the hybrid multi-directional slope-curvature algorithm, the proposed algorithm can reduce computation time by about 50% as well as provide better noise immunity and reconstruction accuracy. Finally, the validity of the proposed algorithm is verified by the null test experiment.  
wavefront reconstruction; wavefront sensor; truncation error; least-squares method Article
## 4 Results
### Variable Importance
In the spectrally-based RFC models, the contribution of SWIR-2 (S2) was striking; it appeared twelve times (out of 20) in the top five most important variables (Table 10). Even though SPL RFC models tended to perform slightly better than CxQ and HPLM RFC models (Table 5, Table 8), more phenometrically-based variables were considered important for classification in the CMB RFC models (Table 10). The LSP-related variables from CxQ (\(\alpha\), \(\beta\), \(\gamma\), HTV, minx) and HPLM (gri, giMD) consistently appeared in the top ten most important variables in the CMB models. Together, phenometrics from CxQ and HPLM appeared 31 times (out of 40) in the top ten most important variables. Consistent appearances of HTV, minx, gri, and giMD as highly ranked important variables in the CxQ and HPLM, as well as in the CMB RFC models, indicated that classification is driven by variables related to the initial green-up phase of seasonal growth. \begin{table}
\begin{tabular}{c l l l l l l l l} \hline \hline  & \multicolumn{4}{c}{**CxQ RFC Models**} & \multicolumn{4}{c}{**HPLM RFC Models**} \\ \cline{2-9} \# & \multicolumn{3}{c}{**2016**} & \multicolumn{3}{c}{**2017**} & \multicolumn{3}{c}{**2016**} & \multicolumn{3}{c}{**2017**} \\ \cline{2-9}  & \multicolumn{1}{c}{**ARD**} & \multicolumn{1}{c}{**HLS**} & \multicolumn{1}{c}{**ARD**} & \multicolumn{1}{c}{**HLS**} & \multicolumn{1}{c}{**ARD**} & \multicolumn{1}{c}{**HLS**} & \multicolumn{1}{c}{**ARD**} & \multicolumn{1}{c}{**HLS**} \\ \hline
1 & \(\alpha\) & \(\alpha\) & \(\alpha\) & \(\alpha\) & \(\alpha\) & gri & gri & giMD & giMD \\
2 & \(\beta\) & \(\beta\) & **HTV** & **HTV** & **giMD** & \(\text{PH}_{\text{HPLM}}\) & **gre** & **gri** \\
3 & **HTV** & \(\text{ymax}\) & \(\text{r}^{2}\) & **minx** & **gre** & vi\_sei & **gri** & **gre** \\
4 & **minx** & **minx** & **minx** & \(\beta\) & \(\text{PH}_{\text{HPLM}}\) & **giMD** & \(\text{PH}_{\text{HPLM}}\) & see \\
5 & \(\text{ymax}\) & **HTV** & \(\beta\) & \(\text{r}^{2}\) & vi\_gre & vi\_gre & **vi\_gri** & \(\text{PH}_{\text{HPLM}}\) \\
6 & \(\gamma\) & \(\text{PH}_{\text{CxQ}}\) & \(\gamma\) & \(\gamma\) & vi\_sei & **vi\_gri** & see & **vi\_gri** \\
7 & \(\text{PH}_{\text{CxQ}}\) & o\_fit & \(\gamma\)max & \(\text{PH}_{\text{CxQ}}\) & see & gre & \(\text{DP}_{\text{HPLM}}\) & vi\_sei \\
8 & \(\text{r}^{2}\) & \(\gamma\) & \(\text{PH}_{\text{CxQ}}\) & ymax & vi\_gri & see & vi\_sei & \(\text{DP}_{\text{HPLM}}\) \\
9 & \(\text{TTP}_{\text{CxQ}}\) & \(\text{r}^{2}\) & o\_per & \(\text{TTP}_{\text{CxQ}}\) & seMD & seMD & vi\_see & vi\_gre \\
10 & o\_fit & o\_all & \(\text{TTP}_{\text{CxQ}}\) & o\_per & \(\text{DP}_{\text{HPLM}}\) & vi\_seMD & vi\_gre & seMD \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Top 10 most important variables of CxQ and HPLM RFC models. Variables highlighted in light yellow are those that consistently appear in the top 6 most important variables (at least three times over four-year data combinations). **Bolded** items are variables related to the initial green-up phase of the seasonal growth.