esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Overall Design of the Moisture Content Detection System for Yinghong No. 9 Tea Leaves  
The composition of the moisture content detection system for Yinghong No. 9 tea leaves based on machine vision is shown in Figure 1, which was composed of an image acquisition system, an image prediction system, and an identity recognition system.  
The identity recognition system uses fingerprint recognition to identify the operator by calling the fingerprint database. The fingerprint information can also be entered into the fingerprint database. After passing through the identity recognition system, the device parameters can be changed.  
The image acquisition system collects the image of the tea leaves through the camera and judges whether there are Yinghong No. 9 tea leaves in the image through the color feature distinction. When there are Yinghong No. 9 tea leaves in the image, the fan will be started, the LED light will be turned on, and the system will wait for the acquisition command. The color indicator in the color feature can strengthen the color feature of the target object. In order to distinguish whether there are Yinghong No. 9 tea leaves, the ExG, via Equations (1)-(3), is used to analyze the image of the Yinghong No. 9 tea leaves [16]. In the case of a given threshold, \(t_{g}\), the presence of Yinghong No. 9 tea leaves is determined when the ExG is greater than \(t_{g}\).  
\[ExG(x,y)=2G(x,y)-R(x,y)-B(x,y) \tag{1}\]  
\[\mathrm{Pg}=\frac{\sum\limits_{i=1}^{w}\sum\limits_{j=1}^{h}g(x,y)}{wh} \tag{2}\]  
\[g(x,y)=\begin{cases}1&(ExG(x,y)\geq t_{g})\\ 0&(ExG(x,y)<t_{g})\end{cases} \tag{3}\]  
Here, \(RGB\) is the three channels of the image, \((x,y)\) is the position of the pixel, \(w\) is the width of the image, and \(h\) is the height of the image.  
Firstly, in the image prediction system, the threshold value of the moisture content and the interval time of the image prediction can be set after the timer is started. Secondly, when a specific duration of time is reached, a collection command is issued to the image acquisition system, and the camera sends the tea leaves image data through the serial port to the main control chip. After that, the main control chip preprocesses the image data, organizes it into multidimensional data, and inputs it into the model for prediction. Finally, the prediction result is displayed on the display screen, and the buzzer is activated to give an alarm when the predicted moisture content reaches the threshold value of moisture content.  
Figure 1: Composition of the Yinghong No. 9 tea leaves moisture content detection system.
## 4 Experimental Results
### Vein Pattern Registration Based on Minutia-Based Matching in the Presence of Affine Transformation  
A dorsal image is captured with our system. The size of the original image is \(420\times 456\) pixels. Proceeding to the ROI process (see Section 2.3.2 A), the ROI vein pattern image is a size of \(145\times 145\) pixels. Preprocessing image enhancement is then performed on the ROI, including a median filter, closing morphological process, thinning morphological process, pruning morphological process and minutia detection (see Section 2.3.2 B). Triangulation is formed on a set of selected minutia triplets, as described in Section 2.4.1. The sequence of triangles both in the query image and in the reference image is in ascending order. A ratio of the consecutive triangle area is formed to derive an absolute invariance. The two sequences are then shifted and compared to find the matching triangle. The vertices of the corresponding matching triangle are used to estimate the affine transformation parameter. With the estimated transformation, the query image is aligned against the reference image. To provide a quantitative measure, the average distance error is computed. Figure 23 shows the matching triangles in the query image and the reference image. Figure 24 demonstrates the alignment of the query against the reference image. The average distance map error before and after the alignment of Figure 24 is shown in Table 1. To test the performance of the alignment further, we randomly selected  
Figure 22: Vein pattern human recognition/verification process.  
a dataset that corresponds to 10 individuals with two vein images from each individual, thereby giving a total of \((10\times 2)=20\) vein image data. The data are divided into two sets, denoted as the reference set and the query set. The vein image data in the query set are aligned against the vein image data in the reference set using the proposed algorithm. The data are shown in Figure 25. In the figure, the reference set is the first row, while the query set is the first column. The average alignment error is also shown in Figure 25.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Error** & **Before Alignment** & **After Alignment** \\ \hline Average distance map & & \\ (in pixels) & 10.915 & 1.773 \\ Alignment errors on average & & \\ (\% of the size of the vein picture) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average distance map and alignment errors of the two vein patterns after performing affine transformation.  
Figure 23: Matching triangles of the template (**a**) and the query (**b**) vein patterns in the presence of affine transformation.  
Figure 24: Two vein patterns before (**a**) and after (**b**) alignment in the presence of affine transformation.
# Statistical Modeling of NaCl and FeSO4 Pretreatment Effect on Refractory Copper Ore Leaching
## 3 Results and Discussion
### Analysis of the Impact of Each Factor on the Response  
This report presents an analysis conducted using single-factor ANOVA to examine the relationship between a continuous variable and categorical factors (the levels of an independent variable). The primary objective is to determine whether there are significant differences among the population means of two or more groups.  
The findings indicate that the \(p\)-value for the ANOVA, which assesses sulfation as a function of time, is less than 0.05. This result suggests that the responses across the various  
Figure 1: Main effects plots of sulfation (%) versus time (h); AAC (%); NaCl (g); and FeSO\({}_{4}\) (g).  
Figure 2: Percent sulfation as a function of time and AAC (a); time and NaCl (b); time and FeSO\({}_{4}\) (e); AAC and NaCl (d); AAC and FeSO\({}_{4}\) (e); and NaCl and FeSO\({}_{4}\) (f).  
levels studied differ significantly, confirming that at least some of the group means are not the same. In addition, the graph illustrating [PERSON]'s simultaneous confidence intervals reveals that the confidence interval for the difference in means between the 24 h level and the 48, 72, 96, and 120 h levels does not cross zero (refer to Figure 3). This indicates that the differences in these means are statistically significant. Conversely, the confidence intervals for the other pairs of means do intersect with zero, suggesting that those differences are not statistically significant (Figure 3a). Moreover, the residual plot derived from the single-factor ANOVA (as presented in Figure 3b) indicates that the data distribution is not skewed and does not exhibit outliers. The \(p\)-value from the normality test of the residuals further confirms that the residuals follow a normal distribution.  
This analysis provides valuable insights into the impact of time on sulfation processes and identifies significant variations among the studied levels.  
Similarly, the one-factor ANOVA for comparing the means of the response variable indicates that the means at each level for AAC are significantly different, i.e., none of the intervals of the differences include 0 (see Figure 4a), while the normality test is presented in Figure 4b.  
The impact of variations in the NaCl level on the level of sulfation indicates that the level of sulfation is higher in the presence of sodium chloride (NaCl \(>\) 0) (see Figure 4c); however, no significant differences are observed at different NaCl levels (30, 61, 91). The analysis of residuals of the single-factor ANOVA for NaCl corroborates that these are normally distributed (see Figure 4d). Finally, the ANOVA for FeSO\({}_{4}\) indicates no differences between the different levels, so it is possible to conclude that there are no significant differences in the level of sulfation at different levels of this independent variable (see Figure 4e,f).  
Figure 3: Individual confidence intervals for the differences in the means of the different levels of the time variable (**a**) and analysis of the model’s residuals (**b**).
# Soft Error-Tolerant and Highly Stable Low-Power SRAM for Satellite Applications
## 3 Simulation Results and Analysis
### Power-Consumption Comparison  
Since SRAM cells predominantly operate in hold mode, the power consumed during the hold operation, referred to as hold power, represents a considerable portion of the total power consumption of an SRAM cell [23]. This power consumption primarily arises from bit-line leakage and the leakage associated with the inverters used in the SRAM cell. Due to carrier mobility, NMOS transistors generate more leakage power than PMOS transistors through the bit lines. Furthermore, in the internal inverter structure, higher resistance in the pull-up and pull-down paths of the transistors leads to lower leakage power. Table 1 shows that the leakage power of the proposed RHSP16T cell is 101.9%, 97.08%, 78.04%, 73.79%, and 73.92% those of QUCCE12T, WE-QUATRO, RHBD10T, RHD12T, and RSP14T, respectively. The proposed 16T SRAM structure, despite having two pairs of access transistors, reduces power consumption by utilizing PMOS transistors for one pair of access transistors. Additionally, by implementing a stacking structure for the internal transistors, it demonstrated lower power consumption compared with most other SRAM cells [23].
## 3 Results
### Analysis of CNN1D Architectures for Flavonoid Content Classification  
To further evaluate our approach and assess the influence of network architecture on classification performance, we conducted additional experiments using an alternative deep learning structure. While our primary results were obtained with the ResNet1D architecture, we sought to understand the impact of its key architectural feature--namely, the shortcut connections or skip connections. To this end, we developed a simpler one-dimensional convolutional neural network (CNN1D) that shares structural similarities with ResNet1D, such as the use of convolutional layers and batch normalization, but crucially omits the skip connections. Using this CNN1D structure with both wavelet and Gabor-like filtering features, we achieved strong performance, with an AUC of 0.7911 \(\pm\) 0.0389 and ACC of 0.7336 \(\pm\) 0.0455. Following the trend observed in our ResNet1D experiments, using wavelet transformation alone yielded superior results, with an AUC of 0.8190 \(\pm\) 0.0248 and ACC of 0.7618 \(\pm\) 0.0169. Conversely, when using Gabor-like filtering alone, performance decreased notably, resulting in an AUC of 0.6809 \(\pm\) 0.0496 and ACC of 0.6817 \(\pm\) 0.0560. Similar to our ResNet1D experiments, using hyperspectral data without feature extraction led to significantly lower performance, with an AUC of 0.6555 \(\pm\) 0.0187 and ACC of 0.6510 \(\pm\) 0.0518.  
These results demonstrate that the relative effectiveness of different feature extraction methods remains consistent across architectures, with wavelet transformation consistently outperforming Gabor-like filtering. However, the overall performance metrics suggest that the ResNet1D architecture's skip connections provide a modest advantage in classification accuracy compared to the simpler CNN1D structure. This indicates that while both architectures are capable of effectively leveraging the extracted features, the additional complexity of ResNet1D's skip connections contributes to enhanced model performance. The complete performance metrics are summarised in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **ResNet1D with Wavelet** & **ResNet1D with** & **ResNet1D with** \\  & **Gas-like Filtering** & **Wavelet Transformation** & **Gabor-like Filtering** \\ \hline AUC & 0.8136 \(\pm\) 0.0159 & 0.8246 \(\pm\) 0.0180 & 0.6869 \(\pm\) 0.0531 \\ ACC & 0.7461 \(\pm\) 0.0326 & 0.7702 \(\pm\) 0.0188 & 0.6948 \(\pm\) 0.0440 \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparison of ResNet1D using different feature combinations (wavelet transformation and Gabor-like filtering).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **CNN1D with Wavelet** & **CNN1D with** & **CNN1D with** \\  & **Gas-like Filtering** & **Wavelet Transformation** & **Gabor-like Filtering** \\ \hline AUC & 0.7911 \(\pm\) 0.0389 & 0.8190 \(\pm\) 0.0248 & 0.6809 \(\pm\) 0.0496 \\ ACC & 0.7336 \(\pm\) 0.0455 & 0.7618 \(\pm\) 0.0169 & 0.6817 \(\pm\) 0.0560 \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison of CNN1D using different feature combinations (wavelet transformation and Gabor-like filtering).
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 3 Experimental Parameters' Effect on Morphology of Nanofibwers
### Effect of pH  
[PERSON] et al., studied the effect of pH on the morphology of CuO nanostructures. In their report, they concluded that a pH value equal to 10.5 is optimal for the formation of NFs [106]. [PERSON] et al., investigated the role of pH conditions on the synthesis of self-assembled _Brevibacterium_ cholesterol oxidase enzyme-containing Cu(II) HNFs (COD-Cu) and found that the optimum value for their formation was pH = 7, and for pH equal to 5 and 3, smaller-sized and not fully grown flower-like structures were obtained, while at pH = 9, no HNFs were formed [104].  
Concerning the production of HNFs, the pH of the reaction system directly affects the net charge of biomolecules, influencing their affinity to metal ions or other biomolecules, so typically, the pH value should be carefully selected according to the isoelectric point (pl) [105; 107]. A pH value lower than the pI can result in repulsion between metal ions and enzymes, while a value equal or higher than the pI can result in a neutral or negative overall change, thus facilitating the formation of NFs. Therefore, this variant has a significant effect on the morphology and properties of the immobilized enzyme. [PERSON] et al., synthesized copper-based HNFs, changing the parameter of the pH (ranging from 7.0 to 8.5) of the buffer solutions in order to test the morphology of the produced HNFs [103]. According to their results, pH values further from the isoelectric point (pI) resulted in a regular HNF morphology and larger flower-like nanocrystals.
## 3 Results  
### Vegetation Indices  
#### 3.1.1 Mean Monthly VIs  
The mean monthly NDVI for the period from 2000 to 2019 and the months from January to December are shown in Figure 3. Mean monthly VIs indicate a growing cycle throughout the year of various vegetation types, and two growing seasons can be seen, winter (Rabi) and summer (Kharif). The first part (Rabi) shows that vegetation growth starts in October and November. The second part (Kharif) indicates another vegetation growth season commencing in late April to the first week of May in the study area. From late December to the first week of January, the vegetation in the entire area tends to be senescent. Low NDVI values were also observed from April to July and October to November.  
Seasonal mean monthly dynamics of vegetation indices demonstrate seasonal growth of the vegetation, representing the trend of development for different types of vegetation. The agricultural area showed high vegetation activity during both seasons (Rabi and  
Figure 3: The NDVI means monthly seasonal dynamics. For the duration from 2000 to 2019, mean VI values were determined every month.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Definition** \\ \hline Start of the season & Time for which the left edge has increased to 30\% of the seasonal amplitude measured from the left minimum level. \\ End of the season & Time for which the right edge has decreased to 30\% of the seasonal amplitude measured from the right minimum level. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of phenological metrics.  
Kharif), while low vegetation from April to July and October to November in many parts of the study area was observed.
## 3 Results and Discussion
### LoRa Communication System  
To check the correct operation of the system, two different tests were performed (Figure 10). In both tests, the beacon was placed in an open environment to guarantee direct communication with the UAV. The objective of the first test was to verify the correct operation of the system. In this case, the UAV was manually controlled from the ground station, directing the drone toward the beacon at a low altitude. Once the communication between the UAV and the beacon was established, the data were successfully transmitted, and, finally, the UAV returned home. As shown in Figure 10a, the beacon is in the lower left part of the image, while the UAV was flying a few meters high, just when the communication between both systems started. The second test consisted of performing an autonomous flight according to a previously planned path. In this case, the UAV took off and flew toward the coordinates of the beacon at a 20 m altitude. Once the UAV reached the target coordinates, the UAV sent a message to the beacon and waited for a reply message. Once the beacon responded, a communication link was established, and the data of the beacon were transferred to the UAV. When the transmission ended, the UAV automatically returned home. To design the path and monitor UAV flight, the Mission Planner application (Figure 10b) was used. In both tests, once the UAV landed, all the information was correctly transmitted to the central system.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 3 Experiments
### Comparison Experiments
#### 3.2.2 Results on the Potsdam Dataset  
The Potsdam dataset is also one of the most widely used image segmentation datasets in remote sensing. As presented in Table 2, the Flauformer model outperformed all other models on this dataset, with an F1 score and OA of 93.1% and 91.5%, respectively. Figure 8 visualizes the test results of different methods. As shown in the upper part, the segmentation results of our model were purer, and the building edges were smoother compared to other methods. The VPANet and UNetFormer misclassified the buildings in the red box into background categories, and the Fast-SCNN's segmentation results had obvious errors. Although the DC-Swin and SegFormer also obtained good results in this region, they were inefficient and not suitable for real-time segmentation tasks. For the lower half, the inner triangle region mainly consists of low vegetation and background categories. Due to a lack of understanding of the global spatial information, the bare grass was misclassified into the impervious surfaces category by VPANet, DC-Swin, etc. Nevertheless, our method obtained better segmentation results in this region due to taking into account both global and local spatial contexts.
# Wideband Eight-Antenna Array Designs for 5G Smartphone Applications
## 8 Conclusions  
An eight-antenna array design operating across the 5G NR-bands (3300-5925 MHz) for smartphone applications has been successfully investigated. The measured 6 dB stacked bandwidth and isolation were 3.15-6.17 GHz and \(>\)10 dB, respectively. The measured total efficiency of the eight-antenna array was 51-84%. Regarding MIMO performance, the ECC remained under 0.1 across the bands-of-interest, and the maximum CC for an 8 \(\times\) 8 MIMO system reached \(>\)40 bits per second per Hertz (bps/Hz). Finally, an indoor throughput measurement was conducted, and the 4 \(\times\) 4 channel throughput was consistently measured at 2023 Mbps across the bands of interest.  
Conceptualization, G.-L.H. and C.-Y.-D.S.; methodology, C.-Y.-D.S.; software, T.-Y.C.; validation, T.-Y.C., C.-Y.-D.S. and G.-L.H.; formal analysis, T.-Y.C. and G.-L.H.; investigation, G.-L.H. and T.-Y.C.; resource, C.-Y.-D.S. and G.-L.H.; data curation, T.-Y.C. and G.-L.H.; writing--original draft preparation, T.-Y.C. and C.-Y.-D.S.; writing--review and editing, G.-L.H. and C.-Y.-D.S.; visualization, T.-Y.C.; supervision, C.-Y.-D.S. and G.-L.H.; project administration, C.-Y.-D.S.; funding acquisition, C.-Y.-D.S. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by the Ministry of Science and Technology (Taiwan), Project Number MOST-111-2221-E-035-022.  
All data are included within the manuscript.  
The authors extend their gratitude to [PERSON] of BWant Co., Ltd., Taiwan, for his assistance with the antenna measurements.  
The authors declare no conflicts of interest.
## 4 Enterprise Network Functionality
### Embedded Security Architecture: Zero Trust and SASE  
Nowadays, advanced security features have been integrated into enterprise networks to protect systems and data. Some of the important security mechanisms are zero trust network architecture (ZTNA) and secure access service edge (SASE).
# GANA-VDC: Application-Aware with Bandwidth Guarantee in Cloud Datacenters
## 4 GANA-VDC System Architecture
### VN Topology Optimization  
Every virtual data center uniquely self-defines VN topology, although some underlying physical link failure may invalidly derive VN path topology. Thus, we need network discovery to synchronize VN topology with the physical network. As for the OpenFlow network, the controller performs network discovery to provide a global topology. The network discovery learns the switch connections in the control domain leveraging LLDP and discovering whether a physical link between switches exists. If a switch receives an LLDP message on one direction of the link Packet-Out on the other side of the link Packet-In, we will know the two switches are connected. Regardless of the network discovery iterations over all physical network ports, it sends out LLDP packets periodically. This provides the  
Figure 5: OpenFlow based source routing.  
rationale for the VDC to obtain its VN topology. Concerning VDC, the requested virtual resources are mapped onto the physical network by an embedding algorithm of FlowVisor. Nevertheless, this does not suggest this algorithm can address connections from the virtual topology to an outside physical network. Indeed, a mapping mechanism remains necessary for this. Depending on the complexity of the mapping mechanism, the mappings per second has an upper limit as to how many FlowVisors it can handle. In some cases, a quick search can be provided using hashing. For example, when the physical link fails, it affects multiple tenants. When the FlowVisor mechanism, the iteration of all tenants, is performed to obtain the real link status, the physical identifier of each tenant is queried. As the number of VDCs expands to a larger scale, heavy burden due to frequent mapping and de-multiplexing the LLDP messages imposes on the FlowVisor degrading performance when too many LLDP messages appear on the switch flowtable.