esa-sceva/satcom-chunk-collection huggingface dataset:
# Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder
## 3 Designing a Machine-Learning-Based Decoder for the Heavy Hexagonal Code
### Error Correction
The measurement syndrome is where the flag qubit turns out to be nontrivial in the \(X\)-type gauge operator measurements when \(X\)-errors occur on qubits, and the measurement of erroneous data qubits occurs during \(Z\)-stabilizer measurements. Similar to \(X\)-error, the \(Z\)-error captures the measurement syndrome of the data qubit by judging the flag qubit's measurement results, thereby inferring the data qubit where the error occurred, and finally, by looping the measurement circuit to extract the syndrome information and analyze the syndrome information to correct errors that occurred on the qubit. However, due to the complexity of the combination of syndrome information, it is difficult for us to select them with commonly used statistical methods. Therefore, we now propose a machine-learning-based CNN decoder [33; 34] where we exploit the translation invariance of the heavy hexagonal code to feed error syndromes to a convolutional neural network for a faster determination of optimal syndromes and achieve the maximum good threshold by optimizing the condition [35]. In this paper, we focus on the degree of local correlation of error syndromes depending on the distance of the heavy hexagonal code [36].
###### Abstract  
GaN films doped with Mg or Zn were obtained via radio-frequency magnetron sputtering on silicon substrates at room temperature and used laboratory-prepared targets with Mg-doped or Zn-doped GaN powders. X-ray diffraction patterns showed broadening peaks, which could have been related to the appearance of nano-crystallites with an average of 7 nm. Scanning electron microscopy and transmission electron microscopy showed good adherence to silicon non-native substrate, as well as homogeneity, with a grain size average of 0.14 \(\upmu\)m, and 0.16 \(\upmu\)m for the GaN films doped with Zn or Mg, respectively. X-ray photo-electron spectroscopy demonstrated the presence of a very small amount of magnesium (2.10 mol%), and zinc (1.15 mol%) with binding energies of 1303.18, and 1024.76 eV, respectively. Photoluminescence spectrum for the Zn-doped GaN films had an emission range from 2.89 to 3.0 eV (429.23-413.50 nm), while Mg-doped GaN films had an energy emission in a blue-violet band with a range from 2.80 to 3.16 eV (443.03-392.56 nm). Raman spectra showed the classical vibration modes A\({}_{1}\)(TO), E\({}_{1}\)(TO), and E\({}_{2}\)(High) for the hexagonal structure of GaN.  
type: GaN; radio-frequency magnetron sputtering; incorporation; film +
Footnote †: journal: Physics Letters B  
_Article_  
Effects in the Optical and Structural Properties Caused by Mg or Zn Doping of GaN Films Grown via Radio-Frequency Magnetron Sputtering Using Laboratory-Prepared Targets  
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## 3 Results and Discussion
### Screening for the Ability to Utilize Locust Bean Gum as the Donor Substrate  
MALDI-TOF MS screening for transglycosylation with the polymeric galactomann local bean gum (0.4 _w/w_% LBG) as the donor substrate was carried out with acceptor concentrations as described in Section 2.5 in the presence of either 12 nkat/mL. _Bo_Gal36A or 8 nkat/mL Aga27A at 37 \({}^{\circ}\)C over 24h. Aga27A maintained its ability to perform transglycosylation (Figure 3 and Figure S7) with all of the screened acceptors except propargyl alcohol (Table 3). For _Bo_Gal36A, on the other hand, a peak corresponding to transglycosylation products was only detected with methanol as the acceptor.  
The ability of Aga27A to utilize a polymeric substrate as a donor substrate with a wide range of acceptor molecules makes it a particularly interesting enzyme. We recently showed that Aga27A was able to produce allyl galactoside when incubated with LBG [25], and to our knowledge, only one other \(\alpha\)-galactosidase was described where a polymeric substrate was used as glycosyl donor, with glycerol being the only evaluated acceptor [21].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Acceptor** & _Bo_**Gal36A** & **Aga27A** & **Product** \\ \hline Raffinose & + & + & Raffinose-Gal \\ \hline
10\% Methanol & + & + & Methyl-Gal \\ \hline
5\% Propanol & + & + & Propyl-Gal \\ \hline
10\% Glycerol & + & + & Glycerol-Gal \\ \hline
5\% Allyl-OH & N.D. & + & Allyl-Gal \\ \hline
2.5\% Propargyl-OH & N.D. & + & Proppargyl-Gal \\ \hline Enzyme, raffinose and acceptor incubations and product analysis with MALDI-TOF MS as presented in Figure 2, Figures S5 and S6. Enzyme concentration 12 nkat/mL, raffinose concentration 0.4 M, acceptor concentration (_w_%): 10% methanol, 5% propanol, 10% glycerol, 5% allyl alcohol, 2.5% propargyl alcohol. Time 24 h, temperature 37 \({}^{\circ}\)C. & \\ \end{tabular}
\end{table}
Table 2: Screening of transglycosylation products from incubations with raffinose as donor and different acceptors with _Bo_Gal36A and Aga27A. N.D., not detected.  
Figure 3: MALDI-TOF MS spectra of incubations with LBG. 8 nkat/mL. Aga27A was incubated with 0.4 _w/w_% LBG alone (**left**) or with the addition of 5 _v/w_% propanol (**right**) at 37 \({}^{\circ}\)C for 24 h. A peak corresponding to galactose (theoretical \(m/z\) for [m+Na\({}^{+}\)]: 203.05), marked G1 in the spectra, was observed in both spectra. When incubated with LBG alone, transglycosylation products were observed in the form of a disaccharide (theoretical \(m/z\) for [m+Na\({}^{+}\)]: 365.10). With propanol added, Aga27A generated transglycosylation products in the form propyl-G1 (theoretical \(m/z\) for [m+Na\({}^{+}\)]: 245.10).
## 3 Tests and Results
### The Evaluation of Low-Temperature Performance for Modified Asphalt
#### 3.2.3 Results of Creep Test  
The curves of creep compliance of BRA compound-modified asphalt in \(-6\)\({}^{\circ}\)C, \(-12\)\({}^{\circ}\)C, and \(-18\)\({}^{\circ}\)C are shown in Figure 9. From analyzing the creep compliance in Figure 9, conclusions can be drawn as below:  
Figure 8: Thermoelectric bending beam rheometer (BBR) and specimen: (**a**) metal die of BBR; (**b**) asphalt beam of BBR; (**c**) thermoelectric bending beam rheometer.  
Figure 9: Creep compliance curve of different compound-modified asphalts: (**a**) the curves of creep compliance of BRA compound-modified asphalt in \(-6\)\({}^{\circ}\)C; (**b**) the curves of creep compliance of BRA compound-modified asphalt in \(-12\)\({}^{\circ}\)C; (**c**) the curves of creep compliance of BRA compound-modified asphalt in \(-18\)\({}^{\circ}\)C.  
1 Comparison under the same temperature 1. With the increase of the amounts of SBR, the creep compliance of BRA/SBR compound-modified asphalt will be increased, which means that SBR can improve the flexibility of BRA-modified asphalt. So, the low-temperature performance of BRA-modified asphalt can be improved. 2. The creep compliance of BRA/nano-CaCO\({}_{3}\) compound-modified asphalt slightly increased with an increase of the mixing amount of nano-CaCO\({}_{3}\) at the same temperature, which means that the low-temperature performance of BRA/nano-CaCO\({}_{3}\) compound-modified asphalt slightly improved. Moreover, the improving effect of nano-CaCO\({}_{3}\) on the low-temperature performance of BRA-modified asphalt was less obvious when the temperature was lower.
2 Comparison under same time 1. When the temperature was lowered, the compliance of two kinds of BRA compound-modified asphalt decreased, and the modified asphalt hardened. 2. The creep compliance of BRA/SBR polymer compound-modified asphalt was larger than BRA-modified asphalt. 3. As the temperature decreased, the creep compliance of BRA/nano-CaCO\({}_{3}\) compound-modified asphalt was close to that of BRA. It means that the improving effect of nano-CaCO\({}_{3}\) on the low-temperature performance of BRA-modified asphalt was not obvious.  
In conclusion, BRA/SBR polymer compound-modified asphalt containing 5% SBR had the greatest creep compliance compared with the other asphalts in the same temperature and the same test time. So, SBR can improve the low-temperature performance of BRA-modified asphalt, and its suitable content is 5% in BRA-modified asphalt.
## 2 Methodology
### Listening Procedure  
All experimental speech stimuli produced by the five participant speakers were randomized for both stimulus items and speakers, and a spoken number preceded each stimulus item. For all speech stimuli, the carrier phrase was removed; hence, listeners were only presented with single words as part of the listening and transcription task. Participant listeners were allowed to move through the listening process at their own pace; however, listeners were asked to listen to the stimulus item only once prior to providing their transcription of the target word. All listeners were presented with a total of 730 stimuli which included 150 stimuli (5 speakers \(\times\) 30 items) from the NIT, 250 stimuli (5 speakers \(\times\) 50 items) from the CRT, and 330 stimuli (5 speakers \(\times\) 66 items) from the W&B list.  
Experimental stimuli were presented through circumferential headphones (JVC H-610) in a sound-treated audio booth (IAC 11648) at a listening level judged to be comfortable by each individual listener. Each listener was permitted to listen to and transcribe their responses to each stimulus item at their own pace. Listening sessions required a total of approximately two and a half hours with opportunities for two brief break periods (approximately 5 min each) provided during the experimental session. However, upon request by several listeners, brief additional breaks were provided. All listeners completed the task without attrition.  
Prior to commencing the listening session, all listeners were instructed that they would be presented with a series of real English words and once presented, they were to manually transcribe their response to each stimulus either phonetically or orthographically based on exactly what they believed they heard [52]. More specifically, listeners were permitted to use standard orthographic only if they believed that they could fully and accurately represent their judgment using orthography for any given target; this option was carefully explained to each listener prior to initiating the listening task. In those instances, listeners were requested to identify any orthographic response by identifying it on the score sheet with the highlighter provided. If listeners were unsure of any stimulus that was presented, they were requested to provide a phonetic transcription of what they heard rather than what they may have thought was the target word. If a particular sound within a word could not be identified by the listener, they were asked to place a hyphen in the location of the word where that occurred. Thus, an open-set response paradigm was utilized. Although all speaker stimuli for any given list were randomized, the presentation of word lists (NIT, CRT, and W&B lists) was counterbalanced to control for potential list order effects. That is, while stimuli within each of the three individual lists were randomized, the order of list presentation varied in a pseudo-random manner across listeners.
# Automatic Selective Encryption of DICOM Images
## 4 Performance Analysis and Discussion
### Approach II
#### 4.2.2 Time Performance and Discussion  
As the main goal of the reported approaches in this section is to reduce the encryption time while maintaining high security, the encryption times of the proposed approaches are compared amongst each other and against the Naive encryption approach, where all frames' pixel data are encrypted using AES cipher with CTR mode of operation.  
The proposed Frame-Based SE is ~10,000 times faster than the Holistic-Based SE and the Naive encryption approach as shown in Table 6. The main reason for this high speed for the Frame-Based SE is the fact that it relies on an optimized matrices multiplication library that significantly reduces the segmentation time. Moreover, the compression greatly reduces the sizes of the selected data to be encrypted as displayed in Table 7. Hence, the Frame-Based SE can significantly reduce the required time to secure the multi-frame DICOM images.  
Figure 12: (**a**) the original image, (**b**) the encrypted image (Frame-Based SE), and (**c**) the encrypted image (Holistic-Based SE).  
Figure 13: (**a**) Original image histogram, (**b**) Encrypted image (Frame-Based SE) histogram, and (**c**) Encrypted image (Holistic-Based SE) histogram.  
Table 7 shows the data sizes before and after encryption. It can be noticed that the Frame-Based SE has a compression ratio of (146:1), which significantly reduces the image size. On the other hand, Holistic-Based SE has the worst compression ratio, which is (1.3:1).  
To sum up, this is a novel approach that selectively encrypts the ROI in a multi-frame DICOM object using a sophisticated encryption algorithm (AES), while encrypting the ROB using a lighter encryption algorithm (XXTEA). The selection process in this approach is executed either in an automatic manner (combining the pre-defined reference frame and automatic thresholding) or by segmenting the pixels from all DICOM frames using the thresholding technique [25] in one go. Accordingly, it was noticed that the Frame-Based SE is very efficient and has a very short processing time with an efficient compression ratio. The novelty of this approach depends on improving redundancy within different frames by segmenting images into ROI and ROB based on a segmentation map that is obtained based on the automatic selection of a reference frame and the thresholding segmentation [25]. Therefore, the proposed approach can be used in various security frameworks to achieve various security goals, such as confidentiality.
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
## 3 Materials and Methods
### Technology Overview and Operation in LoRaWAN
Figure 2: LoRaWAN Classes A, B, and C.  
applications. This means that Class A nodes have the best energy efficiency, but they also have the lowest data throughput. 2. Class B: This class works by periodically opening receive windows to receive downlink messages. These receive windows are called ping slots. The network server broadcasts a time-synchronized beacon periodically through the gateways, which is received by the end devices. The end devices then use this timing reference to schedule when ping slots are opened to receive potential downlinks from the network. The parameters and times shown in Figure 3 correspond to the recommendations found in the standard. The number of ping slots (_pingNb_) available during a beacon window can be derived from the periodicity parameter, referred to as k for the rest of this article. It is fixed by the standard as in (2) by setting the number of ping slots usable by a node in each beacon period from 1 to 128 ping slots, as follows:  
\[pingNb=2^{7-k}\;\text{where}\;0\leq k\leq 7 \tag{2}\]  
The ping period is the delay between two ping slots and it is constant; moreover, it is calculated as follows:  
\[pingPeriod=\frac{4096}{2^{k}}\;\text{slots} \tag{3}\]
3. Class C: This class of LoRaWAN nodes can receive downlinks at any time (provided the sender complies with duty cycle regulations). This gives Class C nodes the highest data throughput, but it also has the lowest energy efficiency. The choice of the LoRaWAN node class depends on an application's requirements. Class A nodes are best suited for applications where energy efficiency is critical, such as environmental monitoring. Class B nodes are a good compromise between data throughput and energy efficiency, and they are suitable for a wide range of applications. Class C nodes are best suited for applications where data throughput is critical, such as actuator control. Finally, polling scheduling then depends on the network devices support for multicast messages, which was introduced by the LoRaWAN Remote Multicast Setup v1.0.0 Specification [17], which preferably needs nodes to support the LoRaWAN 1.1 Specification [15] in their firmware. As many devices are still only compatible with previous versions of this specification, the solutions proposed for polling consider both unicast and multicast support.
# GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
## 3 Methodology  
The GSV-NET was inspired by visual and structural complementary techniques (see reference [15]). We developed a new framework to handle the drawback of MIFN and MVCNN methods mentioned in the related work section. First of all, we choose the GSV representation and a novel 3D wide CNN instead of using a raw point cloud and MLP structure. The 3D CNN performs better than MLP in classification tasks (see reference [20]). Next, we replace the 2D view and panorama view in MIFN with the 2D color image containing region information. The region image provides more visual information than view and panorama view. As a result, GSV-NET reduces the number of images in MIFN from 24 to 3. Furthermore, we change the two-stage 2D CNN to a new 2D wide CNN. Lastly, we use 1D CNN to fuse the 1D vectors of the extracted features while MIFN employs features concatenation. The 1D CNN gives better performance compared to 2D CNN in the classification of the 1D signals (see reference [37]). GSV-NET employs two models, while MIFN uses three models. The numerical result verifies that our framework has the highest performance among methods.  
Figure 1 shows the structure of the proposed framework. Firstly, we describe a point cloud as the input using the Gaussian Supervector representation, then put the GSV result in a 3D CNN architecture to extract the global point cloud feature. We employ a mixture of Gaussians with Gaussian centers on a uniform 3D \(m\times m\times m\) grid with optimal value \(m=8\) for the underlying density design, as mentioned in reference [20]. Secondly, we divide the original 3D point cloud into different regions based on the point locations. Next, we create three 2D point clouds (x-y, x-z, and y-z) and use 2D wide CNN to extract the enhancing region features of these 2D point clouds. Finally, a 1D CNN fuses the extracted features and creates the final point cloud feature. The following section will fully describe our framework: Gaussian Supervector representation in Section 3.1, enhancing region representation in Section 3.2, and 1D CNN feature fusion in Section 3.3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & & & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} **  
Global Point Cloud Feature Extraction with Gaussian Supervector Representation and 3D Wide Inception
## 3 Coplanar Dipole Production Technology  
Three different technological processes were used to produce the patch antennas. An etching technology was used for the production of the patch antenna on the Arlon 25N substrate (Figure 8b). The antenna on the FR4 substrate was produced by CNC technology (Figure 8a). The 3D-printed antenna is shown in Figure 8c. The production of antennas by etching or CNC technology is sufficiently described in the available literature [18]. We only further analyzed the 3D printing using LDM (lights-out digital additive manufacturing) technology.  
Figure 6: Radiation patterns of far-field in the E-plane (**a**) and H-plane (**b**) at 4.7 GHz for a coplanar dipole on 3D substrate and simulated in CST.  
Figure 7: Current distribution on coplanar dipole—frequency 3.1 GHz.
# High-Frequency Workpiece Image Recognition Model Based on Hybrid Attention Mechanism
## 2 Approach
### Improved Lightweight Convolutional Block Attention Module (ILCBAM)
#### 2.3.1 Improved Channel Attention Module (ICAM)  
The collected workpiece image is an RGB three-channel color image. Some color channels contain information that is greatly affected by illumination changes, while some color channels contain information that is insensitive to illumination changes. The purpose of the CAM is to explicitly establish the interdependence between color feature channels in the original image and then automatically obtain the importance of each color feature channel through learning. According to their importance, the attention to useful color feature channels is increased, and the color feature channels that are unfavorable to the current image recognition task are suppressed to minimize the impact of illumination changes. Since the maximum pooling processing mechanism is used in the CAM, a large amount of useful information is lost, so the ICAM uses global power pooling to replace global maximum pooling and then uses one-dimensional convolution to make the network focus on the learning of effective channels with less computation. Its structure is shown in Figure 6.  
The input feature map \(\mathbf{X}_{1}\in\mathbb{R}^{C\times H\times W}\) is subjected to global power pooling and global average pooling to obtain the channel information description maps:  
\[z^{p}=\left(\frac{1}{H\times W}{\sum_{h=1}^{H}{\sum_{w=1}^{W}{\mathbf{X}_{1}(h,w)^{ p}}}}\right)^{\frac{1}{p}}, \tag{2}\]  
\[z^{m}=\frac{1}{H\times W}{\sum_{h=1}^{H}{\sum_{w=1}^{W}{\mathbf{X}_{1}(h,w)}}}, \tag{3}\]  
where \(C\), \(H\), and \(W\) represent the number of feature channels, height, and width of the image, respectively; \(h\) and \(w\) represent the coordinates in the height and width directions, respectively; and \(p\) is set to 2 to highlight the local salient features.  
\(z^{p}\) and \(z^{m}\) accumulate global information in different ways and then perform one-dimensional convolution on the information. After the convolution and activation combination operation, the channel attention weight \(M_{\text{c}}\in\mathbb{R}^{C\times 1\times 1}\) is obtained.  
\[M_{\text{c}}=\sigma\Big{(}F_{p}^{k}(z^{p})\Big{)}\bigotimes\sigma\Big{(}F_{m} ^{k}(z^{m})\Big{)}, \tag{4}\]  
Figure 5: ILCBM structure diagram.  
Figure 6: ICAM structure diagram.  
where \(F_{p}^{k}(\cdot)\) and \(F_{m}^{k}(\cdot)\) represent one-dimensional convolution operations, \(k\) represents the convolution kernel size of one-dimensional convolution, and \(\sigma(\cdot)\) represents the sigmoid function.  
The one-dimensional convolution kernel size \(k\) can be adaptively calculated by formula 5 [29]:  
\[k=\Psi(C)=\left|\frac{\log_{2}(C)+b}{\gamma}\right|_{odd}, \tag{5}\]  
where \(C\) represents the number of channels, \(\gamma\) and \(b\) are set to 2 and 1, respectively, and odd represents the nearest number.
## 4 Results and Analysis
### EV Penetration in Rural Network  
#### 4.2.1 Case 5: 20% EV Penetration in Rural Network  
The set of analysis performed on the rural network, is based on the grid of Figure 12 focusing on the MV/LV transformer feeding 12 LV households.  
The results obtained in this scenario, are presented in Figure 13. The base case shows that the MV/LV transformer is less loaded with respect to the case of the urban network, serving a lower number of customers. Thus, the impact of EV in still evident, but even during the peak period, the maximum value of 86.53% is highly tolerable, in fact the increment, with respect to the base case is around 15%. The voltage profile is very similar in each case, meaning that currently this level of diffusion of EVs is acceptable without any interventions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**EV Penetration** & **Max. Loading (\%)** & **Time Point Max.** & **Min. Loading (\%)** & **Time Point Min.** \\ \hline
20\% & 121.811 & 19:00:00 & 18.471 & 13:00:00 \\
40\% & 132.561 & 19:00:00 & 23.252 & 14:00:00 \\
60\% & 139.561 & 19:00:00 & 28.952 & 14:00:00 \\
80\% & 152.561 & 19:00:00 & 29.742 & 07:00:00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Urban transformer loading data.  
Figure 12: 15 kV rural distribution network.