esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper proposes a framework for the classification of weld defects in bus-bars and interconnect bars. Firstly, a dataset of weld images taken under different illumination and angles is constructed. Then, to generate weld images with defects when the number of defect samples is extremely limited, this paper proposes FastGAN to augment defect data, the qualitative and quantitative superiority of which was demonstrated over BAGAN, BAGAN-GP, RSSA, and WaveGAN. In addition, we combine MobileNetV2 and a Visual Transformer to extract convolutional and self-attention features of input images for classifying weld types. Our experiments demonstrate that our proposed model outperforms networks that only extract convolutional or sub-attention features for the classification of weld seams. And, the model size is small. By adding geometric augmentation and using FastGAN-generated data for training, the accuracy of the network model was further improved.  
The minority sample generation methods and defect classification algorithms proposed in this paper can also be applied to the analysis of other similar images in industrial production, especially in cases where defect samples are scarce. However, the key limitation of this study is the scarcity of real samples available for generating defect images in certain categories, resulting in limited diversity in the generated images and potential challenges in model generalization. Future research could explore the selective addition of noise to enhance diversity within the sample data. As the number of defect samples increases, incorporating semi-supervised learning methods for defect classification could also be considered.  
[PERSON] conceived and designed the study. [PERSON] and [PERSON] provide a lot of guidance and advice. [PERSON] gathered the data. [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52394164) and the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), grant number 311021013.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Model Size** \\ \hline No Fusion & 96.64\% & 98.15\% & 96.70\% & 97.37\% & 30.9 M \\ Fusion & 98.19\% & 99.27\% & 97.99\% & 98.59\% & 39.9 M \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of ablation.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Parameters/M** & **FLOPs/G** & **Run Time** \\ \hline No Fusion & 4.02 & 1.23 & 1:20:35 \\ Fusion & 4.94 & 1.44 & 1:48:01 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Computational complexity and runtime of models.
## 3 Results and Discussion  
### Melissopanlyological Analysis  
Pollen analysis of honey, also known as melissopanlyology, is crucial for quality control. Honey consistently contains numerous pollen grains and honeyde elements, which together provide a detailed fingerprint of the environment from which the honey originates. Therefore, pollen analysis can be useful for determining and verifying the geographical and botanical origins of honey. However, sensory and physicochemical analyses are also necessary for an accurate identification of its botanical origin [28]. The melissopanlyological analysis proved the label declaration of the botanical origin of the honey samples taken in the study (Table 2). It is reported that the _Tilia_ sp. pollen content in linden honey varies widely across the world, with the highest being 50-99% in the Russian Far East and northeast China [29], while in Hungarian samples, _Tilia_ sp. pollen was found to have 5.3-66.4% [6]. According to the analysis of 50 acacia honey samples in Ukraine, it was reported that the pollen rate varied from 5-25% [30]. In Croatia, it was found to be 11-71% in 200 acacia honey types [31]. In Turkiye, it was reported that the pollen rate was 90% or above in 43 chestnut honey samples collected in the province of Sinop [32]. [PERSON] et al. specified that samples of chestnut honey must contain over 86% chestnut pollen because _Castanea sativa_ is a plant with over-represented pollen [28]. In some cases, underrepresented pollen could originate from species in genera such as _Robinia_ (7-60%), _Thymus_ (13-68%), and _Tilia_ (1-56%), and the underrepresented pollen normally belongs to _Citrus_ species, with limits ranging from 2 to 42%. The regulations of Turkiye specify the following pollen lower limits: 70% for chestnut honey, 10% for citrus and thyme, and 15% for acacia honey [33]. _Fallopia_ honey is a new type that is being studied, and the limits of the pollen content are still being refined [34]. The pollen interval for _Rubus_ sp. honey presents a high variability, as also noticed by [PERSON] et al. who determined an interval between 46.4% and 91.3% [35]. These aspects are highly variable when comparing
# Theoretical Optimization of Trapped-Bubble-Based Acoustic Metamaterial Performance
###### Abstract  
Acoustic metamaterials have proven to be a versatile tool for the precise control and manipulation of sound waves. One of the promising designs of acoustic metamaterials employ the arrays of bubbles and find applications for soundproofing, blast mitigation, and many others. An obvious advantage of bubble-based metamaterials is their ability to be relatively thin while absorbing low-frequency sound waves. The vast majority of theories developed to predict resonant behavior of bubble-based metamaterials capitalize on Minnaert frequency. Here, we propose a novel theoretical approach to characterize bubble-based metamaterials that are based on our previous findings for a single bubble trapped in circular cavity modeled as a thin clamped plate. We obtain analytical expressions for resonant frequencies of bubble metascreens using self-consistent approximation. Two geometry factors, distance between bubble centers and distance between bubble center and interface of acoustic impedance change, are taken into account. We demonstrate the existence of multiple bandgaps and possibility of switching between them via adjustment of geometry parameters and reflector properties.  
Keywords:resonant frequency; acoustic metamaterials; soundproofing; oscillating bubbles; coupling +
Footnote †: journal: Applied
## 2 Materials and Methods  
### Dataset and Preprocessing  
The EEG dataset was provided by the University of Sheffield, from an open study and under a relevant NDA. All subjects were informed about the experiment and signed an informed consent form. The dataset consists of 12 s, eyes-open recordings of 20 AD-diagnosed patients and 20 age-matched HC, younger than 70 years of age (Table 1).  
The participants' EEGs were recorded using the International 10-20 system [25]. To reduce volume conduction effects from a common reference [26], 23 bipolar derivations were used in this study. Figure 1 gives an overview of the electrodes and bipolar channels. More specifically, the following bipolar channels were used: F8-F4, F7-F3, F4-C4, F3-C3, F4-FZ, FZ-CZ, F3-FZ, T4-C4, T3-C3, C4-CZ, C3-CZ, CZ-PZ, C4-P4, C3-P3, T4-T6, T3-T5,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **AD** & **HC** \\ \hline
**Size** & \(N=20\) & \(N=20\) \\
**Age** & 60 (\(SD=4.40\)) & 61 (\(SD=6.67\)) \\
**Gender (F/M)** & 8/12 & 12/8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: General information of the AD and HC groups including sample size, age mean with standard deviation, and gender ratio per group.  
P4-PZ, P3-PZ, T6-O2, T5-O1, P4-O2, P3-O1, and O2-O1. These bipolar channels are the most commonly used in clinical practice [27]. During the recording, the participants were instructed to reduce their movements and not to think of anything in particular (i.e., resting state EEG).  
The raw EEG signals were preprocessed in EEGLAB (v.2021.0), MATLAB. First, the signals were downsampled to 500 Hz. Next, a band-pass filter was applied between 0.1 and 100 Hz using EEGLAB functions. This was done following the requirements used for the phase synchronization (see Section 2.3. 'Functional Connectivity Analysis') to avoid phase distortion. Additionally, a notch filter was used to attenuate signals in the 48-52 Hz range.
## 3 Results and Discussion
### Melt Processing of Green Composites
In particular, the generation of these molecular entanglements could result in an increase in torque with time due to the formation of macromolecular structures with higher melt strength. In this regard, one should consider that these compatibilizing agents are chemical substances that are able to establish covalent bonds with different groups of the biopolymer and, in the case of composites, give rise to grafting processes between the functional groups on the surface of the lignocellulosic with the terminal groups of the biopolymer. This process is called \"reactive extrusion\" and it is observed in the processing curves by an increase in torque with time, that is, the molten biopolymer shows a greater resistance to flow after the melting of the biopolymer. Thus, the use of reactive compatibilizing agents produced an increase in viscosity, that is, a melt with a higher torque during mixing, either by an effect of extension or linear growth of the chains (\"chain extension\"), branching, or cross-linking [42]. Finally, one can also observe that, in terms of comparison performance, similar increases in torque were attained for the unfilled PHBV and PHBV/MFC samples with TGIC and DCP and the composite with ESAO. In the case of the isocyanurate, it exhibited higher reactivity with PHBV, while both exerted a large effect on the grafting of MFC onto PHBV. Also, it is noteworthy that the torque increase was seen to take place 20 s faster for TGIC and DCP than ESAO, which can be ascribed to the presence of the peroxide, offering the benefit to facilitate its processability at a larger extrusion equipment. Figure 4: Representation of torque (M) as a function of time during melt mixing of the following: (**a**) poly(3-hydroxybutyrate-_co_-3-hydroxyalerate) (PHBV) for 25 min; (**b**) PHBV without and with microfibrillated cellulose (MFC) at 2.5, 5, and 10 wt % for 5 min; (**c**) PHBV without and with epoxy-based styrene-acrylic oligomer (ESAO) and with triglyceride isocyanate (TGIC) and dicumyl peroxide (DCP) for 5 min; (**d**) PHBV/MFC at 5 wt % without and with ESAO and with TGIC and DCP for 5 min.
# Radar Error Correction Method Based on Improved Sparrow Search Algorithm
## 3 Improved Sparrow Search Algorithm
### Improved Sparrow Search Algorithm
#### 3.2.5 Improved Sparrow Search Algorithm  
The process of the ISSA is shown in Figure 3, which can be roughly divided into the following six steps:  
1. The initialization of the algorithm, randomly generating candidate populations, using chaotic mapping and reverse learning to expand the population range, sorting and selecting well-performing individuals as the initial sparrow population.
2. Calculate individual fitness value and location.
3. Adaptively adjust the number of producers and scroungers according to the iteration schedule.
4. Update the position of producers according to the osprey global search strategy and update the position of scroungers and guards according to the sparrow search mechanism.
5. The dynamic selection of the adaptive _t_-distribution perturbation strategy was used to accelerate the convergence speed and further find the optimal individual, calculate the individual position and fitness values after the disturbance.
6. Determine whether the algorithm has reached the maximum number of iterations. If it has reached the maximum number of iterations, the current optimal solution is output as the radar systematic error estimation result; otherwise, return to step 2.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 3 Vulnerabilities
### Covert Channels  
Covert channels, as an attacker tool, represent a way to transmit sensitive information such as secret keys with a transmission medium, which is often not intended for communication purposes [54]. A classical example includes an optical covert channel, where the intensity of, e.g., an led light is modulated to transmit data. Typical victim devices of covert channels are primarily air-gapped systems and secure-server infrastructure but today, proof-of-concept covert channels with smartphones and IoT gadgets have been presented as well [55]. In the case of LoRaWAN, the authors of [45] presented a hidden communication channel built on top of LoRa signaling. The key component of the method involved embedding-amplitude modulation to a physical LoRa payload. Since LoRa receivers are effectively utilizing frequency demodulation to extract the chirp symbols, an additional amplitude component remains hidden from legitimate users. The authors demonstrated the functionality of the channel with two experimental setups. Firstly, amplitude-modulated LoRa payloads were generated fully in software with gr-lora [53] and transmitted by SDR transmitter. Secondly, amplitude modulation could be achieved with commodity LoRa transceivers by changing the impedance of the transmission line between the LoRa modem and the antenna. The hidden amplitude information can be extracted with an envelope detector and a symbol-recovery mechanism. According to the authors, a covert-communication range of up to 250 m, with approximately 38 bits per a typical LoRa packet, can be achieved by such a signaling technique. Thus, a leakage of **AppKey** (or **AppSKey**) can be devised with only five LoRaWAN packets.  
Figure 5: A software-defined multichannel LoRaWAN receiver in GNU Radio.
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
## 2 Single-Element Antenna
### Parametric Analysis  
Various parameters, like the feedline gap, width of the feedline, radius of the ground plane, and length of the stub, that help in achieving the desired wider bandwidth and good impedance matching were optimized using parametric analysis. However, the ground plane radius (R2) and the length of the stub are the critical parameters that help in achieving the desired bandwidth and therefore are the only ones highlighted in Figure 3. Increasing the ground plane's radius alters the current direction, which lowers the antenna's quality factor and boosts the operational bandwidth. The band of interest is achieved with the help of the stub's length optimization. By increasing the stub length, the antenna's effective length is changed, which moves the frequency to the lower range.  
Figure 2: Simulation results of evolution stages: (**a**) reflection coefficients; (**b**) gain over frequency and (**c**) radiation pattern (E and H plane) calculated at a center frequency of 5 GHz.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 8 Simulation
### Wi-Fi Wireless Network
#### 8.1.2 Main Tank Sector 2  
As shown in Figure 3, sector 2 covers three wells, which are ww1, ww2, and T/1/676/0/96. The results show that the links for wells ww2 and T/1/676/0/96 are achieving the minimum margin at 15 dBm. On the other hand, well ww1 requires a higher Tx power at 19 dBm, and the reason for this is that ww1 is further away from the BS at 2.39 km, resulting in a higher path loss due to the distance, difference in elevation, and terrain as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sector/Link**} & **Distance** & **Antenna Type** & **Antenna Type** & **TX Power** & **Antenna** & **RSSI** & **Fresnel** & \\  & **(km)** & **(Base)** & **(Remote Node)** & **(dBm)** & **Height** & **Margin** & **Clearance** & **Status** \\ \hline Main Tank & & & & & & & & \\ Sector 1 & & ANT793-6 DG & & & 5 & & & \\ \hline T/1/364/0/96 & 2.53 & ANT793-6 DG & ANT793-8 DK & 16 & 5 & 10 & \(\sim\)80 & Feasible \\ \hline \hline \end{tabular}
\end{table}
Table 4: Link Parameters for Sector 1.  
Figure 11: Transmit Power vs. RSSI for wells connected to Sector 1.  
Figure 12: Radio Mobile result for well T/1/364/0/96 at 16 dBm.  
Figure 14 demonstrates that the RSSI margin in ww1 increased by approximately 1 dB for each incremental rise of 0.5 m in antenna height, while the path loss decreased. This trend of increasing signal margin continued to a height of 7 m, beyond which the margin gains were only slight. Therefore, at this height, ww1 was able to reach the margin at 17 dBm, which is an improvement over the previous results as shown in Figure 15. The final link parameters are summarized in Table 5.  
Figure 14: ww1 RSSI margin and path loss values at different heights.  
Figure 13: Transmit Power vs. RSSI for wells connected to Sector 2.
## 5 Results and Discussion
### Datasets  
For academic research, a few malware databases are accessible. Malimg [4] is a frequent dataset. The Malimg dataset was used to assess the performance of the proposed  
Figure 8: ELM model design [35].  
technique. The Malimg dataset includes 9339 malware photos from 25 different families and classes. According to Table 1, each sample belongs to one of the 25 malware families. Therefore, we aim to create a multi-class categorization of malware. Malware graphics come in a variety of widths and lengths. Various malware families, including Dialer, Backdoor, Worm, Worm-AutoIT, Trojan, Trojan-Downloader, Rouge, and PWS, were used to build the photos. It makes sense that the Malimg dataset shares a lot of similarities in terms of texture and size. As a result, classification is simple for a classifier to perform. While variations of some malware families are shown to be extremely unique in other datasets. There is a substantial imbalance in the Malimg dataset. More than 30% of the photos fall under class 2 (Allaple.A), whereas 17% fall under class 3 (Allaple.L) as shown in Figure 9. The Malimg dataset's malware samples are represented as grayscale pictures with a range of (0: black--255: white). Figure 10 illustrates diverse image textures from distinct malware binary file segments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class ID**} & \multirow{2}{*}{**Family Name**} & \multicolumn{2}{c}{**Description**} \\ \cline{3-4}  & & **Malware Kind** & **Sample No.** \\ \hline
**\#1** & **A**dialerC & **D**ialer & **122** \\ \hline
**\#2** & **Agent.FYI** & **Backdoor** & **116** \\ \hline
**\#3** & **Allaple.A** & **Worm** & **2949** \\ \hline
**\#4** & **Allaple.L** & **Worm** & **1591** \\ \hline
**\#5** & **Alueron.gen!J** & **Trojan** & **198** \\ \hline
**\#6** & **Autorun.K** & **Worm** & **106** \\ \hline
**\#7** & **C2 LOP.P** & **Trojan** & **200** \\ \hline
**\#8** & **C2 LOP.gen!g** & **Trojan** & **146** \\ \hline
**\#9** & **Dialplatform.B** & **D**ialer & **177** \\ \hline
**\#10** & **Dontovo.A** & **Downloader** & **162** \\ \hline
**\#11** & **F**akerean & **rogue** & **381** \\ \hline
**\#12** & **Instantaccess** & **D**ialer & **431** \\ \hline
**\#13** & **L**olyda.AA1** & **PWS** & **213** \\ \hline
**\#14** & **L**olyda.AA2** & **PWS** & **184** \\ \hline
**\#15** & **L**olyda.AA3** & **PWS** & **123** \\ \hline
**\#16** & **L**olyda.AT** & **PWS** & **159** \\ \hline
**\#17** & **Malex.gen!J** & **Trojan** & **136** \\ \hline
**\#18** & **Obfuscator.AD** & **Downloader** & **142** \\ \hline
**\#19** & **R**bottgen & **Backdoor** & **158** \\ \hline
**\#20** & **Skintrim.N** & **Trojan** & **80** \\ \hline
**\#21** & **Swizcor.gen!E** & **Downloader** & **128** \\ \hline
**\#22** & **Swizcor.gen!I** & **Downloader** & **132** \\ \hline
**\#23** & **VB.AT** & **Worm** & **408** \\ \hline
**\#24** & **W**ntrim.BX** & **Downloader** & **97** \\ \hline
**\#25** & **Y**uner.A** & **Worm** & **800** \\ \hline  & **Total** & **-** & **9339** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the Malimg dataset families.
# Stationary 3D Scanning System for IoT Applications
## 1 Introduction  
In this article, the problem of developing a low-cost 3D scanning system was addressed. Over the past few years, the popularity of and need for the digital 3D scanning of various objects, especially in industrial applications, have increased rapidly. As a result, new versions of 3D scanning systems have started to appear on the market more and more often. The essence of such systems is the possibility of digitally reconstructing a given object to obtain a three-dimensional model, without invasive interference with the structure of the scanned object and the need to disassemble the components [1]. Three-dimensional scanners can also be used in IoT systems, to diagnose both manufactured elements and assess the wear of machine components. The scanners' average price has increased in parallel with the quality they can guarantee, which is referred to via their parameters such as resolution, measurement range, repeatability, scanning speed, and resistance to external interference. Currently, due to their price, scanning systems with the lowest scan measurement uncertainties, in the order of several tens of \(\upmu\)m, are mainly owned by the best-growing industrial companies with a wide global reach. They are used in fields such as diagnosis, medicine, forensic analysis, quality control, optimization, and metrology [2, 3].  
Therefore, it is necessary to develop relatively low-cost 3D systems for scanning three-dimensional objects that could be used under industrial conditions. The main goal of this research is to develop and build such a system. The first part of the publication contains a review of the literature. The next part describes the kinematics of the device and the design of the mechanical and electronic parts. The operation of the device was confirmed by experimental studies, which allowed for the extraction of the final conclusions.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Proposed Scheme-II (Key Recovery System Using Proxy Re-Encryption)
#### 3.3.2 Objects of Proposed Scheme-II  
The system objects of the key recovery system using proxy re-encryption is shown in Figure 3.  
Participants (_Part_): \(Part\) represents all participants (\(Dev,Med,KRC,KRA\)) who use the encrypted communication provided by \(KGC\). \(Part\) can perform encrypted communication only by using _params_ provided by \(KGC\).  
Key Recovery Center (\(KRC\)): \(KRC\) is an organization in charge of key recovery and plays a central role in key recovery. The key recovery process is performed according to \(Med\)'s request for key recovery, and \(KRF\) is converted into a form that can be recovered using \(KRC\)'s public key. However, in this model, key recovery can only be completed with the help of \(KRA\) to prevent abuse of privileges by \(KRC\).  
Key Recovery Auditor (\(KRA\)): \(KRA\) is a monitoring agency that judges whether a key can be recovered by auditing the validity of key recovery. The \(KRA\) determines whether \(KRF\) is suitable for recovery to prevent abuse of authority through collusion between the \(Med\) and the \(KRC\). If the key recovery request is deemed to be lawful, \(KRA\) will perform the \(KRF\) recovery process with its private key and sends it over to the \(KRC\).