esa-sceva/satcom-chunk-collection huggingface dataset:
# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 2 Related Work  
### Deep Learning-Based Methods for Medical Image Segmentation  
In recent years, deep learning has become an important branch of artificial intelligence. It is widely used in image processing, natural language processing, and other fields. Image segmentation is an important application in deep learning. It divides an image into several regions; the pixels in each region have the same characteristics, providing more refined region-level information for subsequent processing [38]. In 1996, [PERSON] et al. [39] proposed a CNN that was applied to medical image processing. In this work, regions of interest (ROIs) containing biopsy-confirmed masses were extracted from mammograms. A traditional CNN is mainly used for image classification; all images are assigned corresponding class labels separately, while UNet mainly focuses on end-to-end pixel-level medical image segmentation. The UNet structure is generally composed of an encoder and a decoder. The encoder usually contains a convolutional block and a maximum pooling layer. In contrast, the decoder contains upsampling blocks that integrate deep and shallow semantic information and convolutional layers. Compared with 2D images, 3D images can provide more detailed brain tumor information. [PERSON] et al. [40] proposed 3D UNet, which is a variant of the UNet architecture, to process volume data from CT or MR images with depth information. The network layer gradually deepens to learn more abstract and complex features and improve the network expression ability. However, with the gradual increase in network depth, problems such as gradient disappearance or gradient explosion will occur during backpropagation. Many UNet variants have been studied to address such challenges and improve brain tumor image segmentation results. A network architecture named UNet++ with dense connections was proposed by [PERSON] et al. [41] and makes full use of the characteristics of different levels to improve accuracy. In UNet++, the network parameters are greatly reduced to an acceptable range using the deep supervision mechanism. The V-Net architecture based on UNet, which uses a convolutional layer to replace upsampling and downsampling layers, was proposed by [PERSON] et al. [42]. [PERSON] et al. [43] proposed a 3D dilated multifiber network (DMF) architecture to reduce the computational overhead and solve the problem that the model cannot be used in practical large-scale applications. The MBANet, which was developed by [PERSON] et al. [44], achieved more accurate segmentation results using spatial and channel attention. [PERSON] et al. [45] constructed HMNet, which effectively focused on enhancing the relationship between low-level and high-level semantic features. The above methods fully demonstrate the effectiveness of the U-shaped structure in medical image processing, so we chose the U-shaped network as our framework.
## 4 Discussion  
As shown by the experimental data, the use of transistor interleaving for DICE-based FF designs at the 22 nm FD SOI technology node showed superior SER performance compared to a standard FF design. The normal DICE FF with transistor interleaving showed no upsets during any of the tests, including tests where ions impacted the test chip at an angle of 60\({}^{\circ}\) from normal. This showed that transistor interleaving can effectively mitigate SET pulses from occurring simultaneously on two sensitive nodes. There is no doubt that singular upsets occurred on individual nodes of the DICE cell; however, these upsets made no impact on the cell due to the intrinsic single-node immunity of DICE. Additionally, the Guard Gate Stacked DICE FF presented no errors either. This was expected, since if the regular DICE FF design showed no errors, then the guard-gate design should show no errors either because of its additional hardening techniques. As such, for DICE designs at the 22 nm FD SOI node, the use of guard gating is not needed to protect cells from SEUs, given that the sensitive nodes are properly spaced apart.  
The CnRx DICE DFF was the only design that presented upsets during testing. The upsets were observed with a high-LET ion at both a steep incidence angle and a reduced core voltage. This data showed that DICE designs making use of the new CnRx are slightly more likely to experience SEUs. However, since the upsets were only recorded in these extreme conditions, it is likely the construct has a little effect on DICE. As such, designers will have to take into consideration this cost of increased performance depending on the use cases of their designs. DICE FFs at the 22 nm FD SOI node performed well, regardless of what layout construct was used.  
Based on DICE data available for a 28 nm bulk technology node as presented in [17], the interleaved FD SOI DICE designs presented in this work showed superior SER performance. This improvement can largely be attributed to the natural electrical isolation that the FD SOI technology provides, since with bulk technologies, the charge-sharing phenomena will largely impact the SER. However, FD SOI DICE designs not making use of transistor interleaving are still susceptible to upsets, as shown in [18]. Thus, from the results, it can be concluded that the usage of transistor interleaving for DICE FFs at the 22 nm FD SOI technology node provides extremely robust resistance to radiation-induced soft errors.
## Abbreviations  
\begin{tabular}{l l} AWGN & additive white Gaussian noise \\ BER & bit error rate \\ BPSK & binary phase shift keying \\ CEEs & channel estimation errors \\ DM & direct mapping \\ DSE & data symbol error \\ FBMC & filter bank multi-carrier \\ FEC & forward error correction \\ FMT & Filtered Multitone \\ FFT & fast Fourier transform \\ GS & Gold sequence \\ IFFT & inverse fast Fourier transform \\ LDPC & low-density parity-check code \\ MIMO & multi-input multi-output \\ MMSE & minimum mean-squared error \\ MSE & mean square error \\ OCPN & object-composition pertinent \\ OFDM & orthogonal frequency division multiplexing \\ OMP & orthogonal matching pursuit \\ OQAM & offset quadrature amplitude modulation \\ OVSF & orthogonal variable spreading factor \\ PAPR & peak to average power ratio \\ PPNs & polyphase networks \\ PS & power-saving \\ PSNR & peak SNR \\ SNR & signal-to-noise ratio \\ STBC & space time block code \\ UAC & underwater acoustic channel \\ UAMC & underwater acoustic multimedia communication \\ UAMCA & underwater acoustic multimedia communication architecture \\ UMSNs & underwater multimedia sensor networks \\ UMTPAA & underwater multimedia transmission power allocation algorithm \\ USTE & underwater simulation transmission experiment \\ \end{tabular}
# A Game-Theoretic Approach for Network Security Using Honeypots
## 1 Introduction  
Cybersecurity issues are vastly discussed and analyzed by various specialists and researchers from all over the world. Moreover, the pandemic situation decided a lot of companies to use cloud infrastructures more often than before and accommodate their employees to use the new facilities to work from home. In addition, governments and public institutions were forced to implement efficient online solutions to provide services for the population.  
This study focuses on the defender who improves network security by introducing honeypots. Even if the cost of honeypots depends on the performance and applicability, their existence does not interfere with legitimate users but acts as a decoy and distracts attackers from the real virtual machines and can send alarms to the administrator in case of a breach. From the mindset of an attacker, we can also understand that an experienced person should anticipate the existence of honeypots.  
Cyberdeception is used nowadays by both parties, the defender and the attacker. On their side, the defender can use some techniques to give false beliefs to the attacker by using decoys, false information or trying to camouflage the network. These techniques are very similar to those used in military conflicts and can be applied in the cyberworld. On their side, the attacker is using deception and different strategies to protect their identity or let the defender think they are a legitimate user or a service. Moreover, the attacker can use deception techniques in order to gather information about potential victims. Decoys and deceptive signals are mandatory for manipulating the opponent and creating a false perception of reality so the attacker using reconnaissance tools gathers only misleading information and wastes their time.  
In his work [1], [PERSON] stated that human beings are not very keen on detecting deception and the studies run on different classes of workers or students led to the fact thatover 45 % of the subjects did not detect deception. Furthermore, [PERSON], in his book [2], stated that the deceiver was almost always successful, regardless of the sophistication of their victim in the same art.  
The best definition which is widely accepted by authors such as [PERSON] [1] is the one proposed by [PERSON] [3] where cyberdeception refers to all planned actions taken to mislead attackers and to cause them to take or not to take actions that help cybersecurity defense.  
[PERSON] presented in his work [4] the OODA loop, which means to observe, orient, decide and act, as a cyclic process model in order for the defender to be able to assess an event and add an input for the creation of a common operational picture. The cybersecurity game between a defender and an attacker can be seen as an OODA loop race and the winner is decided by who executes this loop faster. Using honeypots, a defender has an advantage in front of the attacker because all the decoys are affecting the attacker's loop.
# Recent Progress in Multiplexed Single-Photon Sources
###### Abstract  
We review the progress in multiplexed single-photon sources, including overviews on heralded single-photon sources and photon-number-resolving detectors, the various approaches to multiplexing, and their successful experimental realizations. We also summarize the recent results on the theoretical description and optimization of multiplexed single-photon sources, focusing on the procedures and methods that enable the improvement of the performance of these sources.  
heralded single-photon sources; multiplexed single-photon sources; spatial multiplexing; temporal multiplexing; spectral multiplexing +
Footnote †: journal: Applied sciences
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 3 Datasets and Experimental Settings
### Dataset Description
#### 3.1.1 All-Sky Ground-Based Cloud (ASGC)  
The cloud images in this dataset were captured by the all-sky camera [54] located in Muztagh, Xinjiang (38.19\({}^{\circ}\)N, 74.53\({}^{\circ}\)E). The all-sky camera consists of a Sigma 4.5 mm fisheye lens and a Canon 700D camera with a maximum field of view of 180\({}^{\circ}\) due to the fisheye lens. Different from conventional GCI, the sky of GCI in this dataset is mapped as a circle, where the center is the zenith and the boundary is the horizon. The frequency of shooting during the day is 20 min, increasing to 5 min a shot at night. In addition, the exposure time will is adjusted between 15 and 30 s according to the moon phase. All images are stored in a color JPEG format with a resolution of 460 \(\times\) 460 pixels. To meet the training requirements, all images were uniformly adjusted to 448 \(\times\) 448 pixels. A total of seven classes of cloud images were included in the original dataset, including altocumulus (Ac), cumulonimbus (Cb), cirrus (Ci), clear (Cl), cumulus (Cu), mixed (Mi), and stratocumulus (Sc). The number of images in each class varies from 210 to 450, and the dataset employs data augmentation because the small number of samples is prone to overfitting. The specific data augmentation methods include: horizontal flip, vertical flip, and random rotation. The example images of each class are shown in Figure 8. The sample distribution of the training set and testing set in the expanded dataset is described in Table 2.
## 2 Smart Grid Background
### Key Components of Smart Grid
A hostile user might take over a power grid of a country, cause a nuclear reactor to malfunction, or shut down the water supply system if SCADA systems are not secured [28]. SCADA networks' architecture has become vulnerable and susceptible to cyberattacks as a result of the use of Internet connectivity, cloud computing, wireless communications, and social engineering. The lack of robust encryption and real-time monitoring is one of the key causes of SCADA's vulnerabilities. From the supervisory level to the field instrumentation level, attacks might happen at any layer. These cyberattacks can be divided into three categories according to how they affect network connections, software, and hardware [29]. A hardware attack occurs when a hacker gains unauthorized access to the hardware and tampers with it or its functionality. Access control is the main key point for SCADA hardware security. On the other hand, a software attack happens as a result of SCADA's software being implemented inefficiently. The SCADA system uses a range of software to meet the functional requirements and increase efficiency. With software attacks, SCADA is susceptible to SQL injection, Trojan horses, and buffer overflows. Finally, an attack can occur on various layers such as the network layer, transport layer, and application layer [30; 31]. The advanced metering infrastructure (AMI) function is to measure and analyze energy consumption while enabling bidirectional communication between customers and the utility company. An essential part of smart grid technology, which is being quickly adopted in the commercial sector, is the smart electric meter (SM) [32; 33]. Wired or wireless networks can be used to link smart electric meters, where these meters achieve their job of remote monitoring and data collections through using different network protocols. Thesesmart meters assist the service provider with remote monitoring and data collection, such as power consumption at a customer premise. Smart meters deliver their data to service providers automatically in almost real time, which makes them very efficient and helpful in various ways [34; 35]. Advanced metering infrastructure (AMI) security has improved along with the introduction of this technology. AMI is used by energy markets, utility companies, and regulators to make it easier to gather real-time data on electric flow and usage. Utility companies will be able to provide demand-side management, dynamic pricing services, and better grid management as a result, but these additional capabilities may increase the attack surface [36]. Smart meters and phasor measuring units (PMUs) are examples of AMI components that are IoT-based and have vulnerabilities similar to those of IoT devices connected to the grid. SMs and PMUs suffer from similar vulnerabilities, such as the lack of appropriate security standards, lack of computing resources for cryptographic algorithms and antivirus software needed for security requirements, and unsecured connections [37; 38].
## 3 Results
### Fuzzy Control Rules  
There are three FLC input variables: The size of the change in wind speed, the error in the controlled variable that is the speed of the generator shaft, and the value of the change in error.  
The membership functions to evaluate the change in the magnitude of the wind are determined according to the statistical analysis of the wind and are determined in three linguistic values: Negative (NC_W), zero (ZC_W), and positive (PC_W). Figure 12 shows the membership functions for the values of the change in wind speed.  
The error values and the magnitude of the error change were adjusted by means of experimental adjustment. Three linguistic levels are assigned for each variable, negative (NS_E), zero (ZS_E), and positive (PS_E) for the error; and negative (NE_D), zero (ZE_D) and positive (PE_D) for the magnitude of the change in the error value. Figure 13 shows the membership functions for the error value of the controlled variable, and Figure 14 shows the membership functions for the magnitude of the change in error.  
Figure 14: Membership functions for magnitude of changes in error.  
Figure 12: Membership functions for changes in wind speed.  
The values assigned to the membership functions for the output variables are obtained within the range of pre-set PID gains for optimal controller response. Each of the gains has five linguistic levels: Very low (VL_KP), low (LO_KP), medium (ME_KP), high (HI_KP), and very high (PB_KP) for \(k_{p}\); very low (VL_KI), low (LO_KI), medium (ME_KI), high (HI_KI), and very high (VH_KI) for \(k_{i}\); and finally, very low (VL_KD), low (LO_KD), medium (ME_KD), high (HI_KD), and very high (VH_KD) for \(k_{d}\). Figure 15 shows the membership functions for outputs.  
There is a response relationship of the rotation speed to the wind speed, depending on the aerodynamic profile of the blades and the mechanical restrictions of the system. The controller gains must be adjusted to give rapid stability to this response at nominal speed. Therefore, the fuzzy rules between the input-output membership functions must be adjusted experimentally for different SHAWTs.  
The authors consider 33 control rules for the FLC; these rules were defined by their experience and knowledge of the wind turbine. The Mamdani-type inference system and the centroid method are used to obtain the value of the output variables. The FLC rules for the output variables are shown in Table 4 for \(K_{p}\), Table 5 for \(K_{i}\), and Table 6 for \(K_{d}\).
###### Abstract  
In this paper, we present various theoretical models that accurately approximate the spectral density of the optical capture cross-section (\(v_{\mathrm{c}}^{2}\)) obtained through the analysis of photo-capacitance transients using the deep-level optical spectroscopy (DLOS) technique applied to semi-transparent Ni/Au Schottky barrier diodes (SBDs) fabricated on n-GaN films. The theoretical models examined in this study involved a variety of approaches, from the Lucovsky model that assumes no lattice relaxation to more sophisticated models such as the Chantre-Bois and the Passler models, which consider the electron-phonon coupling phenomenon. By applying theoretical models to the experimentally determined data, we were able to estimate the photoionization (E\({}^{0}\)), trap level position (E\({}_{\mathrm{T}}\)), and Franck-Condon (d\({}_{\mathrm{FC}}\)) energy, respectively. In addition, the results of our analysis confirm that the photoionization processes of deep traps in n-GaN grown by the metal-organic vapor-phase epitaxy technique (MOVPE) are strongly coupled to the lattice. Moreover, it was shown that the Passler model is preferred for the accurate determination of the individual trap parameters of defects present in n-GaN films grown on an Ammono-GaN substrate. Finally, a new trap level, Ec-1.99 eV with d\({}_{\mathrm{FC}}\) = 0.15, that has not been previously reported in n-GaN films grown by MOVPE was found.  
GaN; DLOS; deep traps; photoionization; Franck-Condon shift +
Footnote †: journal: Physics Letters B
## 3 Results and Discussion
### Optimization of Sample Preparation  
#### 3.2.1 Optimization of Sample Extraction Solution  
Sample preparation plays a critical role in determining the reliability of test results. Therefore, the initial step involved screening extraction solvents capable of effectively extracting the six macrolides from feed matrices. Given the high lipophilicity of macrolide antiparasitic drugs, two extraction methods using acetonitrile ([PERSON] & [PERSON], 2016) [13] and methanol were designed, based on relevant studies ([PERSON] et al., 2013 [14]; [PERSON] et al., 2013 [15]; [PERSON] et al., 2012 [16]; [PERSON] et al., 2016) [17].  
In the preliminary experiments, the premix was selected as a representative matrix to optimize the sample processing procedure. This choice was based on its similarity to biological matrices commonly encountered in analytical studies, such as plasma, serum, and tissue homogenates ([PERSON] et al., 2003) [18]. The premix matrix, composed of a balanced mixture of proteins, lipids, and other endogenous compounds, effectively simulates the complexity of real-world samples. This approach facilitates a comprehensive evaluation of potential matrix effects during method development. Two grams of blank feed and premix feed spiked with the target analytes (at a concentration of 1000 \(\upmu\)g/kg) was weighed into 50 mL centrifuge tubes. Each sample was extracted twice with 10 mL of water-saturated acetonitrile and 10 mL of water-saturated methanol, respectively, to compare the extraction efficiency of the two solvents. The results indicated that both extraction solvents exhibited similar efficiency in extracting the six macrolides (the extraction rates of six drugs using two solvents are shown in Figure 3), meeting the detection requirements for all target analytes. However, the recovery rates for all drugs except AVM were slightly higher when using acetonitrile. This improvement is likely attributed to the reduced interference from co-extracted impurities with acetonitrile. Furthermore, research demonstrates that acetonitrile is selected over methanol as the extraction solvent primarily due to its enhanced ability to precipitate proteins in feed matrices ([PERSON] et al., 2024) [19]. Additionally, a 50% acetonitrile solution is chosen instead of pure acetonitrile as the extraction solvent, as the extraction efficiency of pure acetonitrile in fish feed has been found to be comparatively lower.  
Figure 2: Daughter scan of six macrolide antiparasitic drugs (precursor ions and product ions: 1, acetyl avermectin, \(m/2936.69\)-\(352.13/490.27/368.10\); 2, abametcin B1a, \(m/2895.67\)\(>\)182.86/327.12/751.51; 3, milbemycin, \(m/2556.60\)-\(538.49/520.68\); 4, moxidectin, \(m/2640.30\)\(>\)198.53/528.50; 5, doramectin, \(m/2921.61\)-\(353.15/777.47\); 6: ivermectin H2B1a, \(m/2897.52\)\(>\)609.35/753.30).