esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Background
### Load Balancing Optimization in Ultra-Dense Networks  
Load balancing optimization in UDNs refers to the strategic distribution of network traffic across several densely deployed small cells to prevent congestion, enhance user experience, and improve overall network performance. In UDNs, the asymmetric data loads handled by different types of APs necessitate the development of effective load balancing algorithms to manage these disparities [52]. Traditional load balancing methods, which adjust parameters like CIO to distribute load among neighboring cells, may not suffice in ultra-dense environments due to the complex and dynamic nature of traffic patterns. To address these challenges, advanced techniques such as DRL have been proposed. For instance, a DRL-based mobility load balancing algorithm uses a two-layer architecture to autonomously learn optimal policies, effectively handling large-scale load balancing problems in UDNs [18; 53]. Moreover, D2D communications have been explored as a means to facilitate load balancing without requiring additional spectrum resources. In this approach, data traffic is offloaded from congested small cells to underutilized neighboring cells via D2D links, thereby maximizing the system's sum rate and alleviating congestion [54]. Effective load balancing optimization is crucial in UDNs to manage spatial-temporal fluctuations in mobile data traffic, ensuring efficient resource utilization and maintaining the quality of service in next-generation wireless networks [31].
## 3 Materials and Methods
### Data
The advantage of this approach is that it works quickly and straightforwardly when operating complex scenes. The clusters were identified by associating the pixels from the images with the available information of spectral reflectance and presented as the output maps in Figure 7 (Bumba), Figure 8 (Basoko) and Figure 9 (Kisangani). Figure 6: Executing the k-means clustering classification algorithm in RStudio using the RStoolbox package. Parameters of cluster centroids and sum of squares by cluster are displayed in the console of R. Spatial parameters of the output map are listed. Here is shown the example of Landsat image of Basoko. The unsupervised learning algorithm of R incorporates the parameter estimation for pixels under a partitioning framework using cell's brightness, which corresponds to the spectral reflectance of the objects in a matrix of the raster scene. The algorithm of k-means clustering is computationally demanding by R, yet it is crucial to the concept of remote sensing due to the effectiveness and importance of the output in satellite image processing. Thus, land cover types are not known beforehand and defined by the machine method of image analysis based on the spectral signatures of the objects partitioned into the series of clusters. The data were divided automatically into the ten clusters using the search for the spectral class for each pixel in the image. Using this approach, we defined 10 classes in a process of the unsupervised classification approach to produce the thematic maps with discriminated vegetation and land cover classes using the existing classification adopted from previous studies [103]: swamp forest; humid tropical forest; secondary forest; urban areas and impervious surfaces; inundated grassland; deciduous forest; shrubland and grassland; cropland and mosaic forest; savannah with sparse trees; water bodies. Figure 7: Bumba town and surroundings of the Congo River: raw Landsat-8 OLI/TIRS scene for years 2013 and 2022 and the classified outputs by k-means clustering. Mapping: RStudio. Source: authors. (**a**) Monochrome image for Bumba region: 2013. (**b**) Classified scene using k-means clustering: 2013. (**c**) Monochrome image for Bumba region: 2022. (**d**) Classified scene using k-means clustering: 2022. Figure 8: Basoko town (Tshop Province) and surroundings in the confluence of the Aruwimi River tributary into the main stream of the Congo River: raw Landsat-8 OLI/TIRS scene for years 2013 and 2022 and the classified outputs by k-means clustering. Mapping: RStudio. Source: authors. (**a**) True-colour composite image for Basoko: 2013. (**b**) Classified scene using k-means clustering: 2013. (**c**) True-colour composite image for Basoko: 2022. (**d**) Classified scene using k-means clustering: 2022. Figure 9: _Cont._
# Analysis of ICS and SCADA Systems Attacks Using Honeypots
## 1 Introduction  
Power plants and water treatment facilities are examples of traditional industrial systems that were designed to operate in highly controlled and separated settings. However, the recent exposure of industrial control systems (ICSs) to the Internet has made access and technological adaptation easier, which has led to the exploitation of security holes by attackers to launch attacks against ICSs [1]. These attacks can significantly impact the economics and national security of countries [2]. To identify possible threats and comprehend the terrain of these assaults, ICS honeypots are deployed [3]. One of the primary targets of these attacks is supervisory control and data acquisition (SCADA) systems [4]. There are two different kinds of honeypots: production honeypots and research honeypots. Honeypots may be categorized as low-interaction, medium-interaction, or high-interaction honeypots based on their design and deployment. For this paper, a low-interaction honeypot will be employed.
# Acoustic Material Monitoring in Harsh Steelplant Environments
## 1 Introduction
### Our Approach  
This article evaluates thoroughly the robustness of two-dimensional CNN-based acoustic material monitoring in harsh steel plant conditions. Within the material transport, both desired materials and undesired contaminants are conveyed. These contaminants cause adversarial dropping sounds, which are not characteristic of the material. Therefore, our contribution investigates the impact of detection and removal of adversarial dropping sounds against acoustic monitoring performance. After conducting an explicit literature research, this is the first time that such adversarial dropping sounds are considered within the acoustic material classification for iron- and steelmaking. We present extensive experiments to systematically assess the performance on measured real steelplant environments. Additionally, this research introduces an RMSe-based preprocessing method to tackle vulnerabilities caused by dropping sounds of contaminants in very loud and noisy environments. The results from [3] suggest that two-dimensional CNNs provide better results for environmental sound classification (ESC). Hence, this work focuses on two-dimensional CNNs. Time-frequency distributions serve as inputs for the CNN. Various time-frequency distributions for ESC have been investigated in [4]. Among two-dimensional CNN structures for audio classification, the Log-Mel-based CNN- classifier is frequently used, where psychoacoustic time frequency distribution serves as an input [5]. Therefore, psychoacoustic time frequency distribution was used as an input representation. The proposed CNN-classifier is compared with results achieved by a FF-NN-classifier from [6].  
To implement the proposed approach, three main stages are required, data collection in a real environment, preprocessing the dataset and classification of different materials based on the acoustic sound in the steel industry. Section 3 describes the data collection and preprocessing methods. Section 4 introduces the classifier structure used within this work. Subsequently, Section 5 reports experimental results. Finally, Section 6 discusses, summarizes and concludes this paper by means of the obtained performance and providing directions for future work.
# Unobtrusive Respiratory Flow Monitoring Using a Thermopile Array: A Feasibility Study
## 2 Materials and Methods  
### Background and Challenges  
Modern thermal cameras have a sensitivity in the order of \(10^{-2}\,\mathrm{K}\)[22]. However, a typical thermopile array can only provide a sensitivity in the order of \(10^{-1}\,\mathrm{K}\). Considering that the thermal variations in both adults and neonates have been reported to be higher than tenths of a Kelvin [23], such a sensitivity should be theoretically sufficient for the detection of flow.  
The detection of respiration flow through thermal sensing is not only dependent on the amplitude of the thermal variation. Sensor characteristics also contribute to determining the ability to detect such changes.  
The partial area effect [24] is a known dependency that describes the relationship between the real amplitude (temperature in this case), field of view, number of pixels, distance, size of the target, and the amplitude (temperature) detected by a sensor. It follows that the target size, i.e., the surface involved in thermal heat exchange, is an important parameter in the detection of flow. For example, differences are expected if the measurements are taken in frontal view, i.e., subject facing the device, or side view, with the head on a pillow. In the second case, the pillow material will also take part in the heat exchange process, increasing the area involved. This phenomenon was already observed by [PERSON] et al. [9]. The partial area effect is particularly important in sensors with a broad field of view and a low number of pixels and limits the distance of usage.  
Another challenge can be found in the RR, higher RR will result in lower temperature variation due to the heat capacity. Moreover, oral and nasal respiration show differences in temperature and humidity content depending on the environment. In particular, oral exhaled air results in having a higher temperature compared to the nasal one with an ambient temperature of 20 \({}^{\circ}\)C [25].  
A single thermal sensing element equipped with proper optics would be enough for detecting the respiration flow. However, aiming such a sensor in the correct direction would result in being particularly complicated. Taking into account all the above considerations, a Panasonic Grid-EYE was selected to prove the feasibility of detecting respiration flow. The sensor has a sensitivity of 0.16 K and a resolution of 8\(\times\)8 pixels with an angle of view of 60\({}^{\circ}\), and it is particularly low cost compared to other sensors on the market.
# Palladium-Catalyzed Suzuki-Miyaura Cross-Coupling in Continuous Flow
## 2 Accepted Mechanism of Suzuki Cross-Coupling  
The Suzuki-Miyaura cross-coupling reaction [12; 13; 14; 15; 16] is one of the most versatile and frequently employed method for C-C bond formation. It consists of the coupling of organoboron compounds (organoborane, organoboronic acid, organoboronate ester and potassium trifluoroborate) with aryl, alkenyl and alkylynyl halides. Nowadays, a large variety of boronic acids are commercially available. The general Suzuki-Miyaura catalytic cycle occurs through oxidative addition, transmetallation and reductive elimination [13; 14; 15; 68; 69; 70]. After formation of the catalytic species Pd(0), generated in situ starting from palladium Pd(II) or directly from Pd(0) derivatives, oxidative addition of the aryl halide ArX furnishes the palladium complex (ArPdXLn). The transmetallation step occurs by conversion of the palladium halide (ArPdXLn) in the presence of the base RO\({}^{-}\) to a nucleophilic palladium alkoxy complex (ArPdORLn). This complex subsequently reacts with a neutral organoboron compound Ar\({}^{\prime}\)B(OH)\({}_{2}\) to afford the diaryl complex (ArPdAr\({}^{\prime}\)Ln) in a _cis-trans_ equilibrium. Then, reductive elimination of the _cis_ form gives the biaryl derivative Ar-Ar\({}^{\prime}\) and Pd(0) (Scheme 1) [15].  
Using supported palladium catalysts, Suzuki-Miyaura cross-coupling reaction is a heterogeneous catalysis [71]. During the reaction, the palladium Pd(II) could be release from the surface of the solid support and this leaching palladium could be responsible for the catalysis as a (quasi)homogeneous mechanism (Scheme 2) [72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83].
## 6 Implementation Setup and Evaluation Criteria
### Datasets and Tools  
The dataset, freely available on Kaggle, was used to challenge the access control policy for Amazon employees [15]. The dataset contains 32,769 samples with ten features. The training set contains a single label attribute named \"ACTION\" with the values \"1\" and \"0\" indicating application approval and rejection, respectively. The following features include ACTION, RESOURCE, MGRID, ROLE1, ROLE2, ROLE3, ROLE4, ROLE5, ROLE6 and ROLE7. A data balancing process was performed on the experimental data, which was randomly divided into training data (80%) and testing data (20%). Operating system: Mac; M1 for machine learning performance; 64 GB of memory; and Python 3.10 version were the device's specifications utilised in the implementation.
# Tunneling Current Variations in Small-Sized Devices Based on a Compact Threshold Voltage Model
## 1 Introduction
Through experiments, devices of different sizes were fabricated under the same process conditions, and relevant parameters were extracted using parameter extraction software. The calculation results of the model were compared with the simulation results, which further proved the correctness of the threshold voltage intensive physical model of strained Si NMOSFETs established in this paper. The results provide an important reference for the analysis and design of strained Si devices and also provide an essential theoretical basis for further integrated circuit design. Tunneling current is a crucial parameter in strained silicon devices, as it reflects the electron tunneling through the bandgap or energy barrier in the strained silicon structure. By studying tunneling current, the performance of a device under low-voltage conditions, including conduction capability, energy efficiency, and noise characteristics, can be evaluated. As device dimensions continue to shrink, tunneling current becomes more significant in strained silicon devices. Therefore, research on tunneling current is significant for limiting device size and achieving micro- and nanoscale devices. By understanding and controlling tunneling current, design and process optimization can be conducted to reduce the increase in tunneling current caused by size effects. Tunneling current is also a key factor that affects the reliability of strained silicon devices. Understanding the sources, characteristics, and mechanisms of tunneling current helps to find methods to reduce or suppress tunneling current, thus improving device reliability and reducing the probability of failure. Research on tunneling current in strained silicon devices provides a foundation for developing new technologies and applications. Understanding the characteristics and control methods of tunneling current can provide critical guidance and support for designing novel devices and developing new strained silicon technologies. In conclusion, studying tunneling current in strained silicon devices is important for evaluating performance, limiting device size, enhancing reliability, and advancing strained silicon technology. This research has a significant impact and guiding value in optimizing device design, improving processes, and promoting the application of strained silicon technology in the field of microelectronics.
## 6 Conclusions  
In this work, a flexible mixed parallel and multi-hop DF relaying FSO architecture has been introduced in order to effectively combat weak to saturated atmospheric turbulence effects, along with weak to strong non-zero boresight pointing errors, which significantly mitigate the FSO outage performance and availability. In this context, novel closed form mathematical expressions have been derived for both OP and MOD metrics over NE and G-G modeled turbulence, along with Beckman modeled non-zero boresight pointing errors modeled. By using these expressions, proper numerical results are presented which are further validated by the corresponding simulations. Specifically, it has been demonstrated that by increasing the number of paths of the system, i.e., the number of parallel connected DF relays, significant outage performance improvements can be achieved. Moreover, it has been shown that the outage performance of such a system can be further upgraded but at the expense  
Figure 7: Total mean outage duration time (MOD) of a relay-aided system with both varying path and hop configurations as a function of a wide range of \(\gamma/\mu\), for gamma-gamma (G-G) modeled weak turbulent channels along with different amounts of pointing mismatch.  
of its total coverage area, i.e., by decreasing the number of hops in each path, namely the number of the serially connected DF relays. Consequently, by considering the required technical specifications for the FSO system under design and by using the extracted expressions, we may find the optimal selection for the exact number of parallel and serially connected DF relays, and thus, we may optimally balance any contradicting requirements of coverage area broadening and outage performance enhancement. Thus, the analysis proposed in this work, is a very useful tool for the design, the flexibility, the scalability, the outage performance and the availability of FSO systems.  
Conceptualization, G.K.V., H.E.N. and A.N.S.; methodology, G.K.V. and H.E.N.; validation, G.K.V., A.N.S., C.K.V. and V.C.; formal analysis, G.K.V., H.E.N., G.S.T. and C.K.V.; investigation, G.K.V., H.E.N., A.N.S. and V.C.; writing--original draft preparation, G.K.V.; writing--review and editing, H.E.N. and G.S.T.; supervision, H.E.N., G.S.T.  
This research has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No: 777596.  
The authors declare no conflict of interest.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 3 Base of the SHM System
When performing SHM basins on the ground after each flight, this operation may still prove to be impractical, as the exact detection range of the sensors requires a large number of sensors to monitor the entire aircraft. Due to data processing operators being required to run each sensor one by one, gather all the data and process the data to determine the extent of damage and its location. The total detection procedure can take up to three to four days. Given that each flight's maintenance time takes only 30 min, it is not practical to perform checks after each flight [28; 29]. Nowadays, wireless sensor nodes can be built in a small size and weight-optimized way. However, continuous monitoring and subsequent processing of all data collected by the sensors take a considerable amount of time. The raw data that are accumulated in the SHM sensors must be filtered in order to remove noise, and an intelligent diagnostic algorithm must be applied to convert the raw data into useful damage information. Subsequently, predictive algorithms are used to forecast future damage increases. Given that thousands of sensors are needed to monitor the entire aircraft processing, all the data take a long time. Thus, the cost of signal processing and computation may exceed the cost and benefit resulting from the use of SHM [28; 29]. Therefore, this issue is fundamental, and the analysis of the cost/efficiency ratio of the entire SHM system and its individual processes is even crucial.
# Analysis of Channel Potential Recovery According to the Back Pattern in 3D NAND Flash Memory
###### Abstract  
This study analyzed the recovery of channel potential according to the program states of adjacent cells. When the verify operation ended, and all voltages dropped to 0 V and the decreased potential of the floating channel caused by the down-coupling phenomenon was recovered continuously over time, regardless of the program states of adjacent WLs. The extent of channel potential recovery showed a similar tendency to that of the variation in the electron concentration of the floating channel. The electron-hole pair recombination decreased the electron concentration, resulting in channel potential recovery. When the adjacent WLs were programmed in a low state, additional electron-hole pair recombination and hole diffusion occurred in the floating channel. Therefore, the channel potential recovered quickly when the adjacent WLs programmed in a low state compared to when they programmed in a high state.  
3D NAND flash memory; channel potential; down-coupling phenomenon (DCP); electron-hole pair recombination; carrier diffusion; TCAD
## 3 System Design with Simulation and Experimental Results
### 4 FSK Communication System Simulation Design  
To comprehensively analyze the system performance, we have constructed a simulation platform for an ultraviolet (UV) wireless communication system based on 4-FSK modulation. The composition of the simulation platform is shown in Figure 6.  
Figure 5: Receiver elevation angles and path loss: (**a**) distance 0–500 m; (**b**) distance 4500–4600 m.  
Figure 6: UV communication system simulation platform composition.  
The Carrier Generator is used to generate carriers of four different frequencies. The Carrier Selector selects the carrier with the corresponding frequency for output based on the current symbol. The UV Wireless Channel is designed to simulate the real ultraviolet wireless channel environment. Based on the Poisson distribution probability model and using the Monte Carlo simulation method, it generates signal photons and noise photons to accurately reproduce the photon characteristics in the channel. The Demodulator performs the task of signal demodulation. The Channel Encoder and Channel Decoder are used for encoding and decoding, respectively, and these two modules will play a role in the system with encoding and decoding added. Finally, the BER is calculated by comparing the bit information at the transmitting end (TX) and the receiving end (RX).
# Evaluation of the Prospects for the Use of Microalgae in Functional Break Production
## 3 Results
### The Content of Volatile Compounds in Bread with microalgae  
The content of volatile compounds in bread samples with microalgae is presented in Table 10. The experimental results (Table 10) show that bread samples containing 3% and 5% microalgae contained a slightly higher concentration of biologically active substances (such as sulfur compounds, terpenes, acids, etc.). Bread samples without added microalgae contained the smallest amount of all the studied volatile compounds.