esa-sceva/satcom-chunk-collection huggingface dataset:
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 8 Related Work
In cases of redundancy, the firewall rules are examined to verify that no rule in a firewall is redundant. In this research field, there is much research on integrating the rules of firewalls and verifying their validity [48; 49; 50]. The authors of [48] proposed a firewall compressor by using dynamic programming techniques. In the same context, the authors of [49] formulated the firewall rules as a quantified Boolean formula (QBF) optimization problem, and introduced a new solution that can discover redundant rules in a given ACL. The authors of [50] proved that the firewall verification procedure and redundancy checking are in fact equivalent and give the same results. In this work, we followed the same procedure when we integrated the firewalls' ACLs. Traffic Management and SDN: Researchers proposed utilizing SDN to achieve centralized and efficient control of the traffic of data centers, enterprises, and wide area networks [51]. Since the users have different needs, their traffic presents unequal characteristics. SDN services showed remarkable performance figures in identifying the flow classes and managing them accordingly [52; 53]. On the other hand, the network designers expect the traffic to show a uniform workload between subnets/racks. However, Google, Facebook, and Microsoft researchers discovered a biased traffic workload, and the uniform structure of a wired cloud network is not efficient in dealing with it [54]. As a remedy, wireless networks (mmWave [55] and FSO [56; 57]) were used to offer high-speed yet elastic topology. The topology reconfiguration needs to supply rapid responses and deliberate routing decisions to optimally utilize the available resources. Consequently, SDN is used to collect network statistics and offer fine-grained topology management. Moreover, wireless networks are used for non-data traffic, such as network management traffic [58; 59].
###### Abstract  
Currently, _Internet of Things (IoT)_ has become common in various applications, including smart factories, smart cities, and smart homes. In them, _wireless local-area networks (WLANs)_ are widely used due to their high-speed data transfer, flexible coverage ranges, and low costs. To enhance the performance, the WLAN configuration should be optimized in dense WLAN environments where multiple _access points (APs)_ and hosts exist. Previously, we have studied the _active AP configuration algorithm_ for _dual interfaces_ using _IEEE802.11n_ and _11 ac_ protocols at each AP under _non-channel bonding (non-CB)_. In this paper, we study the algorithm considering the _channel bonding (CB)_ to enhance its capacity by bonding two channels together. To improve the throughput estimation accuracy of the algorithm, the _reduction factor_ is introduced at contending hosts for the same AP. For evaluations, we conducted extensive experiments using the _WIMENT simulator_ and the _testbed system_ using _Raspberry Pi 4B_ APs. The results show that the estimated throughput is well matched with the measured one, and the proposal achieves the higher throughput with a smaller number of active APs than the previous configurations.  
Internet of Things; WLAN; access-points configuration; dual interface; _channel bonding_; WIMNET; Raspberry Pi 4B; IEEE802.11n; 11 ac
# Brushless Field Excitation Scheme for Wound Field Synchronous Machines
## 4 Conclusions  
A brushless field excitation technique for WFSMs was proposed in this paper. Two inverters, i.e., three-phase and single-phase inverters, that were connected to the armature winding and were supplying output current of frequencies were used to generate a unique composite armature current shape. The composite armature current was comprised of fundamental and third harmonic current components that produced fundamental and third harmonic MMF in the airgap. The pulsating third harmonic MMF was utilized to generate harmonic current in the 8-pole harmonic winding. The harmonic current was rectified to inject DC current to the rotor field winding. The field current and torque were provided to realize the idea.  
Unlike the conventional brushless techniques for WFSM, the proposed technique is simple as it involves conventional inverters that do not require any complex control scheme. Furthermore, the proposed technique involves a single-phase inverter which makes it cost-effective as compared to the available dual inverter based brushless WFSM topologies in literature. In addition, the proposed BL-WFSM topology does not require any modification in the machine structure.  
Conceptualization, S.S.H.B. and G.J.S.; funding [PERSON], J.-S.R.; investigation, S.S.H.B., F.A.C. and G.J.S.; writing--review and editing, S.S.H.B. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (2016R1D1A1B01008058), by Human Resources Development (No. 20204030200090) of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Trade, Industry and Energy, and by Korea Research Fellowship Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (2019H1D3A1A01102988).  
The authors declare no conflict of interest.  
Figure 7: (a) Harmonic and field currents and (b) output torque.
## 5 ML Intersection with 6G Enabling Technologies
### ML and THz/mmWave Convergence for 6G Wireless Communication  
The 6G is expected to bring about a transformative impact on the existing 5G structure. Researchers believe that integrating ML in THz networks will further enhance its capabilities [133]. In a study to lower the computational complexity associated with high-frequency THz band communication, [PERSON] [134] integrated an ML approach to handle beam selection in mm Wave MIMO communication. The authors employed two ML methods: random forest classification (RFC) and SVM algorithm to build a multi-class classification model. They found the RFC-based method minimizes complexity by 99.8% while also ensuring maximum uplink sum rate. Figure 3 shows a comparison of the different complexity ratios of the methods considered in the study. In [135], ML, NOMA, and THz technologies converge for the optimization of 6G wireless network performance. Three unsupervised ML clustering algorithms including K-Means, agglomerative hierarchical clustering (AHC), and density-based spatial clustering of applications with noise (DBSCAN) are employed in a NOMA-assisted THz network to enhance the performance of the 6G network. The authors reported that the AHC-based algorithm outperformed all the other two, i.e., K-means and DBSCAN, in terms of system throughput, sum data rates, and connectivity. In Figure 4, the AHC-focused method showed the highest sum data rates with an increasing number of secondary users. A similar convergence study has been investigated for the mm-wave NOMA system [136]. The authors analyzed the sum rate maximization problem of mm-wave-NOMA systems under constraints of the total transmission power and user rate requirements. A K-means-based online user clustering algorithm to reduce the computational complexity was proposed. The proposed approach enhanced the performance of mm-wave-NOMA systems compared to the conventional user clustering algorithms. ML has found applications in THz communications for the optimization of data rate and energy efficiency (EE) of MIMO-NOMA [137]. For mm Wave frequencies, an ensemble learning-based XGBoost classifier was integrated  
Figure 2: Breakdown of the classification of the different ML approaches. into a sub-6 GHz LTE and millimeter (mm Wave) for optimal handover success prediction. The developed scheme enhanced BS's ability to predict handover success on both sub-6 GHz and MM Wave frequencies. A DRL-based ML method was integrated with mm Wave communication as well as mMIMO for an efficient optimization application for wireless communication [138]. The DRL is explored to maximize the network's energy efficiency, and it is utilized for hybrid beamforming in point-to-point communication. The authors investigated two application scenarios: for vehicle-to-infrastructure (V2I) and for high-speed train (HST) communications. The convergence yielded better results compared to other existing methods. Table 4 summarizes all the highlighted studies that integrate THz, ML, and other enabling technologies of 6G.
## 4 Results  
### Deformation Correlation Factors  
Landslide deformation in reservoir areas is significantly influenced by hydrological factors, particularly rainfall and reservoir water level fluctuations [11,38]. The limited sample size of precision measurement data from the underground tunnel group poses challenges in establishing robust correlation criteria for deep-seated deformation. Future research should prioritize the collection of long-term monitoring data to further investigate the relationship between deep-seated landslide deformation and external triggering factors. This study employs monitoring data from multiple GNSS monitoring points on the earth's surface, as well as hydrometeorological monitoring points, encompassing deformation, rainfall, and reservoir water levels. From the perspectives of rainfall and reservoir water levels, four hydrological factors are selected as antecedent items of association rules, while the monthly cumulative deformation increment at the monitoring points serves as the consequent items. The specific details are as follows:  
(1) Reservoir Water Level  
Fluctuations in reservoir water levels can alter the hydrostatic pressure acting on the sliding mass and induce changes in the groundwater table. These changes result in wet-dry cycles in the sliding-zone soils, leading to material softening, reduced shear strength, and a diminished anti-sliding force. This influence exhibits a pronounced lag effect; in the study by [PERSON] et al. [39], a lag time of up to 31 days was reported for the Majiagou landslide, a typical reservoir-induced landslide in the TGRA. Those findings provide valuable reference for the analysis undertaken in this work. Consequently, monthly reservoir water levels are preprocessed, correlating the previous month's reservoir water level with the landslide deformation of the current month. Two hydrological factors are selected to assess the effects of reservoir water levels on landslide deformation: the monthly average reservoir water level (\(\overline{R}/month\)) and the monthly reservoir water level variation (\(\Delta R/month\)).  
Figure 7: The schematic diagram of the FP-Growth algorithm shows **(a)** the frequent-item header table \(L\), and **(b)** the FP-Tree formation.  
Rainfall Rainfall Rainfall infiltration primarily affects landslide deformation by altering the groundwater table. Some research indicates that the effect of rainfall on landslide deformation is delayed, typically by approximately 10 days [40]. Therefore, two monthly rainfall-related variables are selected as hydrological factors to analyze their impact on landslide deformation: monthly cumulative rainfall (\(\sum\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\  
tered into three categories: low, medium, and high. The results indicate that moderate and small deformations predominate across the deformation classifications of all three monitoring points.
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
## 1 Introduction
This is because it is possible that--even though neighbors request old chunks--those chunks can become invalid immediately before the peer actually starts transmitting them. Therefore, it is difficult to use each peer's buffer as the caching area to avoid accessing invalid data. In this paper, we therefore propose a novel buffering scheme in P2P live and time-shifted streaming systems to significantly increase data duplication in buffering periods among peers by adaptively adjusting the ratio of the caching and prefetching areas in the buffer. To this end, we form  
Figure 1: Buffer status of a peer in peer-to-peer (P2P) live streaming systems. groups of peers according to their playback positions and select a super peer from each peer group. A super peer periodically gathers playback position information of all peers belonging to its group. In P2P live streaming systems, each peer should receive data from those whose playback lags are shorter than its own (i.e., those whose playback positions are earlier in time since they have joined earlier). Thus, the peer whose playback position is latest among all peers belonging to a group does not need to cache data that have already been played back, because all the other peers in the group have also already played them back. That is, almost all areas of its buffer need to be used to prefetch subsequent chunks. On the contrary, the peer whose playback position is earliest tends to supply most data that have already been played back to other peers within its group, as long as its buffering period overlaps with those of the peers. That is, the earliest peer is likely to cache the largest amount of data to provide them to other peers within its group because none of the other peers have played them back yet. In our proposed scheme, the ratio of prefetching and caching areas in the buffer of each peer is therefore determined adaptively according to its relative playback position within its group. In other words, by increasing the ratio of the caching area in the buffer of a peer as its playback position moves earlier in time and increasing the ratio of the prefetching area as its playback position moves later, the overlapping buffered data among peers within the same group can be increased significantly. Finally, through extensive simulations, we demonstrate that our proposed adaptive buffering scheme outperforms the conventional buffering technique considerably in terms of startup delay, average jitter ratio, and the ratio of necessary chunks in a buffermap. The remainder of this paper is organized as follows: Section 2 describes work related to buffering techniques in P2P streaming systems. Section 3 describes problems of a fixed buffering technique used in conventional mesh-based P2P systems and explains our adaptive buffering scheme in detail. Section 4 presents the experimental results of our adaptive buffering scheme in comparison with those of the conventional fixed buffering scheme. Finally, Section 5 presents our conclusions.
## 3 Results and Discussion  
### Growth of Au Nanoseeds into AuNRs  
Plasmon resonance properties of AuNRs and Au nanoseeds were studied through UV-Vis spectroscopy. As shown in Figure 3, the absorption spectra of Au nanoseeds exhibited a peak at 521 nm, while those of AuNRs showed two peaks at 520 nm and 903 nm. It is widely accepted that only a  
Figure 2: Fabrication process of gold nanorods (AuNRs): (**a**) formation of Au nanoseeds, (**b**) growth of Au nanoseeds into AuNRs.  
single surface plasmon resonance (SPR) peak is expected from the absorption spectra of spherical Au nanoparticles [30]. In contrast, two observed plasmon resonance peaks of AuNRs are ascribed to transverse and longitudinal SPRs caused by the width and the length of the nanorods, respectively [31].  
The crystalline structures of Au nanoseeds and AuNRs were investigated through TEM. As shown in Figure 4a,c, the solution of Au nanoseeds consisted of clusters of Au nano-sized particles with the average diameter of \(15.37\pm 0.61\) nm (N = 30), and with the crystalline lattices of 0.23 nm, corresponding to the (111) crystal plane of Au (Figure 4a, the inset) [32; 33]. Meanwhile, the TEM images of the AuNRs with high aspect ratios (AR \(>\)3) revealed the presence of rod-like nanostructures with a heterogenous size distribution of \(96.05\pm 10.89\) nm (N = 122) in length and of \(16.44\pm 1.11\) nm (N = 122) in width (Figure 4b-f) [34]. Interestingly, the AR of AuNRs estimated from the discrete dipole approximation (DDA), \(\lambda_{\text{LSPR}}\) = 96 AR + 418 where \(\lambda_{\text{LSPR}}\) is the longitudinal SPR wavelength of AuNRs, was 5.1, which is close to the average AR of AuNRs obtained from the TEM images (AR = \(5.7\pm 0.8\)) and other studies [35; 36]. Moreover, the FFT pattern of the AuNRs along [110] zone axis (Figure 4b, the inset) proved that AuNRs were single crystals with a face-centered cubic (fcc) close packing [37]. It is obvious that the existence of a few nano-sized particles indicates the growth of Au nanoseeds into AuNRs along the [001] direction. It is due to the fact that silver ions are selectively bonding to the high-energy (110) facets of Au nanocrystals, which slows down the growth rate of Au atoms on these facets during the synthesis [38]. It is also noteworthy that the AuNR solution is relatively stable at room temperature due to the presence of CTAB molecules, which prevent AuNRs from aggregation.  
The XRD pattern of AuNRs with characteristic peaks of Au at 20 = 38.1\({}^{\circ}\) (111), 44.3\({}^{\circ}\) (200), 64.5\({}^{\circ}\) (220), and 77.7\({}^{\circ}\) (311) in Figure 5a revealed high crystallinity of AuNRs, which is consistent with the TEM results [39]. Furthermore, the EDX spectra in Figure 5b represented that Au and Si are the major elements of AuNR-coated glass substrate, indicating successful coating of glass substrate with AuNRs. The presence of Ag\({}^{+}\) ions was also observed in the EDX, indicating the deposition of Ag\({}^{+}\) ions (pairing with Br\(-\) from CTAB) on the AuNR-coated glass substrate.  
Figure 3: Ultra-violet-Visible (UV-Vis) spectra of Au nanoseeds and AuNRs.
# A Multi-Domain Model for Variable Gap Iron-Cored Wireless Power Transmission System
## 5 Experimental Validation and Results Comparisons  
### Introduction  
The experimental activities described in this section are carried out to derive an accurate estimation of the measurable constituting parameters of the model, and to provide a comparison and validation of the behavior predictions obtained from the numerical co-simulation analysis.  
In detail, the section is subdivided as follows: the first paragraph describes the WPT device; one paragraph is dedicated to the description of the experimental set-up with particular focus on the controlled gap variation apparatus; and, finally, a paragraph is allocated to a numerical-experimental comparison of the voltage and current trends on both the source and load sides in the time domain, as well as in the frequency domain.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 2 System Model and Conventional Method
### Energy Consumption Model of Sensor Nodes  
We use the first-order model described in [23] to model energy consumption of WSNs. Considering data in each data packet, this paper describes the energy consumption of the node related to receiving and transmitting one data packet as follows.  
\[E_{TX}(l,d)=E_{TX-dec}(l)+E_{TX-amp}(l,d)=\left\{\begin{array}{ll}lE_{elec}+l_{ fs}d^{2}&d\leqslant d_{0}\\ lE_{elec}+l_{fs}d^{4}&otherwise\end{array}\right. \tag{12}\]  
\[E_{RX}(l)=E_{RX-elec}(l)=lE_{elec} \tag{13}\]  
where \(E_{TX-elec}\), \(E_{RX-elec}\), and \(E_{TX-amp}\) are energy consumption of transmitter, energy consumption receiver, and energy consumption amplifier, respectively. \(e_{fs}\) and \(\varepsilon_{mp}\) are coefficient amplify of free space and coefficient amplify of multi-path, respectively. \(E_{elec}\) is energy consumption of one bit data processing, \(l\) is amount of data, \(d\) describe the distance between transmitter and receiver, \(d_{0}=\sqrt{\varepsilon_{fs}\Big{/}\varepsilon_{mp}}\) is effective communication distance between nodes.
# Image Error Concealment Based on Deep Neural Network
###### Abstract  
In this paper, we propose a novel spatial image error concealment (EC) method based on deep neural network. Considering that the natural images have local correlation and non-local self-similarity, we use the local information to predict the missing pixels and the non-local information to correct the predictions. The deep neural network we utilize can be divided into two parts: the prediction part and the auto-encoder (AE) part. The first part utilizes the local correlation among pixels to predict the missing ones. The second part extracts image features, which are used to collect similar samples from the whole image. In addition, a novel adaptive scan order based on the joint credibility of the support area and reconstruction is also proposed to alleviate the error propagation problem. The experimental results show that the proposed method can reconstruct corrupted images effectively and outperform the compared state-of-the-art methods in terms of objective and perceptual metrics.  
Keywords:image error concealment; deep neural network; local correlation and non-local self-similarity; adaptive scan order +
Footnote †: journal:
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 4 Experiment
### System Simulation Verification  
In order to verify the accuracy of the above detection method, a comparison test system is established [18]. The test system includes a laser transmission system, a visibility meter, two cameras, an adjustable brightness target, and a high-pressure spray equipment. The visibility meter measurement range is 10 to 10,000 m, which is unaffected by ambient brightness and weather conditions. The brightness of the adjustable brightness target can be adjusted according to the environment and is suitable for daytime and nighttime. One of the cameras is used to acquire the image of the road surface for the analysis of the road image transmittance; the other is used to collect the images of the target for the analysis of the transmittance, brightness, and contrast of the target images. High-pressure spray equipment is used to adjust the fog concentration at the site. When the fog concentration is insufficient, we will use equipment to supplement the fog to create a different visibility environment.  
Before verification, the placement of hardware devices such as cameras is double checked to meet our requirements: the camera for shooting target is on the same horizontal line as the target; the target installation height is about 1.5 m, 10 m from the camera; the position of the camera shooting the road is 5-10 m higher; the visibility meter installation height is consistent with the target and placed near the target with the laser transmission system.  
In the verification process, first, via the laser transmission system, the current atmospheric transmissivity is estimated. Two cameras respectively collect the road images and the adjustable brightness target images. Combining the DCP algorithm, (\(t_{R}\), \(t_{T}\), \(t_{l}\), \(t_{h}\), \(t_{c}\)) is obtained. Finally, SPSS is used to perform MNLR analyses on six variables for visibility prediction in similar environments. The prediction results should be compared with the results of the visibility meter.