esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization
## 1 Introduction
In China's rotary-cutting production line, log centering is mainly achieved through mechanical centering and halo centering. Neither centering method is suitable for logs with irregular shapes. Compared with digital centering technology, the log recovery rate is reduced by 10-15%, and the card-free processing method is a time-varying nonlinear system. Currently, most equipment adopts open-loop control, which cannot guarantee the quality of veneers. Therefore, to address the low level of intelligence and low utilization of logs in China's rotary cutting production line, this paper establishes a digital twin system for log rotary cutting optimization based on a five-dimensional model of the digital twin, which can autonomously analyze log feature data and derive targeted processing schemes [13]. The system not only includes a digital log-centering machine as the physical entity for the complete acquisition of the log's outer contour but also encompasses virtual entities of other equipment involved in the rotary-cutting process. Through the service system, the system can analyze the optimal centering position of the log based on log feature data and perform real-time calculations for the processing parameters of the log debarker, as well as the veneer peeling machine. These calculations are then verified through simulation using virtual entities. This approach effectively confirms the optimal method for log rotary cutting, improves log utilization and enhances the automation level of the log rotary cutting production line.
## 2 Materials and Methods
### Total Plate Counts, Microbial Isolation and Identification
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Agar Medium** & **Selectivity** & **Incubation Conditions** \\ \hline Luria–Bertani agar (LB) & Aerobic bacteria & Aerobic; 30 \({}^{\circ}\)C; 72 h \\ \hline Luria–Bertani agar (LB) & Anaerobic bacteria & Anaerobic; 30 \({}^{\circ}\)C; 72 h \\ \hline Potato dextrose agar (PDA) & Fungi & Aerobic; 30 \({}^{\circ}\)C; 72 h \\ \hline Yeast extract peptone dextrose agar (YPD) & Yeasts & Aerobic; 30 \({}^{\circ}\)C; 72 h \\ \hline De Man–Rogosa–Sharpe agar (MRS) & Lactic acid bacteria & Anaerobic; 37 \({}^{\circ}\)C; 72 h \\ \hline Plate count agar (PCA) & Bacteria & Aerobic; 30 \({}^{\circ}\)C; 24 h \\ \hline Plate count agar (PCA) & Fungi & Aerobic; 30 \({}^{\circ}\)C; 120 h \\ \hline
**Primer and 5\({}^{\circ}\)–3\({}^{\circ}\) Sequence** & **Target Region** & **Run Conditions** \\ \hline
27F: AGAGTTTGATCCTGGCTCAG & Bacterial 16S rRNA gene & 94 \({}^{\circ}\)C for 5 min, 31 cycles at 94 \({}^{\circ}\)C for 1 min, 58 \({}^{\circ}\)C for 1 min, 72 \({}^{\circ}\)C for 2 min, and then \\
149R: CTACGGCTCACTTGTTACG & & 72 \({}^{\circ}\)C for 8 min, and finally 4 \({}^{\circ}\)C for 30 min \\ \hline NS1: GTAGCTATATGCTTGTCTC & \
\begin{tabular}{c} Yeast 185 gene, the internal transcribed spacer \\ (ITS) segment, and most of the 5.88 gene in \\ ribosomal (r)DNA \\ \end{tabular} &
\begin{tabular}{c} 95 \({}^{\circ}\)C for 3 min; 35 cycles of 60 s at 95 \({}^{\circ}\)C, 60 s \\ at 55 \({}^{\circ}\)C, and 90 s at 72 \({}^{\circ}\)C, and finally an \\ extension step of 5 min at 72 \({}^{\circ}\)C \\ \end{tabular} \\ \hline
**Storage Containers** & **Datalogging and Sampling** & **Storage Conditions** \\ \hline
2 \(\times\) Trays (aerobic) & Temperature and humidity: Middle, bottom & Ambient conditions at Tong Lam Supplies \\
100 cm \(\times\) 100 cm \(\times\) 12 cm & Sampling: Day 0, 3, 7, 14 & (\(\sim\)30 \({}^{\circ}\)C; 70\% to 80\% RH) \\
40 kg wet weight & & \\ \hline
2 \(\times\) Drums (anearobic) & Temperature and humidity: Front, middle, & Ambient conditions at Tong Lam Supplies \\
258 cm \(\times\) 95 cm & back & (\(\sim\)30 \({}^{\circ}\)C; 70\% to 80\% RH) \\
100 kg wet weight & Sampling: Day 0, 3, 7, 14 & (\(\sim\)30 \({}^{\circ}\)C; 70\% to 80\% RH) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental design including agar media and incubation conditions for total plate counts (TPCs) and microbial isolation, PCR primers and run conditions for Sanger sequencing of microbial isolates, and description of storage containers, datalogging, sampling, and storage conditions.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Collaborative Machine Learning for Energy-Efficient Edge Networks in 6G
#### 5.3.1 Energy Efficient Computational Offloading Empowered by Multiagent DRL  
Consider an edge network with \(M\) small cells, \(N\) user equipment, and \(K\) orthogonal channels, where each small cell is covered by an SBS and all small cells reuse the system's whole spectrum. The SBS are equipped with MEC servers and are capable of making decisions. Each EU contains a computationally intensive operation that can be partially downloaded to the MEC server and performed remotely.  
Offloading performance is connected with each other due to the interference between the cells. The problem of efficiently delegating computation activities and distributing resources can be stated as a problem of minimizing total energy usage while also optimizing resource control decisions. This problem can be solved by exploiting a multi-agent DRL model, in which each SBS behaves like an agent that interacts with the environment to improve its performance. Three fundamental elements in the multi-agent DRL process are the status, the action, and the reward:  
* **Status**: each SBS in a real network is unable to acquire the global state of the entire surrounding environment; instead, each SBS has its own limited knowledge. The channel gains of each agent's signals for each channel, the interference power received, and information on the actions requested by the user within its coverage area are all part of the state observed by each agent for the characterization of the environment.
* **Action**: Each instant, each of the agents carries out an action based on its decisions. In this case, the action consists of the computational download decisions, channel and resource allocation for each user served, and uplink power control.
* **Reward**: Through a reward-based learning process, each SBS agent refines its rules. To increase performance and meet the goal of lowering global energy consumption, an incentives system can be devised to encourage collaboration among SBS agents.  
The multi-agent DRL process for offloading computation and resource allocation was proposed in [64]. An SBS agent \(m\) gets a local observation \(s_{m}[t]\) on the status of the local environment each instant of time, and executes an action \(a_{m}[t]\) to decide on the offloading of the computation and resource allocation for the customers it serves. Following that, the SBSagent receives a reward \(r_{m}[t]\) based on all agents combined actions, and the environment advances to the next state. As a result, the SBS agent receives the new observation \(s_{m}[t+1]\).
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
###### Abstract
Initially proposed by the creator of Bitcoin cryptocurrency, blockchain is a practical way to construct and manage a trustworthy decentralized ledger database across the network. By storing transactions into data blocks and linking them together using cryptographic hash functions, blockchain ensures all blocks in the chain reach the consensus effectively. Additionally, blockchain ensures the data in the database becomes computationally infeasible to mutate. In recent years, researchers have envisioned that blockchain can be used in a distributed way to solve many challenging problems in the 5G network [6]. As the number of 5G network infrastructures and mobile devices is growing exponentially, the benefits of blockchain would become more prominent in the future. In this paper, we propose a Secure Blockchain-based Authentication and Key Agreement scheme for the 3 GPP 5G network (5 GSBA). Our 5 GSBA protocol offers these benefits: first, it provides a distributed way to store all subscribers' information safely. By employing the one-time hash secret, authentication-related entries are the hashed digests that work similarly as public keys. Hence, even if the database is disclosed in the future, adversaries cannot use it to impersonate any UEs. Moreover, 5 GSBA prevents not only typical network attacks such as eavesdropping, man-in-the-middle attacks, replay attacks, and IMSI-catching attacks, but also prevents DoS and DDoS attacks effectively and provides perfect session key forward/backward secrecy. The main contributions of this paper are as follows:  
1. We design a novel Authentication and Key Agreement protocol for the 3 GPP 5G network. 5 GSBA works based on the improvement of the existing system architecture of the 5G core network. It can be easily adopted to the 3 GPP access scenario, in which all UEs are connected to the home network via nearby gNBs;
2. Our proposed 5 GSBA protocol is secure and efficient. Using blockchains and other state-of-the-art cryptographic functions, 5 GSBA can guarantee device unlinkability, mutual authentication, and data confidentiality with low computational and energy costs. Most importantly, not only can all typical network attacks be prevented, but DoS and DDoS attacks can be deterred;
3. The security of the protocol is verified with BAN logic and the formal verification tool Scyther. The performance evaluation and simulations also demonstrate its resistivity to DoS and DDoS attacks. The rest of the paper is organized as follows. Section 2 reviews the existing works on 5G authentication and some blockchain-based 5G applications. Section 3 introducesthe system and security model of 5 GSBA. Section 4 discusses the motivations for and the details of 5 GSBA. Section 5 presents the work of security evaluation and Section 6 presents the work of performance evaluation with some simulation results under different attacks. Finally, a conclusion is drawn in Section 7.
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
## 3 Adaptive Buffering Scheme  
Since live broadcast data are generated in real time in P2P live streaming systems, peers can buffer only a limited amount of data in the main memory, unlike in VOD systems. In addition, due to time-shifted viewing and playback lag caused by the nature of P2P streaming, playback positions of the peers watching time-shifted scenes as well as a live broadcast are widely spread over the entire playback period of each video, thereby decreasing data duplication among peers. On the other hand, since the chunks that have already been played back in the buffer are usually considered as unavailable, it is difficult to use each peer's buffer as the caching area to avoid accessing invalid data. In other words, it is common to use each peer's buffer only for prefetching. In this section, we therefore propose a novel buffering scheme to increase the degree of data duplication among peers in P2P live and time-shifted streaming systems by adaptively adjusting the ratio of caching and prefetching areas in the buffer of each peer according to its relative playback position within a group.
## 4 Conclusions  
This article demonstrates that the CPC method can provide very precise discrimination in the color rendering properties of a large number of available LEDs for lighting applications. This method is especially useful for choosing the best LEDs to equip luminaires designed for medical examinations in healthcare facilities. These results show that the specific requirements used in Australia and New Zealand to help the observation and diagnosis of cyanosis can be met with LEDs classified in the P1 class of the CPC (best preferred color rendering qualities). This class also provides the best color rendering of saturated red colors, as measured by the CRI Ra\({}_{9}\) index. However, the CPC class is not a good predictor of the color rendering of the skin, as measured by the \(R_{f,skin}\) index. The \(R_{f,skin}\) values are between 85 and 100 for LEDs of the P1 and P2 classes and between 80 and 95 for class P3 LEDs. However, LEDs with an \(R_{f,skin}\) index between 85 and 90 can be classified as out-of-class with respect to the CPC requirements. As a consequence, the CPC classification is not applicable to choosing LEDs with respect to their color rendering of the skin.  
This work confirms that the standard color rendering index CRI Ra is not adapted to guarantee good observation of cyanosis during medical examination. The use of light sources with a CRI Ra in the range that classifies them as suitable for general lighting applications (between 80 and 100) does not always ensure that the COI parameter is in the required range of values. The issue is that different light sources with the same CRI Ra may or may not meet the COI requirement. The CRI Ra is not a predictor of the COI index.  
This paper presents a tool that allows users to evaluate the quality of light sources in medical examination rooms. This article emphasizes the validity of using the CPC methodology to evaluate the qualitative parameters of LED sources. The article also points out that this type of classification is essential to ensure complete and reliable knowledge of lighting, and that looking only at the energy efficiency of LEDs that meet CRI Ra requirements is not enough to have high-quality lighting in medical examination room applications.  
Conceptualization, I.F.; methodology, M.L., I.F. and C.M.; software, I.F. and M.L.; formal analysis, I.F., M.L. and C.M.; data curation, M.L.; writing--original draft preparation, I.F., M.L. and C.M.; writing--review and editing, I.F., M.L. and C.M.; visualization, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Bialystok University of Technology WZ/WE-IA/3/2023.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The datasets presented in this article are not readily available because they are part of an ongoing study.  
The authors declare no conflicts of interest.
## 3 Experiments
### KITTI Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Initial Disparity**} & \multicolumn{4}{c}{**Final Disparity**} \\ \cline{2-9}  & **O\_all** & **A\_all** & **O\_noc** & **A\_noc** & **O\_all** & **A\_all** & **O\_noc** & **A\_noc** \\ \hline NL [15] & 13.87 & 3.80 & 11.67 & 2.45 & 11.57 & 1.97 & 10.26 & 1.75 \\ ST [16] & 15.66 & 3.86 & 13.50 & 2.53 & 13.44 & 2.15 & 12.16 & 1.93 \\ SGM [22] & / & / & / & / & 9.13 & 2.00 & 7.64 & 1.80 \\ LDESGM [41] & / & / & / & / & 8.22 & 2.40 & 6.01 & 1.40 \\ i5 GM [24] & / & / & / & / & 7.15 & 2.10 & 5.11 & 1.20 \\ w5 GM [42] & / & / & / & / & **6.18** & 1.60 & 4.97 & 1.30 \\ MGM [30] & 12.03 & 3.10 & 9.84 & 2.00 & 11.02 & 2.59 & 8.77 & 1.61 \\ LRNL [18] & 9.29 & 2.88 & 7.49 & 2.00 & 8.51 & 2.07 & 7.14 & 1.67 \\ Our\_Feat & **7.52** & 2.59 & **5.24** & 1.49 & 6.83 & 2.24 & **4.49** & 1.29 \\ Our\_Cen & 8.45 & **2.52** & 6.14 & **1.37** & 6.42 & **1.57** & 4.96 & **1.16** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with state-of-the-art cost aggregation approaches on KITTI 2012 dataset [37]. O\_all: percentage of erroneous pixels in all region (/%). O\_noc: percentage of erroneous pixels in the non-occluded region (/%). A\_all: average disparity error in all region (/px). A\_noc: average disparity error in the non-occluded region (/px). “/”: the results are not available. variant of CT, named Center-Symmetric Census Transform (CST), is adopted in MFSGM to improve the performance of SGM. The error rates of our method using CT in all and non-occluded regions are 5.88% and 5.55%, which are lower than that of MFSGM by 2.36% and 1.36%. Our method outperforms MGM in all metrics. Figure 9 presents the results from the KITTI 2015 dataset. Disparities in large homogeneous regions are successfully predicted, as well as that of complicated geometric structures, such as poles and cars. Most erroneous pixels still lie in the image borders. The reason for this is that these outdoor
# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
## 5 Performance Evaluation  
In this section, the performance of the proposed approach is evaluated and compared with the DBRkM approach [12]. The parameters that were used in the simulation experiments are shown in Table 3. Each experiment with the same configuration was repeated for 10 runs where in each run the same number of sensor nodes is deployed randomly in the network. The average value is then considered for the experiment. The performance metrics that were used in the simulation are: the number of active sensor nodes, total energy consumption, the standard deviation of the remaining energy, and the path length during the network lifetime.  
Figures 4 and 5 show the area of the sensing field and the network conditions using the DBRkM approach and the proposed FUZZY_RPs approach, respectively. As shown in Figure 4, the sensor nodes that are associated with RPs 3 and 4 suffer from high hop count and large distance. On the contrary, Figure 5 shows that the locations of RP 3 and 4 are updated at round 175 to balance the distance between the sensor nodes towards the RPs and to reduce the average hop count. These two figures are presented to show that the RPs are built and changed dynamically in the FUZZY_RPs approach (Figure 5) while the RPs' locations are fixed in the DBRkM approach.  
Figure 4: DBRKM Run-Time Simulation (average hop count = 1.24, tour length = 1034 m fixed at all rounds.  
Figure 5: Fuzzy_RPs Run-Time Simulation at round 175 (average hop count = 1.12, tour length = 1016 m).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Target Area & 220 \(\times\) 220 m\({}^{2}\) \\ Number of sensor nodes & 150–300 \\ Initial Energy of sensor nodes & 2 Joule \\ Communication Range (\(Rc\)) & 40 m \\ Packet Size (\(K_{b}\)) & 4000 bits \\ Speed of mobile sink (\(v\)) & 2 m/s \\ \(E_{dct}\) & 50 nJ/bit \\ \(Mp\) & 0.0013 pJ/bit/m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in the simulation.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### Iso 27017:2015
#### 3.3.2 Benefits  
ISO/IEC 27017 can help enterprises in [32]:  
1. Within the Cloud computing environment, they must safeguard their information assets.
2. Comply with all applicable laws and regulations.
3. Reduce the likelihood of data security problems.
4. Reduce the need for redundant controls to save money.
# Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring
###### Abstract  
To verify the positioning performance and reliability of multi-system combination Precise Point Positioning in landslide monitoring, we carried out a multi-system combination Precise Point Positioning calculation experiment on the monitoring data of a single landslide disaster area in Fujian Province. The coordinates of the monitoring points obtained by a continuously operating reference station and the monitoring station for static relative positioning were used as reference values. The GPS system was used as the standard system and the combined PPP solution mode of G/R/C, G/R/E and G/R/E/C was used to obtain the surface displacement of the landslide area. The research showed that multi-system combination PPP converges to the centimeter level in about 30 min. The average value of internal accordant precision was more than 1 mm after convergence, and that of the external accordant precision was more than 5 cm, which meets the centimeter-level accuracy requirements in rapid landslide deformation monitoring.  
multi-system combination; precise point positioning; landslide; deformation monitoring
# Lightweight Robust Image Classifier Using Non-Overlapping Image Compression Filters
## 3 Materials and Methods
### Adversarial Attacks  
Adversarial attacks can be categorized into two types based on the access level granted to the attacker: white-box and black-box attacks [36]. White-box attacks, such as the Fast Gradient Sign Method (FGSM) [37], Basic Iterative Method (BIM) [38], and Projected Gradient Descent (PGD) [39], necessitate comprehensive knowledge of the target model, including its architecture and parameters. Consequently, these attacks require access to the model's gradients and direct interaction with it. On the other hand, black-box attacks only require the attacker to interact with the target model to obtain its output information, without needing full details about its internal workings [40]. It is evident that black-boxattacks more closely resemble actual attack scenarios in the real world; as such, research into this type of attack is considered more practical.  
The Fast Gradient Sign Method (FGSM) and its multiple variations produce adversarial examples that fool a model with high confidence while requiring only a small perturbation. The aforementioned white-box attacks are described as follows:  
* Fast Gradient Sign Method [37]: This attack uses the sign of the gradients at every pixel to determine the direction of perturbation. Adversarial samples \(x^{adv}\) are generated according to Equation (2): \[x^{adv}=x+e\times sign(\bigtriangledown_{x}Loss(x,y))\] (2)
* Basic Iterative Method (BIM) [38]: This attack extends the FGSM attack [37] by iterating it multiple times with a small step size. Adversarial samples are generated according to Equation (3): \[x^{adv}_{n+1}=Clip_{e}(x_{n}+\alpha\times sign(\bigtriangledown_{x}Loss(x,y)))\] (3)
* Projected Gradient Descent (PGD) [39]: This attack computes the gradient in the direction of the highest loss and projects it back to the \(l_{p}\) norm around the sample, according to Equation (4): \[x^{adv}_{n+1}=\hat{\Pi}(x_{n}+\alpha\times sign(\bigtriangledown_{x}Loss(x,y)))\] (4)  
In Equations (2)-(4), \(Loss(x,y)\) is the loss function used to train the classifier, \(\alpha\) is the iterative step size, and \(Clip(\cdot)\) and \(\Pi(\cdot)\) are the clipping and projection functions, respectively.  
The Square attack [40] is a score-based black-box adversarial attack that does not depend on local gradient information, thereby circumventing the effects of gradient masking. This method employs a strategy of random search, selecting local square updates at random positions, and ensuring that the perturbation in each iteration is roughly situated at the boundary of the feasible set.
###### Abstract  
Background: The study provides real-world data on the impact of [PERSON]'s disease (HD) from the perspective of individuals with HD (IHD) and care partners (HD-CP) and contextualizes these results relative to [PERSON]'s disease (PD) and the general population (GP). Methods: Cross-sectional survey of IHD and HD-CP in the US (July 2019-August 2019) conducted using the Rare Patient Voice panel. Data for individuals with Parkinson's Disease (IPD), the general population (GP), and respective care partners (PD-CP; GP-CP) care from the 2018 US National Health and Wellness Survey. Outcomes included demographics, mental health, clinical characteristics, and health-related quality of life (HRQoL). Results: IHD had greater comorbid anxiety (IHD = 51.2%, IPD = 28.8%, GP = 2.0%), and HD-CP had greater comorbid anxiety (HD-CP = 52.5%, PD-CP = 28.6%, GP-CP = 19.6%) and depression (HD-CP = 65.0%, PD-CP = 29.9%, GP-CP = 19.6%), relative to other cohorts (_p_ < 0.05). Respective of their GP cohorts, IHD exhibited lower HRQoL (EQ-5D: 0.66 \(\pm\) 0.21 vs. 0.81 \(\pm\) 0.17) and greater depression (PHQ-9: 11.59 \(\pm\) 7.20 vs. 5.85 \(\pm\) 6.71), whereas HD-CP exhibited greater depression only (PHQ-9: 6.84 \(\pm\) 6.38 vs. 4.15 \(\pm\) 5.58) (_p_ < 0.001). No differences were observed between HD/HD-CP and PD/PD-CP cohorts on PHQ-9 or HRQoL. Conclusions: HD has a significant burden on patients and care partners, which is higher than GP. Notably, anxiety and depression were greater among HD vs. PD, despite similar HRQoL.  
Huntington's disease; Parkinson's disease; anxiety; depression; caregiver