esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 ML and the Security of Blockchain
### Network Traffic Analysis  
ML-based intrusion detection systems (IDS) are critical for analyzing network traffic in real time and mitigating distributed denial of service (DDoS) attacks. Unsupervised learning methods enable these systems to learn normal network behavior patterns and flag unusual traffic spikes or irregular patterns. Furthermore, reinforcement learning techniques can be applied to develop dynamic traffic-filtering mechanisms that adjust in real time, ensuring continued network availability even during DDoS attacks.
# Experimental Study on Chromaticity Control in Visible Light Communication Systems
## 3 Research on RGB LEDs Dimming
### Dimming Method and Calculation Method
From Equations (10) to (12), the duty cycles of the three channels can be obtained as follows:  
\[D_{r}=\frac{(y_{g}-y_{b})(x_{b}-x)+(y-y_{b})(x_{g}-x_{b})}{(y_{g}-y_{b})(x_{b}-x_{ r})+(y_{r}-y_{b})(x_{g}-x_{b})}\cdot\frac{y_{r}Y}{yY_{r}}, \tag{13}\]  
\[D_{g}=\frac{(y_{b}-y_{r})(x_{r}-x)+(y-y_{r})(x_{b}-x_{r})}{(y_{b}-y_{r})(x_{r}- x_{g})+(y_{g}-y_{r})(x_{b}-x_{r})}\cdot\frac{y_{g}Y}{yY_{g}}, \tag{14}\]  
\[D_{b}=\frac{(y_{g}-y_{r})(x_{r}-x)+(y-y_{r})(x_{g}-x_{r})}{(y_{g}-y_{r})(x_{r} -x_{b})+(y_{b}-y_{r})(x_{g}-x_{r})}\cdot\frac{y_{b}Y}{yY_{b}}, \tag{15}\]  
where \(Y_{r}\), \(Y_{g}\), and \(Y_{b}\) are the maximum output luminous flux when the input duty cycle of the red, green, and blue light sources is 100%; \(Y\) is the output luminous flux of a hybrid light source; \((x_{r},y_{r})\), \((x_{g},y_{g})\) and \((x_{b},y_{b})\) are the color coordinates of red, green and blue, respectively; and \((x,y)\) is the color coordinate of the mixed light source.
## 1 Introduction
#### Research Question and Objectives  
This study evaluated the scope of use of RIP by conducting a systematic review in accordance with the PRISMA guidelines to answer the following question:  
How does the RIP method contribute to the healthcare sector in assessing a person's respiratory health status through the measurement of respiratory parameters, and how can the application of machine learning improve the outcomes of RIP's applications?  
A comprehensive quality assessment of the studies was conducted to minimize the risk of bias. Furthermore, the following research objectives were set to answer the research question:  
1. Conduct a comprehensive systematic review of the existing literature to explore the application and effectiveness of Respiratory Inductive Plethysmography (RIP) for various healthcare purposes, including monitoring respiratory activity and diagnosis.
2. Assess the reliability and medical applicability of RIP as an alternative or supplement to established methods within its designated scope of use, considering factors such as performance and usability.  
3. Investigate the influence of different calibration methods on the performance of Respiratory Inductive Plethysmography (RIP) in terms of measurement accuracy and reliability.
4. Explore the evolution and recent technological advancements of Respiratory Inductance Plethysmography (RIP) devices, focusing on wearability and mobility.
5. Evaluate the applications of machine learning (ML) techniques in the processing of RIP data and assess the practicality of ML-based approaches.
6. Identify areas for future research to advance RIP implementation through both conventional techniques and machine learning-based approaches.
## 5 Simulations
### Real Case Study
Colored boxes represent surgeries while white boxes are idle times. The idle time could be: (i) delay regarding the starting time if the box is at the beginning or (ii) cleaning time if the box is between two surgeries. On the other hand, to check how much the order of patients in the waiting list is respected, the preference order of the patients inside the colored boxes has been included. Since the waiting list of patients has been constructed considering real surgeries, in Figure 5a, the patients are perfectly ordered. However, considering the automatic method (Figure 5b), although the SHA tries to make the scheduling respecting as much as possible, the order of the patients in the waiting list results in being a little disorderly. In order to see it visually, three colors have been used with a different meaning:  
* The patients delayed or advanced at most a time block respect to the manual scheduling (perfectly ordered) are given in green boxes. * The patients delayed or advanced two or three time blocks with respect to the manual scheduling are shown in orange or red boxes, respectively. Remember that the waiting list has been constructed considering the real surgeries performed in 36 surgical blocks, obtaining a waiting list composed of 111 patients. Considering the manual scheduling, patients with preference order from 1 to 73 have been scheduled in the first 24 time blocks. Using CIPLAN, patients with preference order from 1 to 76 (except 65 and 71) have been scheduled in 24 time blocks. The number of patients advanced or delayed two time blocks (orange) is 14 while the number of patients advanced or delayed three time blocks (red) is 9. However only three patients (15, 18, and 57) have been delayed three time blocks and seven patients (22, 25, 47, 54, 58, 60, and 64) have been delayed two time blocks. This means:  
* 4.05% of the patients are delayed three time blocks with respect to the preference order;
* 9.45% of the patients are delayed two time blocks with respect to the preference order.
## 1 Introduction
### Data Collection  
The study area was divided into 182 internal zones and 5 other external zones for primary data collection. In 2016, origin and destination (O-D) surveys (for internal trips) were conducted using a household interview survey (HIS). Approximately 6100 households (4.0% of the total households) were directly interviewed. Additionally, other remaining trips (such as internal-external, external-internal, and external-external trips) in the study area were observed via a roadside interview survey (RIS) at six sites, with a sample size of approximately 5600 interviews. Traffic counts at 44 mid-block locations and their associated traffic compositions were collected. For secondary data collection, most of the data were derived from the SIRDC [32]. Current and future socioeconomic and demographic characteristics (including population, students, and employment), road network physical characteristics (including road hierarchy classes, number of lanes, lane width, and others), current and future highway and transit network data, and land-use characteristics (including land-use types, densities, and others) were obtained from the SIRDC [32].
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 4 Evaluation of Examined Solutions
### Evaluation Results
#### 4.5.2 Variations on the \"k\" Parameter  
As described in Section 4.2 any possible latent potential the experimentation on the variation of other parameters could reserve was not thoroughly explored. Such is the case of the \"k\" variable regarding the utilization of the k shortest path technique in [18].  
Acceptance Ratio (AR)--Figure 8 clarifies that, by relaxing the demand of exclusively using the calculated shortest path for the interconnection of every mapped pair of virtual nodes on the SN, one can achieve better AR percentages. Nevertheless, the range of the successfulness of the preference in longer network paths is dependent on the scale and density  
Figure 6: ANS of the examined embedding strategies.  
Figure 7: Active nodes of the examined embedding strategies.  
of the incoming VNRs. As far as the many-small scenarios are concerned, the improvement on the AR percentages is limited to the initial \"k\" values, after which no positive impact is observed whatsoever. On the other hand, the increment in the success rates in the case of the few-large simulations is more distinctive and ongoing as the \"k\" values advance.  
Running Time (RT)--Although greater \"k\" values provide more successful embeddings, they also inflict significant delays in execution time, more severely so in the case of the denser VNRs. This impact on RT is depicted in Figure 9.  
The two stages solutions were developed with the functionality of choosing between a deterministic and a random way of rounding the solution of the linear program to approximate the values of the binary variables in the original MIP (see Section 3.2.1). However, the authors did not provide any experimental results regarding the interchange between those methods.  
By observing the graphs of Figure 10, it can easily be discerned that almost all randomized trials completed faster compared to the deterministic ones, especially for the less  
Figure 8: Success rate of “k” variations on Greedy-KSP.  
Figure 9: RT of the “k” variations on Greedy-kSP.  
demanding scenarios, meaning that less computational resources are needed to calculate the rounded solution of the MIP's linear programming relaxation stochastically rather than deterministically.  
Both approaches achieved equivalent embedding success with more or less the same cost indices, proving both rounded solutions as equally efficient (Figure 11). Moreover, in most of the less demanding scenario cases (Figure 12a) of the two stages coordination algorithms that utilize the PS technique (see Section 2.1.5) the deterministic approach occupied less substrate nodes under the same or equivalent acceptance circumstances (Figure 11a). Under the light of these discoveries, one can assume that the deterministic approach is able to achieve a greener footprint in contrast to its random counterpart.  
Figure 11: Success of the rounding techniques of R-ViNE-PS.  
Figure 10: RT of the rounding techniques of R-ViNE-PS.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Design Guidelines
#### 8.3.3 Management Plane Design  
The network management plane in KDN should be designed for dynamic network configuration, where the configuration is automated and dynamically adjusted based on real-time traffic analysis, which reduces manual intervention and speeds up the configuration process. The management plane should be designed for real-time monitoring and analytics to monitor network performance. As one of the purposes of the management plane is fault management, data should be collected by the management plane for predictive maintenance to predict potential network failures and recommend proactive measures to prevent them. KDN systems collect a vast amount of data that need to be efficiently stored and processed. Traditional data storage mechanisms may not be sufficient to handle the massive amount of data that KDN collects. Therefore, the performance and capacity of the management plane may be enhanced by intelligent data storage technologies such as distributed databases and object storage. By keeping it in memory, in-memory databases such as Redis can offer quick access to frequently used data. These databases can be used for regularly requested data such as network statistics, topology details, configuration information, etc. [559]. Time-stamped data, such as network performance measurements or telemetry data, may be stored using time-series databases such as InfluxDB, which are built for processing massive amounts of time-stamped data [560]. The management plane collects a large volume of data required for management and knowledge generation, which can be challenging to store and manage. Data compression techniques such as gzip, Snappy, etc. can be used to reduce the storage footprint of network data without compromising its quality [561]. Data deduplication techniques such as content-defined chunking or delta encoding [562] can be used to eliminate duplicate data in the management plane, as duplicate data cause excess data storage and do not effectively contribute to training machine learning models.
# A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism
## 4 Experimental Results and Analysis
### Comparison Experiments  
In order to further verify the overall performance of the improved algorithm, we conducted a total of nine sets of comparison experiments, using precision (Precision), average precision (mAP), frames per second (FPS), and model weights as evaluation metrics. To ensure the fairness of the experimental process, all models were set with a batch size of 8 and epoch of 200, and other detailed parameters are the same. The comparison results of different indexes are shown in Table 4 below.  
As can be seen from Table 4, RebarNet outperforms other algorithms in terms of accuracy and recall in terms of the mAP0.5 of detection results. mAP0.5 of RebarNet is 97.9%; compared to other mainstream target detection algorithms including Faster R-CNN, SSD, RetinaNet, CenterNet, CornerNet YOLOv3, YOLOv4, and YOLOv5s, mAP0.5 improved by 8.1%, 13%, 26.4%, 25.8%, 26.2%, 11.7%, 7.6%, and 9%, respectively. In terms of model weight size, the model weight obtained by training with RebarNet is 17.0 MB, which is 1/11, 1/5, 1/7, 1/9, 1/10, 7/8, and 1/29 of the model weights of Faster R-CNN, SSD, RetinaNet, CenterNet, CornerNet, YOLOv3, and YOLOv4, respectively. Although the model weights are slightly increased compared with the previous YOLOv5s, mAP0.5 is significantly improved by 9%; in terms of frames per second transmission, the FPS of RebarNet is 89 frames, which is slightly inferior to YOLOv5s in terms of detection rate but still satisfies the real-time performance of the rebar detection task. In summary, RebarNet achieves a balance of detection accuracy and detection speed, and its overall performance is better than that of other object detection algorithms, which can meet the basic requirements of rebar cross-section detection.
# Research Status of Pathogenesis of Pneumoconiosis and Dust Control Technology in Mine--A Review
## 4 Research Progress of Shotcrete Dust Control Technology  
Since the new Austrian tunnel construction method was put forward, bolt shotcrete support has been widely used in roadway excavation engineering. Shotcrete is widely used in the construction of mine roadway, subway, tunnel, hydraulic culvert and other projects because of its characteristics of no formwork, fast construction speed, simple process and flexibility [73; 74; 75; 76]. In recent years, the anchor shotcreting operation has mainly adopted dry shotcreting and wet shotcreting processes. Although the dry shotcreting operation is simple, the content of free silica in the dust on the operation site is more than 80% [77; 78], and the risk of miners suffering from pneumoconiosis is very high.  
The shotcrete dust for mining is mainly cement dust, with a large amount of cement, about 440 kg/m\({}^{3}\). Due to the small particle size of cement particles, cement dust escapes during handling, feeding, mixing and spraying. Understanding the characteristics and influencing factors of shotcrete dust has important theoretical significance for controlling shotcrete dust. There are many factors affecting the generation of shotcrete dust, mainly including: shotcrete material, mix proportion, shotcrete process (water pressure, working air pressure, spray distance, spray angle) and shotcrete equipment (spray gun, jet machine, etc.). [PERSON] et al. [6] introduced the characteristics of shotcrete dust and its influencing factors. Starting from the shotcreting process, dry concrete spraying technology, wet concrete spraying technology and their dust production characteristics are introduced, respectively, as shown in Figures 13 and 14. Due to the different ways of adding water to the mixture, the dust production of the two shotcrete technologies is very different. Field measurement shows that dry spraying produces the most dust and wet spraying produces the least. The following discusses the optimization of shotcrete process, the application of admixture materials and the optimization of spray gun structure design.  
Figure 12: Foam-drainage behavior of the surfactant solution with added polymer [71].  
Figure 13: Process flow of concrete dry spraying.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service
### PV-Powered Charging Station with V2G Service with Energy Cost Optimization
#### 2.2.1 Prediction Layer  
In the prediction layer, hourly solar irradiation predictions are provided by Meteo France to calculate the PV power prediction. The PV power prediction is based on solar irradiation (\(g\)) and the ambient temperature (\(T_{amb}\)) from forecasted data [47]. Therefore, the PV power prediction \(p_{PV~{}MPPT~{}prod}\) is calculated in MPPT mode for each time instant \(t_{i}\)[48] as in Equations (1) and (2), and it is introduced into the energy cost optimization layer:  
\[P_{PV~{}MPPT~{}prod}(t_{i})=P_{PV\_STC}.\frac{g(t_{i})}{1000}\cdot[1+\gamma \cdot(T_{PV}(t_{i})-25)]\cdot N_{PV}~{}with~{}t_{i}=\{t_{0},t_{0}+\Delta t,t_{0 }+2\Delta t,\ldots,t_{F}\}, \tag{1}\]  
\[T_{PV}(t_{i})=T_{amb}(t_{i})+g(t_{i})\cdot(NOCT-T_{air-test})/G_{test}, \tag{2}\]  
where \(P_{PV\_STC}\) is the PV power in standard test conditions (STC), \(\gamma\) is the power coefficient of temperature (\(-0.29\%/^{\circ}\)C), \(N_{PV}\) is the number of PV panels, \(T_{PV}\) is the PV cell temperature, and \(t_{0}\), \(\Delta t\), and \(t_{F}\) are the initial time instant, time interval between two samples, and time instant at the end of operation, respectively. \(NOCT\) is the nominal operating cell temperature (41 \({}^{\circ}\)C), \(G_{test}\) is the fixed solar irradiation (800 W/m\({}^{2}\)), and \(T_{air-test}\) is the fixed air temperature (20 \({}^{\circ}\)C).
# Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs
## 4 The Proposed Strategy  
### The Network Model  
It is assumed that all sensors are evenly distributed in a two-dimensional region \(R\) with a radius of \(d\) for the network model. With unlimited energy, a static data sink is located at the center of the two-dimensional region. Each sensor is located within a cluster and transmits the sensed packet to its associated cluster head (CH). Then each CH aggregates and transmits packets along with the constructed shortest paths to the data sink. To evenly assign the CH among sensors, the CH is alternated with an equal probability in the same cluster and all sensors have the same amount of initial energy \(E_{init}\)[9].  
In the network application, each sensor collects parameters such as humidity, temperature, air quality, or other related events at the outside environment and the fused data can be forwarded to the sink via the CHs along the transmission path. Where m is a variable that is represented the total amount of sending packets and forwarding packets from CH \(u\) to the sink. In each data cycle, \(p_{k}\) is the probability that a CH fuses the kth event and generates number of packets \(g_{k}\). As a result, the packet generation rate r for any CH \(u\) in each data cycle can be expressed as:  
\[r_{u}=\sum_{k=1}^{m}p_{k}g_{k} \tag{1}\]  
To simplify the power computation model, the transmission power is the main factor to be taken into consideration in the path loss model since it spends a larger amount of power than the reception and idle situations. Hence, the power consumption for the multi-hop transmission can be represented as \(P_{M}(r_{u})\). Consequently, the network lifecycle of CH node \(u\) is expressed by:  
\[N(u)=\frac{E_{init}}{P_{M}(r_{u})} \tag{2}\]  
where \(E_{init}\) is the initial amount of battery capacity of each sensor and the lifecycle is defined as the network operation round from sensors placement till the first sensor depletes its battery capacity [16].
# Blockchain for Future Wireless Networks: A Decade Survey
## 5 Result Analysis
### Experimental Setup And Tools  
The blockchain and WN are simulated in two different platforms to accomplish a shared objective, i.e., to provide essential 6G services to the application layer. For blockchain, we used the Remix integrated development environment (IDE) and solidity programming language to create digital smart contracts, which helps eradicate the intermediate third-party services and improves the operation of the blockchain network. Furthermore, to provide the benefits of a 6G network, we mimicked the MATLAB-based 5G toolbox by modifying its parameters to behave as a 6G network. The parameters, such as frequency range (95 GHz), channel bandwidth (130 MHz), subcarrier spacing (60 kHz), and many more, are changed in the 5G toolbox. A particular user of the application layer demands the 6G-based service from the WN layer; once the user accesses the 6G services, they can communicate with each other for a particular task (smart home/smart cities/smart healthcare, etc.) of the application layer. Moreover, the blockchain secures the data communication between the application and the WN layer by securely storing the data in the immutable ledger. The proposed architecture is evaluated against different performance metrics, such as scalability, packet loss ratio, and latency. The entire architecture is simulated in a desktop that has configuration, such as Intel core i5, 16 GB RAM, 250 GB SSD, and Intel iRIS graphic card. Due to the involvement of high-end computing software, i.e., MATLAB 2021, and Remix IDE this configuration is a minimal configuration needed to simulate the proposed architecture. Additionally, Table 4 shows the different simulation parameters that are used in the proposed architecture.
## 3 Energy Efficient Schemes for Green C-IoT
### Scheduling Optimization  
With the aim of reducing power consumption and enhancing EE performance, it is a preferable choice to turn the unnecessary nodes into sleeping mode [53]. Thereupon, it is of great necessity to design the optimal scheduling and planning schemes of the MTC devices to decide the on-off states ofthese devices. Besides, the duty cycle needs to be carefully designed to make a proper compromise between lifetime and power consumption [59].  
When referring to sleeping mode, it can be classified into two categories, namely semi-sleeping mode and total-sleeping mode. Semi-sleeping mode means that the sensors are capable of sensing the ambient environment, and the sensed data are not transmitted immediately. In this case, the sensed data can be locally stored in MTC devices, and the cached data will be transmitted if the value of the sensed data reaches the pre-defined threshold. On the other hand, in total-sleeping mode, the MTC devices are not allowed to sense the surrounding environments. Meanwhile, we note that the transitions between active and sleeping modes are also power-consuming. Therefore, combined with the prediction techniques, it is better to estimate the time of the next data transmission and make the decisions about whether or not to switch off the MTC devices to sleep mode when the current transmission process is almost complete.  
As for the wake-up strategies of MTC devices, these can be accomplished in periodical and network-initiate manners. The former strategy is simple, and the devices will be activated in some certain time periods. This method costs little control signaling overhead. The wake-up period needs to be properly decided, and this method fails to deal with the emergent and unexpected scenarios. When confronting these scenarios, the network-initiate strategy can be adopted, where the MTC devices will be woken up if some emergencies happen. However, the latter will result in more control signaling overhead. To this end, there is a tradeoff between these two wake-up strategies, which deserves great consideration in the practical C-IoT deployment [50].