esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In industrial monitoring and control applications, a server often has to send a command to a node or group of nodes in wireless sensor networks. Flooding achieves high reliability of message delivery by allowing nodes to take the redundancy of receiving the identical message multiple times. However, nodes consume much energy due to this redundancy and the long duty cycle. A reliable slotted broadcast protocol (RSBP) tackles this problem by allocating a distinct broadcast slot (BS) to every node using a tree topology. Not only does it remove collision, but it also minimizes energy consumption such that every node remains active only during its parent's broadcast slot and its own broadcast slot to receive and rebroadcast a message, respectively. However, it suffers from low reliability in harsh environments due to the compete removal of redundancy and low responsiveness to the changes in network topology due to the global scheduling of slots. Our approach allocates one distinct broadcast sharable slot (BSS) to each tree level, thus making a BSS schedule topology-independent. Then, nodes at the same level compete to rebroadcast a message to nodes at one level higher within the BSS, thus allowing the redundancy. In addition, it uses a slot-scheduled transmission within BSS that can further improve reliability by reducing message collisions and also enables the precise management of energy. According to simulations and experiments, the proposed approach can achieve high reliability comparable to flooding and low-energy consumption comparable to RSBP.  
sharable slot; slotted broadcast; tree; node mobility; energy-efficient 242019/19, 1236; doi:10.3390/s19051236
## 4 Results and Discussion  
The findings of the conducted systematic literature review are covered in this section. Each of the research questions was also answered in this section. In the Web of Science database from 2010 to 2022. Figure 3 displays the annual number of published papers that can be found and retrieved using the detailed criteria. The bar graph shows a gradual rise in the frequency of publications. The years 2022 and 2021 have the highest publications, clearly showing rising attention in the domain. This inclination was sustained in 2021, observing that this evaluation covers only the first eleven calendar month of the year 2022. Only two publication outlets were considered in this SLR: journal and conference proceedings because two or more reviewers thoroughly reviewed the articles. Figure 4 shows the publications per outlet, and it was demonstrated that there are more publications in the journal outlet than in the conference. 27 (84.4%) articles were published in the journals, while 5 (15.6%) were published in the conference proceedings. Figure 5 shows the journal publishers and the articles each published. It was demonstrated that IEEE in Manhattan, NY, USA published the highest report of 17, followed by MDPI in Basel, Switzerland, with five articles and Elsevier in Amsterdam with three. Springer in Berlin/Heidelberg, Germany published two articles, while Hindawi in Cairo, Egypt; Frontiers in Lausanne, Switzerland, IOP in Bristol, England, Wiley in Hoboken, NJ, USA, and Tech Science in Henderson, NV, USA all published one. Figure 4: Publications per outlets. \begin{table}
\begin{tabular}{c c} \hline \hline
**Database** & **Articles** \\ \hline IEEE & 972 \\ \hline Scopus & 174 \\ \hline Springer & 55 \\ \hline Web of Science & 7119 \\ \hline Wiley Online Library & 15 \\ \hline Total & 8335 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The database and the papers initially retrieved. Table 4 provides a list of the extracted articles. All techniques used were found to be deep learning technologies. It summarizes the information that was taken from the relevant studies. The first column lists the author's name, the year the paper was published, and the technology each contributor utilized. After the subject technology, the aims and contributions made by the research are described. Additionally, the table displayed responses to each research question in the final three columns on the right.
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 4 Simulation Results
### Simulation Results and Analysis
It can be seen that since the system works in the linear region, the simulation results of the linear EH model are consistent with those of the non-linear EH model. The result shows that in our experiments, when the two-hop distances are \(d_{1}=3\) m and \(d_{2}=6\) m, the system works in the linear region and does not enter saturation. This is consistent with many existing works [34; 35]. When the distance between the energy transmitter and the receiver is relatively far or the input energy of the EH module at the receiver is relatively small, the EH circuit may still work in the linear region. Figure 10: Throughput versus energy conversion efficiency.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 11 Discussion on Private Campus Networks
### Research Gaps for Private Campus Networks  
* Radio Access Network (RAN): Publicly available research material contain very little information on how to cover an industrial area by radio. Although there are several kinds of research on WiFi coverage in industrial areas, unfortunately, these researches hardly include any case studies on cellular coverage. Measurements in the industrial sited, under live conditions, provide great feedback to standardization bodies [135]. It is clear that in a manufacturer's environment, different drilling, cutting, pressing and welding machines operate, and radio interference is difficult to model accurately through theoretical methods. In order to get proper base-date, multi-directional measurement execution and analysis must be carried out. Furthermore, the appearance of TSN [136] is an exciting direction, but further investigation is needed on its practical, scalable feasibility.
* Network slicing: Although the theory of network slicing is vast, there is very little information available at this stage on real implementation and feedback from industry. It is necessary to examine how the terminals and standards work and whether they meet the needs of the industrial sector.
* Core network orchestration: It is clear that systems and architectures need to be designed to the need of the industrial enterprise in question. To do this, the mapping of the transmission technology parameters are needed first, which are mostly unknown to the customer, and then build a corresponding network. Although theoretical studies of such methods already exist, detailed elaboration is required.
* Network and service monitoring: The life cycle of industrial mobile networks is unknown [131]. Private campus networks are to be operated in a relatively closed and secured manner, although end-devices has to be made accessible for networking personnel for supervision. The human in the loop appears here, and it is crucial to have a deeper understanding of procedures and task to be covered here, together with the exploration of the proactive maintenance possibilities in this area.
# Application of Deep Learning to Construct Breast Cancer Diagnosis Model
## 2 Materials and Methods
### Image Recognition
#### 2.2.10 Back Propagation Network (BPN)  
The number of neurons in the input layer of BPN is the number of attributes in the BCSC breast cancer risk factor data set (there were 12 neurons in the input layer), and the number of neurons in the output layer is used to determine the presence of invasive breast cancer or noninvasive breast cancer (N = 1). Based on this, to find the best network architecture for BPN, we set the hidden layer of the network to one and determined the number of neurons in the hidden layer using the Equations (21) and (22).  
\[\text{Number of neurons in the hidden layer }=\frac{12+1}{2}=6.5 \tag{21}\]  
\[\text{Number of neurons in the hidden layer }=\sqrt{12\times 1}=3.46 \tag{22}\]  
According to the above equations, the number of neurons in the hidden layer was set to two, three, four, five, six, seven, and tested. The sigmoid function was used for the activation function, and the results were averaged over three times for each number of neurons (Table 1).  
The BCSC breast cancer risk factor dataset was entered into the BPN, and the results were obtained by training the architecture with six hidden layer neurons, 31,901 cases with cancer were correctly classified, while 10,212 cases were classified as not having cancer. Among those without cancer, 12,291 cases were correctly classified, and 34,339 cases were classified as having cancer.  
The experiment performed on our breast cancer risk factors model accumulated the results of the various evaluation metrics. Tables 2 and 3 depicts the accuracy, sensitivity, and specificity for the BPN and SVM. The accuracies of the BPN and SVM are 74.63% and 91.6%, respectively, showing a march between the predicted and the actual instances.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & & & & \\
**Neurons in the** & **First Result** & **Second Result** & **Third Result** & **Average** \\
**Hidden Layer** & & & & \\ \hline
2 & 73.1\% & 74.0\% & 73.6\% & 73.6\% \\
3 & 74.4\% & 73.4\% & 74.1\% & 73.9\% \\
4 & 74.6\% & 74.0\% & 75.2\% & 74.6\% \\
5 & 74.3\% & 74.8\% & 74.8\% & 74.6\% \\
6 & 76.6\% & 76.4\% & 76.7\% & **76.6\%** \\
7 & 76.1\% & 74.9\% & 76.5\% & 75.8\% \\ \hline \hline \end{tabular} Bold text indicates the highest accuracy rate.  
\end{table}
Table 1: Selection of the number of neurons in the hidden layer of 1 BPN.  
The sensitivity and specificity are inversely proportional, meaning as the sensitivity increases, the specificity decreases and vice versa [47]. For instance, the BPN sensitivity is 24.24% whiles the specificity is 75.75%. under cancer-free the sensitivity is 73.64% whiles the specificity is 26.36%.
## 3 Security Issues Facing CVs and Solutions
### Vanet
#### 3.4.4. RSU Assisted Security and RSU Power Abuse
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **C-V2X** & **VANET** \\ \hline Capacity & High & Medium \\ \hline Mobility & Very high (support speed up to 350 km/h) & Medium \\ \hline Coverage & Ubiquitous & Medium \\ \cline{2-3} Delay & Coal is 100 ms (C-plane) and 10 ms round-trip and 5 ms & Goal is 100 ms (safety-critical application) and 500 ms (non-safety-critical application) \\ \cline{2-3} V2I support & Native, due to the centralized architecture with enhancements & Yes, only intermittent and short-lived connectivity \\ \hline V2V support & Potential, through D2D extension & Native, through extensions in MAC protocols \\ \cline{2-3} Network infrastructure & Adopting existing cellular infrastructure for V2I communications & Requiring high investment on network backbone devices \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{4}{*}{Security issues} & (1) & Direct secure communication without a priori configuration of keys by the network is needed. & (1) & Efficient authentication with privacy preservation and key management in group signature. \\ \cline{2-3} Security issues & (2) & Integrity protection on application layer messages. & (2) & Privacy protection and effective pseudonym change strategy. \\ \cline{2-3}  & (3) & Privacy protection of location and operator power abuse. & (3) & Trust management and enhancement. \\ \cline{2-3}  & (4) & RSU assisted security and RSU power abuse. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of features and security issues between C-V2X and VANET.
# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 2 Publicly Available Brain MRI Datasets
### Nfss  
The Neurofeedback Skull-stripped (NFBS) repository [24] is a publicly available dataset. There is a total of 125 brain MRI scans with skull stripped volume in the data (48 are from men and 77 from women). The age of the subjects' ranges from 21 to 45 years old. The matrix size of a volume is \(256\times 256\times 192\) (The first two dimensions constitute size of each slice. Thus, they are the number of pixels. The last dimension represents the number of slices present in each scan.), and the voxel size in each volume is \(1\times 1\times 1\) mm\({}^{3}\).
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 2 Related Work
### Security Assumptions and Models
#### 2.3.2 Development Environment and Tools  
The implementation of CONIKS in Python involves setting up a development environment with essential tools and libraries. Key tools include Python 3.x, which offers modern language features and libraries for cryptographic operations. The primary library used for cryptographic functions is _hashlib_, which provides robust support for hash functions like SHA-256. Additionally, the _'cryptography'_ library is employed for digital signatures,using classes such as _RSAPrivateKey_ and _RSAPublicKey_ to manage key pairs and signing operations. Development environments like Jupyter Notebooks or PyCharm can be used for coding, debugging, and testing.
## 4 Overview of Optimization Methods in Food Science: Linear, Non-Linear, and Evolutionary Approaches
### Evolutionary Algorithms  
In contrast to traditional optimization methods, evolutionary algorithms are heuristic and probabilistic, drawing inspiration from the principles of natural selection. These  
\begin{table}
\begin{tabular}{c c c c} \hline
**Aspect** & **Linear Methods** & **Non-Linear Methods** & **Evolutionary Methods** \\ \hline Complexity & Low & Moderate to High & High \\ \hline Accuracy & Exact solutions & High accuracy & Approximate solutions \\ \hline Problem types & Linear & Non-linear & Non-convex, discontinuous \\ \hline Computation & Fast and efficient & Computationally intensive & Resource-intensive \\ \hline Use Cases & Supply chains, diets & Fermentation, energy use & Recipe optimization, supply chains \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of methods [40; 41].  
methods, which include genetic algorithms (GAs), particle swarm optimization (PSO), and differential evolution (DE), are designed to tackle highly complex, multi-objective, or poorly understood problems. Unlike deterministic methods, evolutionary algorithms do not guarantee exact solutions but are capable of identifying near-optimal solutions in challenging problem spaces [43].  
Evolutionary algorithms excel in addressing optimization problems characterized by non-convex, discontinuous, or high-dimensional relationships. Their iterative nature allows them to explore a wide range of potential solutions, making them ideal for applications where traditional methods fail. For example, they are particularly effective in multi-objective optimization scenarios, where conflicting objectives such as quality, cost, and sustainability must be balanced. However, the resource-intensive nature of evolutionary methods can be a limitation, as they often require substantial computational power and time. Moreover, their reliance on probabilistic processes means that the quality of the solution depends on the algorithm's design and parameters.  
By categorizing optimization methods and problems in food science, a clearer understanding of their applications and limitations emerges. Traditional methods, such as linear and non-linear optimization, offer precise and reliable solutions for well-defined problems, while evolutionary algorithms provide the flexibility needed to tackle complex, multi-objective challenges. Understanding when and how to apply these methods is essential for advancing optimization efforts in food science, enabling the industry to achieve its goals of quality, efficiency, and sustainability [35]. Figure 3 presents a classification of optimization methods, including mathematical programming (both continuous and discrete), metaheuristic optimization, and learning-based approaches. Each category is outlined with key characteristics, highlighting the strengths and specific applications of each method in the context of food science.  
Figure 3: Optimization methods.
## 3 Fuzzy Logic-Based WSN Clustering
The authors validated the performance of the proposed techniques under different network densities and for different locations of base station. Table 2 depicts the network lifetime in rounds based on the HND metric. Average lifetime in rounds was calculated using the fuzzy descriptors proposed by the corresponding authors. The projected data demonstrate the variations in the performance on the basis of the fuzzy descriptors utilized in the fuzzy-based clustering algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol/Algorithm** & **Round-Based** & **Cluster Size** & **Fuzzy-Based** & **Multi-Hop** & **Detecting Hole** \\ \hline Cluster head election & & & & & \\ mechanism using & Yes & Not fixed & Yes & No & No \\ fuzzy logic [31] & & & & & \\ \hline Fuzzy logic-based & & & & & \\ energy-efficient & & & & & \\ clustering for WSN on & & & & & \\ the basis of minimum & Yes & Not fixed & Yes & No & No \\ separation distance & & & & & \\ imposition between & & & & & \\ cluster heads [33] & & & & & \\ \hline Energy aware cluster & & & & & \\ and neuro fuzzy-based & Yes & Fixed & Yes & No & No \\ routing algorithm, [34] & & & & & \\ \hline Fuzzy logic for & & & & & \\ clustering scheme [35] & Yes & Not fixed & Yes & No & No \\ \hline Fuzzy logic-based & & & & & \\ clustering algorithm & & & & & \\ for wireless sensor & & & & & No \\ network [36] & & & & & \\ \hline Self-management & & & & & \\ model [37] & & & & & \\ \hline Clustering approach in & & & & & \\ WSN using fuzzy & & & & & \\ system [38] & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison for the surveyed fuzzy-based clustering protocols. Figure 4: A block diagram summarizing the different parameters discussed in this paper used in cluster formation utilizing fuzzy logic. CH: cluster head, BS: base station.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 5 Evaluation
### Stealthiness  
In this section, we evaluate the stealthiness of the proposed saturation attack considering existing anomaly detection mechanisms. Existing SDN defense mechanisms adopt different detection models but share similar detection metrics. Thus, we demonstrate the stealthiness by (1) the packet-in rates, (2) the controller overhead, and (3) the attack traffic rate, which are the key metrics that many recent defense methods [29, 30, 31, 34, 35] use to detect network anomalies.  
(1) Packet-In rates: We measure the number of packet-in messages sent to the controller with and without the attack. As shown in Figure 23, before the flow table is full (around 250 s), there is no noticeable difference between the packet-in rates for the two scenarios. After the flow table is overflowed by malicious flows, the packet-in rate increases. Compared with the table-miss attack [36], which targets saturating the control channel between the control and data planes, the flow table saturation attack incurs only a few additional packet-in messages. Even after overflowing the flow table, most packet-in requests are caused by benign flows. Therefore, existing defense mechanisms against the controller saturation attack cannot react to the flow table saturation attack.  
(2) Controller overhead: Some detection mechanisms monitor the controller's overhead [37] to capture network anomalies. We measure the CPU usage of the Ryu controller with and without the attack. The results are shown in Figure 24. Before the flow table is  
Figure 22: The number of packet-in requests in the case that the controller adopts a load-aware scheme.  
saturated, there is almost no difference between the two scenarios: the average CPU usage is 29.1% when there is no attack and is 29.3% in the attack scenario. Only after the flow table is overflowed at around 260 s, the CPU usage increases because of the additional packet-in requests. After the flow table is full, the average CPU load in the attack scenario is about 18% higher than the normal scenario.  
(3) Attack traffic rate: We also measure the attack traffic rate to further demonstrate its stealth from the data plane's perspective. Figure 25a shows the attack traffic rates, and Figure 25b shows the traffic rate of background traffic. As the attacker generates more flow rules in the flow table, the required attack traffic rate is higher. When overflowing the flow table, the rate of attack traffic is only tens of Kbps, which is significantly smaller than the benign background traffic.  
Figure 23: The packet-in rate with and without the attack traffic.  
Figure 24: The CPU utilization of the Ryu controller under attack and normal conditions.
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 2 Methods
### SLR 3D Validation Method  
Since SLR range validation can only determine the residuals of the validation in distance and the results are not very intuitive, we propose an SLR 3D validation method on SLR range validation. SLR 3D validation is a method that uses data about SLR stations and the results of SLR range validation to validate the accuracy of orbits. It uses the coordinates of SLR stations provided by the International Laser Ranging Service ([[https://ilrs.gsfc.nasa.gov/](https://ilrs.gsfc.nasa.gov/)]([https://ilrs.gsfc.nasa.gov/](https://ilrs.gsfc.nasa.gov/))) (accessed on 15 March 2022) (ILRS) and RD orbits of LEO to calculate the geometrical distances from the stations to LEO. Then, make differences between geometrical distances and distances obtained by laser ranging, and the root mean square (RMS) of the results are propagated to three directions according to the law of error propagation, and intuitive results of the validation are obtained.  
The latest coordinates of SLR stations (SLRF2014) were downloaded from the ILRS [28], and the coordinates of stations in September 2021 were calculated based on the initial coordinates and the change rate of coordinates provided by SLRF2014. The residuals of distance were obtained according to the principle of laser ranging:  
\[\Delta=SLR-\rho \tag{12}\]  
where SLR is the laser ranging distance and \(\rho\) is the geometrical distance calculated between the SLR station and HY-2D. According to the law of error propagation, the error is transmitted to x, y, and z directions:  
\[\begin{split}\delta_{\Delta}{}^{2}=\delta_{SLR}{}^{2}+\left( \frac{\Delta x}{\rho}\right)^{2}\times\delta_{x1}{}^{2}+\left(\frac{\Delta y }{\rho}\right)^{2}\times\delta_{y1}{}^{2}+\left(\frac{\Delta x}{\rho}\right)^ {2}\times\delta_{z1}{}^{2}+\left(\frac{\Delta x}{\rho}\right)^{2}\times \delta_{x}{}^{2}+\\ \left(\frac{\Delta y}{\rho}\right)^{2}\times\delta_{y}{}^{2}+ \left(\frac{\Delta x}{\rho}\right)^{2}\times\delta_{z}{}^{2}\end{split} \tag{13}\]where \(\delta_{\Delta}\) is the RMS of the SLR range validation; \(\delta_{SLR}\) is the RMS error of the accuracy of range data of the SLR station; \(\Delta x\), \(\Delta y\), and \(\Delta z\) are the coordinate differences between the SLR station and the HY-2D satellite in the x, y, and z coordinates during the continuous observation period; \(\delta_{x1}\), \(\delta_{y1}\), and \(\delta_{z1}\) represent the accuracy of the coordinates of SLR stations in three directions; \(\delta_{x}\), \(\delta_{y}\), and \(\delta_{z}\) are the errors in the results of the validation of orbits in three directions.  
The SLR 3D validation method is an improved method based on the SLR range validation method. The new method can express the accuracy of an orbit in the three directions of x, y, and z, making the results more intuitive.