esa-sceva/satcom-chunk-collection huggingface dataset:
# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 2 Literature Review  
IoTs are resource constraint devices built for a specific purpose. Due to limited resources and their ability to communicate over the internet and work independently, they are exposed to many cyber-attacks. Resource limitations prevent security expertsfrom applying conventional security solutions such as firewalls, antivirus, and patches [1]. In contrast, access to the internet allows cyber-attackers to use sophisticated software to quickly overtake these devices and use them as bots to launch further large-scale distributed attacks on critical infrastructure and websites [15]. There are many attack types that can impact IoT systems. Some of them include distributed denial of service (DDoS), botnet, keylogging, phishing, spamming, click fraud, identity theft, and malware proliferation. However, the most common and the most dangerous are Denial of Service (DDoS) and botnet. Some of these attacks are active attacks because they occur during real-time network communication. They either affect the normal operations or alter the system resources, e.g., data alteration or flooding the target system (DDoS, DOS) to make it unresponsive to prevent it from serving any new requests. Contrary to active attacks, passive attacks monitor and analyze the traffic pattern by eavesdropping to find vulnerabilities such as open ports, low cipher usage, and unencrypted traffic [16]. Protecting the IoT environment is critical. Many research studies have been based on finding the optimal machine learning solution. However, most of them do not provide a comprehensive solution by investigating network traffic from various sources or building robust models that converge quickly and provide the best results. Some researchers trained their models on a single and old dataset (e.g., NSL-KDD), which lacks modern-day attacks [17]. An IDS must detect a large number of attack types because some of the DDoS attacks such as \"Slowloris\" slowly deplete server resources and are hard to detect [18]. In contrast, others such as \"Jamming\" attacks can quickly drain resources and have serious consequences [19].
## 1 Introduction
### Background and Related Work
#### 1.1.2 Brain Segmentation  
The problem of automatic brain tissue segmentation has been very well explored before deep learning algorithms became a standard for semantic segmentation. [PERSON] et al. [10] covers an overview of older methods such as thresholding, clustering or some form of simpler machine learning algorithms. These methods also rely heavily on image preprocessing as in [11] therefore they are not automatic. One of quite frequently used preprocessing method is some form of skull-stripping algorithm, which removes the bone tissue of skull from the input picture before processing as in [12]. One of the most notable works in the brain segmentation field is the FMRIB's Automated Segmentation Tool (FSL) [13]. This method is based on a hidden Markov random field model and an associated Expectation-Maximization algorithm. We used this tool to evaluate our brain segmentation results to a method that does not use deep neural networks. Since deep learning methods achieve higher accuracy even without any preprocessing of input image, most of the ongoing research including this paper is now using some form of deep neural network for brain segmentation. An overview of the current state-of-the-art in the field of brain segmentation using deep learning can be found in [14]. This is an extended version of our paper 3D Dense-U-Net for MRI Brain Tissue Segmentation which was focused only on brain segmentation [15].
## 4 A Hairy Root Platform for the Production of Bioactive Abietane Diterpenes
### Metabolic Engineering to Enrich the Content of Abietane Diterpenes in S. sclarea Hairy Roots
#### 5.2.2 GGPP Availability Limits the Accumulation of Abietane Diterpenes in _S. sclarea_ HRs
A first indirect indication of the potential involvement of the CPPS in the cyclization of GGPP to CPP in the biosynthesis of _S. sclarea_ abietane diterpenes was provided by the high correlation we found between the expression level of the _CPPS_ gene and the abietane diterpene content in _S. sclarea_ HRs [51]. These findings were further confirmed by the _SsCPPS_ overexpression, which boosted a significant 20-fold increase in the aethiopinone content (10.44 \(\pm\) 0.21 mg g\({}^{-1}\) dw) compared to the control HR lines (0.5 \(\pm\) 0.03 mg g\({}^{-1}\) dw). Interestingly, the content of ferruginol (11.50 \(\pm\) 0.38 mg g\({}^{-1}\) dw) and salvipisone (6.65 \(\pm\) 0.72 mg g\({}^{-1}\) dw) was also enhanced by 30 or 9 times, respectively, by overexpressing the _CPPS_ gene in _S. sclarea_ HRs [40]. To the best of our knowledge, this is the first evidence that aethiopinone in _S. sclarea_ might be synthesized from GGPP to CPP by a CPPS, which occurs for other abietane diterpenes [73, 74]. In addition, the availability of the CPP might limit the synthesis of abietane diterpenes in _S. sclarea_, since we have proved for the first time that the _CPPS_ overexpression overcomes this metabolic bottleneck.
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
## 3 CFP Firmware Design Scheme
### FPGA Design
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Addr** & **Name** & **RW** & **Content** \\ \hline
10010 & PRIADR\_REG & RW & PRTADR[4:0] Pin Strap value \\
10012 & PRG\_CNT\_REG & RW & PRG\_CNT\_1\(\sim\)3 Pin value \\
10016 & CTRL\_IN\_REG & RW & TX\_DIS pin, MOD\_LOPWR pin, \\  & & & MOD\_RSTn pin value \\
10017 & TX\_DIS & RW & Tx Disable for each channel \\
10018 & POWER\_REG & RW & Internal CFP power switch \\
1001B & PROT\_REG & RW & [2]: Vendor private protect \\  & & & [1]: User NVR protect \\  & & & [0]: Vendor NVR protect \\
10060 & ACC\_ADDR\_REG & R & [15:0] Address \\
10061 & ACC\_STT\_REG & R & [1]: Read access status \\  & & & [0]: Write access status \\
10062 & ACC\_STT\_REG & R & [1]: Read fail \\  & & & [0]: Write fail \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FPGA Internal Register. Figure 6: Blocking Operation of Micro Controller Unit (MCU) Accessing. The ACC_STT_REG(access status register) signal can be found through FPGA register \(0\times 10061\). The MCU verifies that the status of the ACC_STT_REG signal is IDLE and then performs a write operation. When the memory write operation of the MCU is performed, the ACC_STT_REG signal is enabled after the access is recognized by the MDIO frame. Since the ACC_STT_REG signal is enabled, the write operation is blocked in the FPGA. The WR_FAIL bit at \(0\times 10062\) is activated. However, read operation of the MCU is allowed.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 4 Proposed Model
### Construction of Players  
Every UE will have two different preference lists for uplink and downlink. Therefore, in addition to the existing BS, we create a new set of BSs. The new virtual BS will be a combination of existing ones, where one will serve the uplink traffic and the other one will be for downlink. Gathering BSs into pairs while looking for an ideal matching with maximum gain is a task issue, which we can successfully solve by utilizing the assignment step between BSs. We plan the issue in detail as below:  
The \(b_{in}\) characterizes the benefit of the \(UE_{n}\) served by different BS for uplink and downlink, which is the component of the matrix \(b\).  
\[b_{in}^{(\cdot)}=\max_{\forall i\in\{1,2,\dots,m\}}(\log(\alpha_{i}r_{in}^{( \cdot)})-\phi_{th,n},0), \tag{9}\]  
where \(\phi_{th,n}\) represents the capacity threshold determined by the QoS requirements of \(UE_{n}\). \(\alpha_{i}\) gives scaling effects of \(UE_{n}\) for an uplink and a downlink data rate on the utility. The value of \(\alpha_{i}\) displays the emphasis of \(UE_{n}\) on an uplink or a downlink data rate which often rely upon the type of device (e.g., smart-phone, tablet, laptop). With the objective of maximizing the gain, we outline the coalition task matrix \(h\), with elements representing whether there is a coalition between two BSs:  
\[h_{ij}=\left\{\begin{array}{l}1,\text{ }if\text{ }BS_{i}\text{ }\text{\emph{s} serve the uplink and the }BS_{j}\text{ }\text{\emph{s} serve the downlink},\\ 0,\text{ }\text{otherwise}.\end{array}\right. \tag{10}\]Then, the assignment problem shifts to grouping uplink and downlink problems in a pair of BSs, so as to maximize the overall benefit of \(UE_{n}\). Therefore, the problem is formulated as:  
\[\max_{h}\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{M}h_{ij}b_{ij}, \tag{11}\]  
where \(b_{ij}\) is the sum of the benefits of \(UE_{n}\) served by \(BS_{i}\) for uplink and \(BS_{j}\) for downlink, defined as follows: \(b_{ij}=(b_{in}^{u}+b_{jn}^{d})\). Since the objective is to offer satisfaction to the user for uplink and downlink, the assignment will only be valid if both BSs are beneficial for UE. In other words, the agent BS must check the following condition: \(h_{ij}=1\)_if and only if \(b_{in}^{u}\
eq 0\) and \(b_{jn}^{d}\
eq 0\).
## 5 Performance Analysis
### Computation  
By using chaos synchronization, instead of PF-CP-ABE, to obtain the secret number, the computation can be further reduced. The original encryption means that the user takes their private keys of each attribute, and the secret number can be further calculated. On the contrary, if both Lorenz systems synchronize, the secret number can be generated. Excluding external factors, a comparison of the encryption and decryption times is shown in Figure 9. The horizontal axis is the number of attributes, and the vertical axis is the time (ms). Our system, CS-ABE, could be divided into non-synchronization (Non-syn CS-ABE) and synchronization (Syn CS-ABE). Obviously, the greater the number of attributes, the more time is needed for encryption and decryption. However, for the chaos system, the encryption and decryption times are the same, no matter how great the attributes are. If we use a higher order elliptic curve or a more complex computing technique, both the encryption and decryption times will be even longer. Therefore, the benefit of chaos synchronization determined by the difficulty of elliptic curve calculation.  
Figure 9: Comparisons of encryption and decryption times.
## 2 Mathematical Model
### Boundary Conditions  
A Neumann boundary condition for pressure was used at the outer boundary of the computational domain. The tunnel walls and pavement were taken as an impenetrable, nonslip boundary. It should be noted that the Standard \(k\)-\(\epsilon\) model as a turbulence model for high \(Re\) numbers is not applicable to the near-wall area where the flowing \(Re\) number is low. Therefore, in order to capture the near-wall region's flow structures, the kqRwallFunction and epsilonWallFunction, also called standard wall functions in OpenFOAM 2.1.1 (OpenCFD Ltd., Bracknell, UK, 2012) [(20)], were assigned to the turbulence kinetic energy and dissipation rate, respectively. Using the wall function allowed us to locate the first point adjacent to the wall in the logarithmic law instead of the sublayer. The calculated \(y+\) value should be distributed between 30 and 300 in all simulations. According to \(y+\), the distance of the near-wall node to the solid surface was calculated, which was also corrected continuously through computational simulation. Ultimately, it could be determined that \(y+\) can be ensured between 32 and 132 under all computation conditions when the first layer grid thickness was 0.018 m.  
Figure 3: Velocity profile in different mesh resolution.  
Figure 2: Schematic diagram of gird.  
The tunnel inlet and outlet adopted the Dirichlet boundary condition. According to the ventilation conditions of the actual tunnel and the possible working conditions during the project's operational phase, calculations were separately conducted with respect to the following working conditions: at outlet air velocity \(u_{\text{out}}\) of 1.5 m\(\cdot\)s\({}^{-1}\), 3.0 m\(\cdot\)s\({}^{-1}\), 4.5 m\(\cdot\)s\({}^{-1}\), 6.0 m\(\cdot\)s\({}^{-1}\), and 7.5 m\(\cdot\)s\({}^{-1}\); and inlet air velocity \(u_{\text{in}}\) of 1.5 m\(\cdot\)s\({}^{-1}\), 3.0 m\(\cdot\)s\({}^{-1}\), 4.5 m\(\cdot\)s\({}^{-1}\), 6.0 m\(\cdot\)s\({}^{-1}\), and 7.5 m\(\cdot\)s\({}^{-1}\). The numerical computations in this study were conducted by OpenFOAM, an opensource CFD code with flexible and extendable C++ libraries.
## 2 Methodology and Transfer Matrix Formalism  
Using the transfer matrix method [27], the electromagnetic responses of 1D deformed and non-deformed quasi-periodic crystals arranged in accordance with the Fibonacci sequence are simulated and theoretically studied. The gallium selenide (GeSe) and plasma materials are used to construct the multilayer Fibonacci structures. We aim to study the sensitivity of the PBGs and the localized mode to the plasma material, the geometric thickness of the layers and the aperiodicity of the Fibonacci photonic structure. Therefore, the effect of Fibonacci sequence parameters, the deformation of the thickness of the layers using a mathematical rule and the role of the plasma material in the reflectance response of waves will be discussed. Electromagnetic wave transmission through multilayered stacks is modeled and simulated using the transfer matrix method (also called the Abeles method) [27]. The Abeles method, inspired by [PERSON], is adopted to analyze the behavior of electromagnetic wave propagation within the bulk photonic materials. It takes account of the multiple reflectance within the plate. The scattered electromagnetic waves are considered in two forward and backward waves. We consider a 1D structure consisting of alternating layers of different refractive indices. The amplitudes of the electric fields \(E^{+}\) and \(E^{-}\) of the forward and backward propagating waves and the transmitted wave after m layers \(E^{+}_{m+1}\) are used to calculate the reflectance and the transmittance of waves. Based on Abeles, the product of these propagation matrices, called the resultant matrix (the transfer matrices for the overall layers of the structures that lie between the amplitudes of the forward and backward propagating waves and transmitted wave after m+1 layers), is given by [28; 29]  
\[ \begin{pmatrix}E^{+}_{0}\\ E^{-}_{0}\end{pmatrix} =\prod_{j=1}^{m+1}M_{j}=\prod_{j=1}^{m+1}\frac{C_{j}}{t_{ j}} \begin{pmatrix}E^{+}_{m+1}\\ E^{-}_{m+1}\end{pmatrix}  \tag{1}\]  
where \(C_{j}\) and \(t_{j}\) represent the propagation matrix and the transmittance Fresnel coefficient, respectively. For the \(j\)th layer, from the whole structure, the \(C_{j}\) layer's propagation matrix can be written in the form [28; 29]  
\[C_{j}= \begin{pmatrix}e^{-i\delta_{j-1}}&r_{j}e^{-i\delta_{j-1}}\\ r_{j}e^{i\delta_{j-1}}&e^{i\delta_{j-1}}\end{pmatrix}  \tag{2}\]  
The transfer matrix of the entire structure can be defined as a product of matrices \(\frac{C_{j}}{t_{j}}\)[28; 29]. Here, \(\delta_{j-1}\) represents the phase variation at \((j-1)\)th layer \(\delta_{j-1}=\frac{2\pi}{\lambda}\theta_{j-1}h_{j-1}\cos\theta_{j-1}\), where \(\theta_{j-1},\ h_{j-1}\) and \(\theta_{j-1}\) are, respectively, the refractive indices, the thickness and the incidence angle at the \((j-1)\)th layer with \(\delta_{0}=0\). The transfer matrix can be written as [28; 29]  
\[M=\prod_{j=1}^{m+1}\frac{C_{j}}{t_{j}}= \begin{pmatrix}a&b\\ c&d\end{pmatrix}
## 3 Development of the Generic Implementation Strategy
### Generic Implementation Strategy in the Narrower Sense
#### 3.2.2 Phase 2: Development Phase  
The second life cycle phase serves to connect the real and virtual space, i.e., the transition of the DTP into a preliminary DTI. Referring to the concept of DTs presented in Section 2.3, the development of communication systems and DTs is necessary for the connection of the virtual and real world [17, 49, 54]. To establish this connection, an implementation of the systems addressed is necessary.  
In order for this to take place in a targeted manner, an implementation procedure is defined in this process step, which is based on the results of the previous processes. As described in Section 2.2, the strategy includes the decision whether the DT elements are implemented by means of forward- or re-engineering. Hereby, parts can also be provided and implemented by the already mentioned COTS products. Before the elements are implemented, it is advisable to identify any limitations that may occur with the chosen implementation strategy that may hinder the implementation. After the preparation is complete and all necessary resources are available, the DT elements and the communication system are realized in accordance with the implementation strategy. The focus is on the hardware and software as well as the services of the real and virtual world. Afterwards, an inventory is made of whether the realized DT meets the defined requirements (see Figure 19 and Section 2).  
The next process step serves to assemble the DT elements into a DT that verifiably fulfils the previously defined DT requirements, architecture and design [50, 54]. Before the DT elements can be integrated successively, the necessary services and systems for the process are provided and a procedure is developed. After integration, the interfaces between the elements, but also the relation to the environment, as well as critical quality features and functions are checked. This step is followed by a verification based on defined criteria and a final management of the process, which includes, among other things, the approval of the stakeholders that DT fulfils the defined requirements (see Figure 19).  
Lastly, the developed DT is transitioned to its operational state, in which it can demonstrably provide the requirements and characteristics placed on it. For this purpose, the transition is prepared by various systems and services and then carried out. Once the transition has taken place, a demonstration of the DT's functionalities follows. These are then confirmed by specific validation procedures and the stakeholders, so that the DT can move into its operational phase [54]. This validation and transition build the last process of the GIS in the narrower sense (see Figure 19).
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 8 Stopping Methods
### ANLPP Approach  
All Nodes Least Possible Probability approach (ANLPP), our final implementation of canVisitMoreNodes, illustrated in Algorithm 5 builds on Algorithm 4 and minimizes the chances of wasted power. To minimize these chances, we need to minimize the number of high power nodes in \(v\). This can be achieved by identifying the probability of all the nodes in \(v\) to be of low power. If we utilize our bound for both the highest and lowest power nodes in \(v\), we can determine if all nodes in \(v\) are within a set of lower power nodes in the network.  
ANLPP implementation of canVisitMoreNodes (Algorithm 5) extends the MCR exploration limit under three conditions:  
* sufficient power exists in the MCR to explore further,
* there is a high probability of finding nodes with power level less than the minimum power level node in \(v\),
* there is a high probability of finding nodes with power level less than the maximum power level node in \(v\).  
The algorithm applies all the steps from the Minimum Only approach (Algorithm 4). The algorithm calculates the probability of finding a node with power less than the highest power node in \(v\) as \(pHi\). If \(pHi\) is above the predefined maxThreshold, then keep exploring, since there is a high chance of finding nodes with power less than the maximum power node in \(v\). This minThreshold may be chosen based on the minimum known MCR coverage, or the minimum reasonable certainty needed by the user. This implementation also terminates its exploration once it realizes it can only charge a single node (line 19). The time complexity of ANLPP is also \(O(1)\). Most of the operations are of constant time \(O(1)\), and the method used are the same methods used in Minimum Only leading to applying the same analysis. Next, we will show the baseline algorithm to compare against.  
```
1:\(N\)\(\triangleright\) Set of network sensor nodes
2:\(\lambda\)\(\triangleright\) Localization cost in terms of power
3:\(N_{d}\)\(\triangleright\) Set of distances between nodes \(=\{d_{1},d_{2},...,d_{s}\}\)
4:minThreshold\(\triangleright\) Minimum node probability threshold, tuning parameter
5:maxThreshold\(\triangleright\) Maximum node probability threshold, tuning parameter
6:procedurecanVisitMoreNodes(power, \(v\), \(C\), \(l\), \(v\)Nodes, \(i\))
7:if\(power\geq(d_{i}\cdot A_{m})\)then
8:return True
9:else
10:\(minNeedPaper\leftarrow\lambda+l-min(v)\)
11:if\(C>minNeedPaper\)then\(\triangleright\) Proceed if charging power is greater than power needed to charge lowest node
12:\(pLO\gets findProbLess(min(v))\)\(\triangleright\) Probability of lower power nodes to exist, less than the lowest in \(v\)
13:if\(pLO>minThreshold\)then
14:return True
15:else
16:\(pHI\gets findProbLess(max(v))\)\(\triangleright\) Probability of lower power nodes to exist, less than the highest in \(v\)
17:return True
18:return True
19:else
20:return False
21:endif
22:endif
23:endprocedure
```  
**Algorithm 5** ANLPP approach for canVisitMoreNodes.
## 3 Results and Discussions
### 2,2-Diphenyl-1-Picrylhydrazyl (DPPH) Radical Scavenging Activity Assay  
Buckwheat, especially sprouted buckwheat, is interesting from both a nutritional and pharmaceutical point of view (2019) due to its high antioxidant capacity. The antioxidant activity of buckwheat achenes was determined by the DPPH test--DPPH being one of the most stable free radicals. The buckwheat achenes were characterized as having a high radical scavenging activity of 15%. After 3 days it increased to 37%, and after 7 days of sprouting it reached 87%. This growth is explained by the fact that sprouting increased the accumulation of antioxidant compounds.  
The antioxidant activity measured by this method was positively correlated, significantly, both with the total content of flavonoids (0.985; \(p<0.05\)) and with the free content of polyphenols (0.984; \(p<0.05\)).
## 3 The DRR Traffic Control Algorithm
### DRR Model Layout  
The continuous AGV road network is discretized in square blocks of the same size (see the bottom-left plot in Figure 1). AGVs have the option to either remain stationary or rotate within designated zones, positioned at the center of these blocks. Moreover, they are restricted from advancing to the next blocks during deceleration intervals. Achieving this limitation is feasible by appropriately designing the AGV speed and block size in practical scenarios. In the simulation experiments presented in [10], the layout is discretized using 1 m side blocks, while the AGV size is 0.7 m \(\times\) 0.7 m.