esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
###### Abstract
We reviewed DDoS assaults detection systems based on DL techniques in this research using the SLR protocol, and offer the following findings:  
* Based on common criteria, modern DDoS attack detection technologies involving deep learning algorithms have been identified and grouped. * The methodology, benefits, and drawbacks of current ML/DL systems for detecting DDoS assaults have been outlined. * The different kinds of assaults in the datasets utilized in recent studies as well as the accessible DDoS benchmarked datasets have been compiled. * The core of our review was focused on data pre-processing techniques, hyperparameter adjustments, testing configurations, and the quality measures used by current ML/DL systems for DDoS attack detection. * The main purpose of the study was to identify areas for future research in this field and to highlight current research gaps. The remainder of this review is structured as follows: the SLR protocol is explained in Section 2; Section 3 discusses current ML/DL methods that have been employed in the literature for detection of DDoS assaults; in Section 4, the methodology, advantages, and disadvantages of different studies are discussed; the available benchmarked DDoS datasets and classes of attacks in the datasets commonly used in the literature are described in Section 5; preprocessing techniques and hyperparameters are described in Section 6; \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Review Article** & **[PERSON] et al. [16]** & **[PERSON]** & **[PERSON] et al. [18]** & **[PERSON]** & **[PERSON]** & **This Article** \\ \hline Focused & Cyber security & IDS & NID & IDS & IoT security & DDoS \\  & intrusion detection & & & & & \\ ML/DL & DL & DL & ML/DL & ML/DL & ML/DL \\ Systematic study & \(\times\) & \(\surd\) & DL & \(\surd\) & \(\surd\) & \(\surd\) \\ Taxonomy & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) \\ Preprocessing strategy & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ Types of attack used in & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ existing literature from & & & & & & \\ the datasets & & & & & & \\ Strengths & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) \\ Weaknesses & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Research gaps & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of various research papers in detail (\(\surd=\) Yes; \(\times\) = No). in Section 7, the research gaps in the current literature are shown; finally, in Section 8, our conclusions are explained and future prospects are explored.
## 3 Results
### Question 5: How to Calculate the Individual IEQ Indexes
#### 3.5.2 IAQ Model  
For IAQ models, the most popular ones were developed based on CO\({}_{2}\) concentrations. Among them, the simplest one was the linear regression model (e.g., Equation (7) [13,50]). Likewise, logistic regression models (e.g., Equation (8) [14,49]) and quadratic regression models (e.g., Equation (9) [42]) were also developed. Additionally, other indicators, such as airflow rate [10] and pollutant concentrations [15,48,52,55], were considered to be independent variables of the IAQ models as well. Some models that were developed based on air pollutants considered more than one indicator, as mentioned in question 4. The IAQ index was determined by the minimum value [48] or was calculated based on another linear regression model [52].  
\[\theta_{Ai}=-0.0002 CO_{2}+0.244 \tag{7}\]  
\[\theta_{Ai}=1-\frac{1}{1+e^{(4524-0.257\alpha_{2})}} \tag{8}\]  
\[\theta_{Ai}=6\times 10^{-7}CO_{2}^{2}-0.0025 CO_{2}+1.9416 \tag{9}\]
# Enzymatic Degumming of Soybean Oil for Raw Material Preparation in BioFuel Production
## 3 Results
### Effect of Water Dosage on Degumming Efficiency  
A study on the effect of water quantity on the efficiency of degumming of 1 kg of crude soybean oil was conducted. Water dosage is an important factor that determines the efficiency of degumming and economic feasibility. Within the framework of the experiments, concentrations of 0.50-5.00% water to the amount of oil being processed were tested. The level of phosphorus and free fatty acids in the final product was determined, and the results are given in Table 5. Degumming was carried out for 3 h at fixed values of pH = 6.9-7.1; T = 57 \({}^{\circ}\)C; amount of enzyme Purifine(r) 3G 100 ppm.  
The amount of water determines the formation of the humic phase and its ability to remove hydrolysis products. The best values were obtained when using 5% water to the mass of oil. However, the optimal ratio was 2.5% water to the mass of oil, which ensured a sufficient level of phosphorus 9.1 ppm and minimized VFA (acid value 0.87 mg KOH/g oil) and reduced the amount of wastewater.  
\begin{table}
\begin{tabular}{c c c} \hline
**pH Value** & **Phosphorous Content** & **FFA (Acid Value,** \\  & **(ppm)** & **mg KOH/g)** \\ \hline
6.3–6.5 & 26.2 & 0.59 \\
6.6–6.8 & 12.1 & 0.81 \\
6.9–7.1 & 9.5 & 0.88 \\
7.2–7.4 & 19.2 & 0.85 \\
7.5–7.7 & 37.5 & 0.71 \\ \hline \end{tabular}
\end{table}
Table 4: Determining the optimal pH value of enzymatic degumming with Purifine® 3G enzyme complex.  
\begin{table}
\begin{tabular}{c c c} \hline
**Enzymatic Degumming** & **Phosphorous Content** & **FFA (Acid Value,** \\
**Temperature** (\({}^{\circ}\)C)** & **(ppm)** & **mg KOH/g)** \\ \hline
50–52 & 35.0 & 0.61 \\
53–55 & 15.0 & 0.79 \\
56–58 & 10.0 & 0.86 \\
59–61 & 10.2 & 0.82 \\
62–64 & 18.1 & 0.80 \\
65–67 & 52.3 & 0.80 \\
68–70 & 128.4 & 0.79 \\ \hline \end{tabular}
\end{table}
Table 3: The effect of temperature on the enzymatic degumming process with Purifine® 3G enzyme complex.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 3 Related Work
### D2D Route Selection on Testbeds  
D2D route selection has been investigated in the literature. In [25], there are two types of users: (a) cellular users are PUs; and (b) D2D users are SUs that use available channels owned by PUs to communicate among themselves under observations from a MC BS. It is assumed that the number of D2D users is less than that of cellular users, and each cellular link can be shared with a single D2D user only at a time. The cellular users use a transmission power above a predefined threshold, while D2D users must ensure that its transmission power is less than a predefined threshold in D2D communication (unless there is no transmission from cellular users) to reduce interference with cellular communication. The proposed route selection scheme has shown to increase throughput and reduce interference between cellular and D2D users.  
In [26], a source node selects a route to a destination node using either: (a) cellular communication that goes through the MC BS; or (b) D2D communication that goes through intermediate nodes (or relay nodes) in its proximity regardless of the presence of the MC BS. The source node is aware of the locations of other nodes in the network using global positioning system (GPS). Therefore, the source node selects a neighbor node as the intermediate node if it has: (a) a route leading to the destination; (b) the lowest number of intermediate nodes; and (c) the lowest interference. Investigation shows that network performance improves when a route with less interference is selected in the presence of MC BS's activities, and a route with the lowest number of intermediate nodes is selected in the absence of the MC BS's activities.  
In [27], a source node selects a route to a destination node while garnering the benefits of traffic offloading from a MC BS to SCs. The source node uses either: (a) a route that goes through the MC BS; (b) a route that uses D2D communication that goes through intermediate nodes; or (c) a direct D2D source-to-destination communication if they are in proximity. The MC BS monitors the traffic among the nodes and provides available routes to the source nodes. The proposed scheme has shown to increase traffic offloading using D2D communication under observation and control from MC BS, leading to an increased throughput.
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
## 3 Results
### The Effect of Supercoiling on Transcription In Vitro with Alternative Sigma Factors  
To extend the study, we tested the effect of supercoiling on transcription from promoters dependent on alternative sigma factors: \(\sigma^{\rm B}\), \(\sigma^{\rm D}\), \(\sigma^{\rm E}\), \(\sigma^{\rm F}\) and \(\sigma^{\rm H}\). \(\sigma^{\rm B}\) is a general stress response sigma factor [52; 53], \(\sigma^{\rm D}\) transcribes genes linked with the cell motility and flagella formation [54]. \(\sigma^{\rm E}\) and \(\sigma^{\rm F}\) are sigma factors of early sporulation [55; 56]. \(\sigma^{\rm H}\) is responsible for transcription of early stationary genes [57].  
We tested also \(\sigma^{\rm N}\) (ZpdN) that is present only in the _B. subtilis_ NCIB 3610 strain. This strain possesses a large, low-copy-number plasmid pBS32, which was lost during domestication of the commonly used laboratory strains [58]. pBS32 carries genes responsible for cell death after mitomycin C (MMC) treatment, and this effect is dependent on \(\sigma^{\rm N}\)[24; 25]. MMC is an antitumor antibiotic that induces DNA strand scission by DNA alkylation leading to crosslinking [59; 60; 61]. This DNA damage could lead to the formation of linear DNA fragments.  
Sequences of respective promoters are listed in Supplementary Table S2. We performed transcriptions in vitro on SC and LIN DNA templates with saturating concentration of iNTP. In all but one cases it was the SC DNA that was the better template for transcription, similarly to what we observed with \(\sigma^{\rm A}\) (Figure 6).  
The exception was \(\sigma^{\rm N}\), which displayed about the same or higher activity on LIN DNA than on SC DNA, depending on the promoter (Figure 6B). To show that this effect was not due to some unknown properties of the plasmid DNA bearing these promoters, we also tested a longer _sigN_ promoter construct (_sigN_ P2+P3). This construct contains \(\sigma^{\rm A}\)-dependent _sigN_ P2 and \(\sigma^{\rm N}\)-dependent _sigN_ P3 promoters [25] and allowed us to test the effect of SC vs LIN topology for two sigmas with the same template. The results are shown in Figure 6C: \(\sigma^{\rm A}\)-dependent P2 is more active on SC DNA whereas \(\sigma^{\rm N}\)-dependent P3 prefers LIN DNA for efficient transcription.  
Figure 5: RNAP levels during bacterial growth. Amounts of RNAP were detected by Western blotting from 5 ug of total protein per lane. Representative primary data are shown above the graph. The RNAP level from time point 1 was set as a 1. STA—stationary phase (indicated with the arrow). The experiment was conducted in two independents replicas. The points are averages, the error bars show the range. The dashed line shows a representative bacterial growth.
# Enhanced Photocatalytic Performance of Bi2O2 CO3 Loaded Activated Carbon for Toluene Removal in Air
## 2 Materials and Methods
### Synthesis of Bi\({}_{2}\)O\({}_{2}\)Co\({}_{3}\)  
In a typical experiment, 2.325 g Bi\({}_{2}\)O\({}_{3}\) was dissolved in 450 mL 1 mol/L of HNO\({}_{3}\) solution under ultrasonication conditions. The obtained suspension was kept at 20 \({}^{\circ}\)C in a water bath for 1 h. Subsequently, 400 mL 0.6 mol/L Na\({}_{2}\)CO\({}_{3}\) solution was added into the above suspension at a speed of 30 mL/min, until a uniform solution with pH of about 7 was reached under magnetic stirring. Then, the as-obtained powder sample was centrifuged, washed with de-ionized water and ethyl alcohol for several times, and dried at 70 \({}^{\circ}\)C in an oven. Finally, the powdered Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\) (labeled as BOC) sample was obtained.  
The synthesis process of Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\) nanomaterials follows Equations (1)-(6). CO\({}_{3}\)\({}^{2+}\) is formed via a hydrolysis reaction process between Na\({}_{2}\)CO\({}_{3}\) and H\({}_{2}\)O. Bi\({}_{2}\)O\({}_{3}\) is dissolved in dilute nitric acid to generate (Bi\({}_{2}\)O\({}_{2}\))\({}^{2+}\). Then, a reaction occurs between (Bi\({}_{2}\)O\({}_{2}\))\({}^{2+}\) and CO\({}_{3}\)\({}^{2+}\) to synthesize Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\). Most of the H\({}^{+}\) is neutralized by CO\({}_{3}\)\({}^{2+}\) to generate CO\({}_{2}\) and H\({}_{2}\)O. Only a small part of the H\({}^{+}\) is combined with OH\({}^{-}\) to form H\({}_{2}\)O. Na\({}^{+}\) and NO\({}_{3}\)\({}^{-}\) are washed off by de-ionized water.  
\[\text{HNO}_{3}\rightarrow\text{H}^{+}+\text{NO}_{3}\text{${}^{-}$} \tag{1}\]  
\[\text{Bi}_{2}\text{O}_{3}\xrightarrow{\text{HNO}_{3}}\text{Bi}_{2}\text{O}_{2 }\text{O}_{2}\text{${}^{2+}$}+2\text{OH}^{-} \tag{2}\]  
\[\text{Na}_{2}\text{CO}_{3}\xrightarrow{\text{H}_{2}\text{O}}2\text{Na}^{+}+ \text{CO}_{3}\text{${}^{2-}$} \tag{3}\]  
\[\text{Bi}_{2}\text{O}_{2}\text{${}^{2+}$}+\text{CO}_{3}\text{${}^{2-}$} \rightarrow\text{Bi}_{2}\text{O}_{2}\text{CO}_{3}\downarrow \tag{4}\]  
\[2\text{H}^{+}+\text{CO}_{3}\text{${}^{2-}$}\rightarrow\text{H}_{2}\text{O}+ \text{CO}_{2} \tag{5}\]  
\[\text{H}^{+}+\text{OH}^{-}\rightarrow\text{H}_{2}\text{O} \tag{6}\]
## 1 Introduction
L\(\approx\)r, then the forwarding area (\(\mathrm{a_{Ai}}\)) is smaller than \(\mathrm{a_{i}}\). i.e. \(\mathrm{a_{Ai}}<\mathrm{a_{i}}\) and can be written as the area of the asymmetric lens \(r^{2}\cos^{-1}\!\left(\frac{d}{r}\right)\!-\!d\sqrt{r^{2}-d^{2}}\!<\!\frac{1}{ 2}\pi^{2}\), where, d is a triangular height. The forwarding area for any node in the network is in between \(\mathrm{a_{Ai}}\) to \(\mathrm{a_{i}}\). A node is a stuck node if there is no node in its forwarding area which is based on \(\mathrm{L_{i}}\). Node \(\mathrm{n_{i}}\) is a stuck node in Figure 2. We discuss more about stuck nodes in later sections. The packet should be forwarded to the closest backward node, if no nodes are available in the forward direction to counter this problem. This may cause looping, circumvented when packets are forwarded only toward the destination with positive progress (i.e. reducing distance). Although a dense deployment of sensor nodes can reduce the chances a void in the network, it is still possible for some packets to encounter voids, induced by obstacles, unreliable sensors, weak sensor batteries, sensors destroyed due to natural calamities or disasters, etc. For example, sensors that are deployed to monitor wild habitats could be destroyed if a forest fire broke out. Thus, to acquire data from the remaining sensors, until the defunct sensors are replaced, it is imperative to design a void handling technique for geographic routing in an effective and efficient manner. The communication void problem in greedy forwarding is an important issue in geographical routing. Many protocols have been proposed in the literature to avoid communication voids. Most existing position-based routing protocols have two modes: (a) greedy forwarding mode and (b) recovery mode. If a sender cannot locate a next-hop node that has positive progress toward the packet  
Figure 2: Forwarding area of a sensor in sensor networks, stuck node and void. destination, it switches to recovery mode and attempts to route the packet around the void. Current solutions to this problem are inadequate, as they memorize the path or they cannot find considerably shorter paths. The key contribution of this paper is to design a communication void-free geographic routing for WSNs. Several routing algorithms exist in the literature [7-18,20,21,29-31]. Most of the existing solutions that switch to recovery mode after encountering the communication void have too much routing overheads and consume more energy. After implementing our algorithm, the network becomes communication void-free if there exists a valid route to the destination. It is not necessary to enter a void handling mode. The proposed method does not recover from a dead end, but restricts entry to the dead end. The remainder of this paper is organized as follows. Section 2 overviews prior related literature. In Section 3, we describe our proposed algorithm. In Section 4, we compare the performance of our proposed algorithm to other methods. In Section 5, we summarize the work in this paper and describe possible future research that builds upon this study.
## 2 Materials and Methods
### Evaluation Indicators  
To better evaluate the satellite rainfall products over the TP, several statistical indices were selected, including Relative Bias (RB), Standard Deviation (SD), Root Mean Square Error (RMSE), and Correlation Coefficients (CC). Moreover, the Probability Of Detection (POD) and False Alarm Ratio (FAR) were calculated to check the appearance possibility of rainfall events from satellite products. The definitions of the indicators are as follows:  
\[RB=\frac{\sum_{n=1}^{N}(f_{n}-r_{n})}{\sum_{n=1}^{N}r_{n}}\times 100\% \tag{1}\]  
\[SD_{f}=\sqrt{\frac{1}{N}\sum_{n=1}^{N}(f_{n}-f^{\prime})^{2}} \tag{2}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{n=1}^{N}[f_{n}-r_{n})]^{2}} \tag{3}\]  
\[CC=\frac{\frac{1}{N}\sum_{n=1}^{N}(f_{n}-f^{\prime})(r_{n}-r^{\prime})}{SD_{f }SD_{r}} \tag{4}\]  
\[POD=\frac{H}{H+M} \tag{5}\]  
\[FAR=\frac{F}{H+F} \tag{6}\]  
where \(N\) is the number of samples; \(f_{n}\) and \(f^{\prime}\) stand for individual and averaged satellite rainfall estimates; \(r_{n}\) and \(r^{\prime}\) indicate individual and averaged gauge-based measurements, respectively. \(H\) represents the observed rain by gauge, which is detected by satellite correctly, while \(M\) is the observed rain not detected, and \(F\) is not the observed rain, but detected falsely. The detailed information of the evaluation indices can be found in [29,30].
## 4 The COASYM-MAC Protocol
### Tree Formation
#### 4.2.2 Parent Selection  
In this section, the first hop node sends a \(request\)\(forwarding\) message to its neighbor. The message contains \(<Node\)\(id\), \(d_{BS}\), \(hop\)\(count>\). Following this request message, each node temporarily selects its parent node using Algorithm 1.  
Each node checks all its neighbors to see whether the node can select it as a parent node. When Node \(u\) first finds a request forwarding message, it selects itself as the parent node. Then, it continues checking further neighbor requests as per Algorithm 1. Finally, Node \(u\) selects a temporary parent node.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(G(V,E)\) & Graph of vertex \(V\) and edge \(E\) \\ \hline \(Ne_{u}\) & Neighbor of a Node \(u\) \\ \hline \(p_{u}\) & Parent of Node \(u\) \\ \hline \(d_{u,BS}\) & Distance from Node \(u\) to Base Station \\ \hline \(hcount_{u,BS}\) & Number of hop from Node \(u\) to Base Station \\ \hline \(RE_{u}\) & Residual energy of Node \(u\) \\ \hline \(r_{u}\) & Transferring radius of Node \(u\) \\ \hline \(PReq_{v}\) & Parent request for Node \(v\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the used notation.  
```
1:\(P_{u}=\emptyset\)\(\triangleright\) initially parent is empty
2:\(Q_{u}=\emptyset\)\(\triangleright\) initially buffer for parent is empty
3:for each Node \(i\in N_{u}\)do\(\triangleright\)\(N_{u}\) is the set of all neighbor nodes of u in G
4:if\(P_{u}\) is empty then
5:if\(d_{i,BS}<\)\(d_{u,BS}\)then
6:\(P_{u}\)=\(i\)\(\triangleright\) Assign current node as parent
7: add \(i\) in \(Q_{u}\)
8:endif
9:else
10:if\((d_{i,BS}<\)\(d_{u,BS}\) &&\(d_{i,u}<\)\(d_{u,P_{u}})\) &&\(hcount_{i,BS}\leq hcount_{P_{u},BS}\)then
11:\(P_{u}\)=\(i\)\(\triangleright\) Add current node into the parent queue
12:endif
13:endif
14:endif
15:endfor
16:Sort \(Q_{u}\) according to distance and hop count
17:for\(\forall Q_{u}\) each element in buffer do
18:\(P_{u}=\) Dequeue(\(Q_{u}\))
19: Ack \(\leftarrow\) GetAcknowledgement(\(P_{u}\))
20:if (Ack \(\
eq\) Overflow) then
21: Exit from loop and \(P_{u}\) is the parent of \(u\)
22:endif
23:endfor
```  
**Algorithm 1** Tree Formation.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 6 Assessment of EMS Integration Options
### Discussion of EMS Integration Option 1b
Given proper hardware and software platform choices, billing periods of less than 1 min are technically possible, although they are not allowed to be used for billing purposes according to the _PTB-A 50.8_ requirements [18]. Another factor to consider for a remote EMS is the communication standard used for the communication between EMP and SMGW. There are multiple options including Ethernet, Power Line Communication (PLC) and broadband cellular network technologies such as Long Term Evolution (LTE). Which of these are supported is device specific. While in general all of them may be an adequate choice, case specific solutions may be necessary. It is likely that in the future many SMGWs will use broadband cellular network technologies for WAN communication, although coverage and reliability may not be given everywhere. In this integration option, reliability of communication is especially important. The transparent data tunnel generally needs to be held open, as the reestablishment may take up to 50 s, depending on the implementation of the involved systems [33]. Furthermore, it is necessary to assess the costs for hardware, EMP infrastructure and communication. Given these costs, using a remote EMS solely for the purpose of energy management may not be cost covering.
## 4 Results and Discussion  
### Load-Settlement Curve  
The ZY anchor rod tension gauge was used for manual loading via the displacement control method. That is, for every additional displacement of the pile top (2 mm for undisturbed soil and 0.2 mm for sand), the load value was recorded once, and loads ranging from 0 to 1.224 kN for undisturbed soil and 0 to 1.595 kN for sand were applied. The load-settlement curve obtained through the test is shown in Figure 8.  
Figure 8 demonstrates that, as the load increases, the pile top displacement gradually increases as well. For the pile in the undisturbed soil, there are roughly four stages between the start of the loading and the end of the process. The first stage is the initial loading stage (from 0 to 0.352 kN). As the model pile moves downward, the single pile bearing force is borne by the soil around the pile, the soil under the expanded body, and the soil at the end of the pile. At this time, the load is small, so the load-displacement curve changes slightly. In the second stage (from 0.352 to 0.648 kN), as the load continues to increase, the pile top displacement of the model keeps increasing, the soil above the expanded body separates from the pile body, and the frictional resistance between the pile and soil above the expanded body decreases and may even cease to play a role. At this time, the soil below the expanded body is compressed, and the load is borne by both the soil under the expanded body and the soil at the end of the pile. In the third stage (from 0.628 to 0.897 kN), due to the compression of the soil below the expanded body, its strength is improved, and the load-settlement curve changes slightly. In the fourth stage (from 0.897 to 1.244 kN), the load continues to increase, causing the slope of the load-displacement curve to steepen. For the pile in the sand, the curve of load and settlement of the pile top is relatively smooth. As the load increases, the displacement continues to increase, and there is no obvious stage division.