esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancements and Challenges in CoAP--A Survey
## 5 Qualitative and Quantitative Analysis of Congestion Control Schemes
### Quantitative Analysis  
This subsection describes the quantitative analysis of congestion control schemes discussed in the paper. Most of the congestion control schemes discussed in the paper compare their performance to the default CoAP and the congestion control/advance i.e., CoCoA, while some compare it with CoCoA+. Table 3 shows the quantitative analysis of discussed congestion control schemes of CoAP. The schemes are evaluated with the following performance metrics: settling time, response time, packet loss ratio, throughput, end-to-end delay, network adaptability, energy consumption, and retransmission ratio. Among all the schemes, CoCo-RED, CoCoA, CoCoA+ consider the scenario of burst traffic while rest of them either consider constant, random or periodic network traffic. Moreover, the schemes are tested in different network topologies such as grid, dumbbell, chain, cross, random, and concentric circles. The last column of table presents the average improvement in different performance metrics of proposed schemes compared to default CoAP in percentages. The performance metrics of CoCoA++ and genetic CoCoA++ schemes are compared with the CoCoA only.  
The quantitative analysis performed and presented in Table 3 can help us identify when it is better to use one solution or the other. For instance, if one wants to achieve higher throughput then they can adopt the adaptive congestion control mechanism as it achieves the highest average percentage of throughput (i.e., 56%) than other schemes compared to default CoAP. Similarly, if the target is to achieve low energy consumption instead, one can use CACC scheme as it achieves an average percentage improvement of 41% in energy consumption compared to default CoAP. However, in CACC, there is a tradeoff between energy consumption and throughput. Therefore, depending on the application requirement, one can choose between various schemes.
## 4 Results and Discussion  
### Experimental Validation of the FEM Model  
A single-span third rail setup was developed at the NTU-SMRT Urban Rail Corporate Laboratory. The main objective of conducting the experimental analysis is to validate the third rail's Finite Element Model.  
A roving hammer impact approach is adopted for laboratory modal testing of the third rail. The third rail is impacted at different locations using an impact hammer while keeping the accelerometer sensor's position fixed. The length of the third rail was divided into 11 sections. For each section, the third rail is impacted at five different locations. The laboratory setup of SMRT third rail, impact locations, accelerometer position, and direction of impact is shown in Figure 4a. The SignalCalc [21] analyzer was used to acquire the data from the accelerometer and the impact hammer. Thereafter, MEScopeVES [22] software was used to further process the data. The experimental procedure has a few steps including collection of frequency response functions (FRFs) by striking the hammer at selected impact locations along the rail and aggregating the complete dataset into the analysis software, MEScopeVES. Impact hammer strikes act as an excitation input to the structure, and the response in time domain is captured using an accelerometer. The captured data are then converted into frequency domain [23]. A dataset of 54 FRFs was collected in this experiment. The dataset was then used in MEScopeVES software along with a CAD model of the structure. MEScopeVES software analyses the imaginary part of the FRF to extract the amplitude and direction of the response. Moreover, the software also extracts parameters such as frequency and damping [24; 25]. The software then combines the CAD model of the structure and extracted modal parameters to visually present mode shapes. In this way, the mode shapes and corresponding frequencies were obtained for the single-span third rail using the experimental data.  
The experimental modal testing results are compared with the results obtained from numerical analysis using ANSYS for the single-span Finite Element Model. Figure 4b shows the typical examples of mode shapes obtained from experimental and FEM analysis.  
Figure 4: (a) Experimental set-up for modal testing; (b) impact points’ locations on third rail set up for roving hammer test; (c) comparison between the mode shapes obtained from experimental and FEM analysis.  
Furthermore, Table 2 presents the natural frequencies corresponding to the first five modes. The first three modes were identified accurately, with the difference between experimental and FEM results of less than 5%. The discrepancy between the FEM and experimental results increases for higher modes. The higher modes are very sensitive to small imperfections and boundary conditions; therefore, it is common to have greater discrepancies for the higher modes of vibration. The results show a good agreement between the experiment and the FEM analysis. We conclude that the FEM model and the approach used to analyze the vibration analysis give acceptable results.
# Corrosion Behavior of Stainless Steel in Seawater in the Presence of Sulfide
###### Abstract  
The effect of temperature (from 288 to 308 K) and concentration of sulfide ions (up to 40 ppm) on the corrosion behavior of AISI 304L and AISI 316L stainless steels in seawater was studied with measurements of open-circuit potential, linear and potentiodynamic polarization, and electrochemical impedance spectroscopy. An increase in temperature and pollutant concentration negatively affects the corrosion stability of stainless steels at the open circuit (the resistance, compactness, and thickness of the surface layer decrease and the corrosion current increases), in the passive region (the passivation current increases, the depassivation potential decreases, and the passive potential region narrows), and in the transposable potential region (the rate of metal dissolution increases). The occurrence of pitting corrosion on the surface of the samples was confirmed with optical microscopy and a non-contact 3D profilometer. A few large pits (depth 80-100 um and width 100 um) were formed on the surface of AISI 304L steel, while several smaller pits (depth 40-50 um and width 50 um) were formed on the surface of AISI 316L steel. With increasing temperature and sulfide ion concentration, the width, depth, and density of the pits increased on both steel samples. In the studied temperature and concentration range of sulfide ions, the AISI 316L steels exhibited higher corrosion resistance. Overall, the influence of sulfide ions on steel corrosion was more pronounced than the influence of temperature.  
stainless steel; seawater; sulfide; pitting corrosion; passive film; surface analysis Article
## 4 Analysis of Experimental Results  
### Determining the Detection Threshold  
Since the output of the discriminator ranges from 0 to 1, with values closer to 1 indicating that the input data is more likely to be genuine, selecting an appropriate threshold for determining the authenticity of the input is crucial. The choice of the detection threshold can refer to the general methods for selecting classifier thresholds in machine learning, which involves introducing four parameters:  
False Positive (FP), the number of abnormal samples incorrectly identified as normal; True Positive (TP), the number of normal samples correctly identified as normal; False Negative (FN), the number of normal samples incorrectly identified as abnormal.  
For a classifier, different thresholds applied to the same test sample can result in a confusion matrix, can calculate the corresponding true positive rate and false positive rate.  
True positive rate (TPR) refers to the proportion of normal samples that are correctly identified as normal.  
\[TPR=\frac{TP}{P}=\frac{TP}{TP+FN} \tag{27}\]  
Here, \(P\) represents the number of normal samples in the test set.  
The false positive rate (FPR) refers to the proportion of abnormal samples that are incorrectly identified as normal.  
\[FPR=\frac{FP}{N}=\frac{FP}{FP+TN} \tag{28}\]  
Here, \(N\) represents the number of anomalous samples in the testing set. According to the above formulas, it is evident that both True Positive Rate (TPR) and False Positive Rate (FPR) are fixed for the same test set. Naturally, we hope to maximize TPR and minimize FPR. By plotting the error rate and detection rate corresponding to each threshold as the x and y coordinates, we can obtain the ROC curve, as shown in Figure 5. From the figure, we can see that the closer the ROC curve is to the top left corner, the higher the TPR and the lower the FPR. Therefore, the threshold corresponding to this balance point is what we are looking for, and this balance point selection is shown in Figure 5.  
The main basis for classifying abnormal data is the spatiotemporal correlation of traffic flow data and signal control data. Therefore, when using the trained model to identify abnormal data during different time periods in the same road environment, the classifier leverages the P and N in the test data set to identify anomalies, which is beneficial for quickly finding a balance point.
## 3 Results and Discussion
### Biological Assays  
#### 3.2.1 In Vitro Antimyobacterial Evaluation  
The efficiency of **6a-g** and the INH reference drug was inspected in vitro against _Mtb_ H\({}_{37}\)Ra, _MK_ DSM, _MS_ as well as _M. marinum_ (Table 3) and described with the MIC parameter (in uM units) [39; 40]. Tuberculosis _Mtb_ H\({}_{37}\)R\({}_{\rm y}\) and H\({}_{37}\)Ra were used to confirm (or refuse) if the newly synthesized and traditional anti-TB drugs showed in vitro activity. The question of whether H\({}_{37}\)R\({}_{\rm a}\) might \"substitute in some manner\" a highly pathogenic H\({}_{37}\)R\({}_{\rm v}\) or clinical tuberculous mycobacterial strains was answered clearly by [PERSON] et al. (2018). The authors concluded that the H\({}_{37}\)R\({}_{\rm a}\) strain was as equally useful as the H\({}_{37}\)R\({}_{\rm v}\) one regarding responses to in vitro screened anti-TB drugs. Moreover, the biosafety requirements concerning H\({}_{37}\)R\({}_{\rm a}\) were much more convenient compared to those of H\({}_{37}\)R\({}_{\rm v}\)[69].  
The most powerful antimyobacterials were 1-[2-hydroxyethylpropyl-[(3-trifluoromethyl)-phenyl]carbamoyloxy]-4-(3,4-dichlorophenyl)piperazin-1-ium chloride (**6e**) and 1-[2-hydroxyethylpropyl-[(3-trifluoromethyl)phenyl]carbamoyloxy]-4-(4-diphenylmethyl)piperazin-1-ium chloride (**6g**) showing MIC < 3.80 uM against _Mtb_ H\({}_{37}\)R\({}_{\rm a}\) (Table 3). These compounds were more active than INH (MIC = 58.33 uM).  
Considering previous conclusions [70, 71, 72], it might be stated that a 3-CF\({}_{3}\)-phenylcarbamoyloxy fragment notably improved the in vitro activity of such substituted compounds against a given _mycobacterium_ compared to the efficiency of the derivatives bearing an 2-/3-/4-alkoxyphenylcarbamoyloxy moiety (alkoxy = methoxy or ethoxy).  
In addition, both 1-[2-hydroxpropyl-[(3-trifluoromethyl)phenyl]carbamoyloxy]-4-(4-chlorophenylophenyl)piperazin-1-ium chloride (**6d**) and **6e** were the most effective against _MK_ DSM with MIC = 8.09 uM (**6d**) and 15.13 uM (**6e**), respectively. Their potency was higher than that of INH (MIC = 29.16 uM; Table 3). The molecule **6g** was the most active against both _MS_ (MIC = 14.54 uM) and _MM_ (MIC = 8.09 uM). This substance was even more promising than INH (_MIC_ = 43.75 and 29.16 uM, respectively) as listed (Table 3).  
The presence of an EW (**6c-f**) or even an electron-donating (ED; **6b**) substituent attached to a phenyl ring seemed to be more beneficial in structural variation in order to potentiate in vitro antimycobacterial activity than classical bioisosteric replacement of this substituent with hydrogen (**6a**). Bulky lipophilic diphenylmethyl moiety (**6g**) improved efficiency, especially against _Mtb_ H\({}_{37}\)R\({}_{\rm a}\) and _MM_ (Table 3).  
The compounds **6a-g** fought in applsci-1501121 vitro tested non-tuberculous mycobacteria more effectively than the substances containing an 2-/3-/4-alkoxyphenylcarbamoyloxy moiety (alkoxy = methoxy or ethoxy) despite the presence of a lipophilic 4'-F atom or 3'-CF\({}_{3}\) group within their salt-forming part [19].
## 3 Results and Discussion
### Thin-Film Composite Membranes Morphology  
SEM images reveal the desired TFC membrane morphology (Figure 7). The morphology of the pure PIL \(\mathbf{P_{9}IL_{0}Zn_{0}}\)-based membrane is comparable to the previously reported one [16]. The approximate thicknesses of the selective layer and the sealing PDMS layer were estimated from SEM images and are indicated on the corresponding SEM image. The average selective layer thicknesses were between 5-10 \(\upmu\)m.  
For all TFC, the selective layer lies on top of the PI support (Figure 7). The interface between the selective layer and the support is characterized by gradual textural changes from the smooth dense coating layer to a rougher support. This behavior represents a good adhesion between the PIL/IL/Zn\({}^{2+}\) composites and the PI support, which also prevents the penetration of the selective layer in the porous matrix of the support. The PDMS sealing layer is resting on the selective material film and has a thickness of approximately 3.0 \(\upmu\)m. The transition to the PDMS coating is sharper. The voids under the PDMS coating are probably the result of mechanical damage during fracturing of the sample. Ease of processing the composites into robust TFC membranes by simple solvent-casting method potentially allows their further consideration in flue gas separation applications. However, for the effective exploitation of these materials, improved technical solutions such as the use of a gutter layer and a further reduction of the selective layer thickness should be investigated.
## 4 Data Collection and Engineering
### Features Engineering  
Features engineering removes unnecessary features or extracts new features from existing features to increase the accuracy of the machine learning models [34]. Duplicated records were removed from the original dataset, and there were 164,087 duplications of records. As a result, the dataset had 461,696 records. Table 2 represents statistical information about this research dataset. Moreover, the dataset has the source IP address (Src_IP) and destination IP address (Dst_IP) as features. However, machine learning models cannot sufficiently handle the format of IP addresses, such as \"192.168.0.13\" [36]. Therefore, to solve this issue and help the machine learning models obtain the most use of IP address information, we split the four IP address parts, octet numbers, into features, e.g., Src_IP_oct1: 192 Src_IP_oct2: 168, Src_IP_oct3: 0, and Src_IP_oct4: 13. By doing so, the machine learning model can understand and distinguish between the network and host portions. Furthermore, the IoTID20 dataset has a timestamp as a feature. Therefore, we extracted the following information from the timestamp feature and included them in the dataset as new features: day of the week, hour, and am or pm to use it more efficiently. According to our experiment, those new features helped increase detection accuracy and machine learning classification. Finally, we converted the label and category string values to numerical values. For example, we mapped the values of the label feature normal to 0 and anomaly to 1, as shown while the numerical conversion of the category feature was normal (0), Mirai (1), DoS (2), Scan (3), and MITM (4). Table 3 outline the features that are included in the dataset. The table lists the feature, feature description, and feature data type (Integer: INT or Double: DBL), comprising 71 features. By looking at Figure 9, we can observe that intrusion attacks occurred every day of the week except Monday. In addition, most of the intrusion attacks took place on Thursdays. Figure 10 illustrates whether the network traffic occurred in the morning or evening. It is worth saying that most of the network traffic recorded in the morning was intrusion attacks, and a few pieces of traffic were normal network packets. However, some network traffic occurred in the evening--intrusion activities. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Labels** & **Number of Records** & **Category** & **Number of Records** \\ \hline Normal & 38,598 & Normal & 38,598 \\ \hline \multirow{4}{*}{Anomaly} & \multirow{4}{*}{423,098} & Mirai & 281,102 \\  & & DoS & 59,390 \\ \cline{1-1}  & & Scan & 56,744 \\ \cline{1-1}  & & MITM & 25,862 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IoTID20 dataset statistics.
## 2 Literature Review
### Factors Influencing Incident Duration  
Apart from studies that have sought to predict incident duration, several others have investigated the impact of factors influencing crash-related incident duration on U.S. interstates. Several factors related to the time of the incident, crash type, number of agencies, environmental characteristics, and other crash factors impact incident duration [2; 18; 37]. For example, incidents that occur during nighttime, peak hours, and weekends have been found to be associated with longer incident durations [24; 37; 38]. Nighttime, in comparison to morning and evening time, results in longer ICT due to the darker environment and limited resource availability to response agencies [39]. Also, due to heavy traffic conditions during peak hours, it takes more time for a response team to arrive at the incident scene, which prolongs ICT [15]. During weekends, delays are due to responders often operating with fewer staff. Regarding environmental characteristics, weather conditions, like rain, wind, fog, and snow, resulted in higher incident duration [2; 40; 41]. This can be due to poor visibility for responders, and crashes during these adverse conditions are probably run-off-road collisions [42].  
In terms of crash characteristics, such as crash severity, number of vehicles involved, manner of collisions, heavy truck involvement, heavy occupancy vehicle (HOVs), and number of lanes blocked, have been concluded as significant factors affecting ICT [43; 44; 42]. For instance, severe incidents lead to longer ICT due to the involvement of injuries and fatalities [5]. This is because these types of incidents require a response from different response agencies, like the EMS and the fire department, which may increase ICT.  
Incidents involving HOVs have been found to increase incident duration compared to single-occupancy vehicles (driver only) as more people stuck in a car will require more time to rescue [45]. Additionally, ICT is longer when multiple or all travel lanes are blocked than when a single lane is blocked [46]. This is expected because when multiple lanes are blocked, additional time is required to clear the lane, as single lanes can be cleared quickly.  
It was also observed that crash type significantly impacts ICT [26; 47]. In terms of collision manner, rear-end and sideswipe crashes were found to be associated with decreased ICT in comparison to head-on crashes. This is because rear-end and sideswipe crashes result in fewer injuries in comparison to head-on crashes [48; 49]. Furthermore, incidents involving heavy trucks have been found to prolong ICT in comparison to other vehicles [40; 50]. This is because the incident involving trucks resulted in an increased number of injuries, fatalities, and property damage [51; 52]. Additionally, incidents involving rescue and fire units result in a longer duration, as rescue units may be required in larger incidents [13]. Table 1 summarizes the significant factors that impact ICT.
# A Cross-Layer, Anomaly-Based IDS for WSN and MANET
## 4 System Architecture
The threshold is calculated first by finding an initial threshold \((\delta^{*})\) which is the difference between the maximum of the upper bounds and the minimum of the upper bounds of all NUTs divided by two and added to the minimum of the upper bounds for that iteration. ```
1:Input:\(\mathit{CCI}^{(S_{n})}{}_{1},\ldots,\ldots,\mathit{CCI}^{(S_{n})}{}_{N}\),
2:Output: AMoF
3:At the supernode
4:\(\forall node\in NUT\)
5:Receive \(\mathit{CCI}^{(S_{n})}{}_{1},\ldots,\ldots,\mathit{CCI}^{(S_{n})}{}_{N}\).ST \(\mathit{CCI}^{(NUT)}\) is \(N\times n\)
6:Initialize \(\mathit{Temp}^{(S_{i})}{}_{i}\), \(\mathit{Norm\_Temp}^{(S_{i})}{}_{i}\), \(\mathit{Sigma}^{(S_{i})}{}_{i}\), \(\mathit{AMoF}^{(S_{i})}{}_{i}\)
7:for\(i=1\) to \(N\)do
8:for\(j=1\) to \(n\)do
9:\(\mathit{Temp}^{(S_{i})}{}_{i}{\leftarrow}\big{|}(\mathit{CCI}^{(S_{i})}{}_{i+1 }-\mathit{CCI}^{(S_{i})}{}_{i})\big{|}+\mathit{Temp}^{(S_{i})}{}_{i-1}\)
10:\(\mathit{Norm\_Temp}^{(S_{i})}{}_{i}{\leftarrow}\mathit{Temp}^{(S_{i})}{}_{i}/100\)
11:endfor
12:\(\mathit{AMoF}^{(S_{i})}{}_{i}{\leftarrow}(\mathit{Norm\_Temp}^{(S_{i})}{}_{i}/n)+ \mathit{AMoF}^{(S_{i})}{}_{i-1}\)
13:endfor
```  
**Algorithm 1** Calculating the AMoF for malicious and normal nodes \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mac Layer**} & Tx/Rx & Tx/Rx & Tx/Rx & \\ \cline{2-5}  & RTS & CTS & ACK & \\ \hline \multirow{2}{*}{**Network Layer**} & Tx/Rx & Tx/Rx & Tx/Rx & Tx/Rx \\ \cline{2-5}  & RREQ & RREP & RERR & Hello messages \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross Layer Features. The permanent threshold (\(\delta\)) value is adopted once a small number (say 3) of consecutive initial thresholds achieve the criterion \(|\Delta|\). ```
1:Input:\(AMoF^{(NUT)}\)\({}_{1}\),........., \(AMoF^{(NUT)}\)\({}_{N-1}\)
2:Output: fitted slope (\(\beta\)), detection threshold (\(\delta\))
3:At the super node:
4:\(\forall node\in NUT\) where the number of elements in \(\mathbf{NUT}=l\)
5: Receive \(AMoF^{(NUT)}\)\({}_{1}\),..., \(AMoF^{(NUT)}\)\({}_{N-1}\) S.T \(AMoF^{(NUT)}\) is \(l\times(N-1)\)
6:for k = 1 to \(N-1\)do
7:for j = 1 to NUTdo
8:If k \(\geq 3\)then
9: Find \(\beta_{k}\) by solving (1)
10: Find \(C_{k}\) by solving (2)
11: Find initial threshold \(\delta_{k}^{*}=\left(\frac{max(C_{k})-min(C_{k})}{2}\right)+min(C_{k})\)
12:If\(\delta_{k}^{*}-\delta_{k-1}^{*}\leq|\Delta|\)
13:\(\delta_{k}\leftarrow\delta_{k}^{*}\)
14:If\(\delta_{k}>\delta\)
15: node is normal
16:else
17: node is malicious
18:endfor
19:endfor
```  
**Algorithm 2** Calculating the fitted slope (\(\beta\)) for every NUT, setting the detection threshold (\(\delta\))  
The normal nodes are those which have a higher upper bound value than the permanent threshold. In other words, normal nodes maintain higher fluctuation in the received CCIs compared to the malicious nodes.
## 3 System Concept
### RF Concept and Design
#### 3.1.1 Six-Port Receiver  
Being the core component of the radar system, the overall performance strongly depends on the properties of the receiver architecture. In this case, a so-called six-port structure superimposes its two input signals, the reference signal (\(P_{1}\)) and the receive signal (\(P_{2}\)), and introduces different phase shifts at the outputs (see Figure 4). For a compact realization, the structure can be implemented by a planar microstrip line layout. Therefore, a Wilkinson power divider and three branchline couplers can be used. Since this structure is purely passive, it does not introduce nonlinearities which might distort the signal. In the next step, the power of the signals at the four output ports, denoted as \(P_{3,\ldots,6}\), is converted to proportional voltages (\(B_{3,\ldots,6}\)). This is done by RF power detectors based on nonlinear semiconductor devices, such as Schottky diodes, which convert the power of the RF signal into a proportional DC voltage. For an increased temperature stability and linearity of the transfer curve, commercial active integrated circuits like the chosen Analog Devices ADL6010 contain additional linearizer stages after the power detector.  
Figure 3: Block diagram of the vital parameter radar sensing system.