esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antimicrobial Activity  
In vitro antibacterial activity tests were done using standard bacteria strains, which were selected from the American Type Culture Collection (ATCC) (_Escherichia coli_ (ATCC 25922), _Klebsiella pneumoniae_ (ATCC 700603), _Proteus mirabilis_ (ATCC 29906), _Pseudomonas aeruginosa_ (ATCC 9027), _Staphylococcus aureus_ (ATCC 29213), and _Enterococcus faecalis_ (ATCC 29212)). First, bacteria stored at \(-80\)\({}^{\circ}\)C were revived on Mueller-Hinton agar (Oxoid, Italy) to obtain pure colonies.  
Microdilution was used for determining the antimicrobial activities of C1 and C2 according to the Clinical and Laboratory Standards Institute using a sterile, U-based 96-well plate [26].  
Before starting the dilution step, stock solutions of C1 and C2 were prepared using 10% dimethyl sulfoxide (DMSO) and cation-adjusted Mueller-Hinton broth (CAMHB) (Oxoid, Italy). Then, dilutions of C1 and C2 were prepared with CAMHB starting from the first well in the plate within the range of 1600 \(\upmu\)g/mL to 3.12 \(\upmu\)g/mL. The last two wells were used as negative and positive controls. Sulfarenzazine was used as the reference antibiotic.  
All bacterial strains used in the study were diluted with CAMHB to prepare an inoculum with a final concentration of 10\({}^{5}\) CFU/mL. An equal volume of prepared bacterial suspensions was added to the diluted C1 and C2 in each well of the plate. All of the plates were incubated aerobically at 35 \({}^{\circ}\)C for 24 h. The compound concentration in the first well without bacterial growth and where clarity was observed was determined as the MIC (minimum inhibitory concentration). The tests were run three times.
## 4 Model and Methodology of the Adaptive Protocol  
Q-learning is a model-free reinforcement learning algorithm whose core is the Q-value and reward [31]. In order to maximize the network life cycle and meet the functional requirements of various types of sensors, this section presents an adaptive communication routing algorithm based on improved Q-learning, as shown in Figure 3.  
The algorithm first defines dual Q-values using energy balance and real-time data transmission as metrics. It then introduces an adaptive weighting factor to optimize the  
Figure 3: Double-Q-value adaptive algorithm framework.  
design based on the requirements of the business. Next, it constructs reward models for energy saving and latency, and finally updates the Q-values based on the rewarded data [32]. The parameters and definitions used in this section are shown in Table 4.
## 4 Experiment and Analysis  
### Dataset and Pre-Processing  
The encrypted traffic dataset used in this paper is the UNB ISCX Tor 2016 [21], which contains more than 18 representative applications of Tor and non-Tor encrypted traffic, such as Facebook, Skype, Spotify, Gmail, etc. We categorize the dataset by services, forming the dataset with eight categories, namely, browsing, chat, audio-streaming, video-streaming, mail, VoIP, P2P, and file transfer. The sample size and proportion of each type in the dataset are shown in Table 3.  
According to Table 3, the sample size of the eight different categories in the dataset is extremely uneven, such as VoIP accounting for 54.20%, while browsing, chat, audio-streaming, video-streaming, and mail account for less than 5%. The imbalance in the sample sizes severely affects the classification performance of the model. Thus, we propose several optimization methods to improve the classification accuracy of these minority samples in the dataset.  
Data pre-processing can reduce the noise in the raw traffic and adjust the traffic to fit the input form of the deep-learning model. The following pre-processing steps contain:  
* Data splitting: since the CNN model requires the input data to be of the same size, we split the traffic data at the session level and pick the first 784 bytes;
* Data cleaning: we remove the packets in.pcap format without application layer data to avoid generating bin files with no actual content;
* Image generation: we convert the file with the length of 784 bytes into a grayscale image in binary form, one byte for one grayscale pixel value.
## 4 Algorithm Design
### Adaptive Degressive Ary Number Encoded ADENSGA for Node Placement Problem  
Section 4.2 shows that in the multi-objective problem, the optimal any number will change with evolution. Therefore, the multi-objective genetic algorithm with degressive any number encoding is feasible to solve the problem of node placement in WSNs.  
Through the experiment of N-ADENSGA, the rough degressive rules of any number can be concluded:  
1. To solve the problem that local early convergence leads to poor optimization results and maintain the population diversity in a high-dimensional problem, it is necessary to maintain a high any number \(N\) for certain generations at the beginning of evolution;
2. In the process of \(N\) decreasing, the any number \(N\) should decrease quickly first, then slowly and trend to 2 in the end;
3. The faster the \(Score\) is increased, the slower the any number should be decreased.  
Consequently, it can be supposed that the value of the next generation hexadecimal number can be determined according to the growth rate of \(Score\).  
Then an adaptive degressive rule of any number can be proposed. The equation is shown in Equation (9).  
\[\left\{\begin{array}{ll}N(k)=N(0)&k\leq k_{min}\\ \dfrac{Score(k-1)-Score(0)}{Score_{limit}}\\ \dfrac{k}{k_{limit}+k_{min}}\end{array}\right.=\dfrac{N(0)-N(k)}{N(0)} \dfrac{k-k_{min}}{k_{limit}}\quad k>k_{min} \tag{9}\]  
where \(Score_{limit}\) is the difference between the maximum and minimum values of the theoretical \(Score\), \(Score(0)\) is the initial average \(Score\), \(N(0)\) is the initial any number, \(N(k)\) is the any number of the \(k\)th generation, \(k_{min}\) is the generation when the any number remains at \(N(0)\), and \(k_{limit}\) is an estimation of the generation used when the any number converges to 2. Among them, \(N(0)\), \(k_{min}\), \(Score_{limit}\) and \(k_{limit}\) are the default parameters.  
The any number of \(k\)th generation, \(N(k)\), can be calculated by Equations (10) and (11).  
\[N(k)=\left\{\begin{array}{ll}N(0)&k\leq k_{min}\\ \\ max[2,ceil[N(0)-\\ a\cdot\dfrac{(k-k_{min})\cdot(Score(k-1)-Score(0))}{k}]]\\ \\ \end{array}\right. \tag{10}\]  
\[a=\dfrac{N(0)\cdot(k_{limit}+k_{min})}{Score_{limit}\cdot k_{limit}} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**N** & **2** & **10** & **18** & **26** & **34** & **42** & **50** \\ \hline Average running time/s & 718.3 & 718.0 & 718.5 & 721.6 & 721.0 & 720.2 & 719.8 \\ \hline \(D/\)m & 391.4 & 388.3 & 380.7 & 394.5 & 391.6 & 392.4 & 388.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Running time for different values of \(N\).  
where \(a\) is a parameter value to adjust the decreasing rate of any number. In this paper, according to the experiment environment and experience, set \(Score_{limit}=0.035\), \(k_{limit}=40\). The values of \(N(0)\) and \(k_{min}\) will be discussed in the next section.  
When the adaptive degressive any number encoding is used in ADENSGA, a new improved algorithm called adaptive degressive any number encoded directed evolved NSGA (AD-ADENSGA) is proposed. The process of AD-ADENSGA is shown in Figure 4.
## 2 Development of Key Mechanisms
### Water-Sealed Core-Protecting Drill Bit  
During drilling operations in loose and broken strata, the erosion effect of the flushing fluid is a significant reason for low core recovery rates. To avoid the erosion of the core by high-speed flushing fluid, a water-sealed core-protecting drill bit was developed, as shown in Figure 1(a). The design of the small holes on the inner wall of the bit is primarily based on the bit tooth arrangement parameters and the bit diameter. It aims to determine the optimal crown structure of the bit. Eight small holes are evenly drilled on the inner wall of the bit, and eight corresponding slots are opened on the outer wall. Then, a 1 mm thick steel pipe is welded onto the outer wall of the bit to seal the slots, ensuring that the flushing fluid can flow directly through the slots to flush the bottom of the hole, cool the bit, clean the bottom cuttings, and also assist in breaking the rock [17; 18; 19]. The upper part of the drill bit's body is designed with larger pores to ensure that the enclosed core catcher can smoothly close and cut the core. As illustrated in Figure 1(b), when the inner tube is lowered to the bottom of the hole, the suspension mechanism adjusts the position, allowing the core catcher to hang on the inner step of the drill bit without touching it. The gap between the core catcher and the inner wall of the drill bit is very small, preventing the flushing fluid from flowing out through the gap between the core catcher and the drill bit and eroding the core while also playing a stabilizing role. During the core cutting operation, the core catcher moves downward under pressure, closes along the inner step surface of the drill bit, and completes the core cutting process [20; 21; 22; 23].  
Figure 1: Working Principle of the Flow Control Valve.  
Figure 2: Water-Sealed Core-Protecting Drill Bit.
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 4 Security Requirements for Autonomous Ships
### Derive Security Requirements
#### 4.2.3 Develop Artifacts  
In the existing design process of a ship, each design drawing is produced through the process of conceptual, basic, and detailed designs. In this study, autonomous navigation systems and components were identified via case studies of autonomous ships rather than through the original design process. As shown in Figure 11, the attack path of the attacker in the network modeling was identified using the attack scenarios for an autonomous ship devised earlier via the network logical modeling [49]. Cyber attackers may attempt to attack datasets and neural networks in Zones 1 and 2 and sensor data and protocols in Zones 3 and 4. In each attack scenario, the vulnerable attack vectors for autonomous ships include AI models, sensors, neural networks, and weak protocols.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Term** & **Definition** \\ \hline Attack surface & The set of all possible points where an unauthorized user can access a system and extract data. The attack surface comprises two categories: digital and physical. The digital attack surface encompasses all the hardware and software that connect to an organization’s network. These include applications, code, ports, servers, and websites. The physical attack surface comprises all endpoint devices that an attacker can gain physical access to, such as desktop computers, hard drives, laptops, mobile phones, removable drives, and carelessly discarded hardware. \\ \hline Authentication & Provision of assurance that a claimed characteristic of an entity is correct. \\ \hline Compensating countermeasure & An alternate solution to a countermeasure employed in lieu of or in addition to inherent security capabilities to satisfy one or more security requirements. \\ \hline Computer-based system (CBS) & A programmable electronic device, or interoperable set of programmable electronic devices, is organized to achieve one or more specified purposes, such as collection, processing, maintenance, use, sharing, dissemination, or disposition of information. CBSs onboard include IT and OT systems. CBS may be a combination of subsystems connected via a network. \\  & Onboard CBSs may be connected directly or via public means of communication (e.g., Internet) to ashore CBSs, other vessels’ CBSs, and/or other facilities. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample of terms and definitions.  
The attack scenarios devised in Section 3 are AI model attack methods that do not occur on existing ships but are possible on autonomous ships. As shown in Figure 12, Attacks highlighted in orange will be spoofing on sensor data as jamming further increases in autonomous ships due to the increased connectivity.  
Additionally, to replace misuse cases, cyberattack scenarios for autonomous ships were devised as follows Table 2 through cyberthreats on ship systems and AI.
## 1 Introduction
Subsequently, the multiple Stokes lasers are injected into a KDP crystal for frequency up-conversion into the visible range. Finally, up to 11 visible wavelengths can be simultaneously obtained and are discretely tunable by changing the phase-matching angle of KDP crystal. With maximum output energy of 8.87 mJ, the five visible-wavelength lasers obtain with a SHG/SFG efficiency of 20.1% (total efficiency of 7.4%). These results demonstrate that cascading multiple crystal Raman with second order frequency mixing can expand the wavelength-lines, as desired by their many applications.
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 2 Related Work
Piggybacks on routing messages, probes, and beacons are suggested. Although vehicles could obtain the wide-ranging knowledge of roads with passive monitoring, knowledge of some roads may still be omitted or outdated. Hybrid Traffic-Aware Routing (HTAR) [27] proposed a collaboratively real-time traffic information collection and dissemination mechanism. In HTAR, a vehicle at each junction, called a junc-tracker, is selected. A junc-tracker is responsible to periodically collect traffic information of surrounding road segments, calculate their weights based on traffic information, and disseminate the weights to adjacent junc-trackers and vehicles. Road weights are then used in the junction selection. Junction-based Traffic-Aware Routing (JTAR) [28] is based on HTAR and improves the routing path reactively. In HTAR, a complete delivery path is planned. Contrarily, in JTAR, the junc-tracker dynamically recalculates weights at junctions based on the last updated information and packets can then be detoured in the middle of a delivery. However, HTAR and JTAR share the same drawback. Disseminations of traffic information between junc-trackers may cause a large amount of message exchanges. Below, we discuss issues of existing geographic routing protocols in VANETs, as packets need to be forwarded on a reliable and robust path. Routing protocols prefer to choose a road with a high traffic density. If a packet is transmitted on a road with a sparse density, it is possible not to encounter any appropriate neighbor to forward the packet. The packet will be carried by a vehicle, which results in a serious carry delay. As a result, most existing studies propose to take into account both the traffic density and the distance to the destination in the next junction/road selection. However, most protocols only consider the next road segment. Conditions of the consequent roads are ignored. A road with a high density is not always connected to the destination or other high traffic roads. Examples are shown in Figure 3. In Figure 3a, a packet may be moving away from the receiver, because the routing protocol chooses the road with the highest traffic density but does not consider if the selected road is connected to the destination or not. This situation is similar to the local maximum problem, but at the junction level. Another situation in Figure 3b shows a road with the highest traffic density does not always connect to another road with a high density. As a result, the second best choice, the down direction, is in fact the best choice. On the contrary, in some works, such as HTAR, a complete delivery path is planned. However, the planned path cannot adapt to the highly dynamic traffic, especially in a long delivery path. Therefore, the proposed solution is still based on a dynamic junction selection. When selecting the next road segment, we consider not only the road, but also its following roads.
## 3 Results  
### Validation of the NPP and NEP Calculations  
#### 3.1.1 Validation of the NPP Values  
We compared the monthly NPP estimated by the CASA model with the monthly NPP observed at the flux sites in the FLUXNET2015 database ([[https://fluxnet.org/data/fluxnet2015-dataset](https://fluxnet.org/data/fluxnet2015-dataset)]([https://fluxnet.org/data/fluxnet2015-dataset](https://fluxnet.org/data/fluxnet2015-dataset)) accessed on 1 March 2021). The flux site observed gross primary production (GPP), so this value needed to be multiplied by 0.55 (the ratio of NPP/GPP) to convert it into NPP [47]. For all sites (Table 2), the NPP measured at the site and estimated by the model through cross-validation had a regression coefficient of 0.74 and root mean square error (RMSE) of 25.65 gC\(\cdot\)m\({}^{-2}\) (Figure 2), which indicated a high precision of the CASA model estimation.
# Cornering Stiffness Prediction Based on Geometric Method
## 3 Results
### Load Distribution Validation Based on FE Model
#### 3.1.2 FE Model Validation  
The accuracy of the FE models is confirmed by comparisons to test data. According to the proposed method in the second section, load distribution is symmetric for positive and negative camber angles. Here, we take the positive camber angle result as an example. The corresponding tire test for FE 1\(\#\) is done on the MTS Flat-Trac(r) Tire Force & Moment Measurement Systems [24], while the FE 2\(\#\) tire is tested on the Flat Test Rig in the State Key Laboratory of Automobile Simulation and Control, Jilin University, see Figure 5. Some technical specifications of the Jilin University test rig are given in Table 2.  
Figure 4: The FE modeling process. (**a**) FE 1\(\#\); (**b**) FE 2\(\#\).  
Figure 5: The tire test rigs for FE models’ validation. (**a**) MTS Flat-Trac; (**b**) Flat Test Rig in Jilin University.  
Ideally, the tire load distribution at different camber angles should be measured directly using the test rigs. For a rolling tire, such a measurement would require the test rig to be equipped with force sensors on the road contact. In addition, the tire carcass camber should also be measured to determine the boundary of the two tire parts. These kinds of measurements are rare and hard to realize for most tire test rigs, and hence we use simulations. We had access to the pure side slip and pure camber lateral force test data for these tires. Since the camber thrust should be fairly independent of friction levels, and instead be dictated by the structural properties of the tread and carcass, it is chosen as a suitable property for verifying the FE models.  
The FE models are simulated under the same conditions as in the test, and the excellent agreement with measurement is shown in Figure 6 for both tires. Therefore, the FE models could be used in the following load distribution validation.
## 3 Theory  
### Wavelet Transforms  
A wavelet waveform has a short duration, a tiny waveform, and a specific shape (near zero). A wavelet transform is a tool that divides data, functions, or operators into frequency components and examines a resolution adjusted scale in each component. The analytical band can be fine-tuned with the wavelet transform to achieve precise measurements of low- and high-frequency components. The results of the wavelet transform are displayed in the time and frequency domains to expand signals using either time compression or dilation of a fixed wavelet function known as the mother wavelet. The signals are created by the mother wavelet stretching (scaling: a) or shrinking (shifting: k) of the wavelet itself as it  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
moves along the time axis. A DWT signal analysis can choose from a variety of scales (\(m\)) and positions (\(k\)) for integrated wavelets, resulting in a signal at the scale of interest. The actual input signal is received when integrating the signal at all resolutions. As shown in Equation (1) [58; 59], the wavelet transform scales the analytical results by a factor of two.  
\[DWT(m,n)=\frac{1}{\sqrt{2^{m}}}\sum_{k}f(k)\psi\left[\frac{n-k2^{m}}{2^{m}}\right] \tag{1}\]  
where:  
\(n\): is the number of data points (integer);  
\(m\): is the scaling (integer);  
\(k\): is the shifting (integer);  
\(\psi\left[\frac{n-k2^{m}}{2^{m}}\right]\) is the mother wavelet.  
The mother wavelets, i.e., Daubechies (db), Symlets (sym), Biorthogonal (bior), and Coiflets (coif), were employed in the previous study paper [56] to examine the coefficient values and behaviors for fault classification in transmission line systems and transformers. As a result, Daubechies appears to be suitable for use in fault analysis, so Daubechies was chosen as the mother wavelet in this study.
## 3 Results and Discussion
### Phytochemical Characterization
#### 3.2.2 Essential Oil Yields  
In the two years of data acquirement, from the three replicates each year, it was possible to observe the essential oil production of the accession present in the studied population. Essential oil quantification is shown in Table 4. Genotype L53 has the significant higher value of oil content, with 0.70% of an oil yield value; the accession that showed the lowest oil yield was L41, reaching the value of 0.20%. These data are in accord with prior studies on Italian wild hop and European wild hops [21; 35; 37; 38; 39; 40], where the essential oil yield reached values from 0.2% and seldom exceed 1%.
## 3 Experimental Results and Discussions
### PIV Measurement Results
The peak value of the case \(J_{ji}=0\) shows an increasing trend at the locations of X/B = 0.03 to 0.21 and then a reduction when X/B is more significant than 0.21, which illustrates that the shear layer interaction in the wake flow nearing to the SBG  
Figure 7.0 The velocity profile in the different locations at wake flow in measurement plane I. (**a**) Mean of the streamwise velocity, (**b**) fluctuation of the streamwise velocity, (**c**) fluctuation of the transverse velocity. model is weaker than that of downstream. The maximum value of the fluctuation of the transverse velocity for the case \(J_{\text{sf}}=0.0094\) is obviously alleviated at all positions, indicating that the strong interaction of the shear layer decreased. That leads to the unsteady lift force that is acting on the SBG model when the trailing edge jets attenuated, as shown in Figure 7.1  
The Reynolds shear stress (RSS), which serves as an essential parameter to represent the turbulence in the flow structure, is calculated and given in Figure 7.2 At the near-wake of X/B = 0.03, the RSS possesses a lower value, illustrating the low turbulence level in this area for the case \(J_{\text{sf}}=0\). The maximum value of RSS is increased at first and then decreased with the improvement of the X/B. The maximum and minimum values of the RSS are obtained at the Y/B = \(-\)\(-\)0.01 and 0.06, respectively, because the turbulence level of the shear layer that is separated from the upper and lower surface of the SBG model is strong [33]. This causes a significant fluctuation of the lift force being exerted in the SBG model. As \(J_{\text{sf}}\) sets as 0.0094, the RSS is mitigated at all the positions in wake flow, and the maximum and minimum values are reduced related to the uncontrolled case, which indicates that the interacting strength of the upper and lower shear layer is mitigated in the wake flow behind SBG model. Therefore, the unsteady lift force is decreased by the trailing edge jets.