esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Modify  
This phase involves modifying the dataset, including data cleaning, variable transformation, and feature selection. Therefore, the following section will provide a detailed overview of the process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Feature Name** & **No.** & **Feature Name** & **No.** & **Feature Name** \\ \hline
1 & Flow ID & 29 & Fwd IAT Std & 57 & ECE Flag Cnt \\
2 & Src IP & 30 & Fwd IAT Max & 58 & Down/Up Ratio \\
3 & Src Port & 31 & Fwd IAT Min & 59 & Pkt Size Avg \\
4 & Dst IP & 32 & Bwd IAT Tot & 60 & Fwd Seg Size Avg \\
5 & Dst Port & 33 & Bwd IAT Mean & 61 & Bwd Seg Size Avg \\
6 & Protocol & 34 & Bwd IAT Std & 62 & Fwd Byts/b Avg \\
7 & Timestamp & 35 & Bwd IAT Max & 63 & Fwd Pkts/b Avg \\
8 & Flow Duration & 36 & Bwd IAT Min & 64 & Fwd Blk Rate Avg \\
9 & Tot Fwd Pkts & 37 & Fwd PSH Flags & 65 & Bwd Byts/b Avg \\
10 & Tot Bwd Pkts & 38 & Bwd PSH Flags & 66 & Bwd Pkts/b Avg \\
11 & TotLen Fwd Pkts & 39 & Fwd URG Flags & 67 & Bwd Blk Rate Avg \\
12 & TotLen Bwd Pkts & 40 & Bwd URG Flags & 68 & Subflow Ford Pkts \\
13 & Fwd Pkt Len Max & 41 & Fwd Header Len & 69 & Subflow Ford Byts \\
14 & Fwd Pkt Len Min & 42 & Bwd Header Len & 70 & Subflow Bwd Pkts \\
15 & Fwd Pkt Len Mean & 43 & Fwd Pkts/s & 71 & Subflow Bwd Byts \\
16 & Fwd Pkt Len Std & 44 & Bwd Pkts/s & 72 & Init Fwd Win Byts \\
17 & Bwd Pkt Len Max & 45 & Pkt Len Min & 73 & Init Bwd Win Byts \\
18 & Bwd Pkt Len Min & 46 & Pkt Len Max & 74 & Fwd Act Data Pkts \\
19 & Bwd Pkt Len Mean & 47 & Pkt Len Mean & 75 & Fwd Seg Size Min \\
20 & Bwd Pkt Len Std & 48 & Pkt Len Std & 76 & Active Mean \\
21 & Flow Pts/s & 49 & Pkt Len Var & 77 & Active Std \\
22 & Flow Pkts/s & 50 & FIN Flag Cnt & 78 & Active Max \\
23 & Flow IAT Mean & 51 & SYN Flag Cnt & 79 & Active Min \\
24 & Flow IAT Std & 52 & RST Flag Cnt & 80 & Idle Mean \\
25 & Flow IAT Max & 53 & PSH Flag Cnt & 81 & Idle Std \\
26 & Flow IAT Min & 54 & ACK Flag Cnt & 82 & Idle Max \\
27 & Fwd IAT Tot & 55 & URG Flag Cnt & 83 & Idle Min \\
28 & Fwd IAT Mean & 56 & CWE Flag Count & 84 & Label \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of extracted features.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Class** & **Content** & **Total Samples** & **Selected Samples** \\ \hline Multimedia & Facebook Video, Hangouts Video, Skype Video, Netflix, & 12,500 & 10,000 \\  & Vimeo, YouTube & 168,914 & 10,000 \\ VoIP & Facebook Audio, Hangouts Audio, Skype Audio, & 168,914 & 10,000 \\ Instant message & AIM Chat, Facebook Chat, Gmail Chat, Hangouts Chat, & 16,238 & 10,000 \\  & ICQ, Skype Chat & & 54,680 & 10,000 \\ File transfer & FTPS, SCP, SFTP, Skype File & 275,515 & 10,000 \\ Attack & DDoS, DoS, Probe, Brute-force-attack, Exploitation (R2L), & 275,515 & 10,000 \\  & Web\_attack, Botnet & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset samples selection.
## 2 Materials
### In Situ Data
#### 2.2.1. Topsoil Moisture  
The spatial variability in the topsoil moisture (0-5 cm) was measured quasi-synchronously with satellite acquisitions (i.e., time differences between in situ measurements and satellite acquisitions of less than one day for 77% of cases for TerraSAR-X acquisitions and 81% for Radarsat-2) by using portable probes (ML2x from ThetaProbe). The first protocol aims at determining a calibration relationship for the conversion of the probe's signal, delivered in mV, to volumetric moisture expressed in cubic meters of water per cubic meter of soil (m\({}^{3}\).m\({}^{-3}\)) or percentage (%). The sampling method consisted of synchronously performing measurements with the probe and sampling the soil in the same location. The soil sample was used to estimate the volumetric moisture, with the water content of the sample being determined by weight. The sample was weighed, dried for at least 48 h at 105 \({}^{\circ}\)C and then weighed again. The resulting calibration function is given by Equation (1) [52]:  
\[TSM(\%)=0.04\times mV+0.08\,\left(\mathrm{R}^{2}=0.75,\,\mathrm{RMSE}=4.1\%, \,\mathrm{n}=403\right), \tag{1}\]  
The monitoring of soil moisture in the study area was then performed using similar portable probes to those used for the calibration step. The measurements were collected every 10 or 20 m along transects ranging from 40 to 640 m (depending on the size of the plot).  
The time course of soil moisture measurements collected in bare soil conditions is presented in Figure 3. The average and standard deviation values of topsoil moisture were derived from measurements collected during the 21 dates of observation. The measurements cover a wide range of values, as shown by the average values, which vary between 3.8% (observed during the summer months, after the harvest of the winter crops) and 29.8% (observed during the rainy period in February and May). At the end of the year (in November), the values are comparable to those collected in February (almost 27%). The variability in topsoil moisture values is much more important during the first months of the year (from February to April). The regular measurements of topsoil moisture allow the collection of data in varying conditions (i.e., dry to wet soil), as highlighted by values at the plot spatial scale, ranging from 2.4 to 35.3%.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Soil** & **Variable** & **Measurement** & **Values** \\ \hline
**Descriptor** & **Name (Unit)** & **Date** & **[Min-Max] Mean** \\ \hline Surface Roughness & \(\mid\)h\({}_{\mathrm{rms}}\) (cm) & & [0.6–7.9] 2.2 \\  & \(\mid\)l\({}_{\mathrm{c}}\) (cm) & After each & [1.9–18.5] 7.7 \\  & \(\mid\)l\({}_{\mathrm{rms}}\) (cm) & tillage event & [0.5–5.6] 1.5 \\  & \(\mid\)l\({}_{\mathrm{c}}\) (cm) & & [1.1–14.9] 4.4 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: Time course of topsoil moisture (mean (black dots) and standard deviation (in gray) values) collected on the monitored plots with bare soil conditions.
## 7 Conclusions and Future Research  
This paper implemented a conformance testing system for SGSF-064-1 based on CANoe software. The testing system can monitor the communication of messages between EVs and EVSEs, electrical signals, and the level of voltage or current associated with a proper transfer of power. It also included four testing modes described in the standard to check the behavior of the EVSE, which is the system under test. Through four testing modes of the charger, the developed system has been proven to be an effective and efficient tool for testing the EVSE. In terms of further research, it is imperative to expand the testing system in two directions. First, the system should cover every possible scenario of abnormal termination, including edge cases, which could occur in the EVSE by extensive testing and validation. In this study, only three cases were considered. Secondly, it is also highly recommended to build a conformance testing system for EVs in contrast to the one utilized for EVSEs in this paper.
# Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics
### Capture Techniques  
The IQMs require a digitized version of the reference and/or the reproduction to be used. In the case of printed images, the physical print needs to be digitized prior to QA. Scanners have been used for 2D print digitization [1; 2; 33; 34; 37]. Moreover, cameras have been used for QA of flat surfaces [38]. However, scanners are not suitable in this work because we work with non-flat prints (i.e., 2.5D prints with a surface elevation). The results of the work by [PERSON] et al. [39] support that image QA of flat surfaces (i.e., projection displays) captured through a camera is a relevant approach that works.  
Different techniques can be used to capture 2.5D prints. It depends on which attributes one is interested to capture. For example, art paintings are known to have relief surface structure and [PERSON] et al. [40] were able to capture the topography and color of oil paintings by their proposed hybrid set-up. It consisted of two cameras and a projector. They connected fringe projection with stereo imaging which performed well in color and depth information capturing. The paintings' depth and color information perception with respect to our eyes was mimicked using stereo imaging. The image registration process was avoided by capturing topography and color at the same time. [PERSON] et al. [41] captured the spatially varying gloss of their hand-painted samples. They used high dynamic range images. Their set-up consisted of a camera and a series of light-emitting diode lights. They concluded that the essential attributes for art paintings are translucency and gloss. High accuracy and precision for painting measurement were acquired using multi-scale optical coherence tomography and 3D digital microscopy [42]. However, these measurement tools were slow due to the small field of view. They also tested 3D scanning based on fringe encoded stereo imaging.  
Reflectance Transformation Imaging (RTI) is a common tool to capture appearance under different directional light [43]. The object and camera are fixed perpendicular to each other while the light source moves in RTI. It is common to use RTI for texture visualization purposes of paintings [44] and capturing of low-relief surfaces [45]. For example, [PERSON] et al. [46] performed RTI of cultural heritage data visualization assessment both subjectively and objectively. Recently, [PERSON] et al. [47] assessed the quality of relighting from images acquired through their proposed multispectral RTI system. They captured 3D objects with various colors and translucencies.  
These capture techniques can be applied to different 2.5D prints (maps, signage, etc.). However, some of them might be costly, time consuming, and, most importantly, they might involve post-processing to reconstruct color, depth, gloss, and other attributes that might require (costly) software tools. Overall, capturing the whole appearance features of 2.5D prints or similar objects is challenging. Nonetheless, there are some ongoing works in this direction [48].
## 5 Simulative Assessment
### Simulated Scenarios  
The simulative assessment is performed in realistic scenarios similar to the ones in [62; 93]. The evaluated scenarios consist of a large number of EDs that send messages to the network server while on the move in an industrial plant over a 1000 m \(\times\) 1000 m sensing area.  
The simulated LoRaWAN networks include a network server, a gateway and a varying number of Class A EDs.  
Both the network server and the gateway are stationary nodes placed in the center of the industrial plant. All the EDs are mobile and move with an average speed of 0.3 m/s according to the Chiang-Mobility model, as in [48; 62; 94].  
The EDs generate and transmit unconfirmed messages with a physical payload of 32 bytes, as in [93], following an exponential distribution with a mean of 60 s. No retransmission mechanism is adopted. We consider EDs that are not synchronized with each other, thus allowing multiple EDs to start a transmission attempt at the same time.  
The plain ADR algorithm is disabled for the EDs. In fact, as it was discussed in Section 3.3, the network server could not efficiently perform the runtime configuration of the mobile EDs LoRa parameters due to the dynamics of the radio channel. As a result, each ED has to individually manage the LoRa parameters for transmissions. For this reason, instead of the ADR algorithm, we developed a custom algorithm that runs on each ED using a predefined switching table to change the LoRa configuration parameters. The table is obtained as follows. During the configuration phase, a test application runs on a probe ED. Such a probe ED, while moving in the sensing area, computes the parameters that guarantee a MDelivR higher than a configurable threshold (e.g., 96% in our simulations) in multiple points, which correspond to multiple distances from the gateway. For example, in the simulations we evaluated distances from the gateway in steps of 10 m (a configurable value in the simulator). Based on the outcome of the probe ED assessment, the network designer configures all the EDs so that they switch their LoRa configuration based on their distance from the gateway. If the sensing area RF status is highly changing, the probing procedure may be repeated and the new switching table can be communicated to the EDs through downlink messages sent by the network server.  
The LoRa Path Loss Oulu propagation model [91] was used for the simulative assessment. In particular, we used the parameters of a suburban area that were obtained through experimental evaluations in [95].  
Each simulation runs for 12 h and is repeated five times varying the seed to collect a significant amount of data. OMNeT++ and MATrix LABoratory (MATLAB) were used to analyze the obtained data.  
The configuration parameters are shown in Table 5.  
The performance assessment is performed in several scenarios varying the number of EDs and two MAC parameters, i.e., \(MAC_{nBackoff}^{max}\) and \(U_{backoff}\).
## 2 Results and Discussion
### Different ORP Value Impacts Succinic Acid Production  
The extracellular ORP levels are recognized for their influential role in modulating organic acid fermentation processes. In light of this, our research delved into the correlation between the ORP levels and the biosynthesis of succinic acid within an ORP-regulated MEC framework.  
Our study revealed that the optimized succinic acid concentration peaked at 29.39 g/L when the ORP was meticulously controlled at \(-400\) mV, as delineated in Table 1. This ORP level not only facilitated the highest yield of succinic acid but also delivered a remarkable electronic utilization efficiency of 177.58%. In contrast, the application of a potentiostatic method that maintained a constant potential of \(-1\) V (referred to as CPMEC) did not yield a significant enhancement in succinic acid production when juxtaposed with the aforementioned \(-400\) mV ORP level. This was a pivotal observation, especially considering that the CPMEC method resulted in a substantially higher energy consumption, tallying at 91.23% more than the \(-400\) mV strategy.  
Figure 2: Effects of the voltage applied methods on succinic acid production in an ORP-controlled MEC system. (**a**): Succinic acid; (**b**): biomass; (**c**): ORP value. NR was added to the three MEC systems. The cells were cultured for 15 h with an initial sugar concentration of 50 g/L **\(p\)** < 0.01 between two relevant treatments, ns: not significant.  
The implications of these findings underscore the importance of the extracellular GRP as a critical parameter in fermentation reactions. By sustaining an optimal ORP level, the yields of succininic acid were markedly improved within the ORP-controlled MEC system. Notably, the electronic utilization efficiency of this system was found to be 113.85% higher than that of the conventional potentiostatic approach. It is particularly noteworthy that the electron utilization efficiency exceeded 100%, which suggests that the ORP-controlled MEC system might not only provide a sufficient supply of extracellular electrons but could also potentially enhance the enzymatic activity within the fermentation pathway under suitable ORP conditions.  
This enhanced enzymatic activity could be attributed to the more favorable redox conditions created by the ORP-controlled environment, which may lead to a more efficient electron transfer process and thereby stimulate the metabolic pathways involved in succininic acid production. Consequently, the ORP-controlled MEC system emerges as a highly viable and energy-efficient alternative to traditional potentiostatic methods, paving the way for more sustainable and cost-effective biotechnological applications in organic acid production.
# A Microfluidic Approach for Biosensing DNA within Forensics
## 3 Biosensing of DNA
### Conclusion  
Flow cells, which are part of most NGS machines, have nanopatterned features in the microfluidic channels. These nanosized wells make it possible to obtain a high data density by patterned clustering of DNA fragments [33]. Microfluidic devices or flow cells can be used for STR analysis and for NGS, respectively. With NGS techniques more genetic information can be obtained besides the standard DNA profile, but the complete analysis process takes longer than standard STR analysis.
## 4 Tightness of the Upper Bounds  
In this section, we construct several scenarios to examine the tightness of the derived upper bounds. In particular, we have the following results.  
**Theorem 5**: _When \(r=1\), the upper bound cannot be reduced to be lower than \(D-1\)._  
**Proof.** When \(r=1\), \(R_{i}=R_{t}\). We construct a scenario, as shown in Figure 7. The density \(D\) is 14 in Figure 7. We first draw a sector of radius \(R_{i}/2\) and angle \(\theta_{m}\). Then we place \(D-1\) (13) nodes equally on the arc of the sector with radius \(R_{i}/2\). For each node on the circle, we establish a link with length \(R_{t}=R_{i}\) toward the center of the sector, as shown in Figure 7.  
It is obvious that the node set we have just constructed is of density \(D\) since there are \(D\) nodes within the sector of radius \(R_{i}\) and angle \(\theta_{m}\). For the link set from the constructed node set, the corresponding conflict graph is a \((D-1)\)-clique (_i.e._, each link interferes with each other), which needs exactly \(D-1\) colors to color. So the upper bound cannot be lower than \(D-1\).  
When the interference ratio \(r\) is increased, the upper bound can also be reduced. More specifically, when \(r\geq 2\), we have the following result.  
**Theorem 6**: _When \(r\geq 2\), the upper bound cannot be reduced to lower than \(\lfloor\frac{\beta}{\theta_{m}}\cdot\frac{D}{2}\rfloor+1\), where \(\beta=2\arctan(\frac{\tan\frac{\theta_{m}}{2}\cdot(\sqrt{(2r-1)\tan^{2}\frac{ \theta_{m}}{2}+r^{2}}-(r-1))}{(r-1)\tan^{2}\frac{\theta_{m}}{2}+\sqrt{(2r-1) \tan^{2}\frac{\theta_{m}}{2}+r^{2}}})\), which only depends on the beamwidth \(\theta_{m}\) and the interference ratio \(r\)._  
**Proof.** When \(r\geq 2\), we construct a scenario shown in Figure 8. We first draw a sector with radius \(d\geq R_{i}/2\) and put \(n\) nodes equally on the arc of the sector. For each node on the circle, we establish a link with length \(R_{t}\) directed against the center of the sector, as shown in Figure 8.  
For this example, the node density \(D=2n\) since we can put a sector with radius \(R_{i}\) and angle \(\theta_{m}\) to cover those \(n\) links (each link has two nodes). Then, we need to calculate the number of links that a  
Figure 7: The proof of Theorem 5.
## 1 Introduction
The lubricity measurement is further improved by determining the nature of changesin auxiliary indicators, such as the friction coefficient and the relative reduction in the thickness of the oil film during the lubricity test of the LO and DO mixture. Among the many different methods of evaluating lubricity, three have basically found wide industrial use [30]: the high-frequency reciprocating rig (HFFR) [31], the ball-on-cylinder lubricity evaluator (BOCLE) [32], and the scuffing load ball-on-cylinder lubricity evaluator (SLBOCLE) [33]. The HFFR method is utilized in this experiment because many fuel quality standards specify lubricity determined by the HFFR method, which has several advantages over other lubricity determination methods. Among the most important advantages of the HFFR method are factors such as [34]:  
* Evaluation of the wear pattern of standardized samples only requires one measurement;
* The use of a very small amount of lubricating fluid in a single measurement;
* The low price of the samples and the ability to conveniently configure them to best replicate real friction conditions;
* Good repeatability and reproducibility of measurements;
* The ability to measure changes in electrical contact potential during a lubricity test allows for the evaluation of lubricity-enhancing additives;
* The possibility of conducting additional fretting wear tests on the same bench by using \"long-strokes\". The authors proposed the hypothesis that the nature of changes in lubricity, friction coefficient, and the percentage relative decrease in oil film thickness is similar for lubricating LO/DO mixtures made from fresh lubricating oil (FLO) and used lubricating oil (ULO). The similarity concerns the possibility of presenting changes in each parameter using a linear function, where the input variable is the concentration of diesel oil considering a certain determination coefficient and an average error for each approximation. It must be mentioned that the linear approximation of \(\mu\) and \(r\) trends is just a hypothesis that we made with a certain margin of error we have estimated. There exists a possibility to build more complex models (e.g., polynomial) but with better fitting to the experimental data. Therefore, the laboratory findings for FLO mixtures can be utilized to assess the impact of diluting ULO and, consequently, can be employed to evaluate the properties of oils collected from the circulating lubrication system during engine operation. To verify the truth of this hypothesis, a laboratory experiment is conducted to determine specific quantitative indicators and qualitatively assess the wear scar, the results of which are presented in this article.
# A Wideband Hybrid Envelope Tracking Supply Modulator with Slew-Rate-Enhanced Linear Amplifier
## 2 Circuit Design
### LA Design with an SRE Structure
#### 2.2.3 Simulation Results  
To better understand the effectiveness of the proposed SRE circuit, we compared the simulated LA performances with and without the SRE circuit. As shown in Figure 12, the proposed SRE structure increased the rise slew rate from 122.2 V/us to 2320 V/us and the fall slew rate from 178.5 V/us to 1907 V/us.  
As shown in Figure 13, the UGB of the proposed LA was 226.6 MHz, which is four times the one without the SRE circuit.  
Figure 12: The simulated step response of the LA with the proposed SRE structure.  
Figure 13: Loop gain of the LA (**a**) with the proposed SRE structure and (**b**) without SRE.  
Table 1 shows more details of the comparison between the proposed LA and the conventional LA. The proposed SRE circuit brought about an increase in the slew rate of over tenfold, while the quiescent current only increased by 50%.  
To ensure the robustness of the design, simulations were conducted under different process corners and temperatures. The results are shown in Table 2.  
As we can see, the UGB of the LA was greater than 200 MHz in most cases, and based on the phase margin criteria, the stability of the LA was ensured. The quiescent current increased as the temperature rose. In the worst case (slow process corner and 85\({}^{\circ}\)C), the slew rate of the LA still reached approximately 1000 V/\(\upmu\)s.