esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Computational Problems and Algorithms
### Minimum Cost Problems
** In this problem, whose formalization is shown in Figure 11, we are given a set of vertices in a metric space, a specified vertex \(s\), and a distance bound \(B\). The goal is to find a minimum cardinality set of tours starting at \(s\) that covers all vertices, such that each tour has length at most \(B\). [PERSON] et al. [50] showed that there is a 4-approximation algorithm for mccp. In the same work, the authors also considered a different variant of mccp called mccp_without neighborhoods_ where each IoT device can send its data to a UAV wirelessly, and the UAV can collect the data from the device as long as their Euclidean distance is no greater than a given wireless transmission range. In this case, they proved that there is a constant approximation algorithm. [PERSON] and [PERSON] [51], instead, studied mccp when the cost of each vertex is zero and provided a \((1+\varepsilon,O(\log 1/\varepsilon))\)-bicriteria approximation algorithm, i.e., for any \(\varepsilon\), it obtains a solution violating the length bound \(B\) by a \(1+\varepsilon\) factor while using at most \(O(\log 1/\varepsilon)\) times the optimal number of vehicles. [PERSON] and [PERSON] [51] provided a 2-approximation algorithm on tree metrics. Finally, [PERSON] et al. [52] considered a different variant of mccp in which we are given a required edges \(R\subseteq E\), and the goal is to find the minimum number of closed walks of length at most \(B\) which collectively traverse all the edges in \(R\) such that the number of closed walks used is a minimum. They provided a 5-approximation algorithm for this problem. For the case when \(R=E\), they gave a 4-approximation algorithm.
## 5 Conclusions  
We characterized a fast terahertz TDS system with respect to signal quality and measurement speed, and applied the system to non-contact thickness measurements of a piece of high-resistivity silicon. Employing two synchronized femtosecond lasers rather than a mechanical delay stage, the ECOPS system achieves a maximum measurement speed of 1600 pulse traces per second, at a scan range of 200 ps. With the scan range extended to 700 ps, the system still maintains a measurement  
Figure 8: ECOPS-based thickness measurement of a moving silicon wafer (red). Selected spots on the same sample (not rotating) were measured with the micrometer gauge for comparison (black). The green line in the photograph marks the approximate positions of the measured spots.  
speed of 200 traces per second. This is one to two orders of magnitude faster than state-of-the-art TDS systems with mechanical delay.  
Set to a measurement speed of 1600 traces per second, the ECOPS system achieves a time-domain dynamic range of about 60 dB for a single-shot measurement, and \(\sim\)90 dB with 1000 trace averages, still within a measurement time of less than one second. The time-domain signal-to-noise ratio amounts to \(\sim\)50 dB and \(\sim\)80 dB for 1 and 1000 averages, respectively.  
In the frequency-domain, the ECOPS setup produces spectra with a frequency range of 3.4 THz within 625 us, and 4.8 THz within 625 ms.  
Thickness measurements performed with ECOPS agreed well with results obtained with both a conventional TDS system and a micrometer gauge. Mounting the sample on a record player, we demonstrated the suitability of the system for continuous thickness monitoring in an industrial production line. To the best of our knowledge, our work represents the fastest thickness measurements accomplished with terahertz pulses so far.  
Conceptualization and technology demonstration: N.V., T.P., M.Y.; software: M.Y., A.J.; experiments and data analysis: A.J., M.Y., K.D., A.D.; project supervision, N.V., P.L., B.S. All authors contributed to the manuscript. This publication comprises part of the PhD thesis of [PERSON].  
This research received no external funding.  
We would like to sincerely thank [PERSON] (NPL, UK) for helpful discussions, and [PERSON] (TOPTICA Photonics Inc.) for careful proofreading of the manuscript.  
The authors declare no conflict of interest.
## 3 Model Description
### Environment  
The vehicle agents described earlier require an environment to function within. The model [23] deploys an urban street network that is described as a T-type network [58]. This street network contains similar characteristics to downtown Philadelphia [59] and San Francisco [60]. T-network patterns are like grid-shaped networks but include t-junctions. Several added features such as eight-lane intersections described in [61] are also utilised to add complexity. The street network contains 1295 roads and 354 intersections, which were arbitrarily generated to cover a small town. The individual roads, speed limits and intersection rules are described in the following Figure 2.  
The environment contains three road types with varying fixed and local speed limits and intersections with right of way rules. The environment is a simplification of the real world. Therefore, it does not utilise all intersection types. Moreover, overtaking is not utilised in the model as passing-lanes (overtaking lanes) do not exist in the street network and are commonly found in motorways or multi-lane highways [62]. However, it does contain the basic features of an urban street network which have also been observed in several cities across the United States [59; 60]. The following list describes each road, intersection and the speed limits assigned to these roads from Figure 2:  
* (A) Two-way local road with a speed limit rule of 20 mph.
* (B) Two-way corner road with a speed limit rule of 10 mph.
* (C) Two-way fixed road with a speed limit rule of 30 mph.  
Figure 2: Urban Street Network roads and intersections, (**A**): two-way local road, (**B**): two-way corner road, (**C**): two-way fixed road, (**D**): eight-way intersection and (**E**): two-way T-junction.  
* (D) Eight-way intersection, where right of way is for traffic on horizontal lanes, speed limit rule of 10 mph.
* (E) Two-way T-junction, right of way is for horizontal lanes, speed limit rule of 10 mph.  
The speed limits for the three road types (Figure 2A-C) were derived from UK government sources such as [2], where urban streets consist of local 20 mph and fixed 30 mph zones; however, corner roads sometimes require lower speeds such as 10 mph as vehicles require more room to turn. A UK Government report identifies roads in built-up areas as having a fixed speed limit of 30 mph. However, for dense areas--usually city centres--this may be designated 20 mph by local councils to keep pedestrians safe from collisions [63].  
For comparison, the urban street network in the Urban Traffic Simulator [23] is roughly the same size as the town of Morley, UK (refer to Figure 3). Morley has 1526 roads compared to 1295, which the urban street network possesses.
## 4 Finite Element Analysis
### Parametric Studies
#### 4.3.4 Mechanism Analysis  
The effect of different parameters on the bearing capacity can be analyzed through Figure 20.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Holding Level** & **Load Capacity (kN)** & **Reduction Proportion** \\ \hline
0 & 568 & / \\
80\% & 540 & 5.1\% \\
95\% & 508 & 6.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reduction proportion caused by increasing load holding level.  
Figure 19: Effect of load holding level.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Thickness of Reinforcement Layer** & **Load Capacity (kN)** & **Improvement Proportion** \\ \hline
20 & 568 & / \\
25 & 576 & 1\% \\
30 & 582 & 2\% \\
35 & 591 & 4\% \\
40 & 596 & 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Improvement proportion caused by increasing thickness of reinforcement layer.  
For the effect of steel mesh, the participation of steel mesh improves the bearing capacity of the column. The area of the steel mesh participating to resist the external load increases with the increasing of layer of steel mesh, causing the bearing capacity increases.  
For the effect of thickness of reinforcement layer, when assuming the compressive strain of the concrete at the compressive side reaches the ultimate compressive strain \(\epsilon_{\text{cu}}\), according to the plane section assumption, the tensile strain of resin concrete at the tensile side is much larger than the ultimate tensile strain, indicating that the resin concrete has broken before, as can be seen in Figure 20b. As a result, only increasing the thickness of reinforcement layer has a little effect on the improvement of bearing capacity of the column.  
For the effect of load holding level, as can be seen in Figure 20c, assume that the blue line represents the strain distribution caused by the sustaining load before reinforcement. When the column is strengthened by SMRC at this time, the strain will redistribute, and assume that the red line represents strain redistribution. The strain of the steel mesh is \(\epsilon_{\text{w}^{\prime}}\) at this moment. After the reinforcement, the strain of steel mesh increases to \(\epsilon_{\text{w}}\) with the increasing of the external load. Obviously, the strain of steel mesh resisting to the external load is \(\epsilon_{\text{w}}-\epsilon_{\text{w}^{\prime}}\). With the increasing of the sustaining load, \(\epsilon_{\text{w}^{\prime}}\) increases and the strain of steel mesh resisting to external load decreases, making the reinforcement effect worse.
# Application of DMLF in Pulse Ranging LiDAR System
## 3 Results  
### Simulation Set Up  
Based on the above theory and system flow chart, the simulation models have been set up to verify the progressiveness and accuracy of DMLF. The parameters of pulse ranging LiDAR system and target are set as the following: \(E_{t}=4.8\) uj; \(\lambda=1064\) nm; \(\tau=4.0\) ns; beam diameter is 3.0 mm; laser repetition frequency is 200 kHz; angle of divergence is 0.51 mrad; transmitting and receiving system optical aperture are 32 and 50 mm; APD photosensitive surface size is 3.0 mm; ADC sampling rate, bandwidth and sampling digit are 5 GSa/s, 1 GHz, and 14 bit, respectively; target reflectivity \(\rho=0.2\); optimal axial shift \(d=1.2\) m.  
The waveform of the transmitted pulse and echo pulse of LiDAR system after filtering are shown in Figure 4a. Obviously, the peak point's area of echo signal is disturbed by noise, which makes it difficult to extract useful moment information through the peak position. Therefore, the applicability of the peak method is limited.  
In this situation, the differential method based on linear fitting is used, and the simulation results are shown in Figure 4b. We use the differential method to change PPs  
Figure 4: (**a**) The filtered transmitted pulse and echo pulse in LiDAR system; (**b**) Simulation results of echo pulse profile.  
into PGZCPs, and then calculate the true stop-time moment by the linear fitting curve of points near PGZCPs.  
The reflectivity of different objects varies greatly. Asphalt and cement objects have a low reflectivity approximately 0.2, and the automobile surface metal plate has a higher reflectivity, about 0.75. Therefore, in order to verify the high efficiency of the DMLF algorithm in this paper, we applied it to different reflectivity conditions for simulation, and the simulation results are shown in the Appendix A.  
Considering the effect of noise, we carry out simulation experiments 1000 times with an SNR of 5-15 dB, and range from 30 to 50 m, respectively. The noise added in the simulation is Gaussian white noise with the same mean value. The performance of the peak method and the proposed DMLF will be further compared and analyzed below.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 4 Simulation Experiment and Analysis  
### Stochastic Link Failure Experiment  
This section analyzes the effects of transmission distance and transmitting power on the failure probability in ISLL systems. For each case, transmit gain \(G_{T}\), receive gain \(G_{R}\), transmit efficiency \(\eta_{T}\), receive efficiency \(\eta_{R}\), laser wavelength \(\lambda\), responsivity \(R\), noise variance \(\sigma_{N}^{2}\) and threshold \(a\) are set to \(G_{T}=106.3\) dB, \(G_{R}=118.9\) dB, \(\eta_{T}=0.5\), \(\eta_{R}=0.4\), \(\lambda=1064\) nm, \(R=0.6003\) A/W, \(\sigma_{N}^{2}=4.3\times 10^{-12}\) A\({}^{2}\), \(a=1\times 10^{-6}\). Azimuth pointing error \(\theta_{x}\) and pitch pointing error \(\theta_{y}\) are set to \(\theta_{x}\sim N(\mu_{x},\sigma_{x}^{2})\), \(\theta_{y}\sim N(\mu_{y},\sigma_{y}^{2})\), respectively, where \(\mu_{x}=\mu_{y}=0\) and \(\sigma_{x}=\sigma_{y}=0.75\).  
Table 1 lists the relationships between transmit power, transmission distance and failure probability under the above-mentioned condition. As can be seen from Table 1, in the condition of the same transmit power, the probability of failure is proportional to transmission distance, and it is very small and can be ignored, while the transmission distance is less than 1000 km. In addition, the transmission power is inversely proportional to the probability failure under the condition of the same transmission distance.
## 5 Conclusions  
To assess differential retrieval of _Salmonella_ serovars in enrichment cultures, this study tested growth of four different serovars of _Salmonella_ and revealed similar growth kinetics for the strain in pure cultures in TSB and BPW. Nutrient utilization differences between the serovars provided a basis of experimentation for supplements to enrichment culture media to enhance recovery of serovars of clinical interest that might be missed in routine food surveillance. In-depth analysis of predicted protein sequences of genes involved in phosphorus metabolism indicated differences in _phoR_ sequence among the serovars. Further experimentation regarding the efficiency of various PhoR protein sequences may shed light on the efficiency of nutrient metabolism. Variations in the outcomes of enrichment culture may be affected by many factors, and small populations of specific subtypes can be missed by culture-based methods. Molecular and/or immunogenic methods that detect living cells may be better suited to finding small numbers of contaminants in situations where isolated colonies are not necessary.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/microorganisms11082109/s1](https://www.mdpi.com/article/10.3390/microorganisms11082109/s1)]([https://www.mdpi.com/article/10.3390/microorganisms11082109/s1](https://www.mdpi.com/article/10.3390/microorganisms11082109/s1)), Table SI: Salmonella genomes used in this study; Table S2: Heat map of all 157 phenotype differences between the strains and serovars; Figure S1: Comparisons of nine protein sequences between sequenced strains in this study; Figure S2: Comparisons of nine gene sequences between sequenced strains in this study.  
**Author Contributions:** Conceptualization, L.G.; methodology, L.G.; validation, A.A.N.; formal analysis, L.G. and A.A.N.; investigation, L.G. and A.A.N.; resources, L.G.; data curation, L.G. and A.A.N.; writing--original draft preparation, L.G.; writing--review and editing, L.G.; visualization, L.G. and A.A.N.; supervision, L.G.; project administration, L.G.; funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by funds from U.S. Department of Agriculture under Agricultural Research Service CRIS project 2030-42000-052-00D.  
**Data Availability Statement:** Genome sequence data were deposited with the NCBI under the BioProject ID PRJNA875079. The raw sequencing data were sent to the Sequence Read Archive and are identified by BioSample accession numbers SAMN30597303-SAMN30597310. Strains were identified by their ID numbers: RM15596, RM17366, RM18387, RM18411, RM18433, RM18465, RM19068, and RM20875.  
**Acknowledgments:** The authors thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for advice and helpful discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Results and Discussion
### In Situ Analysis of SDOM Fatty Acids  
#### 2.2.1 Optimal In Situ Derivatization  
Before optimization of the derivatization step, derivatives recovery was estimated. The simple and low cost dichloromethane CH\({}_{2}\)Cl\({}_{2}\) liquid/liquid extraction of the derivatized compounds was selected as the optimized direct immersion solid phase microextraction (DI-SPME) method that showed a peak area decreasing inversely according to the molecular weight of the compounds (Figures 4 and S2).  
The derivatization involving direct methylation of fatty acids with boron trifluoride (BF\({}_{3}\)) in methanol, used on evaporated samples, was then optimized for the direct methylation in water samples to study low molecular weight compounds. To perform in situ derivatization, different ratios of BF\({}_{3}\)/MeOH and MeOH (_v/v_) were studied (Figure 5). BF3-MeOH, MeOH 45/45 (%/%), that showed the best recoveries, was used to analyze water samples.  
Figure 4: **Spectral ionic mass** (SIM) chromatogram of liquid/liquid (**A**) and direct immersion solid phase microextraction (SPME) (**B**) optimal recoveries of iso molar low molecular weight fatty acid methyl esters.  
Figure 3: BF\({}_{3}\)/MeOH derivatization on 500 mL reference (**A**) and amended (**B**) soil solutions from 15 to 100 cm depth after a 40 \({}^{\circ}\)C evaporation step.
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 5 Swarm Behaviours
### Information Consensus
#### 5.2.2 Experimental Results  
A series of experiments was conducted on a real system of four aMussel robots in the Venetian Arsenal. All robots are equipped with acoustic communication devices. The optimal period of message transmission was empirically defined to be \(4s\). Smaller periods have displayed interference in the signals. Moreover, having a somewhat longer communication period ensures correctness of each transmission even after a slight drift in the robots' clocks.  
The initial values of the variable states and the expected stationary state were set to \(x_{0}=(0,50,150,250)\) and \(x^{*}=112.5\), respectively. The gain was set to \(\epsilon=0.01\). The results are provided in Figure 26. As is seen in the graph, the values converge to \(110\) after \(2500\) s \(\simeq 41\) min of the experiment runtime.  
Figure 26: Results of the consensus algorithm applied to four aMussel robots using acoustic communication.
## 3 Performance Analysis
### Empirical Radio Frequency Data Collection in the Park of the Checa Parish

Table 4: Raw format of power level data extracted by the Raspberry Pi 3 near the hotspot. Figure 5: Empirical samples obtained by the Raspberry Pi 3 of hotspot power levels vs distance in meters. Parameters: 560 samples of the reception level power. \[fittedmodel(d)=a+b*\log 10(d). \tag{7}\]  
The coefficients obtained with a confidence interval 95% are  
a = \(-49.32\), between the confidence interval (\(-52.61\), \(-46.02\)),  
b = \(-6.773\), between the confidence interval (\(-7.616\), \(-5.93\)). Therefore, the adjusted model obtained is shown as follows:  
\[fittedmodel(d)=-49.32-6.773*log10(d) \tag{8}\]  
Finally, the power received, \(Pr_{x}\), can be modeled using the following expression:  
\[Pr_{x}=Pt_{x}+fittedmodel(d)-A, \tag{9}\]  
where \(Pt_{x}\) is the transmitter power.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
###### Abstract  
As wind energy development increases, accurate wind energy forecasting helps to develop sensible power generation plans and ensure a balance between supply and demand. Machine-learning-based forecasting models possess exceptional predictive capabilities, and data manipulation prior to model training is also a key focus of this research. This study trained a deep Long Short-Term Memory (LSTM) neural network to learn the processing results of the Savitzky-Golay filter, which can avoid overfitting due to fluctuations and noise in measurements, improving the generalization performance. The optimum data frame length to match the second-order filter was determined by comparison. In a single-step prediction, the method reduced the root-mean-square error by 3.8% compared to the model trained directly with the measurements. The method also produced the smallest errors in all steps of the multi-step advance prediction. The proposed method ensures the accuracy of the forecasting and, on that basis, also improves the timeliness of the effective forecasts.  
Keywords:renewable energy; long short-term memory neural network; power prediction; multi-step prediction +
Footnote †: journal: _Applied Sciences_
# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 1 Introduction
In order to classify IoT devices efficiently and stably, current researchers are attempting to extract features from the network traffic and provide them as inputs to machine learning models to learn hidden patterns from complex traffic features. Many studies have followed this approach and achieved high accuracy. Recent studies have extracted packet header fields or network layer features of IoT devices for device identification [11; 12; 13; 14]. However, the network layer characteristics of many IoT devices exhibit consistency in patterns. Furthermore, most existing methods are not applicable when IoT devices are located behind a NAT (Network Address Translation)-enabled router, as many features change during the NAT process [15]. In this paper, we aim to establish a practical IoT device identification system that accurately identifies IoT devices through sessions; in addition, our method is also applicable to IoT devices behind NAT-enabled routers, as the NAT process does not alter the communication payload. Compared to previous works that focused on identifying IoT devices by extracting features from the header of relatively easily tampered packets, our method focuses on extracting the payload content and features of IoT device sessions to identify IoT devices. It is more universal and suitable for any IoT device, even if they use different protocols. Meanwhile, this method also provides a high accuracy. This work offers the following contributions and differences from previous work: (1) Our method does not rely on packet header fields or network layer features. Instead, we attempt to use the more stable and reliable payload of sessions for identification, which makes our method more robust and widely applicable. (2) We propose a novel two-stage machine learning model based on sessions to identify IoT devices. By establishing machine learning models based on the frequent items and network communication features of sessions, we achieve efficient identification of IoT devices and identify some network communication features. (3) Our method only requires one session to achieve the identification of IoT devices, with an identification accuracy of up to 99.48%. Moreover, the method can accurately identify IoT devices even in network communications with non-IoT devices. The remainder of this paper is organized as follows. In Section 2, we reviewed the relevant work on IoT device identification. Section 3 gives an overview of our method. Section 4 gives an overview of our model selection. Section 5 demonstrates evaluation experiments and results. Section 6 concludes the work.