esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils
## 1 Introduction
In this study, we propose novel sorption models for predicting \(K_{\mathrm{d}}\) of tetrachloroethene (PCE) in low-OC soils. PCE was selected as the model contaminant due to its widespread occurrence and persistence in subsurface environments. This research was guided by four primary objectives: (1) to collect a statistically representative set of fully characterized low-OC soil samples; (2) to measure and analyze PCE sorption isotherms for these samples; (3) to perform detailed statistical analyses to identify the key soil properties influencing sorption behavior; and (4) to develop and validate new advanced predictive sorption models. In addition to the previous models, we involved soil parameters that are practical for routine environmental assessments and are proven to significantly contribute to the sorption. By integrating a comprehensive suite of soil property measurements into the evaluation, this study provides novel insights into the mechanisms governing sorption of hydrophobic pollutants in low-OC soils. Despite their simple implementation, which facilitates application by the wider professional community, these models enhance the precision of risk assessments and support effective remediation strategies for contaminated sites.
# Enhancing Data Security Through VLSM Subnetting and TCP/IP Model in an ENT
## 4 Results
### Simulation Results on Performance Protocols  
The simulation results indicate the performance of different protocols when a device connects to another host in the same subnet--Location A (Laptop2 to PC2) and to another host in a different subnet--Location B (Laptop2 to Laptop0), (Laptop2 to PC1), and Location C (Laptop2 to Smartphone0). The outcomes of the protocols are presented in Figure 17 showing the time taken (in milliseconds (ms)) to deliver packets or messages to the destination devices.  
Figure 16: (**a**) Ping and Tracert from Location A to Location C. (**b**) Ping and Tracert from Location B computer to Location C computer.  
In Figure 17, the graphs with vertical axes are Time in ms, while the horizontal axes are the Protocols (EIGRP, RIPv2, and OSPF) for routing packets, while Figure 18 illustrates comparison of the routing protocols. Our analysis indicates the following:  
1. The connection within the same Location A (Laptop2 to PC2) resulted in the outcome shown in the graph (i).
2. The connection from Location A to Location B (Laptop2 to Laptop0) takes the longest to transfer packets to the destination host as the source. The wireless protocol can be responsible for the delay of message to the destination due to the built-in delay, load, bandwidth, and consistent quality (DLBC) of EIGRP.
3. The connection from Location A to Location B (Laptop2 to Smartphone0) takes about 15 ms with EIGRP compared to OSPF and RIP can be ascribed to distance-vector range, linked-state protocol, and relative to [PERSON]'s model.
4. Laptop2 to PC1 connection from Location A to Location C takes about 10 ms with EIGRP, while OSPF routed the message in slightly less than 10 ms. The EIGRP performance was stronger compared to RIP, while the OSPF routed slightly faster than EIGRP. This performance can be attributed to the DUAL and enhanced security protocol (see Table 7).  
Figure 17: EIGRP, RIPv2, and OSPF performance protocols connecting devices in the network.  
Figure 18: Routing protocols performance comparison.  
We observed that the average time from source to destination within a subnet does not change. However, from source to destination between two subnets across a router, there is a time change or fluctuation. Lower average time is based on the most recent packets received, while the higher average time relates to minimum packets received over time.
## 6 Conclusions  
A compact TFLN modulator maintaining high bandwidth, high modulation efficiency, low phase error and low \(V_{\pi}L\) meeting the fabrication limits has been proposed. The results suggest that the change in orientation of the device majorly impacts the electro-optic response, while effects on RF response are negligible. To meet the performance of a non-rotated device, \(V_{\pi}L\) of the rotated device increases. SFDR calculations have been performed for varying bias voltage and input RF power to quantify the nonlinearity. An SFDR3 of 121 dB/Hz\({}^{2/3}\) is obtained, which is comparable with the high SFDR of conventional LN modulators. SFDR performance of the device can be further enhanced as high as 135 dB/Hz\({}^{2/3}\) by using various linearization techniques available in the literature. The digital performance of the proposed device is analyzed at varying bit rate random binary signal for multi-level higher order modulation schemes. A BER well below the HD-FEC threshold is obtained for 400 Gbps at 100 GHz enabling 400 G communication. Thus, the proposed device shows great potential for applications in state-of-the-art communication technology (5G/6G, satellite downlink, RADAR, etc.) by offering a bandwidth >100 GHz with a high SFDR and low BER for a sub-terabit link. The design and analysis method used in this article is not limited to the LNOI platform only but can also be extended to other hybrid platforms. Currently we are working on the process optimization for complete fabrication of the device for verifying our simulation results.  
[PERSON] conceived the presented idea, developed the theory and performed the computations. [PERSON] and [PERSON] verified the analytical methods. [PERSON], [PERSON] and [PERSON] drafted the manuscript and verified the results. [PERSON] encouraged the investigation of the design and performance of the design. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of this manuscript.  
This research is funded by Ministry of Education, Government of India, under [PERSON] Minister's Research Fellowship scheme.  
Figure 10: BER performance for varying bit rates with and without manipulated voltages.  
Figure 9: Constellation diagram for (**a**) PAM-2, (**b**) QAM-4 and (**c**) QAM-16 signals. Red is the reference, green is received signal without OSNR and blue is received signal with OSNR at 200 Gb/s (100 GHz).  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank [PERSON] from Nano-Device and Sensors Laboratory, Department of Centre for Nano Science and Engineering, Indian Institute of Science, for his inputs.  
**Conflicts of Interest:** The authors declare that there are no conflicts of interest regarding the publication of this paper.  
**Sample Availability:** Not applicable.
# Pseudorange Measurement Method Based on AIS Signals
## 3 Detective Feature of Timestamp  
### Gaussian Filter System Characteristics  
The signal characteristics of the Gaussian baseband waveform are completely determined by the Gaussian filter, which is a low-pass filter in which unit impulse response satisfies the Gaussian distribution where the mean value is 0 [24].  
\[h(t)=\frac{1}{\sqrt{2\pi\delta}}\exp\biggl{(}-\frac{t^{2}}{2\delta^{2}}\biggr{)} \tag{2}\]  
where \(\delta^{2}\) is the variance of Gaussian distribution, and \(\delta\) is standard deviation. The probability density function of the Gaussian distribution is bell-shaped, and the parameter \(\delta\) determines the magnitude of the curve distribution.  
In the field of signal processing, the bandwidth-time product \(BT\) is usually used to characterize the Gaussian filter in which \(B\) is the 3 dB bandwidth of the filter, and \(T\) is the bit period. \(BT\) and \(\delta\) satisfy the following definition  
\[\delta=\frac{\sqrt{\ln 2}}{2\pi(BT)} \tag{3}\]  
Equation (3) is introduced into Equation (2) to obtain another expression of Gaussian filter unit impulse response  
\[h(t)=\frac{\sqrt{2\pi}(BT)}{\sqrt{\ln 2}}\exp\Biggl{(}-\frac{2\pi^{2}(BT)^{2}}{ \ln 2}t^{2}\Biggr{)} \tag{4}\]  
For subsequent discussion, the definition of parameter \(k\) meets the following equation:  
\[k=\sqrt{\frac{2}{\ln 2}}\pi(BT) \tag{5}\]  
Correspondingly, the unit impulse response function of the Gaussian filter expressed by Equation (4) can be simplified as  
\[h(t)=\frac{k}{\sqrt{\pi}}e^{-k^{2}t^{2}} \tag{6}\]  
Thus, it can be seen that the baseband waveform characteristics in the GMSK communication system are only relevant for parameter \(BT\). Usually the smaller the \(BT\) value, the faster the attenuation of the GMSK high-frequency component, but at the same time, the greater the interaction between adjacent bits. Therefore, in GMSK communication systems, the parameter \(BT\) should be considered synthetically.
## 5 Performance Analysis
### Dataset  
In this study, we utilized PEMS04 and PEMS08 [44], which are publicly accessible real-world traffic flow datasets. The California Department of Transportation deployed sensors to capture these statistics in real-time at a frequency of 30 s [45]. The collected data were then aggregated into 5 min time windows, producing 12 observations each hour. Table 1 contains comprehensive details about the datasets [46]. The distances between the sensors in these real traffic road networks were used to construct the adjacency matrices utilized in the experiments conducted in this study.
# Detection of Azo Dyes in Curry Powder Using a 1064-nm Dispersive Point-Scan Raman System
## 3 Results and Discussion
### Spectral Characteristics of Samples
The Raman spectra in this region is a fingerprint of the dye coded with discrete structural information about the dye structure. Interpretation of the spectral \"code\" is essential to minimize false positive results which are possible due to spectral lines close to but not identical to marker frequencies. Interpretation also is critical in assigning marker frequencies which can detect structural analogs, including potential metabolites of the compounds of interest. Metanil yellow and Sudan-I are structural analogs of each other. Each has three aromatic rings and an azo group (-N=N-) between the two of them. Rings III and II plus the azo group between the two at the molecular level are co-planar, which causes the yellow color chromophore in both compounds. Three vibrational modes in Sudan-I and metanil yellow relating to the azo group are similar: 1593 cm\({}^{-1}\) and 1597 cm\({}^{-1}\) (N=N stretching), 1448 cm\({}^{-1}\) and 1452 cm\({}^{-1}\) (N=N stretching + H-C bending in H-C=C-N=), and 1169 cm\({}^{-1}\) and 1147 cm\({}^{-1}\) (C-N=stretching + H-C bending in H-C=C-N=) [34; 35]. The H-C=C-N= bending components in vibrational modes near 1450 cm\({}^{-1}\) corresponds to H-C=C-N= sites on Ring III and are similar for both dyes. The H-C=C-N= bending component in the vibrational mode at 1169 cm\({}^{-1}\) in Sudan-I is present in metanil yellow in its IR spectrum (1171 cm\({}^{-1}\)). Thus 1169 cm\({}^{-1}\) and 1147 cm\({}^{-1}\) are assignable as _sym_ and _asym_ vibrational modes to the same molecular site on Ring III, that is, H-C6=C1. The absence of a Raman peak near 1169 cm\({}^{-1}\) in metanil yellow spectrum could be due to the moiety H-C2=C1- on Ring III being predominantly symmetrical. The metanil yellow peak at 995 cm\({}^{-1}\) due to ring breathing in ring II also confirms the presence of metanil yellow. The Sudan-I peak at 1593 cm\({}^{-1}\) due to N=N stretching is most definitive for its identification. The 1340 cm\({}^{-1}\) and 1387 cm\({}^{-1}\) Sudan-I peaks are due to aromatic ring stretching on the naphthalene ring quite different from metanil yellow and curry vibrational modes. Curry powder formulations can contain a significant amount of the compound Linalool. Linalool has a pleasant smell, and can be a major component in some essential oils. However, sets of vibrational modes corresponding to this compound were not apparent in the Raman sample data. Figure 3: Chemical structure of: (**a**) metanil yellow; (**b**) Sudan-I.
## 2 Theoretical Background of the Uncertainty Theory
### Applications and Benefits of Uncertain Models
#### 2.4.1 Stochastic Programming (SP)  
Stochastic programming (SP) has been widely used in supply chain management, particularly in inventory management, capacity planning, and transportation management. Recent research has applied SP to develop robust methods that account for demand variability, supply chain disruptions, and other uncertainties, thereby enhancing overall supply chain flexibility and efficiency [17; 19]. SP problems are addressed by considering multiple scenarios and their associated probabilities. This technique is effective in contexts such as controlling stock levels when sales volumes are uncertain over a given period or determining manufacturing production levels when rates are uncertain during specific periods [17]. One way to formulate the SP model is through the following:  
\[\text{Minimize }\sum_{s\in S}p_{s}C_{s}(x_{s})\]  
subject to:  
\[\sum_{j\in J}a_{ij}x_{js}\geq b_{is},\quad\forall i,\forall s\]  
Depending on the circumstances \(S\) belongs to, \(p_{s}\) is the probability that scenario \(s\) will occur, \(C_{s}(x_{s})\) represents the cost when the occurrence is scenario-specific, \(a_{ij}\) are the coefficients, and \(b_{is}\) are scenario-specific constraints.
## 6 Discussion  
### Results  
Through the previous simulation analysis, we find that there is a significant correlation between FD, FLR, and the traffic capacity of bus stops. Using the simulation results, we establish a traffic capacity evaluation model of bus stops, and the model has a good effect on the determination of the traffic capacity of bus stops under a CAVs environment. In addition, we also found that the higher the penetration rate of automated driving, the better the capacity of bus stops. From the perspective of influencing factors, we found that bus flow rate, bus dwell time, and bus stop type all have an impact on traffic capacity.  
First of all, in terms of the correlation analysis of permeability, we find that many works in the literature have carried out this research. The mixing of automated vehicles makes the road utilization rate higher. As the car-following distance of autonomous driving is lower, the higher the permeability is, the higher the road capacity is. However, this is only an experimental stage. In some studies, real vehicle verification was carried out [43]. It was found that when the penetration rate of autonomous vehicles is too high, the traffic capacity decreases instead. This is because when congestion is formed, the congestion of autonomous vehicles is more difficult to ease.  
In practical applications, in the absence of actual measurement data to understand the traffic conditions under the CAVs environments and the potential impact of bus stops on CAV traffic flow, this paper's evaluation method provides a convenient way to obtain information on the traffic capacity of the bus stop under the CAVs environments, which can help urban planners to determine the best location of bus stops, and identify bottlenecks and congestion areas, so as to optimize traffic flow. It can also help bus operators to make operational management decisions, which is also of great significance for adapting the urban transportation systems to the impacts of CAV applications. In this regard, when we find that if the traffic volume is greater than or too close to the actual capacity of the road, we can consider increasing the number of berths at the bus stop or reasonably optimizing the bus schedule by dispersing the traffic station lines, and appropriately reducing the bus lines. Passengers' mastery of public transport can also be better understood. Although GPS and other technologies currently have a good understanding of the arrival time estimation and bus frequency of public transport used by passengers to understand travel, errors often occur in the estimated time within a certain distance. Passengers often reduce their travel experience because the estimated time is too compact, and the assessment of traffic capacity can provide a more accurate prediction basis for predicting the time to reach the bus station to a certain extent.
# Comparison of Current Five-Point Cylindicity Error Separation Techniques
###### Abstract  
Cylindricity is a kind of three-dimensional form distortion of a cylinder. An accurate in situ measurement of cylindricity is relatively complex because measuring and reconstructing cylindrical profile and evaluating out-of-cylindricity should be involved. Any method of in situ measuring cylindricity must solve a common issue, i.e., to eliminate spindle error motions and carriage error motions during measurement and reconstruction. Thus, error separation techniques have played an important role in in situ cylindricity measurement through multipoint detections. Although several valuable five-point methods for in situ measurement of cylindrical profile have been proposed up to present, namely the parallel scan, spiral scan, and V-block scan, there are obvious differences in many aspects, such as the arrangement of probes, error separation model, reconstruction method, adaptability to service environment, accuracy and reliability in practical application, etc. This paper presents the evaluation of their advantages and disadvantages in theory and the actual measurement based on the standard ISO 12180. Suggestions for best meeting the requirements of modern manufacturing and the most prospective one for industrial applications are also given.  
error separation technique; cylindricity; form measurement; in situ measurement +
Footnote †: journal: Applied Physics Letters
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 4 Results and Discussion
### Performance Parameters, Evaluation and Comparison
#### 4.1.1 Classification Accuracy  
The first metric used for evaluation of the ProSenAD model is classification accuracy (CA). Although accuracy is prone to biased results in some cases, specifically in multiclass classification and unbalanced dataset, it is important to use in evaluation as it forms a frame of reference for comparison of results with benchmarks and other evaluation metrics. Equation (1) presents the equation for accuracy calculation, where _TP = True Positive_, _TN = True Negative_, _FP = False Positive_, and _FN = False Negative_.  
\[\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
The accuracy shows the value of correctly predicted data instances among all the available instances of benign, and attack classes. Figure 10 shows the average classification accuracy of ProSenAD, GAN-C, ML-LGBM, GB, XGBoost, and LGBM, comprising data from the RPL-based IoT environment. ProSenAD outperforms GAN-C by 8.7% in protocol-specific attack detection, GB by 10.6% in protocol-specific attack detection and 12.2% in SN-inherited attack detection. It exceeds in classifying the target classes by 7% in comparison with the XGBoost where the former's classification accuracy is 0.997 and latter's is 0.927. As for the SN-inherited attack detection scenario, ProSenAD outperforms XGBoost by 7.7% indicating better overall performance in both attack use cases. Baseline LGBM shows an accuracy of 0.952, which is 4.5% less than ProSenAD for protocol-specific attack scenario. As for the SN-inherited use case, the proposed model exceeds the LGBM performance with accuracy improved by 5%. Although ML-LGBM and ProSenAD show similar performance in protocol-specific use cases, the former is not designed for SN-inherited attack detection. Therefore, ProSenAD outperforms in terms of number and simultaneity of attacks for detection. GAN-C is also designed for only one attack scenario; the comparative performance for this method is presented earlier. Similarly, GRU-DL shows negligible difference in performance, but it also works only in the SN-inherited attack detection scenario. It does not address protocol-specific attacks; thus, ProSenAD outperforms the benchmarks, overall.
## 3 Performance Analysis  
In this section, the analytic formulae are determined for the average BER and the ergodic capacity of the drone-assisted FSO downlink system using the derived statistical characteristics.
## 2 Materials and Methods
### Data Analysis  
A radiologist with 25 years of experience on MR pelvic imaging performed the manual segmentation. Regions of interest (ROIs) were obtained drawing manually by the same radiologist, using the diffusion-weighted images with the highest b-value (Figure 1), along the tumor contours, slice by slice in order to obtain the entire volume of interest.  
### Data Analysis  
For each voxel,12 features were derived by DWI data using the mono-exponential model, the Intra-Voxel Incoherent Motion model with the conventional bi-exponential fitting based on Levenberg Marquardt algorithm (using multiple low b-values \(<\) 200 s/mm\({}^{2}\) and higher b-values: 300, 600, 1000 s/mm\({}^{2}\)) and the Diffusion Kurtosis Imaging model (using one low b-value 50 s/mm\({}^{2}\) and multiple high b-values \(>\) 200 s/mm\({}^{2}\): 150, 300, 600, 1000 s/mm\({}^{2}\)).  
DWI signal decay is most commonly analyzed using the mono-exponential model [14; 15]:  
\[ADC=\frac{ln\frac{S_{0}}{S_{b}}}{b_{1}-b_{0}} \tag{1}\]  
where \(S_{b}\) is the MRI signal intensity with diffusion weighting \(b\), \(S_{0}\) is the non-diffusion-weighted signal intensity, \(b_{1}\) and \(b_{0}\) are the gradient factors of sequences \(S_{b}\) and \(S_{0}\), respectively, and \(ADC\) is the apparent diffusion coefficient.  
A bi-exponential model using Levenberg Marquardt fitting algorithm was used to estimate IVIM derived parameters that include pseudo-diffusivity (\(Dp\)), perfusion fraction (_fp_), tissue diffusivity (\(Dt\)), and basal signal \(S_{0}\):  
\[\frac{S_{b}}{S_{0}}=f_{p}\cdot\exp(-b\cdot D_{p})+(1-f_{p})\cdot\exp(-b\cdot D _{t}) \tag{2}\]  
Multi-b DW images were obtained by voxel-wise fitting using the diffusion kurtosis signal decay Equation (3) by a two-variable linear least-squares algorithm as used in previous [18]:  
\[\frac{S_{b}}{S_{0}}=\exp(-b\cdot D+\frac{1}{6}b^{2}\cdot D^{2}\cdot K) \tag{3}\]  
In this equation, \(D\) is a corrected diffusion coefficient; and \(K\) is the excess Diffusion Kurtosis coefficient.  
DWI (ADC), IVIM (fp, Dt, Dp), and DKI (mean of diffusional Kurtosis and MD) maps were obtained using the post-processing software Body Diffusion Toolbox (Siemens Healthcare, Erlangen, Germany).  
Figure 1: Example of segmentation on diffusion-weighted images with the highest b-value (**A**,**C**,**E**,**G**) compared with respective ADC maps (**B**,**D**,**F**,**H**).