esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 2 Methodology
### Trajectory Planning with Time Variant Safety Margin
#### 2.2.2 Supplementary Longitudinal Constraints
Figure 5: Illustration of the constraint sequence for current or target vehicle gap. The black car symbol represents the leading vehicle, whose tail position with respect to time is described by curve \(x_{F}\). The difference \(x_{F}-x_{max}^{g}\) can be divided into two parts: \(s_{F}^{t}+d_{0}\), which remains relatively constant, and \(s_{F}^{a}\), which starts from 0 and increases over time. A similar process is applied to the following vehicle, which is colored in gray. Since the limitation of the vehicle ability to brake in a high velocity, the maximum speed at the end of lane change should be controlled to avoid planning failure. Here we set an upper limit for the longitudinal velocity at the finished time instance \(k_{fin}\) as  
\[vx_{H}(k_{fin})\leqslant v_{F}(k_{fin})+\sqrt{2\left|ax_{min}\right|\Delta x_{fin}} \tag{18}\]  
where  
\[\Delta x_{fin}=x_{max}(k_{fin})-x_{H}(k_{fin}) \tag{19}\]  
With this limitation, the end velocity of \(V_{H}\) can be controlled to satisfy the deceleration capacity, and the planned trajectory can stay out of the safety margin of front vehicles.
# Groundwater Detection Using the Pseudo-3D Resistivity Method: A History of Case Studies
## 1 Introduction
Due to the complicated composition and the deep basement, which make it difficult to apply the conventional geophysical method, we propose a pseudo-3D resistivity method for groundwater and dam leakage detection in order to enhance the efficiency of the 3D electrical exploration. Pseudo-3D resistivity achieves similar exploration efficiency to the 2D electrical exploration with a 3D effective resistivity image. This improved approach is based on 3D DC resistivity, which is successfully applied in groundwater detection [25]. Compared to the 3D DC data array, the pseudo-3D data array provides a more efficient and effective data acquisition system. According to the arrangement of the power supply points, the pseudo-3D resistivity method of the artificial field can be divided into branch methods, such as an inline shape, a crisscross shape, a \"#\" shape, and a \"*\" shape. In addition, based on the arrangements of the electrodes, pole-pole, pole-dipole, and dipole-dipole methods can be used. In the field of exploration geophysics, artificial field electrical exploration is divided into 1D, 2D, and 3D electrical exploration. The 1D electrical method exploration of an artificial field has a low exploration precision and a poor effect. The accuracy and effect of the 2D electrical method exploration of artificial field are better than those of 1D exploration. A pseudo-3D exploration of an artificial field is divided into the 3D electrical method exploration of measuring electrodes with a complete shape, a crisscross shape and a \(\Gamma\) shape [25]. Since the number of power supply electrodes and measurement electrodes are much more than those of 1D and 2D methods, it leads to a much lower efficiency than those of 1D and 2D. Therefore, although this method has a good exploration effect, it is not widely used. It is only used in some very critical exploration areas and when the funds are very sufficient. In this paper, we utilize the pseudo-3D resistivity method, which provides higher data quality, to the Xiaojiashan Dam survey, Zixing groundwater detection survey, and the Huarong groundwater detection survey. In the investigation of the Xiaojiashan Dam, the suspected seepage location of the Xiaojiashan Dam is found through the abnormal resistivity of the inversion results. Then, the dam is checked and the corresponding reinforcement is carried out according to the seepage location. In the groundwater survey of Zixing and Huarong, based on the low resistance anomaly in the data inversion results, the geological structure near the two groundwater detection areas is judged, and the area of groundwater exploitation is divided.
## 2 Materials and Methods
### Methods
#### 2.3.2 EC Flux Footprint Calculation  
This study utilized the flux footprint prediction (FFP) model to estimate the footprint of the EC flux. The FFP model parameterizes the footprint in two dimensions, enabling a robust determination of the spatial extent and characteristics of the EC point footprint.  
For a more detailed understanding of the calculations used in footprint climatology, please refer to [PERSON] et al. [19]. Considering that vegetation's carbon sequestration process primarily occurs during the daytime, footprint mapping was conducted at half-hour intervals throughout daylight hours. Subsequently, the data were aggregated over an 8-day period to generate spatial grids representing the contributions of the footprint. The contribution rate grid was classified by accumulating it around the flux tower in 10% increments, resulting in a classified map where the contribution rate was less than 90%. To facilitate the observation of the seasonal variation characteristics of the footprint contribution rate, monthly maps were also created for the growing season (Figure 3).
# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
The contributions of this paper are the following:  
* We present a new in-the-wild dataset for dementia detection, capturing real-world variability in recording conditions and participant behavior. This dataset provides a valuable resource for evaluating automated dementia detection approaches in unconstrained environments. * We validate key facial cues for dementia detection under in-the-wild settings, demonstrating their effectiveness for both screening and severity assessment. This work addresses the limitations of traditional methods that rely on controlled environments and subjective evaluations, contributing to the development of an accessible, non-invasive, and automated screening tool. Additionally, we show that a visual-only approach achieves competitive accuracy compared to multimodal methods, highlighting its potential as a scalable and cost-effective diagnostic solution. * We improve on the multiple thresholds (MTs) approach for EBR calculation [12], enhancing its robustness for real-world applications. Results confirm EBR as a reliable cognitive impairment indicator even in in-the-wild settings. * We investigate the role of HM features in video-based dementia detection, leveraging a larger in-the-wild dataset than that used in previous work [12], which we present in this paper. HM features enhance classification performance over EBR alone, and their fusion further improves results, highlighting their combined effectiveness. The remaining sections of this paper are structured as follows. Section 2 provides an overview of related work. Section 3 offers a detailed description of the dataset used, including its characteristics, the pipeline of the proposed approach, and how challenges associated with the data are addressed. Section 4 details the experiments conducted and presents the results. Finally, Section 5 provides a discussion of the findings and presents the conclusions.
## 3 Results
### Mercury Environmental Speciation  
* Mercury levels (Table 3) were homogeneous between the rehabilitated sites with an average of 265 ng Hg g\({}^{-1}\). The Yao3 site had significantly higher mercury content with 338.8 ng Hg g\({}^{-1}\). These concentrations were still low compared to the gold mined soil in French Guiana [23]; however, it remained within the normal range of mercury measurements in French Guiana [23]. The total mercury is significantly and positively correlated with microbial biomass, total organic carbon and total iron, and negatively correlated with clay, total aluminum, and pH (Table 4). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Extraction** & **Bel1** & **Bel2** & **Bel3** & **Yao1** & **Yao2** & **Yao3** \\ \hline
**E1** & 6.28 \(\pm\) 2.16 **a** & 7.30 \(\pm\) 0.48 **a** & 6.82 \(\pm\) 0.49 **a** & 18.32 \(\pm\) 1.29 **b** & 8.90 \(\pm\) 0.88 **a** & 4.07 \(\pm\) 0.52 **a** \\
**F1** & 4.87 \(\pm\) 0.09 **b** & 4.97 \(\pm\) 0.12 **b** & 4.21 \(\pm\) 0.08 **b** & 10.39 \(\pm\) 0.82 **a** & 5.55 \(\pm\) 0.45 **b** & 1.71 \(\pm\) 0.21 **b** \\
**F2** & 0.36 \(\pm\) 2.06 **a** & 1.27 \(\pm\) 0.47 **b** & 1.33 \(\pm\) 0.43 **b** & 1.23 \(\pm\) 0.83 **b** & 1.89 \(\pm\) 0.54 **b** & 1.28 \(\pm\) 0.28 **b** \\
**F3** & 1.04 \(\pm\) 0.09 **a** & 1.04 \(\pm\) 0.19 **a** & 1.27 \(\pm\) 0.29 **a** & 6.69 \(\pm\) 0.19 **b** & 1.46 \(\pm\) 0.27 **a** & 1.08 \(\pm\) 0.53 **a** \\
**E2** & 65.8 \(\pm\) 14.9 **ab** & 14.4 \(\pm\) 1.6 **b** & 44.1 \(\pm\) 8.3 **b** & 70.4 \(\pm\) 37.7 **ab** & 103.1 \(\pm\) 5.3 **a** & 25.9 \(\pm\) 4.0 **b** \\
**E3** & 69.2 \(\pm\) 5.1 & 132.5 \(\pm\) 5.9 **b** & 167.8 \(\pm\) 9.8 **a** & 118.6 \(\pm\) 7.1 **b** & 92.8 \(\pm\) 3.6 **b** & 128.6 \(\pm\) 14.9 **b** \\
**E4** & 147.1 \(\pm\) 12.2 **a** & 69.2 \(\pm\) 0.4 **b** & 53.9 \(\pm\) 15.2 **ab** & 10.1 \(\pm\) 3.7 **b** & 23.1 \(\pm\) 1.6 **b** & 184.0 \(\pm\) 4.8 **a** \\ \hline
**Total Hg** & 292.8 \(\pm\) 16.5 **ab** & 224.1 \(\pm\) 7.2 **b** & 273.9 \(\pm\) 4.6 **ab** & 228.5 \(\pm\) 11.5 **b** & 232.7 \(\pm\) 3.9 **b** & 338.8 \(\pm\) 11.6 **a** \\
**Extraction yield (\%)** & 98.5 & 99.7 & 99.5 & 103.1 & 97.9 & 101.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mercury environmental speciation for the six sites. E1: Extraction of highly mobile mercury species, F1: H\({}_{2}\)O soluble mercury fraction, F2: CaCl\({}_{2}\) exchangeable mercury fraction, F3: EDTA bioavailable mercury fraction, E2: Extraction of mobile and toxic mercury species, E3: Extraction of semi mobile mercury species, E4: Non-mobile mercury species. All values are expressed in ng g\({}^{-1}\) soil. Extraction yield represents the quality of mercury extraction in the samples, the value is obtained with the following formula: (E1 + E2 + E3 + E4)/Total Hg \(\times\) 100. For each parameter, values followed by different letters differ significantly with \(p\) < 0.05 with Tukey HSD test.
# Inelastic X-ray Scattering as a Probe of Terahertz Phonon Propagation in Nanoparticle Suspensions
## 5 Results  
### Intra-NP Phonon Excitations  
The first observation of phonon modes propagating through the NP interior dates back to our IXS study on an aqueous suspension of gold nanoparticles (Au-NPs) [22]. An important finding of such a work is illustrated by Figure 4, which compares two IXS spectral shapes measured at low \(Q\)s with the respective best fitting model profiles, along with their two inelastic contributions, determined through the Bayesian inference method described in this paper. We assigned the two inelastic features to acoustic modes of either transverse or longitudinal polarization propagating inside the Au-NPs' interior. The spectrum of pure deuterated water measured in a previous joint IXS and neutron scattering work [36] is also reported in the plot for reference. It is evident that the two broad inelastic shoulders present in the water spectrum essentially disappear upon NP immersion and are replaced by the two small peaks representative of intra-NP phonons. This finding might seem surprising, considering that the dilute nature of the suspension should make the intensity contribution from the gold dynamics relatively small. Indeed, their visibility highlights the mentioned ability of immersed NPs to damp the acoustic modes of the hosting medium.  
In Figure 5, the \(Q\) dependence of the phonon's inelastic shifts, i.e., the sound dispersion curve, is reported as derived through the Bayesian analysis. Our assignment of the two peaks to Au phonons is further supported by the favorable comparison between the dispersion curves and the acoustic branches of gold, as illustrated in Figure 5. Most importantly, the maxima of the two dispersion branches roughly match the energy of the peaks of the density of state of bulk gold, which is reported on the right as computed in Ref. [37].  
Figure 4: Two IXS spectra collected at the indicated \(Q\) from a dilute aqueous suspension of gold nanoparticles (Au-NPs) are compared with the best fitting model line shapes determined through the Bayesian inference method discussed in the text. The IXS spectra of pure water collected at similar \(Q\) values are also reported for reference and vertically offset for clarity. All data are from Ref. [22].  
Figure 5: (**Left panel**) The dispersion curves of the inelastic modes derived from the IXS spectra of the Au-NP suspension (see Figure 4) through the Bayesian inference analysis, as indicated in the legend. Results on liquid gold from Ref. [38]. (**Right panel**) The density of state (DoS) of bulk gold is reported as computed by the lattice dynamics in Ref. [37]. The arrows emphasize the rough correspondence between the dispersion curve maxima and the main DoS features. Data are redrawn from Ref. [22].
## 3 Model and CFD Methods
### Simulated Restraint Model Test  
A series of restraint model tests using CFD were conducted to compare the identification results under the same numerical calculation conditions. These tests included the oblique towing test, pure sway, pure yaw on the horizontal plane, and oblique towing test, pure pitch, and pure heave on the vertical plane. The mesh settings for each test condition are described in Section 3.2. Since the testing methodology for the vertical plane was consistent with that of the horizontal plane, the following paragraphs will discuss only the details of the vertical plane tests. The inflow velocity for all test conditions was set to 0.5 m/s.
# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
###### Abstract  
Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as a result of worries about possible health problems connected to high-frequency radiofrequency transmission. Using the visible light spectrum is one promising approach; three cutting-edge technologies are emerging in this regard: Optical Camera Communication (OCC), Light Fidelity (Li-Fi), and Visible Light Communication (VLC). In this paper, we propose a Multiple-Input Multiple-Output (MIMO) modulation technology for Internet of Things (IoT) applications, utilizing an LED array and time-domain on-off keying (OOK). The proposed system is compatible with both rolling shutter and global shutter cameras, including commercially available models such as CCTV, webcams, and smart cameras, commonly deployed in buildings and industrial environments. Despite the compact size of the LED array, we demonstrate that, by optimizing parameters such as exposure time, camera focal length, and channel coding, our system can achieve up to 20 communication links over a 20 m distance with low bit error rate.  
optical camera communication; IoT; deep learning-based OCC; 2D MIMO-OOK
## 4 General Discussion  
From observing the UV-Vis absorbance results, it was possible to identify that SPs B and D were the most efficient dispersing agents for CNT since they presented the highest absorbance value among the four products studied. FTIR spectra showed a key difference between these two SPs and the others, which was the lack of aliphatic groups in their structure. Additionally, \(\zeta\)-potential results showed that SP D was the only one capable of generating a stable colloid. FTIR results showed that SP D presented a main difference from the others SPs in the ether functional groups.  
These structural differences between SP D and the others were already expected, since SP D is naphthalene-based, while the other three are ether polycarboxylate-based. The best behavior of SP D agrees with results found in [PERSON] et al. [41], who found that naphthalene-based superplasticizers worked better than ether polycarboxylate-based ones, despite being an older-generation plasticizer. [PERSON] [22] has also concluded that polycarboxylates ether are not the most adequate superplasticizer to be used as a dispersant agent and generate stable colloids.  
Regarding the electrical properties of the studied CNT-cement composites, the lower dispersing efficiency of SPs A and C was found to be translated into longer electrical resistance stabilization times and higher electrical resistivity when compared with SPs B and D. The higher dispersing capability of SPs B and D, which was found to be associated with the lack of aliphatic groups in the SP structure, was confirmed to play an important role in generating CNT-cement composites with a low stable electrical resistivity.
# A Cloud-Based Real-Time Mechanism to Protect End Hosts against Malware
## 4 Evaluation
### Limitations  
Experimental results that Skywalker is an effective and efficient solution. But it also has some limitations. First, Skywalker utilizes antivirus engines to detect malware. Hence, Skywalker cannot provide hosts protection that antivirus engines do not provide. Besides, when a program is going to be executed, it takes non-trivial extra time to start the program. A possible solution is to send a program to VirusTotal when it is installed in an end host; hence, when a user executes the program first time, he does not need to wait for a period of time. If Skywalker wishes to speed up the phrase of startup, we can conduct different balances in this trade-offs for this limitation. For instance, we can set an \"offline mode\" which uses larger memory in replace of time; that is, we may establish some meaningful data or patterns in the memory earlier than the startup stage. This is a tradeoff, just like the training phrase of supervisor learning in machine learning field. If Skywalker wants to gain greater precision or precision, we must need to invest other resources. Besides, hosts in a Local Area Network (LAN) can also share their VirusTotal scan results through hash values of programs. Hence, if a host in an LAN has submitted a program to VirusTotal for analysis, other hosts in the LAN can just use the scan result to decide whether it is safe to execute the program. Finally, as mentioned in Section 2, using the private API of VirusTotal for developers can also reduce the longer scanning time problem.
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 2 Materials and Methods  
### Experimental Design  
This study was conducted at the Agricultural Experimental Base of Jilin University in Changchun City, Jilin Province, China, situated within the coordinates 43\({}^{\circ}\)05\({}^{\prime}\)-45\({}^{\circ}\)15\({}^{\prime}\) N and 124\({}^{\circ}\)18\({}^{\prime}\)-127\({}^{\circ}\)02\({}^{\prime}\) E (Figure 1). This region, characterized by flat terrain and fertile black soil, is highly conducive to crop cultivation. During the 2023 corn-growing season, the research integrated remote sensing observations and ground-based biochemical measurements to assess phosphorus fertilization in maize (Xianyu 335). Four phosphorus application levels--0, 50, 100, and 150 kg/ha--were applied based on local soil phosphorus conditions and prior experimental data, with additional nutrient supply of 220 kg/ha nitrogen and 100 kg/ha potassium incorporated before sowing. Each phosphorus treatment was repeated in four groups, resulting in a total of 16 experimental units, from which 96 samples were collected. Data collection was conducted across key growth stages of maize, including jointing, large trumpet, tasseling, silking, grain formation, and milk maturity, with a focus on critical indicators, such as canopy spectral data, plant phosphorus concentration, and aboveground biomass. Canopy spectral data were obtained using a FieldSpec 3 Hi-Res Pro portable spectrometer, manufactured by Analytical Spectral Devices, Inc. (ASD) in Longmont, CO, USA, with measurements taken between 10:00 and 14:00 under clear weather conditions and minimal wind (\(\leq\)2 m/s). The spectrometer sensor was positioned 1 m above the canopy, at a 25\({}^{\circ}\) field of view angle, covering a ground area with a diameter of 0.22 m. Spectral readings, which were averaged from ten measurements per sampling point, were repeated three times per treatment to ensure accuracy in calculating the mean canopy reflectance. Calibration with a standard whiteboard was performed before and after each measurement set to minimize measurement errors. For biomass estimation, three representative maize plants from each plot were selected, and their plant organs were separated and dried at 105 \({}^{\circ}\)C for 30 min to kill green tissue, followed by drying at 75 \({}^{\circ}\)C until a constant weight was achieved. The resulting dry weight was recorded to calculate biomass per unit area. Phosphorus concentration in plant tissues was determined by grinding the samples through a 0.425 mm sieve, followed by digestion using an H\({}_{2}\)SO\({}_{4}\)-H\({}_{2}\)O\({}_{2}\) method and subsequent vanadate-molybdate colorimetric analysis.
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 5 Experiments and Results
### Entropy Domain Results  
Table 3 shows the results of the performance accuracy using seven entropy features, namely, ConFuzEn, FuzEn, impe, MFEmu, RCMFEmu, SampEn, and TsEn. Based on the accuracy and the number of selected channels for best, worst, and mean cases, these entropy features are used to compare the five metaheuristic algorithms, PSO, GWO, FFA, CS, and BAT, respectively. Compared to the other seven entropy features, the best classification performance achieved the highest accuracy using ConFuzEn with the PSO algorithm compared to the GWO, FFA, CS, and BAT algorithms. Other entropy features produced higher accuracy results with the BAT algorithm than the PSO, GWO, FFA, and CS algorithms. The BAT algorithm registered accuracies of 95.5, 95.6, 88.1, 87.2, 91.4, and 84.6 using FuzEn, impe, MFEmu, RCMFEmu, SampEn, and TsEn, respectively. Furthermore, Figure 5 presents the performance evaluation results between the BAT and PSO algorithms concerning the convergence rate and channel distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Feature/Algorithm**} & **PSO** & **GWO** & **FFA** & **CS** & **BAT** \\ \hline \multirow{8}{*}{Conv\(FuzEn\)} & Best & Accuracy & 96.6 & 82.7 & 61.3 & 52 & 96.5 \\ \cline{3-7}  & & No. channels & 3 & 3 & 1 & 1 & 7 \\ \cline{3-7}  & Worst & Accuracy & 76.4 & 64.7 & 45.5 & 42.5 & 87.1 \\ \cline{3-7}  & & No. channels & 11 & 6 & 3 & 2 & 14 \\ \cline{3-7}  & Mean & Accuracy & 90.2 & 73.6 & 54.9 & 45.8 & 93.2 \\ \cline{3-7}  & & No. channels & 8 & 5 & 2 & 1 & 11 \\ \hline \multirow{8}{*}{_FuzEn_} & Best & Accuracy & 93.6 & 81.6 & 60.6 & 49.1 & 95.5 \\ \cline{3-7}  & & No. channels & 2 & 4 & 2 & 1 & 8 \\ \cline{3-7}  & Worst & Accuracy & 82.2 & 68.7 & 50.5 & 41.8 & **88.9** \\ \cline{3-7}  & & No. channels & 10 & 6 & 3 & 2 & 12 \\ \cline{3-7}  & Mean & Accuracy & 88.6 & 74.4 & 55.7 & 45.5 & **92.1** \\ \cline{3-7}  & & No. channels & 7 & 5 & 3 & 1 & 10 \\ \cline{3-7}  & & Best & Accuracy & 94.7 & 78.6 & 59.3 & 49.8 & **95.6** \\ \cline{3-7}  & & No. channels & 5 & 3 & 2 & 1 & 7 \\ \cline{3-7}  & Worst & Accuracy & 75.8 & 56.1 & 47.9 & 44.3 & **84.9** \\ \cline{3-7}  & & No. channels & 10 & 6 & 3 & 2 & 12 \\ \cline{3-7}  & Mean & Accuracy & 85.3 & 69.8 & 52.3 & 46.6 & **90.8** \\ \cline{3-7}  & & No. channels & 8 & 5 & 2 & 2 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance of meta_Heur_istic algorithms with several feature extraction approaches for motor/imaging EEG dataset.  
Figure 5: Convergence rate and channel distribution.