esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Texture Measurement on Complex Geometry Using Dual-Scan Positioning Strategy
## 2 Principle of Confocal Sensing with Internal Scanning  
A laser confocal sensor, Keyence LT9011 (KEYENCE, Osaka, Japan), was utilized for surface height measurement in this study. Figure 1a shows the general principle of a single-point confocal sensor. Only when the measurement point is in focus will the detector receive the peak intensity of the reflected laser. Figure 1b shows the working principle of the Keyence LT9011. The optics are driven by a tuning fork to generate high-frequency vibration, enabling vertical scanning. In the meantime, the focus position can be detected and height can be measured accordingly. The optics are also driven by an oscillating unit for lateral scanning, with a frame rate of 2.8 fps covering a scan length of 1.1 mm. This product was originally developed for height or thickness measurements.  
The vertical resolution of the LT9011 is 0.1 \(\upmu\)m. The laser beam spot size is 2 \(\upmu\)m with a scanning spacing of 2 \(\upmu\)m, which complies with the requirements for surface roughness measurements based on ISO 4287 [9]. In this study, this sensor was further developed for surface texture measurement.  
With the internal scanning mechanism, this sensor is able to measure surface roughness subject to two conditions:  
1. the surface is relatively flat; the surface fluctuation does not exceed the sensing range of 0.6 mm;
2. the surface is relatively smooth; the measurement of roughness parameters does not require a roughness evaluation length longer than 1.1 mm.  
Figure 2 shows the measured surface profile of a calibrated roughness master (178-602, Mitutoyo Corporation, Kawasaki-shi, Japan) with known \(Ra\) and \(Rt\) values of 2.97 \(\upmu\)m and 9.4 \(\upmu\)m, respectively.  
The arithmetical mean roughness (\(Ra\)) can be calculated using Equation (1), where \(N\) is the number of data points acquired in a measurement and \(Z_{i}\) is the \(i\)th data point in height direction of the roughness profile. The total height of roughness profile (\(Rt\)) is defined as the difference between the largest profile peak height and the largest profile valley depth within the evaluation length [9].  
\[R_{a}=\frac{1}{N}{\sum_{i=1}^{N}}|Z_{i}|. \tag{1}\]  
Therefore, with \(Z_{i}\) values captured by the confocal sensor LT9011, the surface roughness parameter \(Ra\) over a scan length of 1.1 mm can be calculated. To verify the measurement capability of the laser confocal sensor LT9011, measurement results of the roughness master are shown in Table 1. The nominal \(Ra\) and \(Rt\) values of the roughness master are 2.97 \(\upmu\)m and 9.40 \(\upmu\)m, respectively. The mean error and standard deviation are 0.06 \(\upmu\)m and 0.04 \(\upmu\)m for \(Ra\) measurements and 0.10 \(\upmu\)m and 0.08 \(\upmu\)m for \(Rt\) measurements. The measurement error is partially due to short sampling length.  
Figure 1: Principle of confocal sensing with internal scanning: (a) principle of confocal sensor; (b) internal scanning mechanism.  
Figure 2: Line scan for roughness measurement using confocal sensing.
## 3 Theoretical Framework
### Step 5: Final Result  
* The algorithm converges when the cluster assignments stabilize and the centroids no longer change significantly. The final result is a set of \(K\) clusters with their respective centroids.  
K-Means clustering technique aims to minimize the within-cluster sum of squares, where the objective function is: \(J=\sum_{i=1}^{K}\sum_{x\in S_{i}}\|x-c_{i}\|^{2}\) where \(S_{i}\) is the set of data points assigned to cluster \(i\) and \(c_{i}\) is the centroid of cluster \(i\). Updating cluster assignments and centroids repeatedly, K-Means seeks to find a partition of the data that minimizes the total within-cluster variance. The iterative nature of the K-mean algorithm allows for efficient assignment and updating of clusters, making it effective for organizing nodes in WSNs. However, K-means has limitations, such as sensitivity to initialization and assumptions about cluster shapes. Therefore, by integrating K-means with BCO, the study will leverage its strengths while addressing its weaknesses, particularly in terms of energy consumption and network lifetime.
## 7 Future Research  
Improvements may arise from analysing and adding further meteorological input features. This could, for example, be a measure of cloud cover or temperature. Care must be taken that no features are included that either worsen the prediction or have no positive impact while making the model more complicated. Adding features could be advantageous for the SVM model, as for SVM, any features can be added, while for FBP, only features that are known in the future can be added.  
The SVM model might be improved by further analysing the correlation of the irradiance with past values. This could reveal correlations with hours that have not yet been used as input features. Adding these would be a promising path to further enhance the model. This also suggests another set of experiments that could be executed to examine the mid-term horizon for both SVM and FBP models. FBP might be better at mid-term forecasts, e.g., 3 months. However, this has not been experimentally investigated. A correlation analysis would be of great use for a mid-term SVM model and would therefore lend itself to being carried out in parallel with a comparative analysis of mid-term SVM and FBP models.  
The long-term FBP model showed that applying the seasonal adaptation to Denver nearly made the extraterrestrial radiation redundant. Both models, with and without extraterrestrial radiation, displayed similar results. This could be useful for datasets that do not possess measurements of extraterrestrial radiation. Therefore, the benefits of only seasonal adaptation instead of adding extraterrestrial radiation to the model should be explored further.  
Conceptualization, P.B. and B.B.; Formal analysis, P.B. and B.B.; Funding acquisition, A.T. and Q.H.A.; Investigation, P.B. and B.B.; Software, P.B. and B.B.; Visualization, P.B. and B.B.; Writing--original draft, P.B. and B.B.; Writing--review & editing, P.B., A.T. and B.B. All authors have read and agreed to the published version of the manuscript.  
This study was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) Grants, EP/T517896/1.  
Not applicable.  
Datasets related to this article can be found at [[https://nsrdb.nrel.gov/data-sets/archives.html](https://nsrdb.nrel.gov/data-sets/archives.html)]([https://nsrdb.nrel.gov/data-sets/archives.html](https://nsrdb.nrel.gov/data-sets/archives.html)), hosted by the National Solar Radiation Database (NSRDB) [35], accessed on 20 October 2021.  
We would like to thank [PERSON], from the University of Kent, UK, for her help in plotting the graphs.  
The authors declare no conflict of interests.
## 3 Data  
### Field data  
#### 3.1.1 Carbon data  
Carbon stocks residing in the vegetation and soils were calculated from field records collected along a 230-km transect in June 2004. The fieldwork was carried out by colleges from the Institutes of Botany and Soils science (Almaty, Kazakhstan). 14 test sites were selected for analysis with four replicates (sub-plot) per site. Each replicate occupied 0.0625 ha. The test sites were classified by the land cover categories in accordance with the land cover map in Figure 1. Of the test sites, 6 were located within the steppe grassland and 8 within the short grassland. The data consisted of grass biomass (g DM/m2), root biomass (g DM/m2), litter mass (g DM/m2), vegetation cover estimations  
Figure 1: Location of the study region on the map of Kazakhstan and distributions of land cover classes in the study region based on the MODIS land-cover map [36].  
(%), and mass of soil carbon at the depths from 10 to 50 cm (g C/m\({}^{2}\)). The peak-season living aboveground biomass (\(AGB_{I}\)), root biomass (belowground biomass, BGB) and litter was measured by destructive sampling of 1.0 m\({}^{2}\), with samples weighted, sub-sampled, and dried at 65 \({}^{\circ}\)C to constant weight to correct for moisture content. Four samples per each replicate (i.e., 16 per plot) were taken. Roots were collected by excavating a square of 1 \(\times\) 1 m\({}^{2}\) to a depth of 50 cm with a narrow, flat-bladed shovel and handsaw. Coarse roots were hand sorted and washed. The remaining sample was dispersed in tap water, passed through a 2-mm sieve, and fine roots were collected without differentiation between live and dead roots. Roots were washed of gross mineral contamination, dried at 65 \({}^{\circ}\)C to constant weight, and weighted. The proportions of litter and roots to aboveground biomass were calculated. The original values of dry matter (g DM) were converted to carbon (g C). The proportion of C in all biomass pools was assumed to be 0.47.  
We reinterpreted the field data with regard to the land cover classes distributed in the study area. From the field records of the individual test sites average values for the two land cover categories, short grassland and steppe grassland, were determined. The results of these calculations are given in Table 1.
## 6 Conclusions  
A quad-band antenna array using a novel near-zero refractive index and double-negative metamaterial is presented. A compact SSRR resonator metamaterial unit cell was proposed and validated numerically, whereas NZRI and DNG properties exhibited negative permittivity, permeability, and a refractive index over several bands for all x and y principal axis wave propagation. The investigated MTM planes were then placed in two scenarios at the reverse and front sides of the proposed CPW antenna. The experimental results show that the proposed antenna integrated with the compact-sized MTM structure, which has proper impedance bandwidth covering the required bandwidths of GSM900, WLAN, LET-A, and 5G Wi-Fi in the 0.865-1.06, 2.24-2.52, 3.25-4.31, and 4.9-6.5 GHz, and stably omnidirectional E plane radiation patterns.  
Conceptualization, S.S.A.-B.; Formal analysis, S.S.A.-B.; Funding acquisition, M.S.I., H.Y.W. and M.T.I; Methodology, S.S.A.-B.; Resources, M.F.J. and M.T.I.; Supervision, M.T.I.; Writing--original draft, S.S.A.-B.; Review & editing, A.N., M.J. and T.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Multimedia University under research grant MMUE-180010, TM R&D and Universiti Kebangsaan Malaysia under research grant KK-2019-003, TM R&D.  
Figure 16: Simulated and measured electric field radiation patterns of the proposed CPW antenna at: the (**a**) XY-Plane; (**b**) YZ-Plane.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency Band (GHz)** & 0.9 & 2.4 & 3.5 & 5.5 \\
**Gain (dBi)** & 1.89 & 5.05 & 6.74 & 5.98 \\
**Bandwidth (\%)** & 20.26 & 11.76 & 27.81 & 28 \\
**Efficiency (\%)** & 62 & 71 & 95 & 83 \\
**Size (mm)** & & \(78.6\times 42.5\times 0.035\) m\({}^{3}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the antenna performance.
# Partitioning Evapotranspirationiration in a Cotton Field under
## 5 Conclusions  
Based on the water-carbon flux coupling, \(uWUE\) was used to partition the \(ET\) of cotton fields under mulched drip irrigation in an arid region, and the effect of environmental factors on it was analyzed. The main conclusions are as follows:  
(1) The changes of \(ET\) and \(GPP\) in cotton fields under mulched drip irrigation in an arid region at different time scales were consistent, which was related to the effects of \(T_{air}\) and \(R_{n}\) on stomatal conductance. The change curves at both the bud stage and the flowering and boll stage were bimodal, with a larger \(uWUE\) and a higher utilization efficiency of irrigation water;  
(2) The change characteristics of \(T\) and \(ET\) of cotton fields under mulched drip irrigation in an arid region were the same, showed a continuous increasing trend from seedling stage to blooming and boll stage, reached the maximum value at blooming and boll stage, and then decreased from boll opening stage to the end of growth stage. Water use efficiency is highest in blooming and boll stage, but there is still the possibility of improving water use efficiency and reducing \(E\) during the boll opening stage;  
(3) The \(ET\) components of cotton fields under mulched drip irrigation in an arid region were mainly affected by \(T_{air}\) and \(R_{n}\), and the components of \(ET\) were positively correlated with \(T_{air}\) and \(R_{n}\). The effective \(ET\) is the strongest when \(T_{air}\) and \(R_{n}\) are the highest and the waste of irrigation water is the least;  
(4) The \(uW\)\(U\)\(U\)\(U\) method of partitioning \(ET\) components doesn't need complicated basic data and has the advantages of fewer intermediate variables and simple calculation steps.  
Conceptualization, Y.L. and C.Q.; Data curation, Y.L.; Formal analysis, Y.L.; Funding acquisition, C.Q.; Investigation, Y.L.; Methodology, Y.L. and C.Q.; Project administration, C.Q.; Supervision, C.Q.; Visualization, Y.L.; Writing--original draft, Y.L.; Writing--review and editing, C.Q. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Key Science and Technology Project Xinjiang Production and Construction Corps of China [grant number 2022 DB024]; the National Natural Science Foundation of China [grant number 51769030]; and the Key Science and Major Science and Technology Project Xinjiang Production and Construction Corps of China [grant number 2021 AA003].  
Available upon request from the corresponding authors.  
The authors thank all members at Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group for providing the experimental site and relevant supports.  
The authors declare no conflict of interest.
# Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
###### Abstract  
Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though the brain activities during tasks (i.e., P300 activities) are considered as biomarkers to diagnose schizophrenia, brain activities at rest have the potential to show an inherent dysfunctionality in schizophrenia and can be used to understand the cognitive deficits in these patients. In this study, we developed a machine learning algorithm (MLA) based on eyes closed resting-state electroencephalogram (EEG) datasets, which record the neural activity in the absence of any tasks or external stimuli given to the subjects, aiming to distinguish schizophrenic patients (SCZs) from healthy controls (HCs). The MLA has two steps. In the first step, symbolic transfer entropy (STE), which is a measure of effective connectivity, is applied to resting-state EEG data. In the second step, the MLA uses the STE matrix to find a set of features that can successfully discriminate SCZ from HC. From the results, we found that the MLA could achieve a total accuracy of 96.92%, with a sensitivity of 95%, a specificity of 98.57%, precision of 98.33%, F1-score of 0.97, and Matthews correlation coefficient (MCC) of 0.94 using only 10 out of 1900 STE features, which implies that the STE matrix extracted from resting-state EEG data may be a promising tool for the clinical diagnosis of schizophrenia.  
effective connectivity; machine learning; resting-state electroencephalography (EEG); schizophrenia; symbolic transfer entropy +
Footnote †: journal:  
0000-0001-5000-5000]C.C.  
0000-0002-1400-0000]K.C.C.
## 5 Impact of Observation Geometry and Multi-Angle Retrieval
### Improvement of Multi-Angle Measurement  
In target mode, to validate satellite data with ground-based observation, TanSat can monitor the same location several times along an orbit. Therefore, several measurements of different viewing geometries for a specific ground pixel can be combined to improve the aerosol retrieval information and accuracy, similar to what has been achieved for POLDER [53]. For simplicity, three along-track measurements at the forward VZA 16\({}^{\circ}\), backward VZA 16\({}^{\circ}\) and nadir viewing angles are combined for the multi-angle retrieval. From Figure 3, it is clear that the DFS in multi-angle mode increase compared to the single-view nadir mode result (such as the red and green bars). The additional information acquired in the multi-angle mode is most striking for the PSD mode radius, whose DFS is limited in nadir viewing and can be enhanced 0.22 on average. For the other parameters, we obtain a large amount of information at only-nadir viewing, so the improvement of multi-angle retrieval is not obvious. However, this improvement for soot is so little that only another 0.04 DFS is provided for the PSD mode radius. This situation can be explained by the weak scattering of soot and the small change of phase function in the chosen geometries. Generally, the DFS of the multi-angle retrieval of dust  
Figure 3: The degrees of freedom for the signal (DFS) of corresponding aerosol parameters at different viewing angles in high and low solar zenith angles for four types of aerosol over the bright and dark surface defined as in Section 4. The left column is for the bright surface, and the right column is for the dark surface. Four rows represent four aerosol types: dust (**a,b**), soot (**c,d**), sea salt (**e,f**) and sulfate (**g,h**). Different colored bars represent corresponding different observation geometries. The \(X\)-axis represents five retrieved aerosol parameters: AOD, real (m\({}_{t}\)) and imaginary part of refractive index (m\({}_{i}\)), mode radius (mod-r) and variance (mod-v) of PSD.  
reaches 4.97, indicating we can retrieve almost all five aerosol parameters independently in multi-angle mode. For sea salt and sulfate, the total DFS are 3.85 and 3.87 in the multi-angle mode, respectively, so we can obtain a large amount of information for the four parameters other than the imaginary part of the refractive index. Unfortunately, for soot, the DFS is still only 3.25 in the multi-angle mode, and the ability to retrieve the real part of the refractive index and PSD radius is not improved. In summary, the DFS in multi-angle retrieval increases by 0.31, on average, compared to the nadir mode.
## 7 Experimental Setup and Results  
To compare the performance of the 5G-enabled MSP with respect to an industrial cabled solution, we describe the use case of workpiece monitoring in a five-axis milling machine. The milling process is complex, and the workpiece needs to be monitored for various parameters that affect quality, namely natural frequency, bending force, temperature of the coolant, etc.  
Figure 4 describes the experimental setup used to measure performance. It comprises the sensors integrated into the test workpiece. The MSP is enabled with vibration, strain, and temperature sensors to perform condition monitoring. These sensors are integrated into the workpiece to be monitored. Another set of sensors is integrated into the workpiece and connected to the industrial data acquisition system, which is the GEM sensor module system from Marposs. The software is configured to match the sampling frequency used by the industrial data acquisition system. The strain sensor samples data at 1 KHz, the vibration sensor at 20 KHz, and the temperature and humidity sensor at 100 Hz. The data between the industrial and 5G solution are logged in the monitoring software.  
Preliminary results show the latency of the industrial solution and the 5G sensor solution is between 11-12 milliseconds. This is in line with the current 3 GPP release 15 of the 5G network. This is significantly lower than other wireless cellular standards. such as 4G, Wi-Fi, and Bluetooth, which have higher latency of data transmission. The latency range is within the acceptable limits in terms of the requirements for data transfer from the sensors, which could be further improved with the upcoming 3 GPP release 16 for 5G.  
The major advantage of the setup is the increased mobility of the sensor platform without the need for additional effort, such as wiring, powering up, and external modules for the sensors. It enables rapid deployment of the sensor to gather data and send it to the cloud for real-time data analytics, such as condition monitoring and predictive analytics. With the SARA-R5 module, the timestamp-integrated data packet is useful for creating real-time digital twins to fuse data from the sensors and the machine.
# Railway Wireless Communications Channel Characterization
###### Abstract  
Railway applications are in continuous evolution with the aim of offering a more efficient, sustainable, and safer transportation system for the users. Generally, these applications are constantly exchanging information between the systems onboard the train and the trackside through a wireless communication. Nowadays, Global System for Mobile communications-Railway (GSM-R) is the technology used by European Train Control System (ETCS), but it is becoming obsolete. Therefore, alternatives for this technology have to be found for the different railway applications. Its natural evolution is to move forward with the latest technology deployed: Long-Term Evolution (LTE), which the Public Land Mobile Networks (PLMN) have already deployed. Therefore, testing the performance of this communication technology in the railway environment could be useful to assess its suitability and reduce the cost of railway network dedicated deployment. In order to do that, a methodology to characterize the communication environment is proposed. The main goal is to measure geolocated impairments of any communication channel in a railway environment being able to determine its behavior of the different communication technologies and find out possible coverage issues. Moreover, it could help in the selection of suitable communication technology for railway. This paper presents a brief description of the communication for railways and its QoS parameters for performance measuring. Afterward, the testing methodology is described, and then, the communication channel measurement campaign on a real track in Spain where the railway environment is variable is presented (tunnels, rural/urban area... ). Finally, the measurements and results on this real track in Spain are shown. The results provide suitability of the 4G technologies based on the delay requirements for the implementation of ETCS over it.  
communication channel; IP impairments; measurements; railway environment; railways +
Footnote †: journal: Applied  
_Article_  
Railway Wireless Communications Channel Characterization  
[PERSON]  
1 Ceit-Basque Research and Technology Alliance (BRTA), [PERSON] 15, 20018 Donostia/San Sebastian, Spain; joya@ceites ([PERSON]); [PERSON]@ceites ([PERSON]); [PERSON]@ceites (I.M.); [PERSON]@ceites ([PERSON]).