esa-sceva/satcom-chunk-collection huggingface dataset:
## Data and Methods  
### SMAP Brightness Temperature Measurements  
The SMAP Level one brightness temperature (L1C_TB) products contain gridded Tb data in a 36 km Equal-Area Scalable Earth grid version 2 (EASE-Grid 2.0) projection format [20], similar to the approximate 40 km native footprint of the SMAP radiometer [21]. The L1C_TB record includes horizontally and vertically polarized Tb from descending (6 AM) and ascending (6 PM) polar orbital acquisitions. Missing daily Tb measurements are primarily caused by orbital swath gaps between satellite overpasses. The missing Tb values are gap-filled on a per grid cell basis by combining up to 3 days of Tb data within a temporal moving window of descending or ascending orbit observations to create continuous daily AM and PM Tb records. The resulting L1C_TB gap-filled daily data are used as inputs for SMAP L3_FT_P global processing to derive daily FT data records in the same 36 km resolution global projection [17].  
An enhanced 9 km grid FT product (L3_FT_P_E) [16] is produced in the same global EASE-Grid projection format using SMAP Tb (L1C_TB_E) records derived using Backus-Gilbert (BG) spatial interpolation [10]; the L1C_TB_E product is optimally interpolated from the L1B_TB swath product onto a 9 km EASE-Grid version 2 fixed Earth grid [10]. A unique feature of the BG interpolation is that it is optimal in the sense that the resulting interpolated data are closest to what would have been measured had the radiometer actually made its measurement with the interpolation points as its bore-sight center [22]. While the L1C_TB_E product is posted to a 9 km grid, the actual resolution is closer to 33 km [23]. The L3_FT_P_E and L3_FT_P (L3_FT_P[E]) products are both derived from the same classification algorithm, but use different Tb inputs (L1C_TB_E vs L1C_TB).
# CAR-Tourist: An Integrity-Preserved Collaborative Augmented Reality Framework-Tourism as a Use-Case
## 5 Results  
To measure and evaluate the prototype implementation concerning latency and throughput of the proposed system, a testbed environment was created. In the testbed setup, the nodes of the blockchain are configured over 2 vCPUs, having a RAM size of 8 GB and a processing speed of an Intel Xeon server. Moreover, appropriate network bandwidth is acquired as well. This study makes use of a fabric-load-gen tool, developed by IBM chaincode, with different test cases, latencies and endorsements. The load-generating scripts configure a mix of write or read transactions as chaincode. The throughput is measured through transactions processed per second \(TPS\) and the latency of the framework is measured through the completion of the end-to-end transaction including the latency of the endorsement, the latency of the broadcasting transaction to the orderer node, and the latency associated with commit and validate. Figure 6 depicts Hyper-ledger performance over different block sizes, the latencies and throughput of the transactions for different transaction arrival rates and the effect of various endorsement policies with various transaction arrival rates. The transactions could be the sign up operation, hiring a guide, initiation of an audio call, etc. It is observed that throughput increased linearly with the increase in transaction arrival rate, as expected until it flattened out at around 150 tps. It can also be observed that latency increases when the arrival rate of the transaction becomes 150 tps and it receives a sudden increase and remains stable thereafter. During the validation of the policy, three operations are found that are CPU intensive. The first is de-serialization of identity (i.e., x.509 certificates), the second is the identity validation with organization MSP, and the last is the signature verification over transaction data. It is also observed that the bytes of the blocks increase with the number of endorsements due to the encoding of x.509 certificates with every transaction. Concerning comparing the results of this study with the literature, this study did not find any identical solution in the literature and, hence, a comparison is not presented. It is further elaborated that the study did not implement earlier versions of Hyper-ledger Fabric and Ethereum. Very powerful and computationally efficient hardware are utilized, i.e., the resources (RAM and the Processors) used are three times higher than the resources utilized in our system. It is evident by observing Figure 6 that by utilizing the same computation resources, our proposed system can achieve better throughput for transaction execution. Moreover, this study configured Hyper-ledger Fabric 1.4 which employs Raft consensus. Raft produces more acceptable performance results compared to other versions of the Hyper-ledger Fabric. These results not only support our ideas of blockchain-based collaborative AR but will also encourage blockchain application in other walks of life.
# Volatile Profiles of Sparkling Wines Produced by the Traditional Method from a Semi-Arid Region
###### Abstract  
Sao Francisco Valley (SFV) is located in Northeastern Brazil, in a tropical semi-arid region where one vine can produce two harvests per year, due to high temperatures, solar radiation rates, and irrigation throughout the year. This is the main characteristic differing this from other winegrowing region in the world. The objective of this study was to characterize volatile profiles of sparkling wines produced by the traditional method, using Chenin Blanc and Syrah grapes, the two main varieties used for white and red wines, respectively, grown in the region. The sparkling wines remained on lees for six months maturing. The sparkling wines were characterized by the parameters density, pH, total titratable and volatile acidities, residual sugars, dry extract, alcohol content, total phenolic compounds, in vitro antioxidant activity and volatile fraction. The volatile fraction extraction was performed by the HS-SPME technique and tentative identification of the volatile compounds was carried out with GC-MS using the scan mode. A total of 33 volatile compounds were identified, among them 11 alcohols, 13 esters, five carboxylic acids, and four different chemical classes. The volatile profile of Chenin Blanc sparkling wine was associated mainly to 2,3-butanediol, 3-ethoxypropan-1-ol, diethyl succinate, and ethyl decanoate, while Syrah sparkling wine was characterized by benzaldehyde, butyric acid, and some acetates. This study reported for the first time volatile profiles of traditional sparkling wines from SFV, as new products, contributing to better understand the quality potential of these beverages for a tropical semi-arid region.  
Sao Francisco Valley; Syrah and Chenin Blanc grape varieties; GC-MS; sparkling wine; volatile compounds 1
## 3 Materials and Methods  
### Target Crop and Regions  
The purpose of this study is to automate the process of identifying cabbage fields at a pixel level in the South Korea highlands. As shown in Figure 1, vegetables on the left and right are different. The left vegetable is called napa cabbage or kimchi cabbage, which is mainly used to make a traditional Korean food called kimchi. The right vegetable is a common cabbage in the West. Our work focuses on detecting cabbage like those in the left figure. We propose a semantic segmentation framework to classify cabbage-growing regions that are invariant to spatiotemporal changes.  
We selected three major highland cabbage cultivation areas in _Gangwon Province_, South Korea (Figure 2); Maebonsan (37\({}^{\circ}\)13\({}^{\prime}\)05\({}^{\prime\prime}\) N, 128\({}^{\circ}\)57\({}^{\prime}\)58\({}^{\prime\prime}\) E), Gwinemi (37\({}^{\circ}\)20\({}^{\prime}\)21\({}^{\prime\prime}\) N, 129\({}^{\circ}\)00\({}^{\prime}\)20\({}^{\prime\prime}\) E), and Anbanduck (37\({}^{\circ}\)37\({}^{\prime}\)31\({}^{\prime\prime}\) N, 128\({}^{\circ}\)44\({}^{\prime}\)21\({}^{\prime\prime}\) E). In South Korea, high summer temperature, humidity, and the proliferation of insects from June to August restrict the successful cultivation of cabbage except for these three highlands [4]. UAV images taken in Maebonsan on 24 July 2019, and 5 August 2019; Gwinemi on 5 August 2019; and in Anbanduck on 6 August 2019, were used. We attached a multispectral sensor, RedEdge-MX (MicaSense, Inc, Seattle, WA, USA), to a rotary-wing UAV (DJI M2010) to collect images from the study areas. The sensor collects image data within specific wavelength ranges consisting of blue (B, 475 \(\pm\) 20 nm), green (G, 560 \(\pm\) 20 nm), red (R, 668 \(\pm\) 10 nm), red edge (RE, 717 \(\pm\) 10 nm), and near-infrared (NIR, 840 \(\pm\) 40 nm) wavelengths. Each image size with ground sampling distance (GSD) of approximately 12 cm is 1280(\(width\)) \(\times\) 960(\(height\)).  
Figure 1: (**Left**) Napa cabbage, which we classify in fields; (**Right**) common cabbage in the West.  
Figure 2: Location of target regions [33]. (**a**) Gangwon Province in South Korea (highlands); (**b**) Maebonsan (bottom red point); (**c**) Gwinemi (middle red point); (**d**) Anbanduck (top red point); (**e**) Maebonsan; (**f**) Gwinemi; and (**g**) Anbanduck.
# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
## 4 Conclusions  
The proposed method has achieved automatic detection and unsupervised clustering-based classification on our datasets. The proposed method extracts the vocal features from the collected waveform in a total of 25.3 h of vocal signals and combines the five cetacean species' vocal signals to build eight classes of datasets for classification. Then, the performances of different clustering methods on our datasets are verified. As a result, K-means and GMM perform well on our datasets. The maximum accuracy rate is 100% for the GMM method. When using the K-means algorithm, the accuracy rate achieved can be as low as 75%. In this work, the proposed method is completely non-manual operation and reduces time costs. The feature vectors have broad applicability and can be used to test other automatic classification methods. Our method inspires future work to detect and classify acoustic signals from cetaceans automatically. However, this method still cannot accurately classify all cetaceans when there are multiple classes. There is still much work to pursue in the future. On one hand, the detection methods should recognize the vocal signals more accurately. On the other hand, when more cetacean species are shown simultaneously, the accuracy of classification needs to be improved. The proposed method is more suitable for pre-processing in acoustic data processing since it is easy to distinguish the vocal signals and the background noise.  
Conceptualization, Y.L. and F.C.; methodology, Y.L.; software, Y.L.; validation, Y.L.; formal analysis, F.C. and Y.W.; investigation, Y.L. and Y.C.; resources, F.C., H.Y. and F.J.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L., F.C. and Y.W.; visualization, Y.L.; supervision, F.C., H.Y. and F.J.; All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62271208, Grant 62192712, and Grant 62341129; and in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515011107.  
Not applicable.  
The data used to support the findings of the study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
# The Effect of Insertion Angles and Depths of Dental Implant on the Initial Stability
###### Abstract  
The objective of this study was to evaluate the effect of the measurement directions of the Periotest value (PTV) and implant stability quotient (ISQ) of dental implant fixtures inserted at various angles and depths. Five groups were organized: vertically inserted implants in an evencrestal position; implants inserted at a 17\({}^{\circ}\) tilt in a distal subcrestal position; implants inserted at a 17\({}^{\circ}\) tilt in a mesial supracrestal position; implants inserted at a 30\({}^{\circ}\) tilt in a distal subcrestal position; and implants inserted at a 30\({}^{\circ}\) tilt in a mesial supracrestal position. The PTV and ISQ were measured along four directions (buccal, lingual, mesial, and distal directions) in all specimens. The PTV and ISQ exhibited a highly negative correlation when the fixture was vertically inserted. Compared to the implants inserted vertically and at the distal subcrestal position, the fixture with a greater tilt and inserted at the mesial supracrestal position had a lower stability. The PTV and ISQ of the tilted implants were affected by the measurement direction, inserted angulation and depth.  
primary stability; tilted implant; insertion depth; Periotest value; implant stability quotient; artificial bone +
Footnote †: journal: Applied Sciences
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 2 Theoretical Method for Determining Prestress Loss of BFRP Tendons  
Generally, the prestress loss in cross-tensioned concrete pavements arise from five causes [21], namely, the loss caused by anchorage deformation and prestress retraction (\(\sigma_{11}\)), the loss caused due to the friction between prestressed tendons and their surrounding casing pipes (\(\sigma_{12}\)), the loss caused because of the elastic compression of concrete (\(\sigma_{13}\)), the loss resulted from the relaxation of prestress tendons (\(\sigma_{14}\)), and the loss arising from concrete shrinkage and concrete creep (\(\sigma_{15}\)).
## 4 Evaluation and Results
### Unit and Integration Testing  
The purpose of unit testing, which is considered the first level of testing, is to validate that each unit of the software performs as designed. Integration testing is the process of taking all program units that have already been \"unit tested,\" combining them with the rest of the software, and testing them as a group [48]. The goal of this level of testing is to ensure there are no faults in the interaction between the integrated units. Our strategy for the integration testing is a bottom-up approach, which begins testing with the smallest and lowest modules in the software and then moving upward [49]. Unit testing was performed for each unit and the functionalities were passed based on the required function, which means they were successful, and integration testing of the Dr. Barea system was performed using the bottom-up approach. The testing of the components integrated the higher modules preceding the lower ones. This approach enabled all Dr. Barea's functions to be integrated successfully.
## 4 Discussion  
### Uncertainty of Estimating GPP by NIRv  
Although, as we know, few studies have examined vegetation productivity in Suzhou, studies in similar cities may provide useful information. From 2000 to 2014, the average rate of GPP in Shanghai, the city next to Suzhou, was between 800 and 1050 g C m\({}^{-2}\) yr\({}^{-1}\) based on the simulations by Vegetation Photosynthesis Model (VPM) [55]. This result is close to our 973.74, 979.51, and 1074.21 g C m\({}^{-2}\) yr\({}^{-1}\) for MODIS, Landsat-8, and Sentinel-2, respectively.  
According to the MODI7A2 product, the previous study also indicated the GPP of crops along the Yangtze River in the northeast of Suzhou as around 1200-1400 g C m\({}^{-2}\) yr\({}^{-1}\)[56], which is lower than our Sentinel-2 GPP, 1250-2000 g C m\({}^{-2}\) yr\({}^{-1}\), in the same region (Figure 4d). Limited by mixed pixel effects, the MODI7A2 product usually overestimates in lower value and underestimates in higher value [57; 58]. The high resolution of Sentinel-2 eliminated the many effects of the mixed pixel. Therefore, the Sentinel-2 GPP was obviously higher than the GPP calculated by MODIS reflectance. Furthermore, Landsat-8 GPP would have to be greater than MODIS GPP if it did not miss images during July and August, when carbon assimilation rates are at their peak.  
Compared to deciduous forests, evergreen forests are inflexible to short-term changes in environmental conditions. As a result, their NIRv is suitable for predicting GPP over a longer period, such as 90 days [25]. Consequently, the NIRv-GPP relationship is a challenge in estimating monthly GPP for evergreen forests. Additionally, for this reason, our GPP of evergreen forests in the southwest of Suzhou was more than 2500 g C m\({}^{-2}\) yr\({}^{-1}\), which is higher than the previously observed and simulated results [59; 60]. Additionally, the original NIRv-GPP relationships across the different land cover types are generated by 0.05 degree AVHRR reflectance [23]. The uncertainty induced by the different remote  
Figure 9: The distribution of urban forests in Suzhou (**a**) and its detailed views (**b**,**c**). Veg. is vegetation. DBF, MF, and EBF are deciduous broadleaf forests, mixed forests, and evergreen broadleaf forests.  
sensing data in terms of field of view angle, signal-to-noise ratio, and spectral width needs to be further evaluated.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 3 Proposed Scheme
### Authentication and Key-Agreement Process
\(TA\) finally transmits the message containing \(\langle RND_{6}^{{}^{\prime}},Z_{TA,n},AUTH_{TA,n},T_{s},T_{TA}^{+}\rangle\) to the \(SN_{n}\) via an open channel. * Upon the arrival of messages from the \(TA\), \(SN_{n}\) firstly checks the message's timeliness by inspecting the condition \(|TC-T_{TA}^{+}\leq\delta T|\). * If true, \(SN\) further checks the condition \(AUTH_{TA,n}\stackrel{{? }}{{=}}H(RND_{3}||V_{temp1}||T_{TA}^{+})\) to authenticate the \(TA\). * If the condition is validated successfully \(SN_{n}\) will compute \(Z_{TA,s}=Z_{TA,n}\oplus V_{temp1}\), \(SK_{ns}\stackrel{{? }}{{=}}H(RND_{6}||Z_{TA,s}\oplus T_{s})\). If \(SK_{ns}=SK_{sn}\), this key safeguards communication among the \(SN_{n}\) and \(S_{s}\).
## 4 Elementary State Components  
The elementary pure states are defined as vectors of the irreducible representations of \(G\) in \(\mathcal{H}\) because these representations provide the smallest linear subspaces, which are kept closed by the time dependence. This concept plays a double role in quantum mechanics. On the one hand, the kinematical use is the construction of the possible physical systems by combining the elementary degrees of freedom. On the other hand, by assuming that the group \(G\) is a symmetry of the dynamics, the time dependence can substantially be simplified by taking into account the closeness of the representation spaces during the time evolution.  
The definition of the elementary pure state can formally be generalized for the mixed states of closed and open dynamics: the elementary closed (open) mixed state components are the elements of the irreducible representation of \(G\otimes G\) (\(G\)) in the Liouville space. While these components remain in a restricted linear space during the time evolution as in the case of pure elementary states, their kinematical role is more restricted. In fact, the physical density matrices form a convex hull rather than a linear subspace in the Liouville space owing to their positivity. Thus, we cannot freely form the linearly superposition of the elementary components since the positivity, a global condition, must be observed.  
We have two different ways to split the Liouville space into the direct sum of the irreducible representations by using the symmetry of either the closed or the open dynamics. Since \(G\subset G\otimes G\), the representation spaces of the closed symmetry contain several representation spaces of the open dynamics. One can introduce two classes of elementary open components, the operators with a nonvanishing (vanishing) trace making up the first (second) class. Each physical state contains at least one first class component, which preserves its norm, and the second class components might be generated during the time evolution without any constraint on their weight in the state. Special attention is needed for state components in infinite three-space with an ill-defined trace, such as plane waves. The master equation of the Lindblad structure preserves the total probability only if constructed by means of bounded operators. The unbounded coordinate operator may generate a time-dependent weight for the components, which can be understood as the result of a nonvanishing particle flux at \(|x|\rightarrow\infty\)[9].  
We briefly discuss the cases of rotations, \(G=SO(3)\), and translations, \(G=R^{3}\), below.
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 5 Case Studies
### IEEE 14-Bus System
#### 5.1.2 Base Load Condition
Further discussion of the comparisons is based on the boxplot of relative errors, as shown in Figure 6.  
Figure 5: Security indices of each method in the base load condition of the IEEE 14-bus system. Figure 6: Boxplot of relative errors in the base load condition of the IEEE 14-bus system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Outage line** & **Errors of MSA-Lasso (\%)** & **Errors of ANN (\%)** & **Errors of SVM (\%)** \\ \hline L 1(1–2) & 0.2909 & \(-0.5716\) & \(-1.1521\) \\ L 2(1–5) & 0.0857 & 0.6023 & \(-0.4704\) \\ L 3(2–3) &  
In the figure, the red crosses represent the mean of errors, and the red lines are the median (50 th percentile) of the errors. From the above figure, it can be seen that although the median of ANN is the best, the mean of MSA-Lasso is closer to zero. Thus, MSA-Lasso has a small deviation in the three algorithms. By comparing the lengths of the boxes that reflect dispersion, it is obvious that MSA-Lasso is the best. Therefore, it can be concluded that the MSA-Lasso algorithm has an advantage in the predictions compared with other algorithms. Then, the security indices are sorted in descending order, and the larger value of \(Pl_{c}\) means that the contingency is more serious. The sorted results of all of the lines are listed in Table 9, and the digits represent branch numbers. From the results, it is clear that the ranking of the MSA-Lasso algorithm is the same as that via NRLF. However, the ANN and SVM algorithms have little differences with NRLF. The results show that the MSA-Lasso algorithm is more suitable for the prediction of security indices. The reason why the others have different orders is that the three algorithms are all biased estimation algorithms [33]; the prediction errors may affect the contingency ranking orders when the values of \(Pl_{c}\) are relatively close. The ranking results with the descending order of index values via the NFLR method are shown in Figure 7.  
From the above analysis, the proposal has proven that it is effective on the base load condition of the IEEE 14-bus system, since it can predict the security index within a small error, and all of the lines can be identified in a proper order.