esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 8 Performance Analysis
### Computational Cost Analysis
's protocol [28], the user performed 1 fuzzy extractor operation, 11 hash operations, and 2 ECC point multiplications; thus, the computational cost of the user is \(1T_{fe}+11T_{hf}+2T_{epm}=4.4773\) ms. The GCS performed six hash operations and one ECC point multiplication; hence, we computed the computational cost of the GCS as \(6T_{hf}+1T_{epm}=1.1268\) ms. The drone performed six hash operations; thus, the computational cost of the drone is \(6T_{hf}=0.0138\) ms. Therefore, the total cost of the operator, GCS, and drone is \(1T_{fe}+23T_{hf}+3T_{epm}=5.6179\) ms. In [PERSON]'s protocol [13], the user performed nine hash operations and one ECC point multiplication. Therefore, we calculated the computational cost of the user as \(9T_{hf}+1T_{epm}=2.2467\) ms. The GCS performed four hash operations; hence, we computed the computational cost of the GCS as \(4T_{hf}=0.0092\) ms. The drone performed three hash operations and one ECC point multiplication; thus, we computed the computational cost of the drone as \(3T_{hf}+1T_{epm}=2.2329\) ms. The total cost of the user, GCS, and drone is \(16T_{hf}+2T_{epm}=4.4888\) ms. In the proposed protocol, the user performed 18 hash operations; thus, we computed the computational cost of the user as \(18T_{hf}=0.0414\) ms. The GCS performed 19 hash operations; hence, we computed the computational cost of the GCS as \(19T_{hf}=0.0437\) ms. The drone performed 10 hash operations and one PUF operation; therefore, the computational cost of the drone is \(10T_{hf}+1T_{PUF}=0.077\) ms. The total cost of the user, GCS, and drone is \(47T_{hf}+1T_{puf}=0.1621\) ms.  
Table 6 presents a comparative analysis of the computational cost for the proposed and relevant protocols. This comparison demonstrates that the proposed protocol presents significantly lower computation overhead than the related protocols, leading to higher computational efficiency. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Protocol** & **Mobile User** & **GCS** & **Drone** & **Total** \\ \hline [25] & 13.3905 ms & 11.1668 ms & 0.0138 ms & **24.5771 ms** \\ [26] & 2.2513 ms & 1.1268 ms & 0.0138 ms & **3.3919 ms** \\ [27] & 6.7079 ms & 4.5164 ms & 4.4796 ms & **15.7039 ms** \\ [28] & 4.4773 ms & 1.1268 ms & 0.0138 ms & **5.6179 ms** \\ [13] & 2.2467 ms & 0.0092 ms & 2.2329 ms & **4.4888 ms** \\ **Proposed** & 0.0414 ms & 0.0437 ms & 0.077 ms & **0.1621 ms** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computational cost comparison.
## 3 Results
### Thermodynamic Parameter Calculations  
Although the phonon dispersion and density of states calculations are quite computationally demanding, they can be very informative as they allow for calculating the thermodynamic properties to assess the stability of the analysed polymorphic forms. The differences (\(\Delta\), Phase I-Phase II) between the chosen thermodynamic properties, including Gibbs free energy (\(\Delta\)G), enthalpy (\(\Delta\)H), and temperature times entropy (TAS) of the structures modelled using PBE TS functional at 20.1 GPa, with respect to the temperature, are presented in Figure 5.  
The positive values of \(\Delta\)G in the whole range of T indicate the higher thermodynamic stability of Phase II at 20.1 GPa, which is in agreement with the experimental observations. In addition, the differences at the level of a few kJ/mol were reasonable, as the lattice energy differences between experimentally observed polymorphs were usually less than 4.2 kJ/mol (1 kcal/mol) per molecule, but could be even lower than 1 kJ/mol [39]. The higher thermodynamic stability of Phase II at 20.1 GPa was a result of both entropic (S) and enthalpic (H) contributions, as Phase II is characterised by higher S and lower H in the whole region of studied temperatures than Phase I.  
Thermodynamic analysis explains the experimentally observed IPT and highlights the important role of phonon DOS calculations required for the thermodynamic analysis, as the analysis of solely lattice energies can be misleading and does not reflect the true order of the stability of polymorphs.
## 4 Experimental Results and Discussion
### Model Testing
#### 4.4.3 Continuous Image Testing Using M4  
In the aforementioned tests, all 330 images in the test set contained at least one object that we wished to detect. However, in reality, the images in the dashcam footage might only contain the objects we do not intend to detect. If such objects were detected (i.e., incorrect detections), the moving vehicle could potentially transmit the wrong information or even brake sharply, which can be dangerous in itself. To showcase the efficiency of M4, we further selected six pieces of dashcam footage (not used in the training set) for testing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **M1** & **M2** & **M3** & **M4** \\ \hline Model mAP & 79.60\% & 81.32\% & 85.60\% & 88.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 20: Model mAP for each model.  
The features of the six videos were as follows: Video 1 [66] showed a vehicle stuck in a rush-hour traffic jam on a normal road in a city under bright and clear weather conditions; Video 2 [67] showed a vehicle traveling smoothly along a hilly road on a sunny day; Video 3 [68] showed a vehicle traveling smoothly along a freeway on a rainy night; Video 4 [69] showed a vehicle traveling along a hilly road on a rainy day; Video 5 [70] showed a vehicle traveling smoothly along a normal road on a rainy day; and Video 6 [71] showed a scooter traveling smoothly along a normal road on a foggy day. The footage was split into different frames for the detection tests, and the test results are presented in Table 21.  
The test results showed that the processing time for each image was 0.28 s. This detection speed is adequate for real-time image recognition, because there were no missed detections caused by late responses. From Table 21, in Video 1, of the 59 images that did not include Class A or Class B objects, only one image was incorrectly detected; in Video 2, of the 54 images that did not include Class A or Class B objects, there were no incorrect detections; in Video 3, of the 50 images that did not include Class A or Class B objects, seven images were incorrectly detected; in Video 4, of the 20 images that did not include Class A or Class B objects, there were no incorrect detections; in Video 5, of the 62 images that did not include Class A or Class B objects, three images were incorrectly detected; and in Video 6, of the 74 images that did not include Class A or Class B objects, 14 images were incorrectly detected. The error rate for each video can be calculated using the following equation:  
\[\text{Error rate}=\frac{\text{Number of images that were incorrectly detected}}{\text{Number of images that did not include Class A or Class B objects}} \tag{2}\]  
From Table 21, Videos 3 and 6 had higher error rates. This is due to the insufficiency of samples for foggy weather and rainy nights in the training set (these samples were difficult to acquire). For the other four videos, the samples were sufficient, and therefore, the error rates were lower or even equal to 0. Therefore, the mean error rate of the six videos was only 6.58%, which attests to the excellence of M4.
## 5 Analysis of Results and Field Testing
### Ablation Experiments  
#### 5.4.1 Influence of Encoder Size  
To verify the rationality of the encoder design in this paper, we conducted tests and analyzed the degree to which model performance is affected by different encoder sizes. The specific test results are shown in Table 3. From the model's performance on the test set, we found that the model's performance improved with increasing encoder size. The largest model of our design, SegFormer-UNet-B3, already outperforms the original SegFormer-B5 model with only half of the parameters and computational effort. This indicates that the encoder design in this paper is reasonable to a great extent.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Params (M)** & **GFLOPs (G)** & **mIoU (\%)** & **mPA (\%)** \\ \hline FCN [20] & 32.95 & 138.86 & 96.06 (2.28.) & 97.84 (1.26\(\downarrow\)) \\ UNet [21] & 24.89 & 225.84 & 96.17 (2.17) & 97.89 (1.21\(\downarrow\)) \\ DeepLab V3+ [24] & 54.71 & 83.42 & 97.47 (0.87) & 98.85 (0.25\(\downarrow\)) \\ PSPNet [33] & 46.71 & 59.21 & 96.66 (1.68.) & 98.17 (0.93\(\downarrow\)) \\ HRNet [34] & 65.85 & 93.83 & 96.88 (1.46\(\downarrow\)) & 98.29 (0.81\(\downarrow\)) \\ SegFormer-B5 [27] & 84.60 & 99.75 & 96.57 (1.77\(\downarrow\)) & 98.22 (0.88\(\downarrow\)) \\ SegFormer-UNet-B3 (Ours) & 46.56 & 49.43 & 98.34 & 99.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of different networks.
# Spatiotemporal Analysis of Evapotranspiration and Effects of Water and Heat on Water Use Efficiency
## 3 Results and Discussion
### Spatiotemporal Characteristic of WUEs Defined by GPP/E, GPP/Et and GPP/P
This phenomenon is mainly due to the relatively large fluctuations caused by farmland and irrigation in southern Xinjiang (Figure 7b). The tWUE change greatly compared with eWUE and pWUE, which indicates that the tWUE of arid and semi-arid areas changes significantly due to the strong fluctuation of the transpiration of vegetation [49]. Figure 6: Spatial distribution of WUEs during 2000–2014; (**a**) eWUE; (**b**) tWUE; (**c**) pWUE. Figure 7: Latitude change trend of WUEs; (**a**) eWUE; (**b**) tWUE; (**c**) pWUE. As shown in Figure 7c, pWUE has only three categories, and most regions show a slight increase, accounting for 80.13%. There are no areas of high increase. In conclusion, eWUE and pWUE have basically the same variation pattern, and the tWUE spatial variation trend is not uniform (Figure 7b). Most areas in northern Xinjiang show an upward trend in tWUE, while the changes around the desert are dramatic. This is due to the rising temperature in northern Xinjiang with increasing precipitation, resulting in increased vegetation photosynthesis and a steaming effect [12; 50]. However, the drastic diurnal temperature difference in the desert leads to the unstable changes of vegetation photosynthesis and water loss, resulting in drastic changes of tWUE. The increase in atmospheric CO\({}_{2}\) emissions caused global warming, which in theory could result in an increase in WUE. High temperatures inhibit photosynthesis and promote an increase in actual steaming emissions, which ultimately led to a decrease in WUE [51; 52].
## 3 Data and Methods
### The SBAS-DInSAR Technique  
Here, we briefly describe the rationale of the SBAS technique. Let us suppose that we have a set of \(Q\) co-registered SAR images, acquired at ordered times \(\left[t_{1},t_{2},\ldots,t_{Q}\right]\). Starting from the available data, the SBAS algorithm relies on the proper selection of SAR acquisitions data pairs, characterized by a short temporal (i.e., the time interval between two acquisitions) and spatial (i.e., the distance between two satellite orbits) baseline. The key objective of this data selection is to mitigate the noise (decorrelation) effects [51] that corrupt the interferograms, thus, maximizing the number of temporally coherent SAR pixels (see [52] for the definition of temporal coherence). This baseline selection may imply that SAR data pairs, used to produce differential interferograms, could be arranged in a few SB subsets, which are separated by large baselines. Displacement time-series are then retrieved by solving a least-squares minimization problem, based on the application of the singular value decomposition (SVD) method, to the sequence of unwrapped interferograms. The residual topographic artifacts, as well as the atmospheric phase screen (APS) signals, are also estimated and filtered out. Additional details on the SBAS algorithm, and the SBAS processing chain, can be found in [53; 54].
## 3 Results
### Health Risk Assessment
#### 3.4.2 Carcinogenic Risk  
Since the SF values of Hg for three exposure pathways were missing and the SF values of Cd, Cr, and Pb were not completed (refer to Table 10), only three exposure pathways of As, two exposure pathways of Cr (hand-to-mouth ingestion, and air inhalation), and one exposure pathway of Cd (air inhalation) and Pb (hand-to-mouth ingestion) were taken into consideration for the carcinogenic risk assessment.  
The results in Figure 5 indicate that: (1) the mean carcinogenic risk indices of hand-to-mouth ingestion, dermal absorption and air inhalation for male workers were \(9.03\times 10^{-5}\), \(2.03\times 10^{-12}\) and \(3.72\times 10^{-7}\), respectively; (2) the mean carcinogenic risk indices of hand-to-mouth ingestion, dermal  
Figure 5: Distribution of carcinogenic risks posed through different exposure pathways.  
absorption and air inhalation for female workers were \(1.06\times 10^{-4}\), \(2.15\times 10^{-12}\) and \(4.37\times 10^{-7}\), respectively; (3) the mean total carcinogenic risk indices for male and female workers were \(9.07\times 10^{-5}\) and \(1.06\times 10^{-4}\), respectively. The risk indices distribution of different exposure pathways indicated that the carcinogenic risk indices of hand-to-mouth ingestion, dermal absorption and air inhalation for male and female workers were distributed in the range of \(10^{-6}\)-\(10^{-4}\), \(10^{-13}\)-\(10^{-11}\), and \(10^{-8}\)-\(10^{-6}\), respectively. The risk indices of hand-to-mouth ingestion was 7 and 5 orders of magnitude higher than that of dermal absorption and air inhalation, respectively.  
The results in Figure 6 show that: (1) the average carcinogenic risk indices of As, Cd, Cr and Pb for male workers were \(3.62\times 10^{-6}\), \(4.21\times 10^{-9}\), \(8.62\times 10^{-5}\) and \(8.45\times 10^{-7}\), respectively; (2) the  
Figure 6: Distribution of carcinogenic risks posed by different heavy metals.  
average carcinogenic risk indices of As, Cd, Cr and Pb for female workers were \(4.25\times 10^{-6}\), \(4.94\times 10^{-9}\), \(1.01\times 10^{-4}\) and \(9.93\times 10^{-7}\), respectively. The risk indices distribution of different heavy metals showed that the carcinogenic risk indices of As, Cd, Cr and Pb for male and female workers were distributed in the range of \(10^{-7}\)-\(10^{-5}\), \(10^{-10}\)-\(10^{-8}\), \(10^{-6}\)-\(10^{-4}\), and \(10^{-8}\)-\(10^{-6}\), respectively. Therefore, Cr contributed the most to the carcinogenic risk for male and female workers, following by As, Pb and Cd.
## 5 The Proposed Solution
### The Extension Method for Arbitrary Polynomial Functions
#### 5.4.1 Univariate Polynomials  
Let \(\mathcal{F}\) be the set of all univariate polynomials of higher order over a finite field \(\mathbb{Z}_{q}\). For any \(f(x)\in\mathcal{F}\), suppose that \(f(x)=f_{0}+f_{1}x+\ldots f_{d}x^{d}\). Let \(m=\lceil\sqrt{d+1}\rceil\) and define  
\[\mathbf{F}=(\begin{array}{cccc}f_{0}&f_{1}&\cdots&f_{m-1}\\ f_{m}&f_{m+1}&\cdots&f_{2m-1}\\ \vdots&\vdots&\cdots&\vdots\\ f_{m^{2}-m}&f_{m^{2}-m+1}&\cdots&f_{m^{2}-1}\end{array}) \tag{12}\]  
as a matrix of order \(m\), where \(f_{i}=0\) for all \(i>d\). Set \(\mathbf{x}=(1,x,\ldots,x^{m-1})^{\top}\) and \(\mathbf{y}=(1,x^{m},\ldots,x^{m^{2}-m})\). Then, we have \(f(x)=\mathbf{y}\cdot(\mathbf{F}\cdot\mathbf{x})\).  
This is a two-phase computation: (i) the computation of \(\mathbf{u}=\mathbf{F}\mathbf{x}\) and (ii) the computation of \(f(x)=\mathbf{y}\cdot\mathbf{u}\). The first phase is a matrix-vector multiplication function that can be delegated to a cloud server using our verifiable computation scheme. It requires \(O(m^{2})=O(d)\) arithmetic operations. The second phase is a substantially fast computation for the user that requires only \(O(m)=O(\sqrt{d})\) arithmetic operations locally.
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 2 Materials and Methods
### Wheat Grain Counting Method
#### 2.3.2 Grain Counting Model  
This study extracted 52 shape, color, and texture features from each spikelet image according to the above image feature extraction. The categories of the spikelet grain quantities were classified by the corresponding spikelet image features through the machine learning algorithm, and the number of the grains in frontal view spike image was counted by adding the number of grains in the corresponding spikelets, as shown in Figure 7. For the sake of determining the optimal method for grain number classification, this study adopted 7 machine learning models, including SVM, Decision Tree (DT), random forest (RF), Gradient Boosting Decision Tree (GBDT), K-Nearest Neighbor (KNN), Naive Bayes (NB), and Linear Discriminant Analysis (LDA). The training and test datasets were divided by 8.2 when modeling the number and image features of wheat spikelet grains.
## 5 Conclusions and Remark
In addition, the horizontal positioning accuracy in GPS/BDS/GLONASS static PPP tests without significant signal obstructions and strong multipath reflectors using the Huawei Mate30 reached 10 cm within 4833 epochs, and it is even faster than the single frequency PPP using data from the geodetic receiver within 5730 epochs. However, the changing multipath and obstructions in urban environments can significantly affect the tracking of satellite signals in smartphones in kinematic conditions, which can lead to the decrease of the reliability of high-precision positioning for smartphones. Nevertheless, with the upgrade of the GNSS chip and antenna of smartphones, the adverse effects of GNSS signals characteristics such as gradual accumulation of phase errors, low carrier-to-noise ratio of the L5 band, high pseudorange residual range, and excessive rate of cycle slips in GLONASS, etc., will be overcome to some extent. By then, we can obtain high-precision and reliable location information using smartphones, even in urban streets where some satellite signals may be blocked. Conceptualization, H.Z., L.X., and D.W.; methodology, H.Z. and J.X. ; validation, H.Z., L.X., and Q.L. ; writing--original draft preparation, H.Z. ; writing--review and editing, H.Z. and D.W. All authors have read and agreed to the published version of the manuscript. This study was funded by the National Key Research and Development Program of China, grant number 2017 YFB0504103; National Natural Science Foundation of China, grant number 41071284; and Fundamental Research Funds for the Central Universities, grant number 17 lgpy43. The authors declare no conflict of interest.
## 2 Experimental Study  
### Materials  
The cement used was CEM I 42.5 R/SR, supplied by Lafarge-Holcim S.A. (Madrid, Spain). The properties of the cement meet the requirements of the EN 197-1 standard [15] and comply with the recommendations of the EHE-08 [16]. For natural aggregates, two fractions of silica sand were used. The sands, with two granulometric fractions 0/0.5 mm and 0.5/1.6 mm, were supplied by Silices La Cuesta (Asturias, Spain). For the additions, densified silica fume (Elkem Microsilica(r) 940) with a mean particle size of 0.15 \(\upmu\)m and micronized quartz with a maximum particle size of 40 \(\upmu\)m, supplied by Silicas Gilarranz S.A (Segovia, Spain), were used. The short steel fibers, with a diameter of 0.2 mm and a length of 13 mm, were supplied by Arcelor Mittal (Asturias, Spain). To achieve a workable UHPFRC, a polycarbonate superplasticizer, provided by ViscoCrete-225 Powder (Madrid, Spain) was used. As an alternative to micronized quartz, granite cutting waste from gang saw cutting machines (GCW-GS) was used (Figure 1).  
Table 1 shows the values of the particle density, water absorption, and humidity of the materials used in this study. The particle density was determined by means of a helium pycnometer, while the specifications of the EN 1097-6 standard were followed to evaluate the water absorption of silica sands [17]. The higher density of GCW-GS, in relation to the additives used in the control concrete and GCW-D, is due to the presence of iron oxide particles.  
Figure 1: Granite gang saw cutting machine.  
Table 2 shows the chemical composition of the waste (GCW-GS), determined by X-ray fluorescence. The differences between this GCW-GS waste and that of the GCW-D used in the previous article [14] are the higher content of Fe\({}_{2}\)O\({}_{3}\) (14.59%), due to the presence of steel particles, and the 4.53% CaO. The higher percentage of SiO\({}_{2}\) is attributable to the difference in the type of granite that was cut to generate the two wastes.  
Figure 2 shows the granulometric distribution of the different materials used in this study. It can be seen that granite waste have a particle size close to micronized quartz, and therefore it is proposed as an alternative to replace this material. Although silica fume has a maximum particle size of 0.15 \(\upmu\)m, the particle size distribution observed in Figure 2 is due to the fact that it is densified.  
The granulometric curve resulting from the combination of all the components of the concrete mixtures with the different substitutions of GCW-GS is also shown in Figure 2. As the percentage of substitution rises, there is a slight improvement in the granulometric distribution, following the criteria of ideal packing curves proposed by authors such as [PERSON] as well as [PERSON] and [PERSON] [18; 19]. It therefore follows that the influence of the granulometry on the final result will lead to a slight improvement in the packing.