esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimal Waveband Selection Using ANOVA Analysis Results
of Spectra**} & \multicolumn{1}{c}{**Accuracy [\%]**} \\ \hline \multirow{8}{*}{Honeydew} & original & 460 & 656 & 2396 & 1548 & 0.98 & 91.79 & 386 & 92.44 \\  & 1:5 & 460 & 660 & 2089 & 1570 & 0.40 & 90.56 & 392 & 91.63 \\  & 1:10 & 460 & 676 & 1306 & 1604 & 0.34 & 87.51 & 400 & 88.67 \\  & 1:20 & 427 & 652 & 809 & 1617 & 0.18 & 85.40 & 404 & 84.36 \\  & 1:50 & 476 & 656 & 344 & 1630 & 0.15 & 79.19 & 407 & 78.23 \\  & 1:100 & 472 & 652 & 95 & 1536 & 0.14 & 74.84 & 383 & 73.43 \\ \hline \multirow{8}{*}{Orange} & original & 456 & 660 & 2614 & 1290 & 0.43 & 92.30 & 322 & 92.36 \\  & 1:5 & 460 & 652 & 2400 & 1620 & 0.63 & 90.96 & 405 & 91.84 \\ \cline{1-1}  & 1:10 & 472 & 640 & 2011 & 1590 & 0.44 & 90.06 & 397 & 90.36 \\ \cline{1-1}  & 1:20 & 460 & 660 & 1358 & 1592 & 0.28 & 88.48 & 398 & 88.62 \\ \cline{1-1}  & 1:50 & 468 & 652 & 767 & 1444 & 0.17 & 84.90 & 360 & 85.79 \\ \cline{1-1}  & 1:10 & 472 & 644 & 270 & 1476 & 0.17 & 74.37 & 368 & 72.02 \\ \hline \multirow{8}{*}{Apple} & original & 472 & 644 & 2058 & 1504 & 0.21 & 93.12 & 375 & 92.90 \\ \cline{1-1}  & 1:5 & 464 & 640 & 505 & 1731 & 0.70 & 89.07 & 432 & 89.53 \\ \cline{1-1}  & 1:10 & 472 & 644 & 1213 & 1533 & 0.20 & 88.22 & 383 & 87.53 \\ \cline{1-1}  & 1:20 & 484 & 656 & 1084 & 1519 & 0.16 & 87.63 & 379 & 83.90 \\ \cline{1-1}  & 1:50 & 488 & 648 & 958 & 1220 & 0.19 & 83.95 & 305 & 76.09 \\ \cline{1-1}  & 1:100 & 472 & 652 & 172 & 1131 & 0.11 & 77.02 & 282 & 72.91 \\ \hline \multirow{8}{*}{Watermelon} & original & 460 & 656 & 1237 & 1687 & 0.56 & 87.31 & 421 & 93.31 \\ \cline{1-1}  & 1:5 & 464 & 640 & 1178 & 1592 & 0.41 & 87.15 & 398 & 90.67 \\ \cline{1-1}  & 1:10 & 472 & 632 & 1131 & 1535 & 0.32
## 3 Results and Discussion
Comparing the mechanical properties of polymer-infiltrated ceramics (PICs) with their contenders is beneficial, specifically when strength and hardness are evaluated. [PERSON] et al. [21] have compared the three-point flexural strength values of some commercial PICs, whose composition was not clearly disclosed, and reported the strength of 180 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ceramic Samples** & **Infiltration Time (h)** & \
\begin{tabular}{c} **Flexural Strength** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Microhardness (HV)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Alglass} & 0 & 9.0 \(\pm\) 2.1 & 455.0 \(\pm\) 36.6 \\  & 6 & 9.4 \(\pm\) 5.1 & 468.8 \(\pm\) 15.5 \\  & 12 & 16.5 \(\pm\) 5.4 & 519.1 \(\pm\) 39.5 \\ \hline \multirow{3}{*}{In-ceram} & 0 & 41.0 \(\pm\) 3.8 & 458.0 \(\pm\) 28.2 \\  & 6 & 61.0 \(\pm\) 7.7 & 704.0 \(\pm\) 41.7 \\  & 12 & 75.9 \(\pm\) 8.0 & 857.0 \(\pm\) 55.1 \\ \hline \multirow{3}{*}{Vitro-ceram} & 0 & 37.5 \(\pm\) 17.6 & 439.0 \(\pm\) 37.3 \\  & 6 & 78.8 \(\pm\) 16.6 & 1030.7 \(\pm\) 52.3 \\ \cline{1-1}  & 12 & 119.3 \(\pm\) 23.6 & 1055.1 \(\pm\) 111.0 \\ \hline \hline PMMA & – & 25.0 \(\pm\) 5.0 & 216.0 \(\pm\) 41.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Flexural strength and microhardness of alumina samples sintered at 1400 \({}^{\circ}\)C and infiltrated with PMMA. and 164 MPa for Vita Enamic (Vita Zahnfabrik Co., Berlin, Germany.) and Lava Ultimate (3M Co., Sumare, SP, Brazil), respectively. These materials have a strength lower than lithium disilicate glass-ceramic (Ivoclar Co., IPS e.max: \(\sim\)270 MPa) and greater than feld-pathic porcelain (Vita Co., Mark II: \(\sim\)138 MPa) or leucite glass-ceramic (\(\sim\)160 MPa). Some other researchers have reported a strength of 100-150 MPa for PICs [22]. The hardness of PICs could also be compared with other materials. The lithium disilicate glass-ceramic shows a hardness of 5.83 GPa, followed by leucite-based glass-ceramic (4.60 GPa) and feld-pathic porcelain (3.46 GPa). Most resin-reinforced PICs demonstrate values between 1.15-1.70 GPa, similar to dental tissues [22]. Therefore, the strength (\(\sim\)120 MPa) and hardness (\(\sim\)1.06 GPa) of Vitro-ceram are in the range of PICs suitable for restorative purposes. Table 6 shows the flexural strength and microhardness measurements submitted to the Tukey test to the 5% statistical significance level. For the flexural test, the samples Vitro-ceram and In-ceram presented statistically different values, while the Alglass showed no differences. The microhardness of Vitro-ceram does not differ statistically when infiltrated for 6 and 12 h; however, the values for the In-ceram fluctuate statistically for different infiltration times and considering the control group. The Alglass sample showed a statistically significant difference when infiltrated for 6 or 12 h. Overall, these results confirmed that increasing the infiltration time, as expected, leads to a statistically significant increase in hardness and strength.
## 4 Interference Analysis
### Parameter Definitions and Relevant Equations
#### 4.1.2 Relevant Equations
It provides a fundamental measure of the system's power efficiency and compares the performance of different modulation/coding schemes, even if they operate at different bandwidths. There is a direct relation between \(\left[\frac{\mathrm{C}}{\mathrm{N}}\right]\) and \(\left[\frac{\mathrm{E}_{\mathrm{b}}}{\mathrm{N}_{0}}\right]\). To calculate the key ratio \(\left[\frac{\mathrm{E}_{\mathrm{b}}}{\mathrm{N}_{0}}\right]\), Equation (6) is used:  
\[\left[\frac{\mathrm{C}}{\mathrm{N}}\right]=10\mathrm{log}\left[\frac{\mathrm{ E}_{\mathrm{b}}}{\mathrm{N}_{0}}\right]+10\mathrm{log}\left(\frac{\mathrm{r}}{ \mathrm{B}}\right) \tag{6}\]  
where \(\left[\frac{\mathrm{C}}{\mathrm{N}}\right]\) can be calculated from \(\left[\frac{\mathrm{C}}{\mathrm{N}_{0}}\right]\), as in Equations (7) and (8):  
\[\left[\frac{\mathrm{C}}{\mathrm{N}}\right]=\left(\frac{\mathrm{C}}{\mathrm{N} _{0}}\right)_{\mathrm{total}}\left(\frac{1}{\mathrm{B}}\right) \tag{7}\]  
\[Or:\ \left(\frac{\mathrm{C}}{\mathrm{N}}\right)_{dB}=\left(\frac{\mathrm{C}}{ \mathrm{N}_{0}}\right)_{Total}-10\mathrm{log}_{10}\left(\mathrm{B}\right) \tag{8}\]
# UHPLC-MS Method for the Analysis of the Molecular Adjuvant Sulfavant [PERSON]
## 1 Introduction  
Sulfur-containing lipids and, in particular, sulfoquinovosyl-diacylglycerollglycerrols (SQDGs) have been proposed as factors in inflammation, immunity and infection. However, despite their dissemination, fast and accurate ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) or ultra high-performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) methods for their quantification are still scarce and mostly focused on sulphate sterols [1; 2; 3; 4] and sulfatides [5; 6; 7]. Most of the lipid analysis, also containing sulfolipids, as reported in the literature, are based on a shotgun lipidomic approach [2; 3] or traditional reverse phase (RP) - HPLC columns usually associated with complex mixtures of solvents used as elements in order to obtain a good quality chromatogram [8].  
In our ongoing drug discovery exploration for new active metabolites, we recently reported the immunomodulatory activity of 1,2-O-distaroyl-3-O-\(\beta\)-D-sulfoquinovosylglycerol, named Sulfavant A (I) (SULF A; Figure 1). SULF A is a synthetic glycolipid that is featured by a sugar unit of sulfoquinovose. The molecule is the prototype for a new class of molecular adjuvants inspired by natural \(\alpha\)-sulfoquinovosyl-diacylglycerrols (\(\alpha\)-SQDGs) occurring in photosynthetic organisms [9]. SULF A (I) triggers in vitro maturation of dendritic cells, the master control of innate immune response, and in vivo antigen-specific immunization [10; 11; 12; 13; 14; 15]. The initiation of a systemic immune response by thestimulation of innate immune cells correlates to adjuvanticity and Pattern Recognition Receptors (PRR) - mediated signaling. SULF A is under preclinical trials as a vaccine adjuvant and its efficacy has been already proven in a murine model of vaccine against melanoma [9]. Interestingly, the product is not cytotoxic, but treated mice do not show progress of the tumour for more than 10 days after subcutaneous injection of B16F10 melanoma cells.  
In the present study, we seek a robust and fast analytical method for the quantification of SULF A in biofluids and tissues during preclinical studies. To this aim, we report the implementation of a novel Ultra High-Performance Liquid Chromatography-High Resolution Mass Spectrometry (UHPLC-HRMS) method, together with the use of deuterated Sulfavant A (d\({}_{70}\)-SULF A) (**2**) as internal standard (IS).
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 3 Reliability Model  
The speed of vehicles on the road makes it difficult to maintain a reliable routing strategy in VANETs due to dynamic factors such as vehicle distribution and mobility patterns [28]. Therefore, to predict the duration of a steady route between vehicles, the vehicular characteristics must be determined which are discussed in the following sections.
## 7 Experimentation and Analysis
### Algorithm Performance Validation
#### 7.1.3 Computation Complexity Comparison  
Although the algorithm execution time is affected by factors such as the hardware, compiler, operating system, compilation language, and input data set, the algorithm execution time can reflect the complexity of the algorithm to a certain extent under specific hardware and software environments.  
Table 6 demonstrates the execution time of the four algorithms on the test functions from F1 to F12. It can be seen that the GLSOAG proposed in this paper, despite the fact that it did not achieve an advantage in the longitudinal comparison with PSO and BSA because of the smaller number of parameters and simple iteration mechanism of the PSO and BSA algorithms, the overall difference is controlled to be less than 1 s. The average difference between the GLSOAG and SOA in the horizontal comparison is even smaller, indicating that its improvement on SOA does not bring an excessive time overhead.  
Figure 10: Performance comparison of the algorithm on the F6, F8, and F12 test functions.Among them, the bold numbers in the table represent the superior values in each group of experiments.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 2 Related Works
### Supervised Hashing  
Unlike unsupervised hashing methods, supervised ones utilize the labels to improve the retrieval performance. For example, Semantic Correlation Maximization (SCM) [34] preserves the pairwise similarities of the training dataset to learn the hash functions. Semantic Preserving Hashing (SePH) [35] constructs a new semantic affinity matrix into a probability distribution, and then learns the hash codes by using an approximate-based scheme. Discrete Cross-modal Hashing (DCH) [36] learns the hash codes in a bit-by-bit manner by using the discrete cyclic coordinate descent (DCC) algorithm. Label Consistent Matrix Factorization Hashing (LCMFH) [37] directly uses semantic labels to guide the hashing learning procedure. Scalable Discrete Matrix Factorization Hashing (SCRATCH) [38] is a two-step hashing method, which first generates the hash codes, and then learns the hash functions based on the learned hash codes. Nonlinear Robust Discrete Hashing (NRDH) [39] uses a nonlinear model to solve the generalization error caused by kernelization, and generates compact hash codes by constructing an asymmetric hash framework and discrete optimization algorithms. Scalable Deep Asymmetric Hashing (SDAH) [40] builds an asymmetric unequal-dimensional hash learning framework by exploring the information content of different modalities. Nonlinear Supervised Discrete Hashing (NSDH) [41] consists of two parts, the first part is a semantic enhancement descriptor, which is used to extract comprehensive latent representations of heterogeneous multimedia data, and the second part is a fast discrete optimization module, which is used to learn discriminative compact hash codes. Subspace Relation Learning for Cross-modal Hashing (SRLCH) [42] handles relationships of labels in a semantic subspace to make similar instances from different modalities closer in the binary Hamming space.
## 8 RAN Decomposition Scheme
### RAN Functional Split  
The RAN functional split is another innovative and practical scheme for alleviating the imposed fronthaul requirements by the C-RAN architecture [23; 25; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. For instance, to address the drawbacks of CPRI-based fronthaul solutions, an eCPRI specification presents additional physical layer FSOns and a packet-based solution. Consequently, unlike the conventional constant data rate CPRI in which the stream significantly depends on the carrier bandwidth, as well as the number of antennas; the eCPRI stream does not depend on either of the factors but the actual traffic load [361]. In essence, apart from being able to alleviate the stringent bandwidth demands, multiple eCPRI streams can also be multiplexed onto a wavelength for onward transmission over the fronthaul network [361; 422; 423].  
In addition, with recent network architecture development, the traditional BBU and RRU have been reformed into different functional entities, which are the CU, DU, and RRU/Active Antenna Unit (AAU). With the configuration, the CU majorly focuses on non-real-time and part of the traditional Evolved Packet Core functionalities [361]. This involves high-level protocol processing like dual connectivity and radio resource man  
Figure 27: Required MFH capacity for different sectors.  
agement. In addition, the DU is responsible for the real-time Media Access Control layer functions like HARQ flow and physical layer function processing. Furthermore, when MIMO antennas are to be employed, certain parts of the physical layer functions can be shifted to the RRU/AAU. The implementation will not only aid in lessening the associated transmission bandwidth between the RRU/AAU and DUs but will also help in reducing the transmission cost considerably. Therefore, a number of FSOns have been presented to reduce the processing and network resources cost considerably. Figure 28 illustrates some potential FSOns with emphasized layer 1 (L1). Moreover, as shown in the figure, each of the options is categorized according to the demarcation point between the CU and the DU. Therefore, depending on the deployment scenarios and use cases, each option offers a different degree of flexibility regarding resource allocation for different service requirements [361,385]. Consequently, to efficiently reduce the associated MFH transport network overhead and the imposed stringent demands, FSOn between the CU and DU could be redefined so as to meet the system requirements. It is noteworthy that, the C-RAN approach corresponds to the 3 GPP Option 8 that has only RF functionality in the DU while other L1, Layer 2 (L2), and Layer 2 (L3) functions are in the CU. It is noteworthy that the L1, L2, and L3 are the PHY layer, data link layer, and network layer, of the LTE protocol stack, respectively [41,47,424].  
Figure 28: Functional split options between CU and DU with emphasised PHY layer (adapted from [143,425]).
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
## 5 Conclusions and Future Work  
To accelerate the verification algorithm, we extended the CLS scheme proposed by [PERSON] et al. [28] and accelerated the algorithm execution through batch verification. The proposed scheme is still based on the Chinese national cryptographic standard (SM2) algorithm and no pairing operation is required during the signing process. This guarantees both efficiency and the requirement of using the standard cryptographic algorithm. In addition, the noninteractive zero-knowledge proof (NIZKP) of the signature is replaced by verifying an equation. This improvement provides efficient batch verification for multiple signatures. The number of pairing operations is constant regardless of the amount of signatures.  
In this paper, we propose a basic certificateless signature scheme derived from the SM2 algorithm without resorting to the use of pairing operations. Signing or verifying a single signature does not involve any pairing operation. In addition, we further show how to improve the scheme to support batch verification. Nevertheless, the verification of multiple signatures requires a constant number of pairing operations. Despite the fact that the number is constant and is independent from the number of signatures in a batch verification, how to achieve a fully pairing-free SM2-based certificateless signature scheme that supports batch verification is worth studying. In addition, the security analysis is based on the random oracle model, which treats the hash function as an oracle. How to construct schemes without random oracles would also improve the security to a greater extent.  
Conceptualization, C.H.; formal analysis, B.Z.; methodology, C.H.; software, Z.X.; Writing--original draft, Y.W.; Writing--review and editing, L.Z. and Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the science and technology project of State Grid Corporation of China \"Research on lightweight cryptographic technology for power IOT terminal\" (Grand No. 5700-202255186A-1-1-ZN).  
Not applicable.  
All the related research data are available upon authors request.  
We would like to thank the anonymous reviewers of this paper for their valuable comments and suggestions.  
The authors declare no conflict of interest.
## 3 Results
### Implementation Details  
A total of 363 patients who underwent ABUS from May 2017 to October 2019 were included. ABUS images of 286 patients presented 434 mass lesions categorized as C2 by BI-RADS, while other 77 images showed no mass lesions. We randomly divided the mass lesions: 304 lesions in the training set, 50 lesions in the validation set, and 80 lesions in the test set. The dataset also includes 3907 nonmass lesions that were randomly cropped from the 77 normal patients' images and randomly divided into 3777 lesions for the training set and 50 and 80 lesions for the validation and test sets, respectively.  
All center coordinates and diameters of masses were obtained from radiology reports annotated by experienced radiologists. We rescaled the original volume images to have voxel sizes of 0.3 mm to 2.0 mm depending on the mass size, and then we cropped the network input volumes at the center coordinate of the mass with a size of \(48\times 32\times 48\) voxels. Additionally, the 3-dimensional patches used for training are rotated three times with rotation angles of \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\) for augmentation.  
The weights of the convolutional layers were initialized by the Xavier uniform initializer [26]. We performed batch normalization (BN) [27] immediately after each convolution layer, and ReLU activation was followed by BN. The models we used were trained with a batch size of 48 with 2 mini batches of the same size, and the losses were optimized by the RMSprop optimizer [28]. All networks in this study were implemented with the TensorFlow 1.12 library and were trained on Nvidia 2080-Ti on an Ubuntu 18.04 system.  
Figure 4: Example of template mask. (**a–c**) Slice image of transverse, coronal, sagittal view of breast cancer on ABUS including diameters of each axis. (**d–f**) template mask matched to (**a–c**). Using average diameter of the lesion, the mask label has two of equal semidiameters of 14.3 mm and 7.15 mm semidiameters, which is the half-length of the other diameters due to _parallel orientation_ property.
## 1 Introduction
In addition to manual selection and preconfigured rulesets, academia has proposed several automated schemes to further reduce false alarms generated by SIDS such as signature enhancement, stateful signature, alarm mining, alert correlation, etc. [14]. Numerous works complement the output of the SIDS with techniques that mostly come from the field of machine learning [15; 16], or others such as Fuzzy misuse detection [2] or heuristic approaches [17]. However, although the results in these works are very promising, implementing these techniques require either to modify or extend standard SIDS, which, in practice, is not available to everyone. In this work, we want to provide insight into the effect that using predetermined configurations of the ruleset has on the detection capability and false alarms of some SIDS in the context of web attacks. In particular, and given the authors' background [18], we conduct an experimental study in which predefined configurations of three freely available SIDS (Snort, ModSecurity, and Nemesida Free) are tested against various public and in-house web datasets to scrutinize their individual and cooperative performance against URI attacks. The results of this study are useful not only to compare the detectors individually but also to find the extent of the benefit of a cooperative decision (e.g., voting scheme) in terms of increasing the detection capability or reducing the false alarm rate. Although there is evidence that different SIDS detect different attacks since they use different rule sets [19; 20], there is no experimental study that gauges the complementarity of their detection capability. In addition to available predefined configurations, manual selection of active rules is still the most reliable method to find the optimal point of operation, but it is costly and there is scarce guidance. In this respect, we introduce a method for systematic determination of which rules deactivate from a ruleset in order to minimize the false alarm rate. Therefore, the contributions of this work can be summarized as follows:  
* We experimentally study the detection rate of standard preconfigurations of three SIDS in the context of URI web attacks. * We experimentally study the false alarm rate and precision of three SIDS using a large web real-life trace. * We study how a cooperative decision (i.e., ensemble of the three SIDS) impacts precision and detection rates. * We propose an efficient method to reduce false positives by deactivating signatures from a full ruleset. The remainder of this paper is as follows. Section 2 provides the theoretical background and related works found in the literature. Section 3 describes the experimental environment, including a description of the datasets and detectors used in our study. Sections 4 and 5 provide the results of our first and second experiments, respectively, and Section 6 provides a tuning method for reducing false positives by deactivating rules from a default ruleset. Section 7 acknowledges the main limitations of this work. Finally, Section 8 concludes the paper.
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 3 Influence Analysis of Error Source
### Error of the Antenna Array
\tag{5}\]  
In this equation, the value \(C\) is calibrated by external calibration method, the parameters \(\alpha\) and \(B\) in the first part, and the phases of the antenna patterns in the third part, are provided by the producer of the antenna. The phase imbalance of entire antenna array under different view angle is obtained and shown in Figure 6. Hence, this phase imbalance is increasing with the increase of the view angle. Polarization data with different beams are corrected using different \(\theta_{t}\) and \(\theta_{r}\) in polarimetric correction.