esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposal
### Using Identity-Based Short Signatures  
As discussed in Section 3, the IBS-1 scheme produces much shorter signatures than the standard ECDSA algorithm, making it a strong candidate for a signature that is compact enough to be embedded even in encrypted images.  
The algorithmic tools that we use in this work offer a wide range of combinations supporting several usage scenarios. For example, there is no constraint on the algorithm that the image owner may choose in order to encrypt the image: any algorithm can be selected. By contrast, IBS-1 is required for the signing phase. However, to fix the ideas, consider the scenario depicted in Figure 2. The image owner encrypts the image using the identity of the intended receiver. Subsequently, the encrypted image is signed, using an hash of it as usual, and the resulting signature is embedded in the encrypted image. The receiver can verify the signature and decrypt the image. Note that the scenario in Figure 2 assumes that the number of users sending and receiving images is high, thus exacerbating the issues of key distribution and key storage that would have arisen if symmetric cryptography had been used. Otherwise, the latter would have been preferable for encryption, in light of its edge on performance over public-key encryption.  
Figure 2: An usage scenario.  
Figure 3 shows an example image (a), the same image encrypted with IBC (b) and the resulting image after embedding the IBS-1 signature into it. The theoretical specification of a cryptographic system should be complemented by a properly engineered and rigorously verified implementation [27]. We have used the jPair [28] cryptographic library to implement the IBS-1 signature. jPair natively supports some supersingular and ordinary elliptic curves over large prime fields, and implements all the arithmetic required for finite fields and Tate pairings. Its implementation is inspired to [29], that introduced the idea of encapsulated point doubling/addition based on projective coordinate system. Although other libraries exist with similar characteristics (e.g., the reference benchmark C library PBC [30] and its Java port jPBC [31]), jPair is a pure Java implementation with no dependencies on external libraries and a very small memory footprint. It combines fast computation over elliptic curves with the benefits of portability and rapid development coming from the use of Java.
## 2 Materials and Methods
### Improved Feature Extraction Algorithm
#### 2.2.3 Improved Eight-Direction Multi-Scale Feature Descriptor  
Figure 3 shows the descriptor construction under different algorithms, where different colors represent different scale information. SAR-SIFT and PSO-SIFT do not use a multi-scale construction method, so they are not represented by color. In Figure 3, lines represent the divided bin, and circles represent the divided concentric circle support area, which is used to store information around feature points. As shown in Figure 3a, in the SAR-SIFT method, descriptors are formed using GLOH, as depicted in Figure 3b, PSO-SIFT further divides descriptors into 17 location bins following the 45\({}^{\circ}\) rule, enhancing their uniqueness. Considering that single-scale support regions may occasionally result in similar appearances for mismatched points, as illustrated in Figure 3c, I-SAR-SIFT proposes using multiple concentric circular support regions sampled from different scales to address this issue. However, this method still divides location bins at 90\({}^{\circ}\), leading to erroneous matches in terrain-similar areas. As shown in Figure 3d, combining the above two improvement methods, in constructing feature descriptors, we first compute feature  
vectors for the current scale feature point neighborhood via the Gaussian blurring of gradient magnitude and direction, extracting the feature vectors for the inner circle from the current layer, for the middle circle from the first Gaussian blur, and for the outer circle from the second Gaussian blur. The final descriptor comprises 3 \(\times\) 8 \(\times\) 8 = 192-dimensional feature vectors, incorporating multiscale spatial information and finer internal division to enhance descriptor uniqueness. Although the constructed feature descriptors in this paper have a relatively high dimensionality, the computational speed in practical experiments does not differ significantly from the methods mentioned above. Following the commonly used NNDR method and SIFT algorithm, distances less than 0.8 are considered correct matches, and the RANSAC algorithm is employed to eliminate mismatched points after NNDR matching.
## 3 Numerical Scheme
### Scheme Update
#### 3.6.2 Horizontal Polarisation  
Similarly to the vertical polarisation, a two-step recursion is derived from Schemes Equations (54a) and (57a):  
\[\mathbf{u}^{n+1} =\mathbf{B}\mathbf{u}^{n}+\mathbf{C}\mathbf{u}^{n-1}-A_{H}\cdot \mathbf{J}_{\mathbf{u}}^{n}\mathbf{f}_{\mathbf{u}}^{n} \tag{73a}\] \[\mathbf{u}_{FB}^{n+1} =\mathbf{B}_{FB}\mathbf{u}_{FB}^{n}+\mathbf{C}_{FB}^{n-1}+k^{2} \mathbf{M}_{FB}^{-1}\mathbf{A}_{FB}\left(\mathbf{f}_{\mathbf{u}FB}^{n}+\mathbf{ f}_{\mathbf{ext}\;u,FB}^{n}\right) \tag{73b}\]  
where \(A\), \(\mathbf{B}\), and \(\mathbf{C}\) are the same as defined in Equation (70). \(\mathbf{A}_{FB}\), \(\mathbf{B}_{FB}\), and \(\mathbf{C}_{FB}\) are \(2\times 2\) matrices, defined as:  
\[\mathbf{A}_{FB} =2\left(2\mathbf{M}_{FB}+k^{2}\mathbf{K}_{FB}+k\Lambda_{FB}\right) ^{-1}\mathbf{M}_{FB} \tag{74a}\] \[\mathbf{B}_{FB} =2\mathbf{A}_{FB}\] (74b) \[\mathbf{C}_{FB} =\frac{1}{2}\mathbf{A}_{FB}\left(-2\mathbf{M}_{FB}-k^{2}\mathbf{ K}_{FB}+k\Lambda_{FB}\right)\mathbf{M}_{FB}^{-1} \tag{74c}\]  
Due to the friction linearity, the horizontal update is also implicit. Equations (73a) and (73b) can be written as a nonlinear system in terms of the vector variable \(\mathbf{v}_{\text{rel}}^{n}\):  
\[\mathbf{v}_{\text{rel}}^{n}+\mathbf{\Lambda}_{3}^{n}\mathbf{f}_{\mathbf{u}}^{ n}+\mathbf{b}_{\mathbf{u}}^{n}=0 \tag{75}\]  
where the matrix \(\mathbf{\Lambda}_{3}^{n}\), and the vector \(\mathbf{b}_{\mathbf{u}}^{n}\) are defined as:  
\[\mathbf{\Lambda}_{3}^{n} =\frac{1}{2k}\left(A\text{diag}\left(\overline{\mathbf{h}} \right)\left(\mathbf{J}_{\mathbf{u}}^{n+1}\right)^{\mathsf{T}}\mu_{i}J_{ \mathbf{u}}^{n}+\mathbf{A}_{\text{obj}}\right) \tag{76a}\] \[\mathbf{b}_{\mathbf{u}}^{n} =\frac{1}{2k}\text{diag}\left(\overline{\mathbf{h}}\right)\left( \left(\mathbf{J}_{\mathbf{u}}^{n-1}\right)^{\mathsf{T}}\mathbf{u}^{n-1}-\left( \mathbf{J}_{\mathbf{u}}^{n+1}\right)^{\mathsf{T}}\left(\mathbf{B}\mathbf{u}^{ n}+\mathbf{C}\mathbf{u}^{n-1}\right)\right)+\left[\mathbf{0}_{\mathbf{N-1}}\left( \mathbf{b}_{\mathbf{u}FB}^{n}\right)^{\mathsf{T}}\right]^{\mathsf{T}}\] (76b) \[\mathbf{b}_{\mathbf{u}FB}^{n} =\frac{1}{2k}\left(\mathbf{B}_{FB}\mathbf{u}_{FB}^{n}+\left( \mathbf{C}_{FB}-\mathbf{I}_{\mathbf{2}}\right)\mathbf{u}_{FB}^{n-1}+\mathbf{A }_{FB}\mathbf{f}_{\mathbf{ext}\;u,FB}^{n}\right) \tag{76c}\]  
where \(\mathbf{A}_{\text{obj}}\) is a \((N_{\mathfrak{D}}+1)\times(N_{\mathfrak{D}}+1)\) matrix with \(\mathbf{A}_{FB}\) at its bottom-right corner and zeros elsewhere; \(\mathbf{I}_{\mathbf{2}}\) is the \(2\times 2\) identity matrix.
## 5 Conclusions  
The Zhuhai-1 video satellite plane array Bayer pattern sensor provides two imaging modes for one satellite, which expands the application capability of a single satellite but also brings up new problems for radiometric calibration. This study developed an on-orbit relative radiometric calibration method for the OVS satellite with a Bayer pattern push-broom imaging mode. The novel aspects of this study include:  
1. reconstructing the order of each detector of the Bayer pattern sensor and converting it into a virtual linear array sensor to separate the RGB three-band components from the Bayer pattern; and  
Figure 17: Anomalous changes in the radiometric response of CMOS edge detectors of the OVS at specific times during calibration of the 2019 data: (**a**) 26 October; (**b**) 28 October; (**c**) 29 October; (**d**) 30 October; (**e**) 1 November; and (**f**) 2 November.  
2. designing a positioning method linked by \"linecounters\" to detect the specific position of each detector of each Bayer pattern. The visual and quantitative assessment of the calibration and correction results revealed the superior performance of the method at reducing image stripes and preserving image details. Furthermore, this study also provides an effective method for the on-orbit radiometric calibration of Bayer pattern sensors and meaningful lessons for the radiometric calibration of similar remote sensing satellites. The Bayer interpolation algorithm is greatly affected by the radiometric response error of the Bayer detectors. Therefore, the relative radiometric calibration method has a pronounced impact on the Bayer interpolation and must be performed for Bayer pattern type remote sensing sensors. From the calibration results, the on-orbit relative radiation calibration accuracy of the OVS was better than 1%, meeting the technical index requirements. The image streaks and strip noise were effectively removed after calibration, and the image radiation quality was greatly improved; however, there were occasional radiation response anomalies in the single CMOS edge detectors of the OVS, which increased the uncertainty of the on-orbit radiation calibration and the subsequent application of the image.  
Conceptualization, L.L. and Y.J.; methodology, L.L.; software, Z.L. and Z.W.; validation, X.S. and J.W.; formal analysis, L.L.; investigation, Z.L.; resources, Z.W.; data curation, J.W.; [PERSON] wrote the paper; and all authors edited the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 41971412, 42171341.  
Restrictions apply to the availability of these data. We thank the research team at Orbita for providing the OHS images. Furthermore, we would like to thank the reviewers for their helpful comments. The authors declare no conflict of interest.
## 2 Materials and Methods
### Multiple Linear Regression of Simulated Results  
In the Nordmann method, Equations (1) and (3) above show that TAC is a linear function of the first equivalent volume (i.e., A in mL). Equations (2) and (4) show that FOS is a linear function of the difference of the two equivalent volumes (i.e., B in mL). Since FOS and TAC interfere in the titration of each other, two new 3-parameter models were proposed in the present work to calibrate the determination of TAC and FOS. This was a multiple linear regression assuming that TAC and FOS depended on both A and B.  
With the simulated data to be validated by experiments, and by using the Scilab solver for multiple linear regression, it was possible to identify the model coefficients to calculate the bicarbonate (or equivalent TAC) and acetic acid concentrations:  
\[\left[\text{NaHCO}_{3}\right]_{\text{new}}=\alpha_{1}\times\text{A}+\alpha_{2} \times\text{B}+\alpha_{3} \tag{6}\]  
\[\left[\text{HAc}\right]_{\text{new}}=\text{FOS}_{\text{new}}=\beta_{1}\times \text{A}+\beta_{2}\times\text{B}+\beta_{3} \tag{7}\]  
where [NaHCO\({}_{3}\)]\({}_{\text{new}}\) (mg NaHCO\({}_{3}\cdot\)L\({}^{-1}\)) is the new sodium bicarbonate content; [HAC]\({}_{\text{new}}\) (mg HAC\(\cdot\)L\({}^{-1}\)) is the new acetic acid content, namely, FOS; A (mL) is the equivalent volume of 0.1 N H\({}_{2}\)SO\({}_{4}\) to obtain pH = 5; B (mL) is the difference of two equivalent volumes at pH = 5 and pH = 4.4; \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) and \(\beta_{1}\), \(\beta_{2}\) and \(\beta_{3}\) are the model parameters for multiple linear regression.  
The simulation range of sodium bicarbonate and acetic acid contents was both extended to 40,000 mg\(\cdot\)L\({}^{-1}\) equivalent to TAC = 23,800 mg CaCO\({}_{3}\cdot\)L\({}^{-1}\) and FOS = 40,000 mg HAC\(\cdot\)L\({}^{-1}\), since the simulation was proved to accurately predict the experimental results (see Sections 3.1 and 3.2).  
By applying Equation (5), [NaHCO\({}_{3}\)] could be converted to the equivalent alkalinity (TAC\({}_{\text{new}}\)). One could also use the FOS and TAC values calculated with the Nordmann formulas (i.e., TAC\({}_{\text{emp}}\) and FOS\({}_{\text{emp}}\)), recalculate the values of \"A\" and \"B\" and then reinject these values into the above formulas in order to correct the initial FOS and TAC (see Section 3.3).
## 4 Our Construction
### System Overview  
Figure 2 shows the overview of the proposed scheme. There are three entities in the system: the client, the server, and the SGX enclave within the server. The client is the data owner, which is assumed to be trusted. We assume that all the entities above act normally and do not consider incorrect queries or wrong operations. As described in Section 3.1.1, although the enclave is located within the untrusted server, the server cannot observe the code or data inside the enclave. Also, we assume that the enclave is fully trusted and resilient to side-channel attacks.  
In the proposed scheme, the client establishes a secure channel with the authenticated enclave using the SGX remote attestation service. The client provisions a secret key \(K\) in the enclave through the secure channel (step 1). During this procedure, the client does not deploy any encrypted database (EDB) to the server as in the other DSSE schemes [5; 8].  
The update procedure in the proposed scheme consists of two different operations: addition and deletion. When adding a document to the cloud storage, the client assigns a unique identifier \(id\) to the document, and encrypts the document with a key \(K\) (Step 2). Then, the encrypted document and its \(id\) are sent to the enclave through a secure channel (Step 3). With the received \(id\) and encrypted document, the enclave extracts all the keywords within the document, and performs cryptographic operations to generate an update token with the encrypted index and state. The update token and encrypted document are then transferred to the server (Step 4). The map of the encrypted index \(M_{I}\) is separately managed in the untrusted area. When deleting a document, the client simply sends the document identifier \(id\) to the enclave (Step 5 & Step 6). (The actual deletion process of the document will be further explained in Section 4.3).  
When retrieving documents from the outsourced EDB, the client sends the search keyword \(w\) to the enclave through the secure channel (Step()& Step()8). The enclave then computes the query tokens, and sends the final search query to the server except the tokens for the deleted documents (Step9). The server then searches over the encrypted index, and returns back the document identifier list to the enclave (Step()0). With the identifier list, the enclave searches over the cached document list; and returns the identifiers not matching any of the cached documents to the server to retrieve the encrypted documents (Step()0). Finally, the enclave sends the matching documents to the client through the secure channel (Step()2).
## 4 Research Method
### Sample and Data Collection  
In this study, research volunteers were systematically recruited through a targeted questionnaire on the \"Credamo\" platform, focusing on professionals in EV supply chain industry. The recruitment process implemented stringent selection criteria: participants were required to provide a brief work histories, demonstrating industry-specific experience. To ensure comprehensive geographic representation across China, a minimum 10 km distance between volunteer locations was established. A total of 713 initial applicants underwent rigorous screening. Exclusion criteria included less than three years of industry experience, lack of supply chain-related work experience, or employment outside the EV supply chain sector. Where multiple candidates from the same company applied, only the highest-position individual was selected. Ultimately, 593 qualified volunteers comprised the research sample pool.  
The determination of sample size follows the \"10-times rule\", which states that the minimum sample size should be at least 10 times the number of measurement items for each construct [80]. In this study, the construct with the most items is Supply Chain Resilience (6 items), so based on the \"10-times rule\", the minimum sample size should be 60. Additionally, statistical power analysis using G*Power was conducted, considering a medium effect size (\(f^{2}=0.15\)) and 80% statistical power. The calculation results indicate that for the basic path model, the minimum sample size should be 85 to effectively detect significant effects [81]. Given that this study involves moderation effects, which increase the complexity of the model, it is recommended that the sample size should range between 150 and 300 to ensure the robustness and effectiveness of the moderation effects.  
Participants for the formal survey were randomly selected from the sample pool. To avoid duplication, those who participated in the pilot study were excluded. A total of 300 questionnaires were distributed, with invalid responses (e.g., incomplete or quickly completed) excluded. This resulted in 249 valid responses, achieving an 83% response rate. The valid sample size falls within the recommended range of 150-300. The sample's demographic characteristics are shown in Table 2.
\(coli\) complexed with its substrates IPP and dimethylallyl diphosphate (DMAPP) (Protein Data Bank (PDB) ID code: 1 RIQ). The diphosphate moiety of the allylic DMAPP is activated by coordination to three Mg\({}^{2+}\) that are, in turn, bound by the characteristic first and second aspartate-rich DDxxD motifs (FARM and SARM), as shown. More specifically, Mg\({}^{2+}\)A and Mg\({}^{2+}\)C are coordinated by the first and last aspartate of the FARM, while Mg\({}^{2+}\)B is largely coordinated by the first aspartate of the SARM. By contrast, the diphosphate moiety of IPP is more directly bound by the basic residues shown. Note that, while the structure contains the thiolo analog of DMAPP, for illustrative purposes the color of the sulfur was changed to red to resemble that of the oxygen found in DMAPP. (\(Z\),\(E\))-FPP is produced by the short-chain _cis_-IDS Rv1086, which uses (\(E\))-geranyl diphosphate (GPP) as its allylic substrate [13]. This unusual bacterial production of the 10-carbon GPP is mediated by the _trans_-IDS Rv0989c (\(grc\)C2) [14]. An intriguing cell wall lipid that appears to be unique to _M. tuberculosis_ is the diterpenoid (20-carbon) nucleoside tuberculosis-adenosine [15]. This is generated by cyclization of (\(E\),\(E\),\(E\))-geranylgeranyl diphosphate (GGPP) to tuberculosis/halima-5,13\(E\)-dienyl diphosphate catalyzed by Rv3377c [16; 17], and subsequent addition of adenosine catalyzed by Rv3378c [18]. A role for this diterpenoid in suppressing acidification of the phagosomal compartments into which _M. tuberculosis_ are taken up, which assists infiltration of the engulfing macrophage that then serve as host cells, was indicated by a genetic screen [19]. Indeed, the diterpenoid itself appears to suppress phagosomal acidification [20]. However, the genetic screen only identified Rv3377c and Rv3378c as essential for this biosynthetic process [19], and not the _trans_-IDS GGPP synthase Rv3383c (idsB) found in the same operon [21]. This is presumably due to the presence of an additional _trans_-IDS that produces the 20-carbon GGPP in _M. tuberculosis_, Rv0562 (\(grc\)C1) [21].
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction
### Feature Selection Algorithms
#### 2.3.7. Mayfly Algorithm  
The Mayfly Algorithm (MA) is a population-based optimization method that combines the advantages of particle swarm optimization (PSO), genetic algorithms, and the firefly algorithm (FA). It is inspired by the behavior of adult mayflies, including their processes of crossover, mutation, gathering in a swarm, nuptial dance, and random walk. The MA algorithm was first proposed by [PERSON] and [PERSON] in 2020 and has been used to solve various optimization problems [21; 58].  
The key steps involved in MA are the following [21; 58]:* Step 1: An initial population of candidate solutions (mayflies) is established within the search space. Each mayfly is classified as male or female and has a given velocity depending on it.
* Step 2: The fitness of each candidate solution is measured using a fitness function.
* Step 3: The position and velocity of male mayflies are updated based on their current position and velocity, their previous best position, and the current global best position. For female mayflies, their position is updated based on their velocity and the position of the current best male solution.
* Step 4: One parent is selected from the male population and the other from the female population. The selection can either be random or based on their fitness. In the latter, the best female breeds with the best male, the second best female with the second best male, and so on. Through this process as well as the use of differential crossover and mutation, new solutions are generated.
* Step 5: Each offspring solution is measured using the fitness function. They are then separated between the two populations, following which the worst older solutions are replaced with the best new ones. At this point, the best positions and best global position are updated.
* Step 6: Steps 2-5 are repeated until a stopping criterion is reached, such as reaching a maximum number of iterations or attaining a satisfactory solution.
## 7 RF-EH Circuit
### Topologies of the Impedance Matching Network (IMN) and the Input RF Filter
#### 7.3.2 IMN Configurations  
It is critical to match impedances in RF circuitry because it is necessary to do so in order to avoid standing waves and to ensure efficient power transfer from the source to the load.  
Multiband Matching Network  
Recently, the author in [149] employed a dual-band multisection MN comprising the configured rectifier circuit. A stack of rectennas with triple dual-band bandpass IMNs on each rectenna and a related rectifier circuit was developed [128]. In addition, a fourth order dual-band IMN was applied in [150] via a combined series and parallel LC pair. Meanwhile, a three-band IMN was employed using a couple of stubs [151].  
[PERSON] et al. [152] implemented a quad-band MN with a series of MNs, where each MN was tuned to a specific resonant frequency. [PERSON] et al. [153] studied the operations of independently associated common matching (CM) and two-branch MNs with a voltage octuple rectifier. For the CM method, the MN was linked between the two-stage rectifier circuit and the input port. In contrast, the TBM methods were carried out by dividing the total feedline impedance of 50 \(\Omega\) equally into two branches of 100 \(\Omega\). Therefore, the two-stage rectifier circuit exhibits a matching impedance of 100 \(\Omega\). The TBM technique obtained better impedance matching than that of the CM technique. Additionally, the rectenna performance with a two-branch MN also showed higher results than a standard MN. The rectenna size increased with an input RF filter, hindering their applications. Moreover, the holding harmonic rejection property of the rectenna suitably fitted the design. Interestingly, the rectenna itself holds the harmonic rejection property [154].