esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 3 The Proposed Signature Scheme
### The Proposed Scheme
#### 3.1.3 Correctness  
The sender calculates \(s_{1}\) and \(s_{2}\) using his/her private keys, where \(s_{1}=m^{k_{s}}\) and \(s_{2}=m^{r_{2}\times p}\); the receiver recovers \(m\) and calculates \(x=m^{pk_{s}}\), where \(pk_{s}=k_{s}+r\times p\) and\(r=r_{1}\times r_{2}\). Then, the receiver calculates \(y=(s_{1}\times s_{2}^{r_{1}})\longrightarrow y=m^{k_{s}}\times(m^{r_{2}\times p})^{ r_{1}}\); this implies \(y=m^{k_{s}}\times m^{r_{1}\times r_{2}\times p}=m^{k_{s}}\times m^{r\times p}\); \(\longrightarrow y=m^{k_{s}+r\times p}=m^{pk_{s}}\); this gives \(y=x\).  
```
0: cipher text \(c\), signature \(sig_{m}\), public keys \((pk_{s},n,n^{\prime},r_{1})\), private key \(k_{r}\)
0: decryption and message integrity functionVerf
2: compute \(m:m\gets Dec_{k_{r}}(c)\) compute \(x:x\gets m^{pk_{s}}\mod n^{\prime}\) compute \(y:y\leftarrow(s_{1}\times s_{2}^{r_{1}})\mod n^{\prime}\) if\(x=y\)then
3:return\(m\) else returninvalid endif
4:endfunction
```  
**Algorithm 2** Verification algorithm.  
Therefore, the proposal is correct and the data receiver can easily verify sender authenticity using the sender's public key.
# Power System State Estimation Approach Considering Transmission Line Temperature
## 1 Introduction
Considering the parameter errors and bad measurements, a robust SE method was proposed, which is formulated as a tractable mixed-integer linear programming optimization problem [26]. The distributed SE operates locally with minimal data exchange with neighbors and is applicable for multi-area power systems [27]. Based on the iterative reweight least square algorithm, a distributed SE method was proposed, where the improved alternating direction method of multipliers is utilized to improve result accuracy and convergence speed [28]. In addition, the placement of PMUs was optimized to improve the reliability of SE results [29]. Considering bad data and missing measurements, a multi-objective PMU allocation method was presented for achieving the desired accuracy of SE [30]. The unscented Kalman filter was used to estimate the states of a permanent magnet synchronous motor (PMSM) [31]. In addition, a multi-area distributed SE method was proposed with the use of the data-driven Kalman filter [32]. In order to estimate the states of electric vehicle batteries (EVBs), a robust adaptive filter, an adaptive smooth variable structure filter has been designed [33]. The aforementioned SE methods are summarized in Table 1.  
For the sake of reducing the temperature-dependent errors, this paper proposes two ETC-SE approaches with the consideration of transmission line temperature. In the augmented Jacobian ETC-SE (A)-ETC-SE) approach, line temperature is integrated into state variables, and the HBE serves as a pseudo-measurement. Compared to the weighted least square SE (WLS-SE), the dimension of the measurement and state vectors increases, but the equation set is still overdetermined. Additionally, the elements in the augmented Jacobian matrix are provided, and the equation set is solved using the Newton method. On the other hand, the improved two-step ETC-SE (ITS-ETC-SE) approach decouples the calculation process of voltage phasors and line parameters. Compared with the AJ-ETC-SE approach, the SE and temperature estimation are solved via alternate iteration so as to reduce the iteration number and computation time. Therefore, the main contributions of this paper are three-fold. Firstly, an ETC-SE model integrating the HBE and WLS-SE is established to consider the impact of line temperature on power system states. Secondly, the AJ-ETC-SE approach is proposed to simultaneously solve the problems of SE and temperature estimation through an augmented Jacobian matrix. Finally, the ITS-ETC-SE approach is proposed to accelerate the solving process, in which the SE and temperature estimation are decoupled and solved via alternate iteration. The rest of this paper is organized as follows. Section 2 develops an ETC-SE model with the consideration of the HBE and meteorological data. The procedure of the AJ-ETC-SE approach is presented in Section 3. Moreover, Section 4 gives the ITS-ETC-SE approach. The case studies of the IEEE 14-, 39-, and 118-bus systems are conducted in Section 5. Finally, conclusions are drawn in Section 6.
## 2 Materials and Methods
### Characterization of the Sol-Gel Silica Materials and of Thin Films
#### 2.3.5 UV/Vis Spectroscopy  
Obtained samples were also characterized as thin films (deposited on the PVC substrates) using a UV/Vis/NIR Jasco V-570 spectrophotometer equipped with a large integrating sphere ILN-472 (JASCO Int. Co., Ltd., Tokyo, Japan), in order to analyze the transmittance in the range of 380-780 nm. Each sample was measured three times (standard deviation \(\pm\) 0.2%, at 550 nm).
# Model-Driven Approach to Cloud-Portability Issue
## 2 Related Works
### Interoperability and Portability of CC Systems  
ITU-T in its recommendation Y.3502 [15] defined several cross-cutting aspects of Cloud Computing. Two of them are interoperability and portability. Interoperability is defined as the ability of a cloud service customer to interact with a cloud service and exchange information according to a prescribed method and obtain predictable results. According to [16], general interoperability is the ability of different systems and organizations to cooperate. In [17], CC interoperability is the ability of public CC environments, private CC environments and other systems to share system and application interfaces, configuration, authentication, authorization, data formats and so on due to mutual cooperation. Interoperability is also the topic of many articles; in the publication [18], the authors refer to the need to standardize CC in all respects so that the public can use it widely.  
Portability is defined according to ITU-T Y.3502 as an ability of CC customers, which allows them to move their application's data between multiple cloud service providers at low cost and with minimal disruption. The amount of cost and disruption that is acceptable can vary based upon the type of cloud service that is being used. When interoperability is defined as communication between different systems, portability is the ability to use systems or their components on different hardware or software platforms [16; 19; 20]. Portability can also be defined as the ability to transfer an individual entity or the entire environment without the need to modify it [21]. In [22; 23], the portability of CC is divided into data, application and platform.  
There are more papers and conference proceedings dealing with cloud migration and portability, such as [24; 25; 26; 27; 28; 29; 30]. There are some papers dealing with cloud and computer interoperability from other scientific fields such as medicine [31; 32], smart cities and IoT [33; 34; 35], transportation [36] or agriculture [37]. But they usually deal with virtual machine (VM) migration. None of these solved environment migration between different cloud environments.  
During our long-term research on the portability and interoperability of the IaaS model, we did not come across any articles addressing or examining this issue. Most research focused on the portability and interoperability of the SaaS model, or, to a lesser extent, the portability of the PaaS model [29; 30; 38]. The IaaS service model contains more technical and hardware details compared to the SaaS and PaaS models, making its description more complex and difficult to understand. This is also related to the portability issue, as the administrator needs to understand the various nuances of both the source and target environments in order to transform the description in the form of code without errors.
# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 3 Detection-Tracking Framework
### Target Tracking with Deep Features  
In the aerial images, the size of the target is small. Compared with the whole image, the number of pixels occupied by the region of the target is small. In this case, the traditional features of the target, such as RGB feature, will reduce the ability to describe the target.  
Convolution neural network use convolution kernel to enlarge the sensory field of each pixel. Through the convolution layer, a low-resolution region can merge the surrounding pixel information. Therefore, aiming at the small target in aerial images, we use the convolution feature to obtain better feature expression ability.  
In order to simplify the model structure and reduce the model parameters, deep features are extracted using their corresponding convolution networks according to different detection modules.  
The convolution network structure for extracting deep features is shown in Figure 2. For Faster R-CNN, the VGG-16 network is used to extract deep features. The conv1 layer and the conv4 layer in the convolutional layer are selected as the deep feature output, andwe obtain DF1 and DF2. For YOLOv3, the Darknet-53 network is used to extract deep features. We select the 2 nd and 27 th convolutional layers as the depth features to obtain the DF1 and DF2 features.  
DF1 obtained from the input image through fewer convolutional layers has a large amount of image spatial information, which represents the low-dimensional image feature of the target itself. DF2 feature has been processed by a large number of convolutional layers and represents the high-dimensional semantic features of the target region.
## 2 Methodology
### Process Modeling  
The solution-diffusion transport model [56; 57], which presumes that the RO membrane does not have porous or imperfections, was utilized. This model is based on considering that each solvent and solute are dissolved in the membrane separately on the feed-brine side and then diffused in individual fluxes through the membrane under the effect of pressure and concentration gradients. This is the most extended model and provides results close to the real behavior of RO systems for both seawater and brackish water [58]. The mentioned transport model was implemented in the algorithm [53] as it usually provides results close to the real behavior of these systems. The transport equations used mean membrane element values, and permeate pressure drops as well as \(T\) changes along the RO system were disregarded. The calculation algorithm considers some simplifications that have been detailed in a previous work [53]. Figure 2 shows the inputs and outputs of the calculation algorithm considering the constraints established by the membrane manufacturer (maximum permeate flow (\(Q_{\text{P-max}}\)), minimum rejection flow (\(Q_{\text{r-min}}\)) and maximum feed flow (\(Q_{\text{f-max}}\)). One of the main limiting factors in BWRO desalination is the presence of poorly soluble compounds in the feedwater that can cause scaling. As a result, antiscalant products are commonly used in BWRO desalination plants to avoid the problems caused by scaling and increase the maximum flux recovery (\(R_{\text{max}}\)). \(R_{\text{max}}\) depends, amongst other things, on the type of antiscalant that is used. The calculation algorithm has a specific function (R function) where \(R_{\text{max}}\) is calculated for various antiscalants [59]. The aforementioned algorithm provides the possible operating points in accordance with the considered constraints and BWRO system configurations. With the inputs, the algorithm calculates the outputs considering the mean operating parameter values per SWMM. The calculation algorithm assumes a negligible pressure decrease on the permeate side, constant pressure drop along the membrane elements on the feed-brine side, constant permeate flow per membrane element, constant feed-brine concentration (\(C_{\text{fb}}\)) on the  
Figure 1: Brackish water reverse osmosis (BWRO) desalination system. membrane surface (\(C_{\rm m_{i}}\)) and constant membrane element feed pressure (\(p_{\rm f_{i}}\)). Equations (1) and (2) [60] were used to determine the outputs of the BWRO system.
# Predictors of Analgesic Consumption in Orthodontic Patients
## 4 Discussion
### Predictors for Analgesic Consumption
The researchers have shown that the orthodontic pain pathway and headaches have related local inflammatory mechanisms transmitted by the trigeminal nerve, which may partially explain the presence of headaches in orthodontic patients [17]. Furthermore, the literature is ambiguous regarding the question of whether people with malocclusion were more likely to suffer from headaches [18]. It has been proved that peak pain intensity level is on the first day of orthodontic treatment [3; 19; 20]. The analgesics intake is correlated with pain intensity throughout the first week of orthodontic treatment [12]. These results are in concordance with the results in this study, therefore the data obtained for the first day of orthodontic treatment were used as representative. Analgesics consumption decreased significantly with each day of orthodontic treatment, reaching such low levels which could not be used for statistical analysis. The pain caused by orthodontic treatment is sometimes underestimated, or even neglected. Patients treated with fixed orthodontic appliances experience pain and have the need to alleviate it with self-prescribed analgesics. The importance of analgesics consumption lies in the fact that there are implications that some analgesics could impact orthodontic tooth movement in terms of reducing it [21]. Monitoring of analgesics consumption has been proposed in order to avoid lengthening of treatment [21]. According to the results of the present study, attention can be also drawn to predictive factors (age, intensity of pain, and headaches). The results of this study indicate the need for further research on the association of orthodontic pain with the occurrence of a headache.
## 4 Experimental Results
### Inputs to Build VKGs: Ontology, Mappings, Queries, Databases, and Ontop
#### 4.4.2 SPARQL Queries and Mappings  
Ontop mainly supports two mapping languages: the W3C RDB2 RDF ([[https://www.w3.org/2001/sw/wiki/RDB2](https://www.w3.org/2001/sw/wiki/RDB2) RDF]([https://www.w3.org/2001/sw/wiki/RDB2](https://www.w3.org/2001/sw/wiki/RDB2) RDF) (accessed on 21 May 2025)) mapping language (R2 RML), a widely used standard, and the native Ontop mapping language, which is easier to learn and use to retrieve external data from external disparate sources. Ontop includes tools for converting native mappings into R2 RML mapping and vice versa. Intuitively, a mapping assertion consists of a source, an SQL query retrieving values from the data sources or bases, and a target, which constructs RDF triples with values from the source [48].  
**Example 2**.: _The part of the ontology in Example 1 can be populated from the database (see Figure 7). The simplified Ontop mapping syntax can be presented as follows (see Table 3):_  
**Example 3**.: _The
# A Cumulant-Based Method for Acquiring GNSS Signals
## 2 Galileo Overview  
Today the main modulation technique used in the Galileo signal is Composite Binary Offset Carrier (CBOC) Modulation [14; 20; 21], which is composed of two different BOC signals. CBOC consists of a lower frequency BOC component (e.g., BOC(1,1)) and a higher frequency BOC component (e.g., BOC(6,1)). The description of CBOC is given as CBOC(m1, m2, r), where m1 and m2 are the two sub-carriers, respectively, and r is the power ratio of the first sub-carrier relative to the total signal power.  
Taking the Galileo E1 signal, for example, the primary code is modulated by CBOC(6,1,1/11). In the following we describe and combine all elements necessary to generate the Galileo L1 OS signal.  
The transmitted bandwidth is 40 \(\times\) 1.023 MHz = 40.92 MHz. The chip length of the ranging code is  
\[T_{c,L1-B}=T_{c,L1-C}=1/1.023\ \mathrm{Mchip/s}=977.5\ \mathrm{ns}. \tag{1}\]The corresponding ranging code rates are  
\[R_{c,L1-A}=2.5\times 1/1.023\;\mathrm{Mchip/s},\]  
\[R_{c,L1-B}=1/T_{c,L1-B}=1.023\;\mathrm{Mchip/s},\]  
\[R_{c,L1-C}=1/T_{c,L1-C}=1.023\;\mathrm{Mchip/s},\]  
and the subcarrier rates are  
\[R_{sc,L1-B,n}=R_{sc,L1-C,n}=1.023\;\mathrm{MHz}\times n.\;(\mathrm{for}\; \mathrm{CBOC}(6,\;1,\;1/11),\;n=1,\;6)\]  
The signal components for channel B results from modulo-2 addition of the navigation data stream \(d_{L1-B}\), the PRN code sequence \(c_{L1-B}\), and the B subcarrier \(sc_{L1-B}\). The final component is denoted as \(e_{B}\). Similarly, the C channel results from the modulo-2 addition of the C channel PRN code sequence \(c_{L1-C}\) with the C channel subcarrier \(sc_{L1-C}\). The component is \(e_{C}\). The signal components are as follows:  
\[e_{B}(t)=\sum_{-\infty}^{\infty}\Bigl{(}c_{L1-B,(i\;mod\;4092)}d_{L1-B,(i\;mod \;4)}\mathrm{rect}_{T_{c,L1-B}}(t-iT_{c,L1-B})\times\mathrm{sign}\;(\sin(2\pi R _{c,L1-B}t))\Bigr{)} \tag{2}\]  
\[e_{C}(t)=\sum_{-\infty}^{\infty}\Bigl{(}c_{L1-C,(i\;mod\;4092)}d_{L1-C,(i\;mod \;4)}\mathrm{rect}_{T_{c,L1-C}}(t-iT_{c,L1-C})\times\mathrm{sign}\;(\sin(2\pi R _{c,L1-C}t))\Bigr{)} \tag{3}\]  
Here \(\mathrm{rect}_{T}\) ( ) is a rectangular function which is equal to 1 for 0 \(<\) \(t\) \(<\) \(T\) and equal to 0 elsewhere. The E1 channel signal can then be represented as follows:  
\[s(t)=\frac{1}{\sqrt{2}}(e_{B}(t)(\alpha sc_{L1-B,6}(t)+\beta sc_{L1-B,1}(t))-e _{C}(t)(\alpha sc_{L1-C,6}(t)-\beta sc_{L1-C,1}(t))). \tag{4}\]  
with \(sc_{X}(t)=\mathrm{sgn}(\sin(2\pi R_{sc,X}t))\). In this case, the parameters \(\alpha\) and \(\beta\) are chosen to match the CBOC(6,1,1/11). That is, \(\alpha=\sqrt{10/11}\) and \(\beta=\sqrt{1/11}\). One period of the sub-carrier for the E1 signal component is shown in Figure 1. Figure 2 compares the primary code modulated by CBOC with the GPS C/A code. It is evident that the autocorrelation of the CBOC signal exhibits three peaks, which presents a significant challenge that many researchers are keen to address.  
Figure 1: One period of the CBOC sub-carrier for E1 signal component.
## 1 Introduction
In order to verify the performance of the proposed antenna design method, simulation and implementation results are presented. Additionally, the achieved performance is compared with those of several antenna designs in the literature. The obtained results demonstrate the superiority of the proposed approach. The contents of this paper are as follows: After this brief introduction, Section 2 presents a brief overview of Support Vector Regression. Section 3 describes the proposed design method for the triple-band antenna. Simulation and implementation results, as well as a comparison with existing designs in the technical literature, are presented in Section 4. Additionally, a comparison with the design carried out with the HFSS optimization tool is provided in Section 5. Section 6 briefly concludes the paper.
# Free Vibration Analysis of Functionally Graded Sandwich Plates with a Homogeneous Core
###### Abstract  
The functionally graded (FG) sandwich plate has attained much attention in recent years due to its potential for exhibiting the merits of sandwich construction and FGM. Accordingly, intensive studies have focused on FG sandwich plates to investigate their mechanical behaviors. However, these mechanical behaviors are still in need of further investigation, particularly with respect to the major parameters. In this context, this paper intends to parametrically investigate the free-vibration behavior of the FG sandwich plate with a homogeneous core by developing a reliable and effective numerical method. This numerical method was based on hierarchical models, developed from the spectral model accuracy, and the 2-D natural element method (NEM). The hierarchical models were derived from the 3-D elasticity and the NEM was characterized by high smooth interpolation functions. From the verification experiments, the proposed method shows a good agreement with the reference and a uniform convergence to the 3-D elasticity. The free vibration characteristics of FG sandwich plates with a homogeneous core were investigated using the proposed numerical method. It was found that the calibrated fundamental frequency was significantly influenced by the type of material composing the core, the volume fraction index, the relative thickness and position of core layer, and the plate aspect ratio.  
functionally graded sandwich plate; homogeneous core; FGM face; free vibration analysis; hierarchical models; natural element method +
Footnote †: journal: Applied Sciences
## 5 Implementation Scheme of Injection Harmonic Current Differential Protection  
### Selection of the Frequency and Amplitude of the Characteristic Harmonic Current  
For the injection protection scheme, the selection of the frequency of the CHC is crucial, and the following constraints need to be considered.  
1. The characteristic frequency should not exceed ten times the fundamental frequency. The resonant frequencies of IBDG filters are generally widely distributed from 10 times the fundamental frequency to 0.5 times the carrier frequency. The characteristic frequency is less than ten times the fundamental frequency, which can avoid the resonance of CHCs with IBDG filters and thus ensure the safe and stable operation of IBDG. In order to suppress the shunting effect of the non-injected source IBDG on CHCs, the characteristic frequency cannot be much smaller than the resonant frequency.
2. The characteristic frequency should be as far from the fundamental frequency as possible. When the distribution network is disturbed, or after a fault occurs, the harmonics will inevitably be generated, mainly low-order harmonics. If the characteristic frequency is far from the fundamental frequency, it can minimize the interference of the generated harmonics to the injection protection.
3. The characteristic frequency should be taken as an integer multiple of the fundamental frequency. The selection of integer multiples of the fundamental frequency facilitates the extraction, analysis, and processing of the Fast Fourier Transform (FFT), wavelet transform, and other standard time-frequency analysis methods, shortens the signal  
Figure 3: Schematic diagram of the distribution network with IBDG.  
processing delay, and improves the engineering practicality of the injection protection method.  
For the selection of the amplitude of the CHC, according to the latest Chinese regulations [33], the total harmonic distortion rates of the current injected into the grid are limited to 5% when the grid-connected inverter is operating at rated power. Therefore, the amplitude of the injected CHC needs to be less than 5% of the output current of IBDG.  
In this paper, the eighth harmonic (with a frequency of 400 Hz) currents are injected into the d-axis and q-axis reference currents of the IBDG current inner loop control link. There will be seventh harmonic (with a frequency of 350 Hz) currents in the output currents of IBDG. The CHC injected into the grid remains at 4% of the IBDG output current amplitude in normal operation and under various fault scenarios. The amplitude \(a\) of the characteristic harmonic current should satisfy the following relation.  
\[a=4\%\times\sqrt{\left(I_{d\_ref}\right)^{2}+\left(I_{q\_ref}\right)^{2}} \tag{16}\]