esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Image Reconstruction Technique for Parallel Phase-Shifting Digital Holography
## 4 Discussion  
Our experiment results prove that the proposed technique can effectively reduce the calculation time for parallel phase-shifting digital holography without the degradation of the reconstructed images. The proposed technique is theoretically equivalent to the conventional technique; therefore, we mainly discuss only the calculation time.  
As shown in Table 1, the calculation time for the proposed technique was mostly occupied by the diffraction calculation. In this study, the FFT program offered by the fastest Fourier Transform in the West library [34], was used for the diffraction calculation. The use of the graphical processor units (GPUs) for the FFT calculation will substantially reduce the calculation time [35]. Ref. [35] reported that the diffraction calculation time using GPU is approximately 5% of that using CPU. When this time reduction ratio is applied to our results, the total calculation time could be reduced to approximately 40 ms, and image reconstruction with a frame rate of approximately 40 Hz can be achieved. Considering recent advances in GPU technology, video rate (60 Hz) image reconstruction can be achieved in the near future.  
The proposed technique replaced the four image interpolation calculations with the two multiplication operations using the fixed complex-number matrices. These multiplication operations can be performed using parallel processing. Therefore, the use of GPUs will also reduce the calculation time for multiplication operations.  
In this study, we used the monochrome polarization image sensor. Color polarization image sensors have also been developed and used for incoherent digital holography [27]. When the color image sensor was used, the four extracted images corresponding to the four  
Figure 6: Magnified images of the reconstructed images obtained using the proposed technique when the interpolation algorithms were (**a**) bilinear, (**b**) bicubic, and (**c**) Lanczos-3.  
phase differences become sparse, and the interpolation kernel size increased. Therefore, the proposed technique is more effective for color digital holography that uses color polarization image sensors.
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 2 Methodology
### Protocol Establishment and Study Scope  
According to [PERSON] et al. (2018), the incorporation of the PICOC method at each stage of the PSALSAR framework ensures transparency and transferability of the study by providing a prescribed structure to decompose research questions and improve the definition of the research scope. The PICOC framework together with the definition of each concept is listed in Table 2.  
Given that the primary research question revolves around the effectiveness of inertial sensors in assessing the BP of adult humans, while taking into account the strengths and limitations of various methods employed for quantifying, evaluating, and mapping changes in BP, the following sub-questions were formulated:  
1. What methods are being employed to enhance IMU-based adult human BP assessment?
2. Which classifiers are being employed and best perform detecting adult human BP?
3. How reliable are IMUs in identifying signals associated with adult human BP?
4. Is there an ideal number of sensors and configurations to accurately assess adult human BP?
5. What are the main challenges and limitations when using inertial sensors in assessing adult human BP?
6. What insights have been gained, and what future directions are envisioned for applying inertial sensors in the assessment of adult human BP?  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Steps** & **Outcomes** & **Methods** \\ \hline Protocol & Define study scope & PICOC (Population, Intervention, Comparison, Outcome, and Context) framework. \\ \hline Search & Define the search strategy & Searching strings Search databases \\ \hline Appraisal & Selecting studies Quality assessment of studies & Defining inclusion and exclusion criteria Quality criteria \\ \hline PSALSAR Framework & Synthesis & Extract data Categorize the data & Extraction template Categorize the data on the iterative definition and ready it for further analysis work \\ \hline Analysis & Data analysis & Qualitative categories, description, and narrative analysis of the organized data \\  & Result and discussion & Identify emerging trends, pinpoint existing gaps, and provide comparative evaluation of the results. \\ \hline Report & Conclusion & Deriving conclusion and recommendation \\  & Report writing & Summarizing the results using PRISMA methodology \\ \hline \hline \end{tabular}
\end{table}
Table 1: Protocol and Reporting result with Search, Appraisal, Synthesis, and Analysis (PSALSAR) framework used in the study.
# Recent Progress in Multiplexed Single-Photon Sources
## 3 Multiplexed Single-Photon Sources
### Spectral Multiplexing  
Recently, a novel type of multiplexed sources called spectrally multiplexed SPS has been proposed and demonstrated experimentally [68; 69; 71; 72]. In this approach, spectral modes are defined within the broadband spectrum of a photon pair source based on spontaneous parametric down-conversion. The spectral modes of the idler beam are demultiplexed into distinct modes before detection. Demultiplexing can be realized, e.g., by diffraction gratings [68; 69; 71] or a dense wavelength division multiplexer [72]. After detecting the idler photons in one of the spectral modes, a feedforward frequency-shift operation is applied to the heralded photons, thus switching signal photons between modes. The frequency of the heralded signal photon from the spectral mode corresponding to the frequency of the detected idler photon is shifted to the prescribed frequency \(\
u_{\text{out}}\) of the output mode. Figure 3 shows the scheme of spectral multiplexing. The frequency of the signal photon can be shifted by using various techniques such as a linear phase ramp realized by an integrated phase modulator [68], four-wave mixing applying different pump frequencies [69], or a travelling wave electro-optic phase modulator [71; 72]. The advantage of spectral multiplexing is that the optical switch realizing the frequency shift has a given loss, and it is not scaled up with the number of multiplexed spectral modes. However, multiplexing several spectral modes can be technically challenging. Moreover, demultiplexing of the idler beam introduces additional losses.
# Influence of Recycled Precast Concrete Aggregate on Durability of Concrete's Physical Processes
## Materials and Methods
### Mix Design  
To carry out this study, two control mixtures were used to ensure a fresh, self-compacting concrete. Furthermore, control mixtures guaranteeing a minimum compressive strength of 30 MPa (HR-30) and 45 MPa (HR-45) were designed. The control mix proportions are intended to meet the standards of precast concrete plants and to cover the widest range of precast products. From the control SCC, the experimental program was done replacing 20%, 50% and 100% of the silica gravel with the same volume of RA. Table 1 shows the mix proportions for manufactured SCCs, in kg/m\({}^{3}\).  
Figure 1: Rejected recast products.  
Due to the high-water absorption coefficient of the RA, its incorporation as a substitute for aggregate 2/12.5 influences the effective water/cement ratio (_w_/_c_). In order to adequately compare the properties analysed in this study, the effective water/cement ratios (_w_/_c_)\({\rm eff}\) of each mix proportions used were calculated. The calculation of the effective water/cement ratios are obtained from Equation (1), where it has been estimated that the recycled aggregate reaches approximately 70% water saturation capacity [29]. Table 1 shows the (_w_/_c_)\({\rm eff}\) for each mixture:  
\[\left\{\frac{w}{c}\right\}_{eff}=\ \frac{W\ -\ \frac{0.7}{100}\sum_{i}(P_{i}A_{i})}{c} \tag{1}\]  
where, \(\left\{\frac{w}{c}\right\}_{eff}\) is effective water/cement ratio; \(W\) is the total amount of water incorporated in the mix; \(P_{i}\) is the weight of the components incorporated during mixing; \(A_{i}\) is the absorption of the components incorporated during mixing.  
A total of eight mixes made up the experimental program. The HR-30-0% and HR-45-0% mixes correspond to the control SCC. The remaining six mixtures correspond to the SCC with RA (Table 2).  
In view of the results, there is a significant decrease in the (_w_/_c_)\({\rm eff}\) in recycled concretes, ranging from a ratio of 0.45 to 0.32, for HR-30 and from 0.35 to 0.25 in the HR-45.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 3 Proposed EPBTL-RDHEI
### Image Encryption and Pixel Grouping
#### 3.1.3 Pixel Grouping  
The pixels in the encrypted image \(I_{e}\) are divided into four groups, namely the reference pixel group \(P_{r}\), the special pixel group \(P_{s}\), the embeddable pixel group \(P_{e}\), and the non-embedded pixel group \(P_{n}\). The non-embeddable pixel group \(P_{n}\) is further divided into the self-recordable subgroup \(P_{ns}\) and the non-recordable subgroup \(P_{nn}\) according to their prediction errors. \(P_{r}\) is composed of the pixels in the top row and the left column of the image. The pixel at the bottom right corner of the image is chosen as the only pixel in the group \(P_{s}\) and is used to record parameter values \(\alpha\) and \(\beta\).  
The remaining pixels are classified into groups \(P_{e}\), \(P_{ns}\), and \(P_{nn}\) based on their prediction errors. The embeddable pixel group \(P_{e}\) corresponds to the group G2 in PBTL. Recall that the number of different codes in G2 is defined in Equation (1). Thus, we exploit these G2 codes to label the embeddable pixels \(P_{e}\) of prediction errors within the range defined by  
\[\left\lceil-\frac{n_{a}}{2}\right\rceil\leq e_{i}\leq\left\lfloor\frac{n_{a}-1 }{2}\right\rfloor. \tag{5}\]  
For a non-embeddable pixel, if its prediction error can be encoded in \((8-\beta)\) bits, it is classified as a \(P_{ns}\) pixel. Otherwise, it is classified as a \(P_{nn}\) pixel. The precise range of prediction error for the group \(P_{ns}\) is given in Equation (6). Figure 8 gives an illustrative example of pixel grouping for \(\alpha=3\) and \(\beta=2\).  
\[\begin{split} e_{i}(P_{n})&\in\left(-2^{7-\beta}- \frac{\left(2^{\beta}-1\right)\times 2^{\alpha-\beta}}{2}+1,-\frac{\left(2^{ \beta}-1\right)\times 2^{\alpha-\beta}}{2}\right]\cup\\ &\quad\left\lceil\frac{\left(2^{\beta}-1\right)\times 2^{\alpha- \beta}}{2}-1,\,\frac{\left(2^{\beta}-1\right)\times 2^{\alpha-\beta}}{2}+2^{7-\beta}-2 \right\rceil.\end{split} \tag{6}\]  
Figure 8: Pixel grouping.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 2 Advanced Technologies and Mechanisms in RF MEMS Switches
### Actuation Mechanism
#### 2.1.1 Electrostatically Actuated RF MEMS Switches  
Figure 1a shows the structure of a typical electrostatically driven RF MEMS switch. Among various actuation principles, electrostatic actuation is the most common in RF MEMS switches due to its simplicity, low power consumption, high-frequency circuit compatibility, and good compatibility with CMOS (Complementary Metal-Oxide-Semiconductor) fabrication processes. These switches play a key role in constructing SPDT (Single-Pole Double-Throw) and more complex SPMT (Single-Pole Multiple-Throw) networks. Core  
Figure 1: Structures of four conventional RF MEMS switches: (**a**) Electrostatically actuated RF MEMS switch [25]. Reprinted/adapted with permission from [25]. Copyright 2011, with permission from Elsevier. (**b**) Piezoelectrically actuated RF MEMS switch [26]. Reprinted/adapted with permission from [26], 2012, IEEE. (**c**) Electromagnetically actuated RF MEMS switch [27]. Reprinted/adapted with permission from [27]. (**d**) Electrothermally actuated RF MEMS switch [28]. Reprinted/adapted with permission from [28], 2020, IEEE.  
performance parameters include the actuation voltage, switch capacitance ratio, on-state insertion loss, and off-state isolation, which are critical metrics for assessing the performance of electrostatic RF MEMS switches.  
Despite many advantages, electrostatically driven RF MEMS switches face two main challenges: reducing the actuation voltage and improving mechanical structure stability. High actuation voltages shorten the lifespan of switches and cause failure due to charge trapping in the dielectric layer. A high actuation voltage is impractical for satellite communication applications, as it necessitates the addition of bulky step-up converter circuits. To achieve efficient voltage conversion, step-up converters need to have sufficient inductors and capacitors to store and release energy [29], which necessitates a certain volume for the converter. [PERSON] et al. [30] confirmed through research that, for capacitive RF MEM switches, a reduction of 5-7 V in actuation voltage can extend the switch's lifespan by 10 years. [PERSON] et al. [31] reported a low actuation voltage RF MEMS switch, requiring a very low actuation voltage (4.8 V). After undergoing 10 million switching cycles, its RF performance showed little change, with no failures, proving that a low voltage can effectively prolong the switch's lifespan.
# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
###### Abstract  
Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R\({}^{2}\)). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R\({}^{2}\)(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R\({}^{2}\) of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.  
soil spectrum; cultivated land quality; HJ-1A imagery; Guangdong +
Footnote †: journal: Applicand
## 4 Numerical Simulation Analysis
Therefore, the overall computational complexity of the proposed IAAM algorithm is given by \(\mathrm{O}\big{(}\mathrm{K}\big{(}\mathrm{N}_{\mathrm{rf}}^{2}\mathrm{N}_{ \mathrm{rf}}^{t}+N_{\mathrm{f}}N_{\mathrm{rf}}^{t}+N_{\mathrm{f}}\big{)}\big{)}\) and K denotes the number of iterations. The SIC-based algorithm has a total computational complexity of \(\mathrm{O}\big{(}M^{2}\Big{(}N_{rf}^{t}S+N_{\mathrm{r}}\Big{)}\big{)}\), where M denotes the number of antennas allocated to each RF chain, and S denotes the number of iterations, which equals 5 after verification [10]. The OMP-based algorithm owns a computational complexity of \(\mathrm{O}\big{(}\mathrm{N}_{\mathrm{t}}^{2}\mathrm{N}_{\mathrm{rf}}^{t} \mathrm{N}_{\mathrm{s}}\big{)}\)[8]. The DC algorithm has a computational complexity of \(\mathrm{O}\Big{(}N_{\mathrm{rf}}N_{\mathrm{rf}}^{t}+LN_{\mathrm{f}}N_{ \mathrm{rf}}^{t}+N_{\mathrm{f}}\Big{)}\)[14]. Table 2 summarizes the complexities and other quantification results of different algorithms. Figure 5: The average Spectral Efficiency versus Iterator times with \(\mathrm{N}_{\mathrm{rf}}^{t}\) = 4, 8, 12 under different SNR. The quantification results given in Table 2 are based on \(\mathrm{N}_{\mathrm{rf}}^{\mathrm{t}}=4\). Additionally, from Figure 5, we know that the value of \(\mathrm{K}\) in the complexity of IAM algorithm is 8, and it will decrease with the increase in \(\mathrm{N}_{\mathrm{rf}}^{\mathrm{t}}\). As the value of \(\mathrm{N}_{\mathrm{t}}\) is always very large, the OMP algorithm has the largest computational complexity in the table. The proposed IAM algorithm has the same magnitude of computational complexity as the SIC algorithm. The complexity of the DC algorithm is the lowest, while its performance is the lowest too. The quantification results also show that the spectral efficiency performance of the proposed algorithm is at least 17.5% higher than the existing two traditional sub-connected algorithms, and it increases at least about 12.8% energy efficiency, compa with the OMP algorithm, even when \(\mathrm{N}_{\mathrm{rf}}^{\mathrm{t}}=4\).
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Integrated Germanium Photodiodes
#### 4.1.1 Photodiodes: Total-Ionizing Dose Effects  
It has been shown that TID does not significantly affect integrated germanium PDs [62; 63; 64]. Figure 4 shows negligible change in responsivity up to 1 Mrad(Si) [64]. The resistance to TID has to do with the fundamental mechanism associated with the generation of TID-induced damage. TID adds positive charge to insulators and creates traps between semiconductor-insulator interfaces. It is expected then, that devices which exhibit charge transport near insulator interfaces (e.g., field-effect transistors) will be especially vulnerable to TID. However, the PD's charge transport path is within the bulk of the _i_-region where the photo-generated carriers originate. In other words, TID-induced damage on the interfaces should have minimal effect on the device's ability to convert photons to electrical carriers.  
Figure 4: Photodiode responsivity for various TID levels (after [64]).  
Figure 3: Typical lateral, germanium-integrated _p-i-n_ photodiode (after [62]).  
The dark current and AC response were also shown to suffer negligible degradation from TID damage. Figure 5a shows only small resultant increases in dark current post-irradiation [62]. However, this small increase could be due to the thermal heating of the _p-i-n_ junction from X-ray radiation, and not the actual damage inflicted on the device. Figure 5b compares the 3-dB optical-to-electrical bandwidth, both before and after irradiation. The small difference in the fit was attributed to the measurement tolerance of the equipment, and the study, therefore, concluded that there was no device performance change due to TID. The PD capacitance and resistance were also measured and showed no differences.
## 4 LPWAN Technologies
### LoRa  
Long Range (LoRa) is a LPWAN standard developed by LoRa Alliance [33, 45]. LoRa operates in unlicensed sub-GHz ISM band. LoRa is a physical layer standard that operates based on chirp spread spectrum modulation whereby the modulation process is done by representing individual bit of payload information by multiple chirps of information [46]. For inference effect mitigation, LoRa standards use Forward Error Correction coding for controlling errors in transmission over reliable communication channels.  
The data rate allowed by the standard varies from 300 bps to 50 kbps, values subjected to the spreading factor (SF) and channel bandwidth settings. LoRa deploys a quasi-orthogonal transmission method with different SFs and they allow multiple transmissions with different SFs at the same time. The standard use ALOHA principle for channel access and like most LPWAN, they mostly use the single-hop technology. Figure 1 presents the structural architecture of a typical LoRaWAN.  
As shown in Figure 1, a typical architectural framework of a LoRaWAN comprises of end-devices, gateways, network server and application servers. The end devices are connected to the network server through a LoRa gateway usually using a star-to-star topology. Generally, the end devices are connected to multiple gateways using single hop packet transfer [10]. Afterwards, the gateways forward the received packets to the network server through a back-haul interface which may be ethernet, Wi-Fi, cellular etc. When the network server receives the packets, it performs important role such as network and security management, verification of end-devices addresses, and removal of packet redundancies [21]. Finally, the network server forwards the messages to the application servers for decryption and the initiation of new actions.
## 4 Bayesian Learning-Based Clustered-Sparse Channel Estimation
### Bayesian Channel Estimation  
To simplify the implementation complexity of Bayesian channel estimation, only known pilot symbols are used for channel measurement. As pilot symbols are sparsely distributed, it is difficult to estimate the non-uniform power spectrum at the location of pilot subcarriers. Therefore, the additive noise in (9) is approximated as white Gaussian noise with noise variance \(\lambda\) in this section. According to (9) and (17), using the Bayes rule, the posterior density of \(\mathbf{h}\) can be obtained as  
\[p(\mathbf{h}|\mathbf{y}_{\mathbf{p}};\lambda,\mathbf{\gamma}_{1},\mathbf{\gamma}_{2} \ldots,\mathbf{\gamma}_{C},\beta_{1},\beta_{2},\ldots\beta_{C})=\mathcal{CN}(\mathbf{ \mu}_{\mathbf{h}},\mathbf{\Sigma}_{\mathbf{h}}), \tag{18}\]  
which is the complex Gaussian distribution with the posterior mean and covariance matrix:  
\[\mathbf{\mu}_{\mathbf{h}}=\lambda^{-1}\mathbf{\Sigma}_{\mathbf{h}}\mathbf{U}_{\mathbf{ P}}^{H}\mathbf{y}_{\mathbf{p}}=\mathbf{\Sigma}_{0}\mathbf{U}_{\mathbf{P}}^{H} \Big{(}\lambda\mathbf{I}+\mathbf{U}_{\mathbf{P}}\mathbf{\Sigma}_{0}\mathbf{U}_{ \mathbf{P}}^{H}\Big{)}^{-1}\mathbf{y}_{\mathbf{P}}, \tag{19}\]  
\[\mathbf{\Sigma}_{\mathbf{h}}=\Big{(}\mathbf{\Sigma}_{0}^{-1}+\lambda^{-1}\mathbf{U}_{ \mathbf{P}}^{H}\mathbf{U}_{\mathbf{P}}\Big{)}^{-1}=\mathbf{\Sigma}_{0}-\mathbf{\Sigma }_{0}\mathbf{U}_{\mathbf{P}}^{H}\Big{(}\lambda\mathbf{I}+\mathbf{U}_{\mathbf{ P}}\mathbf{\Sigma}_{0}\mathbf{U}_{\mathbf{P}}^{H}\Big{)}^{-1}\mathbf{U}_{\mathbf{P}}\mathbf{ \Sigma}_{0}. \tag{20}\]  
Obviously, the maximum posterior (MAP) estimates \(\widehat{\mathbf{h}}_{MAP}\triangleq\mathbf{\mu}_{\mathbf{h}}\) and the covariance matrix \(\mathbf{\Sigma}_{\mathbf{h}}\) are related to the hyperparameters \([\lambda,\mathbf{\gamma}_{1},\mathbf{\gamma}_{2},\cdots,\mathbf{\gamma}_{C},\beta_{1}, \beta_{2},\cdots,\beta_{C}]\). Using \(K_{n}\times M\) null subcarriers \(\mathbf{Y}_{\mathbf{N}}=[\mathbf{y}_{n}^{(1)},\cdots,\mathbf{y}_{n}^{(M)}]\), \(\lambda\) can be estimated as  
\[\lambda=\frac{1}{K}E[\|\ \mathbf{y}^{(m)}\|_{2}^{2}]\approx\frac{1}{MK_{n}} \sum_{j=1}^{M}\sum_{i=1}^{K_{n}}|\mathbf{Y}_{\mathbf{N}}[i,j]|^{2}, \tag{21}\]  
where \(i\) and \(j\) are the row and column indexes of \(\mathbf{Y}_{\mathbf{N}}\), respectively. The remaining hyperparameters \([\mathbf{\gamma}_{1},\mathbf{\gamma}_{2},\cdots,\mathbf{\gamma}_{C},\beta_{1},\beta_{2}, \cdots,\beta_{C}]\) are related to the cluster distribution information.