esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Non-uniquely-decodable (non-UD) codes can be defined as the codes that cannot be uniquely decoded without additional disambiguation information. These are mainly the class of non-prefix-free codes, where a code-word can be a prefix of other(s), and thus, the code-word boundary information is essential for correct decoding. Due to their inherent unique decodability problem, such non-UD codes have not received much attention except a few studies, in which using compressed data structures to represent the disambiguation information efficiently had been previously proposed. It had been shown before that the compression ratio can get quite close to Huffman/Arithmetic codes with an additional capability of providing direct access in compressed data, which is a missing feature in the regular Huffman codes. In this study we investigate non-UD codes in another dimension addressing the privacy of the high-entropy data. We particularly focus on such massive volumes, where typical examples are encoded video or similar multimedia files. Representation of such a volume with non-UD coding creates two elements as the disambiguation information and the payload, where decoding the original data from these elements becomes hard when one of them is missing. We make use of this observation for privacy concerns. and study the space consumption as well as the hardness of that decoding. We conclude that non-uniquely-decodable codes can be an alternative to selective encryption schemes that aim to secure only part of the data when data is huge. We provide a freely available software implementation of the proposed scheme as well.  
non-UD; non-prefix-free codes; selective encryption; massive data security; data coding; data compression; privacy preserving text algorithms; big data delivery +
Footnote †: 2019, 12, 78; doi:10.3390/a12040078
## 10 Conclusions  
The stress analysis of a thin-walled, cantilever beam with open channel sections shows that this is by no means a simple structure. With transverse end loading applied to a symmetric channel's centroid in alignment with its principal directions, the beam bending theory is supplemented by the influence of torsion and flexural shear applied to the shear centre. Thus, in beam design, the maximum net axial stress and maximum net shear stress are found at the fixing. These apply to the application of yield criteria in the assessment of the beam's safe load bearing capacity. Given that torque Equation (4) depends linearly upon \(F_{x}\) the latter may be reduced to lower the shear stress to acceptable levels where necessary. However, a load factor should not be applied until the additive effect of bending upon the net axial stress distribution has been established.  
Further information provided by the sophisticated Wagner-Vlasov theory is illustrated well for a thin-walled, non-symmetrical channel. Following all the necessary geometric calculations that appear as the symbols \(X^{\prime}\), \(Y^{\prime}\), \(I_{x}\), \(I_{y}\), \(I_{xy}\), \(\varnothing\), \(\Gamma_{1}\), \(J\) and \(\mu\) for this particular cross-section, the logical sequence of calculations when transverse forces are applied to the centroid at the free-end of a cantilever beam is as follows:1. Establish the flexural shear flow distribution with equivalent forces for the principal axes transferred to the shear centre.
2. Calculate the unconstrained warping displacements for the resultant St. Venant torque, arising from the transfer in (1), applied to the shear centre.
3. Calculate the axial stress produced by the Wagner torque arising from constraining warping by fixing one end.
4. Calculate the torsional shear flow arising from the Wagner torque in (3).
5. Assess the increased torsional stiffness at length positions arising from (3).
6. Find the net shear flow (stress) distribution from the addition of (1) and (4). Note the stress gradient across the thickness with (1) providing the mean stress and (4) the superimposed linear distribution due to torsion.
7. Find the bending stress distribution from equivalent moments referred to the principal axes with end forces applied at the centroid.
8. Find the net axial stress distribution from the addition of (3) and (7).
9. Apply criteria of yielding/buckling [16; 17; 18; 19; 20] to fixed-end positions stressed most severely under (6) and (8).  
The possibility of a transfer in moments accompanying the shift in forces from G to E has been quantified for both types channel. The conversion to bending stress shows this how influence can augment the net bending stress in consideration of plastic collapse arising at median perimeter points for each section.  
This research received no external funding.  
Further data available on request.  
The author declares no conflict of interest.
# Polyhydroxyalkanoates (PHAs): Biopolymers for Biofuel and Biorefineies
## 2 Sources of PHAs
### Microbial Production of PHA Block Copolymers  
A polymer that contains alternating segments of different polymer compositions, covalently linked via their reactive ends, is called block copolymers. Block copolymers can be in the form of diblock, triblock or repeating multiblocks. Studies have shown that PHA homopolymers and copolymers exhibit many unfavorable properties and a common setback in the commercialization of these polymers is their brittleness, which limits their end-use applications [114]. Therefore, significant research focus has been placed on developing a suitable combination of these PHA polymers to achieve desirable properties [115]. [PERSON] et al., [116] and [PERSON] et al., [115] successfully synthesized P3 HB-\(b\)-P3 HBV block copolymers in _C. necator_ with the periodic addition of substrates (fructose and pentanoic acid). The P3 HB and P3 HBV blocks were synthesized during the fructose utilization and pulse feed of pentanoic acid. Though, the P3 HB-\(b\)-P3 HBV block copolymers still exhibited brittleness due to the brittle nature of scl PHAs. However, the combination of scl and mcl block copolymers could result in better properties compared to pure scl or mcl PHAs copolymers. In this context, [PERSON] et al., [117] reported the synthesis of P3 HB-\(b\)-PHVHHp
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 5 Data Collection IV: Micro-Doppler  
In this section, we present time-frequency analysis of micro-Doppler data collected using a system whose diagram is shown in Figure 14. We construct two rotating blade apparatuses (a single-blade apparatus in Figure 15 and a two-blade apparatus in Figure 16) that are driven by a single-speed 12 V DC motor to generate reflections. The maximum detectable range of a 0 dBm target for this test is \(R_{max}=8.34\) m, found by evaluating Equation (11) with the minimum receive power \(P_{r}=-65\) dBm.  
Figure 14: General block diagram of the FDA radar setup used in Data Collection IV with arrows indicating the direction of wave propagation. Blue arrows indicate propagation from the transmit antenna and red arrows indicate propagation towards the receive antenna.  
\[f_{mD}=\frac{2\Omega L_{Rb}}{\lambda_{n}}\approx\frac{2\Omega L_{Rb}}{\lambda_{0}}. \tag{22}\]
## 2 Study Area and Data  
### Study Area  
This study selects Hunan Province, China, as the research area, focusing primarily on the typical economic crops in the province (as shown in Figure 1). Hunan Province is located in the middle reaches of the Yangtze River (108\({}^{\circ}\)47\({}^{\prime}\)E-114\({}^{\circ}\)15\({}^{\prime}\)E, 24\({}^{\circ}\)38\({}^{\prime}\)N-30\({}^{\circ}\)08\({}^{\prime}\)N), with a total area of approximately 211,800 square kilometers. The province has a subtropical monsoon climate, with an average annual temperature of 16-18 \({}^{\circ}\)C and annual precipitation ranging from 1200 to 1700 mm. The area of cultivated land plays a crucial role in the province's land use, contributing significantly to food production.  
Hunan is one of China's major grain-producing bases, but in recent years, due to insufficient regulation and policy adjustments, some arable land has been converted to non-food crop cultivation, which has impacted food production and posed challenges to the efficient utilization of arable land resources.  
In this context, non-food crops in Hunan, such as cotton, tea, and rapeseed, benefit from the ideal growing conditions provided by the province's distinct four seasons and synchronized rainfall and heat. These crops were selected for this study for the following reasons: Hunan is a major production base for both rapeseed and tea, with rapeseed cultivation accounting for over 10% of the national area, and tea production ranking among the highest in China. Cotton, as a regional specialty economic crop, also holds significant economic value. Therefore, studying these non-food crops is of great importance for enhancing agricultural economic development in the region.
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 2 Background and Motivation
### Current Status of Optical Interconnect Technology  
Photonics technology has been dramatically improved in terms of per-fiber bandwidth, fiber cost, optical module chip size, on-package integration, and power consumption. 100 G (gigabit) Ethernet is one of the mainstream for datacenter-scale networks [11] while 400 G Ethernet is expected to be marketed in a few years. Recently, an optical module vendor, MultiPhy, developed a 100 G single-wavelength optical module chip that adapts the PAM-4 (4-level pulse amplitude modulation) technique [11]. While previous 100 G optical interconnect standards are based on the 25 Gbps-per-wavelength capability, enabling 100 Gbps per wavelength is a key technology for developing 400 G optical interconnects without increasing the number of fibers or wavelengths (e.g., 400 G PSM4 or LR4 standards).  
Besides datacenter-scale networks, on-chip (on-package) optical interconnects have been actively studied. Off-chip and on-chip optical interconnects are functionally similar. Therefore, their transceiver/receiver modules have the same functional blocks (e.g., laser source, modulator, photodetector, and trans-impedance amplifier). However, on-chip interconnect is more cost-/energy-efficient by using on-chip waveguides (instead of using optical cables and connectors) with a limited connection distance. The silicon optical interposer is critical as it can integrate multiple chips, optical components, and waveguides implemented on the interposer in a single package by 2.5D integration. Recently, several real implementations [12; 13] achieved 25-50 Gbps per waveguide on the silicon optical interposer, enabling several Tbps of bandwidth in a package.
# Effect of Wing Corrugation on the Aerodynamic Efficiency of Two-Dimensional Flapping Wings
###### Abstract  
Many previous studies have shown that wing corrugation of an insect wing is only structurally beneficial in enhancing the wing's bending stiffness and does not much help to improve the aerodynamic performance of flapping wings. This study uses two-dimensional computational fluid dynamics (CFD) in aiming to identify a proper wing corrugation that can enhance the aerodynamic performance of the KUBeetle, an insect-like flapping-wing micro air vehicle (MAV), which operates at a Reynolds number of less than 13,000. For this purpose, various two-dimensional corrugated wings were numerically investigated. The two-dimensional flapping wing motion was extracted from the measured three-dimensional wing kinematics of the KUBeetle at spanwise locations of \(r=(0.375\) and \(0.75)R\). The CFD analysis showed that at both spanwise locations, the corrugations placed over the entire wing were not beneficial for improving aerodynamic efficiency. However, for the two-dimensional flapping wing at the spanwise location of \(r=0.375R\), where the wing experiences relatively high angles of attack, three specially designed wings with leading-edge corrugation showed higher aerodynamic performance than that of the non-corrugated smooth wing. The improvement is closely related to the flow patterns formed around the wings. Therefore, the proposed leading-edge corrugation is suggested for the inboard wing of the KUBeetle to enhance aerodynamic performance. The corrugation in the inboard wing may also be structurally beneficial.  
computational fluid dynamics; flapping wings; curved wing; corrugated wing; angle of attack Article
## 3 Results
### Method Validation
#### 3.2.2 Linearity and Lower Limit of Quantification (LLOQ)  
Standard calibration curves were constructed in the range of 0.5-1000.0 ng/mL by plotting peak area ratios (ERDA/IS) against ERDA-spiked concentrations. Linearity was calculated using the least square method (1/\(\chi_{\text{2}}\)). The linearity of the method was \(>\)0.997.  
LLOQ for the determination of ERDA in plasma was found to be 0.5 ng/mL using the proposed method. The LLOQ in the proposed method was very low, which reflects its efficiency for application in the quantitative detection of ERDA plasma for pharmacokinetic studies and TDM. Figure 3 shows the MRM chromatograms of plasma samples spiked with ERDA at its LLOQ, along with blank plasma samples.  
Figure 3: MRM Chromatograms of ERDA and internal standard in blank plasma (**A**,**B** respectively), and plasma spiked at LLOQ (low limit of quantitation) (**C**,**D**, respectively).
## 3 Results
### Adaptability Assessment Tools and Models
#### 3.1.7 The SAGA Method, 2019  
The Spatial Assessment of Generality and Adaptability (SAGA) Method [32] delivers an assessment model of a building's capacity to support changes using a combination of unweighted and weighted graphs. The method introduces a set of five quantitative indicators that are mathematically reproducible to measure a building's passive capacity to change (generality) as well as the active capacity to change (adaptability) in terms of the spatial configuration of layouts. The SAGA method aims to analyze and compare large sets of plan graphs of a building or even at an urban scale, allowing to map generality andadaptability throughout those. It can also inform design decisions, enabling architects and designers to improve the capacity to accommodate change through layout configuration. For example, SAGA can calculate which parts of a unit the use of removable walls would have the highest impact (i.e., the highest increase in generality). The central premise of the method is that graph permeability can serve as a measurement tool for functional uses a layout can accommodate. The method is tested on six representative floor plans of residential typologies. The SAGA model stands out among existing tools for fully automated indicators within Rhinoceros 3D BIM software.  
The method is limited to evaluating the spatial configuration of floor plans (spatial adaptability) which only partially support functional changes. Moreover, although the method was developed for all building typologies, it was only tested for validation on six representative floor plans of residential use.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 4 Problem Formulation and Problem Solution
### Problem Formulation  
In the previous part, we have selected the CHs by Equation (10). In this part, a joint optimization problem of the acquisition time \(\mathbf{T}=\{t_{n},n\in\mathcal{N}\}\) and the sensor association \(\mathbf{X}=\{x_{ij},i\in\mathcal{L},j\in\mathcal{K}\}\) is formulated based on the result of the CHs selection. In order to improve the satisfaction of sensor data acquisition, reduce the energy consumption of the WSN and ensure the stable operation of the microgrids, we jointly optimize the acquisition time \(\mathbf{T}\) and sensor association \(\mathbf{X}\) to maximize data satisfaction and minimize the energy consumption of each round of WSN. Therefore, this paper constructs an optimization problem as follows:\[\begin{split}\mathbf{P}:\underset{TX}{max}&\sum_{n}U(t_{n}) -\beta\left(p_{c}\sum_{n}t_{n}+\sum_{i}E_{CM_{i}}+\sum_{j}E_{CH_{j}}\right)\\ s.t.&\sum_{j}x_{ij}=1,\forall i\in\mathcal{L},\\ &\sum_{i}x_{ij}\leq N_{j}^{max},\forall j\in\mathcal{K}\\ & x_{ij}\in\{0,1\},\forall i\in\mathcal{L},\forall j\in\mathcal{ K}\\ & 0\leq t_{n}\leq T_{\max},\forall n\in\mathcal{N}\end{split} \tag{11}\]  
where \(\beta>0\) is the trade-off between satisfaction and energy consumption. First and third constraints ensure the association of a single CH and the maximum number of CMs that the CH can access are restricted through second constraint. Since the \(x_{ij}\) are binary variables, the optimization problem \(\mathbf{P}\) is a multi-constrained mixed-integer programming problem. However, the mixed-integer programming problem is generally challenging to obtain the optimal solution. If the brute force algorithm is used to obtain it, the algorithm complexity is extremely high. Furthermore, it is still challenging to obtain the optimal solution when the dimensionality of the optimization problem is high.
## 3 Development of a 3 \(\upmu\)m-Thick Laser Structure Grown on InP-SiO\({}_{2}\)/Si
### Broad-Area Laser Performance Comparison  
During the fabrication process, the contact resistivity values of the n-doped InP and the p-doped InGaAs contact layers were measured using a transmission line model. The n-doped contact resistivities were 1.5 \(\times\) 10\({}^{-6}\)\(\Omega\).cm\({}^{2}\) for both structures. The p-doped contact resistivity values were respectively 8.6 \(\times\) 10\({}^{-6}\) and 1.6 \(\times\) 10\({}^{-6}\)\(\Omega\).cm\({}^{2}\) for the structures grown on InPoSi and on InP. This difference was explained by the higher Zn doping level in the InGaAs contact layer for the structure grown on InP as compared to the one grown on InPoSi. The latter is due to growth temperature differences measured between the two substrates by pyrometry during growth, induced by their difference of thermal conductivity. Since Si material has a higher thermal conductivity as compared to InP one, for a given temperature setpoint, growth temperature is 7 \({}^{\circ}\)C higher on InPoSi than on InP.  
Figure 4: Characterizations done on the laser structures grown on InPoSi (blue) and on InP (red): (a) normalized intensity of the photoluminescence signals; (b) HRXRD profiles.  
Figure 5: Cleaved bar of a broad-area laser on InPoSi observed by SEM with an insert observation in the MQW region.  
The light-current densities measurements are presented in Figure 6a. These measurements were done by means of a broad detector composed of a Germanium photodiode with an integrating sphere and a source Keithley 2520 used in pulse regime (duty cycle 0.5%) in order to limit thermal effects typically observed in broad area lasers. Performance were assessed at 20 \({}^{\circ}\)C using a thermo-electric cooler system. Threshold current densities are respectively of 0.4 kA/cm\({}^{2}\) and 0.7 kA/cm\({}^{2}\) for the laser grown on InPSi and on InP. The difference observed, even if not fully inderstood, can be attributed to the different quality of the cleaved facets from both substrates. The latter could have an impact on the losses associated to the reflection in the facets. It could also be attributed to different p-doping profiles due to the difference of growth temperature. Higher Zn doping level on the p-cladding layer of the structure grown on InP could enhance internal losses. The output powers are in the same range of values. The slope efficiency is respectively of 0.092 W/A and 0.095 W/A for the laser on InPSi and for the one on InP. The temperature dependent behavior of the laser was tested up to 50 \({}^{\circ}\)C (Figure 6b). The characteristic temperature T\({}_{0}\) is of 53 \({}^{\circ}\)C for both lasers. Altogether, the performance similarities between the two lasers confirm that the material quality is similar for the structure grown on InPSi as compared to the reference.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 4 Results and Discussion
### Sensitivity Analysis  
Power vs. voltage characteristics, known as the _P-V_ curve, has been widely used as a complementary analytical tool to the dynamic study by many utilities. It depicts the loading and generating capability of each bus in the power network with respect to voltage stability. The _P-V_ curve enables system planners and operators to reduce the risks of systems accidentally entering unstable regions [37].  
In the paper, in order to minimize the possible placements of energy storage systems, thereby reducing the computational complexity of the PSO algorithm, _P-V_ sensitivity analysis is conducted. The IEEE 30-bus system is selected to verify the ability performance of the proposed algorithm. The system consists of five generations and 20 loads, where Bus 1 is the slack bus, Bus 2 is installed with wind generation rated as 113 MW, Buses 5, 8 11, and 13 are defined as PV nodes, and other buses are PQ nodes [38]. Wind generation is added to Bus 2 rated as 113 MW. In order to simulate the extreme actual severe operating condition and expose the weaknesses and limitations of the system, the lengths of branches from Bus 1 to Bus 3 and Bus 1 to Bus 2 are doubled.  
Buses that have more variation according to the change of loading and/or generating conditions are identified as sensitive buses. By placing energy storage systems at sensitive buses, the voltage profiles not only at these sensitive buses but also at adjacent buses will be improved. Therefore, by selecting proper locations for energy storage systems, the overall system voltage profiles can be improved, and system costs and losses thus minimized.  
Figure 6 depicts _P-V_ curves of generation and load areas in the IEEE 30-bus system. Buses 7, 10, and 12 in the generation area shown in Figure 6a, and Buses 25, 26, 28, the 30 in the load area shown in Figure 6b are noticeably more sensitive than other buses; in other words, they are able to contribute more voltage stability than other buses when energy storage devices are installed. It is worth noticing that Buses 1, 5, 8, 11, and 13 are not considered to install ESSs, as they are either connected to the bulk grid or already installed generations.  
Figure 5: (**a**) Simulation result of NARX-BPNN; (**b**) Annual forecast error of NARX-BPNN.
# Discrimination of Severity of Alzheimer's Disease with Multiscale Entropy Analysis of EEG Dynamics
## 2 Materials and Methods
### Feature Extraction in Linear Discriminant Analysis (LDA)  
The scale factor \(\tau\) was limited between 1 and 20 in this study. For each subject, the overall MSE value of the EEG signals from an EEG electrode at each \(\tau\) were obtained as the average of the three MSE values derived from three epochs from the electrode, separately. As a result, there were 20 overall MSE values for \(\tau\) from 1 to 20 per each EEG electrode for each subject.  
For the analysis of each electrode, the number of selected MSE scales \(S\) was from 1 to 5 as input features of LDA. We adopted LDA to derive the weighting factors of MSE values under each scale such that the weighted sum values for AD and healthy groups were well separated. In each of the \(S=1\) to \(S=5\) cases, the combinations of scales among the 20 scales were exhaustively examined. The signals from the 19 electrodes were examined one by one.  
LDA is a technique used to reduce a high dimensional feature set to a lower dimensional feature set, such that the groups can be more easily separated in the lower dimensional space. LDA has an analytical solution via matrices calculation without optimization process, which makes LDA efficient [17].