esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
## 4 Hybrid Model for Run Length Encoding with Adaptive Huffman Encoding
### Hybrid Run Length Encoding with Adaptive Huffman Encoding (H-RLEAHE) Algorithm  
The hybrid algorithm works in two phases. First phase involves the encoding through RLE, where the data item \(\mathbf{d}\) occurs \(\mathbf{n}\) consecutive times in the input stream and is replaced with the single pair \((\mathbf{n},\mathbf{d})\). The H-RLEAHE is shown graphically in Figure 3. However, the findings of RLE are dependent on the data source because it is based on the same sequential input stream. RLE provides the ST file. In the second phase of the tree, both the transmitter and the receiver employ a single node to represent all unsent symbols NYT with a weight of zero. New nodes will be added to the tree when transmission happens in order to maintain track of the symbols that have been transmitted. To begin transmission, the sender and receiver must first settle on a code for each symbol. NYT node codes are transmitted first, followed by the symbol's standard code. The symbol is then removed from the NYT list and given its own node. The tree structure of both the transmitter and receiver is same. Both the transmitter and the receiver utilise the same technique for software updates. Encoding and decoding are therefore always carried out at the same time. Nodes must be arranged in a specific order for the update operation to work. Nodes are sequentially numbered to maintain this hierarchy. The root of the tree is given the highest node number, and the NYT node is given the smallest number. Starting with the NYT node and working our way down the tree, the numbers are allocated in ascending order from lower to higher levels. A block is a collection of nodes with the same weight makes up a block.
# Structural Health Monitoring (SHM) of Civil Structures
###### Abstract  
As newer and more reliable ways of construction were developed, civilization began to spread out further and retain functional infrastructure for longer periods of time. Key building materials such as concrete ushered in a new era of civil engineering that enabled the rapid and low-cost construction of infrastructure that now serves as the backbone of modern society. Many of such buildings constructed in the 19 th to the mid-20 th century are still in operation today as a testament to the robustness of the civil structures enabled by the key building materials and methods. However, robustness has its limits, and the extended service life has inevitably led to the dangerous accumulation of damage in the infrastructure. Aging infrastructure is a problem met around the globe, and in the US, the National Academy of Engineering proclaimed the restoration and improvement of urban infrastructure to be one of the Grand Challenges of the 21 st century.  
For the answer to the challenge of aging infrastructure, we look towards the development of advanced sensor and actuator technologies that hold the promise of heralding the next stage of evolution in civil infrastructure. Popular terms such as Internet of Things and smart structures were coined as a result of the intersection between advances in other engineering disciplines with civil engineering to produce the new field of structural health monitoring (SHM). The field of SHM is now at a vital crossroads, where researchers are challenged to develop technologies for the monitoring and retrofit of older buildings and at the same time to push the boundaries of SHM through the creative use of cutting-edge technologies and data processing algorithms.  
This issue is a snapshot of the newest research in SHM for civil structures, and it includes a range of topics such as data processing algorithms to detect damage, modeling, and simulation [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]; sensor development and experiments [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]; materials studies [27; 28]; state-of-the-art reviews [29; 30]; and case studies [31]. SHM is highly multi-disciplinary, and advances in other areas of study can likely be recruited for the progress of SHM. The future of this field is very bright, and will be a driver for the coming futuristic, intelligent infrastructure.  
The authors gratefully acknowledge the supports provided by the National Natural Science Foundation of China (No. 51378434, 51578456).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Mathematical Modeling Part
#### 2.2.1 Solar Field and Storage Tank Modeling  
The solar thermal efficiency of the FPC (\(\eta_{\rm col}\)) is given by the next expression [30]:  
\[\eta_{\rm col}=0.80-3.56\cdot\frac{\rm T_{m}-T_{am}}{G_{\rm T}}-0.003\cdot\frac {(T_{m}-T_{am})^{2}}{G_{\rm T}} \tag{1}\]  
The mean working fluid temperature (T\({}_{\rm m}\)), the ambient temperature (T\({}_{\rm am}\)) and the solar irradiation on the collector-tilted plane (G\({}_{\rm T}\)) are used in the previous formula.  
The useful thermal product of the FPC (Q\({}_{\rm u}\)) can be calculated as below:  
\[Q_{\rm u}=\eta_{\rm col}\cdot Q_{\rm sol} \tag{2}\]  
Figure 1: The examined system with a solar-assisted heating unit which includes a solar field, a thermal storage tank with auxiliary electrical resistance and a high-temperature heat pump.  
The available solar energy (Q\({}_{\rm sol}\)) is calculated according to the following formula:  
\[\rm Q_{\rm sol}=G_{T}\cdot A_{\rm col} \tag{3}\]  
The fluid outlet temperature of the solar field (T\({}_{\rm out}\)) is found by the next expression:  
\[\rm T_{\rm out}=T_{in}+\frac{Q_{u}}{m\cdot c_{p}} \tag{4}\]  
The working medium mass flow rate (m) in the solar field is selected to follow the next empirical expression [31]:  
\[\rm m=0.02\cdot A_{\rm col} \tag{5}\]  
Moreover, it is useful to add that the fluid's mean temperature (T\({}_{\rm m}\)) can be calculated as below:  
\[\rm T_{m}=\frac{T_{in}+T_{out}}{2} \tag{6}\]  
The thermal energy balance in the insulated tank is given below [32]:  
\[\rm Q_{st}=Q_{u}+Q_{aux}-Q_{loss}-Q_{hp} \tag{7}\]  
The stored energy in the working fluid of the tank (Q\({}_{\rm st}\)) can be expressed by using the tank volume (V), the fluid density (\(\rho\)) and the fluid's specific heat capacity (c\({}_{\rm p}\)):  
\[\rm Q_{st}=\rho\cdot V\cdot c_{p}\cdot\frac{dT_{st}}{dt} \tag{8}\]  
The tank thermal loss (Q\({}_{\rm loss}\)) can be calculated by using the thermal loss coefficient (U\({}_{\rm T}\)), the tank's external area (A\({}_{\rm T}\)), the tank's mean temperature (T\({}_{\rm st}\)) and the ambient temperature (T\({}_{\rm am}\)) [32]:  
\[\rm Q_{loss}=A_{T}\cdot U_{T}\cdot(T_{st}-T_{am}) \tag{9}\]  
The auxiliary heat input (Q\({}_{\rm aux}\)) is activated when the tank's mean temperature is (T\({}_{\rm st}\) < 12 \({}^{\circ}\)C) and it tries to make the fluid reach 12 \({}^{\circ}\)C with a proper control system. The maximum capacity of the auxiliary system is 5 kW.  
The heat input rate in the evaporator (Q\({}_{\rm hp}\)) is dependent on the heating demand and it can be expressed as below:  
\[\rm Q_{hp}=Q_{heat}\cdot\frac{COP_{hp}-1}{COP_{hp}} \tag{10}\]  
The building's heating load of the building (Q\({}_{\rm heat}\)) was calculated by the simulation in the TRSNSYS tool, while the performance of the heat pump (COP\({}_{\rm hp}\)) was calculated by the thermodynamic analysis by the developed code in Engineering Equation Solver.
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 4 Numerical Simulations  
The numeral experiments are executed through a simulated UWA channel. The results are analyzed to show the advantages of the proposed method. The benchmarks include IM-AMP [9], IM-MMSE-AMP [7], IM-LLR and IM-ML [1] and the proposed detector. The OFDM-IM system parameters are defined in Table 1 unless otherwise stated.  
The simulated UWA CIRs are generated from the time-varying UWA channel model [18]. The multipath channel includes \(L=15\) paths and the inter-arrival time follows an exponential distribution with a mean value of 1 ms. The average channel delay spread is 15 ms. The amplitudes of paths follow Rayleigh distribution and the average power of paths decreases exponentially with total power decay 20 dB. The model generates duplicated paths which are merged together. The CFO is randomly generated \(\epsilon\in\left[-\frac{\Delta f}{2},\frac{\Delta f}{2}\right]\). For coherent communication systems, accurate CSI is the precondition to guarantee the reliability of the communication links. By exploiting the sparse characteristics of UWA channels, the orthogonal matching pursuit (OMP) method is used in this paper [19].
## 2 Mathematical Model
### Coordinate System  
The earth-fixed coordinate system \(\mathrm{O}_{0}\)-\(\mathrm{X}_{0}\mathrm{Y}_{0}\mathrm{Z}_{0}\), shown in Figure 2, has its origin in the mean free surface, i.e., \(\mathrm{Z}_{0}=0\), with the \(\mathrm{Z}_{0}\)-axis pointing vertically upwards, and the \(\mathrm{X}_{0}\)-axis denotes wave incidence; \(\omega\) is denoted as wave angular frequency; wave incident angle \(\theta=0^{\circ}\) represents wave propagation in the positive \(\mathrm{X}_{0}\) direction; \(h\) is defined as the water depth. The body-fixed coordinate system is able to define the free surface boundary condition. The position of the origin is fixed at the center of gravity (COG) and changes along with transition or rotation of the floating body.
# A Method for Ferulic Acid Production from Rice Bran Oil Soapstock Using a Homogenous System
## 2 Materials and Methods
### Homogeneous Hydrolysis of \(\gamma\)-oryzanol  
#### 2.3.1 Selection of Solvents  
The solubility of \(\gamma\)-oryzanol in various kinds of solvents (MeOH, EtOH, EtOAc, ethyl ether, acetone, ACN, tetrahydrofuran, isopropanol, and water) was examined. A 9.6 g of \(\gamma\)-oryzanol was put into 100 mL of a solvent in an Erlenmeyer flask with a screwed cap. After closing, the flask was shacked vigorously, and left for a while at room temperature. The solvents in which the solubility of \(\gamma\)-oryzanol is more than 96 mg/mL were EtOAc, ethyl ether, acetone, and tetrahydrofuran. Among these solvents, EtOAc is the most suitable because it has the highest boiling point of 77.1 \({}^{\circ}\)C. EtOAc was used for extraction of \(\gamma\)-oryzanol from soapstock and preparation of standard solution, while EtOH was chosen as a cosolvent to homogenize the \(\gamma\)-oryzanol/EtOAc solution with aqueous alkaline solution.  
It has been reported that the hydrolysis and transesterification reaction proceeds rapidly under homogeneous conditions consisting of water [26]. The experiment of \(\gamma\)-oryzanol hydrolysis was conducted under the homogeneous binary solvents systems consisting of EtOAc and aqueous KOH solution dissolved in a cosolvent of EtOH.
## 2 Materials and Methods
### Validation of the PIRAMID Scoring System  
All statistical analyses were performed using SPSS Statistics (IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 28.0. IBM Corp: Armonk, NY, USA).  
Before we calculated the PIRAMID scores of each territory, analogously to [PERSON] et al., we performed a preliminary analysis to investigate the percentage of BOLD signal reduction that predicted a severe CPR impairment with the highest sensitivity and specificity. For this purpose, we performed a receiver operating characteristic (ROC) curve analysis using [15O]water PET as the dependent variable and the relative BOLD signal reduction as the independent variable. The ROC analysis was performed for the ICA and the ACA/MCA territories separately. The relative BOLD signal reduction with the maximum Youden index was used as a threshold to binarize the CVR of each evaluated territory as severely impaired or not.  
Next, we calculated the PIRAMID scores of each patient's ICA territory, but also of each ACA and MCA territory, by weighting the binary components, as proposed by [PERSON] et al. [17]. For the presence of a prior infarct, one point was given, and, for the presence of an impaired CVR, a mSS \(\geq\) II and the presence of \(\geq\)2 territories with impaired collaterals, three points were given, resulting in a total of up to ten points. The territories were divided into three PIRAMID grades based on the PIRAMD score as follows: Grade 1, 0-5 points; Grade 2, 6-9 points; and Grade 3, 10 points [17].  
To validate the PIRAMID scoring system, we performed an ROC curve analysis using neurological symptomatology as the dependent variable and the PIRAMID score, the PIRAMID grade and all components of the PIRAMID scoring system (prior infarcts, impaired CVR, mSS \(\geq\) II, \(\geq\)2 territories with impaired collaterals) as the independent variables.  
The validation of the PIRAMID score and grade was performed for two regions (ICA territory of each hemisphere) and four regions (MCA and ACA territories of each hemisphere) separately.
## 2 Materials and Methods
### Characterization of the Samples  
The phase composition of the samples was investigated by means of X-ray diffraction (XRD) using a Bruker D2 Phaser diffractometer, Karlsruhe, Germany (Cu K\(\alpha\) radiation; \(\lambda\) = 0.154056 nm at 40 kV).  
An automated apparatus NOVA Win-CFR Quantachrom-Gas Sorption System, Florida, USA was applied to estimate the pore size distribution (DFT method).  
The infrared spectra (4000-400 cm\({}^{-1}\) region) were registered using a Thermo Scientific Nicolet iS5 Fourier-transform IR spectrometer (KBr pellets, resolution of 2 cm\({}^{-1}\)).  
Scanning electron microscopy (SEM) micrographs were recorded on a JEOL JEM-200 CX at an accelerating voltage of 80 keV. The TEM observations were carried out using a transmission electron microscope, JEOL JEM 1011, at an accelerating voltage of 100 kV. Prior to the TEM measurements, the powders were suspended in ethanol and were then deposited on a carbon replica and evaporated on a copper grid (d = 3 mm). For a better contrast, the grid with the sample was re-coated with porous carbon.  
Room-temperature photoluminescence (PL) of powders were performed by means of a Varian Cary Eclipse spectrofluorimeter (excitation wavelength of 325 nm).  
In order to obtain information about the chemical composition of the samples, EDX analyses were carried out by means of a Quantax 220 Bruker apparatus.  
Electronic paramagnetic resonance spectra (EPR) were recorded at room temperature by a JEOL JES-FA 100 EPR spectrometer operating in the X-band with a standard TE\({}_{011}\) cylindrical resonator at a modulation frequency of 100 kHz, microwave power of 3 mW, modulation amplitude of 0.2 mT, time constant of 0.1 s, and sweep time of 2 min.  
The XPS measurements were carried out on AXIS Supra electron spectrometer (Kratos Analitycal Ltd., AlK\(\alpha\) radiation, photon energy of 1486.6 eV) using the commercial data-processing software of Kratos Analytical Ltd. The binding energies (BE) were determined with an accuracy of \(\pm\)0.1 eV. The chemical compositions in the depth of the films were determined monitoring the areas and binding energies of the C1s, O1s, and Zn2p photoelectron peaks.
# Comparison of Automated Extraction Techniques for Volatile Analysis of Whole Milk Powder
## 2 Materials and Methods  
### Preparation of Whole Milk Powder  
Raw milk was produced from 54 Friesian cows at the Teagasc Moorepark dairy farm, Fermoy Co., Cork, Ireland. The milk was pre-heated to 50 \({}^{\circ}\)C in an APV plate heat exchanger (SPX Flow Technology, Crawley, West Sussex, UK), separated by a centrifugal disk separator, and pasteurized at 72 \({}^{\circ}\)C for 15 s. The pasteurized milk was subsequently preheated to 78 \({}^{\circ}\)C and evaporated in a Niro three-effect falling film evaporator (GEA Niro A/S, Soeborg, Denmark) at sequential effect temperatures of 73 \({}^{\circ}\)C, 64 \({}^{\circ}\)C, and 55 \({}^{\circ}\)C. Concentrate feed was introduced to a Niro Tall-Form Anhydro three-stage spray dryer (GEA Niro A/S, Soeborg, Denmark) (air inlet temperature = 180 \({}^{\circ}\)C and air outlet temperature = 85 \({}^{\circ}\)C) at approximately 43% total solids (TS) with a centrifugal atomizer (GEA Niro A/S, Soeborg, Denmark) at Moorepark Technology Ltd. (Fermoy Co., Cork, Ireland). Primary and secondary fluidized beds were maintained at 74 \({}^{\circ}\)C and 24 \({}^{\circ}\)C, respectively. Fines were returned to the cyclone to the top of the spray dryer. WMP samples were stored at room temperature in sealed 900 g aluminum vacuum cans until analysis.  
WMP samples were dissolved at 10% solids (\(w/v\)) using ultra-pure deionized water and stored at 4 \({}^{\circ}\)C overnight to ensure solubility, without overhead lights to prevent light-induced off-flavor formation. Each extraction technique was assessed with or without salting out. NaCl (0.75 g) (Merck, Co., Wicklow, Ireland) was added to 5 mL of the 10% \(w/v\) WMP sample, equivalent to 15% NaCl \(w/v\). This was mixed until soluble (\(\sim\)30 min).
## 3 Materials
### Characterization and Preparation of Capsules  
Polystyrene (PS) capsules with 24 mm length, 10 mm diameter, and 0.7 mm wall thickness were used as healing agent carrier in this study. The capsule has a smooth texture and consists of two parts that were glued together to prevent any separation or leakage that may happen afterward. Modifications on the texture of the capsules were made to enhance their bonding characteristics within the cement matrix and the capability of self-healing agent release. Two surface modifications were applied, (1) by roughening the surface of the capsule, which was done using P400 grit sandpaper, and (2) by engraving inclined lines on the roughened surface of the capsule using Dehaki electric mini drill. The surface texture modifications are shown in Figure 2. Additionally, a hole was made at one end of the capsule and the sodium silicate solution was filled in the capsule through the hole. The hole was then sealed with silicone glue to prevent any leakage.  
The strength of PS capsules was measured using an Instron compression machine with a maximum capacity of 1 kN and the loading rate applied was 0.125 mm/min, as shown in Figure 3. The strength of capsules was measured in vertical and horizontal positions of  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Physical Properties**} \\ \hline  & **OPC** & **Silica Fume** \\ \hline Color & Grey & Dark Grey \\ Specific gravity & 3.15 & 2.22 \\ Specific surface area, (m\({}^{2}\)/g) & 1.65 & 20.55 \\ Form & Powder & Ultra-Fine Powder \\ \hline \multicolumn{3}{c}{**Chemical Compositions (\% by Mass)**} \\ \hline  & **OPC** & **Silica Fume** \\ \hline SiO\({}_{2}\) & 19.5 & 95.3 \\ Al\({}_{2}\)O\({}_{3}\) & 5.3 & 0.7 \\ Fe\({}_{2}\)O\({}_{3}\) & 2.3 & 0.3 \\ Na\({}_{2}\)O & 0.2 & 0.2 \\ K\({}_{2}\)O & 1.0 & 0.8 \\ CaOO & 61.8 & 0.3 \\ MgO & 2.3 & 0.4 \\ SO\({}_{3}\) & 3.8 & 3.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical compositions and physical properties of cement and silica fume.  
Figure 2: Photo of surface texture of capsule (**a**) without modification (smooth), (**b**) roughened, and (**c**) weak lines.  
the capsule placement due to the various orientations of the capsules within the cement composite materials. The strength of PS capsules is tabulated in Table 4.
# Power Management Strategy for an Autonomous DC Microgrid
## 4 Experimental Tests
### Test 1
#### 4.1.2 DG Load-Following Mode  
In load-following mode, the reference of the DG generating power was given as the deficient power, meaning that it only satisfies load demand. There was no consideration of the DG power cost, and there was no excess power to recharge the SC and electrochemical battery bank. Experimental results are shown in Figure 7.  
Figure 7a shows that, before the DG is started up, the power evolution of each component is similar to that given in Figure 6a. In the fourth interval (\(13:52\leq t<18:00\)), DG output power is just enough to avoid load-shedding, while the SC and battery bank are not charged.  
Figure 7b,c show \(SOC_{B}\) and \(SOC_{SC}\) evolutions, which are well within their respective ranges. Since there is no DG power to recharge the SC and electrochemical battery bank in the fourth interval, \(SOC_{B}\) remains in the empty state and \(SOC_{SC}\) is in continuous decline. The DC bus voltage is also steady within a \(\pm 1.5\%\) fluctuation amplitude of the rated voltage, as shown in Figure 7d.  
Figure 6: Experimental results of test 1 with the diesel generator (DG) in duty-cycle mode: (**a**) DC microgrid power evolutions, (**b**) SOC evolution of electrochemical battery bank, (**c**) SOC evolution of the SC and (**d**) DC bus voltage.  
For both operating modes of the DG, the parallel supplying mode of the PV and DG power sources is considered in case of insufficient PV power. Figures 6a and 7a show that the DG supplies power in parallel with the PV source until the duty-cycle time ends or the PV source and storage can satisfy the load power demand.
# Reputation-Driven Dynamic Node Consensus and Reliability Sharding Model in IoT Blockchain
## 4 Experiment and Result Analysis
### Reputation Sharding Test Results  
In Figure 9, with the increase of the proportion of malicious nodes in the total blockchain network under different sharding numbers, it can be seen that the three broken lines of the NCRS sharding construction scheme had a sharding success rate of nearly 100% when the proportion of malicious nodes was less than 0.25. After that, the larger the number of sharding, the lower the success rate; PBFT can only tolerate 1/3 of the number of malicious nodes at most. Therefore, when the proportion of malicious nodes reaches  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Scheme** & **Disadvantage** & **Advantage** \\ \hline \multirow{4}{*}{Participation degree [41]} & The participation table needs to be maintained locally, which consumes storage space; Unable behavior nodes; & Continuous evil nodes can be quickly determined; \\ \hline \multirow{4}{*}{Health status [42]} & The participation table needs to be maintained locally, which consumes storage space; Unable behavior nodes; & Continuous evil nodes can be quickly determined; \\ \hline \multirow{4}{*}{Vote [43]} & It consumes a lot of communication resources and may be used by malicious nodes, resulting in network congestion; & Not need to store other data, saving storage resources; It can accurately identify malicious nodes; \\ \hline \multirow{4}{*}{RE-PBFT} & The participation table needs to be maintained locally, which consumes storage space; & It can solve the problem of evil behavior in different scenarios, and vote only when it is determined that there are nodes that need to be eliminated, which will not cause waste of communication resources; \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of node elimination schemes.  
33%, the sharding success rate is zero, but it has significant advantages compared with the sharding success rate of multi-layer PBFT [36].