esa-sceva/satcom-chunk-collection huggingface dataset:
# Cellulose Nanofibril (CNF) Films and Xylan from Hot Water Extracted Birch Kraft Pulps
## Materials and Methods
### Water Vapour and Oxygen Transmission of CNF Films  
The water vapour transmission rate of the films was determined gravimetrically by the wet cup method, using a modified ASTM-E-96B procedure. Samples with a test area of 30 cm\({}^{2}\) were mounted on circular aluminium cups (68-3000 Vapometer EZ-Cups; Thawing-Albert Instrument Company, West Berlin, NJ, USA) containing distilled water (100% RH). The films were secured between two neoprene rings to prevent any edge leaks, and an aluminium threaded flanged ring was screwed into the cup to firmly hold the sample. The cups were stored at 23 \({}^{\circ}\)C and 50% RH and were weighed periodically until a constant rate of weight reduction was attained. A Climaveneta AXO 10 (Bassano del Grappa, Italy) climate control system was used to control both temperature and humidity conditions in an environmental room. In this test, a 100/50% RH gradient was used. The humidity gradient is the driving force for water molecules to diffuse within a material. Two samples per film were tested.  
Oxygen transmission rates through the films were determined according to the standard ASTM D3985, using the 8001 and 8011 oxygen permeation analysers (Systech Instruments Ltd., Thame, UK). The tests were conducted with 50 cm\({}^{2}\) samples at 23 \({}^{\circ}\)C and 0% or 80% RH, using 100% oxygen as a test gas. The films were clamped between the two halves of the testing chamber. Two samples per film were tested.
# Measurement of Sub-Surface Microstructures Based on a Developed Ultrasonic Atomic Force Microscopy
###### Abstract  
Accurate and non-destructive technology for detection of subsurface defect has become a key requirement with the emergence of various ultra-precision machining technologies and the application of ultra-precision components. The combination of acoustic technique for sub-surface detection and atomic force microscopy (AFM) for measurement with high resolution is a potential method for studying the subsurface structure of workpiece. For this purpose, contact-resonance AFM (CR-AFM) is a typical technique. In this paper, a CR-AFM system with a different principle from commercially available instruments is set up and used for the detection of sub-surface Si samples with grating structures and covered by different thickness of highly oriented pyrolytic graphite (HOPG). The influence of subsurface burial depth on the detection capability is studied by simulations and experiments. The thickest HOPG film allowing for sub-surface measurement by the proposed method is verified to be about 30 \(\upmu\)m, which is much larger than the feature size of the subsurface microstructure. The manuscript introduces the difference between this subsurface topography measurement principle and the commercially available AFM measurement principle, and analyzes its advantages and disadvantages. The experimental results demonstrates that the technique has the capability to reveal sub-surface microstructures with relatively large buried depth and is potential for engineering application in ultra-precision technologies.  
measurement; sub-surface microstructure; ultrasonic AFM; contact-resonance AFM; buried depth +
Footnote †: journal: Applied sciences
## 3 Piperine: Human Clinical Trials  
### Effect of Piperine on Oral Bioavailability of Drugs and Natural Therapeutic Compounds  
Most human clinical studies have been focused on bioavailability enhancement by co-administration of piperine with various drugs (Table 1). In these trials, various groups of drugs were found to be more effective and stayed longer in plasma when administered with piperine. One of the earliest studies was that of [PERSON] et al. [149], which used [\({}^{3}\)H] vasicine and [\({}^{3}\)H] spartein as test drugs, together with piperine, and found that sparteine blood levels increased more than 100%, while vasicine levels increased by nearly 233% when applied together with piperine. Based on this early study, another set of studies on humans investigated the effect of piperine co-administration with phenytoin and found that a single daily dose of piperine decreased the absorption T\({}_{1/2}\), prolonged the elimination T\({}_{1/2}\), and produced a higher AUC in comparison to phenytoin alone [151]; as well as increasing the absorption rate constant (K(a)), AUC (0-48 h)', AUC (o\(-\infty\))', delaying elimination of phenytoin [152], and increasing AUC (0-12 h), C\({}_{\text{max}}\) and K(a) [153]. These results demonstrated the alteration of the pharmacokinetic parameters of this antiepileptic drug, as well as the enhanced bioavailability of phenytoin when applied with piperine. Another study on the effect of piperine on the bioavailability and pharmacokinetics of the drugs propranolol and theophylline demonstrated an earlier T\({}_{\text{max}}\), and a higher C\({}_{\text{max}}\) and AUC in the subjects who received piperine together with propranolol. Considering co-administration of piperine and theophylline, a higher C\({}_{\text{max}}\), longer elimination half-life, and a higher AUC were observed [145]. [PERSON] et al. [156] reported that concomitant administration of piperine with curcumin produced much higher concentrations of this bioactive compound (0.25 to 1 h post-drug), as well as the increase in its bioavailability of 2000%. Combination of dexibuprofen with the multi-ingredient formula (Lipicur composed of lipoic acid, curcumin, and piperine) reduced neuropathic pain by more than 66% in subjects with lumbar sciatica and carpal tunnel syndrome, as well as by about 40% with dexibuprofen use [146].
## 6 Novelty and Perspective  
The novelty of the presented research is based on two aspects that have not been analyzed so far in studies using the NIRS method to assess rSO\({}_{2}\). First, rSO\({}_{2}\) was assessed in 18 independent areas on both sides of the body in 70 healthy people aged 18-30 and elderly people over 60 years of age. This provided the basis for determining rSO\({}_{2}\) reference values in physiological conditions in young and old women and men. The demonstrated significant interarea differences in rSO\({}_{2}\) values under physiological conditions confirm the needs to refer to them in the assessment of pathological changes. Moreover, for the first time the areas where the rSO\({}_{2}\) value decreases with age and increasing fat tissue content have been identified.  
Based on the implementation of the goal of our research in the longer term, we would like to determine whether it is possible to consider measuring regional tissue saturation using the NIRS method to assess the reactivity of microcirculating during hyperemia-stimulating physiotherapy in muscles and tendons. Additionally, to clarify the methodology, it seems necessary to assess the influence of gender and the skin fold thickness at the site of application of the NIRS probe on the accuracy of NIRS measurements in patients with different skin pigmentation. It is also necessary to expanding the sample size, of both women and men.  
Moreover, NIRS may become an element of computer-aided diagnostics (CAD) in the future. CAD, using data-processing techniques and statistical analyses, is an advanced medical diagnostic tool that is becoming more and more accepted in the medical community. CAD systems have proven to be useful, among others, for early detection of breast cancer [59], acute lymphoblastic leukemia [60] or congestive heart failure [61]. A promising prospect is the use of artificial intelligence (AI) and machine learning in combination with NIRS for modern diagnostics and monitoring of the patient's health. Although the application of AI to NIRS data has not yet been widely researched, the literature on the subject includes studies on the detection of cerebral ischemia [62], pain imaging [63] or the detection of cognitive load [64; 65]
## 5 Simulation Experiment
### Parameter Setting Experiment  
Configuring reasonable parameters is crucial for the performance of the algorithm. The key parameters that affect the performance of the DPSO algorithm include the population size (_Popsize_), the range of adaptive inertia constants (\(w_{\text{min}}\) and \(w_{\text{max}}\)) and the learning factors (\(c_{1}\) and \(c_{2}\)). In this paper, the orthogonal experimental design method in reference [6] was used to determine the optimal combination of parameters, and these parameters were set to 5 factors, each of which had 4 levels. Orthogonal experiments based on the FJSP medium-scale test instance MK07 are conducted to adjust the parameters. Each set of parameter combinations is run 10 times, and the average _makespan_ is calculated as the evaluation criterion. The orthogonal experiment table for \(L_{16}\) (\(4^{5}\)) and its experimental results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Number**} & \multicolumn{5}{c}{**Factor**} & \multirow{2}{*}{_Makespan_} \\ \cline{2-2} \cline{4-7}  & _Popsize_ & & \(w_{\text{max}}\) & \(w_{\text{min}}\) & \(c_{1}\) & \(c_{2}\) & \\ \hline
**1** & 100(1) & 0.7(1) & 0.2(1) & 0.6(1) & 0.7(1) & 189.2 \\
**2** & 100(1)  
As shown in Table 4, according to extreme values and ANOVA of parametric orthogonal experiments, the influence degree of five factors on algorithm performance is \(w_{\min}>c_{1}>c_{2}>w_{\max}>\textit{Popsize}\). The \(k_{i}\) (\(i=1\), \(2\), \(3\), \(4\)) value represents the average of the four experimental results for this parameter at the corresponding \(i\) level. As the mean value of _makespan_ is selected as the evaluation index, the smaller the mean value of _makespan_, the better the running result of the current example. Therefore, the smallest \(k_{i}\) value of the parameter corresponding to each column indicates that the \(i\) level parameter corresponding to this factor has a better value. As can be seen in Figure 8, the optimal parameters of the algorithm in this paper are set as _Popsize_ = 300, \(w_{\max}=0.9\), \(w_{\min}=0.4\) and \(c_{1}=0.7\), \(c_{2}=0.9\). Other parameters are set as follows: maximum number of iterations: \(T=100\).
## 3 Results
### Growing Season
The length of the growing season has the highest error compared to the satellite data, where 6 out of 8 models display longer GSL, mostly driven by a late leaf shedding (Table 2). When looking at the hemispheric mean values it is clear that all of the models overestimate the LAI, dormancy and length of the growing season (Table 2). Satellite LAI average for the Northern Hemisphere was 0.83, while LAI from the models varies between 0.98-2.16. Both growing season onset and dormancy  
Figure 6: Mean (1986–2005) growing season dormancy (day) for 8 LSMs and satellite observations over the Northern Hemisphere (30 \(\circ\)-90 N). Spatial correlations and root mean square errors between each model and the observations are given in the white boxes. DOYs above 365 represent DOYs of the following year. were later in all of the models, in some cases by more than a month. The effect of the late offset translates as an increased GSL, with values 9 to 180 days higher than the satellite data (Table 2). However, when the dormancy period is calculated based on GPP the modeled values become much closer to the observations, with an average GSL of 144 \(\pm\)15 days, compared to 184 days in the satellite data. This again suggests a decoupling between the active period of photosynthesis and leaves in the models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **LAI** & **Onset (day)** & **Dormancy (day)** & **Length (days)** \\ \hline
**CLM** & 1.6 & 131 & 351 (288) & 220 (164) \\
**LPJ\_GUESS** & 1.6 & 125 & 314 (285) & 189 (151) \\
**LPJ** & 2.2 & 130 & 319 (278) & 189 (134) \\
**OCN** & 1.2 & 121 & 342 (268) & 221 (142) \\
**ORCHIDEE** & 0.98 & 151 & 323 (268) & 172 (134) \\
**SDGVM** & 1.56 & 122 & 374 (275) & 252(145) \\
**TRIFFID** & 1.11 & 133 & 355 (274) & 222(125) \\
**VEGAS** & 1.98 & 136 & 336 (277) & 200 (139) \\
**LAI3g** & 0.83 & 111 & 295 & 184 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average LAI, growing season onset, dormancy and length for the Northern Hemisphere for each model and the satellite observations. The values for dormancy and length based on GPP are presented in brackets. Figure 7: Mean growing season length (1986–2005) in days for 8 LSMs and satellite observations over the Northern Hemisphere (30 \(\copyright\)90 N). Spatial correlations and root mean square errors between each model and the observations are given in the white boxes.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 2 IoT Tide Gauge Network Design
### Arduino Micro-Controller  
The Arduino MKR FOX 1200 (see Figure 2a) is based on the SAMD21 microchip and an ATA8520 Sigfox module. It has low-power Sigfox communication, 32-bit computational power and can be programmed using the well-supported Arduino Software (IDE) development package.  
Figure 2: (**a**) Arduino MKR 1200 Fox micro-controller, (**b**) Blue Robotics 2-bar (MS5837 pressure sensor), (**c**) Configuration-1 and Configuration-2.  
Power can be supplied via the USB port at 5 V, using a battery pack that does not shut down when the Arduino goes into low-power mode (e.g., the Voltaic Systems USB battery is suitable for this purpose), and at 3.3 V on the I/O pins using a couple of AA, AAA, C or D-cell batteries. The Arduino typically takes around 20 mA, and with the sensors used here and other electronic components, e.g., SD Proto Shield with SD card, gives a total current draw of between 35 and 54 mA, and with a 70 mA current draw during the 1-4 s Sigfox transmission. Bench tests suggest that two alkaline D-cell batteries would last up to 22 days by sampling for 40 s periods and then cycling to sleep mode. Using the Voltaic Systems USB battery with solar panels can allow for continuous recording. The micro-controller is relatively small at 68 mm length and 25 mm width. It is designed to use \"prototyping shields\" such as the MKR SD Proto Shield, which can be directly plugged in to provide the internal logging option.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Renewable Energy Integration in Operation
#### 3.3.1 Operational Challenges Posed by High Penetration of Renewables  
The integration of renewables, as discussed in [122], introduces challenges such as variability, low inertia, and power quality issues, whereas the solutions involve various technologies and strategies, including advanced control systems, optimization techniques, and energy storage solutions. Digital technologies, particularly advanced data analytics and predictive modeling, play a crucial role in forecasting and managing the variable nature of RESs. Additionally, Ref. [183] highlights the need for sophisticated climate modeling tools to anticipate and mitigate the effects of climatic variability on renewable production. Methodologies from the reviewed studies include statistical analyses, power spectrum density plots, and correlation assessments of climate variables and renewable energy production.
## 5 Discussion
### Implications for Geomorphic Analysis
Studies comparing high-resolution (i.e., \(\leq\)5 m) optical satellite DEM quality for geomorphic applications are few; DEM generation is typically carried out in less flexible software compared to the ASP, and processing parameters are not analyzed in detail (e.g., ([PERSON] et al., 2018)). A notable exception is the recent work of [PERSON] and [PERSON] (2018), who used the Surface Extraction from TIN Space-search Minimization (SETSM; (S  
in mountainous regions. Optical satellite data have a rich and growing archive and are valuable data sources for Earth surface analyses. Additionally, the remote sensing and computer vision communities continue to develop and improve algorithms for high-quality depth retrieval from overlapping images. Higher resolution satellites (e.g., \(\leq\)0.5 m, such as those used by [PERSON] and [PERSON] [56]) will likely contribute to vastly improved DEM quality given the increase in image texture that comes with the ability to discern finer features compared to the 1.5 m resolution data used here. Optical DEMs will also be affected by land cover, particularly vegetation, but these effects are avoided in our bare-Earth study area in order to isolate the image-texture uncertainty component. Future studies in vegetated regions could use these findings as a starting point for uncertainty analyses. Figure 12: Difference between tangential curvature calculated in a 3 \(\times\) 3 pixel window and a Gaussian-smoothed version of this curvature map for the BM DEMs. Same area as is shown in Figure 3 (see Figure 2A for location in study area); (**A**) hillshade of the BM DEM with a CK of 15 \(\times\) 15 pixels; (**B**) curvature difference map for this DEM. Larger differences occur in areas with more spiky DEM curvature; (**C**–**F**) similar pairwise (hillshade and curvature difference) results for the CK 25 \(\times\) 25 and 35 \(\times\) 35 pixel BM DEMs.
## 2 Literature Review
[26] proposed an insulator defect detection algorithm based on ResNeSt and a multi-scale region proposal network, and used data augmentation to obtain expanded data, achieving a better accuracy performance compared with other detection networks. [PERSON] et al. [27] established an insulator image data set through GraphCut and Laplace sharpening, while the MobileNet backbone was used to improve the real-time performance of YOLOv4 and a transfer learning strategy was used to better train the model. [PERSON] et al. [28] proposed and used a triplet attention mechanism (TAM) in a feature extraction network to optimize the attention to target outliers, which improved the detection performance of defective insulators with low extra costs. [PERSON] et al. [29] proposed a model based on the CenterMask algorithm to realize the intelligent identification of insulator defects, in which an improved VoVNet was adopted as the backbone network and SAG-Mask was added to FCOS to extract the mask image of the insulator, achieving an improved identification efficiency for insulator defects. These methods all have their advantages, but they have not achieved good performances in both speed and accuracy. In order to achieve the fast and accurate instance segmentation of insulator defects, an improved YOLACT algorithm is proposed in this paper, which improves segmentation speed as well as segmentation accuracy. The main contributions of this paper are as follows:  
* The backbone network is modified to MobileNetV2, aiming to greatly reduce the computation of the model. Meanwhile, an improved scSE module with a serial structure and skip connections is proposed and embedded into the MobileNetV2 to enhance the feature extraction. * A feature fusion structure is proposed in the FPN. We first process P3-P7 with ECA-Net and up-sample the feature maps to the same size. Then, P3 and P4 are added to the top layers, while P5-P7 are added to the bottom layers across double layers. After the feature maps are fused, they are down-sampled to the original size for the next modules in the model. * A bounding box loss function that covers the area deviation, center deviation, and shape deviation is designed to better limit the generation of redundant bounding boxes, namely RIoU Loss. The model is trained to generate bounding boxes more accurately with the new loss function.
# Finite-Time Consensus Tracking Control for Speed Sensorless Multi-Motor Systems
## 5 Simulations  
In this section, a simulation example is provided to verify the effectiveness of the proposed method. Five nonidentical servo motors 0 (L), 1, 2, 3, and 4 are used in the simulation. Motor 0 is assumed to be the virtual leader and motors 1, 2, 3, and 4 are all the followers. Apparently, the reference signal is only transmitted to the virtual leader and all the followers are able to track the motion of the virtual leader. The communication and interaction among motors are shown in Figure 1, and the parameter settings of all motors are shown in Table 1. Each follower realizes the overall consensus through the local information exchange. If motor i and motor j exchange their information, \(a_{ij}=a_{ji}=1\), \(\forall i\
eq j\). Otherwise, \(a_{ij}=a_{ji}=0\), \(\forall i\
eq j\). If motor \(i\) exchanges information with the leader, \(a_{i0}=1\). Otherwise, \(a_{i0}=0\).  
\[A=\left[\begin{array}{cccc}0&1&1&0\\ 1&0&0&0\\ 1&0&0&1\\ 0&0&1&0\end{array}\right],B=\left[\begin{array}{cccc}1&&&\\ &0&&\\ &&0&\\ &&0\end{array}\right]\]  
To verify the effectiveness of the proposed method, four different disturbance signals are adopted: \(d_{1}=15\sin(3t),d_{2}=12\cos(5t),d_{3}(t)=15\cos(5t)e^{-0.3t},d_{4}=13\sin(3t )+3\cos(5t)\), to simulate the uncertainty of each motor. During the simulation experiment, the parameter matrix of the finite-time observer is set as: \(l_{1}=\left[\begin{array}{cc}l_{11}&l_{12}\end{array}\right]^{T}=\left[ \begin{array}{cc}20&45\end{array}\right]^{T}\), \(l_{2}=\left[\begin{array}{cc}l_{21}&l_{22}\end{array}\right]^{T}=\left[ \begin{array}{cc}21&43\end{array}\right]^{T}\), \(l_{3}=\left[\begin{array}{cc}l_{31}&l_{32}\end{array}\right]^{T}=\left[ \begin{array}{cc}22&44\end{array}\right]^{T}\), \(l_{4}=\left[\begin{array}{cc}l_{41}&l_{42}\end{array}\right]^{T}=\left[ \begin{array}{cc}23&45\end{array}\right]^{T}\). The gain of the consensus control protocol is set to \(q=\left[\begin{array}{cc}q_{1}&q_{2}\end{array}\right]^{T}=\left[\begin{array} []{cc}50&10\end{array}\right]^{T}\).
# Recording a Hologram Transmitted over a Communication Channel on One Sideband
## 4 Conclusions
in Figure 1), so it may be concluded that the cycle involving the recording of a 3D image of the object; its transmission through a conventional radio channel with the accepted TV frame rate; the synthesis of a rainbow focused image hologram, which is equivalent to recording flat holograms at 15 incidence angles; the reconstruction of a 3D image of the object; and the observation of the parallax in the image were all carried out successfully. This allows us to conclude that it is possible to transmit holograms in real time through conventional communication channels using a method similar to SSB. Thus, the possibility of implementing a holographic phototelegraph was confirmed experimentally. The authors hope that the data described in this paper will be useful for the developers of dynamic holographic 3D displays to solve the problems of visual reality and holographic TV. The following are available online at [[https://www.mdpi.com/article/10.3390/app112311468/s1](https://www.mdpi.com/article/10.3390/app112311468/s1)]([https://www.mdpi.com/article/10.3390/app112311468/s1](https://www.mdpi.com/article/10.3390/app112311468/s1)): Supplementary Video S1: Parallax of a 3D image reconstructed from a material hologram. Conceptualization, S.A.S. ; methodology, S.A.S. and S.B.O. ; software, A.L.P., I.K.T. and E.A.D. ; validation, S.A.S., A.L.P., S.B.O., I.K.T. and E.A.D. ; formal analysis, S.A.S., A.L.P. and I.K.T. ; investigation, A.L.P. and E.A.D. ; resources, S.B.O. ; data curation, S.A.S. and S.B.O. ; writing--original draft preparation, S.A.S. ; writing--review and editing, A.L.P. ; visualization, A.L.P. ; supervision, S.A.S. ; project administration, S.A.S. ; funding acquisition, S.A.S. and S.B.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The following are available from the authors upon request. The authors thank all reviewers for their helpful comments and suggestions. The authors declare no conflict of interest.