esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of an Innovative Biofeedback Insole on Patient Rehabilitation after Total Knee Arthroplasty
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**TIME** & & & & & \\
**(Days** & **NRS** & **KSS Stn** & **KSS FUN** & **WOMAC** & **Tinetti** \\
**Post-Op**) & & & & & \\ \hline
0 & 6.71 \(\pm\) 1.94 & 53.33 \(\pm\) 6.44 & 65.42 \(\pm\) 23.11 & 45.69 \(\pm\) 15.96 & 8.08 \(\pm\) 2.87 \\ \hline
5 & 6.08 \(\pm\) 1.93 & 49.55 \(\pm\) 19.55 & 11.73 \(\pm\) 10.72 & 51.37 \(\pm\) 15.05 & 5.08 \(\pm\) 1.88 \\ \hline
10 & 4.58 \(\pm\) 1.24 & 50.73 \(\pm\) 11.71 & 45.55 \(\pm\) 9.40 & 47.18 \(\pm\) 15.04 & 6.50 \(\pm\) 1.57 \\ \hline
15 & 3.50 \(\pm\) 1.17 & 63.00 \(\pm\) 15.03 & 49.09 \(\pm\) 11.36 & 41.83 \(\pm\) 17.22 & 7.67 \(\pm\) 1.37 \\ \hline
30 & 4.10 \(\pm\) 3.11 & 78.88 \(\pm\) 12.89 & 71.43 \(\pm\) 15.20 & 14.29 \(\pm\) 7.52 & 9.43 \(\pm\) 1.90 \\ \hline \end{tabular}
\end{table} 
Table 1: Results with different scores: NRS: Numerical Rating Scale; KSS stn: Knee Score System, standard component; KSS FUN: Knee Score System, functional component; WOMAC: Western Ontario McMaster Universities Index Osteoarthritis; Tinetti: Tinetti scoring system. All patients were able to reach a complete load (90% bodyweight or more) before discharge and showed good adherence to the rehabilitation protocol and insole use. The final insole NRS score, ranging from 0 (poor satisfaction) to 10 (maximal approval), had an average of 8.5, with only one patient complaining of some difficulties in using the device; however, she had an ankle sprain that had occurred just before the surgery. The number of patients able or unable to achieve the load targets in function of the number of days post-op are shown in Figure 3.
# A Comprehensive Review on Clay Soil Stabilization Using Rice Husk Ash and Lime Sludge
## 2 Materials  
Disposing of waste materials, such as rice husk ash and lime sludge, resulting from industrial processes poses environmental concerns when deposited in landfills. However, these materials can be repurposed and utilized beneficially, particularly in geotechnical and other engineering projects [71, 72]. This review focuses on applying RHA and LS in clay soil stabilization, exploring their properties, and investigating their chemical composition, as documented by various researchers who worked in the field of using green alternative materials for clay stabilization. Results of laboratory tests, including standard Proctor compaction, unconfined compression strength (UCS), and California Bearing Ratio (CBR) related to soil samples with various mixed proportions of admixtures among different papers, are discussed.
## 3 Results
### Applied to QTL Mapping of Phenotype and Secondary Metabolites
The qwbph8-3 is detected by RM17699-RM264 on chromosome 8 with a LOD value of 3.3 and an explainable phenotypic change of 30%. The qwbph8-3 is detected by RM17699-RM264 on chromosome 8 with a LOD value of 3.3 and an explainable phenotypic change of 30%. In 2020, qwbph4-3, qwbph6-3, qwbph8-4, and qwbph12-1 were detected. The qwbph4-3 is detected by RM280-RM6909 on chromosome 4 with a LOD value of 3.5 and an explainable phenotypic change of 30%. The qwbph6-3 is detected by RM248-RM1134 on chromosome 6 with a LOD value of 3.0 and an explainable phenotypic change of 30%. The qwbph8-4 is detected by RM23230-RM3689 on chromosome 8 with a LOD value of 2.5 and a 30% explainable phenotypic change. The qwbph12-1 is detected by RM1226-RM12 on chromosome 12 with LOD value of 2.7 and 40% phenotypic change explainable. Finally, in the 5 years of QTL mapping results, Chromosomal 8 RM23191 was commonly detected in 2016, 2018 and 2020. In 2016 and 2018, the WBPH resistance score was used for QTL mapping, and in 2020, the concentration of substances generated through HPLP after WBPH inoculation was used for QTL mapping (Figure 1B and Supplementary Materials Table S2). Therefore, the result of using two different characteristics related to WBPH resistance was used to screening for candidate genes related to WBPH resistance by mapping markers in the same region among two different characteristics. For fine mapping, we used R1813, Y89A, C483, Y106, S10588, C1107 near RM23191. As a result, eight candidate genes (_Os08g0453700, Os08g0441500, Os08g0441600, Os08g0459600, Os08g0458600, Os08g046000, Os08g0472000, Os08g0474000_) for the WBPH resistance were detected. _Os08g0453700_ responds to plant defense responses from _Arabidopsis thaliana_ to respiratory burst oxidase protein. _Os08g0441500_ functions as a cinnamoyl-CoA reductase and responds to infection of pathogenic bacteria. _Os08g0441600_ (_OsCM_) has a sequence similar to that of chorismate mutase CM2 and is involved in prephenate synthesis. _Os08g0459600_ is an oxophytodienoate reductase and is involved in jasmonic acid biosynthesis. _Os08g0458600_ is a signal transduction response regulator like Response regulator 33 or A-type response regulator 19. _Os08g0460000_ is a Germin-like protein 1 precursor and is involved in plant defense. _Os08g0472000_ is a transcription factor in abscisic acid-regulated transcription and is involved in plant defense and development. _Os08g0474000_ is an AP2 domain-containing protein, a plant defense regulator.
# Design of Capacitive Paddy Moisture Sensor Based on Electrical Impedance Spectroscopy Analysis
## 1 Introduction
Thirdly, in the process of grain moisture measurement, the bulk density of the paddy samples will change continuously without becoming settled and compacted before the measurement. So, it is difficult to accurately control the bulk density of the paddy during the detection process. This study mainly explores how to improve the detection accuracy of paddy moisture under high temperature conditions. Therefore, the paddy samples used in the sensors will be settled and compacted to eliminate the influence of bulk density. The aim of this study is to design a reliable capacitive paddy moisture sensor. Currently, the main used impedance measurement chips have an excitation frequency range of Hz to MHz, such as AD5933. Therefore, it is necessary to use impedance analysis technology, which is dedicated to studying the dielectric spectrum of the materials on 10\({}^{1}\)-10\({}^{7}\) Hz, to study the impedance characteristics of paddy for the selection of the excitation frequency. This study strictly controlled the temperature and humidity environment of the paddy dielectric properties detected in paddy drying process. It studied the electrical impedance spectroscopy of paddy in different moisture content and different temperature 40-80 \({}^{\circ}\)C by an impedance analyzer, and then established a paddy moisture detection model. Based on the experimental results and established model, a paddy moisture sensor was designed.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 1 Introduction
### Contributions  
Contributions of this paper can be summarized as follows.  
1. We establish a theoretical model that simultaneously takes into account the data freshness and fidelity for jointly optimizing two metrics through adjusting the number of sensor nodes. Explicit characterizations of freshness and fidelity were given by applying the average AoI and MMSE metrics.
2. By endowing the weighting factor to the average AoI and MMSE, the optimal number of sensor nodes optimization problem is formulated. Through appropriate approximations on both metrics, we specify the optimal number of sensor nodes by proposing a sub-optimal solution, which jointly minimizes data freshness and fidelity.
3. Numerical results validate the correctness of the proposed solution and it can achieve the best freshness-fidelity weighted-sum tradeoff with negligible errors.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 3 Identifying Challenges in RSA: Literature Review
### Challenges in Obstacle and Surrounding Information Acquirement and Detection
#### 3.2.1 Narrow View of the Camera  
Prior research found that the camera in use had a relatively limited viewing angle of around 50\({}^{\circ}\), compared with the angle of human vision, which is up to 180\({}^{\circ}\)[9]. Researchers [11; 94] mentioned that the camera should be located appropriately to maximize vision stability along the path. A limited field of camera view affects RSA agents negatively in their guiding performance [13; 108].
## 4 Design and Implementation
### Proposed HFSPDM Modulation
#### 4.3.1 Encoding in HFS-PDM Modulation  
The steps involved in the encoding of data bits using HFSPDM is as shown in Algorithm 6. The \(Setup\) function sets all the required variables and pins connected to the programmable board. The working of \(Frequency\) and \(Send\) Function is the same as explained in BFSOOK Modulation (4.2.1) except for a minor change with respect number of bits sent. In the proposed technique, 2 bits are passed to the \(Send\) function thus, the \(Send\) function has to handle four states. Additionally since, the start bit is represented by a different frequency, the \(Send\) function has to also handle the fifth case of the start bit pattern. For, the purpose of convenience each bit pair is divided into four calls to the \(Frequency\) function. thus, depending upon the bit pair, the corresponding frequency duration can be controlled. For example, if bit pair is 00, then \(F2\) frequency i.e., \(Frequency(NumOf Stripes2, Delay2)\) will be called 4 times. Whereas, if the bit pair is 01, Frequency \(F2\) will be called 3 times followed by Frequency \(F3\) i.e., \(Frequency(NumOf Stripes3,Delay3)\) which will be called only once. Typically, the encoded frequencies follow the Nyquist sampling theorem, and the relation between the number of stripes, delay, and frame rate is provided as follows:  
\[B_{r}=\frac{F_{r}}{2} \tag{6}\]  
\[D_{1}=\frac{1}{2}\times(((1/B_{r})\times 10^{6})/N_{11}) \tag{7}\]  
\[D_{2}=\frac{1}{2}\times(((1/B_{r})\times 10^{6})/(2\times N_{11})) \tag{8}\]  
\[F_{1}=\frac{1}{D_{1}},\textit{(whereF1>F_{ff})} \tag{9}\]  
\[F_{2}=\frac{1}{D_{2}},\textit{(whereF2>F_{ff})} \tag{10}\]  
Here, \(F_{r}\) is Camera Frame Rate, \(B_{r}\) is bit encoding rate, \(N_{l1}\) is number of stripes expected, \(D_{1}\) and \(D_{2}\) is the delay between On-Off cycle, resulting into frequency \(F_{1}\) and \(F_{2}\). These frequencies represent logic 1 and 0. Special care should be taken while assigning the number of lines as it affects the frequency and detection of stripes. The frequency should be more than the minimum flicker-free frequency (\(F_{ff}\)), i.e., 200 Hz.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Data Bit**} & \multicolumn{1}{c}{**Frequency Distribution**} & \multicolumn{1}{c}{**Pulse Shape**} \\ \hline
00 & F2(100\%) + F3(0\%) & \\
01 & F2(75\%) + F3(25\%) & \\
10 & F2(25\%) + F3(75\%) & \\
11 & F2(0\%) + F3(100\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: HFSPDM Frequency Mapping Table.  
Figure 6: Sample Frame Format in terms of Frequency for HFSPDM.
## 4 Results and Discussion
### Model Architectures
#### 4.1.3 Model Quantization  
To examine the effect of weight/bias/kernel quantization level on model accuracy, tests are conducted iterating over the number of bits for the quantization in the range from 2 to 16, and there is no significant difference in achieved results. The comparison of accuracy and FNR for different quantization levels given in Figure 13 shows that accuracy is very similar for all quantization levels, while FNR varies more. Therefore, given that the goal is model minimization in the sense of memory footprint and processing requirements, aggressive 2-bit quantization of model weights/biases/kernels is chosen to be applied for model quantization, even if FNR is higher than, for example, for 6-bit quantized models.  
In the rest of the paper 2-bit quantized CNN is referred to as QCNN, and accordingly, the quantized 2-bit version of MLP is named QMLP.  
Figure 12: A visualization of SHAP local interpretation, calculating the SHAP values for each feature for an individual sample. Red pixels indicate the features that cause the model to predict that an HGCAL image depicts an EM shower, while blue pixels indicate the features that push the model away from predicting that an image belongs to a given class.  
Figure 13: The effect of weight/bias/kernel quantization level on CNN and MLP model: (**a**) accuracy (as a function of bit width), (**b**) FNR (as a function of bit width).  
The performance of the 2-bit quantized version of CNN and MLP is summarized in Table 6. It is evident that the quantized models have the same discrimination power as the baseline architectures. Considering memory and processing limited devices on which the trigger algorithms will be implemented, from now on, the study is conducted just on quantized models. Comparing QCNN with QMLP, it is evident that both models provide very similar results. All metrics are slightly higher for QCNNs, confirming that convolutional layers ensure better performance in computer vision problems.
# Flutter Reliability Analysis of Xiangshan Harbor Highway Cable-Stayed Bridges in Service
## 2 The Flutter Analysis Framework for Flexible Bridge
### Influence of Wire Breaking Rate of Cables on Flutter Stability  
During the long-term operation of Xiangshan Harbor Bridge, wire breakage of stay cables will inevitably occur with the load effect and environment influence, which will affect the cross-sectional area of the cable and lead to the reduction of structural safety performance. For the stay cables of the bridge, [PERSON] [42] pointed out that the wire breaking rate of 10% could be taken as the end point of stay cable life through experiments. In order to better discuss the influence of wire breakage rate on flutter instability wind speed, the wire breaking rates of 5%, 10%, 15% and 20% were analyzed. The wire breaking rate was simulated by the change of the cross-sectional area of each cable, so as to achieve the target wire breaking rate. The critical flutter wind speed of Xiangshan Harbor Bridge was calculated under different wire breaking rates. The calculation results are shown in Figure 14.  
Figure 13: Change curve of flutter critical wind speed with stiffness of main beam.  
It can be seen from Figure 14 that when the wire breaking rate was between 0 and 5%, the flutter instability wind speed of Xiangshan Harbor Bridge was almost unchanged. When the wire breaking rate was between 5% and 10%, the flutter critical wind speed decreased slowly with the increase of wire breaking rate, that is, from 64 m/s to 63 m/s. However, when the wire breaking rate exceeded 10%, the critical flutter wind speed decreased rapidly. The critical flutter wind speed decreased from 63 m/s to 57 m/s. This indicates that if the cable is replaced in time according to the maintenance requirements during the bridge operation, and the wire breaking rate of the cables is below 10%, and the impact on the critical flutter wind speed of Xiangshan Harbor Bridge is very small. Once it exceeds 10%, the critical flutter wind speed will decrease rapidly.
# Analysis of Cyclone Spinning Effect with Different Guide Vane Heights
## 4 Analysis of Cyclone Spinning Effect
### Loss of Mechanical Energy Along the Way  
The relationship between the various moving elements of water flow can be obtained by analyzing the law of energy conservation of water flow. For the water flow through the water section per unit time, its density is \(\rho\), the velocity is \(u\), the axial velocity is \(u_{a}\), the gravity is \(\gamma\), the height from the datum is \(h\), the pressure is \(p\), and the mechanical energy \(E\) (including kinetic energy, pressure energy, and potential energy) can be expressed as follows:  
\[E=\int\limits_{Q}\bigg{(}\frac{1}{2}\rho u^{2}+\gamma h+p\bigg{)}dQ=\int \limits_{A}\bigg{(}\frac{1}{2}\rho u^{2}+\gamma h+p\bigg{)}\cdot u_{a}dA \tag{8}\]  
If the mechanical energy of any section inside the cyclone is \(E_{z}\), where \(Z\) is the distance between the section and the inlet section of the model, the mechanical energy of the inlet section of the cyclone is denoted as \(E_{1000}\), and the mechanical energy of the outlet section is denoted as \(E_{1550}\).  
Then, the mechanical energy loss of the section along the path \(\Delta E_{z}\) can be expressed as follows:  
\[\Delta E_{z}=E_{1000}-E_{z} \tag{9}\]  
The total mechanical energy loss inside the cyclone when spiral flow \(\Delta E\) is generated is expressed as follows:  
\[\Delta E=E_{1000}-E_{1550} \tag{10}\]Figure 9 shows the attenuation value of the inlet section of the mechanical cyclone of the hydrodynamic through the water flow of the cyclone with different guide vane heights per unit time under different Reynolds numbers. As can be seen from Figures 4-11, (1) from the inlet section of the cyclone to the leading edge of the guide vane, there is little difference in the mechanical energy loss of the cyclone with different guide vane heights. (2) When the water flow passes through the leading edge section of the cyclone guide vane, there is a large mechanical energy loss. The mechanical energy loss of water flow increases with the increase in guide vane height and Reynolds number. (3) The mechanical energy loss along the guide vane section increases almost linearly, the mechanical energy loss rate along the straight and twisted sections of the guide vane is close to that of the guide vane, and the greater the height of the guide vane, the faster the mechanical energy loss along the cyclone. (4) At the trailing edge of the guide vane, the speed of mechanical energy loss increases significantly, and the higher the height of the guide vane, the greater the mechanical energy loss of the cyclone.  
Figure 9: Mechanical energy loss along the section \(\Delta Ez\). (a) Re = 105,419; (b) Re = 140,559; (c) Re = 175,699; (d) Re = 210,838.
## 3 Results and Discussion
### Concentration of VOCs in Brine  
The 91 identified and semi-quantified VOC components (Table S1, Supplementary Material) were grouped as acids (10), alcohols (30), carbonyl compounds (12), esters (21), phenols (6), sulfur compounds (2), terpenes (6), and others (4). On average, the predominant VOCs were butanoic acid (V62), acetic acid (V50), ethanol (V10), and (Z)-3-hexen-1-ol (V47) (Supplementary Table S1). The relatively high contents of butanoic acid (V62) in the samples F282 and F283 can be highlighted, affected by the butyric spoilage. Moreover, sample F282 presented high contents of propanoic (V58) and pentanoic (V68) acids. [PERSON] et al. [8] investigated the relationships between microbial communities, metabolites, and sensory spoilage descriptors. These authors found that butyric descriptors exhibited a significant positive relationship with genus _Ruminococcus_, which almost significantly correlated with propionic and butyric acids. [PERSON] and [PERSON] [28] associated the halophilic _Desulfoibrio aestuarii_ with the hydrogen sulfide fermentation of Sicilian-type olives involving the Sevillano variety.  
The data set suggests significant differences among samples for many components. However, they do not reflect relative changes, which depend on their total amounts, highly variable in this work (between 50 and 410 ppb, expressed as 5-nonanol) (Supplementary Table S1). Then, the comparison among samples in the original values is unreliable. Moreover, the spoilages are perceived when their characteristic VOCs are proportionally higher than in normal samples. Because of such characteristics, these VOCs profiles are compositional data, and the interest of the analysis might be focused on the ratios between components, which require appropriate statistical tools [11].
###### Abstract  
Antenna structures for modern applications are characterized by complex and unintuitive topologies that are difficult to develop when conventional, experience-driven techniques are of use. In this work, a method for the automatic generation of antenna geometries in a multi-objective setup has been proposed. The approach involves optimization of a generic spline-based radiator with an adjustable number of parameters using a nested, trust region-based algorithm. The latter iteratively increases the dimensionality of the radiator in order to gradually improve its performance. The method has been used to generate a set of nine antenna designs, representing a trade-off between minimization of reflection within 3.1 GHz to 10.6 GHz and a reduction of size. The properties of the optimized designs vary along the Pareto set from \(-\)10 dB to \(-\)20 dB and from 230 mm\({}^{2}\) to 757 mm\({}^{2}\) for the first and second objectives, respectively. The presented design approach has been validated against a genuine, population-based optimization routine. Furthermore, the smallest Pareto-optimal design has been compared to the antennas from the literature.  
adjustable dimensionality; EM-driven design; multi-objective optimization; nested optimization; UWB antennas +
Footnote †: journal: Applied  
Article EM-Driven Multi-Objective Optimization of a Generic Monopole Antenna by Means of a Nested  
Mdp1
# Impact of Yeast Selection on Volatile Phenol Levels of Wines Produced from Smoked-Exposed Juice
## 2 Materials and Methods
### Chemical Analyses  
Enological parameters, such as residual sugar (glucose + fructose), pH, malic acid, total acidity (TA), ethanol and volatile acidity (VA) of the wines were determined with an OenoFoss(tm) wine analyzer (FOSS Analytical A/S, Hillerod, Denmark). Alcoholic fermentation was monitored and considered complete when the residual sugar concentrations were below 4 g/L.  
Juice and wine samples were analyzed for volatile phenol content using a GC-MS/MS. The method described by [PERSON] [17] and [PERSON] et al. [18] was applied with some modifications. Briefly, ten milliliters of the wine was transferred into a solid phase micro extraction (SPME) vial. Five milliliters of a 20% sodium chloride solution was added into the vial and vortexed. Anisole d\({}_{8}\) was added as internal standard. The headspace of the sample was analyzed using a PDMS/DVB fiber. Analysis was performed on a Thermo Scientific TRACETM 1310 gas chromatograph automated injection coupled to TSQ 8000 MS/MS Triple Quadrupole chromatograph (Anatech Instruments (Pty) Ltd, Cape Town, South Africa). Separation was obtained with a polar ZBwax (30 m, 0.25 mm ID, 0.25 mm film thickness) capillary column. The mass spectrometer was operated in MS/MS mode. The ionization source temperature was set at 250 \({}^{\circ}\)C and emission current of 50 \(\upmu\)A was used with argon as collision gas.  
Ten volatile phenols were quantified: guaiacol, 2,6-dimethyl phenol (2,6-DMP), 4-methylguaiacol (4-MG), \(o\)-cresol, phenol, 4-ethylguaiacol (4-EG), \(m\)-cresol, \(p\)-cresol, 2,3-dimethylphenol (2,3-DMP), eugenol, 4-ethylphenol (4-EP) and 3,4-dimethylphenol (3,4-DMP).