esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The evaluation of whole-brain connectograms, obtained from seeding significant nodes in the lobar analysis, revealed no single pattern for all patients (Figure 4 and Supplementary Figures S3-S16). However, in the majority of cases (80%), the same nodes in HGG showed an equal (20%) or lower (60%) number of significant connections (\(p<0.05\), FDR corrected) when compared to LGG. Global attenuation of the resting-state fMRI signal induced by brain tumors was previously related to remodeling of the neurovasculature [38] and may also reflect the connectivity impairment caused by tumor invasion. This may partially justify the lower clinical performance of HGG patients. This study has some limitations. First, the number of patients was somehow small due to our inclusion criteria, which required presurgical LGG patients with comparable resting-state data and the absence of motion and susceptibility artifacts. We also excluded patients with prior chemotherapy or radiation, further reducing our sample size. We made this choice to avoid connectivity modifications caused by the treatment, as documented in prior studies [39; 40], which would have acted as a confounding factor in our analysis. LGG patients were younger than HGG patients, as expected from different age-specific incidences. Patient groups according to the location labels were variably unbalanced, limiting the generalizability of our results. Comparison of graph-theoretical metrics in tumors involving specific eloquent areas was not performed due to the limited number of subjects. There was a considerable amount of missing BNT data due to patient compliance/clinical state at the moment of the examination. Therefore, correlations between BNT scores and specific graph-theory metrics were not performed. As a final consideration, many variables can potentially affect functional connectivity in the setting of brain tumors. Tumor growth, local aggressivity and invasion likely represent drivers of plastic changes [6] and are affected by genetic mutations, as well as epigenetic alterations. The effect of treatment on connectivity has been clearly demonstrated by previous studies [39; 41]. Although not emerging from our results, the patient's age may affect functional changes since brain ageing is related to diminished plastic potential [42]. The patient's sex may also have an important effect due to well-known differences in connectivity patterns [43]. Tumor size differences may also affect brain connectivity. Future studies are called to confirm our findings in the larger population, to further explore the effects of other patient- and tumor-related variables on brain connectivity, as well as the clinical meaning of functional changes.
## 4 Results and Analysis
### Initial Shear Modulus  
Similarly, as presented for the unconfined compressive strength test results, the porosity/cement index was used for the initial shear modulus (\(G_{0}\)) results for the curing periods studied (2, 4, 7, 28 and 90 days), as presented in Figure 5b. Therefore, an adequate association between \(G_{0}\) and the \(\eta\)/\(C_{iv}\) index (considering the same power shape as for strength) could be obtained as the coefficient of determination (R\({}^{2}\)) varies in the range 0.86 to 0.97 for the studied curing times, in the format of a specific equation (Equation (3)).  
\[G_{0}(\text{MPa})=C\times 10^{4}\times\left[\frac{\eta}{C_{iv}}\right]^{-E} \tag{3}\]  
Scalar \"\(E\)\" has been found to be a constant (\(E=1.3\)) for all curing times studied (from 2 to 90 days), while scalar \"\(C\)\" increases with curing time, as shown in Table 2. \"\(C\)\" changes from 1.46 (for 2 days of curing) to 6.04 (for 90 days of curing). From 2 days of curing to 4, 7, 28 and 90 days of curing, the initial shear modulus (\(G_{0}\)) increase percentage were of 104.1%, 181.5%, 210.3% and 313.7%, respectively. It is interesting to observe that the rate of increase of \(q_{u}\) and \(G_{0}\) was not the same with curing time. The rate of increase of \(G_{0}\) was higher up to 28 days of curing and the rate of increase of \(q_{u}\) was higher from 28 days to 90 days of curing.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 3 SBAUPT-Based Pedestrian Dead Reckoning (S-PDR)
### Step Detection
#### 3.1.3 Sensor Fusion  
In certain types of motion such as phone swinging, other sensors exhibit motion patterns that can be useful for step detection. In the developed technique, a repetitiveness detector is employed to detect such useful information in the gyroscope and magnetometer measurements. The detector identifies the dominant axis of motion for the alternative sensors, and the signals from these sensors are used for peak/valley verification.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Selected Plant Species with Antioxidant Activity
#### 4.11.6 Humulus lupulus L.  
Humulus lupulus L., belonging to the Cannabaceae family, has been used as medicinal plants since ancient times. It is native to temperate regions, predominantly found in deciduous forests and thickets across Europe and Western Asia. The structures produced by the female hops plant after flowering--whether fertilized or not--are commonly referred to as infloresences, cones, or strobiles, with these terms used interchangeably. Hops offer a wide range of health benefits, thanks to their antibacterial, antifungal, cardioprotective, antioxidant, anti-inflammatory, anticancer, and antiviral properties. Hop cones are rich in active substances, including resins, essential oils, proteins, and polyphenols such as quercetin, quercetin, kaempfer, rutin, xanthohumol, and ferulic acid [222; 223].  
According to the online Ingredient Dictionary, Humulus Lupulus (hops) extract serves various functions in cosmetics, including acting as an antimicrobial agent, hair conditioning agent, and a general skin-conditioning agent. Additionally, Humulus Lupulus (hops) oil isrecognized for its role as a fragrance ingredient. The extract is also noted for its potential use as an antiperspirant agent [32].  
In the field of cosmetics, hops are utilized in formulations such as bath lotions and shower gels. Products containing hop cone extracts are particularly valued for their ability to address oily scalp and dandruff. The inclusion of hop extract in hair care formulations is supported by its antifungal and antiseborrheic properties, which contribute to reducing hair brittleness, enhancing nourishment, improving shine, increasing strength, and minimizing hair loss [224].  
The results of the concentration of use survey indicate that Humulus Lupulus (hops) extract is used at up to 0.2% in hair conditioners. The highest reported maximum concentration of use with dermal contact was reported to be 0.13% in eye lotion and in the category of other skincare preparations. Humulus Lupulus (hops) extract is reported to be used in formulations that are used around the eyes in amounts of up to 0.13% and in formulations that come in contact with mucus membranes in amounts of up to 0.084% (e.g., bath soaps and detergents, bubble baths). Humulus Lupulus (hops) extract is used in cosmetic sprays and could possibly be inhaled; for example, this ingredient is reported to be used in amounts of up to 0.0002% in hair sprays [32].  
Humulus Lupulus (hops) extract for cosmetics is produced using water, propylene glycol, ethanol, or butylene glycol as solvents, followed by filtration, clarification, and concentration. Another method involves dispersing dried hops in caprylic/capric triglyceride with stirring, then filtering the solution. The extract is processed to remove sediments and decontaminated before packaging [32].
# Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology
## 3 Failure Physical Analysis for Signal Selection
### The Relevance Analysis between Failure Mechanism and Output Signal
#### 3.1.1 Flutter  
Flutter is an unstable self-excited vibration that occurs in almost all machining processes. During the cutting process, if the system is subjected to an instantaneous accidental disturbance, the relative vibration of the tool and the workpiece will occur. The amplitude of the vibration will gradually decay due to the presence of the system damping, but the vibration will leave a ring of vibration on the existing machined surface. When the workpiece is rotated one revolution, the tool will cut on the surface with the vibrating surface. As a result, the cutting thickness will be too large or too small, which will result in dynamic cutting force. If the various conditions during the cutting process further promotes the vibration, it will develop into flutter. Strong vibrations also generate a lot of noise, irritating the operators [50].  
Because flutter is a strong vibration phenomenon, researchers use the vibration sensor to extract the characteristic quantity from the vibration signal for the monitoring of cutting flutter. In the literature [51], the vibration signal is used to analyze the milling condition. The results show that the vibration signal is more reliable and highly used in related research. In addition, flutter will cause changes in cutting force and generate a lot of noise. Therefore, in addition to the vibration signal, some scholars also use the cutting force and the sound signal. For example, the literature [52] selects the sound signal as the monitoring signal of the milling flutter and establishes the milling flutter monitoring model. The sound signal is more convenient to collect than the cutting force signal and it does not affect the actual machining.
## 2 Materials and Methods
### Structural Analysis  
The microstructural characterization included the use of a Bruker A.X.S. D8 Focus diffractometer (Bruker, Karlsruhe, Germany), with CuK\(\alpha\) radiation (\(\lambda\) = 1.5406 A) operated in a range 20 of 10-110\({}^{\circ}\) at a speed of 4\({}^{\circ}\)/min. The X-ray diffraction (XRD) pattern identification was through the International Center for Diffraction Data (ICDD) database PDF-2(2003). The analysis of sample microstructures was through scanning electron microscopy (SEM) with a JEOL JSM 6701F (JEOL, Tokyo, Japan) microscope. High-resolution transmission electron microscopy (HRTEM) complemented microstructure characterization through an F.E.I. Tecnai G-20 microscope at 200 kV (FEI-Titan Microscope, Hillsboro, OR, USA). The DigitalMicrograph software calculated d-spacing from HRTEM images.  
The Vickers microhardness evaluation was through a compacted sample of mechanically alloyed powder particles inserted on a stainless-steel die using a 3 ton press to obtain a cylinder with dimensions of 10 mm \(\times\)\(\phi\)30 mm. The microhardness of the samples was measured by using the FM microhardness tester (Future-Tech, Kanagawa, Japan) using a diamond indenter applying loads of 2 ton for 10 s. The indentations involved three parallel lines with three points each and the average values for every sample. The average microhardness included different milling times.
## 3 Empirical Results and Analysis
### Econometric Test of the Benchmark Model
#### 3.1.2 Conditions for Application of the DID Model: Parallel Trend Test  
The important premise of using the DID method is that the treatment group and the control group satisfy the parallel trend assumption; that is, before the implementation of the APCP Action Plan, the _GTFP_ of each pilot province should maintain a relatively steady trend of change. In order to ensure this study satisfies this basic assumption, this paper adopts the pilot year 2014 as the base year. For the explained variable ln_GTFP_ in the first two years and the next two years of the base year; the regression analysis with the Panel OLS-DID and the fixed effect (FE) model consistent with the benchmark regression is conducted separately. The regression results are shown in Table 4.  
The coefficients of Before2 and Before1 are not significant in Table 4, indicating that there is no significant difference between the pilot and non-pilot provinces before 2014, which is in line with the parallel trend hypothesis. It shows that the DID method is suitable for the selection of benchmark model. Further, from the dynamic effect of the parallel trend test, the estimated parameters of the policy effect changed from negative coefficient before the environmental policy pilot to positive after the pilot. Therefore, it is distinct to reveal that the impact of the APCP Action Plan on _GTFP_ is significantly positive.  
It is a significant that the change of the estimated parameter coefficient of the policy effect in Figure 1, which draws the conclusion that the assumption of the parallel trend is satisfied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **OLS (1)** & **OLS (2)** & **FE (3)** & **FE (4)** \\ \hline  & _lnGTFP_ & _lnGTFP_ & _lnGTFP_ & _lnGTFP_ \\ \hline Before2 & \(-0.0064\) & \(0.0029\) & \(-0.0064\) & \(-0.0004\) \\  & \((-1.10)\) & \((0.51)\) & \((-1.10)\) & \((-0.10)\) \\ Before1 & \(-0.0085\) &
## 3 Description of Numerical Algorithms
### Contour Integration Methods
Based on (35), it can be proven that, if the column vectors \(\{\boldsymbol{q}_{1},\boldsymbol{q}_{2},\ldots,\boldsymbol{q}_{p}\}\) form an orthonormal basis of \(\text{span}\{\boldsymbol{\psi}_{\sigma},\boldsymbol{\psi}_{1},\ldots,\boldsymbol {\psi}_{p-1}\}\), then the eigenvalues \(\lambda_{i}\) and the corresponding eigenvectors \(\mathbf{u}_{i}\), \(i=1,2,\ldots,p\) can be extracted while using the basis \(\mathbf{Q}_{p}=[\boldsymbol{q}_{1},\boldsymbol{q}_{2},\ldots,\boldsymbol{q}_{p}]\) and applying the Rayleigh-Ritz procedure for the pencil \(s\mathbf{E}-\mathbf{A}\). In order to construct the orthonormal basis \(\mathbf{Q}_{p}\), the contour integral (35) is first approximated through the \(N\)-point trapezoidal rule. In practice, an orthonormal basis \(\mathbf{Q}_{p}\) with size larger than \(p\) may be used to improve accuracy. When compared to the Hankel method, the CI-RR method is typically more accurate and, thus, is preferred most of the time. Finally, another promising contour integration algorithm is FEAST. FEAST was first proposed in [35] for the solution of symmetric eigenvalue problems, but it was later extended to include non-symmetric eigenvalue problems. The algorithm can be understood as an accelerated subspace iteration method combined with the Rayleigh-Ritz procedure [55] and, from this point of view, it has some similarities with the CI-RR method. The unique characteristic of the FEAST algorithm is that it implements an acceleration through a rational matrix function that approximates the spectral projector onto the subspace.
# State Residualisation and Kron Reduction for Model Order Reduction of Energy Systems
## 2 Power System Modelling
### Grid-Forming Converter  
Grid-forming converters (GFMs) are designed to act as voltage sources instead of current sources (which is the case for grid-following converters), and therefore they can replace synchronous machines (which create the voltage in today's power systems). The physical structure of a GFM and its control, as studied in this paper, are shown in Figure 1, representing the classical structure seen in [4; 27; 28; 29]. The GFM is composed of a DC/AC converter (here, the DC voltage source and DC/AC converter are assumed to be ideal) and an RLC filter and transformer, while the control hierarchy consists of external cascaded voltage and current loops.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 2 Review of Early Fault Diagnosis Approaches
### AI-Based Early Fault Diagnosis  
As an emerging field in industrial applications and an effective solution for fault recognition, AI-based early fault diagnosis method has been receiving increasing attention from academia and industry [3]. It is known that the abovementioned FFD-based early fault diagnosis methods require rich expertise of the investigators, which makes them inappropriate for common users. The AI-based early fault diagnosis methods can overcome this limitation effectively by using machine learning techniques.  
The AI-based early fault diagnosis methods often consist of data acquisition, fault feature extraction and fault pattern identification steps, as shown in Figure 4. The key step is to extract the fault features for further classification. In most AI methods, feature selection before the fault identification is also indispensable. With the help of feature extraction, features with more important information will be selected to construct the new feature set, aiming to improve the final classification accuracy. In AI-based early fault diagnosis schemes, the most commonly used classifiers are K nearest neighbor (KNN), neural network (NN), and support vector machine (SVM).
###### Abstract  
A button sensor antenna for on-body monitoring in wireless body area network (WBAN) systems is presented. Due to the close coupling between the sensor antenna and the human body, it is highly challenging to design sensor antenna devices. In this paper, a mechanically robust system is proposed that integrates a dual-band button antenna with a wireless sensor module designed on a printed circuit board (PCB). The system features a small footprint and has good radiation characteristics and efficiency. This was fabricated, and the measured and simulated results are in good agreement. The design offers a wide range of omnidirectional radiation patterns in free space, with a reflection coefficient (S\({}_{11}\)) of \(-29.30\) (\(-30.97\)) dB, a maximum gain of 1.75 (5.65) dBi, and radiation efficiency of 71.91 (92.51)% in the lower and upper bands, respectively. S\({}_{11}\) reaches \(-23.07\) (\(-27.07\)) dB and \(-30.76\) (\(-31.12\)) dB, respectively, with a gain of 2.09 (6.70) dBi and 2.16 (5.67) dBi, and radiation efficiency of 65.12 (81.63)% and 75.00 (85.00)%, when located on the body for the lower and upper bands, respectively. The performance is minimally affected by bending, movement, and fabrication tolerances. The specific absorption rate (SAR) values are below the regulatory limitations for the spatial average over 1 g (1.6 W/Kg) and 10 g of tissues (2.0 W/Kg). For both indoor and outdoor conditions, experimental results of the range tests confirm the coverage of up to 40 m.  
Keywords:button sensor antenna; body centric communication; specific absorption rate (SAR) +
Footnote †: journal:
# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
## 2 Methodology
### Model Effectiveness Experiment  
The dataset used for model training consists of two parts: one is a bridge crack image of old civil bridges and viaducts taken in the field, and the other is a simple background crack image [54]. The dataset contains a total of 2068 crack images with a resolution of 1024 \(\times\) 1024 pixels. The images in the dataset contain a variety of disturbances such as mud stains, water spots, shadows, blurriness, etc., so the network model can learn the crack characteristics and improve the immunity to background noise. The training set, test set and verification set are randomly divided in a ratio of 8:1:1. In the process of model training, the processor used was DELL Intel(R) Core (TM) i7-8700K CPU @ 3.70 GHz and the learning frameworks adopted were PyTorch1.2.0, TensorFlow2.2.0 and Keras2.1.5, respectively. The stochastic gradient descent (SGD) algorithm is used to optimise the network model, with the momentum parameter set to 0.9 and the weight decay set to \(1\times 10^{-4}\). The network is trained for a total of 100 epochs, and the training batch size is 4. The maximum learning rate of the network is \(1\times 10^{-2}\), the minimum learning rate is 1% of the maximum learning rate and the \(\cos\) decreasing strategy is used to attenuate the learning rate.
## 3 Results and Discussion
### Biochemical Composition
#### 3.2.4 Pigments  
_A. platensis_ contains two main photosynthetic pigments, namely chlorophyll-\(\alpha\) and phycocyanin. Both pigments are of interest in the nutraceuticalal sector. Chlorophyll extract is gaining acceptance as a natural colorant or as a super-food because of its functionality, its strong antioxidant and anticancer properties [36; 37]. Likewise, phycocyanin is considered as a very promising source for blue natural coloring, while it has as well functional properties [38]. In the present study, Chlorophyll-\(\alpha\) content did not display a clear pattern with the increase of glycerol in the growth medium (data not shown). However, at the highest glycerol concentrations there was a significant decrease of its content  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Glycerol Concentration g/L** & **5 Klux** & **10 Klux** & **15 Klux** & **NH3-15 Klux** \\ \hline
0 & \(40.53\pm 2.50\) & \(35.18\pm 1.52\) & \(35.78\pm 2.23\) & \(40.48\pm 1.82\) \\
0.5 & \(32.24\pm 2.14\) & \(35.04\pm 1.89\) & \(33.17\pm 3.01\) & \(41.75\pm 2.04\) \\
1 & \(32.22\pm 3.11\) & \(32.78\pm 2.77\) & \(29.82\pm 1.92\) & \(37.98\pm 0.84\) \\
1.5 & \(32.15\pm 1.98\) & \(31.20\pm 1.55\) & \(25.48\pm 0.99\) & \(34.28\pm 3.55\) \\
3 & & & & \(25.79\pm 1.24\) & \(24.20\pm 2.61\) \\
6 & & & & \(28.65\pm 2.33\) & \(19.17\pm 2.24\) \\
9 & & & & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Carbohydrate content of _A. platensis_ cultivated under different glycerol concentrations and light intensities.  
(less than 0.5%) compared to the control and the series where less glycerol concentrations were added (0.8-1.05%). Phycocyanin content displayed a more or less clear trend (Table 5), i.e., it was decreased with increased glycerol concentrations. Indeed, most notable decrease was observed at the highest glycerol concentrations. The photosynthetic activity is crucial for pigment synthesis. In mixotrophic cultures, photosynthesis and heterotrophic metabolism function simultaneously. The photoautotrophic and heterotrophic metabolisms may switch to one another depending on the cultivation conditions. Therefore, the decrease in pigment (chlorophyll-\(\alpha\) and phycocyanin) content was probably due to the domination of the heterotrophic over the photoautotrophic metabolism, where photosynthetic pigments are not utilized and therefore not synthesized [4; 39; 40].