esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effects of _Anthyllis vulneraria_ Hydroalcoholic Leaf Extract as an Adjuvant in Wound Healing
## 2 Materials and Methods
### Obtaining and Characterization of the Vegetal Extract
#### 2.3.4 Methodology of Phytochemical Analysis by LC-MS/MS  
The chemical composition of the plant extracts was analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), based on two validated protocols described in previous studies [31; 32]. All measurements were performed on an Agilent 1100 Series HPLC system (Agilent Technologies, Santa Clara, CA, USA), connected to an Ion Trap SL 1100 mass spectrometer (LC/MSD Ion Trap VL) [33]. The setup included a binary pump, autosampler, column oven, UV detector, and degasser.  
The first method targeted 23 polyphenic compounds. Chromatographic separation was carried out using a Zorbax SB-C18 column (100 mm \(\times\) 3.0 mm, 3.5 um particle size, Agilent). The mobile phase consisted of methanol and 0.1% acetic acid (v/v) and was run in gradient mode starting at 5% methanol, ramping up to 42% over 35 min, held for 3 min, then brought back to 5% over 7 min [31; 32; 33]. The column temperature was maintained at 48 \({}^{\circ}\)C, flow rate at 1 mL/min, and injection volume was 5 uL.  
Detection was conducted in both UV and MS modes. The UV detector was set at 330 nm for the phenolic acids (first 17 min), and at 370 nm for the flavonoids and aglycones (up to 38 min). The MS operated in negative electrospray ionization mode (ESI) with a capillary voltage of +3000 V, nitrogen at 60 psi as the nebulizer gas, and a drying gas flow of 12 L/min at 360 \({}^{\circ}\)C [34; 35].  
The second method focused on eight specific polyphenols: catechchin, epicatechin, gallic acid, syringe acid, vanillic acid, protocatechuric acid, epigallocatechin, and epigallocatechingallate [36]. The same hardware and column were used with a different gradient, starting at 3% methanol, increased to 8% at 3 min, and to 20% at 8.5 min, held until minute 10, and then returned to 3% [37]. All compounds were monitored using extracted ion chromatograms (EICs) under the same ESI conditions. In order to keep the manuscript concise, we have not displayed all EIC traces, following common reporting practices.  
Identification of the compounds was based on matching retention times and MS spectra with those of the reference standards. Quantification was performed via UV absorbance using calibration curves prepared from the corresponding standards. Data were processed using ChemStation (vB01.03) and DataAnalysis (v5.3) software (Agilent Technologies, Santa Clara, CA, USA) [31; 32; 33; 35; 36]. Results are reported as micrograms of compound per milliliter of extract.
# Shallow Velocity Estimation by Multiples for Monochannel Boomer Surveys
## 4 Experimental Data Example
Figure 7a shows the estimated water depth (orange line), which is very smooth as expected by the visual inspection of the seismic section. Less obvious is the smoothness of the estimated offset (grey line), because surface waves and possible rapid turns in busy areas might introduce irregularities. Slightly less stable is the estimated layer thickness (blue line). Its calculation (and that one of the layer velocity) depend heavily on the selection precision of the multiple and the reflection from the layer bottom. Both travel times may be affected by larger errors because their time break is far less evident than that for direct arrivals and water bottom reflections. For this reason, we applied a five-term median filter to the velocity estimate (Figure 7b). The obtained values ranged between 1500 and 1600 m/s and are well consistent with those measured by [22], a dozen kilometers apart from our study area. These values are average velocities between the layer bottom and the seafloor. We observed that the estimated velocity oscillated above and below the seawater velocity. The low values are likely due to some extent of gas saturation, which is known to occur in several parts of the Gulf of Trieste (see also [23; 24], etc.). Figure 6: Seismic section acquired at the Gulf of Trieste (Northern Adriatic Sea) using a Boomer system without (**left**) and with the selected horizons (**right**): direct arrival (red), seafloor reflection (green), primary reflection (violet), and peg-leg multiple (yellow).
# Hybrid Modulation Strategy to Eliminate Current Distortion for PV Grid-Tied H6 Inverter
## 2 H6 Grid-Tied Inverter Operation
The typical control model for the current control loop of a H6 grid-tied inverter is given in Figure 4, where, \(i_{L}\)* is the current reference, and \(G_{r(s)}\) is the controller producing the control voltage, \(V_{c}\). Note in order to offset the impact of grid voltage \(V_{ac}\) for the inductor current control loop, the grid voltage feed-forward control is often adopted within the current loop. This grid voltage is added to the output terminal of the \(G_{r(s)}\) controller in each switching cycle and this produces the control voltage \(V_{c}\). This \(G_{r(s)}\) controller is the traditional PI controller: \(G_{r(s)}=K_{P}+K_{I}/s\), \(K_{P}\) is the P control parameter and \(K_{I}\) is the is the I control parameter. Under the assumption of unipolar modulation, described as modulation mode I earlier, this control voltage is equal to the average terminal voltage \(V_{ab}=\) d\(E\), where d is the duty cycle of switches S1 and S4 or S2 and S3. Inherent in this is the assumption that when S1 and S4 are off, (or S2 and S3 are off), the current freewheels through D1, S5 (or D2, S6), so that the terminal voltage transitions between  
Figure 4: The control model of current loop. Figure 3: The diagram of vector and uncontrollable area. \(E\) and 0 (or \(-E\) and 0). However, when the current and voltage have opposite polarity, the inductor current cannot freewheel normally through either D1, S5 or D2, S6. Thus, the average terminal voltage \(V_{ab}\) in this region of operation is not given by the assumption of unipolar modulation. The averaged value of \(V_{ab}\) over one switching cycle therefore cannot equal the required reference voltage \(V_{c}\), which means the inductor current is out of control and distortion of the current waveforms as illustrated in Figure 5 results. Note also that zero crossing distortion can exist in the grid current in the region shown in Figure 5 even for unity-power factor, because the current cannot reverse and is forced to operate in discontinuous conduction mode in the region of the zero crossing. However, the grid current distortion will be more pronounced when the grid-tied inverter outputs reactive current in non-unity power factor operation. Thus, although the H6 inverter with MMI successfully eliminates common mode leakage current, it will have a problem with grid current distortion under conditions of reactive power provision. In the next section, we illustrate how this problem can be solved by introducing a new modulation approach in the region where voltage and current have opposite polarity.
## 4 Proposed Scheme
### CH-Acquaintanceship  
We present a robust mechanism named CH-acquaintanceship aiming to extend the lifetime of a CH and minimizes packet drop rate in the case of heavy traffic load conditions. To balance the load of CH, a CH may request other CHs having high resources (e.g., residual energy, storage, and computational capability) in its range to balance the load on CH. The overloaded CH sends some of its data to another CH having less traffic load to perform computations on its behalf. By doing so,the CH consumes less energy which in return effectively increases the lifetime of a CH. The overall process of CH-acquaintanceship is explained as follows.  
Let us assume a WSN scenario comprises of various type of heterogeneous nodes such as multimedia (audio, video) and scalar (temperature, humidity) as shown in Figure 3. In each cluster, the deployed sensors sense static events (e.g., temperature, pressure, and humidity) and dynamic events (e.g., intrusion, movement of object etc.). Considering two clusters e.g., CH1 and CH2 (Figure 3).  
We assume that CH1 has high computational capability and low storage whereas CH2 has high storage and low computational capability. It is assumed that CH1 has a greater number of multimedia nodes as compared to scalar nodes in its cluster. Whereas CH2 has a smaller number of multimedia nodes and more scalar nodes. Moreover, the CH1 is located in the area of high alert and has more chances of intrusion detection as compared to CH2. When an event occurs in the range of CH1, the child nodes of CH1 generates a burst of traffic and forward it towards the CH1. Due to low storage at the CH1, the CH1 may not accommodate the incoming traffic from its child nodes and packets drop may occur. As a result, packets are re-transmitted, thereby, create congestion and collisions in the network. The lifetime of CH decreases as it is highly overloaded due to heavy traffic rate. In order to balance the CH load and to increase the performance of the network in terms of CH lifetime, the CH-acquaintanceship feature allows other CHs to collaborate by sharing their resources to achieve the common goal of a network which is energy optimization. Utilizing the CH-acquaintanceship, the CH1 forwards a certain proportion of its stored data to CH2 and vacates its space for incoming packets from cluster members to avoid packets drop. This reduces the re-transmissions and hence extends the lifetime of a CH. In return, CH2 may use the computational capability of CH1 in worst traffic scenario to optimize its energy consumption and delay.  
It is to be noted that the CH-acquaintanceship is strongly dependent on mutual benefits where CHs share resource in return of getting some resources. However, there may be cases where a CH may not have resources to share with other CHs. In such cases, a CH cannot take advantages of CH-acquaintanceship mechanism and may result in degrading the performance of the network. To overcome this issue, we introduced another mechanism named as CH-friendship and is discussed as follows.  
Figure 3: CH-acquaintanceship.
# Automatic Processing of Historical Japanese Mathematics (Wasan) Documents
## 2 Materials and Methods
### Orientation Correction  
Wasan documents frequently include \"pattern lines\" that helped writers maintain a coherent structure in their documents. For us these lines clearly show the tilt that the each page underwent during the scanning process. This tilt can be expressed as the angle between the vertical direction on the scanned image and any of these \"almost vertical\" pattern lines. We focused on finding vertical lines by (a) Detecting all lines, (b) Removing lines with slopes smaller than a set threshold (non-vertical) (c) Grouping neighboring line segments to find longer vertical lines. See Figure 5 for an example.  
Figure 5: Orientation correction.  
We detected lines using the Hough Line transform [33]. Due to the ink density of the original documents, written with calligraphy brushes, most of the long lines present in the documents (most importantly for us, the pattern lines) appear discontinuous in large zoom magnifications. This was a problem for deterministic versions of the Hough transform so we used the The probabilistic version [34] instead. Although we could detect lines, postprocessing was necessary in order to focus on lines pertinent for our orientation correction goal. Specifically lines under a set length where discarded as well as those with slopes that were not close enough to the vertical direction. Subsequently, we observed that many of the remaining lines could be grouped into longer lines. This happened because some parts of the original lines had sections that were faint or not visible. We grouped neighboring line endpoints and converted each group of lines into a \"summary line\".
# Review of Liquid Argon Detector Technologies in the Neutrino Sector
## 1 Introduction  
It has been 90 years since [PERSON] first proposed the existence of particles which he described as \"electrically neutral... that have spin 1/2 and obey the exclusion principle and that further differ from light quanta in that they do not travel with the velocity of light\" [1]. We now of course know these as neutrinos, but despite nearly 70 years passing since the direct observation of the electron (anti)neutrino by [PERSON] and [PERSON] [2], there is still much to be discovered in the neutrino sector of Particle Physics.  
A common detection technique in neutrino experiments (as well as other sectors of Particle Physics) is the use of scintillators-materials that emit photons (\"luminesce\") when exposed to ionising radiation [3]. Scintillators come in a variety of forms: gases, liquids and solids, and depending on the specific material, scintillation light can be emitted at infrared, visible, UV and even X-ray wavelengths.  
One of the most important scintillators for neutrino detectors is liquid argon (LAr). Argon is the most abundant noble gas on Earth, constituting 0.934% of the atmosphere's volume [4], and is commercially extracted, liquefied and purified at large scales worldwide, making it relatively cheap and plentiful. It scintillates in the vacuum ultraviolet (\"VUV\") wavelength range, with a peak emission at approximately 128 nm [5, 6], and in its liquid phase has a relatively high scintillation yield (that is, the amount of light emitted per unit of energy deposited by the incident ionising radiation) of the order of 40,000 photons per MeV [3, 7]. LAr has been used as a scintillator in particle physics experiments since the late 1970s, following [PERSON]'s proposal for a LAr Time Projection Chamber (TPC) [8]. The general TPC concept had been realised a few years earlier [9] as a solution to the simultaneous requirements of high precision position measurement and complete and unambiguous three-dimensional position reconstruction, but was originally envisioned as using a noble gas medium. However, given that particle interaction cross-sections scale with the density of the interacting material, the liquid phase of argon is a vastly moreeffective scintillator than its gaseous phase, the density of which is approximately one thousandth that of LAr [10].  
Liquid Argon Time Projection Chambers (LArTPCs) continue to be at the forefront of experiments in the neutrino sector, growing in size, sophistication and capability in the past fifty years. There are now two main designs for such detectors: single-phase (which utilises only the liquid phase of argon) and dual-phase (which contains both liquid and gaseous forms). This paper will review the detection and design principles, primary hardware and status of a number of currently and recently operating single-phase (Section 2) and dual-phase (Section 3) LArTPCs, as well as a selection of developing technologies that could potentially be used in future experiments.
## 2 Materials and Methods
### Search Methods and Exclusion Criteria  
Three independent reviewers (M.M., K.K., and K.R.) searched MEDLINE and Scopus electronic databases for original studies and reviews assessing native MPFL length change pattern or factors influencing it. The search was performed in February 2021, with no time limits set. The following search term was used: ((Medial Patellofemoral Ligament) OR (MPFL)) AND ((isometry) OR (biomechanics) OR (kinematics) OR (mechanics) OR (length changes)). Inclusion criteria were original research article or review article (any type); healthy study subjects; assessment of MPFL length change pattern; English language. Excluded were studies assessing MPFL length change pattern in patients with patellar instability, after lateral patellar dislocation and after MPFL reconstruction; studies assessing other aspects of MPFL biomechanics (i.e., strain or influence on patellofemoral kinematics such as on the tilt of patella). Three reviewers ([PERSON], [PERSON], and [PERSON]) independently decided whether the study was eligible for inclusion. When there were discrepancies, consensus was reached by discussion.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 4 Experiments and Evaluation
### Comparison of Different Studies  
Table 4 provides a comparison between our proposed approach and previous methods. It is noteworthy that our solution has achieved the best results in terms of FPS and power consumption, which can be attributed to the integration of a series of lightweight techniques. By employing a lightweight feature extraction network that combines depth-wise separable convolutions and utilizing two crucial model compression methods, pruning and quantization, we have ultimately enabled our model to exhibit exceptional detection speed performance. Additionally, when comparing Kv260 with ZCU104, Kv260 demonstrates a power advantage.
## 5 Optimization of LSTM for Fall Detection System
### Normalization  
In this section, the impact of different normalization process of the calculated parameters, including No-normalization, Min-Max normalization, and Z-score normalization, is examined. Figure 9 shows the validation data performance of the STM-LSTM-based fall detection system when applying No-normalization, Min-Max normalization, and Z-score normalization. Accuracies applying No-normalization, Min-Max normalization, and Z-score normalization are 95.93%, 92.85%, and 86.6%, respectively. The best performance is shown when no normalization is applied, and the best accuracy, sensitivity, and specificity are 95.93%, 93.75%, and 97.91%, respectively. It showed the worst accuracy when the Min-Max normalization is applied. To investigate the reason why accuracies of the Min-Max or Z-score normalizations are lower than that of No-normalization, time series data of a running pattern and a forward fall pattern before and after the Min-Max or Z-score normalizations are investigated, as shown in Figure 10. In Figure 10, black and red  
Figure 8: Training data performance of the STM-LSTM-based fall detection system, applying the multiple parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Configuration** & **Parameter Ranges/Methods** \\ \hline Normalization & No-normalization, Min-Max, Z-score \\ Regularization rate, \(\lambda\) & 0.00015 to 0.65 \\ Sampling interval, \(sn\) & 1, 3, 5, 7, 9 \\ Multiple data dimension & Single parameter \(P_{\theta}\) \\ Number of hidden layer nodes, \(n\) & 6, 13, 32, 64, 128, 256 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model configuration.  
lines denote running and forward fall patterns, respectively. After the Min-Max or Z-score normalizations, the running and forward fall patterns are so similar to each other that it is difficult to easily distinguish them, and thus the accuracies by the Min-Max or Z-score normalizations can be reduced, compared to that of No-normalization.  
Figure 10: Comparison of data of running and forward fall before and after normalization. (**a**) min-max normalization, (**b**) Z-score normalization, (**c**) No-normalization.  
Figure 9: Validation data performances of the proposed STM-LSTM-based fall detection system, applying normalization methods.
## 4 Conclusions  
The dissolved salt impregnation technique was used to investigate the effects of boring temperature on the formation of boride layers on grade-2 pure Ti. Detailed characterization of the structure, mechanical, and tribological properties yielded the following conclusions:  
1. Boriding at all tested processing temperatures resulted in the formation of a thick dual borided layer consisting of monolithic TiB\({}_{2}\) and needle-like TiB whiskers, where the relative fractions of the two phases depended greatly on the boring temperature.
2. The introduction of B into the Ti resulted in an initial increase in the thickness and surface hardness, with a maximum thickness of 30 \(\upmu\)m at a boriding temperature of 1150 \({}^{\circ}\)C. As boring temperature increased, increases in the surface hardness were observed for two different applied loads.
3. Comparing the diffusion rate of B in TiB and TiB\({}_{2}\), it was found that the diffusion rate in TiB was higher than that in TiB\({}_{2}\) at the same boriding temperature. Additionally, the diffusion rate of B atoms in the TiB phase along the tip direction was more than 45 times that in the TiB\({}_{2}\) phase.
4. During the wear testing, the untreated sample surface had serious plastic deformation, stains, and scratches. The worn surfaces of the borided samples showed minor deformation and smearing effects at high boriding temperature, with minimum wear depth and width at a boriding temperature of 1150 \({}^{\circ}\)C. The boriding process used in the present study substantially increased the wear resistance of Ti due to high surface hardness.
5. The boriding treatment reduced the fricative value. As the boriding temperature increased, the fricative value of the samples decreased initially and then increased, with a minimum value of 88.9 dB at 1050 \({}^{\circ}\)C.  
Figure 12: Fricative value as a function of wear testing time for the (**a**) untreated sample and those prepared using boriding temperatures of (**b**) 950 \({}^{\circ}\)C, (**c**) 1050 \({}^{\circ}\)C, and (**d**) 1150 \({}^{\circ}\)C.  
**Author Contributions:** Conceptualization, T.C. and S.K.; methodology, S.K.; validation, T.C. and S.K.; formal analysis, T.C.; investigation, T.C.; resources, S.K. and L.Y.; data curation, T.C.; writing--original draft preparation, T.C.; writing--review and editing, S.K.; visualization, S.K.; supervision, L.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** I am pleased to acknowledge experimental support by [PERSON], [PERSON]. This work was also supported by JATCO Ltd, thanks JATCO Ltd, the Functional Interface and Surface Fabrication laboratory at Gunma University, where the samples were produced and macroscopically compressed as part of a different research project.  
**Conflicts of Interest:** No potential conflict of interest was reported by the authors.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 5 Three-Layer Architecture of the Developed System  
The proposed UAV assisted routing and data gathering scheme is developed in multi-layers and multi-phases (see Figure 5):  
1. Layer-1 UAV: UAV is the main part and the top layer,
2. Layer-2 CH: One node per cluster will be selected as CH and it will form a 2 nd middle layer,
3. Layer-3 CM: Composed of ground sensor nodes.  
Each layer further is divided into three phases as shown in Figure 5. The main part of this system is the UAV acting like a data mule, and the process is started by its beacon message. The UAV can operate at two different frequencies: very low power and long-range UHF \(F_{1}\) used for localization and high data rate WiFi \(F_{2}\) for communication. The UAV is performing localization at distance \(D_{1}\) which is considered as constant and should be high enough to satisfy the far field conditions of \(F_{1}\). Data collection is conducted at distance \(D_{2}\) which can be adjusted from 20 to 170 m by considering the CH transmission ability, safe flight distance and WiFi limitations. Top layer (UAV) layer is composed of three phases discuded in detail in next subsection.