esa-sceva/satcom-chunk-collection huggingface dataset:
OPG, a secreted tumor necrosis factor, functions as a receptor for the TNF factor produced by osteoblastic cells, competing with RANK for binding to RANKL [18]. Statins act by inhibiting the mevalonate pathway, a precursor for the formation of steroids and steroid isoprenoids; they also inhibit the activity of 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMG-CoA), reducing the synthesis of cholesterol, which is required for the conversion of HMG-CoA to mevalonate. Thus, by acting to reduce the synthesis of mevalonate, they interfere with the proliferation and activities of cells [15]. One study demonstrated that statins stimulate bone formation associated with increased expression of the BMP-2 gene in bone cells and that they act as markers in enhanced gene expression for vascular endothelial growth factor (VEGF) [19]. Furthermore, several studies demonstrate that during the containment process, cells of the median palatal suture express RANK, RANKL and OPG under physiological aspects and conditions of mechanical stress; thus, these factors regulate the function of osteoclasts and play an important role in the formation of bone. Thus, it was demonstrated that the osteoclastogenesis process is stimulated during the beginning of the healing periods after the REM procedure [20]. Histological description and histomorphometry are the most used methods for quantitative and qualitative analysis of studies that investigate bone structures [21,22]. However, they have been limited to two-dimensional analysis. The micro-computed tomography (\(\mu\)CT), due to its high resolution, is the most reasonable choice as a non-destructive resource for bone morphology analysis when compared with conventional techniques [23]. After acquiring the images, a more refined investigation is possible through microtomographic parameters, such as the bone volume fraction (BV/TV), empty space (EV) of the suture, cortical bone area (Ct.Ar), cortical bone area fraction (Ct.Ar/Tt.Ar) and cortical thickness (Ct.Th). Studies investigating the effect of other drugs, such as strontium male, after RME have already been widely explored. However, so far, the literature regarding in vivo studies that evaluate the effect of simvastatin on bone formation after this procedure is limited. Therefore, it is important to explore this gap in the literature. Through a non-destructive technique, such as \(\upmu\)CT, it is possible to obtain an analysis of linear measurements of the expanded suture and microtomographic parameters of cortical bone, which quantitatively and qualitatively describe this structure. In addition, a histomorphometric analysis in the region of the palatal suture is preponderant in in vivo studies.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## Appendix A Derivation of Noise Transfer Function  
In seismic data acquisition, the signal captured at point O (typically connected to a data acquisition system) must undergo deconvolution of the instrument's response to extract the ground motion velocity signal. The analysis of noise propagation mechanisms and the associated transfer functions follows the physical processes within the system, providing insights into how noise from various components influences the final signal.  
To simplify the equation, we define the following:  
\[L(s)=H_{a}^{d}(s)K_{d}^{c}K_{c}^{v}H_{f}(s)H_{v}^{v}(s)K_{i}^{a}. \tag{1}\]  
Here, the suspension noise, characterized by the PSD \(S_{n_{m}}(f)\), is transmitted through the system to the output at point O, resulting in a noise PSD \(P_{n_{m}}(f)\). The transfer function that governs this process is given by the following:  
\[H_{n_{m}}^{O}(s)=\frac{H_{a}^{d}(s)K_{d}^{c}K_{c}^{v}H_{f}(s)H_{da}(s)}{1+L(s)K _{f}}. \tag{2}\]  
The relationship between the input noise \(S_{n_{m}}(f)\), the output noise\(P_{n_{m}}(f)\), and the transfer function \(H_{n_{m}}^{O}(s)\) can be expressed as follows:  
\[P_{n_{m}}(f)=\left|H_{n_{m}}^{O}(s)\right|_{s=2\pi fj}^{2}\times S_{n_{m}}(f). \tag{3}\]  
Upon removing the instrument response from the output at point O to obtain the PSD of EIN, denoted as \(E_{n_{m}}(f)\), it can be expressed as follows:  
\[E_{n_{m}}(f)=P_{n_{m}}(f)\times\left|\frac{1}{H_{n_{i}}^{O}(s)}\right|_{s=2\pi fj }^{2}. \tag{4}\]  
Here, we define that  
\[H_{a_{i}}^{O}(s)=H_{a_{i}}^{U}(s)H_{da}(s). \tag{5}\]  
Thus, the transfer function \(H_{n_{m}}^{a_{i}}(s)\), which describes the conversion of Brownian noise into EIN in acceleration, is expressed as follows:  
\[H_{n_{m}}^{a_{i}}(s)\big{|}_{s=2\pi fj}=\sqrt{\frac{E_{n_{m}}(f)}{S_{n_{m}}(f) }}=\frac{H_{n_{m}}^{O}(s)}{H_{a_{i}}^{O}(s)}=\frac{1}{K_{m}}. \tag{6}\]
Based on the simplified PWR primary loop model, the simplex method was used for parameter identification with measured data [15; 16]. The multidirectional search method was used for parameter identification based on the reduced PWR nuclear power plant model and for comparison of data from the Reactor Excursion and Leak Analysis Program (RELAP5) Software (Idaho National Engineering Laboratory, Idaho Falls, ID, USA) [17]. The above NPP model parameter identification methods have the shortcoming of high requirements for identified models and initial parameter values, which is satisfactory for a nonlinear system or model signals with noise. The Monte-Carlo method was adopted for state estimation of a simplified third-order neutron flux dynamic model considering xenon poison feedback [18]. The intelligent optimization algorithm is a good solution for the NPP identification problem. Neural networks were used for malfunction transient identification of the Hungarian Paks nuclear power plant simulator [19]. The particle swarm optimization (PSO) algorithm has been used for nuclear engineering applications for fuel reloading, reactor core design, plant transient identification, maintenance scheduling and so on [20; 21; 22; 23]. The PSO computational implementation is much simpler [20]. PSO was also used for a mechanism model of a pressurizer in a PWR nuclear power plant [24]. The NPP primary loop system model has a complex structure, including the following variables: electrical, temperature and pressure. The neutron flux density of a core neutron dynamic module changes rapidly, while the temperature and pressure changes of a steam generator module are slow. The different orders of magnitude for parameters also make it harder to identify parameters. The organization of this paper is as follows. The advanced PWR nuclear power plant model was divided into several sub-modules for parameter identification and validation in Section 2. The parameter identification method and process were developed in Section 3. For each module, we selected appropriate tested data for input and output model variables, then preprocessed the noise reduction, data resampling and data normalization to get the disposed data for parameter identification. After obtaining the variable and parameter constraints, we calculated some parameters according to the operating characteristics and identified the other parameters. Section 4 contains the model initialization through variable steady calculation based on differential equations and a program setting method. The parameters were identified under various conditions using the RP-PSO algorithm in Section 4. The integrated model adaptation was checked in comparison with the PCTRAN software in Section 5. Thus, the primary loop system model parameters of a third-generation PWR nuclear power plant suitable for power system analysis were obtained. The sensitivity analysis of parameters was performed in Sections 4 and 5. The results and discussion were presented in Section 5 as well. Section 6 presents the conclusion drawn thereof.
# The Current State and Future Potential of Microbial Control of Scarab Pests
###### Abstract  
Injury and control costs for the invasive scarab Japanese beetle (Family Scarabeidae, _Popilla japonica_) alone is estimated at $450 million per year in the U.S. Chemical controls are commonly used to control scarab pests, but concerns about human safety and negative impacts on beneficial and non-target organisms, such as pollinators, are increasingly driving the market towards less toxic and more environmentally friendly management options. Microbial entomopathogens are excellent candidates for biopesticides and biocontrol agents. Although microbial pesticides currently make up only 1-2% of the insecticide market, the discovery and development of new microbes are increasing. Microbial products are non-toxic to humans and most are species-specific, reducing non-target effects. While some are slow-acting, others provide rapid control and some can be as efficacious as chemical insecticides, particularly when used in combination. Another major advantage of microbial controls is that many can persist in the environment, and become biocontrol agents, providing long-term control and reducing costs. This article provides a summary of the microbial entomopathogens that are known to infect scarab beetle species including bacterial, fungal, viral, microsporidian, and protozoan taxa, as well as the existing formulations and their efficacy. Lesser-known microbial species are also discussed as potential future controls. We also discuss the development of new techniques for improving efficacy, such as genetic engineering, synergistic interactions, auto-dissemination strategies, and improved formulations.  
white grub; scarabaeidae; _Beauveria; Oavvesicula_; entomopathogenic; fungi; bacteria; protozoa
# High-Performance Microwave Photonic Transmission Enabled by an Adapter for Fundamental Mode in MMFs
## 2 MFA Fabrication  
The experimental setup for the proposed MMF-based MPL is shown in Figure 1. A light wave emitted by a 1550-nm laser is modulated by two tones (1.9 GHz and 2 GHz) that generated by arbitrary waveform generator (AWG) using an intensity modulator (MOD). The power of RF signals is attenuated by an RF attenuator (RF Att), for making the output power of the fundamental frequency (FF) signal and the third-order intermodulation (IMD3) present an approximately linear relationship with the input power. The light wave that has been modulated is amplified by an erbium-doped fiber amplifier (EDFA) then launched into a 17.6-km-long OM3 MMF via an optical circulator, and finally the Brillouin backscattering light power can be measured by the power meter. Besides, the variable optical attenuator (VOA) is used to adjust the incident optical power. Two fabricated MFAs were fused to the two ends of the MMF to excite and filter only the fundamental mode of the MMF. At the end of the link, a photodetector (PD) and an electrical spectrum analyzer (ESA) are applied to detect and analyze the output signal.  
The OM3 fibers, which are widely used in fiber communication, have a 14.5-\(\upmu\)m mode-field diameter for the fundamental mode [13], and the A\({}_{\text{eff}}\) is about 165 \(\upmu\)m\({}^{2}\). As we can see, we need carefully fabricate MFAs to match the mode field of SMF and MMF. To reduce MPI, we need to expand the core of the MFA to \(\sim\)14.5 \(\upmu\)m. We plan to use reverse tapering to fabricate the MFAs by LZM-100, an optical fiber splicer produced by Fujikura. The fiber splicer uses a CO\({}_{2}\) laser, which makes the heating processes highly reproducible and stable. The reverse tapering process can be realized by programming, allowing any fibers to be tapered with high precision [14]. The SMF can be thickened smoothly and slowly then form a plat thickened area using the optical splicer. After that, the MFA is fabricated by cutting off at the thickened area and fusing with the MMF in alignment. We conducted six times of reverse tapering and the cross-sectional images of the MFAs are shown in  
Figure 1: Schematic diagram of the microwave photonic transmission link using an MMF (OM3) with two mode field adapters, in which the large-area fundamental mode can be clearly excited.  
Figure 2a. The side view of the MFA obtained after the 6-th reverse tapering, which is the best match, is presented in Figure 2b. The fiber was thickened slowly from right to left. Two MFAs were fused to the two ends of the MMF, and the losses of them are 0.34 dB and 1.68 dB, respectively.
# Inferring Urban Social Networks from Publicly Available Data
### Related Work  
#### 1.1.1 Synthetic Population  
The approaches used in the literature for the definition of a synthetic population can be broadly classified in two major groups: _Synthetic Reconstruction_ (SR) and _Combinatorial Optimization_ (CO). According to the \(SR\)[23] approach, the population is generated by assigning to each agent some relevant socio-demographic variables drawn from suitable joint-distributions deduced by putting together aggregate data covering the whole population with disaggregated data from a sample (usually gathered from surveys). In the \(CO\)[24] approach, instead, the area of interest is divided into mutually exclusive zones for which a set of marginal distributions is available, then a sample of real individuals from the target population is directly used to generate the whole population through replication/resampling methods. A comparison between SR and CO can be found in Reference [25].  
A peculiar shortcoming of the SR approach in its basic implementation [23] is that it is not possible to satisfy joint distributions of attributes at either household or individual level simultaneously. A way to overcome this problem is the reconstruction algorithm proposed by [PERSON] and [PERSON] [26]. [PERSON] et al. [27] proposed to extend the usual Iterative Proportional Fitting method (usually employed in the SR approach) with the Iterative Proportional Updating (IPU) method where the algorithm adjusts and reallocates weights among households until both household and individual-level attributes match. Unfortunately both CO and SR approaches require the collection of specific data which can be difficult to obtain. To avoid this problem, a few sample-free models were proposed. The main objective of these methods is to achieve a synthesis of the population starting from the most disaggregated level that is actually available. [PERSON] and [PERSON] [28] extended the SR approach by generating a synthetic population based on households' and individuals' attributes for 589 municipalities of Belgium. For a comparison between sample-free and sample-based methods, see [PERSON] and [PERSON] [29].  
[PERSON] et al. [9] proposed a \"first principles\" method for synthesizing populations on both urban and national scale. Their method is capable of reconstructing the contact network of the United States by simulating individuals in the population, their household structure, demographics attributes and a 24-h activity sequence. To do so, they relied on a large data set, including census data, activity location, traffic data, and roads network, as well as several thousand responses to an activity, time-use survey, etc. The definition of our synthetic population, described in Section 2.1 and analyzed in Appendix A, follows a partially similar approach to Reference [9], but it requires significantly less data.
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 4 Incorporating RS as Covariates in DSM
### Remote Sensing Data as Proxies of Soil Properties Controlling Factors in the SCORPAN DSM Model
#### 4.2.4 Parent Material  
Historically, information about parent material in DSM came primarily from digitizing geological or lithological maps, or soil maps in which the parent materials were described. This is still the case for many studies. However, RS products have proven their ability to detect and map certain parent materials through the spectral signature of outcrops, regolith, or upper soil, especially using airborne gamma-ray spectrometry. This has been shown in France in a large number of situations (e.g., [135; 141; 232; 272; 303; 304; 305]). Figure 7 shows four DSM predictions of topsoil texture [305] in a French department, using a large set of covariates and then adding a lithology map, or a gamma-ray spectrometry map, or both. It shows that gamma ray spectrometry captures some rather large spatial patterns linked to the parent material that were not captured by the lithological map.  
Figure 7: Red/green/blue composite of predicted soil textures (clay/silt/sand in g/kg) for the topsoil layer in the Mayenne department (France). White areas are cities. Comparison of DSM predictions using the following: (A): a set of covariates without incorporating lithology or gamma-ray spectrometry maps; (Litho): the set A plus a lithology conventional map, (\(\gamma\)): the set A plus gamma-ray spectrometry maps; (all): all covariates. Extracted from [PERSON] et al. [305]. This article was published in Geoderma Regional, 22, [PERSON], [PERSON] [PERSON], [PERSON] [PERSON], [PERSON] [PERSON], [PERSON], [PERSON] [PERSON], Could airborne gamma spectrometric data replace lithological maps as co-variates for digital soil mapping of topsoil particle-size distribution? A case study in Western France. 22, 1-15, e00295, 2020, doi:10.1016/j.geodrs.2020.e00295. (Figure 12, page 13 in [305]) with permission from Elsevier®. Copyright Elsevier (2020).  
Applications of RS to detect typical parent materials or mineralogy are quite common in tropical environments (e.g., [109; 306]).
## 2 Methodology and Data
### Dataset Description
#### 2.2.1 Agricultural Yield Datasets  
Spanish berry production represents a substantial commercial crop throughout Europe. Spain, as the largest fruit producer in the European Union with a share of (40.1%) [61], stands out, especially in the southwest region which is known for its noteworthy exports of fresh berries. Thus, it served as an ideal case study for this research [62; 63]. The dataset used for this study consisted of production data from three berry cooperatives, covering the period from 2018 to 2021. The final year was allocated to the test set, while the remaining years were utilized for training purposes. This approach was adopted to facilitate weekly yield forecasting, which has the potential to generate significant economic benefits for the farmers.
## 3 Acoustic Emission Monitoring
### Asinelli Tower  
The AE activity on Asinelli Tower was examined in a significant zone for monitoring purposes. This was developed by attaching six piezoelectric sensors to the northeast corner of the tower at an average height of _ca._ 9.00 m above ground level, immediately above the terrace atop the arcade. In this area, the double-wall masonry has an average thickness of _ca._ 2.45 m (Figure 5). AE monitoring began on 23 September 2010 and ended on 10 January 2011, for a total duration of about 16 weeks. By monitoring a significant part of this tower, the incidence of vehicle traffic, seismic activity and wind action on the damage evolution within the structure were assessed [3].  
In this case, among all regional seismic events, we considered 43 earthquakes with a Richter magnitude \(\geq\)1.2, having an epicenter within a radius from 25 to 100 km from Bologna, as the most likely to affect the tower's stability. The strongest earthquakes were the 4.1 magnitude event that occurred on 13 October 2010 at 11:43 p.m. in the Rimini area (epicenter about 100 km from Bologna) and the 3.4 magnitude event on 21 November 2010 at 4:10 p.m. in Modena Apennines (epicenter about 50 km from Bologna). The AE instantaneous rate (averaged over 1 h) and the sequence of the earthquakes obtained during the monitoring are displayed in Figure 6. Also, in this case a correlation between peaks of AE activity in the structure and regional seismicity can be observed (Figure 6).  
The tower, in fact, as in another case investigated by the authors regarding the Medieval Towers of Alba in Italy, is very sensitive to earthquake motions [9, 10].  
Figure 4: Chapel XVII: AE rate (blue chart) and nearby earthquake (red dots) occurrences as functions of time. The AE rate chart illustrates the number of AE events (averaged over 1 h), while seismic events are marked by points indicating occurrence time and Richter magnitude.  
Figure 5: Front views and axonometric view of Asinelli Tower. Faces **(1)** South; **(2)** East; **(3)** North; **(4)** West; **(5)** Axonometric view. The AE transducers were applied to the northeast corner of the tower, in the zones marked with a circle.  
Figure 6: Asinelli Tower: AE rate (blue chart) and nearby earthquake (red dots) occurrences as functions of time. The AE rate chart illustrates the number of AE events (averaged over 1 h), while seismic events are marked by points indicating occurrence time and Richter magnitude.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **N** & **Mean** & **Median** & **Standard Deviation** & **Minimum** & **Maximum** & **Shapiro-Wilk W** & **Shapiro-Wilk \(p\)** \\ \hline pH/bg & 30 & 6.59 & 6.5 & 0.503 & 5.67 & 7.52 & 0.976 & 0.702 \\ pH/1 & 30 & 6.52 & 6.59 & 0.522 & 5.45 & 7.78 & 0.974 & 0.644 \\ pH/5 & 30 & 6.42 & 6.54 & 0.527 & 5.13 & 7.42 & 0.959 & 0.284 \\ pH/10 & 30 & 6.42 & 6.52 & 0.573 & 4.68 & 7.23 & 0.934 & 0.063 \\ pH/30 & 30 & 6.47 & 6.52 & 0.496 & 5.46 & 7.39 & 0.971 & 0.58 \\ O\({}_{2}\)/bg & 30 & 1.04 & 0.308 & 1.55 & 0.0362 & 6.37 & 0.669 & \textless{}0.001 \\ O\({}_{2}\)/1 & 30 & 0.744 & 0.394 & 0.942 & 0.0447 & 3.65 & 0.645 & \textless{}0.001 \\ O\({}_{2}\)/5 & 30 & 0.845 & 0.334 & 1.5 & 0.0656 & 6.92 & 0.509 & \textless{}0.001 \\ O\({}_{2}\)/10 & 30 & 1.05 & 0.467 & 1.35 & 0.047 & 4.78 & 0.692 & \textless{}0.001 \\ O\({}_{2}\)/30 & 30 & 0.798 & 0.333 & 1.38 & 0.0371 & 7.29 & 0.511 & \textless{}0.001 \\ CO\({}_{2}\)/bg & 30 & \(1.69\times 10^{6}\) & \(1.99\times 10^{6}\) & 865,803 & 40,873 & \(2.74\times 10^{6}\) & 0.894 & 0.006 \\ CO\({}_{2}\)/1 & 30 & \(1.76\times 10^{6}\) & \(2.10\times 10^{6}\) & 843,976 & 197,080 & \(2.77\times 10^{6}\) & 0.88 & 0.003 \\ CO\({}_{2}\)/5 & 30 & \(1.77\times 10^{6}\) & \(2.18\times 10^{6}\) & 884,788 & 161,205 & \(2.79\times 10^{6}\) & 0.848 & \textless{}0.001 \\ CO\({}_{2}\)/10 & 30 & \(1.73\times 10^{6}\) & \(2.09\times 10^{6}\) & 893,455 & 75,363 & \(2.92\times 10^{6}\) & 0.88 & 0.003 \\ CO\({}_{2}\)/30 & 30 & \(1.76\times 10^{6}\) & \(2.06\times 10^{6}\) & 864,842 & 127,052 & \(2.97\times 10^{6}\) & 0.896 & 0.007 \\ \hline \end{tabular}
\end{table} 
Table 3: Descriptive data (mean, SD, distribution) of pH, O\({}_{2}\) and CO\({}_{2}\) measurements for the entire sample. \begin{table}
\begin{tabular}{c c c c} \multicolumn{5}{c}{**Pairwise Comparisons (Durbin-Conover)**} \\ \hline  & & **Statistic** & \(p\) \\ \hline O\({}_{2}\)/bg & O\({}_{2}\)/1 & 0.995 & 0.322 \\  & O\({}_{2}\)/5 & 0.332 & 0.741 \\  & O\({}_{2}\)/10 & 1.990 & 0.049 \\  & O\({}_{2}\)/30 & 0.166 & 0.869 \\ O\({}_{2}\)/1 & O\({}_{2}\)/5 & 1.326 & 0.187 \\  & O\({}_{2}\)/10 & 0.995 & 0.322 \\  & O\({}_{2}\)/30 & 1.161 & 0.248 \\ O\({}_{2}\)/5 & O\({}_{2}\)/10 & 2.321 & 0.022 \\  & O\({}_{2}\)/30 & 0.166 & 0.869 \\ O\({}_{2}\teers with acidic initial pH lower than 6.6 values had an increase in salivary pH. In general, the influence of a xylitol gum to pH value is intensive during the first 10 min of chewing when the pH value starts recovering to values close to initial. In three cases (volunteers 15, 19, and 29, Figure 3) pH values dropped lower than 5.5 in at least one moment. Finally, multiple correlation analyses revealed no statistically significant correlations between the age of the participants and any of the investigated variables. Figure 4: Results of O\({}_{2}\) measurements for each volunteer (**a**) male, (**b**) female. Figure 3: Results of pH measurements for each volunteer (**a**) male, (**b**) female. A horizontal line on graphs corresponds to neutral pH and vertical lines separate each volunteer.