esa-sceva/satcom-chunk-collection huggingface dataset:
# PERM1--An Emerging Transcriptional Regulator of Mitochondrial Biogenesis: A Systematic Review
## 4 Discussion
### PERM1 and Heart Tissue
#### 4.2.2 Regulation  
In mouse heart tissue, PERM1 contains a PEST motif with consensus sequences for CK2 phosphorylation, thus PERM1 is phosphorylated by CK2 kinase, a process that regulates the stability and the turnover of PERM1 [25].
## 3 Results and Discussion
### Extraction Experiments
#### 3.1.1 Extraction of Hg  
Before conducting extraction experiments at trace level concentrations, we investigated if the three ILs are adequate extracting agents for Hg. The results for this time-dependent extraction are summarized in Figure 1. All three ILs displayed high extraction efficacies, with [P\({}_{66614}\)][HNA] achieving the highest value of 95.2% \(\pm\) 1.7% after 1 h.  
The results are in good agreement with observations by [PERSON] et al., where increasing alkyl chain lengths, and therefore the hydrophobicity, correlated negatively with the time needed for quantitative extraction of Hg [35]. Leaching values also are in line with previous data, with highly hydrophobic [P\({}_{66614}\)][HNA] achieving the lowest leaching of 80.0 \(\pm\) 10.4 mg C L\({}^{-1}\) (0.40% \(\pm\) 0.05%), while similar but slightly higher values were obtained for [P\({}_{1888}\)][HNA] and [N\({}_{1888}\)][HNA] [28]. These results promise high reusability of the ILs because the relative dissolution ranged from 0.4% to 0.9% and separation from the aqueous phase after extraction was simple. This warrants further experiments regarding the back-extraction of the target metals and subsequent cycles of extraction and back extraction, which was successfully achieved with similar compounds by [PERSON] et al. [15] using, e.g., simple washing steps with 0.5 M HNO\({}_{3}\).
## 2 Materials and Methods
### Data Combination Process  
We used an alert log collector alongside a dominant window detector to collect relevant data [16]. However, the alert log collector only logs the alerts without their respective alert dwell times; meanwhile, the dominant window detector records all alert dwell times without the contents of the alert messages (Figure 1). Therefore, we combined these data.  
First, the data merging program checks for consistency in the computer's IP address and dates between records of user-selected windows (collected by the dominant window detector) and alert log records (collected by the alert log collector) (Figure 2). If consistent, the time gap between the two records is calculated.  
The process of calculating the time gaps between the respective ALC (Alert Log Collector) and DWD (Dominant Window Detector) records is shown in Figure 3. The smallest time gaps between the dominant window records and the alert log records were interpreted to indicate that these records had been derived from the same alert window (thicker line).  
Figure 1: The process of DWD and ALC running on the computer.  
Figure 2: The flowchart of the data merging process.  
For each alert category, titles have corresponding message contents. For example, the message content corresponding to \"Alternative medication remind\" is \"[Drug A] has been suspended. Do you want to use [Drug B] as an alternative option?\" (Table S1). However, the content may contain different drugs. Thus, we use \"(*)\" as a Regular Expression to indicate that this position can be any drug name. Finally, the data merging program checks whether the title of the dominant window and the alert content correspond to predefined pairs of categories. There were 501 predefined pairs in this study. If the window title and alert content were validated, the records were merged successfully. The data merging program keeps checking and merging data until all records have been compared. For our study, the data combination process took about four days to complete.
## 3 Engineering Applications
### Inversion of Stress Field  
Based on the proposed method of stress inversion using surface strain, a three-dimensional numerical model of the underground excavation was established according to the layout of the tunnels. The material properties of the rock mass in the numerical model were assumed based on data in Table 5. With the established model, the six parameters of the stress boundary conditions should be inverted. To facilitate the inversion calculation of the stress field and based on the depth of the tunnels, the following inversion analysis parameters are set: \(\sigma_{y}\subset[0,30]\), \(\sigma_{x}\subset[0,1.5\times\sigma_{y}]\), \(\sigma_{z}\subset[0,1.5\times\sigma_{y}]\), \(\tau_{xy}\subset[-\sigma_{y},\sigma_{y}]\), \(\tau_{yz}\subset[-\sigma_{y},\sigma_{y}]\), \(\tau_{xx}\subset[-\sigma_{y},\sigma_{y}]\), with a population size of 400, crossover probability of 0.8, mutation probability of 0.01, and 300 evolutionary generations. By introducing the mean variance (Equation (1)) for error comparison, the program is terminated by continuously mutating until the mean variance no longer changes. The measured surface strains obtained from measurement Points A to F in Table 6 are input as strain target values into the inversion system. The final inversion results and errors of the measurement points are summarized in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement Point** & \(\varepsilon_{x}(\mu\epsilon)\) & \(\varepsilon_{y}(\mu\epsilon)\) & \(\varepsilon_{z}(\mu\epsilon)\) & \(\gamma_{xy}(\mu\epsilon)\) & \(\gamma_{yz}(\mu\epsilon)\) \\ \hline A & - & \(-80\) & \(-16\) & - & \(-83\) \\ B & - & \(-60\) & \(18\) & - & \(-75\) \\ C & \(-5.2\) & \(-219\) & - & \(-125\) & - \\ D & \(13\) & \(-370\) & - & \(-42\) & - \\ E & \(11\) & \(-168\) & - & \(-25\) & - \\ F & \(14\) & \(-124\) & - & \(-108\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Strain data of measurement points.  
Figure 12: The numerical inversion model.  
As displayed in Table 7, the errors for points \(\varepsilon_{x}\), \(\varepsilon_{y}\), \(\varepsilon_{z}\), \(\gamma_{xy}\) and \(\gamma_{yz}\), except for measurement point D, are 7.74%. The maximum error for the remaining points is 4.59%. Although the errors are relatively large, the absolute values are small and still satisfy engineering accuracy requirements. The corresponding inversion results for the six stress boundary conditions are shown in Table 8. The results indicate that the vertical stress field in the area is minimal, with the maximum stress along the direction of the crosscut tunnel being 34.72 MPa, and the shear stress ranging from 0.134 to 0.239 times the maximum principal stress.
## 3 Results  
### Impact of Pulling Speed to Electrode Friction Forces  
To investigate the impact of pulling speed on electrode friction between the electrode array and the endosteum lining mimicking tissue, friction forces at different pulling speeds (0.1, 0.5, 1.0, 1.5, 2.0 mm/s) were recorded. Data were acquired over a distance of 30 mm along the electrode contacts.  
As demonstrated in Figure 2a, friction forces between 0.014 N and 0.90 N were measured, depending on the distance. Friction forces strongly rise at the beginning of electrode pulling, approaching a slight plateau-like phase as distance increased. Notably, while the plateau phase is characterized by a smooth pattern of the friction force, a pulling speed of 1.5 mm/s induced a more variable course of the friction force, indicated by a fluctuated graph pattern. Moreover, a speed of 2.0 mm/s leads to a second steep of the friction force between a distance of 15-20 mm. The highest friction force maximum were detected at a speed of 1.5 mm/s (0.90 N), followed by 2.0 mm/s (0.73 N), while friction force maxima of 0.1 (0.37 N), 0.5 mm/s (0.35 N), and 1 mm/s (0.31 N) were found to be similar.
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 3 Results
### Trend of the Optimized Sowing Date  
The fluctuations in the optimized sowing date similarly exhibit a two-stage pattern across the two designated SSPs, as depicted in Figure 6. Overall, a warmer climate context empowers agricultural practices to select sowing dates from a broader spectrum, thereby augmenting the likelihood of aligning wheat's phenology curve with the natural seasonal cycle. This synchronization enhances wheat growth prospects [36; 59]. Within the context of SSP126, owing to the advantages conferred by warming conditions, a significant trend emerges, with most of China's production regions experiencing an advancement in the optimal sowing date. Notably, the northwest regions manifest a particularly robust advance due to the more pronounced warming trend. However, with the temperature escalation being regulated around 2060, the optimized sowing date ceases to exhibit statistically significant trends thereafter (\(p>0.05\)). Under SSP370, characterized by a comparatively less constrained greenhouse gas emissions control and, hence, a more intense warming trend, the advance in the optimal sowing date is even more pronounced. This results in an approximate advancement of around ten days across the majority of the country.
## 3 Results
### Comparison with the Image Refocusing Based on Conventional Structural Images  
In the previous section, the propagation distance \(d\) was extracted by evaluating the contrast of the OCTA images and not the conventional OCT images. To confirm the advantage of the proposed method, the image refocusing was performed again by evaluating the contrast of OCT images to predict the optimal propagation distance. With a cross-sectional OCTA image, four different ROIs were established in the depth direction (z direction). In each ROI, the optimum propagation distance was extracted, and with that the OCTA image was reconstructed, as shown in Figure 4a. The middle and right pictures are the ones refocused with the propagation distance \(d\) obtained with the OCTA image and the OCT image, respectively. For structure-based recovery, an average of four repeated B-scans was used as a guiding star. Even though the difference is not very clear, the white arrows in the figure show that the blood vessels appear clearly in the middle picture. To quantify the result, the contrast to noise ratio (CNR) of each image was calculated using the metric [31]. The CNR values of the original image, the image reconstructed by the OCTA image, and the image reconstructed by the OCT image were calculated to be 2.8756, 3.1146, and 2.9155, respectively.  
Figure 3: Contrast change of the digitally focused Images 1–4 as a function of the propagation length. Image 2 in the graph shows the highest contrast. White arrows indicate clear flow signals. Scale bar is 500 μm.  
To explore further cases, we performed automatic refocusing on 500 defocused OCTA images in the C-scan direction. The statistics of propagation distances calculated for 500 images using OCTA images and OCT images, as guiding stars, were determined, respectively. Figure 4b shows the calculated propagation distances, calculated using the OCTA image, shorten as the imaging depth increases (from A to D). The means and standard deviations of the calculated propagation lengths were \(-\)0.73 \(\pm\) 0.16, \(-\)0.64 \(\pm\) 0.14, \(-\)0.57 \(\pm\) 0.18, and \(-\)0.45 \(\pm\) 0.19 mm with respect to A, B, C, and D, respectively. This makes sense because the deeper the region, the closer the focal plane. The focal position was below the ROI, as indicated using a green arrow in Figure 4a. On the other hand, Figure 4c shows the calculated distances not meaningfully dependent on the depth. The means and standard deviations of the calculated propagation distances were \(-\)0.66 \(\pm\) 0.28, \(-\)0.53 \(\pm\) 0.53, \(-\)0.63 \(\pm\) 0.26, and \(-\)0.65 \(\pm\) 0.16 mm, respectively. Therefore, in conclusion, the refocusing based on the OCTA image is better than the conventional one based on the OCT image. Note that the error bars of Figure 4b,c mean the variations of calculated propagation distances between 500 C-scan images, which have slightly different focusing planes.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 2 The HERMES Pathfinder Mission  
### Overview  
The HERMES programme [1; 2; 14; 15; 16; 17; 18] envisages a constellation of six CubeSats as pathfinders for the observation of high energy transient events, such as Gamma Ray Bursts (GRBs) through a set of distributed sensors, three of them supported by the Agenzia Spaziale Italiana--through the HERMES Technological Pathfinder project--the last three financed by a H2020 European Commission study--named HERMES Scientific Pathfinder. In particular, the mission aims at demonstrating the feasibility of reconstructing the position of GRBs by means of triangulation across different satellites, exploiting the time delay of the same event detected by different elements of the constellation. The constellation is planned to be launched in 2023 on circular nearly-equatorial orbits at 550 km of altitude. Once in flight, the nanosatellites will naturally drift apart due to orbital perturbations, quickly reaching a relative baseline distance sufficient to perform GRB triangulation [14; 15].  
For more details on the triangulation procedure the reader can refer to [19] whilst for details about the constellation orbital drift and mission analysis more information can be retrieved in [14; 15].  
All HERMES CubeSats are equipped with an Iridium transceiver that enables a fast link to the ground through the Iridium constellation, deployed at 780 km of altitude [1]. As soon as the PL on board each satellite detects a GRB, an alarm signal is transmitted to the ground and broadcast worldwide to astronomical observatories to enable complementary scientific observations. An overview of the satellite configuration is shown in Figure 1. The HERMES satellite consists in a three-unit CubeSat: the top unit hosts the scientific payload (Figure 1c), while the other two units constitute the Service Module (SM) (Figure 1d). The overall envelope is \(10\times 10\times 30\) cm. The payload occupies 1U, meaning \(10\times 10\times 10\) cm.
# Dual-Band Wearable Antenna Integrated with Glasses for 5G and Wi-Fi Systems
## 2 Materials and Methods
### Final Design  
_Modified IFA_  
The antenna discussed in this paper is a modified inverted F antenna (IFA), with its unique geometry detailed in Figure 5. Unlike conventional IFAs, this design significantly  
Figure 4: Results of impedance matching simulations for IFA, and VSWR coefficient vs. frequency.  
Figure 3: Current distribution in the IFA for 5.8 GHz.  
Figure 2: Current distribution in the IFA for 3.5 GHz.  
enhances performance by incorporating circular elements at both the feed point and the radiating arm's terminus.  
These circular elements were introduced to facilitate dual-band operation at 3.5 GHz and 5.8 GHz. Through an iterative optimization process, the antenna's dimensions were precisely determined to achieve optimal impedance matching across these frequency bands, considering both free-space and near-head operational scenarios. This design also maintains a compact form factor, with a diminutive height of 14 mm and a width of 77 mm, making it highly suitable for integration into eyewear temples. The simulation assumed a conductor thickness of 0.1 mm.  
A key advantage of integrating these circular elements is the resultant greater current values within the 3.5 GHz and 5.8 GHz bands. This increase in current directly translates to improved radiation efficiency and overall antenna performance at these critical frequencies. The current distribution patterns for 3.5 GHz and 5.8 GHz are visually represented in Figures 6 and 7, respectively, clearly demonstrating these enhanced current values.
## 3 Results and Discussion
### Augmented Kenaf Retting Using APPB Combined with Water Solvents  
The kenaf degumming efficacy of APPB with macerating solvents was verified in non-sterile retting conditions. The visual observations of the whole kenaf plants after 6 days of retting treatments are shown in Figure 4. The first steps of retting were initiated by the maceration of solvents, where the plants' cell walls turn soft and soluble proteins make an appropriate environment for the microbes. The four significant retting steps involved in bio-degumming are: (i) absorption of solvents and forming of calcium oxalate crystals; (ii) removing the cortex; (iii) eliminating guns from the middle lamella; and, finally, (iv) eliminating guns from the fiber surface. The first two steps have solvent maceration effects, which also make favorable sites for microbial enzymatic activities [18, 52]. The retting effect by visual observation produced a distinguished state in kenaf plant parts as uninoculated niches (UFW and USW) remained hard and intact with woody core portions compared with what was obtained with the inoculated treatments.  
Figure 2: Quantitative pectinase, xylanase, and cellulase enzymatic activities of the alkalophilic bacterial isolates APPB1 and APPB2.  
Figure 3: _Cont._  
The incorporation of APPB1 with SW showed great value in the fiber bundle separation feasibility (Figure 4c), which is comparable with APPB1 with FW (Figure 4d). The trend of the fibers' separation offered a clear indication of retting achievement. This might positively affect the inoculation of alkalophilic bacterial strains, as they prevent the unwanted endogenous microbial mixture [13]. Similar results were observed for the APPB2 with the same macerating solvents, while with FW, it starts to ret the fibers and additional treatment is required (Figure 4f). However, predicting the actual retting efficacy requires intensive investigation of the retting and fiber properties.
## 3 Graph Clustering Methods  
This section explains the graph clustering benchmark algorithms. The popular Louvain method is introduced [14], centered around optimizing the modularity score. Afterward, the extension, I-Louvain [43], is presented, which combines modularity with the consideration of the statistical proximity of feature nodes. Lastly, the GraphSAGE method [29], a graph neural network utilized for community detection, is discussed in detail.
## 3 LoRaWAN Background
### LoRa Physical Layer  
LoRaWAN builds upon LoRa [40], which is a spread spectrum modulation technique derived from chirp spread spectrum (CSS) technology. The LoRa modulation allows to configure multiple parameters [69] that influence the bit rate, coverage range, energy consumption, and robustness to noise or interference. The main LoRa parameters are the Carrier Frequency (CF), Bandwidth (BW), Spreading Factor (SF), Coding Rate (CR), and Transmission Power (TP). The time required to transmit a LoRaWAN message using specific LoRa parameters is called the Time on Air (ToA) and is obtained through the Equation (1) provided in [70],  
\[\mathit{ToA}=\frac{2^{SF}}{BW}\Bigg{[}NP+4.25+SW+max\Bigg{(}\lceil\frac{8 PL-4 SF+28+16 CRC-20 IH}{4(SF-2 DE)}\rceil(\mathcal{K}+4),0\Bigg{)}\Bigg{]} \tag{1}\]  
where  
* CRC is a boolean flag referring to the Cyclic Redundancy Check (CRC) presence
* DE is a boolean flag referring to the data rate optimization status
* IH is a boolean flag referring to the physical header presence
* NP is the number of preamble symbols
* PL is the Physical payload length in bytes
* SW is the length of the synchronization word
* \(\mathcal{K}\) is the value of the \(\mathcal{K}\) parameter in the formula \(CR=\frac{4}{4+\mathcal{K}}\).  
Based on the region in which the LoRaWAN devices operate, the LoRa parameters can assume different values, as specified in [53]. Configuring these parameters it is possible to tune the bit rate, coverage range and energy consumption. To give an example, in [71; 72] it is shown that an increase in SF broadens the coverage and approximately halves the bit rate, thus approximately doubling both the ToA and the energy consumption.  
According to [73; 74] the SFs are quasi-orthogonal, therefore simultaneous transmissions with different SFs on the same channel are not totally immune to each other. Despite this, the transmitted messages can be correctly interpreted if their Signal-to-Interference Ratio (SIR) is higher than the isolation threshold stated in [73].
## 3 Methods
### Estimation of Gross Primary Production  
The vegetation photosynthesis model (VPM) was used to estimate GPP, as follows [22]:  
\[\text{GPP}=\epsilon_{\text{g}}\cdot\text{FAPAR}\cdot\text{PAR} \tag{4}\]  
\[\epsilon_{\text{g}}=\epsilon_{\text{max}}\cdot T_{\text{scalar}}\cdot W_{\text {scalar}}\cdot P_{\text{scalar}} \tag{5}\]  
where \(\epsilon_{\text{g}}\) (\(\epsilon_{\text{max}}\)) is the actual (maximum) plant light use efficiency (g C mol\({}^{-1}\) PAR); PAR is the photosynthetically active radiation (mol m\({}^{-2}\) (8d)\({}^{-1}\)), and FAPAR is the absorbed fraction of PAR, set equal to EVI [22] but not exceeding 0.95 based on [PERSON] et al. [14]. \(T_{\text{scalar}}\), \(W_{\text{scalar}}\), and \(P_{\text{scalar}}\) are the stress scalar values ranging from 0 to 1, which are used here to account for the effect of temperature, water, and leaf age, respectively, on light use efficiency.  
The \(\epsilon_{\text{max}}\) value was estimated at a time step of 8 days for each pixel via the empirical equation developed by [PERSON] et al. [38] using eddy covariance data in northern China. To derive the \(\epsilon_{\text{max}}\) value at the pixel level, this equation was based on two parameters, the maximum EVI (EVI\({}_{\text{max}}\)) and minimum visible albedo (VA\({}_{\text{min}}\)) within the growing season, as presented below:  
\[\epsilon_{\text{max}}=\exp(1.428\cdot\text{EVI}_{\text{max}}-6.295\cdot\text{ VA}_{\text{min}}-1.211). \tag{6}\]  
Figure 2: Spatial distributions of major land use types in Northern Shaanxi for 2000.  
The temperature scalar (\(T_{\text{scalar}}\)) was calculated using the algorithm of the CASA model [18], and \(W_{\text{scalar}}\) and \(P_{\text{scalar}}\) were computed using the VPM equations [22]:  
\[T_{\text{scalar}}=\frac{1.1814\cdot\left(0.8+0.02\cdot T_{opt}-0.0005\cdot T_{ opt}^{2}\right)}{\left(1+\exp\left(0.3\cdot\left(T-T_{opt}-10\right)\right) \right)\cdot\left(1+\exp\left(0.2\cdot\left(T_{opt}-T-10\right)\right)\right)} \tag{7}\]  
\[W_{\text{scalar}}=\frac{1+\text{LSWI}}{1+\text{LSWI}_{\text{max}}} \tag{8}\]  
\[P_{\text{scalar}}=\frac{1+\text{LSWI}}{2} \tag{9}\]  
where \(T\) is the 8-day mean air temperature, and \(T_{\text{opt}}\) is the optimum temperature for a specific plant type. Here, \(T_{\text{scalar}}\) is set to 0 when \(T\) is lower than 0 \({}^{\circ}\)C. \(T_{opt}\) is set equal to the mean temperature at which the mean EVI value over the 13 years of 2000-2012 reaches its maximum based on previous studies [18; 19]. \(\text{LSWI}_{\text{max}}\) is the maximum LSWI within the plant growing season for individual pixels. From the 13-year LSWI data, we selected the maximum value within the photosynthetically active period as an estimate of \(\text{LSWI}_{\text{max}}\). \(P_{\text{scalar}}\) was set to 1 for grass during the whole growing season. For the other three vegetation types, \(P_{\text{scalar}}\) was estimated using Equation (9) from bud burst to leaf expansion and from leaf falling to wilting; the value was set to 1 during the full expansion period between mid-May and the end of August, as determined from the EVI curve.