esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correction Factor  
Since we found the effect of RH on the measurements of ESCORTAIR or PA, we developed our own correction models for those IRM devices by performing stepwise linear regression. The correction factors were obtained from the regression models (1.11 for ESCORTAIR and 1.92 for PA, \(p<0.01\)), (1.00 for ESCORTAIR and 0.87 for PA, \(p<0.01\)) and (1.15 for ESCORTAIR and 0.70 for PA, \(p<0.01\)) for the measurement of PM\({}_{2.5}\) resulting from indoor pan-frying, SHS or the urban traffic hotspot of South Korea, respectively.  
After adjusting for temperature and relative humidity, the results were unchanged for the indoor tests (1.10 for ESCORTAIR and 1.90 for PA, \(p<0.01\)), (0.97 for ESCORTAIR and 0.81 for PA, \(p<0.01\)) (Table 3). For the outdoor measurement, the change of slopes (0.72 for ESCORTAIR and 0.77 for PA, \(p<0.01\)) was relatively small but the R2 values were changed (Table 4).  
Figure 2: Change of the slope with 95% confidence interval obtained from the regression line of BAM (Beta ray attenuation monitor) on IRMs (Inexpensive real-time particulate matter monitors) or RGMs (research grade monitors) by the degree of relative humidity.
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 3 Data and Methods
### Data
#### 3.2.3 Forest Density Data  
The High-Resolution Layer Tree Cover Density dataset furnishes insights into tree cover density (TCD, represented as a percentage ranging from 0% to 100%), which is defined as the vertical projection of tree crowns to the horizontal surface of the Earth. This product is provided for the reference year 2018 at a pan-European level (EEEA38 countries and United Kingdom) with a high spatial resolution of 10 m.
# Halophytes as Medicinal Plants against Human Infectious Diseases
## 3 Halophytes as a Source of Antimicrobial Compounds
### Antibacterial Effects
Extracts of _Citrullus colocynthis_, used to treat tuberculosis in traditional medicine, were successfully tested against _Mycobacterium tuberculosis_ and the minimum inhibitory concentration (MIC) of a methanolic extract of ripe deseeded fruits (63 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{mL}\)) was lower than the MIC of cucurbitacin acid (25 \(\mathrm{mg}/\mathrm{mL}\)), an antibacterial membrane disruptor present in the extracts [82]. Additionally, there is a strong relation between the antibacterial efficiency of the extracts and the plant part from which they are obtained. Extracts from _E. agallocha_ roots had higher activity against the Gram-negative _Salmonella typhi_, but leaf extracts were much less effective against this bacterium and, in contrast, highly effective against _Enterobacter_ species and _Staphylococcus aureus_[81]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Bacteria** & **Plant Species** & **Reference** \\ \hline \multirow{8}{*}{_Basillus cereus_} & _Salicornia europaea_ & [80] \\ \cline{2-3}  & _Mesembryanthemum edulis_ & \\ \cline{2-3}  & _Suaeda monoica_ & \\ \cline{2-3}  & _Cressa cretica_ & \\ \cline{2-3}  & _Tamarix gallica_ & \\ \hline _Enterobacter_ sp. & _Exocaria agallocha_ & [59] \\ \hline \multirow{4}{*}{_Enterococcus faecalis_} & _Salicornia europaea_ & [80] \\ \cline{2-3}  & _Mesembryanthemum edulis_ & [86] \\ \cline{2-3}  & _Suaeda monoica_ & [87] \\ \cline{2-3}  & _Tamarix gallica_ & [86] \\ \hline \multirow{4}{*}{_Escherichia coli_} & _Arltrochenum macrostachyunt_ & [88] \\ \cline{2-3}  & _Salicornia europaea_ & [80] \\ \cline{2-3}  & _Cressa cretica_ & [89] \\ \cline{1-1}  & _Mesembryanthemum edulis_ & [83] \\ \cline{1-1}  & _Suaeda monoica_ & [84] \\ \cline{1-1}  & _Tamarix gallica_ & [86] \\ \hline \multirow{4}{*}{_Listeria monocytogenes_} & _Mesembryanthemum edulis_ & [83] \\ \cline{1-1}  & _Tamarix gallica_ & [86] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Antibacterial effects associated with extracts of halophyte plants. _Appl. Sci._**2022**, _12_, 7493
## 3 Analysis of Influencing Factors
### Influence of Distance between Tunnel Axis and Basement Center F  
The maximum vertical deformation of the tunnel, \(\mu_{\max}\), affected by the distance between the tunnel axis and the basement center, \(F\), for three different unloading ratios (\(\alpha\) = 0.25, 0.50, 0.75) and four different basement widths (\(B\) = 30 m, 60 m, 90 m, 120 m) is analyzed. Figure 19 shows the relationship between the maximum vertical deformation of the tunnel, \(\mu_{\max}\), and the distance between the tunnel axis and the basement center, \(F\), for different cases, respectively. It can be seen that the maximum vertical deformation of the tunnel decreases with increasing \(F\). The maximum vertical deformation of the tunnel remains unchanged for the lower \(F\), while it begins to decrease when \(F\) reaches a critical level.  
Figure 19: _Cont._  
Figure 18: Relationship between maximum vertical of tunnel displacement and basement width after normalization.  
The ratio of the distance between the tunnel axis and the basement center, \(F\), to half the basement width, \(B/2\), is defined as the offset ratio, \(\beta\), which describes the degree of tunnel offset from the basement center. The value of \(\beta\) ranges from 0 and 1, with a higher \(\beta\) indicating a greater tunnel offset. Figure 20 shows the relationship between the maximum vertical deformation of the tunnel and offset ratio, \(\beta\). It can be seen that vertical deformation decreases with increasing \(\beta\). There exists a critical offset ratio, \(\beta\), of 0.4. The vertical deformation of the tunnel remains unchanged for a value of \(\beta\) less than 0.4, while it decreases rapidly for a value of \(\beta\) larger than 0.4.  
Figure 19: Relationship between maximum vertical tunnel displacement and distance between tunnel axis and basement center: (**a**) \(\alpha=0.25\); (**b**) \(\alpha=0.50\); (**c**) \(\alpha=0.75\).  
Figure 20: Relationship between maximum vertical deformation of tunnel and offset ratio: (**a**) \(\alpha=0.25\); (**b**) \(\alpha=0.50\); (**c**) \(\alpha=0.75\).  
The deformation discount ratio, \(\eta\), is defined to describe the degree of deformation discount for tunnel offset:  
\[\eta=\left(\mu_{\max}-\mu^{\prime}_{\max}\right)/\mu_{\max}, \tag{5}\]  
where \(\mu_{\max}\) is the maximum vertical deformation of the tunnel for \(\beta\) = 0 and \(\mu^{\prime}_{\max}\) is the maximum vertical deformation of the tunnel for \(\beta\) > 0. The value of \(\eta\) ranges from 0 and 1, with a higher \(\eta\) indicating a greater deformation reduction. Figure 21 shows the relationship between \(\eta\) and \(\beta\). It can be seen that \(\eta\) increases with increasing \(\beta\). \(\eta\) is essentially zero for a value of \(\beta\) less than 0.4, while it increases rapidly for a value of \(\beta\) larger than 0.4. The maximum \(\eta\) is approximately 0.3.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 9 High-Level Performance Comparison
Figure 15: **Up**: On-axis angular resolution achieved with the best Machine Learning methods (orange points) against the benchmark (A-SciSoft, blue points). **Bottom**: Comparison between the on-axis angular resolution achieved with the best Machine Learning methods and the one achieved with A-SciSoft. The ratio is calculated so that higher values correspond to better performance. As shown, the new Machine Learning methods provide slightly better differential sensitivity above \(\sim\)1 TeV, a comparable angular resolution in the whole energy range, and a better energy resolution in the range \(\sim\)1-\(\sim\)10 TeV. One the one hand, these results are interesting per se, as they go in the right direction of improving the performance achievable by the data reconstruction. On the other hand, they are also important for confirming that the standard reconstructed methods implemented in A-SciSoft already provide satisfactory, although improvable, results. However, the relevant improvement of the new Machine Learning methods lies in the bias of the energy reconstruction. In this respect, it is worth mentioning that the standard tools used to compute the high-level performance include an energy bias correction routine. This routine uses a polynomial fit of the projection along the reconstructed energy of the energy migration matrix to correct the reconstructed energy of the events. This routine is applied by default in order to provide high-level performance as that is affected as little as possible by energy reconstruction biases. However, the desirability of having a method that performs as well as possible in terms of energy bias prior to the application of this correction is a very important point for the analysis, especially with a view to the analysis of  
Figure 16: Up: On-axis energy resolution achieved with the best Machine Learning methods (orange points) against the benchmark (A-SciSoft, blue points). **Bottom**: Comparison between the on-axis energy resolution achieved with the best Machine Learning methods and the one achieved with A-SciSoft. The ratio is calculated so that higher values correspond to better performance. real data. Figure 17 show the on-axis point-like source energy bias achieved with A-SciSoft methods (blue points) and the best Machine Learning methods investigated in this work (orange points), before and after the application of the bias correction. From the plot on the left, it is evident that the new Machine Learning methods are much more effective, providing less bias in the energy reconstruction than the standard method currently implemented in A-SciSoft. It should be noted that the energy resolution curves shown in Figures 12 and 13 were obtained prior to any application of an energy bias (and without considering optimized cuts for each analysis) and therefore show different behavior from that which is shown in Figure 17
# An Automated Design Method for Plane Trusses Based on User Preference Information
## 1 Introduction  
Computational methods have been widely applied in various aspects of structural design, including both structural analysis and construction drawings. To further improve design quality and efficiency, plenty of automated design methods, which focused on performing conceptual design independently, have been developed in recent years. A representative one is Structural Topology and Shape Annealing (STSA) [1], which simulates the truss design process well and has been successfully used in engineering practice [2, 3]. However, most automated methods such as STSA consider only quantitative objectives and constraints that can be defined mathematically, e.g., minimum weight or stress limit. The ability to analyze qualitative factors reflecting the designer's intuition, experience, and preference is limited. One example is the user's subjective preference for structural shapes. Thus, the qualitative factors can only be considered by designers, which limits the functionality and efficiency of the automated design methods. To overcome this limitation, two main problems are to be solved: first, how to quantify the effects of qualitative design factors so that they can be considered in computational methods; and second, how to coordinate different design goals, including both subjective and objective, so that they can guide the search towards an optimal design.  
This study aims at solving these two problems and proposing an innovative conceptual design method for plane trusses based on user preference. Note that, in this paper, user preference means the subjective satisfaction with the design appearance. Considering the first problem, a prediction model that can quantify user preference is proposed to transform the qualitative information into a quantifiable objective. Regarding the second problem, a cost function based on modified physical programming (MPP) is introduced. It coordinates different design objectives effectively and integrates the prediction model into a multi-objective optimization procedure (STSA). This paper is organized as follows: Section 2 reviews the related studies on applying user preference and multi-objective optimization methods. Section 3 gives the main steps to build a prediction model for user preference. The cost function is described in Section 4. After that, a design example is given to demonstrate the effectiveness of the proposed method in Section 5. Finally, conclusions are presented in Section 6.
## 1 Introduction
It is well known that the transmission performance is better as the number of antennas increases, but the complexity of the signal processing on the receiver becomes higher. A macrodiveristy system composed of a macrodiversity receiver and two microdiversity receivers was proposed in [25], since it had lower implementation complexity than other macrodiversity systems. The macrodiversity reception scheme using microdiversity receiver as distributed remote antenna was considered to be effective against both large-scale fading, such as shadowing and path loss, and small-scale fading, such as rayleigh fading. Many valuable results have been reported on the level crossing rate (LCR) [26; 27] and signal moments [28; 29] at the output of the macrodiversity reception system. Recently, the implementation of the macrodiversity schemes was mature when it was used in the traditional wireless networks. However, its applicability in practical wireless sensor networks to achieve full benefits is still in its infancy [30]. The main issue is the limited computing resources in the sink node. Therefore, it is important to further reduce the implementation complexity of macrodiversity system to adapt to the hardware constraints of the sink node. In this paper, a low complexity macrodiversity reception scheme using multiple receivers which can only output hard decision result is proposed for maritime wireless sensor networks. The scheme is based on a macrodiversity reception architecture consisting of several local convergence modules and a central processing module, which can reduce the effects of fading effectively. Each local convergence module equipped with four radio frequency (RF) antennas is placed in a remote place to receive data. The central processing module aggregates the output of each local convergence module to the processor, and completes the combining of multiple signals on the processor. To reduce the computing complexity of processor in convergence center, a cascaded combining scheme is provided to distribute the computing load of the sink node to each local convergence module. Furthermore, we build a maritime wireless sensor networks macrodiversity reception platform and conduct experiments to analyze the performance of proposed macrodiversity reception scheme. The experimental results show that the macrodiversity reception with two clusters using four antennas scheme has a transmission gain of 3-4 dB compared with the single antenna reception scheme when the package loss rate is \(10^{-2}\) and has greater gain than single cluster using eight antennas reception scheme. The remainder of this paper is organized as follows. Section 2 gives a brief overview of the proposed macrodiversity reception scheme. In Section 3, we describe the reliable transmission method of macrodiversity reception in detail. In Section 4, we conduct experiments in different scenarios and analyze the experimental results in terms of PER and frame error rate (FER). Conclusion is given in Section 5.
## 4 Solution Method
### Assign Patients to the Opened Facilities  
The purpose of this algorithm is to assign patients to the nearest open facility in \(S\) whose capacity is available, and drone constraints are satisfied. Using \(S\) from Algorithm 1 as an input list of opened facilities with size \(p\), along with the list of all potential patients \(I\), several operations should be performed. First, for each patient \(i\) in \(I\), traverse the patients one by one until the list of patients ends, such that the current patient \(i\) is assigned to the first open facility \(f\) in the list \(S\) based on two conditions: (i) \(f\) is the nearest opened facility to \(i\), where \(b_{if}\leq B\); and (ii) there is available capacity in \(f\) to add \(i\) (i.e., \(w_{i}\,\leq U_{f}\)). The second operation recalculates \(U_{f}\) (i.e., the available capacity) and \(\sum_{i\,\in\,\,I}\,\,w_{i}\,\,a_{if}\) (total demand served by \(f\)). Algorithm 2 presents the details of the above steps.  
```
1. \(\backslash\) the list of opened facilities from Algorithm 1
2. \(I\leftarrow\) List of all potential patients
3. For each patient \(i\) in \(I\) Do
4. Pick best \(f\) for \(i\) from \(S\setminus\) the nearest opened facility where \(b_{if}\leq B\) with available capacity \(U_{f}\)
5. \(Assign(i,f)\)
6. Recalculate available capacity \((U_{f})\) and total required demands weights from \(f\) (\(\sum_{i\,\in\,\,I}\,\mathit{w}_{i}\,\mathit{a}_{if}\))
7. Output: \(S\)
```  
**Algorithm 2** Assign patients to the opened facilities.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 3 Materials and Methods
### Scenario  
The CC1352P LaunchPad by Texas Instruments is a multi-protocol wireless micro-controller unit designed for low-power IoT applications. It supports multiple wireless communication protocols, including LoRa, BLE, and IEEE 802.15.4 [48], which enables connectivity with Zigbee and Thread networks. The device operates at both sub-GHz frequencies (868 MHz or 915 MHz for LoRa) and 2.4 GHz for BLE. The modulation techniques used include GFSK for LoRa and FHSS for BLE. It offers a wide communication range, reaching up to 10 km in line-of-sight conditions when using LoRa, while BLE communication is effective within 100 m, depending on environmental factors. Furthermore, the CC1352P is designed for ultra-low power consumption, featuring energy-efficient sleep modes that make it ideal for battery-operated IoT deployments.  
The LPSTK-CC1352R is another multi-protocol device from Texas Instruments, specifically designed as a compact, sensor-oriented platform. Like the CC1352P, it supports LoRa, BLE, and IEEE 802.15.4 protocols, making it highly versatile for various IoT applications. Its communication range and modulation techniques are similar to those of the CC1352P, with LoRa offering long-range connectivity and BLE providing short-range wireless communication. However, the LPSTK-CC1352R is optimized for lower power consumption, integrating additional sensors such as temperature, humidity, and light sensors, which are valuable for environmental monitoring and industrial IoT applications.  
The Laird Sentrius Gateway is a high-performance IoT gateway that facilitates seamless connectivity between LoRa devices and cloud-based applications. It supports LoRaWAN for long-range, low-power IoT communication and Bluetooth Low Energy for direct sensor connectivity. Operating in the sub-GHz band for LoRa (typically 868 MHz or 915 MHz, depending on regional regulations) and in the 2.4 GHz spectrum for BLE, the gateway enables bidirectional data transmission across different wireless technologies. This makes it an essential component for heterogeneous networks where both short-range BLE devices and long-range LoRa nodes coexist.
## 3 Implementation
### Access Layer
#### 3.2.2 FUSE Mount Point  
FUSE, also known as file system in user space, is a software library for computer operating systems that allows non-privileged users to create their own file systems without  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Encoder/Decoder Name** & **Function** \\ \hline compression [9] & Transparent compression \\ seqmeta [10] & Sequence metadata extraction \\ blastdb [16] & BLAST database construction \\ jbrowse [17] & Browse database construction \\ clustal [18] & Clustal database construction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Available encoders/decoders provided by F3 BFS.  
having to probe kernel modules. By supporting FUSE, F3 BFS enables users to easily mount their cluster on an existing file system and access the stored data just like a normal file, whether that is on Linux, Windows, or macOS.  
The access to data with a FUSE mount point is convenient, but in comparison to F3 BFS APIs, the performance is a bit slower, since the FUSE operations need to be transcoded to use the F3 BFS APIs. Essentially, the most significant role played by FUSE is that it enables the traditional applications to connect to the F3 BFS cluster without altering their existing source code to adopt F3 BFS API.
## 4 Discussion
### Why was the Performance of EVI Different between the Short-Term and Long-Term Spatial Analysis?  
Our results suggest that simple models are viable approaches for estimating GPP, but that fundamentally different approaches should be used depending on the spatio-temporal scale of the analysis. Among the four satellite-derived metrics, EVI showed the highest correlation with short-term GPP (Table 3) but not with annual GPP (Table 4). The reason for the higher correlation of EVI than NDVI with short-term measurements of GPP can be attributed to (1) its broader dynamic range compared to NDVI, which is due to non-saturation at high LAI, (2) empirical auto-correction for atmospheric contamination of the reflectance, and (3) consistent retrievability without need of backup algorithms. Other studies also showed that EVI can track the seasonal change in GPP and other phenological events better than NDVI for deciduous forest [21].  
However, when assessed at the level of either non-tropical or tropical sites to annual mean GPP, EVI had almost no correlation with annual GPP, while NDVI had significant correlation (Table 4). This result indicates that the conversion factor from EVI to GPP varies across forest types. That is not surprising considering that vegetation indices are a metric that is only theoretically proportional to leaf amount at any single point [76]. There is no clear biophysical justification for which the GPP/VI ratio should be constant among different biomes. [PERSON] _et al._[77] also reported that the slopes of the linear regression between monthly EVI and monthly GPP depended on biome type. In this study, the correlations between annual maximum VIs and annual maximum GPP were 0.47 (\(p<0.001\)) for NDVI and 0.17 for EVI, indicating that the GPP/EVI ratio was site-specific rather than constant. This explains why annual mean EVI showed no correlation with annual GPP (Table 4).  
To check if these results depended on the assumptions made for winter LAI in the presence of snow, and hence the calculation of the mean values, we set the value of the snow-covered pixels to the annual minimum rather than zero. While this increased the correlations of NDVI and EVI (\(R^{2}\) = 0.63 for NDVI and \(R^{2}\) = 0.62 for EVI), it did not significantly affect the correlation of LAI and FPAR. However, these results did not change how well those metrics were correlated because increases in the winter values proportionally increased the annual mean values. Thus, our results were not dependent on setting the snow values to zero.