esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSSs Based on Structure
#### 4.2.3 Antenna-Filter-Antenna (AFA) FSSs  
A traditional FSS or a first-order FSS cannot cover the demands of wider BW. Additionally, by cascading multiple first order FSSs layers with the interval of quarter wavelength between each other, multi-pole or wideband FSS can be obtained. Resultantly, this technique of the adding dielectric slabs enlarges the thickness of the FSS. The use of advanced FSSs configurations, such as active FSS, fractal FSS, and multilayer FSS, may significantly enhance the bandwidth [41]. Another option of designing a multiband FSS can be accomplished by multilayered surfaces. It has been proved earlier that fractal structures are employed for size reduction, while the stacking models can improve the bandwidth of FSS [29; 92; 2]. Recently proposed, FSS based on the array of Antenna-Filter-Antenna (AFA) are considered as low profile and employed to accomplish high frequency selectivity [74; 75; 106; 107; 108; 106]. It consists of carefully designed receive and transmit antenna elements as the building blocks of FSS, coupled to resonant or non-resonating element structure to synthesize a filter response in a particular frequency band. Use of the resonant element permits an arbitrarily higher order filter response. However, the periodicity of FSS is of primary concern along with the requirement of broader BW, high angular stability, multi-pole characteristics, and stable performance. Smaller period of the FSS element (miniaturization) can help to postpone the inception of grating lobes and it results in the diminution of the FSS distortion. A three layer AFA topology is carried out in [74], as shown in Figure 11, to design passband FSS in C band with the multi-pole response by means of fractal geometric structure (Minkowski fractals). The incorporation of such an AFA arrangement with fractal elements yields a low profile and miniaturizes FSS for high selectivity applications, such as passband waveguide filters, dichroic reflectors, and as narrowband radomes to control the RCS. It is prominent the structure parameters relating to fractal iteration order, ratios of Minkowski patches, and cross-shaped fractal coupling aperture impact the frequency response and control the miniaturization level, as well as the coupling strength.
## 3 Proposed Solution to Predict the AoA
### Support Vector Regression (SVR) for Non-Linear AoA Prediction
This fused prediction constrains the search region for enhanced MUSIC estimation or, if confidence falls below a threshold, can trigger full-range MUSIC fallback at the O-RU. The SVR's prediction error and window curvature are also continuously monitored to dynamically adapt kernel width and fusion weights (see the section Curvature-Adaptive Kernel for Non-Linear Mobility).
## 3 Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**PS Pure**} & \multicolumn{3}{c}{**PS + C60**} & \multicolumn{3}{c}{**PS + CNT**} & \multicolumn{3}{c}{**PS + Graphene**} \\ \cline{3-10}  & & **0.1\%** & **0.5\%** & **5\%** & **0.1\%** & **0.5\%** & **5\%** & **0.1\%** & **0.5\%** & **5\%** \\ \hline \(\lambda\), GPa (\(\pm 0.05\)) & 2.3 & 2.3 & 2.29 & 2.28 & 2.29 & 2.42 & 2.71 & 2.29 & 2.48 & 2.58 \\ \(\mu\), GPa (\(\pm 0.05\)) & 1.4 & 1.42 & 1.4 & 1.41 & 1.4 & 1.45 & 1.5 & 1.4 & 1.46 & 1.68 \\ \(E_{c}\), GPa (\(\pm 0.1\)) & 5.1 & 5.14 & 5.1 & 5.1 & 5.3 & 5.7 & 5.1 & 5.4 & 5.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Linear elastic moduli of PS-based nanocomposites obtained from ultrasonic measurements. Figure 6: Elastic moduli of PS-based composites filled with carbon allotropes at the concentration of 5 wt%. Moduli were obtained at four static and dynamic tests: tensile (\(E_{b}\)), three-point bending (\(E_{f}\)), DMA (\(E_{DMA}\)), and ultrasonic (\(E_{c}\)). The predominant orientation of the layered filler is worth noting (see Figure 1c,f. Filling the PS matrix with spherical (fullerenes) or filamentous anisometric (nanotubes) particles led to the formation of an isotropic structure of the composite, while two-dimensional particles had a preferential orientation caused by the extrusion process. Therefore, the most efficient dispersed filler can be selected depending on the expected type of loading; for example, filamentous structures are preferable for uniaxial loading, while the layered filler is favorable at loading perpendicular to the longitudinal axis in the cross-sectional plane.
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 4 Data Analysis
### Technologies  
To study the influence of the access technologies used in this study, Table 4 shows the number of measurements collected during our test campaign as well as the percentage of measurements made using the 2G (\(EDGE\)), 3G (\(UIMTS\), \(HSPAP\) (\(HSPA+/3G++\)) and \(LTE\).  
As the phones used are not 5\(G\) compatible, the greatest number of measurements are of the 4\(G\)/\(LTE\) type with a percentage of 93%. This implies that 4\(G\) technology is still widely used in France in 2021 due to the modest NSA 5\(G\) deployment for the moment. Moreover, a general lesson to be drawn is that 4\(G\) deployment in the Paris region is good even if it cannot satisfy the requirements of the new generation applications (e.g, cloud Virtual Reality).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Measurements** & **(\%)** \\ \hline \hline \(LTE\) & 152,756 & 92.9(\%) \\ \(HSPA\) & 8226 & 5.0(\%) \\ \(UIMTS\) & 2332 & 1.4(\%) \\ \(EDGE\) & 1112 & 0.7(\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics about the access technologies in the dataset).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Statistics**} & \multirow{2}{*}{**mean**} & \multirow{2}{*}{**std**} & \multicolumn{3}{c}{**Bitrate (kbits/s)**} & \multirow{2}{*}{**50\%**} & \multirow{2}{*}{**75\%**} & \multirow{2}{*}{**max**} \\ \cline{2-2} \cline{6-7}  & & & & & & & \\ \hline Download & 25,264 & 27,835 & 0 & 5112 & 15,340 & 35,735 & 192,963 \\ \hline Video & 32,962 & 75,868 & 0 & 8629 & 20,912 & 39,185 & 237,068 \\ \hline Web & 3503 & 10,313 & 0 & 388 & 1356 & 2771 & 153,461 \\ \hline \multirow{2}{*}{**Statistics**} & \multirow{2}{*}{**mean**} & \multirow{2}{*}{**std**} & \multicolumn{3}{c}{**Launch Time**} & \multirow{2}{*}{**50\%**} & \multirow{2}{*}{**75\%**} & \multirow{2}{*}{**max**} \\ \cline{2-2} \cline{6-7}  & & & & & & & \\ \hline Download & 0.726 & 37 & 0 & 0 & 0 & 2649 \\ \hline Video & 3351 & 8238 & 0 & 183 & 247 & 335 & 30,000 \\ \hline Web & 1050 & 1617 & 0 & 328 & 498 & 902 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of services statistics.  
\(HSPAP\) as well as 3\(G\) are still used to ensure continuity of service in some complex urban sectors (5% and 1.4%, respectively, of our traces). Finally, in all the locations evaluated, at most, 0.01% of the measurements were carried out using a smartphone with 2G access. This is in line with the outcome of recent measurement works [11; 26] done in France and Germany.
## 1 Introduction
### Background  
From empirical models to artificial intelligence, researchers develop models to estimate solar insolation for different time frames. Simplicity, acceptance, adaptability, and low computational cost are the advantages of empirical models [3]. Empirical models are usually based on astronomical, geometrical, physical, and meteorological factors [4]. The meteorological factors include the cloudiness, temperature, and sunshine that describe the condition of the sky. Of these, cloudiness is the most crucial factor for determining solar irradiance behavior. The databases record sunshine duration and temperature [5]; consequently, these variables are most widely used to estimate solar insolation [2]. The implementation of the empirical model depends on data availability and consistency [6]. Given this, the authors of [7] recommend revising the study site's data simultaneity and reliability. In this case, all the weather stations recorded temperature data. Therefore, temperature-based empirical models are a convenient option for estimating global solar insolation [8].  
In 1982, [PERSON] and [PERSON] presented the first temperature-based model for estimating solar insolation based on daily temperature differences [9]. In 1984, [PERSON] and [PERSON] proposed a temperature-based empirical model that inputs the difference between daily maximum and minimum temperatures [10]. In 2011, [PERSON] et al. evaluated the performance of the temperature-based models in China. They found that support vector machine models using maximum and minimum temperatures as input and polynomial kernel functions outperform empirical models [11]. In 2014, [PERSON] et al. presented a temperature-based model for China based on the [PERSON] and Samani model [12]. [PERSON] et al. evaluated temperature-based and sunshine-based empirical models in Ghana [13], and [PERSON] et al. assessed nine temperature-based models for Brazil [14]. In 2017, [PERSON] et al. validated [PERSON] and [PERSON]'s model for Mexico [15], while [PERSON] and [PERSON] compared empirical models for India's subtropical and humid environments [16]. Although several studies analyzed the empirical models' behavior in different places globally, there is no similar amount of research analyzing the efficacy of temperature-based models for tropical and mountainous environments.
# Industry 4.0 and Lean Six Sigma Integration: A Systematic Review of Barriers and Enablers
## 4 Review and Discussion
### Integration Enablers
**External stakeholders' support**--According to [74], the policymakers' role is crucial in moving from a traditional production model to an innovative and sustainable one. In addition, external organisations can facilitate the integration process through initiatives to provide financial support for technology acquisition [75], and governments can help by issuing laws that guarantee the protection and security of company data [76]. **Interconnected IT systems**--From the work in [23] investigating the integration of I4.0 with LSS in Italian manufacturing companies, two characteristics of the IT infrastructure can be inferred as crucial enablers: interoperability and flexibility. By interoperability, we mean the capacity of the IT systems in the company's value chain to exchange and share data [77], whereas flexibility means their capacity to adjust to changes in the environment [78]. In particular, we consider these two measures as part of the features of a highly connected IT environment. Furthermore, from an IT system design perspective, another factor that appears to favour integration is the availability of reference architectures for I4.0 [23; 50; 79]. Reference architecture models are crucial for successfully developing IT system architectures because they provide well-organised templates with standardised terminology [80]. In addition to the five enablers discussed above, our review identified the I4.0-enabling technologies studied in the context of integration with LSS tools and techniques. These technologies can also be considered enablers of integration. The identified technologies are listed in Table 4 according to the percentage of case study articles and surveys that mention them. Although beyond the scope of this study, it is worth noting that, in the broader field of factory digitalisation, the additive manufacturing technology cluster was mentioned in relation to LSS in 27% of the articles reviewed [40; 41; 42; 81].
## 4 Simulation and Discussion
### Algorithms Used for Comparisons and Benchmark Test Functions  
To evaluate the performance of the proposed DM, the following methodology is applied:  
1. [label=(0)]
2. Find in the literature five well-known optimization algorithms, such as: genetic algorithm (GA) [50], particle swarm optimization (PSO) [18], gravitational search algorithm (GSA) [53], teaching learning based optimization (TLBO) [29] and grey wolf optimizer (GWO) [32].
3. Modify the optimization algorithms implementing the proposed DM.  
Figure 2: Flowchart of implantation of the DM for modifying optimization algorithms.  
Define the set of twenty-three objective functions and divide it into three main categories: unimodal [53; 54], multimodal [31; 54], and fixed-dimension multimodal [54] functions (see Appendix A).
4. Implement the present work and the optimization algorithms in the same computational platform.
5. Compare the performance of the modified and the original optimization algorithms using the following metrics: the average and the standard deviation of the best obtained optimal solution till the last iteration is computed.
## 4 Performance Evaluation, Analysis and Discussion
To overcome this issue, all the WBASNs are configured at different communication channel, which means to cover multi-hop scenario, and the coordinator nodes of WBASNs operate on two channels i.e., one is for its internal network and other is for external communications (coordinator to central server). However, still there is some unwanted interference is observed through the received signal strength indicator (RSSI) which is due to human movement in the location as shown in Figure 10.3  
It is observed that there is some interference in the form of variations in RSSI values on a single channel due to human movement; however, it does not significantly affect the packet reception ratio (success [%]). Figure 10.4 Received packet analysis for individual with aggregation and DC. The coordinator nodes can communicate to each other as well as with the central node. Moreover, the coordinator node can be used as a data forwarding node towards the central server. Figure 21 shows the scenario where coordinators of each WBASN communicate with the central server. Figure 22 shows received packet analysis at the central server/coordinator. The inner circle represents the aggregation mechanism at Agg_4, whereas the outer circle represents Agg_2. WBASN1 and WBASN3 have more data to send. Due to aggregation mechanism at the MAC layer, they get more percentage as explained earlier. WBASN3 and WBASN4 send 1/4 th of data traffic as compared to WBASN1 and WBASN3. It is noticed that WBASN3 and WBASN4 packet reception is better than Agg_2 than Agg_4. The reason for this is their slow traffic generation rate.
## 3 Layout and More PEX and EM Simulation Results
### Comparison of PEX vs. Pre-PEX Simulated Results  
Here, we present additional PEX simulated data using three different extraction options: \(R\), \(R\) + \(C\) (\(RC\)), and \(R\) + \(C\) + \(CC\) (\(RCC\)). As shown in Figure 9, the performance of the LNA extracted with the \(RCC\) option exhibited the most significant degradation among the three PEX options, resulting in the poorest bandwidth, \(NF\), and gain. This degradation can be attributed to impedance mismatches and the introduction of parasitics from the extracted layout. In other words, while the pre-PEX simulations of the LNA were originally designed to closely match the optimal gain and noise performance, the addition of extra parasitic elements caused deviations in gain and noise matching, impacting the original source/load impedance and the interstage matching. From the PEX \(RCC\) simulations, the \(S_{21}\) 3-dB gain bandwidth ranges from 16.9 GHz to 41.8 GHz, yielding a fractional bandwidth (FBW, defined under Table 2) of 84.8%. The LNA remains unconditionally stable from these simulations (more stability analysis in Section 3.2).  
Figure 8: Layout of the broadband 2-stage LNA. The red box specifies the core area (0.24 mm\({}^{2}\)) of the amplifier.  
Figure 9: PEX (\(RCC\), \(R\)) vs. pre-PEX comparison in **(a)**\(NF\)**(**b)**\(S_{21}\)**(**c)**\(S_{11}\)**(**d)** comparison table (includes \(RC\)).  
The linearity of the LNA, as measured by the _IP1 dB_ and _IIP3_, is also compared using different PEX methods. In Figure 10, the PEX _RCC_ simulated _IP1 dB_ is observed to be at \(-19.4\) dBm, while the _IIP3_ is at \(-10\) dBm, both simulated at 28 GHz. _IIP3_ was determined through a 2-tone test with a 10 MHz frequency separation. It is worth noting that both _IP1 dB_ and _IIP3_ are extrapolated from a \(P_{in}\) (input power) of \(-60\) dBm, which falls well within the small signal region, as shown in Figure 10. The PEX _RCC_ simulations demonstrate approximately 2 dB better performance in terms of _IP1 dB_ and _IIP3_ compared to the pre-PEX simulations. This improvement is likely due to the approximately 2 dB lower gain, which suggests that the _OP1 dB_ (output-referred 1 dB compression point) and _OIP3_ (output-referred third-order intercept point) are being compressed at nearly the same levels across all cases.
## 4 Discussion  
### S. _indica_ Possesses a Viridiflorol _Synthase_  
The in vitro and in vivo characterization assays showed that SiTPS accepts \(E\),_E_-FPP to produce a mix of sesquiterpenoids with the main product identified as viridiflorol. Viridiflorol is a rare sesquiterpenoid with a \(7/5/3\) tricyclic scaffold belonging to the group of arondamentales, which are terpenids with a characteristic skeleton of a dimethyl cyclopropane ring fused to a hydroazulene ring system [49], usually used in fragrance production [50].  
During sesquiterpenoid synthesis, FPP cyclization can occur either with the closure of \(E\),_E_-FPP or its isomer, (3_R_)-NPP. A mechanism for the formation of viridiflorol was previously proposed, based on quantum chemical calculations, starting with the cyclization of \(E\),_E_-FPP by a ring closure of C1 and C10 [51]. Phylogenetic analysis showing that SiTPS belongs to Clade I (Figure 2), together with enzymes that perform 1,10 cyclization of \(E\),_E_-FPP, supports that activity of SiTPS proceeds through route a (Figure 5). On the contrary, another viridiflorol synthase isolated from the black poplar mushroom, _Cycloybe aegita_ (syn. _Agrocybe aegeira_) [14], named AaVS, was placed in Clade II, together with enzymes that perform 1,10 cyclization of (3R)-NPP, indicating that biosynthesis of viridiflorol can actually occur through both routes (Figure 5).  
Vriridiflorol has also been detected in extracts of many plant species belonging to the families Myrtaceae [52] and Lamiaceae [53,54], but until now only two viridiflorol synthases of plant origin have been identified [52]. The specific compound has also been encountered as a minor product in fungal extracts [55,56] and after AaVS [14], SiTPS is the second fungal enzyme identified and experimentally characterized as a viridiflorol synthase.  
\begin{table}
\begin{tabular}{c c c} \hline _S. _indica_ **Strain** & **2 dpi** & **11 dpi** \\  & **DNAf/DNAp \(\pm\) SEM** & **DNAf/DNAp \(\pm\) SEM** \\ \hline wt & 0.0044 \(\pm\) 0.0003 & 0.0324 \(\pm\) 0.0026 \\ evV & 0.0052 \(\pm\) 0.0001 & 0.0468 \(\pm\) 0.0102 \\ ovII & 0.0054 \(\pm\) 0.0008 & 0.0301 \(\pm\) 0.0040 \\ \hline \end{tabular}
\end{table}
Table 1: Colonization of tomato roots by _S. indica_ wild type (wt), the empty vector-carrying (evV) and the _SiTPS_-overexpressing (ovII) mutants. The colonization ability of each strain was estimated using the ratio of fungal DNA (DNAf) to plant DNA (DNAp) at 2 and 11 days post inoculation (dpi). No statistical differences were observed in between the different treatments (one-way ANOVA in R-studio). Standard error of the mean (SEM) is also presented (_n_ = 3).  
Figure 6: Microtiter plate wells containing PDB supplemented with viridiflorol, methanol or hygromycin and inoculated with 4000 spores of _C. truncatum_. Each treatment was performed in four replicates.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 2 State-of-the-Art and Related Works
### LPWAN Technologies for Smart Agriculture and Wildlife Monitoring
A lower coding rate results in greater robustness at the expense of increased transmission time and high energy consumption. The resulting bit rate equation is given by \[B=SF*(4/(4+CR_{i}))/(2^{SF}/BW)(bits/second)\] (1) where \(i\) takes on values between 1 to 4 in increment steps of 1. The noise floor is given by \[Noise=-174+10*log_{10}(B)\] (2) For a channel bandwidth of 125 KHz and \(SF=7\), this translates to a figure of \(-123\) dBm. Table 1 presents the LoRa main configuration parameters. It is worth noting that different combinations of the aforementioned transmission parameters yield different trade-offs with respect to the range and data rate that can be achieved, and these combinations are also governed by regulatory constraints. A LoRa frame starts with a preamble, mainly used for synchronization purposes, then followed by an explicit header, a payload, and cyclic redundancy check (CRC), as shown in Figure 2. LoRa frame format can be either implicit or explicit, where explicit packet includes a short header containing information about the bytes, CRC and coding rate used in the frame. LoRa MAC layer is basically an ALOHA protocol, controlled by the network server. Significantly, the gateway can receive from multiple end devices simultaneously. [PERSON] (2018) pointed out that a LoRa gateway can simultaneously receive data by 9 devices, due to the orthogonality of transmission sub-bands and quasi-orthogonality of the spreading factors. Moreover, the end devices have to respect the regulatory restrictions with a duty cycle of less than e.g., 1% in each of the European (EU) 868 MHz bands.
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
###### Abstract  
Accurate precipitation data is crucial in many applications such as hydrology, meteorology, and ecology. Compared with ground observations, satellite-based precipitation estimates can provide much more spatial information to characterize precipitation. In this study, the satellite-based precipitation products of Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were firstly evaluated over the Tibetan Plateau (TP) in 2015 against ground observations at both annual and monthly scales. Secondly, random forest algorithm was used to obtain the annual downscaled results (~1 km) based on IMERG and TMPA data and the downscaled results were examined against rain gauge data. Thirdly, a disaggregation algorithm was used to obtain the monthly downscaled results based on those at annual scale. The results indicated that (1) IMERG performed better than TMPA at both annual and monthly scales; (2) IMERG had few anomalies while TMPA displayed significant numbers of outliers in central and western parts of the TP; (3) random forest was a promising algorithm in acquiring high resolution precipitation data with improved accuracy; (4) the downscaled results based on IMERG had better performances than those based on TMPA.  
precipitation; IMERG; TMPA; random forest; downscaling; Tibetan Plateau 11