esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### EVI Trends  
Deciduous-broadleaved forest patches (DBF, Figures 2 and 3) showed mostly positive trends for both EVI\({}_{a}\) (563 out of 569 total patches) and EVI\({}_{m}\) (544 patches) with \(3.5\pm 0.065\times 10^{-3}\) and \(3.4\pm 0.096\times 10^{-3}\) yr\({}^{-1}\) as slopes, respectively. For those patches with the slopes significantly greater than 0 or significantly lower than 0 at \(p<0.05\) (one-tail test), the average slopes are \(3.97\pm 0.062\times 10^{-3}\) and \(4.8\pm 0.116\times 10^{-3}\) yr\({}^{-1}\) for EVI\({}_{a}\) (461 patches) and EVI\({}_{m}\) (298 patches), respectively. Grassland patches showed great spatial heterogeneity in EVI trends (GRASS, Figures 2 and 3), that is, increasing trends of both EVI\({}_{a}\) and EVI\({}_{m}\) in the east, but decreasing in the west. There are 220 patches (out of 311 total patches) showing a positive trend in EVI\({}_{a}\) and 192 patches showing positive trend in EVI\({}_{m}\). As to the significant trends of EVI\({}_{a}\), the averages are \(4.2\pm 0.13\times 10^{-3}\) and \(-4.8\pm 0.29\times 10^{-3}\) yr\({}^{-1}\) for positive significant trends (102 patches, significantly greater than 0 at \(p<0.05\)) in the east and negative significant trends (39 patches, significantly lower than 0 at \(p<0.05\)) in the west, respectively. The histograms of significant slopes detected as greater than 0 or lower than 0 at \(p<0.025\) (one-tail test) are shown in Appendix A Figure A1 for reference.  
Figure 3: Average and stand error of slopes of mean and maximum EVI during May–September (**a**), and slopes of climate variables in wet-year (prior October to this September), growing-season, and nongrowing-season precipitation (**b**), minimum temperature (**c**), and maximum temperature (**d**) in 2001–2015 for grassland (GRASS) and deciduous-broadleaved forest (DBF) patches. EVI\({}_{a}\) and EVI\({}_{m}\) indicate averaged and maximum EVI during growing season, respectively; \(P\), \(T_{\rm min}\), \(T_{\rm max}\) indicate precipitation, minimum, and maximum temperatures, respectively; and y, g, and n indicate wet-year, growing-season, and nongrowing-season, respectively. Summary of all trends is displayed on the left side of the dashed line and summary over the significant trends, i.e., significantly lower than 0 or significantly greater than 0 (\(p\)-value \(<\) 0.05, one-tail test), is shown on the right side of the dashed lines.  
Figure 2: Trends of averaged (**a**) and maximum (**b**) enhanced vegetation index (EVI) (yr\({}^{-1}\)) during growing season, May–September, in 2001–2015 for grassland (GRASS) and deciduous-broadleaved forest (DBF) patches. EVI\({}_{a}\) and EVI\({}_{m}\) indicate averaged and maximum EVI during growing season, respectively; right columns are histograms in blue (all trends) and red (significant trends) bars, and red bars on the left side of the dashed line indicate those trends significantly lower than 0 while those on the right side of the dashed line indicate the trends significantly greater than 0 (\(p\)-value \(<\) 0.05, one-tail test).
## 2 Materials and Methods
In the experiments, an industrial-grade diode-pumped solid-state UV laser (Baltic HP, Ekspla, Vilnius, Lithuania) with a pulse duration of ~10 ns emitting at a wavelength of 355 nm was used. The laser provided light pulses with pulse energy up to 50 \(\upmu\)J at a repetition rate of 20 kHz with an average laser power of 1 W. The laser power was controlled using an external attenuator, which consisted of a polarizing beam splitter cube, a half-wave phase plate, and the beam trap. The beam was expanded using a telescope composed of concave and convex lenses. The beam expander was used to vary the beam diameter on the objective lens and adjust the spot's size on the sample. The beam position on the sample was controlled via a galvanometer scanner (Scancube 10, Scanlab, Puchheim, Germany). The f-theta objective lens with a focal length of 174 mm was used to focus the beam on the surface of the target material. The radius of the transverse focal spot on the sample's surface was measured using the technique described in [23], which was \(w_{0}\approx 20\)\(\upmu\)m.  
The diameters \(D\) of the laser-ablated areas were measured with an optical microscope. The laser fluence sufficient to ablate the material for a fixed number of laser pulses per spot \(N\) is denoted as the ablation threshold fluence \(F_{\text{th}}(N)\). Assuming that a laser beam has the Gaussian spatial beam profile, the relationship between the diameter of the crater \(D\) and the peak laser fluence \(F_{0}=2E_{p}/\pi w_{0}{}^{2}\) in the center of the Gaussian beam can be written as follows [23]:  
\[D^{2}=2w_{0}^{2}\ln\left(\frac{F_{0}}{F_{\text{th}}(N)}\right), \tag{1}\]where \(w_{0}\) is the radius of the Gaussian beam on the sample, and \(E_{\rm p}\) is the pulse energy. The incubation model describes the relationship between the single-pulse ablation threshold \(F_{\rm th}(1)\) and the multi-pulse ablation threshold \(F_{\rm th}(N)\) in the following form [24]:  
\[F_{\rm th}(N)=F_{\rm th}(1)\cdot N^{\xi-1} \tag{2}\]  
Here, \(\xi\) is a material-dependent incubation parameter. \(\xi\) = 1 means that no incubation effect is observable.
## 3 Results
### Phenology  
Seasonal metrics derived from the MODIS NDVI values indicate gradual variations in the vegetative phenology throughout the 2001-2024 study period (Figure 10). However, none of the trails indicate a statistically significant trend toward advancement or delay in either the start-of-season, end-of-season, or peak productivity metrics at the broad scale of this analysis. Temporal analysis of the annual snowmelt conditions indicates snow is melting around 0.65 days earlier per year on the PCT and around 0.72 days earlier per year on the AT.  
The MODIS NDVI boxplots show strong seasonal signatures across all three trails, with increased NDVI values during summer months (Figure 11). The AT shows the highest NDVI values, with the summer median NDVI often exceeding 0.7. In contrast, the PCT is located in a much drier climate and the seasonal growth shows less of a pronounced effect. The CDT includes vast expanses of desert and the median NDVI values are much lower in comparison.  
Figure 10: MODIS NDVI time series plots showing the start of season (SOS), peak of season (POS), and end of season (EOS) for the (**a**) PCT, (**b**) CDT, and (**c**) AT.
###### Abstract
LoRa and Sigfox are excellent networks when any kind of low-data-rate applications are envisaged since they carry flexibility coupled with a small form factor and long battery life. Deployment as carried by any telecommunication service provider (TSP) will require the control of the IoT server or application server for the generation of downlink control packets to streamline the number of messages being transmitted by each device; this will reduce the number of collisions by updating each device with current network situation awareness. In this paper, we consider dense deployment of LoRa and Sigfox for smart city applications where millions of such devices will be contesting to transmit within a limited BW. This enables understanding the scalability of LoRa and Sigfox to calculate collisions. Our motivation is the study of LoRa and Sigfox to evaluate LoRa link behaviour with multiple  
Figure 1: Deployment Architecture of LoRa and Sigfox. SF and fixed bandwidth of 250 KHz and 100 Hz for LoRa and Sigfox, respectively. We practically examine the link behaviour of single LoRa and Sigfox nodes through outdoor experimentation and then use the same data for traffic generators of Sigfox and LoRa to simulate collisions and successful transmissions. We also calculate the number of collisions as we increase/decrease SF; we also use PER and delivery ratio to obtain the number of devices that can coexist in an outdoor environment with fixed transmissions and BW. In the end, we simulate the effects of random access, re-transmissions, path loss, and time on air (ToA) while using the propagation model with urban area settings.
## 1 Introduction
[22] designed a PVC pipe test system to study the effect of buoyancy on the buckling behavior of PVC liners by applying hydrostatic pressure to simulate grouting pressure. Results showed that the presence of buoyancy reduced the critical flexural strength of the liner. In the existing studies, only the initial gap formed by the partial detachment of the liner from the host pipe is considered, ignoring the effect of the annular gap on the buckling behavior of the elliptical liner under loosely fitted conditions and the change of the contact conditions between the liner and host pipe under the action of water pressure, resulting in a specification deviation of the critical buckling pressure calculated by the prediction model in ASTM F1216, which reduces the reliability of the liner designs. This study designed an experimental system to investigate the buckling behavior of the loosely fitted FIPP liner under external pressure. The deformation mode of the buckling lobes can be directly captured by the camera and the critical buckling pressure could be measured by pressure transducer when the liner had a buckling failure. The geometric parameters of the liner were indicated as the dimension ratio and ratio of liner major axis to host pipe radius and ovality, and the 2D finite element models were set to analyze the effect of different geometric parameters of the liners. This study aimed at obtaining the different buckling behaviors of the FIPP liner by changing different geometric parameters and analyzing the sensitivity of different parameters on critical buckling pressure. The results of the study were used to improve the accuracy of the critical buckling pressure prediction model in ASTM F1216 for the design of the FIPP liner by modifying the enhancement factor.
## 3 Results and Discussion
### HUASMC Migration  
It is well known that the proliferation and migration of SMCs on the biomaterials surface could lead to pathological growth of the endothelial cell and further stent restenosis [47]. Therefore, SMC migration tests _in vitro_ can be performed as an evaluation of the anti-hyperplasia of the biomaterials. Figure 8 presents the fluorescence images of SMCs' migration on each sample (Cu0, Cu1, Cu2, Cu3 and Cu4). The statistical results show that there are a large number of cultured SMCs migrated on the surface of the no-donor group Cu0, and which had a long migration distance of \(1175\pm 25\)\(\mu\)m. The doped Cu had a small inhibitory effect on the SMCs' migration, but this effect was not remarkable in the fluorescence images. Notably, the donor group possessed a significant inhibitory effect on the SMCs' migration, and this effect was positively correlated with the amounts of the doped Cu. Accordingly, the Cu4 had the strongest effect in demonstrating SMCs' migration, in which the SMCs only migrated \(545\pm 15\)\(\mu\)m. This result was consistent with the NO release result and the SMC proliferation result. Thus, the results proved that the Cu doped TiO\({}_{2}\) films with suitable SNAP plays an important role in regulating not only SMCs' proliferation but also their migration.  
Figure 7: Investigation of SMC number on the Cu0, Cu1, Cu2, Cu3 and Cu4 surfaces _via_ CCK-8 assay after cultures of one day and three days, respectively (mean \(\pm\) SD, * \(p<0.05\) compared with Cu0, \(\&\)\(p<0.05\) compared with no-donor samples, \(n=3\)).
## 3 Results and Discussion
### Overview of Decision Rules Obtained with DRSA
The positive prediction of the DRSA model is based on the following pair of rules:  
* if MW \(\leq\) 266.2 and SLogP \(\leq\) 3.7 and nHBAcc \(\leq\) 2, then _drug_;
* if MW \(\leq\) 255.1 and nHBAcc \(\leq\) 2, then _drug_. A non-drug example, properly classified by the DRSA model, is supported by the following three rules:  
* if 398.2 \(\leq\) MW \(\leq\) 406.1 and SLogP \(\leq\) 3.7 and nHBAcc \(\leq\) 4 and nHBDon \(\leq\) 3.0, then _non-drug_;
* if 403.2 \(\leq\) MW \(\leq\) 461.1 and SLogP \(\leq\) 3.7 and nHBAcc \(\leq\) 3, then _non-drug_;
* if 379.2 \(\leq\) MW \(\leq\) 408.2 and 3 \(\leq\) SLogP \(\leq\) 3.3 and nHBAcc \(\leq\) 3, then _non-drug_. Table 4 shows the descriptors, structures, and predictions delivered by the DRSA, Lipinski, and Ghose models for both structures (upper and middle parts). In the case of the non-drug example, only DRSA is successful. The [PERSON] and Ghose approaches are too liberal to exclude this compound from the set of potential drugs. \begin{table}  
\end{table} 
Table 3: The strongest rules induced for both classes. Nonetheless, the characteristics of a compound may also align with the rules supporting conflicting assignments. For illustrative purposes, let us consider a molecule presented at the bottom of Table 4 consistent with the following three rules:  
* if MW \(\leq\) 346.1 and nHBAcc \(\geq\) 7 and nHBDon \(=\) 0, then _drug_;
* if MW \(\leq\) 331.1 and SLogP \(\geq\) 1.3 and nHBAcc \(\geq\) 7, then _drug_;
* if MW \(\leq\) 399.1 and 2.9 \(\leq\) SLogP \(\leq\) 3.2 and nHBAcc \(\geq\) 6, then _non-drug_. The rules covering the alternative are used for computing the scores reflecting the support for assigning a compound to each class. The score is calculated by dividing the squared number of examples from the given class covered by these rules by the product of the total number of examples covered by these rules and the size of the given class in the reference set \(A^{R}\)[38]. For the considered compound, the two positive rules jointly cover 9 drugs out of 1767 in the training set. The non-drug rule covers 19 compounds out of 1748 non-drugs. Thus, the score for the class \"drug\" is equal to \(\frac{9^{2}}{9*1767}=0.0051\), and for the class \"non-drug\"\(-\frac{19^{2}}{19*1748}=0.011\). Finally, we recommend the class with the highest score. Therefore, the considered molecule is classified as a non-drug, and again, only the DRSA model predicts the non-drug nature of this compound properly.
## 4 The Example of the Green Stones
### Classical Gemological and Mineralogical Properties
RI = Refractive Index; SG = Specific Gravity; AGG = Aggregate Reaction; DR = Doubly Refractive; STP = Semitransparent; TL = Translucent; STL = Semitranslucent; O = Opaque; LW = Long Wave (365 nm); SW = Short Wave (254 nm). In Table 3, the main mineralogical properties of the phases possibly found in Green Stones are reported. It must be noted that not only silicate minerals but also oxide minerals have to be considered for Green Stones. Moreover, regarding the silicates, all the families are represented. As discussed in depth in the nomenclature section, correctly identifying the green stone phase represents a crucial point for gemology practice and cultural heritage studies. Thus, considering the heterogeneity and complexity of the materials, for their identification, a mineralogical characterization provides extremely suitable support, as attested by many archaeometric applications. Moreover, by the characterization of the phase, several information can be obtained, which could help, for example, in discussions about origins, colors factors, and mechanical properties considerations. Despite all of this variability, observing the data reported in Table 3, several similarities can also be highlighted. Indeed, considering the pyroxene members, jadeite, kosmochlor, omphacite, and diopside are all minerals showing chemical and crystallographic affinities. In some cases, they can be found as components of metamorphic rocks, except for diopside, which can also be found as a component in some magmatic rocks (i.e., alkali olivine basalts). It is interesting to note that pure jadeite is a colorless mineral: only when a small amount of chromium (Cr\({}^{3+}\)) goes inside the crystal structure as a substituting element of aluminium (Al\({}^{3+}\)), jadeite can show its vivid \"Imperial Green\" color (Figure 2a), so called in modern and ancient trades. However, green Cr-rich jadeite does not become a kosmochlor only as a consequence of the substitutions. Indeed, the crystallization of a phase despite another one is not only a matter of chemical availability but also of the pressure and temperature conditions involved in the process. Jadeite genesis is such an example: almost all the known paragenesis involves the albite-jadeite association. If the metamorphic grade increases, the jadeite component decreases, giving way to omphacite
## 3 Mobile Communications Background
### Evolution of Mobile Technology
#### 3.1.1 First Generation (1G)  
During the 1970s and 1980s, the first generation of mobile networks (1G), which used only analog technology and functioned similarly to a landline phone, was introduced. Despite serving its primary purpose of voice communication, 1G technology had several drawbacks. Coverage and sound quality were poor. It provided no roaming between operators and no system compatibility. Worse, call encryption was not possible with 1G, so security was very poor, and any eavesdropper could easily access the message in transmission [42].
## 2 The Bioloid System
### Programming Platform  
The Bioloid kit includes a CD-ROM with its programming platform (RoboPlus), which is based on C-programming and consists of three main software utilities:  
1. RoboPlus Task: This utility is used for the development of \"task code\" (e.g., programming code) for management and control of the Bioloid's hardware resources. Sequences of motions can be coordinated by specific task codes, and complex functions or actions can be accomplished by the coordination of task codes.
2. RoboPlus Manager: This utility is used for the maintenance of the Bioloid's hardware components. Hence, major functions of this utility are updating and restoration of the controller's firmware and testing of the controller and peripheral devices (servomechanisms, sensors).
3. RoboPlus Motion: This utility is used for the development of \"step\" motions for the Bioloid. These motions then can be managed by task code.  
While the RoboPlus software enables the user to develop applications quickly for demonstration purposes, it presents some limitations for the proposed transcription system:  
1. It only allows the development of standalone applications.
2. There is no direct integration with other programming languages.
3. Motions created with RoboPlus Motion can only be accessed via RoboPlus Task code.
4. The creation scheme for motions, which is based on \"steps\", is suitable for the realization of medium-to-large straight or curved trajectories, such as those described in [28]. However, this scheme is not suitable for the realization of fine movements or trajectories required to draw small and curved lowercase alphabet characters.  
MATLAB programming was considered as an alternative for this work. In order to have control over the Bioloid's Dynamixel servomechanisms (and implement the kinematic models), the toolbox developed by the Incognite research group [29] was used. Particularly, the following functions of the toolbox were considered: _setSerialPort_ for enabling the connection of the Bioloid to the PC and MATLAB, _setMovingSpeed_ for setting the speed of each Dynamixel and _setGoalPosition_ for setting the position of each Dynamixel (position ranges from 0-1024). More details about the use of these functions for the transcription task are presented in Section 3.2.  
Figure 1: Dynamixel array (dyn1–8, dyn11–18) of the B-Type humanoid Bioloid.
## 3 Results
### Physicochemical Characteristics of Functional Katik Domokou-Type Cheese  
The physicochemical parameters ranged in usual levels, as published previously (pH 4.3-4.5, moisture 75%) [10; 27], and the results are presented in Table 3. In cheese samples fortified with free or immobilized _P. acidilactici_ ORE5 cells, the pH ranged in lower levels (_p_ < 0.05) (4.08-4.15), while lactic acid concentrations were higher (_p_ < 0.05) (0.11-0.15 g lactic acid/100 g cheese) compared to the control (pH 4.28-4.33, 0.85-0.95 g lactic acid/100 g cheese). After 7 days of storage, a significant (_p_ < 0.05) increase in pH values was observed, whereas acidity was decreased (_p_ < 0.05) in all samples. The addition of free or immobilized cells had no effect on water activity levels, ranging 0.88-0.92. The moisture content ranged at usual levels (72.7-75.5%) in KC and KFP samples. However, in the KIP sample, lower values were observed initially (63.9-65.71%), but increased to that similar to the KC and KFP sample levels after 2 days of storage. After 7 days of storage, a slight increase in moisture content was observed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Agent** & _P. acidilactici_ **ORE 5** & **Cut-Off a**  \\ \hline \multicolumn{4}{c}{**(MIC \(\mu\)g/mL)**} \\ \hline Amoxycillin & 2.41 \(\pm\) 0.95 & n.r. b  \\ Amoxycillin + Clavulanic acid & 0.25 \(\pm\) 0.19 & n.r. b  \\ Ampicillin & 1.28 \(\pm\) 0.58 & 4 \\ Clindamycin & 1.91 \(\pm\) 0.27 & 1 \\ Erythromycin & 1.77 \(\pm\) 0.19 & 1 \\ Gentamycin & 7.23 \(\pm\) 2.08 & 16 \\ Metronidazoleazole & 159.2 \(\pm\) 27.8 & n.r. b  \\ Tetracycline & 10.97 \(\pm\) 1.48 & 8 \\ Tigecycline & 0.49 \(\pm\) 0.08 & n.r. b  \\ Vancomycin & \multicolumn{2}{c}{\(>\)256} & n.r. b  \\ \hline \hline \end{tabular}
\end{table}
Table 2: MIC (\(\mu\)g/mL) of antibiotics for _P. acidilactici_ ORE5 as determined by gradient diffusion using M.I.C. Evaluator® strips.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Cheese 1** & **Days of Storage** & **Water Activity (av)** & **pH** & **Acidity \& Lactic Acid/100 g Cheese)** & **\% Moisture Content** & **Overall Sensory Evaluation 2** \\ \hline \multirow{6}{*}{KC} & 0 & 0.899 \(\pm\) 0.01 & 4.28 \(\pm\) 0.05 & 0.085 \(\pm\) 0.02 & 72.
## 3 Results
### Lattice Structure Design and Part Printing  
Initially, it is worth noting that the lattice geometries are designed using rectangular cross-section beams to eliminate the need for supports during printing. This is achieved by ensuring that the beam inclinations do not fall below 45\({}^{\circ}\). Given that the structure comprises beams as small as 400 to 500 microns in width, it would have been impossible to remove any support material without causing damage to the structure.  
These structures are generated by repeating a single unit cell a consistent number of times along all three spatial dimensions. Specifically, in this study, the unit cell is replicated five times in each direction, resulting in a total of 125 cells. The base lattice structure for case 1 measures 20 \(\times\) 20 \(\times\) 20 mm and consists of beams with a cross-sectional area of 0.4 \(\times\) 0.4 mm. Variations of this geometry are created by applying different aspect ratios, specifically 1:1.25:1, 1:1.25:2, and 1:1.25:5.  
The lattice structures are printed on a solid base that is approximately 3.5 mm thick, which is directly attached to the metal printing platform to mitigate any thermal deformation during the printing process; see Figure 6a. This support prevents warping and ensures the structural integrity of the printed components. After fabrication, the printed pieces are detached from the base using wire EDM, leaving a residual solid base of 2.00 mm beneath the lattice geometries.  
Figure 5: Thermal properties of Inconel 718: (**a**) specific heat, (**b**) thermal diffusivity, and (**c**) density.  
In Figure 5(a), the manufactured lattice structures inside the RenAM 400 PBF-LB machine are shown. For each case, two geometries are manufactured, and, once the repeatability of the machine is ensured, a single lattice for each case is analyzed, as shown in Figure 5(b).