esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond Mitigation: New Metrics for Space Sustainability Assessment +
## 3 Proposed Approach Towards the Definition of a Space Sustainability Index
### BW/FW Compatibility Efficiency  
The degree of BW and FW compatibility is measured by:  
\[e_{bf}=0.5\eta_{BW}+0.5\eta_{FW} \tag{8}\]  
The parameter \(\eta_{BW}\) is related to the backward compatibility efficiency, i.e., a value of \(\eta_{BW}=1\) would imply that the mission or service of the system under design can be carried out without the launch of any additional hardware; on the other hand, \(\eta_{BW}=0\) is an indicator of the fact the mission or service can not exploit any backward compatibility with currently operating systems. The parameter \(\eta_{FW}\) is a measure of forward compatibility efficiency, i.e., \(\eta_{FW}=1\) would indicate that all future missions or services could take advantage of the system under design; \(\eta_{FW}=0\) represents the fact that no future mission could exploit resources from this system.  
Although this component of the \(SSI\), closely related to the concept of sustainability by design, represents a crucial element to identify design choices that could contribute to a more sustainable constellation/mission, the exact expression for \(e_{bf}\) has not been derived in this work. A more comprehensive understanding of the key technological solutions required to ensure BW/FW compatibility is necessary and will be addressed in future research.
# Climate Perspectives in the Intra-Americas Seas
## 3 Regional Climate Features  
### Whwp  
SST gradients between the Caribbean and the eastern tropical Pacific play a role in rainfall distribution [81]. The regional SST is characterised by the WHWP, a warm pool defined as the area enclosed by the 28.5 \({}^{\circ}\)C isotherm that exhibits a marked seasonal cycle [9]. Its role in regional climate is strongly linked to heat balance [82]. The evolution of WHWP components, namely the eastern Pacific (EPWP) and Atlantic (AWP), may be considered independent as they respond to different mechanisms, with the former being characterised by its subsurface oceanic dynamics [83]. The EPWP develops earlier than the AWP, and its onset is seasonally synced with coastal shoaling that marks the development of the Costa Rica Dome (CRD) [84] after March. There is an amplification of its spatial extension across the Central American Pacific coast as far as 120 W on average, until August [85]. In addition to enclosing the biologically important CRD, the EPWP modulates Central American rainfall, triggering deep convection. It is connected with the ITCZ and further activation of intense deep convection [86] as well as cyclone development [87]. Its Atlantic counterpart starts its onset in June and continues expansion to the southern Caribbean by September, with a peak between July and September. The development of the AWP is mainly constrained by boundary layer coupling and modulated by air-sea heat fluxes [88]. Warm waters enclosed by the AWP provide a major part of the moisture transported within the region, playing a relevant role in rainfall contribution [79; 89]. The AWP acts as a moisture supplier to the NAM in connection with the bridge established between the CLLJ and the Great Plains low-level jet [90]. The AWP is important in terms of its regional dynamics beyond its role as a moisture source, as regional rainfall is dependent on the AWP extension. Anomalous extensions of the AWP are associated with a milder MSD, while a smaller AWP enhances the MSD, increasing drier conditions over the Central American Pacific coast [91]. The AWP influences the cyclone development season [92], and the enlargement of the AWP corresponds to a reduced vertical wind shear, which allows an intensification of Atlantic hurricane activity. The activity of both components of the WHWP are responsive to interannual variability [93]. Temperature changes in the WHWP area impose a modulation in the WHWP dynamics, impacting on rainfall. Under a warming climate, temperature changes are predicted to cause variations in warm pool extensions and trigger variations in the rainfall distribution within the IAS domain.
# A Miniaturized Circularly-Polarized Antenna for In-Body Wireless Communications
## 2 Antenna Design and Simulation
### Simulated Results  
Figure 3 illustrates the simulated reflection coefficient together with the axial ratio (AR) of the proposed antenna in main radiation direction towards the outside of human body. The simulated  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Tissues** & **Thickness (mm)** & \(\varepsilon_{r}\) & \(\sigma\) **(S/m)** \\ \hline Skin & 4 & 38.0 & 1.46 \\ Fat & 4 & 5.28 & 0.1 \\ Muscle & 50 & 52.7 & 1.74 \\ \hline \end{tabular}
\end{table}
Table 1: Dielectric properties of different tissues at 2.45 GHz.  
Figure 2: Geometry of the proposed antenna at: (**a**) Top view; and (**b**) side view.  
impedance bandwidth is covered from 2.39 GHz to 2.57 GHz with S11 less than \(-10\) dB while the AR bandwidth can be extended from 2.39 GHz to 2.48 GHz with AR below 3 dB.  
Figure 4 depicts the simulated far-field gain radiation patterns of the proposed antenna in two principal planes (i.e., \(xy\)-plane and \(xz\)-plane) at 2.45 GHz. Its maximum left-handed circular polarization (LHCP) radiation is towards the antenna's boresight at theta = 0\({}^{\circ}\), that is the off-body direction as desired. Due to the fact that the proposed antenna is very compact and implanted in the lossy tissue with the limited space, different from the conventional antenna operating in free space, the peak realized gain is \(-24.8\) dBi at 2.45 GHz.
## 3 Results
### Devices Faturing Pt-ALD Dielectric-Pt Junctions
#### 3.1.1 Initial Experiments: SETs with H\({}_{2}\)O-Based ALD of the Tunnel Barrier
A Coulomb diamond plot or \"charge stability diagram\" [10] of a typical Pt-Al\({}_{2}\)O\({}_{3}\)-Pt device fabricated with nine cycles of ALD Al\({}_{2}\)O\({}_{3}\) (\(\sim\)1 nm) is shown in Figure 3a. One immediately noticeable difference between this device (with Pt-Al\({}_{2}\)O\({}_{3}\)-Pt junctions) and the one reported in [15] (with Pt-Al\({}_{2}\)O\({}_{3}\)-Ti-Pt junctions) is that the value of the zero-bias differential conductance measured in a peak of the CBOs is much closer to the expected value \(G(V_{ds}=0\) mV) \(\approx G_{0}/2\) (Figure 3b). Further analysis of the plot by fitting it to the orthodox model [16; 20] enables estimation of the junction parameters (\(C_{5}=240\) aF; \(G_{S}=35\) aS; \(C_{d}=160\) aF; \(G_{d}=45\) aS; \(C_{g}=32\) aF (the value of gate capacitance can be extracted from the period of CBOs, \(C_{g}=e/dV_{g}\), where dV\({}_{g}\) is a voltage difference between adjacent CBO peaks) and charging energy \(E_{C}=0.185\) meV. The total capacitance (\(C_{\sum}=432\) aF) is lower than expected from the parallel plate model estimate (\(\sim\)700-900 aF) potentially due to the low dielectric constant of very thin Al\({}_{2}\)O\({}_{3}\) films compared to the bulk value [21]. Unexpectedly, the differential conductance of the devices steadily increased over the course of several months, approaching values expected for shorted metal structures with no dielectric barriers (\(\sim\)1 mS). This clearly indicates the instability of the Al\({}_{2}\)O\({}_{3}\) tunnel barrier on a Pt substrate using TMA and H\({}_{2}\)O precursors. We will discuss a probable cause for this below. Another metal studied in combination with H\({}_{2}\)O-based ALD Al\({}_{2}\)O\({}_{3}\) was Pd. However, devices fabricated with Pd as a metal onto which ALD Al\({}_{2}\)O\({}_{3}\) was deposited exhibited a much broader spread of junction resistances compared to Pt-based devices, possibly due to greater surface roughness of the Pd film compared to the Pt film, as shown by atomic force microscopy measurements [22].
# Comparison of Amsler-Krumeich and Sandali Classifications for Staging Eyes with Keratoconus
## 2 Materials and Methods  
The study followed the tenets of the 2013 Declaration of Helsinki and was approved by the local Ethics Committee (Comitato Etico Regione Calabria). Consecutive patients affected by KC who attended the cornea clinic for a routine examination from September 2015 to November 2020 were enrolled. Informed consent was obtained from all patients before data analysis.  
Keratocnus was clinically defined as the presence of signs including stromal thinning, Vogt striae, Fleischer rings, and topographic findings consistent with keratocnus. Eyes with a history of keratoplasty were excluded. For patients who underwent more than one visit during the study period, the first examination was used for statistical purposes.  
Each patient underwent complete ophthalmological examination including visual acuity testing and slit lamp examination; a Casia 1 (Tomey Corp., Nagoya, Japan) was used to perform topographical maps for the calculation of keratometric (K mean) and pachymetric values as well as AS-OCT scans for the detection of corneal layer abnormalities. Data were collected by the same examiner (GG) and also independently analyzed by another operator (GM). All keratoconic eyes of patients enrolled were classified according to the Amsler-Krumeich and Sandali classifications (Table 1).  
\begin{table}
\begin{tabular}{l l} \hline  
All data collected from the study were entered into an electronic database using the Excel 2007 software (Microsoft, Redmond, Washington, DC, USA). Data were expressed as mean \(\pm\) standard deviation (SD) for continuous variables and as individual counts and percentages for categorical variables. The percentage of eyes belonging to each stage of the two classifications as well as the percentage of patients for whom the classifications differ for one or more stages were calculated. When comparing the two classification systems, for statistical purposes, the [PERSON]'s stages 2A-B were considered stage 2, the Sandali's stages 3A-B were considered stage 3, and the Sandali's stages 4, 5A-B were considered stage 4. The percentage of patients undergoing keratoplasty for each stage of both classifications were also calculated.
## 4 Discussion on Noise Effects, Model Selection, and Feature Sensitivity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \multicolumn{2}{c}{**PCA**} & \multicolumn{3}{c}{**AE**} \\ \hline  & & \multicolumn{2}{c}{**Predicted**} & \multicolumn{3}{c}{**Predicted**} \\ \hline  & & Healthy & Damaged & Total & Healthy & Damaged & Total \\ \hline  & Healthy & 70\% & 30\% & 40 & 75\% & 25\% & 40 \\  & Damage Case 1 & 35\% & 65\% & 20 & 5\% & 95\% & 20 \\  & Damage Case 2 & 20\% & 80\% & 20 & 5\% & 95\% & 20 \\  & Damage Case 3 & 100\% & 0\% & 20 & 5\% & 95\% & 20 \\  & Damage Case 4 & 65\% & 35\% & 20 & 5\% & 95\% & 20 \\  & Damage Case 5 & 50\% & 50\% & 20 & 45\% & 55\% & 20 \\  & Damage Case 6 & 75\% & 25\% & 20 & 5\% & 95\% & 20 \\  & Damage Case 7 & 60\% & 40\% & 20 & 15\% & 85\% & 20 \\  & Damage Case 8 & 10\% & 90\% & 20 & 5\% & 95\% & 20 \\  & Damage Case 9 & 5\% & 95\% & 20 & 0\% & 100\% & 20 \\ \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for the numerical study (noise level = 10%): PCA versus AE with OC-SVM. \begin{table}
\begin{tabular}{c c c c c} \hline  & & \multicolumn{2}{c}{**iForest**} & \multicolumn{2}{c}{**OC-SVM**} \\  & & \multicolumn{2}{c}{**Predicted**} & \multicolumn{2}{c}{**Predicted**} \\ \hline  & & Healthy & Damaged & Healthy & Damaged \\ \hline  & Healthy & 95\% & 5\% & 75\% & 25\% \\  & Damage Case 1 & 5\% & 95\% & 5\% & 95\% \\  & Damage Case 2 & 10\% & 90\% & 5\% & 95\% \\  & Damage Case 3 & 30\% & 70\% & 5\% & 95\% \\  & Damage Case 4 & 55\% & 45\% & 5\% & 95\% \\  & Damage Case 5 & 75\% & 25\% & 45\% & 55\% \\  & Damage Case 6 & 75\% & 25\% & 5\% & 95\% \\  & Damage Case 7 & 75\% & 25\% & 15\% & 85\% \\  & Damage Case 8 & 55\% & 45\% & 5\% & 95\% \\  & Damage Case 9 & 0\% & 100\% & 0\% & 100\% \\ \hline \end{tabular}
\end{table} 
Table 5: Detection performance of AE: iForest versus OC-SVM (SNR = 10%). Furthermore, the potential of utilizing latent space features was explored as an alternative indicator sensitive to damage. Figure 13 presents a comparison of the first two latent space features extracted from the training and test data for the selected TFs under varying noise levels. Interestingly, the clustering behavior of the latent features was inconsistent across different TFs. For some TFs, a distinct clustering between damaged and undamaged cases was observed even at higher noise levels, while for others, clustering was more prominent at lower noise levels. Increasing the number of neurons or hidden layers does not necessarily lead to improved results. This suggests that the latent space features' sensitivity to damage may vary depending on the TF, and noise may have a no linear interaction with these features, potentially affecting their robustness. Such inconsistencies require further investigation to better understand the underlying mechanisms that govern the relationship between noise, damage sensitive features, and damage detection performance.
## 2 Transcriptional Regulation in Integrons
### Transcription of Gene Cassettes Independent of Pc and Cassette-Borne Promoters  
#### 2.4.1 Insertion Sequences Elements  
Promoter sequences other than Pc and cassette-borne promoters could also have a relevant role in the expression of promoterless gene cassettes, which also breaks the principle of the cassette position effect. Such promoters are most commonly supplied by the insertion of mobile genetic elements. Several insertion sequences (ISs), such as _IS1999_, _ISEp1_, _ISKpn23_ carry complete and functional outward-oriented promoters (P\({}_{\text{OUT}}\)) that enhance the expression of downstream antibiotic resistance genes and ensure the emergence of resistance phenotype (Figure 4) [77; 78]. Otherwise, some ISs, such as ISI, IS257, and _ISAb125_ supply the -35 hexamer, generating a hybrid active promoter when adjacent to a downstream -10 element with proper spacing [79; 80; 81]. It was the case of the aminoglycososide resistance _aphA-6_ gene, which was expressed from a promoter composed by a -35 hexamer located in the 3\({}^{\prime}\) end _ISAb125_ and a -10 hexamer in the _aphA_-6 flanking sequence [81].  
The transposable elements called Insertion Sequence Common Regions (ISCRs) belong to the IS91-like family elements that act by a mechanism of rolling-circle transposition. The ISCRs code for a putative transposase followed by a recombination crossover site (RCS), and they have been involved with the acquisition and mobilization of antibiotic resistance genes [82; 83; 84]. Among the ISCRs, the ISCR1 is always associated with atypical class 1 integrins, named complex integrons, which present partial duplications of the 3\({}^{\prime}\)CS, separated by ISCR1 associated with antibiotic resistance genes. Several studies experimentally verified that ISCR1 contained two active outward-oriented promoters (P\({}_{\text{CRI-1}}\) and P\({}_{\text{CRI-2}}\)) contributing to the expression of numerous unrelated antibiotic resistance genes found in complex class 1 integrins from different bacterial species, such as the _qnrA_, _dfrA10_, _dfrA19_, _bla_CTX-M-2, and _bla_CTX-M-9 [85; 86; 87; 88]. However, in spite of the role of ISCR1 on the expression and manifestation of antibiotic resistance, it had been verified that the relative transcriptional strengths of P\({}_{\text{CRI-1}}\) and P\({}_{\text{CRI-2}}\) were considerably lower than those observed for other IS elements. A previous study verified that the promoters of ISA_Ab1_, _ISEp1_, and _ISAb125_ presented similar strengths, which was intermediate between those of the variantsPcS and PcW\({}_{\text{TGN-10}}\) found in class 1 integrins, but were 30-fold stronger than those from ISCRI [89].
# An Experimental Investigation of Passive Jet Control Method on Bridge Tower Wake
## 1 Introduction
### Bridge Tower Model  
The bridge tower model was designed using the Xihoumen Bridge as prototype, which is a steel box girder suspension bridge with a main span of 1650 m, located in Zhejiang Province, China. The prototype bridge tower is an overhead gantry type, which can be simplified as two separate independent rectangle cross-section bridge towers when only the wake flow of tower is investigated. We investigate only one tower model, without considering the interaction of the wake of two towers. The cross-section of the model is a 283 mm (\(B\)) \(\times\) 217 mm (\(D\)) rectangle, with a side length of 23 mm square corner cut at each corner, and the scale ratio is set to 1:30, as shown in Figure 1. The data presented in this paper correspond to dimensionless distances of \(B\) = 283 mm and \(D\) = 217 mm in cross-wind and along-wind directions. The height of the column model is 2.9 m, and thus, the remaining 0.1 m to the ceiling can spare space for the clamp device. The model is assembled with thick planks, which provide enough stiffness to limit deformation. The surface of the model is flat and glossy by polishing. A jack system, mounted between the tower model and ceiling, firmly immobilizes the model. The tower model always maintains rigidity and keeps static during the experiment. At the bottom of the tower model, a 20 mm thick wooden turn plate is arranged to adjust the yaw angle to the incoming flow when the jack system is inoperative.  
The yaw angle of model \(\alpha\) is set from 0 degrees to 90 degrees, with an interval of 10\({}^{\circ}\). The blocking ratio of the model varies by the yaw angle, reaching a minimum of 5.4% when the yaw angle is 0\({}^{\circ}\) and a maximum of 8.9% when the yaw angle is 50\({}^{\circ}\).
## 3 Wiener-Granger Causality Analysis Theoretical Fundamentals
### Stochastic Autoregressive Model
From this equation, if the random processes \(C1(t)\) and \(C2(t)\) are statistically independent, then \(F_{\text{C1,C2}}=0\); otherwise there will be causality from one to the other. In the Equation (1), \(\Sigma^{1}_{\text{C1}}\) measures the precision of the auto-regressive model to predict \(C1(t)\), established in the passed samples. In turn, \(\Sigma^{2}_{\text{C1}}\) in the expression (3) measures the precision to predict \(C1(t)\) based on the previous values of \(C1(t)\) and \(C2(t)\) at the same time. Returning to the case of taking only 2 textures at the same time \(C1(t)\) and \(C2(t)\) and according to [11], if \(\Sigma^{2}_{\text{C2}}<\Sigma^{1}_{\text{C1}}\) then \(C2(t)\) is said to have a causal influence on \(C1(t)\). Causality is defined by Equation (7) as follows:  
\[F_{\text{C2}\rightarrow\text{C1}}=ln\left[\frac{\Sigma^{1}_{\text{C1}}}{ \Sigma^{2}_{\text{C1}}}\right] \tag{7}\]  
If \(F_{\text{C2}\rightarrow\text{C1}}=0\) then there is no causal influence of \(C2(t)\) towards \(C1(t)\), at any other value, the result will be nonzero. Also, the causal influence of \(C1(t)\) towards \(C2(t)\) is established by Equation (8):  
\[F_{\text{C1}\rightarrow\text{C2}}=ln\left[\frac{\Sigma^{1}_{\text{C2}}}{ \Sigma^{2}_{\text{C2}}}\right] \tag{8}\]
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation
### Robustness  
In this section, robustness of EDDS was evaluated through two different experiments with the variation of four different network sizes. In the first experiment, energy consumption, latency, and accuracy were measured for varying the query rate, while, in the second experiment, they were measured for four different data distributions.
## 2 Materials and Methods  
### Study Area  
This study was implemented in a planted area dominated by winter wheat, in Hengshui city of the southern Hebei province, China (Figure 1). It extends from 37\({}^{\circ}\)03\({}^{\prime}\) N to 38\({}^{\circ}\)23\({}^{\prime}\) N and 115\({}^{\circ}\)10\({}^{\prime}\) E to 116\({}^{\circ}\)34\({}^{\prime}\) E, covering 8815 km\({}^{2}\) in total and consisting of 11 counties. The study area is a temperate continental monsoon climate, with the average temperature ranging from 9 \({}^{\circ}\)C to 15 \({}^{\circ}\)C, and an average annual rainfall ranging from 400 mm to 800 mm. The crop rotation of winter wheat and summer maize is the main crop planting system in this area [27; 28]. Winter wheat is generally planted in October in the first year and matured in June in the second year. The key phenological stages of winter wheat in this area are the wintering stage before February, the green-up stage in early March, the jointing stage before early April, the heading stage from late April to early May, the grain filling stage before late May, and milk-maturity in early June [29].The climate is seasonally variable, with only 25% of the precipitation occurring from October to May during winter wheat growing season and about 75% occurring in June to September during the maize growing season (Data source: China Meteorological Administration, [[http://www.cma.gov.cn/](http://www.cma.gov.cn/)]([http://www.cma.gov.cn/](http://www.cma.gov.cn/))). Water is a major limiting factor for winter wheat growth in this area.
## 2 Results and Discussion
### Discussion of practical performance  
EpitopeMatch has been implemented in Java 1.6. Java is certainly not the preferred language for large scale computing tasks. Nevertheless, the performance in terms of computing time is satisfactory in all considered cases. As shown in the benchmark application, the algorithm can be easily parallelized  
Figure 9: Comparison of EpitopeMatch (black crosses) with Assam[9] (red crosses), with a PDB-wide search for epitopes similar to the maltose binding pocket in 1 ANF formed by 17 atoms. Along the horizontal axis the names of the PDB structures in which MCS have been identified are given. The vertical axis gives above the zero line the number of matched atoms, below zero the corresponding RMSD.  
with practically linear scaling at various levels. EpitopeMatch can be outperformed by specialized codes such as Dali if explicit use of higher level organization is made, such as secondary structure. On the other hand, such specialized codes typically cannot identify discontinuous epitopes, which is more the application intended for EpitopeMatch.  
EpitopeMatch has still many options for improvements towards more efficient use of time and memory. E.g. we have observed that relatively bad matches in the first iterations of the algorithm usually do not lead to good matches in later iterations. Therefore, in database searches, the efficiency can be increased further by the introduction of an early break of iterations if no cliques are found at low distance tolerances. In the current implementation of EpitopeMatch memory usage is critical for searches of large epitopes in large proteins of several thousand amino-acids, and we have experienced in such cases occasional memory overflows on machines with 4 GB RAM.  
In terms of effectivity, the algorithm performs well, identifying good matches in different application scenarios from whole molecule matching to the matching of epitopes with a considerable flexibility, e.g. due to induced fit. Effectivity can be influenced strongly by considering more than one center per residue. Our implementation allows this, as demonstrated in Figures 5 and 6. All these atoms are matched in parallel, and the CPU-time increases roughly linear with number of atoms per residue taken into account.  
We have already introduced some of the optimization measures to further increase effectivity and time efficiency, so that the code currently available from the authors does not only identify more matches but also is about three times faster than that used in the above benchmark.