esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 NSAS Effectiveness for Underground Structures
### Measurement Definition
Figure 8: Cross-section of Daikai station (unit: mm). Widely accepted as representative of the ground motion (Figure 10a) at Daikai station [40], the north-south component of ground motion recorded at Port Island station during the 1995 Great Hanshin earthquake was used as the seismic input in this study. For this horizontal ground motion with a maximal value of 0.58 g, the strong motion lasted for about 10 to 15 s, and gradually decreased afterward. According to the design code [41], the seismic excitation in the vertical direction can be obtained by modulating the horizontal-acceleration amplitude of the recorded wave. Given the target that deals with the seismic performance of a horizontal isolator, the vertical input was simplified by following the code, and the maximal value was scaled as 0.16 g [10]. The ground motion (see Figure 10b) of the Great Hanshin earthquake recorded at the HIK station was selected for validation analysis. Before investigating the seismic performance of the underground structure controlled with the NSAS or CIS, nonlinear dynamic time-series analyses were performed for the original uncontrolled underground structure as a reference. As summarized, the maximal displacement, shear force and axial force of the central column without any seismic-vibration control systems were 38 mm, 767 kN and 4887 kN, respectively. Consistent with the results given by [PERSON] et al. [37], [PERSON] et al. [40] and [PERSON] et al. [10], excessive seismic responses and failure of the central column caused the collapse of the Daikai subway station. Therefore, the NSAS and CIS were selected to be inserted between the top of the central column and the bottom of the upper slab to provide satisfactory performance for the central column.
# Comparative Transcriptomics for Pepper (_Capsicum annuum_ L.) under Cold Stress and after Rewarming
## 4 Discussion
### TFs Regulate the Cold Resistance of Pepper  
Transcription regulation is a key step in plant response to abiotic stress by regulating their target genes of transcription [70]. TFs can improve the stress tolerance of plants by regulating downstream genes. Most TFs related to cold tolerance in pepper have been reported, including ERF, bHLH, MYB, WRKY, NAC, GRAS, etc. [71; 72; 73; 74]. [PERSON] et al.'s [75] results suggested that _SINAC1_ functions as a stress-responsive NAC protein involved in the abscisic acid-dependent signaling pathway and may have potential applications in transgenic breeding to enhance crops' cold stress tolerances. In Sweet Osmanthus, the expression patterns of most branch '5B' members, especially OfNAC49 and 59, were significantly induced by cold stress, indicating that branch '5B' members could have important roles in cold tolerance in \(O\). _fragrans_[76]. CaNAC064-silenced pepper plants exhibited more serious wilting, higher MDA contents, and chilling injury index in the leaves after cold stress. The CaNAC064-overexpressing Arabidopsis plants exhibited lower MDA content and chilling injury index compared with WT plants under cold stress [77]. In this study, the expression levels of 13 NAC transcription factors increased significantly in T4 group; meanwhile, the _NAC8_ (Capana02g003557) and _NAC72_ (Capana07g002219) in CT were significantly higher than that in CS under T4 group. These results may indicate that NAC plays an important role in regulating the recovery of pepper after cold stress, and the expression levels of _NAC8_ and _NAC72_ determine its recovery ability.
## 4 PVT Results
### Fine-Tuning of the WE Function
#### 4.2.2 Foliage Scenario  
Let us analyze the Foliage scenario for which we have the results shown in Figure 15 and Table 2. In this case, the configuration with an exclusion threshold of \(-1\) dB also provides the best results. The best averaging configuration, though, is the one with an averaging time of \(5\) s. These results are corroborated with Figure 15b, in which we see that the best results in terms of RTK fixed mode are given with \(-1\) dB threshold, for any averaging time greater than \(0\) s. Notwithstanding, we see in Table 2 that the percentage of corrected RTK fixes mode for the reference file (i.e., \(29.57\%\)) is greater than for any other configuration.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{3}{*}{**Configuration**} & \multirow{3}{*}{**Total**} & \multirow{3}{*}{**RTK\_Fixes**} & \multicolumn{3}{c}{**RTK Correct Fixes**} & \multirow{3}{*}{**RTK\_Float**} \\  & & & & \multicolumn{3}{c}{**Error \(>10\) cm)**} & \\ \hline  & **Error (m)** & **Error (cm)** & **\%** & **Error (cm)** & **\%** & **Error (m)** & **\%** \\ \hline Reference & 1.5 & 5.9 & 32.8 & 5.0 & 29.6 & 2.1 & 63.7 \\ Avg5 + Th \(=-1\) & 1.5 & 18.9 & 27.9 & 5.1 & 19.6 & 1.9 & 67.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Foliage scenario: PVT results for the best configuration in terms of exclusion threshold and average configuration (i.e., Avg5 and Threshold \(=-1\)).  
Figure 14: Graphical results of the PVT error when applying (blue curves) or not (red curves) 2 pol exclusion in the urban environment.  
One interesting thing for this scenario can be observed in Figure 15. We see that the configuration with an exclusion threshold of \(-1\) dB, for an averaging time greater than 0 s, also gives the best results in terms of RMS 3D error. This error even gets smaller than the reference one for the configuration with an averaging time of 5 s. In this scenario, the RTK float error is also reduced but not in the same percentage as in the urban scenario. The reason is that in the foliage scenario the reduction in percentage of RTK fixes is not directly translated into an increment of the percentage of RTK float. That is, from the 10% reduction of RTK fixes, only the 4% is translated into float mode; the other 6% moves to code-based positioning.  
The reason for the improvement on the RTK float mode is the same as for the urban scenario. The reduction of the RTK fixes here is due to the fact that when excluding satellites we loose redundancy to fix the phase ambiguities, thus reducing the percentage of RTK fixes. Moreover, in the foliage scenario, the remaining satellites after the exclusion are still affected by multipath, thus worsening the performance to fix the phase ambiguities.
## 3 Wireless Body Area Network (WBAN)
### Pulse oximeters
Airplanes need such a system because passengers with chronic obstructive pulmonary disease (COPD) are at risk from the low pressure, low oxygen levels, and low humidity that can occur within the cabin during the flight. Therefore, it is essential to monitor their health and recognize any irregularities promptly so that they can receive appropriate care.
# Stand Structure Impacts on Forest Modelling
## 2 Factors Driving to Stand Structure Dynamics
This results in growth reduction and also variation in tree allometry, such as the reduction of diameter growth and of crown dimensions [9; 25; 30]. Tree spatial and temporal arrangements, including tree species proportions, create specific complementarity and competition patterns, which influence tree allometry. Regular, wider spacing enables free growth, with trees expressing their genetic traits. Irregular, denser spacing results in the increase of competition and in the variability of tree dimensions. At irregular spacing, the crowns might tend to be less symmetrical, their length depending on the available light and their width on the aerial available space [31; 32]. Several silvicultural practices influence tree allometry as well. Pruning reduces the crown length and to a smaller extent of crown width. During thinnings, tree selection plays a key role both at tree level, allocating more growing space to the most desirable individuals, and at stand level, with the optimisation of the growth and yield [1; 4; 33]. Thinning increases growing space of the remaining stand and allows the increase of stem and crown diameter and, consequently, the variation of the trees' allometric patterns. The method of thinning influences the trees to be removed from the stands. While in some methods the trees with the smaller diameters are removed (thinning from below), others select to remove the larger diameters (compensation thinning and thinning from above), and in others still, the selection is made in all the diameter range (selective or Schadelin and free thinning). Thinning can influence spatial arrangement, with some tending to a regular horizontal spacing (mechanical thinning) while others promote both horizontal and vertical irregularity (free and variable density thinning). The increase of the intensity of thinning tends to increase growing space and thus growth. The variation of tree allometry after thinning is due to the increase of diameter at breast height (larger annual growth rings), the reduction of the _hd_ ratio (due to larger increments of diameter in relation to height), the increase of crown diameter and length (due to the increase of growing space, in particular light), and increase or reduction of crown eccentricity and stem inclination (when the constellation of neighbours is regular) (for more details see [34]). Additionally, fertilisation, increasing growing space, increases volume growth [35]. In short, tree allometry is highly dependent on stand structure and silvicultural practices. Overall, it can be said that tree allometric relations are variable in time and space due to a large set of factors. The incorporation of the variability of tree allometry is of the utmost importance to improving model accuracy and precision.
## 4 Experiments and Results
### Ablation Experiments
#### 4.5.3 Comparative Experiment SCER Module of SCENet with Shuffle Block of Shufflenet V2  
The SCER module and ShuffleNet V2 are indeed somewhat similar, but there are still differences in their structures.  
To demonstrate that the SCER module does indeed perform better than the Shuffle block in the SCENet network, we conducted two sets of experiments. From Figure 10 and Table 5, it can be seen that the WT, TC, ET, and average dice results of the SCER module are 91.15%, 86.40%, 82.17%, and 86.57%, respectively, which, in terms of WT, TC, ET, and average dice, are higher than the shuffle block of 90.73%, 84.99%, 82.39%, and 86.04%, respectively. This result shows that our module outperforms the shuffle block in brain tumor segmentation tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Expt**} & \multirow{2}{*}{**Conv Num**} & \multirow{2}{*}{
\begin{tabular}{c} **Kernel** \\ **Size** \\ \end{tabular} } & \multicolumn{4}{c}{**Dice (\%)**} & \multirow{2}{*}{**FLOPs**} & \multirow{2}{*}{**Parameter**} \\ \cline{3-3} \cline{5-8}  & & **WT** & & & & & \\ \hline A & 1 & 7 \(\times\) 7 \(\times\) 7 & 91.06 & 85.56 & **82.36** & 86.33 & 2763.476 G & 228.368 M \\ B & 2 & 3 \(\times\) 3 \(\times\) 3 & 90.59 & 85.22 & 81.48 & 85.76 & 1410.731 G & 114.840 M \\ C (SCENet) & 3 & 3 \(\times\) 3 \(\times\) 3 & **91.15** & **86.40** & 82.17 & **86.57** & **1537.095 G** & **125.446 M** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of ablation experiments for the number of stacking convolution layers and kernel size in the SCER module.  
Figure 9: The results of ablation experiments for the number of stacking convolution layers and kernel size in the SCER module.
## 3 Control Methods
### Docking Method  
#### 3.3.1 Relative Positioning  
The relative positioning method assumes that the later robot module and the front robot are on the same plane without rolling. The aim of docking is to align the later one with the front one and park them at a certain distance away from each other. The measured feedback variables are distances and angles between two robots as defined in Figure 9b.  
* \(x\) is the lateral distance.
* \(d\) is the longitudinal displacement.
* \(\theta\) is the difference in the heading angle of two robots.  
In different distances between two robots, the precision and perception ranges of relative positioning are different.  
* In the far range (\(d>50\) cm), the color marker of the passive docking panel is recognized by the camera. It has a wide detection field and an easy-to-process algorithm. The color marker indicates that this may be a robot without an ID. The lightness, green value, and red values in LAB color space are used for target segmentation and the markers' geometric center in pixel units is the target position. The binary image with a white block indicating the color marker is shown in Figure 9a.
* In the middle range ( \(50\) cm \(\geq d\geq 10\) cm), the encoded marker (Apriltag) is used to guide docking. The visual processing board can generate the coordinate and bounding-box information (see Figure 9b) of the Apriltag and transfer them to \((x,d,\theta)\). Although the recognition range is minimized, the precise coordinate is given and the robot is identified by its ID.
* In the near range (\(d<10\) cm), the color and Apriltag marker pass beyond the field of view of the camera. The infrared sensors are used to guide the robot based on signal strength, expressed as \(d\), and the weighted average of the lateral distances of three infrared receivers, expressed as \(\theta\).
# Pollen Food Allergy Syndrome in Southern European Adults: Patterns and Insights
## 3 Results
### Allergens Causing PFAS in South Europe
#### 3.4.4 Non-Specific Lipid Transfer Proteins (nsLTPs)  
Non-specific lipid transfer proteins are small proteins (with a molecular weight of 9 kDa) that perform various functions in plants, including as the inhibition of plant pathogens, seed development, and germination [59]. Their highly conserved conformational structure, a basic isoelectric point (pl) between 9 and 11, and high stability against denaturant agents, heat, and proteases are characteristics useful for their functions in plants but are also associated with the clinical symptoms that they cause.  
Sensitization to nsLTPs can lead to clinical symptoms ranging from localized manifestations of PFAS to anaphylaxis. Sensitization typically occurs through the ingestion of foods containing these proteins (e.g., peach, walnut, hazelnut, apple) [60], although sensitization via inhalation is also possible in some cases [61; 62].NsLTPs are significantly more resistant to heat and gastric enzymes than profilins and PR-10s, which explains why LTPs are responsible for most cases of systemic reactions following sensitization via the gastrointestinal tract [61; 62].  
They are considered one of the causes of PFAS in Southern Europe and the Mediterranean area. Allergens of the nsLTP family are found in the pollen of _Parietaria_, _Artemisia_, _Platanus_, and _Olea_ but show limited (_Artemisia_ and _Platanus_) or absent (_Parietaria_ and _Olea_) cross-reactivity with Pru p 3 due to lower sequence identity (<35%) and difference in length [63]. Many patients experience food allergy symptoms after ingestion of the skin of fruits of the _Rosaceae_ family (Mal d 3-apple, Pru p 3-peach) but also with peanut, soybean,walnut, and wheat [20]. It has been proposed that the severity of nsLTP-related allergic reactions is related to concomitant allergies to three or more plant food groups [64].
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 4 Experimental and Results Analysis
### Results Analysis
#### 4.2.2 Comparison Experiments  
To validate the performance of YOLO-SE, this paper conducted comparative experiments with a variety of classical algorithm models, including YOLOv3-Tiny [27], Faster RCNN [10], YOLOv5, YOLOv7 [46], YOLOX [47], YOLOv8, YOLO-DA [48], and YOLO-HR [21].  
The experimental results are presented in Table 4. The Fast R-CNN model has the highest parameter count, yet its AP50 is relatively low compared to that of other models. Subsequent YOLO models have not only increased AP50 but also reduced the model's parameter count, highlighting the excellence of YOLO models. YOLO-SE consistently outperforms the other algorithm models in terms of detection accuracy; the AP50 reached 86.5%, outperforming the state-of-the-art model YOLO-HR by 0.91%. Additionally, YOLO-SE exhibits a lower parameter count, further demonstrating the superiority of the algorithm.  
Figure 12: Confusion matrix on SIMD dataset.  
The comparison results between YOLOv8 and YOLO-SE on the SIMD dataset are illustrated in Figure 13. From the figure, it is evident that YOLO-SE performs well in focusing on small targets when there are large numbers of objects in an image. Additionally, when an image contains multi-scale targets, YOLO-SE effectively handles targets of various sizes. This comparison demonstrates that YOLO-SE outperforms the YOLOv8 algorithm in terms of detection effectiveness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **AP50 (\%)** & **Map (\%)** & **Params (M)** \\ \hline Faster RCNN & 77.7 & - & 41.2 \\ YOLOv3-Tiny & 77.2 & 54.5 & 8.68 \\ YOLOv5s & 83.85 & 66.0 & 6.72 \\ YOLOv7s & 83.80 & 66.5 & 8.92 \\ YOLOv8- & 76.6 & 56.8 & 8.94 \\ YOLO-DA & 80.6 & - & - \\ YOLO-HR & 85.59 & 65.0 & 13.2 \\ YOLOv8s & 84.4 & 68.8 & 11.2 \\ YOLO-SE & 86.5 & 70.7 & 13.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with other algorithms.  
Figure 13: The comparison of detection results between YOLOv8 and YOLO-SE on the SIMD dataset.
## 4 Traditional MPPT Techniques
### Constant Voltage Algorithm  
The Constant Voltage (CV) MPPT algorithm is the simplest approach used to maximize the power output of PV systems by maintaining the system voltage at a fixed value close to the MPP. This fixed voltage is determined based on the characteristics of the PV modules  
Figure 6: Flowchart of the IC MPPT.  
and is typically set to a value where the modules are expected to deliver optimal power under standard conditions [48]. Figure 7 illustrates the flowchart of the CV MPPT where the PV module voltage is measured and compared with the voltage of the module at the MPP (\(V_{MPP}\)), any error detected is used to adjust the module voltage accordingly using a constant (C). The main advantage of the CV algorithm is its simplicity, which makes it cost-effective and suitable for systems with less demanding performance requirements. However, its primary disadvantage is that it lacks adaptability to varying environmental conditions such as changes in irradiance and temperature, which can lead to suboptimal power extraction compared to more dynamic MPPT algorithms like P&O or IC. Consequently, while CV can be effective in stable environments, it may not perform as well in situations with significant fluctuations in sunlight or temperature [49].
## 1 Introduction
### Maritime Emissions and Their Effects
#### 1.1.5 Human Populations in Italian Port and Coastal Areas  
There are about 5 million people living in the cities of the 33 ports in the Italian port system, with three of the cities (Genoa, Napoli and Palermo) accounting for about 2 million. The photo below of Genoa (Figure 1) illustrates the close proximity of large residential areas to port operations. Further, as is often the case, ports have coastal hills that constrain emissions' more widespread dispersal and thus routinely increase the level of local pollutants. Genoa is one of Italy's largest ports in terms of local population, as Table 5 indicates more precisely [56].  
Exposure to maritime emissions is not limited to ports, of course; it also includes coastal residents, and tourists, within the dispersion ranges of the emissions [57]. Of Italy's coastal population, a third live within only 5 km of a coast, slightly more than half live within 15 km, and more than 90% live within 50 km.  
Figure 1: Aerial View of the Port of Genoa.  
In addition to the major metropolitan areas of northern Italy, the concentrations of people in the coastal areas are evident. These coastal concentrations include, of course, many port cities, for instance, Napoli on the west coast, Cagliari on the south coast of Sardinia, Palermo on the north coast of Sicily, Bari and Brindisi on the east coast, and Venice in the northeast. In sum, millions of Italians live in port cities and nearby coasts. These millions are obviously the most vulnerable to the detrimental health effects of ship's emissions. In addition, many millions of tourists experience short-term exposure as coastal visitors. Although their exposure to air pollutants is much shorter, compared with year-round residents, it is nevertheless true that even relatively short-term exposures to black carbon, in particular, have been found to be damaging to lungs.  
\begin{table}
\begin{tabular}{c c} \hline _Core Network Ports (15)_/ & **Population-2020** \\
**Other Network Ports in the Regional Authority (18)** & **(000)** \\ \hline _Genoa_ & 574 \\ Savona & 60 \\ Valdo Ligure & a \\ _La Spezia_ & 93 \\ Marina di Carrara & a \\ \hline _Livorno_ & 157 \\ Piombino & a \\ \hline _Civitavecchia_ & 53 \\ Gaeta & a \\ \hline _Napoli_ & 959 \\ Salerno & 133 \\ Castellamare di Stabia & 65 \\ \hline Gioia Tauro & a \\ Messina & 230 \\ \hline _Cagliari_ & 153 \\ Arbatax-Tottoli & a \\ Golfo Aranci & a \\ Olbia & 61 \\ Oristano & a \\ Torres Castellamare di Stabia & 65 \\ \hline _Palermo_ & 658 \\ \hline Augusta & a \\ Catania & 311 \\ \hline Bari & 322 \\ Brindisi & 86 \\ Manfredonia & 56 \\ \hline _Taranto_ & 195 \\ \hline _Ancona_ & 100 \\ Pesaro & 95 \\ Pescara & 120 \\ \hline _Ravenna_ & 158 \\ \hline _Venezia_ & 259 \\ \hline _Trieste_ & 203 \\ \hline _Subtotal: Core Network Ports_ & 3884 \\ \hline Subtotal: Other Network Ports & 1052 \\ \hline Total & 4936 \\ \hline \end{tabular} a Some port populations are not in the original source.  
\end{table}
Table 5: Human populations in Italy’s Ports.