esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Photosynthesis Was Not Boosted by LED Light Supplements  
In mint plants, monoterenes are biosynthesized from isopentenyl pyrophosphate and dimethylallyl pyrophosphate, which are provided by the plasidial methylerythribtol phosphate (MEP) pathway [27]. The initial compound of the MEP pathway is pyruvate acid, which is supplied from sugars as a photosynthetic by-product. Next, we confirmed whether the photosynthetic activity was the source of monoterenes biosynthesis under light supplementation. We measured chlorophyll contents in the two-week light-treated mint leaves and compared them among the different light wavelengths. Similar to GT density results, BL and FR treatments significantly increased chlorophyll but not anthocyanin (Figure 6A). Anthocyanin biosynthesis was induced by BL treatment to plants. In _Arabidopsis thaliana_, anthocyanin biosynthesis was stimulated by 2.5 W/m\({}^{2}\) of BL (approximately 11 \(\upmu\)mol/m\({}^{2}\)/s) [28], which is higher than the intensity we used in this study (6.7 \(\upmu\)mol/m\({}^{2}\)/s). We considered that the increase in the chlorophyll contents induced by either BL or FR supplements could help the biosynthesis of cyclic monoterenes in GTs. To check the contribution of chlorophyll to photosynthesis, we quantified the quantum yield of photosystem II based on chlorophyll fluorescence.  
Figure 5: The number of glandular trichomes generated on the third leaf under different light treatments for two weeks. (**A**) Quantification on the adaxial side in the three leaf regions (\(n=16\)). (**B**) Quantification on the abaxial side in the three leaf regions (\(n=16\)). The data were obtained from four plants in four independent experiments as biological replications. Error bars indicate standard deviations from the mean. Different alphabets indicate significant differences according to [PERSON]’s HSD test; \(p<0.05\).  
Contrary to our expectations, both \(\mathrm{Fv}/\mathrm{Fm}\) and the quantum yield showed no change with any wavelength of light treatment (Figure 6B). This suggests that the increase in cyclic monoterepenes shown in Figure 3 was not due to the promotion of photosynthesis by the light supplements. [PERSON] and [PERSON] reported no correlation between the chlorophyll content in spinach leaves and photosynthetic activity [29]. In _Arabidopsis_, red and far-red wavelengths supplemented with WL enhanced chlorophyll biosynthesis [30]. A BL supplement was also shown to increase the chlorophyll content in seven plant species [31]. In conclusion, chlorophyll generation observed in light-supplementation experiments might be the result of the transient activation of photoreceptors and does not influence photosynthesis and terpene biosynthesis.
## 3 Materials and Methods  
The methodology used in this research involved utilizing the U-Net framework for the semantic segmentation of aircraft satellite images. To further improve feature localization, we integrated skip connections into the U-Net architecture, allowing for the combination of deep and high-resolution features. Additionally, NVIDIA CUDA technology was utilized for GPU acceleration to expedite the training process and optimize the model. The recognition of aircraft was accomplished through a three-stage workflow consisting of data preprocessing, training, and testing phases. We used the OpenCV library and an alpha compositing algorithm to transparently overlay the color mask over the original grayscale image.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### LV and MV Monitoring and Management
#### 4.7.4 Discern  
LV and MV monitoring is also the survey topic of the \"DISCERN\" project, which involves participants from five countries. The project deals with MV network automation and monitoring as well as LV monitoring plus measurement solutions for AMR. The PLC technology is primarily used for smart metering purposes and especially the PRIME technology. Some end user smart meters are connected to concentrators via GSM, GPRS or 3G, particularly when few customers are connected to the LV side of the transformer. Several methods are available for communication between the data concentrator and the AMR system, like PLC, GSM, GPRS, UMTS or the LTE. SCADA technology is also employed, whereas the MV points communicate through GSM [121].
## 2 Materials and Methods
### RNA Sequencing and Data Analysis  
For the miRNA analysis, HFF cells were infected with HSV-1 and the total RNA was extracted at 8 h.p.i. and 18 h.p.i. Small RNA libraries were generated using the TrueSeq Small RNA Sample Preparation Kit (Illumina, Inc., San Diego, CA, USA) according to the manufacturer's protocol and sequenced by single read sequencing (50 nt) using an Illumina HiSeq 2500 sequencer (Institute Rudere Boskovic, Laboratory for Advanced Genomics, Zagreb, Croatia). The total number of aligned sequence reads (read count) for each host and viral miRNA was normalized using read counts for human let-7a, the expression of which does not change during HSV-1 infection, or to the size of the library (RPM value), as previously described [14]. The sequence reads were analyzed with sRNAtoolbox (Computational Epigenomics Lab, Evolutionary Genomics and Bioinformatics Group. Dept. of Genetics, Inst. of Biotechnology, University of Granada, Spain), a versatile compilation of software for miRNA analysis [31; 32], as previously described [20]. Briefly, microRNAs were mapped using Bowtie and miRbase sequence database (release 22) annotations [33]. Of note, the newly reported HSV-1 miRNAs, miR-H28 and miR-H29, were not represented within the miRbase but were included in the analysis. We looked for reads that did not contain mismatches in the first 19 nucleotides but accepted all sequences that started at most 3 nt upstream and ended at most 5 nt downstream of the reference sequence, and the minimum read length of the input reads was 15. All sequence reads detected at least once in any condition were used for differential expression.  
The transcriptome sequencing was performed using the TruSeq Stranded Total RNA Sample Preparation Guide (Illumina, Inc., San Diego, CA, USA), as previously described [34]. Briefly, samples were rRNA depleted and fragmented, primed for first- and second-strand cDNA synthesis, adenylated 3' ends, adapters were ligated, and DNA fragments were enriched. The libraries were validated using the Agilent Bioanalyzer and sequenced on a NextSeq 500 Illumina platform at the Rudjer Boskovic Institute, Zagreb, Croatia. The sequencing quality was assessed using FastQC (Version 0.11.9, Babraham Bioinformatics,Babraham Institute, Cambridge, UK), and reads were mapped to the reference HSV-1 genome, strain KOS (GenBank accession number JQ673480.1), and the Homo sapiens (human) genome assembly GRCh38 (hg38) from the Genome Reference Consortium using TopHat (2.1.1 release, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA). Mapped reads were then visualized using Integrative Genomics Viewer (IGV; 2.10. release, Broad Institute, Cambridge, MA, USA, and the Regents of the University of California, Oakland, CA, USA). Finally, Cufflinks (2.2.1 release, [PERSON]'s lab, University of Washington, Seattle, WA, USA) was used for differential expression and normalized expression levels were reported in FFKM (fragments per kilobase of exon per million fragments mapped).
# Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
## 4 Experiments  
In this section, we describe our experimental results. We have conducted extensive evaluation and comparison study against state-of-the-art algorithms including U2 Fusion [23],NestFuse [24] and DDcGAN [12]. We first conduct our experiments on the images taken from the RoadScene [23] and our private dataset. In the original patchGAN paper [17], they used an input image of size \(256\times 256\) and a patch size of \(70\times 70\). However, for our experiments, for the input image of \(512\times 256\), we have taken a patch size of \(65\times 65\) and a learning rate of 0.0001. We also change the values of \(\gamma\), but for the qualitative analysis both \(\lambda\) and \(\gamma\) are fixed at 0.5. For the quantitative comparison, we select 20 images with different conditions, including indoor and outdoor, and day and night. For the verification of our results, we choose six quality metrics; Correlation coefficient (CC) that measures the degree of linear correlation between the source images and the fused image, sum of correlation of differences (SCD) [33], \(FMI_{dct}\)[34] calculates the mutual information for discrete cosine feature, modified Structural Similarity (\(SSIM_{a}\)) and multi scale SSIM (MSSSIM) for no reference image which models the loss and distortion between two images according to their similarities in light, contrast and structure information and Peak signal-to-noise ratio (PSNR).
## 1 Introduction
Figure 1 illustrates the three distinct data routing methods. DERM enables each node to transmit packets to a dynamic region accessible to the sink within the delay constraint (called _destination region_) via the shortest path, and then the packets will be collected before their deadlines when the sink arrives. It can be observed that, compared to the other two schemes, DERM achieves a flexible balance between the data delivery delay and energy consumption. The design of DERM is nontrivial as it intrinsically contains three issues: (1) energy-optimal routing towards time-varying regions; (2) efficient sink location estimation; and (3) a reliable fault-tolerant routing mechanism for handling the location errors. Our major contributions are summarized as follows:  
1. To the best of our knowledge, DERM is the first work concerned about both the delivery delay and energy efficiency for data collection in WSNs with a path-fixed and strictly uncontrollable mobile sink. We design a location-based greedy forwarding algorithm for _energy-optimal routing towards dynamic destination regions_, and demonstrate that the right-hand rule can also be used for void handling in this new context after being slightly modified. 2. An _effective location calibration method_ is presented, which can be combined with the rough estimation based on the mobility pattern to determine the sink location. In this manner, the routing performance can be guaranteed with very low control overhead. 3. We propose an approach named _track routing_ to deal with the sink location errors caused by delayed calibration or unpredicted faults. It can guarantee reliable and on-time delivery in an energy-efficient manner, by adopting a \"greedily advance, discreetly step back\" strategy. 4. We verify the effectiveness of our proposed method by extensive experiments and comprehensive performance comparisons. Additionally, we present a delay-constrained rendezvous-based routing, providing a supplementary baseline. The remainder of this paper is organized as follows: Section 2 investigates the related work. Section 3 presents the network model and problem description. Section 4 elaborates the design of DERM. The performance evaluation is provided in Section 5. Finally, Section 6 concludes the paper.
# A Novel Hierarchical Multi-Stable Cylindrical Structure with Superior Energy Trapping
## 5 Results and Discussion
### Energy Trapping and Load-Bearing Behavior
The results show that the HMTSs achieve higher total trapped energy compared to the TMCSs. At \(Q=3.5\), the energy trapping ability of the HMCS increased by 485.84%, and its specific energy trapping ability increased by 225.95% compared to the TMCS. At \(Q=4\), the energy trapping ability showed an increase of 367%, while the specific energy trapping ability increased by 162.54%. At \(Q=4.5\), the energy trapping ability increased by 308.28%, and the specific energy trapping ability increased by 161.65%. At \(Q=5\), the increases were 284.73% and 156.65%, respectively. Therefore, the HMCSs demonstrate a combination of high energy trapping capacity, superior load-carrying capability, and lightweight structural advantages.
# SEIRD COVID-19 Formal Characterization and Model Comparison Validation
## 4 Python Codification and Parameter Fitting  
From these models, we obtain a third codification using Python. This is the codification we will use for the calibration and parameter fitting, and we will compare the results and the assumptions used with the previous two models. To do so, we will be focused on four variables for the model calibration using the observed data. Two refer to data quality and two to confinement quality:  
1. The time lag in the publication of the data.
2. The degree of underreporting in the numbers of newly infected.
3. The timing of the adoption of containment and confinement policies.
4. The containment factor (\(\rho\)) that quantifies the power of these measures.  
A fifth parameter is the transmission rate of the disease represented, as we will see later, by the beta coefficient in the system dynamics equations, see Equations (1)-(6).  
\[S^{\prime}(t)=-\frac{\beta S(t)I(t)}{N} \tag{1}\]  
\[E^{\prime}(t)=\frac{\beta S(t)I(t)}{N}-\alpha E \tag{2}\]  
Figure 8: _PSusceptible_ EMPTY state. No more susceptible individuals exist, hence the model finishes here.  
Figure 9: PROCEDURE _numSusceptible_ that calculates the number of _newExposed_ individuals.  
\[I^{\prime}(t)=\alpha E(t)-\gamma I(t) \tag{3}\]  
\[R^{\prime}(t)=\gamma(1-\mu)I(t) \tag{4}\]  
\[D^{\prime}(t)=\gamma\mu I(t) \tag{5}\]  
\[N=S+E+I+R+D \tag{6}\]  
The alpha (latency rate) and gamma (recovery rate) parameters will be considered constant, based on the measurements made by other studies and \(N\) will be the total population, assuming that there is no pre-existing level of immunization to SARS-Cov2 and that therefore the whole population is initially susceptible (\(S_{0}=N\)).  
The mean incubation period (1/\(\alpha\)) and the mean infectious period (1/\(\gamma\)) can be obtained by the inverse of the above parameters. The fatality rate (\(\mu\)) gives us the fraction of sick people who do not recover. Regarding the transmission rate, this could be represented as the product of the contact rate per day and infectivity to make more detailed modeling.  
Finally, we will use a simulated annealing algorithm called \"dual annealing\" included in the SciPy libraries to proceed with the multiparametric optimization by least-squares minimization. In addition, we will also be able to estimate a value for the effective (Ref) and the basic reproduction number (R0) from the containment factor (\(\rho\)), the transmission rate (\(\beta\)) and the recovery rate (\(\gamma\)) with the Equation (7). The basic reproduction number, R0, see [17], represents the average number of secondary cases that result from the introduction of a single infectious case in a totally susceptible population during the infectiousness period. In turn, the effective reproduction number, Reff, is the same concept but after applying the containment measures.  
\[R_{eff}=\rho R_{0}=\frac{\rho\beta}{\gamma} \tag{7}\]
## 4 Simulation Validation
### Validation 1: Feed-forward Control Algorithm  
The dynamic simulation was conducted using the NREL 5 MW model as the target wind turbine. The simulation conditions involved wind speeds higher than the rated wind speed (18 m/s) with a Class A standard turbulence model, and each condition was simulated for 600 s.  
The results of performance verification on the feed-forward control algorithm using estimated wind speeds according to the wind speed estimation methods are shown in Figure 7. The simulation results display rotor speed, pitch angle, generator torque, and output values, where the solid black line represents the baseline output control algorithm, and the blue and red solid lines depict the simulation results of the feed-forward control algorithm with different wind speed estimation methods. While operational performance appears similar, differences in rotor speed and output standard deviations can be observed. Table 3 presents the comparison results of the means and standard deviations of rotor speed and generator output and confirms that the application of the feed-forward control algorithm improved operational performance compared to the baseline output control algorithm. With the feed-forward control using the 3D look-up table method, the standard deviations of rotor speed and generator output decreased by approximately 6.04% and 8.70%, respectively. For the Kalman filter method, these reductions were approximately 2.05% and 1.93%, respectively. According to the simulation results, the feed-forward control using the 3D look-up table method demonstrated slightly superior performance, indicating that, although the feed-forward control algorithm was applied identically, differences in wind speed estimation methods resulted in varying operational performance of the wind turbine. Therefore, since the accuracy of wind speed estimation can affect the control performance of wind turbines, it is necessary to choose a more accurate or suitable method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{4}{c}{**Wind Speed [m/s, \%]**} \\ \cline{2-5}  & **Mean** & **Error** & **Std.** & **Error** \\ \hline Rotor Averaged & 17.80 & - & 1.94 & - \\ DTM & 18.26 & 2.58 & 2.05 & 5.67 \\ EKF & 18.29 & 2.75 & 2.01 & 3.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of rotor averaged wind speed and results of estimated wind speed.  
Figure 6: Rotor averaged wind speed and results of estimated wind speed.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 2 Related Works
### Hybrid Congestion Control  
The hybrid scheme involves both consumers and routers in network congestion control. At present, PCON [18] is a widely recognized congestion control mechanism in NDN. It detects congestion by measuring the Data queuing time and then signals this information to consumers by explicitly marking certain packets. This allows downstream routers to divert traffic to alternative paths and enables consumers to reduce their Interest sending rates. [PERSON] et al. proposed a method that includes Hop-by-hop Congestion Measurement (HbHCM) and Practical Active Queue Management (PAQM) [33]. This approach judges congestion based on RTT between adjacent routers and provides Explicit Congestion Notification (ECN) feedback to consumers. [PERSON] et al. proposed an NDN Congestion Control Based on Queue Size Feedback (NDN-QSF) [19]. In NDN-QSF, forwarders estimateupstream bandwidth to calculate the forwarding rate of Interests and use queue size as congestion feedback to inform downstream routers to limit the forwarding rate of Interests. [PERSON] et al. proposed a Cooperative Hybrid Congestion Control (CoopCon) scheme [20]. This scheme innovatively introduces four types of congestion markers. Depending on the specific type of congestion marker, consumers will adopt specialized methods to reduce the sending rate of Interests.  
The hybrid congestion control method utilizes the ECN technique to achieve more accurate congestion relief [34]. However, its performance is affected by threshold parameters that cannot adapt to different Data packets. For example, using PCON [35] to forward Data containing 200-byte content results in the loss of 544 packets within 40 s, as shown in Figure 2. This highlights the limitations of fixed thresholds in diverse networks.  
In summary, most existing methods limit the traffic of Data packets by restricting the transmission of Interests. However, these solutions have several drawbacks. Firstly, the varying sizes of Data packets make Interest-based regulation imprecise. Secondly, existing methods limit the overall network throughput to the bandwidth of the bottleneck link. Thirdly, long-distance transmission in LEO satellite networks can lead to delayed responses to congestion, resulting in packet loss. Additionally, prioritizing the forwarding of critical Data is crucial. However, existing approaches lack effective mechanisms for assigning priorities to Data packets, limiting their ability to meet diverse application requirements.  
The essence of congestion control is to regulate the traffic injected into the network. In this paper, we leverage the inherent Content Store (CS) in NDN and introduce a router-driven congestion control method. Each router manages the local scheduling of Data packet forwarding, enabling high-throughput and congestion-free forwarding within the satellite-based cloud platform.
# Backward Locomotion as a Novel Strategy for Enhancing Obesity Management
## 4 Discussion
The metabolic responses further highlight the distinct demands of backward locomotion. The significant direction effect on post-exercise lactate levels (F (1,26) = 20.41, \(p\) < 0.001, \(\eta^{2}\) = 0.43) reflects greater anaerobic energy contribution, which is consistent with studies reporting elevated lactate during backward locomotion in varied contexts, such as aquatic environments [24]. This can be linked to the lactate threshold model, where higher exercise intensity in backward locomotion pushes metabolic demand beyond aerobic capacity, particularly in obese individuals with lower cardiorespiratory fitness [25]. The absence of a significant direction effect on pre-exercise lactate (F (1, 26) = 2.38, \(p\) = 0.13, \(\eta^{2}\) = 0.08) confirms that the observed differences were exercise-induced, reinforcing the specificity of backward locomotion's metabolic impact. The perceptual responses, measured via Borg RPE (F (1,26) = 289.71, \(p\) < 0.001, \(\eta^{2}\) = 0.91), indicate that backward locomotion was perceived as more challenging, aligning with reports of higher perceived exertion due to unfamiliar movement patterns and increased neuromuscular effort [16; 24]. The significant speed-direction interaction for VO\({}_{2}\) (F (1,26) = 49.62, \(p\) < 0.001, \(\eta^{2}\) = 0.65) and heart rate (F (1,26) = 50.41, \(p\) < 0.001, \(\eta^{2}\) = 0.65)suggests that increasing speed amplifies the physiological cost of backward locomotion, a finding that extends prior work on graded backward walking at higher intensities [26]. While this study focused on acute effects, the observed increases in energy expenditure and muscle activation suggest that backward locomotion could enhance weight management and fitness in obese individuals, as supported by studies reporting reductions in waist-to-hip ratios and body fat percentage with backward training [8; 9]. However, the paucity of experimental studies on obese populations limits direct comparisons. Conceptually, backward locomotion may serve as a novel stimulus within the exercise variety model, promoting engagement by introducing movement diversity, which is critical for sustaining exercise adherence in obese individuals [27]. This study's novelty lies in its comprehensive analysis of acute responses in obese males. It addresses a gap in the literature where backward locomotion studies predominantly focus on nonobese populations or long-term outcomes [8; 28]. Integrating cardiorespiratory, metabolic, and EMG data within a robust Latin Square design provides a foundation for optimizing exercise prescriptions for obese individuals.