esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Figure 8: Results for a case study for inclination \(15^{\circ}\): (**a**) maximum radiation; (**b**) radiation variation for \(30^{\circ}\) azimuth; (**c**) maximum radiation loss for \(30^{\circ}\) azimuth; (**d**) axis azimuth for optimal terrain radiation. Figure 10: Losses for different axis and terrain azimuth values. Figure 9: Variation of the maximum radiation affecting the collector with respect to the inclination of the terrain. Figure 11 shows the losses for the conditions \(\gamma=0^{\circ}\) and \(\beta=20^{\circ}\), considering an azimuth range \(\chi\) from \(-60^{\circ}\) to \(60^{\circ}\). For terrains that are also oriented towards the south, or close to it, the losses will be zero or very low, but as the azimuth of the terrain increases, the losses become considerable. Thus, it is possible to establish a relationship between the sensitivity of the collector axis azimuth \(\gamma\) and the inclination \(\beta\) and azimuth of the terrain \(\chi\). It is worth highlighting the novelty of this result since no references have been found in the literature suggesting this relationship between variables. Another key factor when configuring the layout of the collectors was the separation between them. This fact had an influence since the greater the panel separation is, the fewer shading effects are produced and the fewer losses due to a decrease in production. On the other hand, it was not possible to separate the rows of collectors as much as desired, since the costs associated with a higher terrain occupation increase rapidly [21]. Figure 12 shows that, depending on this parameter, the irradiance demonstrated asymptotic behaviour. This was because the shadow effects of one collector on another, when separated by a sufficient distance, were no longer perceived, thus resulting in an irradiance value like that of an isolated collector without adjacent collectors.
# Analysis of Hydrocarbon Enrichment in Tight Sandstone Reservoirs in the Eastern Baiyun Depression
## 4 Basic Characteristics of Paleogene Reservoir Formation Elements in the Eastern Baiyun Depression
### The Target of WXD-5 Region
#### 5.2.2 Typical Seismic Profiles and Seismic Attributes  
Through the analysis of the relevant cross-well seismic profile, the seismic facies characteristics of the sand-oil-rich lithologic assemblage are mainly medium-strong amplitude-to-intermittent reflection, while those of the mud-rich lithologic assemblage in the eastern Baiyun area are mainly medium-weak amplitude-to-continuous reflection. The minimum amplitude attribute map of the target layer shows that the target area has developed beach bar sand bodies formed by lake wave transformation, transportation, and re-sedimentation, with high-energy facies zones and improved physical properties (Figure 8). The seismic profile exhibits strong amplitude characteristics, with sand bodies overlapping the basement in the southeast direction, thinning the sand bodies and weakening the amplitude. The amplitude attribute plan can clearly observe the changes in amplitude strength, which can predict the distribution range and boundaries of the sand bodies.  
The two typical seismic profiles of the WXD-5 region target can clearly show the relevant structural characteristics of the shore-bar reservoir collective (Figure 9). The seismic profiles in both parallel and vertical shoreline directions show abnormally strong amplitude reflection, and the in-phase axis distribution can be seen inside. The target system is a type of shoreline beach bar with a convex inclined end and underwater low uplift developed during the sedimentary period of the Upper Wenchang and Enping formations.  
Figure 8: The minimum amplitude attribute map of the target layer.  
Fractures can be seen in the eastern Baiyun basement, but there is also chlorite development. Chlorite can effectively inhibit the occurrence of quartz secondary enlargement and reduce the filling pores of cement, which plays a role in improving the reservoir. Through analysis, the target of the WXD-5 region is a high-energy sedimentary facies zone, which has the basic conditions for the development of a high-quality reservoir.  
The main source rocks of the WXD-5 region target come from the east wing of the Baiyun main depression and LGH-2 Depression (WC-EP), and there are two different hydrocarbon accumulation modes: (1) the near-source slope transport hydrocarbon accumulation mode mainly supplied by the Baiyun main depression; (2) hydrocarbon supply in LGH-2 depression is dominated by near-source fault (vertical)-sand (lateral) co-controlled transport model.
# FTIR Spectroscopy for Identification and Intra-Species Characterization of _Serpula Iacrymans_
## 2 Materials and Methods  
### Fungal Strains and Growth Conditions  
Seven fungal strains (\(S\)_S. lacrymans_, \(1\)_Rhodonia placenta_ and \(1\)_Trichoderma harzianum_) obtained from the Culture Collection of Universite de Bretagne Occidentale (UBOCC, Plouzane, France) and the culture collection of the Laboratoire de Recherche des Monuments Historiques (LRMH, Champs-sur-Mame, France), were used (Table 1). _S. lacrymans_ and _R. placenta_ were brown-rot fungi. _T. harzianum_ was phylogenetically distant from _S. lacrymans_ and served as an easily distinguished negative control of this species. Preculures of fungi were performed at 23 \({}^{\circ}\)C on Malt Extract Agar (MEA) (Malt extract 20.0 g, Agar 20.0 g per litre distilled water) for _R. placenta_ and _T. harzianum_, and on Malt Extract Agar with Peptone (MEAP) (Malt extract 20.0 g, Peptone 1.0 g, Glucose 20.0 g, Agar 20.0 g per litre distilled water) for _S. lacrymans_.  
One mL of sterile saline solution (NaCl 0.9%) was added to a fungal pre-culture on the surface of a Petri dish. The mycelium was scraped off and the liquid containing the suspended mycelium fragments was collected and used to inoculate broth or agar media. The 100 \(\upmu\)L of mycelium suspension was placed in a tube containing 8 mL of malt extract prepotone broth (Malt extract 20.0 g, Peptone 1.0 g, Glucose 20.0 g per litre distilled water) or plated onto a polyether sulfone (PES) membrane (Merck, France) placed on the surface  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Species** & **Phylum** & **Strain Number 1** \\ \hline _Serpula lacrymans_ & Basidiomycota & UBOCC-A-110074 \\ _Serpula lacrymans_ & Basidiomycota & UBOCC-A-110108 \\ _Serpula lacrymans_ & Basidiomycota & UBOCC-A-111008 \\ _Serpula lacrymans_ & Basidiomycota & LRMH-RX-01 \\ _Serpula lacrymans_ & Basidiomycota & LRMH-SF-Ec3-003 \\ _Rhodonia placenta_ & Basidiomycota & LRMH-Op-001 \\ _Trichoderma harzianum_ & Ascomycota & LRMH-LV-Ec1-001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fungal strains.  
of a new MEA plate. Inoculated broth and agar media were incubated for 12 days at 23 \({}^{\circ}\)C. Broth cultures were then centrifuged for 1.5 min at 13,000\(\times\)\(g\). After the supernatant removal, the mycelium was washed with 10 mL of saline and centrifuged as above. The obtained pellet was dried for 48 h at 50 \({}^{\circ}\)C. Mycelia cultured on filtration membranes were prepared in two ways. Either the membrane carrying the mycelium was dried directly or the mycelium was harvested by scraping the surface of the membrane before drying. The mycelium collection was performed to exclude possible membrane interference on infrared signals. Drying was carried out for 48 h at 50 \({}^{\circ}\)C. For each strain and each culture condition, at least five independent cultures were performed.
## 5 Performance Comparison of the WSN with Different Spatial Node Distributions
### Non-Uniform Random Distribution  
Table 6 exposes the results for a grid of 49 nodes where the coordinator is located in the center of the topology and all nodes generate and send traffic to it. The coverage radio is 50 m and the area is 150 m \(\times\) 150 m. This scenario shows the greater amount of traffic in the center, because the coordinator is the destination. Moreover, we randomly put 40% of nodes near the central part of the topology, and the other nodes are distributed along the whole area in a random way. Results show that the MPH protocol has better performance in all metrics with respect to the other three protocols again. Regarding the retransmission metric, MPH is 20% less than AODV, 19% less than DSR and 19% less than ZTR. With regard to the CSMA retry metric, MPH exhibits an average reduction of 22% against AODV, 18% against DSR and 25% against ZTR. Concerning the overhead metric, MPH is 28% less than AODV and 23% compared to DSR. MPH has only 12% more overhead with respect to ZTR. Regarding the discovered routes metric, MPH has a greater average discovered routes, such as 37% more than AODV, 26% more than DSR and 5% more than ZTR. Regarding the recovery time metric, MPH is faster in the recovery of the topology, having 40% over AODV and DSR and 33% relative to ZTR. Finally, with regard to energy, MPH has an energy savings of 21% compared with AODV, 18% against DSR and 12% in relation to ZTR. Here, we note that MPH is more efficient in this type of scenario with respect to the energy consumption, because routes are better distributed along nodes.
## 2 Related Research
### Strain Gauges  
Strain gauges are the most common force sensor in industry, and several attempts have been made to introduce them into the field of surgery [13; 14; 15; 16; 17; 18]. The resistance of a strain gauge varies when the applied force and/or temperature change. It is commonly implemented by fabricating a thin structure in a mechanical structure to gain flexure and attaching the strain gauge in the proximity of this flexure. [PERSON] et al. presented a flexure structure for forceps blades that allows two-degree-of-freedom force sensing by strain gauges [13]. [PERSON] et al. presented a strain-gauge-attachable three-dimensional (3D) structure that can be implemented at the tip of a surgical instrument [14]. [PERSON] et al. introduced a miniaturized surgical 3-D parallel mechanism that is implemented with a single axis force sensor at each end of the kinematic chain, thus enabling 3D force to be measured [15]. [PERSON] et al. presented a force-sensor integrated MIS robotic tool that allows 6-axis force sensing [16]. [PERSON] et al. presented a flexure mechanism for forceps blades that can augment the strain at the flexure, thus improving the force-sensing capability [17]. [PERSON] studied the effect of autoclave sterilization on a strain gauge  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensing Modality** & **Principle** & **References** \\ \hline Strain gauges & Electric resistance change & [13; 14; 15; 16; 17; 18] \\ Fiber-optic & Light Intensity Modulation (LIM) & [19; 20; 21] \\ Fiber–Bragg Grating (FBG) & [22; 23; 24; 25; 26; 27; 28] \\ Fabry–Perot Interferometer (FPI) & [29; 30; 31; 32; 33; 34] \\ Other methods & [35; 36] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Force sensors in surgical devices.  
attached to a surgical device [18]. The greatest challenge of the use of a strain gauge in surgical applications is that the sensor is not bio-compatible nor noise-tolerant in principle.
# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 5 Conclusions  
Addressing the common issues of insufficient light, low contrast, and blurred details in images captured under conditions such as night, backlight, and adverse weather, this paper proposes a zero-reference dual-path network low-light image enhancement algorithm based on deep curve estimation. This algorithm converts low-light images into multi-scale deep curve estimates using non-reference loss functions and enhances dark details in the image by fitting three types of curve. Firstly, a brightness enhancement curve and a contrast enhancement curve are proposed to improve brightness and enhance contrast, respectively. Then, based on the brightness distribution characteristics of the original image, a multi-scale feature fusion curve is introduced to ensure the preservation of texture and edge information in dark areas while maintaining the authenticity of image colors and avoiding overexposure. Experimental results demonstrate that the proposed algorithm not only significantly enhances the brightness and contrast of low-light images but also avoids issues such as overexposure and color distortion while enhancing dark details.  
However, there is still room for further study in the future. Despite the implementation of corresponding noise reduction measures in this paper, for images that inherently have low resolutions and contain significant noise, noise issues remain inevitable after enhancing dark details, adversely affecting image quality. To further enhance the efficiency and practicality of the algorithm, future research will focus on introducing super-resolution algorithms. This approach aims to achieve noise removal and image resolution enhancement simultaneously, thereby substantially improving image clarity and ensuring the comprehensive optimization of image quality.  
Conceptualization, C.Y.; validation, C.Y. and G.H.; formal analysis, C.Y., G.H. and A.D.; investigation, C.Y., M.P. and A.D.; data curation, C.Y. and M.P.; original draft preparation, C.Y.; review and editing, C.Y., G.H., M.P., X.W. and A.D.; supervision, G.H., M.P., X.W. and A.D.; funding acquisition, G.H. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant number 62175086.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors would like to thank the editors and the reviewers for their valuable suggestions.  
The authors declare no conflicts of interest.  
Figure 21: The ablation study of the contribution of each loss weight.
## 3 Wireless Localization Technology  
As mentioned above, wireless communication technologies are diversely being utilized for localization. In fact, communication signals have various physical factors such as power, delay, and phase, which are used for wireless localization. The algorithms for estimating the position can be divided into three categories: range-based techniques, which are based on physical factors of radio waves, range-free techniques, which use known information (such as pre-processed data) rather than physical factors, and hybrid techniques, which utilize the two techniques appropriately to complement each other. These three categories are summarized as shown in Figure 1.  
Figure 1: Overview of standard localization technology.
## 2 Constellation Design
### Constellation Technical Characteristics
#### 2.2.3 Synergy of Sensors with Multispectral, Hyperspectral, SAR and Ship AIS  
Considering the applicability of multispectral satellites is limited due to the prevalent cloudy and rainy conditions in this region, SAR satellites can retrieve radar images in nearly all weather conditions, and hyperspectral satellites can provide more detailed information. In addition, the AIS can help to achieve the accurate and real-time detection and tracking of ship targets. Synergy among multispectral, hyperspectral, radar sensors, and ship AIS integrates multi-modal spatial information for the ocean observations of Hainan and its surrounding areas to meet the demand for spatial information.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 5 Discussion: Application, Challenges and Future Perspectives
### Optimal Placement of Sensors in Buildings
Finally, the Island topology highlights that (at least one) sensor should be placed in each isolated group if possible [123].
## 4 Jasmonates: Biosynthesis, Signaling, and Functions
### Deeper Mechanistic Insights: How MeJA Modulates Trichome Development and Cannabinoid Biosynthesis
#### 4.2.4 Metabolite Fluxes and Enzymatic Activity  
Cannabinoid biosynthesis depends on two essential precursor molecules: hexanoyl-CoA, which comes from fatty acid metabolism, and geranyl diphosphate (GPP), produced through the isoprenoid pathway [11]. Proteomic analysis shows that ACC1, STADs, and GPPS.Isu reach peak abundance during the secretory phase, highlighting their crucial roles in driving the production of CBGA, the central intermediate in cannabinoid synthesis [54].  
Under MeJA-induced stress, plants typically upregulate enzymes such as LOX (lipoxygenase), AOC (allene oxide cyclase), and HPL (hydroperoxide lyase), which drive the production of short-chain fatty acid derivatives like hexanal [49]. Further enzymatic steps channel these carbon compounds into hexanoyl-CoA, thereby increasing the carbon pool available for CBGA synthesis [41]. MeJA can also alter the supply of GPP by upregulating genes involved in the isoprenoid pathway, ultimately boosting CBGA formation [56]. Some cultivars exhibit a metabolic shift that favors different cannabinoids, such as THCV, rather than pentyl cannabinoids like THC, suggesting that MeJA influences precursor flow or enzyme specificity.  
Although the most conspicuous MeJA-mediated effects often appear as changes in trichome density or precursor availability, certain biosynthetic enzymes may also experience moderate or transient induction. The prenyltransferase CsPT4, responsible for linking GPP and olivetolic acid to form CBGA, can show subtle increases in both transcription and enzyme stability under MeJA [56]. Meanwhile, genes tied to fatty acid desaturation, \(\beta\)-oxidation, and coenzyme A activation may also be upregulated, thereby maintaining a robust supply of hexanoyl-CoA [24]. In parallel, MeJA can induce ABC or MATE transporters, promoting the relocation of intermediates or final products into the subcuticular cavity of glandular trichomes, thus preventing feedback inhibition [41]. The expansion of trichome structures themselves increases storage capacity, helping cannabinoids to accumulate at higher levels without harming the secretory cells. Multi-omics studies, incorporating transcriptomics, metabolomics, and proteomics, confirm that MeJA-treated cannabis exhibits a distinct metabolic signature characterized by enrichment in cannabinoids and terpenoids [49]. By correlating specific gene expression modules--for instance, those containing LOX, AOC, or PT4--with changes in metabolite profiles, researchers can identify master regulators of the MeJA response. Such high-level findings open avenues for breeding or genome-editing strategies that aim to optimize trichome development and cannabinoid biosynthesis with greater precision.
# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
## 3 System Model
### Problem Formulation  
To optimize the utilization of MEC servers and mitigate system overload, it is essential to provide a service cache replacement mechanism that intelligently selects suitable services for deployment on MEC servers. In contrast to content caching, optimizing service caching necessitates investigating the computational resources inside the system. Thus, optimal decision making becomes more challenging.  
\(\xi_{s}^{m}(t_{m})\) represents the likelihood that service \(s\) is requested at MEC server \(m\) during the time interval \(t_{m}\). To optimize the system performance, cache replacement aims to maximize the likelihood of requests for the selected services. Hence, the problem can be formulated as follows:  
\[\max_{\mathcal{S}}\sum_{t_{n}\in t_{m}}\sum_{m=1}^{M}\sum_{s=1}^{ S}\xi_{s}^{m}(t_{m}), \tag{3}\] \[s.t.\sum_{s=1}^{S}c_{s}^{m}\leq C_{m},\forall m\in\mathcal{M}, \tag{4}\]  
where \(c_{s}^{m}\) and \(C_{m}\) denote the computational resources required for service \(s\) in MEC server \(m\) and the computational capacity of MEC server \(m\), respectively. Equation (3) aims to maximize the likelihood of requests being served by the service caches of all MEC servers throughout each operation time slot. Constraint (4) guarantees that the total number of requested computational resources does not exceed that of the MEC server.  
According to (3), determining the likelihood of each requested service is crucial for decision making regarding selecting optimal services. In an environment with limited information exchange, individual MEC servers can use local data obtained from user requests to estimate the probability of certain requested services. Nevertheless, user mobility leads to instability in the distribution of requests observed by each MEC server. Therefore, an efficient FL-based service caching framework for MEC systems is proposed in the following section.
## 3 Results and Discussion  
### Characterization of Jobs before and after Integration  
We found changes in job processing patterns in both periods through job statistics before and after integration. The statistical information is shown in Table 2. The number of jobs processed increased by about 84% in Group A, from 242,963 in 2018 to 447,944 in 2019, and decreased by about 35% in Group B, from 2,788,872 in 2018 to 1,799,363 in 2019. The wall time of submitted jobs increased by about 587% in Group A, from 647,731,692 s in 2018 to 4,450,001,236 s in 2019, and decreased by about 25% in Group B, from 5,342,505,530 s in 2018 to 4,015,125,597 s in 2019, respectively. Job waiting time increased by about 1203% in Group A, from 201,578,262 s in 2018 to 2,625,918,725 s in 2019, and decreased by about 59% in Group B, from 15,587,662,341 s in 2018 to 6,370,466,789 s in 2019.  
The execution time of each job was analyzed to characterize the individual jobs. For Group A, the average execution time in 2018 is 2648.35 s, and the quartiles are 28.00, 84.00, and 956.00 s, while the average execution time of 2019 is 6061.84 s, and the quartiles are 95.00, 1482.00, and 6331.00 s. For Group B, the average time of 2018 is 1890.27 s, and the quartiles are 152.00, 402.00, and 965.00 s, and for 2019, the average time is 1414.18 s, and quartiles are 31.00, 130.00, and 645.00 s. In Group A, the execution time of individual jobs more than doubled from the previous year, while in Group B they tended to decrease. The distribution of job execution time is summarized in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Number of Jobs Processed**} & \multicolumn{2}{c}{**Total WallTime (s)**} & \multicolumn{2}{c}{**Total WaitingTime (s)**} \\ \cline{2-7}  & **Group A** & **Group B** & **Group A** & **Group B** & **Group A** & **Group B** \\ \hline
2018 & 242,963 & 2,788,872 & 647,731,692 & 5,342,505,530 & 201,578,262 & 15,587,662,341 \\
2019 & 447,944 & 1,799,363 & 4,450,001,236 & 4,015,125,597 & 2,625,918,725 & 6,370,466,789 \\ \hline  & +84.37\% & \(-\)35.48\% & +587.01\% & \(-\)24.85\% & +1202.68\% & \(-\)59.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics for submission jobs in 2018 and 2019.  
Figure 5: Individual job committed time distribution.