esa-sceva/satcom-chunk-collection huggingface dataset:
Collectively, these findings underscore the critical role of precise temperature management, both heating and cooling, in optimizing the phenolic profile and sensory quality of EVOO. Importantly, olive cultivar plays a fundamental role in the formation and modulation of phenolic and volatile compounds during thermal treatments. As such, many researchers advocate for the development of cultivar-specific malaxation parameters, particularly when implementing innovative processing technologies, in order to optimize the activity of endogenous enzymes and ultimately enhance oil quality [9; 13; 14; 31; 32; 33]. Despite extensive research on Italian, Spanish, and Greek varieties, there is a lack of scientific data regarding Croatian olive varieties. Croatia contributes approximately 0.22% to European and 0.13% to global olive oil production [34]. Nevertheless, Croatian VOOs are internationally recognized for their high quality [35] which can be attributed to the country's considerable genetic diversity among varieties, favorable agroecological conditions, and the expertise of its olive oil producers. In this context, the primary objective of the present study was to investigate the influence of FTT, including both heating and cooling, on the oxidative stability and antioxidative capacity of virgin olive oils (VOOs) derived from three Croatian autochthonous varieties: Istarska Bjelica, Levantinka, and Oblica. Given their key role in determining these properties, the effects of FTT on phenolic andtocopherol content were also assessed, along with impacts on oil yield and volatile profile. The ultimate goal was to determine variety-specific optimal paste temperatures that support the production of VOO with enhanced oxidative stability and antioxidant potential. To the best of our knowledge, this is the first study to systematically evaluate FTT effects on Croatian olive varieties.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results
### Subcarrier Channel Analysis  
Using 5.9 GHz frequency band, the general sensors transmit 3-, 5-, and 7-packet duplication. In Figure 6, to satisfy 99.999% reliability, we determine the minimum number of subcarrier channels for both mathematical and experimental perspectives. We con  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline General sensors & 90 \\ Subcarrier channels & 60\(\sim\)560 \\ Audio Sensors & 4 \\ Video Sensors & 6 \\ Dedicated subcarrier channels & 10 \\ Carrier frequency & 5.9 GHz \\ Subcarrier bandwidth & 10 KHz \\ Packet size & 200 bits \\ Link speed & 1 Mbps \\ Modulation & BPSK \\ Packet duplication & 3, 5, and 7 \\ Arrival rate, \(\lambda\) & 100\(\sim\)5000 pkt/s \\ Slot duration & 0.2 ms \\ Simulation time & 500 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameter.  
sider 6-clusters for the proposed C-OFSMA scheme and compared it with the OFSMA scheme. Here, Figure 5(b) indicates the experimental result from Algorithm 1 and Figure 5(a) represent Equation (10). For 3-packet duplication and at 5000 pkt/s arrival rate, the OFSMA scheme occupies 560 subcarrier channels to achieve 99.999% reliability, whereas the used C-OFSMA scheme occupies 306 channels. The 5- and 7-packet duplication demands lower subcarrier channels than 3-packet duplication. At the same arrival rate, OFSMA scheme requires 270 subcarriers channels, but the used C-OFSMA scheme can perform with only 198 channels. The 7-packet duplication demands 240 subcarriers and 180 subcarrier channels for OFSMA and C-OFSMA schemes, respectively. All the higher arrival conditions starting from 2500 pkt/s in any packet duplication show much better results for C-OFSMA. With the increment of arrival rate, the requirement of subcarrier channels increases. The 7-packet duplication provides a much better result than 3- and 5-packet duplication. However, our proposed C-OFSMA 5-packet duplication needs less number of subcarrier channels than the OFSMA scheme having 7-packet duplication. Unlike 3-packet duplication, the line generated from the mathematical analysis, most of the time for 5-and 7-packet duplication, represent nearly identical lines like the experimental analysis.  
Figure 6: Determine minimum subcarrier channels to satisfy URLLC’s reliability 99.999% for 3-, 5-, and 7-packet duplication. (a) Mathematical, (b) Simulation.
## 3 Results and Discussion
### Mechanical Testing  
The mechanical properties of the SA-G hydrogel samples were studied through uniaxial tensile testing, as knowledge about retinal mechanical properties is mainly based on uniaxial tensile tests [33]. Since the conventional grips used for uniaxial tensile testing exert localized pressure on soft materials, leading to significant local deformations and fractures, a custom setup able to constrain the sample without causing clamping-induced fractures was designed and developed. The sample fracture site at the end of mechanical testing proved the effectiveness of the new setup (Figure 4a). Figure 4b shows a typical tensile stress--strain curve of the tested SA-G hydrogels. The curve presents a linear elastic region followed by failure, consistent with the literature [34]. As observed in Figure 4b, the fracture was not instantaneous. The stress dropped gradually, indicating a progressive failure of the hydrogel. This behavior may be due to the presence of regions with slightly different crosslinking degrees, although further studies are needed to confirm this hypothesis. The average elastic modulus (E), extrapolated from the stress-strain graph, was 37.5 \(\pm\) 0.36 kPa. According to our previous results, the average E of porcine retinal tissue at room temperature is lower (13.4 \(\pm\) 0.0067 kPa) [27]. However, the test was performed ex vivo on the entire retinal tissue. [PERSON] et al. exploited a phase-resolved acoustic radiation force optical coherence elastography method to determine the elastic properties of different porcine retinal layers in vivo. They found that the stiffness increased from the ganglion layer to the photoreceptors, with the maximum value of 25.9 \(\pm\) 7.36 kPa in the photoreceptor layer [35]. Hence, the stiffness of the SA-G bioink is akin to the stiffness of the native photoreceptor layer. Thus, it can provide in vivo-like mechanical cues, which are essential for promoting cell differentiation into photoreceptor phenotypes [8].  
Figure 4: (**a**) SA-G hydrogel samples after testing. Scale bar = 6 mm; (**b**) Representative stress–strain curve of hydrogel samples when pulled until failure. SA-G: sodium alginate–gelatin.  
Figure 3: (**a**) Swelling degree (%) of the SA-G hydrogels over time; (**b**) Degradation rate (%) of the SA-G hydrogels over time. Error bars represent the standard deviation. SA-G: sodium alginate–gelatin.
## 5 Kubernetes Capabilities for Industrial Edge Cloud
### Kubernetes Resource Management Aspects for Low-Latency Workloads
In the Linux kernel, there are other schedulers available for real-time scheduling: real-time first-in-first-out, real-time round-robin, and deadline scheduler. Currently, the usage of these schedulers is not implemented in Kubernetes. Unfortunately, there is a negative side effect of CPU limits, as the CPU limit is enforced by restricting the total amount of CPU time that a container can use every 100 ms. For example, if the limit is set for 400 m, then the container can run 40 ms in each 100 ms time window; however, when a request is not processed within 40 ms, then 60 ms waiting (throttling the process) will prolong the response time, and this can happen several times until the request processing is finished. Unfortunately, because of a Linux kernel bug fixed only in kernel version 4.19, a container can be throttled even without the CPU usage getting close to the limits. Because of this, it is recommended to define no CPU limits or to disable the enforcement of CPU limits at the Kubernetes platform level, but this can be done only in a self-managed cluster. In addition, without limits, no prevention mechanisms are provided by the Kubernetes platform, and alternative ways are required to prevent high CPU usage for pods, such as monitoring the CPU usage and adjusting the requests accordingly. There are Kubernetes tools that support better performance isolation for selected pods to serve workloads sensitive for, e.g., CPU throttling or context switches. The CPU manager [32] is a beta feature in Kubernetes that can allocate exclusive CPUs to certain pod containers. The pod must be in the guaranteed QoS class, and whole numbers of CPU cores must be specified in the request and limit, e.g., 1000 m or 3000 m, to allocate exclusive cores. This way, the containers within these pods do not share the CPU resources with others, and as a result, better performance is expected. To provide low-latency performance enhancements in a Kubernetes platform, further low-latency features can be configured on the nodes of the cluster. These cover hardware settings and tuning of the software on the nodes, especially the Linux kernel. For the best response times, it is recommended to disable power management options in the BIOS, as various CPU sleep states can affect how quickly the system responds to external events. Another option is to update the kernel to kernel-rt, an optimized kernel designed to maintain low latency, consistent response time, and determinism, in contrast with the normal one, which focuses on throughput-oriented operations and fair scheduling of tasks. The optimized nodes are to be labeled, and node selectors for the pods will ensure the placement on them. The CPU cores of the node can also be partitioned to serve the Kubernetes management processes in one partition and to serve latency-sensitive workloads in another partition not to interfere with each other.
# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
###### Abstract
In [21], the authors found that autumn NDVI is the best period to explain and predict species richness of different breeding bird groups in a region of southwestern France. In this area made of heterogeneous landscapes, the unclassified NDVI acquired in autumn allow a better discrimination between harvested crops (low NDVI), woodlands, hedgerows, and grasslands (high NDVI). This discrimination is required to explain bird behaviors of habitat selection or avoidance, according to their nesting and diet preferences. In this study, we aim at extending this result at national scale by assessing how the the NDVI acquisition period that best model the relationships with bird data varies according to landscape types and their dominant land covers. Instead of using single-date data to explain patterns of biodiversity, recent approaches proposed to explicitely take into account variations in vegetation phenology throughout the year. Among these ones, the Dynamic Habitat Index (DHI) incorporates three components based on the temporal variation of canopy light absorbance through the year including the annual sum, the minimum and the monthly variation [24; 25]. This indicator has been already taken up by studies on different taxonomic groups [26; 27; 28] and a recent study has compared DHI based on different MODIS products to explain breeding bird species richness in North America [29]. However, until now, the added value of this integrative indicator of intra-annual variation, compared to data acquired at a single date, has never been tested. In this study, we used a large bird dataset collected at the French national scale to (i) compare single date NDVI data and DHI to explain and predict species richness patterns of different bird groups; (ii) identify the best NDVI acquisition date to explain and predict bird species richness; and (iii) test whether the best NDVI acquisition date depends on the dominant land cover in landscapes.
## 2 Results and Discussion
### \(H_{2}\)O Adsorption-Desorption Measurement  
The H\({}_{2}\)O adsorption-desorption measurement was used to evaluate the effect of the aminopropyl group and/or NR on the hydrophobic properties of functionalized WMS and NR/WMS materials. Both of the pristine WMS and NR/WMS nanocomposites exhibited type IV isotherms with a hysteresis loop (Figure 8). The presence of rubber incorporated into the mesostructured silica reduced the size of the hysteresis loop, indicating that H\({}_{2}\)O desorbed from the surface of the NR/WMS more easily than that of the WMS. The pure silica WMS and NR/WMS nanocomposites had a \(V_{\rm mH2O}\) of 70.4 and 43.7 cm\({}^{3}\) g\({}^{-1}\), respectively (Table 2). A decrease in the H\({}_{2}\)O affinity observed for the NR/WMS was explained by not only the hydrophobicity of the rubber phase but also the relatively small amount of silanol groups, compared to the WMS, as evidenced by FT-IR (Figure 1) analysis.  
The surface functionalization not only narrowed the hysteresis loops further, but also decreased the H\({}_{2}\)O adsorbed volume of the resulting WMS-NH\({}_{2}\) and NR/WMS-NH\({}_{2}\). With the increasing APS loading level in the nanocomposites, the monolayer adsorbed volume of H\({}_{2}\)O was decreased in the following order: NR/WMS (43.7 cm\({}^{3}\) g\({}^{-1}\)) < NR/WMS-0.05 (42.1 cm\({}^{3}\) g\({}^{-1}\)) < NR/WMS-0.10 (36.2 cm\({}^{3}\) g\({}^{-1}\)) < NR/WMS-0.15 (33.2 cm\({}^{3}\) g\({}^{-1}\)). The result was correlated with the reduced amount of exposed silanol groups in both functionalized materials via the surface grafting of an aminopropyl group (Figure 1). Although the \({}^{29}\)Si CP/MAS-NMR result indicated that the WMS-0.10 had a higher degree of amino-functionalized (Table 1) and a smaller content of surface silanol groups than the NR/WMS-0.10 (Table 2), the former exhibited a higher H\({}_{2}\)O affinity than the latter. The result revealed that the hydrophobic environment created by the entrapped rubber was a major influence on the overall hydrophobicity of the NR/WMS-NH\({}_{2}\) nanocomposites.  
Figure 8: H\({}_{2}\)O adsorption–desorption isotherms of (a) WMS, (b) WMS-0.10, (c) NR/WMS, (d) NR/WMS-0.05, (e) NR/WMS-0.10 and (f) NR/WMS-0.15.
# Synthesis, Structure and Photoluminescence Properties of 2D Organic-Inorganic Hybrid Perovskites
In 2012, [PERSON] et al. studied the optical properties of the perovskite (C\({}_{10}\)H\({}_{7}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{x}\)I\({}_{4-x}\)[29], and found that the near-resonant transition energies between Wannier and Frenkel excitons increases with an enhancement of iodine. In 2015, [PERSON] et al. synthesized double perovskite structure compound Cs\({}_{2}\)AgBiBr\({}_{6}\)[30]. It was significantly more heat- and moisture-stable compared to the perovskite CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) and displayed a long carrier recombination lifetime. In 2017, [PERSON] et al. studied the perovskite CsPbBr\({}_{3}\) nanoplate electroluminescence devices, which displayed a narrow PL peak centered at 530 nm with a narrow full width at half-maximum of 22 nm [31]. In 2017, [PERSON] et al. prepared the perovskite (C\({}_{4}\)H\({}_{9}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) by ternary solvent method and studied the effect of the crystallization temperature and solvent volume ratio on its morphology [32]. In 2018, [PERSON] et al. studied the solar cells with the inverted planar Hetero junction perovskites, yielding a PCE of 21% due to the elimination of nonradiative charge-carrier recombination [33]. The different properties of organic and inorganic components are united in an independent composite. The inorganic component provides the opportunities for high carrier mobility and a wide range of bandgap. The organic component offers the possibility of structural diversity and high PL quantum yield. Moreover, these properties can be easily modified by changing the organic ammonium, metal or halide [34]. Currently, perovskites have been widely studied in the field of solar cells and light-emitting diodes. However, it is rare to study the PL properties of perovskites using two-photon excitation, especially to regulate its spectral properties by changing halogens in organic-inorganic hybrid perovskites. In this paper, four kinds of perovskites--(C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbCl\({}_{4}\), (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\), (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) and (C\({}_{4}\)H\({}_{9}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\)--were synthesized by solvent evaporation. The laser power effect on the PL intensity and lifetime was investigated with femtosecond laser via two-photon excitation. The results show that the PL properties of perovskite can be regulated in a certain range. Using this technology to change the luminescence peak can promote the application of nano-photonics and optoelectronic devices, and promote the development of near-infrared band nano-lasers, which can be better used in optical fiber communication.
# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
## 2 System Model
### Signal Model  
This section describes the model for signal propagation in the network. Let \(p_{s}\) represent the transmission power at source \(s\). To achieve full-duplex communication, a combination of NOMA and OFDM is proposed. Specifically, in the system with the source and multiple users, uplink and downlink transmissions occur on different frequency bands, and NOMA technology is used within each band. The uplink and downlink need to be modeled separately to assist in the subsequent optimization process.
###### Abstract  
The well-being of a honeybee (_Apis mellifera_) colony depends on forager traffic. Consistent discrepancies in forager traffic indicate that the hive may not be healthy and require human intervention. Honeybee traffic in the vicinity of a hive can be divided into three types: incoming, outgoing, and lateral. These types constitute directional traffic, and are juxtaposed with omnidirectional traffic where bee motions are considered regardless of direction. Accurate measurement of directional honeybee traffic is fundamental to electronic beehive monitoring systems that continuously monitor honeybee colonies to detect deviations from the norm. An algorithm based on digital particle image velocimetry is proposed to measure directional traffic. The algorithm uses digital particle image velocimetry to compute motion vectors, analytically classifies them as incoming, outgoing, or lateral, and returns the classified vector counts as measurements of directional traffic levels. Dynamic time warping is used to compare the algorithm's omnidirectional traffic curves to the curves produced by a previously proposed bee motion counting algorithm based on motion detection and deep learning and to the curves obtained from a human observer's counts on four honeybee traffic videos (2976 video frames). The currently proposed algorithm not only approximates the human ground truth on par with the previously proposed algorithm in terms of omnidirectional bee motion counts but also provides estimates of directional bee traffic and does not require extensive training. An analysis of correlation vectors of consecutive image pairs with single bee motions indicates that correlation maps follow Gaussian distribution and the three-point Gaussian sub-pixel accuracy method appears feasible. Experimental evidence indicates it is reasonable to treat whole bees as tracers, because whole bee bodies and not parts thereof cause maximum motion. To ensure the replicability of the reported findings, these videos and frame-by-frame bee motion counts have been made public. The proposed algorithm is also used to investigate the incoming and outgoing traffic curves in a healthy hive on the same day and on different days on a dataset of 292 videos (216,956 video frames).  
digital particle image velocimetry; video processing; image processing; electronic beehive monitoring; insect traffic; honeybee traffic; dynamic time warping +
Footnote †: journal: Applied Soft
# Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion
## 3 Proposed Method
### Exploration Rules
If the graph does not have any data associated to the nodes or edges, the functions take the natural properties of edges (weight) and nodes (degree) which reduces the probability function to:  
\[p_{k}(e_{ij})=\frac{d_{i}^{(out)\alpha}w_{i,j}^{\beta}d_{j}^{(in)\gamma}}{s_{ k}}. \tag{3}\]  
**Uni-edge ball:** The edges are selected uniformly at random without replacement, \(\alpha,\beta,\gamma\) are zero and  
\[p_{k}(e_{ij})=\frac{1}{s_{k}}=\frac{1}{N_{k}^{(out)}}. \tag{4}\]  
Optionally, the selection may take the edge weights into account by defining a probability mass function proportional to the weights, \(\beta=1\), \(g(i,j)=w_{ij}\):  
\[p_{k}(e_{ij})=\frac{w_{ij}}{s_{k}},\text{ with }\ \ s_{k}=\sum_{(i,j)\in E_{k}^{( \text{out})}}w_{ij}. \tag{5}\]  
**Uni-node ball** or **Fireball:** In this configuration, the source nodes are selected uniformly at random without replacement, hence \(\beta,\gamma\) are zero and \(\alpha=-1\)  
\[p_{k}(e_{ij})=\frac{1}{d_{i}^{(out)}}\frac{1}{s_{k}}. \tag{6}\]  
As for the uni-edge ball, optionally, the selection may take the edge weights into account with \(\beta=1\) and \(g(i,j)=w_{ij}\). The name Fireball refers to its similarity with the Forest Fire sampling (see next section for more detailed explanation). **Hubball family:** This family relies on the degrees of the nodes in \(\mathcal{L}_{k}\). The probability is the one of Equation (3) where \(\beta=1\)\(\gamma=0\):  
\[p_{k}(e_{ij})=d_{i}^{(out)\alpha}\frac{w_{ij}}{s_{k}}. \tag{7}\]  
It is a one-parameter family that favors the selection of connections originating from hubs when \(\alpha>0\). Note that it has an influence on the outbound connections from highly connected nodes but not on the nodes themselves: the nodes in \(\mathcal{L}_{k}\) have already been chosen and the choice is independent of the degree of the target nodes. **Coreball family:** This sampling aims at capturing the core of the communities surrounding or including the initial nodes. This is in reference to the \(k\)-core measure that evaluate the hierarchy within a community and its core users. The probability relies on the degree of the target nodes. In Equation (3), \(\alpha=0\), \(\beta=1\), while \(\gamma\) is kept free:  
\[p_{k}(e_{ij})=\frac{w_{ij}}{s_{k}}d_{j}^{(in)\gamma}. \tag{8}\]  
For \(\gamma>0\), it favors the exploration of the core of a community, by collecting with a higher probability the nodes that are the most connected to the ones in layer \(\mathcal{L}_{k}\). However, keeping a non-zero probability for connections with weakly connected nodes, the sampling gets a chance to explore outside of the already captured communities. **Remark:** the Hubball with \(\alpha=0\), the Coreball with \(\gamma=0\) and the Uni-edge ball have the same probability distribution and are therefore the same sampling scheme. The Hubball with \(\alpha=-1\) is the Uni-node ball.
## 5 Results and Analysis
### Success Probability  
For a randomly selected user located at the center of BS, Figure 12, shows the probability of success, with respect to \(q_{ON}\). The simulated results show that the probability of mobile users receiving service when all the femto BSs are in off state is around 10%. Whereas with random sleep mode selection, this probability will increase to a level of 30% to 40%.  
In the load aware strategy, the chances of randomly selected mobile users obtaining the services can be raised up to 90%. However, simulated results indicate that dynamic DOSS outperforms all other techniques by ensuring the highest success probability. It means that dynamically DOSS strategy can provide a better QoS experience for the user along with the improved EE.