esa-sceva/satcom-chunk-collection huggingface dataset:
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 2 Literature Review
### The Measurement Stage
#### 2.1.1 Free Space-Based Sensors  
These types of sensors primarily use distance information to calculate the space needed for parking. Once a space large enough to accommodate a car is detected, the system initiates the parking process. In the current market, free space-based sensors are most commonly ultrasonic sensors [3; 4]. Because of their low price and ease of installation, ultrasonic sensors are widely used in parking assistance systems. However, the precision of ultrasonic sensors is highly dependent on the positioning of adjacent vehicles, and ultrasonic sensors cannot function without reference vehicles. Laser scanners [5] can measure distance with a high degree of precision, but their high cost hinders their commercialization as an automobile accessory. Depth cameras [6] refer to binocular stereo vision or moving stereo vision sensors. These sensors provide not only image information but also depth information. However, because generating a depth map is computationally demanding, depth cameras are difficult to use in real-time settings.
## 4 Discussion
16:0 and 18:0 fatty acids are used as substrates by the stearyl-CoA desaturase (\(\Delta\)-9-desaturase) enzyme. Fatty acids are formed from 16:0 to 16:1 and from 18:0 to 18:1. Stearyl-CoA desaturase enzyme is a very important enzyme for the biochemistry of the cell. It also plays a role in protecting the membrane structure of the cell. Nutrition and hormones are effective in the activity of this enzyme [56]. In this study, while the 18:0 level increases in the Exercise\({}_{\text{only}}\) phase, we think that the decrease in the 18:1n9c level is due to decreases in the activity of the stearyl-CoA desaturase enzyme. Looking at the studies conducted in this context, [PERSON] et al. (2018) reported that after 1.3 km and 15 km races in cross-country skiing athletes, there was no change in long-chain fatty acid levels compared to the athletes' initial values, while a significant increase was observed in the levels of C10:0, C12:0 and C14:0 has been reported [57]. [PERSON] et al. (2019) found that an acute maximal exercise resulted in relatively high plasma levels of free fatty acids C16:0, C16:1, C18:0, C18:1 cis and C18:2, while there was no change in the fatty acid level in erythrocytes. It has been stated that the levels of, C12:0, C14:1, C18:3n-6, C20:4n-3 and C22:1 are relatively low [58]. [PERSON] et al. (2021) found that the 4-week combinatorial group (including exercise and dietary restriction) had significant changes in body composition compared to the control group. Significant changes in SFAs, MUFAs and especially a decrease in the levels of C14:0, C15:0, C18:0, C20:0, C22:0, C16:1n-7, C18:1n-9 and C20:1n-9 were also reported [59]. When studies on the effect of exercise (acute or chronic) on fatty acid levels are examined, it has been observed that exercise can change these levels. There are similarities between the research findings in the literature and the findings of this study.
# Development of an Organic Culture Medium for Autotrophic Production of _Chlorella vulgaris_ Biomass
## 3 Results and Discussion  
### Characterization of The Organic Substrates  
Considering that the OS studied are complex media, it is essential to know the main elements of which they are composed, in order to meet the nutritional requirements of microalgae. Accordingly, the elements that are known to be most important for microalgae growth were determined by elemental analysis (Table 1).  
From Table 1, it is possible to compare the nutrients concentrations of each OS stock solution, so their usage could be adjusted the best way possible with that of the non-organic medium, in order to achieve an equivalent final concentration. All OS displayed very different concentrations for all elements analysed.  
The results obtained revealed that Bioscape clearly presented the highest N and P values (1133.9 mmol L\({}^{-1}\) and 864.2 mmol L\({}^{-1}\), respectively), followed by EcoMix4 (63.8 mmol L\({}^{-1}\) and 566.4 mmol L\({}^{-1}\), respectively), while Nutrimais (11.5 mmol L\({}^{-1}\) and 167.3 mmol L\({}^{-1}\), respectively) and Nutriverde (3.6 mmol L\({}^{-1}\) and 135.4 mmol L\({}^{-1}\), respectively) showed the lowest values. The main macronutrients, N and P, used for microalgal growth, since N is crucial for the biosynthesis of protein and amino acids thereof, while P is essentially used for energy transfer, membrane synthesis (phospholipids), and nucleic acid (DNA and RNA) biosynthesis [15]. However, the optimum N:P ratio must be taken into account, which is within the range of 6.8-10 for the growth of freshwater algae [16]. In this context, Bioscape showed the least balanced N:P ratio (1,289), followed by EcoMix4 (113), Nutrimais (69), and Nutriverde (27). These results show that solubilization of P in the different culture media is low, and that all solutions present an unfavourable N:P ratio.  
Another key element for microalgal growth is Fe, essential for different metabolic pathways and for chlorophyll biosynthesis [17,18]. Among the OS studied, Bioscape was the one that showed the highest Fe concentration, 101.4 mmol L\({}^{-1}\), while in the remaining OS it ranged from 34.6 to 66.8 mmol L\({}^{-1}\). Even though none of the OS displayed ideal concentrations of Fe, the organic Regulation (EU) 2018/848 allows the use of 2% Fe chelated with EDDHSA. Those rules comply with the principles set in the regulation mutatis mutandis concerning phytoplankton production [19].  
Regarding the other micronutrients essential for microalgae growth, Bioscape was once again the OS that presented the highest concentrations, with particularly high contents of Mg (2950 mmol L\({}^{-1}\)) and Mn (28.9 mmol L\({}^{-1}\)). However, no OS contained toxic heavy metals, such as Hg, Pb, Ni, Cu, Zn, Cd, and Cr (data not shown) in concentrations higher than those allowed by law (Regulation (EC) N.o 629/2008).
## 4 Applications of Heterogeneous Photocatalysis in Water Purification
### Pathogen Inactivation and Disinfection  
Disinfection is a critical aspect of water treatment to prevent the transmission of waterborne diseases. Traditional disinfection methods, such as chlorination, may generate harmful byproducts like trihalomethanes. Photocatalysis offers a chemical-free alternative for microbial inactivation, by generating reactive oxygen species that damage cell membranes, proteins, and nucleic acids [113,114]. Figure 8b suggests the mechanism by which photocatalysis inactivates waterborne pathogens through the generation of reactive oxygen species (ROS). These species attack critical cellular components, such as membranes, proteins, and nucleic acids, leading to the destruction of bacteria, viruses, and protozoa. Photocatalysis is emphasized as a chemical-free and effective alternative to conventional disinfection methods, including its potential in addressing antibiotic-resistant microorganisms [115,116].  
Photocatalytic systems have been reported to effectively inactivate a variety of pathogens, including bacteria (e.g., _E. coli, S. aureus_), viruses (e.g., adenovirus, MS2 bacteriophage), and protozoa (e.g., _Giardia lamblia_) [116,117]. Recent advances also show promise in targeting antibiotic-resistant bacteria and genes, contributing to efforts in antimicrobial resistance mitigation [118,119].
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 4 Methods  
We estimate the typhoon damage by identifying coconut canopy density changes between images before and after the event. To that end we have created two different maps using a collection of images sensed in two different time periods as described in Section 3. We first train a model to estimate coconut density in the Philippines using input images and ground truth labels exclusively from before the typhoon. We then apply that model to images from before and after the event to obtain coconut density maps, and analyse the changes in density. See Figure 2 for an overview of the complete method.  
A naive and straightforward approach to evaluate the density difference could lead to inconsistencies, due to two important issues. First, since our density model was not trained with images collected after the typhoon, a potential domain shift might lead to inaccurate predictions. Second, our _after_-map is obtained with notably less samples than our _before_-map which could also increase the uncertainty of the predictions after the typhoon. It is therefore crucial to account for prediction uncertainty, such that change detection can be based on trustworthy predictions.  
In the following we first review the active learning strategy used to ensure accurate density estimation without excessive labelling efforts. We then describe how we model uncertainty and filter out unreliable predictions. Finally, we describe other change detection methods that are used as baselines to compare to.
## 2 Related Work  
The traffic variations and the flexibility of the NFV technology involve resource configurations during the lifetime of a SFC. For example when the traffic decreases, algorithms can be applied in order to consolidate the used resources [19]. The algorithms involve the VM migration towards fewer datacenter and the switching off of the remaining datacenters with advantages in terms of cloud resource costs. To support the VM migration, network paths also need to be reconfigured in order to interconnect the migrated VMs. The reconfiguration cost has been investigated in some research works. It may be characterized by: 1) the gain loss of a network operator due to the information loss during the VM migration and the circuit reconfiguration [20]; the energy consumption occurs when the VM memory content is moved from a server to another [11]. The problem has also been investigatedin [16; 21] in the case in which the datacenters are interconnected by an EON; the reconfiguration cost is characterized by the lost data during the reconfigurations of optical devices in the periods in which VM migrations are needed. Differently from electronic switches, the optical ones, though they have the potential to reduce the overall cost and the power consumption, allow for a lower reconfiguration rate dependently on the used technology. Two main technologies are actually used: i) the 3D-MEMS-based cross-connects [15; 22; 23] that perform transparent optical switching using microscopic tiltable mirrors to reflect light from the input fibers to the input fibers; ii) the Reconfigurable Optical Add Drop Multiplexers (ROADMS) [15; 24; 25; 26] that are often based on 2D-MEMS allowing then to switch more quickly than 3D MEMS. The ROADMs have the advantage of low switching times in the order of 10 \(\upmu\)s but they allow only for the realization of small size optical switches and consequently they are not suitable to interconnect NFV sites. Conversely, 3D-MEMS based cross-connects are cost effective, allow for the realization of high size switches but they have the drawback of slow reconfiguration times, typically ranging from 10 ms to 1 s. [15]. Furthermore when a reconfiguration is performed, the traffic of the reconfigured circuit is lost. The consequence is that, in order to maintain high the average switch throughput, the reconfiguration rate has to be maintained low. For instance, if the reconfiguration time is \(\sigma\), a given circuit configuration has to be held at least for a time of 9\(\sigma\) to get a 90% throughput. That leads to maximum reconfiguration rates of the 3D-MEMS based cross-connects ranging from 100 ms to 10 s. The objective of the paper is to study the impact of the limited reconfiguration rate of the optical switches on the cost saving that reconfiguration policies allow us to obtain in NFV architectures where the NFV sites are optically interconnected. We formulate the optimization problem and due to its complexity we propose a heuristic based on the evaluation of a constrained shortest path [27] in a multi-stage graph.
# In-Field Quantum-Protected Control-Based Key Distribution with a Lossy Urban Fiber Link
## 7 Methods
### Critical Leakage Data  
By picking a particular pair of intensities for bit-encoding pulses, [PERSON] and [PERSON] determine the tolerance for an observed leakage value \(r_{\rm E}\). Figure 7 shows an extended table of critical \(r_{\rm E}\) value as a function of signal intensities. The critical leakage achieves its maximum when the intensity values are close to each other.  
Figure 7: Critical \(r_{\rm E}\) as a function of signal intensities \(\mu_{0}\) and \(\mu_{1}\). The advantage distillation block length is \(M=2\). The BER value after advantage distillation \(\bar{\rho}_{\rm err}\) does not exceed \(5\%\). Cells with a dash (light yellow parts) correspond to the pairs of intensities \((\mu_{0},\mu_{1})\) for which every leakage value is acceptable. The signal intensities are sampled from sets with the boundaries: \(2270\leq\mu_{0}\leq 2770\), \(2320\leq\mu_{1}\leq 2820\).
## 6 Prestall Disturbances
### Nature of Prestall Disturbances  
An analysis of the frequency spectra allows a more precise estimation of the onset of prestall activity. Figure 7 shows the power spectral density (PSD [\(\mathrm{mbar^{2}/Hz}\)]) for selected operating points. The nominal uniform flow at A5 is dominated by the blade passing frequency (BPF). Starting in A12, a broad banded chain of peaks arises that is centered at approximately 45% of the BPF. In detail, the broad band peak is composed of several side-by-side peaks with a constant frequency spacing in-between. The characteristic signature is a typical identification feature of so-called \"Rotating Instabilities\" and will be discussed in more detail in Section 6.2. As the mass flow is reduced, the peak chain grows in amplitude and is being shifted towards smaller frequencies. The rise in flow irregularity happens exactly at the same flow coefficient at which the characteristic peak signature arises, thus answering the question whether there is a link between finite prestall disturbances and Rotating Instabilities positively. The amplitude of the broad peak grows until the compressor becomes unstable. At A19 (and B20), where the stage is operating above the stall point, a peak at 47% of the first engine order (3.4% BPF) indicates the existence of rotating stall cells. Thus, the stall cells propagate at approximately half of the rotational speed. Please note that the peaks in the signature of RI do not represent higher harmonics of the RS peak. Furthermore, the frequency analysis demonstrates that rotating stall and prestall disturbances can indeed coexist. In general, the behaviour of prestall instabilities is very similar for both investigated speeds but at 9000 rpm the amplitudes of the prestall fluctuations are about ten times higher than the amplitudes measured at 5500 rpm. A complementary evaluation of frequency spectra and the skewness allows to clearly identify operating points where prestall activity exists. Within the pressure rise characteristic, the affected operating range is highlighted in red (cf. Figure 3a).
## 3 Results  
Twelve Bladder biopsies originating from eight patients with suspicious lesions which were staged as five pTa, two pT1 and one pT2 and graded as four low-grade and four high-grade urothelial carcinomas by histopathology were studied with a multi-modal approach, starting with OCT and followed by RS. OCT pre-screening was performed on all biopsies to determine the areas of interest of suspicious lesions. In case of small biopsies not fulfilling the full FOV of the OCT protocol, OCT volumes were not acquired for these biopsies as the textural analysis algorithm requires OCT data sets with the same dimensions covering the entire FOV. RS data were collected also of these biopsies as the pathologic region could still be determined by OCT preview (histology distribution regarding the biopsies: Seven low-grade and five high-grade tumors). Hence, we evaluated one OCT data set per patient and one RS dataset per biopsy with the corresponding histology.  
Figure 1: Flowchart of the data analysis process. Optical coherence tomography (OCT) and Raman spectroscopy (RS) data were evaluated separately. The histopathological and the classification results were used to perform staging and grading via OCT and RS, respectively. Solid lines: Prooess; dashed lines: Reference. PCA, principal component analysis; kNN, k-nearest neighbor classification; FOV, field of view.
## 2 Bayesian Functional Mapping
### Parameter Estimation and Algorithm  
Estimation theory  
We will derive a Bayesian approach to estimate the unknown parameters. This approach needs specifying prior distributions for the unknowns. Given the data and the priors specified, the posterior distribution over all unknown parameters can be obtained by using [PERSON] theorem. Let \(\mathbf{Q}=\{Q_{i}\}_{i=1}^{n}\) denote the QTL genotypes for all \(n\) progeny. Then, the posterior density of \(\lambda\), \(\mathbf{\Theta}\), \(\mathbf{\Sigma}\), and \(\mathbf{Q}\) is given by:  
\[\pi(\lambda,\mathbf{\Theta},\mathbf{\Sigma},\mathbf{Q}|\mathbf{y})\propto\pi (\mathbf{y}|\mathbf{Q},\mathbf{\Theta},\mathbf{\Sigma})\cdot\pi(\mathbf{Q}| \lambda)\cdot\pi(\lambda,\mathbf{\Theta},\mathbf{\Sigma}), \tag{6}\]  
where \(\pi(\mathbf{y}|\mathbf{Q},\mathbf{\Theta},\mathbf{\Sigma})=\prod\pi(\mathbf{y }_{i}|Q_{i}=j,\mathbf{\Theta}_{j},\mathbf{\Sigma})\) denotes the probability mass of the observation \(\mathbf{y}\) given the QTL genotypes, \(\pi(\mathbf{Q}|\lambda)=\prod\pi(Q_{i}|\lambda)\) is the probability mass of the QTL genotypes of all \(n\) progeny given their marker genotypes (\(\mathbf{M}\)) and the QTL position (\(\lambda\)), and \(\pi(\lambda,\mathbf{\Theta},\mathbf{\Sigma})\) is the prior imposed on the genetic parameters. It is reasonable to assume the priors are independent for the parameters. Thus, we have:  
\[\pi(\lambda,\mathbf{\Theta},\mathbf{\Sigma})=\pi(\lambda)\cdot\pi(\Sigma) \cdot\prod_{j=0}^{2}\pi(\mathbf{\Theta}_{j}). \tag{7}\]The priors can be chosen from related studies. In principle, if there is reliable information for parameters, such as \(\mathbf{\Theta}\), priors with a small variance may be used. For a parameter without enough information used to determine the prior, such as \(\mathbf{\Sigma}\) and \(\lambda\), noninformative priors or priors with a large variance may be utilized. Here, we choose a uniform distribution on \([0,\mathbf{D}_{m}]\) as a prior of \(\lambda\). Multivariate normal priors with moderate variances are used for \(\mathbf{\Theta}_{j}\) can be obtained. The standard prior distribution for the inverse of the covariance matrix \(\mathbf{\Sigma}^{-1}\) is the Wishart \((\mathbf{R},\rho)\)[25, 26], where the so-called scale matrix \(\mathbf{C}=\mathbf{R}^{-1}\) represents prior structural information about \(\mathbf{\Sigma}\) and \(\rho\) is the degree of freedom, greater than \(T-1\). A small value of \(\rho\) gives a relative flat distribution. The Wishart prior with low degrees of freedom and a specified \(\mathbf{R}\) is regarded as a reference (or noninformative) proper prior. Despite less flexibility, this distribution offers the advantage of being a conjugate prior, leading to a relative simple form for the posterior. Theoretically, the marginal posteriors of the unknown parameters can be obtained from the joint posterior (6) by integrating over the other unknowns. Unfortunately, in practice, the evaluation of such high-dimensional integrals in a closed form is not possible.
## 2 Data and Methods
### Model Descriptions
#### 2.3.2 The GR Model  
The GR model introduced by [PERSON] _et al._[13] has shown high potential for estimating GPP in croplands. [PERSON] _et al._[24] calibrates this model and successfully applies it to forest ecosystems. The GR model can be scaled up using a remote sensing vegetation index as a proxy for the total chlorophyll content:  
\[\mathrm{GPP=m\times VI\times PAR} \tag{7}\]  
In this study, \(m\) is an empirical coefficient with the unit of gC/MJ/eight-day, and VI is EVI. The incoming PAR is acquired from the meteorological observations.
## 4 OLSR with Prediction
At the same pace the predictor of that node determines the predicted TCm, _i.e._, the next TC message that should be received from other nodes (in this case, from Node 1). If no gTCm message is received through the network from a certain node, the routing protocol processes the pTCm. In other case, the routing protocol processes the received gTCm message, and the predictor keeps a local copy of it for the next predictions. Mobile collaborative systems can take advantage of this TC message prediction to increase their energy savings. Typically these systems exchange messages, at the application level, between nodes that can be in the same island or in others. The ideal situation from an energy consumption point of view is that the sender delivers the message when the destination node is reachable. This situation is detectable if the sender receives a gTCm from the destination node. In other case, the sender can delay the message delivery, as much as possible, waiting for a gTCm. If a gTCm is not received after the waiting period, the sender will have to decide if the message will be sent. Depending on the waiting period and node mobility, the sender could assume that the lack of a gTCm indicates that the target node has left the island, and therefore the sender could discard the message to save energy. In a previous work the authors showed that the TC message prediction strategy helps reduce the energy consumption of the nodes in a HWSN [7]. However, these results were obtained considering a particular setting, therefore it is not clear what are the limits of this proposal. In order to try identifying the potential impact of this energy saving strategy on HWSN we have defined five research questions:  
* How does the node density affect the energy consumption? * How does the speed of network nodes affect their energy consumption during the topology control process? * How does the mobility pattern of the nodes affect the energy consumption? * How does the network degree influence the energy consumption of the nodes? * How does the shape of the area where the nodes are deployed influence the energy consumption of the nodes? Several simulations have been done in this study trying to answer these questions. The following section describes the simulation settings and presents the obtained results.