esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The toxic dinoflagellate _Alexandrium catenella_ (Miozoa, Dinophyceae) (Group I, formerly _A. tamarense_ and toxic [1]) is known to produce strong neurotoxins similar to pufferfish (_Takifugu rubripes_, etc.) and cause paralytic shellfish poisoning (PSP) of edible bivalves (_Patinopecten vessoensis, Crassostra gigas, Ruditapes philippinarum_, etc.) through the food chain in coastal seas. The danger of PSP to human health makes it a serious threat in bivalve aquaculture industries worldwide [2; 3]. Occurrences of shellfish poisoning and the resultant harvesting closures have been reported in the farming of bivalves such as oysters and scallops. Hokkaido Island provides the largest production of scallops in Japan, and the incidents of paralytic shellfish poisonings in Hokkaido are frequent and have a long history [4].  
Akeshi-ko Estuary (area, 32.31 km\({}^{2}\)) is located in the eastern part of Hokkaido, and paralytic shellfish toxins in bivalves have only rarely been confirmed in the estuary during the monitoring by the local government of Hokkaido Prefecture. The most notable property of Akkeshi-ko Estuary is the existence of a large-scale seagrass bed that covers approximately 11-25 km\({}^{2}\) of the area (Nakaoka, personal communication).  
Bacteria that have the ability to kill microalgae have been isolated from the leaves of seagrass (_Zostera marina_), and those bacteria were observed to be present in huge densities [5; 6; 7; 8; 9; 10]. These prior investigations were conducted mainly targeting red tide algae, for example, the rapidophytes _Chattonella antiqua_, _C. marina_, _C. ovata_, _Heterosigma akashiuro_, and _Fibrocapsa japonica_, and the dinoflagellates _Karenia mikimotoi_, _Margalefidinium_ (=_Codoliniumium_) _polykrikoides_, and _Heterocapsa circularisquama_. These effective bacteria usually belong to \(\alpha\)-proteobacteria, \(\beta\)-proteobacteria, and the phylum Bacteroides [9]. Although the existence of growth-inhibiting bacteria against the toxic dinoflagellate _Alexandrium_ spp. has been reported [11; 12; 13; 14; 15], the reported bacteria in these studies [11; 12; 13; 14; 15] do not have strong activities. There are only a few studies on the isolation of bacteria with strong growth-inhibiting activities or on their existing densities in nature [8; 10; 16]. In addition, there have been no studies on the dynamics of growth-inhibiting bacteria together with the distribution and temporal fluctuations of _A. catenella_. We conducted the present study to understand the distribution and the composition of growth-inhibiting bacteria against the toxic dinoflagellate _A. catenella_ in Akkeshi-ko Estuary and Akkeshi Bay, eastern Hokkaido. We also investigated the reasons why shellfish poisoning has rarely occurred in the Akkeshi-ko Estuary.
## 1 Introduction
### Objectives  
This research intends to:  
1. Investigate the effectiveness of three NDTs (GPR, hand-held spectrometer, and an IR camera) in detecting leaks in WDI; and
2. Assess the factors that may affect the NDTs' response in detecting the water leaks.  
The three main factors considered in the study are: soil moisture content (2%, 5%, and 10%), pipe material (PVC, PE, and PPR), and types of leaks (vertical crack, hole, and faulty joint). These technologies are dynamic in nature [42]. The spectrometer has never been attempted for leak detection. Evaluation of factors affecting the performance of the NDTs to the authors knowledge was not previously investigated. As evidenced from literature, there has been limited research activities conducted using these technologies. The experimental setup developed in this study is novel in that it represents a simulation of the water distribution system in a much bigger scale than those observed in the literature. Novelty remains in that these experiments were conducted in open atmosphere, not in a controlled weather setting in the laboratory. Additionally, this research is novel in that the focus is on the evaluation of the factors affecting the performance of these leak detection technologies. It is especially crucial since versatility is essential in evaluating the efficiency of these technologies.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Terrain Model Generation
#### 2.3.1 Airborne LiDAR Data  
There are many publications on DTM generation from LiDAR data, where the first and last pulse data exist. Good review papers are [90; 91]. In regions of vegetation, the first pulse measurement corresponds to the top height of the vegetation while the last pulse is reflected from bare-earth, thus defining the terrain height. Therefore, shrubs, trees and whole forests can be easily filtered based upon LiDAR full-pulse data. Man-made objects like buildings, however, are present in both LiDAR measurements and still have to be filtered by appropriate algorithms (or even manually) to get a correct DTM. Since LiDAR provides very accurate height measurements, 3D breaklines are in general very well preserved in LiDAR DSMs. When moving along a 1D profile from street level to a building a sharp steep height jump is observed, which can be used as a distinct feature for the DTM generation process (cf. Reference [92]). One representative algorithm is described in Reference [93], which will serve as the baseline for the algorithmic evolution presented in this paper. A method on hierarchical robust linear prediction employing LiDAR data is introduced in Reference [94].
## 3 Optical Error Analysis
### Optical Errors of FTIS  
Based on the modulation method, FTIS can be classified into TMFTIS, SMFTIS, and TSMFTIS. Across these types of FTISs, the primary sources of error include mirror tilt error, collimation error, and beam splitter error. These errors affect interferograms \(I_{1}\) and \(I_{2}\) simultaneously, necessitating a detailed analysis of their impact on the inversion algorithm model and the super-resolution spectrum. Among these, collimation error can also influence the multiple-beam interference in the FPI, requiring a combined analysis of both effects. Additionally, the mirror tilt error in FTIS and the non-parallelism error in the FPI may interact and superimpose, making it essential to analyze the coupling effects of these errors comprehensively.
## 5 Simulation Results and Prototype Implementation
### Prototype Implementation and Measurements
During experiments, the following results were obtained from edge computing clusters:  
* Average power consumption of one edge computing per one Mbps of information transfer. * The average delay between the end device--the VR application server, depending on the number of hosts in the rendering cluster. In general, the use of edge computing at base stations or near network nodes increases power consumption. The more users accessing this unit increases at times (Figure 9). However, the price of electricity annually falls, and at the same time, the power of elemental bases increases. To improve the QoS of the VR application, we have to solve the problem with energy consumption. In Figure 10, the dependence of the response delay on the number of devices in the VR rendering cluster  
Figure 8: Architecture of the prototype system. Figure 9: Total number of users. was shown. In case of the larger cluster, the VR application will be generated faster. This happens due to computing parallelism.
# A New Under-Sampling Method to Face Class Overlap and Imbalance
## 4 Experimental Set-Up
### Data Sets  
We conducted the experiments on 20 real-life data sets (Table 1) and 12 synthetic data sets taken from the KEEL Data Set Repository ([[https://sci2s.ugr.es/keel/imbalanced.php#subA](https://sci2s.ugr.es/keel/imbalanced.php#subA)]([https://sci2s.ugr.es/keel/imbalanced.php#subA](https://sci2s.ugr.es/keel/imbalanced.php#subA))). All data sets have two classes and various levels of imbalance (the imbalance ratio ranges from 9.35 to 82).  
The experiments on the synthetic data were carried out on three databases with different shapes of the minority class (subclus, clover, and paw) whose instances are randomly and uniformly distributed in a two-dimensional feature space. In all cases, the positive instances are uniformly surrounded by the majority class. Each database comprises 800 instances with an imbalance ratio of 7 and different levels of random noise (0%, 30%, 50%, and 70%) on both classes: data from both the majority and minority classes were randomly mislabeled and some single feature-values were randomly changed.  
In subclus, the positive instances are located inside rectangles that form small disjuncts. Clover represents a more complex, nonlinear problem, where the minority class resembles a flower with elliptic petals. In a paw database, the minority class is decomposed into three elliptic sub-regions of varying cardinalities, where two sub-regions are located close to each other, and the remaining smaller sub-region is separated. Figure 2 illustrates two examples of these data sets: 0% and 70% of noise.
## 3 Results and Discussion
### Residual Levels of Organochlorine Pesticides in Soil
These processes contribute to the more rapid degradation of \(\gamma\)-HCH than other organochlorine contaminants in soil. Despite the above, OCPs are still characterized by high bioaccumulation potential and remain strongly adsorbed by organic matter particles [49; 51]. The concentrations of OCPs are higher in surface soil layers, probably because these contaminants are effectively transported and deposited by wind and precipitation [52]. The concentrations of pesticide residues tend to be higher in soils abundant in \(\mathrm{C_{org}}\)[53]. However, higher \(\mathrm{C_{org}}\) levels stimulate the activity of soil-dwelling microorganisms that degrade OCPs [54]. Soil microorganisms decompose DDT more rapidly than its metabolites, DDE and DDD. However, the degradation rate is generally slow, and it is affected by many factors, including soil type, temperature, moisture content, and the content of \(\mathrm{C_{org}}\) in soil [55; 56]. Figure 4: Residual levels of organochlorine pesticides in soil, ug kg\({}^{-1}\). (**a**) average for year, (**b**) average for treatment, %. Values followed by the same letters do not differ significantly in Tukey’s (HSD) test (_p_ < 0.05). \(\gamma\)-HCH—\(\gamma\)-hexahlorocyclohexane, DDT—1,1,1-Trichloro-bis-2,2 [4-chlorophenyl]-ethane, DDD—dichlorophenoxyyldichloroethane, DDE—dichloroethylene.
## 3 Mathematical Model Combining MoM with FEM
### Verification of Calculation Method with Experiments Results  
The buried conductors was subdivided into about 3000 segments. Boundary conditions were considered in the Green's function when applying MoM. The pipeline conductor domain was subdivided into 45,800 tetrahedron elements. The nonlinear polarization conditions can be treated as a Neumann boundary in each iterative computation when applying FEM. The leakage current density and PSP were measured under 100 A and 150 A grounding current, respectively. The results of the experiment and calculation are shown in Figure 8.  
The results show that, the current density and PSP at the center point are the largest along the pipeline for both test results and calculation results. The maximum test leakage current density changed from 5.987 mA/cm\({}^{2}\) to 8.719 mA/cm\({}^{2}\) when the grounding current changed from 100 A to 150 A. Accordingly, the maximum calculated leakage current density changed from 5.667 mA/cm\({}^{2}\) to 8.604 mA/cm\({}^{2}\). The relative error between the calculated  
Figure 8: Results of test and calculation. (**a**) leakage current density along the pipeline; (**b**) pipe-to-soil potential along the pipeline.  
Figure 7: The relationship between polarization potential and current density for the X80 pipe steel under 100 \(\Omega\cdot\) m, pH = 8 soil condition.  
results and the measured results for leakage current is 1.32-5.35%. In the same way, the relative error for PSP can be obtained, that is 2.63-7.72%. As the grounding current increases, the relative error becomes smaller. Therefore, the validity of the calculation method is verified by experiment results.
# ICN-Based Enhanced Content Delivery for CDN
## 2 Background and Related Work
### Name Resolution Mechanism  
A primary concept of ICN is naming data independently from their physical location. Each Named Data Object (NDO) in ICN is identified with a unique name, which is not directly usable for forwarding or routing within the current IP network. Consequently, the issue of locating and discovering NDOs using their location-independent names has arisen. To address this challenge, a Name Resolution Service (NRS) is required, which provides the mapping between Identifiers (IDs) and Network Addresses (NAs). A NRS in ICN is defined as the service that provides the name resolution function for translating an object name into some other information, such as a locator, another name, metadata, next-hop info, etc., that is used for forwarding the object request [26]. There are generally three kinds of name resolution service approaches:  
1. Explicit name resolution approach: This approach is also referred to as a standalone name resolution approach. In this approach, the name resolution process and content request routing process are decoupled. The NRS first returns to the client the locators of the content and then it is used by the underlying network as the identifier to route the content request. This approach is employed by many ICN architectures, such as PURSUIT [5], DONA [6], NetInf [7] and MobilityFirst [8]. For instance, in the MobilityFirst ICN architecture, the infrastructure of NRS is called the Global Name Resolution Service (GNRS). It is designed as a massively scalable distributed system which provides the dynamic binding between a content's Global Unique ID (GUID) and its NA.
2. Name-based routing approach: Some ICN architectures employ a name-based routing approach which integrates the name resolution process with content request routing. In this approach, the name is used as the basis for routing and forwarding the content request. When a request is initiated, the name is used to search for the NDO in the NRS cache. The NDO is forwarded back to the requester along the same path used for request routing but in the opposite direction. A typical example is NDN [4].
3. Hybrid approach: The hybrid approach entails both an explicit name resolution approach and name-based routing approach to complement each other. For example, if the name-based approach fails at a certain router, the explicit name resolution approach can be utilized as an alternative, and vice versa.
## 3 Materials and Methods
### ZTD Model  
There are two main types of ZTD models, combined and empirical models. The combined model estimates the ZTD via a combination of a meteorological model and a classic ZTD model which requires meteorological parameters, while the empirical model obtains the required meteorological parameters from a look-up table that usually depends on station latitude and height, as well as observation time.
# Class-Ceramics Processed by Spark Plasma Sintering (SPS) for Optical Applications
###### Abstract  
This paper presents a review on the preparation of glass-ceramics (GCs) and, in particular, transparent GCs by the advanced processing technique of spark plasma sintering (SPS). SPS is an important approach to obtain from simple to complex nanostructured transparent GCs, full densification in a short time, and highly homogeneous materials for optical applications. The influence of the different processing parameters, such as temperature, pressure, sintering dwell time on the shrinkage rate, and final densification and transparency, are discussed and how this affects the glass material properties. Normally, transparent glass-ceramics are obtained by conventional melt-quenching, followed by thermal treatment. Additionally, the GC scan is produced by sintering and crystallization from glass powders. Hot pressing techniques (HP) in which the source of heating is high-frequency induction can be also applied to enhance this process. In the case of transparent ceramics and glass-ceramics, spark plasma sintering is a promising processing tool. It is possible to enhance the material properties in terms of its compactness, porosities, crystallization, keeping the size of the crystals in the nanometric scale. Moreover, the introduction of a high concentration of active gain media into the host matrix provides functional glass-ceramics systems with enhanced luminescence intensity through reducing non-radiative transitions like multi phonon relaxation (MPR) and cross relaxations (CR), thus providing longer lifetimes. More effort is needed to better understand the sintering mechanisms by SPS in transparent GC systems and optimize their final optical performance.  
Keywords:spark plasma sintering; glass-ceramics; pressure; density; transparency; optical applications +
Footnote †: journal: Applied Physics Letters
# The Optic Strut--CBCT Pneumatization Pattern and Prevalence
###### Abstract  
(1) Background: The optic strut (OS) is a critical landmark for clinoid and paraclinoid surgical interventions. To our knowledge, the current literature only mentioned the OS as a possibility for a lesser sphenoidal wing (LSW) preunatization path, without a proper study of the preunatization patterns and prevalence within this structure. Thus, our aim was to fill in the missing information. (2) Methods: A retrospective study on 80 cone beam computed tomography (CBCT) files was conducted to assess the prevalence and the origins of pneumonia within the OS. (3) Results: The preunatization patterns of the OS were: 56.25% from the sphenoid sinus, 1.25% from the posterior ethanol air cells (PEAC), and 10% from Onodi cells (ONC). Simultaneous pneumonia of unique origin within the lesser spheroidal lung (LSW) was found in 26.25% from the sphenoid sinus, 1.25% from PEAC, and 5% from ONC. Communication between both LSW roots through preunatization was found in 6.25% of the files. (4) Conclusions: A careful radiological examination should precede clinical diagnosis and surgical interventions in the paraclinoid area to evaluate postoperative surgical risks and possible diffusion patterns for infection. Additionally, preunatization within the OS alters its morphological features and thus, its utility as a landmark.  
optic strut; sphenoid bone; anterior clinoid process; Onodi cell; computed tomography +
Footnote †: journal: Applied Sciences  
The Optic Strut--CBCT Pneumatization Pattern and Prevalence  
[PERSON]  
visual function perturbation, intracranial bleeding, or pneumoephalus, all to the extent of a fatal outcome [4,6].  
Most studies examine the pneumatization within the LSW regarding the anterior clinoid process (ACP), mentioning the OS as a bridge for air cells to reach the ACP. To our knowledge, no study has been conducted strictly on OS pneumatization for prevalence or its source (via sphenoid sinus, ethmoid posterior cells, or Onodi air cells).
## 2 Principles of DAS Fiber Optic
### Procedure  
The DAS acquisition process is a method used to detect and measure acoustic signals using an optical fiber [34]. The process involves several steps that are used to generate, propagate, detect, and interpret the signals.  
1. Generating the excitation signal: a light source, such as a laser, is used to create an excitation signal that is sent into the optical fiber. This signal can be a continuous wave (CW) or a pulsed wave, depending on the specific application and environment.
2. Propagation of the excitation signal: the excitation signal travels along the length of the optical fiber, scattering and reflecting off any changes in the fiber caused by acoustic signals. This scattering and reflection of the excitation signal is known as backscattering.
3. Detection of the backscattered signal: the backscattered signal is then detected and measured by a detector, such as a photodiode. The detector converts the optical signal into an electrical signal, which can be further processed.
4. Signal processing: the electrical signal is then passed through a signal processing system to extract the relevant information. This can include demodulation, filtering, and other signal processing techniques. The specific signal processing techniques used will depend on the type of signals being detected, the distance to the reflection, and the noise level of the system.
5. Data interpretation: the processed data is then interpreted to determine the location and characteristics of the acoustic signals. This can include determining the distance to the reflection and the amplitude and frequency of the signal.
6. Data visualization: the data is then visualized to provide a clear representation of the acoustic signals. This can include displaying the data on a map or in a graph, and can also include the use of data visualization tools such as GIS. This step allows for easy interpretation and analysis of the data.  
It is worth noting that the specific steps and methods used for DAS acquisition can vary depending on the specific application and environment. The choice of light source, detector, and signal processing techniques will depend on the type of signals being detected, the distance to the reflection, and the noise level of the system. These variations are necessary to ensure accurate and reliable data acquisition.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 1 Introduction
In addition to minisatellite- and microsatellite, some larger SAR spacecraft and Cubesat-level non-SAR nanosatellites are also considered. Section 4 concludes with potential use cases of small-satellite SAR in global biospheric monitoring as an extension of larger SAR missions. Figure 2: Classification of small satellites. The nanosatellite example shows a 3U CubeSat platform. Source: NASA, National Centre for Space Studies (CNES), Universitat Politècnica de Catalunya, KTH Royal Institute of Technology, École polytechnique fédérale de Lausanne, MIT [34].