esa-sceva/satcom-chunk-collection huggingface dataset:
# Resilience Analysis for Double Spending via Sequential Decision Optimization
## 6 Attack Planning as an Optimal Stopping Problem
### Attack Optimization in the Optimal Stopping Formulation  
In our numerical approach we use the length of the secret chain \((\tilde{Z}_{t})_{t=0}^{\tilde{T}+1}\) from (8) as our state process. According to the observation (19), the evolution of the underlying state process needs to be examined merely on a finite range  
\[\{(t,x):t=0,1,\dots,\tilde{T}+1,x=0,\dots,t+n\}\subset\mathbb{N}. \tag{24}\]  
Having re-defined the reward (21) in accordance to (19) and (22) as  
\[\tilde{R}_{t}(x)=\left\{\begin{array}{ll}R_{t}(t-x)&\text{for }t=0,\dots, \tilde{T},\quad x=0,\dots,t+n,\\ C&\text{for }t=0,\dots,\tilde{T},\quad x=t+n+1,\dots,\\ 0&\text{for }t=\tilde{T}+1,\quad x\in\mathbb{N},\end{array}\right. \tag{25}\]  
we equivalently re-formulate the problem (23) as  
\[\begin{array}{l}\text{determine a maximizer }\tau^{*}\text{ to }\mathcal{T} \rightarrow\mathbb{R},\quad\tau\mapsto\mathbb{E}(\tilde{R}_{\tau}(\tilde{Z}_{ \tau}))\text{ where }\\ \mathcal{T}\text{ denotes all }\{0,\dots,\tilde{T}+1\}\text{-valued stopping times.}\end{array} \tag{26}\]  
To solve the above optimal stopping problem, we introduce the value functions \((\tilde{V}_{t})_{t=0}^{\tilde{T}}\) to (26) in terms of the standard backward induction, which is initialized by the expected value function  
\[\tilde{V}_{\tilde{T}}^{E}(x)=0,\qquad x\in\mathbb{N}, \tag{27}\]  
and is followed recursively for \(t=\tilde{T},\tilde{T}-1,\dots\), by  
\[\tilde{V}_{t}(x) = \max\{\tilde{R}_{t}(x),\tilde{V}_{t}^{E}(x)\}\quad x\in\mathbb{N}, \tag{28}\] \[\tilde{V}_{t-1}^{E}(x) = \mathbb{E}(\tilde{V}_{t}(\tilde{Z}_{t})|\tilde{Z}_{t-1}=x),\quad x \in\mathbb{N}. \tag{29}\]  
Since \(\tilde{V}_{t}(x)=C\) for all \(x>t+n\), each value function \(\tilde{V}_{t}(x)\) needs to be calculated only for states \(x=0,1,\dots,t+n\). We thus obtain instead of (27)-(29)  
\[\tilde{V}_{\tilde{T}}^{E}(x) = 0,\qquad x=0,\dots,\tilde{T}+n,\] \[\tilde{V}_{t}(x) = \max\{\tilde{R}_{t}(x),\tilde{V}_{t}^{E}(x)\}\quad x=0,\dots,t+n,\] \[\tilde{V}_{t-1}^{E}(x) = \mathbb{E}(\tilde{V}_{t}(\tilde{Z}_{t})|\tilde{Z}_{t-1}=x)\quad x =0,\dots,t+n-1.\]Please note that in the last equality, the conditional expectation can be calculated as  
\[\bar{V}_{t-1}^{E}(x) = \sum_{j=0}^{t+n-x}\bar{V}_{t}(x+j)g(j)+C\sum_{j=t+n+1-x}^{\infty}g (j)\] \[= (1-q)\sum_{j=0}^{t+n-x}\bar{V}_{t}(x+j)q^{j}+Cq^{t+n+1-x},\quad x=0,\ldots,t+n-1.\]  
Having determined the value functions \(\bar{V}_{t}(x)\) for \(t=0,\ldots,\bar{T}\) and \(x=0,\ldots,t+n\), continuation region is obtained by  
\[\mathcal{C}=\{(t,x):t\in\{0,\ldots,\bar{T}\},x\in\{0,1,\ldots,t+n\}:\bar{V}_{t }^{E}(x)>\bar{R}_{t}(x)\}\]  
and the optimal attack time \(\tau^{*}\) is obtained as the first exit time of the process \((\tilde{Z}_{t})_{t=0}^{\bar{T}}\) from the region \(\mathcal{C}\)  
\[\tau^{*}=\inf\{t=0,\ldots,\bar{T}+1:(t,\tilde{Z}_{t})\
ot\in\mathcal{C}\}.\]
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library
### Gaussian Process (GP) Classifier  
GP is a stochastic model used within the machine learning discipline for classification and regression analyses [80]. GP can be considered an infinite collection of random variables that feature multivariate Gaussian (normal) distributions for any finite subset of this collection. The random variables are described by an infinite set of functions, which are further constrained by means of observations to derive posterior distributions over functions. In this way, GP does not make any assumptions about the parameters of the function featuring a \"best-fit\" to observed values, but instead, a priori, it places directly on the space of functions. The closer to training samples a new sample is within this space, the higher probability that the retrieved value will be correct. GP allows for the quantification of this probability. It is characterized by its mean and covariance functions, which facilitate analytical manipulations due to the simple mathematical solutions originating from the properties of a multivariate Gaussian distribution. The mean and covariance functions have to be selected a priori, which may be difficult for a small noisy training dataset. In such a case, the mean function is often set to zero for the sake of numerical convenience, since it is always possible to center a data distribution around zero. Derivation of an appropriate covariance function is more problematic since it is defined in terms of hyper-parameters, which have to be inferred by the GP itself. The definition of hyper-parameters allows for the incorporation of expert knowledge, e.g., about the smoothness or periodicity of the modeled phenomenon. The main advantages of the GP method are related to the derivation of classification probability and to the incorporation of a priori knowledge about the classified phenomena. The main disadvantage of GP is the computational complexity, which quickly scales up with the number of samples; thus, sparse approximation techniques are required for large training datasets.
# Fully Printed Flexible Single-Chip RFID Tag with Light Detection Capabilities
## 2 Materials and Methods
### Characterization  
The antenna characterization has been performed with a ZVA40 Vector Network Analyzer (Rohde & Schwarz, Munich, Germany). The S-parameter differential port between port-1 and port-2 has been defined through a test fixture simulator included in the Vector Network Analyzer (VNA) because of the differential character of the antenna measurement. For characterization purposes, Ultra Miniature Coaxial connectors (U.FL) type (Hirose Electric, Tokyo, Japan) have been attached on the antenna feed point. SubMiniature version A (SMA)to U.FL wires have been employed to connect the antenna to the VNA, using a custom calibration kit that was developed using the U.FL connectors [11].  
Figure 3: Cross-sectional view of the OPD. An inkjet printed Ag line modified with spray-coated PEI is used as reflective bottom-electrode, while a 700 nm thick blend of P3 HT and PCBM is used as photoactive layer. The top semi-transparent electrode is obtained via a mixture of the conductive polymer PEDOT:PSS.  
A commercial RFID reader compatible with EPC Gen2, DK-UHF RFID Radon (AMS AG, Unterpremstaetten, Austria) was employed to measure the read range of the tag and read out its sensor data through an application running in a PC.  
The IV-characterization of the OPDs were performed by means of a Keithley 2602 sourcemeter, (Tektronix, Beaverton, OR, USA), dark current measurements were performed inside a dark chamber, while the illuminated current measurements were performed under a sun simulator at AM1.5. Finally, the External Quantum Efficiency (EQE) of the fabricated devices was evaluated using a 300 W xenon arc lamp chopped at 77 Hz, through an Oriel Cornerstone 260 1/4 m monochromator (Newport Corporation, Irvine, CA, USA) and an Oriel Merlin digital lock-in amplifier (Newport Corporation, Irvine, CA, USA). The performance of the temperature sensor was tested in the climatic chamber VCL 4006 (Votsch Industrietechnik GmbH, Balingen, Germany).
## 3 Materials and Methods
### Materials
#### 3.1.1 Study Area  
Located in the Lungau region (Tamsweg district), Austria (47 deg00'-47 deg13' N, 13 deg23'-14 deg00' E, UTM/WGS84 projection), the study area is managed by the Austrian Federal Forests (OBF AG). Part of the Central Eastern Alps, the area is mountainous with an altitude range of 993-1906 m and has a distinct alpine climate with an average annual temperature of 5.2 degC, mean annual precipitation ranging between 770 and 840 mm, and a snow cover minimum of 1 cm for 105 days per year. The study area, illustrated in Figure 1, has an area of 21,826.55 ha and is predominantly covered with forests (63.9% of the area). The forest area is further divided into commercial forests and protective forests. The commercial forest is managed to maximize the income from timber production while minimizing the risk of forest damage. In contrast, a protective forest's objective is to protect against avalanches, rockfall, erosion, and floods. Although thinnings are planned in both forest types, thinnings in the protective forest aim to ensure its protective function, whereas thinnings, as specified in Section 2.1, are just planned in a commercial forest. In this paper, we are interested in the forests where commercial thinnings are feasible. Hence, we restrict the study area to the commercial forest. The commercial forest has an extent of 9353.54 ha and is stockeded with mainly coniferous forests. The most frequent tree species are 81.0% Norway spruce (_Picea abies_, 81.0%) and European larch (_Larix decidua_, 17.6%). The remaining 1.4% of the area is stocked with deciduous forests.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Name** & **Source** & **Format** & **Bands** & **Res. (m)** & **S.E. (m)** & **Year** \\ \hline RGB + NIR & AI & GeoTIFF & 4 & 0.2 & - & 2018 \\ CHM & AI & GeoTIFF & 1 & 1 & 1 & 2018 \\ DTM & ALS & GeoTIFF & 1 & 1 & 0.15 & 2013 \\ Slope & ALS & GeoTIFF & 1 & 1 & - & 2013 \\ Ground Truth & RD & Shape & 3 & - & - & 2017 \\ \hline \end{tabular}
\end{table}
Table 1: Data sources. NIR: near-infrared, CHM: canopy height model, DTM: digital train model, AI: aerial imagery, ALS: airborne laser scanning, RD: actual thinning reference data, Res.: spatial resolution, S.E.: standard error, and Year: acquisition year. Slope data computed from the DTM.
## 2 Basic Principle, Experimental Setup and Results
### The MPF Principle of Operation  
Figure 1 depicts the basic configuration of the MPF. It is a point-to-point electro-optical communication system at external modulation that includes a multimode laser diode (MLD) as the optical source, an electro-optic Mach-Zehnder-intensity modulator (MZ-IM) an optical link of the SM-SF, and a photo-detector (PD). In [10], it was demonstrated that the frequency response of the system is proportional to the Fourier transform of the MLD spectrum. The interested reader can be addressed to reference [10] for detailed information about the transfer function of the MPF. Additionally, the frequency response is composed of a series of filtered signals or microwave band-pass  
windows that are generated by considering the intermodal separation (\(\delta\lambda\)) of the MLD, the length (\(L\)) of the optical fiber, and its associated chromatic dispersion parameter (\(D\)).  
The corresponding value to the central frequency of the \(nth\) filtered microwave band-pass windows is computed as [10]:  
\[f_{n}=\frac{n}{DL\delta\lambda} \tag{1}\]  
where \(n\) is an integer.  
Moreover, the associated bandwidth at \(-3\) dB of the \(nth\) band-pass window is determined by [10]:  
\[\Delta f_{bp}=-\frac{4\sqrt{ln2}}{\pi DL\Delta\lambda} \tag{2}\]  
where \(\Delta\lambda\) is the full width at half maximum (FWHM).
# Nosemosis in Honeybees: A Review Guide on Biology and Diagnostic Methods
## 5 Diagnostic Methods
### Molecular Diagnostics
#### 5.3.2 Quantitative PCR (qPCR, Real-Time PCR)  
Quantitative real-time PCR allows for both relative quantification and absolute quantification of _N. apis_ and _N. ceranae_ spores in a sample. The precise information to carry out the reaction is described by [PERSON] et al. [44]. The first step is the design of primers which should be species-specific. Usually, the qPCR reaction mixture is created separately for _N. apis_ and _N. ceranae_ using dedicated primers (NaFor and NcFor respectively). Mastermix contains 2 \(\upmu\)L DNA, SsoFast EvaGreen(r) Supermix, 0.4 \(\upmu\)M of dedicated primer, 0.4 \(\upmu\)M UnivRev primer, and 6.4 \(\upmu\)L water. The reaction is carried out under the following conditions: initial enzyme activation step for 15 min at 98 \({}^{\circ}\)C and 40 cycles (denaturation at 98 \({}^{\circ}\)C for 5 s, extension at 63 \({}^{\circ}\)C for 10 s, melt curve analysis from 65-95 \({}^{\circ}\)C one step/10 s) [44]. According to other studies, we can contain two tested samples and the necessary reagents in one real-time PCR reaction mixture [50; 51]. The normalization of the PCR reaction is crucial to carry out it correctly. The most common way is to compare the DNA level of a sample to a reference gene for which the detection level is generally stable [50; 52; 53]. The qPCR test seems to be a fast, efficient, and sensitive method for detecting infections of both microsporidia species (Table 2) [51]. That method was also used to determine the infection level of _N. ceranae_ depending on the ages of the bees and also depending on the way of infecting (individual or collectively infection) [21].  
Another innovative system to quantify _N. ceranae_ and _N. apis_ in the sample uses the light intensities of bands. Products of amplification are separated by electrophoresis in 1.1% gel, photographed, and developed in a dedicated program [52]. Amplification is performed in a standard thermocycler, not a real-time thermocycler. In the HBRC(abbreviation of components of buffer) method buffer consists of 0.03 M hexadecyltrimethyl ammonium bromide, 0.05 M tris hydroxymethyl aminomethane, 0.01 methylenediamine tetra-acetic acid, 1.1 M NaCl, and distilled water. In comparison to the germination-extraction method, [5] the HBRC method is quite inexpensive, less time-consuming, and still highly sensitive (Table 2). To obtain relative quantification, genes of both pathogens' hosts were used additionally [52].  
Both methods (endpoint PCR and qPCR) may be used by scientists and diagnosticians, but they are more expensive than microscopy.
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 1 Introduction
In fact, if a UAV could have installed the same radio-frequency equipment with a similar protocol stack to target both IoT nodes and broadband users, it would be convenient for mobile operators. To this purpose, there exist a number of technologies targeting IoT applications which follow the fourth-generation (4G) numerology [5]. In particular, we will focus on NarrowBand-IoT (NB-IoT) for the design intended to target low-end IoT applications with low data rates, delay tolerance, massive connections, and extremely wide coverage [6]. To this end, this work can be considered an extension of [1], where an initial and simple approach was proposed to UAV-aided NB-IoT networks. In this article, we thoroughly study different approaches to aerial support for NB-IoT networks, in order to provide a general overview of the challenges and potentials of these systems. To properly assess the network performance of UAVs serving NB-IoT nodes, we jointly consider as performance metrics the percentage of completely served nodes, the throughput provided and the latency which has to be expected and the IoT nodes' energy consumption. Our key contributions can be summarized as follows:  
Figure 1: Illustrative example of a scenario with multiple IoT applications and nodes served by UAV BSs (UABs). We propose a UAV-aided NB-IoT scenario with several hundreds of nodes, located in different parts of the area as to simulate diverse applications. Differently from other papers in literature, the NB-IoT technology is considered in details as specified by the 3 GPP documents and studied in all of its features, including the signaling procedures and the parameters' implications of the three NB-IoT coverage classes;
* we investigate the performance in terms of number of completely served nodes, achieved network throughput, perceived latency and energy consumption of nodes;
* we analyze the impact of using different UAV trajectories and the effect of varying UAV parameters, such as speed and height. The article is organized as follows. In Section 2, we give an overview of the literature about UAV-aided networks in IoT applications. NB-IoT and its features are presented in Section 3. Section 4 describes the scenario and the network model. Final simulation results are reported and discussed in Section 5. Finally, Section 6 concludes the article.
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 2 Materials and Methods
### Methodology
#### 2.3.1 Downloading the Base Satellite Data  
Sentinel-1 and -2 data can be downloaded free of charge from ESA's Copernicus Open Access Hub. With the help of a Python script, the Wget program (www.gnu.org/software/wget) was called to search for relevant Sentinel images. A list with images was returned and processed to extract the image URLs. These URLs were fed to Wget, and the files were downloaded sequentially. If, due to overload  
Figure 4: Inland excess water detection workflow based on preprocessed Sentinel-1 and Sentinel-2 data. The elements with a green background indicate the optical data based Modified Normalized Difference Water Index (MNDWI) workflow, the yellow background indicates the optical data based Iterative Self-Organizing Data Analysis Technique (ISODATA) classification workflow and the blue background indicates the radar data-based workflow.  
of the ESA server or other reasons, the file was not downloaded correctly, the process was restarted automatically after a defined time interval. The script was restarted every day in the week that was processed because sometimes data only became available a few days later instead of immediately after acquisition.
# Soil Is Still an Unknown Biological System
## 2 Properties of Soil as a Biological System  
### Physicals Structure and Distribution of Organisms in Soil  
Soil is a peculiar environment for living organisms, being composed of solid, liquid, and gaseous phases. The solid phase prevails, and the extent of the liquid and gaseous phases can change depending on agricultural practices and climatic conditions. The soil structure--that is, the organization and arrangements of soil particles--influences the biological, chemical, and physical properties of soil. Soil particles differ in their size, shape, and chemical composition, and thus they can be linked with different bonds. Soil structure is a hierarchical organization because primary particles bind together to form secondary particles that can interact to form bigger particles, such as microaggregates and macroaggregates [3]. This process varies in time and space [4], and the organization of the solid particles creates differently sized pores that may be filled by water or telluric atmosphere. According to [PERSON] and [PERSON] [5], pores can explain the spatial separation of soil organisms because (i) microarthropods can only inhabit macroopes; (ii) nematodes can also live in intermacroaggregate pores; (iii) protozoa, small nematodes, and fungi can also be present in intramacroaggregate or intremicroaggregate pores; and (iv) intramicroaggregate pores can only be occupied by bacteria and viruses [6]. Indeed, the pore occupancy depends on the organism size; for example, bacterial size is a few micrometres, that of fungi is less than 100 \(\upmu\)m, and that of Acari and Collembola ranges from 100 \(\upmu\)m to 2 mm [6]. Such separation can have important effects on soil organisms because, for example, bacteria inhabiting intramicroaggregate pores escape protozoa predation.  
Soil organisms inhabit less than 1% of the available space. The microsites (hotspots) with the higher microbial abundances are those where nutrients are available, such as the rhizosphere (the soil attached to roots), the detritsphere (the soil around a plant residue), the rhizosphere (soil around biopores created by earthworms), etc. [7; 8]. A plausible explanation of the limited occupancy of soil volume by living organisms is the hostile environment of most soil micro-niches; conditions such as acidity; low water or O\({}_{2}\) availability; competition between different organisms; predation; and frequent disturbances, such as drying-rewetting and freezing-thawing cycles and the presence of toxic compounds, can inhibit the activity of soil organisms [9]. However, as shown in Table 1, the microbial biomass is huge, ranging from 1% to 5% of the organic matter of soil; these values, expressed as microbial biomass C or microbial biomass N, can range from 280 to 1940 kg\(\cdot\)ha\({}^{-1}\) and from 40 to 385 kg\(\cdot\)ha\({}^{-1}\), respectively [10].
## 5 LiDAR 3D Point-Cloud Acquisition Techniques
### Laser Scanning
#### 5.1.2 Solid-State LiDAR Scanners
The design of such a scanner is based on scanning area size, measuring distance, power consumption, and measuring speed [41]. The measuring distance is short. MEMS scanners are used in automotive, machine vision, and laser microscopes. Hamamatsu Corporation [42] and Mirrorcle Technologies [43] are producing MEMS which have different configurations. OPAs are an emerging technology made of arrays of closely spaced (about 1 \(\upmu\)m) optical antennas that radiate coherent light in a broad angular range. The laser beam is sent by splitting into micro-wavelength rays. The beam is steered by the microwave arrays controlling the relationship between the receiving antenna arrays by balancing them. Pulse time or FMCW methods are used for distance measurement. Ambient light can reduce the SNR and cause false detections in ToF ranging. Thus, the FMCW method has high accuracy in range detection. OPA is a fast scanning technique. OPA scanners are solid-state, which have no or fewer moving parts, and are unaffected by vibrations. OPA components are based on mechanical control, such as motor-driven rotating collimation mirrors. The mechanical beam steering provides high efficiency and a relatively large scanning angle, through mechanically moving parts such as gimbals. The bulky mechanical steering approach is less preferred due to acceleration, temperature, and vibration [44]. OPA technology enables very fast scanning (100 kHz) with low beam divergence and a wide FoV angle. A high-power and low-divergence beam is needed to accurately resolve a scene. Detecting a 10 cm object at a 100 m distance away requires an OPA steering at a wavelength of 1 \(\upmu\)m with a circuit consisting of at least 1000 antennas, each spaced 1 \(\upmu\)m apart. The OPA scanning provides a highly reliable and cost-effective 3D LiDAR solution that is important for the automotive industry. It has also been used for mobile mapping by ground vehicles or UAVs. The OPA can be implemented in the visible, near-infrared, and mid-infrared spectral ranges. The main performance parameters of an OPA scanner are FoV, beam width, modulation speed, power consumption, and scalability.
## 4 NFV and SFC Frameworks Taxonomy
### Resource Allocation and Service Orchestration Frameworks
#### 4.1.3 Pishahang Framework  
Pishahang is a multi-domain service orchestration framework for SFCs, proposed by [PERSON] et al. [73; 75] (with the introduction of dynamic service chaining), which combines container-based and VM-based vNFs to create a chain of network services. The framework was implemented across the OpenStack and Kurbenetes domains. In terms of service description, Pishahang uses two descriptors: the first for describing information considered to be _high level_, such as service chaining, microservices, and vNFs, while the second descriptor focuses on a more fine-grained description of vNFs, such as the required resources for running the vNFs. Pishahang is a framework that has been built to chain services across heterogeneous domains.  
Pishahang used the SONATA MANO [[https://www.sonata-nfv.eu/](https://www.sonata-nfv.eu/)]([https://www.sonata-nfv.eu/](https://www.sonata-nfv.eu/)) (accessed on 15 December 2021) framework, which supports the addition of new functionalities following a microservice-based architecture. Service graphs are translated by the SDN adaptor, which is sent to the controller, converted to forwarding rules, and installed on switches. To validate the features of Pishahang in chaining services across multiple domains, VM-based and container-based forwarders were chained to create an SFC with ICMP packets sent end-to-end. The use of containers for the deployment of vNFs is yet to be fully developed because of reasons such as lack of functional isolation, and security.