esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Atmospheric Datasets  
We used satellite-derived rainfall estimates from the Tropical Rainfall Measuring Mission Multisatellite (TRMM) Precipitation Analysis (TMPA) 3B43 product, averaged monthly from 1998 to 2013 at a 0.25-degree spatial resolution [16]. TRMM precipitation is calculated from the radiative and emissive properties of cloud hydrometers at visible, infrared, and microwave wavelengths, which serve as proxies for rainfall rate. The TRMM 3B43 dataset combines rain gauge, infrared, passive-microwave, and precipitation radar estimates and is generally well correlated at a monthly time scale with African rain gauge measurements across the continent [17] and over East Africa's complex topography [18]. To supplement TRMM data, we used the Global Precipitation Climatology Project (GPCP 1979-2012) [19] dataset because it has a longer record, starting from 1979, and is thereby valuable to compare the 2010-2011 drought with other historic droughts. GPCP data are derived from rain gauge and satellite data. We note that the number of rain gauges has declined throughout this region (40 in 1979 to 5 in 2010) [20].  
Soil moisture data integrate precipitation anomalies in time and were used here to depict the spatial distribution of contemporary African droughts. Soil moisture products retrieved from Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) represents soil moisture in the top 1-2 cm of soil at a spatial resolution of \(\sim\)50 km, sensitive to small precipitation events [21]. Land emissivity is a function of soil moisture; AMSR-E employs a low-frequency passive microwave remote sensing approach to measure the brightness temperature at Earth's surface [21]. The ASCAT is an active microwave sensor that measures backscatter from the surface, which is a function of soil moisture. This resulting soil moisture estimate demonstrates potential for drought monitoring [22]. Remotely-sensed soil moisture measurements are used in similar studies to monitor drought, including to characterize the spatial and temporal distribution of the 2010-2011 East Africa drought [23]. This study differentiates itself from previous research in that it characterizes the vegetation response to these conditions.  
Reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to characterize the atmospheric and land surface conditions that led to the successive failure of the 2010-2011 East African monsoons [24]. Variables analyzed for drought monitoring extended from January 1979 to present and were analyzed to assess different drought effects and dynamics. Two m air temperature was selected due to its relationship to surface soil drying (as in [25]), its associated role as a climate driver (as in [26]), and because higher temperatures reduce water use efficiency during photosynthesis [27]; water vapor flux data were selected to monitor regional moisture transport. Sea surface temperature (SST) data were from the HadISST [28].
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 7 Sequential MO Helper Data Scheme
### Security and Achievable Rate  
The main difference between the Sequential MO helper data scheme and the original MO helper data scheme is that the helper data are now updated sequentially in the field. Therefore, an attacker may have access to multiple helper data \(\left(m_{1}^{n},m_{2}^{n},\ldots,m_{t}^{n}\right)\), instead of only the final helper data \(m_{t}^{n}\). However, despite the reveal of multiple helper data, we can show that zero-secrecy leakage is still guaranteed. In particular, a similar Markov Chain as defined in (20) holds in this case  
\[S\leftrightarrow C^{n}\leftrightarrow(W_{1}^{n},W_{2}^{n},\ldots,W_{t}^{n}) \leftrightarrow(M_{1}^{n},M_{2}^{n},\ldots,M_{t}^{n}). \tag{40}\]  
Therefore, we can repeat the same derivations as in Section 4.2 to show that  
\[I(S;M_{1}^{n},M_{2}^{n},\ldots,M_{t}^{n})\leq I(C^{n};W_{1}^{n},W_{2}^{n}, \ldots,W_{t}^{n})=0, \tag{41}\]  
and thus, there is no information leakage about the key by all the revealed helper data.  
Please note that only the helper data are updated, whereas the key that has been generated during the enrollment phase remains the same. Since the key should be reconstructable already after the initial enrollment (since successful reconstruction is required for each update), the maximum achievable rate of the Sequential MO scheme is limited by the number of observations \(t_{0}\) that is used for the initial enrollment, i.e.,  
\[R_{\text{SMO}J_{0}}^{*}=R_{\text{MO}J_{0}}^{*}=I(Y;X_{1},X_{2},\ldots,X_{t_{0} }). \tag{42}\]  
Nevertheless, as we show in the next subsection, the main advantage of the SMO scheme is that the reconstruction observations are exploited to improve the reconstruction error probability over time.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 1 Introduction
The scheme separates the localization process into two modes, low-rate and high-rate transmission mode, by choosing appropriate bit duration which can both increase the range measurement accuracy in multi-hop networks and ensure the transmission rate in UANs. Secondly, we present an Optimal References Selection Scheme that only needs one anchor and can reduce the influence of references' topology on localization accuracy. Above all, the proposed DRL algorithm can be used in multi-hop UANs, increase the localization accuracy and only needs one anchor to accomplish the localization procedure which is more suitable for the underwater environment. The paper is organized as follows: Section 2 describes the structure of UANs in this paper; then the double rate self localization algorithm with one anchor is proposed in Section 3; Section 4 analyses the simulation results of the proposed algorithm; the real experiment results are analyzed in Section 5; finally, Section 6 draws a conclusion.
# A Physical-Layer Watermarking Scheme Based on 5G NR
###### Abstract  
Based on the existing 5G NR system, a physical-layer watermarking scheme is proposed to enhance the physical-layer security in 5G communication systems. A new scheme for watermark generation is proposed to improve the robustness of the authentication. The watermark signal is embedded in the phase of the demodulation reference signal (DMRS), and the influence of the watermark on the demodulation reference signal is reduced by designing the encoder of the watermark. Simulation results show that the watermarking scheme proposed in this paper has good anti-noise and anti-frequency-offset performance, and has good feasibility both in the Gaussian channel and Rayleigh fading channel.  
physical layer security; physical layer watermark; channel coding; 5G NR Article  
A Physical-Layer Watermarking Scheme Based on 5G NR  
2022113184. [[https://doi.org/10.3390/electronics11193184](https://doi.org/10.3390/electronics11193184)]([https://doi.org/10.3390/electronics11193184](https://doi.org/10.3390/electronics11193184))
## 2 Material and Experimental Procedure  
Nine ply laminates of carbon fibre bidirectional plain weave fabric (taffeta weave with 160 g/cm\({}^{2}\)), all in the same direction, and an Ampreg 22 epoxy resin with an Ampreg 22 hardener standard, both supplied by Gurit, were used to produce composite laminates. Plates with overall dimensions of 330 \(\times\) 330 \(\times\) 2.4 \(\pm\) 0.1 (mm\({}^{3}\)) were produced by the hand lay-up process. This system was placed inside a vacuum bag and a load of 2.5 kN was applied for 48 h to maintain a constant fibre volume fraction and a uniform laminate thickness. During the first 10 h the bag remained attached to a vacuum pump to eliminate any air bubbles existing in the composite. The post-cure was carried out in an oven at 45 \({}^{\circ}\)C for 48 h.  
Using the same manufacturing process, composite laminates with epoxy matrix filled with cork powder were also produced. The bulk density of the cork powder used is about 0.11 g\(\cdot\)cm\({}^{-3}\) and the particles' size, in terms of percentile, is d(0.1) = 18.6 \(\upmu\)m, d(0.5) = 78.9 \(\upmu\)m and d(0.9) = 208.3 \(\upmu\)m. More details can be found in [17]. The cork powder was dried in an oven (Heraus, model UT 6060) at about 120 \({}^{\circ}\)C during 2 h and dried in a desiccator until use. Epoxy resin and cork powder were mixed at 900 rpm for 2 h and, at same time, subjected to ultrasonic bath sonicator. Finally, the mixture was degassed in a vacuum oven, followed by addition of the hardener with special care to avoid the presence of air bubbles. The filler content was 3 wt.% of the epoxy resin-hardener mixture as reported in [19].  
The samples used in this study were cut from these plates to square specimens with 100 mm side, which were completely submerged into different solutions and different immersion times, both summarized in Table 1. All solutions have a concentration of 10% by weight (wt.%), which corresponds to a pH of 13.0 for NaOH and 1.5 for acids. Except for seawater at 60 \({}^{\circ}\)C, all the other solutions in which the specimens were immersed are at room temperature. Finally, the samples were washed with clean water and dried at room temperature.  
Low-velocity impact tests were performed using a drop weight testing machine IMATEK-IM10. More details of the impact machine can be found in [31]. An impactor diameter of 20 mm with a mass of 3.005 kg was used. The tests were performed on square section samples of dimensions 75 \(\times\) 75 mm and the impactor stroke at the centre of the samples obtained by centrally supporting the 100 \(\times\) 100 mm specimens. Impact energies of 2, 4, 8, 10, 12, 16, 20 and 24 J were used to analyse the impact strength and the effect of cork powder on the impact strength. The effect of hostile solutions on the impact strength was evaluated for the energy of 12 J. For each condition/environment, five specimens were tested, and the results presented in terms of average values.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Untraceability  
In our scheme, the REQUEST message \(\{H_{i},R_{c}\}\) submitted in the login and authentication phase is different in each communication due to randomly selected integer \(r_{c}\). Similarly, the back ACCEPT message \(\{L_{i}\}\) is also different. Therefore, an attacker \(A\) cannot link any two messages nor trace the client. In this way, untraceability is achieved.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 3 Preliminaries and Problem Definition
### Composition Attack and Privacy Breach  
Given two independently published tables, \(T^{*}\) and \(F^{*}\), a composition attack consists in examining the intersection of the two tables to find the common \(QI\) values and the corresponding sensitive value \(s\) of a person. In the composition attack, \(T^{*}\) and \(F^{*}\) are from two different data publishers, and there is no information shared prior to data publishing. If there is only one common sensitive value shared by two equivalence classes of \(s\) in \(T^{*}\) and \(F^{*}\), the privacy of the person is breached with 100% likelihood.  
From the background knowledge, it is certain that the adversary already knows the \(QI\) values because the adversary has the published tables \(T^{*}\) and \(F^{*}\). In addition, the adversary may gather knowledge about a particular individual from publicly available data sources such as voter registration lists. By using the \(QI\) values from the voter registration lists, the adversary may try to find the \(QI\) values from the intersection of tables \(T^{*}\) and \(F^{*}\) and finally uncover the sensitive values.  
In order to protect the privacy of the individual, we break the association between \(QI\) values by segmenting the microdata table respectively into columns and rows. Breaking the association between the \(QI\) values will confuse an adversary looking for exact \(QI\) values from the intersection of published tables \(T^{*}\) and \(F^{*}\).
## 1 Introduction  
Silicon carbide (SiC) is one of the world's most popular materials due to its excellent physical, chemical, electronic, and mechanical performances, and is widely applied in industries including automobiles, spacecraft, high-speed trains, aviation, electrical systems, and optics [1; 2; 3; 4; 5; 6; 7; 8]. The applications and corresponding properties of SiC are summarized in Table 1.  
Machining precision of these parts by SiC is often required to achieve a micrometer or nanometer level, but the hard and brittle properties of SiC make it difficult to manufacture them because microcracks and microdamages easily occur during processes such as grinding and wiresaw machining. SiC materials, especially SiC single crystals grown by physical vapor transport (PVT) or chemical vapor deposition (CVD), are also expensive; so the occurrence of cracks and damages causes not only performance degradation of these parts, but also increases cost.  
Scratch and indentation technologies are common ways to study brittle material removal. [PERSON] et al. [9] studied the material removal mechanism and crack propagation in single scratch and double scratch tests of single-crystal silicon carbide using abrasives on wiresaw machining. [PERSON] et al. [10] conducted scratching experiments to investigate the influence of heat on material removal and friction behaviors of reaction bonded SiC ceramics under different temperatures. [PERSON] et al. [11] carried out a combination of micro- and nanoindentation techniques to investigate the indentation-inducedpop-in effect and fracture behaviors in yttrium-stabilized zirconia(111) single crystals. [PERSON] et al. [12] established an analytical model of an elastic stress field for brittle material during single abrasive scratching based on elasticity and indentation fracture mechanics.  
A machining method known to be able to avoid fractures on SiC surfaces is ductile-regime machining, but the critical cutting depth of ductile-regime machining for SiC is about 100 nm, which is much larger than the machining accuracy of conventional machining methods used extensively in actual production [27]. In these machining methods, brittle removal is still the main approach. Above all, it is necessary to study the damage and fractures during SiC processing. In this paper, indentation tests were carried out to determine the fracture toughness of 4H-SiC single crystals. Critical condition with no cracks on the edge of the indentation was obtained through a fitting method. The material removal mechanism of 4H-SiC single crystals is discussed herein through ramp-normal force scratch tests. A modified calculation model considering elastic recovery of normal force during the scratching process is proposed, providing important technical parameters for the machining of 4H-SiC single crystals.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 5 Future Perspectives  
As the global food system becomes increasingly complex, the demand for advanced, rapid, and reliable microbiological diagnostic tools will continue to rise. In this context, MALDI-TOF MS is poised to play an even more significant role in food microbiology, both through technological innovations and strategic integration into broader food-safety frameworks. Future developments are expected to focus on improving resolution, expanding database diversity, enhancing automation, and enabling direct-from-sample identification [18; 22; 28].
### Background Knowledge  
The background knowledge required for this work includes zero-knowledge proofs, the fog/edge computing architecture, the mutual authentication protocol, and a distributed database system. These can be given in detail as follows:  
Zero-knowledge proof is a precise scheme that provides the validity of the genuine verifier to the prover by an efficient interaction without disclosing any sensitive information [6]. The cryptographic protocols can be significantly evaluated by using the ZKP properties such as completeness, soundness, and zero-knowledge. The completeness states that the verifier tag will accept the reader as a legal entity when the validity and zero-knowledge proof of the protocol are possessed by a prover. The soundness assures that the verifier tag will be unaccepted as the reader entity possessed by an invalid prover is illegal, whereas zero-knowledge does not reveal any sensitive information about the shared commitments and the verifier only learns about the statement truthness. The ZKP helps to present compelling proofs of the asserted statements. In other words, if no evidence is provided for the claim, then it is considered to be computationally infeasible to mislead with a non-negligible probability. A replay or modification attack may be applied, but this will be an illegitimate interaction and a non-convincible proof, which is applicable to both parties. Therefore, the crucial part is the interaction within ZKP. The fog is a distributed computing architecture [1] including communication, storage, and control, which is brought closer to all the end users, also known as proximity computing within the cloud-to-things continuum. Even though the term fog is optionally used for the edge, it is broader. The fog can be made relevant when providing creative solutions for the cloud's shortcomings with either 5G, the IoT, or embedded artificial intelligence. Mutual authentication, also known as two-way authentication, is a process in which multiple entities authenticate each other in a communication link. In the network environment, the server authenticates the client and vice versa. The blockchain [7; 8] can be defined as a distributed ledger in which records are not required to be stored on multiple servers to avoid redundancy and can be used for transaction auditing. Furthermore, all the records are immutable, ensuring consistency. As the structure of the blockchain is known, it is quite vulnerable and is well known for its limits on transaction processing.
# Improved Interpolation and Anomaly Detection for Personal PM\({}_{\mathbf{2.5}}\) Measurement
## 4 Discussion
As another category of classification, point-anomalous data detection technology detects one datapoint that has abnormal characteristics among much normal data [22]. In contrast, the statistical method extends the point method, and is a technology that detects data when that fall inside or outside a specific range of values in order to find anomalies. The disadvantage of these technologies is that most of these values are set up by hand. Recently, a lot of context-based methods have been studied. These can be seen as techniques for identifying abnormal data depending on the context of a situation. We proposed a method to detect abnormal data that show significant incremental or decremental changes as measured by time. Although the excellence of the proposed algorithms has been proved by experiments, we address the limitations in conducting this study. First, the bootstrapping test was used to prove the excellence of the interpolation method, because we did not have any reference data for our interpolation algorithm. However, under such circumstances, we assumed that the bootstrapping test was the best choice for generating a missing value dataset randomly from a secure dataset. In addition, we did three scenario-specific experiments to evaluate the performance of the proposed method, which could be considered a simplified approach. The patterns of data distribution can be more complicated; since data distribution patterns were not considered in this study, some other methods might work better, depending on the type of data. In addition, we did interpolation by applying the same bandwidth estimated for the flat data pattern to the entire dataset. In the future, it is necessary to conduct research to automatically apply different bandwidth values for each data pattern. Finally, because the method proposed in this study was a kind of context-aware based detection method, we acknowledge that other methods previously proposed could get a completely different result. In addition, the threshold value was chosen empirically by visual inspection at the moment, but we need to develop a sophisticated algorithm to choose the value automatically. We may introduce such an automatic method in our near future study. Despite the limitations of this study, it has a very important academic significance in that it presents a solution to the problem of interpolation and abnormal data of fine-dust data acquired from personal mobile terminals. We believe that these technologies will be a cornerstone for personalized environmental data services in the near future. Future research will include the prediction of PM data concentrations in both indoor and outdoor environments based on machine-learning technologies.
## 4 Discussion  
To date, the application of the eddy covariance technique in the _R. pseudoacacia_ ecosystems has been limited to the determination of CH\({}_{4}\) fluxes [21], and quite recently to the assessment of stand evapotranspiration [9]. Eddy flux measurements have also been performed in a cork oak-black locust mixed ecosystem, where black locust accounted for less than 20% of the forested area [52]. There is, therefore, scarce knowledge about the carbon and water budget of black locust at the ecosystem level, although the species is the second most often used for the establishment of forest plantations globally [24]. To bridge this gap, we established an eddy flux tower in a black locust plantation in NW Greece and assessed the GPP and ET seasonal and diurnal variation for a period of c. 2.5 years.  
In many studies, linear or other pre-defined models have been used to describe the single or combined effect of environmental drivers and GPP or ET (e.g., [53; 54; 55]). However, GAMs have the advantage of separating the influence of each single factor of those that may drive GPP or ET at the same time, and assessing the exact form of the relationship between the dependent and independent variables, without having to assume the form of their relationship (e.g., [56; 57]). This allowed us to assess the exact influence of the tested environmental drivers (Rg, VPD, SWC, EVI) on GPP and ET and to identify certain thresholds that are important for understanding the ecophysiological responses of black locust ecosystems to these variables.
## 9 Routing in CRSNs
### CRSN Routing Modules  
Again, routing protocols in CRSNs are application-specific, _i.e._, there exist no strict standardized protocols for all applications. Thus, depending on the application, protocols are accordingly designed to efficiently suit the specific purpose. However, any routing protocol for a CRSN must consider the major issues of network topology, route setup and route management.