esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 2 Materials and Methods
### Ground Texture Measurements  
During the autumn of 2017, a texture measurement campaign was carried out over 33 bare soil fields, characterized by a large range of soil textures. For each reference field, 5 to 10 ground sample were collected inside a 30 m diameter circle from the upper soil layer (a depth of 5 cm) based on visual assessment of topsoil textural changes. To reduce the risk of bias and to ensure a uniform distribution of the samples, they were selected randomly for each reference field.  
Sieve analyses were used to classify the particles smaller than 2 mm in samples as being clay, silt, or sand. The laboratory technique was based on the international \"Robinson\" pipette method. Firstly, the experiment relies on the destruction of organic matter by hydrogen peroxide. A dispersing solution of sodium hexametaphosphate is then added to neutralize the flocculating action. After stirring, the fine fractions, silt and clay, are separated by sedimentation. The removal of the latter is based on the relationship, linking the sedimentation time to the particle diameter, given by the Stocks' law which describes the settling velocity of particles in a suspension as function of the sedimentation rate. A small volume of the suspension is taken with a Robinson pipette, from which the solid residue will be weighed after evaporation of the liquid. Coarse fractions and coarse and fine sands are separated by sieving.  
Soil texture is defined as the distribution of the soil's constituent mineral particles, classified by size. According to the USDA (United States Department of Agriculture) classification, particles smaller than 2 um in diameter are classed as clay, particles with a diameter between 2 and 50 um are classed as silt, and particles with a diameter between 0.05 and 2 mm are classed as sand.  
From the samples taken during the ground campaign, the measured clay content ranked between 12.7% and 58%, and the sand content between 2% and 73%. The results from the fields analyzed during this campaign are plotted on the soil texture triangle shown in Figure 1. Three dominant soil classes can be distinguished: clay soil, clay loamy soil as a continuous distribution between sandy loam, and sandy clay loam soil. The samples were ranked according to three main classes, depending on their soil clay fraction. The first class corresponds to soils with low clay content (between 13 and 30%), the second class corresponds to clay content values between 30% and 45%, and the third class is used to identify soils with high values of clay content (between 45% and 60%).
## 2 Results
### qRT-PCR Validation of Differentially Expressed Genes  
To confirm the accuracy of the transcriptome sequencing results, twenty differentially expressed genes (DEGs) involved in the terpenoid and alkaloid metabolic pathways were subjected to quantitative real-time PCR (qRT-PCR) analysis. The primers of the twenty randomly selected DEGs defined by primer premier 5.0 are listed in Supplementary Table S4. The qRT-PCR results showed that the expression levels of all these twenty DEGs were consistent with the trend for the transcriptome data (Figure 7). The correlation between the FPKM values of the different genes and the qRT-PCR data were analyzed using [PERSON]'s correlation coefficient (r) [30]. Among them, five DEGs reached a significant level (\(p\leq 0.05\)) and the other fifteen DEGs reached an extremely significant level (\(p\leq 0.01\)) (Supplementary Table S5). In order to explore the hysteresis effect of the genes and metabolites, the relative expression of the above 20 DEGs was further analyzed. We collected samples at 0 h, 0.5 h, 1 h, 2 h, 4 h, 8 h, 16 h, 24 h, 7 d, and 14 d and carried out an analysis of the relative expression levels. Some genes and metabolites were found to have a certain lag effect (Figure S6). In the relative expression analysis for the terpene synthase genes (_LOC110109817_), we found that the difference between the MeJA-treated group after 14 days of treatment (TF14) and the control group (CK) was the most significant--more than 25 times greater. However, due to the hysteresis effect, the gene _LOC110109817_ was significantly overexpressed at 16 h (2399.8 times greater expression than the control group) (Figure S6). BLAST analysis using the NCBI tool revealed that this gene was (\(\pm\))-germacrene D synthase (GDS). Farnesyl diphosphate (FPP) was catalyzed by GDS to produce (\(\pm\))-germacrene D. Previous research has shown that germacrene D is a chiral compound that is present as two enantiomers produced by FPP via enantiomer-specific synthases [31] (Figure S7).  
Figure 6: KEGG classification and enrichment analysis for the CK and TF14 libraries. (**A**) The horizontal and vertical axes represent the ratio of genes annotated to this pathway to the total number of annotated genes and the name of the KEGG pathway, respectively. The label on the right side of the figure represents the classification to which the KEGG term belongs. (**B**) The vertical and horizontal axes represent the KEGG pathway and the enrichment factor, respectively, where the larger the enrichment factor, the higher the degree of enrichment; the larger the dot, the more differentially enriched genes there are; and the redder the dot, the more significant the enrichment.
## 2 Materials and Methods
### MRI Acquisition and Processing  
T1-weighted axial MRI scans, from the L3 vertebra to the toes, were acquired on a Discovery TM MR750w 3.0T scanner (GE Healthcare, Chicago, IL, USA) with the following parameters: scanning sequence--proprietary Dixon sequence, slice thickness--3.94 mm, slice increment--2.0 mm, pixel size--0.4688 mm. Depending on the height of each subject, four or five different sections were acquired, with an overlap of at least 40 slices between consecutive sections. The MRI acquisition lasted around 20/30 min. The selected scan allowed for a reduction in the acquisition time minimizing the motion artifacts while maintaining high-quality images essential for enabling semi-automatic segmentation. The images were imported into Mimics v25 (Mimics Innovation Suite, Materialise, Leuven, Belgium) and merged to obtain the entire volume of interest (full lower limbs) in a single file. Within Mimics, a single expert operator manually or semi-automatically segmented bones, muscles, and soft tissues. In particular, the lower limb muscles were segmented employing an atlas-based approach, namely the muscle segmentation tool (MST), that takes in input masks including the whole muscle tissue of a specific section (i.e., upper or lower leg) and gives as output individual muscle masks. A final manual post-processing was performed to refine the 3D volume reconstructions, with particular attention to the knee extensor muscles (i.e., rectus femoris (RF), vastus intermedius (VI), vastus lateralis (VL), and vastus medialis (VM)), which were primarily responsible for the experimentally measured maximal torque.
# SoK: Delegated Security in the Internet of Things
## 7 Potential Future Research Directions  
Based on the results of our research, the following potential future research directions in the domain of IoT security delegation may be considered particularly interesting:  
_Comprehensive and lightweight delegation solutions:_: Investigate the feasibility of designing delegation-based security frameworks that provide comprehensive protection of confidentiality, integrity, and availability, even in the presence of strong and internal adversaries, while remaining suitable for deployment in small-scale and highly resource-constrained IoT environments.  
_Formalisation of attacks:_ Explore whether common attack scenarios targeting delegation mechanisms in IoT systems can be generalised and expressed as formal mathematical models compatible with our security property definitions.  
_Ensuring security of a delegate:_ Research techniques to enhance the security of delegates, which are often assumed to be trustworthy and robust components. However, as they frequently serve as main components in delegation models, they become attractive targets for adversaries and may be vulnerable to various attacks.  
_Delegation in dynamic environments:_ Design adaptive delegation mechanisms that can respond to real-time contextual factors such as device failure, workload, trust levels, and network conditions, thereby improving both system robustness and efficiency in dynamic IoT environments.  
_Standardisation and interoperability:_ Investigate the feasibility of designing interoperable and standardised delegation solutions capable of operating across heterogeneous IoT environments, addressing the current limitations of many existing approaches that are highly domain- or vendor-specific.  
_Extended property evaluation:_ Explore the extension of the security evaluation model proposed in this paper to include additional security properties beyond those discussed, in order to support a more comprehensive assessment of system security.  
We believe that these research directions provide a solid foundation for future advancements in delegation-based security for IoT systems. Addressing these challenges not only enhances the robustness of existing solutions but also promotes the development of scalable, interoperable, and adaptive frameworks suitable for diverse and resource-constrained IoT environments.
# A Case Study on User Evaluation of Scientific Publication Summarization by Japanese Students
## 6 Discussion
### Evaluation Metric and Request
#### 6.1.3 Automatic Evaluation Metric  
Manual evaluation of summaries is very labor-intensive. Thus, automatic evaluation metrics have been well used in automatic summarization research. Here, we discuss the effectiveness of automatic evaluation metrics in scientific paper summarization.  
ROUGE [12] is a well-known evaluation metric, which measures similarity between generated and reference summaries. We applied ROUGE to the summaries in our experiment with the following settings (see Table 12): (1) AuE with AbE as reference, (2) AbJ and AuJ with MJ as reference, and (3) AuJ with AbJ as reference. As a result, AuJ obtains higher ROUGE score with AbJ as reference than MJ. However, in the comparison between MJ and AbJ (see experiment 1), about 40% of subjects answered \"AbJ is better,\" which was not a significant difference. ROUGE measures the similarity of a generated summary to a reference summary, so the metric does not indicate whether the quality of a generated summary is high,\" but \"the generated summary is similar to the reference.\" Therefore, until we can guarantee the quality of reference summaries, we cannot use ROUGE. In addition, content richness and simplicity are difficult to evaluate from the similarity with reference summaries. This discussion is consistent with the experimental results in [PERSON] et al. [11], where it was reported that manual evaluations such as appropriateness or fluency are not correlated with ROUGE scores.
## 4 Design of the DRFTM
### Access to Evidence of Trust
#### 4.1.4 Environment Trust  
It is inaccurate to judge the trustworthiness of nodes by considering only the interaction behavior between them, and communication between two nodes in an underwater environment is also affected by unreliable underwater acoustic channels. Marine ambient noise is a major factor affecting underwater acoustic propagation, and turbulence, shipping, waves, and thermal noise are the most common sources of marine ambient noise. The empirical equation below gives the continuous power spectral density of the four noise components [30], with a unit of dB re 1 \(\upmu\text{Pa}^{2}/\text{Hz}\), and it varies with the frequency \(f\) of the sound waves:  
\[10\log N_{t}(f)=17-30\log f, \tag{7}\]  
\[10\log N_{s}(f)=40+20(s-0.5)+26\log f-60\log(f+0.03), \tag{8}\]  
\[10\log N_{w}(f)=50+7.5\sqrt{w}+20\log f-40\log(f+0.4), \tag{9}\]  
\[10\log N_{th}(f)=-15+20\log f, \tag{10}\]  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f), \tag{11}\]  
where \(N_{t}\) stands for turbulence noise, \(N_{s}\) represents shipping noise, \(N_{w}\) refers to sea surface noise, \(N_{th}\) is thermal noise, and \(N(f)\) represents the summation of various environmental noises mentioned above. Considering the application scenario, the transmission frequency of UASNs is significantly influenced by shipping noise and sea surface noise.  
The propagation loss in underwater acoustic channels is typically influenced by the distance due to the attenuation of sound waves when propagating through water. In general, the propagation loss of sound waves can be described using the following formula [31]:  
\[A(d,f)=d^{k}a(f)^{d}, \tag{12}\]  
whereas the variable \(d\) represents the Euclidean distance, and \(k\) is the propagation geometric shape expansion factor. Different values of \(k\) reflect the geometric characteristics of sound waves when propagating in water, specifically whether they exhibit a cylindrical or spherical shape. \(a(f)\) denotes the rate at which energy is lost due to absorption during the propagation of sound waves in seawater, expressed as  
\[10\log a(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100+f^{2}}+\frac{2.75f^{ 2}}{10^{4}}+0.003. \tag{13}\]  
By utilizing the ocean environment noise and accounting for underwater acoustic propagation losses, one can derive the signal-to-noise ratio (\(SNR\)) at a distance \(d\) of the receiving node:  
\[SNR(d,f)=\frac{E_{b}}{N(f)A(d,f)}, \tag{14}\]  
where \(E_{b}\) represents the amount of energy used to transmit data per unit bit.  
In the physical layer of acoustic modems, the widely employed modulation technique is binary phase shift keying (BPSK) [32]. Under specific conditions characterized by a distance of \(d\) and a frequency of \(f\), the error probability of BPSK modulation can be expressed as  
\[p_{e}(d,f)=\frac{1}{2}\left(1-\sqrt{\frac{SNR(d,f)}{1+SNR(d,f)}}\right), \tag{15}\]  
The ambient trust (transmission success rate of a packet of received length \(l\)) based on the effect of noise is expressed as  
\[T_{i}^{env}=p(d,f)=(1-p_{e}(d,f))^{l}. \tag{16}\]
## 2 Preliminaries Relevant to the Present Study
### Taylor's Series of Fractional Order  
**Proposition 1**.: _Assume that he continuous function \(f:R\to R,x\to f(x)\) has a fractional derivative of order \(k\phi\). For any positive integer \(k\) and for any \(\phi\), \(0<\phi<1\), then the following equality holds:_  
\[f(x+h)=\sum_{k=0}^{\infty}\frac{h^{\phi k}}{(\phi k)!}f^{(\phi k)}(x), \tag{13}\]  
_with the notation_  
\[\Gamma(1+k\phi)=(\phi k)!,\ 0<\phi\leq 1 \tag{14}\]  
_where \(\Gamma(.)\) is the Euler gamma function. Formally, this series can be written_  
\[f(x+h)=E_{\phi}(h^{\phi}D_{x}^{\phi})f(x) \tag{15}\]  
_where \(D_{x}^{\phi}\) is the derivative operator with respect to \(x\), and \(E_{\phi}(u)\) is the Mittag-Leffler function defined by the expression_  
\[E_{\phi}(u)=\sum_{k=0}^{\infty}\frac{u^{k}}{(\phi k)!}=\sum_{k=0}^{\infty} \frac{u^{k}}{\Gamma(1+k\phi)} \tag{16}\]  
**Remark 1**.: _This fractional [PERSON]'s series does not hold with the standard Riemann-Liouville derivative, but it is applied to non-differentiable functions only._  
**Corollary 1**.: _Assume that \(m<\phi\leq m+1,m\to N-0\) and that \(f(x)\) has derivatives of order \(k\) (integer), \(1\leq k\leq m\). Assume further that \(f^{m}(x)\) has a fractional [PERSON]'s series of order \(\phi-m=:\psi\) provided by the expression_  
\[f^{m}(x+h)=\sum_{k=0}^{\infty}\frac{h^{k(\phi-m)}}{\Gamma[1+k(\phi-m)]}D^{k( \phi-m)}f^{(m)}(x),\ m<\phi<m+1 \tag{17}\]  
_Then, integrating this series with respect to \(h\) provides_  
\[f(x+h)=\sum_{k=0}^{m}\frac{h^{k}}{k!}f^{(k)}(x)+\sum_{k=0}^{\infty}\frac{h^{( k\psi+m)}}{\Gamma(k\psi+m+1)}f^{(k\psi+m)}(x),\ \psi:=\phi-m \tag{18}\]
## 4 Experimental Results and Analysis
### Dataset Distribution
Despite being designed to reflect typical IoV environments, synthetic data may not fully capture the variability and complexity of real-world conditions. Future  
Figure 6: Correlation between connection stability and trust score. Figure 7: The relationship between satisfaction feedback and trust score. work will focus on validating the proposed solution using field-collected data, further demonstrating its robustness and applicability in diverse and dynamic environments. We have established a heterogeneous federated environment by combining MNIST, CIFAR-10, and the synthetic IoV dataset. This diversity of data types and adversarial scenarios highlights Fed-DTB's resilience and ability to maintain robust performance and secure collaboration under realistic IoV conditions.
## 8 Applications of UWSN Technology
### Air Pollution Surveillance  
In order to monitor the concentration of hazardous chemicals for inhabitants, wireless sensor networks were installed in several cities, including Brisbane, London, and Stockholm. Instead of wired systems, these can benefit from ad hoc wireless communications. This also increases their mobility for checking readings in various locations.
# Microstructural, Electrochemical and In Vitro Analysis of Mg-0.5 Ca-xGd Biodegradable Alloys
## 2 Experimental Details  
Obtaining theMagnesium with Calcium and Gadolinium Alloys' Structure and Chemical Composition Investigations  
The manufacturing process of the experimental alloys used master alloys procured from Hunan China Co. [41; 42] with the mass percentages shown in Table 1. The Mg-based alloys were obtained through a classical melting process using an induction furnace in an Ar-protected environment in a 675-700 \({}^{\circ}\)C temperature field for 30 minutes from a square section of the initial materials, as in previous research [43]. The elaboration process resulted in cylindrical mini-ingots, which were cut in smaller round plates with different chemical compositions based on the differences between the Gd element concentrations, with the values shown in Table 2. The round samples are 20 mm in diameter with a thickness of 2 mm. Material load calculations were made on the basis of the metal loss coefficient; the quantities used are shown in Table 2 (with specimen codes) with a net mass of around 23 g per melt. For experimental tests, the specimens were mechanically polished with an Al\({}_{2}\)O\({}_{3}\) suspension solution (2-5 \(\upmu\)m) after metallographic grinding with paper disks of 200-2500 MPi granulation. The surface was cleaned with ethylalcohol for 30 min and the microstructure was highlighted by chemical etching using Mg(CH\({}_{3}\)COO)2 Mg.4H\({}_{2}\)O acetate solution. Microstructural aspects were analyzed by optical microscopy (Leica DMI 5000 equipment, Leica, Wetzlar, Germany) and scanning electron microscopy (SEM)(FEI Quanta 200 3D). Chemical composition determinations were accomplished with an energy-dispersive spectroscopy (EDS) detector (Xflash, Bruker, Germany) and a X-ray diffractionsystem (XRD) (Xpert PRO MPD 3060 equipment, Panalytical, The Netherlands; copper-X-ray tube (K\(\alpha\) = 1.54051\({}^{\circ}\)), 20: 20\({}^{\circ}\)-100\({}^{\circ}\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Mg/Ca/Gd** & **Fe** & **Ni** & **Cu** & **Si** & **Al** \\  & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** \\ \hline Pure Mg & Mg (99 wt.\%) & 0.15–0.2 & 0.17–0.2 & 0.14–0.2 & 0.15–0.2 & 0.16–0.2 \\ Mg15 Ca & Ca (15.29 wt.\%) & 0.004 & 0.001 & 0.003 & 0.013 & 0.011 \\ Mg30 Gd & Gd (28.05 wt.\%) & 0.010 & 0.001 & 0.001 & 0.006 & 0.011 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Magnesium and Mg-based master alloy weight percentages [41; 42].
## 1 Introduction  
The ionosphere is part of the Earth's upper atmosphere, located between 50 and 1000 km above the Earth's surface. It contains ionized particles (ions and electrons) which can influence the behavior of radio waves such as GNSS signals as they pass through the Earth's atmosphere. The ions and electrons contained in the ionosphere can cause refraction, absorption, reflection and dispersion, which all cause signal delay and fluctuations in the amplitude of phase measurements.  
More specifically, ionospheric scintillation refers to rapid fluctuations in the amplitude, phase and polarization of radio waves due to localized, small-scale irregularities in the electron density of the ionosphere. Ionospheric scintillation can be particularly problematic for satellite communications, GPS/GNSS and other radio-based technologies operating in the upper atmosphere. The rapid fluctuations can lead to signal degradation, errors in positioning information and interruptions in communication links.  
The quantification of scintillation requires a measurement to evaluate the severity of scintillation at a specific moment. The amplitude scintillation index (S4) is a measure of scintillation strength and is the square root of the normalized variance in signal intensity (C/N0) over a defined period of time. Strong scintillation is typically associated with an S4 index of 0.6 or higher, whereas an S4 index of 0.3 or lower is associated with weak scintillation [1].  
A high-performance, multi-frequency, multi-constellation GNSS ionospheric scintillation and TEC monitor (GISTM) receiver enclosure with a low-phase-noise oscillator from Hexagon | NovAtel was located in Hyderabad, India, and collected TEC and S4 values over an extended period in 2023 and early 2024. S4 has been previously described in detail [2].
# MIMO Signal Detection Based on IM-LSTMNet Model
## 3 Experiments and Results of Signal Detection
### The Result of the Signal Detection
#### 3.2.3 The Effect of the Cyclic Prefix  
As previously mentioned, CP is necessary to change the linear convolution of a physical channel to a circular convolution and to mitigate ISI. Its transmission consumes time and energy, and less CP contributes to better system performance. In this experiment, we study the effect of CP on system performance based on an FrFT order of 1.6 and subcarrier number of 64.  
Figures 16 and 17, respectively, describe the performance comparison between our proposed IM-LSTMNet method and the traditional ZF algorithm and MMSE algorithm and the performance comparison between the IM-LSTMNet method and the simple LSTM and CNN-LSTM methods with or without CP.  
As shown in Figure 16, without CP, the improved algorithm proposed by us has a lower bit error rate than the traditional algorithm, and the signal detection performance of the FrFT-based MIMO-OFDM system is better.  
As shown in Figure 17, without CP, the improved algorithm proposed by us has a lower bit error rate than simpler CNN-LSTM and LSTM neural network algorithms, and better signal detection performance of FrFT-based MIMO-OFDM system. This result shows that the proposed IM-LSTMNet model could learn the wireless channel's characteristics during the training.
# Novel Algorithm for Multi-Time Data Implantation in a Special Cyber-Manufacturing Architecture
## 4 Proposed Method of Securing Data in Cyber Device
### Proposed Multi-Time Data Embedment Scheme
#### 4.1.1 Secret Implantation Method  
Figure 2 depicts the whole embedding process. The algorithmic steps of the proposed multi time data implantation method are presented in Algorithm 1 and shown in Figure 2 as well.  
Figure 2: Flowchart for generating Stego DNA. This is, indeed, the proposed data hiding method. The method converts nucleotides to binaries. It generates a secret key \(S\) for one time only. This could be the password of the owner. The method employs a binary form of the message \(M_{i}\), secret key \(S\) and the binary of nucleotides \(R\) to generate stego nucleotides. If the message length is too big to hide in nucleotides, the scheme repeats the process.  
The scheme could also serve in both standalone and cloud based applications. In cloud-based application, only the \(\bar{N}_{r}\) will be stored in a cloud space. The intended person will collect \(\bar{N}_{r}\) from the cloud and then will request the sender side/server for a stego key. In that case, only a cloud space of \(L/2\) nucleotides is enough for communicating \(n\times L\) bits of the message.