esa-sceva/satcom-chunk-collection huggingface dataset:
# Shear Behaviour of Aeolian Sand with Different Density and Confining Pressure
###### Abstract  
Different from the other roadbed material, the unique mechanical properties of aeolian sand bring great difficulties to the construction and maintenance of desert highways. However, the main attention was usually paid to the engineering properties of aeolian sand, such as collapsibility, strong permeability, and poor gradation. To investigate the shear behaviour of aeolian sand under different engineering conditions, the drained and undrained tests were performed on aeolian sand with relatively large range of density and confining pressure. Under this condition, both the drained and undrained tests tend to the same critical state line, and the shear behaviour of aeolian sand is directly dependent on its density. Under the undrained condition, the \(q\)-\(c_{1}\) curves and the effective stress paths in triaxial stress space exhibit four types of undrained shear behaviour, such as flow, limited flow, strain hardening, and strain softening. Meanwhile all the specimens exhibit three types of failure, such as flow slip, bulging failure, and shear bands. In the \(q\)-\(p^{\prime}\) plane, the analogous drained and undrained stress paths can be followed by aeolian sand with same initial relative density but different confining pressures, and there are two critical state lines due to the generation of shear bands and for dense sand. In addition, the critical state lines in \(c\)-ln\(p^{\prime}\) plane decrease with increasing initial relative density \(D_{\mathrm{r}}\), that is, the material constant \(c_{\mathrm{F}}\) decreases with increasing \(D_{\mathrm{r}}\), and the \(\lambda\) is also not constant but decreases with the increase in \(D_{\mathrm{r}}\). The results suggest that the strength behaviours of aeolian sand can be fitted by a straight line considering relative density and confining pressure and that two empirical formulas are established to describe this feature.  
density; confining pressure; aeolian sand; triaxial test; stress-strain; critical state Article
# Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
## 1 Introduction
### Protocol Based DDoS Attack  
Protocol based DDoS attacks appear in a variety of forms, such as SYN floods, fragmented pack attacks, ping of death, smurf DDoS, and others. Attacks are measured in packets per second (pps). Such types of attacks use real server resources, as well as those of central communications devices like firewalls and load balancers.
# Methods of Analyzing Microsized Plastics in the Environment
## 2 Microscopy Analyses
### Electron Microscopy--SEM/TEM  
Scanning electron microscopy (SEM) can provide very sharp, high-magnification images even for very small particles, such as nanoplastics [44]. The texture of the particle surface, which can be confirmed through high-resolution images, makes it easy to distinguish between organic particles and microplastics [45]. Transmission electron microscopy (TEM) is also used for the identification of microplastics. Visual identification of microplastics through TEM may vary depending on the user; nonetheless, errors can be reduced by cross-validating results from multiple users through a well-established protocol. Further analysis using energy-dispersive X-ray spectroscopy (EDS) provides the elemental composition of the object in question. Additionally, components of additives, such as Al, Ca, Mg, Na, and Si, or antioxidants in microplastics are captured as markers and used for identification using EDS [25,32]. Determining the surface elemental composition of a particle can be useful for identifying carbon-rich plastics from inorganic particles [39,46]. However, SEM and TEM limit the number of samples that can be processed because the equipment is expensive, and sample preparation and inspection require significant time and effort.
## 2 Principle of the Stitching Linear-Scan Method
Meanwhile, another issue that needs to be addressed is the misalignment of the workpiece along its axial direction. In the developed setup for the proposed stitching linear-scan method, the alignment of the workpiece along its axial direction is carried out with respect to the datum surface determined by the round magnet surface and the side face of the workpiece holder. Therefore, the out-of-flatness of these surfaces could induce the axial misalignment of the workpiece. In this case, as can be seen in Figure 5, different positions on the workpiece surface could be measured in the \(i\)th and \((i+1)\)th arc-profiles at their overlapped region. Since a cylindrical workpiece tends to have a similar circular profile along its axial direction, a certain amount of the axial misalignment can be accepted. Meanwhile, the existence of a local profile error could generate a discrepancy between the obtained arc-profiles in the overlapped region, resulting in a decrease in the cross-correlation coefficient. Figure 4: The influence of the pressure angle of a probe of the linear-scan stylus profiler. Some attempts are thus made in this paper to address the aforementioned issues. An attempt is made to eliminate the data at the edge regions of the arc-profiles in the circumferential stitching process based on Equation (1). A modification is also made to the experimental setup in such a way that a small round magnet is added between the round magnet and workpiece; this modification is expected to reduce the influences of out-of-flatness of the round magnet surface and the side face of the workpiece holder by reducing the contact area between them.
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 2 Materials and Methods
### Regression Models  
Several regression methods were tested to estimate _DMY_ in this study: VI-based univariate regressions and multivariate regressions, including machine learning algorithms.  
Plant biomass-related VIs were identified through a literature search using the following keywords: \"dry matter\", \"nitrogen\", \"chlorophyll\", \"biomass\", \"index\", \"Sentinel-2\", \"satellite\" and \"remote sensing\". The descriptions and calculation formulae are in Table 4. All analyses were conducted using R environment [37].
# Exploring an Integrated Manure-Sewater System for Sustainable Cyanobacterial Biomass Production
## 5 Conclusions  
In the current study, we developed a cost-effective cyanobacterial growth media from integrated manure-seawater nutrients for sustainable cyanobacterial biomass production. Before we tested seawater as a nutrient source, we evaluated the growth of the salt-tolerant cyanobacterial strain HSaC at different NaCl concentrations. Based on the results of the salinity test, we conducted an experiment to evaluate different animal manures, namely, cow manure, pig manure and chicken manure in different salt concentrations of seawater. According to the results, cow manure integrated with 10 dS/m diluted seawater showed the highest biomass production for salt-tolerant cyanobacterial strain HSaC, and cow manure with 0.5 dS/m diluted seawater showed the best biomass yield for salt sensitive cyanobacterial strain LC. The results from integrated cow manure-seawater media were better than or similar to the results from the reference growth media (BG11). However, other animal manures, such as pigs and chickens, exhibited low biomass yield. The biomass production and photosynthetic pigment contents resulting from cow manure with natural seawater and cow manure with artificial seawater did not show a significant difference between 10N and 10A. The results indicated that both natural seawater and artificial seawater at similar dilution can be used interchangeably for biomass production. Overall, the research findings from the current experiment suggested that cyanobacterial biomass produced from integrated cow manure-seawater media could be used for cyanobacterial biofertilizer and biofuel, which are important for improving soil fertility and energy production, respectively. This study will help smallholder farmers to produce their own cyanobacterial biofertilizer, and to produce energy from biofuel with cheap growth media. For more sustainable biomass production, further research should be conducted using different animal manures in combination with seawater.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/18/3888/s1](http://www.mdpi.com/2076-3417/9/18/3888/s1)]([http://www.mdpi.com/2076-3417/9/18/3888/s1](http://www.mdpi.com/2076-3417/9/18/3888/s1)), Figure S1: HSaC cyanobacterial carotenoid content as influenced by different NaCl salt concentration, Figure S2: The effect of cow manure-seawater growth media on carotenoid content of salt tolerant cyanobacteria (HSaC), Figure S3: Chlorophyll content of salt sensitive cyanobacteria (LC) under seawater-cow manure growth media.  
**Author Contributions:** [PERSON] developed the study design, carried out the experiments, created the original draft, revised the manuscript, and prepared the final version.  
**Funding:** This work was supported by the Key Research Program (KFZD-SW-112) and STS Program (KFJ-STS-ZDTP-053) of Chinese Academy of Sciences, CAS-TWAS President's PhD Fellowship Program for International Students ([PERSON]).  
**Conflicts of Interest:** The author declare no conflict of interest.
## 3 Multichannel Signal Acquisition  
### Definitions of Signals, Channels, and Noise  
Let \(s[i],i=0,1,\ldots,N-1\), be the signal that is transmitted for acquisition and packet detection at the receiver side. Upon using the three-channel receiver, the received data vector can be written as  
\[\mathbf{r}=\mathbf{S}\mathbf{h}+\mathbf{n} \tag{3}\]  
In this equation, \(\mathbf{r}\) is a \(3N\times 1\) complex vector that represents the received signal plus noise, defined as follows  
\[\mathbf{r}=\left[\mathbf{r}^{T}[0]\mathbf{r}^{T}[1]\cdots\ \mathbf{r}^{T}[N-1] \right]^{T}, \tag{4}\]  
\[\mathbf{r}[i]=\left[r_{1}[i]\ r_{2}[i]\ r_{3}[i]\right]^{T},i=0,1,\ldots,N-1, \tag{5}\]  
where the three channels of the receiver are labeled 1, 2, 3, instead of \(x,y,p\), for notational convenience. In the above equations, \(\mathbf{S}\) is a \(3N\times 3\) matrix that is composed of \(N\) signal samples, as follows  
\[\mathbf{S}=\mathbf{s}\otimes\mathbf{I}_{3},\ \ \ \mathbf{s}=\left[s[0]s[1]\cdots s[N-1] \right]^{T}, \tag{6}\]  
where \(\otimes\) is the Kronecker product, \(\mathbf{I}_{3}\) is the \(3\times 3\) identity matrix, and \(\mathbf{h}\) represents the underwater channel responses sensed by the three-channel receiver  
\[\mathbf{h}=\left[h_{1}\ h_{2}\ h_{3}\right]^{T}, \tag{7}\]  
with the following covariance matrix  
\[\mathbf{\Sigma}_{\mathbf{h}}=E[\mathbf{h}\mathbf{h}^{\dagger}]=\text{diag}( \eta_{1}^{2},\eta_{2}^{2},\eta_{3}^{2}), \tag{8}\]  
where \(E\) is the mathematical expectation, \({}^{\dagger}\) is the transpose conjugate and diag stands for the diagonal matrix. Additionally, \(\mathbf{n}\) in (3) is a \(3N\times 1\) complex vector that represents the additive noise observed by the three-channel receiver  
\[\mathbf{n}=\left[\mathbf{n}^{T}[0]\mathbf{n}^{T}[1]\cdots\mathbf{n}^{T}[N-1] \right]^{T}, \tag{9}\]  
Figure 1: Schematic representation of the ring vector sensor receiver and the two particle velocity signals that it measures in the \(x\)–\(y\) plane.  
\[\mathbf{n}[i]=[n_{1}[i]n_{2}[i]n_{3}[i]]^{T},\ \ i=0,1,..,N-1, \tag{10}\]  
with the following covariance matrix  
\[\mathbf{\Sigma_{n}}=E[\mathbf{n}[i]\mathbf{n}^{\dagger}[i]]=\text{diag}(\sigma _{1}^{2},\sigma_{2}^{2},\sigma_{3}^{2}). \tag{11}\]  
In Section 3.2 below, we consider and analyze a multichannel signal acquisition method to understand the signal acquisition performance of the ring vector sensor receiver, followed by considering and analyzing a simpler method in Section 3.3 that has lower implementation complexity.
## 2 Background
### Theory of Legendre Sequence  
The research indicates that pseudo-random sequences exhibit similar properties to random sequences and can serve as substitutes in numerous scenarios. Benefiting from this, pseudo-random sequences are widely used in spectrum communication [28], data encryption [29], and many other fields [30]. The Legendre sequence has a more flexible period compared to other pseudo-random sequences.  
Let \(p>2\) be a prime number, and \(a\) be a coprime integer of \(p\); if the congruence  
\[s^{2}=\left(a\right)_{p} \tag{5}\]  
has an integer solution \(s\), then \(a\) is referred to as a quadratic residue of \(\bmod p\); otherwise, it is termed a quadratic non-residue modulo \(p\).  
We define the Legendre symbol as  
\[\left(\frac{a}{p}\right)=\left\{\begin{array}{rl}&0,\;\;\;a\text{ is quadratic residue of mod }p\\ &+1,\;\;\;a\text{ is quadratic non $-$ residue of mod }p\end{array}\right. \tag{6}\]  
For any integer \(g\), it follows  
\[\left(\frac{gp}{p}\right)=0 \tag{7}\]  
and  
\[\left(\frac{0}{p}\right)=0 \tag{8}\]  
Consequently, we can derive a Legendre sequence as  
\[\left(\frac{0}{p}\right)\text{,}\left(\frac{1}{p}\right)\text{,}\left(\frac{ 2}{p}\right)\text{,} \tag{9}\]  
It has been proven that when \(p\) is a prime number in the form of \(4t-1\) (where \(t\) is a positive integer), the Legendre sequence with a period of \(p\) is a pseudo-random sequence [31]. It is necessary to note that there are significantly more prime numbers in the form of \(4t-1\) compared to integers in the form of \(2^{\prime}-1\)(where \(r\) is a positive integer), which represents the periods of most other pseudo-random sequences. For example, the periods of m-sequences between 100 and 1000 are limited to 127, 255, and 511, while the Legendre sequences, in contrast, offer a notably flexible selection of periods, as shown in Table 1.  
Figure 1: Phase diagram for \(l_{1}\) minimization. Dark region: reconstruction success probability above 50%. Light region: reconstruction success probability below 50%.
# Tripartite Quantum Key Distribution Implemented with Imperfect Sources
## Appendix A Estimation of Decoy State Parameters  
In this Appendix, the calculation of the key parameters in Equation (29) is performed. While these parameters are derived in similar fashion to the bipartite case, the formulae are generalized to a tripartite case. Furthermore, the estimation of the decoy parameters for simulation is based on the channel model proposed in [41, 46]. In our analysis, it is assumed that the detection probabilities of photons are independently determined by the channel transmittance, \(t=10^{-0.02l}\), where \(l\) is the channel length, and the detection efficiencies, \(\eta_{B}\) and \(\eta_{C}\), of [PERSON] and [PERSON]'s respective detection systems. According to decoy theory, the overall gain is provided by [46, 47]  
\[\mathcal{Q}_{I_{A}I_{B}I_{C}}^{\gamma}=\frac{1}{2}\Big{[}Y_{0_{A},0_{T}0_{T}}^{ \gamma}+Y_{0_{A},1_{T}1_{T}}^{\gamma}+Y_{1_{A},0_{T}0_{T}}^{\gamma}+Y_{1_{A},1 _{T}1_{T}}^{\gamma}+Y_{0_{A},0_{T}1_{T}}^{\gamma}+Y_{0_{A},1_{T}0_{T}}^{\gamma }+Y_{1_{A},0_{T}1_{T}}^{\gamma}+Y_{1_{A},1_{T}0_{T}}^{\gamma}\Big{]}, \tag{11}\]  
where \(\gamma\in\{\mu,\
u\}\), \(\Gamma,\alpha\in\{X,Y,Z\}\). Moreover, the parameter \(Y_{0_{A},0_{T}0_{T}}^{\gamma}\) is the probability that [PERSON] and [PERSON] obtain a bit value of \(0\) after measuring in basis \(\Gamma\), conditional on [PERSON] preparing the two states \(|\phi_{0\alpha}\rangle\) according to an intensity \(\gamma\). The other conditional probabilities can be described in a similar manner. The coefficient \(\frac{1}{2}\) in Equation (11) denotes the probability that [PERSON] prepares a qubit in state \(|\phi_{0\alpha}\rangle\) or \(|\phi_{1\alpha}\rangle\). Moreover, Equation (11) is a generalised expression for different gains; thus, we evaluate \(Q_{X_{A}X_{B}X_{C}}^{\gamma}\) as an example to demonstrate how to explicitly obtain these gains. First, \(Y_{0_{X}0_{X}0_{X}}^{\gamma}\) is defined as follows:  
\[ \begin{array}{rl}Y_{0_{X}0_{X}0_{X}}^{\gamma}&=\sum_{k=0}^{\infty}p(k|\gamma )\sum_{k=0}^{k}C_{i}^{k}(\eta_{B}t)^{i}(1-\eta_{B}t)^{k-i}(\langle\phi_{0X}| \phi_{0X}\rangle)^{2}\chi(k)\sum_{k=0}^{\infty}p(k|\gamma)\sum_{i=0}^{k}C_{i} ^{k}(\eta_{C}t)^{i}\\ &\quad\times(1-\eta_{C}t)^{k-i}(\langle\phi_{0X}|\phi_{0X}\rangle)^{2}\chi(k )\\ &=e^{-2\gamma\eta_{B}t\eta_{C}t}(1-Y_{0})^{2}\Big{(}e^{\gamma\eta_{B}t(\frac {1+\cos\beta}{2})(\frac{1+\cos\delta_{2}}{2})}+Y_{0}-1\Big{)}\Big{(}e^{\gamma \eta_{C}t(\frac{1+\cos\beta}{2})(\frac{1+\cos\delta_{2}}{2})}+Y_{0}-1\Big{)},\\ &\quad\text{where}\\ &\chi(k)=\begin{cases}1-Y_{0}&\text{if }k>0\\ Y_{0}(1-Y_{0})&\text{if }k=0\\ \end{cases}\end{array}  \tag{12}\]  
denotes the probability of valid detection events conditional on \(k\)-photon states. The other conditional probabilities are analogously obtained as  
\[ \begin{array}{rl}Y_{0_{X}1_{X}1_{X}}^{\gamma}=e^{-2\gamma\eta_{B}t\eta_{C}t }(1-Y_{0})^{2}\Big{(}e^{\gamma\eta_{B}t(\frac{1-\cos\beta}{2})(\frac{1-\cos \delta_{2}}{2})}+Y_{0}-1\Big{)}\Big{(}e^{\gamma\eta_{C}t(\frac{1-\cos\beta}{2 })(\frac{1-\cos\delta_{2}}{2})}+Y_{0}-1\Big{)},\end{array}
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT
### Protection for UAVs
#### 2.5.2 Fleet Management  
Fleet management in autonomous moving objects is a challenging research field that focuses on defining the optimal formation configuration (positioning, speed, height) of a fleet of heterogeneous aerial objects, including decision making in the case of collisions or accretions [77]. Fleet-management techniques may offer various Levels of Automation (LOA) that can range from fully automated flights (no human involvement) to fully human-operated flights. Many works proposed automation architectures [78; 79], but human operations are still usually required [80; 81; 82].  
Fleet-management techniques can be classified into centralized and decentralized schemas [83].  
* In the centralized schema, a formation manager that can be one of the aerial vehicles of the fleet or a ground-based station [82], acts as a supervisor for all aerial vehicles and manages their topology. Centralized schemas present the advantage that important decisions are performed at a higher level, by centralized high-power computer systems, where humans can also interfere. On the other hand, the major disadvantage of this schema is that it requires frequent ground communications, which can be energy consuming and, in case of disruptions/failures, ground management can cause delays [83].
* In the decentralized schema [4], each aerial vehicle has a certain freedom in decision making, while the whole formation must be capable of reconfiguring, making decisions, and achieving mission goals. This schema is energy-efficient and presents reduced reaction times, though it may produce conflicting decisions, jeopardizing the fleet formation and, in cases of critical formation updates, it may require ground-control assistance [83].
# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
###### Abstract  
Authorship attribution for less-resourced languages like Romanian, characterized by the scarcity of large, annotated datasets and the limited number of available NLP tools, poses unique challenges. This study focuses on a hybrid Transformer combining handcrafted linguistic features, ranging from surface indices like word frequencies to syntax, semantics, and discourse markers, with contextualized embeddings from a Romanian BERT encoder. The methodology involves extracting contextualized representations from a pre-trained Romanian BERT model and concatenating them with linguistic features, selected using the Kruskal-Wallis mean rank, to create a hybrid input vector for a classification layer. We compare this approach with a baseline ensemble of seven machine learning classifiers for authorship attribution employing majority soft voting. We conduct studies on both long texts (full texts) and short texts (paragraphs), with 19 authors and a subset of 10. Our hybrid Transformer outperforms existing methods, achieving an F1 score of 0.87 on the full dataset of the 19-author set (an \(11\%\) enhancement) and an F1 score of 0.95 on the 10-author subset (an increase of \(10\%\) over previous research studies). We conduct linguistic analysis leveraging textual complexity indices and employ [PERSON] and Cochran's Q statistical tests to evaluate the performance evolution across the best three models, while highlighting patterns in misclassifications. Our research contributes to diversifying methodologies for effective authorship attribution in resource-constrained linguistic environments. Furthermore, we publicly release the full dataset and the codebase associated with this study to encourage further exploration and development in this field.  
autorship attribution; linguistic features; contextualized embeddings; hybrid Transformer; ensemble learning; linguistic analysis; natural language processing Article
## 2 PTOS Control Scheme
### Summary of PTSTP Control Scheme  
The PTSTP control block diagram is shown in Figure 5, and the control scheme is described as Equation (24), where \(u_{t}\) is the control law of PTSTP.  
\[\left\{\begin{array}{l}\dot{y}=v\\ \dot{v}=au_{t}\end{array}\right. \tag{24}\]  
The block diagram consists of the input section and control section. The operation of the input section is to determine whether the input target position signal is a SRS or WRS according to the absolute value of step input. When it is a SRS, the Switch module in Figure 5 makes \(r_{i}=r_{tp}\), whereas \(r_{i}=r\) for the input signals belonging to WRS. The mathematical expression of Switch model is shown in the Equation (25), which means the WRS is not processed as a \(r_{tp}\).  
Figure 4: The comparison diagram of the step and the TP signal for positive value of \(r_{A}\).  
Figure 5: The control scheme of proximate time-optimal servomechanism based on transition process (PTSTP).  
\[r_{i}\left(t\right)=\left\{\begin{array}{cc}r_{tp}\left(t\right),&\left|r_{A} \right|\leq y_{L}\\ r\left(t\right),&\left|r_{A}\right|>y_{L}\end{array}\right. \tag{25}\]  
Then the control law \(u_{t}\) is selected by \(r_{i}\), which means that \(u_{t}\) is the control law of \(u_{p\_small}\) for \(r_{i}=r_{tp}\), whereas \(u_{t}\) is the control law of \(u_{p\_wide}\) for \(r_{i}=r\). The expression is shown in the Equation (26), where \(u_{p\_small}\) and \(u_{p}\) are the control law of PTOS for SRSs and WRSs tracking, respectively.  
\[u_{t}=\left\{\begin{array}{cc}u_{p\_small},&r_{i}=r_{tp}\\ u_{p\_wide}=u_{p},&r_{i}=r\end{array}\right., \tag{26}\]  
where \(u_{p\_small}\) and \(u_{p}\) are equivalent to Equations (10) and (3), respectively.