esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### K-Nearest Neighbor
#### 3.3.2 Linear Model  
The ensuing algorithm learns a decision function from the dataset to create a boundary for each class. It generates a formula to create a function to predict unknown values. The linear model offers a relatively quick training phase and is straightforward to interpret [20].  
Linear Discriminant Analysis (LDA) is a classifier with a linear decision function to set the boundary for each class [27]. It creates a function from the conditional densities of the labelled dataset using Bayes' rule. LDA assumes the dataset has a Gaussian distribution and each parameter has the same variance. The algorithm estimates the mean and variance of the dataset for each class.  
The One-Class Support Vector Machine (OC-SVM) is an ML algorithm intended to detect a novelty, i.e., a rare event. OC-SVM does not model any probability distribution from the dataset. It learns to find a function for the high- and low-density regions in the dataset based on max-margin methods. A lower density region implies a rare event, or a novelty, within the dataset [28].
## 5 Discussion on Regional Applicability of the Methodology  
While this study is conducted based on specific environmental parameters from the cold region of northern Xinjiang, the established analytical framework (including sinusoidal characterization of thermal loads and coupled calculation methods for soil freeze-thaw processes) demonstrates methodological universality. For engineering applications in other cold regions, the following adaptive adjustments are recommended: (1) modifying the phase parameter in Equation (14) according to local annual temperature amplitude; (2) recalibrating thermal conductivity coefficients for different soil types; (3) validating boundary conditions against regional maximum frost depth observations. The cross-regional applicability of the model will be further enhanced through the establishment of a multi-regional parameter database in future research.
# Fuzzy Adaptive Dynamic Surface Control with Constant Gain for Non-Affine Pure-Feedback Systems
## 2 Problem Formulation and Preliminaries
### System Description and Transformation  
The controlled object is selected as a single-input single-output pure-feedback system with uncertain time-varying disturbance, specifically categorized as the following type [32]:  
\[\begin{split}&\dot{x}_{j}=g_{j}(\bar{x}_{j},x_{j+1})+d_{j}(t)\\ &\dot{x}_{n}=g_{n}(\bar{x}_{n},u)+d_{n}(t)\\ & y=x_{1},\ j=1,2,\ldots,n-1\end{split} \tag{1}\]where the state vector is represented as \(\bar{x}_{j}=\left[x_{1},\ldots,x_{j}\right]^{T}\in\Psi^{j}\), while the system output and control signal are denoted by \(y\) and \(u\), respectively; the differentiable functions \(g_{j}(\bar{x}_{j},x_{j+1})\) and \(g_{n}(\bar{x}_{n},u)\) are considered to be known. Additionally, continuous disturbances \(d_{j}(t)\) and \(d_{n}(t)\) are bounded but remain unknown.  
By employing the differential mid-value theorem, an affine representation is derived for the originally non-affine functions \(g_{j}(\bar{x}_{j},x_{j+1})\) and \(g_{n}(\bar{x}_{n},u)\), resulting in the following form:  
\[\begin{split}& g_{j}(\bar{x}_{j},x_{j+1})=g_{j}(\bar{x}_{j},x_{j+1} ^{0})+\psi_{j}(\bar{x}_{j},\Xi_{j})(x_{j+1}-x_{j+1}^{0})\\ &\psi_{j}=\frac{\partial g_{j}(\bar{x}_{j},x_{j+1})}{\partial x_ {j+1}},\ \Xi_{j}\in\left(x_{j+1}^{0},x_{j+1}\right)\\ & g_{n}(\bar{x}_{n},u)=g_{n}(\bar{x}_{n},u^{0})+\psi_{n}(\bar{x}_ {n},\Xi_{n})(u-u^{0})\\ &\psi_{n}=\frac{\partial g_{n}(\bar{x}_{n},u)}{\partial u},\ \Xi_{n}\in\left(u^{0},u\right),\ j=1,2,\ldots,n-1\end{split} \tag{2}\]  
where these terms \(\psi_{j}\) and \(\psi_{n}\) denote the control gain variables associated with the \(j\)th and \(n\)th subsystems, respectively; \(x_{j+1}^{0}\) and \(u^{0}\) both represent certain equilibrium points, which are usually set to zero. For notational brevity, repeated function arguments in subsequent derivations may be omitted; for example, \(g_{j}(\bar{x}_{j},x_{j+1})\) is written compactly as \(g_{j}\). The substitution of (2) into (1) leads to the derivation of  
\[\begin{split}&\dot{x}_{j}=g_{j}(\bar{x}_{j},x_{j+1}^{0})+\psi_{j}(x _{j+1}-x_{j+1}^{0})+d_{j}(t)\\ &\dot{x}_{n}=g_{n}(\bar{x}_{n},u^{0})+\psi_{n}(u-u^{0})+d_{n}(t) \\ & y=x_{1},\ j=1,2,\ldots,n-1.\end{split} \tag{3}\]
## 2 Materials and Methods
### DNA Extraction, Library Construction, and Amplicon Sequencing  
Twenty-five milliliters of four activated sludge sample (Day0, Day16, Day58, and Day87) were mixed with an equal volume of 50% glycerol and conserved at \(-80\)\({}^{\circ}\)C until analysis. Microbial DNA was extracted from each sample using 1 mL of mixed liquor with ZR Soil Microbe DNA MicroPrep (ZYMO RESEARCH; Irvine, CA, USA) according to the manufacturer's instructions. DNA concentration was measured on a Qubit 2.0 Fluorimeter using the Qubit (r) dsDNA BR assay kit (Invitrogen, Carlsbad, CA, USA) and its integrity was evaluated by electrophoresis on a 0.8% agarose gel. For the amplification of the 16S rRNA gene, libraries were constructed by amplifying the V3-V4 region (\(\sim\)460 bp), with gene-specific primers D-Bact-0341-b-S-17 and D-Bact-0008-a-S-16 selected from [PERSON] et al. [27], whereas for the amplification of the SSU of the 18S rRNA gene, universal primers FR1 and FF390 were selected from [PERSON] et al. [28]. All primers were modified by adding an Illumina (Illumina Inc, San Diego, CA, USA) overhang adapter nucleotide sequence at the 5' end. The sequences of the primers used were 16S_F: 5'-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG-3', 16S_R: 5'-GTC TCG TGG GCT CCGG AGA TGT GTA AAG ACA GGA CTA CHV GGG TAT CTA ATC C-3', 18S_F: 5'-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CGA TAA CGA ACG AGA CCT-3' and 18S_R: 5'-GTC TCG TGG GCT CCGG AGA TGT GTA TAA GAG ACA GAN CCA TAC AAT CGG TAN T-3'. All libraries were constructed as described in the Illumina's 16S Metagenomic Sequencing Library Preparation (15044223 B) protocol. PCR products were purified to remove unincorporated primers and primer-dimer species using NucleoMag(r) NGS Bead Suspension (Macherey-Nagel, Duren Germany). PCR reactions were performed on a Rotor-Gene Q thermocycler (Qiagen, Hilden, Germany). All libraries were quantified with fluorometric quantification using Qubit (r) dsDNA BR assay kit and their molarity was calculated in relation to the size of DNA amplicons after indexing. Quantitative PCR (qPCR) was performed with the KAPA Library Quantification kit for Illumina sequencing platforms (KAPA BIOSYSTEMS, Woburn, MA, USA). Libraries were sequenced in a MiSeq platform using MiSeq(r) reagent kit v3 (2 \(\times\) 300 cycles) at 4 pM ( Illumina Inc, San Diego, CA, USA). All sequences have been submitted to the European Nucleotide Archive database under study PRJEB27589 (sample accession ERR2681858, ERR2681859, ERR2681860, ERR2681861, ERR2681938, ERR2681940, ERR2681941).
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 2 Background
### Deep-Learning-Based IDS
#### 2.4.2 Long Short-Time Memory (LSTM)  
[PERSON] et al. proposed LSTM [23] in 1997, which avoids the RNN's vanishing gradient problem by quickly learning long-term dependencies. A normal RNN generates a new hidden state by using the previous hidden state as input and the current input state. The LSTM serves the same purpose, but it also accepts old cell states. Figure 1 depicts the memory cell in detail. Each modified LSTM cell typically contains three gates, input, forget, and output, calculated using the following equations:  
\[i_{t}=\sigma\;(W_{i}\;[\;h_{t-1},x_{t}\;]+b_{i}) \tag{1}\]  
\[f_{t}=\sigma\;(W_{f}\;[\;h_{t-1},x_{t}\;]+b_{f}) \tag{2}\]  
\[o_{t}=\sigma\;(W_{o}\;[\;h_{t-1},x_{t}\;]+b_{o}) \tag{3}\]  
\[h_{t}=o_{t}\;\alpha\;tanh\;(c_{t}) \tag{4}\]  
\[c_{t}=f_{t}\;o\;c_{t-1}\;+\;i_{t}\;o\;\tilde{c}_{t} \tag{5}\]  
\[\tilde{c}_{t}=tanh\;(w_{c}\;[h_{t-1},x_{t}]\;+\;b_{c}) \tag{6}\]  
Equation (1) is the formula for the input gate, Equation (2) is the formula for the forget gate, and Equation (3) is the formula for the output gate. The \(tanh\) activation function limits the output between \(-0\) and \(1\), which is replaceable with other activation functions. The input data and the previous moment's memory and output are multiplied by the three gates. The formula for memory is Equation (4), which results from multiplying the output data from the current output gate with the cell state after undergoing the \(tanh\) function; the memory represents the short-term memory resulting from the action of the output and the long term memory. The cell state represents the long-term memory and is calculated using Equation (5), which multiplies the previous moment's cell state through the forget gate by the candidate state. Equation (6) calculates the candidate state, which represents the information to be deposited in the cell state, resulting from the action of the current input data and the output data from the previous moment.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 2 The Proposed New One-Dimensional Chaotic Map
### Logistic-Tent Map (LTM)
#### 2.3.1 Boundedness of State Value Distribution of LTM System  
**Properties 1**.: _When \(a\in(0,+\infty)\),\(b\in[0,a]\), system (4) is a mapping: \(x_{n}\in(0,0.5)\cup(0.5,1)\to x_{n+1}\in(0,0.5)\cup(0.5,1)\)._  
**Proof.** The monotonicity of the function in the interval can be proven according to the positive and negative of the function derivative. The derivative function of \(\mathrm{f}(x_{n})\) is  
\[\mathrm{f}^{\prime}(x_{n})=\left\{\begin{array}{l}\frac{4b}{a}(1-2x_{n})+ \frac{2(a-b)}{a},0<\!x_{n}<0.5,a>0,0\leq b\leq a\\ \frac{4b}{a}(1-2x_{n})-\frac{2(a-b)}{a},0.5\leq x_{n}<1,a>0,0\leq b\leq a\end{array} \right.. \tag{5}\]  
It can be concluded from Equation (5) that  
\[\left\{\begin{array}{l}\mathrm{f}^{\prime}(x_{n})>\mathrm{f}^{\prime}(x_{n} =0.5)\Big{|}_{b=a}=\frac{4b}{a}(1-2\times 0.5)+\frac{2(a-a)}{a}=0,0<\!x_{n}<0.5,a>0,0 \leq b\leq a\\ \mathrm{f}^{\prime}(x_{n})<\mathrm{f}^{\prime}(x_{n}=0)\Big{|}_{b=a}=\frac{4b }{a}(1-2\times 0)+\frac{2(a-a)}{a}=0,0.5\leq x_{n}<1,a>0,0\leq b\leq a\end{array} \right..\]  
when \(x_{n}\in(0,0.5)\), owing to \(f^{\prime}(x_{n})>0\), so the function \(\mathrm{f}(x_{n})\) increases monotonically over the interval \((0,0.5)\). It can be seen from this that \(\mathrm{f}(0)<\mathrm{f}(x_{n})<\mathrm{f}(0.5)\), namely: \(0<\mathrm{f}(x_{n})<1\).  
When \(x_{n}\in(0.5,1)\), owing to \(f^{\prime}(x_{n})<0\), the function \(\mathrm{f}(x_{n})\) decreases monotonically over the interval \((0.5,1)\). It can be seen from this that \(\mathrm{f}(1)<\mathrm{f}(x_{n})<\mathrm{f}(0.5)\), namely: \(0<\mathrm{f}(x_{n})<1\).  
Therefore, if \(x_{n}\in(0,0.5)\cup(0.5,1)\), then \(0<\mathrm{f}(x_{n})<1\). The proof is complete. \(\square\)  
Property 1 shows that the state values of LTM are bounded and conform to the attractor condition.
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 11 Open Issues in LTE-U  
A comprehensive study for CSAT based LTE-U exists in the form of [53] in which the fair coexistence of LTE-U has been evaluated with respect to scaling in the number of devices, SCs, and operators. However, a similar comprehensive study for LAA-LBT based LTE-U is needed. From an LAA-LBT perspective, optimal contention window adjustment based on the operating environment needs more research [3]. The optimal channel sense threshold is another key issue in LAA-LBT that requires more attention as discussed in [60; 68]. From the CSAT perspective, future research is required to define a collision-free slot assignment for LTE-U in the presence of multiple LTE-U networks. Learning techniques in [35; 83; 84] can provide distributed collision-free operation. However, further evaluation of these techniques is needed in diverse scenarios for LTE-U [31].  
A more thorough investigation of LTE-U's coexistence with technologies besides WLAN (like RADAR as shown in [2; 88]) is required. A comparative study between the different channel access mechanism for LTE-U is also needed, particularly the LBT and CSAT [2]. Furthermore, more field trials with multi-operator and multi-cell LTE-U deployment is required for performance evaluation of the coexistence mechanisms.  
From a channel selections perspective, both intra and inter-operator coordination needs to be studied in detail for LTE-U along with the stability analysis of the channel selection schemes [44]. The appropriate selection of licensed and unlicensed bands for the operation of LTE-U users needs to be designed for effective utilization of LTE-U's potential as a technology [54]. The study of channel selection schemes in a multi-cell, multi-operator, and multiple WLAN AP environment is even more important to ensure successful commercial deployment of LTE-U based SC's.  
Development of suitable analytical models for various non-saturated traffic models is another important issue that requires more attention in LTE-U [39]. The inherent PHY/MAC differences between LTE-U and WLAN systems must be considered in these models along with inter-cell interference in dense deployments [8]. These models should also consider more practical aspects of LTE-U and WLAN like adaptive modulation and coding, hidden and exposed node problem, etc. [35]. From a practical deployment perspective, specific QoS demands, relay based communication [4], Multiple-Input Multiple-Output (MIMO) systems, and power control [75] are some additional aspects to be studied for LTE-U. A thorough study of both TDD and FDD based LTE-U, with different intra and inter-operator level coordination, is required [61]. Furthermore, the performance of LTE-U for various handover scenarios needs to be evaluated. A novel approach of licensed and unlicensed spectrum utilization for D2D communication and IoT has been considered in [112]. However, a more detailed study is needed.
## 4 Experimental Results and Discussion
### Porosity  
Porosity is a physical quantity that is often used to represent damage and has been successfully used to evaluate the damaged state of rock samples [22; 23; 24]. According to the principle of NMR, the porosity under different external loads can be calculated using NMR, as shown in Figure 6. On the whole, the variation in porosity under an external load can be divided into three stages, with an overall \"\(\surd\)\" shape tendency.  
Figure 6: Change in porosity rock under different external loads. (**a**) Cumulative porosity of 0.6 UCS. (**b**) Cumulative porosity of 0.7 UCS. (**c**) Cumulative porosity of 0.8 UCS. (**d**) Cumulative porosity of 0.9 UCS. (**e**) Cumulative porosity of 0.9 UCS. (**f**) The variation law between porosity and UCS.  
Stage I (0-70%): When the ratio of axial compression is between 0% and 70%, the rock interior is dominated by micropores, and the number of mesopores and macropores is small. The variation in porosity is relatively small, and the growth trend is primarily horizontal. Because of the diversity of the degree of damage and pore expansion in the rock, pores or cracks may be compacted when they develop and expand or penetrate under an external load in this range, slightly reducing the porosity. As can be seen from Figure 7b, although the T\({}_{2}\) spectrum area of the micropores continues to increase, the overall porosity trend is relatively consistent and even, indicating that micropores play a small role in the overall porosity development.  
Figure 7: Microstructure characteristics of rock mass under different loads. (**a**) Rock SEM image magnified 5000 times at 0.7 UCS. (**b**) Rock SEM image magnified 20,000 times at 0.7 UCS. (**c**) Rock SEM image magnified 5000 times at 0.8 UCS. (**d**) Rock SEM image magnified 20,000 times at 0.8 UCS. (**e**) Rock SEM image magnified 5000 times at 0.9 UCS. (**f**) Rock SEM image magnified 20,000 times at 0.9 UCS. (**g**) Rock SEM image magnified 5000 times at 0.95 UCS. (**h**) Rock SEM image magnified 20,000 times at 0.95 UCS.  
Stage II (70-80%): When the ratio of axial compression is between 70% and 80%, the overall slope of the porosity increases. It can be seen from Figure 7b,c that the change in porosity caused by changes in the micropores is small, while the change in porosity caused by changes in the mesopores and macropores has a significant increase. This indicates that the mesopores and macropores play a decisive role in the porosity change in rocks.  
Stage III (80-95%): When the ratio of axial compression is greater than 80% (the critical value of damage), the slope of the porosity has a sharp increase. The porosity caused by the each of the three kinds of pore increases, as shown in Figure 7c-e. Especially when the ratio of axial compression exceeds 90%, the porosity caused by mesopores and macropores accounts for 50% of the total porosity.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 7 Conclusions  
This paper has addressed the routing problem of time-sensitive traffic flow in satellite networks, which is challenging due to the variability of satellite network topology and the specific delay requirements of TS flows, such as delay, and load balancing. We have provided a link-path traffic-scheduling model that aims to optimize several objectives,  
Figure 16: Packet loss rates for low-priority TS flows.  
Figure 14: Maximum delay for low-priority TS flows.  
Figure 15: Average delay for low-priority TS flows.  
including minimizing the number of path handovers, minimizing link utilization, and ensuring deterministic delay by introducing delay constraints.  
We have proposed an approximate algorithm called longest continuous path (LCP). This algorithm selects the longest continuous path within each time slot and then chooses the path with the minimum link utilization that satisfies the latency requirement. By constructing an auxiliary graph, LCP determines a routing solution that also reduces the number of path handovers. LCP takes into account the specific requirements of TS flows and the complex topology of satellite networks, which outperforms in terms of a smaller number of path handovers, a commendable load balancing performance, and fulfilling the latency requirements.  
Conceptualization, F.D. and Y.Z.; methodology, F.D. and Y.Z.; software, G.L.; validation, F.D., G.L., and H.Y.; formal analysis, Y.Z.; investigation, G.L.; resources, F.D. and Y.Z.; data curation, G.L.; writing--original draft preparation, G.L. and H.Y.; writing--review and editing, G.L. and F.D.; supervision, F.D.; and project administration, C.S. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by National Natural Science Foundation of China (Grant No. 92067206).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LEO & Low Earth orbit \\
6G & Sixth generation \\ TSN & Time-sensitive networking \\ SDN & Software-defined networking \\ ILP & Integer linear programming \\ DRA & Distributed routing algorithm \\ LCP & Longest continuous path \\ STK & Satellite tool kit \\ OVS & OpenVSwitch \\ DE & Differential evolution \\ ATS & Asynchronous traffic shaper \\ ONOS & Open-network operating system \\ \end{tabular}
## 4 Semi-Physical Platform Setup and Evaluation for SDN-Based ISTNs
### Introduction of the Semi-Physical Platform  
In Section 3.2, the control plane network protocol and algorithms are successfully tested in the software platform. However, owing to the performance limitations of the VxWorks simulated network card, WindRiver WRTAP has only ten-megabit throughput, and cannot enable label-based AOS frame switching. Real-world end-to-end gigabit-level data transmission cannot be accomplished in the software platform. Therefore, FPGA boards are designed for testing high-level throughput performance based on label switching of AOS frames. As shown in Figure 14,b the semi-physical platform is made up of a network management system, VxWorks Simulators, and FPGA boards. In the semi-physical platform, just three nodes are evaluated for simplicity. The terrestrial network gateway, i.e., vx_simulator_01, is controlled directly by the network management system, and FPGA boards are connected by low voltage differential signaling (LVDS) cables, which are simulated as satellite-terrestrial connections and inter-satellite links. It should be noted that the network management system may communicate with remote nodes, such as nodes vx_simulator_02 and vx_simulator_03, through the **data communication network** overhead in the AOS frame. The simulated nodes in the control plane communicate with the FPGA boards in the data plane through the southbound interface, which might be a serial port, Ethernet port, or 1553B in this semi-physical platform. The northbound interface communication protocol in the semi-physical platform is the simple network management protocol (SNMP), as the SNMP manager operating in the network management system and the SNMP agent running in the VxWorks simulated node (i.e., the controller). In addition, as illustrated in Figure 15, we create an exclusive management information base (MIB) for parameter setup, retrieval, and status informing/trapping.  
Figure 14: The semi-physical platform. (**a**) The semi-physical platform with VxWorks simulated nodes and FPGA boards. (**b**) The network topology of the semi-physical platform.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **vx_simulator\_01** & **vx_simulator\_04** & **vx_simulator\_06** & **vx_simulator\_03** \\ \hline Forward LSP & Out-port: 3 1 ** & In-port: 1, Out-port: 5 & In-port: 1, Out-port: 3 & In-port: 1 \\ Backward LSP & In-port: 4 1 ** & Out-port: 2, In-port: 5 & Out-port: 1, In-port: 3 & Out-port: 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A Bi-directional label switching path between node vx_simulator_01 (SCID 1) and node vx_simulator_03 (SCID 3) under the circumstances of a uni-directional link in the software platform.
## 3 Results and Discussion  
### Chemical Composition of PWPs  
The chemical composition (carbohydrates, proteins, phenolic compounds, chlorophyll) and extraction yield of PWPs were shown in Table 1. _P. major_ L. leaves are generally considered as a rich source of polyphenols and chlorophyll [21; 22; 38; 39; 40]. The initial purification of leaf mass using a series of different solvents (ethanol, chloroform/methanol and acetone) allowed producing a colorless polysaccharide with chlorophyll and polyphenol content less than 0.5%. The extraction yield ofPWPs was estimated at 19 g/kg (1.92% \(\pm\) 0.11) based on fresh leaves. It is important to mention that [PERSON] et al. [25] obtained similar values for polysaccharide content of _P. major_ fresh leaves (1.5-3.3%) using a water extraction. The main compounds of PWPs determined by colorimetric assays were uronic acids (59.34% \(\pm\) 1.05) followed by neutral sugars (39.14% \(\pm\) 0.22). The presented results are similar to those previously reported in our study [41] and to those found in literature [24; 25] where pectic substances were confirmed. Finally, the protein content (0.51% \(\pm\) 0.06) was lower than the value reported by [PERSON] et al. [24] (1.8%) for water-soluble polysaccharide of _P. major_ leaves proving that our extraction process of PWPs was more efficient.