esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Routing Algorithm for Inter-Satellite Network Based on Q-Learning
### Q-Learning Routing System Model  
Various services in the satellite network have specific requirements, such as bandwidth, jitter, delay, etc. In order to better meet the QoS requirements of diverse services, intelligent path selection and traffic distribution are crucial. There are multiple optional  
Figure 4: Q-learning algorithm flow chart.  
paths between the source node and the destination node based on service requests, and each path in the network may have different bandwidth and inherent delay. Therefore, the introduction of an SDN controller can intelligently provide a suitable transmission path for each service flow.  
Figure 5 shows the structural framework of SDN based on Q-learning. The SDN control plane contains the intelligent decision-making module of Q-learning. The intelligent decision-making module effectively generates network policies and realizes global, real-time and customizable network control and management. Specifically, when a service request arrives, the SDN controller obtains the global network state, and according to the link state in the current network, coupled with Q-learning action learning, constantly explores various optional paths. Therefore, the intelligent decision-making module generates the optimal path decision, and sends the forwarding rules to the nodes. At this time, the node can forward the data packet according to the routing table. Through this architecture, routing can be intelligently and rationally selected according to network resources to improve network performance.  
In this section, the inter-satellite routing model in the SDN network is established to provide the best transmission path for business flows, meet its QoS requirements, and reduce the utilization rate of the link with the highest load and transmission delay in the network as much as possible.
### Mittag-Leffler Function, Laplace Transform, and Adomian Decomposition Method  
The Mittag-Leffler function, originally introduced as a generalization of the exponential function, plays a fundamental role in the theory of fractional differential equations. It is typically defined as  
\[E_{\alpha}(z)=\sum_{k=0}^{\infty}\frac{z^{k}}{\Gamma(\alpha k+1)},\quad\alpha>0, \;z\in\mathbb{C},\]  
where \(\Gamma(\cdot)\) denotes the classical Gamma function. The Mittag-Leffler function naturally appears in the solutions of fractional-order differential and integral equations, providing an explicit representation of memory and hereditary properties inherent in such models. Recent studies have extended and applied the Mittag-Leffler framework to various contexts, including new fractional operators [24], Ulam-Hyers-Mittag-Leffler stability [25], multivariate solutions [26], improved discrete kernels [27], and numerical methods such as the Mittag-Leffler-Galerkin approach [28].  
The fuzzy Laplace transform extends the classical Laplace transform to fuzzy-valued functions. Let \(f:[0,\infty)\to\mathbb{R}_{F}\) be a fuzzy-valued function. The fuzzy Laplace transform of \(f\), denoted by \(\mathcal{L}[f](s)\), is defined by  
\[\mathcal{L}[f](s)=\int_{0}^{\infty}e^{-sf}f(t)\,dt,\]  
Concurrently, fuzzy Laplace transform methods have been increasingly employed to solve fuzzy fractional differential equations [29; 30], and specially, the second model market equilibrium in fractional fuzzy economic systems, process medical imaging data [31], and establish foundational techniques for fuzzy differential problems [32]. These contributions underline the relevance and growing importance of operational methods in analyzing and approximating fuzzy fractional systems, providing the context and motivation for the present study.  
The Adomian Decomposition Method (ADM) is a well-known analytical approach for solving linear and nonlinear differential equations. In the fuzzy setting, ADM has been extended to handle fuzzy fractional differential equations, providing a powerful technique that avoids discretization and linearization. [PERSON] [33] applied ADM to Hilfer-type fuzzy fractional differential equations, demonstrating its effectiveness in handling generalized fractional models. [PERSON] and [PERSON] introduced a modified fuzzy ADM for solving time-fuzzy fractional partial differential equations with initial and boundary conditions [34], and further expanded its usage to a broader class of fuzzy fractional PDEs in [35]. These studies underline the robustness and flexibility of ADM in the analysis of fuzzy fractional systems, supporting its use in our own framework.  
Finally, for a review of [PERSON]'s fractional derivative estimation and its application in solving the problem of solute transport in rivers, we refer to [36; 37] respectively.
## 2 System Overview and Design  
The central element of the identification system is the ID generator, a circuit that produces an \(N\)-bit ID number based on small variations in the IC due to manufacturing. The number should be random, with each bit having equal likelihood of being \"0\" or \"1\". The random event is the manufacture of the IC, which occurs only once.  
Many circuits exhibit measurable differences due to manufacturing variation that could be used to generate random, unique data. These include memories, such as SRAM power-up state [10] and static noise margin [11], and DRAM retention fails [12]. Variations in delay lines and arbiters [13], ring-oscillators [9], scan-chain power-up state [14], and cross-coupled inverters [15, 16, 17], as well as direct measurements of the drain current of individual FETs [6] have also been examined for the generation of unique identification data and PUFs.  
All random ID implementations have the property that not all of the random ID bits are necessarily reliable. The ID generator circuit should produce the same ID number every time it is activated, but in such systems some bits are more reliable than others, and unreliable bits may occasionally flip between successive ID generation events. Ultimately, this could cause errors in the identification process. Current literature solves this problem by requiring each sensor to transmit its generated ID number back to the external reader, which increases the amount of data that must be transmitted. However, for such systems, wireless communication is frequently the largest energy consumer, so any reduction in transmitted data directly improves system performance. It has been observed that prior knowledge of which bits are unreliable can be used to increase the accuracy of identification [15,18], and that the reliability of individual bits can be determined by examining the magnitude of the underlying variation [17].  
Ideally, the ID generator bits would not be temporally random: successive reads or power-on cycles would always produce the same ID number for the lifetime of the IC. However, due to the nature of manufacturing variations and the presence of electrical noise, the value of individual bits may vary over multiple read operations. We can model the output of the ID generator, which we will call the generated code, \(G\), as a discrete, \(N\)-bit random variable. For each bit, \(G_{i}\) we can assign a probability, \(p\), such that the probability of bit \(G_{i}\) evaluating to \"1\" is \(p\), and evaluating to \"0\" is \(1-p\). An \(N\)-bit ID generator can then be completely described by a sequence of \(N\) probabilities \((p_{0},p_{1},...,p_{N-1})\).  
Based on this ID generator, each IC can then be assigned an ID code \(C\), where  
\[C_{i}=\begin{cases}1&p_{i}>1/2\\ 0&p_{i}\leq 1/2\end{cases} \tag{1}\]  
In other words, the chip code \(C\) is the most likely value of the random variable \(G\), the output of one read of the ID generator. It is this number that will be used to identify the IC.
# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
## 2 Strategies
### Data Merge  
Some of the phone data files were of particularly low quality and contained continuous cycle slips on all satellites for large portions of the data. The raw data for these files indicated hardware clock discontinuities for every epoch, which were potentially caused by the phone's duty cycling the GNSS tracking during data collection. An example of this can be seen in Figure 1, which shows simultaneous observations taken from two adjacent phones, where the red ticks indicate the flagged cycle slips. With little to no valid carrier phase observations in the degraded files, these solutions tended to have very large position errors. Fortunately, the poor solutions also had very large error estimates, and there was at least one dataset in each collection drive that produced good results. Since the distance between the phones within the same collection scenario was small relative to the position errors, merging the solutions for datasets within a collection proved to be an effective method to minimize errors, not only in the problematic datasets, but also for all of the scenarios. The merge was carried out in the position domain instead of the measurement domain, because valid carrier phase measurements were available in at least one of the phones for each epoch, and the position domain merge was sufficient to leverage the benefits of carrier-enhanced positions. Furthermore, due to the approximate 20 cm physical separation between the phones, if the relative displacement vectors are not accounted for, a successful merge of the carrier phase measurements is difficult.  
The merge was performed by combining the solutions output by RTKLIB for each data collection drive using a weighted approach. The weights were computed as the inverse of the variance of the data naturally computed by RTKLIB. If the weights were approximately zero, they were discarded and the remaining weights which passed this filter were utilized to compute a weighted average merged phone solution. This solution was then distributed back to the individual phones on the drive. The phone merge introduced a minimal error, as the phones were placed approximately 0.2 m from each other on the car dashboard [8], but the improvement to the overall solution that resulted from removing the poor phone data outweighed the small phone distance error.
## 2 Problem Description, Simulation Model and Methodology
### Numerical Simulation Description and Measurement Methodology
Additive white Gaussian noise (AWGN), receiver (RX). To save simulation time, the equalizer length was optimized for each simulated DMD value to \(L_{\text{sym}}=L_{\text{DMD}}+2G_{l}+1\) symbols, where \(L_{\text{DMD}}\) is the DMD value in symbol units and \(G_{l}\) is a guard band of 8 symbols. The number of taps of the equalizer is hence \(L_{\text{taps}}=2\times L_{\text{sym}}\). As shown in Figure 4b, the training sequence was shifted forward by DMD/2 symbols and hence the equalizer frequency response--the estimated inverse of the channel impulse response--did not overflow the equalizer length. For any phase noise and DMD value under study, the OSNR was swept in 0.5 dB intervals and the bit-error rate (BER) vs OSNR curves were linearly interpolated to compute the penalties at a BER \(=10^{-4}\) by averaging the results of 3 independent realizations. The maximum OSNR penalty computed was 5 dB, corresponding to the cases where the impairments were so severe that the BER vs OSNR curve was greater than \(10^{-4}\) for all OSNR values and hence the intersection point did not exist.
## 4 Numerical Simulation Results  
This section details the numerical modelling of the triaxial compression test, performed to assess the effects of confining stress and RFCA on the stress-strain curve, elastic modulus, and strength parameters of the similar material simulating the weak surrounding rocks.  
Using discrete element software, triaxial compression tests were conducted on numerical specimens with seven different RFCAs. By applying servo loading to the lateral walls, stable confining stress was maintained for the numerical samples. Subsequently, the upper and lower walls were loaded at a rate of 0.5 mm/min to yield the stress-strain curve of the numerical specimen. Based on these stress-strain curves under different confining stresses, the elastic modulus and strength parameters of the similar material could be derived.
# Dual aptamer-Functionalized 3D Plasmonic Metamolecule for Thrombin Sensing
## 2 Materials and Methods  
### Design, Folding, and Purification of DNA Nanostructures  
The 3D plasmonic metamolecules were designed as in the previous paper [25], with the only difference being that the staple strands \"arm a\" were replaced with \"arm a thrombin-binding aptamer (TBA)\", \"arm b (57 nt)\" with \"arm b HD22\" and \"arm b (86 nt)\" with \"arm b blank\" (Table 1).  
For the structure folding, 100 nM of staple mix and 10 nM of the scaffold (p7650) were mixed in 1x tris-EDTA (TE) buffer (10 mM Tris, 1 mM EDTA, pH 8) supplemented with 20 mM MgCl\({}_{2}\). Concentrations of aptamer staple strands with the aptamers were set to 1 uM during the folding, to ensure high incorporation yields. The folding mix was heat denatured at 65 \({}^{\circ}\)C, for 5 min, and slowly cooled down to 20 \({}^{\circ}\)C, over 16 h. After folding, the structures were separated from the excessive staple strands by electrophoresis in a 0.7% agarose gel, for 2 h, at 90 V. The visible band of the structure was cut and the structures were recovered by squeezing the gel piece. Purified structures were characterized by negative staining transmission electron microscopy (JEM-1011, JEOL, Tokyo, Japan).
## 3 Generic Algorithm to Determine Maximum Bottleneck Node Weight-Based Data Gathering Trees
### Modifications to the MaxBNW-DG Algorithm to Determine MinBNW-DG Tree  
A MinBNW-DG tree is the one for which the bottleneck node weight on a path is defined as the maximum of the node weights (instead of the minimum of the node weights as in a MaxBNW-DG tree) and the objective would be to determine a DG tree for which the bottleneck node weight for a path from any node to the root node is the minimum. Accordingly, we start with the node having the minimum node weight and accommodate nodes in the increasing order of the _Tree-join-weight_; we modify the pseudo code of the MaxBNW-DG algorithm as shown in Algorithm 2: the lines that are not explicitly shown are the same as in the MaxBNW-DG algorithm.  
```
Input: Graph \(G=(V,E,W_{V},W_{E})\) Output:\(T_{Min}^{BNW}(V,E_{T})\) where \(E_{T}\) is the set of edges in the MinBNW-DG tree Auxiliary Variables: Optimized-Nodes-List, Candidate-Nodes-List, Parent-Node-List Estimated-join-weight, Tree-join-weight, Leader Node Initialization: Optimized-Nodes-List \(=\)\(\phi\); Candidate-Nodes-List \(=\)\(\phi\), Parent-Node-List \(=\)\(\phi\), \(E_{T}\)\(=\)\(\phi\)\(\forall u\in V:Estimated-join-weight(u)=\infty\), Tree-join-weight(\(u\)) \(=\)\(\infty\) Begin MinBNW-DG Algorithm \(1\) Leader Node \(s=\{u\mid Minimum(W_{V}(u))\}\) \(2...4\) (as in the pseudo code for the MaxBNW-DG algorithm, refer Algorithm 1) while (Candidate-Nodes-List \(\
eq\)\(\phi\))do  Node \(u=\{i\mid\begin{array}{c}Minimum\\ i\in Candidate-Nodes-List\end{array}\) (Estimated-join-weights(\(i\))) \(7...8\) (as in the pseudo code for the MaxBNW-DG algorithm, refer Algorithm 1) for( Node \(v\in Neighbors(u)\) AND \(v\
ot\in Optimized-Nodes-List\)) do if(Estimated-join-weight(\(v\)) \(>\) Maximum(\(W_{V}(v)\), Tree-join-weight(\(u\)) ) ) then  Estimated-join-weight(\(v\)) = Maximum(\(W_{V}(v)\), Tree-join-weight(\(u\)) )  Parent-Node-List(\(v\))  endif endfor  endfor  endfor  endfor  endfor
```  
**Algorithm 2**Pseudo Code for the Algorithm to Determine Minimum Bottleneck Node Weight-DG Trees.
# A Typical Small Watershed in Southwestern China Is Demonstrated as a Significant Carbon Sink
## 3 Results
### Variations in Carbon Fluxes
#### 3.2.2 Seasonal and Annual Carbon Fluxes  
Carbon fluxes (NEE, GPP, and Re) at the three sites over the course of two years are shown in Figure 4. The annual NEE values were \(-\)592.8 and \(-\)488.1 gC m\({}^{-2}\) a\({}^{-1}\) for forest, and \(-\)92.5 and \(-\)71.6 gC m\({}^{-2}\) a\({}^{-1}\) for cropland, respectively. The carbon sink for non-timber forests was \(-\)371.0 gC m\({}^{-2}\) a\({}^{-1}\) from October 2022 to September 2023.  
Figure 3: Diurnal variation characteristics of carbon fluxes in forest and cropland sites from October 2021 to September 2023 and in non-timber forest site from October 2022 to September 2023, including (**a1–a3**) diurnal variation of NEE in different months, and (**b1–b3**) diurnal variation of NEE, GPP, and Re during the corresponding data periods. The shaded areas represent the standard deviations for each half-hourly value.  
The variation in daily NEE at the forest site over the two-year period ranged from \(-8.0\) to \(5.9\) gC m\({}^{-2}\) d\({}^{-1}\). Monthly NEE values were negative for 24 months, with the lowest value occurring in July 2022. GPP and Re values increased rapidly from around May each year, peaked in July and August, and then gradually decreased. Daily NEE at the cropland site varied within the range of \(-5\) to \(4.6\) gC m\({}^{-2}\) d\({}^{-1}\). In the two-year period, the site acted as a carbon sink for 197 and 213 days, respectively. Potatoes and maize were planted around the observation site in mid-March and mid-May, which facilitated carbon uptake. There were two phases of decline in GPP and Re after the maturation of potatoes in late August and maize in mid-September. From October 2022 to September 2023, the minimum and maximum daily NEE values at the non-timber forest site were \(-4.5\) and \(1.9\) gC m\({}^{-2}\) d\({}^{-1}\), respectively. Carbon sinks were observed on 88% of the days during this period. Monthly NEE was only slightly positive (\(0.8\) gC m\({}^{-2}\) mon\({}^{-1}\)) in June 2023, and negative in the remaining months. Mid-July was the ripening and harvesting period of _Zanthoxylum bungeanum Maxim_ fruits. At this time, NEE increased (i.e., carbon uptake decreased) and GPP and Re decreased. Subsequently, there was another period of decline in NEE. The defoliation of _Zanthoxylum bungeanum Maxim_ trees started in early November, and both GPP and Re remained low afterwards.  
Figure 4: Changes in (**a1–a3**) daily and (**b1–b3**) monthly NEE, GPP, and Re in forest, cropland, and non-timber forest sites from October 2021 to September 2023.  
Furthermore, significant positive correlations were found between daily changes in NEE across the various ecosystems (\(p\) < 0.001, Figure 5). The correlation between NEE in each of the two ecosystems was consistently greater than 0.45 when analyzing data from the two years of the study period.
## 5 Conclusions  
A preventive MIDA application is of significant importance to the mining industry. However, there are still gaps in the current research and practical applications. The major challenges include the difficulty of obtaining the precise 3D GIS data, the lack of an intuitive visualization environment, and the lack of a unified MIDA framework. To fill these gaps, an oblique photogrammetry-supported unified workflow for MIDA applications is proposed in this paper. In this framework, a multi-scalar oblique photogrammetric scheme is first designed and used to collect and process essential data for a 3D webGIS. The visualization module realizes the 3D visualization of GIS entity data and MIDA results based on WebGL. The proposed framework is applied to a large-scale underground coal mine area, and the results prove the feasibility and reliability of the system. Despite these contributions, some limitations need to be addressed. First, the important step of GIS data collecting and storing in our framework is carried out manually. This process should be further optimized to reduce human involvement. Furthermore, although the GIS entities in our case study present 3D shapes by dynamic rendering (e.g., houses) or manual 3D modeling (e.g., power towers), the corresponding geometric components are dependent on each other. Future research can be carried out by integrating BIM and webGIS environments, as GIS describes the large-scale macro environment. In contrast, BIM describes the micro-information of object geometry and semantic attributes inside an object, complementing each other. This feature is important because when an interesting object is destroyed in a mining area, we can repair or reconstruct it according to the recorded documentation.  
Conceptualization, X.Z., H.Y. and H.B.; methodology, X.Z., H.Y. and H.B.; software, X.Z. and Y.M.; validation, P.X. and B.Z.; formal analysis, H.Y.; investigation, P.X.; data curation, H.B.; writing--original draft preparation, H.B.; writing--review and editing, H.Y.; visualization, B.Z.; supervision, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2017 YFE0119600).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to they are the property of the cooperative mine enterprise.  
The authors declare no conflict of interest.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Robustness Against Common Attack Vectors
#### 5.3.3 Replay Attacks  
Replay attacks are mitigated differently depending on the AES mode used.  
PQC-AES Hybrid PKE (with AES-GCM)  
When using AES-GCM, the GCM mode inherently provides protection against replay attacks. The authentication tag incorporates the initialization vector (IV) and ciphertext, so replaying a message with a different IV (which should be unique for each message) will result in authentication failure.  
PQC-AES Hybrid PKE (with AES-CBC or Other Modes)  
To mitigate replay attacks when not using GCM, we incorporate sequence numbers or timestamps within the HMAC calculation. The receiver tracks these sequence numbers or / timestamps and rejects any messages that are out of order or outside an acceptable time window.