esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Potential Experimental Setup  
The envisioned experimental setup for the all-optical encoder and comparator [24; 25; 26] using a CR-SOA-MZI is designed to perform five essential logic operations--\(\overline{\mathrm{A}}\mathrm{B}\), \(\overline{\mathrm{A}}\mathrm{B}\), \(\mathrm{AB}\) (AND), \(\overline{\mathrm{A}}\)\(\overline{\mathrm{B}}\) (NOR), and \(\mathrm{AB}+\overline{\mathrm{A}}\)\(\overline{\mathrm{B}}\) (XNOR)--using four CR-SOA-MZIs. A schematic representation of this proposed experimental configuration is illustrated in Figure 7. The system is driven by three input signals: A, B, and CW. The data signals A and B are generated using two synchronized laser systems, a rational harmonic mode-locked (ML) laser and a gain-switched distributed feedback (DFB) laser, producing ultra-short pulses with widths of 3.5 ps at repetition rates of 20 GHz and 40 GHz, respectively. A shared 10 GHz RF synthesizer synchronizes these lasers. The CW signal, operating at a wavelength of 1551.3 nm, is used as the reference power to enable efficient XGM in the CR-SOAs, enhancing the logic operations. In the encoder setup, the logic operations \(\overline{\mathrm{A}}\mathrm{B}\), \(\overline{\mathrm{A}}\mathrm{B}\), \(\mathrm{AB}\), and \(\overline{\mathrm{A}}\)\(\overline{\mathrm{B}}\) are performed by four CR-SOA-MZIs, each optimized to handle different logic conditions. The input signals A and B, at wavelengths 1550.7 nm and 1549.3 nm, respectively, are injected into the MZIs along with the CW signal. A 3 dB OC splits the input signals A and B, directing them to the appropriate MZI through WSCs, which ensure the signals are routed based on their wavelength. While the current configuration operates with wavelengths centered around 1549.3-1551.3 nm, the setup is adaptable to a range of wavelengths within the C-band (1530-1565 nm). The optical components in the setup, such as the WSCs, OBPFs, and CR-SOAs, are tunable or can be replaced with versions that support different wavelengths. This flexibility allows for compatibility with wavelength-division multiplexing (WDM) systems, enabling multi-channel optical logic processing. Additionally, the system can be adapted to use wavelengths outside the C-band, depending on the specific application, making it versatile for different communication and computing technologies. In the CR-SOA-MZI1, the \(\overline{\mathrm{A}}\mathrm{B}\) operation is realized at wavelength \(\lambda_{\mathrm{B}}\), with signal A being significantly more powerful than signal B, aided by the CW signal at \(\lambda_{\mathrm{CW}}\) to enhance the XGM effect. Similarly, the CR-SOA-MZI2 performs the \(\overline{\mathrm{A}}\overline{\mathrm{B}}\) operation, where signal B holds higher optical power than signal A, again supported by the CW signal to maximize the
# Enhanced Learning Behaviors and Ability Knowledge Tracing
## 3 Problem and Definition  
In this section, we briefly introduce the knowledge tracing task with the notations used throughout the paper. We summarize them in Table 1.  
Suppose a set of students \(\mathbf{S}=\{s_{1},s_{2},\cdots,s_{i},\cdots,s_{l}\}\), a set of exercises \(\mathbf{E}=\{e_{1},e_{2},\cdots,e_{j},\cdots,e_{l}\}\), and a set of knowledge concepts \(\mathbf{C}=\{c_{1},c_{2},\cdots,c_{m},\cdots,c_{M}\}\), where each exercise is related to specific knowledge concepts. The Q-matrix \(\mathbf{Q}\in\mathbb{R}^{\mathbb{J}\times\mathbb{M}}\), which indicates the relationship between exercises and knowledge concepts. Here, \(Q_{jm}=1\) expresses that the knowledge concept \(c_{m}\) is required for exercise \(e_{j}\), and \(Q_{jm}=0\) otherwise. Each student engages in independent learning, without exerting influence on one another.  
Typically, when an exercise is assigned, the student allocates a specific amount of time to answer it based on their acquired knowledge. In the learning process, the answering behavior will be repeated. The learning process iterates the aforementioned response pattern across various exercises, with an interval period between adjacent answering interactions. Therefore, the student's question-answering history sequence \(\mathbf{H}_{t}=\{(eu_{1},au_{1}),it_{1},(eu_{2},au_{2}),it_{2},\cdots,(eu_{t}, au_{t}),it_{t}\}\), where the tuple \((eu_{t},au_{t})\) represents a basic learning cell in learning process. \(eu_{t}\) denotes the exercise embedding unit, including \(c_{t}\), \(e_{t}\), and \(df_{t}\). \(e_{t}\) is the exercise, \(c_{t}\) is the concept of the exercise, and \(df_{t}\) is the difficulty of the exercise. \(au_{t}\) is the learning ability embedding unit, including \(a_{t}\), \(at_{t}\), and \(ht_{t}\). \(a_{t}\) is the answer to the exercise; \(a_{t}=1\) means correct and \(a_{t}=0\) means wrong. \(at_{t}\) represents the answer time the students spent on answering \(e_{t}\). \(ht_{t}\) represents the hint times. \(it_{t}\) indicates the time interval between adjacent answering interactions.  
**Problem Definition.** Given students' question-answering history sequence \(\mathbf{H}_{t}=\{(eu_{1},au_{1}),it_{1},(eu_{2},au_{2}),it_{2},\cdots,(eu_{t}, au_{t}),it_{t}\}\), the task of knowledge tracing is defined as the process of monitoring the dynamic evolution of students' knowledge states during the learning process and predict the probability that a student will provide a correct answer at time step \(t+1\), i.e, \(p(a_{t+1}=1\mid e_{t+1},\mathbf{H}_{t})\).
# An Adaptive 24 GHz PSO-Based Optimized VCO in Next-Generation Wireless Sensor Networks
## 3 The Proposed Adaptive PSO Algorithm
### Time Complexity  
In optimization algorithms, time complexity is a crucial factor that determines how efficiently an algorithm solves a problem based on the input size. Figure 9 presents a time complexity analysis comparing genetic algorithms (GA), simulated annealing (SA), and adaptive particle swarm optimization (Adaptive PSO).  
The results indicate that the adaptive PSO exhibits lower time complexity compared to traditional GA. This advantage arises because particles in the adaptive PSO traverse the solution space more efficiently. In contrast, GA relies on crossover and mutation operations, which can increase computational complexity, particularly when dealing with large populations. The time required for VCO design using an adaptive PSO is approximately 4.1 s. In comparison, GA takes around 6.09 s, while SA requires about 5.3 s. These findings demonstrate the computational efficiency of an adaptive PSO over GA and SA.  
Figure 8: Histograms of \(f_{3}\), including final values searched for by the adaptive and traditional PSO algorithms over 50 trials.  
Figure 7: Histograms of \(f_{2}\), including final values searched for by the adaptive and traditional PSO algorithms over 50 trials.
# Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution
## 5 Conclusions  
In this work, considering that the fluctuating of the atmospheric turbulence channel has significant influences on the performance and practical security of CVQKD system, we put forward a method to optimize the relevant system. Here, we employ machine learning to CVQKD, which could adjust the modulation variance to an optimal value to ensure the system security and make the system performance optimal. The transmittance is simulated in consideration of not only the atmospheric turbulence, but also the absorption and scattering losses. The LSA algorithm is adopted to generate optimal data as training data of BPNN. Meanwhile, by comparing the two algorithms, we get that a BPNN based CVQKD system furnishes a higher achievable key rate and more efficient parameter optimization. In particular, this approach can be applied to any measurable physical parameter of signals in atmospheric turbulence or fiber channel [35; 39].  
**Author Contributions:** Conceptualization, Y.S., Y.G. and D.H.; software, Y.S.; validation, D.H.; Writing--Original draft preparation, Y.S.; Writing--Review and editing, D.H.  
**Funding:** This work was supported by the Fundamental Research Funds for the Central Universities of Central South University (Grant No. 2019 zzts580) and the National Natural Science Foundation of China (Grant Nos. 61572529, 61871407, 61872390, and 61801522).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: The secret key rate as a function of distance for local search algorithm (LSA) and BPNN in the summer and winter. (**a**) \(\varepsilon=0.01\); (**b**) \(\varepsilon=0.03\).
## 3 Results and Discussions
### Hydrocarbon Type Analysis  
Table 2 shows the hydrocarbon type analysis of the MLCT. The results show that the MLCTs have three dominant groups of compounds: saturates, aromatics, and resins. The saturates are made up of paraffins and cycloalkanes, and the majority of the cycloalkane compounds are 1-3 ring cycloalkanes. The large amount of saturates is an important feature of MLCT. However, the amount of saturates is notably distinct. The total amount of alkanes and olefins is approximately 10 wt% and as high as 30 wt%. By contrast, the content of saturates in MLCT-Z is 6.8% higher than that in MLCT-S. The paraffin and cycloalkane compounds could be used as a cracking feed. The aromatics are mainly composed of 1-5 aromatic-ring compounds and heteroatomic compounds such as sulfur compounds. The abundant aromatic compounds are mainly 1-3 aromatic-ring compounds with and without short side chains. The main monoaromatic hydrocarbons are alkylbenzenes and indans, followed byindenes. Naphthalenes, acenapththenes, and acenapthlyenes account for more than 90% of the diaramatics. Meanwhile, the amount of monoaromatics in MLCT-Z is 10.6% less than that in MLCT-S. Although the two MLCT samples come from different production processes, the resin content is the same. As shown in Table 2, the resins content is 24.7 wt%, which is almost 1/4 of the raw MLCT material.  
Figure 3: Element composition analysis for narrow distillate fractions of MLCT. (**a**) C, H, C/H, (**b**) N, S, O.  
Figure 2: Molecular weight of the distillates.  
In general, resins are the most complex and heaviest fraction of MLCTs. However, the research found that compared to resins from crude oils, the resins from MLCT have essential differences in chemical composition. The hydrocarbon type of MLCT largely determines the performance of MLCT and its products, therefore, the hydrocarbon type results can be used as an important basis for selecting a suitable processing technology.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### RSSI Based Solutions
#### 4.3.3 MoMoRo  
The authors [64] proposed a mobility support layer MoMoRo enabling mobility support and is applicable to existing data collection protocols. MoMoRo supports two operations: first is reconstructing the default root and notifying the unavailability of root to non-root destination. Second, gathering neighborhood information while reconstructing default routes and passing this information to fuzzy estimator for identification of links good for route selection by the routing layer. The link quality estimations are done by collecting information on the number of transmission attempts. MoMoRo is notified during the failure of packet transmission to a node and sends a unicast packet to reach the node by suppressing additional overheads.  
We note that MoMoRo does not take any action if the acknowledgment is received. However, if in a specific time period, the acknowledgment is not received then MoMoRo checks if the node is in the default route and if it is, reconstruction of default route starts, otherwise, the route is broken to the missing destination node, following, the packet's final destination search is made. Node identifies a lost default route and repair of data collection tree is needed. A quick broadcast message is sent by MoMoRo to its neighborhood and a unicast reply is sent by nodes who heard the broadcast. Computation of link ETX, average Received Signal Strength (RSS) on the link, and Symbol Error Rate (SER) variation is done by using the received replies. The simulation results show PRR for downward traffic to and from mobile nodes by MoMoRo is 90% whereas it significantly degrades in the case of RPL. MoMoRO provides large overheads per node by taking aggressive actions and reacting back to link disconnections caused by mobile nodes as compared to RPL showing a minor increase in packet overheads.
# Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans
## 4 Filtering 3D Point Clouds  
To build useful FEMs and FRMs, it is necessary to filter the leveled 3D point clouds. Filtering is composed of three consecutive steps: far points elimination, overhangs removal and maximum height normalization. These steps are performed by the Filter node, and its output is processed by the Map node (see Figure 4).  
The first step is to eliminate far points, concretely those whose projection on the horizontal plane is far from \(d_{max}\)= 10 m from the origin of the 3D scan. The reason for this is the low visibility of the ground from the point of view of the 3D laser scanner, which is limited to \(h\) on Andabata (see Section 2) and because distant zones are sampled with a lower density than closer ones. In addition, the number of points to be processed is reduced and the resulting 3D point cloud focuses on the area where the next movements of the vehicle will take place.  
Overhangs that do not constitute an obstacle for [PERSON], such as the ceiling of a tunnel or tree canopy, need to be removed. For this purpose, the second step of the filter employs the collapsible cubes method with coarse binary cubes with an edge of \(E\) = 0.5 m [34]. Basically, occupied 3D cubes above a minimum vertical gap are omitted. Thus, points inside unsupported cubes are eliminated from the point cloud. Two empty cubes are enough for [PERSON] to navigate safely below overhangs.  
In the third step, the maximum height of obstacles is limited to 3 m above the ground. The objective for this step is that the height of tall and small obstacles can be comparable. For this purpose, the \(z\)-coordinates of 3D points inside coarse cubes with five or more cubes above its vertical ground cube are lowered.  
Figure 7 shows a leveled 3D scan obtained while [PERSON] was moving in an urban park (see Figure 8). It can be observed in Figure 7 that far points (blue) and overhangs from the canopy of the trees (green) are discarded from the filtered 3D scan (red). It is also noticeable that the maximum height of the trees has been limited.  
Figure 6: Details of leveled 3D laser scans obtained with a sensor’s head rotation of 180\({}^{\circ}\) (**left**) and 360\({}^{\circ}\) (**right**) while [PERSON] was moving along a slope.  
Figure 8: [PERSON] navigating on an urban park.  
Figure 7: Lateral (**top**) and aerial views (**bottom**) of a filtered 3D point cloud (red) obtained from a leveled 3D point cloud. Blue and green colors represent far points and overhangs, respectively.
# Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
## 6 Conclusions  
Counterfeit and inferior products not only greatly damage the reputation of businesses but also cause economic losses to consumers and disrupt the normal economic order of society. Anti-counterfeiting technology has become an important means to combat counterfeiting. Due to their own shortcomings and limitations, traditional physical and chemical anti-counterfeiting, code anti-counterfeiting and other technologies generally have problems such as high cost, poor user experience, and poor objectivity. Thus, there is an urgent need for a cost-effective anti-counterfeiting technology in the market.  
At present, the QR code anti-counterfeiting technology integrating anti-counterfeiting patterns has attracted more and more attention due to its high cost performance and pre-scale anti-counterfeiting advantages, but there are still some problems. Many existing identification schemes rely on special equipment such as scanners and microscopes, and there are few methods for authentication on smartphones. In particular, the ability to classify to blurry pattern images is weak, leading to a low recognition rate at mobile terminals. The existing methods also need enough counterfeit patterns for model training, which is hard to collect in practical scenarios.  
Therefore, a deep one-class method for an anti-counterfeiting pattern captured by smartphones is proposed in this paper. Only positive samples are needed to achieve effective identification. The experimental results show that the proposed approach is more able to distinguish the genuine and counterfeit anti-counterfeiting pattern images. The recognition rate for the blurry images of the genuine anti-counterfeiting patterns is particularly improved.  
Conceptualization, H.Z. and C.Z.; methodology, H.Z. and C.Z.; software, C.Z.; validation, C.Z., T.W. and C.Y.; formal analysis, H.Z. and C.Z.; investigation, H.Z. and C.Z.; resources, X.L. and H.Z.; data curation, C.Z., T.W. and C.Y.; writing--original draft preparation, C.Z.; writing--review and editing, H.Z., X.L. and C.Z.; visualization, C.Z., T.W. and C.Y.; supervision, H.Z.; project administration, H.Z.; funding acquisition, X.L. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the scientific research foundation of the Nanchang Institute of Science and Technology (No. NGRCZX-21-02), the Science and Technology Research Project of the Jiangxi Provincial Department of Education (No. GJJ212507), and the Nanchang Key Laboratory of Internet of Things Information Visualization Technology (No. 2020-NCZDSY-017).  
Not applicable.  
The images in this paper are available upon request.  
The authors declare no conflict of interest.
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 4 Dam Deformation Prediction Model Based on IEALL
### Optimization of Model Parameters Based on IGWO  
Compared with other algorithms, gray wolf optimization algorithm has the characteristics of great global search ability and fast convergence speed [28]. Based on this, the IGWO algorithm is developed through the combination of a dynamic weight and a nonlinear convergence factor to enhance the optimization performance of the algorithm, and the IGWO algorithm is then used to optimize the LSTM. In order to increase the model's predictive accuracy, the improved gray wolf optimization method primarily optimizes the LSTM model's parameters and derives the ideal parameter group. The following aspects are primarily involved in the IGWO optimization process:  
(1) optimize the parameter selection  
The main optimization parameters are training times as epoch, batch size as batch_size, number of hidden layer units num as num, learning rate as \(\eta\), and so on.  
2. selecting a fitness function The fitness function is a critical part of the iterative search process used in the IGWO algorithm's optimization process, and it has a direct impact on the convergence rate and optimization performance of the algorithm. The ultimate objective of the combined prediction model of dam deformation is to minimize the \(k=\sum_{t}^{t_{\max}\Sigma}{(\hat{y}_{t}-y_{t})^{2}}\), where \(y_{t}\) is the actual value at the moment of \(t\), and \(\hat{y}_{t}\) is the corresponding predicted value, and \(t_{\max}\) is the length of the time series. The fitness function is set simultaneously according to Equation (18): \[fitness=\sum_{t}^{t_{\max}\Sigma}{(\hat{y}_{t}-y_{t})^{2}}\] (18)
3. EALL Model Process Based on IGWO Optimization The flow chart of optimizing the LSTM model parameters in EALL based on IGWO is shown in Figure 3. Firstly, the parameters of the LSTM model are initialized, and a random uniform distribution function is used to initialize the parameters N of the gray wolf population, the maximum iteration number is \(t_{\max}\), and the coefficient parameter a, A, C. Record the matching places of the top three fitness values as \(X_{a}\), \(X_{g}\), \(X_{g}\), after calculating and comparing the fitness values of all gray wolves. Change the coefficient parameter's value a, A, C as well as the individual gray wolf's position as \(\overset{\rightarrow}{X}(t+1)\). Recalculate and compare the gray wolves' fitness values, choose the top three with the best fitness, and then update \(X_{a}\), \(X_{\beta}\), \(X_{\delta}\). If the current iteration number is less than the maximum iteration number \(t_{\max}\), the gray wolf individual's position and coefficient parameters will be updated continuously. If it is larger than the maximum iteration number \(X_{a}\), the optimal solution to the current parameters will be output. The LSTM model is developed according to the optimization parameters obtained above and trained to obtain an optimal LSTM model. To determine the final LSTM prediction value, the optimized model is utilized to predict the prediction sample.
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 3 System Architecture  
### The Network Architecture of VPP  
Based on VPP deployment experience, we propose a VPP network architecture as shown in Figure 1. It includes a flexible resource aggregation local network with a secure access gateway as the core and a backhaul network between the gateway and VPP monitoring and dispatching platform.  
The local network is responsible for the efficient, flexible, and inexpensive access to various flexible DERs to participate in VPP monitoring and scheduling. The gateway can not only provide RS-232/485, Wi-Fi, power line communication, Bluetooth, and other access interfaces to downward flexible DER, but also be aggregated into a larger DER via Ethernet or optical fiber upward. At the same time, the cascade of multilevel gateways can also aggregate more flexible DERs on a larger scale. The gateway can also support security functions, such as flexible resource secure access and data privacy protection. The backhaul network is mainly responsible for the link between the gateway and the VPP monitoring and scheduling platform. At present, a VPP mainly uses carrier-specific lines or 5G to provide communication services between the gateway and the platform.  
In the actual VPP construction, the local network is usually constructed by the network infrastructure transformation of all units of flexible DERs, so the network topology, link bandwidth, reliability, and timely extension are relatively easy to determine. For the backhaul network, it may pass through the carrier network, and the carrier policy and network topology are unknown. Therefore, as a VPP operator, the difficulty of monitoring and scheduling is usually focused on this.  
Figure 1: The universal network architecture of a VPP.
## 3 Results and Discussion
### Optimization of the Flow Path
However, a significant decrease in the average velocity from 14.91 m/s to 9.12 m/s (i.e., an increase in the average retention time from 13.41 ms to 21.94 ms) was observed after the introduction of the airflow diffuser and perforated metal plates, unlike the cases in the CFD simulations, in which the rate remained at the same level of 10.20 m/s. This result may be attributed to the face that the vortex blower (static wind pressure at 50 Hz: 3.4 kPa) used in this study cannot provide a sufficient wind pressure, resulting in a significantly reduced average velocity, i.e., a lowered gas flow rate. Despite slight deviations between the CFD simulations and practical measurements, these results indicate that the incorporation of the airflow diffuser and perforated metal plates improved the uniformity of the flow velocity per channel in the ten-layer DBD reactor. Figure 4: (**a**) Flow velocity and (**b**) retention time per channel of the three patterns (none, airflow diffuser, airflow diffuser + perforated metal).