esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Main Results
### The Improved Evaluation Mechanism of IWSNs  
Invulnerability represents the network's survival after suffering cascading failure. The higher the invulnerability metric, the less impact the network will have after experiencing cascading failure. The commonly used evaluation method is gain components [26], which can be computed as follows:  
\[G=\frac{N_{r}^{f}}{N_{r}}, \tag{5}\]  
where \(G\) is the gain component; \(N_{r}\) represents the number of nodes, and \(N_{r}^{f}\) represents the normal nodes after cascading failure.  
However, this evaluation mechanism is not applicable in large hierarchical industrial wireless sensor networks. The reason is that, although some nodes survived from the cascading failure, the links to the sink nodes have been severed. They are unable to communicate with the sink nodes. Such nodes should not be counted when evaluating the network. Moreover, even though the communication can still be connected, more relay nodes are required, which greatly affects the communication efficiency problem, and the above evaluation metrics do not describe the communication efficiency. To solve the above problems, the following evaluation metrics are proposed.  
\[E=\frac{\sum_{i\in r}\frac{1}{d_{ik}}}{N_{r}(N_{r}-1)}, \tag{6}\]  
where \(E\) is the efficiency measure, which is a metric to quantify the communication efficiency of nodes; \(d_{ik}\) denotes the shortest path length from relay node \(i\) to sink node \(k\); \(N_{r}\) is the number of nodes;  
\[M=\frac{N_{r}^{(k)}E(t)}{N_{r}E(0)}, \tag{7}\]  
where \(M\) is the new proposed evaluation mechanism. \(N_{r}^{(k)}\) is the number of remaining links between nodes and node \(k\), which removes the surviving but isolated nodes. The ratio of the initial communication efficiency \(E(0)\) to the current communication efficiency \(E(t)\) shows the change in efficiency due to cascading failure. The new proposed evaluation mechanism both ensures the accuracy of the network's surviving nodes and quantifies the network's communication efficiency.
## 5 Module I: Anonymity
### Communication Phase
#### 5.3.2 Transmission as a Forwarder  
When SNi sends the message one-hop uplink to the neighbor SNj, then SNj needs to forward the message to another intermediary node. Upon receiving Mi-j, SNj will match OHPIDi-j in its table, Tj. If there is no match, then the message definitely is not addressed for SNj and it will be dropped immediately. If it matches, then the message is decrypted using ki-j. The message will be forwarded to SNr after (M) is reformatted as in Equation (12):  
\[\mathrm{M_{j\to r}}=\mathrm{OHPID_{j\to r}}\parallel\mathrm{E_{kj\to r}} \left(\mathrm{PID_{i}}\parallel\mathrm{E_{ki\to bs}}\left(\mathrm{D_{i}} \right)\right) \tag{12}\]  
Right after the data is _received_ by SNj and _forwarded_ to the next one-hop SNr, the SNj updates the pseudonym OHPIDi-j. SNj now is ready to exchange another message with SNi using the new pseudonym OHPIDi-j. However, SNj is not yet ready to send data to SNr since SNr does not update the OHPIDi-j until (Dj) is forwarded to the next hop, say NSv. See Figure 3 for the sequence of transmissions for a message from SNi to the BS.  
Figure 3: The sequence of a message transmission from SNi to the BS. (**a**) SNj receives a message from SNi; (**b**) SNj forwards the message to a neighbor SNr; (**c**) BS receives the message and processes it.
_Review_  
A Narrative Review on Functionalized Nanoparticles for the Treatment and Early Detection of Hepatocellular Carcinoma  
**[PERSON] 1,*\(\copyright\), [PERSON] 1,2, [PERSON] 1, [PERSON] 2,3, [PERSON] 2,3 and [PERSON] 2,3**  
1 Department of Physiology, \"[PERSON]\" University of Medicine and Pharmacy, 40000 Clu-Napoca, Romania; [EMAIL_ADDRESS] (T.M.);[EMAIL_ADDRESS] (L.LS.)
2 Nanomedicine Department, Regional Institute of Gastroenterology and Hepatology. 400162 Clu-Napoca, Romania; [EMAIL_ADDRESS] (T.P.); [EMAIL_ADDRESS] (O.M.); [PERSON]SS] (L.M.)
3 3 rd Surgery and Medical Clinic, \"[PERSON]\" University of Medicine and Pharmacy, 400162 Clu-Napoca, Romania
* Correspondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]  
(1) Background: Hepatocellular carcinoma (HCC) is a major global health issue, ranking among the most frequently diagnosed cancers and one of the leading causes of cancer-related mortality. (2) Methods: To identify studies that focus on nanotechnology-mediated treatment and early diagnosis in hepatocellular carcinoma, our group conducted a thorough literature search across major scientific databases. (3) Results: In this narrative review, we demonstrated that nanotechnology--particularly the use of nanoparticles--holds significant potential for both the treatment and early detection of hepatocellular carcinoma. Nanoparticles act as carriers for the targeted delivery of drugs to cancer cells, greatly enhancing treatment efficacy while minimizing adverse effects on healthy tissues. Due to their physicochemical properties, these nanoparticles can also carry contrast agents, enabling precise identification of tumor cells and contributing to the early diagnosis of hepatocellular carcinoma. (4) Conclusions: While significant progress has been made, challenges such as toxicity, cost, and regulatory hurdles remain.  
**Keywords:** hepatocellular carcinoma; functionalized nanoparticles; targeted therapy; early diagnosis; theranostics; photothermal therapy; gene silencing
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Power Allocation (PA)  
Transmit power control is critical for beyond 5G network, as future communications will require EE with green technologies. The power allocation approach must be considered for these networks to ensure that the throughput, EE, and SE are at optimal levels. They are additionally modifying the power that, according to user requirements, can reduce interference, as shown in Figure 5. This section discusses the optimization parameters that were taken into consideration when allocating power, and a summary of those related works is listed in Table 2.  
Figure 4: Resectives management aspects.  
Figure 5: Power allocation in HetNet.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Characteristics** & **Issue** & **Methodologies** & **Advantages** & **Limitations/Future Work** & **Ref.** \\ \hline \hline Minimize the total power of HetNets as well to provide the network capacity and coverage. & Dynamic power optimization model. &
###### Abstract  
Understanding the relationship between leaf phenology and physiological properties has important implications for improving ecosystem models of biogeochemical cycling. However, previous studies have investigated such relationships only at the ecosystem level, limiting the biological interpretation and application of the observed relationships due to the complex vegetation structure of forest ecosystems. Additionally, studies focusing on transpiration are generally limited compared to those on photosynthesis. Thus, we investigated the relationship between stem sap flux density (SFD) and crown leaf phenology at the individual tree level using the heat dissipation method, unmanned aerial vehicle (UAV)-based observation, and ground-based visual observation across 17 species in a cool temperate forest in Japan, and assessed the potential of UAV-derived phenological metrics to track individual tree-level sap flow phenology. We computed five leaf phenological metrics (four from UAV imagery and one from ground observations) and evaluated the consistency of seasonality between the phenological metrics and SFD using Bayesian modelling. Although seasonal trajectories of the leaf phenological metrics differed markedly among the species, the daytime total SFD (SFD\({}_{\text{day}}\)) estimated by the phenological metrics was significantly correlated with the measured ones across the species, irrespective of the type of metric. Crown leaf cover derived from ground observations (CLCground) showed the highest ability to predict SFD\({}_{\text{day}}\), suggesting that the seasonality of leaf amount rather than leaf color plays a predominant role in sap flow phenology in this ecosystem. Among the UAV metrics, Hue had a superior ability to predict SFD\({}_{\text{day}}\) compared with the other metrics because it showed seasonality similar to CLCground. However, all leaf phenological metrics showed earlier spring increases than did sap flow in more than half of the individuals. Our study revealed that UAV metrics could be used as predictors of sap flow phenology for deciduous species in cool, temperate forests. However, for a more accurate prediction, phenological metrics representing the spring development of sap flow must be explored.  
leaf phenology; UAVs; stem sap flow; individual tree-level; temperate deciduous tree species +
Footnote †: journal: _remote sensing_
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 5 Numerical Results
### Transmission Overhead  
Figure 10 depicts the behavior of SASC architecture in comparison with others in terms of transmission overhead under a varying number of nodes. And it is observed from the numerical results that SASC reduces the transmission overhead by an average of 15% as compared to other solutions. Similarly, the numerical analysis in Figure 11 demonstrates that SASC also improves transmission overhead by an average of 12% than the existing solutions based on varying CBR data traffic. Unlike existing solutions, the proposed technique does not impose extra communication overheads on sensor nodes. Furthermore, the existing solutions do not balance the node conditions under heavy nodes and data traffic burden. The design of the proposed SASC architecture is focused on reliability, energy-efficient and secure data routing with lightweight computation, and processing power, thus, results in decreasing transmission overhead. Furthermore, the integration of sensor-cloud  
Figure 8: Average end-to-end delay in a varying number of nodes.  
Figure 9: Average end-to-end delay in varying CBR data traffic.  
infrastructure helps to provide scalable solutions with massive storage and efficient processing in a virtualized manner at minimum network overhead.
## 2 Materials and Methods
### Hardware and Software
#### 2.2.1 Sensor Signal Analysis and Feature Extraction  
We use the spectrum analysis method, whereby we used the sensor signals as input for spectrum feature extraction, in order to obtain the common features of sensor parameters.  
First, we performed an FFT (Fast Fourier Transform) on the data to obtain the power spectrum and determine the corresponding peak value. The frequency value corresponding to the peak area was used as a feature reference for low-pass filtering and data analysis, in order to eliminate high-frequency system interference.  
Classical frequency spectrum estimation methods, such as the indirect method, Bartlett method, and Welch method, usually include a periodic graph. In this study, the common periodogram method was applied for power spectrum conversion.  
The periodogram method regards the N-point observation data \(x(n)\) of the random signal observation data \(x_{N}(n)\) as a signal with limited energy. We used the Fourier transform of \(x_{N}(n)\) to obtain \(x_{N}(e^{i\omega})\), then took the amplitude squared and divided it by N to estimate the true power spectrum \(P(e^{i\omega})\) of \(x(n)\). \(\hat{P}_{per}(e^{i\omega})\) represents the power spectrum estimated by the periodogram method, calculated as:  
\[\hat{P}_{per}\Big{(}e^{i\omega}\Big{)}=\frac{1}{N}|X_{N}(\omega)|^{2}. \tag{1}\]  
As the FFT is used for spectrum transformation, it can be calculated as follows:  
\[\hat{P}_{per}(k)=\frac{1}{N}|X_{N}(k)|^{2}. \tag{2}\]  
In this way, power spectrum conversion of the input information can be completed.  
According to summarization and analysis of the common characteristics of the sensor signal spectrum, effective training data could be gathered by selecting an appropriate input combination of training factors; that is, one that combines the characteristic elements.
###### Abstract  
With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surface (RIS) has received much attention as one of the promising technologies for the sixth generation (6G) due to its ease of deployment, low power consumption, and low price. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude, and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This survey presents a comprehensive study of existing channel estimation methods for RIS. Firstly, channel estimation methods in high and low frequency bands are overviewed and compared. We focus on channel estimation in the high frequency band and analyze the system model. Then, the comprehensive description of the different channel estimation methods is given, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook on the future development of RIS channel estimation.  
wireless communication system; sixth generation (6G); reconfigurable intelligent surface (RIS); channel estimation  
3
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 2 Principle of Operation
### Area Measurement Principle  
#### 2.2.1 Theoretical Basis of [PERSON]'s Formula  
[PERSON]'s formula provides a feasible way to calculate the area of the plane region of the known boundary curve equation [16; 17; 18]. Let D be a closed domain on the xy plane, whose boundary consists of smooth closed curves. If the sum of functions \(P(x,y)\) and \(Q(x,y)\) has a continuous first order partial derivative on D, then  
\[\oint_{L}P(x,y)dx+Q(x,y)dy\ =\ \iint_{D}\bigg{(}\frac{\partial Q}{\partial x}- \frac{\partial P}{\partial y}\bigg{)}dxdy \tag{3}\]  
When a specific function combination \(P(x,y)\ =\ -\frac{1}{2}y\), \(Q(x,y)\ =\ \frac{1}{2}x\) is selected, the right-hand integrand is simplified as  
\[\frac{\partial Q}{\partial x}-\frac{\partial P}{\partial y}\ =\ \frac{1}{2}+\frac{1}{2}\ =\ 1 \tag{4}\]  
At this time, [PERSON]'s formula is transformed into an area integral  
\[S\ =\ \frac{1}{2}\oint_{L}xdy-ydx \tag{5}\]
# Hybrid Shielding for Hypervelocity Impact of Orbital Debris on Unmanned Spacecraft
## 3 Results and Discussion
### Honeycomb Material  
The honeycomb material is a critical design aspect for the optimised Whipple shield as this will not only have a direct impact on the energy absorption, but also on the weight of the structure. Two different numerical models were developed using aluminium alloy (AL2024-T3) and titanium alloy (Ti-6 Al-4V) honeycomb cores, corresponding to models M-AL and M-TI in Table 2, respectively. These models are compared to determine the effect that changing the material of the honeycomb core has on the energy dissipation of the debris particle. Figure 17 shows how the kinetic energy is dissipated for both models in the first 20 us of the impact. These results clearly show that, from early in the impact (\(t\approx 1\) us), model M-TI is more efficient at dissipating the kinetic energy of the debris particle when compared with model M-AL. Although a 27.8% difference can be observed in the kinetic energy of the debris particle between the two different materials at \(t=20\) us, when comparing to the standard Whipple shield, the energy dissipation efficiency is significantly higher, at 57.6% and 69.2% for the M-AL and M-TI models, respectively.  
Figure 18 shows a comparison in debris fragmentation and honeycomb perforation between the two material models, M-AL and M-TI. Although the perforation pattern of the honeycomb is similar between the two models, there is an evident variation, albeit slight, in the distribution of the debris going through the honeycomb structure, with a larger fraction of particles appearing further toward the back plate in model M-AL compared with model M-TI. This agrees with the results in Figure 17, and suggests that the titanium alloy Ti-6 Al-4V honeycomb is more effective in dissipating the kinetic energy from the hypervelocity impact.  
Figure 17: Kinetic energy comparison using Al2024-T3 and Ti-6 Al-4V honeycombs.  
Figure 18: Numerical simulation comparing honeycomb shell materials: Al2024-T3 (model M-AL, **left**) and Ti-6 Al-4V (model M-TI, **right**).
## 1 Introduction
Thus, the maximization of the EE instead of the capacity of the satellite is the novelty in this paper. The issue of optimal energy allocation and admission control is addressed for communications satellites in earth orbit in [22]. The authors in [21] make an overview of EE and satellite networking from a holistic perspective as well as the prospective greener architectures. The energy efficient power allocation problems in multibeam downlink satellite network is analyzed in [23]. Besides, the authors in [24] investigate the relationship between SE and EE for hybrid satellite terrestrial network, where overhead costs, transmission and circuit power, backhaul of gateway (GW), and density of small cells are taken into consideration. The energy efficiency of a multibeam downlink system is investigated in [25], which maximizes the ratio of system throughput over consumed power. However, to the best knowledge of the authors, energy-efficient power allocation problem in cognitive satellite terrestrial networks has not yet been solved in existing literature. In this paper, a novel integrated wireless sensor and cognitive satellite terrestrial network architecture is first presented, where the cognitive satellite user plays the role of the sink for the terrestrial sensor network and the sensing data is transmitted through the satellite communication networks. Then, energy-efficient optimal power allocation schemes are proposed for non-real-time and real-time applications in cognitive satellite terrestrial networks, which aim to maximize the EE of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. To guarantee the quality of the primary terrestrial user, average interference power (AIP) constraint is considered in the proposed schemes. To solve the nonlinear concave fractional programming problem, we combine [PERSON]'s method [26] with Lagrange duality method [27] and decouple the problem into multiple parallel subproblems. Then, an iterative algorithm is presented to search the optimal transmit power of the satellite user. Extensive numerical results evaluate the performance of the proposed energy efficient power allocation schemes and show that the fading of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraints. The remainder of this paper is structured as follows: Section 2 presents the system model and link budget. The energy-efficient optimal power allocation problem is formulated for both non-real-time and real-time applications and the solutions are derived in Section 3. Section 4 presents simulation results. We conclude this paper in Section 5.
## 5 Simulation and Performance Analysis  
### Simulation Scenario and Parameter Settings  
In this study, the four indicators of URSA and CDA, namely, network coverage rate, network energy consumption, network reliability and energy consumption balance, are used to simulate, analyze and compare, in order to verify the validity of URSA. The UWSNs node-deployment process is simulated by MATLAB on the basis of the background of the Xixi Wetland water environment monitoring. During the simulation, the target water area (length \(\times\) width \(\times\) depth) is set to 400 m \(\times\) 200 m \(\times\) 500 m, and the value of each indicator is produced by the average of 30 rounds of simulation data. The parameter settings are as follows:  
1. The parameter settings are based on the CDA in [26], _i.e._, the sensing radius of nodes \(R_{s}\) is 40 m, and the communication radius of nodes \(R_{c}\) in CDA is 100 m. This study also considers the situation wherein the distance between two nodes is greater than \(2R_{s}\); thus, the maximum communication radius \(R_{c}\_Max\) in URSA is also set to 100 m.
2. According to [38], a proportionality constant \(c\) influences the uneven clustering results, namely if the value of \(c\) is small, the cluster size is small, and the number of clusters is large. The purpose of the current study is to appropriately increase the number of clusters near the sink node; hence, \(c\) is set to 0.7.
3. According to the method of calculating the optimum radius of hotspots \(d_{hot}\) in [39] and the parameter of the simulation scene set in the current study, \(d_{hot}\) is set to 50 m.
4. Other parameters are presented in Table 1.
## 5 Data Evaluation
### Comparison to Previous Work  
#### 5.4.1 Motif Concentration Estimation  
Figure 11a-c show the results of our methods for estimating three-, four-, and five-node motif concentrations in comparison with the state-of-the-art methods FANMOD [14], PSRW [12], and Minfer [16] with the same computational time. We set the same edge sampling probability for Mosar and Minfer. We observed that these two methods have almost the same runtime. This is because Mosar and Minfer spend much less time reading the graph files than enumerating and classifying the subgraphs. For example, the computational time needed to pass over the graph file of interest on disk was 3.8%, 6%, 7%, 5%, and 1.9% of the time required to enumerate and classify subgraphs in the sampled graph for Flickr, livejournal, Pokec, Web-Google, and Wiki-Talk, respectively, when using Mosar and Minfer to estimate three-node directed motif frequencies and set \(p=0.01\). Figure 11 shows that Mosar exhibits almost one order fewer errors than the other methods for estimating concentrations of three- and four-node rare motifs, and two orders fewer errors than Minfer for estimating concentrations of five-node rare motifs.
# Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection
## 1 Introduction
In summary, the main contributions of this paper are as follows:  
1. A multi-view co-association matrix optimization objective function with doubly-stochastic constraints is designed, which systematically unifies view-specific spectral embedding from ensemble partitions and better captures the underlying cluster structure across different views. 2. A multi-view cluster structure guided one-class BLS-autoencoder (MOCBLSAE) is proposed, which learns discriminative patterns of normal traffic data by preserving the cross-view clustering structure while minimizing the intra-view sample reconstruction errors, thereby enabling the identification of unknown intrusion data. Based on MOCBLSAE, an intrusion detection framework is constructed and validated on real-world network traffic datasets for multi-view one-class classification tasks, demonstrating its superiority over state-of-the-art one-class methods. The subsequent sections of this paper are structured as follows. Section 2 reviews related works on single-view and multi-view one-class classification. Section 3 introduces the proposed method in detail. Section 4 validates the effectiveness of the proposed method through a series of experiments. Section 5 summarizes this paper and discusses potential future research.