esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 3 The Importance of Quantum Key Distribution (QKD)
### The Challenges of QKD in 5G Implementation
#### 3.1.4 Secure Secret Key Rate  
The fourth challenge is the high channel loss and decoherence in long-distance due to the key rate of QKD that degraded over a long distance. For example, 1000 km fiber can detect merely 0.3 photons, even with the perfect source and detector for every single photon. Recently the distance recorded for QKD in fiber is only 412 km [76], and for that matter, a protocol was proposed to lengthen the distance to 500 km [77]. A proper solution for this challenge is to place a quantum repeater [78] that prove the proposed solution is able to expand up to 500 km. Even though the limitation of the application experiences limitations both in terms of performance and quantum memory [79; 78], one of the temporary substitutions of the quantum repeater is the trustful relay scheme using the current technology along with proper protection on all related nodes [80]. Moreover, the satellite-based quantum communication recorded significantly less chancel loss and negligible decoherence in space and providing great achievable progress lately which makes it the promising solution [81; 82; 83]. Nowadays, enhancing the communication rate is the priority research area to prove the efficiency of QKD technology [84].
## 5 Performance Analysis and Numerical Results
### Results of the Sensitivity Analysis on Properties of Data Dissemination
#### 5.2.3 Radio Signal Transmission Power  
This experiment employed a widely used data transmission mode: 16-QAM 1/2. A total of 40 (10 groups) simulations were completed. The macroscopic results of one group (No. 8 random number seed) are shown in Figure 13, while the comprehensive statistical results are summarized in Figure 14.  
Based on Figure 14, it can be observed that increasing the radio signal transmission power provides an approximately 30% improvement in the number of vehicles that receive data packets and the data dissemination distance. The gain varies depending on different traffic scenarios and transmission power levels; generally speaking, the higher the radio signal transmission power, the lower the gain relative to it. Taking the experimental group illustrated in Figure 13 as an example, using the number of vehicles that receive data packets as a metric, the signal transmission power of 60 W surpass  
Figure 12: Summary of the impact of traffic volume on data dissemination effectiveness.  
while 90 W exceeds 60 W by 28%, and 120 W surpasses 90 W by 21%. The same conclusion is drawn when using the data dissemination distance as the metric.  
Figure 13: Macroscopic results of one group (No. 8 random number seed).
Remote Sens._**2013**, \(5\), 4125-4144; doi:10.3390/rs5084125  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
Assessment Impacts of Weather and Land Use/Land Cover (LULC) Change on Urban Vegetation Net Primary Productivity (NPP): A Case Study in Guangzhou, China  
**[PERSON] 1, [PERSON] 1, [PERSON] 1,*, [PERSON] 1,2 and [PERSON] 1**  
1. School of Geography, South China Normal University, Guangzhou 510631, China; E-Mails: [EMAIL_ADDRESS] (Y.F.); [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (L.X.)
2. China RS Geoinformatics Co. Ltd., Dongguan 523808, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-189-2610-3348; Fax: +86-20-8521-5910.  
_Received: 22 June 2013; in revised form: 13 August 2013 / Accepted: 13 August 2013 / Published: 20 August 2013_  
**Abstract:** Net primary productivity (NPP) can indicate vegetation ecosystem services ability and reflect variation response to climate change and human activities. This study applied MODIS-1 km NPP products to investigate the NPP variation from 2001 to 2006, a fast urban expansion and adjustment period in Guangzhou, China, and quantify the impacts of weather and land use/land cover (LULC) changes, respectively. The results showed that the NPP mean value increased at a rate of 11.6 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)yr\({}^{-1}\) during the initial three years and decreased at an accelerated rate of 31.0 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)yr\({}^{-1}\) during the final three years, resulting in a total NPP loss of approximately \(167\times 10^{6}\) g\(\cdot\)C. The spatiotemporal of NPP varied obviously in the central area, suburb and exurb of Guangzhou driven by three patterns of weather and LULC changes. By the interactive effects and the weather variation dominated effects, NPP of most areas changed slightly with dynamic index less than 5% of NPP mean value in the central area and the suburb. The LULC change dominated effects caused obvious NPP reduction, by more than 15% of the NPP mean value, which occurred in some areas of the suburb and extended to the exurb with the outward urban sprawl. Importantly, conversion from wood grassland, shrublands and even forests to croplands occupied by urban landscapes proved to be a main process in the conversion from high-NPP coverage to low-NPP coverage, thereby leading to the rapid degradation of urban carbon stock capacity in urban fringe areas. It is helpful for government to monitor urban ecological health and safety and make relevant policies.
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Observation Models for Combined BDS-3, GPS, and Galileo RTK
#### 4.1.3 Differential ISBs Correction with TCM  
Since the differential ISBs between overlapping frequencies are generally very stable in the time domain, they can be calibrated in advance and introduced as corrections to strengthen the adjustment model. The double-differenced observation equation corresponding to the correction of ISB parameters is then given by [17; 18; 36]:\[\begin{split}\phi^{1_{G}S_{G}}_{br,i}&=\rho^{1_{G}S_{G}}_{ brr}+\lambda_{i}N^{1_{G}S_{G}}_{br,i}+e^{1_{G}S_{G}}_{br,i}\\ \hat{\phi}^{1_{G}S_{+}}_{br,i}&=\phi^{1_{G}S_{+}}_{ br,i}-\lambda_{i}\delta^{S_{+}}_{br,i}=\rho^{1_{G}S_{+}}_{br}+\lambda_{i}N^{1_{G}S_{+}}_{ br,i}+\varepsilon^{1_{G}S_{+}}_{br,i}\\ P^{1_{G}S_{G}}_{br,i}&=\rho^{1_{G}S_{G}}_{br}+e^{1_{G}S_{ +}}_{br,i}\\ \hat{\rho}^{1_{G}S_{+}}_{br,i}&=P^{1_{G}S_{+}}_{br,i}- \hat{\delta}^{S_{+}}_{br,i}=\rho^{1_{G}S_{+}}_{br}+e^{1_{G}S_{+}}_{br,i}\end{split} \tag{5}\]  
where \(s_{G}\geq 2_{G}\) and \(s_{*}\geq 1\)., \(\hat{\phi}^{1_{G}S_{+}}_{br,i}\) and \(\hat{\rho}^{1_{G}S_{+}}_{br,i}\) are ISBs corrected double-differenced phase and code observations, respectively. Compare to the loosely combined model in Equation (3), the redundancy is increased by 1 per overlapping frequency per system pair. Consequently, improved performance of ambiguity resolution and position estimation is expected with this tightly combined model.  
Similar observation equations can also be obtained if a Galileo or BDS-3 satellite is chosen as reference satellite, which are not presented here.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 2 Energy Trading and Sharing Concept  
Energy trading between large producers and utility companies is well known; however, energy trading and sharing between households or prosumers is a trending topic within the industry and research community. Bilateral energy transactions between prosumers will not only help to better harness the output of distributed generation (DG) systems [14], but also promote effective energy management at the edge of the network. This section analyses the energy trading and sharing concepts reported in the literature.  
To trade, share or buy energy, various 'actors' are involved in the exchange process in addition to prosumers that supply and consume energy [15; 16; 17]. These actors include a trader or local-grid operator that buys energy to trade at a margin [18; 19; 20], a producer that generates energy for sale in large quantities and consumers that rely on those media to meet their energy demand. The actors are equipped with smart meters that will be used to record details of their energy profiles and facilitate bidirectional communication during energy trading and sharing. Based on the structure given in [10], the literature on energy trading and sharing is classified into one of three coverage areas as shown in Figure 1.  
These areas are briefly discussed as follows:  
* Desired outcome: First, the actors decide on what they want to achieve in the trading and sharing of energy. This could be cost optimization [13; 21], to reduce dependence on the main-grid [21], to reduce environmental impact or to improve energy management.
* Enabling technologies: Then, the actors select from available resources the pieces of technologies needed to accomplish their desired outcome. Enabling technologies for prosumer energy trading mainly include a source of energy generation, an energy storage system (ESS) [22] and an ICT [23] for communication among the various sub-systems and for actor-to-actor communication.
* Required framework: Lastly, the TE framework needs to be carefully considered. This includes deciding the appropriate structure to use for the trade, as well as the supporting control mechanisms to adopt. In this regards, one of the key questions is whether the trading structure should be distributed/P2P [24; 25; 26; 27; 28] or centralized through a platform/energy market [29; 30; 31; 32] operation. Likewise, the form of control to be adopted to route information among the actors, whether distributed or central control, needs to be harmonized. Finally, the operational mechanism and operation optimization (for instance, game theory method [33]) must be defined.
## 1 Introduction  
With the rapid development of electronic information technology, people's attention has been attracted by electromagnetic interference. The extensive use of electromagnetic waves (EMW) is potentially harmful to human health, electronic information, and even the national information security [1, 2]. Recently, the negative effect of electromagnetic radiation has been the focus of social attention; the development of EMW absorbing materials, which can be used in microwave interference protection, microwave darkroom, and other military and civilian fields, is of great significance [3]. As the most commonly used construction material globally, the absorbing performance of cement-based materials is limited. To improve the EMW absorption capacity of concrete, various adsorbents have been studied by researchers and scientists, such as ore tailings [4], fly ash [5], and ferrite and steel fiber [6], which can be used to enhance the electromagnetic absorption capacity of the cementitious material. At the same time, nanomaterials [7], chiral materials, conductive polymers, and ceramics were also explored as absorbents [8, 9]. The mechanical properties and durability of cement-based material will be negatively affected to a certain extent by the EMW absorbing additives, which limit the development of cement-based, wave-absorbing materials [10].  
Enormous progress in the field of metamaterials EMW absorption has been made in the past few years. The extraordinary electromagnetic properties of metamaterials [11, 12] leads to its potential application for metamaterial absorbers, which are thin and small. They also have a simple structure and show high absorption capacity, which can be used for electromagnetic stealth [13, 14]. Mie-resonance-based metamaterial has been deeply studied in recent years. In Mie-resonance-based metamaterial, the electromagnetic interaction of dielectric particles with relative high permittivity can exhibit a strong magnetic or electric resonance based on Mie theory. From the oscillation of resulting magnetic or electric dipole, negative permeability or permittivity can be produced [15, 16], and the frequency of magnetic or electric resonance shifts with shapes, sizes, dielectric constant, and permeability of the dielectric cubes [17]. Mie-resonance-based metamaterials with single-band [18], dual-band [19], multi-band [20] and broad-band [21] EMW absorption are widely studied in microwaves, and almost 100% absorption bands of Mie-resonance-based metamaterial absorbers with a wide incident angle and polarization insensitivity was reported [18].  
Here, an alternative approach of absorbing concrete in the idea of combining cement-based materials in civil engineering with Mie-resonant-metamaterial absorbers is studied,where steel bars in the cement-based material are combined with CaTiO\({}_{3}\) ceramic cubes to obtain near-unity absorption. The absorption frequency of the cementitious material can be easily tuned by the structure of the ceramic particles and steel bars.
# Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
## 3 Comparison Experiment
### Comparative Experiments with the VGG19_Simple Model
#### 3.1.2 Global Pruning Analysis  
For the VGG19_Simple model, we compared the Top1 accuracy of the model after pruning using three distinct compression methods, MCP-L1, Lasso regression, and MCP regression, across various pruning ratios. The pruned model underwent retraining at a pruning ratio of 0.3, with a training epoch of 40 and a learning rate of 0.001.  
The results presented in Table 1 and Figure 11 illustrate distinct behaviors among three pruning methods: MCP-L1, Lasso regression, and MCP regression. MCP-L1 solely engages  
Figure 10: VGG19_Simple single-layer pruning diagram.  
in channel pruning without output reconstruction. As the pruning ratio escalates, the model's effectiveness diminishes significantly. When the pruning ratio reaches 0.5, the Top1 accuracy on CIFAR10 drops to a mere 11.61%, akin to random classification, rendering the model nearly ineffective. Lasso regression, coupled with output reconstruction, manages to maintain a relatively high accuracy rate. However, as the pruning ratio increases, its Top1 accuracy experiences a more rapid decline. In contrast, MCP regression, encompassing channel pruning along with output reconstruction, exhibits superior performance compared with the other two methods. A closer inspection of the local zoomed-in graph in Figure 3 underscores that, as the pruning proportion rises, the performance gap between Lasso regression and MCP regression widens progressively.  
The insights derived from Table 2 reveal the substantial advantages of the MCP regression method in model pruning. When the pruning ratio is set to 0.1, the model demonstrates a commendable reduction of 15% in floating-point computation, accompanied by an 8% decline in the number of parameters. Remarkably, the Top1 accuracy of the pruned MCP regression model experiences only a marginal 1% decrease compared with the original model. Upon setting a pruning ratio of 0.4, the Top1 accuracy registers a modest decline of 2.5%, while the reduction in floating-point computation surpasses 50%. Correspondingly, the number of parameters decreases by over 30%, leading to an overall memory usage that is merely 70% of the original model. These results underscore the efficiency of the MCP regression method, as it achieves a balance between computational efficiency and model accuracy. In Table 2, with a pruning ratio of 0.3 and subsequent retraining, the MCP regression model showcases performance closer to the original model compared with other methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **Retrain (0.3)** \\ \hline MCP-L1 & 83.49 & 54.33 & 37.88 & 20.4 & 10.61 & 91.43 \\ Lasso Regression & 91.19 & 89.27 & 87.93 & 86.81 & 82.97 & 91.74 \\ MCP Regression & 91.7 & 91.37 & 90.55 & 90.13 & 88.62 & 91.95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Global pruning comparison diagram of VGG19_Simple.  
Figure 11: VGG19_Simple global pruning comparison chart.
# Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA
## 3 Results
### Spatial Distributions of Aquatic Organisms Based on Meta-Barcoding  
We carried out a survey in June 2018. Relationships among the environmental variables and aquatic organisms based on meta-barcoding were explored by CCA (Figure 2). The first axis explained 33.3% of the variance and distinguished Zones III-2 and III-3 with higher salinity, EC, TIC, and lower Chl-a and TN from the other zones. The second axis (19.6% of variance explained) distinguished the sites from Zone III-1 and the other zones, by having higher TOC and TC, lower temperature, and TP. Nutrient-related factors (Chl-\(a\), TN, and TP) were correlated with _Acropora_ sp. (small polyp stony coral) and _Megabalanus_ sp., (barnacle) and salinity was correlated with _Skeletonema_ sp. (diatoms) and _Centropages_ sp. (copepods).  
Figure 2: Canonical correspondence analysis (CCA) used to evaluate (**A**) the relationships between abundance of operational taxonomic units (OTUs) sequences (based on genus level identification) and environmental factors (temperature (Temp.), Salinity, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), elemental carbon (EC)). (**B**) Sites grouping based on CCA analysis.  
Abundance of assigned OTU sequences showed different patterns among the study sites (Figure 3). The dominant OTU was assigned to _Acropora_ sp. and the sub-dominant OTU was _Acartia_ sp. (copepods). Most common OTUs within the study sites were comprised of phytoplankton, followed by zooplankton and amphipods (Figure 3B). Interestingly, the abundance of OTUs showed different compositions among the three different zones (I, II, III). Zone I showed similar patterns to Zones II and III-I, whereas Zones III-2 and 3 showed different compositions of aquatic organisms which distinguished them from other zones. In particular, Zone III-3 exhibited an entirely different pattern, comprised of marine organisms such as _Euchaeta_ sp. (copepods) and _Tessarabrachion_ sp. (krill) compared with other zone divisions (Figure 3B).
## 2 Mathematical Modeling of UAV
### Problem Statement
#### 2.2.2 Actuator Fault Model  
The four rotors supplied the relevant force and moment of force for the quadrotor aircraft to maintain hovering or other motion attitudes. In ideal situations, the motor's rotational speed \(\Omega_{i}=\Omega_{i,c}\) and \(\Omega_{i,c}\) exhibited a linear relationship with the control quantity \(u_{i}\):  
\[\Omega_{i,c}=k_{o,i}u_{i}+p_{i}\, \tag{12}\]  
where \(u_{i}\) is the control quantity; \(k_{o,i}\) and \(p_{i}\) are the coefficients of normal quantities. However, in practice, a high temperature usually results from the motor's rotation, and \(f_{m,i}\) is no longer linearly related to \(\Omega_{i}^{2}\). When an aircraft collision happens, the rotors or propellers may be damaged, resulting in the deterioration of aircraft performance. The resultant multiplicative fault further impairs the actuator efficiency, while the additive fault causes the output drift of the actuator. Considering the above situations, the traction provided by a single actuator is expressed as  
\[f_{m,i}=c_{T,i}\cdot\big{(}\big{(}1-b_{gain,i}(t)k_{c,i}(t)\big{)}u_{i}+c_{ bias,i}(t)+p_{i}\big{)}^{2}\, \tag{13}\]  
where \(b_{gain,i}(t)\) is the gain fault \(\big{(}0\leq b_{gain,i}(t)\leq 1\big{)}\), \(k_{c,i}(t)\) is the continuous time function of the motor's rotational speed vs. the actual controller signal \(u_{i}\), and \(c_{bias,i}(t)\) is the additive fault of the actuator. The following actuator fault model is considered to facilitate the calculation:  
\[u_{f,i}=(1-b_{i}(t)\cdot k_{i}(t))u_{i}+c_{i}(t)\, \tag{14}\]where \(b_{i}(t)=b_{gain,i}(t)\), \(k_{i}(t)=\frac{k_{i,j}(t)}{k_{i,j}}\) and \(c_{i}(t)=\frac{c_{bias,i}(t)}{k_{i,j}}\). As for the attitude, position, and speed, the system state of a quadrotor aircraft can be calculated using the data from the gyroscope, magnetometer, and other sensors. Here, the system is described with a multi-input, multi-output (MIMO) nonlinear model. For the control system featured by the uncertainty model and actuator faults, and subject to external gust disturbance, the following affine nonlinear system was reconsidered for the design of the FTC algorithm:  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=(f_{0}(x)+\Delta f(x))+(g_{0}(x)+\Delta g(x))u_{f}\\ \quad+d_{w}\\ y=x_{1}\end{array}\right., \tag{15}\]  
where \(u_{f}=(1-b\cdot k)u+c\), \(f(x)=f_{0}(x)+\Delta f(x)\), \(g(x)=g_{0}(x)+\Delta g(x)\), \(\Delta f(x)\) and \(\Delta g(x)\) are the uncertain terms of system modeling and \(d_{w}(t)\in\mathbb{R}^{4}\) is the external wind disturbance of the system.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 3 Methodology
### Feature Extractor  
Convolutional neural networks (CNNs) have been widely applied in a number of computer vision tasks, and their use has gradually extended to other fields because of their advantages of sparse connectivity and weight sharing. Many research works also confirmed that CNNs have a strong feature extraction ability in solving classic signal-processing problems such as spectrum sensing, modulation classification, and SEI. In the LTS-SEI framework, extraction of shallow features of samples also relies on convolutional modules. We designed different feature extractors for Datasets A and B, respectively, as shown in Figure 3a,b. For a 1D convolutional layer, \(fi\) represents the number of filters, \(ke\) represents the size of the convolutional kernel, \(ac\) represents the activation function, and \(st\) represents the convolutional step size. In the final layer of the feature extractor, a 1D global average pooling layer (GAP-1D) was introduced to reduce channel feature dimensions and prevent model overfitting. The following is the extraction process of the feature extractor for the source domain sample feature \(F_{S}\), and the target domain sample feature \(F_{T}\) can be represented as  
\[f_{f}:X_{i}\in\mathbb{R}^{1\times k}\overset{\theta_{f}}{\rightarrow}F_{i} \in\mathbb{R}^{1\times fi} \tag{4}\]  
where \(f_{f}\) represents the mapping function that the feature extractor needs to learn. \(\theta_{f}\) represents the variable parameters of \(f_{f}\). The goal of \(f_{f}\) is to map sample \(X_{i}\) to feature \(\hat{F_{i}}\) that considerably impacts the recognition effect of specific emitters and is not sensitive to domain changes, as shown in Figure 2.