esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 3 Materials and Methods
### Deep Convolutional Neural Network  
Deep learning based neural networks have proven their significance and attracted a considerable attention during recent years. Especially, CNN have been utilized to solve many computer vision issues, for instance, image classification, object detection, and scene recognition, etc. CNNs are based on a large number of convolutional layers, pooling and fully connected layers. The convolutional layer aims at extracting the local features from the input image. The essential role of a pooling layer is to subsample the convolutional layer to reduce the size and dimensionality of each feature map. Pooling layer computes some function on features map and two commonly used functions are'maximum' and 'average'. Pooling layers do not incorporate activation functions; instead, they use rectified linear unit (ReLU) function. The pooling average for each convolutional layer can be calculated by [62]:  
\[X_{ij}^{[l]}=\frac{1}{MN}\underset{m}{\overset{M}{\sum}}\underset{n}{\overset{N} {\sum}}X_{iM+m,jN+n}^{[l-1]} \tag{1}\]  
where \(i\) and \(j\) show the positions of the output map, while \(M\) and \(N\) are the pooling sample sizes.  
A fully connected layer is related to a neural network and serves the purpose of classification. Figure 2 shows the architecture of CNN model used for scene recognition. The network consists of eight convolutional layers and six pooling layers. ReLU is used as activation with convolutional layers four to eight. Pooling layers are used with a stride of 2. Four dropout layers are utilized as well for regularization. Dropout layers are used to prevent complex co-adaptations on the training data and avoid overfitting of the model. Research [63] suggested the use of dropout layers on fully connected layers with a rate of 0.5. It randomly omits each hidden unit with a probability of 50% on each training case for each presentation. The output of fully connected layer two is the number of classes for which classification is to be made. We have selected different points on each floor of a three-floor building to capture camera images which makes it a 15 classes prediction network. Adam optimizer is used to train the CNN. The last layer uses the softmax function to normalize it into the probability distribution of \(K\) probabilities which is equal to 15. It normalizes the network output to a probability distribution over predicted classes. The standard softmax function [64] is denoted by:  
\[\sigma(z)_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{K}e^{z_{j}}} \tag{2}\]  
Figure 1: Architecture of the proposed approach.
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
## 7 Simulations and Experiments
### Gateway Backhaul Simulations  
Based on the design of the above ground gateway network, the results of connection simulation within 24 h are shown in Table 7. Each satellite has multiple connection opportunities in a day.  
As shown in Table 7, a CubeSat has 18 opportunities to build a connection within 24 h. According to different channel environments, different numbers of data packets can be downloaded over a connection. The average number of data packets downloaded by all satellites is shown in Figure 16. The size of each package is 100 Mbit. For a CubeSat, the maximum amount of data that can be downloaded per day is about 320 Gbit. Such transmission capability far exceeds the requirements of NTN-mMTC.  
Figure 15: The simulation results of various GRC flows.  
Those simulations verify the DCS equipment for various signals. Different GRC flows are operated according to task schedules. All task plans can be received from the ground command center. The main purpose of the CubeSat is to collect various ground sensor data and relay it back to the terrestrial B5G network.
## 5 Comparison of Remote Sensing Platforms applications in Northern Peatlands
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Platform** & **Proportion of Studies** & **Applications** \\ \hline Satellite & 36.5\% & Carbon and methane flux estimation (14\%), fire dynamics monitoring (6\%), hydrology (14\%), mapping and monitoring peatland extent (42\%), peatland degradation (4\%), restoration and management monitoring (10\%), vegetation assessment (10\%) \\ \hline Airborne & 20.4\% & Carbon and methane flux estimation (14\%), fire dynamics monitoring (14\%), hydrology (14\%), mapping and monitoring peatland extent (36\%), Other (7\%), peatland degradation (0\%), restoration and management monitoring (4\%), vegetation assessment (11\%) \\ \hline UAV & 13.9\% & Carbon and methane flux estimation (11\%), fire dynamics monitoring (0\%), hydrology (11\%), mapping and monitoring peatland extent (37\%), peatland degradation (0\%), restoration and management monitoring (37\%), vegetation assessment (5\%) \\ \hline In situ & 10.9\% & Carbon and methane flux estimation (20\%), fire dynamics monitoring (0\%), hydrology (7\%), mapping and monitoring peatland extent (13\%), peatland degradation (0\%), restoration and management monitoring (0\%), vegetation assessment (60\%) \\ \hline Mixed & 18.2\% & Carbon and methane flux estimation (12\%), fire dynamics monitoring (0\%), hydrology (12\%), mapping and monitoring peatland extent (40\%), other (4\%), peatland  degradation (4\%), restoration and management monitoring (8\%), vegetation assessment (20\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Proportion of studies on each remote sensing platform and their applications areas in northern peatlands. ogy and vegetation, respectively, notably that UAVs are emerging as a popular tool for restoration and management monitoring purposes. There is an observable gap in studies concentrating on peatland degradation. This area remains underexplored across all platforms, with no studies conducted utilising the airborne, UAV and in situ platforms. Fire dynamics monitoring is another underexplored area, with no studies conducted using the UAV and mixed platforms. There has been a growing interest in using remote sensing to assess carbon fluxes in peatlands, but this is still a relatively unexplored area of research utilising most of the platforms rather than satellites (raw data extracted from the included articles for this section is in the Supplementary Tables S4-S8).
## 2 Materials and Methods
### Gene Expression Profiling  
For 3\({}^{\prime}\)-quant sequencing, library construction was performed using the QuantSeq 3\({}^{\prime}\)-mRNA-Seq library prep kit (Lexogen, Vienna, Austria) according to the manufacturer's instructions. In brief, 500 ng each of total RNA was prepared, and an oligo-dT primer containing an Illumina-compatible sequence at its 5\({}^{\prime}\) end was hybridized to the RNA. As a result, cDNA was synthesized by reverse transcription. After degradation of the RNA template, the second-strand synthesis of the cDNA was initiated by a random primer containing an Illumina-compatible linker sequence at its 5\({}^{\prime}\) end. The double-stranded library was purified using magnetic beads to remove all reaction components. The library was amplified by adding the complete adapter sequences required for cluster generation. The finished library was purified from PCR components. High-throughput sequencing was performed as single-end 75 sequencings using NextSeq 500 (Illumina, San Diego, CA, USA). QuantSeq 3\({}^{\prime}\)-mRNA-Seq reads were aligned using Bowtie2 [46]. Bowtie2 indices were either generated from the genome assembly sequence or the representative transcript sequences for aligning to the genome and transcriptome [47]. The alignment file was used for assembling transcripts, estimating their abundances, and detecting differential gene expression. Differentially expressed genes were determined based on counts from unique and multiple alignments using coverage in BEDTools [48]. The RC data were processed through the quantile normalization method using EdgeR within R using Bioconductor ([[https://bioconductor.org/](https://bioconductor.org/)]([https://bioconductor.org/](https://bioconductor.org/))) [49]. Gene classification was based on searches done on DAVID ([[http://david.abcc.nicifcrf.gov/](http://david.abcc.nicifcrf.gov/)]([http://david.abcc.nicifcrf.gov/](http://david.abcc.nicifcrf.gov/))) and Medline ([[http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)]([http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/))) databases. The genetic network of the differentially expressed genes was framed using String ([[http://apps.cytoscape.org/apps/stringapp](http://apps.cytoscape.org/apps/stringapp)]([http://apps.cytoscape.org/apps/stringapp](http://apps.cytoscape.org/apps/stringapp))) and ClueGo ([[http://apps.cytoscape.org/apps/cluego](http://apps.cytoscape.org/apps/cluego)]([http://apps.cytoscape.org/apps/cluego](http://apps.cytoscape.org/apps/cluego))) [50]. First, experimental data were integrated and analyzed using ClueGO App. of Cytoscape plug-ins and functional analysis was performed using gene ontology (GO). Next, a gene-gene network analysis using interaction data from the STRING database was imported into Cytoscape and visualized. Genes with a change of \(\geq\)2-fold and a log\({}_{2}\)-based normalized value of \(\geq\) 8 (log\({}_{2}\)3) were defined as true positives; genes with an absolute log\({}_{2}\) change value of \(\leq\) 0.1-fold werefined as true negatives.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### Homogeneous Paths  
The next set of experiments considered homogeneous paths, but with non-zero packet loss. In these scenarios, RSF MPTCP started to differentiate itself from the other MPTCP algorithms.  
For Scenario 2, we set the capacity (10 Mbps), delay (150 ms), and Bernoulli packet loss probability (2%) for the links to be the same for both paths. We then studied the impacts on the RSF-DB throughput when downloading files of different sizes (2 MB \(\leq\) size \(\leq\) 8 MB) with different MPTCP algorithms, each operating under the same settings.  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Emulated Network Scenario**} & \multirow{2}{*}{**Regular TCP**} & \multicolumn{3}{c}{**MPTTCP Algorithm (Mbps)**} \\ \cline{3-6}  & & & **LIA** & **OLIA** & **BALIA** & **RSF** \\ \hline
0 & Single Path & 1.58 & 1.53 & 1.57 & 1.58 & 1.58 \\ \hline
1 & Homogeneous Paths: No Loss & 1.58 & 2.52 & 2.58 & 2.54 & 2.53 \\
2 & Homogeneous Paths: 2\% Loss & - & 0.88 & 0.98 & 0.98 & 1.98 \\
3 & Homogeneous Paths: 10\% Loss & - & 0.31 & 0.35 & 0.39 & 0.62 \\ \hline
4 & Heterogeneous Paths: Bandwidth & - & 1.88 & 1.88 & 1.98 & 2.89 \\
5 & Heterogeneous Paths: Delay & - & 2.59 & 2.69 & 2.69 & 4.59 \\
6 & Heterogeneous Paths: Loss & - & 3.13 & 3.10 & 3.12 & 4.48 \\ \hline
7 & Short-Lived Flows & 3.42 & 5.51 & 6.21 & 7.02 & 8.67 \\
8 & Path Failure & 2.21 & 3.88 & 3.97 & 4.19 & 5.71 \\
9 & Competing TCP Flows & 0.25 & 0.66 & 0.67 & 0.61 & 1.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the throughput results from the network emulation experiments.  
Figure 2: Experimental setup: Ethernet links emulate the characteristics of WiFi and 3G paths.  
Figures 3 and 4 illustrate the throughput and fairness results, respectively. Figure 3 shows that, when the size of the file is 4 MB, our proposed _RSF MPTCP_ achieves almost double the throughput of LIA, OLIA, and BALIA. Figure 4 shows that the corresponding Jain fairness index stays quite close to unity. Hence, the throughput is highly improved by using our design without any adverse impact on fairness.  
We also considered Scenario 3 with a much higher Bernoulli packet loss probability (10%) on both paths. While the throughput declined for all MPTCP algorithms, our RSF MPTCP still provided much higher throughput than the others.  
Observation 2/3  
The results from Scenario 2 and Scenario 3 lead to the following observation. The proposed _RSF MPTCP_ outperformed all other MPTCP algorithms in terms of throughput and achieved similar fairness.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 3 Methods
### Feature Sets
#### 3.2.3 Feature Combination  
In order to explore the ability of SAR variables and optical images to distinguish winter wheat, and to obtain the best combination of features to improve the classification accuracy of winter wheat, the following combinations were considered (Table 3).
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies
### Framework and Techniques for Assimilation of Atmospheric CO\({}_{2}\)  
The atmospheric CO\({}_{2}\)-assimilation system is a cutting-edge simulation tool for analyzing carbon sources and sinks in terrestrial ecosystems on global or regional scales. It leverages prior carbon flux data from sources such as land, oceans, biomass burning, and fossil fuel emissions. The system employs an atmospheric transport model as a \"forward\" model and uses CO\({}_{2}\) concentration data as observational input. By integrating Bayesian theory with iterative solving, the system determines the optimal CO\({}_{2}\) flux values and ultimately simulates the spatiotemporal variations of carbon sources, sinks, and concentrations. As illustrated in Figure 23, the framework of the CO\({}_{2}\)-assimilation system includes prior flux data, the atmospheric transport model, CO\({}_{2}\) concentration data, and assimilation algorithms.  
Figure 22: Schematic diagram of DA + NN.
# Conjugation of Urokinase to Water-Soluble Magnetic Nanoparticles for Enhanced Thromolysis
###### Abstract  
In this study, covalent conjugation of thrombol drug urokinase to water-soluble magnetic nanoparticles (NPs) is proposed to enhance the efficiency of thrombolysis. Hydrophobic NPs of oleic acid (OA)-coated Fe\({}_{3}\)O\({}_{4}\) are first synthesized and then surface-modified with the amphipathic copolymer poly(maleic anhydride-_all_-1-octadecylene) (PMAO) to form water-soluble NPs of PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) with monodispersed sizes. PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs display a good water-based stability without aggregation at near neutral pH and show good magnetic separation characteristics. The thrombol drug urokinase is then covalently linked with the former product through dehydration condensation reaction between the amino and carboxyl produced by dehydration of the anhydride under N-Ethyl-N\({}^{\prime}\)-(3-dimethylaminopropyl) carbodiimide (EDC) and N-hydroxysuccinimide (NHS). Transmission electron microscope (TEM) images and dynamic light scattering (DLS) results show that the urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs are uniformly dispersed in water. The in vitro thrombolytic effect based on the manipulation of magnetic coupling, combined with static and alternating current (AC) magnetic fields, in a mimic blood-vascular system was studied. Drug release test shows that AC magnetic field can be used as switch and accelerator for NPs to release drugs. In addition, thrombolitic efficiency is nearly four times that of pure urokinase. This indicates that the coupling magnetic field may be a promising method to improve thrombolytic effect of the prepared magnetic carrier drug conjugates.  
thrombolysis; urokinase; PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) nanoparticles; coupling magnetic field
# A Survey on Network Optimization Techniques for Blockchain Systems
## 5 Network Optimization Techniques Used in Blockchain Networks  
In this section, we will introduce the concept of network optimization as used in blockchain networks and review various optimization frameworks proposed in the literature. The reviewed works have been categorized based on the layer on which they have been implemented. These layers include the consensus layer, the network layer and data layer, as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Ref.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **Blockchain** & **Study Objectives** & **Key Findings** \\ \hline
[64] & ✓ & ✓ & & & & & & & & & Impact of network structure on traffic redundancy & Redundancy increases with number of peers \\ \hline
[61] & ✓ & & & & & & & & & & & & \\ \hline
[47] & ✓ & & & ✓ & & & & & & & & & \\ \hline
[59] & ✓ & ✓ & & ✓ & ✓ & ✓ & & HLF & Study impact of HLFs & Block generation latency increases with transaction arrival rate & Increase in number of peer organizations increases latency \\ \hline
[32] & ✓ & & ✓ & ✓ & & & & & & & & \\ \hline
[66] & ✓ & ✓ & & ✓ & & & & & & & & \\ \hline
[29] & ✓ & & ✓ & & & ✓ & & & & & & \\ \hline
[67] & ✓ & & & ✓ & & & & & & & & \\ \hline
[68] & ✓ & & & ✓ & & & & & & & & \\ \hline
[62] & ✓ & ✓ & ✓ & ✓ & ✓ & HLF & Impact of HLF parameters & Bandwidth requirements of number of nodes & Network delay causes & Inconsistencies and block synchronization problems \\ \hline
[63] & ✓ & ✓ & & ✓ & & & & & & & \\ \hline
[30] & ✓ & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Studies on Blockchain’s Network Characteristics 1 = Mathematical Analysis and Simulations, 2 = Measurement on Real Network, 3 = Varied Network Size, 4 = Varied Block Size and Interval, 5 = Analyzed Transactional Throughput, 6 = Analyzed Latency, 7 = Analyzed P2P Structure, 8 = Analyzed Traffic Load.  
In blockchain networks, network optimization refers to any technique that enhances blockchain's performance by speeding up the communication between peers, reducing bandwidth consumption or deploying techniques that can help sustain acceptable performance for large-scale applications. Existing approaches to network optimization in blockchain improve the P2P topology, minimize duplicates caused by gossiping or by reducing the size of data exchanged between peers.  
Optimization works at the consensus layer include works that improve their communication pattern and hence, making them communicate in a small amount of time, reduce the possibility of congestion or consume less bandwidth. Works at the network layer either improve the gossip algorithm or the peer-to-peer network structure. Data layer approaches are primarily about using schemes to minimize the size of the data that are exchanged on the blockchain.
# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
## 5 Conclusions  
In this paper, we address the detection of air targets by means of the passive bistatic radar that is based on low-EIRP GPS satellite. Extensive cancellation algorithm (ECA) which project the surveillance channel signal in the subspace orthogonal to the clutter subspace is firstly applied in GNSS-based bistatic radar. Subsequently, a new coherent processing method that is based on LIFT is proposed for long time integration. The parameter, RA, is defined to describe the Doppler variation during the dwell time. Based on RA, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Setting Value** \\ \hline Power density & \(\rho\) & \(-158\) dBw \\ Wavelength & \(\lambda\) & \(0.19\) m \\ Code duration & \(T_{c}\) & \(1\) ms \\ Code bandwidth & \(B\) & \(1.023\) MHz \\ Receiver antenna gain & \(G_{r}\) & \(35\) dB \\ RCS & \(\sigma\) & \(20\) dB \\ Noise power & \(P_{n}\) & \(-143\) dB \\ System loss factor & \(\eta\) & \(0.5\) \\ Coherent time & \(T_{coh}\) & \(3\) s \\ \hline \hline \end{tabular}
\end{table}
Table 3: System link budget parameter.  
quadratic phase terms that are caused by the longtime integration can be compensated in the LIFT matrix. A rough estimation of RA is obtained by FrFT and an accurate one is obtained through the optimal detection results. The proposed method can effectively accumulate the target echo during the CPI when compared with the traditional integration method. Simulation experiments verify the excellent performance of the proposed processing method. Our future work focuses on the targets with complex motion and explores the target detection and tracking under low SNR.  
Conceptualization, B.W. and Z.Z.; methodology, B.W.; software, B.W.; validation, B.W.; writing--original draft preparation, B.W.; writing--review and editing, H.C., Z.Z. and B.T.; visualization, B.W.; supervision, H.C.; project administration, B.T.; funding acquisition, B.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 41975005.  
Not applicable.  
The authors would like to thank the editors and the reviewers for their valuable suggestions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
GNSSGlobal navigation satellite system  
ECAextensive cancellation algorithm  
LIFTlong-time integration Fourier transform  
RArelative acceleration  
PBRpassive bistatic radar  
LMSleast mean square  
NLMSnormalized least mean square  
RLSrecursive least square  
LSleast square  
EIRPeffective isotropic radiated power  
CPIcoherent processing interval  
FrFTfractional Fourier transform  
SNRsignal to noise ratio  
DCDoppler centroid  
DMRDoppler modulated rate  
FTFourier transform  
EIRenergy integration ratio  
RDrange Doppler
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 5 The Improved More Efficient Protocol
### Temporary Anonymous Key Generation
Then, \(RSU(ID_{j})\) will check the validity of \(T_{i}\) and \(PID_{i}\). Either of them are invalid, the protocol aborts. Otherwise, \(RSU(ID_{j})\) checks the equation \(R_{2}^{\prime}\cdot e(g_{1},g_{2})^{f(R_{2}^{\prime}||T_{i}||Y)}\stackrel{{? }}{{=}}e(Sig_{OBU},S_{2}^{h(PID_{i})}\cdot U)\). If it holds, i.e., the OBU is authenticated, then \(RSU(ID_{j})\) issues the certificate \(Cert_{i}=(L_{j},T_{i},Y,PID_{i}^{\prime},Sig_{RSU})\), where \(PID_{i}^{\prime}=Enc_{x_{j}}(T_{i},PID_{i})\) and \(Sig_{RSU}=f(R_{2}^{\prime}||T_{i}||Y||PID_{i}^{\prime})A_{j}\), the lifecycle of certification is based on time-stamp \(T_{i}\); otherwise, the OBU fails the authentication since \[e(Sig_{OBU},S_{2}^{h(PID_{i})}\cdot U) =e(S_{i}^{(r_{1}+f(R_{2}||T_{i}||Y))},g_{2}^{h(PID_{i})}\cdot g_{2 }^{h})\] \[=e(g_{1}^{\frac{(r_{1}+f(R_{2}||T_{i}||Y))}{h(PID_{i})+w}},g_{2}^{ (h(PID_{i})+u)})\] (2) \[=R_{2}^{\prime}\cdot e(g_{1},g_{2})^{f(R_{2}^{\prime}||T_{i}||Y)}\]
* Step 3. To verify \(RSU(ID_{j})\) and the validity of certificate \(Cert_{i}\), the OBU checks \(e(g_{2}^{h(L_{j})}\cdot U,Sig_{RSU})\stackrel{{? }}{{=}}e(g_{1},g _{2})^{f(R_{2}||T_{i}||Y||PID_{i}^{\prime})}\). If it holds, \(Cert_{i}\) is valid and the RSU is also authenticated, because the adversary has no ability to recover the secret key \(R_{2}\); Otherwise, the protocol aborts and the RSU cannot pass the authentication since \[e(g_{2}^{h(L_{j})}\cdot U,Sig_{RSU}) =e(g_{2}^{h(L_{j})}\cdot g_{2}^{u},A_{j}^{f(R_{2}^{\prime}||T_{i} ||Y||PID_{i}^{\prime})})\] \[=e(g_{2}^{(h(L_{j})+u)},g_{1}^{\frac{f(R_{2}^{\prime}||T_{i}||Y||PID _{i}^{\prime})}{h(L_{j})+u}})\] (3) \[=e(g_{1},g_{2})^{f(R_{2}^{\prime}||T_{i}||Y||PID_{i}^{\prime})}\] \[=e(g_{1},g_{2})^{f(R_{2}||T_{i}||Y||PID_{i}^{\prime})}\]