esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Takeaways and Recommendations  
In this paper, we examine the impact of different SNR calculation methods on the simulated system performance for the EPA, EVA, and ETU channel models in the LTE DL transmission. It is observed that an LTE subframe is insufficient to capture the long-term ergodic properties (if existing) of these channel models. Specifically, the received signal power averaged over a codeword can significantly differ from the power averaged over an ensemble of channel realizations, a discrepancy that is particularly pronounced in scenarios with small Doppler shifts. Consequently, when the ensemble-average SNR is used in simulations, trials with low channel gain disproportionately influence the overall decoded BER performance. This results in the EPA model, which has a shorter delay spread,  
Figure 12: Decoded BERs at low, medium, and high speeds using the ensemble-average SNR as a basis of comparison.  
Figure 13: Decoded BERs at low, medium, and high speeds using the short-term SNR as a basis of comparison.  
exhibiting higher decoded BER than the EVA and ETU models, which have longer delay spreads, an intuitively counter-intuitive outcome.  
In addition to the conventional ensemble-average SNR, we also utilize the short-term SNR to assess the BER performance of the receiver. Simulation results indicate that the short-term SNR not only yields significantly lower decoded BER compared to the ensemble-average SNR for the same channel model but also reverses the relative BER performance rankings among the channel models, aligning with anticipated outcomes. These observations are consistent across low, medium, and high speeds. However, the application of the short-term SNR has its limitations, including the violation of the uncorrelated scattering property in the channel models and difficulties to perform theoretical BER analysis.  
Although our experiments were based on LTE downlink simulations, we strongly believe that similar scenarios would arise in other communication systems due to the inherent nature of the SNR calculation. Considering these factors, we recommend using the ensemble-average SNR when comparing the performance of the simulated system with other studies, as this SNR definition is predominantly used in the literature. Conversely, if the primary concern is the actual BER performance of the receiver, the short-term SNR could provide a more accurate performance assessment and should therefore also be evaluated.  
Conceptualization, Y.-S.L. and S.D.Y.; methodology, Y.-S.L.; software, Z.-R.J. and M.-F.L.; formal analysis, Y.-S.L.; writing--original draft preparation, Y.-S.L. and S.D.Y.; writing--review and editing, Y.-S.L. and S.D.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Council, Taiwan, grant number NSTC 112-2221-E-027-091.  
The datasets presented in this article are not readily available because all of the experimental data are generated by a random number generator in the experimental program.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
It gives  
Figure 4: Canny edge detection results. more stable and accurate results in cases where there are few water pixels and small water bodies [78]. The lake surface areas of Lake Van according to NDWI, MNDWI, and AWEI indices for the years 2014, 2017, 2020, and 2023 are shown in Figure 5. Accordingly, the lake surface areas determined by the NDWI were obtained as 3738.22 km\({}^{2}\) for 2014, 3578.79 km\({}^{2}\) for 2017, 3784.73 km\({}^{2}\) for 2020, and 3335.98 km\({}^{2}\) for 2023. Lake surface areas determined by the MNDWI were obtained as 3698.02 km\({}^{2}\) for 2014, 2913.13 km\({}^{2}\) for 2017, 3394.72 km\({}^{2}\) for 2020, and 3235.05 km\({}^{2}\) for 2023. Lake surface areas determined by the AWEI were obtained as 3784.73 km\({}^{2}\) for 2014, 3801.96 km\({}^{2}\) for 2017, 3806.76 km\({}^{2}\) for 2020, and 3753.50 km\({}^{2}\) for 2023. As seen in Table 2, Van Lake surface area pixels were accurately classified by SVM, RF, and CART machine learning algorithms utilized by Landsat 8. The satellite images used in the study were taken between 1 June and 1 September. Producer accuracy, user accuracy, overall accuracy, kappa, and f score accuracy assessment parameters were used to determine the lake surface area. Table 2 provides the accuracy assessment of Lake Van, and Figure 5 presents the lake surfaces obtained according to the water indices used. When compared to other water indices in Lake Van in 2014, the AWEI showed the highest performance with 100%, and the MNDWI showed the lowest performance with 97%, according to the producer accuracy, user accuracy, overall accuracy, kappa, and f \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Multiband Index** & **AWEI** & **NDWI** & **MNDWI** \\ \hline  & Optimum thresholds & Optimum thresholds & Optimum thresholds \\
2014 & \(-\)0.7821 & 0.2029 & 0.0548 \\
2017 & \(-\)0.7207 & 0.1412 & 0.0234 \\
2020 & \(-\)0.7204 & 0.3170 & 0.0858 \\ \hline
2023 & \(-\)0.8446 & 0.7809 & 0.0542 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculated threshold values for several water indices within the designated study region. Figure 5: Lake surface areas for NDWI, MNDWI, and AWEI water index. score evaluation criteria. According to the accuracy evaluation parameters of 2017, the AWEI showed the highest performance with 100%, and the MNDWI showed the lowest performance with 89%. According to the accuracy evaluation parameters of 2020, the NDWI showed the highest performance with 100%, and the MNDWI showed the lowest performance with 93%. According to the accuracy evaluation parameters of 2023, the AWEI showed the highest performance with 100%, and the MNDWI showed the lowest performance with 90%. When the success of the classification methods used in determining the water surface is examined, the values of the overall accuracy, user's accuracy, producer accuracy, kappa,
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Classification Methods Used
#### 3.5.5 Multilayer Perceptron Neural Network  
In the context of our research on enhancing last-mile delivery efficiency, the Multilayer Perceptron (MLP) stands out as a pivotal tool. As a feed-forward neural network designed for supervised learning applications, MLP tackles both classification and regression challenges, aligning seamlessly with our goal of optimizing product flows in urban settings [41]. Comprising a minimum of three layers--namely, input, hidden, and output--MLP's input and output layer nodes are determined by the dataset's features and classes. Configuration specifications include the number of hidden layers, nodes allocated to each hidden layer, learning epochs, and the chosen weight learning technique. Within this architecture, hidden layer nodes receive signals from input nodes through weighted connections, applying their output activation function before transmitting data to the output layer nodes. The backpropagation algorithm, widely embraced in MLP training [42], follows a methodical approach. It involves presenting each training sample, computing the output in a forward step, and subsequently determining the squared error by assessing the distinction between predicted and actual outputs. The mean squared error serves as a prevalent metric for gauging error in MLP. Notably, an epoch denotes a complete cycle through the entire training dataset, requiring multiple iterations for robust MLP training. To provide visual clarity, Figure 2 portrays the intricacies of the proposed MLP neural network architecture designed specifically for product classification.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 3 Double Teleportation as Superposition and Parallel Post-Processing  
Multiple teleportation exploits the quantum linearity to extend the traditional teleportation procedure to perform virtual transference of states and processing. In the end, global states could be recovered for concrete tasks. Quantum states obtained by multiple teleportation exhibit interesting non-local properties [29] and they could be used with cryptography purposes [30]. In this section, we will describe the process only for double teleportation (DT) with additional post-processing (PP). Then, in Section 4, we deal with the control problem (TC) to share and transfer concrete quantum states to be used for QKD purposes (QKD) in Section 5. To ease the reading, we first account in Table 1 for the key symbols (states, operators, and related key quantities) through the entire development.
# Estimating the Thermal Conductivity of Unsaturated Sand
## 4 Numerical Models with the MPTC Model
### Assessment of the MPTC Model  
Figure 13 shows that most of the calculated values of temperature and the thermal conductivity of sand calculated by the MPTC model agreed well with the laboratory experimental data. However, a few slight deviations between the calculated and measured values were observed, especially for point C, for two reasons. First, the degree of saturation of sand remained constant for each group, but the spatial distribution of water in sand was altered during the experiment due to temperature gradients that caused moisture migration. However, the effect of moisture migration on heat conduction was not considered in the numerical simulation. Second, the porosity of the sand (\(n=0.45\)) adopted in the numerical model was an average value, and the porosities of the samples in the experiments ranged from 0.44 to 0.47. Even so, the results of the comparison were convincing. In addition, the trends of temperature decreasing with increasing \(S_{\rm r}\) were observed in the simulation, and they were consistent with the trend in the experimental data. Thus, the proposed model had sufficient precision for computing believable predictions, and the thermal conductivity of sand with different \(S_{\rm r}\) can be evaluated by the MPTC model.  
Figure 12: The effect of specific heat of sand particles.
## 3 The Proposed Model
### System Model
#### 3.1.1 Energy Model  
WSNs involve link heterogeneity, energy heterogeneity, and computational heterogeneity. Energy heterogeneity is regarded as the most important to ensure optimal network performance. In the study, we discussed three levels of energy heterogeneity: advanced, normal, and intermediate nodes. Intermediate node initial energy is between advanced and normal node initial energy, given that m and b represent the percentage of advanced nodes and intermediate nodes correspondingly. Advanced node energy is \(\alpha\) times more than that of the intermediate and normal node energy is \(\beta\) times more than normal node energy. Now, the \(\beta\) is connected with \(\alpha\) via \(\beta=\alpha/2\). With \(E_{N}\), \(E_{I}\), and \(E_{A}\) signifying the initial energy of normal, intermediate, and advanced nodes, then energy relationships are formulated, using:  
\[E_{N}=E_{0} \tag{1}\]  
\[E_{I}=E_{0}(1+\beta) \tag{2}\]  
\[E_{A}=E_{0}(1+\alpha) \tag{3}\]
## 3 Materials and Methods
### The APP Feedback Case
#### 3.3.1 The PIC Output Mean with APP Feedback  
Before we begin, it is necessary to clarify some terms relating to the receiver in Figure 4. \(L_{a}\) is an APP LLR output from the SISO, \(L_{e}\) is the extrinsic output from the SISO decoder, and \(L_{p}\) is the LLR input to the SISO decoder. \(L_{a}\), \(L_{e}\) and \(L_{p}\) are related as:  
\[L_{a}=L_{e}+L_{p} \tag{29}\]  
The above LLRs may all be converted to signal estimates via use of the \(\tanh()\) function. Therefore, for user \(k\) at iteration \(n\) we have:  
\[\hat{x}_{k,e}^{(n)}=\tanh\Bigg{(}\frac{L_{k,e}^{(n)}}{2}\Bigg{)} \tag{30}\]  
\[\hat{x}_{k,\mu}^{(n)}=\tanh\Bigg{(}\frac{L_{k,a}^{(n)}}{2}\Bigg{)} \tag{31}\]  
**Another estimate, the residue**: Consider \(\hat{x}_{k,\mu}^{(n)}\), statistical parameters for the case of extrinsic data have already been determined, so it would be advantageous to express the APP estimate in terms of extrinsic feedback as follows:  
\[\hat{x}_{k,a}^{(n)} =\tanh\biggl{(}\frac{L_{k,ext}+L_{k,p}}{2}\biggr{)} \tag{33}\] \[=\frac{\tanh\Bigl{(}\frac{L_{k,ext}}{2}\Bigr{)}+\tanh\Bigl{(} \frac{L_{k,p}}{2}\Bigr{)}}{1+\tanh\Bigl{(}\frac{L_{k,ext}}{2}\Bigr{)}\tanh \Bigl{(}\frac{L_{k,p}}{2}\Bigr{)}}\] \[=\hat{x}_{k,e}+\frac{\Bigl{(}1-\tanh^{2}\Bigl{(}\frac{L_{k,ext}}{ 2}\Bigr{)}\Bigr{)}\tanh\Bigl{(}\frac{L_{k,p}}{2}\Bigr{)}}{1+\tanh\Bigl{(}\frac {L_{k,ext}}{2}\Bigr{)}\tanh\Bigl{(}\frac{L_{k,p}}{2}\Bigr{)}}\] \[=\hat{x}_{k,e}+\frac{\Bigl{(}1-\hat{x}_{k,e}^{2}\Bigr{)}\hat{x}_{ k,p}}{1+\hat{x}_{k,e}\hat{x}_{k,p}}.\]  
The APP feedback is the sum of an extrinsic feedback term \(\hat{x}_{k,e}\) and a _residue_ term \(\hat{x}_{k,R}\).  
\[\hat{x}_{k,a}=\hat{x}_{k,p}+\hat{x}_{k,R} \tag{34}\]  
\[\hat{x}_{k,R}=\frac{\Bigl{(}1-\hat{x}_{k,e}^{2}\Bigr{)}\hat{x}_{k,p}}{1+\hat{x }_{k,e}\hat{x}_{k,p}}. \tag{35}\]  
The PIC output conditional mean is evaluated as follows:  
\[\tilde{\mu}_{k,a}^{(n)} =\mathcal{E}\left[\hat{x}_{k,a}^{(n)}|_{x=1},\hat{x}_{j,e}^{(n-1)},\hat{x}_{j,a}^{(n-1)},\hat{x}_{k,p}^{(n-1)}\lor\forall j\right] \tag{36}\] \[=1+\mathcal{E}\left[\sum_{j\
eq k}c_{jk}\Bigl{(}x_{j}-\hat{x}_{j, e}^{(n-1)}-\hat{x}_{j,R}^{(n-1)}\Bigr{)}|_{x_{k}=1},\hat{x}_{j,e}^{(n-1)},\hat{x}_{j,a }^{(n-1)},\hat{x}_{k,p}^{(n-1)}\lor j\right]\] \[=1-\mathcal{E}\left[\sum_{j\
eq k}c_{jk}\hat{x}_{j,R}^{(n-1)}|_{x _{k}=1}\right]\]  
We can see from (36) that the conditional mean is no longer unity. In addition, calculation of the conditional mean is not straight-forward, as one is needed to determine an  
Figure 4: Receive circuit diagram.  
expectation where there are correlated terms in both the numerator and denominator, since:  
\[\mathcal{E}\left[x_{k}\sum_{j\
eq k}c_{jk}\hat{x}_{j,R}^{(n-1)}|_{x_{k}=1}\right]= \mathcal{E}\left[x_{k}\sum_{j\
eq k}c_{jk}\frac{\left(1-(\hat{x}_{j,e}^{(n-1)})^ {2}\right)\hat{x}_{j,p}^{(n-1)}}{1+\hat{x}_{j,e}^{(n-1)}\hat{x}_{j,p}^{(n-1)}}|_ {x_{k}=1}\right]. \tag{37}\]  
The complexity involved in the calculation of conditional mean implies that the computation of variance will also be difficult. Variance is evaluated in the Section 3.3.2.
# Studying Semi-TCP and Its Application in Marine Internet
## 4 Experimental Simulation and Analysis
### Simulation Scene and Parameters  
This section simulates a ship ad-hoc network, which is a main component of the marine Internet. To reflect the characteristics of the marine Internet, it is necessary to establish a long-distance dynamic simulation scenario, which can be achieved by using the industrial version WiFi. Some nodes at sea move at certain speeds and cooperate with appropriate channel attenuation models. To test the congestion control algorithm, we established a congestion scenario. Therefore, multiple application data flows were added to reflect the differences in network throughput, packet loss rate, and average packet delay. The abstract scene of the simulation is shown in Figure 6.  
The transmission range of each node in the scenario only covers the nodes adjacent to it. The data flow from node A to node C and node F needs to be forwarded by the intermediate node. The data flow from node J to node K is mainly to verify the performance of the improved Semi-TCP algorithm when node A is an exposed terminal, and the two data flows from node B are mainly used to create network congestion.  
The basic parameters of the scene are set as Table 1[21; 22]:  
For the ease of description, the semi-TCP congestion control protocols based on RTS/CTS will be abbreviated as Semi-TCP-RTS, and the improved one as Semi-TCP-RTS-V2. We compared the three algorithms: Semi-TCP-RTS, Semi-TCP-RTS-V2, and TCP-Lite--a lightweight version of TCP.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Network Simulator
#### 3.3.2 Network Topology  
The Starlink V1.0 satellites are equipped with four phased-array antennas and two parabolic antennas, enabling bent-pipe routing through ground stations to provide communication services to terrestrial terminals. Compared to earlier versions, the V1.5 and subsequent models have been optimized and updated, incorporating four laser transmitters to facilitate inter-satellite communication.  
SatScope employs the Grid+ connectivity mode as its default physical connection method: two inter-satellite links connect to neighboring satellites within the same orbital plane, while the other two links connect to adjacent orbit neighbors. Inter-plane communication is achieved using any remaining unused inter-satellite links. In shell clustering analysis, if satellites identified as noise points reside between shells, these can also be utilized for cross-shell communication tasks.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 2 Technology Reviews
### Federated Learning
#### 2.1.2 Inference Attack on Federated Learning  
Although federated learning provides a privacy solution by keeping local data on users' sides, learning parameters can still be subject to various attacks. A malicious entity can see the parameters sent by users, and an honest but curious aggregator can identify a user's contribution when computing the change in the global model. Among the various threat models in federated learning, this work is interested in inference attacks. An attacker infers users' sensitive information as the parameters can leak unintended features of training  
Figure 1: Federated learning consists of a server (coordinator) and a list of connected clients (devices). It takes action processes to train a global AI model in a distributed manner.  
data [4; 5; 30; 31]. The simplest way is to infer the model based on continuous queries and responses. Figure 2 captures a sample scenario of an inference attack on federated learning. Recent research has reported various forms of inference attacks [32; 33]. In order to defend from the attack, many previous works have adopted well-known privacy-preserving techniques: homomorphic encryption, secure multiparty computation, and differential privacy. Among them, this work explores the last one.
## 7 Traffic Traces Analysis
### Normative Noise Tests  
Figure 46 shows the results obtained for the normative noise tests. Test N7 (where filter F9 was used) is not considered since the logs were not recorded by mistake. Nevertheless, based on the spectral measurements (Figure 35), F9 clearly outperformed the rest of the filter configurations in terms of removing the noisy signal. Apart from this, Figure 46 shows that test N1 (where no filter was used) is the worst situation from the communications perspective, as it could be expected. Filter configuration F4 (used in N3) shows the worst performance in this scenario, followed by filter configuration F2 (used in N2). Filter configuration F6 (used in tests from N4 to N6) shows the best performance. In addition, it can be observed that the communications work better as the capacitors in the upper branch of F6 increase and so the dip within the series of noisy pulses move to the beginning of the PRIME frequency band. These results may be related with the fact that PRIME uses DBPSK (c.f., Section 2.2), so the first carrier of the OFDM symbol transmits a well-known value agreed by transmitter and receiver which is used as reference for the subsequent frequency shifts, and invites to conclude that protecting the lower part of the PRIME frequency band yields better communications performance.
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 2 Related Work
### TLS Fingerprint Enrichment  
A large volume of the research into TLS fingerprinting suggests that it is a viable tool for clustering nodes with similar configurations, but that it is not granular enough on its own to enable true classification. As the roll-out of TLS 1.3 and ECH progresses, along with the upswing in the use of CDNs, the variation seen across the TLS stack is decreasing, making TLS fingerprinting less viable as a fine-grained method for malicious domain and infrastructure hunting.  
[PERSON] et al. [7] included the HTTP 'Server' key in their initial scans, to help detect CDN server deployments outside of a CDN's designated AS. The additional HTTP metadata, when combined with TLS, achieved a maximum precision of 97%, but the authors were clear that HTTP header analysis alone was not ideal as a classifier. In contrast to this, [PERSON] et al. [14] developed HSLF, a Locality-Sensitive Hashing algorithm that sequenced all header fields and measured similarities between HTTP sessions. The authors chose to weight specific headers that reflected application-specific details before applying a random forest machine learning algorithm that resulted in an accuracy rating of 0.96 when classifying application-specific traffic.  
HTTP headers have also been used to track and pivot between malicious domains, [PERSON] et al. [15] analysed numerous headers from benign and malicious web-servers and used eight machine learning models to demonstrate the feasibility and effectiveness of only using headers in isolation. The authors chose to invest heavily in data cleaning and the validation process, ensuring that custom headers and mis-spellings were not considered. This approach was in direct conflict to the one taken by [PERSON] and [PERSON] [16], who used headers for hunting malicious infrastructure. Their research considered mis-spellings and unusual sequencing to be valuable anomalies that could aid in the identification of unique command and control servers. The importance of header sequences was echoed by [PERSON] et al. [17], who produced HeadPrint to identify malicious communications in passive traffic. Their fingerprint technique relied on two orthogonal header characteristics that enabled applications to be distinguished by their order and their associated values. A range of machine learning models were evaluated using the fingerprinting technique, and the accuracy ranged from 90.74% to 95.44%.  
Although research into the domain of active HTTP header fingerprinting is limited, the current research does indicate that, at least on an application level, HTTP headers can be effective in fingerprinting [14; 16; 17] and useful for enrichment of TLS fingerprints.
## Theory for the Local Wavenumber Approach
### Identification and Separation of Lamb Wave Modes in the Wavenumber Domain  
Only the fundamental anti-symmetric mode (A0) can be actuated by ACT with the special incidence angle; however, mode conversion still exists due to the interaction between the Lamb wave and the flat bottom defects. The received Lamb wave signals usually comprise the incident, reflected, and converted wave modes. By using three-dimensional fast Fourier transform (3D FFT) [29], the full wavefield data can be transformed into the wavenumber domain, where various wave modes can be distinguished.  
\[U(k_{x},k_{y},f)=F_{3D}(U(x,y,t)) \tag{1}\]  
Figure 1: Dispersion curves of an aluminum plate. (**a**) Phase velocity; (**b**) incidence angle; (**c**) wavenumber.  
Figure 2: Schematic diagram of Lamb wave transmission and reception.  
The entire wavefield signals are filtered by the wavenumber domain filter for the purpose of removing any modes other than the A0 mode.  
\[\widetilde{U}(k_{x},k_{y},f)=W_{k}(k_{x},k_{y},f)U(k_{x},k_{y},f) \tag{2}\]  
\[W_{k}(k_{x},k_{y},f)=\left\{\begin{array}{ll}0&|k|\leq k_{1}\\ 1&k_{1}<|k|<k_{2}\\ 0&|k|\geq k_{2}\end{array}\right. \tag{3}\]  
\[k=\left[\begin{array}{c}k_{x}\\ k_{y}\end{array}\right] \tag{4}\]  
These filtered signals in the wavenumber domain should be transformed back into the time domain by using three-dimensional inverse fast Fourier transform (3D IFFT). \(\widetilde{U}(x,y,t)\) is the filtered wavefield.  
\[\widetilde{U}(x,y,t)=F_{3D}{}^{-1}(\widetilde{U}(k_{x},k_{y},f)) \tag{5}\]