esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 5 NS-3 Simulation Tool
### Channel Link Capacity  
The maximum theoretical channel capacity can be estimated by Shannon's Theory. Shannon's Theory relates the theoretical channel capacity to the signal to noise ratio [52] using (2).  
Figure 3: Two-port network model.  
\[C=B\cdot\log_{2}\left(1+\frac{S}{N}\right), \tag{3}\]  
where \(C\) indicates the maximum channel capacity in Sh/s, \(B\) is the channel bandwidth in Hz, and S/N is the received signal power to the noise signal power at the receiver.  
In power line grid the noise is not invariant and the received signal varies with transfer function. Therefore, the received signal power density spectrum \(S_{RR}\), the noise spectral density spectrum \(S_{NN}\) at the receiver, and transfer function \(H(f)\) must be considered. After that, theoretical channel capacity could be calculated using Equation (4).  
\[C=\int\limits_{B}\log_{2}\left(1+\frac{S_{RR}(f)}{S_{NN}(f)}\right)df=\int \limits_{B}\log_{2}\left(1+\frac{S_{TT}(f)\cdot\left|H(f)\right|^{2}}{S_{NN}(f )}\right)df. \tag{4}\]  
The channel is separated into \(N\) narrowband flat fading sub-channels of bandwidth \(\Delta f=B/N\), where \(N\) is the number of samples of the CTF. After that channel capacity can be derived as follows [53]:  
\[C=\Delta f\cdot\sum\limits_{v=1}^{N}\log_{2}\left(1+\frac{S_{RR}(f)\cdot\left| H(f)\right|^{2}}{S_{NN}(f)}\right). \tag{5}\]  
The power in the particular sub-channels \(v=1\) to \(N\) is:  
\[P_{TT}(v)=\int\limits_{\Delta f_{v}}S_{TT}(f)df \tag{6}\]  
After this, with power the capacity is:  
\[C=\Delta f\cdot\sum\limits_{v=1}^{N}\log_{2}\left(1+\frac{P_{TT}(v)\cdot\left| H(v)\right|^{2}}{P_{NN}(v)}\right). \tag{7}\]  
Finally, the capacity could be computed by:  
\[C=\Delta f\cdot\sum\limits_{i=1}^{N}\log_{2}\cdot(1+SNR_{i}) \tag{8}\]  
where \(SNR_{i}\) is:  
\[SNR_{i}=\int\limits_{(i-1)\cdot\Delta f}^{i\cdot\Delta f}\frac{S_{TT}(f)\cdot \left|H(f)\right|^{2}}{S_{NN}(f)}df \tag{9}\]  
The NS-3 PLC simulator computes the channel capacity for a particular topology and modulation with a given number of modulation states.
## 3 Analytical System Model
2D-OCDMA encoder based on S\({}_{0}\)H\({}_{1}\) sequence (**a**); 2D-OCDMA decoder based on S\({}_{0}\)H\({}_{1}\) sequence (**b**). At the input of the optical modulator, the electrical signal can be written as follows:  
\[s(t)=\sum_{k=1}^{N}\sum_{l=1}^{F}P_{T}c_{k}(t-lT_{c})X_{k}\ e^{(\frac{\pi j(k-1)t }{T_{s}})} \tag{7}\]  
In order to make a comparison between the transmitted signals in the cases of the Fast-OFDM-CDMA configuration and the AO-OFDM-CDMA and OCDMA-WDM systems, we introduce Equations (8) and (9), respectively, as follows:  
\[s_{1}(t)=\sum_{k=1}^{N}\sum_{l=1}^{F}P_{T}c_{k}(t-lT_{c})X_{k}\ e^{(\frac{2\pi j (k-1)t}{T_{s}})} \tag{8}\]  
where \(s_{1}(t)\) represents the transmitted signal in the case of the AO-OFDM-CDMA configuration. \[s_{2}(t)=\sum_{k=1}^{N}\sum_{l=1}^{F}P_{T}c_{k}(t-lT_{c})X_{k} \tag{9}\]  
where \(s_{2}(t)\) represents the transmitted signal in the case of the OCDMA-WDM configuration. Meanwhile, the PAPR is determined by Equation (8), as follows:  
\[PAPR=\frac{max(\left|s(t)\right|^{2})}{E(\left|s(t)\right|^{2})} \tag{10}\]  
where \(max(\left|s(t)\right|^{2})\) and \(E(\left|s(t)\right|^{2})\) are the maximum and the mean of the Fast-OFDM symbol, respectively. A good way to characterize the PAPR is to use the complementary cumulative distribution function (CCDF) of Pc, which is expressed as Pc = Pr[PAPR \(>\) a\({}_{\text{p}}\)]. Pc is defined as the probability that PAPR overtakes a particular value of a\({}_{\text{p}}\). In the latter case, the variance of subcarriers overlap is defined in Equation (11). \[\sigma_{overlap}^{2}=P_{r}\Big{\{}PAPR>a_{p}\Big{\}} \tag{11}\]  
The effects of PAPR and nonlinearities on the system performance are added through the variance of the OFDM subcarriers overlapping. In addition, we consider the particular case in Equation (10) in order to minimize PAPR and simplify the calculations. \[\sigma_{overlap}^{2}=PAPR \tag{12}\]  
The bit-error-rate (BER) expression is derived from the SNR expression in Equation (1), as follows [17]:  
\[BER=\frac{2}{log_{2}(M)}(1-\frac{1}{\sqrt{M}})erfc(\sqrt{\frac{3 SNR}{2(M-1)}}) \tag{13}\]  
where \(M\) is defined as the modulation order.
## Appendix A  
Within this appendix, we are deriving Equation (25) as approximation of Equation (1). First, we are substituting the approximations made Section 2.10 including Equations (8), (22)-(24) into Equations (17)-(21):  
\[I_{C} \approx l_{0}\;\zeta_{0}\;\tau_{s}\;\frac{1}{2} \tag{17}\] \[I_{SD} \approx l_{0}\;\alpha\;(1-\zeta\;\tau_{s}-\zeta_{0}\;\tau_{s}+\zeta\; \zeta_{0}\;\tau_{s}^{2})\;T_{I}^{g}(\tau_{\downarrow},\zeta_{0})\;T_{I}^{g}( \tau_{\downarrow},\zeta)\;\frac{1}{1-2\;\alpha\;\tau_{s}\;E_{2}(\tau_{ \downarrow})\;E_{3}(\tau_{\downarrow})}\] (18) \[I_{CD} \approx l_{0}\;\alpha\;(\tau_{s}-\zeta_{0}\;\tau_{s}^{2})\;E_{2}(\tau_{ \downarrow})\;T_{I}^{g}(\tau_{\downarrow},\zeta_{0})\;\frac{1}{1-2\;\alpha\; \tau_{s}\;E_{2}(\tau_{\downarrow})\;E_{3}(\tau_{\downarrow})}\] (19) \[I_{S1} \approx l_{0}\;\alpha\;\zeta_{0}\;(\tau_{s}-\zeta\;\tau_{s}^{2})\;E_{3} (\tau_{\downarrow})\;T_{I}^{g}(\tau_{\downarrow},\zeta)\;\frac{1}{1-2\;\alpha\; \tau_{s}\;E_{2}(\tau_{\downarrow})\;E_{3}(\tau_{\downarrow})}\] (20) \[I_{CI} \approx l_{0}\;\alpha\;\zeta_{0}\;\tau_{s}^{2}\;E_{2}(\tau_{\downarrow}) \;E_{3}(\tau_{\downarrow})\;\frac{1}{1-2\;\alpha\;\tau_{s}\;E_{2}(\tau_{ \downarrow})\;E_{3}(\tau_{\downarrow})}\] (21) \[I_{SIF} \approx \frac{F_{SIF}^{0}}{\pi}\;(1-\zeta\;\tau_{s})\;T_{I}^{g}(\tau_{ \downarrow}+\tau_{\uparrow},\zeta) \tag{22}\]  
Preparing some terms used for the Taylor series approximation of Equation (1). Function values at \(\tau_{s}=0\):\[I_{C}|_{\tau_{k}=0} = 0 \tag{120}\] \[I_{SD}|_{\tau_{k}=0} = I_{0}\;\alpha\;T_{I}^{\delta}(\tau_{\downarrow},\zeta_{0})\;T_{I}^{ \delta}(\tau_{\downarrow},\zeta)\] (121) \[I_{CD}|_{\tau_{k}=0} = 0\] (122) \[I_{SI}|_{\tau_{k}=0} = 0\] (123) \[I_{CI}|_{\tau_{k}=0} = 0\] (124) \[I_{SIF}|_{\tau_{k}=0} = \frac{F_{SIF}^{0}}{\pi}\;T_{I}^{\delta}(\tau_{\downarrow}+\tau_{ \uparrow},\zeta) \tag{125}\]  
First derivatives according to \(\tau_{s}\) at \(\tau_{s}=0\):  
\[\left.\frac{\partial I_{C}}{\partial\tau_{s}}\right|_{\tau_{s}=0} = I_{0}\;\zeta_{0}\;\frac{1}{2} \tag{126}\] \[\left.\frac{\partial I_{SD}}{\partial\tau_{s}}\right|_{\tau_{s}=0} = I_{0}\;\alpha\;T_{I}^{\delta}(\tau_{\downarrow},\zeta_{0})\;T_{I }^{\delta}(\tau_{\downarrow},\zeta)\;\left(2\;\alpha\;E_{2}(\tau_{\downarrow} )\;E_{3}(\tau_{\downarrow})-\zeta-\zeta_{0}\right)\] (127) \[\left.\frac{\partial I_{CD}}{\partial\tau_{s}}\right|_{\tau_{s}=0} = I_{0}\;\alpha\;E_{2}(\tau_{\downarrow})\;T_{I}^{\delta}(\tau_{ \downarrow},\zeta_{0})\] (128) \[\left.\frac{\partial I_{SI}}{\partial\tau_{s}}\right|_{\tau_{s}=0} = I_{0}\;\alpha\;\zeta_{0}\;E_{3}(\tau_{\downarrow})\;T_{I}^{\delta}( \tau_{\downarrow},\zeta)\] (129) \[\left.\frac{\partial I_{CI}}{\partial\tau_{s}}\right|_{\tau_{s}=0} = 0\] (130) \[\left.\frac{\partial I_{SIF}}{\partial\tau_{s}}\right|_{\tau_{s}=0} = -\frac{F_{SIF}^{0}}{\pi}\;\zeta\;T_{I}^{\delta}(\tau_{\downarrow} +\tau_{\uparrow},\zeta) \tag{131}\]  
First order Taylor series approximation of Equation (1) at \(\tau_{s}=0\) and substituting Equation (13) yields Equation (25).  
\[I\approx\left.I\right|_{\tau_{k}=0}+\left.\frac{\partial I}{\partial\tau_{s}} \right|_{\tau_{s}=0}\tau_{s} \tag{132}\]
###### Abstract  
The small synthetic aperture radar (SAR) technology experimental project (S-STEP) mission aims to develop an innovative spaceborne SAR microsatellite as a constellation of 32 microsatellites featuring a high-resolution stripmap mode of 1 m. The S-STEP is a spaceborne SAR microsatellite technology demonstration program in which innovative approaches have been proposed and investigated for SAR payload system designs for improving the development speed, affordability, size and weight parameters, and quality of SAR satellite systems. In this study, the major design approach includes a bus-payload integrated flat-panel-type SAR payload based on an active phased-array antenna. This study conducted an SAR image performance analysis considering the mission requirements to validate the feasibility of the innovative SAR payload design of the S-STEP. These performance analysis results are presented to demonstrate the effectiveness of the proposed SAR payload design approach under the new space paradigm.  
microastellite; new space; synthetic aperture radar (SAR); X-band; small satellite +
Footnote †: journal: _aerospace_
## 4 Experimental Results
### Datasets
#### 4.1.1 FER2013 Dataset  
The FER2013 dataset (Figure 6), known for its wide range of facial expressions, serves as the foundation for our empirical investigation. FER2013 consists of a total of approximately 35,887 images.  
This total includes images from the training, public-test, and private-test subsets combined. The dataset includes seven different emotion categories--anger, disgust, fear, happiness, sadness, surprise, and neutral--and offers a solid framework for assessing the recognition abilities of various ViT models.
# Research on Communication Stability of Inter-Cannonball Network Based on OPNET
## 3 Routing Protocol Modelling Analysis  
Ad hoc routing protocols are mostly split into two groups based on the driven model: table-driven and on-demand routing protocols. Table-driven routing protocols require each node to maintain a routing table that reaches every other node on the network. The on-demand routing protocol searches for a route only when the node needs to transmit data, and only the route that actually completes the data transmission will be recorded and maintained by the routing table [27].  
When a node has data to transmit, the latency of table-driven routing will be modest as long as a route exists, but it takes a large amount of overhead to maintain the route so that routing updates may keep up with network topology changes as much as feasible. By not constantly broadcasting routing information, on-demand routing conserves some network resources. However, while transmitting data, if there is no route to the target node, the data must be stored until route discovery, resulting in a modest increase in message delay.  
Considering the instability of the inter-cannonball ad hoc network communication link and the bandwidth consumption of the routing protocol, the on-demand routing protocol is more in line with the design requirements. At present, the two more common on-demand routing protocols are dynamic source routing (DSR) and AODV routing protocol [28].
# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
## 4 Experimental Settings
### Dataset Preprocessing  
Efficient data preprocessing is paramount for achieving optimal results in any classification task in ML and DL. Since our FL approach is based on DL, ensuring data quality is fundamental. Consequently, we focused on improving the dataset preprocessing efficacy, specifically addressing the significant imbalance in the dataset, where certain heartbeat types are underrepresented [18]. We extracted the ECG data for each record, focusing on the modified version of lead II (MLII), and labeled the corresponding heartbeats based on annotations provided in the dataset. This involved detecting R-peaks in the ECG signals and extracting adjacent segments for classification. Preprocessing started with cleaning input signals using a wavelet transform, followed by a series of steps including loading the data, marking key points, and labeling the corresponding heartbeats. The processed data consisted of 64,533 segments with a length of 187 for training, 9220 segments with a length of 187 for validation, and 18,439 segments with a length of 187 for testing, with the splits made in a 7:1:2 ratio, respectively. In this research, we divided the training data into multiple parts, using the Independent and Identically Distributed (i.i.d) setting. Specifically, the data were uniformly assigned to each client, ensuring that each client received an equal number of samples. This approach guaranteed that each client had a similar distribution of all five classes of heartbeats [18].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Specification** \\ \hline Disk size & 4.5 TB \\ Processor model & Intel Xeon Processor W-2295 \\ Number of processors & 1 \\ Memory & 16 GB \\ Operating system (Linux) & Ubuntu 22.04.4 LTS \\ GPU & Nvidia A4000 GPU, RTX 4090 \\ Python version & 3.9.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware infrastructure.
# Optimal CNN-Hopfield Network for Pattern Recognition Based on a Genetic Algorithm +
## 4 Knapsack Model for Pattern Recognition  
The key task of our approach is to optimize the storage of the different patterns. To deal with this problem, it is formulated as an assignment optimization problem or a knapsack problem [42]. This optimization problem is a well-known combinatorial optimization problem in which the objective is to find the optimal object from a set of items. The traditional 0-1 knapsack problem is defined by \(N\) items, where \(N=\{1,2,3,...,n\}\) is the set of items. Each item has a weight, \(w_{n}\), and a value, \(p_{n}\). Since one knapsack can store only a maximum capacity or weight of \(W\), the goal is to find the items that can be packed into the knapsack to maximize the packing. The formulation for this traditional 0-1 knapsack is given in (3). The binary decision variable, \(x_{n}\), takes the value of one if the item is packed into the knapsack and zero otherwise. Therefore, the problem can be formulated as follows:  
\[\left\{\begin{array}{c}\text{Maximize}\sum_{i=1}^{n}x_{i}p_{i}\\ \text{subject to,}\sum_{i=1}^{n}x_{i}w_{i}\leq W\\ x_{i}\in\{0,1\}\end{array}\right. \tag{3}\]  
Then, the Hopfield network can be seen as a knapsack that can hold a set of items--in our case, patterns to be stored. Each pattern has a weight \(w_{i}\) corresponding to the absolute energy of the pattern \(x^{n}\) computed by (1) and a value \(p_{i}\) that can be defined by measuring the similarity between the pattern and the other patterns. The capacity (or total weight) of the knapsack \(W\) is defined by:  
\[W=max\{|E_{1}|,|E_{2}|,...,|E_{n}|\}\times P_{max} \tag{4}\]  
The estimation of \(P_{max}\) can be made from the upper bound of the theoretical capacity of the HNN computed by (2). In order to define the values of \(n\) distinct items (patterns), one of the common similarity measures can be used. To select the most dissimilar (orthogonal) patterns among the patterns in the dataset, principal component analysis (PCA) or a similar method can be applied [43; 44].
## 3 Results and Discussion
### Thermal Conductivity
The dispersion of GnP significantly improved the thermal conductivity of the nanocomposite. The reason behind this enhancement of thermal conductivity is the nano-size of the GnP and the two-dimensional geometry of GnP that increases the exposure to the base fluid. By increasing the volume percentage of GnP, the thermal conductivity is found to increase. The average increase in thermal conductivity compared to base fluid are found to be 11.01%, 13.38%, 17.23%, 20.96% and 23.95% respectively for the considered volume percentage of GnP in the liquid state. The thermal conductivity for the 0.5% volume percentage is 23.95% higher compared to that of the base fluid, whereas [PERSON] et al. (2016) obtained a thermal conductivity 16% higher than the base fluid at the same concentration. The superior thermal conductivity is due to the use of chemically functionalized GnP instead of dispersing surfactants in the base fluid for better stability. Whereas, in the solid state as shown in Figure 4, the average surge in thermal conductivity compared to base fluid are 18.67%, 25.7%, 34.75%, 45.08% and 53.05% respectively for the considered volume percentage of GnP. Figure 4: Variation of thermal conductivity with respect to temperature for different volume percentages. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DI Water**} & \multicolumn{2}{c}{**DI Water**} & \multicolumn{2}{c}{**Nan nanocomposite (0.1\%)**} & \multicolumn{2}{c}{**Nan nanocomposite (0.2\%)**} & \multicolumn{2}{c}{**Nan nanocomposite (0.3\%)**} & \multicolumn{2}{c}{**Nan nanocomposite (0.4\%)**} & \multicolumn{2}{c}{**Nan nanocomposite (0.5\%)**} \\ \cline{2-13}
**Temp** & **Standard K** & **Measured K** & **K** & **Percentage** & **K** & **Percentage** & **K** & **Percentage** & **K** & **Percentage** & **K** & **Percentage** \\ (\({}^{\circ}\)**C)** & **(W/mK)  
Thermal conductivity in solid state was higher than in the liquid state. The reason for this sudden increase in thermal conductivity when fluid turns to solid state is due to the orderly solid structure that causes better accelerated molecular vibrations. The sudden fall in thermal conductivity when solid state changes to liquid state might be caused by the orderly stable microstructure in solid turning into a disorderly structure in liquid state. Table 2 shows the tabulated comparison of thermal conductivity of nanocomposite with respect to temperature and % volume fraction. The background for the thermal conductivity enhancement is an attribute to the high aspect ratio, 2-D geometry and stiffness of graphene. The acid treatment of GnP plays a significant role in the enhancement of thermal conductivity in the nanocomposites. Instead of adding surfactant, Nitric acid treatment was used to chemically modify the surface of GnP in order to increase the surface-active sites for electrochemical reactions and also to improve the stability. As surfactants are not used, the effects of degradation in thermal conductivity while adding surfactant are completely avoided in this nanocomposite.
## 4 Signal Pretreatment  
The pretreatment stage includes digital filtering, binarization, and hopping points correction. The purpose of this stage is to provide a pure and stable Morse code signal for the subsequent decoding process. Detailed signal processing is provided in the following subsections.
## 7 Discussion
### Limitations  
* Data Quality: AI models require large amounts of high-quality data that must be representative of the target patient population. Lack of access to appropriate data or insufficient data quality can lead to incorrect predictions. Human error, such as patients' reluctance to provide honest answers, points to the need for the implementation of appropriate methods and techniques for acquiring such data.
* Interpretation of Results: The results generated by AI models can be difficult for medical staff to understand, especially if they are not adequately explained. Therefore, it is crucial to develop tools and applications that are intuitive to use and easy to interpret, which will increase their acceptance and effectiveness in clinical practice. In addition to improving algorithms, it will also be necessary to properly train medical staff. An important aspect is ensuring algorithm transparency so that medical personnel can trust the decisions made by AI-based systems.  
In summary, artificial intelligence holds great potential in post-stroke rehabilitation, particularly in terms of personalizing therapy and dynamically monitoring patient progress. However, to fully realize this potential, further research is needed to expand and improve the quality of data, optimize models, and ensure their interpretability and acceptance in clinical practice.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 7 Conclusions  
The optimal planning of passive single tuned harmonic filter in distorted distribution systems has been proposed in this paper. The proposed method considers the harmonic content resulted from the inverter-based DG units in the distribution system. The optimal planning problem has been solved using WCA with the aid of fundamental and harmonic backward/forward sweep power flow. The simulation results approved the effectiveness of the proposed method in determining the optimal design, number, and placement of the single tuned filter in the radial distribution system. The results also indicate that the harmonic spectrum of the DG units should be considered while designing the harmonic filters in the system to prevent increasing harmonic distortion in the system.  
The noticeable findings of this paper are:  
* The effectiveness of the proposed filter planning method in achieving multi-dimensional objectives with the aid of WCA.
* The WCA is efficient with good convergence characteristics in simultaneously determining the optimal design, placement, and the number of the STF.
* The proposed method not only reduces the THD and improves the voltage profile but also reduces the power loss and filter costs.
* Inverter-based DGs have shown a considerable effect on the harmonic distortion in distribution systems which increases the THD by about 14%.
* By using the proposed method, THD in a 69-bus system has reduced by 75% without considering the harmonic distortion of the DGs, and reduced by 56.5% without considering the harmonic distortion of the DGs.  
The future work will be directed to consider passive filter overcurrent and overvoltage, besides considering different renewable energy sources.  
[PERSON] and [PERSON] designed the idea strategy, studied the data, and wrote the manuscript. [PERSON] and [PERSON] revised and proofread the manuscript and designed some figures. Finally, [PERSON]-S., A.A.E.-E., and [PERSON] reviewing, editing, and supporting different improvements for the manuscript. All authors have read and agreed to the published version of the manuscript. All authors have contributed to the preparation of this manuscript.  
Figure 12: The fundamental voltage of the 69-bus system buses with and without filters in case 2.  
**Funding:** This work was supported by the Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
## 2 Scheme Statement
### Threat Model  
In our proposed scheme, both the participants and the cloud server are required to adhere to the regulations of model consistency and parameter consistency. This means that the server and the participants must maintain synchronization to ensure that all participants utilize the same global model parameters during the same round of training. Additionally, parameters such as the learning rate, which are employed during model training by both the server and the participants, must remain consistent. This uniformity is crucial to guarantee that the direction of model training is aligned across all entities involved. Furthermore, each participant is assumed to be honest and trustworthy, neither curious about the model parameter information of other participants nor maliciously uploading incorrect parameters to disrupt model aggregation, such as uploading parameter label values that do not match the parameter values. Additionally, the cloud servers are classified as one honest and one potentially malicious, which may lead to the following threats.  
* The malicious server attempts to extract user data or privacy information from the learning process by analyzing the model parameters uploaded by participants, thereby compromising the security of the protocol [26];
* The malicious server attempts to return incorrect aggregated results, thereby affecting the training of the global model [29];
* The malicious server attempts to falsify the aggregation results to bypass participant verification, causing participants to accept incorrect aggregation results and thereby disrupting model training [31].