esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 2 Basic Principles of FL over Wireless Networks
### Signal Transmission Models
#### 2.2.2 FDMA-Based Uplink Transmission  
In the FDMA scheme, each client is allocated a distinct, nonoverlapping frequency band to communicate with the central server (CFL) or other clients (DFL), avoiding interference. The uplink rate of the \(k\)-th client (in bps) in this case is expressed as  
\[R_{k}^{\mathrm{u}}=B_{k}\log_{2}\!\left(1+\frac{|h_{k}^{\mathrm{u}}|^{2}p_{k }}{\sigma_{\mathrm{noise}}^{2}}\right), \tag{5}\]  
where \(B_{k}\) denotes the bandwidth allocated to the \(k\)-th client, and \(B_{\mathrm{u}}\triangleq\sum_{k=1}^{K}B_{k}\) is the total uplink bandwidth.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 6 Challenges
### Privacy Challenges
Researchers must provide techniques to protect participants' privacy while ensuring the real-time performance of the system is not affected.
# Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks
## 5 Simulation Studies
### Discussion  
The presented concept is similar to that of [30], where each task/monitoring location has to be serviced by the same MoEl throughout a mission having fixed revisit intervals. In our implementation, these intervals are dynamically calculated among the consecutive paths via the proposed MES scheme. Then, the successor routes for the \(M\) MoEls are calculated again by the MILP problem, resulting in different MoEl routes between consecutive iterations. Thus, in the current work, the static nodes are not assigned to specific MoEls.  
We should note that this formulation does not provide the minimum number of MoEls in order not to experience any data loss. This is a separate problem and requires a reformulation of the overall objective.  
Furthermore, the computational complexity of the suggested MILP problem increases significantly with the number of the MoEls and the static nodes. In this case, sub-optimum solvers need to provide an initial estimate of the a solution, and the algorithm needs to be properly initialized.
# Numerical and Experimental Investigation of Guided Wave Propagation in a Multi-Wire Cable
## 4 Experimental Analysis
### Two Contact Wires  
In order to analyze the CAN phenomena in a multi-wire cable, a wire-to-wire cable was first considered. Two identical 7-mm-diameter steel wires with a length of 1.6 m, used in previous experiments, were tied together tightly and compactly using nylon cable ties along the \(z\)-axial direction, as shown in Figure 11d. The wire that was excited to generate a UGW was labeled the active wire, and another one was labeled the passive wire. The installation of exciting and receiving transducers in Case 2 of Section 4.2. was employed to simultaneously receive L(0,1) and F(1,1) guided waves. The center excitation frequency of the tone burst was 60 kHz. The reception signals of the two wires were recorded and are plotted in Figure 13. The FFT spectra of the two signals were obtained. The theoretical group velocity dispersion curves calculated in Section 2.1. were used to determine the wave modes presented in the signals of two wires. There were two wave packets in the time domain signals of the active and passive wire: the direct waves of L(0,1) and F(1,1). The measured group velocities of two modes calculated from the time-of-flight (ToF) of the wave peak in two wave packets are consistent with the theoretical group velocity dispersion curves at 60 kHz.  
The appearance of the frequency components of 120 kHz in the FFT spectrum of the active and passive wires, which was double the center frequency of the exciting signals, verifies the CAN effect of  
Figure 13: The wave signals and FFT spectrum for the two contact wires with an exciting center frequency of 60 kHz: (**a**) the time domain signal of the active wire, (**b**) the FFT spectrum of the active wire, (**c**) the time domain signal of the passive wire, and (**d**) the FFT spectrum of the passive wire.  
the two contact wires described in Section 2.2. and is consistent with the results of the transient FE simulation in Section 3.2.
# Blind Phase Search with Angular Quantization Noise Mitigation for Efficient Carrier Phase Recovery
###### Abstract  
The inherent discrete phase search nature of the conventional blind phase search (C-BPS) algorithm is found to introduce angular quantization noise in its phase noise estimator. The angular quantization noise found in the C-BPS is shown to limit its achievable performance and its potential low complexity implementation. A novel filtered BPS algorithm (F-BPS) is proposed and demonstrated to mitigate this quantization noise by performing a low pass filter operation on the C-BPS phase noise estimator. The improved performance of the proposed F-BPS algorithm makes it possible to significantly reduce the number of necessary test phases to achieve the C-BPS performance, thereby allowing for a drastic reduction of its practical implementation complexity. The proposed F-BPS scheme performance is evaluated on a 28-Gbaud 16 QAM and 64 QAM both in simulations and experimentally. Results confirm a substantial improvement of the performance along with a significant reduction of its potential implementation complexity compared to that of the C-BPS.  
blind phase search (BPS); carrier phase estimation (CPE); carrier phase recovery (CPR); coherent detection; quadrature amplitude modulation (mQAM); phase noise +
Footnote †: journal: _photonics_  
_Article_  
Blind Phase Search with Angular Quantization Noise Mitigation for Efficient Carrier Phase Recovery
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 4 Discussion
### Topographical Feature Retention  
In general, feature retention should match the degree of simplification. From a geographical perspective, the maintenance of terrain features plays an essential role in the simplification process. To verify the capability of CCSM for topographic feature retention, in this subsection, the retention of typical topographic features (e.g., valley lines and ridgelines) in continuous multi-scale simplified contours is evaluated by visual analysis methods. Figure 19 shows the valley lines and ridge lines were extracted from a contour map at a scale of 1:50,000.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Coefficient**} & \multicolumn{5}{c}{**Coefficient Values of Different Groups**} \\ \cline{2-6}  & **Group1** & **Group2** & **Group3** & **Group4** & **Group5** \\ \hline a & 0.998 & 1.032 & 0.994 & 0.988 & 0.997 \\ b & 0.002 & \(-0.01438\) & 0.004 & 0.009 & 0.002 \\ c & \(-9.37\times 10^{-5}\) & \(-0.04805\) & \(-0.0002\) & \(-2.02\times 10^{-5}\) & \(-3.75\times 10^{-5}\) \\ d & \(-6.075\) & \(-1.02\) & \(-5.542\) & \(-8.531\) & \(-6.361\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coefficient values of the exponential regression functions in different groups.  
Figure 18: Simplified contours generated by CCSM at the 1:100,000 (**a**), 1:200,000 (**b**), 1:250,000 (**c**), and 1:500,000 (**d**) scales.  
As shown in Figure 20, with the progressive simplification of the contour line, some details of the contour lines were continuously reduced, and some ridge and valley lines gradually disappeared. The simplified terrain map only retained some key ridge and valley lines. However, the topological relationship of all simplified contours was also consistent, thereby suggesting that the key corresponding geographical features were maintained. Therefore, CCSM also performs well in maintaining the terrain features of geographical contours with complex linear features. Figure 20 also shows that even though CCSM is used for single line simplification, this method can also well maintain the group multi-scale continuous rendering of simplified contour lines.  
In practice, the target contour \(L_{n}\) is not always easy to obtain or even inexistent at the targeted scale [21]. For the former situation, a general simplification method (i.e., DP algorithm) could be employed to obtain the target contour line \(L_{n}\)[46]. That is, by using the DP algorithm, the significant morphology characteristic points of \(L_{0}\) could be extracted and linked together to form \(L_{n}\) in an orderly manner. The main topographical feature is retained in \(L_{n}\). For the later, it is an extreme case due to the principles of cartographic generalization. The starting and ending points of \(L_{0}\) could be directly linked to form \(L_{n}\). The main topographical feature is disappeared in \(L_{n}\). This is consistent with the inexistent status of \(L_{n}\) in the targeted map.
## 1 Introduction
[PERSON] demonstrated the viability of GNSS PWV [21]. [PERSON] proved that PWV based on GPS has high accuracy [22]. [PERSON] examined the precision of real-time GPS PPP PWV retrieval using real-time data from 20 sites throughout the globe. The results show that the accuracy of real-time GPS PPP PWV is better than 3 mm [19]. [PERSON] conducted real-time GNSS water vapor experiments with GPS, BDS, Galileo, and GLONASS. The findings demonstrate that the real-time water vapor retrieval of multi-GNSS achieves millimeter-level accuracy and high dependability, which is advantageous for atmospheric detection systems, particularly those used in meteorological applications. PWV based on the retrieval of real-time GNSS data may play a significant role in several domains, including weather monitoring and catastrophe warning [23]. However, most of the above studies were carried out under a single weather condition, and there are relatively few studies on the accuracy evaluation of PPP inversion ZTD and ZTD conversion to PWV under different weather conditions. This paper uses observation data from Hong Kong continuously operating reference stations (HK CORS) from June 2021 (DOY 173-DOY 179) and August 2021 (DOY 229-DOY 235) to study and analyze the accuracy of real-time PPP estimation of ZTD and PWV under different weather conditions, with the goal of evaluating its performance in low-latitude areas during varying weather conditions (rainfall and nonrainfall). Before obtaining the overall tropospheric delay through parameter estimation, real-time PPP is utilized for data processing. This is the method used to retrieve PWV. The GNSS and meteorological stations in Hong Kong were measured and analyzed. Utilizing Hong Kong radiosonde (RS) data as a reference, the approach was tested, and its accuracy was assessed using the IGS's final product and the PWV results. This paper is organized as follows: The experimental dataset is provided in Section 2, and the PPP technique for calculating ZTD and converting ZTD to PWV is shown. The positioning performance of real-time PPP is examined in Section 3, along with the precision with which PPP estimates ZTD and with which ZTD is converted into PWV. We discuss the accuracy of PWV in Section 4. Section 5 discusses \"Limitations and Future Direction of the Research\". Section 6 provides a summary of the experiment's conclusion.
# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 1 Introduction
### Contribution of This Study  
1. A Q-learning empowered highly dynamic and latency-aware routing algorithm for ad-hoc networks (QEHLR) is proposed, which combines Q-learning with end-to-end delay improvement to address the issue of ineffective packet routing in highly dynamic FANETs using traditional routing algorithms. The method of Q-learning is used to learn the link status in the network, and effective routing is selected through Q-value to avoid connection loss. The remaining time of the link, or the path lifespan, is included in the routing criteria to maintain the routing table. QEHLR can delete estimated failed links according to the network status, reducing packet loss caused by failed routing selection. Routing experiments designed in this paper show a significant improvement in the packet transmission rate of the improved algorithm.
2. A routing method based on topology change degree improvement is proposed to address the problem, where routing protocols cannot adapt to various mobile model task scenarios in FANET with high dynamic topology due to the diversification of task scenarios and the variability of tasks. The calculation factor for network topology change degree is introduced on the basis of the QEHLR protocol. The experimental results show that the improved routing algorithm can achieve a higher packet transmission rate and a lower delay.
# MIDOM--A DICOM-Based Medical Image Communication System
###### Abstract  
Despite the existing medical infrastructure being limited in terms of interoperability, the amount of medical multimedia transferred over the network and shared through various channels increases rapidly. In search of consultations with colleagues, medical professionals with the consent of their patients, usually exchange medical multimedia, mainly in the form of images, by using standard instant messaging services which utilize lossy compression algorithms. That consultation paradigm can easily lead to losses in image representation that can be misinterpreted and lead to the wrong diagnosis. This paper presents MIDOM--**Med**ical **I**maging and **D**iagnostics **o**n the **M**ove, a DICOM-based medical image communication system enhanced with a couple of variants of our previously developed custom lossless **C**lassification and **B**lending **P**redictor **C**oder (CBPC) compression method. The system generally exploits the idea that end devices used by the general population and medical professionals alike are satisfactorily performant and energy-efficient, up to a point to support custom and complex compression methods successfully. The system has been implemented and appropriately integrated with Orthanc, a lightweight DICOM server, and a medical images storing PACS server. We benchmarked the system thoroughly with five real-world anonymized medical image sets in terms of compression ratios and latency reduction, aiming to simulate scenarios in which the availability of the medical services might be hardly reachable or in other ways limited. The results clearly show that our system enhanced with the compression methods in the question pays off in nearly every testing scenario by lowering the network latency to at least 60% of the latency required to send raw and uncompressed image sets and 25% in the best-case, while maintaining the perfect reconstruction of medical images and, thus, providing a more suitable environment for healthcare applications.  
DICOM; medical imaging; lossless compression; network latency reduction
## 2 Material and Methods
### Time-Domain Methods for V-I Characteristics of EAF Arcs
#### 2.3.2 Hyperbolic Model (Model 2)  
In the hyperbolic model, the correlation between the current and voltage of the electrical arc is articulated in Equation (2) [9; 15]:  
\[V(i)=\bigg{[}V_{at}+\frac{C_{i,d}}{D_{i,d}+\mid i\mid}\bigg{]}\cdot sign(i) \tag{2}\]  
In Equation (2), \(V\) is the arc voltage, \(V_{at}\) is the threshold voltage dependent on the arc length, \(i\) is the arc current, and \(C_{i,d}\) and \(D_{i,d}\) are constants that define the shape of the voltage-current curve. In this model, the arc voltage decreases as the current increases, but instead of following an exponential decay, the decline is governed by a hyperbolic function. The denominator ensures that voltage remains finite and does not drop too sharply for small currents.
## 4 The Proposed QoS-Aware Routing
### Delay Control
The estimation methods for queuing and transmission delays will be discussed in Section 4.5. In the case in which \(|NH_{d}|\) returns a NULL value, _i.e._, there is no neighboring router node satisfying the required velocity level, the packet is dropped immediately in order to prevent the network nodes from expending unnecessary energy. If \(|NH_{d}|\) returns only one node, the packet is sent to the outgoing queue putting that node as the next-hop. On the other hand, if \(|NH_{d}|\) includes several nodes, either the EAGF or the reliability control algorithm is called depending on the packet type in order to select the most appropriate router(s) from this set.
## 3 Proposed GIS-Based Approach
### PSs Optimal Siting
#### 3.2.2 Weighted Voronoi Diagram Approach for PSs Location  
As for the PSO, even the weighted Voronoi diagram approach presented in Section 2 has been shaped for the PS sitting problem. Instead of providing a weight to each PS, a weight is given to each centroid of the squares proportional to the power demand reported in Figure 9. Therefore, turning over the idea behind Equation (5), given \(p\) centroids \(p_{1}\), \(p_{2}\),..., \(p_{n}\) and a weight \(wp_{i}\) for each centroid \(p_{i}\), the weighted distance from a centroid \(p\) to a PS \(PS_{i}\) is defined as in (6):  
\[d_{w}(p_{i},PS_{i})=|p_{i}-PS_{i}|\ w_{pi} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Particles \(i\)** & \(x_{i}^{New\ PS1}(t)\) & \(y_{i}^{New\ PS1}(t)\) & \(x_{i}^{New\ PS1}(t)\) & \(y_{i}^{New\ PS1}(t)\) & \(y_{i}^{New\ PS2}(t)\) & \(y_{i}^{New\ PS1}(t)\) & \(x_{i}^{New\ PS1}(t)\) & \(x_{i}^{New\ PS2}(t)\) & \(x_{i}^{New\ PS2}(t)\) & \(y_{i}^{New\ PS2}(t)\) & \(y_{i}^{New\ PS2}(t)\) & \(y_{i}^{New\ PS2}(t)\) & \(y_{i}^{New\ PS2}(t)\) \\ \hline
1 & 1,511,310 & 5,030,533 & 1,504,760 & 5,033,038 & 1,512,544 & 5,033,515 &... &... & 1,505,567 & 5,038,227 \\ \hline
2 & 1,509,044 & 5,033,231 & 1,519,752 & 5,038,845 & & & & & & & \\ \hline... &... &... &... &... &... &... & & & & & & \\ \hline n & 1,514,184 & 5,027,623 & 1,512,018 & 5,025,146 & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Matrix of new and current PSs location.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Particles \(i\)** & \(x_{i}^{New\ PS1}(t)\) & \(y_{i}^{New\ PS1}(t)\) & \(x_{i}^{New\ PS1}(t)\) & \(y_{i}^{New\ PS1}(t)\) & \(y_{i}^{New\ PS1}(t)\) & \(y_{i}^{New\ PS1}(t)\) & \(y_{i}^{New\ PS1}(t)\) & \(x_{i}^{New\ PS1}(t)\) & \(x_{i}^{New\ PS2}(t)\) & \(x_{The more the weight \(w_{pi}\), i.e., the power demand, the more the centroid \(p_{i}\) appear to be far away from the PS \(PS_{i}\). For the sake of clarity, Figure 15 shows several centroids and PSs. On the one hand, assuming the Euclidean distance as a metric, to minimize the distance between the new PS and the centroids 1 and 2, the location of the PS must be the one depicted in the left picture, characterized, for instance, by \(d(p_{1},PS)=d(p_{2},PS)=100\) m.  
On the other hand, if the weighted distance in Equation (6) is considered, assuming \(w_{p1}=100\) kW and \(w_{p2}=200\) kW, the location of the new PS must be the one depicted in the right picture, much closer to the centroid 2. In this latter case, assuming \(d(p_{1},PS)=200\) m and \(d(p_{2},PS)=100\) m, the weighted distance results in being \(d_{w}(p_{1},PS)=100\times 200=300\) kW\(\cdot\)m and \(d_{w}(p_{2},PS)=200\times 100=300\) kW\(\cdot\)m, perfectly balanced with the advantage of having the PS closer to the square with higher demand.
## 7 Discussion  
### Algorithm Selection  
Using a DM for comparing algorithms allows incorporating the end-user requirements/preferences into the comparison. This adds another dimension to algorithm comparisons, which are usually based solely on the performance on some benchmark, and aids the decision process for selecting the \"right\" algorithm. By using a tool from QFD, the algorithm selection is based on a _systematic framework_. The drawback of the method is that it inserts a set of subjective values into the comparison.  
Two steps are required for building a DM: (1) defining the criteria for the two dimensions; and (2) finding the relationships between the criteria. While the examples in this work and some of the experts involved come from the field of engineering design, we expect that most of the criteria are of interest for optimization problems in other fields. Adapting the DM to specific needs is easily possible and is recommended since it should capture the end-user view.  
Defining the relations between the criteria is a difficult and time-consuming task. In our case, 36 correlations and 81 relationships were required, and not all of them were obvious. We applied \"classical\" QFD, i.e., subjective approach, of finding a consensus from discussions of experts. Hence, DMs built by others (engineers, algorithm designers, etc.) can include different relations, but we don't expect differences in the form of going from no relation to a strong relationship. Finding the correlations could be done differently, for example by involving fuzzy logic [53] or machine learning [11]. Both of these approaches are outside the scope of this work as is the verification of the defined relations.  
An advantage of the proposed DM is that the ranking can be done before the results are available. This allows to quickly obtain a systematic comparison of the algorithms based on prior knowledge about the algorithms (known properties, previous experiences, etc.). The DM can also be combined with standard benchmarks by deriving some of the algorithm ranks from existing benchmark results. The results obtained by the DM aid the algorithm selection by providing results for each criterion and in an aggregate form.
## 5 Conclusions  
In this paper, several channel-model-free methodologies and heuristics are explored for optimal PA and then compared to the unparameterized solution for an all-FSO, multi-aperture satellite downlink between a GEO satellite and an OGS. A DRL-aided power allocation methodology is proposed for optical satellite systems disregarding any channel statistics knowledge requirements. Therefore, the PA problem is formulated as a constrained learning optimization problem with peak and total expected power inequality constraints. A power allocation algorithm was developed, aided by a DNN which is fed CSI observations and trained in a parameterized on-policy manner through a stochastic policy gradient approach. The proposed method does not require the channels' transition models or fading distributions. To validate and test the proposed allocation scheme, experimental measurements from the ARTEMIS optical satellite campaign were utilized. The proposed scheme performs 18% better than Equal Power, 16% better than DQN, and 53% better than Random Power, and it approximates the supervised method, with only 2% less accuracy, for \(P_{avg}=2.0\) W. The predicted average channel powers match very well with the supervised ones, only differing by 0.1-0.2 W, while the allocated channel powers of the other schemes differ significantly more, about 0.1-0.5 W. Two hidden layers, 256 neurons, and two-parameter distributions are the optimal choices for the policy DNN. Finally, the proposed PA strategy is deemed appropriate for difficult optical conditions since it learns explicitly from observation.  
Conceptualization, T.T.K., N.K.L. and A.D.P.; methodology, T.T.K. and N.K.L.; software, T.T.K.; validation, T.T.K.; formal analysis, T.T.K.; investigation, T.T.K.; resources, N.K.L. and A.D.P.; writing--original draft preparation, T.T.K. and N.K.L.; writing--review and editing, A.D.P.; visualization, T.T.K.; supervision, A.D.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Figure 11: Loss function across the training and validation sets for two hyperparameter \(\lambda\) values related to the total power constraint function. \(P_{\text{s}}=1.0\) W, \(P_{avg}=3.0\) W. The relaxed \(\lambda=0.2\) allows the power constraint to be violated, resulting in higher loss values, while the stricter \(\lambda=1.0\) yields lower loss values. No overfitting is observed.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The work presented in this paper was carried out under the ONSET-CCN ESA project.  
**Conflicts of Interest:** The authors declare no conflicts of interest.