esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 1 Introduction
Thus, it is paramount important to give a thorough review on this promising rainfall measurement technology; in particular, to summarize the literature of the past 2 years, in which machine learning has been developing rapidly and has been applied to this realm widely. The main contributions of this paper are as follows:  
* The paper illustrates the main steps in CML-based rainfall measurement, summarizing the state-of-the-art solutions in each step. * The paper analyzes uncertainties and errors involved in CML-based rainfall measurement as well as their impacts on the measurement accuracy. * The paper explores the existing machine learning methods to facilitate CML-based rainfall measurement. To the best of our knowledge, this paper provides the first comprehensive review on machine learning for CML-based rainfall measurement. * The paper summarizes the open-access datasets and codes related to CML-based rainfall measurement, and discusses the current challenges and future directions. The rest of the paper is organized as follows. Section 2 describes the principle and theory of CML-based rainfall measurement. Section 3 outlines the revolution of CML-based rainfall measurement. The steps from CML received signal level (RSL) to rainfall map are presented in Section 4. The machine learning solutions to facilitate measurement are discussed in Section 5. Monitoring phenomena other than rain and the hydrological applications are explored in Sections 6 and 7, respectively. The existing challenges and future directions are presented in Section 8, and finally, Section 9 draws a conclusion.
## 5 Connected Vehicles
### Benefits and Business Opportunities
#### 5.2.4 Predictive Maintenance  
For private vehicles, the after-sales service department will notify the vehicle owners of predictive maintenance based on information such as usage time. Once positive feedback is received from the owner, information about the health of the vehicle components and diagnostics will be uploaded to the after-sales service department for a customized maintenance strategy, allowing the owner to choose the service and order the spare parts to be replaced [95].
# Design and Depth Control of a Buoyancy-Driven Profiling Float
## 4 Results and Analysis
### Field Experiments  
In order to verify the feasibility of the segment PD control, a depth control test t different depths was carried out in three different areas of Qiandao Lake, Zhejiang Province, China, in July. The depth holding time was set to 1 min at the depths of 5 m and 10 m, and 1.5 min at other depths. After the depth setting was completed, the linear actuator was pushed to the outermost side to make the float move to the surface of the lake. In the speed control process of segment PD control, the parameters were set as \(K_{p}=1\), \(K_{d}=1.7\), and in the depth control process, they were set as \(K_{p}=1\), \(K_{d}=13\).  
The final depth retention results at different depths are shown in Figure 7. The process before the hollow red dot is the speed control stage, and the subsequent process is the depth holding stage with the depth error as the PD input. It can be seen that the actual depth retention effect was consistent with the simulation results. We found that 80% of the final depth retention error was within 0.2 m, and the maximum error did not exceed 0.3 m. One set of parameters was suitable for the depth retention at each depth, which reflects the good control of the segmented PD control method for a low-damping and large time-delay system. In addition, the magnified image in the upper left corner shows a simulated throwing of a float. After being thrown into the water, the float first reached a depth of 2 m due to inertial depth, then floated for a short time, and finally dived and maintained a depth of about 10 m. It was found that the throwing layout did not affect the final depth control effect, even if the target depth was very shallow. Figure 8 shows the tracking speed of the float during the retention process at a depth of 60 m. The blue line is the target speed to be tracked. The faint yellow line is the actual speed data that were not processed. The green line is the actual speed curve of the float after passing through the FIR low-pass filter. The red area is the range where the point of the actual float speed fell. It can be seen that the float had a good speed tracking effect and met the requirements.  
Figure 7: Using the segment PD control method to maintain the float at different depths in Qiandao LakeIn addition, by comparing the temperature data of the ascending and descending portions, it was found that the temperature during diving at the same water depth was generally higher than that during ascending. The reason may be as follows: the speed was always kept within 0.2 m/s when diving, the linear actuator was directly pushed to the outermost side when floating, and the floating speed of the float was very high, which affected the temperature sensor, with a low sampling rate. This shows that it is necessary to control the speed during the ascent and the descent of the float.
# Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland
## 2 Materials and Methods
### Model Description
#### 2.1.5 Society  
As we are simulating the spread of COVID-19 in the Republic of Ireland, we aim to match the characteristics of our agent population to the characteristics of the Irish population at the county level. Thus every county in the model has the correct portion of agents of the following characteristics: age, sex and economic status (student, working, retired, unemployed, etc). The counties also have the correct portion of households by type (single, couple, couple with children, etc.), and by number and ages of children (under 15, over 15 and both under and over 15). To make the contact networks in the model more realistic, we allow for agents to have an extended family network. This network connects two households one with agents over 65 and one with agents under 65. The reasons for creating these extended networks are two-fold. The first is to capture interactions and thus transmission between children and grandparents who act as carers. The second is to capture interactions between families during holiday periods, such as Christmas, when inter-generational mixing typically increases. Further discussion of the creation of these networks can be found in the model ODD [26].  
From the 2016 census, the last Irish census collected at the time of model creation, there were 4,757,976 people in Ireland. An agent-based model with that many agents requires a large amount of computing time and power to run such that the results may take days or weeks. Thus we introduce a scaling factor into the model that equates 1 agent to 100 people. This greatly reduces the number of agents needed to simulate the population, from 4,757,976 to 47,580, which allows us to model the social and economic structure of Ireland without decreasing the level of detail in the model or taking an overly large amount of time to run the model.  
Although we still keep the same population structure with the scaling factor, we are changing the total number of agents and need to make sure that the reduction in agents does not impact the model results. Section 2.2 discusses the scaling factor in more detail and the tests run to validate the changes introduced when scaling the model.
# Eye State Identification Based on Discrete Wavelet Transforms
## 2 Theoretical Background  
Brain signals captured by EEG devices need to be analyzed and processed for their posterior classification and subsequent translation into a specific mental state. This task is developed by a signal processing unit, whose two main tasks are feature extraction and feature classification. The feature extraction process aims to find the most relevant values, called features, that best describe the original raw EEG data [32]. These features are sent to a classification algorithm, which is responsible for estimating the mental state of the user. In this section, we will present DWT as the feature extraction technique and LDA as the classification algorithm employed throughout this work.
## 3 Performance Improvement of Multiple Receiver WPT
### Cross-Coupling Rejection
Optimal value of load reactance be subject to the mutual inductance between the coils, load resistance and coil resistances and the equivalent circuit of the selective MRWPT system with compensation is shown in Figure 6. In practical implementations the maximum possible value of the mutual inductance between the coils are calculated and using discrete capacitor tuning the optimal load reactance can be achieved. The simulated response of the selective MRWPT system with cross coupling compensation is illustrated in Figure 7. Figure 7a display the \(dB\) value of \(S\) parameter when the Tx is operated at 1 MHz, it's observed from the graph that the unselected link has less cross coupling effect in the order of \(-50\) dB. Similarly, when the Tx is operated at 1.5 MHz, 2 MHz and 3 MHz resonant frequency the \(S\) parameter responses are shown in Figure 7b-d. In all the cases the unselected load \(S\) parameter value is less than \(-50\) dB, which indicates the much reduction in the cross coupling effect. The calculated capacitance values and circuit parameters are shown in Table 2. However, the effect of frequency splitting needs to be considered in the design whenever the coils are used in dynamic environment. All the simulations are performed at the constant coupling coefficient value of 0.1 between the Tx and Rx coil. \[I_{i}=-\sum_{k=1,\ i\
eq k}^{4}\frac{j\omega_{i}{M_{T1}}{M_{ik}}{I_{T}}}{R_{i}+R_{ k}+R_{Li}};\ i=R_{k},\ L_{k} \tag{10}\]0 _Cont._
## 4 Results
### Assessment of Discharge Simulations Using Multiple SPPs
#### 4.2.1 Scenario I: Discharge Simulations with Rain-Gauge-Calibrated Parameters
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Flood Events**} & \multirow{2}{*}{**Precipitation Inputs**} & \multicolumn{4}{c}{**Scenario I**} & \multicolumn{2}{c}{**Scenario II**} \\ \cline{3-8}  & & \(\mathbf{Q_{p}^{o}}\) **(m/s)** & \(\mathbf{Q_{p}^{o}}\) **(m/s)** & **BIAS\({}_{p}\) **(\%)** & **FTE (b)** & \(\mathbf{Q_{p}^{o}}\) **(m/s)** & **BIAS\({}_{p}\) **(\%)** & **FTE (b)** \\ \hline
20140523 & Rain Gauge & 18,000 & 12,586 & \(-\)30.1 & \(-\)48 & \(-\) & \(-\) & \(-\) \\  & 3B42 RT & – & 24,471 & 36.0 & \(-\)15 & 21,852 & 21.4 & \(-\)9 \\  & 3B42V7 & – & 13,284 & \(-\)26.2 & \(-\)12 & 18,198 & 1.1 & \(-\)6 \\  & IMERG-F & – & 12,424 & \(-\)31.0 & \(-\)9 & 16,855 & \(-\)9.0 & \(-\)9 \\ \hline
20150803 & Rain Gauge & 23,500 & 17,818 & \(-\)24.2 & 66 & \(-\) & \(-\) & \(-\) \\  & 3B42 RT & – & 19,478 & \(-\)17.1 & \(-\)9 & 17,170 & \(-\)26.9 & \(-\)12 \\  & 3B42V7 & – & 11,201 & \(-\)52.3 & \(-\)24 & 15,208 & \(-\)35.3 & \(-\)27 \\  & IMERG-F & – & 11,073 & \(-\)52.9 & 15 & 14,576 & \(-\)38.0 & \(-\)36 \\ \hline
20150908 & Rain Gauge & 21,550 & 24,432 & 13.4 & 24 & \(-\) & \(-\) & \(-\) \\  & 3B42 RT & – & 24,257 & 12.6 & \(-\)30 & 21,672 & 0.6 & \(-\)18 \\  & 3B42V7 & – & 15,711 & \(-\)27.1 & \(-\)21 & 19,677 & \(-\)8.7 & 15 \\  & IMERG-F & – & 15,347 & \(-\)28.8 & 24 & 20,181 & \(-\)6.4 & 18 \\ \hline
20160803 & Rain Gauge & 20,660 & 20,162 & \(-\)2.4 & \(-\)36 & \(-\) & \(-\) & \(-\) \\  & 3B42 RT & – & 16,499 & \(-\)20.1 & \(-\)75 & 15,999 & \(-\)22.6 & \(-\)63 \\  & 3B42V7 & – & 11,229 & \(-\)45.7 & \(-\)72 & 15,845 & \(-\)23.3 & \(-\)69 \\  & IMERG-F & – & 12,387 & \(-\)40.0 & \(-\)66 & 17,096 & \(-\)17.2 & \(-\)63 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of four major flood event simulations at the Monywa station using the rain-gauge-based precipitation data, 3B42 RT, 3B42V7, and IMERG-F SPPs under two simulation scenarios. Figure 10: Comparison of the observed and simulated discharge–duration curves using the rain-gauge-based precipitation data, 3B42 RT, 3B42V7, and IMERG-F SPPs under two simulation scenarios: (**a**) Scenario I and (**b**) Scenario II. satisfactory, which is comparable to that of the rain-gauge-based simulation. The 3B42V7- and IMERG-F-based simulations achieve acceptable and similar hydrological performance. The overall hydrological performance of 3B42 RT, 3B42V7, and IMERG-F SPPs at the four upstream hydrological stations is similar to that at the Monywa station (Figures 55a, 56a, S7a, and S8a).
## 3 Methodology
### Data Preprocessing
#### 3.2.4 Train-Test Dataset Split  
The dataset was split into training and testing sets, allowing the model to learn patterns from the training data and ensuring unbiased evaluation of the testing data for generalization in both binary and multi-class scenarios. The sample distribution of the CICIDS2017 dataset for binary classification is presented in Table 6, showcasing the allocation of samples between the training and testing sets. From the normal class, a total of 79,112 samples were designated for training, with 13,941 samples reserved for testing. The attack class consisted of 62,497 training samples and 11,049 test samples. This distribution ensured that both classes were adequately represented in both the training and testing phases, contributing to a balanced and effective evaluation of the model's performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class Type** & **Number of Samples Before LOF** & **Number of Samples After LOF** \\ \hline PortScan & 12,000 & 11,892 \\ \hline DDoS & 14,000 & 13,544 \\ \hline DoS Hulk & 39,000 & 36,640 \\ \hline DoS GoldenEye & 4000 & 3554 \\ \hline FTP-Patator & 2000 & 1964 \\ \hline SSH-Patator & 900 & 868 \\ \hline DoS Slowloris & 1700 & 1540 \\ \hline DoS Slowhttptest & 1200 & 1099 \\ \hline Bot & 700 & 638 \\ \hline Web Attack—Brute Force & 450 & 436 \\ \hline Web Attack—XSS & 350 & 340 \\ \hline Infiltration & 36 & 15 \\ \hline Web Attack—Sql Injection & 21 & 9 \\ \hline Heartbleed & 11 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sample distribution of CICIDS2017 dataset for multi-class classification using LOF.  
The CICIDS2017 dataset for multi-class classification consisted of multiple attack classes, including PortScan, DDoS, DoS Hulk, DoS GoldenEye, FTP-Patator, SSH-Patator, DoS Slowloris, DoS Slowhttptest, Bot, Web Attack--Brute Force, Web Attack--XSS, Infiltration, Web Attack--Sql Injection, and Heartbleed. The largest class, DoS Hulk, included 31,172 training samples and 5468 test samples, followed by DDoS with 11,519 training samples and 2025 test samples. Other significant classes included DoS GoldenEye, with 3018 training samples and 536 test samples, and FTP-Patator, which contained 1658 training samples and 306 test samples. Smaller classes such as Infiltration and Heartbleed were represented by 13 and 9 training samples, respectively, with even fewer test samples, 2 and 1, respectively. This distribution highlights the varying sample sizes across different classes, with some attack types being more prevalent than others, while rarer attack types like Infiltration and Heartbleed are less frequent, making them critical for testing the model's ability to detect less common attacks. This variation in class size ensured a comprehensive evaluation of the model's performance across both major and minor attack types, as shown in Table 7.
## 5 Discussion  
In this paper, we proposed a method of increasing secret key distribution rates in the existing QKD protocols by encrypting classical communication or delaying it in the case of restrictions imposed on the eavesdropper's quantum memory coherence time. Notably, it is universal (its applicability does depend on the specific protocol; despite the fact that in this work we consider only prepare-and-measure protocols, the method can be applied to entanglement-based QKD as well) and can be implemented just by modifying existing post-processing routines without introducing any changes to the hardware part of QKD realization.  
Under the assumption of limited coherence time of the eavesdropper's quantum memory, the method allows us to show that collective attacks become no more effective than individual ones. If for a given QKD protocol coherent eavesdropping strategies have no advantage over collective, then individual attacks are the only ones to consider, and the key rate formula can be modified to operate with additive quantum accessible information.  
Without any assumptions concerning the technical abilities of a potential eavesdropper, the key rate formula can be modified as well. However, in such a case, the new bound for superadditive accessible information is still, to our knowledge, an open question. Thus, we emphasize that the paper does not claim to provide a full security proof for QKD protocols in case of the method being implemented.  
Note that the method inherits the disadvantages of quantum data locking: the disclosure of one bit of classical information that is meant to be secret (in this case, it is data related to error correction and privacy amplification procedures) may lead to an eavesdropper obtaining more than one bit of additional information. This leads to increased demands on the safekeeping of the classical data. Thus, the method does not provide composable security [31] against an eavesdropper who has access to unbounded quantum resources. Nevertheless, the method provides everlasting security [35] in a narrow sense: if an eavesdropper does not have access to quantum memory with storage time being sufficiently long at the moment of performing an attack (if the legitimate users have strong arguments in favor of this assumption), then no future advances in quantum memory can make an already distributed key less secure.  
Conceptualization, V.A.P. and D.A.K.; Investigation, V.A.P. and D.A.K.; Writing--original draft, V.A.P.; Writing--review & editing, V.A.P. and D.A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** D.A.K. is grateful to [PERSON], [PERSON], and [PERSON] for useful discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
## 2 Problem Definition  
In this work, we assume a pre-deployed WSN in a building that has a connected finite set of sensor nodes and one sink, which are static. While we assume a network with one sink, one can implement a supersink that has connection to many sinks for a multiple sink scenario. The values for parameters used in our tool are based on Tmote Sky hardware [7] and to consider the heterogeneity of device specifications, nodes can have different power levels, sensing ranges and initial energy. For other types of hardware, one can change the values of the parameters accordingly.  
Inside a building, we assume obstacles that can limit communication and sensing capabilities are walls and doors. However, for simplicity, we currently do not consider the types of construction materials used. As manual wall classification is labour intensive and requires architectural information that may not be readily available to application developers or network managers, one could implement an automatic wall classification based on link quality measurement, such as in [8].  
This work assumes that sensor nodes cannot sense through walls and doors. Furthermore, users may require different coverage degrees for special rooms, such as a server room and an electrical room. To do this, users can select a room and specify the coverage requirement. The total coverage percentage is the ratio of the total area satisfying the coverage requirements to the total interior area of the building.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 5 Cloud Services
### Analysis Services  
Analysis services provide deep learning models for the analysis of data gathered during the inspection mission as well as methods for environment reconstruction. Such models perform infrastructure fault recognition and determination to raise a mission's level of autonomy. Recognizing faults during the mission significantly reduces the time until a fault is detected if compared with observing captured images one by one. Reconstruction of the environment offloads heavy computations from the drone's onboard computer and provides context to the local path planner.
# Computing RF Tree Distance over Succinct Representations
## 6 Conclusions  
We provided implementations of the Robinson-Foulds distance over trees represented succinctly, as well as its extension to fully labelled and weighted trees. These kinds of implementations are becoming useful as pathogen databases increase in size to hundreds of thousands of strains, as is the case of EnteroBase [22]. Phylogenetic studies of these datasets imply then the comparison of very large phylogenetic trees.  
Our implementations run theoretically in \(O(n\log n)\) time and require \(O(n)\) space. Our experimental results show that in practice, our implementations run in almost linear time, and that they require much less space than a careful implementation of the Day algorithm. The use of succinct data structures introduces a slowdown in the running time, but that is an expected trade-off; we gain in our ability to process much larger trees using space efficiently.  
Each tree with \(n\) nodes can be represented in \(2n+o(n)+(1+\varepsilon)n\log n\) bits, storing its balanced parentheses representation and its corresponding permutation through bit vectors. Our implementations rely on and extend SDSL [30], making use of provided bit vector representations and primitive operations. In our implementations, we did not explore, however, the compact representation of the permutation associated to each tree. Hence, the space used by our implementations can be further improved, in particular if we rely on that compact representation to store phylogenetic trees on secondary storage. We leave this as future work, noting that techniques to represent compressible permutations may be exploited in this setting, leading to a more compact representation [23]; phylogenetic trees tend to be locally conserved and, hence, that favors the occurrence of fewer and larger runs in tree permutations.  
Conceptualization, A.P.F. and C.V.; methodology, A.P.F. and C.V.; software, A.P.B.; validation, A.P.B. and C.V.; formal analysis, A.P.B. and A.P.F.; writing--original draft preparation, C.V. and A.P.B.; writing--review and editing, A.P.F. and C.V. All authors have read and agreed to the published version of the manuscript.  
The work reported in this article received funding from Fundacao para a Ciencia e a Tecologia (FCT) with references UIDB/50021/2020 (DOI:10.54499/UIDB/50021/2020) and LA/P/0078/2020, and from European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 951970 (OLISSIPO project). It was also supported through Instituto Politecnico de Lisboa with project IPL/IDI&CA2023/PhyloLearn_ISEL.  
Data are contained within the article.  
The authors declare no conflict of interest.