esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations and Discussions
### Simulation of the Reputation Computation Model  
In this subsection, the reputation computation model denoted by Equations (11) and (12) is simulated. The influence of weight \(\lambda\) on the model is illustrated. Both the processes of improvement and decrement of reputation evaluations are simulated, which contain three types of clients. Figure 5 shows the processes of improvement of the reputation computation model denoted by Equation (11). In Equation (11), \(N\) is as 50. In other words, there are a total of 50 direct truth evaluations to combine the values of reputation. Parameter \(\alpha^{(i)}\) is the normal distribution as \(N(0.04,0.01)\), \(i=1,2,...,50\), and service clients are the cautious type, such as \(T_{c}^{(i)}=M=0.5\), and \(t_{0}=200\). Figure 6 shows the processes of decrement of the reputation computation model denoted by Equation (12). There are also total of 50 direct truths to combine the values of reputation. Parameter \(\alpha^{(i)}\) are also normal distribution as \(N(0.04,0.01)\), \(i=1,2,...,50\), and service clients are the risk taking type such as \(T_{c}^{(i)}=H=0.8\).  
Figure 7 shows the processes of improvement of the reputation computation model by Equation (11), which contain three types of service clients. There are total of 150 direct truth evaluations to combine  
Figure 4: The processes of improvement and decrement of trust with three types of clients. **(a)** The processes of improvement of direct trust; **(b)** The processes of decrement of direct trust.  
Figure 8 shows the processes of decrement of the reputation function denoted by Equation (12) which contain three types of service clients. Parameters and notations are the same as before. In Figures 7 and 8, the line of cross tar denotes the reputation values that contain 150 service clients of three different types. The blue line denotes the reputation values containing 50 risk-taking clients. The red line denotes the reputation values containing 50 cautious clients. The green line denotes the reputation values containing 50 conservative clients.  
Form Figures 5 and 6, we see that the values of weight \(\lambda\) of Equations (11) and (12) affect the rates of the processes of improvement and decrement of the reputation evaluation when values of parameter \(\alpha\) are given. Further, the values of reputation evaluation will approach the trust values of a certain type of client in the processes of the improvement of reputation. The values of the reputation evaluation will  
Figure 11 shows the processes of the decrement of total trust denoted by Equation (15), which contains direct trust and reputation. Parameters are the same as in Figure 9. Figure 12 shows the processes of change of total trust which contains direct trust and reputation evaluation. Parameters are the same as in Figure 9, however, direct trust is in the processes of improvement, while reputation is falling down.  
Figure 11: The processes of the decrement of total trust values, direct trust values and reputation.  
Figure 9: The processes of improvement of total trust values, direct trust values and reputation.
## 3 Experimental Results
### Experimental Setup  
Before presenting the results, we describe the general setup that is common to all three proposed architectures. After the video coding feature vectors are generated and the temporal subsampling algorithm is applied, the feature vectors are split in a 20-80% fashion for testing and training, respectively. We apply cross-validation with five folds (K = 5) in which, in every fold, the new testing set shifts by 20% and the older testing set is added back to the training set. The results are then averaged over five folds. The training setup is illustrated in Figure 7.  
We use HEVC features derived from the custom re-encoder mentioned in Section III. We test our setups with and without a dimensionality reduction of the feature space. In addition, the proposed temporal subsampling of video frames methods using HEVC features, PCA projections, and cosine similarity are all tested with the following three learning architectures: 1D-CNNs, LSTM networks and random forests. For each of the four datasets, the top-performing model from each learning architecture is shortlisted and compared against benchmark methods in the literature. First, the results are reported for every dataset, and the best models are then compared with the literature, followed by a thorough discussion of the results.  
The metrics used for comparison are precision (P), recall I and the F-score. The experiments were conducted on a PC with 9 th gen Intel i9, 32 GB of RAM, and an NVIDIA RTX 2070 GPU.
# The Effect of Exposure to an Electromagnetic Field on Entomopathogenic Fungi
## 5 Conclusions  
1. The reaction of _B. bussiana_, _C. fumosroscea_, and _M. anisopliae_ to EMF exposure depended on the investigated fungus species and the applied frequency.
2. Exposure to a 900 MHz electromagnetic field stimulated the linear growth of the mycelium of _B. bussiana_ and _C. fumosroscea_ and the pathogenicity of _C. fumosroscea_.
3. Exposure to a 900 MHz electromagnetic field stimulated the sporulation of the entomophogenic fungus _M. anisopliae_.
4. A 1800 MHz electromagnetic field inhibited the sporulation and spore germination of _B. bussiana_.
5. The study suggests the need for further studies on the effect of a 900 MHz electromagnetic field on the growth and pathogenicity of the entomophogenic fungi _B. bussiana_, _C. fumosroscea_, and _M. anisopliae_.  
Conceptualization, D.R.R., K.F. and K.P.; methodology D.R.R., K.F. and K.P.; software, K.B.; validation, K.B. and M.L.; formal analysis, D.R.R.; investigation, K.F.; resources, K.B.; data curation, M.L.; writing--original draft preparation, D.R.R.; writing--review and editing, D.R.R., K.F. and K.P.; visualization, K.B.; supervision, K.F.; project administration, D.R.R.; funding acquisition, D.R.R., K.F., K.B. and K.P. All authors have read and agreed to the published version of the manuscript.  
The research was founded by the University of Agriculture in Krakow, grant number 010014-11.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
Hence, for the sake of energy conservation, it is desirable to reduce the quantity of sampled data when the water quality remains relatively stable. Meanwhile, if certain parameters in the water are changing abruptly, the sampling frequency should be increased in order to acquire sufficient information about the condition of water. It should be noted that not only will the sampling frequency have a significant impact on the energy usage, the processing and transmitting of the sampled data will also consume extra energy. With this in mind, the proposed data-driven adaptive sampling algorithm (DDASA) has been developed, which dynamically changes the sampling frequency based on the nature of the sampled signal. This algorithm should be universally applicable with respect to conserving energy and prolonging the lifetime of the WSN. This approach is not limited to water quality monitoring but is applicable in other types of monitoring applications using WSN, such as indoor environmental monitoring [6], structural health monitoring [7], climate conditions monitoring [8], and healthcare monitoring [9]. The developed algorithm is implemented using MATLAB R2015a in the PC with a 3.20 GHz Intel Core i5-4460 CPU, 8 GB RAM and compared with a traditional adaptive sampling algorithm (ASA) [10]. The main contributions of this paper are as follows:  
* A DDASA for energy conservation in a sensor network for automated water quality monitoring is presented. * The universal applicability of the algorithm is validated with respect to various parameters with distinct characteristics, making it applicable in other types of practical monitoring situations. * The proposed method is evaluated with respect to two key water-related parameters. * The performance of DDASA is compared with the scheme of sampling at a fixed frequency in terms of data accuracy and energy conservation. * The performance of DDASA is compared to a traditional ASA. The rest of the paper is organized as follows: Section 2 outlines existing schemes for power management and some latest research that is directly related to this paper. In Section 3, the detailed mechanism of the proposed DDASA is presented, including how an abrupt environmental change is detected and how the sampling frequency is changed correspondingly. Section 4 demonstrates the experimental setup, and Section 5 provides some simulation results and a performance evaluation. Section 6 validates the proposed model. Finally, Section 7 concludes the paper.
## 4 System Model and Description of the Proposed Protocols
### Transmission and Receiving Energy Calculations in BF-SPR-Three
#### 4.2.4 Description of Algorithm 2  
In this subsection, the data forwarding algorithm for the proposed protocol (BF-SPR-Three) is discussed. The detail is given below.  
Figure 12: Layer Division.  
```
Input:(nodes, initial energy, sinks, PS, DR, \(T_{p}\), \(R_{p}\), area)
```  
**Algorithm 2** Algorithm of BF-SPR-Three for data forwarding  
Firstly, the input parameters for the routing protocols are initialized. Then, the sensor, sink and relay nodes are deployed in the network with different surface gateways. Afterwards, a beacon message is broadcasted, which helps the neighbor nodes to update their routing table. Moreover, the protocol checks the 3-hop neighbor's availability to avoid the void hole problem. In the end, transmission begins by selecting the next forwarder node given by the 'BF' algorithm (by providing the fastest and shortest path, to route the DP from the source node to the destined sink). Moreover, if the next forwarder is the simple sensor node, it will unicast the DP; otherwise, it will multicast the DP. In the end, if this forwarder node lies within the range of respective sink, then the data reaches the destined sink (using a binary tree generation approach); otherwise, the next forwarder node is selected (provided by the 'BF' algorithm).
## 2 Materials and Methods
### Calculation of Daily and Monthly GPP, R and Ne  
The CO\({}_{2}\) flux value measured during the day was the net crown CO\({}_{2}\) exchange rate (_Ne_), and at night was the night-time crown respiration (\(R_{n}\)). The relationship between crown respiration \(R_{n}\) and air temperature (\(T_{air}\)) was expressed as an exponential function Equation (2) [34]:  
\[R_{n}=a\times exp(b\times T_{air}) \tag{2}\]  
where \(R_{n}\) is night-time crown respiration and \(T_{air}\) is night-time mean air temperature. The constants \(a\) and \(b\) are determined by using non-linear optimization.  
Daytime crown respiration (\(R_{d}\)) was estimated by applying Equation (2) [35]. The sum of \(R_{d}\) and \(R_{n}\) was the total crown respiration (\(R=R_{d}+R_{n}\)). The _Ne_ is the net carbon exchange in a definite temporal scale, crown absorption is defined as negative and crown release as positive. Gross primary productivity (_GPP_) was obtained as follows:  
\[GPP=R-Ne \tag{3}\]  
Plant CO\({}_{2}\) exchange responding to light (or light response curve) was fitted by using a right-angle hyperbola model [36]:  
\[Ne=\frac{\alpha\times PAR\times Ne_{max}}{\alpha\times PAR+Ne_{max}}-R_{d} \tag{4}\]  
where \(\alpha\) is initial quantum efficiency, \(Ne_{max}\) is maximum net photosynthetic rate of crown. The parameters, \(\alpha\), \(Ne_{max}\), and \(R_{d}\) were estimated by iteration method using Origin 8.0.
# Wavefront Aberration Measurement Deflectometry for Imaging Lens Tests
## 6 Conclusions  
In conclusion, we propose WAMD for measuring the wavefront aberration of various imaging lenses. Based on the reverse ray-tracing method, WAMD is elaborated clearly and the feasibility of this method is proved by both the simulation and the experiment. The experimental results show that the accuracy of this method can reach 58.9 nm in the measurement of a single lens, and 166.8 nm accuracy was reached when a telephoto lens was measured. WAMD has the advantages of broad applicability, high efficiency, high resolution, a simple structure, low costs, and reasonable accuracy. It provides a new method for aberration tests. In future research, this method will be developed further to measure the aberration of an inclined lens with a large aperture. Additionally, more complicated lenses will be tested to explore the range of applications of this method.  
Conceptualization, L.Y., D.L., and Y.R.; methodology, L.Y., D.L., and Y.R.; software, L.Y., D.L., Y.R., X.Z., and R.W.; data curation, L.Y., D.L., Y.R., and K.X.; writing--original draft preparation, L.Y.; writing--review and editing, D.L., X.Z., and R.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (grant number: U20A20215 and 61875142) and Sichuan University (grant number: 2020 SCUNG205).  
Not applicable.  
The authors declare no conflict of interest.
# Intrinsic Interference Cancellation Scheme for FBMC-OQAM Systems Based on Power Multiplexing
## 4 Numerical Results  
The parameters in our simulations are as follows. The number of subcarriers of FBMC is \(M=256\), the filter bank adopted is the PHYDYAS filter [2; 22], with an overlapping coefficient equal to 4. The frame structure is shown in Figure 4, where each frame is composed of 14 FBMC symbols, and a pilot is inserted into the seventh FBMC symbol per four subcarriers. The SUI-3 channel proposed by IEEE 802.16 is adopted [24], and channels of different frames are quasi-static. We adopt the suggestions in [25] and set the energy of each pilot symbol to twice the energy of the complex QAM symbol. We use the least squares (LS) algorithm to estimate channel response at pilot positions and then conduct a linear interpolation among them to obtain a channel response of the full data frame.  
It is known that a subframe duration that contains 14 OFDM symbols is 1 ms in LTE [26]. We adopt a similar frame structure in our simulations, but each subframe duration is equal to 0.5 ms, as shown in Figure 4. The reason for this can be explained in Figure 1: the duration of an FBMC symbol is \(4T\), and we overlap and add several FBMC symbols to produce signals transmitted in the time domain. In the receiver, we obtain a full FBMC symbol every \(T/2\) duration thanks to the \"overlap-add\" method in the transmitter, whereupon we assign a subframe duration in our simulation that is half of that in LTE. Note that although FBMC has a higher symbol-receiving rate compared with OFDM, FBMC symbols only carry half information (real or imaginary parts) of complex QAM symbols. As a result, FBMC and OFDM have same spectral efficiency when everything else is the same. Note that the proposed method can be used in the 5G frame structure without modification because of the similar basic frame structures of 5G and LTE, but we chose LTE structure for easy understanding.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 4 Methodology
### Name Server Measurement  
We analyze the name server via three steps.  
**Step 1: Collecting NS record.** We applied zone files as our basic data and gather \(NS\) records to construct the relation between domain names and name servers.  
**Step 2: Mining information of NS records.** We resolved collected \(NS\) records to IP addresses leveraging an open-source project \(Z\)\(D\)\(NS\)[34]. In addition, to expose IP providers that hide behind NS providers, we use MaxMind IPGeoLite2 [35] to collect the Autonomous System Organization (ASO) and Autonomous System Number (ASN) of IPs.  
**Step 3: Exploring centrality.** We performed a comprehensive analysis of the centrality of name servers from the distribution and configuration of name servers.
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 7 Experiments
### Anti-Adversarial Experiment: CTU-13
#### 7.4.2 Evaluation on CTU-13  
Data Pre-Processing  
The User Datagram Protocol (UDP) DDoS data set (no. 10) of CTU-13 was used as the evaluation set and the samples were labeled as benign or malicious. To test the AA-HMM's performance on intensive malicious attacks, we extracted a subset with an intensive pattern change (from no. 440,000 to no. 520,000-80,001 records in total). The benign and malicious samples alternatively dominated the first and second half of the subset, respectively. This subset contains intensive DDoS attacks that are suddenly launched.  
Anti-Adversary and Adaptivity Performance  
As Figure 12 shows, the thicker blue line represents the percentage of benign samples within each window. An intensive malicious attack (DDoS) occurs in this data set. However, the model maintained very high precision toward the benign samples (the thinner red line) when being attacked by an intensive DDoS attack.  
Performance: Imbalance Classes  
In Figure 13, the thinner blue line represents the percentage of malicious instances in each window and the thicker red line is the recall of malicious samples. Although the attacks occur only twice (the magnified two dents in the plot), in the first half of the data set, the recall of malicious samples within the two dents is 100%. AA-HMM is capable of identifying trivial malicious samples (one type of sample), which were overwhelmed by the benign samples (the other sample types). In conclusion, AA-HMM has a strong ability to resolve the imbalanced classes issue.  
Figure 12: Correlation between precision (B) and intensive attacks demonstrated in the drastically fluctuating pattern.  
Since different attacks have different patterns and result in varied WWs, the attacks can be visualized by the DW trajectory. As shown in Figure 14, the first half of the DW trajectory is significantly different from the second half, which is compliant with the actual scenario (the DDoS attacks were launched and dominated the traffic after the middle point). Therefore, AA-HMM can be applied as a novel attack visualization tool to detect if the NIDS is being attacked or to even identify the attack types via the DW trajectory.  
From the perspective of attack distribution, there are two challenges in forming predications on this data set. As shown in Figure 15, (1) spur 1 adapts the model to the first half of traffic from the BM and (2) spur 2 adapts the model to the second half of the traffic (DDoS attacks) from the first half (normal traffic). Based on the trajectories of MD and DT, we concluded that both challenges and adaptation processes were resolved and completed rapidly because the MD and DT were reduced to a very low level within only three windows, which shows that the AA-HMM is responsive to intensive attacks.  
Figure 14: DW trajectory.  
Figure 13: Correlation between recall toward malicious samples and ratio of benign samples.
## 2 Coexistence and Interference in WBASNs  
This section introduces coexistence interference in WBASNs. Inter-WBASN coexistence and interference are reviewed first, and then inter-domain coexistence and interference are summarized. In Figure 1, the inter-WBASN interference can occur in wireless links (1), (2), and (3), whereas inter-domain interference can occur in wireless links (4) and (5).
## 5 Experimental Results
### Challenges  
As shown in Figures 11-13 and Table 3, the TQU-SLAM benchmark dataset contains some challenges for VO construction as follows. Firstly, regarding lighting conditions, the obtained RGB images have very weak lighting conditions, and the light intensity is uneven. There are road sections with adequate lighting, but there are road sections where we had to use additional light from mobile phone lights to assist, as shown in Figure 13. Therefore, the keypoints between two consecutive frames are not detected. Second, the collected environment is highly homogeneous. The image data obtained by the Intel RealSense D435 are mainly floor data and a small part are wall and railing data.  
Figure 14: Illustration of the results of estimating the camera’s moving trajectory on the TQU-SLAM benchmark dataset when using the DPVO model. The ground-truth motion trajectory is the black line, and the estimated trajectory of the DPVO model is the blue line.  
Figure 15: Illustration of the results of estimating the camera’s moving trajectory on the TQU-SLAM benchmark dataset when using the TartanVO model. The ground-truth motion trajectory is the black line, and the estimated trajectory of the TartanVO model is the blue line.  
Therefore, the data do not have great discrimination between frames; in the scene, there are few objects in the environment. Although we posted markers on the floor, the number of keypoints detected in the two frames is not high. This leads to a failure to estimate the 6-DOF camera poses across multiple frames. Although, the DPVO and TartanVO networks were pretrained on many large datasets such as the EuRoC [52], KITTI [9; 10; 11], Cityscapes [45], and TartanAir [51] datasets, when performing feature extraction on the TQU-SLAM benchmark dataset, there is a large number of frames that cannot be detected and whose features cannot be extracted (more than 50% of DPVO). The results are shown in Tables 4-6. Figures 14 and 15 illustrate that the misestimation results of DPVO and TartanVO are very large. This proves that the problem of data enrichment to train the VO estimation model still contains many challenges that need to be implemented.
# Novel MIMO Antenna System for Ultra Wideband Applications
## 5 Conclusions  
A four-port MIMO antenna is designed for UWB applications. The array's single element consists of a fractal circular ring patch radiator backed by a square notch loaded trapezoidal-shaped partial ground plane. The results show that the designed single antenna element resonates from 2.67 GHz to over 12 GHz, and it offers a peak realized gain of 5 dBi and an antenna efficiency of \(>\)75%. Furthermore, a novel MIMO antenna configuration is designed to assess single element performance for UWB MIMO applications. To achieve pattern and polarization diversity, the MIMO antenna elements are placed in a plus-shaped configuration. According to the measured data, the designed MIMO antenna operates in the frequency range of 2.77-12 GHz and has a 9.23 GHz impedance bandwidth. In addition, isolation of \(>\)15 dB is noted between antenna elements with an ECC of \(<\)0.1, DG of \(\approx\)10 dB, and MEG of \(<\)3 dB.  
Conceptualization, A.G.A., U.R. and S.M.A.; methodology, U.R., S.M.A., E.M.A. and M.A.; software, U.R.; validation, A.G.A., U.R., S.U., S.K. and S.M.A.; formal analysis, A.G.A., U.R., S.M.A., M.A. and M.D.; investigation, A.G.A., S.M.A., E.M.A., M.D.; resources, S.K., E.M.A., M.A. and M.D.; writing--original draft preparation, U.R.; writing-review and editing, S.U., S.K., S.M.A., M.A. and M.D.; visualization, U.R.; supervision, S.M.A. and M.A.; project administration, A.G.A., S.K. and E.M.A.; funding acquisition, S.U., E.M.A., M.A. and M.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538.  
The authors would like to appreciate Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme for the funding of this research work under the Marie Sklodowska-Curie Grant 801538.  
Not applicable.  
The authors declare no conflict of interest.