esa-sceva/satcom-chunk-collection huggingface dataset:
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 3 Beamforming-Based Power Estimation Methods
### Matched-Field Processing  
For matched-field processing, the weighting vector is typically represented as the normalized form of the channel transfer function:  
\[\mathbf{w}_{MFP}=\frac{\mathbf{G}}{\left\|\mathbf{G}\right\|_{2}}, \tag{12}\]  
where \(\left\|\ast\right\|_{2}\) denotes the \(l_{2}\) norm of a vector. Substituting the weighting vector to Equation (7), the power estimate with MFP is expressed as [30]  
\[\sigma_{s\,MFP}^{2}=\frac{\mathbf{w}_{MFP}^{\mathrm{H}}\mathbf{R}_{x}\mathbf{w}_{MFP}}{ \mathbf{G}^{\mathrm{H}}\mathbf{G}}. \tag{13}\]
## 5 Numerical Results and Analysis  
In order to test and analyze these two proposed CA-DFC and VL-DFC based MUD algorithms (hereinafter called CA-DFC and VL-DFC, respectively), Monte Carlo simulations are utilized. As Figure 1 shows, a decision-level fusion based MUD receiver for DS-UWB systems is designed; meanwhile, in our experiments, the fusion center applies four DFCs: O-DFC, MV-DFC, CA-DFC, and VL-DFC. The major parameters used for these simulations are summarized in Table 1.  
The performances of CD, SIC, DEC, MMSE, O-DFC, MV-DFC, CA-DFC, VL-DFC, and OMD are compared, including the BER performance _versus_\(E_{b}/N_{0}\), the near-far effect (NFE) resistant capability, the BER performance _versus_ the number of users \(K\), and also their BER performance in the indoor multipath environment. Before them, the computational complexity of CA-DFC and VL-DFC is compared with that of OMD to demonstrate their efficiency.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 5 Discussion: Application, Challenges and Future Perspectives
### Routing Protocols for Energy Efficiency Enhancement
Other key aspects for future  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
research include making sensor networks and routing protocols increasingly self-sufficient (i.e., limiting the need for human intervention for network configuration, calibration, maintenance and repair), further advancements in memory and storage space for data collection and processing, and enhancing fault tolerance within sensor networks (i.e., ensuring that networks continue to function even if certain nodes within the network fail during operation).
# A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
## 3 System Model and Network Controller Design
### Network Architecture
#### 3.1.1 Topology Architecture  
There are six kinds of communication objects in the SDN-based UAV network under a smart jamming environment: the backbone UAV, mission UAV, GPS, ground station, jamming UAV, and jamming station, as illustrated in Figure 1. Among them, the first four belong to the UAV network, and the latter two belong to the jamming source, which will be described in detail in Section 3.4. The UAV network topology studied in this paper is a hierarchical mesh network structure, which is widely used in engineering practice because of its strong scalability. The backbone UAVs, which have wider communication bandwidth, stronger calculation capacity, and larger wireless coverage than the mission UAV, are connected to each other to form the core layer of the UAV network. Each backbone UAV provides a communication relay service for several mission UAVs, which form a cluster as shown by the dotted oval in Figure 1. Each of these clusters is equivalent to a small ad hoc network, and they are the edge layer of UAV network. Mission UAVs can be equipped with many kinds of payload, and they periodically send and receive packets to evaluate the SINR of each channel. All backbone UAVs and some mission UAVs are equipped with GPS modules to obtain their position, speed information, and positioning time. The ground station is connected to at least one backbone UAV.  
The backbone UAVs periodically generate a network situation view report and send it to the ground station and other UAVs. The ground station can dispatch the latest mission plan to the UAV network.
## 2 Improved Penzien Model
### Dynamic Response of the Free Field
#### 2.2.3 Calculation Process of Soil Dynamic Nonlinearity  
The stiffness and damping of each soil layer of the free field in the improved Penzien model are calculated as follows.  
\[k_{zi}=\frac{G_{i}A_{f}}{h_{i}} \tag{10}\]  
\[c_{zi}=\frac{2D_{i}}{\omega}k_{zi} \tag{11}\]  
where \(A_{f}\) and \(\omega\) represent the soil area and fundamental frequency of the free field, respectively. \(A_{f}\) is equal to the soil area of the free field in the ANSYS finite element model. \(G_{i}\), \(D_{i}\), and \(h_{i}\) are the dynamic shear modulus, the damping ratio, and the thickness of the soil layer \(i\), respectively. \(G_{i}\) and \(D_{i}\) are obtained from the results of the ANSYS finite element model of the free field by the following calculation process.  
[PERSON] et al. [68] used the finite element model of a shaking table test to verify the accuracy of the Davidenkov foundation model used in the ANSYS software to simulate the dynamic nonlinearity of soil. The results of the shaking table test were in good agreement with those of the numerical model. Therefore, the Davidenkov model used in this paper is well-proven. The calculation formulas of the stiffness and damping of the soil in the improved Penzien model can be obtained by substituting the calculation results from ANSYS into Equations (10) and (11). Thus, the dynamic nonlinearity simulation of soil is realized in the improved Penzien model. The calculation process of the soil dynamic nonlinearity is as follows: firstly, the initial dynamic shear modulus \(G_{i}\) and the initial damping ratio \(D_{i}\) are substituted for the initial analysis. Enter the post-processor and the average shear strain of soil is calculated. The dynamic shear modulus \(G_{i+1}\) and the initial damping ratio \(D_{i+1}\) are then calculated using Equations (3), (6), (7), and (9), which are the relationship equations between the dynamic shear modulus and shear strain, and between the damping ratio and shear strain. Thirdly, the dynamic shear modulus \(G_{i+1}\) and damping ratio \(D_{i+1}\) are used for the restarting analysis based on the previous analysis. Another dynamic shear modulus and damping ratio are calculated again through Equations (3), (6), (7), and (9). The two groups of the dynamic shear modulus and the damping ratio are compared with each other until the differences between the two rounds of before-and-after calculations are within the permissible values. Fourthly, the average dynamic shear model and damping ratio of each soil layer in this time step are obtained, and the stiffness and damping of each soil layer in the Penzien model are then calculated using Equations (10) and (11). The iteration step stops and the following step then starts, repeating the above calculation process until the earthquake input is completed. The calculation flowchart is shown in Figure 3.
## 3 Results
### Logistic Regression Models Validation--ROC Curves  
After defining the various models presented in Table 2, the area under the ROC curve (AUC) was used to assess their ability to accurately predict fall occurrences. Table 4 and Figure 1 show the results of the ROC analyses and ROC curves for all individuals aged at least 50 years and for the different age groups, comparing the results between the TUG and the Logistic Regression models. In combination with the TUG cut-off, HoF emerged as the strongest self-reported question for distinguishing between fallers and non-fallers among adults aged 50-60 years (AUC = 82.5%; \(p<0.001\)). For community-dwelling adults aged 60-70 years, the combination of UpExt and the number of medications per day with TUG proved to be the strongest (AUC = 75.4%; \(p<0.001\)). Among adults aged 70 years or older, the model combining TUG, HoF, and benzodiazepine use demonstrated significant discriminatory ability (AUC = 70.8%; \(p<0.001\)). However, when considering the entire group of individuals aged 50 years or older, the model combining TUG with HoF, UpExt, and benzodiazepine use exhibited only a weak ability to differentiate between fallers and non-fallers (AUC = 67.1%, \(p<0.001\)).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Cut-Off** & **Sensitivity** & **Specificity** & **AUC** & **95\% CI** & \(p\) \\  & **Scores** & **(\%Fallers)** & **(\%Non-Fallers)** & **AUC** & **95\% CI** & \(p\) \\ \hline TUG & & & & & & \\ \(\geq\)50 years & - & - & - & 54.7 & 48.5–60.9 & 0.136 \\ \(50\)–60 years & 6.9 & 86.7 & 66.7 & 77.7 & 0.66–0.91 & 0.001 \\ \(60\)–70 years & - & - & - & 56.8 & 43.9–69.8 & 0.300 \\ \(\geq\)70 years & - & - & - & 43.4 & 34.6–52.1 & 0.137 \\ Models & & & & & & \\ \(\geq\)50 years & 26.5 & 69.7 & 61.0 & 67.1 & 60.5–73.7 & \(<\)0.001 \\ \(50\)–60 years & 18.1 & 86.7 & 65.7 & 82.5 & 69.3–95.7 & \(<\)0.001 \\ \(60\)–70 years & 17.8 & 77.3 & 65.1 & 75.4 & 64.7–86.1 & \(<\)0.001 \\ \(\geq\)70 years & 24.8 & 71.2 & 61.5 & 70.8 & 62.8–78.8 & \(<\)0.001 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of the ROC analyses for TUG and Logistic Regression models.
## 2 Materials and Methods
### Analyses by High-Performance Liquid Chromatography (HPLC) and Tandem Mass Spectrometry (MS/MS)  
Chromotography experiments were performed using an HPLC system (1260 Infinity II, Agilent, Santa Clara, CA, USA). Water and acetonitrile (HPLC grade) were purchased from Samchun Pure Chemicals (Pyeongtaek, Republic of Korea), and trifluoroacetic acid of Alfa Aesar (Schiltigheim, France) was used. For the identification of each individual peak appearing in the HPLC chromatogram, authentic compounds (assay \(\geq\) 98%) of adenosine (#A9251), guanosine (#G6752), phenylalanine (#P2126), tryptophan (#T0254), tyrosine (#T3754), and uridine (#U3750) were purchased from Sigma-Aldrich (St. Louis, MO, USA). The analytical column was a Shim-pack GIS C18 (5 \(\mu\)m, 4.6 \(\times\) 250 mm, Shimadzu, Kyoto, Japan), and the mobile phase was a mixture of eluent A (0.1% trifluoroacetic acid in water) and B (0.1% trifluoroacetic acid in acetonitrile). Elution was performed with a linear gradient of eluent A and B under the following conditions: 0\(\rightarrow\)5 min, 0\(\rightarrow\)0% B; 5\(\rightarrow\)75 min, 0\(\rightarrow\)75% B; 75\(\rightarrow\)77 min, 75\(\rightarrow\)95% B; 77\(\rightarrow\)82 min, 95\(\rightarrow\)95% B; 82\(\rightarrow\)84 min, 95\(\rightarrow\)0% B and 84\(\rightarrow\)95 min 0\(\rightarrow\)0% B. The flow rate was 1.0 mL/min, the injection volume was 20 \(\mu\)L, and detection was performed at 210 nm. All samples were filtered with a 0.45 \(\mu\)m syringe filter (PTFE, Advantec, Tokyo, Japan) before injection. To identify peaks from the HPLC results, we conducted MS/MS using the 3200 QTRAP system (AB Sciex, Framingham, MA, USA), as described previously [29].
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### Ray Tracing Model  
To estimate locations using the fingerprinting-based approach, it is essential to build a reference database that captures location-dependent signal characteristics. In this study, we utilized a ray-tracing simulation to generate such a database, taking into account physical phenomena such as reflection, diffraction, and transmission of radio waves. The simulation was executed using the Wireless Insite platform, which allows for detailed modeling of propagation in complex environments. Figure 2 illustrates the terrain model adopted in the simulation, while Figure 3 presents the configuration of transmitter positions and candidate UAV observation points. Recent extensions of the 3 GPP/ITU channel model have enabled 3D ray-based simulation in dense urban environments, as proposed by [PERSON] et al. [11]. Additionally, [PERSON] et al. [12] analyzed angular spreads in street-level propagation, which supports our use of directional metrics such as AOA and EOA.  
Figure 1: Flowchart of the proposed method.  
In this study, a simulated terrain model reproducing the Ookayama Campus of the Institute of Science Tokyo was employed. The transmitters in the learning phase and the targets in the estimation phase were placed at a height of 2 m, assuming that an illegal radio wave source is located on the road. The UAV sensor candidate points were arranged in a grid pattern at altitudes of 50, 75, 100, 125, and 150 m. The actual positions of the transmitter antennas and candidate receiver antennas are illustrated in Figure 4.  
Additionally, the parameters for the ray-tracing simulation are presented in Table 1. The antenna is assumed to be the same for both transmitters and receivers, with vertical polarization ensuring ideal polarization matching.  
Figure 3: Layout This layout shows the positions of transmitters and candidate sensor points. Transmitters were placed at a height of 2 m, simulating the emission of illegal radio waves from ground level. UAV sensor points were distributed in a grid at various altitudes (50, 75, 100, 125, 150 m). The left blue region indicates the training area, the central “Target” area includes 144 target positions, and the right blue dots represent 983 UAV candidate positions for flight. This figure is based on previously published work [13], reproduced with proper citation.  
Figure 2: Simulation terrain model representing the Ookayama Campus of the Institute of Science Tokyo. This environment includes multiple buildings and open roads to emulate realistic urban propagation [13].
## 3 Results
### Subjective Results  
The subjective data gathered from the listening tests have been analyzed based on binomial distribution [35] in order to determine the statistical significance of the test results. The inverse cumulative probability is used to evaluate the minimum number of correct answers that are needed to indicate a perceptual difference. The inverse cumulative probability is given as a function of the trials (corresponding to the thirty-one listeners), probability of correct answers (50%), and confidence level (95%). Therefore, the minimum number of correct answers necessary to indicate a significant difference between pairs at a 95% confidence level was found to be 15, i.e., correct answers should result equal or higher than 15. Figure 10 shows the correct answers for each music/signal passage, listening (head), and source position. The dashed horizontal line indicates the minimum number of correct answers necessary to detect a significant perceptual difference between configurations compared in one pair. No significant variations of the location of diffusive surfaces were significantly perceived in any of the compared pairs. Some of the listeners could still indicate a few differences relying on different attributes as presented in Figure 11, which shows the occurrences of each attribute given in the correct answers. Further, according to the feedback of the listeners, for each signal (trumpet, orchestra, pink noise), more than 75% of the correct answers were given by relying on two or more attributes (reverbearance, coloration, and spaciousness). Among them, reverberance is the main attribute when the orchestra and pink noise samples are compared in the reflective condition. Finally, given the small perceived differences, it was not possible to collect reliable results regarding the preference indicated by the listeners.
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 4 Discussion
### Analysis Using Experimental Measurements  
In this analysis, real measurements of \(P_{E}(t)\) and \(P_{C}(t)\) were taken with the experimental setup described in [30]. As shown in Figure 17, both signals show small fluctuations, especially in their amplitude, but also in their phase and frequency. Some noise has also been observed.  
Figure 18 shows the convergence of the DC component estimation for both \(P_{E}(t)\) and \(P_{C}(t)\). The DC component estimation algorithm shows good convergence, stabilizing after approximately three cycles, showing some small fluctuations afterwards.  
Figure 17: Experimental values of \(P_{E}(k)\) and \(P_{C}(k)\) for measured signals, showing amplitude variations (red line).  
Figure 18: Estimation of the DC component of the signal of \(P_{E}(k)\) and \(P_{C}(k)\) for measured signals.  
Figure 19 compares the AC component of \(P_{E}\)(t) and \(P_{C}\)(t), both filtered and non-filtered. The moving window size for the filtering is W = 3 in all the study cases.  
Figure 20 shows the instantaneous power obtained using method 1:  
Figure 21 shows how the estimation algorithms respond to the fluctuations observed in \(P_{E}\)(t) and \(P_{C}\)(t) :  
Figure 19: AC component of \(P_{E}\)(\(k\)) and \(P_{C}\)(\(k\)): non-filtered and filtered for measured signals.  
Figure 20: Instantaneous power obtained using Equation (22) using measured signals.  
Table 7 shows the average power obtained with the proposed methods and compares them to the average power obtained with the reference procedure [17].  
These results show also that a good correspondence is obtained when using the three methods when using real measurements (sampled at approximately 30 measurements per period): the difference is lower than 1.3% (method 1) and 3.7% (method 2).
## 3 Results and Discussion
### Effect of Breed and Finishing Diet on Volatile Compounds
#### 3.2.3 Alcohols
Regarding the involvement of alcohols on the total volatile content, this family was the fifth most abundant in both diet regimes, since it represented 4.45 and 3.83% of the volatile profile for D1 and D2 foals, respectively (Figure 1). Thus, these compounds probably had a low impact on the aroma of meat, considering also their usually high detection thresholds [62]. The effect of diet was particularly observed in JN foals, where JN-D1 presented the highest alcohol total content, while its counterpart, JN-D2, showed the lowest. In addition, significant interactions B \(\times\) D were detected in the total contents of this family, except for glycidol, 1-hexanol, 1-heptanol, diethylene glycol, 2-ethyl-1-hexanol, 1-octanol and 6-undecanol.
# Anomaly Detection in IoT Communication Network Based on Spectral Analysis and Hurst Exponent
## 3 Self-Similarity and Multifractal Spectrum Dependences  
Creating hundreds of thousands of time series based on source and destination when each package can cover more than one class is not a trivial problem. The monitoring level is usually done by collecting NetFlow statistics from IP routers and processing these traffic flows to create time series [9].  
Self-similar processes are interpreted as a sequence of random variables at a specific time. These processes are characterized by specific parameters such as mean value, variance, random moments of higher orders, and probability distribution. Self-similarity, which is also called monofractal, means that the characteristics of some processes are identical or similar to different scales of dimensions or time. The autocorrelation function, Hurst's exponent, and variance are used to describe the monofractal characteristics of time series. These are some of the methods used to determine entropy. In particular, we can include the autocorrelation function which, as a function of time or time delay, can show the correlation between the values of a random process in different periods; the hardtail distribution, which is a class of probability distributions that have heavier tails than the exponential distribution; and the method using the Hurst exponent and re-analyzing the scaled range, where the Hurst exponent associated with autocorrelations is used as a parameter to measure the long-term memory of time series and is usually calculated by re-scaling the range (R/S) [13].  
The determination of the value of the Hurst exponent can be obtained by plotting the \(\log[R(n)/S(n)]\) as a function of \(\log n\) and matching the straight line by the least square method. Referring directly to the definition, the slope of a straight line is precisely Hurst's exponent. The value of the Hurst exponent (H) allows for the determination of the relative trend of time series either to a strong reversion to the average or a cluster in the direction. The range of available variants of the H parameter is from 0 to 1. H in the range 0-0.5 means that time series has long-term switching between high and low values in adjacent pairs, which means that after high values of, e.g., network traffic, the switching trend between high and low values probably would last for a long time in the future. The value of the exponent H = 0.5 indicates that a time series has the law of random walk, known as a stochastic or random process, which describes a path consisting of a series of random steps in a mathematical space, such as integers. The value of the exponent H in the range of 0.5-1 indicates that the time series has long-term positive autocorrelation, which means that a high traffic volume in the series will probably be followed by another high indication of the point value of traffic [8; 13].
# Route Prefix Caching Using Bloom Filters in Named Data Networking
## 3 Route Prefix Caching
### Construction of Bloom Filters  
All the prefixes in a FIB table have their Bloom filters. Each prefix's Bloom filter is constructed using the branch information of its children. Algorithm 1 describes how to construct the Bloom filters in FIB. For a given _fib_, which is an FIB table, and the number of hash functions \(k\), an array of Bloom Filters, BF, is constructed. Each of the hash functions, \(h_{1}\), \(h_{2}\),..., \(h_{k}\), maps component strings to {0, 1, 2,..., \(m-1\)} where \(m\) is the size of a Bloom filter.  
For each prefix in _fib_, its parent prefix is identified as the form of \(c_{1}/c_{2}/\ldots/c_{i}\) where the prefix is \(/c_{1}/c_{2}/\ldots/c_{n}\) and \(i<n\). For example, in Figure 4, if prefix is currently /ride/bike/ha, then parent would be /ride. The _next_component_ after _parent_ is \(c_{i+1}\). In this example, it is \"bike\". Now, hash functions, \(h_{1}\), \(h_{2}\),..., \(h_{k}\), are computed with the input \"bike\" in turn, and the bit indicated by each hash value is  
Figure 6: The organization of overall name lookup.  
Figure 7: The architecture of a Bloom filter-based route prefix cache.  
set to 1 using bitwise-or. Supposing that \(k=2\) and \(h_{1}\) (\"bike\") = 1 and \(h_{2}\) (\"bike\") = 4, we could say that the parent's Bloom filter is 01001000 if the Bloom filter size is 8. Note that the Bloom filter is not completely constructed if there remain other children which are not processed yet. Whenever a child is processed, their parent's Bloom filter is updated. Thus, the Bloom filter of /ride is updated when its child, /ride/wagon, is processed. If \(h_{1}\) (\"wagon\") = 4 and \(h_{2}\) (\"wagon\") = 7, then the parent's Bloom filter is updated as follows using bitwise-or: 01001000 \(\rightarrow\) 01001000 \(\rightarrow\) 01001001.  
```
function(_fib_, \(k\)) Input:_fib_ is a prefix table sorted in component-level order \(k\) is the number of hash functions to indicate bit positions in BF Output: BF is the array of Bloom filters, each of which corresponds to an _fib_ entry
1:foreach prefix in fibdo
2: (\(c_{1}\), \(c_{2}\),..., \(c_{n}\)) is the list of components in prefix
3:for\(i\gets n-1\)to\(1\)do
4:parent\(\leftarrow\)\({}^{\prime}\)/ \(c_{1}\)\({}^{\prime}\)/ \(c_{2}\)\({}^{\prime}\)/... \({}^{\prime}\)/ \(c_{i}\)
5:ifparent in fibthen
6:break
7:done
8:\(p\leftarrow\)parent's index in fib
9:next_component\(\leftarrow\)\(c_{i+1}\)
10:for\(j\gets 1\)to\(k\)do
11:BF[\(p\)] \(\leftarrow\) bitwise-or(BF[\(p\)], Ishift(1, \(h_{j}\)(next_component)))
12:done
13:done
```  
**Algorithm 1**Constructing Bloom filters in FIB