esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Evaluation
### State-Space Estimation  
After training the HDP-HMM, we obtained the state sequence, as shown in Figure 6. The scatter plots in Figure 6a,b illustrate data after sampling, as described in Section 2.3. The \(x\)-axis represents the observations that form clusters, and the \(y\)-axis represents the distances at which these clusters merge. The colored bars represent the individual states. Knowing that the wear rate follows a relation with WDC as described by Equation (1), the wear states should also be capable of tapping the WDC fluctuation in lubricating oil. The state sequence shows a unique starting state. The effects of oil replenishments and oil changes on the state sequence change are visible from the different color bars.  
The blue color bars indicate the states related to oil replenishments and oil changes, the red color depicts the initial degradation state, and the cyan and the yellow colors depict the least degraded and most degraded intermediate states between oil replenishments and oil change in Figure 6a. Figure 6b shows the estimated state sequence at the 100 th iteration, and the scatter plot shows simulation of 300 observations drawn from HDP. Each observation within the cluster is drawn independently from the multivariate Gaussian distribution. Figure 7a shows the inferred hidden states corresponding to the observations. The impact of irregular oil replenishments and oil changes are evident from the expected transition in the degradation states. The corresponding state transition probability for the estimated state-space matrix is plotted in Figure 7b. The matrix quantifies all the  
Figure 5: Log-likelihood convergence trend with an increasing number of data samples corresponding to the number of iterations. The trend converges and saturates towards a global optimum.  
probable transitions from a given state to the next state. The rows in the matrix represent pretransition states, and the columns represent post-transition states. Since state 1 is the default initial state, there is a high probability of transition from state 1 to states 2 and 3. The high probability values along the diagonal are representative of the sticky HDP-HMM algorithm's bias towards self-transition. The remaining values in the matrix are indicative of the possible nonzero, non-negligible, and nonsequential state transitions. With the inferred state sequence as shown in Figure 7a, we shall now present our approach of predicting the residual life of the system in the next section below.
# Predictive Modeling and Experimental Analysis of Cyclic Shear Behavior in Sand-Fly Ash Mixtures
## 5 Data Analysis
At higher contents, such as FA10 and FA15, the fly ash significantly alters the material properties, with increased energy dissipation reflected in the narrower loops. Despite these changes, the NARX model maintains a high level of accuracy. For FA20, which represents the highest fly ash content, the agreement between the experimental data and model predictions remains strong, though slight variations are more noticeable, potentially due to the complex interaction between the sand matrix and the higher proportion of fly ash. The NARX model demonstrates remarkable effectiveness in accurately predicting the cyclic shear behavior of both clean sand and sand-fly ash mixtures, even as the material properties vary with changes in fly ash content. The results of the NARX model underscore the capability of such models to provide reliable predictions for the dynamic performance of composite soils under cyclic loading conditions. The NARX model's ability to capture the nonlinear relationships and time-dependent cyclic behavior of sand-fly ash mixtures are key to its selection. Traditional regression methods or simpler approaches often assume linearity or lack mechanisms to incorporate sequential dependencies in the data. The observed hysteresis loops and stress-strain characteristics in the cyclic tests (Figures 7 and 14 in the manuscript) demonstrate behaviors that simpler models struggle to capture effectively due to nonlinear and path-dependent responses of the mixtures. A step-by-step progression of experimental loading conditions enables a deeper understanding of material behavior under cyclic loads. The primary focus is the development of a time series-based prediction model. Unlike conventional time-dependent models, this approach defines the prediction process based on incremental loading steps rather than fixed time intervals. The fact is that the utilization of fly ash waste from different power stations across the world is a significant step addressing environmental concerns and promoting sustainable construction facilities [26; 69; 70; 71]. Therefore, experimental findings and the models first  
Figure 13: Response plot of NARX model. ever developed here would likely to be used in order to accurately analyze the parameters including cost and operation for the use of fly ash in different countries.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 5 Fuzzy-Based Dynamic Time Slot Allocation
### Fuzzification
#### 5.1.2 Buffer Memory Ratio  
The second input variable is the Buffer Memory Ratio (\(BMR\)), which can be calculated according to Equation (10),  
\[BMR=\frac{m_{a}}{m_{max}} \tag{10}\]  
where \(m_{a}\) is the available memory in a node and \(m_{max}\) is the maximum memory allotted to that node. Equations (11)-(13) explain the partitioning of the variable BMR. Fuzzy rules for BMR and slot allocation are the following:  
1. If BMR is high, then slot allocated value is high.
2. If BMR is medium, then slot allocated value is medium.
3. If BMR is low, then slot allocated value is low.  
\[u_{l}(x)=\begin{cases}1&x\in(0,0.15)\\ 1-2(\frac{x-0.15}{0.35})^{2}&x\in(0.15,0.325)\\ 2(\frac{x-0.5}{0.35})^{2}&x\in(0.325,0.5)\\ 0&x>0.5\end{cases} \tag{11}\]  
\[u_{m}(x)=\begin{cases}e^{-(\frac{(x-c)^{2}}{2\sigma^{2}})}&x\in(0,1)\\ 0&\text{otherwise}\end{cases} \tag{12}\]  
Figure 3: Block diagram of fuzzy model.  
\[u_{h}(x)=\begin{cases}\begin{array}{cc}1&x<0.5\\ 2(\frac{x-0.5}{0.3})^{2}&x\in(0.5,0.65)\\ 1-2(\frac{x-0.8}{0.3})^{2}&x\in(0.65,0.8)\\ 1&x\in(0.81)\end{array}\end{cases} \tag{13}\]
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 3 Proposed Method
### Voxel Selection-Based Refining Segmentation (VS-RS)
#### 3.2.2 Final Selection of Voxels  
After completing the initial selection of voxels. The distribution of the selected voxels is shown by the green voxels in Figure 6a. The distribution of the voxels that have changed their projection plane during the V-PCC refine segmentation process is shown by the red voxels in Figure 6b. It can be found that some remaining voxels have not been selected. These voxels cluster around the initial selection voxels. Based on this, the final selection of voxels was determined.  
At this stage, the reference range of the Projection Uniformity Index is expanded from within voxels to other voxels. They are the nearest neighbor voxels of the initial selection voxels. These nearest neighbor voxels can only be voxels that have not been selected before and then labeled as candidate voxels. The positional relationship is shown in Figure 7. The two red voxels are the initial selection voxels, and the four blue voxels are the candidates for the final selection of voxels. When determining whether the middle red voxel is selected, the lower red voxel is not within the calculation range. Therefore, this calculation only involves 4 blue voxels. We use the KD-tree method to find the nearest neighbor filling voxels.  
The concept of the Expanded Projection Uniformity Index is proposed. It can indicate whether the projection plane remains uniform when compared to the nearest neighbor voxels. The voxels selected based on the Expanded Projection Uniformity Index exhibit a state of relative chaos and meet the requirements for refining segmentation. The Expanded Projection Uniformity Index \(\hat{U}_{j}\) for filled voxels is as follows:  
\[\hat{U}_{j}=\frac{\underset{p\in K}{\max}\overline{S}_{i}(p)}{\overline{T}_{ i}}, \tag{2}\]  
Figure 6: Distribution of the voxels: (**a**) the initially selected; (**b**) projection plane changed in refining segmentation by V-PCC.  
where \(\overline{T}_{i}\) represents the number of 3D points contained in all nearest neighbor voxels of the initial selection voxels. \(\overline{S}_{i}(p)\) represents the number of points projected onto each projection plane in all nearest neighbor voxels as follows:  
\[\overline{S}_{i}(p)=\sum_{j=0}^{N-1}S_{j}(p), \tag{3}\]  
where \(N\) represents the number of voxels that are the nearest neighbors of the initial selection voxels, from 0 to 26. The candidate voxels will be selected based on the values of \(\hat{U}_{j}\). Only when the value of \(\hat{U}_{j}\) is not 1 will some voxels be selected. These selected voxels are all candidate voxels within the range of step size 2. When the value of \(\hat{U}_{j}\) is 1, no candidate voxels will be selected. In general, the final selection of voxels is based on their lack of uniformity compared to their nearest neighbor voxels.
## 4 Result
### Comparison of Forest Burned Area Detection Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Multiclass Classification**} \\ \hline  & - & **OA** & **Recall** & **Precision** & **F1-Score** & **Kappa** \\ \hline \multirow{3}{*}{Scheme 1} & LightGBM & 0.90 & 0.96 & 0.61 & 0.74 & 0.79 \\  & RF & 0.91 & 0.97 & 0.60 & 0.74 & 0.80 \\  & U-Net & 0.94 & 0.90 & 0.91 & 0.90 & 0.87 \\ \hline \multirow{3}{*}{Scheme 2} & LightGBM & 0.91 & 0.97 & 0.67 & 0.79 & 0.81 \\  & RF & 0.92 & 0.97 & 0.63 & 0.76 & 0.81 \\  & U-Net & 0.94 & 0.91 & 0.92 & 0.92 & 0.87 \\ \hline \multirow{3}{*}{Scheme 3} & LightGBM & 0.91 & 0.96 & 0.67 & 0.79 & 0.80 \\  & RF & 0.91 & 0.96 & 0.68 & 0.79 & 0.81 \\  & U-Net & 0.94 & 0.92 & 0.91 & 0.92 & 0.88 \\ \hline \multicolumn{6}{c}{Binary Classification} \\ \hline  & - & **OA** & **Recall** & **Precision** & **F1-Score** & **Kappa** \\ \hline \multirow{3}{*}{Scheme 1} & LightGBM & 0.98 & 0.96 & 0.61 & 0.74 & 0.74 \\  & RF & 0.98 & 0.97 & 0.60 & 0.74 & 0.73 \\  & U-Net & 0.99 & 0.90 & 0.91 & 0.90 & 0.90 \\ \hline \multirow{3}{*}{Scheme 2} & LightGBM & 0.98 & 0.97 & 0.67 & 0.79 & 0.78 \\  & U-Net & 0.98 & 0.97 & 0.63 & 0.76 & 0.75 \\  & U-Net & 0.99 & 0.91 & 0.92 & 0.92 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the burned area detection results for Site B.  
post-fire images, was the lowest accuracy. Because pre- and post-fire images provide change information, it is not surprising that accuracy is high. The use of additional indices (including NDVI, NBR, and dNBR) in Scheme 3 did not significantly improve the detection performance, yielding results comparable to Scheme 2. However, Scheme 3 was more accurate than Scheme 2 in site A, implying that using more indices could be useful in detecting burned areas. The improvement of accuracy is not dramatic in U-Net, showing stable classification accuracy. U-Net conducts image-based learning, while LightGBM and RF conduct pixel-based learning. It demonstrates that image-based learning is more effective in detecting the burned area because it can consider the shape as well as pixel value of the burned area.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Corpus of Binaural Audio Recordings
#### 2.2.1 Raw Audio Material  
The procedure used to generate the audio corpus was as follows. First, a group of 152 multitrack audio recordings was gathered, out of which 112 were selected for training, whereas the remaining 40 items were allocated for testing. They included anechoic and semi-anechoic recordings of classical music orchestra [31] and opera [32]. They also contained typical multitrack studio recordings representing such music genres as pop, orchestral, jazz, country, electronica, dance, rock, and heavy metal. All the recordings were acquired from the publicly available repositories [33; 34; 35]. For most of the selected multitrack recordings, each track represented a single musician or an individual musical instrument. If a given instrument was recorded stereophonically on two tracks, the signals from these tracks were down-mixed to a single monophonic track. It is a common practice taken by sound engineers to record drum-kits using several separate tracks. In this experiment, such tracks were mixed with equal gains to monophonic signals so that the whole drum-kit was stored on a single monophonic track. After such preprocessing, it was assumed by the authors that every monophonic track in the selected multitrack recordings represented an individual foreground audio object, be it a singer or a musical instrument. Finally, all the tracks were RMS-level normalized and stored as separate uncompressed audio files with 24-bit resolution at a 48 kHz sample rate.
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 4 Quantum Entropy as a Service (QEaaS)
### QEaaS Platform Design  
The design of the overall QEaaS infrastructure is shown in Figure 3. A cloud-based infrastructure can be built to host the QRNG. On the cloud provider side, a system can be implemented to manage the quantum entropy pool and develop algorithms for monitoring the entropy pool's capacity and ensuring sufficient entropy is maintained in high-demand scenarios. In addition to that, load balancing shall be considered to dynamically allocate entropy requests to minimize latency and optimize persistence and reliability. IoT clients can request entropy via API or Websocket to securely communicate with the QEaaS provider. Authentication and authorization mechanisms can ensure that only trusted and authorized IoT devices can access quantum entropy. Secure protocols like mutual TLS (Transport Layer Security) or OAuth can be leveraged to ensure that only legitimate IoT devices can request entropy. Moreover, post-quantum cryptography (PQC) can be used to generate digital signatures (e.g., using Dilithium or Falcon) to authenticate clients and ensure the integrity of requests and responses. Ensuring quantum entropy is securely delivered to the IoT clients without tampering or interception is a critical task. End-to-end encryption is required, and quantum-safe cryptographic algorithms such as lattice-based cryptography can be adopted to protect against potential quantum attacks.  
In addition, to guarantee that cryptographic operations are not required to request entropy in real time and can be executed efficiently in environments with intermittent connectivity, IoT devices can maintain a buffer or entropy pool to store quantum entropy locally. The new quantum entropy can be requested periodically or following specific cryptographic operations to ensure that the IoT device maintains high-quality randomness without exhausting its pool.
## 3 Results
### Far-Field Radiation Pattern  
The far-field radiation pattern was measured in a microwave anechoic chamber. The setup for the measurement of H-plane and E-plane radiation patterns are presented in Figure 8. Standard horn antennas were applied as a reference for the normalization of radiation patterns and gains. The standard XB-GH137-20N and XB-GH90-20N horn antennas are used for the frequency bands of 5.38-8.17 GHz and 8.2-12.4 GHz, respectively. The measured H-plane and E-plane radiation patterns of these four antennas are shown in Figure 9. It is noticeable from the figure that the E-plane radiation direction diagram is a little asymmetric and deviates from the center. This is probably due to the fact that the horizontally placed antenna in E-plane measurement was not completely in the center of the rotation axis of test bench. In addition, the presence of the quarter-wavelength converter and feeding line of the side-fed microstrip glass antenna could also result in an asymmetric E-plane radiation pattern. The radiation gains of case one and case four antennas are greater than 2 dB; thus, the shapes of their radiation patterns are similar (such as H-plane). For case two and case three antennas, due to the fact that their radiation gains are lower than \(-4\) dB, their radiation patterns are more vulnerable to measurement noise, errors and side-feeding structure, resulting in slightly different radiation patterns  
Figure 8: Far-field radiation pattern measurement in microwave anechoic chamber. (**a**) H-plane test. (**b**) E-plane test.  
Figure 7: Microscopic images of the radiation patch edge. (**a**) Laser etching technology for case 1 antenna: (**b**) printing technology for case 4 antenna.  
Figure 9: Cont.  
The specific data of the maximum available gain (MAG) of the four antennas are shown in Table 4. Combined with Figure 9, in the four cases, the CCF glass antenna (case 1) has the best radiation performance, with a MAG of 4.60 dB at the operating frequency. In contrast, due to the high resistivity of ITO films, the MAG at the operating frequency is only \(-4.98\) dB for the case two antenna where the radiator is replaced with the ITO structure. Similarly, case three antenna uses CCF as the radiator material, rendering its MAG slightly larger than that of the case two antenna; however, ITO films are for the ground plane, resulting in less suppression of back-scattering. As observed in Figure 9c, the forward and backward radiation pattern gains of case three is close, resulting in low MAG in the forward direction compared with case one. For the case four glass antenna  
Figure 9: Measured H-plane and E-plane radiation pattern at center frequency. (**a**) Case 1. (**b**) Case 2. (**c**) Case 3. (**d**) Case 4. Red line: co-polarization; blue line: cross-polarization.  
composed of CSP layer, due to the better electrical conductivity of CSP material than that of ITO film, the case four antenna reveals a larger MAG. However, the conductivity of the CSP is smaller than that of the CCF, resulting in a gain of 2.10 dB for case four, which is lower than that of case one.
## 5 Results  
The measurement results showed a varying signal strength for each measurement point and surrounding environment. Figures 4-8 depict a few interesting facts. First, the PLEs before \(d_{bp}\) were still approximately 20 from the Fresnel zone clearance effect (see Figures 4 and 5) because the transmitter and receiver antenna heights were above the grass and weed surfaces. In general, the diffraction loss may be neglected if an obstruction does not block the volume contained within the first Fresnel zone [30]. However, in the case of the blockage between the nodes in Figure 6, the RSSIs were in low levels and made the PLEs lower than 20 because of the Fresnel zone non-clearance effect. According to this rule of thumb, as long as 55% of the first Fresnel zone is not kept clear, a further Fresnel zone clearance will significantly alter the diffraction loss. However, the PLEs were more than 20 after \(d_{bp}\), confirming the Fresnel zone theory. Another interesting fact was that the large fluctuation occurred at all distances (see Figure 7) in the case of one blockage moving between the nodes (see Figure 6a). This was because the Fresnel zone clearance was changed by the blockage movement. In contrast, in the case of the blockages fixed at each node (see Figure 6b), large attenuation and fluctuation occurred at only a 1 m distance and small fluctuation occurred in the distance between 5 and 40 m intervals (see Figure 8). The measured RSSIs were fitted to the models and compared as shown below.
## 5 Results and Analytical Discussion
### Computational Efficiency Analysis  
#### 5.2.1 Detection Time Efficiency  
Figure 2 displays the detecting time efficiency of every model. RFC, with a detection time of just 0.584 s, demonstrated remarkable operational efficiency, making it the most appropriate option for real-time IoT deployment scenarios requiring rapid DDoS attack detection. CNN followed with 4.121 s and LSTM needed 13.580 s for detection.  
KNN variants required considerably more time, with K = 5 being the fastest at 36.457 s, followed by K = 7 (37.119 s) and K = 3 (38.923 s). SVM was the slowest model with 93.092 s for detection.  
Regarding IoT deployment situations, these differences are important. IoT systems sometimes run under restrictions including limited processing capacity, power limits, and real-time decision-making requirements. Fast detection times of RFC make it especially appropriate for IoT setups with limited resources as quick DDoS attack detection is required to keep service availability.
## 3 Design and System Description of the Cold Plate  
This study used the SolidWorks 3D drawing software to design three kinds of internal fins with different sizes and shape characteristics for the fluid circulation-type cold plate. Considering the flow rate and temperature of the internal fluid when the heat source power was 210 W, the conjugate heat transfer module of the COMSOL simulation software was adopted to simulate the heat conduction efficiency of different fin configurations for the fluid circulation-type cold plate. The Taguchi analysis method was used to determine the optimal design parameters, considering various factors, such as the temperature, the flow rate of the cooling fluid, the size and appearance characteristics of the internal fins, the distribution quantity, and the volume size of internal fins.  
Based on the size and weight specifications of the T/R module of airborne AESA radars, this study designed a fluid circulation-type cold plate that can match the size of the T/R module, which means that it can fit with the T/R module to form an integrated heat dissipation module. The outer dimensions of the cold plate are as follows: length: 622 mm \(\times\) width: 40 mm \(\times\) thickness: 12 mm; and the internal dimensions are: length: 518 mm \(\times\) width: 36 mm \(\times\) thickness: 4 mm, as shown in Figure 3. In addition, according to the size of the inner and outer spaces of the cold plate, three types of fins, namely, square fins (Type I), round fins (Type II) and finless fins (Type III), were designed and modeled using the SolidWorks 3D drawing software. While they have different shapes, Type I and II fins have the same surface area, the same number of fins (462), and the same center distance between the adjacent fins, as shown in Figure 3.  
Figure 2: Schematic diagram of the operation principle of fluid circulation-type cold plate.