esa-sceva/satcom-chunk-collection huggingface dataset:
# A Prototype for Computing the Distance of Features of High-Pressure Die-Cast Aluminum Products
## 3 Materials and Methods
### Obtaining Distances  
The model outputs feed a polynomial algorithm, observed in Equation (1), that converts pixels into mm using the depth matrix. Figure 7a depicts an aluminum 3D-printed pattern to obtain the indexes by placing it in front of the sensor at 40 different distances. Each iteration builds a matrix and considers a cross-section to locate the initial and final point of the pattern, representing the piece in 200 mm and computing its value in pixels--the distance ratio results from the points at a given depth by dividing them by 200 mm. Figure 7b shows the resulting polynomial behavior.  
Finally, the least square method was applied using Equation (2), and the point represented by [x_center, y_center] in pixels is located in the distance matrix to obtain the spatial location and depth in mm. In a workpiece, given two drilled bore centers measured in pixels, for the first, P0(x, y) = (321, 754) as the origin point, and for the second, P1(x, y) = (318, 435); using Equation (2) results in P0(x, y) = (109.151547, 525.7794206) and a depth of 493 mm, and P1(x, y) = (108.250914, 214.2178846) with a depth of 482 mm.  
\[y=a_{0}+a_{1}x_{i}+a_{2}x_{i}^{2}+a_{3}x_{i}^{3}+\ldots+a_{m}x_{i}^{m}+\epsilon_ {i} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature in Spanish** & **Feature in English** \\ \hline Barreno de fundición & Cast bore \\ Barreno maquinado & Drilled bore \\ Brida maquinada & Machined flange \\ Asiento de tornillo & Screw seat \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features and labels used in prototype.  
Figure 6: YOLOv3 and YOLOv11 training workflow (# indicates related bounding box data).  
Figure 7: Adjustment to obtain ratio of pixels to mm: (a) 3D pattern used to obtain the polynomial indexes; (b) resulting polynomial curve.  
\[\begin{bmatrix}3.063809041-6.292055889x_{pixels}+4.168540127x_{pixels}^{2}\\ 2.701041432-5.547050558y_{pixels}+3.674967808y_{pixels}^{2}\\ depth\end{bmatrix}=\begin{bmatrix}x_{mm}\\ y_{mm}\\ z_{mm}\end{bmatrix} \tag{2}\]  
Furthermore, the Pythagoras theorem is employed to find the relative distance between P0 and P1 with a value of 239.0 mm. Therefore, to calculate the relative distances for any piece, the point on the \(x\)-axis and \(y\)-axis that belongs to the feature closest to the center point of the image was selected as the origin of the piece.
## 3 Proposed Frame Aggregation with Block Acknowledgment Mechanism
### Revisiting MP-EDCA
#### 3.1.1 The MP-EDCA Approach  
The MP-EDCA framework implements a hierarchical quality of service (QoS) mechanism tailored for emergency communication in wireless networks. It categorises traffic into four prioritised emergency classes (Classes 1-4) and a non-priority queue for routine data (e.g., email). Class 1, designated for life-critical scenarios (e.g., life-saving interventions), holds the highest priority, followed by Class 2 (health emergencies), Class 3 (property threats), and Class 4 (environmental risks) [36; 37]. This hierarchy aligns with real-world operational protocols for emergency scenarios.  
The MP-EDCA employs a preemption mechanism to enforce prioritisation, allowing higher-priority traffic to seize channel access from lower-priority traffic. The protocol dynamically adjusts SIFS and slot time parameters within emergency frames, optimising contention windows and backoff intervals to minimise latency for critical services. For instance, Class 1 traffic utilises the shortest SIFS intervals to expedite medium access, while lower classes incrementally scale these timers to balance fairness and urgency. This architecture ensures deterministic performance for mission-critical applications, reflecting the ethical prioritisation of human life, health, and environmental stewardship in networked emergency response systems.
# High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation
## 6 Use-Cases: Modeling the Emilia-Romagna and Lombardy Regions
### Results for Emilia-Romagna  
After finding an optimal TP for the two considered use cases, we simulated the scenarios again. We highlighted when the restrictions, implemented in the simulator, are enabled. First, we simulated the Reggio Emilia province scenario with a TP equal to 0.285.  
Figure 8: Graph of daily infected persons with different TP values—Reggio Emilia province.  
Figure 9: Graph of daily infected persons with different TP values—Emilia Romagna region.  
We took into consideration the daily infections trend (see Figure 8), obtaining an R2 equal to 0.854, a Pearson correlation equal to 0.941, and an RMSE equal to 48.32 (see Figure 10).  
The simulated curve has a very similar trend compared to the real one, which is however very disturbed mainly due to the dissimilarity in the number of daily COVID-19 tests performed in the province. For this reason, we had to create a cumulative curve to better limit data pollution. We got, in this case, an R2 equal to 0.986, a Pearson correlation equal to 0.9988, and an RMSE equal to 608.88 (see Figure 11).  
Secondly, we simulated the use case considering the entire territory of Emilia Romagna. The TP taken into consideration in this case was 0.25. However, we also considered both daily and incremental infections trends. In Figure 12, it is possible to see the curve concerning the daily infections in the region. We obtained an R2 score of 0.881, a Pearson correlation of 0.977, and an RMSE of 319.08. Moreover, for the incremental representation of the infections in Figure 13, we obtained an R2 of 0.922, a Pearson correlation of 0.9993 and an RMSE 10,730.11.  
Figure 11: Positives’ incremental representation—Reggio Emilia province, Emilia Romagna region.  
Figure 10: Positives’ daily representation—Reggio Emilia province, Emilia Romagna region.  
We can infer that the simulator is performing and reliable, and thanks to the achieved result, we can say that our commuting model simulates at best the movement of agents within a bi-dimensional space, whether this space is a province with its municipalities or an entire whole region. Therefore, the simulator can be used to verify the effectiveness of the restriction and predict if they will be effective for lowering the epidemiological curve's trend.
## 3 Conventional Active Disturbance Rejection Control Problem
### Extended State Observer
Consequently, the effect of lumped disturbances is cancelled and the controller actively compensates for the disturbances in real time [37].
## 1 Introduction
One such example is the design of novel electromagnetic materials such as phase-correcting structures [25], artificial magnetic conductors [26], and dielectric lenses [27]. Of particular note is PSO's use in antenna gain or return loss bandwidth enhancement [28; 29; 30; 31; 32; 33]. In our study, the \(-10\) dB return loss bandwidth of an IFA antenna is enhanced by activating or deactivating a set of conductive parasitic patches and does not depend on the reproductive process of GA's. Thus, the PSO with binary variables (so called BPSO [14]) was chosen for this study. The first BPSO algorithm was developed by the original creators of the PSO, [PERSON] [PERSON] [14]. This method utilizes the same basic process as their original PSO [24], but the particle velocity probabilistically determines whether each binary variable changes. This is done by converting the velocity into a probability via the sigmoid function. This probability determines the likelihood of a bit change in a particle's position vector. This algorithm is commonly used in applications where the domain is discretized, and the degrees of freedom can only assume values of 0 or 1 [31; 32; 33; 34; 35]. Other BPSO algorithms have also emerged, such as Quantum BPSO (QBPSO) [37] and Artificial Immune System BPSO (AIS BPSO) [38], which is claimed to produce improved results over the original version [39]. AIS BPSO is especially attractive because both the position and the velocity of the particles are represented as strings of binary digits. In the original BPSO, distances are calculated via arithmetic operations on real numbers. AIS BPSO instead calculates velocities as the Hamming distance using Boolean operations on binary strings, allowing particles to traverse the discretized problem space more effectively. This method of optimization has already found applications in the area of antenna design [40; 41; 42; 43; 44; 45]. AIS BPSO is promising due to its fast convergence, however, when applied to the antenna design it was found to be highly susceptible to converging to local minima [42]. In this paper, a novel form of BPSO is proposed to configure conductive parasitic patches of an IFA for bandwidth improvement, in addition to demonstrate the effectiveness of the evolutionary optimization algorithm in limited ground clearance areas. Here, the AIS BPSO algorithm was modified by introducing a dynamic weighting factor, creating an algorithm that effectively traverses the solution space without significant sacrifice in accuracy or convergence speed. This algorithm is termed the Dynamic Hybrid BPSO (DH-BPSO). In Section 2, we describe the operation of the DH-BPSO algorithm. In Section 3.1, we benchmark the algorithm's performance on common test functions, compare it with other AIS algorithms, and describe the results. In Section 3.2, we apply the algorithm to design an IFA antenna. This is followed by fabrication of the designed antenna and verification of simulated results in Section 3.3. Finally, Section 4 presents the conclusion, discussion of results, and possible topics for future work.
###### Abstract  
Proper orthogonal decomposition (POD) is a widely used linear dimensionality reduction technique, but it often fails to capture critical features in complex nonlinear flows. In contrast, clustering methods are effective for nonlinear feature extraction, yet their application in dimensionality reduction methods is hindered by unstable cluster initialization and inefficient mode sorting. To address these issues, we propose a clustering-based dimensionality reduction method guided by POD structures (C-POD), which uses POD preprocessing to stabilize the selection of cluster centers. Additionally, we introduce an entropy-controlled Euclidean-to-probability mapping (ECEPM) method to improve modal sorting and assess mode importance. The C-POD approach is evaluated using the one-dimensional Burgers' equation and a two-dimensional cylinder wake flow. Results show that C-POD achieves higher accuracy in dimensionality reduction than POD. Its dominant modes capture more temporal dynamics, while higher-order modes offer better physical interpretability. When solving an inverse problem using sparse sensor data, the Gappy C-POD method improves reconstruction accuracy by 19.75% and enhances the lower bound of reconstruction capability by 13.4% compared to Gappy POD. Overall, C-POD demonstrates strong potential for modeling and reconstructing complex nonlinear flow fields, providing a valuable tool for dimensionality reduction methods in fluid dynamics.  
Keywords:POD (proper orthogonal decomposition); clustering-based dimensionality reduction method guided by POD structures (C-POD); dimensionality reduction method; nonlinear model order reduction (NMOR) +
Footnote †: journal: _Algorithms_
# Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments
## 4 Results  
The aim of the analysis in this work is to estimate the general performance characteristics of the identified network architecture configurations so that their performance and the trade-offs between them can be evaluated. To accomplish this, we analyse two key metrics: latency and throughput. First, the results of the experimental methodology will be presented. Following this, the findings as they relate to the outlined configurations will be discussed. It should be noted that the figures presented in this analysis use a log axis for display purposes. In addition, the upper and lower fences of the box plots presented in this section are determined by Equations (1) and (2) [64], where IQR is the inter-quartile range.  
\[LowerFence =Q_{1}-1.5(IQR) \tag{1}\] \[UpperFence =Q_{3}+1.5(IQR) \tag{2}\]
# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 3 Algorithm Model Structure
### Algorithm Module
#### 3.1.2 Feature Extraction  
Sound waves are produced by the vibration of the vocal cords. A speech signal is a time-domain signal that changes in time and amplitude. It can be seen as the sum of periodic signals with different frequencies. It is usually transformed from the time domain to the frequency domain by Fourier transform for analysis [31]. In order to better represent the characteristics of the sound, the acoustic signal needs to be converted into a computer-recognizable form of acoustic feature vectors. Commonly used acoustic feature extraction methods include the mel frequency cepstral coefficient (MFCC), frequency domain features (FBANK), the linear predictive cepstral coefficient (LPCC), etc. [32]. This study adopted the method of extracting the features of the speech spectrogram through experiments. The spectrogram can retain the feature information of the original speech signal more completely, which conforms to the characteristics of human hearing. A schematic diagram of the language spectrum is shown in Figure 2.  
Figure 1: Block diagram of the SE-Res2 Net-CBAM-BiLSTM language identification model structure.  
As seen in the speech spectrum diagram, there are horizontal lines, vertical lines, and scrambled patterns, each of which has a different representation. The horizontal line is a resonance peak, the vertical line is a fundamental tone in the speech signal, and the clutter's depth indicates the noise energy distribution. The frequency and bandwidth of the horizontal lines can determine the frequency and bandwidth of the corresponding peaks. The existence of horizontal lines in an audio signal's speech spectrum indicates whether it is a turbid tone. The vertical bars are perpendicular to the time axis, and each one represents a fundamental tone. The speech spectrogram shows the basic information of the speech signal and can be studied with image processing methods.
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 7 Simulation Experiments and Analysis of Results
### Communication Overhead  
In this section, we will evaluate the communication overhead of the algorithm. Communication overhead refers to the total number of messages exchanged within the system when nodes execute the consensus algorithm. Generally, higher communication complexity correlates with lower consensus efficiency and increased resource consumption during operation. In the experiment, we fixed the number of requests at 200 and conducted 100 continuous tests with the number of participating nodes ranging from 0 to 100 in increments of 10, calculating the average as the result. The number of messages exchanged within the entire network for a single request is used to represent the communication overhead in the consensus process. The communication overhead is as follows:  
\[N_{\text{C}}=\frac{N_{Message}}{n}, \tag{9}\]  
where \(N_{Message}\) is the total number of messages exchanged in the entire network when completing \(n\) requests. The experimental results are shown in Figure 10.  
The experimental results in Table 8 indicate that the communication overhead required by PBFT to achieve consensus is significantly higher than that of the other three algorithms. As the number of nodes increases--particularly when reaching 80 nodes--the communication overhead for PBFT escalates rapidly, while the overhead for the other algorithms remains relatively stable. From an average perspective, the trends for GPBFT and CBFT are similar; however, CBFT demonstrates superior performance. This is attributed to CBFT's division of the consensus process into intra-group and inter-group consensus, which reduces the number of nodes involved in global consensus and minimizes the volume of information exchanged between nodes. AE-PBFT outperforms CBFT, achieving an average reduction in communication overhead of approximately 71.2%. This improvement is primarily due to the algorithm's ability to swiftly update credit points, significantly decreasing the scale of nodes participating in consensus and thus reducing communication complexity while optimizing system performance in real-world environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Nodes** & **PBFT** & **GPBFT** & **CBFT** & **AE-PBFT** \\ \hline
10 & 537 & 623 & 336 & 755 \\
20 & 334 & 417 & 273 & 520 \\
30 & 167 & 195 & 308 & 345 \\
40 & 77 & 87 & 304 & 222 \\
50 & 36 & 46 & 315 & 144 \\
60 & 19 & 26 & 308 & 83 \\
70 & 8 & 12 & 265 & 55 \\
80 & 7 & 11 & 238 & 37 \\
90 & 5 & 7 & 181 & 23 \\
100 & 2 & 3 & 142 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Throughput of different algorithms.
## 3 Numerical Simulation Analysis
### Calculation Efficiency Analysis  
This study analyses the calculation efficiency of the proposed method and compares it with that of the single finite element calculation FEM. An ideal deep-sea channel model is established with a channel depth of 300 m and an elastic sphere target with a radius of 10 m. Table 1 records the calculation of the degree of freedom, memory, and time at frequencies of 50, 60, and 100 Hz using a single FEM calculation and directional point source equivalent situation data.  
\begin{table}
\begin{tabular}{c} **Directional** \\ **Point Source** \\ \end{tabular} \\ \hline
50 & 2,211,712 & 506,657 & 60.27 & 15.32 & 1503 & 121 \\ \hline
60 & 3,679,558 & 825,389 & 192.6 & 30.52 & 4603 & 256 \\ \hline
100 & 15,909,000 & 3,331,737 & \textgreater{}350 & 174.5 & – & 3174 \\ \hline \hline \end{tabular}
\end{table}
Table 1: FEM and directional point source calculation comparison.  
Figure 7: Deep-sea sound scattering field calculation results: (**a**) Deep-sea sound velocity profile of South China Sea; (**b**) Sound scattering field calculation results.  
Table 1 shows that the degree of freedom, memory usage gap, and time gap of the FEM are, respectively, more than four, four, and ten times those of the directional point source method. Thus, the calculation efficiency of the directional point source method is much higher than that of the FEM.
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
## 6 Enzymatic Hydrolysis
Examples of SCO production through SSF include (a) _Cryptoccus curvatus_, cultivated in the presence of cellulase and cellobioses on corn biomass pretreated with IL, resulting in a lipid yield that reached 112 mg of lipid/g of corn stover [250], (b) _Trichosporon cutaneum_ cultivated in the presence of cellulase on pretreated with diluted acid and biodetoxidied corn stover, producing more than 3 g/L lipids [251], (c) _Microspphaeropsis sp_. Cultivated in the presence of cellulase in solid state fermentation on a substrate consisting of steam-exploded wheat straw and wheat bran, producing 74 mg of lipid/g of dry substrate [252], (d) _Rhodococcus opaque_ grown in the presence of a mixture of cellulase and \(\beta\)-glucosidase on diluted acid pretreated residues of pine, poplar and switchgrass, producing approximately 15 mg/L of lipids [253]. For a satisfactory SSF, it is necessary to optimize several parameters, such as the pH, the percentage of sugars and the dose of enzymes, so as to obtain the higher possible yields at the lower costs. Temperature is another key factor to consider, since saccharification and fermentation are optimal at different temperatures [254; 255]. In this context, the use of thermotolerant microorganisms capable of producing significant lipid amounts at elevated temperatures of approximately 40 \({}^{\circ}\)C has been proposed, since at this temperature the enzymatic hydrolysis of cellulose is more efficient. However, the accumulation of lipids by the mesophilic microorganisms is usually reduced at such a high temperature [250; 256].
## 4 ADRC Adaptive Law  
### ADRC Adaptive Law Design  
To overcome the poor anti-disturbance performance of the classical PI adaptive law, an improved first-order linear ADRC adaptive law is proposed based on the premise that the controlled system is an integral series system, illustrated in Figure 3. Theoretically, the ADRC adaptive law has better anti-disturbance performance than the classical PI adaptive law.  
The structure of the first-order ADRC system is shown in Figure 4, where \(v\) is the reference; \(y\) is the feedback; \(u\) is the output of the controller; \(b_{0}\) is the control gain; ESO is an extended state observer; \(z_{1}\) is the estimation of \(v\), \(z_{2}\) is the estimation of disturbance.  
The mathematical description of the first-order linear ADRC:  
\[\left\{\begin{array}{rl}&e_{a}=z_{1}-y\\ &u=-z_{1}-\frac{1}{b_{0}}z_{2}+v\\ &\dot{z}_{1}=z_{2}-\beta_{1}e_{a}+b_{0}u\\ &\dot{z}_{2}=-\beta_{2}e_{a}\end{array}\right. \tag{8}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are the gains of ESO; and generally, \(\beta_{1}=2\omega_{0}\) and \(\beta_{2}=\omega_{0}\)?; \(\omega_{0}\) is the bandwidth of the observer; \(e_{a}\) is the tracking error. To balance the response speed and the anti-disturbance performance of the observer, the linear ADRC with a variable bandwidth \(\omega_{0}\) is designed here, and the value of the bandwidth is set according to Equation (9).  
\[\omega_{0}=\left\{\begin{array}{rl}\omega_{a}&|e_{a}|\leq\delta\\ \omega_{b}&\delta<|e_{a}|\leq n\delta\\ \omega_{c}&|e_{a}|>n\delta\end{array}\right. \tag{9}\]  
Figure 4: First-order ADRC system block diagram.  
Figure 3: Identification signal processing system block diagram.  
where \(\omega_{a}\), \(\omega_{b}\), \(\omega_{c}\) are different bandwidth values tuned according to the experience stated below; \(\delta\) is the fixed value of error interval; \(n\) is a fixed value of multiples; \(n\delta\) is used to separate the observation error generated by noise. When \(n\delta\) is higher, the anti-disturbance performance will be better but the response speed will be lower. Through the variable bandwidth design, the response speed and anti-disturbance performance of the linear ADRC can be improved greatly.  
From Equation (8), let the reference \(v\) be equal to 0, and the feedback \(y\) be equal to \(B(t)\) in Equation (7), [PERSON]'s integral inequality of the inductance identification based on the ADRC adaptive law can be obtained:  
\[\int_{0}^{t_{1}}\left[b-u(t)-\hat{b}(0)\right]B(t)\geq-{r_{1}}^{2} \tag{10}\]  
The derivation process of [PERSON]'s integral inequality of the flux linkage is the same as that of inductance, which is omitted here.
## 4 Results and Discussion
### People Detection and Tracking  
The results shown in Figure 6 are indicative of the performance of the human detection and tracking segment of the proposed system. The first row shows a sequence of frames where people are detected properly and tracked with unique IDs. However, the model fails to perform satisfactorily in specific scenarios. The bottom row gives examples of the cases in which the model can fail. From left, (1) a person is not being identified because of occlusion, (2) the identified bounding box is smaller than the person due to occlusion, and (3) a person is going undetected due to the lack of contrast with the background. The model has an mAP = 65%.  
As observed in Figure 6, a given frame from the output consists of multiple markings. The blue quadrilateral on the ground is the reference used for perspective transformation. The people detected are identified by uniquely colored rectangular bounding boxes. The location of each person in the 2D plane is marked using a black dot on the bottom edge of  
Figure 5: Example frames that were removed from full system evaluation due to disparity in human expert responses.  
the respective bounding box. The threat level for the given frame is numerically displayed. Further details of the relevant markings will be discussed in the subsequent sections.