esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer Vision in Autonomous Unmanned Aerial Vehicles--A Systematic Mapping Study
## 3 Results
### RQ1: How Many Papers Have Been Published on Computer Vision in Autonomous UAVs? According to the classification and mapping process, a total of 144 papers have been published on computer vision for autonomous UAVs during the period studied (until December 2017). The annual trend of the papers is shown in Figure 7, which illustrates the evolution of the number of works focused on computer vision for the navigation and control of UAVs since 1999. Despite an initial slow growth in the early years, a boom started in 2007 when the amount of research began to grow remarkably. Since that year, the extension has been continuous and the number of papers published in recent years has been very significant, demonstrating that the subject matter of this review is of great interest to the scientific community. The 144 papers were published in 68 journals. As shown in Table 3, more than half of the papers originate from 13 top venues. Almost a third of the total works belong to the top five venues, which include five or more papers each. It is noteworthy that the Journal of Intelligent & Robotic Systems contains 23 papers (\(\approx\)16% of the total). The full list of papers from the 68 journals can be consulted in Appendix A. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Validation Process** & **Aircraft** & **Camera** & **Environment** & **Flight** \\ \hline
**Flight** & UAV & Physical & Indoor/Outdoor & Real-Time \\ \hline
**Experimental** & & & & \\ Offline Tests & UAV/Platform & Physical & Indoor/Outdoor & Offline \\ Lab Tests & UAV/Platform & Physical & Lab & Limited \\ \hline
**Simulation** & & & & \\ Hardware-in-the-Loop Simulation (HILS) & Virtual/Model & Physical & Virtual & Simulated \\ Image-in-Loop Simulation (IILS) & Virtual/Model & Physical & Indoor/Outdoor & Simulated \\
3D Virtual Reality & Virtual & Virtual & Virtual & Simulated \\ Numerical Simulation & Model & Model & Model & Simulated \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Validation process. Features of the trials. Figure 7: Annual trend of publications. Considering the Journal Citation Reports (JCR), some conclusions can be drawn. According to data from year 2007, most of the 68 journals have a notable impact factor (IF) for categories such as 'Engineering, Aerospace', 'Robotics', 'Automation & Control Systems', 'Instruments & Instrumentations' and 'Computer Science, Artificial Intelligence'. Figure 8 represents the number of journals (and the papers published) within each JCR quartile. In this respect, 124 of 144 papers (over 86%) were published in indexed journals. These numbers make it possible to highlight the scientific relevance of the papers considered in this systematic mapping study. This also shows that the development of a fully autonomous UAV capable of flying without the control of a human operator is a challenging and multidisciplinary problem. This is a remarkable sign of the complexity involved in the development of solutions based on computer vision for UAV navigation and flight control.
## 5 Perceptual Audio Features Extraction Techniques
### Frequency Domain Perceptual Features
#### 5.2.5 Roughness-Related Perceptual Frequency Features  
In the work by [PERSON] and [PERSON] [183], roughness is defined as a basic psychoacoustical sensation for rapid amplitude variations which reduces the sensory pleasantness and the quality of noises. According to psychophysical theories, the roughness of a complex sound (a sound comprising many partials or pure tone components) depends on the distance between the partials measured in critical bandwidths.  
In fact, roughness is considered a perceptual or psycho-acoustic feature, but it also captures amplitude modulations. In particular, it is defined as the perception of temporal envelope modulations in the range of about 20-150 Hz and is maximal for modulations near 70 Hz.  
To compute the roughness feature, the following pipeline is defined in the work by [PERSON] and [PERSON] [167]: (i) the temporal (Hilbert) envelope of each filter of a bank of Gammatone filters is computed; (ii) a correlation factor for each filter based on the correlation of its output with that from two filters above and below it in the filter bank is obtained; (iii) the roughness estimate is then calculated by filtering the power in each filter output with a set of bandpass filters (centered near 70 Hz) that pass only those modulation frequencies relevant to the perception of roughness ([PERSON] and [PERSON] [175]), multiplying by the correlation factor and then summing across frequency and across the filter bank.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DNN & Deep Neural Network; \\ LSTM & Long Short-Term Memory Neural Network; \\ SBR & Single-Bit Response; \\ SI & signal integrity \\ COM & channel operating margin; \\ eCOM & enhanced channel operating margin; \\ GP & Gaussian process; \\ HPO & hyperparameter optimization; \\ EM & electromagnetic; \\ EMFS & electromagnetic field solver; \\ IBIS-AMI & Input/Output Buffer Information Specification Algorithmic Model Interface; \\ EH & eye height; \\ EW & eye width; \\ RL & return loss; \\ IL & insertion loss; \\ TD & time domain; \\ FD & frequency domain; \\ ML & machine learning; \\ SVM & Support Vector Machine; \\ LS-SVM & Least-Squares Support Vector Machine; \\ FNN & Feedforward Neural Network; \\ RFR & Random Forest Regression; \\ RNN & Recurrent Neural Network; \\ FFE & feedforward equalizer; \\ DFE & decision feedback equalizer; \\ CTLE & continuous-time linear equalizer; \\ ILD & insertion loss deviation; \\ ICR & insertion loss-to-crosstalk ratio; \\ ISI & inter-symbol interference; \\ FOM & figure of merit. \\ \end{tabular}
## 4 Analysis of Experimental Results
### Precise Identification Technology for Emergency Airdrop Stations
#### 4.2.4 Comparative Experiments
The model has fewer Params and lower FLOPs, demonstrating its lower demand for computational resources and the advantage of high efficiency. Although there is a slight reduction in the frames per second (FPS) performance, it still meets the requirements of real-time detection. In summary, APCR-YOLOv8 maintains a high detection rate and reduces computational resource consumption while improving detection accuracy; a significant improvement scheme over other models.
# The Boundary Element Method in Acoustics: A Survey
## 4 The Standard Boundary Element Method in Acoustics
### The Interior Acoustic Problem
#### 4.1.2 The Boundary Element Method for the Generalised Boundary Condition  
Substituting the expressions (53) and (54) into the boundary condition (7) gives  
\[\alpha(\mathbf{p})(L_{k}\sigma)_{S}(\mathbf{p})+\beta(\mathbf{p})\left\{\left(M_{k}^{t}+ \frac{1}{2}I\right)\sigma\right\}_{S}(\mathbf{p};\mathbf{n}_{p})=f(\mathbf{p}). \tag{56}\]  
Following the derivation of the BEM through collocation, this resolves to a linear system of the form  
\[[D_{\alpha}L_{SS,k}+D_{\beta}\left(M_{SS,k}^{t}+\frac{1}{2}I\right)]\widehat{ \sigma_{S}}=\underline{f}_{S}\, \tag{57}\]  
where \(D_{\alpha}\) and \(D_{\beta}\) are diagonal matrices, with the values of \(\alpha\) and \(\beta\) aligned along the diagonal. Equation (56) is solved in the primary stage of the boundary element method, yielding an approximation \(\widehat{\sigma_{S}}\) to \(\underline{\sigma_{S}}\). In the secondary stage, the discrete equivalent of Equation (53) returns the solution at the interior points:  
\[\widehat{\underline{\varphi_{D}}}=L_{DS,k}\ \widehat{\underline{\sigma_{S}}}. \tag{58}\]  
For the direct formulation, the discrete equivalent of Equation (48) (\(\mathbf{p}\in S\)),  
\[(M_{SS,k}+\frac{1}{2}I)\widehat{\underline{\varphi_{S}}}=L_{SS,k}\widehat{ \underline{\varphi_{S}}}, \tag{59}\]  
which is solved with the discrete equivalent of the boundary condition (7),  
\[D_{\alpha}\widehat{\underline{\varphi_{S}}}+D_{\beta}\widehat{\underline{ \varphi_{S}}}=\underline{f}_{S}. \tag{60}\]  
Comparing the linear systems for the indirect method (56) with that of the direct method (59) and (60) illustrates an apparent significant advantage in the indirect approach, the number of unknowns in the system corresponding to the direct method is twice that of the indirect method. However, through exchanging columns, the system for the direct method can be reduced to an \(n\times n\) system, and this method has been automated [115].  
There have been several papers discussing the accuracy of the interior acoustic boundary element method near to the resonance frequencies. It is considered that real eigenvaulues are shifted into the complex plane following discretisation. This phenomenon is termed numerical damping [116; 117; 118; 119].
## 3 Results
### Effects on \(\alpha\)-SMA and COX-2 Expression  
Figure 4 shows the results of immunohistochemical analysis for evaluating liver tissues in the four experimental groups. The level of \(\alpha\)-SMA expression was to evaluate HSC activation (Figure 4a). Compared with the normal liver group, three DMN-treated groups showed increased expression level (stained in brown color). The DMN-only group showed the highest expression level. The DMN + AAE group showed less expression level than the DMN + Lac group. The count of \(\alpha\)-SMA-positive cells of DMN + AAE and DMN + Lac groups were significantly higher than the normal liver group, but significantly lower than the DMN-only group (\(p<0.05\)) (Figure 4b).  
The severity of inflammation between the groups was compared by the level of COX-2 expression (Figure 4c). The DMN-only group showed the highest expression level. DMN + AAE and DMN + Lac groups showed less expression levels than the DMN-only group. The expression level of COX-2 was analyzed by counting the number of cells showing positive expression versus total cell count. The count of COX-2-positive cells in the DMN-only group was significantly higher than that of normal liver (\(p<0.01\)) (Figure 4d). The cell count of DMN + AAE and DMN + Lac groups was significantly lower than the  
Figure 4: Immunohistochemical analysis of four experimental groups. (**a**) Immunostained \(\alpha\)-SMA of liver tissues; (**b**) bar graph of positive cell count showing different levels of \(\alpha\)-SMA expression; (**c**) levels of COX-2 expression in immunohistochemical staining of liver tissues; (**d**) bar graph positive cell count showing different levels of COX-2 expression. Significances are compared between normal liver and DMN-only groups (**, \(p<0.01\)) or between DMN-only and DMN + AAE or DMN + Lac groups (#, \(p<0.05\); ##, \(p<0.01\)).  
DMN-only group (\(p\) < 0.05). Especially, the cell count of the DMN + AAE group was lower than that of DMN + Lac, which may indicate a superior hepato-protective effect on hepatic fibrosis.
## 4 Materials and Methods
### Zymography Assay  
Zimography analyses were performed on the KHS-Cnd treated and untreated culture supernatants of _P. aeruginosa_ clinical and reference strains. Protein samples (0.75 \(\upmu\)g) of unconcentrated culture supernatants were combined with Laemmli sample buffer without a reducing agent nor boiling [45]; then they were separated on a 10% SDS-PAGE gel containing 0.2% gelatin (Sigma-Aldrich, Milan, Italy) with a 4% stacker [46]. Pre-stained molecular mass markers were included (mPAGE Color Protein Standard, Millipore, Milan, Italy) and electrophoretic run was performed until the 23 kDa pre-stained marker band was approximately out of the gel. To renaturate proteins by removing SDS, the gels were incubated with 2.5% Triton X-100 for 1 h, at room temperature. After washing in distilled water, gels were incubated overnight at 37 \({}^{\circ}\)C in a development buffer containing 50 mM Tris/HCl pH 8.0, 10 mM CaCl\({}_{2}\), 1 mM ZnCl\({}_{2}\), and 150 mM NaCl [45].  
Gels were stained for 1 h with 0.5% Coomassieie blue R-250 in glacial acetic/methanol/ distilled water (1:3:6), and destained in distillated water until the clear bands were visible on a blue background. The resulting zymogram images were acquired by using the ChemiDoc XRS+ System (Biorad, Segrate, Italy). Zymogram experiments were repeated twice.
## 3 Experimental Results and Analysis
### Evaluation of GPU Memory Usage  
To evaluate GPU memory usage, we monitored GPU memory consumption during the model training process. We used a single NVIDIA A40 graphics card with 46 GB of memory for the experiments. When training the entire model directly, GPU memory consumption exceeded 46 GB, rendering training infeasible on a single A40. Consequently, we employed a step-by-step training strategy, consisting of three steps:  
* Step 1: Training the global feature extraction module with a batch size of 4, resulting in GPU memory consumption of less than 20 GB.
* Step 2: Training the feature encoding module, feature aggregation module, and information decoding module with a batch size of 1 and GPU memory consumption of 43 GB. Using the gradient accumulation strategy with a batch size of 4 for small image groups reduced GPU memory usage to a maximum of 16 GB.
* Step 3: Training the lightweight convolution module with a batch size of 2 and GPU memory consumption of 26 GB.  
This three-step training strategy substantially reduced GPU memory usage and eased the hardware requirements for model training.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 3 Materials and Methods
### Proposed Methodology  
The proposed methodology aims at improving the accuracy obtained by outstanding state-of-the-art methods for crop classification tasks. The methodology is composed by four main phases as shown in Figure 3, where \(n\) is the number of bands of the image and \(P\) is the patch size. Firstly, the satellite images are stacked to increase the number of features. Second, the stacked images are divided into small patches to analyze local-feature details. Third, the patch classification process is performed by a DL model. Finally, a post-processing phase is added to mitigate the misclassified pixels introduced due to the low spatial resolution of the images.  
Figure 3: Work-flow of the proposed methodology for crop classification from satellite images. This mainly involves stages for image stacking, patch extraction, classification model, and post-processing.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 4 Model Performance and Results Analysis
### Findings of the Highest Performing Approach
These results indicate that subtracting time effects (DTA-IRT, TP-IRT) provides more reliable and generalizable ability estimation than multiplicative scaling. Figure 7 compares the distribution of errors in ability estimation across subtractive and multiplicative models. The width and spread of each box represent the degree of variability in estimation errors. Subtractive models (DTA-IRT, STP-IRT, TP-IRT) exhibit narrower error distributions, indicating greater consistency and precision in ability estimation. In contrast, multiplicative models (TWD-IRT, ART-IRT, NRT-IRT) show wider distributions and more extreme outliers, suggesting higher variability and greater estimation errors. These findings further confirm that subtractive models are more stable for adjusting ability estimates based on response time, while multiplicative models introduce noise rather than improving model interpretability. The figures provide clear empirical evidence supporting the superiority of subtractive response-time models (DTA-IRT, TP-IRT, STP-IRT) over multiplicative models (TWD-IRT, ART-IRT, NRT-IRT). The DTA-IRT and TP-IRT models consistently outperform others across AUC, AIC/BIC, and cross-validation metrics, confirming that subtracting response time as a  
Figure 6: AUC vs. correlation for different models. Figure 7: Comparison of multiplicative vs. subtractive models—error distribution. penalty leads to more accurate and stable estimations. In contrast, multiplicative models (TWD-IRT, ART-IRT) introduce greater variance, lower AUC scores, and wider error distributions, reinforcing the hypothesis that difficulty scaling based on response time is unstable and unreliable. Additionally, the K-fold cross-validation analysis confirms the generalizability of subtractive models, as they maintain high performance across different training and testing splits. Multiplicative models, however, exhibit inconsistency, suggesting that their effectiveness is heavily dependent on the dataset used for training.
## 3 DI-distributed Localization (DIL) Algorithm
### Localization Error Reduction
\[P_{B\to C}^{{}^{\prime}}=\int_{S(f_{B}=f_{C}^{{}^{\prime}})}^{\infty}f_{B}(S)\cdot dS \tag{18}\]  
Thus, the probability of identification of location of beacon \(B\) as location \(A\) based on the new signal strength distribution from a transmitter located at \(A\) with reduced variance can be expressed in the following equation. \[P_{B\to A}^{{}^{\prime}}=\int_{S(f_{B}=f_{A}^{{}^{\prime}})}^{\infty}f_{B}(S)\cdot dS \tag{19}\]  
Using the method of induction, the probability of identification of location of beacon \(C\) as location \(A\) based on the new signal strength distribution from a transmitter located at \(A\) with reduced variance can be expressed in the following equation. \[P_{C\to A}^{{}^{\prime}}=\int_{S(f_{C}=f_{A}^{{}^{\prime}})}^{\infty}f_{C}(S)\cdot dS \tag{20}\]  
And, the probability of identification of location of beacon \(C\) as location \(B\) based on the new signal strength distribution from a transmitter located at \(B\) with reduced variance can be expressed as  
\[P_{C\to B}^{{}^{\prime}}=\int_{S(f_{C}=f_{B}^{{}^{\prime}})}^{\infty}f_{C}(S)\cdot dS \tag{21}\]  
After getting \(f_{B}^{{}^{\prime}}\) and \(f_{C}^{{}^{\prime}}\), as shown in Figure 6, and taking the reduced variance as discussed above, a new PDF \(f^{{}^{\prime\prime}}\) could be designed such that \(S^{{}^{\prime}}(f_{B}^{{}^{\prime}}=f_{C}^{{}^{\prime}})<S^{{}^{\prime}}(f_{C} ^{{}^{\prime}}=f^{{}^{\prime\prime}})\). Similarly, after getting \(f_{A}^{{}^{\prime}}\) and \(f_{C}^{{}^{\prime}}\), and taking the reduced variance, the new PDF \(f^{{}^{\prime\prime}}\) could be designed such that \(S^{{}^{\prime}}(f_{A}^{{}^{\prime}}=f_{C}^{{}^{\prime}})<S^{{}^{\prime}}(f_{C} ^{{}^{\prime}}=f^{{}^{\prime\prime}})\). Finally, the probability of correctly identifying location of all beacons \(A\), \(B\) and \(C\) at the normal node with reduced variance can be expressed in the following equation. \[P_{A\to B}=P(f_{A}(S_{A})<f_{B}(S_{A}))=\int_{S(f_{A}=f_{B})}^{ \infty}f_{A}(S)\cdot dS \tag{14}\]0  
where, \(P_{normal}\) is the reduced probability of wrongly identifying a transmission from an object at location \(A\) as originating from location \(C\). By considering three beacon nodes \(A\), \(B\) and \(C\) at the same time, probability of error correction can be made as the combination among any two of those three beacon nodes, which can be similar to the above equation. Hence, by reducing in variance of each distributions, the location determination error could be reduced as discussed above.
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 1 Introduction
The experimental results prove that SRMCC achieves protocol fairness, improves throughput and total bandwidth utilization, reduces the packet loss rate, and achieves better TCP friendliness than the other three mechanisms. We also verify the effectiveness of the proposed MCC aggregation mechanism by comparing overhead. The remainder of this paper is organized as follows. We review the related work about multicast congestion control mechanisms in Section 2. Section 3 describes the overall design of SRMCC and its main network elements designed for multicast congestion control. In Section 4, the adaptive rate calculation and adjustment method are described in detail. We then evaluate the performance and discuss the simulation experiments results in Section 5. Finally, we conclude the work in Section 6.
## 2 Materials and Methods
### Structure of the Automatic Gain Control
The digitally generated gain value (\(A_{d}\), see signal #2 in Figure 3b) and the peak value (\(V_{oIV}\), see signal #1 in Figure 3b) are multiplied digitally in the memory of the microprocessor. The product is given to the input of the DAC\({}_{2}\) converter (signal #3, see Figure 3b). The DAC\({}_{2}\) transmits the properly amplified input voltage (signal #3, see Figure 3b) to the input of the ADC\({}_{\text{M2}}\) converter, which allows use of its dynamic range. To determine the signal measured (\(V_{oIV}\)) in the microprocessor system (MCU3, see Figure 3b), information about the designated gain, \(A_{d}\) (signal #4, see Figure 3b) is needed. The fuzzy system shown in Figure 3 consists of three controllers operated in parallel. The shapes of the input/output planes of these controllers differ from each other only in the range of the considered interval (X1-axis \(V_{oIV}\)) for each controller, as follows: \"\(s\)\" (\(V_{oIV}\geq 0.2\) V), \"\(l\)\" (\(V_{oIV}\geq 0.5\) V), and \"\(l\)\" (\(V_{oIV}\geq 1.2\) V). The output value of this three-stage structure is selected by the structural program acting as a switch-case. The selection of one output value out of three fuzzy controllers (\(s,l,h\)) depends on the peak value of the signal \(V_{oIV}\), as measured directly by the ADC\({}_{\text{IA}}\) converter (Figure 3). The input ranges of the controllers result from the possibility of spacing the modal of the membership function of fuzzy sets containing the voltage \(V_{oIV}\) and its derivative. There are reports in the literature of AGC circuits based on intelligent algorithms, such as in [12]. Most of the AGC structures are used to amplify signals such as radio waves. To improve the metrological properties of current measurement systems in power inverters, AGC correctors based on programmable amplifiers can be used. The family of integrated electricity meters provides an example of such solutions.
# Cellulases Production by a _Trichoderma_ sp. Using Food Manufacturing Wastes
## 1 Introduction
Accordingly, enzyme cost is a major determinant of the viability of commercial-scale production of bioethanol [19; 20]. Onsite production of enzymes using low-value substrates could be an option to make a bioethanol facility more economical. In addition, using FMWs materials as a substrate for fungal cellulases production would make the biorefinery more environmentally sustainable. In order to improve enzyme performance, extensive research has been carried out on the cellulolytic system of _T. resei_ to elucidate the enzymes and gene regulation involved in the utilization of lignocellulosic materials. Despite this, enzyme production from _T. resei_ still faces two major bottlenecks. The first one is because some of the produced enzymes have low catalytic activity under industrial conditions, due to the presence of high concentrations of inhibitory compounds generated from the degradation of carbohydrates and lignin during the pretreatment of lignocellulosic biomass [21; 22]. The second one is because certain key enzymes are present in limited amounts [18; 23]. Most research to date has focused on overcoming these bottlenecks by improving the performance of _T. resei_ secretion systems and investigating other _Trichoderma_ spp. with higher secretion potential. Few studies have centered on evaluating enzyme production using less expensive lignocellulosic raw materials [24]. Consequently, the main objective of this research was to study the use of FMWs for cellulases production using an isolated fungal strain from olive stones. Production of cellulases from low-cost residues such as FMWS, could support future on-site production of cellulase enzymes from locally available waste materials for bioethanol production.