esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV-Based Airport Lights Inspection Without GNSS Positioning Support
## 3 Materials and Methods
During the UAV flight, the lights at the bottom of the image move large distances, thus hindering reliable calculations of lights correspondences in consecutive images, whereas the top part of the image contains closely located lights that are difficult to correctly identify separately. This study focused on measuring the parameters of PAPI lights in compliance with ICAO regulations [1]. The UAV was positioned 300 m downwind of the PAPI system. Vertical scanning was performed, and the operator determined the heights of the lights of the upper and lower limits of the transition zone from red to white. The idea of the processing algorithm, Figure 2, is based on the tracking light positions during the UAV flight and collecting the set of parameters of the observed lights. The comparison of the content of this set with the airport documentation results in a report on the state of the airport lights. Figure 1: Airport lights: sample view from UAV camera. The following guidelines were set for the design of the processing approach:  
* The video material ensures the size of an image pixel \"ground sampling distance\" (GDS) that enables explicit differentiation of lights;
* The geodesic documentation of the airport is available for use in calculations; And criteria for assessing the effectiveness were set as follows:
* The error of estimation of the lights positions does not impair the ability to track lights;
* The capability to determine the colour and angle of illumination is achieved;
* The speed of processing enables the in-flight tracking of the lights. The guidelines define the measurement field of the UAV and set the necessary camera parameter requirements for performing the inspection. A category 3 inspection UAV was chosen for the missions with a 50 min flight capability. The UAV was equipped with a 20 Mpx zoom camera with focal length range of 6.83-119.94 mm. The camera can record video with resolutions of 3840 \(\times\) 2160@30 fps and 1920 \(\times\) 1080@30 fps and has a sensitivity range of 100-25,600 ISO.
# Modeling and Optimizing the System Reliability Using Bounded Geometric Programming Approach
## 4 Computational Study
### Application to System Reliability Optimization
\begin{split}\text{Model B}\\ \overline{F}=\underset{y}{\text{Maximize}}&\quad\left(\frac{150}{y_{01 }}\right)^{y_{01}}\left(\frac{210}{y_{02}}\right)^{y_{02}}\left(\frac{270}{y_{0 3}}\right)^{y_{03}}\left(\frac{1}{\overline{k}_{s}y_{11}}\right)^{y_{11}}\left( \frac{1}{\overline{k}_{s}y_{12}}\right)^{y_{12}}\left(\frac{1}{\overline{k}_{ s}y_{13}}\right)^{y_{13}}\\ \text{s.\,x.}&\qquad\qquad\qquad y_{01}+y_{02}+y_{03 }=1,\\ &\qquad\qquad\qquad 20y_{01}+15y_{02}+10y_{03}+y_{11}+y_{12}+y_{13}=0, \\ &\qquad\qquad\qquad y_{01}-y_{02}-y_{03}+y_{11}+y_{12}+y_{13}=0, \\ &\qquad\qquad y_{01},y_{02},y_{03},y_{11},y_{12},y_{13}\geq 0 \\ &\qquad\qquad 0.6358\leq\overline{R}_{s}\leq 0.9776,\;\; \forall\;\;i=1,2,3.\end{split}  \tag{35}\]  
The above problems (34) and (35) provide the required upper and lower bound for the problem (33). Upon solving the problem at zero degree of difficulty, the upper and lower bounds for the system cost are obtained as \(\overline{C_{s}}=521.95\) and \(\underline{C_{s}}=216.35\), respectively. However, the system cost at \(\tilde{R}_{s}=0.88\) is found to be \(\tilde{C_{s}}=351.29\). Therefore, the obtained system cost lies between the range of its upper and lower bounds, which shows that it is justified to reduce the system cost at maximum system reliability under variations.
# Satellite Network Task Deployment Method Based on SDN and ICN
## 6 Conclusions  
In this paper, the satellite networks based on SDN and ICN were introduced. We then analyzed the application status of edge computing in the satellite networks. Analyzing the scenario of a multilayer satellite network based on the SDN, we modeled the task assignment problem in edge calculation. We then adopted the genetic algorithm to obtain the solutions to the task deployment problem. We also tested the connectivity of the satellite network and the performance of the proposed satellite network deployment method. The simulation results showed that the proposed task deployment algorithm can reduce the processing delay of computing tasks and the network traffic caused by task processing.  
**Author Contributions:** Conceptualization, Z.L. and X.D.; data curation, Z.L., L.W. and X.D.; formal analysis, Z.L. and X.D.; funding acquisition, Z.L.; investigation, X.D.; methodology, X.D.; project administration, Z.L.; resources, Z.L., J.F. and C.P.; software, X.D.; supervision, Z.L., J.F. and C.P.; validation, Z.L., X.D. and L.W.; visualization, Z.L.; writing--original draft preparation, X.D.; writing--review and editing, X.D., Z.L., L.W. and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the supported by the National Natural Science Foundation of China grant number [No.61931004]. And the APC was funded by Dalian University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 11: The delay performance comparison of different task placement strategies.  
Data Availability Statement: The processed data required to reproduce these findings cannot be shared as the data also forms part of an ongoing study.
# Acoustical Properties of Fiberglass Blankets Impregnated with Silica Aerogel
## 3 Modeling of the Acoustical Properties of Fibrous and Granular Media  
Basic modeling of the acoustical properties of this kind of material requires a mathematical model that takes into account the classical visco-thermal effects in the voids' between the fibers and loose granules of powder. However, a fibrous blanket impregnated with aerogel is a more complicated void structure that has at least three scales of porosity. The fiberglass blanket itself consist of 10 \(\upmu\)m interlaced fibers that form a porous structure with sub-millimeter size pores of approximately 0.1 mm. The aerogel particles are around 20 \(\upmu\)m in size and contain nano-pores of 20 nm in size.  
There are several models that exist that can predict the acoustical properties of classical fibrous media [27]. In this work we attempt to use the model proposed by [PERSON] et al. [28], which is based on the following three parameters: (i) the median pore size, \(\bar{s}\); (ii) porosity, \(\phi\); and (iii) the standard deviation in pore size, \(\sigma_{\bar{s}}\). This reduced number of parameters allows easier inversion of key morphological characteristics of porous media from acoustical data. This model predicts the dynamic density, \(\widetilde{\rho}\), and complex compressibility, \(\widetilde{C}\), of air in the material pores. These quantities are given by the analytical equations, which are presented in reference [28]. The MATLAB code to predict these quantities can be found in reference [29].  
The normalized surface impedance of a hard-backed layer of porous material that is typically measured in the impedance tube is:  
\[Z_{\rm s}=-jZ_{c}\cot(k_{c}d)/\rho_{0}c_{0} \tag{1}\]  
where \(j~{}=~{}\sqrt{-1}\), \(d\) is the sample thickness, \(\rho_{0}\) is the ambient density of air, \(c_{0}\) is the sound speed in air,  
\[Z_{c}=\sqrt{\frac{\widetilde{\rho}}{\widetilde{C}}} \tag{2}\]  
is the characteristic impedance and  
\[k_{c}=\omega\sqrt{\widetilde{\rho}\widetilde{C}} \tag{3}\]  
Figure 2: 2-Microphone impedance tube setup to measure the surface impedance of a porous layer [25].  
is the wavenumber in the porous material. Here, \(\omega\) is the angular frequency of sound. In this work, we use the complex reflection coefficient data  
\[R=\frac{Z_{\mathrm{s}}-1}{Z_{\mathrm{s}}+1} \tag{4}\]  
to fit the model. The work presented in reference [30] shows that the complex reflection coefficient is a reliable quantity to determine the effective values of the three non-acoustical parameters in the model [28] through the parameter inversion. This is the complex acoustical quantity that is measured directly using the standard impedance tube method [25]. The real and imaginary parts of this quantity are bounded between \(-1\) and \(+1\), which makes them attractive to use in the parameter inversion process. The complex reflection coefficient can also be used to predict the acoustic absorption coefficient  
\[\alpha=1-\left|R\right|^{2} \tag{5}\]  
which is a usual measure of the ability of the porous layer to absorb sound.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
###### Abstract  
Natural scene classification, which has potential applications in precision agriculture, environmental monitoring, and disaster management, poses significant challenges due to variations in the spatial resolution, spectral resolution, texture, and size of remotely sensed images of natural scenes on Earth. For such challenging problems, deep-learning-based algorithms have demonstrated amazing performances in recent years. Among these methodologies, transfer learning is a useful technique which employs the learned features already extracted from the pre-trained models from large-scale datasets for the problem at hand, resulting in quicker and more accurate models. In this study, we deployed cross-domain transfer learning for the land-cover classification of remotely sensed images of natural scenes. We conducted extensive experiments to measure the performance of the proposed method and explored the factors that affect the performance of the models. Our findings suggest that fine-tuning the ResNet-50 model outperforms various other models in terms of the classification accuracy. The experimental results showed that the deployed cross-domain transfer-learning system achieved outstanding (99.5% and 99.1%) accurate performances compared to previous benchmarks on the NaSC-TG2 dataset with the final tuning of the whole structure and only the last three layers, respectively.  
natural scene classification; land cover; deep learning; remote sensing; convolutional neural networks; transfer learning +
Footnote †: journal: Medical  
reasons, for example, the base dataset size, variation, structure of deep learning, etc. This is why the base (pre-trained) model is of critical importance in transfer learning.  
In this paper, we applied transfer learning on a NaSC-TG2 dataset [7] using pre-trained deep-learning models. Previously, a VGG-16 model showed outstanding performance (overall accuracy: 89.59%) on a NaSC-TG2, setting a benchmark for further studies. The architecture of a VGG-16 model [8] is shown in Figure 1.
## 5 Implementation and Experiments
### Assessment through Experiments
#### 5.3.2 Evaluating the Cost of Developing the Learning Content  
This criterion evaluates the cost associated with creating content to teach, i.e., the foundational content, examples, and coding exercises.  
We posed a question to the six faculty members in the experimental group regarding the cost of creating learning content for a programming language. From their feedback, we could summarize the average efforts as the following:  
* Average number of units in a programming language: 12 units/P.L.;
* Average number of topics per unit: 3 topics/unit;
* Average time spent creating foundational content per topic: 3.5 hours/topic;  
Figure 18: Evaluation of learning effectiveness in a line chart.  
* Average number of students in a programming class: 28 students/class;
* Average number of coding exercises per unit: 3.5;
* Average time spent creating _a personalized coding exercise_: 0.5 h;
* Average time to grade an exercise submission: 0.25 h. The time required for operating a conventional eLearning system is the following:
* The total time for creating all of the foundational content for a language is computed as follows: _Time_for_Foundation_Content = 12 \(\times\) 3 \(\times\) 3.5 = 126 h_
* The total time for creating personalized coding exercise problems for a language is computed as follows: _Time_for_Coding_Exercises = 28 \(\times\) 12 \(\times\) 3.5 \(\times\) 0.5 = 588 h_
* The total time for evaluating exercise submissions for a language is computed as follows: _Time_for_Evaluating_Exercises = 28 \(\times\) 12 \(\times\) 3.5 \(\times\) 0.25 = 294 h_  
Hence, the total time for creating learning content for a programming language and operating the class with personalized exercise problems and grading exercise submissions is computed as (126 + 588 + 294) which amounts to just 1008 h.  
Now, the time required for operating a ChatGPT-empowered eLearning system is the following:  
* The total time for creating all the _foundational content_ for a language is nearly zero, except for the effort to create a course profile as described in Section 3.4.
* _Time_for_Foundation_Content = 0 h_ _Time_for_Course_Profile = 2 h_
* The total time for creating _personalized coding exercise_ problems for a language is computed as follows: _Time_for_Coding_Exercises = 0 h_
* The total time for evaluating exercise submissions for a language is computed as follows: _Time_for_Evaluating_Exercises = 0 h_
* Hence, the total time for creating learning content for a programming language and operating the class by utilizing ChatGPT is computed as (0 + 2 + 0 + 0) which amounts to just 2 h.  
The difference in total effort for generating the learning content and evaluating exercise submissions between conventional eLearning and GPT-based eLearning is substantial: 1008 h versus 2 h. This significant disparity serves as a compelling indicator of the feasibility and potential of GPT-based eLearning.
## 3 Numerical Results and Discussion
### Cellular Network  
The second numerical application of the model refers to an outdoor cellular network in an urban environment. The scenario consists of a mobile station MS moving along the trajectory from point A to point B passing through the mid-point of two base stations BS1 and BS2. The distance between the base stations is 4 km. The base stations are supposed to transmit with the same characteristics. The propagation loss model used for the subsequent variation of the received power is COST231-HATA. The location variability has been chosen as 6 dB and the shadowing decorrelation distance 10 m. The frequency of operation considered is 3.5 GHz and the mobile is supposed to be moving with the typical speed for vehicular in urban areas of 14 m/s.  
In Figure 6 we plot the theoretical and the simulated outage probability for dual diversity reception from the signals of the two base stations versus distance covered. The dynamic model generates the dual diversity outage probability with great accuracy. Maximum values are noticed as expected in the mean distance between the two base stations. In the same conclusion we arrive for the case of the AOD shown in Figure 7. Here we note again the fact that the simulated AOD does not converge for high values of fade margin near the two base stations. Finally in Figure 8 we plot the hand-off rate. This is defined as a kind of LCR when the strongest signal becomes weaker. The receiver selects in each time instant the strongest signal from the two base stations. As expected the bigger hand-off rate is noticed in the mean distance between the two base stations.  
Figure 6: Theoretical and simulated outage duration for a dual diversity scheme vs. distance covered.  
Figure 7: Theoretical and simulated average outage duration for a dual diversity scheme vs. distance covered.
# Compressive Sensing-Based Sparsity Adaptive Channel Estimation for 5G Massive MIMO Systems
## 2 Sparse Multipath Channel Model  
In a base station (BS) equipped with \(M\) transmitting antenna MIMO systems, the transmitting end sends orthogonal frequency-division multiplexing (OFDM) signals, and the length of each OFDM signal transmitted by each antenna is \(N\), where \(P(0<P<N)\) carriers are selected as the pilot for channel estimation, and the channel length \(L\). The pilot pattern of the \(i\)th transmit antenna is \(p(i)\), \(i=1\), \(2\),..., \(M\), where, \(p(i)\cap p(j)=\emptyset\). If \(i\
eq j\). After the channel is transmitted, the receiving end receives the pilot signal corresponding to each antenna as \(\mathbf{y}(p(i))\), \(i=1\), \(2\),..., \(M\). Abbreviate \(\mathbf{y}(p(i))\) as \(\mathbf{y}^{(i)}\), the basic channel model can be expressed as:  
\[\mathbf{y}^{(i)}=\mathbf{D}^{(i)}\mathbf{F}^{(i)}\mathbf{h}^{(i)}+\mathbf{m}^{(i)},\,i=1,\,2,\,L,\,M \tag{1}\]  
among them, \(\mathbf{D}^{(i)}=\text{diag}\{\mathbf{p}(i)\}\) is a diagonal array of selected pilot patterns, \(\mathbf{m}^{(i)}\) is a Gaussian White Noise with mean 0 and variance \(\sigma^{2}\), \(\mathbf{F}^{(i)}\) is a \(P\times L\) sub-matrix of a Fourier, corresponding to the dimensions \(N\times N\) discrete Fourier transform (DFT) matrix pilot line elements and the first \(L\) columns, \(\mathbf{h}^{(i)}=[h^{(i)}(1),h^{(i)}(2),...,h^{(i)}(L)]^{\text{T}}\) is the channel impulse response (CIR) corresponding to the \(i\)th antenna. Make \(\mathbf{A}^{(i)}=\mathbf{D}^{(i)}\mathbf{F}^{(i)}\), Then, Equation (1) can be further expressed as:  
\[\mathbf{y}^{(i)}=\mathbf{A}^{(i)}\mathbf{h}^{(i)}+\mathbf{m}^{(i)},\,i=1,\,2,\,L,\,M \tag{2}\]  
**Definition 1**.: _supp\(\{\mathbf{h}^{(i)}\}=\{l\colon\mathbf{h}^{(i)}\ (l)\ |>p_{th},\,1\leq l\leq L\}\) is the \(i\)th sub-channel support set index, and \(p_{th}\) is the noise threshold. The research shows that for longer signal transmission distance, the antenna array size of the BS is very small, the delay dispersion characteristics of subchannels between different transmitting and receiving antenna pairs in massive MIMO system are consistent and have approximately the same channel delay. The delay model shows the same sparse support set of sub-channels between different transmitting antennas and users [12], that is:_  
\[\text{supp}\{\mathbf{h}^{(i)}\}=\text{supp}\{\mathbf{h}^{(i)}\},\,i\
eq j \tag{3}\]  
Because the rate of change of channel gain is much larger than the rate of change of delay, the gain coefficients of each subchannel are different, but the positions of nonzero taps of different subchannels are the same, showing a joint sparse characteristic. Based on this characteristic, designing an appropriate recovery algorithm can achieve a more accurate and quick estimate of channel information.
## 4 PSO Based on the PSO-BP Model
### Model Parameter Setting and Data Processing
#### 4.3.3 Key Parameter Configuration for PSO Algorithm  
* Swarm size: 50 particles;
* Maximum iterations: 100;
* Learning factors: c1 = 2.0 \(\rightarrow\) 1.5 (dynamic decay), c2 = 1.5 \(\rightarrow\) 2.0 (dynamic increment);
* Maximum velocity: Vmax = 0.2;
* Inertia weight: w = 0.9 \(\rightarrow\) 0.4 (linear decay).
## 5 Discussion  
Simulations were carried out given the same parameters in [2; 6; 7]. The Rayleigh scenario was included and compared with a practical scenario on a SDR (USRP NI-2922). Results show that the proposed scheme has a better performance compared with the energy detector, by approximately 8 dB for both the AWGN and Rayleigh channels. In experimental results for a selected \(\mathrm{P_{D}}\) = 90%, this difference is equal to 3 dB in the Rayleigh channel and 5 dB below in the AWGN channel, due to the effects of fading in real scenarios. The experimental average value of noise entropy is similar to the simulation value where the Renyi-entropy of noise stands at 2.198 [11] and the average experimental value of noise is 2.161, showing a difference below 2% in simulations. The averaged Renyi-entropy for the PU signal was 1.26. If the Renyi-entropy value remains between 1 and 2.161 then the signal will be classified as a PU.
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 3 Results and Discussion
### Fault Diagnosis
Histograms inform maintenance operators about any deviations of each feature from its normal value. Figure 10 illustrates the detection of an anomaly and a significant temperature increase in the generator bearings. Figure 11 provides an example, where significant deviations in the transformer, generator bearings, and ambient temperature are observed. The results are clear: not all variables are affected by a given problem, regardless of the failed component. This underscores the rationale for minimizing the number of characteristics to be observed while maximizing the relationships each variable may have with a specific issue.
## 2 Materials and Methods
### Determination of Antioxidant Activity of Hydrolystates
#### 2.10.2 ABTS Radical Scavenging Activity  
The ABTS radical scavenging activity of the hydrolysates was determined according to [PERSON] et al. (2019), with some modifications. ABTS radical stock solution was prepared by mixing 7 mmol/L ABTS with 140 mmol/L potassium persulfate, kept in the dark at room temperature for 12-16 h. The ABTS solution was diluted with phosphate buffer (0.2 M, pH 7.4) to an absorbance of 0.70 \(\pm\) 0.02 at 734 nm. A total of 50 \(\mu\)L of sample was added to 150 \(\mu\)L of diluted ABTS solution, followed by incubation at room temperature for 6 min. The absorption was monitored at 734 nm. The scavenging effect for ABTS radical was expressed as follows:  
\[\text{ABTS radical scavenging activity (\%)}=[1-(\text{As}-\text{Ab})/\text{Ac}]\times 100 \tag{2}\]  
where As represents the absorbance of the sample at 734 nm, Ab represents the absorbance of the sample blank without ABTS, and Ac represents the absorbance of the control without sample.
## 4 Experiment and Analysis
### Experimental Results
#### 4.3.4 Comparative Analysis of PR Curves  
The algorithm in this paper is based on an improvement of YOLOv4-tiny, so this paper compared the performance of the improved algorithm with the PR curve of the YOLOv4-tiny algorithm. As can be seen from Figure 8, the performance of the improved algorithm in this paper was better than that of the algorithm before the improvement, with a better accuracy and recall rate and a stronger robustness. According to the PR curve, the algorithm applied to fine sand performed the worst. The reason was that a large part of the fine-sand data-set samples were collected in a dark environment, and the images' distinguishable features were not obvious, resulting in a certain deviation, a low recall rate, and a small performance improvement for this type of images.  
\begin{table}
\begin{tabular}{c c} \hline
**Platform** & **Configuration** \\ \hline Operating System & Ubuntu 18.04 \\ CPU & I-12400F \\ GPU & RTX3060 \\ Memory & 12G \\ Cuda & 11.3 \\ Cudnm & 8.2.1 \\ \hline \end{tabular}
\end{table}
Table 5: Development platform information.  
Figure 7: Detection results of different scenes under low contrast.  
To compare the two algorithms in more detail, we compared each category of the two algorithms separately. The PR curve is shown in the figure. According to Figure 9, the improved algorithm in this paper was better than the original algorithm in terms of comprehensive performance. Therefore, the improved algorithm in this paper met the task requirements.