esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Taillight Detection and Light Signal Identification Based on Image Processing
### Taillight Signal Identification  
#### 4.3.1 Histogram Characteristic Parameter Extraction  
The gray level histogram feature parameters of the image are measured at certain specific pixels or their neighborhoods, which can well describe the gray level features in the image. The histogram shows a global description of the grayscale image, but the histogram is not directly used as a feature. Instead, its mean, variance, energy, and entropy are used as the characteristics of the differences between the categories.  
For an image \(f\), if we assume the total number of pixels is \(N\), the maximum gray level of the image (the grayscale image is 255) is \(L\), the number of pixels with grey level \(k\) is \(Nk\), then the gray-level histogram of \(f\) can be represented as:  
\[h_{k}=\frac{N_{k}}{N}k=0,1,\cdots,L-1 \tag{8}\]  
The mean value is the mean value of the grey level probability distribution, as follows:  
\[\overline{f}=\sum_{k=0}^{L-1}kh_{k} \tag{9}\]  
The variance is the measurement of the discrete type of the image grey value distribution, as follows:  
\[\alpha_{f}^{2}=\sum_{k=0}^{L-1}{(k-\overline{f})}^{2}h_{k} \tag{10}\]  
The energy represents the uniformity of the grey level distribution, as follows:  
\[f_{N}=\sum_{k=0}^{L-1}{(h_{k})}^{2} \tag{11}\]  
Figure 12: The detection results of the taillight pairs. (**a**) The result of the taillight pair detection using only the positional relationship constraint; (**b**) The results of the detection using the taillights of the method in this article.  
The entropy is the measurement of the amount of information in an image, as follows:  
\[f_{E}=-\sum_{k=0}^{L-1}h_{k}\log_{2}h_{k} \tag{12}\]  
The average brightness of the image can be expressed by the mean value. The dispersion of the image grey level distribution can be expressed by the variance. Due to the influence of image sampling on the mean and the variance, the target image is usually normalized for classification. The energy is the secondary moment of the grey distribution to the original point. If the grey value of the image is in an equiprobability distribution, the energy is the smallest. Otherwise, the energy is larger. In information theory, the entropy reflects how much information is contained in an image.
## 3 Optimization Design
### Genetic Algorithm
These two parts are expressed as [39]:  
\[\dot{S}_{gen,T}=\left(\dot{m}c_{p}\right)_{t}\ln\left(\frac{T_{t,\rho}}{T_{t,i }}\right)+\left(\dot{m}c_{p}\right)_{s}\ln\left(\frac{T_{s,\rho}}{T_{s,i}}\right) \tag{15}\]  
\[\dot{S}_{gen,P}=\dot{m}_{t}\frac{\Delta P_{t}}{\rho_{t}}\frac{\ln\left(T_{t, \rho}/T_{t,i}\right)}{T_{t,\rho}-T_{t,i}}+\dot{m}_{s}\frac{\Delta P_{s}}{\rho_{ s}}\frac{\ln\left(T_{s,\rho}/T_{s,i}\right)}{T_{s,\rho}-T_{s,i}} \tag{16}\]  
The total entropy generation rate is given by Equation (17). \[\dot{S}_{gen}=\dot{S}_{gen,T}+\dot{S}_{gen,P} \tag{17}\]  
[PERSON] [40] proposed the \"entropy generation minimization\" theory to optimize the performance of heat transfer equipment including the heat exchanger. The total entropy  
Figure 2: Flow chart of genetic algorithm. generation rate is nondimensionalized into an entropy generation number (EGN) by the larger heat capacity rate, and the expression is given by Equation (18). \[N_{s}=\frac{\dot{S}_{gen}}{inc_{p}} \tag{18}\]  
where \(N_{s}\) is called [PERSON]'s definition of EGN. In order to avoid the \"entropy generation paradox\" arising from the EGN calculation, [PERSON] [41] proposed the modified entropy generation number (MEGN) by drawing the inlet temperature of cold fluid as  
\[N_{s1}=\frac{\dot{S}_{gen}T_{t,i}}{Q} \tag{19}\]  
In the present study, five parameters were taken as the design variables which are the tube--outside diameter (\(d_{0}\)); pitch diameter ratio; ratio of shell--inside diameter to the shell--inside diameter calculated by Equation (11); the length of the tubes (\(L\)); and the layers of tubes. The ranges of these design variables are listed in Table 5.
## 4 Discussion
### Air Renewal Efficiency  
The evolution of CO\({}_{2}\) concentration increased steadily as the children arrived and remained consistently elevated throughout the day. Concentration levels only dropped during short ventilation periods, when windows were partially opened, or after the classrooms were vacated at the end of the day. Ventilation primarily relied on uncontrolled air infiltration through the building envelope, with an estimated air exchange rate (Air changes per hour, _ACH_) of 0.3-0.5. This rate, indirectly calculated using the CO\({}_{2}\) decay method, is typical for classroom structures of this kind in Andalusia [24].  
The monitoring campaign highlighted significant findings in Classroom A. For instance, during the 10 th week (refer to Figure 9), CO\({}_{2}\) concentrations consistently exceeded both the recommended threshold of 550 ppm for spaces with vulnerable, unmasked occupants [36; 87] and the regulatory limit of 770 ppm established for childcare centers in Spain [64; 88; 89]. On the worst days, the CO\({}_{2}\) level peaked at or above 1900 ppm, significantly surpassing acceptable safety standards.  
To contextualize these findings, CO\({}_{2}\) levels were classified into three distinct categories: \"Below the standards threshold\" (<770 ppm), \"Above the Spanish standard threshold\" (770-1220 ppm), and \"Not admissible\" (>1220 ppm). A comparative analysis focusing on weeks 8, 10, and 11 (Figure 9) highlighted significant concerns. In Week 8, indoor air quality was notably poor, with infants exposed to air CO\({}_{2}\) levels exceeding 1220 ppm for over one-third of their time in the classroom. Week 10, which represented average conditions of the monitored period, showed slight improvements but still failed to meet health standards for a significant portion of the day.  
Despite the presence of a mechanical extractor in the bathroom, it was not utilized during the monitored period. Combined with the inefficient operation of the air conditioning system, this suggests that automatic systems integrated with monitoring devices would be more effective than user-controlled systems in improving both energy efficiency and air quality.  
In Week 11, a new ventilation protocol was introduced, instructing staff to leave the main classroom window open by 5 cm throughout the day and fully open the small bathroom windows, providing a total opening area of 0.61 m\({}^{2}\). While this intervention led to modest improvements in CO\({}_{2}\) concentrations, the levels still exceeded the regulatory safe range for over 75% of the classroom's occupied time. Further increasing the window openings and installing transfer grilles in the doors to promote cross-ventilation and thereby enhance airflow would have compromised thermal comfort and energy efficiency. This underscores the inherent limitations of relying solely on natural ventilation, which cannot ensure a minimum air renewal rate with adequate filtration.
## 3 Results and Discussion
### Volatile Compounds in the Soybean Protein Hydrolysate Samples as Identified via GC-IMS
Such a difference in the number of detected species between GC-IMS and GC-MS might be attributed to their detection principles. Most organic acids were less volatile, with only a few being highly volatile. GC-IMS is sensitive to those highly volatile acids. Due to their low relative contents and high odor thresholds, propanoic acid and 2-methylbutanoic acid imparted a minimal impact on the overall flavor of soybean hydrolysate samples. In terms of esters, only three species were detected, with the concentrations of ethyl acetate and isoamyl acetate increasing significantly after fermentation, thereby giving pleasant fruity and sweet aromatic odors. The fewer esters detected might be due to the low lipid content in soybean hydrolyses [35]. In this experiment, the relative contents of some ketones (including methyl isobutyl ketone and 2-hexanone) significantly decreased, while others increased, such as 2-pentanone (sweet fruity), 3-methyl-2-pentanone (cheese), and 2-heptanone (cheese and fruity aroma), compared with levels in the control groups (\(p<0.05\)). These changes were consistent with the results of GC-MS. Furthermore, 3-methylthiopropanol (potato, tomato, and creamy), 2,6-dimethylpyrazine (nutty, roasted, meaty, coffee), and furfural (sweet, woody, baked bread) were also detected, which might be important contributors to the overall flavor of the soybean protein hydrolyses. In summary, the major volatile flavors of soybean hydrolysates were derived from aldehydes and ketones. However, the contribution of aldehydes to the flavor of the final hydrolysate product was reduced significantly after fermentation, while the contribution of esters and alcohols increased. The aldehydes were oxidized to acids or reduced to alcohols, which decreased the effect of the aldehydes on samples. Similar findings were reported by [PERSON], [PERSON], and [PERSON] (2017); the type and concentrations of the volatile compounds, including alcohols, esters, acids, and furan, in the lupine fermented by _T. halophilus_ and _Z. rouxii_ increased significantly.
# Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems
## 5 Results
### Step 2--Defining SQL Query Patterns
#### 5.3.2 Obtaining Interactions between Artifacts  
A list of type-level relations from the main artifact to other artifacts is obtained. Each relation class is used to generate an SQL query that joins the tables of source and target artifacts and selects the identification properties of each artifact. Specifically, direct relation instances are retrieved by joining the two tables based on one or more reference fields defined in a model. For indirect relations, it is possible to connect two tables using the join path attribute which involves multiple joins to be performed. The result is effectively a join table that holds two artifact IDs, i.e., the source and target, which allows us to identify interrelated artifacts. Because of space limitations in this paper, we illustrate one such query pattern in Listing 7.  
```
1:SELECTDirectRelation.SourceArtifact.FieldName_ID
2:DirectRelation.TargetArtifact.FieldName_ID
3:FROMDirectRelation.SourceArtifact.TableName AS S
4:INNERJOINDirectRelation.TargetArtifact.TableName AS T
5:ONS.DirectRelation.SourceArtifact.FieldName_ID =
6:T.DirectRelation.ReferenceField
7:WHEREDirectRelation.SourceArtifact.condition AND
8:DirectRelation.TargetArtifact.condition;
```  
**Listing 7** SQL query pattern that selects direct relation instances.  
To illustrate with a concrete example in our case study, an SQL query is shown in Listing 8, which selects relation instances between purchase order and purchase receipt artifacts. Table 3 shows the excerpt of the query result. Notice that, in this case, two instances of direct relation exist between the purchase order PO1 and receipt documents. R1 and R3. It should also be noted that the examples specific to the case study should not be written manually, but that they would be generated based on the SQL query patterns and the provided model.  
```
1:SELECTOrder_No,Receipt_No
2:FROMPurchase_Order_HeaderASSINNERJOINPurch_Rcpt_HeaderAST
3:ONS.Order_No=T.OrderNo
4:WHERES.Document_Type=\"StandardPurchaseOrder'AND
5:T.Document_Type=\"Purchase receipt';
```  
**Listing 8** SQL query that selects instances of purchase item direct relation.
## 3 Reliability Verification of Finite Element Model
### Fault Frequency Verification  
1. Experimental fault frequency: A QPZZ-II rotating machinery failure simulation experimental bench was used, where the experimental bench was mainly composed of a mechanical drive device, loading device, and a fixed device in four parts, as shown in Figure 2a. The QPZZ-II rotating machinery failure simulation experimental bench can be adjusted through the installation of some parts of the position and an organic combination of the components to achieve a variety of rotating machinery failure fast simulations. The test bench adopts electrical discharge machining wire cutting to simulate the type of bearing failure, the outer ring failure shown in Figure 2b. The vibration signals of the outer ring fault collected during experiments were also subjected to envelope transformation. The obtained time domain and frequency domain waveform is shown in Figure 3b.
2. Simulation fault frequency: The simulated vibration signals in the Y direction near the fault nodes were extracted, as shown in Figure 3a. An envelope spectrum analysis was performed on the original simulation vibration signals, resulting in the frequency domain waveform displayed in Figure 3a.
3. Theoretical failure frequency is calculated by the outer ring failure frequency formula [17]: \[f_{o}=\frac{n_{i}}{2\times 60}\bigg{(}1-\frac{D}{d_{m}}\cos\alpha\bigg{)}z\] (11) A theoretical outer ring fault frequency of 116.14 Hz was obtained.  
Figure 1: Finite element model of bearing.  
Through a finite element simulation analysis of the obtained vibration signals, the envelope spectrum analysis determined the outer ring fault frequency to be 115.2 Hz. Compared to the theoretically calculated fault frequency of 116.14 Hz, the relative error is 0.809%. Similarly, the relative error when compared to the experimentally measured outer ring fault frequency of 115.2 Hz is also 0.809%, as detailed in Table 4. The absence of error between the simulation and experimental values validates the effectiveness of the proposed model and provides a solid foundation for subsequent analyses.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Failure frequency** & \begin{tabular}{c} **Theoretical Values** \\ 116.14 Hz \\ \end{tabular} & \
\begin{tabular}{c} **Simulation Values** \\ 115.2 Hz \\ \end{tabular} &
\begin{tabular}{c} **Experimental Values** \\ 115.2 Hz \\ \end{tabular} \\ \hline
**Inaccuracies** & \begin{tabular}{c} **Theory–Simulation** \\ 0.809\% \\ \end{tabular} & \
\begin{tabular}{c} **Theory–Experiment** \\ 0.809\% \\ \end{tabular} &
\begin{tabular}{c} **Simulation–Experiment** \\ 0 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of failure frequency verification.  
Figure 3: Time domain waveform and frequency domain waveform of simulation signals and test signals: (**a**) simulated signal; (**b**) test signal.  
Figure 2: QPZZ–II rotating machinery fault simulation test bench: (**a**) laboratory bench; (**b**) bearing outer ring failure.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Precision
#### 3.1.2 Green Band
In this case, the mean of all measurements across all systems is shown along with the standard deviation of those measurements. The standard deviation reflects the repeatability that is achievable across multiple teams. Repeatability is \(\pm\)0.01, in general, for bright targets and \(\pm\)0.001 for dark targets. UAS repeatability in the Green band is shown in Table 4. The same target set is used, along with the morning and noon differences. Noon differences seem to be a bit smaller than the morning differences, likely due to target BRDF. The repeatability of bright target measurements varies from 0.004 to 0.058 \(\pm\) 0.02, while for dark targets it is 0.001 to 0.017 \(\pm\) 0.005. On average, UAS bright and dark target repeatability is somewhat larger than that for the ASD. However, the repeatability is comparable excluding Mystery Gray target. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & \multicolumn{6}{c}{A/ASD Representing Across All Features (Green band)} \\ \cline{3-10}  & & \multicolumn{6}{c}{Dimitroid Error Probability} \\ \hline  & & \multicolumn{3}{c}{Permaflect 55\%} & \multicolumn{3}{c}{RIT 30\%} & \multicolumn{3}{c}{Asphalt} & \multicolumn{3}{c}{Mystery Gray} \\  & & \multicolumn{3}{c}{Reflectance} & \multicolumn{3}{c}{Mean} & \multicolumn{1}{c}{Std.} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{Std.} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{Std.} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{Std.} \\  & & \multicolumn{3}{c}{Dev.} & \multicolumn{1}{c}{Dev.} & \multicolumn{1}{c}{Dev.} & \multicolumn{1}{c}{Dev.} & \multicolumn{1}{c}{Dev.} & \multicolumn{1}{c}{Dev.} \\ \hline All & AM & 0.519 & 0.009 & 0.306 & 0.009 & 0.129 & 0.007 & 0.487 & 0.019 \\ \cline{2-10} Teams & Noon & 0.512 & 0.011 & 0.303 & 0.007 & 0.126 & 0.004 & 0.498 & 0.019 \\ \hline \hline \end{tabular}
\begin{tabular}{l l l l l l l l l l l} \hline \hline  & & \multicolumn{6}{c}{Permaflect 55\%} & \multicolumn{3}{c}{RIT 30\%} & \multicolumn{3}{c}{Asphalt} & \multicolumn{3}{c}{Mystery Gray} \\  & & \multicolumn{3}{c}{Reflectance} & \multicolumn{3}{c}{Mean} & \multicolumn{1}{c}{Std.} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{Std.} & \multicolumn{1}{c}{Dev.} & \multicolumn{1}{c}{Dev.} & \multicolumn{1}{c}{Dev.} & \multicolumn{1}{c}{Dev.} \\ \hline All & AM & 0.061 & 0.001 & 0.018 & 0.001 & 0.051 & 0.001 & 0.029 & 0.000 \\ \cline{2-10} Teams & Noon & 0.058 & 0.001 & 0.018 & 0.001 & 0.055 & 0.002 & 0.028 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Green band precision results across all ASD systems. Figure 9: Spectral profile comparison of Permaflect targets.
## 3 Proposed Scheme
### Data Collection and Preprocessing  
We first collect original waveform data \(D_{w}\) from animal sound databases or bioacoustics sensors. To use animal sounds as input to DeepASC, preprocessing into a spectrogram is essential. Compared to the original waveform, a spectrogram contains a frequency-time representation (i.e., 2D visual representation) that shows how the frequency content of a sound signal changes over time. The preprocessing part (Line 1 in Algorithm 1) is organized as follows: (i) all sound segments in the datasets are sampled at a sampling rate of 16 kHz and (ii) padded to a length of one second. (iii) Short-time Fourier transformation (STFT) is conducted on the raw waveform with an FFT size of 256, a window size of 128, and a hop size of 128 to obtain 128 \(\times\) 87 spectrograms that constitute \(D_{w}{}^{\prime}\).
# Serial Decoders-Based Auto-Encoders for Image Reconstruction
## 4 Experiments
### Experimental Conditions  
The experimental software platform is MATLAB 2020b on Windows 10 or Linux. For the small data sets, MNIST, FMNIST, and MMNIST, the experimental hardware platform is a laptop with a 2.6 GHz dual-core processor and 8 GB memory; For the large data set,  
Figure 11: Sample images of experimental data sets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Data Set**} & \multicolumn{1}{c}{**Class Number**} & \multicolumn{2}{c}{**Sample Number**} \\ \cline{3-5}  & & & **Training** & **Testing** \\ \hline \multicolumn{2}{c}{MNIST} & 10 & 60,000 & 10,000 \\ \hline \multirow{4}{*}{EMINIST} & digits & 10 & 240,000 & 40,000 \\  & letters & 26 & 124,800 & 20,800 \\  & balanced & 47 & 112,800 & 18,800 \\  & bymerge & 47 & 697,932 & 116,323 \\  & byclass & 62 & 697,932 & 116,323 \\ \hline \multicolumn{2}{c}{FMNIST} & 10 & 60,000 & 10,000 \\ \hline \multirow{4}{*}{MMNIST} & breast & 2 & 546 & 156 \\  & chest & 2 & 78,468 & 22,433 \\  & derma & 7 & 7007 & 2005 \\  & OCT & 4 & 97,477 & 1000 \\  & axial organ & 11 & 34,581 & 17,778 \\  & coronal organ & 11 & 13,000 & 8268 \\  & sagittal organ & 11 & 13,940 & 8829 \\  & pathology & 9 & 89,996 & 7180 \\  & pneumonia & 2 & 4708 & 624 \\  & retina & 5 & 1080 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Class and sample numbers of experimental data sets.  
EMNIST, the experimental hardware platform is a super computer with high-speed GPUs and substantial memory.  
The components of auto-encoders are made up of fully-connected (FC) layers, leaky rectified linear unit (LRELU) layers, hyperbolic tangent (Tanh) layers, Sigmoid layers, etc. In order to reduce the calculation complexity, the convolutional (CONV) layer is not utilized.  
The composition of the encoder is shown in Figure 12, which consists of input, FC, LRELU, and hidden layers.  
The constitution of the decoder is illustrated in Figure 13, which consists of input, FC, LRELU, Tanh and output layers. The input layer can be the hidden layer for the first decoder, and can be the output layer of the preceding decoder for the latter decoders. The dashed line shows the two situations.  
The organization of the discriminator is demonstrated in Figure 14, which comprises input, FC, LRELU, Sigmoid, and output layers. The input layer can be the hidden layer and can be the output of each decoder. The dashed line indicates the two cases.  
The deep learning parameters, such as image size, latent dimension, decoder number, batch size, learning rate, and iteration epoch, are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Image size & \(28\times 28\times 1\) \\ Latent Dimension & 30 \\ Decoder Number & 3 \\ Batch size & 100 \\ Learning Rate & 0.0002 \\ Iteration Epoch & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The deep learning parameters.  
Figure 12: Composition of the encoder.
## 1 Introduction
We derive a solution for optimal energy beamforming in closed form, thereby developing an efficient algorithm with low complexity in obtaining the optimal solution. * We devise an efficient algorithm to jointly optimize the UAV altitude and energy beamforming in a scenario with multiple EHDs by investigating the optimal conditions as well as the dual problem. Motivated by insights from the design for a single-EHD scenario, we also develop an efficient low-complexity method for determining near-optimal altitude and energy beamforming. Moreover, we explore a sub-optimal design by leveraging weighted-sum energy beamforming in closed form with considerably reduced computational complexity. * The numerical results demonstrate that compared to conventional methods, the proposed methods can significantly reduce the overall charging time while also decreasing the computational complexity. The rest of this paper is organized as follows. In Section 2, we introduce the system model and formulate our design problem. In Section 3, we jointly optimize UAV altitude and energy beamforming for a single-EHD scenario. In Section 4, we propose the methods for jointly optimizing UAV altitude and energy beamforming in a multiple-EHD scenario. We evaluate our proposed methods in Section 5 and conclude our paper in Section 6.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
###### Abstract  
With the explosive growth of unmanned aerial vehicle (UAV) applications in numerous fields, low-altitude networks face formidable challenges in monitoring. In this context, integrated sensing and communication (ISAC) networks through three-dimensional (3D) wide-area sensing have emerged as the key solution. However, the differences in networking mechanisms between communication and sensing, along with the transition from two-dimensional (2D) to 3D networking, complicate the realization of seamless 3D sensing. We aimed to address these challenges by analyzing the sensing capabilities of a single base station and the disparities between communication and sensing. Based on this, an innovative 3D sensing model for ISAC single base stations was proposed, defining the sensing boundaries and providing a foundation for designing the key parameters of ISAC base stations. Additionally, a multi-base station (multi-BS) low-altitude networked 3D sensing cellular-like architecture was proposed, overcoming the limitations of traditional 2D networks and achieving seamless 3D sensing. To validate the effectiveness of the model, comprehensive tests were conducted in both controlled laboratory conditions and real-world commercial network environments. The results show that the model successfully achieved stable and continuous sensing with the expected coverage and accuracy in networked environments.  
Keywords:low-altitude networked sensing; integrated sensing and communication; single base station sensing model; cellular-like architecture; unmanned aerial vehicle +
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