esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of C. cardunculus Extracts on Bacterial Growth  
#### 3.2.1 Antimicrobial Activity of C. cardunculus  
Extracts Using Solid Method  
In this study, the inhibitor agents used were _C. cardunculus_ leaves decoction extract (CL) and inflorescence decoction extract (CI), and chlorogenic acid (CA) to analyze the effect on several bacteria collected in the dairy factory. For this study, rosmarinic acid was also added due to its similar chemical structure to chlorogenic acid, both caffe acid derivatives. The inflorescence aqueous extract is traditionally used in cheese production, so the inflorescence decoction extract was included in this study. Eleven wild strains isolated from the dairy industry and four reference strains from ATCC and NCTC were tested. The results of the antimicrobial activity of the extracts of _C. cardunculus_ and standard compounds against the fifteen bacterial cultures are shown in Table 2.  
CL and CI showed no inhibitory activity on all strains tested. All strains, except _Staphylococcus aureus_ ATCC 25923, were inhibited by CA and RA. In this case, it was verified that the standard compounds were better inhibitors then _C. cardunculus_ extracts. CA and RA showed the same antimicrobial activity as the negative control, for a confidence level of 95%. For example, _Pseudomonas putida_ ESACB 191 showed similar antimicrobial activity for CA and a decrease of approximately 1 mm for RA compared to the negative control (\(p\leq 0.05\)).
### Significance of the Work  
It is well known that regularization for the EIT inverse problem is essential for obtaining proper and stable reconstructions. We investigate methodically the implementation of RBF for EIT with or without regularization, and demonstrate that regularization is required for optimal performance. However, to our knowledge, no prior work has methodically studied the effectiveness of different regularizations for the EIT inverse problems with radial basis function networks. In this paper, we use numerical simulations to (1) determine the optimal number of radial basis functions in the network to avoid overfitting; (2) compare the fixed versus variable Gaussian width with or without regularization; (3) compare image reconstruction with or without regularization, in particular, no regularization, Tikhonov, Lasso, and Elastic Net; (4) compare both mean square and mean absolute error and the corresponding variance; and (5) compare robustness, in particular, the performance of the different methods concerning noise level. We conclude that by looking at the \(R^{2}\) score, one can determine the optimal number of radial basis functions. The fixed-width radial basis function network with regularization results in improved performance. The fixed-width Gaussian with Tikhonov regularization performs very well. The regularization helps recon struct the images outside of the training data set. The regularization may cause the quality of the reconstruction to deteriorate; however, the stability is much improved. In terms of robustness, the RBFs with Lasso and Elastic Net seem very robust compared to Tikhonov.
###### Abstract  
The accuracy of single-mode optical imaging systems for vehicle target recognition is limited by external ambient illumination or imaging equipment resolution. In this paper, a vehicle target recognition method based on visible and infrared image fusion using Bayesian inference is proposed. Based on the significance area detection combined with Spectral Residual (SR) and EdgeBox algorithms, the target area is marked on the visible light image, and the maximum between-class variance method and GrabCut algorithm are used to segment vehicle targets in marked images. The open operation filter is employed to extract the vehicle target features in the infrared image, and the fusion result of the visible light image and the infrared image is obtained by introducing the Intersection Over Union (IOU), and as the parameter of Bayesian inference, the class and attribute of the parameter are defined and substituted into the established naive Bayesian classification model, and the probability of the class is calculated to determine whether the vehicle target is recognized. Experiments under different test conditions were carried out, and the experimental results were as follows: the accuracy of image target recognition reached 77% when the vehicle target was not occluded; when the vehicle target was partially occluded, the accuracy of image target recognition reached 74%; the results verified that the proposed method can recognize vehicle targets in different scenarios.  
vehicle target; visible light image; infrared image; Bayesian inference; intersection over union
# A Novel Method for Constructing the S-Box Based on Spatiotemporal Chaotic Dynamics
###### Abstract  
A novel construction method for a random S-box by using the spatiotemporal nonlinear chaotic system is proposed. The chaotic sequences of the spatiotemporal chaotic system are applied to construct an initial S-box. Then, the permutation operation between independent chaotic sequences is performed to shuffle the elements of the S-box randomly. In comparisons with the former schemes, the results of the performance analysis indicate that the obtained S-box has a better output bit independence criterion and a stronger ability to resist linear password attacks. It also has a high dimensional feature due to the spatiotemporal chaotic dynamical behaviors. The proposed scheme holds superior cryptographic features.  
chaos; S-box; coupled map lattices; non-adjacent coupled map lattices +
Footnote †: journal: Applied Physics Letters
## 7 The Subjective Evaluation Experiment
### Experimental Results of the Subjective Evaluation  
Figure 11 shows the frequency distribution of the responses for distance in the living room environment, and Figure 12 shows the frequency distribution of the responses for distance in the live studio environment. Figure 13 shows a box plot of the correct answer rate for distance. In Figures 11 and 12, the bubbles on the dotted line indicate correct answers. A bubble's size is drawn in proportion to its frequency.  
In the bubble charts in Figures 11 and 12  
As shown in Figures 11-13, the performance of distance estimation using the conventional method and the proposed method decreased compared to that of the real sound source in the living room environment. In this environment, the performance of the proposed method was approximately equal to that of the conventional method.  
On the other hand, in distance estimation, the performance of the proposed method is approximately equal to that of the real sound source in the live studio environment. In contrast, in the live studio environment, the performance of the conventional method is degraded by approximately 20% compared to that of the proposed method.  
Figure 14 shows the frequency distribution of the responses for direction in the living room environment, and Figure 15 shows the frequency distribution of the responses for direction in the live studio environment. Figure 16 shows a box plot of the correct answer rate for direction. In Figures 14 and 15, the bubbles on the dotted line indicate correct answers. A bubble's size is drawn in proportion to its frequency.  
Figure 14: Frequency distribution of answers with direction in the living room.  
Figure 15: Frequency distribution of answers with direction in the live studio.  
Similarly, in the bubble charts in Figures 14 and 15, the horizontal axis represents the presented direction, while the vertical axis represents the answered direction in response to the presented direction, with larger circles indicating a higher frequency of such responses. Conditions where the presented and answered directions are equal are represented by bubbles on the diagonal, which indicate correct responses. Figures 14 and 15 show the overall bubble charts summarizing all of the subjects' orientation responses. Figure 16, on the other hand, presents a box plot of the number of correct responses against the total number of trials for orientation. The percentage of correct responses is calculated for each subject, with individual differences represented in the box plot.  
As shown in Figures 14-16, the performance in direction estimation is consistently high across all conditions. We confirmed the effectiveness of direction control with VBAP using PALs and loudspeakers.  
From the above experimental results, we confirmed the effectiveness of the proposed method. The proposed method demonstrates a high performance for distance presentation, particularly in high-reverberation environments.
## 4 Results
### Comparison Experiment  
The hybrid model structure based on MacBert-BiLSTM is used in this study, and the following experiments can be carried out on the comparison experiments: in performing the benchmark model comparison (baseline: standard BERT model), by only retaining the Bert part to output the classification results directly, the accuracy of the validation set will drop from 93.37% to 87.23%; replacing sequence modeling experiments by replacing BiLSTM with GRU will reduce the accuracy of the validation set from 93.37% to 90.45% on a long text (>300 words) dataset; the accuracy of removing the BiLSTM layer and using the MacBERT is 78.63%; the model that removes BiLSTM and uses the unidirectional LSTM has an accuracy of 85.23% in the validator. BLAF outperforms pure BERT (+6.14% accuracy) and BiLSTM-GRU hybrids (+5.85% F1), demonstrating the efficacy of heterogeneous feature fusion. Table 6 shows the data from the comparative experiment.  
The observed performance degradation when substituting BiLSTM with GRU in long-text scenarios (>300 words) stems from fundamental architectural distinctions in their  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **F1** & **Recall** \\ \hline Benchmark model comparison & & & \\ (baseline: standard BERT model) & 87.23 & 85.56 & 85.56 \\ GRU & 90.45 & 87.43 & 87.43 \\ MacBERT Only & 78.63 & 86.56 & 86.15 \\ LSTM & 85.23 & 83.85 & 83.63 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of experimental data.  
gating mechanisms and directional information flow. While both architectures belong to the recurrent neural network family, BiLSTM's dual-path design (forward and backward passes) inherently captures bidirectional context more effectively than GRU's single-update-gate structure. Specifically, BiLSTM employs separate forget (ft) and input (it) gates to regulate cell state (Ct) updates, enabling explicit control over long-term information retention. This decoupled gating mitigates gradient vanishing when propagating dependencies across extended sequences--a critical advantage for homophone disambiguation where contextual cues may span multiple clauses (e.g., resolving \"\(\bigtriangleup\)\" vs. \"\(\bigtriangleup\)\" requires integrating distant syntactic constraints). In contrast, GRU's update gate (zt) jointly handles forgetting and input, compressing state transitions into a single operation. This simplification enhances parameter efficiency for short sequences but induces systematic attenuation of low-frequency long-range dependencies due to compounded gate saturation. Furthermore, BiLSTM's additive cell state updates (vs. GRU's multiplicative interactions) reduce sensitivity to activation scale fluctuations in deep recursions, stabilizing gradient flow during backpropagation through lengthy texts. GRU's architectural parsimony sacrifices robustness to positional decay of contextual features--a trade-off acutely manifested in tonal languages where homophone resolution demands persistent phonological-semantic alignment.
# Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances
## 3 Materials and Methods
### Channel Attention Mechanism Unit  
An integrated attention model emphasizes significant features by assigning weights based on their relevance to the classification task. This adaptive prioritization enables the model to focus on specific aspects of the input data, thereby enhancing classification accuracy. In this work, a channel attention mechanism is utilized, particularly suited for PQD data, which is represented as time-series signals. Given that the temporal characteristics of these signals are crucial, the proposed approach concentrates on specific time intervals to effectively capture relevant patterns associated with various types of disturbances. Thus, the proposed model can better discern subtle variations in the data, leading to more accurate and reliable classifications of PQDs. The channel attention mechanism typically consists of two main steps: squeeze and excitation. The squeeze operation aims to aggregate spatial information into a channel descriptor. This is accomplished using global average pooling, which summarizes the feature maps across spatial dimensions (height and width).  
\[z_{\epsilon}=\frac{1}{HW}\sum_{i=1}^{H}\sum_{j=1}^{W}F_{\epsilon}(i,j) \tag{6}\]  
where \(z_{\epsilon}\) is the channel descriptor for channel \(c\), \(H\) and \(W\) are the height and width of the feature map, and \(F_{c}(I,j)\) represents the value at position \((i,j)\) in channel \(c\).  
The excitation operation generates attention weights for each channel based on the channel descriptors obtained from the squeeze step.
## 6 Experiments and Results
### Experimental Metrics  
To comprehensively evaluate the performance of the algorithms, the HV [39], IGD [40], and C [41] metrics are used as indicators in our experiments.  
(1) The inverted generational distance (IGD) evaluates both the convergence and diversity of the obtained solutions. It is defined as the average Euclidean distance from each point in the true Pareto front \(PF^{True}\) to its nearest solution in the Pareto front \(PF^{A}\) obtained by algorithm A. The IGD is computed as:  
\[IGD(A)=\frac{\sum_{i=1}^{|PF^{True}|}|d_{i}^{*}}{|PF^{True}|}, \tag{24}\]  
where \(|PF^{True}|\) is the numbers of points in \(PF^{True}\), \(d_{i}^{*}\) is the Euclidean distance from the \(i\)th point of the \(PF^{True}\) to the nearest point of the \(PF^{A}\).  
(2) The C metric is used to compare the dominance relationship between the non-dominated solution sets of two algorithms. Its calculation method is given in Equation (25).  
\[C(A,B)=\frac{|\{b\in B:\exists a\in A,a\succ b\}|}{|B|}, \tag{25}\]  
where \(C(A,B)\) measures the proportion of solutions in \(B\) dominated by those in \(A\), with \(|B|\) representing the total number of solutions in \(B\).  
(3) The hypervolume (HV) metric is used to measure both the convergence and diversity of the obtained Pareto front by computing the volume in the objective space that is dominated by the solution set and bounded by a reference point. The HV is computed as:  
\[HV(A,z)=vol(\cup[f_{1},z_{1}]\times\ldots[f_{n},z_{n}]), \tag{26}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & **Number of** & **Number of** & **Operations Range** & **Machines Range** & **Time Range per** \\  & **Jobs** & **Machines** & **per Job** & **per Operation** & **Operation** \\ \hline MK01 & 10 & 6 & 5–7 & 3 & 1–7 \\ MK02 & 10 & 6 & 5–7 & 6 & 1–7 \\ MK03 & 15 & 8 & 10–10 & 5 & 1–20 \\ MK04 & 15 & 8 & 3–10 & 3 & 1–10 \\ MK05 & 15 & 4 & 5–10 & 2 & 5–10 \\ MK06 & 15 & 15 & 15–15 & 5 & 1–10 \\ MK07 & 20 & 5 &where \(z\) is the reference point. The hypervolume \(HV(A,z)\) quantifies the volume of the objective space dominated by the solution set.  
Specifically, the smaller the IGD value, the better the algorithm's performance. In contrast, the greater the HV and C values, the better the performance of the algorithm. Note that HV and IGD can reflect the comprehensive performance, while C can only measure the convergence of algorithms to some extent. The reference set used for IGD is obtained by merging the non-dominated solutions from all algorithms and extracting the non-dominated solutions from this merged set. The objective values of the non-dominated solutions from all algorithms are normalized using Equation (24):  
\[\widetilde{f}_{i}(\mathbf{x})=\frac{\left(f_{i}(\mathbf{x})-f_{i}^{\min} \right)}{\left(f_{i}^{\max}-f_{i}^{\min}\right)},\ \ i=1,2,3, \tag{27}\]  
where \(f_{i}^{\min}\) and \(f_{i}^{\max}\) represent the minimum and maximum values among all solution sets, respectively. Thus, the reference point for calculating HV is set to (1.1,1.1,1.1) \({}^{T}\).
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 6 Clinical Applications of Commercially Available TD-NIRS Systems by Japanese Researchers
### Group of Professor [PERSON] (Tokyo Medical University)  
In 2000, [PERSON] et al. used TD-NIRS to study the changes in \(C_{oxy-Hb}\), \(C_{deoxy-Hb}\), \(C_{total-Hb}\), and \(S_{02}\) in radial digitorum extensor muscles with arterial occlusion and found reasonable agreements of \(S_{02}\) measured by TD-NIRS and a blood gas analyzer [238]. Later, the group used TD-NIRS to examine the activity of brown adipose tissue (BAT), which can be a counter-measure of obesity and obesity-induced metabolic disorders. Brown adipose tissue has more capillary and mitochondria than other tissues, and the densities of capillaries and mitochondria were found to be strongly correlated with \(\mu_{a}\) and \(\mu_{s}\)', respectively, which are measurable with TD-NIRS [239]. Using TD-NIRS, [PERSON] et al. showed that daily ingestion of capsinoids (thermogenic capsisin analog) for eight weeks increased the BAT density by 46%, resulting in the same results with the conventional method using \({}^{18}\)F-fluoro-deoxy-glucose positron emission tomography (PET) combined with X-ray CT [240]. [PERSON] et al. performed a study with 423 Japanese subjects to confirm the relationship between the BAT density and \(C_{total-Hb}\) measured with TD-NIRS [241].
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 5 Design and Development of Satellite Ground Stations
### Equipment of Ground Station
#### 5.2.3 Antenna Rotator  
To support communication with multiple satellites in GEO and track the movement of satellites in LEO and IGSO, this design requires the addition of a motorized antenna angle adjustment for both the L-band antenna system and the terrestrial and LEO satellite antenna system. The chosen rotator (Figure 6, items 12 and 17) is the Yaesu G-5500 [24], as shown in Figure 9a, which can rotate the antenna in the azimuth axis from 0\({}^{\circ}\) to 450\({}^{\circ}\) and the elevation axis from 0\({}^{\circ}\) to 180\({}^{\circ}\). This rotator is affordable and easily obtainable, with good load-bearing capacity. It features a brushed DC gear motor of 24 V, operating on two separate axes, with a rotation speed of 2.7 degrees per second in the elevation axis and 6 degrees per second in the azimuth axis, and a torque of 14 kg-m in the elevation axis and 6 kg-m in the azimuth axis. The G-5500 outputs the current angle in the form of voltage, which allows for closed-loop control using a microcontroller, as illustrated in the diagram in Figure 9b. The motor speed is adjusted based on the voltage level supplied to the motor drive.  
Test results of the G-5500's angle adjustment, shown in the graphs in Figure 9c,d, indicate a pointing error of \(\pm\)16 degrees when using the factory-supplied controller, which is too high for use with dish antennas that have a narrow half-power beamwidth (HPBW). Therefore, a new rotator controller was developed using a 12-bit ADC. The raw ADC data, ranging from 0 to 4095, were read from the rotator and compared with the actual elevation and azimuth angles measured by the sensors. Multiple linear equations were created for different angle ranges to enhance accuracy in converting raw ADC data to the actual values obtained from the sensors. This improvement resulted in an accuracy within 2 degrees across 0-360\({}^{\circ}\) of azimuth and better than 1 degree across 0-90\({}^{\circ}\) of elevation, which is sufficient for use with a 1.9 m dish antenna.  
Figure 9: Antenna rotator system: (**a**) Yaesu G-5500 rotator, (**b**) the diagram of the rotator controller, (**c**) the error values from the azimuth axis, and (**d**) the error values from the elevation axis.
# Asymmetric Encryption of Invisible Structured Light 3D Imaging
## 2 Invisible Structured Light 3D Imaging System
### Decryption Using the Elliptic Curve Encryption Algorithm  
After receiving the ciphertext map, the receiver uses the elliptic curve decryption algorithm to decrypt the measured object. After receiving the encrypted ciphertext c, user B calculates KB and then calculates the plaintext m to obtain the decrypted images. Decryption key KB is the result of kb \(\times\) Pa and m is the result of c\(-\)KB, and finally the decrypted images can be obtained. The elliptic curve decryption flow chart is shown in Figure 3.
## 5 Forecasting
### Phase Space Reconstruction  
A time series is an abstraction of the data required by the forecasting model. A time series is a sequence of scalar values of a specific variable sampled at a discrete equidistant time. Let \(\mathbb{S}=[s_{1},s_{2},\ldots,s_{t},\ldots,s_{N}]\) be a time series, where \(s_{t}\) is the value of variable \(s\) at time \(t\). The goal is to obtain the forecast of \(\Delta n\) consecutive values, this is \([s_{N+1},s_{N+2},\ldots,s_{N+\Delta n}]\) by employing any observation available in \(\mathbb{S}\). By using a time delay of \(\tau\) and an embedding dimension \(m\), it is possible to build delay vectors of the form \(S_{t}=[s_{t-(m-1)\tau},s_{t-(m-2)\tau},\ldots,s_{t-\tau},s_{t}]\), where \(m,\tau\in\mathbb{Z}^{+}\). These vectors represent a reconstruction of the \(m\)-dimensional phase space that defines the underlying system dynamics that produced the time series [23]. However, the forecasting results exhibit low accuracy using vectors composed only of samples from the variable of interest (i.e., the time series). For this reason, vector \(S_{t}\) includes the day of the week and the hour (in minutes from the start of the day) as control variables. Thus, we append the date and time (hour)when the sample was taken to each vector \(S_{t}\), \(D_{t}=[\text{DoW}(d_{t-1}),\text{MoD}(d_{t-1})],1\leq t\leq N\). (9) shows the resulting vector.  
\[S_{t}=[\text{DoW}(d_{t-1}),\text{MoD}(d_{t-1}),s_{t-(m-1)\tau},s_{t-(m-2)\tau},\ldots,s_{t-\tau},s_{t}] \tag{9}\]  
where \(\text{DoW}(\cdot)\) returns the day of the week (as a real number from 0.0 to 0.8571 starting on Monday), and \(\text{MoD}(\cdot)\) returns the minute of the day (represented as a real number from 0.0 to 0.9993). Those quantities correspond to the day and time when the delay vector ends (i.e., when the forecast is to be produced).