esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Model
### Objective Function  
The objective function is to minimize the total operating cost of the SH for 24-hours, as expressed in (10).  
\[\min\left[\sum_{i=1}^{n}\left(C_{FC,i}+C_{BL,i}+C_{U,i}+C_{B,i}\right)\right] \tag{10}\]  
\[C_{FC,i} =\begin{cases}T\cdot C_{gas}\left(\frac{P_{FC,i}}{\eta_{FC,i}}\right)+ \alpha&\text{if $P_{FC,i-1}=0$, and $P_{FC,i}>0$}\\ \beta&\text{if $P_{FC,i-1}>0$, and $P_{FC,i}=0$}\\ T\cdot C_{gas}\left(\frac{P_{FC,i}}{\eta_{FC,i}}\right)&else\end{cases} \tag{11}\] \[C_{BL,i} =T\cdot C_{gas}\cdot H_{BL,i}\] (12) \[C_{U,i} =\begin{cases}T\cdot T_{b}\cdot C_{U_{b}}\cdot P_{U,i}&\text{if $P_{U,i} \geq 0$}\\ T\cdot T_{s}\cdot C_{U_{s}}\cdot P_{U,i}&\text{if $P_{U,i}<0$}\end{cases}\] (13) \[C_{B,i} =\begin{cases}C_{B_{min}}\cdot P_{B,i}\cdot T&\text{if $P_{B,i} \geq 0$}\\ -C_{B_{min}}\cdot P_{B,i}\cdot T&\text{if $P_{B,i}<0$}\end{cases} \tag{14}\]  
where  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Time Span** & **Normalized Import Price \(T_{b}\) (\(p.u\))** & **Normalized Export Price \(T_{s}\) (\(p.u\))** \\ \hline Peak Tariff & [09:00–12:00] & 1 & 1 \\ Plain Tariff & [13:00–16:00] & 0.9 & 0.8 \\ Valley Tariff & [01:00–08:00] & 0.78 & 0.6 \\  & [23:00–24:00] & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electricity Import and Export Variable Tariffs.  
\begin{tabular}{l l} \(n,T\) & Total time, Span of the time interval (h) \\ \(\alpha,\beta\) & FC Startup, Shutdown costs (\(\$\)) \\ \(C_{FC,i},C_{BL,i},C_{U,i},C_{B,i}\) & Total cost of the FC, Boiler, Utility, BESS at interval \(i\) (\(\$\)) \\ \(C_{gas}\) & Cost for purchasing natural gas (\(\$\)/kW) \\ \(C_{U_{b}},C_{U_{b}}\) & Base cost for Buying, Selling utility electricity (\(\$\)/kW) \\ \(C_{B_{am}}\) & Operation and maintenance cost of BESS (\(\$\)/kW) \\ \(P_{FC,i}\) & FC electrical power output at interval \(i\) (kW) \\ \(H_{BL,i}\) & Thermal power produced by the boiler at interval \(i\) (kW) \\ \(P_{U,i}\) & Electrical power purchased from, or sold to, the utility at interval \(i\) (kW). \\ \(T_{b},T_{s}\) & Multipliers for Buying, Selling tariff as described in Table 1 \\ \(\eta_{FC,i}\) & FC efficiency \\ \end{tabular}  
It should be noted that \(P_{U,i}\) will be positive for purchasing and negative for selling electrical power to the utility.
## 4 Experiments
### Implementation Details  
We implemented our proposed CriSALAD model using PyTorch v2.0.0 and fine-tuned it under the parameter-efficient setup with a single NVIDIA GeForce RTX 4090D GPU. For the adapted DiNOv2 feature extractor, we used ViT-B/14 as our backbone. We set the input image resolution at 224 \(\times\) 224 and the feature tokens with dimensions of 768. Regarding the adapter, we used a bottleneck ratio of 0.5 and a kernel size of 3 \(\times\) 3 for the separable convolution, and the weighting factor in Equation (2) for \(\alpha\) was set to 0.2.  
For the SALAD global aggregator, we set all hidden fully connected layers to 512 neurons, activated by ReLU functions. Following the SALAD default setup, we designed 64 clusters, each with 128 dimensions, and set the global token dimension to 256. The final global descriptor of our model combined the global token and all cluster tokens, resulting in a dimension of 8448.  
For training, we used the GSV-Cities dataset for fully supervised training. The dataset contains 560,000 images from 67,000 locations and provides highly accurate label information. The richness of the dataset and the accuracy of the labels make it an important benchmark for urban visual tasks. We followed the standard framework of the dataset and used the multi-similarity (MS) loss function for training. To further enhance training performance, we also incorporated the online hard example mining (OHEM) strategy to more effectively learn from challenging samples, thereby enhancing the model's robustness and generalization. For inference, our method was evaluated with an inference batch size of 16.  
To implement these strategies effectively, we fine-tuned the model using the AdamW [48] optimizer with an initial learning rate of \(6~{}\times 10^{-5}\) and applied a linear decay to improve learning effectiveness. We used a training batch of 48 places, each with four images. The model was trained on the GSV-Cities dataset until the _Recall@5_ on the Pitts30k [44] dataset stopped improving for three consecutive epochs. In our experiments, the CriSALAD model achieved convergence within five epochs.
## 2 Method
### Labels Processing  
After image processing, corresponding labels should be handled with equal care, ensuring they are saved as int32 to match the expected input types for most machine learning frameworks. This can be achieved using label.astype('int32') to ensure consistency and avoid potential type mismatch issues during model training.  
To save these processed images and labels, numpy arrays can be used. The images can be stored in arrays with a shape that reflects the number of images and their dimensions, for example (num_images, 150, 150, 3) for RGB images. These arrays can then be saved using numpy's np_save function, which preserves the data types and ensures that the images and labels are stored in a format that is quick to load during training. This entire workflow ensures that the data fed into machine learning models is of consistent quality and format, thereby facilitating effective and efficient training processes.  
The number of examples in both the training and testing datasets was determined by examining the shape of the label arrays for each set. This allowed for an accurate count of how many samples were present in each dataset. Specifically, the training dataset comprised a certain number of examples, while the testing dataset contained a different number. This count is critical for understanding the scale of the datasets used in the experiments.  
Additionally, the size of each image was confirmed to be consistent across the dataset. The image size, denoted as IMAGE_SIZE, was uniform for all images, ensuring that each image conformed to the required dimensions for input into the neural network. This uniformity in image size is essential for the preprocessing pipeline and subsequent model training, as neural networks typically require inputs of a consistent shape and size. By reporting the number of training and testing examples, as well as the uniform image size, the research provides clear and detailed information on the dataset's characteristics.  
To visually display the distribution of images across different categories in the training dataset, a pie chart was created, as shown in Figure 1. This chart illustrates the proportion of images belonging to each category (i.e., class, sequence, state machine, UML diagramsor non-UML, images that do not contain UML diagrams), providing a clear overview of the dataset composition. The chart includes labels for each category and displays the percentage of the total dataset that each category represents, formatted to one decimal place. Ensuring the pie chart is a perfect circle enhances its readability and esthetic appeal. The chart is displayed in Figure 1, clearly communicating its purpose. This visualization effectively highlights the relative proportions of images in each category within the training dataset, offering valuable insights into the dataset's structure.
## 4 Discussion
### The Necessity of Spectral Changes Owing to Atmospheric Parameters and Cloud Conditions  
Since summer in Japan has a long sunshine time and strong irradiance, the surface air is warmed, and ascending currents occur frequently. The IMM module is affected by cloud conditions, whichresults in decreasing PR in the summer. This is caused by the overcast conditions decreasing the long-wavelength region of the solar spectrum, which is absorbed by the bottom cell. The IMM module was also greatly affected by atmospheric parameter variation, and the PR markedly decreased  
Figure 11: Comparison of each prediction model to the normal distribution of residual errors.  
Figure 12: External quantum efficiency EQE of single-crystalline Si [39] and IMM solar cells [40] and reference solar spectrum.  
Figure 10: Comparison with PR estimated by each PV output forecasting model.  
in summer because of increasing precipitable water, which absorbed the long-wavelength range of the solar spectrum. As shown in Figure 11b, the absorption of the bottom cell is the water absorption wavelength region. By considering spectral changes owing to atmospheric parameters and cloud conditions, we can reproduce the solar spectrum more accurately and estimate the drop in the photocurrent of the bottom cell. This allows estimating IMM PV output with high accuracy.
## 2 Materials and Methods
### Stimulus
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Class** & **N** & **Description** \\ \hline \multirow{2}{*}{Composition} & I & 123 & \multirow{2}{*}{Class I has a larger blank area compared to Class II.} \\  & II & 126 & \\ \hline \multirow{2}{*}{Tone} & I & 105 & The average brightness of Class I is lower than that of Class II, and the gray level distribution is more discrete. \\  & II & 144 & \\ \hline \multirow{4}{*}{Gabor-Mean} & I & 117 & In the following \((\lambda,\theta)\) combinations—(6, 0), (6, \(\pi\)/2), (9, \(\pi\)/8), (9, 3\(\pi\)/8), (9, 5\(\pi\)/8), (9, 7\(\pi\)/8), (12,0), (12, \(\pi\)/8), (12, 3\(\pi\)/8), (12, \(\pi\)/2), (12, 5\(\pi\)/8), (12, 7\(\pi\)/8), (15,0), (15, \(\pi\)/2), the grayscale mean of the filtered images in Class I is higher than that of Class II. This indicates that Class I has a higher overall brightness in these combinations. Conversely, Class II exhibits more high-brightness features in other combinations. \\ \hline \multirow{2}{*}{Gabor-Variance} & I & 130 & In all the \((\lambda,\theta)\) combinations, Class II has more high-frequency components and greater texture changes after Gabor filter processing, which suggests that it contains more diverse and complex textures. \\ \hline \multirow{4}{*}{Gabor-Energy} & I & 122 & In the following \((\lambda,\theta)\) combinations—(6, 0), (6, \(\pi\)/2), (9, \(\pi\)/8), (9, 3\(\pi\)/8), (9, 5\(\pi\)/8), (9, 7\(\pi\)/8), (12,0), (12, \(\pi\)/8), (12, \(\pi\)/2), (12, 5\(\pi\)/8), (12, 7\(\pi\)/8), (15,0), (15, \(\pi\)/2)—the energy gray level of Class I after Gabor filter processing is greater. The features in Class I are more pronounced and intense, likely containing more edges, lines, or texture features in these combinations. Conversely, Class II contains more prominent edges or complex textures in other combinations. \\ \hline \multirow{2}{*}{Horizontal GLCM (\(\theta=0^{\circ}\))} & I & 123 & In the horizontal direction, compared to Class II, the texture features of Class I are more obvious, with high-contrast edges and lines, higher regularity, and more texture patterns. \\ \hline \multirow{2}{*}{Diagonal GLCM (\(\theta=135^{\circ}\))} & I & 118 & In the diagonal direction, compared to Class II, the texture features of Class I are more obvious, with high-contrast edges and lines, higher regularity, and more texture patterns. \\  & II & 131 & \\ \hline \multirow{2}{*}{LBP} & I & 113 & Compared to Class II, Class I has weaker texture contrast but richer details, uniform texture distribution, and contains more small and intense details. \\ \cline{2-2}  & II & 136 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Image features classification and description.
## 4 Discussion
### Analysis of the Evaluated Metrics for Each Method
#### 4.1.3 Relative Energy Difference (%)  
This metric reveals that V2 GSUN and V2 GGlide experience higher relative energy losses at low SoC levels (<20%), while V2 GSmooth maintains consistently low values across all SoC conditions. This suggests that V2 GSmooth is the most efficient method for minimizing energy losses associated with power compensation, particularly under storage constraints.
###### Abstract  
In this work, thermo-optic (TO) lateral shift apodized sampled waveguide grating for 1550 nm wavelength is designed and fabricated by the metal-printing technique based on fluorinated epoxy-terminated polycarbonates (FBPA-PC EP) and fluorinated epoxy resin (FSU-8) materials. The optical characteristics and thermal stability of the FBPA-PC EP and FSU-8 materials are analyzed. To realize periodic wide-spectrum filtering and suppress the side-lobes of grating, a lateral shift apodized sampled waveguide grating is proposed. The 3 dB bandwidth and wavelength spacing can reach 4.8 nm and 9.7 nm. The side-lobe suppression ratio of proposed device can reach 22.6 dB, which is much better than traditional Bragg grating (6.1 dB). Driving electrical powers of 42.4 mW and 87.2 mW can produce blueshifts of 1.8 nm and 3.5 nm in the measured reflection spectrum, respectively. This device realizes the aim of multiple functions, including periodic filtering, wide-spectrum filtering, and high side-lobe suppression. The device is applicable of realizing signal processing and wavelength division multiplexing (WDM) systems.  
lateral shift apodized grating; fluorinated polymer waveguide; reflect spectrum side-lobes suppression; sampled grating +
Footnote †: journal: Applied Physics Letters  
**J. S.** **(2020), 10, 167**; doi:10.3390/app10010167
# Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel
###### Abstract  
The location, velocity, and flight path angle of an autonomous unmanned aerial vehicle (UAV) landing on a moving vessel are key factors for an optimal landing trajectory. To tackle this challenge, this paper proposes a method for calculating the optimal approach landing trajectory between an UAV and a small vessel. A numerical approach (iterative method) is used to calculate the optimal approach landing trajectory, and the initial lead is introduced in the calculation process of the UAV trajectory for the inclination and heading angle for accuracy improvement, so that the UAV can track and calculate the optimal landing trajectory with high precision. Compared with the variational method, the proposed method can calculate an optimal turning direction angle for the UAV during the landing. Simulation experiments verify the effectiveness of the proposed algorithm and give optimal initialization values.  
unmanned aerial system (UAS); ship; trajectory planning; dichotomy search Article
# Transient Current Density in a Pair of Long Parallel Conductors
## 3 Results and Discussion
### Example 4  
The conductors are the same as in Example 3. At time \(t=0\), they are connected to a source of sawtooth voltage (see Figure 7). The voltage is periodic, in the first period  
\[U(t)=\hat{U}(1-2t/T),\quad\hat{U}=1\mathrm{V}\cdot\mathrm{m}^{-1},\ t\in[0,T). \tag{11}\]  
The solution to Example 4, obtained by solving Equation (6), is given in Figure 7. In the interval \(t\in[0,2T)\), the current \(I(t)\) is transient, and for \(t\geq 2T\) the current can be considered steady.  
In the theory of circuits with lumped parameters, the non-sinusoidal voltage (thus also (11)) is in the calculation of steady current \(I(t)\) approximated by the first \(n\) terms of the Fourier series [14]. For a source voltage that is equal to the \(k\)th harmonic, \(k=1,2,\ldots,n\), the current \(I_{k}(t)\) is then calculated. The principle of superposition is used to obtain the current  
\[I(t)=\sum_{k=1}^{n}I_{k}(t).\]  
An advantage of such a calculation is that it allows the use of phasors. The Fourier series can also be used in the calculation of steady current density, and thus also the steady current, in two parallel long conductors connected to a source of non-sinusoidal voltage via the method published in [1]. When assessing the suitability of such a procedure, it is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(f\) (Hz) & \(\Delta_{I}(-)\) & 16 mm & 32 mm & 0.1 m & 1 m & 1 m, Al \\ \hline
50 & 0.02 & 2 & 2 & 2 & 3 & 2 \\ \(10^{3}\) & 0.02 & 9 & 16 & 26 & 47 & 30 \\ \(10^{5}\) & 0.02 & 525 & 1425 & 2478 & 4574 & 2887 \\ \hline
50 & 0.01 & 2 & 2 & 3 & 4 & 3 \\ \(10^{3}\) & 0.01 & 11 & 18 & 31 & 55 & 35 \\ \(10^{5}\) & 0.01 & 695 & 1695 & 3000 & 5394 & 3405 \\ \hline
50 & 0.005 & 2 & 2 & 3 & 4 & 3 \\ \(10^{3}\) & 0.005 & 13 & 21 & 35 & 64 & 40 \\ \(10^{5}\) & 0.005 & 864 & 1965 & 3380 & 6215 & 3923 \\ \hline \end{tabular}
\end{table}
Table 2: Example 3, \(\alpha=0\): The dependences of time \(t_{\mathrm{sin}}\) (expressed by the number of whole periods \(T\) and rounded up) on \(f,\Delta_{I}\) and \(d\).  
necessary to take into account that the Fourier expansion is an infinite series while the calculation can only be performed with a finite number \(n\) of series terms. The finite number of terms only approximates the given voltage course, which also manifests itself in the resultant current. Instead of \(n\) repetitions of the calculation with individual harmonics, a single calculation can be done of transient current density and current with the given voltage. The transient current at time \(t\geq t_{\text{sin}}\) can be considered steady. Figure 8 gives the sawtooth voltage in Example 4 and its approximation by \(n\) terms of the Fourier series [14]  
\[U(t)=\frac{2}{\pi}\sum_{k=1}^{\infty}\frac{\sin(k\omega t)}{k} \tag{12}\]  
and the transient current calculated with the source voltage approximated by \(n\) terms of the expansion (12) (an accurate transient current is in Figure 7).
## 6 Experimental Results and Discussion for ASCE Benchmark
### FFT-DCNN Testing Result for ASCE Benchmark  
The historical curve of Fold 4 is presented as an example. It can be seen from Figure 17 that there are training, testing, and validation datasets in each epoch. The accuracies of validation and testing reach 80% after Epoch 250. It indicates that the FFT-DCNN has a faster-trained process and convergence speed.  
Four-fold crossvalidation results in the ASCE benchmark dataset are shown in Table 8. In each iteration, the verification accuracy is 88.06% on average, and the testing accuracy is 87.30% on average. The experimental result shows that the FFT-DCNN model is suitable for structural damage detection.  
Figure 18 denotes the best result of Fold-4 for the confusion matrix. It can be seen that the label C1 is undamaged conditions, and the labels C2-C9 denote the damage conditions. The overall identification accuracy of 9 structural damage detection is 88.10%, and the error rate is 11.90%. The accuracy of C2, C6, C7, and C8 is more than 90%. C3, C4, and C9 is 88%, 84%, and 89%, respectively. While the C1 and C5 have wrong identification, but its accuracy is still over 70%, in addition, because the number of samples reaches 31,680 and it is large enough. Thus, the accuracy of FFT-CNN reaches 87.30% on average, which can satisfy the demand for structural damage detection in actual environment.  
Figure 17: Confusion matrix of damage identification for Fold 4.  
Figure 18: Confusion matrix of damage identification for the Fold-4.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 7 Conclusions  
There is still much to be done until the BGP control plane is fully replaced as the main routing core system of the Internet. None of the efforts researched in this work provide all the features to reach that goal. SDN technologies seem to be a prominent technical field for new network architectures, and they are receiving much academic and industrial attention. Beyond the technical requirements, the new proposals to evolve inter-domain routing must be aligned with network operator business vision.
## 4 BER Analysis
### Effects of Different Initial Phases on BER Performance  
In this section, the effects of the co-channel interference with different initial phases on BER performance are simulated. Figure 7 shows the BER performances in different interference initial phases, where assuming \(\mathit{SNR}=0 dB\) and \(\mathit{SNR}=-2 dB\), respectively.  
Obviously, in both cases, BER curves have almost the same change tendency with the increasing of the initial phase. When the initial phase of the interference is from 0 to \(0.4\pi\), the BER agrees with the case of no interference basically. However, when the initial phase is larger than \(0.4\pi\), the BER becomes poor with the increasing of the initial phase.  
When the initial phase of the interference is small, the interference plays the enhancement role for the signal, analyzing as Section 4.1, so the BER performances are similar in the different SIR cases. On the other hand, when the initial phase becomes larger, the interference has negative effect on the original signal, especially greater on the amplitude of the jumping part, so that the waveform amplitude after filtering decreases greatly, and the amplitude difference between jumping part and non-jumping part decreases thereupon, also as shown in Figure 3. Thus, when using the threshold detection in the receiver, the larger the initial phase is, the poorer the BER performance will be.  
Figure 8 shows the SNR loss produced by co-channel interference, _i.e.,_ in order to obtain the same BER, such as \(10^{-4}\), the required SNR is given for the different initial phase, which indicates the effects of the initial phase on BER more clearly.  
Figure 7: Effects of different initial phases on BER performance.
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
## 4 Results
### Delayed XOR
Here, unoptimized networks were distributed according to the starting hyperparameters mentioned above. We note that ESNs were able to perfectly classify the delayed XOR output for lower task delays, but quickly decayed into random guessing as the task delay approached 12. Hyperparameter optimization only resulted in marginal improvements, extending the memory range of the networks to one additional task delay step. Although unoptimized DDNs started out with a higher BER for XOR delays below nine, we observed a slower degradation of performance as the task delay was increased compared to conventional ESNs. For \(k\geq 9\), unoptimized DDNs outperformed ESNs. In addition, we observed a larger effect of hyperparameter optimization for DDNs, and only a marginal effect for ESNs. Optimized DDNs maintained a BER below \(10^{-3}\) up to \(k=14\). However, we note that the use of delays seemed to increase the variability in quality between different network samples.