esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 3 Results
The Pasture class was incorrectly identified as the Highway class. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline
**Actual Class** & **Annual Copy** & **Forest** & **Herbaceous** & **Highway** & **Industrial** & **Pasture** & **Component** & **Residential** & **River** & **Sea Lake** \\ \hline Annual Crop & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Residual & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Residual & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Seg Lake & 0 & 0 & 0 & 0 & 0.053 & 0 & 0 & 0 & 0.9367 \\ Highway & 0.0050 & 0 & 0 & 0.0709 & 0.00140 & 0 & 0 & 0.0011 & 0 \\ Seg Lake & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ Seg Lake & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ Seg Lake & 0 & 0 & 0 & 0.0 & 0.001 & 0 & 0.9899 & 0 & 0 \\ Permanent Crop & 0 & 0 & 0.006 & 0.0026 & 0.0001 & 0 & 0.9914 & 0 & 0.0022 & 0 \\ Seg Lake & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Seguez & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ Shorer & 0 & 0 & 0 & 0.0055 & 0 & 0 & 0 & 0 & 0.9985 & 0 \\ Highway & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ Forest & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Sea Lake & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ Annual Crop & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Forest & 0 & 0,9988 & 0.0004 & 0.0003 & 0 & 0 & 0 & 0 \\ Heterogeneous & 0 & 0 & 0.9998 & 0 & 0 & 0 & 0.0002 & 0 & 0 \\ Vegetation & 0 & 0 & 0.9998 & 0 & 0 & 0 & 0.0002 & 0 & 0 \\ Instance & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ Heterogeneous & 0 & 0 & 0.9998 & 0 & 0 & 0.0001 & 0 & 0 & 0 \\ Vegetation & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ Permanent Crop & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ Industrial & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ Annual Crop & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Forest & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Regional & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ Highway & 0 & 0 & 0 & 0.9996 & 0 & 0 & 0 & 0 & 0.0004 & 0 \\ Permanent Crop & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ Highway & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ Residential & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Feature & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ Forest & 0 & 0 & 0.9934 & 0.0006 & 0 & 0 & 0 & 0 & 0 \\ Heterogeneous & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Instance & 0 & 0 & 0.0018 & 0.986 & 0.0008 & 0.0027 & 0.0007 & 0.0009 & 0.000 & 0 \\ \hline Permanent Crop & 0.0120 & 0 & 0.0136 & 0.0002 & 0.0001 & 0.0039 & 0.9442 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Predicted probability for a sample subset of the test set using ResNet50. The values were rounded off to four decimal places, and if 0.0000 or 1.0000 appeared, they were, respectively, simplified as 0 and 1. The highlighted row indicates that an image from the Pasture class was incorrectly classified as an image from the Highway class.
## 4 Experiments
### Comparison with Previous Approaches  
We initialize our proposed architecture using the weights from ResNet50 pre-trained on ImageNet [44]. Tables 2 and 3 compare our results with those of previous methods on FotW and LFWA datasets, respectively. Our average accuracy is lower than SIAT_MMLAB on the FotW dataset and LNets\(+\)ANet on the LFWA dataset. The SIAT_MMLAB architecture is composed of GNet for gender classification and two SNets for smile classification. GNet and two SNets are trained with different face cropping schemes for better performance. The SIAT_MMLAB architecture adopts the VGG-Faces [45] model, which is pre-trained on a large-scale face identification dataset for face identification and face verification. They use a general-to-specific fine-tuning scheme that fine-tunes the model three times on CelebA [27] (with forty attribute annotations), CelebA (with smile and gender annotations) and FotW (with smile and gender annotations) datasets, respectively. The LNets\(+\)ANet architecture integrates two CNNs LNet and ANet, where LNet locates the entire face region and ANet extracts features for attribute recognition. LNet is pre-trained on ImageNet and fine-tuned by image-level attribute tags. ANet is pre-trained on the CelebA dataset and fine-tuned by attribute tags. Our proposed architecture can perform smile and gender prediction tasks in the end-to-end manner using a single deep neural network. The input face images are processed as mentioned in experimental configurations with no extra face cropping and localization steps. We only use the weights from ResNet50 pre-trained on ImageNet for weight initialization. The results also show the effectiveness of our proposed architecture in comparison with previous state-of-the-art methods.  
Figure 6: t-SNE visualization on the sampled LFWA validation dataset. (**a**) t-SNE visulization of \(FC_{S}\); (**b**) t-SNE visualization of \(FC_{S|C_{G}}\); (**c**) t-SNE visualization of \(FC_{G}\); (**d**) t-SNE visualization of \(FC_{G|C_{S}}\).
## 3 Results and Discussion
### Physicochemical Properties of the Samples
#### 3.2.5 H\({}_{2}\)-TPR Analysis  
The redox properties of ZSM-5 and modified ZSM-5 were studied using H\({}_{2}\)-TPR, the results of which are shown in Figure 8. Regarding virgin ZSM-5, there were a few signs of hydrogen consumption detected, suggesting the absence of reducible species available  
Figure 6: SEM images of (**a**,**b**) ZSM-5 and (**c**,**d**) Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5.  
Figure 7: **(a**) SEM image of fresh Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5. (**b**,**c**) The corresponding EDX elemental mapping images of Co (green) and Fe (red). (**d**) EDX analysis.  
for the capture of Hg\({}^{0}\). For all the modified ZSM-5, there were a number of evident peaks of H\({}_{2}\) reduction. The reduction of Fe-ZSM-5 after deconvolution can be divided into two stages. Located at 346 \({}^{\circ}\)C, the first sub-peak is possibly attributed to the reduction of Fe\({}_{2}\)O\({}_{3}\) to Fe\({}_{3}\)O\({}_{4}\), whereas the reduction of Fe\({}_{3}\)O\({}_{4}\) to FeO can account for the appearance of the peak at 485 \({}^{\circ}\)C [44]. In respect of Co-ZSM-5, the peaks centered at 328 and 357 \({}^{\circ}\)C were associated with the reduction of Co\({}_{9}\)O\({}_{4}\) to CoO and CoO to metallic cobalt, respectively [45]. Co\({}_{4}\)Fe-ZSM-5 manifests four peaks at 316, 361, 404, and 436 \({}^{\circ}\)C. The first one is suspected to result from the reduction of Co\({}_{3}\)O\({}_{4}\) to CoO, the second and third ones are probably attributable to the reduction of CoO and Fe\({}_{2}\)O\({}_{3}\), and the last one is ascribed to the reduction of Fe\({}_{3}\)O\({}_{4}\). Notably, there appears to be a decline of temperature for the reduction peaks, suggesting the improved reducibility of Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 due to the synergistic effect between CoO\({}_{x}\) and FeO\({}_{x}\)[23]. This would facilitate the oxidation of Hg\({}^{0}\), constituting another reason for the elevated Hg\({}^{0}\) removal efficiency of Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5.
## 3 Experiments and Analysis
### Experimental Design

Table 2: Number of data slices for each category in open ocean extraction and sea ice classification for deep learning tasks. the images after setting a fixed step size for the deep learning semantic segmentation using the sliding window. In the actual sea ice extraction task, a step size of 128 and a window size of 256 were chosen for sliding window detection, a soft voting mechanism was used to superimpose the weights of each category after the sliding window, and the final category of pixel points was output based on the weight maximum.
## 1 Introduction
### Satellite and Terrestrial Integrated Networks  
The network traffic has increased tremendously in recent years because of everything connected to the Internet at anytime (IoT) [1] and the increased use of remote services after the COVID-19 pandemic, which required the satellite network to be integrated with the terrestrial network to fulfill the network demands [2]. Hence, satellites are used in the communication industry by providing:  
1. Service continuity in areas where terrestrial networks are not available
2. Service ubiquity to provide resilient service during terrestrial network failures
3. Scalability to load-balance the traffic demands exceeding the terrestrial network's capacity [3].
## 4 Data Transmission
### System description
In the following sections, two proposals to implement the physical layer of this HF radio link will be addressed. One solution is based on DS-SS signaling, the other is based on OFDM.
# Experimental Study on the Current Pretreatment-Assisted Free Bulging of 304 Stainless Steel Sheets
## 3 Results and Discussion
### Influence of Pulse Current on 304 Stainless Steel Sheet Bulging
Figure 11 shows the wall thickness distribution and thinning rate of the sheet after forming under different current intensities. Through observation, it can be found that the  
Figure 10: Schematic diagram of measuring position of wall thickness. Figure 9: Bulging depth of the 304 stainless steel sheet with different current sizes. thickness distribution of the rolled sheet shows great uneveness, and the thinning is the largest at position 9. The thinning rate reached about 48.52%. In addition to position 9, there is also relatively serious thinning at the transition positions of the fillets at 6, 7, and 8. This shows that during the free bulging process, the sheet deforms first at the rounded corners and then gradually flows into the entire mold cavity along with the rounded corners to complete plastic deformation. Significant necking occurs at the rounded corners, resulting in a high rate of thinning. Since position 10 is the first to contact the convex and concave molds during the bulging process, it cannot move under the action of the edge force of the convex and concave molds and is in a stagnant state. Therefore, the wall thickness remains basically unchanged and the thinning rate is low. From positions 1 to 5, the wall thickness shows a gradually increasing trend. Overall, from positions 1 to 10, the wall thickness shows a pattern of first increasing and then decreasing. After being treated with pulse currents of different intensities, the thinning rate of the formed parts was improved accordingly. The thinning rate at fillet position 9 reduced to 35.81% and 27.07% when the current was 20 A and 25 A, respectively. The entire wall thickness distribution also gradually became uniform, and the formability improved. The shape and size of the internal grains of the material are important factors affecting the uniformity after plastic deformation [23; 24; 25]. The internal grains of the cold-rolled sheet are destroyed and distributed in a fibrous shape with severe anisotropy. When subjected to large stamping loads, because of the poor fluidity of the fibrous grains, the necking phenomenon is significant at the fillets, the thinning is high, and the overall thickness distribution is uneven. The pulse current promotes the dynamic recovery of grains, making the structure more uniform and improving fluidity. Therefore, during bulging, the thickness distribution of the entire cross-section is more uniform, the necking phenomenon at the fillets is weakened, and the thinning rate is low.
# Continuous-Service M/M/1 Queuing Systems
## 5 Conclusions  
Situations exist in the real world in which we have systems that require continuous service. We noticed this and set out to model these situations in the form of our continuous-service M/M/1 queuing systems. In doing this, we were able to derive and verify by means of simulations, the common  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(\rho\) = 0.25} & \multicolumn{2}{c}{\(\rho\) = 0.50} & \multicolumn{2}{c}{\(\rho\) = 0.75} \\ \cline{2-7}  & **Theoretical** & **Simulated** & **Theoretical** & **Simulated** & **Theoretical** & **Simulated** \\ \hline \(W\) & 0.2333 & 0.2333 & 0.3000 & 0.2999 & 0.5000 & 0.5000 \\ \(W_{q}\) & 0.1333 & 0.1333 & 0.2000 & 0.1999 & 0.4000 & 0.4000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons between theoretical and simulated results for \(W\) and \(W_{q}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(\rho\) = 0.25} & \multicolumn{2}{c}{\(\rho\) = 0.50} & \multicolumn{2}{c}{\(\rho\) = 0.75} \\ \cline{2-7}  & **Theoretical** & **Simulated** & **Theoretical** & **Simulated** & **Theoretical** & **Simulated** \\ \hline \(P(T>10)\) & 0.7858 & 0.7854 & 0.8452 & 0.8448 & 0.9158 & 0.9158 \\ \(P(T>20)\) & 0.4865 & 0.4863 & 0.6004 & 0.5997 & 0.7636 & 0.7637 \\ \(P(Y>10)\) & 0.4724 & 0.4718 & 0.6065 & 0.6060 & 0.7788 & 0.7790 \\ \(P(Y>20)\) & 0.2231 & 0.2227 & 0.3679 & 0.3672 & 0.6065 & 0.6066 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons between theoretical and simulated results for probabilities that a real customer spends more than 10 time units and more than 20 time units in the system and queue.  
characteristics of interest for our queuing systems: The average number of real customers in the system and queue, and the average times spent by a real customer in the system and queue. We intuitively compared these values to the standard M/M/1 queuing system. It was noticed that the difference between the results of our systems and that of the standard system was by a factor by \((2-\rho)\). We also derived the system time and queue time distributions for our systems and also verified these results with simulations. For any systems that have the server continuously serving, and can be classified as continuous-service M/M/1 queuing systems, our results may be leveraged to analyze, estimate, and optimize the systems. Future work may look into analyzing real-world systems that share similar features of our continuous-service M/M/1 queuing systems.  
This research received no external funding.  
The author declares no conflict of interest.
## 4 Discussion
It has been shown that it is also possible to produce spherically focused transducers with a similar response compared with flat ones, but with the advantage of a better spatial resolution, with a focal spot size \(<\)2 mm. In spite of the reduced peak sensitivity, the huge bandwidth and the observed SNR values still permit to consider the use of this kind of transducers for different water immersion applications related to materials characterization, NDT and medical imaging, in applications where large bandwidth, moderate frequencies (0.5-4.0 MHz) and good axial resolution are required. In addition to the large frequency bandwidth and short signal duration, the FE transducers offer other advantages related to the flexibility of the material and the reduced size of the required backing block. Further studies are needed to determine if the transducer sensitivity can be improved. The first step can be to try techniques that have already been tested for FE air-coupled transducers. They include the use of a high voltage excitation to make the FE film work under the electrostrictive regime and the use of a bias voltage [15]. In addition, spread spectrum signals with programable excitation spectral shape can be used to compensate the 10 dB spectral losses beyond 2.0 MHz, making received signals even more broadband [16]. In addition, further studies of transmission and reception sensitivity and frequency bands could be beneficial in order to optimize transducer design. Conceptualization, L.S. and T.G.A.-A. ; methodology, T.G.A.-A. and J.C.; software, J.C., validation, J.Q., and T.G.A.-A. ; resources, T.G.A.-A., L.S., and J.C.; writing--original draft preparation, J.Q., writing--review and editing, T.G.A.-A., L.S. and J.C.; funding acquisition, T.G.A.-A., J.C. and L.S. All authors have read and agreed to the published version of the manuscript. This work was supported in part by Ministerio de Economia y Competitividad under grants ECERES (DPI2016-78876-R, AEI/FEDER, UE) and AIMUT (RTI2018-099118-A-I00, MCIU/AEI/FEDER, UE) and received partial funding (LS) from European Regional Development Fund (project No. 01.2.2-LMT-K-718-03-0026) under grant agreement with the Research Council of Lithuania (LMTLT). The authors declare no conflict of interest and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results  
### Offline Recognition  
In the offline recognition task, we trained seven models on the open surgical dataset, including the baseline model R3D and six other models, including three classic 3D convolutional models: R(2+1)D [15], I3D [19] and C3D [20], the dual-path model SlowFast [21] and the Transformer-based model Vivit [22] applied in the field of video understanding. Figure 6 illustrates the training process of these seven models, where (a) shows the training loss and (b) displays the results of the test set every 10 epochs. From the figure, it can be observed that at the 40 th epoch, the loss value reaches its minimum and stabilizes, and the test set accuracy also reaches a stable level.  
Table 4 presents the final test results of the models. It can be seen that our proposed R3D-MHA model surpasses the baseline R3D model by 1.9%, 2.0% and 2.0% in terms of accuracy, precision, and recall, respectively. Additionally, it leads in all metrics compared to the other five models.  
Given the imbalance in the sample sizes of our self-built dataset, this study also considers the model's recognition performance for individual gesture categories. We use the recall rate of single gesture recognition to evaluate the recognition accuracy of various surgical gestures. As shown in Table 5, overall, the gestures G1, G2 and G7 are easily confused with other surgical gestures, leading to lower recognition accuracy. This may be because the sample sizes of G1 and G2 are relatively small, and G7 (hand pulling suture) is somewhat similar to G10 (other actions), both of which result in the hand being out of the field of view. Although the R3D-MHA model shows a slight decrease in the accuracy of recognizing G4 and G5 compared to the R3D model, with a reduction of 0.02, it improvesthe recognition accuracy of the three easily confused gestures G1, G2 and G7 by 0.02, 0.03 and 0.09, respectively. Furthermore, it shows varying degrees of improvement for other gestures. This also demonstrates the effectiveness of combining the R3D model with the multi-head attention mechanism.
## 4 Experimental Results  
### Quasi-Steady State Temperatures in the Middle Plane  
Figure 14 presents the temperature distribution inside the refrigerated cabinets with single- and two-layer air curtains in a steady state. The thermocouple measuring tolerance is at \(\pm\)0.5 \({}^{\circ}\)C. When comparing the results with those obtained from the CFD simulation (Figure 13), the temperature of M packages is higher because the side wind (0.2 m/s) and the defrosting time were not considered in the 2D CFD model. The defrosting process takes about 30 min, and then the air temperature in the refrigerated cabinet increases by approximately 2 to 3 \({}^{\circ}\)C. After the defrosting process, it takes about 30 min for the refrigerated cabinet to reach a steady state. The temperature of the discharge air grille corresponds to the set point temperature; in all cases, the set point temperature was set at +3 \({}^{\circ}\)C (Table 1). The measured air temperature of the first layer of the air curtain in all cases was +3.2 \(\pm\) 1 \({}^{\circ}\)C, and the temperature of the second layer was +5.3 \(\pm\) 1.5 \({}^{\circ}\)C. The air temperature of the second layer is significantly influenced by the return air temperature. The temperature of air returned to the refrigerated cabinet with a single air curtain was measured, and it was +6.4 \(\pm\) 1 \({}^{\circ}\)C. For a two-layer air curtain, the intake temperature was also measured and was +5.7 \(\pm\) 1 \({}^{\circ}\)C. The infiltration ratio of the experimental model is higher than that of the CFD model, but the relationship between them remains the same. For the single air curtain version, the infiltration ratio is 14.6%, and in the case of the two-layer air curtain version, the infiltration ratio is reduced to 9.7%. The difference is 4.9%. When comparing the results with the CFD model, the difference was 3.4%.  
When comparing the temperatures of the M packages presented in Figures 13 and 14, a great difference is not observed. The largest difference between the temperatures inside the cabinets with single- and two-layer air curtains was observed for the positions of the M packages F3 and F4; the same dependence remains in the CFD model, and the temperature in the different refrigerated cabinets differs by approximately 1 \({}^{\circ}\)C. The average temperature of the M packages in the experimental model was +5.75 \({}^{\circ}\)C (single air curtain) and +5.17 \({}^{\circ}\)C  
Figure 14: Distribution of the temperature of food products in the middle plane of refrigerated cabinets (experimental data).  
(two-layer air curtain). The worst result in terms of temperature of the M packages was recorded on the bottom shelf for the middle M packages (Figures 13 and 14). It takes much more time to cool the products when there is a large volume of M packages.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 3 Proposed Procedure
### Data Preprocessing  
SSI is assumed to be stochastically excited by stationary and zero-mean random processes. Therefore, at the very beginning, measurements need to be confirmed, provided they are collected under similar assumptions, to excite the structure's modes of interest. If the measurements are mainly acquired during nonstationary events, some techniques can be helpful to differentiate nonstationary excitations. Such techniques include short-time average through long-time average (STA/LTA), normalized Arias intensity, Akaike information criterion (AIC), and so on. For example, Figure 2a,b demonstrates how normalized Arias intensity and AIC determine the initial time of a seismic event. AIC estimates the relative amount of information lost by statistical models, while normalized Arias intensity computes amplitude changes across the time axis. These techniques are generally called P-wave pickers. Once the nonstationary excitations are determined, only the measured response outside nonstationary events can be used to construct the block Hankel matrix.  
Next, the fundamental frequency (or lowest frequency) of the structural system needs to be roughly determined using FFT, and in the meantime, the structure's modes of interest can be checked. Moreover, the SNR can also be conjectured to assess noise levels in measurements. As stated in Section 2, fundamental frequency is crucial for choosing the size of the Hankel matrix, and the overview of the structure's modes provides advantageous information about the representing system order, which is useful for choosing the system order and reducing the computational effort. Similarly, Figure 2c demonstrates how FFT provides the most basic signal and modal information before the automated SSI-based OMA is applied. Consequently, the measurements can be down-sampled once the Nyquist frequency is significantly higher than the structure's modes. The fundamental frequency can be estimated through the Fourier spectrum. Notably, signal and modal information can also be found by other data-driven techniques, i.e., wavelet transform. However, FFT is obviously the best technique because of its few assumptions and less computational effort.