esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Advances in Decoder Research for Building Area Segmentation  
In neural networks, the encoder and decoder are complementary modules. The encoder is in charge of extracting deep-level features of the target from the original image, while the decoder gradually restores these features to generate an output image that matches the original resolution. Currently, decoders commonly used for building area segmentation can be broadly classified into three types. The first type is transposed convolution [20], which increases the resolution of feature maps through stride expansion and convolution kernel weighting operations. Although this can better restore spatial information, transposed convolution may cause checkerboard effects in the feature maps. The second type is based on interpolation techniques, typically using bilinear interpolation [21], which employs non-parametric methods for feature reconstruction, offering advantages in computational efficiency and effectively avoiding checkerboard effects. However, its performance in detail restoration is relatively inadequate. The third type is based on skip connections [22], which fuse encoded features during the decoding process. Although this method helps recover more detailed information, inappropriate fusion may introduce noise, affecting segmentation performance. As a result, to enhance the reconstruction of building area details and prevent the checkerboard effect, we designed a hierarchical decoder combining the advantages of skip connections and transposed convolutions. Meanwhile, in response to the impact of noise, we designed feature refinement modules in the skip connections to effectively reduce noise interference.
# Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
## 3 Results  
### Mapping Accuracies  
The overall accuracy using the 2023 validation samples was 85.6%, with the overall users' accuracy being 87.2%, the overall producers' accuracy 83.8%, and F1-score 85.5%. Figure 7 shows the users' and the producers' accuracy for each of the studied land cover types, including the 10 specific crop types. In general, the broad land cover types, such as urban, water, natural forest, and bare ground, were relatively well identified, with over 80.0% accuracy in most cases. However, there were some discrepancies between the users' and the producers' accuracy for some classes. For example, while urban producers' accuracy was as high as 95.2%, its users' accuracy was barely close to 80.0%. The opposite trend was found for bare ground, where its users' accuracy was noticeably higher. Because unpaved roads are common in the rural regions of Thailand, the high spectral similarity between some unpaved roads and bare ground led to an overestimation of the urban area at the fine scale. However, it did not affect our interpretation of croplands. The majority of natural forests in tropical Eastern Thailand demonstrated a lower variation in phenology than crops, and they were limited to reserved regions (e.g., national parks), which made forest class identification a straightforward task. The only non-agriculture land cover that introduced noticeable uncertainties to crop mapping was grass/shrub, with a high users' accuracy but a low producers' accuracy. Some crops take years to reach closed canopies (e.g., rubber and durian). During the early growing years, the gaps and soil on the ground are typically covered by grass, resulting in mixed spectral signatures. This is particularly true when crops are grown at a relatively low density. However, the underestimation of  
Figure 6: Sample time series observations and the corresponding fitted curves using harmonic regression for urban, forest, rice, durian, and rubber pixels.  
grass/shrub did not cause major issues for crop mapping due to their small coverage (less than 1%) of the study area.  
Mapping accuracies varied across crop types are shown in Figure 7. In general, the majority of crop types reached over 75% of the producers' and the users' accuracy. Particularly for rice, rubber, pineapple, oil palm, and coconut, the accuracies were above 90%. An exception was sugarcane, whose accuracy was lower than 50% of producers'/users' accuracy. Due to the nature of sugarcane as a species of tall, perennial or semi-perennial grass, it was easily misclassified as grass/shrub or other early-stage plantation with ground surface covered by grass of similar phenological traits.
## 2 Operating Principle of the Proposed Circuit  
This section details all operating modes of the established circuit, indicating how the output can be produced as a non-inverting and inverting form or version of the input voltage. This study is detailed with the help of the control signals and current conduction loops formed to transfer power from input to output. The low and high-frequency operating behavior of the active switching devices is governed with respect to the control algorithm, as illustrated in Figure 3. This figure clarifies the non-inverting and inverting behavior of the output voltage with any half cycle or polarity of the input voltage. In the given output voltage waveform, the output voltage is a non-inverting form of the input voltage for the first and second half cycles of the first and second cycles of the input voltage. Similarly, the output is an inverting version of the input for the second and first half cycles of the first and second cycles of the input voltage, respectively. There is a switching of one low and one high-frequency transistor at any time instant during its operation. The role of the full-bridge rectifier circuit shown on the front-end side is to fold the sinusoidal input voltage (\(v_{s}\)) into its absolute form across the input terminal of the inverting voltage circuit. The magnitude of this absolute version of the sinusoidal and input voltage is the same. The role of the inverter circuit is to transform this voltage into its non-inverted and inverted form with voltage regulating capabilities available across the load terminal in the form of output voltage (\(v_{o}\)). With respect to the absolute form of the input sinusoidal voltage, the non-inverted and inverted voltage buck operation can be completed in four operating modes. However, there is a requirement for a detailed operation to show how the input power is transferred from the source side to the output via the filtering inductor and how the stored energy of the inductor is transferred to the output during turn-on and off periods of the high-frequency PWM control, respectively. With the help of all these current conducting loops, the value of the inductor voltage and capacitor current for PWM on and off intervals can be computed to find the state space representation or voltage transfer ratio for any polarity of the input or output voltage. The detail of how this target is achieved with the operation of the active switching devices is described under the heading of non-inverting and inverting operations.
# Study on Stiffness Parameters of the Hardening Soil Model in Sandy Gravel Stratum
## 2 Large-Scale Experiments of Sandy Gravel
### Loading and Unloading Triaxial Compression Test  
As mentioned before, to obtain the unloading-reloading Young's modulus of sandy gravel soil, it is necessary to conduct a loading-unloading triaxial compression test on the sample when the confining pressure is 100 kPa. By calculating the average slope of the hysteresis loop formed during the loading and unloading process, the unloading-reloading Young's modulus \(E_{\text{ur}}\) can be obtained.  
There are four main steps of a loading and unloading triaxial compression test. The first three steps are sample installation, saturation, and consolidation, the same as the triaxial compression test. After that, unloading and reloading shall be carried out. The load applied for the first time is 25% of the failure stress of the sample. When the load approaches the target value, the unloading shall be carried out immediately until the load is about 0, and then reload 50% of the expected failure stress onto the sample. In order to better control the value of the applied load, stress control is used in all loading and unloading processes. The whole test process is shown in Figure 6.  
As shown in Figure 6, when the soil sample is unloaded, the trend of the stress-strain curve varies from steep to slow, indicating that the soil sample has undergone a certain degree of plastic deformation at this time. When the soil sample is reloaded, the curve trend is the same as during unloading. Two unloading-reloading processes are carried out during the test; the average value of their slopes is the \(E_{\text{ur}}\) of the sample, and it is calculated that \(E_{\text{ur}}=1851\) kPa.
# Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage
## 3 Results and Discussion
### The Application of the Machine Learning Algorithms
In the graph, three areas corresponding with the states of the conveyor belt can be distinguished clearly, which overlapped only to a small extent. The results indicate that the data collected from the strain gauge-based monitoring system are closely related to the actual state of the conveyor and the reflected condition of the rubber belt. As such, these data can also be analyzed for the speed and quality aspects of the manufacturing process in terms of a production efficiency analysis [42]. From the collection of records, major faults and critical failures can be assessed using the Fault Tree Analysis (FTA) method through a graphical representation of the faults' causes and potential countermeasures [43]. Moreover, continually collected data can be used for the prediction of the remaining useful life and for appropriate optimized maintenance decisions [44]. \[X_{ms}=\sqrt{\frac{1}{N}{\sum}_{n=1}^{N}[x(n)]^{2}}, \tag{2}\]2 The parallel coordinate plot for the _Coarse Tree_ model.
# Dialog CIN: Contextual Inference Networks for Emotional Dialogue Generation
## 5 Experiments
### Results and Analysis
#### 5.4.3 Ablation Experiment and Result Analysis  
To investigate the impact of different modules in DialogCIN on its performance, we conducted several ablation studies on the LifeDialog dataset. In these studies, we removed different modules that modeled the global and speaker-level information in the Representation Unit and Understand Unit. We found that the model's performance decreased to varying degrees when we ablated the Representation Unit and Understand Unit separately.  
**Effect of Understand Unit**  
When we removed all the Understand Units at the Context and Speaker levels, as shown in Part 3 of Table 5, DialogCIN's PPL, BLEU, Distinct-1, and Distinct-2 metrics on the LifeDialog dataset decreased by 3.8%, 12.5%, 8.1%, and 8.3%, respectively. This demonstrates the effectiveness of the designed Understand Units, which can iteratively perform the reasoning and retrieval process to improve the model's ability to understand context. In addition, as shown in Part 2, when we separately removed the Understand Units at the Context and Speaker levels, DialogCIN's performance experienced varying degrees of decline, indicating that both the global-level and speaker-level Understand Units play a positive role in the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Semantic Coherence** & **Emotional Appropriateness** \\ \hline WSeq & 2.78 & 2.81 \\ HRED & 2.97 & 3.05 \\ DSHRED & 3.13 & 3.17 \\ ReCoSa & 3.32 & 3.41 \\ DialogCIN & **3.78** & **3.65** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of human evaluation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **PPL** & **BLEU** & **Distinct-1** & **Distinct-2** \\ \hline WSeq & 95.78 & 0.0096 & 0.0366 & 0.179 \\ HRED & 101.53 & 0.0084 & 0.0379 & 0.1871 \\ \hline DSHRED & 97.70 & 0.01 & 0.0379 & 0.1929 \\ ReCoSa & 98.41 & 0.0099 & 0.0346 & 0.1868 \\ DialogCIN & **91.36** & **0.0104** & **0.0395** & **0.2151** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of different models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Part**} & \multicolumn{2}{c}{**Representation**} & \multicolumn{2}{c}{**Understand**} & \multirow{2}{*}{**PPL**} & \multirow{2}{*}{**BLEU**} & \multirow{2}{*}{**D-1**} & \multirow{2}{*}{**D-2**} \\ \cline{2-3} \cline{5-6}  & **Context** & **Speaker** & **Context** & **Speaker** & & & \\ \hline
1 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 91.36 & 0.0104 & 0.0395 & 0.2151 \\ \hline
2 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 90.43 & 0.0094 & 0.0384 & 0.2094 \\  & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & 93.21 & 0.0093 & 0.0379 & 0.2063 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of ablation experiment.
# HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration
## 5 Discussions
### Effect of Dataset Characteristics  
Each dataset exhibited unique geometric and distributional properties that influenced registration outcomes. For example, the Monkey dataset featured scattered ISS keypoints distributed across a clearly defined structure. This allowed for the use of fewer points per group (approximately 74) while still achieving reliable convergence. Conversely, the Subway Station dataset was characterized by planar and repetitive structures, which necessitated a larger number of observations per group (about 166) to maintain numerical stability during optimization. In general, sparse structures could be handled with fewer points per group, while dense or repetitive structures required more points to ensure  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metrics** & **Bunny** & **Monkey** & **Dragon** & **Large-Scale** & **Subway (0.03 m)** & **Subway (0.05 m)** & **Statue** \\ \hline ISS points & 330 & 223 & 936 & 7000 & 3698 & 1665 & 244 \\ Stochastic model & E & E & E & E & E & E & E/N \\ Optimal groups & 3 & 3 & 9 & 70 & 20 & 10 & 3/2 \\ Points per group & 110 & 74 & 104 & 100 & 185 & 166 & 81/122 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistics of optimal groups across different scenes.  
robustness and stability. These findings emphasize the importance of adapting grouping strategies to the specific topology and distribution of the data. Moreover, introducing randomness in the point order prior to grouping further ensures statistical homogeneity within each group, enhancing estimation robustness.  
Apart from geometric structure and point distribution, the inherent noise level of a dataset also plays a crucial role in determining the optimal grouping strategy. For example, in the subway station dataset, which exhibited a relatively high level of measurement noise due to environmental interference and large-scale acquisition, the optimal performance was achieved when each group contained approximately 166-185 observations. In contrast, the statue dataset, collected under controlled conditions with high-precision equipment and minimal ambient noise, achieved optimal results with fewer observations per group (81-122 points).  
This contrast suggests that higher noise levels may necessitate more observations per group to stabilize the estimation process and mitigate the influence of random errors. With fewer points, highly noisy groups may suffer from poor conditioning or unstable convergence during iterative optimization. Therefore, datasets with greater noise benefit from coarser grouping--with each group containing more observations--to ensure numerical robustness. Conversely, in low-noise contexts, finer grouping becomes feasible without sacrificing stability, allowing for greater computational efficiency.
# Trapezoidal Cantilevers with Irregular Cross-Sections for Energy Harvesting Systems
## 3 Numerical Investigation of Modified Trapezoidal Cantilever Beams
In order to accurately compare the electrical characteristics of the cantilevers, the ratios between the used volume of the piezo ceramic and the output voltage, current and power were set as critical. Such a means of comparison was chosen due to the differences in piezo ceramic volumes used in each cantilever. Therefore, parameters like V/mm\({}^{3}\), \(\upmu\)A/mm\({}^{3}\) and \(\upmu\)W/mm\({}^{3}\) were used for comparison. The results of the comparison are given in Table 2.6  
Table 2.7 Electrical characteristics of the trapezoidal cantilever with cylindrical gaps. Table 2.8 Electrical characteristics of the trapezoidal cantilever with trapezoidal gaps. According to Figure 12, the voltage generated with modified cantilevers from 1 mm\({}^{3}\) is more than 2.2-times higher in comparison with the conventional cantilever. The generated current has more than a 2.5-times higher value, whereas the power is higher by more than 11.5-times. As a result, Figure 12 shows that the best power/piezo ceramic volume ratio is for a cantilever beam with cylindrical gaps.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 5 Results
### BER vs. OSNR  
Once the step-size is optimized, we evaluate the performance for different optical signal-to-noise ratios (OSNR). In order to track the phase, a carrier-phase estimation (CPE) with a windows size of 64 taps is included for both FD-LMS and FD-SSA (see Figure 4).  
Figure 6 compares the BER performance of the FD-LMS and FD-SSA algorithms for both \(2\times 2\) and \(6\times 6\) implementations. It is observed that FD-LMS and FD-SSA achieve almost identical performance.
## 5 Conclusions
#### Limitations  
As it is briefly discussed in previous sections, even though the presented framework demonstrates promise on industrial rotating machinery condition monitoring, several limitations can be mentioned. First, the reliance on time-domain features especially on mid- and high-frequency ranges of vibration signals may restrict fault discrimination for high-frequency failures (e.g., bearing spalls and gear cracks), which are better captured by frequency-domain analyses. Second, the model's validation on a single machine type (coupled radial suction fan) necessitates caution in generalizing to other rotating equipment. In addition to that, the machinery subjected to analysis here rotates above 3000 rpm, excluding relatively harder cases on slowly rotating machinery, that is, below 60 rpm. Future work will integrate multi-domain features and expand datasets to address these gaps.
## 3 Results and Discussion
### Effect of the Combined Use of Virgin and Recycled Fillers and Fibres on Mortars' Properties
#### 3.2.2 Electrical Resistivity Measurements
When carbon fibres are added, the electrical resistivity is not enhanced at low frequencies, whereas great enhancement is registered at high frequencies with both VCF and RCF at any dosage. The combination of fillers and fibres added to cement-based composites has been already studied by [PERSON] and [PERSON], who substituted 50 wt% of carbon fibres with carbon black [27]. They reported that the filler particles interposing between adjacent fibres enhanced the electrical connectivity of the composite, thereby resulting in a synergistic effect. In general, results in DC are confirmed by those obtained in AC at low and medium frequencies ranges for all the materials tested (Figure 11). This result can be expected considering that in these ranges, frequency goes toward the DC limit, where the effects of the only mortar matrix electrolytic conductivity is prevalent over the combined conductivity of the matrix (ionic conductivity), and in particular, of the fibres (electronic conductivity), which can be observed at high frequencies [39]. Therefore, such combined conductivity takes into account the contribution of both types of conductors with the corresponding and different conduction mechanisms, which cannot be revealed by DC or AC at low frequencies, and in some cases, AC at medium frequencies. Furthermore, when the AC measurements are performed at low frequencies, even the contribution of the polarisation of the interfaces between the electrodes and cement matrix cannot be negligible. This contribution is avoided in DC measurements, taking into account that the method uses a 4-electrode configuration (see \"Direct Current (DC)\" in Section 2.2.3 and Figure 5). On the other hand, the combined conductivity of the mortars' matrix and fibres, with or without fillers, whose contribution is visible in any case (Figure 12), can be partially observed though DC measurements, at least in this work. However, the literature reports that GNP [8] is a good commercial filler and carbon fibres [3; 17; 27; 40; 41] are good reinforcing materials for decreasing the electrical resistivity of mortars through DC measurements. Figure 11: Electrical resistivity (\(\rho\)) of mortars after 28 days of curing measured in both DC and AC. Therefore, the obtained results suggest that electrical impedance measurements should be performed in AC by using high frequency excitation signals in order to better analyse the behaviour of highly conductive cementitious composite materials like those containing GNP and carbon fibres. This avoids errors induced by polarisation of the electrodes or variation of the properties over time due to ion migration [42] and allows the measurement of only the properties of interest, that is, the resistivity of composite systems such as cement-based mortars containing conductive fibres [39] or fillers.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Segmentation with 2D Architectures
#### 3.3.4. UNet 3+  
UNet 3+ [12] introduces a refined approach that augments the capabilities of both the original UNet and its extension, UNet++. The motivation behind the development of UNet 3+ emerges from the inherent drawbacks observed in the earlier UNet++ architecture. Particularly, UNet 3+ endeavors to rectify the issue of insufficient information exploration across full scales. This limitation in UNet++ prevents the model from fully harnessing the rich details and semantics available at various scales, a challenge that UNet 3+ sets out to overcome.  
UNet 3+'s pioneering feature is the incorporation of full-scale skip connections. These skip connections serve as a key differentiator from conventional and nested skip connections utilized in prior models. The full-scale skip connections in UNet 3+ amalgamate low-level details with high-level semantics taken from feature maps at different scales, in contrast to earlier methods that merely combined features from different levels. This unique fusion empowers the model to capture intricate fine-grained details alongside broader coarse-grained semantics, culminating in segmentation outcomes that are more precise and comprehensive.  
Complementing the innovation of full-scale skip connections, UNet 3+ leverages a full-scale deep supervision strategy. This strategy underpins a hybrid loss function designed to capture information at pixel, patch, and map-levels. The fusion of this loss function with the full-scale skip connections results in the model's capacity to learn hierarchical representations from the aggregated feature maps across the full scale. This synergy heightens the discernment of region of interest boundaries and positions, contributing to the model's enhanced segmentation accuracy. Figure 4, which has been adapted from [12], shows UNet 3+'s general architecture.  
In our implementation of the UNet 3+ architecture, the model has a total of 26 million parameters and comprises a total of 114 layers, including layers with parameters and layers without parameters. Among these layers, 61 are equipped with parameters. For a more detailed scheme of UNet 3+, please refer to [12].  
Figure 4: UNet 3+ architecture [12].