esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Preparation and Processing  
To determine possible changes in the surface of the coastal zone of the Biobio region, a total of 12 raw images from the SENTINEL 1 satellite were selected and downloaded from NASA Earth data ([[https://search.earthdata.nasa.gov/search/](https://search.earthdata.nasa.gov/search/)]([https://search.earthdata.nasa.gov/search/](https://search.earthdata.nasa.gov/search/)), accessed on 7 January 2022). Ref. [54] showed that using 12 interferograms is usually sufficient when using StaMPS. The year 2017 was chosen for the study area (Figure 1c) because of the quality and availability of images. Information of the radar images selected, sensor, and date of acquisition of the satellite image is shown in Table 2. The pre-process was run in SNAP 7.0 software, where orbit data and digital elevation model (DEM) are downloaded. The images were co-registered using the selected master/slave pairs, and a co-registered stack was obtained [45]. The master image recommended by preprocessing was selected  
Figure 2: Technical flow chart to represent the methodological process followed in this research.  
based on the parameters of 2 July 2017 (_hyper_ = 0). Subsequently, 12 interferograms were generated, and the topographic phase was removed and then converted into PS Stamps compatible format (PSI Export). Then, the data were analyzed with the persistent scatterer (PS) method [46]. In total, 1,334,951 PS points were selected from 5,947,735 PS candidates, representing points where deformation can be monitored. The PS points distributed over the extension of the study area are represented in deformation velocity maps. The line-of-sight (LOS) represents the measurement of the deformation rate; in other words, it indicates that there was a decrease or increase in the area, based on the geometry of the sensor based on the slant range or across-track direction [55]. Then the PS candidates were processed in StaMPS/MIT [52] software version 4.1b, Linux environment, to select the final PS candidates.  
The interferometric wide-swath (IW) mode of operation was used, which is the one set to perform interferometric and descending orbit processing. To generate quality interferograms, VV polarizations were used, due to their vertical representativeness following the approach of [56], with a range length of 250 km with resolutions of 5 \(\times\) 20 m in range and azimuth, respectively. Single look complex (SLC) has 3 subframes (Subwaths IW1, IW2, and IW3), and IW1 was used for the chosen area.
## 8 VLC-IoT: Challenges, Solutions, and Future Directions
### Future Research Directions  
The advancement in electronics and telecommunication engineering has led to the development of enhanced components and technologies in VLC. Therefore, there is bound to be renewed research efforts in VLC-IoT. We discuss some possible future research directions and provide a summary in Figure 3.  
1. Development of improved handover mechanisms  
Shadowing effects and LOS signal loss due to moving IoT devices as well as a blockade from machines/human operators deployed together with IoT devices are major challenges to the performance of VLC-IoT networks. Therefore, robust handover mechanisms that can guarantee communication connectivity and QoS requirements under LOS blockages should be investigated and developed for VLC-IoT networks for improved performance [65; 66].  
2. Advancement of improved receiver camera for VLC-IoT infrastructure  
It is envisioned that the advancements in electronics engineering will bring about the development of improved electronics components such as the optical communication image sensor which is capable of providing higher bits' rate communication compared to existing camera chips [67]. In the future, when these chips are manufactured and eventually deployed, VLC-IoT communication will occur at a much higher rate in comparison to the current rate. Moreover, camera-based VLC is the candidate for the IEEE 802.15.7 rl optical camera communications standard and a key future component in the envisioned VLC-IoT technology [68].  
3. Deploying VLC for vehicular communications in road tunnels  
The RF signals currently deployed for vehicular communications in intelligent transport systems suffer from some constraints that affect their performance, particularly in road tunnels. The fact that light waves are more reliable than RF signals in closed spaces makes VLC an attractive solution for the future of vehicular communications and IoT technologies in road tunnels. However, more research needs to be carried out with complex simulations scenarios where the channel is estimated as a time-variant system to determine the maximum achievable frame size and link duration. Moreover, external agents that affect the VLC channel inside a tunnel should be investigated [69].  
Figure 3: Future research directions.  
Software-based approach to VLC-IoT infrastructure  
Current research trends show that IoT communications contemplate VLC infrastructure-oriented communications as the preferred solution for high data rates communications. However, in the future, IoT applications with modest bandwidth requirements could run on a software-based VLC framework requiring minimal network interfaces [70]. It is envisioned that in the future, VLC-IoT hardware components would no longer need to take critical control decisions locally as they can be programmed from a remote interface [70]. Therefore, the details of protocols of software-centric approaches that are likely to play key roles in the future of infrastructure-less VLC systems need to be studied and investigated [71].
## 3 RZE Parameter  
### Review and Explanation of ZPP and \(\mu\)  
ZPP [16] is defined as the ratio of the number of image pixels with zero intensity to the total number of pixels, which is used as a quality control parameter to detect rainfall and can be expressed as  
\[ZPP=\frac{P_{0}}{P}\times 100\% \tag{3}\]  
where \(P_{0}\) denotes the number of image pixels with zero intensity, and \(P\) denotes the total number of pixels.  
The echo intensity is the reflection of the echo signal received by the radar receiver on the radar image, which can indicate whether the radar image is contaminated by rainfall. When the constant power electromagnetic wave is transmitted by the radar transmitter, after attenuation, scattering, reaching the target and returning to the receiver, the radar will store the received signal in the form of voltage. Therefore, the voltage value can reflect the echo intensity on the radar image [14].  
Figure 2: (**a**) Furuno raw radar image with co-channel interference; (**b**) Image in (**a**) after co-channel interference removal.  
The mean echo intensity (\(\mu\)) represents the strength of echo signal in a certain area of the radar image, which can be expressed as  
\[\mu=\frac{1}{mn}\sum_{i=1}^{m}\sum_{j=1}^{n}f(x_{i},y_{j}) \tag{4}\]  
where \(m\) and \(n\) are the range value of the selected radar image, \(f(x_{i},y_{j})\) is the echo intensity value of the pixel \((x_{i},y_{j})\) on the radar image and the unit is \(V\).  
The ZPP and \(\mu\) are two classical rainfall detection parameters, which can reflect the rainfall intensity in radar images. Each parameter is compared with the set threshold to determine whether the radar image is contaminated by rainfall, but the detection speed of the single parameter is slow and the detection error is large.
# A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner
###### Abstract
The Bessel filter, known for its nearly constant group delay [27; 28], is chosen because it minimizes pulse distortion. In [26], pulse expansion from 50 ps to about 700 ps was demonstrated using a custom LPF with a 450 MHz cutoff frequency and a commercial filter with a 1300 MHz cutoff frequency. Similarly, Ref. [16] shows pulse expansion using third-order Bessel filters with 1.8 GHz cutoff frequencies, achieving expanded pulses up to 237 ps, although with significant ripple in the pulse tail. Other approaches, such as expanding pulses using time-frequency projection filters [29], are theoretically possible but often too complex for practical use. Dispersive media can also be used [30; 31]. Dispersive devices and elements are commonly used in microwave and optical domains to achieve pulse expansion, as demonstrated in [32; 33]. On-chip implementations have also been explored [34; 35], where dispersive delay filters offer compact pulse expansion solutions, although achieving high expansion factors remains challenging. Finally, active delay lines, split-ring resonators, and metamaterials offer additional methods for pulse shaping [36; 37], although many of these solutions could be more complex for practical applications. The challenge of developing simpler, passive systems for analog pulse expansion remains a topic of ongoing research, as described in foundational works [30; 38; 39]. This paper is organized as follows: Section 2 presents the design of the UWB pulse expander and consists of several subsections. Section 2.1 introduces the initial considerations and theoretical framework; Section 2.2 is devoted to the pulse splitter selection and design; Section 2.3 presents the pulse combiner; and Section 2.4 is devoted to the microstrip delay line implementation and the UWB pulse expander. Section 3 is devoted to the experimental validation in the time and frequency domains of the designs presented in this paper. Finally, Section 4 concludes the presented research.
## 3 Results and Discussion
Statistical Testing: Statistical testing methods can be employed to evaluate the performance of different machine learning models. This involves comparing the predicted crack propagation results from different models using appropriate statistical tests to determine if there are significant differences in their performance or accuracy. It is important to note that the application of formal verification methods to this specific paper would depend on the details of the research and the methodology employed. The computational time and complexity associated with training algorithms exhibit variations contingent upon several factors, including dataset size and algorithmic implementation. When dealing with small to moderate datasets, linear regression techniques, such as Ridge and Lasso, demonstrate efficient training times, characterized by a complexity of O(p\({}^{3}\)), where p corresponds to the number of features. In contrast, the kNN algorithm, which is non-parametric in nature, requires minimal training time due to its reliance on storing data points. However, the computational expense arises during prediction when kNN necessitates distance calculations, resulting in a complexity of O (n d), with n referring to the number of training instances and d representing the number of features. Support Vector Regression (SVR), an algorithm based on support vector machines, generally exhibits reasonable training time for small to moderate datasets. Nevertheless, the process of solving the quadratic programming problem can become time-consuming for larger datasets. Random Forest Regressor, an ensemble algorithm, exhibits efficient training for small to moderate datasets with a complexity of O (M \(\times\) n \(\times\) log(n)), where M denotes the number of trees and n represents the number of training instances. On the other hand, Gradient Boosting Regressor, another ensemble algorithm, typically entails higher computational requirements, especially when considering larger numbers of iterations. Consequently, its complexity generally surpasses that of Random Forests. The selected algorithms can typically achieve training within an acceptable timeframe for small and moderate datasets. However, the actual time and complexity involved are contingent upon specific dataset characteristics, implementation nuances, and the availability of computational resources [1; 21; 22; 23; 24].
## 4 System Analysis
### Data query cost and effective sink-proxies for the data query.  
For analytic tractability, we analyze S2 and S3 schemes in a simple setting. We assume the query area is a square consisting of N nodes. The average energy consumption of a one-hop transmission of a packet is assumed to be \(E_{0}\).  
In the one-to-many model of an S2 query, flooding is typically adopted for disseminating a query to nodes in the network. The energy cost EQ2 of a data query is given by:  
\[E_{Q2}=N*E_{0} \tag{2}\]  
In S3 information retrieval, in contrast, the operation consists of unicasting from sink to sink_proxy, and geocasting from sink_proxy to sensors in the geocast area, which is a combined rectangle area that contains both sink proxy and the query area. Given that b is the ratio of the node number in the geocast to the total node number N, and \(0<b\leq 1\). Therefore, \(b*N\) is the node number in the query geocast area of S3. Let \(R_{u}\) be the hops of transmission in the unicast from the sink to the sink proxy. Then, the energy cost of S3 query can be computed as:  
\[E_{Q3}=b*N*E_{0}+R_{u}*E_{0} \tag{3}\]  
As a result, the ratio \(R_{Q}\) of query cost of S3 to S2 is given as follows:  
\[R_{Q}=\frac{E_{Q3}}{E_{Q2}}=\frac{b*N*E_{0}+R_{u}*E_{0}}{N*E_{0}}=\frac{b*N+R_ {u}}{N} \tag{4}\]  
The smaller \(b\) and \(R_{u}\) are, the smaller \(R_{Q}\) is. This means greater energy savings.  
When a S3 query saves energy compared with the conventional S2 approach, the ratio of the S3 query cost to the S2 query cost is no larger than 1. That is:  
\[R_{Q}\leq 1 \tag{5}\]  
When \(R_{u}\leq N\)(1-\(b\)), we have \(R_{Q}\leq 1\). Therefore, an effective sink proxy should enable \(R_{u}\leq N\)(1-\(b\)). If a query interest area is defined, the value of b and Ru can be determined by the position of a sink proxy. Given a sink proxy candidate with location coordinates of (x,y), and an resolved query area (Xmin, Xmax, Ymin, Ymax), as shown in Figure 4, we can determine whether the sink proxy candidate is a suitable sink proxy for S3 query geocasting by calculating b and Ru if the sink proxy candidate is used. The query geocast area in S3 model can be calculated as (X'min, X'max, Y'min, Y'max), where X'min = min(x, Xmin), X'max = max(x, Xmax), Y'min = min(y, Ymin),Y'max = max(y, Ymax). We can then determine the ratio R of query geocast area to the network area. R equals b, assuming nodes are uniformly deployed. Further, since Ru is the shortest hopcount from the Sink to the sink proxy candidate, it can be calculated according to the Greedy Perimeter Stateless Routing (GPSR) hop-by-hop routing process [26]. Consequently, \(RQ\) can be obtained.  
If:  
\[R_{Q}=\frac{E_{Q3}}{E_{Q2}}=\frac{b*N+R_{u}}{N}\leq 1\,,\]  
then the sink proxy candidate can be selected as a sink proxy for S3 query geocasting.  
**Figure 4**. Selection of effective sink proxy for data query.
## 3 Results
### Physical Characterization Results  
The images taken with SEM for both polyureas are presented in Figure 6. The images show that polyurea A has a heterogeneous surface, while polyurea B has a more homogeneous surface. Some zones of both polyureas were highlighted and then analyzed separately, using the EDX technique to identify the chemical elements present in the materials used in this study.  
The results obtained from the EDX technique, presented in Figure 7, show that the main chemical elements of both polyureas are carbon and oxygen. Nitrogen was identified in some areas of both polyureas, while aluminium, silicon, and titanium were present in only one of the polyureas. Aluminium and silicon were observed only in polyurea A and titanium appeared only in polyurea B. Some other chemical elements are present. However, their presence is residual. It is worth mentioning the appearance of titanium in polyurea B since titanium nanoparticles are commonly used in composites to improve the mechanical properties of materials.  
The physical properties of the polyureas were evaluated through density, water absorption, and dimensional stability tests. The density of both polyureas, presented in Table 1, are similar.  
Both polyureas had slight variations in mass, 0.94% \(\pm\) 0.04% for polyurea A and 1.12% \(\pm\) 0.04% for polyurea B, after being immersed for 24 h in distilled water, meaning the amount of water absorbed by both polyureas was minimum. These values are consistent with the intrinsic characteristics of these types of materials since they are water resistant. Thus, polyureas are being used as coatings in several industries where the products are or could be in direct contact with water [23; 26; 27].  
Figure 5: TGA analysis of polyurea B.  
Figure 6: SEM images of: (**a**) polyurethane A surface (magnification of 1000\(\times\)), (**b**) highlighted zones on polyurea A surface (magnification of 5000\(\times\)), (**c**) polyurea B surface (magnification of 1000\(\times\)), (**d**) highlighted zones on polyurea B surface (magnification of 5000\(\times\)).  
Figure 7: Comparison of the chemical composition of polyureas A and B obtained from the EDX technique.  
The results from the dimensional stability test in Table 2 show that both polyurea used in this study present good dimensional stability since the dimensions in the longitudinal and transversal directions had minor variations.
## 3 Results  
### Psychometric Evaluation of the Instrument  
Model fit was assessed using three psychometric tests following the refinement of the item pool. Prior to Rasch calibration, a Differential Item Functioning (DIF) analysis identified five biased items (44, 63, 68, 23, and 11), which were excluded to ensure fairness and parameter invariance in subsequent modeling.  
With the bias-free item set, three psychometric tests were conducted:  
First, [PERSON]'s Likelihood Ratio Test (LR = 76.125, df = 61, \(p\) = 0.092) did not reject the null hypothesis of parameter invariance, indicating item stability across the ability levels.  
Second, the Martin-Lof Test confirmed unidimensionality (_p_ = 1.0), supporting the interpretation that the test assessed a single latent trait--namely, theoretical competence in anesthesiology.  
Third, the Wald Test (Supplementary Table S1) showed no significant item-level deviations for most items, further validating the Rasch model assumptions.  
Rasch-based ability estimates (\(\theta\)) were then computed for each model anchored to human performance. GPT-o1 achieved the highest ability (\(\theta\) = 2.42), followed by Deepseek-R1 (\(\theta\) = 2.17), and GPT-4o (\(\theta\) = 1.96). LLaMA 3 70B had the lowest (\(\theta\) = 0.52), indicating a weaker alignment with human-level performance under the IRT framework.  
Supporting psychometric visual--including the Item Characteristic Curve (ICC) plots, Item Fit Map, and Person-Item Map--are presented in Supplementary Figures S1-S3.  
Figure 1 illustrates the distribution of the scores of the LLMs and human examinees.
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 4 Results
### Influence of Data Quality and Model Size
Larger models denoted CNN-BiConvLSTM-M with 3.3 million parameters in total, and CNN-BiConvLSTM-L with 10.3 million parameters in total were also constructed and evaluated. Table 3 presents the results. The performances of the CNN-BiConvLSTM model were found to have improved monotonously for all missing patterns as the size of the models increased, indicating that the spatial-temporal variations can be better simulated by models with more parameters and probably by more sophisticated RNN modules. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Missing Patterns**} & \multirow{2}{*}{**Metrics**} & \multicolumn{2}{c}{**CNN-**} & \multicolumn{2}{c}{**CNN-**} & \multicolumn{1}{c}{**CNN-**} \\  & & **BiConvLSTM** & **BiConvLSTM-M** & **BiConvLSTM-L** \\ \hline \multirow{3}{*}{111101111} & MAE & 1.4650 & 1.4357 & **1.4159** \\  & CSI & 0.5085 & 0.5137 & **0.5287** \\  & HSS & 0.7465 & 0.7519 & **0.7548** \\ \hline \multirow{3}{*}{111100111} & MAE & 1.5675 & 1.5431 & **1.5206** \\  & CSI & 0.4828 & 0.4811 & **0.5010** \\  & HSS & 0.7305 & 0.7351 & **0.7382** \\ \hline \multirow{3}{*}{1110000111} & MAE & 1.7777 & 1.7562 & **1.7294** \\  & CSI & 0.4296 & 0.4217 & **0.4477** \\  & HSS & 0.6976 & 0.7018 & **0.7054** \\ \hline \multirow{3}{*}{1100000011} & MAE & 1.9966 & 1.9723 & **1.9420** \\  & CSI & 0.3784 & 0.3684 & **0.3965** \\  & HSS & 0.6651 & 0.6696 & **0.6732** \\ \hline \multirow{3}{*}{1000000001} & MAE & 2.3731 & 2.3486 & **2.3010** \\  & CSI & 0.3067 & 0.2880 & **0.3214** \\ \cline{1-1}  & HSS & 0.6131 & 0.6169 & **0.6234** \\ \hline \multirow{3}{*}{1010101011} & MAE & 1.4670 & 1.4371 & **1.4182** \\ \cline{1-1}  & CSI & 0.5067 & 0.5132 & **0.5273** \\ \cline{1-1}  & HSS & 0.7457 & 0.7515 & **0.7540** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of MAE, CSI-40 scores, and HSS scores for the CNN-BiConvLSTM model with different model sizes and for 6 missing patterns with respect to interpolation. Figure 12: Examples of reconstructing missing radar frames with the CNN-BiConvLSTM model for missing patterns ‘1000000001’ (second line) and ‘1010101011’ (third line). The first line shows the input radar images (ground truths).
## 3 Dog Behavior Recognition Based on Multimodal Data
### Sensor Data Collection and Preprocessing  
The collected sensor data were three-axis accelerometer and three-axis gyroscope da-ta taken once per second from a wearable device placed on the neck of a dog. The collected data were scaled using the median and quartiles values to minimize the influence of outliers on data collected from the wearable device. Next, an upsampling process was performed for each time-series index to display the collected data as a sequence. In the communication process, it was also necessary to process missing values for the time not collected because of the possibility that some data may have been lost. As with the video data, the missing values were replaced using linear interpolation. The sensor data had a shorter length, so to interpolate only data for 1 s out of 4 s, the number of consecutively interpolating missing values was limited to one. Next, for input into the learning model, the sensor data were divided into 4-s windows through a sliding window.  
Next, we proceeded with feature extraction. Feature extraction can generally be divided into handcrafting features and learning features. Handcrafting features require little computation and can be easily extracted, but the sensor type is specific and feature selection is required, whereas learning features automatically learn features from raw data and are robust, but it is difficult to adjust parameters and interpret learned features [8]. Accordingly, we want to improve the behavior recognition accuracy by extracting handcrafting features and reflecting them in the learning model.  
In several studies, statistical features have been extracted and used for behavior recognition; however, this study only used features with little computation because complex features were automatically extracted and input into the next step, a fusion model. The features (\(f\)) selected in this study were the mean (\(f_{mean}\)), variance (\(f_{var}\)), standard deviation (\(f_{std}\)), amplitude (\(f_{amp}\)), and skewness (\(f_{skew}\)); their equations are (1)-(5) for data length \(n\) and data value \(x\). These features were counted 30 times for the three axes of the accelerometer and three axes of the gyroscope, and these features became the input values of the fusion model together with the raw data to determine how much they affect dog behavior recognition accuracy.  
\[f_{mean}=\frac{1}{n}\sum_{i=1}^{n}x_{i} \tag{1}\]  
\[f_{var}=\frac{1}{n}\sum(x-\overline{x})^{2} \tag{2}\]  
\[f_{std}=\sqrt{\frac{1}{n}\sum(x-\overline{x})^{2}} \tag{3}\]  
\[f_{amp}=\max(x)-\min(x) \tag{4}\]  
\[f_{skew}=\frac{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\overline{x})^{3}}{\left( \frac{1}{n}\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}\right)^{\frac{3}{2}}} \tag{5}\]