esa-sceva/satcom-chunk-collection huggingface dataset:
## The Ocean
### Accuracy of SWH Data  
The Comparative Analysis of Ku and Ka-Band Altimeters for Wave Observations  
The measurements from both Ku- and Ka-band altimeters have been available concurrently. This case presented here aims to evaluate the suitability of both frequencies for wave and inland water studies. For this study the SARAL/AltiKa and Jason-2 data for year 2013 has been used. NOAA National Data Buoy Center (NDBC) buoy data have been used extensively to validate both SARAL/AltiKa and Jason-2 data individually for 2013. The SWH from both altimeters are collocated with NDBC buoy data at spatial and temporal intervals of 25 km and 30 min, respectively. Analysis has been carried out for various sea states and for the mid-latitude and tropical belts. The collocated points where difference of altimeter and observation is larger than three times the standard deviation of the difference are excluded from the analysis. The wave data for Cycle 4 from SARAL/AltiKa and Cycle 185 of Jason-2 have been analyzed over the Hudson's Bay area of Canada. This is the second largest bay in the world with average depth below 100 m and maximum depth of 270 m. The bathymetry of this region is shown in Figure 11a. Most of the Ku band altimeters does not show data availability at this shallow depth. Jason-2, for example, does not have data available over this region.  
Figure 11: Availability of SWH data over very shallow depth from SARAL/Altika as compared to Jason-2. The example is Hudson Bay, with average depth of 100 m and maximum depth of 270 m: **(a)** bathymetry of Hudson Bay; **(b)** SWH from Jason-2; and **(c)** SWH from SARAL/AltiKa.  
However, with emergence of SARAL/AltiKa, scientific fraternity is fortunate to get the valuable data from altimeter over this area. This opens the door to several applications for shallow waters including dams and reservoirs using Ka-band altimetry. Apart from being excellent in shallow water, Ka-band altimetry is known for its better approachability towards the coast. For example, over Indian subcontinent, the SWH data across a transect along 18\({}^{\circ}\)N has been shown in Figure 12 for Cycle 4 of SARAL/AltiKa and Cycle 185 of Jason-2. Very clearly, for the coast at about 83.5\({}^{\circ}\)E (marked by the yellow line), the SARAL/AltiKa has much better approachability, up to 84.2\({}^{\circ}\)E, as compared to Jason-2 (85.8\({}^{\circ}\)E). The comparison of SARAL/AltiKa and Jason-2 with NDBC buoys clearly shows that in calm and moderate sea conditions when SWH is between 0-2.5 m the performance of the SARAL/AltiKa is better than Jason-2 (Figure 13). The comparison of SWH with buoy observations for mid-latitudes and tropics clearly indicates that SARAL/AltiKa has excellent capabilities of observing waves for both the belts (Figure 14).  
Figure 12: Availability of valid SWH data from SARAL/Altika and Jason-2 for transect along 18\({}^{\circ}\)N showing better available data near coast marked by green line.  
Figure 13: RMSE of SARAL/AltiKa and Jason-2 SWH with respect to buoy observed wave in various sea state categorized on basis of SWH.
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Measured Parameters** & **Best Settings in Exp. 4** & **Best Settings in Exp. 5** & **Best Settings in Exp. 6** \\ \hline SF & 9 & 8 & 9 & 9 & 9 & 8 & 10 & 9 & 9 \\ BW (kHz) & 125 & 125 & 250 & 250 & 250 & 250 & 250 & 250 \\ CR & 4/5 & 4/8 & 4/5 & 4/5 & 4/6 & 4/7 & 4/7 & 4/8 & 4/5 \\ Packets received (\(\%\)) & 94 & 94 & 87 & 81 & 81 & 73 & 75 & 72 & 71 \\ Average RSSI (dBm) & \(-\)103 & \(-\)98 & \(-\)94 & \(-\)100 & \(-\)101 & \(-\)98 & \(-\)102 & \(-\)105 & \(-\)103 \\ Max. packet distance (meters) & 848 & 807 & 848 & 848 & 764 & 676 & 790 & 860 & 820 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best settings in Experiments 4, 5, and 6 (independently). Regarding the baseline setting, it is highlighted that the RSSI, percentage of packets received, and maximum distance values are considerably close to the values obtained by the best settings in each experiment. This means that this baseline setting should also be considered in future works to perform a larger number of tests for the same or similar services. A mobility service such as the one researched in this work requires the most stable behavior possible of the communication technology at short, medium and long distances, also with a low or high number of LoRa devices sending messages at the same time. The most convenient setting for this type of service was identified by good results in almost all experiments, mainly in experiments 4, 5, and 6 (SF 9, BW = 250 kHz, CR = 4/5). Considering that the maximum distance reached in this setting was relatively low (820-848 m), it is recommended to locate the gateways required for communication at points adjacent to the roads, and at a high enough place to have an adequate LoS over a much greater distance. The results are considered relevant because a significant number of possible LoRa settings were evaluated, considering all the combinations of SF, BW, and CR. 72 settings were evaluated in the first two experiments, 44 settings in Experiments 3 and 4 and 17 settings in Experiments 5 and 6, discarding the settings that did not have adequate results in the previous experiments. In addition, parameters such as distance, number of devices and vehicle speed were scaled. For future testing, it is recommended to use both the baseline setting and the setting with the best results in this work, to further increase the number of devices (eight or more), to use more than one gateway, and to use different routes for vehicles, to get even closer to a real operational environment. Another important aspect in the additional tests is to be able to use a mechanism that allows dynamic modification of the LoRa communication parameters, mainly SF and BW, to allow for changing conditions such as distance, presence of obstacles or number of devices. One possibility to do this is by using the LoRaWAN protocol, or some dynamic allocation algorithms.
# Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information
## 3 Methodologies
### Generating Candidate Line Pairs and Obtain Final Result  
Firstly, it is necessary to calculate the similarity of the line descriptors. The similarity between the two descriptors is determined by calculating the minimum Euclidean distance of the feature descriptor vector. It is worth noting that the line segments in different octaves should be considered when calculating the minimum distance because the same line segments in different octaves are extracted through the image pyramid during the line segment extraction process.  
After that, the NNDR is adopted, which refers to the minimum distance divided by the maximum distance. If the ratio is less than a threshold, then the two line segments are regarded as a set of a candidate matching line. To guarantee the accuracy of matching, the threshold is set to a rough value. In this experiment, it is set to 0.7, which is an empirical value.  
Figure 7: Demonstration of non-local descriptor construction process.  
After screening, there will be some mismatches in the alternate matching line segments. To eliminate these mismatches, the cross-ratio, projection ratio, relative angle of the line segment are taken use of, and the minimum distance of the descriptor obtained in the last section is utilized to establish the link matrix [9; 24]. Then solve the problem through a greedy algorithm [25] to obtain the final matching result.
###### Abstract
In real-world experiments and simulations, we are exploring innovative electro-optical up-converters within the standard modulation structure of a SOA-MZI, which is an extension to our previous work [28] based on photonic differential SOA-MZI modulation. The central receptacle of the administered SOA-MZI is designed to allow the ingress of the sampling signal into the active area of SOA1, while the IF electric subcarrier controls the electric terminal of SOA1. The proposed electro-optical sampled signal dependent on the photonic SOA-MZI exhibits characteristics such as a wide bandwidth and a high conversion efficiency. To further assess the effectiveness and the quality of this electro-optical switching system employing the requested standard modulation approach, we initially obtain simulation results, including error vector magnitudes (EVMs), conversion gains, and bit error rates (BERs), using the VPI emulator. To validate these simulations within the frequency scope up to 59 GHz, we set up experiments based on a photonic SOA-MZI apparatus sourced from the Center for Integrated Photonics (CIP) company. The groundbreaking approach employed in this study has enabled us to analyze EVMs with a data transfer rate of 5 Gbit/s, achieve an impressive frequency scalability of approximately 195.5 GHz, and obtain conversion gains at the discharge location of the operationalized SOA-MZI. These are the key advantages of our current research. This novel method opens up a range of potential applications, including wireless access [29], MMW and microwave connectivity, and ultra-wideband communication. Implementing the standard modulation approach provides significant benefits, such as expansive bandwidth, superior conversion efficiency, and minimal inlet energy prerequisites.
# Single-Carrier Rotation-Interleaved Space-Time Code for Frequency-Selective Fading Channels +
## 4 Simulation Results and Discussion  
In all the simulations, the number of transmit antennas is either two (\(M_{T}=2\)) or four (\(M_{T}=4\)) and the receiver has perfect knowledge of the frequency-selective fading channel which contains uncorrelated and balanced taps with equal variance.
# ISAR Image Quality Assessment Based on Visual Attention Model
## 3 Experimental Setup and Analysis of Results
From the experiment results in Table 1, we can see the performance of different image quality assessment methods (traditional methods, BRISQUE, NIQE, VGG16, Resnet18, Resnet34, Resnet50, ViT, CNN-based Regression, KNN-based Regression, VPSL, EBFF, and \"ours\") on two metrics: the SRCC and the PLCC. These metrics are used to evaluate the performance of the image quality assessment methods and measure the correlation between predicted and actual results. Traditional methods like CQEM, BRISQUE, and NIQE are all manual feature extraction methods, and their performance on this task is relatively low, with SRCC and PLCC not reaching 0.7. VGG16, Resnet18, Resnet34 and Resnet50 are deep learning methods with different architectures. CNN-based Regression and KNN-based Regression [9] use a CNN and KNN to learn the mapping between ISAR image features and their corresponding quality scores. This method, proposed in 2023, features  
Figure 4: Display of the results in the training process. Figure 5: Example images from ISAR image dataset. The first caption row below the images refers to the ground truth scores of the according images, and the second row refers to prediction scores. a relatively rudimentary network architecture and yields lower performance compared to ours. VPSL [17], proposed in 2023, aims to meet the auto-filtering requirements for ISAR images of space targets by introducing a hierarchical evaluation approach based on fusion features. However, the SVM classifier used is not able to effectively extract features from ISAR images. EBFF [18] involves dual-channel input by incorporating the original ISAR images alongside eye-tracking-based heatmaps into a residual network. Utilizing ISAR images and heatmaps can lead to high computational costs and resource demands. The method in this paper is based on the Gram-T backbone architecture and the attention mechanism. According to the results in the table, our method achieves the best results in SRCC and PLCC metrics, which are 0.9103 and 0.8627, respectively, with a total score of 1.773. This indicates that our method has high accuracy and relevance in predicting image quality, outperforming the traditional methods and other typical deep learning models. This table reflects the inference times for each method on a 1080 Ti GPU, highlighting that the ViT model achieves competitive inference times due to its efficient parallel processing, making it suitable for real-time applications, such as ISAR image quality assessment.
## 4 Experimental Results
### Metric  
In this study, we verified the effectiveness of the proposed detection networks by calculating the intersection over union (IoU) metric of TV. The calculation formula was as follows:  
\[\text{IoU}=\frac{TP}{TP+FP+FN} \tag{1}\]  
where True Positive (_TP_) is the number of correctly detected TV pixels; False Positive (_FP_) is the number of background pixels incorrectly classified as TV; and False Negative (_FN_) is the number of TV pixels incorrectly classified as background. In addition, we calculated the precision and recall metrics to evaluate the degree of false alarm and missed detection of TV. The calculation formula was as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{3}\]
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## Appendix E Labelling Uncertainty  
An ECG is an analogue signal recorded with digital devices. Analogue voltages that are very close in value can end up being digitised as identical values due to discretisation, resolution, and gain issues. If this issue occurs during a QRS complex, it means that there are multiple timestamps that can define an R-peak within a given QRS complex (Figure 11), as the digital device is unable to discern the difference between these values. Any method that finds one of these peaks should be considered to have found a \"true\" peak, but can be marked as incorrect if the \"truth\" label is on one of the other identical values in that QRS complex. Since a preprocessed ECG is typically smooth, a local peak correction naturally falls on the closest valid peak to the peak labelled on the preprocessed ECG. The R-peak on the raw ECG closest to the peak on the preprocessed ECG is used in all cases to ensure that if any of these peaks are achieved by a local peak correction, they are marked as \"true\" for a given method.  
Figure 12: Violin plots showing the impact of using local peak corrections with changes in specificity, sensitivity, and positive predictive values for the pre-existing ECG approaches applied to Dataset B, relative to the same approach without local peak corrections. A negative improvement indicates that a metric became worse when a local peak correction was applied.
## 5 Implementation
### Implementation Design  
The proposed method's use case deployment is divided into three parts: implementation of the creation of firmware update smart contract, direct firmware update, and peer-to-peer firmware update distribution. In our implementation, there were five entities. We used the VMWare virtual environment to set up the manufacturer and blockchain network. Our blockchain network was based on Ganache. The IoT gateway and IoT device were hosted on Raspberry Pi. In the IoT gateway,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Manufacturer** & **Blockchain Network** \\ \hline CPU & Intel Core i5-7200U @2.50 GHz \\ Memory & & 4 GB \\ OS & Ubuntu Linux 16.04 LTS \\ Node & v.13.14.0 \\ Ethereum client & x & Ganache-client \\ Library and Framework & Express.js, Web3.js & x \\ Programming language & Node.js & Solidity, Node.js \\ \hline \hline \end{tabular}
\end{table}
Table 2: Development environment for the manufacturer and the blockchain network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **IoT Gateway** & **IoT Device** \\ \hline Hardware & Raspberry Pi 3 Model B \\ Memory & & 1 GB \\ OS & Raspbian Buster \\ Database & SQLite3 & x \\ Server & x & IoTivity Lite 2.0.5 \\ Resources & x & Temperature \\ Library and Framework & Express.js, Web3.js & Express.js, LibCurl \\ Programming language & Node.js & Node.js, C \\ \hline \hline \end{tabular}
\end{table}
Table 3: Development environment for the IoT gateway and the IoT device.  
SQLite3 was used as a database to store metadata from smart contracts and device information. For handling the REST API request, we used Express.js. It supports GET requests for IoT devices and manufacturers. To communicate with the blockchain network, Web3.js was used in the IoT gateway and the manufacturer. The IoT device was connected to a physical temperature sensor. Besides, it hosted the IoTivity server while the client hosted the IoTivity client. All of these entities were in the same network. Figure 6 shows our implementation of IoT network setup. We used a Wi-Fi link to simulate a remote connection to the manufacturer and used a wired link to simulate the local connection between gateways.
[PERSON] [11] used the finite element method to simplify the interaction between foundation soil and structures with fixed longitudinal geometric shapes and established a three-dimensional building model to predict the vibration effect of a train. [PERSON] [12] established a three-dimensional finite element model of a track-soil-depot structure system to predict and control train vibration on over-track buildings, and an in situ vibration measurement was conducted. The particle swarm optimization and genetic algorithm were used by [PERSON] [13] to develop an optimized artificial neural network model for predicting the ground vibration induced by metro trains. To predict the vibration impact of train vibration on different storeys of adjacent buildings, [PERSON] [14] proposed a prediction model (see Equation (1)). \[L_{\text{B}}=L_{\text{r}}+R_{\text{tr}}+R_{\text{tu}}+R_{\text{g}}+R_{\text{b}} \tag{1}\]  
where \(L_{\text{B}}\) is the vibration acceleration level in buildings; \(L_{\text{r}}\) is the track vibration acceleration level; \(R_{\text{tr}},R_{\text{tu}},\ R_{\text{g}},\) and \(R_{\text{b}}\) are the transmission losses of vibration in the track, tunnel, ground, and building, respectively. These attenuation items, \(R_{\text{tr}},R_{\text{tu}},\ R_{\text{g}},\) and \(R_{\text{b}}\), can be calculated on the basis of the impedance theory or obtained through actual measurements. A propagation model of vertical vibration in buildings which can predict the vibration response of building structures in a frequency domain was established by [PERSON] [7] according to the impedance theory. However, this model did not consider the effect of frame beams, infill walls, etc. [PERSON] [15] used field measurement vibration data to train a neural network and built an artificial neural network model to predict the vibration response of buildings. Its limitation is that the effect of soil parameters was not considered. In summary, existing models are mainly suitable for predicting the impact of train vibrations on adjacent buildings along metros and they can only predict the vibration on the ground floor of adjacent buildings. Previous studies on the impact of train vibrations on over-track buildings on the throat area of metro depots are insufficient. The issue of how to reduce the impact of vibration through architectural design is an area of interest for engineering and technical personnel from the fields of rail transit and civil construction. At present, there is no effective model that is convenient for engineering and technical personnel to analyse and evaluate the impact of metro depots on over-track buildings. Previous models cannot be used to directly analyse the impact of changes of main building structural parameters on vibration transmission. Therefore, it is imperative to further investigate the impact of train vibration on over-track buildings and develop a model which can easily and quickly predict the impact of train vibration on over-track buildings in the throat area of metro depots, so as to provide a theoretical and technological foundation for the prevention and control of indoor vibration of over-track buildings.
# Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
## 3 Results and Discussion
### CNN Transfer Learning for Subvisible Particle Classification
The ROC curve and the corresponding AUC are shown in Figure 4. The findings show a very steep cliff pattern in the ROC curve, with an AUC above 0.9885 for seven classes of particles. Taken together, the recall, accuracy, ROC curve, and AUC confirm the usability of the model, avoiding problems that may arise from assessing the model in terms of accuracy alone. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Particle Type** & **Precision** & **Recall** & **\(F\)-Score** \\ \hline Cap fiber particles & 99.60\% & 99.15\% & 99.37\% \\ Glass particles & 94.00\% & 98.70\% & 96.29\% \\ Polystyrene beads & 99.90\% & 100.00\% & 99.95\% \\ Protein particles & 98.90\% & 98.90\% & 98.90\% \\ Silicone oil microdroplets & 97.91\% & 98.40\% & 98.15\% \\ Silicone tubing particles & 95.62\% & 91.70\% & 93.62\% \\ Rubber closure particles & 96.72\% & 95.70\% & 96.21\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recall, precision, and \(F\)-score evaluation of particle classification.
## 3 Methodology
### Distorted Aerial Images  
The workflow used in analog image transmitters and receivers is depicted in Figure 3. An aerial image from a proper source is converted to set of low-frequency tones between 0 Hz and 3 kHz. These tones are frequency-modulated with a radio frequency (RF) carrier. The characteristics of the RF carrier are determined according to the relevance of the situation, how the transmission process must be accomplished, and by considering the possibilities of acquiring a proper license to transmit. For the work conducted in this paper, a 433 MHz carrier frequency was used, which is a part of the non-licensed industrial, scientific, and medical (ISM) band. The modulated RF carrier is amplified and transmitted over the air by using a proper antenna. The image receiver obtains that signal from its antenna, demodulates it, then de-converts it to obtain the image which was sent.  
Figure 2: Sample images from the Dubai dataset [39]. All images were cropped to a size of 320 \(\times\) 256.  
Figure 3: Basic workflow of the image transmission process from image source to image receiver.  
To denote a situation where distortions are inevitable, an analog image transmitter was emulated to capture noisy aerial images. The transmitter and receiver were kept at a distance with many obstacles between them. The details of an image are converted to many low-frequency tones to be frequency-modulated with a radio carrier wave at the transmitter. The narrowband frequency modulation (NBFM) type was used, and the carrier frequency was 433 MHz. Image \(i\) was transmitted through the air, received as image \(i^{\prime}\). With this process, 120 images were collected. Figure 4 shows dissimilarities between the transmitted image and the received image.