esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## 4 Discussion  
### Advantages of the Algorithm  
PWV is an important variable that determines the dynamics of atmospheric motion. This study proposes a PWV retrieval method based on Sentinel-2 data, which can provide products with high accuracy, spatial continuity, and high spatial resolution. It can be seen from Section 3.1 that the PWV retrieved by S2-L2A was different from the products of MOD05 and ESA-L2A. The reasons for the differences between S2-L2A and MOD05 include the following points. First, MOD05 was calculated using apparent reflectance, while the proposed method used radiance. Second, MOD05 used the three-channel algorithm; that is, PWV was calculated from two atmospheric window channels and one water vapor absorption channel. In contrast, S2-L2A used only one atmospheric window channel and one water vapor absorption channel. Third, the transit times of the Sentinel-2 and MODIS differed by tens of minutes. Fourth, although the channels' central wavelength of the Sentinel-2 was close to that of the MODIS, the channels of the Sentinel-2 were narrower and the spectral response functions were slightly different, as shown in Figure 6. The reason for the PWV difference between S2-L2A and ESA-L2A was that ESA-L2A did not distinguish land-cover types and constructed a global PWV inversion model. However, S2-L2A constructed the PWV inversion model considering land-cover types and aiming at local areas.  
Figure 5: PWV inversion results of the 24 Sentinel-2 images.  
The GPS sites deployed on 1 August 2021, and parts of PWV retrieved from S2-L2A, ESA-L2A, and MOD05 are shown in Figure 7. The GPS sites were sparse and only monitored PWV values at a few locations, which could not provide full coverage inversion results. The PWV retrieved by S2-L2A and ESA-L2A had similar spatial distribution to MOD05 but provided more vital details. The spatial resolution of MOD05 was rather coarse, and only a limited number of PWV values could be detected. In addition, spatial variations in the S2-L2A and ESA-L2A PWV tended to be smoothly varying, which removed the mutation in the MOD05. Section 3.1 confirmed that the accuracy of S2-L2A PWV was better than those of MOD05 and ESA-L2A. In general, the spatial performance of S2-L2A PWV was better than that of the other three products.
## 5 Results and Discussion
### Enhanced Phase Shifting Range by Inserting IPs  
We used the designed structure (Figure 4a) to verify the increase in \(\Delta\phi\) by the IPs at 28 GHz. The \(\Delta\phi\) was obtained by using the perpendicular and parallel relative permittivity of ZOC-A001 XX by considering the angle between the RF field and LC directors. As a result, we found that inserting the IPs effectively increased \(\Delta\phi\), as we discussed in the Section 3. To evaluate the tendency among parameters, we regulated the parameters of \(r\) and \(p\). When \(r\) is fixed, \(\Delta\phi\) increased as \(p\) increased (Figure 8a). In addition, when \(p\) was fixed, the \(\Delta\phi\) increased as \(r\) increased (Figure 8b). Therefore, inserting an IP can increase \(\Delta\phi\), and the desired \(\Delta\phi\) can be obtained by changing the \(r\) or \(p\), or both, of the IP.
# An Alternative Series Solution for Free Vibration Analysis of Asymmetric L-Shaped Membranes
## 5 Potential Practical Applications  
To understand performance sensitivity (S), we use the following relationship [49]:  
\[S=\frac{\Delta f}{\Delta m}, \tag{30}\]  
where \(\Delta f\) is the fundamental frequency shift, and \(\Delta m\) is the change in mass. Analyzing the dimensionless parameters \(a/h\) and \(d/h\) within the context of fundamental frequency shift and performance sensitivity provides significant insights that are crucial for practical applications, such as membrane vibration-based sensors [50; 51; 52].  
We modeled a scenario where particle adsorption (e.g., from gases or chemicals) causes a 1% increase in the membrane's mass. This additional mass is represented as point-like particles uniformly distributed across the entire membrane surface. We set the membrane density to 750 kg/m\({}^{3}\) for our analysis and applied a pre-stress of 0.1 MPa. Figure 4a,b illustrate the effects of varying geometric parameters on the asymmetric L-shaped membrane sensors, specifically showing the fundamental frequency shift and performance sensitivities, respectively. These results provide insights into how the sensor's geometry influences its response to mass changes, which is crucial for gas and chemical detection applications.  
Figure 4: (**a**) Fundamental frequency shift (\(\Delta f\)) and (**b**) performance sensitivity (S) versus dimensionless length of the horizontal arm (\(a/h\)) for various \(d/h\) ratios.  
Figure 4a shows a clear trend: As \(a/h\) increases, \(\Delta f\) decreases for all values of \(d/h\). This indicates that longer horizontal arms reduce the impact of asymmetry on the membrane's vibrational characteristics. The rapid decrease in \(\Delta f\) for smaller values of \(d/h\) (e.g., \(d/h=0.1\)) suggests high sensitivity to changes in \(a/h\), stabilizing as \(a/h\) increases. For practical applications, structures requiring stable vibrational properties should opt for higher \(a/h\) and \(d/h\) values. For instance, in the design of sensors and actuators in MEMSs, achieving a stable fundamental frequency is critical for reliable performance. By adjusting \(a/h\) and \(d/h\), engineers can fine-tune the vibrational properties to meet specific operational requirements.  
Figure 4b displays a similar trend to the fundamental frequency shift. As \(a/h\) increases, \(S\) decreases, indicating reduced sensitivity to performance variations. The highest initial performance sensitivity is observed for \(d/h=0.1\), which decreases significantly with increasing \(a/h\). For larger \(d/h\) values, the decrease in \(S\) is more gradual, demonstrating more excellent stability. In biomedical engineering, the design of implants and prosthetics [53] can be improved by understanding the vibrational properties of membrane-like structures. By adjusting \(a/h\) and \(d/h\), the mechanical response of these devices can be tailored to better match the physiological conditions of the human body.
# Performance of Narrow Band Wide Area Networks with Gateway Diversity
## 5 Conclusions  
The study in [19] illustrates the benefit of GW diversity in terms of improved PER with only two gateways measured in an indoor anechoic chamber. This study with realistic system parameters such as outdoor NLOS conditions with shadow fading shows that a coverage area of 1.44 km\({}^{2}\) can be achieved with a TP-99 PSR \(\geq\) 86%. This can be achieved with SF-11 in a suburban terrain type as long as there are multiple GWs providing GW diversity. This observation is made when the packet injection rate is sparse with one packet sent every other minute from the ENs. Such coverage area will shrink when the packet injection rate to the network is increased, causing more collisions in the UL. Although the coverage area size drops to 0.42 km\({}^{2}\) when SF-8 is used, the PHY bitrate is increased by 5.8-fold for the same bandwidth. The LoRa modulation is best suited for stationary end-nodes due to the long packet durations. Future works based on this study may include, but are not limited to, the following: 1. Implement a mobility model and packet repetitions to the large scale simulator and re-assess the coverage area for mobile applications such as asset/pet tracking. 2. Assess system capacity versus packet injection rate of the network. 3. Develop a path loss and interference model for rural and dense urban terrain types and re-assess the coverage area.  
Conceptualization, B.C., B.K., U.B., M.F.G. and T.C.; methodology, B.C., B.K., U.B., M.F.G. and T.C.; software, B.C., M.F.G. and T.C.; validation, B.C., B.K., U.B., M.F.G. and T.C.; formal analysis, B.C. and M.F.G.; investigation, B.C. and M.F.G.; data curation, B.C. and M.F.G.; writing--original draft preparation, B.C.; writing--review and editing, B.C.; visualization, B.C. and U.B.; supervision, B.C., U.B., B.K. and T.C.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Amazon.com, Inc., and performed by the employees of the same. This research received no external funding.  
Not applicable.  
Figure 12: The CDF of distance to the nearest GW with 50 ENs.  
**Acknowledgments:** This work has been sponsored by Amazon Lab126, Sunnyvale, CA. The authors would like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for the NS-3 sub-module code development, and [PERSON] and [PERSON] for their valuable discussions and inputs. We would like to further thank [PERSON] for the RSSI measurements in Mountain View with an SX-1262 chipset, and [PERSON] for the estimation of the path loss exponent. The authors thank [PERSON] for the field measurements in the suburban city of Westheester, CA, USA.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 2 Overall Communication Solution of DSSS Transceiver  
This section describes several core modules of the DSSS transceiver, namely the spreading code module, the modulation and demodulation modules and the carrier synchronization module.
## 3 Results
### Building Monomer and Contour Extraction  
A building model with an area of approximately 1 km\({}^{2}\) was selected and sectioned at different elevation planes. The projection and binarization results of grid model slices are shown in Figure 16, where h represents the elevation of the different planes.  
After obtaining the binarized raster image of elevation planes as shown in Figure 17, filtering and edge detection technologies were used to extract the initial edge contours of the building, as shown in Figure 18.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Precision** & **Recall** & **IoU** \\ \hline Ground & 0.795524 & 0.796856 & 0.661390 \\ Building & 0.872807 & 0.822344 & 0.797025 \\ Trees & 0.766479 & 0.895214 & 0.703374 \\ Other & 0.748356 & 0.823761 & 0.723279 \\ \hline \end{tabular}
\end{table}
Table 4: Test results based on the training model.  
Figure 16: Test results of 3D semantic segmentation for a larger scene: (**A**) original oblique photographic data; (**B**) mesh point cloud data; (**C**) semantic classification results.  
Figure 17: Binarization results of grid projection after slicing at different elevations: (**A**) h = 47 m; (**B**) h = 49 m; (**C**) h = 51 m; (**D**) h = 53 m.  
Figure 18: Initial contours extracted from different elevation cut surfaces at (**A**) h = 47 m; (**B**) h = 49 m; (**C**) h = 51 m; (**D**) h = 53 m.  
As shown in Figure 19, as the elevation difference \(\Delta\)H increases, the accuracy of the monomer increases, whereas the recall rate decreases. This is consistent with the intuitive judgment based on experience. When the elevation difference increases, some low trees and non-building features are filtered out to improve the accuracy. However, extremely large elevation differences may result in the omission of some low buildings during extraction, thereby reducing the recall rate.  
As shown in Figure 20, the accuracy of the building monomer extraction and the recall rate increase with the increase in the area threshold. This is also consistent with the empirical judgment. When the size threshold increases, smaller trees and non-building features are eliminated; however, smaller house structures may also be eliminated.  
Figure 21 depicts the monomer rendering results according to the building contours and height information extracted via monomer segmentation. The monomer segmentation and extraction of an oblique photography 3D model with a range of 5 km\({}^{2}\) took approximately 456 s. Through manual interpretation and verification, the accuracy of this monomer extraction was approximately 90%.  
Figure 20: Curve of accuracy and recall under different elevation differences, \(S_{t}\).
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
## 2 Single-Element Antenna
### Parametric Study  
The antenna dimensions are optimized using parametric analysis, where the major antenna parameters are analyzed to achieve the desired dual-band response. The feed width (\(W\)), ground plane length (Lg), thickness of L-shaped strips, and dimensions of the rectangular cut in the ground are optimized. The corresponding reflection coefficients are compared as shown in Figure 4. The optimal values of the design parameters are finally chosen, providing the desired bandwidth. As exhibited in the figure, the feed width is chosen to be 0.8 mm. Improving the antenna bandwidth is greatly impacted by reducing the ground length. The size of the ground is optimized, and a value of 6.8 mm is chosen which provides a wider bandwidth and requisite frequency of resonance. The widths of L-strips have been a crucial parameter for achieving the desired frequency bands. The width of each branch of the L-strip is chosen as 1 mm, as provided in the figure. The width and height of the rectangular cuts in the ground plane have contributed to good impedance matching. These parameters have also been crucial in achieving a wider bandwidth and frequency of operation for the second band. The height and width of the rectangular cuts are optimized to a value of 1 mm and 1.2 mm, respectively.  
Figure 3: Simulated gain and efficiency plots.  
Figure 2: Reflection coefficient corresponding to different design stages.
# Prediction of Intraday Electricity Supply Curves
## 3 Methodology
### Raw Data  
The raw data were obtained from the OMIE website, [[https://www.omie.es](https://www.omie.es)]([https://www.omie.es](https://www.omie.es)), accessed on 25 April 2024. The information is organized in files with a.1 extension (which can be read as.csv). Each file corresponds to the data of a specific market on a given day. The naming convention for these files works as follows:  
* For the daily market, the names follow the format curva_pbc_uof_yyyymmd.1, where yyyy refers to the year, mm to the month, and dd to the day the file refers to (to the date being negotiated, not the date the negotiation took place).
* For the intraday markets, the structure is similar, curva_pibc_uof_yyyymmddxx.1, where xx refers to the number of the market, going from 01 to 06. For instance, the file curva_pibc_uof_2021032704.1 contains data related to the fourth intraday market on 27 March 2021.  
Each file starts with a block of metadata, followed by the relevant information. The information is presented in Table 2.  
* Hour. This refers to the hour for which offers and demands are made. The number represents the position of that hour in the day, regardless of what the clock shows. For example, on a day when the clock is moved forward, the period from 03:00 to 04:00 corresponds to the third hour of the day, whereas, on a normal day, it would correspond to the fourth hour of the day.
* Date. This refers to the day for which offers and demands are made. It is given in the dd/mm/yyyy format.
* Country. This refers to the market to which the offer to buy or sell is presented. The codes are as follows: MI (Iberian Market: both Portugal and Spain), ES (Spain), or PT (Portugal).
* Unit. An alphanumeric code that is used to identify the current offer or demand.
* Offer type. This refers to whether the offer is for buying (C) or selling (V).
* Buy/Sell Energy. The amount of energy offered or demanded, expressed in MWh and in the Spanish number format.
* Buy/Sell Price. The bid or ask price for the respective energy, expressed in EUR/MWh and in the Spanish number format.
* Offered/Matched. This indicates whether the energy-price pair corresponds to the offers curve (0) or the resulting matching program (C).  
Despite this change, there are few offers above the previous limit of 3000 EUR/MWh. Therefore, we decided, instead of normalizing, to divide by the previous legal range (which remains, in practice, as the current range).
## 4 Results and Discussions of Dynamic Characteristics
### Dynamic Characteristics Under Horizontal Condition  
For analyzing the dynamic characteristics of the tracked vehicle under various velocities, the tracked vehicle is programmed to move on the horizontal ground, and the vehicle speed is set at 2 to 10 km/h. The dynamic characteristics of the tracked vehicle could be expressed by the vertical acceleration of the vehicle body at the mass center and the symmetrical suspensions at the hinge point as shown in Figure 12. As determined from it, the vertical acceleration of the vehicle body at the mass center is lower than that of the symmetrical suspensions at the hinge point at about 35%, indicating that the symmetrical suspensions could decrease the vibration amplitude of the vertical body and improve the move stability of the vertical body. With the increase in vehicle velocity, the vibration of vertical acceleration becomes more severe. For a deeper analysis, the acceleration amplitude is taken out for analysis, and the curve of the acceleration amplitude changing with the vehicle velocity is obtained as shown in Figure 13. As determined from it, the acceleration amplitude of the vehicle body and the symmetrical suspensions increase approximately linearly with the increase in velocity.  
Figure 12: The vertical acceleration of the vehicle body at the mass center and the symmetrical suspensions at the hinge point under various velocities.
# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## 6 Ablation Studies  
We carried out a series of ablation studies to discern the individual contributions of various components in our proposed architecture, the MaskedSkipUNet. The following subsections detail the variants of the model (which can be seen in Figure 8) and their respective performances (which can also be seen in Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **Ped2** & **Avenue** & **SHT** \\ \hline AMC [48] & 2019 & 96.2 & 86.9 & - \\ Mem-AE [36] & 2019 & 94.1 & 83.3 & 71.2 \\ DeepOC [49] & 2020 & 96.9 & 86.6 & - \\ r-GAN [50] & 2020 & 96.2 & 85.8 & 77.9 \\ CDAE [51] & 2020 & 96.5 & 86.0 & 73.3 \\ MNAD [19] & 2020 & 97.0 & 88.5 & 72.8 \\ GOLD [17] & 2020 & 98.1 & - & - \\ IPR [52] & 2020 & 96.3 & 85.1 & 73.0 \\ LDF [53] & 2020 & 94.0 & 87.2 & - \\ CAC [47] & 2020 & - & 87.0 & 79.3 \\ CT-D2 GAN [25] & 2021 & 97.2 & 85.9 & 77.7 \\ AMMCN [32] & 2021 & 96.6 & 86.6 & 73.7 \\ MPN [54] & 2021 & 96.9 & 89.5 & 73.8 \\ AEP [55] & 2022 & 97.9 & 90.2 & - \\ SIGenet [56] & 2022 & 96.2 & 86.8 & - \\ IAAN [57] & 2022 & 92.9 & 80.5 & **80.3** \\ ROADMAP [9] & 2021 & 96.4 & 88.3 & 76.6 \\ ABRA-VAD [8] & 2022 & 97.4 & 86.7 & 73.6 \\ ACP-VAD [31] & 2023 & 96.8 & 88.7 & 76.7 \\ STR-VAD [33] & 2023 & **98.4** & 86.1 & 73.2 \\ PBS [18] & 2023 & **98.4** & 87.1 & 73.6 \\ MAMC [58] & 2024 & 96.7 & 87.6 & 71.5 \\ A2D-GAN [35] & 2024 & 97.4 & 91.0 & - \\ C\({}^{\text{Net}}\) [59] & 2024 & 98.0 & 87.5 & - \\
**MaskedSkipUNet** & **(Ours)** & **98.4** & **91.2** & 76.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: This table presents a performance comparison of different methods for video anomaly detection. The table showcases the anomaly detection accuracy achieved by each method on the Ped2 and Avenue datasets. The MaskedSkipUNet architecture outperforms the existing state-of-the-art methods, demonstrating its effectiveness and superiority in frame-based video anomaly detection. The bold marked figures indicate the best performing scores.
# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 3 System Model  
### Proposed Framework  
In this section, we outline the system architecture for a multi-user SR-supported MMN environment utilizing MEC, as illustrated in Figure 1. The system model consists of multiple MDs labeled as \(N=1,2,\ldots,n\), along with a single MEC server linked to the core network with a network bandwidth of \(B\) and a computing capacity of \(f_{MEC}\). Each MAR client device is assigned a specific network bandwidth \(B_{n}\) and possesses distinct computing resources \(f_{n}\). MAR tasks are represented as a vector comprising task workload density \(\omega_{n}\) defined as CPU cycles per bit, offloading data size \(d_{n}\), and the resolution level of source data \(R_{n}\). Here, we assume that each MAR client executes only one MAR application per decision process, and this application may differ from those of other clients. Consequently, each MAR client has a unique MAR task workload and its data size. In this framework, MAR clients can offload their tasks to the MEC server to manage time-sensitive and computation-intensive MAR tasks given the MDs' limited battery life and computing power. During the offloading process, the MD can transmit low-resolution source images to the MEC server, where the resolution is enhanced using SR to improve accuracy, thereby increasing MAR service satisfaction. Therefore, throughout the entire MAR process, including offloading and SR, MAR clients should optimize offloading and SR control for efficient MAR services.  
Figure 1: Proposed system architecture.  
The proposed system model consists of four main components, as shown in Figure 1. The communication module gathers information from MAR clients, such as the data needed for offloading and SR decisions. The decision controller then calculates the optimal offloading and SR choices, represented by binary variables \(s_{1}\) and \(s_{2}\), which indicate whether to offload or not, and whether to apply SR or not, respectively. Once the optimal values of \(s_{1}\) and \(s_{2}\) are determined, the decision controller sends the results to the MDs. The MAR clients then execute their MAR tasks based on the received decisions. The MEC server processes the offloaded MAR tasks and applies SR as required. This process flow is detailed in Figure 2. Importantly, as illustrated in Figure 2, some MAR clients may choose to offload their MAR tasks (4.1), while others may process their tasks locally (4.2). In the flow of 4.1, MAR clients can take advantage of SR from the MEC server to enhance the resolution of the offloaded images, provided that the additional computational latency introduced by the SR process is within acceptable limits. If the overall latency exceeds acceptable limits, MAR clients will offload their tasks without requesting SR. In this context, the primary objective of the proposed framework is to balance the overall energy consumption, acceptable latency, and service satisfaction from the MAR service throughout the process. The key notations used in this paper are summarized in Table 2.