esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Image Compression Guided by Regions of Interest
## 4 Discussion
Thus, the use of these models has not been investigated at this time, but may be carried out in the future. In general, the gains on dry-land regions are expected to be similar to or better than the gains of the global cloud fraction over dry land, which were 55%, even considering lack of detections in the cloud detection step. This expectation is considering that several tiles in dry-land regions over the globe can be classified as not containing changes. Actually, a cloud is a change, but an undesirable one for the application. With respect to the  
Figure 20: Percentage gains by number of tiles classified as ROI. water regions, the proposed method can significantly reduce the transmission costs for applications which rely on vessel detection. In addition, the number of false alarms due to undetected clouds are lower for the vessel detection algorithm when compared to the change detection algorithm. Finally, a short discussion about the processing time of the algorithms can be given. Processing time experiments were conducted in the Unibap's flight representative hardware for the Unibap iX10-100 space cloud hardware solution (iX10-ADS) [28]. iX10-100 is based on the 14 nm AMD Ryzen V1000 family. It offers up to 8 processor threads and up to 8 CU graphics acceleration units, along with a PCI express generation 3. The baseline algorithms for cloud detection, change detection, and vessel detection were implemented in Python to be evaluated separately using a single core. Processing times measured are as follows:  
* Cloud detection algorithm: 0.47 s (image 11);
* Change detection algorithm: 0.47 s (image 1);
* Vessel detection algorithm: 88.48 s (image 2). The processing time required by the cloud detection and change detection algorithms fits into a small window of time between image acquisitions. However, the processing time for the vessel detection algorithm can be considered not suitable. The large amount of processing time for the vessel detection algorithm is due to the CFAR normalization. The CFAR normalization computes each pixel of the image sequentially, since it was implemented to run in a single core. Thus, an optimized implementation aiming at parallel pixel processing is expected to considerably reduce processing time, for instance, by making use of available GPU cores.
## 5 Discussion
### Limitations of the Current Studies  
Despite the significant theoretical and practical advancements, data-driven CIM still faces challenges in regard to the data quality, computational efficiency, security, and adaptability.  
(1) Data quality and multi-source data fusion  
CIM relies on multi-source heterogeneous data, but inconsistencies, lack of standardization, and quality issues remain problematic. Graph Neural Networks (GNNs) enhance spatial-temporal data integration [80], while self-supervised learning and Large Language Models (LLMs) improve anomaly detection and standardization. However, these methods are computationally expensive and require extensive labeled data. Future research should focus on adaptive data fusion frameworks.  
(2) Computational efficiency and real-time processing  
The real-time processing of large-scale urban data demands high computational resources. Edge computing and neuromorphic computing reduce latency, with autonomous traffic management systems leveraging edge AI for local processing. Spiking Neural Networks (SNNs) have shown promise for low-power environments, but hardware limitations persist. Optimizing efficient distributed computing architectures is crucial.  
(3) Data security and privacy protection  
CIM involves sensitive data, making privacy protection essential. Federated learning (FL) enables AI model training across institutions without sharing raw data and is already tested in smart energy management [81; 82]. Blockchain enhances secure data transactions, as seen in Dubai's real estate sector. However, FL incurs high training costs, and blockchain has significant storage overheads. Future research should develop lightweight privacy-preserving mechanisms.  
(4) Adaptability and cross-city deployment  
CIM adoption varies due to the differences in data structures, governance models, and infrastructure. Reinforcement Learning (RL) supports adaptive urban management, such as Boston's traffic optimization. Digital twins are increasingly used for CIM simulation but face computational constraints. Future efforts should focus on lightweight RL models and hybrid digital twin architectures to enhance the cross-city scalability.
## 3 Simulations and Measurements
### Binaural Room Impulse Responses  
In this section the BRIRs used in this paper are described. For the optimization and subjective evaluation, four rooms were used and from each of these rooms, three recorded BRIRs with the same receiver position are selected. These rooms are described in Table 1 and differ in terms of geometrical dimensions and reverberation time \(T_{60}\). The main BRIR in Table 1 is convolved with a speech source and the resulting left ear signal is used for the optimization to find the weights but is also used for rendering and evaluating the preprocessed speech signal. The second BRIR in this table is used to evaluate the robustness of the algorithm for a different position of the listener compared to the weights obtained for the main BRIR. Thus, the algorithm uses a different IR for the preprocessing than the IR that was originally obtained in the optimization. Finally, the third BRIR is used for convolving with a noise signal to create a binaural noise for both main and robustness evaluation scenarios. The first room (R1) with a relatively short \(T_{60}\) of 0.6 s is selected from a set of BRIR measurements made at our university in Oldenburg. The second room (R2) is a music hall that is selected from the BRAS database [25] with a \(T_{60}\) equal to 1.1 s. The recorded BRIRs in R2 have a relatively long distance between the source and receiver and therefore the direct-to-diffuse ratio is low. The third room (R3) selected again from the BRAS database is a seminar room with a \(T_{60}\) equal to 1.5 s. This room is critical in terms of \(T_{60}\) and speech intelligibility because it has a long reverberant tail that creates a considerable amount of time smearing the source speech signal. Room (4) represents a church selected from Air database [24].  
The \(T_{60}\) is very long (about 5 s) because of the large dimensions of the room and low degree of damping. Because of relatively small source-receiver distances (3 m), however, the selected BRIRs in the church have a high direct-to-reverb diffuse ratio. The distance from speech and noise sources to the listener positions of the main and robustness evaluation scenarios is almost held constant within a room to avoid differences in the direct-to-diffuse ratio. A collection of all used room-acoustical scenarios, reverberation times, selected BRIRs, and the length of \(P\) frames are shown in Table 1. P is a number of future frames that is used to construct the summed-cost-function (SCF) in Equation (12) for the optimization purpose. As previously explained, the length of \(P\) frames is determined according to 25 dB drop of EDC. For a larger \(T_{60}\), more future frames are needed for the optimization.
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 3 Integrated Photovoltaics Options
### Infrastructure-Integrated Photovoltaics
#### 3.4.2 Irrigation Channels  
In many agricultural regions, canal irrigation is a widespread method, especially particularly in regions with either an extensive river network or large artificial water reservoirs or lakes. In general, these canals are open and a water evaporation source.  
Especially in arid regions, irrigation canals not only offer a potential for solar PV installations with channel top PV (channel top PV: CTPV) but can help to reduce water evaporation and offer more water for farming purposes [38, 39]. India has been a pioneer country using this technology [40]. A recent study by the Deutsche Gesellschaft fur Internationale Zusammenarbeit (GIZ) estimates that CTPV could account for 3% or 24 GWp of total PV installation by 2024 [41]. In 2022, Romania announced that it was looking into the possibility to invest EUR 1.8 billion for a total of 1700 km irrigation channel projects with the help of the European Recovery Fund [42].
## 1 Introduction
### Research Gaps  
There are the following research gaps in the field of the distribution network operation:  
* To enhance network operation, including reducing energy losses, voltage profile, and other cases, there are various solutions such as energy management of local resources and system reconfiguration. Yet, it is predicted that the use of several different solutions can achieve a favorable operating situation, which has been stated in fewer studies.
* In addition, there are various indices for system operation, for which in most studies, only the bus voltage limit and the capacity of the distribution lines are considered.
* Furthermore, the minimization of energy losses has been considered in fewer studies although there are various indices regarding the network operation, and different indices may not be correlated with each other, meaning that improving a given index may not help the improvement of other index/indices. Therefore, it is necessary to model them simultaneously in the optimization problem.
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
## 5 Analytical Case Study Assuming Lug Loss Due to Rebar Corrosion
### Summary of Analytical Case Study  
Analytical case studies focusing on lug height loss, lug loss region on cross section, and non-uniform lug loss in axial direction were conducted. Even when the height of the lugs was reduced, sufficient bond strength was maintained in a certain range of height, which was larger than 0.5 mm in this case. On the other hand, as long as stress transfer by interlock can work in a partial region in cross section or axial direction, rebar slip can be prevented and the bond as a whole member can be maintained.  
This study focused on a lug based on the Japanese Industrial Standard for deformed steel bars (JIS G 3112 Steel bars for concrete reinforcement). From our analytical results, when a concentric section loss ratio reaches around 20% to 25%, 75% of contact area between a lug and concrete is lost which leads to bond deterioration. When the section loss ratio reaches around 25% to 30%, interlocking effects against pull-out force are eliminated.  
On the other hand, when the section loss has non-uniformity, interlock in a partial region of rebars can transfer stress between rebar and concrete. Thus, a member can have bond as a whole member even when higher averaged section loss occurred. At minimum, slip and pull-out of an entire rebar can be prevented.
## 1 Introduction
### Avoidance of Interference between Wi-Fi and ZigBee  
To avoid the interference of wireless signals, the Wi-Fi interface in the proposed IoT AP first detects the channels occupied by the other APs and subsequently notifies the _ZigBee device management module_ the information of channels. Afterward, the ZigBee interface chooses the optimal channel for data transmissions, avoiding the starvation problem. This paper had integrated ZigBee and Wi-Fi protocols in the designed IoT AP to automatically avoid the signal interference between ZigBee and Wi-Fi by identifying and selecting an idle channel to sending command messages. Consequently, the proposedIoT AP properly schedules the ZigBee and Wi-Fi transmissions without any signal interference to establish the excellent communication environment in smart home applications.
# Kinematic Modelling and Experimental Validation of a Foldable Pneumatic Soft Manipulator
## 3 Numerical Calculation and Analysis
### Workspace of the Pneumatic Module  
The workspace is an important index to measure the performance of the manipulator. According to the requirements for different mission scenarios, a design scheme of combination structure is proposed. With the combination of pneumatic actuator and straight arm, the workspace of the manipulator can be increased efficiently by changing the length of the straight arm. If the manipulator only consists of a pneumatic actuator, the overall weight of the manipulator will be greatly increased. However, the combined structure of the flexible pneumatic actuator and lightweight straight arm can not only reduce the overall weight of the manipulator, but also greatly expands the workspace of the manipulator. In order to compare the workspace with or without considering the straight arm, two examples are numerically calculated based on the proposed model of the soft manipulator. In these two examples, the three bellows are inflated from 0.01 MPa to 0.1 MPa randomly, and the maximum workspaces with or without considering the straight arm are obtained and shown in Figure 5.  
In Figure 5, the arc surface marked with blue stars represents the workspace of the combination structure of straight arm and pneumatic actuator, while the arc surface marked with red circles represents the workspace of the pneumatic actuator alone. Figure 4(a) shows the comparison of the two kinds of workspace in 3D view; Figure 4(b) shows the projection of the two workspaces in X-Y plane. From these comparisons in different views, it can be clearly known that the straight arm extends the workspace of the soft manipulator largely. It can be seen in Figure 4(a) that the projection of the workspace in the X-Y plane is almost hexagonal. The boundary of the hexagon is limited by the maximum internal pressures of the three bellows, which are limited in 0.1 MPa in these two numerical examples. The air pressures of the six vertices of the hexagon are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Vertices**} \\ \cline{2-7}  & **V-1** & **V-2** & **V-3** & **V-4** & **V-5** & **V-6** \\ \hline Pressure in bellow-1 (MPa) & 0.1 & 0.1 & 0.1 & 0 & 0 & 0 \\ Pressure in bellow-2 (MPa) & 0.1 & 0 & 0 & 0 & 0.1 & 0.1 \\ Pressure in bellow-3 (MPa) & 0 & 0 & 0.1 & 0.1 & 0.1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The air pressures of the six vertices.  
Figure 5: Workspace of the end point of the soft manipulator.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 6 Conclusions  
In this paper, we have proposed a clone-resistant watermarking approach for telemedicine applications. Our scheme extracts the patient name and pertinent features from the original image to generate a watermark using the Jacobian model. A one-time ticket is extracted from the Secret Unknown Ciphers (SUCs) of the medical device to sign the watermark in order to generate a one-time watermark signature. The signed watermark is then embedded in the medical image of the patient using a reversible watermarking technique (Difference Expansion).  
By combining watermarking and SUC, the proposed approach offers several advantages: Resistance to cloning, confidentiality, authentication, non-repudiation, and integrity of the medical image. Moreover, the reversibility of the watermarking technique used in the proposed approach makes it possible to recover not only the watermark but also the original image. Such recovering of the original image is a critical requirement for medical image applications.  
Experimentation results show that the proposed scheme is robust against watermarking attacks (geometric and non-geometric) and provides good bases to withstand other security attacks such as the man in the middle and tampering attacks.  
**Author Contributions:** Conceptualization, S.M., M.T., L.N. and W.A.; methodology, M.T. and S.M.; software, M.T. and S.M.; supervision, A.P., L.N., F.B. and W.A.; validation, L.N., S.M. and A.P.; formal analysis, S.M. and M.T.; visualization, W.A.; writing--original draft preparation, S.M. and M.T.; writing--review and editing, L.N. and W.A.; project administration, W.A. and L.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the DAAD Research Grants Doctoral Programs in Germany, under grant number 57214224 and the German Federal Foreign Office scholarship funding (STIBET) program and indirectly by Microsemi, a Microchip Company, San Jose, CA, USA and Volkswagen AG--Germany. A support was also provided by the MathSTIC Doctoral school of Universite de Bretagne Loire in France.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 3 Results
### General Observations  
Better segmentation performances compared to four-chamber were achieved over the two-chamber dataset, both with and without hierarchical data augmentation. Mean Dice scores of 0.910 and 0.915 were achieved over the two- and four-chamber sets, respectively. The difference was more pronounced during the ES phase, with a Dice score of 0.919 on the two-chamber test set compared to 0.905 on the four-chamber test set. These results are not surprising as the two-chamber orientation contains fewer classes to segment and lesser anatomical shape variance compared to the four-chamber dataset. The difference in the amount of data available between the two sets is another factor that may affect the Dice score. Further experiments were carried out in Section 4.2 to investigate this aspect.  
These factors make the two-chamber dataset easier to converge for the architecture, leaving a smaller margin of improvement that can be achieved with data augmentation. Concurrently, the Hausdorff distances were, on average, lower over the two-chamber orientation, on both the validation and test sets. A mean value of 5.49 mm over the two-chamber test was obtained, as shown in Table 1 with data augmentation. Comparatively, a mean value of 8.22 mm was obtained when computing the average of the ED and ES results over the data-augmented four-chamber set.  
Performance differences between the ED and ES predictions are more pronounced in the two-chamber orientation, as observed in Table 1, especially regarding Dice scores. This difference is related to individual LA and MYO class performances, which show a marked increase in Dice at the ES phase, as can be seen in Figure 4. The difference is explainable by the larger size of these anatomical targets during end systole, making them easier to segment. On the contrary, differences between the average Dice at ED and ES are less pronounced with the four-chamber test set. As can be observed from Figure 4, this is related to the greater amount of anatomical regions with divergent behaviors, leading to a homogenization of the Dice performance between ED and ES, as shown in Table 1.  
The significant variation and lower performance over AO in the four-chamber set can be explained by its lower presence in the dataset. Moreover, this anatomical region presents variations among subjects, and its occurrence depends on the spatial orientation chosen during the acquisition (Table 5).
## Materials
### Environmental Variables  
Monthly air temperature, precipitation, and soil moisture content with a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) were obtained from the ERA5-Land dataset (2000-2022; [[https://cds.climate.copernicus.eu/cdsapp#I/dataset/reanalysis-era5-land-monthly-means?tab=overview](https://cds.climate.copernicus.eu/cdsapp#I/dataset/reanalysis-era5-land-monthly-means?tab=overview)]([https://cds.climate.copernicus.eu/cdsapp#I/dataset/reanalysis-era5-land-monthly-means?tab=overview](https://cds.climate.copernicus.eu/cdsapp#I/dataset/reanalysis-era5-land-monthly-means?tab=overview)) (accessed on 9 June 2023) [30]. In the analysis, we used data for 0-7, 7-28, and 28-100 cm soil depth layers. Monthly values of the drought index SPEI (the Standardized Precipitation Evapotranspiration Index) were obtained from the Global Drought Monitor ([[https://spei.csic.es/map/maps.html](https://spei.csic.es/map/maps.html)]([https://spei.csic.es/map/maps.html](https://spei.csic.es/map/maps.html)); accessed on 9 June 2023) service for period 2000-2022. The SPEI is a measure of air moisture content (or air drought), and it is defined as the difference between total precipitation and potential evapotranspiration. Thus, by definition, the SPEI increase indicated an air drought decrease [31]. We also involved terrestrial water content (or EWTA, Equivalent of Water Thickness Anomalies--hereafter, TW) in the analysis. TW content is the product of gravimetric measurements (in cm\({}^{-1}\)) performed by the GRACE/GRACE-FO satellites with on-ground resolution 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)[32]. Monthly EWTA data were obtained from the GFZ database ([[https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/tellus/L3](https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/tellus/L3)]([https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/tellus/L3](https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/tellus/L3)) (accessed on 9 June 2023)) for period 2002-2022.  
Figure 1: Larch range within permafrost area (marked by brown). Green ellipsoids indicate study areas located in continuous (site #1: “North”), and (sites #2: South1 in Altai–Sayan Mountains; and #3: South2 in the Trans–Baikal zone) in discontinuous, sporadic, and patched permafrost zones. Test sites’ (generalized) locations are marked by red dots. The test site’s number (N) is 28, 32, and 100 in the study area 1, 2, and 3, respectively.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### Distribution of Ranges
#### 4.1.2. Rayleigh Distributed Ranges  
In a Rayleigh fading environment, as shown in Figure 3, shorter-communication ranges are below \(\alpha=2\). We see also that these higher distributions are due to obstructions  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Notation** & **Value** \\ \hline Arrival rate & \(\lambda\) & 0.1 veh/s \\ \hline Transmit power & \(P_{t}\) & 0.5 W \\ \hline Noise power & \(P_{N}\) & 0.01 mW \\ \hline Pathloss constant & \(K\) & 10 \\ \hline Pathloss exponent & \(\alpha\) & [1–5] \\ \hline Threshold SNR & \(\gamma_{th}\) & 10.17 dB \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 2: Lognormal range distribution. (**a**) Lognormal PDF, \(\sigma=1\). (**b**) Lognormal CDF.  
from big vehicle that contribute to overall shadowing which in turn leads to transmitter power variation. Examples of environments where such performance can be experienced are highways and urban settings.