esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Analysis and Proof
### Dataset  
As an active region of the Arctic sea ice, the Fram Strait is chosen as the observation area [54; 55], as shown in Figure 2. The Arctic sea ice remote sensing images were captured during the summer and in a thawing period. They are surface reflectance images. In detail, the interval for acquiring the images was between June and September of 2022, hence the presence of many irregular ice fragments in the images. The operational parameters of the four satellites are described below. Sentinel-2 consists of Sentinel-2A and Sentinel-2B, which can repeatedly photograph the same area in a short period, with a revisit period of 5 days and easily accessible data. It is equipped with a multispectral instrument (MSI) that offers 13 spectral bands with pixel sizes ranging from 10 to 60 m. In addition, the HY-1C satellite is equipped with a payload configuration that includes a coastal zone imager, Chinese ocean colour and temperature scanner (COCTS), ultraviolet imager, calibration spectrometer, and automatic identification system, with a spatial resolution of \(\leq\)1100 m and a swath width of \(\geq\)2900 km. The observation images used in subsequent experiments come from the HY-1C COCTS, with a coverage period of 1 day (single satellite)/0.5 day (two-satellite constellation). For the third experiment, a panchromatic and multispectral image from the ZY3-02 was chosen. The satellite is equipped with three-line-array camera, a multispectral camera, and a laser rangefinder, providing high spatial resolution, wide swath, and high temporal resolution. It has extensive applications in diverse fields, such as land resource surveys, urban planning, environmental monitoring, and agriculture. The fourth experiment used data from HJ-2A, with a revisit period of three days. It is equipped with four types of optical payloads. Among them, the 16 m camera payload consists of four CCD cameras that are visible and provide a multispectral image. The cameras enable a swath width of 800 km via field-of-view splicing. As for the ground truth image, specifically regarding the labeling method, annotation software was used to manually mark the sea ice on each image, thereby obtaining labels for each image. In terms of the lable principles, the data were annotated based on practical application needs such as sea ice identification and oceanic target detection, without considering the type of sea ice in the analysis. In addition, what needed illustration was that different types of sea ice can be seen from the images of the Arctic sea ice in the later experiments. Still, the primary purpose of this paper is to identify the summer sea ice so as to quickly obtain the coverage of the Arctic sea ice, so the classification of sea ice is not essential.  
Figure 2: Observation area.
## 3 Results and Discussion
### Polarization Insensitivity  
The H field (\(\overset{\rightarrow}{H}\)) and E field (\(\overset{\rightarrow}{E}\)) vector direction of the incident EM wave is presented in Figure 9a,b of the regular incident angle (\(\theta\) = 0\({}^{\circ}\)) for TE and TM mode. The k vector towards the z-axis represents the propagation direction of the EM wave. In TE mode, there is no H vector in the z-axis, whereas no E vector exists in TM-mode propagation. Polarization-insensitive behavior of the proposed MMA for normal incident angle is plotted in Figure 10 for both TM and TE modes. The constant absorption plot for different polarization incident angles (0\({}^{\circ}\) to 90\({}^{\circ}\)) increases MMA eligibility for various applications. The reason behind the polarization-insensitive behavior is the symmetrical structural design of the proposed MMA. The designed SSRR is rotationally symmetrical, which indicates no effects on absorption at the rotation of incident EM wave vector on the XY-axis with respect to the Z-axis for circular or any other polarization of the incident wave, as shown in Figure 10a,b. Figure 10c,d shows the oblique incident angle impact TE and TM mode, respectively. In TE mode for the increment of the oblique incident angle, the absorption at 14.12 GHz shows stability up to 45\({}^{\circ}\), but other resonances are either slightly shifted or reduced. On the other side, in TM mode, the absorption at both middle frequencies shows stability, whereas the upper and lower absorption peaks are shifted with the increment of oblique incident angle.  
Figure 8: Dispersion diagram in TM mode.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency** **(GHz)**} & \multicolumn{2}{c}{**Permittivity**} & \multicolumn{2}{c}{**Permeability**} & \multicolumn{2}{c}{**Refractive Index**} & \multirow{2}{*}{**Absorption** **\%**} \\  & \multicolumn{2}{c}{**(Real)**} & \multicolumn{2}{c}{**(Real)**} & & \multicolumn{2}{c}{**(Real)**} & \multicolumn{2}{c}{**(Real)**} & \multicolumn{2}{c}{**\%**} \\ \cline{2-7}  & **TM** & **TE** & **TM** & **TE** & **TM** & **TE** & \\ \hline
12.62 & \(-0.11\) & \(-4.51\) & \(5.68\) & \(-4.05\) & \(2.14\) & \(-4.63\) & \(97\)\% \\
14.12 & \(-3.81\) & \(2.88\) & \(-3.76\) & \(-0.9
## 2 Data and Methods  
### Study Area  
Heihe River originates from the Qilian Mountains, passes through the Hexi Corridor, and ends at Ejin Banner in northern Inner Mongolia [43; 44; 45]; its length of which is about 800 km. The Heihe River basin (HRB) is between 98\({}^{\circ}\)-101\({}^{\circ}\)30\({}^{\prime}\) E and 38\({}^{\circ}\)-42\({}^{\circ}\) N with an area of approximately 1,432,000 km\({}^{2}\), rendering it the second-largest inland river region of Northwest China. Bounded by Yingluoxia and Zhengyixia stations, the basin is divided into upper, middle, and lower reaches with unique and complex geographical characteristics. The upper Heihe River basin (UHRB) is between 37\({}^{\circ}\)30\({}^{\prime}\)-39\({}^{\circ}\)41\({}^{\prime}\) N and 97\({}^{\circ}\)28\({}^{\prime}\)-101\({}^{\circ}\)16\({}^{\prime}\) E (Figure 1), the main runoff-producing area of the HRB, and the study area of this paper, including some regions of Qilian and Sunan counties. Runoff consists of surface runoff, glacier meltwater, and a small amount of groundwater regression [46]. The UHRB is located in a typical continental alpine and semiarid climate area with an elevation of about 2000-5500 m, annual precipitation of 300-700 mm, and a multiyear average temperature of \(-\)5 to 4 \({}^{\circ}\)C. The vegetation of the UHRB is mountain forest steppe, and the distribution of the vegetation belt plays a crucial role in runoff regulation and storage. In addition, there are permanent glaciers and snowfields in the study area.
# Self-Supervised Learning across the Spectrum
## 7 Discussion and Limitations  
We discuss some of the limitations of S4 based on the results of our evaluation.  
**High cloud cover scenarios** As illustrated by Figure 9, S4 does provide improved robustness against cloud cover in optical images due to the incorporation of radar data. However, the method still faces performance challenges when cloud cover exceeds 25%. In such cases, the optical information is so heavily occluded that it provides so little useful information, impacting the overall performance of the model. Potential avenues for future work to solve this solution include the incorporation of historical time series data (years before the crop growth phase). Additionally, training on synthetic data generation or painted cloud images may also be useful in addressing the challenges in training with very highly cloud-covered images.  
**Modalities other than radar or optical** Although we explore the benefits of exploring multimodal self-supervision specifically with radar and optical imagining modalities, satellite imaging has ample additional modalities such as infrared and hyperspectral, which we do not explore in this work. We envision that S4's reconstructive and contrastive frameworks can be extended to support these additional modalities by using multiple modality-specific encoders.  
**Adaptive fusion techniques** In our study, we observed that different modalities excel in specific conditions. For instance, optical images perform well when there is low cloud cover, while radar images are more effective in high-moisture environments, such as near lakes and rivers. Currently, our model, S4, treats all multimodal inputs with equal importance during training and segmentation. However, based on our observations, we believe that enhancing S4 to incorporate adaptive multimodal fusion--where the model dynamically adjusts the weight given to each modality based on the specific conditions--could significantly improve overall performance.  
**Applications of S4** S4's ability to provide accurate segmentation results with limited labeled data makes it beneficial for a variety of different environmental segmentations beyond the agricultural crop segmentation studies discussed above. Some of these applications include land cover classification and ecosystem monitoring, as well as early detection of natural disaster events such as wildfires, mudslides, etc. S4 can be generalized to all these different applications specifically because the amount of actual labeled data needed for its training can be very small.
# Torque Vectoring Control Strategies Comparison for Hybrid Vehicles with Two Rear Electric Motors
## 4 Simulation Results
### 50\({}^{\circ}\) Step Steering  
Figure 7 shows the graph of the right-side EM torque where it is possible to grasp the effect of the different control systems.  
The baseline plot shows the necessary torque requested to maintain the target speed during the maneuver, in this case applied in a balanced manner between left and right, while the other plots display the baseline contribution plus the bias.  
FOSM lowpass shows the sharpest response after the threshold, quickly saturating the actuator at the beginning of the step and establishing a persistent oscillatory behavior, with a continuous signal but with a non-continuous derivative. This is an improvement with respect to the simple FOSM [15] with discontinuous control, but nonetheless it is expected to see a rough effect in terms of vibrations on the vehicle with such aggressive variations.  
The actuation shape displayed by the PID, LQR, and FOSM continuous controllers are quite similar, with a first peak value followed by a stabilized steady-state contribution. The main difference between them is the intensity of the transient value, highest in the LQRand lowest in the PID. This difference is directly related to the optimized gains for each controller, which should balance promptness and stability.  
The response of SOSM twisting is the slowest, achieving its peak only after 0.7 s, while SOSM suboptimal presents a prompt actuation after the beginning of the step. Both second-order SMC systems display sufficiently smooth and stable control outputs, as expected.  
Regarding the main goal of the control systems, Figure 8 shows the yaw rate of the vehicle during the step steering and the error relative to the calculated ideal yaw rate.  
Figure 8: Yaw rate (**a**) and yaw rate error (**b**) in the 50\({}^{\circ}\) step steering.  
Figure 7: The right-side EM torque during the 50\({}^{\circ}\) step steering.  
The relations between the control action and the yaw rate outcome are clear. FOSM lowpass presents a high overcontrol peak and a persistent vibration throughout the maneuver, while PID, FOSM continuous, and LQR display a similar shape of the response, even if it can be noted that the PID controller fails to guarantee a zero error in the 4 s after the step. SOSM twisting is relatively slow to respond and to stabilize while SOSM suboptimal is very precise, with the lowest maximum yaw error.  
Figure 9 shows the lateral acceleration of the vehicle and its relation to the imposed steering angle, as in the goals presented in Figure 1.  
As expected, the baseline vehicle displays the lowest lateral acceleration in each steering condition. Apart from FOSM lowpass, all the other controllers create a similar outcome, with smooth and linear curves. The controller with the biggest impact on the understeering behavior is SOSM suboptimal, generating the most responsive dynamics; however, the difference between it and the second-best results (LQR) is of just shy of 3% lateral acceleration (at 0.04 rad/s steering) versus a 22% increase relative to the baseline.
## 4 Numerical Study  
### System Description  
The ARM-based SLAP methodology was applied to a distribution center in the United States' food retail industry. This distribution center services more than 500 unique stores. The distribution center has a configuration of 24 aisles. There are storage racks in with 135 slots in each side of the aisle. The distance between positions is 40 inches. The distribution center's order-picking process is as follows. Order picking is performed by human operators (i.e., order pickers). The workforce comprises 113 order pickers. The order pickers drive powered vehicles to move around the distribution center. Each order picker processes one order at a time. The traveling routes follow the sequence shown in Figure 1. The operator arrives at the first SKU location to retrieve the required cases from the shelves and then loads the items into the vehicle. The operator then follows the rest of the route and repeats the picking process until all the SKUs contained in the order are retrieved. The order picking time is correlated to the number of items. A completed order is palletized and sent to the docks, loaded into a truck and transported to the retail store.  
The DC receives approximately 200 orders per day, and it performs between 50 and 100 shipments per day. The DC fills up these orders in the morning and replenishes the items at right. The data were cleaned up and transformed. The resulting dataset contains 7881 unique SKUs. The dataset also contains 388 unique orders. Each order contains between 4 and 350 different SKUs. There is a total of 63,000 records, resulting in a \(624\times 7881\) transaction-to-item matrix.  
We set up an experiment to calibrate the association rule mining algorithm. This experiment runs 16 unique combinations of support and confidence, with support changing from 0.10 to 0.25, in increments of 0.05, and confidence changing from 0.5 to 0.8, in increments of 0.10. These combinations generate association rules ranging between 64 and 11,965. The trend indicates that as support and confidence increase, the number of association rules increases; computational time shows a similar increasing trend. The combination that shows the best performance for the dataset considered in this paper is for support = 0.2 and confidence = 0.8. Setting the algorithm parameters of support and confidence at these values results in fewer itemsets with highly correlated items within each itemset.  
Our numerical analysis is based on experimental results obtained with the R package named \"arules\" [19] to extract the association rules. It was run in a MacBook Pro microcomputer with an Intel 43 2.5 GHz Core i7 processor and 16 GB 1600 MHz DDR3 memory. The procedure to generate the layouts was also coded in R.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 2 Related Works
### Depth Estimation Based on Attention Mechanisms  
Humans can efficiently find regions of interest in complex scenes. Inspired by this behavior, attention mechanisms are used in CNNs to make models imitate the human visual system. Attention mechanisms can be viewed as dynamic weight adjustments to input image features. Attention mechanisms can be divided into three types [42]: spatial attention, channel attention, and mixed attention mechanisms. Spatial attention mechanisms [43] learn the relationships between pixels in images, generate plane attention maps, and generate corresponding weights for different pixels in order to select substantial spatial regions. Channel attention mechanisms [44] model the dependencies on the channels and allocate different weights to different channels. Mixed attention mechanisms [45] take advantage of channel and spatial attention mechanisms to generate channel and spatial attention masks and then use them to select valuable features.  
Some researchers have proposed CNN attention mechanisms for DNN-based monocular depth methods [35; 36; 43; 46; 47]. PWA [36] was proposed as an approach that could focus on each local area and learn the relationships between neighboring pixels using corresponding spatial and channel attention networks to generate attention maps and detect essential features. CAD [46] was proposed as a channel-wise attention-based depth estimation network, in which the channel-wise-based structure perception module helped the model to enhance the perception of large-scale structures. The detail emphasis module used weight vectors to refine channel-wise feature maps to highlight local features and improve detail depth prediction. VISTA [47] was proposed as a variational structured attention network, which introduced a structured attention mechanism based on a multi-scale CNN that could model the correlation between a spatial-wise attention tensor and a channel-wise attention tensor to refine object depth estimation. SACRF [35] used an attention mechanism to adjust feature maps at different scales based on a multi-scale CRF model.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 5 Building the Cloud Detector  
In this section, we provide details of the steps involved in the pre-deployment model training stage (Section 3.2). More specifically, we describe the CNN architectures used for onboard cloud detection (Section 5.1) and the training procedure for the source cloud detector \(f(\cdot\,;\,\theta^{s})\) (Section 5.2).
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
## 1 Introduction
### Technical Overview  
From the above analysis, we can see that current studies on certificateless signature (CLS) schemes encounter the limitations of either relying on pairing operations like the scheme [27] built on the SM9 algorithm, or the underlying scheme not being selected as the cryptographic standard. The scheme proposed by [PERSON] et al. [28] is extended from the SM2 algorithm and does not involve any pairing operation. However, it does not support batch verification. We first review the basic idea of [PERSON] et al.'s construction. The core technical transformation from a traditional signature scheme to a certificate-based signature scheme is show in Figure 2.  
A certificateless signature is similar to a certificate-based signature. The main difference is the secret value setting phase. In the CLS scheme, it is not necessary for the user to select a secret value and compute the public key first, before transmitting the public key to the key generation center (KGC) to obtain the partial private key. This means that the user can apply the partial private key from the KGC first; then, generate the secret value and compute the public key later. During the key extraction phase, the user's public key may not yet be generated and, hence, cannot directly use the above transformation. To solve this issue, the user's public key contain two parts: one part is from the key extraction phase, which is similar to the certificate-based setting; the other part is generated by the user itself. These two parts are independently generated but must be used together to sign a message. This paves the way for transforming a traditional signature scheme into a CLS scheme.  
As for the zero-knowledge proof part, we use the property of bilinear pairing to replace the complex proof process. Even though this brings the pairing operation into the scheme, it only appears in the verification phase and the signing phase does not involve any pairing operation. For verification, since our scheme supports batch verification, multiple signatures can be verified simultaneously and the number of pairing operations is constant. This means that the additional time cost caused by the pairing operations is a fixed value and, hence, it will not incur too much computational cost during batch verification. The details of batch verification are depicted in Section 4.2.  
Figure 2: Transformation from traditional signature to certificate-based signature.
# Trustworthiness of Situational Awareness: Significance and Quantification
## 3 Security and Trust of Situational Awareness  
This section elaborates the significance of the security of and trust in SA from an air force perspective. This section first discusses the security and trust issues of a COP presented to the commander. The section then discusses the SA components in an SA system susceptible to attacks, followed by the impacts of security attacks on COP.
# Unleashing the Potential of 5G for Smart Cities: A Focus on Real-Time Digital Twin Integration
## 5 Technological and Policy Recommendations
### Communication Protocol Standardization Roadmap in Urban Digital Twins
Individuals should have greater control over their data, with mechanisms such as explicit consent protocols and opt-in policies for data collection. Ethical considerations must also be considered, ensuring that 5G-powered smart city applications align with societal values [11, 75, 77]. This consists of addressing potential biases in AI-driven decision-making systems and ensuring transparency in how algorithms process urban data. By implementing robust data privacy laws, cities can build public trust in smart city initiatives and encourage the widespread adoption of 5G-enabled services. The successful rollout of 5G in smart cities requires collaboration between governments, private sector stakeholders, and research institutions. Public-private partnerships (PPPs) are essential for addressing financial and technical barriers, enabling quicker and more efficient deployment of 5G infrastructure [10, 68]. Governments can facilitate PPPs by offering incentive-based collaboration models, where private companies invest in infrastructure in exchange for long-term operational benefits. Joint research initiatives between academia and industry can also drive innovation, leading to the development of novel 5G applications tailored to urban needs. Cross-sector partnerships can promote shared infrastructure models, where multiple network operators utilize the same physical infrastructure, reducing redundancy and lowering deployment costs [68, 75, 77]. By fostering an ecosystem of cooperation, cities can accelerate the transition to 5G while ensuring that technological advancements benefit all stakeholders. By tackling crucial technological and policy needs, smart cities can leverage the complete capabilities of 5G to elevate urban life, boost efficiency, and encourage sustainability. Investments in infrastructure, efforts to promote standardization, energy-saving solutions, cybersecurity measures, data privacy laws, and strategic collaborations must all function together to build an inclusive and robust urban ecosystem powered by 5G technology. By achieving the ideal mix of innovation and governance, urban areas can evolve into genuinely smart environments that provide fair access to advanced digital services.