esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Resource Observation and Discovery
#### 3.5.2 Resource Discovery  
A hybrid resource discovery mechanism is proposed in [20] using CoAP. The paper introduces discovery of resources using a hybrid approach which can switch between centralized/distributed resource discovery mechanisms. It is based on the previously developed Proactive Resource Directory (RD) Discovery mechanism proposed in [37]. The centralized approach is called the Proactive RD  
Figure 5: CoAP Resource Observation  
Discovery (PRD) and the distributed is called push-pull resource discovery. In PDR, presence of RD is advertised in network using CoAP POST messages and nodes can cache this information for a specified lifetime. This information can also be updated with PUT or POST CoAP messages. When a CoAP client needs to access a RD, it can generate a CoAP GET request and get it from nearby cached node. Fully distributed push-pull Resource Discovery (FDR) is employed when the RD is absent in nodes of network. In FDR, some nodes in the network proactively advertise (pushes) their cached resources based on the algorithm proposed in [38]. Moreover, FDR forwards the on-demand requests by pull mode. The CoAP GET method is employed to demand the RD in pull mode of FRD.
## 4 Methodology
### Dynamic Resource Scheduling and Task Offloading Based on Value Iteration
#### 4.1.2 Mathematical Formulation for Task Offloading and Resource Scheduling
This objective function considers the impact of task priorities on scheduling, balancing the resources between computational, storage, and bandwidth needs. By adjusting the weights \(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha_{3}\), we can control the trade-off between resource efficiency, computational time, and transmission delay. Incorporating task priorities ensures that higher-priority tasks are allocated resources first, which improves the overall system performance by minimizing the latency for critical tasks while maintaining resource efficiency.
## 3 Case Study  
### Model Parameters  
The open-pit coal mine battery-swapping truck scheduling model can be described with the following specific mathematical parameters: The production system includes 5 shovel points, 4 crushing stations, 30 battery-swapping trucks of the same model, and 3 battery-swapping stations, each with 15 available batteries. The unit cost per kilometer for the trucks is CNY 10 when fully loaded and CNY 7 when unloaded, with the unit energy consumption being 4.58 kWh/km and 2.78 kWh/km, respectively. The average operating speed is 17 km/h when fully loaded and 22 km/h when unloaded. The unit maintenance cost per kilometer is CNY 1, with each truck having a load capacity of 300 tons and a battery capacity of 300 kWh. The impact coefficient of environmental temperature on the battery capacity is 0.8. The production task requirement for each of the 5 shovel points is 4000 tons, while the consumption capacities for unloading points a, b, c, and d are 6000 tons, 7000 tons, 8000 tons, and 4000 tons, respectively. The work shift duration for the trucks is 8 h, with an average loading time of 5 min, unloading time of 3 min, and battery swapping time of 6 min. The minimum battery-swap threshold coefficient is 0.3% and the battery charging speed is 4.5 kWh/min. The simulations were carried out using Python 3 as the programming environment. The hardware specifications include an Intel i5-13400F processor, 32 GB RAM (Intel, Xuzhou, China), and an NVIDIA GeForce RTX 4060 GPU (Intel, Xuzhou, China), running on the Windows 11 operating system.
# Therapeutic Applications of Halloysite
## 3 Halloysite Structure and Applications
### HNTs in Wound Healing  
The absorptive capacity of HNTs has been used in several wound healing applications. An HNT/chitosan oligosaccharidesacharide nanocomposite was tested for its healing capacity in a mouse model [71]. The nanocomposite allowed enhanced skin reepithelialization and reorganization compared to controls and the. Results suggested it has potential as a medical device for wound healing. Chitosan has been combined with HNTs in many would bandage and healing applications. [PERSON] et al. (2014) showed that the HNT/chitosan sponges significantly increased wound closure ratio compared with pure chitosan. HNT addition also aided in re-epithelialization and collagen deposition [72].  
HNTs and other nanoclays such as montmorillonite used as scaffolds also possess the capability of improving the wound healing response. For example, [PERSON] et al. (2020) produced electrospun scaffolds incorporating these nanoclays, and their results showed enhanced fibroblast cell attachment and proliferation with very little to no proinflammatory activity [73]. A final example of potential wound healing applications using HNTs as a key contributor is the study by [PERSON] et al. (2019) [74]. This study loaded electrospun cellulose ether-PVA nanofiber mats with HNTs and gentamicin sulfate. As a result, the mats offered sustained gentamicin release and advanced wound healing in an animal model [74]. The above studies have demonstrated that HNTs, especially chitosan-HNTs nanocomposites, have significant potential for burns, chronic wounds, and diabetic foot ulcers.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 2 Literature Review
### Attention Dilation Link Neural (AD-Linknet)  
In 2019, ([PERSON] et al., 2019) delved into the examination of the attention dilation-linknet (AD-linknet) neural network. Their study embraced an encoder-decoder framework, featuring a pre-trained encoder, a channel-wise attention scheme, and a serial-parallel integrated dilated convolution. This research specifically focused on the application of these components for achieving semantic segmentation in the context of high-resolution satellite images [19].
## 7 Security Analysis and Cryptographic Guarantees
### Mutual Authentication Guarantees
#### 7.2.6 Side-Channel Resistance Considerations  
Although side-channel attack resistance is not the focus of this work, the use of double-base scalar multiplication with point halving naturally reduces the number of elliptic curve operations. Shorter execution paths with fewer field inversions reduce the attack surface for timing and power analysis. Implementers are advised to follow constant-time computation practices and randomized DBC traversal strategies for additional protection.  
The refined protocol preserves all essential cryptographic guarantees of the original GN-AK while introducing a more efficient scalar multiplication method. No additional assumptions are introduced, and security is maintained under established models. The enhancements make the protocol particularly well-suited for constrained or embedded cryptographic environments where performance and energy efficiency are critical.
# Review of Physical Layer Security in 5G Wireless Networks
## 4 Conclusions  
The literature review study on physical layer security in 5G wireless networks concludes by emphasizing the importance of this field of study in addressing the rising demand for secure and dependable communications. The research articles under consideration have highlighted a number of opportunities and difficulties related to physical layer security in 5G networks and have suggested solutions and methods to solve them. The studies, which cover topics including multi-antenna systems, interference exploitation, secrecy metrics, and the effect of fading channels, have made a substantial contribution to our understanding of physical layer security [38]. These inquiries have demonstrated how physical layer security methods can improve the secrecy and integrity of wireless communications in 5G networks. A number of directions require additional investigation, notwithstanding the advancements made. First, improving physical layer security in 5G networks requires researching novel transmission methods [39]. Investigating interference exploitation techniques can also help to improve security precautions. It is crucial to create quantum-safe security mechanisms given the potential threat posed by quantum computing. Furthermore, it is essential to create secure network architectures that successfully incorporate physical layer security. To guarantee the easy acceptance and deployment of secure physical layer solutions in 5G networks, practical implementations, and standardization initiatives need to be given priority [40]. The difficulties in physical layer security can be resolved by addressing these research directions and improving security for 5G wireless networks. This helps with the development and implementation of safe and reliable 5G networks overall while also offering users strong protection against eavesdropping and other security threats [41]. In conclusion, the study recognizes the advancements made in the area and emphasizes the significance of physical layer security in 5G wireless networks. To overcome obstacles and enhance the physical layer security of 5G networks, it also highlights the necessity for more research and development. The objective of realizing safe and dependable wireless communications in the 5G future can be accomplished by concentrating on the indicated research directions.
## 5 Comparison with Other Platforms  
A review of several healthcare platforms shows that the architecture presented in this paper covers all the categories from integration, interoperability, security care, and advanced analytics. Generally, other implementations only focused on one specific area, as shown in Table 15 and taken from the International Conference on Computational Intelligence and Data Science (ICCIDS 2018) and a healthcare frameworks review proposed in the Journal of King Saud University [31].  
We designed and implemented a healthcare platform using big data technologies with actionable insights to augment human decision-making at the organization impacting the population's health, public health, and to capture social determinants of health. This platform comprerehends all the features we use in the comparison. [PERSON] et al. reported a conceptual architecture to present big data analytic outlines in healthcare with no predictive analytic capabilities and no patient 360 view. [PERSON] et al. designed a big data architecture platform to improve data aggregation in the healthcare industry and to provide a reduction in healthcare cost, predicting analytic, preventive care, and drug discovery capabilities but without patient 360 view capabilities. [PERSON] et al. presented a patient-centric healthcare framework--Collaborative Assessment and Recommendation Engine (CARE)--to improve patient-centric treatment and diagnosis without real-time monitoring and 360 view capabilities. [PERSON] et al. implemented a fascinating e-Health service application for diagnosing heart diseases. [PERSON] et al. designed a real-time architecture of big data for Francisco Lopez Lima Hospital in Argentina to process physiological data. This platform did not include predictive analytic and patient 360 view. [PERSON] et al. implemented a genomic data processing platform that provides image analytic and signal processing of psychological data. [PERSON] et al. designed a big data platform for integrating heterogeneous wearable data in healthcare for real-time monitoring and diagnosis. Lastly, [PERSON] et al. presented a real-time big data platform to improve communication and collaboration between patients and providers, increasing the quality of care that clinical teams can provide.
# Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
###### Abstract  
This paper proposed a novel multi-spacecraft tracking and data association method based on the orbit uncertainty propagation. The proposed method makes full use of the dynamic information and thus the data association performance is enhanced. The proposed method is divided into three portions, i.e., the uncertainty propagation, the data association, and the orbit estimation. The second-order solutions derived for state and measurement prediction, on which to base the optimal association, are set up. The optimal association is solved by the contract network algorithm to reduce the computing cost. Finally, a second-order extended Kalman filter is designed to estimate the orbit of each spacecraft. The proposed method is successfully applied for solving a four-spacecraft tracking problem. Simulations show that all the four targets are well tracked. The method demonstrates close to 100% data association precision. The proposed method is proved to be efficient and effective to solve the multi-spacecraft tracking problem.  
multi-spacecraft tracking; data association; uncertainty propagation; orbit estimation +
Footnote †: journal: Applied  
Article  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Analyses
### Analysis on Effect of Probe Offset  
As mentioned above, most studies have proved that, for the FSW of dissimilar materials, if welding is performed without the tool offset or at lower tool offset, then it is not possible to obtain a high-quality weld joint. Therefore, in the current experiment, various probe offsets were considered to analyze their impacts on the weld joint surface.  
From Figure 10b,c, it can be seen that, when the tool probe is fixed at 1 mm and 1.5 mm, the weld joint and the surface quality were poor. However, when the tool probe was fixed at 2 mm for the softer material, Al (Figure 10a), a superior sound weld joint was obtained. From the experiments, it could be observed that, with an increase in the probe offset, the surface of the weld was smoother. This was due to the fact that when the tool probe advanced more towards the aluminum, a higher portion of the relatively softer Al is entered the stir zone, and this helped the smooth movement of the mixed materials due to there being less resistance from Al which produced a better weld surface [17].  
Figure 8: FSW weld joint with Cu (AS) and Al (RS).  
Figure 10: Surface morphologies of welded joints at tool offsets of (**a**) 2 mm, (**b**) 1.5 mm, and (**c**) 1 mm at a rotational speed of 1400 RPM and welding speed of 65 mm/min.  
Figure 9: FSW weld joint with Cu (RS) and Al.
# Effects of Sand-Cement Columns on Primary Consolidation Settlement
## 4 Discussion of the Results  
Figure 6 shows more deformation in the specimen with two columns than in the specimen with no columns. This is because, before a full contact is formed between the loading plate and the sand-cement column, the pores in the sand-cement column provide extra drainage. The extra drainage caused more water to leave the specimen, contributing to the high deformation. The reduced deformation in the one-column specimen could be attributed to the fact that the column stiffness began to contribute to controlling settlement by reducing settlement at the early stage of the test. Figure 7 shows the deformation for the three specimens with and without columns after increasing the stress from 32 kPa to 64 kPa. After forming full contact between the loading plate and sand-cement columns, the specimen with column(s) provided an additional drainage path because of the widened pores developed (Figure 4) in the sand-cement column(s). The extra drainage caused more deformation due to the rapid volume change. The specimen with two columns provided more drainage than the specimen with one column. Therefore, the two-column specimen experienced more deformation than the specimen with one column. Less deformation was observed in the specimen with no column because no extra drainage was created. Figures 8-10 exhibit a similar trend, where increasing stress from 64 kPa to 512 kPa results in the formation of bigger pore openings in the sand-cement columns. Observations from the USC results confirmed that the sand-cement column fails at 255 kPa, as evident when the specimens were analyzed under a microscope. However, at this stage, the stiffness of the columns contributed to reducing deformation as stress increased, and the drainage effect had little influence on the deformation. Therefore, less deformation was observed in the specimen with two columns compared to the specimens with one column and no column. The one-column specimen also experienced the most deformation because of the stress concentration on the single column.  
As the applied stress continued to increase, the confining pressure around the columns forced these openings to progressively close up over time, effectively reducing internal voids or pores. This closure enhances interparticle contact and friction, resulting in an increase in stiffness and load resistance. Figures 11 and 12 also show an identical trend, where specimens with two columns experienced the least deformation due to the stiffness  
Figure 12: Deformation–Time plots after Day 7.  
of the columns, combined with the stress distribution among them. The stiffness provided by the one column also increased with an increase in stress as the bigger pores within the columns closed. Hence, less deformation was observed in the specimen with a sand-cement column than in the specimen without one.
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 3 Double-Step Deep-Learning Framework (DSF)
### Wildfire Detection Task (First Step)  
**Binary-class backbone.** This building block takes the satellite images and assigns to each pixel a binary label (i.e., burnt or unburnt) regardless of the severity values of the wildfire. Its output is a probability map with values in the range [0, 1]. The actual components taken into account are summarized in Section 3.3.  
**Binary loss.** The first backbone training is performed using an intermediate loss function, constructed to compare the probability output with the ground-truth binary mask. At this stage, among all the possible alternatives in the literature, we focus on different standard and custom prediction loss functions depending on the features we want to emphasize. The selection has been performed among the state-of-the-art alternatives in image segmentation, as presented in [66]. In particular, Binary Cross-Entropy (BCE, shortened B), Dice (shortened D), sIoU (shortened S), and two compound loss functions (B + D, B + S) are considered. BCE is used to prioritize a per-pixel adhesion between the prediction and the ground truth, while Dice and IoU involve higher-order correlations between them. Consequently, the compound loss functions are developed to require both features from the network output. They are defined as a weighted sum of the previous functions as this strategy has been shown to improve neural network training in certain domains [66]. In this work we inspected the effectiveness of B + D = 0.5 \(\cdot\) BCE + 0.5 \(\cdot\) Dice, and B + S = 0.5 \(\cdot\) BCE + 0.5 \(\cdot\) LsqU.  
**Binary threshold.** The binary-class backbone output is then passed through a sigmoid function to get a probability map, which is then thresholded to obtain the final binary mask. The choice of the threshold can increase or reduce the number of false-positive of the prediction. Then, it enforces the trade-off between a high recall and a high precision. As we have no evidence that privileging one of the metrics more than the other could improve the performances, we decided on a balanced value fixed to 0.5. In the following, we will provide more intuitions on the effects of an asymmetrical choice on this point.
## 6 Sensitivity Analyses for the Retrieval of GOME-2A SiF Using PCs
### Number of PCs
Comparing retrieved SiF values for the months of January, April, July and October 2013 for the different PC numbers (15, 25, 35, or 45) with flux tower-derived GPP values, we find that the correlation between GPP and SiF increases with five percentage points when using 25 PCs instead of 15 PCs, with 28 percentage points when using 35 PCs instead of 15 PCs but with only 20 percentage points when using 45 PCs instead of 15 PCs. Thus, there is a slight decrease of the correlation when going from 35 to 45 PCs. These results indicate that adding more PCs increases the ability to better retrieve SiF, but that there is indeed an optimum amount at which adding more PCs will deteriorate the retrieval (see also [21]).
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Applications of MFCs
#### 3.4.5 Biosensing  
A biosensor can be defined as a system that utilizes specific biochemical reactions to identify the presence, absence or concentration change of chemical components by electrical, thermal or optical signals [31]. The MFC current output is directly related to the metabolic activity of the electroactive microbial community in the anode zone [247], thus making the system readily usable for multiple biosensing applications. Multiple review works also targeted summarizing biosensor applications of MFCs [247; 248; 249; 250; 251; 31].  
Two most common applications of this category are biochemical oxygen demand (BOD) sensing and various toxicity sensing [248; 31; 249]. [PERSON] et al. [252; 253; 254] reported a copper, i.e., Cu (II) toxicity sensor, a formaldehyde detection sensor and an overall water-quality monitoring system including organic matter, heavy metal (Cu2+), and acidic toxicity detection. [PERSON] et al. [129] reported an intuitive MFC-based biosensor configuration for detection of biofouling occurring at reverse osmosis membranes of marine desalination systems. [PERSON] et al. [255] reported an MFC-based carbon monoxide detecting biosensor depending on the hypothesis that carbon monoxide inhibits bacterial activity in the anode would also decrease electricity.
## 2 Materials and Methods
### Electrical Soil Model  
The electrical soil model is represented as a parallel combination of capacitance \(C\) and conductance \(G\). This relationship is visually depicted in Figure 11 below.  
The expression for the input admittance \(Y_{0}(\omega)\) in free space is given, where \(G_{0}\) and \(C_{0}\) represent the conductance and capacitance in free space, respectively [14]:  
\[Y_{0}(\omega)=G_{0}+j\omega C_{0} \tag{2}\]  
The capacitance \(C_{0}\) is defined as  
\[C_{0}=\frac{\varepsilon_{0}\pi r^{2}}{d}+\varepsilon_{0}\varepsilon_{r}r \bigg{(}\frac{\ln(16\pi r)}{d}-1\bigg{)} \tag{3}\]  
The conductance \(G_{0}\) is given by  
\[G_{0}=\frac{\pi r^{2}}{d}. \tag{4}\]  
In the above equation, the terms are defined as follows:  
* \(\sigma\): conductivity in Siemens;
* \(a\): area of electrical plates in cm\({}^{2}\);
* \(d\): distance between capacitor plates in cm;  
Figure 10: Synoptic of circular parallel plate capacitance.  
Then, we can express the total admittance \(Y\) for the medium:  
\[Y(\omega,\varepsilon_{0},\varepsilon_{r})=G_{0}+j\omega C_{0}\varepsilon_{r} \tag{5}\]  
where \(\varepsilon_{r}\) is the relative permittivity of the material in which the probe is immersed.  
Exploring the relationship between admittance and the reflection coefficient, we conducted simulations at 433 MHz to demonstrate attenuation caused by the reflection and absorption of electromagnetic waves, as illustrated in Figures 12 and 13. Accurate soil modeling was essential for analyzing the underground transmission channel, given its considerable influence on the system's reflection coefficient and admittance.