esa-sceva/satcom-chunk-collection huggingface dataset:
They ignored a technical challenge outside of the image fusion algorithm itself for reconstruction of a cloud-free image time-series, i.e., how to merge the cloud-free surface reflectance measurements within cloud contaminated images that are not used as input data for image fusion processing with synthetic surface reflectance for better quality. To make full use of the cloud-free surface reflectance measurements within cloud contaminated images that are not used as input data for image fusion, this study attempts to extend the spatiotemporal image fusion approach for the reconstruction of cloud-free Sentinel-2 MSI image time-series. Based on the well-developed spatiotemporal image fusion model PSRFM [24; 26], we proposed an extended method for reconstruction of cloud-free Sentinel-2 image time-series. The proposed method goes beyond image synthetization. It combines useful cloud-free pixels of cloud contaminated images with their corresponding synthetic images through successful cloud identification, cloud shadow, and haze pixels. This approach brings us two benefits. First, it maximizes the values of the observation information by keeping the observed cloud-free pixels. Second, using the observed cloud-free pixels as references, the synthetized images can be calibrated for better quality. The rest of the paper details the proposed method as well as the process procedures (Section 2). Section 3 presents a case study. After that, the test results are evaluated and discussed in Section 4. Finally, this study is concluded with a summary in Section 5.
###### Abstract  
Background: Groundwater contamination with chlorinated hydrocarbons (CLHCs), particularly with tetrachloroethylene (PCE) and trichloroethylene (TCE), which are used in industry for degreasing and cleaning, can be considered a serious problem concerning the entire world. In addition to conventional groundwater monitoring from a network of wells, several screening methods have been proposed to identify and delineate groundwater contamination with volatile organic compounds (VOCs), such as soil gas measurement, biindicators, direct-push technologies or geophysical techniques. The main objectives of this study were to confirm the feasibility of active soil gas screening for the characterisation of groundwater contamination with CLHCs under the wider area of the former refrigerator manufacturer (city of Zlate Moravce, western Slovakia) and to evaluate the human health risks through exposure to CLHCs present in groundwater. Methods: a conventional site investigation based on concentration measurements using gas chromatography-mass spectrometry from monitoring wells and soil gas measurements using a portable photo-ionisation detector device were applied. Results: The chemical analyses showed the persistent contamination of groundwater, with PCE, TCE and other CLHCs, such as _cis_-1.2-dichloroethylene (_cis_-DCE) or 1.1,2-trichloroethane (TCA), being most severe in the zone of the former factory (up to 2690, 83,900, 6020 and 156 ug/L for PCE, TCE, _cis_-DCE and TCA, respectively), but also extended into the residential zone located 600 m along the groundwater flow line. Soil gas measurements of VOCs and other chemical parameters (methane (CH\({}_{4}\)), total petroleum (TP), carbon dioxide (CO\({}_{2}\)) and oxygen (O\({}_{2}\))) from a densely designed network of sampling points (\(n\) = 300) helped trace the current state of groundwater contamination. Spatial distribution maps of VOCs concentrations in soil gas clearly marked the areas of the highest CLHCs concentrations in groundwater. Principal component analysis (PCA) confirmed a significant correlation of VOCs and CLHCs with the first principal component, PC1, explaining up to 84% of the total variability of the concentration data, suggesting that VOCs in soil gas were a suitable marker of the extent of groundwater contamination with CLHCs. Despite severe groundwater contamination with CLHCs reaching residential areas, local residents were not exposed to non-carcinogenic risks, but a potential carcinogenic risk was present. Conclusions: based on the results, it could be confirmed that soil gas screening is an efficient and quick tool for identifying the sources of groundwater contamination with CLHCs as well as the level of this contamination.  
contamination; groundwater; chlorinated hydrocarbons; soil gas; human health risk
## 3 Results and Discussion
### Effect of Coir Fiber Addition on Absorption Bands of Geopolymer Specim  
The FTIR spectra of metakaolin-based geopolymer (MGP Control) and coir fiber-reinforced geopolymer composite (MGP--1% PCF) formulations at 28 days as observed in the wavelength 650-4000 cm\({}^{-1}\) are shown in Figure 12. The observed FTIR absorption bands were used to understand some of the changes that took place in the MK-based geopolymer and that incorporated with coir fibers. It can be observed that the spectra of the MK-based geopolymer specimen and geopolymer specimen having coir fibers as reinforcing elements were relatively similar. The MGP control formulation had main characteristic absorption bands at approximately 3340 cm\({}^{-1}\), 2102 cm\({}^{-1}\), 1651 cm\({}^{-1}\), 1398 cm\({}^{-1}\), 947 cm\({}^{-1}\), and 745 cm\({}^{-1}\)[72]. The wide absorption band at 3340 cm\({}^{-1}\) can be attributed to O-H stretching and intramolecular vibrations of H-O-H, Si-OH, and Al-OH bond groups [73,74]. The band observed at 1651 cm\({}^{-1}\) was due to H-O-H bending vibration of absorbed water molecules in the silica matrix. The absorption band at 1398 cm\({}^{-1}\) was assigned to the C-O stretching vibration of carbonate. The MGP Control specimen also exhibited absorption corresponding to Si-O-Al vibrations and Al-O stretching vibrations at 947 cm\({}^{-1}\) and 745 cm\({}^{-1}\), respectively [75,76]. Worthy of note is the high absorption (low transmittance) at the 947 cm\({}^{-1}\) bands, indicating more stretching vibration of the Si-O-Al bonds in the system. The intensity of the IR spectrum at this band may also be attributed to the high polarity of the bond. However, certain shifts in some absorption bands of the spectrum of MGP Control were seen as coir fibers were introduced into the matrices of the MK-based geopolymer (MGP--1% PCF). While the MGP--1% PCF specimen exhibited similar absorption bands to MGP Control at 3340 cm\({}^{-1}\) (O-H stretching) and 947 cm\({}^{-1}\) (Si-O-Al vibrations), the MGP--1% PCF spectrum showed a slight shift towards lower wavenumbers (1651 cm\({}^{-1}\) to 1640 cm\({}^{-1}\) and 1398 cm\({}^{-1}\) to 1387 cm\({}^{-1}\)). However, an exception was observed in wavenumber 2102 cm\({}^{-1}\), which shifted to 2110 cm\({}^{-1}\) of the MGP Control specimen. These slight changes as observed may be attributed to the interactions of the coir fibers with the matrix of the MK-based geopolymer and may have contributed to improved mechanical properties of the CFRMG composites.  
Figure 11: XRD pattern of MK-based geopolymer and CFRMG composite specimen at 28 days curing age.
## 3 Results and Discussion
### Colloidal Stability  
In this work, the long-term stability of St/AA submicrocapsules containing phytoncide oil was investigated by evaluating both the optical transmission and the photon backscattering profiles of the aqueous dispersion of submicrocapsules by using the TurbiscanTM Lab (Formulaction, Toulouse, France) [35]. Measurements were carried out using a pulsed near infrared LED at a wavelength of 880 nm for 24 h. Two different synchronous optical sensors received the light transmitted through and backscattered by samples at an angle of 180\({}^{\circ}\) and 45\({}^{\circ}\) with respect to the incident radiation, respectively. Transmitted and backscattered light flux were correlated as a percentage to those of reference standards. If there was no variation greater than 2% in the backscattering profile, it can be considered as a stable formulation [36].  
The transmission and backscattering profiles of the St/AA submicrocapsule aqueous dispersion prepared at the optimum condition ([AA]/[St] = 0.33; [SDS] = 1.0 \(\times\) 10\({}^{-3}\) mol; [AIBN] = 2.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer) are shown in Figure 6. Variations of transmission and backscattering profiles were not correlated to the destabilization processes below the sample height of 5 mm and over that of 40 mm, the values having been determined by enclosed air in the bottom and/or on the top of the cylindrical glass tube, respectively. Analysis of the submicrocapsule dispersion showed that backscattering was the prevalent signal and there were almost no variations in the backscattering intensity for the entire test height. From this result, it can be confirmed that St/AA submicrocapsules prepared at the optimum condition could be formulated as stable colloids without aggregation.  
Figure 5: Effects of the initiator concentration on (**a**) mean particle size and (**b**) zeta potential of St/AA submicroparticles prepared at 0.33 and 1.0 \(\times\) 10\({}^{-3}\) mol for [AA]/[St] and SDS concentration, respectively.
## 3 Results and Discussion  
### Effect of Mixture Powder Composition on Properties  
#### 3.1.1 Silicon Powder  
Samples 1#, 2#, and 3#, as shown in Table 1, were obtained by carbonizing and then high-temperature sintering. The porosity and open porosity, thermal conductivity, and compressive strength and density of the samples are shown in Figure 2. Compared with the 1# sample, due to the addition of silicon powder with a mass ratio of 24, the density of the 2# sample increased slightly from 0.51 to 0.59 g\(\cdot\)cm\({}^{-3}\), the compressive strength increased from 1.89 to 2.43 MPa with an increment of 28.57%, and the thermal conductivity increased from 0.94 to 1.52 W\(\cdot\)m\({}^{-1}\)K\({}^{-1}\) with an increment of 61.7%.  
Figure 3 shows the X-ray diffraction pattern of the materials from the three samples mentioned above. It can be seen that there was a small amount of unreacted silicon powder, but the silicon carbide (SiC)-reinforced phase formed during the high-temperature sintering. 1# did not contain silicon powder, so there was no silicon carbide peak in the XRD pattern. 2# and 3# contain the same content of silicon powder, and 3# contains 1% expanded graphite, which resulted in a slight difference  
Figure 2: (**a**) Density; (**b**) porosity and open porosity; and (**c**) thermal conductivity and compressive strength of different 3D graphite/ceramic composite parts.  
in diffraction peak strength. It should be noted that the formation of SiC was accompanied by 30% volume expansion, which significantly reduced the porosity from 68.31% to 48.12% and then greatly increased the geometric density. Compared with the samples of the 1# mixture powder, fewer pores existed inside the samples with the 2# and 3# mixture powders. The microstructure and morphology of these samples are shown in Figure 4. It confirmed that the excellent effect of bonding, pores filling, and strengthening of SiC formed from silicon powder in high-temperature sintering led to the increase of thermal conductivity and compressive strength [15; 16; 17; 18].
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### AI's Capabilities and Its Impact on Healthcare
The study conducted at a university hospital highlights the potential of AI to improve healthcare delivery and decision-making, especially in high-pressure, ethically sensitive situations. The AI systems discussed in the paper are primarily designed to assist with complex clinical decisions, especially those that depend heavily on patient preferences, such as resuscitation decisions. These systems can analyze vast data sets to generate suggestions consistent with patient preferences and likely outcomes, potentially reducing the impact of stress, time pressure, personal biases, and conflicts of interest thatmay affect human decision-making. The paper acknowledges the challenges in healthcare decision-making, such as insufficient knowledge about patient preferences, time pressure, and personal biases that guide care considerations. It suggests that AI-based decision support could significantly improve the status quo by helping healthcare professionals and legal representatives make more informed decisions about a patient's DNAR status. The AI system would support rather than replace human decision-making, acting as a consultative tool to enhance decision-making [6]. However, the development of such AI systems is ethically demanding. It requires careful consideration of several preconditions, including the role of AI relative to human decision-making, legal liabilities, and the balance between algorithmic suggestions and human judgment [6]. The paper emphasizes the importance of transparency, explicability, bias monitoring, and user trust in ensuring the ethical application of AI in healthcare [4]. It also highlights the need for compatibility with hospital information systems, practical user training, and awareness of the limitations and risks of AI support in decision-making. The study's conclusion underscores the need for a well-defined ethical framework and evaluation standards for AI-based decision-support systems. As AI systems gain recognition for their accuracy and reliability, they raise critical questions at the intersection of medicine, ethics, and computer science, including bias, fairness, autonomy, and accountability [5]. The paper advocates for including various perspectives, particularly those of patients and legal representatives, in addressing these ethical and policy concerns to ensure the responsible use of AI in healthcare.
## 2 XOR-Based VCS
### The [PERSON] and [PERSON]'s XOR-Based VCS  
[PERSON] [17] proposed three new methods that inherit the advantages of the method proposed in [16]. The schematic from [17] is shown in Figure 3. The difference between [16] and [17] is that [PERSON] and [PERSON]'s methods can input \(n\) covers (the covers can all be different or the same), while [16] can only input one cover. Among their three methods, Algorithm 4 [new method 2] can completely reconstruct the original secret. To be able to import multiple cover images, some steps in Algorithm 3 are changed, as shown below.  
Figure 3: The basic schematic of [PERSON] and [PERSON]’s XOR-based VCS.  
Figure 2: The basic schematic of [PERSON] et al.’s XOR-based VCS.  
```
Input: A binary secret image \(S\) and \(n\) cover image \(C_{1}\),..., \(C_{n}\), all of them with \(H\times W\) pixels, and a parameter \(\beta\). Output:\(n\) meaningful shares \(R_{1}\),..., \(R_{n}\), each of which is \(H\times W\) in size.
1. Generate a random floating point \(d\) (\(0\leq d\leq 1\)).
2. if(\(d<\beta\))  Generate \(n\) pixels \(t_{1}\),..., \(t_{n}\) by Algorithm 2 for secret pixel \(S(i,j)\). \(R_{1}(i,j)\)= \(t_{1}\), \(R_{2}(i,j)\)= \(t_{2}\),..., \(R_{n}(i,j)\)= \(t_{n}\)
3. else if(\(C_{1}(i,j)\)\(\in\)\(C_{2}(i,j)\)\(\in\)...\(\in\)\(C_{n}(i,j)\) = \(S(i,j)\)) \(R_{1}(i,j)\) = \(C_{1}(i,j)\), \(R_{2}(i,j)\) = \(C_{2}(i,j)\),..., \(R_{n}(i,j)\) = \(C_{n}(i,j)\) else  Randomly select a number \(f\) from \(\{1,\)..., \(n\). \(R_{1}(i,j)\) = \(C_{1}(i,j)\),..., \(R_{j}(i,j)\) = \(1-C_{f}(i,j)\),..., \(R_{n}(i,j)\) = \(C_{n}(i,j)\)
4. Repeat Steps 1-3 until all secret pixels have been processed, and output \(n\) shares \(R_{1}\),..., \(R_{n}\), each of which has the same image size as the secret image.
```  
**Algorithm 4****[New method 2].** (\(n\), \(n\)) XOR-based VC scheme with meaningful shares [17]
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 5 Parametric Study
### Influence of Rebar Diameter  
The influence of the rebar diameter on the pull-out behavior of notched perfobond shear connectors is illustrated in Figure 18. When the rebar diameter was increased from 16 mm to 18 mm, 20 mm, 22 mm, and 25 mm, the pull-out resistance increased by 7%, 17%, 12%, and 12%, respectively. It was indicated that the increase of the rebar diameter between 16 mm and 20 mm would lead to an increase in the pull-out resistance of the notched perfobond shear connector. The reason for this might be that increasing the rebar diameter increased the cross-sectional area of the rebar, which was directly related to the contributions of the rebar to the pull-out resistance of the notched perfobond shear connector. When the rebar diameter was greater than 20 mm, the cross-sectional area of the concrete dowel around the rebar was decreased to a certain extent, which stopped the pull-out resistance from increasing.  
Figure 17: Influence of concrete strength. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
Figure 18: Influence of rebar diameter. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
###### Abstract  
Earth observation satellite task scheduling research plays a key role in space-based remote sensing services. An effective task scheduling strategy can maximize the utilization of satellite resources and obtain larger objective observation profits. In this paper, inspired by the success of deep reinforcement learning in optimization domains, the deep deterministic policy gradient algorithm is adopted to solve a time-continuous satellite task scheduling problem. Moreover, an improved graph-based minimum clique partition algorithm is proposed for preprocessing in the task clustering phase by considering the maximum task priority and the minimum observation slewing angle under constraint conditions. Experimental simulation results demonstrate that the deep reinforcement learning-based task scheduling method is feasible and performs much better than traditional metaheuristic optimization algorithms, especially in large-scale problems.  
Earth observation satellite; task scheduling; graph clustering; DDFG; deep reinforcement learning
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 5 Performance Evaluation  
We use computer simulations to evaluate the performance of proposed routing protocol and compare it with other similar protocols, namely with AODV, H-AODV and AOMDV routing protocols. These protocols are all extensions of original AODV protocol, and they are described in [29]. All simulations were carried out in ns-2 network simulator. This software was chosen since it is easy to use, and it still has extensive support in the research community. We note that ultimate verification of any network protocol requires to use field tests. However, such tests are not up to scale, they may not be run in realistic conditions, and are also time consuming and rather costly, so they are rarely assumed in the academic community.
## 4 Results and Discussion
### Sfe  
The SFE refers to the work that a certain material needs to do to create a new interface per unit area in a vacuum. The contact angle values of distilled water, glycerol, and formamide were compared with those of Evotherm M1 warm asphalt, and Sasobit warm asphalt was substituted into Equation (5). The surface energy component of the asphalt can be obtained by solving the equations, and the SFE of the asphalt is calculated according to Equations (1) and (2). The result is shown in Figure 4.  
Figure 4: _Cont._  
The SFE and its components of base asphalt, Evotherm M1 warm asphalt, and Sasobit warm asphalt before and after UV aging are shown in Figure 4. The SFE and its components of base asphalt and warm asphalt decreased after RTFOT aging. UV aging at different times and PAV aging decreased, but the extent of reduction was different. Compared with the base asphalt, the surface energy of the three asphalts decreased by 29.97%, 12.68%, and 39.84%, and the dispersion components decreased by 31.68%, 13.18%, and 36.20%, respectively. The polar components decreased by 21.11%, 9.82%, and 66.26%; after 200 h of UV aging, the surface energy decreased by 81.04%, 72.73%, and 81.53%, the dispersion components decreased by 80.21%, 69.97%, and 87.79%, and the polar components decreased by 85.33%, 88.38% and 89.32%, respectively. The surface energy after PAV aging and its component are between 50 h of UV aging and 100 h of UV aging. The reason for this is that the oxidative aging of asphalt causes the light components in the asphalt to undergo conversion to the heavy components through volatilization. The asphaltene content is significantly improved. Previous studies have shown that the higher the asphaltene content, the smaller the surface energy in the asphalt [32; 33].
## 2 Preliminaries
### Threat Model  
This study adopts a well-defined threat model specifically tailored to the resource-constrained nature of IoT environments and the physical characteristics of cryptographic implementations:  
* Attacker Capabilities: The attacker is assumed to have access to non-invasive tools capable of capturing physical leakages, such as electromagnetic emissions or power consumption traces, generated during the AES encryption process. The primary attack technique considered is Correlation Power Analysis (CPA), which statistically correlates these traces with hypothetical encryption keys to deduce the correct key.
* Attacker Limitations: The attacker is restricted to non-invasive methods and cannot physically tamper with the microcontroller or IoT device. Additionally, the attacker has no access to the device's internal state and relies solely on external observations of emitted signals. The attack is further constrained by real-time requirements, limiting the feasibility of collecting an excessive number of traces in practical operational environments.
* Assumptions: The initial key exchange between the IoT device and the server is conducted securely, ensuring the integrity of the shared key.  
The devices operate in environments where physical access is limited but not entirely restricted, and the cryptographic hardware remains uncompromised during operation. This threat model serves as the foundation for assessing the proposed mechanism's resilience against SCAs. By dynamically updating encryption keys after each cryptographic cycle, the proposed solution effectively reduces the risk of key recovery through CPA or similar statistical attacks.  
Figure 3: Lightweight D-H protocol.