esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Methodology  
### Preparation of jointed Rock Mass Specimens  
The rock employed in this experiment is Finike limestone, a medium-strength calcareous sedimentary rock from Finike, southern Turkey. The rock samples were prepared by cutting Finike limestone, which is free from bedding planes and joints, into dimensions of 360 mm \(\times\) 360 mm \(\times\) 250 mm. To simulate a jointed rock mass, some samples were further subdivided into blocks of uniform thickness. For instance, to represent a joint spacing of 30 mm, one sample was cut into 12 blocks, each measuring 360 mm \(\times\) 250 mm \(\times\) 30 mm. Similarly, for joint spacings of 60 mm and 90 mm, samples were cut into 6 blocks (360 mm \(\times\) 250 mm \(\times\) 60 mm) and 4 blocks (360 mm \(\times\) 250 mm \(\times\) 90 mm), respectively. Each set of blocks was bound together by cement mortar with a thickness of 2 mm, as in previous studies [21; 22], to form an artificial jointed rock mass. The jointed rock mass specimens were fabricated by placing the artificial jointed rock mass in a steel mould and filling the surrounding space with concrete (Figure 2). Similarly, the intact rock specimen was fabricated using the same method.  
The mechanical properties of the Finike limestone, concrete, and cement mortar are detailed in Table 1. The uniaxial compressive strength (\(\sigma_{c}\)), the elastic modulus (\(E\)), and Poisson's ratio (\(\
u\)) were determined according to ASTM D7012-14 [25]. Additionally, tensile strength (\(\sigma_{t}\)) and density (\(\rho\)) were determined using the methods recommended by ASTM D3967-08 [26] and ASTM C97/C97M-15 [27], respectively.  
It should be noted that the strength of the cement mortar used to form the artificial joint plane is significantly lower than that of the Finike limestone. This indicates that the jointed rock mass specimens in this experiment adequately represent joint rock mass in nature. Moreover, the properties of joint planes are consistent across all specimens. Therefore, the effects of joint roughness and joint strength on cuttability can be disregarded.
## 3 Results and Discussion
### Characterization of Microcapsules  
#### 3.2.1 Optical Microscope  
As shown in Figure 1, GE and AG were used as a shell of globules, and some irregular crystals were wrapped in the spheres. Thanks to the characteristics of the core material, our microcapsules could have their internal structures observed more clearly compared to embedded lipids or probiotics [15; 16]. Due to the different RJSR crystals having different growth rates, the size of the RJSR in each microcapsule differed, resulting in a broad size distribution of the microcapsules (Figure 1a). Figure 1b shows that although the shape of the wet capsule was round, a solid rather than a liquid droplet was located inside, thereby affecting the surface morphology of the dried microcapsule. In addition, as shown in Figure 1b, the crystals of RJSR grew and became sharper, preventing the colloid from evenly adhering onto the surface. Therefore, RJSR should be recrystallized to avoid these issues.  
Figure 1: Wet microcapsules under an optical microscope: (**a**) microcapsules contain small crystals, (**b**) Microcapsules contain large crystals.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 2 Related Work
### Dynamic Scheduling and Resource Allocation Optimization Methods  
Dynamic scheduling mainly focuses on solving problems encountered in complex task decomposition in satellite networks, such as task correlation coupling, and researchers have proposed methods based on dynamic scheduling and resource allocation optimization to address the dynamic resource coupling and real-time challenges faced by complex task allocation in satellite networks. Researchers have adopted DRL and hierarchical multi-time-scale methods to solve the delay and resource allocation efficiency problems in dynamic task scheduling through constrained Markov decision process (CMDP) modeling and mixed near-end policy optimization [40; 41; 42; 43]. Based on multi-agent collaboration and genetic programming, they have constructed autonomous satellite collaboration models and centralized distributed architectures, which have improved the task planning capability of multi-satellite systems [24; 44; 45; 46].  
In addition to innovative research work from an algorithmic perspective, some researchers have focused on network architecture innovation and optimized load balancing and economic benefits by designing multi-layer edge computing frameworks and online scheduling mechanisms [47; 48; 49; 50]. For example, [PERSON] et al. [42] proposed a dual decoder strategy network based on the attention mechanism, which utilizes the virtual task adjustment mechanism to achieve flexible scheduling sequence generation; [PERSON] et al. [47] proposed a hybrid cloud satellite multi-layer architecture that dynamically coordinates node selection and computing resource allocation. However, there is an inherent contradiction between real-time assurance and multi-objective trade-offs in existing methods for cross-system collaborative optimization, and further exploration of low-complexity joint optimization theory is needed to balance the global efficiency and local resource constraints of heterogeneous satellite networks.
## 2 Background and Related Works
### Related Works: Energy Management of IoT
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Issue** & **Description** & **Relevant Citations** \\ \hline \multirow{4}{*}{Energy Optimization Technique} & Techniques such as DVFS, duty cycling, or efficient software algorithms are used to reduce energy consumption. & [6,30] \\ \hline \multirow{4}{*}{Hardware Efficiency} & Utilization of low-power microcontrollers, sensors, and other components to optimize energy consumption at the hardware level. & [29,32] \\ \hline \multirow{4}{*}{Software Optimization} & Optimization of software through low-power operating systems, efficient algorithms, and minimizing unnecessary computations to conserve energy. & [5,6,31] \\ \hline \multirow{4}{*}{Communication and Networking Protocols} & Use of low-power communication methods like BLE, Zigbee, and LoRa, along with efficient MAC and routing protocols, to minimize power consumption in communication tasks. & [32, 33, 34, 35] \\ \hline \multirow{4}{*}{Power Source and Sustainability} & Incorporation of renewable energy sources (e.g., solar, wind) to prolong the life of IoT devices, particularly in remote or inaccessible areas. & [36, 37, 38, 39, 40] \\ \hline \multirow{4}{*}{Task Scheduling for Power Minimization} & This paper introduces a task scheduling algorithm that efficiently manages sensing and data transmission periods to reduce power consumption. It uses deep sleep techniques to further minimize energy usage. & \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of energy management approaches in IoT devices. IoT devices will last as long as their power source does since using too much power can cause the device to overheat, which can damage it or make it stop working. So, it is important for IoT computing to use as little power as possible, and using renewable energy sources can help extend the device's monitoring life [37; 38]. It is crucial to use power-efficient components and software to extend the life of IoT devices. Additionally, renewable energy sources such as solar, wind, or kinetic energy can help prolong the device's monitoring life. This can be especially useful in remote or hard-to-reach areas where replacing or recharging batteries is difficult. IoT devices can operate longer and provide continuous monitoring and data collection by combining energy-efficient design with renewable energy sources. Power management in IoT devices is a critical aspect that involves hardware and software issues, including estimating power consumption and scheduling sensor readings [42]. However, actual power consumption may differ from the predicted values. Power management implementation is crucial for disaster monitoring systems, but budget constraints can limit investments in high-performance equipment, resulting in incomplete coverage of surveillance areas [39; 40].
## 4 Test Results and Discussion
### Particle Breakage Characteristics
#### 4.2.2 Mineral Content (Quartz and Feldspar)  
The red-brown, yellow-brown, and gray weathered granite samples were prepared with approximately the same particle size distribution. Standard heavy compaction tests were performed on the samples under optimum moisture content conditions. The results of sieving before and after the test are shown in Figure 16.  
The relationship between relative breakage and quartz (or feldspar) content was analyzed by calculating the relative breakage of different samples before and after the standard compaction test. Figures 17 and 18 show the relationship between relative breakage and quartz (or feldspar) content. It can be seen from the figures that \(B_{r}\) decreased with the increase of quartz content, while \(B_{r}\) increased with the increase of feldspar (=plagioclase feldspar + potassium feldspar). The results show that quartz and feldspar content have an obvious effect on the particle breakage characteristics of weathered granite.  
Figure 16: Sieving curves of different samples before and after the test.  
Figure 17: Relationship curve between quartz content and \(B_{r}\).  
The main reasons that the \(B_{r}\) of samples with high quartz content after compaction test was small are as follows: (1) The probability of breakage of samples with more quartz content is small because quartz has a high strength; (2) samples with high quartz content have strong ability to resist being weathered; and (3) there are few microcracks in samples with high quartz content. On the contrary, feldspar has little strength and is easy being weathered, so the samples with higher feldspar content showed obvious particle breakage characteristics.  
The above conclusions were obtained under the same initial gradation. If the initial gradation changes, the increasing or decreasing trend of relative breakage will change.
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 2 Multi-Functional Energy Storage System Application Development
### Use Cases and Applications  
The selection of use cases UC1-UC5 is based on common services provided mainly by small scale energy storage systems (\(\sim\) > 3.6 kW & < 4.8 kW), see Table 1. An extended list can be found in the study [4] where a categorization by objectives, such as reduction of energy costs, power system stability, and market integration is carried out. A scheme that represents electrical devices connection and communication links, in the frame of those UC, is depicted in Figure 1. A BESS is integrated within the low-voltage grid to mainly support the user to minimize energy costs. This is reached by storing the Photovoltaic (PV) generated energy not consumed by the local load. This mechanism corresponds to UC3 where the customer is the main benefactor. However, those batteries are able to provide support to the grid operator for power system stability purposes. Thus, not only self-consumption (UC3) is pursued but other services such as voltage and frequency regulation (UC1-UC2). Following that premise, the study [5] is also selected (UC4); it defines a multi-functional system embedded with peak-shaving and reduction of price services. An ensemble of storage services is also met by UC5 [1]. That case study gathers voltage control and two other services, it is an appealing example since each service rents one part of the whole capacity of a medium scale BESS. At first glance, it might seem that services do not need to be aligned because of the capacity allocation procedure. However, study [1] analyzes conflict issues regarding a full provision of those services. Since conflict identification is a main requirement to be covered by the EMS-ontology, UC5 is included in the selection of UCs.  
\begin{table}
\begin{tabular}{l l} \hline
**Use Case, Controller Type** & **Description** \\ \hline Volt-VAr mode (UC1) & Reactive power from the battery (\(Q_{bat}\)) is injected to stabilize the voltage at the \\  & Point of Common Coupling (FCC), see Volt-Var mode VV1 in [12]. \\ \hline Frequency-watt (FW) (UC2) & Active power from the battery (\(P_{bat}\)) supports the balancing of frequency of \\  & the grid (\(F_{grid}\)), see FW mode FW21 in [12]. \\ \hline Self-consumption (UC3) & \(P_{bat}\) helps to minimize the energy consumed from the grid (\(P_{grid}\)), see PVBat1 \\  & control strategy in [13]. \\ \hline Min. of costs with peak shaving (UC4) & Electricity costs are reduced and peak-shaving is pursued, see [5]. \\ \hline Multi-functional BESS medium scale (UC5) & Self-consumption is provided to a group of households by injecting active power (\(P_{bat}\)), Primary Control Reserve (PCR) participation follows the logic in UC2. Voltage is controlled by reactive power provision \(Q_{bat}\); to accomplish this, a Proportional Integral (PI) controller is designed as exposed in [1]. \\ \hline \end{tabular}
\end{table}
Table 1: Use cases derived from the integration of BESS into a low voltage grid  
Figure 1: EMS setup with corresponding communication architecture.
## 3 Results
### Impact of the RAE  
No RAE was found in the women's junior disciplines, neither in sprint (\(\chi^{2}\) = 1.75, \(p\) = 0.63; w = 0.19), in keirin (\(\chi^{2}\) = 1.57, \(p\) = 0.67; w = 0.15), in scratch (\(\chi^{2}\) = 6.30, \(p\) = 0.10; w = 0.21) or in the points race (\(\chi^{2}\) = 6.54, \(p\) = 0.09; w = 0.24) (see Figure 3a). In junior men, the RAE was effective in the sprint disciplines, i.e., in sprints with a medium effect size (\(\chi^{2}\) = 8.14; \(p\) = 0.04; w = 0.22), with an underrepresentation of Q4-born riders (z = \(-\)2.31) and in keirin with a large effect size (\(\chi^{2}\) = 16.8; \(p\) < 0.001; w = 0.36), with an overrepresentation of riders born in Q1 (z = 3.01) and an underrepresentation of riders with a birthdate from Q4 (z = \(-\)2.75) (see Figure 3b). No RAE was found for men's endurance disciplines, namely scratch (\(\chi^{2}\) = 5.11, \(p\) = 0.16; w = 0.18) and the points race (\(\chi^{2}\) = 7.07, \(p\) = 0.07; w = 0.20).  
Figure 2: Average corrected score of achievers and non-achievers depending on the sex and the discipline. Notes: F: female; M: male; *: \(p\) < 0.05; **: \(p\) < 0.001.  
No RAE was found in the elites in women's disciplines (Figure 3c) or men's disciplines (\(p>0.05\)) (Figure 3d). No significant influence of the RAE on the average corrected score was observed in any of the studied groups (\(p>0.05\)) (Supplementary File S2).  
Qualitative Data Analysis: Interviews  
Ten individuals from the national teams of France, Italy, and Lithuania participated in the interviews, including six coaches, all of whom had previous experience as athletes; three were active athletes, and one was a former athlete (see Table 3). Four of the six coaches had prior experience as international athletes.  
Figure 3: Birthdate distribution of the riders in the junior women (**a**) and men categories and the (**b**) elite women (**c**) and men (**d**) categories. Notes: Q1: born between January and March; Q2: born between April and June; Q3: born between July and September; Q4: born between October and December; *: \(p\)-value \(<0.05\); **: \(p\)-value \(<0.001\); # post-hoc analysis indicates an overrepresentation; $: post-hoc analysis indicates an underrepresentation.
# New Trends in 4D Printing: A Critical Review
## 4 Shape Memory Materials
### External Transition Stimuli for 4 DP
#### 4.2.5 Magnetic  
A magnetic field can be applied to SMMs to produce induced heat [79].  
Using magnetic fields as actuators offers several advantages. Firstly, they respond quickly [89; 90], allowing for efficient and timely actuation. Secondly, they possess a low safety risk [86], ensuring the well-being of users and minimizing potential hazards. Moreover, magnetic fields enable remote guidance [91], allowing for control and manipulation from a distance. They can accelerate the speed of movement, enhancing their dynamic capabilities.  
Some limitations are also present. The first one is the highly reactive nature and aggregation affinity of magnetic-response materials in biomedical applications [86], which can prevent their usage. Moreover, complications may arise when dealing with magnetic nanoparticles in living systems [89; 92; 93; 94; 95; 96; 97; 98]. Magnetic fields have low operating temperatures and traditional magnetic absorbers exhibit high density. Moreover, under certain circumstances, magnetic fields may cause a rise in temperature during experiments [11].  
Nevertheless, materials that are sensitive to magnetic fields find applications in drug-delivery systems and fastening purposes. These applications leverage the unique properties of magnetic fields to enable controlled release and secure attachments [11].  
Some SMAs are magnetic field sensitive; they are known as magnetic shape memory alloys or ferromagnetic SMAs. These SMAs are also known as magneto-responsive shape memory alloys. A magnetic field is responsible for the structure's change in orientation, i.e., the movement of the twin boundary in the martensitic structure. Magnetization is responsible for the shape alteration, which is a result of the magnetically induced reorientation [82].
## 3 Results
### Range-Height-Indication (RHI) Diagram  
A dedicated routine creates _Range-Height-Indication_ (RHI) diagrams in Cartesian coordinates, based on pBRL return power data at different discrete zenith angles. In an RHI diagram, the horizontal axis represents the horizontal distance from the LIDAR and the vertical axis represents the height above it. LIDAR data obtained at discrete angles fill only a fraction of pixels in an RHI plot (only those that exactly correspond to the lines of specific LIDAR profiles) while blank pixels between successive LIDAR profiles are filled using a barycentric interpolation scheme [71], as described in Figure 23. The value at each interpolated pixel is obtained with:  
\[w(x,y)=\frac{v(x_{1},y_{1})d_{2}+v(x_{2},y_{2})d_{1}}{d_{1}+d_{2}}, \tag{52}\]  
where \(v(x_{1},y_{1})\), \(v(x_{2},y_{2})\) are the closest measured logarithms of RCS values in adjacent profiles and \(d_{1}\), \(d_{2}\) the shortest distances between the measured points and the coordinates of the interpolated pixel.  
Figure 21: Satellite image (_Dust RGB–MSG–0 degree_) of mineral dust over Western Africa and the Atlantic Ocean, including the Canary Islands. The island of La Palma is located in most north-western direction of a series of seven islands found in the centre of the image. Dust is colored in pink, clear sky in blue, and clouds in dark red. The image was taken on 25 August 2021 at 20:00 UTC, simultaneous to our LIDAR measurements. ©EUMETSAT 2021.  
Figure 22: Satellite image (_Volcanic Ash RGB–MSG–0 degree_) of the volcanic plume (cyan, corresponding to enhanced SO\({}_{2}\) concentration) above La Palma. The left image was taken on 22 September 2021, at 03:00 UTC; our LIDAR measurement was performed at 19:30 UTC, and the right image was taken after the measurement, on 23 September 2021, at 04:30 UTC. The wind direction (on the images indicated with a red arrow) changed in a clockwise manner, which blew the plume above the observatory. ©EUMETSAT 2021.  
As the pBRL has pointing capabilities along zenith and azimuth angles, this allows us to make cross-section scans of the atmosphere, yielding 2D maps of atmospheric features. We present RHI diagrams for D-III, D-IV, which are more interesting.  
Figure 24 (left) shows the RHI for the calima event, which clearly shows the extension of the atmospheric event. We can see an irregular aerosol density profile that slowly disperses to a clear atmosphere. In Figure 24 (right), we show the same scan for the volcano event. In the data obtained, two distinct features are visible: an optically thick layer of clouds at altitudes above 2300 m a.g.l. directly above the LIDAR, pointing at zenith; which dissipates out toward the north and a thinner layer located at an altitude of 1500 m a.g.l., which homogeneously covers the scanned sky. Together with the retrieved Angstrom exponents and LIDAR ratios, the lower stratified layer is interpreted as the volcanic dust plume moving over the site.
# Molecular Insights into the Interactions Between Human Serum Albumin and Phospholipid Membranes
## 3 Results and Discussion
### The Affinity of HSA for DPPC and DPPE in the Presence of Mono- and Divalent Cations
For complexes formed with DPPE, domain IA is the primary contributor to hydrogen bond formation, regardless of the metal ions present in the complexes. Likewise, the DPPC system, the domain responsible for the majority of hydrogen bonds, also forms the most ionic contacts. The dynamic nature of hydrogen bonding networks in aqueous environments profoundly impacts the behavior of biomolecules. One intriguing aspect of the interactions between phospholipid bilayers and HSA is their impact on biomechanical properties. This includes indirect contacts, such as water bridges. As is well-known, liposomes exhibit excellent lubricating properties. Interestingly, an appropriately large addition of proteins (such as albumin or \(\gamma\)-globulin) to solutions containing phospholipids can lead to an increase in friction between surfaces filled with model synovial fluids [31,96,97]. Low concentrations of albumin can promote lubrication. This suggests a negative impact of forming a dense network of direct interactions between the components on lubricating performance. A significant excess of water relative to the protein is advantageous, as the formation of water bridges between components of the synovial fluid plays a crucial role in reducing friction [98,99]. This is understandable, as effective lubrication at the molecular level is closely linked to the mobility of synovial fluid components. The structural and energetic details characterizing water bridges are summarized in Table S5 (Supplementary Materials). The majority of hydrogen bonds with water (approximately 80%) are formed by the phospholipid head groups, as expected due to their hydrophilic nature. In the most stable complex examined (DPPE/HSA/Ca\({}^{2+}\)), the average length of these hydrogen bonds is 1.78 \(\pm\) 0.20 A, with a corresponding binding energy of 21.70 \(\pm\) 4.43 kJ/mol. The higher binding energies, albeit with slightly shorter distances between atoms, are observed for hydrogen bonds connecting the phospholipid and HSA through ASP (E\({}_{bind}\) = 22.33 \(\pm\) 3.96 kJ/mol, d = 1.82 \(\pm\) 0.19 A), GLU (E\({}_{bind}\) = 22.26 \(\pm\) 3.74 kJ/mol, d = 1.82 \(\pm\) 0.18 A), GLY (E\({}_{bind}\) = 21.80 \(\pm\) 4.01 kJ/mol, d = 1.89 \(\pm\) 0.16 A), ILE (E\({}_{bind}\) = 22.57 \(\pm\) 2.44 kJ/mol, d = 1.85 \(\pm\) 0.16 A), and TRP (E\({}_{bind}\) = 21.89 \(\pm\) 3.19 kJ/mol, d = 1.96 \(\pm\) 0.17 A). However, the contribution of these interactions is low or negligible (0.02\(-\)4.39%). For DPPC, the most stable complex, i.e., the DPPC/HSA/Ca\({}^{2+}\), is similarly characterized by high E\({}_{bind}\) values (18.77\(-\)23.28 kJ/mol).