esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Damage Mechanism and Evolution Model of Concrete under Freeze-Thaw Cycles
## 4 Experimental Results and Analysis
### Changes in Mechanical Properties  
Figure 7 depicts the alterations in the fundamental mechanical characteristics of concrete samples following various F-T cycles. Concrete samples that were not treated with F-T cycles exhibit clear signs of brittleness in their stress-strain curves. The stress-strain curves of the concrete samples exhibit a clear transition from brittleness to ductility as the number of F-T cycles increases. This is particularly evident for the treatment with 50 F-T cycles, which seems to indicate a yield platform. Furthermore, the samples' compaction deformation stage lengthens as the number of F-T cycles increases during the initial loading stage. This is because there is a large number of F-T cycles creating new pores in the samples, and those pores must be compressed during the initial loading stage.  
A noticeable stress drop occurs in the elastic deformation stage as the number of F-T cycles increases, and as the number of F-T cycles increases, so does the stress needed to generate the stress reduction. This stems from the fact that the samples at this stage produce macroracks, and the actions of the F-T cycles reduce the tension necessary for the production of macroracks. As the number of F-T cycles increases, a clear yield plateau forms at the yield failure stage (pre-peak and post-peak stages). This phenomena is explained by the fact that the F-T cycles clearly damage the samples, causing them to transition from a brittle state to ductile failure. With the increase in the number of F-T cycles, the peak strength and elastic modulus of the samples decreased, while the peak strain increased. This is because the F-T cycles cause the original pores and cracks inside the samples to spread and generate new pore cracks, resulting in damage inside the samples, ultimately reducing the bearing capacity of the samples and their ability to resist deformation. The peak strain increased, but the peak strength and elastic modulus of the samples failed as the number of F-T cycles increased. This is due to the fact that the F-T cycles caused the pores and cracks inherent in the samples to propagate and create new pore cracks, causing internal damage that eventually reduced the bearing capacity and the resistance to deformation.  
Figure 7: Stress–strain, peak strength, elastic modulus, and peak strain for the concrete sample under F–T cycles.
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 3 Method
### Architecture  
An autoencoder is a feedforward neural network that is trained to attempt to copy its input to output by reducing the input to low-dimensional data and reconstructing high-dimensional data as the output [31,32]. The autoencoder consists of an encoder part (contracting path) and a decoder (expansive path). U-net is an autoencoder with skip connections between encoder and decoder [33]. The skip connections help rebuild the high-frequency features, such as border pixels, which could be lost from the deep convolution layer [34]. We use the same concept for the reconstruction of HDR images. Our proposed network is shown in Figure 2. The proposed network is based on the well-known VGG-16 network [25].  
Figure 1: Our framework first converts the color space to HSV and normalizes \(H\) values. Then it creates a mask for each input LDR image and applies a gamma correction on \(V\) of the ground truth. It finally estimates an HDR image by applying an inverse gamma correction to the result from the network.  
In the encoder layer, the spatial feature resolution is gradually reduced by half from 256 to 4, applying \(3\times 3\) convolutions with stride 1. After a bottleneck layer, the decoder reconstructs full-dimensional data from the latent data by upsampling. The decoder process is the reversed encoder process. We use \(4\times 4\) deconvolution for upsampling with stride 2. There are skip connections between encoder and decoder to preserve the detailed information. Each skip connection concatenates the encoder layer and the decoder layer corresponding to the resolution. Then, the concatenated layer is downsampled with \(1\times 1\) convolutions with stride 1 to reduce the number of feature channels by half. We use ReLU activation function at every layer.
## 3 Chemical Modification of Hyaluronan
### 3.2. Esterification of HA at the Carboxyl Moieties  
The esterification of the carboxyl moieties of the polysaccharide with alcohols or alkyl bromides, chlorides, or iodides is a common reaction (Figure 6). The HYAFF family, the benzyl (HYAFF11) and the ethyl ester of HA (HYAFF7), are examples of esterified HA, which are components of Hyalomatrix(r). These derivatives were processed in dermal/epidermal graphs for non-diabetic wound healing [87]. These derivatives were prepared using HA-TBA salt. This type of reaction was used by [PERSON] and collaborators for the preparation of esterified HA (9.6 \(\times\) 10\({}^{4}\) g/mol) with aromatic moieties using benzyl bromide. Electrospun nanofibers based on benzylated hyaluron (HA-Bn) were used for immobilizing tetracycline by increasing \(\pi\)-\(\pi\) interactions in the delivery system. Additionally, a hydrogel based on HA (3.7 \(\times\) 10\({}^{4}\)) modified with dopamine (HA-DOPA)/collagen I was prepared. The hydrogel was loaded with the nanofibrous scaffold containing different concentrations of HA-Bn/tetracycline (0, 0.1, 0.3, and 0.5 wt.%). The stability of the nanocomposite hydrogels depended on the amount of the fibrous scaffolds added being 0.3-0.5% the most stable against collagenase degradation. The in vivo results showed that the composite contracted the wound after 12 days compared with a control group (commercially available Tegaderm film(tm)) [88; 89].  
Another widely used reaction on the carboxyl group is the Steglich esterification reaction. In this reaction, DCC is combined with 4-dimethyl-aminopyridine (DMAP). DCC is a coupling agent used in organic solvents (DMSO, DMF, or formamide), which impairs transfer to industrial scale due to the high price of organic solvents and their liquidation. For example, [PERSON] and collaborators used the esterification of HA (2.5 \(\times\) 10\({}^{5}\) g/mol) with curcumin towards the preparation of a derivative for wound healing by using water/DMSO [90].
###### Abstract  
DC microgrids play a crucial role in both industrial and residential applications. This study focuses on minimizing output voltage ripple in a DC microgrid, including power supply resources, a stochastic load, a ballast load, and a stabilizer. The solar cell serves as the power supply, and the stochastic load represents customer demand, whereas the ballast load includes a load to safeguard the boost circuits against the overvoltage in no-load periods. The stabilizer integrates components such as electrical vehicle batteries for energy storage and controlling long-time ripples, supercapacitors for controlling transient ripples, and an over-voltage discharge mechanism to prevent overcharging in the storage. To optimize the charging and discharging for batteries and supercapacitors, a multi-objective cost function is defined, consisting of two parts--one for ripple minimization and the other for reducing battery usage. The battery charge and discharge are considered in the objective function to limit its usage during transient periods, providing a mechanism to rely on the supercapacitor and protect the battery. Particle swarm optimization is employed to fine-tune the fuzzy membership function. Various operational scenarios are designed to showcase the DC microgrid's functionality under different conditions, including scenarios where production exceeds and falls below consumption. The study demonstrates the improved performance and efficiency achieved by integrating a PSO-based fuzzy controller to minimize voltage ripple in a DC microgrid and reduce battery wear. Results indicate a 42% enhancement in the integral of absolute error of battery current with our proposed PSO-based fuzzy controller compared to a conventional fuzzy controller and a 78% improvement compared to a PI controller. This translates to a respective reduction in battery activity by 42% and 78%.  
DC microgrid; fuzzy controller; particle swarm optimization; electric vehicles; voltage ripple minimization +
Footnote †: journal: _Algorithms_
## 3 Materials and Methods
### Test Device  
A sensor vibration test bench was constructed in this study, as illustrated in Figure 7. The bench was designed primarily for sensor fixation, while an adjustable frequency vibration motor was installed beneath the test bench to simulate the input from a harvester vibration source. A vibration meter was positioned above the sensor to measure the vibration of the stressed plate. Additionally, a charge amplifier was employed to collect and record the charge-discharge voltage from the piezoelectric ceramic. The main instruments of the test bench are the charge amplifier instrument (VT CAMP-2G05) with \(\pm\)0.5% charge accuracy and the vibration motor (HY30 DC12V-G) with a vibration accuracy of \(\pm\)1% Hz.
# Estimation of Gross Primary Productivity (GPP)
## 4 Discussion
### GPP Phenology of SRC Plantations  
The SRC plantation exhibited strong photosynthetic activity, in terms of GPP, until late in the season (Figure 2a). This was in line with previous studies on the same plantation reporting a net carbon uptake until the end of September [11; 19], and on poplar trees [20]. Contrary to SRC plantations, a marked decline of net carbon uptake in early mid-September was observed in deciduous species [18]. The different growth strategy between SRC plantations and other deciduous species could be explained by a high allocation of nitrogen in leaves until late in the growing season, as reported for the same SRC  
Figure 6: Difference, in days, between phonological metrics derived from Gross Primary Productivity (GPP) and VIs (see VIs definitions in Table 1) for each year averaged for filtering methods. (**a**) SOS—start of season; (**b**) EOS—end of season. Error bars represent the standard deviation between the filtering method estimates.  
plantation in [PERSON] et al. (2014). In addition, in this SRC plantation, the production of new leaves quickly decreases around September [100].  
The year-to-year variations in water and temperature regimes (Figure 2b) affected the phenology of the plantation (Figures 2 and 3). Low temperatures in spring 2016 delayed the start of the growing season in that year. Low SWC at the end of season reduced GPP and anticipated the senescence in 2017 and 2018 (Figure 2). However, the SWC recharge after the summer in 2017 contributed to a delay at the end of the season in 2017 compared to 2018. Poplars show a high photosynthetic performance when SWC is not a limiting factor and GPP is controlled by a decrease in solar radiation and air temperature [17]. A reduction of photosynthetic activity under dry conditions might be caused by a decrease in leaf production and stomatal closure during dry periods [17].  
Coppicing in 2017 had a distinct impact on GPP seasonality and interannual variability of the plantation (Figure 2). A delay in the start of the season was observed after copping and was documented for the same plantation in the previous study of [PERSON] et al. (2016). After coppicing, poplars invest energy in the production of new buds from stumps, a process that delays the production of news leaves and the start of photosynthesis. However, this delay in the season start was counterbalanced by rapid growth and resprouting in subsequent growing seasons [101].  
We found a delay of EOS in 2017 compared to 2018 and 2016, but there was no evidence that this delay was directly linked to copping. Based on this, and on previous studies reporting that coppicing does not affect autumn senescence [21], it is possible that SWC limitations might rather have contributed to the delay of EOS observed in 2017.
# Analysis of Numerical Artifacts Using Tetrahedral Meshes in Low Frequency Numerical Dosimetry
## 2 Numerical Formulation  
In low frequency numerical dosimetry, the currents induced in the human body are too weak to modify the source field, hence human exposure can be assessed by means of the well-known scalar potential finite difference (SPFD) technique, where the magnetic field can be considered as the unperturbed source of the problem [16]. This method has been employed extensively in the literature [17; 18; 19; 20; 21; 22; 23]. The finite integration technique (FIT) using the nodal electric scalar potential as unknowns can be considered as a generalization of the SPFD to tetrahedral meshes, and it is used in this paper for both voxel and tetrahedral discretizations. Under this hypothesis, the linear system is  
\[\mathbf{G}^{T}\mathbf{M}_{\sigma}\;\mathbf{G}\boldsymbol{\varphi}=-\mathbf{j }\omega\;\mathbf{G}^{T}\mathbf{M}_{\sigma}\;\mathbf{a}_{\mathrm{S}} \tag{1}\]  
where \(\mathbf{G}\) is the edge-to-node incidence matrix, \(\mathbf{M}_{\sigma}\) is the conductance matrix, \(\boldsymbol{\varphi}\) is the vector of nodal electric scalar potentials, and \(\mathbf{a}_{\mathrm{S}}\) is the vector of line integrals of the source magnetic vector potential along the mesh edges. The right-hand side of (1) can be also evaluated knowing only the magnetic flux density (e.g., measurements) [24]. The linear system (1) is solved using the multigrid iterative solver AGMG [25; 26; 27; 28]. All simulations in this paper have been carried out setting the relative tolerance of AGMG to \(10^{-15}\). This value has been verified to be sufficient to reach the convergence of the solution.
# Contact Lenses as Drug Delivery System for Glaucoma: A Review
## 2 Drug Delivery Systems with Contact Lenses
### Soaking  
Soaking soft contact lenses in a drug solution is the simplest method to load drugs into their hydrogel matrix (Figure 1a). The main advantage of this method is that commercial contact lenses containing HEMA can be used [64]. However, it has the disadvantage that the drug release occurs very quickly because the drug loading is limited by the ionicity and water content of contact lens materials [83; 84], in addition to the concentration of drug solution and its molecular weight. As mentioned previously, the soaking method was the first to be proposed for drug delivery in 1965 [68]. Since then, many studies have used this method to incorporate ophthalmic drugs, such as antibiotics [85], antihistamines [86], nonsteroidal anti-inflammatory, corticosteroids [87], tear secretagogues [83; 84], or glaucoma medication [88]. Despite the fact that most of the studies in the literature were done in vitro, some authors evaluated both the retention time and efficacy of different drugs under in vivo conditions, showing better results with soaked-contact lenses compared to the topical instillation of eye drops [83; 84; 86; 87; 88; 89; 90].  
Figure 1: Representative image of the different methods to load drugs into soft contact lenses: soaking (**a**), functional monomers (**b**), molecular imprinting (**c**), colloidal nanoparticles (**d**), drug-polymer film embedded (**e**), and supercritical fluid (**f**). Each method is explained in sections from 2.1 to 2.6.  
As an alternative to improving the soaking method, the previous incorporation of vitamin E into the matrix of hydrogel creates a diffusion barrier that extends the drug release over time. For example, [PERSON] et al. [91] increased the retention time of timolol in a pHEMA-based contact lens from 1 h (without vitamin E) to 25 h (with 20% vitamin E).
# Modelling of a Non-Transferred Plasma Torch Used for Nano-Silica Powders Production
## 2 Numerical Model  
### Basic Assumptions  
To simulate the heat transfer and the gas flow of the plasma, the following assumptions were used:  
* The plasma is optically thin (when the optical depth approaches to zero, radiation has a small effect on the overall heat transfer process within the plasma, and the opacity can be neglected).
* Turbulence thermal plasma flow is considered (turbulence offers a strong mixing of plasma flow, and its eddies affects the transport processes of nanoparticles).
* The flow is treated as being weakly compressible when the Mach number (the ratio of the speed of the flow to the speed of sound) is smaller than 0.3 (Ma < 0.3), and the density change due to velocity is about 5% in that case.
* High collision frequencies among constituents in thermal plasma leads to a state close to local thermodynamic equilibrium (LTE), in which all plasma components (electrons, ions, and neutrons) have the same temperature.
* The gravity effects are negligible due to a high Froude number (\(\approx 3\ \times\ 10^{6}\)).
* The arc is assumed to be steady and rotationally symmetric.
* Thermodynamic and transport properties of gas plasma expressed as a function of the local temperature and pressure are obtained from the literature provided by [PERSON] et al. study [27].
## 4 Hardware Demonstration  
### Hardware Test Setup  
Figure 10 shows the setup used during the experimental message transmission. The setup shows two SWARM devices not connected to each other, and each is equipped with antennas from the SWARM Eval Kit to enhance transmission speed. An additional power source was utilized for extended experiments to ensure continuous operation. The SWARM devices are capable of sustaining prolonged transmission sessions, and provisions for additional charging can be connected if needed.  
The Eval Kit can be powered through several methods. It can operate on four AA batteries, which provide approximately 16 h of usage. Alternatively, it can be powered  
Figure 10: Experiment setup outside in private backyard, Winnipeg. The setup shows two SWARM devices that are not connected with antennas connected from the SWARM Eval Kit for an easier transmission.  
Figure 9: Lambda setup for the API in AWS.  
via a USB-C connection or through a 4.2-24 V DC input, which is particularly useful for integration with solar power systems.  
In our setup, the Swarm Tile communicates through serial data, which we route to the ESP32 Adafruit microcontroller. ESP32, with its built-in WiFi, forwards these data to a telnet protocol over WiFi, enabling us to send commands to the SWARM Tile from a telnet client.  
Telnet is a protocol that allows two-way communication with a device over a local network. For the SWARM evaluation kit, we connect ESP32 to a WiFi router. This setup lets us interact wirelessly with the SWARM Tile from any client device on the same network.
## 1 Introduction
### Minimum \(k\)-Cut  
In addition to the above biclique problems, we prove an inapproximability result for the Minimum \(k\)-Cut problem, in which a weighted graph is given and the goal is to find a set of edges with minimum total weight whose removal partitions the graph into (at least) \(k\) connected components. The Minimum \(k\)-Cut problem has long been studied. When \(k=2\), the problem can be solved in polynomial time simply by solving Minimum \(s-t\) cut for every possible pairs of \(s\) and \(t\). In fact, for any fixed \(k\), the problem was proved to be in P by [PERSON] and [PERSON] [31], who also showed that, when \(k\) is part of the input, the problem is NP-hard. To circumvent this, [PERSON] and [PERSON] [32] devised two simple polynomial time \((2-2/k)\)-approximation algorithms for the problem. In the ensuing years, different approximation algorithms [33; 34; 35; 36; 37] have been proposed for the problem, none of which are able achieve an approximation ratio of \((2-\varepsilon)\) for some \(\varepsilon>0\) in polynomial time. In fact, [PERSON] and [PERSON] themselves conjectured that \((2-\varepsilon)\)-approximation is intractible for the problem [32]. In this work, we show that their conjecture is indeed true, if the SSEH holds:  
**Theorem 2**.: _Assuming the Small Set Expansion Hypothesis, it is NP-hard to approximate Minimum \(k\)-Cut to within \((2-\varepsilon)\) factor of the optimum for every constant \(\varepsilon>0\)._  
Note that the problem was claimed to be APX-hard in [32]. However, to the best of our knowledge, the proof has never been published and no other inapproximability is known.
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
### Scientific Access to TerraSAR-X Data  
Access to TerraSAR-X data for scientific purposes needs to be gained via a proposal submission and evaluation process. Here, interested users describe the intended research, their project partners and team organization, the expected schedule, an estimate of the number of TerraSAR-X products needed and the location of the test site. A principle investigator (PI) submits the proposal and acts as project lead and single point of contact to DLR. The evaluation is usually completed within two months. The evaluation includes an assessment whether the data is used for scientific purposes and would, therefore, conform to the mandate of DLR.  
DLR issued several calls for proposals, so-called announcement of opportunities (AO). The first was the pre-launch AO, released already in 2004. Seven additional calls followed, four of which addressing specific issues such as promoting the new modes or enabling collaborations with the Canadian Space Agency (CSA) as an international partner were of limited duration. Three calls remain open for submission at any time during the mission. The archived data AO provides simplified access to archived TerraSAR-X data older than 18 months at the time of ordering. Archived experimental data, e.g., in full-polarimetric mode, can be requested through the Special Products AO. New acquisitions or more recently acquired data can be accessed through the general AO.  
Users with accepted proposals get an account with access to DLR's EOWEB GeoPortal EGP [27]. The account's functionality is configured according to the corresponding AO, enabling for example access to future acquisitions or ordering products from the archive. When new observations are needed, the PI gets full flexibility to select and order the best suited mode, polarization and acquisition date. In case of acquisition conflicts, a priority scheme is applied by DLR's mission planning in order to automatically generate the acquisition timeline [25].  
The TerraSAR-X mission is governed by national security regulations because of the high spatial resolution capability. In particular, these take effect when users request the Spotlight modes or StripMap Single Look Slant range Complex products (SSC). The provision of such products may become sensitive depending on the geographical region or the urgency of the data delivery. The latter can usually be simplified if the default latency of five days is acceptable for the user. Otherwise, DLR as the provider has to apply for the data release from the relevant state authority.  
The vast majority of the scientific orders could generally be delivered without any extra actions. Altogether, a total of 154,000 scientific orders requested TerraSAR-X products, either from the archive or new data acquisitions. Only 306 of them needed further clarification. From these, 279 requests were accepted for delivery and, in only 27 cases, the corresponding products could not be provided.