esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Alternate CS2 SWH Data Comparison  
When comparing our new SWH product with existing CS2 SWH data and the accompanying modelled SWH there is a strong, near linear trend for all existing data (see top row of Figure 5). When looking at all data from 2014, for SWHs \(>\)4 m there is close correlation between our new SWH and the RADS SWH. On closer inspection we can see that our SWH collected from LRM mode is greater than the RADS data, while for SAR mode away from the sea ice edge there are small differences, with clear shapes in the NBIAS plots that matches the SAR/LRM collection areas (viewable in the SAMOSA plots which are for SAR mode only). For lower wave heights particularly in regions closer to the ice edge, there is high variability in the RADS data, with biasing up to 3 m higher than our data. As there is higher bias and decreased correlation near the ice edge, contamination of the RADS data with sea ice is likely. The WaveWatch 3 model data that was supplied alongside the RADS data has decreased correlation over the domain, with a particular decrease near the ice edge. Near the sea ice edge our new SWH is typically 20-30% greater than the WaveWatch 3. The bias map fro WaveWatch 3 matches the shape of the CS2 modes. For the SAMOSA data there is a trend of 0.88 to our new data. SAMOSA SWHs are typically are 0.3 m or 5% higher for SWHs greater than 6 m. This bias can be seen reflected in the open ocean and marginal seas. However, there is a close correlation between the two data sets. The ECMWF model shows a reduced correlation similar to the WaveWatch 3 model, but with a reduced spatial pattern of bias.  
Figure 4: Significant wave height and ice concentration 6 September 2014 12:11. To the left is the map of the Arctic with NSIDC ice concentration contours of 15%, 50% and 85%. We plot our retrieved SWH from SAR and LRM modes, with the transition indicated by the black line. To the the right is the detailed SWH for the track from our new data (SAR and LRM modes), RADS (pseudo-LRM and LRM modes), SAMOSA (SAR mode) and the WaveWatch 3 and ECMWF wave models. For the regions where the orbit track intersects the sea ice cover the ice concentration is plotted. Note that the plotted latitude is symmetrical about 88\({}^{\circ}\)N, the northern limit of the CS2 orbit.
## 4 Verification of High-Performance Measurements  
All presented algorithms were utilized to develop a condition-monitoring program, called the Vibration Monitoring and Analysis System (VMAS). It was designed to operate with DAQ devices from National Instruments. As demonstrated in the aforementioned tests, the data packet was generated in advance and required only access and writing. Nonetheless, the process becomes more complex in terms of measurements. A monitoring program is required to perform multiple tasks simultaneously, including:  
* Acquiring sensor reading values from the DAQ device.
* Analyzing the data in real time to detect any anomalies or trends.
* Visualizing the data to provide meaningful insights.
* Encoding the data and writing them into storage.
###### Abstract
[13] conducted research on the energy-saving retrofit of a rural building of Shandong and analyzed the retrofit plan of the envelope insulation based on the cost-effectiveness. [PERSON] et al. [14] proposed a retrofit solution for a farmhouse building in Anhui Province, and obtained a building retrofit solution from the perspectives of thermal comfort, energy saving rate and cost. The studies mentioned above show that researchers have conducted extensive research on rural building retrofits and indoor thermal comfort. The majority of them used indoor air temperature as indoor thermal comfort index to evaluate the buildings retrofitting methods, and only one mentioned PMV as an indicator. Previous studies have not considered the evaluation of building envelope retrofits in terms of indoor PMV values, and hour-by-hour calculations of the relationship between envelope and indoor PMV values in rural areas of north China. Therefore, the present study is the first to consider these characteristics. [PERSON] [15] defined thermal comfort as \"the condition of the mind in which satisfaction is expressed with the thermal environment\", which is influenced by air temperature, mean radiant temperature, air relative humidity, air velocity, metabolic rate and clothing. The Predicted Mean Vote (PMV) is one of the most widely used measures for evaluating thermal comfort conditions in buildings, but rural residents have been in an uncomfortable indoor environment for a long time, they have become more adaptable to the environment, so the adaptive thermal comfort [16] theory is more appropriate to be applied in this paper. Therefore, this paper aims to propose the evaluation of rural residential building retrofits based on thermal comfort, and obtain the relationship between them, thus providing a theoretical basis for an energy-saving building design. There are three specific objectives: (1) To explore the indoor thermal comfort of buildings without insulation and buildings with insulation at the same indoor temperature, and compare the thermal comfort difference. (2) To analyze the energy needs for space heating of insulated buildings and compare the buildings without insulation when the indoor thermal comfort value is the same. (3) To analyze the relationship the heat transfer coefficient and the indoor thermal comfort at a certain temperature.
# Microfabrication Bonding Process Optimization for a 3D Multi-Layer PDMS Suspended Microfluidics
## 2 3D Suspended Polymeric Microfluidic Resonator Fabrication (SPMF\({}^{3}\))
### Fabrication Issues
#### 2.3.3 Bonding Strength  
After fabricating several SPMF\({}^{3}\) samples and conducting a simple flow test, it was observed that fluid leaks out of the microchannel and moves between microsystem layers. This is considered as a failure due to the low bonding strengths with respect to the desired tested flow rates. Therefore, one of the main parameters or issues that has to be monitored during the multi-layer fabrication process is bonding strength. The experimental results are unreliable if the layers are not bonded properly and fluid leaks between layers. In order to avoid this issue and based on best practices in the literature [21], the PDMS curing temperature was lowered from 90 \({}^{\circ}\)C to 65 \({}^{\circ}\)C in order to produce more sticky surfaces after curing. Moreover, the air-vent microchannels will also improve bonding strength by removing trapped air, which is required during fluid experiments with high flow rates.
## 4 Routing in 3D Sensor Network without Internal Holes
### Distributed Mapping Algorithm  
Based on the theory introduced above, we now propose a practical distributed algorithm to realize volumetric harmonic mapping under the spherical boundary condition.  
Let us first consider a solid sensor network with a possibly complex and concave external boundary, but no internal holes (see Figure 6a-c for examples). A UTC mesh is established as discussed in Section 2 (as shown in Figure 6f-h). We construct a volumetric harmonic map with the heat flow method, suchthat the entire UTC mesh is homeomorphically (one-to-one) mapped to a solid tetrahedra ball in \(\mathbb{R}^{3}\) (as illustrated in Figure 6k-m). The proposed algorithm follows two steps, as outlined below sequentially.  
First, we map the boundary of the 3D volume homeomorphically (one-to-one) to a sphere by using spherical harmonic map. The boundary nodes of a 3D sensor network are identified as the nodes on boundary faces. Each boundary node is associated with a three-vector metric, _i.e._, \(u_{i}=(u_{i}^{0},u_{i}^{1},u_{i}^{2})\) for Node \(i\), representing its 3D virtual coordinates. It is initialized by random coordinates on a unit sphere or by the normalized normal of Node \(i\) in order to accelerate the convergence of the algorithm. Then,  
Figure 6: 3D network models and mapping results, where the first row shows original networks, the second row illustrates the established UTC mesh structures with only boundary UTCs, for conciseness, and the third row depicts the results after volumetric harmonic mapping (VHM). The inner boundary is highlighted in magenta. (**a**) Network Model 2; (**b**) Network Model 3; (**c**) Network Model 4; (**d**) Network Model 5; (**e**) Network Model 6; (**f**) UTC mesh of Model 2; (**g**) UTC mesh of Model 3; (**h**) UTC mesh of Model 4; (**i**) UTC mesh of Model 5; (**j**) UTC mesh of Model 6; (**k**) VHM of Model 2; (**l**) VHM of Model 3; (**m**) VHM of Model 4; (**n**) VHM of Model 5; and (**o**) VHM of Model 6.  
the algorithm goes through an iterative procedure. During the n-th iteration, Node \(i\) computes its current spherical harmonic energy:  
\[E_{i}^{n}=\sum_{j=1}^{N_{i}}k_{ij}(u_{i}^{n-1}-u_{j}^{n-1})^{2} \tag{6}\]  
where \(N_{i}\) is the degree of Node \(i\) and \(k_{ij}\) is defined earlier in Equation (5). Node \(i\) then updates its \(u_{i}\) along the negative of the gradient direction of its energy:  
\[u_{i}^{n}=u_{i}^{n-1}-\gamma\
abla E_{i}^{n} \tag{7}\]  
where \(\gamma\) is a small constant (which is set to 0.1 in our simulations). Next, \(u_{i}\) is normalized, such that it is always on the unit sphere. The above process repeats, until the difference between \(E_{i}^{n}\) and \(E_{i}^{n-1}\) is less than a small constant, \(\delta\) (e.g., \(\delta=10^{-6}\)), for all nodes in the network. The final \(u_{i}\) serves as the virtual coordinates of Node \(i\). The algorithm is distributed, where a node only needs to communicate with its one-hop neighbors in each iteration. Moreover, its convergence is guaranteed [37].
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 5 Conclusions  
To balance the computation load of the nodes in the networks, reduce the transmission delay, and improve transmission reliability, we propose a new RLNC-based video transmission optimization scheme for multi-hop D2D networks. In this scheme, some encoding operations are allocated to various destination nodes, which can not only alleviate the computation burden at the source node but also guarantee the video quality. However, the re-encoding and multicasting operation considered in our scheme requires further optimization. we will study how to reduce the delay further and improve the successful video delivery rate in future work.  
Conceptualization, L.W. and Y.L.; methodology, L.W.; software, Y.L.; validation, B.P., Q.W. and J.Y.; formal analysis, L.X.; investigation, B.P. and Qi.W.; resources, J.Y.; data curation, Y.L. and L.X.; writing--original draft preparation, Y.L.; writing--review and editing, L.W. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the [Postdoctoral Science Foundation of China] grant number [2019M651921].  
The authors declare no conflict of interest.
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 3 Proposed Method
### Object Association
#### 3.2.1 Kalman-Filter-Based Object Localization  
The Kalman filter plays an important role in the DBT system by predicting the state of an object in future frames and linking it with existing tracks. Specifically, it uses information about the object's bounding box detected in the current frame to estimate its position in the next frame. In our implementation, we employed a linear constant velocity model for computational efficiency. Although extended Kalman filters can handle nonlinearity, they may diverge if the initial state estimation is incorrect. To address this, unscented Kalman filters can be used with sampling techniques, but these are computationally expensive. Since Kalman filters are suited for continuous systems, they are particularly useful in memory-constrained environments. Furthermore, since they utilize a recursive function, they do not require a history beyond the previous state, which enables efficient memory management.  
The association between the \(i\)th object in the previous track (time \(t\)) and the \(j\)th object detected in the current frame (time \(t+1\)) is calculated. The IoU between the Kalman-filter=predicted bounding box of \(i\), \(B_{i}\) and the detected bounding box of \(j\), \(B_{j}\), to be displayed in the current frame is computed as follows:  
\[IoU(B_{i},B_{j})=\frac{area(B_{i}\cap B_{j})}{area(B_{i}\cup B_{j})}. \tag{2}\]  
For overlapping images, the IoU is calculated as shown in Figure 5. Through the above equation, the object shown in the current frame is calculated to be deeply related to the tracks in the coordinates.
## 5 Prospect of Colloidal Silica as a Grouting Material
### Advantages of CS as a Grouting Material
#### 5.2.3 Low Cost  
The price/performance ratios of CS and some other chemical solutions have been analyzed in detail. [15; 44]. Although the unit price of CS is relatively high, only a spot of CS is effective for liquefaction mitigation. Each cubic meter of treated soil consumes CS that costs 59 USD when CS concentration is controlled to be 5% [15]. However, the treatment costs are about 180, 325, and 500 USD per cubic meter of grouted soil for sodium silica, acrylamide, and epoxy respectively. Thus, CS is such an economical material that its treatment cost is less than one third of the cost of the traditional chemical solutions.  
Meanwhile, with the booming advancement of nanotechnology practices worldwide, the price of nanomaterials is expected to largely decrease [91]. For instance, the price of carbon nanotubes has significantly fallen from 150 USD per gram in 2000 to 50 USD per gram in 2010. Thus, it can be deduced that CS, as a kind of nanomaterial, will become much cheaper than its current price in the future. As noted by [PERSON] and [PERSON] (2016), although the application of CS for ground improvement andliquefaction mitigation is still in experimental study stage, it will finally be successful for commercial use, due to the advantages of CS in terms of both the environmental and cost aspects [15].
## 3 Test Results and Discussion
### Freeze-Thaw Cycles Tests  
F-T cycle tests were conducted to investigate the antifreeze and stabilizing properties of MgCl\({}_{2}\) solution on clay to analyze its impact on the stress-strain behavior of MgCl\({}_{2}\)-treated soils. Experiments were carried out at both \(-10\)\({}^{\circ}\)C and \(-20\)\({}^{\circ}\)C on 28 days cured samples to evaluate the necessity for adjusting the MgCl\({}_{2}\) solution concentrations in response to varying temperatures. These tests aimed to understand how different concentrations of MgCl\({}_{2}\) can maintain soil integrity under mild and extremely cold conditions, providing valuable insights into their adaptability under different thermal stress levels. The samples used for these tests were subjected to a curing period of 28 days.  
Moreover, to show the durability characteristics of the samples subjected to treatment, a durability index (DI) was established. This index is derived by calculating the ratio of the unconfined compressive strength (UCS) of a sample following the freeze-thaw (F-T) cycle (UCS\({}_{i}\)) to its corresponding UCS after the curing period (UCS\({}_{0}\)), as delineated in Equation (1) [57]. This metric is a critical indicator for assessing the material's stability post-treatment under F-T conditions.  
\[DI=\frac{UCS_{i}}{UCS_{0}} \tag{1}\]
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 6 Resin Adhesive Systems
### Functional monomers
#### 6.1.3 Solvents
Furthermore, due to tert-butanol's higher boiling point compared to acetone and ethanol, the dentin collagen matrix is prone to less shrinkage, the evaporation is slower, and the HL in dry or wet dentin is more resistant [17]. Although butanol-based adhesives are indicated for both \"dry\" and \"wet-bonding\" approaches, prior research indicates that BS is higher in wet dentin than in dry dentin [236]. The Dentsply company added tert-butanol (Tertiary butanol) to a two-step ER (ER II) adhesive (Prime & Bond Xp (Dentsply Sirona, York, PA, USA)) because of this solvent's similar vapor pressure to ethanol, which leads to better stability to the chemical reaction with the monomers [2; 17]. Later on, Prime & Bond Universal (Dentsply DeTrey GmbH, Konstanz, Germany) was introduced and launched at IDS 2017. This adhesive, which contains a water solvent of 5-24.5% with a new co-solvent \"isopropanol\" (10-24.5%), was used [237]. Isopropanol has a boiling point of 82.3 \({}^{\circ}\)C (close to that of ethanol (78.5 \({}^{\circ}\)C)) [173], soluble in water at 25 \({}^{\circ}\)C [238], and a vapor pressure of 45.4 mm Hg. The polarity of isopropanol (0.546) is lower than that of alcohol and ethanol (0.654) [239]. The viscosity of isopropanol (2.4 mPa\(\cdot\)s at 20 \({}^{\circ}\)C) is more than 400-fold lower than that of water (1000 Pa\(\cdot\)s at 20 \({}^{\circ}\)C) and twice as high as that of ethanol. This may lead to diverse solubility, wetting, and evaporation properties when combined with water in more complex adhesive mixtures [240]. The following Table presents a solvent overview of common systems used in dental adhesives, including the respective pros and cons [204] (Table 2). In essence, solvents are chemicals that have the capacity to disperse one or more substances. They play a crucial part in removing moisture from the dental substrate during the evaporation procedure and are in charge of enhancing its infiltration throughout the demineralized matrix [241]. Accordingly, considering all the findings discussed, the solvent composition of adhesives and the substrate's moisture content are critical factors that affect the durability of adhesive interfaces over time, as well as the BS and bonding stability of resin-based restorations [2; 7; 9; 241]. Adhesives based on water or ethanol are an excellent choice for bonding agents, yet their BS may quickly deteriorate. Adhesives based on acetone might work better in overly dry environments, but not on excessively moist substrates [17; 33; 233]. Tert-butanol-containing adhesives might be vulnerable to dry conditions when dentin hybridization is taking place [233].
## 5 Camera Placement based on HybVOR Approach
### Generating Voronoi Diagram from Buildings
#### 5.1.3 Ensuring a Maximum Coverage for Roads  
In general, roads are constructed to ensure access to the buildings within a built environment. Therefore, roads are almost located at equal distance from buildings they serve. Hence, the Voronoi edges generated from footprints of buildings will be quite close to roads that are built in the environment. Thereby, the placement of cameras on Voronoi edges will provide a better observability and coverage for roads while minimizing the number of cameras (Figure 8).  
Figure 7: The impact of the foreshortening effect. (**A**) Image without foreshortening effect and (**B**) image with foreshortening effect.
# Resource Allocation for Reconfigurable Intelligent Surface Assisted Dual Connectivity
## 5 Numerical Results
As a result, the throughput of users in coverage starts decreasing as in Figure 10. Further, when \(\alpha\geq 2\), the benchmark (Ben) and RIS throughput surpasses the DC and RISDC system's throughput, respectively. As fairness in the system (\(\alpha\)) increases, the users' rate due to DC in benchmark and RIS system saturates as there is only limited resource available. Hence, RIS and RISDC system improves the coverage and data rate of the user instead of throughput of the system when \(\alpha\geq 2\). This improvement in data rate of the proposed RISDC system leads to small gain in terms of throughput but the gain does not change considerably. However, _the proposed \(\alpha\)-fair scheduling for RIS-assisted DC outperforms other benchmark and DC system in terms of throughput for various BS, user, and blockage densities._  
Figure 7: Variation of throughput with respect to number of users when \(\rho_{bs}=4\), \(\rho_{ris}=6\), \(\alpha=1\), and \(\mu_{b}=10\). Figure 8: Variation of throughput with respect to lowest \(m^{th}\) percentile of users when \(\rho_{bs}=[2,4]\), \(\rho_{user}=60\), \(\alpha=1\), and \(\mu_{b}=10\).
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 3 Data Acquisition and Preprocessing
### Meteorological Data Acquisition and Preprocessing  
The climatic conditions in Xiwuzhumuqin Banner play a key role in the growth of grassland vegetation, especially factors such as precipitation and temperature, which significantly influence the dynamic changes in above-ground biomass. Meteorological data, including precipitation, maximum temperature, and minimum temperature, were obtained from the Xiwuzhumuqin Banner Meteorological Bureau. These data were interpolated and clipped using ArcMap software v.10.8 to generate 30 m resolution meteorological raster data consistent with the study area. This meteorological data provides reliable climate background support for the grassland biomass model.
# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 4 Method
### Data Preprocessing  
Training volumes are split into slices and processed in a 2D manner, in batches, in a random order. The motivation behind using a 2D approach is given by previous research finding the performance of 2D vs. 3D approaches highly depends on the data characteristics. Specifically, large inter-slice gaps and misalignments (which is the case for the imATFIB dataset) negatively impact 3D models, resulting in slice-by-slice approaches, which are basically oblivious to inter-slice relations, to perform better. Data normalization is done by computing the mean value and standard deviation of each particular slice and using those values to normalize. All slices are resampled bilinearly to a specific height and width before being fed to the models. It is worth mentioning that per-slice normalization seems to be the best-performing method when it comes to MRI images [21; 34], and it is the general conclusion from our experiments as well. However, there are also exceptions to this rule, where the best-performing DeepLab model on the ACDC validation set was trained using the mean and standard deviation of the training set to normalize slices.  
With little data, augmentation is especially important, but there should also be a limit when applying augmentation, as too much of it can affect performance. We found that simple augmentations, such as horizontal mirroring and rotations, generally help improve segmentation, whereas elastic deformations, Gaussian noise and blurring can occasionally further boost performance. However, tuning the parameters regarding the intensity/strength of applying these augmentation proved difficult, resulting in inconsistent results (sometimes badly hurting performance). Notably, a popular augmentation technique, random crop, considerably hurts performance. A possible reason could be that heart structures always appear contiguously on MRI images, so a patch comprising only parts of it is not realistic. To be consistent, results presented in this work only use mirroring and rotations.