esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Based on the Kriging Surrogate Model-NSGA-II Algorithm  
The NSGA-II algorithm [20] is a very popular choice for multiobjective optimization problems, which we used to identify the VX gasket's optimum structure. In order to improve the gasket structure's optimization efficiency, we predicted the sealing performance indices using the Kriging surrogate model, as the structure parameters were different. In addition, a random load model based on the Gaussian distribution function was constructed to simulate the randomness of the working load fluctuation. These methods were combined to construct an optimization algorithm based on the Kriging surrogate model [21] and NSGA-II. The concrete flowchart of the algorithm is illustrated in Figure 3.  
Figure 2: The basic structure of the VX gasket.  
_Step 1: Establishing the Random Working Load Model Based on the Gaussian Distribution Function_  
In order to improve the optimal design solution, we regarded the working load \(P\) as a random variable, obeying the Gaussian distribution function [14], where the mean value is \(\mu\) and the coefficient of variation is \(cv\)[22]. The corresponding upper and lower bounds were \(\mu-3\times cv\) and \(\mu+3\times cv\) based on the 3\(\sigma\)-rule [23]. The random load is shown in Equation (4).  
\[P\text{-}N(\mu,(\mu\times cv)^{2}),p\in[\mu-3\times cv,\mu+3\times cv], \tag{4}\]  
where \(P\) is the random variable: working load; and \(p\) is concrete value of the working load.  
_Step 2: Constructing a Random Finite Element (R-FE) Model of the VX Gasket_  
Compared to the simple traditional FE model, the main feature of the R-FE model is that the randomness of the working load is considered during simulation. To reduce computation costs, a two-dimensional axial symmetric model of the gasket was established to simulate the sealing performance, considering the symmetries of the geometric structure and the working load. In order to simulate the plastic deformation behavior of the VX gasket on the sealing surface, the elastic-plastic constitutive equation was applied during the modeling of the R-FE model. During the preload phase, the displacement in the bottom of the wellhead was fixed, and the axial preload \(W\) on the Christmas tree body was calculated using Equation (5) [24; 25]. In the working load phase, the random load calculated using Equation (4) was applied to the inner walls of the wellhead, the VX gasket, and the Christmas tree's body. Then, the R-FE model of the VX gasket was established, illustrated in Figure 4, using the Abaqus software.  
\[W=\frac{\pi D_{m}b_{m}q_{0}}{\cos\rho}\sin(\alpha+\rho) \tag{5}\]  
where, \(b_{m}\) (mm) represents the effective contact width, which can be simulated using the R-FE model, \(q_{0}\)(MPa) is the contact press in the pre-tightening phase, the minimum value is the sealing pressure [26], \(\rho(^{\circ})\) is the frictional angle between the gasket and the sealing groove, and the value is \(8.5^{\circ}\)[27].  
Figure 3: The flowchart of the Kriging surrogate model-NSGA-IIalgorithm. R-FE, random finite element.
## 6 Discussion  
### Memory Application Analysis of the LTSCD-YOLO Algorithm  
To further explore the application environment of the LTSCD-YOLO algorithm, the paper investigated the memory sizes of different types of satellites. Small satellites typically have memory spaces ranging from a few hundred MB to several tens of GB, such as CubeSats (Cube Satellites) [53], which are commonly used for low-cost scientific missions, educational projects, and technology validation, with each satellite having memory sizes of up to 16 GB. Medium satellites have memory spaces ranging from several hundred GB to several TB, such as Landsat 8 [54], which is used for Earth resource monitoring, environmental management, and disaster response, capturing multispectral images for geographic information analysis. Large satellites have memory spaces ranging from several TB to tens of TB, such as GOES-16 (Geostationary Operational Environmental Satellite) [55], used for monitoring and predicting weather and environmental changes. Currently, space-grade NAND flash [56] technology supports storage capacities ranging from 32 GB to 256 GB, with read and write speeds of about 100 megabytes per second. Additionally, flash memory chips have been specially designed for radiation resistance to operate long-term in harsh space environments. The next generation of earth observation satellites will have onboard storage capacities of several TB to handle the collected remote sensing data. The optimal weight file size of LTSCD-YOLO is 5.1 MB, and it requires about 2 GB of memory to operate, making it suitable for use on small satellites like CubeSats for typical satellite component detection tasks.
# A Survey on the Recent Advances of Deep Community Detection
## 2 Preliminaries and Definitions Used in This Survey
However, strategies that belong to these sub-categories often fail to detect very small communities, even in cases they are well-defined. This happens because the initial local structures do not capture these small communities from scratch, and the expansion method used fails to incorporate node-members of a community. Finally, the data structure-based approach is based on the idea of forming a network to some type of data structure (usually in a tree form), which is then analyzed in a way to detect communities (examples are [9; 10]. Generally, the idea of converting a network to a data structure is an interesting one; however, this conversion should be implemented carefully, as it may be very expensive in terms of computation costs, especially when processing networks of millions nodes or edges. Careful parallelism can partially solve this issue. Some definitions required are given below:  
1. **Overlapping Communities:** Overlapping communities are communities that have nodes in common. 2. **Normalized-Cut:** A measure that computes the cut cost as a fraction of the total edge connections to all the nodes in the graph. 3. **Seed nodes:** Key nodes of high influence over the network. 4. **Graph embedding:** A process that learns a mapping from a network to a vector space, while preserving relevant network properties. 5. **Node embedding:** The process of mapping each node into a low-dimensional space, to get insight of its similarity and network structure. 6. **Random walks:** Given network \(G(V,E)\), the random walk is a process used to obtain random sequences of vertices rooted on each vertex \(v\in V\). The walks are randomly sampled starting from the neighbors of the last visited vertex. 7. **Normalized Mutual Information (NMI)**: a measure used to evaluate network partitioning performed by community detection schemes. 8. **Modularity:** A measure of the network structure for the strength of division of a network into clusters or communities.
# Toward Long-Term Implantable Glucose Biosensors for Clinical Use
## 2 Materials and Methods
### Accuracy  
The U.S. Food and Drug Administration (FDA) identified that CGMS were aids in the detection of episodes of hyperglycemia and hypoglycemia, not replacing standard home glucose monitoring devices. In the hearing of the FDA in 2016, the panel gave positive votes to the Dexcom G5(r) integrated continuous glucose monitoring system for use of treatment decision [19]. Such paradigm shift to clinical use is based on the accuracy of CGM sensors. The accuracy of subcutaneous CGM sensors depends on calibration and degree of biocompatibility. Since subcutaneous sensors do not measure blood glucose, but instead measure glucose in subcutaneous interstitial fluid (ISF), signals of subcutaneous sensors have to be calculated to blood glucose concentrations. Recent \"zero finger-picking\" CGM sensors are realized due to a new sensor manufacturing including factory calibration. The factory calibration removes the need of user calibration and associated painful, finger-picking blood glucose measurement. This is possible because of the reproducibility and stability in sensor production. Such factory calibration sensors can increase accuracy with lab-based calibration [20]. The fully-implantable sensor, Eversense(r) (Senseonics), still needs calibration twice per day to maintain 9% mean absolute relative difference (MARD) [21]. To apply subcutaneous CGM sensors into a body, subcutaneous sensors require skin incision, which makes immune responses inevitable. Thereby, subcutaneous sensors require high biocompatibility to guarantee sensor functionality and safety in vivo. Further details of calibration and biocompatibility will be discussed in the following sections. In this section, we describe the CGMS evaluation method and performance requirement of iCGM (integrated CGM).  
Figure 1: Comparison of finger-picking self-monitoring of blood glucose (SMBC) and continuous glucose monitoring (CGM).
## 1 Introduction
### Diagnostics Based on Microwave Radiometry
The review [18] shows the effectiveness of local temperature measurement methods for a wide range of rheumatic diseases. Measurements by the method of multifrequency three-dimensional (3D) radiothermography are promising and are aimed at determining the depth and temperature of a cancerous tumor [47]. The microwave range used makes it possible to determine the temperature at a depth of 2-6 cm, depending on the biological tissue properties and frequency. New technical possibilities for multifrequency measuring the brightness temperature require appropriate mathematical models for a more accurate determination of both the localization and the thermodynamic temperature of the studied areas. Intracavity measurements of brightness temperature using new generations of antennas through natural cavities make it possible to determine the dynamics of three-dimensional \(T_{B}\) distributions [48]. We also point out the possibility of continuous monitoring of the internal temperature of the brain in newborn infants using a multi-frequency microwave radiometer during cooling of the brain after hypoxia-ischaemia [44]. Measurements of temperature difference along the carotid artery underlie the method of presymptomatic diagnosis of myocardial dysfunction, coronary heart disease, and peripheral arterial disease (see pointer 3 in Figure 3) [49], and also provide control of the treatment process [50]. The development of a new instrument base significantly expands the areas of medical application of MWR, including the ability to monitor the pelvic organs [51]. Intracavitary sensors for monitoring intravascular temperature are complete tools for continuous monitoring of important physiological processes [52]. Such approaches make it possible to quickly detect objective changes in the physiological state, including pregnancy planning [53]. Microwave sensors are already capable of detecting inflammation of the ovaries and cervix [54]. Microwave thermometry is not limited to clinical practice and is used in pharmacology and physiological research to create thermal maps of muscles and internal organs in sports and health monitoring [55].
## 1 Introduction  
Metal-Organic Frameworks (MOFs) are a versatile and broad class of crystalline materials with high porosity and a massive internal surface area [1; 2; 3]. These properties and the extraordinary diversity of their organic and inorganic building units make MOFs highly relevant in applications such as catalysis, sensors, batteries, biomedicine, and food safety [4; 5; 6; 7; 8]. Due to their synthetic versatility, long-range order, rich host-guest chemistry, uniform pore size, and high surface area, MOFs are particularly well-suited for the adsorption of a wide range of anthropogenic hazardous pollutants including NOx, SOx, CO, H\({}_{2}\)S, NH\({}_{3}\), HCN, volatile organic compounds (VOCs), and polycyclic aromatic compounds (PAHs). Most of these pollutants are classified as toxic/carcinogenic and pose a global risk to the environment and human health. However, in order to successfully be used in these applications, MOFs must be both stable, featuring exceptional properties, and processed in a form that enables their integration into devices [9]. The use of processing methods that afford a certain degree of cohesion and allow shaping, and the spatial orientation of the crystals as needed, while interfacingwith other components of the device [10]. Indeed, the use of suitable processing methodologies determines the usefulness of a material, allowing it to perform at its best [11]. Notwithstanding, MOF integration into devices is still a challenging task because most synthesis takes place inside sealed glass tubes under solvothermal conditions, yielding very insoluble powders formed by randomly aggregated crystallites making it impossible to apply in most of the currently available technologies. In this context, much of the present research focuses on developing MOF processing methods [12]. Although some MOF materials have already been processed into textiles [13], membranes [14; 15], non-woven fabrics [16], and nanofibers [17], most of the processing methods used are complex and difficult to transpose to industry. Here, we propose a simple and efficient method for processing MOFs into pellets, and to incorporate them into membrane supports. We target the application of these materials in filters in air purifiers. HKUST and MMOF-74 (where M = Mg\({}^{2+}\), Zn\({}^{2+}\), Co\({}^{2+}\), or Ni\({}^{2+}\)) were synthesized and processed in the form of pellets and membranes. These MOFs were selected due to their well-known capability to adsorb harmful indoor gases, namely CO\({}_{2}\), CO, and SO\({}_{2}\)[18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. MOF materials were prepared and characterized by X-ray diffraction and scanning electron microscopy. Because of the strong competition of water molecules with these harmful gases, water adsorption isotherms of materials in the powder, and pellet forms were measured. Pellets were prepared by a simple and cost-efficient extrusion method using only water as the \"binding agent\". The stability of these pellets was evaluated in terms of temperature and humidity.
# Improved Subsidence Assessment for More Reliable Excavation Activity in Tehran
## 3 Subsidence Calculation Due to the Excavation Procedure in the Project  
At first, the excavation pattern of the project was modeled in PLAXIS in order to determine the subsidence; then, this subsidence was compared with the subsidence in the instrument report. According to the excavation method employed in the project, first, the top part was excavated; then, after 14 m, the bench part followed [25]. Figure 5 shows only half of the tunnel.  
We developed the model based on the symmetry of the geometry. After the geometry creation, the mesh was generated, and then, the parameters were input to develop a realistic output model in the third dimension just like in Figure 4.  
After the calculation process, the output was represented as figures. Figure 6 shows a longitudinal profile, in which the vertical axis shows the settlement, and the horizontal axis shows the length of the tunnel. Figure 7 shows the cross-section of the most subsided  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & E & \(\delta_{\text{unsast}}\) & \(\mathbf{v}\) & \(\mathbf{EA}\) & \(\mathbf{EI}\) & \(\mathbf{d}\) (\(\mathbf{m}\)) & \(\mathbf{W}\) (\(\mathbf{kN}\)) \\  & (\(\mathbf{kN}\)/\(\mathbf{m^{2}}\)) & (\(\mathbf{kN}\)/\(\mathbf{m^{3}}\)) & (\(\mathbf{kN}\)/\(\mathbf{m}\)) & (\(\mathbf{kN}\)/\(\mathbf{m^{2}}\)) & & \\ \hline Shotcrete & \(-\) & \(-\) & \(0.2\) & \(6.103\times 10^{6}\) & \(2.215\times 10^{4}\) & \(0.209\) & \(7.2\) \\ Final concrete & \(2.387\times 10^{7}\) & \(25\) & \(0.2\) & \(-\) & \(-\) & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of temporary and final supports.  
Figure 5: Excavation procedure accomplished in the project (numbers show the sequence of excavation).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Definition** \\ \hline E & Elasticity module \\ \(\delta_{\text{unsat}}\) & Unsaturated unit weight \\ \(\delta_{\text{sat}}\) & Saturated unit weight \\ \(\Psi\) & Angle of dilation \\ \(\Phi\) & Internal friction angle \\ V & Poisson’s ratio \\ C & Cohesion \\ K\({}_{0}\) & Coefficient of lateral earth pressure \\ EA & Normal stiffness (axial rigidity) \\ EI & Flexural rigidity \\ d & Equivalent thickness \\ W & Unit weight \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definition of the parameters.  
area, in which the vertical axis stands for the surface settlement, and the horizontal axis indicates the distance from the zero axis, which means the tunnel axis.  
The maximum settlement of the surface was 32.9 mm according to the monitoring and instrumentation report (Monitoring and instrumenting of tunnel behavior, CVR consultant engineers, design consultant of Tehran third south subway line [24; 25]). On the other hand, the subsidence obtained from PLAXIS was 30.01 mm regarding Figures 6 and 7. So, not only is PLAXIS 3D Tunnel reliable for this region, but these numbers are also an approval for the validation and accuracy of modeling with this software.
# Flexible Antennas: A Review
## 8 Performance of Different Types of Flexible Antennas
For wearable antennas, physical deformations increase the SAR value. Miniaturization of antennas can prevent physical distortions or crack due to bending. A performance comparison of the different flexible antennas for the last 3 years (2018-2020) has been shown in Table 3.  
Figure 21: Antenna bending simulation. (**a**) Bending setup. (**b**) S-bending setup. (**c**) Return loss characteristics [235].
## 3 Results and Discussion
### Density, Porosity, and Water Absorption  
Results from the ASTM-C642 test are shown in Table 4 for both concretes. While the density is almost unaffected by the presence of CCADM, a slight decrease of about 3% is observed for water absorption and porosity when the admixture is used. These results show that the admixture has a certain capacity of producing a somewhat more closed porous structure of concrete, thus reducing the permeability to fluids and external aggressive substances.
## 5 Performance Evaluation
### Performance Evaluation for the Number of Copies per Packet  
We calculated how many copies per packet simultaneously exist in the network during the entire experiment. Figure 13 presents the average result. The GPSR and GPSR-Q successfully keep only one packet at a time in the network due to their failure in successfully routing the packets, as discussed in previous subsections. Both LECAR and LER perform well by keeping the number of copies per packet at one in maximum cases and two in the worst cases due to the proper implementation of the ACK mechanism. The lack of such an ACK mechanism is the primary reason that LAROD-LoDiS results in a considerably larger number of copies per packet. Moreover, the highest result obtained by Spray and Wait in terms of the number of copies per packet is expected because it depends on the most increased data dissemination to route the packets successfully.  
Figure 12: Performance comparison of the routing protocols regarding the hop count per packet when the buffer size is (a) 25 MB and (b) 50 MB.
# Neural Radiance Fields with Hash-Low-Rank Decomposition
## 3 Preliminary
### Multi-Level Hash Encoding  
Hash functions have a wide range of applications in computer science [14; 18], with one of the most prominent inventions being the hash table. Using a specific hash function, this lookup method can achieve a performance as fast as \(O(1)\). Given that the grid storage and lookup process in neural radiance fields is essentially similar to a hash table, using hash functions and hash tables to compress the feature grid in neural radiance fields is entirely feasible.  
Since we have already performed low-rank matrix decomposition on a feature grid, we only need to focus on hash mapping and compression in the 2D matrices. Instead of directly decomposing the feature grid into multiple smaller grids by resolution, we partition the grid at multiple resolutions, incrementally increasing the resolution. This naturally leads to higher memory consumption, so we do not directly store features on the grid itself. Instead, we record only the coordinates of the grid vertices and store the corresponding features in a hash table. By mapping the vertex coordinates to an index in the hash table, features can be efficiently queried. When inputting a coordinate \(x\) to query features, the algorithm retrieves the hashed features for that point at all resolution levels and performs bilinear interpolation based on the position of the coordinate. Finally, the features across all resolutions are concatenated to obtain the compressed feature at that point in the scene.  
During this process, redundant features in the edge or empty regions may collide in the hash table with features from high-information-density regions. Given that areas with high information density tend to have higher-feature gradients, we consider the feature gradient as a key factor in competing for space in the hash table. This approach allows regions with high information density to secure more features, thereby fitting finer details with more accuracy.
# Cow Dung Ash in Mortar: An Experimental Study
## 3 Results and Discussion
### Effects of CDA on Hardened Mortar Properties
#### 3.2.5 Sulfate Attack Resistance  
All of the mortar specimens' ability to withstand sulfate attack was determined by analyzing the variations in their compressive strength following exposure to a sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)) solution. Figure 6 illustrates the results of the test depicting the variation in compressive strength of mortar containing CDA, subjected to sulfate attack, in comparison to the control mortar sample, at various curing periods. It is evident that the mortar specimens containing up to 10% CDA are able to withstand sulfate attack without much impact on their compressive strength, as the reduction in strength is negligible. Furthermore, it was observed that the compressive strength starts to decrease beyond 15% CDA content, which means that at CDA20 to CDA30, the sodium sulfate significantly affected the strength of the mortar specimens. This phenomenon can be attributed to the high quantity of CaO and the formation of a gel, particularly when the CDA replacement ratio exceeds 10%, as demonstrated by the microstructural tests discussed in Section 3. Consequently, the density of mortar samples decreases, allowing for the diffusion of sulphate ions into the mortar pores. This accelerates the chemical reaction between the hydration products of cement, such as Na\({}_{2}\)SO\({}_{4}\) and sulphate ions, with Ca(OH)\({}_{2}\) and monosulfate, resulting in the formation of gypsum and ettringite (crystal needles) within the mortar pores [36]. These factors collectively have a negative impact on the mortar's ability to resist sulfate.  
Figure 5: Ultrasonic pulse velocity (UPV) of mortar.  
Figure 6: Compressive strength of the mortar specimens after exposure to sulfate attack.
## 4 4-Chain Based Routing
### Protocol Operations
#### 4.2.4 Data Transmission  
When all the pre-requisites subject to chain(s) organization are performed, all of the nodes transmit their data towards their parent nodes in their allocated Time Division Multiple Access (TDMA) based schedules. To avoid the synchronization problem, we assume that all the TDMA based schedules are assigned by sink. The parent nodes aggregate the data and forward it towards the sink. Later on, data forwarding load is divided between the chain heads of four chains. Problems of distant forwarder node and _long link_ are solved by the interconnection of chains. When all the data transmissions are conducted, next phase begins with the network configuration that plays a vital in deciding the next chain leader node, parent nodes, child nodes, _etc._ Figure 4 shows the data transmission for a 4-chain based routing scheme.  
Figure 4: 4-chain based routing.