esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 3 Proposed MAC Layer for Image Streaming over CRSNs
### The Proposed Algorithm for Delay-Sensitive Data Transferring  
By solving the proposed Equation (18), unknown variables \(\{X^{*},(\tau_{p}^{i})^{*}\}\) can be estimated by the transmitter node. Then it considers the length of its packet to be equal \((\tau_{p}^{i})^{*}\) and sends it. After that, it runs the SS again to check the status of the current channel. If the channel is still free, it will send the second packet with the same time. This procedure can be continued until completing \((X^{*})^{\prime}\)th packet transmissions (providing that no arrival occurred during the transmission time). At that time, the transmitter node attempts to release the current channel and switch to a new alternative channel, even if the current channel is Empty. This is called Proactive Hand Off or PHO. If the current channel has been occupied by the user before the actual \(X^{*}\) transmission, then an HO should be made in a reactive or mandatory manner (MHO). In the following, our mentioned algorithm is well presented in Figure 7.  
Figure 6: The objective function \(f\) vs. optimization parameters: X and packet size; (a) \(f\) vs. \(X\); (b) \(f\) vs. \(\tau_{p}^{i}\);  
In the proposed model, to reduce the computational overhead, the operation of updating the model parameters is defined in accordance with the following two specific events: PHO and MHO. We have named our proposed Dynamic Parameter Updating Procedure as DPUP method and its details are given in the Algorithm 1.  
```
ifProactive\(Handoff\)\((PHO)\)\(occured:\) if\(\tau_{l}^{(new)}>0\)&&\(D_{max}^{(new)}>0\)  ChannelCondition: \(i\to j\) \(i=Current\)\(Channel\) \(j=Next\)\(Channel\), and\(i\
eq j\) Maximum\(Tolerable\)\(Delay:\)\(D_{max}^{(new)}\to D_{max}^{(new)}=D_{max}^{(old)}-X(\tau_{p}^{i}+T_{S})\) \(\tau_{l}^{(new)}=\tau_{l}^{(old)}-X(\tau_{p}^{i})\)  End elseifMandatory\(Handoff\)\((MHO)\)\(occured:\) if\(\tau_{l}^{(new)}>0\)&&\(D_{max}^{(new)}>0\)  ChannelCondition: \(i\to j\) \(i=Current\)\(Channel\) \(j=Next\)\(Channel\) \(i\
eq j\) Maximum\(Tolerable\)\(Delay:\)\(D_{max}^{(new)}\to D_{max}^{(new)}=D_{max}^{(old)}-Y(\tau_{p}^{i}+T_{S})\) \(\tau_{l}^{(new)}=\tau_{l}^{(old)}-(Y-1)(\tau_{p}^{i})\)  End End
```  
**Algorithm 1**_DPUP_  
Figure 7: Our proposed algorithm for delay-sensitive image transmission in cognitive radio networks (CRNs).
## 2 Materials and Methods  
### Site Description and Experimental Design  
This study was conducted at the Sihong Forest Farm (33\({}^{\circ}\)19\({}^{\prime}\)20\({}^{\prime\prime}\) N, 118\({}^{\circ}\)18\({}^{\prime}\)30\({}^{\prime\prime}\) E, approximately 16 m a.s.l.) in Northern Jiangsu, China. The study site is in a poplar plantation dominated by a hybrid of _Populus deltoides_ Bartr \(\times\)_P. euramericana_ [Dode] Guineir. The climate is influenced by a strong subtropical monsoon with a mean annual temperature of 14.4 \({}^{\circ}\)C and a mean annual precipitation value of 972.5 mm. The poplar plantation was es tablished in March 2007 with 1-year-old seedlings planted at a density of 400 stem/ha. The total area of the experimental site was estimated to be 2.5 ha. A randomized block design was employed, which consisted of three 20 m \(\times\) 20 m replicate plots that were randomly arranged at the same topographic position and elevation. In addition, six randomly located 1 m \(\times\) 1 m subplots were selected in each plot. The average diameter at breast height and tree height were 24.4 cm and 23.9 m, respectively, in 2018. The soil is a clayloam Gleysols according to WRB [38], which contains approximately 13.73 mg/g of total organic carbon and 1.12 mg/g of total nitrogen in the top 10 cm of soil. Additional detailed information on soil properties can be found in [PERSON] et al. (2019) [29].  
Three subplots were treated with solid naphthalene balls (TR: 100 g/m\({}^{2}\)/month from January 2019) [23; 39; 40] on the surface of the ground in each plot, while the remaining three subplots received no naphthalene (CK). In May 2019, three soil samples (0-5 cm depth) were collected using soil cores (2.5 cm in diameter) following the removal of herbs and surface litter in each subplot for microbial and chemical analysis. To conduct fungal community analysis, soil samples from the same subplots were then mixed and placed in a thermotank (\(-\)30 \({}^{\circ}\)C) for transport. For soil arthropod extraction, three additional soil cores (4 cm in diameter) were collected up to a depth of 5 cm in each subplot, stored at room temperature, and transported to the laboratory within 24 h.  
Seventy-two 4 mm mesh size litterbags (15 cm \(\times\) 20 cm) were each filled with 10 g of air-dried poplar leaf litter. Of the 24 bags at each plot, 4 were randomly placed on the soil surface in either the CK or TR subplots. The litterbags were collected after 4, 9, 16, and 24 months to examine poplar leaf litter decomposition and nutrient release process (unpublished data). This study focuses on the retrieval of the first 18 litterbags.
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 1 Introduction
### Mechanisms and Objective Overview  
The proposed protocol addresses security, performance, and privacy challenges in smart home networks. In so doing, smart homeowners can be assured that the exchanged packets cannot be compromised in transit. In so doing, security and privacy features such as confidentiality, integrity, availability, non-repudiation, and authentication are upheld. Since the majority of the sensors in the smart home network are resource-limited [11], only lightweight cryptographic operations are executed in our scheme. As such, the objective of the proposed protocol is to prolong the battery life of smart home sensors byreducing energy consumption.
## 4 Experiment and Results
### RS Data Queries Access and Interpretation: HiveQL and MR  
This study integrated the output CSV files in the HDFS. This helps to handle RS data's enormous volume, variety, velocity, and value. HDFS supports arranging, storing, and cleaning the data, making it suitable for analyzing massive parallel processing.  
HDFS is based on a cluster with independent machines in which every node performs its job using its resources. This will help to attach different computers with different OS and configurations. Besides, integrating the pre-processed data in HDFS will automatically  
Figure 10: The CSV output contains the VCD of CH\({}_{4}\) in Morocco (ppm).  
Figure 9: The total daily number of plots (million) during the extraction.  
stripe and run-on commodity hardware, which does not need a very high-end server with a large memory and processing processor. Storing the pre-processed RS data does not require large clusters to be built. We kept on adding nodes. We employed HDFS to store and access the refined easily and to generate value from RS data.  
Hadoop can competently process terabytes of data in a few minutes and petabytes in hours using MR. Importing the ingested RS data in HDFS is also replicated in other nodes in the cluster, which means an alternative copy exists for use in the incident of failure. Importing some GB of data inside the used Hadoop cluster takes approximately a few minutes, and the visualization only a few seconds.  
We also stored the HDFS files inside the HBase system to aggregate and analyze billions of rows of the refined RS datasets. Furthermore, compared to traditional relational models, the data could be shared with other users as end-users quickly and with a small amount of reading and writing time. Storing the output data in HBase also helps to perform online and NRT analytical operations. The importation of massive data from HDFS to HBase involves only a few minutes. HBase does not support SQL requests in contracts and shows a large memory and high CPU performance to process massive inputs and data outputs. The system involves fewer task slots per node to allocate HBase CPU requirements in a shared cluster environment.  
This study stored the pre-processed RS data in Hive external tables, as shown in Figure 11. Hive helps simplify working with billions of rows, using the HiveQL, which is much closer to SQL than Pig and has less trial and error than Pig. Hive also analyzes the massive RS data without strong java programming skills for writing MR programs to retrieve data from the Hadoop system. Importing some GB of data inside the Hive external table takes approximately a few minutes, and the visualization only takes a few seconds [19].
# Unsupervised Driving Situation Detection in Latent Space for Autonomous Cars
## 2 Related Work
The solution demonstrates that this method is faster, easier to use, and produces a higher-quality solution than the typical method of designing a reward jointly across all environments. [PERSON] et al. [25] propose a deep neural network model based on a semantic \"divide and conquer\" approach, they decompose a scene into semantic segments, such as object instances and background stuff classes, and they predict a scale and shift-invariant depth map for each semantic segment in canonical space. Semantic segments of the same category share the same depth decoder, so the global depth prediction task is decomposed into a series of category-specific ones, which are simpler to learn and easier to generalize to new scene types. [PERSON] et al. [26] proposed a \"divide and conquer\" contour design methodology using a bioinspired design methodology for a multifunctional lever based on the morphological principle of the lever mechanism in the Salvia pratensis flower. These kinds of solutions motivate us as a research group to ask us the following questions: How can we avoid subjectivity in the labeling of the data? How can we select an adequate number of policies from the dataset? Finally, how can we avoid the manual labeling of the data? In the above-mentioned cases, the \"divide and conquer\" technique shows that there is a similitude with the idea of dividing the data into driving situations and trying to cluster similar data to accelerate the process of the learning of different policies according to driving situations. The idea of dividing the data into specialized subsets in an unsupervised manner could be a solution to avoid the subjectivity of a person labeling the data and avoids spending a lot of time making the hand-engineered labeling. It also speeds up the process of learning with the specialized subsets to train the individual policies.
# Sphero-Conical Modeling for the Estimation of Very Long Baseline Interferometry Invariant Point
## 3 Mathematical Model for IVP Estimation
### Azimuthal Rotation of the Antenna  
Assume that the local and VLBI-specific frames are aligned with each other. The azimuthal rotation of the targets is depicted in Figure 3, where the traces of the two targets are shown to generate concentric circles of different heights, the centers of which have horizontal coordinates that coincide with those of the VLBI IVP. The radii of the circles differ for each target depending on the location of the antenna.  
The radius of the horizontal circle (\(r_{k}\)) and height of the plane (\(\gamma_{k}\)), both for target \(k\), are highly correlated. Therefore, it is desirable to estimate only the radius of the circle, which is related to the horizontal components. The mathematical model for the circle on the horizontal plane traced by target \(k\) through points \((x_{i},y_{i}.z_{i})\) at height \(\gamma_{k}\) can be expressed as  
\[R_{k}(\alpha,\beta,r_{k})=(x_{i}-\alpha)^{2}+(y_{i}-\beta)^{2}-r_{k}^{2}=0, \tag{1}\]  
where \(r_{k}(k=1,\cdots,n_{k})\) is the radius of the circle centered at \((\alpha,\beta)\), which is the horizontal component of the IVP, and \(n_{k}\) represents the number of targets to be estimated. Thus, as explained earlier, all targets with azimuthal rotation are related to the horizontal component of the IVP, which provides a large degree of freedom to estimate it.
## 3 Micromechanical Analysis
### Future Developments  
Based on the literature in the field, extensively analyzed in the previous sections, the following development directions can be identified in the micromechanical analysis of the creep of fiber-reinforced composite materials:  
* A combined approach of micromechanical models with different physical conditions can be considered. For example, thermal, mechanical, and environmental effects can be integrated. Additionally, behavior in high temperatures or corrosive environments can be considered. Thermal and chemical-mechanical coupling in the development of models can be an area of interest. Also, investigating the effect of residual thermal stresses on creep is a way forward.
* The complexity of engineering systems requires the incorporation of models of time-dependent interface behavior that explicitly account for interfacial creep and debonding mechanisms under long-term loading and consider the effect of interface strength degradation and damage evolution over time.
* It is also necessary to consider, for a better description of creep phenomena, the microstructural heterogeneity, including statistical distributions of different parameters defining the RUC as fiber orientation, volume fraction, and fiber-matrix geometry, and develop stochastic micromechanical models.
* The study of systems using multiscale approaches is a constant in current research in all engineering fields. It can be beneficial to use finite element-based micromechanics with continuum-level models to enable the accurate life predictions of composite components and use hierarchical modeling to bridge scales from a nano (e.g., fiber-matrix interface) to macro (component) level.
* Coupling micromechanical creep models with damage mechanics offers a better prediction in the initiation and growth of microcracks, fiber breaks, or matrix cavitation. At the same time, the simulation of creep rupture processes helps researchers to predict the time to failure under various stress levels.
* Using the specific properties and conditions of a material can provide information that helps develop more accurate models or provide results in a shorter time. This can be used to analyze applications such as turbine blades (ceramic matrix composites), automotive heat shields (metal matrix composites), and aerospace panels (polymer matrix composites).
* As in all fields, the use of machine learning is starting to become important in the field as well. It is possible to combine machine learning algorithms with micromechanicalsimulations to accelerate creep property predictions, use surrogate models trained on high-fidelity micromechanical simulations to explore large design spaces, or apply physics-informed neural networks (PINNs) to incorporate constitutive creep laws into ML models with physical constraints.
# Performance Evaluation of Silane in Concrete Bridge Decks Using Transmission X-ray Microscopy
## 1 Introduction
Other studies have successfully used X-ray absorption spectroscopy (XAS) to probe the chemical speciation of trace elements in cementitious materials and other substrates [35; 36]. There are other techniques such as transmission X-ray microscopy (TXM) that can quantitatively probe the penetration of a tracer into a cementitious system. Therefore, this study uses a direct test method via the TXM technique to investigate the protective effectiveness of silane coatings to reduce ion penetration compared to concrete that is not treated with silane from in-service bridge decks using a quantitative approach. TXM is a novel measurement technique that has been used by other researchers for the investigation of transport properties of different systems [37; 38; 39; 40; 41; 42], but it has not been applied for sealer-coated samples to study and quantify the effectiveness of silanes in reducing salt ingress in order to improve the long-term performance of concrete structures. Other studies that have used X-ray imaging techniques tried to study the presence of silane or to study the penetration depth of the sealers without quantitatively comparing the mass transport properties of coated and uncoated concretes [5; 43; 44; 45]. This study proposed a simple way to measure the penetration depth of silane. Moreover, other studies have shown that TXM is a reliable test method that is faster and more reliable than the titration test method [3; 46]. Therefore, the experimental program implemented in this research can be considered as an alternative to the test method of National Cooperative Highway Research Program (NCHRP) Report 244 [22]. Another study recommended that direct field measurements represent the best method to evaluate sealer performance [20]. Therefore, this study, by applying a direct observation method to field samples that were in service in bridge decks, gives more insight into the effectiveness of silane coatings. The results of this study, along with the strategies offered in [11], can be a helpful guide for state DOTs to gain preliminary insight into the structural conditions of existing bridge decks and then plan an effective repair practice in a reasonable, safe, and costly effective manner.
# Role of Piezoelectric Elements in Finding the Mechanical Properties of Solid Industrial Materials
## 1 Introduction  
The role of piezoelectric elements (PEs) in ultrasonic tests and smart system applications is becoming increasingly difficult to ignore, especially with regard to art using PE in finding the mechanical properties of solid materials. The classical definition of PE is a polarizing ceramic material that can convert mechanical energy to electric energy and vice versa. The mechanical wave or vibration is generated through passing an electric wave through the two electrodes of the PE then, into the material of PE itself. The piezoelectric effect causes a crystal to produce an electrical potential when it is subjected to mechanical vibration. In contrast, the reverse piezoelectric effect causes the crystal to produce vibration when it is placed in an electric field. A crystal exhibits the piezoelectric effect if its structure has no center of symmetry. A stress (tensile or compressive) applied to such a crystal will alter the separation between the positive and negative charge sites in each elementary cell leading to a net polarization at the crystal surface. The effect is practically linear, i.e., the polarization varies directly with the applied stress, and is direction-dependent, so that compressive and tensile stresses will generate electric fields and hence voltages of opposite polarity. It is also reciprocal, so that if the crystal is exposed to an electric field, it will experience an elastic strain causing its length to increase or decrease according to the field polarity [1]. There are many advantages of PEs which are very important in mechanical tests such as (1) the high electromechanical transformation efficiency; (2) a broad range of characteristics can be achieved with different material compositions (high degree of freedom in characteristics design); (3) high stability; and (4) their suitability for mass production and economic production. The purposes of this research are: (a) to summarize published articles that involve PE applications in testing material properties; (b) to report recent progress in this field; and(c) to establish a guideline for the frequency levels or ranges of PE use associated with the dimensions of test specimens for these types of tests.
# Finite Element Analysis on Wall Fluid Shear Stress on Cells under Oscillatory Flow
## 1 Introduction  
Mechanical stimulation such as matrix strain or interstitial fluid flow within a porous bone is an important factor affecting bone adaption. Removal of mechanical stimulation reduces the production of bone matrix protein, mineral content, and bone formation. Compared with matrix strain, interstitial fluid plays a key role in the mechanotransduction of bone cells [1]. Deformations on bone mineral matrix caused by mechanical loading can drive the interstitial fluid to flow within cavities, thereby inducing fluid shear stress (FSS) on the bone surface and adherent bone osteoblasts and osteoclasts [2, 3, 4]. The FSS can reach up to 5 Pa on osteocyte membranes and processes [3, 5, 6]. The existence of a flow field with gradient FSS around microdamaged bones has been previously reported [7].  
Owing to the difficulty of observing the fluid flow within bone cavities in vivo [8, 9], some in vitro loading devices, including the widely used parallel-plate flow chamber, have been constructed to induce fluid flow on cells [10, 11, 12]. We previously designed a modified plate flow chamber, in which the top cover is not parallel to the bottom plate and can provide a globally gradient FSS field on the bottom plate [7]. With this modified flow chamber, we found that osteoclast precursors RAW264.7 have a unique ability of sensing the gradient FSS field and migrating toward the low FSS region rather than along the flow direction. We assumed that this globally gradient FSS field may lead to the polarized distribution of local wall FSS on cell surface and then activate the mechanosensitive cation channels (MSCC), such as transient receptor potential channels or Piezo channels. The induced polarization distribution of intracellular calcium concentration may regulate the orientation of cell migration by mediating adhesion molecules [7, 13, 14, 15, 16].  
Owing to the diversity of daily human activities, the fluid flow on bone cells can be a combination of various forms, e.g., unidirectional or oscillating [17]. The frequency ofoscillatory flow is usually related to the cyclic movement of the musculoskeletal system, such as walking and running. Calcium oscillation in bone cells occurs due to oscillatory fluid flow [18]. Whether oscillatory flow influences the polarity distribution of local wall FSS on the cell surface remains unclear.  
In this study, a finite element (FE) model of modified flow chamber with adherent cells was constructed to investigate the effect of cell spacing and the frequency and magnitude of oscillatory flow on the polarization distribution of local wall FSS on the cell surface.
## 4 Optimization Design Algorithm
### The Latin Hypercube Initialization Method  
The operation of the Latin hypercube initialization method is as follows:  
1. Determine the population size \(N\), which is the number of particles;
2. Divide the optimization range of each dimensional variable into \(N\) equal parts. Randomly select a point in each interval of each dimension as a sampling point;
3. The sample points selected in each dimension space are randomly sorted to form the initial position of particles.  
To illustrate the difference between the Latin hypercube initialization and the random initialization, the distributions of two-dimensional particles using different initialization methods are shown in Figure 10. It is found that the distribution of particles using Latin hypercube initialization is more uniform than those using random initialization, which is more conducive to algorithm optimization.