esa-sceva/satcom-chunk-collection huggingface dataset:
# Pretreatment Method for DNA Barcoding to Analyze Gut Contents of Rotifers
###### Abstract  
We designed an experiment to analyze the gut content of Rotifera based on DNA barcoding and tested it on _Asplanchna_ sp. in order to ensure that the DNA extracted from the rotifer species is from the food sources within the gut. We selected ethanol fixation (60%) to minimize the inflow effects of treated chemicals, and commercial bleach (the final concentration of 2.5%, for 210 s) to eliminate the extracellular DNA without damage to the lorica. Rotifers have different lorica structures and thicknesses. Therefore, we chose a pretreatment method based on _Asplanchna_ sp., which is known to have weak durability. When we used the determined method on a reservoir water sample, we confirmed that the DNA fragments of Chlorophyllceae, Diatomea, Cyanobacteria, and Ciliophora were removed. Given this result, Diatomea and cyanobacteria, detected from _Asplanchna_, can be considered as gut contents. However, bacteria were not removed by bleach, thus there was still insufficient information. Since the results of applying commercial bleach to rotifier species confirmed that pretreatment worked effectively for some species of rotifers food sources, in further studies, it is believed to be applicable to the gut contents analysis of more diverse rotifers species and better DNA analysis techniques by supplementing more rigorous limitations.  
Keywords: gut content of Rotifera; eliminate the extracellular DNA; commercial bleach; pretreatment
# Spatiotemporal Dynamics of Net Primary Productivity in China's Urban Lands during 1982-2015
## 2 Materials and Methods
### Contribution of Variables Based on Multiple Regression Model  
Taking the annual cumulative precipitation, the annual average air temperature, the annual cumulative sunshine duration, and the proportion of vegetation as input data, the multiple linear regression model can simulate NPP change [30]. We built a multiple linear regression model like Equation (2):  
\[\text{NPP}=c+a_{P}\times X_{P}+a_{T}\times X_{T}+a_{SD}\times X_{SD}+a_{\text{ veg}}\times X_{\text{vcg}}+\varepsilon \tag{2}\]  
where \(X_{P}\), \(X_{T}\), \(X_{SD}\), and \(X_{vcg}\) represent annual cumulative precipitation, annual average air temperature, annual cumulative sunshine duration, and the proportion of vegetation (including cropland, forest and, grassland) respectively; \(a_{P}\), \(a_{T}\), \(a_{SD}\), and \(a_{\text{veg}}\) represent the regression coefficients corresponding to these variables; \(c\) is a constant term; and \(\varepsilon\) is an error term. Based on Equation (3), the contribution of different variables to the NPP trends can be estimated as:  
\[C_{i}=\frac{d(a_{i}\times X_{i})}{dt} \tag{3}\]where \(i\) represents different variables, and \(C_{i}\) represents the derivative concerning the time of the product of \(i\) and its coefficient in Equation (2). \(C_{i}\) also represents the contribution of variable \(i\) to the NPP trends [36]. In this paper, \"positive contribution\" indicates that the change in the variable could promote the increase of NPP and contribute to the increasing trend of NPP, while \"negative contribution\" indicates that the change in the variable could inhibit the increase of NPP and contribute to the decreasing trend of NPP. For example, when the \(C_{i}\) of temperature is positive, the change in temperature could promote the increase of NPP and make a positive contribution to the NPP trends.
## 4 Results and Discussion
### Impact Resistance  
RCLMFRRHCC impact test results for concrete pavement emergency repair, according to the type of reinforcement fibers, are shown in Figure 12. Mixes with added reinforcement fibers showed increased numbers of impacts before reaching destruction. In the case of the plain mix with no fiber, the number of impacts was approximately 200 times. Mixes containing reinforcement fibers showed approximately 400 impacts, which was about a two-fold impact improvement in resistance. The mixes using reinforcement fibers showed increased flexural characteristics and energy absorption ability, based on bridging effects and pull out, fiber de-bonding in addition to fiber fracture resistance under impact [10; 11]. Furthermore, the impact resistance due to the addition of the reinforcing fibers is determined by the bonding characteristics of the reinforcing fibers [10; 11]. In this study, bonding with concrete was excellent after applying hydrophilic fibers that achieved strong bonding to concrete and fibers with crimped type fibers. Therefore, the impact resistance was increased. According to the reinforcement fiber type, the number of impacts before destruction using the macro synthetic fiber was approximately 490 times and 410-415 times for added natural jute and PVA fiber mixes, respectively. Natural jute fibers and PVA fibers showed almost the same impact resistance, while the mix using macro synthetic fiber showed better impact resistance. This is because the macro synthetic fiber is a structural fiber with a larger fiber diameter and longer length compared with natural jute and PVA fibers. As the macro synthetic fiber is intended as a structural reinforcement fiber, it shows outstanding resistance against impact, which involves energy absorption ability and flexural characteristics [22; 23].  
Figure 11: Abrasion test results.  
Figure 12: Impact test results.
## 3 Ultra-Low-Frequency Broadband Characteristics
### Influence of the Geometric Parameters on the Ultra-Low-Frequency Broadband Characteristics  
To analyze the relationship between the resonant band gap frequency and the width and the structural parameters in detail, this section investigates the effect of changing the band gap properties of the aligned static metamaterials by a single geometric parameter. Figure 8a shows the variation law of the first band gap with the geometric parameter d/a ratio. The cutoff frequency in Figure 8a shows a gradually increasing trend where the band gap width increases monotonically as \(d/a\) increases. As shown in Figure 8b, the middle part of the I-shaped structure acts as a spring providing equivalent stiffness, while the upper free end acts as a mass body providing equivalent mass. With the increase in the  
Figure 6: Radial vibration displacement field of the quasi-static REMs.  
Figure 7: Radial vibration displacement field outside the band gap of the quasi-static REMs.  
parameter \(d/a\), the equivalent mass \(M_{e}\) in the structure remains almost unchanged, and the equivalent stiffness \(K_{e}\) keeps increasing, which is the reason for the increased cut-off frequency.  
Figure 9a shows the variation in the band gap of the quasi-static REM with the geometric parameter ratio \(e/a\). The cutoff frequency of the first band gap shows a monotonically increasing trend with the increase in \(e/a\) while also affecting its band gap width, which becomes wider with the increase in \(e/a\). To explore the reason for the variation in the band gap width, the eigenmode displacement vector fields for different \(e/a\) values are calculated, and the results are presented in Figure 9b. The results show that with the increase in \(e/a\), the torsional stiffness of the I-shaped structure increases continuously. This continuous increase in the torsional stiffness results in a weakened torsional vibration on the upper and lower sides of the structure. On the other hand, the local radial vibration of the structure is strengthened gradually. The continuous enhancement of the radial vibration coupling between the upper and the lower sides of the structure as well as the middle of the structure is the main reason for the continuous increase in the cut-off frequency and the gradual increase in the band gap width.  
Figure 8: Relationship between the band gap characteristic and the parameter ratio \(d/a\). (**a**) Relationship function curve and (**b**) eigenmode displacement vector field.  
Figure 9: Relationship between the band gap characteristic and the parameter ratio \(e/a\). (**a**) Relationship function curve and (**b**) eigenmode displacement vector field.  
The results show that this is precisely due to the fixed confinement of the lower surface that the energy band transitions, which in turn opens the ultra-low-frequency band gap. Moreover, the cut-off frequency in the ultra-low-frequency band can be adjusted by the resonant frequency of the oscillator.
# Selection of Production Reliability Indicators for Project Simulation Model
## 3 Materials and Methods  
### Research Goal, Hypotheses  
The primary aim of the authors is to apply the Quality Function Deployment (QFD) method for selecting requirements and indicators for professionals in the field of production process simulation. Furthermore, the implementation of selected indicators into a simulation model is proposed:  
Goal 1: To find out what are the most important reliability indicators based on the result of a meta-analysis and Quality Function Deployment.  
Goal 2: Fit the selected reliability indicators to a project simulation model.  
Goal 3: Analyze the dataset gathered from the proposed simulation model.  
Besides the goals of the research, some research questions have arisen, which will be investigated in the results part:  
RQ1: What are the most important reliability indicators for project scheduling?  
RQ2: How can they be implemented in a decision-making process?  
As a preliminary assumption, authors believed that the mean time between failure and mean time to repair are the most important indicators to implement to a simulation model and therefore proposed hypotheses considering the possible implementation opportunities for the simulation model.  
**Hypothesis 1.**_Quality Function Deployment supports the ranking and the selection of reliability indicators._  
**Hypothesis 2.**_MTBF and MTTR reliability indicators are the most important factors of production._  
**Hypothesis 3.**_MTBF can be implemented in stochastic process simulation in a way where every \(n\)-th simulation iteration induces failure._  
**Hypothesis 4.**_Total project time can be estimated with the use of modified stochastic CPM model._
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 4 Evaluation of B2a Signal Reception Performance  
Building upon the established evaluation framework, this section quantitatively assesses B2a signal reception performance degradation under representative high-altitude flight conditions (FL400). The analysis integrates DME beacon network data (including transmit power, spatial distribution, etc.), propagation losses, and receiver characteristics to simulate the interference evaluation results at FL400.  
The RFI threat to the B2a signal primarily originates from DME beacons co-located with VOR stations. According to the latest 2022 statistics from the Civil Aviation Administration of China (CAAC) on en-route radio navigation aids in the Chinese mainland, there are currently 274 operational DME ground beacons, with 212 co-located with VORs. After excluding 98 beacons that present no direct interference risk, the evaluation of DME beacon impacts on B2a signal focuses on 176 ground-based beacons operating in the 64X-126X channels that are co-located with VOR stations.
# Hardware-Based Architecture for DNN Wireless Communication Models
## 4 Proposed DNN-Based MIMO OFDM Models
### Model Type II  
The similarity between model type I and model type II is that both models use the LS algorithm for the channel estimator and the ZF/MLD algorithm for the signal detector. However, the difference between model type I and model type II is that the DNN model is applied to help filter out noise. Noise occurs at the data transmission phase between the transmitters and receivers of the MIMO OFDM system as well as due to the clipped signal which reduces the performance of the MIMO OFDM system. With this flow, the DNN model is expected to learn about noise and then help to remove noise from the received signals.  
Figure 4 shows the structure of model type II in detail. Firstly, we feed the FFT post-phase signals to the signal detection and channel estimator blocks to obtain the preliminary recovered signals \(s(n)\). We then use the preliminary recovered signals \(s(n)\)  
Figure 3: Proposed Model Type I.  
channel information \(H\) from the channel estimation block, and the FFT post-phase signals \(y(n)\) to estimate the noise. Details are shown in the following equations  
\[\hat{w}(n)=y(n)-Hs(n) \tag{14}\]  
From here, we have the estimated noise values \(\hat{w}(n)\), but it is not the exact values we need. Hence, we continue to feed \(\hat{w}(n)\) into the DNN model. Then, the correct noise values are found at the output of the DNN model \(w(n)\). From there, signal values \(y(n)\) are recalculated and then fed back the new values \(\hat{y}(n)\) to the signal detection block to output the final recovered signals.  
\[\hat{y}(n)=y(n)-w(n) \tag{15}\]  
Similar to model type I, we use the LS algorithm for the channel estimator and ZF/MLD algorithm for the signal detector. The DNN models also have five layers, three of which are hidden layers. However, there are some differences between the 2 \(\times\) 2 and 4 \(\times\) 4 MIMO OFDM models. The numbers of neurons for the 2 \(\times\) 2 model in each layer are 256, 512, 512, 512, and 256, respectively. The numbers of neurons for the 4 \(\times\) 4 model in each layer are 512, 1024, 1024, 1024, and 512, respectively. The number of input nodes corresponds to the number of the real and imaginary parts of the estimated noise values before the DNN model. The DNN model is expected to learn the correct noise values so that the number of input nodes equals the number of output nodes. The ReLU function is used as the activation function in hidden layers except in the output layer, where no activation function is applied to predict the correct noise values. The loss function of the DNN model is the same as that of model type I. Thanks to the architecture of the DNN model, all received signals are produced at once. Therefore, only one DNN model is used for one receiver instead of using multiple DNN models like model type I.
# An Iterative Optimization Algorithm for Planning Spacecraft Pathways Through Asteroids
## 5 Other Algorithms for the ARP
### Combinatorial Efficient Global Optimization (CEGO)  
The CEGO algorithm [36] builds upon the well-established Efficient Global Optimization (EGO) method [44], adapting it specifically for combinatorial optimization. Originally designed for continuous domains, EGO utilizes a Bayesian approach to iteratively learn a Gaussian process model which represents a surrogate of the true objective function. Contrarily, CEGO targets combinatorial spaces by employing a distance-based combinatorial surrogate model, substituting the classical Euclidean distance used in continuous spaces with a discrete distance function that is more appropriate for the specific search space at hand.  
In [37], various distance functions suitable for the permutation space are explored. The available implementation of CEGO begins by generating a small set of initial solutions through a max-min distance procedure, which are then utilized to create an initial surrogate model. Then, a genetic algorithm with operators designed for permutations is used to find optimal or good-enough solutions of the surrogate function. The best solution identified during this search is subsequently evaluated using the true objective function, allowing for the acquisition of information about the objective function, thus allowing for the update of the surrogate model maintained by CEGO. This process is iterated until a specified termination criterion is fulfilled.  
Hence, after the initial warm-up phase, each iteration of CEGO involves (i) optimizing the surrogate function, (ii) performing one true objective function evaluation, and (iii) updating the surrogate model. It is important to highlight that, as demonstrated experimentally in [21], updating the surrogate model within the permutation space presents its own set of challenges, necessitating considerable computational time, particularly as the number of training permutations (and their sizes) increases.  
For further information regarding the settings of the genetic algorithm used in CEGO, we recommend consulting [37], while an implementation of CEGO can be accessed at [[https://cran.r-project.org/web/packages/CEGO](https://cran.r-project.org/web/packages/CEGO)]([https://cran.r-project.org/web/packages/CEGO](https://cran.r-project.org/web/packages/CEGO)) (accessed on 10 November 2024).
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
## 2 Overview of Generative Models  
Conventional methods can generate defect images by using computer-aided design [34] or three-dimensional modeling [35]. However, this strategy has limitations: it requires additional ray-tracing, consumes extensive computational resources, and fails to generate defect objects with irregular shapes [32]. With the development of the architecture of deep neural networks, deep generative models are being widely used in defect inspection and gradually replacing the traditional methods. Generative models for surface defect inspection can be broken up into three kinds of network architectures: VAEs, GANs, and DMs, illustrated in Figure 2. Each architecture differs in the strategy that is used to learn a latent representation of the feature. Both VAEs and DMs learn to maximize the likelihood of the input data. In contrast, GANs utilize a minimax game between their two subnetworks, which can be implemented in any form of a differentiable system that maps input data from one space to another. In this section, we only briefly describe the working principle and recent application of these generative models. Readers can refer to the previous reviews and resources for more details on generative models, such as mathematical formulations.  
Figure 2: Comparison of major generative models used in defect inspection.
## 5 Simulation Results and Discussion
### Simulation Results
#### 5.3.1 Case I: Complete Link Failure with No Backup Path  
In this case, we configure the SCN \(\mathcal{N}\) in such a way that there is no backup path. This can be configured by disabling network \(\mathcal{N}_{4}\). Furthermore, network \(\mathcal{N}_{1}\) is disabled for 50 s to 110 s. The SCN is simulated for 110 s.
# A Simulation of Contact Graph Routing for Mars-Earth Data Communication
## 1 Introduction
* Custody Transfer: A node takes responsibility for forwarding the bundle, improving delivery assurance. * Late Binding: Enables routing decisions to be made later, once more info is available. * Opportunistic Forwarding: Uses available contact plans or real-time link availability. The current standard of bundle protocol is BPv7 (RFC 9171). The maximum packet size that can be sent depends on the capacity of the communication link on the rovers, where each rover can send a maximum of 2 Mbps or 258 Kbytes/s. Taking what happened in the implementation of the bundle protocol, we look deeper to be able to solve the problem that arose by building an approach to optimize data delivery via the bundle protocol, optimizing the size of the packet, time, and path chosen for data delivery. This paper constructs a simulation for optimized route pairings for data transmission from Mars to Earth, using a contact graph containing nine nodes. In Section 2, the mathematical variables and parameters are defined for the construction of the model in general terms. In Section 3, the main algorithm, Rover-Satellite Routing, and Satellite-Antenna Routing are shown. The results of the simulation are in Section 4, and the Conclusion is in Section 5.
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 4 Preliminaries
### Network Model  
The cluster-based WSN [26] was organized, as shown in Figure 1. The following assumptions were utilized to develop the network model:  
* In terms of processing time and energy, WSN sensors are identical to one another;
* The Euclidean distance principle is deliberated to calculate the distance between the sensors;
* Once the distance is calculated, the sensors are located in the network region;
* BS assumed the residual energy and distance of the nodes to pick the CHs, using an appropriate CH selection technique. Furthermore, a routing procedure is used to determine the communication route from the CH to the BS.  
For the WSN setup, all of the above qualities and constraints were taken into account. By comparing the received signal strength, the distance between the BS and other nodes was calculated. As a result, with location services such as GPS, no additional system was required. Similarly, a node joined the cluster whose CH it was closest to.  
Figure 1: Organization of cluster-based WSN.
## 6 Simulation Setup and Results
### Uplink Use Case  
In this section, we discuss the uplink use case and examine the impact of NGSO and GSO uplink transmission on the FWA links and the GSO and NGSO satellites. We also examine the ability of the proposed DSS techniques to provide interference protection to both the FWA and the satellite links during uplink transmission. For the uplink case, the FWA link direction between FWA 9 and 10 was reversed so that the FWA link now radiates towards the sky and not towards the earth. This allows us to analyze if the FWA links can be impacted by the GSO and NGSO ESIMs uplink transmission towards space.  
In this simulation set, we allowed the system to allocate the spectrum into five available channels. An initial set of simulations was performed with three available channels, but it was noticed that DSS techniques cannot resolve interference because the two NGSO links are suffering from adjacent channel emissions from the GSO ESIM. It was observed that, although the NGSO links moved out of the channel/slots used by the GSO ESIM to avoid co-channel interference, the NGSO links still suffer a lot of interference in adjacent slots of GSO ESIM transmissions during alignment with the GSO. Such ACI can only be mitigated by moving away further from the GSO ESIM slots. For this reason, five channels are the minimum number of channels that allow NGSO links to avoid both ACI and co-channel interference from the GSO link.
### Related Works  
The construction of error-correcting codes over non-field algebraic structures has received growing attention in recent decades. BCH and Goppa codes laid the foundation for alternant code construction. [PERSON] extended this theory using Eisenstein-Jacobi integers, while [PERSON] and [PERSON] introduced alternant codes over Galois rings with efficient Hamming metric decoding. More recently, codes over Gaussian and Eisenstein integers have been explored for applications in both classical and quantum settings [1; 2; 3; 4; 5; 6; 7]. [PERSON] et al. constructed BCH codes over Gaussian and Eisenstein fields and applied them in cryptographic systems [6; 7]. Other works have linked alternant codes to Grobner bases [8] and semigroup rings [9], while [PERSON] and [PERSON] investigated the role of pullback constructions and integral domain properties [10; 11; 12]. Progress in quantum-safe cryptography has also intersected with this area. [PERSON] et al. and [PERSON] et al. developed quantum error-correcting codes derived from classical BCH variants [13; 14]. [PERSON] et al. and [PERSON] et al. enhanced decoding algorithms for alternant and GRS codes [15; 16]. Different types of efficient codes and decoding strategies have been discussed in [17; 18; 19]. These studies collectively highlight the ongoing interest in applying algebraic structures beyond fields to construct robust, scalable codes.