esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Microstructure of Soil Samples  
MICP technology generates calcium carbonate through bacteria metabolism between soil particles to fill soil pores, thereby improving soil performance. To analyze the effect of the microstructure and morphology on macroscopic mechanical properties, representative samples were collected from the surface of the sample. The specimen surface was sputter coated with gold and then observed using a field emission scanning electron microscope (Quanta 200 FEG03040702) produced by FEI Corporation in the Hillsboro, the State of Oregon, USA.  
Figure 16 presents the SEM images of the MICP treatment samples and without treatment samples. It shows magnified images from the samples at 5000\(\times\) and 10,000\(\times\) magnifications, respectively. The without-treatment samples are relatively loose, and no other particles adhere to the surface of the particles, as shown in Figure 16a,b. The dense and smooth crust layer of precipitated CaCO\({}_{3}\) exists at the surface of the sample after the MICP treatment, as shown in Figure 16c,d. Then, it still has a lot of microscopic voids. The CaCO\({}_{3}\) is formed and accumulated locally. The soil particles are also bonded together by CaCO\({}_{3}\) around the loose particle contacts. Through the interactions of CaCO\({}_{3}\) precipitates and particles, a stable soil structure is generated with high water stability. The disintegration is suppressed significantly due to surrounding CaCO\({}_{3}\) precipitations.  
To investigate the composition of particles attached to the surface of treatment samples, an EDS energy spectrum analysis was performed, as shown in Figure 17. EDS in Figure 17 shows that there are O, C, and Ca, and C occupies about 13.18%, representing a plentiful existence in samples after MICP treatment [22]. The bonding material among the particles on the surface is CaCO\({}_{3}\). CaCO\({}_{3}\) precipitation is produced by the MICP process and bonds  
Figure 16: SEM images of treatment sample and without-treatment sample.  
the soil particles together. Therefore, the bioementation effect significantly suppresses decomposition.
## 3 Experiments and Results
### Experiment 1: Sharpness Assessment  
The fingerprint images blurred to different degrees were entered into the Power Spectrum + Prewitt Filter algorithm developed in this research, the conventional Power Spectrum algorithm, and SSIM. The FIQMs were recorded as a box and whisker plot and line chart (Figure 7). As can be seen from the graphs, the FIQMs of the Power Spectrum + Prewitt Filter approach decreased as the degree of blurriness increased in severity, and this trend was observed in all the sample images. The findings of the sharpness experiments in the conventional algorithm showed similar results to the proposed algorithm, which identified the sharpness of fingerprint images correctly as well; however, the plots indicate that the results of the conventional algorithm had a smaller interval between each kernel group, which means the proposed algorithm could increase the average difference in FIQMs from 0.124 to 0.20 among the 3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7 kernels via Gaussian smoothing. The results indicated that SSIM had good performance in sharpness assessment; namely, the FIQMs of different kernel set (3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7) had clear gaps between each other, and the central tendency of the FIQMs was better than other algorithms. However, SSIM required reference images; for instance, every test sample needed its origin distortionless image for reference; hence, it is not a practical candidate for FIQ  
Figure 5: Fingerprint images with three different levels of blurring.  
Figure 6: Good and faulty fingerprint images.
## 1 Introduction
A spatial correlation analysis of the fluctuating wind speeds over complex terrain was conducted, and a spatial wind field prediction model was established to predict natural wind characteristics. [PERSON] et al. [20] investigated the wind characteristics at a bridge site in a Y-shaped canyon under different oncoming wind directions using a combination of field measurement and wind tunnel testing. The results indicated that the form of the terrain plays a significant role in the variation in wind directions, with the sheltering and channeling effects of the terrain found to contribute to wind deceleration and acceleration at the bridge site, respectively. [PERSON] et al. [21] adopted a wind tunnel test method to study the influence of oncoming wind directions on the wind characteristics at a bridge site in adeep-cut canyon, and a recommended value range for wind attack angles necessary for wind-resistant bridge design in mountain canyon terrain was provided. The decay factor of the coherence function as it changes with location was also investigated. [PERSON] et al. [22] conducted a wind tunnel test of a coastal cliff terrain in Japan with a model scale of 1:2000. The results indicate that it is inappropriate to directly apply power laws to estimate wind speed or turbulence intensity in complex terrain, and significant changes in wind direction and increases in wind force around the river channel of the canyon were also observed. In summary, although some progress has been made in the research on wind characteristics in mountain valleys, previous studies have primarily focused on research variables such as wind direction, slope, and canyon cross-section. Moreover, these studies have only investigated the wind characteristics at bridge sites in mountain valleys under a single wind speed, with the influence of different oncoming wind speeds on wind characteristics not being considered. The remainder of this paper is structured as follows. In Section 2, detailed information about Jinqi Bridge and the topographical features of its vicinity is presented. The wind tunnel and experimental equipment used, as well as the model information, are described in Section 3. A comprehensive discussion of the mean wind characteristics and fluctuating wind characteristics on the main beam and two bridge towers of Jinqi Bridge is provided in Section 4. Finally, the main conclusions of this study are summarized in Section 5.
## 4 Results and Discussion  
The performance of the 5G NR uplink channels, analyzed on the considered SCs types, is presented in the following subsections. The simulation results performed on the IM-DD A-RoF link based on FDMA and TDMA DSP aggregation techniques are evaluated with respect to spectral efficiency and error vector magnitude (EVM), which are the most commonly used parameters to evaluate the quality of radio links [16].
# Fuzzy Frankot-Chellappa Algorithm for Surface Normal Integration
## 4 Fuzzy Frankot-Chellappa Surface Normal Integration
### Fuzzy Discretization of Poisson Equation  
Let \(\tilde{z}(u,v)\) be the unknown fuzzy-valued function to be determined and \(\tilde{f}(u,v)\) be a given fuzzy-valued source function:  
\[\Delta\tilde{z}(u,v)=\tilde{f}(u,v),\quad(u,v)\in\Omega \tag{8}\]  
We discretize the domain \(\Omega=[0,1]^{2}\) using a uniform grid of size \((m+1)\times(n+1)\) with step size \(h\), following the formulation presented by [PERSON] and [PERSON] [17], the Fuzzy Poisson equation takes the form:  
\[\tilde{z}_{i,j}:=\tilde{z}(u_{i},v_{j}),\quad\text{with}\;u_{i}=ih,\;v_{j}=jh\]  
Then  
\[\frac{1}{h^{2}}\big{(}\tilde{z}_{i+1,j}\oplus\tilde{z}_{i-1,j}\oplus\tilde{z} _{i,j+1}\oplus\tilde{z}_{i,j-1}\odot_{gH}4\tilde{z}_{i,j}\big{)}=\tilde{f}_{i,j} \tag{9}\]  
The right-hand side \(\tilde{f}_{i,j}\) is computed using central fuzzy differences:  
\[\tilde{f}_{i,j}=\frac{\tilde{p}_{i+1,j}\odot_{gH}\tilde{p}_{i-1,j}}{2h}\oplus \frac{\tilde{q}_{i,j+1}\odot_{gH}\tilde{q}_{i,j-1}}{2h}\]
# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 5 Methodology
### Injecting Faults  
Chaos-Mesh [61], a robust chaos engineering platform specifically designed for Kubernetes, is used to evaluate the fault tolerance properties of the Micro-FL system. Chaos-Mesh supports a comprehensive array of fault types, including pod and network faults, while also being safe and manageable. For the experiments, Chaos-Mesh version 2.5.2 is employed.  
Kafka broker is considered to be the most essential component of the Micro-FL system, as all communications between services and clients pass through it. Message loss may result from a malfunction in this module, severely impeding the federated learning process. Thus, the fault analysis is restricted to Kafka brokers.  
Federated learning is conducted using the MNIST and CIFAR-10 workloads under two distinct conditions, specifically healthy and faulty scenarios. Under the healthy scenario, the system operates without any injected faults. In the faulty scenario, a POD_FAIL fault is injected every 20 iterations (in 100 iterations) during the federated learning training process and lasts 5 min. This fault is randomly inserted into one of the Kafka brokers. The purpose of this experiment is to assess whether the Micro-FL system can effectively manage these faults and maintain a seamless operation throughout the process.
## 7 Results and Discussion  
The proposed framework comprises two distinct maintenance policies designed to be utilized in different scenarios. Firstly, a PdM maintenance policy with imperfect RUL prognostics (PdMI) based on the k-LSTM-GFT model is used when there is sufficient sensor data for developing the prognostic model. Secondly, for scenarios where constructing an RUL prognostic model is impractical, we propose a dynamic predictive maintenance policy (PMD). For this, we consider that the time between maintenance slots \(d_{S}\) is \(m=10\) days, and that the costs of maintenance actions may follow two distinct scenarios, A and B, represented in Table 2. These distinct scenarios are used to evaluate the different maintenance policies when the costs of maintenance actions change.
## 4 Experimental Results and Analysis
### Experimental Findings and Analysis of the Enhanced Network Model
#### 4.3.3 Analysis of Ablation Experiment Results  
To further substantiate the efficacy of different optimization techniques employed in this investigation and the enhancements in the integrated network model for recognizing non-coal foreign objects on conveyor belts, we executed comparative assessments between the enhanced network model and the baseline YOLOv8n model. The results from the experiments are presented in Table 5.  
According to Table 5, both the improved ShuffleNetv2 network model and the implantation of the SimAM module positively impact the model's recognition accuracy. In particular, compared to the original YOLOv8n model, the YOLOv8n model with the ShuffleNetv2 network as the backbone achieves an accuracy of 94.8%, an increase of 1 percentage point; the recall rate improved to 88.0%, an uptick of 0.2 percentage points; the mAP value reached 93.5%, an augmentation of 1.2 percentage points; the m  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **Parameters (M)** & **Inference (ms)** & **FLOPs (G)** \\ \hline YOLOv8n & 93.8 & 87.8 & 92.3 & 3.1 & 9.9 & 8.1 \\ YOLOv8n-shuffleNetv2 & 94.8 & 88.0 & 93.5 & 1.6 & 3.7 & 4.7 \\ YOLOv8n-shuffleNetv+ & 95.3 & 90.0 & 93.8 & 1.6 & 4.5 & 4.7 \\ YOLOv8n-shuffleNetv+ & 95.8 & 90.2 & 94.1 & 1.6 & 4.0 & 4.7 \\ \hline \end{tabular}
\end{table}
Table 5: Assessment of results from ablation experiments.  
Figure 10: Three types of network identification results. (**a**) YOLOv8n; (**b**) YOLOv8-shuffle; (c) YOLOv8n-shuffleNetv+.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **Parameters (M)** & **Inference (ms)** & **FLOPs (G)** \\ \hline YOLOv8n & 93.8 & 87.8 & 92.3 & 3.1 & 9.9 & 8.1 \\ YOLOv8n-shuffleNetv2 & 94.8 & 88.0 & 93.5 & 1.6 & 3.7 & 4.7 \\ YOLOv8n-shuffleNetv2+SimAM & 95.3 & 90.0 & 93.8 & 1.6 & 4.5 & 4.7 \\ YOLOv8n-shuffleNetv+ & 95.8 & 90.2 & 94.1 & 1.6 & 4.0 & 4.7 \\ YOLOv8n-shuffleNetv+**SM** & **96.9** & **92.7** & **95.6** & **1.6** & **4.5** & **4.7** \\ \hline \end{tabular}
\end{table}
Table 4: Improved ShuffleNetv2 network model results comparison.  
1.6M, reduced by 46.9%; the computation FLOPs is 4.7G, which reduced by 42.0%; after reconstructing the ShuffleNetv2 network, the accuracy reaches 96.9%, the recall rate is 92.7%, and mAP is 95.6%.  
In terms of model performance, the improved network model does not increase the model parameter count and maintains a network computational complexity of 4.7G. This demonstrates that integrating the SimAM attention mechanism and the reconstructed ShuffleNetv2 network model has not negatively affected the YOLOv8n network. On the contrary, it is advantageous in improving the model's recognition accuracy and detection speed.
## 4 Conclusions
The following abbreviations are used in this manuscript:  
5G NR Fifth-generation new radio  
ACS Array coordinate system  
AUT Antenna under test  
CATR Compact antenna test range  
DLO Direct layout optimization  
DoF Degrees of freedom  
FF Far field  
FR Frequency range  
GIA Generalized intersection approach  
LM Levenberg-Marquardt  
MoM-LP Method of moments based on local periodicity  
NF Near field  
NFCS Near-field coordinate system  
POS Phase-only synthesis  
PWG Plane-wave generator  
QZ Quiet zone  
List of Symbols  
The following symbols are used in this manuscript:  
\((\theta,\varphi)\) Angles of incidence in spherical coordinates  
\((\theta,\varphi,\psi)\) Rotation angles that define matrix \(T\)  
\((x_{a},y_{a},z_{a})\) Array coordinate system (ACS)  
\((x_{nf},y_{nf},z_{nf})\) Near-field coordinate system (NFCS)  
\(\delta\) Updating vector that results from the solution of the LM normal equations  
\(\Delta_{\Omega}^{m}\) Magnitude ripple calculated in volume \(\Omega\)  
\(\Delta_{\Omega}^{\Omega}\) Phase ripple calculated in volume \(\Omega\)  
\(\Omega\) Volume in space where the NF is computed  
\(\xi\) Vector that represents the reflectarray layout
## 3 Results
### Genetic Analysis  
Sequencing analysis of SLC2A9 revealed six intron variants (rs2240722, rs2240721, rs2240720, rs28592748, rs13115193 and rs61256984), three synonymous variants (rs13113918, rs10939650 and rs13125646) and three common non-synonymous allelic variants (rs2276961, p.G25R; rs16890979, p.V282I and rs2280205, p.P350L). A sequencing analysis of SLC2A12 revealed one intron variant (rs11231837), four synonymous (rs3825016, rs11231825, rs1630320, and rs7932775), and two heterozygous rare non-synonymous allelic c.973C \(>\) T variants (p.R325W, rs150255373, Figure 1A,B), and a previously identified c.1300C \(>\) T variant (p.R434C, rs145200251) [14]. Segregation analysis was not fully performed because the child's family was not interested.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Table _Cont.**} & **Serum UA** & **EF-UA (\%)** & **Serum Creatinine** & **Identified Variants** \\  & **(\(\upmu\)mol/L)** & & **(\(\upmu\)mol/L)** & **in SLC2A12** \\ \hline \multirow{2}{*}{**Proband**} & 67–70 & 24.3–34.2 & 32 & c.973C \(>\) T (C/T); \\  & 205 & N/A & 62 & c.1300C \(>\) T (C/T) \\ \multirow{2}{*}{**Father of proband**} & 120–360 \({}^{\text{a}}\) & 7.3 \(\pm\) 1.3 \({}^{\text{a}}\) & \multirow{2}{*}{50–110} & \multirow{2}{*}{-} \\  & 120–420 \({}^{\text{b}}\) & 10.3 \(\pm\) 4.2 \({}^{\text{b}}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Biochemical and genetic parameters of the proband and her father. \({}^{\text{a}}\) reference range for women and children; \({}^{\text{b}}\) reference range for men.  
Figure 1: Illustration of allelic variant p.R325W in genetic, protein and functional context. **(A)** Electropherograms of partial sequences of exon 6 showing a heterozygous c.973C \(>\) T variant in the _SLC22A12_ gene. (**B**) Position of identified allelic variant p.R325W in a URAT1 membrane topology model. (**C**) Immunohistochemical analysis of _Xenopus_ oocytes injected with 50 ng of cRNA encoding the wt or p.R325W using anti-URAT1 polyclonal antibodies. The URAT1 signal is green, autofluorescence granules in the cytoplasm of oocytes are blue. Water-injected oocyte without any detectable URAT1 signal.ocyte injected with wt cRNA exhibited a strong linear signal on the plasma membrane and a finely granular intracrytoplasmic signal. The variant p.R325W was characterized by a weak discontinuous URAT1 signal on the plasma membrane, and reduced intracrytoplasmic staining compared to the wt. Scale bar represents 50 \(\mu\)m. (**D**) Uric acid accumulation in _Xenopus_ oocytes transfected with wt URAT1 and p.R325W URAT1 allelic variant after 30 min of incubation in [8-14C] 600 \(\mu\)M uric acid/ND - 96 solution. The data was tested by One-way Analysis of variance (ANOVA). In comparison to wt URAT1, significantly lower UA accumulation (* \(p<0.05\)) was detected in p.R325W URAT1 injected oocytes (n = 5; means \(\pm\) SD). H\({}_{2}\)O injected (mock) oocytes were used as a negative control.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 5 Computational Burden Analysis of the Complete Receiver
### Profiling Analysis of the Complete Receiver Chain
Another aspect to be taken into account is that, as described in Section 3.3, the original 32-bit ARM v7 assembly code has been rewritten into the equivalent 64-bits ARM v8 assembly to make it compatible with ODROID-C4, mainly focusing on the direct translation of the assembly instructions, rather than on the full exploitation of the instruction set features offered by the new architecture. A specific optimization phase could likely reduce the performance gap.
## 3 Models and Methods
### Neural Networks  
Neural networks inspired by biological brains are one of the most versatile and prominent machine learning approaches. Neural networks have been widely applied in various domains including regression, clustering, classification, prediction, pattern recognition, learning, and robotics [5]. Standard feedforward neural network consists of the input layer, one or more hidden layers, and an output layer. In principle, a neuron is a strategic signal processing unit that reads numerous signals as input and then uses the activation function (such as sigmoid) to generate a signal as an output. A sensory route for a network to detect the environment is formed by reading the normalized signal into the input layer. For instance, in the CPU utilization prediction problem, this signal refers to the host CPU demand from previous time steps. This signal is first sent forward via weighted connections through the interconnected layers of neurons. As soon as the signal is propagated through the network, the network then generates an output signal through the final output layer. The output signal correlates to the CPU demand in the future time steps. Several examples in the literature show the effective use of neural networks to solve problems like CPU utilization prediction. It is noteworthy to mention that one of the most challenging tasks in the implementation of neural networks is training the network weights. Training network weights, such that the network could generate a highly accurate output for any given input is a complicated optimization problem, and, therefore, involves the potential use of swarm and optimization algorithms. Functional Link Neural Network (FLNN) is a class of neural networks that utilize higher combinations of inputs. Many applications used FLNN including classification and prediction [26]. The neuron input signal can be expressed as shown in Equation (1).  
\[v_{j}=\sum_{i=1}^{n}w_{ij}a_{i} \tag{1}\]  
where \(i\)th layer is preceding to \(j\)th layer containing N neurons, \(v_{j}\) denotes the input at \(j\)th layer, \(a_{i}\) is the output in the \(i\)th layer, \(w_{ij}\) represents the weights assigned to each output signal when passed to every neuron in the layer. The output value of every neuron always lies in the range between [0, 1], and it is calculated by using the activation function. The activation function (such as sigmoid) is expressed as shown in Equation (2).  
\[a_{i}=\frac{1}{1+exp(-v_{i})} \tag{2}\]
## 5 Mapping Petri Nets to a FlexSim Model
### Conflict  
Transitions T0 and T1 are in conflict in this Petri net: both are enabled, but the firing of either one disables the other, see Figure 6. This situation will arise, for example, when an AGV must choose at an intersection between two different routes. The resulting conflict may be resolved in a non-deterministic or probabilistic way. This situation, in which the entity must choose between two different transitions, can be coded in FlexSim using the **Decide** activity and can also be solved in either of the two ways.  
The **Decide** activity can have one or multiple inputs as well as one or multiple outputs, see Figure 7. Each output is assigned a positive integer number (the first option is 1, the second option is 2, etc.) All inputs enter the **Decide** activity and exit to the assigned output. Therefore, the deterministic or probabilistic solution of the conflict will depend on the number that is \"assigned\" to each token, as the number assigned for each output is not changeable. For a deterministic solution, labels (equivalent to colors in a colored Petri net) can be used. These labels can be assigned before the **Decide** activity or may exist from previous processes.  
Let us imagine that, depending on the weight of the cargo, we will choose one option or another (deterministic decision). Specifically, if the cargo weighs less than 1000 u, it will exit by option 1; otherwise, it will exit by option 2. This can be configured in FlexSim by assigning the labels previously, see Figure 8.  
Figure 8: Example of a deterministic decision with FlexSim (conditional version).  
Figure 6: Conflict in Petri net (**left**) and FlexSim (**right**).  
Figure 7: Example of a deterministic decision with FlexSim (label version).  
Conflict can also be solved probabilistically using this system, but in this case, a statistical expression must be added. For instance, in Figure 9 the condition is selected by a normal distribution. There are several different statistical distributions, such as Bernoulli, Uniform, Poisson, etc.
## 5 Performance Evaluation  
The performance of the TXOP PSM-based mechanism, hereafter referred to as TXOP PSM for simplicity, is evaluated in this section. Results were obtained from the analytical model proposed in the previous section and computer-based simulations conducted in an event-driven simulation program written in the Python programming language. Evaluation of various system parameters such as the traffic load, data frame length, PHY data transmission rate, number of STAs, and amount of \(\alpha\) data frames sent in a burst transmission is considered. In addition, performance comparison between TXOP PSM and different reference mechanisms is presented.