esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The diverse and abundant population of soil bacteria plays a major role in the functioning of the ecosystem; however, different agricultural management strategies primarily drive the bacterial community composition and functioning [1, 2]. Nutrient amendments in agricultural soils are usually used to improve plant productivity, but overuse of fertilizers, like inorganic N, can affect the soil quality by deteriorating soil fertility [3], decreasing crop yield [4], and affecting bacterial diversity [5, 6].  
With the increasing importance of soil microbes, which include bacteria, in maintaining soil quality [7], understanding soil microbial processes under different management schemes is recognized to be important for the sustainability of agricultural ecosystems. Soil fertility management based on the use of organic fertilizers can promote microbial processes and increase crop yield [8]. Increment in microbial processes can help biogeochemical cycles and nutrient cycling [9], which can assist in the enhancement of crop productivity [10]. Extensive research has indicated the beneficial effect of organic matter application in enhancing bacterial diversity and positive interaction with plants [11, 12].  
Livestock manure and crop residue are two major types of organic matter that have the potential to mitigate soil degradation caused by long-term chemical fertilization. Several studies have addressed the impacts of livestock manure and plant residues on soil-dwelling bacterial communities [13; 14]. Crop residues are the most abundant, cheapest, and most readily available organic waste to be biologically transformed, with rice straw being one of them, in many rice-growing countries. They have been used as a common organic material as they contain numerous mineral nutrients, such as nitrogen, phosphorus, potassium, and silicate [15]. The application of straw in soil has been used to improve the activity of soil bacterial community and promote soil nitrogen and carbon sequestration potential [16]. Additionally, livestock manure has shown similar characteristics as crop residues [17]. However, there have been limited studies that concentrate on the effect of compounded compost made of rice straw and livestock manure on soil bacterial diversity and community profiles.  
Thus, this study was conducted to compare and characterize the bacterial diversity and community composition of paddy soils treated with NPK combined with rice straw and livestock manure (+Compost) and that with only conventional fertilizers without compost (\(-\)Compost). We hypothesized that the additional compost amendment will result in changes in the structural and functional composition of the bacterial community.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 6 Technology for Healthcare: Challenges/Pitfalls
### Cost Barriers for 5G  
Many proposals or solutions have been proposed in order to attain the proposed goals of 5G architecture. These solutions include spectrum allocation approaches, massive MIMO, heterogeneous cell architecture, content caching, NFV, etc. Each solution is able to achieve several goals to overcome multiple challenges. However, many of these proposals will require additional changes or modifications in the telecommunication infrastructure to work [90]. The changes such as additional small cell deployments, fibre installation to replace copper and upgrades in the core network require enormous costs. In the GSA survey, it is also reported that the cost is the highest ranked issue to meet the 5G goals [50].
# NDN Content Store and Caching Policies: Performance Evaluation
## 5 Simulation and Discussion
### Simulations Results
#### 5.2.4 Retrieval Delay  
Figure 8 shows the observed delay (in seconds) for each policy per scenario. This is the full delay, which includes the delay observed with re-transmissions (when they are observed).  
With less CS, we have high delay because in this case, the interest is forwarded to more distant nodes, through more hops (as is explained in Section 5.2.2). That is, the response for a given interest is longer because the interest is sent through more nodes (hops) until it retrieves the content. When the number of CS is high, the probability of reaching the content provider with less hops is high. However, in this case, a relatively increased number of retransmissions is necessary when a content is not retrievable nearby and the upstream nodes send back a NACK. The result presented here is similar for both the topologies and both content request ratios. This metric presents a relatively constant value for scenarios with \(\alpha=0.75\), which is due to the absence of retransmission, as presented in the next section.  
Although the results from [5] are inconsistent with the results presented in Section 5.2.1, related to CHR, they are consistent with the results obtained in this section for retrieval delay.  
Figure 8: Full delay content fetch, NDN testbed with content 32 consumers and 10 producers, \(\alpha=1.1\).  
Figure 7: Network traffic, NDN testbed with 32 consumers and 10 producers, \(\alpha=1.1\).
# Validation of Acoustic Emission Waveform Entropy as a Damage Identification Feature
## 4 Experimental Results
### Influence of HDT on AE Entropy
It can be observed in Figure 14f that AE entropy shows no variance with HDT, unlike any other traditional AE feature. AE entropy is calculated from the distribution of the discrete voltage values. Since the distribution and total number of discrete voltage values remained identical between the three waveforms, they all yielded the same value of AE entropy. It is clear from this analysis that AE entropy is independent of HDT. As a result, the loss of information from the sub-waveforms occurring at different time intervals within the waveform can be avoided.
## 4 Discussion
Even if there are some clinicians who prefer to store the dental composite in the fridge, or other clinicians who prefer to heat the composite before applying into the dental cavity, most clinicians store the dental composites at room temperature. As the composite is applied, light curing is suddenly performed (heat transfer from the dental cavity walls to the composite is prevented, as there is no sufficient time for heat to be transferred). Therefore, the base line of the applied composite is room temperature (Figure 2). Instead, in Figure 3, the base temperature line is close to that of the pulp cavity. To simulate pulp temperature levels, in our experiments, we used a Thermoblock system [23] set at 37 \({}^{\circ}\)C. As shown in Figure 1a, the dental root can be assumed to be at the set temperature of 37 \({}^{\circ}\)C, but the dental crown is at a lower temperature level. Before applying the composite, an adhesive interface is realized, and the clinical procedure involves abundant rising, thus cooling the crown. For this reason, the base temperature line of the dentine layer hosting the thermocouple drops to 31 \({}^{\circ}\)C. Since this dental region is located in the tooth crown (see Figure 1a), and the composite restoration is realized immediately after the creation of the adhesive interface, the short time prevents heat transfer from the root heated at 37 \({}^{\circ}\)C to the cooled crown. For this reason, the temperature base line for the experiments of Figure 3 is located at about 31\({}^{\circ}\)C. Our set-up approach tries to simulate clinical conditions as close as possible. Therefore, the reported temperature levels and profiles should be very close to those occurring in clinical conditions. Temperature profiles recorded for the investigated composites show that the temperature rise recorded for EVF is significantly higher than that recorded for SFL (\(p<0.05\)). Again, this difference can be ascribed to the higher resin content of EVF and to the type of initiator system (Table 1). Temperature rates measured for the flowable RBCs (Table 2) are significantly higher than those measured for the packable RBCs (\(p<0.05\)). Temperature peak levels higher than 42 \({}^{\circ}\)C have been observed for the flowable RBCs, thus representing a potential thermal injury for the pulp tissue. Therefore, as flowable bulk-fill RBCs in conjunction with the incremental layering technique is concerned, a curing modality lower than 1000 mW/cm\({}^{2}\) is recommended, especially if the thickness of the occlusal dentin, spacing the restorative material from the pulp chamber, is lower than 1 mm.
## 2 Related Work  
Mobility decisions and possibilities of urban dwellers are affected by their geographic environment [11], so it follows that there will be significant differences between developed and underdeveloped urban regions. The majority of the world's population now lives in cities, with around 3.3 billion people living in urban environments. According to recent data [12], global urbanization is increasing, and developing countries experience the highest growth rates. Currently, around 70 percent live in underserved regions. Urban mobility has been widely studied through simulation [13] or more empirical studies [14]. However, except for some broad characterizations [9; 15], not much attention has been paid to underserved urban zones.  
Studies that aim to understand mobility patterns, such as the one that was conducted by [PERSON] et al. [16], where they track users of public transportation systems through the use of checks ins and outs of transportation cards, are very difficult to duplicate in underserved cities where payments in public transportation are made exclusively in cash. Some studies have focused on the large-scale use of tracking devices [17; 18] in vehicles, such as taxis, motorcycles, bicycles, and buses; these have produced invaluable insights. Again, in underserved regions, this type of study would be impractical, as public transportation is usually not widely available, or it is so basic that a tracking device would be considered an unnecessary luxury (as would also be the case of private vehicles that some residents may own). As these works evidence, our current understanding of urban mobility comes from studies conducted in highly developed countries. In other words, the urban areas in developing regions, where most people live, are ignored.  
Additionally, there are very few studies [19] that focus on explaining the impact that a person's environment has on their subjective wellbeing. However, these studies do not focus on underserved regions. While other mobile sensing platforms (like FunToolkit [20], InCense [21]) are successful in collecting data, they are inadequate for our context where individuals do not have the resources to pay for a monthly data usage plan, and the neighborhood has limited Wi-Fi access and poses several security challenges.
# -- Select and Attempt task -- ```
## 4 Enhancing Performance by Avoiding Unwarranted Task Executions
### The Proposed CA Algorithm for the Early Detection of Bad Providers  
In the proposed algorithm for the early detection of bad providers, each provider maintains a task-specific self-assessment mechanism through the \(i.bad-tasks\ map\), which is initialized with a default value of false for each task (line 1). This means that when a provider is created, it implicitly assumes it is not bad for any task it has not yet performed--there is no need to explicitly initialize entries for unknown tasks. After performing a task, the provider re-evaluates its performance (lines 25-29). If the performance is less than or equal to zero, it considers itself a bad provider for that specific task by setting \(i.bad\_tasks[task]<-true\); otherwise, it maintains or resets the value to false. This enables rapid, task-specific reassessment, allowing the provider to dynamically adapt its trustworthiness profile based on its most recent outcomes. In lines 7-14 of the proposed algorithm, this self-assessment is used to initialize the trust weight of a new connection. If the provider believes it is bad \((i.bad\_tasks[task]=true)\) and the task requires a performance level of PERFECT, GOOD, or OK, the connection is initialized with a trust weight of 0.45 (line 9). Otherwise, the algorithm falls back to the original initialization logic (lines 10-13), which either averages existing weights from similar connections or sets a default trust of 0.5, as described in Section 3.3 for the previous version algorithm1. In lines 15-19, if the connection already exists, the provider re-evaluates whether it should adjust the weight based on its current self-assessment. If the provider considers itself bad and the task requires a performance level \(\geq\) 0 (i.e., PERFECT, GOOD, or OK), it lowers the weight of the existing connection to 0.45, reinforcing a cautious stance toward engaging in the task. This modification enhances the context-awareness and reliability of the decision-making process by ensuring that each provider is consistently informed by its own recent experience when evaluating and responding to task requests. Core functionalities from Algorithm 1--such as broadcasting task requests, handling incoming messages, task selection and execution, connection weight updates, and dynamic profile adjustments--are preserved without modification in Algorithm 2.
## 2 Experimental Section  
### Materials  
Samples of Nafion-112 protonated membrane from Dupont (Flagler County, FL, USA) with the following characteristics, nominal thickness of 51 \(\upmu\)m, density 2000 kg/m\({}^{3}\), ionic conductivity 8.3 S/m, acid capacity 0.89 meq/g and initial water content 5 wt%, were immersed in 1-butyl-3-methylimidazolium tetrafluoroborate (BMIM\({}^{+}\)BF\({}_{4}\)) or phenyltrimethylammonium chloride (TMPA\({}^{+}\)Cl\({}^{+}\)) solutions for 24 h and Nafion-112/IL-doped membranes (Nafion112/BF4\({}^{+}\) and Nafion112/TMPA\({}^{+}\), respectively) were obtained by H\({}^{+}\)/IL-cation exchange mechanism [19], as it was extensively detailed in [2]. Since TMPA\({}^{+}\)Cl\({}^{-}\) is not an ionic liquid (but it is common IL cation), an aqueous solution 40% (\(w\)/\(w\)) was used. Molecular weight (\(M_{\rm w}\)) and molar volume (\(v_{\rm m}\)) of the studied IL-cations are: \(M_{\rm w}\)TMPA\({}^{+}\) = 136 g/mol, \(v_{\rm m}\)TMPA\({}^{+}\) = 189 cm\({}^{3}\)/mol, \(M_{\rm w}\)BMIM\({}^{+}\) = 139 g/mol and \(v_{\rm m}\)BMIM\({}^{+}\) = 182 cm\({}^{3}\)/mol, and their ball and stick models are indicated in Scheme 1. Membrane doping was performed at the laboratory of [PERSON], REQUIMTE/CQFB, FCT, Universidade Nova de Lisboa (Lisboa, Portugal). Membranes were removed from the solution after 24 h of contact with the RTILs and cleaned with paper tissue to remove any RTIL in the membrane surface and used for the different analyses.  
**Scheme 1.** Ball and stick models of **(a)** phenyltrimethylammonium (TMPA\({}^{+}\), (CH\({}_{3}\))\({}_{3}\)NC\({}_{6}\)H\({}_{5}\)\({}^{+}\)), and **(b)** 1-butyl-3-methylimidazolium (BMIM\({}^{+}\), C\({}_{8}\)H\({}_{15}\)N\({}_{2}\)) IL-cations.  
**(a)** (b)
## 4 Results and Discussion
### Polynomial Regression Analysis and Response Surface Analysis
Figure 9: Prediction surface plots of the leveling phase: (**a**) experimental result, initial friction conditions; (**b**) experimental result, changed friction conditions. Therefore, the most suitable polynomial function for this case is a two-dimensional third-order polynomial function in which the input variable \(v_{\text{C}}\) is considered more dynamically, as indicated in Equation (16). \[\Delta X_{n}=\beta_{0}+\beta_{1}v_{n}+\beta_{2}F_{n}+\beta_{3}v_{n}^{2}+\beta_{4 }v_{n}F_{n}+\beta_{5}v_{n}^{3}+\beta_{6}v_{n}^{2}F_{n} \tag{16}\]  
The performance evaluation of the regression model built with experimental data shows solid results during the movement phase, as shown in Table 4. Moreover, the coefficient of determination \(R^{2}\) shows a respectable value of 0.9999. The evaluation metrics SSE and RMSE values of 5.2 and 0.02126 suggest minimal deviations between the predicted results and the mean desired values. Additionally, the regression model built for predicting response error \(\Delta X_{\text{C}}\) shows high predictive accuracy in the movement phase.
## 3 Proposed Architecture for Source Separation
### Loss Function  
The learned output of the proposed architecture is the mask to be multiplied by the input spectrogram. The loss function can be expressed by the following equation:  
\[\text{Loss}=\
orm{Y-X\odot\hat{M}}_{1} \tag{4}\]  
where the input spectrogram is \(X\), the mask estimated in the network is \(\hat{M}\), the ground truth spectrogram is \(Y,\odot\) represents an element-wise multiplication of the matrix, and \(\
orm{\cdot}_{1}\) is 1-norm which represents the  
Figure 8: Dilated convolution. (**a**) Frequency-dilated convolution, (**b**) time-dilated convolution, (**c**) 2-dilated convolution.  
Figure 7: Dilated dense block.  
sum of the absolute values of each element of the matrix. The estimated mask can be represented by \(\hat{M}=f(X;\Theta)\), and \(f(X;\Theta)\) is the neural network model applied to the input \(X\) with parameters \(\Theta\). In our study, the model parameters \(\Theta_{v}\) and \(\Theta_{a}\) were individually trained for each source, such as _vocals_ and _accompaniment_, respectively.
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 2 Background
### Corrosion Detection Process
#### 2.3.4 Feature Extraction  
Feature extraction is the fourth process, and it involves extracting corrosion characteristics from the image that has been segmented [13; 14]. Corrosion features are retrieved to obtain the most significant information from an image. Examples of feature extraction techniques include the Gabor filter, histogram of oriented gradients (HoG), and Laplacian of Gaussian (LoG).
## 1 Introduction
The crumb rubber types are different; their sources are extensive and their components are various, so their modified influence on the asphalt binders will not be the same [13]. CRM was utilized in the modification of asphalt binders to improve its rutting resistance properties by increasing the stiffness and ductility of the asphalt at high service temperatures [14; 15]. It was also used to extend the fatigue life of asphalt through the thickening of asphalt film and accordingly decrease the asphalt ageing rate [16]. Moreover, the application of the crumb rubber modifier as an additive reduces the stiffness of the asphalt binder at low service temperatures, which are convenient for its low service temperature performance [17; 18]. Because of these advantages, there is considerable attention regarding the utilization of tire rubber for modified asphalt binders, which could potentially enhance flexible pavement [18]. Using TPS to modify asphalt has broad applications. In recent years, it has been shown to improve the rutting resistance to permanent deformations [19]. TPS commonly holds the thermoplastics elastomer as an essential component, which is hard to melt in a bitumen binder. However, when combining the polymer implementation with asphalt, it is possible for a plasticizer to become soluble in the virgin bitumen, wherein it modifies it to the high viscous bitumen for a higher quality [9; 19]. The polypropylene (PP) is interacted with virgin asphalt and enhance the asphalt performance characteristics, which are transported by changing the rheological properties of the modified asphalt. When the PP is introduced to the bitumen binder, the flow decreases with an increase in the modified binder concentration, which reflects the increase in the viscosity of the modified binder [3]. The objectives of this study were for the laboratory experiments to evaluate the physical properties and mechanical performance of the modified asphalt binders using CRM, TPS, and PP modifiers. Secondly, the comparison was conducted between the results to determine the most appropriate additive for enhancing the medium- and high-temperature performance and physical properties of the bitumen binders. To achieve these goals various tests such as ductility, rotational viscosity, toughness, and tenacity tests as well as a rheological test such as the dynamic shear rheometer (DSR) test were applied on the modified asphalt binders using CRM, PP, and TPS modifiers.
## 4 Results
### Simulated Data  
The goal of this subsection is to test the tracking based on data association using the Hungarian algorithm and the Kalman filtering and to evaluate the reduction in uncertainty at the output of Kalman filters based on the explicit polynomial fitting method or wavelet-based polynomial fitting method. We designed two categories of experiments to accomplish this goal. The first category of experiments is designed for the evaluation of the reduction in uncertainty at the output of Kalman filter using the explicit polynomial fitting method in the case of multiple targets with linear trajectories. The goal of the second category of experiments is the evaluation of the reduction in uncertainty using the wavelet-based polynomial fitting in the case of a single target with nonlinear trajectory.  
For the first step of the experiments, we designed three simulation scenarios, in each one, three targets were considered. For the simulation of a target, we generated two first degree polynomial trajectories \(x[t]\) and \(y[t]\), one for each geometrical coordinate, \(x\) and \(y\), defining initial points and considering that the targets' movement on each geometrical coordinate is uniform with a defined velocity [21]. Velocity profiles are also generated for each geometrical coordinate \(v_{x}[t]\) and \(v_{y}[t]\). We have registered the analytical expressions of the trajectories and velocities for each geometrical coordinate obtained for further evaluation of the quality of the reduction in uncertainty realized by the explicit polynomial fitting method. To simulate the measurements' uncertainty, an additive white Gaussian noise (AWGN) was added to each signal: \(x[t]\), \(y[t]\), \(v_{x}[t]\) and \(v_{y}[t]\), obtaining the signals \(x_{p}[t]\), \(y_{p}[t]\), \(v_{x_{p}}[t]\) and \(v_{y_{p}}[t]\), \(p=1,2,\ 3\), with a signal to noise ratio (SNR) of 0 dB. After the generation of three trajectories for each scenario, we generated the targets as well, by inserting \"detections\" in each point of each trajectory. Finally, we generated the input data for each experiment, combining the parameters of the three targets: trajectories and velocity profiles \(x_{p}[t]\),\(y_{p}[t]\), \(v_{x_{p}}[t]\) and \(v_{y_{p}}[t]\),\(p=1,2,\ 3\). The second step of the experiments realized with simulated data consists of the tracking simulation, based on the cooperation of the Hungarian algorithm with the Kalman filter algorithm. The final step of each experiment consists of the application of the explicit polynomial fitting method for the reduction in the uncertainty at the output of the tracker.
# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 1 Introduction
### Objective and Significance of This Study  
This paper aims to review state-of-the-art machine learning methods for characterizing subsurface defects using thermography, addressing the limitations posed by small datasets. By enhancing data through synthetic means, such as Finite Element Method (FEM) simulations, this study seeks to improve the robustness and accuracy of defect detection models. The findings underscore the potential of integrating thermography with machine learning to advance the field of corrosion detection and ensure safer and more efficient infrastructure maintenance. The paper is organized as follows: Section 2 provides a literature review of methods for characterizing subsurface defects by thermography. Section 3 discusses the challenges of subsurface defect characterization by thermography and state-of-the-art solutions to overcome these challenges. In Section 4, a case study is presented, demonstrating the automatic detection of subsurface defects in steel pipes using thermography and synthetic datasets.