esa-sceva/satcom-chunk-collection huggingface dataset:
# Field Treatment of Storage Sludge and Stability Analysis of Overlying Municipal Waste Landfilling
## 1 Introduction
However, because of the complex characteristics of sludge ponds, the physical properties of the storage sludge should be determined prior to choosing a sludge treatment, especially in sludge ponds with a large volume and high buried depth. The large volume can greatly increase the variability of the physical properties across the pond. Thus, it is necessary to carry out a detailed field investigation, as well as a physical characterization of the storage sludge. Additionally, the high buried depth translates into differences in the physical characteristics of the sludge due to the sludge composition, age, and confining pressure. It is common that a raw sludge pond can be subdivided into layers by the layered sedimentation [3]. Due to the variability in properties, using a single treatment method appears to be ineffective. Therefore, combined sludge treatments should be explored for the disposal of sludge ponds with large volumes and high buried depths. The ultimate problem that needs to be addressed in order to expand the capacity of landfills is the stability of the slope piled above the sludge pond. [PERSON] et al. [20] analyzed the failure mechanism of an unstable waste slope, which contained a sludge pond, in a Chinese landfill. They pointed out that many factors, including the height of the waste body above the sludge pond, the strength of the sludge, and the leachate level in the slope body, affect the stability of the slope. Some researchers studied the stability of a slope formed by heaped dewatered or solidified sludge [21; 22]. Nevertheless, there are few studies that have investigated the slope stability of landfills constructed above a solidified sludge pond. The particular characteristics of the sludge and its variability within the pond require the use of the shear strength field data, representative of the different zones and depths of the sludge, to analyze the stability of the waste slopes above the pond. To sum up, with a comprehensive consideration of the physical characteristics of the raw storage sludge and the effectiveness of the field treatment, this paper proposes an in situ stratified treatment. Additionally, physical property tests were undertaken on the solidified sludge to evaluate the water content and strength, and to analyze the stability of the overlying waste slope. The aforementioned sludge management provides a reference significance for securely expanding the capacity of the landfill.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Methodology
#### 2.4.4 Random Forest Regression (RFR)  
RFR is based on the decision tree algorithm [76]. The main idea behind RFR is an ensemble of trees with each tree representing a randomly selected subset of variables and associated samples from the dataset. Generally, compared to decision trees, the performance of RFR is better since it creates multiple trees [77]. The results obtained from each tree are based on a majority vote of all associated trees. It is an ensemble of several regression trees, which is why it is sometimes called ensemble learning. The out-of-bag (OOB) performance estimation makes it well-suited for cross-validation and assessing performance. Another advantage of RF over other models is it performs better when data dimensions are high.
# Mesh Stiffness and Dynamic Modeling and Analysis of Modified Straight Bevel Gears
## 3 Finite Element Verification of TVMS
### Finite Element Calculation Method for TVMS  
This section uses a pair of straight level gears with different module sizes to test the accuracy of the calculation method. The three-dimensional finite element model and the modeling process are shown in Figure 7, where the drive gear is the pinion [36]. \(\lambda_{li}=\left(\lambda_{xli},\lambda_{yli},\lambda_{zli}\right)\) represents directional rotation radius [37], transmission error is denoted by \(e_{o}\), and (\(o=LT,NLT\)) represent the working load and minimal load, respectively.  
\[\left\{\begin{array}{l}\lambda_{xli}=-\eta_{yli}r_{zli}+n_{zli}r_{yli}\\ \lambda_{yli}=n_{xli}r_{zli}-n_{zli}r_{xli}\\ \lambda_{zli}=-n_{xli}r_{xli}+n_{yli}r_{xli}\end{array}\right. \tag{30}\]  
To improve computational efficiency, a finer mesh is adopted for the selected mesh pair, while a coarser mesh is used for other mesh pairs. In the coupling relationship settings, the nodes on the inner circle of the gear correspond to the center position of the gear and are placed in the global coordinate system as shown in Figure 2.  
The gear models with geometric parameters and modification parameters of the gear model are shown in Table 2. Using analytical and finite element methods, the time-varying meshing stiffness is calculated, respectively, followed by a comparative analysis to verify the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Pinion 1** & **Gear 1** & **Pinion 2** & **Gear 2** \\ \hline Teeth number: \(Z\) & 37 & 74 & 37 & 74 \\ Module: \(m\) (mm) & 0.5 & 0.5 & 2 & 2 \\ Pressure angle: \(\alpha\) (\({}^{\circ}\)) & 20 & 20 & 20 & 20 \\ Face width: \(B\) (mm) & 5 & 5 & 20 & 20 \\ Young’s modulus: \(E\) (Gpa) & 206 & 206 & 206 & 206 \\ Poisson’s ratio: \(v\) & 0.3 & 0.3 & 0.3 & 0.3 \\ Addendum coefficient: ha & 1 & 1 & 1 & 1 \\ Tip clearance coefficient: \(c\) & 0.2 & 0.2 & 0.2 & 0.2 \\ Torque load: \(T\) (Nm) & 0.5 & 0.5 & 10 & 10 \\ TPM (\(\mu\)m) & 3 & 3 & 5 & 5 \\ LCR (\(\mu\)m) & 3 & 3 & 5 & 5 \\ TM (\(\mu\)m) & 3 & 3 & 5 & 5 \\ LCR (\(\mu\)m) & 3 & 3 & 5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of straight level gears.  
Figure 7: Finite element model of meshing gears.
## 7 Transcription Regulation: How RNA Polymerase Communicates with Transcription Factors
The crystal structure of the \(\lambda\)cl-\(\circ\)4-DNA ternary complex showed that these proteins also use a small interface for recruiting \(\circ\)4 to the \(-35\) element (PDB:1 RIO) [66]. PhoB is a classical two-component response regulator, which interacts with \(\circ\)4 to activate gene expression. In contrast with \(\lambda\)cl-dependent transcription activation, PhoB-dependent promoters lack a canonical \(-35\) element. The structure of the PhoB-\(\circ\)4-DNA ternary complex showed that contact between \(\circ\)4 and the DNA major groove is less extensive; however, interaction between \(\circ\)4 and PhoB compensates for this, allowing recruitment of RNAP to a target promoter lacking the \(-35\) element (PDB: 3T72) [67]. Spx is a global transcription regulator in _Bacillus subtilis_ that interacts with a large interface on \(\alpha\)CTD, thereby forming a stable Spx-\(\alpha\)CTD complex without a DNA platform. Spx regulates gene expression under conditions of disulfide stress, which is sensed by disulfide bond formation between two cysteine residues in Spx. Crystal structures of both oxidized and reduced forms of the Spx-\(\alpha\)CTD complex provided insight into how the disulfide bond affects DNA binding (PDB: 1Z3E, 3 GFK, 3 IHQ) [62, 63, 64]. Bacteriophages produce transcription factors that bind directly with host RNAPs and hijack host transcription machinery to transcribe phage genomes. Several structural models of bacterial RNAP in complex with phage proteins have been used to explain how phage protein remodels RNAP to redirect transcription machinery to the phage genome (Table 2 and Figure 2a) [17, 36, 70, 71, 72, 73].
# Laser Synthesized Graphene and Its Applications
## 3 Applications of Laser Synthesized Graphene
### Pressure and Temperature Sensing  
Pressure sensing has been demonstrated by [PERSON] et al. for LRG based on the piezoresistive response from the obtained rGO pattern, as a voltage output can be measured from the material as pressure is applied, once appropriate circuit elements are connected [73]. Indeed, applying pressure on the system induces more extended contact between graphene layers, thus decreasing resistivity and increasing output voltage. The sensitivity reported here is 19 mV/kPa [73]. This appears to be a very promising outcome, which, if associated with the strain sensing ability that is also demonstrated, could pave the way towards wearable technology based on LRG.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author and Ref.** & **Analyte** & **Functionalization Type** & **Limit of Detection** \\ \hline Cheng [66] & Bisphenol-A & Aptamer & Atto-/Femtomolar \\ Fenzl [68] & Thrombin & Aptamer & Picomolar \\ Marques [72] & AA, AMOX & Polymers & Nanomolar \\ Nayak [67] & AA, DA, UA & Pt nanoparticles & Micromolar \\ Santos [69] & DA & - & Sub-micromolar \\ Soares [71] & Salmonella & Antibodies & 13 colony-forming units \\ Vanegas [70] & Biogenic amines & Cu nanocubes & Micromolar \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the key properties of biosensing systems based on laser-scribed graphene (LSG).  
Figure 12: An example of aptamer-based biosensing on a laser-scribed graphene (LSG) or LIG electrode. Reproduced with permission from Ref. [68]. Copyright 2017, American Chemical Society.  
The same group proposed a temperature sensor based on the same material, deposited by drop-casting between two sets of electrodes [74]. The authors observed a drop in resistance upon increasing temperature, and fitted the obtained data using the equation R(T) = T(T0)(1 + \(\alpha\)AT), where \(\alpha\) is the temperature coefficient. However, while the temperature coefficient reported (\(-\)0.0024) was in line with other values reported for graphene, this was investigated over a limited temperature range (20-40 \({}^{\circ}\)C) [74]; thus, further investigations for this type of sensing activity are required on a more extended temperature range.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 6 Efficient Power and Data Transfer in BackCom
### Scheduling and Resource Allocation  
This subsection considers energy scheduling and data scheduling for the efficient management of power with information transfer. Moreover, the allocation of resources and time is considered, in addition to cognitive radio and optimization techniques, to bring about the best use of resources.
## 1 Introduction
Unlike previous research methods, this study first estimated the ECM model parameters offline, such as internal resistance, polarization resistance, polarization capacity, and time constant, based on voltage measurements obtained using the hybrid pulse power characterization (HPPC) test [35; 36; 37] in the time domain. In this test, pulse charge/discharge current is applied, and only the output voltage is measured, allowing for a simpler and more cost-effective implementation of the ECM model compared to the EIS method. Second, charging time and temperature were selected as the objective functions for optimizing the performance indices of the MSCC charging method. Consequently, the charging temperature and the optimization objective function were directly related, simplifying the calculation process since the charging temperature was measured directly during optimization. Third, among the recently studied metaheuristic algorithms, including PSO, GWO, and JSA, we selected other algorithms such as the war strategy optimization (WSO) algorithm [38], beluga whale optimization (BWO) algorithm [39], levy flight distribution algorithm (LFDA) [40], and African gorilla troops optimizer (AGTO) algorithm [41], which have been relatively frequently cited. We compared these with the DO algorithm applied in this study and evaluated their charging performance indices. By utilizing the DO, the OCP search could be conducted rapidly through application to the ECM-based platform without requiring numerous lengthy experimental processes. The remainder of this paper is organized as follows: Section 2 provides a detailed description of the basic concepts of the proposed charging technique, including the construction of the battery ECM, estimation of ECM parameters using the HPPC in the time domain, derivation of the mathematical relations for the proposed MSCC charging method, formulation of the optimization problem, and definition of the objective function. Section 3 describes the DO algorithm. Comparative simulations and experimental results against several existing methods are presented in Section 4, demonstrating that the proposed method is more valid and effective than the current alternatives. Finally, Section 5 concludes this study.
# Variability of Water Use Efficiency Associated with Climate Change in the Extreme West of Bahia
## 4 Discussion  
Land use changes in the westernmost region of Bahia are mainly related to agricultural expansion, and alter the rate of photosynthesis, water availability, and soil nutrient content [68]. Ref. [69] mention that the change in the mean annual WUE was mainly attributed to trends in human activities that may cause changes in land cover. The western portion of Bahia has been consolidated as the state's largest area of agricultural expansion since 1980. Over thirty years (1985-2015), about 21.7 thousand km2 of the native vegetation of this area was converted into grain plantations, pastures, and other perennial crops [70]. This agricultural expansion implies adequate management of agricultural areas, resulting in a higher WUE. Ref. [71] found an improved crop WUE under drought when irrigation water was >60% or <40% of full irrigation water. Other possible contributing factors are good management practices, cultivar optimization, and irrigation, as they reduce the influence of the climate [72] and regulate the carbon-water balance more efficiently in agricultural areas.  
The effects of WUE anomalies are related to environmental changes induced by the variability of environmental factors such as temperature, precipitation, and vegetation [73; 74; 75]. Thus, the phases of the ENSO mode of variability regulate precipitation availability (dry and rainy years), promoting periods of drought or excessive precipitation, ultimately modifying soil moisture, and significantly impacting the local carbon and water cycles [6; 76]. In the NEB, ENSO events are associated with reductions in precipitation, high local temperatures (El Nino), and high volumes of precipitation (La Nina) [21; 26; 77; 76]. Ref. [78] highlighted that excessive precipitation could cause negative impacts on the crop by causing nutrient leaching losses from the soil. Further, the presence of cloud cover reduces the amount of solar radiation incident on the ground and, consequently, the biophysical activity of the vegetation, prompting low WUE values. These anomalous WUE patterns result from major changes in local vegetation to meet the water needs of the respective biophysical processes. Ref. [79] assessed the interaction between vegetation growth and water vapor from ENSO variations over seven geographic regions of Mainland China. The authors  
Figure 6: Temporal variation of WUE in vegetation types between the years 2001 and 2019.  
found positive (negative) vegetation variations during El Nino (La Nina) periods and high correlation coefficients between vegetation and precipitation in some regions during ENSO variations, implying a change in the WUE of the vegetation. Ref. [79] assessed the correlation between vegetation and ENSO via principal component analysis (PCA) and found a strong relationship in some regions of Africa [80]. Ref. [81] pointed out that ENSO events affected vegetation in a long-term trend, highlighting weather and climate variations, changes in vegetation patterns, and the local WUE.
# Networking of Multi-Robot Systems: Architectures and Requirements
## 2 Types of MRS Systems and Their Applications
### Types of MRS Systems
#### 2.1.2 Intentionally Cooperative Systems  
This type is characterized by robots that have a knowledge of the presence, state, actions, and capabilities of other robots in the team. They work together to accomplish the same objective.  
This type of robots is further divided into two categories: (1) Weakly cooperative and (2) Strongly cooperative. This classification is based on the extent to which the actions of each robot is affected by the state and behavior of other robots in the team.  
* _Strongly cooperative:_ In this class of MRS systems, the robots act cooperatively to achieve a common goal. Consequently, this kind of close coordination require appropriate communication and synchronization which typically has more stringent quality of service (QoS) requirements such as bandwidth, and delay.
* _Weakly cooperative:_ In this class of MRS systems, the robots divide and coordinate the tasks that need to be done among themselves. Afterwards, each robot proceeds to achieve its task with a form of operational independence. In this case, the supporting communication protocols and corresponding QoS requirements are more relaxed.  
Furthermore, intentionally cooperative MRS systems can have heterogeneous robot members, which vary in sensing and acting capabilities. Consequently, they are different than the collective swarm systems, due to the fact that the robots belonging to a certain team are no longer interchangeable.
# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## 2 Related Work  
### Knowledge Grounded Dialogue  
Prior research on Knowledge Grounded Dialogue shows that extractive models often generate more suitable responses than their generative counterparts, as indicated by [8; 10]. Despite this, it has been noted that generative models can produce more captivating responses that resemble natural human dialogue [21; 32]. The primary focus of most KGD generative models is to learn external knowledge representations, often through neural memory networks [7; 8] or intricate attention mechanisms [23; 33; 34; 35]. [PERSON] et al. [9] took a unique approach, encoding the dialogue history and documents separately to imbue responses with facts from the external world. Other researchers, including [PERSON] et al. [16], [PERSON] et al. [36], [PERSON] et al. [37], [PERSON] et al. [38], [PERSON] et al. [39], have integrated knowledge graph representation into the response generation process. A few works concentrate on seamlessly integrating external knowledge with dialogue context. For instance, [PERSON] et al. [40] introduce a two-pass decoding strategy for document grounded conversations. [PERSON] et al. [41] defined knowledge identification as finding relevant knowledge in an extensive document that aligns with a user's current query within the conversation context. In their work [15], introduce a KGD model for document-grounded dialogue generation. The model leverages both structured and unstructured knowledge sources to enhance its performance. It incorporates a comprehensive common-sense knowledge network enriched with named entities, along with a domain-specific factual knowledge base. These knowledge sources are utilized to improve the understanding of utterances and generate more informed and contextually appropriate responses. Overall, the proposed model effectively combines multi-source heterogeneous knowledge to enhance document-grounded dialogue generation. [PERSON] et al. [32] jointly employed a CVAE model to represent context and knowledge within a unified latent variable.  
Contrasting with previous research, our work enhances knowledge fusion methodologies in two distinct ways: (1) By employing interpolating latent variables, we facilitate a fusion of knowledge and context that is both interpretable and controllable. (2) We innovatively manage the fusion ratio of instance-variety knowledge by using deterministic and probabilistic interpolation weight schemes, allowing for dynamic control over the process.
## 7 Conclusions  
Our experimental results proved that, by using inverse Kleinrock's power metric as a cost function in an SNN with reinforcement learning, it is possible to achieve concurrent optimization of both delay and packet loss percentage in a satellite network with uncertain conditions. The CSG approach helps to make forwarding decisions autonomously adaptive to the changing environment to deliver data with a better QoS.  
Our future work will consider exploring offline learning predictions whenever online feedback paths are longer, as in deep space communications such as between Earth and Mars.  
**Author Contributions:** Conceptualization, R.L. and G.V.; methodology, G.V.; software, R.L. and G.V.; validation, R.L. and G.V.; formal analysis, R.L. and G.V.; investigation, R.L. and G.V.; resources, R.L. and G.V.; data curation, G.V.; writing--original draft preparation, G.V.; writing--review and editing, R.L. and G.V.; visualization, R.L. and G.V.; supervision, R.L.; project administration, R.L. and G.V.; funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Grant #80 NSSC22K0259 from the National Aeronautics and Space Administration (NASA).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 3 Results
### Robustness Analysis of Signal Classification
#### 3.3.1 Noise Analysis  
The resilience of the classification model against varying levels of environmental noise was a primary focus. Through extensive noise analysis, the model demonstrated very good performance, maintaining high accuracy even in high-noise scenarios (see Table 1). The different SNR levels were simulated with additive white Gaussian noise incorporated into the transmitted signal. This attribute is crucial for underwater acoustic communications, where noise levels can significantly impact the signal integrity. The model classification accuracy across a broad spectrum of SNR levels was assessed, ensuring its applicability in real-world underwater environments.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Design and Performance Simulation of the Adaptive RC-LDPC Coding Scheme
#### 4.3.2 Simulation Results and Analysis