esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Barriers Limiting Biochar Amendment in GRs
### Policy Barriers  
In general GR research, the adoption of GRs has also been hindered by the lack of attention from policymakers to the benefits of GRs, especially in developing countries [2].  
As a result, the use of biochar in GRs is further constrained, and this is partly responsible for the introduction of this practice only recently. A large amount of sludge and agricultural waste, which are common feedstocks for biochar, has been generated globally. [PERSON], [PERSON] and [PERSON] [44] reported that 6.25 million tons of sludge was produced in China in 2013. Additionally, the United States generates approximately 13.8 million tons of dry sludge annually from wastewater treatment [143]. Thus, converting these wastes into biochar offers significant potential for sustainable urban development. Specifically, the synergy of biochar and green roofs (GRs) can effectively address urban challenges such as air and water pollution, urban flooding, heat island effects, and biodiversity loss [144].  
The use of biochar in GRs and other soil structures necessitates collaboration among various organizations and supportive local policies. Figure 8 demonstrates the geographical coverage of studies on biochar-amended GRs that were reviewed in this research. Most research was undertaken in China, which is followed by Canada, Australia, and Italy. This is consistent with findings in the study by [PERSON], et al. [145]. Local research, using region-specific materials and weather data, is likely to advance the broader adoption of biochar in green roofs (GRs) within those countries. The high number of studies on biochar and GRs in some countries could be attributed to well-developed biochar markets and government support, including funding and policies for biochar research and production. [PERSON] [77] noted that biochar has not yet become widely commercialized due to its recent introduction. Consequently, there are no established guidelines or standards for its production, which could lead to improper manufacturing and potentially negative impacts on soil microbes and plant growth. [PERSON], et al. [146] identified three key policy factors essential for the widespread adoption of biochar, namely funding for research and development, financial incentives, and non-financial supportive policy. While financial incentives contribute to reducing biochar costs, non-financial policies enhance social awareness and investment rates and increase market demand for biochar. [PERSON], et al. [147] also highlighted the importance of policy in the application of biochar. Policy supports biochar productions systems, thereby enhancing system efficiency and lowering costs of biochar collection, transportation, and pretreatment. Without these laws and regulations, research findings on the benefits of biochar may not be effectively translated into practical applications.  
Figure 8: The number of studies on biochar-amended green roofs by country.
# Digital Twin Lean Intralogistics: Research Implications
## 4 Results
### Results of the Quantitative Literature Research  
According to the adopted research methodology (Figure 4), quantitative literature studies were performed after the trend analysis. The research was conducted in January 2021. The results of the quantitative literature research related to terms such as \"Digital twin\", \"digital-twin\", \"digital twin\", \"digital-twin\", \"digital twins\", \"digital-twins\" (H1), \"Intralogistics\", \"intralogistics\" (H2), \"Lean intralogistics\", and \"lean intralogistics\" (H3), and H1 and H3 combined are presented in Table 3. The data in Table 3 refer to the number of publications for which, according to Table 2, Z2 was adopted for the search range.  
Despite the fact that in both WoS and Scopus databases publications whose titles concerned digital twin (H1) or intralogistics (H2) were identified, in the case of \"lean intralogistics\" or \"lean intralogistic\", no work meeting the indicated selection criteria was found.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Keyword** & \multicolumn{2}{c}{**H1**} & \multicolumn{2}{c}{**H2**} & \multicolumn{2}{c}{**H3**} & \multicolumn{2}{c}{**H4**} \\ \hline
**Database** & **WoS 1** & **S 2** & **WoS 1** & **S 2** & **WoS 1** & **S 2** & **WoS 1** & **S 2** \\ \hline till 2014 & 5 & 17 & 8 & 48 & 0 & 0 & 0 & 0 \\
2015 & 1 & 1 & 1 & 3 & 0 & 0 & 0 & 0 \\
2016 & 5 & 8 & 3 & 9 & 0 & 0 & 0 & 0 \\
2017 & 32 & 48 & 5 & 9 & 0 & 0 & 0 & 0 \\
2018 & 129 & 155 & 8 & 15 & 0 & 0 & 0 & 0 \\
2019 & 268 & 417 & 17 & 24 & 0 & 0 & 0 & 0 \\
2020 & 264 & 701 & 4 & 16 & 0 & 0 & 0 & 0 \\
2021 & 7 & 40 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
**TOTAL** & **711** & **1387** & **46** & **124** & **0** & **0** & **0** & **0** \\ \hline first publication & 1973 & 1973 & 2005 & 2004 & & & & \\ \hline max. number of papers per year & 268 & 701 & 17 & 24 & & & \\ \hline \% share of papers & & & & & & & \\ (2019–2020) & 74.82\% & 80.61\% & 45.65\% & 32.26\% & & \\ \hline \hline \end{tabular} 1
[FOOTNOTE:1]Footnote 1: Web of Science, 2
[FOOTNOTE:2]Footnote 1: Web of Science, 2  
\end{table}
Table 3: Summary of the literature review (12.01.2021).  
Figure 9: Global interest in the terms: digital twin + lean + intralogistics + digital twin lean intralogistics (concerns the period 1 Jan 2010–12 Jan 2021).  
The term Digital Twin Lean Intralogistics was not identified in either of the databases, regardless of the scope used (Z1 or Z2), which again proves the existence of a research gap in this area.
## 2 Materials and Methods
### Training/Testing Data Formulation  
The jack-knife 10-fold cross-validation method was utilized for training/testing data validation. This is one of the most widely used methods used for validation when there is a small dataset, to avoid the overfitting. During this process, our data are simultaneously utilized for training and testing. The data are initially divided into 10 folds, where, in training, 9 folds participate, and the remaining folds are used for predictions based on training data performance. The entire process is repeated 10 times to predict each class sample. The unseen sample-predicted labels are finally utilized to determine the classification accuracy. To avoid overfitting, k-fold cross-validation is used where the dataset is small, as one of the standard approaches used by the researchers [14]. In our case, there were a total of 945 images from both classes. For a larger dataset, the holdout method is usually preferred. For model tuning, the dataset is split into multiple train-test bins. In the standard iterative process, the k-1 folds are involved in the training of the model, and the rest of folds are used for model testing. The general k-fold cross-validation method is reflected in Figure 2 below:
# Virtual Signal Processing-Based Integrated Multi-User Detection
## 1 Introduction
By detecting signals transmitted through these ambiguous channels, the uniqueness of the remaining signals' equivalent structure is ensured, allowing for IMUD to effectively decode multiple users' data simultaneously. The rest of this paper is organized as follows. Section 2 describes the system model, while Section 3 details the design of VSP-IMUD. Then, Section 4 evaluates the performance of the proposed scheme. Finally, Section 5 concludes the paper. Throughout this paper, we will use the following notations. Let \(\mathbb{C}\) denote the set of complex numbers. Vectors and matrices are represented by bold letters. \(\mathbf{X}^{T}\), \(\mathbf{X}^{H}\) and \(\mathbf{X}^{-1}\) denote the transpose, Hermitian, and inverse of matrix \(\mathbf{X}\), respectively. \(\|\cdot\|\) represents the Euclidean norm of vectors and matrices, and \(\langle\mathbf{a},\mathbf{b}\rangle\) denotes the inner product of two vectors.
# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 1 Introduction
As illustrated in Figure 1, the input drilling samples are partitioned into three distinct subnetworks based on geological age, geological origin, and geotechnical type. These divisions are achieved through the extraction of relevant features from the drilling data. The processed sample data are subsequently fed into a fully connected deep neural network with N hidden layers and a K-dimensional configuration for each layer. Here, f represents the activation function, while b and w denote the bias and weight parameters connecting individual neurons, respectively. The three subnetworks operate independently and are later amalgamated to establish a geomathematical model. This composite model allows for the prediction of drilling data in uncharted regions through mathematical modeling. A substantial volume of drilling data forms the foundation for establishing a mathematical framework that connects geological entities. Once this mathematical structure is in place, geological entities can be represented in 3D using various formats, such as point clouds, meshes, and 3D bodies. Models generated using this approach can be swiftly adapted to updated data, eliminating the need for reconstruction when data changes or new drilling data are introduced, as is often necessary in traditional modeling methods. Within the framework of deep learning, the loss function serves as a metric for measuring the disparity between the model's output and real-world scenarios. In this paper, we introduce the stratigraphical order rule from the field of geology to design a loss function that enforces constraints on the chronological order of geological epochs. This allows the deep neural network to discern the sequence of different strata during the training process. Simultaneously, considering the distribution characteristics of drilling data, we propose a loss function to address category imbalance. This mitigates the impact of imbalanced category representation in the training dataset, thereby enhancing the model's performance, especially in categories with limited data. Through this implicit modeling method, machine learning, a tool with strong generalization capabilities, is integrated into traditional geological assessment, offering innovative approaches and techniques for high-precision and readily updated 3D geological modeling in urban areas.
## 3 Proposed Model
### Seq2 seq Framework
#### 3.2.1 Embedding Layer  
From Figure 2, one could observe that the procedure of the model is from bottom to the top. First of all, the input layer is the statistics of a country, represented as \(S=\{x_{1}^{k},x_{2}^{k},\ldots,x_{n}^{k}\}\), where \(k\in\{1,2,3,\ldots,36\}\) denotes the specific sub-index, and \(n=7\) denotes the 7 year statistics. The deep learning model is not able to directly process the above numerical data, so this work adds an embedding layer which is able to transform the input numerical sequences to embeddings.
# Dependable and Secure Voting Mechanism in Edge Computing
## 3 System
### Architecture
Byzantine fault tolerant systems are considered by the research community to be state-of-the-art with regard to providing reliability in distributed systems. A typical BFT system consists of \(n=3f+1\) replicas that each provide a finite state machine and execute operations from clients in the same order [29]. This is an expensive solution, but it guarantees safety and liveness properties in harsh environments. Cloud outages are common, and adapting services to use multiple clouds have been the solution to eliminate reliance on any single cloud provider and improve reliability [31]. Using multiple cloud instances to run services is even more expensive, but it has been shown that it could be a good strategy to tolerate BFT and cloud outages [32]. Similar to the mentioned work, this solution uses several cloud instances for the same purpose; however, we add the edges nodes to this architecture to validate the computation performed in the centralized environment. We can consider the cost of adding edge nodes to the solution as negligible when setting up a multiple cloud environment. In terms of communication cost, traditional BFT algorithms require \(n^{2}\) messages exchanged in nice executions, i.e., when there are no failures, and the system is synchronous enough for the leader not to be changed [29] and require five communication steps [33]. Our solution requires fewer communication steps comparing with other BFT algorithms. Moreover, the client is not responsible for the computation in our solution, which contrasts with [32]. The parts responsible for the system execution are the edge network and the cloud instances.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 3 Methodology
### Explainable Artificial Intelligence (XAI) Used
#### 3.5.3 Anchor  
The Anchor algorithm is designed to provide high-precision rules, known as anchors, which offer interpretable and reliable explanations for the decisions of machine learning models [8]. The interpretable explanations are presented as high-precision rules, which are local conditions sufficient to guarantee the prediction with high confidence. An anchor explanation for a prediction can be expressed as:  
\[\text{IF}(A_{1}\wedge A_{2}\wedge\ldots\wedge A_{n})\text{THEN prediction }P\text{ with confidenec} \tag{4}\]  
where \(A_{i}\) are anchor conditions (predicates) for features, \(P\) is the predicted class or outcome, and \(c\) is the confidence level associated with the rule. The algorithm aims to maximize the precision (confidence) of the anchor while ensuring that the coverage (the proportion of instances for which the anchor applies) is above a user-defined threshold. The objective function can be formulated as:  
\[\text{Maximize Precision}(A)\text{ subject to Coverage}(A)>\theta \tag{5}\]  
where Precision(\(A\)) is the probability that the prediction \(P\) is correct given that anchor \(A\) applies, Coverage(\(A\)) is the proportion of instances in the dataset for which anchor \(A\) applies, and \(\theta\) is the coverage threshold.  
Suppose our machine learning model has predicted the adaptability level of students. To provide an interpretable explanation using the Anchor algorithm, consider an instance where the model predicts a student's adaptability level as \"Moderate\". After the application of the Anchor algorithm, the following rule-based explanation is produced:  
IF (Network Type >1) \(\wedge\) (Age \(\leq\) 1) \(\wedge\) (Gender > 0) \(\wedge\) (Institution Type > 0) \(\wedge\) (Financial Condition \(\leq\) 1) THEN Predict 'Moderate' with confidence 97.32%.  
This explanation infers that for this particular student, the presence of certain conditions such as having access to a 3G or 4G network, being in the '16-20' age group, being female, attending a non-government institution, and not being classified as 'Rich' in financial status, are collectively sufficient to predict a 'Moderate' adaptability level with high confidence. This rule-based explanation provides clarity on the factors influencing the adaptability prediction. It suggests that access to quality internet and certain demographic characteristics are significant predictors of adaptability. The high confidence level associated with this anchor allows educators to trust the prediction under the specifiedconditions. By identifying and understanding these anchor conditions, educational institutions can tailor support and interventions to improve student adaptability. For instance, efforts can be made to enhance internet access for students, and special attention can be given to gender-specific educational experiences and resources for students from different socio-economic backgrounds.
## Materials and Methods
### Octyl Caffe Synthesis  
The synthesis procedure of OC was described in our previous work [23]. In brief, 1-octanol was dried over 3 A molecular sieves before starting the experiment. In turn, caffe acid (6 mmol) with an excess 1-octanol (66 mmol) and catalyst (10 mol/L H\({}_{2}\)SO\({}_{4}\)) was transferred in a 50 mL two-neck round bottom flask equipped with a thermometer, a Teflon-coated magnetic stirring bar, and a reflux condenser. Afterwards, activated molecular sieves were put into a round bottom flask to remove any traces of water, and the esterification was carried out at 100 \({}^{\circ}\)C for 2 h. The reaction mixture was cooled to ambient temperature, diluted with ethyl acetate, washed with brine and water, dried over magnesium sulfate, and filtered. In the next step, ethyl acetate was evaporated under a vacuum on a rotary evaporator. The synthesized OC (Figure 1) was purified by crystallization from n-heptane.  
\({}^{1}\)H and \({}^{13}\)C nuclear magnetic resonance (NMR) spectra were collected to confirm the purity of OC at 700 and 170 MHz, respectively, on a Bruker Advance III 700 MHz spectrometer (Bruker Corporation, Karlsruhe, Germany) at 25 \({}^{\circ}\)C. Ester was dissolved in CDCl\({}_{3}\)-containing tetramethylsilane (TMS) as an internal standard. Chemical shifts were recorded in \(\delta\) values in parts per million (ppm), and the coupling constant (\(J\)) was reported in Hertz (Hz).  
Moreover, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectra of OC were recorded by using a Bruker VERTEX 70v FTIR spectrometer (Bruker Optics, Ettlingen, Germany) equipped with a diamond ATR cell. The synthesized OC was directly placed and pressed on the crystal surface of the ATR device and analyzed from 4000 to 400 cm\({}^{-1}\) at a resolution of 4 cm\({}^{-1}\).  
The purified OC (octyl (E)-3-(3,4-dihydroxyoxyphenyl)-2-propenoate) was a white solid compound with the following characteristic: \({}^{1}\)H NMR (CDCl\({}_{3}\)): \(\delta\) 0.90 (t, J = 7.2 Hz, 3H), 1.24-1.37 (m, 9H), 1.37-1.43 (m, 2H), 1.67-1.74 (m, 2H), 4.20 (t, J = 6.8 Hz, 2H), 5.59 (br. s., 2H), 6.28 (d, \(J\) = 15.9 Hz, 1H), 6.88 (d, \(J\) = 8.2 Hz, 1H), 7.03 (dd, J = 8.2, 1.9 Hz, 1H), 7.10 (d, \(J\) = 1.9 Hz, 1H), 7.58 (d, \(J\) = 15.9 Hz, 1H). \({}^{13}\)C NMR (CDCl\({}_{3}\)): \(\delta\) 14.06, 22.64, 25.98, 28.74, 29.19, 29.24, 31.79, 64.75, 114.40, 115.57, 116.12, 122.44, 127.88, 143.66, 144.39, 145.98, 167.56. IR: v 3488, 3313, 2955, 2917, 2870, 2849, 2467, 1680, 1624, 1604, 1531, 1473, 1440, 1309, 1280, 1239, 1177, 1153, 1107, 1031, 972, 813 cm\({}^{-1}\).
## 4 Performance Evaluation
### Simulation Setup
With the intention of to obtain baselines to determine the efficiency of the proposal, we decided to run two groups of simulation, each with 100 simulations were carried out. The first group has no components of the proposal were used. However, this time, SUMO was configured to generate an output file that contained all the information related to each vehicle's trip: the departure time, the time the vehicle wanted to start, the time of arrival, the total duration of the trip, the distance traveled, the number of times the route changed, among other registered values. With these values as a basis, it was possible to determine both the general and the individual performance. Figure 5: Final deep architecture.
# Study on the Vibration Effects of Cyclic Blasting on Bridge Structures under Construction
## 3 Numerical Simulation and Analysis of Blasting Vibration
### Model Overview  
#### 3.1.1 Construction of the Finite Element Model  
The model parameters are obtained from on-site data: the tunnel excavation height is 4.4 m, with a width of 4 m, resulting in a cross-sectional area of 17.2 m\({}^{2}\). The tunnel cross-section is depicted in Figure 1. To mitigate boundary effects during modeling, the model width is set to be five times the diameter of the tunnel. Due to the significant influence of the lower boundary on the tunnel model's blasting vibrations, the model height is set to be three times the tunnel height, with the upper boundary corresponding to the actual average burial depth of the tunnel. The tunnel's advance per cycle ranges from 2 to 2.5 m. In accordance with the actual project, the excavated section of the tunnel face is set to 100 m; to eliminate boundary conditions ahead of the tunnel face, an unexcavated section of 50 m is established.  
The surrounding rock of this tunnel section mainly comprises blocky breccia dolomite. The Hoek-Brown constitutive model is employed to simulate the mountain, with the material parameters for dolomite listed in Table 1.  
The overall dimensions of the model are 1400 m x 400 m x 300 m. To further mitigate boundary effects, non-reflective boundaries are applied to all sides of the model.  
The bridge is constructed using reinforced concrete, and the Hoek-Brown constitutive model is employed to simulate the material properties of the reinforced concrete. Since the concrete has not yet fully solidified, the material parameters are slightly lower than those of fully solidified materials. The parameter settings are determined based on research on the model parameters of reinforced concrete, with specific values provided in Table 1.  
Explosive type 2 emulsion with a detonation velocity of 3500-5000 m/s is used. The finite element models for the bridge and tunnel are illustrated in Figure 4.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 5 Linear Programming-Based Mathematical Formulation  
Linear programming is a mathematical technique that is used to achieve optimal results. To achieve optimal results, an objective function needs to satisfy the defined constraints. The feasible regions are computed for minimal energy dissipation and high network throughput.
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
## 3 LoRaWAN Wireless Technology
### Classes of LoRaWAN Devices by MAC  
LoRaWAN devices [27] can be classified depending on the type of medium access control (MAC) they use, thus defining three types: class A, class B and class C. Each class is intended to meet certain specifications related to latency and power consumption. Each class of device is distinguished by its power consumption profile, which is related to the time (duty cycle) it takes for its receivers to receive a LoRaWAN message. LoRaWAN devices are limited by regulation to transmit below 1% of the time, manage the spectrum and reduce collisions. All three classes are based on the same physical layer characteristics [28], and this is seen graphically in Figure 3:  
* Class A devices are based on the ALOHA transmission protocol. This implies that downlink transmissions will be queued and delayed (higher latency) when initiating two transmissions simultaneously (i.e., devices only receive downlink packets in two short duration windows, RX1 and RX2, immediately after the uplink transmission, TX1). The duration of the windows is configurable, and the reception delay is 1 s for maximum efficiency, depending on the \(SF\) applied.
* Class B devices have higher power consumption, since they are designed to increase the reception windows of the downlink transmissions that are scheduled without requiring an uplink transmission [29]. In this case, a synchronization message (beacon) is transmitted from the gateways that increases the power demand in exchange for more receiving windows.
* Class C devices are application-dependent due to their high power consumption. The operation consists of continuously listening for downlink messages, except when transmitting.  
Figure 3: Classes of LoRaWAN devices according to medium access control (MAC).