esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Forecasting Process by Hybrid LSSVM-IBFOA  
Figure 4 illustrates the formation of a hybrid model of LSSVM-IBFOA where IBFOA will optimize the parameters of LSSVM to achieve accurate forecasting. The data pre-processing is performed through data separation and normalization of the load profile in Peninsula Malaysia. The LSSVM and IBFOA are merged to perform as a hybrid model and will be built separately according to day type for better forecasting performance. The optimization process is initiated with random positions for each bacterium in the predefined dimensions, which represent the LSSVM parameters. The optimized LSSVM parameters are used to train the LSSVM. The trained model is tested on unseen data. The objective function, MAPE, is observed. The modification of IBFOA parameters is performed to optimize the LSSVM parameters, which in turn produces an accurate forecast or lowest MAPE. Table 5 provides the formation step of hybrid LSSVM-IBFOA accordingly.  
Figure 4: The flowchart of LSSVM-IBFOA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Steps** & **Description** \\ \hline \multirow{3}{*}{**Step 1**} & Collecting the historical power load data for the analysis and stored in a MATLAB file format (.mat). The load function in MATLAB is then employed to import these data into the workspace \\ \hline \multirow{3}{*}{**Step 2**} & The power load forecasting dataset is divided into training data, validation data and testing data according to the ratio 70:20:10, and the data are normalized \\ \hline \multirow{3}{*}{**Step 3**} & Parameters setting: p, n, S, Nc, Ns, Nre, Ned, Ped \\  & where p is the number of parameters to be optimized, S is the number of bacteria, Ns is the swimming length, Nc is the maximum number of iterations in chemotaxis, Nre is the maximum number of reproduction, Ned is the maximum number of elimination dispersal, Ped is the probability of elimination-dispersal \\ \hline \hline \end{tabular}
\end{table}
Table 5: Formation step of hybrid LSSVM-IBFOA.
## 3 Capacity Optimization Model  
### Selection of Decision Variables  
Modern CCHP microgrid systems contain a variety of loads [22]. For this reason, every load and power supply source can be regarded as a variable to be optimized for the power capacity allocation. Among variables, the capacity of a photovoltaic power generation system can determine the penetration strength of green energy in the CCHP microgrid [23]. In addition, the capacity of the micro-gas turbine can greatly affect the supply of electric, heat and cold loadin the CCHP microgrid [24]. The upper limit of the exchange power in the grid directly determines the upper limit of the power that the microgrid buys or sells from/to the grid. Therefore, this variable should not be set too large. Otherwise, it may cause an unnecessary impact on the grid. Moreover, the energy storage system and thermal storage tank are important devices for stabilizing power fluctuations. If their capacity configuration is too low, the stored energy is not sufficientto meet the demand of the load. If the capacity allocation is too large,it will inevitably cause unnecessary waste. As the main source of heat, the capacity configuration of the gas boiler must be reasonably distributed to ensure the reliable power supply of the microgrid system.  
As above, this study selects six device capacities as system decision variables.  
\[X=[N_{pv},N_{mt},N_{grid},N_{b},N_{lst},N_{gb}] \tag{11}\]  
where \(N_{pv}\), \(N_{mt}\), \(N_{grid}\), \(N_{b}\), \(N_{lst}\) and \(N_{gb}\) are the installed capacity of PV, MT, interactive with power grid (electricity purchase and sale from/to power grid), battery, TST and GB, respectively.
## 4 Experiments and Results
### Process Models Developing  
The process models were developed with the purpose to simulate and to validate the architecture in a specific way, in other words, each self-organization capacity suggested together with the change of messages used by the agents' platform.  
To simulate the proposed architecture it developed the process modules in language _C/C++_ and it was used as sequence of cycles FSM (_Finite State Machines_), in other words, machines of finite states to organize sequence of steps of each module of developed process. The simulation tool OPNET (_Optimized Network Engineering Tool_) _Modeler_(www.opnet.com) supports for mobile networks and structure to simulate networks in wide climbs. To compile the process modules the Visual compiler C++ version 6.0 was used and later it changed in an automatic way for the simulator _OPNET_, in files in the EMU format (_External Modulates Application_), which are files interpreted by the simulation tool.  
The models of processes presented in the following sections were developed in layers, beginning for the implementation of the agents' platform, later for the self-organization process (being limited to the self-optimization capacities and self-configuration) and finally applying those capacities as extension for the routing protocols OLSR and AODV.
# Characteristics of BeiDou-3 Experimental Satellite Clocks
## 2 Data Collection
Because there are no precise clock products for the experimental satellites, the overlapping arcs of the three-day POD solutions are used to evaluate the precision of the so-called twice difference method [26], which can be expressed as follows:  
\[C(i) =\frac{1}{m}\sum_{s=1}^{m}\delta t_{2}^{s}(i)-\delta t_{1}^{s}(i) \tag{1}\] \[\Delta^{s}(i) =\delta t_{2}^{s}(i)-\delta t_{1}^{s}(i)-C(i)\] (2) \[\text{SD} =\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\Delta^{s}(i)-\overline{ \Delta^{s}}\right)^{2}} \tag{3}\]  
where \(\delta t_{1}^{s}(i)\) and \(\delta t_{2}^{s}(i)\) are the two clock offsets of the overlapping arcs of satellites, \(s\), at the epoch, \(i\), and \(\Delta^{s}(i)\) is the difference of the clock offset after eliminating the average bias \(C(i)\) calculated by \(m\) satellites between these two overlapping arcs. The Standard Deviation (SD) of \(n\) epochs is computed from Equation (3), where \(\overline{\Delta^{s}}\) is the mean of \(\Delta^{s}\). The precision of the two-day overlapping clock offsets calculated by two consecutive three-day solutions is shown in Figure 2. Compared with the result from the regional stations, the precision of the BeiDou-2 MEO satellites estimated by the global stations has visibly improved from 40% to 60%. Therefore, it can be inferred that in the future, the precision of C33 and C34 will be gradually improved through establishing more tracking stations and optimizing the ISL technology. \begin{table}
\begin{tabular}{l l} \hline \hline
**Correction** & **Model** \\ \hline Tropospheric delay & Saastamoinen model [21], global mapping function [22], and two-hourly \\  & Zenith Irpospheric Delays (ZTD) without gradients, which are obtained in \\  & step one and fixed in step two \\ Phase wind up & Corrected [23] \\ Tide displacement & Solid Earth tide, pole tide, and ocean tide loading; according to \\  & International Earth Rotation Service (IERS) Conventions 2010 model [24] \\ Relativity effect & IERS Conventions 2010 model [24] \\ Geopotential & EIGEN\_GL04C up to 12 \(\times\) 12 degrees and orders \\ N-body gravitation & Jet Propulsion Laboratory (JPL) DE405 ephemeris model \\ Solar radiation & ECOM five-parameter model [25] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 2: Standard deviation (SD) of two-day clock offsets of two consecutive three-day solutions.
## 5 Conclusions  
Lignocellulosic materials are present in abundance, readily available, and are environmentally friendly. The efficient use of such potential residues will result in cutoff the menace of burning. In this study, three lignocellulosic materials were combined in different proportions to determine the best reactor that produces maximum methane production. Analysis of methane production trends during anaerobic Co-digestion of lignocellulosic residues i.e., wheat straw and rice straw (WSRS), bagasse, and wheat straw (BAWS), bagasse and rice straw (BARS), bagasse, wheat straw and rice straw (BAWSRS) revealed variation in methane production by each reactor in both experiments. This variation was induced due to different substrate concentrations, lignin content, and hydrolysis rate. The study suggests that a decrease in methane production was due to a lower hydrolysis rate and higher lignin content. From the experimental results can be inferred that the pH of all reactors was neutral except for reactor BARS (S/I 2.5) due to the accumulation of volatile fatty acids that inhibit the methanogenic bacteria to produce methane. Experimental results were further validated by simulation of kinetic models estimating performance parameters. Evaluation of kinetic models demonstrated that the logistic function model was the best fit, and it reproduced the experimental data followed by the modified Gompertz model. Comparison of both experimental and kinetic modeling results denotes that the optimum methane production was exhibited by the reactor WSRS at the substrate to inoculum ratio (S/I) 1.5, with lower lignin content and higher hydrolysis rate. These reactors possess the optimum value of R2, maximum methane production (M) and low initial lag phase (\(\lambda\)), RMSE, and \(\Upsilon\).  
**Author Contributions:** Conceptualization, R.L., S.M.; Formal analysis, Z.S. and A.A.; Methodology, S.M. and A.Z.; Resources, Z.S.; Software, S.R.N., S.M. and A.H.K.; Supervision, R.L.; Write up D.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** We thank the U.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E) at National University of Science and Technology (NUST) Islamabad, Pakistan to carry out this research work.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining
###### Abstract  
This paper describes an autonomous loading system for load-haul-dump (LHD) machines used in underground mining. The loading of fragmented rocks from draw points is a complex task due to many factors including: bucket-rock interaction forces that are difficult to model, humidity that increases cohesion forces, and the possible presence of boulders. The proposed system is designed to integrate all the relevant tasks required for ore loading: rock pile identification, LHD positioning in front of the ore pile, charging and excavating into the ore pile, pull back and payload weighing. The system follows the shared autonomy paradigm: given that the loading process may not be completed autonomously in some cases, it takes into account that the machine/agent can detect this situation and ask a human operator for assistance. The most novel component of the proposed autonomous loading system is the excavation algorithm, and the disclosure of the results obtained from its application in a real underground production environment. The excavation method is based on the way that human operators excavate: while excavating, the bucket is tilted intermittently in order to penetrate the material, and the boom of the LHD is lifted on demand to prevent or correct wheel skidding. Wheel skidding is detected with a patented method that uses LIDAR-based odometry and internal measurements of the LHD. While a complete loading system was designed, the validation had to be divided in two stages. One stage included the rock pile identification and positioning, and the other included the charging, excavation, pull back, and weighting processes. The stage concerning the excavation algorithm was validated using full-scale experiments with a real-size LHD in an underground copper mine in the north of Chile, while the stage concerning the rock pile identification was later validated using real data. The tests showed that the excavation algorithm is able to load the material with an average of 90% bucket fill factor using between three and four attempts (professional human operators required between two and three loading attempts in this mine).  
autonomous loading system; field robotics; mining automation Article
# Fine-Grained Mapping Between Daily Activity Features in Smart Homes
## 1 Introduction  
One of the goals of smart homes is to support residents in living independently by recognizing their daily activities [1]. Smart homes achieve this through the use of non-invasive sensors that monitor residents' activities and status [2, 3]. As residents perform daily activities, these sensors are continuously activated, generating a stream of sensor events. Daily Activity Recognition (DAR) focuses on identifying the category of daily activity based on the generated sensor event streams [4].  
Developing an effective recognition model is the central focus of Daily Activity Recognition (DAR). Numerous approaches have been proposed to generate such models [5, 6, 7, 8, 9, 10, 11, 12]. Among these, data-driven methods treat DAR as a multi-class classification problem, with supervised machine learning techniques being widely applied. While these supervised approaches often achieve high recognition performance, they require a substantial amount of labeled data, which can be labor-intensive to obtain. To reduce the effort of data labeling and make the most of existing recognition models, transferring a trained model from one smart home to another has emerged as a promising area of study [13].  
When transferring a trained recognition model, it is essential to identify shared features of daily activities performed by residents in two smart homes. Sensors are the primary source of such features, making effective sensor mapping between the two environments a fundamental step in generating shared activity features. While the sensor layouts may differ between smart homes, the categories of sensors and functional areas tend to remain relatively consistent. As a result, most existing approaches map sensors on the basis of their categories or the functional areas they are associated with. In these methods,sensors with the same category or those located in similar functional areas are mapped together. Although such coarse mapping methods are straightforward and practical, they often result in only moderate performance for Daily Activity Recognition (DAR) [14]. To enhance DAR performance, this paper proposes a fine-grained sensor mapping approach. In addition to considering sensor categories and functional areas, this method incorporates the significance degree of sensors for mapping. The entire mapping process is automated and does not require manual intervention or labeling information from daily activity instances in the target smart home.  
The major contributions of this paper are summarized as follows:  
1. A novel algorithm for calculating the significance degree of sensors is proposed.
2. A strategy for sensor alignment is presented to improve the mapping process.
3. The proposed approach is evaluated using a public dataset, demonstrating its effectiveness.  
The remainder of this paper is organized as follows: Section 2 reviews related works. Section 3 details the proposed approach. Section 4 presents the experimental evaluation and results. Section 5 concludes the study and discusses potential future directions.
## 2 Data and Methods
### Model Indicator  
In this paper, the accuracy, specificity, sensitivity, F1-Score, Kappa, and MCC were used to evaluate the model performance. The specific evaluation methods are shown in Formulas (1)-(8). In the classification problem, the sample quantity of each category is easily imbalanced, so we introduce the kappa coefficient and MCC evaluation index.  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{1}\]  
\[Specificity=\frac{TN}{FP+TN} \tag{2}\]  
Figure 3: Structural diagram of the Stacking model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Parameter Value** \\ \hline iterations & 1000 \\ learning\_rate & 0.03 \\ depth & 6 \\ grow\_policy & Depthwise’ \\
12\_leaf\_reg & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter values of CatBoost model.  
\[Sensitivity=\frac{TP}{TP+FN} \tag{3}\]  
\[precision=\frac{TP}{TP+FP} \tag{4}\]  
\[F(a)-Score=\frac{(a^{2}+1)\times(precision\times Sensitivity)}{a^{2}\times(precision+ Sensitivity)} \tag{5}\]  
The commonly used index is the F1-score, which means \(a\) = 1, so the formula can be transformed into:  
\[F1\text{-}Score=\frac{2\times(precision\times Sensitivity)}{(precision+ Sensitivity)} \tag{6}\]  
The higher the value of the kappa coefficient, the higher the classification accuracy achieved by the model. The calculation of the k coefficient is based on the confusion matrix, and its expression is shown in Formula (7), where po represents the classification accuracy, that is, the accuracy pe is represented in Formula (8). \(R_{i}\) represents the sum of the elements in row \(i\). \(C_{i}\) represents the sum of the elements in column \(i\), and m represents the elements in the confusion matrix.  
\[Kappa=\frac{po-pe}{1-pe} \tag{7}\]  
\[pe=\frac{\sum_{i}R_{i}\times C_{i}}{(\sum m)^{2}} \tag{8}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{9}\]
# Phytotoxic Substances Involved in Teak Allelopathy and Agroforestry
## 4 Phytotoxic Substances with Allelopathic Activity in Teak
Those compounds were quinones, terpen, apocarotonoids, phenolics, flavonoids, saponins, lignans and norlignans [16; 64]. Teak wood shows resistance to termite and fungal damages, and naptoquinones and anthraquinones contribute a resistance property [65; 66; 67; 68]. Some other compounds were also related to the pharmacological activities of teak [2; 16; 21; 35; 57]. Although those compounds have been associated with the pharmacological effects and property of its wood characteristics, some of those compounds may possess phytotoxic activity. \begin{table}
\begin{tabular}{c c c c c} \hline
**Phytochemical Class** & **Compound** & **Target Plant Species** & **Inhibition** & **Reference** \\ \hline Phenolic & 1: Acetovanillone & Wheat & Plant growth & [56] \\ Benzofuran & 2: Dehydrololipidide & Wheat & Plant growth & [55] \\ Anthra quinone & 3: Naphthotestone & Wheat, onion, tomato, lettuce & Plant growth, germination & [53] \\ Monoterpene & 4: (6 RS)-(E)-2,6-Dimethyl-2,7-octadiene-1,6-diol & Wheat & Plant growth & \\ Sesquerprne & 5: lß-6\(\alpha\)-Dihydroxy-4(15)-eudbenzene & Wheat & Plant growth & [54] \\  & 6: (1S,3 aR,4R,7 aS)-1-(2-hydroxyethylpropan-2-yl)-3a-methyl-7-methyleneoctahydro-1H-inden-4-ol & Wheat & Plant growth & [54] \\ Diterpene & 7: Phytol & Wheat & Plant growth & [54] \\  & 8: Rhinocerotinoic acid & Wheat, lettuce & Plant growth, germination & [54] \\  & 9: 2-Oxokovalenic acid & Wheat, onion, lettuce germination & Plant growth, germination & [54] \\  & 10: Lab-13-en-8\(\beta\)-ol-15-oic acid & Wheat, onion, lettuce & Plant growth & [54] \\  & 11: 19-Hydroxgrruginol & Wheat, onion, lettuce germination & Plant growth, germination & [54] \\ Apocarotonoid & 12: Solidagonal acid & Wheat & Plant growth & [54] \\  & 13: Tectoionol A & Wheat & Plant growth & [55] \\  & 14: Tectoionol B & Wheat & Plant growth & [55] \\  & 15: 3\(\beta\)-Hydrox-7,8-dihydro-\(\beta\)- & Wheat, onion, tomato & Plant growth & [55] \\  & 16: 3\(\beta\)-Hydroxy-7,8-dihydro-\(\beta\)- & Wheat, onion, tomato, & Plant growth & [55] \\ Phenylpropanol & 17: Springaresinol & Wheat & Plant growth & [56] \\  & 18: Medioresinol & Wheat & Plant growth & [56] \\  & 19: Lariciresinol & Wheat & Plant growth & [56] \\  & 20: Balaphonin & Wheat & Plant growth & [56] \\  & 21: Tectonoelin A & Wheat & Plant growth & [56] \\  & 22: Tectonoelin B & Wheat & Plant growth & [56] \\ \hline \end{tabular}
\end{table} 
Table 2: Phytotoxic substances in teak with allebotathic effects on crop plant species. Figure 1: Phytooxic substances in teak.
# Development of Alfa Fiber-Based Mortar with Improved Thermo-Mechanical Properties
## 3 Results and Discussions
### Time-Lag  
Time-lag (TL) is defined as the time duration between two associated occurrences such as a cause and its effect. In our case, it is the time interval during which the heat flow peak passes through a flat slab from one side to another. The time-lag was calculated based on measured thermal diffusivity values using the following expression [47]:  
\[\mathrm{TL}=\frac{1}{2}\mathrm{e}\sqrt{\frac{T_{0}}{\pi\alpha}} \tag{18}\]  
where T\({}_{0}\) is the periodic cycle of temperature variation (h) (T\({}_{0}=24\) h) and e is the thickness of the sample (m).  
The calculated time-lag as function of AF mass content is displayed in Figure 12 for samples with a thickness of 4 cm. As expected, one can notice that the time-lag increases with AF mass content increase. It reaches 2.21 h for an AF content of 5%. This means that the reinforcement of mortar with AF enhances the insulation performance of mortar.  
Furthermore, Table 4 compares the time-lag of this AF-based composite with 5% to those of different materials used in the building construction sector. All compared materials have the same wall thickness of 20 cm. It can be seen that AFRM5% has a good time-lag. It is greater than that of hemp concrete (9.6 h) followed by cellular concrete (8.6 h). This result indicates that the composite AFRM material developed in this study can reduce outdoor weather conditions' propagation through the building envelope by about 10.37 h for a composite thickness of 20 cm. That is, the midday heat does not reach the indoor building until night and vice-versa for the mid-night cold.  
Figure 12: Time-lag variation as function of AF mass content.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 4 Microencapsulation of Polyphenols
### Impact of Carriers on Polyphenol Stability and Release  
The selection of an appropriate carrier material is critical for ensuring the stability and controlled release of encapsulated polyphenols. For instance, carbohydrate carriers, such as maltodextrin and starch, are excellent for stabilising polyphenols in dry formulations, protecting them from environmental degradation. Nevertheless, these carriers may not always provide sufficient control over release kinetics, particularly in liquid systems [173]. On the other hand, protein-based carriers, such as casein and gelatine, offer more versatile release mechanisms. In particular, they can protect polyphenols from oxidation and enzymatic breakdown while allowing for controlled release, particularly in response to digestive conditions. Moreover, the amphiphilic nature of proteins enables them to interact with both hydrophobic and hydrophilic polyphenols, improving their encapsulation efficiency and bioavailability [174]. Similarly, lipid-based carriers, particularly lecithin, are highly effective at encapsulating hydrophobic polyphenols and improving their solubility and absorption in the body. These carriers are particularly well-suited for liquid formulations and offer sustained release profiles. Likewise, biopolymer carriers like chitosan provide pH-sensitive release mechanisms, which can enhance the targeted delivery of polyphenols to specific regions of the digestive tract, optimising their bioactive effects [173]. In conclusion, the choice of encapsulation carrier significantly influences the stability, bioavailability, and controlled release of polyphenols. For example, carbohydrates provide good protection in dry systems, proteins offer versatile release in both aqueous and solid matrices, lipids enhance the absorption of hydrophobic polyphenols, and biopolymers such as chitosan enable targeted release in the digestive system. Thus, each carrier type brings unique advantages that can be tailored to the specific application and desired release profile of polyphenols in food products [174]. Table 2 presents the carriers, applications and challenges of applying microencapsulation of polyphenols in food technology.