esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Architecture Configuration  
The architecture configuration can be mainly divided into Hardware layout optimization, Manufacturing resource description, and Software environment configuration.
## 1 Introduction
It is thus essential to evaluate the chemical resistance of geotextiles that are greatly dependent on their polymer compositions, manufacturing parameters, fabric structures, as well as external factors, including liquid composition and _in situ_ conditions [15; 16; 17; 18]. In the study by [PERSON] _et al._, polyester and polypropylene geotextiles were immersed in saline, strong acidic solution (pH 3), and strong alkaline solution (pH 10) at room temperature and 95 \({}^{\circ}\)C, thereby examining the influence of the accelerated aging experiment on the properties of geotextiles. The test results indicated that the influence of groundwater chemistry on the durability of geotextiles was important when geotextiles were used in hazardous waste landfills, slopes, subgrades and earth retention systems [16]. As indicated in ISO 13434 (Geosynthetics--Guidelines for the Assessment of Durability), the pH value of the environment reaches 9-11 for specified soils or a special construction process [19]. In addition, the pH value of soils in Taiwan is between 4 and 6. Due to a limited time, this study thus uses extreme conditions to examine the influence of adding recycled Kevlar(r) fiberson tensile strength of nonwoven geotextiles. In order to examine possible factors that change the durability and mechanical properties of geotextiles in diverse environments, this study immerses recycled Kevlar(r)/PET/LPET nonwoven geotextiles in neutral, acid, and alkaline solutions that is at different temperatures, and measures their tensile strength every month for four consecutive months. Strong alkali and acid solutions are thus used for a maximum of a 4 month immersion in order to exert as much severe influence over the tensile strength of the nonwoven geotextiles. Statistical analyses are then used to analyze the differences in the tensile strength of the nonwoven fabrics, in relation to fiber ratios, thermal treatment, and immersion periods. The influential levels of different variables that are demonstrated on recycled Kevlar(r)/PET/LPET nonwoven geotextiles are determined by using multiple regression analyses. The experimental results can serve as a reference for designing novel geotextile products with their associated environmental requirements.
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 5 Experiments
### Ablation Study
We divide our analysis into three parts: the first two parts are the cases for restoring a single point, and the last part is recovering a long trajectory:  
The results in Figures 6 and 7 show that the MLP-based model tends to recover the position on the trajectory line in the model, while the other models can capture the offset of the real place and the base station. Moreover, this figure also shows that our model can also locate the real position when a user is just wandering across several places. Each of the signaling trajectories has a timestamp on it, and the black point with a timestamp on it is the timestamp of the ground truth position. We also apply our model to a long sequence and use the middle time of the two timestamps of the nearing two signaling sequences as the predicted timestamps. The blue part is the signaling sequence of a single mobile phone use, while the red one is the recovered trajectory of users. As the results show in the pictures, our model can be used for long-sequence recovery. The first two pictures in Figure 8 show that our model can deal with city-scale moving patterns, which may related to vehicles. The third one is the moving between some fixed locations, despite some small offsets, our model still can capture the moving patterns of the signaling data and generate a probable long trajectory. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**MSD**} & \multicolumn{3}{c}{**Geolife**} \\ \cline{2-7}  & **RMSE** & **MAE** & **Rate150** & **RMSE** & **MAE** & **Rate150** \\ \hline MLP & 253.44 & 190.43 & 53.03 & 266.02 & 194.04 & 44.69 \\ LSTM & 219.67 & 156.15 & 64.82 & 183.98 & 155.88 & 53.02 \\ BiLSTM & 219.18 & 149.24 & 69.48 & 171.98 & 145.46 & 57.37 \\ Transformer & 170.989 & 114.869 & 79.485 & 161.56 & 139.06 & 59.57 \\ TSMixer & 204.63 & 145.48 & 66.54 & 155.62 & 136.07 & 60.34 \\ GenTrajRec & **161.28** & **103.73** & **81.26** & **150.24** & **124.62** & **67.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results without graph embedding. Figure 8: Results for long sequence. Figure 6: The recovering cases for some of the moving results. Figure 7: The recovering cases for some of the staying results.
## 5 Experimental Hardware Realization
### Experimental Rotor-Position Results  
The proposed observer's performance in determining the rotor position on the experimental platform is presented in Figure 15(b)-e. The position responses are shown for four speed conditions: 0.15, 0.25, 0.45, and 0.65 p.u-speed. In general, the observer's rotor positions and actual rotor positions nearly matched, and the average position error was close to zero (0.0607 p.u-position). For example, Figure 15(b) presents the rotor position at 0.15 p.u-speed (615 rpm). The duration of one electrical position cycle was 0.026 s; therefore, the time for one mechanical cycle was 0.104 s. The rotational frequency was 9.6154 Hz (576.92 rpm). The results for the remaining three speeds are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Speed (PU)**} & \multirow{2}{*}{**Speed (rpm)**} & **Electrical** & **Mechanical** & **Speed** & \multirow{2}{*}{**Error rpm**} \\  & & **Rotation** & **Rotation** & **Response** & **(rpm)** & **(\%)** \\ \hline
0.15 & 615 & 0.026 & 0.104 & 576.92 & 6.19 \\
0.25 & 1025 & 0.015 & 0.060 & 1000.00 & 2.44 \\
0.45 & 1845 & 0.008 & 0.032 & 1875.00 & 1.63 \\
0.65 & 2665 & 0.006 & 0.024 & 2500.00 & 6.19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Speed calculations from the rotor-position responses for the experimental platform.  
Figure 15: Experimental hardware platform.  
Figure 16: Experimental results for (**a**) rotor speed responses and for rotor positions at (**b**) 0.15, (**c**) 0.25, (**d**) 0.45, and (**e**) 0.65 p.u-speed.
# Performance Evaluation of Laetitic Subgrade Soil Treated with Lime and Coir Fibre-Activated Carbon
###### Abstract  
The subgrade layer's stability considerably influences the long-term performance of pavement systems. This study investigates the influence of lime as a traditional stabiliser and activated carbon with coir fibre (ACF) as waste materials and an environmentally friendly binder to stabilise lateritic subgrade soil. Experiments, including the one-dimensional consolidation and unconfined compressive strength (UCS) tests, have been conducted to investigate the geotechnical properties of stabilised soil in various percentages of additives 3%, 6%, 9%, and 12% lime and 1%, 2%, and 3% ACF. The results demonstrate that 3% ACF and 12% lime can significantly improve the strength parameters and decrease the void ratio and permeability in the stabilised soil. Furthermore, microstructural analysis was performed before and after stabilisation for optimum content. The microstructural analysis proves that AC and lime particles fill soil voids, and gel formation binds the soil particles in the stabilised soil matrix. The results show that 3% ACF stabilised soil is comparable with 12% lime in UCS value and decreasing void ratio. Furthermore, both are suitable for subgrade of low-volume road stability according to Malaysian standards.  
laetitic soil; activated carbon; fibre; lime; consolidation; compressive strength; microstructure
## 4 The Proposed TOCA-IoT Framework
### Data Preprocessing  
The first phase consisted of comprehensive data preprocessing to ensure data quality and model compatibility. This involves several steps, including data cleaning to handle missing values (using imputation techniques where appropriate and removing rows with excessive missing data), feature engineering to derive relevant features from raw network traffic data, and one-hot encoding of categorical features to convert them into a numerical representation suitable for machine learning models. Categorical variables were encoded, and continuous variables were scaled to a common range to facilitate model training and performance evaluation.  
Figure 2 shows a highly imbalanced class distribution across eight categories representing different types of network activity. The vast majority of instances (533,392) are classified as distributed denial of service (DDoS) attacks, indicating a significant focus on this type of attack within the dataset. Denial of service (DoS) attacks represent a considerably smaller but still substantial portion (126,763). Mirai attacks, a specific type of DDoS attack using compromised IoT devices, account for 41,533 instances. The remaining categories, such as Spoofing, Web-based attacks, Reconnaissance (Recon), and Brute-Force attacks, are significantly underrepresented compared to DDoS and DoS, with counts ranging from 7703 down to just 212 instances. The small number of 'BENIGN' instances (17,187) further emphasizes the dataset's bias towards malicious activity. This imbalance is a critical consideration for any analysis or model training using this dataset, requiring techniques to address class imbalance to prevent biased results.  
Figure 1: The main steps of the TOCA-IoT model.  
Feature engineering is employed to determine the important features for analysis. This involves the application of dimensionality reduction techniques to reduce noise and improve model performance. The recursive feature elimination (RFE) method is used in this work to find important features. The dataset is then split into two parts: the training set, which contains 70% of the dataset, and the testing set, which contains 30% of the dataset. The training set is used to train the proposed model, the validation set for hyperparameter optimization and model selection, and the testing set for final performance evaluation.
## 3 Results
### Excess Properties  
#### Excess Molar Volume  
The excess molar volumes have been calculated from the experimental densities data using the following equation:  
\[V^{E}=\frac{[x_{1}M_{1}+x_{2}M_{2}]}{\rho}-\left[\frac{x_{1}M_{1}}{\rho_{1}}+ \frac{x_{2}M_{2}}{\rho_{2}}\right] \tag{16}\]  
where \(x_{1}\) and \(x_{2}\) are the mole fractions of the components, \(M_{1}\) and \(M_{2}\) are the molar masses of components 1 and 2, \(\rho\), \(\rho_{1}\) and \(\rho_{2}\) are the respective densities of the solution and of the pure components. The results of excess molar volumes are illustrated in Figures 1 and 2.  
The experimental excess molar volumes are negative for the DEG + DMSO system and positive for the MDEA + DMSO binary system in the whole composition range at all temperatures.  
The negative values are a consequence of the following effects: (1) strong intermolecular interactions due to the charge-transfer complex, dipole-dipole and dipole-induced dipole interactions, and H-bonding between unlike molecules finally leading to more efficient packing in the mixture than in the pure liquids; (2) structural effects which arise from suitable interstitial accommodation giving a more compact structure of solutions [50].  
The negative excess volume values for the DEG + DMSO system indicated that the volume of the mixture was less than the sum of the volumes of the pure components, possibly due to contraction of the mixing volume caused by structural effects and strong intermolecular interactions between DEG and DMSO. Similar behavior was observed by [PERSON] al. [12] for the binary system tri-ethylene glycol + dimethyl sulfoxide.  
The value became less negative with increasing temperature and arrived at the minimum around molar fraction 0.40 for DEG at all temperatures. These values indicate that there is a maximum volume contraction on mixing DEG with DMSO at a rate of 2:3.  
Dimethyl sulfoxide is a highly polar solvent, not forming H-bond networks and tending toward self-association [1]. The molecular dynamics simulations demonstrated that in liquid DMSO, the H-bonds C-H... O=S are formed [51]. Amines are moderately polar but not as polar as alcohols of comparable molecular weights, and the polar nature of N-H results in the formation of hydrogen bonds with other amine molecules, or other H-bonding systems [52; 53]. In addition, DMSO can provide an S=O group and the hydroxylamines can provide OH or C-H groups for interactions [54].  
The positive values for the MDEA + DMSO system indicate that there were no strong intermolecular interactions. The positive values are due to expansion of the solution volume due to mixing caused by the hydrogen bond rupture and dispersive interactions between unlike molecules [55]. These positive values of excess volume for the MDEA + DMSO system can be explained by the fact that DMSO forms a strong associative structure and by the self-association of MDEA molecules. The effect of temperature on the excess volumes shows a systematic decrease with rising temperature.
# Extraction and Synthesis of Silicon Nanoparticles (SiNPs) from Sugarcane Bagsase Ash: A Mini-Review
## 1 Introduction  
Valorization of sugarcane bagasse has been studied extensively over the years, with its niche applications in the sugar industry and bioenergy production. Silicon dioxide (SiO\({}_{2}\)), often known as silica, is a useful inorganic multifunctional chemical substance that is one of the basic materials. Silica can be found in nature as quartz, sand, or flint. Gel and amorphous forms are also possible, with both crystalline and amorphous forms found in the earth's crust [1, 2]. In their study, [PERSON] and [PERSON] reported numerous silica content in different sources: Sorghum (88.75%), wheat (90.56%), corn (64.32%), bamboo (57.40%), bagasse (73%.00%), lantana (23%.38), sunflower (25.32%), rice husk (93.00%), rice straw (82.00%), and bread fruit tree (81.80%) [3].  
The silica comes from the soil in the form of silicic acid, which the sugarcane plant absorbs and collects around the cellulose micro-compartments. The quantity of silicon in the soil influences silica concentration. Sugarcane roots play a key role in absorbing silica acid from the soil and delivering it to the shoots, where it is stored as amorphous silica. Amorphous silica predominates in sugarcane bagasse ash with other metallic contaminants [2, 3].  
In this study, the use of bases such as \(NaOH\), \(KOH\) and acids like \(HF\), \(H_{2}SO_{4}\), \(HNO_{3}\), and \(HCl\) for the pre-treatment of the ash, and yield silica with desirable properties. Thesugarcane bagasse ash is rich with silica of about 73%, and it is economically feasible due to the conversion of raw material to the production of silica gels and powders [4; 5]. The sugarcane bagasse has been investigated as the potential raw material and source of sugar, juice, and fuel in the ethanol industry (shown in Figure 1) and valuable material in the cementious industry [6].  
Many industrial processes (shown in Figure 1) in the transformation of sugarcane into sugar and ethanol, namely, the burning process of the biomass for energy production, result in ashes with high impurity content, such as iron and aluminum oxides, hindering the process of obtaining silica with satisfactory purity values [6]. Thus, the resulting ashes require additional purification processes HCl acid leaching allied or not to a sol-gel method [6; 7].  
The silica nanocomposites (soluble silicates) are widely used in the glass, ceramic, and cement industries as a major component and in cosmetic, pharmaceutical, and detergent industries as bonding and adhesive agents. Notably, silica has been used as a major precursor in a variety of applications as inorganic and organometallic materials, which have desirable applications in synthetic chemistry as a catalyst, coating for electronic optical materials, and thin films [7].
# Improved Control Strategy of MMC-HVDC to Improve Frequency Support of AC System
## 4 Improved Inertia Control of MMC-HVDC  
### Effective Short-Circuit Ratio (ESCR) for AC Systems  
The improved inertia control proposed in this study can be used to control the frequency stability of AC systems of various strengths. For the single-line HVDC-fed system, consideration of the interaction effects from other lines is unnecessary. Therefore, in this study, the effective short-circuit ratio (ESCR) is used to assess the robustness of the onshore AC system. In addition, the ESCR takes into consideration the reactive power injected into the system from the reactive power compensator. The ESCR accurately represents the robustness of the AC system. The ESCR formula is as follows:  
\[ESCR=\frac{S_{AC_{rated}}-Q_{CAC}}{P_{DC_{rated}}} \tag{8}\]  
where \(S_{AC_{rated}}\) is the rated MVA of the AC system, \(Q_{CAC}\) is the reactive power injected into the AC system, and \(P_{DC_{rated}}\) is the rated active power of the HVDC.
## 6 Conclusions  
In this paper we present a global IP-WSN architecture with special attention to addressing and routing. These are the most challenging issues that must be addressed with reference to energy efficiency. Since the proposed global IP-WSN architecture is based on a localized mobility management protocol, it meets the demand for energy efficiency in terms of reducing signaling costs, mobility costs, end to end delay and increasing packet delivery ratio. Here we also present the detailed software architecture, mobility scenarios, messages formats, addressing issues and evaluate its performance by analyzing signaling costs and mobility costs, packet delivery ratio and end to end delay. Our experiments show that with respect to the number of IP-WSN nodes, the proposed global IP-WSN scheme reduces the signaling cost by 60% and 56%, as well as the mobility cost by 62% and 57%, compared to MIPv6 and PMIPv6, respectively. The simulation results also show that in terms of the number of hops, proposed IP-WSN scheme based on SPMIPv6 decreases the signaling cost by 56% and 53% as well as mobility cost by 60% and 67% as compared to MIPv6 and PMIPv6 respectively. It also indicates that proposed scheme reduces the level of energy consumption. Moreover the proposed scheme also enhances the packet delivery ratio and end to end delay significantly. In this paper, we focus on 6 LoWPAN based IP-WSN of the same vendor and protocol stack. In future we will focus on the federated IP based sensor networks consisting of multi-vendor and heterogeneous protocol stacks.
## 1 Introduction
A 50% increase in metal deposition rate to 10.4 kg/h was demonstrated when iron metal powder was added in SAW with input parameters of 500 A, 31 V and 60 cm/min travel speed in a two weld wire set-up, compared to 6.3 kg/h deposition rate achieved in the absence of iron metal powder addition, in the same SAW set-up with the same welding parameters used [22]. Limited published studies are available on the application of unconstrained (not fluxed cored wire or metal cored wire) metal powders for alloying purposes in SAW. In particular, the behaviour of nickel when used as an alloying powder is not clear since the percentage yield appears to be much lower than the 100% yield typically reported for solid weld wire alloying [12; 17]. The objective of this study is to demonstrate the application of unconstrained nickel and aluminium powders in SAW to alloy the weld metal and control the weld metal oxygen content. Thermodynamic analysis is applied to elucidate the chemical interaction between Ni and Al and its effects on Ni yield from the unconstrained metal powder to the weld pool. Application of weld metal alloying from unconstrained metal powders will yield productivity gains in weld metal deposition rate and in eliminating the expensive time consuming step of manufacturing alloyed wire and alloyed powder because metal powders can be used directly in the unconstrained format [14; 15; 21; 22].
## 2 Evaluation Study and Design
### Electromagnetic Simulations
#### 2.2.1 Device Design and Electromagnetic Simulation in Free Space  
Initially, we conducted an analysis of the electromagnetic device within a simulation environment. The E4 Shield device (Elettronica SpA, Rome, Italy) was meticulously modeled in CST to replicate the actual design of the object, as illustrated in Figure 3.  
The antenna designed is a miniaturized patch antenna fed by a microstrip (Figure 4). The pattern of the device was simulated in free space and is shown in Figure 4.  
To acquire the near-field data, the field source was evaluated. Utilizing the field source in place of the actual device can significantly streamline subsequent simulations conducted in complex environments.  
Figure 3: E4 Shield device.  
Figure 2: Geometric model for the environment ‘Office’.
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 2 Research Methodology
### Identification Phase  
A bibliographic search was performed to identify the studies related to this area; the query comprises mixed keywords and includes \"route guidance system\", \"traffic assignment model\", \"Intelligent Transportation Systems\" in different variations and combined by the operator \"OR\"; \"integration\", \"data integration\", \"information integration\" in different variations and combined by the operator \"OR\"; and \"vehicle-to-vehicle\", \"V2X\", \"vehicular communication\" in different variations and combined by the operator \"AND\". The four selected databases were SCOPUS, ScienceDirect, Researchgate, and IEEEXplore Library. The documents found were identified using search strings related to the research terms, adding some recommended references from external sources. The search was limited to full text documents published over the last ten years. This search was concluded in Dec 2023.
## 4 Materials and Experiments  
### Dataset  
The current experiment is a DEAP dataset consisting of two parts of emotion recognition and physiological signals [31], while the acquisition process is shown in Figure 5. The DEAP dataset collected physical signals and emotional assessments from 32 subjects. Subjects autonomously rated videos from 1 to 9 on 4 dimensions based on arousal, valence, dominance, and liking [32]. The official provides two data forms, one is the original signal including various interferences, such as EMG, EOG, and other interference; the other is preprocessed data, including down-sampling data to 128 Hz, removing EMG artifacts, 4.0-45 Hz band-pass frequency filter filtering, etc. Preprocessing removes the non-emotional information from the previous 2 s to avoid affecting the training of the subsequent models.  
The states of valence, arousal, dominance, and liking in the DEAP dataset can be classified according to a given threshold. Emotion states were studied in a quadratic dichotomy of high and low valence, high and low arousal, high and low dominance, and high and low liking with a threshold of 5. The two-dimensional model with four categories consists of a combination of four different affective states: high arousal and low valence (HALV), high arousal and high valence (HAHV), low arousal and low valence (LALV), low arousal and high valence (LAHV) [33].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Head**} & **Test Accuracy (\%)** & \multirow{2}{*}{**F1-Score**} & **Test Accuracy (\%)** \\  & **(Binary Classification)** & & **(Four Classification)** \\ \hline
50 & 94.58 & 93.61 & 85.57 \\
100 & 94.43 & 92.77 & 84.23 \\
150 & 93.98 & 93.12 & 84.10 \\
200 & 94.33 & 93.07 & 83.79 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different epoch times ablation experiments.