esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
## 2 Research Methods
### Determination of Rock Mass Mechanical Parameters
This value resides within the conventional empirical range of 1/3 to 2/3, demonstrating strong agreement between theoretical predictions and practical engineering observations. The technical basis for this modification stems from comprehensive mechanical analyses. The high degree of structural connectivity (>65%) creates pervasive weak planes that dramatically reduce the rock mass's effective stiffness. Concurrently, the moderate RQD value (\(\approx\)55%) quantitatively confirms the compromised integrity of the rock mass structure. Our integrated analytical approach, combining the Hoek-Brown criterion with empirical correction methods, provides a robust framework for determining appropriate parameter modifications that balance theoretical soundness with practical applicability in engineering design and stability assessments. Based on the actual engineering geological conditions and data of the mine, the [PERSON] method was selected to weaken the cohesion (\(C\)). According to [PERSON]'s experimental research on over ten types of rocks, the internal friction angle (\(\varphi\)) of intact rock is nearly equal to that of the rock mass. However, there is a significant difference in cohesion (\(C\)) between them. Therefore, [PERSON] proposed the following reduction formula (Equation (1)):  
\[C_{m}=C_{K}[0.114e^{-0.48(i-2)}+0.02] \tag{1}\]  
In the aforementioned formula, i = joint density of the rock mass (here, based on mine data, taken as 1.8 joints/m); \(C_{m}\) = cohesion of the rock mass (MPa); \(C_{K}\) = cohesion of the intact rock specimen (MPa). Based on the joint density (1.8 joints/m), the calculated reduction factor for rock cohesion is 0.143. If the empirical method is applied, the reduction factor typically ranges between 1/7 and 1/10 (0.1-0.15). Based on the comprehensive analysis of the results of the empirical method and the [PERSON] method, the reduction coefficient of 0.143 meets the accuracy requirements. Therefore, the polymerization reduction coefficient of 0.143 is selected. In this numerical simulation study, the mechanical parameters of backfill materials were determined by extensively referencing both domestic and international research findings on backfill mechanical properties [35; 36]. Additionally, actual in situ strength test data of mine backfill structures were incorporated. The reduced mechanical parameters of the rock mass, as applied in the theoretical calculations and numerical simulations in this study, are summarized in Table 1.
## 4 Discussion
### Comparison with Other Studies  
In this study, a synergetic wetland classification method combining GF-3 full-polarization SAR and OHS hyperspectral images is proposed to generate a high-accuracy and reliable wetland classification of the YRD. The OAs obtained by the ML, MD, and SVM methods for the synergetic data sets are 97.3%, 89.0%, and 97.2%, and the Kappa coefficients are 0.96, 0.84, and 0.96, respectively. Compared with the single data set, the synergetic classification method combined with OHS and GF-3 can further improve the accuracy and meet the demand of a high-accuracy wetland classification in the YRD, which has great potential in other wetland classification scenarios.  
Previous studies have provided a number of synergetic classification cases for multi-source SAR and optical remote sensing data integration, such as Sentinel-1, Sentinel-2, and PlanetScope, to generate high-precision land cover and wetland classification results, regardless of seasonal variations and tidal effects [23; 40; 42; 44; 45]. For example, [PERSON] et al. [40] used Sentinel-1 and Sentinel-2 data to provide a more detailed spatial distribution of the St. Lucia wetland habitats without considering wetland dynamics. [PERSON] et al. [42] demonstrated the good performance of the object-based stacked generalization method by combining multitemporal optical and SAR data, with an overall accuracy of 92.4% and a Kappa coefficient of 0.92, respectively. Nevertheless, some prediction errors may exist in the low-resolution prior images if the temporal land surface changes are too indiscernible.  
Some scholars have conducted preliminary studies on the classification of coastal wetlands in the YRD without using full-polarization SAR data or hyperspectral data. In particular, there are few synergetic wetland classification studies that evaluate the GF-3 and OHS data. For example, [PERSON] et al. [36] proposed a multibranch convolutional neural network (MBCNN) to fuse Sentinel-1 and Sentinel-2 images to map YRD coastal land cover, with an overall accuracy of 93.8% and a Kappa coefficient of 0.93. [PERSON] et al. [7] mapped the distribution of salt marsh species with the integration of Sentinel-1 and Sentinel-2 images. However, only the Sentinel-2 vegetation index and Sentinel-1 backscattering feature are used, but the polarization feature of SAR images is not fully utilized.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 6 Discussion and Future Work  
Travel route planning is a crucial procedure among the routing protocol designing with a mobile collector. The scheduled route has a significant influence on the performance of the network. If the scheduled path is too long, it will take the mobile collector much time for traveling and the network will experience serious network latency. If the scheduled path is too short, many sensors may not communicate with the mobile collector directly so that much energy will be wasted for data forwarding. Therefore, it is difficult to schedule a short path as the coverage rate of sensors is high at the same time. In this paper, we not only focus on the coverage rate of the SPs but also take the overlapped coverage rate into consideration. The overlapped covered sensors waste the efficiency of the usage of the mobile collector. Additionally, the overlapped coverage rate is generally in inverse proportion to the coverage rate which accelerates the convergence of the fitness function of our proposed schema.  
A single mobile collector contributes limited improvement in the performance of the network. With the incessant improvement of the software and hardware, the cost of the mobile collector is decreasing and more and more researchers transform their attention to WSNs with multiple mobile devices. Following are some problems which need to be addressed for multiple mobile sink-based routing protocol. Firstly, a communication mechanism should be designed for communication between the mobile collectors. Then, a cooperative mechanism which contains the moving paths and the target areas division for each collector should be elaborately designed. Additionally, the speed of the mobile collectors should also be controlled to confirm that the data of the network is synchronized. Our future  
Figure 13: Comparison of different numbers of SPs.  
work will mainly focus on the problems mentioned above and further improve the performance of the network.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 1 Introduction
### Contributions  
The development of an ADRC for attitude tracking of quadrotor aerial vehicles with bounded inputs is the central proposal. It is assumed that the aerial robot is subject to unknown external perturbations. Then an ESO, designed to estimate the disturbances, is employed. Further, a control law is designed for attitude tracking, which takes these estimated disturbances into account. The proposed controller uses quaternions to parameterize the attitude, thus avoiding singularities and being computationally efficient. Besides, the control strategy uses nested saturation functions, considering the constraints/capacity of the actuators to reject the disturbances. The stability analysis is studied for the nonlinear closed-loop system in the input-to-state stability (ISS) sense, concluding a robust stability [52]. The proposed strategy is validated through simulation results using the model of a small quadrotor. The proposed strategy is validated through simulation results using a small quadrotor model and physical parameters, namely, the Crazyflie 2.0 [53]. Although this quadrotor is considered in this work, the approach can be applied to spacecraft and aerial robots with VTOL capacities, e.g., ducted-fan tail-sitters and helicopters.  
The rest of the paper is structured as follows. The mathematical background is introduced in Section 2. The multirotor and disturbance models are presented in Section 3. Section 4 details the design of the extended state observer (ESO) and the attitude control law. Section 5 describes the simulations carried out, as well as their results, where the effectiveness of the proposed algorithm is highlighted. In Section 6, some conclusions are shown and some future work is mentioned. An appendix with the stability proofs is attached.
## 3 Related Work
### Architectural Views and Patterns of the Proposed Systems  
The analyzed works [11-25] outline system architectures for emergencies using (1) physical views such as N-Tier, client-server, and cloud computing styles; (2) implementation views like component-based and layered architectures; and (3) communication/process views based on service-oriented architectures, including REST. Specifically, 60% adopt a client-server architecture [11-13,16,18,21-24], 14% use SOA [14,19], 13% implement N-Tier [15,17], and 13% employ layered architectures [20,25]. However, none provide a comprehensive architecture to standardize development, leading to issues such as a lack of consistency and limited quality in addressing requirements for victims and rescue brigades before, during, and after earthquakes, as outlined in civil protection programs [5-8].
## 3 Simulation Results and Analysis  
Simulations utilizing 90 nm CMOS technology were conducted to evaluate the performance of the RSLP16T cell. The superiority of the RSLP16T was highlighted through comparisons with various conventional cells, such as WE-QUATRO [12], QUCCE12T [9], RHD12T [14], RSP14T [15], and RHPD14T [16]. During this process, the cell ratios of the transistors comprising the comparison cells were implemented according to the conditions presented in their respective studies.  
In this research, the most significant improvement in performance is leakage power, as SRAM cells predominantly operate in the hold state without frequent recharges. Minimizing leakage power during this state significantly influences the overall power consumption of the cell [1, 10]. The simulation results comparing leakage power in the hold state (HPWR) are outlined. In the hold state, leakage power arises from two primary sources: leakage through the bit lines and leakage from the internal inverter structure. The leakage through the bit lines contributes more significantly to total leakage power due to the higher carrier mobility of NMOS transistors compared to PMOS. Meanwhile, the leakage power from the internal inverter structure is reduced when there is higher resistance in the pull-up and pull-down paths of the inverter. The RHPD14T cell, which has access transistors made up of two pairs of NMOS, experiences significant leakage power due to its characteristics, resulting in the highest leakage overall. In contrast, the RSLP16T also contains two pairs of transistors but achieves the lowest leakage power. This advantage arises from the higher ratio of PMOS transistors within its internal structure, with two of the four access transistors being PMOS. Moreover, as illustrated in Figure 3, the arrangement of internal transistors in a stacking configuration [19] effectively minimizes leakage power, further contributing to the efficiency of the RSLP16T design.
## 4 Experimental Environment and Results  
This study presents the findings of systematic field research and the empirical analysis of a seamless technology framework for elderly-centered interactive systems. The research was conducted in two phases: phase one involved field testing across 25 sites with 659 participants, while phase two was expanded to more diverse settings with 1338 participants. Both quantitative tools (the SUS usability scale) and qualitative methods (observations and interviews) were employed to evaluate the system's effectiveness.
## 3 Results  
### Synthesis of ADIP-PS with Various Molecular Weights  
ADIP-PS was synthesized as an antibacterial additive. Using toluene as the solvent at 40 \({}^{\circ}\)C for 24 h, ADIP-PS (AD-15K) with an \(M_{n}\) of 15,000 and \(M_{w}\) of 54,000 was obtained in 40% yield. Because of the relatively low molecular weight of AD-15K, we attempted to synthesize higher-molecular-weight ADIP-PS by increasing the reaction temperature or extending the reaction time. ADIP-PS (AD-23K), synthesized over 48 h, had a \(M_{w}\) of 120,000 and a 56% yield, whereas ADIP-PS (AD-40K), synthesized at 60 \({}^{\circ}\)C, had a \(M_{w}\) of 310,000 and a 33% yield (Table 2). The ADIP content in PS was calculated by determining the number of nitrogen atoms exclusively in ADIP, revealing the proportion of ADIP relative to the entire molecule decreases as \(M_{w}\) increases (Table 2). FTIR analysis confirmed the absence of residual styrene, indicated by the disappearance of the band at 1683 cm\({}^{-1}\), which corresponds to the double-bond stretching vibration of the vinyl group. The presence of ADIP functional groups was confirmed by bands at 1150 and 1270 cm\({}^{-1}\), characteristic of trifluoromentanesulfonate (triflate) ions, likely corresponding to CF\({}_{3}\) and SO\({}_{3}\) stretching modes (Figure S1). Additionally, strong bands at approximately 750 and 700 cm\({}^{-1}\), associated with out-of-plane CH bending in the benzene ring, were observed, and these are characteristic of PS. Based on these findings, the synthesis of ADIP-PS was confirmed. Notably, when AD-23K, a representative ADIP-PS, was left at 90 \({}^{\circ}\)C for 72 h and analyzed by FTIR, no significant changes were observed in the bands derived from the functional groups of ADIP (Figure S2). This suggests that ADIP is stably bonded with the PS. Given that ADIP-PS with a \(M_{w}\) similar to GPPS was obtained, further investigation proceeded using these three ADIP-PS samples. All samples were molded into sheets via hot pressing, and they exhibited significant antibacterial activity (Figure 2). The antibacterial properties of ADIP-PS sheets were evaluated according to ISO 22196:2011, a standard for plastics [33]. Although ISO 22196 typically assesses antibacterial activity against _E. coli_ and _S. aureus_, only _S. aureus_ was used in this study. Although antibacterial ADIP-PS was synthesized using propan-2-ol in previous studies [25], the ADIP-PS synthesized with toluene also showed high antibacterial activity.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \(M_{n}\)1  & \(M_{w}\)1  & \(M_{w}\)1  & Yield (\%) & \(N\)Content & ADIP Residue \\  & & & & & **(wt\%)** & **Content (mol\%)** \\ \hline AD-15K & 15,000 & 54,000 & 3.6 & 40 & 0.83 & 4.3 \\ AD-23K & 23,000 & 120,000 & 5.3 & 56 & 0.66 & 3.4 \\ AD-40K & 40,000 & 310,000 & 7.7 & 33 & 0.55 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Yield, molecular weight, and ADIP residue contents of the obtained ADIP-PS.
# Fault-Tolerant Model Predictive Control Applied to a Sewer Network
## 5 Results and Discussion
### Fault Detection, Diagnosis and MPC Reconfiguration Tests
#### 5.3.2 Scenario 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **No Control** & **Normal MPC** & \
\begin{tabular}{c} **MPC with u3** \\ **Fault** \\ \end{tabular} &
\begin{tabular}{c} **Reconfigured** \\ **MPC** \\ \end{tabular} \\ \hline \(N_{ov,1}\) & 0 & 5 & 5 & 5 \\ \(N_{ov,2}\) & 0 & 1 & 4 & 4 \\ \(N_{ov,3}\) & 2 & 5 & 7 & 7 \\ \(N_{ov,4}\) & 5 & 5 & 5 & 5 \\ \(N_{ov,5}\) & 1 & 4 & 4 & 4 \\ \(N_{ov,WTP}\) & 7 & 6 & 6 & 6 \\ \(T_{ov,1}\) & 0 & 0.0604 & 0.0604 & 0.0542 \\ \(T_{ov,2}\) & 0 & 0.0250 & 0.0500 & 0.0542 \\ \(T_{ov,3}\) & 0.0396 & 0.0729 & 0.6708 & 0.6667 \\ \(T_{ov,4}\) & 0.1271 & 0.1333 & 0.1167 & 0.1187 \\ \(T_{ov,5}\) & 0.0187 & 0.0813 & 0.0750 & 0.0750 \\ \(T_{ov,WWTP}\) & 0.4562 & 0.7708 & 0.7354 & 0.7354 \\ \(V_{ov,1}\) & 0 & \(6.9554\times 10^{3}\) & \(6.4800\times 10^{3}\) & \(5.6885\times 10^{3}\) \\ \(V_{ov,2}\) & 0 & \(385.1159\) & \(1.0779\times 10^{3}\) & \(1.4689\times 10^{3}\) \\ \(V_{ov,3}\) & \(1.1834\times 10^{3}\) & \(2.5109\times 10^{3}\) & \(1.3599\times 10^{4}\) & \(1.3222\times 10^{4}\) \\ \(V_{ov,4}\) & \(1.1874\times 10^{4}\) & \(1.8044\times 10^{4}\) & \(1.3507\times 10^{4}\) & \(1.3430\times 10^{4}\) \\ \(V_{ov,5}\) & \(3.2433\times 10^{3}\) & \(1.9535\times 10^{4}\) & \(1.7682\times 10^{4}\) & \(1.7603\times 10^{4}\) \\ \(V_{ov,WWTP}\) & \(7.1094\times 10^{4}\) & \(219.6231\) & \(200.5045\) & \(201.2536\times 10^{3}\) \\ \(V_{ov}\) & \(8.7395\times 10^{4}\) & \(4.0695\times 10^{4}\) & \(4.6066\times 10^{4}\) & \(4.5926\times 10^{4}\) \\ \(Q_{WWTP}\) & \(3.7001\times 10^{4}\) & \(4.1641\times 10^{4}\) & \(4.0910\times 10^{4}\) & \(4.0938\times 10^{4}\) \\ \(G_{u}\) & 61.6680 & 69.4022 & 67.2835 & 68.8092 \\ \(S\) & - & \(6.3711\times 10^{10}\) & \(6.3251\times 10^{10}\) & \(6.3165\times 10^{10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: System performance in every case: fault in tank 3 gate (scenario 3).
# MIMO Relaying UAVs Operating in Public Safety Scenarios
## 1 Introduction
### UAV in D2D Communication Using MIMO  
UAVs add a degree of complexity to sidelink communications due to the joint UAV mobility control and communication resource allocation problem, the unique channel of UAV-ground links, and the possible doppler effects related to speed differences between cars and UAVs. Massive multiple-input-multiple-output (mMIMO) technology is the next feature to be standardized. In Release 16, only a 2 \(\times\) 2 in 2 layers is mentioned to enhance system performance [27] and mMIMO can exploit array gain, spatial multiplexing, and channel reciprocity to provide communication using sidelink protocols in UAVs [30]. [PERSON] et al. [31] compares mMIMO in single-user and multi-user scenarios. [PERSON] et al. [32] describe achieving the throughput in relay systems by only increasing power. [PERSON] [33], uses MIMO techniques to estimate the channel between UAVs and internet of Things (IoT) devices. Communication constraint studies, such as [PERSON] et al. [18], suggest that the implementation of MIMO technology in D2D relay systems improves power management through energy harvesting techniques. The author analyzes outage probability and throughput.  
Two well-known MIMO techniques related to diversity and spatial multiplexing are the Alamouti [34] and zero-forcing (ZF) algorithms [35], respectively. The Alamouti (2 \(\times\) 2) provides a second-order diversity advantage and the message is decoded with a simple algorithm [36]. In this case, the operation \({}^{\star}\) represents the complex conjugate, the receiver signal \(y_{1}\) and \(y_{2}\) can be expressed as:  
\[\left\{\begin{array}{c}y_{1}=h_{1}c_{1}+h_{2}c_{2}+n_{1}\\ y_{2}=-h_{1}c_{2}^{\star}+h_{2}c_{1}^{\star}+n_{2}\end{array}\right. \tag{2}\]  
where \(h_{1}\) and \(h_{2}\) represent the channel while \(n_{1}\) and \(n_{2}\) are the complex random variables related to noise and interference and \(c_{1}\) and \(c_{2}\) are the transmitted signal from the first and second antenna respectively.  
In the zero-forcing algorithm, the receiver signal \(y\) (Equation (3)) is combined linearly and it can be expressed as a composition of the signal transmitted \(s\) and precoded using the Zero forcing-ZF technique, the transmit filter \(P\), the channel \(H\), and, the noise \(n\) are received at several receivers. \(G\) is the receiver filter with is unitary. In this case, the operation \({}^{\star}\) represents convolution.  
\[y=H*P*G*s+n\ \in\ \mathbb{C} \tag{3}\]  
where \(P\) is given by:  
\[\mathrm{P}=H^{\prime}*\left(H*H^{\prime}\right)^{-1} \tag{4}\]  
Both techniques are compared in the NS3 LTE physical layer, where the simulator granularity is the resource block.
## 3 Nonthermal Methods
### Intense Pulsed Light (IPL)  
Intense Pulsed Light (IPL) sterilization technology, a nonthermal method, uses a gas lamp (e.g., Xenon or Krypton) to generate light with wavelengths similar to sunlight. IPL, also known as high-intensity pulsed ultraviolet light, effectively reduces microbial contamination of food. Studies show that IPL treatment has resulted in significant reductions in pathogens such as _Salmonella_, _Campylobacter_, _Escherichia coli_, and _Listeria_. The efficiencyvaries depending on factors such as treatment conditions and product characteristics, with minimal impact on the quality of the treated food products [82].  
All these modern nonthermal methods not only meet the increased requirements for safety and quality but also contribute to environmental protection by reducing the use of chemicals and energy consumption. Therefore, nonthermal technologies offer efficient and innovative solutions for the control of _L. monocytogenes_ contamination in the food industry. The combined use of these methods can provide more effective control, reducing the risks of food poisoning and extending the shelf life of products. These approaches contribute to increasing food safety while preserving the quality and freshness of food products [83].