esa-sceva/satcom-chunk-collection huggingface dataset:
# Microgrid Cyber-Security: Review and Challenges toward Resilience
## 4 Perspective-Based Interventions Addressing Cyber Attacks
### Microgrid Control
#### 4.3.2 Automation Control against Cyber-Attacks
Later, the same DC microgrid model was used in another experiment using artificial intelligence in [56]. A Nonlinear Auto-Regressive Exogenous (NARX) neural network was trained over the previously mentioned control method during offline operation, capturing and storing its behavior, only to be used then as an online estimator for DC voltages and output currents of each unit. The FDI attacks detectability of this method was built on the estimation errors making it suitable for a larger spectrum of DC microgrid, in contradiction to the cases presented in [49,57] that only suits those functioning with cooperative consensus-based algorithms. The FDI problem shaping in terms of determining the aspects that could be altered by such an attack was the subject of [58], in which a detection method was built on the assumption of the attack capability to modify the invariant values required in the secondary distributed control layer. A new technique for optimal dynamic state estimation, based on a distributed algorithm for multiple connected DC microgrids under FDI attack, was proposed and tested over malicious and normal load disturbance in [47], proving its capability of distinguishing between the two cases. Unlike previous literature that dealt with DC microgrids as quasi-static models, this work employed a dynamic microgrid paradigm where the three DC connected microgrids employed in the study collaborated under a control configuration, that enabled each of them to verify the security status of the other two, making it possible to isolate the potentially infected entity.
# Solution-Processed CdTe Thin-Film Solar Cells Using ZnSe Nanocrystal as a Buffer Layer
## 1 Introduction
[26] fabricated CdTe-TiO\({}_{2}\) heterojunction solar cells by introducing p type spiro-OMeTAD as the hole transport layer for hole collecting. [PERSON] et al. obtained a PCE coupled with 0.71 V of \(V_{\infty}\) as high as 6.56%. The \(V_{\infty}\) of NC solar cells was further improved when the Sb-doped TiO\({}_{2}\) was used as the electron acceptor material instead of the un-doped TiO\({}_{2}\)[27]. It is noted that CdSe or CdS is low bandgap semiconductor (about 1.8 eV for CdSe and 2.4 eV for CdS), which will render the spectrum response in a short wavelength for CdSe (or CdS) NC thickness up to 100 nm. Therefore, it is necessary to quickly develop new electron acceptor materials with a wide bandgap for the further commercial application of CdTe NC thin-film solar cells. Compared to CdS or CdSe, ZnSe is a wide bandgap (2.7 eV) and Cd-free semiconductor. Therefore, it is expected that the short wavelength response of CdTe solar cells will be improved using ZnSe buffer layer materials since it allows more light to pass without being absorbed. Sputtering ZnSe thin film was applied as a buffer layer successfully in CdTe thin-film solar cells [28; 29] prepared by the close space sublimation method. However, there is still no report on CdTe NC solar cells with a ZnSe NC buffer layer prepared by solution-processing. In this paper, we report on the synthesis of high-quality ZnSe NC using the hot injection method. Devices with a structure of ITO/ZnO/ZnSe/CdTe/MoOx/Au were fabricated through layer-by-layer sintering. The effects of ZnSe NC thickness, the annealing temperature, and ligand treatment on the NC solar cells were investigated in detail. Our research study illuminates that, compared to devices without 1,2-ethanedithiol (EDT) treatment, higher \(V_{\infty}\) and PCE can be attained in devices with EDT treatment, which accounts for the low defect intensity and high junction quality.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 4 Simulation Results
### Aggregate EPFD
Use filters with better cut-off characteristics and an amplifier with a better linearity to work in the linear region of amplification as far as possible. * Adopt the interference avoidance zone of the RAS telescope, as well as a sharper roll-off antenna of the NGSO satellites. The above interference avoidance methods can reduce the interference of NGSO systems with RAS telescopes, thus further enabling coexistence between them.
# Calibration of Model Parameters for Soda Saline Soil-Subsoiling Component Interaction Based on DEM
###### Abstract  
To apply the discrete element method (DEM) to simulate the interaction process between soda saline-alkali soil and subsoiling component in Northeast China, establishing the soda saline-alkali soil particle model and selecting more accurate simulation parameters are important. In this paper, we studied the soda saline-alkali soil of the Songpan Plain in China. First, we studied the geometric shape of soda saline-alkali soil particles and proposed a modeling method for single soil particles based on the multisphere combination method. Considering the cohesion of soda saline-alkali soil particles, the Hertz-Mindlin with JKR (JKR) model was used as the contact model between soil particles. Then, the calibration method was used to obtain simulation parameters of soils that are difficult to obtain experimentally. We conducted soil angle of repose (AoR) tests, the Plackett-Burman (PB) tests, and steepest ascent (SA) tests in turn to perform a sensitivity analysis for microscopic contact parameters and select the parameters that have a significant effect on the response value (static AoR), i.e., soil surface energy, soil-soil static friction coefficient, and soil-soil rolling friction coefficient. Then, the optimal combination of simulation parameters was obtained via the Box-Behnken (BB) tests, using ANOVA to optimize the multiple regression equation. Finally, the optimal parameter combination was verified by the AoR test and the direct shear (DS) test. The results showed that the parameters had good adaptability for the AoR test. However, the simulation results of the DS test were significantly different from the experimental values. Therefore, the contact model needs to be further modified by adding Bonding bonds between soil particles based on the JKR model and further correcting for Rayleigh time step, shear modulus, and surface energy. By comparing the simulation and the experimental results, it was found that the simulation results obtained from both the DS test and AoR test had relatively small errors relative to physical tests, the two trends are the same, and the values are similar. This verified the feasibility and effectiveness of the soda saline-alkali soil particle modeling method and parameter selection proposed in this paper.  
soda saline-alkali soil; contact model; parameter selection; DEM 2023  
20 October 2023
# X-ray Photoelectron Spectroscopy in Mineral Processing Studies
## 4 Towards in Situ XPS  
### XPS Studies of Solid-Liquid Interfaces  
The application of in situ and _operando_ XPS [171, 172, 173, 174, 175, 176, 177, 178, 179] is a vital problem in catalysis, materials science, electrochemical power sources, and other fields, and the minerals processing is not an exception. The behavior of minerals occurs at the interface between the aqueous phase and the (oxidized) minerals, which decays upon routine sample preparation and drying. XPS operation in the systems containing aqueous solutions is complicated by the high pressure of water vapor \"forbidden\" for the UHV installation and the attenuation of photoelectrons in gas and liquid phases. At present, there are two main approaches to this problem.
# Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation
## 3 Materials and Methods
### Paths Tested
#### 3.1.3 Propensity Versus Accuracy  
To assess whether Propensity score is a good indicator for prediction accuracy (Question 4), we compare both measures across the different datasets and synthesizers and check if they are correlated.  
Propensity score calculation involves the construction of a classification model. The vast majority of published propensity score analyses uses logistic regression to calculate the score, however they rarely indicate how the model is constructed, and when they do, they only consider first order interactions to build the model [38; 44]. In [41], the authors emphasized that the choice of the propensity score model is critical for its efficacy in comparing synthesized datasets. The authors then evaluated different logistic regression propensity score models, and found that it was important to include higher order terms (including cubic terms). In a more recent investigation, [PERSON] at al [38] expanded the propensity score models to include CART. They present a recommendation for calculating propensity: For simple synthetic data sets, logistic models with first-order interactions are to be tried first. For more complex data, logistic models with higher order interactions should be considered (cubic if computationally feasible) as well as CART models. We follow the recommendation of [PERSON] et al. [38] for Propensity score calculation (we note that in almost all datasets, CART offered a better model for distinguishability).  
When calculating propensity, missing values in categorical features are treated as a new category, and missing values in continuous features are processed, as per the recommendation of [PERSON] and [PERSON] [45], by adding an indicator variable for each such feature and setting missing values to 0.
# Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization
## 3 System Proposed
### WSN Energy Comparison and Selection
(3) A final option is choosing a complete solution offered by companies like Actility [46], Loriot [47] or Senet [48], among others. In some cases, they provide turnkey solutions for the infrastructure, data storing and even data analytics. Table 3 presents options for the implementation of the LoRaWAN network server (and all the software involved) along with some of their features. For our system, we selected TTN [84] as LoRaWAN the server network because of its collaborative deployment and free hosting option. This allows the use of the deployed network by all of the TTN community and provides a service that integrates with third-party applications at the same time. Consequently, the deployment of a LoRaWAN network benefits the entire area where the system is implemented. It provides the region with an infrastructure to develop applications based on this kind of technology and therefore, improves the technological development of the area. The system proposed in this work is decoupled from the LoRaWAN network server and application server so that any change in this element does not affect the other parts of the system.
# Mobile Robotics in Agricultural Operations:
## 2 Materials and Methods
### Methodological Approach  
This research applied the 'traditional narrative' review approach to analyze the extant literature pertinent to planning aspects of mobile robots in agriculture, while aiming to provide insights in the field [35]. In particular, the 'traditional narrative' review approach involves informal mechanisms to organize and analyze the existing literature in a field [36]. To that end, a small number of articles in peer-reviewed scientific journals were selected which helped to identify key authors and other relevant studies to the particular topic of planning agricultural robotics. The selection of the 'traditional review' approach, instead of a more systematic review, was motivated by the intention to close the gap on fundamental knowledge in the field of agricultural robotics planning which currently lacks any concise and formal definitions beyond scattered and random empirical knowledge [37]. Instead, we attempted to identify a set of papers that investigate planning aspects of mobile robots (both UGVs and UAVs) in main agricultural operations to gain insights.  
The taxonomy of the selected articles is based on the attributes of the presented planning approaches. As previously presented, in agricultural mobile robots, seven planning attributes have been recognized. These attributes can grouped into three main categories: (i) High-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion-task connection and capacity constraints, and iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics (Figure 4).  
Furthermore, the articles are also taxonomized based on the findings of the corresponding approaches. These findings include:  
1. Operational environment. Typically, three types of agricultural environments are recognized, including: (a) unstructured environments (arable farming, e.g., wheat and corn); (b) semi-structured environments (open-air horticulture, e.g., orchards and vineyards); and (c) structured environments (greenhouses).
2. Operation. This regards physical agronomic operation, e.g., harvesting, spraying.
3. Approach validation. This regards the validation means of the presented approach, namely, through simulation, lab experiments, or trials in a physical environment.
## 4 Integrated Approach for QoS Assurance in IP/Optical Networks
### Traffic-driven Lightpath Provisioning Based on the Service Plane  
One of the key roles of the service plane is to forecast the behavior of traffic entering the IP/optical network and to automate the generation of provisioning requests by IP routers to the GMPLS control plane. This is based on applying the concept of self-managed networks described in Section 2.2. In a first stage, the service plane will be optimized for a wavelength-routed all-optical network, and then upgraded to Ethernet over this network. Taking this into account, the basic functionalities of an SCE, especially those related to the characterization of IP traffic into flows that can be translated into lightpath services are to be validated in the ADRENALINE Testbed(r). In other words, the SCE is acting as the self-management engine of the proposed approach, and performs the functions depicted in Figure 2. The IP flows considered in this work include heavy-hitter flows and elephant IP flows, and are analysed and characterized by the 'Traffic Characterizer' component of the SCE. This component is fed with real-time measurement information from the IP router using one or more techniques, such as packet sampling, provided by the 'Monitor' element in Figure 4. These techniques are applied to the traffic already being transported in the network, so that proactive actions can be taken in a timely manner if it can be inferred from the measurements that the IP layer should be decongested, _i.e._, by offloading. Note that most network standards focus on assuring QoS in the set up process, that is, for new services. For example, in Carrier Ethernet networks, Ethernet probing can be applied during provisioning to validate that Ethernet services are turned up according to the SLAs. In MPLS with Traffic Engineering (MPLS-TE) networks, end-to-end routing paths are set with QoS parameters before forwarding data, reserving resources in one aggregated class. In both cases, once the services have been set up, continuous SLA conformance reporting through proactive service and network monitoring is the basis for any detection of SLA violations and subsequent corrective action.  
A key aspect for traffic-driven lightpath provisioning is the necessary trade-off between flow duration and decision time, which in the proposed approach will be calculated by the 'Decision Maker'  
Figure 4: Proposal for QoS-oriented service plane architecture.  
component of the SCE taking into account the applicable QoS policies. Note that timeliness in decision making, _i.e._, selecting a flow to be offloaded to the optical layer, is very important because if the decision is taken too soon there is a high probability that the flow is not going to last long enough, while if it is postponed, the flow will be consuming resources at the IP layer unnecessarily [1]. For example, [PERSON] _et al._ propose to choose an elapsed duration of 5 minutes to define a flow as being persistent in time because flows with this or higher duration represent over 70% of the total traffic [1].
## 3 System Architecture and Modeling
### Load Categorization
#### 3.1.1 Thermal Load  
This category includes the electrical load consumed by the appliances which are thermal in nature, i.e., their system dynamics include temperature as a system-state, e.g., heat pump, room heater, water heater, refrigerator, etc.
## 4 Analysis of Factors Affecting the Reflection Coefficient
### Effects of Ice Thickness on Reflection Coefficients  
Figure 6a depicts the reflection coefficients at different thicknesses of the first layer while keeping the second layer invariable. On the contrary, the second layer's ice thickness varies and the thickness of the first is constant in Figure 6b. An apparent phenomenon in these two figures is that the reflection coefficients decrease with the increase in thickness in most cases. This is because the length of tubular pores increases with the thickness, and the waves trapped in pores could be reflected more times, thus causing more energy losses. Furthermore, it can be seen in Figure 6a that the change in the first layer's ice thickness at small values has a minor effect on the reflection coefficient. Contrarily, Figure 6b shows that the reflection coefficient is deeply influenced by the second layer's ice thickness. To further illustrate this matter, a comparison was made between the double-layered porous model and the single-layered porous model at the same thickness in Figure 6c. The solid line represents the 1.5-m thickness of the single-layered model and the other two lines signify the 1- and 0.5-m thickness of the two layers in the double-layered model, in which the dashed line represents a first layer of 1 m and a second layer of 0.5 m and the dotted line is the opposite. As Figure 6c shows, these lines have the same trend to some degree but differences in detail. Consequently, the layering and the distribution of thickness of sea ice have an effect on the reflection coefficient, and it is necessary to establish the layered model.
## 4 Results and Discussion
### Horizontal Openings Effect on Compartment Fire Dynamics
#### 4.2.1 Steady-State Burning  
A dwelling will achieve its highest heat release rate and thus the highest values of heat flux to the environment during the post-flashover \"steady-state\" burning regime. It is therefore important to identify the likely start (usually at flashover) and end (beginning of the decay phase) of this regime. However, for some of the experiments with larger horizontal opening areas, the behaviour exhibited did not necessarily conform to classic criteria for flashover. Therefore, it was necessary to define two criteria for identifying the start and end of the steady-state burning period:  
* Classic behaviour Start--the first instance at which the gas temperature at the roof reached 525 \({}^{\circ}\)C (flashover). End--the point at which the heat flux 30 cm from the door first returns below the value it was at flashover.
* Well-ventilated behaviour Start--the first instance at which the heat flux 30 cm from the door exceeds 2 kW/m\({}^{2}\). End--the first instance after the peak value that the heat flux 30 cm from the door drops below 2 kW/m\({}^{2}\).
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 5 Simulations and Discussion
### Performance Comparison of AC-ETO and a Greedy Algorithm  
We further compare the performance of the proposed AC-ETO algorithm with a greedy algorithm shown in Algorithm 1. Initially, the feasible table--i.e., FCL Table of each node--is established to store the CLs within the communication coverage. All CLs in the table are grouped by type in increasing order of the communication distance. To set up a path, each node along the path is selected by checking CLs in the FCL Table of the prior node in an order, until the BS is reached. The network construction completes when all paths are found for any MP.  
As shown in Figure 7(a), the deployment cost of AC-ETO is lower than that of the greedy algorithm. In Figure 7(b), it is found that the energy consumption of the greedy algorithm in different scenarios is higher than that of AC-ETO. Accordingly, the network lifetime of AC-ETO is longer than that of the greedy algorithm in Figure 7(c). In the greedy algorithm, it is favorable to select a path with the minimum cost and, accordingly, the minimum energy consumption, which does not guarantee the cost and energy consumption of the overall network. While AC-ETO guides ants to find the optimal (approximate optimal) solution through two pheromones. Figure 7(d) shows that running time increases with the network size. For instance, in Scenario 7, the deployment cost obtained by AC-ETO is 447 gcu while the deployment cost obtained by greedy algorithm is 841 gcu, and the energy consumption obtained by AC-ETO is 5% less than that obtained by greedy algorithm. Thus, AC-ETO achieves better performance than that of the greedy algorithm in different scenarios, at the cost of increased but affordable time complexity.  
Figure 7: Comparison of Gurobi and AC-ETO in terms of deployment cost, energy consumption, network lifetime, and time complexity under various network scenarios.  
Based on the above analysis, it is observed that the AC-ETO algorithm outperforms the greedy algorithm and approaches the optimal solutions in different scenarios which may be difficult for Gurobi to solve.
## 5 Simulation Experiments and Numerical Results Analysis  
To evaluate the effectiveness of the proposed speed guidance model, a simulation experiment was designed using Python and SUMO to construct the simulation scenario. By comparing parameters such as platoon delays and the number of stops before and after guidance, the impact of the speed guidance strategy under different traffic volumes and penetration rates was analyzed.