esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical and Experimental Results
For a clearer description of the results of reinforcement learning, the blue shaded area in Figures 13-18 and Figures 20 and 21 presents the replanning of the original strategy when the actual speed at the micro level deviates from that planned by the macro model, and the grey shaded areas A and B in Figures 19 and 22 present the detailed difference for the engine operating area between the original strategy and the replanning strategy. The principle of re-optimization is to move the engine operation from the low efficiency area to the high efficiency area, that is, to replace area A with area B. The purpose of the replanning strategy is to convert the energy utilization of area A into that of area B, since the engine's fuel consumption rate for area B is lower than that for area A. The fuel consumption for the original strategy and the replanning strategy for Mode 1 and Mode 2 are shown in \[\frac{\partial k(x_{end},t)}{\partial t}=0,t\leq 1000\;\mathrm{s} \tag{27}\]0 Table 2 shows that the replanning strategy can reduce the fuel consumption by 9-10% compared with the original strategy, indicating that the replanning strategy can make further utilization of the engine's fuel saving potential by improving the efficient operation of the engine at high power and reducing its inefficient operation at low power. The replanning strategy has strong adaptability and can make energy management close to global optimization, despite the deviation in actual driving behavior from the initial predicted value, which may be caused by inevitable errors of the macro traffic flow model and traffic environment changes. The novel PHEV self-driving, from the perspective of the  
\[\frac{\partial k(x_{end},t)}{\partial t}=0,t\leq 1000\;\mathrm{s} \tag{27}\]1 The difference between the original and the replanning strategies for Mode 2.  
full space-time chain, achieved the integrated management of transportation planning and energy utilization.
# Analysis and Application of Selected Control Charts Suitable for Smart Manufacturing Processes
## 2 Theoretical Background  
In the technical literature, numerous examples of the utilization of parametric statistical process control when data come from the normal distribution can be found. Unfortunately, in practice, there are many processes where normality cannot be met. The results of classical parametric statistical process control may not be correct in these cases (see [23; 24; 25]). These sources provide sufficient justification for the development and practical application of control charts in cases of noncompliance with data normality or other data preconditions for the use of parametric control charts. NPCCs and distribution-free control charts have been designed to achieve this aim. The term nonparametric is not entirely accurate, as this would mean that no parameter needs to be involved. This is not true because at least the assumption of a continuous distribution is required. The notion of distribution-free seems to be more correct because it captures the essence of these control charts It means they are not dependent on a specific probability distribution, mostly the normal distribution. However, these terms are taken as synonyms, and the term nonparametric is used more frequently [10].  
In the same source, a very valuable overview of the literature on NPCCs can be found. The authors divided NPCCs into four classes: Shewhart-type charts, CUSUM-type charts, EWMA-type charts and other methods.  
Shewhart-type control charts are practically applicable because of their relative simplicity. The first nonparametric Shewhart procedure for monitoring the location parameter of a continuous process when the value of the parameter for the statistically stable process is not specified was described in [26]. Other Shewhart-type methods are discussed in [27; 28].  
CUSUM-type control charts are more suitable for the detection of small changes in processes. In 1975 [PERSON] [29] introduced the nonparametric CUSUM based on sequential signed ranks of observations. Other contributions to nonparametric CUSUM-type charts can be found in [30; 31; 32].  
Various EWMA-type charts, also suitable for detecting small changes, are described for instance in [33; 34; 35; 36; 37; 38].  
In addition to the previous three classes of nonparametric methods, the fourth class of NPCC defined in [10] covers various special cases that were introduced for instance in [15; 21; 39; 40; 41]. [PERSON] and [PERSON] in [15] compared the classical Shewhart X-bar control chart with the MAD (median absolute deviation) control chart. In [21], a new NPCC MIN is presented. Interesting special nonparametric methods are discussed in [39]. [PERSON] and [PERSON] in [40] suggested an NPCC working with the Mood statistic.  
In the following subsections there are described in more detail some NPCCs that were selected for analyses described in this paper.
## 4 Experimental Evaluation
### Scenario 3: Multiple STAs  
As primary investigations, the above two scenarios evaluated the communication quality between one STA and one AP. However, multiple STAs exist in an actual environment. Therefore, this section explains how to assess the scenario, and it examines the impact of communication quality by multiple STAs. As an example, we employ the two STAs located at different distances. The scenario will give more interesting insights than a scenario for two STAs at the same distances. Section 4.4.1 explains the measurement environment, and Sections 4.4.2 and 4.4.3 examine the impact of communication quality on the IP and wireless network layers, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Tx Bitrates on TCP Sender Side**} \\ \hline Mb/s & 10 m & 20 m & 30 m & 40 m & 50 m & 60 m & 65 m \\ \hline N/A & 0 & 0 & 0 & 0 & 0 & 0 & 1.00 \\
27.5 & 0 & 0 & 0.08 & 0.33 & 0.08 & 0.08 & 2.50 \\
385.0 & 0 & 0 & 0.16 & 0.33 & 1.83 & 0.33 & 1.41 \\
770.0 & 0 & 0 & 0.83 & 0.91 & 12.91 & 5.41 & 5.50 \\
962.5 & 0 & 1.00 & 3.58 & 3.41 & **47.50** & 8.50 & 9.66 \\
1155.0 & 0 & 1.83 & 14.41 & **93.91** & 34.58 & **85.50** & **71.16** \\
1540.0 & 0 & 19.16 & **73.50** & 1.08 & 2.83 & 0.16 & 8.75 \\
1925.0 & 4.08 & **70.58** & 7.41 & 0 & 0.25 & 0 & 0 \\
2310.0 & **95.91** & 7.41 & 0 & 0 & 0 & 0 & 0 \\ \hline Mb/s & 70 m & 75 m & 80 m & 85 m & 90 m & 95 m & 100 m \\ \hline N/A & 0 & 0 & 0 & 0 & 0 & 29.08 & 33.75 \\
27.5 & 10.33 & 4.25 & 6.00 & 0.16 & 2.50 & **63.08** & **60.50** \\
385.0 & 19.83 & 18.58 & 30.41 & 7.00 & 11.58 & 7.25 & 5.66 \\
770.0 & **43.00** & **75.75** & **62.58** & **87.16** & 36.66 & 0.58 & 0.08 \\
962.5 & 26.83 & 1.41 & 0.91 & 5.66 & **47.08** & 0 & 0 \\
1155.0 & 0 & 0 & 0.08 & 0 & 2.16 & 0 & 0 \\
1540.0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
1925.0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
2310.0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Tx bitrates on sender side (Scenario 2).  
Figure 11: Packet loss of TCP and UDP communications on both sides (Scenario 2).  
Figure 12: RSSI of both sides for TCP and UDP communications (Scenario 2).
## 4 Calculation of Environmental Impact and Economic Effect
### Economic Effect of CV Joint Replacements  
Remanufacturing typically requires less energy, labor, and raw materials. Consequently, the cost of remanufactured automotive parts is relatively lower than that of new ones. Due to the differences in retail prices and production costs between new and remanufactured parts, profit calculations are performed to evaluate the economic effects of increasing the proportion of remanufactured parts. The profits for new and remanufactured parts are calculated using Equations (2) and (3).  
\[\pi_{N}=P_{N}-C_{N}\;for\;a\;new\;CV\;joint \tag{2}\]  
\[\pi_{R}=P_{R}-C_{R}\;for\;a\;remanufactured\;CV\;joint \tag{3}\]  
where \(\pi_{N}\) and \(\pi_{R}\) are the profit of new and remanufactured parts, respectively, \(P_{N}\) and \(P_{R}\) are the retail price of new and remanufactured parts, respectively, and \(C_{N}\) and \(C_{R}\) are the estimated total cost of new and remanufactured parts, respectively.  
Although the retail prices of automotive parts (both new and remanufactured) are well known, estimating their production costs is challenging. Therefore, certain assumptions have been made to calculate the profit of remanufactured automotive parts. Obtaining the exact manufacturing cost of automotive parts is almost impossible, as it is treated as proprietary information. Consequently, the cost is estimated using the company's operating profit-to-sales ratio in the automotive parts industry.  
The detailed parameters used to calculate the profit from automotive parts replacement are presented in Table 2. New and remanufactured automotive parts have different retail prices, production costs, and profits. The total profit can also vary depending on the standard vehicle age and consumer preferences. Sensitivity analysis can be conducted by varying the preference ratios, retail prices, and production costs.
## 2 Materials and Methods
### Characterization Methods  
The pore size, pore volume, and specific surface area of the produced activated carbons were analyzed based on nitrogen adsorption-desorption isotherms at the temperature of liquid nitrogen using a Micromeritics ASAP 2020 system (Norcross, GA, USA). Prior to measurements, samples were pretreated at low pressures and high temperatures to clean the surfaces. Sample tubes were immersed in liquid nitrogen (-197 \({}^{\circ}\)C), nitrogen gas was added to the samples in small steps, and the resulting isotherms were obtained. Specific surface areas were calculated from adsorption isotherms according to the Brunauer-Emmett-Teller (BET) method [24] and pore size distributions were calculated using the Barrett-Joyner-Halenda (BJH) algorithm [25]. Sample degassing was performed by elevating the temperature to 50 \({}^{\circ}\)C under restricted pressure drop to 15 mm Hg followed by a 10-min hold at 50 \({}^{\circ}\)C. After the hold period, the samples were heated 10 \({}^{\circ}\)C/min up to 140 \({}^{\circ}\)C under free evacuation. Finally, samples were degassed at 140 \({}^{\circ}\)C for 3 h. This procedure gives a final, constant pressure of 2 \(\upmu\)m Hg.  
The content of carbon present in each sample, given as total carbon (TC) percent, was measured using a solid-phase carbon analyzer (Skalar Primacs MCS, Breda, The Netherlands). Dried samples were weighted in quartz crucibles and combusted at 1100 \({}^{\circ}\)C in an atmosphere of pure oxygen, and the formed CO\({}_{2}\) was analyzed by the infrared (IR) detector of the instrument. Carbon content values were obtained by reading the signal of the IR analyzer from a calibration curve derived from known masses of a standard substance, oxalic acid. The total mass of carbon was calculated as a percent of the mass initially weighted and was measured after the carbonization and activation steps.
## 2 Materials and Methods
### Preparation of P-BC-LDHs  
Rice straw loaded with LDHs (RS-LDHs) was initially fabricated through the chemical coprecipitation of aluminum and magnesium on the biomass surface, followed by pyrolysis under optimal conditions. To synthesize RS-LDHs, AlCl\({}_{3}\cdot\)6H\({}_{2}\)O (0.01 mol) and MgCl\({}_{2}\cdot\)6H\({}_{2}\)O (0.03 mol) were dissolved in 100 mL of deionized water, and 10 g of rice straw powder was simultaneously immersed in a solution with pH maintained at 10 \(\pm\) 0.5, using 1.0 M NaOH solution. After the mixture was incubated at 80 \({}^{\circ}\)C for 3 d, the feedstock precoated with LDHs was washed with deionized water and dried at 105 \({}^{\circ}\)C for 24 h. The production of BC-LDHs was performed in a horizontal electric tube furnace (New Separation Muffle AMF-N with Ceramic Electric Tubular Furnace ARF-30K, Asahi-Rika Co., Ltd., Aichi, Japan). Prior to commencing the experiment, the reactor was flushed with highly purified nitrogen gas for 30 min at a flow rate of 200 mL/min to provide an oxygen-deficient atmosphere. Subsequently, RS-LDHs filled in a quartz boat were subjected to a heating zone at 475 \({}^{\circ}\)C for 2 h, which is a suitable condition for converting biomass into biochar and calcining LDHs simultaneously [2]. An inert atmosphere was established throughout the pyrolysis process until the BC-LDH samples had cooled to room temperature. When phosphate adsorption was achieved with as-prepared BC-LDHs in phosphate-containing water prepared from KH\({}_{2}\)PO\({}_{4}\), the post-adsorption products were rinsed with deionized water and oven-dried at 105 \({}^{\circ}\)C for 24 h to obtain P-BC-LDHs.
###### Abstract  
We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evaportanspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.  
combined drought indices; SHAP; XGBoost; crop yield; agricultural drought Article Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Turkiye
## 1 Introduction
* Challenge 2: The existing simulation environment is based on the traditional network graph model, which only includes the basic elements and connection relations of the network and does not incorporate the security attributes and elements related to penetration tests. To address these challenges, we first propose a network graph model for the penetration test by extending the security-related properties on the basis of the traditional network graph model. By analyzing the characteristics and mechanism of social engineering methods, the modeling of social engineering exploitation actions by the network graph model is improved. Then, on the basis of the model, a penetration test model is established by considering various factors in the penetration test. The penetration test model is combined with the Markov decision process, which is the paradigm of the reinforcement learning method in the field of artificial intelligence, to establish an intelligent penetration test model extended by social engineering factors. Finally, on this basis, an intelligent penetration test simulation environment with extended social engineering factors is established. The proposed method can be used in various intelligent penetration test simulation environments. In summary, the main contributions of this paper are as follows:  
* To improve the exited network graph model and integrate it into security-related attributes and elements in the penetration test. We propose an improved network graph model for penetration test (NMPT); it could be better used to describe the penetration test process. * To expand the penetration tester agent actions and incorporate social engineering factors into the intelligent penetration test. We propose an intelligent penetration test simulation environment construction method based on social engineering factors (SE-AIPT). The integration of social engineering actions provides a new way and possibility for the penetration tester agent to discover the attack path during a penetration test. The research in this paper can further provide an interactive penetration test simulation environment and a platform for RL algorithms to train penetration tester agents. The rest of the paper is structured as follows: In Section 2, we review the related work of intelligent penetration tests and introduce the background knowledge. In Section 3, we present the improved network graph model NMPT and the technical details of the SE-AIPT method. In Section 4, we conduct a series of experiments and analyze the experimental results. In Section 5, we summarize the paper and analyze the prospects of future research.
## 1 Introduction
### Reference Building to Test the Efficacy of the Method  
The 3D two-story R/C building considered in the present investigation is schematically represented in Figure 3. All the structural details of this building are given in [2; 3]. The seismic behavior of this building, designed according to an old Italian regulation, was assessed in [2; 3], where the effectiveness of the seismic retrofit of the structure with fiber-reinforced polymer (FRP) was also evaluated. Non-linear time-history analyses (with spectrum-consistent earthquakes) were performed in [2], while the results of both pushover analysis and standard multi-record IDA were presented in [3].  
The building has a rectangular plan (9.80 m \(\times\) 15.50 m) with a fixed elevation height between floors (3.00 m) and fixed column sections (0.40 m \(\times\) 0.40 m). An exterior R/C staircase gives the structure an irregular configuration. The slabs are ribbed in the \(y\)-direction, which defines two types of frames: frames that support predominantly seismic loads (in the \(y\)-direction) and frames that support both gravitational and seismic loads (in the \(x\)-direction). The characteristics of the structural materials are shown in Table 1. Further details about the building geometry, its steel reinforcement, and the seismic retrofit with FRP can be found in [2; 3].  
A 3D finite element model was implemented with SeismoStruct [34], and the building non-linear behavior was introduced through a distributed plasticity model (fiber model). Each element section was split into at least 200 fibers. Structural members were divided into four elements to identify  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Type** & **Strength (MPa)** & **E (MPa)** & **G (MPa)** \\ \hline Concrete & C20/25 & R\({}_{\text{ck}}\) = 25 & 30,200 & 13,728 \\ Reinforcement & FeB44k & F\({}_{\text{yk}}\) = 430 & 20,600 & 79,231 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the structural materials.  
Figure 3: (a) Scheme of the reinforced concrete - building strengthened with FRP (b) rendering.  
different steel confinement zones, while the areas wrapped by FRP sheets to improve the member behavior (namely, at the ends of the ground-floor columns, see Figure 3) are considered separately, cf. [2,3]. The constitutive models of [PERSON] et al. [11] and [PERSON] [12] were adopted for concrete and reinforcing steel, respectively. The properties of the first six modes (namely, mode type, frequency, period, effective mass in the \(x\)- and \(y\)- direction) of the building are provided in Table 2.
## 1 Introduction
Furthermore, researchers currently realize that these two technologies may have a complementary relationship when combined to implement a fully secure and smart IoT system. On one hand, the capacity of deep learning for intelligent data analysis and decision making could make blockchain applications more effective and feasible. On the other hand, blockchain has the potential to aid deep learning by providing a large amount of data in a decentralized manner. To the best of our knowledge, no literature survey of the application of both deep learning and blockchain techniques for IoT security has been conducted to date. In this work, we summarize the existing works on the application of deep learning and blockchain to enable secure decentralized IoT systems. Furthermore, we discuss the importance and future challenges of integrating deep learning and blockchain in IoT systems. The main findings of this paper are summarized as follows. * We provide a literature survey of security methods for the IoT, including deep learning and blockchain. We also provide a summary of the primary benefits and drawbacks of various methods. * We discuss how deep learning and blockchain technology can overcome the current difficulties associated with ensuring the safety and reliability of IoT devices. * We highlight the contrast between decentralized blockchain-based security solutions and deep learning methods. The rest of this survey paper is organized as follows. In Section 2, we present an overview of the scope of this survey. We detail the current deep learning IoT security solutions and blockchain applications in IoT security in Sections 3 and 4, respectively. In Section 5, we present a discussion and explored future challenges associated with deploying deep learning and blockchain for IoT security.
###### Abstract
Compared with the results of various decomposition scales, the optimal decomposition scale is found to extract the frequency band signal of gear fault to eliminate the interference of coal flow load and scraper chain impact to the greatest extent. Aiming at the strong fundamental frequency interference in the current, the method of amplitude demodulation is used to eliminate those interferences. The demodulation signal can effectively retain the fault components in the current signal, and the Hilbert transform (HT) is a small amount of calculation amplitude demodulation method. Therefore, the envelope of the current demodulation signal obtained by HT can effectively eliminate  
Figure 1: The fully mechanized coal face in the mine. the current fundamental frequency interference. After the preprocessing operation, the frequency component of gear fault can obviously be enhanced. The current fundamental frequency component and gear fault frequency component will make the current of the scraper conveyor have obvious nonlinear characteristics. In addition, the scraper conveyor bears a relatively large time-varying load in the process of operation, so the operation state of each transmission part changes all the time. The instability of the transmission parts will cause the Gauss noise in the scraper conveyor current. Considering the characteristics of bispectral (BA), such as phase information retention, Gaussian noise elimination and good nonlinear current signal recognition [23; 24], the fault frequency component under time-varying load conditions can be effectively extracted by BA. Under different load conditions, the fault information intensity from the current of scraper conveyor is also different. The intensity of the gear fault component in the current can cause the change of energy distribution in different frequency bands of the current. Therefore, from the energy point of view, the gear fault information in the current can be quantitatively analyzed under time-varying load conditions. Overall, a method for the local fault diagnosis of the gear based on the BA of an analytical signal envelope of stator current is proposed. The remainder of our paper is outlined as follows. Section 2.1 establishes the mathematical model of the fault current. Section 2.2 proposes a current preprocessing method based on WPMD and HT. Section 2.3 proposes a fault feature frequency extraction method based on the BA of signal envelope in the fault frequency band and a fault quantitative analysis method based on bispectrum entropy (BE). Section 3 is the experimental part of our paper. Section 4 is the experimental results and discussion. Section 5 summarizes the work of our paper.