esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Framework  
In this work, a hybrid prediction model for the local resistance coefficient of water transmission tunnel maintenance ventilation based on machine learning is proposed. The research framework is composed of three steps: the construction of a training sample set, building the IAJS-HKRVM prediction model, and a case study, as shown in Figure 1.  
**Step 1: Construction of training sample set.** Sample points are selected in the input parameter design space, and the cross-section mean speed and pressure under different working conditions are calculated based on the three-dimensional numerical model of the local resistance of water transmission tunnel maintenance ventilation, and then, the local resistance coefficient of maintenance ventilation under different working conditions is obtained. The numerical simulation results are verified to construct the training sample set of the prediction model for the local resistance coefficient of water transmission tunnel maintenance ventilation.  
**Step 2: IAJS-HKRVM prediction model build.** Firstly, a Gaussian kernel function with excellent local learning ability and Sigmoid kernel function with excellent global generalization ability are combined by the weighted method and are introduced into the RVM model to establish the HKRVM model, to accurately excavate the deep nonlinear characteristics of the local resistance coefficient of maintenance ventilation. Secondly, in order to determine the optimal kernel parameters of the HKRVM model, the IAJS algorithm is used to optimize the kernel parameters. Fuch chaotic mapping, lens-imaging reverse learning, and adaptive hybrid mutation strategies are introduced to improve the population initialization and location update methods of the AJS algorithm, so as to overcome the shortcomings of local optimal and premature convergence, to improve the optimization accuracy and efficiency of the algorithm. The performance of the IAJS algorithm is compared with other swarm intelligent optimization algorithms on the benchmark test function.  
Finally, the hybrid prediction model for the local resistance coefficient of the combined tee structure of water transmission tunnel maintenance ventilation based on machine learning is established.  
**Step 3: Case study.** The method proposed in this paper is applied to a long-distance water transmission project and a case study is carried out. Firstly, the results of the numerical simulation and prediction are compared to verify the applicability of the proposed method in this paper. Then, the error indexes such as the relative coefficient square (\(R^{2}\)), mean absolute error (_MAE_), and root mean square error (_RMSE_) are selected to compare the prediction performance of the proposed method in this paper with different kernel RVM models and different prediction models to verify the superiority of the proposed method.
## 4 Concept
### Data Processing
#### 4.3.3 Contextual Data Analysis  
In this task, the actual contextual data analysis of the data takes place to solve the ML task. Ideally, the data should be complete and free of errors as a result of the previous steps. During the data analysis, the data is prepared for further analysis. For example, the data is formatted as required by the analysis software without changing the meaning of the data. During data exploration, new data sets are also often transformed or constructed from the primary data. For example, ratios to the data sets such as averages, derivatives, etc., are calculated to be able to visualize the physical behavior of the CPPS with appropriate models. Effects such as dead time or compensation behavior of the underlying processes also must be taken into account or used to restore data quality. Thus, the dynamic process properties provide decisive information regarding the data quality. Figure 5 shows data quality problems that can only be identified and corrected in context.  
In data analysis, different modeling variants are usually developed, tested, and finally evaluated in terms of meaningfulness. Model development is an iterative process in which the data are prepared in different ways and model parameters are adjusted to improve the informative value and also the performance of the model. All revisions to the models must be documented.  
In this task, data quality is affected by the preparation of the data. When using image data or sound recordings, the labeling of the data is of crucial importance for the data quality. Here, the individual scenes are assigned to certain interpretations so that the model learns the meaning of contained features. Improper labeling affects the accuracy of the models. Otherwise, analysis accuracy reflects the choice and training state of the models. There are different assessment methods for this purpose. In classification tasks, for example, the confusion matrix is used to assess the quality of the classifier. In this task, the control of the statement accuracy of the algorithmic solution takes place regarding the system behavior which can be represented.
## 4 Discussion
### Potential Future Developments  
The SENSHOME technology is not intended to replace the users, causing them to reduce their activities or choices. On the contrary, it works by stimulating the users to act and choose autonomously by clearly illustrating the alternatives available to them (contained objects, adjustment of environmental conditions), encouraging independence not only from caregivers but also avoiding forms of dependence on technological support systems. Therefore, the designed SENSHOME environment (or at least parts of it) can be valuable support not only for living environments but also for collective spaces such as schools, museums, and commercial spaces.  
Potential future developments would include a deployment of the system to monitor environments and identify common sensitivity thresholds for its occupants. Short-term future developments foresee a necessary phase of evaluation of the furniture and the integrated system by potential users. The validation of SENSHOME could easily be implemented even for different frailties (elderly people) or disabilities (physical or mental). Long-term future developments will depend on the market interests in developing lines intended for specific users; perhaps discovering that what makes a space more inclusive can become more livable by all.
## 2 Materials and Methods
### Methods
#### 2.2.4 Scanning Electron Microscopy (SEM)  
The surface and structure of the samples were analyzed by scanning electron microscopy using a Nova NanoSEM 450 (FEI, Hillsboro, OR, USA) (available at CIGS, Centro Interdipartimentale Grandi Strumenti, of the University of Modena and Reggio Emilia) provided with LVD detector, under low vacuum conditions (80 KPa) and with an acceleration of 10 kV. Images were captured with different magnifications (500\(\times\)-3000\(\times\)) and tilts (0\({}^{\circ}\)-40\({}^{\circ}\)), thus enabling the visualization of both surface and cross section.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Layers** & **Nominal Coating** & **Application** & **Bioplastic** & **PEG** \\
**Code** & & **Thickness (\(\upmu\)m)** & **Speed (mm/s)** & **Bioplastic** & **(\% \(w\)/\(w\))** \\ \hline \hline \multicolumn{5}{c}{SET 1} \\ \hline
80s80 & 1 & 80 & 80 & PHBV & - \\
80s50 & 1 & 80 & 50 & PHBV & - \\
80d & 2 & 80 + 80 & 80 & PHBV & - \\
100s & 1 & 100 & 80 & PHBV & - \\
100d & 2 & 100 + 100 & 80 & PHBV & - \\  & & & SET 2 & & \\ \hline PHBV & 2 & 80 + 80 & 80 & PHBV & - \\ PHBVpeg5 & 2 & 80 + 80 & 80 & PHBV & 5 \\ PHBVpeg10 & 2 & 80 + 80 & 80 & PHBV & 10 \\ PHBVpeg20 & 2 & 80 + 80 & 80 & PHBV & 20 \\ PCL & 2 & 80 + 80 & 80 & PCL & - \\ PCLpeg5 & 2 & 80 + 80 & 80 & PCL & 5 \\ PCLpeg10 & 2 & 80 + 80 & 80 & PCL & 10 \\ PCLpeg20 & 2 & 80 + 80 & 80 & PCL & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coating conditions and codification of samples from the first and second set.
## 4 Attacks and Misbehaviors
### Colluding Nodes  
This type of attacks is somehow advanced, where two nodes are colluding to disrupt the network or bias it to their advantage. An example of colluding nodes that can really impair the proper operation of the model is the case where an upstream node A is colluding with a downstream node B to drop its extra undeclared packets. This attack aims at decreasing the trust value of benign nodes in the network and banning them. Attacker A sends more packets than it later declares in its activity report, and its benign DL forwards all of its packets; however after some hops down the path, node B drops the extra packets (upon previous agreement) from source A. The goal is to trick the BS into flagging the DL of A as a lying node.  
In CENTERA, after the BS detects the difference in the packets sent by A and those forwarded by its DL, and the difference between the packets sent by the UL of B and those forwarded by B, the BS puts both pairs on probation and changes the links between them. In following periods, the colluding nodes persist in the same attempt to disrupt the network, while the other nodes continue operating normally. So, after the number of probations of A and B reaches the maximum allowed, the BS flags them as bad nodes and isolates them from the network. Note that as the number of colluding nodes increases, the BS is faced with more and more misleading reports, until a limit where the logic flips and the BS's decisions start to be inaccurate and erroneous; _i.e._, the system fails.
## 3 Experimental Section
### Synthesis of Compounds
#### 3.2.7 Methyl 2-(Dodeccanoylamino)propanoate (\(N\)-dodecanoyl alanine methyl ester)  
Using the same reaction and isolation procedure as that for \(N\)-nonanoyl alanine methyl ester above, \(N\)-dodecanoyll alanine methyl ester was prepared using dodecanoic acid (817.6 \(\mu\)L, 4 mmol), CDI (648.6 mg, 4 mmol) and methyl l-alanine (558.3 mg, 4 mmol) in THF (8.79 mL), producing a white, waxy solid (1.108 g, 3.88 mmol, 97%). Purification by recrystallisation was attempted in CH\({}_{2}\)Cl\({}_{2}\)/hexane and EtOH/hexane, but the product formed an organoel, so the crude product underwent the saponification procedure described below. A small portion was purified for characterisation by column chromatography on silica (80% CH\({}_{2}\)Cl\({}_{2}\)/EtOAc, R\({}_{\rm f}\) = 0.47) to give the title compound as a white crystalline solid, which was \(>\)99% pure (HPLC-MS), mp 58.8-61.2 \({}^{\circ}\)C. [\(a\)]\({}_{\rm o}^{\rm o}\) = +5.0 (\(c\) 0.600, CHCl\({}_{3}\)). \({}^{1}\)H-NMR (MeOH-\(d_{4}\)) \(\delta\) 6.10-6.02 (1H, m, N\(H\)); 4.61 (1H, app q, \(J\) = 7.2 Hz, H\({}_{14}\)); 3.75 (3H, s, H\({}_{16}\)); 2.20 (2H, t, \(J\) = 7.7 Hz, H\({}_{3}\)); 1.63 (2H, app q, \(J\) = 7.4 Hz, H\({}_{2}\)); 1.40 (3H, d, \(J\) = 7.1 Hz, H\({}_{15}\)); 1.35-1.20 (16H, m, H\({}_{4\mbox{-}11}\)); 0.88 (3H, t, \(J\) = 7.0 Hz, H\({}_{12}\)); \({}^{13}\)C-NMR (MeOH-\(d_{4}\)) \(\delta\) 173.87 (C\({}_{13}\)); 172.83 (C\({}_{1}\)); 52.56 (C\({}_{16}\)); 47.94 (C\({}_{14}\)); 36.67 (C\({}_{2}\)); 31.04 (C\({}_{10}\)); 29.56 (C\({}_{4}\)); 25.67 (C\({}_{3}\)); 22.77 (C\({}_{11}\)); 18.70 (C\({}_{15}\)); 14.06 (C\({}_{12}\)). Melting point and spectral data were in complete accord with those previously reported [48].
## 3 Results
### Complex Correlation Analysis
#### 3.2.2 NPP Variation Characteristics of Different Soil Types  
Overall, the NPP among various soil types within the research area demonstrated a fluctuating increase from 2001 to 2021 (Figure 11). The NPP of the nine soil types, in descending order of magnitude, was as follows: alfisol soil (376.8 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), ferroaluminous soil (373.5 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), primordial soil (346.1 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), semi-hydromorphic soil (343.4 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), semi-alisfosol soil (341.9 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), anthropogenic soil (326 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), hydromorphic soil (319.8 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), saline soil (312 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)), and calcareous layer soil (253.1 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)). The study showed that the degree of NPP change varied among different soil types in northwest Liaoning. Firstly, the NPP growth rate was the largest in the calcareous soil zone at 4.5/a, followed by the ferroaluminous soil zone at 4.3/a. At 0.4/a, the hydromorphic soil zone had the smallest growth rate. The probable reason for this is that temperate grasses and miscellaneous grass meadow steppe, which have a high demand for moisture, were the main vegetation types in the calcareous soil zone, along with other conditions such as the fluctuating rise in precipitation in the study area. Second, the mean annual NPP values showed mostly similar trends across the alfisol, semi-alisfosol, primordial soil, and semi-hydromorphic soil zones. However, NPP levels in the alfisol zone surpassed those in the other three, which was due to the fact that the vegetation types here were mainly temperate deciduous thickets, which had a stronger ability to adapt to drought and cold stress and had better growth conditions. Finally, the growth rate of NPP was less than 1/a in the hydromorphic, saline, and anthropogenic soil zones while the NPP values were also lower, indicating that the development and growth of the indigenous vegetation were subpar and less susceptible to the influence of external factors.  
Figure 10: Regional differences in annual average NPP change driven by various factors in northwest Liaoning province from 2001 to 2021.
## 2 Results and Discussion
### Physicochemical Characterization
(2015), evaluating acerola pulp and acerola blended with seriguela, found in acerola RS levels of 6.00 g glucose/100 g, TS of 6.87 g glucose/100 g and NRS of 0.87 g sucrose/100 g, and in the prepared blend, RS of 6.3 g glucose/100 g, AT of 8.3 g glucose/100 g and NRS of 2.0 g sucrose/100 g. [PERSON] et al. (2015), analyzing the same parameters, obtained results of 0.01 g glucose/100 g (RS), 3.26 g glucose/100 g (TS) and 3.08 g sucrose/100 g (NRS) for acerola pulp and 0.02 g glucose/100 g (RS), 5.30 g glucose/100 g (TS) and 5.02 g sucrose/100 g (NRS) for mixed pulp of acerola and jambolan. In the guava juice evaluated by [PERSON] and [PERSON] (2015), the results were 4.51 g glucose/100 g for RS and 9.04 g sucrose/100 g for NRS; in guava nectar, the levels presented were 5.82 g glucose/100 g for RS and 10.40 g sucrose/100 g for NRS. In the mixed drink of guava and coconut water, studied by [PERSON] et al. (2015), RS ranged from 2.75 to 4.12 g glucose/100 g, TS ranged from 4.12 to 7.59 g glucose/100 g and NRS ranged from 1.46 to 3.54 g sucrose/100 g.  
So, given these levels of sugar in tropical fruit pulps and their blend when compared with the cited data from the literature, there were large amplitudes in the levels demonstrating great variability, probably due to the region of cultivation, variety, maturation stage and mix of fruits, among other factors.
## 3 The Main Parameters and Indices of MOT
### Conventional Performance Indices  
The MOT functions to slow down and trap gaseous atoms to create cold atomic groups. As a result, the performance of the MOT is typically evaluated on the basis of the physical characteristics of the trapped atomic groups. These characteristics primarily include the number of atoms, density, temperature, and lifespan.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 4 Performance Analysis
### Control Overhead
#### 4.2.2 Under NDMR  
Under NDMR, the number of request and reply messages transmitted and re-transmitted in the network depends on whether a two-way handshake or three-way handshake discovery process is used.
## 1 Introduction  
With the isomorphic nature of nickel (Ni) and copper, the Ni-Cu system results in a complete solid solution which paves way for the development of single-phase alloys over a wide range of compositions [1]. The Monel family of Ni alloys which is based on the Ni-Cu alloy system is characterized by their relatively good strength, good weldability, excellent corrosion resistance, high toughness, and fracture toughness over a wide range of temperature. As a result, they are found in many fields of application [2; 3; 4; 5]. The single-phase Monel series is a solid solution alloy primarily hardened conventionally by cold working and typically finds its application in the chemical and petroleum industry and in food processing, as well as in the marine industry for the production of valves, pumps, heat exchangers, boiler feed heaters, pressure vessels, shafts, and fasteners, among others [2; 6; 7]. Comparatively, Monel is known to have better corrosion resistance than stainless steelsand copper with regard to marine applications [8; 9]. It does not exhibit a ductile to brittle transition (DTB) and, therefore, performs very well, even at subzero temperatures [10]. Several variations of the Monel family of alloys, such as Monel 400, Monel 401, Monel 404, Monel R-405, and Monel K-500, are commercially available [2; 11; 12]. G-NiCu30 Nb (DIN 2.4365/9.4365) is a typical casting alloy which is modified after the Monel 400 NiCu30 Fe alloy but with significant niobium (Nb) and Si content which promotes better weldability and castability, respectively [13; 14; 15]. They are typically manufactured in the as-cast state but may be soft annealed at about 900 \({}^{\circ}\)C depending on the wall thickness [16].  
As additive manufacturing (AM) technologies continue to receive attention, various ranges of metal alloys of steel, titanium, aluminum, cobalt-chromium, gold, silver, and nickel-based superalloys continue to have their application via this generative technology [17]. AM of nickel-based alloys predominantly involved IN625, IN718, IN738 LC, and Hastelloy X, whereas others like the Nimonic 263, Chromel, and MAR-M 247 were also severally investigated for the LPBF process [18]. Not much is known in published literature concerning the application of Ni-Cu-based alloy series for the AM process, especially with LPBF. Wire arc additive manufacturing (WAAM) of Monel K500 Ni-Cu alloy was investigated by [PERSON] [19], who concluded that WAAM manufactured components had higher strength, hardness, and ductility than those hot rolled under various heat treatment conditions. Finite element modeling (FEM) of heat transfer, as well as residual stress build-up in laser metal deposition (LMD) of Monel 400, was investigated by [PERSON] [20], while laser and plasma arc direct energy deposition (DED) fabrication of Monel 400 bimetallic structures was also carried out by [PERSON] [21]. This present work, however, focuses on the processability, microstructure, and mechanical properties of the Ni-Cu-based G-NiCu30 Nb alloy for LPBF.
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 2 Current Landslide Monitoring Methods
### Limitations of Existing Methods
Such strategies ideally require low-cost sensors that are robust enough for on-site autonomous operations, coupled with reliable analytical models to provide reasonable lead times for response. Additionally, attenuation remains a major challenge, impeding the direct sensing of granular slope deformation by electromagnetic, acoustic, or mechanically coupled waves. An ideal approach must identify strategies to overcome these restrictions, and the AE technique holds promising capabilities in this regard, as will be highlighted subsequently.
# Warranty Cost Analysis for Multi-State Products Protected by Lemon Laws
## 1 Introduction  
### Motivation  
Lemon laws had been enacted since 1982 in the state of Connecticut, US, and in the next five years, all states in the US had implemented lemon laws ([1]). Customers' veto power over car quality is transferred to them, and automobile manufacturers are responsible for quality-related financial losses. Numerous nations, including China, Canada, Europe, Australia, Singapore and South Korea, have enacted lemon laws to protect consumers, and other items are also covered by these regulations ([2, 3, 4]). After the official introduction of lemon laws, the warranty cost arising from warranty claims has become an enormous financial burden for manufacturers, compelling them to focus more on enhancing product quality. Consequently, an accurate calculation of warranty service costs is essential to guide product pricing, quality control, and warranty policy development ([5]). In this article, warranty cost modeling and analysis of lemon-law-protected products are examined.  
According to lemon laws, if the number of repairs or the time required for a single repair action exceeds predetermined levels, the product will be deemed defective and the producer will be required to either replace the device or compensate the consumer financially. For instance, according to the lemon laws of Illinois and Indiana, the number of repairs should not exceed four, and the duration of each repair action should not exceed 30 days. Moreover, to guarantee the safety of customers, catastrophic failures during thewarranty period are not permitted. According to the lemon laws of Maryland, for instance, only one repair action is permitted for brake or steering system failure, the number of minor repairs should not exceed four, and the duration of each repair action should not exceed 30 days ([4]). Based on the aforementioned background, two warranty cost models, the \(\{\tau,N\}\) model and \(\{\tau,N_{1},1\}\) model, are built in this paper. Under the \(\{\tau,N\}\) model, \(\tau\) is the threshold repair time, while \(N\) is the threshold number of failures. The product will be replaced with a new and identical one, and warranty terms are renewed at no charge to customers if the repair time for a failure (regardless of the failure type) is more than \(\tau\) or the number of failures is more than \(N\) within the warranty period. Under the \(\{\tau,N_{1},1\}\) model, the product will be replaced with a new and identical one, and the warranty is renewed at no charge to the customer if the repair time of a failure activity (regardless of the failure type) exceeds \(\tau\), the number of minor failures exceeds \(N_{1}\) or a catastrophic failure occurs within the warranty period. Cost analysis is conducted under the assumption that the failure process of the product can be characterized with a homogeneous Markov chain.
# A Novel Carbon Fiber Composite Material for the Simulation of Damage Evolution in Thick Aquifers
## 3 Damage Self-Sensing Performance of CFSSM
### Effect of Carbon Fiber Content on the Resistivity of CFSSM  
During the curing process, the resistivities of the carbon fiber similar simulation material were monitored, and the results are shown in Figure 6. The initial resistivity of the specimens gradually decreased with the increase in the carbon fiber content. During curing, the resistivity of the specimens continuously increased, but the rate of increase diminished as the carbon fiber content rose. The initial resistivity of the baseline group was 310.0 \(\Omega\cdot\)m, which increased to 5170.2 \(\Omega\cdot\)m after 28 days of curing, a 16.6-fold increase. With the addition of 0.5%, 1%, and 1.5% carbon fiber contents, the initial resistivity of the samples decreased to 230.0 \(\Omega\cdot\)m, 110.9 \(\Omega\cdot\)m, and 37.2 \(\Omega\cdot\)m, respectively. After 28 days of curing, the resistivity increased to 1120.5 \(\Omega\cdot\)m, 260.1 \(\Omega\cdot\)m, and 71.7 \(\Omega\cdot\)m, respectively. When the carbon fiber content reached 2%, the initial resistivity dropped to 8.7 \(\Omega\cdot\)m, and after 28 days of curing, it rose to 9.8 \(\Omega\cdot\)m, an increase of only 12.6%. This indicates that the  
Figure 4: The micromorphology characteristics of the materials; (**a**) carbon fiber; (**b**) CFSSM.  
Figure 5: The mineral composition of CFSSM.  
addition of carbon fibers significantly enhances the conductivity of the composite material, and as the carbon fiber content increases, the material's conductive performance becomes more stable.