esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### OFMSW as a Resource  
OFMSW is an inhomogeneous mixture with characteristics depending on its origin. The composition varies in its proportions of biodegradable and non-biodegradable organics, and mineral components [1]. The different composition depends on the season, lifestyle, waste management regulations, and regional economic frameworks [16; 17; 18]. The organic fraction of the MSW is larger in lower-income countries than in higher-income countries [19]. The fluctuations in the composition of OFMSW are also reflected in the material properties. While dry matter (DM) contents of OFMSW collected from 22 different countries fluctuate between 15.0-50.2% fresh mass (FM) with a mean value of 27.2 \(\pm\) 7.6% FM, contents for organic dry matter (oDM) vary between 7.4-36.1% FM (mean 22.9 \(\pm\) 6.3% FM), which is equal to 43.0-95.0% DM (mean 84.6 \(\pm\) 9.9% DM). The average concentrations for C (46.6 \(\pm\) 4.4% DM), H (6.6 \(\pm\) 0.62% DM), N (2.9 \(\pm\) 0.6% DM), and S (0.3 \(\pm\) 0.26% DM) are also specified, and the potential CH\({}_{4}\) yields at standard conditions fluctuate between 61 and 580 L/kg\({}_{\text{oDM}}\) with an average of 415 \(\pm\) 138 L/kg\({}_{\text{oDM}}\)[1].  
Due to the variety of ingredients and changing characteristics, OFMSW can serve as a resource for several treatments and utilization pathways, e.g., the share of biodegradable components in OFMSW is relevant for CH\({}_{4}\) generated in AD [7]. Furthermore, OFMSW represents a resource for the creation of value-added products such as organic fertilizers, biopesticides, and bioplastics [2]. In general, OFMSW can be recovered with technologies using biochemical, thermochemical, or physicochemical principles, either as single technology or in combination.
## 6 Numerical Simulation Analysis of the Dual-Electrode Excitation Method
### Analysis of Influencing Factors on the Distribution of the Magnetic Induction Intensity Formation
#### 6.4.2 Analysis of Influencing Factors on Electrode Distance  
The electrode distance \(L\) is adjusted, and the numerical simulations are conducted with the values of 2 m, 5 m, 10 m, 25 m, and 50 m, respectively.  
Figure 6 illustrates the impact of different electrode distances \(L\) on the casing current. With increases in \(L\), the casing current demonstrates a near linear growth. However, once \(L\) reaches a certain threshold (e.g., 25 m), the top of the casing current curve flattens out and the maximum value of the casing current increases slowly. The corresponding maximum casing currents for electrode distances of 2 m, 5 m, 10 m, 25 m, and 50 m are recorded as follows: 0.17 A, 0.48 A, 0.96 A, 1.9 A, and 2.5 A. These results indicate that, as the electrode distance \(L\) increases, the amplitude of the casing current also increases while the attenuation rate along the axis decreases, which is conducive to the further flow of the current into the casing.  
Figure 6 illustrates the impact of varying electrode distances \(L\) on the induced magnetic field intensity along the axis of the drilling well. The attenuation characteristics of \(B_{c}\) are similar to those of \(l_{c}\). The maximum magnetic field intensity corresponding to different electrode distances \(L\) is measured at 2.3 nT, 6.5 nT, 13.4 nT, 30.4 nT, and 46.4 nT,  
Figure 6: The amplitudes of \(l_{c}\) and the \(B_{c}\) with different electrode distances.  
respectively. Additionally, the magnetic field intensity at the magnetic field measurement position (\(\mathrm{z^{\prime}=\pm 2.5~{}m}\)) is 1.3 nT, 3.65 nT, 7.2 nT, 13.52 nT, and 16.45 nT for different electrode distances \(L\).
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 5 Results and Discussion  
In this section, we present some results obtained when using the proposed methodology to evaluate WVSNs' dependability. That way, some examples are given showing different types of analysis allowed by the proposed methodology. For all examples, it is assumed that each device uses the same model of battery, which is an alkaline battery (AA/R6) with the following specifications:  
* \(c_{0}=3000\) mAh;
* \(H=25\) h;
* \(I=100\) mA;
* \(\eta=1.3\);
* \(DC=50\%\).  
For this case, it would take 25 h to discharge the battery considering an average operation current of 100 mA. Considering a 50% duty-cycle, this will increase the operating time up to 50 h. It is assumed that a node cannot work properly with a battery capacity lower than \(c_{\min}=500\) mAh. Thus, the modeling of the battery by an approximation to a stochastic process is given as follows. Suppose that the battery discharges through \(n=4\) stages, and then, according to Section 3.3, each stage discharges \((c_{0}-c_{\min})/n=625\) mAh. This means that each stage duration \(\tau_{i}=t_{i+1}-t_{i}\) (\(i=0,\ldots,n-1\)) can be found solving Equation (5) to the values \(c_{0}=c(t_{0})=c(0)=3000\) mAh, \(c_{1}=c(t_{1})=2375\) mAh, \(c_{2}=c(t_{2})=1750\) mAh, \(c_{3}=c(t_{3})=1125\) mAh and \(c_{4}=c(t_{4})=500\) mAh. This implies in \(\tau_{0}=4.1235\) h, \(\tau_{1}=6.0297\) h, \(\tau_{2}=7.0465\) h and \(\tau_{3}=7.8003\) h. Thus, according to Equation (6), \(\lambda_{bft0}=0.1213/\)h, \(\lambda_{bft1}=0.0829/\)h, \(\lambda_{bft2}=0.0710/\)h and \(\lambda_{bft3}=0.0641/\)h. Once discharged, we consider that it takes 2 h to repair (replace or recharge) the battery, so \(\mu_{bft}=1/2=0.5/\)h. It is important to remark that increasing the number of battery stages results in a better approximation to the real battery discharging behavior [60].  
With respect to the visual nodes and theirs parameters, we assume a viewing angle \(\theta=60^{\circ}\) and a sensing radius \(R_{s}=150\) m for all considered visual sensors. In addition, we consider that the radio communication range is \(R_{c}=180\) m. These device parameters are used for the communication scenarios of Examples (Sections 5.1 and 5.2).
# An Improved Cohesive Zone Model for Interface Mixed-Mode Fractures of Railway Slab Tracks
## 3 Simplified PPR Traction-Separation Law
### Mixed-Mode Bending (MMB) Test Verification  
The simplified PPR traction-separation law is verified here and compared to the original PPR model by simulating the mixed-mode bending (MMB) test. The MMB test has been widely used to validate the applicability of CZM for mixed-mode fracture [37]. The configuration of the test is shown in Figure 10. Following the geometry parameters of the MMB test, specimens were considered: L = 51 mm, h = 1.56 mm, a\({}_{0}\) = 33.7 mm, c = 60 mm, and B = 25.4 mm.  
Numerical simulations of the mixed-mode fracture were implemented using the commercial software ABAQUS with a user-defined element (UEL) subroutine, and such a subroutine for the PPR model was given in the work of [34]. In this paper, user element subroutines (UEL) were also utilized to implement the simplified PPR traction-separation law. Since the mesh, FE element type, boundary conditions, as well as the solving method are all the same as in [34], those items are not covered again here.  
In this study, two cases were tested, one with the same fracture energy (\(\phi_{n}=\phi_{t}=1\) N/m) and another with different fracture energies (\(\phi_{n}=1\) N/m _and_\(\phi_{t}=2\) N/m). The cohesive strength \(\sigma_{max}=\tau_{max}=200\) MPa, shape parameter \(\alpha=\beta=3\), and the initial slope indicator \(\lambda_{n}=\lambda_{t}=0.02\) were the same for both cases. The numerical results were compared to the analytical solution given in [32].  
For the same fracture energy, the computational results for different models are illustrated in Figure 10(a). The results for the SPPR model and PPR model were the same, and coincided with the analytical solutions. For the case of different fracture energies (Figure 10(b)), the computational results for the SPPR model were in better agreement with analytical solution compared with the PPR model under the same conditions. The results for the PPR model were relatively small, as shown in Figure 10(b). The reason is that the effective region for the PPR model is smaller than for the SPPR model when \(\phi_{n}\
eq\phi_{t}\), leading to the smaller tractions and energies under mixed-mode fractures as mentioned before.  
Figure 10: Mixed-mode bending (MMB) test configuration.
# Domestic Use Simulation and Secondary Shelf Life Assessment of Industrial _Pesto alla genovese_
## 3 Results and Discussion
### Chemical Analysis  
The lowering of \(\mathrm{a_{w}}\) and the reduction of pH are crucial factors for the stabilization of pesto sauce, to achieve microbiological stability and lowering degradative reactions such as browning, lipid oxidation, enzymatic reactions and protein denaturation [26]. The stabilization of pesto sauce is reached both by lowering the pH and/or the \(\mathrm{a_{w}}\), depending on the producer strategy and on possible taste consequences of the formulation. Low values of \(\mathrm{a_{w}}\) are generally reached by the addition of humectants such as sugars, salts, polyols and protein derivates [26], while the pH is lowered by formulation with lactic, citric and ascorbic acid, and glucono-\(\delta\)-lactone.  
Along with the two pesto brands object of this study, the pH and \(\mathrm{a_{w}}\) of 8 other commercial brands of shelf-stable _pesto alla genovese_ saucces were determined. Results are provided in Table 5, which show \(\mathrm{a_{w}}\) ranging from 0.8447 (in P10) to 0.9667 (P9) and pH values ranging from 4.00 (P3) to 5.64 (P1). The \(\mathrm{a_{w}}\) values of the pesto saucces used in this study are higher than reported in the literature by [PERSON] et al. [27] for industrial pesto (0.82), while they agreed with data reported by [PERSON] et al. [26] (ranging from 0.914 to 0.956). As it can be inferred, the pH and \(\mathrm{a_{w}}\) values of the samples object of the study (P1 and P2) fall in the range of commercial products, except for the pH of P1 which was the highest among commercial brands. Hence, based on stability-related parameters, P1 and P2 well represent the pesto category, and the observed results for these two brands may be extended to the commercial product category of pesto sauces.  
Figure 2: (**a**) Overall color variation (\(\Delta\)E) and (**b**) Browning Index (BI) for pesto 1 (triangle) and pesto 2 (circle) during household storage after first opening. Each line reports the mean value of samples from the five households and managed under the two scenarios. Different letters within each line indicate significant differences (\(p\) < 0.05). If letters are not provided, no significant difference was observed.  
The monitoring of pH (Figure 3) did not show significant differences based on the level of domestic use in both pesto sauces (\(p\) = 0.21 and 0.06 for P1 and P2, respectively), therefore the values from both scenarios were analyzed together. Very small changes, though statistically significant, were shown during the period after first opening (\(p\) < 0.05) in both pesto brands. In spite of that, the pH of Pesto samples after 20 d from the first opening was very similar to the initial one, thus confirming the intrinsic stability of the products.
# Optimization Method of Assembly Tolerance Types Based on Degree of Freedom
## 1 Introduction
These relationships provide the theoretical basis for optimizing tolerance types with DOF of the tolerance zone. To summarize, based on the analysis of the geometric functional requirements of the assembly, the relationship between the CPDF that affects the geometric functional index value and the DOF of the tolerance zone of different tolerance types on the assembly feature surface is obtained. This article proposes a method for the selection and optimization of assembly tolerance types based on degrees of freedom; studies the Boolean relationship between the degree of freedom vector of the tolerance zone and the control parameter degree of freedom vector of the assembly body under different tolerance types and tolerance zones of assembly feature elements, and achieves the optimization of assembly tolerance projects. The remainder of this paper is organized as follows: Section 2 introduces the relevant work of tolerance specification design and preferred tolerance types. Section 3 defines DOF of tolerance zones and vector representation of DOF, and introduces the meaning of CPDF of assembly and its vector representation. Section 4 introduces the process of obtaining the control parameter degrees of freedom of an assembly and summarizes the mapping relationship between typical tolerance bands and the control parameter degrees of freedom obtained. Section 5 introduces Boolean operations to construct a Boolean operation method for obtaining CPDF, and obtains an automatic tolerance item generation algorithm for optimizing the selection of assembly body shape and position tolerances based on DOF. Section 6 conducts engineering verification. Section 7 discusses the method of selecting and optimizing tolerance types based on DOF.
## 2 Preliminaries  
### Bayesian Optimization  
Bayesian optimization (BO) was first proposed by [PERSON], et al. of the University of Illinois Urbana-Champaign (UIUC) in 1998 [25]. It finds the optimal value of the function by constructing a posteriori probability of the output of the black box function when the limited sample points are known. Because the BO algorithm is very efficient, it is especially useful when the evaluation cost of the objective function is high, the derivative of the independent variable can not be obtained, or there are multiple peaks. BO method has two core components, one is a probabilistic surrogate model composed of prior distributions, and the other is the acquisition function. BO is a sequential model, and the posterior probability is updated by the new sample points in each iteration. At the same time, in order to avoid falling into the local optimal value, BO algorithms usually add some randomness to make a tradeoff between random exploration and a posteriori distribution. BO is one of the few hyper-parameter estimation methods with good convergence theory [26].  
Taking the optimization of hyper-parameters of models with BO as the research direction, the problem of finding the global maximum or minimum value of black objective function is defined as (this paper takes finding the maximum value of objective function as an example):  
\[x^{*}=argmax\quad f(x) \tag{1}\]  
where \(x\in X\),\(X\) is hyper-parameters space. The purpose of this article is to find the maximum value of the objective function. Suppose the existing data is \(D_{1:t}=(x_{i},y_{i})\), \(i=1,2,\cdots,t\), \(y_{i}\) is the generalization accuracy of the model under the hyper-parameter \(x_{i}\). In the following, \(D_{1:t}=(x_{i},y_{i}),i=1,2,\cdots,t\) was simplified as \(D\). We hope to estimate the maximum value of the objective function in a limited number of iterations. If \(y\) is regarded as a random observation of the generalization accuracy, \(y=f(x)+\varepsilon\), where the noise \(\varepsilon\) satisfies \(p(\varepsilon)=N(0,\sigma_{\varepsilon}^{2})\), independent and identically distributed. The goal of hyper-parameter estimation is to find \(x^{*}\) in the \(d\)-dimensional hyper-parameters space.  
One problem with this maximum expected accuracy framework is that the true sequential accuracy is typically computationally intractable. This has led to the introduction of many myopic heuristics known as acquisition functions, which is maximized as:  
\[x_{t+1}=argmax\quad\alpha_{t}(x;D) \tag{2}\]  
There are three commonly acquisition functions: probability of improvement (PI), expected improvement (EI) and upper confidence bounds (UCB). These acquisition functions trade off exploration against exploitation.  
In recent years, BO has been widely used in machine learning model hyper-parameters estimation and model automatic selection [27; 28; 29; 30; 31], which promotes the research of BO method for hyper-parameters estimation in many aspects [32; 33; 34; 35]. The flow of the BO algorithm is shown in Figure 1.  
Figure 1: Flow chart of Bayesian optimization.
## 2 Experimental Design
### Material Properties  
To ascertain the mean compressive strength of the test beams, six concrete cube specimens were concurrently fabricated throughout the concrete placing process of the test beams. Following the curing process, the specimens underwent uniaxial compression testing. Furthermore, the plain steel and CFRP tendons utilized in the beams were subjected to tensile testing to determine their average yield and ultimate tensile strengths. Table 2 displays the material parameters of concrete, steel, and CFRP tendons. Figure 2 shows the stress-strain curve for each material.  
As shown in Figure 2a, at the peak strength point of the stress-strain curve, a maximum stress value of 2123 MPa was observed, with an associated strain of 8866 \(\mu\epsilon\). This point represents the ultimate capacity of the CFRP tendon prior to rupture. Rupture of the CFRP tendon is closely linked to the behavior observed in the stress-strain curve; this curve demonstrated a linear relationship, with the stress being uniformly applied to the CFRP tendon until the ultimate strain was reached. With increasing applied moment, the material undergoes elastic deformation until it reaches the ultimate strain. The rupture point is characterized by a sudden drop in stress, indicating the failure of the CFRP tendon. The strain value at rupture yields crucial information regarding the material's ductility and brittleness. In this study, the CFRP tendon ruptured at a strain of 8866 \(\mu\epsilon\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Type** & **f\({}_{\text{c}}\) (MPa)** & **f\({}_{\text{y}}\) (MPa)** & **f\({}_{\text{u}}\) (MPa)** & **E (GPa)** \\ \hline Concrete & C40 & 40.9 & - & - & 32.8 \\  & D = 16 mm & - & 447.8 & 604.5 & 196.7 \\  & D = 8 mm & - & 457.7 & 619.7 & 201.3 \\ CFRP tendon & D = 10 mm & - & - & 2123 & 160.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of concrete, steel reinforcement, and CFRP tendons.  
Figure 2: Stress-strain curves of materials: (**a**) CFRP tendon; (**b**) concrete; (**c**) reinforcing bars with a diameter of 16 mm; (**d**) reinforcing bars with a diameter of 8 mm.  
undersoring the material's capacity to withstand substantial deformation prior to ultimate failure. This analysis of the stress-strain curve not only augments our understanding of the mechanical behavior of the CFRP tendon but also offers valuable insights for structural design considerations and safety assessments.
###### Abstract  
To reduce the negative effect on sound source localization when the source is at an extreme angle and improve localization precision and stability, a theoretical model of a three-plane five-element microphone array is established, using time-delay values to judge the sound source's quadrant position. Corresponding judgment criteria were proposed, solving the problem in which a single-plane array easily blurs the measured position. Based on sound source geometric localization, a formula for the sound source azimuth calculation of a single-plane five-element microphone array was derived. The sinusoids and cosines of two elevation angles based on two single-plane arrays were introduced into the sound source spherical coordinates as composite weighted coefficients, and a sound source localization fusion algorithm based on a three-plane five-element microphone array was proposed. The relationship between the time-delay estimation error, elevation angle, horizontal angle, and microphone array localization performance was discussed, and the precision and stability of ranging and direction finding were analyzed. The results show that the measurement precision of the distance from the sound source to the array center and the horizontal angle are improved one to threefold, and the measurement precision of the elevation angle is improved one to twofold. Although there is a small error, the overall performance of the sound source localization is stable, reflecting the advantages of the fusion algorithm.  
microphone array; sound source localization; fusion algorithm; performance analysis Article
# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
## 5 Result and Discussion
### Distribution from Wholesaler to Selected DCs  
In the current transportation system with only one DC, the wholesaler of each province (central market) delivers the products to DC based on demand. In this research, as in the current system, we assumed that the demand of each candidate DC was assigned to the nearest wholesaler. If the nearest wholesaler could not support the demand, another wholesaler would be selected to support the remained demand by considering the remained capacity of wholesaler and the distance from DC to the wholesaler. For example, in Figure 14, five candidate DCs in scenario 6 were supported by four wholesalers. Therefore, we should investigate and evaluate the performance of scenarios considering the distribution from wholesaler to selected DCs.  
The results of LRP from wholesaler to the selected DCs of each scenario are summarized in Table 7. Similar to the results of the LRP analysis from DCs to retailers, scenario 1, which used only the existing DC, showed the worst performance, as all wholesalers should deliver their product to only single DC. Similarly, in scenario 3 with only two DCs, the performance was the worst among alternative scenarios. As the number of DCs increased, total travel time and distance tended to decrease. However, the biggest number of DCs could not guarantee the best performance. As it can be seen in Table 7, scenario 6 with five DCs showed the best performance with the shortest traveling distance and time among alternative scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Number of Selected DCs** & \multicolumn{2}{c}{**Number of Required**} & \multicolumn{2}{c}{**Travel Time (h)**} & \multicolumn{2}{c}{**Travel Distance (km)**} \\ \cline{3-7}  & **DCs** & **Tracks** & **Total** & **STDEV** & **Total** & **STDEV** \\ \hline Scenario 1 & 1 & 60 & 121.58 & 1.02 & 3647.52 & 30.52 \\ Scenario 2 & 8 & 62 & 56.92 & 0.64 & 1707.52 & 19.11 \\ Scenario 3 & 2 & 61 & 72.20 & 0.62 & 2165.91 & 18.49 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of distributions from the wholesale market to DCs.  
Figure 14: Examples of location-routing problem (LRP) from wholesaler to DCs (scenario 6).