esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Ultrasonic Bonding Strength with Optoacoustic Methods
## 2 Theory  
First, we describe formation and enlargement of the bonded area in an ultrasonic bonding process. The ultrasonic bonding process consists of two main steps: a clamping step and a vibrating step. At the clamping step, a normal force is applied to the bonding part by the ultrasonic horn and the faying surfaces get into an intimate contact under the exertion of the normal force. During the vibrating step, the ultrasonic horn vibrates parallel to the contact area with the bonding tip at the ultrasonic frequency, causing relative motion between the plates to be bonded. At the initial phase in the bonding mechanism, oxide films on the faying surface break, and new surfaces get into contact. By achieving intermetallic bonding, micro-bonds are created. These micro-bonds create a convex contact part, so that micro-bonds are formed dispersedly at the macroscopic bonding interface. With increase in the vibration time, each micro-bond gradually enlarges, leading to an increase of the bonding strength.  
In our study, we consider the bonded specimen that consists of two pieces of metal sheet as shown in Figure 1. From the bonding mechanism described above, we hypothesize an elastic modulus of the micro-bond is the same as the elastic modulus of other areas. The apparent spring constant of the total bonded area K (N/mm) is expressed by the spring constant per unit area k (N/mm \(\cdot\) mm\({}^{2}\)) as K = k \(\cdot\) A, where A (mm\({}^{2}\)) is the total area of micro-bonds. Since the total area of micro-bonds is small at the initial phase during bonding, the number of springs is also small. Thus, it is considered that the spring constant, K, is small if the bond is weak. The micro-bonds develop with the vibration in the latter bonding phase, resulting in an increase of the total bonded area, A, and the total spring constant, K. Considering the specimen as a spring-mass system in which two plates are connected via micro-bonds, the resonant frequency f is given as Equation (1).  
\[\text{f}=\frac{1}{2\pi}\sqrt{\frac{\text{K}}{\text{M}}} \tag{1}\]  
where K is the apparent spring constant at the bonded part. M is the mass of the bonded specimen. As it is considered that the mass is constant during the bonding, the resonant frequency of the bonded specimen is solely affected by the apparent spring constant K. Namely, the bonding strength could be evaluated by measuring the resonant frequency.
## 1 Introduction
To better interpret such huge datasets, multivariate data analyses are often used, e.g., principal component analysis (PCA), in addition to supervised methods, viz., orthogonal projection to least squares discriminant analysis (OPLS-DA), which can simplify metabolite data complexity and ease sample classification. To the best of our knowledge, this is the first report of the determination of the quality characteristics of _Prunus mahaleb_ seeds, i.e., aroma and nutrient metabolites, in the context of different cultivars and post-roasting using GC-MS, assisted by multivariate data analysis tools. For aroma collection, headspace SPME was used as a more sensitive method for volatile analysis.
# _forests_
## 4 Discussion
### Influence of Environmental Factors on Subsoil \(\tau\)
#### 4.2.2 Vegetation Effects  
In our study, subsoil \(\tau\) decreased with the increase in vegetation variables (GPP, NDVI, EVI) (Figure 3D-F, \(p<0.001\)), which was similar to previous research results that vegetation could affect subsoil C turnover time [42; 43]. The influence of vegetation on subsoil \(\tau\) mainly included three aspects. (1). Vegetation can indirectly affect the subsoil \(\tau\) by changing photosynthate [42]; [PERSON] et al. (2020) [42] also found that photosynthate drives SOC mineralization in mature forests (\(>\)50 years old), which may have an effect on subsoil \(\tau\). (2). Vegetation type affects the subsoil C turnover time by changing the litter quality; the better the litter quality, the faster the subsoil \(\tau\)[44]. (3). Vegetation could also affect subsoil \(\tau\) by changing microclimate, soil water content, and forest structure [45]. However, in the results of our study, subsoil \(\tau\) of DBF with better litter quality was slower than EBF, which was related to the distribution of the sample points. In the terrestrial ecosystem of China, EBF was mainly distributed in subtropical areas, while DBF was widely distributed (Figure 1). However, in a fixed study area (such as subtropical areas), the subsoil \(\tau\) of DBF was 55.9 years, which was faster than that of EBF (59.9 years).  
We also found that forest age was an important biotic factor determining subsoil \(\tau\) (Figure 2D), with the fastest subsoil \(\tau\) observed in young forests and the slowest in mature forests (Table A1). This result may be associated with C allocation patterns at different forest ages [46]; the C storage in vegetation increases with the development of forest age, which affects the C turnover in the subsoil [47]. In addition, the decrease in stand NPP with the increase in tree age may be another reason [48], which is caused by the decrease in soil nutrients and photosynthesis during vegetation development [49].
## 4 Comprehensive Link Budget Analysis of the BDS-3 ISL
### Analysis of Inter-Satellite Link Margin  
For BPSK and QPSK modulation schemes, the following relationships hold:  
\[P_{b}=\frac{1}{2}erfc\sqrt{E_{b}/N_{0}} \tag{19}\]  
where \(erfc(\cdot)\) is the complementary error function, and \(P_{b}\) is the system Bit Error Rate (BER).  
We can calculate the normalized signal-to-noise ratio based on the system BER requirements. The extent to which the actual normalized signal-to-noise ratio exceeds the required normalized signal-to-noise ratio can be defined as the link margin, which can be expressed as follows:  
\[M_{BDS\_ISL}=\frac{(E_{b}/N_{0})_{real}}{(E_{b}/N_{0})_{req}} \tag{20}\]  
where \(M_{BDS\_ISL}\) is the link margin of the BDS-3 ISL, \((E_{b}/N_{0})_{real}\) is the actual normalized signal-to-noise ratio, and \((E_{b}/N_{0})_{req}\) is the required normalized signal-to-noise ratio. Combining Equations (17), (18) and (20), the link margin can be expressed in dB as follows:  
\[[M_{BDS\_ISL}]=[\text{EIRP}]+[G/T]-[L_{BDS\_ISL}]-[L_{P\_BDS}]-[L_{D\_BDS}]-[ k]-[k]-[k_{b\_ISL}]-[L_{M}]-[(E_{b}/N_{0})_{req}] \tag{21}\]
###### Abstract  
Accessibility differences across individuals are a core topic in the transport equity debate. Space-Time Accessibility measures (_STAs_) have often been used to show such differences, given their sensitiveness to individual spatial and temporal constraints. However, given their complexity, _STAs_ cannot properly isolate the specific role of the transport system in individual accessibility differences, since it is mixed with several other spatial, individual and temporal factors. To isolate the role of the transport system, this study introduces a Space-Time Transport Performance measure (_STTP_) that (a) grounds on the individual daily schedule of fixed activities, (b) calculates the generalised transport costs each individual has to bear to perform such schedule, and (c) weights it against the Euclidean distance between the activities of such a schedule. _STTP_ is tested together with _STA_ for a small sample of individuals living and performing their daily activities within the 22 nd district of Vienna. This test provides two main findings: first, individual differences registered by _STTP_ tend to be smaller than those highlighted by _STA_, according to the former's more narrowed and transport-specific approach. Second, individuals with the highest _STA_ do not necessarily register the highest _STTP_ (and vice versa). Indeed, some may experience limited transport performances when running their mandatory daily schedule, while registering a high degree of access to discretionary activities according to their constraints and opportunities at disposal (and vice versa). Considering these results, _STTP_ may be seen as a complementary indicator to be used together with _STA_ to analyse both general and transport-specific individual accessibility differences. Its role is particularly important for transport policy makers, who should understand which accessibility differences are directly linked to the performances of the transport system and could be remediated through transport policies.  
transport equity; distributional analysis; accessibility; space-time model; transport policy +
Footnote †: journal: Applied  
1 Faculty of Architecture and Spatial Planning, Vienna University of Technology, Karlsgasse 11, A-1040 Vienna, Austria; [EMAIL_ADDRESS]  
2 Eurace Research, Institute for Regional Development, Viale Druso 1, I-39100 Bolzano, Italy2
## 2 SRAM PUFs
For this reason, if general-purpose SRAMs are to be used as PUF, it is necessary to implement some procedure to generate a reliable PUF response from a group of cells. There are mainly two methods to achieve this goal. The first method is based on using error correction codes, repetition codes or temporal majority voting techniques. In the last case, the final output of a cell is obtained after challenging it several times and determining the most common challenge response [25]. The main drawback of this approach is that it is time consuming. In addition, the overall reliability of the result depends on how reliable the involved cells are. Under a certain individual reliability threshold, the result can be poor. Also, if error correction codes are used, usually implementing helper data algorithms (HDAs), the chances of a secure key being tampered are increased [12]. Furthermore, environmental conditions like temperature, aging or radiation can affect the result because they can modify cell reliability, either by worsening or even improving it [26; 27; 28]. The second method intends to obtain a more consistent response minimizing the effect of environmental conditions by using as PUF-cells only those cells that show a  
Figure 1: (**a**) Schematic of the six-transistor SRAM cell (6T cell) (**b**) Layout of the 6T cell without metal lines. more constant behavior. These cells will be less susceptible to effects like aging, noise or temperature, and thus provide a more reliable overall result. To implement this method, it is necessary to identify the more reliable cells using an appropriate selection method. Once identified, only the most reliable cells are included in the PUF, while the others are masked out. The location of the suitable cells can be saved and reused [14; 29]. However, this subset of cells may vary in time due to aging, and they may require revision. This work focuses on this second method, which is compatible with memories that have not been specifically designed for PUF applications. Its aim is to develop a methodology to estimate the statistical distribution of cells that are suitable for being used as PUF as a function of its start-up probability. The proposed approach uses an imbalance factor obtained from Monte Carlo analysis to quantify the cell mismatch and relate the resulting distribution to start-up experimental data.
# Reversible Hydrogen Storage Using Nanocomposites
## 2 Nanocomposite Materials for Hydrogen Storage
### Metal Organic Frameworks (MOF)  
There is a pressing need for the discovery and development of new reversible materials. One new area that may be promising is that of high surface area hydrogen sorbents based on microporous metal-organic frameworks (MOFs). Such materials are synthetic, crystalline, and microporous and are composed of metal/oxide groups linked together by organic struts. Hydrogen storage capacity at 77 K (-195 \({}^{\circ}\)C) has been reported as high as 10 wt.% [104] via an adsorptive mechanism, with a room temperature capacity of approximately 1 wt.% [105]. However, due to the highly porous nature of these materials, the volumetric capacity at a room temperature remains a significant issue. Besides, their significant potential for applications such as novel storage media for natural gas and hydrogen, the MOFs suffer from very high structural instability when exposed to atmospheric moisture [106].
# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 2 Local Zernike Moments Transformation
### Zernike Moments  
Zernike moments consist of Zernike orthogonal polynomials defined in polar coordinates [27]. Two-dimensional Zernike moments with moment order \(n\) and repetition \(m\) of an image \(f(i,j)\), whose pixel coordinates (\(x_{i}\) and \(y_{j}\)) scaled to range of \([-1,1]\) using (3), are represented as follows [27]:  
\[Z_{nm}=\frac{2(n+1)}{\pi(N-1)^{2}}\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}V_{nm}(\rho_ {ij},\theta_{ij})f(i,j), \tag{1}\]  
\[V_{nm}(\rho,\theta)=R_{nm}(\rho)e^{-jm\theta}, \tag{2}\]  
\[x_{i}=\frac{2i+1-N}{\sqrt{2}(N-1)},\quad y_{j}=\frac{2j+1-N}{\sqrt{2}(N-1)}, \tag{3}\]  
where \(\rho_{ij}=\sqrt{x_{i}^{2}+y_{j}^{2}}\) and \(\theta_{ij}=tan^{-1}(y_{i}/x_{i})\). In (1), \(V_{nm}(\rho,\theta)\) represents the Zernike polynomials which are defined in polar coordinates within a unit circle, and \(R_{nm}(\rho)\) represents real-valued radial polynomials. These polynomials are defined as:  
\[R_{nm}(\rho)=\sum_{k=0}^{\frac{n-|m|}{2}}(-1)^{k}\frac{(n-k)!}{k!(\frac{n+|m|} {2}-k)!(\frac{(n-|m|)}{2}-k)!}\rho^{n-2k}. \tag{4}\]  
In the above equations, \(n\) and \(m\) parameters take values as \(0\leq n\), \(0\leq|m|\leq n\), and \(n-|m|\) is even. Zernike polynomials calculated using \(n\) values up to 5 are illustrated in Figure 1.  
Figure 1: Zernike polynomials calculated using values up to \(n=5\) (**left**), and \(7\times 7\) local Zernike moments (LZM) filters corresponding to these polynomials (**right**). Negative values of \(m\) represent imaginary, while positive values represent real components.
## 2 Raw Materials, Experimental Methods and Theory Background
### Theory Background of Damage Model and Prediction Model  
#### 2.4.1 Logistic Damage Model  
A logistic model is a widely used non-linear statistical model, which is mainly used to discuss and quantify the relationship between the \"damage\" parameters and the independent variables for the calculation of damage probabilities [24]. The logistic model was developed from Malthus model expressed by Equation (5):  
\[\mathrm{d}N/\mathrm{d}t\times 1/N=r, \tag{5}\]  
where \(N\) is the dependent variable. \(t\) is the independent variable, and \(r\) is a constant.  
Then the analytical solution of Equation (5) is given by  
\[N(t)=N_{0}\times e^{rt}, \tag{6}\]  
on the basis of Malthus model, assuming that the model is linearly constrained gives  
\[\mathrm{d}N/\mathrm{d}t\times 1/N=-r/Q\times N+r, \tag{7}\]  
where \(Q\) is the saturation factor and \(r\) is the growth rate factor.  
Rewriting Equation (7) in the expression of logistic model gives  
\[\mathrm{d}N/\mathrm{d}t=rN\times(1-N/Q), \tag{8}\]  
the analytical solution of logistic model can be solved as  
\[N(t)=Q/[1+(Q-N_{0})/(N_{0}\times e^{rt})], \tag{9}\]  
Equation (9) can be rewritten as  
\[N(t)=Q/[1+e^{(a-rt)}], \tag{10}\]  
and the damage degree can be defined as  
\[D=(1-I_{i}/I_{0})\times 100, \tag{11}\]  
where \(D\) is the damage degree, \(I_{0}\) is the initial index, and \(I_{i}\) is the index of the \(i\)th freeze-thaw cycle.  
Figure 4: Indirect tensile stiffness modulus test in this study.  
Therefore, the damage degree in Equation (12), based on the logistic model, was established in order to analyze the freeze-thaw damage degree of asphalt mixtures.  
\[D=(A_{1}-A_{2})/[1+(x/x_{0})^{p}]+A_{2}, \tag{12}\]  
where \(A_{1}\) is the minimum value of regression curve, \(A_{2}\) is the maximum value of regression curve, which reflects the degree of damage, and \(x_{0}\) is the x-coordinate value when \(D=0.5A_{2}\), which reflects the damage grow rate.
# Fatigue Life Prediction of Radial Tire Bead Using a Maximum Strain Energy Density Range Method
###### Abstract  
The damage that occurs around the tire bead region is one of the critical failure forms of a tire. Generally, the prediction of tire durability is carried out by the experimental method. However, it takes a lot of money and time to conduct experiments. Therefore, to determine the fatigue life of radial tire bead, a reasonable prediction method is proposed in this paper. Fatigue testing of bead rubber compounds to determine the \(\Delta SED\)-number of the cycle (\(N_{j}\)) was applied. The maximum strain energy density range (\(\Delta SED_{\text{max}}\)) of several bead compounds was obtained by steady-state rolling analysis with a finite element method. This quantity is then inserted into a fatigue life equation to estimate the fatigue life. The experimental results of the 175/75R14 tire were compared with the estimated value, which showed a good correlation. It is concluded that the method can be effectively applied to the fatigue life prediction of the tire bead.  
Keywords:tire bead; fatigue life prediction; maximum strain energy density range; finite element method +
Footnote †: journal: Applied Sciences
# Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method
## 2 Backgrounds
### Markov Chain Monte Carlo and the Flat Histogram Method  
Markov Chain Monte Carlo (MCMC) is a sampling method that has been widely used for statistical physics. Sampling from a state (configuration) space \(S\) is the process of generating samples from a probability distribution \(\pi\) defined over \(S\). The aim is to estimate the distribution or to compute the states having a maximum (minimum) probability. A MCMC algorithm samples the space in a way that the transition probability from the current state \(\sigma\) to the next state \(\sigma^{\prime},p_{\sigma\rightarrow\sigma^{\prime}}\), depends only on \(\sigma\), but not on any previously sampled states. If \(\pi(\sigma)>0\) for any \(\sigma\in S\), and \(p_{\sigma\rightarrow\sigma^{\prime}}\) satisfies the _detailed balance property_,  
\[\pi(\sigma)p_{\sigma\rightarrow\sigma^{\prime}}=\pi(\sigma^{\prime})p_{ \sigma^{\prime}\rightarrow\sigma}. \tag{1}\]  
then the sampling process will reach a stationary distribution.  
In statistical physics, the flat histogram method [14] was firstly used to estimate the _density of states_ (DOS) of a state space \(S\) w.r.t. an energy function,  
\[g:S\rightarrow\mathbb{N}.\]  
The DOS of a given energy level \(E\) is,  
\[n(E)=\mid\{\sigma:g(\sigma)=E\}\mid.\]  
To estimate \(n(\cdot)\), the flat histogram method visits a state \(\sigma\) in the energy level \(g(\sigma)\) with a probability \(p(\sigma)\) that is inversely proportional to \(n(g(\sigma))\). As a result, a flat visit histogram to each energy level is generated. Specifically, the method defines a random walk process with the following transition probability  
\[p_{\sigma\rightarrow\sigma^{\prime}}=min\{1,\frac{1/n(E^{\prime})}{1/n(E)}\} =min\{1,\frac{n(E)}{n(E^{\prime})}\}. \tag{2}\]  
where \(g(\sigma)=E\) and \(g(\sigma^{\prime})=E^{\prime}\). The process satisfies the detailed balance equation  
\[P(\sigma)p_{E\to E^{\prime}}=P(\sigma^{\prime})p_{E^{\prime}\to E}. \tag{3}\]  
when \(P(\sigma)\) is proportional to \(1/n(E)\). This strategy guides the process to visit states at every energy level equally often.
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 3 Coupling Mechanism Design and Array Switching Control Strategy
### Coupling Mechanism Design
#### 3.1.3 Receiving Coil Design
This trade-off is critical for AUVs, prioritizing stable power transfer over large offsets in turbulent waters. Given the trade-off between mutual inductance magnitude and anti-offset performance--where larger \(L_{2}/L_{1}\) ratios reduce peak mutual inductance but significantly improve misalignment tolerance--the final receiving coil side length is determined as \(L_{2}\) = 1.75\(L_{1}\) = 350 mm. This selection balances both mutual inductance and anti-offset capability to ensure robust power transfer under dynamic underwater conditions. The system coupling mechanism model is illustrated in Figure 8.
# A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification
## 3 Validation by Model Experiment
### Experimental Process  
The experimental procedure consisted of three main steps. First, the foundation pouring was completed by mixing cement, sand, and water in the specified ratio, and the mixture was layered into the test bin. During this process, preserving the grouting holes was critical. Second, the anchor cable installation was carried out. After a curing period of 7 days, the anchor cable bundle was positioned along the reserved holes, and the prepared slurry was injected into the holes in the required quantity to complete the grouting. Finally, the pull-out test was conducted. After another week of curing, the experimental setup was installed and connected, as shown in Figure 1. Following the Chinese Geotechnical Anchor Code, the anchor cables were loaded in stages. This staged loading involved incrementally adding weight (10 kg) for 5 min intervals until the anchor cable failed.
## 5 Results Analysis and Discussion
### Discussion  
We propose an innovative method that integrates orthogonal experimental design and a semi-supervised clustering algorithm, aiming to analyze the advantages and disad  
Figure 11: Comfort clustering results after PCA dimensionality reduction.  
Figure 12: Number of persons eligible for comfortable working conditions.  
vantages of urban tunnel landscape design from the perspective of physiological quantity data. This method provides a new idea for optimizing the combination design of tunnel landscape elements, which is expected to improve the safety and comfort of tunnel driving.  
The results of this study are somewhat different from those of [PERSON] et al. [34]. They believed that a simple pavement design can help improve driving safety but has little effect on comfort. However, our study found that a simple pavement can meet both safety and comfort requirements when texture design is used on the sidewalls and roof. Compared with traditional evaluation methods, the machine learning method used in this paper is objective, accurate, and repeatable. By analyzing the physiological parameters of drivers, the impact of different landscape designs can be quantitatively evaluated, avoiding subjective bias. At the same time, the introduction of a semi-supervised clustering algorithm can automatically identify the optimal combination without manual intervention.  
In addition, the results of this study show that a safe and comfortable tunnel landscape design is not the only optimal solution. As long as the standard of \"textured landscape design is adopted for the tunnel sidewalls and vaults, while the road surface remains simple and texture less\" is met, it can be regarded as a reasonable design solution. This finding provides flexibility for tunnel landscape design, which is conducive for designers to design according to local conditions and specific circumstances.  
It should be pointed out that this study mainly focuses on the landscape design of the tunnel body, while the design of the tunnel entrance and exit is not taken into consideration. In addition, the physiological data of drivers are also affected by individual differences, and the sample size needs to be further expanded in the future to improve the reliability of the results.  
In general, this study provides new ideas and methods for tunnel landscape design, but there is still room for further improvement. It is believed that through continuous research and exploration, more contributions can be made to improve the safety and comfort of tunnel driving.