esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Directional Model in UASNs  
#### 3.1.1 Multi-Modal Transducer Model  
The sound field radiated by multi-modal transducer at different spatial azimuth angles can be expressed as the following formula [10]:  
\[p(\theta)=\frac{B_{0}}{2}+\sum_{n=1}^{\infty}B_{n}\cos(n\theta) \tag{1}\]  
In Equation (1), \(p\) denotes the sound pressure with reference \(\mu Pa\), \(\theta\) denotes the azimuth in \(rad\), and \(B_{n}\) denotes the weight of the \(n\)th modal. While \(\cos(n\theta)\), the normalized directional function can be calculated by Equation (2):  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Beamwidth** & **Mode** & **Range** & **Complexity** & **Reference** \\ \hline \(\alpha<\pi/3\) & Fixed Orientation & Not always guaranteed & NPC & [22] \\ \(\alpha=\pi/2\) & Fixed Orientation & \(14\sqrt{2}\) & \(O(n\log n)\) & [20] \\ \(\alpha=\pi/2\) & Fixed Orientation & 7 & \(O(n\log n)\) & [24] \\ \(\pi/3<\alpha<2\pi/3\) & Fixed Orientation & \(4/\cos(\alpha/2)\) & \(O(n\log n)\) & [25] \\ \(\alpha=2\pi/3\) & Fixed Orientation & \(5\) & \(O(n\log n)\) & [26] \\ \(2\pi/3<\alpha<\pi\) & Fixed Orientation & \(2\cos(\alpha/2)+2\) & \(O(n^{2})\) & [25] \\ \(\pi\leq\alpha\leq 5\pi/3\) & Fixed Orientation & \(2\sin(\alpha/2)+1\) & \(O(n\log n)\) & [23] \\ \hline \end{tabular}
\end{table}
Table 1: Researches for symmetric connectivity problem under condition of one beam.  
\[B_{n}=\frac{2}{\pi}\int\limits_{0}^{\pi}p(\theta)\cos(n\theta)d\theta \tag{2}\]  
Therefore, the appropriate parameters can be selected according to the requirements to obtain the intended directivity performance.  
Except for the monopole modal (exactly omni-directional mode), most transmitting transducers can also produce dipole modal directivity, and a few may provide the directivity of the quadrupole modal, and thus Equation (1) can be consequently reduced as follows:  
\[\frac{p(\theta)}{p(0)}=R(\theta)=\frac{1+A_{1}\cos\theta+A_{2}\cos 2\theta}{1+A_{ 1}+A_{2}} \tag{3}\]  
For different combination of \(A_{1}\) and \(A_{2}\), there forms different normalized directional performance, as shown in Figure 1 below:
## 4 Analysis of Approaches to Modelling Agricultural Systems
### Probabilistic Approach  
The probabilistic approach consists of adding stochastic components to a deterministic approach. This approach is based on statistical, frequentist, or Bayesian statistical models, using historical dataset to capture variability, and the use of optimisation techniques (Figure 2). Multiple linear regression, logistic regression, Poisson regression, generalised Pareto distribution, Monte Carlo simulation, weights of evidence, and geographically weighted regression are commonly used in probabilistic approaches [72; 73]. Probabilistic models are less sensitive than deterministic ones to the non-stationarity of model parameters (i.e., great variations or disruptions). For example, probabilistic approaches work well for flood hazard and estimation of the 1-in-100 year flood extent where the model question is already a probability that needs mapping representation [74]. Many statistical models are developed using quantitative data table that can be coupled with a deterministic system response (i.e., crop yield, milk production, or farmers-consumers' prices) [39]. The probabilistic approach, however, lacks extrapolation power because of the data dependency for parameterisation.  
The probabilistic approach is also useful for understanding statistical relationships between drivers of changes. This type of approach works well for Land Use and Land Cover Change (LULCC) modelling by coupling statistical methods like Markov chain for quantifying change over years, and to integrate probability in changes in maps [75; 76], or for agricultural-economic production relationship analysis [77], historical data, and reconstruction of crop sequences [78], or allocation-optimisation based on statistical multi-criteria analysis [79].  
**Strengths**:  
* At a regional to national scale, this approach is well designed for land-use change dynamic analysis and for policy adaptation [75; 80; 81].
* With a large amount of data available freely, the probabilistic approach can be easily considered both for statistical and spatial trend analysis.
* Coupling deterministic and probabilistic approaches can lead to powerful modelling of the climate change adaptation for agriculture and land-use strategies.  
**Weaknesses**:  
* Statistical models are inherently unsuited to predict the impact of major disruptions because of the historical data/parameterisation dependency for this approach.
* Analysis tools are freely available (R, Q-GIS), but advanced skills in data processing are needed.
## 2 Background
### Structural Damage Detection  
Any occurrence of structural damage could lead to variations in dynamic responses, which are closely associated with structural parameters, e.g., mass and stiffness. Structural damage therefore can be localized and quantified by tracking the changes in mass and stiffness. In this study, we are only concerned with stiffness and assume that the mass is exactly known to avoid the coupling effect of concurrently updating mass and stiffness [31]. A global stiffness matrix of a structure is parameterized as  
\[\mathbf{K}_{d}(\theta)=\mathbf{K}_{0}+\sum_{l=1}^{N_{\theta}}\theta^{(l)} \mathbf{K}_{ud}^{(l)},\;l=1,2,\cdots,N_{\theta}, \tag{11}\]  
where \(\theta=\left(\theta^{(1)},\theta^{(2)},\cdots,\theta^{(N_{\theta})}\right)\) is stiffness parameter vector in a damaged state and \(N_{\theta}\) is the number of stiffness parameters. \(\theta^{(l)}\) represents the resulting \(l\)-th stiffness value after damage occurs to the structure. \(\mathbf{K}_{0}\) is the non-parameterized component of the global stiffness matrix that can be set as zero. \(\mathbf{K}_{ud}^{(l)}\) is a contribution stiffness matrix of an element or substructure corresponding to the \(l\)-th stiffness parameter under an intact state. With Equation (11), structural damage detection is finally realized by the identification of \(\theta\). For each set of stiffness parameters, the analytical modal parameters in Equation (9) can be computed using a standard eigenvalue problem:  
\[\left(\mathbf{K}_{d}(\theta)-\mathbf{M}\omega_{i}^{2}\right)\mathbf{\Phi}_{i} =\mathbf{0},\;i=1,2,\cdots,N_{m}, \tag{12}\]  
where \(\mathbf{M}\) is the mass matrix, and \(\omega_{i}\) and \(\mathbf{\Phi}_{i}\) are the \(i\)-th circle natural frequency (\(\omega=2\pi f\)) and the mode shape vector, respectively. Given one set of stiffness parameters, resulting in a set of modal parameters, the likelihood function in Equation (8) is then iteratively evaluated using MCMC methods.
## 5 Conclusions  
This paper investigated the extraction and processing method of dispersion curves from small-scale station array microtremor data based on SI. For multiple dispersion curves acquired using SI, the superimposed averaging method is quick but does not completely exploit the effective dispersion information of station pairs with varied distance, and the exploration accuracy is constrained. In response, this paper proposed a station-spacing-based dispersion curve processing method called hierarchical frequency fusion. Based on the correlation between the station pair spacing and the gathered signal frequency components, the reliable frequency intervals for dispersion curves were determined, and the fusion criterion was proposed. The dispersion curves of the various distance station pairs were fused in accordance with the fusion criteria to obtain the final dispersion curve. This method makes full use of the dispersion information of the various spacing station pairs and allows the dispersion curves of each distance station pair to be focused on their reliable frequency intervals to produce a more precise geological structure from shallow to deep.  
Microtremor survey experiments were carried out in Xiamen City, with a rectangular exploration array arranged to explore and study the geological formations of the site. The station array's dispersion curves were extracted and processed using the hierarchical frequency fusion method and superimposed averaging method, and the stratigraphic structures were determined using conversion of the subsurface transverse wave velocity structure. Compared with the superimposed averaging method, the stratigraphic structure obtained using the hierarchical frequency fusion method is in better agreement with the borehole data, which can effectively improve the accuracy of microtremor surveys and has certain guiding significance for similar projects in the future.  
Conceptualization, X.M.; methodology, X.M. and X.W.; software, X.M. and X.W.; validation X.W.; formal analysis, X.M. and X.W.; resources, H.L., J.Z. and X.Z.; writing--original draft, X.M. and X.W.; writing--review and editing, X.M., X.W., H.L. and J.Z.; supervision, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 18: Stratification error of two methods.
## 2 Materials and Methods
### **ECC-33 Model**  
This was extrapolated from the original measurements by [PERSON] and optimized to be fit for the fixed wireless access (FWA) system. It is useful for the ultra-high-frequency (UHF) band and covers up to 3.5 GHz [8]. The model is presented in (8):  
\[L_{dB}=L_{FS}+L_{bm}-G_{t}-G_{r} \tag{8}\]  
where \(L_{FS},\ L_{bm},\ G_{t},\) and \(G_{r}\) (in dB) are the free space attenuation, basic medium path loss, transmitting antenna height gain factor, and the receiver antenna height gain factor, respectively. They are given as follows:  
\[L_{FS}=92.4+20\text{log}_{10}(d)+20\text{log}_{10}(f), \tag{9}\]  
\[L_{bm}=20.41+9.83\text{log}_{10}(d)+7.89\text{log}_{10}(f)+9.56\text{[log}_{1 0}\Big{(}f)]^{2}, \tag{10}\]  
\[G_{t}=\text{log}_{10}\left(\frac{h_{t}}{200}\right)\left[13.98+5.8\big{(} \text{log}_{10}(d)\big{)}^{2}\right], \tag{11}\]  
For medium-sized cities,  
\[G_{r}=\big{[}42.57+13.7\ \text{log}_{10}(f)\big{]}\left[\text{log}_{10}(h_{m})-0.585\right], \tag{12}\]  
and for big cities,  
\[G_{r}=0.759h_{m}-1.862 \tag{13}\]  
where \(f\) is in GHz, \(d\) is in km, and \(h_{t}\) and \(h_{m}\) are in meters.
# Modeling and Design Enhancement of Electothermal Actuators for Microgripping Applications
###### Abstract  
Microgrippers are miniature tools that have the capability to handle and manipulate microgr and nano-scale objects. The present work demonstrates the potential impact of the incorporation of perforations on a 'hot and cold arm' electrothermal actuation mechanism in order to improve the operation of microgrippers in terms of arm opening and operating temperature. By applying a voltage to one arm and setting the other as a ground, the current passes through the electrothermal actuator and induces its displacement along the in-plane direction. The difference in the geometry of the two arms causes one arm to expand more than the other and this results in transverse bending. A computational model was developed using a finite element analysis tool to simulate the response of the thermal actuators with varying geometries and investigate the impact of incorporating perforations on the arms of the thermal actuators to enhance its performance in terms of deflection and operating temperature. The simulation results were compared to their experimental counterparts reported in the literature. A good agreement between the numerical and experimental data was obtained. A novel design of a microgripper, made of perforated electrothermal actuators, was introduced. Its main characteristics, including the tip opening of the gripper arms, the applied voltage, and the stress and temperature distributions, were analyzed using the developed computational model. Different perforation shape and distribution were investigated. The present study demonstrates the capability of perforations to enhance the operation of microgrippers as manifested by the obtained higher tip displacement and lower tip temperature in comparison to conventional microgripper designs made of non-perforated thermal actuators. Furthermore, the highest stress generated on the microgripper elements was found to be much lower than the yield strength of the constituent material, which indicates proper functioning without any mechanical failure.  
electromechanical actuator; microgripper; numerical simulations; design enhancement; experimental verification 2023
## 6 Security Analysis
Therefore, for a correct decryption, these two conditions must be satisfied: \(m\times s_{2}+r<s_{1}\) and \(r<s_{2}\). To further enhance the robustness of the protocol, incorporating additional security measures such as nonce values or time stamps in the message structure can provide protection against replay attacks. These elements introduce freshness into each transaction, ensuring that messages cannot be reused or duplicated by an adversary. Therefore, the proposed HMPS cryptographic protocol effectively combines asymmetric and symmetric cryptographic techniques to achieve a secure and robust communication framework. The ElGamal encryption-based signature ensures message authenticity and integrity, while the symmetric encryption scheme protects message confidentiality. By addressing potential threats and implementing best practices for key management and hash function selection, the protocol can offer strong security assurances against both external and internal adversaries. This comprehensive approach highlights the strength of leveraging multiple cryptographic primitives to achieve layered security and demonstrates the importance of meticulous protocol design in safeguarding sensitive information.
## 2 Basic Principle of Cross-Well Elastic Wave CT
### Fundamental of Reconstructing Velocity Field  
The probe profile was meshed when reconstructing the velocity field as shown in Figure 2. The initial slowness (the reciprocal of the velocity) was assigned to each mesh to form the slowness matrix S. The propagation path length of the elastic wave in each mesh forms the path matrix A. The theoretical travel time matrix B can be derived from the slowness and path matrix as shown in Equation (1).  
Since the theoretical travel time B is different from the measured travel time B\({}^{\prime}\), the difference between the two \(\Delta\)B1 is brought into Equation (2) to obtain the corrected slowness matrix S1, and the theoretical travel time B1 is calculated again; then, the difference between the theoretical and measured travel times is repeatedly iterated until the difference between the two reaches the error limit or reaches the number of iterations [7]. At this point, the corresponding slowness matrix S\({}^{\prime}\) is the result of the inversion.  
\[\begin{bmatrix}a_{11}&a_{12}&\cdots&a_{1j}&\cdots&a_{1N}\\ a_{21}&a_{22}&\cdots&a_{2j}&\cdots&a_{2N}\\ \vdots&\vdots&\cdots&\vdots&\cdots&\vdots\\ a_{i1}&a_{i2}&\cdots&a_{ij}&\cdots&a_{iN}\\ \vdots&\vdots&\cdots&\vdots&\cdots&\vdots\\ a_{I1}&a_{I2}&\cdots&a_{Ij}&\cdots&a_{IN}\\ \end{bmatrix}\begin{bmatrix}s_{1}\\ s_{2}\\ s_{j}\\ s_{N}\end{bmatrix}=\begin{bmatrix}b_{1}\\ b_{2}\\ \vdots\\ b_{j}\\ b_{I}\end{bmatrix} \tag{1}\]  
\[A\cdot S=B \tag{2}\]
# A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype
## 4 Proposed HS (MOHSg)
#### Description  
The main differences between MOHSg (described by Algorithm 6) and the original mono-objective HS version lie in the \(HM\) ranking and in the pitch adjustment operation by genotype. The algorithm requires the presetting of the harmonic memory consideration \(HMCR\) and pitch adjustment \(PAR\) parameters. The execution starts generating a random initial harmonic memory \(HM_{1}\). For each iteration, a new harmonic memory \(HM_{2}\) is generated with the same dimensions as the initial one and is made up variable by variable applying the memory usage operators, pitch adjustment by genotype and randomization. Both memories are combined to form an extended matrix \(HM_{1-2}\) that is ranked according to the ranking criteria of [PERSON] et al. [31]. Finally, the ranked matrix is truncated in half to form the \(HM_{1}\) of the next iteration.  
```
1 define objective functions; define\(HMCR\), \(PAR\);
2 generate randomly an initial \(HM_{1}\); evaluate the objective functions with \(HM_{1}\) ;
3\(\text{rank}\,HM_{1}\) according to [PERSON] et al. [31];
4 generate\(bw\)by \(Cr\), applying the crowding with genotype described in Algorithm 5;
5 while\(g\leq m\text{max number of iterations}\)do
6 while\(H_{2}\)is not completedo
7 while\(i\leq N\)do
8 if\(rand<HMCR\)then
9\(r=rand(1,k)\) ;
10 if\(rand<PAR\)then
11\(new\,X(i)=HM(r,i)+rand(-1,1)\times bw(r,i)\);
12 else
13\(new\,X(i)=HM(r,i)\)
14
15 else
16\(new\,X(i)=rand(Lib_{i},UB_{i})\)
17
18
19
20
210
22
23
24 rank\(H_{1-2}=HM_{1}\cup HM_{2}\), according to [PERSON] et al. [31];
25
26 truncate\(H_{1-2}\) and assign to \(HM_{1}\) ; generate\(bw\)by \(Cr\) with genotype
```  
**Algorithm 6** Proposed algorithm MOHSg
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
###### Abstract  
The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches.  
caching; cooperative network; multi-tier cellular network; content popularity; machine learning +
Footnote †: journal: Applied Sciences
###### Abstract  
In this paper, a depth-bistatic bottom reverberation model that employs the ray theory is presented. The model can be applied to an active towed array in the ocean. The reverberation time series are modeled under the depth-bistatic assumption and their Doppler shift is calculated based on the actual source-receiver geometry. This model can handle \(N\times 2D\) range-dependent bathymetry, the geometry of a triplet array, and the Doppler motion of the source, targets, and receiver. The model predictions are compared with the mid-frequency reverberation data measured by an active triplet towed array during August 2015 in the East Sea, Korea. These data are collected with a variable depth source at mid-frequency and the triplet line array in a deep-water environment. Model predictions of the beam time series and its spectrogram are in good agreement with the measurement. In particular, we discuss the effects of the source and receiver depths on the reverberation in deep water observed in both the measured and modeled results.  
active triplet towed array; bottom reverberation; East sea experiment; time-spectral signal processing 2020
# Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table
## 2 Robotic Deburring Cell
### Kinematic Model of the Rotary Table  
Optimal placement of the workpiece allows postures with maximum stiffness to be used in robotic machining. In a static rotary table position, the drilling accuracy could be increased [42]. To exploit this finding, the current research focuses on optimizing the table rotation during deburring.  
The kinematic model of the one DOF rotary table is assembled from the following two components: the fixed table base and the rotary clamping plate (Figure 4). The translation vector \(t_{1}^{table}\ =\ [1.815,\ 0.750,\ 0]\) represents the position of the table base \({}_{0}T^{table}\) relative to the robot base \({}_{0}T^{robot}\). The translation vector \(t_{2}^{table}\ =\ [0,\ 0,\ 0.550]\) represents the position of the rotating clamping plate frame relative to the table base frame and forms the final homogeneous transformation matrix \({}_{0}^{2}T^{table}\). The rotation of the rotary table is limited to the interval \(rot_{PT,max}\in[-2\pi,\ 2\pi]\).
## 2 Mineral Acid Fractionation
### Conditions and Yields
#### 2.3.1 Low Concentrations of Acid and High Temperatures
Dilute acid treatments are considered inexpensive given the low cost of acids, are relatively efficient given the hydrolysis of hemicelluloses into monomeric sugars (generally yield of about 90%), and given the yield of the following step: the enzymatic hydrolysis of cellulose. They are also fast compared to other fractionation processes, for instance mild alkaline fractionation that usually requires several hours of reaction duration [54; 55; 56]. However, they are carried out at a low S:L ratio and at high temperatures, which impact the cost efficiency of the process. Besides, acids under high temperatures are corrosive, so resistant reaction vessels are required. This process also generates fermentation inhibitors (furans, aliphatic carboxylic acids, phenol derivatives) that have to be removed before the enzymatic reaction step. Purifying these molecules for further valorization can also enhance the profitability of the acid fractionation process. The dilute acid fractionation process was also used to produce the degradation products of sugars, which present a high value, some being referred as platform chemicals by the U.S. Department of Energy, such as levulinic acid or furfural [6]. The Biofine process, developed by [PERSON] in the 1990s, produced levulinic acid and furfural from a dilute acid treatment of lignocellulosic biomass without enzymatic hydrolysis or fermentation step [57; 58]. Sulfuric acid at concentrations between 1 and 5% (_w/w_) was contacted with biomass for short periods of time (from a few seconds to a few minutes) in a two reactor system at high temperatures (in the range of 195-230 \({}^{\circ}\)C) to obtain high yields of levulinic acid and furfural from the degradation of the hexoses and pentoses [57; 58]. It led to the conversion of approximately 50% of the mass of six-carbon sugars to levulinic acid, with 20% being converted into formic acid and 30% being incorporated in the residual \"char\" material, which also contains all of the acid insoluble lignin and 50% of the pentoses that did not convert into furfural [4]. A commercial facility run by GF Biochemical in Caserta, Italy, processes 50 tons per day of waste paper, municipal wastes, and agricultural residues to produce levulinic acid and furfural based on the Biofine process [4].