esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Studies  
### Parameters of Simulation  
We compare our work against well-known clustering-based routing protocols such as [13; 14; 17; 19; 20; 21; 22]. The evaluation is based on hop count, delivery ratio average, throughput, and end-to-end delay. To evaluate the ACRP in relation to mobility patterns and vehicle movement, we have simulated a DTN with specific configurations given in Table 1. Using the ACRP, we strategically convert this simulated DTN into a network employing TCP/IP protocols through the adaptive clustering of nodes. This transformation is a pivotal component of our protocol's capability to enhance network connectivity and performance. We utilized the Opportunistic Network Environment (ONE) simulator. ONE is an open-source simulation tool for the development and testing of routing protocols, based on Java [23]. It can be extended for analyzing protocols and network mobility models for delay-tolerant networking. With ONE, it is easy to integrate contact records, route modules, applications, and reports [24]. For the ACRP, we used the default ONE simulation map from Helsinki, Finland. The geographical space for this experiment is 4000 \(\times\) 3500 m\({}^{2}\) in size, with 50, 100, 150, 200, and 250 nodes distributed at random. In this setting, 64% of the nodes represent vehicles moving from their current location to a randomly selected point via the shortest route. These vehicles travel in one direction at speeds of 2.7 to 13.9 m/s. The remaining nodes follow predefined routes like tram lines and travel at a speed between 7 and 10 m/s. All nodes have a transmission range of 20 m, except trans, which can transmit up to 200 m. Each message generated in the network has a time-to-live value of 300 s. The generated messages range in size from 250 to 750 KB. The nodes' buffer sizes are 5, 10, 15, and 20, respectively, and the starting point is 5 MB. The coordinates of the cluster head nodes in each clustering can be acquired during a specific round of data collection, following the cluster head election procedure described in Equation (7). Table 1 shows the summary of the parameters.  
Figure 2: Routing based on dynamic clustering through common members where S and D are source and destination nodes respectively.
###### Abstract  
Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.  
vibration-based damage identification; response-based method; model-based method; objective function; optimization algorithm; residual useful life; damage Prognosis; decision making
## 3 Results and Discussion
### Yeast Identification
#### 3.3.2 Strain Level  
The identification at the strain level is collected in Table 2. PCR-RAPD showed that in pork meat there were 26 different profiles from 88 isolates (29%). Regarding each species, _K. servazzii_ from Factory B presented a low number of strains in comparison to its isolates, unlike _C. metapsilosis_ in pork products.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & **Factory** & **No. of Yeast Isolates** & **No. of Strains** & **Biodiversity \%** \\ \hline C. _metapsilosis_ & A & 4 & 3 & 75 \\ C. _zeylanoides_ & A & 2 & 2 & 100 \\  & A & 27 & 9 & 33.3 \\ _D. hansenii_ & B & 8 & 4 & 50 \\ _K. servazzii_ & B & 30 & 2 & 6.6 \\ _M. guillermondii_ & A & 1 & 1 & 100 \\ _S. cerevisiae_ & A & 4 & 1 & 25 \\ _T. delbrueckii_ & B & 1 & 1 & 100 \\ _Y. lypoliticia_ & A & 5 & 1 & 20 \\  & B & 3 & 1 & 33.3 \\ _W. anomalous_ & A & 3 & 1 & 33.3 \\ \hline
**Species** & **Factory** & **No. of Isolates** & **No. of Strains** & **Biodiversity \%** \\ \hline _A. pullulans_ & E & 1 & 1 & 100 \\ _C. santamariae_ & E & 1 & 1 & 100 \\  & C & 10 & 4 & 40 \\ _D. hansenii_ & D & 1 & 1 & 100 \\  & E & 1 & 1 & 100 \\ _H. valbyensis_ & E & 8 & 5 & 62.5 \\ _K. servazzii_ & D & 3 & 3 & 100 \\ _K. unispora_ & D & 2 & 1 & 50 \\ _Rh. mucilaginousa_ & D & 2 & 1 & 50 \\ _W. anomalous_ & E & 5 & 1 & 20 \\  & E & 1 & 1 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of the isolates and strains found in pork (Factories A and B) and game (Factories C, D, and E) fermented sausages.  
In contrast, the 35 isolates from game meat products were classified into 20 different strains with a higher biodiversity (57%) than pork samples (29%). The species such as _A. pullulans_, _C. santlamariae_, _D. hansenii_, _K. servazzii_, and _Wickerhamomyces anomalous_ showed the highest number of strains identified, while _Rh. mucilaginousa_ showed the lowest one. A high genetic variability is desirable owing to the studied biotechnological capacities, which are strain-dependent. Therefore, the highest number of strains identified, the better the biotechnological study.  
In general, it has been noted that a greater number of strains have been identified from game meat in comparison with the small number of yeast isolates and species identified. Referring to the factory results, in Factory A, seven species and 18 strains were identified and in Factory E a total of six species and 10 strains were observed, with these two curing rooms having the best yeast variability.
## 3 Real-Time Measurement Scheme for Light Intensity Flicker and Fiber Coupling Efficiency
The test link is shown in Figure 3, where firstly, the scintillation index and fiber coupling efficiency were measured for a link distance (L) of 3000 m, and secondly, the scintillation index and fiber coupling efficiency were measured for a link distance of 12,000 m for a blimp descending from 1000 m to 600 m. The communication mode and related system parameters are the same as in Table 1, the actual received photocurrent \(I_{APD}\) values measured in 800 s are shown in Figure 5, and the scintillation index measurements are shown in Table 2.  
The optical power meter samples and stores 3000 data points in 1 s; the core diameter of the single mode fiber is 10 \(\upmu\)m. In turbulent environments, the calculated instantaneous coupling efficiency is a fluctuating value due to the flickering of light intensity that causes fluctuations in the fiber coupling efficiency, so the measured coupling efficiency is recorded as the average optical power. The experimental results are shown in Table 2.  
From the experimental results, it can be seen that the scintillation index has greater coupling efficiency and is lower when the airship is at 1000 m altitude, and the link distance is 12,000 m. At a communication distance of 12,000 m, the maximum current flicker index was measured to be 0.63. At this time, the airship was at an altitude of 600 m, and the coupling efficiency was 0.05. In order to ensure the accuracy of the experiment, multiple different experimental measurements were carried out. The statistical results of the measured fiber coupling efficiency are shown in Figure 6.  
It can be calculated from Figures 1 and 6 that the maximum deviation between the experimental value and the theoretical value of the coupling efficiency does not exceed 20% as the link distance and the height above the ground change. Due to environmental restrictions, the maximum height of the airship platform in the experiment is 1000 m. The coupling efficiency can be calculated using the deviation formula \(P_{\eta}=\eta_{experiment}-\eta_{simulation}/\eta_{simulation}\) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link Distance** & **Blimp Altitude** & **Link** & **Scintillation Index** & **Coupling Efficiency** \\ \hline
3000 m & 1000 m & Air-Ground & 0.09 & 0.53 \\
12,000 m & 1000 m & Air-Ground & 0.44 & 0.14 \\
12,000 m & 800 m & Air-Ground & 0.50 & 0.09 \\
12,000 m & 600 m & Air-Ground & 0.63 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results. Figure 5: Actual received optical current value of APD. which solves the limitation that a higher platform cannot be completed due to environmental restrictions.
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 4 Example Analysis and Solution Validation
### Solution Validation  
The theoretical calculation and numerical simulation results of the three considered cases are summarized in Table 4. It should be noted that in the numerical simulation, the lateral resistance \(P_{1}\) and end resistance \(P_{2}\) were all obtained through the Fish programming language embedded in FLAC3D. The results showed that the error between the numerical solution and the theoretical solution for the lateral resistance in the three cases was only 6.0%, whereas that for the end resistance was relatively large; however, it was still within 30%, and so the maximum error of the ultimate pullout capacity was 7.7%. This indicated that the proposed theoretical model demonstrated high reliability in the analysis of the ultimate pullout capacity of the expanded anchor cable.  
Figure 15: Curve representing relationship between pullout force and slipping displacement.  
In order to further illustrate the reliability and superiority of the present calculation method, the theoretical formula for the ultimate pullout capacity of an expanded anchor cable described in [18] was used for calculation, and the results were compared and discussed.  
[PERSON] et al. [18] proposed an algorithm for the ultimate pullout capacity of an expanded anchor cable that could be divided into three parts, as follows:  
\[T=T_{1}+T_{2}+T_{3} \tag{30}\]  
where \(T\) denotes the ultimate pullout capacity (i.e., \(P\)); \(T_{1}\) denotes the ultimate lateral resistance of the free segment, which was neglected in the present paper; \(T_{2}\) denotes the ultimate lateral resistance of the anchorage segment (i.e., \(P_{1}\)); and \(T_{3}\) denotes the ultimate end resistance (i.e., \(P_{2}\)). \(T_{2}\) and \(T_{3}\) are given by:  
\[T_{2} =\pi D_{2}L_{2}\tau_{fd} \tag{31}\] \[T_{3} =\frac{\pi}{4}(D_{2}^{2}-D_{1}^{2})P_{D} \tag{32}\]  
All the variables in Equations (31) and (32) are identified in [18]. The values of the variables were as follows: \(D_{1}=0.2\)m, \(\tau_{fd}=31\)kPa, \(\xi=0.5K_{a}\), and the values for the other variables are provided in Section 4.1. The results of the three considered cases are summarized in Table 5.  
According to this table, the results showed that the error between the numerical solution and the theoretical solution based on [18] for the lateral resistance in the three cases was only 3.45%. However, the error for the end resistance was relatively large, resulting in a maximum error of 68.95%, which was mainly due to the high value of \(\xi\), in the range of \((0.5\sim 0.95)K_{a}\). Combined, the maximum error of the ultimate pullout capacity was 22.57%, which was significantly higher than the error between the numerical simulation and the theoretical solution proposed in this paper. Compared with the technique presented in [18], the calculation method for the ultimate pullout capacity of the expanded anchor cable proposed in this paper was more accurate and reasonable, and has high applicability in practical engineering.
## 4 Implementation
### Libp2p Host
#### 4.5.2 Upgrading Connections  
Since there are several ways to provision libp2p capabilities, the connection update process uses protocol negotiation, as described in the preceding section, to choose which specific protocols to use for each capability. The protocol negotiation procedure employs multistream selection. When a connection requires both security and multiplexing, a secure communication channel is first established, and stream multiplexing is negotiated via an encrypted channel.  
Figure 9 shows an example of the connection updating procedure. The following are the main steps:  
* Both peers provide the multistream protocol identifier, indicating that they will utilize multistream-select to negotiate protocols for the connection update.  
Figure 8: Protocol negotiation flow diagram.  
* The starting peer recommends the TLS protocol [39] for encryption, which the destination peer rejects since it does not support TLS.
* The initiating peer then offers the Noise protocol [38], which is accepted by the destination peer, who acknowledges it by returning the Noise protocol identification.
* At this point, the Noise protocol takes over, and the peers perform the Noise handshake to create a secure channel. If the Noise handshake fails, the connection formation procedure terminates. If successful, the peers will utilize the safe channel for all further messages, including the remainder of the connection update procedure.
* Once the secure communications channel has been established, peers decide the stream multiplexer to be utilized. The negotiation method is the same as the previously described, with the calling peer offering a multiplexer and delivering its protocol identification, and the listening peer answering by returning the admitted identifier or sending \"na\" if the multiplexer is not compatible.
* Once both security and stream multiplexing are established, the connection update procedure is complete, and both peers can utilize the resultant libp2p connection to create new secure multiplexed streams.  
Figure 9: Connection update process sequence diagram.
## 3 Materials and Methods
### Land Use and Land Cover Simulation  
The CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model was chosen to simulate LULC change between 2020 and 2029. In particular, the Markov Chain model was first used to assess future land demand based on the annual rate of each LULC class from the transition area matrix of LULC change between 2014 and 2019. In general, the Markov chain is a stochastic process model that describes the probability of change from one state to another, i.e., from one land-use type to another, using a transition probability matrix [27]. Then, the selected driving factors of LULC change were applied to identify the LULC type distribution using binomial logistic regression analysis (Equation (1)) for allocating LULC types. In this study, the physical and socio-economic driving factors of LULC change included elevation, slope, distance to water bodies, distance to to roads, distance to settlement, soil fertility, population density at the sub-district level, and average income per capita at the sub-district level, which was successfully applied to Phuket Island by [PERSON] and [PERSON] [28]. Finally, future LULC data were simulated using the CLUE-S model. The conversion matrix, the elasticity of LULC change, and land use demand were simultaneously combined to allocate LULC data according to the driving factors of LULC change for a specific LULC type at a given location. The basic concept and the development of the CLUE-S model were explained in more detail by [PERSON] et al. [29; 30; 31].  
\[\text{Log}\left(\frac{\text{P}_{\text{i}}}{1-\text{P}_{\text{i}}}\right)= \beta_{0}+\beta_{1}\text{X}_{1,\text{i}}+\beta_{2}\text{X}_{2,\text{i}}\ldots \ldots+\beta_{n}\text{X}_{n,\text{i}} \tag{1}\]  
where Pi is the probability of a grid cell for the considered land-use type in location i, and the Xs are location factors. The coefficients (\(\beta\)) were estimated through logistic regression using the actual land use pattern as the dependent variable.
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
## 1 Introduction
### Main Contributions  
Motivated by the above observations, in this article, we propose an altitude control algorithm for quadcopter unmanned aerial vehicles. The main advantages of this new method are twofold: (1) while our method inherit the advantages of the PID approach, i.e., it is simple and straightforward, its multi-loop approach ensures that unlike other control algorithms [33; 34; 35; 36; 37; 38], our controller requires less computational resources and exhibits a smooth performance at all times; and (2) compared to conventional PID controllers, our control algorithm delivers a greatly improved performance with regard to both raising time and error convergence speed.  
We use the Lyapunov stability theory to validate the system's stability. We prescribe a maximum acceleration/deceleration of the vehicle which allows the controller to have the vehicle move at an appropriate acceleration before fast and hard approaching a target. This contributes to a safer and smoother flight performance of both single vehicles performing specific missions [5; 14; 41; 42] and multi-agent systems flying in formation or switching their inter-communication topology [43; 44]. For safety reasons, we firstly evaluate the performance of our new controller with numerical simulations and then we will conduct experimental flights to demonstrate the effectiveness of the proposed algorithm in real flight conditions.
## 3 Calibration Method
### Calibration Parameters Solution  
The magnetic measurement data are substituted into Equation (20) to calibrate the misalignment error by matrix \(\mathbf{R}\) which is a composite matrix consisting of multiple rotations [17].  
\[\mathbf{V}_{m}(i)=\mathbf{R}\cdot\mathbf{H}_{m}(i) \tag{20}\]  
Let:  
\[\mathbf{P}_{m}(i)=\mathbf{V}_{m}(n)-\mathbf{V}_{m}(j)\quad(n\
eq j) \tag{21}\]  
According to Equations (17) and (19), we can get:  
\[\mathbf{T}_{h}r(i)\times\mathbf{T}_{v}(i)=0 \tag{22}\]  
\[\mathbf{P}_{m}(i)\cdot\mathbf{N}_{v}=0 \tag{23}\]  
The cross product of two parallel vectors is zero which is used as a constraint to estimate the \(\mathbf{R}\) matrix. According to Equations (22) and (23), the objective function of obtaining the matrix \(\mathbf{R}\) can be set as Equation (24):  
\[f(i)=\sum_{i=1}^{n}\left|\mathbf{P}_{m}(i)\cdot\mathbf{N}_{v}\right|\cdot\left|\mathbf{T} _{h}r(i)\times\mathbf{T}_{v}(i)\right| \tag{24}\]  
where \(n\) denotes the measurement points.  
The value of the objective function is zero when the misalignment error is calibrated completely. Therefore, matrix \(\mathbf{R}\) can be estimated by solving the minimum value of the objective function. The nonlinear least squares (NLS) function provided by the MATLAB (R2014b, Mathworks, MA. US) optimization toolbox is used to solve the parameter optimization of matrix \(\mathbf{R}\). The objective function in the process of solving calibration parameters using the NLS function is as Equation (25):  
\[\varepsilon=\text{min}f(i) \tag{25}\]  
In particular, this method has high operability. Because the normal vector and tangent vector of magnetic data trajectory are used in the calibration process, the two trajectories can completely coincident theoretically. In this method, only one set of rotational experimental data are required to uniquely determine the rotation matrix \(\mathbf{R}\) without any requirement of the rotating axis. This characteristic has high value in a practical application.
# Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis
## 3 Uncertainty Flow Framework
### I. Bayesian Neural Network  
The core part of proposed **Uncertainty Flow** is the dual BNNs. Under Bayesian learning, a deep neural network--a stacked multiple non-linearity transformations of affine computations--is perceived as sequential layer-wise prior-to-posterior inferences. To allow the model to be flexible enough, we resort on the hierarchical architecture. In a standard classification task set-up, where both input and output variables are observable, i.e., \(\{X|x_{n}Y|y_{n}\}\), and each input is comprised of \(D\) features, i.e., \(x_{n}\in\Re^{D}\), the likelihood functions for our dual BNNs are specified in following Equations (1) to (3):  
\[p(y_{n}|\theta,x_{n},\sigma^{2})=Categorical(y_{n}|NN_{train,test}(x_{n};\theta), \sigma^{2}) \tag{1}\]  
\[NN(x_{n};w)_{source}=softmax\circ\left(tanh\circ\sum_{i=1}^{n}(x\cdot\theta)\right) \tag{2}\]  
\[NN(x_{n};w)_{target}=sigmoid\circ\left(tanh\circ\sum_{i=1}^{n}(x\cdot\theta)\right) \tag{3}\]  
Figure 1: Uncertainty Flow Framework. The graphic model explanation of our proposed **Uncertainty Flow** framework shows four essential elements. Le., the dual Bayesian neural networks in **I**, e.g., a source and a target BNN(separate by different colours in Figure 1); the weakly informative prior in **II**; the quantification of model uncertainty in **III**; and three proposed uncertainty indexes in perfecting the final multi-label categorisation in **IV**.  
For simplicity, only \(tanh\) non-linearity is considered as the activation function in BNN. Notice here, we tailer the target BNN in concord with the differentiated task demand. I.e., to allow a Bayesian neural network to produce multiple outputs, the soft-max function in the source BNN is replaced with a real-value function, e.g., sigmoid function, in the target BNN.  
Armed with foregoing likelihood functions, a full hierarchical Bayesian neural network is derived from following Equations (4) to (6):  
\[\sigma\sim Normal(0,I) \tag{4}\] \[\theta\sim Normal(w|0,\sigma^{2})\] (5) \[y_{n}|\theta,\sigma^{2}\sim Categorical(y_{n}|NN_{sourcetarget}( x_{n};\theta),\sigma^{2}) \tag{6}\]  
Here, we narrow our discussion in the most simplified version of a hierarchical Bayesian neural network, which contains one hyper-parameter, i.e., \(\sigma\). This hyper-parameter, e.g., \(\sigma\) directly controls the variance of a prior in production of weight posterior.
# Screening Mechanism and Properties of a Cantilevered Vibrating Sieve for Particles Processing
## 6 Conclusions  
1. The proposed cantilevered vibrating sieve (CVS) is composed of cantilevered sieve rods. During the screen body oscillations, the sieve may experience additional bending vibrations. When the vibration frequency of screen body closely approaches the natural frequency, the additional bending vibration of cantilevered sieve rod reaches its peak value, and different amplitudes exist in the cantilevered rod from its fixed end to the free end.
2. The CVS has a higher screen-penetrating probability than the traditional sieve, and the bending vibration of cantilevered sieve rod can crack the clusters of wet sticky materials on the sieve, cause near-aperture particles to experience rubbing motion, and then reduce sieve plugging.
3. According to DEM analysis of the effect of the geometric parameters and vibration parameters of the sieve on screening efficiency, with the increase of sieve inclination angle and rod length, screening efficiency has a declining trend. Screening efficiency is at a high level under vibration direction angle of about 50\({}^{\circ}\), vibration frequency of 25-30 Hz, and amplitude of about 2 mm.  
Conceptualization, L.P., H.F.; Methodology, L.P.; Software, H.F.; Formal Analysis, H.W.; Data Curation, Z.W.; Writing--Original Draft Preparation, H.H.; Writing--Review and Editing, L.P.; Writing--Revision, Z.W., H.Y.  
This work is supported by the National Natural Science Foundation of China (Grant NO. 51705131, 51605138), the Fundamental Research Funds for the Central Universities (2018B22614), the Shandong Provincial Natural Science Foundation (Grant No. ZR2019 PEE024), and the Project funded by China Postdoctoral Science Foundation (Grant NO. 2018T110568).  
The authors are grateful to the anonymous reviewers for their constructive comments.  
The authors declare no conflict of interest.