esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A Two-Stage Optimal Scheduling Model for Wind-PV-Thermal-Storage System
### Intraday Optimal Dispatching Model
#### 4.2.2 Intraday Low-Carbon Economic Scheduling Model  
\[\min F^{\prime}=F_{1}+\sum_{t=1}^{T}[C_{\max}(E_{C,t}-E_{D,t})]+\Delta f \tag{38}\]  
where \(\Delta f\) is the average adjustment cost. Due to the uncertainty of wind speed, solar radiation, and load demand prediction, this cost caused by intraday thermal power unit plans deviating from the day-ahead scheduling plan.  
The demand response cost in the above low-carbon economic dispatching objective function is composed of day-ahead price demand response cost \(C_{PDR,t}\) and intraday IDR cost \(C_{IDR,t}\). In addition, it can be expressed as:  
\[f_{\text{DR}}=\sum_{t=1}^{T}\left(C_{PDR,t}+C_{IDR,t}\right) \tag{39}\]  
The intraday IDR has a certain scheduling cost, which is to give the corresponding economic compensation to the users who adjust the load on demand.  
\[C_{IDR,t}=\left\{\begin{array}{l}N_{IDR}^{\text{IPR}}\omega_{t}\Big{(}s_{IDR, t,d}^{+}m_{t,d}^{+}\Big{)}\text{Increasing Load}\\ \sum_{d=1}^{N_{IDR}}\omega_{t}\Big{(}s_{IDR,t,d}^{-}m_{t,d}^{-}\Big{)}\text{ Decreasing Load}\end{array}\right. \tag{40}\]  
where \(\omega_{t}\) is the 0-1 variable, which represents that the virtual unit participates in or not participates in intraday scheduling at time \(t\), \(N_{IDR}\) is the number of ladder price segments, \(s_{IDR,t,d}^{+}\) and \(s_{IDR,t,d}^{-}\) are respectively the increase or decrease of electric quantity of the IDR virtual unit when it is in the \(d\)-th ladder price at time \(t\), \(m_{t,d}^{+}\) and \(m_{t,d}^{-}\) are the unit cost of increasing or decreasing electric quantity respectively.  
To fully mobilize the flexibility of the demand response on the load side and realize the absorption of wind farms and PV stations in the multi-energy cooperative scheduling system. In this paper, the load response of the IDR users will not transfer to other periods, and meets the following constraints:  
\[0\leq\sum_{t=1}^{T}\big{|}P_{IDR,t}\big{|}\leq P_{IDR,\max} \tag{41}\]  
where \(P_{IDR,\max}\) is the maximum value of IDR in the whole scheduling cycle.
# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
## 5 Experimentation  
### Experimental Environment  
The data construction and data extraction of this experiment and the training and evaluation of the model were conducted in the environment of Windows 10, and the specific environment settings are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware Environment** & **Software Environment** \\ \hline GPU NVIDIA GTX 3060 (NVIDIA, Santa Clara, CA, USA) & Windows 10 \\ Memory 32 GB & Python 3.8.19 \\ CUDA11.6.0 & Torch 1.13.0 \\ cuDNN 8.0.5 & Anaconda 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment settings.  
In order to evaluate the performance of the model proposed in this experiment for link prediction, the AUC and F1 metrics were used in this study, and the F1 metric considers both the precision and recall of the classification model, as shown in the specific evaluation metrics Equations (13)-(16).  
The AUC and F1 value of binary classification were the evaluation indicators used in this experiment. The AUC can comprehensively measure classification algorithms, and when the AUC score exceeds 0.5, it can measure the performance of link prediction algorithms. In the comparison of \(a\) independent scores, when there are \(a^{\prime}\) times when the positive edge score is higher than the negative edge score, and \(a^{\prime\prime}\) has the same score, the calculation equation for the AUC is defined as per Equation (13) [28]:  
\[AUC=\frac{a^{\prime}+0.5a^{\prime\prime}}{a} \tag{13}\]  
Precision represents the ratio of the number of samples correctly predicted as positive cases to the number of samples of all true positive cases and is calculated by Equation (14) [29]:  
\[P=\frac{TP}{TP+FP} \tag{14}\]  
Recall represents the ratio of the number of samples correctly predicted as positive cases to the number of all samples predicted as positive cases and is calculated by Equation (15) [30]:  
\[R=\frac{TP}{TP+FN} \tag{15}\]  
The F1 value is used to measure the values of precision and recall and is the reconciled mean of these two values, where [31]:  
\[F=\frac{\left(1+a^{2}\right)PR}{a^{2}(P+R)} \tag{16}\]  
The confusion matrix has four markings: \(TP\) (true proportion), \(FP\) (false proportion), \(FN\) (false negative) and \(TN\) (true counterexample), which denote true positives, false positives, false negatives and true negatives, respectively. Generally speaking, a higher value of these three evaluation indexes represents a better performance of the classification model.
## 3 Regarded System Architecture  
We adopt the sensor network model in Figure 1, which is proposed by [PERSON] et al. [25]. In this model, the network consists of a large number of sensor nodes distributed over an area of interest. There is at least one command node in charge of the system. The model also introduces some super-nodes, called gateway nodes, in addition to the sensor nodes. The gateway nodes have more computational, energy, memory and communication resources. They act as a gateway between sensor nodes and the end user, as they typically forward data from the WSN on to a command node [26]. As depicted in Figure 1, the gateways partition the sensors into different distinct clusters. Each cluster is composed of a gateway node and a set of sensor nodes. The operations within a cluster are independent of each other. The cluster sensor nodes transmit the gathered and integrated data to the gateway node of their cluster.  
It is assumed that the gateway nodes are deployed in safe places and cannot be captured by the adversary; while the other nodes in the cluster may be captured and compromised. For the key management, we adopt the scheme proposed by [PERSON] and [PERSON] [27]. The management scheme triggers the rekeying phase regularly to handle the timing attacks from inside or outside attackers, and our goal is to indicate the optimal rekeying rate. In our approach, the sensor nodes use a symmetric key mechanism. A cluster key is used for the encryption and decryption operations of all data between the sensors and the gateway node. Before being deployed, the cluster key is programmed into the memory of the sensors. Sensor and gateway keys are stored in the flash RAM and, hence, can be renewed in the rekeying phase. We assume that the time to perform a rekeying operation is measured based on a generic Diffie-Hellman protocol (GDH) [28; 29]. At the same time, random delays are padded for each gateway node response message to mitigate the information leakage of the timing side-channel.
## 5 Finite-Dimensional Model  
Aimed to validate asymptotic results, a finite-dimensional model of bridge for galloping analysis is formulated. The finite-difference method is applied to transform the partial integro-differential Equation (8) into a set of ordinary differential equations, to be numerically integrated for given initial conditions.  
The space domain (changed in \([0,1]\) for simplicity) is divided in \(N>2\) equispaced subintervals of amplitude \(\Delta=1/N\). The following notation is adopted:  
\[\begin{split} s_{j}&:=j\,\Delta,\qquad\quad j=-1, \ldots,N+1,\\ v_{j}&:=v\left(s_{j},t\right),\quad j=-1,\ldots,N+1. \end{split} \tag{34}\]  
Figure 2: Qualitative galloping response to smooth flow, and hysteresis loop, for prismatic bodies with a 2:1 side ratio.  
in which two external nodes, \(j=-1,N+1\), have been introduced.  
The space derivatives are expressed via the central finite differences:  
\[\begin{split} f^{\prime\prime\prime\prime}&=\frac{f_{j-2 }-4f_{j-1}+6f_{j}-4f_{j+1}+f_{j+2}}{\Delta^{4}}\\ f^{\prime\prime}&=\frac{f_{j+1}-2f_{j}+f_{j-1}}{ \Delta^{2}}\end{split} \tag{35}\]  
and the integral term via the trapezoidal rule:  
\[\int\limits_{0}^{1}f\,\mathrm{d}x=\Delta\sum\limits_{j=1}^{N-1}f_{j} \tag{36}\]  
in which the boundary conditions \(f_{0},f_{N}=0\) have been accounted for. The field equation in (8), accordingly, becomes:  
\[\begin{split}&\tilde{v}_{j}+\varrho^{2}\frac{v_{j-2}-4v_{j-1}+6v_{j}-4v _{j+1}+v_{j+2}}{\Delta^{4}}+\varrho^{2}\eta_{b}\frac{v_{j-2}-4\dot{v}_{j-1}+6 \dot{v}_{j}-4\dot{v}_{j+1}+\dot{v}_{j+2}}{\Delta^{4}}+\\ &-\frac{v_{j+1}-2\,v_{j}+v_{j-1}}{\Delta^{2}}-\frac{\dot{v}_{j+1 }-2\,\dot{v}_{j}+\dot{v}_{j-1}}{\Delta^{2}}\eta_{c}+\Lambda^{2}\Delta\sum \limits_{j=1}^{N-1}v_{j}+\Lambda^{2}\eta_{c}\Delta\sum\limits_{j=1}^{N-1} \dot{v}_{j}+\\ &+(c_{e}+b_{1}u)\,\dot{v}_{j}+b_{3}\frac{1}{u}\dot{v}_{j}^{3}+b_ {5}\frac{1}{u^{3}}\dot{v}_{j}^{5}=0\qquad\qquad\qquad j=1,\ldots,N-1\end{split} \tag{37}\]  
and the boundary equations read:  
\[\begin{split}& v_{0}=0\\ &\varrho^{2}\frac{v_{1}-2\,v_{0}+v_{-1}}{\Delta^{2}}+\frac{\dot{v} _{1}-2\,\dot{v}_{0}+\dot{v}_{-1}}{\Delta^{2}}\eta_{b}\varrho^{2}=0\\ &\varrho^{2}\frac{v_{N+1}-2\,v_{N}+v_{N-1}}{\Delta^{2}}+\frac{ \dot{v}_{N+1}-2\,\dot{v}_{N}+\dot{v}_{N-1}}{\Delta^{2}}\eta_{b}\varrho^{2}=0 \end{split} \tag{38}\]  
Equations (37) and (38) are a set of \(N+3\) (ordinary and algebraic) equations for the \(N+3\) unknown nodal displacements \(v_{j}\left(t\right)\), \(j=-1,\ldots,N+1\).
# Application of Microbially Induced CaCO\({}_{3}\) on the Reinforcement of Rock Discontinuity
## 2 Materials and Methodology
### Production Rate for Calcium Carbonate  
The process of calcium carbonate precipitation is a complex biochemical process that requires enough carbonate ions and calcium. The amount needed depends on such factors as pH value, calcium concentration, nucleation sites, and inorganic carbon concentration [61]. Additionally, temperatures between 10 \({}^{\circ}\)C and 37 \({}^{\circ}\)C significantly impact the performance of microbially induced calcium carbonate precipitation (MICP) and the growth rate of _Sporosarcnia pastevii_[65; 66]. This study considers two ambient temperatures, namely 10 \({}^{\circ}\)C and 30 \({}^{\circ}\)C. Various domestication schemes were tested to measure the productivity of CaCO\({}_{3}\)[20]. The specific process is outlined below.  
The bacteria obtained from different domestication schemes were cultured at 150 rpm and removed for use after 48 h. The MICP calcification test was carried out in a tissue culture tube with a breathable film. A 20 mL culture solution of urea and calcium chloride with molar concentrations of 0.5 M/L was prepared. The same volume of suspension and bacterial liquid was mixed for a precipitation reaction. The reaction temperature was  
Figure 5: Growth rate of _Sporosarcnia pastevii_ in a saline–alkaline environment.  
controlled at 10 \({}^{\circ}\)C and 30 \({}^{\circ}\)C, respectively. After 6 days of reaction, the mass of calcium carbonate was weighed, and the productivity was calculated. The measurement method of calcium carbonate productivity was as follows [67]:  
1. The mixed solution after the precipitation reaction was washed and filtered with deionized water three times. The tissue culture tube and filter paper were then dried in the oven at 110 \({}^{\circ}\)C for 24 h and weighed to obtain mass M\({}_{1}\);
2. Next, the tissue culture tube and filter paper were soaked in a prepared hydrochloric acid solution (0.5 M/L), rinsed with deionized water twice, and dried in a 110 \({}^{\circ}\)C oven for 24 h. After drying, they were weighed to obtain mass M\({}_{2}\);
3. The actual mass of calcium carbonate produced is calculated by finding the difference between the two masses, M1-M2. The theoretical mass of calcium carbonate is obtained by multiplying the calcium ion concentration (m) used in the gel solution by the volume (V) used in the culture solution;
4. The final calcium carbonate productivity is determined by dividing the mass of calcium carbonate experimentally produced by the mass of calcium carbonate theoretically generated.
## 3 Results and Discussion
### Solid-State Production of A. olivimuriae Extracellular Glycosyl Hydrolases and Lyaases
Figure 2: Specific activities, referred to unit mass of initial substrate, of endo-\(\beta\)-1,4-xylanase (**A**), \(\beta\)-xylosidase (**B**), \(\beta\)-mannanase (**C**), and \(\beta\)-mannosidase (**D**) in 14-day-old solid-state cultures conducted on residual thistle biomass (RTB), spent coffee grounds (SCG), barley straw (BS) and digestate solid fraction (DSF) in the presence and absence of wheat bran (WB) as the amendment. Data are the mean \(\pm\) standard deviation of four replicates. In addition to these hydrolases' activities, the activities of pectin lyase (EC 4.2.2.10) and spectate lyase (E.C. 4.2.2.2) were also measured. These enzymes break the \(\alpha\) (1 \(\rightarrow\) 4) bond between D-galacturonic residues adjacent through a \(\beta\)-elimination mechanism that leads to the production at the non-reducing end of one of the two fragments of an unsaturated residue between C4 and C5 (4-deoxy-alpha-D-galact-4-en-uronosyl groups). Table 5 shows that, among the non-amended substrates, the best polymethylgalacturonase, polygalacturonase and pectin esterase activities were found in cultures carried out on RTB (2.30 \(\pm\) 0.18, 1.27 \(\pm\) 0.21 and 1.24 \(\pm\) 0.20 IU g\({}^{-1}\) substrate, respectively). Significantly increased polymethylgalacturonase activities, due to the presence of WB compared to the non-amended counterpart, were found on RTB (2.99 \(\pm\) 0.27 vs. 2.30 \(\pm\) 0.18 IU g\({}^{-1}\) substrate) and barley straw (1.49 \(\pm\) 0.10 vs. 0.99 \(\pm\) 0.13 IU g\({}^{-1}\) substrate). In the case of polygalacturonase, the only cultures to benefit from the presence of WB were those carried out RTB (1.93 \(\pm\) 0.09 vs. 1.27 \(\pm\) 0.21 IU g\({}^{-1}\) substrate) and pectin esterase activities (2.16 \(\pm\) 0.33 vs. 1.24 \(\pm\) 0.20 IU g\({}^{-1}\) substrate). Although detected in all the solid-state cultures, the lyase activities were significantly lower than those of hydrolases (Table 5).
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 3 Analysis and Discussion
### Performance Comparison Between OISL-OSU and OISL-MPLS
As depicted in Figure 12, compared with OISL-MPLS, OISL-OSU reduces jitter by 75.00%, 66.67%, 66.67%, 66.67%, and 0% for frame lengths ranging from 68B to 1522B, respectively. Finally, Figure 13 displays the back-to-back performance. Compared with OISL-MPLS, OISL-OSU reduces average burst frames by 80.91%, 80.53%, 80.29%, 80.15%, 80.08%, and 80.05% for frame lengths ranging from 68B to 1522B, respectively. The experimental data indicate that OISL-OSU outperforms OISL-MPLS in terms of bandwidth utilization, delay, jitter, and reliability.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 1 Introduction
### Restrictions on Signal Parameters  
To give a comprehensive analysis of the integrated navigation and communication signal design, some parameters of the signal have to be restricted, and the IRIDIUM signal system is considered as the basis.  
The IRIDIUM signal system is combined of Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA). The TDMA frame length is 90 ms [12]. The frequency access is 41.667 kHz with the occupied bandwidth being 31.5 KHz. The total bandwidth is 10.5 MHz (1616-1626.5 MHz), and there are 240 duplex frequency access bands and 12 simplex frequency access bands in total.  
In the following, the frequency access band will be referred to as unit band with the symbol \(B_{u}\), and the occupied bandwidth of every unit band will be expressed as \(B_{c}\). The total number of unit band is \(N_{B}=252\).The data rate during the data part is 50 Kb/s. The orbit altitude of IRIDIUM is 780 Km.  
In this paper, the design of the signal is based on the parameters above. Some other restrictions are set as follows:  
1. Direct Sequence Spread Spectrum (DSSS) and BPSK are considered for the navigation signal.
2. Assume that the orbit altitude is 24,000 km for medium Earth orbit (MEO). Then, the power deterioration difference for MEO and LEO will be \(20\times\log(24000/780)=29.76\) dB. This means that when the launch power of the signal is the same, the received power of signal from LEO will be 29.76 dB higher than that from MEO for receivers on the ground. In the following, the CN0 difference is assumed to be 30 dB for simplicity.
3. The power of each unit band is assumed to be identical, which means that every unit band gets 1/252 of the total power.
4. The CN0 of GNSS signals mainly falls in 30-50 dBHz in urban areas. Therefore, the analysis assumes that the highest CN0 of the LEO signal is 80 dBHz.  
The following analysis is based on the restrictions above.
## 2 Implementation of the Algorithms of Fatigue and J-Integral in FEM
### Bond Damage and Deletion  
With the horizon region of the crack tip identified in the URDFIL subroutine, the USDFLD subroutine was used to compute the change in remaining life parameter \(\lambda\) according to Equation (8).  
\[\frac{\partial\lambda(N)}{\partial N}=-A\epsilon^{m} \tag{8}\]  
The parameters \(A\) and \(m\) were established prior to the start of the simulation. The remaining life parameter \(\lambda\), and cyclic bond strain \(\epsilon\), were evaluated from the static simulations. In this study, a constant load ratio \(R\) was used where \(R\) is defined as  
\[R=\frac{Load_{min}}{Load_{max}} \tag{9}\]  
Given the assumption of linearity, the load ratio combined with the maximum load condition allowed the cyclic strain range to be computed by only simulating the maximum load state. The processes of [PERSON] and [PERSON] [20] were used in this study to calculate the cyclic strain range  
\[\epsilon_{cylic}=|\epsilon_{max}-\epsilon_{min}|=|(1-R)\epsilon_{max}| \tag{10}\]A time mapping was introduced which defined a relation between fatigue cycles and simulation steps. A similar relation to the one given by [PERSON] and [PERSON] [20] was used in the present work but rather than define mapping between fatigue cycles and fictitious simulation time, simulation step number was used. As shown in Equation (11), the number of fatigue cycles at any instance of the simulation, \(N\), is given by the current time step, \(n_{i}\), and a constant mapping coefficient, \(\mu\). For all horizon sizes, a value of \(\mu=900\) was utilized.  
\[N=\mu(n_{i}-1) \tag{11}\]  
All elements were initialized to have remaining life equal to one. A conditional statement was included within the USDFLD subroutine which checked the remaining life value for all elements during each time step. The SDV used for monitoring element status was used as a deletion flag and was updated based on the value of remaining life. Any element with remaining life below 0 had the status SDV changed to inactive and the element was removed from the model.
# A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method
## 4 Results  
Two kinds of excitation signals, namely a sinusoidal burst signal and a pulse signal, were used to excite the transducer. The ultrasound signal was detected by the ultrasonic transducer and laser sensing system.  
The pulse signal was a high voltage sharp pulse generated by the reflectoscope, which is shown in Figure 5a. In this situation, the transducer worked in transmit-receive mode. The ultrasonic signal is shown in Figure 5b. The details of the initial wave and echo wave are shown in Figure 5c,d. The width of the initial wave is quite wide and noisy. It is difficult to determine the time of start of the TOF. The laser sensing system provided a noncontact detection for ultrasound. Although the laser beam was placed near the surface of the transducer, the initial wave (also seen as a reference wave here) was cleaner and shorter than the ultrasonic transducer, which is shown in Figure 5f. However, the ultrasound emitted by the sharp pulse was generated by self-vibration of the piezoelectric materials. The stability and the signal-to-noise radio (SNR) were not good enough for precise measurement. The initial signal was also unsuitable for cross-correlation calculations.  
Figure 5: Ultrasound excited by pulse signal: (**a**) Pulse signal; (**b**) ultrasound signal detected by ultrasonic transducer; (**c**) ultrasound signal detected by laser sensing system; (**c**,**d**,**f**,**g**) zooms of the signal.  
To transmit a stable and controllable ultrasonic signal, we instead used a sinusoidal burst signal. The sinusoidal burst signal was a 5-cycle 1 MHz burst signal generated by the signal generator, which is shown in Figure 6a. The transducer worked in transmit mode, which only transmits ultrasound. The laser sensing system was used for ultrasound detection. Figure 6b-d show the ultrasound signal detected by the laser sensing system. In this case, the ultrasound was generated by forced vibration. We clearly found 5-cycle rising pulses and several small vibrations in the initial and echoed wave.  
The ultrasound excited by the sinusoidal burst signal was stable and controllable, and therefore could be a good reference signal for the cross-correlation method. The laser sensing system provided an ultrasound detection method without disturbing the ultrasound field. Therefore, the sinusoidal burst signal and laser sensing system were used in the following experiment.
## 3 Results and Discussion
### Hirshfeld Surface Analysis  
Hirshfeld surface analysis provides an effective tool to analyze the differences in crystal packing between structurally related compounds. It is a useful for visualizing 3-dimensional electron density, especially that related to intermolecular interactions. We used the Crystal Explorer program [(39)] for this task. Hirshfeld surface plots and 2D fingerprints of intermolecular interactions for compounds 1-4 are presented in Figure 5. In all compounds, we observe four significant contacts, i.e., O\(\cdots\)-H (16.6-35.2%), N\(\cdots\)-H (11.3-26.3%), H\(\cdots\)-H (12.6-39.3%), and C\(\cdots\)-H (6.4-12.4%). In addition, a Cl\(\cdots\)-H (25.7%) interaction also occurs in 2 (Figure S1, Supplementary Materials). For compound 1, the most significant contributions come from H\(\cdots\)-O (35.2%) contacts, which correspond to the N-H\(\cdots\)-O/C\(-\)H\(\cdots\)-O interactions. However, for compounds 2 and 3, a higher contribution comes from Cl\(\cdots\)-H (25.7%) and H\(\cdots\)-H (39.3%) contacts, respectively. However, for 4, the predominant interaction is N\(\cdots\)-H (26.3%). In these compounds, C\(\cdots\)-C (1.2-3.3%) contacts occur to a lower extent, which significantly proves that they do not have relevant \(\pi\)-\(\pi\) interactions.  
Figure 5: Calculated Hirshfeld surfaces of compounds **1** (**A**), **2** (**C**), **3** (**E**), and **4** (**G**). The red spots on the Hirshfeld maps correspond to close intermolecular interactions, whereas blue regions are the domains without close intermolecular contacts. 2D fingerprint plots of **1** (**B**), **2** (**D**), **3** (**F**), and **4** (**H**) exhibit different intermolecular interactions.