esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
In this paper, we proposed the idea that putting the datacenters into the grid integrated with renewables as dynamic loads to maintain the normal operation of the grid. In addition, we designed an optimal load allocation strategy based on stochastic searching ideas, which could achieve a reasonable distribution of the load to get the minimum power losses when deploying multiple datacenters in the grid. Our results show that multiple datacenters in the smart grid as dynamic loads can effectively reduce the power losses and especially when adopting the optimal load-allocation scheme obtained by the GA-based method to adjust the power usage of datacenters; the advantages would more obvious. Stability issues of the grid were also considered in the problem we defined and the experiments we conducted, including bus voltage variations and the overloading of transmission lines. Besides, we also incorporated practical factors into the experiments such as the flexibility and action delay during the demand response process of datacenters. Moreover, in order to eliminate the inescapable influence of the action delay when adjusting the power of the datacenters as much as possible, we adopted forecasting techniques to predict the renewable energy generation amount looking ahead, under both sunny and cloudy/snowy weather conditions. Comparing the results under different action delay settings, it can be seen that adopting the forecasting method could reduce the extra power losses effectively. During our experiments, the relationship of computational time and the achieved accuracy was also investigated, which illustrated the feasibility of the proposed method based on forecasting.  
From the study about forecasting results and forecasting time, we also found that the time cost of the forecasting method could further be optimized. As the next step, we will focus on the combination of multiple kinds of demand response strategies and approaches in the datacenter towards the requirements of the electricity providers. Besides, we will also pay more attention to the power management and other kinds of reliability issues of the smart grid after integrating renewable energy sources and take frequency and voltage excursion as the knobs to facilitate better demand response in the future work.  
Conceptualization, P.L. and X.W.; methodology, P.L.; software, H.J.; validation, Y.L., X.Y.; formal analysis, P.L.; investigation, H.J.; resources, P.L.; data curation, Y.L.; writing--original draft preparation, P.L.; writing--review and editing, X.W.; supervision, X.W.; project administration, X.W.; funding acquisition, Y.W.  
This paper is partially supported by The National Natural Science Foundation of China (No. 61762074, No. 91847302, No. 61563044 and No. 61862053) and National Natural Science Foundation of Qinghai Province (No. 2019-ZJ-7034, No. 2015-ZJ-725, No. 2017-ZJ-902).  
The authors declare no conflict of interest.  
Figure 10: The relationship between forecasting error and forecasting time. (**a**) sunny day; (**b**) cloudy/ rainy/snowy day.
# A Review of MEMS Scale Piezoelectric Energy Harvester
## 2 Research Progress of PEH
### General Model of Energy Harvester  
In 1995, [PERSON] and [PERSON] [24] proposed a model which was based on linear system theory to simulate the energy harvester. The model consists of a seismic mass, \(m\), a spring, \(k\), and damping, \(d\). The model can accurately simulate the electromagnetic energy harvester mentioned in their article, but it is only feasible when the damping and stiffness are linear. For PEHs, the model needs to be changed because the impact of the electrical system on the mechanical system is not linear. Even so, important conclusions of the model can be promoted on PEHs. Based on this, [PERSON] [25] further decomposed the damping into electrically induced damping and mechanical damping in 2002. The improved mass-spring-damping system is shown in Figure 1. The system consists of a mass \(m\), a spring with a stiffness \(k\) as well as the electrically induced damping \(b_{e}\) and the mechanical damping \(b_{m}\). While applying an external sinusoidal displacement load \(y(t)=Y_{0}\cos(\omega t)\) with the amplitude \(Y_{0}\) on the model, the relative motion of the mass with respect to the housing would be \(z(t)\).  
The differential equation of the model can be expressed by Equation (1):  
\[m\ddot{z}+(b_{e}+b_{m})\dot{z}+kz=-m\ddot{y} \tag{1}\]  
The power converted is the product of the force on the mass and its velocity. Thus, it can be shown as Equation (2):  
\[P=kz\big{(}\dot{y}+\dot{z}\big{)} \tag{2}\]  
Combining Equations (1) and (2), the power converted can be expressed as:  
\[P_{c}=\frac{m\zeta_{e}\omega_{n}\omega^{2}\Big{(}\frac{\omega}{\omega_{n}} \Big{)}^{3}{Y_{0}}^{2}}{\Big{(}2\zeta_{T}\frac{\omega}{\omega_{n}}\Big{)}^{2 }+\left(1-\Big{(}\frac{\omega}{\omega_{n}}\Big{)}^{2}\right)^{2}} \tag{3}\]  
where \(\zeta_{e}\) represents electrical damping ratio; \(\zeta_{T}\) indicates the combined damping ratio (\(\zeta_{T}=\zeta_{e}+\zeta_{m}\)); \(\omega\) is the input frequency; and \(\omega_{n}\) suggests the natural frequency (or resonant frequency) of the model.  
When the model's resonant frequency matches the input frequency, the energy harvester can obtain the maximum conversion energy, as shown in Equation (4). Furthermore, the equation also shows that as the input frequency increases, the power converted \(P_{c}\) rises.  
\[P_{c}=\frac{m\zeta_{c}\omega^{3}Y^{2}}{4\zeta_{T}^{2}} \tag{4}\]
# Three Dimensional CS-FEM Phase-Field Modeling Technique for Brittle Fracture in Elastic Solids
## 3 Numerical Modeling
### Crack Propagation in Thick Walled Cylinder  
We performed another numerical example to demonstrate the advantages of using the phase-field method to simulate crack propagation. We modeled two parallel cracks at different elevations in a thick walled cylinder under uni-axial tension. The dimensions were: \(t\) = 0.1 mm, \(r\) = 1 mm, \(a\) = 0.2 mm, and \(h\) = 1 mm (Figure 14). The material and length scale parameters were: \(E\) = 210 kN/mm\({}^{2}\), \(\
u\) = 0.2, \(l_{c}\) = 0.075 mm, and \(G_{c}\) = 2.7 \(\times\) 10\({}^{-3}\) kN/mm. We observed the crack merging phenomenon in this case. The cracks initiated from the respective notches but, when the damage zones were influenced by each other, the two cracks merged (Figure 15). This phenomenon was simulated based on the physics of the system without any ad-hoc criterion set up for the crack propagation path, thereby restating that the phase-field technique can be used to simulate many such cases that would traditionally be a bit more complex using discrete treatment.  
Figure 13: Crack propagation in a bi-material specimen: (**a**) representational 2-D geometry; thickness = 5 mm; (**b**) crack initiation; (**c**) crack propagation through the soft material; (**d**) crack reaches the material discontinuity junction; and (**e**) crack branching.
# Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering
## 2 Fusion Algorithm
### Overall Design of GNSS/IMU/Visual Fusion Positioning
The final fusion of the integrated navigation pose and visual odometry pose can be represented by the state equation as shown in Equation (4):  
\[x(t)=[(\delta r_{vis}^{n})^{T},(\delta v_{vis}^{n})^{T}]^{T} \tag{12}\]  
where \(\delta r_{vis}^{n}\) represents the visual position error; \(\delta v_{vis}^{n}\) represents the visual velocity error. The observation equation is as follows:  
\[z(t)=\widetilde{r}_{vis}-\widetilde{r}_{nav}=H(t)x(t)+v(t) \tag{13}\]  
where \(\widetilde{r}_{vis}\) represents the visual position observation; \(\widetilde{r}_{nav}\) represents the integrated navigation position. The overall framework of GNSS, IMU, and visual fusion can be illustrated as shown in Figure 1.
## Appendix A Specific Measures of the Radar Data Quality
### Beam Height above the Terrain  
Some quality factors only influence certain types of radar products. The beam height above the terrain should be useful especially for rainfall estimating products--the estimated rainfall is expected to be more precise when the target bin is near the surface of the terrain. The related QI is computed as follows:  
\[qi_{2,i}=\begin{cases}0;&h<0\\ 1-\frac{h}{h_{max}};&0\leq h\leq h_{max}\\ 0;&h>h_{max}\end{cases} \tag{2}\]  
where \(h\) is the bin height above the terrain and \(h_{max}\) is the user defined maximum height above the terrain. Note that the case \(h<0\) occurs when the radar beam hits the ground, i.e., the projected radar beam is under the surface.  
The quality index \(qi_{2,i}\) also belongs to the fundamental ones, implemented for instance by [PERSON] et al. [18]. [PERSON] et al. [20; 21] adopted this concept in a slightly different way: they preferred working with the height of the lowest radar scan, which, for a given radar represents a constant value, depending on the actual altitudes of the surrounding terrain.
###### Abstract  
We first propose and demonstrate a novel approach for achieving a focused laser differential interferometer (FLDI) system with tunable spatial resolution. The spatial resolution of the FLDI can be adjusted continuously between 83 \(\upmu\)m and 382 \(\upmu\)m. The density fluctuation of a supersonic shear flow is measured using the FLDI system with a spatial resolution of 182 \(\upmu\)m, and the density fluctuations at different locations of the supersonic shear flow are measured and analyzed. The ability to adjust the spatial resolution in this work is of great significance for enhancing the spatial resolution and flexibility of the FLDI system.  
focused laser differential interferometer; tunable spatial resolution; density fluctuation measurement +
Footnote †: journal: Applied Sciences
## Methodology
### Model Formulation  
#### 3.2.1 Notation  
To facilitate the model presentation, all definitions and notation used hereafter are summarized in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Indices** & \\ \hline \(i,j,m\) & Vehicular node (demand point, dispatch center, and urban rail station) index \\ \(k\) & Vehicle index \\ \(h\) & Time window \\ \hline
**Sets** & \\ \hline \(I\) & Set of demand points \\ \(K\) & Set of vehicles \\ \(D\) & Set of dispatch centers \\ \(M\) & Set of rail transit stations \\ \hline
**Parameters** & \\ \hline \(q_{i}\) & Number of passengers at the demand point \(i\); \(\forall i\in I\) \\ \(l_{ij}^{h},e_{i}^{h}]\) & The \(h\)th travel time window of the demand point \(i\); \(\forall i\in I\) \\ \(L_{i}\) & The maximum expected ride time of the demand point \(i\); \(\forall i\in I\) \\ \(St_{i}\) & The minimum expected ride time of the demand point \(i\); \(\forall i\in I\) \\ \(Q\) & Maximum capacity of the vehicle \\ \(D_{\max}\) & Maximum length of the vehicle \\ \(T_{\min}\) & Minimum travel time of feeder bus route \\ \(d_{ij}\) & Distance from the vehicular node \(i\) to the vehicular node \(j\); \(\forall i,j\in I\cup D\cup M\) \\ \(t_{ij}\) & Travel time from the vehicular node \(i\) to the vehicular node \(j\); \(\forall i,j\in I\cup D\cup M\) \\ \(t_{M}^{k}\) & The time of vehicle \(k\) arriving the rail transit stations \\ \(t_{i}^{k}\) & The time of vehicle \(k\) arriving the demand point \(i\); \(\forall i\in I\) \\ \(t_{i}^{k}\) & Number of passengers at customer point \(i\) assigned to vehicle \(k\); \(\forall i\in I\) \\ \(g(t_{M}^{k}-t_{i}^{k})\) & A function to calculate the passenger satisfaction at demand point \(i\) based on his/her ride \\ \(c_{1}\) & Operational cost yuan/km \\ \(c_{2}\) & Satisfaction cost yuan/person \\ \(H\) & A very large fixed value \\ \hline
**Decision Variables** & \\ \hline \(x_{ij}^{k}\) & Whether the vehicular node \(i\) precedes the vehicular node \(j\) on the vehicle \(k\), or not; \\ \(y_{i}^{k}\) & Whether the vehicular node \(i\) is covered by the vehicle \(k\), or not; \(\forall i\in I\cup D\cup M\), \(\forall k\in K\) \\ \(U_{k}\) & An auxiliary (real) variable for sub-tour elimination constraint in vehicle \(k\); \(\forall k\in K\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and variables in the mathematical model.  
Figure 2: Graphical representation of the integrated DRT problem.
# Manipulator Smooth Control Method Based on LSTM-XGboost and Its Optimization Model Construction
## 4 The Stability Prediction Model of Manipulator Based on LSTM-XGboost  
In recent years, deep learning algorithms have been widely used in the field of robotic arms. With the popularity of intelligence, more and more robotic arms come into contact with the environment to perform complex and dangerous tasks. When the external environment contact force is generated, the safety and flexibility of the manipulator are very important. A variety of impedance control strategies are designed from the perspective of control. For example, Reference adopted a position-based adaptive impedance control method for the grinding robot, effectively tracking the end's ideal position and solving the grinding trajectory's compensation correction problem. For the lower-limb rehabilitation robot based on impedance control, the human-machine interaction torque is set as the constraint condition for real-time control. The parameters of the end impedance model of the robot are adjusted online to make the robot adapt to the external environment to realize the robot's compliant control.  
The manipulator is a complex multi-input multi-output system. In actual control, the dynamic model and joint friction of the manipulator are unknown in most cases, affecting impedance control's robustness. In order to improve the compliance control effect of the manipulator, this chapter proposes a stability prediction model of the manipulator based on LSTM-XGboost.
## 5 Security Analysis
In the proposed authentication framework, secrets, such as _sdij_, \(x_{ij}\) and _sbij_ are long-term secrets that are created and then used for many authentication requests. Temporal secrets, such as _degij_ and _chebyshev_val_, are freshly generated for each authentication request. The generation of long-term secrets is a straightforward process that can be carried out frequently to ensure secrecy. Since temporal secrets are created for each authentication request, if these secrets are compromised for a session, it will not affect previous or future authentication requests. This aspect of the proposed framework supports forward and backward secrecy of authentication requests. Moreover, because of the use of the Chebyshev polynomial, compromise in one session of an IoT device and Blockchain communication will not affect the overall authentication requests. This is because, for successful authentication, the _chebyshev_val_ of each IoT device should be the same as Blockchain is expecting, as explained in Figure 1.
## 1 Introduction
### Variable Identification in the Sand Retention Test
#### 1.1.1 Sand Characteristics  
The PSD of \(d_{1}\), \(d_{5}\), \(d_{10}\), \(d_{30}\), and \(d_{50}\) are used to analyze plugging performance and sand production [3; 4; 5; 6; 7; 8]. The PSD of \(d_{\rm x}\) refers to the percentage of sand in the sample with a specific size. For example, if \(d_{1}\) is equal to 300 microns, then 1% of the sample has a sand size of 300 microns. The PSD of \(d_{5}\), \(d_{10}\), \(d_{30}\), and \(d_{50}\) of the same sample will have a smaller sand size than \(d_{1}\) because the higher the value of \(\rm x\), the smaller the sand size in the sample. PSD of \(d_{1}\), \(d_{5}\), and \(d_{10}\), which represent the large sand grains, shows a good correlation with sand retention, while PSD of \(d_{50}\), representing the median sand size, gives a weaker relationship [3]. [PERSON] et al. [5] used only one point on the particle distribution curve, which is \(d_{10}\), but it cannot accurately predict plugging nor sand production. In other words, parameters other than \(d_{10}\) must be considered when choosing the slot width of the sand control screen. In contrast, [PERSON] and [PERSON] [4; 6] used PSD of \(d_{10}\) and \(d_{30}\) as an indicator to select the screen slot size, which can control the amount of sand produced for a particular screen. For good sand retention, the screen slot size should be smaller than \(d_{10}\) because the bigger the screen slot size, the higher the amount of sand produced [7; 8].  
Next, the other sand characteristics used in SRT are fines content, sorting, and uniformity coefficients. [PERSON] and [PERSON] [4] found that a high fines content has a shallow impact on the sand production compared to the largest grains in the sand distribution. If the size of the sand in the sample is mostly less than 45 microns, then the amount of sand that passes through the screen will be high. The sorting and uniformity coefficients show a bad interaction with the amount of sand passing through the weave [4; 6]. However, the combination of the fines content with the sorting coefficient gives a good interaction, where a poorly sorted sand with many fine particles results in a high risk of sand production. In contrast, the well-sorted sand with high fines content results in a smaller amount of sand passing through the weave [3; 5].
## 3 Results
### Occupant Behaviors
#### 3.1.2 Timeseries Occupancy Patterns  
To formulate an indoor environmental control strategy that is commensurate with occupants' activities, time-series data pertaining to the activities of individual occupants are imperative. By augmenting the location data from the preceding chapter with temporal data, we conducted a time-series analysis of occupant activities within each zone. Figure 7 presents the occupancy status of each zone at 15 min intervals during the measurement period. On weekdays, occupants were consistently detected in Zone 2 (bedroom) from 00:00 to 07:00, indicating sleep. They typically initiated their activities around 7:00 and left the space by 8:30. From 08:00 to 18:00, the occupants were categorized as being in Zone 0 (unoccupied). Occupancy resumed between 17:00 and 18:00, with individuals returning to Zone 2 around 22:00. On the weekends, occupancy frequency increased, particularly in Zones 1 and 3. However, Zone 2 still exhibited a relatively high occupancy rate compared to weekdays. The time-based occupancy frequency provides insights into the occupant's schedule. A representative schedule can be constructed as follows: sleep from 00:00 to 07:00, preparation and activities from 07:00 to 08:00, unoccupied from 08:00 to 18:00, study and activities from 18:00 to 22:00, and sleep again from 22:00 to 24:00. It is evident that the occupancy status and activity levels vary significantly different by time during weekdays  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Occurrence** & **Rate** \\ \hline Non-occupied & 238 & 35.41\% \\ Zone 1 & 90 & 13.39\% \\ Zone 2 & 303 & 45.08\% \\ Zone 3 & 41 & 6.1\% \\ Total & 672 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Presence occurrence in each zone.  
Figure 6: Spatial occupant presence for five weekdays.  
compared to the constant occupancy conditions provided in the benchmark operation model for the multi-family residential building sector [43]. Operating the heating system in an occupant-centric manner is expected to yield significantly different energy consumption characteristics. Furthermore, the energy model can be adapted by resetting the schedules for occupancy status and occupancy environments (activity levels and clothing levels) each time.
## 3 D2 HCP (Distributed Dynamic Host Configuration Protocol)  
The Distributed Dynamic Host Configuration Protocol (D2 HCP) is an auto-configuration protocol that manages the addition and departure of nodes in a MANET. The protocol makes the MANET nodes collaborate with each other to manage the assignment of unique and correct IP addresses in a distributed manner. All the network nodes have the same role; there is no special type of node that centralizes the management of the same.  
Nodes have a synchronization system based on the OLSR [30] routing protocol. Thanks to this mechanism, the synchronization is done passively, by monitoring the mentioned routing protocol, thus no network traffic overhead is generated compared to that generated by the OLSR protocol.  
Due to the fact that all the nodes are responsible for managing the addition of any new node to the network, this process can be done quickly. A node that wishes to join a network tries to contact any node still belonging to it, and may receive several responses from multiple nodes. This makes the chances of successfully joining the network high, because of the high availability and redundancy that distributed management provides.  
Here we introduce the D2 HCP specification: it begins with the data structures used, continues with an explanation of the messages exchanged between nodes for joining and departing the network, and then details how synchronization takes place in the protocol. Finally, we explain the format of the messages exchanged during the auto-configuration process, detailing how to solve the problem of possible message loss in the network using appropriate timers and performing certain actions when they expire to restore the auto-configuration process, as well as state diagrams for each operation mode that a node can adopt.
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
## 2 Multi-Frequency RTK Positioning Mathematical Model
### Kalman Filtering Theory for Long Baseline RTK Positioning  
#### 2.3.1 Double-Differenced Ionosphere Model  
The propagation speed of electromagnetic wave signals changes as they pass through the ionosphere, and the degree of change depends mainly on the electron density and signal frequency in the ionosphere [10]. The initial value of the ionosphere delay can be corrected by using the Klobuchar model, and the residual part can be estimated as an unknown parameter in Kalman Filtering. The ionosphere delay between different satellites is usually considered to be uncorrelated. The DD ionosphere is generally defined as:  
\[I_{rb}^{jk}=\left[M(E_{r}^{j})I_{z,r}-M(E_{b}^{j})I_{z,b}\right]-\left[M(E_{r} ^{k})I_{z,r}-M(E_{b}^{k})I_{z,b}\right] \tag{13}\]  
In Equation (13), \(I_{z}\) is the zenith ionosphere delay; \(E\) is the elevation of the satellite; \(M\) is the mapping function. Taking the average of the mapping functions of the rover and the base-station, Equation (13) can be converted as:  
\[I_{rb}^{jk}=\overline{M}(E^{j})[I_{z,r}-I_{z,b}]-M(E^{k})[I_{z,r}-I_{z,b}] \tag{14}\]  
where:  
\[\overline{M}(E^{j})=\frac{M(E_{r})+M(E_{b})}{2}\]