esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Figure 11(a) presents the battery's measured temperature graph with no cooling system turned on, in the Kelvin unit (K), which is the top graph, and in the Celsius unit (C), which is the bottom graph. The Kelvin unit was used because the SOC estimators required it to function properly; the Celsius unit was used because it is the most common temperature unit used by the public. Hence, it is for the user to read. The graphs show that, as the number battery cycles completed increases by time, so does the battery temperature increase. Temperature increased from 298 K to 308 K or from 24 \({}^{\circ}\)C to 34 \({}^{\circ}\)C.  
Figure 11(b) illustrates the battery's measured temperature graph with the cooling system turned on. It shows that, as the battery temperature reached 299 K or 25.8 C, the cooling system turns on automatically, which drops the battery temperature and prevents it from exceeding the assigned limit of 299 K or 28 \({}^{\circ}\)C, hence the battery's cooling system is operating exceptionally at keeping the battery operating safely. Figure 12(a),b displays the transmitted battery parameters on the IoT \"ThingSpeak\" platform in the visualised format such as the LEDs indictor which shows the green LED being turned on, indicating that the battery is operating safely. The platform uses the LCD display to display the measured battery voltage of 3.82 V, current of 15 Amps, temperature of 26.75 \({}^{\circ}\)C, and the SOH level of 100% as its first cycle to indicate the battery is brand new; the real SOC level is approximately 30% and the estimated SOC is 35% because the initially given SOC level to the SOC estimator is 35%. Hence, in start estimations, there is a slight error but the BMS will adjust to more accuracy with more battery cycles; the internal resistance of 0.008 ohms is also measured. Figure 11: Estimated R0 Graphs. Figure 14 presents the BMS's battery overheating alert indicator system functioning properly, which can be seen as the battery is overheating: the red LED has been turned on and the green LED has been turned off, which informs and alert the end-user of the battery overheating status. Even the battery overheat alert LED graph shows a spike of value being 1 when red LED is turned on. Figure 12: (**a**) Show Measured Temperature Graphs with Cooling and (**b**) without Cooling. Figure 14: Red LED Turning on When Battery Overheating. Figure 13: (a,b) Show Battery Parameters Displayed on IoT Platform.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 12 Applications of HCFs in Networks and Systems
### Low Latency Applications  
#### 12.1.1 Reduction in Latency by Using HCF  
The effective refractive index (n\({}_{\text{eff}}\)) of HCFs is seldom reported. The estimated values for HC-PBGF and NANF fibers are 1.005-1.010, and 1.001-1.003 at 1550 nm, respectively. Solid-core single-mode fibers [30,31] have n\({}_{\text{eff}}\) in the 1.460-1.470 range, with the lowest values in Pure Silica Core Fibers (PSCF) covered by ITU-T Recommendation G.654 [78].  
The latency of HCF equals 68-69% of the latency in SMF; a typical difference per unit length is 1.53 \(\upmu\)s/km. HCF has a similar latency advantage over twisted pair and coaxial cables with solid insulation made of low-density polyethylene (LDPE).  
If the network is latency-limited, the replacement of solid-core fiber with HCF can increase the reach by \(\approx\)45%, and the area covered by network from a single site by \(\approx\)100%, bringing in savings on facilities (e.g., edge data centers in a 5G network), equipment, and power.  
In latency-sensitive applications, n\({}_{\text{eff}}\) is not the only parameter of importance. Except for transmission over short distances (how short depends on the bit rate and line code), when the accumulated loss and dispersion (CD and PMD) are within the limits for \"streamlined\" transponder without time-consuming signal processing like FEC, dispersion compensation, large constellation coding/decoding, etc. (Section 12.1.2), the loss and dispersion can force network designers to adopt measures increasing the latency of data link and ultimately losing the low latency advantage of HCF, including the following:  
* Adding amplifiers or repeaters to bridge excessive loss;
* Activating dispersion compensation to avoid errors;
* Activating FEC, if errors are still experienced.  
When a minimum latency at all costs is the goal, for example, in HST (Section 12.1.3), the most effective solution is adding regenerators with the simplest signal path inside.  
Figure 23: Simplified comparison of SMF, HC-PBGF and NANF properties important for applications in (**a**) low-latency single-wavelength links, data centers, etc., (**b**) high-capacity core or metro DWDM networks. Larger distance from center indicates better properties, e.g., lower attenuation.
# A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
## 2 Macroscopic Saturated Porous Media Model
### Porous Media Flow Models
#### 2.6.1 Darcy Flow  
[PERSON]'s law is widely used as a constitutive assumption for the description of mostly fully saturated pore flow conditions in porous media. It is primarily applied to steady-state, incompressible, laminar flow in media with small pore spaces, where the drag force dominates over the frictional force. Thus, to recover a Darcy-like flow model, the frictional force (related to \(\mathbf{T}_{E}^{F}\)) is neglected from the fluid momentum balance in comparison with the drag force (related to \(\mathbf{\hat{p}}_{E}^{F}\)). This neglect can be mathematically justified following a dimensional analysis, as discussed in detail in [1]. In particular, having \(\mathrm{div}\big{(}\mathbf{T}_{E}^{F}\big{)}\ll\mathbf{\hat{p}}_{E}^{F}\) yields \(\mathrm{div}\big{(}\mathbf{T}_{E}^{F}\big{)}\approx\mathbf{0}\,\). Thus, the fluid momentum balance in (13) yields after neglecting \(\mathrm{div}\big{(}\mathbf{T}_{E}^{F}\big{)}\) and considering the definition of the effective momentum production (12) the Darcy-like flow model, expressed as  
\[\mathrm{grad}\,p=-\mu^{F}\,\big{(}\mathbf{K}^{S}\big{)}^{-1}\mathbf{w}_{F}+ \rho^{FR}\,\mathbf{g}\,, \tag{14}\]  
which accounts for both pressure-driven and gravity-driven flow. Although the gravitational force (\(\rho^{FR}\mathbf{g}\)) can play a significant role in some fluid mechanics applications, they are often negligible in small-scale systems where pressure gradient effects dominate. In our study, this holds true when applying [PERSON]'s law in an inverse technique to estimate the permeability parameter based on data from LBM simulations conducted on mesoscale RVEs (cf. Section 3.3). Consequently, an additional simplification can be applied to Equation (14), resulting in a simple flow model  
\[\mathrm{grad}\,p=-\mu^{F}\,\big{(}\mathbf{K}^{S}\big{)}^{-1}\mathbf{w}_{F}\,. \tag{15}\]  
In analogy to [PERSON]'s law, this equation establishes that the flow velocity of a fluid through a porous medium is linearly proportional to the pressure gradient driving the flow, with the intrinsic permeability \(\mathbf{K}^{S}\) and the fluid dynamic viscosity \(\mu^{F}\) acting as the proportionality constants. Using \(\mathbf{K}^{S}\) in the above formulation provides a more robust and fluid-independent measure of a porous medium's ability to transmit fluids. Since \(\mathbf{K}^{S}\) depends primarily on the micro-morphology of the porous medium, it can be accurately derived from \(\mu\)-CT images of the material, as will be discussed in Section 3.3.
# First SIMO Harmonic Radar Based on the SFCW Concept and the HR Transfer Function
## 2 The HR Equation and HR Transfer Function  
In order to understand the basic operation of a SFCW HR system measuring in the frequency domain, it is beneficial to analyze the two components of a single discrete tone of the measured complex harmonic return signal phasor, the amplitude, and its phase argument conveying the range information, separately. Figure 1 shows the typical scenario of an HR for a reference measurement without material under test (MUT) between the antennas. According to [14; 24], the harmonic return signal power \(P_{RX,2}\) at the interrogator's receiving antenna feed point can be calculated using the HR equation by evaluating  
\[P_{RX,2}=\frac{G_{RX,2}\;\lambda_{2}^{2}\cdot(P_{TX}\cdot G_{TX})^{2}\cdot \sigma_{2}}{(4\pi)^{4}\cdot R^{6}}\;, \tag{1}\]using the nonlinear pseudo harmonic radar cross section (RCS) \(\sigma_{2}\) of the tag, defined as  
\[\sigma_{2}=d_{2}\cdot G_{TXT}\cdot\left[\frac{G_{RXT}\lambda_{1}^{2}}{4\pi} \right]^{2} \tag{2}\]  
with \(G_{RX,2}\) for the gain of the interrogator harmonic receiver antenna, \(G_{TX}\) for gain of the interrogator's fundamental frequency illumination signal antenna, \(G_{RXT}\) for the gain of the tag's illumination signal reception antenna, and \(G_{TXT}\) for the gain of the tag's harmonic re-transmission antenna. Furthermore, \(\lambda_{1}\) and \(\lambda_{2}\) denote the free-space wavelengths of the illumination and harmonic return signals, while \(R\) denotes the slant range between the interrogator and the tag. Finally, \(P_{TX}\) denotes the input power at the interrogator's illumination signal transmit antenna feed point, while \(d_{2}\) is a proportionality factor which describes the specific harmonic conversion efficiency of the frequency doubler used in the HR tag. A graphical representation of all these key HR system variables is presented in [18] and an extensive experimental validation of the validity of Equation (1) can be found in [18; 24]. When comparing Equation (1) with the classical (monostatic) radar equation, two important observations fundamental to the optimization and design of harmonic radar systems can be made. The first is the different proportionality of the return signal strength for a given system and constant RCS target when the transmission power or range to target is varied--the HR return signal  
\[P_{RX,2}\propto\frac{P_{TX}^{2}}{R^{6}}\,,\qquad\text{in contrast to}\qquad P_{RX} \propto\frac{P_{TX}}{R^{4}} \tag{3}\]  
for a standard primary radar system. Additionally, it is easily observed that all fundamental frequency signal path components up to the input of the tag's frequency doubler show a square law dependency with regard to the harmonic return signal, which is caused by the nonlinear input power dependent transfer function of the doubler circuit. An important metric for optimizing the nonlinear frequency doubler is the actual received power \(P_{RXT}\) at its input.
## 3 Microalgal/Cyanobacterial Metabolites and Phytohormones with Potential Interest for Agriculture
### Free Fatty Acids  
FFAs are non-esterified fatty acids typically found in microalgal and cyanobacterial crude extracts. According to the length of the hydrocarbon chain, FFAs can be classified as short, medium, long and very long chain fatty acids. Moreover, they can be divided into unsaturated (e.g., lignoercic, palmitic, capric, behenic, cerotic, caprylic, lauric, stearic, myristic and arachidonic acids) and saturated (e.g., cis- and trans-fatty acids) fatty acids [7]. FFAs were identified in microalgal biomass from different genera (e.g., _Anabaena_, _Chlorella_, _Dunialella_, _Porphyridium_, _Scenedesmus_ and _Spirulina_), which include green, red and blue-green algae [29; 33]. In addition, several studies have reported that microalgae and cyanobacteria can accumulate up to 70% of fatty acids per dry weight [2]. For example, in the study performed by [PERSON], et al. [32], extracts from the marine microalgae _P. tricornutum_, _Nanochloropsisulata_ and _Porphyridium cruentum_ presented 37.3-82.4% of saturated fatty acids and 27.5-38.5% of polyunsaturated fatty acids. Due to current demands for renewable energy resources, FFAs from microalgae and cyanobacteria have been extensively explored as a resource for biodiesel production, as it can be obtained from the transesterification of fatty acids [58]. In addition to their potential use as a renewable energy resource, FFAs are also recognised for their anticarcinogenic, antibiotic, antifungal, antioxidant and antiviral activities, which make them promising candidates for the development of pharmaceutical, nutraceuticalil and cosmetic products [2; 7; 30; 31]. Biological activity, namely antimicrobial, antifungal and antioxidant properties, is also an important advantage for agriculture, as application of FFAs extracts can act on crops' protection.
## 4 Overview: Signal Processing Methods
### Discrete Wavelet Transform
In contrast, decomposition at level 4 is optimal as it retains the unique characteristics that capture the essential information in the signal.
# A Blockchain Network Admission Control Mechanism Using Anonymous Identity-Based Cryptography
## 3 Proposed Mechanism
### Ulser Admisson Control
#### 3.4.3 Admission and Network Layer Data Filtering  
Network layer data filtering involves the inspection and handling of incoming data packets at the network layer, determining whether the data should be accepted, rejected, or processed. The detailed steps are outlined in Algorithm 2.  
```
Input: IP datagram packet, Whitelist W, Blacklist B, Allowable Error Time etime Output: Verification Result
1. IP = packet[src_ip]//Extract IP from packet source
2. ID = packet[options].ID//Extract ID from the options part of the packet
3. If IP exists in B:
4. //If IP is in the blacklist, update the countdown for the IP entry
5. B[IP].time = updateTime()
6. Filter IP packet
7. Return False
8. For each w in W://Search for whitelist elements that meet requirements
9. If w[ID] == ID and w[IP] == IP and getTime() <= w[time]:
10. Accept IP packet
11. //if in the whitelist, algorithm stops
12. Return True
13. //If no matching whitelist element is found
14. Create a new element b in B
15. b.ip = IP
16. b.id = ID
17. //Set blacklist countdown with allowable error time
18. b.time = setTime(getTime() + etime)
19. B.add(b)
20. Filter IP packet
21. Return False
```  
**Algorithm 2** Network layer Admission Control  
_Etime_ is an allowable error time, meaning that we consider a small amount of unverified data acceptable for errors. For such cases, a shorter blacklist time is set compared to the normal penalty time. However, if the source of this error is already on the blacklist, meaning the IP sender has transmitted multiple data packets within a short period, an update method is used to gradually increase the blacklist duration for this IP, effectively extending the filtering time.
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
## 2 Data and Processing Strategy  
In this section, the reanalysis product (ERA-Interim) and the ZTD estimation method as well as the used GPS data and data processing strategy will be described.
# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### The Scale Discriminative Correlation Filter  
When tracking the target, the target object experiences different scenes, and its appearance and scale will change with time. This situation brings great problems to the tracking, how to update the target appearance and scale change effectively in a timely manner, which is the key to improving tracking performance. We found that [16] proposed an accurate scale estimation method. This method can estimate the target scale from the best score by training a scale discriminative correlation filter. Based on this, we learn a scale discriminative correlation filter to handle the problem of target scale change and get an ideal scale correlation filter \(h\) by the following function:  
\[\varepsilon=\|\sum_{l=1}^{d}h^{l}s^{l}-g\|^{2}+\lambda\sum_{l=1}^{d}\left\|h^{ l}\right\|^{2}. \tag{7}\]  
In Equation (7), \(g\) represents the optimal correlation output, \(l\) represents the dimension of the feature, and \(\lambda\) is a regular coefficient. The above solution in the frequency domain is given by:\[H^{l}=\frac{\overline{G}F^{l}}{\sum_{k=1}^{d}\overline{F}^{k}F^{k}+\lambda}=\frac{D_ {t}^{l}}{E_{t}}. \tag{8}\]  
To better calculate the results, the numerator and denominator of \(H^{l}\) in Equation (8) are respectively updated as follows:  
\[D_{t}^{l}=(1-\eta)D_{t-1}^{l}+\eta\overline{G}_{t}F_{t}^{l} \tag{9a}\] \[E_{t}=(1-\eta)E_{t-1}+\eta\sum_{k=1}^{d}\overline{F}_{t}^{k}F_{t}^{k} \tag{9b}\]  
\(\eta\) represents a learning rate parameter. the response value of the scale filter that we need can be calculated by following equation:  
\[\hat{g}_{\text{s}}=\mathcal{F}^{-1}\Bigg{\{}\frac{\underline{\underline{ \underline{\underline{\underline{\underline{\underline{\underline{\underline{ \underline{\underline{\underline{\underline{\underline{\underline{\underline{\underline \cdot}}}}}}}}}}}}{D^{l}Z^{l}}{E+ \lambda}\Bigg{\}} \tag{10}\]  
We estimate the target scale by getting the maximum scale response value and utilize Equations (9a) and (9b) to update the scale discriminative filter model.
# Improved Sliding Mode Finite-Time Synchronization of Chaotic Systems with Unknown Parameters
## 1 Introduction
For example, in works such as [24; 25], relevant model parameters are directly set and regarded as unknown. Since system models contain multiple parameters, careful analysis and argumentation are needed when choosing the unknown parameters. Additionally, in general, several parameters cannot be regarded as unknown. Building on the presented discussion, this paper proposes a novel finite-time control method for chaotic systems with unknown parameters. The control strategy is based on sliding mode control technology. The controller is model-based, but the controller unknown parameter adaptation only depends on the sliding surface instead of directly selecting the relevant unknown parameters in the system model, and the sliding surface has new forms. In this way, the selected parameters might be the outside unknown variables of the system model that affect chaotic motion, which are challenging to measure. The proposed approach avoids the difficulties of the unknown parameters selection and mitigates the limitations of choosing the relevant parameters within the system model only. The paper demonstrates the proposed method's finite-time convergence. The adaptive law of unknown parameters is then presented to make the synchronization error system reach the sliding mode surface in finite-time. Simultaneously, simulation verification is conducted using chaotic systems with the same, as well as different, structures. The results show a good control effect. This paper discusses the mathematical theory enabling the realization of the proposed approach. However, further analysis is required to determine the specific parameters' value for different chaotic phenomena applications. The rest of the paper is structured as follows. Section 1 presents the fundamental lemmas used in the research. In Section 2, a new sliding surface is proposed for a general nonlinear chaotic system. Additionally, the sliding mode controller that deals with unknown parameters independent of the controlled system model is designed within this section. The adaptive law of unknown parameters is described, and the finite-time stability is proved. In Section 3, simulation verification is performed, demonstrating the same-structure synchronization of the parametric excitation rolling chaotic system of a ship, as well as the different structures' synchronization of the Liu system and the Lorenz system. Section 4 concludes the paper and discusses the directions for further research.
# Unified Analytic Melt-Shear Model in the Limit of Quantum Melting
## 2 Methods
### Electron and Ion Subsystems Combined  
Now consider the actual substance at \(T=0\), with (mass) density \(\rho\), composed of nuclei of mass number \(A\) and atomic charge \(Ze\) (atomic number \(Z\)), by combining its electron and ion subsystems. Let \(n\) be the average number density of electrons, and \(r_{s}\) the dimensionless density parameter defined analogously to \(s\) above:  
\[n^{-1}\equiv\frac{4\pi}{3}(r_{s}a_{B})^{3},\]  
so that  
\[\rho=2.674\ \frac{A}{Zr_{s}^{3}}\ \ \mathrm{g/cm^{3}}. \tag{16}\]  
Here \(r_{s}=1.388\ (A/\rho\,Z)^{1/3}\) measures the mean inter-electron distance in the units of the Bohr radius.  
If \(M\) is the mass of the charged particles (protons) and \(Ze\) their charge, then \(a_{0}=\hbar^{2}/M(Ze)^{2}\) is the length scale (the ion Bohr radius), and \(\mathrm{Ry}=\hbar^{2}/2 Ma_{0}^{2}=M(Ze)^{4}/2\hbar^{2}\) the energy scale (the usual rydbergs for electrons). \(M\) is related to \(A\) as \(M=Am_{p}\).  
The coupling parameter of a quantum ion-electron plasma reads\[R_{s}=\frac{(Ze)^{2}}{aE_{F}}=\frac{a}{a_{0}}=\left(\frac{m_{p}}{m_{e}}\right)AZ^{ \gamma/3}r_{s}, \tag{17}\]  
where \(E_{F}\) is the ion Fermi energy and \(a\) the mean inter-ionic distance. It then follows from (16) and (17) that  
\[\rho=2.674\left(\frac{m_{p}}{m_{e}}\right)^{3}\frac{A^{4}Z^{6}}{R_{s}^{3}}\ \ \mathrm{g/cm^{3}}. \tag{18}\]  
With \(m_{p}/m_{e}=1836.153\), one finally obtains  
\[\rho=1.655\cdot 10^{10}\ \frac{A^{4}Z^{6}}{R_{s}^{3}}\ \ \mathrm{g/cm^{3}}. \tag{19}\]
# Dynamic Modeling and Response Analysis of an Optical Machining Robot with Prismatic Joint Clearance
## 2 Modeling of Spatial Prismatic Joint Clearance
### Contact Force Calculation Under Different Contact Modes
#### 2.2.1 Contact Force Calculation for Plane Contact  
After calculating \(F_{N}\), \(F_{T}\) can be determined using the Coulomb friction model. \(F_{T}\) is shown as follows:  
\[F_{T}=\mu F_{N} \tag{2}\]  
where \(\mu\) represents the coefficient of sliding friction.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Order** & **Diagram** & **Description** & **Order** & **Diagram** & **Description** \\ \hline
2 & Double points of contact & 10 & Three lines of contact \\ \hline
3 & Three points of contact & 11 & Four lines of contact \\ \hline
4 & Four points of contact & 12 & Single line and single plane of contact \\ \hline
5 & Single point and single line of contact & 13 & Double lines and single plane of contact \\ \hline
6 & Single point and double lines of contact & 14 & One plane of contact \\ \hline
7 & Double points and single line of contact & 15 & Two planes of contact \\ \hline
8 & Single line of contact & Single line of contact & Single line of contact \\ \hline \end{tabular}
\end{table}
Table 1: Cont.  
Since the Coulomb friction model cannot describe the relationship between friction over time, a dynamic friction coefficient based on the change in velocity is used to more accurately describe the friction process [17].  
\[\mu=\begin{cases}\mu_{d}&|V|>V_{d}\\ \mu_{s}\sin(\dfrac{\pi}{2}\dfrac{|V|}{V_{s}})&|V|<V_{s}\\ \dfrac{(\mu_{s}+\mu_{d})}{2}+\dfrac{1}{2}\bigg{[}(\mu_{s}-\mu_{d})\cos(\pi \dfrac{|V|-V_{s}}{V_{d}-V_{s}})\bigg{]}&V_{s}\leq|V|\leq V_{d}\end{cases} \tag{3}\]  
where \(V_{s}\) and \(V_{d}\) represent the velocities for transitions from stick-slip and static-to-sliding friction; \(\mu_{s}\) and \(\mu_{d}\) represent the coefficients of static friction and sliding friction, respectively.  
The slider and the guideway form a rectangular contact area. The \(\mathbf{F}_{N}\) (\(\delta\)) between the slider and the guideway can be expressed as follows [18]:  
\[\mathbf{F}_{N}(\delta)=\dfrac{EA\delta^{\prime}}{l} \tag{4}\]  
where \(E\) represents the material's elastic modulus, \(A\) denotes the area of the contact part, \(\delta\) indicates the compression depth, and \(l\) corresponds to the material's thickness along the primary compression axis.