esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Extrusion Test
Because of the limited extrusion distance (about 13.5 mm) by each driving velocity in our test, the slight reduction of frictional resistance was hard to be clarified. The decreasing trend was also not clear in the results of [PERSON] et al. [26]. Thus, we assumed the inner wall friction was consistently the same in the measuring zone. The extrusion pressure (\(P\)) in the measuring zone could be calculated by \(P=4F/\pi D^{2}\), where \(F\) is related to the average extrusion force under 0.25 mm/s of piston rate in the measuring zone and \(D\) represents the inner diameter of the barrel. The extrusion pressures of mixtures MIX-L, MIX-M and MIX-H at different ages are demonstrated in Figure 6b. The mix design with a higher content of MK showed the higher extrusion pressure with time. As explained by [PERSON] et al. [43], most SCMs, including MK, could be regarded as fillers at the early-age, due to the pozzolanic reaction was mainly dependent on the hydrated cement products which were generated after alite hydration. One of the principal mechanisms about filler effect was illustrated as the finer SCMs could provide extra surface as nucleation sites for the hydration products. Mixtures MIX-H and MIX-M had a much higher growth rate of extrusion pressures with time to compare with that of mixture MIX-L. The extra MK in the matrix might accelerate the phase change of cement particles from flocculation to structuration. That could be one reason to explain the decreased initial setting time with an increased amount of MK addition. Besides, the HPCC used in this study had very small average particle diameter size (d50 = 3.75 \(\upmu\)m, see Figure 1) which might physically increase the inner friction between particles and the number of available nucleation surfaces in the system. Figure 7 gives the extruded filaments collected after each extrusion test. Even at the age of 10 min, the extruded filaments of mixtures MIX-L, MIX-M and MIX-H showed stable shapes. The curves of mixtures MIX-H and MIX-M showed significantly high extrusion pressures as well as substantial standard deviations after the initial setting time. The high load was potentially harmful to the experimental setup.
## 3 Results
### Lambert W-Function Simplified Equations for Solar Cell/Panel Modeling
Depending on the magnitude of \(x\) (that is, depending on how closely it approaches \(0^{+}\)), \(W_{0}^{+}\) can be reasonably estimated as:  
\[W_{0}^{+}=x-0.98639x^{2}+1.1976x^{3}\approx x-0.98639x^{2}\approx x, \tag{29}\]  
The negative branch of the [PERSON] W-function, \(W_{-1}\), needs to be calculated at:  
\[x=f\big{(}I_{pv},I_{0},a,R_{s},R_{sh},n,T,V\big{)} \tag{30}\]  
when estimating the series resistance, R\({}_{s}\), for the 1-D/2-R equivalent circuit model (Equation (8) to (12)). Since the above equation for the different solar cells/panels from the database gives values of \(x\) within the range \([-2.77\cdot 10^{-3},\ -10^{-35}]\), two expressions were defined:  
\[ \begin{array}{l}W_{-1}(x)=9.7117\cdot 10^{-5}\ln(-x)^{3}+6.8669\cdot 10^{-3} \ln(-x)^{2}+1.2\ln(-x)-1.1102,\\ x\in\big{[}-10^{-2},-5\cdot 10^{-13}\big{]}\end{array}  \tag{31}\]  
\[ \begin{array}{l}W_{-1}(x)=1.6705\cdot 10^{-6}\ln(-x)^{3}+4.4514\cdot 10^{-4} \ln(-x)^{2}+1.0511\ln(-x)-2.3364,\\ x\in\big{[}-5\cdot 10^{-13},-10^{-40}\big{]}\end{array} . \tag{32}\]  
The error for these equations was below 0.42% (Equation (31)) and 0.02% (Equation (32)) (see Figure 9). Equation (32) can be applied for values of up to \(x=-10^{-50}\), with errors of below 0.07%. Figure 8: Positive branch of [PERSON]’s W-function (right-side), \(W_{0}^{+}\), calculated at \(V=0\) and \(V=V_{\infty}\) for points \(x\) (Equation (25)) corresponding to the 1-D/2-R equivalent circuit model, based on the data for the solar cells/panels. Our approximation for \(W_{0}^{+}\) (Equation (26) to (28)) is also shown. Finally, if the explicit models proposed by [PERSON] and [PERSON], and [PERSON] are used, W-1 needs to be calculated at points x defined by the characteristic points of the _I-V_ curve (\(I_{sc}\), \(V_{mp}\), \(I_{mp}\), and \(V_{sc}\) (see Equations (16) and (19)):  
\[x=f\big{(}I_{sc},I_{mp},V_{oc},V_{mp}\big{)} \tag{33}\]  
as shown in Figure 9. Based on the post-processed data for the 90 solar cells/panels, the values of \(x\) calculated using these explicit methods were within the range [\(-0.304\), \(-0.1\)]. The following equation for \(W_{-1}\) was initially suggested, which was characterized by an error of less than 1.6%22 within this range:  
\[W_{-1}(x)=248.42x^{4}+134.24x^{3}+4.4258x^{2}-14.629x-4.9631 \tag{34}\]  
In order to extend both the range of validity and the accuracy of the above equation, a splitting into two sub-branches at the point where the curvature of the function changes its sign (\(x=-0.27\)), the following equations were suggested:  
\[W_{-1}(x)=-1-\sqrt{42.949x^{2}+37.694x+8.0542},\ x\in[-0.36785,-0.27] \tag{35}\]  
\[W_{-1}(x)=0.14279\ln(-x)^{3}+1.04416\ln(-x)^{2}+3.92\ln(-x)+1.65795, \tag{36}\]  
\[x\in[-0.27,-0.0732]\]  
The error for the above equations was below 0.23% (Equation (35)) and 0.02% (Equation (36)) (see Figure 9). It should also be emphasized that caution needs to be used with regard to Equation (35), as it gives complex solutions when \(x\rightarrow-1/e^{+}\) (that is, when \(x\in[-1/e,-0.36785]\)).
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 4 Joint Digital Signal Processing Schemes for Carrier Recovery
### Transmission Effects  
Fiber transmission will affect the phase coherence between the channels in mainly two ways. First, dispersive walk-off will cause a delay between the channels, which will affect the performance of the joint processing. Here, it is important to realize that this cannot be compensated for by simply applying electronic delays to the detected channels since the the phase noise originates from both the carrier and the LO laser. If the channels are time-shifted relative to each other before detection, they will mix with different parts of the LO phase noise. Thus, the phase noise differences cannot be undone by simply time-shifting the detected signals. Second, fiber nonlinearities may introduce phase noise on the channels. However, the effects of this on joint processing remains to be studied.
## 3 Laser Tracker as a Measurement System
### Laser Tracker Measurement Technologies  
Currently, there are two different types of technologies used in LTs to determine the distance measurement: incremental distance measurements with an interferometer and a frequency-stabilized helium-neon laser (IFM), and absolute distance measurement (ADM) with an electronic control that analyzes the beam signal to determine its time of flight, providing the distance from the LT to the retroreflector. Meanwhile the measuring beam cannot be broken in IFM mode; if it is broken in ADM mode, the beam can be recovered and the measurement can continue. However, ADM is less accurate than IFM. The difference in accuracy between the two measurement techniques is shown in [24].
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 1 Introduction  
In the 5G/6G era, mobile edge computing (MEC) has emerged as a computing architecture enabler, as this technique migrates computing from the core network to the network edge [1]. As a result, MEC significantly reduces transmission latency and improves the user's quality of experience [2].  
Edge servers are the key elements of MEC [3]; they provide resources for processing data and tasks and are deployed closer to users than the core network server. However, it is difficult to determine the required number of edge servers and their deployment locations based on the deployment cost and desired performance, including the required delay, energy efficiency, and system reliability [4]. Providing full coverage for all users is not always feasible. Further, deploying edge servers in areas far from users induces resource waste and high costs, but deployment of only a small number of edge servers in dense areas degrades the user service experience. Most previous studies on this problem focused on static edge-server placement only, in which the locations are fixed once the edge servers are deployed [4, 5]. However, edge-server placement should be dynamically adjusted to handle changing conditions such as variations in demand, cost, and efficiency.  
To overcome these challenges, this study explores efficient and intelligent dynamic edge-server placement by considering time-varying network scenarios and deployment costs. The main contributions of this paper can be summarized as follows:  
* The edge servers are classified as static or dynamic. Static servers are deployed in densely populated areas, whereas dynamic servers move over time in response to dynamic changes in user locations; the number of dynamic servers can be increased or decreased.  
* The snake optimization (SO) algorithm [6] is improved to determine the optimal server placement and to implement operations such as increasing, decreasing, and moving the servers via a dynamic edge-server placement algorithm. The minimum placement-cost algorithm is used to calculate the minimum edge-server movement distance.  
The rest of this paper is organized as follows. Firstly, we review the related work in Section 2. Then, we present the system model and problem description in Section 3. In Section 4, we propose the algorithm for dynamic placement of edge servers. Additionally, we provide simulation results and analysis in Section 5. Finally, conclusions and future perspectives are presented in Section 6.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 5 Design  
### Feedback/On-Demand Forwarding  
According to our mechanism, the relay starts to forward after receiving a feedback message from the network server. This feedback message contains a list of current packets received by the network server from the previous feedback. Each packet is identified by packet id and sequence number, allowing the LoRaWAN packet to bring a long list without exceeding the LoRaWAN packet size's maximum capacity. This packet ID is generated by the network server during the join process, derived from a unique device ID given by the manufacturer.  
To avoid collisions between feedback messages and forwarding messages or packet messages, we utilize separate channels. Any relay that receives a feedback message will do the filtering process:  
* Packet Filtering: The relay compares the packet id and packet sequence of the received packets to the feedback message's information. It selects only the missing packets from the feedback message and then forwards them to the gateway. However, the selected packets will not be removed as it can be used for the next feedback process if the packet is still lost. The packet is removed from the relay if it is included in the feedback message.  
* Time Filtering: There are cases wherein the gap between the feedback message transmission time in the network server and its reception time at relays may lead to unnecessary forwarding. Suppose that the network server already generated a feedback message without including a packet that had just arrived. If the relay receives this feedback message, it unnecessary forward the late packet. Therefore, a packet that is received during feedback message transmission will be forwarded in the next feedback message.
# Glassy Carbon Electrochemical Sensor for Gallic and Vanillic Acid Detection in Aqueous Solutions
## 2 Experiments  
The electrochemical experiments were carried out at 25 \({}^{\circ}\)C and atmospheric pressure in a one-compartment electrochemical cell using a three-electrode setup. A bare glassy carbon electrode (with a geometrical surface of 0.76 cm\({}^{2}\)) was used as the working electrode located at the bottom of the cell. Prior to any experiment the working electrode was polished with alumina paste. The counter electrode was a platinum spiral (0.5 mm in diameter) and the reference electrode was a mercury/mercury sulfate electrode (Hg/Hg\({}_{2}\)SO\({}_{4}\)) connected to the working electrode via a Luggin capillary. The solution of each phenol (i.e., gallic or vanillic acid) was stirred with 3D water at room temperature for 30 min, while the resulting aqueous solution was stored in the fridge until the analysis. The cell was then filled with 50 mL of aqueous KNO\({}_{3}\) (i.e., electrolyte)-phenol solution, and prior to any experiment a purging with He was performed. All the electrochemical measurements were carried out with an AUTOLAB potentiostat/galvanostat (PGSTAT302N) using the three-electrode system described above. The concentration of each solution ranged from 20 to 200 mg\(\cdot\)L\({}^{-1}\), while the pH value was measured prior to each experiment and without any extra adjustment between three and four (increasing slightly in the lower phenol concentration values). The pH value remained almost constant during the course of the experiments. This behavior was expected since the concentration of the phenol did not significantly change after the run of two cycles. Cyclic voltammetry (CV) measurements were performed in a potential window from \(-0.8\) V to +1 V in two scan rates of 25 and 50 mV\(\cdot\)s\({}^{-1}\) using the above potentiostat/galvanostat system. Experiments were also carried out at higher scan rates, however, they resulted in nonseparable oxidation peaks. All the experiments were repeated at least twice with a deviation of less than 5%.
## 2 Results and Discussion
### How a TAS System Based on the MS-Enose Works  
The platform adopted allowed experiments to be run with an high throughput Total Analysis System (TAS) [36], which consisted of an autosampler for fully automated HS-SPME sample preparation on-line, and was directly combined to a mass spectrometer (MS), through a void column that was thermostatted in a GC oven, whose output signal (data) was elaborated on-line and then processed using chemometric software. The HS-SPME-MS TIC (Total ion Current) pattern is a single peak whose mass spectrum is representative of the fingerprints of the whole coffee volatile fraction (Figure 2).  
The corresponding mass spectral fingerprint is highly reproducible and ideally suited for further chemometric elaboration as it only consists of whole masses [37; 38].  
Compared to conventional GC-MS, mass spectral fingerprints provide total information about each sample and may even be more helpful and meaningful, in routine control screening, than the characterisation of each individual component in that sample. The reliability of the mass spectral fingerprint is demonstrated by the comparison of the high degree of overlap between the average mass profile of an Arabica volatile fraction obtained using the non-separative techniques (HS-SPME-MS-enose) and the average total spectrum over the total analysis time from a conventional HS-SPME-GC-MS analysis, as reported in Figure 3.  
Figure 2: Schematic representation of the Total Analysis System (TAS) system used.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 2 NDN and QoS
### Flow Classification  
Flow classification is the foundation of QoS. Flow classification is a problem of grouping the packets and identifying the priority at which forwarding resources must be allocated for the said group [14].  
* In an IP network, flow classification is executed using a source address, destination address, source port, destination port, and protocol type identified with the packet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Schemes** & **QoS Support** & **PIT Management** & **CS Management** \\ \hline LCE [17] & No & No & Yes \\ SDC [18] & No & No & Yes \\ SNN [19] & No & No & Yes \\ Probability cache [20] & No & No & Yes \\ GrIMS [21] & No & No & Yes \\ FairCache [22] & No & No & Yes \\ CPHR [23] & No & No & Yes \\ HILR [24] & No & Yes & No \\ IFS [25] & No & Yes & No \\ PRP-MC [26] & No & Yes & No \\ PIT lifetime & No & Yes & No \\ mechanism [27] & & Yes & No \\ EQPR [28] & Yes & Yes & No \\ PRWR [29] & Yes & Yes & No \\ PRR [29] & Yes & Yes & No \\ QLPCC & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Uniqueness of QLPCC when compared with existing schemes.  
NDN packets cannot be identified by their source and destination addresses for flow-identification purposes. Therefore, an alternative mechanism has been proposed by the Internet Engineering Task Force (IETF);
* The equivalence class name component type (ECNCT) [15] introduces name components which identify a particular flow and infer the equivalence classes of the traffic. This mechanism does not need any alterations in the NDN paradigm and easily integrates with the existing framework. The name component can be encoded at any granularity of the name hierarchy of the content, and this facility can be used to identify equivalence classes of streams and sub-streams of content at any desired granularity. Consider the named content \"Netflix/Show1-Prompt/Frame ID/#Segment\", represented in Table 2: all content of the streaming service Netflix may not require QoS, but a subset of the content may. An equivalence class identifier with encoded naming conventions can establish the equivalence class without any additional overhead if the naming conventions are recognized by the network stakeholders. The content consumer issues \"interest packets\" to the NDN network, indicating the equivalence class. The content producer/intermediate NDN node streams \"data packets\" indicating the corresponding equivalence class which may be served to multiple destinations, either through a direct path or through a CS in a non-synchronized way. The ECNCT does not add any additional overhead to the existing packet structure, but requires the addition of a \"Flow Table\", which can identify the name prefixes for establishing class equivalences. Flow classification has useful applications for enforcing forwarding-rate control for the content of equivalent classes, the estimation of unique flows traversing through a given bottleneck, which is useful in congestion control, and to make caching decisions.
## 2 Methodology
### Short-Time Fourier Transform  
While the fast Fourier transform (FFT) serves as a powerful and widely utilized tool for transforming signals from the time domain to the frequency domain, it exhibits inherent limitations when applied to non-stationary signals. In contrast, the short-time Fourier transform (STFT) could provide greater flexibility and effectiveness in analyzing such signals by offering time-frequency localization, capturing dynamic characteristics, enabling multi-resolution analysis, and fulfilling practical application requirements.  
Through the window function \(w(t)\), the traditional Fourier transform is extended to the short-time Fourier transform (STFT) [37], which could provide the localized frequency components of the signal \(s(t)\).  
\[S_{t}(\tau,\omega)=\int_{-\infty}^{\infty}s(t)w(t-\tau)e^{-i\omega t}dt \tag{13}\]  
As shown in Equation (11), if the acceleration responses of these contact points are utilized for beam mode shape extraction, there are two residual accelerations.  
\[\Delta\ddot{u}_{1,2}(t)=\ddot{R}_{1}(t)-\ddot{R}_{2}(t+\Delta t),\Delta\ddot{ u}_{2,3}(t)=\ddot{R}_{2}(t)-\ddot{R}_{3}(t+\Delta t) \tag{14}\]  
\[S_{\Delta 12}(\tau,\omega)=\int_{-\infty}^{\infty}\Delta\ddot{u}_{1,2}(t)w(t- \tau)e^{-i\omega t}dt,S_{\Delta 23}(\tau,\omega)=\int_{-\infty}^{\infty}\Delta\ddot{u}_{2,3}(t)w (t-\tau)e^{-i\omega t}dt \tag{15}\]  
The ratio \(\varphi_{n+1,n}^{i}\) at the natural frequency \(\omega_{i}\) corresponding to Equation (12) with the distance of \(d=V_{e}\Delta t\) between the neighboring trailers can be expressed.  
\[\varphi_{n+1,n}^{i}=\frac{S_{\Delta 12}((n+1,\Delta t),\omega_{i})}{S_{\Delta 23} (n\Delta t,\omega_{i})} \tag{16}\]  
The mode shape \(\phi_{x_{n}}^{i}\) at the beam coordinate \(x_{n}=nV_{e}\Delta t\) could be estimated in a moving-reference-based manner [13; 38].  
\[\phi_{x_{n}}^{i}=\varphi_{n+1,n}^{i},\text{when }n=1,\phi_{x_{n}}^{i}=\varphi_{n+1,n }^{i}\phi_{x_{n}-1}^{i},\text{when }n=2,\ldots \tag{17}\]
## 2 System Description
### Road Holding and Handling Stability Analysis  
In the above analysis, the guarantee for sprung mass displacement \(x_{1}\) to remain within the prescribed performance boundaries has been obtained, i.e., the first performance of ride comfort is achieved. In this section, two objectives of road holding and handling stability can be ensured by adjusting the control parameters appropriately. To analyze the road holding and handling stability of suspension systems, we suppose that the tracking errors \(e_{1}=0\) and \(z_{2}=0\) are bounded, i.e., the vertical displacement \(x_{1}\) and speed \(x_{2}\) converge to a small zero neighborhood. For this purpose, we focus the system states of the unsprung mass with \(x_{3}\) and \(x_{4}\) in the system (3). Then, substituting (3) into (63), we obtain  
\[\dot{X}=EX+FZ_{0}+Z \tag{83}\]  
where \(X= \begin{bmatrix}x_{3}\\ x_{4}\end{bmatrix} \); \(E= \begin{bmatrix}0&1\\ -\frac{k_{1}}{m_{a}}&-\frac{c_{1}}{m_{a}}\end{bmatrix} \); \(F= \begin{bmatrix}0&0\\ \frac{k_{1}}{m_{a}}&\frac{c_{1}}{m_{a}}\end{bmatrix} \); \(Z_{0}= \begin{bmatrix}z_{r}\\ z_{r}\end{bmatrix} Z= \begin{bmatrix}0\\ \psi\end{bmatrix} \); \(\psi=\frac{m_{a}}{m_{a}}\big{(}W_{h}^{T}S(X)+\eta(X)\big{)}+\frac{1}{m_{a}} \big{(}-m_{s}x_{5}-m_{s}d_{0}\big{)}\). Besides, the tracking errors \(z_{1}\), \(z_{2}\), and \(z_{3}\) with be bounded according to (82), so \(\psi\) is bounded and there exists a constant \(\overline{\psi}\) such that \(\|\psi\|\leq\overline{\psi}\). Then, the Lyapunov function is selected as  
\[V_{a}=X^{T}PX \tag{84}\]  
where \(P\) is a positive definite symmetric matrix. Then, we can describe the time derivative of \(V\) as follows  
\[\dot{V}_{a}=\dot{X}^{T}PX+X^{T}P\dot{X} \tag{85}\]  
Substituting (83) into (85), we have  
\[\dot{V}_{a}=X^{T}\Big{(}E^{T}P+PE\Big{)}X+2X^{T}PFZ_{0}+2X^{T}PZ \tag{86}\]  
There exists a positive definite symmetric matrix \(Q>0\) which is chosen to satisfy the Lyapunov equation \(E^{T}P+PE=-Q\). Based on [PERSON]'s inequality theorem for two nonnegative real numbers \(a\) and \(b\), we have \(ab\leq a^{2}/2\gamma_{i}+b^{2}\gamma_{i}/2\). Therefore, we can apply these results for \(2X^{T}PFZ_{0}\) and \(2X^{T}PZ\) as follows  
\[ \begin{array}{l}2X^{T}PFZ_{0}\leq\frac{1}{\gamma_{1}}X^{T}PFF^{T}PX+\gamma_ {1}Z_{0}^{T}Z_{0}\\ 2X^{T}PZ\leq\frac{1}{\gamma_{2}}X^{T}PPX+\gamma_{2}Z^{T}Z\end{array}  \tag{87}\]  
where \(\gamma_{i}>0\), \(i=1,2\) are the control parameters. Applying (87) into (86), we obtain  
\[\dot{V}_{a}\leq-\bigg{[}\lambda_{\min}\Big{(}P^{\frac{1}{2}}QP^{\frac{1}{2}} \Big{)}-\frac{1}{\gamma_{1}}\lambda_{\max}\Big{(}P^{\frac{1}{2}}FF^{T}P^{ \frac{1}{2}}\Big{)}-\frac{1}{\gamma_{2}}\lambda_{\max}(P)\bigg{]}V_{a}+\gamma_ {1}Z_{0}^{T}Z_{0}+\gamma_{2}Z^{T}Z \tag{88}\]  
where \(\lambda_{\min}\) and \(\lambda_{\max}\) denote the minimal and maximal eigenvalues of the matrix.
## 2 Experimental Arrangement  
### Test Set-Up  
Figure 1 shows the adopted experimental arrangement at the field site, where the impulse generator generates high voltages of up to 100 kV and high current impulses of up to 1.5 kA. The current measurement was achieved using a current transformer with a sensitivity of 0.01 V/A and a response time of 20 ns. The voltage measurement was achieved using a resistive divider with a ratio of 1000:1 and a response time of 40 ns. The voltage and current traces were captured using a Digital Storage Oscilloscope (DSO). The remote ground consisted of a large square grid measuring 10 m by 10 m, which was further subdivided into a four-quadrant sub-grid. The ground resistance value was 8 \(\Omega\).