esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Current Trajectory Embedding
#### 4.1.1 Spatial Reconstruction of Trajectory  
The spatial reconstruction method for the current trajectory is the method introduced here. We first use the dual representation method of GPS trajectories to construct a trajectory road network through trajectory spatial clustering. Then, when the vehicle is traveling in the trajectory road network and facing a path selection problem, we use the Bayesian method to select the path with the highest probability among the candidate paths as the vehicle's driving path. Through this method, for a trajectory \([(as_{A},at_{A}),(as_{B},at_{B})]\) traveling between adjacent point pairs \((C_{A},C_{B})\), the spatial trajectory in the middle is reconstructed as \([s_{1},s_{2},\ldots,s_{m}]\), where \(s_{i}\) represents the i-th spatial point in the reconstructed spatial trajectory.  
Figure 4: Framework of spatiotemporal trajectory reconstruction model.
## 3 Dependability
### Availability  
Availability is realized once reliability has been achieved. Reliability is the probability that the system is working, and availability is the probability of it working at a given time. Availability ensures that no denial of authorized access to the system occurs [29]. The advantage of distributed systems is that additional nodes and communication paths  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dependability** & **Challenge** & **Potential Future Research Directions** \\ \hline \multirow{4}{*}{Availability} & Due to the distributed control, & \multirow{2}{*}{The architecture should be modular and distributed as it is, and designed} \\  & availability can be increased in & \\  & principle, however, availability can & such that the effects of cascading failures are avoided, where availability \\  & be compromised through & \\  & weaknesses in security, reliability & \\  & and safety. & \\ \hline \multirow{4}{*}{Safety} & Safety is a rarely researched topic & \multirow{2}{*}{The main work needed in increasing safety of future communications} \\  & from technical perspectives and is & \\  & intertwined with security. & \\ \hline \multirow{4}{*}{Security} & Security in 6G is extremely & \multirow{2}{*}{First, it will be important to know early whether to build 6G security on} \\  & complicated in terms of new & \\  & technologies, modular distributed & \\  & design, and the increasingly & \\  & vanishing physical-cyber borders & \\  & leading to highly complex network & \\  & architectures. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._help hiding any failure that might exists. Current research trends in edge computing aim at improving system availability by carefully planning task and data offloading from end devices towards edge servers with frameworks that are even capable of performing the offloading based on network statistics and the edge servers' computation capabilities. Another characteristic helping availability is the reassignment of tasks from failing nodes, although common node failures are still a problem, since a task that crashes a node can be moved to another node and causes the same type of crash. Since availability and reliability work together, it is important to notice they can also work at cross purposes; with this in mind, both concepts must be weighted against one another, as different systems might require a different degree of each.
## 3 Thermal Damages and Weight Loss after Temperature Conditioning  
Samples submitted to thermal treatments experiment a typical loss of weight, which evidences the damage suffered by mortars and concretes under the action of high temperatures. In this experimental campaign all the cylindrical specimens (50 mm \(\times\) 100 mm) were weighted before and after being placed in the furnace. Four specimens per each temperature level and for each mortar type were analyzed for a total of 56 specimens.  
In Figures 4 and 5 the weight loss after thermal treatments is presented for MNS and MHS, respectively. The histogram views report the specific weights of the sample before and after the heating (thermal conditioning). Also the values of the \(Diff(\%)=\left(W_{before}-W_{after}\right)/W_{before}\times 100\) are addressed in these figures, where \(W_{before}\) and \(W_{after}\) denote the specimen weights before and after heating, respectively. In both figures, the error bars indicate the scatter of the results, highlighting the difference between the mean value and the min/max among four sample repetitions.  
Figure 6 reports the relative weight loss for both mortars. It can be observed that the high strength mortars are more affected by high temperature exposures than normal strength mortars. Samples submitted to the highest level of temperature exposure (i.e., 600 \({}^{\circ}\)C) have a mean weight loss of 9.71% for MNS and 12.65 (significantly higher) for MHS. The same trend can be also observed by analyzing the mid-to-high ranges of temperature exposures (300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C). Contrarily, comparable weight loss can be seen for low temperature exposures (namely 100 \({}^{\circ}\)C and 200 \({}^{\circ}\)C).  
Figure 4: Weight loss due to thermal treatments for the normal strength mortar MNS.  
Figure 5: Weight loss due to thermal treatments for the high strength mortar MHS.  
This different behavior between normal- and high-strength mortars can be attributed to those parts of the non-evaporable water (trapped in closed porosity) which are normally greater in high strength composites. The latter, are characterized by being less porous and often, by having less interconnection inside the poro-structure, in comparison with normal strength mortars, and consequently, capable to store a significant amount of non-evaporable water. At mid-to-high temperatures, the occurrence of microcracks leads to a further connection of the closed porosity which allows the water to emigrate and evaporate, resulting in a higher weight loss at composite level.
# Mechanical, Electrical and Fractural Characteristics of Carbon Nanomaterial-Added Cement Composites
## 1 Introduction
GNPs possess elastic modulus, strength, electrical resistivity, diameter, thickness, surface area, and aspect ratio of 1 TPa, approximately 10-20 GPa, 50 \(\upmu\Omega\)-cm, 1-20 \(\upmu\)m, approximately 30 nm, approximately 2630 m\({}^{2}\)/g, and 50-300, respectively [56]. Multiple studies have demonstrated the positive impact of GNP addition on various properties of cement composites. Studies conducted by numerous researchers have reported significant improvements in the compressive strength [14; 15; 57; 58; 59], flexural strength [14; 58; 59], durability [57], fracture energy [60], electrical properties [15; 29; 61; 62], and thermal properties [29] of cement composites. The influence of different CNM types on the mechanical properties of cement composites has been extensively investigated. However, studies on the effects of CNM types such as GNP and graphene on the fracture characteristics of cement composites remain scarce. Additionally, research on the fracture characteristics of CNM-added cement composites, where CNMs are dispersed while maintaining low fluidity, is still limited. This study aims to bridge this gap by investigating the fracture characteristics of low-fluidity cement composites incorporating CNTs, CNFs, GNPs, and graphene. In this research, a comparison was made between four different nanomaterials (CNT, CNF, GNP, and graphene) with content ratios of 0.1, 0.3, 0.6, and 1.0 wt. %, along with a cement-only sample, that were fabricated to examine their mechanical properties (compressive strength, and flexural strength), fracture characteristics (fracture energy, and fracture toughness), electrical properties and porosity. Factors such as the dispersion method, dosage rate, and curing age were consistent in all the prepared cement composites.
## 3 Results
### ESV Analysis
#### 3.2.1 Time Variation Analysis of the ESV
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**2000** & \multicolumn{5}{c}{**2020**} \\ \cline{2-7}  & **Farmland** & **Foresland** & **Grassland** & **Water Areas** & **Built-Up Land** & **Barren Land** \\ \hline Farmland & 6,401,275.11 & 32,459.40 & 197,398.08 & 8153.10 & 12,473.91 & 14,915.97 \\ Forestland & 152,720.37 & 4,495,579.20 & 180,160.65 & 793.71 & 2379.33 & 1240.74 \\ Grassland & 441,281.97 & 71,274.15 & 7,299,567.00 & 4270.68 & 1729.17 & 17,701.57 \\ Water areas & 18,791.28 & 1273.68 & 6219.27 & 150,438.78 & 614.88 & 1443.24 \\ Built-up land & 158,224.86 & 7000.92 & 45,515.07 & 1613.25 & 297,252.81 & 18,149.13 \\ Barren land & 3875.04 & 4972.95 & 16,767.36 & 484.57 & 15.39 & 421,799.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Land-use transfer matrix of Shaanxi Province from 2000 to 2020. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Land-Use Type** & \multicolumn{5}{c}{**ESV (\(\times\)10\({}^{8}\) USD)**} & \multicolumn{5}{c}{**Change Rates (\%)**} \\ \cline{2-10}  & **1990** & **2000** & **2010** & **2020** & **1990–2000** & **2000–2010** & **2010–2020** & **1990–2020** \\ \hline Farmland & 461.81 & 462.66 & 438.90 & 428.63 & 0.18 \(\uparrow\) & \(-5.13\downarrow\) & \(-2.34\downarrow\) & \(-7.18\downarrow\) \\ Forestland & 1480.06 & 1486.32 & 1540.04 & 1549.53 & 0.42 \(\uparrow\) & 3.61 \(\uparrow\) & 0.62 \(\uparrow\) & 4.69 \(\uparrow\) \\ Grassland & 1514.97 & 1527.21 & 1549.10 & 1538.62 & 0.81 \(\uparrow\) & 1.43 \(\uparrow\) & \(-0.68\downarrow\) & 1.56 \(\uparrow\) \\ Water areas & 381.68 & 381.68 & 368.96 & 365.56 & 0 & \(-3.33\downarrow\) & \(-0.92\downarrow\) & \(-4.22\downarrow\) \\ Built-up land & \(-42.22\) & \(-48.12\) & \(-65.70\) & \(-80.58\) & \(-13.99\downarrow\) & \(-36.52\downarrow\) & \(-22.64\downarrow\) & \(-90.86\downarrow\) \\ Barren land & 6.51 & 5.15 & 4.90 & 4.74 & \(-20.88\downarrow\) & \(-4.84\downarrow\) & \(-0.34\downarrow\) & \(-27.23\downarrow\) \\ Total & 3802.82 & 3814.90 & 3836.20 & 3806.50 & 0.32 \(\uparrow\) & 0.56 \(\uparrow\) & \(-0.77\downarrow\) & 0.10 \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Changes in the ESV of different land-use types in Shaanxi Province from 1990 to 2020.
# Frequency-Dependent Schroeder Allpass Filters
## 3 Proposed Frequency-Dependent Schroeder Allpass
### Filter Structure  
We start with the filter version of the Schroeder formulation (13). Let \(g(z)\coloneqq\frac{b(z)}{a(z)}\) be an IIR filter normalized such that \(a_{0}=1\). By substituting with the flip operation (1) we get  
\[\frac{g^{*}(z)+z^{-m}}{1+g(z)z^{-m}}=\frac{\frac{b^{*}(z)}{a^{*}(z)}+z^{-m}}{1+ \frac{b(z)}{a(z)}z^{-m}}=\frac{a(z)}{a^{*}(z)}\frac{b^{*}(z)+a^{*}(z)z^{-m}}{a( z)+b(z)z^{-m}}=\frac{a(z)}{a^{*}(z)}\frac{\overset{\overline{b}}(z)z^{l_{b}}+ \overset{\overline{=}}{\overline{a}}(z)z^{-m+l_{a}}}{a(z)+b(z)z^{-m}}. \tag{19}\]  
Removing the allpass components \(a(z)/a^{*}(z)\) and \(z^{l_{b}}\) leaves us with  
\[H_{\text{Proposed}}(z)=\frac{\overset{\overline{=}}{b}(z)+\overset{ \overline{=}}{\overline{a}}(z)z^{-m-l_{b}+l_{a}}}{a(z)+b(z)z^{-m}}. \tag{20}\]  
Due to (8), the proposed structure (20) is clearly allpass. Figure 5 shows the corresponding block diagram.  
Figure 4: Numerical example of a Dahl absorbent allpass filter. (**a**) Poles and zeros of the gain \(H_{\text{Dahl}}(z)\) (\(\times/\circ\)) and proportional magnitude response \(|c(e^{\omega u})|/delay\) (solid red). (**b**) Magnitude response of \(|H_{\text{Dahl}}(z)|\).  
Figure 5: Proposed frequency-dependent allpass for an IIR gain \(g(z)=\frac{b(z)}{a(z)}\).  
In contrast to (13) and (15), this formulation is realizable for any stable filter \(g(z)\). A special case for \(g(z)=b(z)\) being a FIR filter is (see Figure 6):  
\[H_{\text{Proposed FIR}}(z)=\frac{\overset{=}{b}(z)+z^{-m-l_{b}}}{1+b(z)z^{-m}}. \tag{21}\]  
Each filter component is applied only once for the feedback and once for the feedforward path. In that sense, the computational cost of the proposed formulation is minimal. Therefore, it has about a third of the numerical operations compared to [PERSON]'s formulation (15). As we have now established a flexible and efficient formulation, we proceed with studying the frequency-dependent character of the proposed allpass filter.
## 3 Background and Related Work
### Applications of DL in ED  
DL has emerged as a valuable tool in emergency medicine, with its application to ED challenges growing significantly over the past five years. DL techniques have demonstrated considerable potential in enhancing various aspects of ED care, including diagnosis, imaging interpretation, triage, and clinical decision-making. These systems can rapidly analyze multimodal data (e.g., vital signs, EHRs, imaging) to uncover complex patterns that complement clinician judgment.
## 4 Theoretical Optimization of Converter Inductor
### Frequency and Ripple Optimization for One Inductor Value
#### 4.1.4 Optimal Switching Frequency for the Specific Inductor  
To finalize the design optimization with the operating point defined above and the 47 uH inductor considered as a first example, all losses are computed. Figure 18 is presenting the resulting total losses with respect to the frequency and the ripple current magnitude. As expected, the losses are reducing with respect to the switching frequency up to a minimum value. Then losses are rising again with respect to switching frequency. We can see that for this specific inductor value the optimal operating point is a little bit over 1 A of current ripples (>40% of the DC current in the example).  
From this first result, it appears that reducing the ripple current would lead to minimum losses. It is interesting to notice that quite reduced ripple current may not lead to minimum losses in this particular case. Nonetheless, it is important to keep in mind that the differences are not very important and could come from uncertainties. Let us now investigate what design optimization we can reach if an entire inductor series is considered.
# A Review of Flywheel Energy Storage System Technologies and Their Applications
###### Abstract  
Energy storage systems (ESS) provide a means for improving the efficiency of electrical systems when there are imbalances between supply and demand. Additionally, they are a key element for improving the stability and quality of electrical networks. They add flexibility into the electrical system by mitigating the supply intermittency, recently made worse by an increased penetration of renewable generation. One energy storage technology now arousing great interest is the flywheel energy storage systems (FESS), since this technology can offer many advantages as an energy storage solution over the alternatives. Flywheels have attributes of a high cycle life, long operational life, high round-trip efficiency, high power density, low environmental impact, and can store megajoule (MJ) levels of energy with no upper limit when configured in banks. This paper presents a critical review of FESS in regards to its main components and applications, an approach not captured in earlier reviews. Additionally, earlier reviews do not include the most recent literature in this fast-moving field. A description of the flywheel structure and its main components is provided, and different types of electric machines, power electronics converter topologies, and bearing systems for use in flywheel storage systems are discussed. The main applications of FESS are explained and commercially available flywheel prototypes for each application are described. The paper concludes with recommendations for future research.  
energy storage systems (ESS); flywheel energy storage systems (FESS); power electronics converters; power quality improvement +
Footnote †: journal: Applied Sciences
## 4 Comparison with Experimental Results
### Ultimate Strength
The initial imperfection might be another possible reason why the ultimate strength predicted by the FE analysis was quite different from that in the experimental results. In the experimental preparation process, the initial imperfection of the curved plate had not been measured. However, initial imperfection would have been implied, and to take this into account in the analysis stage, the Smith equation was applied to the FE model. As a result, a different initial imperfection was considered between the experiments and FE analysis. This could be one possible reason why the FE analysis showed a different trend in comparison to the experimental results. In the experimental preparation stages, the V-type groove, which was used to enable plate edge rotation, was designed to implement the simply supported condition. However, owing to the friction between jigs and plates, it is difficult to perfectly implement the moment free condition. For these reasons, the values of the ultimate strengths obtained from the FE analysis and experiments are different. Figure 14 shows the comparison of ultimate strengths obtained from FE analysis and experiments for various values of flank angle and slenderness ratio. In general, for a curved plate under a longitudinal compressive load, the ultimate strength obtained from the FE analysis is higher than that obtained from experiments, while the ultimate strengths of flat plates are similar in both cases. The difference in ultimate strength in the two cases increases as the flank angle increases or as the slenderness ratio decreases (the plate thickness increase in case of present study). In other words, in the case of a thick curved plate with a large flank angle, the ultimate strength obtained from the FE analysis has low accuracy. In addition, structural design based on the FE analysis using the existing computational method can overestimate the real load capacity of a structure and cause the danger of an accident, such as the failure of a panel, because proper safety factor is not considered. Therefore, the empirical formula derived from the FE analysis should be modified based on the results of buckling tests. Based on these results, a new empirical formula for a curved plate under a longitudinal compressive load is suggested in the following section. The results derived by FE analysis and experiment are summarized in Table 4.  
Figure 13: Elastic buckling mode under varying flank angles-(**a**) \(2^{\circ}\), (**b**) \(3^{\circ}\), (**c**) \(5^{\circ}\), (**d**) \(10^{\circ}\)[1].
## 2 E-Sail Experiment Design for Solar Wind in Lunar Orbit
### Spin Rate Modification with E-Sail Torque  
The goal of the spin rate modification experiment is to measure the propulsive E-sail effect acting on the tether when it is moving either downstream or upstream of the solar wind. The optimum scenario for the experiment is represented in Figure 2, with the spin plane perpendicular to the solar wind direction. This scenario happens twice per year, with the angle changing by \(1^{\circ}\) per day.  
The change in spin rate is  
\[\Delta\omega=\frac{Fr\cos(\beta)t}{\pi M_{I}} \tag{1}\]  
where \(F\) is the nominal E-sail force as given above; \(r\) is the arm length position on the tether where the average E-sail force acts (in the middle of the tether), as measured from the centre of mass (CoM); \(\beta\) is the angle between the tether and the solar direction that defines the circular sector where the tether is charged (see Figure 2); \(t\) is the time duration of the experiment; and \(M_{I}\) is the moment of inertia of the spacecraft-tether system. Equation (1) was originally derived by [PERSON] et al. (2014) for the LEO ionosphere, and here, we have adapted it for the solar wind case.  
As discussed in Section 3.2, the ESTCube-LuNa CoM is 3.35 m outside the spacecraft when the full 2 km tether is deployed. In a general case, the charged tether on the spacecraft side of the CoM would contribute negatively to spin rate modification. For ESTCube-LuNa,  
Figure 2: ESTCube-LuNa spin rate modification experiment. The solar wind, arriving from the right, meets the nanospacecraft, which is rotating anti-clockwise, and exerts the E-sail force, which is turned into a torque. The torque is changing the ESTCube-LuNa’s angular velocity. The spin rate modification experiment can be performed twice a year when the inertially fixed spin plane is aligned with the solar wind flow. Artwork credit: [PERSON], [PERSON] and [PERSON].  
the CoM shift is insignificant in terms of the spin rate because it is inside the Debye length and inside the sheath. However, this would change if a remote unit (750 g [43]) was used instead of a passive endmass, introducing a significant shift in CoM when compared with the ESTCube-LuNa endmass of 2.5 g.  
Considering an estimated E-sail force of 1 mN, an arm length of 1000 m, a \(\beta\) angle of 20\({}^{\circ}\) and a moment of inertia of 70,380 kg m\({}^{2}\), Equation (1) yields a change in angular velocity of \(2.44\times 10^{-4}\) deg /s. This equates to approximately 1 deg /s if the experiment is run for 4200 s, something that can easily be detected by the on-board attitude systems of cubesats or the proposed visual navigation system in Section 5.5.2.
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 2 Overview of Concept Drift
### The Process of Concept Drift Adaptation Methods under Deep Learning Framework  
The general adaptation process of concept drift under the deep learning framework when dealing with unstable state data streams is shown in Figure 3. First, the data stream input (single input or batch input) is generally trained and learned by the deep learning model (single model or ensemble model) to obtain the basic prediction results. Next, if concept drift occurs during this process, a concept drift adaptation method will be triggered to update the deep learning model to accommodate concept drift and maintain the model [16; 19]. The concept drift adaptation method can be divided into two parts: concept drift detection and model update. Among them, concept drift detection contains both active and passive modes, and model updates can be divided into structure updates and parameter updates.  
Figure 3: The general process of concept drift adaptation methods under deep learning framework.  
Active modes mean that the learning process of a deep learning model contains a concept drift detection algorithm. When concept drift is detected, the concept drift adaptation method will be triggered to update the model. Passive mode means that the method continuously adjusts its model as data are continuously input during the learning process. Instead of using a drift detection algorithm, it uses a concept drift adaptation method to passively update the model continuously. After triggering the concept drift application mechanism, the deep learning model is generally updated to adapt to the concept drift through a model parameter update or a structure update [30]. Model parameter updates can be divided into full parameter updates and partial parameter updates. In particular, parameter updates also include parameter updates between ensemble models. Here, parameter updates are also weight updates. In addition, model structure updates can generally be performed by adjusting the width and depth of the network.
## 4 Discussion
The complexity that other parameters, such as LOS conditions or building density, add is addressed as separate features. Despite this, demonstrating a general inverse relationship between distance and the electric field, specifically for 5G frequencies, underscores the relevance of distance as a reliable predictive feature. This insight is especially valuable as it supports the integration of distance as a core input for AI-based EMF prediction models, contributing to their accuracy in 5G EMF exposure assessments. In the context of this study, such models are primarily intended for spatial prediction, estimating EMF levels at unmeasured locations based on features such as distance from BS, BS technical specifications, and urban morphology (e.g., built area).