esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of the Transmission Delay
### One-Hop Delay/Distance for Intra-/Inter-Lane Scenario  
The random variable \(T_{L}\) for a generic link in a path setup phase is defined as the minimum among the backoff timer levels set by the \(N=n\) vehicle nodes reached by the path setup message issued byan elected BV node \(A\). Those vehicles run their respective backoff timers to decide which one is going to forward the message to elect itself as next hop BV node \(B\), as explained in Section 4.  
Let \(T_{j}\) denote the backoff timer level set by the \(j\)-th vehicle node, triggered by the reception of the path setup message issued by node \(A\). Then, \(T_{L}=\min\{T_{1},T_{2},\ldots,T_{n}\}\), given that \(N=n\) vehicle nodes are running their timers at the considered hop. Note that \(N\) is the number of vehicle nodes within the range of \(A\) that have not already received the path setup message.  
We will focus on the random variable \(T_{L}\) conditional on the lane that \(A\) and \(B\) belong to, i.e., we define \(T_{L}^{(1,1)}\) to be the random variable \(T_{L}\) conditional on \(X_{k}=l_{1}\) and \(X_{k+1}=l_{1}\); \(T_{L}^{(1,2)}\) to be the random variable \(T_{L}\) conditional on \(X_{k}=l_{1}\) and \(X_{k+1}=l_{2}\). In the sequel, we drop the subscript \(L\) whenever there is no ambiguity, for the sake of simple notation. Analogously, we denote \(T_{j}^{(1,1)}\) the timer of the \(j\)-th vehicle node (\(j=1,\ldots,n\)) running its timer in the considered hop, conditional on \(X_{k}=l_{1}\) and \(X_{k+1}=l_{1}\).  
The conditional timer delay distribution calculation will be divided into two parts: the intra-lane case for the random variable \(T_{L}^{(1,1)}\) and the inter-lane case for the random variable \(T_{L}^{(1,2)}\), which will be derived separately.
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 4 Applications of DLC Coatings
### Mechanical Applications of DLC Layers  
In addition to the attractive properties of DLC, the material is additionally hard and artificially solid to make it helpful for securing metal objects from scratching and chemical reactions. This has been seen in various moving parts inside vehicle motors, and it has effectively reduced wear rates. The frictional properties of DLC were studied by [PERSON] et al. [156], utilizing a ball-on-disc apparatus. They noticed an increase in the coefficient of friction from 0.01 to 0.19 \(\upmu\) with increasing humidity. A sharp increase in \(\upmu\) that occurred for relative humidities was around 1%. These results were in opposition to those for graphite and diamond reported by [PERSON] and [PERSON] [157], who noticed a reduction in \(\upmu\) with expanding water emission pressure. [PERSON] et al. [158] studied the impacts of O on the frictions and wear of DLC films. Moreover, they considered the use of DLC films as protective overcoats on a thin-film magnetic recording disc. Comparative applications have been discussed by [PERSON] and [PERSON] [110] and by [PERSON] et al. [159], who related their perceptions with Raman and resistivity measurements. The utilization of DLC in magnetic and optical recoding discs would seem to offer promising market opportunities.
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 5 Additive Manufacturing in Printing Nanocellulose Composites
### Electrospinning  
Electrospinning is a broadly adopted nanofiber manufacturing technique to produce cellulose-based composite nanofibers [157]. The technique utilizes a high-voltage supply to produce a liquid jet. In this technique, solid fibers are produced as an electrified jet composed of a high-viscosity polymer mixture and are continually extended via the electrostatic repulsion among solvent evaporation and surface charges [158]. Nanofibers are produced when a liquid polymer mixture is introduced to a strong electric field through a syringe needle or capillary tube. A Taylor cone is generated when the electrostatic forces overcome a liquid's surface tension, and it causes the thin jet to be swiftly accelerated towards various collecting plates. Instability in the jet causes a whipping motion that lengthens and narrows the jet, enabling some solvent to evaporate or cooling the melt to obtain nanofibers on collecting plates. As a result, random non-woven films, electrospun nanofibers, and uniaxially aligned sheets are produced [159]. The nanofibers produced by the electrospinning method have a broad range of applications in the medical field. Incorporating antimicrobial agents, nanoparticles, and drug molecules into nanofibrous dressings can lower the chances of infection and can be utilized as a potential candidate to reduce inflammation and antibacterial properties [160; 161].
## 3 Results
### Time Stability Performance of the THz-TDS Setup
For each of the waveforms, the peak amplitude position of the reference pulse was determined by a numerical fit. Its numerical value was next subtracted from the time data, setting the reference \"zero time\" point for all the waveforms in the measurement series (Figure 6b). Since we have checked that the reference and cuvette transmitted pulses always shift simultaneously, we can consider that any pulse delays measured with the cuvettes filled with gasoline samples are likely caused by admixture concentration change and not the drift of the pulse itself. While the absolute peak amplitude positions of the reference pulses could be determined in CDA with an accuracy of 1-2 fs, the instabilities of the THz pulse position  
Figure 6: Time correction procedure of the THz pulse: (**a**) Actually measured waveforms of the reference pulse before correction; (**b**) The same waveforms brought to the common “zero delay” time after correction. The applied correction is also valid for the pulses propagating through gasoline samples during the measurements. The color code of the arrows above the pulses is used consistently with previous figures; (**c**) The measured evolution of the peak amplitude of the THz reference pulse during approx. 2.5 h-long series of the measurements. The positions of the peaks with fit errors were determined by numerical fit with a predefined function. The inset of the graph shows small-scale oscillations of the pulse position in detail. measured with gasoline samples (or ambient air) were higher, \(\sim\)10-100 fs. They were strongly dependent on admixture type, length of the cuvette and absorbance properties resulting from the admixture concentration in the solution. These instabilities were reasonably smaller than typically measured pulse TOFs and were most likely caused by inhomogeneity and density fluctuations of the liquids.
## 5 Performance Evaluation
### Performance Comparison for PNCS-GHA under Different Transmission Rounds  
In this subsection, the \(n\) was set to 100 and the AUV's moving velocity was set to 2.5 m/s. The value of transmission rounds was changed for different values of parameter \(p\) to determine how the PNCS-GHA performs. A minimum probabilistic neighborhood covering set was constructed through simulation, and then the PNCS-based data collection path for the AUV was generated to traverse all the neighborhoods in the constructed covering set. We do not consider the variable \(n\) here,  
Figure 10: (a) Average information gain VS transmission rounds; (b) Average completion time VS transmission rounds.  
Figure 9: (a) Average information gain VS \(n\); (b) Average completion time VS \(n\); (c) Average gain to cost ratio VS \(n\).  
but concentrate on the impact of transmission rounds. Simulations were run for the PNCS-GHA of different neighborhood sizes with variable transmission rounds.  
Figure 10 shows the average information gain and the average data collection completion time for variable parameter \(p\) with different transmission rounds. With increased transmission rounds, the average information gain as shown in Figure 10 gradually approaches saturation and the average data collection completion time in Figure 10 increased steadily. In addition, the information gain and data collection completion time increased with the increase of \(p\).  
In Figure 11, we show the gain to cost ratio for different \(p\) values with variable transmission rounds. It can be seen that with increased transmission rounds, the gain to cost ratio decreased after increasing initially. For different values of parameter \(p\), the gain to cost ratio reached the maximum on individual transmission rounds. When the \(transmission\)\(rounds=5\) and \(p=0.1\), the gain to cost ratio reached a global maximum. It is important to point out that the gain to cost ratio reached the maximum on distinct transmission rounds due to the communication parameter setting.  
Finally, in Figure 12, we present an intuitive chart of information gain versus data collection completion time for variable parameter \(p\) and transmission rounds. By increasing the transmission rounds, our proposed algorithms allow a tradeoff between data collection completion time and information gain.  
Figure 11: Tradeoff between completion time and information gain.  
Figure 12: Tradeoff between completion time and information gain.
# ML-Based 5G Network Slicing Security:
## 4 Machine Learning Based Network Slicing
For resource mapping in 5G network slicing, a deep reinforcement learning model derived from the communal relationship between a node and link mapping (RLCO) is proposed in [44]. The RLCO algorithm proposed is based on this reward function, which results in network slice mapping reaching the global optimum. The dynamic network slicing or slice configuration in WiFi networks can be achieved by applying DRL approaches and can achieve promising results in highly dynamic and complex environments without expert knowledge [74].
###### Abstract
Compared with ALS systems, they are more cost-efficient and suitable for continuous monitoring over large areas with good availability [25; 26]. Point clouds are generated from the digital images through the structure from motion (SfM) algorithm [27], which is one of the digital aerial photogrammetry (DAP) techniques. For single-layered forests, forest attributes such as canopy height model (CHM) and LAI can be captured well from DAP and show good agreement with ALS-based results [15; 28]. For more complex forest environments, even though there are difficulties for DAP to estimate under-canopy structural and terrain information, accurate upper canopy structure can be obtained with the input digital terrain models (DTMs) [29; 30]. Therefore, more efforts are still needed to explore the potentiality of DAP to estimate canopy structural variables for forestry applications. This study aimed to compare the performance of airborne LiDAR and digital imagery in the estimation of canopy cover and LAI in tropical forests. Airborne LiDAR and digital imagery were first processed. Methods to estimate canopy cover and LAI from two data sources were then presented. The estimated results from both airborne LiDAR and digital imagery were compared together with the results from Sentinel-2 data and the relationship between canopy cover, LAI, and tree height were investigated. Finally, we discuss the implications of our results for further forestry inventory.
## 4 Test Validation
### Test for BeiDou Satellites in Synchronous Orbits  
In order to evaluate the capability of the method model for simultaneous multi-signal source direction finding and to determine the deceptive signal recognition threshold by establishing the two-dimensional orientation accuracy towards on-orbit satellites, multiple BeiDou on-orbit satellites are used as targets for two-dimensional orientation.  
Figure 7 illustrates that the two-dimensional direction of the signal source has an error of less than 0.1 rad, which aligned with the positioning error expected for the signal source.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Gyroscope** & **Accelerometer** \\ \hline Range & \(\pm\)300\({}^{\circ}\)/s & \(\pm\)10 g \\ Bias & 0.02\({}^{\circ}\)/h & 0.01 mg \\ Bias Stability & 0.02\({}^{\circ}\)/h & 0.01 mg \\ \hline Position Accuracy & Horizontal & 0.02 m \\ Vertical & 0.03 m \\ Attitude Accuracy & Heading & 0.01\({}^{\circ}\) \\ Velocity Accuracy & Leveling & 0.01\({}^{\circ}\) \\ Horizontal & 0.02 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: MP-POS620 performance parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Gyroscope** & **Accelerometer** \\ \hline Range & \(\pm\)500\({}^{\circ}\)/s & \(\pm\)30 g \\ Bias & 1\({}^{\circ}\)/h & 200 \(\mu\)g \\ Bias Stability & 1\({}^{\circ}\)/h & 400 \(\mu\)g \\ \hline \hline \end{tabular}
\end{table}
Table 2: EIMU630 performance parameters.
## 1 Introduction
However, the training samples, usually based on manual collecting or interpretation from satellite images, stopped them from being used widely, especially on large-scale applications. Furthermore, the timeliness of these methods did not allow them to be used for emergency cases [22; 23]. Subsequently, an automatic sampling strategy for retrieving high-quality samples has become more and more important, especially for large-scale remote sensing land cover mapping [8; 24]. Because samples from historical land cover maps and classification cases contain the prior information and knowledge that is helpful to the classification of the current satellite images, transfer learning is employed to solve the problems [25; 26; 27; 28] and it has achieved good results for applications at the local scale. However, the accuracy is degraded without considering the samples incorrectly introduced by historical samples. In addition, these methods have not fully used the historical land cover maps with multi-year coverage [22; 29]. Therefore, we propose a new time series land cover mapping method based on machine learning and it is applied to HRB for verification purposes. Firstly, the high-quality land cover datasets at HRB from 2011-2015, which were retrieved using the LCMM method and can be downloaded at [[http://westdc.westgis.ac.cn/data/6](http://westdc.westgis.ac.cn/data/6) bbf9a3f-e7d8-4255-9 ecb-131e1543316d]([http://westdc.westgis.ac.cn/data/6](http://westdc.westgis.ac.cn/data/6) bbf9a3f-e7d8-4255-9 ecb-131e1543316d), accessed on 19 April 2021, are used for quickly and accurately making training samples. Secondly, a strategy for transferring the training samples after 2011 to earlier years is established. Thirdly, the random forest model is employed to train the selected yearly samples and the land cover maps for earlier years are subsequently made. Finally, comprehensive analysis and validation are carried out for evaluation.
## 5 The Strength Calculation for the Removable Module  
The acceleration value obtained by means of model (3) was included in the strength calculation for the removable module. The strength of the removable module was determined with the finite element method in SolidWorks Simulation.  
The design model included the following forces: the vertical loading \(P_{v}\) (Figure 13) to the removable module and the longitudinal loading \(P_{l}\) to the corner fittings.  
The removable module was secured in the areas of support on the flat wagon. The finite element model of the removable module was built with isoparametric tetrahedrons. The number of nodes in the model was 9814 and the number of elements was 33,490. The maximum element size was 110 mm and the minimum element size was 22 mm.  
The results of the calculation have made it possible to conclude that the maximum equivalent stresses emerge in the contact areas between side longitudinal beams and fittings; they have amounted to 282.2 MPa (Figure 14) and have not exceeded the allowable values.  
The distribution of the maximum equivalent stresses along the side beams is given in Figure 15. The stresses were taken on the bottom tube wall of the side beam with a step of 600 mm by applying the standard options of SolidWorks Simulation.  
The maximum stresses were recorded in the end parts of the beam, and in the middle part they were lower--about 180 MPa.  
Figure 13: Design diagram of the removable module.
# Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing
## 4 Mean-Field Games Equilibrium and Optimal Strategies
### Optimality for the Attacker: The \(\varepsilon\)-Nash Equilibrium  
In the edge computing environment, if the defenders obtain the optimal attacker strategy, the defense strategies can obtain an approximated Stackelberg equilibrium solution. The definition is given as follows:  
**Definition 2**.: _The set of strategies \(\big{\{}u_{0}^{*},u_{i}(u_{0}^{*}),\cdots,u_{N}(u_{0}^{*})|i=1,2,\cdots,N\big{\}}\) satisfies an \(\varepsilon_{2}\)-Nash equilibrium concerning the cost \(J_{0}\), if there exists \(\varepsilon_{2}>0\), such that we have_  
\[J_{0}(u_{0}^{*},u_{i}(u_{0}^{*}))\leq\inf_{u_{0}}J_{0}(u_{0},u_{i})+\varepsilon_ {2} \tag{38}\]  
**Theorem 2**.: _For the optimal strategies \(\big{\{}u_{0}^{*},u_{i}(u_{0}^{*}),\cdots,u_{N}(u_{0}^{*})|i=1,2,\cdots,N\big{\}}\), we have_  
\[J_{0}(u_{0}^{*},u_{i}(u_{0}^{*}))\leq\inf_{u_{0}}J_{0}(u_{0},u_{-i}(u_{0}^{*})) +\varepsilon_{2} \tag{39}\]  
Proof.: Similar to the Proof of Theorem 1, and due to the fact that \(E\big{\int_{0}^{T}\left\|x_{0}(t)\right\|^{2}\!dt<\infty\), we have  
\[\begin{array}{l}J_{0}\big{(}u_{0}^{*},u_{i}(u_{0}^{*}))-J_{0}(u_{0},u_{-i}(u _{0}^{*}))\\ \leq E\big{|}\!\int_{0}^{T}\big{(}x_{0}(t)-z(t)\big{)}\!\alpha_{0}\big{(}Ax^{N }(t)-Az(t)\big{)}\Big{|}\!dt\\ \leq|\alpha_{0}||A|\Big{(}E\!\int_{0}^{T}\left\|x_{0}(t)-z(t)\right\|^{2}\!dt \Big{)}^{\frac{1}{2}}\times\Big{(}E\!\int_{0}^{T}\left\|x^{N}(t)-z(t)\right\| ^{2}\!dt\Big{)}^{\frac{1}{2}}\\ =O\Big{(}\frac{1}{\sqrt{N}}\Big{)}\end{array} \tag{40}\]  
This completes the proof.
## 2 Related Work
### Model Architecture Components
#### 2.2.3 Domain Adaptation and Cross-Domain Generalization  
Models trained in one domain (e.g., FOMC texts) often experience performance degradation when applied to another related domain (e.g., ECB texts), a phenomenon known as domain shift [21]. This challenge is particularly pronounced in central banking research, where institutional differences, linguistic variations, and policy framework distinctions can significantly impact model transferability. Domain adaptation techniques aim to build models that are robust to such changes. Unsupervised domain adaptation is particularly important as it only requires unlabeled target domain data.  
Domain-adversarial neural networks [21] use a domain classifier and gradient reversal layer to train the feature extractor to generate domain-invariant representations [21]. The adversarial training paradigm encourages the model to learn representations that are discriminative for the target task while being indistinguishable across different source domains. This approach has proven particularly effective in scenarios where labeled data is abundant in one domain but scarce in target domains. Other methods like AdapterFusion [34] learn to dynamically combine domain-specific knowledge modules. These approaches represent different adaptation paradigms: DANN seeks unified domain-invariant representations, while AdapterFusion combines domain-specific knowledge. For tasks requiring robust performance across different central banks or economic cycles, domain adaptation techniques are key for enhancing generalization capabilities.  
While existing research has made significant strides in each of these individual areas, there remains substantial room for optimizing the integration of efficient long-sequence processing, parameter-efficient fine-tuning, and cross-domain generalization. The systematic combination of these technologies, specifically for the challenging task of nowcasting from lengthy central bank texts, represents a significant gap in the literature that our framework aims to address. The Methodology section details how these components are integrated into a cohesive and effective system.
# Context-Based Energy Disaggregation in Smart Homes
## 3 Related Work  
Non-Intrusive Load Monitoring (NILM) [6] is a research field that has been studied for more than 20 years and has recently received particular attention for its expected benefits in energy monitoring and conservation policies. As mentioned above, NILM techniques aim at disaggregating consumption data, obtained from a metering device (e.g., smart meter) connected to the electric network, in order to identify the energy consumed by single devices in private households. The Non-Intrusive qualification refers to the fact that these approaches do not require the use of metering hardware dedicated to each single appliance; this implies a shorter installation time and negligible user involvement.  
The first NILM method, developed by the [PERSON]'s working group [6], was based on the continuous monitoring of the active and reactive power measured at the electric meter. This method allowed detecting only the status change of bi-state (ON-OFF) devices and those modeled by Finite State Machine (FSM). The obtained results showed poor accuracy, mainly because of the poor reliability and precision of the measuring instruments that were available in the early 90s.
# A Review on Microfluidic Platforms Applied to Nerve Regeneration
## 3 Microfluidic Platforms Applied to Nerve Repair and Outgrowth
### Neurite Outgrowth  
[PERSON] [77] developed a microfluidic in vitro culture platform to simulate axon entrapment during nerve regrowth and to investigate the mechanism by which glial cell line-derived neurotrophic factor (GDNF) causes the axon entrapment. As shown in Figure 5A, the device consists of three culture chambers connected by two sets of micro-channels that prevent cell soma from moving between chambers, but allow neurons to grow between chambers. The structure allows the transport of proteins from the distal chamber to the somatic chamber to ensure that factors in the distal chamber elicit responses from neurons separated by micro-channels. The platform features a superior ability to enable cell signaling through exogenous growth factors and cell-cell interactions. [PERSON] et al. [78] proposed a 3D motor neuron-schwann cell (MN-SC) co-culture on a microfluidic biochip to evaluate the influence of neural activity on axon outgrowth triggered by optogenetically mediated light stimulation. The microfluidic biochip consists of five different channels separated by four micropillar arrays. There are three central channels for hydrogel formation, one channel for SC culture, and one channel for MN culture. The biochip channels guide MN axon growth from the MN reservoir toward the SC reservoir and allow myelin formation from the SC in the hydrogel. [PERSON] [79] developed a hybrid microfluidic platform that generates a dynamic microenvironment by placing neatly aligned PDMS microgrooves on the surface of degradable polymers as a physical guidance cue to control the neural differentiation of hiPSCs. The device consists of a network of micro-channels with 32 cell culture compartments integrated and bonded to a poly lactic-co-glycolic acid (PLGA) coated substrate. The top layer is PDMS and the bottom layer is a glass substrate coated with PLGA nanofibers.