esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of Knowledge
### Optimization of Vehicle Movements in Ro-Ro Terminals  
Ro-Ro terminals face unique logistical challenges due to the constant movement of vehicles and the need for optimized internal flows [19]. Efficient management of vehicle movements within the terminal is crucial to avoiding congestion, minimizing waiting times, and streamlining loading operations. To address this, advanced route planning models and smart technologies have been introduced to improve space allocation and traffic flow [13].  
Several approaches have been proposed to enhance mobility in Ro-Ro terminals, including search algorithms such as A*, Dijkstra, and reinforcement learning techniques [20]. These models calculate optimal routes in real time by considering factors such as available space, distance to the boarding point, and vehicle departure priority. Additionally, IoT integration allows for dynamic route adjustments based on real-time terminal traffic conditions [21].  
Despite advancements in movement optimization, many terminals still rely on static planning models, limiting their adaptability to operational changes. A lack of coordination among different port logistics agents often leads to inefficiencies, increasing waiting times and reducing overall capacity [22]. Implementing a multi-agent system to optimize coordinated vehicle movements could significantly improve efficiency in these terminals.
## 4 Climate Projections for Impact Studies  
Nowadays, climate models are the major source of information about future climate. They are process-based dynamical models [25] based on various assumptions, which operate across the entire globe or in a limited region of the world, simulating the physics and chemistry of the atmosphere and oceans to obtain projections of temperature and other meteorological variables. Indeed, they can be run in different modes either considering observed changes in atmospheric composition to reproduce historical climate or according to different scenarios of potential and plausible changes of forcing agents to simulate future climate. In most cases, these scenarios refer to changes in the emission of greenhouse gasses [26], which, manifestly, depend on economic and social development, as well as on political choices.  
One of the most relevant parameters in the climate model is the resolution; that is, the dimension of the single cell of the geographical grid used for the simulation. Obviously, finer grids provide more accurate results requiring, in turn, higher processing time.  
In this paper, high-resolution regional climate models, based on a 12.5 km resolution (EUR-11) grid, developed within the EURO-CORDEX initiative [27; 28], are analyzed.  
These regional simulations are obtained by downscaling the new Coupled Model Intercomparison Project (CMPI5) for global climate predictions [29], where future projections are run according to a new set of scenarios, containing emission, concentration, and land-use trajectories, commonly referred to as Representative Concentration Pathways (RCPs) and exhaustively described in [30].  
In this study, the RCP4.5 and the RCP8.5 emissions scenarios have been considered for the investigated climate models: RCP4.5 is a midrange mitigation emissions scenario, and RCP8.5 is the highest emission scenario.  
Due to the huge processing time needed to provide predictions until 2100, usually each climate model is run once; thus, different climate models are often combined aiming to improve the soundness of the results. A frequently used combination is the so-called multi-model ensemble.  
The rationale of multi-model ensemble is that each climate model can be considered as an independent and more or less credible realization of the future climate: if all the models provide credible outputs, they are just combined; if, on the contrary, some models are judged more reliable than the others, for example, considering their ability to reproduce past climate, suitable weights are introduced to differentiate the contribution of single models in the ensemble.  
Dealing with multi-model ensemble, a detailed review of different approaches to combine climate models' output is discussed in [31].  
In the present study, unweighted models have been used for the evaluation of changes in environmental parameters. This approach is usually called the Ensemble Mean [32].
## 6 Conclusions  
In this paper, the simulator SHINe for SpaceFibre and SpaceWire networks has been presented. The main building blocks needed to set up a network have been illustrated in detail, together with some additional modules implementing advanced features like RMAP and the Hardware-in-the-Loop capability. Finally, all the steps to run a simulation of an example network have been shown, from the node deployment to the analysis of the results.  
SHINe allows to quickly and easily implement and test SpaceFibre or SpaceWire-based networks and evaluate typical metrics such as packet latency and link utilization, as well as protocol-related measurements (FCT counters and buffer status). The simulator, based on the open-source framework OMNeT++, is modular and easily extensible, allowing the user to implement custom applications. SHINe can effectively help both the study of networks before their actual implementation and the development of new protocols on top of SpaceFibre and SpaceWire.  
The research and the article preparation have been carried out by [PERSON] and [PERSON] under the supervision of [PERSON], [PERSON] and [PERSON].  
IngeniArs SpaceFibre technologies have been developed in the framework of the SIMPLE project (Spacefibre IMPLementation design & test Equipment). This project received funding from the European Unions Horizon 2020 research and innovation programme under Grant Agreement No. 757038.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source Node/VC** & **Minimum** & **Average** & **Maximum** \\ \hline SpFi, VC0 & \(3.211\times 10^{-3}\) & \(3.389\times 10^{-3}\) & \(3.701\times 10^{-3}\) \\ SpFi, VC1 & \(2.020\times 10^{-3}\) & \(34.910\times 10^{-3}\) & \(40.519\times 10^{-3}\) \\ SpFi, VC2 & \(1.086\times 10^{-3}\) & \(1.189\times 10^{-3}\) & \(1.520\times 10^{-3}\) \\ SpFi, VC3 & \(0.915\times 10^{-3}\) & \(0.997\times 10^{-3}\) & \(1.311\times 10^{-3}\) \\ SpW Sim & \(40.201\times 10^{-3}\) & \(40.688\times 10^{-3}\) & \(41.845\times 10^{-3}\) \\ SpW HIL & \(40.112\times 10^{-3}\) & \(40.392\times 10^{-3}\) & \(41.448\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Packet latency for each source node to the sink (seconds).
# Dimensioning of Reactive Power Compensation in an Autonomous Island System
## 4 UC-ED-Oriented Cost-Benefit Analysis
### Reactive Compensation Requirements and Feasibility Analysis
To perform the cost-benefit analysis, the equivalent annual cost (EAC) of inductors is calculated using Equation (35), while the fundamentals of [31] apply. For the purposes of this study, the CAPEX is assumed equal to EUR 150 k/MVAr, the annual OPEX is 2% of the CAPEX, the discount rate is 5%, and the investment lifetime is assumed 15 years. \[EAC=capex\cdot\left(\frac{d}{1-(1+d)^{-y}}+opex\right) \tag{35}\]  
The net system benefit after the implementation of reactors can be derived by subtracting the EAC of the investment from the achieved system cost savings. The results are illustrated in Figure 10, with the optimum reactor size being in the order of 8 MVAr, resulting in a net system benefit around EUR 0.8 M/year. Almost the same benefit can also be obtained with the introduction of an additional 4 MVAr of inductors (12 MVAr in total). Overall, any level of inductive compensation between 8 and 20 MVAr achieves an annual economic benefit above EUR 0.7 M.  
The sensitivity of the results to the cost of reactors is presented in Figure 11 for a CAPEX ranging between EUR 50 and 200 k/MVAr. The optimum reactor size increases as their CAPEX decreases, reaching up to 16 MVAr, with the system benefit exceeding EUR 0.9 M per year. In the increased CAPEX scenarios (EUR 150 and 200 k/MVAr), the optimum size falls to 8 MVA, with an annual gain of EUR 0.75 M in the worst-case scenario. Figure 10: Cost-benefit analysis for various reactor sizes. Figure 9: Reduction of system annual variable operating cost as a function of the installed reactor capacity.
# A Hybrid Adversarial Attack for Different Application Scenarios +
## 4 Experiments
### Evaluation  
#### 4.2.1 Black-Box Attack Based on Differential Evolution  
We randomly sample 200 correctly classified examples from the IMDB test set to evaluate our algorithm. We choose examples of correct classification to avoid the impact of model accuracy. Our purpose is to make the model prediction error, that is, the original prediction is positive. The model will be judged as negative after the adversarial attack, so that our attack is considered successful.  
We limit the attacker to maximum G = 20 iterations. We also fix the maximum percentage of each text change to 10% and 20%. We believe that the quality of the text will decrease if the modification ratio exceeds 20%. Although the success rate of black-box attack based on a genetic algorithm has reached 97%, its word replacement rate has reached 25%. If an attack fails within the iteration limit or exceeds a specified threshold, it is considered a failure. Table 1 shows the attack success rate under different F-values and percentage of modified words. From our results, we can see that we can achieve a high success rate by making small changes to the text. In addition, our algorithm is significantly better than the genetic algorithm in word modification percentage and attack success rate.  
It is worth noting that, under the 10% replacement rate threshold, GA and DE have only about a 60% attack success rate, which is far lower than the white-box attack algorithm mentioned later. It shows that there is still a lot of room for improvement in a black-box attack. Once the threshold of the word replacement rate is increased to 20%, we see that both GA and DE exceed 90% in the attack success rate, and our method even approaches 100%. This shows that the word replacement rate is  
Figure 1: The whole attack process of white-box attack.  
an important factor affecting the success rate of attack. Algorithms that do not strictly limit the word replacement rate are not good algorithms.  
The mutation factor F can be used to control the degree of scaling of the difference vector between two random individuals. F value has a great influence on the success rate of the final attack. Experiments show that the smaller the F value, the higher the attack success rate at low replacement rates. When the F value is larger, the attack success rate is higher at a relatively higher replacement rate. Moreover, when F is 0.3, the attack success rate of our algorithm is higher than that of the genetic algorithm in terms of 10% and 20% word substitution rate. The example output generated by a black-box attack is shown in Table 2. From the results, the examples we generated can maintain the original sentence form to some extent.
# A Decentralized Receiver in Gaussian Interference
## 4 Ergodic Bounds
### Path Diversity versus Performance  
It is reasonable to expect that the best strategy for a distributed receiver to use will change depending on its environment. If the receivers observe the signal through a single line-of-sight path, all else being equal, the signal and noise at each receiver will have similar statistics. In contrast, in environments with many scatterers, the channel statistics will vary more across spatially distributed receivers.  
The level of attenuation at a receiver can be modeled with a Rician distribution [24]. The Rician distribution follows the magnitude of a circularly-symmetric complex Gaussian with nonzero mean and can be parameterized by two nonzero values: a scale \(\Omega>0\) representing the average receive SNR and a shape \(K>0\) (called a _K-factor_ in other literature) denoting the ratio of signal power received from direct paths to the amount of power received from scattered paths. By construction, if \(K=0\), then a Rician distribution is equivalent to a Rayleigh distribution with mean \(\Omega\). In contrast, if \(K\rightarrow\infty\), then the distribution approaches a point mass at \(\Omega\) (Reference [25]).  
The type of scattering environment does not greatly affect the average rate achievable by any bound other than the Broadcast bound (Figure 11). This is because the Broadcast bound's only utilization of receive diversity is in channel variances which is small when the channel is dominated by line-of-sight receptions. Despite the invariance of most bounds to \(K\), the profile of helper-to-base bits does change. Figure 5 shows that, no matter the other parameters, in high scattering, it is most helpful for the base to draw most of its information from the highest SNR helper while mostly ignoring low-SNR helpers' observations. The imbalance is less pronounced in interference and when SNR is less varied across receivers (high \(K\)), since in these regimes, gaining diverse observations for combining is more beneficial than using the strongest helper's observation.  
When the base collects its own full-precision observation, the helper rate profile is unaffected by interference and scattering, where most helper information is provided by the highest SNR helper. In practice, in this situation, the base might use its observation as an estimate of the interferer and subtract it from a compression of the strongest helper's observation.  
Figure 4: Bound performance versus LAN constraint (that is, the total rate available to hub from all the helpers combined). Four helpers with 0 dB average receiver SNR, averaged over 1000 channels with gains \(\sim\mathcal{CN}(0,I)\). Interferer present at each receiver with a uniform random phase and specified INR. Single-user decode-and-forward is greatly affected by interference (Compare the Broadcast curves from \(-20\) dB to \(20\) dB INR). Distributed compression only offers significant benefits over Gaussian compress-and-forward in strong interference and when LAN resources are scarce.
## 2 Numerical Simulation Methods
### Dynamic Mesh Techniques  
In the process of aircraft flight simulation, the motion of the aircraft relative to the inertial coordinate system and the deformation relative to the body axis system are involved. The dynamic mesh approach can handle both of these problems and is practical for simulating such a complex process. The basic idea of the deformable mesh method [24] is to keep the mesh topology unchanged and to absorb the movement of the solid wall boundary by adjusting the nodes' position in the field grid. Although this method is only applicable for small-scale motion, it has the advantages of preserving the correlation information between meshes and avoiding interpolation at each step. The spring-tension-based deformation method is utilized to adjust the field grid nodes position.  
The spring-tension method models the connection between each two grid nodes by a spring. The displacement of a given boundary node produces a force, which is directly proportional to the displacement of all springs connected to that node [25]. According to [PERSON]'s law, the force imposed on the mesh node can be expressed as follows:  
\[\overrightarrow{F}_{i}=\sum_{j}^{n_{i}}k_{ij}\Big{(}\Delta\overrightarrow{x}_{j }-\Delta\overrightarrow{x}_{i}\Big{)} \tag{25}\]  
where \(\Delta\overrightarrow{x}_{j}\) and \(\Delta\overrightarrow{x}_{i}\) represent the displacement of node \(i\) and its adjacent node \(j\), and \(n_{i}\) denotes the number of nodes nearby the node \(i\). \(k_{ij}\) indicates the elasticity coefficient between nodes \(i\) and \(j\), which is defined as follows:  
\[k_{ij}=\frac{k_{fac}}{\sqrt{\left|\overrightarrow{x}_{i}-\overrightarrow{x} _{j}\right|}} \tag{26}\]  
where \(k_{fac}\) is the spring constant. In the equilibrium condition, the resultant force exerted by all springs connected to the central node is zero, leading to the following iterative equation:  
\[\Delta\overrightarrow{x}_{i}^{m+1}=\frac{\sum_{j}^{n_{i}}k_{ij}\Delta \overrightarrow{x}_{j}^{m}}{\sum_{j}^{n_{i}}k_{ij}} \tag{27}\]  
where \(m\) represents the number of iteration steps.
# Determining Optimal Parameters of Regular Microrelief Formed on the End Surfaces of Rotary Bodies
## 3 Results
The formula determined the area of the DEF sector:  
\[S_{s4}=\frac{\pi\cdot\Delta_{t2}^{2}\cdot\alpha_{t1}}{360^{\circ}} \tag{35}\]  
where \(\Delta_{t2}\) is the radius of sector \(DEF\), mm. The formula determined the sector radius:  
\[\Delta_{t2}=A_{k1}+A_{k2}-S_{o}+\rho_{k}\bigg{(}1-\frac{1}{\sin(\alpha_{k1}/2 )}\bigg{)} \tag{36}\]  
Taking into account Formula (36), Formula (35) takes the following form:  
\[S_{s4}=\frac{\pi\cdot\Big{[}A_{k1}+A_{k2}-S_{o}+\rho_{k}\Big{(}1-\frac{1}{\sin (\alpha_{k1}/2)}\Big{)}\Big{]}^{2}\cdot\alpha_{t1}}{360^{\circ}} \tag{37}\]  
The area of existence for this formula was determined by the limit value of the axial pitch of PRMR, which was determined from the dependence:  
\[\left\{ \begin{array}{l}S_{o}>A_{k1}+A_{k2}+\rho_{k}\Big{(}1-\frac{1}{\sin( \alpha_{k1}/2)}\Big{)}\\ S_{o}>A_{k1}+A_{k2}-\rho_{k}\Big{(}\frac{1}{\sin(\alpha_{k1}/2)}+\frac{1}{\sin (\alpha_{k2}/2)}\Big{)}+\frac{\frac{2\cdot p_{k}}{\sin(\alpha_{k1}/2)\cdot 2 }}{1-\frac{p_{k}\cos(\alpha_{k2}/2)}{\sin(\alpha_{k1}/2)}}\\ \alpha_{t2}>\alpha_{k1}\end{array} \right. \tag{38}\]  
Figure 6: The scheme of overlapping of V-shaped grooves of PRMR (stage IV). Stage V. Consider the option when the intersecting grooves form two intersecting diamonds (Figure 7). The overlap area for this case was calculated by Formula (13), taking into account the absence of the circular sector area with radii \(\Delta_{t1}\) and \(\Delta_{t2}\), which was determined by the formula:  
\[S_{ov}=S_{s2}+S_{t1}+S_{t2}-S_{BCD}-S_{s4}-2\cdot S_{CDE} \tag{39}\]  
The area of figures \(S_{t1}\) is determined by Formula (23), \(S_{t2}\)--by Formula (25), \(S_{s2}\)--by Formula (27), \(S_{s4}\)--by Formula (37). Area \(\Delta\)\(BCD\) was determined by the formula:  
\[S_{BCD}=CL\cdot LD=\left[\Delta_{t1}-\rho_{k}\cdot\left(1-\sin\Bigl{(}\frac{ \alpha_{k1}}{2}\Bigr{)}\Bigr{)}\right]\cdot\rho_{k}\cdot\cos\Bigl{(}\frac{ \alpha_{k1}}{2}\Bigr{)} \tag{40}\]  
Taking into account Formula (31), Formula (40) takes the following form:  
\[S_{BCD}=\left[A_{k1}+A_{k2}-S_{o}+\rho_{k}\biggl{(}1-\frac{1}{\sin(\alpha_{k2} /2)}\biggr{)}-\rho_{k}\cdot\left(1-\sin\Bigl{(}\frac{\alpha_{k1}}{2}\Bigr{)} \Bigr{)}\right]\cdot\rho_{k}\cdot\cos\Bigl{(}\frac{\alpha_{k1}}{2}\Bigr{)} \tag{41}\]  
Area \(\Delta\)\(CDE\) was determined by the formula:  
\[S_{CDE}=\frac{1}{2}\cdot CD\cdot CE\cdot\sin\biggl{(}\frac{\alpha_{k1}-\alpha_{ s}}{2}\biggr{)} \tag{42}\]  
The length of segment \(CD\) was determined by the formula:  
\[CD=\frac{\rho_{k}\cdot\cos(\alpha_{k1}/2)}{\sin(\alpha_{s}/2)} \tag{43}\]  
The length of segment \(CE\) can be found from the sine theorem. \[\frac{CD}{\sin\Bigl{(}\frac{\alpha_{k1}+\alpha_{k2}}{2}\Bigr{)}}=\frac{CE}{ \sin\Bigl{(}\frac{\alpha_{k1}+\alpha_{k2}}{2}-\frac{\alpha_{k2}-\alpha_{s}}{2 }\Bigr{)}} \tag{44}\]  
Figure 7: The scheme of overlapping of V-shaped grooves of PRMR (stage V).
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 3 Results
### System Architecture and Key Features  
3.4.1. Modular Architecture  
AERQ is designed with a modular architecture, where functionalities are distributed across independent layers (front end, back end, ETL, and HPC). This ensures that specific tasks are isolated and executed in a well-defined sequence. In the event of an issue during the simulation workflow, the system marks the simulation as incomplete in the database and logs the error. Intermediate data and inputs are preserved, allowing for diagnostics and, if necessary, the resumption of the process without starting from scratch. Additionally, the front end remains operational, allowing users to access pre-processed simulation results and visualizations even during the processing of new simulations.  
The system ensures efficient data exchange through optimized communication channels between components, all of which operate over a high-speed internal network. This setup reduces latency and ensures smooth data flow between the front end, back end, ETL, and HPC layers, enabling fast and reliable interactions across the system.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles
### Delayed Self-Heterodyne Method
#### 2.3.3 Gain Compensation Loop Delay Self-Heterodyne Method  
The structure of gain compensation loop delay self-heterodyne measurement technology is shown in Figure 1e, which constructs the delay and frequency shift optical path in the MZI delayed self-heterodyne system into a fiber ring structure. The fiber optic ring mainly consists of couplers, AOMs, erbium-doped optical fiber amplifiers (EDFAs), and delay fibers [43; 44]. The beam circulates in the fiber-optic ring according to a certain splitting ratio and undergoes an AOM frequency shift once per cycle. The delay of the light also increases with the number of cycles. The EDFA is used to compensate for losses caused by optical field cycling [45; 46].  
[PERSON] et al. first proposed the cyclic delay self-heterodyne interferometer [47], while [PERSON] et al. conducted a theoretical analysis [48]. Its basic principle is similar to that of the MZI delayed self-heterodyne system, but the difference is that the delayed beam of the cyclic gain compensation system is a set of light waves with a center frequency and delay time in an equal sequence. Beat frequency curves with several peaks can be obtained by using a spectrograph, achieving a high linewidth resolution.  
The electrical signal converted from the output light of the system by the photodetector is the nth-order complete current:  
\[I_{out}(t)=I_{0}(t)+\sum_{n=1}^{\infty}I_{n}(t) \tag{11}\]where \(I_{out}\) is the DC output component, n is the order of the spectral line, \(I_{n}\) is the nth-order optical direct current, and \(I_{0}\) is the beat frequency current output with a center frequency of \(n\omega_{0}\), which is expressed as:  
\[I_{0}(t)=E^{2}(\alpha/2)^{2}cos[nf_{AOM}t-nwf_{0}\tau_{0}+\varphi(t-n\tau_{0})+ \varphi(t)] \tag{12}\]  
where \(\alpha\) is the transmission coefficient of the delay fiber and AOM, and \(f_{AOM}\) is the frequency shift of the AOM. The delay time is much longer than the coherence time of the laser, and the normalized n-order beat current power spectrum is:  
\[S_{n}(w)=(\alpha/2)^{n}(\triangle f/\pi)\Big{[}\triangle f^{2}+(w-nf_{AOM})^{ 2}\Big{]} \tag{13}\]  
The loop output only changes the power spectrum gain and does not change the power spectrum type. The effective order of the beat frequency peak is selected, and the average bandwidth is taken. The width of the beat frequency spectrum line is twice the linewidth of the laser to be measured. The effective order delay time should be greater than six times the coherence time to eliminate the influence of the delta pulse function, and the corresponding output power should be able to maintain a complete beat frequency curve. The gain compensation loop delay self-heterodyne has extremely high linewidth resolution but requires multiple cycles within the fiber-optic loop, which is extremely sensitive to the testing system parameters and environmental noise.