esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 2 System Model and Related Works
### Related Works
Additionally, since this method assumes changing channel states and therefore requires channel state information, it cannot be applied within the system model for the blind rendezvous considered in this paper. To address these limitations, this study proposes an advanced reinforcement learning approach that extends beyond previous methods by incorporating the learning of both states and policies. By carefully designing the reward structure to prioritize minimal attempts and configuring states based on prior actions, the proposed method enables the learning of optimal policies. This, in turn, improves rendezvous success rates and reduces the time-to-rendezvous (TTR), thereby enhancing overall network performance. Recently, algorithms utilizing multiple radios for faster channel exploration have been developed [14]. However, this paper limits the discussion to the use of a single radio without the addition of multiple wireless interfaces. Figure 2: JS hopping sequence for \(M=4\), \(P=5\) in [6].
## 7 Experimentation and Analysis
### Validation of Resource Optimization Methods
#### 7.2.2 System Architecture Performance Validation  
Firstly, a validation experiment is conducted on the architecture of the \"Unmanned Emergency Support System\". Different task volumes are set to run in three network modes, namely, \"terminal side\", \"terminal-edge side\" and \"center-edge-terminal side\", based on the random traffic diversion policy. In the Figure 11, the total overhead of the above architectures is roughly the same at the beginning stage of the task volume increase; as the task volume continues to increase, the terminal low-computing power equipment cannot meet the overhead of the large model inference task, and the system faces overload, which makes the latency and energy consumption increase rapidly; the \"edge-terminal\" collaborative architecture is able to handle the medium-sized inference task better, and the \"edge-terminal\" collaborative architecture can handle the medium-sized inference task better than the MEC server computing power and the MEC server computing power. The \"cloud-edge-terminal\" architecture has no obvious advantage in a small number of tasks, and the performance is jittery and slightly better than that of the edge-end architecture because the experiment is based on a random offloading strategy, while the \"cloud-edge-terminal\" architecture has no obvious advantage in a small number of tasks, and the performance is slightly better than that of the edge-terminal architecture because the experiment is based on a random offloading strategy. In the case of the \"cloud-edge-terminal\" architecture, the performance of the edge-end architecture is slightly better than that of the edge-end architecture. The \"cloud-edge-terminal\" architecture does not have significant advantages for a small number of tasks, and there is performance jitter, which is slightly greater than that of the edge-end architecture, because the experiment is based on a random offloading strategy, and this system has significant advantages for large-scale tasks.
# Impact of Chirp in High-Capacity Optical Metro Networks Employing Directly-Modulated VCSELs
## 3 Simulations
For comparison, also the transmission capacities are given for DSB and SSB DMT modulation in case of chirp typical of electro-absorption modulated lasers (EML), i.e., \(\alpha=0.4\), are reported in Figure 7a,b respectively. As can be seen by comparing the dashed and continuous curves in Figure 6, the tunable VCSEL performance is better than the short-cavity VCSEL one, due to the lower chirp parameters. This is particularly true for DSB modulation, and is confirmed by Figure 7a, where the chirp of the EML is even lower. On the other hand, this is less evident for SSB modulation, where only for a higher number of crossed WSS the short-cavity VCSEL transported capacities are significantly lower than the tunable VCSEL ones, i.e., when chirp-filtering interaction is more detrimental. Moreover, by comparing Figures 6 and 7 it can be seen that, in case of lower OSNRs, a limited amount of chirp could be useful, since the performance of the EML are worse than the VCSELs. This effect could be due to the decrease of the carrier to signal power ratio (CSPR) in presence of chirp. CSPR has a strong impact on the capacity of DMT-modulated signal [19]; hence, its reduction due to chirp could mitigate the capacity decrease in the SSB condition. Figure 5: (**a–f**) Tunable VCSEL: SSB DMT for single WSS filtering (9.5-GHz detuned) and 40-dB OSNR. (**a**) Received electrical spectrum; (**b**) Bit loading and corresponding EVM; (**c**) BER per subcarrier; (**d**) Bit-loading SNR; (**e,f**) Examples of received constellations. Finally, Tables 2 and 3 summarize the transmitted capacities and SSMF reach as a function of the span length and of the number of crossed WSS respectively for DSB and SSB DMT modulations for the VCSEL based transmitters. The results are very encouraging for the exploitation of VCSELs in S-BVT transmitters targeting around 50 Gb/s capacity per polarization in presence of COH-D. In fact, especially for SSB DMT modulation also in presence of 5 WSS consecutive filtering, more than 50 Gb/s data rates are expected for OSNRs above 30 dB. This allows us to bridge more than 250 km SSMF when using EDFAs with typical NF performance and span length of 65 km; with shorter span length, even longer transmission distances can be supported, covering wider MANs. However, DMT DSB performance is heavily impaired by chirp and its interplay with filtering, limiting the achievable capacities to below 50 Gb/s also for 100-km transmission ranges. Figure 6: Transmission capacities vs number of crossed WSS for: 40-dB OSNR (blue square), 35-dB OSNR (red circle), 30-dB OSNR (green triangle), and short-cavity VCSEL (continuous line open symbols), tunable VCSEL (dashed line full symbol) (**a**) DSB DMT modulation; (**b**) SSB DMT modulation. Figure 7: Transmission capacities vs number of crossed WSS for EML: 40-dB OSNR (blue square), 35-dB OSNR (red circle), 30-dB OSNR (green triangle). (**a**) DSB DMT modulation. (**b**) SSB DMT modulation.
# Predictive Modeling of Photovoltaic Energy Yield Using an ARIMA Approach
## 3 Results
### Specific PV System Yield
This suggests that the second model achieves a better fit and thus we expect better forecasts. Figure 13 (analogously to Figure 11) presents the values and the confidence interval of ACF and PACF errors under the ARIMA (3,0,5) model for the total yield time series. The number of the ACF errors and the PACF errors outside the confidence interval is less than those for the ARIMA (2,0,1) model and they are less than \(p\) and \(q\), respectively. This indicates a good model, and trying to make it better would result in overfitting. Figure 14 (analogous to Figure 12) shows the real data for the total yield time series in red, the approximated values by the ARIMA (3,0,5) model in blue, the predicted values in bold blue, and their confidence intervals with the dashed line in purple. In this case, the forecast seems very plausible--both the seasonality and the range match the historical trend, so we choose this model. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \hline \hline \multicolumn{1}{c}{**Model Fit**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline
**Fit Statistic** & **Mean** & **SE** & **Minimum** & **Maximum** & **5** & **10** & **25** & **50** & **75** & **90** & **95** \\ \hline
**Stationary** & 0.856 &. & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 \\
**R-Squared** & 0.856 &. & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 & 0.856 \\
**RMSE** & 92.303 &. & 92.303 & 92.303 & 92.303 & 92.303 & 92.303 & 92.303 & 92.303 & 92.303 & 92.303 & 92.303 & 92.303 \\
**MAPE** & 15.720 &. & 15.720 & 15.720 & 15.720 & 15.720 & 15.720 & 15.720 & 15.720 & 15.720 & 15.720 \\
**MaxAPE** & 85.819 &. & 85.819 & 85.819 & 85.819 & 85.819 & 85.819 & 85.819 & 85.819 & 85.819 \\
**MAE** & 70.398 &. & 70.398 & 70.398 & 70.398 & 70.398 & 70.398 & 70.398 & 70.398 & 70.398 & 70.398 \\
**MaxAE** & 242.351 &. & 242.351 & 242.351 & 242.351 & 242.351 & 242.351 & 242.351 & 242.351 & 242.351 & 242.351 \\
**Normalized BIC** & 9.405 &. & 9.405 & 9.405 & 9.405 & 9.405 & 9.405 & 9.405 & 9.405 & 9.405 & 9.405 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Numerical characteristics of ARIMA (3,0,5) model: total yield. Figure 13: The values and confidence interval of ACF and PACF errors of the ARIMA (3,0,5) model, for total yield: (**a**) residual ACF; (**b**) residual PACF. In the next section, we will comment on the forecasts for the specific PV system yield and the total yield.
## 4 Discussion
### Research Limitations  
This study validates the effectiveness of the R-barricade interactive barricade design in improving driver attention and reaction time through eye-tracking experiments in a laboratory setting. However, there are still some limitations that need to be addressed. We recognize that these limitations may affect the generalizability and applicability of the research results, and we plan to take specific measures to address these issues.  
Limitations of the current sample size: The eye-tracking experiment in this study involved a small sample size, including 14 participants. Due to cognitive biases or procedural errors, the final valid data were obtained from only 10 participants. Although these data provide initial insights, the limited sample size may not fully represent a broader driver population, particularly those of different ages, genders, and driving experiences.  
Differences between laboratory conditions and real-world environments: The study demonstrated the effectiveness of the R-barricade design in improving driver attention and reaction time through eye-tracking experiments conducted in a laboratory setting. However, we acknowledge that while the laboratory conditions provided the necessary controls to ensure experimental accuracy, they cannot fully replicate the complexity of a real motorway construction site. The current research lacks field testing, primarily due to the challenges of gaining access to motorway construction sites, as well as the high cost and potential safety risks of field testing.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 2 Group Communication
### CoAP-Based Group Communication
#### 2.2.3 Unicast Group Communication  
In our previous work [9], we have introduced our alternative unicast-based group communication solution for CoAP-enabled devices. Our aim was to create an intermediate level of aggregation to be able to easily manipulate a group of resources across multiple smart objects. Such a group of resources is called an entity, and the resources themselves are called the entity members. An entity can be created, used or manipulated through a single CoAP request.  
We call the component that manages the entities the Entity Manager (EM). This component, which can reside, e.g., on the border Gateway (GW) of the LLN, is responsible for maintaining entities that are created from groups of resources residing on CoAP servers (i.e., sensors and actuators) inside the LLN. Clients on the Internet can interact with an EM to create new entities and/or customize how these entities should behave. The EM exposes entities to the Internet as any other regular CoAP resource. Figure 3 shows an overview of the involved components. The EM acts as a \"proxy\" between the client and the constrained devices. Client requests are sent to the EM, which analyzes and verifies the requests and then issues the appropriate requests to the constrained devices using CoAP. Once the EM receives responses from the constrained devices, it combines them according to the needs of the client and sends back an aggregated response to the client.  
Since an entity is exposed by the EM as yet another CoAP resource, entities can be members in other entities. This makes it possible to construct nested entities that can, for example, reflect a certain hierarchy of the members.  
When a client tries to create a new entity consisting of a group of resources inside LLNs, the EM performs a sanity check on the request in order to make sure that the resulting entity would make sense. We call this sanity check the entity validation.  
Figure 3: Clients create entities consisting of several smart object resources on the entity manager.  
Furthermore, we have introduced in [9] the notion of profiles for the created entities. The use of entity profiles allows the client to specify in more detail how the entity should behave (e.g., if it should use confirmable or non-confirmable CoAP messages) and, through updating the profile, allows manipulation of this behavior. By building upon standardized concepts, the impact on the constrained devices was limited.  
An important advantage of our approach is its reliability, since it uses unicast messages that can make use of the CoAP built-in reliability mechanism. Its main disadvantage is the increased communication overhead to achieve this reliability. For more details, we refer the reader to [9].
## 3 Results and Discussion
### Influencing Factors
#### 3.3.3 Effect of pH  
Figure 7 shows that the solution pH substantially affected the removal of Ni\({}^{2+}\) by nZVI@ATP. For an initial pH of 2.0-5.0, the Ni\({}^{2+}\) removal rate sharply increased from 73.2 to 143.87 mg/g, and the solution pH increased from 2.75 to 7.42 after the reaction. For an initial pH of 5.0-7.0, the Ni\({}^{2+}\) removal rate by the nZVI@ATP decreased slightly, and the solution pH increased from 7.42 to 7.88 after the reaction. Notably, for the initial pH of 2.0-5.0, the excess H\({}^{+}\) in the solution competing with Ni\({}^{2+}\) for the active sites of the material necessitated a lower removal rate. As the initial pH of the solution kept increasing, increasing hydroxide ions in the water promoted the generation of Ni(OH)\({}_{2}\) precipitation, resulting in an improved removal performance [31]. We obtained the isoelectric point of nZVI@ATP as 9.40 (Figure 3). At pH < 9.40, the nZVI@ATP surface was positively charged, and electrostatic repulsion existed between the nZVI@ATP and Ni\({}^{2+}\), which hindered the removal of Ni\({}^{2+}\) by the nZVI@ATP. In addition, with the increasing pH, the deprotonation was enhanced, and the surface electronegativity of the nZVI@ATP gradually increased; this competitive relationship gradually weakened, the Ni\({}^{2+}\) removal rate by the nZVI@ATP increased, and the highest Ni\({}^{2+}\) removal rate by the nZVI@ATP was reached at a pH of 5. The increasing pH of the solution after the reaction can be attributed to the reaction between the nZVI and water to produce excess OH\({}^{-}\), which increases the pH of the solution [24]. In addition, ATP, as an alkaline substance, plays a role in increasing the pH after the reaction [14].  
Figure 6: Adsorption results of different dosing amounts on Ni\({}^{2+}\) over time by nZVI@ATP (initial concentration of 200 mg/L; initial pH of 5; temperature of 298 K; and stirring time of 12 h).
## 2 Materials  
### Study Area  
Yadong County, situated at the southern foot of the middle section of the Himalayas, falls under the jurisdiction of Shigatse City, Tibet Autonomous Region (Figure 1). The study area, spanning over 4240 km\({}^{2}\), is home to a population of over 120,000 individuals. The region is characterized by a high mountain landform, with an average elevation of 3500 m (ranging from 1747\(\sim\)7057 m). The study area is geographically divided into two distinct parts, namely north and south, based on the line from Pali Town to Kangbu Town, owing to the significant difference in altitude. Consequently, two distinct climates have emerged in these regions [5]. The study area exhibits distinct geographical and geological characteristics. The northern region is situated at an elevation of 4300 m above sea level and experiences a cold and dry climate, with an average annual rainfall of approximately 410 mm. Conversely, the southern region has a lower average altitude of 2800 m, a humid climate, and an annual precipitation of 873 mm. The geological composition of the study area is primarily composed of shale, limestone, and dolomite, with the southern region being characterized by faults and folds. There is a degree of VIII on the modified Mercalli index, indicating a high seismic intensity. The southern area also exhibits a higher density of geological disasters, such as collapses and landslides, while the northern region is primarily affected by debris flows. Figure 1 also shows the location of landslides that occurred over the years and roads distributed in the study areas.  
On 18 September 2011, a magnitude 6.8 earthquake occurred in Sikkim, India. The study area was affected by the earthquake, which induced a series of secondary geological disasters, causing great harm to local residents and roads (Figures 2 and 3). Therefore, it is of great significance to compile an accurate landslide susceptibility map for the study area.  
Figure 1: Location of the study area showing elevation and landslide samples.  
Figure 2: The Sikkim earthquake triggered landslides in Yadong County: (**a**) S204 road was interrupted; (**b**) houses collapsed.
## 2 Experimental Method
### Specim Design  
Figure 2 depicts the specimen designs following the ASTM E466 standard [29] used in this study. The specimens are made of high-strength Al7075-T6 alloy that meets the ASTM B209 standard [30]. The specimens are designed to have a one-sided stress concentration factor (\(K_{t}\)) to allow for controlled initiation of fatigue cracks for imaging purposes. Several specimen geometries are simulated using Solidworks and eventually, two geometries yielding \(K_{t}\)s of 7.1 and 8 are down-selected. For brevity, the specimens having \(K_{t}\) of 7.1 are attributed as \(K_{t1}\) and the specimens having \(K_{t}\) of 8 are attributed as \(K_{t2}\). Both sets of specimens, owing to their distinct \(K_{t}\) values, will exhibit different failure lives. A higher \(K_{t}\) implies larger localized stress fields. These fields can be easily computed through a finite element simulation, as shown in Figure 3. The figures depict the stresses in both notches for a tensile load of 2 kN. The simulations are performed using a Linear Elastic Isotropic model with an elastic modulus of 72 GPa, and a Poisson's ratio of 0.33. These differences ultimately impact the instance of crack detection and the fatigue lives of the specimens, as observed in Figure 4. \(K_{t2}\), with a more damaging impact, lowers the fatigue life and accelerates the crack appearance instance as compared to \(K_{t1}\). The instance of crack detection (or appearance) corresponds to a crack opening displacement of 3 \(\upmu\)m [21], as detected by the confocal microscope. An elaborate description of the assistance of the confocal microscope in these experiments is presented later in Section 3.2.  
Figure 3: Local stress field visualization for **(a)**\(K_{t1}\) and **(b)**\(K_{t2}\) specimens using a finite element-based simulation.  
Figure 2: Specim designs for **(a)**\(K_{t1}\) and **(b)**\(K_{t2}\) (all dimensions are in mm).
## 4 Comparison between Numerical Simulation and Analytical Methods
### Data Comparison  
As shown in Figure 5, A1, B1 and C1 represent the upstream, middle and downstream piers, respectively. The data obtained from the filed measurement, the numerical simulation and the calculation are compared. Except for the flow force along the incoming direction,  
Figure 4: Verification of the Strouhal number.  
the lateral and back force of the temporary pier are negative due to the action of the lateral flow and the rear vortex, and the pressure increases on increasing the flow velocity. Compared with the resistant and lateral force, the absolute negative pressure on the side of these three piers is greater than the positive pressure against the incoming surface; thus, more attention should be paid to the structural design process. There is little difference between the measurement and numerical results, and the change pattern is basically similar.  
The average errors of water pressure on the temporary pier among the measurement results and those obtained via numerical simulation and the Morison equation for A1 are 10.2% and 36.3%, respectively, as shown in Table 2. The average errors for B1 are 7.9% and 20.0%, respectively. Due to the lack of detailed regulations on the shielding coefficient of three column piers in the specifications, the comparison with the Morison equation for the C1 pier is not provided. The numerical simulation results agree well with the measured ones for A1 and B1, indicating that the numerical method can accurately predict the water pressure.  
Figure 5: Comparison of water pressure.
## 4 Discussion
### Model Accuracy  
Deriving an expression for the energy harvester power output, based on the lumped parameter model of Equation (1), and electric models of Equation (4), has been performed frequently in the previous literature. We compared our derived expression to those in [1,7,8,14] and found that, although different approaches are implemented and certain assumptions made, the resulting power expressions are equivalent. Measurements by [PERSON] et al. [2] showed that the model significantly underpredicted measured PEH power output close to resonance and anti-resonance but was in good agreement off-resonance. Applying the model to the MEMS-scale EMEH in [4] results in a similar underprediction of the power at resonance. However, by reducing the input acceleration, an accurate prediction can be made even at resonance. This result is reasonable as the model is linear and will naturally break down outside of the linear regime. [PERSON] et al. finds a good match between predicted and measured EMEH data at resonance while using a beam similar to that used by [PERSON] et al., yet under a significantly larger base acceleration.  
[PERSON] et al. deduced the reason for their discrepancy to be the small-signal linear piezoelectric constitutive model, which underpredicted the piezoelectric constant at large strain. [PERSON] et al. [19] later confirmed that this nonlinear behavior is significant unless the tip displacement is small. Similarly, for the assumed case of a magnet traveling along the symmetry axis of a cylindrical coil, there are nonlinear effects in the electromechanical coupling of the EMEH arising from the nonlinear expansion of the magnetic field (along the symmetry axis) [4,5]. Thus, the requirement of small displacement is valid for an EMEH as well. Likewise, if we measure the coupling coefficient at a specific displacement amplitude and keep this parameter constant during measurements and calculations, the discrepancy between model and measurement due to nonlinear electromechanical coupling should be reduced.  
For the model under inertial load, a constant base acceleration amplitude is assumed. This has a significant effect on the model output characteristics. As base acceleration amplitude equals \(\omega^{2}y_{0}\), where \(\omega\) is angular excitation frequency and \(y_{0}\) is the base displacement amplitude, this implies that either \(\omega\) and \(y_{0}\) are constant, and \(\gamma\) varies only due to spring stiffness and mass, or that \(y_{0}\) is inversely proportional to \(\omega\). The latter case breaks down as \(\gamma\) approaches zero as this implies \(y_{0}\rightarrow\infty\). The alternative is that \(\omega\) remains constant and the ratio of spring stiffness to mass approaches infinity, which is a feasible case.