esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Deployment of WDM-PON Protection Schemes
### The Evaluation of the Power Budget in WDM-PON Protection Schemes  
For the PB calculation, results for the P2 MP WDM-PON protection schemes based on specific parameters' values from Table 2 are presented in Figure 2. The highest power budget is determined for the unprotected P2 MP network. The Type B protection satisfies requirements for correct evaluation of the power budget. For more protection, the dual-parented Type B or Type C protections can be adequately selected. These protection scenarios are suitable for rural geographical areas up to 33 km fiber length.  
Upon designing the WDM-PON network with P2 MP protection schemes, it is important to accommodate values of specific parameters for acquiring a correct, reliable and convenient optical power budget of the final selected protection scheme.  
For the PB calculation, results for the RING WDM-PON protection schemes based on specific parameters' values from Table 2 and another adequate basic data selected for this topology type are presented in Figure 3. The 13 km fiber length is used for covering a metropolitan area with a higher number of users. The highest power budget must be assured for the unprotected metro-access network. However, the protected network architecture is more suitable because this architecture ensures the protection by means of  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **P2 MP** & **RING** \\  & **Architectures** & **Architectures** \\ \hline Transmitted optical power \(P_{T}\) [dBm] & 8 & 10 \\ EDFA gain \(G\) [dB] & - & 31 \\ Specific attenuation \(\alpha\) [dB/km] & 0.25 & 0.25 \\ Feeder fiber length \(FF\) [km] & 25 & 10 \\ Distribution fiber length \(DF\) [km] & 8 & 3 \\ Splice loss \(L_{S}\) [dB] & 0.2 & 0.2 \\ Connector loss \(L_{Low}\) [dB] & 1 & 1 \\ AWG loss \(L_{AWG}\) [dB] & 5 & 5 \\ Mux/Demux loss \(L_{Max/Demux}\) [dB] & 2 & - \\ OLT with Mux/Demux loss \(L_{OLT\_Max/Demux}\) [dB] & - & 2 \\ ONU with Mux/Demux loss \(L_{ONU\_Max/Demux}\) [dB] & - & 2 \\ Number of ONU units & - & 10 \\ Number of optical power splitters & - & 16 \\ System margin \(SM\) [dB] & 3 & 3 \\ Receiver sensitivity \(R_{S}\) [dBm] & \(-\)35 & \(-\)28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific parameters’ values for power budget calculations in WDM-PON networks.  
Figure 2: The comparison of power budget requirements for P2 MP WDM-PON protection schemes.  
the FF fiber redundancy, whereby no system breakdown influencing a high number of users is occurring in a case of the fiber failure.  
Upon designing of the WDM-PON network, an arrangement of specific parameters is also possible for these ring protection schemes. However, the power budget analysis is again necessary for acquiring a correct, reliable, and convenient optical power budget in a given selected protection scheme.
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 5 The DOTO Algorithm  
### Algorithm Overview  
In this section, we will design an online computing offloading strategy function \(\Omega\) based on deep reinforcement learning in WPT-MEC with a parameter of \(\omega\). Input the wireless channel gain \(c_{t}\) at the beginning of each time block, and the online computing offloading strategy function can immediately output the optimal offloading decision. The policy function is expressed as:  
\[\Omega_{\omega}:c\to u^{*} \tag{14}\]  
We introduce \(N\) auxiliary variable \(u=[u_{1},\dots,u_{n}]\), where \(u_{i}=0\) (or \(u_{i}=1\)) denotes that the computing mode of the \(n-\)th UAV is local computing, i.e., \(i\in U_{0}\) (or offloading computing, i.e., \(i\in U_{1}\)). For each group of offloading actions \(u\), the optimal time resource allocation and the corresponding maximum residual energy \(E\) can be obtained by solving problem \(P2\). After all the generated offloading actions are calculated, the group with the largest residual energy is selected through comparison. The DOTO algorithm will gradually learn the policy function from experience and constantly iterate and update to allow the strategy function \(\Omega\) to achieve the best effect. The structure of the DOTO algorithm is shown in Figure 3.  
The DOTO algorithm consists of two stages. The first stage is the iteration of the offloading function, and the second stage is the update of the offloading policy. The two stages are alternately performed until the training converges.  
In the offload function iteration, we use DNN to generate the offload action, and the DNN contains built-in parameters. Specifically, at the beginning of the \(t-\)th time block, the wireless channel gain \(c_{t}\) is input, and the DNN outputs a relaxed offloading action \(\widetilde{u}_{t}\) based on the current parameter \(\omega_{t}\) and the offloading strategy \(\Omega_{\omega_{t}}\). Each number in \(\widetilde{u}_{t}\) is between 0 and 1. Then, \(\widetilde{u}_{t}\) is quantized into \(K\) binary offload actions. By solving the \(P2\) problem, a set of decisions \(u_{t}^{*}\) with the largest residual UAV energy that can satisfy all constraints among the \(K\) offloading decisions is calculated. The optimal time resource allocation is \((a_{t}^{*},b_{t}^{*})\). The MEC system executes the offloading action \(u_{t}^{*}\), and the DOTO algorithm accepts the reward \(E^{*}(c_{t},u_{t}^{*})\), and puts the state-action \((c_{t},u_{t}^{*})\) into the experience replay pool. The new offloading strategy generates a new optimal offloading decision \(u_{t+1}^{*}\) based on the new input \(c_{t+1}\) in the next time block. With continuous training iterations, the DNN offloading strategy will gradually improve until the training converges. We will describe these two stages in detail below.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 6 Resin Adhesive Systems
### Etch-and-Rinse Adhesive Strategy
HEMA monomer should be added in higher concentrations to stabilize the mixture of ER II and to facilitate the infiltration of the self-priming adhesive inside the demineralized collagen fibrils [15]. The phenomenon of over-wetting in which water prevents the formation of a continuous adhesive seal, intensifies the importance of the issue of removing excess water from the surface of etched-and-rinsed dentin before applying the adhesive [269]. Conversely, a collapse of collagen can occur as a result of intense drying. The difficulty which the clinician now faces is finding the correct degree of dentinal moisture to provide optimal adhesive penetration [266]. Unfortunately, it is very difficult to control this state. Several techniques have been proposed for this purpose: progressive air-drying, elimination of excess water by simple aspiration with the saliva ejector, absorption of excess water by using wet cotton pellet or \"micro-brushes\" or drying the cavity with compressed air followed by rehydration by wet cotton [270]. ER II adhesives containing acetone are considered more sensitive to the moisture control of dentin than those containing alcohol [9; 271]. ER adhesives, such as ER III and ER II, have several advantages [9; 10; 33]. They rely on an efficient diffusion-based bonding mechanism, enabling profound micro-mechanical interlocking at both the enamel and the dentin surfaces. The aggressive nature of the etchant, normally H\({}_{3}\)PO\({}_{4}\), guarantees complete removal of the smear layer, thereby improving bonding performance. ER techniques are mainly effective for enamel bonding, with a recognized long-term adhesion durability. Specific ER adhesives, like ER III OBFL (Kerr Co., Orange, CA, USA) have a longstanding presence in the dental market, signifying reliability over more than two decades. Furthermore, independent clinical trials accomplished over 10 years acknowledge the persistent bond efficiency of ER adhesives, yet the outcomes may vary based on the product. But ER adhesives also present disadvantages. The aggressive nature of H\({}_{3}\)PO\({}_{4}\) can result in excessive demineralization of the dentin, affecting its structural integrity. Complete dissolution of dentinal HAp and exposure of the collagen matrix can occur, necessitating the formation of a thicker HL within a limited clinical timeframe. This thick HL might be prone to microleakage, enzymatic degradation, and weak secondary chemical interactions, potentially destabilizing the long-term bond durability [10; 15; 33].
## 2 Fundamentals of Frequency-Selective Surfaces
### Basic FSS Design Performance  
As an example, the electromagnetic behavior of each unit cell design shown in Figure 2 was simulated using an electromagnetic simulation suite, such as CST Studio Suite [18]. Each unit cell, measuring 10 mm by 10 mm, was modeled on a 5 mm-thick concrete substrate (representing a one-year-old concrete wall). The results of these simulations are presented in Figure 3. In the plots, the x-axis represents frequency (in GHz), while the y-axis indicates the transmission coefficient (in dB). A transmission coefficient of 0 dB signifies that the surface is fully transparent at that frequency, meaning the radio wave passes through the material without any change or attenuation. The more negative the transmission coefficient (i.e., lower in dB), the greater the attenuation of the radio wave attempting to pass through the FSS. A reduction of 3 dB corresponds to a halving of the power.  
All the designs introduce some level of attenuation, as the concrete wall itself contributes to the overall attenuation. The plots reveal differing performances among the unit cells: the patch and loop (slot) demonstrate opposite behaviors, with the patch design exhibiting the least attenuation and the loop design exhibiting the most, both at similar frequencies. The meandered cell performs similarly to the loop design, although its resonant frequency (i.e., the frequency at which attenuation is strongest) is lower due to meandering, which effectively simulates a larger loop within a more compact size. The cross cell design, when constructed with the same dimensions and metallic strip width as the loop, introduces less attenuation in the passband but shifts its resonance to a higher frequency.  
During the design stage, the dimensions of the FSS unit cell must be carefully adjusted to tune the attenuated frequency bands for the desired effect. Typically, the goal is to block or mitigate radio waves from specific telecommunication services while allowing other frequencies to pass through, ensuring that devices on the opposite side of the FSS remain functional. Given that the electromagnetic spectrum is a limited and valuable resource, it is tightly regulated to ensure the efficiency and efficacy of telecommunication systems.  
A full wall or building envelope can be constructed by replicating the unit cell design into a rectangular matrix pattern. Figure 4 illustrates this process, showcasing two samples of FSS with distinct unit cell designs.  
Figure 3: Electromagnetic behavior of the basic FSS cell designs.
## 9 Conclusions  
In the present study, we have highlighted the capability of surface remote sensing as a crucial method in avoiding the ambiguity of statistically-based satellite aerosol/cloud interaction studies. Our overall goal is to describe the instrumentation and algorithms needed to provide simultaneous aerosol and cloud measurements at time resolutions suitable to isolate aerosol/cloud interactions that modify the cloud droplets. Due to the fairly small dynamic range that can be expected for the ACI index, a more thorough sensitivity study to account for realistic errors from both the measurements themselves and the variability of the environment were accounted for and it was shown that these errors which were \(\sim\)8% were sufficiently small to resolve realistic ACI coefficients. A demonstration of the algorithm together with the retrieval uncertainty was given for a case where surface measurements alone did not result in any observable ACI. In particular, we emphasized the potential improvement that may occur if aloft aerosol measurements were used based on Raman Lidar measurements of aerosol extinction. In this case, a realistic ACI value (\(\sim\)0.23) was found although systemic deviations from the trend line were observed which makes the above result less than certain.  
Although hardly conclusive, we believe the use of aloft aerosol measurements from lidar is important in obtaining better ACI measurements when cloud heights are sufficiently high. In such cases, the surface aerosols are expected to be quantitatively less representative of actual particles forming CCN nuclei.
## 4 Discussion
### Relationship between Clay Minerals and MOC  
Among the three grassland transects, the highest content of Sme (2:1 swelling clay minerals) and It (2:1 non-swelling clay mineral) was the LP, and the lowest was the MP. The contents of Ver (2:1 swelling clay mineral) and I/Sme were the highest in MP soil. Studies have shown that soil (It, Sme, Ver) dominated by 2:1 type minerals has higher MOC content than soil (Kao) dominated by the 1:1 type [17]. Sme enhances SOC stability through its cationic bridge [68]. Ver as a 2:1 swelling mineral could improve SOC stability by stabilizing agglomerates [69]. The intercrystalline space of illite is full of low-hydrated K\({}^{+}\), and it is difficult to expand in water. Therefore, the ability to fix organic carbon is lower than Ver [70]. In contrast, I/Sme indirectly affected the MOC content by influencing the pH, clay content, and bacteria content (Figure 5). However, the correlation between MOC and mineral species differed at different temperature gradients, and the correlation between MOC and minerals gradually increased as the temperature decreased. Only Ver was positively correlated with MOC in the LP with the highest temperature. Therefore, the low Ver content was one of the reasons affecting the lowest MOC content in the LP. Ver was positively correlated with MOC in the MP. MOC in the TP with the lowest temperature was positively correlated with Sme and It. MOC was negatively correlated with I/Sme. To some extent, this explained the pattern of the highest MOC content in TP soils.
## 5 Prototype and Experimental Results  
### Prototype Design  
In order to verify the validity of the design, a prototype with 30-kg load ability was built, as shown in Figure 9. To apply our robot for delivery, the robot had functions of object recognition, automatic navigation, and so on, which enabled it to be operated in the right way. However, these functions are not the focus of this paper; thus, they are not discussed here. For the design of the robot, there were three key points, described below.
## 1 Introduction
### Research Questions  
This study focuses on digital twin software solutions and their inclusion in industrial cobot applications. By exploring the integration of digital twin software solutions with industrial cobot applications, it aims to investigate the potential synergies and benefits that arise from combining these technologies. It examines how digital twins can enhance the capabilities of cobot and contribute to operational efficiency in various industries. The study is driven by several research questions such as:  
* What are the key challenges and opportunities associated with utilizing digital twin software solutions in conjunction with industrial collaborative robotics applications?
* How does the integration of digital twins and collaborative robots contribute to improved operational efficiency, productivity, and decision-making processes?
* What are the implications of the integration of digital twin software solutions with industrial cobot applications for different industries, such as manufacturing, energy, healthcare, and transportation?  
The findings of this research review will contribute to a deeper understanding of these technologies and their impact on operational efficiency, paving the way for further advancements and practical applications in various industries.
# Dynamic Impedance Estimation: Challenges and Considerations
## 4 Dynamic Impedance Estimation
### Illustrative Results  
Using this technique, the current transfer from A to D and C to D (Figure 9) was found. As shown in Figure 14 a,b, the present transfers values higher than the 1 for several frequencies. It means that harmonic sources injecting current at bus A or C can be amplified at bus D. It is also possible to observe the behavior of distributed parameters transmission line model by the resonance points.  
Figure 15 shows the total impedance seen on bus A. Note that a current injection at this node will cause a high voltage value due to the resonance frequencies.  
Figure 14: Current transfer function: (**a**) from bus A to bus D; (**b**) from bus C to bus D.
## 2 System Model and Complexity Analysis
### Complexity Analysis for General Linear Block Codes and LDPC Codes
These contribute significantly to increasing complexity in LDPC decoding. Next, we consider polar codes for our multi-way relaying system, analyze complexity, and compare them with the results of the LDPC codes.
## 4 The Methodology for the Proposition Validation
### Distribution of Train Delay Times  
Freight train delays negatively impact the reliability of intermodal freight networks and are an important, but under-researched element of combined transport [78]. Freight trains are typically less reliable regarding the planned arrival times than trucks, which is a planning challenge for freight owners. Reasons for freight train arrival delays can be grouped into two sets of factors [79]. The first set describes a deviation of the departure time from shunting yards or hubs. The distribution of these deviations in Swedish shunting yards has been investigated by [80]. In their paper, the authors find that the log-normal and the gamma distribution best approximate the departure delays and early departures, respectively. The second set describes delays during the journey of the train. Network capacity utilization, weather conditions, construction sites, and many more factors determine the actual arrival time of trains. To model the delays of freight trains in our simulation, we thus cannot rely on the departure time delays, as the influence of the journey would be neglected. As creating models that predict the arrival time by combining both sets of factors is its own body of research [79], we concentrate on approximating the actual arrival delay using a probability distribution. Some work in this field has already been done. For example, the delay time of arriving passenger trains was approximated for Chinese railway stations through exponential distribution [81]. Although passenger train punctuality is much higher than freight train punctuality in Europe (see, e.g., [82; 83]), we argue that the nature of the factors influencing arrival delays are similar, and thus, the type of distribution approximating passenger train delays also approximates freight train delays. Therefore, we use the exponential distribution with the probability density function:  
\[f(d)=\lambda e^{\lambda d},d\in\mathbb{N}\]  
to model the delay \(d\) of a freight train. For parameterizing, we use the punctuality information of the Austrian Railways, reporting that the share of combined wagonload traffic with less than 30-min delay in Austria in 2022 was 52% [82]. By calculating the cumulative distribution function:  
\[F(d)=1-e^{-\lambda d}\]we approximate \(\lambda\approx 0.02531\) so that \(F(29)\approx 0.52\). This results in a mean delay of \(E=\frac{1}{\lambda}=39.51\) min and a median delay of \(m=\frac{\ln(2)}{\lambda}=27.39\) min. In the simulation, we incorporate the delay by adding it to the scheduled lead time \(t_{s}\), thereby defining the actual lead time \(t_{a}\) of a railway service as:  
\[t_{a}=t_{s}+d\]
# Seamless Indoor-Outdoor Localization Through Transition Detection
## 6 Conclusions  
Our proposed method does not require a time-consuming labeling process in training the model for indoor positioning. The outdoor positioning algorithm also does not require any additional GNSS receivers other than the internal GNSS module of an Android smartphone, as it only uses the FLP measurements from Google Android's LocationManager API.  
For three buildings (Maru360, Keumha, and Namsan Green), the accuracy of the estimated position was compared to the FLP and GPS from the Android LocationManager using three different smartphone models (Samsung Galaxy S10, S20, and S21). The accuracy of classifying the current state was also compared between the state estimated using the proposed method and the state estimated based on the FLP location. The result shows a significant enhancement in the indoor/outdoor classification of the proposed method compared to the FLP and GPS. The position estimation accuracy of the proposed method is also better than the FLP and GPS for the S10 and S20. However, the results with the S21 showed the proposed method's vulnerability to the effect of multi-path errors in GNSS measurement updates.  
To address the issue of GNSS multi-path error, we are currently in the process of investigating the utilization of the raw satellite measurements provided by Android for analyzing the multi-path error. Moreover, the manually designed and tuned weight function applied to the GNSS measurement's error covariance matrix in the outdoor positioning engine needs to be replaced with a more data-driven approach to reduce the effort in tuning parameters for each place, which will be conducted in future studies.  
This work was supported by a Sungshin Women's University research grant, No. H20220092.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The author declares no conflicts of interest.