esa-sceva/satcom-chunk-collection huggingface dataset:
# Programmable Adaptive BVT for Future Optical Metro Networks Adopting SOA-Based Switching Nodes
## 1 Introduction
In [15], a comparison between the filterless option and active switching, in terms of cost and performance, is provided. In the case where SOAs are combined with wavelength-filtering technologies, we consider SOA-based wavelength selectors as part of the Optical Add/Drop (OAD) nodes. These nodes have the capacity to insert or drop traffic to the optical network, by means of a SOA array with a splitter, a combiner and filtering elements based on Wavelength Division Multiplexing (WDM) technology [13]. WDM technology allows the multiplexing of multiple signals with different optical carrier wavelengths. In visible light communications, the performance of WDM technology is limited due to transmission losses [16,17]. Some studies propose the design of a novel \(1\times 4\) optical demultiplexer based on multimode interference [16] or the design of \(1\times 4\) silicon-alumina wavelength demultiplexer based on multimode interference [17], to overcome this problem. In the range of C band for optical communications, an 8-channel wavelength multimode demultiplexer is demonstrated to work with low cross-talk [18]. WDM cross-connect switches combined with SOA technology have been assessed in [10,19] for optical data center networks. The system is validated considering three different types of modulation formats, such as Non-Return Zero-On Off Keying (NRZ-OOK), Pulse Amplitude Modulation (PAM) and DMT. Potential lossless operation and low cross-talk (\(\textless-30\) dB) has been demonstrated. Using BVTs, the bandwidth and bit rate can be dynamically adjusted by adopting a modulation format per each subcarrier. Therefore, more flexibility and adaptability can be obtained [7]. In this paper, we propose a combined use of BVT and SOA technologies to design flexible and cost-effective solutions for future optical metro networks. Bit loading (BL) and power loading (PL) algorithms can be implemented in the transceiver Digital Signal Processing (DSP) module, to enhance the capacity, adaptability, and the resilience towards transmission impairments [5,6]. We will present preliminary results on the use of BVT in the context of elastic optical metro networks with switching nodes based on SOA technology, particularly analyzing the case of a cost-effective implementation of the BVT. The paper is organized as follows. In Section 2, the S-BVT and its cost-effective implementation are described. Then, in Section 3 further details on optical metro network adopting switching nodes based on SOA technology are provided. In Section 4, we present the proposed system, adopting BVT and SOA technology. The experimental setup and system optimization are presented. In Section 5 the results obtained are discussed for the different analyzed scenarios and finally the conclusions are drawn in Section 6.
# Operational Temperature Effect on Positioning Accuracy of a Single-Axial Moving Carrier
## 2 Mathematical Theoretical Models and Experimental Methods
### Positioning Accuracy Measurement of the Moving Carrier  
The precision of a measurement system, also called reproducibility or repeatability, is the degree to which repeated measurements under unchanged conditions show the same results. In the fields of science, engineering, industry, and statistics, the accuracy of a measurement system is the degree of closeness in measurements of a quantity to that quantity's actual value [23].  
The positioning accuracy of the moving carrier was measured by a laser optic measurement system composed of an XL-80 (laser measurement equipment from the Renishaw company, Gloucestershire, UK), a laser generator, an optic lens module, temperature sensors, and a temperature compensating component XC-80, as shown in Figure 4[24]. This experiment set the Renishaw laser interferometer on a single-axis stage. The laser beam was aligned with the direction of the axial movement, and an adjustment was performed for the linear positioning lens group with the principle of adjusting the proximal end and the angle. The results of the analysis are in accordance with the VDI3441, which is the standard to measure positioning precision and repeat accuracy. This method uses software that can measure the reversal error average, positional scatter average, maximum reverse error, positioning accuracy, maximum repeat accuracy, and position deviation.  
Figure 4: Schematic diagram of a laser optic measurement system [24].  
Figure 3: Schematic diagram of environmental control system in the study.  
The equipment properties used in the experiment are presented as follows:  
1. PT100 temperature sensor The temperature sensors used in this paper are in line with the international IEC 751 standard for temperature sensors, with the temperature ranging from \(-\)200 to +850 \({}^{\circ}\)C, having a resistance of 100 ohms at 0 \({}^{\circ}\)C and 138.4 ohms at 100 \({}^{\circ}\)C. The measured temperature data were collected by a data log (GRAPHTEC GL-840-M) with an interval of 5 s.
2. Laser measurement system * Laser frequency stability of \(\pm\)0.05 ppm over one year and \(\pm\)0.02 ppm over 1 h is achieved by dynamic thermal control of the laser tube length to within a few nanometers. * Linear measurement accuracy of \(\pm\)0.5 ppm is achieved over the whole environmental range, i.e., from 0-40 \({}^{\circ}\)C (32-104 \({}^{\circ}\)F) and 650-1150 mbar. Readings can be taken up to 50 kHz, with a maximum linear measurement speed of 4 m/s and a linear resolution of 1 nm, even at maximum speed.
3. The air velocity at the inlet vent was measured by a flow meter (Fluke 923) with an accuracy of \(\pm\)2.5%.
## 3 Results
### Muscle Activation  
Muscle activation pre- and post-training was examined for healthy (Table 3) and stroke (Table 4) participants. For healthy participants (FW and BW), the right RF (quad muscle) showed significant increases post-training. Further, for the healthy group, the right BF and the right MG exhibited significant increases in activation for FW. For the stroke group, the right TA showed significant increases post-training for BT-W. Also, for the stroke group, the right BF (FT-W) and left MG (BW) also showed significant increases. Due to the smaller number of stroke participants, we also calculated [PERSON]'s \(d\), as shown in Table 5. This measurement was calculated via the mean differences between the pre- and post-training means divided by the pooled standard deviation. A lower [PERSON]'s \(d\) indicates the necessity of larger sample sizes. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Walking Condition**} \\ \hline \hline  & \multicolumn{2}{c}{**FW**} & \multicolumn{2}{c}{**BW**} & \multicolumn{2}{c}{**FT-W**} & \multicolumn{2}{c}{**BT-W**} \\ \hline
**Muscle** & **Pre** & **Post** & \(p\)**-Value** & **Pre** & **Post** & \(p\)**-Value** & **Pre** & **Post** & \(p\)**-Value** & **Pre** & **Post** & \(p\)**-Value** \\ \hline \multirow{2}{*}{RRF} & 0.78 & 0.79 & 0.79 & 0.76 & & 0.8 & 0.8 & 0.8 & 0.79 & 0.7 & \multirow{2}{*}{0.14} \\  & (0.05) & (0.05) & & (0.04) & (0.04) & & (0.02) & (0.03) & & (0.05) & (0.04) & \\ \hline \multirow{2}{*}{LRF} & 0.79 & 0.79 & & 0.81 & 0.7 & & & & & & & \\  & (0.03) & (0.02) & & (0.05) & (0.07) & & & & & & & \\ \hline \multirow{2}{*}{RTA} & 0.77 & 0.75 & & 0.69 & 0.81 & 0.06 & 0.65 & 0.74 & 0.14 & 0.63 & 0.72 & \multirow{2}{*}{0.02} \\  & (0.03) & (0.04) & & (0.05) & (0.03) & & (0.03) & (0.07) & & (0.02) & (0.05) & \\ \hline \multirow{2}{*}{LTA} & 0.71 & 0.74 & & 0.69 & 0.61 & & & & & & & \\  & (0.06) & (0.03) & 1 & & (0.06) & (0.05) & & & & & & \\  & (0.06) & (0.03) & & & 0.76 & 0.85 & & 0.71 & 0.8 & \multirow{2}{*}{0.07} & 0.78 & \multirow{2}{*}{0.14} \\  & (0.06) & (0.04) & & (0.04) & & (0.04) & & (0.04) & & & & \\ \hline \multirow{2}{*}{RBF} & 0.68 & 0.76 & & 0.66 & 0.74 & & & & & & & & \\  & (0.06) & (0.04) & & (0.06) & & & & & & & & \\ \hline \multirow{2}{*}{LMG} & 0.71 & 0.77 & & 0.22 & & 0.73 & 0.76 & 0.02 & 0.68 & 0.75 & 0.07 & 0.67 & 0.79 & \multirow{2}{*}{0.07} \\  & (0.05) & (0.03) & & & (0.04) & (0.05) & & & & & & & \\ \hline \multirow{2}{*}{RMG} & 0.68 & 0.75 & & 0.63 & 0.75 & & & & & & & & \\  & (0.06) & (0.05) & & & (0.11) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stroke participants’ pre- compared to post-training muscle activation for walking conditions (FW, BW, FT-W, BT-W). Means are shown with standard deviation in parentheses. The color-coding highlights significance level: \(p\) < 0.05 (blue).
###### Abstract
The main objectives of this research were as follows:  
1. To develop a comprehensive MCDM framework that accounts for both individual and cooperative contributions of alternatives by integrating AHP/ANP with Shapley values. 2. To demonstrate the applicability and efficacy of the proposed method through a numerical example of budget allocation. 3. To provide insights into the implications of considering cooperative alternatives in resource allocation decisions and compare the results with traditional AHP/ANP methods. In this paper, we put forth a novel method that amalgamates the AHP/ANP with Shapley values to tackle the cooperative alternatives of a problem. This proposed method first discerns the problem structure, e.g., independent or interdependent, and utilizes the AHP/ANP to obtain weights for the criteria and scores of the alternatives. Subsequently, we compute the Shapley values of the alternatives based on the coalition values of the alternatives. Finally, we can synthesize the scores from the results of the AHP/ANP and Shapley values to achieve the ultimate resource allocation decision. The innovation of this study lies in integrating AHP/ANP with Shapley values, offering a comprehensive approach to tackling the nuances of cooperative alternatives. The integration with Shapley values, which calculates the contributions of alternatives based on coalition values, allows for a more nuanced and equitable resource allocation. To illustrate the effectiveness of our approach, we present a numerical example of budget allocation for transportation facilities, showcasing significant insights when Shapley values are considered. This paper contributes to the MCDM field by presenting a novel framework that simplifies complex decision-making and ensures fairer resource distribution. The structure of our paper is organized as follows. Section 2 offers a literature review of the recent developments in addressing the interdependence between criteria and alternatives in MCDM. Section 3 presents our proposed method that combines the AHP/ANP with Shapley values, outlining the theoretical foundation and the step-by-step process for implementing this approach. Section 4 demonstrates the proposed method by applying it to a budget allocation problem for transportation facilities as a numerical example and compares the result with the conventional AHP and ANP. Section 5 discusses the implications of our findings and accentuates the benefits of incorporating Shapley values. Finally, in Section 6, we conclude our study and delineate potential future research directions to further explore the applicability of our proposed method in various fields and scenarios.
## 4 Discussion
### POS and the Timing of GPPmax  
It is worth noting that the POS in this study corresponded to the maximal vegetation index, which represents the timing of the seasonal peak canopy structure, and did not match with the timing of GPPmax. [PERSON] et al. [74] found that the seasonal peak for photosynthesis was earlier than the peak for canopy structure in more than 87.5% of the ecosystems in the Northern Hemisphere (>30 degN), and this mismatch increased due to increasing atmospheric CO2. The timing of GPPmax is important to characterize seasonal carbon uptake [75]. GPPmax is not only affected by canopy structure but also affected by light use efficiency, which is regulated by environmental conditions, and the potential maximum GPPmax is achieved when the densest canopy matches the ideal resource availability [68]. We found the timings of GPPmax were about 2.2 d and 6.7 d earlier than the POS for Period 1 and Period 2 in the SRYR, respectively.
# Near-Optimal Weather Routing by Using Improved A* Algorithm
## 3 Improved A* Algorithm
### Heuristic Function Using Haversine Method  
#### 3.2.1 Optimality Condition of Haversine Function  
[PERSON]'s formula and the haversine function are used for calculating the distance between two points on a spherical surface. [PERSON]'s formula calculates the distance between two points on a surface of a spheroid using two related iterative methods used in geodesy, and the haversine function determines the distance between two points on a sphere given their longitudes and latitudes. [PERSON]'s formula offers higher accuracy than the haversine function, but it is more computationally intensive and slower [22; 23]. In this study, because the ship route optimization required many route cost calculations and did not require extremely accurate distance calculations, the haversine function was used as a heuristic function.  
However, before the haversine function is used as a heuristic function, it is necessary to determine whether it satisfies the optimality condition, including admissibility and consistency, for optimal path finding. In spherical geometry, even though the shortest distance between two points is an arc of a great circle, it is known that triangular inequality holds [24; 25] if it is the length of the minor spherical line segment connecting other endpoints. This kind of heuristic that obeys triangular inequality is called a \"consistent\" or \"monotonic\" heuristic.  
For example, if one arbitrary node \(u\in N\) and the target node \(t\in N\) on the sphere are considered, one can obtain the following relation by the triangular inequality, which satisfies the consistency condition. The haversine function value for the target node is 0, so that the one for any arbitrary node, \(h(u)\), is always less than the distance from any arbitrary node \(u\) and the target node \(t\), indicating that the haversine function is admissible.  
\[\begin{split} h(u)&\leq d^{*}(u,t)+h(t):\text{ \emph{Consistency}}\\ &\leq d^{*}(u,t)+0\\ &\leq d^{*}(u,t):\text{\emph{Admissibility}}\end{split} \tag{2}\]  
Consequently, the heuristic function is admissible if it is consistent. Furthermore, if it is supposed that there exists node \(v\in N\), which is the successor of the node \(u\in N\), the optimality of the A* algorithm can be derived under the condition of the admissible heuristic:  
\[\begin{split} f(v)&=g(v)+h(v)\\ &\geq g(u)+d^{*}(u,v)+h(v)\\ &\geq g(u)+h(u)=f(u):\text{\emph{Admissibility}}\end{split} \tag{3}\]  
Thus, nodes expand in the nondecreasing sense of \(f(n)\), which results in the optimal solution of the A* algorithm. Therefore, because the haversine function satisfies the consistency and admissibility conditions, they were used as the heuristic function to find the optimal ship routes in this study.
## 3 Multi-Scale Pooling with Inception4 Module and PSalign  
We believe that the responses of objects with various sizes on the feature map are also different. However, in Faster R-CNN, the proposals generated by the RPN use the ROI (Region of Interest) pooling layer to extract the corresponding response directly on the output from conv5, in which the size of the receptive field for each neuron is the same. Therefore, the network is not robust to multiscale object detection. In order to solve this issue, we decided to obtain feature maps of corresponding proposals from different hierarchies (conv5, conv4). We then introduced an inception4 [28] module to further explore multi-scale features. As shown in Figure 1, after conv5, we added three convolution layers (kernel size 1) to divide the channels of the feature map into 256, 128, and 128. After 1 \(\times\) 1b, we employed a 3 \(\times\) 3 convolution layer. In order to speed up training, we replaced it with two 1 \(\times\) 3 and 3 \(\times\) 1 convolution layers. Using the same strategy, 1 \(\times\) 5 and 5 \(\times\) 1 convolution layers were added after 1 \(\times\) 1c to substitute 5 \(\times\) 5 convolution layers. After that, the outputs of three stacks were concatenated by a concat layer, and the number of channels was adjusted by a 1 \(\times\) 1 convolution layer to 512. Moreover, we applied a shortcut module, and a ReLu layer was used. Eventually, we used a ROI align pooling layer to project the region of the proposals predicted by RPN to the size of 7 \(\times\) 7. Using an ROI align pooling layer overcomes the misalignment problem of the ROI pooling layer. This can locate the position of proposals more accurately on the feature map by means of quadratic difference, which is very helpful for the detection accuracy of small objects [20].  
In addition, due to the lack of background information in infrared images, training of the network should focus on the foreground information. In this paper, we added a 1 \(\times\) 1 convolution layer after conv4, which outputs a feature map with K\({}^{2}\)(C+1) channels. This means that we divided the proposal into K\({}^{2}\) regions, and each was encoded by (C+1) channels in the feature map. C represents the number of categories [29]. Similar to Light head R-FCN [30], we directly set the category number to 9, increasing the number of channels of feature map to enrich the appearance. We then used a position sensitive ROI align (PSalign) pooling layer to project the corresponding feature map to 7 \(\times\) 7 \(\times\) 10 channels. Furthermore, we used a concat layer to merge the output of the PSalign pooling layer and the ROI align pooling layer. After that, a 1 \(\times\) 1 convolution layer was added to reduce the channels of the feature map to 512. Additionally, we applied two 4096 full-connection layers to encode the information of all feature maps. Ultimately, we employed two full-connection layers for location regression and classification.
Some papers have been published on the effect of the water content in EMs [20] and have observed that the lower the soil resistivity, the lower the fall of resistance from its steady state. However, this work was mostly carried out in a laboratory, and there is still a need to investigate it using field measurements, which can provide more realistic data on the impact of the characteristics of EMs on soil under high-impulse conditions. In [20], a significant fall in the resistance value was noticed as the percentage of water increased from 1% to 10% in sand mixed with 5% sodium chloride (NaCl), which was more than a 90% difference between the resistance at 1% and that at 10%. When [PERSON] et al. [14] carried out tests on a ground rod installed under three conditions--natural, foundation concrete and slurry bentonite--in summer and autumn, using power frequency tests and under impulse conditions with EMs, they noticed that the resistance installed in bentonite had the best performance in autumn and winter due to its high water absorption. In their study [6], an impulse voltage level of up to 11 kV was used, where the ionisation effect may not have been so observable. Due to the various grain sizes and conductivity of EM, the characteristics of ground electrodes installed in EMs with various percentages of water content would be expected to appreciably change. In this paper, a small grid was constructed, where the soil was treated with EM, and some amount of water content was poured on top of the electrode to imitate light and heavy rain in typical tropical countries. The results were discussed in terms of voltage and current traces, impulse impedance, current rise and discharged times. A peculiar observation, where the voltage and current traces swung to opposite impulse polarity during the discharge, was seen for (ii) the ground electrodes with the EM and (iii) the ground electrodes with the EM and water content. Though the traces swinging to opposite polarities at the discharged times has also been observed in several studies [7; 8], no detailed studies have attempted to explore the factors affecting it. Further analysis with computational methods shows that that the magnitude of the swing is dependent on the \(R_{DC}\) and voltage/current magnitudes. This study shows that regarding the interest in fulfilling the design criteria by having a low ground resistance value, it must be acknowledged that other problems may arise, where the inductive effect can become more significant in ground electrodes with a low ground resistance value.
## 3 Results
### Section D: Antimicrobial Use for Varroa Control
More specifically, many of the beekeepers used only two active ingredients (30.6% in the Americas, and 32.2% in Europe), a lower percentage used three of them (16.3% in the Americas, and 10.7% in Europe), while only 2.7% of the American beekeepers and 2.3% of the European beekeepers used four active ingredients annually. The vast majority of the respondents (94.1% in the Americas and 82.7% in Europe) stated that they did not need to obtain a prescription to buy varroacides. As far as acquiring varroacide products, a higher percentage of the American respondents reported obtaining medicines on the internet (36.7%) compared to 23.1% of the Europeans (Table 3). An additional 23.1% of the European beekeepers bought miticides through Agro Chemical Supply or from beekeepers' associations (21.5%). Nearly 26 percent of the beekeepers in the Americas and nearly 17% in Europe purchased medicines in \"local bee supply store retailers\". A smaller percentage of the users in each continent (4.1% of Americas and 15.8% of Europeans) reported making purchases through Veterinarians, Pharmacies, and extension services. For varroa control, both the European (79.4%) and American respondents (68%) agreed that veterinary medicines should be administered simultaneously to all the hives within the apiary (Figure 4). Similarly, all the respondents agreed on the importance of the adoption of all the \"good beekeeping practices\" listed in Figure 4, categorized as \"Medium\" or \"High\"  
Figure 3: Active ingredients, grouped as “Hard” and “Soft” acaricides, used by American and European beekeepers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Purchasing Channel** & **The Americas (\%)** & **Europe (\%)** \\ \hline Internet & 36.7 & 23.1 \\ Local bee supply store retailers & 25.9 & 16.8 \\ Agro Chemical Supply & 15.7 & 23.1 \\ Beekeepers’ Association & 8.8 & 21.5 \\ Other beekeeper & 11.6 & 6.1 \\ Veterinarian & 2 & 7.1 \\ Extension Services & 1.4 & 1.7 \\ Pharmacy & 0.7 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Where the American and European beekeepers purchase medicines against varroa. relevance. Only one of the procedures listed in the survey, \"treat only the varroa affected hives\", showed a lower level of agreement, considering the low percentages reached both in the Americas (27.9%) and in Europe (7.8%) (unpublished data). Regarding the question on the correct administration of the treatments, the majority of the beekeepers in each continent think that treatments against varroa mites are not carried out correctly following the label instructions. Combining all the categories (from \"always\" to \"sometimes\"), about 90% of the respondents indicated that labels were not correctly followed. In detail, the data of American and European beekeepers showed about the same percentages in all categories. On the other hand, only a low percentage (6.9% in the Americas and 8% in Europe) thought that the beekeepers \"never\" use medicines against varroa without following the label instructions.