esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Economic Analysis  
The total revenue requirement method is applied to evaluate the processes from an economic point of view. Figure 7 presents a comparison of the specific total capital investment and the cost per ton of produced methanol. The cost is calculated as the ratio of the TRR and the capacity of the plant.  
The total capital investment for Case I amounts to 520 mil. US$, while it is 420 mil. US$ for Case II and 640 mil. US$ for Case III. As shown in Figure 7, the total capital investment per ton of methanol is the highest for Case I. The plant design requires large air compression units and an SMR furnace, which are both capital intensive. Also in case III, the largest investment is assigned to the air- and syngas compressors (CM-03-CM-06) as well as the furnace R-06. The investment for the synthesis reactor R-07, the DMR R-04 and the GHR R-05 is of minor significance, because these components are designed as tubular shell and tube heat exchangers with high temperature materials. Despite of the higher production capacity of the plant in Case II, the investment is lower than for the design in Case I. This is due to the smaller sizes of the air compression unit, the furnace, and all other components in the flue gas line. The current price (May 2017) per ton of methanol of the main supplier Methanex on the largest sales market Asia is 350 US$. Taking this into account, the plant in Case I is economically  
Figure 6: Comparison of the cases using results from the exergy analyses.  
Figure 7: Comparison of the design cases using some results from the economic analyses.  
not feasible. Due to the economies of scale effect, the plant designs with a combination of different reforming technologies at higher capacities can produce at a competitive cost.
## 2 Literature Review
Existing research on MAC and routing protocols for UWSNs tackles notable hurdles including high propagation delay, decreased data rates, elevated energy consumption, and collision control. Handshaking-based protocols and receiver-initiated protocols improve channel utilization and reduce collisions but suffer from synchronization issues as well as increased latency. Optimized Aloha variants improve throughput but perform badly in dense networks since they entail constant collision risks. Reinforcement learning-based MAC protocols like Q-learning methods such as DR-D3 QN-MA and MR-SFAMA-Q are time-slot allocation and multi-receiver handshaking flexible but are not implemented in real-world applications, are plagued by scalability issues, and have high computational complexity. Hybrid MAC-routing methods like EE-UWSNs are energy efficient but remain limited in multi-hop transmission and real-world application. TDMA-based scheduling reduces collision rates but is wasteful in case of changing traffic conditions, while traffic-aware MAC protocols optimize fairness but lack the integration of dynamic routing. Contention-free MAC gives priority to emergency data but leads to data starvation for low-priority traffic, and spatially fair MAC offers improved network stability but lacks adaptability with changing network conditions. While the current literature has made significant contributions, most of them fail to include deep reinforcement learning (DRL) techniques, for instance, DQN, for scalability and efficiency enhancements. The proposed work aims to overcome this by designing a DQN-augmented MAC-routing model that adjusts both layers dynamically to improve scalability, efficiency in multi-hop communication, and energy efficiency at reduced computational complexity. In contrast to conventional RL techniques that require enormous processing capacities, our method employs optimized state-action representations that reduce complexity without compromising on strong network performance. To tackle the gap between theoretical models and real-world applications, we engage in intense simulations inspired by real-world underwater communication scenarios to achieve a more efficient, scalable, and energy-aware underwater communication system.
## 4 Manipulability Maximization and Constraints Satisfaction
### Joint Position Constraints
#### 4.2.1 Joint Limits Avoidance  
To construct the weighting matrix for joint limits avoidance \(W_{Jlim}\), a well-known criterion function [17] is used:  
\[H_{Jlim}(\mathbf{q})=\sum_{i=1}^{n_{i}}\frac{1}{4\gamma}\frac{(q_{i}^{+}-q_{i}^{-}) ^{2}}{(q_{i}^{+}-q_{i})(q_{i}-q_{i}^{-})},\]  
where \(q_{i}\) is the \(i\)th joint angle, \(q_{i}^{-}\) and \(q_{i}^{+}\) are its lower and upper limits, respectively, and \(\gamma\) is a scalar constant that adjusts the rate of change of \(H_{Jlim}(\mathbf{q})\). This criterion function increases as the joint gets closer to its limits and goes to infinity at the joint bounds. The elements of the gradient of this function are given by  
\[\frac{\partial H_{Jlim}(\mathbf{q})}{\partial q_{i}}=\frac{1}{4\gamma}\frac{(q_{ i}^{+}-q_{i}^{-})^{2}(2q_{i}-q_{i}^{+}-q_{i}^{-})}{(q_{i}^{+}-q_{i})^{2}(q_{i}-q_ {i}^{-})^{2}}. \tag{18}\]  
Each element \(\partial H_{Jlim}(\mathbf{q})/\partial q_{i}\) is equal to zero if \(q_{i}\) is at the middle of its joint range and goes to infinity at either limit.
## 5 The Simulation Validation and Results Analysis
### The Effect of Different Communication Topology Optimization Strategies
#### 5.2.1 Group A: The Minimum Spanning Tree Topology  
Figures 18 and 19 show the trajectories at 10 s and 100 s, respectively. The transmission probabilities of \(A_{1}\) and \(A_{2}\) to base station are shown in Figure 20. The trace of the FIM for \(A_{1}\), \(A_{2}\) and base station are shown in Figure 21. We can conclude that, due to \(A_{2}\) being closer to the base station than \(A_{1}\), the communication topology structure based on the weighted minimum spanning tree is \(\{A_{1}\to A_{2}\to B\}\). With \(A_{2}\) acting as the relay of \(A_{1}\), \(A_{2}\) should to make a tradeoff between  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**UAV \(A_{i}\)** & **Position (\(x_{i},y_{i}\))/(m)** & **Heading Angle \(\
abla_{i}\)/(c)** \\ \hline \(A_{1}\) & \((-800,\,200)\) & 45 \\ \(A_{2}\) & \((-400,\,-700)\) & 180 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The initial settings of the two UAVs in Scenario 2.  
communication and sensing. Therefore, \(A_{2}\) finds the optimal location \(O_{2}=(-520.4,\)\(-557.6)\) m, where the information gain and the probability of a successful transmission with the base station are both optimal. Due to the dynamic constraints, \(A_{2}\) continue to orbit around the optimal position \(O_{2}\). At the same time, \(A_{1}\) continue to orbit around the target \(T\).  
Figure 19: Trajectories of UAVs at \(t=100\) s (Group A in Scenario 2).  
Figure 20: The probabilities of successful transmission of \(A_{1}\) and \(A_{2}\) to base station (Group A in Scenario 2).
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 2 Study Area  
China is an ideal test bed for the development of new hydrological monitoring methods because the country hosts several major river systems (Yangtze, Yellow River, Pearl River, Songhua River, and Heilongjiang-Amur River) that span a wide variety of climatic and physiographic conditions, as well as major water infrastructure projects. China is home to roughly 6% of the world's total freshwater resources, but accommodates 20% of the global population [16]. Especially during the past decades, the combinations of unevenly distributed water resources, rapid economic development and climate change have pushed China towards a situation with increasingly severe water scarcity [17]. To date, the increasing scarcity of water resources has not been effectively managed, with existing laws and regulations usually focusing on principles, but lacking mechanisms and procedures for enforcement [18]. Better and more timely information on surface water dynamics can help decision-makers and practitioners to implement more evidence-based planning, evaluation, compliance monitoring, and provide statistics for reporting in response to the global water agenda.  
Many studies have demonstrated how EO can be applied to investigate various aspects and drivers of surface water changes in China [19; 20; 21]. In 2014, the first complete picture of inland water bodies in mainland China was published using Landsat imagery acquired over the 2005 to 2008 period [22]. This has been followed by additional studies also using Landsat data to provide a 2015 nationwide map of surface water extent [23] and to characterize long-term changes to inland water bodies [24]. Satellite altimetry data has been used in combination with surface water extent maps from JRC-GSWE to assess the spatial variation of lake and reservoir water storage [25]. Most recently, a 10 m monthly Water Body Dataset was generated from Sentinel-1 imagery, yet due to over estimations caused mainly by snow, the final product was constrained to be within the maximum water extent of JRC-GSWE [26]. To the best of the author's knowledge, there has been no attempt to use an optical-SAR sensor fusion approach to map the extent and track the recent changes in China's surface water resources.
## 2 Related Research
### LEACH Protocol for WSN
#### 2.1.1 Setup Phase  
In the setup phase, each sensor node independently decides whether to become a CH for the current round by generating a random number between 0 and 1 and comparing it to a predefined threshold. If the number is below the threshold, the node becomes a CH; otherwise, it remains a normal node, allowing for balanced energy distribution across the network. That is, the CH is randomly elected by the probability threshold equation, and the cluster configuration is performed.  
The selection of CHs following this manner leads it to distribute energy between all the network nodes. The probability threshold \(T(n)\) used to elect CHs for a node \(n\) in the LEACH protocol is defined to ensure that each node has a fair chance of becoming a CH while balancing energy usage across the network. The threshold function typically can be expressed as Equation (1), and it has a value between 0 and 1.  
\[T(n)=\left\{\begin{array}{cc}\frac{p}{1-P\times\left({\it{mad}}\left(\frac{p }{p}\right)\right)}&\mbox{if}&n\in G\\ 0&\mbox{otherwise}\end{array}\right. \tag{1}\]  
where \(P\) is the desired percentage (probability) of CHs in the network, and this determines the portion of the nodes that should act as CHs in each round, allowing the network to regulate the number of CHs effectively. The variable \(r\) is the current round or epoch number, and \(G\) is the set of nodes that have not yet been selected as CHs in the last \(1/P\) rounds. By ensuring that only nodes in \(G\) are considered, LEACH avoids repeatedly selecting the same nodes as CHs, balancing the energy consumption over time. The variable \(({\it{mad}}(1/P))\) helps to cycle through the nodes, ensuring each node has a turn at becoming a CH approximately once every \(1/P\) rounds. It resets every \(1/P\) rounds, providing fair CH rotation across nodes. When a node n generates a random number between 0 and 1 at the start of each round, it compares this number to \(T(n)\). If the random number is less than \(T(n)\), the node elects itself as a CH for that round. This approach maintains a balanced and probabilistic selection of CHs, distributing energy consumption evenly across the network nodes over time.  
After the CHs are elected by Equation (1), the CHs broadcast advertising messages to notify surrounding nodes of their status. Each sensor node then selects a CH to join based on the signal strength received from nearby CHs, with stronger signals indicating closer CHs. When a node decides on a CH, it sends a request message to the chosen CH, asking to join its cluster. Once clusters are established, each CH provides its members with a TDMA  
Figure 1: The formation for the cluster round in the LEACH protocol and the architecture of LEACH for the WSN.  
schedule, allowing each node to know its designated time slot for transmitting collected data to the CH.
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 2 Numerical Calculations  
The optimization parameters inherent to DC SQUID device design typically encompass factors such as device noise and energy resolution. In this section, we will derive expressions specifically pertaining to these design parameters. Furthermore, our study confines its scope to uncoupled SQUIDs, specifically focusing on the fundamental \"bare\" DC SQUID device model solely comprising washers.
# An Efficient AdaBoost Algorithm with the Multiple Thresholds Classification
## 2 Background
### AdaBoost  
AdaBoost is an effective ensemble learning algorithm that can take full advantage of a limited number of training examples by updating the weight of training examples. The main steps of AdaBoost are presented in Algorithm 1.  
```
1. Input: Training dataset: \(S=\{(X_{1},Y_{1}),(X_{2},Y_{2}),\cdots,(X_{m},Y_{m})\}\); Weak Learn algorithm; Ensemble size T.
2. Initialization: Initialize the training set with uniform weight distribution \(\omega_{i}^{1}\):\(\omega_{i}^{1}=\frac{1}{m},i=1,2,\cdots,m\).
3. Do for\(i=1,2,\cdots,T\) (3.1) generates a weak classifier based on the weak classifier learning algorithm with current weight distribution \(\omega^{t}\); (3.2) calculate the weighted training error \(\epsilon_{t}\): \(\epsilon_{t}=\sum_{i=1}^{m}\omega_{i}^{t}\), \(Y_{i}\
eq h_{t}(X_{i})\); (3.3) assign\(h_{t}\) a weight of \(\alpha_{i}\): \(\alpha_{t}=\frac{1}{2}\ln(\frac{1-\epsilon_{t}}{\alpha_{t}})\); (3.4) update the weights of the training examples: \(\omega_{i}^{t+1}=\frac{\omega^{t}\exp(-\alpha_{i}y_{i}\cdot h_{t}(X_{i}))}{ \sum_{i=1}^{m}\omega_{i}^{t}}\); \(Z_{t}=\sum_{i=1}^{m}\omega_{i}^{t}\cdot\exp(-\alpha_{i}\cdot y_{i}\cdot h_{t} (X_{i}))\) is the normalization.
4. Output: the ensemble classifier: \(f(X)=sign(\sum_{i=1}^{T}\alpha_{t}\cdot h_{t}(X))\).
```  
**Algorithm 1** AdaBoost  
In training, dataset \(S\), \((X_{i},Y_{i})\) is the \(i\)th example with \(X_{i}\in R^{d}\) showing the attributes and \(Y_{i}\in\{-1,1\}\) doing the label \((i=1,2,\cdots,m)\), and the weight distribution over all samples is initially set uniform in step 2. Then, AdaBoost calls Weak Learn algorithm repeatedly in a series of iteration as shown in step 3. In the \(t\) th iteration, the Weak Learn trains a classifier \(h_{t}\) and the distribution \(\omega^{t}\) is updated after each iteration according to the prediction results on the training samples. \"Easy\" samples which are correctly classified by \(h_{t}\) obtain lower weights, and \"hard\" samples that are misclassified become higher weights according to step 3.4.  
In the next iteration, there will be a new classifier \(h_{t}\) in case of a new distribution \(\omega^{t+1}\). Due to this, AdaBoost focuses on the samples with higher weights, which seem to be harder to use for the Weak Learn algorithm. It continues for T iterations, and, finally, AdaBoost linearly combines each component classifier to form a single final hypothesis \(f\).
## 3 Experimental Study on In-Service LG Beams
### Derivation of Experimental Fundamental Frequencies  
The analysis of the experimental results was based on post-processing of the collected acceleration-time data, see Figure 10. Given the availability of a single control point only for each test specimen, special care was spent for the fundamental frequency of LG beams, disregarding higher experimental modes, or vibration shapes and damping related issues.  
Figure 10: Examples of test records: (**a**) time-acceleration data and (**b**) Power Spectral Density (PSD) function for selected specimens.  
Figure 9: Example of delaminations (i.e., bubbles and shadows) for a selection of tested LG beams (photos by [PERSON], courtesy of So.Co.Ba.).  
All of the LG specimens proved to offer a beam-like behaviour, but strictly related to the effect of mechanical supports and PVB layers. In Figure 11, for example, the experimental frequencies are proposed for the tested beams, as a function of \(L_{0}\). Analytical estimates from Equation (2) are also collected, as obtained for LG members having nominal experimental dimensions, but classical boundaries (S-S or C-C). Disregarding the uncertain PVB shear stiffness of the test specimens, moreover, three configurations are analytically taken into account in Equation (2) and Figure 11, that is the limit _\"layered\"_ and _\"monolithic\"_ theoretical configurations, and the \(\mathrm{PVB_{A}}\) properties of Figure 3.  
It is worth of interest that most of the test predictions are comprised within the lower limit of S-S beams with \"rigid\" shear connection (\"_monolithic_\" curve of Figure 11a) and the upper limit of C-C beams with \"weak\" mechanical bonding between the glass layers (\"_layered_\" curve of Figure 11b).
## 4 Gearbox Noise Signal Analysis
### Experimental Signal Analysis  
To reduce the influence between the sources, the signals were measured near-field. The measured near-field sound pressures of the gearbox \(x\) and the engine \(y\) are shown in Figure 15. The FT of \(x\) and \(y\) are shown in Figure 16, and the traditional correlation coefficient was calculated as shown in Figure 17. The most common signals of the rotating machinery were the fundamental frequency and the multiples of the fundamental frequency. As can be seen in Figure 16, the amplitude peaks of gearbox occurred mainly at 86.25 Hz, 173.75 Hz, and  
Then, we applied the ST and the SSST to \(x\) and \(y\), and the TF spectra obtained are shown in Figure 18. As can be seen, the ST energy below 500 Hz was mainly gathered at the frequencies around 86 Hz and 173.65 Hz, which corresponds well with multiple rotating frequencies. For the SSST, the bandwidth around 86 Hz and 173.65 Hz was much smaller, showing the advantages of the SSST. The energy distributions on the TF spectrum were concentrated around the real instantaneous frequency of the signal. Then, we carried out the SSST-TD TF correlation analysis. The correlation coefficient is shown in Figure 19.  
Figure 19: The SSST-TD TF correlation coefficient between \(x\) and \(y\).  
Figure 18: The TF representations of signals: (**A**) the ST of \(x\), (**B**) the ST of \(y\), (**C**) the SSST of \(x\), and (**D**) the SSST of \(y\).  
Taking 86.25 Hz and 173.75 Hz as an example. From the ST and the SSST, it can be seen that the signals were basically continuous at 86.25 Hz, so the correlation coefficient was also expected to be continuous. The result shows consistency with that of the traditional method, as shown in Figure 20A. However, there were many discontinuities and fluctuations near 173.65 Hz, which means the correlation coefficient was not as continuous as that at 86.25 Hz, as shown in Figure 20B. The other main frequencies can also be analyzed similarly.  
As previously analyzed, the correlation coefficient varied with the phase. The ST/SSST time frequency spectrum should be considered to judge the correlation between the signals. Therefore, the traditional method is the overall correlation coefficient, which cannot reflect the time-varying characteristics, while the SSST-TD TF correlation method is able to obtain the time-varying correlation and obtain a better local correlation between the signals.
## 4 Angle Selection and Ray Categorization  
Our proposed transmission angle (beam) selection technique aids nodes in conserving energy by minimizing beam overlaps and ensures maximum communication coverage by eliminating shadow zones. The balance of this section presents how to overcome shadow and overlapping zones by exploiting the beamforming capability of the used acoustic transducer to select best angles for establishing communication links. Then we show how nodes can individually map the 3D operational environment into an equivalent 2D one that captures the SSP along the depth. Once the 2D environment with the established links is obtained, nodes begin the process of classifying links based on the experienced propagation path. Finally, nodes divide the underwater environment into distinct regions based on the observed SSP coupled with the ray types. Specifically, each region is obtained by determining the coverage area of distinct beam types.
## 2 FES for SCI
### FES Rowing and Cycling
#### 2.3.1 FES Rowing  
[PERSON] et al. reported the combination of rowing with FES as a hybrid exercise for people with SCI in 1993 [43]. The hybrid FES rowing system itself was developed by [PERSON] and [PERSON] and co-workers, who reported that using the RowStim II system significantly increased rowing distance, peak oxygen consumption, and peak oxygen pulse in people with SCI [44; 45]. The hybrid system was developed to enable participation in a major international rowing event in 2004 [46].  
Our group also developed an FES rowing machine for preventing lower limb muscle atrophy and improving overall fitness for paraplegic patients after SCI [47]. Using this machine, the upper limbs perform voluntary movements whereas the paralyzed lower limbs perform rowing movements via FES, which are controlled by a switch attached to the handle. In paraplegic patients, FES stimulation can be performed in smooth synchronization with the rowing motion under an appropriate load [47] (Figure 2). The rowing motion exercises both the upper and lower limbs, as a whole-body exercise; in contrast, cycling exercises only the lower limbs. The FES-rowing exercise was significantly more effective at increasing peak oxygen consumption during exercise compared with arm-only exercise [44; 48; 49]. A recent systematic review found that the FES rowing system was a viable exercise system for individuals with SCI that can improve cardiovascular performance and reduce bone density loss [50].
# A Simple Alternative Method for Preservation of 2-Methylisoborneol in Water Samples
## 3 Results and Discussion  
### Impact of Chlorine on 2-MIB Concentration in Deionized Water  
Figure 1 shows the effect of the chlorine dosages on the concentration changes in the 2-MIB in deionized water spiked with 100 and 20 ng/L of 2-MIB and stored at 4 and 25 \({}^{\circ}\)C. As shown in the figure, under all testing conditions, within 14 days, the 2-MIB concentrations were only changed by 5%-16%, suggesting that chlorine and permanganate are not able to destroy 2-MIB at the tested dosages. [PERSON] et al. [22] studied the destruction of 2-MIB and geosmin by chlorine, permanganate, and two other oxidants. Their results indicated that under 10 mg/L dosages, only 10% of the 2-MIB  
was destroyed, which is similar to our observation. In addition to the effect of oxidation, in the current experimental setup, other loss mechanisms for 2-MIB, such as volatilization and biodegradation [32], were also insignificant.  
In the current study, deionized water was used and was expected to be free from or low in bacteria. Although 2-MIB is known to be biodegradable [27], biodegradation was expected to be negligible, leading to the results of almost constant 2-MIB concentrations in the samples through the experimental period. Another reason to lower 2-MIB biodegradation in the system is the addition of oxidants. Both chlorine and permanganate are known to be able to inactivate bacteria [33]. In the current experimental systems, the residual chlorine and permanganate (shown in Appendix A Figure A1) within the experimental time remained relatively high, all \(>\)1 mg/L. Considering the unfavorable growth conditions due to high oxidant concentrations, the bacteria concentration present in the water was therefore expected to be even less, and low biodegradation in the system was also predicted.  
Since both permanganate and chlorine have been shown to have the potential to be used as chemical preservatives for 2-MIB in deionized water, i.e., low reaction with 2-MIB and no biodegradation in the samples, the two oxidants were further tested for preservation of 2-MIB.