esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell +
## 3 Controller Design
### Backstepping and STSM Control Design  
Figure 2c depicts the (BS + STSM) control diagram for the two loops, where the inner loop is regulated by the backstepping controller and the outer loop is regulated by the STSM controller. This diagram illustrates how the two control algorithms collaborate to achieve the voltage and current regulation objectives. Integrating these two approaches enables leveraging the specific advantages of each method, thus ensuring enhanced overall control performance. For further information on this control method and parameter synthesis, we recommend consulting Ref. [11]. This source provides a detailed analysis of the method and insights into the parameter synthesis procedure.  
The STSM control law for the voltage loop \((V_{STSM})\) can be defined as  
\[I_{ref}=-\int_{0}^{t}\alpha_{1}sgn(S_{v})dt-\alpha_{2}\sqrt{|S_{v}|}sgn(S_{v}) \tag{57}\]  
where \(sgn(.)\) represents the sign function, and \(S_{v}\) is the sliding surface of voltage. \(\alpha_{1}\), \(\alpha_{2}\) are the designed controller parameters.  
The backstepping control law for the current loop \((I_{BSj})\), \((j=1,\ldots,4)\) can be defined as  
\[\dot{u}_{j}=\frac{C}{C\lambda_{j}-2\beta_{1}|s|^{-1/2}sgn(s)i_{j}}.\Delta \tag{58}\]  
where  
\[\dot{\lambda}_{j}=\frac{c_{1}z_{j}-\bar{I}_{ref}}{1-u_{j}}+\frac{\dot{u}_{j} \lambda_{j}}{1-u_{j}} \tag{59}\]  
and  
\[z_{1j}=i_{j}-I_{ref} \tag{60}\]  
and  
\[s=V_{out}-V_{ref} \tag{61}\]  
\[\left\{\begin{array}{c}\Delta=[c_{1}^{2}-(1-u_{j})^{2}]z_{1j}+[(c_{1}+c_{2}) (1-u_{j})]z_{2j}\\ +\frac{4(1-u_{j})^{2}i_{j}}{L\mathcal{C}}-\frac{(1-u_{j})}{fL\mathcal{C}}V_{ out}+\frac{1}{4}\alpha_{1}|s|^{-3/2}\dot{V}_{out}^{2}\\ -2\alpha_{1}|s|^{-1/2}(1-u_{j})z_{1j}sgn(s)+\frac{\alpha_{1}\dot{V}_{out}^{2}| s|^{-1/2}}{2f\mathcal{C}}\end{array}\right. \tag{62}\]  
where \(c_{1}\) and \(c_{2}\) are the control parameters, and \(z_{2j}\) is a virtual control law that is given as  
\[z_{2j}=\frac{V_{out}}{L}-\lambda_{j} \tag{63}\]
# Vacuum Ultraviolet Single-Photon Positionization of Amino Acids
## 3 Results
### Applications in relative quantitative measurement of analytes  
One drawback of UV-MALDI is the difficulty in the application of quantitative analysis. Shot-to-shot fluctuation, sweet spot effects, large differences in the ionization efficiency of various analytes because of different proton affinities, and suppression effects cause difficulties in the application of UV-MALDI in quantitative analysis. Because the ion signals obtained through VUV-SP-positionization were larger than those obtained through UV-MALDI and no suppression effects were expected in postionization, quantitative analysis performed using the VUV-SP-positionization data was a suitable approach.  
Quantitative analysis of UV-MALDI data for glycine is impossible because of the low ion signal of glycine from conventional UV-MALDI MS. However, quantitative analysis can be straightforwardly performed using VUV-SP-positionization. Figure 5 shows the relative VUV-SP-positionization signals of glycine and histidine from glycine/histidine mixtures with various molar ratios. Glycine parent (_m/z_ 75), glycine fragment (_m/z_ 30), and histidine fragment (_m/z_ 82) ions were considered for the quantitative analysis. The relative ion intensities of both _m/z_ 82 to 30 and _m/z_ 82 to 75 were linearly related to the molar ratio of glycine and histidine, as illustrated in Figure 5a,b, respectively. The result indicated that VUV-SP-positionization can be used for the quantitative analysis of glycine/histidine mixtures.  
The quantitative analysis of tryptophan and phenylalanine was also performed. Both tryptophan and phenylalanine can be detected using conventional UV-MALDI and VUV-SP-positionization. Figure 6 illustrates the relative ion signals (obtained through UV-MALDI or VUV-SP-positionization) of tryptophan and phenylalanine mixtures with various molar ratios. Protonated tryptophan and phenylalanine ion signals were used to analyze the UV-MALDI data. Fr  
Figure 5: Relative VUV SPI ion signals of glycine and histidine from the glycine/histidine mixtures with various molar ratios. The glycine parent ion at _m/z_ 75, glycine fragment ion at _m/z_ 30, and histidine fragment ion at _m/z_ 82 were used for the quantitative analysis.  
\(m\)/z 74 and 120 were used for identifying phenylalanine, whereas one fragment ion corresponding to \(m\)/z 130 was used for tryptophan. Because the sensitivity of VUV-SP-positionization is higher than that of UV-MALDI, the dynamic range of VUV-SP-positionization for quantitative analysis is approximately one order of magnitude larger than that of UV-MALDI (Figure 6).
## 3 Results and Discussion
### The Variation of Micro- and Macro-Elements within AD
#### 3.2.1 Macro-Elements Content
A different trend occurred only in the case of N, for which a reduction in its concentration was observed in the three-component series (R6-R8) compared to that in the control (R1). This fact may be related to the effective N utilization for the synthesis of amino acids and proteins within the AD process. However, the N content in all of the series reached a high value, and was at levels typically observed at Polish WWTPs, ranging between 30.5 to 80.50 g/kg [49; 50; 51]. While comparing digestates from the different AcoD experiments, one exception was noted concerning series R7, with lower contents of Mg, Ca and S versus those achieved for the control. Other crucial nutrients in the further application of digested SS included P and K. The former is known as a catalyst in biochemical reactions in plants, while the latter plays an important role in the resistance of plants against various stress conditions in the environment [52]. In all of the AcoD series, their contents were enhanced as compared to SS digestate where mono-digestion was conducted. It should be pointed out that the contents of both macro-elements in all of the experimental series were relatively high, as compared to those of other studies. Typically, digested sludge contains 13.3-16.6 g/kg of P and up to 10 g/kg of K [28]. This difference may have resulted from the characteristics of the wastewater that was discharged to the municipal WWTP. In some Polish studies, a high content of P, ranging up to 30.00 g/kg, was also noticed [49]. Sulfur is another essential nutrient that participates in the regulation of growth and development in plants [53; 54]. As in the case of the other analyzed nutrients, in all of the AcoD series its content was improved, and reached a relatively high level. At municipal WWTPs, it typically varies between 11 and 13.5 g/kg [41]. Importantly, regarding the most important nutrients, the digestate demonstrated a valuable composition that can compete with commercial fertilizers [55; 56]. Similarly to the main nutrients regarding Ca and Mg, the introduction of co-substrates resulted in their increased contents in digestate, as compared to SS mono-digestion (R1). The content of both macro-elements in digested SS was related to the size of the WWTPs and adopted treatment technology. Generally, their contents did not exceed a level of 30 g/kg. Both metals are valuable and desirable components of fertilizers. They play an important role in physiological and biochemical functions of plants, e.g., growth and photosynthesis [57]. Additionally, previous studies indicated that the application of alkaline substances to contaminated soils may reduce the content of HMs [58; 59]. In the case of Na, a lower content of this macro-element was obtained in R7 as compared to the control. In other cases, minor increases, not exceeding 10%, were found.
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 4 Weighted Coplanar Feature Clustering
### Fast Estimation of Normal Map  
Since superpixel segmentation is used as a preprocessing method, it requires a fast normal map estimation algorithm. However, the point cloud algorithms estimate the normal map with either low accuracy, a lot of noise, or low speed. That is because these algorithms choose a number of neighboring points to fit a local plane to determine each normal vector. The increasing number of neighboring points can improve the accuracy of normal estimation but slows down the computing speed. Therefore, a fast, normal map estimation algorithm was used.  
This algorithm uses three planes retrieved from the point cloud as the calculation inputs. A point cloud generated from a given depth has the same resolution as the depth map, and each point has a coordinate (\(x\), \(y\), \(z\)); therefore, \(x\)-planes, \(y\)-planes, and \(z\)-planes corresponding to the point cloud can be obtained. In addition, the pixels in the \(x\)-, \(y\)- and \(z\)-planes, depth images, and color images display a one-to-one correspondence.  
After achieving the three input planes, it is time to estimate the normal map. First, a squared mean filter is applied on each plane to remove the noise, because our normal estimation algorithm is sensitive to noise. As a result, the three smoothed planes, denoted by \(\mathbf{u}\), \(\mathbf{v}\) and \(\mathbf{w}\), have the same resolutions as the depth and color images. Second, \(\mathbf{u}\), \(\mathbf{v}\) and \(\mathbf{w}\) are separately filtered with horizontal and vertical Sobel filters so that the derivatives for horizontal changes and vertical changes, expressed by \(G_{x}(\cdot)\) and \(G_{y}(\cdot)\), can be acquired. Finally, the cross-product of the horizontal and vertical derivatives is calculated, which can be represented as follows:  
\[\mathbf{n}=\left[\begin{array}{c}G_{x}(\mathbf{u})\\ G_{x}(\mathbf{v})\\ G_{x}(\mathbf{w})\end{array}\right]\times\left[\begin{array}{c}G_{y}( \mathbf{u})\\ G_{y}(\mathbf{v})\\ G_{y}(\mathbf{w})\end{array}\right]. \tag{4}\]  
Additionally, the cross-product is the normal map, because it is perpendicular to both derivatives simultaneously.
## 3 Towards Autonomous SC with Tenderfone
### Autonomy vs. Security  
Trust issues may arise since byzantine interpreters or validators may technically forge spurious transactions out of thin air, with the purpose of triggering a smart contract in an illegitimate way. This is always possible, as spontaneous transactions are generated by interpreters after consensus has been achieved on a block, and nothing prevents interpreters from cheating after that. More importantly, this is a potential violation of smart contract computational autonomy, other than an undesirable security threat--for instance, vulnerable to DOS attacks.  
To discriminate among legitimate and spurious, possibly forged, spontaneous transactions, Tenderfone exploits the existence of multiple copies for any legitimate spontaneous transaction. In particular, Tenderfone interpreters not misbehaving guarantee that only spontaneous transactions  
Figure 6: Request–response protocol enacted by each validator–interpreter couple in Tenderfone to execute the transactions contained in a block. Notice that spontaneous transactions are generated by interpreters and returned to validators.  
for which \(V\) copies have been observed are actually executed, and their effects persist on the blockchain, otherwise they are just registered on the blockchain unexecuted. In other words, from a smart contract perspective, its outgoing messages are actually considered \"sent\" only when the \(V\)th copy of the corresponding transaction has been committed on the blockchain by the consensus algorithm. Faulty or malicious interpreters may of course still forge transactions, but unless other interpreters are faulty or malicious too, they will be simply ignored.  
In practice, spontaneous transactions are cryptographically signed in Tenderfone by the interpreter generating them, even if their sender's identifier is a SC one. This is how spontaneous transactions are distinguished from ordinary ones. To prevent the forgery of spurious transactions, Tenderfone actually waits for \(V\) copies to appear on the blockchain, and requires such copies to be signed by all \(V\) interpreters.  
As a last note, it is worth to be mentioned that Tenderfone is not just a theoretical model. Despite being a proof of concept, Tenderfone's ambition is to become a full-fledged smart contracts-enabled blockchain technology. To this end, it includes a number of practical features that are not discussed in this paper since they are not strictly concerned with autonomy. For instance, concerning security, the current implementation comes with a Role-Based Access Control (RBAC) sub-system based on public-key cryptography, which makes every entity composing the system (including end users) validators, and smart contracts to be individually accountable for their actions, thus easing the task of recognizing legitimate spontaneous transactions. Furthermore, concerning the termination of callbacks execution, Tenderfone leverages strict temporal bounds to prevent attackers to perform DoS attacks by deploying and triggering infinite recursion within some smart contract KB.
## 4 Results and Discussion
### Combined ORC-Single Evaporator VCR Cycle Results
Using R600a, T-s and P-h diagrams of the system can be observed from Figure 10a,b, respectively. Exergy destruction rate and exergy destruction ratio of each component in the system can be seen from Figure 11a,b, respectively. Calculations were performed using input values in Table 2 at 25 \({}^{\circ}\)C ambient temperature. It can be seen from these figures that highest exergy destruction in combined system occurs in boiler with 35%. In addition, the component that has the highest exergy destruction in refrigeration cycle is compressor. Figure 8: Influence of \(\eta_{\text{turb}}\) on CO\({}_{\text{sys}}\) (**a**) and \(\eta_{\text{ex,sys}}\) (**b**) in combined ORC-single evaporator VCR cycle. Figure 9: Effect of T\({}_{0}\) on CO\({}_{\text{sys}}\) in combined ORC-single evaporator VCR cycle. Second law efficiency and degree of thermodynamic perfection for each component in the system can be observed from Figure 12a,b, respectively. Figure 12a explains that the pump has the greatest second law efficiency in combined system. The reason why second law efficiencies of both of the condensers are zero is that exergy transferred with heat in condensers are equal to zero since their  
Figure 10: T-s (**a**) and P-h (**b**) diagrams for combined ORC-single evaporator VCR cycle. heat sink temperatures are equivalent to ambient temperature. While heat is transferred to ambient, exergy of the working fluid is totally lost in the condenser. In addition, there is zero exergy transfer in expansion valve. There is only exergy destruction in the valve and so its second law efficiency is equal to zero. The component giving the highest degree of thermodynamic perfection is pump according to Figure 12b. If Figure 12a,b are compared, it can be seen that there is a difference between second law efficiency and degree of thermodynamic perfection of some components. This situation results that second law efficiencies of components can be improved as much as their difference from degree of thermodynamic perfection, thereby some more exergy can be gained. Figure 11: Exergy destruction rate (**a**) and exergy destruction ratio (**b**) of components in combined ORC-single evaporator VCR cycle.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Predictive Coordination Between NTN and RIS
#### 4.2.1 Channel Prediction with Mobility Awareness  
We implement a recurrent neural network (RNN)-based predictor that leverages a sliding window of past channel estimates \((h(t-1),h(t-2),\dots,h(t-n))\), with the help of user trajectory, velocity vectors, and known RIS and NTN locations.  
This model predicts the future states of the cascaded channels \(h(t+1)\) and \(g(t+1)\), as well as the direct path \(h_{d}(t+1)\), allowing preoptimization of the RIS phase-shift matrix \(\Phi(t+1)\). The CSI prediction model is trained offline on synthetic and simulated NTN mobility data and updated online using observed channel behavior.
## 7 Conclusions  
In a scenario of the virtualization of WSNs, physical networks can undergo dynamic change, such as the addition or removal of nodes or links or resource assignment to fulfill new user requests. Therefore, reliability is important even when such environmental changes occur. To tackle these problems, we show an overall architecture of constructing and running VWSN services with considering the environmental changes. We define that reliability consists of robustness and adaptivity. In our previous work, we proposed a method for constructing a robust VWSN topology against node failure. In this paper, we conduct a simulation of the practical situation to evaluate the adaptivity of our proposed VWSN topology in consideration of an actual environment.  
The results of the simulation experiments showed that the adaptivity of the VWSN topology constructed by BICM(ll,LL) was the highest against target attack, which is consistent with the robustness results. However, when there is a first-tier module whose central area is densely connected, the zeroth-tier virtual topology in the first-tier module becomes fragmented quite easily. To address this problem, it is necessary to more deeply consider the method of modular division and the modular structure. We intend to do so in future work.  
We also discussed the memory needed for the tables used in our proposed routing. In our evaluation environment, the size of a zeroth-tier routing table is the most dominant.  
However, the number of entries of each table depends on the method of modular division, the number of connected modules and nodes and the number of virtual long-distance links. Therefore, further investigation into the relation between the chosen method of modular division and memory efficiency will be needed.  
In this paper, we analyzed the properties of a virtual topology composed of only sensor nodes. Therefore, we need to consider the following in future work. First, the method of realizing a virtual link in a physical network should be investigated; packets forwarded along a virtual long-distance link should be conveyed with only a short delay. As candidate methods for this, we intend to consider creating directional beams, increasing omnidirectional transmission range and multi-hop forwarding with variable priority. Second, because there may be multiple demands for constructing VWSNs that compete for resources, such as energy, memory and bandwidth, it is necessary to consider a method that can construct resource-efficient VWSN topologies. Third, we hope to create a protocol for evolving the VWSN topology in response to environmental changes, such as changes in traffic patterns. Due to the modular structure, a small adjustment of a few virtual links between modules should be sufficient to achieve that.  
This work was supported by a Grant-in-Aid for JSPS Fellows, No. JP26-1639.  
[PERSON], [PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] performed the experiments and analyzed the data. [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
###### Abstract  
The size of the sound field reconstruction area has an important influence on the beamforming sound source localization method and determines the speed of reconstruction. To reduce the sound field reconstruction area, stereo vision technology is introduced to continuously obtain the three-dimensional surface of the target and reconstruct the sound field on it. The fusion method can quickly locate the three-dimensional position of the sound source, and the computational complexity of this method is mathematically analyzed. The sound power level can be estimated dynamically by the sound intensity scaling method based on beamforming and the depth information of the sound source. Experimental results in a hemi-anechoic chamber show that this method can quickly identify the three-dimensional position of the moving source. When the depth of the moving sound source changes, the estimated sound power is more stable than the sound pressure on the microphone.  
moving sound source; beamforming; 3D surface; sound state +
Footnote †: journal: Applied Sciences
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 5 Performance Evaluation
### Simulation Setup  
In this section, we develop a simulator in Python language and implement our scheme to evaluate the performance of SUDC. The version of Python Interpreter is Python 3.9. To evaluate its performance, we generate a random topology containing 100 nodes, which is stored as an adjacency matrix. On the basis of this topology, we hypothesize the transmission delay of each link and a comprehensive metric for resource overhead during packet transmission within each link segment.  
The comprehensive metric for various resource overheads on any link ranges from 5 to 50, including 5 and 50. The transmission delay of any link ranges from 5 ms to 15 ms, including 5 ms and 15 ms. We assume a time interval of 1 ms between consecutive policy transmissions from the controller. Twenty nodes with the lowest degree in the topology are selected as entry switches. There are eight distinct test sets, each containing 5000/10,000/25,000/50,000/100,000/250,000/500,000/1,000,000 SRv6 TE policies, respectively.  
Subsequently, we employ the Shortest Path First (SPF) algorithm to compute the least comprehensive resource overhead paths between any two nodes, simulating the SRv6 TE policy set derived from the controller calculation. These simulated SRv6 policies are stored, and eight test sets of different sizes are randomly drawn from all these simulated SRv6 policies. We utilize the SPF algorithm to calculate the minimum transmission delay paths from the controller to the other nodes, therefore simulating the packet propagation trajectory when policies are unicastably distributed by the controller to each node individually. We utilize the Prime algorithm to build a transmission delay minimizing the spanning tree, including all nodes, and, therefore, simulating the packet propagation trajectory when the SID blocks are distributed by the controller to the nodes in a multicast manner. We select some of the switches within the topology as ingress switches in order to evaluate the performance of the two-phase update scheme.  
Finally, simulations are conducted on the three metrics of ordered update, two-stage update, and SUDC. To test the performance of SUDC, the policy splitting algorithm is executed on each test set to obtain multiple policy distribution SID blocks before simulation.