esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of the Current Birmingham PEPT Cameras
## 3 Development of Positron Cameras at Birmingham
### SuperPEPT  
SuperPEPT was constructed principally by reconfiguring components from an ECAT HR+ PET scanner. As originally configured, this scanner comprised 24 buckets, each consisting of 12 high resolution BGO blocks, each approximately 32 \(\times\) 35 \(\times\) 30 mm\({}^{3}\) and subdivided into 64 pixels 4.0 \(\times\) 4.4 mm\({}^{2}\). To extend the FOV, in SuperPEPT we have replaced some of these high-resolution blocks with the lower resolution blocks from an EXACT31 scanner, which are 52 \(\times\) 52 \(\times\) 20 mm\({}^{3}\) and divided into 64 6.4 \(\times\) 6.4 mm\({}^{2}\) pixels.  
As shown in Figure 2, SuperPEPT comprises three cylindrical sections: a central section made with the original high-resolution HR+ blocks and two end sections made with the same electronics but the lower resolution EXACT31 blocks. The internal diameter is approximately 40 cm throughout. The central section consists of 16 buckets, configured as six rings of detector blocks with 32 blocks per ring, and has an axial length of 234 mm. Each end section consists of four buckets of EXACT31 blocks, configured in two rings (24 blocks per ring) and has an axial length of 110 mm. Axial gaps of 45 mm between the central section and the end sections bring the overall axial length to 544 mm.  
To increase the effective efficiency of the detectors, all the blocks were operated with a lower-level discriminator (LLD) value of 150 keV rather than the value of 350 keV usually adopted for PET imaging. Data from the HR+ coincidence processor are acquired in listmode using a new data-acquisition system (Scintron 7.2, from MiE GmbH, Seth, Germany). Initial results from SuperPEPT operating with only the central section and one end section were recently reported [7]. Since then, the second end section has been brought into use. A full report on the performance of the complete system will be published shortly; meanwhile, this paper presents its key parameters.  
SuperPEPT has an overall FOV of 40 cm diameter and 54 cm axially, comparable to the ADAC Forte. For a source at the centre of the FOV, the central section has a geometrical acceptance of approximately 0.5 and the addition of the end sections increases this to approximately 0.67.
## 2 Related Works
### Camera Selection in Tracking  
Camera selection is a crucial aspect of multi-camera tracking systems. Processing data from all cameras simultaneously in a large-scale camera network can lead to inefficient computation. The goal of camera selection is to optimize resource utilization and tracking performance by selecting the camera where the target is most likely to appear next. Camera selection decisions can be addressed using various reinforcement learning methods. [PERSON] et al. [1] employed a traditional Q-learning algorithm, to effectively solve the cross-camera handover problem and learn a selection policy. [PERSON] et al. [2] also utilized deep Q-learning combined with n-step bootstrapping, to optimize the scheduling of re-identification queries in a multi-camera network. Additionally, [PERSON] et al. [28] improved camera selection performance by leveraging state representation learning and adopting a semi-supervised learning approach to train the policy. These methods refine camera selection decisions and improve tracking efficiency.  
Sequential characteristics can also be considered in tracking and camera selection. To handle these time-series characteristics, [PERSON] et al. [15] utilized deep learning models specialized for time-series data, such as long short-term memory (LSTM) and gated recurrent unit (GRU) models, to predict the next camera. They also explored methods such as the shortest distance, transition frequency, trajectory similarity with the training set, and handcrafted features for next-camera prediction.
## 3 Experiments and Analysis
### MDF Hamming Coding  
The deviation information of the MDF sheet is calculated by using the Hamming coding method. For sensor \(S_{s}\), code the error level bits string \(B_{es}\) into ECW \(W_{s}\). The bit numbers of \(B_{es}\) and \(W_{s}\) are separately \(N_{Bs}\) and \(W_{s}\). They should satisfy Equation (13). It is also more than \(N_{Ws}\). Standard \(B_{0}\) is constant. Actual error level string \(B_{es}\) is variable and reflects the real error condition, but is not transferred. Therefore, it is not necessary to receive an \(N_{Bs}+N_{Ws}\) bit Hamming code for nuclear synergy. ECW \(N_{Ws}\) with bits is enough for the decision. It is learned from Equation (13) that transferring the object is ECW \(N_{Ws}\) with bits, but the number of error levels \(N_{Bs}\)\(N_{Ws}\) does not grow with \(N_{Bs}\) in a logarithmic function, e.g., not linear. It is shown in Figure 6a, within a certain limit, that employing ECW to represent error data permits the increase of the error levels' number. It makes the optimization of the controller more convenient. More detection channels are appended to the increasing tendencies of \(N_{Bs}\) and \(N_{Ws}\) with \(S_{s}\). The usage of ECW reduces the bit numbers of representation and operation. It is beneficial for not only saving hardware resources to transfer and calculate, but also improves efficiency in recognition. Obviously, in any case, to develop control performance, refining error levels or appending an efficient operation flocking system, the number of sensors increases. For \(S_{s}\) and also satisfying Equation (13). Figure 6b shows the error from the five sensors.  
\[N_{Bs}+N_{Ws}+1\leq 2^{N_{Ws}} \tag{13}\]
## 5 Conclusions  
This study investigates the protective effects of plant phenols on Trp. Rib, an effective photosensitizer, possesses a triplet excited state with strong oxidizing power capable of oxidizing Trp to its radical cation (Trp\({}^{\bullet+}\)). Plant phenols, known for their antioxidant properties, can reduce the Trp\({}^{\bullet+}\) and regenerate Trp. The efficiency of Trp regeneration depends on the concentration of plant phenols, pH, and the driving force of the reaction. The pseudo-first-order rate constants for Trp, calculated by exponential fitting, were found to increase linearly with the plant phenol concentration. As the pH shifts from acidic to alkaline, the bimolecular reaction rate of \(\mathrm{Syr}\) with Trp\({}^{\bullet+}\) rises from 4.8 \(\times\) 10\({}^{6}\) M\({}^{-1}\)-s\({}^{-1}\) at pH 2 to 1.7 \(\times\) 10\({}^{8}\) M\({}^{-1}\)-s\({}^{-1}\) at pH 10. Similarly, the rate for vanillic acid rises from 1.4 \(\times\) 10\({}^{6}\) M\({}^{-1}\)-s\({}^{-1}\) at pH 2 to 4.6 \(\times\) 10\({}^{7}\) M\({}^{-1}\)-s\({}^{-1}\) at pH 10, corresponding to an increase in the proportion of \(\varphi\)-O\({}^{-}\) and \(\varphi\)-O\({}^{2-}\) from 0% to nearly 100%. Furthermore, the reduction rate of Trp\({}^{\bullet+}\) by \(\varphi\)-OH is influenced by the driving force of the electron transfer reaction between \(\varphi\)-OH and Trp\({}^{\bullet+}\). Plant phenols with moderate reducing power exhibit the fastest reduction rate for Trp\({}^{\bullet+}\) (1.7 \(\times\) 10\({}^{8}\) M\({}^{-1}\)-s\({}^{-1}\)). These findings are significant for advancing our understanding of protein oxidation and antioxidant mechanisms. The rate of Trp regeneration from Trp\({}^{\bullet+}\) by plant phenols is influenced by factors such as concentration, pH, and the driving force. Additionally, the reverse reaction between plant phenols and Trp may play a critical role in a protective mechanism via rapid electron transfer. This mechanism helps reduce or prevent the oxidation and degradation of protein-based foods, thereby contributing to the protection of human health.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15073524/s1](https://www.mdpi.com/article/10.3390/app15073524/s1)]([https://www.mdpi.com/article/10.3390/app15073524/s1](https://www.mdpi.com/article/10.3390/app15073524/s1)), Additional information on the formation of Trp\({}^{\bullet+}\) from Trp by oxidation of SO\({}_{4}\)\({}^{\bullet-}\), effect of concentration of Trp and the effects of oxygen.  
[PERSON] performed the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] contributed to data analysis, result discussion, and manuscript revision. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of China (NSFC, Nos. 22173115; 32070264; 22003075).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no competing interests.
# Optimal Mobility-Aware Wireless Edge Cloud Support for the Metaverse
## 3 System Model
### Wireless Resource Allocation and Channel Model  
With \(B_{j}\), we express the bandwidth of the resource block and \(\gamma_{rj}\) denotes the signal to interference plus noise ratio (SINR) of the user \(r\) at node \(j\). With \(P^{\textit{fran}}_{rj}\), we denote the transmit power of user \(r\) at node \(j\), and \(P_{i}\) is the transmission power at the base station. Furthermore, \(H_{rj}\) is the channel gain, \(N_{j}\) is the noise power and \(a\) is the path loss exponent, whilst \(d_{rj}\) is the distance between the user and the base station. Finally, a nominal Rayleigh fading channel is used to capture the channel between the base stations and the users [24]. More specifically, the channel gain \(H_{rj}\) can be written as follows [25],  
\[H_{rj}=\sqrt{\frac{1}{2}}(t+t^{{}^{\prime}}J) \tag{7}\]  
where \(J^{2}=-1\), \(t\) and \(t^{{}^{\prime}}\) are random variables following the standard normal distribution. Based on the above, the SINR \(\gamma_{rj}\) can be expressed as follows [25; 26],  
\[\gamma_{rj}=\frac{P^{\textit{fran}}_{rj}H^{2}_{rj}d^{-a}_{rj}}{N_{j}+\sum_{i\in \mathbf{M},i\
eq j}P_{i}H^{2}_{ri}d^{-a}_{ri}} \tag{8}\]The data rate is denoted as \(g\in\mathbb{G}\) and the binary decision variable \(e_{rg}\) decides whether to select the data rate \(g\) for user \(r\),  
\[e^{rg}=\begin{cases}1,\text{ if data rate $g$ is selected for user $r$},\\ 0,\text{ otherwise}.\end{cases} \tag{9}\]  
Noticing that the chosen data rate can also be written as \(B_{j}\log_{2}(1+\gamma_{rj})\), after choosing a data rate as \(ge_{rg}\) for the user, the transmit power \(P_{rj}^{tran}\) can be written as follows,  
\[P_{rj}^{tran}=\frac{N_{j}+\sum_{i\in\mathbf{M},i\
eq j}P_{i}H_{n}^{2}d_{ri}^{- 2}}{H_{rj}^{2}d_{rj}^{-a}}(2^{\frac{ge_{rg}}{B_{j}}}-1) \tag{10}\]  
Note that \(2^{\frac{ge_{rg}}{B_{j}}}=(1-e_{rg})+e_{rg}2^{\frac{rg}{B_{j}}}\) and should satisfy the following constraint to ensure that a single data rate is selected per user,  
\[\sum_{g\in\mathbb{G}}e_{rg}=1,\forall r\in\mathbb{R} \tag{11}\]
## 4 Experimental Results and Discussion
### Regressive Anchor Box Selection  
Experiments were performed on the default YOLOv3 network and the proposed tweaked YOLOv3 network with two sets of anchor boxes. The first set was obtained from the default YOLOv3 anchor box selection algorithm. And the second set was obtained from the proposed regressive anchor box selection algorithm for the GTSDB dataset. Both sets of anchor boxes were tested on the default and the proposed YOLOv3 network. Here by default YOLOv3 network, we mean the default method of image resizing, and the proposed YOLOv3 network includes pruned network with patch-wise technique. The comparative results are depicted in Table 1. Results show that for the proposed Regressive algorithm the recall percentage and AUC have improved, and the proposed tweaked network model helps in obtaining near to 100% recall percentage for the tuned network as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Anchor Box Selection Algorithm** & **Evaluation Metric** & **Default YOLOv3 Network** & **Proposed YOLOv3 Network** \\ \hline Default-Kmeans & Recall percentage & 77.23\% & 96.83\% \\  & AUC & 75.57\% & 91.82\% \\ \hline Ours-Regressive & Recall percentage & 85.32\% & 98.13\% \\  & AUC & 78.34\% & 93.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of anchor box algorithms and YOLOv3 network model.
# Digital Shearing Speckle Pattern Interferometry Based on Rochon Prism and Its Application
## 3 Comparing RP with WP
If the shearing distance is \(\Delta x\), which means that each point interferes with a corresponding point that is \(\Delta x\) away, the inherent phase difference of the interference system is:  
\[\varphi_{\Delta}=\frac{\tan\theta}{\lambda}\cdot\Delta x. \tag{11}\]  
An immobile phase difference means that RP would not introduce phase difference noise into the system, which is the same as WP. However, although the inherent phase difference of RP does not change due to the deformation of the object, the shearing distance of the prism is still reflected in the fringe distribution through Equation (10). The larger the shearing distance, the narrower the measured zone. The fringe pattern is sparser and the SNR is reduced with a large shearing distance. A smaller shearing distance, as shown in Equation (2), leads to a tighter pattern and lower contrast, which reduces the intensity and accuracy of the result [9]. To verify the conclusion, we conducted a simulation, as shown in Figure 3. Different shearing distances led to different distributions in the phase variety. In Figure 3a, the surface after loaded is supposed to be Gaussian function. From Figure 3b-d, the shearing distance was set from 3 to 12, which progressively lowered the peak of the difference distribution. In reference [13], the relationship mentioned above was analyzed and the fringe patterns under different shear were given, as shown in Figure 4. The shearing distance is set to 10 mm in Figure 4a, 20 mm in Figure 4b, and 30 mm in Figure 4c. When the shearing distance is too small, the stripes become dimmer and less visible because the phase difference between the interfered light sources is small and the overall phase changes is minimal, so the intensity at each pixel is not so different. As shown in Figure 2, an excessive shearing distance reduces the maximum value of the displacement gradient, which also reduces the contrast of the interference pattern. Therefore, we should consider both when adjusting the shearing distance. In some cases, the sensitivity is desired to be adjustable [14; 15]. In Michelson interferometers with one of the mirrors tilted [9], the shearing distance can be changed while the angle of the mirror is changed. As far as splitters such as WP and RP are concerned, their sensitivity, which depends on their shearing distance, is determined by their angles of divergence and their distance to the measured object. Under limited measuring conditions, such as a certain measuring distance and a fixed system resolution, we must choose a beam splitter with an appropriate emergent angle. Generally, the WP angle of divergence is larger than that of RP, which means that to maintain the proper shearing distance generated by WP, we have to reduce the measured distance and the measured area. Figure 3: The simulation of difference distribution in various shearing distance: (**a**) Gaussian function, (**b**) \(\Delta x=3\), (**c**) \(\Delta x=6\), and (**d**) \(\Delta x=12\). Without loss of generality in conclusion, the units are omitted.
## 4 Proposed Method
### Description of OAA Algorithm  
Suppose \(U\) be the objective function as in (8a) subject to \(\varphi_{\text{C}_{1}-\text{C}_{6}}\) (8b) to (SSg) and \(Y=\{\mathbb{P}_{UE},\mathbb{P}_{BS},\mathbb{P}_{RIS}\}\). Whereas \(X=x\cup Y\). The following hypothesis are applied to (8a).  
1. \(Y\) is the set with objective \(U\). It possesses constraints \(\varphi_{\text{C}_{1}-\text{C}_{6}}\).
2. \(U\) and \(\varphi_{\text{C}_{1}-\text{C}_{6}}\) are continuous differentiable.
3. Optimal solution obtained by fixing the \(X\) satisfies all the constraints.
4. If we have solution of \(X\), we can obtain the optimal solution of \(U\).  
All these hypotheses convert problem (8a) to NLP complex problem, and OAA gives \(\epsilon\)-optimal solution at convergence. We can achieve the main problem by adjusting \(x\). Integer variables of OAA are iterated \(x\)th times. The problem can be written as:  
\[\begin{cases}\arg\min_{Y}\quad\quad-U(X,Y)\\ \text{s. t.}\quad\varphi_{C_{1}-C_{6}}(X,Y)\leqslant 0\end{cases} \tag{9}\]  
The solution of (9) is set to \(Y^{\kappa}\) and applying the upper and lower limit. We can get the master MIP problem by first converting (9) to integer space and then applying linearization. The problem in integer space \(X\) can be written as:  
\[\begin{cases}\min_{X}\min_{Y}\quad\quad-U(X^{\kappa},Y)\\ \text{s. t.}\quad\varphi_{C_{1}-C_{6}}(X^{\kappa},Y)\leqslant 0\end{cases} \tag{10}\]  
The master problem in (10) can be represented as:  
\[\min_{X}\quad\quad-\theta(X) \tag{11}\]  
where  
\[\begin{cases}\theta(X)=\min_{Y}\quad\quad-U(X^{\kappa},Y)\\ \text{s. t.}\quad\varphi_{C_{1}-C_{6}}(X^{\kappa},Y)\leqslant 0\end{cases} \tag{12}\]  
The equation in (11) is the projection of the (8a) with distinct variables. Then according to OAA linearization is applied to get master MIP problem. The master MIP problem can be written as:  
\[\begin{cases}\min_{X}\min_{Y}-U(X^{\kappa},Y^{\kappa})-\
abla U(X^{\kappa},Y^ {\kappa})\begin{pmatrix}Y-Y^{\kappa}\\ X-X^{\kappa}\end{pmatrix}\\ \text{s. t.:}-\varphi_{C_{1}-C_{6}}(X^{\kappa},Y^{\kappa})-\
abla\varphi_{C_{1} -C_{6}}(X^{\kappa},Y^{\kappa})\begin{pmatrix}Y-Y^{\kappa}\\ X-X^{\kappa}\end{pmatrix}\end{cases} \tag{13}\]  
If the algorithm does not converge then a new variables are introduced and can be written as:  
\[\min_{X,Y,\eta}\,\eta\,\,\text{s. t.:}\begin{cases}\eta\geqslant-U(X^{\kappa}, Y^{\kappa})-\
abla U(X^{\kappa},Y^{\kappa})\begin{pmatrix}Y-Y^{\kappa}\\ X-X^{\kappa}\end{pmatrix}\\ -\varphi_{C_{1}-C_{6}}(X^{\kappa},Y^{\kappa})-\
abla\varphi_{C_{1}-C_{6}}(X^{ \kappa},Y^{\kappa})\begin{pmatrix}Y-Y^{\kappa}\\ X-X^{\kappa}\end{pmatrix}\leqslant 0\end{cases} \tag{14}\]  
OAA solves the MINLP problem (8a). For the resources allocation we use Algorithm 2.
# Neighbor Discovery for IPv6 over BLE Mesh Networks
## 2 IPv6 over BLE Mesh Networks
### BLE Mesh Networks  
BLE mesh networks use scanning and advertising to realize communications in the same way as the BLE technology. In a mesh network, both the flooding mechanism and the routing protocol can be used to achieve the communications between nodes. There are several features in a BLE mesh network, among which only the relay feature can forward the received packets to the other nodes. Nodes that have the relay feature are called relay nodes. Meanwhile, a BLE device that joins the BLE mesh network is called a node. The mesh node receives the packets with a 100% duty cycle to scan the channels and sends packets according to a random back-off time, instead of the advertising interval. Therefore, for those low power nodes that cannot enable a 100% duty cycle, a friend node is used to store the received message and relay the message to the network for them; this feature is called friendship. A typical BLE mesh network topology and its features are shown in Figure 1.
## 3 Manufacturing Difficulties  
### Thermal Expansion  
A contiguous metallic object exhibits a coefficient of thermal expansion (CTE), which must be taken into account even for purely mechanical reasons. The same problem exists for ordinary railway tracks, which can be irreversibly damaged by the mechanical buckling process due to excessive heat in direct sunshine in hot summers.  
In railway tracks this problem is solved in several ways, for example, by providing suitable dilatation gaps that can accommodate the maximum thermal expansion expected in its nominal operating conditions.  
The prototyping track was installed in the Port of Valencia (south-east cost of Spain) and would have to operate in the rather hot summers of Spain, possibly in direct sunshine, which can heat up metal structures up to 90 \({}^{\circ}\)C (depending on the emissivity of the surface and the cooling conditions). Therefore, the design of the magnetic core had to take into account not only the maximum operating temperature of the system but also the actual temperature on the day of installation so that the appropriate expansion in the dilatation gaps could account for not only the hot summer but also the corresponding contraction during a cold winter.  
The CTE of electrical steel is on the order of 10 \(\mu\)m/m per \({}^{\circ}\)C [14]. Therefore, in the worst-case scenario, with the highest temperature of +90 \({}^{\circ}\)C (direct sunshine in the summer) and the lowest of \(-\)10 \({}^{\circ}\)C (winter), the maximum span could be 100 \({}^{\circ}\)C, which could mean a linear expansion of up to 10 mm for a 10 m long magnetic core. However, the stator core was planned to be installed underground, without direct sunshine reaching it. So for this reason, it was decided that from the thermal viewpoint, the best compromise of performance, manufacturability, and thermal properties was achieved when the magnetic core was to be segmented and made from 1.3 m long parts, with appropriate dilatation spacing between the segments in the longitudinal direction. Taking into account precision of cutting, mounting, vibrations, and additional movements, the lateral dilatation gap was set as 2 mm (at the day of installation).  
This gap was proved to be sufficient because each module had enough room to expand with respect to its neighbour modules. As checked experimentally and by further calculations, the temperature changes between 15 \({}^{\circ}\)C (night in the spring) and over 50 \({}^{\circ}\)C (afternoon in the summer) resulted in a sufficient remaining gap even if the temperature were to rise more. Therefore, this solution was technologically viable for any given length of the stator, starting from the initial 10.6 m (housed in a white tent; see the white wall in the background of Figure 1) to the final 60 m (which possibly could be still exposed to direct sunshine), as required by the research partner.