esa-sceva/satcom-chunk-collection huggingface dataset:
# A Roadmap for Gigabit to Terabit Optical Wireless Communications Receivers
## 5 Conclusions  
Results have been reported which confirm that SiPMs with narrow output pulses should be preferred when selecting SiPMs for incorporation into VLCs or OWCs receivers. Furthermore, the irradiance required to achieve a particular BER increases rapidly once the bit time is shorter than the output pulse width.  
Although the pulse width is a very important parameter, SiPMs' non-linearity must be taken into account. In particular, it is suggested that a SiPM should operate with a count rate of less than 40% of its maximum count rate.  
The need to detect a constant number of photons per bit means that increasing the area of a SiPM should reduce the irradiance needed to support a particular data rate. Unfortunately, increasing the area of a single SiPM of a particular type increases its pulse width. The trade-off between area and pulse width can be avoided by using diodes to add the outputs of SiPMs acting in parallel; however, using this method to significantly increase the data rate would be expensive. This cost increase could be reduced by using a small change in the last stages of the manufacturing process of individual larger SiPMs to create single SIPMs with multiple outputs that have narrower output pulses.  
Reasons for a change in transmitter wavelength from 405 nm to 850 nm have been highlighted; however, results have been presented which suggest that the benefits of this change will be relatively small.  
Finally, a brief survey of systems made by stacking arrays of SPADs onto a second chip has been presented. This survey suggests that this new technology could dramatically improve the performance of receivers. The result would be a receiver that is significantly better than existing receivers for fiber-optic communications operating at 25.78 Gbps. Factors that would make it possible to create receivers operating at significantly higher data rates have been highlighted.  
Conceptualization, W.M. and S.C.; methodology, S.C.; software, W.M.; validation, S.C. and W.M.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, W.M. and S.C.; supervision, S.C. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/R00689X/1.  
The data presented in this study, and the accepted version of this paper, are openly available in the Oxford Research Archive (ORA), [[https://doi.org/10.5287/bodleian:ORodKRbm0](https://doi.org/10.5287/bodleian:ORodKRbm0)]([https://doi.org/10.5287/bodleian:ORodKRbm0](https://doi.org/10.5287/bodleian:ORodKRbm0)) (accessed on 13 January 2023).  
[PERSON] would like to thank the EPSRC for the grant to support his DPhil.  
The authors declare no conflict of interest.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 5 Route Discovery in the Suggested Connectivity-Based Clustering Scheme
### Route Discovery in Inter-Cluster to the Destination  
In our proposed cluster-based scheme, the discovery to a particular destination in inter-cluster is performed by the Frontier Access Protocol (FAP). The mechanism of FAP is the same as that for the Border Gateway Protocol (BGP) [24]. In FAP, all clusters connect to a unique RSU that forms an Autonomous System (AS). FAP enables inter-AS and intra-AS communications. Our current model is only limited to intra-AS communication, which enables inter-cluster communication. The discovery process of FAP is given below.  
* FAP enables each CH to obtain cluster reachability information from RSU. The reachability information refers to the list of connected nodes to a particular RSU and/or CH. It is similar to a routing table that stores information of all accessible nodes.
* Next, RSU propagates the reachability information to all CHs. In Figure 1, a source vehicle in cluster \(X\) discovers a destination \(d\). The source vehicle first creates a route request message that disseminates inside the cluster through CEG-Dijkstra. Once the Dijkstra fails to reach the destination \(d\), the \(CH\) forwards the request to the RSU. Since the RSU has the reachability information of all clusters using FAP, it thus forwards the request to the subsequent \(CH\). At the end, \(CH\) discovers the particular destination using CEG-Dijkstra and forwards back to the source in the reverse path.
# Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning
## 1 Introduction
When computing services need to be frequently migrated between multiple MEC nodes, this will incur additional operational costs. A resource allocation strategy based on VM migration is proposed in Reference [11]. It is considered that the MEC server is deployed near the terminal, and the MEC controller allocates the servers accessed by the terminals in different areas to maximize the system Energy Efficiency (EE). User equipment can harvest radio frequency energy from multiple frequency bands as they move. Energy-efficient mobility management is an important issue in modern networks. In Reference [12], the author introduces handover costs into total energy consumption. Therefore, the effective dynamic service layout strategy should pay attention to: (1) coordinating communication delay and computing delay to minimize user-perceived delay; (2) weighing performance and cost in a cost-effective way. In this paper, the VM migration method is used to keep the VM synchronized with the user all the time to ensure the quality of service. MEC places computing resources on the edge of the network closer to mobile devices in a distributed way. Considering that it is difficult to ensure the real-time exchange of information in distributed systems, this paper proposes a VM migration strategy based on Multi-Agent Deep Reinforcement Learning (MADRL). The strategy adopts the combination of centralized training and distributed execution, evaluates the migration action through the global information during training, and transmits the evaluation in reverse to train the state value network, so as to learn the migration strategy with the lowest energy consumption. During execution, only local observation information is needed to obtain the migration action. Compared with the centralized control method, the proposed method alleviates the communication bottleneck of the VM migration strategy based on MADRL. Compared with other distributed control methods, this method only needs local information and does not need communication between servers. it speeds up the perception of the current environment, and the migration strategy can be generated more quickly. Finally, the simulation results show that the proposed migration strategy has a better effect than the contrast strategy in terms of convergence, energy consumption and migration success rate.
###### Abstract  
Wireless Sensor Networks (WSNs) connected to the Internet of Things (IoT) are increasingly employed in commercial and industrial applications to accomplish various tasks at a low cost. WSNs are essential for gathering diverse types of data within physical environments. A key design objective for WSNs is to balance energy consumption and increase the network's operating lifetime. Recent studies have shown that mobile base stations (BSs) can significantly extend the lifetime of such networks, especially when their location is optimized using specific criteria. In this study, we propose an algorithm for selecting the optimal BS location in a large network. The algorithm computes a distance metric between sensor nodes (SNs) and potential BS locations on a virtual grid within the WSN. The selection process is repeated periodically to account for dead SNs, allowing the BS to relocate to a new optimal position based on the remaining active nodes after each iteration. Additionally, the inclusion of a relay node (RN) in large networks is explored to improve scalability. The impact of path loss within WSNs is also discussed. The proposed algorithms are applied to the well-known Stable Election Protocol (SEP). Simulation results demonstrate that, compared to other algorithms in the literature, the proposed approaches significantly enhance the lifetime of WSNs.  
WSNs; IoT; mobile base stations; network lifetime optimization Article
## 2 Materials and Methods  
### Experimental Site and Sampling  
The experiment was performed in the Korla region (41\({}^{\circ}\)44\({}^{\prime\prime}\)59\({}^{\prime\prime}\) N, 85\({}^{\circ}\)48\({}^{\prime}\)30\({}^{\prime\prime}\) E) of Xinjiang, China, a typical cotton production area, which is located in the southern foot of the Tianshan Mountains, and the northeastern edge of the Tarim Basin. It has a temperate continental arid climate, with a total annual sunshine duration of 2990 h, an annual average temperature of 11.4 \({}^{\circ}\)C, a minimum of -28 \({}^{\circ}\)C, an average annual precipitation of 58.6 mm, and an annual maximum evaporation of 2788.2 mm. Cotton was planted with a large scale and simple planting structure, where cotton aphids were the main cotton pests. The field data experiments were conducted in the budding stage from 3 July to 11 July 2019. Any disease or insect inoculum had not been treated and no pesticide was applied until after data collection in the cotton fields. During these periods, sampling plots were selected randomly for the canopy hyperspectral reflectance and corresponding aphid damage grade measurements.
## 5 Nanoindentation of Diffusion-Bonded Interfaces
### Aluminium/Steel
Phases at the interface were Al\({}_{3}\)Fe\({}_{2}\) and Al-Fe-Si containing Cu. The hardness of the reaction layer was significantly higher than that of either Al or steel parent alloys. At the steel side of the interfacial region the phase formed was Al\({}_{5}\)Fe\({}_{2}\), irrespective of the Al alloy to which it was bonded. In contrast, the Al alloy composition affected the phase formed at the Al side of the bond. When the low-carbon steel was bonded to base Al (i.e., no additions of Si and Cu), the reaction layer was Al\({}_{3}\)Fe at the Al side. However, when the Al alloys contained elevated Si and/or Cu, Al-Fe-Si layers exhibited a significantly lower hardness; this was more pronounced in the higher Cu content alloy. The differences in properties were ascribed to different crystal structures of the different intermetallic phases. The reaction layer at the interface was between 2 and 4 um in thickness. This work was continued in a follow-up study [53], in which bonding was carried out at 512 \({}^{\circ}\)C for 30 min. EPMA showed the interfacial reaction layer to be enriched in Si and Cu; this was ascribed to Al\({}_{12}\)Fe\({}_{3}\)Si. Nanoindentation mapping of the interface indicated that the region of the interface closest to the Al varied in hardness depending on the Si or Cu content in the Al alloy parent as this determined the phase that formed during the bonding process. The Al\({}_{12}\)Fe\({}_{5}\)Si phase was lower in hardness than the Al\({}_{3}\)Fe. In contrast, the hardness of the interfacial region nearest to the steel (Al\({}_{5}\)Fe\({}_{2}\)) was not significantly different, irrespective of the alloying elements. [PERSON] et al. [19] formed sandwich layers (steel/Al/steel) from Al alloy 6082-T4 and stainless steel type 316L, each layer 1 mm in thickness, which were roll bonded at elevated temperature and then given heat treatments at temperatures of 400-550 \({}^{\circ}\)C for various durations followed by water quenching. SEM and TEM revealed the interfacial region to consist of four discrete layers which were identified as: (i) \(\alpha_{\text{C}}\)-Al\({}_{15}\)(Fe,Cr,Mn)\({}_{3}\)Si\({}_{2}\), (ii) \(\tau_{1}\)-FeNiAl\({}_{9}\), (iii) a mixed phase layer of \(\theta\)-Fe\({}_{4}\)Al\({}_{13}\) and \(\tau_{11}\)-Al\({}_{5}\)Fe\({}_{2}\)Si and (iv) a region predominantly of \(\eta\)-Fe\({}_{2}\)Al\({}_{5}\) which also contained nanoscale precipitates. Nanoindentation of these four layers, together with the two parent materials, showed that the region with the highest hardness was the \(\eta\)-Fe\({}_{2}\)Al\({}_{5}\) region which formed closest to the stainless steel. Its mean hardness was 13.7 \(\pm\) 0.4 GPa, which compares with hardness values of 1.1 \(\pm\) 0.1 GPa and 4.9 \(\pm\) 0.4 GPa for the aluminium and stainless steel, respectively. Microensile tests showed that a thicker intermetallic region at the interface reduced the bond strength and caused brittle interfacial fracture. Figure 5 shows a backscattered electron micrograph of the interface together with indentations, which were made at a maximum load of 3 mN.
## 3 Research on BPL Decoding Algorithm for Polar Codes Based on Information Geometry
### List Decoding Algorithm for Polar Codes from the Perspective of Information Geometry
#### 3.1.2 Statistical Manifold Formed by Bernoulli Distributions  
The probability distribution of each codeword is a Bernoulli distribution, which we usually use as follows:  
\[\begin{pmatrix}0&1\\ p&1-p\end{pmatrix}. \tag{6}\]  
The exponential deformation of Bernoulli distribution is s follows:  
\[p(x)=p^{x}(1-p)^{1-x}=\exp[x\ln x+(1-x)\ln(1-p)]. \tag{7}\]  
Let \(\lambda=LLR=\ln\frac{p}{1-p}\), and then \(p=\frac{e^{\lambda}}{1+e^{\lambda}}\); it follows that:  
\[\begin{array}{l}p(x;\lambda)=\exp[x\ln\left(\frac{e^{\lambda}}{1+e^{\lambda} }\right)+(1-x)\ln\left(1-\frac{e^{\lambda}}{1+e^{\lambda}}\right)]\\ =\exp[\lambda x-\ln(1+e^{\lambda})].\end{array} \tag{8}\]  
The statistical manifold on which the \(LLR\) (Log-Likelihood Ratio) lies is:  
\[M=\{p(x;\lambda)=\exp[\lambda x-\ln(1+e^{\lambda})]|x\in\{0,1\},\lambda\in R\}. \tag{9}\]  
This is a one-dimensional manifold.  
1.3 Statistical Manifold Formed by the Joint Probability Density Distribution of Bernoulli Distributions  
For polar codes with a code length of \((N,K)\), the output consists of soft information for \(K\) information bits. As mentioned earlier, the soft information for one information bit can be represented as a point on a statistical manifold formed by a one-dimensional Bernoulli distribution. Therefore, the soft information for \(K\) information bits can be represented as a point on a \(K\)-dimensional statistical manifold, which is composed of the joint probability distribution of \(K\) one-dimensional Bernoulli distributions. The joint probability distribution can be expressed as:  
\[p(x;\lambda)=\prod_{i}\exp[\lambda_{i}x-\ln(1+e^{\lambda_{i}})]=\exp[\sum_{i }\  
The Fisher information matrix is:  
\[I(\lambda)=-E[\frac{\beta^{2}}{\partial\lambda^{2}}\ln p(x;\lambda) ]=-E\{\frac{\beta^{2}}{\partial\lambda^{2}}[\lambda x-\ln(1+e^{\lambda})]\}\] \[=\left[\begin{array}{cccc}\frac{e^{\lambda_{1}}}{\left(1+e^{ \lambda_{1}}\right)^{2}}&&&\\ &\frac{e^{\lambda_{2}}}{\left(1+e^{\lambda_{2}}\right)^{2}}&&\\ &&\cdots&\\ &&&\frac{e^{\lambda_{K}}}{\left(1+e^{\lambda_{K}}\right)^{2}}\end{array}\right] \qquad. \tag{14}\]  
Therefore, the metric matrix is:  
\[G(\lambda)=I(\lambda). \tag{15}\]  
Obviously, the metric matrix of the statistical manifold formed by Bernoulli distributions is not an identity matrix, which means that this manifold is not a Euclidean space. Hence, Euclidean distance is not suitable for calculating the difference between two points on this manifold. This aligns with our analysis in Section 3.1.1.
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 3 Materials and Methods
### Image Acquisition  
An aggregate of 6000 RGB ordinal pictures of weeds and wheat plants were reserved at the university research farm Koont Chakwal field, using a mobile camera (Samsung A31s) and Logitech C920 Pro HD webcam with a resolution of 1080 \(\times\) 2400 (FHD+) (Full HD 1080p/30 fps HD 720p/30 fps) pixels. The images were taken from the field of PMAS Arid Agriculture University research farm Koont (Lat N 33.06, 59.7, N\({}^{\prime\prime}\) long 73.00, 40.6 E). Besides the other physical characteristics of wheat and weed plants modified throughout their life cycle, the dataset was split into three groups (Table 1). Some sample images of _Cirsium arreuse_ are shown in below Figure 1. The weed images obtained with an RGB camera were transferred to a computer to build deep learning models. A diagram of the weed images' collection is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Weed Type**} & \multirow{2}{*}{**Network Model**} & **Total Images** & **Total Images** & **Total Images** \\  & & **(Training)** & **(Validation)** & **(Testing)** \\ \hline \multirow{4}{*}{_Cirsium arreuse_} & YOLOv5s & 640 & 180 & 180 \\  & YOLOv5m & 640 & 180 & 180 \\ \cline{1-1}  & YOLOv5l & 640 & 180 & 180 \\ \cline{1-1}  & YOLOv3-Tiny & 3600 & 200 & 200 \\ \cline{1-1}  & YOLOv4-Tiny & 3600 & 200 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training dataset under different growth stages.  
_Crisium arrenese_ is one of the worst kinds of weed; it destroys many plants and crops by consuming their resources (such as water and nutrients), causing a loss in final production. It is also referred to as creeping thistle. In plants affected by this weed, plant shape is deformed, and growth is affected too.  
There are basically two classes (class labels) involved, i.e., _Crisium arrenese_ and wheat plants. A total of 1200 images of weeds in wheat plants were captured. Some 1000 images were available for final use after blurred and ineffective images were removed. For equal distribution, every class has 1000 images, of which 640 weed and wheat plant images were used to train the dataset. As mentioned in Table 1, 30% of pictures of every session were used for validation, 180 for every three classes, and 30% of 180 for testing all weeds and wheat plants. All of the images were taken in a variety of weather situations, including bright, overcast, and partially cloudy conditions, at varied heights (1, 1.5m), at 60, 80, and 90 degrees from the ground's surface, and with shadow and tree shadow.  
Figure 1: Overview of samples of _Crisium arrenese_.  
Figure 2: Flow chart of the weed detection process.
## 3 Test Results
### Experimental Verification of Optimal Analysis Results  
The maximum value of the unconfined compressive strength of the tailing sample with seven grouting rounds was considered as the target optimization value, and the independent variable parameter values of the model were optimized with the help of the numerical function in the CCD module. The optimal ratio of the unconfined compressive strength of seven grouting cycles was obtained, and there were three sets of test schemes in R1-R3. According to the above recommended scheme, an MICP-solidified uranium tailing test was conducted, and the actual value was compared with the predicted value to verify the feasibility of the model. According to Table 4, to facilitate the slurry configuration and reduce the error during the test, the actual optimized test conditions were as follows: bacteria\(-\)cementation ratio = 1, cementation solution concentration = 1.3 mol/L, and grouting amount = 70 mL to perform the verification test. Three parallel groups were established in the optimization group, and two control groups were established using only tap water and the cementation solution. After grouting was completed, uranium tailing samples were collected for the unconfined compressive strength test. Table 5 presents the results of the study. The stress\(-\)strain curve is plotted in Figure 6. The test results showed that the measured value of unconfined compressive strength is close to the predicted value, indicating that the response surface method was more accurate in this test. From the compression test and stress\(-\)strain curve, it can be found that there is local inhomogeneity in the uranium tailings solidified by optimized grouting curing; however, the unconfined compressive strength of the uranium tailings is 27 times higher than that of the control  
Figure 5: Three-dimensional response surface of the compressive strength of the seven grouting rounds; (**a**) response surface and contour plots of the bacteria-cementation ratio, cementation solution concentration, and their interaction on UCS, (**b**) response surface and contour plots of the bacteria-cementation ratio, grouting amount, and their interaction on UCS, (**c**) response surface and contour plots of cementation solution concentration, grouting amount, and their interaction on UCS.  
group with only water injection, and six times higher than that of the control group with only the cementation solution. Therefore, it is feasible to optimize MICP grouting parameters using the response surface method.
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
## 4 Security Analysis
### The UE\({}_{k}\) Is Semi-Honest  
Like the previous case, only \(S_{1}\) needs to be constructed so that when the input \(q_{i}\) and output \(r_{z}\) of \(P_{1}\) are known, the corresponding view can be obtained according to (5). Similarly, because the SQRT scheme is based on the security of \(OT_{N_{k}+\Delta}^{N_{k}}\), there is an algorithm \(S_{1}^{OT_{N_{k}+\Delta}^{N_{k}}}\) to obtain the input \(z\) and output \(r_{z}\), and the view of \(U\!E_{k}\) executing \(OT_{N_{k}+\Delta}^{N_{k}}\) can be obtained, i.e.,  
\[\left\{S_{1}^{OT_{N_{k}+\Delta}^{N_{k}}}(z,r_{z})\right\}\stackrel{{ c}}{{=}}\left\{\text{VIEW}_{1}^{OT_{N_{k}+\Delta}^{N_{k}}}(z,r_{i})_{i=1, \ldots,m}\right\} \tag{9}\]  
The view of the output of \(S_{1}\) is represented as  
\[\{S_{1}(q_{i},r_{z})\}=\left\{q_{i},s_{a},\mathbf{B_{k}},S_{ij}^{\prime}\Big{|}_{i,j=1,\ldots,n,i\
eq j}\right\},\left\{S_{1}^{OT_{N_{k}+\Delta}^{N_{k}}}(z,r_{ z}),r_{z}\right\} \tag{10}\]  
Since \(s_{a}\) and \(\mathbf{B_{k}}\) are randomly generated and satisfy \(\sum\limits_{a=1}^{w}s_{w}\cdot b_{ai}=1\), \(i=1\), \(2\), \(\ldots\), \(m\), \(s_{a}\) and \(\mathbf{B_{k}}\) can be obtained from the view \(\left\{\text{VIEW}_{1}^{\Pi}(q_{i},\{d_{1},d_{2},\ldots,d_{m}\})\right\}\). Because of their randomness, we can ascertain that (\(s_{a}\), \(\mathbf{B_{k}}\)) and (\(s_{a}^{\prime}\), \(\mathbf{B_{k}^{\prime}}\)) are indistinguishable. Similarly, \(S_{ij}^{\prime}\Big{|}_{i,j=1,\ldots,n,i\
eq j}\) and \(S_{ij}\big{|}_{i,j=1,\ldots,n,i\
eq j}\) are computationally indistinguishable [28].  
Thus, combined with (9), we can obtain  
\[\left\{\mathbf{q_{i}},\mathbf{s_{a}},\mathbf{B_{k}},S_{ij}^{\prime}\Big{|}_{i,j=1,\ldots,n,i\
eq j}\right\},\left\{S_{1}^{OT_{N_{k}+\Delta}^{N_{k}}}(z,r_{ z}),\mathbf{r_{z}}\right\}\] \[\stackrel{{ c}}{{=}}\left\{q_{i},\mathbf{s_{a}},\mathbf{B_{k}},S_{ ij}^{\prime}\Big{|}_{i,j=1,\ldots,n,i\
eq j}\right\}\left\{\text{VIEW}_{1}^{OT_{N_{k}+\Delta}^{N_{k}}}(z,r_{i})_{i=1, \ldots,m},r_{z}\right\} \tag{11}\]Whereas we can deduce that  
\[\{S_{1}(q_{i},r_{z})\}\stackrel{{ c}}{{=}}\left\{\text{VIEW}_{1}^{ \Pi}(q_{i},\{d_{1},d_{2},\ldots,d_{m}\})\right\}\]  
Therefore, the security of the SQRT scheme can be proven under the semi-honest model.