esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antennas Design and Working Principle Analysis
### Backward Endfire Radiation  
The parallel SSPPs-TL is a slow-wave guiding structure, as shown in Figure 2, the SSPP waves of which are confined tightly in the TL. A set of open stubs with uniform length \(h_{u}\) are periodically introduced in the TL, as shown in Figure 4. By adjusting the groove height and the period of the unite cell, the \(n=-1\) order space harmonics mode is exited. The dispersion curve of \(n=-1\) mode is shown in Figure 5, and a part of the dispersion curve near the cutoff frequency is around the line of \(-1\).  
The maximum radiation angle of the linear source antenna array can be approximately determined by  
\[\theta_{m}\cong\arcsin\biggl{(}\frac{\beta_{-1}}{k_{0}}\biggr{)} \tag{1}\]  
where \(\beta_{-1}\) is the phase constant of \(n=-1\) space harmonics mode and \(k_{0}\) is the propagation constant in free space.  
Based on the Hansen-Woodyard (H-W) condition, for the maximum directivity of endfire radiation, the phase constant of an antenna needs to satisfy the following equation:  
\[\beta\ =\biggl{(}k_{0}+\frac{2.94}{L}\biggr{)} \tag{2}\]  
where \(L\) is the length of the effective radiation aperture. The normalized phase constant line of the H-W condition is also given in Figure 5.  
From 6.2 GHz to 6.7 GHz, the phase constant of the antenna is between the H-W condition and the air propagation constant, so the antenna can realize backward endfire radiation based on Equations (1) and (2). From 6.7 GHz to 7.4 GHz, although the phase constant of the antenna is slightly larger than the vacuum wavenumber, the maximum radiation angle of the antenna still directs at the backward endfire direction because of superposition of dual low-elevation beams in the upper and lower spaces.  
Overall, from 6.2 GHz to 7.4 GHz, the antenna in Figure 4 has the backward endfire radiation property, and radiation patterns of the antenna given in Figure 6 have the backward endfire direction to further conform the deduction. Figure 7 gives the electric field distribution of the antenna in cross section. The electric energy mainly concentrates around the backward endfire area.  
Figure 3: S parameters of the parallel SSPPs-TL with \(h=4\) mm.  
Figure 4: The structure of the proposed antenna with uniform stubs. \(p=12\) mm, \(h=4\) mm, \(b=3\) mm, \(h_{u}=6\) mm.  
Figure 5: Normalized phase constant.  
Figure 6: Radiation patterns of the antenna.  
Figure 7: Electric field distribution of the antenna in Figure 4 at 6.6 GHz in YOZ plane.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What Are the Main Types of Learning Techniques Used to Solve 5G Problems?
#### 3.2.2 Reinforcement Learning  
Reinforcement learning has received a lot of attention in the last years. This paradigm is based on trial and error, where software agents learn a behavior that optimizes the reward observing the consequences of their actions [86]. The works we reviewed addressed different problems while taking into account context information and solving optimization problems. For instance, authors in [3] used reinforcement learning to determine phase shift and amplitude of each antenna element with the purpose to optimize the aggregated throughput of the antennas. In [62], authors used reinforcement learning to improve the URLLC energy efficiency and delay tolerant services through resource allocation. In [73], the authors also considered a URLLC service but this time they worked on optimizing packet scheduling of a multipath protocol using reinforcement learning. In [57], the authors adopted reinforcement learning for network slicing in RAN in an attempt to optimize resource utilization. To handle the cache allocation problem in multiple RRHs and multiple BBU pools, the authors in [55] used reinforcement learning to maximize the cache hit rate and maximize the cache capacity. In [77], reinforcement learning was used to configure indoor small cell networks in order to optimize opinion score (MOS) and user QoE. Finally, in [2], reinforcement learning was used to select radio parameters and optimize different metrics according with the scenario addressed.
# Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game
## 5 Conclusions  
Communication nodes are particularly important in combat systems. They play an important role in the transmission of command flow, energy flow, and material flow. Attacks on the enemy's important communication nodes can often produce better effects than other nodes. Therefore, the confrontation scenario can make full use of this feature and have a multiplier effect on combat effectiveness. At the same time, it also provides a basis and reference for the protection and camouflage of communication nodes in the confrontation scenario by determining which nodes and hubs should be protected and which nodes should be repaired first so that the combat effectiveness of the combat system can be maximized.  
Figure 13: Changes in the indicators of the same distributed combat network under \(\alpha=0.2\).  
The communication network is a small-world network composed of an outer layer and an inner layer with a core structure. The outer nodes tend to connect more important nodes. At the same time, the hierarchical nature of the communication network prevents the conflict of information flow in the entire combat network. In order to improve the efficiency and cost of information circulation, the degree distribution of the communication network is a mixed distribution. Compared with the ordinary randomly distributed ER network and the power-law distributed BA network, it has stronger robustness and survivability, and it is also more realistic. Reflecting the communication network topology in the fighting, it can also provide a basis and reference for the calculation of combat effectiveness of the next combat system. On the basis of modeling and simulation, quantitative analysis of combat effectiveness at a certain moment.  
Conceptualization, T.L.; methodology, T.L. and Y.Z.; software, K.C.; validation, K.C.; formal analysis, K.C.; investigation, Q.D.; writing--original draft preparation, T.L. and Y.Z.; writing--review and editing, T.L. and K.C.; visualization, K.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the National Natural Science Foundation of China (Grant No. 61906207).  
Not applicable.  
Not data were used to support this study.  
The authors declare that there are no conflict of interest regarding the publication of this paper.
# Optimization of Pico-eNB Tx Power and the Effects of Picocell Range Expansion in Multiband HetNet
## 2 System Overview  
Figure 1 shows a multiband HetNet combining macrocells operating at a 2.0 GHz band and picocells covered by a low-power pico-eNB operating at a 4.5 GHz band. In this paper, we assume that the signal bandwidth of pico-eNBs is 10 times wider than that of a macro-eNB. We also assume that the macrocells comprise three sectors and a couple of picocells are distributed in each macro-sector.  
Figure 1: Multiband HetNet combining macrocell operating at 2.0 GHz and picocells with a wide bandwidth operating at 4.5 GHz.  
In HetNets, the cell selection procedure which allows the UE to camp onto a cell is executed by measuring a downlink reference signal from macro- and pico-eNBs. For example, cell selection can be based on the downlink RSRP, as shown in Figure 2. The UE first receives reference signals from both macro- and pico-eNBs (i.e., RSRPmacro and RSRPico). Then the UE replies with the measurement reports including RSRP macro and RSRPico to a macro-eNB. If the RSRPmacro is larger than RSRPico, the UE is connected with the macro-eNB and a radio resource control (RRC) connection is established between the UE and the macro-eNB. In 3 GPP, the RSRP is defined as the average power of the resource blocks that carry the cell-specific reference signals.  
In a typical case, the transmission power of macro-eNBs is larger than that of pico-eNBs. If the carrier frequency of macro-eNBs is the same as that of pico-eNB (i.e., in the case of a singleband HetNet), interference between macro- and pico-eNBs, as well as among pico-eNBs, should be avoided. By contrast, if the carrier frequency of macro-eNBs is different from that of pico-eNB (i.e., in the case of a multiband HetNet), interference among pico-eNBs should mainly be avoided. Either way, the transmission power of pico-eNBs can never be larger than that of macro-eNBs. Therefore, the amount of UE connected with macro-eNBs is greater than what is possible with pico-eNBs as the transmission power of macro-eNBs is larger than that of pico-eNBs. However, for the UE in HetNets, when macro-eNBs lack radio resources, some UE can be connected with pico-eNBs, even though the RSRPmacro is larger than RSRPico. This traffic offload from macrocells to picocells will be more significant when the traffic in the macrocell increases drastically.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 5 PicPose: A Picture-Based Pose Method
### PicPose Picture Detection
#### 5.1.2 Online SSD Detection  
The bounding boxes that contain detected pictures are produced using Caffe-based SSD when the trained neural network model is applied. Each bounding box has four coordinates, \((P_{1x},P_{1y}),(P_{2x},P_{2y}),(P_{3x},P_{3y}),(P_{4x},P_{4y})\). If there are many target pictures detected in a camera image, picture \(i\) has four corner coordinates: \((P_{1x}^{i},P_{1y}^{i}),(P_{2x}^{i},P_{2y}^{i}),(P_{3x}^{i},P_{3y}^{i}),(P_{4x }^{i},P_{4y}^{i})\).  
Computational speed is always the pain point of deep learning based object detection in real-time services. There are two possible ways to speed up the detection. One is using hardware to provide more powerful computing capability for deep neural network algorithm such as Intel Movidius or commodity GPUs. Tuning the parameters of SSD, such as scale increasing rate, input size and batch  
Figure 6: PicPose Workflow.  
size, is another way not only to increase computing speed but also to improve accuracy. However, it is always a conflict of goals between accuracy and speed when tuning parameters. For example, if the detection speed is unable to ensure the efficiency of the whole system for real-time localization, some parameters must be modified to increase speed such as reducing the input image resolution, even though it may affect the accuracy of bounding boxes produced.
## 6 Quality Analysis of the Reconstructed Image
### Review and Analysis of Objective Measurement Results
#### 6.2.2 Objective Quality of the Test Image Beauty  
The reconstructed images for each compression method and ratio are shown in Figure 15. At the lowest compression ratio (top row), the quality of the JPEG image appears to immediately drop; JPEG 2000 has some edges, while EEDC-BSSP is smooth. JPEG has the worst image quality for all compression ratios, while JPEG 2000 and EEDC-BSSP are on par with only a slight difference. EEDC-BSSP is smoother in detail. At 0.05 bpp, the face is not recognizable on the JPEG 2000 image, while recognizable facial features are preserved on EEDC-BSSP. The results of the objective quality assessment are shown in Tables 12-14.  
From Table 12, it can conclude that EEDC-BSSP performs better than JPEG 2000 only at 0.1 and 0.05 bpp, while it is better than JPEG at all compression ratios except 0.8 bpp.  
The SSIM, MS-SSIM parameters in Table 13, and VIF in Table 14 show similar results to the AAE, MSE, and SNR values. EEDC-BSSP is very similar to JPEG, but JPEG 2000 outperforms this new compression method at 0.2, 0.4, and 0.8 bpp, respectively.
# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 6 Evaluation and Results
### KS and PS Recognition Scenarios  
The Ar_WSDS system defined its recognition model based on the discovery of the patterns defined in Figure 7 and Equation (6), where it is established that, if the key signals could not recognize a pattern, they were recognized by partial signals. Success rate tests for each of the recognitions in the nine QoS parameters were conducted, and Table 4 presents the results.  
For each experiment, the number of signals to be recognized was nine (i.e., there was a variation between partial and key signals), such that the system could recognize one group of signals if it did not recognize the other. All recognition modules were dynamically created in this scenario. This setup means that, as the constraints increased in both recognitions, the recognition success rate decreased. In practice, this condition implies that, if a service with high restrictions in its non-functional requirements is requested, few services will satisfy this quality condition.
## 3 Results and Discussion
### General Characteristics of De Novo Bacidia rubella Genome
#### 3.1.1 Atranorin
Specifically, we compared the unconstrained ML tree recovering _Bacidia_ + Cladoniaceae as monophyletic (Topology 1) versus the constrained tree recovering Cladoniaceae + Parmeliaceae as monophyletic and _Bacidia_ sister to that branch (Topology 2; Figure 1). Our results showed that the topology constraining Cladoniaceae + Parmeliaceae as monophyletic is significantly less likely (0.8%, Figure 1, Table 3). Possible explanations are that the ancestor of _Bacidia_ may have acquired the PKS23 gene from an ancestor of Cladoniaceae via horizontal gene transfer or that the PKS23 genes were generated by convergent evolution. It is obvious that these hypotheses require additional testing with augmented sampling of PKS23 sequences from Cladoniaceae, Parmeliaceae, and Ramalinaceae. Both scenarios may also explain the scattered occurrence of atranorin in Ramalinaceae. Figure 2: Organization of putative atranorin BGCs in _Bacidia rubella_ and _Cladonia rungiferina_. The genes PKS23 (_atr1_), Cytochrome 450 (_atr2_), _O_-methyltransferasearse (_atr3_), and transporter gene (_atr4_) are present on the left side of the gene arrow plot. The BLAST percent similarity between two genes is 73% for _atr1_, 74% for _atr2_, 78% for _atr3_, and 91.5% for _atr4_. APH, phosphotransferase enzyme family (PF01636); DUF, domain of unknown function; FMO-like, flavin-containing monooxygenase family (PF00743); HCP60, HSP60 chaperone family (PF00118); HP, hypothetical protein; MFS, Major Facilitator Superfamily, transporter gene (PF07690); R-PKS, reducing PKS (in this case from R-VIII group); OrsD, Orsellinic acid/F9775 biosynthesis cluster protein D family (PF12013); TFIIH, Ssl1-like protein, subunit of the transcription factor II H complex (PF04056); Sugar_tr, Sugar transporter family (PF00083); WD-40, WD40 repeat (PF00400). Even though a PKS16 gene from _Cladonia grayi_ was first shown to be involved in grayanic acid production by [PERSON] et al. (2018), a homolog of this gene from _C. rangiferina_ was later suggested to be involved in atranorin production by [PERSON] et al. (2016). In our phylogeny, these genes group together with genes from several other _Cladonia_ and lichen-forming fungi (PKS16 clade, See Figure 3 in Section 3.3: Type I PKS Phylogeny), but not all of them are known as atranorin producers (Table 2). Moreover, atranorin is a B-orcinol depside, and a corresponding biosynthetic gene would require a CMeT domain to add a methyl group to the depside ring. This is not the case for PKS16, which is thought to be involved in the synthesis of orcinol depsides (2015). Our comparison of known metabolites in lichen-forming fungi containing PKS16 does not reveal a clear pattern either (See Figure 3 in section: Type I PKS Phylogeny, Group NR-I, PKS16 clade; Table 2). As such, the biosynthetic role of PKS16 genes remains elusive.
## 3 Results
: Let \(E=H\) in Theorem 1. Since the duality mappings \(J_{E_{1}}^{q}\), \(J_{E_{1}}^{p}\) and \(J_{E_{2}}^{p}\) are the identity mapping in a Hilbert space \(H\), from Theorem 1, we obtain the desired conclusion. **Corollary 5**. : _Let \(H_{1}\), \(H_{2}\) be two real Hilbert spaces and \(C\), \(Q\) be nonempty closed convex subsets of \(H_{1}\), \(H_{2}\), respectively. Let \(A:H_{1}\to H_{2}\) be a bounded linear operator and \(A^{*}:H_{2}\to H_{1}\) be the adjoint of \(A\). Suppose that the problem (SFP) has a nonempty solution set \(\mathcal{F}\). Let \(T:C\to C\) be a right Bregman strongly quasi-nonexpansive mapping such that \(F(T)=\widehat{F}(T)\
eq\emptyset\), \(I-T\) is demiclosed at zero and \(F(T)\cap\mathcal{F}\
eq\emptyset\). Suppose that \(\{\beta_{n}\},\{\gamma_{n}\}\) and \(\{\delta_{n}\}\) are sequences in \([0,1)\) such that \(\beta_{n}+\gamma_{n}+\delta_{n}=1\), \(\beta_{n}\leq b<1\), \((1-\beta_{n})a<\gamma_{n}<\delta_{n}\), \(a\in(0,\frac{1}{2})\). For any fixed \(u\in C\), let the sequences \(\{x_{n}\}\) and \(\{u_{n}\}\) be iteratively generated by \(u_{0}\in E_{1}\) and_  
\[\left\{ \begin{array}{l}x_{n}=P_{\mathrm{C}}\big{(}u_{n}-s_{n}A^{*}(I-P_{ \mathrm{Q}})Au_{n}\big{)},\\ u_{n+1}=P_{\mathrm{C}}\big{(}\beta_{n}u+\gamma_{n}x_{n}+\delta_{n}Tx_{n}\big{)} \end{array} \right. \tag{47}\]  
_for each \(n\geq 1\). Suppose the following conditions are satisfied:_  
1. \(\lim\limits_{n\to\infty}\beta_{n}=0\)_;_
2. \(\sum\limits_{n=1}^{\infty}\beta_{n}=\infty\)_;_
3. \(0<s\leq s_{n}\leq k<\frac{2}{\|A\|^{2}}\)_._  
_Then the sequence \(\{x_{n}\}\) and \(\{u_{n}\}\) converges strongly to a point \(\Pi_{F(T)\cap\mathcal{F}}u\)._  
Proof. : Let \(E=H\) and \(\alpha_{n}=0\) for each \(n\geq 1\). Since the duality mappings \(J_{E_{1}}^{q}\), \(J_{E_{1}}^{p}\) and \(J_{E_{2}}^{p}\) are the identity mapping in a Hilbert space \(H\), from Theorem 1, we obtain the desired conclusion. **Remark 1**. : _A prototype for the sequences \(\{\alpha_{n}\}\), \(\{\beta_{n}\}\), \(\{\gamma_{n}\},\{\delta_{n}\}\) and \(\{s_{n}\}\) in Theorem 1 are as follows:_  
\[\alpha_{n}=\frac{1}{n+1},\ \ \beta_{n}=1-\frac{2 an+1}{n+1},\ \ \gamma_{n}=\frac{\frac{1}{2}an}{n+1},\ \ \delta_{n}=\frac{\frac{3}{2}an}{n+1},\ \ a\in \Big{(}0,\frac{1}{2}\Big{)}\]  
_and_  
\[s_{n}=\Big{(}\frac{n}{n+1}\Big{)}\Big{(}\frac{q}{C_{q}\|A\|^{q}}\Big{)}^{\frac {1}{q-1}}\]  
_for each \(n\geq 0\)._
## 3 Test Results and Discussion
### Deformation of Concrete on the Surface of Different Sections of the Beam at Midspan  
Figure 6 illustrates the deflection across different zones in the specimens. In Figure 6a, the compressive zone exhibited the least deformation: the steel-reinforced concrete beams measured 1.66 mm and 1.30 mm, the GFRP-reinforced concrete beams measured 1.45 mm and 2.73 mm, and the GFRP-reinforced beams measured 3.45 mm and 1.01 mm. In Figure 6c, the neutral zone demonstrated steel-reinforced concrete beams with 3.94 mm and 3.13 mm, GFRP-reinforced concrete beams with 3.77 mm and 4.38 mm, and GFRP-reinforced concrete beams with 6.86 mm and 7.95 mm. Figure 6c, representing the tensile zone, showed maximum deformation: the steel-reinforced concrete beams with 3.96 mm and 3.42 mm deformations, the GFRP-reinforced concrete beams with 7.04 mm and 9.05 mm, and the GFRP-reinforced concrete beams with 8.43 mm and 10.76 mm.  
When comparing the values of concrete deformations at the same levels of beam loads, it was concluded that in the compressive zone, the highest deformations recorded in the case of concrete beams that were reinforced with GFRP and GFRP bars were 3.45% and 2.73%, respectively. These values represented a 107.8% and 64.46% increase compared to the highest deformation recorded by the concrete beams that were reinforced with steel bars, which was 1.66%. The most significant deformations in the compressive zone occurred in the case of concrete beams reinforced with GFRP bars.  
In the mid-section of the beam, the highest deformation was recorded on concrete beams reinforced with GFRP, amounting to 7.95%, which represented a 101.78% increase compared with the highest deformation recorded on concrete beams reinforced with steel bars, which was 3.94%. Conversely, the highest deformation recorded for concrete beams reinforced with GFRP was 4.38%, indicating an 11.17% increase from the deformation of concrete beams with steel reinforcement.  
In the last section, which is the tensile zone, the highest deformation was observed for concrete beams that were reinforced with GFRP bars, measuring 10.76%, and with GFRP bars, measuring 9.05%. These values represented a 171.72% and 128.53% increase, respectively, compared to the highest deformation of concrete beams reinforced with steel bars, which was 3.96%.  
Figure 5: Deflection vs. position curves of beam specimens at 20 kN applied load (deflection data collected from gauges at the midpoint and both ends of the beams, measured at 2 kN load intervals).
# Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems
## 3 Analysis of OFDM/FHMA System Based on Mini-Slot Frame Structure
### Latency Analysis of Mini-Slot Scheduling Mechanism  
In the transmission over a single mini-slot interval, the reliability of the OFDM/FHMA system may not meet the stringent requirements in some applications such as wireless control and commanding data transmission in power systems. In these scenarios, the retransmission scheme becomes a natural solution to enhance the reliability, but in turn, it leads to the increase of the transmission latency. In this section, on the basis of the OFDM/FHMA signal, we investigate the latency due to the retransmission based on the mini-slot scheduling strategy.  
To facilitate the analysis, the retransmission strategy adopted in this paper could be depicted as follows: If the BER exceeds a given threshold, the quick retransmission will initiate at the next mini-slot. Thus, given the BER \(P_{b_{\text{mini-slot}}}\), which was obtained by the signal analysis in Section 3.1, the probability of successfully delivering at the \(m\)-th retransmission is denoted as  
\[p_{m}=\left(1-P_{b_{\text{mini-slot}}}\right)P_{b_{\text{mini-slot}}}^{m-1} \tag{12}\]  
Assuming the interval of the mini-slot to be \(T_{\text{mini-slot}}\), which is determined by the factor \(L\) and the OFDM numerology, the latency experienced over these \(m\) retransmissions is \(N_{m}=mT_{\text{mini-slot}}\). In practical power systems, the allowable maximum latency is normally fixed, i.e., \(NT_{\text{mini-slot}}\), where \(N\) is the allowable maximum amount of retransmissions. Through the mini-slot scheduling/retransmission, the probability of successfully delivering within \(N\) retransmissions is expressed as  
\[P_{N}=\sum_{m=1}^{N}p_{m}=1-P_{b_{\text{mini-slot}}}^{N} \tag{13}\]  
In turn, given a successful delivering probability \(P_{N}\), the amount of retransmissions required can be calculated from the following expression.  
\[N=\lceil\log_{P_{b_{\text{mini-slot}}}}\left(1-P_{N}\right)\rceil \tag{14}\]  
It should be reminded that \(P_{b_{\text{mini-slot}}}\) is the average BER (reliability) of a single transmission over a mini-slot, which can be obtained by the Monte Carlo simulation method. The numerical tradeoff between the retransmission latency and the reliability of the OFDM/FHMA system based on the mini-slot frame structure has been established.