esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a novel J-DBN equalizer for the 144-Gbps QPSK signal transmission system over a 20-km SSMF and 3-m THz-band wireless link at 500-GHz is experimentally demonstrated, which consists of two steps including a DBN-1 adaptive equalizer based on the CMA algorithm and a DBN-2 blind equalizer based on the DD-LMS algorithm. We compare the J-DBN equalizer with the classical equalizer in terms of the BER performance. Meanwhile, our proposed J-DBN equalizer in the adaptive equalization step can reduce the computational complexity and obtain better training accuracy during the self-recovering blind equalization process. The experimental results show that the J-DBN method has good training accuracy, a smaller requirement for training sequences, and satisfactory computational complexity. Thanks to our proposed J-DBN scheme, an improvement of 0.2 dB and 0.8 dB over the J-DBN equalizer in receiver sensitivity and SNR gains at a BER of 3.8 \(\times\) 10\({}^{-3}\) is achieved compared with conventional equalizers. Moreover, the computational time is effectively improved, being up to 46% lower than the conventional equalizer. To our knowledge, this is the first time that joint DBN equalizers have been deployed in the THz-band wireless transmission link. Our proposed J-DBN equalization scheme is promising for future 6G fiber-optical and THz-wireless seamless integration communication systems.  
Conceptualization, X.L. and J.Z.; methodology, X.L. and J.Z.; software, W.T. and Y.W.; validation, B.H., M.L. and Y.C.; formal analysis, X.L., S.G. and J.Z.; investigation, X.L. and J.Z.; resources, X.L.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L., Y.Z. and J.Z.; supervision, M.Z.; project administration, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (62101121 and 62101126), the project funded by the China Postdoctoral Science Foundation (2021M702501 and 2022T150486), the open project (2022 GZKF003) of the State Key Laboratory of Advanced Optical Communication Systems and Networks of Shanghai Jiao Tong University,  
Figure 8: Complexity analysis of the 36 Gbaud QPSK signals with different equalization options when the input power into AIPM is 12.8 dBm. (**a**) Loss function versus iteration numbers. (**b**) Comparison of the computation time.  
the Transformation Program of Scientific and Technological Achievements of Jiangsu Province (BA2019026), the Key Research and Development Program of Jiangsu Province (BE2020012), and the Major Key Project of Peng Cheng Laboratory (PCL 2021A01-2).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Novel Composite Powders with Uniform TiB\({}_{2}\) Nano-Particle Distribution for 3D Printing
## 3 Results and Discussion
According to the work by [PERSON] and co-workers [8], the faces of the TiB\({}_{2}\) particles in Al that provide the greatest contact area are the basal faces which form OR1. This enhances the engulfment of particles. It should be noted that three of 34 small TiB\({}_{2}\) particles of the 10 \(\upmu\)m powder and four of 13 big particles of the 50 \(\upmu\)m powder within the Al grains form neither OR1 nor OR2. This indicates that particle engulfment can take place without a coherent interface in the condition of fast-cooling. However, a well-documented crystallographic orientation relationship can lower the solid-particle interfacial energy to improve particle engulfment during solidification [5]. Most TiB\({}_{2}\) particles segregated at grain boundaries (Table 2) rarely develop any relationship with Al. It is proposed that such a phenomenon mainly results from the restriction of \(\alpha\)-Al solid volume fractions (v\({}_{\text{fs}}\)). At the beginning, when the v\({}_{\text{fs}}\) is low, TiB\({}_{2}\) particles are relatively unconstrained and can reorient freely to the growing \(\alpha\)-Al in order to reduce the interface energy of TiB\({}_{2}\) and \(\alpha\)-Al \(\sigma_{\text{SP}}\) by forming OR1 or OR2. As v\({}_{\text{fs}}\) increases, reorientation of TiB\({}_{2}\) becomes increasingly difficult due to the impingement by either neighboring TiB\({}_{2}\) particles or \(\alpha\)-Al from multiple directions. Further, during the final stage of solidification, a high v\({}_{\text{fs}}\) along with a high degree of particle-particle interaction will hinder particle motion. Only relatively unconstrained particles will be able to reorient and obtain OR1 or OR2 [6; 24]. From the statistical analysis of the orientation relationship, it can be concluded that a uniform dispersion of TiB\({}_{2}\) particles is favored by a coherency interface, supercooling and a nanoscale particle size. As shown in Figure 5, the composite powder has a reflectivity of \(\sim\)43\(\%\) and a corresponding K-M absorption factor of \(\sim\)0.37 at a wavelength of 1.06 \(\upmu\)m, which is typically used for most SLM processes. The K-M absorption factor is comparable to most Al-Si alloy powders between 0.3-0.4. So the laser absorptivity increased significantly due to the addition of TiB\({}_{2}\). The resulting Al-based composite powders with improved laser absorptivity provide promising candidates for nanocomposite synthesis via AM. The reasons for this are three-fold: (1) the composite powders with higher laser absorptivity will benefit the melt formation during SLM [14]; (2) the introduced nano-sized TiB\({}_{2}\) was pre-embedded mainly into the powder and only a limited proportion was distributed on the powder surface, thus not imposing any negative effect on the flowability of the matrix Al-Cu powder; (3) the interfacial bonding between the nano-sized TiB\({}_{2}\) and the Al-Cu matrix was strong in the gas-atomized composite powder, which can help limit the interface de-bonding during rapid solidification in SLM.
## 4 Proposed Routing Protocol
### Routing Decision Learning Phase
#### 4.2.2 DDQN with Prioritized Experience Relay Algorithm
The parameters of the target \(Q^{\prime}\) network can be updated by the newest parameters of the current \(Q\) network at regular intervals. We use the moving-mean method to update the target network parameter \(\theta^{\prime}_{t}=\alpha\theta^{\prime}_{t-1}+(1-\alpha)\theta_{t}\), which can save storage space and avoid large gaps in the update process that can accelerate learning. In this paper, the training and run phases adopt two different strategies. In the run phase, we use the \(\varepsilon-\text{greedy}\) method to select the next-hop node, which is depicted as  
\[a^{*}=\left\{ \begin{array}{ll}\underset{a}{\operatorname{argmax}}Q(s,a; \theta),&P=1-\varepsilon\\ \text{others}\;,&P=\varepsilon\end{array} \right. \tag{14}\]  
where \(\varepsilon\) is the exploration probability that is used to balance exploitation and exploration in order to avoid falling into a local optimum. Generally, according to our experience, \(\varepsilon\) is set as 0.05. The \(1-\varepsilon\) probability is used to select the next hop with the maximum Q value. The \(\varepsilon\) probability is used to randomly select a node as the next-hop forwarding node. In addition, we use the softmax function to choose the next hop in the test phase, which can distribute traffic among nodes that have nearly identical Q values. When node \(i\) needs to forward packets, the softmax policy of node \(i\) is given by  
\[\pi(a\mid s_{i})=\frac{\exp(Q(a\mid s_{i}))}{\sum_{a_{i}\in A_{i}}\exp(Q(a_{i} \mid s_{i}))} \tag{15}\]
## 2 Materials and Methods
### Evaluation of Created Models
#### 2.4.2 Evaluation of Classification Models  
The performance of our classification model was assessed using the confusion matrix and the area under the curve (AUC) metric. The confusion matrix serves as a representation of the alignment between the classification model's predictions and the actual classes. It does so by displaying the distribution of predictions across different actual classes, providing a clear picture of the model's accuracy in classification tasks.  
**Confusion Matrix**: This matrix is crucial for visualizing the performance of the classification model. It shows the numbers of correct and incorrect predictions divided into each category by the model, allowing us to evaluate the model's performance across various class labels. The confusion matrix was particularly useful in identifying how the model performed for each actual angle versus the predicted angle, enhancing our understanding of its precision and areas where improvement was needed.  
**AUC**: AUC is a metric derived from the receiver operating characteristic (ROC) curve, which illustrates the trade-off between the true positive rate and the false positive rate of a classification model at various threshold settings. The AUC metric, ranging between 0 and 1, serves as a summary of the model's ability to correctly classify the instances across all possible thresholds. A value closer to 1 indicates higher model performance. In our study, the AUC was used to quantify the overall effectiveness of the classification model in distinguishing between different classes, providing a single measure of performance irrespective of any particular threshold.  
Utilizing these evaluation methods allowed for a comprehensive assessment of the classification model's performance. The confusion matrix provided detailed insight into the model's accuracy for each class, and the AUC offered a holistic measure of its ability to discriminate between classes. Combined, these approaches enabled a robust analysis of the model's performance, with the level of precision and reliability evident.
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 6 Conclusions  
This paper suggests multi-dimensionality and a methodology to improve the throughput in an MU-MIMO system. This paper also presents the transceiver structure of the proposed scheme. As a result, the multiple dimensions (space and power) are exploited at the same time, and the overasll system spectral efficiency is improved. If more dimensions are used without degradation or with a little tradeoff in performance, the system throughput can be increased. Also, various system models can be implemented by using additional dimensions.  
the algorithm and provided the experimental materials for better computational simulations and revised critical errors of the manuscript  
**Funding:** This research was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2017-0-00217, Development of Immersive Signage Based on Variable Transparency and Multiple Layers) and was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-2018-0-01423) supervised by the IITP(Institute for Information & communications Technology Promotion).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods  
### Materials  
In this study, a cement type 2 with a density of 3.15 gr/cm\({}^{3}\) and blaine fineness of 3210 cm\({}^{2}\)/gr (confirming ASTM C150 [34]) was utilized in all mixes. The related properties are presented in Table 1.  
Fine and coarse aggregates were extracted from a local mine. Grading tests were done on the coarse and fine aggregates, and results are shown in Table 2; Table 3. According to Table 3, the fine aggregate is coarse-grained (sieve No. 4 has 17% remaining mass), and the fineness modulus is 3.8. Therefore, fine aggregates are modified to reduce the fineness modulus to meet the recommended amount in the standard of ASTM C33, [35]. In this regard, the aggregates of sieve No. 4 and half of sieve No. 8 were assumed as a part of coarse aggregates. This matter is considered in the mixture designation of concrete, and the required water content of concrete is modified due to this issue.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Chemical Analysis (\%)** & **Cement Type 2 Portland** \\ \hline CaO & 63.35 \\ SiO\({}_{2}\) & 21.32 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.77 \\ MgO & 2.44 \\ SO\({}_{2}^{1-}\) & 1.98 \\ Na\({}_{2}\)O & 0.24 \\ K\({}_{2}\)O & 0.63 \\ Cl\({}^{-}\) & 0.01 \\ Blaine (cm\({}^{2}\)/g) & 3210 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition and properties of cement type 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sieve No. (BS)** & **Remaining Mass on** & **Remaining Mass on** & **Cumulative Fraction** & **Cumulative Fraction** \\  & **Sieve (gr)** & **Sieve (\%)** & **Passing (\%)** & **Remaining (\%)** \\ \hline
1\({}^{\prime\prime}\) & 0 & 0 & 100 & 0 \\
3/4\({}^{\prime\prime}\) & 150 & 5 & 95 & 5 \\
1/2\({}^{\prime\prime}\) & 1000 & 33.4 & 61.6 & 38.4 \\
3/8\({}^{\prime\prime}\) & 870 & 29 & 32.6 & 67.4 \\
#4 & 930 & 31.1 & 1.5 & 98.5 \\ Pan & 40 & 1.3 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grading of coarse aggregates.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sieve No.** & **Remaining Mass (gr)** & **Remaining Mass (\%)** & **Cumulative Fraction** \\  & **Sieve No.** & **Remaining Mass (gr)** & **Remaining Mass (\%)** & **Remaining** \\ \hline \#4 & 170 & 17 & 17 \\ \#8 & 280 & 28.5 & 45.5 \\ \#16 & 140 & 14.2 & 59.7 \\ \#30 & 180 & 18.3 & 78 \\ \#50 & 130 & 13.2 & 91.2 \\ \#100 & 70 & 7.1 & 98.3 \\ \#200 & 10 & 1.02 & 99.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grading of fine aggregates.  
Nano-MgO particles are used as admixture by the percentage of cement weight. The nanoparticles are less than 40 nm, and the density of nano-MgO is 3.58 g/cm\({}^{3}\) with a purity of 99%. Table 4 presents the details of nano-MgO analysis.
## 6 Proposed MAC Protocol for Wireless Healthcare Environments--WhMAC
### TDMA Frame, Packet and Wake-Up  
The whMAC TDMA frame is presented in Figure 4. It consists of a beacon (B), sent by MN, and a number of time slots. Every measured quantity is assigned to one or more time slots, depending on its size. A single value, such as heart-rate, or blood pressure can be sent using only one time slot, and hence it is given only one time slot. On the other hand, streaming of ECG needs multiple time slots in order to achieve the required data-rate. Some slots can be also merged and the packet can be sent across all of them to reduce overhead. Some reserve time slots should be left for a real-time data stream, in the case of errors occurring. The decision on what quantities are going to be measured is made by the MN, using the predefined scenarios, or can be selected using the MN or MS. For example, if the patient has flu, the slot \"S1\" could be used for temperature, \"S2\" for blood pressure and so on. In another example, if the patient needs to be monitored for ECG, for example \"S1\" would be heart rate, and \"S2-12\" would be used for the ECG data stream. Every measured quantity should also be associated with a preferred sensor, and if possible, a backup sensor (or sensors).  
The whMAC communication packet is shown in Figure 5. Its MAC layer consists of:  
* _WBAN ID_--WBAN ID, given during network forming.
* _Source Address_--Sensor or MN address
* _Destination Address_--Sensor, MN or broadcast address
* _Type of packet_--Beacon, Stream, Information, Commands, ACK/NACK
* _Payload_--Sampled data or additional information
* _Check_--CRC or FEC  
These communication packets can be of different size, depending on the type. For example, information packets sent in one time slot can be shorter than the stream packets, sent across multiple time slots, to reduce overhead.  
Figure 4: whMAC TDMA frame.  
Figure 5: whMAC data packet: wake up command, beacon, data, status and ACK/NACK packets.  
A whMAC wake-up packet can be seen in Figure 6. It consists of:  
* _Preamble_--used to generate the wake-up signal
* _Frequency channel_--Channel in which to expect a command.
* _WBAN ID_--WBAN ID, given during network forming.
* _Sensor address_
* _Error detection_--CRC or FEC  
The wake-up packet is always followed by a communication command packet, intended for the same address, at the frequency channel sent in the wake-up packet. The wake-up communication has its own carrier frequency and it cannot use multiple channels. As for the timing of the wake up packet, it is important to be as close to the end of the time frame (to be followed by the beacon), in order to reduce idle listening for a beacon to synchronise. Therefore, at the end of the time frame, certain number of time slots should be freed if needed for wake up packets.
## 4 Results  
A total of 158 LSFE procedures were evaluated, among which only 18 met the inclusion criteria. Overall, 20 teeth (1 third molar, 10 s molars, 1 first molar, 7 s premolars, and 1 first premolar) in 18 patients (12 females and 8 males) were tested as vital. In 4 out of the 18 patients, a single tooth was missing. There were no intraoperative (excessive bleeding, Schneiderian membrane perforation) or postoperative (surgical site infection, abscess formation, or dehiscence with drainage and maxillary sinusitis) complications within the 18 evaluated cases.  
Based on the preoperative CBCT scan, the spatial relationship to the maxillary sinus was classified according to the criteria described by [PERSON] and colleagues (Table 1) [15].  
There were no statistically significant differences between the examiners. The reliability of the measurements was excellent (ICC = intraclass correlation coefficient = 0.999). All results are presented as the mean of the measurements obtained by the two examiners (Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Type 1** & **Type 2** & **Type 3** & **Type 4** & **Total** \\ \hline PM1 & & 1 & & 1 \\ PM2 & 4 & 2 & 1 & 7 \\ M1 & & & 1 & 1 \\ M2 & & 3 & 6 & 1 & 10 \\ M3 & & & 1 & 1 \\ Total & 4 & 6 & 9 & 1 & **20** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship between the tooth type and Shabazian classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Apex to Sinus Floor Distance** & **Ridge Height at the Edetulous Area** \\ \cline{2-4}  & **Baseline** & **6 Months** & **Baseline** & **6 Months** \\ \hline Mean \(\pm\) SD & \(1.05\pm 1.71\) & \(8.25\pm 3.86\) mm & \(4.33\pm 2.27\) & \(17.29\pm 3.02\) \\ \hline Range & 0–5.63 & 1.11–18.12 & 0.8–8.81 & 7.82–22.18 \\ \hline Mean difference \(\pm\) SD & \(7.17\pm 3.35\)* & \(12.95\pm 3.38\)* \\ \hline N & 20 & 20 & 18 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean measurements at baseline and 6 months after sinus augmentation.  
There was a statistically significant difference in the ridge height between the baseline and 6 months postoperatively and in the distance from the apex to the sinus floor between pre- and 6 months postoperatively (\(p<0.01\)). In addition, a positive statistically significant correlation was found between the preoperative distance from the apex to the sinus floor and the preoperative edentulous ridge height (\(p=0.03\)).
## 3 Results and Discussion
### Validation of the Quantitative Analytical Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Analyte**} & \multirow{2}{*}{\begin{tabular}{c} **Precision** \\ **(RSD, \%, n = 6)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Repeatability** \\ **(RSD, \%, n = 6)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Stability** \\ **(RSD, \%, n = 6)** \\ \end{tabular} } & \multirow{2}{*}{**Recovery**} \\ \hline
1 & N-R1 & 1.78 & 3.12 & 4.43 & 99.25 & 3.45 \\
2 & G-Rg1 & 3.23 & 4.93 & 3.93 & 101.21 & 2.45 \\
3 & G-Re & 3.65 & 2.86 & 4.43 & 103.19 & 0.94 \\
4 & G-Rf & 4.87 & 4.05 & 4.60 & 99.92 & 2.74 \\
5 & G-F3 & 4.52 & 4.86 & 3.53 & 100.60 & 3.22 \\
6 & G-Rg2 & 1.32 & 4.19 & 3.79 & 101.40 & 1.90 \\
7 & G-Rh1 & 4.72 & 4.11 & 2.09 & 100.22 & 2.68 \\
8 & G-Rb1 & 4.59 & 4.10 & 2.02 & 104.10 & 0.69 \\
9 & G-Ro & 2.99 & 1.40 & 1.02 & 100.38 & 2.84 \\
10 & G-Rc & 4.16 & 2.90 & 2.60 & 102.00 & 2.37 \\
11 & G-Rb2 & 3.78 & 3.70 & 4.43 & 102.29 & 2.61 \\
12 & G-Rb3 & 2.29 & 4.26 & 4.56 & 102.67 & 1.56 \\
13 & CS-IV & 2.70 & 4.32 & 2.47 & 100.98 & 2.68 \\
14 & CS-IVa & 3.18 & 4.74 & 1.67 & 101.55 & 1.89 \\
15 & G-Rd & 4.21 & 3.34 & 3.72 & 100.73 & 3.14 \\
16 & G-Rg3 & 2.72 & 3.41 & 3.31 & 101.67 & 2.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The precision, repeatability, stability, and recovery of the 16 analytes.
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 3 Existing Methods for Vbda
### Vary Beam Divergence by Adjusting the Focal Length F of the Collimating Lens
#### 3.1.2 Using Zoom Lens
It was the first study on a WDM FSO system capable of non-mechanical optical beam steering and divergence control realized by liquid lenses.
# Unsteady-State Creep of an Asphalt Concrete
## 4 Results and Discussion
### Steady-State Creep Rate  
It is accepted in the theory of viscoelasticity that all the strains of the materials are plastic ones on the steady-state creep site [45; 46; 47]. In other words, the material \"flows\" under the impact of load like a viscous liquid on this site. The works [37; 38; 39; 51] show experimentally that a creep curve of an asphalt concrete is approximated on the unsteady-state creep site with a high accuracy by a straight line, and therefore, it can be characterized by only one parameter--the steady-state creep rate \(\dot{\epsilon}_{II}\).  
Inserting Expression (12) for the unsteady-state creep duration into Expression (13), we will have the following expression for the steady-state creep rate of the asphalt concrete:  
\[\dot{\epsilon}_{II}=\epsilon_{0}\cdot\delta\bigg{[}1.3\cdot 10^{-25}\cdot \exp\bigg{(}\frac{16733.96}{T_{k}}\bigg{)}\cdot\sigma^{-(15.05-0.038-T_{k})} \bigg{]}^{-\alpha}. \tag{15}\]  
The steady-state creep rate is an important indicator for an asphalt concrete, as it characterizes directly the process of the plastic deformation. Furthermore, it can be included into the models for the prediction of a rut on an asphalt concrete pavement of a highway.  
The obtained Expression (15), therefore, is of important practical value. Usually, the creep test of the material should be performed to the end of the steady-state creep site to determine experimentally the value of the steady-state creep rate, i.e., a long duration of the experiment is required. The peculiarity of Expression (15) is in the fact that it enables us to determine the numerical value of the steady-state creep rate according to the creep test results of an asphalt concrete only to the end of Site I (unsteady-state creep site). It enables us to reduce considerably consumption of time and material resources, as well as the volumes of the calculation works. For example, the experimental results have shown that the average relative duration of Sites I, II and III (Section 4.1) of the creep curves for the asphalt concrete are equal to 13, 63 and 24% respectively at the temperature of 20 \({}^{\circ}\)C [37]. In other words, the methodical approach, according to which Expression (15) has been obtained, enables us to reduce the duration of the experiments almost by five times.  
Another important peculiarity of Expression (15) is the fact that all the parameters, included into it, are of clear physical nature: the thermodynamic temperature T\({}_{\mathrm{k}}\), the stress \(\sigma\), the creep parameters \(\epsilon_{0}\), \(\delta\), \(\alpha\). From Expression (15) it is seen that the steady-state rate \(\dot{\epsilon}_{II}\) is directly proportional to the strain rate at load duration for 1 s (\(\epsilon_{0}\cdot\delta\)), and the effect of the unsteady-state creep duration on it is characterized by the parameter \(\alpha\) (the logarithmic strain deceleration).