esa-sceva/satcom-chunk-collection huggingface dataset:
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 4 Approaches to Engineering Whole Organs
### Building Organs from Synthetic Biomaterials  
Synthetic scaffolds and grafts are a tool for structural engineering and development of implantable organs. In the late 1970s and early 1980s, many researchers and scientists began developing new approaches to shaping polymers into sophisticated and complex structures, which resembled the needed architectures of whole organs and tissues. The manufacturing methods resulted in reproducible and tunable scaffolding materials for the purpose of tissue regeneration. Synthetic polymer scaffolds have the advantage of possessing high mechanical strength, adjustable porosity and surface area, reproducible manufacturing, and engineered surface chemistry [14].  
The starting scaffolding is the essential foundation for building functional and viable whole organs. This is because the three-dimensional anatomic orientation must be similar to naturally occurring scaffolding in order to be successful. With synthetic biomaterials, these key scaffolding characteristics and attributes can be engineered to the specific application they are being designed for. Standard synthetic scaffolding designs have typically consisted of surgical meshes, polymer fibers, sponges, foams, sheets, and various other configurations. Different types of synthetic biomaterial scaffolds, including microsphere scaffolds, porous scaffolds, hydrogel scaffolds, fibrous scaffolds, polymer-bioceramic composite scaffolds, and acellular scaffolds, are all used in these various applications [14].
# SSTNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems
## 5 Experimental Simulation and Result
### Experiments on NSGA II with Test on Problems DTLZ1 and DTLZ2  
In order to investigate the performance of the proposed sorting algorithm utilized in MOEAs, the test problems DTLZ1 and DTLZ2 are used as benchmarks [30]. The adopted evolutionary algorithm is NSGA II as described in [2], and the parameters are: the population size is \(N=200\), the iterations are 200, the selection operator is binary tournament, and the simulation binary crossover (SBX)  
Figure 8: Number of fronts, comparisons and running time for random populations varying from 50 to 10,000.  
Figure 9: Number of comparisons and running time (in seconds) of 1000 solutions with increasing fronts from 1 to 50.  
(with distribution index \(mu=20\)) and polynomial mutation operator (with nonnegative real number, \(num=20\)) are used in NSGA II, as in paper [2]. The dimension of variables is set as 20. The crossover rate and mutation rate are 0.8 and 1/\(n\), respectively. The number of objectives \(M\) to be tested are 2, 5, 10 and 15. The number of comparisons and running time are also considered to be the performance indicators. Each algorithm runs independently ten times to get the average value of the performance indicators.  
Table 4 gives the mean values of comparing times and running time of the NSGA II algorithm with FNDS, AP, CS, DS, ENS-SS, BOS and SETNDS on DTLZ1 and DTLZ2, respectively. SETNDS works better than the others with a minimum number of comparisons as shown in Table 4. At the same time, we can also get the information that the running time of the NSGA II algorithm with SETNDS is the smallest, and algorithm with FNDS performs the worst. The sorting algorithm we improved can hugely shorten the running time in the same cases.
## Appendix D Asymptotic Passive Decoy-State BB84 Transmitter  
To evaluate the secret key rate formula given by Equation (12) in this scenario, we consider that \(p_{s}\approx 1\), and we assume that [PERSON] and [PERSON] can estimate the relevant parameters \(Y_{0}\), \(Y_{1}\), and \(e_{1}\) perfectly. Moreover, we use the channel and detection models introduced in Appendix B. In this situation, it turns out that the yields \(Y_{0}\) and \(Y_{1}\) are given by \(Y_{0}=\epsilon_{\text{B}}(2-\epsilon_{\text{B}})\) and \(Y_{1}=1-(1-Y_{0})(1-\eta_{\text{sys}})\).  
The single-photon error rate \(e_{1}\) can be calculated using Equations (28)-(32) with \(\sigma_{i,\psi}=|1_{\psi}\rangle\langle 1_{\psi}|\). After a short calculation, we obtain  
\[e_{1}=\frac{1}{2Y_{1}}\Bigg{\{}Y_{0}+(1-\epsilon_{\text{B}})^{2}\eta_{\text{ sys}}-\frac{4(1-\epsilon_{\text{B}})\eta_{\text{sys}}}{\pi-4\Omega}\sin\left( \frac{\pi}{4}-\Omega\right)\Bigg{\}}. \tag{43}\]  
The resulting lower bound on the secret key rate is illustrated in Figure 3 (red line) for the optimized parameters \(\zeta\) and \(\Omega\).
# Opportunistically Exploiting Internet of Things for Wireless Sensor Network Routing in Smart Cities
## 4 The Integration Platform
### IoT Discovery and Negotiation in IT Platform  
When a sensor has data, it first tries to send it via IoT layer. A hello message is broadcasted to find any IoT device in vicinity. If an IoT receives such message and its direction is towards Sink, it replies to the sensor. The reply message contains IoT's distance from the Sink, and the speed. When multiple IoTs reply, on discovering them the sensor will select one based on RSSI value, distance and speed of  
Figure 4: The internal architecture of a Sensor and an Internet of Things (IoT) device.  
Figure 3: The two layers used by our platform.  
IoT using Algorithm 1. In lines 7 and 8, while calculating the score of an IoT device, the RSSI value is given increased importance as compared to distance and speed. _bestValue_ is the score of so far best IoT device. The sensor sends a confirmation message and on receiving the acknowledgement of that message, the data is sent to the selected IoT device. The IoT acknowledges the data reception.  
```
1:\(\text{iots}[]\) = //array of IoT objects
2:\(\text{iot.rssi}\) = //received signal strength indicator value
3:\(\text{iot.distance}\) = //Maximum distance between two points **minus** distance of IoT from Sink
4:\(\text{iot.speed}\) = //Maximum allowed speed in which communication is possible **minus** speed of IoT
5:proceduregetBestIot(iots[])
6:for each\(p\in iots\)do //remove it replenig with less than threshold RSSI.
7:if\(p.rssi<\text{threshold}\)then
8:\(\text{iots.remove}\) (p)
9:endif
10:endfor
11: best = iots[1]
12:for each\(p\in iots\)do
13: bestValue = 2 * best.rssi + best.speed + best.distance
14: pValue = 2 * p.rssi + p.speed + p.distance
15:if\(pValue>\text{bestValue}\)then
16: best = p
17:endif
18:endfor
19:return best
20:endprocedure
```  
**Algorithm 1** getBestIoT  
When a sensor doesn't get any reply from IoT, it uses its WSN routing protocol and sends the data to the next hope accordingly. If the next hope is in IoT mode (modes discussed in next sub-section), the sensor knows the next hope (neighbor's) time slot and sends data accordingly. When next hop is in Normal mode, its Rx will be ON all the time and can receive data from the sender sensor at any time.
## 3 Proposed Identification Methods
### Quasi-Newton Method
```
0: Given starting point \(x_{0}\), convergence tolerance \(\epsilon>0\)
1: set \(k=0\), \(H_{0}=I\)
2:while\(||\
abla f_{k}||>\epsilon\)do
3: Compute search direction, \(d_{k}=-H_{k}\cdot\
abla f_{k}\)
4: Compute next solution, \(x_{k+1}=x_{k}+\alpha_{k}d_{k}\) using line search
5: Define \(s_{k}=x_{k+1}-x_{k},y_{k}=\
abla f_{k+1}-\
abla f_{k}\)
6: Compute \(H_{k+1}\) using Equation (15)
7: set \(k=k+1\). 8:endwhile
```  
**Algorithm 2** QN (BFGS). The built-in MATLAB function 'fminunc' is commonly utilized for solving optimization problems [58; 59]. It utilizes the QN method as the default optimization algorithm and is designed to be robust and efficient. 'fminunc' can handle various optimization problems, including non-smooth and non-convex functions, providing flexibility for solving different types of optimization problems [58; 59].
# Trustworthy Transaction Spreading Using Node Reliability Estimation in IoT Blockchain Networks
## 4 Trustworthy Transaction Spreading
### Determining the Next Hop for Transaction Traverse: Q-Learning
When a low-hop size (one or two hops) is selected as the action, the transaction spreading is late; in the opposite case, the transaction spreading speeds up. Figure 4 illustrates the overall flowchart of the transaction traverse. Given an origin node and a destination node for each traverse, the agent selects the hop size sequentially from the origin node. Finally, a transaction is delivered to the destination node through border nodes. At each step, the rewards for hop size are calculated and the Q-table is updated. Table 1 shows an example of a Q-table as a result of Q-learning. Each row of the Q-table represents a node, and each column represents an action (hop size: 1-4). Each Q-value is a reward that can be obtained when a specific action is performed at each node. Table 1a,b indicate the significant differences. (a) is more likely to select a low-hop size as an action, and (b) is the opposite. This difference occurs due to the node's reliability. A policy is learned to select an action: decreasing the spreading hops in the less reliable nodes and increasing the spreading hops in the highly reliable nodes. The node reliability can be determined by applying Q-values to the message-passing structure of the GCN. Whenever one transaction traverse is completed, the proposed GCN classifies nodes based on their reliability levels using the Q-values of each node.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 4 Discussion
### Implications and Broader Context  
The implications of this study are significant for various sectors relying on precise indoor localization. In healthcare, for example, accurate patient and asset tracking within hospitals can enhance operational efficiency and patient care. Similarly, in large retail environments, improved localization can facilitate smart shopping systems, guiding customers efficiently and enhancing their shopping experience.  
The reduction in localization error achieved through our methods also has broader implications for the development of smart cities. Accurate indoor localization systems can support various applications, from efficient space utilization in public buildings to enhanced navigation systems in complex environments such as airports and universities.
## 1 Introduction
### Contribution  
As pointed out by a number of researchers, there are still relatively few studies on human-robot interaction in collaborative learning situations [19]. There is also little research about collaborative learning activities in small groups supported by social, anthropomorphic robots, especially in the complex context of a real classroom environment [18]. This paper aims to fill some of these research gaps by focusing on applying a social robot to facilitate a collaborative learning activity in small groups of students in a real classroom environment. The contributions of this paper include the following:  
* A game-based, robot-supported application \"Planning poker with NAO\" for collaborative learning of agile estimation in small groups, using a combined macro and micro script [24] based on the model of self-regulated learning by [PERSON] [25], which can be used to enable a scalable in-person, robot-supported collaborative learning experience for a larger number of small groups;
* A shortened 20-item scale with high internal consistency, which can be used for the evaluation of the quality of social learning experiences supported by social robots;
* Quantitative and qualitative results from the evaluation with 46 adult participants showing that planning poker with the robot NAO as a robot-supported collaborative learning activity in a team can provide university students with an enjoyable and, at the same time, effective social learning experience that supports understanding and learning through play in a team.
* Comparison of the learning experience during a robot-supported collaborative learning activity of students with and without prior experience in interacting with a robot.  
Following this introduction (Section 1), the next sections of this paper present materials and methods used to design, implement, and evaluate the study (Section 2), quantitative and qualitative evaluation results related to the key three research questions (Section 3), and a discussion with recommendations for future research (Section 4).
## 4 MAC Protocol for Scheduling Concurrent Transmissions
### Estimation of Channel Parameters  
So far we have assumed that the channel parameters, i.e., both \(\beta\) and \(\sigma_{dB}\), are known. In practice, these parameters may not be given _a priori_. After the initial deployment, the nodes need to measure received signal powers and estimate these parameters, assuming the location information of neighboring nodes are known. We describe how to estimate \(\beta\) and \(\sigma_{dB}\) in the following.  
According to the shadowing channel model, the received power \(P_{r,dB}\) is governed by (1), and \(X_{r}\) is normally distributed. Thus, (1) can be categorized as the ANOVA (Analysis of Variance) model I (also known as a fixed-effects model ANOVA) [36]. During network operation, each node computes the distance to other transmitters based on location information, and measures the received powers. Assume that a node has made a set of \(n_{T}\) observations, which consists of \(n_{i}\) observations \(P_{r,dB}^{ij}\), \(j=1,\cdots,n_{i}\), for each corresponding distance \(d_{i}\), \(i=1,2,\cdots,N\). Note that \(\sum_{i=1}^{N}n_{i}=n_{T}\). According to the least squares criterion, the sum of the squared deviations of the observations around the expected values, denoted by \(Q\), should be minimized:  
\[Q=\sum_{i=1}^{N}Q_{i}=\sum_{i=1}^{N}\sum_{j=1}^{n_{i}}\left(P_{r,dB}^{ij}-\mu_{i} \right)^{2}\]  
where \(Q_{i}=\sum_{j=1}^{n_{i}}\left(P_{r,dB}^{ij}-\mu_{i}\right)^{2}\).  
Differentiating \(Q\) with respect to \(\mu_{i}\), we obtain that:  
\[\frac{dQ}{d\mu_{i}}=\frac{dQ_{i}}{d\mu_{i}}=\sum_{j=1}^{n_{i}}\left[-2\left(P_ {r,dB}^{ij}-\mu_{i}\right)\right]\]  
Setting the derivatives to zero and replacing \(\mu_{i}\) with the least squares estimator \(\hat{\mu}_{i}\), we obtain that:  
\[\hat{\mu}_{i}=\frac{1}{n_{i}}\sum_{j=1}^{n_{i}}P_{r,dB}^{ij} \tag{15}\]  
Since \(\mu_{i}=P_{0,dB}-10\beta\log\left(\frac{d_{i}}{d_{0}}\right)\) according to the attenuation model (i.e, \(X_{r}\) is zero mean), the estimator of the path loss exponent \(\beta\), denoted as \(\hat{\beta}\), can be obtained as follows:  
\[\hat{\beta}=\frac{\hat{\mu}_{i}-P_{0,dB}}{-10\log\left(\frac{d_{i}}{d_{0}} \right)}=\left(\frac{1}{n_{i}}\right)\sum_{j=1}^{n_{i}}\frac{P_{0,dB}-P_{r,dB }^{ij}}{10\log\left(\frac{d_{i}}{d_{0}}\right)} \tag{16}\]  
With (16), we next derive \(\hat{\sigma}_{dB}^{2}\), the estimator of \(\sigma_{dB}^{2}\). Since the expected value of mean square error (MSE) is equal to \(\sigma_{dB}^{2}\), we derive \(\hat{\sigma}_{dB}^{2}\) as:  
\[\hat{\sigma}_{dB}^{2}=MSE=\frac{\sum_{i=1}^{N}\sum_{j=1}^{n_{i}}\left[P_{r,dB }^{ij}-\hat{\mu}_{i}\right]^{2}}{n_{T}-N} \tag{17}\]
# Effect of the Transition Points Mismatch on Quanta Image Sensors
## 4 Conclusions  
The effect of an ADC transition points mismatch on a QIS is discussed, and related mathematical models are established. The BER is higher when the mismatch of 1-bit and \(n\)-bit ADC transition points is stronger. The tolerance for the mismatch is determined to ensure a low BER and good image quality. The standard deviation of the 1-bit ADC transition point should be less than 0.15e\({}^{-}\) if the reference value of the BER is 1% for a single-bit QIS; the standard deviation of \(n\)-bit ADC transition points should be less than 0.21e\({}^{-}\) if the reference value of the BER is 5% for a multi-bit QIS.  
Imaging models for a QIS are established to observe the imaging quality when a low BER is guaranteed. The models prove that, while the image quality is affected by the mismatch of 1-bit and  
Figure 11: (**a**) A reconstructed image contaminated by shot noise and read noise (\(U_{n}=0.15\)e\({}^{-}\)) without a mismatch of ADC transition points (\(U_{In}\) (\(k\)) = \(k\)-0.5) for \(m=4\), \(z=16\), and \(n=4\), and the GSI is 0 LSB; (**b**) a reconstructed image with a mismatch of ADC transition points (\(U_{thr}=0.21\)) added on the basis of (a), and the GSI = 1.13 LSB; (**c**) \(m=4\), \(z=32\), \(n=3\), and the GSI = 1.93 LSB; (**d**) \(m=1\), \(z=256\), \(n=4\), and the GSI = 2.06 LSB.  
Figure 12: The FPN of reconstructed images with \(m=4\), \(z=16\), and \(n=4\).  
\(n\)-bit ADC transition points, a higher bit depth of a multi-bit QIS and a larger spatial oversampling factor could reduce its effect. The imaging models also prove that the FPN increases with a stronger transition point mismatch.  
Investigation, [PERSON] and [PERSON]; Methodology, [PERSON] and [PERSON]; Supervision, [PERSON] and [PERSON]; Writing (original draft), [PERSON]; Writing (review & editing), [PERSON] and [PERSON].  
This work was supported in part by National Natural Science Foundation of China (61704119, 61434004), and in part by Tianjin Research Program of Application Foundation and Advanced Technology (17 ZXRGGX00040).  
The authors declare no conflict of interest.
## 7 Conclusions  
In conclusion, the proposed decentralized P2P video broadcasting system demonstrates substantial advantages in scalability, cost-efficiency, and fault tolerance over traditional centralized architectures. Its lightweight chain-based topology enables easy deployment across diverse environments with minimal hardware requirements, reducing both CAPEX and OPEX while supporting integration with legacy and low-power devices. The system's robust performance under adverse network conditions, enabled by self-healing mechanisms and efficient load distribution, ensures high video quality and sub-second  
Figure 13: The VMAF score under different bitrate (Mbps).  
recovery. Future works will focus on energy-aware relay selection, economic benchmarking against CDN models, adaptive protocols for mobile networks, and enhanced fault resilience via AI and Byzantine fault tolerance. Integration with 5G, edge computing, and intelligent resource scheduling will pave the way for scalable, production-ready deployments.  
Project administration, G.L. and T.G.; Conceptualization, H.X., Z.S. and G.L.; Methodology, T.G. and Z.S.; Software, Z.S. and H.X.; Validation, T.G., H.X. and G.L.; Formal analysis, Z.S. and T.G.; Investigation, Z.S., H.X. and G.L.; Data curation, H.X. and T.G.; Writing--original draft preparation, T.G. and Z.S.; Writing--review and editing, T.G., Z.S. and G.L.; Visualization, H.X.; Supervision, G.L.; Funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (No. 62401342), the Guangdong Basic and Applied Basic Research Foundation (No. 2025A1515011826), and the Natural Science Foundation of Shandong Province (No. ZR2024 QF092).  
The code supporting the findings of this study is available on GitHub at [[https://github.com/Tamakilroha3/ChainCast](https://github.com/Tamakilroha3/ChainCast)]([https://github.com/Tamakilroha3/ChainCast](https://github.com/Tamakilroha3/ChainCast)) (accessed on 15 July 2025).  
The authors would like to thank [PERSON] for his support and guidance in paper writing and express their gratitude to [PERSON], Executive Deputy Director of the Information Technology Office at Shandong University, for his guidance in project design and implementation as well as for providing equipment and hardware support.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:
# Strain-Softening Analyses of a Circular Bore under the Influence of Axial Stress
## 3 Strain-Softening Analyses and Solution Procedure
### \(\sigma_{z}\): 3 rd Principle Stress  
The yield criterion is  
\[\sigma_{\theta(i-1)}+\Delta\sigma_{\theta(i)}=\alpha_{i}\cdot(\sigma_{z(i-1)} +\Delta\sigma_{z(i)})+Y_{i} \tag{39}\]  
The plastic potential is  
\[G(\sigma_{\theta},\sigma_{z},\eta)=\sigma_{\theta}-\beta\cdot\sigma_{z} \tag{40}\]  
We also have  
\[\Delta\varepsilon^{p}_{z(i)}+\beta_{i}\Delta\varepsilon^{p}_{\theta(i)}=0 \tag{41}\]  
\[\Delta\varepsilon^{p}_{r(i)}=0 \tag{42}\]  
Considering Equations (17)-(19), we have  
\[\Delta\varepsilon_{\theta(i)}=\Delta\varepsilon^{e}_{\theta(i)}+\frac{1}{ \beta_{i}}\Delta\varepsilon^{e}_{z(i)} \tag{43}\]  
\[\Delta\varepsilon_{r(i)}=\Delta\varepsilon^{e}_{r(i)} \tag{44}\]  
By substituting Equation (22) into Equations (43) and (44), and then into Equation (13), including equilibrium Equation (9) and yield criterion (39), we produce a second-order differential equation for incremental radial stress. \[r^{2}\frac{d^{2}\Delta\sigma_{r}}{dr^{2}}+g_{1}\,r\frac{d\Delta\sigma_{r}}{ dr}+g_{2}\,\Delta\sigma_{r}+g_{3}\ =0 \tag{45}\]  
where \(g_{1}=(3+\frac{3}{\alpha_{i}\beta_{i}}-4\frac{v}{\beta_{i}}-\frac{2v}{\alpha_ {i}})/T\), \(g_{2}=(\frac{1}{\alpha_{i}\beta_{i}}-2\frac{v}{\beta_{i}})/T\), \(g_{3}=-\frac{1}{\alpha_{i}\beta_{i}}(Y_{i}+\alpha_{i}\sigma_{z(i-1)}-\sigma_{ \theta(i-1)})/T\) and \(T=(1+\frac{1}{\alpha_{i}\beta_{i}}-\frac{v}{\beta_{i}}-\frac{v}{\alpha_{i}})\). The solution for Equation (45) is in the same form as in Equation (25) with \(H=-\frac{g_{3}}{82}\), \(n_{1}=\frac{1-g_{1}+\sqrt{(1-g_{1})^{2}-4g_{2}}}{2}\) and \(n_{2}=\frac{1-g_{1}-\sqrt{(1-g_{1})^{2}-4g_{2}}}{2}\). \(F_{i}\) and \(G_{i}\) in Equation (25) will be determined by boundary conditions. By means of \(\frac{\Delta u_{(i)}}{r}=\Delta\varepsilon_{\theta(i)}\), Equations (22), (43) and (44), complex manipulations produce  
\[\Delta u_{(i)}=\frac{r}{E}[F_{i}\cdot(n_{1}+g_{4})r^{n_{1}}+G_{i}\cdot(n_{2}+g _{4})r^{n_{2}}+g_{4}H+g_{5}]\,T \tag{46}\]  
with \(g_{4}=(1+\frac{1}{\alpha_{i}\beta_{i}}-v-2\frac{v}{\beta_{i}}-\frac{v}{\alpha_ {i}})/T\) and \(g_{5}=-(\frac{1}{\alpha_{i}\beta_{i}}-\frac{v}{\alpha_{i}})(Y_{i}+\alpha_{i} \sigma_{z(i-1)}-\sigma_{\theta(i-1)})/T\). In a manner similar to the process in Section 3.3, \(F_{i}\) and \(G_{i}\) can be determined for all plastic annuli, and thermicrenmental radial stress and displacement can be obtained by Equations (25) and (46). In the plastic region, both \(\sigma_{r}\) and \(\sigma_{\theta}\) decrease when the pressure \(p_{in}\) decreases further. Once \(\sigma_{r}=\sigma_{z}\), there will be 2 plastic zones. For the outer plastic zone with \(\sigma_{\theta}>\ \sigma_{r}>\sigma_{z}\), the solution for radial stress and displacement increments is discussed above. For the inner plastic zone with \(\sigma_{\theta}>\sigma_{r}=\sigma_{z}\), the solution for radial and tangential stresses is the same as those in [PERSON] et al. [16]. The stress in the axis of the bore is \(\sigma_{z}=\sigma_{r}\).