esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Routing Protocol Design
### TSBR Design
Assume \(A\) has more sub-nodes than \(B\), and the sub-node number of \(A\) in the communication range of \(A\) and B is k. The sub-node number of \(A\) out of the common communication range is \(k_{1}\); \(k_{2}\) is the sub-nodes number of \(B\). The different value of sub-node number between A and B is:  
\[\alpha=k_{1}+k-k_{2}\hskip 36.135 pt\alpha>1 \tag{3}\]  
The different value of sub-node number between A and B exceeds common communication range is:  
\[\beta\ =\ k_{1}-\ k_{2} \tag{4}\]  
We calculate the probability change according to the follow two situations:  
\[\bullet if\ k=0\hskip 28.452756 ptP=0\] \[\bullet if\ k>0\hskip 28.452756 pt\left[ \begin{array}{cc}& \mbox{if}\ \beta<0\hskip 28.452756 ptP\in(0,1/\ 2)\\ &\mbox{if}\ \beta=0\hskip 28.452756 ptP=1/\ 2\\ &\mbox{if}\ 0<\beta<k\hskip 28.452756 ptP\in(1/\ 2,1)\\ &\mbox{if}\ \beta\geq k\hskip 28.452756 ptP=1\end{array} \right.\]  
We analyze the situations of \(\hat{a}<0\) and \(0<\hat{a}<k\) when \(k>0\). Based on the definition of equilibrium, we realize equilibrium means to make the sub-node number of each node as equal as possible. We can achieve the target by giving \([\hat{a}/2]\) nodes to \(B\) when node number k in the common communication range is greater than 0, and \(\hat{a}<0\), \(0<\beta<k\), the change probability of a sub-node in the common communication range is \([\hat{a}/2]/\)k.  
Therefore, we can conclude the change probability of \(k\) nodes based on the above analyses and large amount data fitting as follows. \[p=\left\{ \begin{array}{cc}0&\mbox{$k=0$}\\ 1/\ 2&\mbox{$k\
eq 0$, $\beta=0$}\\ 1&\mbox{$k\
eq 0$, $\beta<k$}\\ \left|\left|\frac{\alpha}{2\,k}\right|\right|&\mbox{$others$}\end{array} \right. \tag{5}\]  
In the process of route establishment, we can use Equations (3-5) to conclude the change probability of a node in common communication range to dynamically adjust the sub-node number of each node.
## 2 Materials and Methods
### Sorption Experiments  
#### 2.3.1 Kinetic  
The kinetic behavior was determined using a batch technique. 10 mL of Pb(II) aqueous solution with a known initial concentration (100 mg L\({}^{-1}\)) were added to 0.10 g of non- and acid-modified clinoptiolite-rich tuffs in a 12 mL conical flask. The mixtures were agitated at 150 rpm in an orbital shaker at 25 \({}^{\circ}\)C for different time intervals (15-1440 min). At the end of the each contact time, the tubes were centrifuged at 4500 rpm for 2 min, and the adsorbent was removed by filtration. The concentration of remaining Pb(II) in the supernatants was analyzed using atomic absorption spectroscopy (Thermo Scientific iCE 3000 Series).  
The sorbed amount of Pb(II) (\(q\), mg g\({}^{-1}\)) was calculated by a mass balance relationship equation as follows:  
\[q=\frac{(C_{0}-C_{t})V}{W} \tag{1}\]  
where \(V\) is the solution volume (mL), \(W\) is the amount of sorbent (g), \(C_{o}\) and \(C_{t}\) are the initial and final metal concentrations (mg L\({}^{-1}\)), at time \(t\) (min).  
In order to describe the kinetics of the adsorption process of Pb(II) by the zeolite materials, pseudo-first-order and pseudo-second-order models [32] were applied in this work. Lineal forms for these models are represented in Equations (2) and (3), respectively.  
\[ln(q_{e}-q_{t})=\ln q_{e}-k_{1}t \tag{2}\]  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}}^{2}+\frac{t}{q_{e}} \tag{3}\]where \(q_{t}\) (mg g\({}^{-1}\)) and \(q_{e}\) (mg g\({}^{-1}\)) are the amounts of metal ions adsorbed at time \(t\) and equilibrium, respectively. \(k_{1}\) (min\({}^{-1}\)) and \(k_{2}\) (g mg\({}^{-1}\)min\({}^{-1}\)) are the pseudo-first order and pseudo-second order reaction rate constants, respectively.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 1 Introduction
### Wiscanet and B5g/6g  
The primary objective of the WISCANet SDR testbed is to accelerate the development cycle of RF applications by reducing the cost of early implementation and validation stages. Many emerging B5G/6G technologies are algorithmic innovations to existing hardware; WISCANet allows algorithm experts to quickly test their new ideas without the hassle of building and operating custom hardware. WISCANet's features specifically enable many of the B5G/6G techniques identified in this special issue, some of which are enumerated below.  
**Size and Scalability**: WISCANet supports dozens of simultaneous radio devices, allowing a user to quickly configure and deploy OTA network applications without the usual overhead of actually building a network. This enables rapid OTA validation of novel network techniques that are typically only characterized in simulation. Emerging B5G/6G network techniques in this area include wireless resource allocation; mobility management; multi-access; internet-of-things (IOT); spectrum sensing and sharing; and distributed coherence.
**Flexibility**: WISCANet allows users to quickly reprogram radios for a variety of different tasks, enabling a range of customizable mission scenarios for characterizing channel estimation and predicition; interference avoidance, management, and cancellation; and localization.
**Programmability**: WISCANet automatically configures the connected radios with minimal user input, so end users can easily test a variety of adaptive techniques that modify the processing chains in real-time, including dynamic spectrum sensing and access; adaptive waveforms and fluid protocols; and novel modulation techniques.
**Hardware Integration**: WISCANet easily integrates with hardware accelerators like CUDA GPUs, FPGAs, and RFSoCs to accelerate computation-heavy applications. These accelerators have proven extremely useful in enabling a variety of machine learning (ML) and artificial intelligence (AI) techniques [32, 33, 34, 35].
**Compatibility**: WISCANet is compatible with the industry-standard Ettus USRP SDRs and several programming languages (MATLAB, Python, C/C++, Rust, etc.), making it accessible and budget-friendly for academic research institutions or independent developers. This further facilitates portability and interoperability between different devices, enabling a variety of IoT applications.
**Upgradability**: WISCANet can be augmented with high-performance equipment like OctoClock distribution modules to enable precise synchronization and time control for sensitive applications such as ultra-reliable, low-latency communications; precise localization; and distributed coherence.
**Stability**: WISCANet features phase synchronization and stability features between connected radios, enabling massive multiple-input, multiple-output (MIMO) applications and numerous multi-antenna techniques.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 3 Proposed Approach: Stpa-Rl
### Model Simulation and Analysis  
#### 3.3.1 Recording Environmental State Trajectories  
To trace the environmental state transition during the trained model simulation process, lists of trace paths are generated and stored. When a safe action is performed from the current state, the corresponding state and action are linked and stored in the trace path. When an unsafe action(\(a_{unsafe}\)) is performed, it is linked to the risk denoted as \(H\{num\}\) in the trace path, where \(\{num\}\) represents the hazard number. Additionally, the length of the path up to the current episode termination is also stored.  
During the evaluation of the A2C model, we trace the state-action transitions in the environment. Starting from the initial state \(s_{0}\), the actor network outputs a probability distribution over actions \(\pi(a|s,\theta_{actor})\). Based on this distribution, we select an action \(a_{t}\) and observe the next state \(s_{t+1}\) and the corresponding reward \(r_{t+1}\). The trajectory of state transitions, actions, and rewards is represented as \(\sigma\):  
\[\sigma=\langle(s_{0},a_{0},r_{1},s_{1}),(s_{1},a_{1},r_{2},s_{2}),\cdots,(s_{ t},a_{t},r_{t+1},s_{t+1})\rangle\]  
But to identify as a loss scenario, the transitions' trace path should end with the unsafe action and reach the hazardous state. The hazard trajectory of a loss scenario is represented as \(\sigma_{H}\):  
\[\sigma_{H}=\Big{\langle}\Big{(}s_{0},a_{(0,safe)},r_{1},s_{1}\Big{)},\Big{(} s_{1},a_{(1,safe)},r_{2},s_{2}\Big{)},\cdots,\Big{(}s_{t},a_{(t,unsafe)},r_{(t+1, unsafe)},s_{(t+1,hazard)}\Big{)}\Big{\rangle}\]
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Emotion Means, Maxima, and Relative Frequencies of Differing Data Points**} \\ \hline \hline  & **Subject** & **Neutal** & **Happy** & **Sad** & **Angry** & **Surprised** & **Scared** & **Disgusted** & **Contempt** \\ \hline \multirow{4}{*}{\#27} & Mean & \(-2.3\times 10^{-6}\) & \(3.37\times 10^{-6}\) & \(1.2\times 10^{-6}\) & \(7.29\times 10^{-7}\) & \(-2.4\times 10^{-5}\) & \(1.13\times 10^{-6}\) & \(4.27\times 10^{-5}\) & \(4.46\times 10^{-8}\) \\ \cline{2-9}  & maximum & \(0.001467\) & \(0.001972\) & \(3.79\times 10^{-6}\) & \(0.000158\) & \(0.011318\) & \(2.54\times 10^{-6}\) & \(0.007265\) & \(1.49\times 10^{-7}\) \\ \cline{2-9}  & rel. freq, in all data & \(0.084117\) & \(0.027527\) & \(0.056829\) & \(0.026808\) & \(0.08287\) & \(0.045943\) & \(0.025993\) & \(0.032611\) \\ \cline{2-9}  & rel. freq, in valid data & \(0.10206\) & \(0.033399\) & \(0.068951\) & \(0.032526\) & \(0.100547\) & \(0.055743\) & \(0.031537\) & \(0.039567\) \\ \hline \multirow{4}{*}{\#33} & Mean & \(-6.7\times 10^{-6}\) & \(9.14\times 10^{-8}\) & \(8.03\times 10^{-6}\) & \(-6.7\times 10^{-7}\) & \(-1.1\times 10^{-6}\) & \(-4.1\times 10^{-7}\) & \(-4.6\times 10^{-8}\) & \(-2.4\times 10^{-8}\) \\ \cline{2-9}  & maximum & \(0.005283\) & \(5.29\times 10^{-5}\) & \(0.002069\) & \(1.79\times 10^{-6}\) & \(1.39\times 10^{-5}\) & \(9.04\times 10^{-7}\) & \(1.1\times 10^{-7}\) & \(1\times 10^{-7}\) \\ \cline{2-9}  & rel. freq, in all data & \(0.216552\) & \(0.101549\) & \(0.109482\) & \(0.105168\) & \(0.159352\) & \(0.175821\) & \(0.040314\) & \(0.166868\) \\ \cline{2-9}  & rel. freq, in valid data & \(0.263016\) & \(0.12338\) & \(0.132973\) & \(0.127733\) & \(0.193543\) & \(0.213545\) & \(0.048963\) & \(0.202671\) \\ \hline \multirow{4}{*}{\#46} & Mean & \(3.72\times 10^{-6}\) & \(-9\times 10^{-6}\) & \(-1.6\times 10^{-7}\) & \(-2.8\times 10^{-8}\) & \(-1.3\times 10^{-5}\) & \(1.31\times 10^{-7}\) & \(-2.2\times 10^{-8}\) & \(1.32\times 10^{-7}\) \\ \cline{2-9}  & maximum & \(0.004441\) & \(0.003993\) & \(6\times 10^{-7}\) & \(1.32\times 10^{-7}\) & \(0.003331\) & \(3.86\times 10^{-5}\) & \(1.67\times 10^{-6}\) & \(5.37\times 10^{-7}\) \\ \cline{2-9}  & rel. freq, in all data & \(0.297395\) & \(0.215251\) & \(0.133531\) & \(0.218219\) & \(0.261457\) & \(0.24436\) & \(0.104281\) & \(0.203288\) \\ \cline{2-9}  & rel. freq, in valid data & \(0.310698\) & \(0.224879\) & \(0.139504\) & \(0.22798\) & \(0.273152\) & \(0.28529\) & \(0.108946\) & \(0.212381\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Residual differences between test\({}_{2}\) and test\({}_{3}\) intensity data for the 7 cases which did not “stabilize” for the test\({}_{2}\)–test\({}_{3}\) comparison. All maxima are absolute values. Frequencies of differing data points are shown relative to (i) the number of all data points: “rel. freq. in all data”, and to (ii) the number of “valid” data points with numerical intensity values (thus excluding data points with FIT_FAILED or FIND_FAILED error signals): “rel. freq. in valid data”.
## 4 Results and Discussion
### PEMD Scenarios Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Case**} & \multicolumn{2}{c}{**C5**} & \multicolumn{2}{c}{**C6**} & \multicolumn{2}{c}{**C7**} & \multicolumn{2}{c}{**C8**} \\ \hline
**Peak Price** & **200\%** & **250\%** & **300\%** & **200\%** & **250\%** & **300\%** & **200\%** & **250\%** & **300\%** & **200\%** & **250\%** & **300\%** \\ \hline
**Peak Reduction (\%)** & 2.729 & 4.093 & 5.458 & 2.729 & 4.093 & 4.582 & 2.729 & 4.093 & 5.458 & 2.729 & 4.093 & 5.458 \\ \hline
**Load Factor** & 0.780 & 0.791 & 0.802 & 0.780 & 0.791 & 0.795 & 0.779 & 0.789 & 0.799 & 0.779 & 0.789 & 0.799 \\ \hline
**Cost Saving (5/day)** & 44,323 & 63,726 & 63,586 & 38,517 & 53,856 & 52,213 & 63,264 & 90,487 & 105,859 & 56,801 & 87,448 & 95,989 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Cases [C5–C8] results. performance. The power plant shares for the C7 having the best cost saving at 300% peak price can be seen in Figure 24, where the share of power purchased from IPP3 was reduced from 1.49% to 0.68%.
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 2 Related Work
### Hybrid Adaptation  
In Mutual Feedback (MutFed) [21], the SINR is measured on the receiver side. After 10 frames a new rate selection decision is made, either to stay at the same rate or to change the rate. This decision is made based on a table that maps SINR threshold ranges to rates. The newly selected rate is fed back to the sender by sending an ACK with the proposed rate. On the transmitter side, if two frame transmissions fails, then the transmitter automatically lowers the MCS for the following transmission. The main drawback of MutFed is not differentiating the result of packet loss that may result in lowering the rate when the packets are not lost due to the channel degradation.  
EasiRA [22] measures the link quality by two means. First, it calculates the FLR and combines it with mobility and other sensor information. Secondly it obtains the environmental signal strength (ESS) information to help differentiate the causes of packet loss. When a packet cannot be successfully received due to bit errors, the receiver sends back a special control frame, named \"non-acknowledgement NAK\", to the transmitter in order to inform it that it may suffer collision. If the transmitter does not receive neither an ACK or a NAK then it reduces the rate. Finally, it combines the random and deterministic rate adaptation mechanisms together. The main drawback of EasiRA is the fact that it tries to identify collisions while using external information, such as sensor-hints and ESS, which may not be available in all of the devices.
# Perspective of Using Vertically Oriented Graphene as an Electrochemical Biosensing Platform
## 3 Results and Discussions
### SEM and TEM Analysis  
The typical structure of the VG, characterized by raised walls and ridge profiles, is visible in the SEM analysis (Figure 2a,b). The height of the walls measures approximately 500 nm in cross-section, and the growth rate of the VG is 9.16 nm/min. The VG film appears uniform with slight densification. In a previous study [20], distinct sheets of VG can be observed at temperatures between 600 \({}^{\circ}\)C and 700 \({}^{\circ}\)C. As the temperature increases, these sheets' deposition rate and interlacing become more pronounced. Beyond 750 \({}^{\circ}\)C, the VG layer becomes more compact, with densely packed sheets. The temperature of the substrate plays a crucial role in the PECVD process as it dramatically affects the surface reaction kinetics. A higher substrate temperature increases nucleation sites, promoting VG growth rate. Additionally, higher temperatures promote the formation of secondary nucleation sites on the VG sheets, contributing to increased interlinking at the active edges.  
Figure 1: Raman spectrum of VG and table with Raman parameters.  
Figure 2: SEM images of VG film: (**a**) top-view of VG, (**b**) cross-section of VG and TEM images of VG film, (**c**) cross-section at 200 nm, and (**d**) cross-section at 100 nm.  
As in the case of the SEM images (Figure 2a,b), the specific morphology of the vertical graphene can be observed in the TEM images (Figure 2c,d) of vertically oriented graphene walls and peaks of different sizes and heights. The SEM and TEM analyses are correlated. This morphology of VG on electrodes allows cell attachment and detection. VG has a high surface area, which provides cell attachment and interaction. Another characteristic is biocompatibility. As we saw in our article [20], based on fluorescence microscopy, the density of the U251 glioblastoma cells is higher on the VG surface, and the absorbance is about 1.138 um, which involves the viability of the cells.
## 3 Materials and Methods
### ECG Preprocessing  
The importance of pre-processing has been widely described in the literature for any kind of signal, but in biological signals is it undoubtedly a key stage. In the case of the ECG, it is extremely relevant as it is not possible to physically set a noise-free environment, as a number physiological events are simultaneously taking place side by side to heart beat. Examples of that are breathing, and involuntary muscle activation among others. In this work we designed the preprocessing in three phases, namely, _signal settlement_, _QRS detection_, and _beat template_ creation.  
The _signal settlement_ process included three different processes as well: (i) Starting with a low-order band-pass filtering from 0.5 Hz to 100 Hz, to reduce the out-of-band noise without changing the wave forms; (ii) Then applying a set of notch filters centered on 50 Hz and its harmonics, to reduce the power-line interference, to the signal; and (iii) finally applying a baseline wander filter, based on cubic spline interpolation [24].  
In the _QRS detection_ according to the implementation in [25], after filtering the input record, the derivative feature signals are computed, and QRS complexes are detected through an adaptive threshold.  
According to physiological knowledge, the fragmentation is supposed to be present in almost every beat scoping the heart region where this affection is visible. Therefore, the _creation of a beat template_ for each lead could be understood as a a morphology-emphasizing statistical filtering, as far as this affection in concerned. Beat templates were computed by averaging highly correlated beats of each lead, according to [26].
## Appendix D Secondary Cognitive Outcomes  
Further analyses on the sub-scores of the MMSE and the DFS support the therapeutic benefits of a serious game-based cognitive training method (i.e., _Neuro-World_) for improving the cognitive function and attention in chronic-stage stroke survivors over no therapy. From the two-way mixed model ANOVA, statistically significant interactions for intervention and time in all the assessed outcomes,including the MMSE sub-scores and the DFS scores, were observed (Table A5). The [PERSON]'s post-hoc test showed that among the MMSE categories, the experimental group achieved statistically significant increases in the orientation (BASE: \(7.93\pm 1.82\) vs. POST: \(8.07\pm 1.49\), \(t.ratio(27)=2.96\), \(p<0.05\)), register (BASE: \(2.57\pm 0.51\) vs. POST: \(3.00\pm 0.00\), \(t.ratio(27)=3.30\), \(p<0.05\)), attention & calculation (BASE: \(2.57\pm 0.51\) vs. POST: \(3.00\pm 0.00\), \(t.ratio(27)=4.85\), \(p<0.01\)), and recall (BASE: \(1.21\pm 0.80\) vs. POST: \(2.93\pm 0.27\), \(t.ratio(27)=9.55\), \(p<0.01\)) categories and non-statistically significant increase in the language (BASE: \(7.71\pm 0.83\) vs. POST: \(8.14\pm 0.53\), \(t.ratio(27)=1.29\), \(p=0.58\)) category (Tables A6 and A7). On the other hand, the control group exhibited non-statistically significant decreases in the orientation (BASE: \(8.07\pm 1.49\) vs. POST: \(6.73\pm 2.79\), \(t.ratio(27)=-2.60\), \(p=0.07\)), the attention & calculation (BASE: \(2.13\pm 0.83\) vs. POST: \(1.40\pm 0.74\), \(t.ratio(27)=-1.44\), \(p=0.49\)), the recall (BASE: \(0.67\pm 0.62\) vs. POST: \(0.33\pm 0.62\), \(t.ratio(27)=-1.92\), \(p=0.24\)) categories and statistically significant decreases in the register (BASE: \(2.13\pm 0.83\) vs. POST: \(1.40\pm 0.74\), \(t.ratio(27)=-5.85\), \(p=<0.01\)) and the language (BASE: \(7.87\pm 0.64\) vs. POST: \(6.7\pm 1.29\), \(t.ratio(27)=-3.74\), \(p=0.01\)) categories. This trend of improvements in cognitive function is further supported by the analysis results of the DFS. The experimental group exhibited non-statistically significant increase in the DFS (BASE: \(7.36\pm 2.17\) vs. POST: \(9.07\pm 2.87\), \(t.ratio(27)=2.48\), \(p=0.09\)), while the control group showed marginal decrease in the DFS (BASE: \(6.13\pm 1.25\) vs. POST: \(4.93\pm 2.22\), \(t.ratio(27)=-1.79\), \(p=0.30\)). Despite the non-statistical significance, these changes are still clinically meaningful, considering the minimal clinically important changes of the DFS (i.e., 1 DFS point) [54]. [PERSON]'s post-hoc tests further revealed that while the experimental and the control groups showed no statistical difference in all the MMSE categories and the DFS at baseline, there was a trend toward greater improvements for the experimental group when compared to the control group. These results collectively suggest that _Neuro-World_ training for a three-month period can improve the overall cognitive level and attention in chronic-stage stroke survivors.
## 4 Discussion
### Opportunities and Challenges Related to the Use of Generative AI in Personalized Patient Care
#### 4.1.2 Challenges  
1. Data privacy and security: Generative AI models often require access to sensitive patient data. Strong privacy and security measures are needed to protect patient information and meet regulations like HIPAA;
2. Ethical considerations: The ethical use of Gen AI in healthcare, including issues related to bias, transparency, and accountability, must be carefully addressed to maintain trust and fairness;
3. Interoperability of data: Healthcare data are often stored in disparate systems that may not easily communicate with each other. Achieving interoperability and seamless integration of data from different sources is a challenge;
4. Validation and regulation: Gen AI models need rigorous validation and regulatory approval to ensure their safety, efficacy, and reliability before widespread clinical adoption;
5. Explainability of models: Understanding the decisions made by Gen AI models is crucial for healthcare professionals. Ensuring the explainability of these models is a challenge, especially when dealing with complex neural network architectures;
6. Integration with clinical workflows: Integrating Gen AI seamlessly into clinical workflows is challenging. The technology should complement existing practices without causing disruptions;* Bias in data and models: If the training data used to develop Gen AI models are biased, the models can perpetuate or amplify these biases. Ensuring fairness in AI applications is an ongoing concern;
* User acceptance and adoption: Healthcare professionals and patients may be skeptical or resistant to adopting Gen AI technologies. Ensuring user acceptance and addressing concerns is crucial for successful implementation;
* Ongoing model maintenance: Gen AI models need continuous monitoring, updating, and refinement to adapt to evolving medical knowledge, changing patient demographics, and emerging healthcare trends.  
Balancing these opportunities and challenges requires collaboration between AI developers, healthcare practitioners, regulatory bodies, and patients to create a framework that maximizes the benefits of Gen AI in healthcare while addressing ethical, privacy, and operational concerns.
## 4 Characteristics of Different Time Distribution Systems
### Precision Time Protocol
#### 4.2.3 Flexibility  
Like NTP, PTP is a network-based time synchronization standard, which makes it easy and flexible to access, with no need to install and maintain dedicated timing cables. But in order to improve synchronization quality, PTP is typically used in conjunction with hardware support with regard to the generation of timestamps [80], which is different from the software-based NTP.  
With the hardware-based Ethernet time synchronization method, the cost of PTP can be higher than NTP due to the need for dedicated hardware. However, it remains relatively inexpensive.