esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
###### Abstract
The story of digital twins in healthcare began to gather momentum in the 2010s, with the rise of the Internet of Things and artificial intelligence (AI)-based data analytics enabling the real-time monitoring of patient health through wearable devices. Early applications focused on creating digital replicas of organs, such as the heart, to simulate conditions and optimize treatment plans. The concept has been further developed through the integration of AI and machine learning, enabling personalized medicine through predictive modelling of patient outcomes. By the mid-2010s, digital twins were being used in surgical planning, where virtual models of a patient's anatomy helped surgeons practice and refine procedures before actual surgeries. The COVID-19 pandemic has accelerated interest in digital twins as a tool to manage and predict the spread of the virus and assess the impact of public health interventions. Advances in genomics have also enabled the creation of digital twins at the cellular level, facilitating more targeted therapies. Hospitals began implementing digital twins of entire facilities to optimize patient flow and resource management, improving overall efficiency. This concept extended to drug development, where virtual models of human physiology were used to simulate drug interactions, reducing the time and cost of clinical trials. The increasing availability of high-quality medical data and advanced computing power have made digital twins more accessible and accurate, driving their widespread adoption. From the 2020s, AI-based DT technologies in healthcare are poised to  
Figure 1: DTs development against the background of AI development (own version). revolutionize patient care, offering unprecedented levels of personalization and predictive power (Figure 2) [7; 8; 9; 10; 11].
## 5 Results
### Performance Evaluation
#### 5.3.2 Dynamic Checkpointing for CiC Algorithms
To conclude this section, based on results presented in Figures 6-8, we can argue that our approach correlated both cost (QoS parameters: response time, CPU, Memory, reliability) and benefit (how well the system behaves) in the following way:  
* When QoS was very high it implied saving checkpoints, because there were enough resources available, and the cost of storing checkpoints was minimal. * When QoS was very low it implied not saving many checkpoints, because there were not enough resources available, yet our approach saved some checkpoints for preserving consistency upon a failure. * The granularity to monitor the system performance through non-functional parameters allowed us to detect smalls changes, and the system evaluation was enriched; hence fewer forced checkpoints could be generated when the system behavior was good.
# Day and Night: Locating the General Practitioner's Panel after Hours
## 2 Materials and Methods
### Solution Approaches
#### 2.2.2 Sequential Approach  
For this approach, the standard p-Median problem is used to solve for one time setting, and the solution for that time setting constrains the other time setting. This is more robust than the independent approach, since the results of one time setting are used to constrain the other. This is expected to more closely model the real-world use of primary care facilities.  
The sequential approach differs depending on which time setting is solved first. If the regular setting is solved first, the solution set of regular hour facility locations becomes the set of possible locations for the after-hours p-Median problem. For example, consider a situation where three facilities are to be placed during the regular setting, two are to be located after-hours, and the nodes \(I\in[1,2,3,\ldots,n]\) are possible facility locations. If the model locates facilities at nodes 1, 2, and 3 in the regular setting, then the after-hours setting facilities must be placed among nodes 1, 2, and 3 only.  
The process for the sequential model when the regular setting is solved first (abbreviated as \"Rta\") is as follows:  
* Solve the standard p-Median problem for the regular setting (identical to the independent approach for the regular setting).
* Create a set (\(I^{\prime}\)) of nodes from the solution containing nodes where regular time setting facilities are located.
* Solve a p-Median problem for the after-hours using \(I^{\prime}\) instead of \(I\) as the set of possible facility locations.  
If the after-hours problem is solved first, the nodes selected for after-hours facilities must be included in the regular setting solution. For example, consider a situation where one facility is located for the after-hours setting and three are located in the regular setting. If the model locates a facility at node 2 in the after-hours setting, then a facility must also be set at node 2 in the regular setting solution. This is enforced by a constraint (shown by Equation (15)) added to the regular hours problem so that regular hours facilities must be located at nodes where there are after-hours facilities. Note that this constraint requires that there must be at least as many regular facilities as after-hours facilities.  
\[y_{i}-y^{\prime}_{i}\geq 0 \forall i\in I \tag{15}\]  
The process for the sequential model when the after-hours setting is solved first (abbreviated as \"AtR\") is as follows:  
* Solve the standard p-Median problem for the after-hours setting (identical to the independent approach for the after-hours setting).
* Solve a p-Median problem for the regular setting with additional constraint (15).  
This approach should yield more practical results than the independent approach due to practical costs of setting up additional facilities for either time setting. However, this approach does not consider the second time setting when solving for the first, and it should be more constrained than an approach which considers both time settings simultaneously.
## 4 Comparative Analysis
### Distributional Analysis via the Kolmogorov-Smirnov Test  
The one-sample Kolmogorov-Smirnov (K-S) test was used in this study to assess whether the execution time distributions for Apriori and FP-Growth conformed to a normal distribution, which is a prerequisite for applying parametric tests like ANOVA.  
This is a non-parametric statistical test used to determine whether a given sample comes from a specific distribution, most commonly, the normal distribution.  
The K-S test compares the empirical distribution function (EDF) of a sample with the cumulative distribution function (CDF) of a reference distribution (e.g., normal and uniform). It evaluates the goodness-of-fit between them. The test calculates the maximum absolute difference between the empirical CDF of the sample, and the theoretical CDF of the expected distribution.  
The results are summarized below.  
Figure 1: Average number of rules generated by Apriori and FP-Growth across different support thresholds.  
* FP-Growth values followed a normal distribution for all tested support levels (\(p>0.05\)).
* Apriori results exhibited non-normal distributions for support = 0.15 and 0.20 (\(p<0.05\)), but normality was accepted at support = 0.10.  
These findings justify the use of ANOVA for FP-Growth comparisons and Kruskal-Wallis tests for Apriori.
## 5 Performance Evaluation and Comparative Analysis
### Performance Evaluation
Therefore, the average waiting time for a packet in a queue (\(Wq\)) is as follows:  
\[Wq\ =\ Lq/\lambda\]  
Assume \(Lq\) is 1940, 1169, and 780 for the arrival rates (\(\lambda\)) of 6.6, 4.0, and 1.9, respectively, observed for Camera, LiDAR, and RADAR sensors at the SDN gateway during the third test. \(Wq\) is approximately 294, 292.3, and 410.5 s, respectively (as illustrated in Table 5). This is the total time taken (average flow duration) by the SDN gateway to pre-process and transmit all vehicular traffic to the gNB for dynamic offloading. Using the equation below, the average delay (\(Li\)) at the SDN gateway is approximately 0.15, 0.25, and 0.5 s/packet, respectively, during the third test. This implies an average delay per packet of approximately 150, 250, and 500 milliseconds, respectively, for Camera, LiDAR, and RADAR sensors at the SDN gateway (as illustrated in Table 6). This corresponds with the observed delay per packet observed during our simulations. \[Li=Wq/Lq\]  
Applying the same formula and considering the average number of packets offloaded at the gNB for all four vehicles (as illustrated in Table 7) and the average delay observed at gNB during offloading (as illustrated in Table 8), the average delay per packet (\(Li\)) at the gNB during the third test is approximately 60, 90, and 170 milliseconds for Camera, LiDAR, and RADAR sensors, respectively (as illustrated in Table 9). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **TEST 1 (S)** & **TEST 2 (S)** & **TEST 3 (S)** \\ \hline CAMERA & 0.43 & 0.22 & 0.15 \\ LIDAR & 0.71 & 0.36 & 0.25 \\ RADAR & 1.67 & 0.77 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average delay per packet transmitted at SDN gateway (s). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **TEST 1 (Packets)** & **TEST 2 (Packets)** & **TEST 3 (Packets)** \\ \hline CAMERA & 1945 & 1948 & 1940 \\ LIDAR & 1167 & 1170 & 1169 \\ RADAR & 778 & 780 & 780 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average packets transmitted by a single vehicle (packets). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **TEST 1 (S)** & **TEST 2 (S)** & **TEST 3 (S)** \\ \hline CAMERA & 845.7 & 423.5 & 294 \\ LIDAR & 833.6 & 417.9 & 292.3 \\ RADAR & 1296.7 & 600 & 410.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average delay for all transmitted packets by the SDN gateway (s). The overall delay per packet for pre-processing vehicular traffic at the SDN gateway, using vehicle on-board resources and dynamically offloading to edge application servers during the third test phase, where the best overall performance was observed, is approximately 210, 340, and 670 milliseconds for Camera, LiDAR, and RADAR, respectively (as illustrated in Table 10).
## 3 Experimental Results
### Drainage Manager  
In this study, the performance of the drainage system utilizing a centrifugal humidifier was evaluated to minimize water circulation while enabling re-misting. This system was applied in a vertically constructed grow tent made from a regular tent fabric, and the experiment tested the minimum amount of water required for re-spraying and the viability of cultivation over the period. The experimental setup, as illustrated in Figure 7, involved a nutrient solution diluted with water in a reservoir, which was then sent to the humidifier and sprayed inside the vertical grow system, with the remaining solution not returning to the reservoir. The results demonstrated that the plants could be sustainably grown using only about 40 L of water over three weeks, highlighting the system's efficiency without the implementation of the air circulation detailed in Figure 3.  
During this experiment, a new variable identified was the correlation between the amount of light provided by artificial sources and plant growth. As depicted in Figure 8, although Area A received more light compared to Area B under artificial lighting, the growth performance was notably poorer in Area A. This observation suggests that the amount of water absorbed by the plants through their roots is determined by the photosynthesis and transpiration processes, and insufficient water supply via mixing can lead to plant dehydration. Plants located on the periphery, receiving less light, utilized more water for their growth and consequently showed better development. This result implies that optimizing the relationship between water supply to the roots and lighting in a hydroponic system using fine mist can establish an ideal cultivation environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Overall View of Cultivation** & **Spray Start Position** & **Opposite the Spray Start Position** \\ \hline Guide & & & \\ Pipe & & & \\  
The system tested in the experiment could accommodate a total of 250 crops and used about 40 L of water during the three-week cultivation period, equating to approximately 0.16 L per plant. Assuming a planting distance of 15 cm, it would be possible to cultivate 49 plants per square meter. Comparing this to traditional field cultivation data from the Jeju Agricultural Technology Center, where water usage is significantly higher, the system demonstrated a water savings of about 93.5%. Utilizing this system within an air-inflated enclosed smart farm allows for water harvesting through air circulation, projecting a 99% reduction in water usage compared to open-field agriculture.
## 4 Choice of Occurrences
### IRR with Occurrence Optimization  
We can now define a variant of IRR, called _Iterative Repeat Choice with Occurrence Optimization_ (IRCOO) whose pseudo-code is given in Algorithm 2. Different from IRR, what is maintained is a set of terminal strings, and the current grammar in each moment is a Minimal Grammar Parsing over this set of strings. Recall that \(\textit{cons}(\omega)\) gives the only terminal string that can be derived from \(\omega\) (the \"constituent\"). Again, we define IRCCO-MC, IRCOO-MF, IRCOO-ML the instances of Algorithm 2 where the score function is defined as \(f_{MC}\), \(f_{MF}\), \(f_{ML}\).  
The computation of the _argmax_ function depends only on the number of repeats, assuming that \(f\) is constant, so that its complexity lies in \(\mathcal{O}(n^{2})\). Like for IRR, the total number of times the while loop is executed is bounded by \(n\). The complexity of this generic scheme is thus \(\mathcal{O}(n\times(n^{2}+n\times m^{2}))\).
```
1:\(s\) is a sequence, and \(f\) is a score function on words
2:\(\mathcal{C}\leftarrow\{s\}\)
3:\(\mathcal{P}\leftarrow\{S\to s\}\)
4:while (\(\exists\omega\ :\ \omega\leftarrow\text{argmax}_{\alpha\in\text{ repeats}(\mathcal{P})}f(\alpha,\mathcal{P}))\wedge|\textit{ mgp}(\mathcal{C}\cup\{\textit{cons}(\omega)\})|<|\mathcal{P}|\)do
5:\(\mathcal{C}\leftarrow\mathcal{C}\cup\{\textit{cons}(\omega)\}\)
6:\(\mathcal{P}\leftarrow\textit{mgp}(\mathcal{C})\)
7:endwhile
8:return\(G(\mathcal{P})\)
```
**Algorithm 2** Iterative Repeat Choice with Occurrences Optimization.  
As an example, consider again the sequence from Section 3.2, where \(\alpha\), \(\beta\) and \(\gamma\) have length one. After three iterations of IRRCOO-MC the words \(xax\), \(xbx\) and \(xcx\) are chosen, and a MGP of these words plus the original sequence results in \(G^{*}\).  
IRRCOO extends IRR by performing a global optimization at each step of the replaced occurrences but still relies on the classical score functions of IRR to choose the words to introduce. But the result of the optimization can be used directly to guide the search in a hill-climbing approach that we introduce in the next subsection.
# AWEncoder: Adversarial Watermarking Pre-Trained Encoders in Contrastive Learning
## 3 Proposed Method
### Watermark Embedding  
After generating \(\mathbf{w}_{\text{adv}}\), the next step is to embed \(\mathbf{w}_{\text{adv}}\) into the pre-trained \(E_{\theta}\). It is realized by further training \(E_{\theta}\) according to a combined loss \(\mathcal{L}_{\text{comb}}\), which consists of two components, that is, the contrastive loss \(\mathcal{L}_{\text{con}}\) and the watermarking loss \(\mathcal{L}_{\text{wat}}\). In other words, we have \(\mathcal{L}_{\text{comb}}=\mathcal{L}_{\text{con}}+\alpha\mathcal{L}_{\text {wat}}\), where \(\alpha\) is a parameter balancing the loss and we use \(\alpha=40\) by default. On one hand, \(\mathcal{L}_{\text{con}}\) has already been defined in Section 2, referring to Equations (1) and (2). Notice that \(\mathcal{L}_{\text{con}}\) needs to be adjusted when other contrastive learning algorithms are applied. On the other hand, \(\mathcal{L}_{\text{wat}}\) is defined as the Kullback-Leibler (KL) divergence [29] between the adversarial embedding and the non-adversarial embedding processed with the softmax function \(\sigma\), that is,  
\[\mathcal{L}_{\text{wat}}=\mathbb{E}_{\mathbf{x}^{\prime}_{i}\sim\mathcal{D}^{ \prime}}\big{[}\text{KL}(\sigma(E_{\theta}(\mathbf{x}^{\prime}_{i})),\sigma(E_ {\theta}(\mathbf{x}^{\prime}_{i}+\mathbf{w}_{\text{adv}})))\big{]}, \tag{5}\]  
where \(\mathbf{x}^{\prime}_{i}\) is sampled from the augmented dataset \(\mathcal{D}^{\prime}\). Thus, \(E_{\theta}\) can be watermarked by updating its parameters during training. We will use \(E^{+}_{\theta}\) to represent the marked version of \(E_{\theta}\).  
Figure 1: General framework for the proposed method. We use the same dataset for Phase I, Phase II and Phase III (**a**) so that the watermark can be successfully embedded into \(E_{\theta}\) while avoiding degrading the encoding performance of the encoder. In Phase II, each clean image is augmented into two images, from which only one randomly selected image was used for generating the corresponding adversarial image that will be used for watermark embedding.
###### Abstract  
Novel silver compounds in higher oxidation states, Ag (II) and Ag (III), have emerged as desirable alternatives to existing forms of antimicrobial silver compounds. Offering enhanced efficacy without sacrificing biocompatibility. Unique physiochemical characteristics associated with higher oxidation state silver confer desirable therapeutic traits. However, these same characteristics create challenges in terms of long-term stability and chemical compatibility with conventional biomedical materials. Core-shell methodologies, utilizing silica as a mesoporous or amorphous shell, have been adopted to enhance the stability of reactive active ingredients or cores. These methodologies commonly utilize controlled condensation of silicic acids in non-aqueous media by way of hydrolyzing alkyl silicates: the Stober process or modified processes thereof. However, these strategies are not conducive to cores of higher oxidation state silver wherein hydroxyl organic precursors and by-products are incompatible with strong oxidizing agents. Addressing these challenges, we present a strategy herein for the preparation of a self-directed silver oxynitrate-silica, Ag\({}_{7}\)NO\({}_{11}\)SiO\({}_{2}\), framework. The method described utilizes pH gradients generated from the oxidation reaction of soluble silver, Ag (I), with a strong oxidizing agent/alkaline silicate media to facilitate spatial control over the protonation and subsequent condensation of silicic acid from aqueous solution. The resulting Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\) framework confers enhanced long term and thermal stability to silver oxynitrate without impairing aqueous degradation profiles or subsequent antimicrobial and antibiofilm activities.  
Keywords:silica; silver oxynitrate; stability; core-shell; antibiofilm; antimicrobial +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion  
### Wireless Module  
An ECG signal from dry electrodes based on zeolites and PDMS is transferred to the proposed wearable wireless module using NI's myDAQ. Before the participation of volunteers in this study, the necessary institutional review board (IRB) approvals were obtained. The raw ECG signal had a signal-to-noise ratio (SNR) of 7.21 dB. SNR is improved to 30.9 dB by filtering the power line noise frequency of 60 Hz and all higher-order frequencies above 100 Hz. The ECG signal obtained while the subject is walking is shown in Figure 8. It is observed that the peaks of \(P\), \(Q\), \(R\), \(S\), \(T\) are present. The denoising and baseline wandering are effectively taken care of using discrete wavelet transform MATLAB code [26].  
Figure 8: ECG signal acquired while the subject is walking.  
Figure 7: NVIDIA Jetson size and specification.  
The nRF24L01 transceiver wireless module transmits the ECG signal, while an EVAL board utilizes another nRF24L01 as a receiver. A distance of 5 m separates the EVAL board from the wireless transmitter. For the SCA database, the sampling frequency is 250 Hz, for which a data rate of 250 kbps is sufficient. A higher data rate can be used in the case of high sampling rate biosignals, but this will increase power consumption. While transmitting the digitized ECG signal at a rate of 250 kbps, the transceiver and the ADC module draws 3.4 mA of current for an ECG signal of 45 s, and the system with a 3.3 V supply has an average power consumption of 11.2 mW. The battery life can be estimated using the battery's nominal capacity and the load's average drawn current. The capacity of a battery is typically measured in milliamp-hours (mAh). For a 3.4 mA current (during transmission) and 240 mAh capacity battery (with CR032), the battery life of this wireless module is estimated to be 70 h. The SNR analysis is performed to determine the loss during transmission. For a 2m distance between the receiver and transmitter, the SNR of the ECG signal before transmission is 16 dB, and at the receiver is 6 dB, indicating a 10 dB loss. The plot of the ECG signal before and after the transmission is shown in Figure 9. Table 4 compares this work to similar works with the same supply voltage of 3.3 V. In comparison [27], our work has a 50% reduction in current and minimizes the power consumption. Using MATLAB code, the bit error rate is estimated between (0-0.1)%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & [28] & [27] & [11] & **This Work** \\ \hline Current (mA) in & & & & \\ TX & 11.3 & 7.22 & 8.5 & 3.4 \\ \hline Current (mA) in & & & & \\ RX & 13.5 & 6.75 & 12.5 & 1.4 \\ \hline Data rate (Mbps) & 2 & 2 & (1–2) & 0.25 \\ \hline Power (mW) & 37.3 & 23.8 & 28 & 11.2 \\ \hline Distance (m) & NA & NA & NA & 5 \\ \hline Bit Error Rate & NA & NA & NA & (0–0.1)\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with other nRF24L01 works at 3.3 V.  
Figure 9: Comparison of the ECG signal at the transmitter and receiver.