esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Respiratory rate (RR) is a vital sign with continuous, convenient, and accurate measurement which is difficult and still under investigation. The present study investigates and evaluates a stretchable and wearable inkjet-printed strain gauge sensor (IJP) to estimate the RR continuously by detecting the respiratory volume change in the chest area. As the volume change could cause different strain changes at different body postures, this study aims to investigate the accuracy of the IJP RR sensor at selected postures. The evaluation was performed twice on 15 healthy male subjects (mean \(\pm\) SD of age: \(24\pm 1.22\) years). The RR was simultaneously measured in breaths per minute (BPM) by the IJP RR sensor and a reference RR sensor (e-Health nasal thermal sensor) at each of the five body postures namely standing, sitting at 90\({}^{\circ}\), [PERSON]'s position at 45\({}^{\circ}\), supine, and right lateral recumbent. There was no significant difference in measured RR between IJP and reference sensors, between two trials, or between different body postures (all \(p>0.05\)). Body posture did not have any significant effect on the difference of RR measurements between IJP and the reference sensors (difference \(<\)0.01 BPM for each measurement in both trials). The IJP sensor could accurately measure the RR at different body postures, which makes it a promising, simple, and user-friendly option for clinical and daily uses.  
Keywords: inkjet printing; respiratory rate; strain gauge; stretchable and wearable sensors; silver nanoparticles; clinical evaluation; body posture +
Footnote †: journal: Applied Soft 2020, 10, 480; doi:10.3390/app10020480
## 3 Description of Research Methods
### Adversarial Example Restoration  
Adversarial example restoration is a process whose goal is to recover the original, unperturbed data from the tampered adversarial example. Given a known adversarial example \(x_{adv}\), our objective is to find a close, untampered data point. This can be represented as an optimization problem where we attempt to minimize some distance measure between \(x_{adv}\) and its corresponding original sample \(x\). A common approach is to use the Euclidean distance as the metric and recover through the following optimization problem:  
\[\min_{x^{\prime}}\|x_{adv}-x^{\prime}\|_{2}^{2} \tag{6}\]  
here, \(x^{\prime}\) is the unperturbed data sample we are attempting to recover. The crux of the restoration lies in finding an efficient method or algorithm to solve the above optimization problem, yielding \(x^{\prime}\), which should be very close to the original sample \(x\) and distinctly different from the adversarial example \(x_{adv}\).
## 6 Conclusions and Future Works  
### Conclusions  
In this paper, we analyzed scalp images using machine-learning algorithms, including deep learning, BOW with machine-learning classifiers and HOG/PHOG with machine-learning classifiers. The results show the good performance of deep learning, which presented an accuracy rate of 89.77%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Time Spent** & **Accuracy** \\ \hline \multirow{2}{*}{**Deep Learning**} & **(Second Fastest)** & **(Best)** \\  & 52,817.67 & 89.77\% \\ \hline \multirow{2}{*}{**BOW**} & **(Slowest)** & **(Worst)** \\  & 447,959 & 80.50\% \\ \hline \multirow{2}{*}{**PHOG**} & **(Faster)** & **(Second Best)** \\  & 8232.574 & 53.30\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Running time and accuracy comparisons among the four methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Time** & **Testing Time** & **Accuracy without Normalization** & **Accuracy with Normalization** \\ \hline PHOG & 6715.337 & 1517.237 & 39.77\% (70/176) & 44.31\% (78/176) \\ HOG & 106,267.868 & 6294.642 & 34.65\% (61/176) & 37.5\% (66/176) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training time, testing time and accuracy for PHOG and HOG.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification Learners** & **PHOG** \\ \hline Decision Tree & 39.3\% \\ LDA & 33.2\% \\ SVM & 53.0\% \\ K-NN & 41.3\% \\ Ensemble & 50.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of PHOG based on different machine-learning classifiers.  
This value is far higher than that achieved by the other two combination methods and higher than the human inspection accuracy of 70.2% of human inspection.
## 2 Background and Literature Review
### Named Data Networking (NDN) Overview  
In order to efficiently distribute material such as streaming videos, music, and P2P services intended for data sharing, NDN is regarded as a future design with respect to the internet. In addition, it uses names to route packets rather than IP addresses and stores data in a cache [6; 7; 8]. Interest and data packets are the two categories of packets used in NDN. Moreover, the data packet includes information like a digital signature, signed documents, and so on. The interest packet also contains information like the name of the content being sought. The CS, PIT, and FIB are the three tables that make up the NDN node as shown in Figure 2. To store the data locally, CS is utilized, however, the PIT is applied to retain the interface between FIB and the consumer, it is also used as a forwarding strategy as illustrated in Figure 2. Each table has a distinct function. The router verifies the contents of data with the interested party's name in the CS when they show up at the router for placement of content. The PIT entails an interest list for future data, which is where the router searches if the data is not discovered in the CS. It is sent to the consumer if the operation accomplished on the look-up table has been successful; if not, it is verified with PIT before sending the packet of interest to the source. The router comprising of the whole interests that have conveyed but have not yet been satisfied are kept in the PIT. In this scenario, the interest packet is ignored since we know the router does not have the content and does not know how to deliver it from the source if no concerned entity is located. The forwarding strategy is maintained by the FIB, which also chooses how and when to convey interest [18].
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 3 Fast Computation Strategies
### Explicit MPC  
Reducing the computational load has been tackled by resorting to general optimization methods, some of which have proven effective in cases where control actions are quantized, as in FSMPC. In Explicit Model Predictive Control (EMPC) [41], a quadratic optimization problem is decomposed into multiple local subproblems, with their solutions computed offline. Implementing EMPC involves identifying the relevant subproblem (via region determination) and retrieving the appropriate control law from a precalculated table [42; 43]. One of the main challenges with reported EMPC applications is the application of a general region-splitting methodology that lacks physical insight, leading to unnecessarily high complexity. Additionally, the inclusion of complex cost functions can make the EMPC problem more challenging.  
Another general technique is the Sphere Decoding Optimization algorithm [44]. Likewise, Laguerre function-based MPC with a modulator has been proposed for long-horizon problems [45]. However, these methods focus on long horizons and do not completely eliminate the need for combinatorial search.
# Building Topic-Driven Virtual IoTs in a Multiple IoTs Scenario
## 5 Topic-Guided Virtual IoTs in a MIoT and Approaches to Constructing Them
### Unsupervised Approach  
The unsupervised approach begins with the construction of a support network \(\mathcal{N}\) starting from the MIoT \(\mathcal{M}\). In particular:  
* For each node \(n_{i_{k}}\) of \(\mathcal{M}\), a node \(\overline{n_{i_{k}}}\) is added in \(\mathcal{N}\).
* For each i-arc \((n_{i_{k_{i}}},n_{i_{q_{k}}})\) in \(\mathcal{M}\), an (unoriented) arc \((\overline{n_{i_{k}}},\overline{n_{i_{q_{k}}}})\) is added in \(\mathcal{N}\). The arcs of \(\mathcal{N}\) are weighted. The weight of the arc \((\overline{n_{i_{k}}},\overline{n_{i_{q_{k}}}})\) is obtained by applying the operator \(J^{*}\) on the topic sets \(topicSet_{j_{k}}\) and \(topicSet_{q_{k}}\) of \(\iota_{j_{k}}\) and \(\iota_{q_{k}}\), respectively. Therefore, the weight of an arc in \(\mathcal{N}\) belongs to the real interval \([0,1]\); the higher this weight the higher the semantic similarity between the topics of the profiles \(\overline{\mathcal{P}_{j_{k}}}\) and \(\overline{\mathcal{P}_{q_{k}}}\) of \(\iota_{j_{k}}\) and \(\iota_{q_{k}}\), respectively.
* For each c-arc in \(\mathcal{M}\), which relates two instances \(n_{i_{k}}\) and \(n_{i_{q}}\) of the same object \(o_{j}\) in two different IoTs \(\mathcal{I}_{k}\) and \(\mathcal{I}_{q}\), the two nodes \(\overline{n_{i_{k}}}\) and \(\overline{n_{i_{q_{k}}}}\) in \(\mathcal{N}\), corresponding to the nodes \(\eta_{i_{k}}\) and \(n_{i_{q_{k}}}\) in \(\mathcal{M}\), are merged into a unique node \(\overline{n_{i_{j}}}\). This node inherits all the arcs of \(\overline{n_{i_{k}}}\) and \(\overline{n_{i_{q}}}\). At the end of these steps, it could happen that two or more arcs relate the same nodes \(\overline{n}\) and \(\overline{n^{\prime}}\) in \(\mathcal{N}\). In this case, all these arcs must be merged into a single arc. Clearly, it is necessary to determine the weight of this arc. Here, it appears reasonable that it must be higher than or equal to the maximum weight of the merged arcs. To reach this objective, our approach operates as follows. Let \(\{(\overline{n},\overline{n^{\prime}},\overline{w^{1}}),(\overline{n}, \overline{n^{\prime}},\overline{w^{2}}),\cdots,(\overline{n},\overline{n^{ \prime}},\overline{w^{3}})\}\) be the arcs to merge, ordered by decreasing weight. The new arc \((\overline{n},\overline{n^{\prime}},\overline{w})\) will have a weight equal to: \[\overline{w}=min\left(1,\overline{w_{1}}+\alpha\sum_{k=2,s}\overline{w_{k}}\right)\] (29)In other words, in the computation of \(\overline{w}\), the arcs with the maximum weight will contribute with all their weight. All the other arcs will contribute to a lesser extent, with a fraction of their weight. This last is determined by means of the coefficient \(\alpha\).  
Once the construction of \(\mathcal{N}\) has been completed, the thematic views are derived by applying on \(\mathcal{N}\) a graph clustering algorithm among the ones already existing in the literature (see [59] for a survey on them).
## 3 Experiment Results and Discussion
### The Effect of Asymmetric Stroke Angles on the Motion and Efficiency of the Flapping-Wing Rotor  
Based on the measuring method depicted in Section 2.2, the flapping frequency (\(f_{flap}\)), rotary speed (\(\omega_{r}\)), lift-to-power ratio (\(L_{arg}/P_{input}\),average lift over input power of the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline _Cases_ **1** & \(\phi\) & \(V_{input}\) & _Cases_ **2** & \(\phi\) & \(V_{input}\) & _Cases_ **3** & \(\phi\) & \(V_{input}\) \\ \hline
1-1 & \(-20^{\circ}\)-\(50^{\circ}\) & 3 & 2-1 & \(-35^{\circ}\)-\(35^{\circ}\) & 3 & 3-1 & \(-50^{\circ}\)-\(20^{\circ}\) & 3 \\
1-2 & \(-20^{\circ}\)-\(50^{\circ}\) & 3.5 & 2-2 & \(-35^{\circ}\)-\(35^{\circ}\) &motor), and peak inertial forces for the FWR are tested as shown in Figure 9. According to Figure 9, despite the lift-to-power ratio going down with the increase of the input voltage, the flapping frequency, rotary speed, and peak of the inertial forces are positively correlated with the input voltage.  
According to Figure 9a, similar flapping frequencies are found for Cases 1 (\(\phi\in[-20,50]\)), Cases 2 (\(\phi\in[-35,35]\)), and Cases 3 (\(\phi\in[-50,20]\)) under a different input voltage in general. Thus, the peaks of the inertial forces under these cases also stay at the same level as shown in Figure 9d. Compared with the cases in Cases 2 (\(\phi\in[-35,35]\)), the cases in Cases 1 (\(\phi\in[-20,50]\)) or Cases 3 (\(\phi\in[-50,20]\)) share a relatively higher rotary speed given the same input voltage. The rotary speed for Cases 3-8 (7 rev/s) especially are 1.3 times larger than that for Case 2-8 (5.375 rev/s). Among the cases in Cases 1 (\(\phi\in[-20,50]\)), Cases 2 (\(\phi\in[-35,35]\)), and Cases 3 (\(\phi\in[-50,20]\)), it is found that the positive average stroke angle in Cases 1 (\(\phi\in[-20,50]\)) leads to the largest lift-to-power ratio (about 1.25 times those in Cases 2 and about 1.6 times those in Cases 3). The negative average stroke angle tends to generate the smallest lift-to-power ratio despite the negative average stroke angle facilitating the generation of a higher rotary speed.
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
###### Abstract  
This paper describes a three-screen television system using a block recovery rate (BRR)-based unequal error protection (UEP). The proposed in-home wireless network uses scalable video coding (SVC) and UEP with forward error correction (FEC) for maximizing the quality of service (QoS) over error-prone wireless networks. For efficient FEC packet assignment, this paper proposes a simple and efficient performance metric, a BRR which is defined as a recovery rate of temporal and quality layer from FEC assignment by analyzing the hierarchical prediction structure including the current packet loss. It also explains the SVC layer switching scheme according to network conditions such as packet loss rate (PLR) and available bandwidth (ABW). In the experiments conducted, gains in video quality with the proposed UEP scheme vary from 1 to 3 dB in Y-peak signal-to-noise ratio (PSNR) with corresponding subjective video quality improvements.  
Keywords:scalable video; forward error correction; block recovery rate; unequal error protection; layer switching +
Footnote †: journal: Applied Sciences
## 3 Inferring an Agent's Desires
### Reward Function Discrimination
#### 3.1.1.2 Feature Expectation Matching  
[PERSON] and [PERSON] [42] contributed modifications to the max-margin approach under somewhat stricter constraints: \(R\) is bounded in absolute value to 1, which requires \(||\theta^{*}||_{1}\leq 1\), and therefore \(||\theta^{*}||_{2}\leq 1\). Casting the problem as apprenticeship learning (AL), deviating slightly from the IRL premise, their goal is to find a policy \(\pi\) that performs close to the expert policy under the unknown reward function \(R^{*}\). Their focus is on the feature expectations: the estimated policy \(\pi\) must obtain (empirical) feature expectations close to the expert's, i.e., satisfy \(||\psi^{\pi}-\psi^{\pi_{E}}||_{2}\leq\epsilon\). In other words, the true goal is not uncovering the reward function: although the algorithm guarantees finding a policy, the feature expectations of which are within \(\epsilon\) of the expert's, the reward function recovered as part of this process may not be correct. Because the \(\ell 2\)-norm of the linear approximation weights \(\theta\) is restricted to be less than 1, this is equivalent to minimising the difference between the value functions of the expert's (\(\pi_{E}\)) and estimated (\(\pi\)) policies. Such policy (and pertaining feature expectations) can be obtained almost identically to Algorithm 1, with the key difference being in the weights' update step, as per Algorithm 2, and the change in the loop exit condition to \(t<\epsilon\). They provide an additional, simpler algorithm based on computing the orthogonal projection of the feature expectations onto the segment between previous iterations' expectations and demonstrate its faster convergence compared to their max-margin method.  
```
ProcedureUpdateWeights(\(\theta\), \(\phi\), \(\tau^{\pi_{E}}\))
1\(\theta^{\pi}\leftarrow\)EstimateFeatureExpectations(\(\tau^{\pi}\), \(\phi\)) Equation (16)
2\(\vec{\
u}^{\pi_{E}}\leftarrow\)EstimateFeatureExpectations(\(\tau^{\pi_{E}}\), \(\phi\))
3\(L\leftarrow\min_{\eta_{L}\in\Pi}\{\theta^{\mathsf{T}}(\theta^{\mathsf{T}}( \theta^{\pi_{E}}-\phi^{\pi_{L}})\}\)
4\(\theta\leftarrow\)Optimise(\(L\), \(\theta:||\theta||_{2}\leq 1\))
5\(t\gets L(\theta)\) Terminal condition in loop
6 return\(\theta\)
```  
**Algorithm 2**Excerpt from the algorithm in [42], with adapted notation.  
Figure 1: Diagram of the _max-margin_ IRL algorithm (see Algorithms 1 and 2). Given trajectories \(\tau^{E}\), the observer constructs a model of the actor comprising a policy \(\pi\) and reward function \(R\) (dashed green), employing a model of the environment (i.e., a model of the MDP\(\backslash\)R, dashed yellow, which is usually assumed to be a priori known by the observer and equal to the actual environment, yellow) to generate candidate trajectories \(\tau^{\pi}\). Both trajectories are compared (blue) with the aid of features \(\phi\) (orange) that are intrinsic to the observer to update the weights \(\theta\). The weights characterise the reward function in conjunction with the features. Iteratively repeating this process yields a suitable reward function.
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 3 Results
### Noise Tolerance and Rotor Detection  
Our numerical experiments demonstrated that the features used for constructing rotor location maps exhibit varying degrees of sensitivity to different types of noise, including spikes, loss of samples, PLI, and loss of resolution. Specifically, maps derived from STDand ShEn showed low tolerance to spike artifacts, whereas mean-based maps exhibited the highest robustness, followed closely by SampEn. These findings are consistent with [42], reinforcing the suitability of these features for mitigating spike-related distortions. Regarding PLI noise, visual inspection confirmed that mean-based maps remained unaffected, while maps generated from other features were highly susceptible to interference. Notably, SampEn emerged as the second most resilient feature, further confirming prior findings [19]. When assessing the impact of a loss of samples, ShEn and the mean remained stable, with the latter being the most effective against this type of noise. In scenarios where multiple noise sources were present simultaneously, both SampEn and the mean exhibited strong resilience, with the mean demonstrating superior noise tolerance and SampEn providing additional insights regarding rotation direction. The robustness of the mean may be attributed to smooth noise fluctuations, whereas SampEn demonstrated strong capability in distinguishing CFAE from non-CFAE, though certain noise configurations limited its performance, in agreement with [4].  
To systematically evaluate feature stability across different noise conditions, Pearson correlation was employed to quantify similarities between quality measures at various levels of the JDL model. The results confirm that Pearson correlation effectively captures the relationships between quality metrics and provides a reliable framework for interpreting noise tolerance across different map construction strategies. While alternative similarity indices could be considered [55], our analysis establishes that Pearson correlation is well suited for this application, offering a robust measure of feature consistency and reinforcing the validity of the proposed framework.
# Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder
## 4 Simulation and Analysis
As the physical error rate increases, the logical error rate also increases gradually. As shown in the schematic diagram of the network iterative training above, in convolutional neural networks, the number of iterations and layers have a significant impact on model accuracy. By adjusting the number of iterations and using different network layers, we can improve training accuracy. Initially, we used the RestNet7 network for model iteration prediction. When the number of iterations exceeded approximately 300, the neural network's accuracy reached around 9.2%. Subsequently, as the number of iterations increased, the neural network's performance remained largely unchanged and was prone to overfitting. We then increased the convolutional layer to obtain more accurate training results and used the RestNet14 network to improve the convolutional performance. As shown in the figure, by comparing the training results of RestNet7 and RestNet14 networks,  
Figure 8: The figure displays the variation in the logic error rate of the CNN decoder for the heavy hexagonal code at different code distances. The threshold is indicated as 0.0065. The heavy hexagonal threshold is close to the optimal threshold, and the logical error rate increases gradually as the physical error rate increases. Figure 7: The figure shows the variation in the logic error rate of the MWPM decoder for the heavy hexagonal code at code distances of 5 (marked with green squares), 7 (marked with orange triangles), and 9 (marked with blue circles). The threshold is indicated as 0.0045.  
we can see that prior to 320 iterations, the accuracy of the RestNet14 network was slightly better than that of the RestNet7 network, with each training interval being approximately 0.2% higher. The RestNet14 network achieved the highest training accuracy at about 320 iterations. After more than 320 iterations, the accuracy of both networks had minor fluctuations. Thus, through a comparison of the two networks' training, the RestNet14 network approached the optimal decoder's performance after full-depth training, while the decoding efficiency and accuracy were significantly improved.
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 5 Conclusions  
In this paper, a novel QoS-aware backup padding ARQ scheme for MU transmissions in IEEE802.11 ax WLANs was proposed to enhance the transmission efficiency and the sliding of the aggregation window. The simulation results showed that the proposed scheme increased the throughput by up to 171% and 250%, reduced the latency by up to 98.2% and 98.9%, and increased the threshold of BER where the packet loss rate exceeded 0.1% by up to 60% and 700% compared to the BAW-ARQ and the ASR-ARQ, respectively. Moreover, the proposed scheme also enhanced the performance of traffic with different priorities in the priority-based network. Consequently, this work is critical for achieving low-latency and reliable communications and satisfying the QoS requirements of emerging applications. Nevertheless, the proposed scheme may suffer from frequent retransmissions when the BER is further increased. Hence, it is recommended to study the proposed scheme together with rate adaptation in the future.  
Conceptualization, C.L. and B.W.; Methodology, C.L., L.W., and Y.T.; Software, C.L. and Y.T.; Validation, C.L. and L.W.; Writing--original draft preparation, C.L.; Writing--review and editing, C.L., L.W., Z.W., Y.T., and B.W.; Project administration, L.W. and B.W.; Funding acquisition, B.W.; Supervision, B.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Major Science and Technology Program of China grant number 2013 ZX03004007.  
The authors declare that there is no conflict of interest.  
Figure 15: The QoS performances vs. the BER in the uplink. (**a**) The end-to-end delay of the AC_VO and AC_VI traffics. (**b**) The packet loss rates of the AC_VO and AC_VI traffics. (**c**) The throughput of the AC_BE traffic in each station.