esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Powered, Hybrid, Multifunctional Sensor for a Human Biomechanical Monitoring Device
## 3 Results and Discussion
In this paper, the time-sequence hybrid signal provided by the SPMS was used as the signal for research and analysis, and the gait signals of three subjects provided biometrics as personal identification. Three testers were required to perform five different actions while wearing a rehabilitation device; the tester information is shown in Supplementary Information Table S1. The LSTM network was trained to identify the activities of the testers. The SPMS output sequence hybrid signal was the database for the personal gait judgment system with the LSTM model. In the training program, the hybrid signals involved stepping on the SPMS randomly to decompose into several sets; every training database had three seconds of action, and a training set contained at least one action cycle (press to release). The database for LSTM contained eight groups as the training set and two groups as the test set. In this paper, data received from the SPMS sensor was set on the leg-rehabilitation device given the time series data. These time-series data represent the SPMS readings with five different movement sequences of three characteristics and the same array length. The LSTM network architecture set the number of input features as 3 sequences and 200 hidden layers, outputs as the complete sequence, and the final standard of a completely connected layer of size 5 (five different actions: walk, run, jump, hop, and limp). Figure 6b displays the confusion matrix obtained from the test with the SPMS action identification results. The plurality of misclassification is the classification of limp and hop behaviors in the confusion matrix. The signals are partly similar in analysis processes. In contrast, the best motion classification results are run and hop, in which the predictive value of true positive was close to 99%. Experiments show that the real-time OCV/SCC signals of the five feature data set models can identify different actions, and the overall training accuracy rate was 81.8%. If the data (used to avoid bias) and the extraction of the time-frequency moment feature increases, which can overcome the inconstancy of training data and class disparity, the accuracy can be improved. Figure 6: _Cont._
## 3 Results and Discussion  
### Mineralogical Characterization of Raw Materials  
Figure 1 shows the mineralogical characterization of all the raw materials using FTIR and XRD. Regarding the most significant FTIR results, the presence of an intense band located around ca. 800-1000 cm\({}^{-1}\) is observed in all materials, which is attributed to the presence of SiO\({}_{4}\) groups, specifically to asymmetric tensions of the O-Si-O bonds [8]. If the position of this band is compared among materials, it is possible to observe notable differences. In the waste corresponding to blast furnace slag, this band lies at around 900 cm\({}^{-1}\). However, in the case of fly ash and both ceramic wastes, it appears slightly above 1000 cm\({}^{-1}\). This is because all the materials are aluminosilicates, but fly ash and ceramic wastes have a higher aluminum content, causing the band to shift toward a higher wavenumber. The presence of characteristic bands related to the presence of carbonates, around ca. 1400 cm\({}^{-1}\), should also be noted. This band can be found in CEM I, blast furnace slag and porous ceramic residue, mainly due to the high CaO content it presents. The CaO content with the presence of ambient CO\({}_{2}\) and humidity causes the formation of calcite-type carbonates (CaCO\({}_{3}\)). In the CEM I system, the band corresponding to the presence of sulfates (SO\({}_{4}\)\({}^{2-}\)) is observed, located at around 1100-1200 cm\({}^{-1}\), which is attributed to asymmetric stress vibrations.  
Regarding the different diffractograms presented (Figure 1b), clear differences can be observed for each of the studied materials. In the case of CEM I, the presence of numerous crystalline phases is observed, such as calcium silicates, C\({}_{3}\)S and C\({}_{2}\)S (alite and belite), as well as some reaction products generated after the slight hydration that the cement underwent due to its atmospheric exposure [26]. In the diffractogram corresponding to the blast furnace slag, a halo is present between the values of 20 = 25-35\({}^{\circ}\) due to the fact that the material is quite amorphous. Fly ash, on the other hand, has a somewhat higher crystallinity since crystalline phases of quartz (Q, SiO\({}_{2}\); PDF card 331161) and mullite (M, Al\({}_{6}\)Si\({}_{2}\)O\({}_{13}\); PDF card 150776) appear. Finally, the diffractogram corresponding to the ceramic residues (porcelain and porous) shows a similar mineralogical behavior, with the presence of quartz (Q, SiO\({}_{2}\); PDF card 331161) and mullite (M, Al\({}_{6}\)Si\({}_{2}\)O\({}_{13}\); PDF card 150776). Different feldspars, such as sodium feldspar (albite, A, NaAlSi\({}_{3}\)O\({}_{8}\); PDF card 190926), and traces of diopside (D, CaMg(SiO\({}_{3}\))\({}_{2}\); PDF card 190239) are also identified [11].
## 4 Results and Discussion  
### Benchmarks for Comparison  
To quantify the performance of our proposed composite metric approach, we compare Algorithm 1 against the following benchmarks:  
1. The energy minimization routing approach from [22];
2. The ACO meta-heuristic approach from [21].  
It is worth noting that the study in [22] presented a routing algorithm that minimizes the energy consumption using an \(A^{*}\)-like algorithm. In fact, the \(A^{*}\) algorithm attemptsto reduce the number of Dijkstra iterations, at the expense of possible sub-optimal results when the \(A^{*}\) is guided by an admissible heuristic [22]. As a benchmark for comparison, we minimize the energy consumption metric of [22] via a _Dijkstra_ algorithm. Thereby, we are essentially comparing against the _best possible_ results achievable by the approach of [22].  
Note also that the ACO parameter values have been optimized in [21]. However, in this work we validate the optimal values of the parameters that produce the best possible results with fast convergence.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 6 The Soft-Decision Helper Data Scheme
### Description of (Regular) SD Helper Data Scheme  
The SD helper data scheme, see Figure 7, observes the one-probability vector \(\theta^{n}\) of all the cells, and derives the dominant values \(u^{n}\), defined as  
\[u\stackrel{{\Delta}}{{=}}\begin{cases}0&\text{if }0\leq\theta\leq 1/2, \\ 1&\text{if }1/2<\theta\leq 1,\end{cases} \tag{29}\]  
and the error probability \(\psi^{n}\) (regarding the dominant value of the cell), defined as  
\[\psi\stackrel{{\Delta}}{{=}}\min(\theta,(1-\theta))=\begin{cases} \theta&\text{if }0\leq\theta\leq 1/2,\\ 1-\theta&\text{if }1/2<\theta\leq 1,\end{cases} \tag{30}\]  
with \(\psi\in[0,1/2]\). As in the MO helper data scheme, a key is generated uniformly at random, and encoded using a linear error-correcting code into a codeword \(c^{n}\). Then the codeword is XORed with the dominant values \(u^{n}\) to create the helper data sequence  
\[w^{n}=c^{n}\oplus u^{n}. \tag{31}\]  
Besides the helper data sequence \(w^{n}\), also the reliability values \(\psi^{n}\) of the SRAM cells are stored publicly. For reconstruction, another observation \(y^{n}\) of the SRAM-PUF is XORed with the helper data, resulting in a noisy codeword  
\[r^{n}=w^{n}\oplus y^{n}=c^{n}\oplus(u^{n}\oplus y^{n}), \tag{32}\]  
where \((u^{n}\oplus y^{n})\) is an error vector, and an error \((r_{j}=c_{j}\oplus 1)\) occurs when the reconstruction observation \(y_{j}\) is flipped regarding the dominant value \(u_{j}\) of a cell. The decoder reconstructs the key \(\hat{s}\) based on the received noisy codeword \(r^{n}\) and the error probabilities of the cells \(\psi^{n}\).
# Sirtuin 1 and Hormonal Regulations in Aging
## 4 Natural Activators of SIRT1 and Their Use in the Prevention of Aging
### Buyang Huanwu Decoction (BHD) as a SIRT1-Activating Anti-Aging Prescription  
Buyang Huanwu Decoction (BHD), a traditional Chinese medicine formula composed of seven herbs, upregulates SIRT1 expression in the brain, which promotes neuronal survival, reduces apoptosis, and enhances neurogenesis in neural stem cells from rat embryo [97] and in the Sprague-Dawley cerebral rat ischemia model [98]. BHD has been used for post-stroke recovery and to enhance general health in rat models of middle cerebral artery occlusion and transient ischemic injury [99]. By activating SIRT1, BHD also upregulates vascular endothelial growth factor (VEGF) expression, leading to improved vascularization and blood flow in the rat brain, which is critical for preventing neurodegenerative diseases [100]. Animal studies have demonstrated that BHD reduces infarct size and improves functional recovery after ischemic stroke in Sprag-Dawley rats, effects that are dependent on SIRT1-mediated neurovascular repair [100]. Furthermore, BHD supplementation was shown to alleviate age-associated cognitive decline in mice by activating autophagy and reducing oxidative stress [101].  
Furthermore, BHD-induced activation of SIRT1 modulates autophagy pathways by promoting the clearance of damaged proteins and reducing oxidative stress, thereby protecting against cellular senescence in the Sprague-Dawley cerebral rat ischemia model [98]. Furthermore, BHD demonstrates an anti-ischemic effect due to the dependent control of mitochondrial quality by caveolin-1 in caveolin-1 KO mice [102]. Preclinical studies have also suggested that BHD may improve cardiac function in male Sprague-Dawley rats, aged 6 weeks, by enhancing mitochondrial biogenesis and reducing myocardial apoptosis through SIRT1 activation [103].  
Although clinical studies on BHD are less extensive, observational studies suggest that its long-term use improves post-stroke recovery outcomes and reduces frailty in elderly patients [101]. Further RCTs are needed to validate its efficacy and safety in aging populations. These findings suggest that BHD may serve as a promising natural prescription for the prevention and management of aging.
## 3 The Key Technologies of Extracting Transmission Characteristics  
### Obtaining The Transfer Function \(H_{t}(f)\)  
\(H_{T}(f)\) is related to the structures of circuits and is disparate for different transmission lines and connectors. In this study, a coplanar waveguide with a through-hole SMA connector soldered at the end was employed. The equivalent circuit of \(H_{T}(f)\) is shown in Figure 2a. The coupling capacitance \(C_{p}\) between the printed trace and the probe tip play an important role in coupling, which is only related to the geometric structure. A plane-parallel capacitor is appropriate for this structure. The value can be estimated according to  
\[C_{p}=\varepsilon\frac{S}{h}, \tag{11}\]  
where \(\varepsilon\) denotes the permittivity of the medium, \(S\) denotes the area of the plates, and \(h\) denotes the distance between the plates. The equivalent circuit composed of RLC elements was deduced [15] for the microstrip line electric probe. The loss resistance \(R_{s}\) (=3.6 \(\Omega\)), the self-inductance \(L_{s}\) (=16 nH), and the shunt capacitance \(C_{g}\) (=8.5 pF) of the probe effect the transfer factor drastically at a high frequency.  
To characterize and validate \(H_{T}(f)\), a coupling measurement and a commercial finite element field simulation were arranged. Figure 2b illustrates the model of the probe and PCBs with connectors in a simulator. As shown in Figure 3, a VNA and a probe fixture are used to maintain the position between the probe and the printed trace being tested. There are three ports. Port 1 is one end of the printed trace, and port 2 is the other end connected to a 50 \(\Omega\) terminal. Port 3 is the probe output, and port 1 and port 3 are connected to the VNA. The measurement results with standardized calibration and simulation of \(H_{T}(f)\) are shown in Figure 4 with different distances (0.5 mm and 1.5 mm) between the fixed probe and tested printed trace, and they are compared with those of the equivalent circuit model.  
The distance between the probe and printed trace is the major factor affecting \(C_{p}\) in this system because the area is constant when the probe is right above the printed trace. The values are 118 fF and 37 fF, respectively, when the distances are 0.5 mm and 1.5 mm, according to Equation (10). Figure 4 shows that the equivalent circuit model and field simulation agree well with the measurement curves at a low frequency (<4 GHz). The reason for this is that the fringe effect cannot be ignored at a high frequency. Therefore, at a high frequency, the measurement data are chosen to characterize \(H_{T}(f)\).  
Figure 4: Comparison of \(|H_{T}(f)|\) from circuit simulation, field simulation, and measurement.  
Figure 3: Setup for the transfer function measurement.  
Figure 2: Full-wave model of the probe and printed trace.
## 2 The Proposed VA-RAIM
### Vision Model with Calibration
#### 2.2.1 Vision Model
(1) Fault on \(\delta\mathbf{d}\)  
If there is a fault \(\Delta\mathbf{p}_{i}\) in the location of the \(i^{\text{th}}\) landmark, the error of position difference can be obtained as \(2(\mathbf{p}_{i}-\mathbf{p}_{j})^{T}(\delta\mathbf{p}_{i}+\Delta\mathbf{p}_{i}-\delta\mathbf{p}_{ j})\)=\(\delta d_{i}^{2}\) + \(\Delta d_{i}^{2}\), where \(\Delta d_{i}^{2}=2(\mathbf{p}_{i}-\mathbf{p}_{j})^{T}\Delta\mathbf{p}_{i}\) is a bias caused by the location fault. As the location results are prior given, the bias fault is time-invariant during the flight operations. Thus, \(\delta\mathbf{d}\) can be updated to \(\delta\mathbf{d}^{\prime}=\delta\mathbf{d}+\Delta\mathbf{d}\), where \(\Delta\mathbf{d}=[\Delta d_{l2}^{2}\), \(\Delta d_{2s}^{2}\), \(\Delta d_{sj}^{2}]^{T}\). (2) Fault on \(\delta\mathbf{c}_{(k)}\)  
If there is a fault \(\Delta\mathbf{p}_{i(k)}^{C}\) in the feature detection result during image processing, combined with Equation (6), \(\delta c_{ij(k)}\) can be updated to  
\[\delta c_{ij(k)}^{\text{ }}=\delta c_{ij(k)}+\mathbf{\mu}_{ij(k)}^{T}\Delta\mathbf{p}_{(k)} ^{C}+\mathbf{\mu}_{ji(k)}^{T}\Delta\mathbf{p}_{j(k)}^{C} \tag{7}\]  
Denote \(\Delta c_{ij(k)}=\mathbf{\mu}_{ij(k)}^{T}\Delta\mathbf{p}_{i(k)}^{C}+\mathbf{\mu}_{ji(k)} ^{T}\Delta\mathbf{p}_{j(k)}^{C}\), then \(\delta\mathbf{c}_{(k)}\) can be updated to \(\delta\mathbf{c}_{(k)}^{\text{ }}=\delta\mathbf{c}_{(k)}+\Delta\mathbf{c}_{(k)}\), where \(\Delta\mathbf{c}_{(k)}=[\Delta c_{12(k)}\), \(\Delta c_{23(k)}\), \(\Delta c_{31(k)}]^{T}\). The probabilities of the vision fault can be statistically obtained with real computer vision data, and the relatively high probabilities often reflect the challenging environment of the data collection, which will be further researched. (C) Vision Measurement Equation  
Similar with the linear GPS measurement equation [20], the linear vision measurement equation with \(N_{V(k)}\) landmarks can be modeled as:  
\[\mathbf{z}_{v(k)}=\mathbf{H}_{v(k)}\mathbf{x}_{(k)}+\mathbf{\varepsilon}_{v(k)}+\mathbf{b}_{v(k)} \tag{8}\]  
where \(z_{v(k)}\in\ \ \mathbf{R}^{{}^{N_{v(k)}}}\) is the vector of vision measurements obtained by the vision pseudoranges at time \(k\), \(\mathbf{H}_{V(k)}\in\ \mathbf{R}^{{}^{N_{v(k)}},{}^{4}}\) is the vision observation matrix. \(\mathbf{x}_{(k)}\in\mathbf{R}^{4}\) is the state vector includes three position elements (\(x_{(k)}\), \(y_{(k)}\) and \(z_{(k)}\)) and the receiver clock bias \(C_{b(k)}\). \(\mathbf{\varepsilon}_{V(k)}\in\mathbf{R}^{{}^{N_{v(k)}}}\) and \(\mathbf{b}_{V(k)}\in\mathbf{R}^{{}^{N_{v(k)}}}\) are the random error and bias of vision pseudoranges, respectively. According to Equations (3) and (8), \(\mathbf{\varepsilon}_{V(k)}\) and \(\mathbf{b}_{V(k)}\) with the consideration of vision fault \(\Delta\mathbf{d}\) and \(\mathbf{\Delta c}_{(k)}\) can be obtained as:  
Figure 3:0  
\[\mathbf{b}_{v(k)}=\mathbf{A}_{(k)}^{-1}\mathbf{B}_{(k)}(\delta\mathbf{d}+\Delta\mathbf{d})+\mathbf{
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## 8 Future Research Directions  
In the future, researchers should focus on the identification of local damages by improving the accuracy and noise-robustness of damage identification algorithms [187; 188]. Furthermore, they must think about introducing a novel platform for the implementation of commercialized SHM software that is versatile enough to deal with the diversity of techniques in a damage detection system. Providing a user-friendly platform for the implementation of the SHM algorithm will improve the general usage of SHM software in solving real-life engineering problems. Furthermore, the extensible architecture would enhance the applicability of the software by enabling users to modify the existing base code and add their own extensions. A modular and flexible architecture enables a wide variety of reported methods to deal with within an integrated framework. Enhancing the SSI properties to deal with real-world applications, such as noise inclusion, short length data, and gluing sensor data, will enhance accuracy to provide more reliable damage detection results.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 5 Performance Comparison
### Achievable Information Rates  
Here, we numerically study the information rates of CCDM, MPDM, and SpSh in the finite blocklength regime. As the figure of merit, the finite-length information rate for BMD is used as defined in (Equation (15) in [55]):  
\[\text{AIR}_{n}=R_{\text{BMD}}-R_{\text{loss}}. \tag{16}\]  
Here \(R_{\text{BMD}}\) and \(R_{\text{loss}}\) are as defined in (12) and (10). We note that (16) converges to (12) for asymptotically optimum shaping architectures when \(n\to\infty\). Although the finite length information rate \(\text{AIR}_{n}\) in (16) is not an \"achievable\" rate in the strict sense, it has been employed to compare CCDM and ESS for the optical fibre channel in [61; 62; 63]. We note here that (16) is an instance of the rate expression (Equation (1) in [101]) provided for the _layered PS_ architecture. We refer the reader to (footnote 3 in [61]) for a discussion on the derivation of (16) from (Equation (1) in [101]).  
In Figure 9, \(\text{AIR}_{n}\) (in bit/1-D) is shown versus SNR (in dB) for CCDM, MPDM and SpSh with 8-ASK. We use shaping blocks of length \(n=216\), which is compatible to the \(n_{\text{c}}=648\)-bit LDPC codes of IEEE 802.11 [78] that will be employed in PAS in subsequent sections. Shaping algorithms operate  
Figure 8: Rate loss vs. blocklength for various shaping architectures.  
at a rate of \(k/n=1.75\) bit/1-D, i.e., \(k\) is set to 378 bits. We note that this means we plotted the curves for fixed distributions and did not optimize them at each SNR, unlike (Figure 4 in [28]) or (Figure 5 in [55]). For comparison, the Shannon capacity \(\frac{1}{2}\log(1+\text{SNR})\) and the GMI for uniform 8-ASK are also plotted. We observe that SpSh and MPDM close most of the shaping gap. From the inset figure, we see that SpSh and MPDM are roughly 0.72 dB more SNR-efficient than uniform signaling at rate \(R=2.25\) bit/1-D. We note that this rate corresponds to \(\gamma=R-k/n=0.5\), and thus, \(R_{\text{c}}=5/6\) in the PAS context. As a reference, the maximum possible capacity gain for 8-ASK at this rate that can be obtained by using the optimum MB distribution was shown to be 0.83 dB in Figure 5. The remaining gap of 0.11 dB is due to the finite blocklength nature of the shaping architectures. Finally, from the inset of Figure 9, we see that SpSh and MPDM are approximately 0.23 dB more SNR-efficient than CCDM due to their energy-efficient use of the signal space.  
We conclude from Figures 8 and 9 that from a practical point of view, SpSh and MPDM perform almost the same at blocklengths larger than a few hundreds. Therefore, to make a choice among these at such values of \(n\), required storage, computational complexity and latency of the algorithms that can be used to realize SpSh and MPDM should be considered. We will discuss these aspects of shaping algorithms in Section 6.
# Developing a Universal Mirror-mirror Laser Mapping System for Single Event Effect Research
## 4 Discussion  
The testing of op-amps was used to verify the features of the mirror-mirror BSM system for scanning large size of ROIs. The results show that the BSM can locate the sensitive areas with high accuracy and velocity. Two factors need to be noted for testing at this scale. First, the maximum size of a ROI was limited by the field of view (FOV) of the objective lens. If there is a need to scan a ROI larger than the FOV, this BSM system can work with a movable supporting stage. Second, the effect of injection angle needs to be considered. As shown in the previous section, a sufficient injection angle will introduce an offset to the laser beam. As a result, if the DUT has components in several micrometers size located far away from the center, additional adjustment should be considered in the mapping software.  
The testing of SRAM cells was used to verify the spatial resolution of the BSM system. Step size of 0.18 \(\upmu\)m was used for the testing of the Regular_11T SRAM cell. The results were consistent with what was predicted by circuit analysis and further confirmed by results from the TFIT simulation software. Furthermore, the results confirm that the actual spatial resolution of a laser beam in mapping testing can be much smaller than the calculated spot size (0.36-\(\upmu\)m resolution vs. 2.7-\(\upmu\)m spot size). This result can extensively extend the application of pulsed lasers in SEE testing. Another significant and easily neglected factor was keeping the SRAM testing system in strictly stable condition throughout the mapping testing process. Because the SRAM cell in testing has a size as small as about 1 micron,  
Figure 21: TFIT simulation sensitivity distribution with all ’0’ stored data pattern.  
any slight vibration of the system can introduce unacceptable offsets to the result. In the actual testing, an enclosure was made to enclose the entire testing system in order to shield the system from air flows and all the cables were fixed with clamps.
###### Abstract  
The echo state network (ESN) employs a huge reservoir with sparsely and randomly connected internal nodes and only trains the output weights, which avoids the suboptimal problem, exploding and vanishing gradients, high complexity and other disadvantages faced by traditional recurrent neural network (RNN) training. In light of the outstanding adaption to nonlinear dynamical systems, ESN has been applied into a wide range of applications. However, in the era of Big Data, with an enormous amount of data being generated continuously every day, the data are often distributed and stored in real applications, and thus the centralized ESN training process is prone to being technologically unsuitable. In order to achieve the requirement of Big Data applications in the real world, in this study we propose an algorithm and its implementation for distributed ESN training. The mentioned algorithm is based on the parallel particle swarm optimization (P-PSO) technique and the implementation uses Spark, a famous large-scale data processing framework. Four extremely large-scale datasets, including artificial benchmarks, real-world data and image data, are adopted to verify our framework on a stretchable platform. Experimental results indicate that the proposed work is accurate in the era of Big Data, regarding speed, accuracy and generalization capabilities.  
Keywords:Echo State Network; distributed algorithm; spark framework; Parallel Particle Swarm Optimization; machine learning; Big Data +
Footnote †: journal: Applied Sciences