esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 An Efficient Speech CBSS Algorithm in Multipath Channels
### BSS of Speech Signals with Chaotic Masking in Multipath Channels Based on Fast IVA Algorithm  
Considering the information security transmission of speech signals under multipath channels in the wireless transmission environment, this paper processes the confidentiality of speech signal before sending. Due to the characteristics of initial sensitivity and internal randomness of the chaotic system itself, the chaotic sequence of its output is unpredictable, which provides a new design idea for secure communication. In this paper, the message signal and the chaotic signal are superimposed on each other, and the useful signal is covered by the pseudo-random and noise-like characteristics of the chaotic system to realize the confidential transmission of the message signal. Since the speech signal is a small signal, the magnitude of its energy and amplitude is far from that of a chaotic signal. Even though they are both multi-frequency signals, the broadband power spectrum characteristics of the chaotic signal still ensure a good masking effect on the speech signal.  
In this paper, chaotic signals are introduced into the source signal for chaotic masking to achieve the secure transmission of speech signals. In addition, this paper also proposes using a Fast IVA algorithm to solve the CBSS in the case of multipath speech secure transmission. Taking three signals (two speeches and one chaotic signal) as an example, we simulate the multipath transmission situation of speech signals and the whole process of blind observation signal separation to extract the source speech signal in the actual environment, as shown in Figure 3.  
From the above model, it can be seen that this paper introduces chaotic masking technology to hide the multiplexed speech signals into the random signals generated by the Chen chaotic dynamical system to ensure the transmission security of speech information. Secondly, the masked speech signals are transmitted via multipath channels to reach the receiving end. The multipath effect made the observed signals inevitably affected by phase and time delay and became a superposition of multiple source signals. The Fast IVA algorithm is applied to solve the FD-CBSS of the unknown observed signals, which efficiently achieved blind separation or extraction of speech signals without an additional sorting process. This greatly reduced the computational complexity of the algorithm and improved the efficiency of the FD-CBSS.
# Direct Mobile Coaching as a Paradigm for the Creation of Mobile Feedback Systems
## 6 Discussion
### Privacy and Data Protection  
In the domain of medical and sport sciences, the declarations of Helsinki [71] and Taipei [72] include regulations on how studies should be conducted. While the former concerns general principles for conducting research involving humans, the latter lays down principles for \"health databases\" (\"systems collecting, organizing and storing health information\" [72]) and thus also includes studies where information about movement is collected. Both declarations include the principle that any participant at any time can request the complete removal of their data to be withdrawn from the study. Consequently, the use of proprietary technology can be a potential problem, as control over data replication is not always possible. GCIS is a commercial product with a proprietary server and cannot serve as a database for all types of studies as it is not clear how the data are stored and backups are created. The models of DMC, as well as MobileCoach and RADAR, allow running a database with restricted access to data, thus being compliant with data privacy and ethical standards. DMC further incorporates the concept of roles, which can be associated with access privileges to data, thus providing further possibilities when used in large scale studies.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### Third Generation Partnership Project (3 GPP)  
The 3 GPP group is presently evolving its technologies to ensure use for IoT applications [4; 13; 28; 49]. They have stripped their cellular standards of unnecessary complexities, reduced costs, extended transmission range, and ensured prolonged battery lifetime. LTE technologies are being enhanced particularly for narrowband (NB)-IoT applications [50]. NB-IoTs are designed to provide low cost, long coverage, low data rates and low power consumption rates to satisfy LPWAN requirements. For example, in order to extend battery lifetime, the 3 GPP are planning to deploy either PSM or extended discontinuous reception (eDRx) schemes [4; 12; 13]. Both methods use deep sleep mode operation for end devices to provide longer hours or even days of network activities. The NB-IoT is a narrow band technology with low bandwidths of 180 kHz with the possibility to serve as many as fifty thousand end devices in a particular cell [4]. It uses frequency division multiple access (FDMA) and OFDMA in the uplink and downlink channels, respectively. With an established infrastructure based on older 3 GPP cellular technologies, NB-IoT is expected to be a strong competitor in the IoT market in the coming years.
# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 4 Analysis and Discussion of Experimental Results  
### Training Results of YoloV5s  
Figure 11 presents the curves of training loss and mAP. It is evident that the curves show relatively smooth changes. Particularly, the [EMAIL_ADDRESS]:0.95 indicator reached 87.60%, indicating high recall and precision of the model.  
Furthermore, as shown in Figure 11b. The left image demonstrates the detection results in the presence of various object interferences. The YoloV5s model accurately identifies the real lifting hole instead of adjacent interfering hole. The other three images depict environments with strong lighting, weak lighting, and lighting shadows, respectively. From the confidence of bounding boxes and the anchor boxes, it can be observed that the model is still capable of effortlessly handling target localization under these conditions.  
Based on the experimental result images, YoloV5s consistently and accurately identifies the positions of lifting holes, regardless of different lighting conditions or shooting angles. This indicates that the model possesses excellent generalization performance and can reliably achieve rough positioning of lifting holes in the described lifting scenarios. These experimental results provide compelling evidence to demonstrate that YoloV5s can consistently acquire ROI and ensure the safety of lifting operations.
## 3 Results  
Varied Species Richness and Diversity of Phyllosphere Microbes among Two Sphagneticola Species and Their F1 Hybrid Hybrid  
High-throughput sequencing of _S. trilobata_, _S. calendulacea_ and their F1 hybrid yielded 3,418,736 raw sequences, with 3,413,498 high-quality sequences retained after filtering. Rarefaction curves indicate sufficient sequencing depth (Figure S1). \(\alpha\)-diversity analyses revealed the following: 1 Chao1 index: F1 hybrids (1082.00 \(\pm\) 78.89) showed significantly higher species richness than both parents. No significant difference was observed between _S. calendulacea_ (774.76 \(\pm\) 122.40) and _S. trilobata_ (763.33 \(\pm\) 114.79) (Figure 1A). 2. Shannon index: _S. trilobata_ (4.14 \(\pm\) 0.30) exhibited significantly higher diversity than _S. calendulacea_ (3.60 \(\pm\) 0.13) (Figure 1B). The F1 hybrid (4.01 \(\pm\) 0.18) did not differ significantly from either parent (Figure 1B). 3. Simpson index: No significant differences were found among _S. calendulacea_ (0.90 \(\pm\) 0.04), _S. trilobata_ (0.95 \(\pm\) 0.02), and the F1 hybrid (0.93 \(\pm\) 0.01) (Figure 1C).  
Distinct Phyllosphere Microbial Community Structure at Family and Genus Levels among Three Sphagneticola Groups  
Taxonomic annotation revealed significant differences in community structure between the host plant groups at both the family and genus levels. Taxa with <0.01 relative abundance were grouped as \"others\". At the family level, 10 bacterial families dominated the phyllosphere communities, comprising > 50% of relative abundance (Figure 2A). The family _Beijernicacea_ was most abundant on _S. trilobata_ (32.0%) and the hybrid (23.7%), but  
Figure 1: \(\alpha\)-diversity indices for the 16S rRNA gene sequences. Box plots of the Chao1 (**A**), Shannon (**B**), and Simpson (**C**) indices in phyllosphere samples from both _S. calendulacea_ (SC), _S. trilobata_ (ST) and their F1 hybrids (SH). Whiskers represent the minimum and maximum values. The bar represents the median. [PERSON] correction-adjusted p-value was calculated from [PERSON]’s test of multiple comparisons using rank sums. The different letters represent significant differences between groups (_p_ < 0.05).  
lower on _S. calendulacea_ (22.4%). In contrast, the family _Oxalobacteraceae_ was most prevalent on _S. calendulacea_ (23.8%) but rarer on _S. trilobata_ (4.8%) and the hybrid (5.9%). At the genus level (Figure 2B), _Methylobacterium-Methylorubrum_ dominated on _S. trilobata_ (31.7%) and the hybrid (22.9%), but were less abundant on _S. calendulacea_ (21.9%). _Duganella_ was most prominent on _S. calendulacea_ (22.5%) but rarer on _S. trilobata_ (4.3%) and the hybrid (5.3%). While the hybrid's microbial community was intermediate between its parents at the family level, it more closely resembled _S. trilobata_ at the genus level.
###### Abstract  
Fiber fermentation by gut microbiota yields short-chain fatty acids (SCFAs) that are either absorbed by the gut or excreted in feces. Studies are conflicting as to whether SCFAs are beneficial or detrimental to cardiometabolic health, and how gut microbiota associated with SCFAs is unclear. In this study of 441 community-dwelling adults, we examined associations of fecal SCFAs, gut microbiota diversity and composition, gut permeability, and cardiometabolic outcomes, including obesity and hypertension. We assessed fecal microbiota by 168 rRNA gene sequencing, and SCFA concentrations by gas chromatography/mass spectrometry. Fecal SCFA concentrations were inversely associated with microbiota diversity, and 70 unique microbial taxa were differentially associated with at least one SCFA (acetate, butyrate or propionate). Higher SCFA concentrations were associated with a measure of gut permeability, markers of metabolic dysregulation, obesity and hypertension. Microbial diversity showed association with these outcomes in the opposite direction. Associations were significant after adjusting for measured confounders. In conclusion, higher SCFA excretion was associated with evidence of gut dysbiosis, gut permeability, excess adiposity, and cardiometabolic risk factors. Studies assessing both fecal and circulating SCFAs are needed to test the hypothesis that the association of higher fecal SCFAs with obesity and cardiometabolic dysregulation is due to less efficient SCFA absorption.  
gut microbiota; SCFA; butyrate; adiposity; metabolic dysregulation; hypertension; gut permeability
## 2 Hybrid Numerical Model
### Governing Equations of the Numerical Model
\(S_{\rm\varepsilon}\) represents the average energy source for electrons. The Poisson equation is used to solve the electric field according to the density distribution of electrons and heavy particles, then the calculation results are input into Equations (1)-(3) and the Boltzmann equation to update the density of charged particles, the average energy density of electrons, and the chemical reaction rate. The expression of [PERSON]'s equation is as shown in Equation (4) and the Boltzmann equation is shown in Equation (5):  
\[\left\{ \begin{array}{l}\
abla^{2}\phi=-\frac{\rho}{\varepsilon_{0}}=-\frac{e \left(n_{\rm p}-n_{\rm n}-n_{\rm e}\right)}{\varepsilon_{0}}\\ E=-\
abla\varphi,\end{array} \right. \tag{4}\]  
where \(\rho\) is the amount of charge, \(\varepsilon_{0}\) is the dielectric constant of vacuum gas, \(n_{\rm i}\) is the number density of charged particles (The subscripts \(e\), \(p\), and \(n\) respectively represent electrons, positive ions, and negative ions), and \(\varphi\) represents the electric potential. \[\frac{\partial f}{\partial t}+\
u_{1}\cos\theta\frac{\partial f}{\partial z}- \frac{e}{m_{e}}E(\cos\theta\frac{\partial f}{\partial\
u_{1}}+\frac{\sin^{2} \theta}{\
u_{1}}\frac{\partial f}{\partial\cos\theta})=C(f), \tag{5}\]  
where \(f\) is the probability distribution function of electrons and ions, \(\
u\) is velocity, \(t\) is time, \(\theta\) and \(z\) are the position coordinates in the cylindrical coordinate system, \(E\) is the electric field intensity, and \(C(f)\) represents the change in \(f\) caused by electron collision. Assuming that the electric field and the electron collision are in a uniform space,\(f\) in the six-dimensional phase space (three-dimensional position and three-dimensional velocity) is symmetrical and only changes along the direction of the electric field.
## 3 Long-Term Durability of Sensor Coupling and Mounting and of the Measurement Equipment
### Long-Term Sensor Coupling
#### 3.1.3 Examples of Long-Term Coupling for AE Monitoring
The sensor system used in the three cases reported in the paper consisted of fiber optics with FBG and linear variable differential transducers (LVDT) for measuring loads or strains in selected load-bearing components made from CFRP or their anchorage. The stability and reliability of the measurement system is essential, and this required a major effort with long-term reference experiments in the laboratory. The fiber optics FBG system proved suitable for integration into CFRP structures and components, and the data indicate sufficient long-term performance without maintenance or repair. Even though fiber optics in this case was not for measuring AE or GUW, developing similar fiber optics systems for SHM of infrastructure monitoring with AE or GUW looks promising.
## 3 Results
### The CVs of PAR during Different Sky Conditions
Figure 6: The diurnal dynamics of SIF retrieved by the SFM, 3 FLD, and SVD methods and the diurnal dynamics of PAR under different sky conditions, such as (**a–c**) for the clear day (DOY 224); (**d–f**) for the rapidly fluctuating illumination condition day (DOY 227); and (**g–i**) for the cloudy day (DOY 230). The blue dots represent the retrieved SIF at 760 nm by the three algorithms. The red circles represent the reserved SIF. The orange dots illustrate the average values of the high–frequency PAR data during the \(L\) measurements. Figure 8: The relationship between PAR and SIF retrieved by the **(a)** SFM, **(b)** 3 FLD, and **(c)** SVD methods from 17 days across the measurement period. Black square dots represent the retrieved SIF at 760 nm by SFM, 3 FLD, and SVD. Red square circles represent the reserved SIF. The PAR values were the average of the high-frequency PAR values during the L measurements. Figure 7: The relationship between _PAR_ and SIF retrieved by the SFM, 3 FLD, and SVD methods under three sky conditions: **(a–c)** for the clear day (DOY 224); **(d–f)** for the rapidly fluctuating illumination condition day (DOY 227); and **(g–i)** for the cloudy day (DOY 230). Black square dots represent the retrieved SIFs at 760 nm by SFM, 3 FLD, and SVD. Red square circles represent the reserved SIF. The _PAR_ values were the average of the high-frequency _PAR_ values during the \(L\) measurements. To demonstrate how illumination affects SIF retrieval accuracy, we examined two typical cases. In the first case, the mean of the _PAR_ values during the measurement of the \(E\)1 spectrum (_PARE1_) was approximately 75% of the mean of the PAR values during the measurement of the \(L\) spectrum (_PARL_) (Figure 9a). This indicates that the real \(E\) was stronger during the measurement of the \(L\) spectrum. When the measured \(E\) was assumed to be correct, the fluorescence fill bulge of the apparent reflectivity curve in the absorption band increased (Figure 9b), and the SIF values retrieved by 3 FLD and SFM were overestimated (case 1 in Figure 7d,e). In contrast, in the second case, the mean of the _PARE1_ values was greater than the mean of the _PARL_ values (Figure 9c). Assuming the measured E to be true, the apparent reflectivity curve appeared concave (Figure 9d) rather than convex at the \(O_{2}A\) absorption band. The SIF values retrieved by 3 FLD and SFM were underestimated (case 2 in Figure 7d,e). In both cases, however, the SIF values retrieved by SVD were not abnormal.
## 4 Experimental Validation
Nevertheless, the cross-polarized RCS reduction is verified both by the simulation and measurement and the effective reduction of the cross-polarized RCS by the absorber in the proposed DLTSA is experimentally verified. Figure 8: 4-by-4 array DLTSA. (**a**) Photo of the built prototype. (**b**) Radiation pattern measurement environment (CATR). (**c**) Simulated and measured reflection coefficients. (**d**) Simulated and measured \(\theta\)-sweep radiation patterns (\(\phi\) = 90\({}^{\circ}\)). (**e**) Measured \(\theta\)-sweep radiation patterns (\(\phi\) = 90\({}^{\circ}\)). Furthermore, we simulate the cross-polarized monostatic RCS when the beam is steered. When a \(90^{\circ}\) phase delay along the \(y\)-axis is applied as shown in Figure 11a, the radiation pattern at 35 GHz shows a main lobe at \(\theta=-10^{\circ}\), as shown in Figure 11b. The cross-polarized monostatic RCS result at \(\theta=\phi=0^{\circ}\) in the X-band of the 4-by-4 array with beam steering is shown in Figure 10a. The black dashed line in Figure 10a shows that the RCS value is similar to the cross-polarized monostatic RCS one without the beam steering. The simulated result shows that the cross-polarized monostatic RCS is still effectively reduced by the absorber even when the beam is steered. Figure 10: RCS results. (**a**) Simulation. (**b**) Measurement. Figure 9: RCS measurement environment.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology
### Extraction of Full-Field Displacement Time History  
#### 2.3.1 Displacement Extraction Based on Edge Tracking  
The noise and other factors in the image may result in the detection of false edge points; that is, there could be more than one edge point at the same coordinates around the edge. As shown in Figure 5, the red points are false edge points. In order to eliminate those false edge points and obtain the genuine edges, a threshold \(T_{1}\) is used to make the selection. For example, there are two points at the \(i\)+1 th row in the first frame of Figure 5. The point with a horizontal distance to the reference point \(x_{i}(1)\) larger than \(T_{1}\) is treated as false edge point (i.e., the red point \(x^{\prime}{}_{i+1}(1)\)), and only one point with a distance smaller than \(T_{1}\) is the genuine edge point (i.e., point \(x_{i+1}(1)\)).  
Figure 4: Comparison of pixel-level and subpixel-level edge detections: (**a**) straight edge; (**b**) curved edge.  
Moreover, in order to improve the robustness of edge tracking, the average of \(2a+1\) edge points is used to calculate the position of the edge point. For example, as for the edge point \(x_{i}(1)\) in the first frame, all the edge points in the range of \(a\), i.e., from point \(x_{i-a}(1)\) to point \(x_{i+a}(1)\), are used for averaging to calculate the coordinate of the point. For instance, if \(a=1\), three points are used for the average. Thus, the updated position of the edge point \(x_{i}(1)\) is determined as  
\[\widetilde{x}_{i}(1)=\frac{[x_{i-a}(1)+x_{i-a+1}(1)+\cdots+x_{i+a-1}(1)+x_{i+a }(1)]}{(2a+1)}, \tag{8}\]  
The position of the same edge point in each video frame is tracked to calculate the movement of the edge point, in such a way that the displacement of each edge point is obtained. In order to match the edge point in adjacent frames, a threshold of \(T_{2}\) is used:  
\[|x_{i}(t)-x_{i}(t-1)|<T_{2}, \tag{9}\]  
where \(x_{i}(t)\) and \(x_{i}(t-1)\) are the horizontal coordinates in the frame \(t\) and \(t-1\), respectively. The average process is repeated for each frame to calculate the updated position.  
After the updated positions of the edge point in all frames are obtained, the horizontal movement of the edge point in the \(t\)-th frame relative to the first frame is calculated as  
\[\overline{x}_{i}(t)=\widetilde{x}_{i}(t)-\widetilde{x}_{i}(1), \tag{10}\]  
The time history of point \(i\) can be obtained as \(\{\overline{x}_{i}(1),\overline{x}_{i}(2),\cdot\cdot\cdot,\overline{x}_{i}( n)\}\), where \(n\) is the total number of video frames, as shown in Figure 5. By applying the same procedure on all the edge points, the full-field displacement time history of the structure edge can then be obtained.
# Advanced Technology in ATSC3.0: Boosting Data Signal Rates Using Polarization Properties and FSS +
## 5 Conclusions  
This paper proposed a novel method to improve the transmission rate in data communication by utilizing polarization properties to suppress interference without affecting broadcast waves. Simulation results show that the proposed method not only enhances the transmission rate in data communication, but also achieves improved BER within the tolerance range. Next, we demonstrated that the BER performance can be further improved by employing FSS. Finally, the effectiveness of adaptive modulation in improving the system performance was clarified: the XPD value was fixed at 15 dB and the system throughput was evaluated for various modulation schemes, and the results showed that QPSK is used when SNR is less than 24 dB, and 16 QAM is employed when SNR is greater than 24 dB. The throughput was confirmed to be improved using QPSK when the SNR is less than 24 dB. In addition, the use of FSS as the spreading code is expected to significantly reduce  
Figure 10: Throughput performance with different modulation methods.  
Figure 9: BER performance in relation to FSS spreading length (XPD = 15 dB).  
the amount of control information in adaptive modulation because the received SNR is more uniform.  
[PERSON] is the main author. [PERSON], [PERSON], and [PERSON] provide the methodology, software, and constructive feedback on every part of the manuscript. [PERSON] is our supervisor. All authors have read and agreed to the published version of the manuscript.  
Grant of Science Research from the Japan Society for the Promotion of Science (JSPS) 22K04085.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.