esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### N-Glycan Data from the Off-Line IdeS Digestion Method  
The IdeS enzyme can specifically cleave the bond between G242 and G243 in the hinge range of mAbs. Therefore, it is widely used to generate the fragments of Fc/2 and F(ab')2 [23]. To the best of our knowledge, all the mAb drugs currently available on the market typically exhibit N-glycans exclusively within the Fc domain, with Cetuximab (Erbitux) being a notable exception, as it has been found to possess N-glycans within the Fab domain [27]. In line with this trend, the mAbs used in our study also exhibit N-glycans solely within the Fc domain, so fragments of Fc/2 are chosen for N-glycan analysis in this study. At the subunit level of Fc/2, LC-MS can analyze N-glycan with higher sensitivity than that at the intact protein level. Herein, we used the IdeS enzyme to digest mAb A to generate Fc/2 off-line and then used LC-MS to analyze N-glycan. Figure 3B shows representative deconvoluted MS spectra of the Fc/2 fragment of mAb A from the off-line IdeS method. The same six major glycans as those from the reduction method were identified with small mass errors compared to the theoretical molecular weight. Additionally, C-terminal lysine truncation was the main PTM. Representative raw MS spectra of the Fc/2 fragment of mAb A from the off-line IdeS digestion method is shown in Supplementary Materials, Figure S1B. The method was run in triplicate, and the detailed information are shown in Supplementary Materials, Table S2. The average glycan ratios (n = 3) of G0F-GN, G1F-GN, G0F, G1F, G2F, and Man5 were 5.29%, 2.80%, 43.64%,  
Figure 3: Representative deconvoluted MS spectra of mAb A. (**A**) Deconvoluted MS spectra of HC from the reduction method. (**B**) Deconvoluted MS spectra of Fc/2 from the off-line IdeS digestion method. (**C**) Deconvoluted MS spectra of Fc/2 from the 2D-LC-MS method.  
36.82%, 8.04%, and 3.41%, respectively. The RSD (n = 3) of G0F-GN, G1F-GN, G0F, G1F, G2F, and Man5 were 7.16%, 9.07%, 2.81%, 1.68%, 3.42%, and 6.02%, respectively.
## 3 Correlation and Implementation
### Construction Site Description  
Two real construction earthwork sites were selected for this research. One construction project involves the replacement of contaminated soil and compaction operation over an area of 8300 m\({}^{2}\). The other project is a land development project for the construction of an event stadium which also requires extensive earthwork operations, including compaction with an area of 11,500 m\({}^{2}\). The same IC roller was used for both projects, equipped with a Leica DGPS and a RADIAN accelerometer. The project overview and compaction operation are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Soil Type** & **Compaction Meter Value** \\ \hline Rock fill & 60–100 \\ Gravel & 30–80 \\ Sand & 20–50 \\ Clay and Silt & 5–30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Typical CMV values.  
Figure 6: Research experiment environment.
# Compact Ultra-Wideband Series-Feed Microstrip Antenna Arrays for IoT Communications
## 5 Conclusions  
In this paper three ultra-wideband log-periodic antenna arrays are presented for IoT communications, combining high gain and efficiencies to the ultra-wideband. The array uses microstrip technology and log-periodic structures, scaled from a central 28 GHz inset-fed patch element. The bandwidths obtained are 6.91 GHz, 8.3 GHz and 12.2 GHz for the five, seven, and nine elements, respectively. The simulated and measured values of the array were compared, and overall, they present a good agreement. Their small and compact structures together with the high gains and efficiencies, makes these arrays good solutions to be incorporated in IoT sensors.  
Conceptualization, T.V., J.C., and J.N.M.; methodology, T.V. and J.C.; software, J.C.; validation, T.V., J.C., and J.N.M.; formal analysis, J.C.; investigation, J.C.; resources, T.V. and J.N.M.; data curation, T.V. and J.C.; writing--original draft preparation, T.V.; writing--review and editing, T.V. and J.N.M.; visualization, T.V.; supervision, T.V. and J.N.M.; project administration, J.N.M.; funding acquisition, J.N.M. All authors have read and agreed to the published version of the manuscript.  
This work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020-UIDP/50008/2020.  
Data sharing not applicable.  
The authors declare no conflict of interest.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 3 Hardware and Software of WT in Sport
### Sensors Layer
#### 3.1.1 Physiological Sensors
These devices exhibit the characteristic of becoming the gold standard, particularly in terms of energy expenditure and performance measurements. Wearable smart masks comprise a multitude of sensors such as gas sensors, flow sensors, heart rate sensors, temperature sensors, and humidity sensors [42; 43]. However, the primary sensors consist of oxygen and carbon dioxide gas sensors along with a flow measurement sensor. Other sensors serve auxiliary roles, and in some commercial products, they are positioned in a manner that could be more effective outside the mask, incorporating additional systems such as ECG. Wearable smart masks hold considerable potential applications within sports science and medicine. They enable accurate and continuous measurements of fundamental parameters, namely, respiratory gas exchange and metabolic rate, for evaluating aerobic capacity, anaerobic threshold, energy expenditure, substrate utilization, respiratory efficiency, lung function, and more. These parameters can aid in assessing and optimizing physical performance, including monitoring training intensity and duration, recovery, and adaptation processes, as well as identifying health issues or risks. Wearable smart masks also have the capability to offer personalized feedback and guidance tailored to users' goals and preferences.
## 5 Conclusions  
Based on preliminary natural Safi gypsum rock characterization, several plasters were fabricated by adding natural, synthetic, and mineral additives. Seven natural additives (Stipa Tenacissima (ST), Petiol of Palm (PPa), Sisal Fiber (SF), Straw (Sw), Corn Bristles (CB), Cork (Ck), and Chips-Sawdust (ChSd)) were collected in the same area and mixed with the gypsum plaster at 20% by weight. The composites' thermo-acoustic performance was measured and compared with plasters with synthetic and mineral additives. The most promising composites were identified to promote the use of materials with low environmental impacts in building refurbishment. Based on the experimental characterization, the following results were obtained:  
* An increase in thermal performance with bio-additives: thermal conductivity in the 0.118 W/mK (with Petiol of Palm, PPa) to 0.238 W/mK (with Corn Bristles, CB) range, with a reduction up to 30% compared to pure gypsum plaster.
* An improvement in the sound absorption properties with all types of additives. Sound absorption average index (SAA) in the 0.06 (cork, Ck) to 0.12 (Stipa Tenacissima, ST, and Sisal Fiber, SF) range were measured.
* Higher sound insulation properties with ST and PPa (TL = 48-59 dB). However, straw and Sisal Fiber did not seem to affect the performance.
* The density of the samples affects the performance. The reduction in density leads to better thermal performance with comparable sound absorption values. TL is slightly reduced, but significant values were measured.  
The experimental campaigns highlighted the good thermal and acoustic behavior of bio-based gypsum plasters, especially when Petiol of Palm and Stipa Tenacissima were added to the composites. However, the mechanical and hygothermal performance of the most promising solutions will have to be investigated in order to optimize and identify the best solutions from a thermal, acoustic, mechanical, and vapor permeability point of view. The great added value of additives from material recycling and reuse chains is the lower environmental impact of production processes; this aspect could be investigated by means of life-cycle analysis in terms of the embodied energy and energy carbon contributions of each additive. Moreover, the long-term impact of bio-additives in the plasters will be analyzed by means of accelerated aging processes.  
Conceptualization, C.B. and M.T.; methodology, C.B. and F.M.; investigation, F.M. and S.B.; writing--original draft preparation, F.M. and S.B.; writing--review and editing, C.B.; supervision, C.B. and M.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 2 Related Work on Video Action Recognition Methods
### Methods for Capturing Motion Tempos of Sub-Action  
Accurately capturing sub-action information at varying motion tempos is essential for improving a model's ability to capture the temporal dynamics of video actions. Sub-actions often exhibit considerable differences in their motion tempos. To address this challenge, SlowFast [29] employs a dual-pathway architecture that captures motion information of slow-fast tempos. MotionSqueeze [16] utilizes adjacent RGB features to generate frame-level motion representations, which aids in capturing fast-tempo sub-action information, whereas struggles to adequately capture slow-tempo sub-actions. Similarly, AGPN [30] uses RGB features sampled at two different frame intervals to capture both fast-tempo and slow-tempo sub-action motion information.  
However, these methods are limited to fully capture the sub-action information that is most pertinent to action classification. To capture important sub-actions with varying motion tempos, we propose a Multi-Granularity Adaptive Fusion Module (MgAFM) to extract a crucial sub-action's intrinsic information with fast and slow motion tempos, and fuses them adaptively at different granularities.
## 4 Industry Perception Study
### Analysis of the Results of the Survey
#### 4.2.2 Awareness about SDP Technique  
The sample pool was given an insight into two decades of research on SDP. It was informed that SDP has the potential to evolve as a robust process and become a step in the software development life cycle. Then, respondents were asked to rate the level of their knowledge about SDP.  
About 61% of respondents did not believe that they knew enough about SDP (\(\chi^{2}\) = 6.76, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.0093). About 16% of the respondents had a deeper knowledge of SDP. Only 3% of respondents were aware of advanced capabilities of SDP such as defects count prediction. None of the respondents was aware that the research has progressed to identify risky changes. None of the respondents believed that SDP could estimate the severity of defects.
## 4 Engineering Application Case  
### Simulation Environment and Data Acquisition  
The engineering applications were carried out as part of the East China Sea offshore wind energy project in Zhangjiagang. The surveillance camera used in this application case was located in the port of Zhangjiagang City, at about 800 m from the target vessel. Zhangjiagang port is an important hub for the transportation of goods and large equipment by sea in China. This port has become one of the largest international trading ports in China, with a large flow of seaworthy vessels, mainly including bulk carriers, container vessels, cargo vessels, etc. Figure 8 shows a real video image taken from the port of Zhangjiagang; the video has a duration of 1 min and 30 s, and its resolution is about \(640\times 480\). A server was used as the simulation and development environment; its memory was 4 GB, the system platform was the 32-bit version of WinXP, the software implementation platforms were VS2010 and OpenCV 2.4.8, and the video frame rate was 24 fps.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 1 Introduction
### Related Literature
The effect of the antenna position on MIMO communication in LoS and NLoS system is studied in [30] using the method of moments and the finite-difference time-domain. The authors in [31] have derived analytical expressions for the achievable rate of asymptotic ergodicities of the system under a zero-forcing (ZF) detector. The capacity of a \(2\times 2\) short-range LoS MIMO channel in three-dimensional space is studied and the spherical wave model (SWM) is used to model the corresponding channel with arbitrary antenna directions in three-dimensional space in [32; 33; 34]. There are some literature studies related to short-range communication. In [35], the transmit design for short-range MIMO channels with the SWM is studied and the analytical expression of phase differences is derived, which makes the channel matrix optimally well-conditioned. A LoS MIMO channel model is proposed in [36], which considers an additional phase in the transmission path. The channel performance can be improved by adding a dielectric material. A constraint for antenna arrangement is designed as a function of frequency and distance for the LoS MIMO communication in [37]. The authors in [38] performed a novel analysis of the sensitivity of the optimal design parameters and derived analytical expressions for the eigenvalues of the pure LoS channel matrix. Two channel phase measurement methods of microwave LoS MIMO systems are investigated in [39]. In [40], the SWM enables the performance of the short-range LoS MIMO system to be significantly improved by properly adjusting the array geometries. According to the authors of [41], when the distance between the transmitting and receiving array antennas is short enough, the array antenna with the optimally narrow element spacing can give low spatial correlation.
## 2 Materials and Methods
### Detrended Cross-Correlation and EEG Analysis  
#### 2.4.1 Detrended Cross-Correlation Computation  
The detrended cross-correlation approach was originally developed based on the reverse correlation technique and the convolution of the derivatives [12]. The derivative is most commonly used as a pre-whitening method to reduce the effect of spurious correlation caused by trends and internal oscillations in time-series inputs [26; 27; 28]. Detrended cross correlation analysis was used to estimate the impulse response function of the LTI system. Here, it assumes that a brain can be modeled as an LTI system, which is characterized by the impulse response of \(h(t)\). Then, the output \(y(t)\) of EEG signals in response to speech envelope \(x(t)\) can be described as follows:  
\[x(t)\;*\;h(t)=\sum_{\tau}x(t-\tau)h(\tau)=y(t), \tag{1}\]  
where \(x(t)\) is the system input, and \(h(t)\) is the impulse response function. Equation (1) yields the derivative of the convolution as  
\[\
abla x(t)\;*\;h(t)=\sum_{\tau}\
abla x(t-\tau)h(\tau)=\
abla y(t), \tag{2}\]  
where \(\
abla x(t)=x(t)-x(t-1)\),\(\
abla y(t)=y(t)-y(t-1)\) (Appendix A). From Equation (1), if the input \(x(t)\) is the white noise, the impulse response function \(h(t)\) can be measured by cross-correlating the output \(y(t)\) with the input \(x(t)\)[12]. The impulse response function \(h(t)\) can be estimated using the detrended signals of \(x(t)\) and \(y(t)\), i.e., \(\
abla x(t)\) and \(\
abla y(t)\), respectively, according to Equation (2). The detrended cross-correlation function, \(C(\tau)\), between \(x(t)\) and \(y(t)\) is obtained as  
\[C(\tau)=<\
abla x(t)\
abla y(t-\tau)>. \tag{3}\]  
We examined the autocorrelation of the original and differentiated speech envelopes to assess the similarity between the autocorrelation and impulse functions. When the autocorrelation of the differentiated input signal converges on the impulse function, the detrended cross-correlation function can be computed as  
\[C(\tau)=<\
abla x(t)\
abla y(t-\tau)>=Kh(t), \tag{4}\]  
where \(K\) represents the power spectral density, which is computed by dividing the root mean square of the input \(\
abla x(t)\) by the spectral bandwidth [29]. Although the derivation action can generally cause noise in the signals, the cross-correlation between the input and output derivatives effectively mitigate noise in the signals. Figure 1 shows examples of speech envelopes of sentence stimuli, the autocorrelation of speech envelopes, and the autocorrelation of the corresponding differentiated speech envelopes. The shape of the autocorrelation of the differentiated speech envelope more closely resembles an impulse function than that of the original speech envelope, indicating that the differentiated speech envelope is more suitable for identifying the brain system.  
Figure 1: Examples of autocorrelation of speech envelopes from two sentences in input stimuli. (**a**) speech waveform and speech envelope for sentences #1 and #6, (**b**) the autocorrelation of the speech envelopes, and (**c**) the autocorrelation of the differentiated speech envelopes.
## 3 Synthesis of Optical Coherence Function  
The visibility of interference fringes caused by a signal and reference lightwave in an interferometer is changed as a function of the optical path-length difference between the two waves. The visibility change just corresponds to the absolute value of the \"optical coherence function\", which inherently has a complex value [21]. The complex optical coherence function is calculated as the Fourier transform of the power spectrum shape of the light source [21].  
Then, we thought that the shape of the optical coherence function might be synthesized by manipulating the time-averaged shape of the light source power spectrum. This has lead to the \"synthesis of optical coherence function (SOCF)\" technology [15; 16; 17]. When a delta function-like optical coherence function is synthesized, we can measure the reflected or backscattered lightwave that is caused at a coherence peak position along an optical fiber position-selectively. By shifting the position of the coherence peak, distributed measurement can be realized without using any mechanical moving parts nor any data calculations. In practice, varieties of distributed/multiplexed sensing and related schemes have been proposed and demonstrated by the SOCF technology [13; 14].  
In this chapter, the principle of the SOCF is explained, as well as a description of its historical aspects. SOCF applications to the fiber optic nerve systems are also shown.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 4 Dataset  
### Experimental Data and Environment  
The dataset of the denoising autoencoder includes eight modulation signals generated by MATLAB; they are BPSK, QPSK, 8 PSK, CPFSK, GMSK, OQPSK, 16 QAM, and 64 QAM, and the detailed parameters of the dataset are shown in Table 2. The SNR of the modulation signals ranges from \(-12\) dB to \(8\) dB, and the SNR interval is \(2\) dB. In the transmission process of the signal, the transmitter completes the modulation and up-conversion of the signal, and then transmits radio frequency signals into the channel. The receiver first performs down-conversion, and subsequently estimates the carrier frequency and phase, restores the received signal to the baseband signal, and performs modulation recognition, demodulation, and other operations. Without the loss of generality, this paper takes the baseband signal as an example to generate the dataset of the modulation signals. The rate of the baseband signal is set to \(256\) kHz, and the sampling frequency of the signal is \(1024\) kHz--that is, the oversampling rate of the signal is \(4\) times higher to increase the fault tolerance rate of the signal. The modulated signals are shaped and filtered using a root raised cosine filter with a roll-off factor of \(0.3\), and the transmission channel is modelled as an AWGN channel. Pytorch is used as the deep learning framework, and an NVIDIA GeForce RTX2080 Ti graphics card is used to implement the GPU parallel acceleration calculation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Description** \\ \hline SNR & \(-12\):\(2\):\(8\) (dB) \\ Symbol rate & \(256\)k Baud \\ Sample rate & \(1024\) kHz \\ Oversampling rate & \(4\) \\ Forming filters & Root raised cosine filter \\ Roll-off factor & \(0.3\) \\ Channel & AWGN channel \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for data sample sets
## 2 The Interacting Multiple Model Filters
### The Framework of the Interacting Multiple Model Filter
The calculations of the mixing probabilities with mode switching probability matrix \(\pi_{ij}\) and the Gaussian mixing probabilities are expressed by:  
\[\mathbf{\mu}_{k-1|k-1}^{i|j}=\frac{1}{\mathbf{c}_{j}}\mathbf{\mu}_{k-1}^{i}, \ i,j=1,\dots,r \tag{8}\]  
where \(\mathbf{c}_{j}^{-}\) is a normalization factor given by  
\[\mathbf{c}_{j}^{-}=\sum_{i=1}^{r}\pi_{ij}\mathbf{\mu}_{k-1}^{i}\]  
where \(\mathbf{x}_{k-1|k-1}^{0j}\) and \(\mathbf{P}_{k-1|k-1}^{0j}\) are the mixed initial conditions for mode-matched filter \(j\) at time \(k-1\). Notice that \(i\) and \(j\) denote the corresponding model index and \(n\) is the total number of sampling particles. Filtering  
The state estimate equation and covariance equation are inputs to the filter matched to \(\mathbf{M}_{k}^{j}\), which employs \(\mathbf{z}_{k}\) to yield \(\mathbf{x}_{k|k}^{j}\) and \(\mathbf{P}_{k|k}^{j}\). The likelihood function corresponding to the \(r\) filters  
\[\Lambda_{k}^{j}=p[\mathbf{z}_{k}\Big{|}\mathbf{M}_{k}^{j},\mathbf{Z}_{k-1}] \tag{9}\]  
which are computed through the mixed initial condition given by  
\[\Lambda_{k}^{j}=p[\mathbf{z}_{k}\Big{|}\mathbf{M}_{k}^{j},\mathbf{x}_{k-1|k-1} ^{\alpha_{0j}},\mathbf{P}_{k-1|k-1}^{0j}] \tag{10}\]  
Model probability update  
The model probability \(\mathbf{\mu}_{k}^{j}\) is updated according to the model likelihood and model transition probability governed by the finite-state Markov chain:  
\[\mathbf{\mu}_{k}^{j}=\frac{1}{c}\Lambda\mathbf{c}kj_{j} \tag{11}\]where  
\[c=\sum_{j=1}^{r}\mathbf{c}_{j}\Lambda kj \tag{12}\]  
and \(\Lambda kj\) is a likelihood function given by  
\[\Lambda_{k}^{j}=\frac{1}{\sqrt{2\pi\big{|}\mathbf{P}_{zz}^{j}\big{|}}}\exp\! \left[-\frac{1}{2}\mathbf{v}_{k}^{j\mathrm{T}}(\mathbf{P}_{zz}^{j})^{-1} \mathbf{v}_{k}^{j}\right] \tag{13}\]  
(4)  
Combination of state estimation  
The model-conditioned estimates and covariances, respectively, are combined according to the mixture equations  
\[\mathbf{x}_{k|k}=\sum_{j=1}^{r}\mathbf{u}_{k}^{j}\mathbf{x}_{k|k}^{j} \tag{14}\]  
\[\mathbf{P}_{k|k}=\sum_{j=1}^{r}\mathbf{u}_{k}^{j}\Bigg{\{}\mathbf{P}_{k}^{j}+[ \mathbf{x}_{k|k}^{j}-\mathbf{x}_{k|k}^{\Lambda}][\mathbf{x}_{k|k}^{j}-\mathbf{ x}_{k|k}^{\Lambda}]^{\mathrm{T}}\Bigg{\}} \tag{15}\]