esa-sceva/satcom-chunk-collection huggingface dataset:
# Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data
## 2 Hidden Markov Modeling of Activity Recognition  
Currently, many mathematical models for activity recognition system have been developed, such as Hidden Markov Model (HMM) [21], Bayesian Network [22], Fuzzy Systems [23] and Neural Network [24]. Each model has its features and benefits. However, none of them can be assured to be entirely accurate, truthful, and totally free of error. However, HMM represents the most activity recognition model that is easy to develop and deploy.  
HMM is one of the simplest forms of the Dynamic Bayesian Networks (DBNs), where it has one discrete hidden and observed nodes per segment. When dealing with spatio-temporal information, the accuracy of the HMMs is superior. Therefore, most of the activity recognition literature is based on HMMs. The main components of the HMM are represented by 5-tuple \(\lambda=(S,V,A,B,\pi)\) as follows [25]:  
1. \(S=s_{1},s_{2}\ldots s_{N}\) is the set of the hidden states, \(N\) is the number of the states. The active state at a time instant, \(t\), is \(q_{t}\). In the activity recognition model considered herein, the states represent the conducted activity in this specific instant, \(t\);
2. \(V=v_{1},v_{2}\ldots,v_{M}\) is the set of observable events and \(M\) is the number of observation events. The observable events in our case are the sensor readings;
3. \(A=a_{ij}\) is the state transition probability distribution, where \(a_{ij}=P[q_{t+1}=s_{j}|q_{t}=s_{i}]\),\(1\leq i,j\leq N\);
4. \(B=b_{j}(k)\) is the observation event probability distribution for state \(s_{j}\) where \(b_{j}(k)=P[v_{k}\)_at_\(t|q_{t}=s_{j}]\),\(1\leq j\leq N,1\leq k\leq M\);
5. \(\pi=\{\pi_{1},\pi_{2}\ldots\pi_{N}\}\) is the set of initial state probability distribution where \(\pi_{i}=P[q_{1}=s_{i}]\), \(1\leq i\leq N\).  
Figure 1 shows the structure of an HMM. For illustration purposes, only three activities along with two gaps are shown. The annotated activity transitions are represented by solid arcs, \(a_{ij}\), while the activity gaps are dimmed out.  
For modeling the activity recognition problem by HMM, any activity is represented by a sequence of hidden states. At any time \(t\), the user is assumed to be at one of these states and each state releases  
Figure 1: Graphical representation of the used HMM for activity recognition. Annotated events are represented by solid-circle states. Annotation gaps are shown in dimmed circles.  
events with certain observation event probabilities \(b_{j}(k)\). In the following time slot, \(t+1\), the system goes to another state, resulting in another activity, according to the transition probabilities between the states \(a_{ij}\). During the training phase, an annotated dataset is used to calculate the transition and observation event probabilities, transition, and emission models respectively, by solving decoding problem, i.e., estimating the most expected state sequence that achieves the events [26]. In the classification stage, the estimated transmission and emission models are used to estimate hidden states, which are the sought activities.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 2 Mission Overview
### Spatial Resolution  
The cross-track spatial resolution of the iGNSS-R altimeter in this experiment was primarily determined by the hybrid-code ambiguity function (using the half-power beamwidth criterion). Based on the satellite's orbital altitude, we conducted simulations to evaluate the minimum resolution cells for the BDS, GPS, and Galileo systems across elevation angles ranging from 55\({}^{\circ}\) (incidence angle = 35\({}^{\circ}\)) to 90\({}^{\circ}\) (incidence angle = 0\({}^{\circ}\)) and azimuth angles ranging from 0\({}^{\circ}\) to 360\({}^{\circ}\). The simulation results are shown in Figure 6. As can be seen from the figure, the minimum cross-track resolution and along-track spatial resolution of BDS and GAL are relatively close, with the cross-track resolution being less than 4.75 km and the along-track resolution being better than 6 km. In contrast, the cross-track resolution of GPS is less than 7.75 km, and the along-track resolution is less than 10 km.  
To mitigate the effects of speckle noise and enhance altimetry accuracy, it is essential to apply incoherent accumulation processing to the DM waveform and to smooth the sea surface height in the along-track direction. Consequently, the along-track spatial resolution of the iGNSS-R altimetry product is contingent upon the duration of along-track incoherent accumulation and the extent of sea surface height smoothing. The iGNSS-R altimeter has a sampling rate of 5 Hz, meaning that incoherent processing has already been performed for 0.2 s onboard. Since the average moving speed of the specular point is about 5.6 km/s, the cross-track and along-track spatial resolutions shown in Figure 6 correspond to the cross-track and along-track spatial resolutions of a single DM waveform. The purpose  
Figure 5: Comparative analysis of GPS L1 C/A and iGNSS-R DMs. (**a**) L1-band iGNSS-R DM; (**b**) L5-band iGNSS-R DM; (**c**) conventional L1 C/A DDM; (**d**) SNR and leading-edge slop performance comparison: L1 C/A DM vs. L1 and L5 iGNSS-R DMs.  
of this experiment was to verify the interferometric altimetry accuracy under a spatial resolution of 40 km. Therefore, after inverting the sea surface height using the 5 Hz DM waveform, it was necessary to smooth the sea surface height over an along-track distance of 40 km. In summary, the along-track resolution of the final iGNSS-R sea surface height was close to 40 km.
# The Structure-Activity Relationship of the Antioxidant Peptides from Natural Proteins
## 3 The Relationship of Chemical Structure and the Antioxidant Ability of Peptides
### The Relationship of Peptide Structure and Its Antioxidant Activity
#### 3.2.4 Secondary Structure  
Taking the secondary structure into consideration, [PERSON] _et al._ found by bioinformatics that 58 out of 77 bioactive peptides (accounting for 75% of total peptides) contained at least one \(\beta\)-turn, followed by helices (60%), and \(\beta\)-strands were present in just 13% of the total peptides studied [56]. Based on the conformational prediction and fourier transformed infrared spectroscopy, a \(\beta\)-sheet structure exists in HLFGPPGKKDPV (MW: 1291.51 Da), derived from fertilized hen eggs [8]. The two thiol-bearing peptide of apolipoprotein (apo) A-IMilano (R173C) possesses novel antioxidant properties on phospholipid surfaces in a dose-dependent manner. The contextual constraints of the amphipathic \(\alpha\)-helices play the key role in the antioxidant [57]. \(\alpha\)-Helices and \(\beta\)-sheet secondary structures exist in the structure of antioxidant peptides, but results verifying a role of the secondary structure on the activity are quite scarce. According to the previous sections, the peptides with significant antioxidant capacity are usually short chains with 4-6 residues. Therefore, the secondary structure may be a minor factor for the antioxidant peptides due to their low molecular weight.
# Characteristics and Applications of Spatiotemporally Focused Femtosecond Laser Pulses
## 5 Applications of SSTF Technique in Manipulation of Plasma Dynamics  
In the previous section, we demonstrated the applications of the SSTF technique in femtosecond laser filamentation. More careful examination shows that the plasma behaviors in the SSTF field are intriguing. By recording the time-resolved shadowgraphs of the transient plasma induced by the SSTF pulses inside fused silica glass, it was discovered that the track of the plasma is significantly curved [26]. Figure 10 shows the time-resolved shadowgraphs of plasma produced by the SSTF pulses when the polarization conditions and pulse energy were varied. The plasma formation appears more pronounced when the polarization direction of the pump pulse is parallel to the spatial chirp of the SSTF pulses, as stronger photoionization can be achieved with such a polarization configuration. Moreover, the plasma track will bend itself more severely when photoionization appears stronger from the shadowgraph images. The bending of plasma tracks can be attributed to the asymmetric expansion of the plasma in the SSTF pulses, as the PFT of the SSTF pulses will generate unbalanced ponderomotive forces in the directions parallel and anti-parallel to the pulse front tilting [26]. It is noteworthy that when the plasma density distribution becomes asymmetric along the direction perpendicular to the optical axis, and the focused laser beam will be deflected toward the area where the plasma density is higher (i.e., similar to an optical fiber in which the light is reflected from the cladding where the refractive index is lower than the core), which leads to the bending of the plasma tracks.  
Furthermore, in 2014, [PERSON] et al. reported an unexpected intense second harmonic generation in the gas chamber filled with 1 atm argon using the SSTF scheme, which seems counterintuitive in centrosymmetric media [25]. As it can be seen in Figure 11, when the polarization direction of the pump pulses is along \(x\) axis (i.e., linearly polarized along the direction of the spatial chirp), the SSTF pulses can generate a second harmonic efficiently with an extremely high signal-to-noise ratio. By changing the polarization of input laser pulses to the \(y\) axis (i.e., linearly polarized perpendicular to the direction of the spatial chirp), the second harmonic beam shows two lobes separated from each other and the signal-to-noise ratio is dramatically decreased. The underlying mechanism for such a second harmonic generation by the spatiotemporal focusing scheme is ascribed to the gradient of non-uniform plasma induced by the PFT of the SSTF pulses [25].  
Figure 9: Filament-induced breakdown spectroscopy generated on iron (**a**) and aluminum; (**b**) targets with (blue solid lines) and without (red dashed lines) SSTF pulses. Reproduced with permission from [37], Copyright The Optical Society, 2012.
## 3 DCPSO with Cooperation
### Distributed Cooperative Particle Swarm Optimization with Cooperation
#### 3.2.5 Cooperative Path-Planning with DCPSO  
In conclusion, the formation rendezvous problem of multi-UAV based on DCPSO can be solved by the following steps:  
Step 1. Initialize all sub-swarms:  
(1) For the \(N\) UAVs, the size of each sub-swarm is \(M\), \(T\) is the maximum iteration number, and the current iteration is set to \(t=0\);  
(2) The velocities and positions of all the particles are initialized within the bounded search space \([-V_{\text{max}},V_{\text{max}}]\) and \([Z_{\text{min}},Z_{\text{max}}]\), respectively;  
(3) Each particle's fitness is calculated using Equation (11); the best particle in each sub-swarm is selected as its representative. Each particle's fitness is modified cooperatively using Equation (14). Then, the initial personal and global best solutions are easy to obtain.  
Step 2. The particles' velocities and positions in each sub-swarm are updated using Equation (8) and Equation (9), which are limited within the prescribed search space and velocity range. Then, each particle's fitness is calculated using Equation (11). To improve the searching efficiency of the DCPSO algorithm, an elite keeping method is used. In each sub-swarm, the best particle of the previous generation is used to replace the worst one in the current generation.  
Step 3. The group best location of each sub-swarm is selected as the representative and cooperative groups are formed. Then, each particle's fitness is modified in each cooperation group using Equation (14).  
Step 4. For each sub-swarm \(i\), compare every particle \(z_{ij}\) with the personal best location \(p_{\text{best}ij}\); if the particle is better, let \(p_{\text{best}ij}=z_{ij}\), \(i=1,\ldots,N,j=1,\ldots,M\).  
Step 5. For each sub-swarm \(i\), compare every particle \(z_{ij}\) with the group best location \(g_{\text{best}i}\); if the particle is better, let \(g_{\text{best}i}=z_{ij}\), \(i=1,\ldots,N,j=1,\ldots,M\).  
Figure 3: Collision checking for two Pythagorean hodograph (PH) paths.  
Step 6. If \(t=T\), end the iteration and output the best solutions of each sub-swarm; otherwise, let \(t=t+1\) and go back to Step 2.
## 3 Materials and Methods
### RGB-D and IRT Images Fusion Module
#### 3.4.1 Sensor Calibration  
It is important to have a good camera calibration of the different sensors, to be able to merge their information. The used sensors were placed in a horizontal parallel configuration, as shown in Figure 7. For this reason, stereo calibration is carried out to obtain accurate intrinsic and extrinsic parameters that relate the local coordinate systems of the RGB-D camera and with the IRT camera, assuming a pinhole camera model for both. Furthermore, with the stereo calibration, the fourth-order coefficients of the radial and tangential distortion for each of the cameras are calculated. The distortion model employed is [PERSON]'s model [54]. In addition to radial distortion, it also models tangential distortion, which occurs when the lens is not aligned with the sensor. These distortions are compensated with the following equations:  
\[\begin{split} x_{u}&=x_{d}+\overline{x}(\kappa_{1}r^{2 }+\kappa_{2}r^{4}+\kappa_{3}r^{6}+\cdots)+\\ &\quad\quad[\rho_{1}(r^{2}+2\overline{x}^{2})+2\rho_{2}\overline {x}\,\overline{y}](1+\rho_{3}r^{2}+\cdots)\\ y_{u}&=y_{d}+\overline{y}(\kappa_{1}r^{2}+\kappa_{2} r^{4}+\kappa_{3}r^{6}+\cdots)+\\ &\quad\quad[\rho_{2}(r^{2}+2\overline{y}^{2})+2\rho_{1}\overline {x}\,\overline{y}](1+\rho_{3}r^{2}+\cdots)\end{split} \tag{15}\]  
where \([x_{u},y_{u}]^{T}\) is the undistorted image point, \([x_{d},y_{d}]^{T}\) is the distorted image point, \([x_{c},y_{c}]^{T}\) is the center of distortion, \(\overline{x}=x_{d}-x_{c}\), \(\overline{y}=y_{d}-y_{c}\), \(r^{2}=\overline{x}^{2}+\overline{y}^{2}\), \(\kappa_{n}\) is the \(n\)th radial distortion coefficient and where \(\rho_{n}\) is the \(n\)th tangential distortion coefficient.  
The factory calibration of the RGB-D camera is used to have the depth sensor registered on the color sensor, such that it is only necessary to calculate the intrinsic parameters of the thermal camera \(K_{T}\) and the extrinsic parameters \(R_{C,T}\) and \(t_{C,T}\) seen in Figure 7. The extrinsic parameters are constant, due to the fixed positioning of the sensors relative to each other. To perform the stereo calibration, the method of [PERSON] et al. [55] is used. In detail, the calibration is calculated using multiple images from different points of view and scales of the calibration target pattern, whose 3D points are known and constant, and performing a RANSAC [56] optimization to minimize the reprojection error.  
Although a traditional calibration target, such as a chessboard, is usually used to calibrate a color camera, to calibrate the thermal camera, it was necessary to design a special calibration target; this was because the contrast obtained in the thermal image was not enough to calculate accurate correspondences between points precisely.
###### Abstract  
Steel slag (SS) is a secondary material from steelmaking production with little commercial value. Its volumetric expansion and low reactivity limit the use of SS in Portland cement (PC)-based materials. This study investigated the potential use of basic oxygen furnace (BOF) slag as a single precursor in alkali-activated matrices (AAMs). Six AAM pastes were assessed by changing the silica modulus (0.75, 1.50 and 2.22) and the sodium concentration (4% or 6% Na\({}_{2}\)O--wt. SS). The early hydration was assessed using isothermal calorimetry (IC), followed by the assessment of the mechanical performance (compressive strength), apparent porosity, and structure and microstructure characterization (X-ray diffraction, thermogravimetric analysis and scanning electron microscopy). The results indicated that although the BOF slag may be considered a low-reactivity material, the alkaline environment effectively dissolved important crystalline phases to produce hydrates (reaction products). An optimized combination of activator sources was achieved with 4% Na\({}_{2}\)O and a silica modulus of 1.50-2.22, with a compressive strength up to 20 MPa, a significant amount of reaction products (C-S-H/C-A-S-H gels), and low initial and cumulative heat release. Those properties will help to promote SS recycling use in future engineering projects that do not require high-strength materials.  
basic oxygen furnace slag alkali-activated materials; waste recycling activation parameters; microstructure properties +
Footnote †: journal: Applied Sciences
## 4 Experiment Results and Analysis  
The data sets collected at ZHNZ station from DOY 198 to DOY 203, 2021 are used to evaluate the proposed method. During these days, a flood occurred from DOY 200 to DOY 202. The GPS observation station (34.5\({}^{\circ}\)N, 113.1\({}^{\circ}\)E), which is located in Zhengzhou City in Henan Province, was affected during the flood. The sample interval of the data set is 30 s. The elevation cut-off angle is 10 deg. The GNSS receiver is set on an observation pier above 3 m of the ground, thus the receiver is not affected by the flood. Information about the flood can be found on the following website by searching the keywords: [[https://view.inews.qq.com/k/20210721A07](https://view.inews.qq.com/k/20210721A07) DRD00?web_channel=wap&openApp=false&autoopenapp=ampzkqw&pgv_ref=amp]([https://view.inews.qq.com/k/20210721A07](https://view.inews.qq.com/k/20210721A07) DRD00?web_channel=wap&openApp=false&autoopenapp=ampzkqw&pgv_ref=amp) (accessed on 20 July 2021).
# Monitoring Theophylline Concentrations in Saline Using Terahertz ATR Spectroscopy
## 2 Experimental Section  
We used techniques of a typical THz time-domain spectroscopy (THz-TDS) system for this experiment. Samples were measured with the THz-TDS system, equipped with dipole low-temperature-grown GaAs photoconductive antennas (Hamamatsu Photonics KK, Hamamatsu, Japan) as the emitter and detector, and a fiber laser (IMRA femtolite HFX-400, IMRA America, Inc. Ann Arbor, MI, USA, wavelength 780 nm, pulse width 48 fs, laser power max. 100 mW). The procedure  
Figure 1: (**a**) Path of the propagating THz wave inside the Si prism. (**b**) Image of the measurement system using a typical THz-TDS system.  
to measure the sample, with the exception of the ATR method, is similar to that described in our previous paper [11].  
It is difficult to measure aqueous samples with a transmission-type THz-TDS because of their strong absorption at terahertz frequencies. Thus, we chose an ATR THz-TDS using a silicon prism. The prism was 30 mm in width; the long axis of the triangle was 73 mm and it was 50 mm tall. The principle of the measurement using the prism is based on that of the Dove prism, although we used a triangular prism. The path of a THz wave propagating inside the Si prism is shown in Figure 1. The THz wave is guided by the two off-axis parabolic mirrors between the emitter and detector; thus, the shape of the THz wave entering the prism is a parallel beam (Figure 1). This shape is intended to use a wide area of the sample surface. The diameter of the parallel THz wave is about 20 mm, according to a knife-edge measurement.  
A container with the sample solution was placed onto the surface of the Si prism. This was made of plastic walls (20 \(\times\) 20 \(\times\) 40 mm) and a resin adhesive. The sample solution in the container was connected directly to the Si prism. The entire propagating THz wave reflected at the border of the Si prism and the sample solution, and propagated onto the detector. Since we used the Si prism and plastic container, these materials had no influence on the measurement results. We used an aluminium mirror to measure the reference THz wave. We performed our analysis by comparing the reflected THz wave from the sample solution and the mirror.  
The aim of this work was to assess the possibility of blood drug concentration measurements using THz techniques. We used saline as solvent for the drug. We prepared the saline by dissolving NaCl (Wako Pure Chemical Industries, Osaka, Japan) in pure water. Furthermore, we prepared a NaCl solution with various concentrations to measure the NaCl concentration dependence of the strength of reflected THz waves. We also used theophylline (Wako Pure Chemical Industries) as a test drug. Theophylline is an anti-asthmatic drug and has been used in clinical practice for over 30 years. The pharmacological effects of theophylline are closely related to its blood concentration, but high levels of theophylline in blood can result in various toxic effects, such as generalized convulsions and impaired consciousness [12]. Thus, TDM of theophylline was necessary for its safe administration.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 4 Our Proposal
### Registration  
At this phase, \(SM_{ij}(i=1,2,\cdots,k,j=1,2,\cdots,n)\), \(FN_{i}(i=1,2,\cdots,k)\), AC and the SP make registration requests. RKG generates and distributes keys for them. Precisely, the Registration phase includes the following steps:  
1. \(SM_{ij}\) registration: For each \(SM_{ij}\), RKG selects \(x_{ij}\in Z_{q}^{*}\) randomly as the private key of \(SM_{ij}\) and computes \(pk_{ij}=g^{x_{ij}}\), which is set as the public key.
2. \(FN_{i}\) registration: For each \(FN_{i}\), RKG randomly selects \(x_{FN_{i}}\in Z_{q}^{*}\) as the private key of \(FN_{i}\) and computes \(pk_{FN_{i}}=g^{x_{FN_{i}}}\), which is set as the public key.
3. \(AC\) registration: RKG randomly selects \(x_{AC}\in Z_{q}^{*}\) as the private key of AC and computes \(pk_{AC}=g^{x_{AC}}\), which is set as the public key.
4. \(SP\) registration: RKG randomly selects \(x_{SP}\in Z_{q}^{*}\) as the private key of the SP and computes \(pk_{SP}=g^{x_{SP}}\), which is set as the public key.  
RKG securely transmits the private keys of all entities to them through the secret channel (as in the scheme of [13], we assume that the adversary cannot capture the private key transmitted in the secret channel), computes the common public key as \(PK=pk_{AC}.pk_{SP}=g^{x_{AC}+x_{SP}}\) and finally publishes the system parameters \(\text{par}=(G,G_{\tau},e,q,g,pk_{ij},pk_{FN_{i}},pk_{AC},pk_{SP},PK,H)\).