esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Reduced EMMPRIN Expression Enhances Markers of Dorancy
CT26-WT or CT26-KD cells (\(1.5\times 10^{5}\) cells) were each cultured alone or in co-culture with RAW 264.7 cells that were seeded in the upper chamber of the inserts at a ratio of 1:1, in serum-starvation medium (final volume 650 \(\upmu\)L) for 48 h. Alternatively, single cultures of CT26-WT or CT26-KD cells (\(1.5\times 10^{5}\) cells/650 \(\upmu\)L) were cultured with or without the addition of recombinant TGF\(\beta\) (10 ng/mL) or recombinant EMMPRIN (25 ng/mL or 250 ng/mL). Lysates were extracted from the CT26 cells in lysis buffer in the presence of proteinase inhibitors, and the concentrations of phosphorylated ERK1/2 and p38 were determined by ELISA. The ratio pERK/pP38 was calculated for (**A**) the co-culture (\(n=5\)), (**B**) the addition of TGF\(\beta\) (\(n=5\)), and (**C**) the addition of recombinant EMMPRIN (\(n=6\)). (**D–G**) CT26-WT and CT26-KD cells (\(3\times 10^{4}\) cells each) were incubated as in (**A–C**), mounted on cover slips, and stained for the expression of the proteins E-cadherin (green) and vimentin (red), and using DAPI (blue) to stain cell nuclei. Bar size is 25 \(\upmu\)M. (**D**) Representative images of the staining in co-culture, quantification of vimentin expression (**E**) in the co-cultures (\(n=6\)), (**F**) with addition of TGF\(\beta\) (\(n=5\)), and (**G**) with addition of recombinant EMMPRIN (\(n=4\)). Data are presented as means \(\pm\) SE, and analyzed using a two-way ANOVA followed by [PERSON]’s post-hoc test. Next, we examined the gene expression of the EMT-TFs and a few markers associated with dormancy. As dormant cells must also exhibit EMT plasticity and stemness, and EMT-TFs may also drive the process of dormancy and stemness [48; 49], we expected to see this reflected in the patterns of expression. Indeed, relative to the single cultures of CT26-WT cells, the expressions of the dormancy markers _NR2F1_, _p21_, and _p27_ (Figures 4A,B and S3B), as well as the stemness marker _SOX2_ (Figure S3A) were unaffected by the co-culture, whereas the CT26-KD cells exhibited increased levels of these genes. Incubation with the macrophages reduced the expression of these genes in the CT26-KD cells, but, for the most part, their expression remained higher relative to the co-cultured CT26-WT cells. The expression of the EMT-TFs presented the same trends (Figures 4C,D and S3C,D). Likewise, upon addition of TGF\(\beta\) (Figure 4E-H) or EMMPRIN at higher concentrations (Figure 4I-L), the CT26-KD cells showed a reduced expression of _NR2F1_, _p21_, _Slug_ and _Zeb1_ genes relative to the untreated CT26-KD cells, reaching levels similar to the CT26-WT cells. These findings further support our previous conclusion that the reduced expression of EMMPRIN in the CT26-KD cells pushes the cells towards dormancy, and that the presence of macrophages can drive an exit of these cells from a dormant state.
## 5 Simulated Jamming Results Analysis
### High jamming Scenario
#### 5.2.2 Second Trajectory  
The GPS DF, GPS TF, and GPS TF + Galileo SF-based positioning solutions fail to provide a reliable continuous solution, as indicated in Figures 17 and 18. Additionally, by using the GPS TF + Galileo TF, or the GPS TF + Galileo TF + GLONASS SF, the positioning solutions show continuous behavior. However, there are large spikes for both the horizontal and vertical positioning components, respectively. By using the GPS TF + Galileo TF + GLONASS DF signals, the positioning solution maintains continuous and reliable positioning performance, as shown in Figures 17b and 18b. This is because of the availability of a minimum of 10 satellites during jamming, as shown in Figure 19.  
It can be seen from Table 5 that the GPS TF + Galileo SF-based solution provides better accuracy than the GPS TF + Galileo TF counterpart for the vertical component of the positioning solution. However, it is worth mentioning that the GPS TF + Galileo TF solution is available for the whole jamming interval, and the GPS TF + Galileo SF solution is not available for about 90% of the jamming interval. Additionally, it seems that the RMSE of the GPS TF + Galileo TF-based solution is better than the GPS + Galileo TF + GLONASS DF-based solution. However, the latter combination provides a smoothed solution without large spikes, as the maximum horizontal and vertical positioning errors are 5.137 m and 11.658 m compared to 7.245 m and 13.756 m for the GPS TF + Galileo TF solution.  
Figure 17: (a) Horizontal positioning error for different GNSS signal combinations [second trajectory]. (b) Zoomed view of horizontal positioning error for different GNSS signal combinations [second trajectory].  
Figure 19: Number of visible satellites for different GNSS signal combinations [second trajectory].  
Figure 18: (a) Vertical positioning error for different GNSS signal combinations [second trajectory]. (b) Zoomed view of vertical positioning error for different GNSS signal combinations [second trajectory]. [second trajectory].
# Betatron Radiation and Bremsstrahlung in the Interaction of Intense Laser Pulse with Solid Target
## 3 Results
### Effect of Laser Intensity and Spot Size
Panels (**a,b**) are for \(L=5.0\)\(\mu\)m. Curve with: triangles is for \(a_{0}=18.0\) and \(r_{0}=3.0\)\(\mu\)m, circles is for \(a_{0}=6.0\) and \(r_{0}=3.0\)\(\mu\)m, squares represent \(a_{0}=18.0\) and \(r_{0}=1.0\)\(\mu\)m. Panels (**c,d**) are for \(L=0.5\)\(\mu\)m. Curve with: triangles is for \(a_{0}=6.0\), circles denote \(a_{0}=18.0\), squares represent \(a_{0}=38.0\). (**e**) \(\eta_{\gamma,b}\) versus \(t\) for \(L=0.5\)\(\mu\)m from 1D simulations. The blue, orange, and green curves are for \(a_{0}=6\), 18, and 38, respectively. The gradients of the curves in (**b,d,e**) are also explicitly given. Decreasing \(r_{0}\) also decreases the electron energy gained by the DLA, so that \(\eta_{\gamma,s}\) remains at a relatively low level and there is no large variation in \(\eta_{\gamma,b}\), as can be seen in Figure 5a,b. This behavior can be attributed to the fact that divergence of the laser pulse increases with a decrease of its focal spot, so that the laser electric field decays rapidly as it propagates in the preplasma. The dispersion angle of the Gaussian laser pulse is \(\theta\sim\lambda/\pi r_{0}\), with \(r_{0}\) the focal spot radius at the beam waist. The laser electric field decreases with the propagation distance \(d\), or  
\[\frac{a_{0}}{a_{w}}=\frac{1}{\sqrt{1+\left(\frac{d\lambda}{\pi r_{0}^{2}} \right)^{2}}}, \tag{1}\]  
with \(a_{w}\) being the value of \(a_{0}\) at the beam waist. If the beam waist is at the preplasma boundary (\(n_{e}=0.05n_{c}\)) and \(d=L/2\ll\Delta x_{1}\), for preplasma with \(L=5.0\)\(\upmu\)m and \(r_{0}=3.0,1.0,0.5\)\(\upmu\)m, we get \(a_{0}/a_{w}=0.996,0.844,0.366\), respectively, showing that the laser field attenuation is due to tight focusing. For preplasma with \(L=0.5\)\(\upmu\)m and \(r_{0}=3.0,1.0,0.5\)\(\upmu\)m, we get \(a_{0}/a_{w}=1.00,0.998,0.969\), respectively, indicating that for short preplasmas, suppression of \(\eta_{\gamma,s}\) is not obvious, which was also verified by simulations (not shown).
# A Collaborative Human-Robot Framework for Visual Topological Mapping of Coral Reefs
## 2 Method  
In this section, we present our method for creating topological maps of a coral reef from visual information provided by different agents. The overview of the proposed method is presented in Figure 1. The topological map is represented as a graph \(G=\{V,E\}\) with nodes \(V\) and edges \(E\). Each of the nodes and edges of the graph contains the following information:  
* Nodes: Each node contains an identification number \(l\), an image \(I_{l}\) and its visual features \(D_{l}=(F_{l},Z_{l})\). \(F_{l}=\{f_{l,0},f_{l,1},...,f_{l,1}\}\) is the list of \((x,y)\) coordinates of each visual feature and \(Z_{l}=\{z_{l,0},z_{l,1},...,z_{l,k}\}\) is the list of 1D vectors describing the appearance of each feature. For this work, we have utilized SURF [2] as feature extractor since it has shown good results for tracking in underwater environments [34]. However, other methods can be used for extracting visual features. In addition to that information, the graph contains the pose \(p_{l}=(x_{l},y_{l},\theta_{l})\) of the image with respect to the first node.
* Edges: An edge \(e_{ij}\) contains the spatial relation between two nodes \(V_{i}\) and \(V_{j}\), i.e., their relative position with respect to each other encoded as \(p_{ij}=(x_{ij},y_{ij},\theta_{ij})\) and \(p_{ji}=(x_{ji},y_{ji},\theta_{ji})\), where \((x_{ji},y_{ji})\) is the center of the image \(I_{i}\) with respect to the image \(I_{j}\) and \(\theta_{ji}\) is the orientation of image \(i\) with respect to the \(x\)-axis of the image \(j\) and vice versa. An edge is added when a valid relative position between the node \(i\) and \(j\) exists.  
With respect to the images, the following assumptions were taken into account:  
1. The images are taken from above the coral reef with the image plane parallel to the seafloor.
2. During the exploration, the agent keeps approximately the same distance from the coral reef. However, in different explorations, the agent can move to other distances.
3. The images from a single agent should be presented to the method in the same order as they were captured during the exploration. This is intended to exploit their temporal and spatial coherence.
4. The proposed method is intended to be used in an offline and online mode, that is, the map can be created once the exploration has finished and all of the images are available (offline mode) or while exploring the environment (online mode). To deal with both cases, the method is designed to process the images incrementally, that is, they are processed one by one as they are extracted from a camera during exploration or read from a storage device after exploring. Therefore, when processing image \(I_{t}\), we do not know any information about \(I_{t+1}\). The main parts of the method are described in detail in the following sections.
## 2 Materials and Methods
### Schematic Architecture  
The schematic architecture of the tunable multi-tone 5G+ MMW frequency synthesizer based on the EO-combs is shown in Figure 1. It consists of QDMLL, a commercial LiNbO\({}_{3}\) DD-MZM with the \(V_{\pi}\) of 5.8 V, an RF signal generator, an UTC-PD with a beating broadband of 110 GHz, a power divider and tunable frequency filter. The emission spectrum directly from the QDMLL is in the C-band wavelength range from 1520 nm to 1558 nm, using a GaAs/InP Fabry-Perot laser cavity.  
The output spectrum of the GaAs/InP QDMLL has many attractive useful characteristics [29], such as ultra-low relative intensity noise (RIN) (less than \(-160\) dB/Hz), small optical linewidth (less than 158 kHz) and a large optical signal-to-noise ratio (OSNR) (up to 72 dB), etc. In the proposed architecture of tunable multi-tone MMW generators, the output spectrum of the QDMLL is modulated by the DD-MZM, which is driven by two opposite-phase sinusoidal wave RF signals.  
The bias voltage is used to set the operating point of the DD-MZM, important for improving the flatness of the optical comb lines generated. Compared to the reported MMW generators, this scheme does not use polarization controllers, optical phase lock loops and optical filters. As a result, it is considered to be more compact and flexible, which makes it simple to generate tunable and low-phase noise MMW signals [32].  
Figure 1: Schematic architecture of the tunable multi-tone 5G+ MMW frequency synthesizer using ultra-wide broadband electro-optic frequency combs.
## 2 Materials and Methods
### Land Cover Classification  
A detailed description of the mapping process is found in Figure 3 and the sections below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Description** \\ \hline Deciduous Forest & _Deciduous forest_ was sometimes referred to as ‘dry or arid zone’; this area is mostly lava substrate dominated by native species like _Acacia_ spp., _Bursen gravelens_, _Piscidia carthagenensis_, _Croton scouleri_, or _Opuntia_ spp., and ranges from 0 to \(\sim\)200 masl [42; 46]. \\  & The _Evergreen forest and shrubland_ is sometimes referred to as the _‘Scalesia’_, ‘brown’, and _‘Miconia’_ zones. This region contains well-developed soil with vegetation characterized by an abundance of shrubs, herbs, ferns, and trees like _Darwinithamus tenuifolius_, _Lycopodium_ spp., _Scalesia pedunculata_, _Miconia robinsoniana_, and _Zantoxylum fagara_. Epiphytes like mosses and liverworts commonly cover the trunks and branches of trees in this zone [42; 46; 51]. \\  & _Evergreen seasonal forest and shrubland_ is an ecosystem previously referred to as ‘transition zone’, where soil and understory are more developed than the deciduous forest and species like _Clerodendrum molle_, _Cordia lutea_, _Chiococcus alba_, _Psidium galapageium_, _Tournefortia_ spp. can be found [42; 46; 51]. \\  & _Humid tallgrass_ is a region previously known as ‘pampa zone’, which contains virtually no trees or shrubs, and its vegetation consists mainly of ferns, grasses, and sedges like _Cyathea weatherbyana_, _Habenariia monorrhia_, _or _Pteridium aquilinum_[42; 43]. \\  & The _pioneer_ category describes herbaceous, quick-growing native vegetation that thrives in recently disturbed areas, such as _Plumbago scandens_, _Senna occidentalis_, _Mentzelia aspera_, _Sida rhombifolia_, or _Heliotropium_ spp., but likely also includes introduced herbaceous species like _Amaranthus_ spp., or _Pothomorple pellata_[46]. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptions of native land cover categories.  
Figure 3: Workflow overview for land cover classification.
## 2 Background: Data and Autoregressive Models
### AutoRegressive Integrated Moving Average (ARIMA) Models
#### 2.4.3 Advantages and Disadvantages  
The ARIMA technique presents several advantages, among which are the use of an online learning environment, the independence between sample size and storage costs, as well as the fact that parameter estimation can be performed online, in an efficient and scalable way. The disadvantages of this technique are the subjectivity of its progress evaluation, the fact that the reliability of the selected model may depend on the skill and experience of the specific forecaster, and the existence of several limitations to the parameters and classes of possible models. A consequence of all these limitations is the fact that the final choice of the prediction model can be a difficult task [17].  
Another important aspect of the application of the ARIMA to time series forecasting is the linearity of the dataset it aims to predict. The basic form of the ARIMA algorithms is designed to handle linear data relationships. However, their scope of successful application in forecasting can be significantly extended, considering the various transformations available, which extend the method's capability to handle non-linear time series. A widely used family of such transformations, including both logarithmic and power transformations, is the Box-Cox toolset [18], depending on the \(\lambda\) parameter and defined as:  
\[w_{t}=\begin{cases}log(y_{t})&if\quad\lambda=0\\ \frac{y_{t}^{\lambda}-1}{\lambda}&otherwise\end{cases}. \tag{11}\]  
Based on the value of \(\lambda\), the new time series can be transformed in order to improve the forecasting model.
## 1 Introduction  
Speech signals received within a room usually contain reverberation which impairs the intelligibility of speech in communication scenarios such as mobile phones and hearing aids. Reverberation will also degrade the recognition performance of automatic speech recognition systems. Hence, speech dereverberation is still an important issue nowadays.  
Dereverberation techniques can be divided into reverberation cancellation [1] and reverberation suppression [2, 3] depending on whether or not the acoustic impulse response (AIR) needs to be estimated during the dereverberation [4]. The major part of most reverberation suppression methods is the estimation of late reverberant spectral variance (LRSV), which remains a challenging task due to its high time variability [5]. [PERSON] proposed a single-channel LRSV estimator [3] based on a generalized statistical model [6] to suppress late reverberation, and it still performs outstanding nowadays [5]. However, in [PERSON] LRSV estimator, two parameters (i.e., the reverberation time \(T_{60}\) and the parameter \(\kappa\) which is related to direct-to-reverberation ratio (DRR)) should be given in advance or estimated online.  
To the authors' knowledge, there are numerous reverberation time estimation methods, whereas there are few single-channel DRR online estimation methods [7]. Besides, according to practical experience, it may also be inappropriate to obtain \(\kappa\) indirectly by estimating DRR, because even the \(\kappa\) calculated via measured ground truth DRR may not be the optimal \(\kappa\) for the [PERSON] estimator. A detailed discussion can be found in Section 5. Therefore, unlike other traditional methods using estimated DRR to calculate \(\kappa\), the present work aims to propose a blind adaptive \(\kappa\) estimator which can improve the performance of the Habets LRSV estimator and makes it more practical. Inspired by the optimally-modified log-spectral amplitude (OM-LSA) algorithm [8], this letter differentiates between the direct sound presence/absence hypotheses and derives the conditional direct sound presence probability to give a time-varying recursive average on the estimated \(\kappa\). The proposed \(\kappa\)estimator is evaluated and compared with existed \(\kappa\) estimator [9] and \(\kappa\) calculated using measured ground truth DRR. The evaluation results show that the proposed \(\kappa\) estimator performs better than the conventional \(\kappa\) estimator or measured \(\kappa\) under all evaluation conditions. Besides, the quality of the dereverberated speech is also evaluated and compared to a method using recursive maximum-sparseness-power-prediction-model (MSPP) [10].
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 4 Simulation Case Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy (m)**} & \multicolumn{2}{c}{\begin{tabular}{c} **Availability** \\ **(\%)** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Distance** \\ **penalty (\%)** \\ \end{tabular} } \\ \hline  & **A*** & **Nav-A* & **A*** & **Nav-A* & **A*** & **Nav-A* \\ \hline \
\begin{tabular}{c} Scenario-1 \\ Single-frequency \\ \end{tabular} & 8.1 & 6.8 & 39.1 & 51.7 & - & 5.8 \\ \hline
\begin{tabular}{c} Scenario-2 \\ Dual-frequency \\ \end{tabular} & 3.2 & 2.00 & 100 & 63.1 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of simulation results. Figure 12: HRMS error time-series (Dual-frequency). Minimum performance requirements have not been standardised for UAM navigation systems at the time of writing. Therefore, a limiting threshold value approximately equal to one-third of an average urban street width (10 m) is set to compute the system availability, which lies between the standard and precision PBN boundaries presented in Section 2. Figure 13 plots the PDOP and number of visible satellites for both trajectories. The number of redundant visible satellites dictates the fault detection and isolation capability of onboard systems such as RAIM. As described in [3], a minimum of six satellites is required for RAIM-based fault detection and isolation. Only fault detection is possible with five visible satellites, and integrity monitoring is unavailable with only four visible satellites. The availability is computed as the percentage of time over the flight for which the accuracy, integrity and continuity requirements are jointly satisfied. This inclusion of navigation parameters in the path-planning cost map has clearly resulted in an improvement in overall system availability over the trajectory. However, this improvement is marginal in the single-frequency scenario since the ionospheric error dominates in this scenario, and is considered uniform over the relatively small test-domain. The navigation performance-augmented path incurs a penalty in terms of distance, but minimizes accuracy violations as compared to the conventional A* algorithm.
# Experimental Investigations of a Tunnel Lining Segment Strengthened by In Situ Spraying Mortar
## 2 Experimental Overview
### Experiment of Lining Segment Structure Strengthened by In Situ Spraying Mortar  
#### 2.2.1 Test Structure  
The strengthened tunnel lining segment structure adopted a Wuhan subway lining ring, as shown in Figure 4. The lining ring had an outer diameter of 6200 mm, an inner diameter of 5500 mm, a segment thickness of 350 mm and a ring width of 600 mm. The ring was divided into six blocks, including one cap block (F), two adjacent blocks (L1 and L2), two standard blocks (B1 and B2) and one base block (B3). Starting from the center of the F block in a clockwise direction, the six seam positions are located at 10.75\({}^{\circ}\), 78.75\({}^{\circ}\), 146.25\({}^{\circ}\), 213.75\({}^{\circ}\), 281.25\({}^{\circ}\) and 349.25\({}^{\circ}\). Each longitudinal joint is connected by one M30 bending bolt. The segment concrete grade is C55, and the reinforcing bar is an HRB335 rib bar.  
[PERSON] found that after the interior of the concrete pipe is repaired by cement mortar, if the shear strength of the old and new interfaces is low, the old and new interfaces of the composite structure will strip under the external loads, which will affect the bearing capacity of the structure [19]. Therefore, in order to improve the shear strength of the interface, when the segment is strengthened, the first step should be to plant reinforcement for the segment. The planting bar is an HRB400 ribbed steel bar with a diameter of 12 mm; the depth of the original concrete is 100 mm, and the length of the embedded spray mortar layer is 30 mm. The mortar layer adopts circumferential \(\Phi\) 10 @ 100; longitudinal \(\Phi\) 10 @ 200 steel mesh reinforcement; the inner lining ring width is 80 cm, the steel mesh is HRB 400 threaded steel with a welding method and the lap length is not less than 10 D (100 mm). The strengthened ring is made of in situ spraying mortar, and the overall strengthened thickness is controlled at about 50 mm. The layout of the reinforcement cage is shown in Figure 5. After the spraying was completed, it was cured to the test age. The strengthened structure is shown in Figure 6.  
Figure 4: Indication diagram of tunnel lining segment structure strengthened by in situ spraying mortar.
## 3 LPWAN Technologies and Design Goals
### Topology and Scalability  
LoRa and Sigfox both use star topology for forwarding messages to gateways as being used by cellular networks, and end devices are hooked to a single gateway; however, their transmission may be received by multiple nearby gateways. Key requirement for LPWAN devices is scalability while working on low volumes of traffic and still coping with increasing density of devices by making use of more number of gateway controllers and adaptive data rate for collision-free communication. In order to increase scalability, LoRa configures multiple communication parameters to increase or decrease data rate and ToA. Mesh and tree topology is also used by some technologies, but with limited adaptation as the same will require complex protocols and higher consumption of battery, which are in contravention of the design goals of LPWAN. LoRa and Sigfox are meant for machine-to-machine communication on lines of cellular networks to support an IoT grid for sensors while keeping the capital expenses (CAPEX) and operation expenses (OPEX) to minimum.