esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 2 Background and Related Works
### The ARQ Scheme in the Aggregation-Enabled WLANs  
The ARQ scheme is one of the most important techniques to ensure the reliability of WLANs. For the aggregation-enabled WLANs, the BlockACK agreement should first be established to allow the A-MPDU transmissions via the add BlockACK (ADDBA) handshake. The BlockACK agreement maintains an aggregation window by the starting sequence number (SSN) and the window size, \(W\). The aggregation window slides when the received packets are forwarded to the upper layer. The submission of the received packets follows the order of sequence numbers. If some packets are lost due to the channel noise, those successfully transmitted packets whose sequence numbers are greater than the failed packets should also wait in the reordering buffer for the retransmission of the failed packets. There are two typical ARQ schemes for aggregation-enabled WLANs, i.e., aggregation  
Figure 1: The frame exchange procedure of the downlink (DL) multi-user (MU) transmission in IEEE802.11 ax WLANs. A-MPDU (aggregate MAC protocol data unit). BlockACK (block acknowledgment). ACK (acknowledgment).  
Figure 2: The frame exchange procedure of the uplink (UL) MU transmission in IEEE802.11 ax WLANs.  
selective repeat ARQ (ASR-ARQ) [5; 6; 7; 8] and block Acknowledgement window ARQ (BAW-ARQ) [3; 9]. The ASR-ARQ aggregates the retransmitted packets and the queued packets, respectively. In the BAW-ARQ, the packets whose sequence numbers are in the window are allowed to be aggregated into an A-MPDU. Figures 3 and 4 show the examples of the ASR-ARQ and the BAW-ARQ, respectively.
# The Complex Relationship between Virulence and Antibiotic Resistance
## 5 Biofilms Enhance the Transmission of Virulence and Antibiotic Resistance  
### Biofilm Formation  
The Centers for Disease Control (CDC) has revealed that the majority of bacteria occur in a biofilm, or a community of bacteria protected by a secreted exopolysaccharide, slimy matrix. Interestingly, the apparent form of bacterial altruism, leads to community behavior, including synchronized regulation of virulence genes. Biofilms are already up to 1000 times more resistant to antibiotics when compared to their free-floating, planktonic counterparts. Furthermore, this coordinated community, protected by a slimy matrix called the glycocalyx also facilitates the spread of antibiotic resistance [62; 64; 175; 176]. Antibiotic resistant strains of _P. aeruginosa_ were shown to have enhance biofilm production and a coordinating regulatory protein to switch on and off antibiotic resistance in _P. aeruginosa_[177].  
Although single species biofilms have proven deadly, as evidenced by extensive study of _P. aeruginosa_ single species biofilms in patients, particularly children, with cystic fibrosis [178], it is now widely believed that biofilms usually are made up of multiple species of bacteria, which have (1) proven more difficult to treat and (2) caused more persistent infections than mixed species of planktonic bacteria (e.g., _P. aeruginosa_, _Pseudomonas protegens_, _and Klebsiella pneumoniae_). These multispecies bacterial communities also had slower development trends and distinct structures in comparison to the single species biofilms [179]. These in vitro observations have been born out in vivo. In an in vivo study, a mixed species biofilm of _P. aeruginosa_ and _S. aureus_ caused longer wound healing times and increased host immune response in a rabbit model [180; 181]. Similar results were not obtained from single species infecting pathogens. Regardless of the homogeneity in biofilm composition, it is clear that the establishment of a biofilm enhances virulence.
# Electrochemical Removal of Chromium (VI) from Wastewater
## 3 Results and Discussions
### Electro-Reduction of Chromium (VI)
#### 3.1.4 Effect of Current Intensity  
The free electron supplied by the DC power supply was the main reducing agent which reduced chromium (VI) to chromium (III). The results shown in Figure 4 summarize the effect of current density on the reduction efficiency, while other conditions--the initial concentration of chromium (VI) of 1.000 g/L, concentration of H\({}_{2}\)SO\({}_{4}\) of 100 g/L, reaction temperature of 70 \({}^{\circ}\)C, and stirring rate of 500 rpm--were kept constant. It could be seen that the reduction efficiency increased when current density was increased, even though it was not increased by very much. The reduction efficiency was up to 86.45% at a current density of 50 A/m\({}^{2}\). In other words, a higher current density could achieve a high reduction efficiency.
## 2 Virulence Factors
### Biofilm Formation  
In addition to the specific role of the exopolysaccharides amylovoran and levanin in the biofilm formation of _E. amylovora_, as mentioned above, a recent study has suggested that type I fimbiae, flagella, type IV pili, and curli of _E. amylovora_ may contribute to biofilm formation in static and flowing environments and that defects in many of the genes encoding these appendages result in decreased virulence in planta [19].  
Previous results of the same group demonstrated that _E. amylovora_ forms a biofilm _in vitro_ and _in planta_[19, 21]. Pathogenesis and biofilm formation appear to be linked, but without identifying genes encoding traits independent of amylovoran production, the mechanistic role in virulence of biofilm formation in _E. amylovora_ could not be studied. Interestingly, mutants with reduced biofilm-formation ability appear unable to successfully establish large populations in apple xylem. Colonization of xylem is critical to the systemic movement of the pathogen through plants [19, 22]. Therefore, biofilm-deficient mutants remain localized within an inoculated leaf and are strongly impaired in the ability to invade the rest of the plant [19].  
In a study using a bioinformatic approach and the recently sequenced genome of _E. amylovora_[19, 42, 43], genes encoding putative cell surface attachment structures were identified [19]. A time course assay indicated that type I fimbiae function earlier in attachment, while type IV pilus structures appeared to function later in attachment. Deficiencies in type I fimbiae lead to an overall reduction in _E. amylovora_ virulence. By deletion of individual genes and gene clusters and using a combination of _in vitro_ attachment assays and plant virulence assays it was demonstrated that multiple attachment structures are present in _E. amylovora_ and play a role in mature biofilm formation, which is critical to pathogenesis and systemic movement in the host (Figure 2). In contrast, a fully functional biofilm was not necessary to survival and growth _in planta_[19].  
Although the mechanistic details behind biofilm formation remain largely unknown and genetic and structural analyses should be done for full characterization of these structures, it is suggested that they are formed in response to environmental triggers [24, 44] and quorum sensing signals [24, 45].
## 3 D2 HCP (Distributed Dynamic Host Configuration Protocol)
### Joining and Departure of Nodes
#### 3.2.2 Node Departure  
The node departure mechanism does not require the exchange of any messages. The node that wants to leave the network does not have to notify any other node of its departure, avoiding the overhead that these messages cause. The other nodes in the network will become aware of the departure node through the periodic route updates that the OLSR protocol performs every so often. They will note that they have lost the path to that node, and therefore they remove it from their _Free_IP_Blocks_ table, adding its free address block to the corresponding node as explained in the previous section.
## 1 Introduction
### Main Contributions  
This paper proposes a TCN-BiGRU method for wind power prediction based on VMD decomposition and SSA optimization. The original wind power data are first decomposed via VMD to obtain a number of intrinsic mode function (IMF) components, and each component is combined with the remaining features of the original data; then, TCN-BiGRU is used to predict the combined data separately, and SSA is used to optimize the number of iterations, learning rate, and number of neurons of TCN-BiGRU to improve the prediction accuracy of the model, and finally, the results of each component of the prediction are summed to obtain the final prediction results. In this paper, the model is used to predict the power of wind turbines in domestic wind farms, and the wind power prediction results of different models obtained every 5 min are compared to verify the effectiveness of the proposed model. The proposed model can accurately predict the wind power output and prepare for scheduling in advance to ensure the smooth operation and safe stability of the power grid. The following are the major contributions:  
1. By employing the VMD method to decompose the original power data, noise can be reduced, and the lag in the prediction model can be addressed.
2. The TCN-BiGRU combination model is utilized to forecast the decomposed power components, and the accumulated predicted components yield the final prediction result.
3. To further enhance the prediction accuracy of the model, the SSA algorithm is employed to optimize the hyperparameters in the TCN-BiGRU combination model. This optimization process helps to obtain the optimal parameters, leading to an improvement in the prediction accuracy of the model.
## 1 Introduction
Both of the designs try to combine electric and magnetic dipoles for achieving isotropy and have reported gain variations of 7.9 dB and 12 dB, respectively; [23] has proposed a 3D antenna structure for dual band isotropic pattern with gain variation as small as 0.9 dB at one of the operating frequencies; [24] proposes an SRR based structure printed on a flexible material to design a quasi-isotropic antenna with gain variation of 3 dB; while, [25] again uses a combination of electric and magnetic dipole to achieve isotropy where the achieved gain variation is 5.2 dB. Continuing on the effort of previous researchers, this paper presents a two-dimensional (2D)-planar near-isotropic antenna. The presented design is proposed to be used in DoA estimation. The main highlights of the proposed design are as follows:  
1. The proposed design achieves an overall gain variation of \(\leq\)0.5 dB in the total gain which is amongst the best isotropic radiation pattern. The measured variation in total gain is almost same as in [10] i.e., \(\leq\)2 dB. 2. The proposed design is low cost and simple enough to be constructed by printing it on a substrate. Table 1 gives a comparison of the proposed antenna with some of the significant existing designs reported in the literature. It can be seen that the proposed antenna has minimum _Gain variation_. In comparison to previous designs, the antenna also has a simple, planar configuration. Section 2 of the paper describes the methodology for the design of the proposed isotropic antenna, while Section 3 presents the simulated results of the antenna. Section 4 describes the transformation of planar wire antenna to planar printed antenna. Section 5 shows the measured results of fabricated antenna with comparison of simulated results, while Section 6 concludes the paper.
# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Current Status of SSIM Research in Medical Imaging
#### 4.6.3 Contour Extractor  
Contour extraction for tongue image sequences may be beneficial for speech recognition, speech production, and swallowing study. The tongue images can be obtained utilizing the US modality. In 2016, [PERSON] et al. compensated the manual tongue contour tracking by employing CW-SSIM for re-initializing the tongue contour position automatically [61]. They considered CW-SSIM due to its invariant with a few rotations, translations, and scaling distortions. They started the procedure by computing the similarity score between the reference image (the first frame image in this case) and the current frame image. In total, they can have a thousand images depending on the number of frames. Then, they set a threshold value of CW-SSIM for the purpose of re-initializing the contour position to the first frame if the similarity score was greater than the designed threshold value (0.8). Their results indicated that the CW-SSIM along with similarity constraint achieved the best performance by showing the less mean sum of distances (MSD) for three subjects, i.e., \(3.36\pm 0.86\), \(3.65\pm 1.02\), and \(2.96\pm 0.95\).
# A Novel Continuous-Discontinuous Multi-Field Numerical Model for Rock Blasting
## Abbreviations  
List of symbols  
\begin{tabular}{l l l} \(Q_{i}\) & (J) & The work of external force \\ \(L\) & (-) & Lagrangian function \\ \(\ddot{u}_{i}\) & (m/s\({}^{2}\)) & Generalized acceleration \\ \(\ddot{u}_{i}\) & (m/s) & Generalized velocity \\ \(u_{i}\) & (m) & Generalized displacement \\ \(t\) & (s) & Time \\ \(\Pi_{e},\Pi_{f},\Pi_{m}\) & (J) & System kinetic energy, elastic energy,potential energy \\ \(\rho,\rho_{0}\) & (kg/m\({}^{-3}\)) & Density of material, charge \\ \(\sigma_{ij}\) & (-) & The mean strain tensor of the element \\ \(V\) & (m\({}^{3}\)) & Volume of element \\ \(\mu\) & (N/(m/s)) & Damping \\ \(T_{i}\) & (N/m\({}^{2}\)) & The surface force on the element \\ \(\mathbf{M}\) & (-) & Element mass stiffness matrix \\ \(\mathbf{C}\) & (-) & Damping matrix \\ \(\mathbf{K}\) & (-) & Stiffness matrix \\ \(\mathbf{F}(t)\) & (-) & External load array of nodes \\ \(F_{n_{r}},F_{\tau}\) & (N) & Normal,Tangential contact force of adjacent elements \\ \(K_{n_{r}},K_{\tau}\) & (-) & Normal,Tangential stiffness of the spring \\ \(\Delta u_{n_{r}},\Delta u_{\tau}\) & (m) & Normal,Tangential displacement of adjacent elements \\ \(\sigma_{n1},\sigma_{n2}\) & (MPa) & Normal stress of contact point pair \\ \(\sigma_{\tau 1},\sigma_{\tau 2}\) & (MPa) & Tangential stress of contact point pair \\ \(A\) & (m\({}^{2}\)) & The area of the contact point pair \\ \(c\) & (N) & Cohesive force \\ \(\phi\) & (\({}^{\circ}\)) & Internal friction angle \\ \(V_{i}\) & (m/s) & Velocity of node explosive gas flow \\ \(P_{g}\) & (MPa/m) & Pressure gradient of explosive gas \\ \(x_{i}\) & (-) & The coordinate component of the direction i of a node \\ \(k_{s}\) & (m\({}^{2}\)/Pa-s) & Gas permeability coefficient \\ \(\omega\) & (m) & The width of crack \\ \(\mu_{g}\) & (Pa-s) & The dynamic viscosity of fluid \\ \(P_{w}\) & (MPa) & The peak pressure of explosive \\ \(D\) & (m/s) & Detonation velocity \\ \(\gamma\) & (-) & Gas adiabatic index \\ \(t_{w}\) & (s) & Blast stress wave action time \\ \(r_{s}\) & (m) & The radius of pulverized zone \\ \(V_{cp}\) & (m/s) & Crack propagation velocity under shock wave \\ \(P_{k}\) & (MPa) & Critical pressure, \(P_{k}\) = 200 MPa is considered in this work \\ \end{tabular}
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 3 Results
### Lower Limb Motor Functions: Long-Term Effects  
For the long-term effects of combined robotic-training and tDCS protocols, the analysis on the four comparisons from four studies showed a significant effect size: \(SMD\) = 0.692; \(SE\) = 0.258; variance = 0.067; 95% CI = 0.186-1.198; \(Z\) = 2.678; \(p\) = 0.007; Figure 5. The levels of heterogeneity across the individual effect sizes were relatively minimal: (a) Q-value = 2.715 with \(p\) = 0.438 and (b) \(I^{2}\) = 0.000%. Moreover, two publication bias tests reported minor asymmetry levels in individual effect sizes: (a) a revised funnel plot with one imputed value after performing the trim and fill technique (Figure S4); and (b) [PERSON]'s regression test: \(\beta_{0}\) = 2.652 and \(p\) = 0.400. In addition, given that all four comparisons from four studies used RCT design and recruited chronic stroke patients, moderator variable analyses were not necessary. Taken together, these findings indicate that robotic training combined with tDCS protocols showed significant positive transient and sustained effects on lower limb rehabilitation post-stroke as compared with those individuals in the control groups (i.e., robotic training only).
## 2 Materials and Methods
### Sensor-Based Assessment of Gait  
We used a total of six wireless inertial sensors (Shimmer Research, Dublin, Ireland) attached by straps on the thighs, shins, and feet (Figure 1). Results from the data of shank and foot IMUs will be presented in this study.  
Each sensor includes a triaxial accelerometer, gyroscope, and magnetometer and is able to measure linear acceleration, angular velocity, and magnetic heading in three dimensions. The data from sensors were acquired via a Bluetooth wireless connection at a sampling frequency of 256 Hz.  
Participant walked a distance of 4 m (13 feet) at the self-selected usual pace. The distance of 4 m for a walk test was chosen because it is a gold standard test for functional assessment in older adults, which is used in the Short Physical Performance test, gait speed test [33; 34]. Data from three trials were used. From all the data obtained from the inertial sensors, we selected shank angular velocity and foot linear acceleration to determine heel-strike and toe-off characteristic points. These data were filtered using a Butterworth second order low pass filter with an 8 Hz cut-off frequency and an additional least square method 25 th order filter with a 10 Hz cut-off frequency for composite foot acceleration data. A gait event detection algorithm was made by picking toe-off points from the angular velocity data [35; 36] and heel strike points from composite foot acceleration data [37]. Gait parameters were calculated based on gait events determined by the toe-off (TO) and heel-strike (HS) events detected by the algorithm. Full gait cycle (also referred as stride) was defined as two consecutives HSs of the same leg, stance phase begins with the HS and concludes with TO of the ipsilateral foot, swing phase begins with the TO and ends with the HS of the same leg. Double support occurs twice in gait cycle and it starts at the beginning and end of stance phases of contralateral legs. Time calculated as number of samples between respective gait event markers divided by sampling rate. The following quantitative gait parameters were calculated: stance phase time, swing phase time, stance and swing phases normalized to a percent gait cycle, gait speed, stride time, on right and left leg accordingly, double support time, and cadence (steps/min).  
Figure 1: Wireless sensors fixed on the lower limb segments.
## 2 Materials and Methods  
### Sample Collection and Chemical Analyses  
The Qingshan Lake reserve watershed area is approximately 620 km\({}^{2}\), and is the important potable water sources of Lin'an city. There are mainly six tributaries adjoining the residential area and inflowing to the Western basin of the lake. Three samples representing the three main nutrient levels (i.e., ammonium; total nitrogen, TN; and chemical oxygen demand, COD) were collected in Qingshan Lake from the tributary water to the reservoir area. All the sludge samples were collected from the surface sediments (5 cm) at a water depth of 30 to 50 cm. High-quality water samples of Qingshan  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **TN** & **NH\({}_{4}\)\({}^{\star}\)-N** & **NO\({}_{3}\)\({}^{\star}\)-N** & **NO\({}_{2}\)\({}^{\star}\)-N** & **COD** & **pH** & **DO** \\ \hline QL-H & 0.21 & 0.08 & 0.06 & ND\({}^{\text{a}}\) & 13 & 6.83 & 4.91 \\ QL-V & 16.82 & 14.36 & 1.15 & 0.14 & 75 & 6.97 & 5.71 \\ SR-W & 121.32 & 93.41 & 5.67 & 0.87 & 331 & 7.71 & 3.35 \\ \hline \hline \end{tabular} \({}^{\text{a}}\) ND not detectable, unit mg/L.  
\end{table}
Table 1: General physicochemical parameters of three environmental waters of the pristine Qingshan Lake and its branch, the Sewage River.  
Lake (QL-H) and those closest to the village of Qingshan Lake (QL-V) were taken from a reservoir area and the adjoining residential area, respectively. The other Sewage River sample (SR-W) was taken from a domestically polluted tributary. A sample was taken by collecting at least six individual samples at the same depth in the same area to form a composite sample in the study. Metagenomic DNA was isolated from the sludge samples with the Mo Bio PowerSoil(r) DNA Isolation Kit (Mo Bio Laboratories, Inc., Carlsbad, CA, USA). To remove any coextracted humic substances and other contaminants, the metagenomic DNA was further purified and concentrated with ethanol precipitation, as previously described [17].  
The dissolved oxygen (DO) concentrations were measured with the Winkler method [18]. The concentrations of ammonium, nitrate, and nitrite were determined with Nessler's reagent spectrophotometry at 420 nm, \(N\)-(1-naphthalene)-diaminoethane spectrophotometry at 410 nm, and ultraviolet (UV) spectrophotometry at 540 nm, respectively. Total nitrogen (TN) was measured with the standard UV spectrophotometry method (DR6000, Hach, Loveland, CO, USA).
## 3 Results and Discussion  
After the model calibration, the structure was implemented into the model according to the available layout plans. The groundwater flow in the case study location was simulated for the calibrated year 2006. Each of the piles was individually inserted into the model using the principle of \"blocking\" the grid cells inside the pile to the depth of 9-13 m, practically forming a no-flow zone at the position of the pile itself. For the study area location as described above, shown in Figure 2b in the form of cells in the horizontal plane, the impact of the structure on the groundwater in the surrounding area was analyzed by comparing the groundwater level change over time in three control points: KT-1, KT-2, and KT-3, in cases with and without the structure, as well as by examining the flow net at the location of the structure itself. Comparison diagrams for each control point are given in Figure 7. The  
Figure 5: Calculated (black dots) and observed (green dots) levels in well S-102, along with the obtained deviations (green lines).  
Figure 6: Distribution of the groundwater level for 10 August 2006.  
Figure 8: Maximum velocity values in case without structure.  
Figure 9: Maximum velocity values in case with structure.  
The obtained results indicate no influence of the piles on the groundwater flow in the study area. If the numerical oscillations of the level at the control points from April to May are excluded, the diagram lines of groundwater level change are nearly coincident for the cases with and without the structure. These oscillations are solely a consequence of the drying and wetting of cells and layers due to the temporal variation in the groundwater level. This process is numerically accompanied by minor oscillations of the level due to the permanent wetting and drying of cells. This congruence of results suggests that the structure itself does not in any way affect the groundwater levels in its immediate surroundings or in the broader region around it. On the other hand, the diagram of the maximum flow velocity values shows a slight increase in velocities in the space between the piles, i.e., along the lines of the shortest distance between two piles (Figure 9). However, the analysis of the maximum velocity values clearly demonstrates that there is only a slight (negligible) increase in the velocity in the zone between the piles. Therefore, when observing the case without the structure (Figure 8), it can be noticed that the maximum velocity values amount to around 0.35 m/day, while in the case with the structure they reach the value of 0.40 m/day (Figure 9).