esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Organic-Inorganic Hybrids in Chemical Analysis, Bio- and Environmental Monitoring
## 3 Biosensors as a Targeted Class of OIH
In summary, combination of ZnO with PDA layers helped to improve optical, electronic, and sensitivity properties of ZnO/PDA toward targeted molecules. Besides, the PDA coating would prevent the photocorrosion of ZnO during photoelectrochemical procedures [77]. Because of the recent progress in nanoengineering, we may see that current research efforts pay more attention to nano-based hybrids. Using nano-based OIH in biosensor development enables to increase the biosensor stability and sensitivity, to enlarge the operating range by extension of lower and upper detection limit, to improve their shelf and storage time. Besides, the development of nanob biosensors provides an opportunity for the fabrication of small non-invasive sensing platforms whose production was considered to be impossible with older technology. On the one side, there is no doubt that nano-based OIH possess much better analytical microsystem characteristics compared to the measurement systems based on pure bulk materials. On the other side, it requires the use of novel methods of validation and \"testing\" of produced nanoanalytical measurement systems. Therefore, special approaches or methodologies of nanoanalysis and nanometrology for nano-based OIH and nanob biosensors should be systematized or developed. Figure 4: (**a**) General ZnO-based optical biosensor scheme. Reproduced with permission from [71]. Copyright Elsevier, 2016. (**b**) Photoluminescence. Reproduced with permission from [72]. Copyright Elsevier, 2019. (**c**) Reflectance. Reproduced with permission from [76]. Copyright Elsevier, 2018, for detection of leukemic cells detection and paraquat/atrazine, respectively.
## 5 Discussion and Future Directions
### Limitations of SCI Device-Based Therapies
#### 5.2.4 Usability, Training, and Maintenance  
The successful implementation of an SCI device depends on the user's competency level as well as the continuous system upkeep. Exoskeletons require extensive training for safe use, which may take extra patient time, energy, and determination. Misalignment with joint axes can cause discomfort or injury. FES systems demand calibration and electrode placement expertise, which may not be available in a clinical setting. Not having the right professional available to the patient may mean malfunction of the device. There are further maintenance burdens, which include software updates, battery management, and hardware servicing, which can overwhelm the rehabilitation teams without the dedicated technical support [48].
## 5 Conclusions and Future Works
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Trend**} & \multicolumn{1}{c}{\multirow{2}{*}{**TWE**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2330**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2454**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2317**}} & \multicolumn{1}{c}{\multirow{2}{*}{**6505**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2414**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2881**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2882**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2303**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2603**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2308**}} \\ \cline{3-3} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & & & & & & & & \\ \hline \multirow{3}{*}{Precision} & Up & 60.3\% & 63.54\% & 55.25\% & 61.89\% & 60.96\% & 58.25\% & 58.48\% & 51.87\% & 57.14\% & 56.70\% \\  & Flat & 91.3\% & 83.54\% & 78.18\% & 83.12\% & 85.33\% & 86.11\% & 80.24\% & 79.47\% & 80.26\% & 78.63\% \\  & Down & 49.4\% & 51.87\% & 46.51\% & 52.62\% & 56.89\% & 54.95\% & 52.80\% & 47.86\% & 46.31\% & 46.25\% \\ \hline \multirow{3}{*}{Recall} & Up & 64.2\% & 43.64\% & 35.91\% & 44.29\% & 49.90\% & 47.62\% & 38.83\% & 33.21\% & 39.64\% & 39.22\% \\  & Flat & 83.2\% & 68.82\% & 64.18\% & 69.11\% & 67.65\% & 64.85\% & 69.25\% & 61.60\% & 64.19\% & 64.66\% \\  & Down & 67.6\% & 40.72\% & 32.32\% & 38.34\% & 41.13\% & 42.37\% & 35.79\% & 37.11\% & 33.05\% & 30.52\% \\ \hline \multirow{3}{*}{
\begin{tabular}{l} F1 Measure \\ sure \\ \end{tabular} } & Up & 62.2\% & 51.74\% & 43.53\% & 51.63\% & 54.88\% & 52.40\% & 46.67\% & 40.49\% & 46.81\% & 46.36\% \\  & Flat & 87.1\% & 75.47\% & 70.49\% & 75.47\% & 75.47\% & 73.99\% & 74.34\% & 69.40\% & 71.33\% & 70.96\% \\  & Down & 57.1\% & 45.63\% & 38.14\% & 44.36\% & 47.74\% & 47.85\% & 42.66\% & 41.80\% & 38.57\% & 36.77\% \\ \hline \multicolumn{3}{l}{Accuracy} & \multicolumn{1}{c}{\multirow{2}{*}{}} & \multicolumn{1}{c}{\multirow{2}{*}{**79.2**\%}} & \multicolumn{1}{c}{\multirow{2}{*}{**71.38**\%}} & \multicolumn{1}{c}{\multirow{2}{*}{**65.38**\%}} & \multicolumn{1}{c}{\multirow{2}{*}{**71.19**\%}} & \multicolumn{1}{c}{\multirow{2}{*}{**72.25**\%}} & \multicolumn{1}{c}{\multirow{2}{*}{**70.86**\%}} & \multicolumn{1}{c}{\multirow{2}{*}{**69.31**\%}} & \multicolumn{1}{c}{\multirow{2}{*}{**64.71**\%}} & \multicolumn{1}{c}{\multirow{2}{*}{**66.64**\%}} & \multicolumn{1}{c}{\multirow{2}{*}{**66.04**\%}} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision, Recall, F1 Measure and Accuracy of the top 10 weighted stocks in TWE. **Acknowledgments:** The author would like to thank the Ministry of Science and Technology (MOST) for their support during this study. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Evaluating Satellite-Retrieved Far-Red SiF with Data on Vegetation Activity
### Comparing Far-Red SiF with GPP Derived from Australian Flux Tower Observations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **GOME-2** **SIF KNMI vs. Flux Tower GPP** & **GOME-2** **SIF NASA (v26) vs. Flux Tower GPP** \\ \hline  & **Slope** & **Intercept** & **\(R\)** & **Slope** & **Intercept** & **\(R\)** \\ \hline Sturt Plains & 11.5 & \(-\)3.0 & 0.739 & 13.4 & \(-\)2.0 & 0.776 \\ Daly Uncleared & 7.9 & 2.5 & 0.699 & 8.6 & 3.4 & 0.637 \\ Adelaide River & 17.5 & \(-\)1.7 & 0.835 & 12.6 & 3.0 & 0.691 \\ Daly Pasture & 20.0 & \(-\)6.0 & 0.831 & 24.0 & \(-\)4.4 & 0.871 \\ Dry River & 5.4 & 4.0 & 0.622 & 8.4 & 3.4 & 0.821 \\ Fogg Dam & 12.1 & \(-\)0.2 & 0.763 & 7.0 & 3.6 & 0.554 \\ Howard Springs & 12.2 & 5.1 & 0.554 & 15.0 & 6.4 & 0.646 \\ Riggs & 8.2 & \(-\)1.0 & 0.627 & 11.8 & \(-\)1.0 & 0.781 \\ Wallaby & 3.7 & 4.0 & 0.265 & 7.6 & 3.0 & 0.366 \\ Whroo & 2.5 & 5.0 & 0.371 & 2.2 & 5.8 & 0.299 \\ Wombat & 4.4 & 8.6 & 0.397 & 6.8 & 8.7 & 0.433 \\ Yanco Jaxa & 4.4 & 0.0 & 0.570 & 6.5 & 0.3 & 0.800 \\ All & 6.8 & 2.8 & 0.456 & 9.7 & 2.9 & 0.510 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Sun-induced Fluorescence (SiF) time series from KNMI and NASA (v26) with Gross Primary Productivity (GPP) derived from Australian flux tower sites for the period January 2007 to June 2015. For each site the slope, intercept and correlation between the time series are given: the linear relation reads as GPP = intercept + slope \(\times\) SiF. Figure 6: Sample scatterplots for the Adelaide ((**left**) high correlation between GPP and SiF); and Wallaby ((**right**) low correlation) sites.
## 1 Introduction
From the feasibility study results, we show that the API not only provides similar data statistics compared to the FI, but can also improve overall network performance. Moreover, the API can be a more advanced initialization approach than conventional simple initialization methods for a UWASN in the sense that it partially initializes the network considering application requirements and promptly responds to poor underwater environments via repetitive initialization. In addition, due to the partial initialization characteristic, the API can be favorable for any underwater applications requiring high energy efficiency. These applications periodically generate a volume of data from multiple under nodes for a long time like the Internet of Underwater Things (IoUT). These underwater applications are exemplified as follows [18]:  
* The environmental observation-based applications that periodically measure various environmental information such as water quality, pollution, salinity, and temperature. * The disaster prevention-based applications that monitor floods, earthquakes, and tsunamis in real time. * The fisheries applications such as fish and shellfish farming. We organize the rest of this paper as follows: In Section 2, we explain the network model applying the API. In Section 3, the algorithm of the API is described in detail. In Section 4, we analyze the performance of the API via extensive simulation tests in order to investigate its feasibility. Section 5 concludes this paper.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 5 Conclusions  
This research introduces a new routing protocol, called VGRQ, specifically designed for query-driven Wireless Sensor Networks (WSNs) with a mobile sink. It is a grid-based routing protocol that binds all the cell headers in chains. Randomly moving mobile sinks update cell headers about their location. This restricts the sharing of sink locations over the entire sensor field and conserves the overall network energy expenditure. The chains are utilized to share mobile sink location information and transmit query and data messages. The performance of VGRQ is assessed and analyzed against QDVGDD and QRRP. And it is found that VGRQ is more energy-efficient and delivers the data with less data delivery delay in comparison to both QDVGDD and QRRP. Thus, VGRQ is suitable for time-sensitive scenarios.  
In the future, we would like to implement multiple mobile sinks and evaluate the performance of VGRQ in this environment. Although multiple mobile sinks can improve the network performance in terms of data delivery delay and data delivery ratio, providing the location of multiple mobile sinks to sensor nodes will be a challenging task. Furthermore, faulty node detection and void grid cell identification also need to be examined.  
Conceptualization, S.K.J., R.B. and N.S.; methodology, S.K.J. and N.S.; software, S.K.J., N.S. and S.S.; validation, M.F.H., S.S. and N.D.B; formal analysis, S.S. and R.B.; investigation, S.K.J., R.B., S.S., M.F.H. and N.D.B; resources, N.S., M.F.H. and N.D.B.; writing--original draft preparation, S.K.J., R.B., N.S., S.S., M.F.H. and N.D.B.; writing--review and editing, S.K.J., R.B., N.S., S.S., M.F.H. and N.D.B.; visualization, S.K.J. and N.D.B.; supervision, R.B., N.S., M.F.H. and N.D.B.; project administration, S.S., M.F.H. and N.D.B.; funding acquisition, M.F.H. and N.D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors have no conflict of interest.
## 2 Materials and Methods
### Characterization Techniques  
Composition of glasses was determined by means of field emission scanning electron microscopy microscope (FESEM) with energy dispersive X-ray detector (EDX) (Carl Zeiss, Jena, Germany). EDX technique allows the determination of most elements present in concentration above 0.1% with an estimated accuracy of \(\pm\)5%.  
Absorption spectra were recorded with a Cary 5 spectrophotometer. Steady-state emission were obtained by exciting the sample with an argon laser and a Ti-sapphire ring laser (0.4 cm\({}^{-1}\) linewidth) in the 770-920 nm spectral range. The fluorescence was analyzed with a 0.25 m Jobin-Ybon monochromator (Horiba, Kyoto, Japan), and the signal was detected by a Hamamatsu R928 photomultiplier and finally amplified by a standard lock-in technique. Infrared emission at 1.5 \(\upmu\)m was detected with an extended IR Hamamatsu R5509-72 photomultiplier (Hamamatsu, Hamamatsu-city, Japan).  
Lifetime measurements were performed by exciting the samples with a dye laser pumped by a pulsed nitrogen laser and a Ti-sapphire laser, pumped by a pulsed frequency doubled Nd:YAG laser (9 ns pulsewidth) (Coherent, Santa Clara, USA), and detecting the emission with Hamamatsu R928 and R5509-72 photomultipliers (Hamamatsu, Hamamatsu-city, Japan). Data were processed by a Tektronix MDO3104 oscilloscope (Tektronix-Inc, Beaverton, OR, USA).
## 3 Offline Optimization and Adaptation Under Nonlinear Task Constraints
### Constraints Definitions in Joint Space and Cartesian Space
#### 3.2.3 Hyperplane  
During motion, the robot is required to be confined to specific planes called the hyperplane or the virtual wall. Hyperplane constraints are defined in the Cartesian space related to the positions of the robot's end effector or any other interest points. We can formulate a hyperplane constraint function as follows:\[c_{k,t}=\mathbf{n}_{k,t}^{T}(T(\mathbf{q}_{t}(\mathbf{\mu}_{t},\mathbf{\sigma}_{t}))-\mathbf{b}_{k,t}) \tag{25}\]  
where \(\mathbf{n}_{k,t}\) and \(\mathbf{b}_{k,t}\) are the normal and bias vectors of the hyperplane. The one-side inequality constraint is expressed with:  
\[P_{\mathbf{\mu}_{t},\mathbf{\sigma}_{t}}\left(\mathbf{n}_{k,t}^{T}(T(\mathbf{q}_{t}(\mathbf{\mu}_{t },\mathbf{\sigma}_{t}))-\mathbf{b}_{k,t})\leqslant 0\right)\geqslant\alpha_{k,t} \tag{26}\]  
The distribution of \(c_{k,t}\) is a linear transformation of the robot's 3D positions \(T(\mathbf{q}_{t}(\mathbf{\mu}_{t},\mathbf{\sigma}_{t}))\); however, \(T(\mathbf{q}_{t}(\mathbf{\mu}_{t},\mathbf{\sigma}_{t}))\) is nonlinear in \((\mathbf{\mu}_{t},\mathbf{\sigma}_{t})\). We thus use our moment-matching method via Equation (23) to obtain the estimated mean \(\mathrm{E}[T]\) and variance \(\mathrm{V}[T]\). The overall distribution of \(c_{k,t}\) can be derived by:  
\[c_{k,t}\sim\mathcal{N}\Big{(}\ \mathbf{n}_{k,t}^{T}(\mathrm{E}[T])-\mathbf{b}_{k,t} \,\mathbf{n}_{k,t}^{T}\mathrm{V}[T]\mathbf{n}_{k,t}\ \Big{)} \tag{27}\]  
Subsequently, The CDF of \(c_{k,t}\) is computed using Gaussian distributions to represent the hyperplane constraint probability via Equation (26). Note that we can impose hyperplane constraints at a timestep or a temporal interval, and combine several hyperplane constraints to represent more complex convex domains.
## 2 Materials and Methods
### Quantitative Assessment of Caco-2 Cell Kinetics in xCELLigence RTCA S16  
#### 2.4.1 Preparation of the Cells  
When the Caco-2 cells reached 80-90% confluence on T75 cell culture flasks, they were harvested with 0.25% trypsin-EDTAA and resuspended in pre-warmed fresh complete cell culture media (20% of FBS), followed by counting and adjusting the cell concentration to obtain cell densities of 5000, 10,000, 20,000, and 40,000 cells per well.
# Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018
## 4 Results
### The Distance of Light Pollution to the Protected Areas
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & \multicolumn{3}{c}{**The First Polulted Buffer (km)**} & \multicolumn{3}{c}{**The High-Intensity Buffer (km)**} \\
**Type** & **Buffer \(\leq\) 10** & **10 \textless{} Buffer \textless{} 25** & **Buffer \(\geq\) 25** & **Sum** & **Buffer \(\leq\) 10** & **10 \textless{} Buffer \textless{} 25** & **Buffer \(\geq\) 25** & **Sum** \\ \hline Ia & 521 (44\%) & 427 (36\%) & 229 (20\%) & 1177 & 226 (17\%) & 342 (27\%) & 719 (56\%) & 1287 \\ Ib & 817 (57\%) & 434 (31\%) & 174 (12\%) & 1425 & 273 (17\%) & 418 (27\%) & 889 (56\%) & 1580 \\ II & 1329 (57\%) & 707 (31\%) & 270 (12\%) & 2306 & 903 (32\%) & 759 (26\%) & 1196 (42\%) & 2858 \\ III & 502 (44\%) & 397 (34\%) & 248 (22\%) & 1147 & 297 (22\%) & 347 (26\%) & 696 (52\%) & 1340 \\ IV & 3801 (74\%) & 1113 (12\%) & 239 (5\%) & 5153 & 2912 (38\%) & 1796 (23\%) & 3013 (39\%) & 7721 \\ V & 3195 (80\%) & 656 (16\%) & 173 (4\%) & 4024 & 4231 (65\%) & 1616 (25\%) & 668 (10\%) & 6515 \\ VI & 696 (55\%) & 461 (36\%) & 120 (9\%) & 1277 & 515 (15\%) & 462 (14\%) & 2372 (71\%) & 3349 \\ Sum & 10,861 (66\%) & 4195 (25\%) & 1453 (9\%) & 16,509 & 9357 (38\%) & 5740 (23\%) & 9553 (39\%) & 24,650 \\ \hline \end{tabular} Note: strict nature reserve (Ia), wilderness area (Ib), national park (II), natural monument of feature (III), habitat/species management area (IV), protected landscape/seascape (V), protected area with sustainable use of natural resources (VI). The averaged first polluted buffer is defined as the average of the first polluted buffers during 1992 to 2018, as does the high-intensity buffer.  
\end{table} 
Table 2: Number and types of polluted PAs with different distances to the averaged first and high-intensity polluted buffer. Figure 6: The distance of PAs to the averaged first (**a**) and high-intensity (**b**) polluted buffers. The visualization of maps is composited by channels of distance to buffers (buffer \(<\)= 10, R; 10 \textless{} buffer \textless{} 25, G; buffer \textgreater{}= 25, B). Those red points with distances beyond 40 km are mainly caused by unstable light sources (e.g., ports, logging, and shipping).
# Performance Improvement for PMSM Driven by DTC Based on Discrete Duty Ratio Determination Method
## 4 Discrete Duty Ratio Determination Method
### Calculation of the Duty Ratio Scale  
The scale between the master vector duty ratio and the slave vector duty ratio is defined as  
\[k_{\text{ms}}=\frac{d_{\text{m}}}{d_{\text{s}}} \tag{12}\]  
The calculation process of the discrete duty ratio is described as follows:  
(1) The relationship between the duty ratio scale and the sector angle of the synthetic vector is determined, as shown in Table 3.  
Figure 5: Process of duty ratio discretization.  
(2) The duty ratio scale between the applied master vector and slave vector is selected from Table 3 according to the sector angle.  
(3) The final duty ratio can be calculated based on the duty ratio scale.  
As torque is the most important variable in the control system, the determination of the duty ratio values of the master vector and the slave vector should meet the prerequisites that the torque error should be compensated to the desired condition. The relationships of the variables inEquation (5) can be rewritten as  
\[r_{\mathrm{T}}=\mu_{\mathrm{Tm}}\cdot d_{\mathrm{m}}+\mu_{\mathrm{IS}}\cdot d _{\mathrm{s}}=d_{\mathrm{m}}\cdot\left(\mu_{\mathrm{Tm}}+\frac{\mu_{\mathrm{ TS}}}{k_{\mathrm{ms}}}\right) \tag{13}\]  
Therefore, the final duty ratio of the master vector can be calculated as  
\[d_{\mathrm{m}}=\frac{r_{\mathrm{T}}}{\left(\frac{1}{2}+\frac{1}{k_{\mathrm{ms} }}\right)\cdot\sin\theta_{\mathrm{sv}}+\frac{\sqrt{5}}{2}\cos\theta_{\mathrm{ sv}}} \tag{14}\]  
where \(\theta_{\mathrm{sv}}\) is the active angle between the phase-lag active vector and the stator flux linkage.  
Additionally, the final duty ratio of the slave vector can be calculated according to the duty ratio scale and the master vector duty ratio. It is worth mentioning that, the duty ratio of the master vector should be defined as 1, while the sum of the master vector duty ratio and the slave vector duty ratio is greater than 1, which can avoid the offset of the error compensations supplied by the two active vectors effectively.
## 3 Results and Discussion  
### Two Stages Reaction Processes  
Figure 1a-c are at different coordinating scales. In Figure 1a, the reaction is divided into two stages. The first stage occurs after about 1.3 ms. For the phase of low temperature oxidation, the reaction of mixed gas is exothermic, and the temperature increases from 800 K to 1000 K. H\({}_{2}\)O\({}_{2}\) and HO\({}_{2}\) are mainly produced at this stage and are considered as indicator products for the two-stage reaction. Decomposition of H\({}_{2}\)O\({}_{2}\), H\({}_{2}\)O\({}_{2}\) + M <=> 2 OH + M (#16 elementary reaction in Chen mechanism plus GRI mech3.0) happens at 6.9 ms. For the combustion reaction, the large amount of OH generation marks the start of the burning. The three-body reaction H + O\({}_{2}\) + M <=> HO\({}_{2}\) + M (#9 elementary reaction in Chen mechanism plus GRI mech3.0) is an important radical recombination step, plays an important role as a chain terminating process at the second explosion limit and leads to relatively long induction times at conditions near the second limit [33]. The paramount condition that the system can enter the high temperature stage after the low temperature stage is that the low temperature reaction must be able to release enough heat to increase the temperature of the system. When the temperature reaches to 1000 K, the temperature rises sharply, and the mixture enters the high temperature oxidation stage. The high temperature reaction process releases a lot of heat, which provides the conditions for NO\({}_{x}\) generation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Value** \\ \hline Fuel mixture (mol. \%) & C\({}_{6}\)H\({}_{5}\)CH\({}_{3}\): 37\%, iC\({}_{5}\)H\({}_{12}\): 27\%, iC\({}_{8}\)H\({}_{18}\): 27\%, \\  & nC\({}_{5}\)H\({}_{12}\): 5\%, nC\({}_{7}\)H\({}_{16}\): 4\% \\ Dynamic viscosity (kg:m\({}^{-1}\cdot\)s\({}^{-1}\)) & 3.1 \(\times\) 10\({}^{-4}\) (at 25 \({}^{\circ}\)C, 1 atm) [29; 30] \\ Density (kg:m\({}^{-3}\)) & 804 (at 25 \({}^{\circ}\)C, 1 atm) [29; 30] \\ RON & 95.4 [30; 31] \\ MON & 87.8 [30; 32] \\ Oxidizer mixture (mol. \%) & O\({}_{2}\): 21\%, N\({}_{2}\): 78\%, Ar: 1\% \\ Initial condition & T: 800 K, \(P\): 8 atm, \(V\): 65 cm\({}^{3}\) \\ \(\lambda\) & 0.6, 0.8, 1, 1.2, 1.4 \\ Water-oil mass ratio & 0, 10\%, 20\%, 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the oil and experimental settings.  
Figure 1: The iterative process of the intermediate products, NO\({}_{x}\) and temperature (\(\lambda=1\), WOMR = 0). (**a**) Global comparison; (**b**) Detail comparison 1; (**c**) Detail comparison 2.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 2 Background on LTE-Advanced
BSRs are sent in band, possibly trailing a data transmission, whenever the UE is scheduled and has enough space to do so (a BSR can take up to 24 bits). New backlog for a previously idle UE can instead be signaled through an out-of-band Random-access (RAC) procedure. RAC requests are contention-based, and the eNB responds by scheduling the UE in a future TTI. Unanswered RAC requests are re-iterated. H-ARQ protects UL transmissions as well, and it works like the DL one, the main difference being that it is synchronous: the eNB schedules UL retransmissions after a constant lag of eight TTI.