esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 4 Conclusions  
In this paper, we studied the adoption of TCH codes as a promising low-complexity solution for enabling reliable MC. Adequate detection and decoding methods which do not require CSI acquisition were presented and evaluated through end-to-end simulations. Furthermore, to validate the reliability achieved with the proposed approach, we implemented a macroscale MC platform where the channel is an aqueous solution and the information to be transmitted is differentiated by the pH value of the medium, as this is a good approach to a biological environment for in-body communications. At the transmitter, different encoded pulses were considered, namely, NRZ and Manchester, combined with either OOK and BCSK modulations. Other channel codes were evaluated as benchmarks for the TCH codes, namely, Hamming codes and the ISI-mtg codes that were designed specifically for this context.  
The results obtained from the simulation implementation, showed that the new introduced coding scheme based on TCH codes, performed better without introducing more complexity to the system. In fact, TCH codes with higher rates were able to perform better than other codes with lower rates. In the developed macroscale experiment TCH codes proved to work when applied to a physical MC scenario. In addition, Manchester pulses for coded and uncoded situations proved to be essential when operating in a channel that can saturate, as in the case of the pH-based experimental tests. These pulses also proved to work better in the simulation than the NRZ pulses, giving more robustness to the communication in an MC channel with an absorbing receiver.  
Conceptualization, N.S., F.C. and S.F.; Methodology, N.S. and F.C.; Software, S.F. and N.S.; Validation, F.C. and N.S.; Formal Analysis, F.C. and N.S.; Investigation, N.S., F.C. and S.F.; Resources, S.F. and N.S.; Data Curation, S.F.; Writing--Original Draft Preparation, S.F.; Writing--Review and Editing, N.S., F.C. and S.F.; Visualization, S.F.; Supervision, N.S. and F.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work is funded by FCT/MCTES through national funds and when applicable co-funded by EU funds under the project UIDB/50008/2020.  
The authors declare no conflicts of interest.
# Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM
## 3 Experiment
### Feature Selection and Classification  
In this study, three different welding speeds were taken into consideration, and the welded seams are shown in Figure 3 as Seam 1 (3 m/min), Seam 2 (4.5 m/min) and Seam 3 (6 m/min). During the welding process, the data collected at the beginning and end of welding were obviously unstable. Only 5500 images captured during welding were analyzed, including 2500 images from Seam 1, 1500 images from Seam 2, and 1500 images from Seam 3. Fifteen features were generated from each image to form a 5500 \(\times\) 15 data set. The 5500 samples in this data set were classified according to the welding quality. The analyzed regions were labeled to clarify (see Figure 3). As shown in Figure 3, Seam 2 was divided into three regions; Region A (1, 500), Region B (501, 1000) and Region C (1001, 1500). The welded seam width of Region A, Region C and Seam 1 was generally large and fluctuated in a small range, which meant high welding quality. Comparatively, the welded seam width of Region B and Seam 3 was either small or decreased sharply, which indicated inferior welding quality. Therefore, Region A, Region C and Seam 1 were treated as one class, denoted as Class R, Region B and Seam 3 was treated as another class, denoted as Class E.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & \\ \hline Feature & \(A_{p}\) & \(p_{p}\) & \(C_{p(i)}\) & \(C_{p(j)}\) & \(H_{p}\) & \(W_{p}\) & \(G_{p}\) & \\ \hline
**No.** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline Feature & \(A_{s}\) & \(P_{s}\) & \(C_{s(i)}\) & \(C_{s(j)}\) & \(G_{s}\) & \(Q_{s(a)}\) & \(Q_{s(l)}\) & \(Q_{s(r)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fifteen extracted features.  
Figure 3: _Cont._  
SVM and another widely applied classification algorithm, Back-Propagation (BP) neural network [27; 28], were adopted to build classifiers. The SFFS algorithm was used to detect the optimal feature subset with \(d\) features. The classification accuracy of the SVM or BP classifier by 10-fold cross validation was chosen to evaluate the effectiveness of feature subsets. More specifically, for each candidate feature subset, the data set was equally divided into 10 different parts, 9 of them were used to establish a SVM classifier and the rest one was for testing. Experiments were conducted 10 times, and the average classification accuracy was taken as the performance of the model, as well as the effectiveness of the feature subset. In this study, all possible values of \(d\) were tested, so as to recognize the optimal feature subset and explore the relation between the feature quantity and the classification accuracy.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 6 Conclusions  
In this article, we presented a novel communication approach by exploiting purposefully-interfering out-of-tune signals of two or more wireless sensor nodes, which produce the wake-up signal as the beat frequency of superposed carriers. We discussed the theoretical approaches of wake-up messages generated from beat frequencies for the first time and demonstrated their suitability to improve wake-up robustness and reliability. We experimentally verified the theoretical approaches and developed two novel network protocols that apply this technique. First, we developed a simple and highly reliable algorithm that includes concurrent wake-ups into existing protocols. We achieved a quick and reliable dissemination of wake-up messages in a large wireless sensor network, by designing a novel flooding protocol based on concurrent wake-up transmissions. We implemented the algorithms and verified their performance experimentally with the help of an outdoor test setup and an indoor deployment. The tests confirmed the expected gain of up to 3 dB for two concurrent senders and nearly 100% wake-up rates in less than 20 ms achieved by the flooding protocol. Our future research will further investigate the presented protocols and compare their performance to direct transmission-based approaches.  
The article processing charge was funded by the German Research Foundation (DFG) and the University of Freiburg in the funding program Open Access Publishing.  
All authors contributed to the main idea of this paper. [PERSON] and [PERSON] designed the algorithms and experiments. [PERSON] wrote the article, and [PERSON] supervised the overall research work, critically evaluated and revised the paper.  
The authors declare no conflict of interest.
# Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading
## 3 Load Analysis
### Variable Loads
#### 3.2.2 Wind Loads  
The wind action was evaluated according to the Italian recommendations in [43]. Firstly, the peak kinetic pressure (\(q_{p}\)) was evaluated as follows:  
\[q_{p}=\frac{1}{2}\cdot\rho\cdot v_{r}^{2}\cdot c_{e}(z) \tag{2}\]  
where \(p\) is the kinetic pressure, while:  
* \(\rho\) is the air density;
* \(v_{r}^{2}\) is the reference wind velocity;
* \(c_{e}\) is the exposure coefficient, varying with the elevation \(z\) of the structure.  
For this purpose, the equivalent longitudinal or drag forces, \(f_{D}\), and transverse or lift force, \(f_{L}\), are evaluated as follows:  
\[f_{drag}=q_{p}(z)\cdot l\cdot c_{drag};\qquad f_{lift}=q_{p}(z)\cdot b\cdot c_{ lift} \tag{3}\]  
where  
* \(q_{p}(z)\) is the _peak kinetic pressure_ evaluated at height \(z\);
* \(l\) is the characteristic element size;
* \(b\) is the reference transverse dimension of the section;
* \(c_{drag}\) and \(c_{lift}\) are the longitudinal and transverse dynamic coefficients. Drag D and Lift L forces are reported in Tables 15 and 16.
## 3 Proposed System Model
### Integration of DQN-Base Approach
#### 3.3.2 Action Space A  
The action set \(A_{t}\) defines the courier node as receiver node's decision-making process to optimize energy efficiency and communication reliability:  
\[A_{t}=\Big{\{}P_{\text{wake up}},P_{\text{bc}},T_{\text{slot}},Q_{\text{ priority}},T_{\text{wake up}}\Big{\}}.\]
* **Wake-Up signal Power Adjustment (\(P_{\text{wake}}\)):** The courier node as the receiver node optimizes energy usage by transmitting a low-power wake-up signal, gradually increasing the power only when necessary to reach distant nodes. This wake-up power, measured in milliwatts (mW), is categorized into discrete levels such as low, medium, and high, each corresponding to specific power values. The decision-making process involves evaluating the state of the current environment to ensure energy-efficient and reliable communication. This state encompasses factors such as distance from neighboring nodes, 3D position, residual energy levels, traffic load, packet type, and required communication reliability. Using Bellman update function, the system calculates Q-values for each potential power adjustment level, balancing energy efficiency with communication performance. The agent selects the power level with the maximum Q-value, indicating the optimal trade-off between minimizing energy consumption while maintaining robust connectivity.
* **Data Transmission Power Adjustment (\(P_{\text{bc}}\)):** Transmission power is adjusted based on the node's distance and environmental conditions (e.g., SNR). Lower power is used for close-range, clear communications, while higher power is employed for distant or interference-prone connections.
* **Dynamic Slot Allocation (\(T_{\text{slot}}\)):** We considered round-based data collection in which the receiver dynamically allocates TDMA slots, prioritizing nodes with higher traffic loads, critical data packets, or higher residual energy. Transmitter nodes send data during their allocated time slots determined by their receiver and then return to an idle state until the next frame. This ensures prompt communication and prevents congestion.
* **Data Prioritization (\(Q_{\text{priority}}\)):** Critical packets (e.g., emergency data) are prioritized over routine data, ensuring that urgent transmissions are delivered first, even when the network is under heavy load.
* **Wake-Up Time intervals Adjustment (\(T_{\text{wake up}}\)):** The receiver adjusts the wake-up intervals to optimize the communication cycle. If the network is underutilized, the wake-up period can be extended to save energy. However, in times of high activity or critical data transmissions, the wake-up interval may be shortened to reduce latency and ensure faster responses.
# Groundwater Prediction Using Machine-Learning Tools
###### Abstract  
Predicting groundwater availability is important to water sustainability and drought mitigation. Machine-learning tools have the potential to improve groundwater prediction, thus enabling resource planners to: (1) anticipate water quality in unsampled areas or depth zones; (2) design targeted monitoring programs; (3) inform groundwater protection strategies; and (4) evaluate the sustainability of groundwater sources of drinking water. This paper proposes a machine-learning approach to groundwater prediction with the following characteristics: (i) the use of a regression-based approach to predict full groundwater images based on sequences of monthly groundwater maps; (ii) strategic automatic feature selection (both local and global features) using extreme gradient boosting; and (iii) the use of a multiplicity of machine-learning techniques (extreme gradient boosting, multivariate linear regression, random forests, multilayer perceptron and support vector regression). Of these techniques, support vector regression consistently performed best in terms of minimizing root mean square error and mean absolute error. Furthermore, including a global feature obtained from a Gaussian Mixture Model produced models with lower error than the best which could be obtained with local geographical features.  
time series data; pixel estimation; full image prediction; gaussian mixture model; global features; feature engineering; square root transformation +
Footnote †: journal:  
_Article_
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 6 Application and Evaluation
### Evaluation of Computation Time  
Provided with the three outputs (i.e., the workflow transition system, an execution summary and DFA, Table 1), the computation includes building the SMC model, and generating and solving the linear systems of FPT moments. In the empirical evaluation, we parametrised the value of \(i\) in the DFA in Figure 6 for JobDeciderWorkflow and created SMC models in different sizes. The experimental data, as summarised in Table and depicted in Figure 7, were collected in a Linux system with a 2.8 GHz Intel Core i7 CPU and 32 GB memory. As shown in the figure, the computation time is relevant to the size (i.e., the number of states) of the SMC models, which is also the size of the linear systems. Within 16 s, we can compute the first four moments for SMC models with up to 5000 states, and within 70 s, we can compute the same moments for models with up to 10,000 states. The superlinear increment in computation time with respect to model size is mainly due to the need of solving linear systems (c.f., Proposition 2). However, we believe that the meaningful time analysis of most real-world workflows in our approach results in SMC models with no more than 10,000 states. In Figure 7, the largest SMC model is built from the production of the JobDeciderWorkflow transition system with 13 states and a DFA with 2400 locations. Another observation from Figure 7 is the linear increment in computation time as the order of the FPT moment increases (Note that the computation of the first FPT moment also includes the model building time). This conforms with the fact that the linear systems for FTP moments are of the same size regardless of the moment orders.  
Figure 6: Trace component DFA \(\mathcal{A}_{i}\) for \(\mathcal{W}_{\text{jobDec}}\) (where \(A=\) “Submit Job to ECS”, \(S=\) “Emit “Successful”, and \(\
eg(A\wedge S)\) refers to all other steps except \(A\) and \(S\))  
Figure 7: Plot of computing time of FPT moments
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 7 Perceptual Pre-Weighting and Post-Weighting  
The computational complexity with the perceptual weighting filter inside the loop as in Figure 4 is computed as follows. If the computational complexity of \(W(z)\) is \(C\) operations, and \(B=2^{n}\) is the number of children of each tree node for the \(n\) bits/sample tree, then the  
Figure 4: Block diagram of a tree coder with a perceptual weighting filter.  
complexity of releasing one symbol is \(M\cdot B\cdot L\cdot C\) operations. [PERSON], [PERSON], [PERSON], and [PERSON] [32] have employed adaptive pre-filtering and post-filtering in lossless audio coding. They showed that lossless audio coding with pre- and post-filtering maintains high quality. In addition, [PERSON] and [PERSON] [33] employed perceptual pre-weighting and post-weighting in a G.726 ADPCM codec [24] and a modified AMR-NB CELP codec. They showed that the performance of lossy coding with pre- and post-weighting also improves over having no weighting. As shown in Figure 5, the computational complexity of our Multimode Tree Coder is reduced to 2C operations for releasing one symbol using pre-weighting and post-weighting filters.  
Let \(S(z)\) be the input speech, \(X(z)\) be the pre-weighted speech, \(X^{\prime}(z)\) be the pre-weighted speech output, and \(S^{\prime}(z)\) be the output speech after post-weighting. From Figure 5, the relation of \(S(z)\) and \(X(z)\) is  
\[S(z)W(z)=X(z), \tag{16}\]  
and the relation of \(S^{\prime}(z)\) and \(X^{\prime}(z)\) is  
\[X^{\prime}(z)\frac{1}{W(z)}=S^{\prime}(z). \tag{17}\]  
The design of the pre-weighting filter \(W(z)\) and post-weighting filter \(\frac{1}{W(z)}\) is to mask the reconstruction error at the output by the input spectrum. The spectral response of the perceptual weighting filter \(W(z)\) should have the form of that shown in Equation (15).  
However, the input spectrum information is not available at the decoder in our codec; so our post-weighting filter uses the pole-zero coefficients from the backward adaptive predictor. The post-weighting filter is of the form,  
\[H_{post}(z)=\frac{1+\sum_{i=1}^{6}m_{2}^{i}b_{i}z^{-i}}{(1+\sum_{i=1}^{6}m_{3} ^{i}b_{i}z^{-i})(1-\sum_{i=1}^{2}m_{1}^{i}a_{i}z^{-i})}, \tag{18}\]  
where \(a_{i}\)'s are pole coefficients and the \(b_{i}\)'s are zero coefficients, and we have added an additional denominator term mainly to tune the spectral tilt.  
To choose the weighting filter parameters, \(m_{1}\), \(m_{2}\), and \(m_{3}\), in Equation (18), experiments were conducted to match the frequency response of the perceptual post-weighting filter generated with 5 th order linear prediction coefficients calculated from the input, Equation (19),  
\[\frac{1}{W(z)}=\frac{1-\sum_{i=1}^{5}(0.86)^{i}a_{i}z^{-i}}{1-\sum_{i=1}^{5}a_ {i}z^{-i}}, \tag{19}\]  
with the frequency response of the filter generated with the ADPCM predictor coefficients. After experimentation, the parameters were chosen to be \(m_{1}=0.2\), \(m_{2}=1.0\), and \(m_{3}=0.85\) in both pre- and post-weighting filters.
## 3 Results  
### Reliability  
The between-session reliability results for the CMJ, DJ, and CMRJ are presented in Table 1. The CMJ showed good to excellent relative reliability and good to poor CV for almost all variables (ICC = 0.75-0.98; CV \(\leq\) 20.95%), excluding the positive ankle contribution that showed only moderate reliability (ICC = 0.73). The reliability measured in CMRJ1 followed a similar trend to the CMJ, with all metric but negative hip work (ICC = 0.73) being \(\geq\)0.80 and deemed good to excellent reliability with moderate to poor CV (\(\leq\)17.63%). Finally, apart from the hip joint related variables that showed moderate to good reliability with poor CV (ICC = 0.64-0.89; CV \(\leq\) 25.04%), all other variables measured in DJ and CMRJ2 present good to excellent relative reliability with moderate CV (ICC = 0.82-0.97; CV \(\leq\) 9.91%).
## 3 Simulation
### Meshing and Independence Verification  
In this study, the structured mesh (Figure 3a) is used and local mesh at the position of the gap is refined (Figure 3b). As shown in Figure 3c, mesh independence test is performed and the results show that the grid-independent results can be obtained when the mesh element number exceeds 750,000.  
Figure 3: _Cont._  
To achieve the most suitable turbulence model, different turbulence models were used to perform the simulation under same boundary conditions. The comparison between the simulation results of different turbulence models and the experimental data was performed, and the leakage deviation between the simulation value and the experimental data is shown in Figure 4. It can be found that the simulation value of Transition \(k\)-\(kl\)-\(\omega\) model agrees well with the measured data. First, the Transition \(k\)-\(kl\)-\(\omega\) model is generally applied in the simulation of jet flow and wall constrained flow. Then, it is found that the difference is the least between experiment and Transition \(k\)-\(kl\)-\(\omega\) model. Therefore, the Transition \(k\)-\(kl\)-\(\omega\) model is used for simulation.
## 3 Results
### Pulse-Based Fed-Batch Cultivation
The scattering signal \(\mu_{\mathrm{s}}\)' increased until 70 h, whereas the absorption coefficient \(\mu_{\mathrm{a}}\) only slightly increased after 52 h. At 52 h, nitrogen was depleted again. The cells accumulated 66 wt% PHB until the end of the cultivation and the CDW increased to 53 g\(\cdot\)L\({}^{-1}\). When the whole cultivation was repeated, the PDW spectroscopy signals showed an equivalent course and a final CDW of 52 g\(\cdot\)L\({}^{-1}\) CDW containing 64 wt% PHB, indicating a high robustness, i.e., repeatability, of the process. Figure 3: Pulse feeding cultivations for PHB production by _R. eutropha_ H16. The cultures were initially started with 0.5% (w\(\cdot\)v\({}^{-1}\)) rapeseed oil and 4.5 g\(\cdot\)L\({}^{-1}\) urea (150 mM nitrogen). The dashed vertical lines represent time points of pulse additions: 0.5% (w\(\cdot\)v\({}^{-1}\)) rapeseed oil at 8.2 h, 1% (w\(\cdot\)v\({}^{-1}\)) rapeseed oil at 14.3 h, and 2% (w\(\cdot\)v\({}^{-1}\)) rapeseed oil at 21.1 h. At 31.7 h a bolus consisting of 110 mL urea solution (122 g\(\cdot\)L\({}^{-1}\)), 15.6 mL 0.5 M K\({}_{2}\)SO\({}_{4}\), 30 mL 0.32 M MgSO\({}_{4}\), 30 mL 0.042 mM CaCl\({}_{2}\), and 3 mL trace element solution was added to the bioreactor. A final bolus of 4% (w\(\cdot\)v\({}^{-1}\)) rapeseed oil was added at 48.4 h. Data from the reference experiment is shown in color (indexed P1) and the biological duplicate (with an independent seed train) in transparent grey (indexed P2). Absorption coefficient \(\mu_{\mathrm{a}}\) (upper graph, solid line, \(\times 10^{-3}\) mm\({}^{-1}\)), RCDW (upper graph, empty circles, g\(\cdot\)L\({}^{-1}\)), PHB content (bottom graph, squares, wt%), CDW (bottom graph, filled circles, g\(\cdot\)L\({}^{-1}\)), and reduced scattering coefficient \(\mu_{\mathrm{s}}\)’ (bottom graph, solid line, mm\({}^{-1}\)) are shown. Error bars indicate minimum and maximum values of technical duplicates. The pulse-based fed-batch experiment showed the possibility to control the rapeseed oil-based cultivation for PHB production using the _in-line_ PDW spectroscopy probe. The highly reproducible course of the PDW spectroscopy signals strongly imply the connection of biological events with this measurement technique. The coefficients \(\mu_{\mathrm{s}}\)' and \(\mu_{\mathrm{a}}\) show the same trend as the CDW and RCDW, respectively. Nevertheless, a more significant change was observed for the \(\mu_{\mathrm{a}}\) signal after addition of the nitrogen pulse than the _off-line_ determined RCDW. Regardless of this discrepancy, the hypothesis was that the \(\mu_{\mathrm{s}}\)' and \(\mu_{\mathrm{a}}\) correspond and can be correlated with the CDW and RCDW, respectively.