esa-sceva/satcom-chunk-collection huggingface dataset:
in [6] introduced a robotic assembly method designed specifically for the production of large segmented composite structures. The methodology tackles three essential stages in the assembly process, in particular (i) panel localization and pick-up, (ii) panel transport, and (iii) panel placement. The proposed approach is based on the utilization of several stationary and robot-mounted cameras in order to provide information for the processes of part localization and alignment. Furthermore, a force/torque sensor mounted on the robot end effector enables secure and gentle panel pick-up and placement of the composite parts. Human-assisted path planning is utilized to ensure collision-free motion of the robot with a heavy load in tight spaces. The process flow and user interface are governed by a finite state machine which allows for process interruption and the return to the previous known state in case of error conditions or when secondary operations are needed. For the validation of the proposed method, a high-resolution motion capture system is used in order to set a reference for the ground truth. An experimental testbed integrating an industrial robot, vision, and force sensors, and representative laminated composite panels was developed to demonstrate the feasibility of the proposed assembly process. Experimental results have indicated that sub-millimeter placement accuracy with shorter cycle times, lower contact force, and reduced panel oscillation than manual operations is feasible. This work exemplifies the adaptability of sensor-guided robotic assembly operation in complex end-to-end tasks using the open-source Robot Operating System (ROS) software framework. Finally, the literature review presented in [7] by [PERSON] et al. emphasizes flexible automation and customized product manufacturing using technologies such as the Internet of Things (IoT) and cyber-physical systems (CPS). The literature review expands also on the most pertinent developments in the field of digital twins. Digital twins are expected to play a crucial role throughout the product lifecycle as the manufacturing sector increasingly adopts digitized products, processes, and services. Furthermore, collaborative robots are being introduced into the shop floor to assist human operators in task completion through human-robot collaboration. This paper provides insights into the creation of digital twins for human-robot collaboration and the challenges involved. Among the key contributions of this research work is the examination of different approaches for the creation of digital twins, the discussion of the function and importance of digital twins in human-robot collaboration scenarios and highlighting of the challenges associated with creating digital twins, such as modeling the digital twin of human-robot collaboration and ensuring its exactness with respect to the physical system.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 4 Measurements  
This section first presents the setup used to measure the duration and energy consumption of each state of each slot type. Afterwards, the measurements of the time slot state durations are discussed together with how the duration values are affected by the packet size. Finally, the consumption of each device state is presented with a detailed discussion for each of the two evaluated radios.
## 8 MATLAB Simulation and Experimental Data Analysis
### MATLAB Simulation
The noise variance of the AUV heading is 0.01. The noise variance of the forward speed of AUV is GMD, which contains two single Gaussian distributions. The parameters of GMD are the weights(\(w\)), mean (\(\mu\)) and variance (\(\Sigma\)), which are shown in Table 5:  
\begin{tabular}{c c c c} \hline \hline  & **Weight** & **Mean** & **Variance** \\ \hline
1 & \(w_{1}=0.6\) & \(\mu_{1}=0\) & \(\Sigma_{1}=0.2^{2}\) \\
2 & \(w_{2}=0.4\) & \(\mu_{2}=0\) & \(\Sigma_{2
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### At-Home Therapeutic Treatments of Dentin Hypersensitivity
#### 6.1.3 Stannous Fluoride  
Stannous fluoride (SnF\({}_{2}\)) has shown remarkable effectiveness in sealing dentin tubules, which can be attributed to the strong attraction of Sn ions to the mineral surfaces of tooth hard tissues [73]. It creates a protective layer on the tooth's surface by establishing bonds with exposed tissues. More specifically, tin ions (Sn\({}^{+2}\)) become part of the near-surface layer of the tooth, penetrating to a depth of around 20 \(\upmu\)m and providing extended protection, guarding the underlying surface against further external stimuli. Moreover, the bond between F\({}^{-}\) and Ca\({}^{+2}\) resulting from the application of various fluoride salts is less robust compared to the bond established on tooth surfaces treated with SnF\({}_{2}\)[74]. In a previous study [75], it was observed that after 8 weeks of using a toothpaste containing 0.454% SnF\({}_{2}\), the dentin surfaces displayed effective coating, with the open dentin tubules being sealed. In contrast to the control toothpaste, the experimental toothpaste showed a significant  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{4}{c}{**At-Home Treatments for Dentin Hypersensitivity**} \\ \hline
\begin{tabular}{c} 2 times daily for \\ 2–4 weeks \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: The at-home therapeutic treatments for dentin hypersensitivity.  
reduction in tooth sensitivity, underscoring its notable efficacy. In addition, in a recent investigation, it was reported that the ability of a SnF\({}_{2}\) gel to occlude dentin tubules reached 99.5% [76]. Nonetheless, despite its effectiveness, SnF\({}_{2}\) presents some disadvantages, such as a metallic taste, the potential for tooth staining, and the possibility of diminished taste sensation with prolonged use [74].
# A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks
## 1 Introduction
For the states, a fully connected deep neural network is designed to identify state type. The contributions of this paper are summarized as follows. (1) The reward function is designed as a negative reciprocal of the system energy efficiency to avoid the slow speed of convergence and the possibility of fulling into local optimum. If the magnitude of the reward in RL is too large, it is easy to fall into the local optimization, and too small value can cause the problem of system oscillation or slow speed of convergence. In this paper, directly using energy efficiency as the reward function causes the reward value too large and it is easy to fall into the local optimum. As shown in our experimental results, our designed reward function works well. (2) The TQL is proposed by combining traditional Q-Learning and multistage decision process which has a loop iteration structure, each sub-Q-Learning solving each sub-problem which is from an original optimization problem. It effectively deals with the dimensional explosion problem caused by the action space increasing in RL and greatly reduces the complexity of optimization problems. (3) The relevant parameters of each sub-problem can be adjusted independently. Thus, the complexity is low in our proposed TQL algorithm. Simulation results show that the TQL algorithm can solve the original problem with efficiency and flexibility. The rest of the paper is organized as follows. Related works are summarized in Section 2. Section 3 introduces the system model. In Section 4, the energy efficiency model is formulated. Our proposed algorithm is presented in Section 5. Section 6 shows the simulation framework and numerical results, and conclusions are drawn in Section 7.
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 3 Fifth-Generation V2X System Simulation Parameters and Scenarios  
In our simulation scenarios, we implemented the BCJR, logBCJR, maxlogBCJR, SOVA, polar and LDPC coding schemes. Turbo-based algorithms were implemented using four iterations according to [33; 37]. Polar coding and decoding were implemented according to [32; 50], and LDPC was implemented based on the offset min-sum algorithm using 12 iterations [37], where the base matrices are provided by the 5G NR standard specification TS 38.212 [32].  
The coding rate was equal to one-third. Additionally, binary phase-shift keying (BPSK) modulation and demodulation were used at the transmitter and the receiver, respectively, as illustrated in Figure 1. The carrier frequency was set to 5.9 GHz, similarly to [34; 37]. We also assumed that the vehicles were moving at 50 km/h in urban fabric and at 100 km/h on highways. Furthermore, additive white Gaussian noise (AWGN) was added to the signal.  
In our simulations, we assumed 128-bit frames as representatives for 128-, 256- and 512-bit frames since similar quality results are extracted in all three different data-frame sizes [37]. Furthermore, we implemented five different simulation scenarios, which are summarized in Table 1, and we transmitted 100,000 data frames for each scenario.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System Simulation Scenario** & **Data Frame Size (Bits)** & **Traffic Model** & **Channel State** \\ \hline S1 & 128 & Highway & LOS \\ \hline S2 & 128 & Highway & NLOSv \\ \hline S3 & 128 & Urban & LOS \\ \hline S4 & 128 & Urban & NLOSv \\ \hline S5 & 128 & Urban & NLOSv \\ \hline \hline \end{tabular}
\end{table}
Table 1: System simulation scenarios.  
Figure 1: Simulation model using V2X stochastic channel model, additive white Gaussian noise and BPSK modulation [37].  
MATLAB was used for the implementation of the coding/decoding schemes and for the implementation of the 3 GPP V2X channel in each simulation scenario of Table 1. The details of the large-scale and small-scale parameters for each system simulation scenario were derived from [8,9] and are fully analyzed in [37].  
**4. Fifth-Generation V2X QoS Simulation Parameters and Scenarios**  
The six QoS scenarios that we use in our simulations are derived from [13], reproduced from [51] and fully described in [35]. The QoS requirements for each of these scenarios are shown in Table 2. In this table, the distance between the two vehicles is short when it is smaller than 200 m, medium when it is between 200 m and 500 m and long when it is larger than 500 m, according to [51]. Thus, from now on, we will consider 200 m for Q2 and Q4 scenarios, 375 m for Q1 and Q3 scenarios and 500 m for Q5 and Q6 scenarios as maximum typical range values for our simulations.
## 3 Results and Discussion
### Vegetative Performance of Eco-Soil  
Figure 8 illustrates the effect of hydrogel active ingredients' dosage on the pH of eco-soil. As shown in Figure 8, soil treated with the hydrogel system maintained pH values within 6.5-8.0, distinct from the high alkalinity of traditional Portland cement-based materials. At the optimal 2.0% dosage, pH values for 7 d, 14 d, and 28 d specimens were 7.3 \(\pm\) 0.1, 7.5 \(\pm\) 0.1, and 7.6 \(\pm\) 0.2, respectively, transitioning from weakly alkaline to neutral--consistent with the vegetation-friendly pH range (5.5-8.5) specified in GB/T 51435-2021 [26]. The hydrogel's three-dimensional network binds to soil colloids via hydrogen bonds, while carboxyl groups in the acrylic acid-acrylamide copolymer backbone buffer OH\({}^{-}\) concentrations, lowering pH compared to cement-based systems.  
Figure 8: Effect of active ingredients in hydrogel on pH of eco-soil.  
Figure 7: Effect of heating duration on UCS of eco-soil.  
Figure 9 displays the growth dynamics of different vegetation combinations in eco-soil containing hydrogel curing agents. Adding HSZ resulted in the vegetation length being approximately 15% shorter compared to when HSZ was not added. However, it showed good growth over time and reached sufficient length for the landscape. As shown, both tall fescue and alfalfa achieved an average leaf length exceeding 8 cm after 10 d of seeding, indicating no inhibitory effect of the hydrogel matrix on early-stage germination. With the curing time extended to 60 d, the vegetation exhibited significant development: the average leaf length of tall fescue reached 200 \(\pm\) 5 mm, accompanied by dense fibrous root branching structures. Figure 10 presents a time-dependent visualization of tall fescue growth in the eco-soil. By 30 d, lush foliage had fully developed in the tall fescue group. Continuous monitoring until 90 d revealed no wilting or lodging across all vegetation groups, with roots visibly penetrating the eco-soil layer. These results demonstrate that the hydrogel-vegetation composite system, through bionic structural design, achieves synergistic enhancement of soil mechanical performance and ecological functionality. This provides an innovative solution for eco-friendly construction in applications such as slope restoration.
## 3 Proposed Work  
### Communication Model  
The communication module given in Figure 1 is considered as a study case in proposed protocol.  
If the distance between the cluster head and its associate node is short, then the free-space model is considered, otherwise if the distance is longer, then the multipath fading model is used [15].  
Figure 1: Radio communication model.  
The power consumed to transmit \(k\) bits packet of data to the sensor node allocated \(d\) distant away can be given as:  
\[\begin{array}{c}E_{TX}(k,d)=E_{TX,elec}(k)+E_{TX,amp}(k,d)\\ E_{TX}(k,d)=\left\{\begin{array}{cc}E_{elec}*k+E_{fs}*k*d^{2},&d\leq d_{0}\\ E_{elec}*k+E_{amp}*k*d^{4},&d>d_{0}\\ E_{RX}(k)=E_{RX,elec}(k)+kE_{elec}\\ d_{0}=\sqrt{\frac{E_{fs}}{E_{amp}}}\end{array}\right.\end{array} \tag{23}\]  
where \(d\) is the Euclidian distance from the sending node to the receiving node, \(E_{elec}\) is the receiver/transmitter energy consumption per bit, to receive \(k\) bits packet of data \(E_{elec}*k\) amount of energy is spent on the radio module, \(E_{\beta}\) and \(E_{amp}\) are the free space and the multi-path fading amplifier energies respectively.
## 4 SAR Raw Signal Processing
### Omega-k Algorithm for LFM-CW SAR System
#### 4.2.2 Residual Video Phase Removal  
Then, we proceeded to eliminate the residual video phase by multiplying Equation (32) by \(exp(j\pi k_{r}\Delta t^{2})\) to eliminate said unwanted phase. This phase should be eliminated because it negatively affects image quality due to its negative impact on distance measurement applications, errors in velocity measurement, and causing blurring in the LFM-CW SAR image. Equation (45) is used to remove the residual phase of the video.  
\[H_{RVP}=exp\Big{(}j\pi k_{r}\Delta t^{2}\Big{)} \tag{45}\]
## 4 Experimental Setup
### Performance evaluation  
Several metrics will be used to evaluate the performance of the proposed algorithm, aimed at capturing the effectiveness of the methodology for both the identification of the activation status and the estimation of energy consumption.  
For a given instant \(t\), \(a_{i}(t)\) indicates the activation status of a device \(i\), unitary if on and null if off, and we indicate with \(y_{i}(t)\) the power absorbed at the same time. We denote with \(\hat{a}_{i}(t)\) and \(\hat{y}_{i}(t)\) the activation status and power consumption estimates obtained from the model. The accuracy of the prediction is evaluated on a time series sampled at times \(t_{j}\) with \(j\in[0,N_{\text{s}})\).  
We also define True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN). TP refers to the number of instants of the time series in which a device is correctly identified as running, while TN is the count of instants in which the device is correctly evaluated switched off. On the contrary, FP indicates the number of instants for which an activation status is reported while the device is actually inactive, and finally FN is the count of instants in which the device is wrongly evaluated as off.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
\[F_{1}=2\frac{Precision\times Recall}{Precision+Recall} \tag{8}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{9}\]  
Precision in Equation (6) denotes the ratio of TPs to all instants classified as positive, while Recall in Equation (7) represents the ratio of the number of instants for which the appliance is correctly identified as operating to all instants for which the appliance is actually in function. The Accuracy defined in (5) denotes the ratio of all correctly classified instants (for both states, on or off) to all instants of the verification set. The F1 measurement defined in (8) represents the weighted average between Precision and Recall, and it is within the range of \([0,1]\), high values indicate a better identification of the state of the appliance. Finally the Matthews Correlation Coefficient is defined in (9), whose values are in the range \([-1,1]\). A value equal to 1 denotes an exact classification, 0 denotes a random prediction while a value equal to \(-1\) indicates a totally wrong classification.  
The accuracy of the estimate of consumption for the individual devices is measured using the Mean Absolute Error (MAE) and the Signal Aggregate Error (SAE).  
\[MAE_{i}=\frac{1}{N_{\mathrm{s}}}\sum_{j}\hat{y}_{i}(t_{j})-y_{i}(t_{j}) \tag{10}\]  
\[SAE_{i}=\frac{\sum_{j}\hat{y}_{i}(t_{j})-\sum_{j}y_{i}(t_{j})}{\sum_{j}y_{i}(t_ {j})} \tag{11}\]  
where MAE is a measure of the average deviation at each instant of the estimated power with respect to the measured power, while SAE is a measure of the relative error in the estimation of the energy used during the entire evaluation period.
## 3 Mathematical Modeling of the System Components
### Modeling of the Battery  
The principal focusing parameters of the battery mathematical model are the voltage and current. The current can be determined by the change in the terminal voltage of the battery. The current production is driven by the transmission of electrons from one electrode to another [43]. The potential difference between the positive and negative electrodes determines the open circuit voltage of the battery [44]. The following equations present the charging and discharging voltages of the battery:  
\[\rm V_{discharge}=E_{0}-V_{op}{}^{+}-V_{op}-IR_{pol} \tag{16}\]  
\[\rm V_{charge}=E_{0}+V_{op}{}^{+}+V_{op}+IR_{pol} \tag{17}\]  
\[=E_{0}-K\Big{[}\frac{Q}{Q-it}\Big{]}i-R_{0}I \tag{18}\]  
\[\rm V_{battery}=E_{0}-\left(\frac{K}{SOC}\right)i-R_{0}I. \tag{19}\]  
\[\rm V_{discharge}=E_{0}-K_{dr}\frac{Q}{Q-it}i_{1}-R_{0}i-K_{dv}\frac{Q}{Q-it} it+e(t) \tag{20}\]  
\[\rm V_{charge}=E_{0}-Kc_{r}\frac{Q}{it+\lambda Q}i_{1}-R_{0}i-Kc_{v}\frac{Q}{Q- it}it+e(t) \tag{21}\]  
\[\rm e(t)=Bi\big{[}e(t)+Au(t)\big{]} \tag{22}\]  
\[V_{discharge}\!=E_{0}\!-\!K_{dr}\!\left(\frac{K}{SOC}\right)\!I\!-\!R_{0}I\!- \!K_{v}\!\left(\frac{K}{SOC}-1\right)\!+\!e(t) \tag{23}\]The composed relation model allows us to modify Equations (18) and (19). \(\rm V_{op}\) is the open circuit voltage at the battery and \(\rm V_{op}+\) is the open circuit voltage at the positive electrode of the battery. \(\rm E_{0}\) represents the open circuit voltage of the battery expressed in Volt, I is the battery current, _i.t._ is the discharged capacity (Ah), K is the polarization coefficient (\(\rm\Omega\)), Q represents the battery capacity (Ah), R is the internal resistance, A is the exponential voltage coefficient in V, B is the exponential capacity in \(\rm(Ah)-1\), R\({}_{0}\) is the ohmic resistance in \(\rm\Omega\), and SOC is the battery state of charge in %.  
Equations (18) and (19) present some limitations such as (i) battery aging and self-discharge, (ii) the current amplitude not having an impact on the battery capacity, and (iii) no consideration of the temperature coefficient. To surmount these limitations, it is crucial to consider the aspects that have an impact on the life of the battery [45]. The analysis of the SOC condition is conducted at each instant and is calculated with the threshold capacity using Equation (24).  
The following equation determines the net power produced by a DC microgrid structure:  
\[\rm P_{net}=P_{PV}+P_{wind}+P_{bio} \tag{24}\]
# Potassium Recovery from Potassium Solution and Seawater Using Different Adsorbents
###### Abstract  
The potassium (K) sorption characteristics with three adsorbents, natural zeolite, ammonium acetate-treated zeolite, and manganese nodule, were studied and compared to see the potential use of manganese nodule as an alternative K adsorbent. In general, the Langmuir isotherm could fit the K sorption in the KCl solutions at different pH conditions better than the Freundlich isotherm. Based on the Langmuir parameters, the maximum K sorption was greater for the zeolite-based adsorbents (i.e., 40-42 mg g\({}^{-1}\)) than the manganese nodule (i.e., 2.0 mg g\({}^{-1}\)) at acidic conditions, while the manganese nodule (i.e., 9.7 mg g\({}^{-1}\)) showed better K sorption at neutral conditions. With the seawater samples, the zeolite-based adsorbents showed higher K recovery (4-14%) than the manganese nodule (0-8.8%). The K sorption on the zeolite-based adsorbents followed the pseudo-second-order kinetics and the K sorption rates were higher for the treated zeolite than the natural zeolite. The repeated sorption tests showed that the natural zeolite could potentially be reused up to three times without any significant loss of K sorption capacity, while the ammonium acetate-treated zeolite lost its K sorption capacity after the single sorption test. Overall, the results show that the manganese nodule may potentially be the alternative to zeolite for K recovery under certain conditions, yet the zeolite-based adsorbents are generally better than the manganese nodule. Thus, more studies to enhance the K recovery using zeolite, including surface modified zeolite, are recommended.  
potassium; sorption; seawater; manganese nodule; zeolite +
Footnote †: journal: Applied Sciences  
14 August 2021  
14 September 2021  
12 September 2021