esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Redundant Transmission Control Clustering Approach
### Cluster Head Selection Phase  
If the basic measure of CHs selection should not be taken into account for head node selection the network lifetime may end earlier. The balanced CH selection also leads towards the less frequent re-clustering process. In our CHs selection phase, only those measures are taken into consideration that equally divide the energy and data load over the network. The CHs selection process of our model is discussed as follows:  
* Each FN resets it's timer during in the beginning of the CHs selection phase Then, FN \(i\) can compute it's timer \(T_{i}\) value through the following equation: \[T_{i}=T_{max}\times\frac{R_{EN}}{I_{EN}}\times\frac{d_{F,RH}-d_{i, RH}}{d_{F,RH}}.\] (13) \[d_{F,RH}=r\sqrt{(x^{2}+y^{2}+z^{2})}.\] (14) where \(R_{EN}\) and \(I_{EN}\) are used to express the remaining and the preliminary energies of the FN, respectively. While, \(d_{F,RH}\) is used to express the maximum distance between the cluster and RH. The \(T_{max}\) is the maximum timer value.
* After computing the timer values, each of the FN in each of the cluster transmit a CHs advertisement message \(ADV\_CH\_SELECT\) at the communication range \(r\). This message \(ADV\_CH\_SELECT\) enclose the FN's information: FN cluster identification \((x,y,z)\), FN location \((i,j,k)\), distance of FN from the RH \(d_{i,RH}\), \(R_{EN}\) and \(I_{EN}\) energies of the FN.
* Upon receiving the CHs advertisement message \(ADV\_CH\_SELECT\), each of the FN analyzes the \(ADV\_CH\_SELECT\) and checks it's possibility as a CH through the given expression: \[CH_{i}=\frac{R_{EN}}{d_{i,RH}}.\] (15)Each node has already computed its CH possibility through the above equation. So, it compares the possibility of that FN \(CH_{j}\) with own \(CH_{i}\). If the \(CH_{j}\) is greater then \(CH_{i}\) the FN update the information its table in a descending order in comparison with other \(ADV\_CH\_SELECT\).
* If a FN receives \(ADV\_CH\_SELECT\) message after the defined time slot and the CH is not chosen yet. If so and if the \(T_{i}\) of the information equals to 1, the tag \(T_{i}\) of this FN is set to 0. Meanwhile, the FN who sends the \(ADV\_CH\_SELECT\) message is set as a CH node. Otherwise, the received message is abandoned.
* At the end of timer values, each FN is well aware with the CH possibility of all the other FNs. The FN with maximum possibility is chosen as a CH and each FN send the joint request to that FN according to their cluster table.
###### Abstract  
The escape probability of Solar-induced chlorophyll fluorescence (SIF) can be remotely estimated using reflectance measurements based on spectral invariants theory. This can then be used to correct the effects of canopy structure on canopy-leaving SIF. However, the feasibility of these estimation methods is untested in heterogeneous vegetation such as the discontinuous forest canopy layer under evaluation here. In this study, the Discrete Anisotropic Radiative Transfer (DART) model is used to simulate canopy-leaving SIF, canopy total emitted SIF, canopy interceptance, and the fraction of absorbed photosynthetically active radiation (fAPAR) in order to evaluate the estimation methods of SIF escape probability in discontinuous forest canopies. Our simulation results show that the normalized difference vegetation index (NDVI) can be used to partly eliminate the effects of background reflectance on the estimation of SIF escape probability in most cases, but fails to produce accurate estimations if the background is partly or totally covered by vegetation. We also found that SIF escape probabilities estimated at a high solar zenith angle have better estimation accuracy than those estimated at a lower solar zenith angle. Our results show that additional errors will be introduced to the estimation of SIF escape probability with the use of satellite products, especially when the product of leaf area index (LAI) and clumping index (CI) was underestimated. In other results, fAPAR has comparable estimation accuracy of SIF escape probability when compared to canopy interceptance. Additionally, fAPAR for the entire canopy has better estimation accuracy of SIF escape probability than fPAR for leaf only in sparse forest canopies. These results help us to better understand the current estimation results of SIF escape probability based on spectral invariants theory, and to improve its estimation accuracy in discontinuous forest canopies.  
SIF; spectral invariants theory; discontinuous forest canopy; DART; 3-D radiative budget; total emitted SIF +
Footnote †: journal: Remote Sensing, 2020, 12, 3962; doi:10.3390/rs12233962
# High Throughput Data Relay in UAV Wireless Networks
## 6 Conclusions  
In this paper, we consider a dual sampling bandwidth efficient transmission technique in regard to the UAV flight trajectory, so as to maximize the minimum data transmission throughput among all the victims. In order to solve the problem, we propose an iterative algorithm which alternately optimizes the victim bandwidth scheduling and UAV trajectory. In addition, power balance is implemented in each iteration of the algorithm for supporting the DS method. We compare the DS scheme with two non-DS schemes, that is, a fair bandwidth allocation scheme and a bandwidth contention scheme. The DS scheme outperforms the non-DS schemes in terms of the optimal max-min average data rate among all the victims. The theoretical analysis reveals that the proposed solution is very close to the ground true optimal value. The optimal UAV flight trajectory for the DS scheme is different from the non-DS bandwidth contention scheme and non-DS fair bandwidth allocation scheme, as the UAV flies to positions that are not necessarily close to each victim. The UAV trajectory derived by the proposed algorithm is pre-configured before the UAV is dispatched and we assume the UAV is explicitly guided to follow the optimal trajectory. In regard to the UAV propulsion energy consumption, for shorter time periods, the non-DS fair bandwidth allocation scheme consumes the least energy. For longer time periods, the DS method consumes the second least energy but achieves the highest max-min average data rate among all victims. However, if the UAV hovering episodes are replaced by small circular movements, the propulsion energy consumption for DS method can be significantly reduced.  
Moreover, the assumption that the UAV starts and ends at fixed locations is reasonable. As in a disaster scenario, maybe only some certain places are able for launching and landing the UAV. In addition, a feasible path can be either a straight line, a circular curve or in other types, as long as the UAV can follow this path to fly through the area to connect all the victims. Meanwhile, a feasible path helps the staff collect the UAV back.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(T=60\) s & \(T=40\) s & \(T=30\) s & \(T=20\) s \\ \hline non-DS bandwidth contention scheme & 88.43 & 63.83 & 51.21 & 34.14 \\ non-DS fair bandwidth allocation scheme & 69.77 & 45.26 & 36.26 & 30.84 \\ DS method & 76.83 & 69.78 & 62.24 & 52.24 \\ DS method (adjusted) & 62.22 & 46.91 & 39.68 & 30.82 \\ \hline \hline \end{tabular}
\end{table}
Table 2: UAV propulsion energy (kJ) comparison.  
Figure 8: UAV propulsion power consumption.  
**Author Contributions:** Conceptualization, F.J.; methodology, F.J.; software, F.J.; validation, F.J.; writing--original draft preparation, F.J.; writing--review and editing, C.P.; supervision, C.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
## 5 Performance Evaluation
### Task Arrival Rate Prediction Accuracy  
To quantitatively compare the accuracy of the task arrival rate prediction, we calculate the mean absolute error (MAE) and the root mean square error (RMSE) and show the results in Table 3. We observe that STGCN can predict the task arrival rate more accurately than LSTM regardless of the location of an MECS in the topology. The difference stems from the fact that, unlike LSTM, which independently considers the temporal correlation of the task arrival rate at each MECS, STGCN comprehensively considers the spatio-temporal relationships of the task arrival rates across all MECSs when predicting the task arrival rate. For example, compared to the LSTM predictor, the STGCN predictor decreases MAE and RMSE by 11.24% and 11.01%, respectively, when all MECSs are considered.  
To further understand the prediction behavior, we inspect the distribution of the prediction error (i.e., \(\hat{\lambda}_{i}(t)-\tilde{\lambda}_{i}(t)\)) in Figure 4. In this figure, for ease of visual comparison, we plot all subfigures with the same ranges for both the x-axis and y-axis. As we can see in Figure 4, the prediction errors are more densely clustered around zero when using STGCNcompared to LSTM. In addition, we observe that STGCN exhibits smaller variations in the errors compared to LSTM.  
We also examine the prediction delay. Since LSTM processes inputs sequentially, the LSTM method takes an average of 450 ms to predict task arrival rates. On the contrary, STGCN processes inputs in parallel by using graph convolutions. Therefore, the time required to predict task arrival rates decreases to as small as 10 ms when STGCN is used. This corresponds to a 93.33% reduction compared to the LSTM method.
## 4 Joint Routing and Resource Management with Conjecture Based Multi-Agent Q-Learning
### Multi-Agent Q-Learning  
Among various algorithms in the RL framework, Q-learning is a practical approach adopting Q-value. Q-value is the total expected discounted reward for the pair of state-action and describes the value of choosing a particular action in a given state. It weights and ranks the probabilities of different actions, i.e., the action with a higher Q-value is more valuable and given higher selection probability, and vice versa. To achieve this object, the Boltzmann distribution is used to calculate the probability of choosing action \(a_{i}\) at time slot \(t\):  
\[\zeta_{i}^{t}(s_{i},a_{i})=\frac{e^{Q_{i}^{t}(s_{i},a_{i})/\eta}}{\sum_{b\in A _{i}}e^{Q_{i}^{t}(s_{i},b)/\eta}}, \tag{19}\]  
where \(Q_{i}^{t}(s_{i},a_{i})\) is the Q-value for the pair of state-action \((s_{i},a_{i})\) at time step \(t\), \(\eta\) is a positive number called the temperature. The larger the temperature is, the more balanced the probability of action selection becomes, and vice versa.  
\(M\) players are considered and every player is fitted with a Q-learning agent that learns its own strategy through limited cooperation with other agents. Thus, the Q-value is updated according to multi-agent Q-learning rule:  
\[\begin{split} Q_{i}^{t+1}(s_{i},a_{i})&=(1-\alpha)Q_{i }^{t}(s_{i},a_{i})+\alpha\bigg{[}E[R_{i}(s_{i},\zeta_{i},\zeta_{-\mathbf{i}})]+ \beta\underset{b_{i}\in\mathbf{A_{i}}}{\max}Q_{i}^{t}(s^{\prime}_{i},b_{i}) \bigg{]}\\ &=Q_{i}^{t}(s_{i},a_{i})+\alpha\bigg{[}E[R_{i}(s_{i},\zeta_{i}, \zeta_{-\mathbf{i}})]+\beta\underset{b_{i}\in\mathbf{A_{i}}}{\max}Q_{i}^{t}(s^ {\prime}_{i},b_{i})-Q_{i}^{t}(s_{i},a_{i})\bigg{]}\end{split} \tag{20}\]  
where \(\alpha\in[0,\ 1)\) is the learning rate, and \(\beta\) is the discount factor, and \(E[R_{i}(s_{i},\zeta_{i},\zeta_{-\mathbf{i}})]\) is the expected reward for SU \(n_{i}\) at time slot \(t\) considering other \(M-1\) competing SUs. The variation of Q-value is proportional to the expected reward plus the difference between the target and evaluated Q-value. The detailed definition of \(E[R_{i}(s_{i},\zeta_{i},\zeta_{-\mathbf{i}})]\) is given by:  
\[E[R_{i}(s_{i},\zeta_{i},\zeta_{-\mathbf{i}})]=\sum\limits_{(a_{i},a_{-i})\in A }\left[R_{i}(s_{i},a_{i},a_{-i})\prod\limits_{j\in M\backslash\{i\}}\zeta_{j} (s_{j},a_{j})\right], \tag{21}\]  
where \(\prod\limits_{j\in M\backslash\{i\}}\zeta_{j}(s_{j},a_{j})\) represents the joint probability of \(SU_{i}\)'s competing SUs choosing actions \(\mathbf{a}_{-\mathbf{i}}\) in their respective states. From Equations (20) and (21), in multi-agent Q-learning, the agent needs not only its own transmission strategy but also the complete information of competing SUs' strategies \(\zeta_{j}(j\in M\backslash\{i\})\) to update the Q-value of SU \(n_{i}\). However, it is not always practical to observe other SUs' private information in multi-hop CRN with finite cooperation. Therefore, designing a quasi-cooperative multi-agent learning scheme, which only needs private strategy and information exchange with its previous nodes, is challenging.
## 3 Results
### Tyrosol Stimulates the Expression of CD11b and CD14 Surface Antigens in HL-60 Cells  
Tyrosol-exposed cells were assessed with flow cytometry for changes in the expression of cell surface markers. Tyrosol treatment was found to enhance the surface expression of CD11b and CD14 with peak expression in both cases happening 3 days after exposure as with the monocytic differentiation positive control PMA (Figure 5a,b, respectively).  
CD11b is an integrin marker expressed by leukocytes during the inflammatory response and is associated with leukocyte adhesion and migration. High CD11b populations  
Figure 4: (**A–H**) [PERSON]’s stained HL-60 cells exposed to RPMI—Medium only negative control (**A**), Vehicle control 0.5% Ethanol (**B**), 1.6% DMSO on day 3 (**C**), 1.6% DMSO on day 5 (**D**), 10 nM PMA on day 3 (**E**), 10 nM PMA on day 5 (**F**), 9.23 ppm tyrosol on day 3 (**G**), 9.23 ppm tyrosol on day 5 (**H**). Magnification 1000\(\times\).  
were recorded at 23.70% (day 3) for tyrosol and were comparable to the 29.31% (day 3) recorded with the positive control (PMA) associated with the induction of monocyte differentiation (Figure 5a). There was no statistically significant difference between CD11b expression following tyrosol treatment and CD11b expression following PMA treatment.  
CD14 is an antigen expressed by monocytes and macrophages and is associated with participation in binding of lipopolysaccharide. For CD14, the effect of tyrosol was again similar to that of PMA with CD14 high populations reported at 14.11% (day 3) and 4.38% (day 5) after tyrosol treatment compared to 16.61% (day 3) and 4.03% (day 5) after PMA application (Figure 5b). There was no statistically significant difference between CD14 expression following tyrosol and PMA exposure. Both these results confirm that tyrosol is inducing differentiation along a monocytic pathway.  
**CD11b expression following tyrosol treatment**  
Figure 5: _Cont._  
Figure 5: **(a)**: A graph representing the % Gated CD11b high population on days 3 and 5 and representative histogram overlays and subtractions for each condition on day 3. Histograms from left to right = Untreated, 1.6% DMSO, 10 nM PMA, and 9.23 ppm tyrosol. Untreated = negative (medium only) control; 1.6% DMSO = granulocyte positive control; 10 nM PMA = monocytic positive control. Each bar represents the median value where \(n=3\) per condition. Error bars represent differences between the median and upper and lower quartiles. Statistically significant differences from the negative control are denoted by (**) \(p<0.01\). (b): A graph representing the % Gated CD14 high population on days 3 and 5 and representative histogram overlays and subtractions for each condition on day 3. Histograms from left to right = Untreated, 1.6% DMSO, 10 nM PMA, and 9.23 ppm tyrosol. Untreated = negative (medium only) control; 1.6% DMSO = granulocyte positive control; 10 nM PMA = monocytic positive control. Each bar represents the median value where \(n=3\) per condition. Error bars represent differences between the median and upper and lower quartiles.
## 5 Discussion
### Impact of Preprocessing Techniques  
The extensive preprocessing steps were instrumental in preparing the data for effective modeling. Converting text to lowercase, removing URLs, mentions, and special characters, and handling abbreviations and slang using Byte Pair Encoding (BPE) ensured that the textual data were normalized and free from noise. The lemmatization process reduced words to their base forms, reducing dimensionality and improving the model's ability to generalize.  
Handling informal language, which is prevalent in tweets, was crucial. By converting emojis into their textual descriptions and expanding abbreviations, the model preserved semantic information that could be critical for accurate classification. These preprocessing steps mitigated the challenges posed by the informal and succinct nature of X data.
## 4 Conclusions  
This paper demonstrated the experimental implementation of an RS acquisition-based camera capturing in MIMO-OCC, employing an LED array in an indoor static environment. The neopixel's light source could be used either as a single LED chip or an array display with similar optical characteristics to those of commonly used RGB LEDs. We showed the transmission spans of 7 cm and 1 m using  
Figure 11: MIMO-OCC performance analysis: (**a**) the peak signal-to-noise ratio (PSNR); and (**b**) the percentage success of received bits with respect to \(L\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Neopixels** & \(N_{\text{groups}}\) & \(L\) **(cm)** & \(N_{\text{visible}}\) & **Data Throughput (kbps)** \\ \hline \(16\times 16\) & 16 & 40–140 & 14 (max)–1 (min) & 3.36 (max)–0.240 (min) \\ \(24\times 24\) & 24 & 60–160 & 22–1 & 7.92–0.360 \\ \(32\times 32\) & 32 & 80–180 & 28–1 & 13.44–0.480 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Theoretical (approximation) data throughput based on different Tx configurations.  
the Tx without, and with, the LED grid and a diffuser, respectively. The proposed system with the multi-channel LED array-based Tx offered the same performance at the SS of 200 and 400 \(\upmu\)s over an \(L\) of 80 cm. However, at higher SS, the captured images were saturated, which resulted in increased PSNR and reduced percentage success of received bits. The maximum PSNR values of \(\sim\)18 and \(\sim\)19 dB and a 100% success rate of received bits were measured for the SS of 200 and 800 \(\upmu\)s at the link span of 20 cm. A theoretical approximation (see Table 3) to further extend the \(L\) up to 1.8 m and the data throughput up to 13.44 kbps within the proposed MIMO-OCC using a grouped LED array by employing a Tx with a larger illuminating surface (large size) for practical indoor environments was provided. We conclude that the proposed Tx design can provide flicker-free transmission by employing multiple channels Tx with perfect inter-LED synchronization and an RS-based camera for use in short-range and low data rates indoor IoT applications, such as display-to-camera communications. The complexity of the proposed RS-based MIMO-OCC scheme will be further investigated for its implementation in practical scenarios, such as mobility, rotational support, and multiuser, based on ROI detection and bit or image pattern recognition within neural network algorithms.  
The contributions of the authors in this paper are the following: conceptualization: S.R.T. and V.M.; investigation: S.R.T. and V.M.; methodology: S.R.T.; project administration: S.Z., R.P.-J., and Z.G.; software: S.R.T.; validation: S.Z., R.P.-J., S.V., and Z.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Union's Horizon 2020 Research and Innovation Programme, under the [PERSON] grant agreement no 764461 (VisIoN).  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## Appendix L Description of Mechanisms for Vehicles
### Proof of Event and Location [616]  
Proof of Event and Location is a mechanism to form consensus over traffic events. As a permissioned mechanism, it relies on centrally managed roadside units that act as oracles to validate events and calculate trust values. Roadside units form the heart of 'trust zones', spatially separated areas, in which a set of vehicles operate. Due to the centralised nature of roadside units, no Sybil attack resistance is provided by the algorithm.
## 3 Results and Discussion
### Continuous Welding Process and Electrode Sticking
It was easy to remove the aluminum oxide film on the aluminum surface when using the abraded electrode during the RSW process because the surface roughness of the abraded electrode was higher than that of the as-received electrode. Figure 17 shows the measured dynamic resistance between the base material and the electrode according to the electrode type. As shown in Figure 17, the average dynamic resistances of the as-received electrode and the abraded electrode were 14.5 \(\upmu\Omega\) and 11.5 \(\upmu\Omega\) at welding time of 83.5 ms, respectively. The dynamic resistance was lower by 3 \(\upmu\Omega\) when the abraded electrode was used. The resistance between the abraded electrode and the aluminum base material was small, and the resistance heat generated was low. In contrast, because the amount of heat generated between the as-received electrode and the base material due to the presence of the oxide layer was higher, the melting was enhanced during the welding process, which resulted in sticking of the electrode. Figure 14: Carbon imprints of the abraded electrode at the electrode force of 600 kgf during continuous RSW. Figure 16: Cross sections of nuggets obtained using the as-received and the abraded electrodes during continuous RSW at electrode force of 600 kgf. Figure 8:0 Measured height of the nuggets obtained using the as-received and the abraded electrodes at electrode forces of (**a**) 400 kgf and (**b**) 600 kgf.
# Femtosecond Laser Textured Surfaces for Radiative Cooling: Black Metals
## 3 Results and Discussion
### Radiative Cooling Effect of Black Metals  
Black (anti-reflective) metal surfaces have great potential for applications in radiative cooling when the low reflectance is in the mid-IR 1.5-18 \(\upmu\)m spectral range [47; 48; 49]. Physical effects, such as surface roughness are known to alter emissivity, which is also affected by the surface composition/chemistry [50; 51; 52]. Fs-laser texturing is a simple and increasingly practical method to create nano-micro rough textures over large surface areas [27; 53]. Such fs-laser-textured surfaces are of interest for catalytic applications [30] not only due to the larger surface area but also due to reduced reflectivity in photo-catalytic applications [54]. The spectrally broad reduction in IR reflectivity demonstrated here can expand the application potential of narrow IR band emitters based on perfect absorbers [4], which can also be made out of high entropy alloys (HEA) [55; 56] which is promising for industrial applications including at high temperatures.  
Tungsten is a unique material due to its highest melting temperature, very large mass density, chemical inertness, and comparatively low thermal expansion. Its applications can be found from ink spreading ball in a ball pen, served/serves as a lightning filament in incandescent lamps to industry/defense/space industries where surfaces are subjected to extreme temperatures in plane/rocket engines or power plants. The W and WC surfaces with anti-reflective features made by fs-laser texturing can be efficient emitters based on Kirchhoff's law when heated at very high temperatures \(\sim\)3000 \({}^{\circ}\)C to cool down surfaces. Moreover, thermal energy harvesting can be more efficient as predicted by [PERSON]'s limit of efficiency \(\eta_{C}=1-T_{cold}/T_{hot},T_{cold}=3\) K at outer space [47]. Thermal radiation harvesting is promising due to its high efficiency [57].