esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Occurrence of PhACs in the Five WWTPs and Surface Waters
Figure 2: Composition profiles of PhACs in influents (**A**) and effluents (**B**) of target WWTPs. The occurrence profile of upstream and downstream samples of each WWTP is represented in Figure 3 and detailed in Tables S7-S11 of Supplementary Material. Unlike the influent results, upstream samples considerably presented different profiles. For instance, in the samples collected upstream from Evora WWTP, 14 out of 26 PhACs were detected, namely erythromycinycin (ERT) and azithromycin (AZM), bezafibrate (BZF) and IBUP, metoprolol (MTPL), propranolol (PPNL), and SPD, which were only detected in these samples. DCF in Evora presented the second-highest median concentration with 1.59 \(\upmu\)g/L. Moreover, ATN (0.20 \(\upmu\)g/L) and CAF (0.17 \(\upmu\)g/L) showed the highest median concentrations of all samples. Upstream of the Reguengos de Monsaraz WWTP, DCF and CBZ had the highest median concentrations of all five locations, 2.54 and 0.47 \(\upmu\)g/L, respectively. Despite serving the smallest population, the Portel WWTP upstream samples had the highest concentrations of APAP and NPX, 0.26 and 1.02 \(\upmu\)g/L, respectively. Figure 3: Composition profiles of PhACs in upstream and downstream samples of target WWTPs. As for the downstream results, the profiles between samples were different. DCF was the most representative PhAC in Evora (1.72 \(\upmu\)g/L) and Reguengos de Monsaraz (2.10 \(\upmu\)g/L) WWTPs. For Borba WWTP it was CAF (2.19 \(\upmu\)g/L) and DCF (1.44 \(\upmu\)g/L). For the Redondo WWTP, CBZ and DCF had the highest concentrations of 0.88 \(\upmu\)g/L and 0.72 \(\upmu\)g/L, respectively. All the remaining detected PhACs had concentrations smaller than 1 \(\upmu\)g/L.
## 4 Results
### Power Consumption
The choice of the epoch size has marginal effects on the power consumption, and only a 3% reduction is observed when we increase the epoch size from \(N=128\) to \(N=768\).
## 4 Conclusions  
In this study, the ability of _M. albus_ Med. to accumulate PTE was assessed. This plant species was selected with regard to its use in sustainable agriculture, especially in growing systems of mixed culture of _Maize_ and _Fabaceae_ with the subsequent use for biomass production as an energy source in biogas stations. The results suggest that the _M. albus_ Med. can absorb PTEs from the soil. The measured values thus indicated that the _M. albus_ Med. would be able to accumulate heavy metals in its biomass at reduced efficiency, TF values did not exceed 1.0. On the other hand, there is a potential for the plant to resist abiotic stress caused by increased heavy metal concentration in the soil. The highest PTE concentrations in the plant biomass were those of zinc (av. 12.638 mg/kg in above-ground biomass and 10.560 mg/kg in underground biomass) and copper (av. 4.0 mg/kg in above-ground biomass and 5.128 mg/kg in underground biomass). The bioconcentration and bioaccumulation factors were always lower than 1. The plant can effectively translocate only Zn, Hg, and Pb from roots to shoots. The bioconcentration factor shows inefficiency of PTE accumulation in the shoots of _M. albus_, which leads to the conclusion that _M. albus_ is not suitable for phytoremediation processes. The measured results showed that _M. albus_ Med., Meba does not pose a danger to processes of anaerobic digestion in biogas plants by the excessive accumulation of PTEs in its biomass, when grown on soils comprising PTEs in naturally abundant concentrations. Therefore, its use in biogas production should not jeopardize biomethane production due to the high PTE concentration. Further research should focus on growing _M. albus_ on soils with the increased content of PTEs, e.g., brownfields. Biomass would be then used for testing in the laboratory fermenter with the aim to analyze in details all processes in the fermenter.  
**Author Contributions:** Conceptualization, A.K., J.S., V.L. and J.E.; methodology, A.K., J.S. and I.H.; validation, [PERSON], J.S., I.H.; formal analysis, A.K., T.H., M.B. and J.E.; investigation, A.K., J.S., M.R. and J.E.; resources, A.K.; data curation, A.K., J.S. and I.H.; writing--original draft preparation, A.K., J.S., I.H. and J.E.; writing--review and editing, A.K., V.L. and J.E.; visualization, A.K., J.S. and J.E.; supervision, A.K. and J.E.; project administration, A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.  
Supported by the Ministry of Agriculture of the Czech Republic, institutional support MZE-RO1722.  
Not applicable.  
The data that support the findings of this study are available from the corresponding authors ([PERSON] and [PERSON]), upon reasonable request.  
The authors declare no conflict of interest.  
List of abbreviations.
## 4 Experimental Results
### Experimental Results on Real-World Image  
In order to evaluate the performance of our presented approach for real diffuse surfaces, we performed it on a real-world image and compared the reconstructed result with the existing approach of [PERSON] and [PERSON] [8], the variant approach of [PERSON] and [PERSON] [8], and the approach of [PERSON] and [PERSON] [20]. The image is the real-world case used in the surveys [4; 5], which is shown in Figure 5a. Figure 5b gives the ground truth referred to [5]. Figure 5c-f illustrate the reconstructed height maps using the approach of [PERSON] and [PERSON] [8] with exact height values at each singular point, the variant approach of [PERSON] and [PERSON] [8] with the known Dirichlet boundary condition \(z=\varphi\) on \(\partial\Omega\), the approach of [PERSON] and [PERSON] [20], and our presented approach with the known Dirichlet boundary condition \(z=\varphi\) on \(\partial\Omega\), respectively.  
Similarly, we calculated the MAE and RMSE errors of these approaches and the comparisons results are listed in Table 2. From the reconstructed results illustrated in Figure 5c-f, we can see that  
Figure 5: Experimental results of the real-world case: (**a**) real intensity image; (**b**) ground truth referred to [5]; (**c**) reconstructed height map using the approach of [8]; (**d**) reconstructed height map using the variant approach of [8]; (**e**) reconstructed height map using the approach of [20]; (**f**) reconstructed height map using our presented approach.  
our presented approach and the variant approach of [PERSON] and [PERSON] [8] are more vivid than the approach of [PERSON] and [PERSON] [8]. The same conclusion can be drawn from Table 2: our presented approach shows best performance since our approach employs the advanced reflection model for diffuse surfaces and high-order accurate numerical scheme. Recall that the approach of [PERSON] and [PERSON] [8] assumes the Lambertian reflection model for diffuse surfaces. As previously, the approach of [PERSON] and [PERSON] [20] shows a slightly worse performance as can be seen from Figure 5e.
## 2 Mathematical Model
### Generalized Model Description
#### 2.2.2 Scheduling Constraints: Job Pulse Start-End Formulation  
We define this set of constraints as pulse start-end formulation because in this formulation we use binaries \(S_{jt}\) and \(E_{jt}\) which take the value 1 only in period of job start and end, respectively. We have variable job duration, so it is not sufficient to use only start pulse decision variable \(S_{jt}\), we also need variables \(E_{jt}\). Firstly, we imply Constraints (8) and (9) for \(S_{jt}\) and \(E_{jt}\) to start and end the job only once:  
\[\sum\limits_{t\in T}S_{jt}=1,\ \forall j\in J; \tag{8}\]  
\[\sum\limits_{t\in T}E_{jt}=1,\ \forall j\in J. \tag{9}\]  
Secondly, we force the job duration variable \(d_{jt}\) to get zero value in periods \(t\) when job \(j\) is not implemented. The index of starting period for job \(j\) equals \(\sum\limits_{t\in T}tS_{jt}\), and ending period index is \(\sum\limits_{t\in T}tE_{jt}\). So we state that duration \(d_{jt}=0\) outside the interval \([\sum\limits_{t\in T}tS_{jt},\sum\limits_{t\in T}tE_{jt}]\) with the following constraints. If job \(j\) is started in some period after the period \(t\) or it is finished in some period before the period \(t\), then \(d_{jt}=0\):  
\[d_{jt}\leq d\ (1-\sum\limits_{k=t+1}^{m}S_{jk}-\sum\limits_{l=1}^{t-1}E_{jt}),\ \forall j\in J,\ \forall t\in T; \tag{10}\]Next constraints have the same sense as Constraints (4). In any period between start and end of the job \(j\) it must be implemented without preemptions inside the period, so \(d_{jt}=d\) inside the interval \((\sum\limits_{t\in T}tS_{jt},\sum\limits_{t\in T}tE_{jt})\). If job \(j\) was started before the period \(t\) and it was finished after the period \(t\), \(d_{jt}=d\):  
\[d_{jt}\geq d\ (\sum\limits_{k=1}^{t-1}S_{jk}+\sum\limits_{l=t+1}^{m}E_{jt}-1),\ \forall j\in J,\ \forall t\in T. \tag{11}\]  
The precedence constraints require two constraints for binaries and continuous job duration for each pair \(\{j_{p},j_{s}\}\in P\), representing precedence constraints on periods and inside each period. In this formulation we can just compare start period index of successor \(j_{s}\) and end period index of predecessor \(j_{p}\):  
\[\sum\limits_{t_{1}\in T}t_{1}E_{jt_{1}}\leq\sum\limits_{t_{2}\in T}t_{2}S_{jt_ {2}},\ \forall(j_{p},j_{s})\in P; \tag{12}\]  
\[d_{jt}+d_{jt}\leq d,\ \forall t\in T,\ \forall(j_{p},j_{s})\in P. \tag{13}\]
## 5 Simulations and Results
### Performance under extreme conditions:  
To rigorously assess the robustness of the UAV's control algorithm in extreme real-world conditions, a test case is designed with simultaneous dynamic variations, external disturbances, and sensor noise. The UAV follows a continuous ramp reference trajectory, challenging its tracking performance under changing setpoints. Sudden impulsive disturbances of \(\pm\)2.0 V magnitude and 100 ms duration are injected at discrete intervals to simulate wind gusts or mid-air collisions, while band-limited white noise is added to the control input to  
Figure 12: Step reference tracking response of the UAV under step disturbance.  
Figure 13: Step reference tracking response of the UAV under model uncertainties.  
replicate turbulence and sensor noise. Additionally, a 20% increment in the UAV's servomotor gain \(K_{s}\) at t \(\approx\) 8.0 s is introduced to assess robustness against model-induced parametric uncertainties. The time-domain responses of the UAV's pitch angle, under the influence of each controller, are presented in Figure 14.
## 5 Principle and Mechanism of CuIC  
According to the conducted research, and based on the results of characterization tests, we here describe and explain the process of CuIC of a-Si in three main steps, as illustrated in the top row of Figure 13. In the first step, the deposited a-Cu and a-Si layers start to mix (forming a Cu\({}_{3}\) Si compound) in several local centers of their interface, initiated from centers where the effective surface of the contact between Cu-Si is larger. The existence of Cu\({}_{3}\) Si was verified by EDS scanning of the surface of a sample annealed at a temperature close to Cu-Si eutectic point.  
Then, in the second step, the formation of Cu\({}_{3}\) Si increases with extending the thermal annealing time or increasing the annealing temperature. These Cu\({}_{3}\) Si bulks migrate into a-Si layer-like ribbons (or needles), leading to the crystallization of the a-Si layer as a mixture of nano- and micro-Si ribbons which become longer and bigger. As mentioned above, the micro-Si cannot start to grow from everywhere, and cannot grow in all directions; it can only evolve into a-Si layer within a \(\sim\)100 micron radius area from the Cu\({}_{3}\) Si ribbons, as observed in Figure 7 [28]. In fact, studying the lateral crystal growth for CuIC of glass/a-Si/Cu in [31] revealed that the majority of the micro-Si ribbons had \(<\)110\(>\) orientations with respect to the a-Si film surface normal, and that they grew in the \(<\)111\(>\) and \(<\)211\(>\) directions. Therefore, the migration of Cu\({}_{3}\) Si ribbons mainly takes place perpendicular to the interface plane between the Cu and Si layers.  
The crystal orientation can be explained by the thermal strain energy generated in the micro-Si film due to differences in the thermal expansion coefficients between a-Si and a-Cu, as well as a-Si and glass (or C-Si wafer). By extending the thermal annealing time of the CuIC process, micro-Si growth in the \(<\)111\(>\) direction becomes dominant due to the lower energy needed for crystal formation in this  
Figure 12: EQE responses of the solar cells fabricated by CuIC micro-Si thin film.  
Figure 13: Illustration of the principle and mechanism of micro-Si film fabrication by CuIC.  
orientation. Furthermore, since Cu\({}_{3}\) Si has a vacancy in its crystal band structure, it can passivate the a-Si films in a similar way to a-Si passivation by hydrogenation.
# Experimental Study on Repairing Corroded Cracks by Electrophoretic Deposition
## 4 Results and Discussion
### Physical and Chemical Principle of Electrophoretic Deposition  
Figure 16 shows a schematic of the electrophoretic deposition repair process. According to our observation of the whole repair process and the  
Repair Stage: as shown in Figure 16(2), the corroded steel bar and anode plate are, respectively, used as the positive and negative poles of the electric field. An electric field is formed between these poles under a direct current. Because the electrical resistance is lowest inside cracks, the current density at this location is higher than that at the sides of the cracks. As shown in Figure 16(3), under the action of an electric field force, the colloidal particles are electrophoresed. Because the cationic epoxy resin and curing agent molecules are positively charged, according to the like-charges-repel/unlike-charges-attract principle, they aggregate on the negative reinforcement. Given the high current density in the cracks, charged molecules are more likely to gather at these locations. When the charged molecules encounter steel bars or concrete, they cannot continue to electrophorese; instead, they gradually deposit and consolidate. As the molecules continue to accumulate, the epoxy resin layer changes from a milky colloidal state to a light-yellow solid state (Figure 4).  
Curing Stage: when the cracks are completely plugged and the epoxy film gains sufficient thickness, the repair device is removed (Figure 16(4)) and the deposited wet epoxy film is exposed to air. As the water molecules evaporate, the distance between the deposited cationic epoxy resin and curing agent molecules decreases, and a tight accumulation is formed under the action of capillary pressure. The curing reaction between the two types of molecules is accelerated (Figure 16(5)), and the epoxy resin finally achieves consolidation hardening. The epoxy resin deposited in the cracks and on the surface of the reinforcement can bond the reinforced concrete damaged by rust expansion and cracking. The epoxy resin film deposited on the surface of the concrete can also prevent external erosive media (e.g., carbon dioxide, water, chloride ions, etc.) from coming into contact with the damaged concrete. In addition, it can be seen from Figure 6 that the resistance of the reinforced mortar system increases significantly after the repair, and, when the applied voltage is constant, the greater the resistance of the system, the smaller the current that can reach the internal reinforcement. Thus, it can be seen that electrophoretic deposition repair can improve the performance of the repaired specimen against stray current erosion (Figure 16(6)). Due to the crack location, the resistance is smaller and the molecular aggregation to the middle is easier. Therefore, when the repair work is completed, the epoxy resin film deposited on the surface of the concrete presents an inverted trapezoidal distribution that is thick in the middle and thin at both ends.
## 1 Introduction
#### Motivations and Impact  
The need for ML-driven energy management solutions is increasing with the net-zero carbon by 2050 target set by the UK government [15]. Several contributing parameters to managing energy in our society include demand, energy usage behaviour, environmental factors, etc. In this paper, we addressed the question of how to accurately forecast solar irradiance. This plays a crucial role in choosing the most optimal energy system management strategy, and optimising the integration of solar cells [16]. Moreover, we aim to present a methodological foundation of algorithm and feature selection, and evaluation metrics for other studies to follow.  
The main contributions of this paper are as follows:  
Figure 1: Increase in solar power generation worldwide [9].  
Data-driven and contextual optimisation forecasting (DCF) algorithm, which accurately predicts solar irradiance in the short- and long term. DCF is a hybrid algorithm that utilises state-of-the-art ML algorithms and optimises their accuracy using contextual information;
* A comparative study of two ML algorithms (Support Vector Machine and Facebook Prophet), in which an investigation was made into the effect of adding extraterrestrial radiation as a feature;
* Comprehensive validation of the forecasting accuracy for short- and long-term predictions in three cities to ensure that the model is not specific to one location. This was evaluated by computing the coefficient of determination (R\({}^{2}\)), mean absolute error (MAE), and root-mean-squared error (RMSE).  
The rest of the paper is organised as follows: Section 2 reviews previously proposed algorithms for solar forecasting. Section 3 presents the dataset used for training the ML algorithms and the evaluation methods, respectively. The DCF algorithm is introduced in Section 4, while Section 5 discusses the forecasting results. Finally, Section 6 concludes this paper, and Section 7 suggests potential future research.
## 2 Materials and Methods
### Simulation Setup for Extended Scenarios
#### 2.3.1. Apparel Closed-Loop Scenario  
The forward flow (manufacturing and distribution) follows the same steps as the original: Raw material is produced, sent to textile manufacturing, then to garment assembly, and then finished products are delivered to the retailer. We simulate an order of a certain size (e.g., 1000 garments) flowing through this chain. When products reach the retailer, we then simulate customer purchases and returns. This flow is captured in Figure 4a.  
For simplicity, we assume all 1000 garments are sold to consumers (the sale event can be logged or assumed off-ledger for now). Over a period, a fraction of these (say 10%) are returned for end-of-life processing--perhaps they are worn out or part of a take-back initiative. We model return arrivals as a random process (e.g., each item has a certain probability of return per period). In the simulation timeline, these returns trigger the retailer (RE) agent to invoke RecordReturn on each returned item. Subsequently, the recycler (RC) agent processes batches of returned items by invoking RecycleProduct. For example, RC might accumulate 50 returned garments and then process them in one batch, yielding, say, 30 kg of recycled cotton and 20 kg of polyester (assuming garments are poly-cotton blends). In our model, to keep it simple, assume garments are 100% cotton for now. So, RC would produce some quantity of recycled cotton fiber. That recycled fiber is then supplied to the  
Figure 3: Industry-agnostic sequence diagram outlining key closed-loop supply chain scenarios. Solid arrows indicate actor-initiated transactions, while dashed arrows show the corresponding on-chain confirmations or query responses.  
raw material stage of a new production order for, say, 200 garments. After the initial cycle, we initiate a second production cycle that uses a mix of virgin and recycled cotton. The simulation ensures that the RC agent provides the recycled fiber asset ID to the RM agent, who subsequently includes it in their production (and therefore chaincode invocation) for the new cycle. This tests the full loop closure: product A is sold, returned, and recycled into material for product B, and so on....  
We run this simulation with parameters that can be varied: return rate (0% vs. 10% vs. 50%), recycling yield, etc. The output of interest will be both ledger data (the blocks/transactions created, chaincode function calls, state of assets) and performance metrics (time taken, CPU usage, etc., during simulation).  
Figure 4: State transition diagrams capture how the flow of product states differs across BDCCs. (**a**) Apparel supply chain state transition diagram; (**b**) furniture supply chain state transition diagram.
## 4 Computational Complexity of the Time Stepping Algorithms  
Here, we briefly compare the computational complexity of the considered time-stepping algorithms.  
LU factorization (or any direct Gaussian elimination method) for solving dense linear systems requires \(O(N^{3})\) arithmetic operations. More precisely, the computational complexity of the factorization (forward elimination step) is \(O(N^{3})\) while solving the system with the factorized matrix (backward substitution step) costs \(O(N^{2})\) operations.  
Alternatively, as discussed in Section 3, the solver based on HSS compression requires \(O(rN^{2})\) arithmetic operations, where \(r\) is the maximum rank of the off-diagonal blocks generated in the compression process. The costs of the three consequential steps here are as follows: HSS compression--\(O(rN^{2})\); ULV-like factorization--\(O(r^{2}N)\); solving system with a factorized matrix--\(O(rN)\).  
Let us first consider the case of the backward Euler scheme with a uniform time step \(\tau\). Then, the matrix \(\tilde{K}=K+M^{L}/\tau\) does not change during the time-stepping procedure, that is, the factorization is performed only once. Thus, the following estimates of the computational complexity of the time-stepping algorithm hold:  
\[\mathcal{N}_{\text{uniform LU}} =O(N^{3})+O(mN^{2}),\] \[\mathcal{N}_{\text{uniform HSS}} =O(rN^{2})+O(mrN).\]  
A numerical comparison between these two methods can be found in [22]. Then, in [20], a special case of different time steps is considered, where the time domain is divided into (by default) a small number of subdomains with piecewise constant time steps.  
The performance analysis that will be presented in the rest of the paper only applies to the case of adaptive time stepping. In the forward-backward Euler adaptive scheme, the matrix \(\tilde{K}_{i}\) can generally change at each time step. In practice, this does not allow us to reuse the factorization computed at the previous time step.  
Let us write the number of time steps \(m\) in the form  
\[m=m_{1}+m_{2},\]  
where \(m_{1}\) denotes the number of times the adaptive forward-backward Euler scheme has changed the step size. Then,  
\[\mathcal{N}_{\text{adaptive LU}}=O(m_{1}N^{3}+mN^{2}).\]  
As shown in Section 3.2, the HSS compression is performed only once for the adaptive scheme, while the ULV-like factorization must be performed \(m_{1}\) times. Therefore, the computational complexity \(\mathcal{N}_{\text{adaptive HSS}}\) can be estimated by  
\[\mathcal{N}_{\text{adaptive HSS}}=O(rN^{2}+m_{1}r^{2}N+mrN).\]  
We should note that the number of steps of the adaptive method \(m\) as well as the part \(m_{1}\) will depend on the parameters set by the user and the behaviour of the right-hand side \(f(x,t)\).  
In conclusion, the computational complexity analysis shows that we can expect the adaptive scheme to outperform the uniform one if the number of adaptive time steps \(m\) is significantly smaller than the number of uniform steps required to cover the same time domain \(m_{\text{uniform}}=T/\tau_{0}\) and also when \(m<N\).
## 2 Routing Algorithms for Wireless Sensor Networks  
Routing protocols for wireless sensor networks can be classified as data-centric, hierarchical or location-based.