esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance-Enhancing Sulfur-Doped TiO2 Photoanodes for Perovskite Solar Cells
## 3 Results  
### UV-Visible Spectroscopy  
Given the boiling temperature of elemental sulfur at 444.6 \({}^{\circ}\)C, evaluating the behaviour of the S-doped TiO\({}_{2}\) thin film towards the change in sintering temperature is interesting. Figure 1a shows the UV-vis absorption spectra of various TiO\({}_{2}\) thin films sintered at 450 \({}^{\circ}\)C and 500 \({}^{\circ}\)C. The sintering temperatures were selected based on the temperature required to produce highly-crystalline TiO\({}_{2}\) thin films, that is, at least 450 \({}^{\circ}\)C [18]. 550 \({}^{\circ}\)C is not suitable due to the formation of rutile TiO\({}_{2}\) at this point, and this type of TiO\({}_{2}\) is less photo-active than anatase TiO\({}_{2}\)[19]. Firstly, all TiO\({}_{2}\) samples exhibit the typical absorbance behaviour of TiO\({}_{2}\) materials, in which the absorbance is low at higher wavelength and gradually increases at the UV region. Compared with the un-doped TiO\({}_{2}\) photo-anode, the S-doped TiO\({}_{2}\) displayed a small red shift of the absorption edge due to the new energy levels within the band gap caused by the doping of S. This phenomenon has been reported in a previous study [20,21]. Based on a previous study, the mechanism of TiO\({}_{2}\) band gap reduction could be attributed to the interaction between the S dopant (d and p orbitals) with the TiO\({}_{2}\) energy levels [22]. The bandgaps were reduced from 3.56 eV (un-doped TiO\({}_{2}\) photoanode) to 3.41 eV (10% S-doped TiO\({}_{2}\) photoanode) as estimated from the Tauc plots in Figure 1b. When the sintering temperature was increased to 500 \({}^{\circ}\)C, the absorption spectrum of the S-doped TiO\({}_{2}\) photoanode was reduced. This phenomenon suggested the S dopant has diffused out of the thin film, which was also found in a previous study [15]. Thus, to produce S-doped TiO\({}_{2}\) with anatase phase, 450 \({}^{\circ}\)C was selected as the sintering temperature.  
The different doping levels of S were investigated using UV-vis spectroscopy. Figure 1b shows the absorption response of all S-doped TiO\({}_{2}\) photo-anodes with various concentrations of S. With 5% S dopant, the absorption bands were improved compared with those of the un-doped TiO\({}_{2}\) photo-anode. According to the plots, 10% S-doped TiO\({}_{2}\) showed the highest light absorbance. Thus, 10% was selected as the optimum concentration for sulfur doping.
## 2 Materials and Methods
### Experimental Protocol  
The experimental setup includes either ten treatments (two tillage types x five depths) for soil properties evaluated before the experiment or 30 treatments (two tillage types x three nitrogen doses x five depths for soil properties evaluated at the end of the experiment or two tillage types x three nitrogen doses x five varieties for wheat yield. The experimental design was a randomized complete block with three replications. Therefore, the corresponding experimental designs were either a split plot for soil properties determined before the start of the experiment (tillage type in main plots and depth in subplots) or a split-split plot for those determined at the end of the experiment and for wheat yield. For the former, tillage types were allotted to main plots, nitrogen doses were allotted to subplots, and depths were allotted to sub-subplots. The same is true for wheat yield, except varieties were allotted to sub-subplots. The two tillage types were conventional tillage (CT) and no tillage (NT). The three nitrogen doses were 20, 40, and 60 kg N ha\({}^{-1}\). On 21 December 2020, the base fertilizer NPK 10-20-20 was applied at 150 kg ha\({}^{-1}\), and on 20 January 2021 and 8 February 2021, ammonitrate 33.5% was supplied at a rate of 100 kg ha\({}^{-1}\). The five varieties of durum wheat (_Tritcium durum_ Desf.) were Louiza (INRA-Morocco, 2011), Faraj (INRA-Morocco, 2007), and Nacht, as well as two germplasms (M.G and I.C). Some of the main characteristics of the three varieties are presented in Table 1; however, those relative to the two germoplasm are not yet publicly available.  
Grain and straw yields were determined for each block, variety, nitrogen dose, and tillage type using two plots with a 1 m \(\times\) 1 m square metal stem quadr  
Figure 1: Map showing the location of the experimental site (in red).
## 3 Antimicrobial Peptides (AMPs)
### Application of AMPs Against Foodborne Pathogens and Persisters
#### 3.2.3. _E_radication of Persisters in Infection  
Even though persister-cells can survive in stress conditions such as low nutrition and antibiotic exposure, they still need an intact membrane to remain viable. In this case, the ability to perturb the bacterial membrane makes AMPs powerful drug candidates against persisters. In addition, extensive research proved that many membrane-targeting AMPs show anti-biofilm activity, which also contributes to persister eradication. For instance, LL-37 displays broad-spectrum bactericidal activity by membrane-perturbing, pore-forming, and biofilm removal activities [158]. The artificial AMP SAAP-148, derived from LL-37, has high efficiency against _S. aureus_ persisters as well as its biofilms without resistance selection [126]. [PERSON] et al. [159] reported that cationic AMPs containing a varying number of arginine and tryptophan repeats effectively kill _E. coli_ persisters and significantly enhance the susceptibility of biofilms to antibiotics. Human blood platelet-based TC-19, thrombocidin-based TC-84, and the designed peptide BP2 can inhibit the outgrowth of germinated _Bacillus_ spores by perturbing their inner membrane [128]. Except for directly killing persister-cells by targeting cellular membranes or biofilms, novel AMPs were developed in silico to inhibit TA complexes that induce persister formation, and are proposed as a potential method against _L. monocytogenes_ persisters [160].  
The combination of AMPs and other bactericidal methods is another, potentially synergistic, way to combat persisters. [PERSON] et al. [159] discovered that, while using AMP (RW)4-NH2 alone can kill up to 99% _E. coli_ persisters in planktonic culture and 98% persistent cells in a mature biofilm, the synergy of ofloxacin and (RW)4-NH2 can completely eradicate viable cells including persisters. Based on the cell-penetrating characteristic of intracellular AMPs, [PERSON] et al. [161] re-engineered antibiotics by adding 12 residues AMP on tobromycin to give it a membrane-penetrating activity. This composite antimicrobial agent named pentobra shows high killing efficiency against _E. coli_ and _S. aureus_ persisters. [PERSON] et al. [162] illustrated that the synergy of nisin and ampicillin is a successful method to kill \(S\). Typhimurium persisters in the presence of mannitol, which is used to restore the antimicrobial susceptibility of persisters. Hence, the combination of AMPs, antibiotics, and metabolizable sugars also provides a potential strategy for persister eradication. Furthermore, the conjunction of nisin and physical methods, such as high pressure or high temperature, to control microbial contamination have a demonstrated stronger ability to eliminate recalcitrant _Bacillus_ spores [117].
## 2 System and Channel Models
### Channel Statistical Model  
The probability distribution of the composite coherent THz wireless channel \(h=h_{l}h_{a}h_{p}\) can be expressed as  
\[f_{h}(h)=\int f_{h|h_{a}(h|h_{a})}f_{h_{a}}(h_{a})dh_{a} \tag{10}\]  
Because \(h_{l}\) is a fixed parameter that serves as a scaling factor, the conditional distribution can be written as \(f_{h|h_{a}}(h|h_{a})=\frac{1}{h_{l}h_{a}}f_{h_{p}}(\frac{h}{h_{a}h_{l}})\), and the composite PDF for the three distributions of LN and GG can be calculated. We derive the average BER of LN fading with pointing error by substituting Equations (4) and (9) into Equation (10). The composite PDF can be found as  
\[f_{\rm e,LN}(h)=\frac{\gamma^{2}\exp(u_{a})}{2(A_{0}h_{1})^{\gamma^{2}}}h^{ \gamma^{2}-1}\operatorname{erfc}(\frac{\ln(\frac{h}{A_{0}h_{1}})+\mu_{b}}{ \sqrt{8}\delta_{X}})^{n} \tag{11}\]  
where \(\mu_{b}=2\sigma_{X}^{2}(1+2\gamma^{2})\), \(\mu_{a}=2\sigma_{X}^{2}\gamma^{2}(1+\gamma^{2})\).  
Similarly, for the GG channel model of Equations (5) and (9) into Equation (10), the composite PDF of the channel can be written as  
\[f_{h_{a,GG}}(h)=\frac{2\gamma^{2}(\alpha\beta)\frac{\alpha+\beta}{2}h\gamma^{ 2}-1}{(A_{0}h_{1})^{\alpha^{2}}\Gamma(\alpha)\Gamma(\beta)}\times\int_{\frac{ h}{A_{0}^{\alpha}\beta_{0}}}^{\infty}h_{a}^{\frac{\alpha+\beta}{2}-1-\gamma^{2}}K_{ \alpha-\beta}(2\sqrt{\alpha\beta h_{a}})dh_{a} \tag{12}\]
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 10 Conclusions  
This piece of research effectively addresses the limitations and gaps identified in previous wireless network design for a real-life water distribution network. It justifies the selection of specific wireless technologies, bridging the connectivity gap between wireless modules and field devices to ensure compatibility and system integrity. The integration of the secondary water tank into the wireless network was successfully executed. Moreover, the designs were systematically and rigorously validated, exploring various approaches, and maintaining a defined threshold of functionality across two distinct networks with different designs and behaviors.  
Trusted Wireless, though less commonly used, showcased its strengths through a flexible design enabled by its mesh topology and parent-child communication strategy. The capability to incorporate various external antennas is particularly advantageous, allowing for tailored configurations that enhance network coverage and signal quality. In contrast, Wi-Fi technology excelled in providing higher data rates and operating across a wider range of frequency bands. This versatility is instrumental in reducing interference, a common challenge in densely populated network environments.  
A notable limitation of this research lies in the performance testing of the two networks concerning error rates and noise levels. Due to the proprietary nature of SCALANCE's iPCF medium access control mechanism and the lack of simulation support for Trusted Wireless, comprehensive performance assessments could not be conducted using simulation software. Future studies should focus on empirical testing under controlled conditions to validate the theoretical models and simulations presented in this work.  
For future developments, high-throughput Wi-Fi could be utilized to enhance security at the water station, particularly through the deployment of advanced security cameras. Additionally, integrating the automation network with a cloud computing provider such as Amazon Web Services (AWS) could significantly improve monitoring and control capabilities. However, it is crucial to rigorously assess and mitigate potential security vulnerabilities associated with this enhancement.  
Furthermore, future research could explore hybrid solutions that combine the strengths of both wireless technologies. By integrating Trusted Wireless for its robust mesh topology and long-range capabilities with Wi-Fi's high data rates and broader frequency options, a hybrid approach could improve overall network performance, reliability, and scalability in water distribution automation.  
Conceptualization, M.M.N. and P.L.D.J.; methodology, M.M.N. and P.L.D.J.; software, M.M.N.; validation, M.M.N.; writing--original draft preparation, M.M.N.; writing--review and editing M.M.N. and P.L.D.J. All authors have read and agreed to the published version of the manuscript.
# 2D Triangulation of Signals Source by Pole-Polar Geometric Models
## 2 The Proposed Geometric Models
### Tangent Lines with Minimal Angles Model (MAI)  
A complementary explanation is necessary before presenting this model. We have to consider three disjoint circles centered at \(C_{0}(x_{0},y_{0})\), \(C_{1}(x_{1},y_{1})\), and \(C_{2}(x_{2},y_{2})\), as Figure 11a shows. Two tangent lines pass by \(C_{0}\) through the circle at \(C_{1}\) and two more through to the circle at \(C_{2}\). Consider the angles \(\theta_{k}\), \(k=0,1,2,3\), formed between two tangent line pairs that touch the circles at \(C_{1}\) and \(C_{2}\). The tangent lines that touch the same circle are not important to this analysis. Discard all combinations of tangent  
Figure 11: **(a) Illustrations that define the Tangent Lines with Minimal Angles Model (MAI). (b) All intersections (red-star) with non-minimal angle tangent lines (orange-lines) must be eliminated. (c) All points inside the ROI formed by the convex hull polygon among the circle centers are the required points (blue-star).**  
lines pairs that touch the same circle. For the illustration presented by Figure 10(a), four angles can be obtained by combining all possible pairs of tangent lines that touch different pairs of circles.  
For each circle pair, the two tangent lines that form the lowest angle must be preserved, while others must be removed. These two preserved lines are used by MAI (Algorithm 5). Figure 10(a) shows that the two lines combinations with lowest angle are the tangents lines \(t_{11}\) and \(t_{02}\). Repeat this process to obtain all combinations of Tangent Lines with Minimal Angle that pass by different circles center to other \(m-1\) circles for each different pair of circle. Executing this procedure, Figure 10(b),c shows in orange-color the lines that must be eliminated.  
Figures 12 and 13 show some examples of results produced by applying the MAI. The results Figure 11(a),b and Figure 13 are consistent with the expected results due to the signals range errors.  
The signal range used in Figure 11(c),d are accurate and the solutions are exact. But, for Figure 11(c) the MAI do not found this exact solution. MAI did not find the exact solution because the geometric arrangement of the devices of the localization system is not symmetrical. In case Figure 11(d) there is this geometric symmetry and the exact solution was found. However, it can be seen both in Figure 11(c),d that there are polar points marking the exact solution and, if the exact solution exists, the best way to find it is to apply the solution provided in Section 2.1 of this work.  
Figure 12: Results produced by MAI model. (**a**,**b**) show the possible presence of noise in acquired data. (**c**,**d**) represents the best case for acquired data. (**d**) Application using four receivers (the legend box is omitted for best view). For the cases (**c**,**d**) is better applying the exact solution provided by Section 2.1.
## 2 Background
### Code-Based GNSS Inter-User Ranging Techniques
#### 2.1.6 Double Differences Ranging  
This technique was introduced in [53; 54; 55]. When the same pair of satellites \(r\) and \(s\) are visible to both receivers, a Double Differences (DD) measurement can be obtained as the difference of two SDs:  
\[D^{sr}_{ij,k}=S^{s}_{ij,k}-S^{r}_{ij,k}=\Delta R^{sr}_{ij,k}+\Sigma_{ij,k}, \tag{11}\]  
where \(S^{s}_{ij}\) is an SD computed according to Equation (9), \(S^{r}_{ij,k}\) is an SD taken with respect to a satellite chosen as reference for the DD algorithm, while \(\Sigma_{ij}\) is a random variable collecting residual error contributions that cannot be canceled due to the non-correlation between the measurements such as multipath, second-order noise components of the receiver front-ends and residual, unmodeled noise contributions [14]. However, DDs allow the cancellation of:  
* the differential clock bias \(\Delta b_{ij,k}\) between the cooperating users when uncorrected pseudoranges are used,
* or the differential residual error on the clock bias estimation when pseudoranges corrected from the estimated receiver clock bias are used \(\Delta b_{ij,k}\).  
The term \(\Delta R^{sr}\) can be expressed highlighting the dependency from the baseline vector as:  
\[\Delta R^{sr,k}=\begin{bmatrix}\mathbf{h}^{r}_{*,k}-\mathbf{h}^{s}_{*,k} \end{bmatrix}^{T}\mathbf{d}_{ij,k}. \tag{12}\]  
This technique will be named DD Haiding and DD Haided when the _aiding user_ and the _aided user_, respectively, are used as origins of the steering vectors in the results presented in Section 4.
# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 3 Methods
### TGA-Gs  
We designed a multimodal Gaussian training methodology, as shown in Figure 2. By deeply integrating thermal radiation physics constraints with geometric priors, our approach achieves high-precision 3D reconstruction and cross-modal super-resolution generation in low-light scenarios. Specifically, we first train a foundational Gaussian Splatting (GS) model using low-light RGB images, then fine-tune it with thermal maps. The entire training process consists of two phases: pre-training and end-to-end training.  
Pre-training Phase: During this phase, we train a baseline low-light GS model using low-light RGB images from the same scenes intended for end-to-end training. Although pre-training with low-light RGB images may lead to texture detail loss and inaccurate depth estimation during training, it enables initialization based on partially observable information, thereby significantly reducing convergence time in subsequent end-to-end training.  
End-to-End Training Phase: As illustrated in Figure 2, this phase fine-tunes the pre-trained low-light GS network using raw thermal maps and low-light RGB images. The outputs of the GS network are then fed into thermal SR and RGB SR modules to generate  
Figure 1: AgNWs/PVB ethanol solution and standard checkerboard calibration.  
Figure 2: Illustration of our proposed TGA-GS: **(a)** Multimodal Gaussian Networkfor joint RGB-thermal training and **(b)** Thermal and RGB SR Networks with cross-modal guidance.  
high-resolution thermal and RGB images. This stage comprises two key steps. (1) The first is the multimodal Gaussian network. As illustrated in Figure 2a, we extend the pre-trained 3 DGS by jointly optimizing it with low-resolution low-light RGB and thermal images. The thermal modality provides robust geometric priors through thermal gradients (e.g., object boundaries in darkness), while the RGB modality guides texture recovery. This mutual constraint enhances the model's ability to resolve ambiguities in low-light 3D perception. (2) The second is the thermal and RGB SR networks. As depicted in Figure 2b, the thermal maps and low-light RGB outputs from the multimodal Gaussian network are processed through dedicated thermal SR and RGB SR networks. These modules not only enhance their respective modalities but also exchange cross-modal guidance signals--thermal gradients inform texture recovery in RGB SR, while RGB edges refine thermal detail reconstruction.
## 4 Methodology and Setup
### Setup  
For the transmitter, receiver and interferer devices we use the OpenMote-B [16; 17] shown in Figure 4. The OpenMote-B is equipped with a Texas Instruments CC2538 SoC (System on Chip) and a Microchip AT86 RF215 radio transceiver. The CC2538 [18] includes an ARM Cortex-M3 micro-controller (32 MHz, 32 kB RAM, 512 kB Flash) and a radio transceiver compatible with the IEEE 802.15.4-2006 standard. The AT86 RF215 [19] is a dual-band (sub-GHz and 2.4 GHz) radio transceiver compatible with the IEEE 802.15.4-2015 standard, which supports all Multi-Rate PHY options defined in the IEEE 802.15.4g-2012 amendment for Smart Utility Networks (SUN).  
As depicted in Figure 3, the boards are connected using an Agilent 87302C RF power combiner that operates in the 0.5-26.5 GHz band and provides an input attenuation of 3 dB for the input channels towards the output channel, and a return loss of 24 dB. The output of the RF power combiner is further attenuated using a Minicircuit 30 dB RF attenuator to ensure that the received signal at the receiver device is within the receive power limits of the AT86 RF215 radio transceiver (\(-5\) dBm according to the data-sheet).  
To coordinate the actions between the different devices, a computer running Ubuntu 18.04 LTS is used. The computer is connected to the OpenMote-B boards over USB, and executes a Python script that orchestrates an experiment. For each board, the script uploads the radio configuration to be used (i.e., modulation type, transmit power and packet length, among others) and starts transmission/reception. The OpenMote-B boards execute a custom firmware that receives commands over UART (Universal Asynchronous Receiver Transmitter), execute the action commanded (i.e., transmit a packet, start receiving, etc.) and return the result (i.e., packet successfully received or not).
## 2 Related Work
### A Comparison of the Proposed Method and Related Works  
The proposed method adopts WuR [5] instead of duty cycling [7] for power saving by reducing the sensor node's active time. Also, a mobile sink [3] is adopted to distribute traffic load. However, the proposed method solves the problems of a mobile sink by integrating asymmetric communication [4] and WuR. Aperiodic queries are monitored with a low-power WuRx. The proposed method also reduces the overhead of updating routing information by transmitting a WuC to activate sensor nodes per hop. By asymmetric communication, a WuC directly reaches all sensor nodes, and each sensor node only needs to receive a WuC from the sink node and does not need to relay the WuC.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions
### Factors for HSN Latency
#### 5.6.2 Results and Discussion  
The main factors directly affecting network latency in the HSN system included the propagation delay, transmission delay, processing delay, waiting delay, and protocol overhead. However, in hybrid networks, propagation delays can be neglected due to the fast transmission speed of optical fibers and free-space communication. Similarly, processing delays can be disregarded since the same hardware platform and unified application-layer design are employed in hybrid networks. Therefore, the remaining significant factors are transmission delays, waiting delays, and protocol overheads (40). In practice, network latency is also influenced by various other factors such as media type, distance, geographic location, network topology, routing, network load, and network congestion. However, this paper does not delve into an in-depth analysis of these additional factors, and it welcomes further research from other interested scholars in exploring these aspects.  
\[\begin{split} T&=T_{trans}+T_{wait}+T_{protocol}\\ &=\frac{W}{\eta_{p}\times L}\times\frac{L}{R_{t}}+\left(\frac{W}{ \eta_{p}\times L}-1\right)T_{idle}+\left(n_{jump}-1\right)\times T_{cycle}+T_ {pro-cost}\end{split} \tag{40}\]  
As illustrated in Figure 16, the average latency of the hybrid network decreased with an increase in the number of wireless links. This is because with more wireless links, there are more transmission options available to choose the Li-Fi channel for transmission. Since Li  
Figure 15: Bandwidth performance with cycle periods.  
Fi offers higher rates and fewer forwarding delays, the average latency tended to decrease due to the reduction in the transmission delay. However, the average latency increased with an increase in the scheduling of the small cycle. This is primarily due to the prolonged waiting time for multi-hop transmissions, which constitute a significant proportion of the total latency. In other words, as the scheduling of the small cycle increases, there is a higher likelihood of waiting for the availability of resources for multi-hop transmissions, leading to increased latency.