esa-sceva/satcom-chunk-collection huggingface dataset:
# Planning of Optical Connections in 5G Packet-Optical xHaul Access Network
###### Abstract  
One of the main challenges in dense 5G radio access networks (RANs) is provisioning of low-cost connectivity between a large number of antennas, located at remote sites, and a central site (hub) in which baseband processing functions are performed. Packet-switched Ethernet and wavelength division multiplexing (WDM) are two principal transport network technologies enabling the reduction of the demand for direct optical fiber connections between the antennas and the hub. Whereas Ethernet allows for statistical multiplexing of multiple xHaul (fronthaul/midhaul/backhaul) flows and their aggregation in a high-capacity transmission link, WDM makes it possible to establish a number of such links (using different wavelengths) in a single optical fiber. Additional savings in the amount of fibers required can be achieved by means of optical add-drop multiplexers (OADMs) that allow for obtaining access to unused wavelengths by intermediate remote nodes, whenever the capacity on the WDM system is not fully utilized by the end remote node. In this work, we focus on the problem of planning optimal fiber connections, including the placement of OADMs for a set of wavelength demands at remote sites, with the aim of minimizing the amount of fibers used in a packet-optical xHaul access network carrying 5G traffic. We consider a passive WDM system in which the maximum transmission distance, estimated using an optical power-budget model, depends on the number of OADMs that are present on the transmission path. To formulate and solve the optimization problem, we make use of integer linear programming (ILP). We apply the ILP model in network analysis. In particular, by means of numerical experiments performed for two different network topologies, we study the impact of traffic load (in terms of the number of requested wavelengths) and optical multiplexer loss on the number of transmission paths that have to be established in the network. Obtained results show that the savings in fiber connections of up to 65% can be achieved in a packet-optical xHaul network if OADMs are used when compared to the scenario without OADMs.  
5G networks; centralized radio access network; packet-switched xHaul network; optical network; WDM; network planning; network optimization; ILP modeling +
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## 3 Experimental Results
### Denoise Preprocessing of Sensor Signals  
Taking the daily cumulative amount of lubricating oil entering the turbo-lubricating oil pump as an example, combined with the measured data and the empirically assumed original fluctuation signal data of the lubricating oil system, it is assumed that the oil pump turbine operates normally during the first 80 months, with a fault occurring between the 80 th and 100 th months, resulting in an increased amplitude of lubricating oil fluctuation.  
The EEMD method was used to reduce the noise of the fluctuation signal, \(k\) = 0.2 and \(N\) = 100 were used to reduce the noise of the original vibration signal, and 14 IMF components and the remaining items were decomposed. The remaining items were denoted as IMF15. The correlation coefficient between the original signal and the IMF was calculated as shown in Table 7.  
The correlation coefficient is chosen to be greater than 0 as described previously. A positive correlation coefficient (greater than 0) indicates that the IMF component contains some meaningful information related to the original signal rather than just noise. If the correlation coefficient were negative or close to zero, it would mean that the IMF component does not significantly contribute to the original signal or may even introduce artifacts, making it unsuitable for reconstruction. The IMF1 component of 6 reconstructed the signal to complete the noise reduction preprocessing of the original signal. The original vibration signal and the signal after noise reduction are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IMF Component** & \begin{tabular}{c} **Coefficient of** \\ **Correlation** \\ \end{tabular} & **IMF Component** & \
\begin{tabular}{c} **Coefficient of** \\ **Correlation** \\ \end{tabular} & **IMF Component** &
\begin{tabular}{c} **Coefficient of** \\ **Correlation** \\ \end{tabular} \\ \hline
1 & 0.9417 & 6 & 0.0683 & 11 & 0.0014 \\
2 & 0.1262 & 7 & 0.0061 & 12 & 0.0019 \\
3 & 0.1503 & 8 & 0.0035 & 13 & 0.0026 \\
4 & 0.1836 & 9 & 0.0040 & 14 & 0.006 \\
5 & 0.1148 & 10 & 0.0010 & 15 & 0.0049 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Correlation coefficient of original signal after EEMD decomposition.  
Figure 6: Comparison diagram of sensor signal denoising.
## 3 Results
### Phosphate Adsorption in Batch Experiments
#### 3.3.3 Adsorption Isotherms  
The adsorption isotherm was used to describe the distribution of the adsorbate molecules on the adsorbents. BS-GMA-EDA-Q and BS-GMA-PEHA-Q adsorption isotherms were conducted at 25 \({}^{\circ}\)C with a phosphate concentration range of 25-250 mg/L.  
The correlation between the equilibrium adsorption of \(q_{e}\) (mg/g) and the equilibrium concentration \(C_{e}\) (mg/L) in the solution is shown in Figure 9a. The adsorption capacity of BS-GMA-EDA-Q and BS-GMA-PEHA-Q increased with the increasing phosphate concentration, and finally reached a constant.  
Langmuir and Freundlich isotherm models were used to fit the adsorption data at various concentrations. The linear equation can be described by Equations (7) and (8) [39].  
\[\frac{C_{e}}{Q_{e}}=\frac{C_{e}}{Q_{m}}+\frac{1}{K_{L}Q_{m}} \tag{7}\]  
\[\ln Q_{e}=\ln K_{F}+\frac{1}{n}\ln C_{e} \tag{8}\]  
where \(C_{e}\) referred the adsorbate concentration (mg/L) at equilibrium, \(Q_{e}\) and \(Q_{m}\) were the mass adsorbed on 1 g adsorbent and the maximum adsorption capacity (mg/g). Table 2 summarizes the constants of the Langmuir and Freundlich models.  
The Langmuir isotherm model assumes that the adsorbate has a monolayer adsorption on the adsorbent through a homogeneous surface. Once an adsorbate molecule was adsorbed on an active adsorption site, no more molecules can be adsorbed on the same site. The maximum adsorption capacity was achieved when all the adsorption sites of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multicolumn{3}{c}{**[PERSON]**} & \multicolumn{3}{c}{**Freundlich**} \\ \cline{2-7}  & **Q\({}_{\text{m}}\) (mg/g)** & **K\({}_{\text{L}}\)** & **R\({}^{\text{2}}\)** & **K\({}_{\text{F}}\) (mg\(\cdot\) L\({}^{-1}\))** & **n** & **R\({}^{\text{2}}\)** \\ \hline BS-GMA-EDA-Q & 85.985 & 0.0620 & 0.9972 & 23.055 & 4.0297 & 0.9752 \\ BS-GMA-PEHA-Q & 152.21 & 0.0296 & 0.9931 & 13.209 & 2.1187 & 0.9568 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Langmuir and Freundlich isotherm parameters and correlation coefficients for the adsorption of phosphate ion.  
Figure 9: Equilibrium studies of phosphate adsorption: relation of the adsorption capacity with the equilibrium concentration (a), Langmuir (b), and Freundlich model (e).  
the adsorbent were saturated with the adsorbate. The Freundlich model was applied to describe adsorption on homogeneous and heterogeneous surfaces. The fitting curves of the Langmuir and Freundlich models are shown in Figure 9b,c, respectively. Comparatively, the Langmuir isotherm model can better describe the adsorption isotherms. The R\({}^{2}\) value (0.9972 and 0.9931) of the Langmuir model was very high, suggesting that the adsorbed phosphate forms a monolayer onto the surface of BS-GMA-EDA-Q and BS-GMA-PEHA-Q [40]. The obtained maximum adsorption capacity (Q\({}_{\text{max}}\)) for phosphate by BS-GMA-EDA-Q and BS-GMA-PEHA-Q were 85.99 and 152.21 mg/g, respectively. The values were relatively higher compared with other similar adsorbents shown in Table 3.
# Non-Compliance Distalization Appliances Supported by Mini-Implants: A Systematic Review
## 4 Discussion
### MI Placement in Palate vs. in Interradicular Area  
The preferable site for the miniscrew insertion is the median or paramedian region of the palate, according to the literature. In our review, 18 studies selected the paramedian region, 2 articles the median palate region, and 8 of the studies utilized the interradicular area. The paramedian site is considered the safest solution for MI insertion since it presents high cortical bone thickness while being apart from the neighboring root teeth. Buccally, the optimal site is between the second premolar and first molar together with the first and second molar.
# A Rectangular Toroidal Current-Based Approach for Lung Biopsy Needle Tracking
## 3 Results
### Equivalent Material Simulation of Biological Tissues  
In this study, equivalent materials were used to simulate biological tissues instead of conducting real biological experiments. This approach was chosen due to the complexity of real experiments, ethical considerations, and the challenges associated with in vivo testing. The selected materials simulate muscle (conductivity: 0.4 S/m; relative permittivity: 260; permeability: 1) and bone (conductivity: 0.05 S/m; relative permittivity: 15; permeability: 1), as their electromagnetic properties closely resemble those of human tissues, making them suitable for experimental validation. These equivalent materials effectively replicate the electromagnetic characteristics of biological tissues and can be applied in the validation of electromagnetic tracking systems.  
In the electromagnetic tracking system, calibration was performed based on the procedures outlined in previous sections, and the system was established accordingly, as shown in Figure 14. The equivalent muscle and bone materials were then introducedinto the system, and random positioning experiments were conducted to obtain absolute positioning error data, as illustrated in Figure 14c. The results indicate that the inclusion of human-equivalent tissues and organs in the tracking system does not affect the overall positioning performance and accuracy. Experimental results confirm that skin-depth effects do not significantly weaken the induced signal, ensuring accurate localization. The field strength remains within acceptable limits, and any signal loss in biological tissues does not impact the system's overall performance.  
Since the magnetic permeability of biological tissues is close to that of a vacuum (\(\mu\approx\mu_{0}\)), the magnetic field can penetrate the human body with minimal loss, particularly in the case of low- and mid-frequency electromagnetic fields, without inducing significant eddy current losses as seen in metal shielding. While this approach provides a controlled and repeatable testing environment, we acknowledge that the heterogeneity in real biological tissues--such as variations in conductivity across different organs--may introduce discrepancies between experimental results and real-world conditions. In future research, we plan to conduct in vivo experiments to further validate the applicability of this system in real medical scenarios.
## 2 Propulsion System Structure
### Improved Storage Tank Structure  
In order to withstand the greater pressure load and for ease of manufacturing, conventional storage tanks in a pico-satellite have a hollow structure inside and require a high wall thickness. Moreover, a fixed storage tank requires additional accessories which reduces its volume utilization rate and increases the overall weight. In addition, in traditional processing methods, such as electrode welding, the processing cycle is long, or unable to fully complete, and the cost is very high. In order to solve these problems, this paper presents an improved mesh storage tank structure obtained using 3D metal printing by changing the shape of the conventional storage tank. When the overall size of the envelope is fixed, increasing the internal reinforcement structure and reducing the wall thickness to use the volume of a conventional storage tank is impractical. A solution requires an improved compressive stress, increased volume utilization rate, and reduced weight.  
The internal structure utilizes a mesh structure in order to further optimization. This is a single column structure with a square or circular cross shape. When the envelope of the storage tank uses the shape shown in Figure 2, the structure of the storage tank is determined by the length \(d\) of the mesh structure, three direction intervals \(d_{x}\), \(d_{y}\), and \(d_{z}\), wall thickness \(T\), and the connecting distance to the side wall \(D\), as shown in Figure 3.  
In order to ensure an equal length design of the storage tank in all the three directions, a cylindrical structure with the same length is adopted into the three directions, and the connection distance to the side wall is equal. According to the characteristics of the mesh structure, the distance to the wall, which is equal in the three directions, can be expressed as:  
\[d_{i}=(i-2d-2D)/(n_{i}-1)\ \ \ \ \ i=x,y,z \tag{1}\]  
Figure 2: Shape and dimension of the conventional storage tank.  
where, \(x,y,z\) represent the envelope size of the tank in three directions, respectively, and \(n_{i}\) represents the number of columnar structures in three directions.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 6 Fiber Optic SPR Sensor Based on MITs
### Developments on FO-SPR-MIP-Based Sensors
#### 6.3.9 Erythromycin  
Erythromycin (ERY) is a medicine which restricts the activities of Gram positive and Gram negative bacteria, hence is used for the treatment/prevention of bacterial diseases such as skin infections, coughs, etc., but its wide use results in its residues being present in foodstuffs and their derivatives which makes its sensing important. An approach for the detection of ERY in aqueous medium was reported incorporating nanotechnology for the synthesis of MIPs [179]. Nanotechnology was introduced by preparing the ERY imprinted nanoparticles to use as the sensing medium for ERY detection. These were prepared by a two-phase mini-emulsion method [93]. Nanoparticles having ERY as template were coated over the Ag coated fiber core using a dip coating method. The sensor was characterized for the ERY concentration range from 0 to 50 uM. Figure 17a,b show the SPR response and variation in resonance wavelength as a function of ERY sample concentration. The total shift in resonance wavelength was found to be around 72 nm while the sensor showed a detection limit of 5.32 nm/uM.
## 4 Methodology
### Data Preprocessing  
The first step in the methodology is to preprocess engine data. The dataset, stored in a MATLAB file, includes engine SNs, cycle counts, and sensor measurements. Features are extracted from columns and organized into cell arrays where each cell corresponds to a specific engine. Data preprocessing involves normalizing features to ensure uniformity and that they are on a similar scale. The dataset used in this study contains sensor readings and operational parameters for multiple engines. Each engine has a varying number of cycles, and the goal is to classify the engines into health states (Healthy, Middle, Unhealthy) and predict their RUL.
## 2 Materials and Methods  
The vanadium alloy reacts readily with oxygen at elevated temperatures even in a low gas pressure environment [21]. Owing to this behavior, the alloy is usually covered with an alloy jacket (e.g., steel) to avoid oxidation during the hot processing [22]. Since we wished to study the diffusion bonding of the V-4 Cr-4 Ti alloy to commercially pure Ti metal, a V-4 Cr-4 Ti alloy rod and a Ti metal pipe as the covering jacket were employed for the bonding (see Figure 1a). The chemical compositions of the V-4 Cr-4 Ti alloy and the pure Ti are listed in Table 1. The V-4 Cr-4 Ti alloy rod was machined to a final size of 28 mm (diameter) \(\times\) 75 mm (length) and then underwent turning and fine grinding. The Ti pipe was machined to be the jacket with a size of 28.2 mm (internal diameter) \(\times\) 75 mm (length) \(\times\) 8 mm (thickness). The V-alloy rod and Ti pipe were ultrasonically cleaned in acetone and dried prior to assembly. The two ends of the pipe were joined with pure Ti plates (44 mm in diameter \(\times\) 10 mm in thickness) by electron beam welding in a vacuum. The welded specimen adopted a cylindrical geometry after hot forging and lathe turning for later bonding (see Figure 1b).  
Figure 1: Schematic diagrams illustrating the specimen preparation and hot forging process: **(a)** the assembly of materials; **(b)** the assembled piece prior to forging; **(c)** the geometry of hot forging; **(d)** the assembled piece after forging.  
The specimen was heat treated in a furnace at 1150 \({}^{\circ}\)C for 2 h before the hot forging. The geometry of the forging die is illustrated in Figure 1c. The internal diameter of the die is a little smaller than the specimen in order to bond the Ti jacket to the V-alloy through a radial deformation process. Several dies with different diameters (\(\Phi\) = 42 mm, 39 mm, 36 mm, 33 mm, and 29 mm) were sequentially used. The forging temperature measured by a handled digital thermometer was always kept in the range between 1150 \({}^{\circ}\)C and 950 \({}^{\circ}\)C. The specimen was reheated in the furnace when cooled down to about 950 \({}^{\circ}\)C during the forging. Lastly, it was forged to 29 mm in diameter and then air cooled to room temperature. Cross-sections of the specimen (see Figure 1d) were sliced and mirror polished, which ensured that the specimen was ready for observation by conventional technique. The interface was etched for 10 s in an acetic solution (30 mL) of HNO (10 mL) and HF (10 mL) followed by optical microscope observation (OLYMPUS BX51M, Tokyo, Japan) to reveal the structural evolution. The joint was also examined with a scanning electron microscope (SEM, Zeiss Auriga, Germany) to observe the finer structure and determine the chemical composition distribution across the joint and the fracture surface by using energy dispersive X-ray spectroscopy (EDS, Oxford Instruments, Oxford, UK). A Vickers hardness tester (WOLPERT 430 SVD, Norwood, MA, USA) with a 10 g load for 30 s was used to investigate the micro-hardness distribution across the joint.
# Review of Traffic Assignment and Future Challenges
## 3 Traffic Assignment: Theoretical Background
### Static Traffic Assignment (STA)
#### 3.1.2 Stochastic User Equilibrium (\(Sui\))  
The Stochastic assignment was introduced to address issues arising from the \(UE\) assumption [19] within the framework of the static assignment. For various reasons, users might have imperfections and variations in their perceived generalized costs of travel. One reason is that users may have different experiences in the same segment during the study period. Another reason for this is that users do not have perfect knowledge of the costs associated with different options. Hence, a route is chosen based on a probability relative to its travel time. More precisely, the stochastic assignment is based on the following principles [20; 21]:  
* All reasonable options can be chosen, even if their probability of selection is very low (In [20], the author describes the concept)
* If two options have the same cost, the probability of selection is the same.
* The probability of choosing options depends on their costs: a route with a higher cost has a lower probability of being chosen.
* The user of the \(SUE\) model must have some control over the probability of diverting routes.  
The principles of \(SUE\) incorporate a term of random error in the generalized cost of the route. Generally, this term is assumed to follow Gumbel, normal, and Weibull distributions, which correspond, respectively, to choice models based on logit, probit, and weight models [22; 23; 24]. The logit-based model is the most commonly used in the literature on traffic assignment ([24; 25]). According to the logit model, the probability of choosing route \(k\) is as follows:  
\[P_{k}=\frac{\exp{(-\theta\cdot c(k))}}{\sum_{i\in K^{ad}}\exp{(-\theta\cdot c (i))}}, \tag{11}\]  
where \(\theta\) is the diversion factor. This is the controlling variable for variations in users' perceptions of travel costs. When \(\theta=0\), all routes will have an equal probability of being selected. As \(\theta\rightarrow\infty\), the least costly route will be chosen. With this probability expression, [PERSON] [23] extended the formulation of the \(UE\) problem (7) to encompass the multinomial logit model of \(SUE\). The problem is formulated as follows:  
\[\begin{split}\min_{k_{a}}&\quad\sum_{a\in A}\int_ {0}^{r_{a}}c_{a}(z)dz+\frac{1}{\theta}\sum_{o\in O}\sum_{d\in D}\sum_{k\in K^ {ad}}r_{k}^{ad}\ln\left(r_{k}^{ad}\right)\\ \text{s.t.}&\quad(8)\\ &\quad r_{k}^{ad}\geq 0,r_{a}\geq 0\end{split}. \tag{12}\]  
Other works have extended the objective function [26; 27; 28; 29] of the problem posed in (12). This extension aims to consider the overlap between routes by introducing a conditional probability of route choice.
## 2 Materials and Methods  
### Chemicals and Reagents  
Methanol (MeOH), acetonitrile (ACN), ethyl acetate (AcOEt), and water LC-MS grade and HPLC gradient grade were acquired from Chem-LabNV (Zedelgem, Belgium). Sodium chloride was purchased from Sigma-Aldrich (St. Louis, MO, USA). Anhydrous magnesium sulfate pro analysis grade was supplied by Alfa Aesar (Karlsruhe, Germany). Ammonium acetate LC-MS grade was purchased from Fluka (Buchs, Switzerland). Primary secondary amine (PSA) was supplied by Agilent Technologies (Santa Clara, CA, USA). Acetic acid 99.5% pro analysis grade was purchased from Carlo Erba reagents (Barcelona, Spain), and LC-MS grade was acquired from Fluka. Formic acid 99% was supplied by Carlo Erba reagents. The standard compounds abancetin 98%, acetamipid 98%, acrinathrin 98%, alternariol (AOH) 96%, alternariol monomethyl ether (AME) 98%, boscalid 98%, bupirimate 98%, carbendazim 97%, chlororyrifos 98%, citrinin 98%, clofentezine 98%, clothianidin 98%, cyprocanazole 98%, cyprodinil 98%, deltamethrin 98%, difenoconazole 95%, etofenprox 98%, fenbuconazole 98%, fenpyroximate 98%, flonicamid 95%, fluidoxonil 98%, flyopyram 98%, fluquinconzole 98%, flutirafol 95%, hexythiazox 98%, imazali 98%, indoxacarb 95%, kresoxym methyl 95%, lambda-cyhalothrin 97%, methoxyfenzo 98%, myclobutanil 98%, patulin 98%, penconazole 98%, pirimicarb 98%, pyraclostrobin 98%, pyrimethanil 98%, pyriproxyfen 98%, spinosyn A 95%, spinosyn D 95%, spirodiclofen 98%, tau-fluvalinate 95%, tebuconazole 98%, tebufenoxide 98%, tebufenpyrad 98%, thiabendazole 99%, thiacolpid 98%, thiamethoxam 98%, and trifoxystrobin 98% were acquired from Sigma-Aldrich (St. Louis, MO, USA).