esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 6 Open Issues
### Data Link and Network Layers Issues  
In hierarchical based networks, several challenges exist when applying ML and DL techniques in MAC and routing protocols. For example, various parameters should be considered during the protocol design phase, such as the computation efficiency, the accuracy requirements, the acquisition of the communication parameters from all the network elements, the centralized or distributed control performance, and the architecture design. Distributed UAV swarm networks in varying formations and without centralized control should be also studied in order to handle inter-swarm communication optimization issues. To coordinate the data transmission in multi-UAV cooperative scenarios, ML methods that enable the grouping the UAVs into several clusters based on their location, type of sensors, and type of data are indispensable. In network planning and user association problems, energy efficiency for the UAV-aided networks should also be considered. Specifically, additional constraints in the optimization strategy could be included, such as the flight time and the total energy consumption, in order to obtain a trade-off between mobility and radio coverage and to maximize the amount of ground nodes and the flight time of the UAVs while satisfying the QoE. Moreover, in cluster-based UAV networks, resource management may be performed in a centralized or distributed manner. The first approach has a high computational cost, while cooperative decisions are very challenging, since the information should be shared among all the UAVs. The adoption of a suitable solution depends on various parameters, including the network architecture, system requirements, and the wireless environmental conditions. Proactive caching by UAVs is also a research field that can greatly benefit from ML as the wealth of available data related to user-centric information and network conditions can improve the placement of content, leveraging on the dynamic UAV deployment [88]. Regarding UAV scheduling, in Reference [137], a mathematical framework for the optimization of UAV-aided video monitoring of a set of points of interest (PoI) distributed in a large urban area was proposed. Using this framework, which is based on Mixed Integer Linear Programming (MILP) techniques and real experimental data, particular energy-constrained UAVs are selected for recharging using public transportation buses, which also transfer the UAVs to desired PoIs in order to increase reliability and coverage. Still, ML-based solutions relying on DNNs can enable MILP-based optimization to reach its full potential, as suggested in Reference [138].
## 4 Dynamic BWP Allocation: Dynamic Multiplexing vs. Orthogonal Slicing
### ThrouInput Analysis for Dynamic Multiplexing and Orthogonal Slicing
Consequently, the number of RBs assigned to eMBB AVs is \(\Phi_{eMBB}=\Phi-\Phi_{\text{URLLC}}\), and the number of RBs assigned to eMBB AV decreases at a rate of \((\Phi-\Phi_{\text{URLLC}})/\Phi\). Then, the number of bits for the eMBB AV u is given as  
\[r_{u}=\phi_{u}\cdot b(I_{\text{MCS},u})\cdot(\Phi-\Phi_{\text{URLLC}})/\Phi \tag{6}\]  
Figure 3: Modeling of error rate for code block group (CBG). Then, the throughput of the entire eMBB AV is given as follows:  
\[\sum_{u\in\mathcal{U}_{\text{eMBB}}}\phi_{u}\cdot b(I_{\text{MCS},u})\cdot(\Phi- \Phi_{\text{URLLC}})/\Phi \tag{7}\]  
Furthermore, the throughput of one CBG in an eMBB AV \(u\) in OS is given as  
\[\frac{\phi_{u}\cdot b(I_{\text{MCS}}^{(u)})}{N_{\text{CBG}}}\cdot\left(1-\frac {D}{b(I_{\text{MCS}}^{\text{URLLC}})}\cdot\frac{1}{\Phi}\right) \tag{8}\]  
Comparing (5) with (8), when the URLLC load increases, the performance degradation of eMBB becomes more severe in DM than in OS. This is attributed to the fact that DM relies on the error-correction capability of the CBG-based MDS code to compensate for the performance loss. However, when the URLLC load is small, the punctured bit can be properly corrected using MDS code. Therefore, we expect that a critical level of URLLC load exists, such that OS outperforms DM. We denote the threshold of URLLC load as \(D_{\text{threshold}}\), where the eMBB throughput of OS outperforms that of DM. To determine \(D_{\text{threshold}}\), we can analytically obtain the value that achieves equality in the following inequality:  
\[1-P_{\text{CBGER}}(\delta_{\text{CBG}})\leq 1-\frac{D}{b(I_{\text{MCS}}^{ \text{URLLC}})}\cdot\frac{1}{\Phi} \tag{9}\]  
where only the comparison between the latter part of the eMBB throughput given in (5) and (8) is shown. Furthermore, by calculating (9), the following theorem is established:  
**Theorem 1**. : _In the given system model, \(D_{\text{threshold}}\) can be expressed as follows:_  
\[D_{\text{threshold}}=N_{\text{CBG}}\cdot l\cdot\left(\frac{\Phi}{\phi_{u}} \right)\cdot\left(\frac{b(I_{\text{MCS}}^{\text{URLLC}})}{b(I_{\text{MCS}}^{ \text{(u)}})}\right) \tag{10}\]  
Proof of Theorem 1.: See Appendix A. Equation (10) affords a closed-form value of the threshold of one CBG for an eMBB AV, given in our NR-based system model. Therefore, the proportional relationships and average tendency of \(D_{\text{threshold}}\) can be exploited to switchingreferenced to determine between DM and OS. As shown in (10), the value of \(D_{\text{threshold}}\) tends to increase with an increase in the number of CBGs \(N_{\text{CBG}}\) and the relative MCS of eMBB and URLLC \(b(I_{\text{MCS}}^{\text{URLLC}})/b(I_{\text{MCS}}^{\text{(u)}})\). In the following subsection, the performance of eMBB throughput for DM and OS is analyzed by varying the URLLC load to verify the relationship shown in (10).
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
###### Abstract  
This paper presents a method of fusing the ego-motion of a robot or a land vehicle estimated from an upward-facing camera with Global Navigation Satellite System (GNSS) signals for navigation purposes in urban environments. A sky-pointing camera is mounted on the top of a car and synchronized with a GNSS receiver. The advantages of this configuration are two-fold: firstly, for the GNSS signals, the upward-facing camera will be used to classify the acquired images into sky and non-sky (also known as segmentation). A satellite falling into the non-sky areas (e.g., buildings, trees) will be rejected and not considered for the final position solution computation. Secondly, the sky-pointing camera (with a field of view of about 90 degrees) is helpful for urban area ego-motion estimation in the sense that it does not see most of the moving objects (e.g., pedestrians, cars) and thus is able to estimate the ego-motion with fewer outliers than is typical with a forward-facing camera. The GNSS and visual information systems are tightly-coupled in a Kalman filter for the final position solution. Experimental results demonstrate the ability of the system to provide satisfactory navigation solutions and better accuracy than the GNSS-only and the loosely-coupled GNSS/vision, 20 percent and 82 percent (in the worst case) respectively, in a deep urban canyon, even in conditions with fewer than four GNSS satellites.  
Keywords:visual odometry; upward-facing camera; motion estimation; satellites; GNSS; tightly-coupled integration; vehicle navigation; image segmentation; clustering algorithms +
Footnote †: journal: Sensors
## 3 A Decision Support System for Detailed Scheduling
### DSS Functionality for the Scheduling Process
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Functionality** & **Functionality Type** & **Methods/Algorithms** \\ \hline Production order and work center **data** & & \\
**extraction** from PPC system & Supplementary & The common database schema, ODBC \\ \hline
**Order releasing** & Core &
\begin{tabular}{c} Manual selection by planner, automatic \\ inclusion or exclusion of late orders \\ \end{tabular} \\ \hline
**1 st level dispatching** & Core & Weighted earliest due date heuristic (WEDD) \\ \hline
**2 nd level dispatching** & Core &
\begin{tabular}{c} Weighted priority dispatching rules: shortest \\ processing time (SPT), longest processing \\ time (LPT), slack, presubcontractor first \\ (PSF), setup, combo) \\ \end{tabular} \\ \hline
**Schedule representation** (supervision of \\ order information, workload and bottleneck \\ work centers) & Supplementary & Gantt chart \\ \hline
**Bottleneck identification** & Core &
\begin{tabular}{c} Capacity calculation algorithm, Processing \\ time estimation \\ \end{tabular} \\ \hline
**Order transferring** (either to alternative \\ production planning periods or alternative \\ work centers) & Core & Manual input, preemption algorithm \\ and **Order preemption** & & \\ \hline
**Order transferring** in “freeze” mode & Core & Manual input \\ \hline
**BOM precedence constraints satisfaction** & & \\ (supervision and control of BOM precedence \\ constraint violation, component availability & Core &
\begin{tabular}{c} TS/VNS, BOM tree impact heuristic, \\ ATM algorithm, \\ BOM relationship representation method \\ \end{tabular} \\ check and impact estimation of order \\ transferring according to BOM tree) & & \\ \hline
**What-if analysis** & Supplementary & Alternative scenarios management \\ \hline Construction of **final production schedule** & Supplementary & Report generation \\ \hline PPC and ERP **database update** & Supplementary & Common database schema, ODBC \\ \hline \hline \end{tabular}
\end{table} 
Table 3: G-DSS’s functionality and methods. analysis. Finally, the process concludes with the PPC shop floor control module sending the production control data to the ERP system. Figure 2: Algorithms for core DSS functionality.
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 4 System Efficiency
### Atmos
The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) reported the delayed mode return rate of CTD sensors operating at 1, 10, 20, 40 and 100 m water depth in the range 80-90% for temperature and 75-87% for salinity [26]. The data were averaged over all the selected mooring periods. In the North Indian Ocean buoys, the sea water conductivity sensors recorded an average annual data availability of 72%, with 47% of the failures being environmentally induced [27]. Figure 15: ATHOS percentage of the quality approved data, the rejected data during quality control (QC) as well as the percentage of non-operation period of the station or sensors for temperature (a) and salinity (b).
## 4 Results
### Ablation Study on Backbone Networks  
To assess the contribution of the hybrid backbone in the proposed AEA-GAN architecture, its performance was compared against two individual backbone settings: using MobileNetV3 alone and EfficientNetB7 alone. As shown in Table 1, the hybrid approach achieved a notable improvement in all evaluation metrics, with an mIOU of 89.46%, outperforming MobileNetV3 (85.21%) and EfficientNetB7 (87.38%).  
The results data in Table 1 indicate that combining the fast, lightweight feature extraction of MobileNetV3 with the deep semantic representation of EfficientNetB7 provides a more robust and efficient backbone, especially beneficial for complex scene understanding in autonomous driving applications.
## 3 Materials and Production
### Industrial Products and By-Products Suitable as Bed Material
#### 3.2.2 Copper Smelting  
Another large industry in Sweden is copper mining and smelting. Boliden minerals operates two large coppers smelters: one in Ronnskar in Sweden and one in Harjavalta in Finland. Copper production volumes during the years 2004-2017 are presented in Figure 7. A by-product of the copper smelting process is copper smelter slag. Approximately 1.5 Mton of slag is produced yearly by Boliden across Sweden and Finland, 1/3 of which is water granulated into amorphous particles [80]. 250-270 kton water granulated slag is produced in Ronnskar (Sweden) and sold under the name Jarnsand (\"iron sand\"). According to Boliden, the production of water granulated slag could be increased if there was a growing interest on the market. The Swedish water-granulated slag is today used as an construction aggregate, abrasive media, and as an iron source in cement clinker and glass manufacturing.  
Copper smelter slag has an iron content of around 35 wt.-% [81] (or 43 wt.-% FeO according to the data in Table 4), bound in oxides and silica oxides, which suggests that it has oxygen-carrying properties. Another aspect that makes it especially interesting as a bed material is that the water granulated slag is produced directly as particles in the size range of 0-2 mm. This means that it could potentially be utilized as a bed material as-is, with no or little further preparations, aside from sieving.
# Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane
## 3 Results
### Case 8: LIDAR-Based Pathfinding  
LIDAR-based pathfinding enables automatic scanning of crane environment with a LIDAR sensor and pathfinding to a target with obstacle avoidance. The application was developed at a combined master level mechatronics and automation project course [58] with [PERSON] as the advisor for crane use. The team won an innovation competition with the application [59].  
The LIDAR was installed as an additional sensor to the crane and connected via Ethernet to a Raspberry Pi microcomputer which sent the sensor data to a Windows computer via WiFi. The Windows computer performed data processing and sent resulting control commands to the crane with OPC UA python library.  
The team was mainly independent in their work and crane support included mainly just handing the documentation of the interface and the crane python library for accessing the OPC UA server. The team noticed an error due to an update and fixed the library. The source code for the application was not published at that time.  
This case was not developed for a digital twin and is implemented as a local solution. Nevertheless, this application provides a new local operational feature for the crane. It generates a lot of data about the environment that could be provided to other parties via the digital twin of the crane. The digital twin could also relay control requests to the pathfinding application from external parties, such as a forklift.
## 1 Introduction  
In the last decades, solid lipid nanoparticles (SLNs) have been widely investigated as carriers for topical drug delivery [1; 2; 3; 4; 5; 6; 7]. These colloidal systems, consisting of a solid lipid matrix stabilized by different types of surfactants, have been proposed for skin delivery of various active ingredients including antifungal, anticancer, anti-inflammatory, and antioxidant agents [8; 9; 10; 11; 12; 13; 14; 15]. The interest in SLNs arises from their many advantages compared to other colloidal carriers such as possibility of incorporating both hydrophilic and lipophilic drugs, ability to achieve drug-controlled release and targeting, thus improving drug topical bioavailability while reducing systemic side effects [1; 4]. However, the highly ordered structure of the solid lipid matrix of SLN could result in low incorporation of lipophilic drugs and drug leakage during storage. To overcome these drawbacks, liquid lipids were mixed together with solid lipids to obtain a less packed lipid matrix, leading to a second generation of lipid nanoparticles defined as nanostructured lipid carriers (NLC) [2; 3; 6]. To obtain this last type of nanocarrier, a variety of oils without any intrinsic pharmacological activity has been investigated. However, to improve NLC effectiveness, oils that have a synergic activity with the loaded drug could be used together with solid lipids to form the NLC core.  
Previously, we evaluated in vitro skin permeation of idebenone (IDE), an analogue of coenzyme Q\({}_{10}\) with strong antioxidant activity [16], from SLNs with different compositions [17]. We observed an interesting IDE-targeting effect in the upper skin layers from SLNs prepared using oleth-20 as a surfactant and cetyl palmitate as the solid lipid. These SLNs showed good stability during storage at room temperature but limited IDE loading.  
In this work, we designed IDE-loaded nanoparticles (NLC) containing tocopheryl acetate (vitamin E acetate, VitE) as a liquid component to obtain both a synergic effect between IDE and VitE and to improve IDE loading. Vitamin E is a well-known radical scavenger agent with skin photo-protective properties [18] for which topical beneficial effects are well known [19].  
To evaluate the effect of co-loading vitamin E acetate and IDE in the matrix of lipid nanoparticles, we assessed technological properties, in vitro antioxidant activity, and cytotoxic and photo-protective effects of IDE loaded SLN and NLC on human fibroblast cells HS-68. In addition, we evaluated the effects of topical application on human volunteers of gel formulations containing IDE-loaded SLN and NLC.
###### Abstract  
This study investigates a load-based, track-irregularity-analysis technique for ballasted tracks on a serviced railway line with respect to excavation work conducted in adjacent sites. A numerical analysis and field measurements (railbed-settlement-monitoring sensor, track-geometry-measurement system, wheel-load measurements) were analyzed comparatively to demonstrate the correlation between the track irregularities and the Wheel-Rail interaction force. In this way, we highlight the necessity for load-based track-irregularity-management methods. The analyzed results show that the maximum dynamic wheel load was measured in the range of approximately 10 m before and after the location where the maximum track irregularities occurred, and that even if the maintenance criteria of track irregularities were satisfied, the design dynamic wheel load could still be exceeded depending on the train speed, thus indicating that track damage can be caused by the impact load.  
track irregularity; evaluation method; ballasted track; service railway line +
Footnote †: journal: Applied  
doi: Article  
Dynamic Wheel-Rail Force-Based Track-Irregularity Evaluation for Ballasted Track on Serviced Railway by Adjacent Excavation
# Bioremediation of Persistent Organic Pollutant--Oxybenzene with _Pleurotus diamor_
## 2 Materials and Methods
### HPLC Analysis  
The content of organic compounds was analyzed using the RP-HPLC-DAD method, which relied on standard calibration curves and assumed a linear relationship between peak area and reference standard concentration.  
Indole compounds, phenolic compounds, lovastatin, ergothioneine, and L-phenylalanine concentrations were determined via RP-HPLC. All information regarding the identification of the studied compounds has been described or cited in previous work [24]. UV detection was set at 280 nm for indole compounds, 238 nm for lovastatin, and 257 nm for ergothioneine. The calibration equations and R\({}^{2}\) values for the analyzed compounds are as follows: L-tryptophan: y = 3 \(\times\) 10\({}^{7}\)x \(-\) 236,885, R\({}^{2}\) = 0.9983; 6-metylo-D,L-tryptophan: y = 3 \(\times\) 10\({}^{7}\)x \(-\) 236,885, R\({}^{2}\) = 0.9983; p-hydroxybenzoic acid: y = 2 \(\times\) 10\({}^{8}\)x \(-\) 71,530, R\({}^{2}\) = 0.9998; ergosterol: y = 5 \(\times\) 10\({}^{7}\)x \(-\) 3 \(\times\) 10\({}^{6}\), R\({}^{2}\) = 0.9861; lovastatin: y = 5 \(\times\) 10\({}^{7}\)x + 760,417, R\({}^{2}\) = 0.9964; ergothineine: y = 7 \(\times\) 10\({}^{7}\)x + 493,847, R\({}^{2}\) = 0.9985; L-phenylalanine: y = 2 \(\times\) 10\({}^{6}\)x \(-\) 4982.9, R\({}^{2}\) = 0.9999. Oxybenzene concentration was measured using RP-HPLC following the method by [PERSON] et al. [25]. The separation was performed on a Hitachi HPLC system (Merck, Tokyo, Japan) with an L-7100 pump and a Purospher(r) RP-C18 column (200 mm \(\times\) 4 mm, 5 \(\upmu\)m), using a methanol-water (95:5, \(\upupsilon/\upsilon\)) mobile phase at a flow rate of 1 mL/min, with UV detection at 280 nm. Qualitative analysis of oxybenzene involved comparing sample peak retention times with those of the standard. To confirm oxybenzene presence, a standard solution was added to the samples, and an increase in peak height at the corresponding retention time indicated its presence. Additionally, an MS/MS analysis was conducted. The quantification of oxybenzene was performed using a calibration curve constructed with standard solutions in the concentration range of 0.025 to 0.25 mg/mL, described by the equation y = 3 \(\times\) 10\({}^{8}\)x + 374,701 and an R\({}^{2}\) value of 0.9989.  
Phenolic compounds, L-phenylalanine, and sterols were analyzed using a Hitachi-Merck HPLC VWR liquid chromatograph (Darmstadt, Germany) as previously described. All compound quantities were reported in milligrams per 100 g of dry weight (d.w.).
## 3 Results
### Structural Equation Modeling for Effluent T-P  
#### 3.2.1 Path Model of Effluent T-P  
The initial path derived for effluent T-P concentration consisted of five related variables affecting the target: influent T-P concentration in the bioreactor (B_in_T-P), DO concentration of the aeration tank (B_DO), PO\({}_{4}\)-P concentration in the anaerobic tank (Anaero_PO\({}_{4}\)-P), sludge-recycling ratio from the settling tank to the bioreactor (B_Sludge return ratio), and PO\({}_{4}\)-P concentration of the aeration tank (Oxic_PO\({}_{4}\)-P) (Figure 5a). The fitness of this initial path model was acceptable for all indices and is listed in Table 7. There were two paths with an insignificant \(p\)-value, from influent T-P concentration (B_in_T-P) to PO\({}_{4}\)-P concentration of the aeration tank (Oxic_PO\({}_{4}\)-P), and from B_DO to Oxic_PO\({}_{4}\)-P. This is due to the fact that PO\({}_{4}\)-P level was kept low in spite of the variation of influent T-P loading in the bioreactor. For the relationship between B_DO and Oxic_PO\({}_{4}\)-P, DO concentration is a variable that can largely be affected by the amount of carbon oxidation and nitrification, not only by excessive phosphorus accumulation. There are two paths that have strong causality, from PO\({}_{4}\)-P concentration in the aeration tank (Oxic_PO\({}_{4}\)-P) to effluent T-N, and from influent T-P concentration and PO\({}_{4}\)-P concentration in the anaerobic tank. The former path is natural because PO\({}_{4}\)-P in the aerobic tank would pass the settling tank almost without any change. The latter would also be the result of adding the effect of phosphorus release in the anaerobic tank to the influent loading effect. DO concentration of the aeration tank (B_DO) was included to complete the fitness of path model but did not have significant path-correlation values in either the path to or from that variable. The two paths explain the effect of the sludge amount for phosphorus release and uptake. The higher the number of micro-organisms for phosphorus uptake, the lower the measured phosphorus concentration after uptake, so the path coefficient is interpreted as having a weak negative value. The fitness of the initial path model for effluent T-N was acceptable for all indices, with 1.659 for Q, 0.974 for GFI, 0.931 of AGFI, 0.063 of RMSEA, and 0.981 for CFI. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Criterion** & **Result (Test)** & **Result (Validation)** & **Fitness/Not** \\ \hline Q value & \textless{}3 & 2.455 & 2.604 & Fitness \\ GFI & \textgreater{}0.9 & 0.924 & 0.919 & Fitness \\ AGFI & \textgreater{}0.8 & 0.847 & 0.838 & Fitness \\ RMSEA & \textless{}0.05 (\textless{}0.1) & 0.094 & 0.098 & Fitness \\ CFI & \textgreater{}0.9 & 0.917 & 0.912 & Fitness \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fitness of structural equation model for effluent T-N. Modified Path Model for Effluent T-P  
The final confirmed path model for effluent T-P (Figure 5b) with the acceptable fitness results listed in Table 7, as influent BOD concentration (B_in_BOD) was added to reflect the influent effect