esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study introduces a low-cost, ArduinoTM-based data acquisition (DAQ) system that overcomes key limitations of existing methods by integrating automated resistive load control and rapid data acquisition in a single setup. Unlike traditional systems that require manual adjustments, this system can automatically vary the resistive load and acquire real-time data, which is crucial for accurate PV panel characterization, especially in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Arduino-Based** & **HT I–V500w**[8] & **Solmetric PVA-1500**[52] \\ \hline Voltage Range [V] & 0–35 & 15–1000 & 20–1500 \\ Current Range [A] & up to 5 & 0.1–15 & up to 30 \\ Voltage Accuracy & \(\pm 0.5\%+0.01\) & \(\pm 0.5\%\) rdg + 2 dgt & \(\pm 0.5\%\) + 0.25 \\ Current Accuracy & \(\pm 2\%+0.00001\) & \(\pm 1.0\%\) rdg + 2 dgt & \(\pm 0.5\%\) + 0.04 \\ Power Accuracy & \(\pm 4\%\) & \(\pm 1.0\%\) rdg + 6 dgt & \(\pm 1.7\%\) + 1.0 W \\ Full Sweep Time [s] & \(\sim\)45 & 5 & 0.05–2 \\ Irradiance/Temp Sensor & External & Yes & Yes (SolSensor) \\ IoT/Automation & Manual (expandable to ESP32/Wi-Fi) & USB + SW & Wi-Fi + SW \\ Estimated Cost [USD] & \(<\)100 US \$ & 2 k–4 k+ & 10 k+ \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison between the proposed Arduino-based I–V tracer and commercial systems.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**G [W/m\({}^{2}\)]** & \multicolumn{2}{c}{\(R^{2}\) (Current)} & \multicolumn{2}{c}{RMSE (Current) [A]} & \multicolumn{2}{c}{\(R^{2}\) (Power)} & \multicolumn{2}{c}{RMSE (Power) [W]} \\
**Before** & **After** & **Before** & **After** & **Before** & **After** & **Before** & **After** \\ \hline
913.86 & 0.9655 & 0.9646 & 0.2152 & 0.2178 & 0.9364 & 0.9686 & 4.0370 & 2.8360 \\
827.59 & 0.9174 & 0.9976 & 0.2888 & 0.0495 &  
field conditions where environmental factors such as solar irradiance and temperature can fluctuate. The data acquisition time of 45 s minimizes the impact of these variables.  
The single-diode model provided good results under standard conditions (manufacturer's data--MAPE of 4.40% for current values). However, discrepancies arose under real operational conditions, particularly at lower solar irradiance levels (with a relative difference of about 27% at G = 246.33W/m\({}^{2}\)). This highlights the importance of real-time experimental data for optimizing PV models, as standard laboratory conditions may not reflect actual field performance. When experimental data were used to optimize the model, the accuracy improved, reducing the MAPE for I-V curves and output power from 18.23% and 18.54% to 7.06% and 7.22%, respectively.  
The proposed system offers a cost-effective and flexible alternative to commercial I-V curve tracers, making it especially valuable for educational institutions, small-scale renewable energy projects, and researchers in developing countries. Its open-source hardware and software design ensures that it can be easily replicated and adapted to meet various experimental needs, further enhancing its accessibility.
## 3 Background  
### Deep Metric Learning  
Deep metric learning aims to automatically learn an embedding space model so that similar samples are placed into nearby space, while dissimilar samples are pushed away using the Euclidean or cosine distance. The common objective functions used in deep metric learning are contrastive loss and triplet loss which were first proposed in image processing and computer vision tasks [58; 59; 60].  
In the contrastive learning, the simplest and the oldest method, proposed by [58; 59], there is a pair of embedding vectors \((x_{i},x_{j})\) and a label, either 1 or 0. If the embedding pair is from the same class, this label will be 1 and objective function tries to reduce the distance between them. Otherwise, the label will be 0 and objective function tries to increase the distance between them.  
In triplet learning, which was first introduced for face recognition in [60], the loss is computed over triplets of an anchor, a positive and a negative sample \((x_{i},x_{i}^{+},x_{i}^{-})\), so that the distance between anchor and positive pairs must be less than the distance between anchor and negative pairs. The simple triplet loss is computed as:  
\[L_{TL}=\max\big{(}D\big{(}x,x^{+}\big{)}-D\big{(}x,x^{-}\big{)}+m,\ 0\big{)} \tag{1}\]  
where \(D(\cdot)\) is a metric function for measuring distance, which can be Euclidean or cosine distance, and \(m\) is a margin, which is a hyper-parameter used to determine how far the dissimilar images (or dissimilar sentences) should be from the anchor image (or anchor sentence).
# Multi-Span Tension Control for Printing Systems in Gravure Printed Electronic Equipment
## 4 Performance Study of the ADRC Decoupling Controller for the Printing Tension System
### Experiments  
The PID control experiment and the ADRC decoupling control experiment for the printing tension system were conducted on the experimental platform built earlier. The parameters for both the PID controller and the ADRC controller were fine-tuned based on the simulation parameters and remained unchanged throughout the experiment. The web tensions of the three spans of the printing tension system were controlled to produce step changes of 10 N at different times. The step responses of the web tensions for each span using the PID controller are shown in Figure 9, while the step responses of the web tensions for each span using the ADRC decoupling controller are shown in Figure 10.  
Figure 8: Performance simulations of the ADRC controller for the printing tension system.  
When using PID control, the step change in web tension of the previous printing span will have an impact on the web tension of all subsequent printing spans, which cannot solve the problem of multi-span coupling. When using ADRC decoupling control, the tension step changes of each printing span will not affect the web tension of other printing spans, demonstrating good decoupling performance.  
In addition, ADRC decoupling control has good steady-state performance. The steady-state tension fluctuation amplitude range in ADRC decoupling control is \(\pm 0.7\) N, while the steady-state tension fluctuation amplitude range in PID control is \(\pm 1.1\) N, which means that the steady-state tension-control accuracy has been improved by 36.4%. The steady-state tension-control accuracy of ADRC decoupling control is within \(\pm 1.4\)%, which is much lower than the steady-state tension-control accuracy index of \(\pm 5\)% for traditional image-text gravvure printing. This is related to the anti-interference ability of ADRC itself, which can provide timely estimation and compensation for roller speed fluctuations, mechanical vibrations, electrical interference, etc.  
By organizing the above experimental data into Table 4, it can be seen that the ADRC decoupling controller of the printing tension-control system has significantly better performance than the PID controller.  
Figure 9: Performance experiment of PID control for the printing tension system.  
In summary, the experimental and simulation results of the printing tension system are basically consistent. Compared with traditional PID control, the ADRC decoupling control method better solves the coupling problem of the printing tension system and has good interference suppression performance.
# Computational Modeling of Passive and Active Cooling Methods to Improve PV Panels Efficiency
## 2 Modeling and Computational Details
### Description of the Physical Processes  
Considering the different physical processes that are presented in this research (heat transfer, radiation, fluid dynamics, and power analysis of PV panels), the appropriate modules that provide physical interfaces and tools to model the main physical phenomena were chosen. To find the required solutions, in this work fluid mechanics was combined with numerical methods using three key elements: turbulence modeling, mesh construction, and algorithm development. These three elements can be introduced in the simulation with COMSOL Multiphysics, which leads to finding a relationship of the study variables.  
Calculations were performed using the turbulent flow modules, where the RANS (Reynolds-averaged Navier-Stokes) k-\(\epsilon\) model was chosen together with the non-isothermal flow multiphysics. The model of heat transfer in solids by radiation was used to study the behavior of the panel.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Computational** & **Domains** & **Sides** & **Edges** & **Points** \\ \hline Standard panel & 3 & 17 & 32 & 20 \\ Passive panel & 3 & 117 & 332 & 220 \\ Active panel & 4 & 23 & 44 & 28 \\ Hybrid panel & 4 & 124 & 348 & 232 \\
**Mesh Specifications** & **Domain Elements** & **Boundary Elements** & **Edge Elements** & \\ Standard panel & 1415321 & 55018 & 1441 & \\ Passive panel & 3731403 & 107882 & 2931 & \\ Active panel & 920878 & 45450 & 1259 & \\ Hybrid panel & 3042372 & 135418 & 3329 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of the domains and meshes implemented.  
Figure 2: Meshing the geometry of the PV panels.
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
## 4 Experimental Results and Discussion
### RO-PUF Quality Factors
#### 4.3.3 Uniqueness  
Figure 11 presents the inter-die Hamming distance obtained using Equation (3). Once again, a loss on the uniqueness can be observed and is proportional to the accumulated dose.
## 5 Results and Evaluation  
With the help of publicly available benchmark datasets like DupPR-basic and DupPR-complementary data, we validate our proposed HDL-ODPR model in this section. This model is implemented in the open-source Spyder cross-platform with the Python language. Our proposed HDL-ODPR model's simulation results are contrasted with those of state-of-art models in terms of saved reviewing effort (SRE), F-measure, recall, precision, and accuracy.
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 4 Discussion
### Advanced Control Strategies
In fact, they foresee the self-adaptation of the control law to the changing conditions by means of the correction in the time-variant parameters. In [139], in the framework of the project OPTi, it has been demonstrated for the Lulea district heating system how the multi-input multioutput (MIMO) adaptive controller outperforms a standard multi-loop PID implementation, reducing the oscillations in the controlled variables that is a consequence of the different interaction of components such as generation units, pumps and valves in substations that are present in DH systems. Finally, conversely from traditional MPC, multi-agent systems (MAS) control can be considered a hierarchy-free solution where different entities interact, e.g., as in a peer-to-peer market and operate without mandatory signals from a centralized higher-level controller. Even if they are not so widely applied in the DHC sector, some interest is appearing for the operation of decentralized active substations in 5 GDHC with good results [75]. Nevertheless, for the MAS control approach, the following positive (+) and negative (\(-\)) aspects can be derived [140,141]:  
(+) Based on the goals, agents can act autonomously in their environment, which could be cooperative or competitive. (+) Decreased need for massive data manipulation. (+) The other agents modify and continue the system functions if one of any controller loses. (+) According to some rules, allow the manufacturers or loads to embed programmable agents in the equipment controllers. (+) Agents are able to learn from their behaviors and past activities. (\(-\)) Communication languages, protocols and the design of agents' ontologies should be based on common standards. (\(-\)) Since plenty of multi-agent platforms have been developed, selecting the most appropriate is a tricky task. (\(-\)) The design of an intelligent agent is challenging.
## 1 Introduction  
The recent advent of heterogeneous services and use cases with diverse requirements have made modern wireless communication networks highly complex systems, which need to be carefully designed and operated to offer immersive experience to their customers, while keeping both capital expenditure (CapEx) and operational expenditure (OpEx) low. The increasing complexity of wireless ecosystems makes network planning, optimisation, and orchestration arduous tasks. Communication system engineers have made remarkable progress in modelling various digital and analogue transceivers and simple network functions, e.g., at the MAC and link layers in wired/wireless local networks. However, in the 5G/6G landscape, the traditional models for network optimisation often fall short, as they mostly enhance the performance of network processes or signal processing blocks one-by-one, with limited end-to-end (E2E) performance or cross-block considerations. Simulations can be useful in some cases, but their efficacy and applicability are often limited too.  
New paradigms, ushered in by the recent developments in the field of artificial intelligence (AI), and, most specifically, data-driven machine learning (ML) techniques, offer new possibilities in operating complex systems, such as modern communication networks. Unlike traditional approaches, the aspiration is that a massive amount of data, which is now within our reach to collect and process within practical timeframes, can help alleviate the need for tedious mathematical modelling and simulations for operating complex systems. Deep neural networks (DNNs) and modern learning theories can enable computers to learn models and apply optimisations that are beyond human comprehension and intellectual capabilities. Not surprisingly, AI/ML is already accepted as an indispensable enabler for the closed-loop control and optimisation of several network processes in emerging wireless networks as recognised by several recent vision papers, such as [1; 2; 3; 4; 5].  
Motivated by the aforementioned potentials, this article aims to provide the interested readers with a comprehensive analysis of the most recent progress in the use of data-driven and ML-based approaches in the study of modern communication systems and networks with a focus not only on the key, most recent publications, but also on the developments in the industry, major research projects and potential for new research presented by the gaps in the literature.
###### Abstract  
A new method for the isolation and enrichment of ofloxacin enantiomers from fish samples was developed using magnetic molecularly imprinted polymers (MMIPs). These polymers can be easily collected and rapidly separated using an external magnetic field, and also exhibit a high specific recognition for ofloxacin enantiomers. The preparation of amino-functionalized MMIPs was carried out via suspension polymerization and a ring-opening reaction using _rac_-ofloxacin as a template, ethylenediamine as an active group, glycolyl methacrylate and methyl methacrylate as functional monomers, divinylbenzene as a cross-linker, and Fe\({}_{3}\)O\({}_{4}\) nanoparticles as magnetic cores. The characteristics of the MMIPs were assessed using transmission electron microscopy (TEM), X-ray powder diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), and vibrating sample magnetometer (VSM) measurements. Furthermore, the adsorption properties were determined using Langmuir and Freundlich isotherm models. The conditions for use of these MMIPs as magnetic solid-phase extraction (MSPE) sorbents, including pH, adsorption time, desorption time, and eluent, were investigated in detail. An extraction method using MMIPs coupled with high performance liquid chromatography (HPLC) was developed for the determination of ofloxacin enantiomers in fish samples. The limits of quantitation (LOQ) for the developed method were 0.059 and 0.067 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) for levofloxacin and dextrofloxacin, respectively. The recovery of ofloxacin enantiomers ranged from 79.2% \(\pm\) 5.6% to 84.4% \(\pm\) 4.6% and ofloxacin enantiomers had good linear relationships within the concentration range of 0.25-5.0 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) (R\({}^{2}\) > 0.999). The obtained results demonstrate that MSPE-HPLC is a promising approach for preconcentration, purification, and simultaneous separation of ofloxacin enantiomers in biomatrix samples.  
magnetic solid-phase extraction; ofloxacin enantiomers; magnetic molecularly imprinted polymers; chiral HPLC
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 4 Findings and Discussion
### Summary and Insights  
The majority of studies investigating tunneling and forwarding functions are implemented within the UPF and edge-to-core section and have focused on lowering latency as well as enhancing throughput. The performance achieved in these studies is heavily influenced by the type of device used. Programmable hardware switches have been found to achieve higher throughput compared to FPGA boards and smart NIC implementations, whereas software switches demonstrate the lowest line rates. Nonetheless, hybrid designs aim to leverage the programmability features of smart NICs or software switches to offload more complex user-defined rules and achieve lower latencies.  
Network slicing implementations are mostly deployed in the edge-to-core segment of systems, with use cases such as e-health, industrial IoT, and smart grids being evaluated to demonstrate the efficacy of programmable devices in achieving slice isolation and fairness. While a study has explored the deployment of network slicing in the NG-RAN section of the architecture, to date, no work has integrated this approach with transport network slicing to achieve an end-to-end programmable data plane solution.  
Articles pertaining to cybersecurity focus on strategies for blocking malicious traffic and content permutation. However, this type of implementation is restricted due to the computational constraints of programmable devices.  
INT applications reviewed in this study utilize programmable devices to append additional headers containing specific data from 5G and beyond networks. These data are subsequently collected by a control entity to optimize key parameters and processes, such as QoS, traffic steering, and network slicing. Notably, the reviewed deployments in this domain are currently limited to software switch implementations only.  
Control plane offloading using programmable data planes is effective for handling simple signaling components that can take advantage of the high-speed properties of these devices.  
On the other hand, the reviewed literature reveals other additional uses of programmable data planes in 5G and beyond architectures. These applications include data placement, data retrieval, data aggregation, publish-subscribe schemes, service function chaining (SFC), handover processes, and beamforming calculations.  
While programmable data planes have demonstrated a broad range of applications for 5G and beyond architectures, there are general limitations that need to be addressed. For instance, programmable devices do not currently support complex functionalities such as automatic repeat request (ARQ) and ciphering. Furthermore, handling state transfer for numerous UEs can be challenging due to memory constraints. Finally, a lack of realistic experimental settings can make it difficult to obtain reliable results.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 6 Implementation Details
### Communication Using HTTP Clients  
Modern architectures based on microservices require effective communication between different system components [13]. Within the developed system, communication is achieved using HTTP requests, employing dedicated client implementations. The se lected implementation primarily takes into account interoperability with the types and mechanisms in each paradigm while also providing efficient interactions.  
For the asynchronous application, it was decided to use the HTTP client provided by Java. This interface integrates with the primitives specific to the paradigm and offers robust support for asynchronous operations. Requests can be launched asynchronously using methods such as _sendAsync_, with the results made available in a _Future_ object. The interface also allows the configuration of a dedicated _ExecutorService_ to achieve greater control and flexibility in concurrency management. The main downside of this solution is the lack of integration with Spring, with response transformations and data conversions being handled programmatically, introducing additional logic and complexity.  
In the reactive approach, it was opted for the usage of _WebClient_ from Spring WebFlux. Similar to _HttpClient_, it provides native integration with _Mono_ and _Flux_ reactive types but also supports automatic serialization and deserialization of data, increasing the clarity and efficiency of the implementation. As an integrated part of the reactive ecosystem, _WebClient_ is developed with paradigm principles in mind so that requests and responses can be handled in a non-blocking way, making it a natural choice for scenarios implying heavy traffic conditions and high-performance requirements.
# Current Harmonic Suppression Algorithm for Asymmetric Dual Three-Phase PMSM
## 4 Proposed Current Harmonics Suppression Strategy  
### Feedforward Voltage Compensation  
The feedforward voltage compensations can be calculated according to Equations (4), (5), and (6) as described in the previous section, so the key issue of the feedforward voltage compensation is to determine the polarities of the phase currents.  
This paper proposes a current vector method that uses the current vector angle to judge the polarities of the six-phase currents. The current vector angle that is shown in Figure 5 is defined as follows:  
\[\phi=\theta+sign(i_{q})*\pi/2+\delta \tag{11}\]  
Twelve different combinations can be derived according to the polarities of the six-phase currents, which divide the complex plane of the 360-degree electric angle into 12 sectors. Every sector occupies 30 degrees. The corresponding relationship between the current vector angle and the polarities of the six-phase currents are shown in Figure 6.  
So, the polarities of six-phase currents can be defined according to the current vector angle, as shown in Figure 6. Meanwhile, the current vector angle can be calculated according to the current vector in space coordinate, as shown in Equation (11). \(\delta\) can be given as  
\[\delta=sign(i_{q})*\arctan\left|\frac{i_{q}}{i_{q}}\right| \tag{12}\]  
According to Equations (11) and (12), the current vector angle can be obtained as follows:  
\[\phi=\theta+sign(i_{q})*(\pi/2+\arctan\left|\frac{i_{q}}{i_{q}}\right|) \tag{13}\]