esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Tunnel Test on Windblown Sand Two-Phase Flow Characteristics in Arid Desert Regions
## 3 Experimental Results and Discussion
### Turbulence Intensity in the Windblown Sand Flow Field  
To study the effect of concentration and bed surface with different particle sizes on turbulence intensity, Figure 12 provides the relationship between the turbulence intensity and height at three different wind speeds in the impurity-free wind and windblown sand flow field. The results show that the turbulence intensity decreases with increasing height. The sand particle movement significantly enhances the turbulence intensity when the height is less than 0.3 m and has a small influence on the turbulence intensity when the height is over 0.3 m in the windblown sand flow field. Similar to the wind profile, the degree of influence of sand particles on the turbulence intensity at different heights is directly related to the vertical distribution characteristics of the sand concentration, as shown in Figure 9. The higher the sand concentration is, the faster the turbulence intensity increases. Moreover, at the same indicated wind speed, the degree of influence of coarse sand, fine sand and mixed sand on the turbulence intensity increase, in turn.  
To quantitatively analyze the influence of particle movement on the turbulence intensities at different heights, we introduce turbulence intensity impact value \(\Delta I_{z}\) and impact factor \(IF_{L}\), respectively. The formula is as follows:  
Figure 12: The turbulence intensity: (**a**) At the indicated wind speed of 14.74 m/s; (**b**) At the indicated wind speed of 16.68 m/s; (**c**) At the indicated wind speed of 18.61 m/s.  
Figure 13: The influence of particle movement on the turbulence intensity: (**a**) At the indicated wind speed of 14.74 m/s; (**b**) At the indicated wind speed of 16.68 m/s; (**c**) At the indicated wind speed of 18.61 m/s.  
It is worth noting that turbulence characteristics of the flow field are significantly affected by the sand particle movement. Since the turbulence characteristics represent the intensity of fluctuating wind, the windblown sand flow can enhance the fluctuating wind loads on infrastructures and civil structures compared with the impurity-free wind flow. At the same time, the sand particle movement is significantly affected by the turbulence characteristics in the windblown sand flow field. The more disordered the flow field is, the more violently the sand particles move.
## 2 Hybrid Microgrid Topology and Its Sub-Microgrid Control
### AC-DC Sub-Microgrid Control
#### 2.2.2 DC Sub-Microgrid Control  
The PV power generation system in a DC sub-microgrid consists of PV panels and boost circuits, and a maximum power tracking algorithm is used to keep the PV at maximum power output. The main circuit and control block diagram are shown in Figure 4.  
Firstly, the output voltage \(U_{\text{pv}}\) and current \(I_{\text{pv}}\) of the PV panels are collected, the reference voltage \(U_{\text{pv}}^{*}\) is obtained by the maximum power point tracking algorithm based on perturbation and observation, and the deviation of \(U_{\text{pv}}^{*}\) and \(U_{\text{pv}}\) is obtained by the PI controller to control the duty cycle, which can control the conduction and shutdown of the boost switching tube.  
Figure 3: Control diagram of PCS.  
In this paper, the energy storage battery in the DC sub-microgrid is controlled by droop control, which enables the DC DGs to distribute power proportionally according to the droop coefficient under no-communication conditions. The droop characteristic expression is shown in Equation (2).  
\[{U_{\mathrm{dc}}}^{ref}={U_{\mathrm{dc}}^{s}}-{k_{\mathrm{dcj}}}\Big{(}P_{ \mathrm{dcj}}-P_{\mathrm{dcj}}^{s}\Big{)}, \tag{2}\]  
where the subscript \(j\) (\(j\) = 5, 6) denotes the number of DGs in the DC sub-microgrid; \(P_{\mathrm{dcj}}\) is the active power output from DG\({}_{j}\) in the DC sub-microgrid; \(U_{\mathrm{dc}}^{s}\) is the rated voltage of the DC sub-microgrid; \(P_{\mathrm{dcj}}^{s}\) is the rated power of the DC sub-microgrid DG\({}_{j}\); \({U_{\mathrm{dc}}}^{ref}\) is the DC reference voltage; and \(k_{\mathrm{dcj}}\) is the droop coefficient of DG\({}_{j}\) in the DC sub-microgrid, which is related to the capacity of DG\({}_{j}\).  
The energy storage battery system consists of a battery and a boost-buck circuit, which realizes the charging and discharging of the battery by controlling the boost and buck switching tubes. The output of the system is connected to the DC bus. The main circuit and control block diagram of the energy storage battery are shown in Figure 5.  
First, the output power is calculated from the collected DC voltage \(U_{\mathrm{dc}}\) and the system output current \(I_{\mathrm{dc}}^{\mathrm{bat}}\). Then the DC voltage reference value is obtained from the droop characteristics of the DC sub-microgrid. The deviation value of the obtained voltage reference value from the DC voltage is passed through the PI controller to obtain the reference value of the battery output current \(I_{\mathrm{b}}\). Finally, the deviation of the reference value of \(I_{\mathrm{b}}\) from the actual value is used to obtain the duty cycle through the PI controller, which is inverted by PWM to control the on-off switch of the two tubes, respectively.  
Figure 4: Control diagram of PV power generation system.  
Figure 5: Energy storage battery system control block diagram.
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
## 1 Introduction
### Structure of the Paper  
When looking at the literature studies in general, it is observed that bifacial panels are used in limited amounts in BIPV systems. For this reason, in the conditions of Sanlurfa, the bifacial panel technology--a new panel technology--was used to increase the amount of electrical energy obtained per unit area in BIPV systems.  
Bifacial panels are two-sided panels. Bifacial PV panels produce electrical energy by collecting radiation from both the front and back surfaces. Monofacial panels, on the other hand, are traditional single-sided panels and produce electrical energy only from the radiation received from their front surface. Bifacial panels are named based on the peak electric power produced by their front surface, similar to monofacial panels. Moreover, there is no significant price difference between bifacial and monofacial panels with the same peak production. However, because of the radiation reflected or scattered on the back surface of the bifacial panel, additional electricity production is achieved [40].  
When analyzed compared with monofacial cells, bifacial PV panels are seen as a potential opportunity to reduce the cost of solar energy conversion. The bifacial panel is a new technology that increases the electrical energy production per square meter of the PV module by utilizing light absorption from albedo [41].  
In this study, PV panels with the same potential were installed on various facades of a building. Using panels of the same capacity on different facades, the energy production levels on the facades were investigated. The aim of this study was to compare the effect of panel technology by comparing bifacial panel technology with monofacial panels of the same direction and capacity. In addition, after obtaining experimental data, the aim was to verify the data using the PVsyst program on the computer and to determine the annual production amount.
## 3 Results
### Simulation Validation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Actual execution of work orders (manual input)**} \\ \hline
**Code** & **Workstation** & **Actual start** & **Actual stop** & **Off** & **Short stop** & **Long Stop** & **Working** & **Quantity/m2** \\ \hline Micro-batch- & Press & 17/10/2024 & 17/10/2024 & 00:00.00 & 00:00.00 & 00:00.00 & 00:00.00 & 00:41:38 & 115.9 m2 \\
44-025-CAA & KJ-PR050 & 112631 & 12/08:10 & & & & & \\ Micro-batch- & Press & 17/10/2024 & 17/10/2024 & 00:00.00 & 00:00.00 & 00:00.40 & 00:27:25 & 73.88 m2 \\
44-023-CAA & KJ-PR050 & 17/10/2024 & 17/10/2024 & & & & & \\ Micro-batch- & Press & 17/10/2024 & 17/10/2024 & & & & & \\ Micro-batch- & Press & 17/10/2024 & 17/10/2024 & & & & & \\ Micro-batch- & Press & 17/10/2024 & 17/10/2024 & & & & & \\ Micro-batch- & Press & 17/10/2024 & 17/10/2024 & & & & & \\ \hline \multirow{2}{*}{44-032-CM} & KJ-PR050 & 9/9:51 & 100:54 & & & & & \\ Micro-batch- & Press & 17/10/2024 & 17/10/2024 & & & & & \\ \hline \multirow{2}{*}{44-035-CM} & KJ-PR050 & 1300.30 & 135:52 & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline \multicolumn{10}{c}{**Virtual factory simulation data**} \\ \hline
**Shift** & **Workstation** & **Start** & **End** & **Off** & **Short stop** & **Long Stop** & **Working** & **Quantity/m2** & **Availability** & **Performance** & **OEE** \\ \hline \multirow{2}{*}{17.10.2024} & Press & 17/10/2024 & 17/10/2024 & & & & & & \\  & & & & & & & & \\  & & & & & & & & & \\
17.10.2024 & KJ-PR050 & 7.00:00 & 150:00.00 & 00:00.00 & 05:40.00 & 02:20.00 & 400 & 29\% & 99.9\% & 29\% \\ \hline \multicolumn{10}{c}{**DIMUSA real–time data**} \\ \hline
**Shift** & **Workstation** & **Start** & **End** & **Off** & **Short stop** & **Long Stop** & **Working** & **Quantity/m2** & **Availability** & **Performance** & **OEE** \\ \hline \multirow{2}{*}{17.10.2024} & Press & 17/10/2024 & 17/10/2024 & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Press workstation activity on 17 October 2024, based on manually reported task feedback, simulation data, and DIMUSA sensor logs. Table 4 presents the press workstation data from 17 October 2024, following the same structure. Unlike the plotter, the press exhibited more variation between simulated expectations and actual execution. Several micro-batches experienced delays or extended idle periods between processing steps. In some cases, operator-logged reasons included material unavailability or coordination delays. DIMUSA readings confirmed these delays through prolonged inactive states. The comparison underlined the importance of accounting for coordination dependencies and manual handling variability when calibrating the simulation model. It also reinforced the need for complementary validation layers-manual reporting, real-time monitoring, and simulation achieve an accurate representation of production behavior.
# Research Frontiers in the Field of Agricultural Resources and the Environment
###### Abstract  
From the perspective of project and paper datasets, research frontier recognition in the field of agricultural resources and the environment using the Latent Dirichlet Allocation (LDA) topic extraction model was studied. By combining the wisdom of domain experts to judge the similarities and differences of clustering topics between the two data sources, multidimensional indicators, such as the emerging degree, attention degree, innovation degree, and intersection degree, were comprehensively constructed for frontier identification. The methods for hot research frontiers, emerging research frontiers, extinction research frontiers, and potential research frontiers were proposed. The empirical research in the field of agricultural resources and the environment showed that the \"interaction mechanism of plant-rhizosphere-microbial diversity\" was a hot research frontier in the years 2016-2021. The themes of \"wastewater treatment technology and efficient utilization of water resources\", the \"value-added utilization of agricultural wastes and sustainable development\", the \"soil ecological response mechanism under agronomic management measures\", and the \"mechanism of soil landslide, erosion, degradation and prediction evaluation\" were judged as potential research frontiers. The theme of \"ecosystems management and pollution control of agricultural and animal husbandry\" was recognized as an emerging research frontier. The results confirm that the fusion method of extracting topics from project and paper data, combined with expert intelligence and frontier indicators for fine classification of frontiers, is an optional approach. This study provides strong support for accurately identifying the forefront of scientific research, grasping the latest research progress, efficiently allocating scientific and technological resources, and promoting technological innovation.  
LDA model; research frontier; agricultural resources and the environment; frontier indicators; theme fusion +
Footnote †: journal: Medicalresources in the direction with the most strategic value, to promote the effective allocation of resources and promote continuous progress. Therefore, research frontier identification is of great significance for gaining insight into research trends, targeting frontier fields, developing knowledge services, and promoting technological innovation.  
Currently, many scholars have carried out fruitful research on frontier recognition, which can be summarized as methods based on experts' subjective judgment, methods based on objective data analysis, and methods based on content mining (Table 1).
## 3 Description of the Methodology  
The methodology we exploit to distribute and balance the computation on edge nodes works through the annotation, via pre-defined parallelization directives, of the sequential implementation of an algorithm. The approach, has been designed within the research activities of the TOREADOR project, has been specifically developed to distribute computational load among nodes hosted by different platforms/technologies in multi-platform Big Data and Cloud environments, using State of the Art orchestrators [15]. Use cases where edge computing nodes represented the main target have been considered and demonstrated the feasibility of the approach in Edge and Fog Computing environments [16].  
The methodology requires that the user annotates her source code with a set of **Parallelization Primitives** or parallel directives, which are then analysed by a compiler. The compiler determines the exact operations to execute on the original sequential code, thanks to a set of transformation rules which are unique for the specific parallel directive, and employs Skeletons to create the final executable programs. Directives are modelled after well known Parallelization Patterns, which are implemented and adapted according to the considered target. Figure 1 provides an overview of the whole parallelization process, with its three main steps:  
1. **Code**: in the first step, we suppose the user owns a good knowledge of the original algorithm to be transformed from a sequential to a parallel version. The user will annotate the original code with the provided Parallel Primitives.  
2. **Transform**: The second step consists in the actual transformation of the sequential code, that the user has annotated with the aforementioned primitives, operated by Skeleton-Based Code Compiler (Source to source Transformer). The compiler will produce a series of parallel versions of the original code, each one customized for a specific platform/technology, according to a 3-phases sub-workflow. 1. _Parallel Pattern Selection_: on the base of the used primitives, the compiler selects (or asks/helps the user to select) a Parallel Paradigm. 2. _Incarnation of agnostic Skeletons_: this is the phase in which the transformation takes place. A parallel agnostic version of the original sequential algorithm will be created, via the incarnation of predefined code Skeletons. Transformation rules, part of the the knowledge base of the compiler, guide the whole transformation and the operation the compiler will perform on the Abstract Syntax Tree. 3. _Production of technology dependent Skeletons_: the agnostic Skeletons produced in the previous phase are specialized and multiple parallel versions of the code are created, considering different platform and technologies as a target
3. **Deployment**: Production of Deployment Scripts.
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 6 Conclusions
### Future Directions  
This work has interesting extensions that could be studied in the future. Some suggested ones are listed below:  
* More realistic experimental simulation of FRESH protocol versions. More specifically, in this work, one key assumption made was that the storage space of the DTN nodes (buffer) has an infinite capacity, which can be relaxed for a more realistic implementation. Understandably, nodes in real DTN systems have limited memory and storage resources. Therefore, a future simulation where nodes have finite buffer space may further highlight the benefits of using the proposed modified FRESH protocol not only in terms of packet age and freshness but also in system throughput.
* Conducting simulations of the proposed protocol in real Delay Tolerant Networks. In the present work, for the experimental evaluation of the modified FRESH protocol, synthetic networks were constructed, in which the widely used Random Waypoint Model mobility model was applied for the mobility of the nodes. It would be very interesting to conduct simulations on actual real-life Delay Tolerant Networks in order to study the effectiveness of the proposed protocol, as well as to further demonstrate the benefits of using it.
* Study of Age of Information in DTNs in order to improve the energy consumption of such networks as well, and probably jointly provide delay guarantees. Since the main topic of this paper is not to produce an energy optimal operation for DTNs, but rather demonstrate the efficacy of employing AoI in order to provide delay guarantees in delay-tolerant routing protocols, this is a topic of its own merit and we will explore it in our future work.
* Introduction of the Age of Information concept to other routing protocols in DTN. In the relevant literature, a multitude of routing protocols have been proposed that aim to make this type of subnets run as smoothly as possible. Possibly, the concept of AoI can match and co-exist smoothly with many of these protocols, especially those that belong to the Encounter-based Protocols and Time-based Protocols categories. In this way, it is possible to improve the quality metrics of DTNs with what this implies for the future evolution of the quality of life itself in the world.  
Conceptualization, all authors; methodology, G.K. and V.K.; software and validation, G.K.; investigation, all authors; writing, all authors; visualization, G.K.; supervision, V.K. and S.P.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable, the study does not report any data.  
The authors declare no conflict of interest.
## 1 Introduction
Our investigation encompasses an evaluation of different microcontroller platforms, as well as an in-depth examination of communication protocols in electronics, including detailed comparisons of their power requirements. To ensure the system's reliable operation, even in the event of potential failures, the system integrates a rechargeable Lithium-Ion battery as a backup power source. We have also explored various strategies for receiving transmitted data and decoding embedded information. These strategies aim to minimize power consumption and enable a dependable and autonomous system operation, excluding the battery. Our proposed dual-frequency-vibration-based energy harvester designs play a crucial role in achieving this goal. These harvesters allow us to assess their overall performance under real-world applications. Through an extensive study, we have investigated the influence of broadcast frequencies. The results have successfully demonstrated that the harvesters, when subjected to a harmonic excitation of 0.5 g, can ensure reliable and fully autonomous operation of both the sensing and data transmission system. Notably, in scenarios requiring high broadcast frequencies, such as real-time data reading and transmission, which typically present the most demanding power requirements, our system can extend the battery lifetime by a factor ranging from three to nine times, depending on the microcontroller platform.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 4 Examining Noise in Quantum Computing
### Relevance of the Noise Models  
A QECC comprises two main components: the actual circuit involving logical qubits and gates, and syndrome measurements featuring ancilla qubits and stabilizer measurements.  
The code-capacity model evaluates the maximum error rate a code can correct, focusing exclusively on the actual circuit and its data qubits, disregarding the syndrome measurement part. This model also evaluates the error propagation in the data qubits over time, covering the impact of cumulative errors on the overall computation. Contrarily, the phenomenological model estimates the accuracy threshold considering faulty syndrome measurements. It scrutinizes the ancilla qubits and the repeated stabilizer measurements, overlooking the detailed circuit-level error propagation. Lastly, the circuit-level model encapsulates errors across all elementary operations in the entire circuit, including data and ancilla qubits. It simulates a realistic setting where all operations are noisy and provides an estimation of the accuracy threshold considering error propagation within the circuit [44].  
Figure 3: The code-capacity model gauges the error rate for data qubits, ignoring syndrome measurements. The phenomenological model estimates error thresholds with faulty syndrome measurements but disregards circuit-level error propagation. The circuit-level model comprehends errors in all operations, covering data and ancilla qubits.  
Using these noise models, we can compare various QECCs under equivalent conditions, allowing fair assessments of their intrinsic error tolerance.