esa-sceva/satcom-chunk-collection huggingface dataset:
### Literature Review  
Although the research idea of this study is intuitive, there are only a few studies on data-driven BSS configurations in the literature, let alone modeling the configuration problem of modular BSS. Due to the lack of detailed EV trajectory data, the existing optimization models for configuring BSSs are usually solved with the assumption that the \(BSD\) satisfies the uniform or empirical distributions [7; 20]. This ideal assumption simplifies the analysis  
Figure 1: A typical modular battery swapping station.  
of the performance of BSSs that have battery-to-grid (B2G) capability or that cooperate with microgrids. However, it does not match the real-world _BSD_ patterns that demonstrate temporal fluctuations [13]. Therefore, [PERSON] et al. [21] and [PERSON] et al. [22] highlighted the significance of dynamic _BSD_ and proposed data-driven approaches for deploying BSSs using GPS trajectory data on actual electric freight vehicles and shared electric vehicles. They extracted the trip and _BSD_ patterns to parameterize the BSS configuration model, so as to help improve the model's realism and accuracy. With advances in data collection and storage technology for ETs, [PERSON] et al. [23] characterized the driving behavior of ETs by mining and modeling the trajectory data and proposed a joint modeling approach for user charging behavior decision making and charging station planning. However, few studies use a data-driven approach to configure the ET BSS.  
The EV charging stations and BSSs often contribute with heavy power loads to the public grid. Under the scenario of ToU price, it is necessary to consider the battery charging scheduling at the operating stage before configuring the BSS. For example, [PERSON] et al. [24], [PERSON] et al. [25] and [PERSON] et al. [26] have proposed scheduling methods for battery charging at BSSs, considering _BSD_ and charging costs. However, the practicality of these studies is limited by assuming that _BSD_ is predetermined.  
Although extensive literature studies on BSS document positive results and insights into BSS operations, they have to consider ET drivers' heterogeneous driving range requirement and then make active responses on the supply side. A handful of related studies mined and analyzed ETs trajectory data to incorporate vehicle heterogeneity into charging infrastructure planning [1]. However, they only passively adjusted the number of charging piles and did not respond regarding the battery swapping or charging service. Moreover, the concept of modular battery swapping is similar to the concept of individual battery cell replacement, which has been validated to be viable and more economically beneficial than replacing the entire battery, given that the design requirements are fulfilled [27].
## 3 Selecting and Defining the Scenarios
### Selected Scenario  
Considering all the parameters and dependent variables mentioned above, approximately 400 scenarios can be defined related to railway and road coexistence scenarios [7]. For this practical work, five different feasible scenarios are considered that are designed, developed, and executed using the considered tools. The selected scenarios have nomenclature SX(Y)Z, where: S represents the term \"Scenario\", X represents the \"Telecommunication Network Parameters\", Y represents the \"Mobility Parameters\", and Z represents the \"Topological Elements in Civil Engineering Infrastructure\".  
1. **S1(5/6)1: Different Access Network and Different Core, Single Serving Technology, Track Parallel to Road**: This scenario is considered the baseline scenario to investigate the coexistence of railway and road scenario telecommunication services infrastructure. In this scenario, both domains, i.e., railways and roads, have their own dedicated radio access network (RAN) and dedicated core. Considered access pointsand cores work on a single technology/radio frequency. Along with this, railway tracks are kept parallel to roads [7].
2. S1(5/6)4: **Different Access Network and Different Core**, **Single Serving Technology**, **Track Perpendicular to Road**: In this considered scenario, the network parameters are similar to scenario S1(5/6)1, i.e., railways and roads have dedicated radio access networks with dedicated cores, but in this scenario, railway tracks are perpendicular to roads [7].
3. S2(5/6)1: **Different Access Network and Shared Core**, **Single Serving Technology**, **Track Parallel to Road**: In this scenario, railway and road domains have different radio access networks, and both domains share backhaul and core network infrastructure. In this considered scenario, railway tracks are perpendicular to roads [7].
4. S4(5/6)1: **Shared Access Network and Shared Core**, **Single Serving Technology**, **Track Parallel to Road**:
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 3 Methodology
### Scenarios for the Ad Hoc Implementation  
Three en-route scenarios from the Madrid ACC were selected for the implementation of Ad Hoc separation. These sectors are LECMZMU (Reference Scenario), LECMTLU and LECMDGU (Figure 4).  
LECMZMU sector is located in the west part of the Madrid ACC (Figure 4), on the border between FIR Madrid (LECM) and FIR Lisbon (LPPC). The demand in the sector is strongly conditioned by the proximity to Madrid-Barajas [PERSON] (LEMD) and Lisbon [PERSON] (LPPT) airports. Regarding the vertical limits, the lower and upper vertical limits for LECMZMU are FL345 and FL660, respectively. The declared capacity of the LECMZMU sector is 45 aircraft/hour. According to the IATA definition, the busy day is the second busiest day in an average week during the peak month [44]. Pre-pandemic traffic levels were considered for this analysis (2019 year). Therefore, the peak month of traffic in LECMZMU during the year 2019 was July, and the busy day was the 13 th of July. AIRAC 1907 data were used, obtained from EUROCONTROL DDR2 [45]. LECMZMU was designed as reference scenario.  
The LECMTLU sector is located in the southern part of the Madrid ACC (Figure 4), on the border between the Madrid ACC and the Sevilla ACC, and over the Madrid TMA. Regarding the vertical limits, the lower and upper vertical limits for LECMTLU are FL345 and FL660, respectively. The declared capacity of the LECMTLU sector is 45 aircraft per hour and the busy day for the year 2019 was 6 th of July.  
The LECMDGU sector is centrally located in the Madrid ACC (Figure 4). It is bordered by the LECMZMU sector to its left, the LECMTLU sector to the south, the LECMPAU sector to the east, and the LECMBLU sector to the north, all belonging to the Madrid ACC.  
Regarding the vertical limits, the lower and upper boundaries for LECMDGU are FL345 and FL660, respectively. The declared capacity of the LECMDGU sector is 44 aircraft per hour and the busy day for the year 2019 was 7 th of July.
# Value Assessment of UGC Short Videos through Element Mining and Data Analysis
## 2 Literature Review  
### Study on the Value Assessment of UGC Short Videos Using Technical Methods  
Two main methods are commonly used to evaluate the value of UGC short videos: traditional technical methods and deep learning [6]. The traditional technical approach involves utilizing computer vision and signal processing techniques to process and analyze UGC short videos, aiming to determine their value level. For instance, [PERSON] et al. [7] developed a VQA model that effectively assesses the value of UGC videos. They achieved this by training an end-to-end spatial feature extraction network, enabling the model to directly learn value-aware spatial features from the original pixels of UGC videos. [PERSON] et al. [8] enhanced the performance of the reference-free VQA video value assessment model (BVQA) by incorporating a feature selection strategy, which demonstrated favorable results. [PERSON] et al. [9] compared the performance of three mainstream computer vision technology models (YOLOV2, Google Cloud Vision, and Clarifai) for evaluating UGC value. Their findings revealed that the Google Cloud Vision model outperformed the others, especially in assessing brand promotional UGCs. Deep learning methods, being the prevailing AI technology, enable automatic learning and extraction of high-dimensional and nonlinear data features through the construction of deep neural network models. This facilitates the efficient evaluation of UGC's value. For instance, [PERSON] et al. [10] introduceda framework based on deep neural networks (DNNs) that considers features like technical value and video compression level. This framework aims to perceive the significance of UGC video value and ultimately assign a value score for assessment. [PERSON] et al. [11] developed a hyper-network-based NR-IQA model that can dynamically adjust video value prediction parameters. Their model exhibited superior performance in differentiating UGC values.
# Engineering Hydroponic Systems for Sustainable Wastewater Treatment and Plant Growth
###### Abstract  
This study aimed to optimize hydroponic systems for simultaneous wastewater treatment/nutrient recovery and plant growth. Various hydroponic systems (geyser pump, full flow, ebb and flow, nutrient film techniques, aeroponics, misting) were constructed using 160 mm PVC waste pipes supported on a 200 L reservoir. Secondary wastewater was used to cultivate rice (_Oryza sativa_), ivy (_Hedera helix_), tomatoes (_Solanum lyopersicum_), and wheatgrass (_Triticum aestivum_). Parameters such as plant height, biomass, retention time, temperature, conductivity, pH, dissolved oxygen, ammonia, nitrite, nitrate, total phosphorus, COD, BOD, TDS, TSS, and TS were monitored. Results indicated minor variations in pH, EC, and TDS over time in systems with and without plants, with no significant differences. Turbidity decreased significantly (\(p\leq 0.001\)) in all systems, while TOC levels reduced significantly (\(p\leq 0.05\)) only in the presence of plants. BOD and COD levels exhibited similar reductions with and without plants. Ammonium levels decreased in plant systems, while nitrite levels remained unchanged. Nitrate levels increased significantly in plant systems, and phosphate levels showed no significant difference. Additionally, significant (\(p\leq 0.001\)) plant length (12.84-46.75%) and biomass (31.90-57.86%) increases were observed in all hydroponic systems, accompanied by higher levels of dissolved oxygen (36.26-53.65%), compared to the control (4.59%). The hydroponic system that created a moist atmosphere, either through misting or aeroponics, thus allowing maximum access to oxygen, showed the greatest growth. This study confirmed the importance of oxygen availability to the rhizosphere for plant growth and wastewater treatment. It also identified limitations and investigated the impact of dissolved oxygen concentration on plant-microorganism interactions. Optimal oxygen availability was achieved when plant roots were exposed to a moist atmosphere created by the hydroponic system through aeroponics or misting. The findings have practical implications for hydroponic system design in urban vertical farms, benefiting wastewater treatment, mitigating eutrophication, and reducing food miles.  
hydroponics; design; municipal wastewater; rhizosphere; oxygen; urban farm Article
## 2 Literature Review
### Decision-Making Methods  
Selecting the most appropriate multi-criteria decision-making methods from the existing literature is complex. No single MCDM model can address all the multifaceted challenges encountered in the construction industry [32]. Among the approaches employed to select retrofit alternatives, the joint application of the Analytical Hierarchy Process (AHP) for criteria weighting and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking alternatives has gained widespread acceptance within the scientific community. This combined approach generates a comprehensive ranking of alternatives with minimal parameters required from the decision-maker (DM) [26]. However, research on the application of MCDM in the construction industry remains limited. Researchers and decision-makers must integrate emerging MCDM methods to fill this gap.  
The weighting stage of criteria is a critical step in the decision-making process. According to the literature, the Best-Worst Method (BWM) outperforms both the AHP and the Analytical Network Process (ANP) [27]. BWM offers a systematic approach that requires fewer pairwise comparisons than AHP, leading to more consistent judgments [33]. For the ranking of alternatives, several state-of-the-art methods have gained prominence. These include EDAS (Evaluation Based on Distance to Average Solution), MABAC (Multi-Attribute Boundary Approximation Area Comparison), CODAS (Combinatorial Distance-Based Assessment), and MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution). These advanced techniques offer robust frameworks for evaluating and ranking alternatives effectively.
# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 4 Results
### Other Model Training Results  
#### 4.2.1 Ridge Regression Model Results  
The ridge class from the scikit-learn library is implemented, where the alpha parameter specifies the strength of the regularization term. When the alpha parameter takes a small value, the regularization strength is weak, and the model tends to fit the training data more closely, leading to overfitting. Conversely, it may result in underfitting [42, 43]. The fit() method is used to train the model. The default solver = 'auto' is used to automatically select a suitable solver for the data. Automatic selection means that in most cases ridge regression  
Figure 7: Three-dimensional surface diagram of voltage, temperature, and moisture.  
will select a suitable solver for the data, eliminating the need to specify it manually. By changing the value of the alpha parameter from 0.1 to 10, continuous model training is performed, as shown in Figure 8, where panel (a) is the training model with an alpha value of 0.1, and panel (b) is the training model with an alpha of 10.  
When adjusting the alpha parameter, the correlation coefficient is 0.92, the determination coefficient is 0.86, and the root mean square error is 3.84 at an alpha value of 0.1. Similarly, at an alpha value of 10, the correlation coefficient is 0.92, the determination coefficient is 0.83, and the root mean square error is 4.20. Figure 8a,b, as well as the correlation coefficient, indicate that ridge regression is not very suitable for predicting this dataset due to a significant deviation.  
The reasons for choosing ridge regression as a comparative algorithm to build the model are as follows:  
(1) Handling multicollinearity: ridge regression addresses the issue of collinearity among features by adding an L2 regularization term to the loss function, which helps in dealing with highly correlated datasets.  
(2) Preventing overfitting: the regularization term penalizes the complexity of the model, aiding in reducing overfitting on the training data and improving the model's generalization ability.  
(3) Baseline for linear models: as a linear model, ridge regression is often used as a baseline for comparison to assess whether nonlinear models can significantly outperform it on the same problem.
# Testing the Galileo High Accuracy Service User Terminal (HAUT) in Static Scenarios +
## 3 The HAUT Project  
As indicated in the introduction, EUSPA procured the implementation of the Galileo HAS User Algorithm for the Initial Service within a User Terminal (UT) equipment that was later used in the service validation. This project (HAUT) was launched under the Fundamental Elements (FE) program.  
The project was led by Spaceopal and conducted during one year (throughout the COVID-19 pandemic) together with its industrial partners: ANavS (responsible for algorithm definition and overall hardware design and assembly), DLR Institute of Communication and Navigation (in charge of the production of synthetic data and the provision of their GNSS testing infrastructure), IABG (responsible for housing and physical and environmental tests) and Iguassu Software Systems (in charge of the human-machine interface on the computer). Spaceopal conducted the project management and overall verification and validation tasks.  
The requirements for this work targeted a wide range of applications, configurations, dynamics and environmental constraints. Five receivers were delivered to the customer for service validation and the algorithm was also provided as an offline tool, useful for experimentation and scenario replay. In the following sections, details about the UA and UT will be provided.
## 3 Technical and Operational Challenges
### Security and Compliance Gaps  
Industrial applications demand stringent data security, yet many low-code platforms prioritize ease of use over robust safeguards. In energy sectors, grid monitoring apps built with drag-and-drop tools often expose vulnerabilities in data encryption, as highlighted by [PERSON] et al. [28] in their analysis of edge computing deployments. Similarly, aerospace compliance workflows require adherence to standards like DO-178C, but most platforms lack built-in audit trails for automated documentation, forcing manual validations that negate efficiency gains.  
Emerging solutions propose hybrid models. Duke Energy's hybrid SCADA system combines Mendix for real-time dashboards with Python -based anomaly detection scripts, achieving ISO 27001 [64] compliance while retaining low-code's agility. However, such approaches require specialized expertise, undermining low-code's promise of democratization.
_Appl. Sci._**2013**, \(3\), 515-544; doi:10.3390/app3020515  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**What We Can Learn about Ultrashort Pulses by Linear Optical Methods**  
**[PERSON], [PERSON] and [PERSON] ***  
Department of Optics and Quantum Electronics, University of Szeged, P.O. Box 406., H-6701 Szeged, Hungary; E-Mails: [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (A.P.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +36-62-544-223; Fax: +36-62-544-658.  
_Received: 10 December 2012; in revised form: 10 April 2013 / Accepted: 10 April 2013 / Published: 26 April 2013_  
**Abstract: Spatiotemporal compression of ultrashort pulses is one of the key issues of chirped pulse amplification (_CPA_), the most common method to achieve high intensity laser beams. Successful shaping of the temporal envelope and recombination of the spectral components of the broadband pulses need careful alignment of the stretcher-compressor stages. Pulse parameters are required to be measured at the target as well. Several diagnostic techniques have been developed so far for the characterization of ultrashort pulses. Some of these methods utilize nonlinear optical processes, while others based on purely linear optics, in most cases, combined with spectrally resolving device. The goal of this work is to provide a review on the capabilities and limitations of the latter category of the ultrafast diagnostical methods. We feel that the importance of these powerful, easy-to-align, high-precision techniques needs to be emphasized, since their use could gradually improve the efficiency of different _CPA_ systems. We give a general description on the background of spectrally resolved linear interferometry and demonstrate various schematic experimental layouts for the detection of material dispersion, angular dispersion and carrier-envelope phase drift. Precision estimations and discussion of potential applications are also provided.**  
**Keywords: pulse characterization; spectral interferometry; ultrashort pulses; dispersion**
## 3 Results and Discussion
### Assessment of Cell Viability  
A Live/Dead assay was performed at 24, 48 and 72 h after printing to evaluate the DAOY and HUVEC cells' viability. The BioVERVE-printed cells showed a decrease in viability of about 22% (DAOY) and 18% (HUVEC) 24 h post printing, which was probably due to the printing stress, and showed a recovery to 93% (DAOY) and 98% (HUVEC) of their pre-printing value 72 h after printing. The survival curve obtained by the printing cells using the RegenHU high-precision plunger dispenser showed a similar trend, with a decrease in viability of about 16% (DAOY) and 25% (HUVEC). The cell viability was recovered for both cell lines to 87% (DAOY) and 91% (HUVEC), respectively, 72 h after printing (Figure 6). The survival results indicate similar survival kinetics obtained with the BioVERVE home-made bioprinter or with the professional RegenHU bioprinter. In fact, both 3D bioprinters showed an impact of the printing process on the initial rate between live and dead cells, followed by an increase in this value at longer time points. Moreover, a significant higher cell vitality was observed after BioVERVE printing for both the analyzed cell lines and at almost all of the time points considered. These results suggest that the home-made BioVERVE 3D bioprinter can be used in in vitro experiments with optimal survival performances. This allows for researchers to have a wider access to 3D bioprinting technologies.  
Figure 5: (**a**) Medulloblastoma cell model. (**b**) Representative images of bioprinted constructs and ImageJ analysis; DAOY and HUVEC cells are labelled with the green and red dyes, respectively. The corresponding gray images are 8-bit images converted using ImageJ 1.53i software and processed in order to analyze the number of cells. (**c**) Percentage of fluorescent areas in the printed constructs analyzed using ImageJ software. Four images were processed for each bioprinter. Data are plotted in Figure 5c as mean \(\pm\) SEM.
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 5 Conclusions  
In this work, we proposed the new mobility model, RDPZ (Random Destination with Pheromone Zone), for reconnaissance with multi-UAVs. The RDPZ mobility model guided uniform reconnaissance by dividing the scanning area into \(N\) zones in order to solve the imbalance problem. To evaluate the proposed model, we measured the coverage rate and inter-visiting time of the Random Waypoint model, the Pheromone Repel model, and RDPZ model. When we simulated for 10,800 s, 90%-coverage reaching time was about 51.7% better than the Random Waypoint model and about 25% better than the Pheromone Repel model. Similarly, the average inter-visiting time was about 34% better than the Random Waypoint model and about 15.1% better than the Pheromone Repel model. In addition, the experimental results according to the size of the scanning area show the RDPZ models with the half size of the scanning area has the best performance.  
Our on-going work includes enhancing the proposed model in irregular shaped areas including hazard region. In addition, we are also planning to analyze the performance results (e.g., the effect of reconnaissance time) in order to enhance the proposed model. We will apply target tracking, collision avoidance, etc. to RDPZ model and compare other mobility models. After that, we will redesign the mobility model by focusing on different environments.  
[PERSON]-[PERSON] and [PERSON] proposed the mobility model. [PERSON]-[PERSON] implemented the model, conducted the experiments, and wrote manuscript under the supervision of [PERSON], M.F.F assisted and performed model comparison experiments. All the authors have reviewed and revised final manuscript.  
This work was supported partly by the Gyeongsang National University Fund for Professors on Sabbatical Leave, 2017, and by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Ministry of Trade, Industry and Energy (No. 20194030202430).  
The authors declare no conflict of interest.  
Figure 11: The coverage rate results: (**a**) 20 km \(\times\) 20 km area; (**b**) 30 km \(\times\) 30 km area; and (**c**) 40 km \(\times\) 40 km area.