esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of the Measurement System and Experimental Test
### Experimental Set-Up  
To test the method efficiency, an experimental set-up was arranged. To this aim, a 50 m long cable strand was tensioned in the Cable Dynamics Laboratory of Politecnico di Milano. The Cable Dynamics Laboratory is mainly used for evaluating the mechanical properties of cable and damping devices, namely the conductor self-damping, the damper, and the spacer-damper dynamic stiffness and damping properties [32]. The knowledge of these parameters allows to correctly estimate, through numerical models, the HVTL dynamic response to the wind action.  
The peculiar features of the facility are the 50 m long span, which represents a strict requirement for many tests performed by standards, and the presence of an electromechanical actuator and a load cell, which permit to easily control and regulate the conductor tensile load.  
The instrumentation available in the laboratory comprises an electrodynamic shaker used to excite the cable vibration and some current suppliers (for a total power of 50 kW) to perform conductor heating and thermal tests.  
The cable employed for the tests is a high temperature low sag (HTLS) conductor characterized by an external diameter of 30.45 mm. The main features of the cable are summed up in Table 2.  
One sensor node was used for the tests, and was mounted at a distance sufficiently high (i.e., approximately 1 m) from the conductor extremity to avoid the zone influenced by the high bending stiffness due to the presence of the clamp (Figure 4a). The sensor node acquisition was ruled by a gateway positioned in the laboratory, which continuously stored the received data.  
The tensile load of the conductor was imposed and controlled by means of programmable logic control (PLC) managing the electromechanical actuator and the load cell. A linear position sensor (Celesco encoder model PT5A-100-S47-FR-10K-M6 with a sensitivity of 265 mm/V) was used to measure, with accuracy, the sag at the midspan during the different tests illustrated in the following sections (Figure 4b).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Measurement range & \(\pm\)2 g, \(\pm\)4 g, \(\pm\)8 g, \(\pm\)16 g \\ Sensitivity (_acc\_sens_) & 4 mg \\ Noise & 1.1 LSB rms \\ Maximum output data rate & 3200 Hz \\ Operating voltage & 3.3 V \\ Supply current & 140 \(\mu\)A \\ Operating temperature range & \(-\)40 \(\div\) 85 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of the MEMS accelerometer adopted for the sensor nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Conductor Characteristics** & \\ \hline External diameter & 30.45 mm \\ Mass per unit length & 1.485 kg/m \\ Ultimate tensile strength (UTS) & 180 kN \\ Maximum operative temperature & 120 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main characteristics of the conductor employed in the tests.
# Application of Ultrafiltration for Recovery of Bioactive Phenolic Compounds from Rose Wastewater
## 1 Introduction
The use of ultrafiltration and nanofiltration offer high selectivity and scalability for industrial applications [23]. These processes have been particularly effective in recovering phenolic compounds from diverse sources, such as winemaking residues, olive oil byproducts, and fruit processing wastes, thereby supporting the circular economy in agro-food systems [7; 9]. They not only enhance the efficacy of polyphenol recovery but also address environmental concerns by converting industrial byproducts into valuable ingredients [24; 25]. Ultrafiltration is commonly integrated before nanofiltration or reverse osmosis to enhance phenolic concentration, supporting sustainable circular economy models through its chemical-free operation [26]. By operating on size exclusion, ultrafiltration membranes, particularly those with a low molecular weight cut-off, effectively separate phenolic compounds from larger macromolecules while maintaining stable permeate flux and rejecting chemical oxygen demand [27]. Ultrafiltration is effective in the recovery of polyphenols from extract made from dry rose petals after rose oil distillation [28], but has never been used in the distillation of its other byproduct--rose wastewater. The aim of this research is to study the possible application of ultrafiltration using three different membranes (UF1-PAN, UF10-PAN and UF25-PAN) in the recovery of phenolic compounds from rose wastewater. The resulting retentates would be used to develop functional additives for food, cosmetic, and pharmaceutical applications, aligning with the principles of green technology and circular economy.
# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 2 Materials and Methods
### Methodology
#### 2.1.4 Machine Learning for Enhanced Predictive Insights--Phase 4  
Subsequently, the data from each plan were organized and divided into two distinct groups: a training set and a validation set. This was achieved through the use of the partitioning node, allowing the algorithm to be trained with the initial dataset and subsequently tested with the second dataset within the partition diagram before the learning phase [80].  
The partitioning node was configured to perform stratified sampling, as indicated by the nominal variable \"Holidays_Festivities\". Stratified sampling entails the random division of rows in a dataset, thereby ensuring that the distribution of the nominal variable remains similar in both partitions. This method was selected in order to ensure an even distribution of weekdays, holidays, and bridges between the two datasets. Furthermore, a random seed was established to guarantee the consistency of the results [81]. This enables the choices of the partitioning node to be fixed and the same output to be obtained against the same input, regardless of the machine on which the workflow is executed. Regarding the choice of learning algorithm, the use of a tree ensemble learning algorithm was opted for, which is particularly suitable for the estimation of numerical variables, such as the occupancy of a floor [82]. This technical choice enables the use of multiple decision trees, thereby facilitating the generation of more accurate and robust predictions.  
Ensemble learning is a ML algorithm in which multiple learners are trained and combined to solve the same problem [83]. When multiple learners are used, the ability of the ensemble to generalize can be much stronger than that of a single learner (the \"wisdom of the masses\" principle applies). In this specific case, ensemble learning was applied to decision trees. The tree ensemble uses a set of regression trees (like random forest variants). Typically, each tree is constructed with a different set of rows (records) and/or columns (attributes) used alternately. The output model describes a set of regression tree models and is applied in the corresponding predictor node using a simple average of the individual predictions [84]. In a regression tree, the (numerical) predicted value for a leaf node is the average target value of the records within the leaf [85]. Thus, predictions are better (compared to the training data) if the variance of the target values within a leaf is minimal.  
Figure 11: Data after the data cleaning and transformation phase.
# Numerical Simulation of Oil Pipeline Leakage Diffusion in Dashagou Yellow River Crossing Section
## 4 Results and Discussion
### Analysis of Groundwater Solute Transport
#### 4.4.2 Analysis of Crude Oil Migration and Diffusion  
The aquifer parameters, initial conditions and boundary conditions were substituted into the water quality model, and MODFLOW (version 6) and MT3D software (version 3) were used to jointly run the water flow and water quality models to obtain the prediction  
Figure 23: Migration and diffusion distribution of benzene at different time periods.  
results of pollutant transport after oil spillage with total petroleum hydrocarbon (TPH) saturation of 10 mg/L as the continuous source concentration. Figure 24 shows the comparison of the range of petroleum hydrocarbons transported and dispersed in different time periods.  
According to the simulation results of groundwater solute-phase pollutant transport, the following conclusions are summarized: When crude oil leakage occurs in the pipeline, the solute phase of crude oil enters into the pebble layer and arrives at the on-line monitoring site after about 300 days. Under the condition of continuous source strength of 10 mg/L, the solute-phase pollutant of crude oil reaches quasi-static equilibrium when it migrates downward about 190 m after 850 days of groundwater transportation, and the pollutant does not enter into the Yellow River but forms a stable equilibrium state in the Dashagou; due to the low solubility of crude oil in water, the solute-phase pollutant of crude oil is significantly weaker in the migration ability in the groundwater as compared with that of benzene in the groundwater. The solute phase of crude oil is significantly weaker in groundwater than benzene due to the lower solubility of crude oil in water.
# BIM-Based Management System for Off-Site Construction Projects
## 5 System Application and Effects
### Logistics Management  
By integrating BIM and RTLS, the proposed management system rendered the process of the factory shipment of the components to the onsite import more efficient in the transport phase of the test projects. Because the correct and timely procurement of components is essential in OSC projects, the project participants tried to closely track each component's shipment status, transportation, and import. Notably, before the proposed BIM-based system was applied, it was challenging to manage each of the thousands of components per project. Before testing, the projects were associated with unique systems, some of which involved advanced technologies. Because the data were not automatically linked, the personnel in charge had to review the data sequentially before proceeding to the next step. Accordingly, the errors in communication between the factories, vehicles, and sites occurred frequently in addition to the human errors that led to incorrect shipment or the omission of components. These errors required the correct components to be reshipped and transported or, in severe cases, to be remanufactured. These additional inputs of resources, such as cost, time, and workforce, resulted in project losses.  
After the application of the proposed management system, the errors in the logistics stage were significantly decreased because the BIM environment system matched the data collected from the sensor to the appropriate object using global unique identifiers (GUIDs). Using this technical basis, the logisticians could streamline the shipment, transportation, and import procedures. Figure 8 shows the logistics management performed using this system based on the BIM and RTLS technologies.  
Once a tag is attached to an item after production and setting, the system can store the information received from the tag as a sub-property of the component. Consequently, the managers could register the production status of the components in the system through tagging without creating production documents. Even when a component was being transported, the GPS information (from sensors mounted on the vehicle) was transmitted to the system, allowing the users to identify the component location in real time. The system recognized a component arriving at the site through a sensor installed at the gate. The testers did not have to wait at the gate and manually list each vehicle and component entry. In this manner, the proposed system enabled time and human resource savings compared to the traditional system and prevented errors or omissions.
# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
## 5 Discussion
### Optimization Objective Function  
In the line chart of Figure 12, when the weight coefficient of total cost \(\gamma\) increases in a range from 0.0 to 0.8, the starting point and ending convergence point of the optimization objective function value gradually rise. Meanwhile, compared to the former weight coefficient of total cost, the average increments of optimization objective function value are 33.9%, 24.1%, 28.4%, and 15.9%, respectively, while for weight coefficient of seismic decrease coefficient \(\alpha\), weight coefficient of maximum story drift ratio \(\lambda\), there is no significant variation of objective function value. For example, fixing weight coefficient \(\gamma\) at 0.2, when weight coefficient \(\alpha\) increases from 0.0 to 0.8, and the weight coefficient \(\lambda\) decreases from 0.8 to 0.0, the value of objective function increases by--7%, 4.3%, 2.6%, and 6.4% respectively. If fixing weight coefficient \(\alpha\) at 0.2, when weight coefficient \(\gamma\) increases from 0.0 to 0.8, and weight coefficient \(\lambda\) decreases from 0.8 to 0.0, the value of objective function increases by 28.6%, 32.1%, 28.5%, and 15% respectively. If fixing weight coefficient \(\lambda\) at 0.2, when weight coefficient \(\alpha\) increases from 0.0 to 0.8, and weight coefficient \(\gamma\) decreases from 0.8 to 0.0, the value of objective function decreases by 14.4%, 22.1%, 19.2%, and 23.9% respectively. This indicates that, for the example structure, the value of optimization objective function is more sensitive to the weight coefficient of total cost \(\gamma\) relatively. The possible reason is that the size of isolation bearings to be employed is relatively large due to the comparatively large building volume. Then, the size of isolation bearing tends to the upper boundaries of bearing size in Tables 1 and 2, thus the optimized size of the bearing is close to the initial size during the optimization process, resulting in the third item in Formula (1) being relatively larger than the first two items. Therefore, the total cost has a greater impact on the objective function compared with the seismic decrease coefficient and maximum story drift ratio.
# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 2 Related Work  
The techniques and methods used for indoor positioning use a wide variety of resources and methods, as discussed in Section 1. In these studies, a large amount of data or additional infrastructure such as proximity sensors are required to increase the positioning accuracy. In addition, errors caused by biases of inertial sensors are still a problem to be resolved. This section introduces marker-based and vision-based positioning, which are relatively free from infrastructure installation and the collection of large amounts of data. In addition, we describe how these studies proceed with regard to positioning, strengths, and limitations.
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
## 3 The S-LMB-GOM Filter
### An Approximate Method for Calculating Point Spread Function  
The idea for fast calculation of point spread function is approximating the \(c_{t}\left(\mathbf{x}\right)\) to the nearest subdivision grid \(c_{t}\left(\mathbf{\tilde{x}}\right)\) and approximating \(H\left(\mathbf{x}\right)\) to \(H\left(\mathbf{\tilde{x}}\right)\), where \(H\left(\mathbf{x}\right)=\left\{h_{A}^{i}\left(\mathbf{x}\right):i\in T\left( \mathbf{x}\right)\right\}\) and \(H\left(\mathbf{\tilde{x}}\right)=\left\{h_{A}^{i}\left(\mathbf{\tilde{x}} \right):i\in T\left(\mathbf{\tilde{x}}\right)\right\}\). \(T\left(\mathbf{x}\right)\) and \(T\left(\mathbf{\tilde{x}}\right)\) are the index sets of \(C\left(\mathbf{x}\right)\) and \(C\left(\mathbf{\tilde{x}}\right)\), respectively. The method is effective because the number of particles is large and the number of likelihood functions required computation is also large.  
The details of the method can be summarized as: from Figure 6, the grid of cell \(c_{i_{0}}\left(\mathbf{x}\right)\) corresponded by the target \(\mathbf{x}\) is divided into ten equal divided cells and then \(c_{t}\left(\mathbf{x}\right)\) is approximated by the nearest divided cell \(c_{t}\left(\mathbf{\tilde{x}}\right)\). The point spread function \(H\left(\mathbf{x}\right)\) of the target \(\mathbf{x}\) is approximately equal to \(H\left(\mathbf{\tilde{x}}\right)\) of the target \(\mathbf{\tilde{x}}\). The number of \(H\left(\mathbf{\tilde{x}}\right)\) is finite so we can calculate and store all the point spread function \(H\left(\mathbf{\tilde{x}}\right)\). When computing the likelihood function, we can approximate \(c_{t}\left(\mathbf{x}\right)\) to \(c_{t}\left(\mathbf{\tilde{x}}\right)\) and find the corresponding \(H\left(\mathbf{\tilde{x}}\right)\) through the index.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 6 Discussion  
Our innovative design incorporates the Montgomery radix-4 algorithm, fixed window algorithm, and two-stage pipelining technique to significantly improve the throughput/area ratio. The Montgomery radix-4 algorithm reduces the number of required modular multiplications, leading to faster computations and improved performance. The fixed window algorithm performs point operations based on multiple bits of the scalar simultaneously within a fixed window. This approach significantly reduces the required number of expensive point additions and doublings, which results in faster point multiplication. The two-stage pipelining technique maximizes hardware resource utilization and reduces latency by enabling the parallel processing of cryptographic operations.  
The aforementioned optimization techniques contribute to improving the throughput/area ratio by reducing the number of operations, optimizing modular exponentiation, and maximizing hardware resource utilization through pipelining. Our paper emphasizes scalability and the applicability of the proposed optimizations to various FPGA designs. The optimizations are designed with adaptability in mind and evaluated on different FPGA platforms, including Virtex 4, Virtex 5, Virtex 6, and Virtex 7, showcasing their efficiency across diverse hardware configurations. Moreover, our design is flexible, allowing it to adapt to different ECC parameters and hardware platforms. This flexibility ensures that our design can meet the performance requirements of various applications and integrate seamlessly with existing systems.  
The advantages of our optimization techniques include improved throughput/area ratio, reduced computational complexity, and enhanced overall efficiency. However, it is important to consider the drawbacks associated with these techniques, such as increased implementation complexity, and the need for the careful consideration of data dependencies and synchronization in the pipelining process. By addressing these challenges and leveraging the benefits of our design, we can unlock the full potential of efficient and high-performance cryptographic systems.
###### Abstract  
Diversified noise sources pose great challenges in the engineering of an ANC (active noise control) system design. To solve this problem, this paper proposes an ANC method based on VMD (variational mode decomposition) and Ensemble Learning. VMD is used to extract IMFs (Intrinsic Model Functions) of different types of noise and obtain the approximate entropy of each IMF. Clustering analysis on the output of VMD is conducted based on the PCA (principal component analysis) dimension reduction method and k-means++ method to get classification results for different noises. On the basis of the clustering results, different GBDT (gradient boosting decision tree) regressors are constructed for different noise types, in order to create a high-performance ANC system for multiple noise sources. To verify the effectiveness of the proposed method, this paper designed four simulation schemes for the ANC: obstacle-free rectangular enclosed space, rectangular enclosed space with obstacle, obstacle-free trapezoidal enclosed space and trapezoidal enclosed space with obstacle. When machine gun noise is used as an example, noise attenuation by the proposed method in four simulation schemes is \(-23.27\) dB, \(-21.6\) dB, \(-19.08\) dB and \(-15.48\) dB respectively.  
active noise control; variational mode decomposition; gradient boosting decision tree; principal component analysis; k-means++ +
Footnote †: journal: Medical Imaging and Control
## 2 Materials and Methods
### Nutrient and Pigment Analysis  
For nutrient analysis (all except N and C), samples were thawed and dried at 50 \({}^{\circ}\)C prior to analysis. Afterwards, the samples were homogenized, and 200-300 mg (dry weight) of each sample was taken for nutrient content analysis. Each sample was then mixed with 10 mL of HNO\({}_{3}\) and 1 mL of H\({}_{2}\)O\({}_{2}\). The samples were inserted into a MARS 5--microwave digestion system (CEM Corporation, Matthews, NC, USA), and digested for 10 min at 175 \({}^{\circ}\)C. After cooling, the resulting extract was filtered and analysed with the ICP-MS--mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA), using the method by [PERSON] [54]. The N and C concentrations of the leaves were analysed separately using a Variomax CN Analyzer (Elementar Analysensysteme, Hanau, Germany). To achieve dry weight, the thawed samples were dried for eight hours at 50 \({}^{\circ}\)C and homogenized with a mortar and pestle, after which 250 mg (dry weight) of each sample was analysed using the dry combustion method, where the samples are burned at 1000 \({}^{\circ}\)C in pure oxygen [55].  
For leaf Cab and carotenoid pigment sampling, samples were thawed and 70 mg (fresh weight) of each sample was transferred into new tubes along with a pair of homogenization pellets and 1.8 mL of dimethyl sulfoxide (DMSO), after Wellburn [56]. Following a two-minute homogenization at 25 Hz, the samples were extracted for four hours in 50 \({}^{\circ}\)C. After extraction, the samples were centrifuged for 10 min at 3600 rpm (5810-R, Eppendorf, Hamburg, Germany) and the extracts analysed with a Shimadzu UV-1800 photometer (Shimadzu Corporation, Kyoto, Japan). Chlorophyll a and b and carotenoid values were estimated from absorbance measurements using equations by [PERSON] [56] for DMSO:  
\[\text{Chlorophyll a}=12.47\times\text{A665.1}-3.62\times\text{A649.1} \tag{1}\]  
\[\text{Chlorophyll b}=25.06\times\text{A649.1}-6.5\times\text{A665.1} \tag{2}\]  
\[\text{Carotenoids}=(1000\times\text{A480.0}-1.29\times\text{Chl a}-53.78\times \text{Chl b})/220 \tag{3}\]  
where A denotes the absorbance at a given wavelength in nm.
## 2 The definition of scale and relevant terminologies
### Scaling and scale effects  
Apart from the concept of scale, we have to pay attention to the terms, \"scaling\" and \"scale effects\". Scaling, is just defined as transferring information across scales [16]. When one speaks of scaling, one must distinguish between two cases: up-scaling and down-scaling. Up-scaling is a process that transfers information from local scale to large scale. It concerns the extraction of global parameters from local measurements and has been much more studied [4]. On the contrary, down-scaling is to go from large scale to local scale. In general, up-scaling and down-scaling may also be called aggregation and disaggregation, respectively [18]. Scale effects refer to the contrast of information or the different characteristics at different scales. For example, the production of farmland estimated from TM and AVHRR are significantly different. The difference varies with the region and lacks regularity. It is mainly due to the dependence on the scale. How to select the appropriate scaling method and determine scale effects will be clearly analyzed next.