esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comprehensive Analysis of Attention
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stimulus Classification** & **AOI Number** & **AOI Name** & **TTF (s)** & **Average** & **Attention** & **Subtotal** \\  & **Number** & & **(s)** & **Revisits** & **Rate (\%)** & **(\%)** \\ \hline \multirow{6}{*}{Decoration} & \multirow{3}{*}{AOI 01} & Small video & \multirow{3}{*}{3.09} & \multirow{3}{*}{2} & \multirow{3}{*}{8.86} & \multirow{3}{*}{\multirow{3}{*}{7.06}} \\  & & Screen & & & & \\  & & (Decoration) & & & & \\  & & Hanging papers & \multirow{3}{*}{3.59} & \multirow{3}{*}{2} & \multirow{3}{*}{4.11} & \multirow{3}{*}{29.76} \\  & & (Decoration) & & & & \\  & & Vertical garden & \multirow{3}{*}{3.70} & \multirow{3}{*}{2} & \multirow{3}{*}{9.83} & \multirow{3}{*}{7.06} \\  & & (Brand/Decoration) & & & & \\  & & Wood boxes & \multirow{3}{*}{3.13} & \multirow{3}{*}{3} & \multirow{3}{*}{1.18} & \multirow{3}{*}{7.06} \\  & & (Decoration) & & & & \\  & & AOI 05 & Decoration & 3.64 & 2 & 2.31 & \\  & & AOI 06 & Decoration & 4.09 & 2 & 3.47 & \\  & & AOI 07 & Accessories & 3.30 & 2 & 3.83 & \\  & & AOI 08 & Accessories & 3.13 & 2 & 3.23 & \\  & & AOI 09 & Product & 2.54 & 2 & 1.20 & \\  & & AOI 10 & Product & 4.51 & 3 & 2.63 & \\  & & AOI 11 & Product & 3.98 & 2 & 1.54 & \\  & & AOI 12 & Product & 4.59 & 1 & 1.69 & \\  & & AOI 13 & Product & 2.66 & 1 & 1.35 & \\  & & AOI 14 & Product & 4.14 & 2 & 1.81 & \\  & & AOI 15 & Product & 3.78 & 3 & 4.54 & \\  & & AOI 16 & Product & 2.81 & 2 & 2.00 & \\ Product & AOI 17 & Product & 4.76 & 1 & 1.38 & 35.32 \\  & & AOI 18 & Product & 4.35 & 2 & 1.65 & \\  & AOI 19 & Product & 4.53 & 3 & 4.30 & \\  & & AOI 20 & Product & 4.90 & 1 & 2.04 & \\  & & AOI 21 & Product & 3.75 & 1 & 1.34 & \\  & & AOI 22 & Product & 3.39 & 2 & 2.73 & \\  & AOI 23 & Product & 4.64 & 1 & 0.97 & \\  & AOI 24 & Product & 4.67 & 1 & 1.72 & \\  & AOI 25 & Product & 4.48 & 3 & 2.43 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Footwear store area. Time to First Fixation (TTF) and Attention percentage of the total number of participants to each AOI. Source: Prepared by the authors. Figure 5: Attention rate (%) dedicated to the product, relative to other elements, for both packaging and store area. Source: Prepared by the authors. The previous data about group behaviour regarding attention to the product (Table 6) showed that in the case of toy packaging it is lower than in the footwear store area. A significant percentage of attention is dedicated to other elements/items that accompany the product, so it is important to consider this in the design phase.
# Adaptive Versioning in Transactional Memory Systems +
## 3 Model and Preliminaries
### Conflict Detection and Resolution  
Conflict detection and resolution comes into play when concurrently executing transactions on both persistent/non-persistent TM systems read/write the same memory locations and certain combinations of read/write patterns cannot allow multiple transactions to proceed to commit. _Conflict detection_ mechanism signals such an overlap between the _write set_ (data written) of one transaction and the write set or _read set_ (data read) of other concurrent transactions. Conflict detection is called _eager_ if it detects offending loads or stores immediately and _lazy_ if it defers detection until later when transactions commit. Conflict detection depends on whether lazy versioning is used or eager versioning. Table 2 illustrates some existing non-persistent TM systems that use lazy versus eager conflict detection with the versioning mechanism (lazy or eager) they use. For example, TCC [11] uses lazy conflict detection with lazy versioning and LogTM [12] uses eager conflict de tection with eager versioning. _Contention management_ technique is then used to decide on which conflicting transaction(s) to continue and which transaction(s) to wait (or abort and restart) the execution. This is typically done through a contention management strategy. There is a huge amount of work in this area giving many different strategies with and without provable properties on the guarantees they provide, e.g., [60; 61; 62; 63; 64; 65; 66; 67; 68].
## 3 Methodology
### UAS-Aided Survey at Egermuoi  
The raw image data acquisition was conducted with a rotor-wing UAS (DJI Phantom 4 RTK) equipped with a stabilized, built-in 20M camera (of 8.8 mm focal length) bundled on a two-axis gimbal. This unmanned platform has been chosen due to its relatively reasonable cost and easy on-site operation in combination with the equipped miniaturized  
Figure 4: Basic steps of the applied methodology.  
Geodetic Navigation Satellite System (GNSS) antenna, which provides sufficient precision, particularly for the horizontal positioning required to facilitate proper alignment of the images captured during the survey [95]. Although ground control points (GCPs) are a vital tool for ensuring geolocation accuracy, in this case study, in which the morphology of the area consists of very steep slopes with limited accessibility, the use of the RTK antenna with which the drone is equipped is efficient enough for highly accurate results. Nevertheless, we used the GNSS receiver in the Network Real-Time Kinematic (NRTK) mode, connected to the SmartNET provider accuracy service [96] for obtaining subjective geolocation, which is also necessary for the comparison between independent surveys [49].  
The UAV flight survey was designed to cover an area of 0.3 km\({}^{2}\), of a 160 m steep slope consisting of fragmented limestones that fall on the coastal zone, especially after the occurrence of earthquakes [78]. The take-off point was placed at an elevation of 190 m, and the UAV was programmed to fly along four transects almost parallel to the coastline, at the absolute height of 230 m. The camera was set pointing 30 degrees off radir, with an image overlap value of 75%. This resulted in the acquisition of 592 images that were processed within Agisoft Metashape photogrammetry software, to produce (a) DSM (0.12 m resolution), (b) a vertical ortho-mosaic image of the coastal zone (0.06 m resolution) and (c) an ortho-mosaic image normal to the steep morphological discontinuity (0.06 m resolution) (Figure 5), based on a dense point cloud that consisted of 31 million points. Regarding accuracy, the processing resulted in a root mean square error (RMSE) of 0.08 m on the xy-axes and 0.18 m on the z-axis, which are rather acceptable values.  
Figure 5: The SfM technique led to the construction of several high-resolution products such as (**a**) DSM, (**b**) ortho-image and (**c**) vertical photo-mosaic of the Egremnoi steep slope.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Polynomial Model Construction  
After evaluating various possible approaches for implementing the model, a polynomial structure was selected. The model is formulated as a multivariate polynomial expression, which is efficiently computed using [PERSON]'s rule [PERSON] (1963). The polynomial coefficients are obtained through linear regression employing the Generalized Least Squares (GLS) algorithm [PERSON] (1963). Details regarding [PERSON]'s rule and the GLS algorithm application are presented in the following sections. While this model technically falls within the domain of machine learning, it distinguishes itself from conventional ML techniques and neural network-based models due to its simplicity, computational efficiency, and ease of deployment. The entire project was developed using the Python Python (2016) (version 3.10) programming language, which was chosen for its robust open-source ecosystem, flexibility, and rapid development capabilities, as well as its extensive collection of well-documented libraries for data processing and visualization.  
The modeling process begins with the evaluation of the noise figure from the signal power spectra measured before and after amplification by the EDFA. This procedure follows the methodology outlined in various OSA user manuals OSA (2016), which serve as a reference for the subsequent sections. Once the active channel frequencies are identified, a dataset is constructed. Each row in this dataset corresponds to a unique combination of four inputparameters--input power, target gain, target tilt, and channel central frequency--along with the associated noise figure value.
# Effect of Nanofluids on Boiling Heat Transfer Performance
## 3 Effects of Nanofluid Physical Properties and Heated Surface on Boiling Heat Transfer
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Nanoparticles** & **Base Liquid** & **Concentration** & **Heated Surface** & **CHF/HTC** & **Reasons** \\ \hline \multirow{4}{*}{ZnO} & \multirow{2}{*}{Water} & 0.01, 0.05 wt.\% & horizontal rod heater & HTC deterioration & Nanoparticle deposition causes a reduction in surface roughness. & [80] \\ \cline{3-6}  & & 0.01, 0.02 wt.\% & surface of rod heater & HTC enhancement & SDS significantly reduces surface tension. & [87] \\ \cline{2-6}  & \multirow{2}{*}{EG} & 0.35, 0.62, 0.93, 1.6, 2.6 vol.\% & cylindrical copper & HTC firstly increase and decrease with the concentration & It is affected by the surface roughness of the nanoparticle deposition. & [100] \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & \multirow{2}{*}{EG/DW} & \multirow{2}{*}{5,25, 7.25, 8.25 wt.\%} & \multirow{2}{*}{Ni-Cr wire} & \multirow{2}{*}{Ni-Cr wire} & \multirow{2}{*}{HiTC enhancement} & & \\ \cline{3-6}  & & & & & \\ \hline \multirow{4}{*}{Water} & \multirow{4}{*}{Water} & 0.04, 0.4 1 kgm\({}^{3}\) & copper surface & CHF enhanced & It depends on the concentration of the nanoparticles. & [79] \\ \cline{3-6}  & & 0.001, 0.01, 0.055, 0.075, & 0.01 \% & flat heater plate & HTC deterioration & The bubbles have a more spherical shape and their sizes is reduced. & [89] \\ \cline{3-6}  & & 12, 15 wt.\% & copper surface & HTC enhanced 124-138\% & Verified by [PERSON] correlation [102] & [103] \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & 0.00005, 0.0001, 0.0005, & Horizontal circular plates & HTC firstly increase and concentration, which & Subject to surface roughness. & [104] \\ \cline{3-6}  & & 0.01 wt.\% & & & 0.001\% is best. & \\ \hline \multirow{2}{*}{EG} & 0.01, 0.025, 0.055, 0.075 vol.\% & cylindrical brass surface & HTC deterioration & The bubble size decreases with increasing concentration. & [105] \\ \cline{3-6}  & & & & & \\ \hline \multirow{2}{*}{RI4 lb} & 0.01, 0.025, 0.055, 0.075 vol.\% & cylindrical brass surface & HTC deterioration & The bubble size decreases with increasing concentration. & [105] \\ \cline{3-6}  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Results and Discussion
### Characterization of MGONCs
#### 3.1.6 Bet  
The N\({}_{2}\)-adsorption/desorption isotherm (shown in Figure 9) confirmed that MGONCs composite showed a type II with H\({}_{4}\)-type hysteresis loop, which indicated the mesoporous structure of the composite. Moreover, the specific surface area of MGONCs was 206.4397 m\({}^{2}\)/g. The results showed that MGONCs obtained a good specific surface area and could be applied for the adsorption of chemical agents.  
Figure 8: Thermal analysis of MGONCs: (**a**) TGA; (**b**) DSC.
## 3 EP in Immunogen Electrotransfer  
Electrochemotherapy (ECT) represents one of the possible utilizations of EP and applies EP to deliver cytotoxic drugs, such as bleomycin and cisplatin into the cell [54]. In situ vaccination elicits an immune response against tumor-associated antigens (TAA), which are released from the tumor cells by ECT [55; 56]. EP also induces inflammation through cytokine release, which results in immune cell recruitment [57]. Another technique based on EP is gene electrotransfer (GET), which constitutes a powerful method of DNA delivery that offers numerous medical applications. For example, it allows plasmids with encoded genes to be transferred into the tumor cells [56]. GET can also transmit immunomodulatory genes into the target tumor tissue, which boosts the antitumor effect [58]. Systemic administration of cytokines is associated with high toxicity, which might even lead to death, whereas GET allows the accumulation immunomodulatory cytokines in the vicinity of the tumor without systemic severe adverse effects [54; 59; 60].  
The specific combination of ECT and immunogen electrotransfer leads to significant changes in the tumor microenvironment. The electrochemotherapy and the IL-12 production create a proinflammatory environment. Because of that, immune cells embark on infiltrating the tumor tissue, and the tumor cells begin to die by immunogenic death, which contributes to the release of TAA and danger-associated molecular pattern molecules (DAMP). Dendritic cells uptake TAA and relocate to the tertiary lymphoid structures, where they can induce the formation of tumor-specific effectors and memory cells. T cells then migrate to both the primary tumor site and metastases, where they can initiate regression of the tumor (Figure 3). It was noticed that Treg lymphocytes, which are present in the tumor microenvironment, have a massive impact on the immune response. They make this environment immunosuppressive, and it is crucial to inhibit their activity to increase the treatment's effectiveness [58]. The effectiveness of in situ vaccination may differ depending on the size and immunogenicity of the tumor. Electrochemotherapy is more effective in high-immunogenic tumors but immunomodulatory gene electrotransfer significantly impacts low-immunogenic types [61]. GET increases a tumor's immunogenicity by activating immune cells, which may elicit a systematic response [62].
## 3 Results--Effectiveness of Pull and Push Strategies in Production and Distribution  
### The Model and Its Assumptions  
The model is a modification of models already developed to evaluate the efficiency and economic effectiveness of the degree of centralization of the warehouse network in the distribution field ([PERSON], 2020 [47]; [PERSON], [PERSON] 2022 [48]). The model is applicable to situations where standard products are produced, and the decision problem is whether to \"Push\" products after production, or whether warehouses should order products based on their needs (\"Pull\"). So, one could say that warehouses are \"customers\" for the production. The model presented in this article is a modification of those models because it takes into account not only the number of warehouses but also how stocks are replenished in those warehouses.  
The following section presents the results of a simulation conducted to compare the effectiveness of Pull and Push strategies in the field of distributing goods to customers.  
The first step was to present the results of a simulation of the impact of the distribution strategy on inventory levels and the availability of stocks (AoS) (Section 3.2).  
Subsequently, it was shown how the results of these simulations could be used to calculate the economic efficiency of these strategies (Section 3.3).  
The simulations were carried out for two demand distributions: Gaussian and Gamma, and for each of these distributions, for different deviations of demand from the average demand. The model was supposed to take into account different situations in which a given strategy (or a combination of different strategies) would be the most effective. Since the \"Push\" system is based on forecasts, and \"Pull\" reacts to actual demand, the effectiveness of these systems was tested with different fluctuations in demand (and therefore different predictability) and different demand characteristics.  
Figure 3 shows how strategic decisions affect resource and process parameters and, consequently, economic efficiency.  
The functioning of the model is shown in Figure 4.  
Figure 4: Processes and product flows in the simulation model of “Pull” and “Push” systems.  
Figure 3: Impact of decisions on production and logistics processes, resources, and costs.
## 2 Materials and Methods
### UPLC-MS/MS Conditions  
Metabolome analysis was conducted on a Thermo Vanquish (Thermo Fisher Scientific, Waltham, MA, USA) ultra-high-performance liquid chromatography (UPLC) system using an ACQUITY UPLC(r) HSS T3 (2.1 \(\times\) 100 mm, 1.8 \(\upmu\)m) (Waters, MiRecord, Milford, MA, USA) column at a flow rate of 0.3 mL/min and an injection volume of 2 \(\upmu\)L at a column temperature of 40 \({}^{\circ}\)C. In the positive ion mode, the mobile phases were 0.1% formic acid in acetonitrile (B2) and 0.1% formic acid in water (A2). The gradient elution program was as follows: 0-1 min, 8% B2; 1-8 min, 8%-98% B2; 8-10 min, 98% B2; 10-10.1 min, 98%-8% B2; 10.1-12 min, 8% B2. In the negative ion mode, the mobile phases were acetonitrile (B3) and 5 mM ammonium formate in water (A3), and the gradient elution procedure was as follows: 0-1 min, 8% B3; 1-8 min, 8%-98% B3; 8-10 min, 98% B3; 10-10.1 min, 98%-8% B3; 10.1-12 min, 8% B3.  
Mass spectrometry analysis was performed using a Thermo Q Exactive Focus instrument (Thermo Fisher Scientific, USA) equipped with an electrospray ionization (ESI) source operating in both positive and negative ion modes. Instrument parameters were set as follows: spray voltages at 3.50 kV (positive) and \(-2.50\) kV (negative), sheath gas flow 40 arb, auxiliary gas 10 arb, capillary temperature 325 \({}^{\circ}\)C. Full scan acquisition was conducted with 70,000 resolution over m/z 100-1000, followed by data-dependent MS/MS acquisition (top 3 precursors) using higher-energy collisional dissociation (HCD) at 30 eV with 17,500 resolution. Dynamic exclusion was applied to minimize redundant fragmentation [36].
# A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System
###### Abstract  
With the increasing importance of renewable energy and flexible loads, the operation of the distribution system is becoming more stochastic and complex, and it is necessary to monitor the power system in real-time. Considering the gradual applications of intelligent electronic devices in the distribution systems, a hybrid state estimator based on supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements is proposed in this paper, which consists of the improved robust estimation and linear state estimation. At the time of SCADA data acquisition, the improved robust estimation combining the SCADA measurements with PMU measurements is performed. To eliminate the effect of bad data, the internal student residual method is introduced, and the robust thresholds are adjusted adaptively. Then the linear state estimation is performed at the time of PMU data acquisition based on the results of the previous estimation time and the PMU measurements, which can quickly correct the robust estimation results and track the changes of the distribution system. Finally, the effectiveness and performance of the proposed method are verified in a modified IEEE 33-bus distribution system and a real distribution system in China.  
distribution system state estimation; phasor measurement units (PMUs); improved robust estimation; fast state tracking +
Footnote †: journal: Applied Sciences
## 5 Conclusions
Furthermore, since not all monitoring applications require a high level of accuracy, such as the water level monitoring of dams, low-cost sensors can be combined to make a low-budget ranging device for these applications. Finally, with the open source and open hardware of the proposed measuring device, changes can be applied efficiently. The adverse effects of ambient light on ToF sensors were not amended significantly through a similar sensor combination. This issue needs to be studied by combining ToF sensors' estimations with ultrasonic sensors that are not affected by excessive light in the environment. Furthermore, the use of ToF sensors in this system has an indirect relation with the luminous response of a light detector sensor. More precise instruments, such as Linear Variable Differential Transformer (LDVT) sensors, are needed to perform the distance measuring experiments more accurately. Finally, individual sensor responses must be postprocessed to remove the proposed system's inherent noises. Conceptualization, J.T. and J.-A.-L.-G.; methodology, S.K. ; software, B.M. ; validation, S.K. and J.T. ; formal analysis, S.K. ; investigation, J.T. ; resources, H.M.; data curation, B.M. ; writing--original draft preparation, S.K. ; writing--review and editing, S.K. ; visualization, J.-A.L.-G.; supervision, J.T. ; project administration, H.M.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript. The Spanish Ministry of Economy and Competitiveness and Feder Funds: BIA2017-86811-C2-1-R; the Spanish Ministry of Economy and Competitiveness and Feder funds: BIA2017-86811-C2-2-R; the Secretaria d' Universitats i Recerca de la Generalitat de Catalunya, Catalunya, Spain: 2017 SGR 1482; Spanish Agencia Estatal de Investigacion del Ministerio de Ciencia Innovacion y Universidades and Fondo Social Europeo: PRE2018-083238. Not applicable. **Data Availability Statement:** All data and results are presented in this paper. **Acknowledgments:** The authors are indebted to the Spanish Ministry of Economy and Competitiveness for the funding provided through the research project BIA2017-86811-C2-1-R, directed by Jose Turmo, and BIA2017-86811-C2-2-R, directed by Jose Antonio Lozano-Galant. All these projects are funded with FEDER funds. Authors are also indebted to the Secretaria d' Universitats i Recerca de la Generalitat de Catalunya, Catalunya, Spain for the funding provided through Agaur (2017 SGR 1482). It is also to be noted that funding for this research has been provided for Seyedmilad Komarizadehasl by the Spanish Agencia Estatal de Investigacion del Ministerio de Ciencia Innovacion y Universidades grant and the Fondo Social Europeo grant (PRE2018-083238). **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Tests
### Tests Method  
To evaluate the accuracy and stability of placement methods, we proceed by testing each application on site and measuring differences of local coordinates between the real (d0) and the virtual environments (d1) by a topographic method (Figure 8a). The accuracy (e) and stability of placement were quantified using RMSE and deviation standards based on metric measurements of 12 quantifiable and significant points on site, such as the corners of the building, stairs, window frames, doors, corners of beams, etc., (indoors and outdoors) by using a robotic total station equipped with a laser system. The process is illustrated in Figure 8b.  
Figure 8: (**a**) Error of placement. (**b**) The adopted workflow of the experimental tests.  
The tests were performed in the same conditions (Table 1).  
For the marker-based techniques, we used several types of markers in terms of forms, colors and the number of characteristic points in the form (QR codes, printed photographs, images by a camera phone, etc.) (Figure 9). We should notice that the markers are kept as AR scene triggers, which implies that they must be detectable in the real world. Figure 9 presents their distribution for outdoor testing (Figure 10).