esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The main aim of this investigation was to determine whether the position of the copper tip of the oxygen lance relative to the inner wall of the BOF had any effect on the wear of the refractory lining in the areas analyzed. A proposal for the rotation management of the 4-hole, 5-hole, and 6-hole copper tips of the oxygen lance used in the BOF was determined. The article also discusses the possibility of saving on gunning mixtures in the area of BOF trunnions. The effect of controlled copper tip turning on MgO-C refractory wear uniformity during the campaign is also described. The results of the above analyses make it possible to formulate the following practical recommendations for the purpose of increasing the lifespan of MgO-C refractory linings in BOFs and for reducing the consumption of the gunning mixtures and thus saving costs.  
* Turn and weld the new 5-hole copper tip of the oxygen lance by 36\({}^{\circ}\) from its original position when replacing a worn tip (Figure 9). When replacing a 4-hole copper tip, rotate and weld the new tip 45\({}^{\circ}\) from its original position (Figure 10a). When replacing a 6-hole copper tip, turn and weld the new one by 30\({}^{\circ}\) from its initial position (Figure 10b).
* Using a 5-hole tip and alternating Periods 1 and 2, the copper tip rotates, exposing only one trunnion area of the MgO-C refractory lining to direct oxygen jets and molten metal and slag splashes at a time. In Period 1, the S3 zone is exposed while the opposite S9 zone is preserved. In Period 2, the roles reverse, saving the gunning mixture on the non-exposed side. Other MgO-C refractory zones in the BOF are repaired primarily through slag splashing and high-basic slag coating techniques.
* The following position management is used when using 4-hole or 6-hole copper tip. In Period 1, nozzles expose the S3 and S9 trunnion zones to oxygen jets and molten metal and slag splashes, causing refractory wear and consuming the gunning mixture. After turning the copper tip (30\({}^{\circ}\) for 6-hole, 45\({}^{\circ}\) for 4-hole) in Period 2, no oxygen jets directly hit the trunnion areas, reducing wear and conserving the gunning mixture. Laser measurements and consumption analysis confirm no excess gunning mixture usage in non-exposed areas. Slag splashing and high-basic slag coating techniques will prioritize repairing other refractory zones.
## 2 Experimental Setup and Procedure  
The ORC unit is a non-regenerative system consisted of a plunger pump, two plate heat exchangers (PHEs), a scroll expander, and a liquid receiver charged with R134a. The heat source is provided by five electrical heaters heating the diathermic oil, and the heat sink is cooled down by a chiller with water as the cooling medium. The organic fluid and the diathermic oil flow rates are adjusted using pump inverters, while the water flow rate provided by the chiller pump is fixed in all measurements. A photo of the ORC system studied in this work, its process and instrument diagram (P&ID) and the temperature-entropy (T-S) diagram for a given operating condition during the experimental tests are shown in Figure 1.  
The measured data of the steady-state performance were recorded with a time interval of 1 s for 10 min (600 data points), and then the average of the recorded data was used. To determine whether the system was at a steady-state condition, the methodology proposed by [PERSON] et al. [9] and also used by [PERSON] et al. [10] was applied. More precisely, the average of the first 30 data points was compared with the average of the last 30 data points, meaning two sample data with a 9-min delay. If the average of the two groups shows a negligible deviation, the system is considered at steady-state. Considering the characteristics of the test bench, 84 useful data points were recorded representing the off-design conditions of the system as far as the system boundaries and component limitations allowed. A description of the main components of the ORC test bench is reported in Table 1, and the characteristics of the sensors are given in Table 2.  
Figure 1: The photo of the organic Rankine cycle (ORC) test bench (**a**), the system schematic (**b**), and the temperature-entropy (T-S) diagram for a given operating condition during the tests (**c**). The following points are noteworthy regarding the experimental procedure and the subsequent database:  
* The operation of the system was adjusted by varying the rotational speed of the refrigerant and hot fluid pump, the hot and cold fluid inlet temperatures to the evaporator and condenser, respectively; and the electric load, hence a total of five degrees of freedom;
* The electric power consumption of the organic fluid pump and the electric power produced by the generator were measured using electric data loggers;
* The water flow rate was measured with a pulse flow meter, and an average value of 10 measurements was used for each data set. * The hot fluid flow rate was indirectly calculated considering the hot fluid pump specifications and applying an empirical model available from the literature.
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 1 Introduction  
Eighty percent of current offshore turbines in regions with water depth less than 40 m are supported by pile foundations [1]. Waves can cause the cyclic responses of piles and accumulate pore water pressure in the seabed, which is related to the permeability of seabed sediments. Understanding the responses of piles under wave loading is essential to secure offshore wind turbines.  
The available literature on pile foundations subjected to wave loading covers two aspects, i.e., cyclic response of piles [2, 3] and seabed response under wave loading. Relatively to the first aspect, a common simplification is to use cyclic concentrated loads to represent wave loading. For example, normal gravity tests [4, 5, 6] and centrifuge tests [7] were conducted, showing that displacement accumulation can be appropriately represented by a power function of cycle number \(N\) for rigid piles in dense sand [8, 9, 10]. [PERSON] et al. [11] and API [12] reported that soil stiffness and strength degraded during laterally cyclic loading, with a recommended reduction of 10% for sand. Some different views were also recently presented in the literature [6, 13, 14, 15, 16], e.g., it was found that an increase in pile capacity can be led by cyclic loading. This reveals the complexity of this problem.  
Regarding the second aspect of seabed responses, theoretical studies were carried out to describe the oscillation of pore water pressure [17, 18, 19] based on [PERSON]'s theory [20] and mostly adopted linear theory for both elastic solid skeletons and compressible fluids.  
The accumulation of pore pressure is often studied through experiments because of the relatively complex mechanism [21; 22; 23; 24]. In these studies, pile foundations are commonly neglected. In reality, pile foundations [25; 26] and seabed are in an integrated interaction status, whereby separating the two aspects may not necessarily lead to conservative results.  
A more promising approach to the study of piles in seabed is to use an integrated method by coupling the two aspects. This study utilized flume experiments via which the response of piles and seabed under wave loading were measured. Two types of seabed soil, fine siliceous sand with \(d_{50}=0.15\) mm and silt with \(d_{50}=0.06\) mm, were considered. In total, 12 tests were conducted, through which the effects of pile diameter, pile cross-section, pile stiffness and wave variations were investigated.
## 6 Unbalanced PSI-CA Protocol Based on Single-Cloud Assistance
### Experimental Analysis
#### 6.5.2 Protocol Running Time  
For this protocol, as shown in Table 4, the paper conducted experiments and recorded the running time of the protocol under various data volume combinations. Because preprocessing can be completed offline, the running time of the protocol refers to the total time of the outsourcing process and the intersection process. Table 4 also compares the running times of the unbalanced PSI-CA protocol based on Cuckoo filter and the unbalanced PSI-CA protocol based on single-cloud assistance. Here, Protocol 1 refers to the unbalanced PSI-CA protocol based on Cuckoo filter, and Protocol 2 refers to unbalanced PSI-CA protocol based on single-cloud assistance.  
From the experimental analysis, the following conclusions can be drawn: In cases of smaller data volumes, the performance differences between the two protocols are not significant. However, as the data volume increases, the running time differences between different protocols gradually become apparent. This is because, at certain specific levels, the proportion of communication time is relatively high when the data volume is small, significantly impacting the results. For larger data volumes, where computation time dominates, Protocol 2, by placing computational tasks on the more powerful cloud server, gradually widens the running time difference from Protocol 1. Overall, the use of cloud resources in the unbalanced PSI-CA protocol based on single-cloud assistance significantly reduces running times, especially when dealing with large-scale datasets.
# Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
## 3 Materials and Methods
### SURF Feature Points
#### 3.1.1 Feature Points Detection  
In order to ensure the scale invariance of SURF feature points, the algorithm performs feature extraction in the same scale space as the SIFT algorithm. The scale space of the image is defined as  
\[\mathrm{L(x,y,\sigma)=G(x,y,\sigma)*I(x,y)} \tag{1}\]  
In the above formula, \(\sigma\) represents the scale space, \(G(x,y,\sigma)\) is the two-dimensional Gaussian function, and \(*\) represents two-dimensional convolution.  
To improve the convolution speed, the SURF algorithm uses the box filters (as shown in Figure 2) and uses the integral image to accelerate the image; the calculation efficiency is more than three times higher than that of the SIFT algorithm.  
The extreme values of the pixel points are obtained using the Hessian matrix. The definition of the Hessian matrix at the position \(\sigma\) of the image \(I(x,y)\) is:  
\[H\,=\,\begin{bmatrix}L_{xx}(x,\sigma)&L_{xy}(x,\sigma)\\ L_{xy}(x,\sigma)&L_{yy}(x,\sigma)\end{bmatrix} \tag{2}\]  
In the above formula, \(L_{xx}(x,\sigma)\) is the two-dimensional convolution of the second-order partial derivative of the Gaussian function and the image; \(L_{xy}(x,\sigma)\) and \(L_{yy}(x,\sigma)\) are defined similarly. Nonmaximal suppression is performed in the stereo neighborhood in the range of \(3\times 3\times 3\) around the point (as shown in Figure 3), and the candidate feature points must be extreme in the neighborhood range. The candidate feature points are interpolated in the scale space and the image space to obtain the feature point positions and the corresponding scales.  
Figure 1: Tile map registration flowchart.  
Figure 2: Approximations using box filters. (**a**) x direction. (**b**) y direction. (**c**) xy direction.
# Implementation of MS Circle Map in Digital Image Encryption
## 3 Results and Discussion
### Correlation Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Data**} & \multicolumn{3}{c}{**Correlation Coefficient Values of**} & \multicolumn{3}{c}{**Correlation Coefficient Values of**} \\  & \multicolumn{3}{c}{**Plain Color Images**} & \multicolumn{3}{c}{**Encrypted Color Images**} \\ \cline{2-7}  & **Horizontal** & **Vertical** & **Diagonal** & **Horizontal** & **Vertical** & **Diagonal** \\ \hline
1 & 0.96568 & 0.94434 & 0.92287 & \(-\)0.0029 & 0.00052 & 0.0038 \\ \hline
2 & 0.96617 & 0.94438 & 0.92639 & \(-\)0.00256 & 0.00015 & 0.00338 \\ \hline
3 & 0.97512 & 0.97521 & 0.96592 & \(-\)0.0011 & \(-\)0.0004 & \(-\)0.00047 \\ \hline
4 & 0.86378 & 0.85873 & 0.79948 & \(-\)0.00104 & \(-\)0.00091 & \(-\)0.00116 \\ \hline
5 & 0.90500 & 0.89435 & 0.84902 & \(-\)0.00001 & 0.00023 & \(-\)0.00015 \\ \hline
6 & 0.96200 & 0.95648 & 0.93273 & 0.00066 & 0.00012 & \(-\)0.00033 \\ \hline
7 & 0.98914 & 0.98481 & 0.98135 & 0.00022 & \(-\)0.00013 & \(-\)0.00002 \\ \hline
8 & 0.99281 & 0.98962 & 0.987836 & 0.00023 & 0.0001 & \(-\)0.00053 \\ \hline
9 & 0.99415 & 0.99176 & 0.9895 & 0.00041 & 0.00055 & \(-\)0.00063 \\ \hline
10 & 0.96107 & 0.95283 & 0.9337 & \(-\)0.00256 & 0.0008 & 0.00321 \\ \hline
11 & 0.96845 & 0.96614 & 0.95483 & \(-\)0.00436 & \(-\)0.00166 & \(-\)0.0015 \\ \hline
12 & 0.98193 & 0.98199 & 0.969853 & 0.00053 & 0.00013 & \(-\)0.00006 \\ \hline
13 & 0.81418 & 0.74386 & 0.65015 & \(-\)0.0016 & 0.00252 & 0.00222 \\ \hline
14 & 0.86347 & 0.78645 & 0.71327 & \(-\)0.00126 & \(-\)0.00027 & 0.00058 \\ \hline
15 & 0.91477 & 0.87933 & 0.82351 & 0.000013 & 0.000016 & \(-\)0.00026 \\ \hline Average & 0.9412 & 0.9234 & 0.8934 & \(-\)0.0010 & 0.0001 & 0.0005 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Correlation coefficient of the color images.
###### Abstract  
We propose an efficient logical layer-based reconciliation method for continuous-variable quantum key distribution (CVQKD) to extract binary information from correlated Gaussian variables. We demonstrate that by operating on the raw-data level, the noise of the quantum channel can be corrected in the low-dimensional (scalar) space, and the reconciliation can be extended to arbitrary dimensions. The CVQKD systems allow an unconditionally secret communication over standard telecommunication networks. To exploit the real potential of CVQKD a robust reconciliation technique is needed. It is currently unavailable, which makes it impossible to reach the real performance of the CVQKD protocols. The reconciliation is a post-processing step separated from the transmission of quantum states, which is aimed to derive the secret key from the raw data. The reconciliation process of correlated Gaussian variables is a complex problem that requires either tomography in the physical layer that is intractable in a practical scenario, or high-cost calculations in the multidimensional spherical space with strict dimensional limitations. To avoid these issues, we define the low-dimensional reconciliation. We prove that the error probability of one-dimensional reconciliation is zero in any practical CVQKD scenario, and provides unconditional security. The results allow for significantly improving the currently available key rates and transmission distances of CVQKD.  
Keywords:continuous-variable quantum key distribution; quantum Shannon theory +
Footnote †: journal: Applied Physics Letters
# Quality and Safety of Dried Mushrooms Available at Retail Level
## 2 Materials and Methods
### Microbiological Enumeration and Detection  
Usually, dried mushrooms are rehydrated in water before further processing and being used in dishes. Therefore, 50 g of each sample was soaked in a sterile plastic bag with 100 mL of sterile distilled water at room temperature for one hour according to ISO 6887-4:2017 [18]. Subsequently, a 75 g portion of the rehydrated mushroom sample (corresponds to 25 g dry matter) was diluted in 225 mL buffered peptone water (BPW) (Biokar, Groupe Solabia, Pantin Cedex, France), homogenized for 60 s in a laboratory blender (Stonacher, Seward Limited, West Sussex, UK), and serial decimal diluted in Ringer's solution (B. Braun, Melsungen, Germany). AMC, _Enterobacteriaceae_ (EB), ppBC, yeasts, and molds were enumerated according to the corresponding ISO standards or in-house methods (Supplemental Table S1). Microbiological counts were calculated according to ISO 7218:2007/Amd.1:2013 [19]. For the detection of _Listeria_ spp., _L. monocytogenes_, and _Salmonella_ spp., a 75 g portion of rehydrated mushrooms each was enriched and streaked on selective agar according to the ISO standards (Supplemental Table S1).  
Typical colony morphologies (1-5 isolates) for EB, pBC, _Listeria spp., L. monocytogenes_, and _Salmonella_ spp. were selected and sub-cultivated on Tryptic Casein Soy Agar with 0.6% yeast extract (TSAYE, Biokar) for further confirmation. Colonies that were typical for coliform bacteria on Violet Red Bile Glucose Agar (pink to red or purple colonies with and without precipitation halos) were further differentiated on Brilliance _Escherichia_ (_E._) _coli_/Coliform Agar (Thermo Fisher Scientific Inc., Oxoid, Waltham, MA, USA) according to the manufacturer's colony morphologies (presumptive _E. coli_: purple, coliform bacteria: pink). These results were used to enumerate _E. coli_ and coliforms in dried mushroom samples. Presumptive BC grown on MYP were further streaked on _Bacillus_ Chromo Select Agar with Polymysxin B (Sigma-Aldrich, St. Louis, MO, USA) and 10.0% egg yolk suspension (Thermo Fisher Scientific Inc., Oxoid). Blue growing colonies with an opaque halo were used for the final confirmation steps of the pBC. For further confirmation by PCR, the purified _Salmonella_, _Listeria_, and pBC isolates were stored at \(-80\)\({}^{\circ}\)C in 25% glycerol stocks in the strain collection of the Unit of Food Microbiology (University of Veterinary Medicine, Vienna, Austria).  
The microbiological quality of dried mushrooms was assessed as satisfactory according to the values for the AMC load \(\leq\) 6.0 log cfu/g; for EB \(\leq\) 4.0 log cfu/g; and for pBC \(\leq\) 2.0 log cfu/g (according to the recommended limits of the German Society for Hygiene and Microbiology (DGHM) [20], as well as for yeasts \(\leq\) 4.0 log cfu/g and molds \(\leq\) 3.0 log cfu/g (according to our internal standard). The critical limit of pBC was defined by the recommendations of the DGHM [20] for dried mushrooms, with \(\geq\)3.0 log cfu/g.
# Voltage Ride through Control Strategy of Modular Multilevel Converter under Unbalanced Voltage Sag
## 4 Simulation Results and Analysis
Figure 7: Dynamic response of the GC-MMC to the ramp-up and ramp-down changes of input power command. In Phase A, voltage sag is considered to drop 20% at t = 2.5 s over 20 ms. In the beginning, the MMC was delivering 0.5 MW active power and 0.2 MVAr reactive power to the grid. As illustrated in Figure 8, there were oscillations in active and reactive powers during the unbalanced voltage fault. It is observable that the offered current controllers allowed sustained power as demanded by the DC power source during and after the fault. In spite of the imbalance of the AC voltages, the current  
Figure 8: Time domain response of the GC-MMC during (**a**) unbalanced voltage sag due to a single-line-to-ground fault, (**b**) unbalanced voltage sag due to a line-to-line fault and (**c**) unbalanced voltage sag due to a double-line-to-ground fault. controller was able to balance the output AC currents. As a result, the active, alongside the reactive powers, started oscillating with the double frequency of an AC grid. Owing to the decoupling between the AC and DC parts, the voltage sag was compensated by the stored energy inside the MMC. Finally, as has been verified, the variation of capacitor voltages were within satisfactory bounds and were not influenced by grid voltage faults, excluding the voltage sag case (c). As illustrated in Figure 9c, the voltage sag due to the double-line-to-ground fault created many oscillations on the active and reactive powers, as well as many distortions of the capacitor voltage. In this situation, a supercapacitor with a high power density would be proposed to absorb the power oscillation during voltage sag and satisfy the voltage ride through condition at the output of the GC-MMC [19]. To observe the performance of the proposed controller, an experimental setup was developed. The test system was connected to a 750-W solar system. Since the system required many input/outputs and a sampling frequency up to 10 kHz, the dSPACE MicroLabBox was used as the real-time interface for the control of the MMC. The experimental setup consisted of a low-power MMC with three levels, as shown in Figure 9. The parameters of the experimental setup are listed in Table 2.  
The MMC was examined under unbalanced voltage sag. The voltage waveform captured on the oscilloscope for the three-level MMC can be seen in Figure 3:0 The voltage waveform obtained on the oscilloscope had a peak voltage of approximately 10 V.  
Figure 3:1 Voltage waveform across the load. Figure 3:2 Setup of a three-level, single-phase MMC. The voltage across the capacitor of module 1 as obtained on the control desk can be seen in Figure 3:3 As the voltage was obtained at \(\
u_{\mathrm{dc}}\approx 20\) V, it was expected that the average value when the algorithm is active, across any submodule, at any given time, should be approximately 10. The obtained results show that the MMC was stable under voltage sag condition and the proposed controller kept the capacitor voltage in the viable boundary.
## 4 Results and Discussion
### Performance on House B  
In addition, we conducted comparative experiments on the House B data from the ARAS multi-resident activity dataset. The models were evaluated using five-fold cross-validation, and their performance is presented in Table 5. As shown in the table, the Gated Recurrent Unit (GRU) achieved the highest performance on House B, followed by the decision tree (DT), while the Gaussian naive Bayes (GNB) had the lowest performance. These results are similar to those observed for House A.  
To gather more empirical information, we analyzed the impacts of different training-test splitting ratios on the House B data from the ARAS dataset. Our experiments yielded comparative results, which we present in Figure 5. Similar to our findings for House A, we observed that the GNB and KNN classifiers exhibited varying levels of performance in terms of the recall, macro F-1, and weighted F-1 scores for different splitting ratios. On the other hand, the remaining classifiers displayed more consistent performance across different metrics.  
Figure 4: The figures presented here demonstrate the relative performance of various models on the House A data from the ARAS dataset. The sub-figures **(a–f)** display the accuracy, precision, recall, F-1, macro F-1, and weighted F-1 scores, respectively, obtained by applying ten machine learning and deep learning models at different splitting ratios for the training and testing sets. These ratios include 80:20, 70:30, and 60:40 for training and testing.  
Figure 5: _Cont_.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F-1 Score** & **Macro Average F-1** & **Weighted Average F-1** \\ \hline Decision Tree ** & 1.0 & 0.98 & 0.98 & 0.98 & 0.99 & 1.0 \\ Gaussian Naive Bayes + & 0.95 & 0.35 & 0.94 & 0.49 & 0.74 & 0.96 \\ Random Forest & 0.99 & 0.99 & 0.96 & 0.98 & 0.98 & 1.0 \\ LGBM & 1.0 & 0.97 & 0.96 & 0.98 & 0.98 & 1.0 \\ Support Vector Machine & 1.0 & 0.99 & 0.95 & 0.97 & 0.95 & 0.99 \\ Logistic Regression & 1.0 & 0.93 & 0.92 & 0.91 & 0.97 & 1.0 \\ k-Nearest Neighbors & 0.99 & 0.97 & 0.85 & 0.90 & 0.89 & 0.98 \\ Gradient Boosting Machine & 1.0 & 0.97 & 0.86 & 0.93 & 0.95 & 1.0 \\ LSTM & 1.0 & 0.98 & 0.97 & 0.98 & 0.98 & 1.0 \\ GRU Technique * & 1.0 & 0.99 & 0.99 & 0.99 & 0.99 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: This report presents a comparison of the performance of ten machine learning and deep learning classifiers applied to the House B data from the ARAS multi-resident dataset. The performance evaluation was conducted using 5-fold cross-validation. In the table, an asterisk (*) indicates the overall best performance, (**) indicates the second-best performance, and (+) indicates the worst performance.