esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Key-Value Data Placement for ZNS SSD
## 6 Experiments
### I/O Performance  
Figure 16 shows the normalized I/O performance of _\"fillseq + overwrite\"_ workload on 4 million keys. As a result, the lifetime-based zone allocation method improved the IOPS by 5% compared to the conventional method. Otherwise, the proposed garbage collection logic could use more key ranges but showed that valid page copying resulted in performance degradations. The introduction of garbage collection into the conventional method resulted in a 9% performance degradation and the proposed \"hot/cold + GC\" technique showed a 3% performance degradation.  
The reason for the above evaluation results is the zone allocation latency, as described in Figure 17. According to Figure 17a, you can see that the zone allocation method based on the lifetime without garbage collection had a smaller latency for zone allocation than the conventional method. In other words, the lifetime-based zone allocation method was interrupted by finding free zone less often and this resulted in a better performance than that of the conventional method.  
Figure 16: Input/Output Operations Per Second (IOPS) of _“fillseq + overwrite\"_ for 4 million keys.  
Figure 17: Latencies of _”fillseq + overwrite\"_ for 4 million keys (**a**) without garbage collection and (**b**) with garbage collection.  
On the other hand, Figure 17b shows the latency required to obtain space when garbage collection was introduced. Although it is not represented in the graph, the latency was less than that of the method that did not use garbage collection up to the 90 th percentile, but it could be confirmed that latency increased sharply after the 90 th percentile.  
It was found that the cause of the increase in the latency over the 99 th percentile in the garbage collection case was valid data copying. Moreover, the zone allocation method based on lifetime with garbage collection had more latency in the 99 th percentile than the conventional one. This is because the proposed method produced a zone with all data invalidated more quickly and caused garbage collection processing to occur earlier than conventional methods.  
Nevertheless, the zone allocation method based on lifetime with garbage collection had fewer latencies over the 99.9 th percentile than the conventional method due to the valid page copying reduction. Because the latencies were reduced, I/O could be processed faster. For this reason, the proposed method resulted in a relatively high IOPS compared to conventional zone allocation with garbage collection.
## 3 Processing and Extraction
### Pulsed Electric Field-Assisted Extraction
As the above results for PEF-assisted extraction from by-products only concern blueberry pomace treatment, an example of extraction of polyphenic compounds from black-currant juice preceded by PEF application could be mentioned (not included in the following table). The PEF procedure was conducted before juice production under optimized conditions, as determined by RSM modelling. The chosen conditions for the electric field  
Figure 4: A scheme of PEF impact on plant material. 1318 V/cm and 315 pulses (pulse width 100 ms)--followed by methanol (for TPC and AA determination) or ethanol (for monomeric anthocyanin content determination) liquid extraction of blackcurrent juice resulted in extracts with significantly lowered pH but increased TPC, AA and monomeric anthocyanin content compared to the control (blackcurmat juice untreated with PEF) [123]. Conditions of extraction also influence the effectiveness of PEF, as listed below. Impact of Processing Conditions  
Field strength: Most studies show that a higher field strength results in increased antioxidant compound yields. When field strength is greater, the potential difference outside and inside the cell membrane is higher than the critical membrane potential and this improves the dissolution rate of the cell membrane. However, applying too strong a field may promote antioxidant degradation [119]. Number of pulses: Study results are inconclusive regarding the influence of the number of PEF pulses on the antioxidant content and capacity of extracts. [PERSON] et al. [113] claim that the highest number of pulses used (100) resulted in the highest antioxidant content and capacity, while [PERSON] et al. [122] claim that 12 pulses was less efficient than 10, this being the optimal number of PEF pulses for the anthocyanin extraction process. Solvent type: The properties of solvents which may determine extraction efficiency are described in the paragraph focused on SLE. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{4}{c}{**PEF Conditions**} \\ \multicolumn{4}{c}{**Material**} & \multicolumn{2}{c}{**(E—Equipment, P—Power, FS—Field Strength, f—Frequency, W—Pulse Width,—Energy Input, S—Solvent, L
## 2 Magnet Anomaly Motion Target Feature Detection
### Standard Orthogonal Basis Decomposition  
The standard orthogonal basis serves as a method to dissect magnetic anomaly signals. It seeks to decompose the magnetic field, under the framework of the target magnetic dipole model, into several orthogonal basis functions. Subsequently, energy detection is achieved by summing up the squares of the weighted coefficients. Within this method,  
Figure 2: Relationship between the magnetic field \(\mathbf{B}\) of the magnetic target and the geomagnetic field \(\mathbf{B}_{E}\).  
magnetic anomaly signals manifest as a linear combination of three distinct functions, depicted in Equation (6):  
\[S=\frac{\mu\mathbf{M}}{4\pi R_{m}^{3}}\sum_{n=1}^{3}\tau_{n}\varphi_{n}(w) \tag{6}\]  
where \(\tau_{n}\) symbolizes the coefficient of the basis functions, with \(w\) equating to \(dm/Rm\). The foundational basis functions are classified as \(\varphi_{1}(w)=\frac{w^{2}}{|1+w^{2}|^{\frac{5}{2}}}\), \(\varphi_{2}(w)=\frac{w}{|1+w^{2}|^{\frac{5}{2}}}\), and \(\varphi_{3}(w)=\frac{1}{|1+w^{2}|^{\frac{5}{2}}}\). After undergoing Schmidt orthogonalization and normalization, the following orthogonal basis functions emerge:  
\[f_{1}(w)=\varphi_{1}(w)\sqrt{\frac{128}{3\pi}},\,f_{2}(w)=\varphi_{2}(w)\sqrt {\frac{128}{5\pi}},\,\,\text{and}\,\,f_{3}(w)=\left[\varphi_{3}(w)-(\frac{5}{3 })\varphi_{1}(w)\right]\sqrt{\frac{24}{5\pi}}.\]  
These functions have orthogonality and normalization properties, as illustrated in Equation (7):  
\[\begin{cases}\int_{-\infty}^{+\infty}f_{1}(w)f_{j}(w)dw=0,i\
eq j\\ \int_{-\infty}^{+\infty}f_{1}^{2}(w)dw=1,i=1,2,3\end{cases} \tag{7}\]  
Relying on the orthogonality of the OBF, the decomposition coefficients \(\tau_{1}\), \(\tau_{2}\), and \(\tau_{3}\) are derived from the orthogonal basis decomposition, with their respective solution formulas presented as:  
\[\tau_{n}=\left(\frac{\mu_{0}\mathbf{M}}{4\pi R_{m}^{3}}\right)^{-1}\int_{- \infty}^{+\infty}f_{n}(w)Sdw,\,\,n=1,2,3 \tag{8}\]  
During real-time magnetic target detection, the magnetic anomaly signal \(S\) is contemporaneously measured with discrete values. The timeliness of this signal is pivotal. To uphold this real-time characteristic of the magnetic anomaly signal \(S\), one can employ a sliding time window for decomposition. This process is visually represented in Figure 3.
## 2 Description of a Simultaneous Service Process
### Example 3  
Let the service time of a customer, which receives service in the system alone, have the \(PH\) distribution with an irreducible representation \((\beta,S)\). Let the \(LPS\) service discipline be used, and the maximal number of customers that can receive service simultaneously is equal to \(N\). Recall that under this discipline, all admitted customers are processed at an equal rate that is inversely proportional to the number of serviced customers. Thus, if \(n\) customers receive service, then the transitions of the underlying process of service of each of these customers are described by the sub-generator \(\frac{1}{n}S\).  
Formulas for computation of the matrices \(H_{n}^{0}\), \(H_{n}^{-}\), and \(H_{n}^{+}\) in the description of simultaneous service for such a system can be found in Lemma 1 in [6]. Namely,  
\[H_{n}^{0}=\frac{A_{n}}{n},\ n=\overline{1,N},\ H_{n}^{-}=\frac{L_{N-n}}{n},\ n= \overline{1,N},\ H_{n}^{+}=U_{N-n},\ \ n=\overline{0,N-1},\]  
where the meaning of matrices \(\ A_{n},\ L_{N-n},\ U_{N-n}\) is explained in the previous example and recursive algorithms for their computation are given in [35; 36].  
It is worth noting that besides the classical \(LPS\) discipline, where all admitted customers equally share the system's bandwidth (egalitarian \(PS\)), there exist modifications of this discipline, e.g., discriminatory \(PS\) and the generalized \(PS\), see [38; 39]. These modifications can also be analyzed using the results of this paper.
## 2 Train Modeling
### Train Dynamics  
In this study, the applied train model is assumed to be a single point mass model, as expressed in Equation (1), and the total weight of the train is considered as the full load; the change in weight is not considered [16; 21; 22].  
\[ma=F_{t}-\left(F_{r}+F_{g}\right), \tag{1}\]  
where \(m\), \(a\), and \(F_{t}\) are the mass of the train, the acceleration of the train, and the traction and braking force implemented in the propulsion and braking system, respectively. Here, \(F_{r}\) and \(F_{g}\) denote the running resistance affected by the running speed of the train and the gradient resistance given by the inclination of the running track, respectively. In addition, the forces that interfere with the running of the train act in various complex manners, such as curve and tunnel resistances. In practical applications, determining a highly precise optimal solution according to the environment of the operating route by using precise route model data is highly advantageous.  
The traction and braking force of the train are defined by Equation (2) of the propulsion and braking force and are approximated according to the characteristics of the train's propulsion system [20].  
\[F_{tMax}=\left\{\begin{array}{cc}F_{acMax},&v\leq V_{ac}\;\cap\;a\geq 0\\ F_{acMax}\cdot V_{ac}/v,&v>V_{ac}\;\cap\;a\geq 0\\ F_{dcMax},&v\leq V_{dc}\;\cap\;a<0\\ F_{dcMax}\cdot V_{dc}/v,&v>V_{dc}\;\cap\;a<0\\ \end{array}\right.. \tag{2}\]  
Here, \(v\), \(V_{ac}\), \(F_{acMax}\), \(V_{dc}\), and \(F_{dcMax}\) are the speed of the train, the speed that distinguishes the motor characteristic area, the maximum torque in the steady torque area, the speed that distinguishes the motor characteristic area during braking, and the maximum braking torque in the constant torque area, respectively.  
The Davis equation, which is widely used to calculate rolling and air resistances, is used for calculating the running resistance, as shown in Equation (3) [23].  
\[F_{r}=a_{r}+b_{r}\cdot v+c_{r}\cdot v^{2}, \tag{3}\]  
where \(a_{r}\), \(b_{r}\), and \(c_{r}\) are the coefficients of the running characteristics of the train.  
The gradient resistance is calculated using Equation (4), according to the inclination of the train's running track.  
\[F_{g}=mg\cdot sin(\theta), \tag{4}\]  
where \(g\) is the gravitational acceleration, and \(\theta\) is the slope angle. Since the slope data of the running track are recorded in per mil units, the \(\tan^{-1}\) (Gradient [m]/1000) function is applied to convert it into \(\theta\) radian units to apply Equation (4). Table 1 presents the train model parameters used in this study.
# Probabilistic Analysis as a Method for Ground Freezing Depth Estimation
## 2 Material and Methodology
### Estimation of Gumbel Distribution Parameters
#### 2.3.1 Least Square Estimation  
This method is relatively easy to use as double logarithm of Gumbel distribution results in the equation of a straight line. The parameters of the regression function are approximations of the Gumbel distribution parameters. Good approximation is obtained when noticeable linear relationship is present. The estimation method of Gumbel distribution parameters based on the least square method gives the best results as the coefficient of correlation R \(\geq\) 0.95. However, it is very sensitive to the cases when some points are located far outside the straight line. Such cases are often occurring, for example, in snow load on the ground. Such points are increasing the steepness of the regression line, which leads to the overestimation of the predicted values. In extreme cases, the linear regression do not fit the approximated empirical distribution at all, which then disqualifies this method.  
The straight line was fitted to the points placed on the probability grid so that it runs as close as possible to all points, i.e., the sum of the squares of vertical distance of points from this straight line is as small as possible. The inverse of the slope coefficient of this line and its Z-intercept are the parameters of the Gumbel distribution--being the results of the least squares estimation (LSM).
# Spectroscopic and Imaging Analyses on Easel Paintings by [PERSON]
###### Abstract
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Painting and Code** & **Location** & **Panel Size** & **Supports Description** & **Date** & **Non-Invasive Analyses** \\ \hline _Muses_ (Clio, Melpomene, Erato, Calilope, Etturepe, Polyhrynmia, Terpische)—MC & Florence, Galleria Corsini & 83 \(\times\) 40 cm & 1 board & 1470–1480 (or 1480–1490) & Vis, IRR, IRC, Vis-RS, ED-XRF, DM \\ \hline _[PERSON] [PERSON] in the_ _Sarcophagus with the Symbols_ _Passion_–GSCSP & Urbino, Galleria Nazionale delle Marche & 25 \(\times\) 20 cm & 1 board & 1475–1482 & Vis, IRR, IRC, UVF, Vis-RS, ED-XRF, DM \\ \hline _Apostles_ ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON])—GST & Urbino, Galleria Nazionale delle Marche & 97 \(\times\) 40 cm & 1 board & ante 1480 (14757) & Vis, IRR, IRC, Vis-RS, ED-XRF, DM \\ \hline _[PERSON] administers the_ _Commento to [PERSON]_–CRISP & Urbino, Casa Raffaello & 110 \(\times\) 79 cm & 3 vertical boards, poplar & About 1474–1480 & Vis, IRR, IRC, ED-XRF, DM \\ \hline _Martydom of [PERSON]_–GSM & Urbino, Casa Raffaello & 210 \(\times\) 166 cm & 5 vertical boards, poplar & 1487–1488 & Vis, IRR, IRC, Vis-RS, ED-XRF, DM \\ \hline _[PERSON] Supported by Two Angles_–GSCMA & Urbino, Galleria Nazionale delle Marche & 35 \(\times\) 23.5 cm & 1 board & 1483–1485 & Vis, IRR, IRC, DM \\ \hline _Sacred_ & Gradara, Rocca di & & & & \\ _Conversation_–GRAD & Gradara & 210 \(\times\) 164 cm & 8 vertical boards & 1484 \({}^{\star}\) & Vis, IRR, IRC, Vis-RS, ED-XRF, DM \\ \hline _Sacred_ _Conversation_–GSMF & Fano, Museo Civico & 221 \(\times\) 184 cm & 7 vertical boards, poplar & 1484–1489 & Vis, IRR, IRC, Vis-RS, ED-XRF, DM \\ \hline _Anntunciation_–GSA & Urbino, Galleria Nazionale delle Marche & 233 \(\times\) 162 cm & 12 horizontal boards & 1485–1490 & Vis, IRR, IRC, UVF, Vis-RS, ED-XRF, DM \\ \hline _Dead Christ and the Virgin_–GSCM & Urbino, Galleria Nazionale delle Marche & 54 \(\times\) 41 cm & 1 board & 1482–1490 & Vis, IRR, IRC, UVF, Vis-RS, ED-XRF, DM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List and description of [PERSON]’s paintings involved in the diagnostic campaign, with the code given to each work and the related analysis. The dates marked with an asterisk (*) were deduced from inscriptions, documentary sources and references; the others are those generally hypothesized by art historians [3; 5]. This paper presents the results of in situ investigations, including photographic, reflectographic, spectrometric, spectroscopic and microscopic methods, focused on easel wood and canvas paintings, as they represent a quite homogeneous group from a technical point of view. In addition, a multivariate data treatment (PCA and HCA analysis) was applied to a large set of ED-XRF data acquired for the different hues investigated to verify whether the chemometric approach, scarcely ever used for paintings, can be useful to obtain more information on the painter's palette.
# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 2 Materials and Methods
### Splitting of Soil Samples  
In this study, the LUCAS 2015 topsoil dataset (0-20 cm), comprising 21,859 samples, was used as the reference data. Since spectral data were available for only 21,782 samples, all soil samples with spectral data, including both organic and mineral soils, were utilized without accounting for additional factors such as land use classification or soil type. First, the Latin Hypercube Sampling (LHS) Minasny and McBratney [35] method was employed to divide the dataset into a training set (85%, 18,516 samples) and a test set (15%, 3266 samples). A stratified sampling strategy was applied by dividing the variable range into equal intervals and randomly selecting a value from each interval. This approach minimizes sampling bias and ensures representative coverage of the dataset's variability. The training set was then divided equally into six parts for six-fold cross-validation. Each fold was alternately used as the validation set, while the remaining five folds were merged to form the training set, with this process repeated six times. The training set was used to learn model parameters, while the validation set helped to prevent overfitting and optimize hyperparameter combinations. Due to the computational cost and the primary focus of this study on evaluating the model's effectiveness, hyperparameter optimization was performed using manual fine-tuning on the validation set. In the final testing phase, the test set obtained through data partitioning was used exclusively for model evaluation. This test set remained independent of the cross-validation process, ensuring that it was not involved in model training or validation, thereby providing an unbiased assessment of model performance.
# Monitoring of Atmospheric Corrosion of Aircraft Alloy AA2024 by Acoustic Emission Measurements
## 3 Results and Discussion
### Frequency Analysis of Atmospheric Corrosion AE Data
However, the maximum frequencies reached were lower than those in the initial phase where the full droplet was present on the specimen. The rapid reduction in the RH at the end of the experiment led to a short but significant increase in the frequencies, with peak values of nearly 2 MHz. A detailed view of these results revealed that this happened about 30 min after the start of drying out at an RH of 37% and ended about 12 min later, which fits well with the end of the increases in the above-mentioned cumulative quantities. The corresponding videos showed that at the beginning of this time span, the specimen surface was already dry but the remaining NaCl was still slightly soaked with some water. In the following 10 min, the last of the water evaporated, leaving dry NaCl deposits on the specimen's surface, as can be seen in Figure 5e. Thus, it was expected that this high-frequency AE activity also originated from sources other than corrosion, e.g., the very fast drying of the surface and its deposits. However, due to the very short durations of these possibly disturbing additional AE sources, their effects on the experimental findings of the cumulative AE hit energy were excluded. The frequency analysis shows that further information can be extracted from the AE data. The low bubble activity in the pits could not be fully detected with this frequency domain analysis. However, the transitions of the dominant frequency activities attributed to corrosion to the frequencies of the background noise and vice versa shifted the AE measurements closer to the corresponding observation points of the cumulative video event count.
## 3 Results and Discussion
### Volatile Fatty Acid Evolution  
In Figure 3A, the total acid concentration at the beginning and at the end of the tests are presented. The initial total acid concentration, measured as acetic acid equivalents, was similar for C and MNO, while MN was slightly lower (2.5, 2.4 and 2.2 g acetic acid/L, respectively).  
It is important not to attempt to achieve a high total acid concentration through ozone dosing, as this does not guarantee an improvement in methane production efficiency. Previous studies suggest that the ozonation of sludge improves the hydrolysis rate and, consequently, the VFA production rate. However, a VFA production over the uptake capacity of methanogens can significantly inhibit methane production [36].  
At the end of the tests, the control test showed a concentration of 0.06 g equiv acetic/L, while MN and MNO exhibited very similar values (0.09 and 0.10 g equiv acetic/L, respectively). In terms of total acidity, there was a reduction of more than 95% in all tests, indicating the proper conversion of VFAs into methane during the methanogenic stage [37]. Notably, the MNO test stands out, achieving a reduction of 98%.  
Figure 3: Total acidity concentration (**A**) and individual volatile fatty acid concentrations (**B**) at the beginning (I) and at the end (E) of the Biochemical Methane Potential P test. Control (MC), nanoparticles (MN) and nanoparticles together with ozone pre-treatment (MNO).  
Figure 3B shows the acid profile at the beginning and at the end of the test. It is noticeable that all acids were completely consumed except for acetic acid, which showed an 87% reduction in the MC and MN tests and a 95% reduction in the MNO tests.  
In the literature, it has been described that intermediate doses of ozone ranging from 0.049 to 0.157 g O\({}_{3}\)/g COD showed high acetoclastic activity with low lag phases [12]. This is consistent with the results obtained in our study, where ozone pre-treatment at doses similar to those used (0.045 g O\({}_{3}\)/g TCOD) increased biomass viability and acetoclastic methanogenic activity, thereby reducing the lag phase. However, at higher doses, such as 0.192 g O\({}_{3}\)/g COD [12], it was observed that ozone pre-treatment reduced biomass viability and acetoclastic methanogenic activity, resulting in an initial lag period of 10 days. Subsequently, after this lag period, ozone treatment leads to an increase in methane production.
## 1 Introduction
The possibility of using recycled food waste as poultry feed has been demonstrated in previous studies [23; 30; 31]. For example, previous studies have shown that broilers fed diets added with 10 to 30% dried bakery waste had similar body weight and feed efficiency compared to those fed conventional diets based on corn and soybean meal [30; 31]. Similarly, processed recycled food waste from various sources, such as fermented fish, leftover Korean food, fruits and vegetables, dried kitchen waste, fermented apple pomace, and restaurant food waste, has been illustrated to provide suitable ingredients for poultry diets [32; 33; 34; 35; 36; 37]. However, previous research examined the effects of incorporating only food waste from one source into poultry diets at moderate inclusion rates. Recent research at our laboratory has illustrated the possibility of formulating a diet based almost 100% on food waste materials that meet the nutritional requirement of laying hens and safety and hygiene standards [23; 38]. Furthermore, the results of our previous study showed that food waste-based diets maintained egg production and egg quality while improving the feed efficiency of laying hens compared to conventional control diets [23; 38]. Although gut health and mineralization status play crucial roles in the nutrient digestibility, production performance, and general health conditions of laying hens, limited information could be found in the literature regarding the effects of feeding food waste-based diets on the gut health and mineralization status of laying hens. This study is an extension to our previous studies [23; 38], and the main objectives are to explore the effects of feeding food waste-based diets on gut pH and morphology, nutrient digestibility, bone morphology, bone mineral composition, and overall laying performance, which may elucidate the mechanisms generating the improved performance of laying hens offered food waste-based diets. To the best of our knowledge, this is the first study investigating the effects of feeding diets based almost 100% on food waste materials on the abovementioned health- and production performance-related parameters of laying hens.