esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation
### Experimental Results and Analysis  
To better compare the effectiveness of the proposed Privacy-Preserving Federated Learning method for Space-Air-Ground Integrated Networks (PPFL-SAGINs), we compared it with existing federated learning methods including FedAvg [16], Asynchronous Federated Learning (FedAsync) [39], and Two-Tier Federated Learning (FedAsyncISL) [40]. To ensure the accuracy of the experimental results, the data presented in this paper are the average values obtained from three runs.
## 2 Materials and Methods
If the initial data are presented in a one-hour resolution and the forecasting horizon is short-term, then the mathematical equation for the abstract forecasting model can be written as follows:  
\[\dot{y}_{i}=f(\overline{X}_{i-1}),\]where \(\hat{y_{i}}\) is the PV generation output for the considered hour; \(f(X)\) is the particular forecasting model; \(\overline{X}_{i-1}\) is the array of meteorological features values for the previous hour. To train individual models for the selected data clusters, a comparison of the considered types of regression models was made (Linear Regression, Decision Tree Regression, Random Forest Regression). These models were used to predict solar power plant (SPP) generation and the prediction results were compared as follows:  
* The general model was trained on data from all the three clusters;
* Three models for different clusters were trained on the data of these clusters, respectively;
* The forecasting results of the general model were compared to the forecasting results of the composite model (obtained using three models trained on the data of the selected cluster). To visualize data points in a 2D-space, a principal component analysis (PCA) [54] was used. The PCA method helps to transfer data from higher dimensional space to lower dimensional space and is commonly used for visualization purposes. The main idea of the PCA is to use a linear combination of the original features of the dataset in order of decreasing importance. The PCA process can be described using following steps. Data standardization:  
\[Z=\frac{X-\mu}{\sigma}, \tag{7}\]  
where \(\mu\) is the mean of independent features; \(\sigma\) is the standard deviation of independent features. Computation of covariance matrix:  
\[\text{cov}(x_{1},x_{2})=\frac{\sum_{i=1}^{n}\left(x_{1\,i}-\overline{x_{1}} \right)\cdot\left(x_{2\,i}-\overline{x_{2}}\right)}{n-1}, \tag{8}\]  
where \(x_{1},x_{2}\) are the values of two independent features. Eigenvalues and eigenvectors computation:  
\[ \begin{array}{l}AX-\lambda X=0\\ (A-\lambda I)X=0,\\ |A-\lambda I|=0\end{array}  \tag{9}\]  
where \(\lambda\) is the eigenvalue. Data projection to the lower feature space:  
\[\Pr_{Pi}\left(\overrightarrow{u}\right)=\frac{P_{i}\cdot\overrightarrow{u}}{ |u|}, \tag{10}\]  
where \(P_{i}\) is the vector consisting of the eigenvalues placed in order of decreasing importance.
# A New String Edit Distance and Applications
## Appendix A
### A Worked Example  
The example below is shown with unit costs for all operations. The motif is ACG. First an original Levenshtein example is computed in detail, followed by an RFL example. The distance being computed is from ACG to ACGTCG.  
Each matrix entry is equal to the distance from the parent string (up to that row) and the child string (up to that column). For example, the bold 4 in Table 11 corresponds to the distance from A to ACGTC. The value at the bottom-right of Table 11 (i.e., 3) is the least costly solution for the edits from parent to child. This follows the intuition that in the base Levenshtein distance, the necessary edits to get from ACG to ACGTCG are three insertions in sequence, each with a cost of 1.  
The RFL distance between the same two strings with the same motif as in Table 11 is computed in Table 11.  
The following paths can be described from Table 2: A to ACG requires two edits, either via A \(\rightarrow\) AC \(\rightarrow\) ACG or A \(\rightarrow\) AACG \(\rightarrow\) ACG, while A to ACGT requires three, via A \(\rightarrow\) T \(\rightarrow\) ACGT \(\rightarrow\) ACGTC, and AC to ACGTCG also requires three, via AC \(\rightarrow\) ACACG \(\rightarrow\) ACTCG \(\rightarrow\) ACGTCG.  
The final function value is given in the bottom right corner of the matrix in Table 11, representing the RFL distance from ACG to ACGTCG. This value of 2 is obtainable via the edit path ACG \(\rightarrow\) ACGACG \(\rightarrow\) ACGTCG.  
Changing a few individual edit costs will give different optimal paths. In the following example, the cost of inserting a motif (forward stutter) is 2, backward stutter costs 0.5, switching from A to T costs 1.5, switching from A to C costs 0.5, and inserting a C or a T costs 2. All other costs remain equal to 1. The dynamic programming matrix computed with modified costs is shown in Table 12.  
The previous three examples are shown below, but now with updated costs.  
In the previous example, there were two paths from A to ACG that each cost 2. A \(\rightarrow\) AC (+2) \(\rightarrow\) ACG (+1) now costs 3, and A \(\rightarrow\) AACG (+2.5) \(\rightarrow\) ACG (+1) now costs 3.5. The former path cost matches the cost of 3 in Table 11, and is thus still a minimal path, but the latter is no longer minimal.  
In the previous example, A to ACGTC cost 3. Now the same path A \(\rightarrow\) T (+1.5) \(\rightarrow\) ACGT (+2.5) \(\rightarrow\) ACGTC (+2) costs 6, which matches Table 11.  
For AC to ACGTCG, there was a path that cost 3, but now the cost should be 5 to match Table 11. AC \(\rightarrow\) ACACG (+2.5) \(\rightarrow\) ACTCG (+1.5) \(\rightarrow\) ACGTCG (+1) costs 5, as desired.  
Other cost combinations are possible. For example, if the cost of forward stutter was raised higher than 3 while keeping unit costs for insertion, forward stutter would never be reflected in a minimal edit path because it would always be cheaper to insert the motifs individually instead of stuttering them together.
# Update: Heart Failure in Systemic Lupus Erythermatosus
## 5 Treatment of Systemic Lupus Erythematosus and Heart Failure
### Intravenous Immunoglobulin (IVIg)  
Intravenous immunoglobulin (IVIg) has an immunomodulatory effect by increasing the suppressor activity, blocking the Fc receptor, and influencing the complement components. Furthermore, IVIg is involved in the regulation of T and B lymphocytes, the interferon pathway, and inhibits the elimination of immune complexes [112; 113; 114]. Data on IVIg treatment in SLE are scarce and mainly concern myocarditis [66]. Several studies have shown that IVIg reduces inflammation and increases LVEF in patients with HF [115; 116; 117; 118; 119; 120; 121], although two studies did not show any changes in the above-mentioned parameters [69; 122].
## 2 Data and Methodology
### Cloud Layers in CPR Profile  
Using surface type information in 2B-GEOPROF first screened out the profile data over land. For the remaining oceanic data, the cloud mask information in 2B-GEOPROF was used to identify clouds in each bin. The valid values of the cloud mask parameter in 2B-GEOPROF range from 0 to 40, with large values indicating a high probability of clouds. According to [PERSON] et al. [26], as the value of cloud mask parameter reaches 30 and 40, the likelihood of wrong cloud identification is reduced to 4.3% and 0.6%, respectively, which suggests at least 95% reliability of the presence of clouds for cloud mask values that are larger than 30. Hence, the value of 30 was taken as a threshold for differentiating cloudy or clear situations for each bin in the CPR profile.  
In a CPR profile, successive cloudy bins are considered as a cloud element, which actually represents a sampling column in a broad domain of a real cloud layer. Given such measurements, single-layered or multilayered clouds are readily derived by summarizing all 125 bins. This is very useful for quantifying the overlapping characteristics of clouds over the global oceans. Without any restriction on cloud type, the frequencies of all cloud profiles with one or more cloud layers are shown in Figure 1. It is evident that single-layered clouds are the dominant cloud form. Multilayered clouds occur more infrequently, but they cannot be ignored due to their nearly 30% contribution. Double-layered clouds account for about 20% of the total cloud profiles. This percentage of double-layered clouds is lower than ~26% from [PERSON] et al. [19], which could likely be attributed to the land-ocean contrast. It is very likely that double-layered clouds occur more frequently over land than over ocean. Since the overall frequency of cloud appearances with three or more individual layers is below 10%, double-layer is indeed the predominant form of multilayered cloud systems. Only single-layered and double-layered ones were analyzed in this study while taking into account such a sample distribution.
## 3 Verification of the Main Starting Hypotheses of the Interstitialcy Theory
### Increase of the Interstitial Concentration before Melting  
As mentioned above, the IT argues that melting of metals is related to a rapid increase of the concentration of dumbbell interstitials. This is a crucial statement of the theory. Since dumbbell interstitials produce tenfold bigger shear softening as compared with vacancies, this effect can be detected experimentally provided that there are no contributions coming from other defects (e.g., dislocations) in the crystal. Such experiments were recently performed on single crystal aluminum and coarse-crystalline indium [35; 36]. The main results of these measurements are shown in Figure 3. Despite the usual opinion that the equilibrium interstitial concentration \(c_{i}\) is negligible at any temperature [37], it is seen that \(c_{i}\) rapidly increases for both metals upon approaching the melting temperature \(T_{m}\). In aluminum, this concentration remains smaller than the vacancy concentration \(c_{v}\) while for indium \(c_{i}\) becomes even bigger than \(c_{v}\) near \(T_{m}\). Besides that, the data on Al clearly demonstrate an increasing tendency of \(c_{i}\)-growth at high temperatures \(T\geq 926\) K (0.99 \(T_{m}\)) while the formation Gibbs free energy start to rapidly decrease in this region [35]. These features agree with the predictions of the IT. It is worth noting that increasing concentration of dumbbell interstitials can explain the non-linear growth of the heat capacity of simple metals near \(T_{m}\), whose nature remains hitherto unclear [38]. It should also be mentioned that temperature dependence of the interstitial concentration obtained for aluminum [35] allowed an estimate of their formation entropy \(S_{i}^{F}\), which was found to be \(\approx\)7 \(k_{B}\)[39], in full agreement with [PERSON]'s value [14].  
Figure 2: Histogram illustrating the distribution of the ratio of dilatation \(U_{bulk}\) and shear \(U_{shear}\) components of the elastic energy for dumbbell interstitials in 63 polycrystalline elemental metals. The same data for interstitial-type defects in 189 metallic glasses are also shown [27]. With permission from JETP Letters, 2019.
## 3 Data Collection and Experiment Analysis
### Outliers Detection for Real-Time Positioning  
We use offline data for unsupervised learning and training to obtain the signal type identification rule. This rule is finally used for real-time GNSS observation classification and position calculation, and we call the real-time observed data--online data. Due to the good scalability of the K-means algorithm, there is no need for re-supervised learning on offline data (the labels have been obtained through the above process), but the clustering rules obtained by training are directly used for real-time detection of signals. Therefore, the above model is then employed to identify the observation outliers containing multipath/NLOS signal on the online observation dataset D2 for GNSS/INS real-time positioning. Feeding new GNSS/INS observations into the classifier, outliers can be obtained in real time. Similar to the offline system, the accuracy and availability of INS/DGNSS double-differenced pseudorange positioning results are used to evaluate the performance of anomaly detection algorithms after excluding multipath/NLOS signals.  
Figure 13 indicates the comparison of the number of satellites participating in the position calculation before and after excluding the multipath/NLOS observations.  
Figure 14 exhibits the comparison of the 3D position of the vehicle obtained by different positioning methods, where the red solid line is the ground truth value of the vehicle. From Figures 4b, 13 and 14, we can see that the observation conditions of the first part of the epoch of the data set D2 are poor. Multipath/NLOS signals seriously affect the positioning accuracy, which causes the position result to deviate from the reference value to a large extent. Without detecting and eliminating multipath/NLOS on the observational data, the trajectory diagram of the vehicle's 3D position can even produce spurs. The observation  
Figure 12: Comparison of PDOP values before and after excluding multipath/NLOS signals.  
Figure 13: Comparison of the number of satellites for D2 before and after excluding multipath/NLOS signals.  
circumstance of the subsequent epochs is gradually expanded, and the results obtained by the two algorithms are close to the reference value. This demonstrates that in urban complex scenarios, detecting and eliminating multipath/NLOS signals plays an important role in improving the performance of GNSS/INS integrated real-time positioning.  
Further, this paper calculates the RMS value of the position error of the real-time positioning of the online dataset, as shown in Table 4. It can be seen that in the urban harsh scenarios, the K-means clustering algorithm has a better effect on the improvement of the positioning results. The positioning accuracy of the D2 online dataset is improved by 21% and 41% in the horizontal and vertical directions, respectively.
## 3 Influence of Key Parameters on the Response
### Craig-Bampton Reduction: Influence of the Retained Contact Nodes
#### 3.3.2 Density of Nodes Incremented Along the Strip Short-Edge Direction Y  
The purpose of this section is to show why a higher density of retained nodes not always results in a stiffer system. In detail, by increasing the number of retained nodes along Y direction, i.e., increasing node rows, (while keeping the same number of nodes along the X direction, i.e., unchanged node columns), the system natural frequency decreases (see Figure 6b). This apparently strange phenomenon can be easily explained by considering the following:  
* as more rows of nodes are retained, the local contact stiffness values at each node are reduced (see Section 2.1);
* as a result, the rows of springs connected to the _high mobility region_ will progressively offer a weaker constraint;
* conversely, the additional rows created far from the _high mobility region_ do not significantly modify the stiffness of the system as the nodes they are connected to are not very mobile.  
This behavior is well represented in Figure 10 where the blade-strip contact patch, already shown at \"high resolution\" in Figure 7, is discretized with 2 rows of elements (on the left) and with 6 rows (on the right). As expected, most of the \"additional\" nodes introduced with the 6 rows are positioned in low mobility regions (low relative displacement, represented in green), that means that their constraints do not contribute significantly to the overall stiffness. At the same time the nodes in high mobility region (represented in red) see a reduced stiffness (each one has a local stiffness 1/90 of the total stiffness vs. 1/30 for the two-rows case represented on the left). The result is a global weaker constraint and a decrease of the natural frequency.  
In conclusion, Figure 6b suggests that it is important to retain a sufficiently high number of nodes to capture effectively the dynamic behavior of the system. This is due to the kinematic of the strip/blade contact interfaces, as shown with the _high mobility region_. Therefore, despite the effort in keeping the global contact stiffness to the same value, reducing below a given threshold the contact nodes density (and thus introducing high \"concentrated\" constraints) is not a viable alternative. The contact nodes density which guarantees a proper representation is fixed as the boundary of the asymptotic region in Figure 6b (i.e., approximately 4.5 node columns per cm and 7 node rows per cm respectively) and can be used as a starting point for cases addressing strip dampers of similar thickness. This topic will be addressed further in Section 4.
# Sorption of \({}^{137}\)Cs and \({}^{90}\)Sr on Organic Sorbents
## 3 Results
### Sorption of Cs (I) and Sr (II)  
Since the studied sorbents have different specific surface areas, in order to compare their sorption characteristics, the sorption value was calculated not only as a percentage per unit mass (Figures 4a and 5a), but also normalized per surface unit (Figures 4b and 5b). As a result of the experiments carried out, the following regularities are observed. Sorption of Cs (I) increases with increasing pH, and the highest sorption capacity is observed in samples of brown coal (up to 40%) and high-moor peat (up to 24%). For the hard coal and shungite samples it is less Cs (I)--up to 20% and 11%, respectively (Figure 4a).  
Figure 4: Dependence of the amount of sorbed Cs (I), in % (**a**) and in mmol/m\({}^{2}\) (**b**), on the pH of the equilibrium solution.  
Figure 3: Scanning electron microscope images [7].  
The amount of Cs (I) sorbed on 1 m\({}^{2}\) of the surface of brown coal and high-moor peat turned out to be significantly higher than that on hard coal and shungite (Figure 4b).  
Thus, for the studied samples, a correlation is observed between the value of sorption, CEC and SSA of the sample.  
The maximum sorption of Sr (II) reaches 95-100% on high-moor peat and brown coal, and 56% and 64% on shungite and hard coal, respectively. Sorption of Sr (II) on high-moor peat significantly increases with increasing pH and reaches a maximum at equilibrium pH values in the range from 5.2 to 6.7. At pH > 6.7, Sr (II) sorption decreases (Figure 5a,b).  
A significant increase in the Sr (II) sorption on brown coal occurs in the pH range from 5.9 to 7.7. A further increase in pH does not lead to an increase in the sorption of Sr (II) ions. Sorption of Sr (II) on shungite and on hard coal increases in the pH ranges of 2.1-5.4 and 6.3-8.7, respectively (Figure 5a,b).  
The maximum amount of Sr (II) per 1 m\({}^{2}\) is sorbed on high-moor peat. Brown coal sorbs half as much Sr (II). Shungite sorbs much less Sr (II) than both high-moor peat and brown coal, but almost two times more than hard coal (Figure 5b).
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 3 Proposed Methodology
### Feature Extraction
#### 3.3.2 Statistical Features  
The statistical features are hence an important component in characterizing the underlying patterns and behaviors of the cleansed data across different times. Extracting statistical features is essential because it allows quantifying, interpreting, and making informed decisions based on data patterns. By calculating statistical features, insights into the central tendencies, variability, and distribution of data can be gained. Statistical features help to identify patterns and detect trends. By analyzing historical data, we can uncover recurring behaviors or changes over time. Here's an explanation of each statistical feature, along with its mathematical equation:  
Mean  
The mean is a measure of the central tendency, which gives an average value in the dataset. The mean is obtained by adding all individual data points and dividing the sum by the total number of data points. In demand forecasting, the mean establishes a benchmark for comparison in understanding what is considered average, from around which these fluctuations ensue.  
\[M=\frac{1}{n}\sum_{f=1}^{n}S_{i}^{*} \tag{4}\]  
In this case, \(M\) stands for the mean value; \(S_{i}^{*}\) is an individual data point and \(n\) refers to how many numeric values there are in total within the dataset.  
Variance measures the scatter or dispersion of data points from their mean value. Variance provides the average of squared deviations for each data point from its mean. High variance implies more variations in the demand over time.  
\[V=\frac{1}{n}\sum_{f=1}^{n}(S_{i}^{*}-M)^{2} \tag{5}\]  
This is the square root of the variance and it stands as a measure of average deviation between the dataset points from the mean. Dispersion is commonly measured by using standard deviation. It measures the average or typical distance between every data point and its mean. In the supply chain context, it aids in determining possible variations of demand.  
\[S=\sqrt{V} \tag{6}\]  
Skewness  
Skewness describes the lack of symmetry in the distribution among data points. Skewness represents the form of distribution. A positive skewness implies that the graph is right-skewed, showing more of the high-demand periods. Negative skewness means left-skewed distribution, which reveals more periods of low demand.  
\[SK=\frac{\sum_{f=1}^{n}\bigl{(}S_{i}^{*}-M\bigr{)}^{3}}{\biggl{(}\frac{1}{n} \underline{\sum_{f=1}^{n}\bigl{(}S_{i}^{*}-M\bigr{)}\biggr{)}^{3/2}}} \tag{7}\]
## 5 Discussions
### Spatial Distribution of SSI around Korea
#### 5.2.1. \(\mathrm{GK2A/AMI}\) SSI

Table 3: Mean, minimum, and maximum values of monthly mean daily GK2A/AMI SSI considering administrative district around Korea from 1 January 2020 to 31 December 2021, where Mean, Min., and Max. indicate mean value, minimum value, and maximum values, respectively. Figure 12: The distribution of monthly mean daily SSI, from 1 January 2020 to 31 December 2021, estimated from GK2A/AMI using the CNN approach with respect to different administrative districts around Korea in **(a)** January, **(b)** February, **(c)** March, **(d)** April, **(e)** May, **(f)** June, **(g)** July, **(h)** August, **(i)** September, **(j)** October, **(k)** November, and **(l)** December. heavy rainfall events for the period from mid-June to early September in 2020, and record-breaking rainfall events were reported by KMA [56]. The heavy rainfall events over Korea during Changma are common due to the monsoon climate; however, the intensities and durations of the rainfall in 2020 were higher than normal. This extreme summer rainfall was accompanied by intense cloud coverage and caused a sharp decrease in the mean daily SSI values. Conversely, there were fewer heavy rainfall events in 2021 than in 2020, which caused the mean daily SSI to be higher in 2021 than in 2020.
###### Abstract  
Video content and streaming services have become integral to modern networks, driving increases in data traffic and necessitating effective methods for evaluating Quality of Experience (QoE). Accurately measuring QoE is critical for ensuring user satisfaction in multimedia applications. In this study, an optimized adaptive neuro-fuzzy inference model that leverages subtractive clustering for high frame rate video quality assessment is presented. The model was developed and validated using the publicly available LIVE-YT-HFR dataset, which comprises 480 high-frame-rate video sequences and quality ratings provided by 85 subjects. The subtractive clustering parameters were optimized to strike a balance between model complexity and predictive accuracy. A targeted evaluation against the LIVE-YT-HFR subjective ratings yielded a root mean squared error of 2.9091, a Pearson correlation of 0.9174, and a Spearman rank-order correlation of 0.9048, underscoring the model's superior accuracy compared to existing methods.  
video quality; video streaming; high frame rate; evaluation; quality of experience; multimedia applications Article