esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Algorithm Application and Clustering Results
#### 3.2.3 Clustering Results  
In Figures 8-10, the comparisons between the original PSs dataset distribution in the three areas and the clustering results, are proposed. In Figures 8a, 9a and 10a, the buildings' footprints are clearly marked with a black shape. There are 19 buildings in Area 1, 12 buildings in Area 2 and 20 buildings in Area 3. Moreover, the PSs, distributed all over the areas, marked with red circles for ascending PSs and green circles for descending PSs, are shown. In Figures 8b, 9b and 10b, the results of the clustering are reported, where each cluster has been delimited by a rectangle. The clustering results have highlighted that the procedure is very performant. Each building having more than one floor is identified by a cluster. The only exception is represented by the one-floor manufact of Area 2, that is not included in the clustering results due to the additional \(\delta\) considered for the height cut, aimed to avoid noise PSs (belonging to the ground), as previously exposed in Section 2.3.1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Area Number [-]** & **Ground Level \(\delta\) [m]** & \(\delta\) **Limit [m]** \\ \hline
1 & 2.59 & 8.09 \\
2 & 2.78 & 8.28 \\
3 & 2.64 & 8.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Topography ground level \(\delta\) and \(\delta\) limit values.  
Figure 7: Points sorted by distance to the \(k^{th}\) nearest neighbor for: Area no. 1 (**a**), Area no. 2 (**b**), and Area no. 3 (**c**).  
Figure 8: Area no. 1: top view of ascending and descending PSs datasets (number of buildings: 19) (**a**), and DBSCAN result (number of clusters: 19) (**b**).  
Figure 10: Area no. 3: top view of ascending and descending PSs datasets (number of buildings: 20) (**a**), and DBSCAN result (number of clusters: 20) (**b**).  
Figure 9: Area no. 2: top view of ascending and descending PSs datasets (number of buildings: 11) (**a**), and DBSCAN result (number of clusters: 11) (**b**).
# Assessment of Word-Level Neural Language Models for Sentence Completion +
## 4 MSR Sentence Completion
### Results with the Official Training Data
The x-axis represents the number of epochs and the y-axis represents the validation accuracy for the MSR dataset. Table 2 compares the accuracy results of previous work with ours. An ensemble of differently initialized RNNs having the same dimensions was defined by selecting the option that minimizes the average loss obtained from the RNNs. All of our results in Table 2 were obtained by using the full scoring strategy. To present the approximate network sizes, we specify the dimensions in the first column as (\(d_{emb}\)-\(d_{hid}\)-\(d_{out}\)). Interestingly, even the performance of our unidirectional word RNN was on par with or slightly above the previous best accuracy results on the MSR dataset. As examined in Figure 3, most accuracy gains compared to the LSTM of [PERSON] et al. [10] were attributed to the appropriate embedding and hidden sizes, and miscellaneous training settings contributed to additional gains. When compared to the RMN [10], which contains a memory block consisting of two lookup tables for additional word embeddings and calculates an attention distribution over context words at every timestep, our RNN is simple and computationally inexpensive. In addition, our bidirectional version achieved the best result among the individual models while the ensemble of those five networks further improved the performance by leveraging the stochastic nature of deep learning. As mentioned above, adding more nodes to the unidirectional word RNN (200-600-400) was not helpful. This implies that the benefit \begin{table}
\begin{tabular}{l c} \hline \hline
**Model (\(d_{emb}\)-\(d_{hid}\)-\(d_{out}\))** & **Accuracy** \\ \hline RNNLMs [39] & _55.4_ \\ Skip-gram [6] & _48.0_ \\ Skip-gram + RNNLMs [6] & _59.2/58.7_ \\ NPMI + Co-occ. Freq. + LSA + CBOW + CSKIP [7] & _48_ \\ PMI using Unigrams + Bigrams + Trigrams [8] & _61.44_ \\ Contex2 vec (300-600-600) [9] & _66.2/64.0_ \\ LSTM (256-256-256) [10] & _56.0_ \\ Unidirectional-RM (512-512-512) [10] & _69.2_ \\ Bidirectional-RM (512-512-512) [10] & _67.0_ \\ \hline Unidirectional word RNN (200-600-400) & 69.1 (0.9)/69.6 (0.8) \\ Bidirectional word RNN (200-600-400) & 72.5 (1.4)/72.0 (2.0) \\ Unidirectional word RNN ensemble & 72.0/71.5 \\ Bidirectional word RNN ensemble & **74.1/74.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy results (%) for the MSR Challenge dataset. The accuracies in italic were from their corresponding publications. We present the accuracy results of the development and test sets using a slash if they were measured separately. We report the average accuracy with the standard deviation if a model was optimized repeatedly. Figure 3: Performance comparison for different numbers of nodes. of bidirectional modeling did not come from mere increase of the number of learnable weights but from incorporation of future context information. These results disagree with [PERSON] et al. [10], whose bidirectional model compared unfavorably with the unidirectional model.
# Viscosupplementation in the Therapy for Osteoarthritic Knee
## 4 Polynucleotide/Polydeoxyribonucleotide
### PN/PDRN Physiology
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Publication** & **Agent** & **Study Design** & **Comparison Agent** & **Patients Number (Treal/Control)** & **Injection Times** & **Result** \\ \hline
2010 [118] & PN & RCT & HA & 60(30/30) & 5 &
\begin{tabular}{c} VAS, KOOS, NSAID consumption improved: no significant differences in outcome between two group \\ \end{tabular} \\ \hline
2013 [119] & PN & Open label & non & 95 &
\begin{tabular}{c} Radiologic \\ OA: 1 \\ Severe OA: 3 \\ \end{tabular} & KOOS, NRS, ROM improved. \\ \hline
2013 [120] & PN & RCT & HA & 30(15/15) & 3 &
\begin{tabular}{c} VAS, KOOS improved in both group; \\ PN statistically significantly greater improvements at KSS. \\ improvements after 6 month. \\ \end{tabular} \\ \hline
2013 [121] & PN & Retrospective & HA & 60(30/30) & 4 &
\begin{tabular}{c} VAS, KOOS improved in both group; \\ PN statistically significantly greater improvements after 6 month. \\ \end{tabular} \\ \hline
2014 [122] & PN & RCT & HA & 72(36/36) & 3 &
\begin{tabular}{c} VAS, KOOS improved in both group; \\ PN showed a significant difference after 2 weeks \\ and HA after 18 weeks \\ \end{tabular} \\ \hline
2020 [123] & PN & RCT & HA & 100(50/50) & 3 &
\begin{tabular}{c} PN + HA vs. HA \\ VAS, KSS improved in both group: \\ PN + HA group significantly greater improvements at knee function, pain. \\ \end{tabular} \\ \hline
2021 [124] & PN & Retrospective & HA, CLHA & 15(5/5/5) & 3 &
\begin{tabular}{c} Function or weight-bearing pain, PN decreased \\ the most by 6 weeks, and three was no difference between groups in VAS, K-WOMAC, EuroQoL, and painDETECT \\ \end{tabular} \\ \hline
2021 [125] & PN & RCT & HA & 98(49/49) & 3 &
\begin{tabular}{c} PN + HA vs. HA \\ WOMAC, KSS improved in both group: PN + HA group significantly greater improvements at knee function, pain after 2 years. \\ \end{tabular} \\ \hline
2019 [116] & PDRN & RCT & HA & 30(15/15) & 3 & \
\begin{tabular}{c} POM + HA vs. HA \\ WOMAC, KSS improved in both group: PDRN + HA group significantly greater improvements at VAS, WOMAC. \\ \end{tabular} \\ \hline \hline \end{tabular}
\begin{tabular}{c} PN, polynucleotide; PDRN, polydeoxyrribonucleotide; K-WOMAC, Korean version of Western Ontario and McMaster Universities Osteoarthritis Index; KOOS, knee injury and osteoarthritis outcome score; ROM, range of motion; KSS, _Kur_ Society clinical rating system. \\ \end{tabular}
\end{table} 
Table 4: Clinical studies evaluating the efficacy of intra-articular PDRN/PN in knee osteoarthritis. known that they have a protective effect on chondrocytes in vitro [127]. However, in an experiment in which PDRN was administered to a collagen-induced arthritis mice model, it was confirmed that the onset of arthritis was significantly attenuated compared to that in the control group [115].
## 4 Materials and Methods
### SPME-GCMS Data Processing  
We used the Automated Mass spectral Deconvolution and Identification System (AMDIS) software to process the SPME data generated by GC-MS. The compounds were identified using the National Institute of Standards and Technology (NIST) 2017 mass spectral library, only considering those with a match quality above 90% (putative identification). The identity of selected compounds was confirmed using authentic standards. An in-house R package was used for automated integration of reference ion peak area. Each identification was individually screened, and manual retention time correction and subsequent re-integration was performed where required. Only compounds that increased in abundance or were produced de novo were considered for further characterisation experiments.  
To determine the fold-change increase in volatile metabolites after fermentation, the analyte peak areas were divided by the total ion count (TIC) of the corresponding data file. The peak abundances in proportion to the TIC were subsequently divided by the raw peak area of hexamethyl cyclotrisiloxane as a means of external standard normalisation. The resulting data were log-transformed and Pareto-scaled to make the features more comparable within the data. Student's t-test was applied to determine whether the relative abundance of compounds detected in a different group of samples (data classes) was significantly different between growth conditions, which was defined as \(p\)-value < 0.05 after adjustment by false discovery rate (FDR).
# An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering
## 1 Introduction
[PERSON] [16] proposed a scheme to preserve space by utilizing compressed LSB planes, but the embedding capacity of this method was relatively low. [PERSON] and [PERSON] [17] proposed a method to reserve embedded rooms using distributed source coding. In 2014, joint and separable DH-EI schemes using bit flipping and prediction error techniques were proposed by [PERSON] and [PERSON] [18]. [PERSON] et al. [19], in 2016, proposed a DH-EI framework for encrypting images using block-wise stream cipher and shuffling. Since block-wise stream cipher and shuffling are used, the correlation between encrypted blocks of pixels is preserved so that DH techniques can be used directly in the encryption domain. In 2018, [PERSON] et al. [20] combined the block-wise stream cipher and selected peaks for histogram shifting. In 2020, [PERSON] and [PERSON] [21] used an improved block-based joint DHEI algorithm to obtain higher embedding rates. In 2022, [PERSON] et al. [22] proposed a method to embed secret data using rotated pixel-blocks. [PERSON] et al. [23] used MSB replacements to embed data and then used the complexity among neighboring pixels to restore the image. Our novel scheme focuses on using VRAE to hide data and incorporates vector quantization codeword index reordering [4], Stream Cipher, and LSB replacement [5] techniques to implement the scheme. Our research goal is to achieve a larger embedding capacity than other methods without degrading the visual quality of recovered images. The core contributions of the scheme are described below:  
* The scheme offers large embedding capacity. * It sustains the visual quality of encrypted images. * Extracting secret messages and recovering images can be independent. The rest of the paper is structured as follows: Section 2 discusses the Background of the Works, including the LSB encryption, the applied sorting, and the key index reordering method. Section 3 describes the details of the novel scheme, and Section 4 shows the experiments conducted and their analyses. Finally, Section 5 is the conclusion.
After validating the model against experimental data, we predict the PBTs' removal in a 3-hollow fiber/module configuration. Although some equilibrium constant data for the affinity of PBTs to albumin are available, to the best of the authors' knowledge, there are no literature data concerning the binding and dissociation rates of the specific PBTs to albumin. A parametric analysis is also conducted of the effect of the binding rate on the removal of p-cresyl sulfate (PCS) and indoxyl sulfate (IS). These compounds are examples of protein-bound uremic toxins, which, needless to say, do not account for the entire cardiovascular risk present in this type of patient. The particular toxins were chosen due to the fact that, not only do they bind to albumin in the blood, but they also competitively bind to the same site on human serum albumin (HSA) [PERSON] _et al._ (2016). As a result, PCS and IS free fractions are strongly intercorrelated, and secondary to this, dialogic clearances of indoxyl sulfate and p-cresyl sulfate are also closely related [PERSON] (1936), creating interesting dynamics during their association with albumin. The main objective is the numerical validation and quantification of the role of activated carbon particles to enhance the removal of PBTs.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 4 Results
### Kalman Filter Algorithm Implementation
#### 4.2.3 Setting Parameters
The green line is the concentration of CO at 0:00 on the 16 February 2019 of the monitoring site, which will be the target of this prediction. As can be seen from the figure, the model converges quickly when \(Q\) is constant and optimal. Not only the error of the observation is corrected, but also the predicted result (the last point on the blue line) is very close to the true value after the model converges. According to Equation (7), with the iteration of the KF, the value of the error covariance matrix \(P_{k}\) will constantly change. When the system enters a steady state, the value of \(P_{k}\) converges to a minimum estimated variance matrix. The Kalman gain \(K_{k}\) at this time is also optimal. Therefore, in the process of prediction and correcting CO concentration by the KF algorithm, we can also check the convergence of \(P_{k}\) to judge whether the system has entered the steady state. As shown in Figure 10, \(P_{k}\) basically stabilizes when iterating about 50 times, indicating that the model has converged at this time. Figure 10: The convergence process of the error covariance matrix \(P_{k}\) when Kalman Filter algorithm predicts the CO concentration at 0:00 on 16 February 2019 with \(Q(CO)=0.089\). Figure 9: The convergence process that the Kalman Filter algorithm predicts the CO concentration at 0:00 on 16 February 2019 with \(Q(CO)=0.089\).
## 5 Numerical Modelling of the Investigated Structure  
The aim of the numerical modeling was to confirm the accuracy of the developed optimization method. A commercial Finite Element package, ABAQUS/CAE, was used for the numerical modeling of the sandwich structure. Despite its higher computational cost, we believe that using ABAQUS/CAE provides greater flexibility in terms of geometry and load cases, particularly when dealing with composite sandwich structures.  
The honeycomb core material properties were defined in the \"Material Properties\" module and then assigned using the \"Assign Section\" module. The mechanical properties of the FML materials were defined for each individual constituent using the main engineering constants listed in Table 1. The FML composite was simulated using the \"composite layup\" function in ABAQUS, which allows for the definition of the layers' sequences of the FML laminate and the fiber orientations in each FRP layer. To reduce the computational cost of the sandwich structure, the honeycomb core was simulated as a solid layer with homogeneous mechanical properties of the detailed honeycomb core [38; 39]. Therefore, the three-dimensional stress element C3D8R was used to simulate the aluminum honeycombs.  
The common shell element S4R was used to simulate the laminated composite face sheets. A general contact with a tangential friction property and a normal \"hard\" contact was specified. The honeycomb core was meshed with 2925 solid elements, while 4557 shell elements were created to mesh the face sheets. The distribution load defined in Section 2 was applied to the upper face. The geometric model of the support seats was also modeled and defined as a rigid body. A coupling constraint between the support seats and the reference points (RP) was used, and then a fully fixed constraint was applied to RP, which follows the fixation nature of the subpanel. As highlighted in Section 4, the failure criterion of the face sheets was considered a crucial optimization constraint. Therefore, non-feasible designs that did not meet the failure criterion were excluded from further analysis. Conversely, all feasible designs that fulfilled the failure constraints can be adopted under elastic load conditions that are significantly lower than the failure load. Based on these considerations, the current FEM model was created to validate the optimization results without considering the failure of the FML face sheet. This fact is evident in the results section, as the stresses of the face sheet layers are significantly lower than the failure strength of each constituent. The main parameters for optimal design points used in FEM modeling are elaborated in Section 6, while the general FEM model of the composite sandwich structure is shown in Figure 6.
## 3 Results and Discussion  
### Nonuniform Rust Distribution  
Figure 6 illustrates the nonuniform rust distribution in the PC-bar and transverse rebar cross-sections from 0 to 75 years based on Equations (1)-(8). The rust thickness increased over time, forming a symmetrical distribution across the cross-sections. The maximum rust thicknesses of the PC-bar and transverse rebar in the 75 years were 1.1448 mm and 1.6061 mm, respectively. These results illustrate that transverse rebar,  
Figure 7 illustrates the PC-bar's corrosion degree and corrosion rate over time. After 75 years, the corrosion degree reached 18.56%. The corrosion rate was found to decrease exponentially over time. A year after the beginning of the corrosion propagation, the corrosion rate was 1.9033 \(\upmu\)A/cm\({}^{2}\), and it had decreased to 0.5442 \(\upmu\)A/cm\({}^{2}\) at 75 years later.  
Figure 8 shows volumetric strain expansion in the model representing the 75-year rust expansion. The maximum strain expansions of the PC-bar and transverse rebar in the 75 years were 1.1448 mm and 1.6061 mm, respectively.  
Figure 6: Nonuniform rust distribution. (**a**) PC-bar. (**b**) Transverse rebar.  
Figure 7: Time-dependent corrosion parameter. (**a**) Corrosion degree. (**b**) Corrosion rate.
## Results
### Construction of Aroma Fingerprinting of Flat Peaches from Xinjiang  
#### 3.3.1 The Composition and Content of Volatile Compounds of Flat Peaches from Xinjiang  
Volatile aroma substances are the primary or secondary metabolites of fruits, which can reflect their aroma pattern and affect their taste and flavor expression [74]. HS-SPME-GC-MS was used to analyze the volatile aroma compounds of samples of flat peaches (Qingpan, QP; Ruipan 2, R2; Ruipan 4 R4; Wanpan, WP). A total of 60 volatile aroma compounds were identified from the flat peaches, including four alcohols, two acids, eighteen aldehydes, five ketones, nine terpen, sixteen esters, seven lactones, and two terpen (Supplementary Table S4). As shown in Figure 5a, the first-ranked volatile compounds in the tested flat peaches were aldehydes (10.09-31.98%), followed by esters (1.15-2.16%) and terpenes (0.04-1.63%). It was noteworthy that no terpenoids were detected in R4. Lactones, alcohols, and ketones accounted for 0.40-1.00%, 0.04-0.28%, and 0.07-0.24% of the total volatile compounds in the flat peaches, respectively. Acids only accounted for 0.03-0.07% of the total volatile compounds of flat peaches. With regards to our study, benzaldehyde was the most abundant volatile compound in the flat peaches and comprised 4.74-27.28% of the total volatile aroma compounds and approximately 5.47-31.46% of the total aldehydes. 2-Hexenal (4.23-9.35%), which comprised approximately 3.67-8.11% of the total volatile compounds, was the second-most abundant aldehyde. 3-hexenyl acetate (1.51-18.69%) and phenethyl acetate (3.52-7.97%) were important components of esters in flat peaches (Supplementary Table S4). In addition, we found that a total of 27 volatile aroma substances  
Figure 4: The Pearson rank correlation matrix between the phenolic compounds and antioxidant activity of flat peaches from Xinjiang. High and low correlations are shown by the large and small circles, respectively. When the scale bar is red and the value is 1, this indicates a perfectly positive correlation; when the scale bar is green and the value is \(-1\), this indicates a perfectly negative correlation. Significant correlations with a level of \(p<0.05\) are shown by \({}^{*}\).  
were evaluated in all four tested flat peaches (Figure 5b). In summary, aldehydes (especially benzaldehyde and 2-hexenal) and esters were the main aroma compounds of flat peaches from Xinjiang.
## 3 Proposal of Positioning Approach in Nondestructive Environments
### Proposal of UWB-PPM-ATS
Thus \(\zeta\) will be selected that makes the value of \(R_{G4}(\tau-T_{\text{PPM}}-\zeta)\) reaches to the minimum point of the \(R_{G4}\) function which is denoted by \(R_{0 pt}\) as shown in Figure 7. It is clear that, in the UWB-PPM-ATS scheme, the negative values of \(\zeta\) give the better correlation function than positive values. The variation of the correlation function with the different of \(\zeta\) values is illustrated in Figure 8. In Figure 8, the different values of \(\zeta\) lead to the different shapes of the correlation function; the optimal value of \(\zeta\) in this case is \(\zeta_{opt}=-0.08\) ns which makes \(R_{1i}(0)\) have the maximum value. When the magnitude of the \(\zeta\) increases close to the pulse width (\(\zeta=-0.16\) ns, \(-0.25\) ns in Figure 8), the corresponding correlation functions will not reach the global maximum near the value of \(\tau=0\) and its local maximum points have the values close to the global maximum. This leads to the higher error in determining the global maximum point of these functions. Therefore, depending on the specific parameter configuration of each UWB-PPM system, the value of \(\zeta\) will be chosen such that \(R_{1i}(0)\) in the (14) has the largest value. Figure 7: Correlation functions of the conventional UWB-PPM and the proposed UWB-PPT-ATS systems with different time shift. The procedure for estimating distances is based on the traveling time mentioned above and can be applied to a well-known propagation environment; however, it is unavailable for an unknown propagation environment. Consequently, we propose to apply LMFA [28] into the calculation of distances, relative permittivities, and determination of buried object location (depth and horizontal direction) in the case of the unknown propagation environment. The proposed method comes from the fact that the traveling time depends on the relative permittivities, buried object location, and transceiver position. So in order to find the value of these parameters, we change the position of the receiver horizontally and calculate the traveling time corresponding to each position of the transceiver. Based on the transceiver location and computed traveling time, the remaining parameters will be estimated.