esa-sceva/satcom-chunk-collection huggingface dataset:
# Region-of-Interest Based Coding Scheme for Live Videos
## 3 Proposed Approach
### Region \(\lambda\) Infer
Additionally, the base quantization parameter for the entire frame is \(QP_{frame}\). By utilizing \(\lambda_{roi}\) and \(\lambda_{rou}\) with the pre-configured coding table, we can determine the quantization parameters \(QP_{roi}\) and \(QP_{rou}\) for the ROI and ROU regions, respectively. To estimate the increased bitrate allocation for the ROI area, the calculation method proceeds as follows:  
\[\Delta R_{i}=R_{i}-R_{i}^{\prime}=\alpha\times C_{roi}/QP_{roi}-\alpha\times C _{roi}/QP_{frame} \tag{5}\]  
\[\Delta R_{u}=R_{u}-R_{u}^{\prime}=\alpha\times C_{rou}/QP_{rou}-\alpha\times C _{rou}/QP_{frame} \tag{6}\]  
where \(\alpha\) is the rate Proportional coefficient. The terms \(\Delta R_{i}\) and \(\Delta R_{u}\) signify intermediate quantities that capture the incremental changes in bitrate attributed specifically to the ROI and ROU, respectively. As depicted in Figure 2, we employ a shallow 4-layer neural network, which takes into account the real-time constraints in live streaming scenarios where each frame has specific encoding speed requirements. This network is configured with 8 input variables: \(R_{frame-1}\), \(R_{over}\), \(C_{roi}\), \(C_{rou}\), \(P_{roi}\), \(P_{rou}\), \(K_{roi}\), and QP of the previous frame \(QP_{frame-1}\). The architecture includes a first hidden layer with 52 intermediate feature nodes, followed by a second hidden layer consisting of 36 intermediate feature nodes, culminating in two output nodes that represent the predicted values for \(\lambda_{roi}\) and \(\lambda_{rou}\) for the upcoming frame. The complete encoding and rate control flowchart is shown in Figure 3. In the preparatory analysis and encoding stages, systematically acquire data on the positional information of the ROI and ROU, their associated encoding complexities, the employed quantization parameters across encoded frames, along with the actual bit count per frame. Feed these empirical measurements into the reservoir model to dynamically update its bit rate and buffer occupancy status, serving as inputs to the CNN model. Ultimately, the CNN model predicts and outputs the rate control parameters \(\lambda_{roi}\) and \(\lambda_{rou}\) that correspond to and differentiate between ROI and ROU. By utilizing this neural network model, we calculate the adjusted \(\lambda\) parameters for the respective regions and proceed to apply these values in the coding process. This approach ensures that our rate control algorithm efficiently balances bitrate allocation between ROI and ROU while meeting the stringent latency and performance demands inherent in live video streaming environments. After one frame encoding is completed, the reservoir \(R\) updating formula is  
\[R_{frame}=R_{frame-1}+\Delta R_{i}+\Delta R_{u} \tag{7}\]  
\[R_{frame}=R_{frame-1}+\alpha\times C_{roi}/Q{P_{roi}}+\alpha\times C_{rou}/Q{P _{rou}}-\alpha\times(C_{roi}+C_{rou})/Q{P_{frame}} \tag{8}\]
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 3 Denoising Based on the Speech Segment Detection
### Filtering Methods  
#### 3.2.1 Energy-Based Filtering  
In general, the signal energy remains the basic component to the feature vector [29]. Most of the standardized algorithms use energy besides other metrics to make a decision [29]. Thus, we present an energy-based filtering method to extract the segments containing the speech based on the energy. A common way to calculate the energy of a speech signal is the root mean square energy (RMS energy), which is the square root of the average sum of the squares of the amplitude of the signal samples [29]. In Equation (4) [29], we present the RMS energy \(EN(t)\) for a time point \(t\). Here, \(n\) is the number of contiguous time points; \(x(k)\) is the amplitude for a time point \(k\). In the experiment, we use 100 for \(n\). Figure 5(a) illustrates the normalized amplitude (range: \(-1.0\)-\(1.0\)) of a sample audio signal; Figure 5(b) shows the RMS energy (range: \(0.0\)-\(1.0\)) of the same sample according to Equation (4).  
\[EN(t)=\sqrt{\frac{1}{n}\times\sum_{k=t-n/2}^{t+n/2}x(k)^{2}} \tag{4}\]  
In Equation (5) [29], we define the threshold. \(\lambda\) is used as a weight from 0 to 1 between the maximum and minimum RMS energy. Then, if \(EN(t)\) for a time point \(t\) in a time frame is greater than the threshold, we determine it as the speech segment; otherwise, we determine it as the non-speech segment.  
\[Threshold=(1-\lambda)\times\text{max}(EN)+\lambda\times\text{min}(EN) \tag{5}\]  
In the energy-based filtering method, \(\lambda\) in Equation (5) is an important parameter affecting the accuracy of the method. Figure 7 shows the recall, precision, and F1 score of energy-based filtering as \(\lambda\) is varied from 0.1 to 1.0. For all the figures showing the recall, precision, and F1 score of the speech segment detection methods, we use a total 824 validation noise-speech data files and the corresponding clean speech data files (See Section 4). We use the same data files to measure the accuracy of all the methods in this section. We indicate that the most accurate result of energy-based filtering is observed when \(\lambda\) is 0.9, where the recall and F1 score are the highest. We note that we can increase the recall to 100% by setting \(\lambda\) as 1.0, but precision dramatically decreases. This implies that we have an adequate \(\lambda\) for a target environment.  
Figure 6: Normalized amplitude and RMS energy of a sample audio signal.  
Figure 7: The accuracy variation of energy-based filtering as \(\lambda\) is varied.  
filtering is observed when \(\lambda\) is 0.6, where the F1 score is the highest. Figure 12 illustrates the result of entropy-based filtering where the audio signal is presented in orange and the speech segment is in blue.
## 4 Simulation and Analysis
### Simulations
#### 4.1.2 LM iteration Effectiveness  
To test the feasibility and effectiveness of the MLE and the LM algorithm, we set the residual carrier as 25 Hz and the phase as 0.5 rad of the input TC-OFDM signal. The SNR is \(-25\) dB and other parameters are same as Table 1. The convergence curve of frequency and phase using MLE and iteratively by LM algorithm is shown in Figure 8. From Figure 8, after iteration to the 9 th, the residual carrier and phase values remain stable and are close to the set value. The final residual carrier value is stable at 25.8 Hz and the phase value is stable at 0.53 rad, which verifies the feasibility of using MLE to estimate the residual carrier and phase of the carrier loop. For the convenience of analysis, we selected the iteration number fixed at nine in the following tests.  
Figure 8: The residual carrier and phase convergence curve estimated by LM algorithm.  
Figure 7: The RMS Frequency Tracking Error with SNR under different sample observations.
## 3 Results  
Nine ion-exchange materials were subject to comparative testing. The results obtained, which were based on manually performed chemical testing of the beaker-stored samples, consisted of nine datasets from at least 16 runs of softening-regeneration cycles.  
The ionic capacity results of the nominally equivalent resins were different under the applied conditions. A comparison of the nominally calculated molar operating capacity is shown in Table 1.  
Figure 6: Resin test product’s timing, solution volumes, and operation logic used in the study.  
The results of the molar capacity per kilogram are indicative of the best performing material from a chemical perspective. However, industrial cost and engineering approach requires the prices of the materials to be compared. Due to the confidential nature of commercial conditions, the prices are not displayed. The indicated cost shown was calculated based on the ratio of the actual prices, with the highest value of 1 attributed to the most expensive material and logistic costs being included, giving a comparative price for 1000 metric tons per year under DAP (Delivered at Place) Incoterms conditions for Poland.  
A comparison of the materials' relative costs is shown in Table 2 and Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resin Product** & **Molar Capacity per Liter** & **Molar Capacity per kg** & **Test Std** \\  & **(mmol)** & **(mmol)** & **(\%)** \\ \hline Resin A & 213.39 & 250.30 & 1.591 \\ \hline Resin B & 208.17 & 252.94 & 0.787 \\ \hline Resin C & 201.90 & 227.98 & 6.361 \\ \hline Resin D & 217.48 & 235.88 & 0.857 \\ \hline Resin E & 203.68 & 249.61 & 1.457 \\ \hline Resin F & 182.47 & 197.91 & 9.506 \\ \hline Resin G & 161.31 & 173.45 & 7.313 \\ \hline Resin H & 190.77 & 189.26 & 3.685 \\ \hline Resin I & 214.68 & 232.84 & 3.017 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of calculated averaged molar capacities of resin products and test result standard deviation per sample in %.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Resin Product** & **Relative Cost of 1 kg of Material** \\ \hline Resin A & 0.7457 \\ \hline Resin B & 1 \\ \hline Resin C & 0.7213 \\ \hline Resin D & 0.7543 \\ \hline Resin E & 0.7439 \\ \hline Resin F & 0.7426 \\ \hline Resin G & 0.7424 \\ \hline Resin H & 0.7538 \\ \hline Resin I & 0.7891 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The relative cost of 1 kg for resin products under DAP Incoterms.  
Figure 7: Comparison of average molar capacity in given test conditions of the resin products.
## 6 Result and Discussion
### Site Seismic Hazard Analysis  
The site of the taken dam is located in South Korea, which is situated in a moderate seismic zone. The site-specific magnitude is appropriate for use in seismic risk assessment because there are only a few strong motion data and seismological information for the dam sites in Korea. As a result, according to the [PERSON] et al. (2010) for Korea, there are four seismic source models as shown in Figure 17 along with the dam site. More than 2000 earthquake records were used for making the seismic source models (Korea, 2000), and the magnitude-frequency relationship was presumed the same as the Gutenberg-Richter study ([PERSON] and [PERSON], 1995). The site-specific magnitude range is important to predict potential issues. Which GM intensity will contribute more to the hazard may cause the vulnerability to risk  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Aging-Effect** \\ **(year)** \\ \end{tabular} } & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**16\% Fractile**} & \multicolumn{2}{c}{**84\% Fractile**} \\ \cline{2-7}  & **Serviceability** & **Safety** & **Serviceability** & **Safety** & **Serviceability** & **Safety** \\ \hline
0 years & 0.259 & 0.425 & 0.269 & 0.422 & 0.227 & 0.405 \\ \hline
25 years & 0.250 & 0.438 & 0.220 & 0.338 & 0.200 & 0.390 \\ \hline
50 years & 0.210 & 0.400 & 0.159 & 0.382 & 0.170 & 0.375 \\ \hline
75 years & 0.159 & 0.375 & 0.175 & 0.351 assessment of the CGD. Therefore, all the sources in each seismic model have been used to illustrate the hazard curve as shown in Figure 18a-d. The total hazard curve for each source model can then be extracted using the ground motion prediction equation (GMPE) from [PERSON] et al. [67], and all the total hazards along with the average curve have been visualized in Figure 18e.  
The idealized reason for failure in the aged-CGD was characterized by the function of time in the previous study of the author of this manuscript [23]. The connection between the seismic capacity and the modulus of elasticity was correlated with the analysis of some specifically selected GMs. In this case, the aging effect has been more characterized by the real scenario of the site seismic risk analysis. To determine the safety, the dam reliability index is strongly supported by the mean annual rate of earthquake exceedance. The failure probability of the reliability estimation covers the probable failure pattern by considering all the epistemic and aleatory uncertainties.  
Figure 17: Seismic source model in Korea.  
Figure 18: Seismic hazard curve on the site Bohyeonsan concrete gravity dam for seismic source model in South Korea; (**a**) Model A, (**b**) Model B, (**c**) Model C, (**d**) Model D, (**e**) Total and average hazard curve of all sources.
## 1 Introduction
It was also demonstrated that compounds combining adamantane/heteroadamantane and monoterene fragments are active against Tdp1 [46; 47]. Moderate activity at micromolar concentrations (IC\({}_{50}\)\(\sim\)15 \(\upmu\)M) was demonstrated for diazaadamantanes containing citronellal and citral moieties **23** and **24** (Figure 2) [46]. The adamantane derivatives **25** and **26** (Figure 2) had activity at IC\({}_{50}\) = 6.0 and 3.5 \(\upmu\)M, respectively [47]. A synergistic effect was found for the simultaneous action of **25** or **26** and topotecan. Derivative **26** increased cytotoxic effect of topotecan five-fold whilst **25** increased cytotoxicity two-fold in the colon carcinoma HCT-116 cell line [47]. Monoterpene derivatives of 1- and 2-aminoadamantane **27** and **28** decreased Tdp1 activity by 50% at 6.6 and 5.5 \(\upmu\)M, respectively [48]. Previously we synthesized a number of esters of 1-adamantane carboxylic acid containing fragments of acyclic, monocyclic and bicyclic monoterpenoids. A potent Tdp1 inhibitory activity  
Figure 1: The chemical structures of known Tdp1 inhibitors, a remarkable diversity is accommodated by the enzyme. Figure 2: Monoterpenes and adamantane/diazaadmanatane derivatives with Tdp1 inhibitory activity. with IC\({}_{50}\) ranging from 0.9 to 4 \(\upmu\)M was achieved [49]. It was found that acyclic derivatives **29** (IC\({}_{50}\) = 0.89 \(\upmu\)M) and **30** (IC\({}_{50}\) = 0.92 \(\upmu\)M) are in Figure 2) had more potent activity than monocyclic or bicyclic derivatives. Additionally, ester **29** increased topotecan cytotoxicity [49]. Drugs containing the ester moiety often have metabolic instability due to hydrolysis by carboxylesterases [50]. To mitigate this problem, amides can be used. Therefore, we investigated the substitution of esters by amides and thioamides in combining adamantane and acyclic monotereng fragments on Tdp1 inhibitory activity.
# On the Privacy-Utility Trade-Off in Differentially Private Hierarchical Text Classification
## 5 Reference Datasets
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hierarchy Level** & **Dataset** & **Classes** & **Assigned Products** \\ \hline \multirow{3}{*}{Level \(L_{1}\)} & BestBuy & 19 & 51,390 \\ \cline{2-4}  & DBPedia & 9 & 337,739 \\ \cline{2-4}  & Reuters & 4 & 804,427 \\ \hline \multirow{3}{*}{Level \(L_{2}\)} & BestBuy & 164 & 50,837 \\ \cline{2-4}  & DBPedia & 70 & 337,739 \\ \cline{2-4}  & Reuters & 55 & 779,714 \\ \hline \multirow{3}{*}{Level \(L_{3}\)} & BestBuy & 612 & 44,949 \\ \cline{2-4}  & DBPedia & 219 & 337,739 \\ \cline{2-4}  & Reuters & 43 & 406,961 \\ \hline Level \(L_{4}\) & BestBuy & 771 & 26,138 \\ \hline Level \(L_{5}\) & BestBuy & 198 & 5640 \\ \hline Level \(L_{6}\) & BestBuy & 23 & 346 \\ \hline Level \(L_{7}\) & BestBuy & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classes and assigned records per level per dataset. 800,000 manually categorized news articles [48]. Per news article, a headline, text block and topic codes representing the classes in the hierarchy are provided. In our experiments, we use the concatenation of headline and text block as input for the respective classifiers. The resulting texts have an average length of 237 words. Table 1 shows the number of classes and assigned documents for each hierarchy level of the Reuters dataset. To ensure comparability with state of the art, we follow the approach of [PERSON] et al. [27] and randomly assign 80,443 texts to the test dataset and assign each news article to the least frequent topic code. This approach is based on the assumption that the least common topic code is the one that most specifically characterizes the document. A differentially private HTC model trained on a non-public article dataset such as RCV1 can prevent leaking sensitive information represented by individual articles. **DBPedia. ** DBPedia is a community project that extracts structured knowledge from Wikipedia and makes it freely available using linked data technologies [49]. The DBPedia dataset for HTC ([[https://www.kaggle.com/danofer/dbpedia-classes](https://www.kaggle.com/danofer/dbpedia-classes)]([https://www.kaggle.com/danofer/dbpedia-classes](https://www.kaggle.com/danofer/dbpedia-classes)), (accessed on 2 June 2022)) is used as a reference dataset in many state-of-the-art publications [35; 50; 51] on text classification. Overall, the dataset contains the introductions of 337,739 Wikipedia articles, of which 240,942 are pre-assigned to the training dataset and 60,794 to the test dataset. Per article, the dataset contains a description of on average 102 words and three one-class label per hierarchy level (\(L_{1}\), \(L_{2}\), \(L_{3}\)). Table 1 shows the number of classes on each level of the DBPedia dataset hierarchy and indicates that all texts are assigned to a class on all levels, which means that the labels are _full depth_. Training a differentially private HTC model on a dataset such as DBPedia prevents leaking information about participating institutions and people if the underlying encyclopedia is not public.
## Appendix E Comparisons Between Real-World Datasets and Synthetic Cohorts
Overall, while strong performance in matching the mean and standard deviation is a foundational requirement for any synthetic data tool, the distinctive strength of ourmodel lies in its capacity to go beyond basic summary statistics and emulate the structural interdependencies characteristic of real-world data. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Variable** & **Real Data** & **Scenario A** & **Scenario B** & **Scenario C** & **Scenario D** & **Scenario E** & \(p\)**-Value** \\ \hline
**N** & 9360 & 9360 & 9360 & 9360 & 9360 & 9360 & 9360 & \\ \hline
**Sex (Male)** & 4152 (44.4) & 4152 (44.4) & 4152 (44.4) & 4152 (44.4) & 4152 (44.4) & 4152 (44.4) & 1.000 \\ \hline
**Total cholesterol,** & 241.56 (45.17) & 241.55 (45.01) & 241.55 (45.06) & 241.55 (45.08) & 241.55 (45.08) & 241.55 (45.06) & 1.000 \\ \hline
**Age, mean (SD)** & 54.25 (9.45) & 54.25 (9.25) & 54.25 (9.26) & 54.25 (9.25) & 54.25 (9.25) & 54.25 (9.23) & 1.000 \\ \hline
**SBP, mean (SD)** & 135.72 (22.59) & 135.72 (22.44) & 135.72 (22.43) & 135.73 (22.48) & 135.73 (22.48) & 135.72 (22.39) & 1.000 \\ \hline
**DBP, mean (SD)** & 82.93 (11.75) & 82.93 (11.82) & 82.93 (11.68) & 82.93 (11.66) & 82.93 (11.66) & 82.93 (11.61) & 1.000 \\ \hline
**Current smoker, yes, n (\%)** & 5246 (56.0) & 5246 (56.0) & 5246 (56.0) & 5246 (56.0) & 5246 (56.0) & 5246 (56.0) & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary statistics of NHANES real data versus five scenarios. The test of significance used is the Chi-square test for nominal variables and ANOVA for continuous variables. The level of significance applied is \(p<0.05\).
## 4 Visualization Proposal
### Heatmap Visualization
The final heatmap graph consisted of a two-dimensional distribution map with sentiment words and tiny triangular cells. Every cell was initialized at numerical value, 0. The value then increased depending on the TF-IDF score of the sentiment word located in the pertinent cell. As the numerical value of the cell increased, the color of the cell changed, making it easier to discover the value of TF-IDF score of a pertinent sentiment word. Furthermore, as high-valued cells influenced the values of surrounding cells, the heatmap graph became gradually significant. Figure 4b is a heatmap graph representing the distribution map of sentiment words from movie reviews written by viewers about the movie \"Snowpiercer.\" This graph shows high frequencies of emotions such as \"Pitiful and boring\" as well as \"Funny and great.\" Some reviews noted \"Not so impressive, below expectation. It was pitiful and boring.\" \"It was crueler than I thought, and it lasts pretty long in my head.\" and \"The movie was pretty pitiful, probably because my expectations were too high.\" As shown in these reviews, there were various spectators with different emotions about this movie, including disappointment. Figure 3: 36 sentiment words multidimensional scaling (MDS) map. Reproduced with permission from [6]; published by [IEEE], 2015. Therefore, a heatmap showing movie sentiment words can be divided into two cases. Thus, there are two types of heatmaps [7; 8]. The first one is a type including movies with a single frequent sentiment word. The table shown above presented a ranking of sentiment words in a movie called \"Don't cry mommy\" (Figure 4a), while the table shown below is its actual movie reviews. Since the movie describes a sexual crime, emotions related to \"Furious\" and \"Outraged\" are prominent. Such parts are shown in red in the heatmap. The second is a type with various frequent sentiment words. For instance, the word frequency table and reviews on the movie \"Snowpiercer\" (Figure 4b) revealed different sentiment words such as \"Boring, Cruel, Funny, and Pitiful.\" Thus, its heatmap suggests patterns based on such information. Furthermore, heatmap visualization helps us easily understand the types of emotions that viewers have experienced in a movie by reflecting the frequency of each sentiment word in the movie.
## 4 Experiments and Results
### Results and Analysis of MSTAR Experiments
#### 4.3.2 Results from SOC Experiments  
In the SOC experiment, detailed information on 10 types of SAR vehicle targets is shown in Table 3. There were 2747 and 2452 samples of SAR vehicle data at 17\({}^{\circ}\) and 15\({}^{\circ}\) pitch angles in the training and test datasets, respectively.  
The confusion matrix of the recognition results of the proposed model for 10 types of vehicle targets in SOC experiments is shown in Figure 5. The data in the test set were basically concentrated on the diagonal, indicating that these targets were correctly classified into their categories, and the overall recognition accuracy reached 99.26%. In SOC experiments, the average precision, recall, and F1-score of the proposed model reached 99.18%, 99.29%, and 99.23%, respectively. The precision of the three types of 2S1, BRDM2, and BTR70 reached 100%. The confusion matrix shows that the precision of ZIL131 is the lowest, and some ZIL131 data are wrongly identified as BRDM2 and BTR60, potentially due to the high similarity of ZIL131, BRDM2, and BTR60 as trucks. In general, the proposed model demonstrated good recognition performance in SOC experiments, effectively validating its effectiveness.