esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Statement and First-Cut Method  
### Problem Statement  
Given a sequence of original histogram bins, \(H=\{H_{1},H_{2},\cdots,H_{n}\}\). Without a loss of generality, assume that the bin proportions are \(p=\{p_{1},p_{2},\cdots,p_{n}\}\), respectively, where \(0<p_{1}\leq p_{2}\leq\cdots\leq p_{n}<1\) and \(\sum_{i=1}^{n}p_{i}=1\). Publish a histogram \(H=\{\widetilde{H}_{1},\widetilde{H}_{2},\cdots,\widetilde{H}_{n}\}\) satisfying \(\epsilon\)-DP, where \(\widetilde{H}_{j}=H_{j}+n_{j}\), \(n_{j}\sim Lap(\lambda)\) for \(j\in\{1,2,\cdots,n\}\), and \(Lap(\lambda)\) is the Laplace distribution satisfying a scale parameter \(\lambda=\frac{1}{\epsilon}\).  
Obviously, the absolute error of each bin in the histogram is \(\mathrm{E}[|\widetilde{H_{j}}-H_{j}|]=\lambda\). Note that the mean square error (MSE) is given by \(E[(\widetilde{H_{j}}-H_{j})^{2}]=2\lambda^{2}\). So, we can deduce that the relative error of each bin is  
\[E[\frac{|\widetilde{H_{j}}-H_{j}|}{H_{j}}]=\frac{\lambda}{H_{j}}. \tag{5}\]  
Given that \(p_{1}\leq p_{2}\leq\cdots\leq p_{n}\), it follows that \(H_{1}\leq H_{2}\leq\cdots\leq H_{n}\). Consequently, it has Formula (6).  
\[E[\frac{|\widetilde{H_{1}}-H_{1}|}{H_{1}}]\geq E[\frac{|\widetilde{H_{2}}-H_{2 }|}{H_{2}}]\geq\cdots\geq E[\frac{|\widetilde{H_{n}}-H_{n}|}{H_{n}}]. \tag{6}\]  
According to Formula (6), the relative error of each bin with different proportions is different. If the data distribution is very uneven, the relative errors of bins with smaller counts (small bins) can be disproportionately higher compared to those with larger counts. This disparity may be detrimental in application scenarios where critical decisions rely on accurate representations of these smaller bins.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 2 Preliminaries on Routing Protocols for Ad-hoc Networks
### Optimized Link State Routing (OLSR) Protocol  
The Optimized Link State Routing Protocol (OLSR) is a proactive routing protocol designed to work with large and dense Mobile Ad-Hoc NETworks (MANETs) and using a hop-by-hop strategy to achieve a performance optimization higher than other classic link state routing algorithms. OLSR has been designed to work in dynamic scenarios where the communicating peers change over time and, since routes are maintained for all known destinations at all times [7], the control packets in the network are very limited and are usually handled only by selected nodes, denoted as MultiPointRelays (MPRs)--key element of the OLSR protocol--whose tasks are: (i) route creation and selection, and (ii) relay of messages between nodes.  
In detail, a MPR reduces redundant retransmissions in the network region it is handling, thus minimizing the overhead of flooding messages. In general, a node obtains the information about its neighbors through periodic _hello_ messages received from the neighbors themselves. A generic intermediate node \(N_{x}\) selects a set of nodes at 1-hop distance as its MPRs. Then the MPRs advertise link-state information for their \"child\" nodes periodically in their control messages. OLSR uses packets encapsulated in UDP datagrams transmitted on the reserved port 698. Moreover, the packet format is unified for all data (control messages, actual data messages) in order to have a fast and easy extensibility of the protocol maintaining compatibility with older versions of the protocol itself.
###### Abstract
Specifically, four different simulation schemes are designed: (1) driving the WRF model with Final Operational Global Analysis (FNL) data; (2) driving the WRF model with European Reanalysis Interim (ERA5) data; (3) applying the Holland empirical parametric typhoon model; and (4) applying the Jelesni-anski empirical parametric typhoon model. By comparing the simulated typhoon wind fields from each scheme with SMAP satellite data, this study aims to investigate differences in simulation performance under high wind speeds and assess accuracy. Furthermore, we apply these four wind field datasets to the SWAN wave model to simulate sea surface conditions during typhoons, analyzing differences in significant wave height generated by each wind dataset. The results provide valuable insights and references for future researchers in the field of super typhoon numerical simulation.
## 3 Results  
The results of the research work are described below. Table 4 presents the results of the compressive strength, and Tables 5-9 presents the comparison of the results of the leaching of environmental pollutants from the samples tested with emphasis on the emission of formaldehyde. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Sample No.**} & \multicolumn{2}{c}{**Flexural Strength (Mpa)**} & \multicolumn{2}{c}{**Compressive Strength (Mpa)**} \\ \cline{2-5}  & **Average Value** & **Standard Deviation** & **Average Value** & **Standard Deviation** \\ \hline WM1 & 2.96 & 0.97 & 11.34 & 1.73 \\ WM2 & 3.12 & 0.27 & 9.24 & 1.28 \\ WM3 & 3.68 & 0.40 & 8.53 & 1.79 \\ WM4 & 1.48 & 0.51 & 17.78 & 1.77 \\ WM5 & 2.58 & 0.14 & 17.07 & 1.07 \\ WM6 & 1.90 & 0.18 & 5.99 & 1.16 \\ \hline \end{tabular}
\end{table} 
Table 4: Compressive and flexural test strength results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & & & & & **Criteria for Waste Landfilling Acceptance**[26] \\ \hline  & & & & & & & **Other Than** \\  & & & & & & & **Neutral and** & **Hazardous** \\  & & & & & & & & **Hazardous** \\ \hline Arsenic, As & mg/kg s-m & \(<\)0.01 & \(<\)0.01 & \(<\)0.01 & 0.5 & 2 & 25 \\ Barium, Ba & mg/kg s-m & \(1.80\) & \(2.36\) & \(2.52\) & 20 & \(100\) & 300 \\ Cadmium, Cd & mg/kg s-m & \(<\)0.01 & \(<\)0.01 & \(<\)0.01 & 0.04 & 1 & 5 \\ Chromium, Cr & mg/kg s-m & \(3.23\) & \(2.04\) & \(1.33\) & 0.5 & 10 & 70 \\ Copper, Cu & mg/kg s-m & \(0.14\) & \(0.15\) & \(0.17\) & 2 & 50 & 100 \\ Mercury, Hg & mg/kg s-m & \(0.0004\) & \(0.0004\) & \(0.0002\) & 0.01 & 0.2 & 2 \\ Molybdenum, Mo & mg/kg s-m & \(0.73\) & \(0.41\) & \(0.37\) & 0.5 & 10 & 30 \\ Nickel, Ni & mg/kg s-m & \(<\)0.01 & \(<\)0.01 & \(<\)0.01 & 0.4 & 10 & 40 \\ Antimony, Sb & mg/kg s-m & \(<\)0.01 & \(<\)0.01 & \(<\)0.01 & 0.4 & 10 & 40 \\ Selenium, Se & mg/kg s-m & \(<\)0.20 & \(<\)0.20 & \(<\)0.20 & 0.1 & 0.5 & 7 \\ Lead, Pb & mg/kg s-m & \(<\)0.01 & \(<\)0.01 & 0.01 & 4 & 50 & 200 \\ Selenium, Se & mg/kg s-m & \(31\) & \(31\) & \(46.6\) & 800 & 15,000 & 25,000 \\ Fluorides, F\({}^{-}\) & mg/kg s-m & \(2.50\) & \(2.50\) & \(3.40\) & 10 & 150 & 500 \\ Sulphates, SO\({}_{4}\)\({}^{2-}\) & mg/kg s-m & \(1870\) & \(1600\) & \(1550\) & 1000 & 20,000 & 50,000 \\ Dissolved organic carbon, DCC & mg/kg s-m & \(37.4\) & \(38.5\) & \(44.8\) & 500 & 800 & 1000 \\ Total dissolved solids (TDS) & mg/kg s-m & \(5400\) & \(4210\) & \(4040\) & \(4000\) & 60,000 & 100,000 \\ Phenolic index & mg/kg s-m & \(0.870\) & \(1.35\) & \(0.620\) & 1 & - & - \\ \hline \end{tabular}
\end{table} 
Table 6: Leachability of selected contaminants from cement mortar, expressed as mg/kg dry weight compared with maximum acceptable waste disposal rates.
## 4 Results and Discussion
### Compressive Strength  
The compressive strength tests on specimens prepared with the 13 various concrete mixes mentioned in Table 2 were conducted after 28 days of casting, and the results (average of three samples with a maximum variation of 7% in values) are presented in Figure 8. As indicated, the compressive strength exhibited by all RAC mixes satisfied the minimum requirement of strength for structural concrete given in ACI 318. Among all mixes, the maximum strength of 24.6 MPa was attained by the control mix containing 100% fine and coarse NA [F(NA) + C(NA)]. However, replacement of 100% fine NAs with fine RBAs [F(RBA) + C(NA)] in this mix resulted in a drop in compressive strength of 6.1%, while the drop in compressive strength was 8.9% when 100% fine NAs were replaced with fine RCAs [F(RCA) + C(NA)]. Since brick waste generally consists of aluminates and silicates, its pozzolanic characteristics [68] result in a significant drop in the compressive strength of concrete mix. Concrete mixes containing 100% fine and coarse RBAs attained 21.9% lower compressive strength compared to the control mix; however, the replacement of 100% coarse RBAs in this mix with coarse RCAs and coarse NAs increased the compressive strength by 2.6% and 20.3%, respectively. Similarly, concrete mix containing 100% fine and coarse RCAs (i.e., F(RCA) + C(RCA)) exhibited 20.7% lower compressive strength compared to the control mix (i.e., F(NA) + C(NA)); however, the replacement of 100% coarse RCAs in this mix with coarse RBAs decreased the strength by 4.1% while replacement of 100% coarse RCAs with coarse Nas increased the compressive strength by 14.9%. For concrete mixes with the same fine RAs, altering the replacement level of coarse NAs with  
Figure 7: Concrete density (CONT = control mix, NA = natural aggregate, RBA = recycled brick aggregate, and RCA = recycled concrete aggregate).  
coarse RAs caused a gradual change in the compressive strength. Compared to a mix containing 100% fine RBAs and 100% coarse NAs [F(RBA) + C(NA)], the replacement of coarse NAs with coarse RBAs in the mix at rates of 25%, 50%, 75%, and 100% decreased the compressive strength by 5.2%, 9.5%, 12.5%, and 16.8%, respectively. Similarly, in comparison of concrete mix containing 100% fine RCAs and 100% coarse NAs [F(RCA) + C(NA)], replacement of coarse NAs with coarse RCAs in the mix at the rate of 25%, 50%, 75%, and 100% decreased the compressive strength by 4.0%, 8.5%, 10.3%, and 12.9%, respectively. The results of compressive strength clearly revealed that although fine and coarse RAs imparted a negative effect on the concrete strength, in the presence of fine RBAs in the mix, the drop in compressive strength of RAC, compared to the control mix, was lower than that of other RAC mixes. This may be attributed to the cementing ability of fine RBAs, as highlighted by the previous research studies. On the contrary, the detrimental effect on the concrete strength was higher with coarse RBAs compared to coarse RCAs.
# A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
###### Abstract  
Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children's Medical Center dataset.  
deep learning; transfer learning; medical image processing; computer-aided diagnosis +
Footnote †: journal: Applied Soft
# A Survey of the Applications of Text Mining for the Food Domain
## 7 Text Mining in Food Safety Regulation
### Analysis of the Causes of Foodborne Diseases  
The surge in foodborne illnesses, caused by contamination with microbial, viral, or parasitic agents, poses a serious threat to public health and safety, impacting both individual well-being and the broader healthcare and economic landscape. Extensive textual data serve as a valuable resource for understanding the causes behind these diseases. [PERSON] et al. [130] have pioneered a novel data mining methodology to uncover patterns in the incidence of foodborne diseases. Using a combination of attribute selection, decision tree training, and association rule generation, they revealed important correlations among different categories of foodborne afflictions, implicated food sources, and consumption locations.  
Additionally, emerging sources of information, such as social media, have been leveraged to enhance the detection of foodborne disease outbreaks [119]. [PERSON] et al. [131] developed nEmesis, a system that intelligently identifies potential public health threats from food establishments by analyzing Twitter discourse. This showcases the potential of social media analytics in public health and provides actionable intelligence to strengthen public health responses. Similarly, collaborations between health departments and academic institutions, like the one between the New York City Department of Health and Mental Hygiene and Columbia University [132], have utilized online restaurant reviews on platforms such as Yelp to identify previously unreported outbreaks of foodborne illnesses, offering valuable insights into the origin and impact of these diseases [133; 134].  
Text data analysis of foodborne illness has challenges such fragmentation, compartmentalization, and quality inconsistencies. The development of extensive text analysis tools and the establishment of defined methods for text annotation are crucial in addressing these difficulties. [PERSON] et al. [135] presented TWEET-FID, an annotated dataset designed for the multifaceted detection of foodborne illness events. They provided a detailed overview of the robust framework for creating this dataset and the fine-grained annotation process and showed how state-of-the-art deep learning methods can be applied to various tasks using the TWEET-FID corpus.  
In conclusion, a comprehensive and effective analysis of foodborne disease etiologies necessitates the integration of various methodologies. Text data offer new perspectives for exploration through the use of text classification, information extraction, and sentiment analysis, and continued efforts to advance text intelligence analytics are critical to ensuring food safety.
## 3 Experimental Results
### Strains  
Figure 8 describes the curves of the axial load versus the steel plate strain, which are the average values of the mid-height vertical strains, with the negative value corresponding to the compression. From test experiments, it was shown that the steel plate of specimen HGW-1 yielded at 10.81 MN load, while the steel plate of specimens HGW-2 and HGW-3 yielded at the ultimate load. Therefore, the yield strength of the steel plate can be used in the subsequently calculated formula of the axial compressive bearing capacity. As shown in Figure 7, the strain of specimen HGW-1 increased more rapidly than specimen HGW-2, mainly because the vertical stiffening ribs on both sides increased the rigidity of the steel plate, and the horizontal distribution of the steel bar through the holes of the ribs strengthened the cooperative performance of the steel plate and concrete.  
Figure 9 shows the changing-rule curves of the vertical strain and circumferential strain of specimen HGW-3, which were the average value of each side. The vertical strain was expressed by the compressive strain, with a negative value on the left side of Figure 9, while the circumferential strain corresponded to the tension strain, with a positive value on the right side of Figure 9. It was observed that four circumferential strain curves were basically coincident, which revealed that the deformations of each side were uniform. During the whole loading process, the increase rate of the vertical strain was greater than that of the circumferential strain before the ultimate load, which indicated that vertical deformation became the major part in this stage. The vertical strain of the steel plate marginally reduced after the ultimate load, while the circumferential strain gradually increased, chiefly because the constraint effect of the steel plate on the concrete was no longer increased after the  
Figure 7: Rigidity degradation curves. (**a**) HGW-1 and HGW-2; (**b**) HGW-3.  
yielding of the steel plate, so circumferential deformation of the concrete and circumferential tension stress of the steel plate could rapidly increase.
## 3 Research Methods, Research Platform, and Data Sources
### Research Platform  
This study selected Weibo 15.1.2, Bilibili 8.36.0, and Douyin 33.2.0 as data sources to analyze Chinese users' comments on AIGC. These three platforms are well-known social media platforms in China, facilitating user interaction and information dissemination through their real-time information-sharing mechanisms. According to relevant statistics, Weibo's monthly active users have reached 583 million, with daily active users totaling 256 million. In the fourth quarter of 2024, Bilibili recorded 340 million monthly active users and 102 million daily active users. Meanwhile, Douyin surpassed 700 million DAUs in 2024, with users spending over 60 min daily on average. The selection of these platforms as data sources is justified not only by their significant advantages in terms of user scale, activity level, and representativeness but also by the strong scientific basis and persuasive evidence underlying the data. This choice ensures the comprehensiveness, diversity, and representativeness of the research data, providing a robust foundation for analyzing public sentiment and opinion dynamics.
## 4 Results and Discussion
### Functionality Verification Results  
#### 4.3.1 ISO 10328 Cyclic Tests  
Figure 28 shows how the elastic elements were deflected when the foot was subjected to the ISO 10328 cyclic test.  
The foot prosthesis had its maximum plantarflexion around \(-6.4^{\circ}\) and maximum dorsiflexion around \(15^{\circ}\). Both the values are comprised in the ranges specified in the _Requirements_ section (Section 2.4), as also highlighted in Figure 29.
# A Machine-Learning-Based Cyberattack Detector for a Cloud-Based SDN Controller
## 4 Integration of an ML-Based Cybersecurity Detector and Mitigator in the TeraflowSDN Controller
### Attack Detection and Mitigation Workflows  
This section presents the main workflows that illustrate how the Cybersecurity NetApp interacts with other TeraFlowSDN core components to implement the detection and mitigation of network attacks at the IP layer.  
When a new service is created on TeraFlowSDN, the Cybersecurity NetApp communicates with the core components during the different stages of the process. The Cybersecurity NetApp starts by subscribing to service events from the Context component during start-up. When a service request is received, the service setup stage is triggered, which involves changes to several components of TeraFlowSDN. The service identifier is then returned to the customer who requested the service. The KPI setup stage then starts with the Context component notifying the Cybersecurity NetApp about the new service.  
The Cybersecurity NetApp will then begin performing the attack detection and mitigation process on the service and tracking relevant KPIs through the Monitoring component. To implement the detection and mitigation of network IP-level attacks, four workflows were created with each workflow focusing on a specific part of the system.  
The first workflow focuses on the DAD component and covers the process of collecting traffic statistics from IP-level traffic and reporting to the CAD component. The second workflow focuses on the CAD component and covers the process of processing the traffic statistics reported by the DAD component and making inferences with the ML model to detect IP-level attacks. The third workflow focuses on the AM component and covers the process of computing attack-remediation solutions in response to attack notifications from the CAD component. Finally, the fourth workflow focuses on the monitoring of cybersecurity KPIs and covers the process of collecting and storing KPIs related to the security status in the network.
## 5 Comparative Analysis Results and Discussion
### Performance Comparison for All Models
The ROC curve comparison for all the models we evaluated for this study is presented in Figure 12. Compared to the other models we tested, our ClaimsBERT classifier performed better, because ClaimsBERT ROC curve is closer to the upper-left corner than the other models (Figure 12). The Area Under the ROC curve (AUC) is another important parameter for analyzing classifier models. In general, a higher AUC score indicates a better classifier performance [69]. Table 10 and Figure 12 presenting the AUC value for all language models, which indicate that ClaimsBERT had the best AUC value amongst all language models we have tested for this study. Figure 12 shows that the highest AUC belongs to ClaimsBERT, which was 0.968. This demonstrate that ClaimsBERT is better at identifying classes than any other language models we evaluated in this study. This figure also shows that our model (ClaimsBERT) improves the AUC by more than 19 percentage points compared to BertForSequenceClassification's AUC. The Table 10 reports the required for completing model training after optimum learning rate determination for each evaluated model architecture. We found that BERT + BiLSTM training took the longest of all the models, followed by BERT + LSTM. We can also observe that the ClaimsBERT model training time is smaller than CyBERT's training time, even though it has more trainable parameters (Table 10). An explanation for the faster training process in the ClaimsBERT classifier is observable in Figure 11: Because of the effectiveness of feature map selection via CNN, the model starts out with more informative parameters and can therefore converge faster than CyBERT. Training these models does not need to be repeated, unless changes to the training dataset are necessary. Based on results reported in Table 10, we observe that adding LSTM and BiLSTM on top of BERT encoders increase the training time, and does not improve the accuracy compare with adding neural network to BERT (CyBERT model). Figure 12: ROC comparison for all language models.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 4 Deep Learning-Based Point Cloud Upsampling
### Supervised Upsampling
#### 4.1.1 Feature Extraction Components
Unlike MPU and PU-GCN, it introduces residual connections between different convolution blocks instead of dense connections. PUGeo-Net [15] adds a feature re-calibration module on the basis of using DGCNN to extract features. Multi-scale features are recalibrated through one layer of MLP and one layer of softmax. [PERSON] et al. [35] introduced a channel attention mechanism in PUI-Net to extract features. They calculate the feature mean of each channel, and control the features of each dimension through two fully connected layers, which are spliced with the extracted features to form the output features. The feature extraction method based on graph convolution can extract local and global features more effectively, has fewer parameters and is easy to train, and has been widely used.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 3 Methods
### Motion Correction Strategies
#### Prospective Approaches  
A number of modern commercial devices include additional hardware for correcting movement, such as retinal trackers [35], although the measurements generated by these instruments are not interchangeable [36]. A few examples are: Spectralis OCT (Heidelberg Engineering, Heidelberg, Germany), which uses auxiliary Scanning Laser Ophthalmoscopy (SLO) images; RTVue (Optovue Inc., Fremont, CA, USA), which uses a full-field fundus image; or tracking OCT from Physical Sciences Inc. (PSI, Andover, MA, USA), which uses a separate tracking beam for observing motion related reflectance changes of the retina [37]. Custom made hardware procedures based on new instruments or configurations have also been proposed [38; 39; 40; 41]. For more information on prospective/hardware-based approaches to correct retinal motion on OCT imaging, we refer to the article by [PERSON] et al. [16].