esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware Update through Digital TV Signals
## 3 Hardware Data Broadcast through a Digital TV Signal
### Data Broadcasting Mechanisms  
An essential feature of DTV standards is data broadcasting capability. Broadcast data are generally used to describe and identify the HDTV broadcast content. For instance, the European DTV standard, known as digital video broadcasting (DVB), offers four data transport mechanisms: data piping, data streaming, multiprotocol encapsulation (MPE), and carousels [45; 46].  
Data piping is the most straightforward mechanism, which consists of inserting raw data directly into the TS packet payload areas. Data streaming is more complex when compared with data piping and can arrange data using private sections [42] or packetized elementary stream (PES) packets. Private sections can be split into units of at most 4084 bytes of payload (actually, it presents a variable-length; see field private_section_length in Table 1), plus 8 header bytes and 4 bytes of a cyclic redundancy check (CRC), which amounts to 4096 bytes. Some advantages of private sections are contiguity control, given by the section number field, and error detection control, obtained via redundant information (e.g., CRC_32) [47].  
That being said, it is worth noticing that receivers can use field CRC_32 to detect corrupted sections, which confers safety and avoids using erroneous data. This way, when corrupted sections are identified, receivers can choose to discard those and then wait for the next ones, as this kind of mechanism is usually employed for sending data in repeating cycles. Consequently, any data reception procedure is concluded only when all sections with correct CRC_32 fields are obtained, which results in a reliable channel.  
[PERSON], for instance, describes the data streaming method by PES packets [48]. MPE uses the logical link control subnetwork access protocol (LLC/SNAP) encapsulation, which allows the use of any network protocol, in addition to unicast (i.e., datagram sent to a single receiver) and multicast (i.e., datagram sent to receiver sets).  
Carousels are techniques used to deliver data in a continuous cycle [49; 50], as defined by the digital storage media command and control (DSM-CC) standard [51; 52], which is adopted by both the Digital Audio Video Council (DAVIC) and DVB repeatedly. DSM-CC specifies two types of carousels: data and object carousels. The latter extends the data carousel by setting a file system directory structure (e.g., media files, applications, image files, and directories).  
Section 5.1 addresses the presented broadcasting methods by evaluating the core data characteristics and the most suitable approach to implement hardware reconfiguration schemes.
## 3 Results and Discussion
### Cluster Analysis of VFCs  
In order to further analyze the VFCs in each sample, the data were clustered via the Manhattan distance method and Euclidean distance method. Cluster analysis reflects the correlation degree of samples through visual graphics. The heat map clustering of pickled BLS with different salt concentrations is shown in Figure 5, and the original data had been standardized. The depth of the color block in the figure indicates the level of the VFC content, with redder meaning a higher relative content of the flavor substance and bluer meaning a lower relative content of VFCs. The horizontal direction represents the clustering of each sample, and the vertical direction represents the clustering of VFCs. Clustering into the same category indicated a high degree of correlation between the two, and a shorter Euclidean distance indicated a higher degree of correlation. According to the clustering results of each sample, the five samples were divided into four categories; G1 and G2 belonged to one category, and G4, G3, and G5 belonged to one category, respectively, among which G1 and G2 had the highest degree of correlation, indicating that G1 and G2 were relatively close in flavor. Judging from the clustering results of VFCs, some VFCs showed a strong correlation; 3,5-octaleni-2-one and (E,E)-2,4-heptadienal were highly correlated in the cluster result.  
Figure 4: Relative content of various VFCs in pickled BLS with different salt concentrations.
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 4 Application Research and Method Comparison
### Prediction of Cumulative Displacement  
The cumulative displacement prediction model could be obtained by accumulating the predicted values obtained by substituting the trend item, periodic item, and random item datasets into the model. The results are shown in Figure 11a-d.  
The evaluation index values are shown in Table 6. From the graph, we can see more intuitively that the model fit best when the training set was 40 weeks, and the real value of the cumulative displacement was closest to the model prediction. The data in Table 6 show that the prediction effects of Model 2 were the best. At this time, MAPE was 0.1883%,  
Figure 11: Cumulative displacement prediction results of different training sets: (**a**) prediction results of Model 1; (**b**) prediction results of Model 2; (**c**) prediction results of Model 3; (**d**) prediction results of Model 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Number**} & \multicolumn{4}{c}{**Evaluation Index**} \\ \cline{2-5}  & **MAPE (\%)** & **MSE** & **RMSE** & **R2** \\ \hline Model 1 & 32.96 & 0.0049 & 0.0699 & 0.8326 \\ Model 2 & 112.88 & 0.0031 & 0.0560 & 0.8926 \\ Model 3 & 33.87 & 0.0057 & 0.0758 & 0.8028 \\ Model 4 & 13.73 & 0.0007 & 0.0261 & 0.9765 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation index values of different models of random displacement.  
MSE was 0.0377, R\({}^{2}\) was 0.9891, and RMSE was 0.0469. However, in conjunction with the prediction results in Sections 4.2-4.4, the optimal prediction model for the trend component was Model 2, the optimal prediction model for the periodic component was Model 1, and the optimal prediction model for the random component was Model 4. Although the prediction effect of Model 2 was optimal on a whole, it was not locally optimal in all places, and there was room for improvement. After combining the optimal models corresponding to the trend term, periodic term, and random term, the test set was predicted. The results are shown in Figure 12, and the value of each evaluation index is shown in the last row of Table 6. Compared with Model 2, the evaluation index values tended to be more optimal.
# Usability Evaluations Employing Online Panels Are Not Bias-Free
## 1 Introduction
### Professional Respondents and Data Quality  
An Online Panel (OP; also known as crowdsourced community) is a group of people enrolled in a web platform who have agreed to participate regularly in online studies and/or in the execution of simple and repetitive operations. An OP is a form of access panel, defined by ISO 20252 as \"a database of potential users who will collaborate on future data collections if selected.\" OPs have many advantages: they are fast, inexpensive, and allow for the implementation of complex research studies (e.g., questionnaires with audiovisual stimuli, usability tests). Recruitment methodologies often rely on the self-selection of users who choose to become members of an online panel [13]. Additionally, researchers commonly use incentives to engage users, such as money or bonus points that can be exchanged later [14]. Moreover, the OPs are increasingly challenging the people involved with a wide range of studies and activities such as answering market research surveys, participating in usability evaluation, and managing bug-hunting tasks.  
The combination of self-selection, incentives, and increased use of OPs has led to a growing concern about \"professional respondents\" (PR) (also referred to as \"frequent survey responders\", see [15]) and how they might affect the data quality. [PERSON] [16], defined this category of users as \"respondents who participate in many online surveys, only when offered an incentive.\" Other authors describe them as users who participate in many surveys or are members of several POs [17; 18; 19]. These users may only be interested in receiving the incentive [15; 17]. As a result, they may participate in a survey using shortcuts and heuristics to complete it with minimal effort and/or they could present skill sets hardly comparable with the general population characteristics. This response behavior is called \"satisficing\" [20; 21], and can lead to less accurate responses and poor data quality [22]. Indeed, the supporting literature does not provide evidence against the latter hypothesis [13; 15; 23; 24]. Moreover, and this is our main concern, professional respondents may no longer represent a \"typical\" target user and instead provide feedback more akin to a subject-matter expert [25].
# Prediction of Vibration-Mode-Induced Noise of Structure-Acoustic Coupled Systems
## 5 Conclusions  
This study was aimed at identifying the correlation between the vibration and acoustic modes for predicting the sound pressure of the acoustic domain involving a structure, considering the structural vibration. The results demonstrated that the acoustic domain could be predicted from the vibration mode through a time domain structure-acoustic coupled simulation.  
The vibration and acoustic modes were found by applying mode decomposition to the shell vibration and sound pressure measured by an FEM analysis of a reverberation chamber offering sound barrier conditions. Through the mode decomposition, the relationship vibration mode to acoustic mode and the fact that coupling system between vibration and sound affected the former were verified. Through the time-domain analysis, it was also proved that vibration diffused in shell structure after converting a shape of the sound. Therefore, coupling effect by predictive formula was verified and it was confirmed that the acoustic mode can be predicted from the vibration mode by the correlation between the two modes based on the former result. More precise prediction of the acoustic mode would require natural frequency and damping ratio of the given acoustic mode, and in particular, the damping ratio at the sound barrier domain.  
To expand the acoustic mode prediction technique presented in this study from structure acceleration levels to heavy weight floor impact sound levels, a field experimental technique is required to identify the number of measurement sensors, the contribution level of vibration and sound pressure from each mode, and the modal characteristics of the acoustic domain. For this, it is judged that continuous research in this area is needed.  
Conceptualization, J.H.; writing--original and draft preparation, J.H. and S.K., writing--review and editing, J.H., J.R., M.S. and S.K.; Supervision, J.H.; All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from Technology Advancement Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government (grant 21 CTAP-C164107-01). And this work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1F1A1070349).  
The authors declare no conflict of interest.
# The Value of Fractal Analysis in Ultrasound Imaging: Exploring Intricate Patterns
## 3 Results
### Lung Cancer Detection  
Endobronchial ultrasound (EBUS) is a minimally invasive procedure used primarily for the staging and diagnosis of lung cancer. EBUS combines traditional bronchoscopy with US technology to provide detailed images of the lungs and surrounding lymph nodes. This method allows for real-time needle aspiration of lymph nodes and masses, aiding in the accurate staging of lung cancer and other thoracic diseases [19]. Fractal dimension analysis has been applied to EBUS images to differentiate between malignant and benign mediastinal nodes. This technique measures the complexity of structures within the US images, with malignant nodes typically exhibiting lower fractal dimension than benign nodes [20]. This distinction is due to the irregular and chaotic nature of cancerous tissue growth, which is less complex geometrically than benign tissues [20]. Studies have shown that fractal dimension analysis significantly improves the ability to differentiate between malignant and benign nodes. In one study, the fractal dimension was found to be significantly lower in malignant nodes, with an area under the ROC curve of 0.76, indicating good diagnostic performance [19; 20]. EBUS-TBNA (transbronchial needle aspiration) is less invasive than traditional surgical methods, reducing patient risk and recovery time. It provides a high diagnostic yield with minimal complications, making it a preferred method for lung cancer staging [21]. The combination of EBUS with advancedimage processing techniques, including fractal dimension analysis, enhances the visual and quantitative assessment of lymph nodes. This allows for better targeted biopsies and more accurate diagnoses [19; 20; 21]. EBUS is particularly useful in staging non-small cell lung cancer (NSCLC) by assessing the involvement of mediastinal and hilar lymph nodes. Accurate staging is crucial for determining the appropriate treatment plan and prognosis [20]. The ability to distinguish between malignant and benign nodes helps to identify metastatic disease, which is critical for comprehensive cancer management [20]. The implementation of fractal dimension analysis in routine EBUS procedures could standardize and improve diagnostic accuracy, offering a reliable tool for thoracic oncologists [19; 20; 21] (Figure 2).
# A High-Density Crowd Counting Method Based on Convolutional Feature Fusion
## 2 Feature Fusion CNN for Crowd Counting
### Ground Truth Density Map  
To apply supervised training on FF-CNN by means of density map regression from an input image, the image with labeled pedestrian heads should be converted into a ground truth density map. The ground truth density map generation method was used in FF-CNN [14].  
Suppose a pedestrian head is at pixel \(x_{i}\), which can be represented as a delta function \(\delta(x-x_{i})\). For N labeled pedestrian heads in an image, it can be expressed as follows:  
\[H(x)=\sum_{i=1}^{N}\delta(x-x_{i}) \tag{1}\]  
Convert it into a continuous density function with Gaussian kernel \(G_{\sigma}\)[7]:  
\[F(x)=\sum_{i=1}^{N}\delta(x-x_{i})\times G_{\sigma}(x) \tag{2}\]  
where \(N\) is the head count, \(\sigma\) is the variance of Gaussian kernel. The sum of the density maps is equivalent to the total number of pedestrians.  
Considering the perspective transformation, it is necessary to obtain the Gaussian spread parameter \(\sigma\) according to the size of each pedestrian head in the image. However, in a high-density environment, it is almost impossible to accurately obtain the size of the occluded head manually. It is observed that the distance between two adjacent pedestrian heads in a crowded scene is proportional to the size of the pedestrian head. Therefore, a geometrically adaptive Gaussian kernel can be used to generate a density map [16].  
Suppose that the crowd is distributed evenly around each pedestrian head, the average distance between each pedestrian head and its \(k\) neighbors may provide a viable geometric distortion estimation method. For a given head coordinate \(x_{i}\), suppose its distance from \(k\) neighbors is \(\{d_{1}^{i},d_{2}^{i},\cdots,d_{k}^{i}\}\), the average distance is \(\overline{d}^{i}=\frac{1}{k}\sum_{j=1}^{k}d_{j}^{i}\) and set \(\beta\) as the initial variance. The density function is:  
\[F(x)=\sum_{i=1}^{N}\delta(x-x_{i})\times G_{\sigma_{i}}(x),\ \sigma_{i}=\beta \overline{d}^{i} \tag{3}\]  
Although we used the adaptive density map generation method of Ref. [16], the value of \(\beta\) was optimized to obtain the ground truth density map with higher quality, which will be introduced in Section 3.2.
## 1 Introduction
Figure 1: RFID collisions. (**a**) Reader-to-Reader Interference; (**b**) Reader-to-Tag Interference. Concurrent requests, the primary cause of reader collisions in RFID systems, occur when multiple readers contend for access to the communication channel concurrently. This phenomenon may stem from various environmental conditions, such as high reader density, reader proximity, and system congestion. Understanding the implications of concurrent requests is crucial for developing effective collision mitigation strategies. By delving deeper into the causes and impacts of collisions in RFID systems, researchers and practitioners can devise more robust and efficient solutions to address these challenges. In our paper, we introduce the Distributed Time Slot Anti-Collision protocol (DTS-AC), a novel solution designed to tackle the challenges of Reader-to-Reader Interference (RRI) and Reader-to-Tag Interference (RTI) collisions in RFID systems. Through a combination of innovative techniques, DTS-AC not only resolves these issues but also maximizes the number of active readers while minimizing reader collisions and reading delay.
## 6 Simulation Results  
In this section, we evaluate the performance of TS via simulations. The performance metrics include the delivery rate, energy consumption and search delay. Moreover, the performance of TS in the WSAN with multiple actors is also studied. The performance of ERS [5] and XYLS [8] are evaluated for comparison.  
The simulations are executed in the OMNeT++ network simulator [28]. In the simulations, there are 400 sensor nodes uniformly deployed in a 400 m \(\times\) 400 m area. The source is randomly selected from the network. The original location of the actor is randomly selected and its mobility strategy follows the Random Waypoint model. To create an initial trail in the network, the actor movement starts 200 time slots earlier than the generation of the source. The TTL values of both TS and ERS are set to be 8 and the increment of TTL is also 8. The transmission range is 30 m. The waiting time of packet transmission \(\tau_{P}\) is fixed at 1. Each simulation runs 500 times to obtain statistical results.
###### Abstract  
Exploration geochemical data are typically collected and analyzed according to standard map sheets. Different map sheets may be tested by different companies, at different times, or using different instruments. As a result, \"systematic errors\" inevitably arise, which are directly reflected in the \"shift\" effect between different map sheets. This study compares various leveling methods, including contrast return (CR), boundary leveling (BL), and multi-scale correction (MSC), based on compositional data transformation of six map sheets from the Baiyinchagan-Maodeng area. The results show the following features: the raw data from multiple map sheets contain certain errors, and these raw data cannot be directly used to create geochemical maps; the CR method is unsuitable for leveling in areas with small scales or significant geological background differences; the BL method yields more reasonable results when there is a similar geochemical background (geological background) between map sheets, and it is better suited for leveling between map sheets in areas with small scales or significant regional differences in geochemical backgrounds; finally, the MSC method yields results similar to the BL method, but it does not require boundary consistency, thus having fewer limitations, although it requires multi-scale geochemical data. In practical terms, this study provides valuable guidance for selecting appropriate geochemical data leveling methods in exploration projects, thereby improving the accuracy and reliability of geochemical mapping and enhancing the effectiveness of mineral exploration.  
data leveling; compositional data analysis; boundary leveling method; multi-scale correction method 2025157208
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 5 Experimental Results and Discussion
### Comparative Analysis
#### 5.2.1 Network Throughput  
It is defined as the sum of data transmitted successfully to the destination node. However, the increase in throughput shows higher performance. It is an important \(QoS\) metric that shows the quality of the network in data transmission. Hence, maximizing the \(QoS\) performance is important in WBAN. Mathematically, it is calculated as follows,  
\[NetworkThroughput=P(s)/T(t) \tag{16}\]  
where \(P(s)\) is the size of the arrived packet and \(T(t)\) is the transmission time for a single packet. Figures 8 and 9 represent the throughput analysis for emergency and non-emergency packets, respectively.  
Figure 8: Network throughput vs. simulation rounds (emergency packets).  
Figure 7: Block diagram for CVD patients diagnosis.  
From the results obtained, it is proven that when the number of rounds increases, the network throughput increases gradually for the proposed B-DEAH work. However, the throughput performance is affected by long route acquisition delays, high routing overhead, high traffic over a particular route, higher rated buffers, and so on. In PCA, a large number of nodes are deployed but less effective in data transmission. The larger communication overhead introduces low throughput. In the E-HARP protocol, nodes are sparsely distributed, and communication is simple, which results in higher throughput than PCA and B-DEAH. In the proposed B-DEAH method, efficient routing is performed by conducting clustering using the SHO algorithm, which increases the throughput, thereby increasing the \(QoS\).  
The extracted routing information for communication leads to higher throughput in the B-DEAH model. For example, PCA attained 22.5 Kbps for 300 rounds, which is 50% less than B-DEAH, and other works have more than 50% lower performance than B-DEAH. Due to the path selection by the MOORA algorithm, throughput is obtained in a higher range. For both, emergency and periodical packets are running simultaneously since two different kinds of sink nodes are deployed for individual packet transmission. In B-DEAH, \(RDF\) is utilized, whose main role is to remove the fault data packets over a path and thus eliminate the transmission of the faulty packets. Therefore, throughput is increased when the network rounds also increase.
## 5 Limitations and Future Work  
The API utilized in this study, RapidAPI, has certain limitations. The collected data may be incomplete. Some information may not be accessible through the API due to privacy restrictions such as limited access to private posts or accounts. As a result, the available data may be limited or incomplete.  
In spite of these limitations, it may be argued that they contribute to the study's authenticity. An attacker attempting to gather information about an organization from social networks would likely encounter similar challenges. Data collection may not be perfect, or private profile information may not be accessible. As a result, this study simulates the challenges an actual attacker may encounter, increasing our findings' practical relevance.  
As part of our future research, we plan to implement additional deep learning methods to identify censored objects in images. We also plan to apply these methods to additional organizations. Furthermore, since social media posts often include textual content in addition to images, we are planning to extend our method to automatically identify confidential information in text as well. The enhancement will enable the detection and protection of both visual and textual confidential information.