esa-sceva/satcom-chunk-collection huggingface dataset:
# Reversible Data Hiding for DICOM Image Using Lifting and Companding
## 2 Key Features of DICOM File Format  
Digital Imaging and Communications in Medicine (DICOM) is the international standard for formatting, exchanging, and storing medical images and related data for clinical use. It also includes a network communications protocol that uses TCP/IP to communicate between systems [23]. DICOM files can be interchanged between two persons that are capable of receiving images and patient information in DICOM file format. Digital medical images could be generated form diagnostic modalities such as Ultrasound, Nuclear Medicine, digital radiography, X-ray and hospital information system, etc. [24]. Nowadays, DICOM is used in radiology, cardiology imaging, radiotherapy device, ophthalmology, and dentistry. In other words, DICOM is widely used in most of the healthcare messaging standards in the world [24]. The digital watermarking techniques should employ necessary steps so that the quality of medical images are not degraded and are still conform with Digital Imaging and Communication in Medicine (DICOM) format. Any degradation of the medical image's quality could lead to misdiagnosis, and that is somewhat unacceptable. However, in the case of natural images, there is no such hard restriction about the quality of the watermarked natural image.  
Each DICOM file has a header which contains the patient's name, type of scan, patient demographic information, acquisition parameters, practitioner & operator identifiers, and image dimensions, etc. The remaining portion of the DICOM file contains the image data. Because they often contain multiple high-resolution images, DICOM files tend to be large and are frequently compressed before storage and transfer [25]. An illustration of the basic file structure is shown in Figure 1. The header consists of a 128 byte File Preamble, followed by a 4-byte DICOM prefix. A Data Set represents an instance of a real-world Information Object. A Data Set is constructed of Data Element. Data Elements contain the encoded values of attributes of that object. Figure 2 shows the DICOM data set and data element structures. The Data Element structure consists of the following fields: Tag, Value Representation, Value Length, and the Value [26].
## 4 Experimental Test Results
### Split Tensile Strength  
Figure 9a shows the average split tensile strength of concrete samples at 28 days. In addition, the relative strength ratios of the ERA series, ERB series, and ERR mix are shown in Figure 9b-d, respectively, compared to the split tensile strength of the control specimens (CNC). In contrast with the relation of the MOR to compressive strength, the split tension to compressive strength lineally follows the compressive strength trend. In [PERSON]'s study [47], the split tension of concrete to compressive strength was suggested to be 8.5% with a compressive strength of 4000 psi (27.6 MPa) and 7.7% with 6000 psi (41.4 MPa). Test results in Table 4 shows that the split tension to compression ranges from 9.6-10.7%, except for the ERR3 with a value of 8.4%. Overall, except for the ERR3 mix, a slightly higher split tensile strength values than expected were obtained.  
From Figure 9b,c, the relative split tensile strengths of the ERA concrete series and ERB concrete series were compared to the CNC concrete, respectively. As discussed before, due to variation in the FA/a ratio, split tensile strength was ERA1 > ERA2 and ERB1 > ERB2. Poor water absorption of the RA  
Figure 9: Split tensile strength results of concrete samples (**a**) strength, (**b**) relative value of ERA series, (**c**) relative value of ERB series, and (**d**) relative value of ERR mix.  
(3.82%) and RB (6.61%) aggregates resulted in a decrease of 9-11% (except for 18% of the ERA2) in the split tensile strength results, in contrast to the good water absorption of the NA aggregate (0.37%). It is plainly seen that the strength gap in the split tension narrowed relative to the reference mix, compared to the values of 11-20% (except for 24% of the ERA2) in the compressive strength results.  
Comparing the test results between the ERA series and ERB series, except for the ERA2 mix, very similar tensile strength trends were observed. Once again, very poor density of ERA2 in Figure 9 might result in it having the lowest tensile strength.  
Test results in Figure 9d shows that the ERR3 mix exhibited 90% of the relative tensile strength to the control mix. According to [PERSON]'s interpretation of the relation between the tensile strength of concrete to compression, other mixes (9.6-10.7%) had somewhat greater tensile strength gains, in comparison to the compressive strength gains, than the ERR3 mix (8.4%), as tabulated in Table 4.
## 3 Results and Discussion
### Biochar Composites Characterisation
#### 3.2.1 Thermal Behaviour of the PLA/PBAT/BC Composites
A suitable printing temperature (220 \({}^{\circ}\)C) was used without nozzle clogging and degradation. The 3D-printing-nozzle temperature of 220 \({}^{\circ}\)C was safer, since this was much lower than the onset of degradation of the PLA/PBAT blend and the PLA/PBAT/BC composites. Another critical piece of information is the percentages of char residue at 550 \({}^{\circ}\)C, which for the PLA/PBAT blend was only 1.37%, but was around 5.38% for composites with 5 wt% biochar, while for composites with 10 wt% of biochar it was \(\sim\)12.95%. Therefore, it appears that the char residue of the composites mainly depends on the amount of biochar added to them [39]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \(\mathbf{T_{c}}\) [\({}^{\circ}\)C] & \(\mathbf{T_{m}}\) [\({}^{\circ}\)C] & \(\Delta\mathbf{H_{cc}}\) [J/g] & \(\Delta\mathbf{H_{m}}\) [J/g] & \(\mathbf{X_{c}}\) [\%] \\ \hline PLA/PBAT (80/20) & 101.54 & 176.05 & 17.41 & 34.30 & 22.53 \\ PLA/PBAT/BC (79/20/1) & 96.76 & 177.77 & 18.17 & 36.63 & 24.93 \\ PLA/PBAT/BC (77.5/20/2.5) & 96.50 & 177.23 & 16.54 & 38.27 & 29.92 \\ PLA/PBAT/BC (85/10/5) & 98.03 & 177.86 & 20.72 & 44.47 & 31.68 \\ PLA/PBAT/BC (80/15/5) & 98.76 & 176.90 & 17.05 & 38.76 & 28.96 \\ PLA/PBAT/BC (75/20/5) & 99.01 & 176.76 & 17.71 & 36.85 & 27.23 \\ PLA/PBAT/BC (70/20/10) & 98.03 & 176.82 & 15.75 & 36.12 & 31.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Thermal properties of the PLA/PBAT/BC composites. Figure 4: DSC of PLA/PBAT/BC composites.
## 5 Experiments
### Datasets  
We conducted experiments on four popular datasets to evaluate the effectiveness of the proposed transformer in various learning scenarios. Table 1 presents the statistical details of these datasets. We briefly introduce these datasets as follows:  
* Algebra05 ([[https://www.kdd.org/kdd-cup/view/kdd-cup-2010-student-performance-evaluation/Data](https://www.kdd.org/kdd-cup/view/kdd-cup-2010-student-performance-evaluation/Data)]([https://www.kdd.org/kdd-cup/view/kdd-cup-2010-student-performance-evaluation/Data](https://www.kdd.org/kdd-cup/view/kdd-cup-2010-student-performance-evaluation/Data)), accessed on 10 March 2024) was introduced during the KDDcup 2010 Educational Data Mining challenge [37]. It comprises student responses to algebra questions from 2005 to 2006.
* Bridge06 ([[https://www.kdd.org/kdd-cup/view/kdd-cup-2010-student-performance-evaluation/Data](https://www.kdd.org/kdd-cup/view/kdd-cup-2010-student-performance-evaluation/Data)]([https://www.kdd.org/kdd-cup/view/kdd-cup-2010-student-performance-evaluation/Data](https://www.kdd.org/kdd-cup/view/kdd-cup-2010-student-performance-evaluation/Data)), accessed on 10 March 2024) is similar to Algebra05, but it includes more learners, questions, and interactions from 2006 to 2007 [37].  
* Assist09 ([[https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data](https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data)]([https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data](https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data)), accessed on 10 March 2024) is a dataset collected from ASSISTment, an online tutoring system for grade school math exercises, which contains student responses to different types of questions, such as multiple choice, text, and open-ended questions [38].
* Assist17 ([[https://sites.google.com/view/assistmentsdatamining/dataset](https://sites.google.com/view/assistmentsdatamining/dataset)]([https://sites.google.com/view/assistmentsdatamining/dataset](https://sites.google.com/view/assistmentsdatamining/dataset)), accessed on 10 March, 2024) is another collection from ASSISTment for a data mining competition in 2017, with learners having significantly longer learning sequences [39]. This dataset allows learners to attempt a single question multiple times until they reach the correct answer. Therefore, it becomes crucial for models to take into account the cumulative effect throughout the learning process.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 4 Proposed Methodology
### Data Extraction Using Alphapose
#### 4.2.1 Motion Calculation and Graph Construction  
In this study, we focus on the dynamic fall detection dataset, where motion is a critical characteristic for an effective fall detection framework, particularly in terms of movement, alignment, and data structure efficiency. Motion directly influences the dynamics inherent in fall-related data. To quantify motion, we utilize all landmark positions for \(x\) and \(y\) coordinates, represented as a two-dimensional vector. The motion for a given joint is computed by differentiating the joint positions between consecutive frames:  
\[X_{motion}=M_{j}^{t}=J_{j}^{t}-J_{j}^{t-1} \tag{1}\]  
where \(M_{j}^{t}\) represents the motion of joint \(j\) at time \(t\), \(J_{j}^{t}=(x_{j}^{t},y_{j}^{t})\) is the position of joint \(j\) at frame \(t\), \(J_{j}^{t-1}=(x_{j}^{t-1},y_{j}^{t-1})\) is the position of the same joint in the previous frame. Here, \(X_{motion}\) represents the motion dataset, which serves as a structured representation of human movement, capturing the two-dimensional coordinates of various joints over time. The classification of fall and non-fall events is based on analyzing multiple frames, utilizing the sequential arrangement of relative joint positions. To model spatiotemporal relationships, we construct a graph-based representation that integrates both spatial and temporal domains while considering the anatomical relationships among joints. The primary graph structure, which is initially underconnected, is formulated as follows:  
\[G=(V,E) \tag{2}\]  
Here, \(V\) and \(E\) denote the nodes and edges of the graph. Then, the node can written as \(V=v_{(i,t)}\mid i=1,\ldots,N,t=1,\ldots,T\), which considers the whole body skeleton as a graph. Consequently, the adjacent matrix can be defined as in Equation (3).  
\[f(x)=\begin{cases}0&\text{if they are not adjacent}\\ 1&\text{if the nodes are adjacent.}\end{cases} \tag{3}\]
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 5 Conclusions  
This paper presented a novel lossless, high-compression-rate with a low-complexity EEG compression algorithm that is based on a fuzzy decision, dynamic voting prediction and tri-stage entropy coding. The proposed VLSI design had significantly increased the compression rate to 14.6% and significantly reduced the hardware cost to 37.3% compared to previously reported designs in literatures. A limitation of the proposed design is its power consumption which can be reduced by improving the power consumption of the registers for future works. The proposed design is suitable for applications in WBAN, WBSN, wearable devices, as well as in Internet of Things (IoT) devices.  
Conceptualization, C.-A.C., C.W. and S.-L.C.; Software, C.W. and S.-L.C.; Validation, P.A.R.A. and C.-A.C.; Formal Analysis, C.-A.C., C.W., P.A.R.A. and S.-L.C.; Investigation, C.-A.C., C.W. and S.-L.C.; Resources, C.-A.C. and C.W.; Data Curation, C.-A.C., C.W., P.A.R.A. and S.-L.C.; Writing-Original Draft Preparation, C.W. and C.-A.C.; Writing-Review & Editing, C.-A.C., P.A.R.A. and S.-L.C.; Visualization, C.-A.C., P.A.R.A. and S.-L.C.; Supervision, S.-L.C.; Project Administration, C.-A.C. and S.-L.C.; Funding Acquisition, C.-A.C. and S.-L.C.  
This work was supported by the Ministry of Science and Technology (MOST), Taiwan, under grant numbers of MOST-107-2218-E-131-002, MOST-107-2221-E-033-057, MOST-107-2622-E-131-007-CC3, MOST-106-2622-E-033-014-CC2, MOST-106-2221-E-033-072, MOST-106-2119-M-033-001, and the National Chip Implementation Center, Taiwan.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Performance Parameters** & **Bluetooth** & **Zigbee** \\ \hline _Etx\_original_ (nJ/bit) & 143 & 296 \\ _Etotal\_original_ (nJ/bit) & 143 & 296 \\ Compression Rate & 2.35 & 2.35 \\ _Etx\_CR_ (nJ/bit) & 60.85 & 125.96 \\ _Ecomp_ (nJ/bit) & 0.005 & 0.005 \\ _Etotal\_CR_ (nJ/bit) & 60.855 & 125.965 \\ Energy Savings (\%) & 57.44 & 57.44 \\ \hline \end{tabular}
\end{table}
Table 4: Energy saving for Bluetooth and Zigbee wireless transmitters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Performance Parameters** & **[PERSON] et al. [6]** & **[PERSON] et al. [17]** & **This Work** \\ \hline Compression Rate & 2.05 & 1.37 & 2.35 \\ Process (\(\mu\)m) & 0.1 & 0.065 & 0.18 \\ Frequency (MHz) & 100 & 24 & 100 \\ Voltage (V) & 1.80 & 1.00 & 1.80 \\ Gate Count (k) & 13.4 & 53.9 & 8.4 \\ Area (k\(\mu\)m\({}^{2}\)) & 134 & 58 & 84 \\ Normalized Area & 1.59 & 5.29 & 1.00 \\ Power Save (\%) & 51.21 & 27.00 & 54.77 \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the proposed lossless EEG encoding algorithm and its VLSI architecture with a previous work.
## Materials and Methods
### Analysis Methods
#### 2.3.2 Pre-Processing of the Spectral Feature Sets  
In order to improve the accuracy of identification of the different stages of severity of FHB, and to ensure a high-efficiency operation, it is necessary to select the optimal spectral features from the candidate spectral features. The conditions of selection are as follows:  
1. The spectral features are significantly correlated with the severity of FHB and the threshold determination coefficient is greater than 0.49 (R\({}^{2}>0.49\)).
2. For the three classes of samples (healthy, mild, and severe), the independent T-test was used to select spectral features that showed significant heterogeneity to each disease class (\(p<0.001\)).
3. The spectral feature sets selected by the above two conditions were intersected to obtain an optimal spectral feature set sensitive to FHB and with significant heterogeneity between the three classes of disease severity.
4. The above steps were repeated from three angles--front, side, and erect--to obtain the optimal spectral feature set suitable for FHB identification.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 4 Conclusions  
This paper proposed a new secure chaos-based cryptosystem system for a block cipher in CBC mode and evaluated its performance. The proposed cryptosystem achieved high confusion diffusion effects thanks to a robust pseudorandom number generator of chaotic sequences, a strong circular substitution based on the proposed S-box, and a solid diffusion layer. The proposed PRNG-CS was formed by four discrete 1-D chaotic maps weakly coupled by a predefined coupling matrix M, to avoid the divide-and-conquer attack, and to increase the randomness of the sequences produced as well as their lengths. The diffusion layer was performed by a 2-D modified cat map and a horizontal addition diffusion (HAD) followed by a vertical addition diffusion (VAD). The experimental results and the security analysis demonstrated that the proposed cryptosystem could successfully resist various attacks known in the literature, such as statistical attacks, brute force attacks, noise attacks,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Crytosystem** & \(NCpB\) \\ \hline Proposed & 1568 \\ [PERSON] et al. [25] & 77,386 \\ [PERSON] et al. [52] & 95,368 \\ [PERSON] et al. [53] & 15,642 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of NCpBs from different encryption schemes.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Image** & **Size** & \begin{tabular}{l} **Encryption Time** \\ **(Milliseconds)** \\ \end{tabular} & \(ET\) (Mbps) & \begin{tabular}{l} \(NCpB\) \\ **(Cycles/Byte)** \\ \end{tabular} \\ \hline \hline \begin{tabular}{l} \hline \begin{tabular}{l} \begin{tabular}{l} \begin{tabular}{l} \begin{tabular}{l} \(256\times 256\times 1\) \\ \end{tabular} \\ \end{tabular} \\ \end{tabular} & \begin{tabular}{l} \(39.3\) \\ \end{tabular} & \begin{tabular}{l} \(1.589\) \\ \end{tabular} & \begin{tabular}{l} \(1501\) \\ \(1516\) \\ \end{tabular} \\ \begin{tabular}{l} \begin{tabular}{l} \(512\times 512\times 1\) \\ \end{tabular} \\ \end{tabular} & \begin{tabular}{l} \(163.9\) \\ \end{tabular} & \
\begin{tabular}{l} \(1.5249\) \\ \end{tabular} &
\begin{tabular}{l} \(1564\) \\ \(1915\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 12: The proposed encryption system’s computing performance.  
and occlusion attacks, and therefore could be used to secure sensitive data. In a future study, we plan to address the hardware implementation of the system.  
Conceptualization, F.D.; Formal analysis, R.L.; Methodology, F.D.; Writing--original draft, F.D.; Writing--review & editing, F.D. and S.E.A.; Validation, W.E.H.Y., M.M. and R.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Results and Discussion
### Experiment 2  
The second experiment adopted the same underlying prioritization method, the AHP, but the difference was the increased number of SPs, resources, and tasks. The main reason for this experiment was to assess the applicability of the proposed model with a larger number of resources and tasks. An experiment was conducted to assign 12 tasks to 8 resources belonging to 5 SPs (see Table 5).  
The prioritization of tasks was acquired after prioritizing the four criteria. The results in this experiment differed and showed the following weights: reputation, 0.25%; trust, 0.40%; performance, 0.20%; cost, 0.15%. Table 6 shows the final weights and prioritization of the 12 tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**T** & **Weight** & **Rank** \\ \hline
1 & 0.13 & 3 \\ \hline
2 & 0.06 & 8 \\ \hline
3 & 0.11 & 4 \\ \hline
4 & 0.03 & 12 \\ \hline
5 & 0.13 & 2 \\ \hline
6 & 0.15 & 1 \\ \hline
7 & 0.05 & 9 \\ \hline
8 & 0.03 & 11 \\ \hline
9 & 0.10 & 6 \\ \hline
10 & 0.04 & 10 \\ \hline
11 & 0.08 & 7 \\ \hline
12 & 0.10 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Preference-based ranking of tasks (Experiment 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SP** & **Weight** & **Rank** & **R** & **Weight** & **Rank** \\ \hline
1 & 0.28 & 1 & 1 & 0.03 & 8 \\ \hline
2 & 0.24 & 3 & 2 & 0.07 & 6 \\ \hline
3 & 0.26 & 2 & 3 & 0.22 & 1 \\  & & & 4 & 0.14 & 5 \\ \hline
4 & 0.002 & 5 & 5 & 0.22 & 2 \\  & & & 6 & 0.14 & 4 \\  & & & 7 & 0.04 & 7 \\ \hline
5 & 0.21 & 4 & 8 & 0.15 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Preference-based ranking of SPs and their resources (Experiment 2).  
The upper threshold was 0.65 and the lower threshold was 0.35, which resulted in the first five tasks tending to the SP-tendency category and seven tasks tending to the R-tendency category. Similarly, Equations (1) and (2) were adopted for the prioritized tasks, and they were matched with the best option in either the pool of prioritized SPs or the pool of prioritized resources based on each task's tendency. Table 7 shows the final assignment of each task with the best match.
# Comparison of Selected Characteristics of SARS-CoV-2, SARS-CoV, and HCoV-NL63
### Morphology, Structure, and Replication of SARS-CoV-2
#### 1.3.2 Structural Proteins  
The downstream ORFs of the RNA genome encode the four structural proteins: Spike (S), nucleocapsid (N), envelope (E), and membrane (M). These are essential for the assembly of the new RNA genome during replication. The S protein is the largest structural protein encoded by the RNA genome. The S protein functions in recognizing angiotensin converting enzyme 2 (ACE2) on the host cell. This protein consists of two functional units. The S protein may be a target for inhibition of viral entry and the development of antibody-based therapeutics to prevent the disease. The S protein has two functional units: S1 (bulb part) for receptor binding and S2 (stalk part) for membrane fusion; their functions may need other human receptors or proteases such as transmembrane protease, serine 2 (TMPRSS2), and furin to activate and facilitate viral entry into host cells. The E protein is a small membrane protein that plays a role in virion assembly and has additional effects on the infected cells of the host. The E protein alters coronavirus virulence by regulating the cell stress response and apoptosis during host cell infection. The M protein is the most abundant in the viral envelope. It provides structural support for the virion and plays a role in the assembly and construction of virion particles. N complexes with the genome RNA to form the virial nucleocapsid within the viral membrane and interact with the M protein in the assembly. The N protein may play a role in replicating RNA [22] (Figure 2).
# Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System
###### Abstract  
Chongqing mountain citrus orchard is one of the main origins of Chinese citrus. Its planting terrain is complex and soil parent material is diverse. Currently, the citrus fertilization, irrigation and other management processes still have great blindness. They usually use the same pattern and the same formula rather than considering the orchard terrain features, soil differences, species characteristics and the state of tree growth. With the help of the ZigBee technology, artificial intelligence and decision support technology, this paper has developed the research on the application technology of agricultural Internet of Things for real-time monitoring of citrus soil moisture and nutrients as well as the research on the integration of fertilization and irrigation decision support system. Some achievements were obtained including single-point multi-layer citrus soil temperature and humidity detection wireless sensor nodes and citrus precision fertilization and irrigation management decision support system. They were applied in citrus base in the Three Gorges Reservoir Area. The results showed that the system could help the grower to scientifically fertilize or irrigate, improve the precision operation level of citrus production, reduce the labor cost and reduce the pollution caused by chemical fertilizer.  
Internet of Things technology; single-point multi-layer detection; fertilization and irrigation decision support +
Footnote †: journal: Sensors  
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