esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Message Extraction Algorithm
#### 3.2.1 Message Extraction by Weighted Modulus and Image Restoration  
In this phase, dual stego images are processed to extract the message vector, \(\mathbf{d_{du}}=(d_{k}^{1},\,d_{k}^{2},\,d_{k}^{3})\) concealed in pixels, \(\mathbf{P^{\prime\prime}_{sl}}=(r_{sl}^{m},\,s_{sl}^{m},\,b_{sl}^{n})\) in \(I_{sl}^{m}\) and \(\mathbf{P^{\prime\prime}_{sl}}=(r_{sl}^{m},\,s_{sl}^{m},\,b_{sl}^{n})\) in \(I_{sl}^{m}\) and \(\mathbf{P^{\prime\prime}_{sl}}=(r_{sl}^{m^{\prime\prime}},\,s_{sl}^{m},\,b_{sl}^{n })\) in \(I_{sl}^{m}\), respectively. First, we construct a dual-pixel, \(\mathbf{P^{\prime\prime}_{du}}=((r_{sl}^{m^{\prime\prime}},\,r_{sl}^{m^{\prime}}), \,s_{sl}^{m^{\prime\prime}},\,s_{sl}^{m^{\prime}})\), \((s_{sl}^{m^{\prime\prime}},\,s_{sl}^{m^{\prime\prime}})\) from \(\mathbf{P^{\prime\prime}_{sl}}\) and \(\mathbf{P^{\prime\prime}_{sl}}\) and represent each component pair as \(\mathbf{P^{\prime\prime}_{1}}=(r_{sl}^{m^{\prime\prime}},\,s_{sl}^{m^{\prime}})\), \(\mathbf{P^{\prime\prime}_{sl}}=(s_{sl}^{m^{\prime\prime}},\,s_{sl}^{m^{\prime}})\)\(\mathbf{P^{\prime\prime}_{sl}}=(b_{sl}^{m^{\prime\prime}},\,b_{sl}^{n^{\prime\prime}})\). We then input the corresponding 2-tuple weight vectors, \(\mathbf{W_{b}}=[w_{1},\,w_{2}]\) and the parameter \(k\) for each component pair to extract the secret message vector, \(\mathbf{d_{du}}\), by Equation (11). Finally, we restore the pixel, thereby producing the recovered pixel, \(\mathbf{RP^{\prime\prime}_{ouect}}\), from each component pair in the dual-pixel, \(\mathbf{P^{\prime\prime}_{du}}\), using Equation (12), where \(\lfloor\,\rfloor\) represents the floor function. We can certainly produce the recovered optimal color-transfered image, \(R^{I^{\prime\prime}_{ouect}}\), after restoring every pixel. We remark that since the recovered pixel, \(\mathbf{RP^{\prime\prime}_{ouect}}\), is identical to the stego pixel, \(\mathbf{P^{\prime\prime}_{ouect}}\), produced in the sender part, the recovered image, \(R^{I^{\prime\prime}_{ouect}}\), is also indistinguishable from the stego image, \(I^{\prime\prime}_{ouect}\), produced in the sender part, thereby implying that our scheme can fully restore the stego image.  
\[\mathbf{d_{du}} =(d_{k}^{1},\,d_{k}^{2},\,d_{k}^{3})=\left(\left[\mathbf{P^{\prime \prime}_{sl}}\mathbf{W_{b}}\ \right]\bmod k,\,\left[\mathbf{P^{\prime\prime}_{gl}}\mathbf{W_{b}}\right]\bmod k, \,\left[\mathbf{P^{\prime\prime}_{bl}}\mathbf{W_{b}}\right]\bmod k\right) \tag{11}\] \[\mathbf{RP^{\prime\prime}_{ouect}} =\left(\left[\frac{r_{sl}^{m^{\prime\prime}}+r_{sl}^{m^{\prime \prime}}}{2}\right],\,\left[\frac{s_{sl}^{m}+s_{sl}^{m^{\prime\prime}}}{2} \right],\,\left[\frac{b_{sl}^{m^{\prime\prime}}+b_{sl}^{m^{\prime\prime}}}{2} \right]\right) \tag{12}\]
# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 4 Results
### Comparative Assessment  
The superiority of the MGTO-CapsNet model was analysed by comparing its results with those of the two existing models introduced by [PERSON] et al. [33] and [PERSON] et al [34]. [PERSON] et al. [33] employed the AAL algorithm to segment RoI from sMRI images, which were collected from the ADNI dataset. Next, a voxel based hierarchical feature extraction approach was utilized to extract feature vectors, which were finally passed as input into the CNN model to classify the types of AD classes. In addition, [PERSON], et al. [34] integrated the Otsu thresholding technique, fuzzy-based elephant-herding optimization algorithm, and DA-MIDL model for accurate detection of AD. This framework obtained a 93% sensitivity, an 86.50% accuracy, and a 93% specificity on the AIBL dataset, whereas the proposed MGTO-CapsNet model obtained an impressive classification performance, with a 99.88% sensitivity, a 99.94% accuracy, and a 99.91% on the same dataset. The comparative results between the MGTO-CapsNet model and the existing models (CNN [33] and DA-MIDL [34]) are specified in Tables 5 and 6. MGT-CapsNet produces better solutions and faster convergence than CNN because the use of a customized optimizer adjusts to the issue. Also, CapsNet captures the complicated connection between data by extracting features in a more robust and equivariant manner. Due to the effective modified optimizer in MGT-CapsNet, it trains more quickly than do standard CNNs. As CapsNet represents data in a hierarchical fashion, results can be more easily analysed and interpreted. MGT-CapsNet provides more interpretable data since the features are represented hierarchically, allowing clinicians to comprehend the underlying disease causes. This makes it possible to interpret how the model is describing the data in a more understandable way, particularly in crucial applications such as healthcare.
# Natural Frequencies of a Tethered Satellite System
## 4 Natural Frequencies of a Tensioned Beam Model
As shown in Figures 16a and 17a, when one satellite mass is fixed and the other is increased, the average tension increases by nearly the same amount regardless of whether the mass \(m_{1}\) or \(m_{2}\) increases. The reason why the tension appears almost identical in both figures can be explained as follows. By neglecting the mass of the tether and replacing \(r_{1}+L\) with \(r_{2}\), the average tension, according to Equation (49), can be expressed as  
\[\overline{\sigma}A=CA=\frac{GMm_{1}m_{2}(r_{2}-r_{1})\left(r_{1}^{2}+r_{1}r_{ 2}+r_{2}^{2}\right)}{r_{1}^{2}r_{2}^{2}(m_{1}r_{1}+m_{2}r_{2})}. \tag{54}\]  
Since the difference between \(r_{1}=6600\) km and \(r_{2}=6610\) km is small, swapping the masses \(m_{1}\) and \(m_{2}\) in the equation does not result in a significant change in the average tension. Therefore, the difference in the tensions shown in Figures 16a and 17a is negligible. In both cases--when \(m_{2}\) is fixed and \(m_{1}\) is varied (Figure 16) and when \(m_{1}\) is fixed and \(m_{2}\) is varied (Figure 17)--the tensions are nearly identical. Consequently, the changes in the natural frequencies are also almost identical in both scenarios. As previously discussed, an increase in tension leads to an increase in the transverse natural frequency of the tether (Figures 16b and 17b). Conversely, an increase in satellite mass results in a decrease in the longitudinal natural frequency, as shown in Figures 16c and 17c. Figure 15: First natural frequencies of the tethered satellite system for the variation of the total mass of \(m_{1}\) and \(m_{2}\): (**a**) average tension of the tether, (**b**) natural frequency of the transverse motion, and (**c**) natural frequency of the longitudinal motion. Figure 16: First natural frequencies of the tethered satellite system for the variation of the satellite mass \(m_{1}\): (**a**) average tension of the tether, (**b**) natural frequency of the transverse motion, and (**c**) natural frequency of the longitudinal motion. Figure 17: First natural frequencies of the tethered satellite system for the variation of the satellite mass \(m_{2}\): (**a**) average tension of the tether, (**b**) natural frequency of the transverse motion, and (**c**) natural frequency of the longitudinal motion.
# Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review
## 3 Recent Mobility Techniques
This accelerated pace can affect the precision of time-sensitive events or protocols. Furthermore, the disparity between the duration of the simulation and actual time might impact the accuracy of outcomes, particularly in situations where time is of utmost importance. Simulations may need help in effectively capturing the dynamic characteristics of real-world networks by accurately capturing the complexities and intricacies of deploying NDN in dynamic and diverse situations. The availability of updates, bug fixes, and community support may differ for this simulation software. Nevertheless, the stability and functionality of the simulation tools may be affected by a lack of community support or the use of outdated software versions. Therefore, modifying models or incorporating novel components into simulations can be intricate and time-intensive. Researchers need help when trying to simulate particular facets or experimental circumstances, which restricts the adaptability of the simulation system. Attaining interoperability among various simulation tools might pose challenges, hindering the integration of distinct models or the execution of cross-tool analysis. Consequently, the absence of smooth interoperability may impede joint research endeavors and the ability to compare outcomes across various simulation environments. Researchers frequently utilize ndnSIM to examine mobility scenarios to evaluate the performance of the NDN architecture in dynamic situations. There are several examples of research focusing on mobility techniques, including dynamic routing strategies [45], vehicular communication [39; 73], content caching [74], and forwarding strategies [75].
## 4 Results and Discussion
### Ventilation Performance  
The variation of the ventilation mass flow rate with sections of the Y-axis for different structures is demonstrated in Figure 4. When the ambient temperature was 298 K and 303 K, the mass flow rate of S1 CSC was the maximum and that of S2 TW was the minimum. The mass flow rate was the maximum for S2 CSC and the minimum for S1 TW when the ambient temperature varied from 308 K to 313 K. It increased from 0.096 kg/s to 0.128 kg/s for S1 CSC with a growth rate of 33.3% at an ambient temperature of 298 K. With the variation of the ambient temperature from 298 K to 313 K, the mass flow rate of S1 CSC with sections of the Y-axis increased by 33.3%, 31.2%, 29.4%, and 28.6%, respectively. The growth rate of the S2 CSC mass flow rate with sections of the Y-axis were 27.8%, 29.4%, 31.7%, and 32.3%, respectively. The mass flow rate of S1 CSC decreased with an increase in the growth rate, which was the reverse for S2 CSC. The results illustrated that the ventilation performance of S2 CSC gradually improved with the increase in the ambient temperature compared with that of S1 CSC. The mass flow rate of the IRSC had no significant changes with the increase in the ambient temperature and ranged from 0.7 to 0.8 kg/s. With the changing of the ambient temperature from 298 K to 313 K, the average mass flow rate of S1 CSC decreased from 0.112 kg/s to 0.095 kg/s with a reduction rate of 15.2%. Similarly, the variations of the mass flow rates for S2 CSC, S1 TW, and S2 TW were 0.089-0.113 kg/s,  
Figure 5: The temperatures of the thermal gap and interior wall at different ambient temperatures: (**a**) average temperature of the air channel and (**b**) the temperature of the internal wall.  
0.076-0.058 kg/s, and 0.057-0.073 kg/s, respectively. Consequently, the mass flow rate of S1 CSC and S1 TW decreased gradually whereas that of S2 CSC and S2 TW increased with the increase in ambient temperature.  
In brief, when the ambient temperature was 298 K or 303 K, the average air temperature and the internal wall temperature of S1 CSC were the lowest and the mass flow rate was the maximum. In this situation, the natural ventilation mode of the combined solar chimney (S1 CSC) in the summer was more suitable without overheating. However, when the ambient temperature reached 308 K or above, the average air temperature in the channel and the internal wall temperature began to rise and the mass flow rate decreased gradually. In this case, the anti-overheating mode (S2 CSC) was more suitable. The selection of a suitable operation mode can effectively solve the indoor overheating problem in the summer.
# Impact and Stability of mandibular Setback after Intraoral Vertical Ramus Osteotomy
###### Abstract  
(1) Background: The purpose of this study was to evaluate stability and complications following mandibular setback using intraoral vertical ramus osteotomy (IVRO) and provide an assessment of IMF (Inter-maximally Fixation) compliance. (2) Methods: This is a retrospective cohort study. It included a total of 39 patients who underwent 78 IVRO for the treatment of mandibular prognathism between 2005 and 2021 at Sheba Medical Center. Radiographic and clinical examinations were performed preoperatively (T0), 3 days post-surgery (T1) and 8 months post-surgery (T2). Measurements of dental and skeletal stability, as well as neurosensory disturbances and TMJ dysfunctions, were statistically analyzed. (3) Results: The mean mandibular setback was 5.6 mm. A relapse of less than 2 mm was observed at T2. A short-term neurological disturbance was reported in 38.46% of the 39 patients, and 17.94% of them showed full recovery by T2. In 21 patients who underwent IVRO without genioplasty, only 14.28% reported long-term sensory disturbance. In addition, when a vertical osteotomy was performed, neurological damage was 174% higher as compared to oblique osteotomy. The IMF compliance rate for the 6 weeks postoperatively was 100%. (4) Conclusions: IVRO is still a relevant and valid technique for the treatment of mandibular prognathism that provides stable results with minimal complications rate.  
IVRO; mandibular prognathism; mandibular setback; IMF +
Footnote †: journal: Medical Society
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 2 Related Works
[34] extended this method to support both data integrity and deduplication by using the convergent encryption technique. The existing data integrity checking methods assume that the data owner's secret key is secure, but this is not always true. [PERSON] et al. [35] proposed an integrity checking method to mitigate the damage of the data owner's key exposure issue, in which the secret key of the data owner is updated by employing the binary tree structure and the pre-order traversal technique. Based on the principal characteristics of the decentralized blockchain, many authors have studied schema verification of the data integrity based on blockchain. [PERSON] et al. [36] considered that the reliability of the TPA-based framework was not satisfactory and proposed a framework based on blockchain to verify the data integrity. Similarly, [PERSON] et al. [37] suggested a framework to verify the data integrity based on blockchain for cloud storage;the authors used a Merkle tree with a random number for data integrity verification without relying on TPA. [PERSON] et al. [38] suggested a scheme to verify data integrity based on blockchain, but with significant improvement in the security and efficiency of the verification process to avoid excessive reliance on TPA. While existing systems using blockchain to verify data integrity can avoid the issues associated with TPA, they entail extensive communication and processing costs [39]. There are numerous types of systems and some guarantee the validity of remotely stored data within complex safeguarding schemes, but are expensive and thus inappropriate for devices with limited sources. The proposed system has a greater efficiency, is lightweight, and is better protected than the previously discussed systems. It is founded on a cryptographic hash function with the key chain method and can be integrity-checked through a semi-trust server. This scheme [10; 11; 18; 20; 22; 25] is designed for the data integrity verification of the whole dataset. Unfortunately, it suffers from a very high computational cost resulting from its reliance on bilinear mapping, so it cannot guarantee that the data validation results are entirely correct [39; 40]. Thus, this study proposes that the suggested system is more suitable than its predecessor in terms of smart devices with modest sources or capabilities. Additionally, the safeguarding evaluation and investigative outcomes reveal that the recommended system is vigorous, safe, and successful. Table 1 illustrates the contrasts between the safeguarding characteristics of numerous systems.
## 4 Connectivity Architecture
### Ship Gateway  
Different systems in a ship may use different radio interfaces and wired connections. An autonomous vessel connects to other vessels and remote locations using satellites and terrestrial technologies. Successful connectivity management demands a gateway providing interoperability between different networks and devices. The gateway uses protocol conversion to connect different parts and technologies in the network and acts as a connection point in the ship linking the sensors and actuators with the decision making and the outside world.  
The ship gateway provides a communication link to the public and private clouds, enabling offline services and real-time control over the in-ship equipment. It sends monitoring and sensor data from the ship to the cloud and remote operators, and receives controlling data, providing authenticated access to the in-ship system. An authenticated gateway hardens the network against attackers from the outside world. Without authentication, malicious users cannot be blocked from the network and denial-of-service (DoS) attacks will be threats.
## 3 Results from Patent Data Mining across the Countries
### Hottest Technologies around the World  
This section explores the knowledge needed to answer the question, 'What were the hottest technologies around the world during the years in question?' As 'hottest technologies' refers to the areas that have gained more attention, the relevant analysis can be accomplished by measuring the total number of patents applied for in each individual area during the study period, such that the 'hottest technologies' can be ranked and observed.  
In the data repository, the patents that have the same 3-tuple values in Sections G and H (i.e., {section, class, subclass} values, as mentioned in Section 3.1) are counted for each country. This is followed by a process in which each 3-tuple is accumulated over all the countries. This results in a table which carries the information of \"the total number of times patents have received a given IPC code prefix in the top 9 technological powers during the period\". By sorting this table, the top 10 'hottest technologies' can be observed along with the top 10 IPC codes. These are visualised in a histogram in Figure 6.  
Figure 6: Top 10 ’hottest technologies’ in the 9 technological power countries. (see Appendix E for the related technological domains as are revealed by these IPCs)  
As shown in Figure 6, three main technological categories accounted for the top ten hottest technologies, namely, 4G wireless communications (i.e., the four IPC codes for signal transmissions of handset devices, transmission technologies for images, cells and inductances), optical lenses (i.e., the two IPC codes for digital cameras and optical modulation/demodulation) and fin-techs (including data-driven forecasting and prediction). These findings perfectly meet the observable trends in the smartphone-relevant industries, as the optical lens industry, for example, was boosted by the smartphone market, and fin-tech was boosted by mobile-based value-added services over the 9 years in question. These results also reveal the strategic global patenting policies during the study period, i.e., they were not limited to any particular high-tech company or any particular country.
## 5 Optimal Gain Design Strategy  
### Optimal Gain Design Using the RMS Index  
Conventional gain design strategies based on tracking indices [10; 22] have the following difficulties:  
* The selection of an appropriate model (e.g., RA, random-velocity) is not considered. Thus, this selection is conducted empirically [4].
* There are no general rules for the determination of a tracking index [12]. [PERSON] did not discuss how to set the tracking indices for the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter [31].  
To resolve these problems, we have adapted the strategy based on optimizing the RMS index presented in [4] to the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter. Given Equations (20) and (26) and the normalization of the RMS index in Equation (28) and substituting Equation (17), we define the  
Determine \(\alpha\), \(\beta\) and \(\theta\) by solving Equation (32).
4. Determine \(\eta\) with Equation (17).  
With respect to the proposed strategy, note that:  
* Equation (32) can be solved by simple gradient descent with several initial values [33]. This is because the range of parameter searching is not so wide due to the stability conditions.
* This design process is conducted only once before using the filter. Although the computational costs of the above optimization process are not small, this does not affect the simple tracking process of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filters.
## Materials and Methods
### Configuration of Parameters for Urban Areas  
In order to reduce the calibration efforts, several parameter values are determined from bibliographic works. For instance, the initial loss for different urban land-uses are defined by considering the findings of [PERSON] et al. [23]; besides, in accordance with the propositions of [PERSON] [24], we fixed two parameters for the Green and Ampt method [17] (suction head at the wetting front and the residual soil moisture content), as well as three parameters used in the sewer network module, including the initial loss of roof wetting, the Manning's N value for conduits, and virtual sub-basins. The trial and error procedure is performed for calibrating the Manning's N value and the saturated conductivities (Ksat) by using the DTM + LiDAR topographic data and the detailed land-use information. Parameters are calibrated for the event of 15 November 2014, and then validated for the other studied rainfall events. The Nash-Sutcliffe coefficient (NSE) [25] are used to evaluate the model performance. The optimized Manning's N is equal to 0.015 and 0.2 for the impervious and pervious surfaces, respectively. The calibrated Ksat is equal to 1.0 \(\times\) 10\({}^{-8}\) m/s and 1.0 \(\times\) 10\({}^{-5}\) m/s for the impervious and pervious surfaces, respectively. Comparing with the commonly used parameter values in literature [10; 24; 26], these parameter values are in agreement with typical values for such land-uses.  
Figure 5: Input land-use information (**a**) obtained by combining multiple data sources, (detailed land-use); (**b**) extracted directly from the BD-TOPO® database (coarse land-use).