esa-sceva/satcom-chunk-collection huggingface dataset:
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 4 Proposed Scheme
### Data Verification Process
#### 4.4.1 Correctness Verification  
To check the correctness of the fog node \(g_{o}\), the user first encrypts \(g_{o}\) to obtain \(c(g_{o})\) by using \(AES\) and further obtains \(prf_{k}(c(g_{o}))\) with \(k\). Then, with the set of \(l\) hash functions \(H\), the user begins to calculate \(\{h_{1}(prf_{k}(c(g_{o}))),\ldots,h_{l}(prf_{k}(c(g_{o})))\}\) and checks the corresponding positions in \(VO_{g_{o}}\). According to the above comparison, the user can confirm whether the returned data are sent by the fog node \(g_{o}\). To check the correctness of the query result \(c(ds)_{\omega}\), the user first calculates \(\{h_{1}(prf_{k}(c)),\ldots,h_{l}(prf_{k}(c))\}\) with the set of \(l\) hash functions \(H\) for each encrypted description file \(c\). Then, the user makes a comparison between \(\{h_{1}(prf_{k}(c)),\ldots,h_{l}(prf_{k}(c))\}\) and \(vo_{\omega}\). If one of the counters in \(VO_{\omega}\) is 0, this \(c\) is incorrect. The process of correctness verification is shown in Algorithm 2.  
```
1:\(g_{\omega}\), \(VO_{g_{\omega}}\), \(k\), \(H\), \(c(ds)_{\omega}\), \(VO_{\omega}\)
2: The correctness of \(g_{\omega}\) and \(c(ds)_{\omega}\)
3: Calculate \(prf_{k}(c(g_{\omega}))\);
4: Calculate \(\{h_{1}(prf_{k}(c(g_{\omega}))),\ldots,h_{l}(prf_{k}(c(g_{\omega})))\}\) using \(H\);
5: Check all positions \(h_{1}(prf_{k}(c(g_{\omega}))),\ldots,h_{l}(prf_{k}(c(g_{\omega})))\) in \(VO_{g_{\omega}}\). If one of them is equal to 0, \(g_{\omega}\) is incorrect;
6:for each \(c\in c(ds)_{\omega}\)do
7: Calculate \(prf_{k}(c)\);
8: Calculate \(\{h_{1}(prf_{k}(c)),\ldots,h_{l}(prf_{k}(c))\}\) using \(H\);
9: Check all counters \(\{h_{1}(prf_{k}(c)),\ldots,h_{l}(prf_{k}(c))\}\) in \(VO_{\omega}\). If one of them is equal to 0, \(c\) is incorrect;
10:endfor
```  
**Algorithm 2** Correctness verification.
## 3 Results and Discussion
### [PERSON]'s Heuristics
#### 3.1.2 Keep Consistency  
Scratch uses many elements in its interface that clearly suggest their function, e.g., a button marked with a magnifying glass with a \"+\" or \"-\" sign. These types of markings are used on many sites and users are already familiar with them. An important aspect of maintaining compatibility between the system and reality is to avoid technical jargon that is incomprehensible to the average user. Scratch uses a very simple and understandable language. Descriptions of specific programming blocks are formulated in such a way that they clearly describe how they function.  
App Inventor also uses elements in its interface that suggest their function. Elements from a visual language palette remind users of real-life equivalents, e.g., an element handling camera activities reminds users of a camera, an element handling voice calls  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Visual Programming Environments**} \\ \cline{2-5}  & **Heuristics** & **Scratch** & **App Inventor** & **StarLogo** \\ \hline
1. & Show system status & 5 & 5 & 4 \\
2. & Keep the consistency between the system and reality & 5 & 5 & 3 \\
3. & Give the user full control & 4 & 4 & 4 \\
4. & Stick to the standards and be consistent & 5 & 5 & 4 \\
5. & Prevent errors & 4 & 4 & 4 \\
6. & Show instead of forcing to remember & 4 & 4 & 3 \\
7. & Ensure flexibility and efficiency & 3 & 3 & 3 \\
8. & Take care of aesthetics and moderation & 4 & 4 & 2 \\
9. & Ensure effective error handling & 3 & 3 & 3 \\
10. & Take care of help and documentation & 5 & 5 & 1 \\ \hline  & Total & 42 & 42 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Aggregated results of Nielsen’s heuristics evaluation.  
reminds users of a handset, etc. Despite addressing a complex domain of mobile application development, it was possible to keep the language user- and domain-oriented.  
StarLogo also uses suggestive elements in its interface, e.g., a button with scissors for the \"cut\" action or a button with a basket for removing an element. These types of markings are used on many sites and users are already familiar with them. An important aspect is avoiding language that is incomprehensible to the average user. Nevertheless, StarLogo uses a slightly complicated language, especially in describing some programming blocks, which can cause some difficulties, especially for beginners.
## 4 Discussion and Validation
### Intelligent Maintenance vs. Maintenance 4.0  
There exists a large number of terminologies that are supposed to define maintenance management in Industry 4.0 such as, e-Maintenance [32], intelligent maintenance [33; 34],smart maintenance [35], deep digital maintenance [36], and Maintenance 4.0 [37]. However, this paper adopts the terminology of intelligent maintenance, which intentionally differs from other terminologies e.g., smart maintenance [35], e-maintenance [32], as the focus is not primarily based on data analysis i.e., detection, diagnosis, and prognosis. However, this paper extends the scope to also consider enterprise-level data e.g., spare part management, seasonal loadings, available resources, in order to provide a solid foundation for the maintenance decision management that shall ensure that the right maintenance takes place at the right time. Furthermore, the term Maintenance 4.0 might bring the question about other technologies like robotics, augmented reality, additive manufacturing, i.e., 3D printed spare parts.
# A Method for Precise Tracking Control of Pneumatic Artificial-Muscle-Driven Exoskeletal Robot
## 1 Introduction
In 2020, [PERSON] proposed an Upper Limb Exoskeleton Rehabilitation Robot based on PMA, which can plan the rehabilitation trajectory through curve fitting, and use fuzzy synovial controllers (FSMC) for rehabilitation control [18], but the way to enhance the accuracy of the model is yet to be determined. In 2021, [PERSON] et al. developed a multi-joint soft exosuit for upper limb assistance and rehabilitation using dynamic GC and joint trajectory tracking controllers [19]. IMU sensors were used to control the pressure for the pneumatic actuators. In 2022, [PERSON] developed a 4-DOF wearable upper limb rehabilitation assistance exoskeleton system (WURAES) that is suitable for assisting in the rehabilitation of patients with upper limb injuries [20]. a proxy-based output feedback sliding mode control (POFSC) was developed to provide appropriate rehabilitation assistance power for the upper limb exoskeleton and to maintain smooth and safe contact between the WURAES and the patient. Therefore, the pneumatic muscle output force can affect its control over the motion of the exoskeletal robot. Currently, Upper Limb Exoskeletal Robots' coupling with humans has reduced their auxiliary performance due to actuator control. Furthermore, they fail to take load variation into consideration. This study takes the perspective of the hysteresis of pneumatic muscle output force, trying to introduce elastic and friction forces into the existing PAM model to enhance it. In the meantime, a BP neural network will be employed  
Figure 1: Self-assembled McKibben-style pneumatic muscle. in reverse modeling. Furthermore, this study will endeavor to incorporate the principle of torque balancing to handle variable loads, construct a control system according to the inverse dynamics of the exoskeletal robot accompanied by the inverse model of PAM's output force, and finally, render tracking control of the elbow angle during the auxiliary process possible.
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 3 Results
### Ablation Study
#### 3.2.2 Orthogonal Initialization  
Kaiming uniform random initialization [52] is used for all layers of the network apart from the final **F** layer, which is initialized to an orthogonal basis for the subspace using PCA. If **F** is also initialized with Kaiming initialization, training improves noticeably slower than when it is initialized with orthogonal weights (Figure 8), and the final results are not as good, as the bar labeled \"Kaiming initialization\" in Figure 6 shows. On the other hand, parametrizing **F** so that its weights are always orthogonal also impedes learning and gives similarly worse results, as shown in Figure 6 by the \"Orthogonal parametrization\" bar.
# ICT Adoption and Stock Market Development: Empirical Evidence Using a Panel of African Countries
## 3 An Overview of the African Stock Markets

Table 1: Overview of empirical studies. The Namibia Stock Exchange (NSX) was founded in 1904 during the diamond rush at that time. However, within six years the diamond rush ended and the stock exchange was closed. In 1992, NSX was relaunched with funds contributed by 36 leading businesses in Namibia. The companies contributed USD 10,000 each, as start-up capital for the exchange. Moreover, the Namibia Stock Exchange (NSX) in 1998, via a telecommunications link to the JSE, and in 2002 joined the JSE in adopting the LSE's trading system technology, which is known as Stock Exchange Electronic Trading System (Namibia Stock Exchange 2019). The Nigerian Stock Exchange (NSE) was founded in 1960, and like most African stock exchanges it went through several reforms from inception. The NSE is the largest stock exchange in West Africa and serves the largest African economy (Nigerian Stock Exchange 2019). Among the several ICT-related developments and reforms in the Nigerian Stock Exchange (NSE) are the introduction, in 1997, of the automated clearing, settlement and delivery system--the Central Securities Clearing System (CSCS)--to ease transactions and foster investors' confidence in the stock exchange. Further, performance information on the NSE was linked to the Reuters International System for the timely dissemination of relevant market information to subscriber investors ([PERSON] and [PERSON] 2011). The CSCS enables shares to exist in electronic form in a central depository and, thus, helps eliminate risks of the loss, mutilation and theft of certificates, as well as reduce errors and delivery delays. Other ICT adoptions include the CSCS trade alert, phone-in-service, e-bonus and e-dividend payments ([PERSON] et al. 2009).
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 2 Image Watermarking Background
### Detection Phase
#### 2.2.1 Blind Watermarking  
The watermarking technique is qualified to be blind if the hidden information is detected or extracted directly from the watermarked image without requiring the original image [40]. Blind watermarking is widely preferred in most applications since the original image is usually not available at the stage of detection/extraction.
# Comprehensive Energy Demand Response Optimization Dispatch Method Based on Carbon Trading
## 2 The Mechanism of Integrated Energy Demand Response Participating in Low-Carbon Economic Dispatch  
This paper incorporates carbon trading into the integrated energy system, and the constructed energy hub model is shown in Figure 1. The purple, blue, and red lines represent the flow of electricity, natural gas, and thermal energy flows, respectively. On the energy supply side, there are natural gas networks, coal-fired units, and wind turbines. There are electrical, gas, and thermal loads on the load side. The coupling equipment between electricity and gas includes a gas turbine and _P2G_. The coupling equipment between electricity and thermal includes electric boilers and heat pumps. The coupling equipment between the gas and thermal has a gas boiler.  
In this paper, the carbon-trading mechanism and integrated energy demand response are introduced into the integrated energy system, which is beneficial to reduce the carbon emission and energy cost of the system. First of all, the introduction of carbon trading makes the carbon cost factor added to the dispatch mode, allowing the system to realize the coordination between low carbon and economy. Secondly, a comprehensive energy response model is introduced under the carbon-trading model. On the one hand, it can promote users to actively adjust the load, so that the power system can reduce the power supply during the peak load period. The gas thermal load can be converted into electrical load when the wind power is large, and the amount of abandoned air in the system can be reduced. For the integrated energy system, it can reduce the overall energy demand and reduce carbon emissions. On the other hand, the carbon emissions of units can be fully considered when dispatching the load for a demand response. Support units with lower carbon emissions are given priority regarding output.
# Potential Marker Genes for Predicting Adipogenic Differentiation of Mesenchymal Stromal Cells
## 4 Discussion
However, the precursor cells already committed to certain lineages have only limited proliferative activity [4; 31]. MSCs derived from adipose tissues have also been shown to have an ability to differentiate into various types of cells including adipocytes, as described above [27; 28; 29]. However, the difference between preadipocytes and MSCs is not clear. Further studies are warranted to evaluate the potential of preadipocytes and MSCs. In our previous study, we found eight gene markers capable of predicting the potential to differentiate into chondrocytes [7]. In this study, five out of the eight genes were also identified as marker genes for predicting adipogenic differentiation. In addition, we identified the gene for MCAM (CD146), a cell surface marker capable of isolating potent MSCs for chondrogenesis [8], as a prediction marker for adipogenesis. These findings suggest the existence of a common genetic basis for chondrogenesis and adipogenesis. Accordingly, a significant correlation of differentiation marker levels after induction was observed between chondrocytes and adipocytes derived from 17 ilium MSCs [11]. In the present study, we selected CDKN2D, ITGA5, and CD74 for 3D analysis. As we described above, the expression levels of CDKN2D and CD74 in ilium MSCs were approximately 20 times higher than those in jaw MSCs (Table 2), making them promising candidates. Although ITGA5 showed the highest correlation coefficient (Table 1), there is another candidate for 3D analysis. When MCAM, whose expression levels were 13.4 times higher in ilium MSCs (Table 2), was used instead of ITGA5, the 3D analysis showed similar results (Figure S3). CDKN2D and CD74 used for the 3D analysis in this study were also used for the 3D analysis for chondrogenic potential in the previous study [7]. However, the third chondrogenic prediction marker gene, TGM2, was not identified even as a candidate gene in this study. Therefore, two types of 3D analysis with different combinations of the four genes (CDKN2D, ITGA5, CD74, and TGM2) can be used to predict the differentiation of MSCs for adipogenesis as well as chondrogenesis.
# Kolmogorov-Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity
###### Abstract  
Kolmogorov-Arnold Networks (KANs) are a recent development in machine learning, offering strong functional representation capabilities, enhanced interpretability, and reduced parameter complexity. Leveraging these advantages, this paper proposes a KAN-based reduced-order model (ROM) for unsteady aerodynamics and aeroelasticity. To effectively capture temporal dependencies inherent in nonlinear unsteady flow phenomena, an architecture termed Kolmogorov-Arnold Gated Recurrent Network (KAGRN) is introduced. By incorporating a recurrent structure and a gating mechanism, the proposed model effectively captures time-delay effects and enables the selective control and preservation of long-term temporal dependencies. This architecture provides high predictive accuracy, good generalization capability, and fast prediction speed. The performance of the model is evaluated using simulations of the NACA (National Advisory Committee for Aeronautics) 64A010 airfoil undergoing harmonic motion and limit cycle oscillations in transonic flow conditions. Results demonstrate that the proposed model can not only accurately and efficiently predict unsteady aerodynamic coefficients, but also effectively capture nonlinear aeroelastic responses.  
Kolmogorov-Arnold Networks; reduced-order model; unsteady aerodynamics; nonlinear aeroelasticity Article
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## Results
### Motion Correction  
We used two wavelet motion correction (MC) methods from HOMER2, specifically 'hmrMotionCorrectWavelet' (MC#1) and 'hmrMotionCorrectKurtosisWavelet' (MC#2). Figure 6 shows the comparison between two motion correction methods, as well as the comparison against no motion correction. The motion correction methods differed significantly in how much they changed the relevant time course (correlation applied after bandpass filter). MC#2 had significantly (\(p\) \(<\) 0.05) lower correlation with the uncorrected data (mean \(\pm\) std.: \(r\) = 0.73 \(\pm\) 0.1) in comparison to MC#1 (_r_ = 0.92 \(\pm\) 0.0). The mean correlation between both methods was \(r\) = 0.78 \(\pm\) 0.1. The right side of Figure 6 shows example time courses the channel above M1 (channel 12) in subject #1 after bandpass filter and PCA analysis in three conditions--no motion correction, MC#1, and MC#2. The resulting time courses showed a systematic overcompensation of motion artifacts for MC#2.
## 3 Materials and Methods
### Shipboard Data Acquisition
Each sentence contains, from left to right, delimiter character \"5\", address field denoting talker identifier (TT) and sentence formatter (SSS), delimiter character \",\", text representing data content, as well as the delimiter character \"** followed by the checksum field (xx) consisting of two bytes in a hexadecimal format. Table 1 contains codes for data source devices. These device codes are used to identify data items in the transmitted data and are specific to the shipboard data acquisition and transmission system of TS _Fukae Maru_, as this advanced system is not yet widely used on larger merchant vessels. The amount of data collected by all devices that make up the advanced data acquisition system reaches about 7.5 million lines in one day of the ship's voyage. Therefore, about 87 lines of data are transmitted in NMEA sentence format in every second of ship operation. The total size of data received from devices, transmitted, and recorded as ASCII text in one day is about 400 MB. In order to transfer the data collected onboard to the land, we consider using a data line like Inmarsat FB, so this transmission rate is not unfeasible with the current bandwidth of satellite communications. However, the relatively high cost of using satellite communications and their already high utilization must be considered. In addition, the demand for Internet use by the ship's crew and passengers is increasing rapidly. On larger cruise ships, this is a challenging task due to the limitations of narrowband satellite communications. Therefore, methods are needed to reduce the amount of data transmitted onboard. \begin{table}
\begin{tabular}{c c} \hline \hline
**Device Code** & **Data Source Device** \\ \hline
0 & Voyage Data Recorder ( VDR) \\
1 & GPS \\
2 & GPS compass \\
3 & GPS compass \\
4 & Doppler sonar \\
5 & Gyrocompass \\
6 & Magnet compass \\
7 & Electromagnetic (EM) log \\
8 & Doppler log \\
9 & Accelerometer \\ N & Navigation data collection equipment \\ D & Weather (Analog) \\ A & AIS \\ R & Radar 1 \\ S & Radar 2 \\ W & Weather transmitter \\ E & Engine data logger \\ C & Central Processing Unit (CPU) control system \\ H & Acoustic Doppler Current Profiler (ADCP) \\ B & Sea water monitor \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Codes of the data source devices. Figure 9: Structure of the NMEA data format.
## 7 Open Issues and Challenges
### Network's Dynamic Character  
Sensor nodes have long been thought to be stationary by many researchers. However, because of variable network sizes, sensor node moves, topology changes, and unanticipated operational problems, it is necessary to address the dynamic character of WSN. Even node or sink mobility might be difficult, necessitating clusters to alter over time [68].
## 3 Results and Discussion
### Differential Scanning Calorimetry  
The DSC curves recorded effects, such as: the temperature of the beginning of the thermal effect (T\({}_{\text{oneset}}\)), the temperature of the end of the thermal effect (T\({}_{\text{k}}\)), the temperature of the extreme point (T\({}_{\text{max}}\)), and enthalpy (H) [46]. The DSC graph of cinnamon-filled foams (series C_0t) containing a flame retardant in the composition has one endothermic peak P1 and two and exothermic peaks P2 and P3 (Figure 6a,b). The absence of flame retardant in foams (series CU_0t) results in a change in the course of the DSC curves (Figure 6c,d). We could observe two endothermic peaks P1 and P2 and one exothermic P3 in the figure. From the graphs (Figure 6), it can be observed that the increasing amount of cinnamon extract (from 5% by weight to 15% by weight) increases the enthalpy H\({}_{1}\) in the first endothermic peak (Figure 6a) from 26.47 J/g (C5_0t) to 36.53 J/g (C15_0t); see Table 10. Enthalpy H\({}_{1}\) was associated \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Foam** & **CR (\%)** & **LOI (\%vol. of O\({}_{2}\))** & **HT** \\ \hline W & 83.4 \(\pm\) 1.6 & 24.7 \(\pm\) 0.2 & \\ C5 & 91.2 \(\pm\) 1.8 & 24.4 \(\pm\) 0.2 & \\ C10 & 91.6 \(\pm\) 1.8 & 24.6 \(\pm\) 0.2 & \\ C15 & 91.7 \(\pm\) 1.8 & 23.7 \(\pm\) 0.2 & self-extinguishing \\ WU & 37.7 \(\pm\) 0.8 & 18.5 \(\pm\) 0.1 & \\ CU5 & 650 \(\pm\) 0.9 & 21.0 \(\pm\) 0.2 & \\ CU10 & 63.3 \(\pm\) 0.9 & 20.3 \(\pm\) 0.1 & \\ CU15 & 63.3 \(\pm\) 0.9 & 20.2 \(\pm\) 0.1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Flame properties of foams: combustion residue (CR); limited oxygen index (LOI); and horizontal test (HT). with the evaporation of water from foams. Its increase was caused by the increasing content of the biofililer in the foams. H1 for the foam without cinnamon extract (W_0t) was 25.75 J/g. The maximum temperature (T\({}_{\text{max}}\) in P1) also increased from 71.8 J/g (W_0t) to 85.6 J/g (C15_0t). There was a reduction in the intensity (size) of the first exothermic peak (P2) associated with the decomposition of urethane bonds and ether bonds from polyol at T\({}_{\text{max2}}\) around 295 \({}^{\circ}\)C (for W_0t foam) and its goes towards a lower temperature, i.e., 270 \({}^{\circ}\)C for C15_0t foam). The T\({}_{\text{max3}}\) of the second exothermic peak (P3) was practically unchanged. The curves of CU_1t foams series (Figure 4b) degraded one week were similar to C_0t series. However, the H\({}_{2}\) and H\({}_{3}\) enthalpy values or P2 and P3 peaks were smaller.