esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 4 Experimental Results and Analyses
### Subjective Performance Evaluation  
Figure 5 shows the amplitude spectrums of standard-deviation curves for test video sequences in different formats. It can be seen that the spectrum amplitudes of the original videos are small and distributed evenly while all forged videos have significant spikes. For QCIF format, the forged video not only has a large spike, but also its spectrum amplitude changes rapidly, owing to the limited spatial resolution of QCIF format. With insufficient pixel samples, the Gaussian distribution cannot fully describe the noise statistics, which can weaken the periodicity of the standard deviation. For other formats, sufficient pixel samples guarantee a high accuracy of standard-deviation estimation, so the spectrum amplitude is uniformly distributed except the spike. It can be seen from the above results that the proposed method is suitable for high-definition video sequences. Moreover, the variations of motion and color in video sequences have little effect on the performance of the proposed method, e.g., the proposed method generates large spikes for both the _Soccer_ sequence with fast motions and the _Tractor_ sequence with image zooming. To sum up, the proposed method has a stable detection performance for the video sequences in different contents and formats.  
Figure 5: _Cont._
## 3 Results and Discussions
### Friction Interpolators
These two interpolators maintained their ability to mimic the spatial pattern even at 900 m. Comparatively, RF and the RFSI at 900 m predict what was essentially a straight line, as shown in Figure 8.  
When RF and the RFSI were combined with OK, both interpolators saw an improvement in their ability to mimic the spatial pattern. Previously, these two interpolators began to perform poorly at 300 m. After the inclusion of OK, RFOK could mimic the spatial pattern until 900 m and RFSIOK could mimic for up to 400 m; it can be said that adding OK to ML models can boost performance. Figure 9 below depicts the interpolation improvements made through the inclusion of OK.  
Figure 8: Interpolator performance at a separation distance of 900 m.  
Figure 7: Interpolator performance at a separation distance of 300 m.  
Ultimately, identical results were obtained from the interpolated values' error comparison and visual inspection. OK was the best of the six interpolators, requiring the fewest input variables while having the lowest error and the ability to mimic the spatial pattern closely. However, it is important to point out that methods involving kriging have much higher data demands than ML-based methods due to the need for spatially dense data to determine the spatial covariance structure. In comparison, the ML models only require the observation data and nothing else. Hence, if both accuracy and data demand were considered, RF is perhaps the better choice.
# A Review on Methods and Systems for Remote Collaboration
## 4 Visual Display Technologies
### Augmented Reality (AR)  
Augmented reality places digital objects or annotations onto the real world. Historically, augmented reality was used with HMD devices capable of displaying digital content overlaid on a user's view of the real world.  
In 1957, augmented reality appeared in the form of the Sensorama [75], an invention that could deliver visuals, sounds, vibrations, and smells to a viewer. Nevertheless, the term AR started to be used in 1990, when several workers wearing HMDs were guided in assembling electrical wires in aircraft. Over the last decade, different applications and devices that employ AR have emerged; for example, the design tool ARToolkit appeared in 2009, Google Glass in 2013, and HoloLens in 2015 [75].  
One system that implemented AR was the Tele-Assistance-Collaborative system developed by [PERSON] [76], in which the operator was equipped with a specific AR display device. Its design enabled it to capture a video flow of exactly what the carrier's eye saw (flow A) and a wide-angle video flow (flow B). On the other side, the expert had a catalogue of images and annotations that he could use by applying them on the video stream seen in real time by the operator.  
[PERSON] [12] presented another solution where AR was used with mobile devices to overlay objects onto the real environment.  
In most collaboration systems where AR was utilized, head-mounted displays were employed. [PERSON] et al. [77] proposed an AR application that used edge computing. This solution consisted of three main components. The local user had a mobile AR device that captured images of the site and displayed the incoming video. These images were processed by an edge server, which sent them to the remote user and then back to the operator after overlaying the expert's annotations. The helper used a laptop/PC device that displayed the incoming video flux and a software program that allowed him to draw instructions on the images.  
[PERSON] et al. [78] focused on the dense reconstruction of the local user's surroundings by generating a three-dimensional mesh automatically. The local user wore a pair of AR glasses equipped with a depth sensor used for scene reconstruction. The remote user could explore the scene independently, add annotations, and create 3D animations.  
SceneCam [79, 80] was a multi-camera remote collaboration system that allowed a remote user to have multiple views of the task space. The local user wore an AR HMD that collected information about the local space and ran an algorithm that determined which camera view captured his actions the best. Based on this selection, the system drew the remote user's attention to the optimal view. Another technique implemented by the system was the automatic camera view selection, which implies that, based on the algorithm, the system selected the optimal view for the remote user, making it their primary view.
## 5 Validation of RPAL SCAOF in Simulations
### Validation of Energy-Aware Routing Metrics and SCAOF Performance  
The RPL and RPAL models are validated in terms of network lifetime, packet delays, packet loss rate, Round-Trip Time (RTT) for CoAP resource request/reply, routing table size, overheads and path hop distance. For testing these performance metrics, we built two simulation scenarios.
## 3 Results and Discussion
### Torrefaction Index (TI)  
A study by [PERSON] et al. [40] suggests that the degree of torrefaction or energy densification alone cannot determine the quality of the torrefied product and proposes the use of the torrefaction index. [PERSON] and [PERSON] [41] also supports this statement that energy yield alone cannot be used to define the optimum operating condition for torrefaction. As a matter of fact, as the severity of the process escalates, there is a reduction in the energy yield, which in turn reduces the net usable energy of raw material. Therefore providing the optimized condition for torrefaction would aid the reduction of weight loss but would boost the calorific value.  
Table 2 depicts the comparison of the experimental torrefaction index obtained in this study, with the torrefaction index for different torrefaction regimes according to [PERSON] et al. [40]. It demonstrates that the values obtained in this study aligns well with the values provided by [PERSON] et al. [40]. At 350 \({}^{\circ}\)C, not much improvement in the torrefaction index is demonstrated. Therefore, torrefaction of sewage sludge above 350 \({}^{\circ}\)C will not be required. If the polymeric composition of the biomass is known for specific torrefaction temperatures, than TI can be used as a potential tool for preliminary design or the selection of biomass ahead of operating absolute test [40].  
Figure 4: Changes of (**a**) H/C (**b**) O/C molar ratios as a function of torrefaction residence time.  
Figure 5: Gas analysis from torrefaction of sewage sludge as a function of the temperature.
## 4 Results and Discussion  
It is an important goal of the response surface optimization to get the optimal results with the appropriate values assigned to each variable. Equation (8) is the quadratic response surface model with single objective optimization, which can be solved quickly by quadratic programming. By comparing the maximum electric field intensities on the electrostatic ring before and after optimization in Figure 8, the decrease of the maximum electric field intensity can be observed more intuitively. The result shows that the maximum electric field intensity has been significantly reduced.  
Figure 8: Comparison of electric field intensity of electrostatic ring before and after optimization.  
Figure 7: 3-D plotof the interaction between two variables: (**a**) \(R_{1}s\), (**b**) \(hw\), (**c**) \(R_{1}h\), and (**d**) \(R_{1}w\).  
In order to verify the validity of the RSM, we compared the result of RSM with that of traditional FEM. The FEM taking much time and requiring many experiments, to ensure the fairness of the experiment and save the calculation time, thirty experiments were carried out by using the FEM. As can be seen from Table 10, the results obtained by RSM and FEM were consistent, while the time the of RSM experiments was much less, which demonstrates the effectiveness of RSM and also shows the high optimization efficiency and good prediction ability of this method.  
In Figure 9, a comparison of the variations of variables predicted by the FEM and those actually obtained by the RSM was made. It can be observed that the variations of variables in this paper are monotonic, so FEM can obtain the optimal results by inference in this special case. However, this method is no longer applicable when the variables are not monotonic. The RSM has a wider range for applications and the result obtained by it is more rigorous and convincing.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of IoT in PV Systems
#### 3.2.2 Optimization Techniques: MPPT, Solar Tracking, and Cleaning Systems
Additionally, the high installation costs of IoT infrastructure--such as sensors, communication devices, and cloud services--can be prohibitive for smaller installations or markets in developing regions. Security and privacy concerns also pose risks, as the data collected from these systems are susceptible to cyberattacks, potentially leading to malfunctions or data breaches. Furthermore, the integration of IoT with legacy systems and the lack of standardization across platforms complicates seamless adoption.
## 3 Results and Discussion
### Engine Performance
#### 3.1.1 Combustion Characteristics
Exhaust gas temperatures were similar (390 K) at all blends at 0 Nm engine load, but at a 40 Nm engine load, the temperature increased from 485 K to 493 K with increasing blend ratio from PD0 to PD30. The exhaust gas temperature dropped to 489 K when PD100 was applied. The combustion efficiency was reduced by the poor atomization under low idle conditions with increasing injected fuel quantity, however, it increased due to rapid combustion after the ignition delay in the main injection for PD100, while the combustion at pilot injection and the beginning of the main injection deteriorated. As the injected fuel quantity increased, the combustion efficiency was reduced due to the poor atomization in this condition. However, the combustion efficiency of PD100 increased due to rapid combustion after the ignition delay of the main injection, even though the combustion of pilot injection and the beginning of the main injection deteriorated. As shown in Table 5, BTE at an engine load of 40 Nm decreased as the blend ratio increased from PD0 to PD30, but it increased at PD100. The stability of combustion can be observed by the COV of IMEP, where a lower COV means higher stability. If the COV of IMEP exceeds 10%, it means the engine has a problem to operate. but less 5%, it is judged to have a stable combustion state [55; 56]. The stability of combustion is better at an engine load of 40 Nm, and it became worse as the blend rate increased.
## 2 Materials and Methods
### Oxidative Stress Parameters Measurement  
#### 2.7.1 Lipid Peroxidation Activity  
Lipid peroxidation (LPO) levels were detected with an MDA Assay Kit (Cat. N* MAK085, Sigma-Aldrich Co. LLC, St. Louis, MO, USA). As an end product of lipid peroxidation, MDA reacts with thiobarbituric acid that forms a colorimetric (532 nm) substance, the intensity of which is proportional to the extent of lipid peroxidation. The values were expressed in nmoles MDA/mg protein, with a molar extinction coefficient of 1.56 \(\times\) 10\({}^{5}\) M\({}^{-1}\) cm\({}^{-1}\).
## 4 Conclusions  
In this paper, the formula for connecting two-dimensional distribution function with the copula function is derived and a modeling method is given for the three-dimensional joint distribution of wind speed, wind direction, and height. The method started with a one-dimensional wind speed distribution model and used mvM to describe a wind direction distribution. A two-dimensional joint distribution model of wind speed and wind direction at each height was established through the AL model. Then, the two-dimensional joint distributions at each height were connected by the copula function, from which a three-dimensional joint distribution model of wind speed, wind direction, and height was formed. Our main conclusions are as follows:  
(1) The copula function can connect not only one-dimensional distribution but also two-dimensional or even multi-dimensional distribution. In practice, the joint distribution function of variables that have a clear relationship between each other can be obtained first, and then the copula function can be used to connect these joint distribution functions to form the overall joint distribution function.  
(2) This kind of joint distribution model has a good fitting effect, can make full use of the original data, and is not affected by the characteristics of wind speed data. The example in this paper is based on the measured data in a flat area. Because of the compatibility of its function, it can be applied to complex terrains, such as mountain canyons, in subsequent research.  
(3) The two-dimensional joint distribution model can describe the relationship between wind speed and wind direction; a suitable two-dimensional distribution model can be adopted to fit the data. The distribution model proposed in this paper can be used for three-dimensional distribution fitting. The number of different heights will not affect the establishment of a low-dimensional model. This will only affect the parameter value of the copula function. In practical applications, the influence of bad data points on the final results can be avoided.  
(4) The wind speed in the area chosen for this study is in good compliance with the Weibull distribution parameters, and the north and south wind prevails in this area. In addition, the probability of prevailing wind direction decreases as height increases.  
(5) The three-dimensional distribution model was used to obtain the wind field characteristics and to further calculate the wind load of the structure. It provides a basis for wind resistance design of high-rise buildings or long-span bridges. The model can also be applied to the field of wind power to evaluate the potential wind energy in a certain area.  
Not applicable.  
The authors declare no conflict of interest.
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 5 Experiment and Results
### DDoS Attack Mitigation  
To evaluate the system's security capabilities, a DDoS attack simulation was conducted, in which a hacker launched an HTTP flood attack, sending 50,000 requests per second to disrupt the network. Such an attack could shut down machinery or halt production, resulting in substantial financial losses. The system responded swiftly, with edge nodes detecting the surge in network traffic within 12 s, which is significantly faster than traditional tools, which typically take up to 120 s to respond. The AI algorithms flagged the anomaly, triggering automatic IP blocking through blockchain-based smart contracts. Within 28 s, 95% of malicious traffic was blocked, and the system was restored to normal operation. The edge nodes handled 78% of the CPU (central processing unit) load during the attack, compared with central servers, which would have reached 98% utilization and likely crashed. Table 5 lists all anomalies detected in factory A over a 72 h period, including timestamps, temperature values, and Z-scores. It highlights how statistical methods, such as the Z-Score (standard score), identify unusual data points. Table 6 provides a granular view of the DDoS attack simulation, detailing network traffic levels, system actions, and outcomes at specific time intervals.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device ID** & **Sensor Type** & **Value** & **Timestamp** & **Data Hash** \\ \hline
**device\_001** & Temperature & 25.4 \({}^{\circ}\)C & 2023-04-08T12:00:00 & a1b2c3d4... \\ \hline
**device\_002** & Humidity & 65\% & 2023-04-08T12:00:05 & e5 fg57h8... \\ \hline
**device\_003** & Pressure & 101.3 kPa & 2023-04-08T12:01:00 & i9j0k112... \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transaction examples from blockchain integration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Timestamp** & **Temperature (\({}^{\circ}\)C)** & **Z-Score** & **Anomaly Flagged** \\ \hline
2023-04-08T03:00:00 & 28.0 \({}^{\circ}\)C & 3.2 & Yes \\ \hline
2023-04-08T15:30:00 & 27.8 \({}^{\circ}\)C & 3.1 & Yes \\ \hline
2023-04-09T02:45:00 & 28.1 \({}^{\circ}\)C & 3.5 & Yes \\ \hline \hline \(\ldots\) (12 total events) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Anomalies detected in factory A (72-h period).
## 5 Discussion  
The developed solution provides robust pose estimation for any mobile robot using only a monocular camera. It was designed and developed for robots operating in dense public spaces, such as museums and hospitals, where determining a robot's location is particularly challenging. While it can also be applied to industrial environments, achieving optical performance in these settings would require more advanced hardware.  
Compared with other pose-estimation methods in the state of the art, the proposed system offers the advantage of achieving high precision with a single downward-facing sensor. This disposition offers the advantages of ensuring better control of image-capturing conditions and being immune to external perturbations that may interfere with location estimation, as can occur with systems employing VO odometry with outward-facing cameras or LiDARs. Furthermore, unlike traditional odometry, where the position is estimated by summing the movements of each motor, VO is immune to wheel slippage, which is common on polished indoor surfaces. Finally, this method is applicable to any mobile robot, unlike other state-of-the-art solutions, which may focus only on certain models, such as those in [12; 13], whose study was limited to robots with the Ackerman model.  
As is typical of odometry solutions, the system presents a degree of drift, as seen in the testing results. We did use the calibration procedure to mitigate this error, but unlike other odometry solutions, in our system, this issue can be addressed by strategically placing floor markers in an installation. These markers can be used by the system to determine its exact, absolute position, enabling the constant correction of the system. This can be achieved using stickers placed directly onto a floor, such as QR codes, or by using more subtle geometric patterns like dot or line patterns to encode information. For example, this can be applied directly by using floor-tile boundaries to minimize drift where available. These solutions allow the system to determine the robot's absolute position or control the behavior of the robot with minimal installation costs.
# Thermal Characteristics and Parametric Analysis of an Improved Solar Wall
## 4 Thermal Performance Optimization of Solar Walls
### Effect of Vent Size on the Thermal Performance of Solar Walls  
The structure size of the vent affects the volume flow in the air gap. Figure 14 shows the variation of volume flow and heat supply with the vent size. It can be seen that the volume flow of air in the channel was increased with the diameter of the vent, so as the heat supply. A larger ventilation volume can strengthen the heat transfer between the air and the absorber board, and send the heated air into the room.  
According to Figure 16, the increase of vent diameter would contribute to improving thermal efficiency. However, the strengthening effect of heat supplied by the solar wall was not obvious when the vent diameter was larger than 200 mm. At the same time, the larger size of the vent may bring the inconvenience of engineering construction. Therefore, in the actual design, the ideal diameter of the vent should be about 200 mm.
## 2 Materials and Methods--Porosity Classification and Fracture Identification  
This section elaborates on the developed image analysis methodology for porosity classification and fracture identification of rock. To make our proposition more robust, we assume the input images are of low quality, primarily grayscale, and less statistically divergent. Here, less statistical divergence in the dataset (\(\mathcal{M}\)) points to the high similarity among its various image samples, which subsequently might make the mini-batch samples very similar and might lead to the mode collapse for the deep learning models.  
The basic idea of the proposed methodology is multi-objective--to find the \(k\)-immediate neighboring dark pixels of every discovered dark pixel within a specific region in an input grayscale image \(\mathbb{G}_{n}\in\mathbb{M}\) and then to classify it as a pore or a fracture. This is because, from the image processing point of view, we consider a pore or a fracture as a region within an image that contains a large number of contiguous dark pixels, even if not all of them are dark. Hence, we name the developed algorithm as \"Pixel-wise \(k\)-Immediate Neighbors\" (Pixel-wise \(k\)-IN) approach, where the set of \(k\)-immediate neighboring pixels (\(k\)-IN) of any specific pixel located at (\(x\), \(y\)) in \(\mathbb{G}_{n}\), \(n\) = 0, 1 \(\cdot\cdot\cdot\) are listed as \(k\)-IN(\(x\), \(y\)) = (\(x-1\), \(y-1\)), (\(x+1\), \(y-1\)), (\(x-1\), \(y\)), (\(x+1\), \(y\)), (\(x-1\), \(y+1\)), (\(x+1\), \(y+1\)), (\(x+1\), \(y+1\)), where \(x\), \(y\), \(x-1\), \(x+1\), \(y-1\), \(y+1\)\(\mathbb{G}\)\(\mathbb{Z}^{+}\). In short, the \(k\)-IN of (\(x\), \(y\)) can be defined as \(k\)-IN(\(x\), \(y\)) = (\(x\pm i\), \(y\pm j\)); \(\forall x\), \(y\)\(\in\)\(\mathbb{Z}^{+}\); \(i\), \(j\) = 0, 1; \(i\), \(j\) \(\
eq\) 0 simultaneously).  
For more clarity, in Figure 1a, the set of \(k\)-INs computed for pixel 6 is {1, 2, 3, 5, 7, 9, 10, 11}, where that for the pixel 1 is {2, 5, 6}. The procedure to classify every individual pixel in an input grayscale image as dark or bright is shown in Section 2.1. Later, we articulate the procedure to identify the \(k\)-immediate neighboring dark pixels of any specific pixel and classify a region containing a large number of contiguous dark pixels as pore or fracture, as is shown in Section 2.4. and Algorithm 2.