esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Transparency Design Based on Shared Situation Awareness in Semi-Automatic Driving
## 4 Workload  
The results of the NASA-TLX scale (Figure 9) showed that although Design1 could improve participants' information perception, it also increased the attention workload.  
According to the correlation analysis of the scale data of the three designs in Table 3, there was a significant difference between NASA-TLX and ASQ (\(p\) = 0.011), which showed a negative correlation (r = \(-\)0.364 *). The NASA score had a greater impact on the efficiency in ASQ (r = \(-\)0.487 **, \(p\) = 0).  
Figure 8: Scores from the ASQ questionnaire. The first column of each design is the ASQ score, and the next three columns are its three-dimensional scores.  
Figure 9: Scores from the NASA-TLX questionnaire. The first column of each design is the NASA-TLX score, and the next six columns are its six-dimensional scores.  
According to the scale data (Figure 8), increasing the transparency design stage greatly improved the interface's usability, especially in terms of helpfulness. The three designs showed significant differences in helpfulness (F = 4.442, \(p\) = 0.017 *). Among them, the helpfulness score of Design3 was higher than that of Design1, which means increasing the transparency design according to situation awareness can significantly improve the interface's usability and user satisfaction. Moreover, through correlation analysis (Table 4), helpfulness had a strong correlation with SA3 (projection) (r = 0.488 **, \(p\) = 0). This implied that increasing the transparency design of the SA3 stage (projection) can greatly benefit users by improving their satisfaction with the interface and improving the interface's usability. Therefore, the transparency design of the SA3 stage (projection) can significantly improve the usability of the interface and improve user satisfaction.  
According to the interview, 12 of the 16 participants preferred Design3 (75%). In the comparison between Design1, Design2, and Design3, the participants indicated that the change of color conveyed a change in safety level (yellow indicated vigilance, red indicated danger) and attracted their attention. If the color did not change, they would have to pay long-term attention, which could cause stress, exhaustion, or neglect of the change. Comparing Design2 and Design3, the participants said that the color block reminded them in advance, attracted their attention, and provided additional environmental information. In addition to this, based on user feedback, increasing the transparency design resulted in higher workload, possibly owing to the increased amount of information.
## 4 Applications for IoT Technology in Smart Buildings
### IoT for Power Generation  
The footstep produced electricity and saved data such as produced power, stored power information, and remaining storage space gadgets. The literature determined the possibilities for generating the power using piezoelectric sensors and the different sensors associated with the framework [109]. The processor handles the gathered feedback signals and conveys consistent data to the innovative gadgets. The power generation from this technology is not continuous, and it is based on the user's corresponding footsteps. The notification can be transferred to the authorized people via mobile as an alert message when capacity arrives at its 90 to 100% limit [110]. The piezoelectric arrangement is further associated with an analog to digital converter to change over the sensors detected force changed over from the simple to the advanced yields, then the associations proceed through the ATMEL AT89552 (Microcontroller).  
Implementing the IoT-based intelligent technology to tackle energy challenges in a growing society, the IoT smart energy management service has become mandatory. To maximize energy efficiency by collecting, managing, and sharing/trading information about energy utilization [111]. It is feasible to create and distribute new energy services to address national society's concerns, such as the constant rise in energy consumption, avoiding power peaks, and dealing with future trends, as shown in Figure 9. The use of IoT smart energy management services has been requisite in several settings, including smart cities and building regulations, to enhance energy efficiency and promote renewable energy sources. Utility companies increasingly mandate that residential and business customers build these systems as part of demand response programs, while governments provide incentives for their adoption. The development of energy service-oriented technology, such as M2M energy information services, facilitates automated monitoring, data analytics, and predictive maintenance. Home Energy Management Systems (HEMS) can independently control lights and appliances, improving efficiency and lowering energy expenses [112]. In summary, these IoT solutions enhance energy efficiency and facilitate sustainability objectives.  
Recently, there has been an increase in interest in renewable energy in the environment, which may be achieved by adopting IoT in smart buildings. In addition to the above applications, IoT plays a role in several aspects of the energy industry, including fuel extraction, energy generation, operation, and maintenance (O and M), testing and development (T and D), as well as the final usage of energy.
## 4 Numerical Results
### Results and Discussion
#### 4.3.2. Single Secondary User Identification Performance in Different Identification Environments  
With the growing popularity of social media platforms and the rapid development of image processing tools, it has become increasingly easy for malicious attackers to tamper with, forge, and distribute images. When digital images are used as forensic evidence, both the image content and source are often highly sensitive, especially in security and legal contexts. Therefore, protecting the privacy of images during the source camera identification process is critical. To address this, we implemented the privacy-preserving strategy described in Section 2.2.2, ensuring that image privacy is maintained throughout the identification process. To demonstrate that the privacy-preserving strategy does not affect the identification performance, and considering that the GLRT theoretically relies on specific statistical assumptions (e.g., the noise obeys a normal distribution and the variance is known), we further empirically validated the encrypted image data to assess whether these statistical assumptions still held in the privacy-preserving environment. In our experiments, Canon served as the known camera model \(C_{0}\), while Sony, Samsung, Nikon, and Fuji were used as unknown camera models \(C_{1}\). For each case, we determined whether the images originated from the Canon camera, comparing results in both before and after encryption environments. The ROC curves, shown in Figure 8, visualize the identification results before and after applying the privacy-preserving strategy. As seen in Figure 8, the AUC for the four different cases were similar before and after encryption. This results indicates that the privacy-preserving strategy did not affect the identification performance. Thus, the strategy can be used to protect the authentic content and source of images during the source camera identification process.  
Figure 7: ROC curves of identification results before encryption for Pentax OptionW60, comparing our proposed method with the comparison method [12].
## 3 Instrument and Simulation Assumptions
### Simulation Assumptions
#### 3.2.1 Aerosol Model
In the simulation, we assume that the profile of the aerosol extinction coefficient follows an exponential-decay profile, which is often used in atmospheric correction algorithms and can be written in the form of [7; 32; 36]:  
\[\int_{TOA}^{Z}\tau(z)dz=\tau_{0}\exp\left(-\frac{z}{H}\right) \tag{10}\]  
Figure 3: Scattering phase functions (**a,b**) and polarized phase functions (**c,d**) of ten typical aerosol models at a wavelength of 388 nm. (**a,c**) represents fine mode aerosol types and (**b,d**) represents coarse mode aerosol types. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**SSA** & **F-BLW** & **F-BNS** & **F-NM** & **F-ULW** & **F-UHS** & **C-ULW** & **C-BNM** & **C-BHM** & **C-UHS** & **C-UHS** & **C-UHS** & **UNW** \\ \hline SSA (388 nm) & 0.9541 & 0.9020 & 0.9572 & 0.9277 & 0.9413 & 0.7642 & 0.7906 & 0.7666 & 0.6194 & 0.6338 \\ SSA (443 nm) & 0.9498 & 0.8895 & 0.9569 & 0.9261 & 0.9377 & 0.7817 & 0.8082 & 0.7842 & 0.6333 & 0.6496 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SSA of ten aerosol models at 338 nm and 443 nm. where \(\tau_{0}\) is the columnar aerosol optical depth (AOD), and \(H\) is the scale height, which represents the height of the aerosol layer when the columnar aerosol optical depth (AOD) decays to \(1/\text{e}\) of the ground. To simplify this, we only include \(H\) in our retrieval's state vector, while the other aerosol-related characteristics (AOD, effective radius, variance, and refractive index) are specified as model parameters.
## 4 Result Analysis
### AUROC Curve Analysis  
The true-positive and false-positive rates were plotted against each other at different threshold settings on an AUROC probability curve, revealing how well the model differs across classes. The model is more accurate in classifying 0 classes as 0 and classifying 1 classes as 1 with higher AUROC values.  
The AUROC curves for ML models without feature selection and with feature selection via FPA, PSO, and GA are shown in Figure 5.  
According to the results, ML models using the FPA feature selection algorithm also perform well in terms of AUROC values.
## 3 Materials and Methods
### Mapping in Dense Vegetation by the Fusion of FPCS Components from Different Data Sources Using GEE
#### 3.3.3 Fusion of the Selected Crosta Components  
Both the raw and stretched data were used to incorporate more information, as a high degree of stretching means the loss of information for minor pixels [42]. The proposed criteria assigned an equal weight to all the datasets involved in the final FCC. However, the final FCC was supposed to contain two components from the same dataset, and then, the weight assignment was based on the information of a particular mineral in a component (i.e., granite and marble in this case). After the assignment of weights, components were linearly combined to derive a final FCC. The three best PCs, retaining significant granitic spectral features, were selected and linearly combined after assigning weights to the components. Each component image was used to validate the known locations of four granite leases and marble quarries of Shewa-Shahbaz Garhi and Ambela complexes. The known mining leases of granite in these regions were marked as ML-1, ML-2, ML-3, and ML-4, while the marble mining leases were marked marble mining leases (MMLs) in Figure 2. The criterion for the assignment of weight for each component is presented as:  
\[FCC=w_{1}PC_{d}^{i}+w_{2}PC_{d}^{j}+w_{3}PC_{d}^{k}, \tag{4}\]  
where \(d\) can be any combination of the chosen components among the FPCS applied to raw or stretched Sentinel-2, ASTER, Landsat-8 OLI, and Landsat-8 TIRS data; \(w_{1}\), \(w_{2}\), \(and\)\(w_{3}\) are the weights assigned to generate an \(FCC\) for the chosen components \(PC_{d}^{i}\)\(PC_{d}^{j}\), and \(PC_{d}^{k}\); \(i\), \(j\), and \(k\) indicate the \(PC\) of dataset \(d\) subject to the following constraints:  
\[w_{1}+w_{2}+w_{3}=3. \tag{5}\]  
If the chosen components are from the different datasets, equal weights are assigned to each component, i.e., \(w_{1}=w_{2}=w_{3}=1\). If any two components are from the same dataset, then the sum of weights of these two components is half, i.e., 1.5, and the third component is assigned as the remaining 1.5 as per Equation (5). This 1.5 weight is split among the two components from same dataset, such that the Crosta component with more explicit indications about the targeted mineral is given a weight between 2/3 and 3/4 while the other is given the remaining weight. This is conducted to incorporate more information from the component about the mineral/rock type. In this way, the weights can be distributed among components to produce the best results for different rock types and data.  
The proposed methodology was also extended to map the well-known granitic rock bodies of Karoonjhar Mountains in Nagarparker (Sindh Province of Pakistan) and the Kotah Dome, Malakand (Khyber Pukhtunkhwa Province of Pakistan).
## 2 Materials and Methods
### IRIS Software  
The IRIS software developed by the company NHAZCA Ltd. (Rome, Italy) can be used to implement advanced image-processing algorithms to monitor ground and structural displacements and changes; IRIS was conceived to work with terrestrial, aerial, and satellite imagery of any data type (radar, optical, thermal, or near-infrared). This software allows users to reach subpixel accuracy in displacement monitoring with several digital image correlation (based on the most common DIC algorithms, such as [23; 24] and change detection algorithms (based on the SSI Index [25]), allowing a multitemporal approach with the generation of displacement time series. From image preparation, pre-processing, and manual/automatic processing to post-processing, IRIS implements several analysis modules. IRIS can be used individually or combined with other monitoring systems without the installation of contact sensors/reflectors, and it allows real-time analyses, with results ready within minutes after image acquisition.
# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
## 3 Method
### Instantiations  
SMG can be implemented in standard visual RGB detection networks and convert those networks to infrared detectors. To illustrate this point, we apply SMG in both anchor-free and anchor-based detection frameworks, which is described next.  
We first consider CenterNet [19], an anchor-free model, as an instantiation, and the framework we proposed is named SMG-C. As shown in Figure 3, CenterNet predicts center points of targets directly by producing a heatmap \(\hat{Y}\in[0,1]^{H\times W\times class}\), where _class_ is the number of categories (for RGB models trained on COCO, _class_ = 80 ). Therefore, the sum of the heatmap represents foreground prediction, and we use it as \(\mathbf{S_{FG}}\) to transfer knowledge. For the infrared detection network of SMG-C, only a BSM is inserted in between FEN and DH in comparison with CenterNet.  
SMG is also applied in YOLOv3 [18], an anchor-based model, and Figure 4 shows the framework of SMG-Y. YOLOv3 predicts bounding boxes at 3 different scales by extracting  
Figure 3: The framework of SMG-C.  
features from 3 scales. As a result, we add 3 BSMs in the infrared detection network. Furthermore, YOLOv3 sets \(k\) anchors with different sizes, and thus, the prediction in every scale is a \(k\)-d tensor encoding location, confidence, and class. The confidence reflects whether there is an object in the anchor, and we adapt it as the foreground soft label \(\mathbf{S_{FG}}\) directly. In this work, we set \(k=3\) according to the original paper [18].
# Mapping Automated Cyber Attack Intelligence to Context-Based Impact on System-Level Goals
## 4 Mapping Cyber Attacks to Statistical Distributions
### Network Attacks
#### 4.1.2 Remote Control/Packet Alteration  
This kind of attack targets devices that lack authentication mechanisms (e.g., TP-Link Smart Plug). As a result, the IoT devices accept a range of commands that can either change their state or provide sensitive information. Examples of such commands include: turning the device on/off, returning device information, returning cloud connectivity information, scanning for nearby access points, resetting the device to factory settings, returning real-time voltage/current/power, and returning system time [40]. Evidently, this attack can directly impact the IoT device and make it behave in an irregular manner, whilst it can also provide the attacker with crucial system and device information.  
As Figure 8 shows, and as explained in Table 3, the attack is of medium difficulty to detect, as control packets will stand out in the netflow traffic, so we map this to an exponential distribution. The attack has high damage potential, possibly completely disabling the device, so it is mapped to a rapid decay.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Attack** & **Denial of Service (DoS)** \\ \hline Damage Potential & High—DoS attack can severely impact the internet of things devices [37]; can completely interrupt the communications in the network [38]; and can severely disable the device by blocking communication. \\ \hline Detection & Hard—DoS attack is very hard to detect before the network is unavailable [39]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Denial of Service Attack (DoS)—qualitative summary.  
Figure 7: Denial of Service attack (DoS).
## 2 Multi-Dimensional User Importance Indicator System and Ranking Model
### Brief Introduction to the Proposed User Importance Indicators
#### 2.1.4 User Demand Response Rate \(\Delta L\)  
The degree and willingness to participate in the demand response varies from one user to another. Generally speaking, the greater a customer's involvement in demand response, the more flexible their load becomes. This flexibility enhances the likelihood of successfully shifting their electricity consumption from the moment of the fault to normal times or reducing it altogether. Consequently, during load-shedding operations, such customers can be prioritized for load reduction, indicating a lower importance level in load shedding.  
The electricity price elasticity coefficient \(\varepsilon\) indicates the ratio of the rate of change in demand for electricity to the rate of change in its selling price and is calculated as follows:  
\[\varepsilon=\frac{\Delta L}{L}\frac{c}{\Delta c} \tag{8}\]  
where \(L\) and \(\Delta L\) are the quantities of electricity demanded and changed, respectively, and \(c\) and \(\Delta c\) are the prices of electricity sold and changed, respectively.  
When the price elasticity coefficient of electricity is larger, the degree of load participation in the demand response is greater, and the sensitivity to price changes is greater. After the implementation of the peak-valley time-sharing tariff, the electricity demand of users in a time period is affected by the current price level and price level of the remainder of the time period. The value of the load in each time period can be calculated through price elasticity matrix E, as follows:  
\[L=L_{0}+\Delta L \tag{9}\]  
\[\Delta L=\begin{bmatrix}L_{\rm P0}&0&0\\ 0&L_{\rm P0}&0\\ 0&0&L_{\rm v0}\end{bmatrix}E\begin{bmatrix}\Delta c_{\rm P}/c_{\rm P0}\\ \Delta c_{\rm f}/c_{\rm f0}\\ \Delta c_{\rm v}/c_{\rm v0}\end{bmatrix} \tag{10}\]  
\[\Delta L\leq\Delta L_{\rm max} \tag{11}\]  
where \(E=\left(\varepsilon_{ij}\right)_{3\times 3^{\prime}}L_{0}=\left[L_{\rm P0},L_{ \rm P0},L_{\rm v0}\right]^{T}\); \(L_{\rm P0}\), \(L_{\rm P0}\), and \(L_{\rm v0}\) are the original peak and valley hours of electricity consumption; \(c_{\rm P0}\), \(c_{\rm f0}\), and \(c_{\rm v0}\) are the original peak and valley hours of the electricity price; \(\Delta c_{\rm P}\), \(\Delta c_{\rm f}\), and \(\Delta c_{\rm v}\) are the peak and valley hours of the electricity price change; and \(\Delta L_{\rm max}\) is the user's maximum demand response. When the user's maximum demand response is greater, the user can participate in the demand response to a greater extent, and their relative user importance is lower.
## 3 Operational Requirements
### Technical Specifications
#### 3.4.6 Gathering Capacity  
Robotic arms are critical components in a fruit-harvesting robot, designed to perform the physical task of harvesting fruits from plants. To adapt to the variety of fruits in a field, these arms are equipped with adaptive grippers and advanced technologies that allow for efficient and careful picking.  
The robotic arms are designed with multiple degrees of freedom, allowing for a wide range of motion. This mobility is essential to reach fruits in different positions and heights within the tree or plant, ensuring a complete and efficient harvest [70]. The grippers on the robotic arms are designed to accommodate different sizes and textures of fruits. They use soft, flexible materials to grip delicate fruits without damaging them. In addition, the grippers can adjust their grip strength in real time, based on touch sensors, to prevent bruising or damage.  
The time it takes for the robot to pick each fruit is a critical balance between speed and care. An optimized picking time ensures that the robot can cover large areas quickly without compromising the quality of the harvested fruits. Route planning algorithms optimize the movement of the robotic arm to minimize travel time between fruits. This includes calculating the most efficient sequence for harvesting fruit in a given area, reducing the total operating time. Precise synchronization between different robot components, such as arm movement and robot movement, ensures smooth and continuous operation. This reduces downtime and increases harvesting efficiency.  
The load capacity of the harvesting robot is another essential aspect that determines its operational efficiency. Adequate load capacity allows the robot to operate for extended periods without the need to empty its fruit container, thus increasing productivity. Cargo containers are designed to support a significant amount of fruit, using durable materials that can handle the weight and conditions of the field. This allows the robot to harvest large volumes of fruit before requiring emptying [71]. The storage system inside the container is designed to protect the harvested fruits. This includes padded compartments and mechanisms that ensure that fruits are not damaged by the movement or weight of other stacked fruits.