esa-sceva/satcom-chunk-collection huggingface dataset:
# Wormhole Whispers: Reflecting User Privacy Data Boundaries Through Algorithm Visualization
## 3 Design Method
### Interaction Design & User Study
\begin{table}
\begin{tabular}{c c c} \hline
**Participants** & **Age** & **Reaction** \\ \hline P1 (F) & 20 & _I definitely have a more intuitive understanding of the privacy policies,_ _including some of the algorithms._ \\ P2 (F) & 22 & _Honestly, I’ve always thought agreeing to terms and conditions is kind of funny._ \\ P3 (M) & 22 & _Regarding the latter part about cluster analysis, when I first started_ \\  & 22 & _using TiTok, I thought, ”Wow, so this is how most people are”. Until I_ \\  & & _stumbled upon the concept of the “information bubble”._ \\ P4 (M) & 23 & _Whenever I show interest in something, suddenly everything related to_ \\  & & _it, just flood in from everywhere._ \\ P5 (F) & 23 & _Our privacy data is rapidly transmitted to various unknown places,_ _indeed like traveling through a wormhole_ \\  & & _I really hope that the designers and tech teams can do more to educate_ \\ P6 (F) & 23 & _regular people like me and push those capitalists to stop spying on_ _and make some real changes._ \\  & & _This kind of game makes me feel like the internet has gone too far. Now I_ \\ P7 (M) & 23 & _get why I always get calls from people trying to sell me a car or a house._ \\  & & _Most apps tend to overreach when it comes to permissions, and some_ \\ p8 (F) & 24 & _even block you from using them if you don’t agree to everything._ \\  & & _It’s just like those forced pop-ups in games—towadays, so many apps_ \\ P9 (M) & 25 & _bombard you with permission requests when you open them._ \\  & & _If apps could just stick to privacy agreements and keep users’_ \\ P10 (F) & 26 & _information safe, I think that would be amazing!_ \\ \hline \end{tabular}
\end{table} 
Table 2: Feedback from general users. privacy decisions. However, some participants were pessimistic about their ability to control privacy decisions and expressed a desire for the right to negotiate with platforms. Additionally, we received several suggestions regarding privacy policies. Participants recommended simplifying the service agreements that pop up when opening an app to show only the conditions needed for main functions, with secondary functions being negotiated with users later. They also suggested adding design elements to long agreements, such as icons and cartoons, to aid understanding.
###### Abstract
presented a fault prediction method based on enhanced time series forecasting models, which could evaluate the development of the fault before a flashover occurs [22]. [PERSON] implemented a short-term prediction of energy consumption based on a standard Kalman filter [23]. [PERSON] proposed a grey GM(1, n) model for predicting the short-term power load [24]. Modern prediction methods include the support vector machine, artificial neural network, wavelet analysis, random forest, decision tree [25; 26; 27; 28; 29], etc. [PERSON] et al. proposed a hybrid support vector regression algorithm and, through it, performed a short-term load forecasting in a Microgrid [25]. [PERSON] et al. used an artificial neural network to obtain the consumption data of the Greek interconnected power system [26]. [PERSON] et al. predicted the fault in electrical power grids by a long short-term memory model based on wavelet transform [27]. [PERSON] et al. carried out short-term load forecasting using the random forest method [28]. [PERSON] used decision the tree method to clarify the relationship between the load and the relative variables and implement a long-term load forecasting [29]. Based on the current research, we found that most of the prediction models have combined two or more prediction methods to obtain useful information. However, it is still difficult to find an accurate prediction method that can be applied to any region and any situation. This is because the real system is complex and diverse, and it will also be affected by many external factors, such as economy, policy, climate and holidays. In this paper, we analyze the characteristics of Suzhou's industrial power consumption data and set up a grey model with a seasonal index adjustment to predict the industrial power consumption. The remaining parts of this paper are organized as follows. Section 2 analyzes the data of Suzhou's industrial power consumption between 2003 and 2005 and finds its characteristics. Section 3 introduces the grey model with a seasonal index adjustment. Section 4 discusses the empirical analysis results of Suzhou's industrial power consumption. The conclusion is given in Section 5.
# Processing Fresh-Cut Potatoes Using Non-Thermal Technologies and Edible Coatings
## 5 Emerging Processing Technologies: Operating Principles and Examined Parameters
### Conventional and Novel Anti-Browning Agents
[PERSON] et al. (2021) investigated the impact of varying concentrations of ginger essential oil within a chitosan-based edible coating on the microbiological and sensory attributes of fresh-cut sweet potatoes during a 6-day storage period at 4 \({}^{\circ}\)C. Findings revealed that incorporating 0.3% (\(w/v\)) ginger essential oil into the chitosan coating displayed significant potential in extending the shelf life and maintaining the quality of fresh-cut sweet potatoes. However, the addition of 0.5% (\(w/v\)) ginger essential oil to the coating resulted in a notable deterioration in their sensory attributes [219]. Finally, [PERSON] et al. (2018) demonstrated the efficacy of navel orange peel essential oil to significantly improve the microbiological quality of potato slices [209]. In summary, a wide range of anti-browning agents, from inorganic compounds to plant-based extracts and essential oils, have proven effective in inhibiting enzymatic browning and extending the shelf life of fresh-cut potatoes. Chemical agents like ascorbic and citric acid offer affordable and efficient browning control, while concerns over health and safety drive interest in natural alternatives. Plant extracts, phenolic compounds, and essential oils not only inhibit browning but also enhance microbial stability, offering promising clean-label solutions that align with consumer demand for natural ingredients. These innovative approaches, particularly when tailored to specific potato cultivars and applied at optimal concentrations, provide a versatile foundation for future developments in fresh-cut potato preservation.
## 2 Methodology
### Image Feature Extraction  
The foremost task in this paper is to extract region-level image features. Existing studies show that CNNs can accomplish the task of deep semantic feature extraction from images excellently. Here, this paper used ResNet152 [31] pretrained over the dataset ImageNet as the image feature extractor and removed the final fully connected layers and the global average pooling layer from the network to acquire the region-level semantic features of images. Bypassing the global average pooling layer means the advanced semantic features of all image areas are preserved intact with their weights undetermined, which can be further allocated and selected by the CAL. The specific process is shown in Figure 3. During training, we fine-tuned the parameters of ResNet152 to adapt them to our specific task. Given the set X of images, the image feature extraction process can be formulated as \(F=f(X,\theta_{v})\).  
One thing to note is that, since the global average pooling layer was removed, the features extracted from a single image were no longer a one-dimensional vector but a two-dimensional matrix, each row of which would serve as the feature representation of an image area.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 3 Methodology
### Finding Rotation Candidates  
When a point-cloud frame is recorded, it is processed for template matching. To keep the template-matching run time to a minimum, only a few possible rotations for the point cloud are considered. We therefore determine a set of rotation candidates that are most likely to match the recorded point cloud with the BIM model. Using the knowledge of our domain, we can assume that the walls recorded in the point cloud must face the same direction as the walls in the reference model. We use a convolution of normals in Fourier space to find the best rotational candidates. The rotation candidates are determined as follows:  
1. Calculate normals for the point cloud using a normal estimation algorithm. All normal vectors are assumed to be unit vectors.
2. Project every normal vector onto the \(XY\) plane.
3. Transform the projected vector from a two-dimensional coordinate system to a polar coordinate system, which yields a rotation and magnitude for every normal vector.
4. Filter out vectors by magnitude. Vectors with a magnitude below a certain threshold can be ignored, as they are most likely situated on a floor or ceiling of the point cloud.
5. Generate a histogram based on the rotation of the vectors. This histogram requires a form of binning.
6. Compute a convolution between the histogram of normal rotations and a target wave, and determine the best fit. The target wave has to match the general directions of walls in the reference model. A reference model with only perpendicular walls would yield four rotational candidates after a convolution with a sine wave \(f(\phi)=\sin\left(4\phi\right)\). Since the histogram is binned, the convolution can be calculated efficiently by transforming the histogram into Fourier space using a discrete fast Fourier transform algorithm.  
The maximum value of the convolution is the optimal rotation \(\phi_{0}\) of the point cloud to the reference. Additional rotation candidates \(\phi_{k}\) can be calculated by adding (in the case of perpendicular  
Figure 2: Inverse distance map \(\hat{d}_{ij}\) of a Building Information Modeling (BIM)-model cross section at 1 m above the floor: Higher values are closer to walls. The cutoff distance is set to 2 m in this case. The same scene can be seen as a point cloud in Figure 3. Note that the value range of the map can be neglected, as the map is later used for convolution.  
walls) \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\) to \(\phi_{0}\). Depending on the geometry of the building, more rotational candidates may be defined.
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 5 Experimental Results and Discussion
### Comparative Experiments Results
For instance, GRU (Figure 11b) and LSTM-GRU (Figure 11c) show distinct regions where points consistently deviate to one side of the 45\({}^{\circ}\) line, reflecting areas of prediction bias. In Figure 11f, the fitted curves of all the models are compared against the 45\({}^{\circ}\) line. The proposed model's fitted curve is closest to the 45\({}^{\circ}\) line, further validating its superior predictive performance. The network's parameters are optimized using stochastic gradient descent (SGD) during the backpropagation process. However, for hyperparameter optimization, we employed the TSO algorithm. To provide a thorough comparison, we evaluated TSO against other commonly used hyperparameter optimization methods, including random search (RS), Grid Search (GS), and Genetic Algorithm (GA). The performance of each method is presented in Table 5.  
Table 5 presents the results of various hyperparameter optimization methods, including Grid Search (GS), Genetic Algorithm (GA), random search (RS), and tuna swarm optimization (TSO) algorithm, across three prediction tasks: unit flow rate, guide vane opening, and maximum water guide tile temperature. For unit flow rate, the TSO algorithm achieves the lowest MSE (294.481) and a higher \(R^{2}\) (0.891) compared with GS, GA, and RS, indicating superior predictive performance. Similarly, for guide vane opening, the TSO method not only reduces the MSE (8.943) and MAE (0.553) but also achieves the highest \(R^{2}\) value of 0.913, demonstrating improved accuracy over the baseline methods. In the case of maximum water guide tile temperature, the proposed method shows a significant improvement, with the lowest MSE (0.058) and MAE (0.097), and an \(R^{2}\) of 0.994, outperforming the other optimization techniques in all the key metrics. These results highlight the efficacy of the TSO algorithm in achieving lower prediction error and higher accuracy across all the prediction tasks compared with conventional optimization methods.
## 3 Preliminaries
### Trusted Execution Environment  
A trusted execution environment (TEE) is a secure computing environment designed to protect the security and privacy of sensitive data and codes. Typically composed of both hardware and software, a TEE provides a secure execution environment on standard operating systems, ensuring that malicious software cannot access or tamper with the data and code within the TEE. Examples of TEEs include Intel Software Guard Extensions, (Intel SGX), ARM TrustZone, and so on.  
Intel SGX [27; 28; 29; 30; 31; 32] is a set of new instructions and modifications to the memory access architecture of Intel CPUs. As shown in Figure 2, we mainly focus on the following two features of the Intel SGX: remote attestation and memory isolation.  
* Remote attestation: Remote attestation offers cryptographic verification to ensure that the enclave operates securely on the cloud server. When the enclave is created, a remote attestation signature is generated with the assistance of the SGX component, which is known as quote enclave (QE), to ensure the security of the enclave. Additionally, a secure channel is established for secret sharing between the enclave and the client using Elliptic-curve Diffie-Hellman (ECDH) [33].
* Memory isolation: When a project runs on an SGX platform, the project is divided into an untrusted (or semi-trusted) storage area and a trusted isolation area, called an enclave. The enclave is an independent physical RAM block where the code and data contained within it are protected and inaccessible to privileged software, operating systems, hypervisors, and system firmware. When the SGX program is suspended or closed, the encrypted sensitive data separated from the enclave are stored in an untrusted area. When the SGX program is running, these data can only be accessed within the enclave through a dedicated interface designed for decryption and integrity checking.
## 3 Materials and Methods
### Characterization Techniques  
Thermal analysis of the initial solution was investigated via thermogravimetric analysis (TGA) and derivative thermogravimetry analysis (DTG) (PerkinElmer, Waltham, MA, USA), model TGA7/DTA7, in the presence of N\({}_{2}\) with a 10 \({}^{\circ}\)C/min heating rate from room temperature to 1000 \({}^{\circ}\)C to optimize the heat treatment program. FT-IR was used to study the chemical composition of samples using PerkinElmer Spectrum 1650. Energy dispersive X-ray (EDX) measurements were performed under a variable pressure scanning electron microscope (VPSEM, LEO 1455, Carl Zeiss, Germany) with an Oxford INCA EDX 300 microanalysis attachment. The crystal phase of prepared samples was determined via the X-ray diffraction (XRD) technique using a Shimadzu 6000 diffractometer (Lab XRD-6000 SHIMADZU, Kyoto, Japan) utilizing Cu K\(\alpha\) (0.154 nm) radiation. The morphology and average particle size of the nanocrystalline powder were evaluated using a transmission electron microscope (HTACHI H-7100 TEM, Chula Vista, CA, USA) operating at an accelerating voltage of 100 kV. The average size and size distribution of nanoparticles were determined using Image tool software from TEM images. The optical reflectance spectra of samples were recorded using the UV-Vis spectrometer (Shimadzu-UV1650 PC SHIMADZU, Columbia, MD, USA), and the band gap energy was evaluated from the reflectance spectra using the Kubelka-Munk equation.
## 4 Results and Discussion
### Field Test  
A validation experiment was conducted in the Liyue Road Tunnel in Chongqing. The rock surrounding the experimental section is primarily sandstone with low strength, developed rock stratification, and poor engineering geological conditions. The rock density is 2010 kg/m\({}^{3}\), uniaxial compressive strength is 33.7 Mpa, modulus of elasticity is 5.6 Gpa, and Poisson's ratio is 0.26. Rock drilling trolley drills 42 mm diameter blast holes with a depth of 2.0 m, and the hole spacing of the peripheral holes is 50 cm. The type of explosive is a waterproof #2 rock emulsion explosive. Several rounds of experiments were conducted in the field to eliminate any potential chance effects of the tests. Therefore, stemming materials are efficiently produced using a PNJ-B Mine Explosion-proof gunite machine to save time and money. And making specialized water bags in large quantities using the KPS-60 sealing machine produced by China Mining Company, with a capacity of 700 bags/person per hour. The water bags have a diameter of 35 mm and a length of 200 mm. The experiment was carried out on the right side of the tunnel. The right peripheral hole was filled with water bags and explosives by CS#7, while the left side had a conventional air-spaced charge structure, as shown in Figure 16.  
After completing three rounds of testing, statistics showed that the half-hole trace rate under the water interval charge structure was maintained at a high level, and the blasting effect performance was stable. Figure 17 illustrates the typical blast effects. The actual charge is reduced by approximately 26%, compared to the water interval and air interval charge structure, and the half-hole trace rate increases to around 75%. The contour surface  
Figure 16: Consumables, fabrication tools and test charge structure: (**a**) the KPS-60 water bag machine and Stemming material machines, (**b**) emulsion explosive, (**c**) test charge structure.  
is flat, without any visible fractures, avoiding over- and under-blasting phenomena, and operational efficiency is greatly improved.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 5 Role of Pulsed Light Technology in Food Safety
\begin{table}
\begin{tabular}{c c c c c} \hline
**Food Products** & **Pulse Energy (J/cm\({}^{3}\))** & **Treatment Time (us) \#** & **Logis Reductions** & **Reference** \\ \hline \multirow{2}{*}{Tomates} & 4 & \multirow{2}{*}{-} & 1 of surface microflora & \\ \cline{3-4}  & 2.2 & & 23 of _S. cerevisiae_ & [97] \\ \hline \multirow{4}{*}{Strawberries} & \multirow{4}{*}{3.9} & \multirow{4}{*}{200 s} & 2.2 of Aerobic mesophilic bacteria & \\ \cline{3-4}  & & & 1 of yeasts and molds & \\ \cline{3-4}  & & & 1.5 of _B. cereus_ & [67] \\ \cline{3-4}  & & & 1.1 of _L. monocytogenes_ & \\ \hline \multirow{2}{*}{Orange Juice} & \multirow{4}{*}{71.6} & \multirow{4}{*}{60 s} & Up to 1 of _L._ _innovates_, & \\  & & & Up to 2 of _E. coli_ & \\ \cline{3-4}  & & & Up to 15 of _S._ _E._ & \\ \cline{3-4}  & & & Up to 0.5 of _S. cerevisiae_ & \\ \hline \multirow{4}{*}{Apple juice} & \multirow{4}{*}{71.6} & \multirow{4}{*}{60 s} & Up to 0.45 of _L._ _innovate_ & [98] \\ \cline{3-4}  & & & Up to 2 of _E. coli_ & \\ \cline{3-4}  & & & Up to 4 of _S._ _Enteritidis_ & \\ \cline{3-4}  & & & Up to 6 of _S. cerevisiae_ & \\ \hline \multirow{2}{*}{Melon juice} & \multirow{4}{*}{71.6} & \multirow{4}{*}{70 s} & Up to 0.6 _L._ _innovate_ & \\  & & & Up to 5 of _S._ _Enteritidis_ & \\ \cline{3-4}  & & & Up to 7 of _S. cerevisiae_ & \\ \hline \multirow{2}{*}{Blueberry wine} & \multirow{2}{*}{-} & \multirow{2}{*}{60 s} & 9.6 \(\pm\) 0.9 of _S. cerevisiae_ & [99] \\ \cline{3-4}  & & & 5.9 \(\pm\) 0.5 of _S. cerevisiae_ & \\ \hline \multirow{2}{*}{Blueberries} & \multirow{2}{*}{5-56.1} & \multirow{2}{*}{360 s} & 3.8 \(\pm\) - 6.7 of _E. coli_ & [27] \\ \cline{3-4}  & & & 4.8 - 5.7 of _slammids_ & \\ \hline \multirow{2}{*}{Apple juice} & \multirow{2}{*}{17.5} & \multirow{2}{*}{-} & 3.6 _L. laevis_ & [100] \\ \cline{3-4}  & & & Up to 3.2 of total microbial count & [101] \\ \hline \multirow{2}{*}{Packaged cheese} & \multirow{4}{*}{53.4} & \multirow{4}{*}{40 s} & 1.25 of _P. nonspecific_ & \\ \cline{3-4}  & & & 2.98 of _Listeria monocytogenes_ & \\ \hline \multirow{2}{*}{Unpackaged cheese} & \multirow{2}{*}{53.4} & \multirow{2}{*}{40 s} & 1.32 of _P. nonspecific_ & \\ \cline{3-4}  & & & 3.08 of _L. monocytogenes_ & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 3 Approach
### Incremental Enhancement and Improvement  
In case that there is any probabilistic SSReq (safety and security requirement) that is not valid in the previous step, it is possible to enhance the satisfiability of the probabilistic SSReq by strengthening the operational power of some problematic process by upgrading its version. Once the upgrade is performed, it goes through the validation procedure for the probabilistic SSReq in the newly generated execution model of the system with the upgraded version of the processes. If the validation requirement is satisfied, it is classified as a _satisfiable_ probabilistic SSReq for the system with the upgraded processes. If not, meaning that there are no other better versions of the processes that satisfy the probability requirement, the SSReq is classified as an _unsatisfiable_ probabilistic SSReq.  
_Process enhancement_ is defined as increasing the value of probabilities on the transitions in a process defined by the discrete level representing the capability of IoT device in smart systems. Furthermore, _system improvement_ is defined as replacing the problematic processes with the enhanced processes to increase the performance or capabilities of the target IoT systems.  
For example, as shown in the previous example, there are two SSReqs that are unsatisfied for the existing \(P_{1}^{\prime}B_{1}^{\prime}C_{1}^{\prime}\): \(R_{1}^{\text{sc}}\) and \(R_{1}^{\text{sf}}\). For enhancement, the problematic process \(P_{1}^{(0.60)}\) is to be upgraded to its upper version \(P_{2}^{(0.70)}\) by increasing the probability for its choice operation, as shown in Figure 4. The improved system with the new enhanced version of Process \(P_{2}^{(0.70)}\) becomes System \(P_{2}^{\prime}B_{1}^{\prime}C_{1}^{\prime}\). Figure 5 shows the probabilistic execution model for the improved system, and the corresponding probabilistic SSReqs are shown in Table 10. Now, the satisfaction probabilities for the SSReqs are 0.205, 0.290, 0.205, and 0.085, respectively, and were obtained as follows:  
* \(p_{r}\big{(}R_{2}^{\text{sc}}\big{)}=p_{e}(ePath_{1})+p_{e}(ePath_{10})=0.196+0.009=0.205\);
* \(p_{r}\big{(}R_{2}^{\text{sc}}\big{)}=p_{e}(ePath_{1})+p_{e}(ePath_{4})+p_{e}(e Path_{7})+p_{e}(ePath_{10})=0.196+0.049+0.036+0.009=0.290\);
* \(p_{r}\big{(}R_{1}^{\text{sf}}\big{)}=p_{e}(ePath_{1})+p_{e}(ePath_{10})=0.205+0.009=0.205\);
* \(p_{r}\big{(}R_{2}^{\text{sc}}\big{)}=p_{e}(ePath_{4})+p_{e}(ePath_{7})=0.049+0.036=0.085\).  
Figure 4: Overview of the incremental system improvement for the PBC (Producer–Buffer–Consumer) example.