esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
### Panoramic Image Segmentation in Indoor Construction Site  
Panoramic images are lightweight compared to other data formats, providing efficient storage and processing capabilities while offering 360-degree environmental coverage. This characteristic eliminates blind spots and allows for comprehensive spatial analysis. Additionally, they can capture the entire space in a single shot, offering a more efficient and accurate means of environmental analysis compared to traditional perspective images with a limited field of view [1,2]. Due to these advantages, panoramic image segmentation has been widely utilized in such applications as autonomous driving, machine vision inspection, and medical imaging, and has recently been extended to the construction domain as well [20,21]. However, applying panoramic image segmentation to indoor construction sites presents considerable challenges, as models must be capable of understanding the dynamic and complex characteristics of these environments.  
Indoor construction environments exhibit distinct characteristics compared to outdoor settings. In outdoor construction sites, large-scale structures can be reliably captured across wide areas using drones or fixed-position cameras under uniform natural lighting and relatively simple background conditions that facilitate the application of segmentation models. In contrast, indoor construction sites are spatially constrained and highly complex and are frequently subject to visual noise caused by uneven brightness from artificial lighting, the movement of workers and materials during ongoing construction, and unfinished surfaces and finishes. These environmental conditions pose inherent limitations on the ability of segmentation models to accurately identify object boundaries [6; 7; 8]. Moreover, existing panoramic segmentation models have predominantly been trained on static, fully completed indoor spaces, such as residential units and offices [22; 23; 24]. As a result, applying these pre-trained models to dynamic and physically complex indoor construction sites without domain-specific knowledge injection often leads to a decline in segmentation performance.  
To address these challenges, additional training using domain-specific datasets is necessary. However, constructing such datasets is a labor-intensive and time-consuming process [7]. This study focuses on addressing this challenge by developing a method that enables existing segmentation models to accurately segment objects in panoramic images of indoor construction sites without requiring additional dataset construction.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Image Encryption
#### 5.5.1 Network Types  
* Back propagation: [PERSON] et al. [229] proposed a Backpropagation (BP)-based neural network utilizing fractional-order memristive hyper-chaos to encrypt images. Another proposal by [PERSON] et al. [230] uses a similar network to encrypt large satellite images. This algorithm is unique in that it is not affected by geometrical image distortions such as translation, size, and rotation.
* Bidirectional: Memristive bidirectional associative memory neural networks have been applied to image encryption in proposals by [PERSON] et al. [231] and [PERSON] et al. [232]. Simulations and experimental analysis demonstrated both algorithms to be highly secure and resist common attacks. Several image encryption schemes utilizing cellular neural networks have been proposed. These include one by [PERSON] [233] using parallel computing, one by [PERSON] et al. [234] using compressive sensing to achieve encryption and compression, and one by [PERSON] et al. [235] using a fractional-order quantum variant of the network. [PERSON] et al. [236] also proposed an algorithm that uses the network to generate a chaotic stream, which is further used to power Latin squares. Another proposal by [PERSON] et al. [237] implemented an encryption/decryption scheme using cellular neural networks that can decrypt the image even if parts of it were corrupted in transmission. A similar technique was used by [PERSON] et al. [238], where noise removal was performed on a transmitted image using the special Cohen-Grossberg neural network. Noise removal is key, otherwise the decryption will not work correctly due to the avalanche property of encryption algorithms.
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 5 Power Profiling Model
### Power Profile Assignment  
The power profile assignment stage assesses the input power usage data and clusters it into a particular power load type. It measures the similarity between the input load data time series with both load patterns sets of WoLP and WeLP using the DTW algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Characteristics**} & \multicolumn{2}{c}{**Power Load Patterns**} \\  & & **Workday Load** & **Weekend/Holiday** \\  & & **(WoLP)** & **Load (WeLP)** \\ \hline DTW clustering & DTW distance & 1165.80 & 1244.75 \\  & St. deviation & 362.45 & 402.53 \\ Power usage & Peak (W) & 309.24 & 484 \\  & Mean (W) & 48.92 & 41.36 \\ Daily load factor (\(l_{f}\)) & 22.71\% & 21.35\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power load patterns.  
Depending on the measured similarity, the load data are categorized as either WoLP or WeLP. For instance, Figure 5 depicts the categorization of the refrigerator's load data points on a sample workday. It shows the Euclidean distance, warp path, and the straight-line fit between this workday's load pattern set and the load data sequences of WoLP (for categorizing to workday profile, shown in Figure 5a) and WeLP (for categorizing to weekend profile, shown in Figure 5b). The load pattern set of this sample workday has a distance of 1030 to the WoLP's load data sequence and 1872 to the WeLP's. Due to the smaller similarity distance, it is matched to the WoLP (Table 2) and classified as a workday. By recognizing the power consumption patterns and matching them to a load type, the load type's profile is detected, and the consumer's consumption behavior can be predicted.
## 2 Material and Methods  
### Material  
Three types of fuels were used (fossil fuel and two types of biomass) of approximately \"same shape\"--briquettes (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fuel**} & **Moisture** & **VM** & **FC** & **Ash** & **C** & **H** & **N** & **S** & **O** & **GCV** \\ \cline{2-10}  & & & & & **\%** & & & & & **MJ/kg** \\ \hline CB & max 19 & 50.61 & 42.89 & 5.50 & 58.97 & 4.92 & 0.67 & \textless{}0.50 & 29.94 & 19.2 \\ REKORD 7 & max 8 & 75.23 & 23.82 & 0.95 & 46.27 & 5.42 & 1.64 & 0.71 & 44.10 & 17.8 \\ PW (SaverBrik) & max 8 & 79.65 & 7.15 & 13.2 & 38.30 & 5.29 & 0.11 & \textless{}0.10 & 43.30 & 16.1 \\ \hline \hline \end{tabular} Explanation: GCV—Gross calorific value.  
\end{table}
Table 1: Proximate and ultimate analysis of combusted fuels in dry matter.  
Coal briquettes (CB) with the trade name \"REKORD 7\" come from lignite in the Lumber Range (Welzow-Sud and Nochten mines) which are further processed by Lausitz Energie Bergbau AG (LEAG) at the Schwarze Pumpe plant. The briquettes are produced without binders and are available at all fuel suppliers in the Czech Republic. The sales price plus VAT is EUR 2.67/10 kg. The advantage of CB briquettes for domestic heating is a higher fixed-carbon content.  
Paper briquettes ((PB) SaverBrik POWER type RUF-P (manufacturer SaverBrik, the Czech Republic)) are produced from mixture of coarsely crushed cardboard and recovered paper using high pressure. The sales price of these briquettes ranges from 0.85 to 1.25 EUR/10 kg with VAT.  
Briquettes made from waste wood from furniture production (WM) with the addition of wood-processing waste containing polymer materials--veneer, wood, wood composites, and MDF (medium-density fibrewood) products come from a plant (Ostravice, Moravian-Silesian Region, the Czech Republic) corresponding to category 1.2.3 \"homogeneous mixtures and mixtures\" defined by ISO 17225-7 (WM) [22]. The sales price plus VAT is EUR 2.24/10 kg. Therefore, the costs of obtaining 100 MJ are comparable for briquettes CB and WM (EUR 1.3 and EUR 1.2), while for PB, it is half the cost (EUR 0.6).  
In addition to the input fuel, combustion products were analysed: ash (A)--samples taken from the bottom of the boiler and deposits (D)--samples taken from the wall of the boiler and heat exchange surface (Table 2). Char/soot terminology is used for high-carbon material in atmospheric deposition. Char retains the morphology of the source material with diameters ranging mainly from 1 to 100 \(\upmu\)m [23]. Soot is submicron particles (<1 \(\upmu\)m) formed from the condensation of hydrocarbon radicals at high temperature (>600 \({}^{\circ}\)C) via conversions in flue gas [24]. As the samples taken did not fit either definition (particle size, presence of a primary particle structure from the fuel), they were marked as deposits.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 3 Magnetometer Calibration Based on Least-Squares Fitting Algorithm
### Ellipsoid Model  
For magnetometers with three sensitive axes installed perpendicular to each other, the originally collected data represents a spatial ellipsoid that is roughly distributed along three axes parallel to the body axis. The aim of ellipsoid calibration is to use statistical methods to determine the ellipsoid equation with the smallest average variance of the collected data. In this study, we used the least-squares method to calibrate the magnetometer.  
The general equation of the ellipsoid can be expressed as:  
\[a_{1}x^{2}+a_{2}y^{2}+a_{3}z^{2}+a_{4}xy+a_{5}xz+a_{6}yz+a_{7}x+a_{8}y+a_{9}z=1 \tag{8}\]  
We write the standard equation in the following form:  
\[1=(x^{2}\ y^{2}\ z^{2}\ xy\ xz\ yz\ xy\ z)\begin{pmatrix}a_{1}\\ \vdots\\ a_{9}\end{pmatrix} \tag{9}\]  
From a geometric perspective, the points distributed on the ellipsoid can be expressed in the following matrix form:  
\[\begin{bmatrix}x-c_{x}&y-c_{y}&z-c_{z}\end{bmatrix}\begin{bmatrix}r_{11}&r_{12 }&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}^{T}\begin{bmatrix}\lambda_{1}&0&0\\ 0&\lambda_{2}&0\\ 0&0&\lambda_{3}\end{bmatrix}\begin{bmatrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}\begin{bmatrix}x-c_{x}\\ y-c_{y}\\ z-c_{z}\end{bmatrix}\\ =1+\begin{bmatrix}c_{x}&c_{y}&c_{z}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}^{T}\begin{bmatrix}\lambda_{1}&0&0\\ 0&\lambda_{2}&0\\ 0&0&\lambda_{3}\end{bmatrix}\begin{bmatrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}\begin{bmatrix}c_{x}\\ c_{y}\\ c_{z}\end{bmatrix} \tag{10}\]  
The above equation can be expressed as:  
\[\begin{bmatrix}X-C\end{bmatrix}M[X-C]T=1+CMC^{T}\\ \begin{matrix}XMX^{T}-2 CMX^{T}+CMC^{T}=1+CMC^{T}\end{matrix} \tag{11}\]  
where:\(X=\begin{bmatrix}x&y&z\end{bmatrix}\) represents a point on the ellipsoid and \(C=\begin{bmatrix}c_{x}&c_{y}&c_{z}\end{bmatrix}\) is the coordinate of the center of the ellipsoid.  
\(M=R^{T}BR=\begin{bmatrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}^{T}\begin{bmatrix}\lambda_{1}&0&0\\ 0&\lambda_{2}&0\\ 0&0&\lambda_{3}\end{bmatrix}\begin{bmatrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}=\begin{bmatrix}a_{1}&a_{4}/2&a_{5}/2\\ a_{4}/2&a_{2}&a_{6}/2\\ a_{5}/2&a_{6}/2&a_{3}\end{bmatrix}\)  
According to the ellipsoid model, the following ellipsoid parameters can be obtained:  
Sphere center coordinates:  
\(C=0.5\begin{bmatrix}a_{7}&a_{8}&a_{9}\end{bmatrix}M^{-1}\)  
\(X-\text{axislength}:x_{scale}=\sqrt{\frac{5S}{\lambda_{1}}}\)  
\(Y-\text{axislength}:y_{scale}=\sqrt{\frac{5S}{\lambda_{2}}}\)  
\(Z-\text{axislength}:z_{scale}=\sqrt{\frac{5S}{\lambda_{3}}}\)  
where: \(SS=CMC^{T}+1\)
# A Novel Real-Time Virtual 3D Object Composition Method for 360\({}^{\circ}\) Video
## 3 Proposed System and Experiment
### Pre-Visualization  
The purpose of the pre-visualization system is to confirm the composition result while recording the 360\({}^{\circ}\) video. For this purpose, we connect the 360\({}^{\circ}\) camera and stereo-vision ZED to a PC through a USB 3.0 port to send a video signal and trajectory data within the 3D program. In this study, we used the Unity game engine, which synchronizes the external parameters using the virtual camera from the ZED and generates a 360\({}^{\circ}\) virtual space for streaming the 360\({}^{\circ}\) camera video feed of the texture of a spherical object in real-time. The spherical object is set to 2.5 m in radius so as not to interfere with the placement of the virtual object. It also follows the virtual camera. It streams the video feed at 4K resolution at 60 fps, with a delay of 0.212 s. If the frame rate and time code do not match, the 3D composition will fail. To avoid this, the update function in Unity is set to 60 updates per second using FixedUpdate which has a static update rate, and a 0.212 s delay is given to the ZED data to match the time code.  
The pre-visualization system uses simple 3D objects such as a box, cylinder, and a human-shaped figure. The real-time lighting and texture composition mentioned in various studies can be applied to our proposed method, although the purpose of our system is to confirm the possibility of such composition, and not perfect its application. Therefore, our system does not consider real-time lighting and texture composition techniques. Figure 6 shows the pre-visualization system and a simple 3D object.
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 4 Simulation Results and Evaluation  
In this section, the evaluation of the localization improvements of the uplink-based positioning methods is performed.
# An Energy-Efficient Convolutional Neural Network Processor Architecture Based on a Systolic Array
## 2 System Architecture
### Analysis of the Low Power design  
Hardware overhead and data access are the main sources of power consumption, and these two aspects are discussed below. We discuss the advantages of systolic arrays over other architectures in these two aspects.  
1. [label=(1)]
2. Hardware overhead: According to the feature of dataflows, we can summarize acceleration into the following three categories: weight stationary (WS) [6; 10; 11; 14], row stationary (RS) [3] and output stationary (OS) [4; 5]. We take the convolution with image size = 5 and filter size = 3 as an example to evaluate the number of PEs required for each computing strategy. The WS architecture needs nine PEs due to each weight value being fixed on one PE. In the RS strategy, a 2D convolution is decomposed into many 1D convolution primitives, and each PE completes its corresponding single 1D convolution primitive, so it occupies nine PEs to meet the needs of computing. The OS architecture adopts the strategy that each PE completes the computation of a fixed output feature map value, which needs nine PEs if segmentation is not applied.  
Figure 7: Convolution in two processing sequences. In this example, image size = 8, filter size = 5, step = 1, mp_row0-mp_row3 denote the intermediate psum rows produced in process 1, and p_row0-p_row3 denote the psum rows of the results. The unit times in process 2 are indicated in light blue.  
The proposed strategy based on a systolic array is somewhat similar to the RS. The difference is that each PE completes all 1D convolution primitives of one weight row, so PEs are reused, and only three PEs are required to complete the 2D convolution of the example, and the power decreases accordingly with fewer PEs.
2. Data reuse: Supposing a PE array contains N systolic arrays composed of three PEs, since image rows are broadcast to all PEs simultaneously, image values can be reused 3 \(\times\) N times. In practical applications, it is usual to use register files (RF) to temporarily store the weight rows read from SRAM for each PE in the systolic array to realize local data reuse. The weight values in RF are updated after the convolution of the current channel is completed, so the weight values are reused by (image size--filter size)/step + 1 times. In other words, it is equal to the number of rows of the output feature map. The data reuse mentioned above can effectively reduce the number of memory access, thus achieving low power in computing.
## 5 Experiment and Results
### Compliance with NIST Standards and Guidelines [SP 800-57 Pt. 1 Rev. 4]  
Key management provides the foundation for the generation, storage, use, distribution, and destruction of keys [2]. For secured key generation and storage, we used an Intel SGX secured environment. Then, the key is moved to the USB-connected blinking device for encoding the key to a blinking LED stream. We used a handy blinking LED device attached to the sender computer via a USB connector. According to the device specifications, the blinking speed can be up to 1000 blinks per second. However, the collecting smartphone camera we used is limited to 30 blinks per second. Therefore, we have tuned the blinking device to 30 images per second, which seems to be enough for the purpose of our experiment to prove the feasibility of using basic wireless optical communications for key transmission, eliminating the need for a permanent, long, and costly setup. It is mobile and available everywhere anytime, requiring simple connections to operate. It is similar to the described OC system by [PERSON] in [8]. The experiment of a key distribution prototype complies with the NIST guidelines outlined in [9] Sections 8.1.5.2.1 and 2.  
According to these guidelines, during the distribution, the key must be protected throughout the entire distribution via a communication channel. Only approved key-wrapping or public key-transport schemes shall be used. For symmetric key-wrapping schemes, the distributed key is not disclosed or modified. For an asymmetric key-transport schemes, the private key and the distributed key are not disclosed or modified, and the parity of private-public keys is maintained. The keys are protected in accordance with the guidelines for protection requirements for cryptographic information. It should provide the following assurances: each entity in the distribution process knows the identifier associated with the other entity, the keys are correctly associated with the entities involved in the key-distribution process, and the keys have been received correctly. Our experiment complies with these guidelines, as it is practically embedded already as a standard in current implementations. In our experiment, we comply with these guidelines by encrypting the encoded key (blinking LED) by an asymmetric cryptography used just for key wrapping. For extra protection, we use VPN for encoded key distribution.
# Fabricating Sustainable All-Cellulose Composites
## 3 Dissolution of Cellulose
### Solvents for Cellulose  
Many efficient cellulose dissolution solvent systems are currently in use (Figure 4). The most used industrial scale solvent systems are the viscose and lyocell processes. Other processes such as DMAc/LiCl, cellulose carbamate, aqueous NaOH-with additives, ionic liquids and deep eutectic solvents (DES) are in different stages of technological maturity and constantly developed further.  
Some of the solvent systems developed especially in past have a negative impact on the environment [67]. Therefore, finding new ways to effectively dissolve cellulose and produce regenerated cellulose materials in an environmentally friendly way has recently been of major interest within research on cellulose refining technologies [76; 77; 78].  
Cellulose solvents can be divided into two categories: derivatising and non-derivatising solvents. Non-derivatising solvents physically dissolve cellulose by breaking hydrogen bonds. Therefore, these solvents only change intermolecular interactions. Non-derivatising solvents can, more specifically, be divided into aqueous and non-aqueous sub-groups. Derivatising solvents convert cellulose polymers into soluble intermediates by derivatizing cellulose's hydroxyl groups. These solvents can form hydrolytically unstable, organo-soluble cellulose intermediates, such as ethers, esters, or acetal derivatives [79].  
Figure 3: Schematic presentation of parallel-packed cellulose I converting to antiparallel-packed cellulose II during mercerization. Reaction scheme adapted from [75].
# Integration of High Voltage AC/DC Grids into Modern Power Systems
## 1 Introduction  
Electric power transmission relies on AC and DC grids. The large integration of the conventional and non-conventional energy sources and power converters into power grids has resulted in a demand for High Voltage (HV), Extra-High Voltage (EHV), and Ultra-High Voltage (UHV) AC/DC transmission grids in modern power systems [1; 2; 3]. To ensure the security, adequacy, and reliable operation of power systems, practical aspects of interconnecting HV, EHV, and UHV AC/DC grids into the electric power systems, along with their economic and environmental impacts should be considered. The stability analysis for planning and operation of HV, EHV, and UHV AC/DC grids in power systems is regarded as the other key issue in modern power systems [4; 5]. Moreover, interactions between power converters and the other power electronics devices (e.g., FACTS devices) installed on the network are the other aspects of power systems that must be addressed [6]. This Special Issue aims to investigate the integration of HV, EHV, and UHV AC/DC grids into modern power systems by analyzing their control, operation, protection, dynamics, planning, reliability, and security along with considering power quality improvement, market operations, power conversion, cybersecurity, supervisory and monitoring, diagnostics, and prognostics systems.
# Intelligent Measurement of Coal Moisture Based on Microwave Spectrum via Distance-Weighted kNN
###### Abstract  
Realizing the rapid measurement of coal moisture content (MC) is of great significance. However, existing measurement methods are time-consuming and damage the original properties of the samples. To address these concerns, a coal MC intelligent measurement system is designed in this study that integrates microwave spectrum analysis and the distance-weighted \(k\)-nearest neighbor (DW-kNN) algorithm to realize rapid and non-destructive measurement of coal MC. Specifically, the measurement system is built using portable microwave analysis equipment, which can efficiently collect the microwave signals of coal. To improve the cleanliness of modeling data, an iterative clipping method based on Mahalanobis distance (MD-ICM) is used to detect and eliminate outliers. Based on multiple microwave frequency bands, various machine learning methods are evaluated, and it is found that coal MC measurement using broad frequency signals of 8.05-12.01 GHz yields the best results. Experiments are also carried out on coals from different regions to examine the regional robustness of the proposed method. The results of on-site testing with Z7 additional samples show that the method based on the combination of microwave spectrum analysis and DW-kNN has a potential application prospect in the rapid measurement of coal MC.  
Keywords:coal moisture content; microwave spectrum; distance-weighted \(k\)-nearest neighbor; non-destructive measurement +
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