esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Microtops II(r) Sunphotometer and Ozonometer Measurement of Atmospheric Conditions  
Microtops II(r) Sunphotometer and Ozonometer were used to measure atmospheric information. Both have been widely used in vicarious calibration of satellite imaging sensors [3; 4; 41; 44]. The measurement factors, including AOD, water vapor, and ozone, were used to run the 6S model as input parameters (Figure 8a). These instruments have the advantage of being lightweight (600 g), small in size (10 \(\times\) 20 \(\times\) 4.3 cm), easy to operate, and relatively low in cost. The Sunphotometer has two visible and three near-infrared (NIR) channels (440, 675, 870, 936, and 1020 nm) to measure AOD and water vapor. The Ozonometer has three ultraviolet B (UVB) channels (305, 312, and 320 nm) to measure total ozone. The instruments were fixed on a tripod to measure the atmospheric information, which  
Figure 6: Hyperspectral reflectance curves corresponding to the KOMPSAT-3 and -3A spectral range (350–900 nm) measured by the dual ASD Fieldspec® 3 radiometer system. On 14 May, 27 July, 19 and 20 September in 2017, respectively (**a–d**).  
Figure 7: Relative spectral response function (SRF) for the blue, green, red, and near-infrared (NIR) spectral ranges of KOMPSAT-3 and -3A.  
typically results in a significant standard deviation of approximately 0.23% according to Microtops II(r) user guide (version 2.43); however, this can change according to user skill or atmospheric conditions.  
The 6S model used to determine the TOA radiances for the KOMPSAT-3 and -3A requires the AOD to be in the 550 nm channel [11; 21]; however, this does not exist in the Sunphotometer. In this study, the Angstrom exponent [45] was used to estimate the AOD in the 550 nm channel. The exponent is an empirical function indicating that the dependence of AOD on the spectral wavelength is inversely related [17; 44; 46]. Therefore, when the AODs for the two wavelengths are obtained, the AOD between both wavelengths can be estimated through the following empirical Angstrom equation:  
\[\tau_{\lambda}=\tau_{\lambda_{0}}\cdot(\lambda/\lambda_{0})^{-\alpha}, \tag{3}\]  
where \(\tau_{\lambda}\) is the AOD at wavelength \(\lambda\), \(\tau_{\lambda_{0}}\) is the AOD at reference wavelength \(\lambda_{0}\), and \(\alpha\) is the Angstrom parameter. The following equation calculates this parameter:  
\[\alpha=-(\ln\frac{\tau_{1}}{\tau_{2}})/(\ln\frac{\lambda_{1}}{\lambda_{2}}), \tag{4}\]  
where \(\tau_{1}\) and \(\tau_{2}\) are the AOD of two different spectral wavelengths \(\lambda_{1}\) and \(\lambda_{2}\), respectively. In this study, the AODs at 440 and 870 nm were used to determine the Angstrom parameter (Figure 8b). Table 3 shows the measured values of water vapor (g cm\({}^{-2}\)), total ozone (cm-atm), and AOD in 550 nm used as the atmospheric input parameters of the 6S model.
## 2 Related Work
### PLC Channel Modeling Techniques  
Physical impacts on power line networks such as cable losses and multipath signal propagation are modeled analytically as complex transfer functions in [19]. A small set of parameters are used to formulate a multipath model that can derive the complex frequency response of the PLC channel. The approach is convenient for scenarios where exact network parameters are unknown. This model assumes the propagating signal as an aggregate of all possible forward components in different paths to the destination [20; 21; 22; 23; 24]. Transfer functions are evaluated based on the measured values. However, the quality of received signals deteriorate due to reflection and can be expressed as [25]:  
\[H(f)=\sum_{i=1}^{N}g_{i}A(f,d_{i})e^{-j2\pi f\tau_{i}} \tag{1}\]  
Here \(g_{i}\) represents the reflection attenuation while \(A(f,d_{i})\) denotes the line attenuation. \(N\) and \(e^{-j2\pi f\tau_{i}}\) represents the number of considered delays due to distance. Impedance mismatch occurrence in the branch lines may result in the traveling waves and their reflections. The traveling wave reflection is given as,  
\[w(z)=W_{1}e^{-\gamma z}+W_{2}e^{\gamma z} \tag{2}\]  
where \(W_{1}\) and \(W_{2}\) represent the forward and reflected waves. The reflection coefficient is defined as  
\[r=\frac{W_{2}e^{\gamma z}}{W_{1}e^{-\gamma z}}=\frac{\frac{w_{k}-Z_{0}k}{2}e^ {-\gamma(l-l)}}{\frac{w_{k}+Z_{0}k}{2}e^{\gamma(l-l)}}=\frac{Z_{k}-Z_{0}}{Z_{ k}+Z_{0}} \tag{3}\]  
where \(Z_{k}\) and \(Z_{0}\) are the load and characteristic impedance, respectively. The wave transmitted part is shown by Equation (4) [26].  
\[T=1-R \tag{4}\]  
\(e^{-\gamma l_{i}}\) represents the attenuation of the line. As the parameters for the power line are not known, then \(\gamma\) can be approximated by using the following equation [26; 27].  
\[\mu(f)=\mu_{0}+\mu_{1}f^{k} \tag{5}\]  
whereas the line attenuation can be expressed as  
\[A(f,l_{i})=e^{-\mu(f)l_{i}}=e^{-(\mu_{0}+\mu_{1}f^{k})l_{i}} \tag{6}\]  
where the constants \(k\), \(\mu_{0}\), and \(\mu_{1}\) can be estimated by the measurement data.  
In the frequency domain, delays in the waves can be represented as \(e^{-2\pi f/\tau_{i}}\). The delay \(\tau_{i}\) can be calculated by the following equation [27].  
\[\tau_{i}=\frac{l_{i}\sqrt{\epsilon_{r}}}{c_{0}} \tag{7}\]  
where \(c_{0}\), \(l_{i}\), and \(\epsilon_{r}\) represent the speed of light, length of the route, and relative permittivity of the conductor, respectively.  
In [25], the bottom up statistical approach is used for PLC channel modeling. Random topology technique is employed to improve the computational efficiencies for producing the transfer function. In the first step, the authors construct a network topology for European homes by employing a statistical approach. For any topology, methodology is proposed in the second step to determine the transfer function between any pair of outlets. Furthermore, a simulator was developed to observe the characteristics of the power line communication channel to evaluate the necessary parameters [26; 27].
## 4 Discussions
### Limitations and Future Works
#### 4.5.1 Energy Consumption in Blockchain-Based Storage Systems  
While blockchain technology offers advantages in decentralized data management and security, it also imposes significant energy costs. Proof-of-Work (PoW)-based blockchains are known for their high energy consumption due to mining processes that require substantial computational power [45; 88]. In networks such as Bitcoin and Ethereum, mining activities consume large amounts of electricity, increasing their carbon footprint. In contrast, alternative consensus mechanisms such as Proof-of-Stake (PoS), Delegated Proof-of-Stake (DPoS), and Proof-of-Authority (PoA) improve blockchain sustainability by significantly reducing energy consumption [92; 101].  
Comparative Energy Consumption of Blockchain Consensus Mechanisms: Table 14 presents the energy consumption levels of different consensus mechanisms and their advantages in terms of sustainability. This comparison demonstrates that PoS and PoA-based blockchain solutions enhance energy efficiency, contributing to more sustainable blockchain storage systems.  
* _Sustainability Strategies for Blockchain-Based Storage:_ Several innovative strategies have been proposed to improve the sustainability of blockchain-based data storage solutions:  
1. Green Blockchain Networks: Developing carbon-neutral blockchain projects and promoting the use of sustainable energy sources [106];
2. Layer-2 Solutions and Sharding: Reducing transaction load and minimizing energy consumption through Layer-2 scaling solutions and sharding mechanisms [94; 108];These strategies aim to enhance the long-term sustainability of blockchain-based data storage systems while minimizing their environmental footprint.
# Assessment of Laser Galvanometer Scanning System Accuracy Using Ball-Bar Standard
## 2 Materials and Methods
### Determination of Reference Standard Placement in the Test Space  
The ball-bar standard placement in the workspace of the laser projector was designed considering the ISO 10360 group recommendations. Two axial settings were defined: X and Z and two diagonal two-dimensional settings: XZ1 and XZ2. The layout of the standard is shown in Figure 4.
## 3 Results
### Parts-of-Speech Tagging  
Using a corpus of one-page long Amharic text, [PERSON] [47] has developed a part of speech tagger using the stochastic Hidden Markov Model approach that can extract major word classes noun, verb, adjective, auxiliary), but fails to determine subcategory constraints such as number, gender, polarity, tense case, definiteness, and unable to learn from new instances. [PERSON] et al. [48] have developed an Amharic POS tagger using three different tag-sets. Similarly, [PERSON] and [PERSON] [49] have prepared 210,000 manually annotated tagged tokens and developed a factored language model using SVM and HMM. [PERSON] et al. [50] have employed a memory-based tagger, which is appropriate for low-resourced languages like Amharic.  
Despite the several works on Amharic POS tagging, there is, as far as we know, no publicly available POS tagger model and benchmark dataset that can be used for downstream applications.  
For our Amharic POS tagging experimentation, we have trained different POS tagging models using the dataset compiled by [PERSON] and [PERSON] [19]. The dataset is comprehensive in the sense that texts from the different genres are incorporated. The dataset from the Ethiopian Language Research Center (ELRC) of Addis Ababa University [51] consists of news articles covering different topics such as sport, economics, politics, etc. amounting to a total of 210,000 words. The ELRC dataset supports 11 basic tags. The work by [PERSON] and [PERSON] [19] extends the ELRC dataset by annotating texts from Quranic and Biblical texts (called ELRCQB). In total, 39,000 sentences are annotated for Amharic POS tags using 62 tags. The dataset is split into training, development and testing instances. The development set is used to optimize parameters during training.  
Table 6 shows the experimental results using different models. We built two types of models, the Conditional Random Field (CRF) tagging model and the sequence classifier model using the FLAIR deep learning framework. For the CRF model, the \"sci-kit-learn-crfsuite\" python wrapper of the original CRF implementation is employed (Model **CRF-Suite** in Table 6). The features for the CRF model include (1) the word, previous word, and the next word, (2) prefixes and suffixes with lengths ranging from 1 to 3, and (3) checking if the word is numeric or not. The remaining models are built using the FLAIR sequence classifier based on the different semantic models using the parameters shown in Table 2 with a GPU:GeForce GTX 1080 11 GB server.
# Best-Suited Communication Technology for Maritime Signaling Facilities: A Literature Review
## 3 Results and Discussion
### Range of Communication Technologies  
The range of communication technologies used for transmitting signals from maritime signaling facilities is an important technical parameter, especially considering that these facilities are often located in remote marine locations. This means that maritime signaling facilities are frequently far from the coastline and, consequently, from terrestrial transmitter base stations. Moreover, due to the geographical configuration and navigational requirements for marking hazardous maritime locations, maritime signaling facilities are also distant. The range of communication technologies, i.e., the strength of the transmitted signal, is proportional to the level of the received signal or its field [15; 30]. The concept of range can also be associated with coverage and connectivity [63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75]. All these factors pose challenges in establishing communication with such objects, and Table 3 presents the analysis of the range for each communication technology with corresponding paper references. Papers in which a particular communication technology was used, but the achieved range was not specified (n/a), have also been recorded.  
Table 4 shows that the highest achieved range is 120 km using VHF communication technology, as described by [PERSON] et al., 2022 [39]. The smallest range was achieved using ZigBee communication technology (0.43 km), as defined by [PERSON] et al., 2016 [62]. The ranges achieved for 3G and GSM communication technologies were not definitively specified in the analyzed papers, representing an important limitation in the literature. It is crucial to emphasize that, out of the 36 analyzed papers, sixteen papers (44.44% of the total) did not provide information about the range of communication technologies used. This lack of range data poses a significant drawback to the analysis, as access to this information would contribute to more comprehensive and accurate final analysis results. Based on the analysis conducted in the context of the achieved range, it can be concluded that the lack of a standardized approach is a common shortcoming of the available papers. Standardization is important in ensuring consistency and comparability across different studies or experiments. By utilizing a standardized approach for setting up the network and transmitting signals uniformly, the reliability of the results can be enhanced.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**AIS**} & \multicolumn{2}{c}{**LoRa WAN**} \\ \hline
**Range (km)** & **Ref.** & **Range (km)** & **Ref.** \\ \hline n/a & [36] & 8 & [40] \\ \hline n/a & [23] & 15 & [56] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the paper analysis in the context.  
Figure 5: The key technical criteria for establishing communication with maritime signaling facilities (Source: authors).
## 3 Validation
### Workspace Calculation  
To validate the manipulator's workspace, a mathematical model was used to calculate the manipulator's workspace without the effects of gravity, arm material, or geometry. Figure 6 depicts the manipulator's arm under the influence of the retracted tendon. For simplicity, the depiction shows only one linear actuator. The motion effect of the other two actuators is shown by the thick red arrows.  
In this model, \(L\) represents the length of the bending segment, \(ab\) represents the tendon's length between the two ends of the bending segment, and \(cd\) represent the tip's displacements in \(Y\) axis direction. Initially, at rest position, the bending segment's length \(L\) is calculated as:  
\[L=ac+cd \tag{5}\]  
When the tendon retracts and the manipulator bends, an angle \(\varphi\) is generated between the tendon and the vertical line representing the manipulator's body at rest, thus:  
\[ab=L\,\cos\varphi \tag{6}\]  
Figure 5: Manipulator concept based on the methodology and selected configuration. The tendon pulling the manipulator’s tip is routed through the ACTR towards the relative motor at the base.  
Figure 6: A sketch depicting the setup of the manipulator with a single external actuator. The ACTR can be relocated anywhere along the manipulator’s body.  
\[bc=ab\ \sin\varphi \tag{8}\]  
\[cd=L-ac \tag{9}\]  
Equation (9) provides the \(Y\) axis' (vertical) displacement of the manipulator's tip. In Figure 6, angle \(\theta\) represents the manipulator's rotation around the \(Y\) axis; hence, _de_ and _ef_ provide the \(X\) and \(Z\) axes' displacements respectively.  
\[de=bc\cos\theta \tag{10}\]  
\[ef=bc\sin\theta \tag{11}\]  
The volumetric workspace of the manipulator can be obtained by using Equations (9) and (10), and by stepping through the values of \(\varphi\) and \(\theta\), as illustrated in Figure 7.
# Decentralized Identity Authentication with Auditability and Privacy
## 3 Overview
### System and Threat Model  
Our system consists of three types of entities: the blockchain, service providers, and clients.  
* **The blockchain** is a blockchain system, such as Hyperledger Fabric or Ethereum, in which users can register their _dids_ and retrieve valid proof that a transaction has been added to the blockchain.
* **The client** represents the user. It stores secret data, such as private keys associated with the user's _did_, and it runs the DID protocol on behalf of the user.
* **The service provider (or service)** provides resources that the user wants to access. It runs a DID authentication protocol to authenticate the user before giving them access to a resource.  
We define our threat model with respect to each entity in the system as follows. The blockchain is trusted to protect the integrity of the stored data. It is also highly available, i.e., not subject to denial-of-service attacks. The client is trusted to follow the protocol, but the adversary can steal its credentials (corresponding to the client's identity). The service provider can be compromised. In this case, it can collude with an adversary that has stolen a user's credentials to prevent the user from detecting the credential misuse. When not compromised, the service provider is semi-honest; that is, it follows the authentication protocol correctly, but it wants to learn information other than what the authentication protocol reveals. In particular, it may want to learn which client authenticated with which (other) service providers, since knowledge of clients' behavior may give a service provider an important competitive advantage.
# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 2 Related Work
### Super-Resolution  
The HDR image fused by multi-exposure images is composed of LDR images. LDR images contribute pixel values and then are fused. Fusion inevitably causes distortion of pixel values in the resulting image. In order to restore the clarity of the image, many scholars have studied how to improve the resolution of the image from a single image. In 2015, [PERSON] et al. proposed a method to learn an end-to-end mapping between different resolution images and extend the network to process three color channels simultaneously, improvingreconstruction quality [22]. In 2016, [PERSON] et al. used a deep convolutional network to utilize information over a big image area by cascading filters multiple times to achieve a high-precision, single-image super-resolution method [23]. In 2016, [PERSON] et al. proposed a CNN architecture that introduced an efficient sub-pixel convolution layer to learn a series of upsampling filters to replace the bicubic filter and upgrade the low-resolution input image to the high-resolution space [24]. In 2018, [PERSON] et al. provided the enhanced SRGAN (ESRGAN) to obtain better visual quality and more realistic and natural textures by improving the network architecture, adversarial loss, and perceptual loss [25]. In 2018, [PERSON] et al. extracted local features through densely connected convolutional layers and then used global feature fusion to learn global hierarchical features to achieve image super-resolution [26]. In 2019, [PERSON] et al. proposed an embedded block residual network (EBRN) in which different modules recover texture in different frequencies to achieve image super-resolution [27]. In 2021, [PERSON] et al. proposed Real-ESRGAN, which takes into account the common ringing and overshoot artifacts, introduces a modeling process from ESRGAN to simulate complex degradation, and extends it to practical restoration applications [28]. In 2021, [PERSON] proposed a hardware limitation-aware real-time super-resolution network (XLSR), using Clipped ReLU in the last layer of the network, maintaining the image quality of the reconstructed image super-resolution with less running time [29].  
We have briefly introduced the methods proposed by scholars and shortcomings when overcoming artifacts and loss of details when reconstructing HDR images. We will step by step introduce the MDHDR solution and the evaluation method used in this paper in the methods. In the result, we compare the MDHDR solution with various state-of-the-art models for reconstructing HDR images and show experimental results on processing artifacts, detail loss, and image blur. In the discussion, we explain why our model is outstanding according to the experimental results. Finally, we show the contribution of the model proposed in this paper to the reconstruction of HDR images in the conclusion.
## 3 Analytical Solutions for Beams with One, Two, and Three Equal Spans
### Influence of Additional Mass over Length \(\lambda\)  
The frequency \(\varpi_{r}\) for this case is determined from Equation (17) in accordance with Relations (19)-(21) as follows:  
\[G_{r}=\frac{\mu_{q}}{M_{r}}\int_{x_{0}-\frac{1}{2}}^{x_{0}+\frac{\lambda}{2}}V _{r}^{2}(x)dx=\frac{\mu_{q}}{M_{r}}\int_{x_{0}-\frac{\lambda}{2}}^{x_{0}+\frac {\lambda}{2}}sin^{2}\Big{(}\frac{r\pi x}{l}\Big{)}dx=\frac{\mu_{q}}{M_{r}} \frac{l}{2}.\bigg{(}\frac{\lambda}{2}-\frac{1}{r\pi}sin\frac{r\pi\lambda}{l} cos\frac{2r\pi x_{0}}{l}\bigg{)}, \tag{22}\]  
\[\overline{\varpi}_{r}=\frac{\omega_{r}}{\sqrt{1+G_{r}}}=\omega_{r}\bigg{[}1+ \frac{\mu_{q}}{M_{r}}\frac{l}{2}.\bigg{(}\frac{\lambda}{2}-\frac{1}{r\pi}sin \frac{r\pi\lambda}{l}cos\frac{2r\pi x_{0}}{l}\bigg{)}\bigg{]}^{-\frac{1}{2}}, \tag{23}\]  
\[\overline{\omega}_{r}=\omega_{r}\bigg{[}1+\frac{\mu_{q}}{M_{r}}\frac{l}{2}. \Phi_{a}(\lambda,x_{0})\bigg{]}^{-\frac{1}{2}}, \tag{24}\]  
where:  
\[\Phi_{a}(\lambda,x_{0})=\bigg{(}\frac{\lambda}{l}-\frac{1}{r\pi}sin\frac{r\pi \lambda}{l}cos\frac{2r\pi x_{0}}{l}\bigg{)}. \tag{25}\]  
Equation (24) is applicable to beams with one, two, or three equal spans. For such beams, the generalized mass \(M_{r}\) should be determined as specified in Equation (21). The specific expressions for the frequencies of systems with these span configurations are provided in Table 1.  
Changes in fundamental frequency due to additional mass \(\mu_{q}\) along length \(\lambda=0.2l\), obtained using the expressions in Table 1 and depicted through the ratio of circular frequencies, are presented in Figure 3 for bridges with one, two, and three equal spans. One may conclude that the additional mass in the mid-span has a larger effect on the natural frequency. Increasing the ratio of additional mass to bridge self-mass ampl  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Dynamic Model of the Bridge**} & \multicolumn{1}{c}{**Circular Frequency**} \\ \hline \hline  & & \(\overline{\omega}_{r}=\omega_{r}\Big{[}1+\frac{\mu_{q}}{\mu}\cdot\Phi_{a}( \lambda,x_{0})\Big{]}^{-\frac{1}{2}}\) \\  & & \(\overline{\omega}_{r}=\omega_{r}\Big{[}1+\frac{\mu_{q}}{\mu}\cdot\Phi_{a}( \lambda,x_{0})\Big{]}^{-\frac{1}{2}}\) \\  & & \(\overline{\omega}_{r}=\omega_{r}\Big{[}1+\frac{\mu_{q}}{2\mu}\cdot\Phi_{a}( \lambda,x_{0})\Big{]}^{-\frac{1}{2}}\) \\  & & \(\overline{\omega}_{r}=\omega_{r}\Big{[}1+\frac{\mu_{q}}{3\mu}\cdot\Phi_{a}( \lambda,x_{0})\Big{]}^{-\frac{1}{2}}\) \\  & & \(\overline{\omega}_{r}=\omega_{r}\Big{[}1+\frac{\mu_{q}}{3\mu}\cdot\Phi_{a}( \lambda,x_{0})\Big{]}^{-\frac{1}{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frequency due to the influence of additional mass \(\mu_{q}\) over length \(\lambda\).