esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The increasing use of powerful electronic devices and the availability of networks that provide ubiquitous and high-performance connectivity allow applications to transfer huge volumes of data in brief periods of time. Several transactions and secure connections are performed using reliable schemes of authentication and privacy based on complicated mathematical problems, which have remained unsolved up to now. The starting point of secure communications requires previous secret sharing or authentication, using for this purpose, public key cryptography (PKC). Though several cryptographic algorithms exist, only a few protocols are used in real-world applications due to their proven resistance and easy implementation: the well known procedure due to Rivest-Shamir-Adleman (RSA) [1], based on the factorization problem, and the Digital Signature Standard (DSS) [2] based on the discrete logarithm problem on finite groups. These algorithms are the base of several digital signature techniques, and authentication and identification protocols, which are commonly used for e-commerce, banking transactions, and government services, among others, and their applications have been increasing with the introduction of multifactor authentication and cryptocurrencies.  
The rapid development of cryptanalysis techniques and quantum computers endanger these security measures, with the most alarming threat being the existence of an algorithm that can solve the factorization problem efficiently, provided a quantum computer can ever be built [3]. These issues make clear that new techniques must be studied and developed in preparation for possible realizations of these threats. Recently, zero-knowledge proofs (ZKP) have been considered as an alternative to design authentication and identification protocols. Protocols based on ZKP are built upon problemswhich have not been solved yet by quantum computer algorithms; many of them originated from graph theory and NP-complete problems.  
In addition to authentication and identification services, novel technologies (e.g., blockchain and cryptocoins [4]), which require anonymity services, have demonstrated in ZKP systems, a reliable technique to prove knowledge of specific data without disclosing details; say, whether an account has enough credit to buy an item. Current uses have also been reported in the direction of authentication in cloud storage [5] and Internet of Things (IoT) [6], encouraging the development of these sorts of protocols.  
The method defined in this work produces key pairs from an associated isomorphism between a pair of graphs. The public key will be given by a system of equations. The private key will consist of a solution to the system. It will be shown that finding this solution is at least as difficult as finding an isomorphism between the associated graphs. At present, the fastest algorithm for solving the graph isomorphism (GI) problem runs in quasi-polynomial time [7]. However, an authentic prover will be ready to provide a solution efficiently.
# A Method to Accurately Estimate Fish Abundance in Offshore Cages
## 2 Proposed Fish Counting Algorithm
### Algorithm  
This section explains how the above theory is used in the algorithm. For each echo ping sent, a typical echosounder provides \(s_{v}\) data measured at each water column (layer) of \(z\) m height between the sampled range interval \(R1\) m and \(R2\) m measured from the top, where \(R1\) is closer to the surface. The algorithm assumes that \(R2\) is larger than the cage bottom. The algorithm takes \(s_{v}\) data as an input. Additionally, the algorithm requires length frequency key and cage backscattering strength as inputs, which are explained below.
## 3 Results
### Experimental Results and Analysis of Variance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Indicators**} & **Sources of** & **Sum of** & **Degrees of** & **Mean Square** & **F-Measure** & \(p\)**-Value** & **Significant** \\  & **Variation** & **Squares** & **Freedom** & **Mean Square** & **F-Measure** & \(p\)**-Value** & **Significant** \\ \hline \multirow{4}{*}{Dtt} & Model & 277.86 & 9 & 30.87 & 84.95 & \textless{}0.0001 & Yes \\  & Residuals & 17.81 & 49 & 0.3634 & & & \\  & Misfitting term & 14.50 & 33 & 0.4395 & 2.13 & 0.0551 & NO \\  & Error & 3.30 & 16 & 0.2065 & & \\ \hline \multirow{4}{*}{AT} & Model & 3.753 \(\times\) 10\({}^{5}\) & 12 & 31,273.03 & 43.49 & \textless{}0.0001 & Yes \\  & Residuals & 33,074.27 & 46 & 719.01 & & & \\  & Misfitting term & 26,382.28 & 30 & 879.41 & 2.10 & 0.0596 & NO \\  & Error & 6691.98 & 16 & 418.25 & & \\ \hline \multirow{4}{*}{PC} & Model & 0.7948 & 15 & 0.0530 & 24.58 & \textless{}0.0001 & Yes \\  & Residuals & 0.0927 & 43 & 0.0022 & & \\  & Misfitting term & 0.0709 & 27 & 0.0026 & & \\  & Error & 0.0218 & 16 & 0.0014 & & \\ \hline \multirow{4}{*}{FC} & Model & 3.02 & 16 & 0.1885 & 22.95 & \textless{}0.0001 & Yes \\  & Residuals & 0.3450 & 42 & 0.0082 & & \\  & Misfitting term & 0.2160 & 26 & 0.0083 & 1.03 & 0.8773 & NO \\  & Error & 0.1290 & 16 & 0.0081 & & \\ \hline \multirow{4}{*}{AC} & Model & 659.89 & 20 & 32.99 & 17.64 & \textless{}0.0001 & Yes \\  & Residuals & 71.09 & 38 & 1.87 & & \\  & Misfitting term & 44.89 & 22 & 2.04 & 1.25 & 0.3301 & NO \\  & Error & 26.20 & 16 & 1.64 & & \\ \hline \multirow{4}{*}{APC} & Model & 15,217.98 & 15 & 1014.53 & 55.19 & \textless{}0.0001 & Yes \\  & Residuals & 790.48 & 43 & 18.38 & & \\  & Misfitting term & 557.73 & 27 & 20.66 & 1.42 & 0.2341 & NO \\  & Error & 232.74 & 16 & 14.55 & & \\ \hline \hline \end{tabular} Note: total drying time, Dtt: effective accumulated temperature, AT. The protein content of undried rice was 7.58 g/100 g; fat content was 1.9 g/100 g; amylose AC was 6.62 g/100 g; amyloseptic ATC was 118.7 g/100 g.  
\end{table} 
Table 4: Results of variance analysis of regression model.
# Novel Processing Methods of Low-Clinker Multi-Component Cementitious Materials--A Review
## 4 Microwave Treatment (MW-T) Methodology  
Microwave treatment (MW-T) is the term for non-ionizing electromagnetic radiation with wavelengths between 1 mm and 1 m and spatial frequencies between 300 MHz (100 cm) and 300 GHz (0.1 cm) [101; 102; 103; 104]. By rapidly attenuating MW-T through the intense vibration of polar molecules, it can increase the temperature of the material and accelerate chemical reactions. The entire specimen becomes heated as a consequence of the movement and friction this causes between molecules [104; 105; 106; 107; 108; 109]. The frequencies of 915 MHz and 2450 MHz are currently mostly used on cement and concrete for industrial and scientific objectives [110]. Microwaves interact with matter more sensitively than ultrasonic and electromagnetism, which can penetrate dielectric materials and convert electromagnetic energy into heat energy without needing to first warm up a heating cavity. Microwave-absorbing materials generate volumetric heat as a result of the interaction between propagating waves [111; 112; 113]. Short reaction times, easy control, improved reaction kinetics, reduced heat loss, clean heating processes, good energy efficiency, and environmental protection are only a few advantages of MW-T over conventional heating techniques [114; 115; 48; 109]. These heating qualities make it possible to cure concrete in an eco-friendly way.  
Precast concrete, in particular, has undergone heat curing to expedite the development of strength [48; 116]. The skin effect, which is produced by conventional heating methods and is more efficient for surface heating [101], is the fast evaporation of water from treated materials' exterior surfaces. It is difficult to accomplish consistent heating because of the steep temperature gradient, and heated materials lose a large amount of energy through heat conduction and convection.  
In contrast, MW-T may promote a temperature rise as a consequence of the polar molecules' ability to absorb heat and form localized heating spots that are independent of position, which is beneficial to generating uniform volumetric heating [104; 105; 106; 107; 117]. The utilization of MW-T is based on the internal energy loss associated with the stimulation of molecular ions and dipoles under electromagnetic fields [118]. The specimens' microwave heating characteristics can be varied by the secondary material's propensity to absorb, transmit, or reflect MW-T [119]. It has been proven that microwave-assisted heating can cut processing times and lower the expenses of additive manufacturing [104; 105; 106; 107; 108; 120; 121]. Combining these approaches for the best materials processing could be plausible (Figure 10), and it would follow the properties of power ultrasound, microwave, and magnetic processing stated in the preceding chapters.
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 2 Related Work  
### RGBT Tracking  
Siamese network and MDNet network architectures are two mainstream frameworks for RGBT tracking algorithms, especially the former. [PERSON] et al. [11] suggested a fusion tracking method based on the MDNet [12] framework in 2018, and, subsequently, many RGBT trackers based on the MDNet framework appeared. [PERSON] et al. [1] proposed the dense feature aggregation and pruning network (DAPNet), proposing a recursive strategy to densely aggregate the hierarchical features of the network densely and by channel pruning to reduce redundant features and remove noise. [PERSON] et al. [13] proposed a multi-adapter convolutional network (MANet) to fully exploit the potential value of mode-sharing, mode-specific, and instance-aware information. [PERSON] et al. [2] proposed the cross-layer bilinear pooling network (CBPNet), which aggregates features at different levels by performing bilinear pooling operations on any two layers through cross-products, and uses a quality-aware network to fuse the bilinear pooled features of different modalities. [PERSON] et al. [14] proposed the Adaptive Learning Attribute Representation Network (ADRNet), where the core of the algorithm is adaptive attribute representation and adaptive modality fusion, by learning how to distinguish the target from the background based on the target attribute representation.  
The first Siamese framework to be applied to RGBT tracking is SiamFT [15], which considers SiamFC [16] as the baseline and uses a doubled Siamese network, visible and infrared network, which can process visual and infrared images separately to meet the real-time requirements. DSiamMFT [17] further employs dynamic online learning transformation strategies and multi-level semantic features based on SiamFT. SiamIVFN [18] utilizes complementary feature fusion networks and contribution aggregation networks to handle not only the misalignment of image pairs but also the fusion of features based on the contributions of the two modalities, giving the network high accuracy and a speed of 147.6 FPS, making it the fastest deep learning-based RGBT tracker available. It is worth mentioning that algorithms based on the Siamese framework rely on a large amount of data to train the network. However, since the existing RGBT dataset does not have such a large amount of data, the current algorithms of the Siamese framework usually use a large-scale RGB dataset and train the network with the grayscale maps generated from RGB images. However, the imaging principle of thermal infrared images differs from that of visible images, resulting in the generated grayscale maps lacking reliability to some extent. In this paper, we conducted a study using the MDNet framework.
## 4 Discussion  
The performance of our hybrid algorithm was rigorously evaluated. A key strength of the deep learning stage is its ability to achieve retrieval accuracy for CTH and CWP from AHI infrared observations. The performance of the two cloud products is comparable to the Terra MODIS C6.1 MOD06 products, which benefit from both visible and infrared channels. This demonstrates the model's power in extracting cloud properties even in the absence of solar reflectance data, a capability that is crucial for diurnal estimation. The subsequent physical stage, which uses an ECWC LUT constrained by both cloud properties and environmental variables, allows for the rapid and physically consistent inference of CGT and CBH under real atmospheric conditions.  
Validation against spaceborne active sensors confirms the effectiveness of the algorithm. In daytime, our CBH retrievals show good agreement with CloudSat-CALIPSO measurements, achieving a high correlation coefficient of 0.94 and a small bias of \(-0.14\pm 1.26\) km. The algorithm's capability to capture cloud vertical structures throughout the diurnal cycle was further validated using EarthCARE-CPR measurements, yielding a daytime bias of \(-0.21\pm 1.66\) km and a nighttime bias of \(-0.35\pm 1.84\) km. Furthermore, comparisons with four ground-based MMCRs highlight the algorithm's capability to estimate CBH continuously and effectively. This capability is vital for studying the diurnal evolution of cloud structures.  
While the hybrid algorithm demonstrates promising results, it is crucial to acknowledge its limitations and potential error sources. A primary source of uncertainty originates in the initial CNN stage, stemming from potential inaccuracies in the MODIS training labels and the inherent spatiotemporal mismatches between the geostationary AHI and polar-orbiting MODIS. The subsequent physical calculation introduces further error, as our ECWC LUT represents a climatological average that may not perfectly represent the microphysics of a specific cloud scene. Critically, this two-stage process is subject to significant error propagation, where inaccuracies in the initial CTH retrieval are directly transferred and can be amplified in the final CBH estimation.
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 5 Data Sets
### Simulated Data  
Due to the small amount of data provided from the robot's test runs and the lack of reference robot routes in these data that would allow for the evaluation of the quality of the proposed algorithm's operation, a measurement data simulator was developed. The simulator generates synthetic sensor outputs based on a sequence of movement instructions and the characteristics of the robot used in tests. These characteristics include the dimensions of the robot's wheels, its speed, encoder resolution, and sampling rate. Movement commands include distance, both left and right encoders readings updated incrementally based on distance traveled, and speed and encoders characteristics. Rotation commands include desired angle, and the rotation is simulated by adjusting one encoder upwards and second downwards--left or right--based on desired direction. At the same time, the IMU angle is updated. The X and Y position of the robot stem from simulated odometry and IMU angle. The block diagram is presented in Figure 7. The simulator allows for added slippages as a post-processing step.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Stage** & **No. of Runs** & **Description** \\ \hline
1 & 11 & Simple runs (driving in a straight line vertically, horizontally, diagonally, rotation). \\
2 & 3 & 2 vertical runs up and down, 1 horizontal run. At this stage the IMU sensor position was changed, which resulted from the conclusions from the analysis of data from the previous stage. \\
3 & 4 & The runs were delivered with videos showing the robot driving sideways along a route marked with tape. \\
4 & 2 & 2 test runs to show the slippage problem. At this stage, the IMU sensor position was changed due to design reasons. \\
5 & 7 & Test runs with videos made on the floor. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of test runs.  
The reference route designated using the simulator is shown in Figure 8.  
Slippages were simulated in the simulator, as shown in Figure 9.  
Figure 8: Reference route determined using the simulator.  
Figure 7: Block diagram presenting functional components of the simulator.  
The route determined only by formulas that take into account the positions of the encoder (to show that the slippages have been simulated) is shown in Figure 10.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 2 Related Works
### Proposed Solutions  
This work is an improvement of our previous study [29] that we propose different solutions by employing on-demand, opportunistic forwarding, and network coding approach. Random Linear Network Coding (RLNC) is a network coding approach that encodes a linear combination of multiple packets with a coefficient selected from the Galois Field (GF). This packet can be decoded if the receiver receives sufficient linear independent combinations. However, the coefficient needs to be included in the packet header, resulting in header overhead that can exceed the maximum packet size allowed in LPWAN. Many researchers present a compression strategy by limiting the number of combined packets to \(m\)[30; 31]. The authors of [30] improve the compression strategy by using a pair of a small set of the allowed coefficient from the GF.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Pursue Mobility Model (PMM)
#### 6.4.5 Average Power Consumption  
Figure 21 presents a bar chart illustrating the correlation between APC, measured in watts, and the number of nodes in a mobile network employing the PMM mobility model, both with and without the Hello Flood attack. In this context, mobile nodes represent scenarios without attack, whereas attacker nodes denote those subjected to attack. The network density ranges from 10 to 60 nodes, with the percentage of mobile and attacker nodes varying from 10% to 40%. In a mobile environment without attack, changes in the number of nodes and the percentage of mobile nodes have a gradual effect on Average Power Consumption (APC). Conversely, in the presence of the Hello Flood attack, APC shows a gradual increase as the number of nodes and the percentage of attacker nodes change. Notably, the figure highlights the significant impact of the Hello Flood attack on the mobile network, with the introduction of the attack leading to a notable rise in APC from approximately 5 watts in the mobile environment without attack to 10 watts in the mobile network with attack.  
The combination of the PMM and the Hello Flood attack significantly raises the Average Power Consumption within the network environment. This increase in power usage stems from several interconnected factors. Firstly, the structured movement patterns inherent in the PMM model can precipitate congestion hotspots within the network as nodes pursue a target, leading to heightened packet collisions and transmission delays. Simultaneously, the Hello Flood attack inundates the network with an overflow of traffic, escalating interference and necessitating increased power consumption to manage the surge in data volume. Secondly, the movement patterns introduced by the PMM model may disrupt routing stability, resulting in suboptimal routing decisions and heightened packet loss, further exacerbating power consumption. When combined with the disruptive effects of the Hello Flood attack, these routing instabilities exacerbate the strain on network resources and intensify power usage. Lastly, the heightened network activity induced by both the PMM model and the Hello Flood attack intensifies competition for limited resources, such as bandwidth and processing power, thereby driving up power consumption as network components contend to meet the heightened demands. In essence, combining the PMM mobility model with the Hello Flood attack results in a significant increase in Average Power Consumption due to congestion, routing instabilities, heightened network activity, and resource contention.  
Figure 20: ETX without and with attack using PMM.
# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 2 Materials and Methods
### Data Processing Methodology
#### 2.3.2 Climatic Data  
The reference crop evapotranspiration (ETo) is calculated within the model using the provided meteorological data, including minimum (Tn) and maximum (Tx) air temperature, dew point temperature (Tdew), wind speed at x meters above the soil surface (u(x)), solar or shortwave radiation (Rs), and rainfall. These data are obtained from the ERA5-Land Daily Aggregated dataset with a resolution of 9 km through the Google Earth Engine platform.  
The relationship between precipitation and ETo (reference evapotranspiration) throughout the simulation period is presented in Figure 3. The overall trend of the graph indicates significantly higher levels of precipitation and ETo during summer months, while these values noticeably decrease in winter months.  
In the context of the AquaCrop model, evapotranspiration is a complex process that involves the evaporation of water from the soil surface and plant transpiration. This process is sensitive to various factors such as atmospheric conditions, biological characteristics of the plant, crop management, and environmental variables. Evapotranspiration from the reference surface, also known as reference evapotranspiration (ETo), is a key parameter in the AquaCrop model. ETo represents the amount of water evaporated from the reference plant surface.  
The FAO Penman-Monteith method is used to calculate ETo in accordance with the calculation procedures described in FAO Irrigation and Drainage paper Nr. 56. [23], which takes into account various variables such as air temperature, air humidity, wind speed, and solar radiation. This equation allows the precise determination of the rate of evapotranspiration based on available meteorological data.  
Data such as air humidity, solar radiation, air temperature, and wind speed necessary for calculating ETo were also obtained from the ERA5-Land Daily Aggregated dataset through the Google Earth Engine platform.  
AquaCrop employs a reference atmospheric CO\({}_{2}\) concentration of 369.41 parts per million by volume (ppm), representing the average CO\({}_{2}\) concentration for the year 2000 at Mauna Loa Observatory. This specific value is crucial for normalizing the biomass water productivity (WP) in the model. The normalization for atmospheric CO\({}_{2}\) involves adjusting the WP to account for variations in CO\({}_{2}\) concentration. This adjustment is necessary because crop is influenced by atmospheric CO\({}_{2}\) levels, and AquaCrop aims to provide accurate simulations by considering conditions at the reference location [24].
## 4 Visual Odometry
### Motion Model
Their control quantities are \(u_{v,i}\) and \(u_{a,i}\), assuming that the error obeyed a Gaussian distribution with a zero mean \(\mathbf{q}_{m,i}\sim N(0,\sigma^{2})\) and assuming they were independent of each other. The motion model of the vehicle was nonlinear and approximated by the first order Taylor series. The finite difference method was used to approximate the derivative of the forklift state variables. \[x(t+\Delta t)\approx x(t)+\dot{x}(t)\Delta t \tag{12}\]  
Therefore, the approximate discrete time model of the system was established. \[ \begin{split} x_{i+1}&=x_{i}+v_{i}\cos\theta_{i} \delta t+q_{x,i}\\ y_{i+1}&=y_{i}+v_{i}\sin\theta_{i}\delta t+q_{y,i}\\ \theta_{i+1}&=\theta_{i}+\frac{v_{i}}{L}\tan\alpha_{i }\delta t+q_{\theta,i}\\ \alpha_{i+1}&=\alpha_{i}+u_{\alpha_{i}}\delta t+q_{x,i}\\ v_{i+1}&=v_{i}+u_{v_{i}}\delta t+q_{v,i}\\ u_{\alpha_{i+1}}&=u_{a_{i}}+\delta u_{a}+q_{u,i}\\ u_{v_{i+1}}&=u_{v_{i}}+\delta u_{v}+q_{u,i}\\ \end{split}  \tag{13}\]Next, we needed to find two states \(\delta u_{\alpha}\) and \(\delta u_{v}\). By solving the following least squares problem for \(N\) continuous frames, we could obtain the optimal estimates of \(\delta u_{\alpha}\) and \(\delta u_{v}\) to calculate the line speed and wheel rotation angle of the vehicle at the next moment, which could be used to estimate the pose of the vehicle at the next moment:  
\[\min_{\delta u_{\alpha},\delta u_{v}}\frac{1}{2}\sum_{i=k}^{k+N}\|q_{x,i}+q_{y,i }+q_{\theta,i}+q_{\alpha,i}+q_{v,i}+q_{u_{\alpha},i}+q_{u_{\alpha},i}\|_{2}^{2} \tag{14}\]