esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Energy Optimization of UAV-Assisted Cellular Networks
#### 8.2.2 Machine Learning Approaches  
The preceding sections considered the few conventional techniques that have been applied for energy efficiency optimization in UAV-assisted cellular networks comprising both UAV-BSs and fixed BSs. Since the research on UAVs for wireless communications is still in its early stages, most of the work that has been carried out in the area of joint optimization of the energy consumption of both the UAV-BS and fixed BSs has mainly applied conventional approaches. The areas where machine learning techniques have been applied for energy optimization are in the areas of energy-efficient UAV trajectory design [226], energy-efficient UAV positioning [200], energy-efficient UAV resource allocation [255], and energy-efficient UAV transmission scheduling [30]. This means that there is a need to conduct research work where machine learning techniques are employed to both quantify  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Category**} \\ \cline{3-4}  & & **CA** & **ML** & **Specific Algorithm** \\ \hline
[107] & 2019 & ✓ & & Heuristic \\ \hline
[106] & 2021 & ✓ & & Heuristic \\ \hline
[105] & 2017 & ✓ & & Heuristic \\ \hline
[266] & 2021 & ✓ & & Heuristic \\ \hline
[267; 268] & 2019,2021 & ✓ & & Heuristic \\ \hline
[269] & 2020 & ✓ & & Heuristic \\ \hline
[270] & 2018 & ✓ & & Heuristic \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of energy optimization in UAV-assisted cellular networks.  
and optimize the combined energy consumption of both UAV-BS and fixed BS. This would ensure that the advantages achieved by the deployment of the UAVs with fixed BSs are not overwhelmed by the increase in the overall energy consumption of the network. This will also help overcome the limitations that conventional techniques face in terms of scalability and adaptability to changing environmental conditions.
# Enhanced Spectral Amplitude Coding OCDMA System Utilizing a Single Photodiode Detection
## 3 Performance Analysis  
### System Description  
The block diagram of the proposed system of three users of SAC-OCDMA using EDW code is shown in Figure 2. The transmitter consists of an LED that is sliced into nine wavelengths using wavelength division multiplexing (WDM) to generate the OCDMA codes. The information signals are generated from the pseudorandom bit generator with the non-return-to-zero (NRZ) line coding before being modulated with the codes using an external Mach Zehnder modulator. Then, the signals are combined using an ideal multiplexer before launching to the single mode fiber (SMF). A DCF and an EDFA are connected in series to the SMF. At the receiver, FBGs are used with the same bandwidth but with different Bragg wavelengths to decode the signal. The signal is then converted from the optical domain to the electrical domain through the APD. The resultant signal is filtered by a fourth-order Bessel low pass filter, which is used to reject noise and interference components.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**User One (Decoder)** & **010011** \\ \hline First interference of user one (X) & 110100 \\ Second interference of user one (Y) & 001101 \\ (Decoder \(\times\) X) & 010000 \\ \(\sum\)(X \(\times\) Decoder) & 1 \\ \(\overline{\text{Decoder}}\) & 101100 \\ (X \(\times\) Y) & 000100 \\ S-Decoder = \(\overline{\text{Decoder}}\) \(\times\) (X \(\times\) Y) & 000100 \\ (X \(\times\) S-Decoder) & 000100 \\ \(\sum\)(X \(\times\) Decoder) \(-\sum\)((X \(\times\) S-Decoder) & \(1-1=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Logical interference cancellation.  
Figure 1: Single photodiode (SPD) detection.  
Figure 2: Block diagram of three users of spectral amplitude coding-optical code division multiple access (SAC-OCDMA): (**a**) transmitter using enhanced double weight (EDW) code, (**b**) channel, and (**c**) receiver using EDW code.
# Terahertz Raman Measurements Using a Spatial Heterodyne Raman Spectrometer
## 4 Results and Discussion
### THz Raman Spectra of Nucleobases  
#### 4.5.1 Spectra of Solid Mixtures  
The performance of the system was further tested by measuring mixtures. Figure 10a shows the THz Raman spectra of a mixture of BA powder and 2-hydroxy BA powder with a ratio of 2:1, and Figure 10b shows the THz Raman spectra of a mixture of cytosine powder and uracil powder with a ratio of 1:2. The components of each mixture can still be clearly distinguished in the spectra. Therefore, the spatial heterodyne THz Raman spectrometer can easily be used to identify the compositions of such samples, which further broadens the application range of this system.
# On the Performance Analysis for CSIDH-Based Cryptosystems
## 2 Montgomery Curve and Tweaked Montgomery Curve  
This section introduces two types of Montgomery elliptic curves, which will be used throughout the paper. Then, we analyze the computational cost of elliptic curve arithmetic and isogeny computation on both curves, which are the main building blocks for implementing CSIDH-based algorithms.  
Let \(K\) be a field with the characteristic not equal to 2 or 3. The Montgomery curves over \(K\) are denoted by  
\[M_{a,b}:by^{2}=x^{3}+ax^{2}+x,\]  
where \(b(a^{2}-4)\
eq 0\). We shall write \(M_{a}\) when \(b=1\) throughout the paper. Moreover, the tweaked Montgomery curves over \(K\) are denoted by  
\[M_{a,b}^{t}:by^{2}=x^{3}+ax^{2}-x,\]  
where \(b(a^{2}+4)\
eq 0\). We shall write \(M_{a}^{t}\) when \(b=1\) in this paper. Similar to the arithmetic on \(M_{a}\), the elliptic curve arithmetic on \(M_{a}^{t}\) can also be constructed using only \(x\)-coordinate.  
For the remainder of this section, we introduce the elliptic curve arithmetic and isogeny formulas and analyze the computational cost for each operation on both curves. As the projective curve coefficient and projective coordinate are used for implementing isogeny-based cryptography, we shall evaluate the computational cost on both curves in these circumstances. For the elliptic curve arithmetic, we mainly focus on differential addition and doubling formula. For isogeny computation, we consider odd-degree isogenies.
## 3 Results
### Influence of the Degradation Level on Model Parameters Inference
#### 3.3.1 Medium Level of Degradation  
When increasing the level of excitation to a medium value (i.e., PGA of 6 m/s\({}^{2}\)) and keeping the same reference damage, all the models were still able to predict the correct value of the initial stiffness \(k_{i}\). However, an increase of the uncertainties in the model prediction was registered, this time due to the greater values of the discrepancy variance. The choice of the MAP (or of the mean of the distributions) as point estimate led to a slight overrating of the initial stiffness. As the estimation error was negligible, the adopted discrepancy term still performs as a good function to embody all the model errors. However, increasing the level of the input without accounting for a growth of the damage level appears to be unrealistic. This is because higher seismic excitations generally involve greater dissipation of energy. Therefore, a loss up to 40% of the initial stiffness \(k_{i}\) was taken  
Figure 12: (**a**) Kernel density estimations (KDE) of the initial stiffness model parameter \(k_{i}\) (on the left) and; (**b**) of the discrepancy variance \(\sigma^{2}\) for each model (low-level of degradation, PGA \(\approx\) 1 m/s\({}^{2}\)). (**c**) Boxplot representations (based on the five-number summary: minimum, first quartile, median, third quartile and maximum) of the posterior samples for the model parameter \(k_{i}\) and; (**d**) \(\sigma^{2}\). The horizontal and vertical dotted lines in the plots represent the calibrated reference value of \(k_{i}\).  
into account as well, resulting in Model 1 (i.e., the one consistent with the BWBN degrading model, which is able to predict the correct value of the initial stiffness). On the contrary, all the models inconsistent with the BWBN model (i.e., Models 2, 3 and 4) overrate the initial stiffness value in a non-negligible proportion (Figure 13), leading to higher level of uncertainty in the model response prediction. It is also worth noticing how the estimation error was amplified for non-parametric models. This is a considerable indicator of the inadequacy of the discrepancy function adopted. In order to recover the response records, more accurate and well-suited discrepancy models should be investigated, but this is beyond the scope of this paper.
# LW-FIRE: A Lightweight Wildfire Image Classification with a Deep Convolutional Neural Network
## 4 Results
### Classification Performance of the Proposed Framework
The next model, LW-FIRE150\({}_{42}\), has 1,106,753 parameters. The best validation accuracy is reached after the 35 th epochs of training and it is 99.36%, while the testing accuracy is 97.25%. An image is processed in 39 ms, which implies that the model can predict 25.6 images (frames) per second. In terms of the performance on the test dataset, this model is the best model of all models that share this image tessellation scheme. The model LW-FIRE300\({}_{42}\) has 3,236,673 parameters. The training time increased because the amount of data in the training phase also increased. The best validation accuracy is reached after the 6 th epoch, and it is 98.1%, while the testing accuracy is 96.4%. On average, a test image is processed in 51 ms, which indicates a processing speed of 19.6 frames per second. In Figure 6, we present the overall performance of the best performing model (LW-FIRE150\({}_{42}\)) in terms of accuracy on the testing subset. The model displays stability and robustness. It also shows that adding the batch normalization and dropout layers prevented overfitting. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Image** & **No. Parameters** & **Epochs** & **Validation Accuracy** & **Testing Accuracy** & **FPS** \\ \hline RT-LW-FIRE100\({}_{12}\) & 100 \(\times\) 100 & 877,377 & 27/50 & 97.8\% & 96.1\% & **31.25 fps** \\ \hline LW-FIRE150\({}_{12}\) & 150 \(\times\) 150 & 1,106,753 & 29/50 & 98.13\% & 95.83\% & 27.7 fps \\ \hline LW-FIRE300\({}_{12}\) & 300 \(\times\) 300 & 3,236,673 & 18/50 & 97.5\% & 95.4\% & 19.6 fps \\ \hline LW-FIRE100\({}_{42}\) & 100 \(\times\) 100 & 877,377 & 32/50 & 98.44\% & 97.1\% & 27 fps \\ \hline LW-FIRE150\({}_{42}\) & 150 \(\times\) 150 & 1,106,753 & 35/50 & 99.36\% & **97.25\%** & 25.6 fps \\ \hline LW-FIRE300\({}_{42}\) & 300 \(\times\) 300 & 3,236,673 & 6/50 & 98.13\% & 96.4\% & 20 fps \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Different configurations and their performance measures for \(N=12\) and \(N=42\).
## 5 Evaluation
### Results for Traditionally Ordinal Data Sets  
The evaluation of the detection of ordinal structures in combination with the traditionally ordinal data sets is provided in Table 4. From Table 4, we can make the following observations. First, based on the LSM mapping DR, eight out of the eleven data sets are identified as FS-ordinal, whereas based on the SVM-Acc measure, seven data sets are identified as FS-ordinal. Both approaches found the _correct_ structures, i.e., the structures corresponding to the data sets' natural class order. Second, six out of the eleven data sets are identified as FS-ordinal by both mappings simultaneously, i.e., the data sets CMC, LEV-4, SWD, ELS-5, LEV, and BVDB. Third, the data set Cars is identified as FS-ordinal in combination with the SVM-Acc measure, while not being identified as FS-ordinal based on the DR measure. On the other hand, the data sets ERA-7 and ERA are identified as FS-ordinal in combination with the DR measure, while not being identified as FS-ordinal based on the SVM-Acc measure.  
The data sets Nursery and ESL were not identified as FS-ordinal by either of the two measures: DR and SVM-Acc. Obviously, FS-ordinal structures and traditionally ordinal structures constitute two different categories. Traditionally ordinal structures are defined simply based on class label names, i.e., on a semantic level. However, the corresponding order is not necessarily reflected in the feature space. As a consequence, it is not always possible to detect any structure in combination with traditionally ordinal data sets. This outcome has also been observed and discussed in [3; 20; 21].  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Data Set** & **Cl** & **Fea** & **Sam** & \#\(\omega_{1}\) & \#\(\omega_{2}\) & \#\(\omega_{3}\) & \#\(\omega_{4}\) & \#\(\omega_{5}\) & \#\(\omega_{6}\) & \#\(\omega_{7}\) & \#\(\omega_{8}\) & \#\(\omega_{9}\) \\ \hline CMC & 3 & 9 & 1473 & 629 & 511 & 333 & – & – & – & – & – & – \\ \hline LEV-4 & 4 & 4 & 1000 & 93 & 280 & 403 & 224 & – & – & – & – & – \\ SWD & 4 & 10 & 1000 & 32 & 352 & 399 & 217 & – & – & – & – & – \\ Cars & 4 & 6 & 1728 & 1210 & 384 & 69 & 65 & – & – & – & – & – \\ Nursery & 4 & 8 & 12,958 & 4320 & 328 & 4266 & 4044 & – & – & – & – & – \\ \hline ESL-5 & 5 & 4 & 488 & 52 & 100 & 116 & 135 & 85 & – & – & – & – \\ LEV & 5 & 4 & 1000 & 93 & 280 & 403 & 197 & 27 & – & – & – & – \\ BVDB & 5 & 194 & 8700 & 1740 & 1740 & 1740 & 1740 & 1740 & – & – & – & – \\ \hline ERA-7 & 7 & 4 & 1000 & 92 & 142 & 181 & 172 & 158 & 118 & 137 & – & – \\ \hline ESL & 9 & 4 & 488 & 2 & 12 & 38 & 100 & 116 & 135 & 62 & 19 & 4 \\ ERA & 9 & 4 & 1000 & 92 & 142 & 181 & 172 & 158 & 118 & 88 & 31 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data Set Properties (Traditionally Ordinal Data Sets). Cl: Number of Classes. Fea: Number of Features. Sam: Number of Samples. #\(\omega_{i}\): Number of samples in class \(\omega_{i}\).
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 6 Challenges to AIoT Implementation in Aquaculture
For example, a model developed using data from Atlantic salmon farming in net pens might not perform effectively when used for shrimp aquaculture in ponds or tilapia in recirculating systems, as their behaviours, environmental needs, and disease profiles differ significantly. This limited flexibility often requires repeated retraining or tailored adjustments to the model for each new application, which can be time-consuming and expensive, thus slowing down the broader implementation of AIoT technologies across the diverse aquaculture industry. Finally, the environmental impact of AIoT systems must be considered. The energy-intensive nature of data processing and monitoring equipment can contribute to environmental degradation, potentially undermining aquaculture's sustainability goals. Furthermore, the successful deployment of AIoT systems relies heavily on adequate physical and digital infrastructure. In many remote aquaculture locations, existing infrastructure may be insufficient to support the required stable power supply and reliable internet connectivity for extensive AIoT deployments. Establishing and maintaining the necessary communication networks through technologies like LoRa, 5G, or satellite internet can be challenging and costly in these areas, limiting the feasibility of real-time data transmission and analysis. The physical robustness of equipment in harsh marine or aquatic environments, subject to factors like biofouling and corrosion, also presents an infrastructure challenge requiring durable and easily maintainable designs.
# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
## 2 Background
### Drone Attack Models
#### 2.1.3 GPS Signal Jamming/Spoofing  
GPS spoofing entails the presentation of inaccurate geo-location/coordinates to a UAV in-flight. GPS blueprints are widely available and can therefore easily be spoofed [13]. Such a malicious attack can be perpetrated simply by setting up a power amplifier and a transmission antenna that relays RF signals to a target. As drones are programmed to accept unencrypted GPS signals, spoofing of the data is a feasible attack and can lead to catastrophic results for the target drone. A varied coordinate signal could cause the drone to change its flight path and trajectory and may lead to a crash, with affected entities, including human citizens of the smart city, power lines, vehicles in commute and other ground objects [14].  
Table 1 summarizes security property compromised, threat types and their impact analysis.
## 3 Results
### Changes in Athletes' Physical Function Indicators during Subalpine Training
#### 3.1.1 Changes in Leukocyte and Red Blood Cell-Related Indicators in Athletes' Blood
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**WBC/(10\({}^{9}\)/L)**} & \multicolumn{2}{c}{**RBC/(10\({}^{12}\)/L)**} & \multicolumn{2}{c}{**Hb/(g/l)**} \\ \cline{2-6}  & **Male** & **Female** & **Male** & **Female** & **Male** & **Female** \\ \hline Before training & 4.80 \(\pm\) 0.91 & 4.97 \(\pm\) 1.49 & 5.30 \(\pm\) 0.24 & 4.46 \(\pm\) 0.35 & 153.28 \(\pm\) 7.78 & 136.13 \(\pm\) 11.12 \\
1 st month & 4.94 \(\pm\) 0.81 & 4.78 \(\pm\) 1.17 & 5.18 \(\pm\) 0.26 \({}^{\rm a}\) & 4.54 \(\pm\) 0.34 & 156.08 \(\pm\) 7.25 & 131.38 \(\pm\) 12.35 \\
2 nd month & 5.15 \(\pm\) 1.03 & 4.85 \(\pm\) 1.23 & 5.08 \(\pm\) 0.29 \({}^{\rm a}\) & 4.35 \(\pm\) 0.37 & 157.27 \(\pm\) 7.39 \({}^{\rm a}\) & 137.50 \(\pm\) 11.41 \\
3 rd month & 5.57 \(\pm\) 1.08 \({}^{\rm a,n}\)cc & 5.43 \(\pm\) 1.50 & 5.13 \(\pm\) 0.33 \({}^{\rm a}\) & 4.39 \(\pm\) 0.34 & 157.48 \(\pm\) 8.13 \({}^{\rm a}\) & 138.50 \(\pm\) 11.60 \({}^{\rm b}\) \\
4 th month & 5.34 \(\pm\) 1.10 & 5.09 \(\pm\) 0.99 & 5.00 \(\pm\) 0.32 \({}^{\rm a,b}\) & 4.38 \(\pm\) 0.35 & 158.76 \(\pm\) 8.88 \({}^{\rm a}\) & 137.50 \(\pm\) 10.86 \\
5 th month & 5.71 \(\pm\) 1.41 \({}^{\rm a,c}\) & 5.96 \(\pm\) 1.21 & 5.07 \(\pm\) 0.28 \({}^{\rm a}\) & 4.23 \(\pm\) 0.35 & 161.56 \(\pm\) 6.51 \({}^{\rm a}\)\({}^{\rm b,0c}\) & 137.75 \(\pm\) 11.67 \({}^{\rm b}\) \\
6 th month & 5.31 \(\pm\) 1.03 \({}^{\rm a}\) & 4.79 \(\pm\) 0.99 \({}^{\rm f}\) & 5.04 \(\pm\) 0.29 \({}^{\rm a,b}\) & 4.39 \(\pm\) 0.39 \({}^{\rm a,b}\) & 158.72 \(\pm\) 6.76 \({}^{\rm a,f}\) & 141.00 \(\pm\) 11.15 \({}^{\rm b}\) \\ Time effects & \(p=0.001\) & \(p=0.001\) & \(p=0.000\) & \(p=0.001\) & \(p=0.014\) \\ \hline \hline \end{tabular}  
* Notes: (1) a, a means \(p<0.05\), \(p<0.01\) compared with before subalpine training (2), b, bb means \(p<0.05\), \(p<0.01\) compared with the second compared with the first month of subalpine training. (3) c, cc means \(p<0.05\), \(p<0.01\) compared with the third month of subalpine from. (4) d, dd means \(p<0.05\), \(p<0.01\) compared with the fourth month of subalpine training. (5) e, ee means \(p<0.05\), \(p<0.01\) compared with the fourth month of subalpine training. (6) f, ff means \(p<0.05\) and \(p<0.01\) compared with the fifth month of subalpine training.  
\end{table} 
Table 5: Results of leukocyte and red blood cell-related indicators in athletes’ blood. There is no significant difference in the WBC of different genders (\(p>0.05\)), male and female have the same trend over time; there are significant differences in the RBC and Hb of different genders (\(p<0.01\)), and the RBC and Hb of male athletes are higher than those of female athletes (Figure 2).
## 1 Introduction
Our method aims to reduce the time and resources needed for label quality assurance activity by offering immediate estimates of label quality to annotators and computer vision project stakeholders. While we focus on object detection as an example, our method can be extended to other tasks and artificial intelligence subfields. Figure 3: Segmentation: cat and background pixels distinguished. Figure 2: Recognition: cat identified and tag is assigned to the image. In order to provide a clear overview of the paper's structure, we present an article map outlining the content of each section. * **Related Work**: This section explores the crucial role of data requirements in artificial intelligence projects, distinguishes them from traditional software engineering needs and surveys various methodologies for label quality assurance, such as manual review, inter-annotator agreement, deep active learning and algorithmic approaches. * **Proposed Process for Data Requirements within Requirements Engineering**: This section outlines a proposed process for incorporating data requirements within requirements engineering for artificial intelligence projects, placing the \"Data Requirements\" phase between \"Requirements Elicitation\" and \"Requirements Analysis\" activities. * **Machine Learning-Based Label Quality Assurance**: This section discusses a machine learning-based label quality assurance method that involves a series of steps to build a quality assurance model that classifies object detection labels as good or bad, helping annotators focus on samples with potentially incorrect labels. * **Results and Discussion**: This section details the performance of a machine learning-based label quality assurance model utilizing ResNet-18 components and a fully connected neural network that attains 82% accuracy in discerning good and bad labels for object detection tasks, even when trained on a small dataset. * **Conclusions**: This section underscores the significance of data requirements for artificial intelligence development and underlines a pioneering machine learning methodology for automating label quality assurance, which, while demonstrated for object detection, offers flexibility for adaptation across a variety of computer vision tasks and other machine learning subfields.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Vertebrates
#### 2.2.1 Frog Inspired Jumping Robots  
[PERSON] et al. studied the jump performance of five different kinds of bull frogs [139]. The measured muscle-mass-specific power ranges from 225 W/kg to 1000 W/kg, exceeding with large margin the power available from direct muscle contraction. This could be due to a redistribution of power during take-off, obtained by means of elastic energy storage, that permits to produce the power peak in the exact instant that is required. Recently, researchers demonstrated the existence of the catapult mechanism in frogs' jumping [140; 141; 142] by using high-speed marker-based biplanar X-ray cinefluoroscopy. In fact, they observed the two principal characteristics of catapult mechanism, i.e., the contraction of muscles prior to joint movements (during tendon stretch) and the rapid joint movement during tendon recoils that gives power to take-off.  
[PERSON] et al. [143; 144] proposed an equivalent six-bar mechanism model for frogs' jump, manufacturing an open chain jumping robot moved by serval pneumatic muscle actuators. They applied an adaptive cascade controller to the proposed robot. This jumping robot has the capacity to jump following a desired trajectory based on optimized results. [PERSON] et al. developed three generations of hopping robots for celestial exploration [145; 146]. The second generation \"Frogbot\" used a six-bar geared mechanism to turn the linear spring force into a nonlinear contacting force. In their third generation of jumping robots, the take-off angle adjustment, self-righting and steering ability are also achieved. The six-bar gear mechanism is widely adopted in jumping robots [147; 148; 149; 150; 151; 152; 153] to achieve desired contact force profile and to avoid premature jumping.