esa-sceva/satcom-chunk-collection huggingface dataset:
# Output Feedback Tracking Control for Vessel with Collision-Avoidance and Performance Constraints
## 6 Conclusions
\begin{tabular}{l l} \((\cdot)^{T}\) & Transpose of a matrix \\ \(\|\cdot\|\) & Euclidean norm of a vector \\ \(\mathrm{diag}\{\cdot\}\) & Diagonal matrix \\ \(I_{n}\) & \(n\times n\) dimensional identity matrix \\ \(0_{n}\) & \(n\times n\) dimensional zero matrix \\ \((x,y),\psi\) & Position and yaw of dinamicaly positioned vessel \\ \(u,v\,,r\) & surge velocity, sway velocity and yaw rate of dinamicaly positioned vessel \\ \(\eta\) & \(\eta=[x,y,\psi]^{T}\) the vector containing position and yaw angle \\ \(v\) & \(v=[u,v,r]^{T}\) velocity vector \\ \(\tau=[\tau_{u},\tau_{v},\tau_{r}]\) & Input signals of vessel control system \\ \(M\) & The positive definite inertia matrix included added mass \\ \(C\) & The skew-symmetric matrix of Coriolis and centripetal term \\ \(D\) & The positive definite damping matrix \\ \(R\) & The rotation matrix \\ \(d\) & The external environmental disturbances \\ \(\tau_{\max}\) & The maximum constraint constraint of input signal \\ \(\tau_{\min}\) & The maximum constraint of input signal \\ \(\tau_{c}=[\tau_{uc},\tau_{cc},\tau_{rc}]\) & The control inputs calculated by the proposed controller \\ \(\omega\) & The mismatch function between input without saturation and with saturation \\ \(\zeta\) & The unknown term \(\zeta=M^{-1}(-C\
u-D\
u+d)\) \\ \(\hat{\eta},\vartheta,\zeta\) & The estimates of \(\eta,\
u,\zeta\) \\ \(\hat{\eta}=\hat{\eta}-\eta\) & The position-heading estimation error \\ \(X=[\hat{\eta}^{T},\hat{\
u}^{T},\hat{\xi}^{T}]^{T}\) & estimation error \\ \(C\hat{r}\) & Collision risk index \\ \(T_{B}\) & The true bearing of the target vessel from the own vessel \\ \(D_{cpu}\) & The closest point of the approach \\ \(T_{cpu}\) & The time to the closest point of the approach \\ \(t_{2}\) & The time that the vessel sail from current position to the minimum \\ \(t_{1}\) & The time that the vessel sail from the point of evasive actions to the \\  & closest point \\ \(\tau_{ppc}\) & The control inputs of the prescribed performance controller \\ \(\tau_{acac}\) & The control inputs of the collision-avoidance controller \\ \(p_{v}=\|p-p_{t}\|\) & the distance between target vessel from the own vessel \\ \(p=[x,y]^{T}\) & the position of own vessel \\ \(p_{t}=[x_{t},y_{t}]^{T}\) & the center of the obstacle \\ \end{tabular}   
**Abbreviations**  
The following abbreviations are used in this manuscript: \begin{tabular}{l l} CASC & Collision-avoidance switch control \\ PPC & Prescribed performance controller \\ ADS & Auxiliary dynamic system \\ CAC & Collision-avoidance controller \\ ESO & Extended state observer \\ DCCR & Dynamic calculation of collision risk \\ \end{tabular}
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
Furthermore, applying changes such as Gaussian blur and Wavelet transform to the pre-processed inputs improves classification accuracy when using a ConvLSTM compared to its original input as image noise is reduced, improving performance for low-quality deepfakes. Also, research by [PERSON] et al. [37] deduces noise as an identifier of deepfake content, concluding that reconstructed deepfake videos have a low Signal-To-Noise ratio. Therefore, further research into using noise as a detection measure could improve the accuracy of deepfake detection models. Table 6 presents the factors that influenced the detection accuracy the most for each of the selected publications. Where 'Other' is selected, the factor is either minimal or applicable to a specific type of CNN-LSTM architecture. **RQ5: Is CNN with LSTM more effective for video deepfake detection compared to models that do not utilise LSTM? **  
Researchers utilise many performance metrics to determine how successfully a model can correctly classify unseen input as real or deepfake. The most commonly used metric is accuracy to determine how often the outcome is predicted correctly. \[\text{Accuracy}=\frac{\text{Number of correct predictions}}{\text{Number of total predictions}}\times 100\]  
A robust model should be accurate, detecting faces with no false positives or negatives. Another measure is the Area Under the Receiver Operating Characteristics Curve (AUC), which combines the true positive rate and false positive rate using logistic regression. A true positive rate refers to the number of positive class samples the model correctly predicted, whilst a false positive rate refers to the number of negative class samples the model predicted incorrectly. A true negative rate refers to the number of negative class samples the model correctly predicted, and a false negative rate refers to the number of positive class samples the model incorrectly predicted. These values are often used in confusion matrices, as seen in Figure 9, to represent how correctly data are classified. With these values, further calculations such as precision, recall, and the F1 score can be computed.
## Data Used in this Study
### Raster Data
#### 2.1.4 Land Cover Data  
To analyze the potential biome-dependency of vegetation response to drought, we used land cover information derived from MODIS land cover product (MCD12C1, 2011, 0.05\({}^{\circ}\)). The land cover map in Victoria is shown in Figure 1. Crop and evergreen broadleaf forest consist of main land cover types in Victoria. 0.5\({}^{\circ}\) pixels were determined as crop land or evergreen broadleaf forest land if over 50% of the 0.05\({}^{\circ}\) pixels (i.e., more than 50 pixels) within the grid were identified as crop land or evergreen broadleaf forest land.
# Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images
## 3 Experimental Results
### Lidc-Idri  
LIDC-IDRI [19] consists of 1018 CT scans case, and 244,617 CT images. Pulmonary nodule diameters range from 3 to 30 mm, including only 4702 images of small nodules (<3 mm). For differential diagnostic imaging features, the larger the size (maximum diameter) of the nodule, the higher its malignancy: \(\leq\)8 mm is mostly benign and \(>\)8 mm is more likely malignant. Other types are very faint with smaller lesions called ground-glass opacity (GGO): \(\leq\)10 mm is mostly benign; \(>\)10 mm is more malignant. We performed experiments according to the combination of experiments in the OA table to improve the reliability of the experimental results. We obtained observations by combining experiments with three replications. The values of the parameters, the measured accuracies of the 2D CNN for lung cancer recognition, and S/N ratios in each run are listed in Table 4. The S/N ratio values of the accuracy were calculated by implementing of the-higher-the-better concept. A larger S/N ratio means less quality loss, that is, higher quality. Based on the results of the 36 runs, the maximum mean accuracy of 98.48% was achieved with Run #31, whereas the minimum mean accuracy of 78.29% occurred for Run #30.  
The mean responses for the S/N ratios and the ANOVA table are shown in Tables 5 and 6. Table 6 shows that four factors-C1_S, C2_S, C1_KS, and C2_F-have a significant impact. Table 5 and Figure 6 show that the factors affecting the S/N ratios based on yield extraction method are affected for each level of the parameters. The most significant factors can be determined by the larger difference in the S/N ratio. The experimental results indicate that factor C2_F has noticeable delta value of 0.845%, meaning that factor C2_F has the big significant parameter on model response. The importance of parameter was ranked from C2_F > C1_KS > C2_S > C2_P > C1_S > C2_KS > C1_F > C1_P. Hence, the best levels are A1 (level 1 for C1_S), B1 (level 1 for C1_P), C1 (level 1 for C2_S), D2 (level 2 for C2_P), E1 (level 1 for C1_KS), F1 (level 1 for C1_F), G3 (level 3 for C2_KS) and H3 (level 3 for C2_F). Optimum parameter C1_S is 1, C1_P is 0, C2_S is 1, C2_P is 1, C1_KS is 3, C1_F is 4, C2_KS is 7 and C2_F is 32.  
Figure 5: Architecture of the proposed CAD system.  
To compare our 2D CNN performance with Taguchi parametric optimization with the other neural networks such as deep fully convolutional neural network (DFCNet) [22] and deep features + k-nearest neighbor (kNN) + minimum redundancy maximum relevance (mRMR) [2] based on LIDC-IDRI dataset. The results are shown in Table 7. The best accuracy was 99.51% for the Deep features + kNN + mRMR, but the best sensitivity was produced by 2D CNN with Taguchi parametric optimization with a sensitivity of 99.97%, the best specificity of 99.93%, and FPR of 0.06%.
# LoRaWAN Behaviour Analysis through Dataset Traffic Investigation
## 4 LoRaWAN Analysis  
In this section, we analyse several aspects of the LoRaWAN protocol. To this aim, we divided our study in six blocks, each focused on one specific behaviour of the network:  
1. Gateway radio indicators;
2. Packets loss;
3. Device-Gateway interaction;
4. End-Device addressing and activation;
5. Duty-Cycle enforcement;
6. Performance of De-duplication procedure.  
Let us first discuss what the dataset contains in terms of number of packets and types of packets. LoRaWAN foresees different \"types\" of packets, e.g., packets requiring an acknowledgment, join request messages, etc. The information characterizing the type of a packet is encoded with three bits in the field _mtype_ in the MAC Header, which is sent as clear-text with the packet and it is reported inside the dataset. All the possible _mtype_ values with their description is reported in Table 2.  
To derive an initial characterization of the traffic in the dataset, we counted the total number of packets per _mtype_.  
As described in Section 2.2, LoRaWAN protocol foresees the reception of duplicate copies of the same packet. In the dataset, such duplicate copies are included, therefore we counted the number of packets in two different ways, with and without duplicate copies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gateway Number** & **Gateway ID** \\ \hline
00000f0c210281c4 & GW1 \\
00000f0c22433141 & GW2 \\
00000f0c210721f2 & GW3 \\
00000f0c224331c4 & GW4 \\
00800000a0001914 & GW5 \\
00800000a0001793 & GW6 \\
00800000a0001794 & GW7 \\
7276 ft002e062804 & GW8 \\
0000024b0b031c97 & GW9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gateway numbers to gateway IDs.  
Table 3 reports the total number of the packets contained in the dataset and the number of packets after removing the duplicate ones. In the dataset there are 11,263,001 collected packets and 8,266,868 distinct packets. The packets identified as \"Unknown\" are those packets which are captured by the LoRa antennas on the gateways but do not follow the LoRaWAN protocol. Most of them have an empty PHYPayload, and could be beacon packets for some proprietary systems over LoRa. We consider all these packets as distinct since we cannot determine whether a duplicate reception takes place.  
In the following sub-sections we first introduce the features we want to analyse, then we present our strategy to carry out the specific analysis and the results obtained applying the functions of our library.
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 2 Motivation  
The novelty of the proposed scheme is to use multiple dimensions as much as possible. Figure 1a,b represent examples of resource usage in NOMA and BD schemes using only one dimension, respectively. Figure 1c represents example of resource usage for the proposed scheme. In Figure 1a,bwhich represent existing techniques, the two techniques cannot be simply combined since all existing schemes utilize each technique for multiple access. However, in the proposed scheme, the SC is used to separate each symbol by utilizing the power domain. In conclusion, it is possible to utilize both power and space domain at the same time and improve performance by changing usage in SC application. In addition, through proper combination, the proposed scheme compensates the shortcomings of existing schemes and has advantages. First, overhead of the BS is reduced. A dynamic user scheduling and grouping strategy needs the feedback information at the BS. The proposed scheme can reduce the feedback overhead significantly by not considering intra-beam interference. Second, proper fairness among users is assured. The interference is treated as noise to users and does not guarantee fairness among users. In the proposed scheme, a certain error probability is ensured for all users. Third, the complexity is reduced for each user. In the case of the existing NOMA scheme, it is necessary to decode even if it is not the user's own signal. However, in the proposed scheme, all the decoded symbols become the user's own data. Therefore, it is possible to reduce the complexity of the user in demodulating unnecessary data. As a result, the proposed scheme allows more flexibility in spreading user signals over the multiusers.  
Figure 1: _Cont._  
By adopting the multiple dimensions, the same resources can be shared at each domain. In this paper, both the power and space domain are exploited. Figure 2 shows an example of additional dimension usage. Four dimensions (frequency, time, power, space) are used and the same resources can be appropriately shared. Therefore, the more data symbols can be transmitted to more users.  
This paper considers a downlink single-cell MU-MIMO system as shown in Figure 3. In the proposed scheme, multiple access is accomplished by SDMA and the power domain is exploited to transmit more data symbols to each user. As a result, the proposed scheme has significant potential to improve spectral efficiency and provide better wireless services to many users. Also, this paper offers advantages in various design issues to meet the requirements and characteristics of the system by using multiple dimensions.  
Figure 1: Examples of resource usage in various dimensions (**a**) Example of resource usage for conventional NOMA; (**b**) Example of resource usage for conventional BD; (**c**) Example of resource usage for proposed scheme.  
Figure 2: The spectral efficiency using multiple dimensions.
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 2 Background
### Full-Duplex Jamming Practical Issues
#### 2.5.1 Control of [PERSON]'s Jamming Power Level  
There are two constraints that dictate the power level of [PERSON]'s jamming signal. The first is comprised of the limits on jamming peak power and duration imposed by [PERSON]'s power supply capability. When finite constraints must be applied, [PERSON] is free to jam at a power level that matches his constraints. When the constraint on [PERSON]'s power consumption is not strict, another aspect of the communication system can help to guide this decision. At the physical layer the main goal is to assure reliable communication between [PERSON] and [PERSON], with PLS schemes working as an \"add-on\" layer to increase security against eavesdroppers while not compromising communication between [PERSON] and [PERSON]. In the scheme of Figure 1, reliable communication between [PERSON] and [PERSON] is a function of the SINR experienced by [PERSON], which is affected by his capacity to cancel the self-interference caused by his own jamming signal. Therefore, a simple procedure to be used by [PERSON] while jamming is to reduce the jamming power to a level that bounds the signal quality at a level that guarantees successful decoding by [PERSON].
# Effect of Nano Hydrolcite on the Aging Resistance of a High Binder Content Stone Mastic Asphalt
###### Abstract  
Hydrolcite, a type of layered double hydroxide (LDH), reveals an interesting potential for the modification of bitumen. The LDH can induce a barrier effect that prevents the loss of volatiles, retards oxidation, and protects against ultraviolet radiation. Such properties can enhance the aging resistance of the bitumen. However, there is a gap in knowledge regarding the effects of the modification with hydrolcite in the properties of the asphalt mixture. To contribute to fill such a gap, the current study presents a characterization of the effects of the modification with nano hydrolcite in the surface characteristics, mechanical performance, and aging resistance of an asphalt mixture. To better explore the effects of the modification, the selected asphalt mixture was a high binder content (7.5%) Stone Mastic Asphalt (SMA). The experimental study indicates that the binder-rich SMA presented adequate performance for application in surface courses. If compared to conventional mixtures, the binder-rich SMA presented better initial mechanical performance (unaged conditions). Furthermore, it presented smaller variation in the parameters between unaged and aged conditions, indicating enhanced aging resistance. The modification with nano hydrolcite induced smaller evolution in the fatigue resistance parameters, indicating enhanced aging resistance; however, in the remaining tests, the trends were not clear.  
stone mastic asphalt; nanomaterials; hydrolcite; mechanical performance; surface characteristics; aging resistance +
Footnote †: journal: Applied Sciences  
1
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 5 Simulation
### Manage Trust Certainty with Entropy
#### 5.2.1 Certainty Rises with Increasing Experiences under Fixed Conflict  
Assuming that agent \(a_{i}\) perceives evidence with the fixed satisfactory degree \(S_{ij}\) towards agent \(a_{j}\), we can ascertain how evidence certainty varies with an increasing amount of interaction. Logically, evidence certainty grows with increasing interaction. For example, compare 3 positive feedback instances out of 5 with 12 positive feedback instances out of 20, and we recognize that the satisfactory degree \(S_{ij}\) is the same \((\frac{3}{5}=\frac{12}{20}=0.6)\) in both cases. Nevertheless, the certainty in the second case is intuitively greater than the first one. Definition 12 defines the generated BPAs, and Equation (16) is employed to calculate the certainty with the entropy of the Dempster-Shafer theory of evidence. We have certainty of 0.60 from \((3,2)\), and as a comparison, certainty of 0.69 for \((12,8)\). In more detail, Figure 6 indicates how certainty changes with an increasing amount of interaction (\(x\)-axis: from 0 to 20) and the fixed satisfaction \(S_{ij}=0.5\).  
As displayed in Figure 6, certainty increases with the amount of interaction when the satisfaction degree is fixed to 0.5, and it reaches a fixed value of 0.7 with sufficient interaction. Next, we check how certainty changes with increasing conflict under a fixed amount of interactions.
## 3 Results  
### Spectral Profiles  
After spectra extraction and spectral preprocessing, rice seeds were randomly divided into a training set with 3000 samples of each variety and a hold-out test set with 2664, 2394, 1933, and 1916 samples of Xiushui 134, Zhejiang 99, Zhongjiazao 17 and Zhongzao 39, respectively. To train models with different sizes of training set, subsets of 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000 samples of each variety were selected from the full training set. The test performance of all models was evaluated on the same hold-out test set.  
The spectral range of 441-948 nm (Spectral range 1) for visible/near-infrared system and 975-1646 nm (Spectral range 2) for near-infrared system were analyzed. The average spectra of 4 varieties of rice seeds in two different spectral ranges are shown in Figure 3a,b, and corresponding average spectra preprocessed by second derivative (polynomial order: 2, smoothing points: 7) are presented in Figure 3c,d.  
In the two spectral ranges, only slight differences existed among the average spectra of different varieties. As seen from the second derivative spectra in the Spectral range 1, more obvious differences could be observed at the wavelengths of 451, 456, 463, 478, 678, 698 and 716 nm. These wavelengths were mainly related to the color variations of the rice seeds. In the Spectral range 2, the wavelengths at 1136, 1177, 1251, 1325, 1386, 1440, 1494 and 1619 nm showed greater differences. The wavelengths at 1136, 1177 and 1251 nm are attributed to the second overtone of C-H stretching mode [26]. The wavelength at 1386 nm (around 1387 nm) might be attributed to the first overtone of stretching and anti-symmetric O-H bond [27]. The wavelengths at 1440 and 1494 nm are attributed to water [26,28]. The wavelength at 1619 nm (around 1620 nm) might be attributed to first overtone of C-H group absorption [29]. However, for both preprocessed and unpreprocessed average spectra, the spectral differences among different seed varieties were not significant.
# The Generalized Mohr-Coulomb Failure Criterion
## 6 Comparisons with Commonly Used Failure Criteria
### L-D and SMP Criterion  
The L-D criterion to participate in comparisons takes the form  
\[\frac{I_{1}^{3}}{I_{3}}=k_{LD} \tag{30}\]  
where  
\[I_{1}=\sigma_{1}+\sigma_{2}+\sigma_{3} \tag{31}\]  
and  
\[I_{3}=\sigma_{1}\sigma_{2}\sigma_{3} \tag{32}\]  
\(k_{LD}\) is the material constant related to the friction angle \(\phi_{0}\) associated with \(b=0\) by  
\[k_{LD}=\frac{\left(3-\sin\phi_{0}\right)^{3}}{1-\sin\phi_{0}-\sin^{2}\phi_{0}+ \sin^{3}\phi_{0}} \tag{33}\]  
For \(b>0\), given by the L-D criterion is a real root of the cubic equation as follows [46]  
\[\begin{array}{l}\left(2b-1\right)\left[k_{LD}+\left(2b-1\right)^{2}\right] \sin^{3}\phi_{b}+\left[k_{LD}+9(2b-1)^{2}\right]\sin^{2}\phi_{b}\\ +\left(27-k_{LD}\right)(2b-1)\sin\phi_{b}+27-k_{LD}=0\end{array} \tag{34}\]  
The SMP criterion, as another failure criterion to participate in the comparison, takes the form  
\[\frac{I_{1}I_{2}}{I_{3}}=k_{SMP} \tag{35}\]  
where  
\[I_{2}=\sigma_{2}\sigma_{3}+\sigma_{3}\sigma_{1}+\sigma_{1}\sigma_{2} \tag{36}\]\(k_{SMP}\) is the material constant defined by  
\[k_{SMP}=8\tan^{2}\phi_{0}+9 \tag{37}\]  
For \(b>0\), given by the SMP criterion is a real root of the cubic equation as follows [46]  
\[\begin{array}{l}(k_{SMP}-1)(2b-1)\sin^{3}\phi_{b}+\left[2(2b-1)^{2}-3+k_{SMP} \right]\sin^{2}\phi_{b}\\ +(9-k_{SMP})(2b-1)\sin\phi_{b}+9-k_{SMP}=0\end{array} \tag{38}\]  
As for the GMC, we emphasize again \(c_{b}\) and \(\phi_{b}\) in the criterion are obtained by the Lagrange interpolation stated in Section 3.