esa-sceva/satcom-chunk-collection huggingface dataset:
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Cluster-Based Routing Protocols
#### 4.3.2 Energy Aware Cluster-Based Routing Protocol (EALC)  
[PERSON] et al. [49] consider the energy factor and proposed an energy aware cluster-based (EALC) model. First, the transmission power of UAVs was adjusted by predicting the operational needs of UAVs. The transmission distance reaches the optimal value, which will reduce the packet  
Figure 9: Energy-Efficient Clustering Algorithm (EECA) algorithm.  
loss rate (PLR), improve the link quality, and reduce the energy consumed by communication. The transmission power is tightly coupled with transmission range:  
\[P_{T}=P_{R}+20 log(\frac{4\pi R}{\lambda})-G_{T}-G_{R}. \tag{7}\]  
where \(G_{T}\) and \(G_{R}\) are the antenna gain of the transmitting and receiving side, respectively. \(\lambda\) is the wave length of transmitting frequency, and \(P_{R}\) is the receiver sensitivity.  
Second, k-means density clustering algorithm was used to select cluster heads. K-means sorts fitness, takes the fitness value of the node as input, and provides cluster heads and its associated members as output. The fitness value in EALC is calculated by using  
\[Fitness=\frac{\omega 1\times Energy_{R_{es}}}{(\omega 2\times avg\_dis)( \omega 3\times delta\_diff)}. \tag{8}\]  
where \(Energy_{R_{es}}\) represents residual energy level of nodes, \(avg\_dis\) is average distance to the neighbor nodes, and \(delta\_diff\) is the delta difference. \(\omega 1,\omega 2,\omega 3\) are weight factors, respectively, for energy, average distance, and delta difference. The original k-means density only used one metric, namely, neighborhood degree. In this paper, EALC added two parameters, namely, energy level and distance, to optimize the selection of cluster heads for neighbor nodes, thus increasing the life cycle of the cluster. In terms of clustering quantity, clustering construction time, clustering life cycle and energy consumption, this model is superior to existing artificial intelligence technologies (ant colony optimization clustering algorithm and gray wolf optimization clustering algorithm).  
**Advantages:** (1) EALC selects cluster heads according to the energy level of clusters, the distance from neighboring regions, and the degree of neighboring regions, so as to improve the cluster lifetime. (2) EALC adjusts the transmission power of nodes according to the operation requirements to reduce the resource wasted of nodes.  
**Disadvantages:** In the process of clustering formation, this protocol ignores the characteristics of nodes' high-speed moving in FANET, and the network is liable to be interrupted.
## 9 Conclusions  
This paper examined and tested the suitability of six machine-learning algorithms for ransomware detection. The experiments performed in this study utilised Windows API call frequency analysis as an approach for identifying ransomware efficiently. The produced samples were balanced to avoid under-sampling/over-sampling [26]. However, it is essential to acknowledge that the number of positive samples (malware) under contextual circumstances could significantly vary between applications in real-world scenarios (e.g., would be less in normal circumstances), which poses a limitation and a challenge for this type of research.  
In response to our research questions ((1) which error parameters and evaluation matrices should be utilised by the state-of-the-art to discuss the reliability of ML models for the detection of ransomware, and (2) which ML models offer reliability for the detection of ransomware?), we have utilized and tested several metrics, such as the Positive Likelihood Ratio, Negative Likelihood Ratio, and Diagnostic Odds Ratio. However, the study emphasized the importance of false negatives in preventing a potentially large cost of a ransomware infection.  
Overall, we trained and evaluated three Artificial Neural Network (ANN) models based on six algorithms: Support Vector Machine, K-Nearest Neighbours, Logistic Regression, Decision Tree, Random Forest, and Artificial Neural Network. After the models were trained and evaluated, metrics were calculated to allow a comparison to be made. The ANN model 1, with a hidden layer of 16 nodes using ReLU activation function and trained for 100 epochs, was the most time-consuming to train. The decision tree and early iterations of the neural network models ANN Model 1 and ANN Model 2 (trained for 40 epochs) suffered from overfitting. After considerable experimentation, overfitting of the ANN models was overcome in ANN Model 3 by using the tanh function and by adjusting the number of epochs over which the model was trained. Although the Random Forest algorithm did not achieve the highest accuracy and reached the lowest precision metric, as it was more likely to classify non-malicious examples as malicious and less likely to classify malicious examples as non-malicious, it might be considered the safest of the six algorithms for ransomware identification. Finally, the ANN Model 3 achieved the highest from the models' true-negative rate and precision values. If the false-negative rate could be reduced with further tuning and/or training on a larger dataset, ANN Model 3 would be the optimum choice for ransomware classification. A more comprehensive source of benign samples from vendors other than Microsoft would also reflect different coding styles and may increase the number of false positives observed.  
Writing--original draft, R.B.; Writing--review & editing, H.A.-K. and N.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
## 3 Methods
### Dual-CNNSELSTM Model  
Based on some studies, standard LSTM is challenging to use directly on sequences where the input is spatial. Therefore, to perform tasks that need sequences of images to predict relevant classes, we need a more sophisticated model. Hence, the combination of CNN and LSTM network is an architecture designed explicitly for dynamic pattern classification problems with spatial inputs, like a sequence of images or videos. Considering the advantage of adding CNN, we propose a novel two-stream model Dual-CNNSELSTM, utilizing the SE-LSTM concept combined with CNN to improve the dynamic pattern classification task. When considering models with CNN layers on top of LSTMs, we have achieved better accuracy than models with only with a sequence of LSTMs [46]. Because of the ability to find patterns in images through looking for existing patterns, a CNN is good at identifying objects in images. Also, _Max Pooling_ layers only select and keep significant features and ignore the rest. This means it can classify small and big objects into the same class accurately and helps to overcome the feature extraction issues while directly inputting to the LSTM. Therefore, CNN helps the learning models to attain better performance than LSTM models.  
The Dual-CNNSELSTM architecture consists of five CNN layers followed by an LSTM layer, including a dropout layer. A fully connected layer follows a softmax layer, which computes the classification probabilities. Within the CNN layers, we reduced the input size at lower layers since it helps to decrease the computational cost. Therefore, for low layers such as layer 1 and layer 2, we wanted to cut the size of the input in half for each layer. To do this, we set the stride size to 2 when the input is passing through the CNN layer. The kernel size of the first CNN layer is \(5\times 5\) and for the other four layers it is \(3\times 3\). To avoid high information loss, our input feature planes were set to 32 in the first layer. The feature maps consisted of four filter sizes such as 32, 64, 128, and 256, and we doubled them up within each CNN layer to increase the depth. Each CNN layer was followed by a batch normalization layer, a ReLU activation layer, and a MaxPooling layer with a pool size of 2 \(\times\) 2. The Batch Normalization layer addressed the vanishing and exploding gradients problem and made the training faster; it also removed the internal covariate shift problem. Spatial and motion features were extracted from the five layers of CNN architecture and fed into the SeqLSTM, which has 256 hidden layers.
# Stress Estimation Using the Acoustoelastic Effect of Surface Waves in Weak Anisotropic Materials
###### Abstract  
This paper proposes a novel stress measurement method using the acoustoelastic effect of surface wave to estimate the stress of a homogeneous material plate with orthogonal anisotropy, in which the surface wave velocities are measured in three different directions before and after loading stress. The effectiveness of the proposed method was verified by numerical simulations and experiments. For the simulations, the surface wave velocities in three directions were obtained from a conventional perturbation model for weak anisotropic materials. The simulation results showed that the stress estimation error was less than 3% for an anisotropic rate up to 2% under stress conditions up to 90 MPa. Two specimens were prepared for the experiments, one was almost isotropic and another that had a relatively larger anisotropy rate of 2.6%. Then, the stresses loaded by a tensile test machine were estimated. The results showed good agreement with the given stresses for both specimens. These results confirm that the proposed method can be applied to estimate the surface stress state in anisotropic material plates. The proposed method is simple, practical, and is expected to be useful for monitoring changes of surface stress before and after machining such as the punching or bending of plate.  
acoustoelastic effect; stress estimation; surface waves; weak anisotropy material +
Footnote †: journal: Applied Sciences
# An Intelligent Collaborative Charging System for Open-Pit Mines
## 2 Current Development Status of Key Technologies and Equipment in Open-Pit Mining  
### Current Status of Rock Drilling and Blasting Technology and Equipment in Open-Pit Mines  
Rock drilling and drilling, charging, and blasting are pivotal technologies in open-pit mining, directly impacting mine production efficiency and operational costs [29]. Bench blasting serves as the predominant method in open-pit mining operations. Among its variants, deep-hole bench blasting is widely adopted due to its compatibility with large-scale mining equipment, feasibility for automation, and high production efficiency [30; 31; 32; 33]. The integration of IoT, artificial intelligence, and photogrammetric technologies (e.g., 3D laser scanning and UAVs) [34] has enabled predictive analysis and quantitative design in deep-hole bench blasting. This advancement drives the maturation of intelligent blasting systems, significantly enhancing the efficiency and automation of open-pit mining operations [35; 36].  
In recent years, with the rise of intelligent construction in mines, rock drilling and perforation machinery equipment has been continuously upgraded [37]. Currently, the primary equipment for bench drilling in open-pit mining includes down-the-hole (DTH) drills and rotary drill rigs [38]. The literature shows that the development of foreign open pit drilling equipment came earlier, and the leading suppliers include Atlas Copco, Sandvik, etc. Research and development for rock drilling and perforation equipment in domestic mines started relatively late, but the development speed is fast [39]. Currently, mature ones include XCMG, Sany Heavy Industry, Shanhe Intelligent, etc. Some open-pit mines have achieved localization of rock drilling and perforation equipment [40].  
Driven by rapid technological advancements and intelligent manufacturing, rock drilling and perforation equipment is evolving toward intelligent systems. Modern technologies including 3S, 5G communication, IoT, AI, and automatic control have enabled intelligent guidance, precise positioning, and autonomous drilling functions in rock drilling equipment, becoming pivotal technologies for open-pit mine intelligence [41; 42]. By integrating satellite positioning and 5G technologies into equipment such as down-the-hole (DTH) drills and rotary blasthole drills, equipped with 3D electronic positioning systems, these devices achieve intelligent guidance and millimeter-level positioning accuracy. This facilitates line-of-sight remote control, teleoperation, and fully autonomous drilling operations. For instance, the Qidashan Iron Mine has developed a digital intelligent drilling system by implementing navigation positioning and information management modules, enabling full-cycle intelligent workflows for rotary blasthole drills, including precise positioning, autonomous hole searching, and high-efficiency drilling [43; 44; 45; 46].
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 4 Experiments
### Receiver Signal Processing  
The received waveform from the test-bed is stored in a binary form and transferred to the control PC. Note that, with 512 MSps sampling speed, 16-bit integer format, and complex I and Q data, the data rate to store corresponds to 2 GB for every second of the experiment. Most of the subsequent processing is performed in a MATLAB environment, except for the persistent homology computation, which is a Python code obtained from [32].  
The received signal is for the first time synchronized via cross-correlation of the received time-domain signal with its known replica, then fine synchronization via an FFT-based approach [33] is performed. Next, the received signal is gain-compensated for a  
\begin{table}
\begin{tabular}{l l} \hline \hline \(M\), number of subcarriers [-] & 1900 \\ \hline \(M_{0}\), number of zero subcarriers [-] & 100 \\ \hline \(N\), number of symbols in OTFS slot [-] & 2000 \\ \hline \(\Delta_{d}\), delay resolution & 2.1 ns \\ \hline \(\Delta_{D}\), Doppler resolution & 128 Hz \\ \hline Number of pilot guards in delay domain [-] & 48 \\ \hline Number of pilot guards in Doppler domain [-] & 48 \\ \hline Sampling frequency & 512 MHz \\ \hline Signal bandwidth & \(\approx\)490 MHz \\ \hline Data symbols & 4-QAM, uncoded \\ \hline Communication data rate & \(\approx\)920 Mbit/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: OTFS system parameters.  
Figure 8: Measurement scenario with physical dimensions of the monitored environment.  
one-tap complex attenuation factor. This makes the synchronization task simple. In real communication scenarios, some kind of synchronization preambles can be used.  
Subsequently, after the cyclic prefix removal, all the OTFS frames are processed with the function inverses to Equations (1) and (2) to transform the received signal to the delay-Doppler domain. As our primary interest is on the sensing part, all the data symbols are discarded and only the guard band around the embedded pilot (see Figure 1) is further processed. Note that the size of the guard band in both delay and Doppler domains has been set to eliminate the interference between the data and the reflected pilot. If this is not the case, the modulation symbol cancellation technique from [17] should be used.
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
## 3 Study Area and Datasets
### SAR Imagery  
RADARSAT-2 images (HH and HV polarizations) acquired on 29 dates during the ice season of 2014 were used for this study. All images were obtained in ScanSAR Wide beam mode with a pixel spacing of 50 m and a scene size of 500 km \(\times\) 500 km in range and azimuth. This mode of image acquisition is used in operational ice monitoring due to wide swath and appropriate spatial resolution for this task. The temporal resolution is 1-3 days and incidence angle ranges from 20\({}^{\circ}\) to 49\({}^{\circ}\) for both Lake Erie and Lake Ontario. The list of dates and acquisition times as well as information regarding the dataset split into train, test, and validation, can be found in Table 1. Of the 29 images that cover Lake Erie and/or Lake Ontario, ten are used for training and ten are withheld as independent test data. The trained model is evaluated on 19 dates in total (due to scene coverage six are used to evaluate ice mapping in Lake Ontario, and all 19 images are used to evaluate ice mapping in Lake Erie).  
Figure 2: 2–m air temperature (averaged hourly) of (**a**) Lake Erie recorded by Erie AUT, Long Point AUT, Southeast Shoal AUT, and Point Pelee CS. (**b**) Lake Erie recorded at Erieau AUT, Long Point AUT, Southeast Shoal AUT, and Point Pelee CS weather stations. Air temperatures obtained within the hour of RADARSAT–2 image acquisitions are shown as orange circles [35].  
RADARSAT-2 images (HH and HV polarizations) acquired on 29 dates during the ice season of 2014 were used for this study. All images were obtained in ScanSAR beam mode with a pixel spacing of 50 m and a scene size of 500 km \(\times\) 500 km in range and azimuth. This mode of image acquisition is used in operational ice monitoring due to its wide swath and appropriate spatial resolution for this task. The temporal resolution is 1-3 days, and the incidence angle ranges from 20\({}^{\circ}\) to 49\({}^{\circ}\) for the two lakes. The list of dates and acquisition times as well as information regarding the dataset split into Train, Test, Validation, can be found in. Of the 29 images that cover Lake Erie and/or Lake Ontario, ten are used for training and ten are withheld as independent test data. The trained model is evaluated on 19 dates in total (due to scene coverage six are used to evaluate ice mapping in Lake Ontario, and all 19 images are used to evaluate ice mapping in Lake Erie).
## 5 Conclusions  
In contrast to spaceborne SAR, the motion error of the airborne platform is not negligible. The traditional channel error estimation method, which neglects the motion error, would miscalculate the error. After compositing the error with the traditional method, the azimuth ambiguity component in distributed SAR HRMS imaging is still apparent.  
In this paper, a signal model for the distributed SAR on a single airborne platform with channel error and motion error is constructed. A joint estimation method of the channel phase error and motion error for this distributed SAR based on the TDCM is then proposed. Finally, the effectiveness of the proposed method is verified by simulation and real data processing. The proposed method effectively solves the problem of joint estimation of channel error and motion error. Moreover, the azimuth ambiguity component in HRMS imaging is effectively suppressed after compensating for the estimated signal error. The proposed method provides a solution for correctly estimating the signal error for HRMS imaging. The estimation of signal error for distributed SAR on several airborne platforms with different error motions needs to be studied.  
Conceptualization, H.L. and C.Z.; methodology, C.Z.; software, C.Z.; validation, C.Z., H.L. and L.L.; formal analysis, H.L. and C.Z.; investigation, C.Z.; data curation, C.Z. and L.L.; writing--original draft preparation, C.Z.; writing--review and editing, H.L., S.L. and C.Z.; supervision, Z.D.; project administration H.L. and Z.D.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Program of the National Natural Science Foundation of China, grant numbers 11833001 and 61931002.  
The authors declare no conflict of interest.  
Figure 15: Azimuth spectrum comparison.
## 1 Introduction
### Problem Statement  
Deploying MDAO in system development allows to explore large parts of the design space, identify technical variants with the best objective values and resolve conflicting objectives appropriately. However, several challenges arise in classical document-centered system development approaches (Figure 1, left). These include for MDAO:  
* high initial efforts due to distributed data
* high reconfiguration efforts
* black box behavior due to lack of formalization The high initial effort arises in the setup of MDAO. Various development data of the system are required for MDAO. This includes, among others, requirements, models and parameter sets. This development data is usually distributed among different sources and participants in the project and must be gathered costly. Once executed, the results of MDAO must be distributed accordingly to each development data source with corresponding effort. Which development data and system aspects are ultimately included in MDAO and how they are linked is often not formalized, so MDAO exhibits black box behavior. The high reconfiguration effort arises because changes to development data are communicated via several different paths. The lack of formalization makes it difficult to implement changes.  
The linking of MDAO and MBSE is shown in Figure 1, right. In this case, the MBSE system model acts as a single source of truth (SSO) for MDAO, as all data (requirements, models,...) related to the developed product are centrally stored. The initial effort for MDAO is reduced because the required development data exists in the MBSE system model in parts or completely [26]. Results of MDAO are systematically transferred to the linked model elements of the system architecture. The formalization of MDAO in the MBSE system model resolves its black box behavior. Therefore, which and how development data are considered in MDAO is always traceable. Changes to the development data have an immediate effect on MDAO, so that the reconfiguration effort of the MDAO problem is reduced. Due to these benefits, numerous research engages with methods for linking MBSE and MDAO.
## 2 Antenna Design
### Parametric Analysis  
A parametric analysis is used to investigate the variation in dimensions of the projected antenna as well as its influence on antenna properties. This section displays a thorough antenna construction study in order to achieve UWB and dual band-notch frequency characteristics. Initially, a circular monopole antenna is designed with the full ground plane. To attain a wider impedance bandwidth, the length of the ground plane (b6) is reduced successively with a step size of 4 mm. The reduction in a ground plane influences the Q-factor due to the alteration of transmission line properties and increases the operating bandwidth.  
Further, to obtain the UWB spectrum, the patch's radius (b1) varies from 3.6 mm to 7.6 mm. The optimal radius is chosen as 5.6 mm, considering the compactness of the antenna. A rectangular stub with the dimensions of b2 and b3 is attached to the circle of the patch to enhance impedance bandwidth and reflection coefficient values. Finally, a rectangle slot of b7 and b8 dimensions were carved on the ground plane to improve the S11 curve on the higher frequency side. Figure 6 depicts the changes in various UWB antenna parameters and their influence on the S11 curve. Table 1 shows the ideal values for these parameters.  
Figure 6: The variations in the parameters of the UWB antenna and their impact on the S11 curve (a) b1, (b) b2, and b3 (c) b6, (d) b7, and b8.  
Variations in the n5 and n6 parameters considerably affect frequency band notching, as seen in Figure 7. The n5 is modified in 0.1 mm increments from 0.05 to 0.35 mm, and when the width increases, the notching frequency band moves to the higher frequency. The optimal value of n5 for the required band notch of the WLAN frequency spectrum is 0.25 mm. For the second band notch, n6 is gradually increased from 0.5 to 0.9 mm. To cover the whole X-band satellite communication band notching, the optimal value of n6 is chosen as 0.8 mm; with other values of n6, the bandwidth of notching is expanded.