esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks
## 2 Background
### Destination-Sequenced Distance Vector (DSDV)  
DSDV is a proactive protocol [8] that involves multiple hops to reach the target node. This protocol uses routing tables located on every node for the transmission of data packets in the network. The presence of a routing table facilitates the reduction of high routing overhead and avoids loops [6]. DSDV is particularly suitable for creating a network with a lesser number of nodes; it requires a consistent update of the routing tables, which involves battery power usage and bandwidth.
# Assessment of Affordable Real-Time PPP Solutions for Transportation Applications
###### Abstract  
With the availability of multi-frequency, multi-constellation global navigation satellite system (GNSS) modules, precise transportation applications have become attainable. For transportation applications, GNSS geodetic-grade receivers can achieve an accuracy of a few centimeters to a few decimeters through differential, precise point positioning (PPP), real-time kinematic (RTK), and PPP-RTK solutions in both post-processing and real-time modes; however, these receivers are costly. Therefore, this research aims to assess the accuracy of a cost-effective multi-GNSS real-time PPP solution for transportation applications. For this purpose, the U-blox ZED-F9P module is utilized to collect dual-frequency multi-GNSS observations through a moving vehicle in a suburban area in New Aswan City, Egypt; thereafter, datasets involving different multi-GNSS combination scenarios are processed, including GPS, GPS/GLONASS, GPS/Galileo, and GPS/GLONASS/Galileo, using both RT-PPP and RTK solutions. For the RT-PPP solution, the satellite clock and orbit correction products from Bundesamt fur Kartographie und Geodasie (BKG), Centre National d'Etudes Spatiales (CNES), and the GNSS research center of Wuhan University (WHU) are applied to account for the real-time mode. Moreover, GNSS datasets from two geodetic-grade Trimble R4s receivers are collected; hence, the datasets are processed using the traditional kinematic differential solution to provide a reference solution. The results indicate that this cost-effective multi-GNSS RT-PPP solution can attain positioning accuracy within 1-3 dm, and is thus suitable for a variety of transportation applications, including intelligent transportation system (ITS), self-driving cars, and automobile navigation applications.  
intelligent transportation system (ITS); urban transportation; transport; ZED-F9P module; RT-PPP; RTK; vehicular navigation +
Footnote †: journal: _Algorithms_
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Application** & \multicolumn{2}{c}{**Main Input Datasets or Causative Factors**} & \multicolumn{2}{c}{**Algorithms Used**} & \multicolumn{1}{p{113.8 pt}}{**Algorithm with the Best Accuracy**} \\ \hline
[90] & Landslide susceptibility mapping & Elevation, slope, slope aspect, general curvature, plan curvature, profile curvature, surface roughness, TWI, SPI, slope length, NDVI, LULC, and distance from roads, rivers, faults and railways & Logistic regression, GBDT, VFI, SVM, DT, neural networks, Naive bayes, RF, deep learning, majority-based voting ensemble & Majority-based voting ensemble \\ \hline
[91] & Spatial downscaling of precipitation data & Enhanced vegetation index, altitude, slope, aspect, latitude, and longitude & RF & — \\ \hline
[92] & Downscaling soil moisture soil moisture related indices derived from MODIS and a digital elevation model & GBDT & — \\ \hline
[93] & Estimating daily reference evapotranspiration & Maximum and minimum air temperature, relative humidity, wind speed at 2 m height, and solar radiation & RF, SVR, MLP, kNN, stacking, blending & Stacking \\ \hline
[94] & Downscaling precipitation & North American multi-model ensemble model outputs & Quantile mapping, wavelet SVM, wavelet RF & Wavelet SVM and wavelet RF \\ \hline
[95] & Downscaling LST & Landsat 8 and Sentinel-2A images, SRTM data, and daily minimum and maximum air temperatures & Landsat 8 and Sentinel-2A images, MICGWML, thermal image sharpening (TsHARP), high resolution thermal sharpener for cities & Multi-factor geographically weighted machine learning (MFGWML), thermal image sharpening sharpener for cities & MFGWML \\ \hline
[96] & Growing stem volume estimation & & Bagging (CART), Bagging (kNN), Bagging (SVR), Bagging (ANN), AdaBoost (CART), AdaBoost (kNN), AdaBoost (SVR), Random ensemble with an improved weighted average (IWA) & IWA \\ \hline
[28] & Forest biomass estimation & Leaf area index, canopy height, net primary production, and tree cover data, climatic data, and topographical data & SVR, MARS, MLP, RF, ERT, SGB, GBRT, CatBoost & CatBoost \\ \hline
[58] & Forest biomass estimation & Satellite-derived leaf area index, net primary production, forest canopy height, tree cover data, climate data, and topographical data & CatBoost, GBRT, MLP, MARS, SVR, stacking & Stacking \\ \hline
[97] & Mapping fine spatial resolution precipitation & MODIS NDVI, daily land surface temperature, and SRTM DRM data & RF, CART & RF \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# A Secure and Efficient Group Key Management Scheme for Clusters of String Inverters
## 7 Conclusions
This accounted only for 9.5% of the maximum time allowed for group key distribution, i.e., 60 s. As to memory overhead, still considering a group of 50 inverters (i.e., maximum cluster size), we showed that the virtual memory size of the prototype process consisted of less than 15 MB. Of these, around 5 MB was the part resident in the main memory. This only accounted for around 1% of the overall inverter's main memory, i.e., 512 MB. Summarizing, experimental results indicate that the scheme was capable of scaling up to clusters composed of 50 inverters with an efficiency of 90.5% in terms of latency for group key distribution and 99% in terms of memory overhead. On future research studies, they may assess the limitations of the presented work and investigate possible modification to the presented scheme. Limitations were related to the aforementioned relevant metrics, namely latency for group key distribution and memory overhead. Those metrics are expected to get worse (e.g., increase) in the case of PV plants having a capacity higher than 2 MW and/or in the case of PV plants having string inverters with a nominal output power less than 50 KW. As a matter of fact, in these cases, the number of involved string inverters composing the cluster was expected to be higher than 50 nodes. Particularly, limitations should be assessed considering typical PV plants' capacity in commercial and industrial applications (i.e., ranging from 1 MW up to 6 MW) and/or considering the minimum nominal output power of typically employed inverters (i.e., 10 KW). In general, all possible combinations should be explored. If latency ends up being the main limitation, tree-based contributory approaches may be investigated. In fact, in tree-based contributory approaches, the overall communication overhead is logarithmic in the group size, rather than linear. Though, the cost introduced with tree-based approaches must also be taken into account. The cost is intended both in terms of increased complexity of the key management scheme but also in terms of increased memory overhead. In this respect, group members are indeed required to maintain a tree of cryptographic keys. Specifically, for a balanced tree, each group member is required to store (at most) (log\({}_{2}\) N) + 1 keys, with N equal to the size of the group.
# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 1 Introduction
DenseNet with attention mechanisms and multi-level TL surpassed 84% accuracy [36], while Few-Shot Learning integrated with TL achieved 95.6% accuracy and 0.97 AUC [37]. In infrared thermography, ensemble models with attention-guided Grad-CAM achieved 98.04% accuracy [38], while CNN-IoT frameworks supported real-time diagnosis with 95% accuracy [39]. In FS research, LASSO-enhanced Random Forests reached 90.68% accuracy [40], and grid-searched RF and AdaBoost models hit 99% and 98%, respectively [41]. For metastasis prediction, XGBoost and RF yielded 93.6% accuracy and 91.3% AUC [42]. Hybrid techniques such as TLBO-SSA [43] and BinJOA-S [44] achieved 98.46% accuracy and advanced multi-objective FS. Outside of BC, flower pollination-based segmentation improved lung cancer detection [45], while bacterial foraging and emperor penguin optimization hybrids excelled in BC classification with >98% accuracy and AUC > 0.998 [46]. Inspired by tumor growth dynamics, the Liver Cancer Algorithm (LCA) reached 98.704% accuracy on the MAO dataset [47]. FS wrappers using RF, SVM, and DT achieved AUCs up to 0.98 in early detection [48]. In ultrasound-based CAD systems, Autoencoders and DeepLabV3+ achieved 97.4% accuracy with strong Grad-CAM interpretability [49]. Recent segmentation strategies usingcluster-enhanced Transformer architectures, such as CenterFormer, have demonstrated robust performance in delineating complex structures in unconstrained imaging scenarios [50], which may inform future exploration of transformer-based breast cancer models. SHAP-based XAI integrated with RF outperformed clinical nomograms in NSLN metastasis prediction [51], while CNNs combined with XAI methods (LIME, SHAP, Saliency Maps) showed high interpretability [52]. Patho-Net (GRU + U-Net) achieved 98.9% accuracy in histopathology via GLCM and resizing-based preprocessing [53]. Other studies have used DenseNet201 with LIME and Grad-CAM++ for up to 99% accuracy in mammograms [54], shape-based models with t-SNE and UMAP for 98.2% AUC in 3D tomosynthesis [55], and condensed CNNs for 96.67% accuracy on MIAS + DDSM [56]. Lastly, ResNet50, optimized by the Improved Marine Predators Algorithm, attained 98.88% accuracy on MIAS and CBIS-DDSM [57]. Table 1 summarizes the methods and performance metrics of these studies. Despite these advances, most prior studies rely on single-modality imaging, lack hybrid optimization, or offer limited interpretability. To address these gaps, our work introduces a hybrid framework that integrates mammography and ultrasound data, uses MobileNet fine-tuned with DOA and FLA, and incorporates Grad-CAM for clinically reliable visual explanations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 3 Materials and Methods
### General Thermal Resistance Model for GTCs
#### 3.1.2 Thermal Resistance Model for DGTCs
Specifically, \(h_{rad(igt-gt)}\) [W/(m\({}^{2}\)K)] is the radiation heat transfer coefficient between the internal and the external glass tubes (Equation (8)), \(h_{com(igt-gt)}\) [W/(m\({}^{2}\)K)] is the convection heat transfer coefficient between the internal and external glass tubes (Equation (9)), \(A_{egt,arg,tot}\) [m\({}^{2}\)] is the total average area of the external glass tube, \(D_{egt,z}\) [m] is the external diameter of the external glass tube, \(D_{egt,z,tot}\) [m\({}^{2}\)] is the internal diameter of the external glass tube, \(A_{egt,z,tot}\) [m\({}^{2}\)] is the total external area of the external glass tube, \(h_{rad(gt-o)}\) [W/(m\({}^{2}\)K)] is the radiation heat transfer coefficient between the external glass tube and the ambient air (Equation (10)), \(h_{com(gt-gt)}\) [W/(m\({}^{2}\)K)] is the convection heat transfer coefficient between the external glass tube and the ambient air (Equation (11)), \(q_{igt-gt}\) [W/m\({}^{2}\)] is the specific heat flux exchanged between the internal and external glass tubes, and \(q_{gt-o}\) [W/m\({}^{2}\)] is the specific heat flux exchanged between the external glass tube and the ambient air. \[h_{rad(igt-gt)}=\frac{\left(T_{igt}^{2}+T_{egt}^{2}\right)\,\left(T_{igt}+T_{ gt}\right)}{\frac{1}{\sigma_{igt}}+\frac{A_{igt,tot}}{\Delta q_{egt,tot}}\left(\frac{1}{ \sigma_{egt}}-\frac{1}{\sigma}\right)} \tag{8}\]  
Figure 5: Heat transfer and resistance network for DGTC with an air layer (**a**) and DGTC with a vacuum layer (**b**): \(T_{gt}\) [K] is the external glass tube temperature. Figure 6: Triple iterative calculation algorithm for DGTC with an air layer [38; 39; 40; 41]. \[h_{conv(igt\text{-}eqt)}=\frac{Nu_{eq}k_{eq}}{D_{egt,i,eq}} \tag{9}\]  
\[h_{rad(gt\text{-}o)}=\sigma\varepsilon_{egt}\Big{(}T_{egt}^{2}+T_{o}^{2}\Big{)} \ \big{(}T_{egt}+T_{o}\big{)} \tag{10}\]  
\[h_{conv(egt\text{-}o)}=2.8+3W \tag{11}\]  
where [39; 40]\(Nu_{eq}\) [-] is the Nusselt number of the external air layer [41; 42; 43; 44], \(k_{eq}\) [W/(mK)] is the thermal conductivity of the external air layer, and \(D_{igt,i,eq}\) [m] is the equivalent diameter of the external air layer. In step I of the triple iterative calculation algorithm (Figure 6), the initial values are \(T_{abs}\), \(T_{igt}\), and \(T_{egt}\). All Equation (7) values are determined in step II using Equations (3), (4), and (8)-(11). The control equations (step III) now are \(q_{abs\text{-}gt}\), \(q_{igt\text{-}eqt}\), and \(q_{egt\text{-}o}\)[45]. When the control heat flux results were approximately equal (\(q_{abs\text{-}gt}\approx q_{igt\text{-}eqt}\approx q_{egt\text{-}o}\), with the same deviation, step IV), the calculation would be finished (step V).
## 3 Data Analysis and Forecasting Process
### Load Forecasting Based on Models  
In this section, the short-term forecasting framework is given in Figure 4. The framework starts with data pre-processing for the inputs. In this case, the following input features are used:  
1. The charging load sequence of 24 points per day from April 2017 to June 2018 is denoted as C.
2. The charging time sequence of 24 points per day from April 2017 to June 2018 is represented as T.
3. The sequence of real-time electricity prices for peak and valley periods is denoted as E. The real-time electricity price has a greater impact on the charging load. Most electric vehicles will choose to charge during the valley price period (23:00-7:00), and the charging load will reach the peak within one day. The charging load is usually at a minimum during the peak price (9:00-12:00, 14:00-17:00, 19:00-21:00).
4. The corresponding binary holiday marks H involving the weekday and the weekend are 1 and 2 respectively, and a special holiday is 3.  
Figure 4: Deep learning model based forecasting framework.  
Due to that, the deep learning models are sensitive to data scale, in this study, we adopt min-max normalization for C, T, E, and the holiday marks H are encoded by one-hot encoder. Eventually, the inputs of the models are 4-dimensional vectors shown as below:  
\[\vec{X}_{t}=[\vec{C}_{t},\vec{T}_{t},\vec{E}_{t},\vec{H}_{t}]. \tag{20}\]  
Before establishing the deep learning load forecasting model, we save the data as a file in _.csv_ format and read it in the form of (samples, time steps, features), where the input charge is the charging load at time t-1, and the charging load at time t is taken as the training label, which is shaped like (samples, 1). The model and the label are fed into the network to obtain the pre-training model, the model dimension conversion diagram is shown in Figure 5.  
In the training process of the deep learning model, the choice of loss function and optimizer is very important. In this study, we chose mean squared error (MSE) as a loss function, as follows:  
\[MSE=\frac{1}{N}\sum_{i=1}^{n}{(\hat{y}-x)^{2}} \tag{21}\]  
where N is the number of samples, \(\hat{y}\) is forecasting value, and \(x\) is actual value.  
An effective optimizer is crucial in the deep learning model. In this paper, several optimizers including Adam [27], classic stochastic gradient descent (SGD), Adagrad [28], Adadelta [29] and RMSProp [30], and Adam achieved better results than other optimizers. Therefore, Adam was selected as the optimizer for the training process.
## 3 Results
### Phase I: Biotrickling Filters Operation
#### 3.1.2 Analysis of the Liquid Phase and Pressure Drop across the Bed  
The pH measurements started during the biotic phase of the process in the recycling liquid. The pH remained roughly stable in the BTF-FAC, with initial values of 6.4 \(\pm\) 0.0 and a final value of 6.1 \(\pm\) 0.0 in the last operating stage at a GRT of 7.5 s (Figure 3a). On the contrary, a significant pH drop was observed in the BTF packed with plastic rings, reaching minimum values of 2.8 after 15 days of operation. After this initial drop, the pH in BTF-K stabilized at \(\sim\)2-2.2, slightly decreasing in the last operating stage to average values of 1.7 \(\pm\) 0.3. In the case of BTF-F, after the initial pH stabilization by day 20, a sharp drop was recorded along with the increase in H\({}_{2}\)S load, reaching values of \(\sim\)3.3 by day 36. From this day onwards, a steady decrease was observed in the pH of the recycling liquid, reaching values of 2.0 \(\pm\) 0.1 when operating at a GRT of 7.5 s. This decrease in the pH of the recirculation liquid solution was associated to the aerobic oxidation of H\({}_{2}\)S to sulphate and the concomitant accumulation of this byproduct. Finally, a stable sulphate concentration of \(\sim\)1650 mg SO\({}_{4}\)\({}^{-2}\) L\({}^{-1}\) was recorded in the trickling liquid of the BTF-F for the first 22 days of operation. Then, sulphate concentration fluctuated from a minimum value of 1186 mg SO\({}_{4}\)\({}^{-2}\) L\({}^{-1}\) on day 51 to a maximum of 1811 mg SO\({}_{4}\)\({}^{-2}\) L\({}^{-1}\) on day 36. Sulphate concentration increased from 796 to 1605 mg SO\({}_{4}\)\({}^{-2}\) L\({}^{-1}\) and from 264 to 753 mg SO\({}_{4}\)\({}^{-2}\) L\({}^{-1}\) by days 9 to 16 of operation in the BTF-FAC and the BTF-K, respectively. Then, the concentrations of SO\({}_{4}\)\({}^{-2}\) gradually increased reaching a maximum of 1897 mg SO\({}_{4}\)\({}^{-2}\) L\({}^{-1}\) in BTF-FAC by day 71 of operation and 1672 mg SO\({}_{4}\)\({}^{-2}\) L\({}^{-1}\) by day 64 in BTF-K. These concentrations were far below inhibitory concentrations of sulphate for H\({}_{2}\)S degrading bacteria.  
The pressure drop remained stable in the three systems at values of \(\sim\)9 mmHgO m\({}_{\text{bed}}\)\({}^{-1}\) in BTF-FAC and BTF-K, and was slightly lower in BTF-F (\(\sim\)6 mmHgO m\({}_{\text{bed}}\)\({}^{-1}\)) when working at a GRT of 2 min. Concomitant increases in the pressure drop were recorded when decreasing the GRT, reaching values of 15 mmHgO m\({}_{\text{bed}}\)\({}^{-1}\) at 1 min, 19 mmHgO m\({}_{\text{bed}}\)\({}^{-1}\) at 30 s, 35 mmHgO m\({}_{\text{bed}}\)\({}^{-1}\) at 15 s and \(\sim\)5 mmHgO m\({}_{\text{bed}}\)\({}^{-1}\) at 7.5 s regardless of the BTF (Figure 3b).
## 5 Experimental Evaluation
### Performance Comparison  
In this paper, the proposed OPO algorithm is compared with DQN, Double DQN, and Dueling DQN benchmark methods, and the following performance metrics are used: the average delay, energy consumption, and the number of task throw volume to verify the performance advantages of the proposed algorithm. The experimental results are shown in Figure 8. Five edge servers and 50 terminal devices are used for simulation experiments, and it can be obtained that the proposed algorithm significantly outperforms the other benchmark methods in all the above three performance metrics. This is because a combination of predictive and decision methods to deal with complex scenarios can make excellent use of the characteristics of the tasks and the load on the edge servers.  
Figure 6: Effect of threshold size on LSTM prediction task features. (a) Threshold size = 0.5 M; (b) Threshold size = 0.1 M.
## 3 Literature Review Results
### Systematic Review  
The systematic review was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. The searches were performed in the three databases mentioned in Section 2.1 (IEEE Xplore Library, EBSCO, and ScienceDirect). The documents found in such databases were identified using some search strings related to research terms, adding some recommended from  
Figure 1: Strategic diagrams based on the number of documents published: (**a**) 2014–2015 time period; (**b**) 2016–2017 time period; (**c**) 2018–2020 time period.  
external sources. The initial search yielded a total of 2139 documents. These documents were evaluated and filtered according to the PRISMA methodology. In the final phase, 56 documents of interest were selected, and quantitative and qualitative analysis was performed for such documents.  
Figure 2 shows the exclusion and inclusion results obtained in each of the phases, and the percentage of distribution of documents in each group.  
Groups identified in the included phase were: (1) articles related to mobility services of fleet control and management systems, (2) articles that are related to mobility services other than fleet control and management, (3) articles that are related to implementation algorithms and improvements, and (4) articles that are related to information security.  
The following section presents only the results obtained in the last phase of the systematic review (quantitative and/or qualitative synthesis for each one of the four groups of documents).
## 4 The Development of the MRFCO-ZTS Approach  
### The Architecture of MRFCO-ZTS-Secured LEO Satellite Network  
When applying a ZTS concept to satellite networks, classifying attackers and detector users is difficult because of the distinctive characteristics of satellite communication and the potential for sophisticated attacks. In addition, the cost function of conventional ZTS was higher in satellite networks. Hence, the MRFCO-ZTS approach is implemented by integrating the Manta Ray Foraging Optimization algorithm and ZTS for attack and detector classification via continuous verification. With the assistance of ZTS, the developed MRFCO-ZTS-secured LEO satellite system verifies satellite users continuously during communication sessions by using the rule of 'Never trust, always verify', whether the satellite users are inside or outside the network boundary. By using the ZTS protocol, the developed MRFCO-ZTS-secured LEO satellite system re-authenticates satellite users with each access request for every session by optimizing the cost function of attacker/detector classification. From this, the MRFCO-ZTS approach provides higher security levels through adding an extra layer of protection and ensuring that only authorized users and devices can access valuable resources in satellite networks. The architecture diagram of the MRFCO-ZTS-secured LEO satellite network is presented in Figure 2.  
As presented in the above diagram, the MRFCO-ZTS approach initially considers zero-trust satellite networks with many satellite users. Then, the MRFCO-ZTS approach generates the cost function of ZTS by considering multiple factors, including attack likelihood, cumulative user risk, and computational delay. Subsequently, the MRFCO-ZTS approach utilizes Manta Ray Foraging Optimization (MRFO) to optimize the cost function of ZTS for continuous user verification with better resource efficiency. As a result, the MRFCO-ZTS approach can provide stronger security for data communication in satellite environments by finding honest and attack users in satellite networks via classification.  
Figure 1: MRFCO-ZTS LEO Satellite Network Architecture (Reference from [1]).