esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Simulation
### Performance on the Tree Topology
#### 5.2.1 Finding the Appropriate \(n_{base}\)  
As discussed in Section 4.2, we use \(n_{base}\) to specify a base number of copies for the topology. Firstly, we need to find the appropriate \(n_{base}\). The base number of copies \(n_{base}\) is supposed to reflect the properties of the topology. For any content, the upper limit of its number of copies equals the number of caching nodes in the topology (in this case, every caching node will cache one copy). Thus, an intuitive assumption is that \(n_{base}\) is proportional to the number of nodes. As the number of caching nodes is 40, we set \(n_{base}\) to (10, 40, 160) and unlimited (without controlling the number of copies).  
Figure 3 shows the performance of PB-NCC with different \(n_{base}\) when the Zipf parameter \(\alpha\) changes, while Figure 4 shows the performance when the cache size ratio changes. From the simulation results, we can find that applying the Number of Copies Control (NCC) will bring a higher cache hit ratio and lower server load. As for the network latency, applying NCC will reduce it in most parameters. Larger \(n_{base}\) results in a poor cache hit ratio but could reduce network latency, for the reason that, as the number of copies increases, content copies are more likely to be closer to users. In general, setting \(n_{base}\) equal to the number of caching nodes will get near the highest cache hit ratio and the lowest server load, as well as the lower latency in most cases. Thus, we set \(n_{base}\) equal to the number of caching nodes in the following experiments.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Topology & Tree topology/ISP like topologies \\ Traffic workload & Zipf distribution/YouTube video traffic \\ Range of Zipf parameter \(\alpha\) & 0.4–1.0 \\ Size of the content set & \(10^{5}/5.81\times 10^{5}\) \\ Request number & \(2\times 10^{5}/1.46\times 10^{6}\) \\ Request distribution & Poisson distribution \\ Request rate & 10 req/s \\ Cache allocation & Uniform \\ Content replacement & LRU \\ Number of experiments & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameters.
## 2 Battery Modeling Methods
### Equivalent Circuit Model
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Expression** \\ \hline _Rint_ model [38] & \\ \(\bullet\)\(U_{\alpha c}\) & \(U_{t}=U_{\infty}-I\cdot R_{0}\) & \\ \(U_{t}\) is the terminal voltage, \(U_{\alpha c}\) indicates the OCV. \(I\) is the discharging current and \(R_{\alpha}\) is the \(O\)_lmn_ resistance. \\ \hline _Thevenin_ model [39; 40] & \\ \(R_{0}\) & \(U_{t}=U_{\infty}-U_{1}-I\cdot R_{0}\) & \\ \(R_{1}\) is the polarization resistance and \(C_{1}\) is the polarization capacitance, \(U_{1}\) is the voltage of the RC network. \\ \hline PNGV model [41] & \\ \(R_{0}\) & \(U_{t}=U_{\infty}-U_{cap}-U_{1}-I\cdot R_{0}\) \\ \(C_{cap}\) is the bulk capacitance. \\ \hline \(U_{\alpha c}\) & \\ \(R_{1}\) & \(R_{2}\) & \(I\) \\ \(R_{0}\) & \(U_{t}=U_{\infty}-U_{cap}-U_{1}-U_{2}-I\cdot R_{0}\) \\ \(C_{cap}\) & \(C_{1}\) & \(R_{2}\), \(C_{2}\) are the concentration polarization resistance and capacitance. \\ \hline \(C_{cap}\) & \\ \(C_{cap}\) & \\ \(C_{cap}\) & \\ \(C_{cap}\) & \\ \(C_{cap}\) & \\ \(C_{cap}\) & \\ \(C_{cap}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The expressions of the four common ECMs. model on the SOC and State-of-Power (SOP) estimation are also discussed in [45]. The experimental results on the New European Drive Cycle (NEDC) show that two RC ECM is an optimal choice for the energy storage system and one RC ECM with hysteresis voltage is preferred for battery with strong hysteresis effect in the terminal voltage. Therefore, adding more RC networks generally improves the modeling accuracy, but having more than two RC networks also increases the computational burden. ECMs can be modified in several ways to improve their ability to describe the terminal voltage. In order to describe the inherent electrochemical property of the battery, a physically based ECM is proposed through analyzing the Warburg element from the Electrochemical Impedance Spectroscopy (EIS) measurement [46]. The difficulty of online EIS measurement in real-time applications may decrease the accuracy of the physically based ECM. For a better interpretation of electrochemical and thermal behaviors, a multiphysical battery model with 11 parameters is proposed in [47]. The modeling accuracy is less than 2% for the electrical part and the mean error is 2.45% for the thermal part in the experimental test. For real applications, there should be a good tradeoff between the complexity and accuracy before applying ECM to a specific application.
## 3 Network Architecture
### Design Goals  
The SB can disconnect the power line in case of a fault in its line, in which case, the SB tries to communicate with the CC to hand over the load to another line as fast as possible. The goals of this design are as follows:  
* Secure the communication channel between the CC and the SBs to avoid cyber-attacks.
* Reduce the overhead of the network in terms of computation and communication costs.
* Anonymous IDs for the SBs and the CC to avoid receiving a fabricated faked fault.
* Prevent different types of attacks.  
Figure 3: Network Architecture.  
Figure 2: The single-line diagram of IEEE 33-bus network.
# A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification
## 3 Experiments and Results
### Hierarchical Classification Results  
Table 5 presents the results of precision, recall, F1, accuracy, and specificity for the 2-class, 5-class, and 7-class hierarchical classification of Herlev database nuclei images. The 7-class classification without hierarchy allows us to analyze and determine if the hierarchical classification improves the task. The best results are highlighted in bold. It is possible to observe that the RF classifier performs better than k-NN, Ridge, and DT, considering all metrics and number of classes. The results also reveal that the hierarchical methodology improves the classification.  
In turn, Table 6 shows the results of 2-class, 3-class, 6-class hierarchical classification, and 6-class classification without a hierarchy of the CRIC database nuclei images. These results are similar to those for Herlev: our findings show that the RF is the best classifier, and the hierarchical methodology improves the classification. In RF, an ensemble learning technique [61], multiple decision trees are combined in a committee, known as boosting [65], whose final performance is better than the base classifiers. Each decision tree is trained with different features and is responsible for predicting diverse data in the classifier. Thus, the decision boundary becomes more stable and accurate with more trees. Simultaneously, the unpruned and diverse trees result in a high resolution in the feature space and a smoother decision boundary between the classes. These essential characteristics of RF, combined with the nonlinearity correlation of features, contribute to the good classification prediction [66].  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & **Prec.** & **Rec.** & **F1** & **Acc.** & **Spec.** \\ \hline \multirow{4}{*}{7-class classification without hierarchy} & RF & **0.8187** & **0.8172** & **0.8139** & **0.9476** & **0.9695** \\  & k-NN & 0.7031 & 0.7092 & 0.6916 & 0.9168 & 0.9515 \\  & Ridge & 0.7544 & 0.7490 & 0.7406 & 0.9282 & 0.9581 \\  & DT & 0.7069 & 0.7012 & 0.
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 3 System Design
### Sub-Clustering Algorithms
#### 3.4.2 Random Forest (RF) Classifier  
Random forest (RF) is an ensemble tree-based supervised learning algorithm. The building blocks of RF are decision trees (DTs) and the final prediction of the RF is obtained by combining the majority vote of different DTs [36]. RF exhibits great performance with heterogeneous feature space and high dimensional data. RF is insensitive to overfitting, however, it is computationally demanding with high-dimensional data and large forest size [37]. DT makes predictions by applying feature-based splits, and it depends on the impurity of the dataset. Features with the lowest impurity or Gini index are treated as root nodes.
## 3 Exploring Speaker-Independent Dysarthric Speech Recognition
### Datasets and Data Split
#### 3.2.2 In-Domain Canonical Speech  
We utilized seven dysarthric speech datasets covering the five languages mentioned above to provide both in-domain canonical and dysarthric speech data. Table 1 provides the statistics of these datasets, including the length as well as the number of speakers and utterances for the canonical and dysarthric parts.  
Six of the dysarthric datasets include recordings of the same content as the dysarthric speech within the dataset spoken by canonical speakers. These recordings are used as the in-domain canonical speech for target speakers within the respective datasets, referred to as **C_indm**. For the Dutch dataset **Domotica**, which lacks canonical speakers, we use the canonical part of the **COPAS** dataset as its in-domain canonical data. The **COPAS** dataset covers 21% of the words and 100% of the phones in **Domotica**. The dysarthric part of **COPAS** is excluded in this work due to its limited number of recordings per speaker.  
In model training involving **C_indm** data, 90% of the data from each dataset are used for training, with the remaining 10% for validation. For canonical speakers in the **UASPEECH** dataset, which provides a predefined data split, data from block 1 and 3 are used for training, and block 2 is used for validation.
# Analysis of Life Cycle Environmental Impact of Recycled Aggregate
## 3 Analysis of Life Cycle Impact Assessment (LCIA)
### Result of LCIA  
The life cycle impact assessment (LCIA) of recycled aggregate (dry) was 2.94 \(\times\) 10\({}^{-2}\) kg-CO\({}_{\rm 2 eq}\)/kg, 2.93 \(\times\) 10\({}^{-5}\) kg-SO\({}_{\rm 2 eq}\)/kg, 5.44 \(\times\) 10\({}^{-6}\) kg-PO\({}_{4}\)\({}^{3}\)\({}_{\rm eq}\)/kg, 4.70 \(\times\) 10\({}^{-10}\) kg-CFC\({}_{\rm 11 eq}\)/kg, 1.25 \(\times\) 10\({}^{-5}\) kg-C\({}_{\rm 2}\)H\({}_{\rm 4 eq}\)/kg, and 1.60 \(\times\) 10\({}^{-5}\) kg-Antimony\({}_{\rm eq}\)/kg for GWP, AP, EP, ODP, POCP, and ADP, respectively, as shown in Table 4.  
LCIA of recycled aggregate (wet) was 3.81 \(\times\) 10\({}^{-2}\) kg-CO\({}_{\rm 2 eq}\)/kg, 4.13 \(\times\) 10\({}^{-5}\) kg-SO\({}_{\rm 2 eq}\)/kg, 6.33 \(\times\) 10\({}^{-6}\) kg-PO\({}_{4}\)\({}^{3}\)\({}_{\rm eq}\)/kg, 5.80 \(\times\) 10\({}^{-10}\) kg-CFC\({}_{\rm 11 eq}\)/kg, 1.53 \(\times\) 10\({}^{-5}\) kg-C\({}_{\rm 2}\)H\({}_{\rm 4 eq}\)/kg, and 2.10 \(\times\) 10\({}^{-5}\) kg-Antimony\({}_{\rm eq}\)/kg for GWP, AP, EP, ODP, POCP, and ADP.  
This shows that the LCIA of recycled aggregate (wet) was greater than that of recycled aggregate (dry), and that GWP and ADP of recycled aggregate (wet) were 30% higher than for recycled aggregate (dry). ODP and POCP were also greater by 20% and AP was greater by 40%. EP showed a difference of about 16%, because it was determined according to the electric power of the additional facility (impact crusher) used for producing recycled aggregate (wet). This was because more carbon dioxide (CO\({}_{\rm 2}\)), trichlorofluomethane (CFC-11), chlorodifluoromethane (HCFC-22), phosphate (PO\({}_{4}\)\({}^{3-}\)), crude oil, ammonia (NH\({}_{\rm 3}\)), nitrogen oxides (NO\(\times\)), and ethylene (C\({}_{\rm 2}\)H\({}_{\rm 4 eq}\)), which impact the environment as more electric power was consumed, were directly and indirectly released.  
Among environmental impact categories, the manufacturing stage of the life cycle constituted approximately 81%, 72%, 70%, and 83% of GWP, AP, EP, and ODP, respectively.  
However, the raw material stage was responsible for the highest proportion of POCP at 66% and the transportation stage for 81% of ADP.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Division**} & \multicolumn{1}{c}{**GWP**} & \multicolumn{1}{c}{**AP**} & \multicolumn{1}{c}{**EP**} & \multicolumn{1}{c}{**ODP**} & \multicolumn{1}{c}{**POCP**} & \multicolumn{1}{c}{**ADP**} \\  & **(kg-CO\({}_{\rm 2 eq}\)/kg)** & **(kg-SO\({}_{\rm 2 eq}\)/kg)** & **(kg-SO\({}_{\rm 2 eq}\)/kg)** & **(kg-PO\({}_{4}\)\({}^{3-}\)\({}_{\rm eq}\)/kg)** & **(kg-CFC\({}_{\rm 11 eq}\)/kg)** & **(kg-C\({}_{\rm 2}\)H\({}_{\rm 4 eq}\
# A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis
## 4 Case Study  
### Experimental Setup  
To demonstrate the efficacy of the proposed error confinement technique, we adopt image encoding and decoding as case study of error confinement, which can better visualize the quality of protection under fault injection. As shown in Figure 7, six typical images are chosen to verify the feasibility of proposed method for images with various features. The quality of the output image compared to the original one is evaluated using the peak signal to noise ratio (PSNR) [29]. A typical PSNR value for an acceptable lossy image is 30 dB.  
Our experimental platform is based on Ubuntu 16.04.1 LTS, and software tools are GCC 5.4.0 and QMake 2.01a. The PCA-based error confinement is implemented in C++, where the flow of experiment is presented in Figure 8.  
Figure 7: Six images to test feasibility of proposed method for memory protection.  
The storage space occupied by \(\overline{\mathrm{X}}_{\mathrm{i}}\) is much smaller than the one occupied by the projection data \(Y_{\mathrm{i}}\) or the eigenvectors\(V_{\mathrm{Ki}}\), so we can roughly treat that the probability of an error occurring in \(\overline{\mathrm{X}}_{\mathrm{i}}\) is extremely low and can even be ignored. The following experiments address the fault tolerant reconstruction of the original data when errors occur in the projection data \(Y_{\mathrm{i}}\) or the eigenvectors \(V_{\mathrm{Ki}}\).  
To demonstrate the validity of the proposed error confinement technique, we first use random functions to generate random numbers which determine the location of the errors, then we force the bit flipped errors in those location, which can be used to simulate real random errors.  
For comparison, we replicated a similar infrastructure by adopting conventional single error correction double error detection (SECDED) ECC of Hamming coding format H(38, 32), which requires six parity bits for encoding each 32 bit memory word.
## 2 Literature Review
They are categorized into two classes including heuristic and infrastructure-based. Heuristic path planning is a widely used technique to reactively find the paths for UAVs to move. They usually utilize a greedy function to find routes according to the applications or user requirements, such as reduced delay. For example, IEMF (Itinerary Energy Minimum for First-source-selection) [13] is a greedy algorithm that heuristically finds the closest visiting location via minimum battery consumption path. This allows UAVs to start from a starting location, and finds the next location with minimum distance and consumed energy. On the other hand, infrastructure-based UAV path planning forwards UAVs through an infrastructure (e.g., tree or chain). Tree-Based Itinerary Design (TBID) [14] proposes a tree-based infrastructure (spanning tree) for mobile agents (e.g., UAVs) to move through and collect sensory data. For this, TBID forms a set of concentric zones around the (single) user access point. In turn, a tree is formed from the most inner zone to the outer ones through minimized euclidean distance links. The inter-zone links form the tree trunk, whereas the intra-zone links shape the tree branches. RL-enabled UAV route planning approaches offer remote sensing applications a number of benefits as compared to heuristic and infrastructure-based path planning. Heuristic routing addresses increased UAV resource consumption and data collection latency, especially when the field (e.g., farm) is wide. This forwards UAVs to find the path according to a greedy function with no environment exploration and interaction. On the other hand, infrastructure-based path planning needs to set-up a routing infrastructure for the UAV to go through. This results in significantly increased UAV resource consumption, especially if the environment is highly dynamic. Due to these issues, this paper aims to design a RL-enabled path planning algorithm through which UAVs interact with the environment and learn how to move throughout the field to complete the mission with minimized recourse consumption or delay, and maximized data collection.
## 4 Results
### Proximate Analysis  
The results obtained in the proximate analysis are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Materials** & **Test** & **C (wt\%)** & **H (wt\%)** & **N (wt\%)** & **O (wt\%)** \\ \hline \multirow{3}{*}{Sugarcane bagasse} & As received & 47.30 & 6.56 & 0.601 & 45.54 \\  & 300 \({}^{\circ}\)C & 78.60 & 6.46 & 0.381 & 14.56 \\  & 400 \({}^{\circ}\)C & 77.50 & 7.59 & 0.305 & 14.61 \\ \hline \multirow{3}{*}{Cashew nutshells} & As received & 53.50 & 6.39 & 0.435 & 39.68 \\  & 300 \({}^{\circ}\)C & 69.50 & 5.19 & 0.962 & 24.35 \\  & 400 \({}^{\circ}\)C & 76.70 & 3.49 & 1.060 & 18.75 \\ \hline \multirow{3}{*}{PKS} & As received & 52.00 & 5.43 & 0.322 & 42.25 \\  & 300 \({}^{\circ}\)C & 58.50 & 3.56 & 0.603 & 37.34 \\  & 400 \({}^{\circ}\)C & 56.00 & 2.90 & 0.613 & 40.49 \\ \hline \multirow{3}{*}{Coconut shells} & As received & 46.70 & 5.35 & 0.869 & 47.08 \\  & 300 \({}^{\circ}\)C & 70.10 & 3.60 & 1.120 & 25.18 \\ \cline{1-1}  & 400 \({}^{\circ}\)C & 74.10 & 3.28 & 1.030 & 21.59 \\ \hline \multicolumn{2}{c}{\(p\) value: feedstock} & \multicolumn{1}{c}{0.0232} & 0.0174 & 0.0013 & 0.0042 \\ \multicolumn{2}{c}{\(p\) value: thermal treatment} & \multicolumn{1}{c}{<0.001} & 0.5446 & 0.5944 & \multicolumn{1}{c}{<0.001} \\ \hline \end{tabular}
\end{table}
Table 2: Results obtained in the elemental analysis (average values shown).  
Moisture content does not show a statistically significant variation with respect to either feedstock or thermal treatment. Moisture presents a generalized downward trend in all materials, except for sugarcane bagasse, wherein the values are, respectively, 3.31%, 3.79%, and 3.67%. In the case of cashew nutshells, there was a decrease from 5.85% to 3.55%, but followed by an increase to 5.24% for the test carried out at 400 \({}^{\circ}\)C, likely related to the fact that the samples were not stabilized in the desiccator after being removed from the muffle. The volatile content shows a downward trend in all samples. The ash content shows an upward trend in all samples, related to the concentration of non-volatile materials and directly proportional to the mass losses which were, for 300 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, and for each of the materials in the order of Table 1, of 60.16% and 73.63%, 37.77% and 76.67%; 35.79% and 75.46%; and 59.46% and 64.72%, respectively. The fixed carbon content shows an upward trend in all analyzed samples.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 4 Experimental Evaluation
### Evaluation of the Remote Sensing Image Retrieval Algorithm
#### 4.3.2 Performance Test  
To validate the performance of the developed remote sensing image retrieval algorithm based on scene graph similarity, the algorithm scene graph similarity-based remote sensing image retrieval algorithm (SGSRSIIR) proposed in this research was compared with the remote sensing image retrieval algorithms [49] asymmetric multimodal feature matching network (AMFMN), metric-learning-based deep hashing network [50] (MiLaN), and asymmetric hash code learning [51] (AHCL). The experimental results are summarized in Tables 5 and 6.  
In Tables 5 and 6, it is observed that the remote sensing image retrieval algorithm developed in this research improved Precision@K and Recall@K metrics compared to previous methods on the RSSG dataset. In the comparison to previous methods, AMFMN completes retrieval by extracting the salient features of images, while MiLaN and AHCL achieve retrieval by transforming deep image features into hash codes. Unlike previous methods, this study proposes a remote sensing image retrieval algorithm that does not use global image features but rather constructs a scene graph based on the semantic information of objects contained in the image. On the basis of the traditional graph convolutional network, an edge-feature-based EGCN graph feature extraction network is designed. The category information of objects in the image and their positional relationships are transformed into features in the scene graph, while the Euclidean distance and corresponding angles between two objects are used as edge features in the scene graph. This allows for a more effective utilization of edge features, improving the network's feature extraction capabilities and enhancing retrieval performance. A neural tensor network (NTN) is employed to replace Euclidean distance and cosine similarity for calculating the similarity between feature vectors. Compared with traditional similarity calculation methods, the NTN network is better suited to learning the nonlinear relationships between two graph feature vectors, resulting in a better retrieval performance.
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 4 Experiments
### Overall Comparisons
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **AC** & **PR** & **RC** & **F1** \\ \hline \multirow{4}{*}{USTC-TFC2016} & MT-FBERT & 0.9996 & 0.9901 & 0.9993 & 0.9993 \\  & FlowPrint & 0.8904 & 0.6471 & 0.8462 & 0.7333 \\  & TFE-GNN & **1.0000 *** & **1.0000** & **1.0000** & **1.0000** \\  & YaTC & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ \hline \multirow{4}{*}{CICMalAnal2017} & MT-FBERT & **0.8688** & **0.8700** & **0.8655** & **0.8683** \\  & FlowPrint & 0.4936 & 0.4382 & 0.4389 & 0.4376 \\  & TFE-GNN & 0.2696 & 0.2959 & 0.2696 & 0.2061 \\  & YaTC & 0.7283 & 0.7314 & 0.7277 & 0.7055 \\ \hline \multirow{4}{*}{IoT-22} & MT-FBERT & 0.9998 & 0.9998 & 0.9998 & 0.9940 \\  & FlowPrint & 0.9574 & 0.9574 & **1.0000** & 0.9783 \\  & TFE-GNN & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\  & YaTC & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ \hline \multirow{4}{*}{IoT-23} & MT-FBERT & **0.8023** & **0.7986** & **0.8065** & **0.8266** \\  & FlowPrint & 0.5647 & 0.5179 & 0.5415 & 0.5172 \\ \cline{1-1}  & TFE-GNN & 0.6962 & 0.7300 & 0.6962 & 0.6907 \\ \cline{1-1}  & YaTC & 0.7449 & 0.7438 & 0.7449 & 0.7433 \\ \hline \hline \end{tabular}  
* A number in bold means that the corresponding method performs better than its counterpart does, for the same metric and the same dataset.  
\end{table} 
Table 2: Overall comparisons of four methods on four malicious traffic datasets. where \(n_{1}\) and \(n_{2}\) are the number of all metrics of MT-FBERT and FlowPrint. Third, the degree of freedom (DOF) is calculated based on \(DOF=n_{1}+n_{2}-2\), and the critical value \(t_{critical\_value}\) is retrieved from the T-distribution table with respect to DOF at a 95% confidence interval. The results can be found in Table 4. We can see that there are significant improvements when we compare MT-FBERT with FlowPrint and TFE-GNN. Although the improvements are not significant between MT-FBERT and YaTC, MT-FBERT can protect the data privacy based on the FL training, but YaTC is trained based on the centralized mode, which can pose privacy concerns.