esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Transmission Delay and Total Queuing Delay of Multi-Node End-to-End Transmission  
Figure 7 presents the simulation results of the transmission delay and propagation delay for multi-node end-to-end transmission within a single period, under the condition of an unchanged load. From the figure, it can be seen that the optimization algorithm proposed in this paper is capable of reducing the end-to-end transmission delay for multiple nodes, thus enhancing the schedulability of the network. As shown in Figure 7, different satellite formations mainly affect the inter-satellite distances. Therefore, different satellite formations will not affect the performance of this mechanism.  
Figure 8 shows the probability density function of the total queuing delay for multi-node end-to-end transmission within one cycle obtained from the simulation. As shown in the figure, the average queuing delay before optimization is 2.12 ms, and the average queuing delay after optimization is 1.07 ms. The queuing delay after optimization is only 50.47% of the delay before optimization. Thus, the optimization algorithm proposed in this paper can reduce the total queuing delay of packet transmission, thereby reducing end-to-end transmission delay.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Total number of satellites (\(N\)) & 66 \\ Satellites in each orbital plane (\(SN\)) & 11 \\ Number of particles (\(NP\)) & 20 \\ Maximum number of iterations (\(T\)) & 1000 \\ Learning factors (\(c1\), \(c2\)) & 1.5 \\ Inertia weight (\(\omega\)) & 0.9 \\ Maximum position of each particle (\(X_{max}\)) & (200, 7) \\ Minimum position of each particle (\(X_{min}\)) & (1, 2) \\ Maximum Transmission Unit (MTU) & 1500 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter table.  
Figure 7: Transmission delay and propagation delay for multi-node end-to-end transmission.
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 4 Proposed Trust Model
### Model Overall Trust  
In this section, we demonstrate how to model the overall value of trust. The overall evaluation of the service provider \(s_{j}\) from the perspective of the service client \(c_{i}\) derives from two parts, namely direct trust and indirect reputation. In general, a client trusts itself more compared to the information provided by third-party agents. At least, agents do not have self-conception. However, a client agent should better trust others if a system is unstable. Thus, the pretreatment of direct trust and indirect reputation proposed in [64] is adopted; Equation (18) shows the detailed processes. In the equation, \(\psi\) indicates the weight of direct trust, and \(1-\psi\) corresponds to the indirect reputation. The value of \(\psi\) depends totally on the stability in the MASs; a small \(\psi\) indicates agents' performances often change. Generally, \(\psi>(1-\psi)\), we use \(m_{ij}^{pre}\) to represent the pretreated BPA, then we have,  
\[\begin{array}{l}m_{ij}^{pre}(T)=\psi m_{ij}^{d}(T)+(1-\psi)m_{ij}^{ind}(T)\\ m_{ij}^{pre}(nT)=\psi mt_{ij}^{d}(nT)+(1-\psi)m_{ij}^{ind}(nT)\\ m_{ij}^{pre}(T,nT)=1-m_{ij}^{pre}(T)-m_{ij}^{pre}(nT)\end{array} \tag{18}\]  
After the pretreatment, [PERSON]'s combination rule is applied to combine \((m_{ij}^{pre}(T)\), \(m_{ij}^{pre}(nT)\), \(m_{ij}^{pre}(T,nT))\) one time as [64] stated. The client agent, therefore, can receive the final evaluation of the service provider agent. Subsequently, the resource client selects one of the most reliable service providers to increase interactions. The interactive feedback outcome is achieved after the interaction, which could be either binary results (either success or failure) or an evaluation score in the range \([0,1]\). In the following section, we describe how personal credibility is updated.
# Fine-Grained High-Utility Dynamic Fingerprinting Extraction for Network Traffic Analysis
## 3 Data Processing
### Network Traffic Segment
The information entropy only proves that the method of division by IP address proposed in this paper, IAT, is more complete and accurate than that by time interval. Specific results of two different division methods for network data flow are calculated in the experimental part based on actual network communication data according to Formula (3) and (6). The following defines IAT: Given a data stream \(seq_{T}=\left\{p_{1},p_{2},\cdots,\ p_{i},\ p_{j},\cdots,p_{N}\right\}\), any data packet \(p_{i}\) has an attribute time and IP address pair \(\left(\Delta t_{i},\{IP_{src},IP_{dst}\}\right).\) If any of the IP address pairs \(\left\{IP_{src},IP_{dst}\right\}\) of two adjacent packets \(p_{i},p_{j}\) change, the data stream is divided into two \(\left\{p_{1},p_{2},\cdots,p_{i},p_{j},p_{j+1},\cdots,p_{j}\right\}\) segments. The sequence exchange of IP addresses is not considered as the change of IP addresses. A segment of network traffic obtained by IP address is classified into \(N\)-gram types based on message types. Here, it is first expressed in sequence, and each message is represented by a label defined above. For example, 42 packets in Figure 3 can be divided into 17 segments, as shown in Figure 6.  
Some of the fragments in Figures 2-5 contain only one data packet. Most of such fragments are caused by the out-of-order and retransmission of data sent by the terminal. In this way, the obtained fragments of network traffic are meaningless and are represented by labels in Tables 1 and 2, as shown in Table 3.
# Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories
## 3 Results and Discussion
### Modeled Concentrations Versus Observations  
The inversion results were evaluated by comparing the modeled concentrations to observations of ground-based stations and GOSAT values shown in Table 2. Three years of observation, from 2010 to 2012, were used in each simulation, and the model outputted the forward concentrations (simulated with prior fluxes) and optimized concentrations (simulated with posterior fluxes). The bias (model minus observation data mismatch) and the root-mean-square error (RMSE) were calculated for cases S0 and S1. The bias was defined as the average mismatch between the observations and model (model minus observations) throughout the observation period. The RMSE was an aggregated form of the residuals (the difference between observed values and simulated values). Improvement of model simulation for reproducing the observations was found when changing the prior emissions from case S0 to S1. Table 2 shows that the mean RMSE decreased from prior to posterior emissions in both cases, and the optimized RMSE for GOSAT data was lower for the S1 case. RMSE was improved by flux optimization by almost half for both ground-based and GOSAT data. Additionally, the mean RMSEs and bias decreased from S0 to S1 for ground-based data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Number of** & \multirow{2}{*}{**Bias (ppb)**} & \multirow{2}{*}{**RMSE (ppb)**} \\  & **Observations** & & \\ \hline S0-prior ground & 89,059 & \(-\)6.49 & 45.19 \\ S0-posterior ground & 76,772 & \(-\)4.61 & 26.62 \\ S1-prior ground & 89,059 & \(-\)5.17 & 44.80 \\ S1-posterior ground & 76,481 & \(-\)4.24 & 26.40 \\ S0-prior GOSAT & 329,483 & \(-\)12.30 & 24.39 \\ \hline S0-posterior GOSAT & 272,101 & \(-\)5.29 & 12.26 \\ \hline S1-prior GOSAT & 329,483 & \(-\)14.56 & 24.14 \\ \hline S1-posterior GOSAT & 254,657 & 5.59 & 8.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean bias and the root-mean-square error (RMSE) for cases S0 and S1.  
Figure 6: Posterior natural methane fluxes (S0 (**a**) and S1 (**c**)) and scaling factors (S0 (**b**) and S1 (**d**)). (2010–2012 average, unit mg CH\({}_{4}\) m\({}^{-2}\) d\({}^{-1}\)).  
The time series of atmospheric methane concentrations at selected stations for case S1 is shown in Figure 7, including the global background in the South-West Pacific (MLO: Mauna Loa), the regional European background (PAL: Pallas), a European mountain station in Switzerland (JF): Jungfraujoch), and a station influenced by anthropogenic emissions in Asia (RYO: Ryori). The optimized emissions induced better reproduction of the methane concentrations with lower RMSE values at each site.
## 3 Results and Discussion
### Adsorption and Desorption Experiments
#### 3.2.4 Adsorption Capacity  
Figure 7 shows the effect of various concentrations on the adsorption capacity of Cd-I-MA-_co_-AN and NI-MA-_co_-AN for Cd(II). As presented in the figure, the adsorption capacity increased with the increasing concentration of Cd(II) before reaching saturation. The saturated adsorption capacities of Cd-I-MA-_co_-AN and NI-MA-_co_-AN were 20.46 and 7.64 mg g\({}^{-1}\), respectively. Cd-I-MA-_co_-AN has a higher adsorption capacity for Cd(II). The saturated adsorption capacity of the Cd-I-MA-_co_-AN was almost three times that of NI-MA-_co_-AN. This is due to the synergistic effect on imprinted ions and functional groups [36].
## 3 Results
### NO Reduction  
At the beginning, the samples were tested with a 70 W light source without any treatment (the samples were dusty). Then, the samples were cleaned with pure water, dried, placed again into the  
Figure 4: Spectraobtained by applying two UV light sources. (**a**) Original spectra of 70 W bulb, (**b**) original spectra of 300 W bulb, (**c**) spectra after passing the desiccator glass, 70 W bulb, and (**d**) spectra after passing the desiccator glass, 300 W bulb.  
The obtained results indicate that after seven years of service the tested paving blocks were still able to reduce NO content in the air (Table 1). The effectiveness of this phenomenon strongly depended on UV light intensity. The percentage of NO abatement was significantly higher when a 300 W light source was used. The surface cleanliness had a minor influence on NO abatement when the 70 W light source was used.  
During the experiments, an increase of NO\({}_{2}\) content was observed. This phenomenon occurred, because of the complex characteristics of the photocatalytic process. Before NO finally oxidizes to NO\({}_{3}^{-}\) or HNO\({}_{4}\), the formation of HNO\({}_{3}\) and NO\({}_{2}\) occurs. NO\({}_{2}\) is about 5 to 25 times more toxic than NO [20]. This indicates that in some cases, the air quality may be degraded rather than improved. Therefore, when studying the air purification performance of photocatalytic materials, both NO removal and NO\({}_{2}\) formation must be considered [21]. The maximum average increase of NO\({}_{2}\) content was 8 ppb compared with a 43 ppb average reduction in NO content. Only in the case of weak light and a dusty sample was the \(|\Delta\)NO\({}_{2}|\)equal to \(|\Delta\)NO\(|\).
# Laplacian Controllability and Observability of Multi-Agent Systems: Recent Advances in Tree Graphs
## 3 Laplacian Controllability and Observability of Starlike Graphs  
In this Section, we derive a set of results which constitute the complete theoretical characterizaton of node selection for controllability and observability for the class of starlike tree graphs.  
It is worth mentioning that the Laplacian eigentructure of starlike graphs has been widely studied also by mathematicians in the recent years for their peculiar features. For example, contrary to path graphs, which always have simple Laplacian eigenvalues, in starlike graphs Laplacian eigenvalue multiplicity may range from \(1\) to \(n-2\). Consider the two starlike graphs in Figure 1. Graph depicted in Figure 1a has \(\Lambda_{a}=\{0,1,1,1,5\}\), so that \(m_{g}(1)=3\), and at least three nodes must be selected to achieve reachability. Conversely, graph in Figure 1b has \(\Lambda_{b}=\{0,0.26,0.62,1.4,2.27,3.1,4.3\}\), and hence all eigenvalues are simple. Considering the problem of interest, the minimum number of nodes necessary to achieve complete controllability and observability in case of Figure 1a is three in the first case (and hence more than half of the nodes constituting the whole network), while in the case of Figure 1b, it is possible to have complete reachability and observability by properly selecting only one node. We show later that the differences between the two graphs are even more evident; indeed, in Figure 1a, such nodes must be chosen judiciously, while in Figure 1b reachability and observability is achieved by choosing any node of the network.  
Figure 1: Examples showing large differences in eigenvalue multiplicities. Graph (**a**) has \(m_{a}(1)=3\) and it is controllable by selecting at least three leaf nodes, Graph (**b**) has all simple eigenvalues and it is controllable by any single node.
## 3 Materials and Method
### Machine and Deep Learning Techniques
#### 3.5.1 K-Nearest Neighbors  
The K-Nearest Neighbors (KNN) algorithm is a supervised learning technique employed for classification tasks. It is recognized as one of the simplest and oldest classification algorithms. Unlike the NB (Naive Bayes) approach, the KNN algorithm does not rely on probability values but directly utilizes the data for classification without constructing a pre-defined model. In the KNN algorithm, the adjustable parameter is denoted as \"k\", which represents the number of nearest neighbors to consider when estimating the class membership of an item. By adjusting the value of \"k\", the algorithm determines the number of neighbors to be examined during the classification process.  
In this study, the KNN algorithm was applied with the parameter n_neighbors = 7, indicating that it considered the seven nearest neighbors for classification. The range of \"k\" values is explored, starting from 1 up to the maximum number of neighbors, which corresponds to the number of data points in the training set. The algorithm's performance  
Figure 6: DNN model.  
is assessed for each \"k\" value, and the \"k\" value associated with the highest accuracy is determined by identifying the maximum element and its index in the evaluation array.  
Regarding the calculation of distances between neighbors, the study utilizes the Minkowski [44] method, which computes the distance between two neighbors based on multiple variables. Formula (2) displays the formula for computing the distance using the Minkowski method for N variables. In this particular case, a value of \(p=1\) is chosen for the Minkowski distance calculation. The selection of the \(p\)-value impacts the model's performance speed, and by opting for a \(p\)-value of 1, the model achieves a trade-off between speed and accuracy.  
\[(\sum_{i=1}^{n}\mid p_{i}-q_{i}\mid^{p})^{1/p} \tag{2}\]
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 2 Backgrounds  
### Model for Collusion Attack  
In this section, we will illustrate the frequent types of collusion attack. Collusion attack is a type of unauthorized removal of fingerprint when an adversary obtains several copies of a content marked with different fingerprints [4]. The adversary combines the fingerprinted copies of given contents to attenuate the power of fingerprints of the each copy. The most frequent type of collusion attack is linear collusion. The simplest example of this type of attack is averaging the copies. Figure 1 illustrate the process of linear collusion. Alice's original content \(x\) is distributed with fingerprints \(w_{1}\) to \(w_{n}\). Suppose [PERSON] obtained the marked copies \(y_{1}\) to \(y_{n}\). [PERSON] mix those copies by averaging and add noise \(z\) and acquired attacked content \(y\). Attacked content \(y\) has reduced power of each fingerprints, the attacked contents \(y\) become similar to the original work \(x\). Therefore, [PERSON] may issue that the content \(y\) is his original content. As the number of copies increases, the power of each fingerprint becomes weaker, and harder to declare the original ownership.  
If the marking algorithm spreads the fingerprint throughout the entire contents, cut-and-paste has a similar effect to averaging attack. Algorithm such as spread spectrum-based embedding in transform domain, mixing the part of copies by cut-and-paste also reduces the power of each fingerprints. For example, suppose [PERSON] takes \(1/n\) parts equally from each copies \(w_{k}\), and paste them into new copy \(y\). If the fingerprints are embedded in transformed domain such as frequency domains, the power of each fingerprints will be attenuated. Another type of collusion attack is non-linear collusion. The concept of this type of attack is taking maximum, minimum or median, rather than taking the average, of among the observed copies.
# Profitable Double-Spending Attacks
## Appendix D  
_Comparison of Attack Success Probabilities of DS Attack and Pre-Mining Attack_  
In [12], a special case of pre-mining strategy has been considered, where the condition for a DS attack success was different from Definition 1. Specifically, the only condition was to have the fraudulent chain to grow longer than the honest chain by \(N_{BC}\), i.e., \(A(t)>H(t)+N_{BC}\) (see Section 7 of [12]). We refer to \(\mathbb{P}_{\text{pre-mine}}\) as the probability of satisfying this condition. The literature has shown that satisfying this condition suffices a success of DS attack [12]. What they have not shown, however, is that this condition is not a necessary one. Thus, we here aim to show that their condition is indeed not a necessary condition, by showing that \(\mathbb{P}_{DSA}>\mathbb{P}_{\text{pre-mine}}\) for all \(p_{A}\in(0,0.5)\). First, it has been given that \(\mathbb{P}_{\text{pre-mine}}=(p_{A}/p_{H})^{N_{BC}+1}\). Under the condition of [12], it is required that the fraudulent chain catches up with the honest chain with additional \(N_{BC}\) blocks. The catch-up probability has been derived by [PERSON] in [1] using the gambler's ruin approach as \((p_{A}/p_{H})^{k}\), where \(k\) is the number of blocks that the honest chain leads by at the initial status. Next, we refer to an intermediate step in the derivation of \(\mathbb{P}_{DSA}\) by [PERSON] [6]:  
\[\mathbb{P}_{DSA}=\sum\limits_{k=0}^{N_{BC}+1}\left(\begin{array}{c}N_{BC}+k-1 \\ k\end{array}\right)p_{H}{}^{N_{BC}}p_{A}\Bigg{(}\frac{p_{A}}{p_{H}}\Bigg{)}^{N_ {BC}-k+1}+\sum\limits_{k=N_{BC}+2}^{\infty}\left(\begin{array}{c}N_{BC}+k-1 \\ k\end{array}\right)p_{H}{}^{N_{BC}}p_{A}{}^{k}.\] (A41)Finally, clear inequalities can be used to show \(\mathbb{P}_{DSA}>\mathbb{P}_{\text{pre-mine}}\):  
\[\mathbb{P}_{DSA}>\sum\limits_{k=0}^{N_{\text{BC}}+1}\left(\begin{array}{c}N_{ \text{BC}}+k-1\\ k\end{array}\right)p_{H}{}^{N_{\text{BC}}}p_{A}\!\!\left(\frac{p_{A}}{p_{H}} \right)^{N_{\text{BC}}-k+1}+\sum\limits_{k=N_{\text{BC}}+2}^{\infty}\left( \begin{array}{c}N_{\text{BC}}+k-1\\ k\end{array}\right)p_{H}{}^{N_{\text{BC}}}p_{A}\!\!\left(\frac{p_{A}}{p_{H}} \right)^{N_{\text{BC}}+1}\] \[>\left(\frac{p_{A}}{p_{H}}\right)^{N_{\text{BC}}+1}\sum\limits_{k= 0}^{\infty}\left(\begin{array}{c}N_{\text{BC}}+k-1\\ k\end{array}\right)p_{H}{}^{N_{\text{BC}}}p_{A}{}^{k} \tag{100}\]  
For numerical example, when \(p_{A}=0.35\) and \(N_{\text{BC}}=5\) the probabilities can be computed as \(\mathbb{P}_{DSA}=0.2287\) and \(\mathbb{P}_{\text{pre-mine}}=0.0244\). As the gap is significant, it is shown that the DS attack success condition defined in [12] was indeed only a sufficient condition, set to be too strict.
# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 4 Results
### Experimental Setup  
The experiments utilized the AdamW optimizer with a batch size of four, learning rate of 0.0002, and learning rate drop at 40 epochs. Training spanned 70 iterations with a confidence threshold of 0.85. All implementations used Python 3.10 and PyTorch 2.1.0 on an RTX-4090D GPU (24 GB) with CUDA 12.1 in an Ubuntu 22.04 environment. A random seed of 42 was fixed for reproducibility.  
The proposed architecture and methodology are evaluated using several standard metrics: Recall, F1, Score, mean average precision (mAP), False Negative Rate (FNR), and Intersection over Union (IoU). The mathematical formulations of these metrics are presented below:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{26}\]  
\[\text{Precision}=\frac{TP}{DT} \tag{27}\]  
\[\text{F1}=2\cdot\frac{Precision\cdot Recall}{Precision+Recall} \tag{28}\]  
\[\text{mAP}=\frac{\sum_{i=1}^{k}\text{AP}_{i}}{k} \tag{29}\]  
\[\text{FNR}=\frac{\text{FN}}{\text{TP}+\text{FN}} \tag{30}\]  
\[\text{Score}=\frac{1}{J\cdot K}\sum_{j=1}^{J}\sum_{k=1}^{K}S_{0,j,k,0,-1} \tag{31}\]  
The terms in the aforementioned equations are defined as follows: True Positives (\(TP\)) denote the number of instances accurately predicted as positive. False Negatives (\(FN\)) refer to positive instances erroneously predicted as negative. Precision is defined as the ratio of TP instances to the detective target (\(DT\)), while False Positives (\(FP\)) indicate negative instances incorrectly predicted as positive. Recall quantifies the proportion of actual positive instances correctly identified. Average precision for class/query \(i\) (\(AP_{i}\)) represents the area under the precision-recall curve for a specific class or query, contributing to the calculation of \(mAP\). The parameter \(k\) signifies the total number of classes or queries evaluated in mAP. For the score metric, \(S_{0,j,k,0,-1}\) denotes the confidence score for a detection at an Intersection over Union (\(IoU\)) threshold of 0 (e.g., IoU = 0.5), recall threshold \(j\), category \(k\), area range  
Figure 8: The format of dataset made by using labelimg and the app version is 1.8.6.  
0 (all areas), and maximum detections (e.g., 100), where \(J\) and \(K\) represent the number of recall thresholds and categories, respectively.
# Dynamic Impedance Estimation: Challenges and Considerations
## 3 Harmonic Synchrophasor Estimation
### Where to Go  
In order to obtain reliable harmonic phasor estimations, it is necessary to advance the research on the following points:  
* Develop resampling algorithms to ensure synchronous sampling that is robust to additive noise or to evaluate noise reduction techniques in pre-processing;
* Investigate the effect of the delay of the frequency estimation on the interpolation performance over time. Since the frequency value used in the resampling process is always delayed about the current frequency value;
* Define standards for HSpE, such as the TVE error allowed, the dynamic test conditions, the report's rate, etc.
## 4 WSN Platforms and Energy Harvesting Techniques Investigations
### Energy Harvesting Techniques
#### 4.2.4 RF Energy Harvesting Systems  
The RF signals (from 3000 Hz and 300 GHz) signals are generated from millions of radio stations. Based on these RF signals, the RF energy harvesting process is defined as the phenomenon of taking energy from some ambient RF sources. Typical examples of ambient RF sources are MF (AM Radio, 526.5-1705 kHz); FM (87.5-108 MHz); TV (41-250 MHz, 470-950 MHz); GSM (850/1900 or 900/1800 MHz); CDMA, 3G, 4G, and ISM (industrial scientific medical, 2400 MHz); and Wi-Fi (2.45/5.0 GHz) [82]. Compared with the ambient energy sources mentioned above, RF energy is independent of environmental conditions, including weather, climate, and temperature. These advantages make it an attractive choice as an ambient energy source for powering wireless sensor nodes for SHM applications.  
Table 14 presents an overview of the selected research studies for RF energy harvesting techniques. The second column identifies the output voltage. The sensitivity of the RF harvester is expressed in the third column. The fourth column presents the performance of the conversion process in terms of maximum output power and conversion efficiency. The fifth and sixth columns express the rectification and the integration processes, respectively. There is only one selected study in this SLR that has deployed the RF energy harvesting technique. Probably, this is the only work in this category that meets the selection and rejection criterion of this SLR.