esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 4 Experiments
### Ablation Studies
#### 4.4.4 Results of Ablation Studies  
To validate the effect of each component, we formed a table of ablation results by summarizing optimal results in the previous three sections, as shown in Table 6.  
From Table 6, we find that the two components, feature fusion and focal loss, are both capable of gaining considerable performance improvement. As improvement adopted by focal loss (7.19 points) is higher than that adopted by feature fusion module (6.24 points), it implies that:  
* As for the class imbalance dataset, it is of the highest urgency to balance importance on positive and negative samples;
* As for the deep feature fusion module, the considerable optimization on model performance also validates its effectiveness.  
Optimal performance is obtained through adopting the dual branch network, which integrates these two components. Hence, we can conclude that the dual branch network proposed in this paper has advantages of these two components complemented and makes effective integration. Furthermore, it will make better effects on the task of multi-label object classification in X-ray security inspection images.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Backbone** & **Feature Fusion** & **Focal Loss** & **mAP/\%** \\ \hline Baseline & ResNet34 & & & 74.83 \\ \hline \multirow{3}{*}{Ours} & \multirow{3}{*}{ResNet34} & \(\surd\) & & 81.07 \\  & & & \(\surd\) & 82.02 \\ \cline{1-1}  & & \(\surd\) & \(\surd\) & **83.10** \\ \hline \end{tabular}
\end{table}
Table 6: Ablation results of each component based on SIXray10 subset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\lambda_{1}\) & \(\lambda_{2}\) & **mAP/\%** & \(\lambda_{2}\) & \(\lambda_{1}\) & **mAP/\%** \\ \hline \multirow{4}{*}{1} & 0.25 & 79.52 & & 0.25 & 81.28 \\  & 0.5 & 80.09 & & 0.5 & 80.83 \\  & 1 & 82.52 & & 1 & 82.52 \\  & 2 & 81.92 & & 2 & 81.73 \\  & 3 & **83.10** & & 3 & 81.78 \\  & 5 & 81.92 & & 5 & 82.05 \\ \hline \end{tabular}
\end{table}
Table 5: Effect of loss weight of each branch on dual branch network performance.
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 6 Simulation Result
### Subareas  
A set of subareas in c_ and sqr was defined for a targeted area. Based on the subareas having the same area, the numbers (\(n_{\text{area}}\)) of subareas generated by c_ and sqr were compared as follows. Table 4 provides the values of \(n_{\text{area}}\). When the radius was 1/16 to 1/256, \(n_{\text{area}}\) of c_max ranged from 10 to 707, \(n_{\text{area}}\) of c_min ranged from 7 to 694, and \(n_{\text{area}}\) of c_avg ranged from about 8 to 702. Based on the same radius, \(n_{\text{area}}\) of sqr ranged from 4 to 552, which was less than that of c_max, c_min, and c_avg. When the radius was 1/16, compared with c_max and sqr, sqr reduced the number to 6 (=\(10-4\)) subareas, and the reduction rate was 60% (=6/10). Figure 11 provides the subarea reduction rates for c_max and sqr, c_min and sqr, and c_avg and sqr, which are represented by c_max#sqr, c_min#sqr, and c_avg#sqr, respectively. When the radius was 1/16, the reduction rates were about 60%, 43%, and 51%. When the radius was reduced to 1/256, the reduction rates were about 22%, 20%, and 21%, respectively. Overall, the subarea reduction rate was between 60% and 20%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Radius** & **c\_max** & **c\_min** & **c\_avg** & **sqr** \\ \hline
1/16 & 10 & 7 & 8 & 4 \\
1/32 & 22 & 19 & 20 & 13 \\
1/64 & 63 & 57 & 60 & 41 \\
1/128 & 199 & 192 & 196 & 146 \\
1/256 & 707 & 694 & 702 & 552 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The number of subareas.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{3}{c}{**O\_PtInPy**} & \multicolumn{3}{c}{**L\_Rect**} \\ \cline{2-7}  & \(n_{cand}\) & \(n_{conf}\) & **Rate** & \(n_{cand}\) & \(n_{conf}\) & **Rate** \\ \hline City 1 & 1,112,188 & 1,112,188 & 12.47 & 138,678 & 138,678 & 1.55 \\ \hline City 2 & 1,112,188 & 1,112,188 & 7.09 & 190,507 & 190,507 & 1.21 \\ \hline City 3 & 1,112,188 & 1,112,188 & 7.60 & 256,725 & 256,725 & 1.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of candidate points, confirmation points, and inner points. The rate is \(n_{\text{cand}}\) divided by \(n_{\text{inner}}\).
# Intelligent TCP Congestion Control Policy Optimization
## 3 Methods
### Deep Reinforcement Learning
#### 3.1.5 Proximal Policy Optimization Algorithm  
The Proximal Policy Optimization (PPO) algorithm, which is one of the most effective model-free policy gradient methods, achieves state-of-the-art performance in many reinforcement learning continuous control benchmarks [13]. It is derived from the TRPO algorithm [20], but is easier to implement and has better sample complexity. Therefore, PPO is used to train a defined congestion-controlled reinforcement learning strategy. PPO is an actor-critical algorithm, so it uses a multi-step return of TD(\(\lambda\)) as a function of training values and a generalized advantage estimator (GAE) to compute the policy gradient [21].
## 4 Research Directions for System Designs and Optimization Concepts
### Optimization in PHY Security: Current Status and Main Issues
#### 4.2.1 Convex Optimization Techniques  
Convex optimization has been widely used in PLS because it can effectively evaluate information-theoretic security metrics to find the most favorable transmission solutions. In PLS, the optimization problem refers to the problem of determining the performance metrics, such as power consumption, secrecy outage probability, secrecy capacity or secrecy rate, etc., which were discussed in Section 3. In the existing studies on PLS, there are some convex optimization techniques that are widely used to resolve optimization problems, such as in [109]. As mentioned earlier, due to the properties of the logarithmic subtraction functions in the performance metrics, most of the optimization issues in PLS are either non-convex or quadratic programming functions. These non-convex optimizations are adopted to solve problems that are related to non-linear constraints and objective functions. Therefore, non-convex optimization problems can be used in the design of complex global and optimal solutions. Some common examples of non-convex optimization problems include secure power allocation, power minimization and beamforming. In order to achieve optimal solutions, the original non-convex problems need to be converted into convex problems using other functions, such as SDP. Semi-definite programming (SDP) can be used to optimize a linear constraint function under non-negative and linear equality constraints. In general, in order to solve the problems of non-convexity optimization in PLS system design, many optimization methods have been discussed in the literature, such as semi-definite relaxation (SDR), alternating search, dual decomposition, etc. [37; 97; 110].
# Seismic Analysis of a Large LNG Tank Considering Different Site Conditions
## 2 Methodology
### Coupled Smoothed Particle Hydrodynamics-Finite Element Method (SPH-FEM)  
The relationship between the SPH and FEM algorithm is a key concept in the coupled SPH-FEM method. Velocity and displacement are transferred between SPH particles and FEM nodes in each time step. SPH particles provide boundary conditions for FEM, and FEM prevents the boundary effect by maintaining the continuity of the particles [23]. In this fluid-structure interaction study, the interactive forces between SPH and FEM are coupled by exchanging velocity and displacement fields on the solid-fluid interface. Figure 1 shows a schematic computational flowchart of the SPH-FEM coupling method.  
Figure 1: Computational flowchart of Smoothed Particle Hydrodynamics-Finite Element Method (SPH-FEM) coupling method.
## 4 Conclusions  
Ni\({}_{0.05}\)Ce\({}_{0.95}\)O\({}_{2-\delta}\) catalysts, with nanorod and nanocube morphologies, were synthesized by hydrothermal methods. The most active catalyst for CO\({}_{2}\) methanation was the one with nanorod morphology, due to its strong metal-support interaction and its high reducibility, which favors the presence of oxygen vacancies. A high methane selectivity of about 90% was achieved over a wide temperature range up to 400 \({}^{\circ}\)C. No carbon residues were detected after the catalytic tests. The reaction rate essentially depended on the partial pressure of hydrogen. The DRIFT characterization showed that CeO\({}_{2-\delta}\) strongly interacted with the CO\({}_{2}\) molecule, adsorbed as various carbonates, while Ni\({}^{0}\) participated in the H\({}_{2}\) spillover. The absence of metal carbonyls and the considerable formation of chemisorbed formates on the CeO\({}_{2-\delta}\) support indicate a direct formate-type reaction mechanism. Ni\({}_{0.05}\)Ce\({}_{0.95}\)O\({}_{2-\delta}\) is thus a bifunctional system. At high temperatures (i.e., above 400 \({}^{\circ}\)C), CO can be formed by the dissociation of CO\({}_{2}\)\(\rightarrow\) CO + 1/2O\({}_{2}\), with a redox mechanism involving the oxygen vacancies of CeO\({}_{2-\delta}\). The presence of formate was also observed in the hydrogenation of CO, together with the formation of carbonyls absorbed on Ni. This finding suggests an important role of formate species also in the hydrogenation of CO.  
Figure 10: DRIFT spectra of Ni-nR catalyst exposed to reaction mixture CO:H\({}_{2}\):He = 5:15:30 cm\({}^{3}\) min\({}^{-1}\) from 200–400 \({}^{\circ}\)C between 4000–2600 cm\({}^{-1}\) (**a**), and 2150 and 1150 cm\({}^{-1}\) (**b**). Before the experiment, the sample was reduced in situ at 500 \({}^{\circ}\)C. Symbol definition: \(\blacklozenge\) formate, + CH\({}_{4}\), \(\blacksquare\) linear CO on Ni.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/pr9111899/s1](https://www.mdpi.com/article/10.3390/pr9111899/s1)]([https://www.mdpi.com/article/10.3390/pr9111899/s1](https://www.mdpi.com/article/10.3390/pr9111899/s1)), Figure S1: Average size distribution: Ni-nR CeO\({}_{2}\) oxide width (a), Ni-nR CeO\({}_{2}\) oxide length (b), Ni-nR Ni oxide particles (c), oxide width of CeO\({}_{2}\) in the Ni-nC (d), Ni oxide particles in the Ni-nC (e).  
**Author Contributions:** Conceptualization, I.L.; formal analysis, I.L., investigation, I.L., J.K.K., S.M.S.K., K.S.; resources, S.T.; data curation, I.L., J.K.K., U.P.L., S.T., S.S.; writing--original draft preparation, I.L.; writing--review and editing, S.T., S.S., U.P.L.; supervision, I.L.; project administration, G.I. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Measurement of Diurnal Variation in Needle PRI and Shoot Photosynthesis in a Boreal Forest
## 5 Conclusions  
We found that during high irradiance conditions (clear sky around midday), the needle PRI of fully-grown Scots pine trees was correlated with LUE, but not with PPFD. This supports the use of PRI as an indicator of photosynthetic activity providing information complementary to incident radiation flux and leaf conditions. The photosynthetic downregulation measured by shoot chambers in the afternoon was successfully reflected by needle PRI. Canopy PRI measured from a tower had an asymmetric daily cycle with a minimum in the afternoon similar to these of needle PRI and shoot photosynthesis. The afternoon downregulation in PRI present during the campaigns is not normally present at typical remote sensing satellite overpasses at 9-11 a.m. local time. Hence, the satellite measurements may not be representative of daily averaged LUE and additional data is required to scale the instantaneous remote measurements to longer (diurnal, seasonal) time scales. We suggest that for the successful mapping of boreal forest productivity from space, the mechanisms creating daily PRI cycles at both needle and canopy levels should be quantified and modelled.  
Conceptualization and methods: M.M.; leaf-level measurements: R.H.-C., V.P., and V.M.; shoot- and canopy-level measurements: J.A., J.B., and C.J.N.; and analysis: M.M.  
The work was supported by Academy of Finland (266152, 272989, and 202633); Finnish Centre of Excellence in Atmospheric Sciences (Academy of Finland grant 272041); ICOS ERIC; Horizon2020 (ACTRIS, eLTER).  
We acknowledge [PERSON] for his supervision of the Skye PRI sensor and [PERSON] for overall assistance with shoot chamber measurements and Skye PRI sensor setup at SMEAR II. Carbon exchange and meteorological data were provided by the Station for Measuring Ecosystem--Atmosphere Relations (SMEAR).  
The authors declare no conflict of interest.
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 3 Experiment and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **TROIKA**[17] & **JOSS**[18] & **WFPV**[9] & **CASINOR**[8] & **SPECMAR**[15] & **Adaptive** **Heart Rate** & **Proposed** \\  & & & & & & & **Search**[19] & \\ \hline
1 & 2.87 (2.18\%) & 1.33 (1.19\%) & 1.25 (1.15\%) & 1.22 (-) & 1.22 (-) & 1.10 (1.07\%) & 1.07 (0.97\%) \\
2 & 2.75 (2.37\%) & 1.75 (1.66\%) & 1.41 (1.30\%) & 1.64 (-) & 1.51 (-) & 1.31 (1.23\%) & 1.34 (1.47\%) \\
3 & 1.91 (1.5\%) & 1.47 (1.27\%) & 0.71 (0.59\%) & 0.63 (-) & 0.75 (-) & 0.65 (0.53\%) & 0.64 (0.55\%) \\
4 & 2.25 (2.0\%) & 1.48 (1.41\%) & 0.97 (0.88\%) & 1.51 (-) & 1.26 (-) & 0.73 (0.68\%) & 0.95 (0.86\%) \\
5 & 1.69 (1.22\%) & 0.69 (0.51\%) & 0.75 (0.57\%) & 0.78 (-) & 0.75 (-) & 0.64 (0.92\%) & 0.94 (0.74\%) \\
6 & 3.16 (2.51\%) & 1.32 (1.09\%) & 0.92 (0.75\%) & 1.46 (-) & 1.87 (-) & 1.30 (1.24\%) & 0.88 (0.69\%) \\
7 & 1.72 (1.27\%) & 0.71 (0.54\%) & 0.65 (0.50\%) & 0.94 (-) & 0.80 (-) & 0.68 (0.53\%) & 0.68 (0.53\%) \\
8 & 1.83 (1.47\%) & 0.56 (0.47\%) & 0.97 (0.83\%) & 0.79 (-) & 1.07 (-) & 1.12 (0.77\%) & 0.67 (0.60\%) \\
9 & 1.58 (1.28\%) & 0.49 (0.41\%) & 0.55 (0.48\%) & 0.60 (-) & 0.65 (-) & 0.52 (0.46\%) & 0.58 (0.53\%) \\
10 & 4.00 (2.49\%) & 3.81 (2.43\%) & 2.06 (1.29\%) & 2.95 (-) & 2.24 (-) & 3.15 (2.00\%) & 1.41 (0.93\%) \\
11 & 1.96 (1.29\%) & 0.78 (0.51\%) & 1.03 (0.68\%) & 1.00 (-) & 1.39 (-) & 1.13 (0.72\%) & 0.98 (0.66\%) \\
12 & 3.33 (2.30\%) & 1.04 (0.81\%) & 0.99 (0.70\%) & 0.83 (-) & 1.09 (-) & 1.07 (0.73\%) & 0.98 (0.76\%) \\ Average & 2.42 (2.42\%) & 1.28 (1.03\%) & 1.02 (0.81\%) & 1.20 (-) & 1.21 (-) & 1.12 (0.90\%) & 0.93 (0.77\%) \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of MAE and MAPE results for the public dataset. Figure 11: Comparison of results for the tenth set of data. (**a**) Results of the proposed algorithm on the tenth set of data; (**b**) WFPV on the tenth set of data.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation
### Random Topologies' Generation
#### 5.1.1 Brite Tool  
BRITE [41] is a widely used tool designed to generate random network topologies that simulate real-world network structures. In the context of this project, BRITE was used to create random topologies representing different configurations of SGs with the aim of importing them into the DEN2 DE algorithm and simulating various network conditions. BRITE allows the definition of multiple parameters and topological models, such as node distribution, degree connectivity, and link probabilities, bringing flexibility and making BRITE particularly useful for generating diverse scenarios. In addition, the tool supports several models to generate network topologies, and two of them are established [42]:  
* Waxman model [43]: This model is a probabilistic approach where nodes are placed randomly, and the likelihood of a connection between two nodes decreases with their distance. It is suitable for simulating the physical distance constraints in power grids.
* Barabasi-Albert model [44]: This model creates topologies based on preferential attachment, where new nodes are more likely to connect to nodes with higher degrees. This reflects the real-world nature of power grids, where certain nodes are hubs with many connections.  
As shown in Figure 9, both topological models are depicted with an identical number of nodes. Node size reflects the number of connections within the topology, with larger nodes representing those with more connections. In the Waxman model (see Figure 9a), the topology exhibits a relatively random structure, resulting in an approximately uniform average number of connections per node. Conversely, in the Barabasi-Albert topological model (see Figure 9b), initial nodes have a higher likelihood of connecting with newly added nodes, thus creating what is known as a scale-free topology.
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 4 System Model  
### Scenario Definition and Attacker Classification  
The reference scenario, as depicted in Figure 1, is comprised of a drone that is subject to a jamming attack. The drone communicates with a ground station and periodically samples the Received Signal Strength (RSS) of the signal it receives. According to the edge processing and networking paradigm, the drone is equipped with a jamming detection module which could be integrated as a part of a more complex security architectural framework. In particular, this module implements a simple unsupervised machine learning module, trained on nominal traffic RSS values. The module takes the sampled RSS values and classifies them as either normal or anomalous, making the drone able to determine whether or not a jamming attack is taking place.  
We assume that a jammer is deployed in the network and that it starts a jamming attack at a certain instant by transmitting a high-power signal on the same frequency as the one used by the drone to communicate with the ground station. We assume that the jammer is able to completely block the communication between the drone and the ground station, meaning that the drone has to rely on its internal resources to determine if a jamming attack is taking place and react accordingly. We assume that the jammer type is unknown to the drone.  
In our scenario, the drone is subject to two types of jamming attacks: a constant and a periodic jamming attack that can be attributed to two different types of jammers. The constant jamming attack could be caused by a fixed ground jammer, as maintaining a constant jamming signal requires a large power source, while the periodic jamming attack could be caused by a mobile jammer, for example a drone, as drones are usually battery powered and can only jam for a limited amount of time. Employing a periodic jamming attack allows the malicious drone to preserve energy and lowers the chances of detection.  
The attacker classification [21] of the proposed scenario is detailed in Table 2:
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 3 Results
### Binary Classification
#### 3.1.3 Pair-Wise Classification  
The goal of pair-wise classification was to compute and compare classification accuracies of different terrains against each other as pairs.  
The best classification accuracies were achieved with all features computed from the lower back sensor data and trained with SVM, as shown in Figure 6. The best pair-wise classification accuracies of above 98% were observed between carpet and asphalt, carpet and soil, asphalt and tiles, and soil and tiles. The lowest pair-wise classification accuracy of 91% was seen between grass and soil. The precision, recall, and f1-score remained above 93% in all cases. Detailed pair-wise classification results are presented as bar graphs in Figure 7 and in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Classification Category**} & \multirow{2}{*}{**Sensor**} & \multicolumn{10}{c}{**Accuracy (\%)**} & \multicolumn{10}{c}{**Statistical Analysis**} \\ \cline{3-14}  & & **All Features** & **Temporal Features** & **Spectral Features** & **3D Accelerations** & **3D Angular Velocities** & **Precision** & **Recall** & **f1-Score** \\ \hline  & & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF \\ \hline \multirow{3}{*}{**Senary**} & Lower Back & 87.55 & 88.70 & 82.51 & 89.10 & 80.10 & 81.67 & 81.46 & 84.08 & 74.78 & 81.74 & 86.9 & 87.2 & 86.7 & 86.5 & 86.8 & 86.6 \\ \cline{2-14}  & Chest & 85.78 & 85.99 & 79.54 & 87.56 & 77.91 & 76.76 & 81.45 & 84.76 & 69.13 & 69.67 & 85.7 & 86.1 & 85.7 & 86.2 & 85.6 & 86.1 \\ \hline \end{tabular}
\end{table}
Table 7: Classification results of quinary and senary classification for each sensor location. The classification results are presented for each feature set and statistical scores are presented for the set of all features only.
# Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse
## 3 Engineering Situation and MS System
### MS System Profile  
Based on the analysis of the occurrence and mining situation of the 30# ore body, there is a risk of large-scale roof caving in the overlying goat and rock mass instability caused by goat penetration in the mining area. In response to these hidden dangers, this plan establishes a 24-channel microseismic monitoring system produced by the Institute of Mine Seismology (IMS) and a 16-channel high-accuracy MS positioning enhancement system to conduct real-time online monitoring of the collapse mining process. Within the monitoring range, there are no apparent fault or joint structures at depths above 530 m.  
This plan involves constructing an 8-channel IMS monitoring system (Figure 1e) on the surface and deploying an 8-channel IMS system (Figure 1c) in the middle section at 530 m. The 8-channel IMS monitoring system (Figure 1d) is installed in the middle section at 450 m to form a spatial envelope for comprehensive monitoring of the collapse mining process. Through network analysis, it is shown that under ideal conditions, the positioning error of the IMS monitoring system within the monitoring range is about 10 m, which can basically satisfy the requirements of the monitoring area.  
Figure 1: Positioning accuracy of the MS monitoring system. (**a**) Positioning error; (**b**) sensitivity; (**c**) 530 m microseismic monitoring point; (**d**) 450 m microseismic monitoring site; (**e**) surface microseismic monitoring site.  
In order to achieve higher measurement accuracy, this plan additionally adopts a 16-channel high-precision MS positioning enhancement system to improve positioning accuracy and reduce positioning errors (Figure 1a). The high-accuracy MS positioning enhancement system is mainly installed in the middle section at 530 m and 450 m to assist in improving the positioning accuracy of the IMS monitoring system. The installation position is the same as that for the IMS monitoring system, i.e., two sensors are installed in the same borehole. Additionally, with the gradual expansion of production into deeper areas, MS sensors at the surface, 530 m, and 450 m can enable the monitoring network to converge to disaster areas. The sensitivity of microseismic systems to earthquake magnitudes is shown in Figure 1b.