esa-sceva/satcom-chunk-collection huggingface dataset:
# PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving
## 4 Experimental Results
### Ablation Study  
To explore the contribution of each PIFNet component, ablation experiments are performed, and the results are summarized in Table 3. We removed the EDF module and fusion module, one by one, to verify their effectiveness.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Modality**} & \multicolumn{4}{c}{**3D Detection**} & \multicolumn{4}{c}{**BEV Detection**} \\ \cline{3-10}  & & **Easy** & **Mod.** & **Hard** & **mAP** & **Easy** & **Mod.** & **Hard** & **mAP** \\ \hline Point RCNN & L & 86.96 & 75.64 & 70.70 & 77.77 & 92.13 & 87.39 & 82.72 & 87.41 \\ \hline Fast Point R-CNN & L & 85.29 & 77.40 & 70.24 & 77.76 & 90.87 & 87.84 & 80.52 & 86.41 \\ \hline PV-RCNN & L & 90.25 & 81.43 & 76.82 & 82.83 & 94.98 & **90.65** & 86.14 & 90.59 \\ \hline Voxel RCNN & L & 90.90 & 81.62 & 77.06 & 83.19 & - & - & - & - \\ \hline MV3D & L + R & 74.97 & 63.63 & 54.00 & 64.20 & 86.62 & 78.93 & 69.80 & 78.45 \\ \hline F-Point Net & L + R & 82.19 & 69.79 & 60.59 & 70.85 & 91.17 & 84.67 & 74.77 & 83.54 \\ \hline MMF & L + R & 88.40 & 77.43 & 70.22 & 78.68 & 93.67 & 88.21 & **91.99** & **91.29** \\ \hline EPNet & L + R & 89.81 & 79.28 & 74.59 & 81.22 & 94.22 & 88.47 & 83.69 & 88.79 \\ \hline PIFNet (Ours) & L + R & **92.53** & **83.03** & **79.92** & **85.16** & **95.89** & 89.08 & 87.91 & 90.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons with state-of-the-art methods on the testing set of the KITTI dataset (Cars). L and R represent the LiDAR point cloud and the RGB image.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Running Time (s/frame)** \\ \hline MV3D & 0.36 \\ \hline F-Point Net & 0.17 \\ \hline MMF & 0.08 \\ \hline PIFNet (Ours) & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Running time comparisons with several camera-LiDAR fusion-based methods.  
The fusion module can utilize the point-wise features and image-wise features by combining the two features to achieve good performance. The EDF module effectively extracted image features and boosted the performance more than that of the fusion module. It is also possible to apply our EDF module and fusion strategies to other detectors to improve their performance.  
To explore the effect of the number of fusion modules on the object detection performance, we compared mAP values of architectures with different number of fusion modules. The performance results for 3 architectures with 3, 4, and 5 fusion modules are summarized in Table 4. In the experiments, the depth of EDF and the number of SA/FP layers are adjusted accordingly. As shown in Table 4, the architecture with larger number of fusion modules shows improved performance. When the number of fusion modules is increased from 3 to 4, mAP is improved by 1.73. However, when the number of fusion modules is increased from 4 to 5, mAP is improved only by 0.05, while the training time is increased significantly from 73 h to 116 h. Therefore, we choose 4 fusion modules in our normal architecture.
# A Review of Early Injection Strategy in Premixed Combustion Engines
## 3 Injection Timing
### Two-Stage Early Injection  
In PCCI combustion, a two-stage early injection strategy is utilized: Part of the fuel is first injected into the cylinder to form a homogeneous mixture prior to ignition, and this part undergoes the HCCI type of clean combustion. The remainder of the fuel is injected close to the TDC to control the combustion phase, and this part undergoes conventional combustion.
## 4 QoS Aware Multipath Routing Protocols for WMSNs
### Geam  
Instead of using deviation angle like DGR [46] to control directions of multiple paths, the geographic energy-aware non-interfering multipath routing protocol GEAM [50] divides the topology into separate districts for individual paths. Precisely, as shown in Figure 10, the topology in GEAM is divided into three areas by virtual coordinates similar to DGR. The source area \(A_{src}\) and the sink area \(A_{sink}\) are confined by vertical lines located at \(R\) distance away from the source and sink respectively (\(R\) is the transmission range). Rows of non-interfering area \(A_{ni}\) are in between the two vertical lines with width \(R\). Therefore, a district \(D_{i}\) consists of sections from all three areas. When sending packets, the source piggybacks the boundary information of the chosen district on every packet. In doing so, intermediate nodes can use the greedy perimeter stateless routing (GPSR) [122] to route the packet through its corresponding district.  
Figure 9: An Example of Z-MHTR.  
To balance loads and energy distribution, GEAM organizes data transmission into runs of fixed lengthes. To guarantee non-interfering multipath routing, each run is further divided into three rounds, in which a district \(D_{i}\) belongs to round \(j\) if \(D_{i}\%3=j\). Loads are equally distributed to all districts during the first run. At the end of each run, the sink gathers statistics such as the minimum residual energy level \(E_{i}\) among nodes in each district. Such statistics are feedback to the source. Based on the \(E_{i}\)'s, the source adjusts the utilization rate of each district, consequently the duration of each round in the next run in a way that districts with higher \(E_{i}\) will have more loads assigned. In the occurrence of holes, the same perimeter routing in GPSR is used. When the route is reconstructed, the districts are also realigned to ensure non-interfering paths.  
Pros and Cons. By dividing network topology into different districts and by organizing data transmission into rounds with mutually distant active districts, GEAM realizes non-interfering routing and at the same time achieves balanced energy consumption and traffic loads. The protocol also scales well with the underlying GPSR routing method. However, the overhead is increased since each packet is piggybacked with border information of the designated district and is required to collect network statistics on-the-fly. The main drawback of GEAM is it does not take into consideration the QoS metrics such as delay and reliability which are important to multimedia delivery in WMSNs.
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 3 Proposed Method
### Ensemble  
By leveraging the unique features learned from each input data type, we constructed a final ensemble network based on the VLLM and achieved high accuracy. VLLM adapts a model originally used in natural language processing to address challenges in the vision domain. It delivers results through visual and language-based interactions in various applications, such as responding to visual questions and image searches.  
Due to variations in how each input data influences network performance, we aimed to achieve high accuracy by constructing a final ensemble network. Specifically, we focused on analyzing glaucoma data more intensely than normal data to improve detection accuracy. Even if the classification from the original or cup-disk images is incorrect, if the classification from the vascular images or color palette is accurate, we weighted the results of the vascular and color palette classification models more heavily to obtain the final outcome. The classification results from each dataset are received and arranged in a flattened layer. The positions in this flattened layer contain the probability values from each of the input data before applying the softmax function. The scores are calculated by comparing these probability values with the actual correct answers. If a classification result is uniquely correct, its weight is increased. The newly determined ensemble score is turned into a positive number through exponential functions in the softmax process and normalized to a value between 0 and 1. This process is similar to the attention score mechanism in transformer models. This process can be checked in Algorithm 1 in the form of pseudocode.  
```
1:Inputs:
2:\(Q\): Before Softmax Probability (batch_size, num_queries, \(d_{k}\))
3:\(K\): True label (batch_size, num_keys, \(d_{k}\))
4:\(V\): Ensemble Score (batch_size, num_keys, \(d_{v}\))
5:Output:
6:Binary Classification
7:procedureEnsembleWeights(\(Q,K,V\))
8:\(d_{k}\leftarrow\) dimension of \(K\) along axis 2
9:\(ensemble\_scores\leftarrow\) dot product\((Q,\text{transpose}(K,(0,2,1)))/\sqrt{d_{k}}\)
10:\(ensemble\_weights\leftarrow\) softmax\((ensemble\_scores,\text{axis}=-1)\)
11:\(output\leftarrow\) matmul\((ensemble\_weights,V)\)
12:return\(output,ensemble\_weights\)
13:endprocedure
```  
**Algorithm 1** Ensemble Weights
# An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis
###### Abstract  
As essential but easily damaged parts of rotating machinery, rolling bearings have been deeply researched and widely used in mechanical processes. The real-time detection of bearing state and simple, rapid, and accurate diagnosis of bearing fault are indispensable to the industrial system. The bearing's inner ring and outer ring vibration acceleration can be measured by high-precision sensors, and the running state of the bearing can be effectively extracted. The empirical wavelet transform (EWT) can adaptively decompose the vibration acceleration signal into a series of empirical modes. However, this method not only runs slowly, but also causes inexplicable empirical modes due to the unreasonable boundaries of the frequency domain division. In this paper, a new method is proposed to improve the empirical wavelet transform by dividing the boundaries from the spectrum, named the fast empirical wavelet transform (FEWT). The proposed method chooses different points in the Fourier transform of the spectrum (key function) to reconstruct the trend component of the spectrum. The minimum points in the trend component divide the spectrum into a series of bands. A more reasonable set of boundaries can be found by choosing appropriate trend components to obtain effective empirical modes. The simulation results show that the proposed method is effective and that the acquired empirical mode is more reasonable than the EWT method. Combining kurtosis with fault feature extraction of inner and outer rings of bearings, the method is successfully applied to the fault diagnosis of rolling bearings.  
Keywords:reasonable band; real-time detection; trend component; empirical wavelet transform; fault diagnosis +
Footnote †: journal: Applied Sciences
## 4 Numerical Results
### OP Results
In addition,  
when \(P_{j}\) is large, the OP curves of the system tend to the same asymptotic bound, which is independent of \(P_{j}\) and the SNR threshold \(\gamma_{\text{th}}\), thus verifying Remark 1. Figure 6 shows the OP versus \(P_{j}\) for different \(\theta_{\text{FoV},m,j}\) when \(M=2\) and \(Z_{m,j}=0.5\) km. As seen in the figure, the OP gradually decreases as \(P_{j}\) is increased for a given \(\theta_{\text{FoV},m,j}\). However, when \(P_{j}\) is large, the OP performance does not improve as \(P_{j}\) increases, but tends to different asymptotic bounds due to different \(\theta_{\text{FoV},m,j}\) values. This indicates that the system's OP performance cannot always be improved by increasing \(P_{j}\) without limitation, which verifies the accuracy of Remark 1. Figure 7 shows the OP versus the FoV \(\theta_{\text{FoV},m,j}\) for different average transmitted optical powers \(P_{j}\) when \(M=2\) and \(Z_{m,j}=0.5\) km. It can be observed that the OP decreases first and then increases as the \(\theta_{\text{FoV},m,j}\) increases for a given \(P_{j}\). This indicates that there is an  
Figure 5: System OP versus average transmitted optical power \(P_{j}\) for different \(\gamma_{\text{th}}\) when \(M=2\) and \(Z_{m,j}=0.5\) km. optimal \(\theta_{\text{FoV},m,j}\) value for obtaining the best outage performance, and the optimal \(\theta_{\text{FoV},m,j}\) value gradually increases with the increase in \(P_{j}\). Figure 8 shows the OP versus \(P_{j}\) for different \(\sigma_{\text{o}}\) when \(M=2\) and \(Z_{m,j}=0.5\) km. In this figure, the OP gradually decreases with the increase in \(P_{j}\) for a fixed \(\sigma_{\text{o}}\). When the transmitted optical power conditions are the same, the system OP increases with the increase in \(\sigma_{\text{o}}\), which suggests that a better OP performance for the system can be obtained by decreasing the value of \(\sigma_{\text{o}}\). Moreover, when \(P_{j}\) is large, the OP performance does not decrease with the increase in \(P_{j}\) but approaches different asymptotic bounds due to different \(\sigma_{\text{o}}\) values. This indicates that the system's OP performance cannot be enhanced by increasing \(P_{j}\) without limitation, which verifies the accuracy of Remark 1. From Figures 2-8, it can be observed that the performance gaps between all theoretical and simulation results are so small that they can be ignored. This indicates that the obtained  
Figure 8: System OP versus \(P_{j}\) for different \(\sigma_{\text{o}}\) when \(M=2\) and \(Z_{m,j}=0.5\) km. Figure 7: System OP versus FoV for different \(P_{j}\) when \(M=2\) and \(Z_{m,j}=0.5\) km. theoretical expression of the OP can be utilized to evaluate a system's outage performance without the need for time-intensive simulations.
# On the Use of FOPID Controllers for Maintenance Phase of General Anesthesia
## 2 Materials and Methods
### FOPID Controller
The optimal controller parameters were then determined by solving an optimization problem that minimizes the maximum sensitivity \(M_{s}\), defined as  
\[M_{s}:=\max_{\omega}\lvert S(j\omega)\rvert=\max_{\omega}\biggl{\lvert}\frac{1} {1+C(j\omega)P(j\omega)}\biggr{\rvert}, \tag{12}\]  
subject to the following frequency constraints:  
* \(\omega_{c}=\bar{\omega}_{c}\) (desired gain crossover frequency);
* \(\phi_{m}=\bar{\phi}_{m}\) (desired phase margin);
* \(\frac{d\phi_{m}}{dx}=0\) (isodamping condition). Two sets of values of \(\bar{\omega}_{c}\) and \(\bar{\phi}_{m}\) were selected (thus obtaining two sets of optimal FOPID parameters). These sets were determined by considering the frequency response of the loop transfer function \(L(s)=C(s)P(s)\), where each of the linearized models of the 13 patients is in series with either the PID controller tuned with the optimization method described in Section 2.2 or the PID controller presented in [7], where an individualized approach is employed. In particular, for each of the 13 patients, the values of the gain crossover frequency and of the phase margin were determined, and the resulting average values were used as constraints in the isodamping optimization problem. The first case (from the PID controller in Section 2.2) is denoted by ISO1 and has \(\bar{\omega}_{c}=0.08\) (rad/s) and \(\bar{\phi}_{m}=57.5^{\circ}\). The second case (from the PID controller designed in [7]) is denoted by ISO2 and consists of \(\bar{\omega}_{c}=0.03\) (rad/s) and \(\bar{\phi}_{m}=65^{\circ}\). Note that the second set should provide a slower and more robust controller. In fact, from the first set, a worst-case maximum sensitivity of 1.204 is obtained, while using the second set of constraints yields \(M_{s}=1.053\). Indeed, two different design options were considered in order to thoroughly evaluate the effectiveness of the isodamping approach in different contexts. Genetic algorithms were also exploited in this case to solve the optimization problem. However, in this scenario, the problem is subject to some equality constraints, which causes genetic algorithms to solve many subproblems at each iteration. For this purpose, the default mutation function'mutationadaptfeasible' for constrained GA problems was used. This function randomly generates directions that are adaptive with respect to the last successful or unsuccessful generation. The mutation chooses a direction and step length that satisfy bounds and linear constraints. A constraint tolerance equal to \(1\times 10^{-4}\) was used. The resulting optimal values of the controller parameters are shown in Table 1. Note that \(N\) is fixed to 5 for all controllers and all design procedures.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 3 Deep-Reinforcement-Learning-Based Cooperative Downloading Scheme
### Overview  
To maximize the amount of downloaded data, all of the satellites have to fully utilize their contact times with GSs. However, satellites have different amounts of data and contact times according to their trajectories. Although ISL can be utilized to overcome mismatches between their contact time and retaining data, the high mobility of LEO satellites and its impacts on the dynamic formation of ISL/SGL make it more challenging. In this regard, we formulate MDP to obtain the optimal policy, which makes LEO satellite networks take the optimal decision regarding whether to offload or download data depending on the currently observed network state information, which ultimately leads to maximizing the amounts of downloaded data.  
Figure 2 shows the overall DRL-based cooperative downloading framework, which is composed of LEO satellite network environments and an SAC agent with an actor network to approximate the optimal cooperative downloading policy and critic networks to evaluate the policy. At the beginning of every time slot, the SAC agent defines a state with currently observed network state information from the network environments as a graph. The SAC agent then utilizes the actor network to map the state into an action and controls the environments with the action. Its corresponding results are observed as a reward and a transition of the state (i.e., next state). The SAC agent stores all of this information (i.e., a tuple (state, action, reward, next state)) as an experience to the experience buffer. At the end  
Figure 1: System model.  
of each time slot, the agent samples a mini-batch of a few experiences from the experience buffer and calculates losses to train the optimal policy. The detailed procedure is elaborated in Section 3.3.  
Meanwhile, the actor and critic networks follow identical neural network designs as shown in Figure 3, which consists of (1) an input layer with GAT [33], (2) hidden layers with FC layers, and (3) output layers with parallel FC layers. At the input layer, GAT, which is one of the graph convolutional networks, is adopted to efficiently process graph-oriented network states. It propagates the feature vector of each node to neighboring nodes and aggregates received vectors with an attention mechanism. After that, latent features of the graph-based states are extracted while maintaining the relationship between nodes. The following FC layers of hidden layers merge the extracted latent features obtained from each node and extract hidden features with respect to the entire graph. Lastly, in the output layer, each of \(|\mathrm{L}|\) parallel FC layers maps the hidden features into logit for each LEO satellite. In the case of the critic network, the logit is utilized as soft-\(Q\) values. Meanwhile, for the actor network, it is mapped into the action probabilities by applying a softmax function.  
Figure 3: Neural network designs.  
Figure 2: DRL-based cooperative downloading framework.
## 4 Enhanced Barrier Membranes
### Membranes with Growth Factors  
Growth factors have been the subject of numerous studies, being included both in bone addition material and in the structure of barrier membranes. These factors include the group of bone morphogenetic proteins (BMP), stromal cell derived factor 1 alpha (SDF-1\(\alpha\)), transforming growth factor beta (TGF-\(\beta\)), platelet-derived growth factor (PDGF), growth factor rich in platelets (PRGF) or fibroblast growth factor-2 (FGF-2) [55].  
Bone morphogenetic proteins are among the most powerful osteonductive proteins; they have modulatory effects on the differentiation and functionality of cells involved in bone formation [127]. [PERSON] et al. [128] and [PERSON] et al. [129] investigated the treatment of ePTFE and titanium membranes, respectively, with BMP-2, both studies providing favorable data on new bone formation. One study reported beneficial results of BMP-9 incorporation into the collagen barrier membrane; BMP-9 stimulated alkaline phosphatase activity as well as osteoblastic gene expression [130].  
A number of studies have shown that BMP-6 induces bone neo-formation, favoring bone augmentation procedures [8,131]. [PERSON] et al. [127] developed a biodegradable chitosan membrane whose porous surface that comes in contact with the bone surface was enriched with hydroxyapatite and BMP-6; in order to improve the resistance of chitosan, the membrane was treated with glycerol solution. The authors concluded that this membrane demonstrated osteogenic activity and prevented the migration of epithelial cells [127]. In another study, [PERSON] et al. [132] showed that the application of BMP-6 improved the osteoblastic differentiation of mesenchymal stem cells derived from bone marrow in vivo. A polymeric membrane loaded with BMP-6 on the hydroxyapatite coated membrane surface was patented (WO2016186594A1) [133], with the aim of providing memory and osteogenic activity, supporting bone regeneration, preventing epithelial cell migration and eliminating inflammation.  
Incorporation of BMP-9 into the collagen membrane in vivo has also generated favorable results for bone neoformation in horizontal defects [134]. [PERSON] et al. [135]demonstrated favorable results regarding the differentiation of osteogenic cells in GTR therapies with chitosan-fibroin-hydroxyapatite membrane in which BMP was incorporated.  
PDGF-enriched PLA membranes have been associated with improved regenerative effects [136, 137]. Other studies investigating PLA membranes with TGF-\(\beta\) in alginate/nanofiber hybrid mesh have found effective regeneration in bone defects [138]. In another in vivo study, a six-fold increase in bone volume was observed when using a PCL/gelatin membrane enriched with SDF-1\(\alpha\) compared to the use of titanium membrane [139].  
Major disadvantages of growth factors supplementation in the structure of barrier membranes include the high production cost, but also the fact that the doses are usually over-physiological, with potential adverse effects [55, 140].
# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 2 Materials and Methods
### ML Custom-Made Versus Pre-Implemented Algorithms for AQI
It provides insight into the average discrepancy between predicted _AQI_ values and actual observations, offering a straightforward metric for assessing the accuracy of the AQ forecasts;
* _Mean Absolute Percentage Error (MAPE)_ evaluates the accuracy of the _AQI_ predictions as a percentage of the actual _AQI_ values. _MAPE_ is particularly useful in this context, as it allows for a relative assessment of prediction accuracy across different levels of air pollution, accommodating the variations in pollutant concentrations;
* Median Absolute Error (_MedAE_) indicates the median absolute errors between predicted and actual _AQI_ values. This metric offers a measure of prediction accuracy less affected by outliers, especially during extreme pollution events that could distort the results. These indicators enable comparative analysis between different customized models. Assessing performance through metrics such as \(R^{2}\), _MAE_, _MAPE_, and _MedAE_ identifies which modeling approach (ML or DL) yields the most accurate predictions of AQ.