esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Performance Evaluation
The HEM+BASE model showed an mAP value that was 13.47%p higher than that of the pre-existing base model. It seemed that the model to which hard-negative mining and hard-positive mining were applied showed better performance in detecting crosswalk occlusion objects compared to the pre-existing model, and the model demonstrated the effective true-positive detection of hard-example objects serving as causes that degrade detection performance. The APO+BASE model showed an mAP value that was 12.81%p higher than that of the pre-existing base model. It seemed that the model to which augmentation policy optimization were applied showed better performance compared to the pre-existing model. Thus, it demonstrated that more precise detection of occlusion objects can be achieved by applying CutMix, Mosaic, and Grayscale policies. In addition, the HEM+APO model showed an mAP value that was 1.88%p higher than that of the HEM+BASE mode. This model effectively overcame the data imbalance problem that is likely to occur in the object detection process for road driving through augmentation policy optimization. In this study, we proposed a detection model of occluded objects based on YOLO using hard-example mining and augmentation policy optimization. In comparison to the pre-existing model, the proposed model more precisely detected the occluded objects during road driving, and the accuracy performance was enhanced as well. However, this study is limited in that the occlusion objects relating to multiple classes were not taken into consideration, because the proposed model was designed to detect occlusion objects relating to a single class known as a crosswalk. In addition, since the size of the IARA and GOPRO data sets for model training was not big enough, there is a possibility that the performance may be improved. In the future study, continuous tests must be conducted to take into consideration the occlusion phenomenon involving diverse objects of interest other than crosswalk objects that may occur in a road driving environment. In addition, diverse types of training data sets should be additionally collected and used for neural network learning and the pre-existing limitations should be supplemented to improve the performance of the occluded object detection model developed.
# Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling
## 6 Conclusions  
In this study, a unique deep-learning-based signed network embedding model with hierarchical graph pooling of the networks is proposed. Specifically, we utilize a pooling layer with a top-k selection process, which can select necessary nodes with higher scores at each update step. Moreover, the high-order relations between the node pairs have been taken into account with the constraint of balance theory. In conclusion, our proposed SHGP model can accurately anticipate the sort of links that exist inside social networks and also be able to accurately forecast how negative associations are utilized within network systems, which helps to influence the design of social computing applications. The proposed model can be able to infer the underlying attitudes of users based on the data from the network and may be used to better recommend friends or favorite items to users in apps on social media. Extensive testing on three large real-world datasets shows the effectiveness of SHGP in signed link prediction tasks. One conceivable approach for the future is to extend the scope of this framework to heterogeneous networks.  
Conceptualization, J.C. and Z.W.; methodology, J.C.; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON] and Z.W.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and Z.W.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Sichuan Provinces Key Research and Development Plan Information Secure Sharing and Privacy Protection Technology for IoT Project (2020 YFG0292) and the Science and Technology on Communication Security Laboratory of China (6142103190207).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Unsigned Network Embedding** & \multicolumn{4}{c}{**Signed Network Embedding**} \\ \hline  & DeepWalk & Node2 vec & SiNE & SIDE & SGCN & SHGP \\ Bitcoin-A & 0.641 & 0.727 & 0.779 & 0.757 & 0.801 & 0.854 \\ Bitcoin-O & 0.614 & 0.734 & 0.780 & 0.764 & 0.804 & 0.841 \\ Wikirfa & 0.542 & 0.643 & 0.740 & 0.693 & 0.796 & 0.812 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Signed Link Prediction Result With AUC.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Unsigned Network Embedding** & \multicolumn{4}{c}{**Signed Network Embedding**} \\ \hline  & DeepWalk & Node2 vec & SiNE & SIDE & SGCN & SHGP \\ Bitcoin-A & 0.836 & 0.863 & 0.895 & 0.835 & 0.899 & 0.936 \\ Bitcoin-O & 0.823 & 0.870 & 0.876 & 0.802 & 0.908 & 0.925 \\ Wikirfa & 0.779 & 0.787 & 0.804 & 0.787 & 0.895 & 0.914 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Signed Link Prediction Result with F1-score.
## 2 Related Work and Theoretical Background  
### Process Mining for Operational Excellence in Manufacturing  
Process Mining has established itself as a pivotal data-driven discipline to understand and improve real-world processes based on event data readily available in modern manufacturing information systems [6]. Its core strength lies in moving beyond static, idealized process models to reveal how operations actually unfold. Key applications in manufacturing include automated process discovery, where algorithms like Inductive Miner or Heuristics Miner can automatically generate process models from event logs, providing unprecedented visibility into complex production flows, including undocumented variations and deviations. Performance diagnosis enables the detailed analysis of operational Key Performance Indicators (KPIs). Studies have successfully used it to pinpoint bottlenecks that cause delays [7], measure and optimize cycle times [8; 9], quantify idle times of resources [10], and analyze throughput variations across different process paths. Conformance checking compares event logs against normative process models to identify deviations, skipped activities, or incorrect sequences that can negatively impact quality or efficiency [11], helping to ensure compliance and pinpoint areas that need corrective action.  
Limitation : Despite its power in operational analysis, conventional process mining focuses mainly on the efficiency, time, and cost dimensions derived directly from the event logs. Although some research explores resource utilization, the explicit and dynamic integration of environmental KPIs (such as real-time energy consumption per case, emissions per activity, or waste generated during specific process instances) directly into the process mining analysis loop is often lacking. The focus remains largely operational, missing the opportunity to directly correlate process flow characteristics with their immediate environmental consequences using the same granular event-level data.
## 4 Results
### Training Evolution  
The training process of the DQN algorithm is performed by considering a MC-capable UE moving along the scenario following trajectories according to random walk and with required bit rate \(R_{u}=50\) Mb/s, while at the same time background UEs also generate traffic as explained previously. The DQN agent decides the MC connectivity configuration of the UE and, based on the obtained rewards, the decision-making policy is progressively updated as explained in Section 3.2. Figure 2 intends to graphically represent the scenario used for conducting the training and evaluation processes. At the beginning of the training the UE is located in the coordinates [X\({}_{1}\) = 50, Y\({}_{1}\) = 450]. Then, it moves with speed of 1 m/s following a random walk model in which it changes the direction (between \(\pm\frac{\pi}{4}\)) with probability \(p_{dir}=1/20\) at each time step. The green arrows in Figure 2 exemplify this process.  
The training is executed until reaching the maximum number of training steps Max Number Of Training Steps = \(10^{6}\). To illustrate how the trained policy evolves when increasing the number of training steps, we obtained the learnt policy every 2500 training steps of the training process. Then, this policy was applied to an evaluation scenario in which an illustrative MC-capable UE follows the blue trajectory in Figure 2, starting from point [X\({}_{1}\) = 50, Y\({}_{1}\) = 300] and following a straight trajectory at 1 m/s up to the point [X\({}_{2}\) = 450, Y\({}_{2}\) = 300]. As a result, we measured the average reward obtained when applying the policy along this trajectory. Figure 3 presents the evolution of this average reward for the policies learnt every 2500 training steps.  
The results show that at the beginning the average reward increases significantly, meaning that the training process is able to progressively learn better policies. Then, the reward tends to stabilize at around 40 \(\times\) 10\({}^{4}\) training steps, which gives an indication of the number of training steps needed to learn the policy. After this time, the reward fluctuations are only around 2%, which reflects the stability of the algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**DQN Algorithm Parameters**} \\ \hline
**Parameter** & **Value** \\ \hline Initial collect steps & 5000 \\ \hline MaxNumberOfTrainingSteps & \(1\times 10^{6}\) \\ \hline Experience Replay buffer maximum length (\(l\)) & \(1\times 10^{5}\) \\ \hline Mini-batch size (\(J\)) & 256 \\ \hline DNN updating period (\(P\)) & 2500 s \\ \hline Discount factor (\(\tau\)) & 0.9 \\ \hline Learning rate (\(\alpha\)) & 0.0003 \\ \hline \(\varepsilon\) value (\(\varepsilon\)-Greedy) & 0.1 \\ \hline \multirow{3}{*}{DNN architecture} & Input layer: 17 nodes \\  & Two hidden layers: 100 and 50 nodes \\ \multicolumn{3}{c}{Output layer: 58 nodes} \\ \hline Time window (\(\Delta T\)) & 1 s \\ \hline Granularity \(\Delta\beta\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: DQN algorithm configuration parameters.
###### Abstract
For example, [22; 23] proposed an autoregressive model to automatically determine P-wave first-motion polarity, [24] used the Bayesian probability density function to identify the polarity of a P-wave, and [25] focused on mutual information and maximum order statistics in information theory to determine the P-wave first-motion polarity. However, when the signal-to-noise ratio of the waveform is low, these automatic algorithms based on rules are unable to achieve reliable first-motion polarity discrimination, and manual review is required. In addition, these algorithms only use limited discriminant parameters, and it is difficult to reflect the complete waveform characteristics, which restricts the accuracy of the algorithm. With the rise of deep learning, data-driven algorithms have made breakthroughs in tasks such as object detection and classification, and have been widely used not only in image recognition [26; 27] and speech recognition [28; 29], but also developed rapidly in the field of seismology, such as earthquake detection [30; 31], seismic phase picking [32; 33; 34; 35], earthquake classification [36; 37], and focal mechanism inversion [38; 39]. The P-wave first-motion polarity is usually divided into three categories: upward, downward, and uncertain. Therefore, the discrimination of incipient polarity can be regarded as a classification problem, and the automatic identification of first-motion polarity can be realized by using a large number of known labels for supervised learning. Specifically, [PERSON]. [40; 41; 42; 43] used data from Southern California, Japan, and Taiwan, China, and used a convolutional neural network to try to realize the automatic determination of the first-motion polarity of P-waves, and they achieved good results. In this paper, based on a deep-learning P-wave first-motion picking model named DiTingMotion [44; 45], we detected the P-wave first motions of the 2021 Yangbi M86.4 earthquake sequence and compared them with the manual picking results and the rule-based automatic algorithm. The P-wave first-motion polarity discriminative effect of the model was evaluated using 2389 P-wave first-motion polarities detected by DiTingMotion; the focal mechanisms of 112 earthquakes were retrieved by the CHNYTX program [16]. The FocMech-Flow process for the automatic inversion of focal mechanisms using P-wave first-motion was established. Compared to the results of other methods, the feasibility of automatic inversion of the focal mechanism solution was analyzed.
## 3 Results and Discussion  
### Preparation of the Verified Model, Model Calibration and Verification  
The SWMM model was applied to perform watershed-scale and site-scale modeling. The SWMM includes an LID module, which is very useful for assessing LID performance. In the two-stage optimization process, the watershed-scale model finds the total reduction loads and the site-scale model finds the optimal LID size required for each tea farm.  
1. Watershed-scale model  
The Jinggualiao stream is one of the streams into the Feitsui Reservoir, and its high TP concentration is of concern. The Jinggualiao watershed is divided into 7 subwatersheds according to their topography. The land use and DEM of the Jinggualiao watershed are shown in Figure 4. The rainfall data from the nearest official weather station were used as model inputs. Water discharge and water quality stations were used for observation data to verify the model simulations. The rainfall and flow discharge data were in the form of daily data, and the water quality data were in the form of monthly data.  
Figure 4: The land use and DEM of the Jinggualiao watershed.  
The data from 2015 to 2016 were used for model calibration, and the data from 2017 were used for model verification. The model calibration and verification results are attached as supplemental files. Figure S1 shows the results of the flow simulations, and Figure S2 shows the results of the TP simulations, which are the target pollutants in this study. R2 and the mean absolute percentage error (MAPE) were used as the indicators to define the acceptable model results. When R2 is greater than 0.5 and the MAPE is less than 50%, the simulations are acceptable [31]. The verification results are summarized in Table S1.  
2. Site-scale model  
The SWMM was verified for the Jinggualiao watershed in the first stage. In this stage, its feasibility for site-scale performance and its LID function must be tested. A 0.78 ha tea farm (24.918023, 121.681830) located within the Jinggualiao watershed was used to verify the site-scale simulations of the SWMM. On this tea farm, a bioretention cell with a 3.7 m2 area was built to treat runoff from the farm. Figure 5 shows a photograph of the site and its bioretention cell. The simulation results are shown in the supplemental files; please see Figure S3 and Table S3.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion  
This section presents the results of the power and quality measurements performed for each technology. Based on these data, the design of the path loss models and the relationship of the BER with the designed models will be discussed.
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 5 Future Directions
### Multi-Model Integration Problem  
Our review shows that online integration methods have been more popular in concept drift adaptation methods, such as OARIMA-RNN. Ensemble algorithms can effectively prevent overfitting and provide better prediction performance. However, their computational complexity is high, and they take up more resources, so how to optimize their performance is also a question worthy of deep consideration [110].
## 4 Problem Formulation
### Inner Loop of Joint Optimal Problem Formulation
#### 4.2.2 Joint Optimal Problem Formulation for RIS-Assisted MANET  
To jointly optimize the beamforming for the receivers \(\mathbf{W}=[\mathbf{W}_{TR,1},\ldots,\mathbf{W}_{TR,N_{T}}]\) and the phase shifts for the RIS \(\mathbf{\Theta}=[\mathbf{\Theta}_{1},\ldots,\mathbf{\Theta}_{\mathbf{R}}]\), the optimal design problem for an RIS-assisted mobile ad-hoc network (MANET) can be formulated to maximize the following expression:  
\[\max_{\mathbf{\Theta}_{i},\mathbf{W}_{i}}\sum_{t=1}^{T_{F}}\left[\sum_{i=1}^{ N}\eta_{EE,i}(t)\right] \tag{15}\]  
where \(\mathbf{\Theta}\) and \(\mathbf{W}\) represent the controlling variables for RIS phase shifting and transmission power allocation, respectively, and \(g(\cdot)\) is a positive definite function; the objective function aims to maximize a certain expression.  
Here, \(\eta_{EE,k}(t)\) signifies the energy efficiency of pair \(k\), which is defined as the ratio of the instantaneous data rate \(R_{k}(t)\) to the corresponding power consumption \(P_{k}(t)\) at time \(t\). Utilizing Equations (7) and (13), \(\eta_{EE,k}(t)\) can be expressed more explicitly as  
\[\eta_{EE,i}(t)=\frac{B_{i}log_{2}(1+\gamma_{i}(t))}{(\mu\mathbf{W}_{i}^{H} \mathbf{W}_{i}+P_{RIS,j})(t)+P_{D_{i}^{t}}+P_{D_{i}^{t}}} \tag{16}\]  
With the optimization problem formulated in (15), the optimal policies can be obtained as  
\[[\mathbf{\Theta}^{*},\mathbf{W}^{*}]=argmax\sum_{t=1}^{T_{F}}\left[\sum_{i=1} ^{N}\eta_{EE,i}(t)\right] \tag{17}\]
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 5 Performance Evaluation and Results
### Results
#### 5.5.4 Results Summary  
The results showed that the DYNASTI reaches significant improvement in traffic performance for non-critical and critical applications as well as coexistence between regular and sporadic applications. DYNASTI also shows itself as a more scalable solution for a greater number of nodes and applications. Furthermore, it achieves better service differentiation for critical applications.
## 3 Experimental Setups
### Tensile Tests with Digital Image Correlation  
The tensile test machine Instron 4411 was used to perform the tensile tests. The loading rate was fixed at 1 mm/min. A camera with a resolution of 1280 pixels \(\times\) 960 pixels was used to record a snapshot every 0.6 s while performing the test. This frequency gives an accurate reproduction of the tensile curves. The speckle was applied manually through a black paint spray. After collecting the snapshots, the DIC software NCORR [28] allows a quantitative analysis of the strain. It tracks the displacement of the speckles on each snapshot during the loading phase and uses it to interpret the kinematic field maps on a specific region of interest (ROI). A subset size of 15 pixels and a spacing of 2 pixels were used for data treatment. The images were treated as described in [29]. The method uses specific dummy sensors placed next to the jaw's location in order to calculate the true value of displacement.
## Abstract  
Pneumatic spraying is especially sensitive to spray drift due to the production of small droplets that can be easily blown away from the treated field by the wind. Two prototypes of environmentally friendly pneumatic spouts were developed. The present work aims to check the effect of the spout modifications on the spray quality, to test the convenience of setting the liquid hose out of the spout in cannon-type and hand-type pneumatic nozzles and its effect on the droplet size, homogeneity and driftability in laboratory conditions. Laboratory trials simulating a real sprayer were conducted to test the influence of the hose insertion position (HP), including conventional (CP), alternative (AP), outer (OP) and extreme (XP), as well as the liquid flow rate (LFR) and the airflow speed (AS) on the droplet size (D50, D10 and D90), homogeneity and driftability (V100). Concurrently, the droplet size spectra obtained by the combination of aforementioned parameters (HP \(\times\) LFR \(\times\) AS) in both nozzles were also classified according to the ASABE S572.1. Results showed a marked reduction of AS outside the air spout, which led to droplet size increase. This hypothesis was confirmed by the droplet size spectra measured (D50, D10, D90 and V100). A clear influence of HP was found on every dependent variable, including those related with the droplet size. In both nozzles, the longer the distance to CP, the coarser the sprayed drops. Moreover, LFR and AS significantly increased and reduced droplet size, respectively. A higher heterogeneity in the generated drops was obtained in XP. This position yielded V100 values similar to those of the hydraulic low-drift nozzles, showing an effective drift reduction potential. The classification underlines that the variation of HP, alongside AS and LFR, allowed varying the spray quality from very fine to coarse/very coarse, providing farmers with a wide range of options to match the drift-reducing environmental requirements and the treatment specifications for every spray application.  
pesticide application; pneumatic nozzle; homogeneity; airflow speed; liquid flow rate; spray drift reducing; spray quality
## 4 Working Mechanism of ESRVR
### Data Transmission and Void-Hole Detection and Mitigation  
This section describes in detail the process of data transmission and the strategy by which a void-hole is avoided.