esa-sceva/satcom-chunk-collection huggingface dataset:
# Technical Interoperability for Machine Connectivity on the Shop Floor
## 3 Method and Project Description  
The methodology applied to this work is the design science approach. Design science research centers around artificial artifacts, designed and developed with the researchers deeply involved. This methodology has been successfully used in information systems research, in which an artifact can be software, hardware, a method, a theory, etc. [42]. The crucial aspects of design science for information systems research are illustrated in the information systems research framework developedby [PERSON], et al. [43]. An artifact is designed and evaluated in a design/test cycle. The design/test cycle gets its relevance in an iterative process of adapting to real-life business needs and evaluating the results in the intended environment, which, in this case, is the shop floor. The design/test cycle is built on theories drawn from the common knowledge base, and new learnings from the design/test cycle can often result in new theories that should be communicated to contribute to new knowledge and expand the knowledge base. This is called the rigor cycle.  
As mentioned above, the 5 GEM project investigates how the next generation cellular networks, 5G, can support manufacturing industries in implementing smart manufacturing systems. Part of the investigations was to deploy a cellular network on the shop floor, connect a grinding machine, collect data from the grinding process, and to utilize that data in real-time and analytics applications. The results from the applications provide the system feedback loop that can interconnect with an automated system, e.g., controlling the machine directly, or be displayed to manufacturing operators through a mobile support system (mobile application) already in operation on the shop floor. Beyond the fast and scalable connectivity that the infrastructure provided, this required software and hardware designed to collect and transfer the data between source and utilization, which are the relevant artifacts for this research. To connect a producing grinding machine to a cloud network seems like a straightforward task. However, there are many things to consider regarding the technical aspects. First, there is a question of what data should be collected. Then there is the problem of how to collect that data, and then that data needs to be transferred. Solutions for the latter are the relevant artifacts further described in the results chapter. Figure 2 shows how these artifacts relate to the project goal and infrastructure. The artifacts connect a grinding machine to a mobile operator support system and an analytics platform. Furthermore, they are enabled by the connectivity infrastructure, i.e., the cellular and back-end network, handling the low-level data transmissions.
## Materials and Methods
### Calculation of Amplitude, Phases and Periods for LAI Time Series  
We use metaCycle [70] to calculate the periodic characteristics (periods, phases, and amplitude) for the monthly field, MODIS, GEOV2 and V3 LAI time series. This method can calculate the periodic characteristics for time series with missing values [70], which frequently occurred for the satellite LAI times series. MetaCycle incorporates three methods: ARSER (Autoregressive spectral analysis) [71], JTK_CYCLE(Jonckheere-Terpstra-Kendall algorithm) [72], and Lomb-Scargle [73], for periodic signal detection, and it could output integrated analysis. The integrated period from MetaCycle is an arithmetic mean value of multiple periods, while phase integration based on the mean of circular quantities calculated from the above three mentioned methods.  
MetaCycle recalculates the amplitude with the following model (Equation (6)):  
\[Yi\ =\ B+TRE*\left(ti-\frac{\sum_{i=1}^{n}ti}{n}\right)+A*\cos\left(2*\pi*\frac{ ti-PHA}{PER}\right), \tag{6}\]  
where \(Yi\) is the observed value at time \(t_{i}\); \(B\) represents the baseline value (mean value) of the LAI time series; \(TRE\) is the trend level of the time-series profile; \(A\) is the amplitude of the waveform. \(PER\) and \(PHA\) are the integrated period and phase, respectively. \(PER\) represented the periods of the LAI time series; here, it is 12 months. \(PHA\) represented the phases of the LAI time series at the period of 12 months. \"\(i\)\" is the time points of the time series. \"\(n\)\" represents the total number of the time points of the time series. In this model, only \(B\), \(TRE\), and \(A\) are unknown parameters and can be calculated using ordinary least squares. [PERSON]'s method is implemented in MetaCycle for integrating multiple \(p\)-values.  
Relative amplitude (rAMP), which is the _A/B_, can be used to compare the amplitudes between time series with different baselines levels.  
In this study, each LAI time series is decomposed into the baseline value (mean value), the trend, and the wavelength with specific amplitude, period, and phase. The integrated phases can be used to compare whether the LAI time series is lead, lag, or synchronous. The amplitude can be used to quantify the magnitude of seasonal fluctuations for LAI time series and the relative amplitude can be used to compare the LAI seasonal fluctuations from different sites and data source. We apply this method to the monthly averaged field LAI time series at BNF, and the monthly averaged retrieved satellite LAI at each site because only BNF has frequent enough field measurements over multiple months from the year 2005 to 2017.
## 4 Evaluation Experiments
### Experimental Conditions
In Figure 7b, the setup with reflection, no point is set for parameter determination, and some points located on the path of the demodulated sound are set for evaluation. The root mean square error (RMSE) was used to evaluate the error between the simulation and measured results, which can be given as  
\[E_{\mathrm{rms}}=\sqrt{\frac{1}{G}\sum_{g=0}^{G-1}\bigl{(}\mathrm{SPL}_{ \mathrm{d}}^{\mathrm{sim}}-\mathrm{SPL}_{\mathrm{d}}^{\mathrm{meas}}\bigr{)}^ {2}}, \tag{35}\]  
where \(g\) (\(g=0,1,\cdots,G-1\)) denotes the index of each evaluation point, \(G\) denotes the total number of evaluation points, \(\mathrm{SPL}_{\mathrm{d}}^{\mathrm{sim}}\) denotes the simulated sound pressure level (SPL) in decibels of demodulated sound, and \(\mathrm{SPL}_{\mathrm{d}}^{\mathrm{meas}}\) denotes the measured SPL of demodulated sound. The SPL can be calculated from the sound pressure in Pascals. For example, the SPL of demodulated sound can be given as  
\[\mathrm{SPL}_{\mathrm{d}}=20\log_{10}\frac{P_{\mathrm{d}}}{P_{0}}, \tag{36}\]  
where \(P_{0}\) denotes the reference sound pressure (\(P_{0}=2\times 10^{-5}\) Pa). Moreover, the average absolute error is also used to evaluate the error between the simulation and measured results, which can be given as  
\[E_{\mathrm{abs}}=\frac{1}{G}\sum_{g=0}^{G-1}\bigl{|}\mathrm{SPL}_{\mathrm{d}} ^{\mathrm{sim}}-\mathrm{SPL}_{\mathrm{d}}^{\mathrm{meas}}\bigr{|}. \tag{37}\]
## 3 Proposed Security Improvement Framework (SIF) Using Zigbee Protocol
### Replay Attack Control Object  
Replay attack could easily happen in a Zigbee network where security is not enabled, which leads to a Zigbee network operating without any encryption, authentication, or a frame counter. In this case, an attacker can sniff the packet using another Zigbee device connected to a computer and capture the packets transmitted. Since authentication and frame counter are disabled in the network, an attacker can replay the same packets, or even change the data contained in that packet, and send it using any Zigbee device, causing an unaccepted behavior in the network. In the Replay Attack Control Object, pseudocodes are used to detect and protect Replay Attack, as presented in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm: Replay_Attack_Protect (T_data, R_data, H, L)** \\ \hline \multicolumn{2}{l}{**Data:** Transmitting data=T_data, Receiving data=R_data, Authorize bit=H, Unauthorized bit=L. \\
**Result:** Replay_attack=R_attack. \\ \hline (1) & Start \\ \hline (2) & Connection==Serial Communication();/*Check serial communication between router and coordinator*/ \\ \hline (3) & If connection==Fail \\ \hline (4) & Return Start; /*Step 1*/ \\ \hline (5) & End \\ \hline (6) & If connection==True then \\ \hline (7) & T\_data==Encrypt(data); /*AES 128 bit encryption*/ \\ \hline (8) & If R\_data==H’ OR ’L’ then /*128 bit decryption*/ \\ \hline (9) & If R\_data==H’ OR ’L’ \\ \hline (10) & Initialization i, data; \\ \hline (11) & Fori=1 to 10 do \\ \hline (12) & Data=data+1; \\ \hline (13) & Delay==200 ms; \\ \hline (14) & If Data>7 \\ \hline (15) & R\_Attack==Yes; \\ \hline (16) & Update Web(R\_attack);/*Update web page with Replay\_attack data*/ \\ \hline (17) & Send Email(Admin); /*Send email to Admin notifyingReplay\_attack*/ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pseudocodes used to protect Replay Attack in the proposed SIF using Zigbee protocol.  
Figure 4: Sequence diagram of Router, Coordinator, Webserver, and Admin to detect Physical Attack in the proposed Security Improvement Framework (SIF).
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### NSL-KDD Results
#### 4.2.3 Majority-Class Performance  
Figure 1 illustrates the F1-score comparison for majority classes (DoS, Normal, and Probe) across traditional oversampling methods and various GAN-based augmentation strategies, including SNNL-enhanced variants.  
For DoS and Normal classes, all methods--both traditional and GAN-based--achieve consistently high F1-scores (approximately 87-89% for DoS and 79% for Normal), indicating these classes are well represented and reliably detected regardless of the augmentation strategy.  
In the Probe category, more noticeable differences are observed. Traditional methods such as SMOTE and ADASYN achieve F1-scores of 73% and 75%, respectively, while ROS shows the lowest performance at 68%. Most GAN variants (WGAN, CWGAN, and their SNNL-enhanced versions) maintain F1-scores around 70-71%. However, WGAN-GP+SNNL achieves a notably higher F1-score of 79%, suggesting that SNNL significantly improves detection for WGAN-GP.  
These results indicate that GAN-based augmentation methods, particularly when combined with SNNL, can preserve or even enhance majority-class performance. The inclusion of SNNL does not compromise the classification of well-represented classes, further supporting the overall robustness of the proposed approach in imbalanced intrusion detection tasks.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 3 Results and Analysis
### Comparison with Traditional DBSCAN Method  
In the segmentation method based on the traditional DBSCAN, the value of recall varies from 80.36% to 89.36%, with a total value of 83.88% (Table 1). The value of precision varies from 87.10% to 95.45%, with a total value of 91.24%. The value of F1-score varies from 0.84 to 0.92, with a total value of 0.87. The recall, precision and F1-score values of Plot 1 are the highest. In this paper, the results of individual tree segmentation based on the improved DBSCAN method is compared with the traditional DBSCAN method. As shown in Figure 4, individual tree segmentation of TLS based on the improved DBSCAN method improves the recall, precision, and F1-score. Compared with the traditional DBSCAN method, the recall, precision, and F1-score in Plot 1 are improved by 6.38%, 2.38% and 0.05, respectively. The recall, precision and F1-score in Plot 2 were improved by 5.77%, 2.39% and 0.04, respectively. In Plot 3, recall, precision, and F1-score are improved by 8.69%, 6.87% and 0.08, respectively. In Plot 4, recall, precision, and F1-score are improved by 9.09%, 4.36% and 0.08, respectively. Compared with the traditional DBSCAN method, the total recall, precision and F1-score of our method are increased by 6.96%, 4.14% and 0.06, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Plot** & **Actual Tree Number** & **TP** & **FP** & **FN** & **Recall (\%)** & **Precision (\%)** & **F1-Score** \\ \hline Plot 1 & 47 & 42 & 2 & 5 & 89.36 & 95.45 & 0.92 \\ Plot 2 & 52 & 43 & 4 & 9 & 82.69 & 91.49 & 0.87 \\ Plot 3 & 46 & 40 & 4 & 6 & 86.96 & 90.91 & 0.89 \\ Plot 4 & 33 & 27 & 4 & 6 & 81.82 & 87.10 & 0.84 \\ Total artificial forest & 178 & 152 & 14 & 26 & 85.39 & 91.57 & 0.88 \\ BR04 & 56 & 45 & 5 & 11 & 80.36 & 90.00 & 0.85 \\ KA09 & 39 & 32 & 3 & 7 & 82.05 & 91.43 & 0.86 \\ Total mixed forest & 95 & 77 & 8 & 18 & 81.05 & 90.59 & 0.86 \\ Total & 273 & 229 & 22 & 44 & 83.88 & 91.24 & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Segmentation results based on the traditional DBSCAN segmentation method.  
Figure 8: Segmentation results of small trees under tall trees. (**a**) The small light blue tree is detected. (**b**) The small purple tree is detected.
## 5 Results and Discussion
### Spatial Analysis
#### 5.3.3 Observation and Prediction Difference  
The EB method relies on two crash values: the observed value and the predicted value. Analyzing the difference between these observed and predicted values can provide significant insights. Locations with large observation and prediction differences often warrant additional attention because they may either experience a high frequency of accidents or fewer accidents than expected [6,7]. We visualized this observation and prediction difference (OPD) in Figure 8. Figure 8(a) employs high opacity in the QGIS layer rendering parameters to present each grid's color, while Figure 8(b) uses low opacity and a burn blending mode to highlight grids with large absolute OPD and to reveal the road network within each grid.  
In our study, OPD ranges from \(-44\) to \(11.7\). Notably, most grids exhibit small OPDs, demonstrating the efficacy of the SPF model across most grids. However, there are some grids with large absolute OPDs. Grids with OPD greater than 5 are colored dark red, while grids with OPD less than \(-5\) are colored dark blue.  
Dark red grids represent areas with a higher-than-expected crash frequency. As evident from Figure 8(a), these grids are predominantly located near two tunnels, in the  
Figure 8: Observation and prediction difference between observation and model prediction.  
vicinity of Penn Station, and along the highway. For the grids situated on the highway, the road network is characterized by risk factors such as curves [66] and interchanges [67]. For other grids, it is possible that high-risk intersections inside them which need further studies. On the other hand, the dark blue grids as seen in Figure 8b, which depict locations with fewer accidents than predicted, are situated on or near the linkage roads of bridges and tunnels (Lincoln Tunnel, Queens Midtown Tunnel, and Queensboro Bridge), or covering a roundabout (Columbus Circle). The road networks within these grids are often complex and with large road lengths. Such patterns may lead to the NB model's inadequacy, which is mainly fitted by grids with a simple road network, in accurately predicting the crash frequency for these abnormal grids.
## 4 Model Validation and Analysis  
In this section, we will validate the superiority of the proposed model for time-series non-food crop classification tasks through detailed descriptions of the dataset, experimental setup, ablation experiments, and comparison experiments. We created a time-series remote-sensing image dataset for non-food crops using remote-sensing data. Given the limited availability of datasets for non-food crop semantic segmentation, we also utilized the Canadian Cropland Dataset (CCD) ([[https://github.com/bioinfoUQAM/Canadian-cropland-dataset-github](https://github.com/bioinfoUQAM/Canadian-cropland-dataset-github)]([https://github.com/bioinfoUQAM/Canadian-cropland-dataset-github](https://github.com/bioinfoUQAM/Canadian-cropland-dataset-github)), accessed on 6 February 2025), which contains 78,536 high-resolution geo-referenced images for 10 major crop types over 5 months (June to October) and 4 years (2017-2020). Each image includes 12 main spectral bands as well as a series of bands corresponding to vegetation indices (GNDVI, NDVI, NDVI45, OSAVI, and PSRI), covering agricultural croplands in Canada [36]. In the comparison experiments, we compared the classification results of PAM with those from other deep-learning models, evaluated the classification results on both the self-made dataset and the Canadian Cropland Dataset, and also examined the classification results when the decoder in our model was replaced with another type of decoder. In the ablation experiments, we sequentially removed the phenology-aware module based on phenology information and the fusion module based on MTAF from the original model to verify the effectiveness of the entire model.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 5 Experimental Results and Discussion
### Experiment 6: GA-KL Method Stability Test in a Narrow Operating Environment  
To further verify that the GA-KL method can improve the efficiency of AGVs in a narrow operating environment, we compare the GA-KL method with four path planning methods: a three-layer control model-based path planning method [19] (referred to as GP-AP), a BP neural network-based path planning method (referred to as BP-P) [20], a path integral-based reinforcement learning algorithm [23] (PIRL), and a hybrid A* algorithm with a reinforcement learning path planning approach (HARL) [24]. We carried out five path planning experiments in real life with the same start point and end point. As shown in Figure 17, the field experiment environment consists of obstacles (another AGV) and walls. The experimental results are shown in Table 8.  
Figure 16: Side avoidance. A and B are the serial numbers of AGVs respectively.  
Figure 15: Frontal avoidance. A and B are the serial numbers of AGVs respectively.  
As shown in Table 8, comparing the GP-AP and BP-P path planning methods based on neural network reinforcement, although the path length of the GA-KL method is longer, the running time is much shorter than that of the other two methods. Comparing the hybrid path planning methods of HARL and PIRL, the GA-KL method achieves satisfactory results in terms of path length and running time because the GA-KL algorithm can coordinate the path of the two AGVs in advance according to the node busy factor, whereas the remaining two methods start their own obstacle avoidance algorithms to achieve avoidance, which wastes time, considerably reducing the efficiency. In a real factory environment, we want the total working time of multi-AGVs to be as short as possible, so the GA-KL method is most suitable for a real factory environment.
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters  
Defining a device as \"smart\" means that it has the ability to operate efficiently and autonomously with limited operator intervention required. The role of an inverter in a microgrid is to operate as an interface between energy generation and consumption points. Therefore, its role is not limited to AC DC conversion or vice versa (depending on the type of the converter) but also to control the power flow, sense faults, disconnect when necessary and other functions. Since these are the main factors that concern the microgrid control, it can be stated that the inverters are the thinking and processing components of a microgrid, which collect data and configure themselves in order to operate in a safe, controlled and effective environment. Furthermore, each power converter acting as an interface for a distributed energy resource (DER) to the grid has to fulfill the requirements of the IEEE 1547 standards series [30]. These standards have been implemented and periodically revised to deal with aspects such as voltage and power quality, grounding, islanding detection, etc [5; 8; 13]. The latest version, IEEE 1457-2018, was modified for inverter based microgrids. What is more, in the last 2018 release of the standard the DERs require to contain grid-supporting features such as voltage and frequency ride through capabilities and continue to function in case of parameter unbalances. This is in contrast to the original version, IEEE 1457-2003, that trip the DER offline in these situations [30; 31; 32; 33].  
In [34], the major indicators of smartness for an inverter were described. Each of these indicators contains some operational features that are described in separate sections and sub-sections in this article. Figure 1 is an illustration of the \"smartness\" indicators and their operational features.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
## 6 Conclusions  
Several aspects are evolving while exploring an underwater environment, such as monitoring underwater resources, investigating parameters, and planning military action. The extent of battery power is the network's primary focus because UWSN can only carry out specific tasks. This study compares the performance of the routing protocols AODV, DSR, DYMO, LAR1, Bellman-Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA in UWSN networks with variable deployment applications such as FTP, CBR, and VBR for 60 and 120 nodes, respectively. Several metrics were tracked, such as average transmission delay, average jitter, utilization rate, and energy used in transmit and receive modes. The simulation results show that, when compared to the DSR, DYMO, LAR1, Bellman-Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA routing protocols, the AODV routing protocol generates the least overall energy with a slight variation of additional nodes as well as 88.6 percent less average transmission delay. Additionally, compared to the AODV, DSR,  
Figure 21: Average jitter of all routing protocols for 120 nodes.  
Figure 20: Average jitter of all routing protocols for 60 nodes.  
DYMO, LAR1, Bellman-Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA routing protocols, the Fisheye routing protocol achieves a 91.4% higher percentage of utilization. The average jitter produced by STAR-LORA is 85.3% lower than that of the other routing protocols for 60 and 120 nodes.  
K.S.; Conceptualization, Data curation, Formal Analysis, Methodology, Software, Writing--original draft; C.V.R.; Supervision, Writing--review & editing, Project administration, Visualization; A.R.; Visualization, Investigation, Formal Analysis, Software; G.P.; Data curation, Investigation, Resources, Software, Writing--original draft, Methodology. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Methodology
### Performance Measurements  
The predictive performance of the Bayesian GLM and XGBoost models was evaluated by computing various performance and error metrics (Table 2). For Bayesian GLM, trace plot and R-hat, mean prediction, 95% credible intervals were used to assess the performance. Additionally, the linear correlation coefficients \((r)\) between the measured Vs values and the predicted values for the validation dataset were computed for both models.  
Figure 3: Seismic cone penetration (SCPT) data used for model validation.  
The predictive performance of XGBoost model was further evaluated using several metrics, including the coefficient of determination (\(R^{2}\)), index of agreement (IA), Kling-Gupta efficiency (KGE), mean squared error (MSE), root mean squared relative error (RMSRE), mean absolute error (MAE), mean absolute relative error (MARE), mean square relative error (MSRE), mean bias error (MBE), and maximum absolute relative error (MaxARE) [40; 41; 42]. These metrics provide a comprehensive quantitative evaluation of the models in predicting the Vs. IA, KGE, and \(R^{2}\) indicate overall agreement and explanatory power, with ideal values close to unity, while error metrics quantify prediction errors (lower values indicate high performance).