esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 3 Results
### K-Means Clustering for Pilot Overhead Reduction  
This section investigates the application of the k-means clustering algorithm (k-clustering) to optimize system performance by reducing pilot overhead. The effectiveness of k-clustering is evaluated across various cluster configurations (2, 4, 8, and 16 clusters). The impact of k-clustering on system parameters is analyzed, particularly focusing on pilot overhead reduction compared to the baseline scenario (classical pilot placement technique).  
Figure 5 illustrates the impact of k-means clustering on pilot overhead reduction. By employing k-means clustering, a significant reduction in pilot overhead is observed,  
Figure 4: Pilot placement hybrid beamforming SE versus SNR.  
Figure 3: Pilot placement hybrid beamforming BER vs. SNR.  
especially at low SNR values. The initial overhead of approximately 350 bits is reduced to around 63 bits, representing an impressive 82% reduction. This significant improvement demonstrates the effectiveness of k-means clustering in optimizing system performance under challenging conditions.  
Figure 6 illustrates the BER performance of the hybrid beamforming system after applying k-means clustering for pilot placement optimization. The results demonstrate a significant improvement in BER, especially at higher SNR values. Notably, a configuration with 16 clusters yielded the lowest BER, achieving the best improvement at 30 dB SNR, aligning with the findings in [24].  
Figure 7 illustrates the system's spectral efficiency (SE) as a function of signal-to-noise ratio (SNR) after pilot placement optimization using k-means clustering. Compared to the baseline system (Figure 4), which achieved an SE of approximately 12.5 \(\mathrm{bps}/\mathrm{Hz}\), the k-means-optimized system significantly improves SE to around 31 \(\mathrm{bps}/\mathrm{Hz}\), representing a 2.5-fold increase. This result surpasses the clustering-based pilot allocation scheme in [7], which demonstrated a 17% reduction in pilot overhead, with an 8-14% SE improvement. In contrast, our proposed system offers a substantial 150% increase in SE and an 82% reduction  
Figure 5: K-Clustering Pilot Overhead vs. SNR.  
Figure 6: BER vs. SNR after pilot placement optimization using K-Clustering.  
in pilot overhead. It is also noted that the spectral efficiency reaches a near-optimal value at k = 4. This plateau can be attributed to the fact that increasing the number of clusters beyond this point does not significantly enhance pilot allocation efficiency. The diminishing returns indicate that the channel variations are effectively captured with fewer clusters, aligning with the observed pilot overhead reduction trends.
## 5 Use Cases
### Analysis Using Graph Neural Networks
#### 5.7.2 Model Performance  
The performance metrics of our GNN implementation provide an empirical validation of the theoretical principles outlined by [PERSON] and [PERSON] [22] regarding adaptive decision-making structures. Cross-validation experiments across five folds revealed consistent performance that demonstrates the framework's ability to maintain institutional trust through reliable outputs:  
* Prediction accuracy: values of 73.4% \(\pm\) 0.4% across folds.
* Training convergence: stable loss reduction from 0.70 to 0.46.
* Model stability: small variance in performance metrics (\(\sigma\approx 0.004\)).
* Learning dynamics: effective early stopping around epoch 280-330.  
To validate the robustness of our GNN implementation, we conducted a comprehensive five-fold cross-validation analysis, visualized in Figure 8. The training dynamics reveal several key insights into the model's learning behavior. First, the validation loss shows consistent convergence patterns across all folds, starting from approximately 0.60 and stabilizing around 0.47, indicating robust generalization capabilities. Second, the validation accuracy demonstrates stable improvement, reaching a plateau at approximately 73% across all folds with minimal variance (\(\sigma\approx 0.004\)). The training loss exhibits smooth decay characteristics, while the learning rate adaptation through the OneCycleLR scheduler shows effective exploration of the parameter space during early training followed by fine-tuning in later epochs. This example provides an instantiation of a dashboard for the control of advanced AI techniques.  
Figure 8 shows the training dynamics across five-fold cross-validation of the GNN model showing convergence patterns: (top row) validation loss, training epochs progression, and validation accuracy; (bottom row) training loss and learning rate adaptation. The consistent patterns across folds demonstrate the model's stability, with validation accuracy converging around 73% and losses steadily decreasing across training steps. The learning rate curve shows effective adaptation through the OneCycleLR scheduler, supporting robust model optimization.
## 2 The Proposed Method  
The data flow diagram of the proposed method is illustrated in Figure 1; the rectangle box in the graph represents the data, and the oval box represents the operation. The data flow is as follows:  
1. Based on previously estimated pose at time T-1, which is obtained by the fusion of 6 DOF IMU data and LiDAR, and the current output of IMU sensor, the initial pose of LiDAR at time T is estimated based on EKF estimation. The output of EKF is then forwarded to participate in scan matching. The results of the scan matching can be involved in the EKF prediction at the next time. Here, a scan-to-submap strategy is employed to greatly reduce the time consumption.
2. After a scan matching, the system will carry out a closed-loop detection. If a loop closure is found by a suitable matching, the result will be added as a constraint to the back-end optimizer. The back-end optimizer will run one time every 5 s, and output the LiDAR pose at all moments. We can get all optimized static maps by making use of all point cloud data.
3. The scan-to-submap matching strategy is used to solve the data association problem. A local map (submap) is composed by a number of consecutive LiDAR data frames. When a frame is inserted into the corresponding local map, we will estimate the best LiDAR pose with the existing frames in the local map. The estimation is actually to align the current point cloud with the local map to find the optimal matching, which is a nonlinear least squares problem. To solve the problem, the occupancy grid map is continuous with the bicubic interpolation method.
4. To further improve the map accuracy, a sparse pose adjustment (SPA) algorithm [30] is periodically activated for the back-end optimization. Owing to the merits of the SPA algorithm, for example, it is robust and tolerant to initialization value, with very low failure rates (getting stuck in local minima) for both incremental and batch processing, and the convergent rate is very fast as it requires only a few iterations of the LM method (this is one of the key factors in our application). With the periodic optimization process, the accumulated error can be limited. It can improve the success rate of closed-loop detection in low-texture environments.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 3 Design and Implementation  
This section describes methodologies and implementation for the generation and forwarding of LID utilising PAN within LPWANs and transferring towards DRA. Types of calls and networks as well as call setups are also described to show how the actual information flows from the origination to the destination in case of an emergency.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 9 Conclusions and Future Directions  
This review has presented a comprehensive and integrative synthesis of recent advances in the integration of computer vision and artificial intelligence (AI) within collaborative robotic systems. By systematically examining foundational technologies--such as object detection, scene understanding, human pose estimation, reinforcement learning, and explainable AI--we have illustrated how the convergence of perception and cognition is transforming collaborative robots (cobots) into perceptive, adaptive, and context-aware partners.  
Recent developments indicate a clear evolution from reactive, rule-based systems to data-driven, proactive robots capable of interpreting complex visual environments and anticipating human intentions. Deep learning has substantially improved visual recognition, while multimodal sensor fusion has enriched human-robot interaction (HRI) with greater nuance and responsiveness.  
However, several critical challenges persist. A major gap remains between laboratory performance and real-world deployment. Generalizability remains a significant hurdle: vision-AI models often overfit to controlled environments and degrade in performance under real-world conditions such as variable lighting, occlusions, sensor degradation, and environmental noise. Multimodal fusion systems, while promising, bring their own challenges--including calibration complexities, latency, and synchronization issues across heterogeneous sensors like vision, tactile, auditory, and force feedback. Moreover, the reliance on data-intensive deep learning methods poses limitations, particularly in domains where annotated data is scarce or ethically constrained, such as healthcare and public security. The lack of standardized benchmarks and evaluation protocols across application domains further complicates both reproducibility and cross-comparative validation. Ethical concerns, data efficiency, real-time inference under hardware constraints, and the need for explainable decision-making remain pressing issues.  
To address these barriers, future research must prioritize several strategic directions.
## 5 Conclusions  
We propose an asymmetrical CMOS SPDT switch with a low IL, good isolation, and high linearity for a 28 GHz 5G NR. The SPDT switch is designed and implemented in a 90 nm CMOS process with a top metal thickness (TMT) of 3.4 \(\upmu\)m (SPDT SW1) and in a 0.18 \(\upmu\)m CMOS process with a TMT of 2.34 \(\upmu\)m (SPDT SW2). Compared with SPDT SW2, SPDT SW1 exhibits better overall performance (such as a 0.1-0.4 dB enhancement in IL) mainly due to a thicker TMT (i.e., lower transmission loss). The TX path is a \(\pi\)-network consisting of a parallel DTMOS-R M1, a series \(\lambda\)/8 TL, and a parallel capacitance, Cant, for a low IL (0.58-1 dB over 17-34.9 GHz for SPDT SW1) and a high P1 dB (28.5 dBm at 28 GHz for SPDT SW1) in the TX mode and good isolation in the RX mode. The RX path consists of a series impedance (of parallel L1 and DTMOS-R M2) and a parallel DTMOS-R M3 for a high P1 dB (24 dBm at 28 GHz for SPDT SW1) in the RX mode and decent isolation (25.6-62.3 dB over 17-34.9 GHz for SPDT SW1) in the TX mode. The prominent performance of SPDT SW1 indicates that it is suitable for a 28 GHz 5G NR.  
Conceptualization, Y.-S.L. and J.-F.C.; methodology, Y.-S.L.; software, C.-Y.H.; validation, Y.-S.L., C.-T.H., C.-Y.H. and J.-F.C.; formal analysis, Y.-S.L.; investigation, Y.-S.L., C.-T.H., C.-Y.H., N.-W.T. and Y.-H.C.; resources, Y.-S.L.; data curation, Y.-S.L.; writing--original draft preparation, Y.-S.L.; writing--review and editing, Y.-S.L.; visualization, Y.-S.L.; supervision, Y.-S.L.; project administration, Y.-S.L.; funding acquisition, Y.-S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council (NSTC) of Taiwan, grant number MOST111-2221-E-260-017.  
The authors are very grateful for the support from the Taiwan Semiconductor Research Institute (TSRI) for chip fabrication and measurements.  
The authors declare no conflict of interest.
## 2 Theory Background
### Machine Learning Model
#### 2.4.1. Random Forest (RF)  
As shown in Figure 2, random forest is an ensemble method based on decision trees. The basic idea is to combine multiple weak classifiers into a strong classifier [23]. In the regression and prediction performance of industrial data, random forest has high prediction accuracy, strong generalization ability, low sensitivity to outliers and noise, few hyperparameters, and easy-to-adjust parameters, and it is widely used in various industrial scenarios.
## Appendix B Optimization Algorithms
### Energy Cost Minimization  
The energy cost \(EC\), is the first service minimized using the genetic the GA. The costs of the battery and PV energy, included in the objective function (see Equation (A11)), correspond to the constant non-corrected nominal values \(C^{nom}\), presented in Table A1. The correction due to battery-life reduction and PV curtailment, expressed in Equations (A3) and (A6), is computed once the grid power profile for the entire test period has been generated. This has to be this way since, as expressed in Equations (A2) and (A5), a prior grid profile is required to compute the correction factors \(B_{LR}\) and \(PV_{CF}\). The optimization problem, including the objective function and its constraints, is expressed in the set of Equations (A11).  
\[\begin{split}\underset{P_{bat}}{Min}\sum_{h=1}^{24}\Delta t\cdot(P_{ grid}^{h}\cdot C_{grid}^{h}+P_{batt}^{h}\cdot C_{batt}^{h}+P_{pv}^{h}\cdot C_{pv}^{h}+M \cdot P_{grid}^{h})+L\cdot SoC_{dev}+K\cdot SoC_{out}\\ where:\begin{cases}C_{batt}^{h}=0&\text{ if }P_{batt}^{h}<0\\ C_{grid}^{h}=0&\text{ if }P_{grid}^{h}<0\\ M=0&\text{ if }P_{grid}^{h}>0\\ s.t.&\\ P_{grid}^{h}+P_{batt}^{h}+P_{pv}^{h}+P_{load}^{h}=0\\ \Delta t\cdot\sum_{h=1}^{24}P_{batt}^{h}=0\ \left( SoC^{h-24}= SoC^{h=1}\right)\\ P_{pv}^{h}=P_{pv}^{h\,mppt}\\ 0\%\leq SoC_{batt}^{h}\leq 100\%\\ 0\,kW\leq P_{batt}^{h}\leq 27 kW\end{split}\] (A11)  
where \(P_{pv}^{h\,mppt}\) is the maximum possible PV power output of hour \(h\) (i.e., the power delivered by the maximum power point tracker -mppt- controller). The energy delivered by the PV panels is the product of its average-hourly power and the time resolution (\(E_{pv}^{h}\)=\(P_{pv}^{h}\Delta t\)). The factors M, L and K represent respectively the penalizing weights of: the energy sent to the grid (\(P_{grid}^{h}<0\)), the deviation from the desired SoC at the end of the day (\(SoC_{dev}\)) and SoC values that go out-of-bounds (\(SoC_{out}\)), for a given battery profile, as previously explained in this section.  
It can be observed that the costs of grid and battery energy are zero when these resources consume energy. This is done this way because, in the case of the battery, the \(TOE_{min}\) given by the manufacturer refers to delivered energy, while in the case of the grid energy, our study-case SB is not paid if energy is sent back to the grid. The optimization is supposed to be performed once a day (at midnight) when the PV output power forecasts for the next day become available. The SoC of the battery is constrained to be left, by the end of each day, at the same value as it was at the beginning of it, which in this study is set to 50%.
# Visual Saliency Detection Using a Rule-Based Aggregation Approach
## 3 Experimental Results
### Evaluation
#### 3.3.1 Comparison of Diverse Combinations  
In this section, we present the procedure to determine the best set of saliency models to be used as input features by our system. We combined the candidate saliency models incrementally following two strategies, from best to worst model and from worst to best model, resulting in thirteen representative combinations. In Table 1, we list the performance of the seven candidate saliency models on the iCoseg dataset by ranking the F-measure.  
In Table 2, we present the F-measure performance obtained on the iCoseg dataset by each considered combination. The best performance is highlighted in bold. The evaluation and analysis of the two combination strategies are detailed below.  
**Superior ranked models aggregation** In the first combination strategy, we aggregated models incrementally, from the model with the best performance to the model with the worst performance. That is, we selected the best saliency model for the first test, the two best saliency models for the second test and so forth. The results presented in Table 2 indicate that the combination that obtained the better performance in F-measure metric includes the four better models RC, SF, MDC, and MBS.  
**Inferior ranked models aggregation** In the second combination strategy, we aggregated models, from the lowest performance model to the highest performance model. Thus, we utilized the worst performance model for the first test, the two worst performance models for the second test and so forth. In Table 2, we can notice that the combination that obtained the best performance includes the models CA, HC, FT, MBS, MDC, and SF.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **F-Measure** \\ \hline RC (2011) & 0.720 \\ SF (2012) & 0.707 \\ MDC (2017) & 0.680 \\ MBS (2015) & 0.665 \\ FT (2009) & 0.601 \\ HC (2011) & 0.572 \\ CA (2010) & 0.467 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of models ranked by their F-measure. The individual models are ranked according to the obtained F-measure on the iCoseg dataset from higher to lower score.  
According to the results depicted in Table 2, the best combination of inferior models performed lower than the best combination of superior models. In general, the best performance was achieved by aggregating the superior models. In view of the obtained results from the two combination strategies, we selected the models RC, SF, MDC, and MBS as input features of our system. In fact, the highest score was obtained by the combination including two models that estimate saliency in a region-based manner and two models that compute saliency in a pixel-wise manner. Additionally, the four models utilize the CIELAB color space.
## 2 System Design and Experimental Setup
In our experimental research works, by simplifying the conventional approach used in [15], the physical bandpass optical filter corresponding to each dedicated wavelength was in turn used in front of a PD to investigate the data transmission performance of bidirectional communications based on the proposed OT architecture. Although the optical filters that were used have the central wavelength of 450 nm, 550 nm and 650 nm respectively (Thorlabs, FB450-40, FB550-40 and FB650-40), their transmittances are not identical at each corresponding wavelength and therefore a varied degradation of system performance resulted. An amplifier with a gain of 11 dB (Minicircuits) was used to amplify the received signals from the PD again before these signals were recorded by an oscilloscope which has a real-time sampling rate of 5 GSa/s (Keysight DSOX 4104A). A VSA (vector signal analysis) software (Keysight VSA89600) was then used directly to analyze these recorded signals which have not processed with any signal processing technique, i.e., equalization from the real-time oscilloscope.
## 2 SLM Process Modeling in Moving Coordinate System
### Governing Equations  
Consider the SLM process shown in Figure 1. An effective volumetric heat source is required instead of surface heat flux on the boundary to accurately predict the size of the molten pool. Moreover, to account for the moving heat source, the coordinate system transforms from the x, y, and z fixed coordinate system to the \(\xi\), y, and z coordinate system according to a constant moving heat source by the transformation, as shown in Equation (1)  
\[\xi=x-vt \tag{1}\]  
where \(v\) is the scan speed of the laser \((\mathrm{mm/s})\).  
Using the abovementioned transformation, the three-dimensional heat transfer equation of the system shown in Figure 1 can be rewritten as Equation (2) for the moving coordinate system  
\[\rho c\bigg{(}\frac{\partial T}{\partial t}-v\frac{\partial T}{\partial\xi} \bigg{)}=\frac{\partial}{\partial\xi}\bigg{(}k\frac{\partial T}{\partial\xi} \bigg{)}+\frac{\partial}{\partial y}\bigg{(}k\frac{\partial T}{\partial y} \bigg{)}+\frac{\partial}{\partial z}\bigg{(}k\frac{\partial T}{\partial z} \bigg{)}+Q(\xi,y,z) \tag{2}\]  
where \(\rho\) is the material density \(\big{(}\mathrm{kg/m^{3}}\big{)}\); \(c\) is the heat capacity \(\big{(}\mathrm{J/kgK}\big{)}\); \(t\) is the time \((\mathrm{s})\); \(\xi\), \(y\), and \(z\) are the coordinates according to a constant moving heat source \((\mathrm{\mu m})\); \(k\) is the thermal conductivity \(\big{(}\mathrm{W/mK}\big{)}\); \(Q(x,y,z,t)\) is the volumetric moving heat sources per unit volume \(\Big{(}\frac{\mathrm{W}}{\mathrm{m^{3}}}\big{)}\); and \(T\) is the current temperature \((\mathrm{K})\).
## 6 Existing Routing Protocols  
### LEACH (Low Energy Adaptive Clustering Hierarchy)  
LEACH [7] is a self-organizing, adaptive clustering protocol. It uses randomization for distributing the energy load among the sensors in the network. The following are the assumptions made in the LEACH protocol:  
1. All nodes can transmit with enough power to reach the base station.
2. Each node has enough computational power to support different MAC protocols.
3. Nodes located close to each other have correlated data.  
According to this protocol, the base station is fixed and located far from the sensor nodes and the nodes are homogeneous and energy constrained. Here, one node called cluster-head (CH) acts as the local base station. LEACH randomly rotates the high-energy cluster-head so that the activities are equally shared among the sensors and the sensors consume battery power equally. LEACH also performs data fusion, _i.e._ compression of data when data is sent from the clusters to the base station thus reducing energy dissipation and enhancing system lifetime. LEACH divides the total operation into rounds--each round consisting of two phases: set-up phase and steady phase.  
In the set-up phase, clusters are formed and a CH is selected for each cluster. The CH is selected from the sensor nodes at a time with a certain probability. Each node generates a random number from 0 to 1. If this number is lower than the threshold node [T(n)] then this particular node becomes a CH. T(n) is given as follows:  
\[T(n)=\frac{p}{1-p}[r\bmod(\bigvee_{p}p)],\ n\in G=0,\,\text{otherwise}\]  
where p is the percentage of nodes that are CHs, r is the current round and G is the set of nodes that have not served as cluster head in the past 1/p rounds.  
Then the CH allocates time slots to nodes within its cluster. LEACH clustering is shown in Figure 2.  
In steady state phase, nodes send data to their CH during their allocated time slot using TDMA. When the cluster head gets data from its cluster, it aggregates the data and sends the compressed data to the BS. Since the BS is far away from the CH, it needs high energy for transmitting the data. This affects only the nodes which are CHs and that's why the selection of a CH depends on the remaining energy of that node.