esa-sceva/satcom-chunk-collection huggingface dataset:
# Buffer-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection
## 3 Materials and Methods
### Methods
#### 3.2.4. YOLOv8 Algorithm  
The four primary parts of the SOTA YOLOv8 network architecture are the input, the backbone network, feature enhancement (neck), and the decoupling head (head). An adaptive anchor frame calculation, adaptive grayscale filling, and mosaic data augmentation are some of the main improvements on the input side. The YOLOv8 backbone network uses the CSP (Cross Stage Partial) concept and lightweight CSPLayer_2 Conv module. Finally, the widely used SPPF (Spatial Pyramid Pooling with Factorized Convolutions) module completes the backbone network, adding to its strong feature extraction capabilities. The neck section uses PAN-FPN (Path Aggregation Network-Feature Pyramid Network) for feature improvement. PAN-FPN is a bi-directional pathway. Three down-sampled inputs are integrated using this feature pyramid network via channel fusion via up sampling. In the end, three branches receive the output, which is supplied in the direction of the decoupling head. The separation of the prediction and regression branches is the responsibility of the decoupling head. Both the category and localization components are used in the loss computation in the regression branch. The binary cross-entropy loss function is used in the adoption of Varifocal Loss for category loss. The components of loss associated with localization are the CloU (Complete IoU) and DFL (Distribution Focal Loss). The overall YOLOv8 network architecture is visually depicted in Figure 8.  
Figure 7: Comparison between the original and filtered images. (**a–d**) Original images. (**e–h**) Filtered images.  
Using cross-entropy, DFL describes the position of the target detection frame as a global distribution. Equation (12) demonstrates the network's capacity to rapidly focus on the target spot. Interval orders are denoted by \(y_{i}\) and \(y_{i+1}\), the output of the network's sigmoid function is represented by \(s_{i}\) and \(s_{i+1}\), and \(y\) is a label.  
\[\text{DFL}(S_{i},S_{i+1})=-((y_{i+1}-y)\log(S_{i})+(y-y_{i})\log(S_{i+1})) \tag{12}\]
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.6 United Kingdom  
C-Roads UK pilot builds on the core elements of the A2/M2 CVC-A2/M2 Connected Vehicle Corridor project [84], which, in turn, represents the UK part of the InterCor programme. The beneficiary is the UK Department for Transport (DfT) and the partners involved are Highways England (HE), Transport for London (TfL), and Kent County Council (KCC). In particular, HE and KCC intend to deliver their services through both ITS-G5 and cellular networks in a hybrid framework, whilst Transport for London intends to deliver the services via cellular technologies alone. According to the planned timeline, the four services GLOSA, IVS, RWW, and PVD are currently operational, and the demonstration will be held until May 2019. It is worth mentioning the demonstration event Hybrid Testfest, held in October 2018 in the UK, with the collaboration of other InterCor partners that aims at testing the implementation of the above Day-1 services within a hybrid G5/cellular framework.
## 3 Methodology for Data Pre-Processing
### Preprocessing
#### 3.2.2 Oversampling  
After the undersampling process, the data were still not perfectly distributed over all feature ranges due to a lack of data in some edge areas. Undersampling deeper than necessary would lead to a lack of information to train the model. The oversampling algorithm employed for the proposed model used underrepresented data points, added artificial noise to the features and appended the result to the original dataset. The noise was chosen such that it had no noticeable influence on the target value. This flexibility was due to the errors in the measured variables, which are present in the data anyway, as well as due to the inertia of the features. A temperature change by a few Celsius has little influence on the result but, in contrast to a simple duplication, it prevents over-representation of the experimental data.  
The noise range was set from \(-2\) to \(2\)\({}^{\circ}\)C in \(1\)\({}^{\circ}\)C steps, such that every under-represented data point was quintupled. The final data distribution can be seen in Figure 2. The current was almost equally distributed, except in two areas. Very low and very high currents were still under-represented, due to the fact that the system rarely operates in these areas. The range around zero current was over-represented, which was due to some rest periods. Undersampling this area would lead to an inability to perceive the open-circuit voltage (OCV). Due to warming in the battery during operation, there were many more data for higher temperatures than for lower.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Training Set** & **Validation Set** & **Test Set** \\ \hline Mean Power [W] & \(-234.7\) & \(30.7\) & \(-64.4\) \\ RMS Power [W] & \(3.795\) & \(2.998\) & \(1.847\) \\ Peak Power [kW] & \(17.136\) & \(15.905\) & \(10.567\) \\ Mean C-Rate [\(1/\mathrm{h}\)] & \(-0.499\) & \(0.084\) & \(-0.100\) \\ RMS C-Rate [\(1/\mathrm{h}\)] & \(8.410\) & \(6.679\) & \(4.073\) \\ Peak C-Rate [\(1/\mathrm{h}\)] & \(38.440\) & \(35.740\) & \(23.068\) \\ Temp max [\({}^{\circ}\)C] & \(65\) & \(62\) & \(57\) \\ Temp min [\({}^{\circ}\)C] & \(-21\) & \(0\) & \(-25\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power, C-rate and temperature values for the three created datasets: training set, validation set and test set.
# Potential of Photovoltaic Generation in the Putumayo Department of Colombia
## 4 Discussion
This situation usually occurs in the installation of photovoltaic systems in the tropical zone where the optimum angle is usually between 2\({}^{\circ}\) and 6\({}^{\circ}\); however, systems are usually installed between 6\({}^{\circ}\) and 10\({}^{\circ}\). The calculations of the installation PR and the energy produced were obtained for each one of the zones. The PR with the highest value occurred in the Andean region where the temperature is lower, whereas the lowest PR occurred in the Amazon region where the temperature is higher. The maximum PR for an optimal installation is at the Encano station with a value of 0.799 and a minimum for the Tagua and Valparaiso stations of 0.750. A photovoltaic system in the Amazon region would produce 1429.96 kWh/year, the Andean-Amazonian 1167.45 kWh/year, and the Andean 775.47 kWh/year, approximately half of the Amazon region. According to the results, the municipality that would produce the most energy is Puerto Asis with a value of 75,281.5 MWh/year and the one that produces the least is Santiago with a value of 2828.3 MWh/year. The Putumayo would produce a total of 277.7 MWh/year. The Amazon region would produce 67% of the Putumayo total, 24% would be produced in the Andean-Amazon region and 9% in the Andean region. These results were obtained with an average PR value and considering an available area of 30% on roofs in the populated centers of the municipal capitals, with 250 Wp solar panels. With the municipal results, a total of 277.7 GWh/year can be generated in Putumayo. With the energy produced, it is possible to cover more than what has been consumed in Putumayo in recent years. According to the SUI report, in 2016 Putumayo consumed a total 67.0 GWh/year, of which 4.7 GWh/year were from ZNI consumption in the municipalities of Puerto Guzman and Puerto Leguizamo. In 2017, 69.1 GWh/year were consumed without adding the energy consumption of the ZNI of Puerto Guzman, and in 2018 69.9 GWh/year were consumed without adding the energy consumption of the ZNI of Puerto Guzman and Puerto Leguizamo. According to the SUI report in 2016, the utility company Agua Viva de Puerto Guzman billed the cost of energy at USD 0.35/kWh for a consumption of 1.1 GWh/year, and the Puerto Leguizamo Utility Ditlity Company billed the cost of energy at USD 0.25/kWh for a consumption of 3.6 GWh/year. The average cost of energy in Putumayo in 2016 was USD 0.17/kWh and the average for Colombia was USD 0.14/kWh. According to these data, the cost of energy in Putumayo is above the national average, and it is more expensive in the ZNI. For photovoltaic solar system, the calculated LCOE was USD 0.10/kWh, this value is still below the reported ones for the Putumayo department.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 4 ML Techniques in Energy Prediction
### Supervised ML Solutions
#### 4.1.5 Artificial Neural Network Algorithms  
Artificial neural networks (ANNs) are popular models used to solve classification and regression issues. Popular ANN algorithms include Perceptron, Back-Propagation, the Hopfield Network, and the Radial Basis Function Network (RBFN). ANNs offer benefits like less statistical training, potential correlations between predictor variables, and complex non-linear interactions [61]. However, they have drawbacks such as the 'black box' aspect, high computational cost, and susceptibility to overfitting. Applications of smart buildings have been used for improved energy efficiency, security applications, context-aware services, and activity identification [62].
# Camera Calibration in High-Speed Robotic Assembly Operations
## 1 Introduction
Intrinsic calibration determines the optical properties of the camera lens, including the focal point, principal point, and distortion coefficients [9], while extrinsic calibration refers to the camera orientation concerning the measuring plane. Intrinsic calibration can be performed once for each use of the camera. However, when using moving cameras, the main challenge is determining the actual camera position relative to the measuring plane. Regarding the characteristics of the robots used in assembly applications, two of the most important parameters are accuracy and repeatability. It should be taken into account that most assembly operations are performed by articulated arm or SCARA robots. For these robot types, repeatability is typically around 0.01-0.02 mm. While, in most cases, the accuracy value is not published by the producers, robotic assembly operations generally require it to be around 0.2-0.3 mm. This article proposes a procedure to determine the measuring plane using a 3D laser camera. For validation purposes, the camera coordinates and orientation will be verified using robot coordinates. This procedure is an essential element for camera calibration. The development of the presented procedure was considered necessary as a step towards optimizing the integration of robotic vision systems in assembly applications. A better calibrated camera has the potential to provide better recognition results, which are essential in this field. These improved results can then be used to increase the accuracy and repeatability of the robot. The article is structured as follows:  
* Section 1 presents the scientific context, the main goal of the research, and the importance of the studies aspects in the field of robotic manufacturing systems. * Section 2 presents the state of the art in the field of camera calibration for robotic vision systems. * Section 3 presents the materials and methods used for research, including the methods used, the experimental setup and procedure, and the implemented calculation algorithms. * Section 4 presents the measurement results, and the calculation outputs, followed by a discussion that validates the experimental results. * Section 5 presents the conclusions that highlight the main results and contributions of the research.
## 4 Mathematical Modeling and Analysis
### Simulation Results
#### 5.2.1 Scenario 1: Different Channel Model  
In this section, we evaluate the performance of the proposed scheme through a numerical computational analysis. We firstly perform the evaluation of the isolated D2D pair at the cell edge area when a D2D pair shares resources with a CUE located in a neighboring cell. In addition, we compare the performance of the proposed scheme (FFR-ABS) with the conventional FFR proposed in [15]. In this scenario, we assume that a target DUE-Rx is located 20 m from its corresponding DUE-Tx, and its power is set to 20 dBm (100 mW). The locations of interferers (DUE and CUE) are randomly designated between 20-60 m. During the mathematical analysis, we compare the results obtained for the conventional system using normal sub-frames (FFR only) and the proposed FFR-ABS to protect the victims from CUE interference.  
Figures 8 and 9 show the SINR and the received throughput at the target DUE receiver (DUE-Rx). The SINR and the throughput of the proposed scheme is higher compared to the conventional method because a certain spatial distance can be maintained between DUE-Rx and the interferers (CUEs and DUE pairs). In the conventional system, the target DUE-Rx experiences strong interference from the CUE in neighboring cells that share the same resources. The proposed scheme also achieves a higher SINR at the cell edge area as opposed to the existing scheme. This is due to usage of the hybrid FFR-ABS mechanism; during ABS time, the CUE would not transmit data. The muting sub-frames would help the target DUE-Rx to receive dedicated data from its corresponding DUE-Tx. As shown in Figures 8 and 9, as the interferers are located closer to the target DUE-Rx, less SINR and throughput are gained at the target DUE-Rx. Therefore, the proposed scheme achieves better performance than the conventional scheme in terms of the received SINR at the target DUE-Rx. As seen in Figure 9, the throughput can be increased up to 60% by the proposed FFR-ABS in case the distance between the target DUE-Rx and interferer is less than 30 m.  
Figure 7: Scenario 3: Random distribution with typical traffic loads.
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 3 Performance Degradation Trend Analysis and Fault Early Warning of Marine Diesel Main Engines
### Performance Degradation Analysis and Fault Early Warning  
#### 3.4.1 Performance Evaluation Metrics  
Calculation of Mahalanobis Distance  
Following operational condition partitioning of marine diesel main engines using the CLIQUE clustering algorithm, a time series of Mahalanobis distances was established by calculating the distances between performance samples at different time points under identical operational conditions. This study established an initial performance dataset \(Y=[Y_{1},Y_{2},T_{3},\ldots,Y_{100}]^{T}\) comprising the first 1000 sets of performance samples, where each synchronized parameter configuration constitutes a performance sample. We subsequently calculated the Mahalanobis distance between temporal performance samples and this reference dataset. For a given performance sample \(X\) at time \(t\), its Mahalanobis distance to dataset \(Y\) is formulated as  
\[MD=\sqrt{(x-\mu)S^{-1}(x-\mu)^{T}} \tag{10}\]  
In the equation, \(\mu\) denotes the mean vector of the initial performance dataset \(Y\), and \(S\) represents its covariance matrix. These statistical descriptors are formally defined as  
\[\mu=\frac{1}{n}\sum_{i=1}^{n}Y_{i} \tag{11}\]  
\[S=\frac{1}{n-1}\sum_{i=1}^{n}(Y_{i}-\mu)(Y_{i}-\mu)^{T} \tag{12}\]  
In the equation, \(n\) = 1000, denoting the sample size of the reference dataset. The Mahalanobis distance serves as a robust metric for marine diesel engine performance assessment, quantitatively characterizing the engine's degradation trajectory. A monotonic relationship exists between the distance magnitude and degradation severity: larger Mahalanobis distances indicate more pronounced performance deterioration, while smaller values correspond to preserved operational integrity.
## 4 Results and Discussion
### Characterization Results of Diplexers  
#### 4.1.1 Hairpin Diplexers  
A comparison of the simulated and measured results of the fabricated hairpin diplexers is shown in Figure 13. The s-parameters (S\({}_{11}\), S\({}_{21}\) and S\({}_{31}\)) are measured on separate coupons and combined with the simulated results for comparison. As evident from Figure 13, the non-ideal behavior of high-frequency resistors used as broadband terminators starts appearing in the stopband region of one filter, which has the same frequency as the passband of the other filter. This implies that the impedance of the broadband terminator is not purely resistive; rather, it has some reactance associated with it. The maximum passband insertion loss of both diplexers is 3 dB, the return loss is better than 14 dB and the VSWR is less than 1.5. The isolation between the two filters in both diplexers is better than 50 dB. The cross-over point of the hairpin diplexer (bands n257 and n260), shown in Figure 13a, is at 33.12 GHz with insertion loss of 30.6 dB. Similarly, the cross-over point is at 32.07 GHz with 37 dB insertion loss for the hairpin diplexer (bands n258 and n260), as depicted in Figure 13b.  
Figure 12: Comparison of simulated and measured results of filters: (**a**) hairpin for band n257, and (**b**) interdigital for band n258.
## 3 Simulation Results
### Case When Forecasting Error Follow A Log-Normal Distribution

Table 2: Tick loss function values corresponding to \(\phi\) = 0.70 and \(\phi\) = \(-\)0.70 when forecasting errors follow a lognormal distribution. Figure 3: Tick loss function values in the case of log-normal distribution for \(\phi\) = 0.70 (**a**,**c**) and \(\phi\) = \(-\)0.70 (**b**,**d**) and lead times of one week (**a**,**b**) and four weeks (**c**,**d**). Figure 4 shows the tradeoff curves in the case of lognormal distribution for \(\phi\) = 0.70 (on the left) and \(\phi\) = \(-\)0.70 (on the right) and lead times of one week (upper panel) and four weeks (lower panel). This figure plotted the backorders versus inventory investment. Each curve for each panel contained four specific points corresponding to the four targets CSL (85%, 90%, 95% and 99%). It is difficult to determine the best method since for instance in the left upper panel and at the 99% level, the CEVT provides a lower level of backorders but gives a higher inventory investment. Contrary, the GARCH method gives a lower inventory investment but provides a higher level of backorders. Despite these conflicts, it is clear that the GARCH-based methods and especially GARCH always give good results for all panels and almost all CSL targets.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 3 THz Channel Modeling
### Modeling of Statistical Channels  
The previously presented deterministic models provide accurate channel modeling results at the cost of high computational complexity. As a result, elaborating deterministic channel models can be far too time-consuming [103]. In addition, geometric knowledge of the propagation environment is required for those models. Statistical propagation models were proposed and designed to overcome those limitations, and numerous models are found in the literature. Some common models are the Rayleigh [104], Nakagami [105], and Rice [106] models. However, in general, they are used to represent narrowband fading and do not provide a complete analysis of the physical phenomena that occur in the THz band.  
For fast channel realizations, a statistical impulse response model for the range 275-325 GHz was presented in [107]. The proposed approach includes fully polarimetric path amplitudes, amplitude-frequency dispersion, phases, direction of departure (DoD), direction of arrival (DoA), time of arrival (ToA), propagation phenomena, and antenna characteristics, and is suitable for multi-antenna systems. The authors conducted RT simulations for 3201 frequency points and then compared data histograms with analytical distribution models. Based on the results obtained by the authors, they provided parameter values that can be used for a universal stochastic spatiotemporal model to generate ultra-broadband THz indoor radio channel realizations.  
In addition, [108] proposes a RT-statistical hybrid channel model for THz indoor communication in the range 130-143 GHz. The measurement campaigns were made in an indoor environment, and the calculations considered the same environment. The approach provided insights into THz waves propagation including LoS path loss, power-delay-angular profiles, delay and power spreads, and correlations among THz multipath characteristics such as antennas separation and the number of clusters. The hybrid model showed better results due to its adaptive capacity within the measurements in different positions between the transmitter and the receiver.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 3 Our Merging Approach: Ring Reunion Algorithm
### Example of Merging of Several Rings  
As the example in Figure 9 highlights, the Ring Reunion Algorithm is capable of merging multiple rings simultaneously. It terminates properly and creates one single ring. Figure 9 shows a scenario in which two nodes try to merge one node (27) at the same time. Since one MERGE message will always be received first, the second incoming Ring Reunion instance will always merge a ring that has been merged before. In this example, node 11's MERGE message is received by node 27 shortly after node 18's MERGE message has arrived at node 27. Arrows describe forwarded MERGE messages, and solid lines denote the successor pointers from each node to its successor.  
Figure 8: Distribution algorithm: furthermost finger contacts are invited to start instances of the merging algorithm as well.  
Figure 7: Ring Reunion Algorithm with parallelization.
## 3 Simulation and Experimental Results
### Experiment  
The numerical analysis in the simulations verified the effectiveness of the FNA method, while this section will use the experiment to verify the effectiveness of the FNA method more directly. For the machining path shown in Figure 8b, the feedrate was constrained by the angle-constraint method and FNA method, respectively, and the CNC machining was performed under the same machining conditions. In the machining, a ball milling cutter W2 QX4408 (Weidiao, Shanghai, China) was used, and the machining material was AlMg1 SiCu aluminum. The command feedrates were both 3000 mm/min, and the machining results are shown in Figure 10.  
Figure 10: The machining results of the angle-constraint method and FNA method.  
When the angle-constraint method was used, the obtained feedrate was overlarge and unstable and the overcutting occurred at many places, resulting in machining damage represented by \"pits\". However, when the FNA method was used, the obtained feedrate was reasonable and stable and the machining quality was obviously improved. In addition, the machining times of the two methods were 22 min 45 s and 22 min 8 s, respectively. Therefore, the proposed FNA method can both improve the machining efficiency and the machining quality. The machining experiments and algorithm simulations corroborate each other and they both verify the effectiveness of the FNA method proposed in this paper.