esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments
### Performance of Isomorphic Models  
Even under identical model architectures, the random sampling of training data and mini-batch dynamics induce divergent biases in temporal segments, feature representations, or modality alignment. Such discrepancies lay the groundwork for knowledge transfer between structurally identical models. To assess the MMCL framework's efficacy and potential constraints in same-architecture collaborative learning, we designate 2D-TAN as both Model A and Model B and conduct experiments on the Charades-STA dataset, employing the hyperparameters outlined in Section 4.3.2. According to Figure 4, the models utilizing the MMCL framework demonstrate a degree of performance enhancement relative to the baseline model. This finding suggests that even with a uniform model design, differences in data inputs and the stochastic nature of feature learning give rise to variations  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Publication**} & \multicolumn{4}{c}{**Rank@1**} & \multirow{2}{*}{**mIoU**} \\ \cline{3-5}  & & **IoU = 0.3** & & **IoU = 0.5** & **IoU = 0.7** \\ \hline CTRL [1] & ICCV2017 & 18.32 & 13.30 & – & – \\ ACRN [50] & SIGIR2018 & 19.52 & 14.62 & – & – \\ TGN [48] & EMNLP2018 & 21.77 & 18.90 & – & – \\ SM-RL [42] & CVPR2019 & 20.25 & 15.95 & – & – \\ ACL [41] & WACV2019 & 22.07 & 17.78 & – & – \\ ABLR-aw [49] & AAAI2019 & 18.90 & 9.30 & – & 12.50 \\ DEBUG [54] & EMNLP2019 & 23.45 & 11.72 & – & 16.03 \\ CMIN [58] & SIGIR2019 & 24.64 & 18.05 & – & – \\ CBLN [45] & CVPR2021 & 38.98 & 27.65 & – & – \\ SeqPAN [60] & ACL2021 & 31.72 & 27.19 & 21.65 & 25.86 \\ MMN [59] & AAAI2022 & 38.57 & 27.24 & – & – \\ PS-VTG [28] & ITM2022 & 23.64 & 10.00 & 3.35 & – \\ D3G [46] & ICCV2023 & 27.27 & 12.67 & 4.70 & – \\ \hline
2D-TAN [23] & AAAI2020 & 37.29 & 25.32 & 13.32 & 25.19 \\ MRTNet (2D-TAN) [51] & ICASSP2024 & 37.81 & 26.01 & 14.95 & 26.29 \\ MMCL (2D-TAN) & – & **39.50** & **27.50** & 15.12 & **27.85** \\ \hline VSLNet [22] & ACL2020 & 29.61 & 24.27 & 20.03 & 24.11 \\ MRTNet (VSLNet) [51] & ICASSP 2024 & 32.35 & 25.84 & 21.31 & 26.14 \\ MMCL (VSLNet) & – & 34.15 & 26.81 & 22.57 & 27.83 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison with the state-of-the-art models on TACoS dataset.  
in predictive outcomes. The MMCL framework capitalizes on these variations for inter-model knowledge transfer, thereby achieving a synergistic boost in overall performance.
# Spectrum Decision-Making in Collaborative Cognitive Radio Networks
## 4 Conclusions  
This work analyzes the level of information that secondary users need to share to establish a proper decision-making process. The number of failed handoffs is assessed according to the segmentation structure with five percentages of collaboration (10%, 20%, 50%, 80%, and 100%). The decision-making process was carried out with two multi-criteria techniques that have shown excellent results and are widely used in these types of problems: Feedback Fuzzy, Analytical Hierarchical Process, and Simple Additive Weighting. The analysis is developed in three stages. The first stage is a collaborative model, which divides the training matrix. The second is a decision-making block that implements two multi-criteria techniques. Finally, the third stage consists of a search algorithm that quantifies  
Figure 22: Relation failed handoffs for SAW: (**a**) Best effort (BE) and high traffic (HT); (**b**) Best effort (BT) and low traffic (LT).  
the performance metrics. For the process of training and validation, real power measurements are used for the commercial GSM frequency band in operation.  
For both multi-criteria techniques (SAW and FFAHP) under two traffic levels (high and low) and two types of classes (BE and RT), two analysis criteria are implemented. The first one involves the total number of failed handoffs, and the second one quantifies the collaboration level for the failed handoff rates according to 100% collaboration. For the criterion, the behaviors are proportional. Since these are cumulative metrics, the failed handoff rate grows according to the transmission time. The tendency of the number of failed handoffs is equivalent for all five percentages of collaboration analyzed. In the second criterion, based on the amount information shared and the average increase rates, the percentage of collaboration leads to efficient results between 20% and 50% for an operator GSM band.  
Conceptualization, C.H. and E.R.-C.; methodology, C.H.; software, D.G.; validation, D.G. and C.H.; formal analysis, C.H., D.G. and E.R.-C.; data curation, C.H.; writing--original draft preparation, D.G.; writing--review and editing, C.H., D.G. and E.R.-C.; project administration, C.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Center for Research and Scientific Development of Universidad Distrital Francisco Jose de Caldas and Ministerio de Ciencia, Tecnologia e Innovacion--Mincincias.  
The authors would like to thank the Center for Research and Scientific Development of Universidad Distrital Francisco Jose de Caldas and Mincienciencas for supporting in this investigative project. [PERSON] wish to thank the Council of Science and Technology of Mexico (Conacyt) for their support through the SNI program.  
The authors declare no conflict of interest.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 3 Noise Techniques
### Novel Noise Techniques Reflecting Industrial Noise
#### 3.1.2 Harmonic Noise  
Harmonic noise comprises signal components that are integer multiples of a fundamental frequency, creating a complex periodic signal. These components increase in frequency but decrease in amplitude as the harmonic order rises, contributing to a wider frequency spectrum. Harmonic noise can blur or complicate the original signal by overlapping multiple frequency components. It can be represented as:  
\[H(t)=\sum_{n}^{N=1}\frac{A}{n}\cdot\sin(2\pi f\cdot n\cdot t+\mathcal{O}_{n}) \tag{2}\]* _A_: Base amplitude.
* _n_: Order of harmonics.
* _f_: Fundamental frequency.
* _\(\mathcal{O}_{n}\)_: Phase offset for each harmonic component.
## 5 Experiments and Data Analysis
### 5G Commercial Base Station Testing  
Based on the 5G experimental base station experiment, a measurement experiment using a 5G commercial base station is also designed. In this measurement scenario, the accuracy of target speed estimation is verified using a commercial 5G base station. Unilateral rotating target models with radii of 0.3 m and 0.15 m are tested. The measurement scenario is shown in Figure 18a, and the obtained CSI-RS signal parameters of the 5G base station are shown in Table 5.  
The measurement results of the unilateral target rotating model with a radius of 0.3 m are shown in the red line in Figure 18b and Table 6. The measurement results of the unilateral target rotating model with a radius of 0.15 m are shown in the blue line in Figure 18b and Table 7. When the rotation radius of the unilateral target is 0.3 m and the theoretical speed is 0.75 rps, the reason for the error of 100% is that the Doppler frequency exceeds the measurement range, resulting in Doppler blur.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Theoretical Speed (rps)** & **Measured Speed (rps)** & **Error** \\ \hline
0.125 & 0.122 & 2.4\% \\
0.25 & 0.244 & 2.4\% \\
0.5 & 0.5005 & 0.1\% \\
0.625 & 0.6225 & 0.4\% \\
0.75 & 1.5 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of unilateral targets with a radius of 0.3 m.  
Figure 18: (**a**) 5G commercial base station testing measurement scenario; (**b**) results of unilateral target with a radius of 0.3 m and unilateral target with a radius of 0.15 m.
## 4 Equalization Technologies
### Equalization Based on Machine Learning
#### 4.3.2 Neural Network  
The neural network (NN) is computational model loosely inspired by its biological counterparts [88]. In recent years, it has been proposed to mitigate the nonlinear impairments in optical communication system [89; 90; 91]. For short-reach PAM4 optical links, various research concerning the NN method has been performed to improve transmission performance [43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. The schematic of NN based nonlinear signal processing is presented in Figure 19a, where the leftmost part consists of a set of neurons representing the input features and the rightmost part is a non-linear activation function [48]. In the middle, a weighted linear summation is employed to represent connections between neurons. Figure 19b shows a two-layer neural network to classify PAM4 signals. Rectified Linear Units (ReLU), as indicated in the inset of Figure 19b, is always applied as the activation function. Compared with sigmoid function or Tanh function, it is more like a real neuron in our body and results in much faster training. A softmax function is commonly selected as the activation function for the output layer. The output of the softmax function is the class with the highest probability.  
An example for BER performance of NN is shown in Figure 20a [49]. Thanks to the NN method, about one order of magnitude is decrease compared to the BER with VNLE after 60 km transmission. Figure 20b shows the BER performance versus OSNR after 80 km SSMF. The required OSNRs to reach the FEC threshold for VNLE and NN are 39 dB and 35.5 dB, respectively. Thus, we can conclude that  
Figure 19: (**a**) Schematic of neural network (NN) based nonlinear signal processing for a hidden layer and (**b**) simple structure of two-layer neural network (reprinted from [48] with permission from authors).  
Figure 18: BER curves for (**a**) 40 Gbps and (**b**) 50 Gbps PAM4 signal using CBT-SVMs (reprinted from [42] with permission from IEEE).  
compared to conventional equalizers, a better BER performance can be achieved using the NN method, which is an attractive solution for short-reach PAM4 optical links.
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
###### Abstract  
This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT)-enabled relay networks with the decode-and-forward relaying protocol, where the effect of the energy triggering threshold at the relay on the system performance is considered. The closed-form expressions of the system outage probability and throughput are derived in Rician channel fading. Monte Carlo Simulation method is used to verify the accuracy of the derived closed-form expressions. The effects of some system parameters on the system performances are discussed via simulations, which show that the system outage probability increases with the increase of the minimum transmission rate required by the users and also decreases with the increase of the energy conversion efficiency. Besides, the system throughput increaseswith the increment of the transmit power of the source node, as well as the energy conversion efficiency. Additionally, the outage performance of the system with the equal two-hop distance is better than that of the system with unequal two-hop distance.  
energy collection; outage probability; Rician channel; circuit energy threshold; decode-and-forward Article
## 4 Simulation Results and Discussion
### Results of Case Studies
#### 4.3.2 Case 2  
In order to have a better understanding of ESSs depreciation, this case study was conducted. The objective function in the previous case study was acquired by considering the depreciation cost of ESS. In this case, simulations were performed with and without considering ESSs depreciation as described in subsection (Section 3.1.4). The patterns of ESSs SOC were depicted for the understudy day (Figure 13). Some significant reductions occurred when ESSs was discharging to obtain maximum profits from the market. According to the previous description, the initial and final values of SOC should be equal in the day-ahead operation of ESSs. The minimum values of SOC for SB and PHEV on the day were achieved as 1.25 kW and 1.14 kW, respectively. These two values were regarded in the first case study and the objective function achieved as $20.353. However, if the ESSs depreciation term \(D_{d}^{ESS}\) has not been regarded in the mentioned objective function, the value of US$21.248751 would have been achieved, which shows an increase in the operation cost of the prosumer. The difference between the two values was US$0.8647 for a typical summer day. With regard to the ESS lifetime, a higher value will be obtained, which shows the effect of battery depreciation on the system operation cost. Moreover, the daily value of minimum SOC (\(SOC_{d}^{\text{min},SB}\),\(SOC_{d}^{\text{min},PHEV}\)) changes based on the operational parameters, such as load data (including prosumer load data and contracted load with the consumer) and availability of RESs. As a result, long-term optimization seems necessary in order to analyze the precise value of ESSs depreciation cost.
## 4 Cost Function to Be Minimized  
As outlined in Section 2, the optimization tool identifies the combination of system variables which minimizes the total cost of the cable system \(C_{tot}\). Therefore, it is relevant to have a comprehensive cost function which can be applied consistently to different cable designs, whether superconducting or conventional. The objective function proposed in this study is reported here, whose indexes correlate to the system variables and, consequently, to the resultant cable design. The authors tried to make this function as comprehensive as possible, but users can possibly modify or expand it depending on their specific requirements:  
\[\begin{array}{c}C_{tot}=C_{HTS}\;^{\%}+C_{ins}\;^{\%}+C_{LN2}\;^{\%}+C_{ Cu}+C_{cool_{op}}\;^{\%}+C_{cool_{op}}+C_{crystat}\;^{\%}+C_{vac}+\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+C_{ tal}+C_{cab}\;^{\%}+C_{man}+C_{dis}+C_{comm}+C_{install}+C_{other}\end{array} \tag{14}\]  
where \(C_{HTS}\;^{\%}\), \(C_{ins}\;^{\%}\), \(C_{LN2}\;^{\%}\), and \(C_{Cu}\) are the costs of the superconducting material, the electrical insulation, the liquid nitrogen, and the copper required for the neutral conductor layer, respectively, \(C_{cool_{op}}\;^{\%}\) and \(C_{cool_{op}}\) are the capital and operating costs for the cooling stations, respectively, \(C_{crystal}\;^{\%}\), \(C_{vac}\), and \(C_{ter}\) are the costs of the cryostat, the vacuum pump system, and the cable current leads, respectively, \(C_{land}\) is the cost of purchasing the land to install the cable, \(C_{cab}\;^{\%}\), \(C_{man}\), and \(C_{dis}\) are the cabling, maintenance, and dismantling costs, respectively, and \(C_{comm}\), \(C_{install}\), and \(C_{other}\) represent the costs related to a cable project's commissioning and management, the civil engineering activities required for on-site installation, and the other costs which cannot be categorized in the previous indexes, respectively.  
Hereafter, the cost indexes expressed with uppercase letters (C) will indicate the total cost, while lower case letters (c) will be used to represent their unitary cost.
# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 3 Methodology  
This review considers research articles that outline the occupancy estimation methods and algorithms involving two or more networked occupancy sensor nodes. It tabulates the studies highlighting gaps in using networked sensor nodes for occupancy sensing. These occupancy sensing gaps exist in several application areas. Each application area demands a specific set of parameters from the networked sensor nodes and the occupancy estimation methods to address application area challenges. These parameters are termed as ADPs. These ADPs are identified from the reviewed studies and are tabulated and associated with these studies. This phase is referred to as Occupancy Sensing Gap and ADP Identification phase. Articles in the literature that address the identified gaps are then evaluated. Comments are added to each article on whether the proposed solutions in the articles contain the ADPs demanded by a particular application area. This phase is termed the Solution to Application Mapping phase. The review is concluded by comparing the accuracy delivered by each solution. A discussion is added at the end of this chapter about the occupancy estimation methods used by each solution, and the reasons behind the reported accuracies are highlighted. Explanations are also listed as to why specific proposed solutions are not suitable to some application areas despite the contrary claims of the authors. This phase is termed the Accuracy and Suitability Analysis phase. The review thus presents a complete picture to the reader, from identifying sensing gaps, to the suitability of each available solution in the literature, to the gaps and application areas. It must be mentioned here that although the review's primary focus lies on networked sensor nodes-based occupancy estimation solutions, the underlying sensor modalities for each solution are listed. The reviewed estimation methods may not be agnostic to the underlying sensing technology. An illustration summarizing the review methodology is shown in Figure 2.
# Temperature Comparison of Looped and Vertical Carbon Nanotube Fibers during Field Emission
###### Abstract  
Carbon nanotube (CNT) fiber-based emitters have shown great potential to deliver stable, high current beams for various potential applications. Because of joule heating, CNT field emitters are heated to high temperatures during field emission. It is important to improve the thermal management of emitters to increase their reliability and prevent premature failure. This paper compares the field emission characteristics and the temperature distribution of a new configuration of a looped CNT fiber emitter with a traditional single vertical CNT fiber emitter. It is found that the maximum temperature of the looped fiber emitter (\(\sim\)300 \({}^{\circ}\)C) is significantly reduced compared to that of the vertical fiber (\(\sim\)600 \({}^{\circ}\)C) at the same emission current of 3 mA. The experimentally measured temperature distribution is compared with a recent theory on joule heating of a one-dimensional conductor. This study provides new insights into the design of high performance field emitters.  
Keywords:field emission; carbon nanotube fibers; joule heating; temperature dependent electrical and thermal conductivities; thermal stability +
Footnote †: journal: Applied Physics Letters
# Directly Matching an MMIC Amplifier Integrated with MIMO Antenna through DNNs for Future Networks
###### Abstract  
Due to the exponential growth of data communications, linearity specification is deteriorating and, in high frequency systems, impedance transformation leading to power delivering from power amplifiers (PAs) to antennas is becoming an increasingly important concept. Intelligent-based optimization methods can be a suitable solution for enhancing this characteristic in the transceiver systems. Herein, to tackle the problems of linearity and impedance transformations, deep neural network (DNN)-based optimizations are employed. In the first phase, the antenna is modeled through the DNN with using the long short-term memory (LSTM) leading to forecast the load impedances in the a wide frequency band. Afterwards, the PA is modeled and optimized through another LSTM-based DNN using Multivariate Newton's Method where the optimal drain impedances are predicted from the first DNN (i.e., modeled antenna). The whole optimization methodology is executed automatically leading to enhance linearity specification of the whole system. For proving the novelty of the proposed method, monolithic microwave integrated circuit (MMIC) along with the multiple-input multiple-output (MIMO) antenna is designed, modeled, and optimized concurrently in the frequency band from 7.49 GHz to 12.44 GHz. The proposed method leads to enhancing the linearity of the transceiver in an effective way where DNN-based PA model gives rise to a solution for achieving the most optimal drain impedance through the modeled DNN-based antenna.  
deep neural network (DNN): linearity; long short-term memory (LSTM): multiple-input and multiple-output (MIMO) antennas: multivariate newton's method: monolithic microwave integrated circuit (MMIC): optimization +
Footnote †: journal: Sensors
###### Abstract  
In football, accurately pinpointing key events like passes is vital for analyzing player and team performance. Despite continuous technological advancements, existing tracking systems still face challenges in accurately synchronizing events and positional data accurately. This is a case study that proposes a new method to synchronize events and positional data collected during football matches. Three datasets were used to perform this study: a dataset created by applying a custom algorithm that synchronizes positional and event data, referred to as the optimized synchronization dataset (OSD); a simple temporal alignment between positional and event data, referred to as the raw synchronization dataset (RSD); and a manual notational data (MND) from the match video footage, considered the ground truth observations. The timestamp of the pass in both synchronized datasets was compared to the ground truth observations (MND). Spatial differences in OSD were also compared to the RSD data and to the original data from the provider. Root mean square error (_RMSE_) and mean absolute error (_MAE_) were utilized to assess the accuracy of both procedures. More accurate results were observed for optimized dataset, with _RMSE_ values of RSD = 75.16 ms (milliseconds) and OSD = 72.7 ms, and _MAE_ values RSD = 60.50 ms and OSD = 59.73 ms. Spatial accuracy also improved, with OSD showing reduced deviation from RSD compared to the original event data. The mean positional deviation was reduced from 1.59 \(\pm\) 0.82 m in original event data to 0.41 \(\pm\) 0.75 m in RSD. In conclusion, the model offers a more accurate method for synchronizing independent datasets for event and positional data. This is particularly beneficial for applications where precise timing and spatial location of actions are critical. In contrast to previous synchronization methods, this approach simplifies the process by using an automated technique based on patterns of ball velocity. This streamlines synchronization across datasets, reduces the need for manual intervention, and makes the method more practical for routine use in applied settings.  
positional data; events data; synchronization; football; match analysis +
Footnote †: journal: Applied sciences