esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Potential Applications and Broader Impact
### Broader Societal and Economic Impact  
The widespread adoption of MHMM-based systems promises significant societal and economic benefits. By enabling early detection of fatigue, distraction, and skill degradation, MHMMs can support timely interventions that reduce burnout and work-related stress. Notably, research highlights the prevalence of mental health challenges faced by healthcare professionals, exacerbated by factors such as excessive workloads and the COVID-19 pandemic [78; 79]. The findings presented by [78]; illustrate the critical need for support mechanisms for mental health professionals, indicating that actionable measures like MHMM could mitigate issues such as compassion fatigue. Likewise, [PERSON] et al. (2025) discuss strategies to bolster the mental health of healthcare workers, underscoring the urgency of protecting this workforce [78; 79].  
Furthermore, the proactive risk mitigation capabilities of MHMM (particularly in medical settings, aviation, and industrial operations) can prevent costly errors and improve overall safety and operational efficiency. Reports on the impact of MHMM implementations in high-stakes environments support claims about operational improvements, with potential cost savings aligning with findings from studies advocating for enhanced protocols in sectors such as healthcare [79; 80].  
Beyond traditional applications, MHMM's scope extends into fields like renewable energy and autonomous logistics.  
These adaptations are crucial as organizations strive toward Industry 5.0's human-centric vision, which prioritizes workers' well-being amidst technological advancements. Studies highlight the necessity of scalable AI solutions like MHMM in ensuring resilience within these emerging applications, underscoring this approach's relevance in addressing the increasing complexity of today's operational environments [81].  
The transparent nature of the MHMM framework supports trust and accountability in human-AI interactions, addressing ethical imperatives crucial for responsible AI deployment.  
Moreover, the importance of human-AI synergy for workforce resilience, particularly in the context of Industry 5.0, highlights the potential societal benefits of MHMM implementation. Studies suggest that engaging with AI positively impacts employee mental health and promotes a supportive work environment, essential for sustaining a healthy workforce [79; 81].  
The potential benefits of MHMM-based systems encapsulate a wider societal and economic impact that includes enhanced mental health support for workers, improved operational safety across sectors, and a commitment to ethical AI deployment. As industries adopt these systems, the integrated approach of MHMM could facilitate transformative changes that foster resilience within the workforce while addressing the challenges presented by modern work environments.
# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 3 Combining Knowledge Graph and Collaborative Filtering Recommendation Algorithm
### Collaborative Filtering Similarity Calculation
#### 3.2.2 Item based Collaborative Filtering  
Assuming there are \(u\) users and \(v\) chapters in the dataset. The user set can be represented as \(X=\{X_{1},X_{2},X_{3},\ldots,X_{u}\}\), and the chapter set can be represented as \(Y=\{Y_{1},Y_{2},Y_{3},\ldots,Y_{v}\}\). Based on Equation (9) and the preference rules set in this paper, we can form the user behavior matrix \(M_{u\times v}\).  
In Table 1, the user behavior matrix, \(M_{ij}\) represents the user \(i\)'s preference level for chapter \(j\). For example, if user \(i\) has a video learning progress of 70% and a text learning progress of 20% in chapter \(j\). When using the video-dominant preference rule, \(M_{ij}=0.7*1+0.2*0.5=0.8\). When using the text-dominant preference rule, \(M_{ij}=0.7*0.5+0.2*1=0.55\). Under the two preference rules, different user behavior matrices are formed.  
In item-based collaborative filtering, using the user behavior matrix, recommendations are made based on the user's historical behavior. To achieve this, the similarity between chapters is computed using cosine similarity, resulting in the similarity matrix for the collaborative filtering method. When the similarity approaches 1, it signifies a higher frequency of occurrence or a stronger connection between these two chapters in user behavior data. This suggests that users tend to select these two chapters together. The notation \(simB(x,y)\) represents the chapter-to-chapter similarity obtained through the collaborative filtering algorithm.
###### Abstract  
The response of any engineering design components to stresses should be predictable, While the response of a material to complex loading, such as high strain rates experienced during service, is difficult to represent with simple tests, the Taylor impact test is one of a number of tests devised for high strain rate complex loading. To expedite the acceptance of LPBF Ti6 Al4V (ELI) for use in demanding structural applications, there is a need to develop numerical models based on the internal microstructural state variables to predict the performance of the alloy over a wide range of high strain rates using such complex tests. This paper documents the numerical simulation of Taylor impact tests for direct metal laser-sintered and post-processed Ti6 Al4V (ELI--Extra Low Interstitial) alloy. A microstructural variable-based constitutive model was used to predict the mechanical properties (stresses and evolution of plastic strains) of the material. The corresponding material parameters of the model were based on the specific microstructure obtained upon post-process heat treatment. The model was first implemented as a user material subroutine in the explicit finite element program ABAQUS using the VUHARD subroutine. Subsequently, the symmetrical Taylor impact tests of Laser Powder Bed Fusion (LPBF) Ti6 Al4V (ELI) parts were numerically simulated using the VUHARD subroutine at different impact velocities. The equivalent von Mises stress and plastic strain obtained from numerical simulations were compared with the analytical solutions based on the strain rates obtained. It was shown that the instantaneous and average absolute errors between the numerical and analytical values of the model were generally less than 5%. The mushroom end, commonly observed in a Taylor test specimen, was also seen in the numerical model.  
T6 Al4V (ELI); subroutine; VUHARD; ABAQUS; high strain rate; simulation +
Footnote †: journal: Applied Sciences
# Opportunistic Weather Sensing by Smart City Wireless Communication Networks
## 4 Open Issues in Opportunistic ISAC and IoT
### Wireless Networks Quality of Service Optimization  
Up to this point, we presented how OISAC can be used for weather monitoring, specifically, in the smart-city scene. However, one can think in the opposite direction, using opportunistic sensitivity to improve communication. As weather phenomena influence the performance of communication networks, and since CMLs and SCWNs report attenuation (and other signal-based data) back for network management purposes, it is possible to add an additional layer of processing, which could detect disturbances in the network throughput in real-time or near real-time, and use these disturbances to predict future disturbances. In a recent work [68], we presented how one can detect such disturbances (caused by a storm) and predict these disturbances due to the storm movement up to a few minutes ahead of time. Such predictions can make the network management proactive (e.g., by changing the network switching and rerouting profiles ahead of time), and thus, improve the quality of service (QoS) of the network dramatically.
## 5 Conclusions  
This study assessed the spatial and temporal performances of the VIUPD-derived VCI in comparison with the NDVI-derived VCI, TCI, SMCI, PCI in various climate divisions across the CONUS. The remote-sensing-based drought indices (VIUPD-derived VCI, NDVI-derived VCI, VUAbased TCI, MODIS based TCI, TRMM based PCI, and AMSR-E based SMCI) were correlated with the _in situ_ drought indices (PDSI, Z-index, SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24).  
The VIUPD-derived VCI had the strongest correlation with _in situ_ drought indices in different sub-periods of the growing season and in a greater number of climate divisions, with higher r-values than the NDVI-derived VCI. This indicates that the VIUPD has the potential for monitoring drought conditions in different environments. Compared with the other single remote-sensing-based drought indices (TCI, PCI, and SMCI), the VIUPD-derived VCI was more suitable for longer-term drought monitoring, such as agricultural droughts, because it had stronger correlations with SPI-6, SPI-9, SPI-12, SPI-24, and PDSI. The VIUPD-derived VCI can also overcome some of the defects of SMCI for short-term drought monitoring in areas with a high density of vegetation cover. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI. The VIUPD-derived VCI is recommended as the optimum remote-sensing-based single drought index for long-term drought in the warm regions over the CONUS.  
This work was supported under the Natural Science Foundation of China (Project Numbers 41371362, 41201348, and 41202234) opening fund of the National Key Laboratory of Remote Sensing Science (OFSLRSS201508).  
[PERSON], [PERSON] and [PERSON] conceived and designed the study; [PERSON] and [PERSON] carried out the investigation; [PERSON] and [PERSON] reviewed the manuscript; [PERSON] wrote the manuscript. All co-author provided supervision of the manuscript throughout the process.  
The authors declare no conflict of interests.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NDVI & Normalized Difference Vegetation Index \\ VIUPD & Vegetation Index based on the Universal Pattern Decomposition Method \\ VCI & Vegetation Condition Index \\ TCI & Temperature Condition Index \\ PCI & Precipitation Condition Index \\ SMCI & Soil Moisture Condition Index \\ CONUS & Continental United States \\ PDSI & Palmer Drought Severity Index \\ SPI & Standardized Precipitation Index \\ EVI & Enhanced Vegetation Index \\ MCARI & Modified transformed Chlorophyll Absorption Ratio Index \\ TVI & Triangle Vegetation Index \\ TRMM & Tropical Rainfall Measuring Mission \\ NASA & National Aeronautics and Space Administration \\ MODIS & Moderate-resolution Imaging Spectroradiometer \\ GPP & Gross Primary Production \\ CD & Climate Division \\ UPDM & Universal Pattern Decomposition Method \\ \end{tabular}
## 3 System Model
### Scheduling Model  
Figure 2 shows a schematic diagram of a typical alternate sensing scheduling scheme. A sensor wakes up its sensing unit when transitioning to the working state and turns it off when the sleep interval comes, and the length of the sleep interval is dynamically adjusted according to the event occurrence of the previous interval. Despite the scheme's prevalence, how to adjust the intervals properly is as yet an open issue.  
Now, we establish a baseline scheduling strategy: fixed-time scheduling (denoted as FT for convenience). In FT, we set the working duration and the sleep duration in advance and they remain unchanged. Then, we introduce an adaptive scheduling strategy, in which we dynamically fine-tune the sleep duration as per the event statistics and user-specified parameters. The mathematical formula is:  
\[S_{k}=\begin{cases}S_{k-1}+\delta S_{k-1},&Sgn(W_{k-1})=0\\ S_{k-1}-\theta S_{k-1},&Sgn(W_{k-1})=1\end{cases}, \tag{14}\]  
where \(\delta\) and \(\theta\) denotes the step-size increasing/diminishing factor, \(0\leq\delta\leq 1\), \(0\leq\beta\leq 1\). \(Sgn(\cdot)\) is sign function:  
\[Sgn(t)=\begin{cases}1,&\text{If an event is detected during time $t$}\\ 0,&\text{Otherwise}\end{cases}. \tag{15}\]  
\(\delta\) and \(\theta\) provide the ability to maintain a synergy between the event capture and energy expenditure. For instance, a larger \(\delta\) renders more energy-saving but also increases the probability of missing an event.
## 4 Performance Evaluation and Analysis
### Results and Discussion
#### 4.3.3 Analysis of Model Performance Based on Defect Depth and Size
The Pre-models generally demonstrated higher performance compared to the Post-models. Thus, it can be inferred that the transformation module effectively improves model performance by reducing the domain gap between synthetic and real data. Furthermore, the Post-ViT model exhibited the most stable and superior performance regardless of defect size and depth. It consistently maintained high accuracy under various defect conditions, indicating that the transformer-based architecture effectively captures complex defect patterns. However, there remains a need to enhance overall defect detection performance. Future research could focus on developing hybrid models or employing ensemble techniques to address the weaknesses in individual models and further improve robustness.
## 2 Literature Review
The deployment of hardware facilities is the key to the positioning system implemented using these technologies. This research used the wired network inside the vehicle to collect and transmit information. Limited by the problem of hardware deployment in the experimental field, discussion of the positioning system discussion of the Internet of vehicles is temporarily excluded from this research. Some accurate and reliable location data are necessary for autonomous vehicles' requirements. It can be seen from the previous discussion that positioning information alone cannot meet these requirements. Researchers have proposed many information fusion methods to solve the accuracy problem [23; 24; 25]. The information fusion method can solve some of the system's disadvantages. This method integrates multiple sensor systems that can provide some positioning information in different scenarios. To address these shortcomings, researchers have used different approaches to solve them. Information fusion combines some of the benefits of other systems, making the positioning system more precise and reliable. GPS and IMU integrate the extended Kalman filter (EKF) as one of the methods. In addition, the coordinate data fusion method has the function of repairing inaccurate information coordinates. The additional error detection mechanism of the positioning system correcting the coordinate information is a method by which to solve the error of the positioning system. There are still some differences between these methods. We propose a method that can be used to improve the stability and reliability of positioning systems; it supports the positioning system with an error detection mechanism. Considering the sensing systems and methods configured on the vehicle, this mechanism is based on the RTK, SLAM, IMU, and chassis systems for implementation of the function.
# Effective Blind Frequency Offset Estimation Scheme for BST-OFDM Based HDTV Broadcast Systems
###### Abstract  
The integrated services digital broadcasting-terrestrial (ISDB-T) system is designed in order to accommodate high-quality video/audio and multimedia services, using band segmented transmission orthogonal frequency division multiplexing (BST-OFDM) scheme. In the ISDB-T system, the pilot configuration varies depending on whether a segment uses a coherent or differential modulation. Therefore, it is necessary to perform a joint estimation of carrier frequency offset (CFO) and sampling frequency offset (SFO) independent of the segment format in the ISDB-T system. The goal is to complete those synchronization tasks while considering an information-carrying transmission and multiplexing configuration control (TMCC) signal as pilot symbols. It is demonstrated through numerical simulations that the differential BPSK-modulated TMCC information can be efficiently used for a least-squares estimation of CFO and SFO, offering an acceptable performance.  
orthogonal frequency division multiplexing; ISDB-T; carrier frequency offset; sampling frequency offset; least-squares estimation; TMCC Article
## 4 Results and Discussions
### Experimental Results
#### 4.3.1 Ablation Experiment  
To verify the effectiveness of each module of DRFENet, the important functions in our model are separated to complete the ablation experiment: (1) Use the strategy of placing the skip connection before dilated convolution in the RDB module and converting scale after fusion. (2) Do not use skip connections to aggregate different scales in the RDB module. (3) Remove the DEB module in the shallow layer of the network. (4) Remove the attention mechanism of weight multiplication in SIRB. (5) Implement DRFENet without FEB and SIRB without multiplication of weights. (6) Remove the RDB module. (7) Complete DRFENet. In order to ensure the effectiveness of the ablation experiment, we conducted experiments in Set12 and BSD68 datasets, consisting of 12 images and 68 images, respectively. The average value of ten experiments was selected as the experimental result, and the same noise map was used to attack the images in each batch of experiments. Comparison tests are shown in Table 3. It can be seen from the results that when the skip connection is placed before dilated convolution, the strategy of transforming the scale after fusion is not as good as the progressive reuse strategy we proposed in obtaining more feature information. When long skip connections are abandoned in RDB, the communication between multiple scales is weakened, and the integrity of the network is damaged. When there is no DEB in DRFENet, a large amount of feature information is lost in the shallow layer of the network, resulting in insufficient global feature learning in the network. When the multiplication of weights is removed from the SIRB, the model loses the ability to extract more prominent noise features at the end. When DRFENet does not have the FEB, the network loses the opportunity to revisit global features at a deep level. The RDB is the backbone module in DRFENet that has the most frequent feature learning. When DRFENet does not have the RDB, the reasoning ability of DRFENet also plummets.
# Real-Time Penetration Recognition Based on Voltage Signal in K-TIG Welding
## 3 Experimental Results and Discussion  
To ensure the safety of the acquisition card, a differential amplifier is utilized in the present study. Its role is to decrease the voltage by a factor of 1/200 of its original value proportionally. Figure 2a portrays the voltage time-domain signal acquired during the welding of 304 austenitic stainless steel with a welding current of 525 A and a welding speed of 240 mm/min. The voltage demonstrated a continuous rise from the arc initiation and ultimately stabilized after a certain duration of time. For this study's purpose, the signal analysis primarily focuses on the time period that excludes the arc initiation and termination phases. It is worth noting that the voltage's time domain signal is analyzed with meticulous care in this research.  
Figure 2: The time domain and frequency domain of the signal collected in the experiment. (**a**) Time domain; (**b**) frequency domain.  
Figure 2b depicts the frequency domain signal of the 304 austenitic stainless steel experiment, revealing two prominent frequency bands: 36-37 kHz and 72-73 kHz. Within each band, two frequency peaks were evident, with frequencies of 36,133 Hz and 36,310 Hz being dominant and frequencies of 72,265 Hz and 72,620 Hz being harmonic. These frequency characteristics can be extracted from the voltage signal and utilized for further analysis.
## 1 Introduction
Dual-order Raman based mid-link OPC was tested in a 256 Gb/s dual-polarization 16-QAM transmission over 2 \(\times\) 50.4 km single-mode fiber (SMF) [17], in which the performance evaluation in Q-factor achieved 7 dB reduction in nonlinear threshold and 5 dB optimum launch power, respectively. [PERSON] presented an experimental test of 8 \(\times\) 32 Gbaud PDM 16-QAM sub-channels over a 912 km WDM link with OPC based on 500 m HNLF, [18], in which 3 dB nonlinear threshold gain was achieved comparing to the case of no OPC. However, the effect of HNLF length was not discussed [18]. [PERSON] reported an experimental test of a 604.7 Gb/s 16-QAM OFDM rather than CO-OFDM over 800 km fiber with mid-link [19], in which 4.8 dB Q-factor improvement was achieved comparing to the case of no OPC. In [20], 4-QAM CO-OFDM with multiple OPC sections was studied using an analytical model, in which the BER improvements were investigated for dispersion unmanaged (DU) and dispersion managed (DM) system, respectively. This paper presents a case study of multiple 16-QAM super sub-channels over 800 km CO-OFDM link with mid-link OPC using HNLF. When 16-QAM CO-OFDM signal, including carrier frequency and bandwidth, is given, the investigation focuses on OPC parametric optimization, including the optimization of HNLF length and signal/pump power that inputs into OPC, where four different HNLFs, as illustrative examples, are considered. Furthermore, the investigation focuses on the effects of fiber dispersion, fiber nonlinearity, and ASE noise on the transmission of 16-QAM CO-OFDM signal and the compensation efficiency of using mid-link OPC with different HNLFs. The performance evaluation focuses on the transmission quality of 16-QAM CO-OFDM with mid-link OPC, including conversion efficiency, the received signal constellation, Q-factor gain, and BER at the receiver end. This is important because 16-QAM CO-OFDM signal status is heterogeneous and the mitigation of transmission impairments to the signal is still unclear. On the other hand, the case study of 16-QAM CO-OFDM with mid-link OPC presented in this paper is different comparing to [17; 18; 19], which focus on OPC with OPSK [17], WDM link [18], and OFDM link [19], respectively. Although the performance evaluation in [20] focused on BER versus the signal launch power but without considering of HNLF length and transmission distance.