esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Processing Efficiency  
The heat transfer efficiency (\(\eta_{th}\)) [26] is the efficiency of energy transmission from the source to the workpiece and defined as  
\[\eta_{th}=\frac{P_{a}}{P_{i}} \tag{1}\]  
where \(P_{a}\) is power actually absorbed by the workpiece and \(P_{i}\) is total power available at the laser source. The processing efficiency (\(\eta_{p}\)) is the thermal efficiency of energy conversion in the workpiece and defined as  
\[\eta_{p}=\frac{P_{p}}{P_{a}} \tag{2}\]  
where \(P_{p}\) is the power required to melt, evaporate, and thus remove a given volume of material. From the measured widths and geometrical information, the power required to remove the material can be estimated. In addition, the total laser power available at the laser source is also estimated. Therefore, these efficiencies can be combined as  
\[\eta_{c}=\eta_{th}\eta_{p}=\frac{P_{a}}{P_{i}}\frac{P_{p}}{P_{a}}=\frac{P_{p}} {P_{i}} \tag{3}\]  
Since there is no clear definition of this term, this term is named as the cutting efficiency (\(\eta_{c}\)). Hence, first, the assumptions required to use the cutting efficiency need to be explained. Furthermore, the power required to melt, evaporate, and remove a given volume of material, as well as the total power available at the laser source, needs to be calculated.
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 2 Related Work
In order to optimise the energy efficiency, C-RAN and RT-based scheduling models were employed, and packet delay jitter was used with a fixed delay budget that considered real-time behaviour pertaining to various types of traffic with services that demanded different quality requirements. Moreover, [15] investigated the energy efficiency EE pertaining to the UE, as well as the delay jitter over the LTE downlink. The study stressed the voice-over LTE traffic that is regarded as a thoughtfully-used service. For both EE and jitter delay due to the fixed delay budget, multi-target optimisation was carried out by utilising two low-complex exploratory algorithms. The results showed a basic comparison between the user equipment's energy efficiency versus the delay jitter packets. A recent work put forward in [18] focused on handling real-time High Definition (HD) video traffic with regards to delay jitter in LTE technology throughout the uplink channel. As per the related works, the delay jitter associated with video streaming is poorly assessed with regards to downlink scheduling in the LTE cellular network. Ref. [19] put forward enhancement for the EXPRule scheduler algorithm to improve the EXPRule algorithm by defining the drawback pertaining to this algorithm in terms of high delay and high packet loss ratio, as well as low fairness. This was achieved by integrating EXPRule, as well as an enhanced eEXPRule, in order to deliver considerable performance with regards to voice and video services in LTE downlink via decreasing head-of-line packet delay. This, in turn, is achieved through computation of transmission metrics with regards to traffic separately, which results in an overall increase in the throughput of UE.
## 1 Introduction  
Modern supply chains have evolved into complex, globally distributed ecosystems that demand high levels of data integrity, system transparency, and secure collaboration to function reliably. As these systems grow in scale and complexity, ensuring transparency, traceability, and secure data sharing has become increasingly difficult. Cyberattacks, counterfeiting, data tampering, and a lack of trust among actors undermine the effectiveness and resilience of supply chain ecosystems. These vulnerabilities are particularly severe in critical domains, such as food traceability, pharmaceutical distribution, and humanitarian logistics, where failures may lead to regulatory, economic, or even humanitarian consequences. One of the key drivers behind this complexity is the growing diversity of products,dynamic geopolitical factors, and the exponential increase in data generated throughout supply chain operations [1; 2; 3].  
Traditional SCM systems, which often rely on centralized and opaque infrastructures, struggle to manage these realities effectively. They remain vulnerable to data tampering, counterfeiting, and cyber threats. In response, blockchain technology has emerged as a promising enabler, offering decentralized, tamper-resistant, and transparent ecosystems that may align better with the demands of modern SCM [4; 5].  
Blockchain's potential in SCM lies in its ability to automate trust through smart contracts, enhance traceability via immutable ledgers, and ensure data integrity through consensus mechanisms and cryptographic techniques. However, while the technology has shown promise across various industries, its integration into real-world SCM operations is not without barriers. Organizational readiness, standardization gaps, and legal uncertainty remain key obstacles [5; 6].  
Although blockchain promises significant improvements in transparency, data reliability, and decentralized trust across supply chain networks, these benefits may not always be fully realized in practice. Real-world implementations demonstrate that aligning blockchain technology with the complex and evolving nature of SCM remains a nuanced task. Challenges such as adoption resistance, fragmented standards, smart contract limitations, and persistent interoperability constraints across diverse contexts continue to hinder the widespread implementation of blockchain in real-world supply chain systems [1; 6].  
Moreover, the continuous evolution of systemic demands, driven by factors such as quantum threats, the proliferation of IoE systems, and AI-enhanced logistics, has redefined what security, traceability, and data integrity mean within supply chain ecosystems. These technologies not only introduce new capabilities but also shift the architectural expectations of blockchain-enabled systems [7]. In this dynamic landscape, ensuring trust, traceability, and data integrity is no longer a static design concern but a forward-looking imperative that requires continuous technological alignment and adaptive policy frameworks [8].
# Three-Dimensional Human Pose Estimation with Spatial-Temporal Interaction Enhancement Transformer
## 3 Method
### Preliminary  
Due to the excellent performance of Transformer across tasks, our model also adopted the Transformer-based architecture. In this part, we briefly describe the components of Transformer [31], including scaled dot-product attention, multi-head self attention, and multi-layer perceptron.  
Scaled dot-product attention is shown in Figure 2a, and its input consists of queries, keys, and values. First, the dot-product of the query and all the keys are calculated and scaled, then the Softmax function is applied to obtain the weights and finally multiplied with the values to obtain the output. The process is expressed formulaically as follows:  
\[SA(Q,\;K,\;V)\;=\;\text{Softmax}(\frac{Q\cdot K^{T}}{\sqrt{d}})\cdot V. \tag{1}\]  
Figure 1: The pipeline of our proposed framework (STFormer) for video 3D human pose estimation. Feature Extraction module for independently extracting features from spatial and temporal domains. The Cross-Domain Interaction module then receives the spatial and temporal features extracted in the previous stage for feature enhancement. Two regression heads to regress the 3D poses in the spatial and temporal domains, respectively.  
To enhance the expressive capacity of the network, a multi-head self attention (MSA) mechanism is further proposed. MSA divides queries, keys, and values into \(h\) heads, with each head performing scaled dot-product attention in parallel (Figure 2b). The information in different representation subspaces at different locations is jointly modeled with multiple heads. After that, the values that are generated by the output of each head are aggregated and linearly projected to generate the final output.  
\[MSA\ =\ f(Concat(head_{1},head_{2},\ \ldots,head_{h})),head_{i}\ =\ SA(Q_{i},\ K_{i},\ V_{i}), \tag{2}\]  
where \(i\in[1,...,h]\) is the index of the head and \(f(\cdot)\) is the linear function.  
Multi-layer perceptron (MLP) consists of two linear layers and an activation function for non-linearity and feature transformation. The process is defined as  
\[MLP(x)=\sigma(xW_{1}+b_{1})W_{2}+b_{2}, \tag{3}\]  
where \(W_{1}\) and \(W_{2}\) are the weights of the two linear layers, \(b_{1}\) and \(b_{2}\) are the bias terms, and \(\sigma\) denotes the GELU activation function [38].
# Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension
## 2 Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension
### System Model of the Proposed Algorithm
Considering that noise power in the wireless environment has a random change, we resort to the dynamic detection method in each sensing cycle, where the best decision thresholds are decided by the real-time calculation of the noise SFD. \[ \begin{cases}D<\lambda_{1}&H_{1}\\ \lambda_{1}<D<\lambda_{2}&\Omega\\ D>\lambda_{2}&H_{0}\end{cases}  \tag{26}\]  
Condition 1: if \(D>\lambda_{2}\), then the decision is \(H_{0}\). Condition 2: if \(D<\lambda_{1}\), then the decision is \(H_{1}\). Condition 3: if \(\lambda_{1}<D<\lambda_{2}\), this region is called \"decision buffer\" and the decision is the uncertain state \(\Omega\) due to the fact that the SFD of the received signal falls within the random fluctuation range of the noise SFD. SUs participating in the CSS send the local decision results \(H_{1}\) and \(H_{0}\) as hard decision information to the fusion center (FC). If the decision is uncertain state, the test statistic \(D\) is sent as the soft decision information to the FC for decision. Assume the FC receives \(Z\) local decision results sent by SUs, in which the number of the hard decision information is \(K\) and correspondingly the number of the soft decision information is \(Z-K\). The fusion rule for the soft decision information at the FC is shown as follows:  
\[\gamma= \begin{cases}\frac{1}{Z-K}\sum\limits_{i=1}^{Z-K}\alpha_{i}<\Gamma&H_{1 }\\ \text{else}&H_{0}\end{cases}  \tag{27}\]  
where \(\alpha_{i}\) represents one of the soft decision information and \(\Gamma\) is the threshold for the reliability fusion at the FC. In practice, \(\Gamma\) can be calculated by  
\[\Gamma=\lambda+\Delta \tag{28}\]  
Figure 4: Double-threshold decision. where \(\lambda=(\lambda_{1}+\lambda_{2})/2\), \(\Delta\) is the correction, \(\Delta\in(-1,1)\). Since the two thresholds are set in terms of the maximum and minimum values of the noise SFD, their range of variation is related to the current spectral environment and the noise background, and thus \(\Delta\) needs to be adjusted according to the actual situation. The OR rule is adopted as the final decision so as to maximize the global detection probability. The FC makes the fusion decision based on the hard decision information and the soft decision information. The final decision is expressed as:  
\[\Lambda{=} \begin{cases}\gamma+\sum\limits_{i=1}^{K}\beta_{i}<1&H_{1}\\ \text{else}&H_{0}\end{cases}  \tag{29}\]  
where \(\Lambda\) is the final decision result at the FC.
## 3 Results
### The Object-Based Anomaly Score Map
(4) In the buffered anomalous area, only 8 mineral spots falling outside, and the recognition rate increases to 88.24%. (3) Only a few ore spots fall within the image objects with the brightest tone, namely with the largest anomaly scores. Figure 11: (**upper**) the object-based anomaly-score base map with all the known ore spots (red squares) overlaid on it; (**lower**) the buffered anomalous regions (green- and orange- colored patches edged with white) with ore spots overlaid on it. Note: the mineral species of the ore spots include Cu, Au, Fe, Mo, Hg, Ag-Pb-Zn-Fe, Ag-Pb-Zn, Pb-Zn, Cu-Zn, Ag-Pb, Pb, Zn, W, Nb-Ta, Rb-Nb-Ta, Fluorite, W-Mo, Beryl, U, etc. Other legends in this figure are consistent with those in Figure 9 if not specified. Given the above, OGE's performance on S-series elements is even better than that on I-Series elements. The main drawback of our methodology is that the delineated anomalous area is too large and many of the anomalous patches are barren, which is not helpful for guiding the follow-up anomaly verification. As stated before, these results may demonstrate the possible pitfalls of using object-level anomaly scores due to their possible misrepresentation of the within-object geochemical characteristics [10]. That is to say, the anomalies delineated by OGE must be further modified through the within-object multi-elemental statistical analysis.
## 2 Related Work
### Bearing Diagnostic Methods  
Some studies decompose and analyze vibration signals to extract fault characteristic frequencies, thereby enabling the identification and classification of fault signals [13; 14; 15]. For comprehensive feature extraction from vibration signals, a hybrid approach integrating Quadratic Wavelet Packet Energy Entropy (QWPEE) with t-distributed Stochastic Neighbor Embedding (t-SNE) was introduced in reference [13], effectively enhancing pattern recognition capabilities for bearing faults. To address the challenges brought by non-stationary signals, reference [14] employed Continuous Wavelet Transform (CWT) and Wavelet Packet Transform (WPT) to reconstruct signals exhibiting non-stationary characteristics, thereby enhancing the prominence of bearing fault-related frequencies and ultimately improving fault detection accuracy. Similarly, reference [15] adopted an approach combining envelope analysis with wavelet packet transform for feature extraction, followed by support vector machine (SVM)-based classification, where the SVM was primarily utilized to distinguish between normal and faulty signals or to classify different fault types. However, the selection of wavelet basis functions and the setting of parameters have a significant impact on the analysis results, and they have a large amount of calculation in the train-bearing scenario with high real-time requirements. In reference [16], a novel Maximum L-Kurtosis Deconvolution (MLKD) algorithm was proposed, accompanied by an iterative filter specifically designed to deconvolute unknown fault signals through L-kurtosis maximization. Reference [17] proposed a new method for diagnosing faults of rolling bearings, using wavelet packet decomposition for feature extraction and the chaotic sparrow search optimization algorithm to optimize the parameters of the deep belief network. In addition, some studies have adopted the back-propagation neural networks to diagnose bearing faults [18; 19; 20]. To avoid the problem of information loss of vibration signals during the feature extraction process, reference [18] proposed an intelligent fault diagnosis method for rolling bearings based on short-time Fourier transform and convolutional neural network (STFT-CNN). Aiming at the problem of poor ability of deep learning models in extracting comprehensive spatiotemporal features, reference [19] proposed a diagnostic method that integrates convolutional neural networks (CNNs), Long Short-Term memory (LSTM) networks, and gated recurrent unit (GRU) models. Reference [20] proposed a hybrid framework based on rolling bearing fault diagnosis by integrating variational mode decomposition--Discrete wavelet Transform (VMD-DWT) with depth-separable CNN-BiLSTM based on hybrid attention (HADS-CNN-BiLSTM). However, neural networks are relatively sensitive to the quality and noise of the training data, and it is difficult to achieve good generalization ability.
## 2 Modeling Methods
### Application Scenario
#### 2.2.2 Experimental Validation  
To validate the results of this hybrid approach, some experimental measurements were performed on the real back-to-back test rig. The two gear-meshing frequencies--850 Hz (meshing frequency of the test gears) and 1700 Hz (meshing frequency of the slave gears)--were clearly visible in the measurements (Figure 5). Other peaks corresponding to 2550 Hz, 3400 Hz, 4250 Hz etc. were the harmonics of the GMFs. The agreement between the predicted and measured frequencies was good; the amplitudes differed, but it must be considered that the damping factors introduced in the model were not calibrated--the goal was to predict the frequencies only.  
The lowest frequency was an eigenfrequency of the system. The numerical value results were higher since the inertia of the e-motor was neglected.
## 2 Materials and Methods
### Data Sources and Preconditioning  
#### 2.2.1 Data Source and Preprocessing  
Various influencing factors were collected, including statistical, climatic, and socio-economic data (Figure 2). These driver data were then standardized into a 100 m resolution grid, and the corresponding distance grid was calculated using Euclidean distance. The sources of the potential drivers are detailed in Table 1. The LU data are categorized into six types: arable land, woodland, grassland, water areas, construction land, and other land. This study used data from different years, mainly following the principles of data availability and integrity. Climate data and some socio-economic data were based on the latest observational and statistical information, and were interlinked in time span, reflecting the socio-economic pattern and infrastructure distribution at the time and reflecting the dynamic changes in the study area to the greatest extent. Through data standardization, the consistency of the data and reliability of the results were ensured. The technical route is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Data** & **Year** & **Attribute/Spatial Resolution** & **Data Resources** \\ \hline Land use datasets & Land use data & 2000, 2010, 2020 & TIFF/30 m &
\begin{tabular}{c} Globeland30 \\ ([[http://global30.org/](http://global30.org/)]([http://global30.org/](http://global30.org/)) \\ (accessed on 20 November 2024)) \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Spatial basic data source.  
Figure 2: Spatial distribution of the driving factors affecting land use and CS.
## 2 Related Work
### Segment Anything Model (SAM) Related Work  
The Segment Anything Model (SAM), introduced by [PERSON] et al. [11], acts as a foundational model for universal image segmentation and has been trained on an expansive dataset exceeding one billion unique masks. While SAM demonstrates impressive capabilities in generating accurate object masks using prompts or autonomously, its efficacy for segmenting medical images is constrained by the significant disparities that exist between natural images and those found in the medical domain. SAM may encounter challenges, particularly in tasks with faintly defined boundaries commonly found in medical images [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. Recognizing this limitation, [PERSON] et al. [9] introduced MedSAM, which leverages a diverse array of medical image datasets and employs fine-tuning techniques to adjust SAM specifically for medical image segmentation tasks. [PERSON] et al. [36] presentedSAM-Med2D, a comprehensive exploration of applying SAM to 2D medical images. This approach incorporates adaptive layers in the image encoder, fine-tunes the prompt encoder, and updates the mask decoder through interactive training. Furthermore, SAM has been applied in weakly supervised segmentation tasks. For example, [PERSON] et al. [37] introduced using SAM to generate pseudo-labels, which were subsequently utilized in training other weakly supervised semantic segmentation models. In addition, [PERSON] et al. [38] used sparse annotations as prompts to generate segmentation masks, enhancing the training of other hidden target segmentation models.  
Compared to previous studies [9; 36], which required full supervision during fine-tuning and necessitated precise pixel-level annotations, our method circumvents this requirement by using weak annotations to guide SAM in generating pseudo-labels as supervision masks. In contrast to [37; 38], which introduced additional segmentation models, we chose to use pseudo-labels to guide the fine-tuning training of SAM.
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 2 Predicting Rain Attenuation Impairing FSO Links  
A key element limiting the development of rain attenuation models for FSO links is the lack of experimental data [7; 16; 17; 18], especially long-term statistics, which are typically required in the system design phase to identify the power margin needed to cope with rain attenuation. Indeed, while a large set of rain attenuation data have been collected by links operating in the 10-100 GHzfrequency range and made available by the study group 3 of ITU\(-\)R for the development/test of rain attenuation models, this is not the case for optical wavelengths. In order to tackle the lack of experimental data, in this work, we rely on a methodology able to synthesize realistic rain fields on a global basis, i.e., multi exponential cell (MultiEXCELL) [19]. Afterwards, the relationship between rain rate and specific attenuation (i.e., attenuation per unit path length) is derived through fundamental scattering theory. Finally, rain attenuation impairing a link of an arbitrary length is obtained by integrating specific rain attenuation along the path. The results are presented in the form of the complementary cumulative distribution function (CCDF) of rain attenuation, which is the key element typically supporting the design of wireless communication systems.
## 4 Experimentation and Results
### Performance of the Recommendation System
#### 4.10.2 Operational Efficiency  
The results also show notable improvements in operations. The DeepSORT system and YOLOv5 have allowed merchants to improve inventory control. Overstocking and stockouts can be prevented by more precisely adjusting stock levels through the real-time monitoring of client preferences and product interactions. Inventory waste has significantly decreased due to this enhanced inventory control, as seen in Figure 19, where waste levels decreased from 88% to 75% after the system was implemented. These operational improvements demonstrate the system's worth in maximizing resource utilization and reducing wasteful expenses.  
Figure 19 shows how YOLOv5 + DeepSORT affects inventory waste levels. At the end of each month, the percentage of unsold items compared to the total inventory stock determines the inventory waste level. The retailer's improved capacity to modify stock levels in response to real-time insights from YOLOv5 + DeepSORT is demonstrated by the two curves in Figure 19, which show a consistent, monotonic decline in inventory waste over time. By adjusting inventory to reflect observed consumer interest and demand patterns, overstocking was reduced, and stockouts were prevented. The steady decrease in inventory waste observed in the baseline and implementation years illustrates how YOLOv5 + DeepSORT enabled more precise inventory management, resulting in notable operational improvements.