esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
## 2 Related Work and Contribution  
### CTR Prediction State-of-the-Art Models  
Deep CTR prediction models traditionally adopt an embedding-plus-MLP architecture, which efficiently handles high-cardinality categorical features and learns non-linear feature interactions. Pioneering work like Google's Wide & Deep [14] and Deep Crossing [15] demonstrated the benefit of combining memorization (via wide features) and generalization (via deep layers), while Facebook's DLRM extended this design to web-scale ad ranking with large embedding tables and deep networks. These models established the embedding-MLP structure as a production standard.  
To enhance interaction modeling, researchers introduced explicit feature-interaction mechanisms, such as in DeepFM [16] and xDeepFM [17], which fuse factorization machines with deep architectures. The Deep & Cross Network (DCN) series introduced cross layers that model bounded-degree interactions with low additional latency, improving performance in real-world systems like Google Ads. More recently, transformer-based models have gained traction due to their superior ability to capture high-order dependencies. Self-attention architectures such as AutoInt and STEC dynamically learn feature importance and co-dependencies, often boosting performance for rare events [18]. Retrieval-Augmented Transformers further integrate external memory or content indices, aiding in long-tail item recommendation without overwhelming model complexity [19].  
Sequence modeling has emerged as a major trend, particularly for capturing temporal user behavior. Instead of relying solely on static feature aggregates, transformers now process raw event timelines to track interest evolution over time. In production settings, Meta's sequence-based ad recommender has shown measurable conversion improvements by modeling recency and engagement order.  
While these approaches efficiently handle high-cardinality categorical features, they face three key limitations: (1) performance degrades significantly with feature sparsity, as rare categories fail to learn meaningful representations; (2) they require extensive historical interaction data (typically millions of examples) to achieve adequate performance; and (3) they exhibit severe cold-start problems, struggling to represent new users/items without substantial interaction history.
# Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding
## 2 Related Work
### Semantic Matching Models  
In the realm of knowledge graph applications, semantic matching models hold indispensable significance. An eminent contender in this class is RESCAL proposed by [PERSON] et al. [24], which characterizes a knowledge graph with a binary three-dimensional tensor \(\mathcal{X}\in\mathbb{R}^{|E|x|E|x|R|}\) and depends on one-hot encoding. While RESCAL offers a foundational approach, it has limitations. It fails to capture the rich semantic nuances within the graph and scales poorly with the expansion of the size of the knowledge graph, making it less suitable in industrial-scale scenarios. Several variations of RESCAL have emerged to address these shortcomings. DistMult [25] simplifies the RESCAL model by constraining the relation matrix to a diagonal form, effectively reducing the overall parameter complexity. Going a step further, ComplEx [26] incorporates complex embeddings, enabling a more accurate representation of non-symmetric relations. [PERSON] [27] employed a decomposition algorithm to break down the tensor into entity and relation matrices, offering a holistic approach to semantic matching. ConvE [28] takes a unique perspective by separately reconstructing head entity and relation embeddings and applying a 2D convolution layer to extract semantic features, enhancing its capability to handle complex relations. InteractE [29] identifies a limitation in previous models, particularly ConvE, which restricts feature interaction between entities and relations. To improve completion performance, InteractE prioritizes increasing feature interaction, yielding better results.  
While these semantic matching models have significantly advanced knowledge graph applications, they often come with a high parameter count, posing challenges in terms of overfitting and scalability. Inspired by TuckER, we propose the introduction of a shared parameter tensor to mitigate these issues, allowing for a more effective learning in large-scale knowledge graph scenarios.  
In summary, the evolution of semantic matching models has significantly enriched the landscape of knowledge graph-based applications. Each model offers unique insights and improvements over its predecessors, and our proposal of shared parameters seeks to further enhance efficiency and scalability in this domain.
## 2 Feature Extraction
### GLCM and Haralick Features
#### 2.2.2 Haralick Features
* Sum average feature: \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]0 (13) where \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]1 (14)
* Sum entropy feature: \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]2 (15)
* Sum variance feature: \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]3 (16)
* Difference average feature: \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]4 (17) where \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]5 (18)
* Difference variance feature: \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]6 (22) where \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]7 (23) \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]8 (24) \[f_{2}=-\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}P_{d,\theta}(i,j)\cdot\log P_{d,\theta} (i,j).\]9 (25) \[f_{3}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}\lvert i-j\rvert^{2}\cdot P_{d,\theta}( i,j).\]0 (26) \[f_{3}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}\lvert i-j\rvert^{2}\cdot P_{d,\theta}( i,j).\]1 (27) \[f_{3}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}\lvert i-j\rvert^{2}\cdot P_{d,\theta}( i,j).\]2 (28) \[f_{3}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}\lvert i-j\rvert^{2}\cdot P_{d,\theta}( i,j).\]3 (29)  
Figure 8 shows an example of a \(4\times 4\) grayscale image to illustrate the above equations. After we obtain a co-occurrence matrix of the \(4\times 4\) gray-level image, \(13\) textural features can be calculated. The results of the \(13\) textural features are shown in \[f_{3}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}\lvert i-j\rvert^{2}\cdot P_{d,\theta}( i,j).\]4  
\[f_{3}=\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}\lvert i-j\rvert^{2}\cdot P_{d,\theta}( i,j).\]5 GLCM example with \((d=1,\theta=90^{{}^{\circ}})\).
# Traffic-Aware Optimization of Task Offloading and Content
## 4 Joint Optimization for Offloading and Content Caching with Traffic Stream Prediction  
In this section, FOST [29], an industry-wide, highly versatile, and easy-to-use spatio-temporal forecasting open source tool, was first used to solve the problem of predicting traffic streams. The BikeNYC dataset is preprocessed to provide a dataset format adapted to the model, after which FOST extracts temporal and spatial correlations on the dataset and integrates them to predict traffic streams, thus improving the accuracy of predicted traffic streams. Then, MOEA/D [30] is used to search for the optimal solution for the joint optimization of task offloading and content caching to determine whether to cache the vehicle-generated tasks to the edge or offload them to each platform.
# Multigap Resistive Plate Chambers for Time of Flight Applications
## 3 Applications in Large Experiments
### CBM Experiment in GSI
#### 3.2.1 The CBM-ToF System  
The CBM ToF wall [62; 63] is currently under development and construction, and it is designed to be composed of MRPC detectors. To achieve hadron identification of momentum of 4 \(\rm GeV/c\), a system time resolution below 80 ps and efficiency above 95% are required. To match the high-density particle fluxes, the detector's rate capability is the most challenging issue. These high rate requirements are no longer achievable with MRPCs built in the standard technique with float glass resistive plates. The rate capability [64; 65] is limited by the voltage drop of the resistive plates and can be increased by reducing the electrode thickness and resistivity. The efficient way is to look for low resistivity materials for the electrodes to increase the operating current without reducing the voltage applied to the gas.  
A kind of low resistive glass [66; 67] was developed at Tsinghua University. Its main parameters are shown in Table 2. The bulk resistivity is about \(10^{10}\)\(\Omega\) cm. To meet the requirement of CBM-ToF, around 1000 m\({}^{2}\) of low resistive glass is produced. As a result of the fragility of glass, the dimensions of one piece of low resistivity glass is limited to 32 cm \(\times\) 30 cm. Large area detectors can be achieved by the mosaic MRPC [68]. Similar MRPC prototypes composed of such low resistive glass were validated in a very high rate  
Figure 5: The top two panels show the dE/dx (in units of multiples of \(\sigma_{\rm dE/dx}\), \(n_{\rm eff/dx}\)) of \(\rm\overline{{}^{3}He}\)/\(\rm\overline{{}^{4}He}\) and \(\rm\overline{{}^{3}He}\)/\(\rm\overline{{}^{4}He}\) as a function of mass. The bottom panel shows a projection of entries in the upper two panels onto the mass axis. Reproduced with permission from Star Collaboration, Nature; published by Springer Nature, 2011 [60].  
environment. As shown in Figure 6, at the rate of 70 kHz/cm\({}^{2}\), the efficiency above 90% and time resolution around 80 ps [66] are achieved.  
Figure 6: Measured efficiencies (**a**) and time resolutions (**b**) for high rate MRPCs as a function of the average particle flux determined with reference scintillators. The gray band shows the data region and leads the sight. Reproduced with permission from [PERSON] et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment; published by Elsevier B.V., 2013 [66].  
The high-performance electronics for the whole CBM ToF wall is based on the PADI [69] and the GET4-ASIC chips [70]. The PADI board has 32 channels, and the threshold can be set via slow control. It can be directly connected to the MRPC readout electrodes inside the gas box, which suppresses the electromagnetically induced noise from outside and matches the impedance from electrodes to the preamplifier itself.
## 2 The Experimental Campaign
### Instrumentation and Loading History  
In order to check the proper function of the experimental setup (e.g., whether parasitic rotation of the specimens was occurring), and to document the behavior of the interfaces as exhaustively as possible, measuring devices were provided on both faces of the specimens at as many locations as allowed by the dimensions of the tested interfaces. Furthermore, strain gauges were used to measure the development of tensile strains in the interface reinforcement. The measurements during testing were (a) the shear slip along the interface, (b) the interface resistance corresponding to the imposed shear slip, and (c) the crack opening, perpendicular to the interface. To measure the shear slip and the crack width, in total, 8 LVDTs (Linear Variable Displacement Transducers) were used, positioned on both faces of the specimen and both ends of the interface. The measuring devices are shown in Figure 3; LVDTs 5 to 8, parallel to the interface, were used for the measurement of the shear slip, while LVDTs 1 to 4, perpendicular to the interface, measured the crack opening. In addition, the strains that developed in the two end reinforcing bars were measured, using electrical strain gauges. The strain gauges were positioned on the bars at a distance approximately equal to 10 mm to 20 mm from the interface on both blocks, \"A\" and \"B\".  
Each specimen was subjected to stepwise increasing cyclic shear slips under displacement control, after the application of the external normal stress \(\sigma_{\text{N,ext}}\), where applicable, and the corresponding shear resistance of the specimen was obtained using the load cell of the vertical actuator. For each relative displacement value, the recorded force constitutes the resistance of the interface to the imposed slip, and this term is used in the relevant plots. Three full reversals were imposed at each slip step, starting with a shear slip value equal to \(\pm\)0.10 mm, with the exception of specimen R-16/B/12/0.2 (Table 2), for which the first cycle slip was \(\pm\)0.20 mm. The test was terminated when the interface resistance degradation exceeded 50% of the maximum or when extensive damage of the interface hindered the continuation of testing.  
Figure 2: Test setup of the specimens with external compressive stress: (**a**) sketch of the test setup; (**b**) photo of a specimen in testing position.
# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 2 Methods
### Network Design  
The feature extractor part of our network consists of a cascade of MLCA blocks, illustrated in the purple region of Figure 1. We input a SAR image with dimensions \((h,w,3)\) into the network and acquire multi-scale features with dimensions \((\frac{h}{8},\frac{w}{8},\,\delta c)\), \((\frac{h}{16},\frac{w}{16},\,16c)\), and \((\frac{h}{32},\,\frac{w}{32},\,32c)\) after passing through the third, fourth, and fifth layer MLCA block, respectively, where c is set to 32.  
Drawing inspiration from YOLO framework [18], we incorporate its straightforward and efficient feature fusion and detection methodology, showcased in the green and yellow regions of Figure 1. The task of the neck part is to fuse multi-scale features from the feature extractor. Through a series of operations of the conv-batch normalization-SilU (CBS) module, C2f module, and upsampling module, the network has information from different semantic spaces. In the head part, multiple prediction bounding boxes at different scales are derived by performing regression and classification on feature maps across three distinct scales, then an NMS [41] algorithm is employed to remove redundant detections exhibiting substantial overlap, yielding the final output in the form of \((label,conf,x,y,h,w)\), where \(label\) is the label index value corresponding to the target, and \(conf\) is the confidence score of the target. \(x\), \(y\) and \(h\), \(w\) represent the position information and scale information of the marking box, respectively.
# SSDLiteX: Enhancing SSDLite for Small Object Detection
## 3 Comparison of SSD and SSDLite
### Fully Connected Layer Substitution  
An image classification CNN usually has a few fully connected layers at the end to collect the information and make a decision. SSD replaces the fully connected layers with convolutional layers. For example, the fc6 and fc7 fully connected layers of VGG16 are replaced with a 3 \(\times\) 3 convolutional layer and a 1 \(\times\) 1 convolutional layer. The fc6-replacement layer uses a dilation of six to have a larger receptive field. However, SSDLite does not include such layers. Using such layers can increase the complexity, but may also improve the detecting capability.
# Comparison of Crack Detection Performance of Low and High Contact Ratio Spur Gears
## 4 Dynamic Analysis of LCR and HCR Gears with Tooth Cracks
The dynamic force changes obtained during three complete pinion revolutions were examined for each gear design examined in this study. The power is transferred through at least two gear pairs in HCR gears, whereas power is transferred through at least one gear pair in LCR gears. Therefore, it is seen that the dynamic forces for HCR gears are lower than the LCR gears. During power transmission through healthy teeth, the force occurring on each tooth is similar. However, when the contact is in the cracked tooth, the dynamic forces increase suddenly. The amount of dynamic force increases with increasing crack length in both LCR and HCR gears. The amount of dynamic force increases with increasing crack length. The dynamic force in LCR gears is instantly zero in the cracked tooth, as seen in Figure 10C,D. This situation indicates that the contact between \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Pinion speed (rpm) & 1500 \\ Mass moments of inertia of pinion and gear (J\({}_{\text{P}}\)-J\({}_{\text{g}}\)) (kgm\({}^{2}\)) & 0.00257 \\ Mass moments of inertia of motor and load (J\({}_{\text{M}}\)-J\({}_{\text{L}}\)) (kgm\({}^{2}\)) & 0.00771 \\ Shear modulus (N/mm\({}^{2}\)) & 80,000 \\ Torsional stiffness of shafts (K\({}_{\text{s1}}\)-K\({}_{\text{s2}}\)) (Nm/rad) & 11,000 \\ Damping of shafts (C\({}_{\text{s1}}\)-C\({}_{\text{s2}}\)) (Nms/rad) & 0.08 \\ \(\lx@overaccentset{{3}}{\mathcal{\tilde{2}}}_{\text{1}}\)-\(\lx@overaccentset{{3}}{\mathcal{\tilde{2}}}_{\text{2}}\) & 0.01 \\ \(\lx@overaccentset{{3}}{\mathcal{\tilde{2}}}_{\text{1}}\) & 0.1 \\ Power (kW) & 10 \\ Bearing stiffness (K\({}_{\text{2}}\)-K\({}_{\text{3}}\)) (N/m) [33] & \(8\times 10^{9}\) \\ Damping of bearings (C\({}_{\text{2}}\)-C\({}_{\text{3}}\)) (Ns/m) [33] & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated constant values used in the dynamic model. the teeth is momentarily lost. The effect of the crack on the dynamic force decreases as the contact ratio increases.
## 4 Simulation and Test Verification  
To validate the effectiveness of the proposed algorithm, relevant simulations and experiments were conducted. Without loss of generality, the Iridium satellite system in LEO constellation was selected for both simulations and experiments as the signal radiation source. Among them, the Iridium system is a polar-orbiting satellite constellation with an orbital altitude of 780 km, uniformly distributed across 6 near-north-south orbital planes. Each plane contains 12 satellites (including one spare), with an orbital inclination of 86.4\({}^{\circ}\) and an orbital period of 100.13 min, enabling global coverage [35,36]. The user link employs FDMA/TDMA/SDMA/TDD multiple access methods. The 48 spot beams of each satellite are divided into groups of 12 adjacent beams, using spatial division multiple access (SDMA) for frequency reuse over the total available bandwidth. Within each beam, the frequency band is divided into multiple TDMA channels using FDMA. In each TDMA channel, time division multiplexing (TDD) is adopted for the uplink and downlink of the same user, that is, the uplink and downlink are located in different time slots of the same frame of the same TDMA carrier. The total bandwidth allocated to Iridium is 1616.0 MHz-1626.5 MHz,of which 1616.0 MHz-1626.0 MHz is a duplex channel used as a traffic channel, and 1626.0 MHz-1626.5 MHz is a downlink simplex channel with a downlink bandwidth of 500 kHz, used as a signaling channel. The received SNR of Iridium signals is generally in the range of 15-30 dB due to their low orbital altitude [37].
## 4 Materials and Methods
### Total RNA Extraction and Real-Time qPCR Assay  
The extraction of the total RNA and the transcript analysis of the key LOX-HPL pathway genes followed a published method [26; 27]. Briefly, a mortar was heated at 200 \({}^{\circ}\)C for 4 h before the extraction of the total RNA in the grapes to avoid any contamination. Afterwards, after removing seeds and stems, 10 grape berries from each cultivar were ground into a powder using liquid nitrogen in the mortar. The grape powder (100 mg) was used for the total RNA extraction, and extraction was conducted using a Spectrum(tm) Plant Total RNA Kit (Sigma-Aldrich, Beijing, China sector) according to the manufacturer's instructions. After the extraction, the quality of the RNA was verified using agarose gel electrophoresis, whereas the concentration of the RNA was determined using the absorbance ratio (A260/A280, 1.8-2.0) on a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). Subsequently, the corresponding cDNA was synthesized using the qualified RNA as the template through a Reverse Transcription System Kit (Promega). Quantitation of the relative expression of the LOX-HPL pathway key genes (including _VvLOXs_, _VvHPL1_, _VvADHs_, and _VvAAT_) was carried out under real-time qPCR using the SYBR green method on an ABI/300 Real-Time System (Applied Biosystems, Foster City, CA, USA). The reaction system (10 \(\upmu\)L) consisted of 5 \(\upmu\)L SYBR Premix Ex TaqMan(2\(\times\)), 0.2 \(\upmu\)L ROX Reference Dye (50\(\times\)) (TaKaRa, Japan), 0.1 \(\upmu\)L cDNA (1 \(\upmu\)g/\(\upmu\)L), 0.2 \(\upmu\)L primer mixture (forward primer and reverse primer, 20 \(\upmu\)M), and 4.5 \(\upmu\)L ddH2O. The real-time PCR was programmed as follows: denaturation at 95 \({}^{\circ}\)C for 30 s, followed by 40 cycles of amplification with 94 \({}^{\circ}\)C for 10 s and 60 \({}^{\circ}\)C for 31 s, and a melt cycle from 60 \({}^{\circ}\)C to 95 \({}^{\circ}\)C. The primers used in this study are listed in Table S1. The specification of the primer pairs was verified by determining the melt curves and analyzing the size and nucleotide sequence of the PCR product using gel electrophoresis and nucleotide sequencing, respectively. EF1-\(\upalpha\) (GenBank accession: EC959059), Actin (GenBank accession: EC969944), and UBQ-L40 (GenBank accession: EC929411) were used as internal controls according to previous research [26; 27; 46]. The difference between the cycle threshold (C\({}_{\text{t}}\)) of the target gene and the reference gene (\(\Delta\)C\({}_{\text{t}}\) = C\({}_{\text{t}\,\text{Target}}\) - C\({}_{\text{t}\,\text{RefGene}}\)) was used to calculate the normalized expression of the target genes (2\({}^{-\Delta\)C\({}_{\text{t}}}\)) [47]. For each sample (including three independent biological replicates), two independent extraction procedures were performed and three technical replications of real-time qPCR analysis were undertaken. The expression levels of each gene were expressed as a ratio relative to the E-L 35 stage of the \"Xiangfei\" cultivar, which was set at 1.