esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
The sizes of saturated soil pores are strongly associated with the percentage of water types. Energy exchange of the water phase transition is mainly controlled by the pore size. Accounting for the specific surface area, the water in smaller pores contains more absorbed water than larger pores. Damage from freezing action is affected by many factors, including freezing temperature, thawing temperature, and gap time. Exploring the changing trend and mechanism of PSD under different freezing condition can help make a wise decision during AGF construction. Using NMR technology, the microstructural change in soil could be observed and a new method to determine the \(T_{2}\) cut-off value was proposed. There are several conclusions from this well-controlled experiment as follows:  
* Under the freezing effect, the size of small pores in the soft clay was increased by frost heave. After thawing, the structure of soft clay was destroyed, and the structure went looser.
* For the soft clay samples volume in this test, the freezing stage was mostly completed during the first four hours. During the second hour, the freezing rate reached the maximum, and slowed after four hours. So, there was also a turning point freezing duration in practical engineering.
* After the freezing progresses, some water in small pores and almost all water in medium pores was frozen; the frozen water in small pores interconnected and new medium pores were formed. The medium pores, especially from 0.03 to 0.2 \(\upmu\)m, played a key role in the structural failure of soft clay.
* The second freezing action could disrupt the recover progress of the enlarged pores. The shorter the gap time is, the less damage cause by refreezing. Accidental breaks should be controlled in a specified range to avoid more microstructure damage.
* Considering the comprehensive effect of soft clay structure looseness and melting settlement, a thawing temperature above 15 \({}^{\circ}\)C was much better for the stable of  
Figure 18: Cutoff values tested by three methods.  
soft clay structure. Therefore, arranging the cross-passage construction in spring and autumn was a good choice.  
Conceptualization, B.K. and Z.D.; methodology, S.H. formal analysis, S.H.; investigation, T.X.; resources, T.X.; data curation, B.K.; writing--original draft preparation, B.K.; writing--review and editing, S.H.; visualization, Z.D.; supervision, Z.D.; project administration, Z.D.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Chinese National Natural Science Foundation (51508506); Joint Fund of Zhejiang Provincial Natural Science Foundation (LHF20E080001); Hangzhou Science Technology Plan Project (20172016A06, 20180533B06, 20180533B12, 20191203B44), and The Science and Technology Development Fund, Macau SAR (File no. SKL-IOTSC-2018-2020).  
Not applicable.  
Not public.  
The authors declare no conflict of interest.
## 2 Equivalent Circuit Modeling of the Lithium-Ion Batteries
### Modeling of the Equivalent Circuit Model and Parameter Identification of the Lithium-Ion Battery
To obtain the internal resistance, the sudden change in the battery terminal voltage at the time points of discharge and cessation had to be analyzed, both of which were caused by the internal resistance. The internal resistance value can be calculated as  
\[R_{0}=(|V_{1}-V_{2}|+|V_{4}-V_{3}|)/2I. \tag{1}\]  
The time constant is obtained as follows:  
\[\tau=-(t_{5}-t_{4})/\ln(\frac{V_{1}-V_{5}}{V_{1}-V_{4}}). \tag{2}\]  
After obtaining the time constant \(\tau\), the polarization resistance \(R_{p}\) can be calculated as follows:  
\[R_{p}=(V_{2}-V_{3})/[I(1-e^{-(t_{3}-t_{2})/\tau})]. \tag{3}\]  
The polarization constant \(C_{p}\) can be obtained using the following relationship:  
\[\tau=R_{p}C_{p}. \tag{4}\]  
Figure 3: Current pulse charge/discharge variations. Figure 2: Experimental battery platform. The parameter identification results are presented in Figure 5. The simulation model for the Thevenin ECM was constructed using the Simscape and Simulink packages of the MATLAB software R2024b. The thermal model is constructed as follows:  
\[Q=C_{T}A\Delta T, \tag{5}\]  
where \(Q\) denotes the convective heat, \(C_{T}\) represents the heat transfer coefficient, \(A\) indicates the cell area, and \(\Delta\)T refers to the temperature increase. The charging temperature was set to 25 \({}^{\circ}\)C. The CC-CV charging results at 1C for the Panasonic NCR18650 PF LiB based on the identified parameters are shown in Figure 1:0 The root mean square errors (RMS) for the charging current, voltage, and temperature rise between the constructed model and the real battery were calculated as 1.3%, 2.2%, and 22%, respectively. The RMS value of the temperature rise was relatively high, but since the maximum charging temperature was more critical for the battery performance, this deviation was acceptable. Figure 1:1 HPPC experimental voltage curves. (**a**) Pulse power tests. (**b**) A single pulse power test.
# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
## 2 Related Work and Contribution
### Cold-Start-Related Approaches  
Because the above deep learning models struggle in cold-start scenarios, various strategies have been proposed to mitigate this.  
1. Content-based initialization uses ad metadata, visual descriptors, or textual content to seed embeddings for unseen entities. This allows models to assign a \"best guess\" representation even before interaction data accumulates. For example, meta-learning approaches such as RGMeta and Graph Meta Embedding create pseudo-cold-start episodes during training, enabling the model to infer embeddings based on feature similarities or graph neighborhoods [7]. Nevertheless, content-based initialization depends heavily on high-quality metadata or descriptors, which are often incomplete or uninformative for novel entities, limiting embedding quality. Also, meta-learning methods require artificially constructed training episodes that may not reflect real-world cold-start dynamics, risking poor generalization.
2. Exploration-driven approaches treat the early recommendation phase as a contextual bandit problem, allocating impressions to gather information while minimizing immediate revenue loss. Large-scale video platforms have reported over 60% improvement in new-ad performance by applying such strategies in production deployments. Contextual bandits improve new-ad performance but incur exploration costs and require careful reward balancing to mitigate short-term revenue loss.  
3. Graph neural networks (GNNs) represent features or users as nodes and propagate relational signals through edges, making them robust to sparse data. Fi-GNN and other graph-enhanced CTR models improve representation learning by capturing inter-feature and inter-entity structures [20]. However, graph neural networks suffer from computational inefficiency at scale.
4. Two-tower architectures have gained popularity by separating user and item encoders, enabling efficient candidate retrieval via approximate nearest neighbors. The short-listed candidates are then re-ranked using richer single-tower or cross-attention-based models, balancing efficiency and accuracy [21]. Nevertheless, two-tower architectures trade interaction modeling for speed, potentially missing key cross-feature signals.  
Overall, hybrid architectures aim to bridge memorization and generalization, making the system more robust across both dense and sparse data regimes.
# Evaluation of Positioning Accuracy of Smartphones under Different Canopy Openness
## 3 Results  
### Influence of Canopy Openness on the Number of Observation Satellites and Signal Quality  
#### 3.1.1 Influence of Canopy Openness and Equipment on the Number of Available Satellites  
The number of available satellites is mainly affected by its spatial geometric distribution and the ability of the receiver to search for satellites. According to the statistics of the number of satellites available for each device (Figure 6), multi-system devices can capture enough satellites during positioning measurement when GNSS devices are carried out in an open environment. With the increase in the canopy openness, the number of satellites available for each device increases exponentially. For example, the average number of available satellites of the two dual-frequency smartphones is higher than that of the single-frequency smartphones. The number of available satellites for the dual-frequency smartphone P50 is higher than that of survey-grade devices (the average number of satellites searched is 34). Even in the most sheltered environment (\(p<0.3\)), each device can obtain the support of at least 15 satellites. This, by some distance, meets the requirements for the number of satellites measured by GNSS.  
Figure 5: Methodology and data processing flow chart.  
In SPSS24, one-way ANOVA and post-LSD test were performed on the number of available satellites of each piece of equipment under different opening conditions (0.14-0.92) to test the difference in devices under different opening conditions (Figure 7). It is found that the stability of satellite search of single-frequency smartphones is poor. When dual-frequency smartphones are in a range of openness above 0.3, the number of available satellites tends to stabilize. The average number of available satellites of NEO is 23 and the average number of available satellites of P50 is 34. Due to the high requirements for satellite signal quality, survey-grade GNSS devices can track satellites more stably when the openness is more than 0.5. The average number of available satellites is 24. In addition, the poor satellite search stability of single-frequency mobile phones makes no significant correlation between the number of available satellites and the openness of the forest (Table 2). However, the number of available satellites of dual-frequency smartphones and measurement-level devices is moderately correlated with the openness of the forest (\(p<0.05\)).
# New Magnetstrictive Transducer Designs for Emerging Application Areas of NDE
###### Abstract  
Magnetostrictive transduction has been widely utilized in nondestructive evaluation (NDE) applications, specifically for the generation and reception of guided waves for the long-range inspection of components such as pipes, vessels, and small tubes. Transverse-motion guided wave modes (e.g., torsional vibrations in pipes) are the most common choice for long-range inspection applications, because the wave motion is in the plane of the structure surface, and therefore does not couple well to the surrounding material. Magnetostrictive-based sensors for these wave modes using the Wiedemann effect have been available for several years. An alternative configuration of a sensor for generating and receiving these transverse-motion guided waves swaps the biasing and time-varying magnetic field directions. This alternative design is a reversed Wiedemann effect magnetostrictive transducer. These transducers exhibit a number of unique features compared with the more conventional Wiedemann sensor, including: (1) the use of smaller rare earth permanent magnets to achieve large, uniform, and self-sustained bias field strengths; (2) the use of more efficient electric coil arrangements to induce a stronger time-varying magnetic field for a given coil impedance; (3) beneficial non-linear operating characteristics, given the efficiency improvements in both magnetic fields; and (4) the ability to generate unidirectional guided waves when the field arrangement is combined with a magnetically soft ferromagnetic strip (patch). Reversed Wiedemann effect magnetostrictive transducers will be presented that are suitable for different inspection applications, one using electromagnetic generation and reception directly in a ferromagnetic material, and another design that integrates a magnetostrictive patch to improve its efficiency and enable special operating characteristics.  
magnetostrictive transducers; guided waves testing; reversed Wiedemann effect; nonlinear harmonics +
Footnote †: journal: Materials
# Simulation-Oriented Analysis and Modeling of
## 5 Model Calibration
### Model Performance at the Individual Level  
(1) On expressways with high traffic volume  
As shown in Figure 4(a), on expressways with high traffic volume, the speed of the following vehicle gradually increases as the level of distraction changes from mild to  
Figure 4: Distribution of remaining distraction parameters.  
excessive. For those experiencing excessive distraction, the following speed remains higher than that of the leading vehicle, without timely adjustments. This can be attributed to increased reaction times, preventing the quick speed adjustments necessary to maintain a safe following distance. In this environment, the following behavior during mild distraction aligns most closely with the leading vehicle, likely because high traffic volume requires drivers to stay more vigilant to respond to frequently changing traffic conditions.  
(2) On expressways with low traffic volume  
As depicted in Figure 4(b), on expressways with low traffic volume, the following speeds in moderate and mild distraction patterns are consistently slightly higher than that of the leading vehicle. However, under excessive distraction, the speed increases more rapidly over the duration of the distracted epochs. This indicates that drivers under excessive distraction have poorer speed perception, which affects their ability to match the speed of the leading vehicle accurately.  
Figure 5: Model performance at the individual level.  
(3) On surface roads  
As illustrated in Figure 5c, on surface roads, when the leading vehicle's speed increases smoothly, following vehicles under excessive distraction accelerate noticeably, whereas those under moderate and mild distraction opt for slower acceleration to maintain the following distance. This suggests that on surface roads, excessively distracted drivers perceive following distance quite differently compared to those who are moderately or mildly distracted.
## 2 Conceptual Background  
### Product Development Process  
Product development is carried out in the following order: Planning-Concept Design-Design-Test-Manufacturing, as shown in Table 1[2]. In recent years, due to the segmentation of each role and the sophistication of each stage, different parts have been performing their roles separately at each stage. The conversion of this process to show an engineer's point of view is indicated in Figure 2.  
Product design involves identifying the characteristics required by the user, \"Customer Attributes\" (CA), and setting up \"Functional Requirements\" (FRs), and \"Design Parameters\" (DPs) for functional implementation [3]. Design is the process of mapping the relationship between FR and DP and has been actively studied through methodologies such as axiomatic design. Recent products require a more delicate approach to the relationship between CA and FR, as user demand characteristics have changed.  
Figure 1: Product classification according to technical/market uncertainty.  
For example, there is a greater demand for diversification of functions and increased emotional needs of users. Previously, CA and FR were thought of using the House of Quality (HOQ) methodology [4; 5] and the Delphi technique [6; 7], but these methods depend on engineers' intuitions, and there is a risk of distortion or misunderstanding.  
The existing product development method defines design requirements (FR) based on the judgment of the engineer. It will also determine the importance of each FR and, at the same time, establish a relative product design requirement by comparing the performance of competing products. Each of these processes is independent and lacks connectivity. Because there is no objective and integrated index or method that can be used in the entire product development process, errors in each step can be combined to present the wrong development direction. In addition, for multi-functional products, the engineers responsible for each function may be different, and decision making may be difficult due to the lack of an objective comparison of each performance. This is why we need an index that can quantitatively and objectively compare all performances and, in view of the complex user needs, this needs to be extracted directly from the user base, not from the intuition of the engineer.
## 5 Use Cases
### Dynamic Trends in Market Leadership: Animated Bar Race  
The animated bar race shows the evolution of top countries contributing to cancellations over time. Bars represent cancellation counts, with their lengths and ranks changing dynamically as the timeline progresses.  
This visualization reveals:  
* Temporal dynamics: highlighting shifts in market behavior over time.
* Competitor analysis: comparing country-level performance in managing cancellations.
* Longitudinal patterns: supporting predictive models by visualizing trends in a dynamic context.
## Abbreviations  
\begin{tabular}{l l}
**AgNP** & silver nanoparticle \\
**ATM** & 2-aminooxy-_N,N,N_-trimethylethan-1-ammonium iodide \\
**CAR** & Carboxen \\
**CRDS** & cavity ring-down spectroscopy \\
**DNPH** & dinitrophenylhydrazine \\
**DVB** & divinylbenzene \\
**EB** & exhaled breath \\
**e-nose** & electronic nose \\
**FA** & fatty acid \\
**FT-ICR-MS** & Fourier-transform ion cyclotron resonance mass spectrometry \\
**GC-MS** & gas chromatography-mass spectrometry \\
**HC** & healthy control \\
**4-HHE** & 4-hydroxy-2-hexneal \\
**4-HNE** & 4-hydroxy-2-nonenal \\
**IMS** & ion mobility spectrometry \\
**LC** & lung cancer \\
**LoD** & limit of detection \\
**LPO** & lipid peroxidation \\
**MDA** & malondialdehydedehyde \\
**MUFA** & monounsaturated fatty acid \\
**NSCLC** & non-small cell lung cancer \\
**NR** & not reported \\
**OS** & oxidative stress \\
**PC** & phosphatidylcholineroline \\
**PDMS** & polydimethylsiloxane \\
**PE** & phosphatidylcholineethanolamine \\
**PFBHA** & (pentafluorobenzyl)hydroxylamine \\
**PG** & phosphatidylcholineylycrol \\
**PL** & phospholipid \\
**POSS** & polyhedral oligomeric silsesquioxane \\
**PS** & phosphatidylcholineserine \\
**PTR-MS** & proton transfer reaction-mass spectrometry \\
**PUFA** & polyunsaturated fatty acid \\
**ROS** & reactive oxygen species \\ \end{tabular}
## 4 Simulation and Evaluation
### Network Stabilization  
The original random node deployment is shown in Figure 5, the structure of the network after the movement of 10% of the nodes is shown in Figure 6, and the clustering topology of the network for a new round after the nodes' movement is shown in Figure 7.  
Comparing Figures 6 and 7 we can find that the movement of the nodes has a great influence on the network's topology. Not only have the cluster heads been changed, but also the number of the nodes in each cluster has been changed. As shown in Table 1, when 10% of the nodes are moved, the No. of the nodes which move within the cluster are 1, 75, 12, 88 and 31, and the No. of the nodes  
Figure 5: The original random deployment of the nodes.  
Figure 6: The structure of network after the movement of 10% of the nodes.  
Figure 7: The clustering topology of network for a new round after nodes’ movement.  
with previous cluster heads in this process. Although it can more quickly recover the link between the moving nodes and the new cluster head than LEACH-M, its data transmission quantity is still worse than that of the proposed TCM method.
# Dynamic Frequency Support from a DFIG-Based Wind Turbine Generator via Virtual Inertia Control
## 3 Model and Control of the DFIG-Based WTG
### Two Masses Model  
For indicating the dynamics between wind turbine and generator, a two masses model is used [23]. The dynamics between mechanical torque TM and low speed shaft torque (\(T_{ls}\)) are represented as:  
\[2H_{t}\frac{d\omega_{t}}{dt}=T_{m}-T_{ls}, \tag{7}\]  
where \(H_{t}\) means the inertia constant of the turbine; \(\omega_{t}\) is the turbine rotor speed.  
Figure 1: Typical configuration of the doubly-fed induction generator (DFIG)-based Doubly-fed induction generator (WTG).  
Dynamics between the electromagnetic torque of a generator (\(T_{em}\)) and high-speed shaft torque (\(T_{bs}\)) can be defined as:  
\[2H_{g}\frac{d\omega_{r}}{dt}=T_{bs}-T_{em}, \tag{8}\]  
where \(H_{g}\) means the inertia constant of the induction generator; \(\omega_{r}\) is the induction generator rotor speed.  
\(T_{bs}\) is derived by:  
\[T_{bs}=K(\theta_{t}-\theta_{bs})+B(\omega_{t}-\omega_{bs}), \tag{9}\]  
where \(K\), \(\theta_{t}\), and \(\theta_{bs}\) are spring constant, angular displacement of the wind turbine, and angular displacement of the low speed shaft, respectively. \(B\) and \(\omega_{bs}\) are the damping constant and the low speed shaft rotor speed, respectively.
## 3 Results and Discussion
### Physicochemical Properties  
#### 3.2.1 Weight Loss  
Due to the varying batch compositions, the initial dry matter content also varied (see Table 1). In previous experiments with similar matrices, the dry matter content had proven to be decisive, especially for textural properties [60]. The sausages were hence dried and ripened after production until equal dry matter contents of 55% and 65% for each batch were reached. The dry matter of traditional raw-fermented sausages usually ranges between 55 and 85% [61; 62; 63], whereas plant-based salami analogs usually have between 29 and 64% dry matter [64]. The weight loss development for all batches during the first 8 days of drying is shown in Figure 4.  
The drying kinetics were primarily influenced by the pectin concentration and only slightly by the total water content. Samples with 1% pectin lost weight the fastest, followed by samples containing 3% and 5% pectin. Samples containing only 10% gluten also consistently lost weight faster than their 15%-gluten counterparts. According to [PERSON], [PERSON], [PERSON] and [PERSON] [37], water binding can be considered a key attribute to evaluate binder functionality, along with stickiness and hardness. Hence, pectin addition increased binder functionality in this regard distinctly. Low-ester pectin is known to enhance the structure of gluten networks through increased intermolecular hydrogen bonds as well as the conversion of sulfhydryl groups (-SH) to disulfide bonds (-S-S-), which increases the water holding capacity of gluten protein matrices [41; 42]. Additionally, formation of random coils can occur, leading to competition for water between gluten proteins and polysaccharides, resulting in further structural changes depending on available water [65]. Additional visual representations of the raw-fermented sausage analog cross sections are shown in Figure 5.  
Figure 4: Weight loss and pH courses of the different plant-based salami analogs, all with identical base masses, varying only in binder composition (pectin/gluten), during 8 days of drying.