esa-sceva/satcom-chunk-collection huggingface dataset:
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 4 The Proposed Scheme  
In our approach, we improve the concept of carrier sense adaptive transmission (CSAT), which performs time-division multiplexing between LTE and Wi-Fi to share the same unlicensed spectrum. More specifically, we evaluate the upper and lower bounds of the time ratio, \(\delta\), of the multiplexing according to the traffic demands of LTE and Wi-Fi. We can then decide on an optimal \(\delta\) value by optimizing a utility function considering both Wi-Fi's minimal QoS requirements and the total throughput.  
The contention-free period (CFP) is exploited and modified compared to the LTE-U standard to support the Wi-Fi system's QoS provisioning and resolve the bias problem of the CSAT mechanism towards LTE networks. It is not solely reserved for LTE users but is further divided into two sub-intervals. The first subinterval is to provide a contention-free operation for Wi-Fi QSTAs. During the first sub-interval, QSTAs can access the unlicensed band by a PCF polling mechanism. The second CFP subinterval is LTE users' turn to access the unlicensed band. If no LTE users would like to access the resource, Wi-Fi stations can access the resource during the entire repetition interval. Therefore, the adjustment of the lengths of CP and CFP is the core issue of this study. For example, assuming that the transmission time for a polled QSTA is \(T_{QSTA}\), the duration that \(N_{QSTA}\) of QSTAs would need \(T_{QSTA}\cdot N_{QSTA}\) in the CFP sub-interval if we want to guarantee that all QSTAs can be served as in Figure 3.  
The procedure for LTE eNB to utilize the unlicensed band is as follows. If the eNB wants to deliver packets using the unlicensed band, firstly, it performs channel detection to select the least utilized channel. The nearby HAP informs the Wi-Fi AP using the selected channel. Then, based on the QoS requirement of Wi-Fi and the traffic demands of LTE, the Wi-Fi AP calculates an adequate \(\delta\) to maximize the total throughput and maintain fairness in the meantime. The \(\delta\) value will be passed back to the LTE eNB through HAP. As the eNB receives the \(\delta\) information, it can decide the start and end time of the CFP duration to access the unlicensed band.  
Figure 4 shows the procedure for finding the optimal \(\delta\) in our approach. First, we define the utility function and some constraints which need to be satisfied. We calculate the transmission probability and throughput threshold of Wi-Fi stations. Next, the lower bound of \(\delta\) can be obtained using the constraints and threshold of Wi-Fi stations. The upper bound of \(\delta\) can be obtained by exercising the user filtering mechanism. By substituting all feasible solutions into the utility function, the solution maximizing the utility function is the optimal value of \(\delta\). In the following, we explain successful access probability, threshold adjustment, lower and upper bounds of \(\delta\), user filtering mechanism, and time complexity in detail.
# Experimental Investigation on Contaminated Friction of Hydraulic Spool Valve
## 3 Contaminated Friction Experimental Analysis of the Spool with Standard Morphology  
### The Contaminated Friction Curve of the Spool  
The real-time contaminated friction along the movement of spools with standard morphology was measured, which in the different oil of particulates suspended under \(0.1,0.2,\)\(0.3,\)\(0.4,\)\(0.5,\)\(0.6,\)\(0.7,\)\(0.8,\)\(0.9,\) and \(1\) times the size of clearance, obtained the curves of the clamping stagnation with time, as shown in Figure 12.  
In the process of the experiment, under the particulates is \(1\) times the size of clearance. During the experiment there existed complete stagnation, showing that the spool and stepper motor stopped. Therefore, the curve is only showing before the time of \(7\) s, as shown in Figure 12j.  
Figure 11: The natural friction curve of the spool.  
Figure 12: _Cont._  
Figure 12: The real-time curve of contaminated friction. (**a**) The size of particulates is 0.06 mm. (**b**) The size of particulates is 0.12 mm. (**c**) The size of particulates is 0.18 mm. (**d**) The size of particulates is 0.24 mm. (**e**) The size of particulates is 0.30 mm. (**f**) The size of particulates is 0.36 mm. (**g**) The size of particulates is 0.42 mm. (**h**) The size of particulates is 0.48 mm. (**i**) The size of particulates is 0.54 mm. (**j**) The size of particulates is 0.6 mm.  
Figure 12 reveals that contaminated friction along the path has the characteristics of pulsation and fluctuates in a certain range, which indicates that contaminated friction caused by the spool intermittently appears with the movement of particulates. When there is a contact between the outer contour of solid particulates and the surface of fit clearance, the peak of contaminated friction appears, the contact separates, and the friction return to the troughs.  
From Figure 12f-j, it can be seen that the fluctuation frequency of the real-time curves decreases with increase of particulates size; it is opposite for the maximum value friction. This occurs because the small size of solid particulates move easily in the clearance flow field and the tri-contacts of the external contour surface and two surfaces of fit clearance are very short at the same time. However, the movement of large-sized solid particulates is restricted and the tri-contacts occur over a longer period of time, so the friction amplitude is higher and relatively stable.
# Deep Learning Methods for Space Situational Awareness
## 2 Related Works
### DL-Based Methods
#### 2.2.1 CNN-Based Methods
Finally, a parallel spatial pyramid pooling (ASPP) structure is designed to improve the contextual information of the network. To verify the effectiveness of the method, experiments are conducted on a spacecraft segmentation dataset. The experimental results show that the encoder + attention + decoder structure proposed in this paper can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. In [31], The authors explore DNN (deep neural networks) using light curve data. Conventional classification algorithms, such as k-nearest neighbor (k-NN), are implemented and compared in terms of accuracy with the proposed DNN-based classification algorithms, including the popular CNN and the recurrent neural network (RNN). The concept of an RNN is different from CNN. The hidden level of RNN is time-related. For time series data, the input is processed sequentially from different levels. Originally, RNN had the vanishing gradient problem, which meant that the gradient became extremely small with the number of propagation layers. This implies that network weights might not be updated during the learning process. To solve the problem, long-short-term memory (LSTM [102]) has been proposed, which can be seen as an improvement to solve the original RNN problem. Both CNN and RNN show promising results over the conventional k-NN-DTW (k-nearest neighbor combined with Dynamic Time Warping) algorithm [31]. Two-dimensional CNN also shows promising results, providing an alternative way to use time series data directly.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 2 Related Research
### Research on Ship Interaction  
In order to solve the problem of ship collision avoidance in complex situations, we can directly study the collision avoidance algorithm. Many scholars have made some research achievements in ship collision avoidance [21]. Alternatively, as stated in the foothold of this article, we solve this problem indirectly through ship-to-ship interaction. [PERSON], [PERSON] et al. [22] proposed a collision avoidance method for unmanned surface vehicles (USVs) based on negotiation protocol, and constructed a collision avoidance ad hoc network for negotiation and communication between USVs. [PERSON], [PERSON] et al. [23] proposed and improved the negotiation framework in the case of high collision avoidance costs under COLREGs to achieve a more economical overall collision avoidance scheme. [PERSON] [24; 25] proposed a new collision avoidance solution, assuming that all ships encountered, whether in restricted waters or open waters, communicate with each other and negotiate their maneuvers, without involving any external management or communication center. The process includes registration, data collection, negotiation, trajectory search, and result distribution. In addition, the negotiation process can be designed considering non-cooperative ships, and the trajectory of each ship can be planned, allocated, and optimized among cooperative ships [26; 27].
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver
### Classical Block Interleaver  
Classic block interleavers have an \(L\) period and are represented by Equation (12).  
\[L=I\cdot J \tag{12}\]  
The interleavers are described in terms of a matrix of \(I\times J\) order. These interleavers are characterized through processes in which their data inputs are written in the rows of a memory matrix and read along the columns. There are four basic variations of classic block interleavers with an \(L\) period: the schemes vary according to the order in which the columns are read (from left to right or from right to left) and the order in which the rows are read (from top to bottom and from the bottom to the top) [31]. These schemes allow for obtaining a permuted output matrix.
## 3 Regulatory Mechanisms of Volatile Terpenoids in Abiotic Stress Responses
### Protective Roles and Mechanisms of Volatile Terpenoids Under Abiotic Stress
For example, in Iranian basil, both linalool (a monoterepene) and \(\gamma\)-cadinene (a sesquiterpene) showed significant increases in response to moderate drought stress, reflecting inducible metabolic reprogramming [154]. In _S. dolomitica_, studies under varying stress conditions reveal divergent metabolic responses: under warming, terpenoid emission was reduced while levels of ascorbate and APX activity increased, suggesting a compensatory reliance on non-volatile antioxidants; in contrast, during drought stress, terpenoid release was maintained, indicating a carbon-based defense strategy. This pattern suggests that terpenoid-mediated antioxidative responses are highly plastic and tailored to specific stress types [155]. Furthermore, (E)-\(\beta\)-farnesene has been observed to rapidly react with ozone in experimental chambers, significantly lowering O\({}_{3}\) concentrations and mitigating ozone-induced oxidative damage. This positions certain sesquiterpenes not only as cellular antioxidants but also as environmental oxidative shields. Collectively, these findings demonstrate that both monoterepenes and sesquiterpenes are indispensable components of plant antioxidant defenses under abiotic stress [156]. Their structural diversity, regulated storage and emission patterns, and integration with stress signaling networks confer them with multifunctional roles in maintaining redox homeostasis, stabilizing photosynthetic machinery, and enhancing overall plant resilience. Abiotic stress is one of the major external factors influencing the emission of plant volatile compounds. We provide a concise overview of the regulatory cascade governing volatile terpenoid emission in plants under abiotic stress conditions (Figure 3). Upon exposure to environmental stimuli, plants rapidly accumulate ROS and Ca\({}^{2+}\) signaling, which serve as early signals [157]. These primary signals subsequently activate downstream phytohormone pathways. The hormone signals are further integrated into specific TFs, which, once activated, bind to the promoter regions of key structural genes involved in volatile terpenoid biosynthesis [158; 159; 160]. Ultimately, the coordinated expression of these biosynthetic genes promotes the synthesis and emission of volatile terpenoids, playing vital roles in plant stress adaptation and ecological communication [161; 162].
# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 4 Experiments and Results
### Network Architecture  
For a fair comparison, we employ the identical feature extractor \(F_{\theta}\) as utilized by the majority of few-shot learning methods. \(F_{\theta}\) consists of four convolutional layers, each comprising a convolution block with 64 channels, a \(3\times 3\) convolution kernel, a BatchNorm layer, and a ReLU layer. Among these layers, only the first two contain a \(2\times 2\) max-pooling layer at the end. The input image size is set to \(84\times 84\times 3\). The multi-scale feature generator comprises five parallel parts, as illustrated in Figure 2. The relation networks module adopts the main structure of RN [9], which consists of two convolution blocks and two fully connected layers, each with 64 channels. Each layer utilizes a \(3\times 3\) convolution kernel, a BatchNorm layer, a ReLU layer, and a \(2\times 2\) max-pooling layer. The output size of the last convolution block is set to 64. The dimensionality of the first fully connected layer is reduced to 8, followed by a ReLU layer. Finally, the last fully connected layer outputs a one-dimensional classification score. The relation networks module in this paper consists of two branches. The relation network in Branch 1 incorporates an adaptive global average pooling layer to resize the input feature map to \(10\times 10\), and a Kronecker-product module is used to obtain spatial correlation maps of feature pairs. Meanwhile, the input of Branch 2 does not undergo a global average pooling layer, and because of the different sizes of feature maps, an adaptive global average pooling layer is employed before the fully connected layer.
## 4 Applications of InSAR in Landslide Monitoring
### Recent Advances in Landslide Monitoring with InSAR
#### 4.2.2 Expanding Dimensions: 3D Monitoring with InSAR
\(\alpha\) is the heading angle of SAR satellites, and \(\theta\) is the incidence angle of SAR images. (Software: Microsoft Visio 2021 Professional). terrains, where geometric distortions, especially shadows, may affect the availability of the InSAR data source, only ascending or descending single-orbit data may be suitable for analysis. Based on the selected literature, there are relatively few studies among selected articles addressing this issue. To the best of the authors' knowledge, the utilization of single-orbit InSAR for 3D deformation monitoring in specific scenarios still requires exploration.
## 3 Methodology
### Satellite Image Processing
#### 3.1.3 Contribution of Sun-Glint as an Error Source  
Sun-glint can appear at the slope of waves and this potentially influences the returned signal. Sun-glint is a problematic issue in bathymetric surveys with satellite imagery. For example, [PERSON] (2008) [46] demonstrated that sun-glint in high resolution imagery can result in errors of up to 30% of the water depth. It is most common where the sensor is facing towards the sun [47], however, with a relative azimuth of 51 \({}^{\circ}\)(solar illumination elevation angle minus the satellite view angle) significantly less than 90 \({}^{\circ}\), it can be anticipated that sun-glint was not a significant issue for this RapidEye image [48]. To further confirm this conclusion, three additional tests were carried out:  
1. A visual inspection of the image--the RGB image of the test site was inspected to identify the presence of sun-glint. None were apparent. However the spatial resolution could potentially have masked the effect.
2. In theory, energy in the NIR portion of the spectrum should be absorbed in water, and therefore any NIR that is recorded over water is due to sun-glint. In reality, there is always a small portion of NIR recorded by the sensor and it rarely equates to a DN of 0 [49]. The NIR values for the test site were inspected looking for significant variation which would imply areas of sun-glint. None were identified.
3. A final dual-wavelength test was designed to ensure no sun-glint was present in the area. A process developed by [PERSON] _et al._, [50] demonstrates a method for removing sun-glint and this was adapted and used to assess whether sun-glint was a significant contributory factor. An area of deep water, where the spectral brightness could be considered as homogenous, was located. The values for the Blue band in the visible portion of the spectrum and the NIR were then plotted against each other and a linear regression line was applied to the data. Here a very low correlation of 0.08 between NIR and Blue band values implied that minimal sun-glint is present, as the NIR values do not increases as the Blue band values increase. The slope of the linear regression line is used by [PERSON] _et al._, [50] to apply a de-glinting correction to the pixels. However, our results and the clear shallow slope of the linear regression line from the test area demonstrate that this is not necessary.
# Assessment and Evaluation of the Intake of Sugars in European Countries
## 3 Results
### Intake of Free Sugars  
None of the surveys presented figures for free sugars among infants. Ireland (9.0, 11.0 and 13.4 for the toddlers of 1, 2 and 3 year of age), the Netherlands (15.6 among males and 17.4 among females), and the UK (11.3) reported the en% of free sugars among toddlers. Among 3-9-year-old children, the proportion of energy coming from free sugars was lowest in Portugal (9.1 for males and 9.6 for females) and Ireland (9.5), and highest in the Netherlands (19.0 for boys and 19.5 for girls). Among these children the UK has an intermediate position with 13.6 for boys and 13.4 for girls. For adolescents, data are available from Germany (about 16 en%), Portugal (9-10 en%), Spain (about 10 en%), the Netherlands (16-18 en%), and the UK (about 14 en%).  
In Table 2 the results of the intake of free sugars among adult population groups (except the elderly) are presented. The range in the mean intake of free sugars is 7-18 en%. Austria has the highest mean intake with all the adult groups having a mean intake above 16 en%. Other countries with a mean intake above 10 en% for free sugars are Germany, Switzerland, the Netherlands, and the UK. Spain, Portugal, and Ireland (except for 18-35-year-old women) have mean values for free sugars below 10 en%. In most countries women have for free sugars a higher mean en% than men. Among the adult population groups there is no clear association between the intake of free sugars and age in the three countries that presented intake values for different age groups. The standard deviations are relatively high, indicating that the distributions are skewed, probably to the right. Among elderly people the range of the intake of free sugars is 4.4-12.5 en%. All four mean values for the UK are above 10 en%. Ireland and the Netherlands have mean values between 8 and 10 en%, whereas those for Portugal and Spain are between 4.4 and 6.2 en%. Except for the UK, women obtain more energy from free sugars than men.
# Enhancing Diffusion-Based Music Generation Performance with LoRA
## 2 Related Work
### Text-to-Music Generation Models  
Building on the foundations of PEFT, this subsection turns to recent advances in text-to-music generation. We outline three leading models--MusicLM, MusicGen, and AudioLDM--that illustrate the progression from hierarchical token decoding to diffusion-based synthesis and establish a motivating context for applying LoRA to music generation tasks.  
Text-conditioned music generation has emerged as a key frontier in multimodal AI, with models seeking to align audio outputs with rich semantic descriptions. MusicLM [3] generates audio via a hierarchical sequence-to-sequence framework: textual input is first mapped into MuLan semantic embeddings, followed by coarse-to-fine decoding into audio using SoundStream. This design enables high-fidelity synthesis but relies on large-scale data and complex pipelines. In contrast, MusicGen [4] streamlines generation by directly predicting EnCodec-quantized audio tokens using a Transformer decoder conditioned on text features from a w2v-BERT encoder, reducing latency while preserving semantic controllability. AudioLDM [5] adopts a latent diffusion approach, encoding waveforms into latent space via a VAE and aligning audio-text representations through CLAP embeddings. This enables noise-conditioned generation with high diversity and fidelity. Each model leverages different conditioning strategies and audio tokenization schemes: while MusicLM employs cascaded quantizers and MuLan, MusicGen integrates EnCodec for token compression, and AudioLDM relies on VAE-based latents and CLAP for joint embedding. Collectively, these models reflect a trend toward increasingly efficient, semantically aligned, and flexible text-to-music systems, offering fertile ground for integration with PEFT techniques such as LoRA
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 3 Approach
### Number of Floors Estimation
#### 3.7.1 Identification of \(K\)  
Because the number of floors is unknown (\(K\)), it has to be estimated in the first step. For this purpose, the elbow method [26] was chosen. The elbow method is a clustering analysis method, and it enables the interpretation and validation of the consistency within the cluster analysis. It takes into consideration the percentage of variance explained as a function of the number of clusters: the optimum number of clusters is reached when adding another cluster no longer improves the modeling of the data. If we plot the variance as a function of the number of clusters, the first clusters will add much information, but with an increasing number of clusters, the marginal gain will drop and the graph will flatten out, indicating the optimum number of clusters. Identifying the correct number for \(K\) is essential, as it corresponds to the number of floors. A wrong estimation of \(K\) can lead to large errors in the estimated altitude of each floor.
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 2 FBG Temperature Sensing Principle
### Heat Transfer Model
#### 2.1.2 Multipoint Model
Thus, the estimated junction temperature \(T_{j,k}\) for the kth device module referenced to the temperate sensor is provided as  
\[T_{j-k}\ =P_{di}\ R_{eq(j-h),k}\ +T_{s,k} \tag{15}\]  
The equivalent \(R_{eq(j-h)}\) for all the modules, i.e., \(R_{eq(j-h),1}\), \(R_{eq(j-h),2}\ldots\ R_{eq(j-h),k}\), is parallel combination of all the modules on the heatsink and is provided as  
\[R_{eq(j-h)}\ =\left[\frac{1}{R_{eq(j-h),1}}+\frac{1}{R_{eq(j-h),2}}+\ldots \frac{1}{R_{eq(j-h),k}}\ \right]^{-1} \tag{16}\]  
while the expression for the equivalent \(R_{th(j-a)}\) is provided as  
\[R_{th(j-a)}=\left[\ \left(R_{eq(j-h)}\right)n_{R}+\left(R_{eq(j-h)}\right)n_{ I}\ldots\ldots+\left(R_{eq(j-h)}\right)n_{o}\right]+R_{th(h-a)} \tag{17}\]  
where \(R_{eq(j-h)n_{o}}\) represents the thermal resistance of junction-to-heatsink for other PSS devices mounted on the heatsink; \(n_{R}\), \(n_{I}\), and \(n_{o}\) represent the number of rectifiers, IGBT modules, and other PSS devices stacked on the heatsink, respectively. Considering the equivalent sum of \(P_{di}\) at each semiconductor module, the mean junction temperature for all the semiconductors combined on a heatsink, \(T_{j(mem)}\), is provided as  
\[T_{j(mem)}\ =\ \left[\frac{P_{di}R_{th(j-a)}}{F}\right]+T_{s,mem} \tag{18}\]  
where \(F\) is the safety factor for operation temperature. To avoid using devices beyond the maximum allowable temperature, the manufacturers typically recommend a safety factor of 0.85 [30].