esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In their proposed model, the spectral data of apples were investigated and determined using a random frog algorithm; and DL was used to train and test the detection of geographical origin with spectral data as input. Partial least squares (PLS) were used to create individual calibration models, and then to estimate soluble solids. Competitive adaptive reweighted sampling (CARS) was used to select the optimal wavelengths. Compared to the individual source model, the proposed multi-source model obtained more accurate results for predicting soluble solid content of apples from multiple geographical origins, obtain an RP of 990 and RMSEP 0.274. [PERSON] et al. [19] estimated soil nutrients also using spectroscopy and DL. The simulation results indicated that the proposed model was able to improve the efficiency of obtaining the features with high reliability. This solves the problem of traditional models. Cucumber is a fruit that is rich in useful nutrients, containing some compounds and antioxidants, which may help improve, and even prevent, some diseases in humans [20]. Reliable diagnosis of the nutritional status of agricultural products is an important aspect of farm production, since both excess and deficiencies of nutrients can lead to damages and reduced yields. According to the literature, most of the previous research works used statistical methods, simple perceptron neural networks and DL neural networks with predefined structures, such as ImageNet and LSTM [21]. Although all these methods were successful on their own, more accurate methods are needed for everyday use. Consequently, the present paper describes a new one-dimensional (1D) convolutional neural network to estimate the nitrogen content of cucumber leaves. The innovation of this paper resides in the application of nitrogen overdose at 3 different levels, in order to investigate whether it is possible to detect nitrogen-rich cucumber on site and in real-time. Hyperspectral analysis of fruits and plants has been a very active area of research in the literature, and particularly in our research group, as previously seen. The main novelty of the present work resides in the proposal of a new 1D-CNN architecture with 12 layers. It contains six convolutional layers and three max-pooling layers, finishing with a dense layer, which produces the final estimation of nitrogen content. Another important element is the addition of a dropout layer that, by randomly removing some weights of the previous layer, avoids problems of overfitting.
# Performance Optimization of the Low-Capacity Adsorption Oxygen Generator
## 3 Methods  
### The Experimental Set-Up  
The test stand was built using a standard, commercially available low-capacity PSA oxygen generator. It consists of an air compressor, air dehumidifier and filters, air tank, oxygen generator, and oxygen tank. The schematic diagram of the stand is shown in Figure 1. The view of the PSA oxygen generator is presented in Figure 4.  
The air is compressed using the compressor with a capacity of 0.34 m\({}^{3}\)/min. After it has been filtered and dried, it is sent to the buffer tank, from where it goes to the oxygen generator. The oxygen generator consists of two 20 dm\({}^{3}\) adsorption tanks and the control system. The adsorption tanks are filled with 5A zeolite. The nominal oxygen separation capacity, specified by the manufacturer, is 0.6 normal m\({}^{3}\)/h. After the separation process, oxygen is collected in the oxygen tank.  
The generator is equipped with several sets of sensors monitoring the process. One set of sensors monitors the temperature and relative humidity of the air inflowing the generator. The second set tracks the pressure inside the generator. The third set of sensors reads the purity, the temperature, and the pressure of oxygen received from the generator. The test stand was supplemented with the measurement of the acquired oxygen stream capacity and the measurement of the supplied electricity. The oxygen flux was measured with a flowmeter with an accuracy class of \(\pm 1.0\%\) RD + 0.5% FS. For the  
Figure 3: The cycle of changes in pressure and adsorbers of the bed inside a single PSA tank. \(a_{max}^{T}\)—the maximum nitrogen adsorption at the process temperature; 1—air compression; 2—oxygen transfer to the oxygen tank; 3, 6—pressure equalization with the adjacent tank; 4—bed regeneration.  
measured capacities, this corresponds to a maximum calibration uncertainty of \(\pm 0.041\,\mathrm{normal\,m^{3}}\). Electric energy consumption was measured with an energy meter with an accuracy class of 2%.
# Optimization Study on Longitudinal Joints in Quasi-Rectangular Shield Tunnels
###### Abstract  
There are large bending moments in quasi-rectangular shield tunnels due to their deviation from the circular shape, and as for other types of shield tunnels, the longitudinal joints are the most critical parts in the lining structure. A new type of joint with ductile iron joint panels (DIJPs) was installed in quasi-rectangular tunnels to solve these problems. The distance from the bolts to the segment's inner surface was improved for better performance under specific bending moment types. Both tests and finite element modeling (FEM) simulations were conducted to investigate the effect of the bolt position improvements. The resistances to crack appearance increased by 33.6% and 18.0% for positive and negative moment cases, respectively. The resistances to crack penetration increased by 13.8% and 18.4% for positive and negative cases. From the FEM approach, it was found that the behavior of the joint under the design bending moment range can be divided into three stages, whereby the bolts are only active from the second stage on. The effects of other optimizing methods, such as enhancement of concrete properties and increase of bolt diameters and numbers, are explored. Through comparison, it is believed that optimizing the joint section to increase the lever arm between bolts and the compression zone can improve the joint behavior most effectively. This optimization direction is recommended when designing a shield tunnel joint with DIJPs.  
joint behavior; joint with panels; quasi-rectangular shield tunnel; bearing capacity; numerical model +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effect of Hydrolysis Conditions  
The effects of temperature (80-100 \({}^{\circ}\)C), TFA concentration (0.5-1.5 mg/mL), and hydrolysis time (30-90 min) on hydrolysis efficiency were investigated. As shown in Figure 2, fructose concentration increased with hydrolysis temperature from 80 to 90 \({}^{\circ}\)C. However, the fructose concentration decreased when higher temperatures (>90 \({}^{\circ}\)C) were used, mainly due to fructose decomposition [24]. Updating of TFA concentration from 0.5 to 1 mg/mL facilitated the hydrolysis reaction, while more TFA also led to fructose decomposition [25]. A similar trend was also observed for hydrolysis time, presenting a decrease in fructose concentration of hydrolysates after prolonged reaction time (>60 min). Thus, the optimal hydrolysis parameters applied for this study were: hydrolysis temperature 90 \({}^{\circ}\)C, TFA concentration 1 mg/mL, and hydrolysis time of 60 min.  
Figure 1: Schematic presentation of combined trifluoroacetic acid hydrolysis and HPLC-ELSD analysis for inulin determination.
###### Abstract  
The frequency synthesizer plays a crucial role in atomic clock technology. In this study, we demonstrate a direct microwave frequency synthesizer for a cesium atomic clock, employing frequency multiplication and a dual-phase-locked loop mode. A mathematical model of the frequency synthesis chain is established to estimate its performance. The phase-settling time and system stability are analyzed and studied in detail, and the obtained results are verified by experiments. An optimized realization of the frequency synthesizer shows that the phase-settling time can be adjusted within the range of 644.5 us to 1.5 ms. Additionally, we measure the absolute phase noise values to be \(-63.7\) dBc/Hz, \(-75.7\) dBc/Hz, \(-107.1\) dBc/Hz, and \(-122.5\) dBc/Hz at 1 Hz, 10 Hz, 1 kHz, and 10 kHz offset frequencies, respectively.  
frequency synthesis; phase noise; phase-settling time; stability analysis +
Footnote †: journal:
# Volatile Organic Compound from _Trichoderma asperelloides_ TSUI: Impact on Plant Pathogenic Fungi
## 2 Materials and Methods
### Sealed Plate Method  
To test the effect of volatiles released from _Trichoderma_ TSU1 in suppressing the fungal growth of plant pathogens, the sealed plate method was used [40] with some modifications. _Trichoderma_ TSU1 was grown on PDA in 8 cm Petri dishes (Citotest, Jiangsu, Haimen, China) for 3 days. A mycelial plug (0.5 cm in diameter) was cut from the culture plate, inserted centrally, and the lid of each Petri dish was removed. The bottom plate containing PDA inoculated with tested fungi was replaced, and the two bottom plates were then sealed together with Parafilm. For the control, the bottom plate with PDA alone was replaced, instead of the tested fungi, in the same manner. The experiment was conducted in five replicates and repeated twice. Tested plates were incubated at ambient temperature (28 \(\pm\) 2 \({}^{\circ}\)C) for 3 days. Colony diameters of plant pathogens were measured and converted into the percentage of inhibition through the following formula  
\[\text{Percentage inhibition (\%)}=[(\text{D}c-\text{D}\text{f})/\text{D}c]\times 100 \tag{1}\]  
where D\(c\) is the mycelial growth of the plant pathogen on the control plate, and D\(t\) is the mycelial growth of the plant pathogen on the tested plate.
## 3 Mobile Mapping Systems
### Commercial MMSs  
Commercial MMSs cover a wide range of data-capturing applications with diverse settings that are easy to handle by their operators. Despite the relative ease of use, high price tags made them unavailable for many possible applications that need geospatial information. In this section a brief list of commercial MMSs has been listed and discussed.  
Figure 2: (**a**) Customized mobile mapping system developed by FGI (Dr. [PERSON]). (**b**) FGI Roamer mounted on a car, (c) FGI Sensei (images b and c courtesy of NLS).  
A modular MMS with a LiDAR, 4 cameras, GNSS, and IMU was presented by Viametris MS-96. In this system, two LiDARs are located, one in a horizontal direction on top of the system and the second one in front of the system with a tilt angle. The localization accuracy is reported as \(\pm 1\) cm, which is typical for most commercial MMSs, and the system is able to plug and play new sensors. Leica developed several MMSs, two of which are demonstrated in Figure 2. In this setup, two LiDARs are placed on top of the system, both with tilt angles. The GNSS antenna of the MS-96 seems to be better located in comparison to TRK 100 and TRK Neo. The point position accuracies of 11 mm and 19 mm were reported for TRK 100 and TRK Neo, respectively. Leica has also developed trolley-based MMSs such as PoroScan, which could be moved by an operator on the ground. A painted point cloud captured by TRK 100 is demonstrated in Figure 3, which shows the benefits of integrating cameras with LiDARs in an MMS.  
A commercial MMS with extensive usage of cameras is shown in Figure 4 (RIEGL VMX-2 HA). In this MMS setting, two tilted LiDARs have been integrated with several cameras in a complex configuration. A dual-LiDAR tilted configuration could be seen in many other commercial MMS settings. A separate 6-head multi-camera is placed for 360 image generation, which demonstrates the usability of multi-cameras in complex MMS setups. A simple setup of a 6-head multi-camera with two tilted LiDARs has been employed in the Trimble MX50 (Figure 5). This setup demonstrates a simple, useful, yet efficient setup by the MMS designer.  
Despite the vast design differences demonstrated in commercial MMSs, some structural similarities show that optimum configurations are achievable, at least for general-purpose MMSs.  
Figure 4: Leica Pegasus TRK 100, painted point cloud example. Image courtesy of Leica.  
Figure 3: (**a**) Viametris MS-96 mobile mapping system (image courtesy of Viametris). (**b**) Leica Pegasus TRK 100, (**c**) Leica Pegasus TRK Neo (b and c images courtesy of Leica).
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 3 Empirical Formulae for Damping Ratio of RC Buildings for Wind Design  
The formulae for damping ratio can be categorized into (1) single-value damping ratio and (2) frequency- and amplitude-dependent damping ratio. The single-value damping ratio type formulae provide specific damping values regardless of the natural frequency of the building and the magnitude of building response under wind load. By contrast, the frequency- and amplitude-dependent damping ratio type formulae yield different damping values according to the natural frequency and response magnitude of a building.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 5 Conclusions  
In this paper, we propose an end-to-end learned point cloud attribute compression method with non-local attention optimization and a modulation network achieving variable rate compression. The proposed method achieves state-of-the-art performance compared with other existing learning-based methods, in terms of coding performance measured by BD-rate. When compared with traditional compression methods, the proposed method achieves a large improvement over TMC13v6 and further narrow the gap between the state-of-the-art TMC13v14. Specifically, for the BD-rate, the proposed method outperforms TMC13v6, Deep-PCAC and SparsePCAC by 31.5%, 50.7% and 6.3% reduction, respectively, and reduces the gap with TMC13v14 to 29.3%. For practical applications, the variable rate compression model is achieved to overcome the limitation of storing one network per bitrate. In future work, we would like to utilize the geometry information of the point cloud to extract the latent features of the attribute more reasonably and further optimize the corresponding distribution model.  
Conceptualization, X.H., S.Z. and F.Y.; methodology, X.H., S.Z. and F.Y.; software, X.H.; investigation, X.H. and S.Z.; writing, X.H. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (62171353, 62101409 and 61801364).  
Not applicable.  
The dataset used in this study can be found at [[http://www.scan-net.org/](http://www.scan-net.org/)]([http://www.scan-net.org/](http://www.scan-net.org/)) (accessed on 15 March 2022). The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Calibration
The uncertainty of a transmitter (or receiver) can be estimated as:  
\[\mathrm{u(T_{i})}\approx\left\|\mathrm{T_{i}}\right\|\frac{\mathrm{u_{r}}}{ \mathrm{r}} \tag{9}\]  
For suppressing random components of ToF measurements averaging is necessary. The problem with averaging over a longer time is that an assumption of constant measurement conditions, especially temperature, is not realistic. This problem can be circumvented by assuming that the mechanical system is stable and the physical distances stay the same during the calibration process. As speed of sound is a scaling factor of the ToFs, the ratios have to remain the same. Multiple measurements \(\mathrm{t_{i}(k)}\) are performed where the scaling factor \(\mathrm{c(k)}\) is defined by the sum of the 24 ToFs where the first measurement is arbitrarily used as reference. \[\mathrm{c(k)}=\frac{\sum_{i=1}^{N}\mathrm{t_{i}(k)}}{\sum_{i=1}^{N}\mathrm{t_ {i}(1)}},\mathrm{k}>1,N=24 \tag{10}\]  
Applying such a correction to each measurement retains the ratios but compensates the temperature. Using all of these techniques described above results in a high quality calibration and suitability of the approach was verified by simulations and a practical experiment.
# Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites
###### Abstract  
The orientation of fibers in composites reinforced with short fibers can provide insight into the microstructure of the material and considerably affect its macroscopic characteristics. However, the present standard techniques for detecting fiber orientation and length based on microscopic image processing have faults in practical applications, including high effort, low efficiency, and unreliable measurement results. In this study, a method for measuring fiber orientation based on 3D reconstructions of scanning electron microscope (SEM) images is provided. The geodesic active contour (GAC) model is applied to segment the fibers in the SEM images. Matching the fiber contours with the scale-invariant feature transformation (SIFT) algorithm successfully extracts 3D orientation information from the fiber contours. The unit vector of the fiber axis is fitted from the extracted point cloud using the ordinary least squares (OLS) method. With a maximum deviation of 3.83\({}^{\circ}\) and an average deviation of 1.50\({}^{\circ}\), the measurement findings of this method are substantially comparable to those of the image-measuring instrument. This paper offers a quantitative approach to studying the microstructure of short fiber-reinforced composites, thereby furnishing objective evidence to support the development and research of such materials.  
fiber orientation; SEM image 3D reconstruction; image processing; fiber-reinforced composite
## 3 Experimental Results and Discussion
### Strain Responses of Energy Absorbers  
Figure 24 presents the maximum strain responses of low-yield-point steel energy absorbers at the bottom floor under different loading intensity levels of three seismic waves. When the PGA of the earthquake record was less than 0.7 g, the line connecting the envelope strain points of energy absorbers at each level was approximately straight, and the strain discrepancies under the same intensity level for different seismic waves were quite small. However, the discrepancies became more significant after the increasing PGA was greater than 0.7 g, in agreement with the initially intense vibration of the overall specimen, accompanied by the visible displacement observed in the tests. The maximum tensile strain in the energy absorbers was 476 \(\mu\epsilon\), reaching and slightly exceeding its average yield strain of 470 \(\mu\epsilon\) calculated from Table 5, when the PGA of the Kobe wave was 1.1 g. It reveals that the LYP plates gradually begin to dissipate the earthquake energy. With the further increase of loading intensity level, the envelope tensile strain of LYP energy absorbers became much larger and reached 718 \(\mu\epsilon\), far exceeding its yield strain, under PGA of 1.6 g nearly rare earthquake intensity 9 at the end of the test. A considerable plastic deformation was formed in the metallic energy absorbers, and its energy dissipation performance was gradually shown during the strong excitations.  
It could be concluded, from the aforementioned analyses, that the frame columns or beams remained in the elastic stage with minor deformation, but energy absorbers significantly yielded and underwent permanent deformation. In other words, the structural failure of the steel frame retrofitted by energy absorbers primarily concentrated at the LYP plate, which utilized its superior ductility to form hysteretic bending plastic deformation, with an abundant amount of energy dissipation, so as to prevent the parent structure from damage under the severe earthquake.  
Figure 24: Maximum strains of LYP energy absorbers subjected to different intensity levels.
# Estimating Road Segments Using Kernelized Averaging of GPS Trajectories
## 2 From Dynamic Time Warping to Time Elastic Kernels Averaging
### Time Elastic Kernels  
From the DTW formulation, several attempts have been made to build kernel measures more suitable for machine learning purpose, in particular in the context of support vector machine. Distance substituting kernels were first introduced [15; 16], and, although such kernels are not definite positive, they have shown mixed success.  
Later, the global alignment kernel was introduced [17], which is, in most practically encountered conditions, positive definite (\(\mathcal{K}_{\text{ga}}\)), and takes the following form:  
\[\mathcal{K}_{\text{ga}}(\mathbf{x},\mathbf{x}^{\prime})=\sum_{\pi\in A}\ \prod_{(t,t^{\prime})\in\pi}\kappa(x_{t},x_{t^{\prime}}^{\prime}) \tag{2}\]  
where \(\kappa(.,.)=exp(-\gamma\cdot||.,.||^{2})\) is a local kernel and \(\mathcal{A}\) is the set of all admissible alignment paths.  
[PERSON] and [PERSON] [10] proposeda general procedure to construct positive definite time elastic kernels from general time elastic distances. In particular, \(\mathcal{K}_{\text{rdItw}}\), based on the design of a global alignment positive definite kernel for each single alignment path as given in Equation (3), has been defined such as close to the DTW matching scheme.  
\[\mathcal{K}_{\text{rdItw}}(\mathbf{x},\mathbf{x}^{\prime})=\sum_{\pi\in C \subset\mathcal{A}}\ K_{
## 2 Related Work
### RNN-Based Models  
To capture the sequential dependencies inherent to user mobility patterns, recurrent neural network (RNN)-based models have been widely adopted for next-location prediction. Early RNN variants, such as ST-RNN [12], incorporate temporal and spatial intervals into standard RNN architectures, enabling the modeling of time-aware and location-sensitive user trajectories. To further address the challenge of heterogeneous user preferences, GTSAR-RNN [13] introduces a group-specific modeling strategy, where users are clustered via probabilistic matrix factorization, and separate RNNs are trained to capture intra-group behavioral dynamics. By integrating sentiment-aware location prediction aspects, geographical categories, and temporal context, this model significantly enhances personalization and relevance, especially under sparse data conditions. More recent approaches, such as NeuNext [14], enhance traditional LSTM networks with specialized time and distance gates, effectively capturing both short-term transitions and long-term interests through adaptive spatio-temporal gating mechanisms. Meanwhile, LSTPM [15] advances sequential modeling by combining geo-dilated LSTMs with a nonlocal attention mechanism, allowing the model to simultaneously attend to long-range dependencies and local contextual cues. Despite their notable success in modeling sequential behaviors, RNN-based methods often struggle to represent high-order or multi-relational structures among users and location predictions, limiting their expressiveness in capturing complex spatial or collaborative influences embedded in real-world check-in data. These models primarily focus on pairwise relationships, which makes it difficult to capture complex interactionsthat involve multiple users or locations simultaneously. As a result, RNNs struggle to model collaborative co-visitation patterns, spatial dependencies between locations, and other higher-order relationships that are crucial for accurate location prediction.