esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamical Effects of the Increase of the Axle Load on European Freight Railway Vehicles
## 1 Introduction
It follows that in the European context, the approach to high axle load, and therefore the adoption of heavy haul strategy for freight, up to at least 30 tonnes/axis, is working its way. The aim of this work is to investigate the effect of the increased axle load on the dynamical parameters adopted to verify vehicle safety. This study is performed considering the dynamical characteristics of a typical European vehicle which currently operates with lower axle loads. Therefore, it is evident that some aspects are not analyzed in this work, such as the required modification to the vehicle in order to strengthen its structure and to manage the higher load. The paper is limited to the dynamic analysis of the performance of the vehicle under different loads on a realistically modeled track, in order to study the feasibility of the increased axle solution under the actual regulatory environment. The wagon chosen to carry out the simulations is a Tadns hopper wagon equipped with Y25 type bogies. The vehicle has a distance between bogie pivots of 18.4 m and is used for the transportation of bulk goods with controlled discharge. The standard bogie is suitable for a maximum load of 65.5 tonnes relative to the axle load of the category D4 lines (22.5 tonnes/axle). For the purpose of this paper, the behavior of a vehicle based on standard Y25L bogies, currently adopted in Italy up to 22.5 t/axle, was compared with the same bogie where an increase of the axle load up to 35 t/axle has been implemented. To compare the effect of the different axle load under realistic conditions, the suspension system has been redesigned. The primary suspension stiffness has in fact been optimized for the higher loads. It is evident that the considered vehicle is nowadays homological for a maximum load of 25 t/axle; therefore, the possibility of using this bogie for higher loads may require structural improvements and a new homologation process. It can be expected that the structure of the bogie should be reinforced using plates of moderately greater thickness and additional reinforcement elements in specific positions. The wheelset of the bogie should be replaced, adopting components suitable for higher axle load that are already available since they are used in the North American bogie. The axle-box and bearing units for heavy haul suitable with the Y25 bogie have already been designed. SKF has developed a new design of the Y25 axle-box and bearing unit (CTBU 130 \(\times\) 240), which can exceed 25-tonne load. Furthermore, ContiTech and Skf have developed an innovative suspension (Gigabox), interchangeable with the current Y25 suspension, based on a rubber suspension and an integrated hydraulic damper (hydrospring [18]). This suspension can be easily adapted to different axle loads by modifying the characteristics of the rubber, which due to its stiffening behavior, is also capable of bearing heavier loads.
# Effects of Glazing with Preservatives on the Quality Changes of Squid during Frozen Storage
## 1 Introduction  
Squid, the most important group of cephalopods, has high yield [1]. Squid has many advantages, such as low fat content and high quality proteins, which has a positive impact on the health of consumers [2]. Generally, squid is easily susceptible to deterioration after being caught through long-distance fishing. To inhibit microbial growth, oxidation and enzymatic autolysis, freezing is a widely used technique in squid preservation [3]. However, traditional freezing may lead to deterioration of fish quality because of lipid oxidation, protein denaturation and surface dehydration during frozen storage [4].  
Glazing, a widely used technique is used to protect the quality of fish from the effects of long-term frozen storage. A layer of ice on the surface of frozen fish prevents the contact between the air and the food's surface, reduces the rate of oxidation and controls moisture loss [5]. The coating thicknesses are closely related to many factors, such as dipping time, fish temperature and coating temperature [6].  
The quality of the product would be affected by the coating thickness, for example if the coating thickness was too low (\(<\)6%), it might not be able to play protective function completely over storage time, but excessive glazing (\(>\)12%) may harm the interests of customers [7]. A new regulation was issued by the Brazilian Agricultural Animal Husbandry and Supply Department: the thickness of ice on the surface of frozen fish products, with or without additives, cannot exceed 12% [8].  
Water is usually used as traditional ice-glazing (IG), but this water glaze cracks easily and loses antioxidative effects during long-term frozen storage. Edible sodium polyacrylate (SP), as a water-soluble hydrophilic polymer, could dissolve in water to form a transparent and viscous solution [9]. Its viscosity is about 15-20 times that of carboxymethyl cellulose and the alginate; therefore, SP is popular in the food industry because of its thickening characteristics [10]. Sodium polyacrylate has many advantages, such as excellent mechanical strength and thermal stability, was approved as a safe food additive by U.S. Food and Drug Administration [10,11].  
Antioxidants have been used as glazing materials for better protective effects on the quality of frozen food products during frozen storage such as chitosan [12; 13; 14], rosemary extract [15], saponin-free quinoa extract [16] and essential oil (EO) [17]. As a stereoisomer of sodium ascorbate, sodium erythorbate can effectively prevent or delay food oxidation [18; 19]. Furthermore, it was reported that sodium erythorbate was an effective ascorbic acid for stabilizing muscle color, but sodium erythorbateis is cheaper than ascorbic acid [20]. The aim of the present study was to assess the effects of sodium polyacrylate and D-sodium erythorbate as the ice-glazing layer on squid during six months' frozen storage. The quality of squid was investigated by physicochemical properties such as texture, color, protein denaturation, and lipid oxidation.
# Experimental Research on the Effects of Suction Ports on Twin Screw Expander Performance
## Highlights  
* 1. The effects of a suction port on twin screw expander performance are discussed.
* 2. An experimental investigation is carried out with different rotation speeds, suction pressures, and discharge pressures.
* 3. Lower rotation speeds result in lower pressure loss values under certain working conditions.
* 4. With certain suction ports, a higher suction pressure causes a higher pressure loss value.
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
## 3 Results
### Measurements under Rotation
#### 3.4.1 Influence of the Rotation in the Temperature Measurements
In the current case, the lack of resolution is attributed to the instability of the link budget caused by the excessive vibration of the couplers,which, in turn, produces a large parasitic frequency shift with a negative impact in the accuracy of the measurements.
## 2 Antenna Geometry and Data Generation
### Antenna Geometry  
The geometry of the proposed wire antenna, built on an infinite ground plane at \(z=0\), is shown in Figure 1 where the copper wire with the radius of 0.5 mm is bent on the \(x\)-\(z\) plane (\(y=0\)). The antenna body, connected to the feed with the characteristic impedance (\(Z_{0}\)) of 50 \(\Omega\), is divided into three sections that are determined by both the lengths of wire-sections (\(l_{n}\)) and the bending angles (\(\theta_{n}\)) at the bending points (\(P_{n}\)) (where \(n=1\), \(2\), and \(3\)). Thus, the location of (\(n+1\))th bending point (\(P_{n+1}\)) is derived from \(n^{\text{th}}\) bending point (\(P_{n}\)) and its design parameters \(l_{n}\) and \(\theta_{n}\), as shown in Table 1.
# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 3 Two Layers LSTM Network for Wavelet Prediction  
### The Structure of Two-Layer LSTM Model Network  
Recurrent neural networks (RNNs) are commonly applied to explore relations in sequential data. The special network structure can selectively store the past information and use them together with current input to speculate the future information. Long Short-Term Memory (LSTM) is a variation of RNN with the capability to prevent gradients decaying or exploding. It can fully explore the non-linear relationship between variables and process complex long-term time series dynamic information [17].  
Figure 3 shows the cell structure of LSTM network, which has a new memory unit \(R_{t}\) and three control gates, namely input gate 1, forget gate 2, and output gate 3. The computing flow is expressed as the following equations [18].  
\[f_{t}=\sigma(w_{F}[y_{t-1},x_{t}]+b_{F}) \tag{1}\]  
\[i_{t}=\sigma(w_{I}[y_{t-1},x_{t}]+b_{I}) \tag{2}\]  
\[Rt_{t}=tanh(w_{R}[y_{t-1},x_{t}]+b_{R}) \tag{3}\]  
\[o_{t}=\sigma(w_{O}[y_{t-1},x_{t}]+b_{O}) \tag{4}\]  
\[R_{t}=f_{t}*R_{t-1}+i_{*}*Rt_{t} \tag{5}\]  
Figure 1: Detection of buried targets by ground penetrating radar. (**a**) Schematic diagram of GPR measurement. (**b**) Time accumulation diagram.  
Figure 2: Correlation coefficients of the adjacent A-Scans in Figure 1.  
Here, \(y_{t}\) is the output and \(x_{t}\) is the input. \(\sigma\) is the sigmoid function. \(tanh\) is the hyperbolic tangent function. \(w\) and \(b\) are the weights and biases, respectively. The subscripts \(I\), \(F\), \(O\), and \(R\) represent the input gate, forget gate, output gate and memory unit, respectively. The symbol \"\(*\)\" means convolution. As is well known, the wavelet sequence of a given GPR system and detection environment is always stable. The characteristics of LSTM network makes it possible to extract the wavelet sequences from the GPR echoes. In the following, multiple A-scan echoes on one survey line are connected end to end to form a longer sequence for wavelet prediction.  
The network structure contains two-layer LSTM, two-layer Dropout, and one-layer Dense. The dropout probability of 0.2 is set to prevent overfitting. The timestep is 100, batch size is 200, epoch is 200, and learning rate is 1 \(\times\) 10\({}^{-6}\). The input length of each timestep is 100, and the output length is 1. If the loss does not decrease within 10 epochs, the learning rate is dynamically adjusted to be 0.1 times that of before.
## 2 Proposed Networks
### Proposed LSA-Net for Leaf Disease Identification
Here, a semicolon (\(\mathbf{:}\)) is utilized to indicate a new row in the matrix. The later fusion is indicated at the center right of the SAC-SubNet. The later fusion concatenates two features \(\mathbf{v}\) and \(f_{\theta_{seg}}^{(i)}\), as expressed in Equation (4), where \(f_{\theta_{seg}}^{(i)}\) denotes the output feature maps of the \(i\)th layer of the VGG model. In this study, the VGG16 model was adopted for SAC-SubNet. In Equation (4), it is noted that \(f_{\theta_{seg}}^{(i)}\) takes two inputs, \(\mathbf{I}\) and \(\mathbf{F}^{early}\), which is different from the conventional VGG model. In addition, the later fusion function \(f_{c}\) is incorporated into the VGG architecture. Through the later fusion, two types of high-level features \(\mathbf{v}\) and \(f_{\theta_{seg}}^{(i)}\) are fused. This enables a spot-aware classification. The proposed ROI-aware FES provides spot areas and encoded spot features, which are fed into the SAC-SubNet through early and later fusions to complete the entire network  
Figure 3: Proposed LSA-Net for leaf disease identification. to be trained in an end-to-end manner. Therefore, the ROI-aware FES is a novel spot feature extractor that encodes spot information, and subsequently teaches the SAC-SubNet regarding which areas and features should have a decisive role in classifying leaf diseases. The ROI-aware FES serves as a guide to achieve more accurate leaf disease identification. Because the spot features are utilized in SAC-SubNet, the leaf spot attention mechanism is reflected. Even though spatial and channel weight maps are not designed like other attention models in [22; 42; 43], our experiment revealed that simple concatenation is sufficient to develop a leaf spot attention mechanism and improve leaf disease identification. The architecture of the ROI-aware FES in Figure 3 is inspired by semantic segmentation in [41]. Even though the proposed ROI-aware FES includes the leaf segmentation module, the goal of this study is different from that in [41]; in other words, our goal is not to divide the input image into multiple regions, but to encode spot features and achieve apple leaf disease identification. Naturally, the proposed architecture is different from those of TL-based methods [15; 16; 17; 18; 19; 20; 21] because two types of subnetworks are connected to create a whole network that is subsequently trained in an end-to-end manner. In other words, conventional TL-based methods do not include the ROI-aware FES. If the ROI-aware FES and ROI-aware feature fusion are removed from Figure 3, the proposed architecture becomes identical to the conventional VGG network. Therefore, whether recognition accuracy can be increased must be verified by comparing the performance between the proposed LSA-Net and the conventional VGG network [13].
## 2 Materials and Methods  
### Participant Recruitment and Ethics  
The sample size was calculated using G*Power 3.1 ([PERSON], Germany) with two tails, an effect size dz of 0.8, an \(\alpha\) error probability of 0.05, and a power (\(1-\beta\)) of 0.8, resulting in a minimum requirement of 15 participants for the study. Recruitment information for volunteers was disseminated online by members of the research team, where volunteers registered their names and contact details. Researchers collected necessary personal information including name, age, gender, and basic health status, and screened individuals based on trial inclusion and exclusion criteria. Throughout this period, volunteers remained uninformed about the specifics of the experiment, only being aware of a running task. Initially, we recruited a total of 23 participants. After screening, the final experiment recruited 18 healthy male running enthusiasts (age: 21.65 \(\pm\) 1.41 years, height: 1.73 \(\pm\) 0.03 m, weight: 67.70 \(\pm\) 8.20 kg, body mass index (BMI): 22.40 \(\pm\) 2.30 kg/m\({}^{2}\)) for the running test. Inclusion criteria for participants were as follows: (1) age between 18 and 60 years; (2) male; (3) absence of health issues and/or neuromuscular diseases and/or known gait abnormalities; (4) participants not sustaining lower limb injuries within the past 6 months; (5) right leg dominance (defined as the preferred leg for kicking a football); (6) running technique involving heel-to-toe movement. Exclusion criteria for participants were as follows: (1) age below 18 or above 60 years; (2) female; (3) presence of neuromuscular, musculoskeletal diseases, and gait abnormalities; (4) lower limb injury within the past six months; (5) involuntary participation in the trial; (6) left leg dominance; (7) running technique involving forefoot striking. Prior to the experiment, all participants received and signed informed consent forms approved by the Institutional Review Board. Ethical procedures for this study were approved by the Ethics Committee of Ningbo University, number RAGH20231210.
## 2 Results and Discussion
### Analysis of Aroma-Active Volatiles in Cooked Sorghum by Using GC-O/MS  
The volatile compounds of the cooked glutinous and non-glutinous sorghum samples were directly extracted using the optimized headspace-SPME method and analyzed by GC-O/MS. The analysis results showed that 51 and 48 aroma-active volatiles were detected in glutinous and non-glutinous sorghum, respectively (Table 3). Most of the aroma compounds detected in the cooked sorghums have also previously been identified in Chinese Baijiu [6; 7; 14; 15], indicating that the aroma compounds in sorghum contribute to the overall flavor of Baijiu. The 52 different identified aroma volatiles included 14 aldehydes, 13 terpenes, 8 acids, 5 ketones, 5 alcohols, 5 heterocyclic compounds, 1 sulfur-containing and 1 aromatic compound. Numerically, the aldehyde group comprised 27% of the total aroma-active volatiles in the cooked sorghum samples, followed by terpene (25%), acid (14%), ketone (10%), alcohol (10%), heterocyclic (10%), aromatic (2%), and sulphur-containing (2%) compounds. The aroma intensity of (\(E\))-2-heptenal, nonanal, (\(E\))-2-octenal, benzaldehyde, (\(E\))-2-undecenal, (\(E\), \(E\))-2,4-decdenalal, \(\alpha\)-limonene, (-)-\(\alpha\)-Cedrene, thujopsene, \(\beta\)-elemene, \(\beta\)-damascenone, geranyl acetone, 2,3-octanedione, acetophenone, 2-pentylfuran, 2-acetylpyrrole and dimethyl disulfide, present significant differences between the two samples (\(p<0.05\)). (\(E\))-2-undecenal was undetected in glutinous sorghum, and (\(E\), \(E\))-2,4-decdenalen, \(\alpha\)-limonene, thujopsene, and \(\beta\)-elemene were undetected in non-glutinous sorghum (Figure 4). To estimate the contributions that these aroma-active volatiles had on the overall aroma impression, the 52 compounds were grouped into eight general aroma categories: sweet/fruity, green, floral, fatty, volatile acids, citrus-like, roasty/nutty, and miscellaneous (sulphury/smoky/mushroom). To compare the differences in the aroma intensities of each group between the cooked glutinous and non-glutinous sorghum samples, the intensities of each group were summed (Table 4).
# Trend Prediction of Event Popularity from Microblogs
## 3 Methodology
### Popularity Trend State Partition  
In order to explore and predict the popularity trend of events from microblog, this paper considers the change between each time node and the previous node into state characteristics. Firstly, this paper needs to difference the denoised time series and calculate the state trend value \(H(t)\) by Formula (10).  
\[H(t)=x_{t+1}-x_{t}(1\leq t\leq m-1) \tag{10}\]  
However, the same data set may cover multiple hot events, and different hot events may cause different responses from netizens, which may eventually lead to a large deviation in the popularity of multiple events derived from a period of dataset. In order to explore the systematic change of event popularity, the box plot method is used to divide the popularity trend into various states. The box plot algorithm defines a standard for identifying outliers, which are usually defined as values less than \(Q_{L}-1.5 IQR\) or greater than \(Q_{U}+1.5 IQR\), where the symbol \(Q_{L}\) and \(Q_{U}\) represent the lower quartile and the upper quartile respectively, and the difference between \(Q_{L}\) and \(Q_{U}\) is defined as \(IQR\). The box plot method does not require data to follow a certain distribution, so it is reasonable to use it to judge the state change of event popularity. The structure of the box plot is shown in the Figure 2.  
Actually, the popularity trend is divided into four states, defined by Formula (11), according to the box plot distribution.  
\[\begin{array}{l}S_{1}=\text{Rapid rise}=[Q_{U}+1.5 IQR,\overline{H}_{\text{ max}}];\\ S_{2}=\text{Slowly rise}=[0,Q_{U}+1.5 IQR];\\ S_{3}=\text{Slowly fall}=[Q_{L}-1.5 IQR,0];\\ S_{4}=\text{Rapid fall}=[\overline{H}_{\text{min}},Q_{L}-1.5 IQR];\end{array} \tag{11}\]  
In the paper, the lower bound and the upper bound of the box plot are set to \(Q_{L}-1.5 IQR\) and \(Q_{U}+1.5 IQR\), respectively. And the paper uses \(\overline{H}_{\text{max}}\) and \(\overline{H}_{\text{min}}\) to represent the maximum and minimum values of event popularity trend correspondingly.
# Behavioural Realism and Its Impact on Virtual Reality Social Interactions Involving Self-Disclosure
## 3 Results
### Results for the Perceived Realism Measure  
The ANOVA for the perceived realism ratings showed a significant effect of avatar type (\(F\)(1,56) = 15.70, \(p\) < 0.01, \({\eta_{p}}^{2}\) = 0.22), where the iClone UNREAL avatar (\(M\) = 3.01, \(SD\) = 0.96) had higher ratings for realism compared to the Vive Sync avatar (\(M\) = 2.45, \(SD\) = 0.94). [PERSON]'s measure of effect size (\(d\) = 0.58) indicated a moderate to large effect (see Figures 5 and 6). There was also a significant effect of disclosure source (\(F\)(1,56) = 19.73, \(p\) < 0.01, \({\eta_{p}}^{2}\) = 0.26). Disclosures from the interviewer (\(M\) = 2.80, \(SD\) = 0.80) resulted in higher perceived realism ratings compared to disclosures from the participant (\(M\) = 2.66, \(SD\) = 0.80). [PERSON]'s measure of effect size (\(d\) = 0.17) indicated a small effect (see Figure 5). Further, there was a significant effect of disclosure type (\(F\)(1,56) = 11.99, \(p\) = 0.01, \({\eta_{p}}^{2}\) = 0.17). Positive disclosures (\(M\) = 2.78, \(SD\) = 0.80) led to higher perceived realism ratings compared to negative disclosures (\(M\) = 2.69, \(SD\) = 0.79). [PERSON]'s measure of effect size (\(d\) = 0.11) indicated a small effect (see Figure 6). No significant interaction effect was observed between avatar type and disclosure source (\(F\)(1,56) = 1.25, \(p\) = 0.27, \({\eta_{p}}^{2}\) = 0.02), avatar type and disclosure type (\(F\)(1,56) = 1.94, \(p\) = 0.17, \({\eta_{p}}^{2}\) = 0.03), disclosure source and disclosure type (\(F\)(1,56) = 0.08, \(p\) = 0.78, \({\eta_{p}}^{2}\) < 0.01), or between all three (\(F\)(1,56) = 0.02, \(p\) = 0.90, \({\eta_{p}}^{2}\) < 0.01), suggesting that the effects of avatar type, disclosure source, and disclosure on perceived realism ratings were independent of each other. These results demonstrate that the iClone UNREAL avatar, disclosures from the researcher, and positive disclosures led to higher realism ratings.  
Figure 5: Realism ratings for avatar types according to disclosure source. Error bars represent 95% confidence intervals.
###### Abstract  
We constructed a synchronized femtosecond optical laser system with spatiotemporal stabilization for pump-probe experiments at SPring-8 Angstrom Compact Free Electron Laser (SACLA). Stabilization of output power and pointing has been achieved with a small fluctuation level of a few percent by controlling conditions of temperature and air-flow in the optical paths. A feedback system using a balanced optical-microwave phase detector (BOMPD) has been successfully realized to reduce jitter down to 50 fs. We demonstrated the temporal stability with a time-resolved X-ray diffraction measurement and observed the coherent phonon oscillation of the photo-excited Bi without the post-processing using the timing monitor.  
XFEL; ultrafast laser; pump-probe experiment; timing synchronization +
Footnote †: journal: Applied Sciences