esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Back Extrusion  
The technique of back extrusion is often used to test foods to identify the flow behavior of complex fluids. This method estimates the rheological properties of products under sticking boundary conditions [42].  
Figure 2 presents the results of the measuring force values of the desserts using back extrusion. In the tests carried out using the back-extrusion method, it was shown that the increase in the force values of the tested products correlated with the increase in the content of the WPC80 addition in the 5-11% range. The sample with 13% WPC80 had a lower value (436 g) compared to the dairy dessert with 11% (499 g). Therefore, these results indicate the formation of an increasingly dense gel structure with the increasing content of whey-protein concentrate. They correlate with storage (G\({}^{\prime}\)) and loss (G\({}^{\prime\prime}\)) moduli results as well as hardness. There is little research discussing the reasons for an uneven increase in product hardness relating to supplied whey proteins. Carrageenan can interact with proteins through electrostatic or ionic bonding. Its molecules carry negative charges and can therefore be combined with positively charged proteins or particles like potassium (c.a. 520 mg), which is found in WPC80. For example, when carrageenanan combines with a milk protein such as casein it can form a three-dimensional gel network [43]. Additionally, [PERSON] et al. [2] suggested that carrageenanan affects the increase in the hardness of dairy desserts, but at a higher concentration it may cause adverse changes in the tested sample texture due to the interaction between milk proteins and carrageenanan [2]. Other studies have suggested that the rheological properties of gels prepared with the addition of carrageenanan may be influenced by the separation of the phase between carrageenanan and the proteins. This can occur between \(\beta\)-lactoglobulins and k-carrageenanan based on depletion interactions in a suspension of small proteins immersed in a solution of polysaccharides that then form an entangled network [26]. These theories can explain the decrease in the back-extrusion measurement value while the hardness of the product increases with the addition of 13% WPC.  
Figure 2: Effect of different WPC80 contents on force values of high-protein fat-free dairy desserts. Letters (a–d) indicate significant differences at \(p<0.05\) ([PERSON]’s HSD test).
# Automatic Docking Trajectory Design-Based Time-Varying-Radius Dubins for Unmanned Surface Vessel
## 4 Simulations and Experiments
### Simulation Results  
The automatic docking trajectory results of the above two proposed algorithms for translational dockyard are compared in Figure 12. The docking trajectory lengths of ADTA-ACO and ADTA-TRD, respectively, are 50.62 m and 56.82 m. The voyage times of ADTA-TRD and ADTA-ACO, respectively, are 1900.28 s and 2276.65 s. Therefore, the docking trajectory length and voyage time of ADTA-TRD is better than that of ADTA-ACO.  
The automatic docking trajectory results of the above two algorithms for the water-influenced mobile dockyard are compared in Figure 13. The docking trajectory length of ADTA-ACO and ADTA-TRD, respectively, are 28.4 m and 30.23 m. The voyage time of ADTA-TRD and ADTA-ACO, respectively, are 1443.65 s and 1611.6 s. Therefore, the docking trajectory length and voyage time of ADTA-TRD is better than that of ADTA-ACO.  
The paper selects the radius influence factor \(k\) 1, 2, 3, 4, 5 and other simulation parameters in Table 2 for analyzing the influence of radius influence factor on voyage time. Through several round simulations, the average value of the results is obtained for comparison. As shown in Table 3, with the increase in the radius influence factor \(k\), the docking trajectory length gradually increases, leading to a gradual increase in the voyage time of ADTA-ACO and ADTA-TRD. Meanwhile, the voyage time of ADTA-TRD is lower than that of ADTA-ACO.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Value** \\ \hline \(k\) & 2 \\ \(V_{min}\) & 1 \\ \(V_{max}\) & 5 \\ \(R_{min}\) & 1 \\ \(R_{max}\) & 9 \\ \(W\) & 100 \\ \(L\) & 200 \\ \(n\) & 50 \\ \(\theta\) & \(\pi/4\) \\ \(h\) & 3 \\ \(\epsilon\) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation values.  
Figure 12: Automatic docking trajectories with translational dockyard.  
The paper selects the minimal speed of the USV \(V_{min}\) 1, 2, 3, 4 and other simulation parameters in Table 2 for analyzing the influence of minimal speed of the USV on voyage time. Through several round simulations, the average value of the results is obtained for comparison. As shown in Table 4, with the increase in minimal speed of the USV \(V_{min}\), the speed search range gradually decreases, leading to a gradual increase in the voyage time of ADTA-ACO and ADTA-TRD. Meanwhile, the voyage time of ADTA-TRD algorithm is lower than that of ADTA-ACO algorithm.
## 4 Results: Analysis of the Integration of VR, AR, and MR in the Design Process  
This section presents the results obtained from the analysis of the usability of immersive technologies applied to product design. Information is organised at two levels: (1) individual results for the nine performance indicators defined in the methodology (Table 2), which allows for a detailed analysis of the behaviour of VR, AR, and MR (Section 4.1); (2) aggregated results by usability dimension (effectiveness, efficiency, and satisfaction) according to the ISO 9241-11:2018 standard (Section 4.2).  
The Design Units encompass a wide range of design situations and conditions of user-immersive technology interaction, which allowed evaluation of the different defined usability indicators: (1) **type of design task**, involving varying technical and creative capabilities of the designer in terms of control, precision, and expressiveness, including sketching, 3D modelling, geometric modifications on meshes, and morphological adjustments aimed at ergonomic adaptation (for example, 3D sketching of a vehicle, detailed modelling of a ring, or morphological adaptation of a reading chair); (2) **work environment**, which enabled observation of how the type of immersive setting influences perception, control, and the quality of the obtained result, including (i) VR environments without physical references (such as the outdoor chair), (ii) virtual spaces incorporating anatomical models or elements from the usage context (such as the mouse in a virtual hand or the reading chair with a human figure), (iii) AR and MR environments where the design is performed directly on the visualisation of the real physical environment (such as the candle holder modelled on the real candle); (3) **cognitive and motor demands placed on the designer**, aimed at analysing important variations in mental workload, human error, or learning, including sequential and mechanical tasks, tasks with high attentional requirements, or free exploration tasks; (4) **scale of intervention**, covering different spatial magnitudes, such as small pieces requiring precise manipulation in a compact setting (for example, jewellery), medium-sized components with functional adjustment needs (such as furniture), and complex volumes involving movement and navigation within an extended immersive space (such as the vehicle); (5) **ergonomic conditions**, to analyse the physical-spatial relationship between the user and the designed product, observing the degree of anthropometric and biomechanical adaptation and the precision achieved under simulated usage conditions (such as the mouse adapted to the hand, the reading chair, or the outdoor chair modelled with postural criteria); (6) **interoperability and physical validation**, with designs exported and used in other modelling and 3D printing software, aiming to verify the quality of workflows between process stages and the reliability of immersive technologies as integrated tools within the design process.
# A Multi-Head LSTM Architecture for Bankruptcy Prediction with Time Series Accounting Data
## 9 Results
### LSTM Training and Validation  
The two LSTM architectures were trained with exactly the same parameters for a fair comparison. The main hyper-parameters were the following:  
* Epochs = 1000.
* Learning rate = \(10^{-4}\).
* Batch size = 32 (default Keras value).  
To make sure the results were unaffected by unrelated factors, we chose to keep the parameters of both LSTM architectures the same and only compare the performance differences between them based on architectural differences. Instead of investigating the impact of different settings on the results, we wanted to see how their designs affected their performance. However, it is worth noting that, while both architectures began with comparable parameters, they were independently optimized using the validation set and weight initialization approaches. Moreover, we used the early stopping technique to prevent the network from overfitting, and we employed the validation set to select the hyper-parameters. In particular, to deal with the imbalance in the training set, we performed 500 runs for each LSTM architecture by considering a randomly balanced training set every time.  
After this, we first compared single-input LSTM, multi-head LSTM, and previous results achieved with Random Forest and CatBoost on the same validation set. Figure 7 shows the model comparison for the temporal windows of 4 and 5 years. Table 5 summarizes  
Figure 6: Top image: The multi-head LSTM setting where each financial time series serves as the input to a different shorter and smaller LSTM unit. The representation learned by each head is then concatenated and exploited as the input to a subsequent feed-forward network. Bottom image: The classical RNN setting where the input for our bankruptcy task is a single matrix with 18 rows and j columns.  
this result in terms of the average AUC achieved in the 500 runs. For each run, the models' weights were randomly initialized. In light of this result, it is clear that, at least in terms of the AUC, the Recurrent-Network-based deep learning models outperformed traditional classifiers.
## 4 Truck-UAV Collaborative Delivery Planning Algorithm Based on Improved Evolutionary Algorithm
### Algorithm Design
#### 4.2.3 Evolutionary Operations  
In light of the heterogeneous characteristics of truck routes and drone tasks, a two-layer hybrid crossover strategy is employed. Initially, the tournament selection strategy is utilized to identify parent individuals from the population [31]. The formula for calculating the selection probability is as follows:  
\[\mathrm{P_{select}(x_{i})}=\frac{\exp(-\beta\cdot\mathrm{rank(x_{i})})}{\sum \
olimits_{j=1}^{5}\exp\left(-\beta\cdot\mathrm{rank(x_{i})}\right)} \tag{28}\]  
Figure 4: Comprehensive overview of the enhanced evolutionary algorithm.  
In this context, beta represents the selection pressure coefficient, while \(\text{rank}(\text{x}_{\text{i}})\) denotes the non-dominated sorting rank of the i-th individual within the tournament subset S. To facilitate the crossover of truck paths, the order crossover (OX) method is employed to ensure the feasibility of the paths. Specifically, starting and ending points, denoted as \(\text{a},\text{b}\), are randomly selected from the parent paths \(\text{P}_{1}\) and \(\text{P}_{2}\). The nodes within the interval \([\text{a},\text{b}]\) of \(\text{P}_{1}\) are then combined with the nodes outside this interval from \(\text{P}_{2}\) to generate a new offspring solution. For the assignment of drone tasks, uniform crossover is utilized. The task vectors of the drones are encoded in binary format. Let the parent tasks be represented as \(\text{U}_{1}\) and \(\text{U}_{2}\), and bitwise operations are conducted in accordance with the mask matrix \(\text{M}_{\text{ij}}\sim\text{B}(0.5)\). The formula for calculating the offspring is as follows:  
\[\text{C}_{1}^{\text{U}}=\text{M}\odot\text{U}_{1}+(1-\text{M})\odot\text{U}_{2} \tag{29}\]  
Among the various equations, \(\text{B}(\cdot)\) represents the Bernoulli equation. Ultimately, it is essential to implement a greedy selection process for overlapping tasks in order to mitigate conflicts, while preferentially retaining the scheme that demonstrates a lower energy consumption.
## 4 Discussion
### The Impact of Vegetation Factors on WUEc Dynamics  
As an essential indicator of vegetation growth, the LA affects both plant development and the allocation of evapotranspiration between transpiration and evaporation. Consequently, it plays a key regulatory role in GPP and actual ET, ultimately influencing WUEc [68; 71]. However, the effect of LAI on WUEc via its influence on the GPPc/ETc ratio remains debated. For instance, [PERSON] et al. and [PERSON] et al. reached opposing conclusions based on remote sensing data: [PERSON] et al. reported that increased LAI elevated ET, thereby decreasing WUE [72], while [PERSON] et al. found that LAI significantly enhanced GPP, leading to improved WUE [73]. In this study, approximately 83% of the region exhibited a negative correlation between LAI and WUEc. This trend may result from changes in croppingstructure, which have led to increased LAI and higher agricultural water consumption. Strong evaporative demand and the continued use of traditional surface irrigation in the study area have exacerbated leaf water loss, limiting the ability of photosynthesis to offset the increase in Etc and ultimately reducing WUEc. In contrast, the correlation between the EVI and WUEc was relatively weak, aligning with the findings of [PERSON] et al. in arid regions [25]. However, some studies have reported a positive influence of EVI on WUEc. For instance, [PERSON] et al. [74] integrated eddy covariance tower data with MODIS-derived EVI to investigate WUEc drivers and found a significant positive correlation. The divergence between their findings and ours may stem from the dual role of EVI. On the one hand, higher EVI enhances vegetation cover and photosynthetic capacity, potentially boosting GPPc and improving WUEc. On the other hand, increased transpiration leads to greater water loss, which may offset these gains. This trade-off likely accounts for the weak overall correlation between EVI and WUEc observed in this study [75; 76].
## 3 Results and Discussion
### Load-Crack Width Relationship
Equations (14) and (15) consider a coefficient \(k_{b}\) for the bond between the concrete and FRP reinforcement. For different types of FRP reinforcement, resin formulations, and surface treatments, ACI represents the values of the coefficient \(k_{b}\), ranging from 0.6 to 1.72, with an average of 1.10. ACI-06 [34] and ACI-15 [29] suggest a conservative \(k_{b}\) value of 1.4, an ISIS-07 [35] value of 1.2, and a CSA-12 [30] value of 1.0 for deformed FRP bars. For FRP bars with similar adhesion conditions using concrete containing steel bars, a \(k_{b}\) value of 1.0 is considered. Experimental crack widths in the critical sections during loading are compared with the predictions obtained according to ACI-06 [34], ISIS-07 [35], CSA-12 [30], EC2-13 [31], and CNR-06 [33] in Figure 9a,b. For the beams series 1 with ribbed bars, the experimental maximal crack widths in the midspan are less than those calculated by the current codes during the entire loading process. This provides evidence of the good bond strength between GFRP bars and concrete. For the middle support, significant deviations occur. In the beams G1-0 and G1-15, the measured maximal crack widths are the closest to the values obtained by CSA-12 [30], while in the beam G1-25 the maximal crack widths are higher than those predicted by CSA-12 [30]. The best-suited predictions for the beam G1-25 are by ISIS-07 [35] and EC2-13 [31], indicating slippage between GFRP bars and concrete in the middle support. This was expected because beam G1-25 had the smallest amount of reinforcement om the support out of the beams series 1 as a consequence of the designed moment redistribution of 25%. For the beams series 2, for the appropriate load level, the maximum crack widths obtained experimentally in both the midspan and the middle support section are greater than the calculated values. The best match to the experimental results is shown by CNR-06 [33], which also gives the highest values for the maximum crack width. The main reason for this occurrence is probably the small number of cracks formed in the critical sections, which consequently had large widths. It is evident that different surface treatments for GFRP bars directly affect the values of maximal crack widths for beams series 1 and 2. It is also noticeable that the deviations from the experimental values for the maximum crack widths in the middle support area compared to the prescribed regulations are larger for the cases of beams that are designed to redistribute moments (G2-15 and G2-25). It can be concluded that the axial stiffness of the GFRP reinforcement is clearly expressed on the maximal crack width in middle support section. \[s_{r,max}=3.4\cdot c+0.425\cdot k_{1}\cdot k_{2}\cdot\frac{\varnothing}{ \rho_{peff}} \tag{7}\]0 _Cont._
# Cryogenic System for FTIR Analysis of Hydrocarbon Fuels at Low Temperature and Atmospheric Pressure
###### Abstract  
This study presents a novel approach to FTIR spectroscopy at low temperatures under atmospheric pressure. The work aimed to confirm the efficiency of a fundamentally new cryogenic setup that enables material research under the specified conditions. The new technique combines a nitrogen-based cryogenic capillary cooling system with precise temperature monitoring via a PID controller, along with DRIFT spectroscopy for hydrocarbon materials. New fundamental data were obtained on the properties and behavior of hydrocarbon compounds such as methanol, kerosene, and ethanol. The IR spectra of these samples contain key characteristic vibrations of hydrocarbon functional groups, which demonstrate the effective operability of the cryogenic device. A detailed description of the setup and measurement technique is provided, along with a thorough comparison of the results with data from other authors. The application scope of the cryogenic device, the relevance of the research, and potential future developments are also discussed.  
FTIR; cryogenic; hydrocarbon; atmospheric pressure; low temperature; methanol; kerosene; ethanol; spectroscopy Article
## 2 Materials and Methods
### Data Collection and Processing  
Players were instructed to synchronise their side-to-side movements with the tempo set by the metronome. The duration of lateral displacement was standardised to ensure consistent data collection. After a familiarisation period, data were collected by attaching an IMU to the lower back of each player at the L4-L5 level using an elastic belt (Figure 2). IMUs were configured to collect data at a sampling frequency of 1000 Hz and were calibrated on a flat surface, consistent with established protocols [20; 22; 31; 33]. This placement was chosen to optimise the capture of whole-body movement data [34]. The IMUs employed in this study have demonstrated excellent accuracy and reliability in previous research [35; 36; 37; 38].  
Figure 1: Study design; NVNS: no vibration and no stick; NVS: no vibration with stick; VNS: vibration without stick; VS: vibration with stick.  
For the analysis, 10 lateral slides (5 with the dominant leg and 5 with the non-dominant leg) characterised by push-off actions were extracted from each participant's time-series data to eliminate any potential influence from the initial and final phases of the movement. The first 10 s of push-offs were discarded to allow stabilisation of behaviour. Subsequently, the raw acceleration data collected from the IMUs were analysed using WIMU software (V1.0.0, SPRO, Realtrack Systems, Almeria, Spain). The acceleration signals were processed using a summation of vectors (AceIT) in three axes, namely, vertical (x), mediolateral (y) and anteroposterior (z), following the method described by previous authors [36; 39].  
SampEn was calculated for the AceIT signals according to the algorithm described by [PERSON] et al. [40], using custom-written Matlab(r) routines (version R2020a, The MathWorks, Natick, MA, USA). Additionally, the mean value (MeanAceIT) and peak value (PeakAceIT) of AceIT were calculated.  
To establish the values for the dominant and non-dominant legs, the exercise always started by pushing off with the dominant leg. This was performed so that in the subsequent analysis, the first push-off was omitted, and from that point on, odd peaks corresponded to the non-dominant leg, and even peaks to the dominant leg.
## 2 Instrument Design
### Scaffold and Elevation Tracker  
The scaffold consists of a structure attached to the antenna horn (the antenna holder) and the suspension to mount the system either on a tower platform or on the cantilever of a crane (Figure 1). The construction is made of a space framework of rectangular hollow steel (EN 10219 S355J2H) sections welded together and hot-dip galvanized for corrosion protection. The cross beams with the most loads have cross-sections of \(60\times 60\) mm\({}^{2}\) and thickness 3 mm, whereas the stabilizing cross beams have smaller dimensions (\(30\times 30\) mm\({}^{2}\)).  
The antenna holder is pivoted on the suspension which allows the antenna to be automatically angled to different elevations using a mechanical drive (elevation tracker). Elevation angles in the range \(30^{\circ}\leq\alpha\leq 330^{\circ}\) are supported (\(\alpha=180^{\circ}\) is the zenith direction), enabling the observation of two diametrical footprints without rotating the instrument around its vertical axis. This is achieved by placing the suspension sufficiently high and by using a horseshoe-shaped base.  
The elevation tracker comprises a two-stage worm gear (Atlanta, type BWS 58, reduction 1:39), attached to the antenna rotation axes and a planetary gear (Neugart, reduction 1:40) connected in series and propelled by an AC servo motor (JVL, type MAC141-A3 AACA with MAC00-B4 extension module). This configuration results in the maximal mechanical torque of \(\approx\)1000 Nm, and features repeatable elevation positioning. The manufacturer of the gears rates the operational temperature range to be -20\({}^{\circ}\)C to +80\({}^{\circ}\)C.  
The selected motor is equipped with an encoder that keeps the antenna at a constant orientation even under windy conditions. Furthermore, an inductive switch between the rotating part and the fixed scaffold is mounted to allow absolute positioning the antenna. The motor is powered and controlled through the embedded servo-drive, comprising an RS-232 interface that allows various state parameters also to be monitored, such as speed and torque. The motor conforms to IP67 and has a nominal operational temperature range of 0 \({}^{\circ}\)C to +40 \({}^{\circ}\)C, and a storage temperature range of -20\({}^{\circ}\)C to +85 \({}^{\circ}\)C. The electrical power consumption is 140 W at 48 V AC for 4,000 min-1. The entire system, including the scaffold, the elevation tracker, the antenna, and the radiometer electronics, weights approximately 500 kg.