esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 4 Results
### Performance Experiments  
All of the performance results were measured with the metrics described in Section 3.6. First, the experiment of the reference implementation's paper was replicated, in which the 26 subjects of the WISDM dataset were used for training, which left the remaining 10 subjects of the data for testing. This train/test split of the dataset, although user-independent, was arbitrary. Consequently, a 10-fold cross-validation was implemented, in which the dataset was split into 10 groups of users, in order to keep the evaluation user-independent. The process was repeated on the Actitracker dataset,which contains data from a considerably higher number of users and collected in an uncontrolled environment, thus being more realistic.
## 5 Analysis of the Dynamic Response Characteristics of the Soil  
### Analysis of Acceleration of Soil with Vibration Load  
Figure 18 shows the acceleration time curve of the soil layer below the tunnel invert. Table 6 displays the statistics of the peak acceleration of the soil layer below the tunnel invert. Similar to the tunnel structure, the acceleration of the soil points decreases as the distance from the vibration source increases. This is mainly because the existence of material damping causes the vibration wave in the propagation process. When the tunnel base has a void, the acceleration value of each measuring point in the soil layer decreases. This is mainly because the existence of the base void reduces the density of the vibration wave propagation medium and weakens the conduction efficiency of the vibration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **No Base Void** & **With Base Void** & **Growth Rate (\%)** \\ \hline A5 & \(1.55\) & \(1.25\) & \(19.4\) \\ A6 & \(1.24\) & \(1.06\) & \(14.5\) \\ A7 & \(0.86\) & \(0.79\) & \(8.1\) \\ A8 & \(0.64\) & \(0.62\) & \(3.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Peak acceleration of soil layer measuring points (m/s\({}^{2}\)).  
Figure 17: Relationship between peak stress and allowable stress at the measuring points of the invert.  
Figure 18: Acceleration time curve of soil measuring points below tunnel invert: (**a**) no base void; (**b**) with base void.  
Figure 19 shows the peak acceleration of the soil layer points above the tunnel vault. The peak acceleration of each measuring point is significantly smaller than the peak acceleration of the soil layer below the tunnel invert. As the distance from the center of the source increases, the acceleration of different measuring points in the soil layer gradually decreases. The peak acceleration at point A20 is significantly smaller than that at point A19. The possible reason for this is that the measuring point A19 is close to the tunnel structure, and the vibration propagation efficiency of the tunnel is better than that of the soil layer. The peak acceleration of A20 is 0.21 m/s\({}^{2}\), and the peak acceleration of A23 is 0.11 m/s\({}^{2}\). Comparing the peak acceleration of measuring point A5 and measuring point A8, the decay rate of the acceleration of the soil layer above the vault is significantly reduced.  
When the base has a void, the peak acceleration of measuring point A19 is 0.589 m/s\({}^{2}\), with an increase of 0.037 m/s\({}^{2}\) and a growth rate of approximately 6.7%. The A20 acceleration also increases slightly. However, the peak accelerations of measuring points A21-A24 are almost unchanged.
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 4 Design of the VR Therapy for USN
### VR Therapy Module  
During a VR therapy session, the patient will see an arrangement of several musical keys, similar to a xylophone, aligned in a semi-circle around them, as shown in Figure 5. Using the controllers, notes can be played by hitting a key with a percussion millet, i.e., the motor modality of the therapy. The notes produced by the keystrokes are spatial sounds, i.e., originating from their location in space, where its pitch also depends on its location in the virtual environment, i.e., the auditory modality. Finally, when hit, the keys light up in a different colour for approximately 1 s before turning back to their original colour, corresponding to the visual modality.  
The VR therapy module contains four tasks, called Assessment, Scales, Memory and Free-to-Play. The Assessment task aims to assess the severity of the spatial neglect of the patient and their progress in the rehabilitation process. The other three tasks aim to change the behaviour of the patient by training them to respond to triggers in their environment, a top-down approach.  
The use of a VR environment enables us to adapt the setup of the xylophone to the current task and difficulty level. More specifically, the number of keys of the instrument and their location in space can instantly be changed to meet therapy needs. The location of the keys changes based on the objective of the task. Since the _Assessment_ task aims to assess progress, the keys will be symmetrically spaced on the semi-circle, as shown in Figure 6. However, for the three training tasks, the keys will be located off-centre towards the neglected side of the patients' environment, as shown in Figure 7, a bottom-up approach. More specifically, two keys will always be located on the ipsilesional side, while the other keys are situated in the neglected side of the environment. In this setup, the key at the extremity of the instrument is an extra key, producing a special sound. This modification is necessary as people with USN benefit from these anchor points in their neglected side of space. Moreover, a red target will occasionally appear during these training tasks. The users are required to look directly at this target for a few seconds, before continuing with the task. This addition is necessary to keep the patient aware of the location of their mid-line. Finally, the difficulty level of each task is reflected in the number of keys shown, namely difficulty 1 to 4, which corresponds to 6 to 9 keys, respectively. Each task, except the _Assessment_, consists of several of these levels, and thus a different number of keys based on the set difficulty level, as shown in Figure 8.  
Next, each task will be discussed in more detail.  
Figure 6: General setup of the symmetrical setup of the _Assessment_ task in schematic **(left)** and virtual environment **(right)**.
## 4 Challenges and Future Research Directions
### Privacy and Cybersecurity  
Maintaining the cybersecurity and privacy of gathered data is crucial in UAV-enabled ITSs. Extensive research has been conducted on these issues. In UAV-enabled ITSs, system components, including UAVs, various vehicles (air, ground, water, and rail), and control units, are mainly interconnected through wireless networks. This connectivity increases the network's vulnerability to potential cyberattacks. In addition, the cloud contains a lot of data about vehicles and passengers, which need to be protected. These data include vehicle identity, location, and traffic data, which can be hacked and used maliciously. Moreover, failing to protect these data could compromise user privacy. As ITS technology advances, cybersecurity challenges will increase, putting the system and users at greater risk without robust privacy and security protocols. Potential initiatives to address these challenges include developing intrusion detection systems, improving UAV communications, optimizing security and performance through data encryption, and applying advanced technologies.
## 1 Introduction
They then investigated the effect of FFF-based nozzle temperatures of 200 \({}^{\circ}\)C, 210 \({}^{\circ}\)C, 220 \({}^{\circ}\)C, and 230 \({}^{\circ}\)C on the crystallinity and rheological and mechanical properties of the composite. Rheological studies were carried out in the same temperature range as used in the actual FFF process, and the results showed an improvement in rheological properties at higher nozzle temperatures. Overall, the results confirmed that TCP-PLA filaments can be made in house, and optimization of nozzle temperature is essential to develop 3D-printed composite parts with suitable mechanical properties. [PERSON] et al. investigated the effect of printing temperature on the mechanical and structural properties of parts. Poly-(phenylene sulfide) (PPS) was chosen as the material because of its mechanical performance [28]. The results showed that a printing temperature of 340 \({}^{\circ}\)C produced parts with the highest tensile properties, with a degree of crystallinity superior to as-received PPS. [PERSON] and [PERSON] examined the effects of annealing temperature and time on the mechanical properties of PLA plastics produced with a 3D printer [29]. PLA samples were annealed at 70 \({}^{\circ}\)C, 85 \({}^{\circ}\)C, and 100 \({}^{\circ}\)C for 30, 60, and 90 min. As a result of the study, it was shown that the annealing process had a significant effect on the mechanical properties of PLA plastics. In addition, the results provide an important method for optimizing the mechanical properties of materials produced with 3D printing technology. It is seen that the mechanical properties of the final component produced by additive manufacturing are highly affected by the nozzle temperature. This study investigated the relationship between nozzle temperature and the dimensional and mechanical properties of samples prepared by FFF, one of the AM methods. The samples were fabricated with ABS material at nozzle temperatures of 220 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, 240 \({}^{\circ}\)C, 250 \({}^{\circ}\)C, 260 \({}^{\circ}\)C, and 270 \({}^{\circ}\)C. The temperature range was determined according to the nozzle temperature recommended by the manufacturer. Tensile tests and hardness tests were applied to the samples produced according to ISO standards. ANOVA was applied to examine the effect of temperature change on the findings obtained as a result of the experimental study. The results showed that the nozzle temperature significantly affected both the mechanical strength and dimensional properties of the samples.
## 4 Results Analysis
### Deformation and Vibration Behaviors of the Main Shaft
#### 4.4.2 Vibration Behaviors  
The central point of main shaft was chosen for the vibration behavior analysis in this work, where the impeller is mounted. Its time-varying acceleration and velocity curves under different flow conditions are shown in Figures 21 and 22, respectively. In addition, the corresponding frequency distributions were shown in Figures 23 and 24.  
It can be seen from Figures 23 and 24 that for both acceleration and velocity, the dominant frequency of the shaft vibration is \(f_{\text{BPF}}\) (148 Hz), and its harmonics (296 Hz, 444 Hz,... ) are also obvious. Besides, harmonic component of both the vibration acceleration and velocity is more obvious under part-load conditions, so there are some distinct small peaks for the corresponding time domain curves in Figures 21 and 22. Nevertheless, harmonic component is weakened gradually with the increase of flow rate and there are little harmonic components under large flow rates, where the time-domain sine curves are relatively smooth.  
Figure 21: Time-varying acceleration under different flow conditions: (**a**) X direction; (**b**) Y direction.  
In the engineering practice, the peak and RMS values in a certain period of time are counted for the vibration acceleration and velocity signals, respectively. The acceleration peak indicates the external force induced the vibration, and the RMS velocity reflects the strength of vibration energy. Figure 25 compares the above two vibration indicators in the center of the main shaft under different flow conditions.  
It can be seen from Figure 25 that acceleration peak and velocity RMS are relatively low around \(Q_{des}\). Given large flow (\(Q_{1.3}\) or higher) or part-load conditions (\(Q_{0.7}\) or lower), both acceleration peak and velocity RMS increase significantly with the increase of deviation from \(Q_{des}\), and this phenomena is more obvious under part-load conditions.  
Figure 23: Frequency distribution of the acceleration under different flow conditions: (**a**) X direction; (**b**) Y direction.  
Figure 22: Time-varying velocity under different flow conditions: (**a**) X direction; (**b**) Y direction.
# Enzymatic Degumming of Soybean Oil for Raw Material Preparation in BioFuel Production
## 2 Materials and Methods  
### Materials  
Soybeans were grown in the Lviv region, Ukraine, on the acreage of the <<Agro Center>> farm. Soybean vegetable oil was obtained by extraction using hexane as a solvent at the production facilities of D-MIX LLC (Zolochiv, Lviv region, Ukraine). The physical and chemical properties of soybean oil are given in Table 1.  
The phospholipase enzyme complex under the brand name Purifine(r) 3G, manufactured by DSM Food Specialties (Royal DSM), belongs to the third-generation enzymes. It is a combination of three separate phospholipases obtained by three independent degumming processes. Phospholipase C (PL-C), which catalyzes the hydrolysis of phospholipids to diglycerides and water-soluble phosphate residues, is cultivated using a strain of _Pichia pastoris_. Phospholipase C specific for phosphatidylinositol (PI), which hydrolyzes phosphatidylinositol to water-soluble residues, is produced by a strain of _Pseudomonas fluorescence_. Phospholipase A2 (PL-A2), which catalyzes the hydrolysis of phosphatidylcholine (PC) and phosphatidylethanolamine (PE), forming lysophospholipids, is produced by the _Aspergillus niger_ strain. Enzymatic processing of soybean oil using the Purifine(r) 3G enzyme complex is carried out under strictly regulated conditions that ensure optimal process efficiency. The temperature regime is set within 50-60 \({}^{\circ}\)C, which is optimal for achieving maximum enzyme activity. The pH value of the reaction medium is maintained in the range of 6.5-7.5, which ensures the highest efficiency of enzymatic hydrolysis of phospholipids. The reaction duration depends on the type and quality of the oil and varies within 1-3 h. The enzyme dosage is determined by the concentration of phospholipids in the raw material and is in the range of 50-200 ppm. Compliance with these parameters is critical to ensure effective degumming, reduce the content of phospholipids, and obtain high-quality raw materials for further use in biofuel synthesis.
## 2 Materials and Methods
### Proposed Joint CE and Turbo MUD  
The signals are transmitted into blocks and it is transmitted using the \(N_{T}\) transmitter antenna. The signal is converted into blocks before transmitting the data, which are transmitted along with the CP. At the transmitter and receiver sides, the inverse FFT and FFT are used for data transmission. First, the input data streams are transmitted to the turbo encoder at the transmitter side to obtain the encoded signal and this encoded signal is conveyed in blocks. After this, the encoded signal is fed to the interleaver. Following this, the output from the interleaver is given to the 16-QAM modulator block to produce symbol blocks. We obtain the frequency domain symbol vector after N-point DFT operation. Furthermore, the subcarrier mapping, IFFT and cyclic prefix (CP) operations are conducted for the data symbol vector for all subcarriers. The same process is repeated at the recipient side. MAI can reduce the performance of MIMO-OFDM systems. The detailed architecture is illustrated in Figure 1.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 4 Experiments and Results
### E2: SoC Estimation Using Fusion Hybrid Model
This means the train set had 188 sessions, the validation set had 40 sessions, and the test set had 41 sessions. This same setting was used in Section 4.1. **Model**: The Fusion Hybrid Model has 2 CNN layers with 256 neurons and LSTM layers with 66 neurons per layer. It has a feed-forward NN with three layers to capture the static features. The output of this model is the combined linear layer, which takes the individual output of the dynamic and static part of the model. Dropout was used to prevent overfitting. **Training procedure**: Training involved an initial phase of hyperparameter tuning using Optuna [51] with Tree-Parsing Sampler, while the experiments were subsequently recorded on Neptune.ai [52] as an experiment tracking tool. The Fusion Hybrid Model trained for 1500 epochs with an initial learning rate of 0.0028408 and a batch size of 32, which was scheduled by a cosine scheduler. Early stopping was used with the Adam optimizer, which terminated after 300 epochs if the loss had not changed. This was done to prevent overfitting. The resulting SoC estimation from the Fusion Hybrid Model was evaluated based on the metrics in Section 3.6. **Results**: Utilizing the stratified session split method with the Fusion Hybrid Model, the SoC estimation results remained excellent across the train, validation and test sets when evaluated using the evaluation metrics. In terms of SoC estimation, the Fusion Hybrid Model was able to maintain a MSE lower than \(5\times 10^{-4}\) and a MAE lower than \(20\times 10^{-3}\). This is similar to the results of the LSTM and the GRU, with only a difference of up to \(0.5\times 10^{-4}\) in MSE and a difference of up to \(0.6\times 10^{-3}\) in MAE The results are available in Table 4, and the SoC estimations can also be visually confirmed in Figure 6. Additionally, an extended version of Table 4 can also be found in Appendix A's Table A1. Figure 6: Fusion Hybrid Model SoC Prediction: SoC predictions (blue) versus real SoC (orange). The Fusion Hybrid Model was able to produce accurate SoC predictions based on visual comparison.
## 2 Data and Methods
### Sensitivity Analysis Methods
#### 2.3.1 Morris Screening Method
The calculation formula for the elementary effect (\(EE\)) of the model parameters is shown in Equation (1). \[EE_{i}=\frac{f(x_{1},\cdots,x_{i-1},x_{i}+\Delta,x_{i+1},\cdots,x_{M})-f(x_{1},\cdots,x_{M})}{\Delta} \tag{3}\]  
where \(EE_{i}\) is the elementary effect value of the \(i\)th model parameter. \(x_{1},\cdots,x_{M}\) is the model parameter value. Generally, the [PERSON] screening method adopts two calculation indices, the mean and standard deviation of the elementary effect, to judge the importance or sensitivity of parameters. To avoid the negation of the positive and negative when \(EE_{i}<0\), [PERSON] et al. (2015) suggested replacing the mean \(\mu\) with the modified mean \(\mu\)*. The calculation formula is as follows:  
\[\mu_{i}^{*}=\frac{1}{n}\sum_{j=1}^{n}\lvert EE_{i}(j)\rvert \tag{4}\]  
\[\sigma_{i}=\sqrt{\frac{1}{n-1}\sum_{j=1}^{n}\biggl{[}EE_{i}(j)-\frac{1}{n} \sum_{j=1}^{n}EE_{i}(j)\biggr{]}^{2}} \tag{5}\]  
where \(EE_{i}(j)\) is the elementary effect value calculated from the \(i\)th model parameter and the \(j\)th sample. \(n\) is the number of model parameter samples. \(\mu_{i}^{*}\) is the modified mean elementary effect value of the \(i\)th model parameter. \(\sigma_{i}\) is the standard deviation of the elementary effect value of the \(i\)th parameter. The larger the modified mean elementary effect value of a parameter in the SWAT model is, the higher the sensitivity of the parameter. The larger the standard deviation of the elementary effect value is, the greater the interaction between this parameter and other SWAT model parameters is.
# The Housing System Contributes to Udder Health and Milk Composition
## 4 Discussion  
The somatic cell count is an indicator of inflammatory processes in individual cows and, more importantly, of the udder health status of a herd and the quality of raw milk in both the herd and the entire population [14]. In the present study, tie-stall housing and milking directly in stalls resulted in a lower SCC than free-stall housing and the use of milking parlors. [PERSON] et al. [27] and [PERSON] et al. [28] demonstrated that free-stall barns were characterized by a much lower risk of mastitis. Higher SCCs in free-stall barns may be related to poor organization of the milking process, excessive parlor waiting time, and inadequate pre-milking teat preparation [4]. In the free-stall barn analyzed in this study, wood sawdust was used as bedding and manure was removed once daily, which could negatively affect under hygiene and increase the SCC. According to [PERSON] et al. [29], dairy cattle cleanliness is essential to maintaining hygienic milk production. In the current experiment, milk yield decreased and milk composition deteriorated with increasing SCC, which could have been due to impaired synthesis of milk components and increased permeability of the milk-blood barrier [30]. The lactose content of milk decreases during mammary gland infections [31], as a consequence of epithelial cell damage and increased permeability. The osmotic pressure of milk is maintained by an equilibrium between the concentrations of lactose and soluble minerals. Changes in the sodium and potassium contents of milk lead to reductions in lactose synthesis and milk production [32]. In the present study, the milk's lactose percentage was negatively related to intramammary inflammation and, therefore, could be a potential indicator of udder health. In addition, the daily milk yield decreased with increasing SCC at a slower rate in the tie-stall barn than in the free-stall barn, which could have resulted from the fact that, in the former housing system, cows were handled individually during milking and feeding. Milk samples were collected only from cows that did not exhibit clinical symptoms of mastitis; in the group with the highest SCC, the milk contained 1.2 mln somatic cells per mL on average. [PERSON] et al. [33] also examined cows that did not show clear clinical mastitisti symptoms; the group with the highest SCC was characterized by 2.8 mln somatic cells per mL of milk on average, and this state was referred to as severe subclinical mastitisti. S subclinical mastitisti poses a serious challenge because, in the absence of clinical mastitisti symptoms, milk is not discarded and can be used for consumption or processing. However, increased SCC renders milk unsuitable for, e.g., cheese production, due to an unfavorable casein-total protein ratio, as well as reduced lactose and calcium levels [34]. Differences in SCC and milk composition, including endogenous enzyme profiles, can lead to different degrees of lipolysis. It is difficult to establish a clear relationship between enzyme activity and SCC in milk, and their effect on the quality of the end product [17].
## 2 Regions and Methods
### Model Building  
#### 2.2.1 Mathematical Model  
In the analysis of porous flow of the slope under rainfall conditions, the flow of rainwater in unsaturated soil and in saturated soil below the groundwater level is a two-dimensional saturated-unsaturated problem. The Darcy's Law is still used to describe the law of saturated and unsaturated seepage. The two-dimensional saturated and unsaturated seepage control equation has the following form [17]:  
\[\frac{\partial}{\partial x}\bigg{(}k_{x}\frac{\partial h_{w}}{\partial x}\bigg{)} +\frac{\partial}{\partial y}\bigg{(}k_{y}\frac{\partial h_{w}}{\partial y} \bigg{)}+Q=m_{w}\gamma_{w}\frac{\partial h_{w}}{\partial t} \tag{1}\]  
where \(k_{x}\) and \(k_{y}\) are the hydraulic conductivity of the soil in \(x\) and \(y\) directions, \(h_{w}\) is the height of the head, \(Q\) is sink/source term, \(\gamma_{w}\) is the specific weight of the water, and \(m_{w}\) is the slope of the soil moisture characteristic curve.  
The finite element equations are solved in combination with the following boundary conditions.  
Head boundary:  
\[k\frac{\partial h_{w}}{\partial n}\bigg{|}_{\Gamma_{1}}=h(x,y,t) \tag{2}\]  
Fluid boundary:  
\[k\frac{\partial h_{w}}{\partial n}\bigg{|}_{\Gamma_{2}}=q(x,y,t) \tag{3}\]  
The initial conditions for the non-constant seepage:  
\[h(x,y,0)=h_{0}\hskip 14.226378 pt(x,y)\in\Omega \tag{4}\]