esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation-Based Design for Inlet Nozzle of Vortex Tube to Enhance Energy Separation Effect
## 4 Energy Separation Efficiency Improvement  
### Nozzle and Internal Shape Change of Vortex Generator  
There are various approaches to improving the energy separation efficiency of a vortex tube, one of which is to modify the nozzle and internal geometry of the vortex generator. As shown in Figure 3, the vortex generator typically has a cylindrical shape, with air introduced through a side nozzle, resulting in a cross-sectional shape where a circle intersects with a rectangle. In conventional designs, the outer boundary is constructed such that the circle and the outer edge of the rectangle meet. This configuration can cause interference between the incoming flow and rotating flow generated inside the vortex tube, potentially reducing momentum and diminishing performance.  
In this study, the nozzle and internal shape of the vortex generator were modified by referring the 80% parabolic bell shape, which is considered optimal for rocket nozzles, and applying the RAO construction method [34; 35]. The goal of these modifications was to reduce the interference between the incoming and rotating flows. The contraction-expansion structure typical of rocket nozzles was applied in reverse to enhance flow dynamics within the vortex tube.  
Figure 15 outlines the key parameters used in designing the bell-shaped rocket nozzle. The throat radius of the nozzle was set to 2 mm, corresponding to the cross-sectional height and width of the vortex generator nozzle. Both the parabolic entry and exit angles were set to 15 degrees. The expansion ratio was varied as 4, 6, 8, and 10 to modify the nozzle and internal geometry of the vortex generator. The definitions and values of these parameters are summarized in Table 6. The original and modified vortex generator shapes, incorporating these design changes, are presented in Figure 16.  
Figure 14: Comparison of temperature difference versus cold fraction between experimental data and CFD results [27; 33].
## 5 Results
### Analysis of Gaze and Driving Behavior
#### 5.2.6 Fixations and Saccades  
Fixations and saccades were identified as the most meaningful gaze indicators. The analysis of the speed, amplitude, duration, and frequency of fixations and saccades revealed that the significance of these indicators varied depending on the driving scenario. In urban areas, the number of fixations and saccades decreased, fixation duration increased, and saccade amplitude decreased significantly. Saccade speed, however, remained essentially unchanged. On country roads, only fixation frequency and saccade amplitude showed significant differences, while on highways, no significant differences were observed.  
The results align with findings in the literature, where alcohol consumption is associated with tunnel vision. This manifests as a reduction in the number of fixations and saccades, longer fixation durations, and a narrower field of vision, as indicated by a decreased saccade amplitude. Scenario-specific differences can be explained by varying demands on the driver. For example, urban driving requires frequent changes in the line of sight due to turning and lane changes, whereas highway driving involves a more constant, forward-focused vision. As a result, significant differences in gaze behavior are harder to detect on highways.
# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 3 System Description
### Link Scheduling
The rank of the nodes used to define the priorities of N member nodes is given as  
\[\max_{(pn_{1},pn_{2},\cdots pn_{N+1})}\sum_{i=1}^{N+1}pn_{i}RN_{i} \tag{2}\]  
where \(pn_{i}\) represents the priority of node \(i\) and \(RN_{i}\) represents the rank of node \(i\), respectively. The sink node that has the highest priority and is assigned to rank 1 is also considered in Equation (2). Priority of member nodes is given as  
\[\max_{(pn_{1},pn_{2},\cdots pn_{N+1})}\sum_{i=1}^{N+1}\frac{IPN_{i}}{pn_{i}} \tag{3}\]  
where \(IPN_{i}\) is the IPN measured at node \(i\). The free space equation is used to calculate interference from node \(j\) to node (4) \(i\). The study in [62] describes the interference from member node \(j\) to member node \(i\) as follows  
\[P_{r,ij}=P_{TR}Y^{2}\left[\frac{\lambda}{4\pi\big{(}L_{ij}+B\big{)}}\right]^{2} \tag{4}\]  
Figure 4: Time slot of the 6 TiSCH wireless network. where \(\lambda\) represents wavelength, \(L_{ij}\) represents the distance between node \(i\) and node \(j\), \(P_{TR}\) represents transmit power, and \(B\) represents the Rayleigh block fading random variable parameter, respectively [63]. \(IPN_{i}\) is expressed as  
\[IPN_{i}=\sum_{j=1,j\
eq i}^{N}P_{r,ij} \tag{5}\]  
The priorities of all nodes based on the number of packets to transmit within a specific slot frame are assessed as  
\[\max_{(pn_{1},pn_{2},\cdots pn_{N+1})}\sum_{i=1}^{N+1}\frac{PT_{i}}{pn_{i}} \tag{6}\]  
where \(PT_{i}\) is the number of packets to transmit by node \(i\). For the proposed link scheduling algorithm, the priorities of member nodes are determined by the optimization established on the united objectives as follows:  
Figure 1:0  
where \(W_{1}\), \(W_{2}\), and \(W_{3}\) represent the weights of individual objectives. These weights are subject to \(W_{1}+W_{2}+W_{3}=1\). As the values of \(\sum_{i=1}^{N+1}pn_{i}RN_{i}\) and \(\sum_{i=1}^{N+1}\frac{PT_{i}}{pn_{i}}\) are greater than the value of \(\sum_{i=1}^{N+1}\frac{PN_{i}}{pn_{i}}\), normalized components are applied in Equation (7). Assuming \(RN_{ni}=\frac{RN_{i}}{\sum_{i=1}^{N+1}RN_{j}}\), \(IPN_{ni}=\frac{IPN_{i}}{\sum_{i=1}^{N+1}IPN_{j}}\), and \(PT_{ni}=\frac{PT_{i}}{\sum_{i=1}^{N+1}PT_{i}}\), Equation (7) can be expressed as  
Figure 1:1  
After the completion of broadcasting, link scheduling commences with the priorities of the nodes established by the sink node. The priority of the child node determines the link between the parent node and the child node. Link scheduling is implemented based on the node priority.
## 3 Results and Discussion
### OAV-Based Characteristic Aroma Analysis
C1: ethyl acetate; C2: isobutyl acetate; C3: ethyl butyrate; C4: isoamyl acetate; C5: isobutanol; C6: 1-butanol; C7: isoamyl alcohol; C8: ethyl caproate; C9: ethyl lactate; C10: ethyl octanoate; C11: decanal; C12: isobutyric acid; C13: ethyl decanoate; C14: isovaleric acid; C15: ethyl 9-decenoate; C16: hexy alcohol; C17: 2-phenylethanol; C18: octanoic acid; C19: decanoic acid. while Merlot 343 was distributed in the second quadrant, close to most of the fermentation aroma components. Malbec VCR6 was distributed in the fourth quadrant, surrounded by most of the fatty acid ethyl ester components. The analysis showed that Cabernet Sauvignon 170, Cabernet Sauvignon VCR11, and Malbec 598 had high contents of high-quality fermentation aroma components, mainly including fatty acids, fatty acid ethyl esters, and acetate, which accounted for the high-quality fruit flavors in the wines. The compositions and contents of the fermentation aromans of the wines from the four Pinot Noir clones were similar. Although the compounds responsible for the fermentation aromans of the wine samples from different clones of other varieties were similar, their contents varied widely. Among the four Cabernet Franc clones, Cabernet Franc 396 produced wines with higher levels of ethyl acetate, ethyl 9-decenoate, and decanal, which impart floral, cream, and cheese aromas [22]. The other three clones led to similar distributions of wine samples with higher levels of isobutanol, ethyl lactate, and 1-butanol, which provide aromas of pineapple, honey, butter, and caramel [23]. Among the different clones, Cabernet Sauvignon was found to be more dispersed. The distance between Cabernet Sauvignon 15 and Cabernet Sauvignon 169 was short, and the contents of isoamyl alcohol, isobutanol, isovaleric acid, and phenylethanol were higher; they produce aromas reminiscent of rose, bread, fermentation, grains, and sweet fruits [24]. Cabernet Sauvignon 170 had higher levels of isobutyl acetate, isoamyl acetate, ethyl caproate, capric acid, and caprylic acid, producing more sweet fruit, candied fruit, banana, and oil aromas [25]. Cabernet Sauvignon VCR11 had higher levels of ethyl caprylate and ethyl caprate, adding pleasant fruit flavors. The wine samples from the two Merlot clones were farther apart, with Merlot 343 having a higher content of isoamyl alcohol and more prominent fermented and grainy flavors, while Merlot VCR1 had a higher content of ethyl lactate and more creamy and buttery flavors. Malbec VCR6, which was rich in tropical fruit flavors, had higher contents of ethyl acetate and ethyl 9-decenoate. Malbec 598 had higher levels of isobutyl acetate and ethyl caprate, which resulted in banana, candied fruit, and coconut aromas. In summary, fermentation aromas contributed more to the sensory flavor of the wine samples than varietal aromas. Therefore, more volatiles belonging to the class of fermentation aromas were selected during the analysis using the PLSR model.
# A New Method of Time-Series Event Prediction Based on Sequence Labeling
## 3 Experiment
### Baseline Method  
This article compares the proposed CX-LC model with eight other models:  
**Classification event prediction method.** For the prediction of time series events, most of the current research is to take each subsequence set \(T_{m}^{\mathrm{r}}\) as an independent individual, extract the information in the subsequence set \(T_{m}^{\mathrm{r}}\) as the input of the classifier, and use the event prediction result \(\hat{Y}_{m+\tau}\) as the output of the classifier. This article uses the most advanced sets of frameworks as the baseline model. X-HMM [11] is a sequential model, XGBoost can capture the relationship between different observation features, and then HMM can find the most likely hidden state sequence of a given observation sequence, so as to make event prediction. Time2 Graph [9] adopts shapelet to extract states; it aggregates the graphs at different times as a static graph and conduct DeepWalk to learn graph's representations, which then serve as features for event predictions. Evolutionary State Graph Network models (EvoNet) [10] both the node-level (state-to-state) and graph-level (segment-to-segment) propagation, and captures the node-graph (state-to-segment) interactions over time, and then uses it as a function of event prediction. Since the original EvoNet model combines and scrambles the subsequences of different samples before training the classifier, in order to highlight the comparison effect, this article added the _EvoNet_Separate_ model ( _EvoNet_S_), which trains different samples separately. Call the original model _EvoNet_Merge_ (_EvoNet_M_). These four methods are all classification algorithms that do not consider the time factor between the subsequence sets \(T_{m}^{\mathrm{r}}\).  
**Labeling event prediction method.**The CX-LC model proposed in this paper first uses the cluster algorithm for pattern recognition, uses XGBoost for feature selection, then uses the LSTM algorithm for event prediction, and finally uses the CRF model to improve the continuity of the prediction results. In order to explore whether the first, second, and last steps have an effect on improving the prediction accuracy, this article used the XGBoost-LSTM-CRF (X-LC) model with the pattern recognition step removed, the cluster-LSTM-CRF (C-LC) model without the step of feature selection, and the cluster-XGBoost-LSTM (CX-L) model without smoothing are tested on five data sets. As we expected, the complete CX-LC model has better forecast results.
# A Novel Hybrid Path Planning Method Based on Q-Learning and Neural Network for Robot Arm
###### Abstract  
Path planning for robot arms to reach a target and avoid obstacles has had a crucial role in manufacturing automation. Although many path planning algorithms, including RRT, APF, PRM, and RL-based, have been presented, they have many problems: a time-consuming process, high computational costs, slowness, non-optimal paths, irregular paths, failure to find a path, and complexity. Scholars have tried to address some of these issues. However, those methods still suffer from slowness and complexity. In order to address these two limitations, this paper presents a new hybrid path planning method that contains two separate parts: action-finding (active approach) and angle-finding (passive approach). In the active phase, the Q-learning algorithm is used to find a sequence of simple actions, including up, down, left, and right, to reach the target cell in a gridded workspace. In the passive phase, the joints angles of the robot arm, with respect to the found actions, are obtained by the trained neural network. The simulation and test results show that this hybrid approach significantly improves the slowness and complexity due to using the simplified agent-environment interaction in the active phase and simple computing the joints angles in the passive phase.  
path planning; hybrid method; Q-learning; neural network; robot arm; target reaching; obstacle avoidance +
Footnote †: journal: Applied Sciences
## 3 Test Results and Discussion
### Deformation and Cracking Process
(2) Specimen of D10  
Figure 14 illustrates the principal strain fields of specimen D10 corresponding to key points (a)-(e), shown in Figure 15. At the end of the static loading phase, the principal strain of the specimen is evenly distributed (Figure 14a). As the stress increases from point \"a\" to point \"b\", the strain localization zone (Figure 14b) appears in the lower right corner of the specimen, and the strain gradient within this region is significantly larger than that outside of this region. Subsequently, a crack appears in the lower right corner of the specimen in a short time, and another strain localization zone appears on the left side (Figure 14c). Point \"d\" corresponds to the peak stress, at which point the main crack starts to form and extends. After a sudden stress drop, the crack eventually penetrates through the specimen, almost parallel to the indentation direction. Figure 14e shows the strain contour at the end of fracture propagation, and the failure mode is determined. Figure 16 illustrates the failure process of specimen D10. The initial crack appears on the surface of the specimen, accompanied by the spalling and ejection of small rock fragments from the surface. Subsequently, multiple cracks are generated and extended simultaneously. As shown in the Figure 16, there are two main cracks. The cracks continue to propagate until the rock specimen is ruptured into three parts, and the right part is quickly broken into several small pieces, resulting in disintegration and ejection. The strain localization can better identify the crack initiation and propagation processes based on the above analysis. The formation of the strain localization zone can be considered a precursor stage of crack propagation. Comparing specimens D6 and D10 under the same static pre-stress but different dynamic loading rates, we find that both of the strain localization zones appear on the specimen surface before cracks are created, but the corresponding stress values are different. When the dynamic loading rate is \(2\times 10^{-3}\) s\({}^{-1}\), the initial strain localization zone is formed at 84.19 MPa (95.8% of the SUCS) while the initial strain localization zone is formed at 81.44 MPa (90.6% of the SUCS) when the dynamic loading rate rises to \(10^{-2}\) s\({}^{-1}\). Figure 13: Image of the failure process of specimen D6. Figure 14: The principal strain fields corresponding to point (**a**–**e**) under different loading stages in Figure 13.  
Figure 16: Image of the failure process of specimen D10. Figure 15: The stress–strain curve and five key stress points of specimen D10.
## 2 Materials and Methods
An important issue in the research was determining the common frequency range for ES and physiological speech, i.e., the range in which frequency components are present for both laryngectomised patients and healthy people. A natural feature of physiological speech is that the average male voice falls within the frequency range of between 50 Hz and 600 Hz. For average female voices, it is 100 Hz to 800 Hz, respectively. However, in the case of children, the upper value of the frequency range can reach up to 2000 Hz, especially while shouting, which accompanies play among peers. The above cases differ significantly from the properties of oesophageal speech (ES), which on the low side of the frequency range may take on the character of a \"cereaky\" voice and a voice that is much lower than physiological speech. In addition, the speech of laryngectomised women does not have to be higher than that of laryngectomised men, which is very common in the case of the speech of healthy people. Figure 2 shows the F0 frequency of oesophageal speech (ES) for selected words and selected people following laryngectomy surgery. The figure shows that the F0 frequencies within individual words are very similar despite the gender of the speaker. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Average Duration of the Word Studied [s]**} \\ \hline
**In Polish** & **In English** & **Healthy** & **Laryngectomised** \\ \hline jajko & an egg & 0.6050 & 0.7688 \\ beczka & a barrel & 0.6035 & 0.6758 \\ bulka & a bread roll & 0.6701 & 0.8442 \\ paczka & a package & 0.6687 & 0.6901 \\ snieg & snow & 0.3283 & 0.4685 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean durations of the studied words for healthy and laryngectomised people. In further stages of the research, the frequency analysis range from 50 Hz to 200 Hz was established, taking into account the following settings of the Praat program:  
1. Pre-processing: attenuation at ceiling = 0.03
2. Finding a path: * Silence threshold = 0.09
* Voicing threshold = 0.50
* Octave cost = 0.055
* Octave-jump cost = 0.35
* Voiced/unvoiced cost = 0.14
3. Time step: fixed time step = 0.01 s
4. The filtered autocorrelation analysis method was chosen. For each tested word (physiological speech and oesophageal speech [ES]), the following parameters were analysed: the number of F0 components obtained in the \"pitch listing\" from Praat, the average of values F0, and the minimum F0 and maximum F0 values of each word.
## 1 Introduction  
In higher education, increased autonomy is required from students because external regulation is generally limited [1]. Students need to rely on their own evaluation of performance to regulate their learning processes. Unfortunately, not all students can accurately evaluate their performance, which can result in poor learning choices [2]. The use of e-learning environments in higher education has emerged over the last decades [3], facilitating administration of online formative assessments that can help students interpret the meaning of their past performance and determine what they should be studying and practicing to increase their performance [4, 5].  
In online formative assessments, students answer assessment items and receive feedback based on their responses via learning dashboards. In current practice, this feedback often consists of information regarding correctness of responses and percentage correct scores. However, if students know the correct answer to an item and can compare it with their given answer, this does not imply that they can accurately infer which knowledge or skills are lacking. For example, [PERSON]. [6] found that students find it difficult to determine which features of statistics items are relevant for learning. Furthermore, the interpretation of percentage correct scores can be problematic, since these scores cannot be directly compared across different item sets and differences in percentage scores cannot be used to express change [7]. It would be beneficial to provide students more detailed, _diagnosticfeedback regarding their knowledge and skills [8]. Diagnostic feedback is information about how students' current levels of performance relate to desired levels of performance, indicating whether or not they have mastered the skills that are required to solve certain tasks [9]. This allows students to determine where they should focus their attention and effort; hence, this information is actionable [10].  
In order to provide diagnostic feedback, valid and reliable measurement of students' skills is required, which is the focus of the current study. Diagnostic information can be extracted from students' item responses with cognitive diagnostic assessment, which brings together cognitive science and psychometrics to measure knowledge structures and processing skills in order to provide information about cognitive strengths and weaknesses [8].
## 2 Materials and Methods
### Quality Control Metrics and Property Links  
The monitoring system supports QC by computing the following: (a) \(T_{P}\), \(t_{P}\), \(dT/dt\,\mathrm{l}_{0\text{-}12\text{h}}\), and \(\Delta T\) (surface-core) to assess heat of hydration and thermal gradients; (b) \(dRH/dt\,\mathrm{l}_{0\text{-}24\text{h}}\) and stabilized RH to screen for surface desiccation risk; and (c) a maturity index M(t) from internal temperature histories. Using the Nurse Saul formulation, \(M(t)=\Sigma(T^{a}-T_{0})\Delta t\), with \(T^{a}\) the concrete temperature, \(T_{0}\) a datum temperature, and \(\Delta t\) the logging interval. When calibration data for a given mix are available, strength can be estimated via a maturity-strength function f_c(t) = F(M(t)) determined experimentally for that mix. In this study, we report the QC metrics (\(T_{P}\), \(t_{P}\), \(\Delta T\), \(dRH/dt\)) and compute M(t) from the recorded temperature curves as a basis for plant-level curing management; quantitative f_c-M calibration is identified as future work for each plant mix.