esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Push Force during Screw Phase of the Motion  
A typical mean push force curve of participant P3 for ten cycles is shown as an example in Figure 5. The standard deviation (Std. dev.) is marked as the blue area, the start and endpoint of the screw phase with black dashed lines. The mean push force started at a lower level between 10 N and 40 N from 0% to 50% of the motion cycle. This was then increased slightly from 40 N to 75 N until the start of the screw phase (62%) and reached a plateau at 110 N during the screw phase from 62 to 87% of the motion cycle. The push force dropped back to a lower level at the end of the screw phase (87%). All further evaluations of the push force were considered only the mean value during the screw phase of the motion for each cycle.  
Considering task T1 in Figure 6, the mean push force of participants P1, P4, and P5 were at different levels between 83-93 N (P1), 88-100 N (P4), and 103-108 N (P5) for the different conditions S0, S50, and S100, respectively. Only participant P3 showed a larger dispersion of the mean push force between the support conditions (68-109 N). The dispersion of the push force was larger for participants P1 and P3 than for P4 and P5. A larger mean value of the push force at condition S50 compared to S0 could be observed for participants P3, P4, and P5. Subject P1 did not show this effect. However, P1 had two outliers for condition S50. All participants tended to show a lower push force at support condition S100 compared to S50, which was particularly evident for subject P3. Due to the overlapping of the boxes, no difference in push force was suggested between the support levels for participants P1, P4, and P5.  
Figure 5: Exemplary course of the mean push force for participant P3, task T1, and support condition S50 with the start- and endpoint of the screw phase. A push force plateau could be recognized during the screw phase.  
Considering task T2 in Figure 7, the mean push force varied between the participants from 66 N to 135 N. Here, the participants showed different force levels. The push force was at different levels for participants P1 (66-74 N), P3 (70-89 N), P4 (124-135 N), and P5 (87-100 N) for the three support conditions. The dispersion of the mean push force was comparable for participants P1, P3, and P5. Participant P4 showed a larger dispersion for S100 and a decrease in dispersion at higher support levels. At the same time, there was a slight increase in the mean push force for participant P4 with a support level of S100 compared to S0. However, due to overlapping of the boxes, there was no difference in the mean push force of participants P2, P4, and P5 at different support conditions. Only participant P3 had a lower mean push force for support levels S50 (71 N) and S100 (70 N) compared to S0 (89 N).
## 2 Materials and Methods
### Material Measure Design  
The basis of a calibration is to compare a known input with a measured output which, in a DIC application, involves measuring a set of defined deformation states. In order to establish confidence in the values delivered by the calibration methodology, the defined deformations against which measurements are compared must have a known accuracy. This is usually known through a traceability chain; a series of measurement standards that allow the accuracy of a measurement to be traced back, via a hierarchy of calibrations, to a national or international standard [14].  
A material measure is \"a device intended to reproduce or supply, in a permanent manner, values of a given quantity\" [22]; in this case, deformation values. The material measure facilitates a meaningful measurement calibration by being traceable back to a suitable standard level, i.e., the inaccuracy of the material measure is known. The final uncertainty measurement that is obtained through a calibration is the sum of the inaccuracy of the material measure, plus the uncertainty contributions that are a result of the measurement process.  
Strain, which is the quantity that will be used to reflect deformation, is derived from a relative change in length and DIC essentially measures length changes to compute strain; length, therefore, is the obvious measurement chain for traceability [12], especially for large deformations.  
DIC systems make measurements by tracking a number of surface points created from subsets within the digital images of a surface undergoing a deformation. A non-periodic stochastic pattern, referred to as a'speckle pattern', is usually applied to the surface of interest to create suitable intensity fields for data point creation and to aid in the unique correspondence of subsets between images. This pattern adheres to and deforms with the surface during deformation and measurements are thus derived from relative movements of the surface pattern.  
The synthetic speckle pattern approach was deemed the most appropriate as it enabled full control of speckle characteristics and the pattern could be deformed in a known manner to create multiple stages of deformation. The patterns were printed onto separate rigid boards to produce high-resolution material measures that could be imaged individually by the DIC sensors; the stages of the deformation could be precisely controlled and, more importantly, measured and traced.
# New Insight on the Formation of 2-Aminoacetophenone in White Wines
###### Abstract
[3] recently showed that the addition of tannins in grape must prior to fermentation was more efficient than glutathione in preventing the formation of 2-AAP in wine, with gallotannin showing an effect almost comparable to ascorbic acid. Another important factor to consider in wine oxidation chemistry is the role of transition metal ions. This has been well documented for some important chemical reactions like the oxidation of ethanol into acetaldehyde [12]. On the other hand, the impact of transition metal ions on ATA development has been only suggested by sensory study without any quantification of 2-AAP [13]. Finally, a preliminary study performed in our laboratory (data not published) suggested that some varieties might be more prone to the formation of 2-AAP than others. This was intriguing, as while historically, Riesling wines have been known to be particularly sensitive to ATA, this off-flavor was observed in different types of white wines all around the world [2]. This study aimed to improve the understanding of 2-AAP formation in white wines by investigating three main questions. Firstly, the potential varietal effect on 2-AAP concentration in white wines was assessed by analyzing two set of commercial white wines, and included the quantification of 2-AAP after artificial ageing and 2-AAP precursors. Secondly, the effect of different types of antioxidants on 2-AAP formation was investigated, as previously published results were unclear on the topic. Finally, the influence of transition metal ions present in wine (Fe, Cu and Mn) on 2-AAP concentration was performed for the first time.
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 4 Annotation Scheme
### Client Utterance Attribute (Talk Type)  
According to the MI literature [1], clients usually feel ambivalent about adopting positive behaviour change, and thus the desirable outcome of MI is for the client to pick up pro-change arguments and talk themselves into changing, provided that it aligns with their aspirations and values. This type of talk that favours change is known as \"change talk\". Conversely, a \"sustain talk\" conveys resistance to behaviour change and favours the status quo. On the other hand, a \"neutral talk\" indicates no preference for or against change. Hence, we name Change Talk, Sustain Talk, and Neutral Talk as three types of the client Talk Type attribute. Table 8 presents some examples of these talk types in different scenarios, such as reducing alcohol consumption.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Cenario 1 — Smoking Cessation** & **Reflection Type** \\ \hline \multirow{4}{*}{Client} & Um, I try really hard not to smoke around him, but I-I’ve been & \\  & smoking for 10 years except when I was pregnant with him. & \\  & But it– everything is so stressful being a single mom and-and & \\  & my having a full-time job. And so it’s just– that’s why I started & \\  & smoking again. & \\ \hline Therapist 1 & Things are very stressful for you right now. & Simple \\ \hline \multirow{2}{*}{ Therapist 2} & You have a lot of things going on and smoking’s kind of a way & \\  & to relax and de-stress. & \\ \hline \multicolumn{3}{c}{**Scenario 2 — Reducing Alcohol Consumption**} \\ \hline \multirow{2}{*}{**Speaker**} & **Utterance** & **Reflection Type** \\ \hline \multirow{2}{*}{Client} & Um, I’ve been really trying not to, but, you know, weekends & \\  & come around, and, um, all my friends are kind of partying and & \\  & stuff, and it’s been hard to, like, break that habit. & \\ \hline Therapist 1 & It’s quite a challenge for you. & Simple \\ \hline \multirow{2}{*}{ Therapist 2} & Mm-hmm. So, there’s this external pressure coming from the & \\  & people you care about to sort of stay in the scene. & Complex \\ \hline \hline \end{tabular}
\end{table}
Table 7: Example labelling for therapist Reflection based on the dataset.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Utterance** & **Talk Type** \\ \hline Yeah, I just want to do what’s right. & Change \\ \hline Well, that was fine until I came here, um, but now that I know about the health risk, & Change \\ um, I have something I gotta think about. & \\ \hline Um, I mean, the 10 drinks seems like not a lot for me and my tolerance. & Sustain \\ \hline Yeah, whatever. I Know you got to do your job, but I don’t care. & Sustain \\ \hline Yeah, I would like to play soccer in college. & Neutral \\ \hline And um, I think she used to look after me because she used to do the cooking and & Neutral \\ stuff like that. & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Example labelling for client Talk Type from the dataset.
## 4 Results
### Experiments
#### 4.2.3 Trajectory Segmentation  
The enriched trajectory was segmented into smaller parts (segments) based on specific criteria and thresholds defined by the user. Segmentation criteria constituted (a) the altitude variations of the drone and (b) the time interval and distance between two consecutive points (geo-tagged photos). Figure 11 illustrates the results of the segmentation using the segmentation criterion of distance with a threshold equal to 50 m. The segmentation process was implemented in the reconstructed trajectory of the Konya University dataset using the Phantom 4 drone.
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
###### Abstract  
Outdoor solutions aiding the navigation of visually impaired individuals can seamlessly transition to indoor environments. Take, for instance, the adaptation of special lanes and configurations on the floor. However, these existing solutions fall short when it comes to addressing obstacles above ground level, such as open windows, as highlighted in a previous article on the use of ultrasonic glove for visually impaired users. In response, the present proposal is a user-friendly, cost-effective solution that is capable of detecting elevated obstacles. Importantly, this solution aligns with a user's language preferences, eliminating the need for learning new languages or possessing IT skills. Users simply specify their desired language for the prototype to communicate in, ensuring a personalized experience. The system alerts users to the presence of obstacles through varying levels of warning, calculated based on the distance between the obstacle and the user's current position. This approach not only enhances safety but also prioritizes accessibility and ease of use. easy to use; low cost; visually impaired user; indoor; any language +
Footnote †: journal: Medical  
The current solutions for indoor navigation [3; 4; 5; 6; 7; 8] are described in Table 1.  
The current solutions for indoor navigation include prototypes that are considered add ons which enhance the awareness of the user regarding the surrounding area. The main function of the developed prototype[11; 12] is to detect obstacles that are not connected to the floor like open windows, cabinets, etc. The main advantage of the Pulse Code Modulation (PCM) and WAVE prototypes is that the user is not required to have IT expertise; the prototypes are user-friendly, reliable, and cost-effective and the language for the messages can be adjusted according to user need. The prototypes are user-friendly because the user can select the language and the tone for the alert messages that they will hear. They can even record their own voice and it can be transformed and used. This fact means that the messages are very clear. Furthermore, the weight of the prototype is less than 200 g, with the head or chest part containing only the sensor and the audio module. The reliability of the device is determined by the fact that the parts are protected by plastic cases and the connections are welded and secured with connectors. The hardware expenses for constructing the prototype amount to less than EUR 20-25, with the micro-controller constituting the priciest component at around EUR 9 per unit, in the context of a small-batch order. It is worth noting that, for larger quantities, the overall hardware cost is likely to decrease further; the prototypes are slightly more expensive than conventional walking sticks, which can be acquired for EUR 10-15. Their cost-effectiveness thus becomes evident when compared to alternatives like Orcam glasses (priced at approximately EUR 6000 per pair) or mobile applications that necessitate a smartphone and a long-term subscription.
# Experimental Damage Identification of a Model Reticulated Shell
## 4 Experimental Results  
From the impact experiments on the single layer shell model, we obtained the acceleration responses of six nodes under different damage conditions. The sampling frequency was 500 Hz and the recording duration was 20 s. The 10,000 data points from the 5 th second to the 25 th second were chosen. In order to check the robustness of the method, 3% noise was added to the acceleration signal to build the AR model. Taking GK7, GK8, GK9, and GK10 as examples, a comparison of the first four coefficients of the constructed AR model between the undamaged condition and damaged condition is shown in Table 5. As we can see from Table 5, compared with the undamaged condition GK1, the AR model coefficients \(\varphi_{i}\) were changed under the damaged condition of GK7, GK8, GK9, and GK10, which indicates that damage has occurred.  
Damage factors \(DS_{i}\) for the damaged conditions and the undamaged condition damage were calculated according to Equation (2). Based on the training conditions in Table 4, the network sample database was constructed. The structure of the BPNNs was set as 24 input nodes, 70 hidden nodes, and 5 output nodes. The maximum training times was 500. The training accuracy was \(10^{-10}\), and the learning factor was \(\eta=0.01\). The momentum coefficient was \(\alpha=0.9\) and the transfer function was the logarithmic sigmoid function. The neural networks were able to be established based on the L-M algorithm. After training, the neural networks were able to map any nonlinear relationships between the inputs and the outputs [49]. Then the experimental results were inputted into the BNPP model to identify the damage locations. The training conditions and experiment conditions are shown in Table 4. The network training process is shown in Figure 8 and the results of the network experiment are shown in Table 6. From Table 6, we can conclude that the damage identification method based on the time series model and L-M algorithm for a long-span spatial shell structure can accurately identify the damage locations. It can be proven that this method is accurate, feasible, and has a certain degree of anti-noise ability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of the Nodes**} & \multicolumn{2}{c}{**Undamaged Condition**} & \multicolumn{4}{c}{**Damaged Condition**} \\ \cline{2-7}  & & **GK1** & **GK7** & **GK8** & **GK9** & **GK10** \\ \hline \multirow{4}{*}{Node 9} & \(\varphi_{1}\) & \(-3.08\) & \(-3.165\) & \(-3.496\) & \(-3.708\) & \(-3.209\) \\  & \(\varphi_{2}\) & \(5.627\) & \(5.403\) & \(6.802\) & \(7.637\)
# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 3 Multi-Layer Feature Fusion-Based Community Evolution Prediction
### Community Features  
Similar to other community evolution prediction algorithms, MCEP also needs to construct a training set by calculating related features. This section will introduce the features used in MCEP.  
The features used in MCEP can be divided into three types: overall community features, node features, and community mesoscopic features. Compared with the first two features, the calculation of community mesoscopic features is more complicated. We will introduce it in detail in Section 3.3. This section mainly introduces the overall community features and node features used in MCEP. It is worth pointing out that because different networks have different properties, finding a suitable feature set to make it effective for all networks is difficult. The experimental part will also illustrate this point. The features listed below are just some of the general features. In practical applications, the user should make a choice based on specific conditions.  
The overall community features measure the community from an overall perspective. This measurement mainly reflects the features of the community at the macro level. The overall community features used by MCEP are community size, density, and cohesion. The community size measures the number of nodes in the community. Both the density and the cohesion are measures of the closeness of the connections within the community. The density of the community pays more attention to the connections within the community. The cohesion considers the number of internal connections while also considering the connections outside the community.  
The overall community features are macroscopic reflections of the community features. In addition to the macro features, the community also has its micro features. Micro features refer to the features of nodes in the community. To obtain the micro features, MCEP calculates the features of each node in the community and then calculates its average value. The average value is the micro features of the community. The node features used in MCEP are degree, clustering coefficient [18], betweenness centrality [19], closeness centrality, and eigenvector centrality [20]. Among them, clustering coefficient and closeness centrality mainly measure the tightness of connections around nodes. Centrality and eigenvector centrality mainly measure the importance of nodes in the network.  
The relevant calculation formulas and symbol meanings of the above features are given in Table 2.
## 3 Results and Discussion
### Shrinkage Results and Analysis
Figure 17: Shrinkage of the samples before and after washing. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Knit Macrostructure** & **Sample 1** & **Sample 2** & **Sample 3** & **Sample 4** & **Sample 5** \\ \hline Shrinkage Length, \% & 26.170 & 4.76 & \(-6.52\) & \(-7.29\) & 11.11 \\ \hline Shrinkage Width, \% & 36.25 & \(-6.28\) & \(-7.50\) & 6.25 & 10.005 \\ \hline Area Shrinkage, \% & 30.78 & \(-1.22\) & \(-14.51\) & \(-0.58\) & 20.00 \\ \hline \end{tabular}
\end{table} 
Table 8: Shrinkage of the samples. The sample expands in length and shrinks in width, which can distort the overall proportions of the design. This is particularly relevant for structured motifs such as text, where geometric fidelity is critical. In applications such as scarves, where visual and symmetrical form are important, this asymmetrical change may disrupt the proportions of the text motif, lead to distortion of the letters, and finally render the product visually unsatisfactory. Thus, area stability does not always equate to shape stability--a key distinction in textile design. Sample 5 presents a significant (\(\text{Shav}=20.0\%\)) but symmetric shrinkage. The deformation is balanced between length and width (\(11.11\%\) and \(10.00\%\), respectively), which helps preserve the original shape, even as the overall size is reduced. This makes Sample 5 suitable for products where proportional scaling is acceptable (e.g., scarves, panels), but dimensional predictability is still required. Area shrinkage is not just a summary metric--it provides meaningful insight into how evenly and predictably a structure responds to washing. Some samples may lose area but maintain shape, while others retain area but deform visibly. For intelligent textile design, both numerical shrinkage and deformation symmetry must be considered in the evaluation of post-production behaviour.
## 4 Results
### Comparison of Algorithm Performance
#### 4.3.2 Data Rate Comparison  
In remote area grid scenarios utilizing satellite communications for data transmission, inter-satellite handovers must prioritize the user data rate. This emphasis is crucial, as the data rate directly impacts the user's quality of service (QoS), which is essential for ensuring communication reliability, power grid stability, and the accurate, timely transmission of monitoring and control commands. Furthermore, higher data rates minimize the impact of handover, prevent data rate fluctuations from causing disruptions to grid operations or poor decision making, and avoid wasted resources.  
We compared the data rates of the GNN+DQN-based handover strategy proposed in this study with other strategies. As shown in Figure 10, the pink squares represent the average value of multiple experiments, the red stars represent abnormal experimental values. The maximum service time-based strategy exhibits the lowest user data rate. This is due to the strategy prioritizing satellites with the longest service times to minimize handover, resulting in the service time of satellites to be too long, when the channel conditions will be poor and unfavorable for communication. Our proposed strategy's data rate is close to that of the maximum elevation angle-based strategy and exceeds the data rate of the DQN-based strategy by approximately 0.7 Mbps. This is because we start from the perspective of global optimization and focus on the impact of user data rate, so that we can better ensure the stable operation of the power grid in remote areas and efficient data communication. Additionally, the maximum elevation angle-based strategy achieves the highest data rate because it consistently selects the satellite closest to the ground power terminals, and the main loss in the signaling process of our design is the free-space propagation loss. More realistic channel conditions will be designed later.  
Figure 9: Comparison chart of satellite handover frequency.
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 2 Materials and Methods
### Data Collection  
#### 2.2.1 UAV Imagery Collection  
A multi-rotor UAV, Phantom 4 (DJI Co., Shenzhen, China) was used for data collection, as shown in Figure 2. Phantom 4 is able to fly autonomously or by a remote controller with an integration of a GPS receiver. The technical characteristics of the UAV information are presented in Table 1. Its onboard camera is a standard Red-Green-Blue (RGB) camera, and the captured imagery is 4000 \(\times\) 3000 pixels in size. The UAV imagery of the HLB and the AS fields was collected at the height of 80 m, with the spatial resolution of 3.4 cm.