esa-sceva/satcom-chunk-collection huggingface dataset:
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 6 Dataset Analysis
### Frequent 3-Grams  
Table 13 lists the most frequent 3-grams in the therapist utterances of each behaviour and in the client utterances of each talk type. It is clear from the table that an MI-adherent therapist tends to use \"it sounds like\" to initiate a reflection--as is recommended by MI guidelines--more often than a non-adherent counsellor. Otherwise, however, the frequent 3-grams reveal little about the characteristics of utterances of different types or MI qualities. This suggests that utterance-level semantic differences are more nuanced and contextualised.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Therapist**} & \multicolumn{2}{c}{**Client**} \\ \hline \multicolumn{2}{c}{**High-Quality MI**} & \multicolumn{2}{c}{**Low-Quality MI**} & \multicolumn{2}{c}{**High-Quality MI**} & \multicolumn{2}{c}{**Low-Quality MI**} \\ \hline \multirow{4}{*}{Reflection} & “it sounds like” (78) & “re gon na” (4) & & “Id do n’t” (188) & “I do n’t” (8) \\  & “sound like you” (56) & “you do n’t” (3) & & “do n’t know” (68) & “I guess I” (6) \\  & “a little bit” (51) & “you’ re here” (3) & Change Talk & “I’ m’ not “ (42) & “I think I” (5) \\  & “you do n’t” (43) & “you re gon” (30) & & “do n’t want” (30) & “do n’t know” (4) \\  & “a lot of” (39) & “you ’ve already” (2) & & “I think I” (30) & “I-I guess I” (4) \\ \hline \multirow{4}{*}{Question} & “do you think” (91) & “do you think” (11) & & “I do n’t” (261) & “I do n’t” (27) \\  & “a little bit” (62) & “you think you” (6) & & “do n’t know” (142) & “I m’ not” (7) \\  & “time a little? (35) & “a lot of” (5) & Neutral Talk & “I’ m’ not “ (53) & “do n’t know” (7) \\  & “little bit about” (33) & “that you re” (5) & & “do n’t really “ (47) & “I’ve been” (6) \\  & “I’m wondering” (30) & “you “re not” (5) & & “Id not n’t” (27) & “I have n’t” (5) \\ \hline \multirow{4}{*}{Input} & “a lot of” (32) & “a lot of” (15) & & “Id do n’t” (135) & “Id do n’t” (44) \\  & “a little bit” (27) & “you need to “ (11) & & “do n’t know” (57) & “I’m not” (8) \\  & “one of the” (16) & “that you re” (9) & & “I’ m not” (28) & “do n’t know” (5) \\  & “that you re” (13) & “s gon na” (6) & & “it’ s not “ (23) & “It’ s just” (5) \\  & “you do n’ be” (13) & “s not you “ (7) & & “do n really” (23) & “I just need” (4) \\ \hline \multirow{4}{*}{Other} & “for coming in” (12) & “that’ s certainly” (2) & & \\  & “that you re” (8) & “so it ’s” (2) & & \\  & “a little bit bit” (8) & “you ’ re not” (2) & & \\  & “coming in today” (7) & “you re still” (2) & & \\  & “I do n’t” (7) & “be able to “ (2) & & \\ \hline \end{tabular}
\end{table}
Table 13: Most frequent 3-grams of (1) therapist utterances of different (main) behaviours and (2) client utterances of different talk types, in high- and low-quality MI.
## 4 Experiments and Results
### Results  
#### 4.4.1 Performance Evaluation of Different Backbone Networks  
This paper compared multiple backbone models to attain the best classification performance. Due to the extensive and difficult lesions' distribution in the fundus images, we prioritized Inception-ResNet-v2 [20]. With its residual connections, this hybrid architecture solves the gradient vanishing problem in deep networks and performs very well at capturing the features at various sizes. This preserves training stability for deeper networks. This paper also compared VGG16 [20], Inception-v3 [21], ResNet101 [22], and EfficientNet-B4 [23], four additional high-performing models, to strengthen the persuasiveness of our findings. The best results were obtained when using InceptionResnetV2 as the backbone network, as shown in Table 3, with a 93% ACC, 82.13% Final_score, 83.74% F1_score, 67.52% \(\kappa\), 7% HL, and 95.12% AUC. Among the models compared, Inception-v3 and ResNet101 had their strengths, with ACCs of 92.28% and 90.70%, respectively. However, they slightly underperformed compared to the composite InceptionResNetV2. EfficientNet-B4 focuses on computational efficiency and resource utilization, which tends to sacrifice some feature extraction capabilities in complex tasks, resulting in poorer performance. VGG16 lacks an explicit multiscale feature extraction mechanism and residual connections, leading to relatively inferior results.  
Table 3 also illustrates the impact of the preprocessing module on experimental outcomes. The results demonstrated that incorporating the preprocessing module improved all the metrics by 1-3%. This indicated that addressing the input image distortion could significantly enhance the model's feature recognition capability. Furthermore, by significantly augmenting the diversity of image data and improving the quality of input images, the preprocessing module effectively reduced the risk of model overfitting.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{4}{c}{**No Preprocessing**} & \multicolumn{4}{c}{**Preprocessing**} \\ \cline{2-13}  & **ACC** & **AUC** & **F1\_Score** & **Final\_Score** & \(\kappa\) & **HL** & **ACC** & **AUC** & **F1\_Score** & **Final\_Score** & \(\kappa\) & **HL** \\ \hline Vgg16 [20] & 88.15 & 85.66 & 73.38 & 68.60 & 46.77 & 11.85 & 89.97 & 91.42 & 74.86 & 72.08 & 49.96 & 10.03 \\ Inception-v3 [21] & 90.42 & 90.68 & 77.44 & 74.35 & 54.94 & 9.58 & 92.28 & 94.62 & 82.49 & 80.70 & 65.00 & 7.72 \\ Resnet101 [22] & 89.35 & 90.45 & 71.99 & 68.95 & 44.42 & 10.65 & 90.70 & 93.52 & 78.02 & 75.88 & 56.11 & 9.30 \\ EfficientNet-B4 [23] & 88.58 & 89.39 & 65.47 & 62.43 & 32.43 & 11.42 & 89.50 & 91.46 & 70.32 & 67.75 & 41.48 & 10.50 \\ InceptionResnetV2 [24] & 90.95 & 92.69 & 77.52 & 85.15 & 55.23 & 9.05 & 93.00 & 95.12 & 83.74 & 82.13 & 67.52 & 7.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of comparative experiments of the backbone model.  
Figure 7: Fundus image preprocessing. The images in (**a–c**) are the original, cropped, and uncropped directly trained images, respectively.
###### Abstract  
Deep learning-based Automatic Modulation Classification (AMC) has achieved excellent classification accuracy. However, most deep learning-based AMC methods have an inherent drawback. They exhibit a strong dependency on massive labeled samples, which is precisely difficult to obtain in real-world scenarios. This paper presents a few-shot AMC approach that integrates signal transformation and meta-learning. The former enhances class separability, while the latter addresses challenges posed by limited sample sizes. The results of simulation experiments conducted on the RadioML.2018.01a dataset demonstrate that the proposed technique achieves a classification accuracy of 74.21% when using one sample per class and increases to 82.27% when using five samples. The few-shot AMC efficacy of this proposed approach exhibits an outperformance over the classical deep learning methods.  
automatic modulation classification; few-shot learning; meta-learning; singular spectrum analysis +
Footnote †: journal: Applied Sciences
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 2 Materials and Methods
### Epipolar Geometry-Based Extrinsic Parameters Determination
#### 2.3.2 Five-Point Algorithm-Based Essential Matrix Estimation  
The projective geometry between the left and right views of a stereo camera system is the epipolar geometry. The epipolar geometry can be represented by the essential matrix:  
\[\mathbf{q^{T}Ep}=0 \tag{7}\]  
where \(\mathbf{E}\) is the essential matrix connecting the pose relations between the two cameras through points in space, \(\mathbf{p}\) and \(\mathbf{q}\) are the normalized image coordinates of the corresponding points in the left and right images of the stereo camera pair matched by DIC. For a pair of cameras whose intrinsic parameters have been calibrated, the essential matrix \(\mathbf{E}\) takes the form  
\[\mathbf{E}=\left[\mathbf{t}\right]_{\times}\mathbf{R} \tag{8}\]  
where \(\left[\cdot\right]_{\times}\) is defined as a skew-symmetric matrix of 3-vector, while \(\mathbf{R}\) and \(\mathbf{t}\) represent the rotation matrix and translation vector between the left and right cameras, respectively.  
Since the essential matrix has two equal and non-zero singular values, this leads to the following important three-dimensional constraints on the essential matrix:  
\[2\mathbf{EE^{T}E}=\text{trace}\Big{(}\mathbf{EE^{T}}\Big{)}\mathbf{E}. \tag{9}\]  
The five-point algorithm is used to compute the reliable essential matrix through the corresponding points. For a detailed implementation process one can refer to the classical work in the literature [41] and easy-to-use, effective open-source codes [42]. It is important to note that due to the scale equivalence of the essential matrix, it can only reflect the relative position relationship independent of the specific scale and cannot describe the absolute position relationship between two cameras.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 6 Results and Analysis
### Computational Time and Memory of ML Models  
Figure 19A,B reveal that Polynomial Regression, Extra Trees, Decision Tree Regression (DTR), and AdaBoost required over 250 s for wind speed (WSF) and wind power forecasting (WPF) computations, respectively. In contrast, Figure 19C shows that XGBoost, Random Forest Regression (RFR), and DNNs had the lowest computational times, with XGBoost being the fastest, completing data fitting in just 3 s. Regarding memory usage, Figure 19D illustrates that XGBoost, DNNs, Regression, Stochastic Gradient Boosting (SGB), AdaBoost, DTR, RFR, and SVM used less than 200 MB of memory.  
Figure 18: (**A**) Performance of Extra Tree models for 36 h ahead of WSF and (**B**) performance of bagging for 36 h ahead of WPF.
## 4 Conclusions  
To conclude, we successfully obtained low-cost PZT/Terfenol-D magnetoelectric composites from commercially available materials. The composites utilize a product property of two ferroic phases (magnetostrictive Terfenol-D and piezoelectric PZT). Of particular note is the use of P1 MFC piezoelectric sheets as a substitute for chemically synthesized PZT in our study. The final outcome is the ability to measure the voltage signal as a response to magnetic stimulation. The room-temperature ME coupling was examined via the lock-in technique and confirmed in our samples. Resonant peaks were observed for resonance states at different frequencies. Instead of detecting single or separated peaks as shown in the literature, we observed multiple resonant peaks that are grouped together in a band-like fashion in the kHz range. Based on U\({}_{\text{OUT}}\)(H\({}_{\text{DC}}\)) and U\({}_{\text{OUT}}\)(f) characteristics, it can be seen that the first resonant peak (close to 30 kHz) is clearly separated from the group and has a different origin. Its orientation is DC field-dependent. Furthermore, we can observe that the strongest ME effect occurs for a certain magnetic DC bias value and then becomes weaker as the field continues to increase. This behavior can be connected with the interplay of two effects: (i) saturation of the Terfenol-D magnetostrictive properties, and (ii) increasing damping of mechanical vibrations due to stress. Additionally, the number, frequency, and height of resonant peaks observed in our study depend on the sample mass and the number of layers. The resonant frequencies also undergo shifting towards higher values with respect to the rising magnetic bias for each composite. This effect can be ascribed to the growth of stress caused by magnetostriction. Our study shows that the functionality of the commercial P1 MFC piezoelectric sheet can be extended by joining it with a magnetostrictive material. As a result, the sheet becomes susceptible to two kinds of stimulus, i.e., strain and magnetic field. Such composite configuration can be of interest in the context of energy harvesting and multifunctional sensors.  
Conceptualization, J.G. and T.P.; methodology, J.G., R.M. and T.P.; software, J.G.; validation, T.P. and R.M.; formal analysis, J.G. and T.P.; investigation, J.G. and T.P.; resources, J.G. and T.P.; data curation, J.G.; writing--original draft preparation, J.G.; writing--review and editing, T.P. and R.M.; visualization, J.G.; supervision, T.P.; project administration, T.P.; funding acquisition, T.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 2 Theoretical Framework and Methods
### Subway Station Flood Resilience System
#### 2.1.3 Resilience Framework for Flooding at Subway Stations
The resilience analysis framework for subway station flood disasters is shown in Figure 1. The figure was created by the author. Subway stations serve as significant hubs characterized by extensive underground structures and equipment, as well as sites of high-density, dynamic activities and interactions. Flood disasters represent a complex interplay between the natural water cycle and human societal factors. Consequently, the analysis of flood resilience emphasizes the adaptive responses of disaster-affected entities to the evolving nature of such disasters. Achieving resilience in the face of flood disasters necessitates not only robust engineering systems, such as buildings and infrastructure, but also the collaborative support of various interconnected systems, including economic, social, and informational frameworks.
## 2 CFD Analysis
### Details of CFD Analysis  
Stationary Reynolds-averaged Navier-Stokes (RANS) simulation was performed using a k-\(\omega\) shear stress transport (SST) turbulence model [29]. The wall functions were used to do so-called high Re-type flow simulation. In the project, the cruise condition was defined as 240 knots (134 m/s) flow velocity and the standard atmosphere at an altitude of 25,000 feet (air density \(\rho\) = 0.549 kg/m\({}^{3}\)). The landing condition was defined as 140 knots (72 m/s) flow velocity and the sea-level atmosphere (\(\rho\) = 1.23 kg/m\({}^{3}\)). The Reynolds number per 1 m amounts to 1.42 \(\times\) 10\({}^{7}\) and 1.47 \(\times\) 10\({}^{7}\) for cruise and landing conditions, respectively.  
The 2D profiles in Figure 2 were first extruded by 1 m along the span-wise direction. Each of them was then placed in a computational domain of 80 m \(\times\) 80 m \(\times\) 1 m. The periodic boundary condition is imposed on both ends in order to do 3D analysis by assuming infinite span length. In this domain, a hexahedral-dominant mesh was generated with 9-step refinement towards the aerofoil. The element size was approximately reduced from 1 m to 2 mm. The entire surface of the aerofoil was covered with 3 boundary layers. The total number of cells was about 1.7 million. The y+ on the aerofoil was confirmed to be between 30 and 300 to let the wall functions properly work. The generated 3D CFD mesh for profile S5 HL is shown in various view angles in Figure 3. The analysis was carried out using the incompressible stationary flow solver simpleFoam provided by OpenFOAM.
## 8 Discussion
### Hardware-Related Considerations
#### 8.2.2 Assessing the Timeliness of the Sampling Process  
In order to discuss the feasibility of the proposed sensing scheme with respect to the employed COTS hardware platform, we monitor and analyze the delay generated by the various operations needed to perform the in-node channel sensing. In Figure 16, we show the partial duration of the tasks implemented in TelosB motes in order to acquire and store the RSSI samples. Two of the most demanding tasks in terms of delay are the request for accessing and releasing the I/O resources, requiring 212 us and 74 us, respectively. In addition, the tasks of setting the CSn (chip select) pin for reading the CC2420 RSSI register lasts 12 us, while the actual operation of sampling the value of RSSI register takes 112 us to be completed.  
The total delay for collecting and storing one sample is then 429 us, while we use a sampling frequency of 2 kHz, corresponding to a 500 us sampling period. With the current approach, the sampling frequency could be theoretically pushed up to 4.6 kHz if the CC2420 resources are not released until the end of the whole sampling process. In other approaches (e.g., [26]), the implementation for the channel sensing in Contiki OS allows for a sampling rate up to 8.13 kHz. Nevertheless, a higher sampling frequency means more data to process, as well as a more stressful and energy-consuming sampling process. Therefore, in this work, we have employed a more relaxed sampling timing, while we rely on the approach of an advanced classification algorithm, in order to maintain high classification performance while ensuring a lower memory footprint.
## 3 Nutritional, Anti-Nutritional and Health Beneficial Constituents of Lima Beans
### Fermentation  
Fermentation is a traditional food processing technique that improves the nutritional content of foods through the biosynthesis of essential amino acids, and vitamins, and through antinutrient degradation [63; 110; 111]. A decline in the protein content of lima bean flour, however, was reported by [PERSON] et al. [63], with authors attributing this to an increase in the catabolism of the protein matrix by microorganisms during fermentation. The same authors ascribed fat reduction during the fermentation of the bean to the hydrolysis of lipids by lipases, which are produced by fermenting microorganisms [63]. The fermentation of lima beans also has a decreasing effect on iron, copper, calcium and phosphorus, possibly linked to the reduction in phytate, tannins and HCN [54; 63].  
In a study, ground lima beans were fermented with starter cultures (_Rhizopus stolonifera_, _Saccharomyces cerevisiae_ and _Aspergillus fumigatus_), and their nutritional and anti-nutritional compositions were investigated [84]. Significant reductions were seen in all the anti-nutritional factors (oxalate, saponin, phytic acid, tannin, and cyanide) of the starter culture fermented samples over the naturally fermented samples [84]. Significant reductions in anti-nutrients in the starter culture-fermented samples indicate that the fungi _Rhizopus stolonifera_, _Saccharomyces cerevisiae_ and _Aspergillus fumigatus_ were liable for these reductions [84]. Variations in the mineral contents of the starter cultures and naturally fermented samples were observed, but these were significantly lower than in the raw samples. The starter culture-fermented samples contained high fat, ash and protein contents, and low carbohydrate and crude fiber, over the naturally fermented samples [84].  
The reported decrease in carbohydrate and crude fiber contents of starter culture-fermented samples over the naturally fermented samples could be attributed to the secretion of extracellular enzymes, for example, hemicellulases ligninases and cellulases by the fungi [112] These fungi are able to hydrolyze carbohydrates and crude fiber into simple sugars, while the organisms could be used as a carbon source to convert them to other macromolecules such as fats and proteins [113]. The substantial quantity of ash content in the starter culture fermented sample over the naturally fermented sample could indicate that these fungi might have played unique roles through hydrolytic or biosynthetic mechanisms to enhance the inorganic mineral elements in the samples [84].
## 8 Cache Size and Traffic  
The second most important parameter of the performance evaluation of OppNets, after mobility, is the amount of messages in the network. Usually, this parameter is driven by four independent other parameters, i.e., cache size, traffic generation, time-to-live of messages and simulation time. However, the explored studies do not homogeneously explore those parameters in the same way, thus an analysis is not possible. Instead, we analyze the network-wide traffic in number of messages per hour against the cache size (in number of messages). The results are provided in Figure 8. Not all studies provide  
Figure 6: Properties of standard mobility models (**left**); and extension opportunities for trace-based models (**right**).  
Figure 7: Used mobility models in the here explored studies. There is a slight trend towards using more than two traces or at least one trace and one mobility model.  
enough details, thus only a subset are depicted in the graph. In the same graph, we have marked three different areas: high, medium and low cache pressure.  
In low cache pressure scenarios, the generated traffic will never exceed the cache size and thus no buffer drops will be observed (unless time-to-live of messages is used). In medium cache pressure scenarios, buffer drops will occur, but rarely and will likely not affect the work of the protocol significantly. In high cache pressure scenarios, however, buffer drops will dominate.  
Ideally, any protocol should be evaluated in all three scenarios to cover all possibilities. The protocols which get close to this goal are ORWAR and MaxProp. In Figure 8, we have also marked newer protocols (since 2013) in grey. We can observe that many of these newer studies have considered high to medium cache pressure scenarios. Older protocols often do not provide enough details about their traffic generation patterns, which is the reason why they are missing from this graph (few white boxes).  
The most often used traffic generation frequency is by far 25-35 s. This comes from the standard settings of the ONE simulator. Thus, it is highly important how simulators are configured by default, since many researchers use these parameters under the assumption that they are somehow more \"correct\". We consider this risky, as often such default settings are chosen either at random or are historically motivated (old settings from other scenarios). The traffic pattern and frequency depends highly on the scenario used, ranging from few messages per day for human-generated messages to hundreds of messages per second for sensor-generated ones.  
**Take home message**: _Cache sizes and traffic have not been considered very seriously in existing studies, taking default values. However, the parameter settings need to be well adjusted towards the application scenario taken into account._  
Figure 8: Cache sizes against network-wide traffic. Not all studies provide sufficient details, thus only a subset is depicted.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 4 Experiments and Analysis of Results
### Cross-Validation  
In our work, we apply repeated K-Fold cross-validation, employing the observations from available radar images (2980 objects). The experimental design contains 10 K-Folds and 10 iterations for the tuned model, totaling 100 experiments. In each iteration, the position of the elements is ordered randomly. This approach allows the largest amount of data to be used for training and reduces the risk of the trained models showing biases [55; 56; 57]. This scheme was used in our previous work [58]. Finally, the model performance is evaluated using metrics with the untrained image set.