esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurement Results Analysis  
A prototype was fabricated, as displayed in Figure 11a. The antenna was tested using an Agilent Vector Network Analyzer (PNA-X) and an anechoic chamber developed by Electronic Forty, as displayed in Figure 11b,c. The simulated and measured S11 results are illustrated in Figure 12a, and the simulated an measured AR results are displayed in Figure 12b. The impedance bandwidths of the two bands in the biogel are 0.37 GHz (1.15-1.52 GHz) and 0.41 GHz (2.20-2.61 GHz), respectively, and the AR at 1.4 GHz is 0.38 GHz (1.32-1.71 GHz). In the freshly chopped pork tissue, the impedance bandwidths of the two bands are 0.35 GHz (1.17-1.52 GHz) and 0.40 GHz (2.20-2.60 GHz), and the AR bandwidth is 0.4 GHz (1.33-1.73 GHz) in the 1.4 GHz band. The measured data are in good agreement with the simulation data, but there are some deviations in the high-frequency band. One cause is that although the test environment is very close to the simulation environment, they are not the same. Another reason is that the higher the frequency, the more significant the errors introduced by RF connection lines and connectors are.  
Figure 11: Antenna prototype and experimental overview. (**a**) Prototype, (**b**) the antenna in biogel, and (**c**) the antenna in minced pork.  
Figure 12: Comparison of simulated and measured S11 and AR values under different conditions. (**a**) S11 and (**b**) AR.  
Figure 13 shows the measured and simulated radiation patterns at the two operating frequency points. The measured results agree with the simulated results in that the antenna's magnetic field has quasi-omnidirectional radiation characteristics field at the two operating frequency points.
## Discussion
### Challenges and Weaknesses of Existing Predictive Models  
In our survey quest, we were enlightened about several challenges and underexplored areas prevailing in the existing learning outcomes prediction models. Future studies implementing machine learning models to prognosticate the attainment of student learning outcomes should pay close attention to the research challenges below and take necessary actions to mitigate them.
* Research challenge one: The prediction of academic performance of student cohorts to assist in the automation of course and program-level outcomes assessment.
* Research challenge two: The use and availability of multiple datasets from various disciplines to strengthen the validity of the predictive model. The datasets should comprise a large sample size of students to draw any meaningful conclusions.
* Research challenge three: The inspection of the effects of different features on the attainment of student outcomes to contribute to academic corrective interventions in higher education, i.e., the shift from predictive analytics to explanatory analytics.
* Research challenge four: The use of multiple performance evaluation metrics to assess the quality of the learning outcomes predictions.
* Research challenge five: The lack of unsupervised learning techniques devised to forecast student attainment of the learning outcomes.
* Research challenge six: The application of automated machine learning (i.e., AutoML) to the problem of student outcomes prediction was rarely conducted, except in [84]. Addressing this challenge would enable the development of ML models that automate the machine learning pipeline tasks, making the tasks of featurization, classification,and forecasting efficient and accessible to the non-technical audience (e.g., education leaders and course instructors) in different disciplines.
## 3 System Model Design
### Calculation of Node Similarity
#### 3.2.2 Social Similarity Calculation  
As described in the previous sections, we have already analyzed the degree of mobile similarity between nodes. In this section, we will assess the social similarity between nodes by exploring the correlations of social attributes of nodes.  
Firstly, we define the following Equation (7) as a feature vector of the node \(u\), where \(A_{u},B_{u},C_{u},D_{u}\ldots N_{u}\) represents different values of social attributes of the node \(u\). In fact, \(A_{u},B_{u},C_{u},D_{u}\ldots N_{u}\) are different sub-vectors corresponding to different social attributes of the node \(u\). To obtain detailed comparisons of the social similarity, we also define different feature words for eachsub-vector in the feature vector, such as the interest sub-vector \(A_{u}=(a_{1},a_{2},a_{3},a_{4})\), where \(a_{1},a_{2},a_{3},a_{4}\) respectively represent different feature words (such as basketball, game, music or film). If the node \(u\) possesses feature words in the sub-vector, the corresponding value is set to 1; otherwise it is set to 0. For instance, if the node \(u\) likes basketball and film, its sub-vector of interest would be \(A_{u}=(1,0,0,1)\). Moreover, for the same sub-vector between a pair of nodes \(u\) and \(v\), we use the following formula  
based on its historical experience, the corrected weight value \(w_{j}^{*}\) can be computed by the following Equation (14).  
\[w_{j}=\frac{d_{j}}{\sum\limits_{j=1}^{n}d_{j}} \tag{13}\]  
\[w_{j}^{*}=\frac{\lambda_{j}w_{j}}{\sum\limits_{j=1}^{n}\lambda_{j}w_{j}} \tag{14}\]  
In conclusion, the purpose of exploring the social similarity is to divide nodes in the network into different communities, and the nodes belonging to the same community may have more opportunities to transmit data packages to each other, which signifies that the efficiency of data transmission between these nodes can be improved greatly.
# Design of a Phase Calibrator to Evaluate Non-Matched Microphones for Intensity Probes
## 4 Further Prototype Implementation and Discussion  
Given the results of the measurements performed on the prototypes, configuration A with F-F microphones was the one of choice. The calibrator was scaled into a smaller, more portable version (3 cm diameter) (Figure 37). Both the original and scaled A F-F configurations exhibited good behavior when compared to a state-of-the-art acoustic coupler.  
Figure 35: Configuration A F-F—phase difference for calibrated measurement.  
Figure 36: Configuration A F-F—phase difference for calibrated measurement.  
After proceeding with the calibration, the device showed very good consistency up to 4000 Hz. The result in the [60-4000] Hz frequency range was very satisfying, given that the calibrated phase mismatch is contained in a [\(-\)0.02 0.06] deg range. Starting from f > 4000 Hz, the peak phase mismatch is set at 2.5 deg are reported in Table 9 and Figures 38 and 39.  
Both the full-size and the scaled configurations showed good results when compared to the acoustic coupler in a [60-7000] Hz frequency range. Clearly, the better results were obtained in the [1000-4000] Hz frequency range, which is the one targeted in the numerical model as reported in Figure 40.  
Figure 40: Comparison between the phase difference for calibrated measurement for prototypes and reference instruments in the [60-7000] Hz frequency range.  
Figure 39: Configuration A F-F, scaled—phase difference for calibrated measurement.  
In the [60-1000] Hz range, both the calibrators showed good results when compared to the reference instrument (Figure 41).  
In the [1000-4000] Hz range, the performances were still very competitive regarding the reference instrument. The phase mismatch of the scaled model suffered from bias error (Figure 42).  
Figure 41: Comparison between the phase difference for calibrated measurement for prototypes and reference instruments in the [60-1000] Hz frequency range.  
Figure 42: Comparison between the phase difference for calibrated measurement for prototypes and reference instruments in the [1000–4000] Hz frequency range.  
In the [4000-7000] Hz frequency range, both the calibrators experienced poor performances when compared to the acoustic coupler (Figure 43).
# An Equivalent Non-Uniform Beam-Like Model for Dynamic Analysis of Multi-Storey Irregular Buildings
## 2 The Beam-Like Model  
The proposed model consists of a 3D shear-torsional cantilever beam able to reproduce the dynamic behaviour of multi-storey buildings. In particular, when the building columns cross sections decrease along the height of the building, as is usual in structures that were designed to resist gravity  
Figure 1: Aerial view of (**a**) residential blocks and (**b**) their beam-like idealization.  
loading only, the equivalent beam has a non-uniform virtual cross section. Furthermore, planar irregularities due to the unsymmetrical distribution of the columns or shear walls can be taken into account. This kind of irregularity must be properly considered since it induces eccentricity between the centers of stiffness and mass, thus causing unneglectable torsional effects. Each portion of the beam represents a building inter-storey, whose shear and torsional stiffness are initially approximately evaluated according to a geometric reference model. Once the beam-like model of the entire building is defined, it can be calibrated on a reduced number of modal properties that could be identified by means of ambient vibration techniques. It is worth noting that, although only elastic, the proposed beam-like model could also be used for defining an operativity limit state index based on inter-storey drift [25; 26].
## 3 Results and Discussion
### Color Analysis  
The change in color of crumb and crust of breads fortified with increasing levels of GPP is summarized in Table 6. In the control bread (GP0), the crust color was darker than the crumb due to different Maillard and caramelization reactions [60]. The GPP inclusion reversed this trend because of the GPP-darkened color parameters. According to [PERSON] et al. [24] and [PERSON] et al. [50], GPP inclusion caused a significant reduction in brightness (L*) in samples GP5 and GP10, both in the crumb and the crust. As expected, an increase in the a* parameter was evident according to the GPP increment in the bread crumb, while an opposite trend was observed in the crust. This could be due to the fact that anthocyanins, responsible for the red GPP pigmentation, are less degraded in the crumb since it undergoes lower heat treatment and maintains a higher moisture level than the crust [61]. Finally, a significant decrease in the b* parameter was observed with the progressive supplement of GPP in both crumb and crust. The total color difference (\(\Delta\)E) is generally used to describe the color variation. The \(\Delta\)E values revealed that GP5 and GP10 led to high color variation as the concentration of added GPP increased. This trend was observed both in the crumb and the crust but was less pronounced in the latter.
## Appendix D Explanation of Layer Shapes for SVD Convolutional Layer  
In this section, we provide an explanation of the tensor shapes of SVD convolutional layers--compressed layers resulting from the SVD decomposition of a convolutional layer's weight tensor.  
SVD decomposition is applied only to convolutional layers with a kernel size of \(1\times 1\). Let the weight tensor of the original convolutional layer be \(W\in\mathbb{R}^{T\times S\times 1\times 1}\).  
The tensor \(W\) can be represented using the SVD decomposition with rank \(R\) as follows:  
\[W(t,s,1,1)=\sum_{r=1}^{R}w_{1}(t,r,1,1)w_{2}(r,s,1,1)\]  
where \(w_{1}\in\mathbb{R}^{S\times R}\) and \(w_{2}\in\mathbb{R}^{R\times T}\). The convolution operation of the layer can be expressed as follows:  
\[P(n,t,x,y)=\] \[=\sum_{s=1}^{S}\sum_{r=1}^{R}w_{1}(t,r,1,1)w_{2}(r,s,1,1)Q(n,s,x,y)\] \[=\sum_{r=1}^{R}w_{1}(t,r,1,1)\Bigg{(}\sum_{s=1}^{S}w_{2}(r,s,1,1) Q(n,s,x,y)\Bigg{)}\] \[=\sum_{r=1}^{R}w_{1}(t,r,1,1)\hat{Q}(n,r,x,y)\]  
This operation can be compactly represented as  
\[P=\text{conv}(w_{1},\text{conv}(w_{2},Q,R,S,1,1),T,R,1,1)\]  
This shows that the original convolutional layer can be replaced by a sequence of two consecutive convolutional layers:  
1. With \(S\) input channels, \(R\) output channels, and a \(1\times 1\) kernel size.
2. With \(R\) input channels, \(T\) output channels, a \(1\times 1\) kernel size, and group size \(R\).
# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 1 Introduction
However, the Synthetic Aperture Radar (SAR) is a form of radar that is used to create more useful images over a target region to provide fine spatial resolution [12; 13], which can operate day and night and is unaffected by cloud cover, smoke, atmospheric water, or hydrometeors. Moreover, the signal can penetrate through foliage (wavelength dependent), and therefore can provide more complete information about the inundation state [14; 15]. SAR based flood mapping methods are usually more complex than those based on optical sensors because of the process added to mitigate the error propagated from one or more of the error sources [11; 16]. Accordingly, in the present study, we investigated the potential use of the Sentinel-1 SAR images as a powerful and complete tool for flood mapping, and as a crucial element of flood management [17]. This methodology was applied in the case of extraordinary floods that occurred during the month of April 2018 in the Ebro River due to the snow that was melting in the headwater basins. The specific goals were: (1) Analysis of the affected area and hydraulic parameters characteristic of the Ebro flood case; (2) selection of the optimal processing and correction options (orbital parameters, radiometric, noise reduction--speckle, and geometric geometric geometric) to perform an accurate and thorough treatment of Sentinel-1 SAR images, in order to optimize the detection and analysis of flooded areas; (3) application of two detection algorithm approaches (RGB--Red, Green, Blue color model - composition and calibration threshold technique) of free water surfaces to derive flood cartography from SAR images, which permits the delimitation of non-permanent water surfaces; and (4) final comparison and analysis between the flooded area maps derived from SAR images, the official orthophotography provided by the Ebro Hydrographic Confederation (CHE, acronym in Spanish), and flood risk areas by the National Flood Zone Mapping System (SNCZI acronym in Spanish) provided by the Spanish Ministry for the Ecological Transition (MITECO, acronym in Spanish).
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 2 Background and Related Work
### Available Resources  
MI conversation resources are scarce. As real-world therapy often contains sensitive topics and information, counselling dialogues are mostly privately owned or proprietary (e.g., therapy transcripts from Alexander Street [[[https://alexanderstreet.com/products/](https://alexanderstreet.com/products/)]([https://alexanderstreet.com/products/](https://alexanderstreet.com/products/)) counseling-therapy-video-library], accessed on 14 February 2023). As for resources, annotated MI corpora such as [16] have been built from sources such as wellness coaching phone calls and leveraged for tasks such as utterance-level code prediction [17] and empathy prediction [18], but they mostly remain publicly inaccessible.  
To the best of our knowledge, the only freely and publicly available MI corpus to date is [13], created based on automatic transcripts of MI videos on YouTube/Vimeo. [PERSON] et al. [13] also collected annotations with respect to reflections and questions for the corpus and conducted related analyses, but these annotations are not available at the time of writing. Moreover, considerable transcription errors from automatic captioning are present in the corpus (Section 3.2), thus limiting the quality of the dataset.
# An Incremental Local Outlier Detection Method in the Data Stream
## 2 Materials and Methods
### Incremental Outlier Detection
: \(x_{\text{del}}\) is deleted from the kNN of some objects after the insertion of \(x_{\text{new}}\), which results in the break of the shared neighborhood relationship between data in \(\text{RkNN}(x_{\text{del}})\). When only one new data as the \(x_{\text{new}}\) is inserted, the amount of \(x_{\text{del}}\) obviously equals the amount of kNN-affected data, which is \(\left|\text{RkNN}(x_{\text{new}})\right|\), as proved in Theorem 4. Then, \(\left|\text{SkNN}\right|_{\text{affected}}\) equals \(\left|\text{RkNN}(x_{\text{new}})\right|\cdot\left|\text{RkNN}(x_{\text{del}}) \right|\leq F^{2}=\Theta\big{(}k2^{2D}D\big{)}\). \(\square\)  
According to the above theoretical analysis, it is proven that the amount of affected data in the incremental update strategy for outlier detection is limited. Therefore, the asymptotic time complexity for insertion into the incremental strategy is:  
Figure 2: The influence of inserting new data to the local neighborhood. \[T_{\text{insertion}}=O(\left|\text{kNN}\right|_{\text{affected}}\cdot T_{\text{kNN}}+ \left|\text{kkNN}\right|_{\text{affected}}\cdot T_{\text{RKNN}}+\left|\text{SkNN} \right|_{\text{affected}}\cdot T_{\text{SkNN}})\]  
where \(T_{\text{kNN}}\), \(T_{\text{RKNN}}\), and \(T_{\text{SkNN}}\) are respectively the time consumptions in kNN, RkNN, and SkNN searching methods, and can be approximated by \(T_{\text{kNN}}\) = \(T_{\text{RKNN}}\) = \(T_{\text{SkNN}}\) = \(O(\log n)\) when efficient indexing structures for inserting data are used in a fixed sliding window with \(n\) width [29]. Then:  
\[T_{\text{insertion}}=O\Big{(}\Big{(}k+2F+F^{2}\Big{)}\cdot\log n\Big{)} \tag{3}\]  
When all updates to the dataset of size \(N\) are applied, the time complexity of the incremental update algorithm is \(O(N\log N)\), which is the same as the state-of-the-art methods [7; 8]. As the processes of insertion and deletion in the sliding window are opposite to each other, they have the same time complexity. Then, because of the limitation of length, no additional proof of the deletion process within sliding window is discussed here. Finally, the pseudocode of CLOF algorithm is presented in Table 1.  
In the CLOF method, if CLOF(\(x_{i}\)) is continuously greater than 1 and its outlier count is greater than or equal to the threshold \(t\) (\(1\leq t\leq n\)), \(x_{i}\) is an outlier. The basis of outlier judgment is under the consideration that data streams are dynamically changing, and that a local outlier should be significantly different from its prior and post data. Therefore, the novel method uses the prior and post (\(n-1\)) data for each data \(x_{i}\) to detect its outlierness, where the outlierness of data \(x_{i}\) (\(n\leq i\leq N-n+1\)) can be calculated \(n\) times (\(n\) is the sliding window width and \(N\) is the total amount of data in \(X\)).
## 1 Introduction
### Literature Review
#### 1.1.2 Maintenance Performance Indicators  
From a strategic perspective, companies are forced to constantly improve their capacity to add value for their clients and improve the cost effectiveness in their operations [15]. Therefore, maintenance of assets associated with large investments, which was once considered to be a \"necessary evil\", is now considered a key element to improve cost effectiveness, adding value by providing a better service to clients [16]. Consequently, it becomes crucial to measure, control, and improve the performance of maintenance, with maintenance performance measurement defined as the multidisciplinary process of measuring and justifying the value generated by maintenance investment, considering the strategic requirements of the stockholders from a global business perspective [17].  
In general, performance measurement in any organisational area is materialised through indicators enabled by several measures and metrics available in the process or organisation. According to the latter, [PERSON] [18] defines key performance indicator (KPI) as the measure of how well an organisation or individual performs operational, tactical, or strategic activities critical to the current or future success of the organisation. These indicators can be further classified into four categories [19]:  
1. Asset development;Organisation and management;
* Performance management;
* Maintenance efficiency.  
This classification prematurely points out that there are several types of indicators and that their usage depends on the decisions to be assessed or the features the need to be measures. In this regard, when the purpose of the indicators is to help evaluate and control the performance of the maintenance function, they are called maintenance performance indicators (MPI) [20]. These are applied to control; expose weaknesses; and find new ways to reduce downtime, cost, and waste; in other words, to operate more efficiently and realise maximum capacity on production lines.  
Furthermore, correct selection of MPI can ensure that performed tasks support the strategy and objectives [21] of the organisation. In fact, \"performance cannot be managed, if it cannot be measured\" [22], but in order to efficiently measure performance, it is necessary to also accurately select the most representative and influential indicators.  
Aligned with the goal of improving efficiency, some selection processes arise from the organisational vision to deliver a selection framework capable of focusing on metrics that support the main objectives and vision of the organisation. In general terms, MPIs such as availability, MTBF, or MTTR are considered the most significant [23] indicators regarding maintenance decisions in the majority of cases, emphasising the importance of accurately accounting for availability. More computational approaches consider the use of a fuzzy sets approach and genetic algorithms [24] to help optimise maintenance performance.
# An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band
## 3 System Model
### Outage Probability  
The outage probability of a randomly located UE at distance \(r\) with the tagged AP is shown as follows [28]:  
\[\begin{split} Pr_{i}^{\rho}(\lambda_{I},\lambda_{J},\beta,\alpha) &=E_{r}[Pr[SINR\leq\beta|r]]\\ &=1-E_{r}[Pr[SINR>\beta|r]]\\ &=1-\int_{r>0}Pr[\frac{p_{iu}^{t}\beta r^{-\alpha}}{\mathbf{Ic}_ {i}+\sigma^{2}}>\beta|r|f_{r}(r)dr\\ &=1-\int_{r>0}Pr[g>\beta{p_{iu}^{t}}^{-1}r^{\alpha}(\mathbf{Ic}_ {i}+\sigma^{2})|r]f_{r}(r)dr,\end{split} \tag{10}\]  
where \(\beta\) is the threshold for the received signal-to-interference-plus-noise ratio \((SINR)\). \(\mathbf{Ic}_{i}\) and \(\sigma^{2}\) are co-channel interference power set and white Gaussian noise, respectively.  
With the fact that \(g{\sim}exp(\mu)\)[31], we can obtain  
\[\begin{split} Pr[g>\beta{p_{iu}^{t}}^{-1}r^{\alpha}(\mathbf{Ic}_ {i}+\sigma^{2})|r]&=E_{\mathbf{Ic}_{i}}[Pr[g>\beta{p_{iu}^{t}}^{- 1}r^{\alpha}(\mathbf{Ic}_{i}+\sigma^{2})|r,\mathbf{Ic}_{i}]]\\ &=e^{-\mu\beta{p_{iu}^{t}}^{-1}r^{\alpha}}2_{\mathbf{Ic}_{i}}( \mu\beta{p_{iu}^{t}}^{-1}r^{\alpha})\end{split} \tag{11}\]  
and  
\[\begin{split} L_{\mathbf{Ic}_{i}}(\mu\beta{p_{iu}^{t}}^{-1}r^{ \alpha})&=E_{\mathbf{\Phi},\xi}[\prod_{i\in\mathbf{\Phi}}E_{g}[ exp(\mu{\beta{p_{iu}^{t}}^{-1}r^{\alpha}})]]\\ &=E_{\mathbf{\Phi},\xi}[\prod_{i\in\mathbf{\Phi}}\int exp(\mu{ \beta{p_{iu}^{t}}^{-1}r^{\alpha}})\mu exp(-\mu g)d_{g}]\\ &=E_{\mathbf{\Phi}}[\prod_{i\in\mathbf{\Phi}}\frac{1}{1+\beta{p_{ iu}^{t}}^{-1}r^{\alpha}v^{-\alpha}}]\\ &=exp(-2\pi\phi\int_{r}^{\infty}\frac{\beta}{\beta+(\frac{\rho}{ r})^{\alpha}}vdv)\end{split} \tag{12}\]  
with \(r^{2}\to v\) and \(\mathbf{\Phi}\) is the interference set for the downlink communication between AP \(i\) and UE \(u\).