esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Scattering in 3-D Model  
In this section, we explore the 3-D scattering effects from defects to assess the suitability of the Mindlin model for tomographic imaging applications. Figure 11 presents the subtracted scattered wavefield of the \(A_{0}\) mode at a defect with both sharp and smoothed edge thinning. The wave is excited at 0 degrees and interacts with the defects at the center of the plate, with most of the scattered wavefield observed on the opposite side of the plate. Qualitatively, both models produce similar scattering patterns; however, the scattered field in other directions is slightly more intense in the FEM model (FEM) compared to the Mindlin model (PsM). This observation aligns with previous 2-D studies that indicated weaker reflections in the Mindlin model compared to the FEM model.  
The absolute values of the reflection and transmission signals measured at points Tx and Rx, respectively, are presented in Figure 12. The signals from both the Mindlin and FEM model are normalized by the sum of their respective maximum amplitudes of transmitted and reflected signals. Although this normalization approach is not physically accurate, since the energy content also depends on the spatial width of the wave packet, it provides a reasonable basis for initial comparison and visualization of the signals. The reflected signal is longer in duration compared to the transmitted signal, consisting of a weaker initial component from a larger, smoother defect and a stronger component from a smaller, deeper defect with sharp edges. The agreement between the models is excellent, showing similar amplitudes and envelope shapes.  
Figure 10: Transmission from non-symmetric rectangular defect: (**a**) transmission coefficients from FEM model (FEM), (**b**) transmission coefficients from Mindlin model (FDM), (**c**) relative difference between the reflection coefficients.
## 5 Diagnostic Performance of VI-RADS
[13] was that a cut-off VI-RADS \(\geq\) 4 should be applied when using imaging only to predict muscle invasion and not operating TURBT prior to radical cystectomy, whilst a cut-off of VI-RADS \(\geq\) 3 would perform better in sensitivity and negative likelihood ratio, therefore decreasing the misdiagnosis of MIBC. This should be then used to evaluate patients who will receive TURBT for confirmation of muscle invasiveness via histopathology. A multicentre prospective study published by [PERSON] et al. in 2021 [23] reported a combined positive predictive value (PPV) and a negative predictive value (NPV) of 92.5% and 83.6% in VI-RADS \(\geq\) 4, respectively, among different readers. Additionally, [PERSON] et al. in their multicentre prospective study published in 2022 [20] showed that reporting bladder MRI using VI-RADS provided a sensitivity ranging 83-93%, specificity 80-92%, PPV 67-81%, NPV 93-96%, and accuracy of 84-89% for more experienced readers; whilst a sensitivity ranging 83-95%, specificity 73-86%, PPV 60-71%, NPV 92-97%, and accuracy 80-85% for the less experienced readers. The AUC was 0.95 for the more experienced readers and 0.93 for the less experienced readers. Different factors, including the number of patients, magnetic field strength (3 T vs. 1.5 T), T2-weighted image slice thickness (3 mm vs. 4 mm), VI-RADS cut-off score (\(\geq\)3 vs. \(\geq\)4) [14] and study design and radiological characteristics [16] were commonly considered to be significant factors affecting the heterogeneity of the studies reported in the literature. Another strength of VI-RADS is represented by the good performance in staging patients after BC treatment [24] and it is also good at identifying patients who need to undergo re-resection TURBT [25]. Some anatomical and technical radiological factors can affect the accuracy of this scoring system, among these the flat appearance of the tumour should be mentioned [26].
## 3 Results of the Experimental Validation
### Determining the Less Error Prone Master Device's Location on the Backpack
The environment with best results proved to be a laboratory environment with no geometric obstacles and otherwise stable environmental conditions. The right thigh IMU unit achieved 0% packet loss; the other IMU units also had negligible values. The highest values for packet loss were found for the left and right shaft IMU units (4.8% and 2.0%, respectively). Most other readings were in the range from 0.005% to 0.02%, which corresponds to approximately 1 loss in 10,000 samples (or 1 in 100 s), which is acceptable. The placement of the master unit (6) proved to be less error prone, with a low packet-loss rate while the subject was walking in a public hall and on a treadmill.
# Influence of the Analytical Segment Length on the Tram Track Quality Assessment
## 2 Materials and Methods
### Selection of the Track-Quality Index
The following expression defines these individual indicators along a particular analytical segment:  
\[\text{W}_{\text{i}}=\frac{\text{N}_{\text{P}}}{\text{N}}, \tag{2}\]  
where \(\text{W}_{\text{i}}\) represents the quality indicator of the observed track geometry parameter along the analytical segment; N represents the total number of measured values of the observed track geometry parameter; and \(\text{N}_{\text{P}}\) represents the number of measured values of the observed track geometry parameter exceeding a certain tolerance. The track geometry quality is expressed concerning the values of the synthesized track geometry quality index W along the analytical segment, as follows:  
* 0.0 \(\leq\) W < 0.1 indicates new or reconstructed track;
* 0.1 \(\leq\) W < 0.2 indicates good quality of track geometry;
* 0.2 \(\leq\) W < 0.6 indicates the satisfactory quality of track geometry;
* 0.6 \(\leq\) W \(\leq\) 1.0 indicates poor quality of track geometry. The synthetic track quality index J provides a quantitative evaluation of the track. The index is based on the standard deviation of four track geometric parameters: longitudinal level (Z), horizontal alignment (Y), twist (W), and gauge (e). The following expression determines it:  
\[\text{J}=\frac{\text{S}_{\text{Z}}+\text{S}_{\text{Y}}+\text{S}_{\text{W}}+ 0.5\text{S}_{\text{e}}}{3.5}, \tag{3}\]  
where \(\text{S}_{\text{Z}}\) represents the standard deviation of longitudinal level; \(\text{S}_{\text{Y}}\) represents the standard deviation of horizontal alignment; \(\text{S}_{\text{W}}\) represents the standard deviation of track twist; and \(\text{S}_{\text{e}}\) represents the standard deviation of track gauge. The following equation calculates the standard deviation for each measured parameter:  
\[\text{S}_{\text{i}}=\sqrt{\frac{1}{\text{n}}\sum_{\text{i=1}}^{\text{n}}( \text{x}_{\text{i}}-\overline{\text{x}})^{2}}, \tag{4}\]  
where n represents the number of signals registered on the analyzed track section; x\({}_{\text{i}}\) represents the parameter value at point i; and \(\overline{\text{x}}\) represents the average signal value. Table 1 presents the specified allowable deviation of the J index given in [32], determining the track condition for the state defined by the track operating as appropriate on one side and the track requiring maintenance on the other. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline Speed (km/h) & 30 & 40 & 50 & \(\dots\) & 90 & \(\dots\) & 120 & \(\dots\) & 160 & \(\dots\) & 200 \\ \hline J index (mm) & 12.0 & 11.0 & 10.0 & \(\dots\) & 6.2 & \(\dots\) & 4.0 & \(\dots\) & 2.3 & \(\dots\) & 1.4 \\ \hline \end{tabular}
\end{table} 
Table 1: Allowable deviations of the J index depending on speed.
# Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems
## 5 Performance Analysis
### Optimized Threshold \(\gamma\)  
In this section, we try to relate the optimized threshold \(\gamma\). For \(P_{FA}\), the \(Q\)-function is monotonically decreasing for \(\frac{\gamma-\mu_{a}}{\sigma_{a}}\geq 0\). Therefore, from (21), the threshold \(\gamma\) is calculated as  
\[\gamma=\mu_{a}+\sigma_{a}Q^{-1}(P_{FA}) \tag{24}\]  
Substituting the above in (23), we get  
\[P_{D} = Q\left(\frac{\mu_{a}+\sigma_{a}Q^{-1}(P_{FA})-\mu_{p}}{\sigma_{p}}\right) \tag{25}\]
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 2 Materials and Methods
### Focusing Retroreflectors from Surface Geometric Form  
Physically realizable focusing retroreflectors were modeled based on square trihedra, the surfaces of which were parabolically warped. These are referred to as 'parabolic trihedra'. The specifics of the design (Figure 4) are explained in Appendix A. Four configurations were modeled:  
* A single'small' parabolic trihedron with 1 m edge lengths;
* A single 'large' parabolic trihedron with 1.65 m edge lengths;
* An array of seven small parabolic trihedra;
* An array of seven large parabolic trihedra.  
With a central source-to-vertex distance of 3.15 m, the small and large single trihedra have the same solid angle as a 1.2 \(\times\) 1.2 m and a 2.4 \(\times\) 2.4 m patch at a 2 m distance (0.34 and 1.09 steradians), respectively. The arrays of seven small and large trihedra have solid angles of 1.30 and 3.21 steradians, respectively. By way of comparison, the 2.4 \(\times\) 2.4 m and 7.2 \(\times\) 7.2 m square reflectors (Section 2.1.2) have solid angles of 1.09 and 3.52 steradians, respectively, at a 2 m distance.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Octave Band** & **Speech Spectrum 1** & **A-Weighting** & **Speech-A-Weighting 2** \\ \hline
125 Hz & 49.9 & \(-\)16.1 & \(-\)23.6 \\
250 Hz & 54.3 & \(-\)8.6 & \(-\)11.7 \\
500 Hz & 58.0 & \(-\)3.2 & \(-\)2.6 \\
1 kHz & 52.0 & 0.0 & \(-\)5.4 \\
2 kHz & 44.8 & 1.2 & \(-\)11.4 \\
4 kHz & 38.8 & 1.0 & \(-\)17.6 \\
8 kHz & 33.5 & \(-\)1.1 & \(-\)25.0 \\ \hline \end{tabular} \({}^{1}\) Omnidirectional speech sound pressure level at 1 m, from ISO 3382-32022 [65]. 2 Combined speech-A-weighting adjusted to energetically sum to 0 dB (\(w_{\text{oct}}\) in Equation (11)).  
\end{table}
Table 1: Speech- and A-weighting values in decibels for the seven octave bands spanning 125 Hz–8 kHz. Values are in decibels.  
Reflected energy levels from each configuration were evaluated using FDTD, as described in Section 2.1.2. Twenty-five source positions were in a 2.4 \(\times\) 2.4 m square offset from the reflector configuration, and receivers were in the same plane. However, the transformation of square patches was not needed here, and a higher sampling rate (144,000 Hz) was used to represent the curved geometry more accurately.
### Key Integration Features  
1. Central Configuration and Management: * The system is managed through a central equipment management system (EMS) that provides status updates on each QKD node. * Syslog and SNMP feeds are available for integration into the customer's management system, ensuring secure monitoring with SNMPv3.
2. Interoperability: * The Cerberis3 QKD system can interface with link encryptors from major vendors, including Optical Transport Network (OTN) vendors such as ADVA and L2/L3 encryption. * ID Quantique actively participates in standardization processes, particularly at ITU and ETSI, to enhance interoperability with other security systems.
3. Key Management and Monitoring: * The KMS software performs add/drop or forward functions to distribute keys based on the recipient's location. * The system supports up to 80 encryptors through standard ETSI API or proprietary interfaces.
4. Network Flexibility: * QKD blades can be housed in the same chassis for point-to-point or relay topologies or in add-on chassis connected to a QNC for more complex topologies. * The system supports operation over dark fibers and channel multiplexing over a single core, allowing for flexible deployment in various network environments.
## 3 Model Establishment
### Weather Similarity Analysis  
From the perspective of weather similarity, we analyze the relationship among meteorological observations at different times and classify weather into several categories based on meteorological observations data from the wind farm, which contains wind speed, wind direction, temperature, air pressure, and humidity. For each type of weather, a wind power prediction model is established.  
For the preprocessed meteorological observations data, the k-means algorithm is used to accomplish the classification of weather. The k-means algorithm finds \(k\) clusters from the dataset and the number of \(k\) clusters is decided by users. Every cluster is described by its centroid, which is the center of all points belonging to this cluster. The inputs of the k-means algorithm include wind speed, wind direction, temperature, pressure, and humidity. First, \(k\) points are selected randomly as centroids of \(k\) clusters. Then, each point of the dataset is assigned to a cluster. Then, the nearest centroid for each point of the dataset is found and assigned to the cluster corresponding to the nearest centroid. Next, let the centroids of different clusters be equal to the center of all points belonging to this cluster. Repeat the above operations until the centroids of different clusters are unchanged. The number of clusters is determined by cross-validation. The value of \(k\) corresponds to the best WPF results.  
The pseudo code of the above process is in Algorithm 1[30].  
```
Input: Dataset \(S=\{\mathbf{x_{1}},\mathbf{x_{2}},\ldots,\mathbf{x_{h}}\}\)  Procedure:
1: select \(k\) samples from dataset \(S\) as the original centroid vector \(\mathbf{c}=\{\,\mathbf{c_{1}},\,\mathbf{c_{2}},\ldots,\,\mathbf{c_{k}}\,\}\) 2: for every sample \(\mathbf{x_{i}}\) then 3: calculate the euclidean distance between \(\mathbf{x_{i}}\) and \(\mathbf{c_{j}}\) (\(j=1,2,\ldots,k\)) 4: find the nearest centroid \(\mathbf{c_{j}}\) and let \(u_{i}=j\) where \(u_{i}\) represent the corresponding cluster of \(\mathbf{x_{i}}\) 5: end for 6: \(\mathbf{u}=\{\,u_{1},\,u_{2},\,\ldots,\,u_{k}\,\}\) 7: for every cluster then 8: calculate the centroid of every cluster \(\mathbf{v_{i}}\) 9: end for 10: \(\mathbf{v}=\{\,\mathbf{v_{1}},\,\mathbf{v_{2}},\ldots,\,\mathbf{v_{k}}\,\}\), \(\mathbf{u}=\{\,u_{1},\,u_{2},\,\ldots,\,u_{h}\,\}\) 11: if \(\mathbf{v}==\mathbf{c}\) then 12: return \(\mathbf{u}\) 13: else 14: \(\mathbf{c}=\mathbf{v}\) and go to step 2 15: end if Output: vector \(\mathbf{u}\) which shows the cluster label of every sample
```  
**Algorithm 1********K-means Algorithm
## 1 Introduction
This gap is significant because in real-world applications, NMASs often operate in environments where all three challenges are present, and a comprehensive solution is essential for ensuring system reliability and performance. To address this gap, this paper proposes an event-triggered active fault-tolerant predictive control method for NMASs. The proposed method aims to simultaneously mitigate the effects of RCCs, compensate for actuator faults, and reduce network congestion by optimizing data transmission. The novelty and significance of the proposed method lie in its comprehensive integration of three key components: event-triggered control, active fault-tolerant control, and predictive control. By utilizing an event-triggered mechanism, the method dynamically adjusts the data transmission schedule based on the current system state, thereby reducing the amount of data sent over the feedback channel and alleviating network congestion. This is in contrast to traditional time-triggered methods that transmit data at fixed intervals, regardless of the system's actual needs. The active fault-tolerant control aspect of the proposed method involves actively estimating and compensating for actuator faults using a Kalman filter. By integrating system output information conveyed through the feedback channel, the method can accurately estimate both the system state and potential faults, thereby facilitating timely and effective fault compensation. This represents an enhancement over passive FTC methods that lack dynamic fault compensation capabilities. The predictive control component of the proposed method conducts iterative predictions based on the established system model and generates a predictive sequence of control inputs. These inputs are precisely applied to the actuators at designated moments using timestamping technology, ensuring that the system can adapt to changing conditions and maintain stability even in the presence of RCCs and actuator faults. Compared with existing solutions, particularly the method presented in [21], a flexible event-triggered mechanism is proposed to reduce the communication burden of the feedback channel. The main advantages and contributions of the proposed method can be summarized as follows. First, it provides a unified framework for addressing RCCs, actuator faults, and network congestion, representing a significant advancement over previous approaches that have focused on individual challenges. Second, the event-triggered mechanism reduces data transmission without compromising system performance, making it more suitable for resource-constrained communication networks. Third, the proposed method comprehensively considers dual-channel communication constraints and actuator faults, and the stability condition of closed-loop system is derived.
## 3 Materials and Methods
### Selection of Products and Website Development  
A website was developed as a platform on which participants had access to use the different technologies to display a product. Four different website versions were generated based on the shown product and the used technologies.  
Two well-known common home appliances with varied characteristics (shape, size, materials, etc.) were selected: a sideboard (as a piece of furniture) and a gooseneck lamp (as a household accessory). Selection followed the following criteria: (1) products had to be widely known and used by all the participants; (2) they had to be products with very different characteristics (dimensions, shapes, materials, etc.); (3) products had to be able to be shown by the technologies used in the study.  
Two of the versions showed the sideboard, and the rest displayed the gooseneck lamp. To avoid participants' fatigue, the used technologies were either 2D, 360\({}^{\circ}\) and AR or 2D, 360\({}^{\circ}\), ni_VR and i_VR.  
The 2D product's information was shown on a carousel of rendered images from different perspectives (Figure 1): isolated, integrated into an environment, showing some details, and depicting detailed measures. A product description was also available next to the image carousel.  
The 360\({}^{\circ}\) rotation interaction was allowed by displaying the product with a three-dimensional model on an app (Sketchfab) (Figure 2). Allowed actions included rotation from any angle, zoom, and, with the sideboard, also opening drawers and doors. Instructions about how to perform these actions were included.  
This app was also used to obtain virtual product representation, which could be shown on a smartphone screen over a real location to display the product in the environment for which it was intended. Additionally, product orientation realistically changed in accordance with users' movements (Figure 3).  
The VR representation considered two different displays: on the one hand, for ni_VR, an app (VR Media Player) had to be downloaded on a smartphone so that a virtual representation of the product located in a virtual room could be displayed. Users could modify the orientation of the environment by sliding their fingers across the screen. For i_VR, participants used a head-mounted display, which they were previously provided with to hold their smartphone (Figure 4). This system allowed them to view a complete scene in an immersive way.  
Figure 1: Rendered images displayed on a carousel along with a written description (translated from Spanish) on the simulated sale website. Left: the sideboard; right: the gooseneck lamp.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 4 Estimation of Forwarder Search Delays
### Average Progress When Selecting the First Forwarder  
The forwarder node will be the first one waking-up inside the half-disk of center \((0,0)\) with radius 1 with \(x\geq 0\). Its position is a random variable \(X_{1}\). Assuming that nodes are distributed uniformly and since wake-up time is independent from positions, the density of probability of the random variable \(X_{1}\) is: \(f_{1}(x)=\frac{4}{\pi}\sqrt{1-x^{2}}\). The average value of \(X_{1}\) is:  
\[E(X_{1}) = \int_{t=0}^{t=1}tf_{1}(t)dt\] \[= \left[-\frac{4}{3\pi}\left(-x^{2}+1\right)^{\frac{3}{2}}\right] _{0}^{1}\] \[= \frac{4}{3\pi}\]  
This is the average amount of progress per hop towards the gateway when selecting the first forwarder (expressed in \"radio range\" units).
# Machine Learning Approach for Personality Recognition in Spanish Texts
## 2 Personality Survey to Gather Data
To conduct the analysis, the handwriting was transcribed to electronic texts. On average, the texts had 90 words and a lexical diversity of 0.19. To measure lexical diversity, the type-token ratio (TTR) measure was used. This measure is expressed as the number of different words in a document divided by the total number of words in that document [25]. The text processing includes eliminating stop-words since, as is well known, they do not provide relevant information to the analysis because they are common words. There is not a unanimously accepted comprehensive list of stop-words since these words can depend on the context and specific application. However, there is agreement on most words that are considered stop-words. A proposed list of Spanish stop-words was used [26]. This list contains articles, pronouns, adverbs, prepositions, and verbs. We used AntConc, which is a corpus analysis toolkit for concordance and text analysis which allows the extraction of data such as word frequencies, collocations, concordances, and so on [27]. We eliminated stop-words, computed the number of words with and without stop-words, and the number of different words. Every word was lemmatized, i.e., it was converted to its root. The FreeLing software suite was used for this process. FreeLing is an open-source software suite for natural language processing. This library provides a wide range of analyzers for several languages. It offers natural language application developers text processing and language annotation facilities [28]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Gender** & **Schoeling** & **Civil** & **Occupation** & **Preferred SN** & **Friends in** & **Personality** \\ \hline
1 & Male & College & College & Student & Twitter & 120 & Dominance \\
2 & Female & College & College & Student & Facebook & 1150 & Influence \\
3 & Male & High School & College & Student & Facebook & 100 & Steadiness \\
4 & Female & College & Married & Student & Facebook & 80 & Compliance \\ \hline \hline \end{tabular} \({}^{1}\)SN stands for social network.  
\end{table} 
Table 2: Examples of answers of the participants in the survey. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Personality** & **Women** & **Men** & **Total** \\ \hline Dominance & 8 & 6 & 14 \\ Influence & 10 & 16 & 26 \\ Steadiness & 24 & 38 & 62 \\ Compliance & 7 & 11 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the personality survey. With this data, we built an annotated linguistic corpus for Spanish, which was useful for the construction of the predictive model for personality recognition.