esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results
Note that _(i)_\(K\) follows from the Riccati Equation (51), whose solution comes from MATLAB's function _icare()_; _(ii)_ the eigenvalues of the matrix \(A_{c}-KC_{c}\) have a negative real part and are distinct (i.e., \(\sigma(A_{c}-KC_{c})=\{-0.459,-0.197+0.529i,-0.197-0.529i\}\)); _(iii)_ since the matrix \(A_{c}-KC_{c}\) is diagonalizable, \(\overline{\Phi}_{\eta}(t,\tau)=e^{v((A_{c}-KC_{c}))(t-\tau)}=e^{-0.197(t-\tau)}\)_; _(iv)_ regarding initial values, as \(y_{1}(t)\) is known (and therefore \(x_{2}(t)\)), the chosen \(\hat{x}_{2}(t_{0})\) is equal to \(x_{2}(t_{0})\); see Table 3. (b) Then, \(\hat{\beta}(t)\) and \(\hat{x}(t)\) followed from the solution of Equations (53), (55) and (56). (c) Assuming that the upper bound \(\overline{\pi}\) is known (refer to Table 4 for its value), the bounds \(\overline{\phi}_{w}\) and \(\overline{w}\) were calculated with Equations (89) and (88), respectively; see Table 5. Then, assuming that \(\overline{\theta}\) and \(\overline{q}\) were also known, given the expressions (79)-(81), \(\hat{\theta}_{E}(t)\) and \(\hat{z}_{E}(t)\) were computed. Note that all the integrals appearing in those expressions were solved with the function _trapz()_. From the maximum estimation errors \(\hat{\beta}_{E}(t)\) and \(\hat{x}_{i,E}(t)\) for \(i=1,2,3\) calculated with Equations (92) and (96), the time-varying maximum estimation bounds \(\hat{\beta}_{E}^{+}\), \(\hat{\beta}_{E}^{-}\), \(\hat{x}_{i,E}^{+}\) and \(\hat{x}_{i,E}^{-}\) for \(i=1,2,3\) were computed. See Figures 3 and 4, where the areas \(I_{E}^{B}\) and \(I_{E}^{\varepsilon_{i}}\) defined by the Equations (93) and (97), respectively, have been shaded with a light blue color. (d) Assuming that the average values of \(\overline{\overline{\theta}}\), \(\overline{\overline{\overline{q}}}\) and \(\overline{\overline{\overline{\alpha}}}\) were known (refer to Table 4 for their values), the functions \(\theta(t)\), \(q(t)\), and \(\alpha x_{3}(t)\) were approximated as shown in Equations (98) and (99) from Remark 8. The values for \(\overline{\overline{\phi}}_{w}\) and \(\overline{\overline{w}}\) were computed with the Equations (101) and (100), see Table 5, and Equations (53) and (102)-(104) were used to compute \(\hat{\theta}_{e}(t)\) and \(\hat{z}_{e}(t)\). Then, with the adapted versions of Equations (92) and (96) as stated in Remark 10
# Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal
## 3 Methods
### Classification
Similarly, based on F-test results in Table 7, \(m=1\) and \(r=0.10\) was set for SampEn and FuzzyMEn in model B. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Tolerance**} & \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Embedding Dimension SampEn**} & \multicolumn{2}{c}{**Embedding Dimension FuzzyMEn**} \\ \cline{3-8}  & & \(m=1\) & \(m=2\) & \(m=3\) & \(m=1\) & \(m=2\) & \(m=3\) \\ \hline \multirow{4}{*}{\(r=0.10\)} & Norm & \(1.58\pm 0.44\) & \(1.43\pm 0.46\) & \(1.34\pm 0.46\) & \(1.03\pm 0.27\) & \(1.34\pm 0.46\) & \(0.92\pm 0.27\) \\  & CHF & \(1.61\pm 0.42\) & \(1.43\pm 0.44\) & \(1.43\pm 0.44\) & \(1.28\pm 0.37\) & \(1.43\pm 0.44\) & \(0.97\pm 0.28\) \\  & \(p\)-value & \(0.95\) & \(1.00\) & \(1.00\) & \(6\times 10^{-5}\) ** & \(0.12\) & \(0.37\) \\ \hline \multirow{4}{*}{\(r=0.15\)} & Norm & \(1.09\pm 0.33\) & \(0.96\pm 0.33\) & \(0.89\pm 0.33\) & \(0.71\pm 0.23\) & \(0.87\pm 0.27\) & \(0.67\pm 0.23\) \\  & CHF & \(1.35\pm 0.40\) & \(1.18\pm 0.41\) & \(1.09\pm 0.41\) & \(0.99\pm 0.36\) & \(1.02\pm 0.34\) & \(0.74\pm 0.25\) \\  & \(p\)-value & \(1\times 10^{-3}\) ** & \(3\times 10^{-3}\) ** & 0.02 * & \(3.8\times 10^{-6}\) ** & \(0.01\) ** & \(0.15\) \\ \hline \multirow{4}{*}{\(r=0.20\)} & Norm & \(0.88\pm 0.28\) & \(0.76\pm 0.30\) & \(0.70\pm 0.30\) & \(0.53\pm 0.20\) & \(0.67\pm 0.23\) & \(0.52\pm 0.20\) \\  & CHF & \(1.06\pm 0.37\) & \(0.90\pm 0.37\) & \(0.82\pm 0.36\) & \(0.79\pm 0.34\) & \(0.83\pm 0.32\) & \(0.59\pm 0.22\) \\ \cline{1-1}  & \(p\)-value & \(0.06\) & \(0.23\) & \(0.35\) & \(9\times 10^{-7}\) ** & \(3\times 10^{-3}\) ** & \(0.10\) \\ \hline \hline \end{tabular}  
*: statistical difference (\(p\)-value \(<\)0.05); **: significant difference (\(p\)-value \(<\)0.01).  
\end{table} 
Table 6: The statistical Kolmogorov-Smirnov (KS) test results of the sample entropy (SampEn) and fuzzy measure entropy (FuzzyMEn) with different combinations of \(m\) and \(r\) for the normal and congestive heart failure (CHF) groups.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 4 Discussion
In addition, the 16-dimensional representation is computationally lighter, taking a fraction of the time to train the supervised network (i.e., 10%). However, supervised methods that train an internal representation explicitly specialized for the task have better classification results over all unsupervised techniques. This is expected if we take a look at the input mapping for the desired output \(y\) with a classification model \(C(\cdot;w)\) parameterized by \(w\). For example, (considering that the input \(\vec{x}\) should be \((\vec{x},\vec{\delta})\)), the VRAE\({}_{t}\) will estimate \(\hat{y}=C(f(\vec{x};\phi);w)\) by learning \(w\) with \(\phi\) fixed, since the representation \(\mu=f(\vec{x};\phi)\) is already learned (in the unsupervised auto-encoder objective without knowing \(y\)). However, the supervised version of this method (RNN\({}_{t}\)) will estimate \(\hat{y}\) by learning \(\phi\) as well (in the supervised objective knowing \(y\)). Despite this, it is worth noting that our proposed S-VRAE\({}_{t}\) achieves a performance quite close to the maximum model _capacity_ for this specific task (RNN\({}_{t}\)) by learning \(\phi\) without labels (\(y\)). In this case, we refer to the capacity to extract the information from the input \(\vec{x}\), and it would be given by the structure of \(f(\vec{x};\phi)\), e.g., a linear function or not, in the case of a neural network, the architecture, or how \(\phi\) is organized within the network.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 2 Current In-Car Network Architectures and Technologies
### Automotive Functional Domains  
Table 1 presents the traditional functional domains and the correspondent legacy in-car communication technologies, which will be discussed in Section 2.2.  
The main automotive communication requirements are described in the following.  
_Communication predictability_ is the property that enables the network designer to assess whether all the real-time frames will be delivered to the destination within their deadlines, before starting the network operation. This means that all the components of the frame end-to-end delay can be calculated, in an exact or stochastic way [6; 7; 8; 9; 10].  
_Fault-tolerance_ is the network ability to deliver a service even following the occurrence of a fault (e.g., message faults, defective circuits, line failure). Redundancy, either spatial or temporal, and bus guardians are examples of measures used to achieve fault-tolerance.  
Figure 1: Example of domain-based architecture.  
_Bandwidth_ requirements highly depend on flows. Some flows, i.e., the raw video flows originating from the cameras used for ADAS or for automated driving require high bandwidth, while the flows of the typical control loops in the powertrain domain do not need it.  
_Low cost_ is always beneficial in the automotive domain. For instance, the LIN and CAN network protocols, which will be described in Section 2.2, are very successful and broadly used as they work well and are cheap.  
_Security_ is becoming increasingly important with the growing of the inter-domain communications and with the increased openness of the car to flows coming from the external world (e.g., car-to-x communications) [11]. CAN, for example, was not designed with security in mind, and therefore it contains vulnerabilities that hackers have already exploited in well-known attacks that were abundantly reported in the press and in social media in recent years.  
_Energy efficiency_ is an important aspect, especially when the engine is not running. To save energy, automotive networks offer sleep and wake-up modes and partial networking. One requirement is that when the car is started or restarted, it needs to be fully operational within two seconds.  
All the above-mentioned aspects are important, however, to what extent one of them dominates over the other ones very much depends on the specific application/functional domain. For instance, in the body and comfort domain, low cost is more important than bandwidth, while powertrain applications demand higher bandwidth and predictable communications. Very high bandwidth, predictability and security are required for the multimedia and infotainment domains. ADAS and Automated Driving require very high bandwidth, deterministic communications, security, and fault-tolerance.
## 4 Validation of the Proposed Method
### Case 1: Experiment Results on the CWRU Bearing Dataset and Performance Analysis  
#### 4.2.1 Data Collection  
In this section, vibration signals of different fault states collected by the motor experimental bench of Case Western Reserve University [34] were used as experimental data to verify the effectiveness of the proposed method in rolling bearings fault diagnosis. As shown in Figure 5, the experimental platform mainly consisted of a torque transducer, a load motor and a three-phase induction motor. In the test, vibration signals collected from the drive end (DE) were treated as the research target, where the sample frequency was 12 kHz and the rotation speed was 1730 r/min under the rated load of 3 hp. The experimental device adopted electric spark machining technology to simulate three fault states of rolling bearings: ball fault, inner race fault and out race fault, and the depth of the faults was 0.011 inches. Each fault state included three degrees of fault with width of 0.007 inch, 0.014 inch and 0.021 inch, respectively.  
In this experiment, the data set contained 1000 groups of training data, and each training sample contains 864 sampling points. The standardized fault samples under each fault state were randomly divided into training data set and test data set with the scale of 7:3. Since different fault diameters at the same position were regarded as different fault types, so there were ten categories of health conditions in total. We selected the same amount of data under each operating condition, 70 samples were used as the training set and 30 samples were used as the test set. In this way, this method ensured the balance of data and facilitated the diagnosis model to get correct diagnosis results.
## 4 Local Railway Level Crossing Case Study
### Level Crossings Data  
The efficiency of the decision-making process is strongly influenced by the quality of the data available for analysis.  
For each level crossing, several categories of data are collected. General information includes details like the LC's name, the section, and road it is located on, its position along the railway, and whether it is in an urban or suburban area. Information on the level crossing protection system covers the type of barriers, how they are operated, the presence of visual or acoustic signals, railway signaling, and the visibility between the road and the train. Operational data consist of metrics such as the Average Annual Daily railway Traffic (\(\text{AADT}_{\text{rail}}\)), the Average Annual Daily road Traffic (\(\text{AADT}_{\text{road}}\)), and the average speed of trains. Lastly, faults data include a log of all accidents and precursors that occurred at LCs.  
The first type of data is intended to identify the LCs and the road section concerned. The second type of information is crucial for the classification of LCs according to the seven classes described in Table 2.  
The Average Annual Daily road Traffic (\(\text{AADT}_{\text{road}}\)) was obtained from the combination of the information road traffic counts published by the local authorities and from traffic simulation models performed on the analyzed network. \(\text{AADT}_{\text{rail}}\) and estimated train speeds at the LC were provided by the infrastructure manager.  
Finally, data on the reliability and availability of the protection systems of the LC were obtained from the fault database owned by the Local Railway Infrastructure Manager, which reports all relevant events detected during monitoring and maintenance activities on the line. The database reports all the failures of the LC systems from 2018 to 2021 for a total of 1227 reports, each characterized by the following:  
* Number, description, and date of input of the report;
* Status of the report (i.e., pending, solved, etc.);
* Start and end date of the repair and its duration;
* Name and type of the LC involved;
* Type of failure, part of the system involved, possible cause, and repair.  
The faults described have been filtered in order to exclude non-safety-related events, which are not able to influence the risk level of the LC. The relevant faults refer to the following categories:  
1. Train detection pedal failure;
2. Faults in logic, control, and power systems;
3. Vandalism;
4. Barriers failure;
5. Acoustic signals failure;
6. Warning lights failure;
7. Accidents due to rush or distraction of car drivers.  
As Figure 3 shows, the most recurrent fault is linked to the barriers, in particular to the 'Barrier' does not lower' fault, an event, however, solved on average in a few hours. Vandalism, on the other hand, is a critical event as, despite having an average frequency, it requires the most time to repair.
## 2 Materials and Methods
### Statistical Analysis  
To analyze cephalometric changes before and after CAT, and to assess the consistency between the projected ClinCheck mean intrusion and angular control compared to the actual clinical CAT-based mean intrusion and angular control of mandibular incisors on lateral cephalometric images, paired sample _t_-tests were employed. Differences between the growing and non-growing groups were evaluated using a repeated measures ANOVA with Tukey post hoc analysis. For statistical significance, an alpha level of 0.05 and a power of 80% were applied. With a pooled standard deviation of 1 mm, a sample size of 48 ClinCheck-based intrusion predictions and 48 pre-post sets of cephalometric images would be necessary to detect a 0.58 mm difference between the two groups. The statistical software utilized was SAS Release: 3.81 (Enterprise Edition), Copyright (c) 2012-2020, SAS Institute Inc., Cary, NC, USA.  
Figure 1: Lower incisor intrusion measurement landmarks. Point A (incisal tip): most coronal point of lower mandibular anterior tooth from sagittal view at buccolingual and mesiodistal midpoint. Point B (root apex): most apical point of lower mandibular anterior tooth from sagittal view at buccolingual and mesiodistal midpoint. Centroid-C Point: intersecting perpendicular point of Centroid-C line with mandibular plane. Centroid point: central point of lower central incisor identified from midpoint formed from buccal and lingual CEJ. AB line: line segment created from A point to B point and total estimated length of tooth. Centroid-C line: line segment from tooth centroid to mandibular plane forming right angle at mandibular plane and estimated spatial vertical position. Overbite: overlapping of mandibular central incisor teeth by maxillary central incisor teeth. Overjet: extent of anterior–posterior of maxillary central incisor to mandibular central incisors.  
Figure 2: Lower incisor angular analysis landmark. Mandibular plane: two-dimensional plane constructed by line tangent to mandibular border from constructed gonion to menton (Me-Go). T to APOE angle: angle formed from lower incisal axial inclination relative to plane of maxillary A point to pogon. T to NB angle: angle formed from lower incision axial inclination relative to plane of nasion to mandibular B point. IMPA: angle formed from lower incision axial inclination relative to mandibular plan.
# Low Discrepancy Sparse Phased Array Antennas
## 2 Sampling Points on a Planar Aperture
### Random Sampling Approaches  
#### 2.1.1 Random Sampling  
The element locations on a random lattice are defined by  
\[(x_{n},y_{n})=(\alpha_{n}x_{\text{max}},\beta_{n}y_{\text{max}}) \tag{3}\]  
where \(\alpha_{n}\) and \(\beta_{n}\) are uniformly distributed random variables between 0 and 1, and \(x_{\text{max}}\) and \(y_{\text{max}}\) are the maximum lattice size in \(x\) and \(y\). Random element sampling is hierarchical, because adding an additional element does not require recalculating the previous element locations.
## 4 Material and Methods  
### Patients and Tumor Biopsies  
The study was carried out after obtaining ethical approval by the ethical commission University of Erlangen (ID 3784). For the ICPL analysis, 20 patients with histopathology-proven rectal adenocarcinoma (stage UICC II-IV)--which were recruited for a standardized clinical study (CAO/ARO/AIO-04)--were selected after informed consent [12]. Patients with synchronous second colon carcinomas or younger than 18 years of age were excluded. Tumors with a distance of maximal 16 cm from the anal verge (measured by a rigid endoscopy) were classified as rectal carcinomas. Patients received a total dose of 50.4 Gy radiation, fractionated in 28 \(\times\) 1.8 Gy single applications. During radiation they received either 5-Fluorouracil (5-FU: 1000 mg/m\({}^{2}\)/day; nCRT I) or 5-FU (250 mg/m\({}^{2}\)/day + Oxaliplatin 50 mg/m\({}^{2}\)/day; nCRT II) [12]. Patient characteristics are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Patients** & **nCRT I** & **nCRT II** \\ \hline n & 10 & 10 \\ Male & 9 & 7 \\ Female & 2 & 3 \\ \hline ypT-category & & \\ yT0 & 1 & 3 \\ yT1 & 0 & 0 \\ yT2 & 3 & 1 \\ yT3 & 5 & 6 \\ yT4 & 1 & 0 \\ \hline ypN-category & & \\ yN0 & 4 & 7 \\ yN1 & 5 & 3 \\ yN2 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Patients and histopathological tumor characteristics of rectal adenocarcinoma which underwent laser capture microdissection and proteome analysis by isotope coded protein label (ICPL); nCRT I: 50.4 Gy + 5-FU; nCRT II: 50.4 Gy + 5-FU/Oxaliplatin.  
Tumor biopsies of all patients were performed prior to nCRT by rigid rectoscopy. The samples used for ICPL were immediately harvested in liquid nitrogen and stored by \(-80\)\({}^{\circ}\)C until further processing. Samples used for immunohistochemistry examination underwent a formalin fixation and paraffin embedding procedure (FFPE). Standardized surgery such as total mesorectal excision (TME) was performed in all cases.
# Virtual Design and Construction (VDC) Framework: A Current Review, Update and Discussion
## 4 VDC Case Studies
### Case Study 5: Civil Engineering Laboratories (Peru)  
The Civil Engineering Laboratories of the University of Lima, Peru, are grouped in a shed with 1440 m\({}^{2}\) and were built in 2017 for a budget of $20M approximately. This in -frrastructure contains nine central laboratories. The first three laboratories (Materials, Soils, Topography and Geomatics) were implemented in 2017 and the remaining six laboratories (Structures, Environmental Engineering, Pavements, Sanitary Engineering, Hydraulics and Hydrology) were implemented in 2021. However, due to the limited space available to install the equipment for the remaining six laboratories, a detailed plan was developed, supported by VDC methodology, to avoid unforeseen issues during installation and operation [67].  
The VDC application was developed during 2019-2020 and focused on the conceptual design for the last six labs and the respective purchase order of the equipment before the established end date of 30 December 2020. The conceptual design depended on the equipment definition. Therefore, the design had to consider the appropriate dimensions, spaces, operation, maintenance, use and installation for this equipment [67].  
Virtual ICE sessions were developed weekly due to the COVID-19 pandemic restrictions. The laboratory managers, technicians, suppliers and other actors coordinated and resolved issues involving the entire facility management. A detailed BIM was built and managed, integrating the specialties and lab equipment, containing the product's information on a cloud-based platform. All this was developed through a PPM system focused on solving issues for each session and reaching the desired conceptual design according to the needs [67].  
The conceptual design was achieved by solving 100% of the issues. Likewise, the equipment purchase orders were successfully requested one month before the deadline.
## 2 Methods
### Clinical, Functional and Radiological Analyses  
Patient-reported outcomes (PROMs) were collected pre-operatively, and at one, three, six and twelve months after surgery. These included the Knee Osteoarthritis Outcome Score (KOOS) [31], here considered as a total score averaged across all domains and as individual domains (KOOS, 0 = worst condition, 100 = best condition), and the visual analogue pain scores [32] during activity (VAS, 0 = no pain, 10 = worst pain).  
Instrumented gait analysis was also performed prior to the surgery and at 6 months follow-up (Figure 2). A nine-camera motion capture system (Vicon(r), Nexus motion-capture Software v.2.12.1, with B10 Bonita Optical cameras, Oxford, UK), together with two GRF-measuring platforms (KistlerTM, model 9291B, Winterthur, Switzerland) and a wireless EMG system (Zerowire, Cometa, Milan, Italy) for the myoelectric activity of the main muscles of the lower limbs were employed. An established protocol (IOR-gait) [33] with tested data reproducibility [34] and consistency [35] was used to position reflective markers on the skin in well-defined anatomical landmarks and to calculate 3D kinematics and kinetics of the hip, knee and ankle joints.  
Gait analysis data were collected during five repetitions of level walking. All patients were asked to walk at their self-selected speed. The standard marker set was enriched with four additional knee-related fiduciary markers used for the subsequent medical imaging analysis, morphological reconstruction and data registration purposes (Figure 2) according to a recently reported original methodology developed by the present authors [25], where the reproducibility of the combination of stereophotogrammetric and force platform data was also assessed. Whilst the patients were still wearing these additional knee-related fiduciary markers, long-leg radiographs and cone beam CT examination were undertaken for the knee in the weight-bearing condition; this imaging was needed before surgery for the planning, as detailed above, and was repeated at 6 months follow-up. Three-dimensional bone morphological models for the tibia and the fiduciary markers were reconstructed from the CT imaging. Next, the fiduciary marker trajectories obtained from gait analysis were registered to the corresponding marker coordinates from the CT-based reconstruction, allowing superimposition of the GRF data to the reconstructed tibia morphology. A plane matching the border of the tibial plateau was defined, and its intersections with the GRF vectors were calculated.