esa-sceva/satcom-chunk-collection huggingface dataset:
# A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support
## 1 Introduction
### The Mobile Crowdsensing Paradigm  
In MCS, users share data from integrated sensors in their mobile devices, complementing the wide adoption of IoT (Internet of Things) devices. With the support of IoT infrastructure, opportunistic sensing can be integrated to take advantage of the ubiquity of personal devices, GPS and Mobile Networks [10].  
Participatory sensing is a form of MCS, requiring a strategy for motivating the users, tapping into the social aspects (social networks) and gamification (game theory). Challenges include privacy and security (GPS, disclosure of location information), quality of data (low-quality sensors, number of participants, data evaluation), trustworthiness (trust by reliability for anonymous participants, trust by decision for trusted participants, reputation systems such as collaborative scores, vote-based), energy (battery level, influenced by device model, sensors, GPS and networking usage, sampling frequency), incentives (rewards for quality of data, increasing the involvement of participants), agent-based strategies (AR/VR) [11].  
MCS frameworks assume an opportunistic assignment of sensing tasks, with reputation-aware schemes that can have a significant effect on platform utility in the context of urban water reporting. Optimal user assignment according to the reputation and sensing cost is a challenge in MCS that involves geolocation, reliability (quality of sensing data, robust and secure services, encryption, truthful participation) and incentives (gamification, game theory, auction-based models, reward-based mechanisms) [12].  
MCS requires an incentive scheme to be effective, while the amount and accuracy of sensing data should be based on the active participation of citizens. Some of the main challenges in robust MCS systems are given by the trust, reliability and security aspects (e.g., privacy protection, malicious behavior, security threats) of crowd-sensed data from anonymous sources [13]. Solutions include reputation systems (trust, reliability) and incentives (increasing participation, compensating for privacy concerns). In this sense, an optimization problem can be formulated based on the level of trust and reward-driven mechanism, while the data exchange and storage requirements should ensure an optimal security scheme that ensures the required level of trust for service providers.  
Game theory can be applied to define the incentive model where multiple players are involved and the interaction between players in the form of cooperative and non-cooperative games. Incentive mechanisms can be defined for crowdsensing in the context of a CPSS, such as the classification of users based on the level of trust [14], while different incentive schemes for MCS can be classified in terms of platform-centric and user-centric approaches [15].
# Directional Suppression of Monotone Noises with A Parametric Array Loudspeaker
Considering such issues from VCLs, a novel type of directional speaker termed parametric array loudspeaker (PAL) [23] has gained interest in ANC research. Due to nonlinear effects in acoustic wave propagation, low-frequency audible signals can be transmitted by ultrasound carrier waves through amplitude modulation (AM) techniques. PAL comprises multiple ultrasound transducers arranged in an array to project audio signals directionally based on the high directivity of carrier wave beams produced by limited aperture ultrasound arrays [24]. This facilitates localized ANC using PALs with minimal impact on non-interested sites, which has been validated in the literature [25]. Inspired by previous research, this paper explores the feasibility of localized monotone noise suppression with PALs embedded in a feedforward ANC system constructed with off-the-shelf, consumer-grade equipment. The algorithm is designed based on FxLMS architecture and solely implemented in software through a generic personal computer (PC). A customized hexagonal PAL functioned as the secondary source for the directional projection of monotone noise-countering signals. Experimental verification and comparison of noise reduction results are performed for the proposed method versus the results from using generic VCLs. The main contribution of the paper is summarized as follows:  
* Demonstration of the enhanced directivity and operation distance for monotone noise suppression with PALs. * Exploration of the feasibility of implementing the PAL-driven ANC system with a PC-powered platform and generic audio hardware. The remaining paper is organized as follows. Section 2 describes the implemented FxLMS algorithm including offline secondary path identification and adaptive noise filter design. This section also briefly depicts the origination of the super-directivity of PALs. Section 3 discusses the realization of the PC-based ANC system. Section 4 presents the experimental characterization of PAL's pressure field in terms of the directivity and axial pressure distribution. Section 5 describes the experimental evaluation of using the PAL for directional monotone noise suppression and provides comparisons against conventional VCLs to demonstrate the advantage of PALs in terms of operating distance and directivity. Section 6 concludes the paper and discusses the possible future improvements.
# Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems
## 4 Simulation Methodology
### Scenario 3  
In this scenario, the interference occurred at an Immarsat receiving Earth station installed in an aircraft flying from Greece to the UAE, operating within the service area of the Immarsat satellite. The aircraft was in the cruise phase at an altitude of 11,500 m and was moving at a velocity of 700 km/h. The aircraft's antenna was pointed towards the serving Immarsat satellite. The desired link between the aircraft and the Immarsat satellite was assumed to be continuously active. The simulation was conducted with a time step of 1 s, enabling a precise estimation of the interference along the entire route of the aircraft. Figure 10 illustrates the simulation results, depicting the interference caused by the 5G NTN satellites at the Immarsat Earth station, which was located within the aircraft.
## 1 Introduction
VSM technology uses prefabricated circular shafts, and during construction, the lateral deformation of the pit is smaller than that of rectangular shafts [23]. With respect to the impact of circular shaft construction on the surrounding soil, [PERSON] et al. [24] proposed two new empirical equations for predicting soil displacement in actual engineering, which were derived from centrifuge tests and available field measurement data. [PERSON] et al. [25] proposed an equivalent volume loss method for predicting surface deformations caused by circular shaft structures and assessing the applicability of the method through engineering cases and centrifuge tests. [PERSON] et al. [26] established a numerical model on the basis of a circular shaft construction case in Shanghai, and summarized the factors contributing to soil heave, such as excavation unloading, dewatering, diaphragm wall constraints, and the mechanical properties of the soil. Currently, the deformation pattern of the surrounding soil during the sinking of the VSM in soft soils remains ambiguous. Therefore, this study systematically analyzed the impact of the new VSM technology on the deformation behavior of soft soils on the basis of the VSM construction project in soft soil areas and integrated monitoring data and the Plaxis[30] 24.1 software. The research process is shown in Figure 1. Additionally, a soil impact zoning method based on deformation characteristics, which divides the surrounding area of the shaft, is proposed. The zoning results are discussed in terms of their guidance for construction optimization and environmental protection. This study provides not only important references for controlling ground deformation during shaft construction in soft soil areas but also scientific support for construction safety management and environmental protection measures in complex urban underground engineering projects.
# Deep Neural Networks Classification via Binary Error-Detecting Output Codes
## 3 Results and Discussion
### Out-of-Distribution Performance  
Error detection is an approach to determine whether the senseword (a codeword assigned to the test sample) has agreed with properties that define a particular subspace of the codespace [15]. As we train the network to transform training patterns into the error detecting codewords, we assume that we allocate these samples into a subspace keeping the error detection ability. We hypothesize that if a sample is qualitatively different from the training patterns, the output codeword will have other properties, i.e., it will lie outside of the code subspace generated by the training set. Although the main goal is to show that binary coding can be trained to achieve high accuracy, out-of-distribution (outliers) detection is a side effect; therefore, we also comment on it.  
In our case, the in-distribution training set was the CIFAR-10 dataset. As the out-of-distribution dataset, we chose datasets with various complexities, from the simplest (MNIST, Fashion-MNIST) through similar (CIFAR-100) to the most complex (white noise). The natural (and very undesirable) property of the recognition systems that maps all input patterns into one of the output classes is their defenselessness against outliers. It is the case of the softmax decision in our study. On the other side, error-detecting codes have an implicit ability to filter sensewords outside the subspace with class-codewords. This sorting generates rejected sensewords and improves recognition reliability. Table 5 shows the percentage of detected outliers from different out-of-distribution datasets. We can see that outliers are naturally detected by networks trained directly for error detection even without applying the threshold.  
Similar to the reliability, the out-of-distribution performance can be improved by applying a threshold during decision-making. Figure 4 shows that in both decision rules, the out-of-distribution rejection significantly increases with the threshold. For CNN1/CNN2 architectures with CIFAR-100/Fashion MNIST datasets, we can see a similar pattern in the accuracy-reliability characteristic. For high in-distribution FCA levels, the performance of both methods is very similar. For lower in-distribution FCA levels, one-hot encoding with softmax provides better out-of-distribution performance. CRC7 with the Zadeh decision performs better on the noise dataset, especially for ResNet20v2 architecture. On the contrary, one-hot encoding with softmax shows better performance in the rest of the ResNet20v2 experiments.  
Figure 4: Out-of-distribution rejection (from left to right: CIFAR-100, Fashion MNIST, white noise) versus in-distribution FCA (full codespace accuracy) in studied networks and decision rules.
## 4 Experiments Datasets
### Visualized Analysis  
The HSTAN and STGCN models both employ stacked hierarchies to capture spatial and temporal information. However, there are differences in the specific techniques used within each spatial-temporal block. The STGCN model utilizes 1-D temporal convolutions and spectral-based graph convolutions in each spatial-temporal block. On the other hand, the HSTAN model incorporates the temporal attention layer and spatial attention layer based on the attention mechanism. Thus, the forecasting outcomes of the HSTAN and STGCN models, employing two real datasets, are visually presented to facilitate a comprehensive understanding of the models' performance. For the SZ-taxi dataset, we selected one road segment and visualized the results from 27 January to 1 February 2015. The visualization results for the 15, 30, 45, and 60 min time series are displayed in Figure 5. Similarly, for the Los-loop dataset, we visualized the data from one loop detector. The visualization results for the 15, 30, 45, and 60 min intervals are shown in Figure 6.  
Figure 4: Comparison of model variants using the SZ-taxi and Los-loop datasets, which are listed as (**a**) MAE and RMSE for the SZ-taxi set; (**b**) MAE and RMSE for the Los-loop dataset.  
Overall, the predicted traffic speeds exhibit a consistent variation trend compared to the actual traffic speeds across different time series lengths. This indicates the competence of our model in the task of traffic forecasting. In Figure 5, our model aptly captures the variation trends in traffic speed and identifies the start and end points of rush hours. It also demonstrates a smoother traffic prediction than the STGCN model, showcasing its enhanced generalization ability. Moreover, the superiority of the multi-headed self-attention mechanism and graph attention mechanism over traditional graph convolution and time series convolution is clearly evident, as they enable more precise traffic prediction.  
Figure 5: Comparison of the prediction effect of the SZ-taxi dataset, which are listed as (**a**) description of the predication in 15 min; (**b**) description of the predication in 30 min; (**c**) description of the predication in 45 min; (**d**) description of the predication in 60 min.  
Figure 6 exemplifies the accurate anticipation of traffic congestion during rush hours, further validating the efficacy of our model in real-time traffic forecasting.
## 1 Introduction
It is used in the root canal first as a penetrating instrument, and once reaching the working length (WL), it is used with an additional 15 pecking motion strokes to the working length [12]. The resultant final root canal size after XP-endo shaper instrumentation corresponds to #30/0.04. The XP-endo finisher file has a circular whip-shape and was initially introduced as a complementary universal instrument that can be used after root canal preparation with any file system and is aimed for cleaning complex morphologies and difficult-to-reach areas within the root canal system [1; 3; 13]. Recently, the manufacturers have introduced the XP-endo shaper plus sequence, which includes three stages: first, hand K-files (#10 and #15) are used to create a glide path, and then the XP-endo shaper is used, to be followed by the XP-endo finisher. Several studies have addressed the issue of apical extrusion of debris by the SAF system [3; 5; 14]. It has been established that the SAF extruded less debris than rotary and reciprocating NiTi files [3; 5; 14]. However, it was recently suggested that the issue of apical extrusion of debris should evaluated for the full sequence of instruments of a given file system, as the initial stages of glide path preparation may contribute to the total amount of extruded debris and should not be omitted from such measurements [3; 5; 14]. The individual elements of the XP-endo system (XP-endo shaper, XP-endo finisher) have also been studied for their potential to extrude debris [1; 3; 15; 16]. The XP-endo finisher, which preceded the  
Figure 3: Adaptation of the self-adjusting file (SAF) to an oval canal. Radiographs of a mandibular premolar with an oval canal, with the SAF adapted to an oval canal. (**a**) Bucco-lingual projection. (**b**) Mesio-distal projection. XP-endo shaper, was also found to extrude debris when used after ProTaper Next files [3]. The more recently introduced XP-endo shaper was found in some studies to extrude less debris than Reciproc [16] or One-Shape files [15], while others have found that it extruded similar amounts of debris as Reciproc or HyFlex EDM files [1; 16]. Only recently were these two instruments introduced as elements of the XP-endo shaper plus system. A literature search revealed no studies investigating the amount of debris that may be apically extruded by the full XP-endo shaper plus sequence. Thus, the present study aimed to assess the amount of apical extrusion of debris when preparing oval root canals with either the full-sequence SAF system or the XP-endo shaper plus sequence. The null hypothesis was that the two file systems will not differ in terms of apical extrusion of debris when used in oval canals.
## 3 Finite Element Analysis
### Material Properties  
The SS400 and HSB600 steel grades used in the test are materials with the yield and tensile strength shown in Table 3. The tensile strengths of SS400 in the tensile and sub-column tests are almost the same, but the yield strength shows a significant difference. This is because permanent deformation occurs in the curved section when the CHS is manufactured. However, the yield and tensile strength of HSB600, which is a high-strength steel, are almost the same. The yield strength was determined by the 0.2% offset method. In the FE analysis, a bi-linear material curve using the yield and tensile strength of the stub-column test was applied. The elastic modulus and strain at tensile strength were determined to be 205 GPa and 10%, respectively. This strain of 10% is also in good agreement with the tensile test, and the Poisson's ratio is defined by 0.3.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Specimen** & **Steel Grade** & \(D\) **(mm)** & \(t\) **(mm)** & \(l_{b}\) **(mm)** & \(t_{b}\) **(mm)** & \(P_{ro}\) **(kN)** \\ \hline LH-N700-0.0 1  & SS400 & & & 700 & & 1797 \\ LH-N350-0.0 & & & & & & 0 \\ LH-N350-0.6 & & & & & & 0 \\ LH-N700-0.0 & & & & & & 3364 \\ LH-N700-0.6 & & & & & & 0 \\ LH-N700-0.6 & & & & & & 3364 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specimens for validating the finite element (FE) model.  
Figure 2: LH-N350-0.6 specimen (LH is the longitudinal horizontal plate, N350 is the plate width, and 0.6 is the utilization ratio).  
The true stress-strain curve used in the FE analysis is converted from the nominal stress-strain by using [PERSON]'s law, as shown in Equations (2) and (3).  
\[\sigma_{true}=\sigma_{nom}(1+\varepsilon_{nom}), \tag{2}\]  
\[\varepsilon^{pl}_{ln}=\ln(1+\varepsilon_{nom})-\sigma_{true}/E, \tag{3}\]  
where \(\sigma_{true}\) is the true stress, \(\varepsilon^{pl}_{ln}\) is the log strain, \(\sigma_{nom}\) and \(\varepsilon_{nom}\) are the nominal stress and nominal strain, respectively, and \(E\) is the Young's modulus. The plates and the welds were assumed to have the same material properties as CHS [31].
## 2 Corpora
### ItalianPVS  
Each speaker in the publicly available ItalianPVS corpus [33] ([[https://ieee-dataport.org/open-access/italian-parkinsons-voice-and-speech](https://ieee-dataport.org/open-access/italian-parkinsons-voice-and-speech)]([https://ieee-dataport.org/open-access/italian-parkinsons-voice-and-speech](https://ieee-dataport.org/open-access/italian-parkinsons-voice-and-speech)), last accessed on 9 January 2022) recorded five sustained vowel sounds, as in Neurovoz, with two iterations each. This study considers two subsets from this group--the Healthy Elderly group (HC) with 22 participants, and the Parkinson's Disease group (PD) with 28 participants. All speakers included in this corpus were recorded in Bari (Puglia region), Italy. None of the subjects reported any language or speech disorders. Each recording session occurred under a controlled environment, considering factors including room temperature, microphone distance, time of day and having a conversation with the subject to warm up their vocal muscles. The sampling rate was 16 kHz. Table 1 contains the number of recorded phonations per vowel, and Table 3 includes the corpus demographic information. More information can be found in [33], although the severity of the disease for participants is not reported.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Neurovoz**} & \multicolumn{2}{c}{**ItalianPVS**} \\ \hline
**Vowel** & **PD** & **HC** & **PD** & **HC** \\ \hline /a:/\({}_{1}\) & 44 & 47 & 28 & 22 \\ /a:/\({}_{2}\) & 43 & 44 & 27 & 22 \\ /e:/\({}_{1}\) & 44 & 20 & 28 & 22 \\ /e:/\({}_{2}\) & 37 & 45 & 27 & 22 \\ \hline /i:/\({}_{1}\) & 44 & 47 & 28 & 22 \\ /i:/\({}_{2}\) & 43 & 38 & 27 & 22 \\ \hline /o:/\({}_{1}\) & 36 & 47 & 28 & 22 \\ /o:/\({}_{2}\) & 43 & 44 & 27 & 22 \\ \hline /u:/\({}_{1}\) & 41 & 47 & 28 & 22 \\ /u:/\({}_{2}\) & 5 & 18 & 27 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of sustained vowel recordings per class and corpus for Neurovoz and ItalianPVS.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Female**} & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Total**} \\ \hline  & **PD** & **HC** & **PD** & **HC** & **PD** & **HC** \\ \hline Subjects & 21 & 23 & 23 & 24 & 44 & 47 \\ \hline Age, average (std) & 70 (8.6) & 69.5 (7.4) & 67.0 (10.2) & 61.0 (7.5) & 68.4 (9.5) & 65.1 (8.5) \\ \hline Age range & 56–86 & 58–86 & 41–80 & 53–77 & 41–86 & 53–86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Age demographics from the Neurovoz corpus, organized by gender and class. Each category has similar numbers of participants, age means, standard deviations and ranges.
# Adjustable Capacity Evaluation Method Based on Step-by-Step Power Mapping of Offshore Wind Farms
## 4 Result
### Analysis and Calculation of Submarine Cable Loss  
The power curve of the wind farm is shown in Figure 7.  
As can be seen from the figure, before about 14:00, the power of the wind farm was maintained at a high level, and the total power was greater than 150 MW. After 14:00, the total power of the wind farm dropped significantly, and the overall power was lower than 100 MW. Moment power is close to zero.  
The loss of the wind farm is shown in Figure 8.  
Combined with Figure 7, it can be found that the submarine cable loss has a strong positive correlation with the power, so the power is selected as the input of the neural network. The evaluation index of the calculation results adopts the Mean Absolute Error (\(MAE\)) and the Root Mean Square Error (\(RMSE\)).  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}}{\left|{{{\hat{y}}_{i}}-{{y}_{i}}}\right|}, \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}{\left({{{\hat{y}}_{i}}-{{y}_{i}}}\right) ^{2}}}}, \tag{15}\]  
where \(N\) is the number of samples, \({{\hat{y}}_{i}}\) is calculated value, \({{y}_{i}}\) is actual value.  
Figure 7: Power curve of offshore wind farm.  
Figure 9 shows the result of calculating the loss of the submarine cable using the wind turbine power, the number of hidden layer nodes is set to 8. Figure 10 shows the result of using the collector system, the number of hidden layer nodes is set to 5. The error comparison is shown in Table 1.  
Figure 8: The loss of the wind farm.  
Figure 10: Calculation of cable loss results based on the power of the collector system.  
From the comparison of the calculation results and errors, it can be seen that the calculation using the power of the collector system can better reflect the real change trend of the submarine cable loss, and the error is significantly lower than that of the wind turbine power. This is because the dimension of the fan power data is high, which affects the fitting effect. The dimension of the data is reduced after using the power of the collector system, so the error is smaller.
## Abbreviations  
ANOVA Analysis of Variance  
BAET Bis(2-aminoethyl) terephthalalamide  
BBR Bending Beam Rheometer  
BHETA Bis(2-hydroxyethyl) terephthalalamide  
BMD Balanced Mix Design  
cPE Commercial Polyethylene  
DOC Dissolved Organic Carbon  
DSR Dynamic Shear Rheometer  
EVA Ethylene Vinyl Acetate  
FM Fluorescence Microscopy  
FTIR Fourier-Transform Infrared Spectroscopy  
GHG Greenhouse Gas  
HDPE High-Density Polyethylene  
LCA Life Cycle Assessment  
LDPE Low-Density Polyethylene  
LLDPE Linear Low-Density Polyethylene  
MCP Microplastic Contaminant Particle  
MP Microplastic  
PAHs Polycyclic Aromatic Hydrocarbons  
PAV Pressure Aging Vessel  
PE Polyethylene  
PET Polyethylene Terephthalalate  
PP Polypropylene  
PS Polystyrene  
PU Polyurethane  
PVC Polyvinyl Chloride  
rABS Recycled Acrylonitrile Butadiene Styrene  
RTFOT Rolling Thin-Film Oven Test  
SBS Styrene Butadiene Styrene  
SCB Semi-Circular Bending  
SEM Scanning Electron Microscopy  
SPLP Synthetic Precipitation Leaching Procedure