esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Gender Bias in Auditory-Perceptual Ratings of Tracheoesophageal Speakers
## 7 Conclusions  
This study empirically examined the potential relationship between speaker gender and listener perceptions of tracheoesophageal (TE) speech. The study involved collecting auditory-perceptual judgments of two descriptive dimensions--speech acceptability (SA) and listener comfort (LC)--from a group of 16 listeners who were naive to TE speech. Auditory-perceptual judgments were made on two occasions in an effort to determine if knowledge of the speaker's gender influenced a listener's ratings. During the first session, listeners were deceived about the gender of the voice samples presented, and, during the second session, listeners were informed of the true gender of the voice samples. Datarevealed that statistically significant differences in listener ratings were observed for SA when a speaker's correct gender was known. Thus, the same voice samples were rated differently when a speaker was believed to be male versus when they were believed to be female. A similar finding was observed for ratings of LC; however, this trend did not reach significance. Females were judged by listeners to be more unacceptable and to some degree more uncomfortable to listen to when they were known by the rater to be female. When judgments of male speakers were analyzed independent of their female counterparts, no significant differences between how they were rated on SA or LC emerged. Collectively, these findings suggest that at least some degree of gender bias exists in perceptions of TE speech, and that female TE speakers may be disproportionately penalized when compared to their male TE-speaking counterparts. Consequently, consideration of gender and voice quality in those who undergo TE puncture voice restoration deserves continued exploration in the context of postlaryngectomy voice and speech rehabilitation and related outcomes.  
J.L.B.: study conception, design, data acquisition and analysis, interpretation, drafting of manuscript, revising, and final approval. N.J.: data acquisition and analysis, data interpretation, drafting of manuscript, revising, and final approval. P.C.D.: study conception, design, data acquisition and analysis, data interpretation, drafting of manuscript, revising, and final approval. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Board of Western University (#104645), June 2017.  
Informed Consent Statement: Informed consent was obtained from all participants involved in this study.  
Data Availability Statement: Data is contained within the article.  
The authors would like to thank [PERSON] for his assistance in preparing digital voice stimuli and for measuring the raw listener data, respectively.  
The authors declare no conflicts of interest.
## 3 Mechanical Parameters of Rock Mass Under the Influence of Freeze-Thaw Cycles
### Uniaxial Compressive Mechanical Properties of Rock Specimens Under Freeze-Thaw Cycles  
Low temperatures induce the freezing of pore water into ice within rock masses, which generates volumetric expansion, leading to progressively increasing frost heave pressure. This process drives the propagation of existing fissures and nucleation of new cracks, resulting in severe microstructural damage. Consequently, the strength and deformation characteristics of the rock are significantly degraded. The complete stress-strain curves for dacite, magnetite, and skarn under varying freeze-thaw (F-T) cycles are presented in Figure 3.  
As shown in Figure 3, the stress-strain curves of the three rock types demonstrate notable similarities. Specimens subjected to 0 and 30 freeze-thaw cycles exhibited a longer elastic phase with no distinct yielding stage, showing pronounced brittle failure characteristics. This phenomenon can be attributed to the relatively low damage degree in  
Figure 3: Stress-strain curves of rocks under uniaxial compression conditions. (**a**) Dacite; (**b**) magnetite; (**c**) skarn.  
Figure 2: Experimental equipment. (**a**) JS-DW-40 ultra-low temperature freezer. (**b**) GAW-2000 electro-hydraulic servo rigid pressure tester. (**c**) TAW-2000 electro-hydraulic servo three-axis testing machine.  
specimens after 0 and 30 freeze-thaw cycles. However, for specimens subjected to 60 and 90 freeze-thaw cycles, failure does not occur abruptly. Instead, after experiencing a yielding stage prior to peak stress, the rock strength gradually decreases post-peak with an extended softening phase. This behavior arises from the combined effects of enhanced micro-pores and newly generated cracks formed by water-ice phase transitions during repeated freeze-thaw cycles; progressive crack propagation under frost heave pressure; cumulative damage accumulation leading to increased ductility; and transitional rock behavior from brittle to plastic failure modes with cycle intensification. Mechanical parameters derived from uniaxial compression tests under varying F-T cycles are summarized in Table 1.  
The damage morphology of the three rock specimens under uniaxial compression testing is shown in Figure 4. As can be seen, the damage morphology of the three rock specimens mainly includes tensile damage, conical damage, and diagonal shear damage, with tensile damage being predominant. The specimens demonstrate progressive proliferation of fracture cracks and intensified localized spalling phenomena with increasing freeze-thaw cycles.
# Assessing Evolution of Microservices Using Static Analysis
## 4 Evaluation Case Study
### Service View Metric Results  
The results of the two service view metrics (S1 and S2) demonstrate the evolution across the three versions in terms of microservice growth and communication intensity of their connections.  
**S1: Number of Microservices (\(\#\mathbf{\mu_{s}}\))**.  
It decreased from 46 in v0.0.1 to 40 in v0.2.0 and then slightly increased to 42 in v1.0.0. This indicates a reduction in the system's high-level components or a consolidation of functionality.  
**S2: Number of Microservices Connections (\(\#\mathbf{C\mu_{s}}\))**.  
It decreased from 135 in v0.0.1 to 91 in v0.2.0 and remained relatively stable at 90 in v1.0.0. This suggests a decrease in the interdependence between microservices or a more optimized communication pattern.
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 3 Results
### Model Implementation
\begin{table}
\begin{tabular}{c c c} \hline
**Date and Time** & **Abnormal Component** & **Preventive Action Taken** \\ \hline
5 April 2023 10:10:00 & Overloaded Transformer & Preventive Maintenance \\
12 April 2023 15:45:00 & Damaged Transmission Line & Equipment Replacement \\
18 April 2023 13:20:00 & Failed Substation & Substation Replacement \\
26 April 2023 09:15:00 & Blizzard Interruption & Line Repair \\
2 May 2023 20:30:00 & Control System Failure & Restoration of Control \\
9 May 2023 14:55:00 & Transformer Overload & Load Reconfiguration \\
16 May 2023 18:40:00 & Deterioration in Connectors & Connector Replacement \\
23 May 2023 07:10:00 & Storm Outage & Real Time Monitoring \\
30 May 2023 22:25:00 & Substation Damage & Substation Repair \\
6 June 2023 16:50:00 & Disruption due to Accident & Equipment preparation \\ \hline \end{tabular}
\end{table} 
Table 7: Record of anomalous components and preventive actions taken. Implementing these models in a smart city environment not only contributes to the security of electrical infrastructure but also has a positive impact on citizens' quality of life. Early detection of anomalies allows you to avoid unplanned power outages, reducing interruptions in power supply and improving energy efficiency. A complete evaluation of anomaly detection models in a smart city framework requires considering their generalizability, robustness, and scalability. The results are promising, but analyzing how these models can adapt to new data sets or unforeseen scenarios is crucial. The ability of models to identify anomalies in different contexts is essential for their applicability in constantly changing urban environments. Cross-validation should be performed on additional data sets or data collected at later time points to evaluate the generalizability of the models. In addition to generalization, it is necessary to analyze the robustness of the models. This involves verifying how the models behave when faced with data with varying levels of noise or disturbances. For example, in an urban environment, data can be affected by unexpected events such as storms, network outages, or sensor malfunctions. Models must be able to maintain adequate performance even in adverse situations. Sensitivity tests can be performed by introducing artificial noise or anomalous data into the test set and observing how the models respond. The ability of the models to handle larger data sets or larger urban environments is a crucial aspect to consider. As a smart city grows and accumulates more data, models must be scalable to remain efficient and effective. How models perform when faced with data sets significantly more significant than the original training set should be investigated. This may involve the use of parallel or distributed data processing techniques.
## 3 Periodic Wireless Sensor Network Model
### Network Topology Model  
It is assumed that a wireless network node \(v_{i}\in S_{D}\) can correctly receive data from another wireless network node \(v_{j}\in S_{D}\) when it is in listening mode (\(e_{2}^{i}=1\)) and the power level \(Pr^{i}\) of the signal received from node \(v_{j}\) is greater than an assumed threshold value--receiver sensitivity--\(Ps^{i}\) and greater by a value \(\gamma\) than the noise and interference power \(Pn^{i}\) ([32]), where \(\gamma\) corresponds to the minimum value of the coefficient \(SNIR_{min}\) assumed by the designer. These conditions are defined by parameter \(A_{jk}^{i}\), which takes value 1 when the transmission from the node \(v_{j}\in S_{D}\), which is in the energy state \(k\in S_{D}\), to the node \(v_{i}\in S_{D}\) operating in the listening mode is possible. Otherwise, the parameter takes a value equal to 0. The situation where too low a power level of the signal received by node \(v_{i}\) does not allow the signal to be decoded correctly and at the same time increases the level of interference when receiving other signals is described by the parameter \(B_{jk}^{i}=1\).  
\[Pn^{i}= \max\{Ps^{i},Pn^{i}+\gamma\} \tag{4}\] \[A_{jk}^{i}= \left\{\begin{array}{ll}1&Pr^{i}\geq Pm^{i}\quad:k\in\{3, \ldots,K\},\\ 0&\text{ wireless communication is not possible},\end{array}\right.\] (5) \[B_{jk}^{i}= \left\{\begin{array}{ll}1&\quad kTB<Pr^{i}<Pn^{i}\quad:k\in\{ 3,\ldots,K\}\\ 0&\text{no interference},\end{array}\right. \tag{6}\]
## 3 Conceptual Design  
The IIP aims to bridge the communications between everyday objects (_i.e._, information providers) and Internet-based services (_i.e._, information consumers), acting as a service broker between the two entities. This broker is expected to break the information reusability issue in a vertical \"silo\" integration approach that has been chosen by many personal wearable device vendors, including within the healthcare sector; see Figure 1a.  
Patients are faced with various healthcare-related devices from different vendors in their daily activities, and the information these devices gather is commonly only used by specific services provided by the devices' vendors. Other Internet-based services have no or very limited possibilities to utilize such information, so it is common that similar information is redundantly gathered by different devices for their own services. This tight coupling between devices and services can be solved if device vendors provide open APIs that enable service developers to make use of the gathered information in their services. However, the integration normally still follows a point-to-point approach, as shown in Figure 1a, where each service is directly communicating with each device that provides the information. The downside of such point-to-point integration is, from the information providers' (_i.e._, device) perspective, that they have to send newly gathered information to different services that are interested in using it. This is particularly an issue for battery-powered wireless devices. A brokered approach, as shown in Figure 1b, tackles this issue by delegating the information distribution task to the broker, so that information providers only need to send newly collected information once to the service broker. The service broker provides convergence for information gathering from various different devices that the patients encounter.
# Precision Agriculture: Assessment of Ergonomic Risks of Assisted Driving System
Precision agriculture (PA) is an approach that allows farmers to precisely observe, measure, and respond to variability in soil and crop characteristics within and between fields [5]. It is a strategy that utilizes technologies to integrate data from multiple informational layers for decision-making in the management of agricultural systems. It focuses on optimizing productive and qualitative efficiency; reducing business costs; optimizing inputs, thereby minimizing environmental impacts; and creating entrepreneurial opportunities such as consultancy firms, contract farming, and innovation brokers [6]. A system for implementing precision agriculture is the use of prescription maps and Global Navigation Satellite Systems (GNSSs) with the aim of reducing the consumption of water, fertilizers, and pesticides by adapting them to changes in soil conditions, thus reducing environmental impact and, at the same time, increasing yield and productivity [5; 7]. In this field, it is important to pay attention to the risks that the use of new agricultural technologies can entail to the workers. Previous studies have highlighted the prevalence of ergonomic risks that can cause musculoskeletal disorders (MSDs) in the use of agricultural tractors [8; 9; 10; 11]. In fact, vibration exposure and incorrect postures maintained for a long time can produce stress on the musculoskeletal system with the risk of developing MSDs such as pathologies of the spine, cervical and back-lumbar areas, and shoulders [8]. A previous study investigated the ergonomic comfort of operators driving different types of agricultural tractors: traditional, satellite-assisted, and semi-automatic [11]. It was observed that the semi-automatic driving mode required minimal muscular effort compared to the other driving modes. In semi-automatic driving, the operator only steered during turning maneuvers at the field's end. Meanwhile, the automatic control system, guided by the GNSS receiver atop the tractor's cabin, autonomously directed the tractor along the predetermined field line, controlling the steering wheel and front axis [11]. It is also important to highlight how satellite-assisted and semi-automatic systems included the presence of a display inside the cabin where the route was displayed on the map. However, this previous study did not take into account the position of the display in the tractor with respect to the driver [11]. The aim of this study was to evaluate the comfort and muscular activation of the operators, in relation to the position of display of the driving system applied to a tractor for semi-automatic driving during the harrowing operation.
## 4 Implementation and Results
### Training and Validation
#### 4.4.2 Enhancing Accuracy  
In order to enhance the optimum structure of the model and its accuracy, we perform hyperparameter tuning. Performing hyperparameter tuning can minimize loss function and give good results in the classification. For instance, [PERSON] et al. defined hyperparameters for three machine learning models (ANN, SVM, and KNN) to enhance the result of predicting the performance of the stratified thermal energy storage tank [62].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Distance to Oil Palm** & **Distance to River** & **Distance to Road** \\ \hline distance\_op\_mean & distance\_rivers\_mean & distance\_road\_mean \\ distance\_op\_min & distance\_rivers\_min & distance\_road\_min \\ distance\_op\_max & distance\_rivers\_max & distance\_road\_max \\ \hline \hline
**Distance to Vector** & & \\ distance\_concession & & \\ distance\_between\_patches & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assessment methods for the evaluation of distance criteria.  
We used hyperparameter tuning to identify the best-fitting parameters among the ones given to the model. Therefore, to determine the best-fitting parameter, we define parameters and their corresponding ranges. Our model tuning is based on 35 samplings of hyperparameter values. Table 2 shows the range of the parameters given to the model.  
This tuning was performed with the'mean' dataset. The highest accurate model is performed with a threshold for oil palm \(\geq\)30% and palm oil area \(\geq\)1 ha.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 6 Future Research Directions
### Adaptive Spectrum-Sharing Scheme  
In cognitive satellite-terrestrial networks, it is important to effectively use the underutilized spectrum resources without deteriorating the primary communication. Although the considered underlay mode could improve the spectrum utilization, the accompanied CCI is the main concern, but not for overlay mode, where secondary satellite communications have minimal collisions with terrestrial communications by continuously sensing spectrum holes. It is attractive to maximize the long-term throughput of the secondary user with minimal interference to primary users and low sensing overhead. To achieve this goal, an adaptive spectrum-sharing scheme should be developed, which enables the satellite communication to switch between overlay and underlay modes flexibly.
# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
### Waveform Retracking
#### 3.2.1 Novel and Improved Retracking Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Publication** & **Satellite Mission** & **Data** & **Retracker** & **Validation Data** & **Accuracy Metrics** \\ \hline \multirow{3}{*}{[64]} & \multirow{3}{*}{CryoSat-2 (InSAR)} & Baseline C Level-1b & \multirow{3}{*}{ImpMWaP} & Retrackers (NPPTR & RMSEs = 0.085 m-0.573 m, \\  & & (L1b) and Level-2 & & [0.5], NPPTR [0.8], & lowest mean RMSE = 0.175 m \\  & & (L2) & & NPOR, MwaPP, & and lowest Std = 0.23 m \\  & & (L2) & & ESAL2) and in-situ & obtained by ImpMWaP \\ \hline \multirow{3}{*}{[75]} & \multirow{3}{*}{T/P, Jason-1/-2/-3} & SGRD versions-T/P: & Modified algorithm & Retrackers (Ice, \\  & & MGDR-B, J-1: E, J-2: & to use the first & MLE3, MLE4, T, ST, \\  & & D, J-3:D & & subwaveform & MST) and in-situ & \\ \hline \multirow{3}{*}{[120]} & \multirow{3}{*}{Sentinel-3A} & \multirow{3}{*}{L1b and L2} & A new approach to & In-situ gauge, & RMSE = 0.077 m and R = 87\% \\  & & & & Level-2 data, and & using optimal sub-waveform, \\  & & & & & RMSE = 0.08 m and R = 85\% \\  & & & & & using Mean-all sub-waveform. \\  & & & peak of a waveform & & Sea-ice) & RMSE = 0.14 m and R = 65\% \\ \hline \multirow{3}{*}{[67]} & \multirow{3}{*}{CryoSat-2 (InSAR), Sentinel-3, Jason-2/3} & \multirow{3}{*}{-} & \multirow{3}{*}{AMPDR (AMPDTR + AMPDORR)} & In-situ gauge and 7 & mean-RMSEs = 0.149, 0.139, and 0.181 m and Std = 0.16, \\  & & & & [0.5], NPPTR [0.8], NPPCR, MWAPP, & 0.16, and 0.24 m obtained by \\  & & & & NPOR, MWAPP, & Cryosat-2, Sentinel-3, and \\  & & & & SWMOSA, ALES+) & \\ \hline \multirow{3}{*}{[73]} & \multirow{3}{*}{CryoSat-2 (LRM, InSAR)} & Baseline-C L2 for LRM and L1B for InSAR & \multirow{3}{*}{APD-PPT} & Jason-2 LRM, \\  & & & LRM and L1B for & In-situ gauge, and & Postrackers \\  & & & & (WWMPW, PPT, \\  & & & OCOG, MBP, ALES) & \\ \hline \multirow{3}{*}{[66]} & \multirow{3}{*}{CryoSat-2 (InSAR)} & \multirow{3}{*}{L1b} & \multirow{3}{*}{INPPTR} & Retrackers (NPPTR, \\  & & & NPPOR, MWAPP, \\  & & & and In-situ gauge & \\ \cline{1-1}  & & & & \\ \end{tabular}
\end{table} 
Table 4: Publications that include novel or improved retracking algorithms for the measurements. RMSE stands for Root-mean-square-error, r: Pearson correlation, MAD: median of absolute deviation, subRMSE: unbiased RMSE, std: standard deviation.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 2 Mission and Payload Operations
### Short Revisit Time at High Latitudes  
**Mission Goal 2** states that the revisit time of dedicated areas of interest should be 3-72 h, to monitor the temporal changes on an hourly time-scale of ocean color.  
One example of low revisit times is a series of three consecutive captures taken over the same location in Greenland (81.7\({}^{\circ}\)N, 27.3\({}^{\circ}\)W). RGB composites are shown in Figure 3. A common region in each of the pictures is shown with red rectangles. The different imaging angles for each pass complicate co-registration. The low revisit time can be valuable and utilized when the satellite is part of targeted research campaigns, as described in the next section.
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 2 Materials and methods
### Methods
#### 2.3.2 Coefficient of Variation  
The coefficient of variation (\(C_{p}\)) is a statistical index that describes the ratio of a variable relative to its mean, and is regarded as a useful indicator of interannual variability in an ecosystem. Specifically, a lower \(C_{p}\) value indicates a lower fluctuation and greater stability of the vegetation, while a higher \(C_{p}\) value indicates less stable vegetation. The calculation is as follows:  
\[C_{p}=\frac{1}{\overline{NDVI}}\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\big{(}NDVI_{i }-\overline{NDVI}\big{)}^{2}} \tag{6}\]  
where \(NDVI_{i}\) is the NDVI value in year \(i\) and \(\overline{NDVI}\) is the average NDVI value from February 2000 to December 2019.  
In this study, the stability of the interannual variation of the NDVI in Inner Mongolia was expressed using \(C_{p}\) to indicate the fluctuation of NDVI in each region over a 20 year period. The \(C_{p}\) of the NDVI in Inner Mongolia during 2000-2019 was calculated pixel-by-pixel.
## 3 Assessment of Precise MGEX Orbit and Clock Products
### Orbit Comparisons  
Satellite orbits from different ACs were compared at 15 min intervals for the along-track, cross-track and radial components. The comparison results will be discussed according to the individual Galileo, BDS and QZSS constellation.