esa-sceva/satcom-chunk-collection huggingface dataset:
# DIVA Meets EEG: Model Validation Using Formant-Shift Reflex
## 2 Materials and Methods
### Experimental Phase
#### 2.2.2 Experimental Setup  
This work reports reflexive responses in controls tested in an altered auditory feedback paradigm such as that utilized in [18].  
Participants were seated in a comfortable chair inside a double-walled, sound-attenuating booth meeting the ANSI S3.1-1999 standard. A microphone (B&K 4961) was positioned approximately 10 cm from the participants' mouth at a 45-degree offset in the axial direction.  
The acoustic signal was calibrated to physical units of dB SPL (dB re 20 \(\mu\)Pa) using a Larson Davis calibrator (model CAL200, Depew, NY, USA).  
Speech was sampled at 48 kHz using a MOTU Microbook IIc sound card and the CueMix FX software. Participants' voices were played back to them over closed-back, over-the-ear AKG K240 Studio Headphones, with a mean latency of \(\sim\)18 ms. This latency is lower than that at which feedback delays are perceived (50 ms) [52]. The speech level of the participant determined the amplitude of the speech playback.  
Participants were instructed to read a series of texts presented on a screen (white font on a black background) positioned 70 cm away and adjusted in the vertical axes to the eye level of the participants at a comfortable conversational pitch and loudness. The text series comprised repetitions of the Spanish monosyllabic words: /mes/, /pep/, and /ten/. Words were presented for 2.5 s, at a presentation rate of 0.25 Hz (one word every 4 s to prevent the participants from developing a constant rhythm and the automatic character of their production). A total of 648 stimuli were presented, distributed in 6 blocks of 108 trials. In each block, stimuli were distributed in a random order. Participants were asked to sustain the vocalization of the vowel until the end of each word's presentation. No additional instructions were provided.  
A 10-trial training session was conducted prior to the start of the experiment to ensure that participants were familiar with the experimental setup, familiar with stimulus timing, and comfortable with sustaining vocalizations.
## 3 Network Slicing in 5G Networks
### Energy Demand of Network Slicing Service Types  
Every NS in the 5G network is uniquely identified through the Single Network Slice Selection Assistance Information (S-NSSAI) identifier [23]. This identifier is signaled between the UE and the 5G network during the signaling procedure and it assists the network in selecting a particular NS instance that provides the UE with a specific service across the fixed/wireless access and core part of the network. The S-NSSAI consists of the  
Figure 3: Concept of 5G network slicing [10]. eight-bit Slice/Service Type Identifier (SST ID) that specifies the network slice's features and services and a 24-bit Slice Differentiator (SD), which is an optional element that, when combined with SST, provides additional information for distinguishing between multiple slices with the same SST. Currently, up to eight different signaling messages S-NSSAIs that are exchanged among the network and UEs are allowed by the 3 GPP. This means a single UE may be served by a maximum of eight different NSs at the same time. It is possible to use **non-standardized S-NSSAIs** that can use only SST ID alone or SST and SD identifiers, and this **non-standardized S-NSSAI** enables operators to specify their own services in specific NSs. However, to achieve global interoperability for NS, which enables MNOs to support roaming between NSs more efficiently, the SST values for the most commonly used NSs are standardized in [24; 25; 26; 27]. Currently, standardized **S-NSSAIs** with only SST IDs are presented in Table 2, along with the general description of the main use cases of NSs and their energy demand. The currently standardized SSTs assume next NS use cases in 5G networks that include the following: enhanced mobile broadband connectivity (eMBB), ultra-reliable low-latency communication (URLLC), massive internet of things (MIoT), vehicle-to-everything (V2X) services, high-performance machine-type communications (HMTC), and high-data-rate and low-latency communications (HDLLC). In terms of the management of energy consumption, different SST types play an important role in the deployment and differentiation of mobile networks with NSs [4], since every NS SST ID or use case has a different energy demand due to differences in the practical implementation scenarios of a specific SST type.
## 2 Study Area and Data
### Data
#### 2.2.2 Yield Data  
Australia's major dryland crops are selected for yield estimates and validation, including 35 fields of canola, 123 fields of wheat, and 52 fields of barley (Table S1, Supplementary Materials). The average area of these fields is \(\sim\)112 ha with a standard deviation of \(\sim\)69 ha. Using various yield monitoring systems mounted on grain harvesters operated by farmers, these observed data were collected at these sites across the country from 2009 to 2015 (Figure 1). The data obtained by these commercially available yield monitors were used to construct a yield data image at 5-m resolution [44], which was upscaled to 25 m and 250 m to match the Landsat and MODIS resolutions, respectively.
## 4 Discussion
### Clinical Implications  
The findings of our research aim to provide information to people from various domains, such as long-distance walkers, by offering insights into the decision-making process in route selection and strategies that consider individual characteristics while accommodating special needs and travel preferences [1]. Combining individuals' specific needs with the changing walking conditions along a route may enable walkers to anticipate the challenges posed by uphill and downhill sections, allowing them to adjust their pace and conserve energy accordingly. Public health providers can also benefit from understanding the potential effects of walking and advocating for it as a therapeutic intervention to enhance overall physical health. Indeed, studies have demonstrated that the efforts required to overcome the challenges faced during uphill and downhill walking are correlated with enhanced cardiovascular health [3; 4], increased muscle activation [7; 8], and the management of chronic conditions like diabetes [6], obesity [5], COPD [13] and osteoarthritis [66]. In addition, healthcare professionals, including physical therapists and rehabilitation specialists, can utilize spatiotemporal parameters to adjust and optimize rehabilitation programs [28; 29] by improving certain functional abilities (e.g., muscle strength) or identifying specific gait abnormalities and tracking changes over time. Understanding ground reaction force and GRF patterns may also facilitate the implementation of intervention programs to improve weight distribution and reduce joint stress, thus alleviating asymmetries or excessive joint loading. This would be particularly important in the prevention of musculoskeletal injuries resulting from frequent and prolonged walking on various surfaces, such as blisters [67] and ankle sprains [68]. Likewise, COP data may allow for the design of exercises that specifically target balance deficits and enhance overall stability. Understanding the impact of gait mechanics and ground reaction forces can assist footwear designers in developing shoes with suitable cushioning and support to minimize impact forces and reduce the risk of injury during activities like hiking [27]. Finally, such findings may assist government agencies and organizations responsible for the management of natural parks and open spaces in improving and maintaining existing footpaths [69] and designing new ones to ensure public safety and enjoyment [26]. Knowing how people move on different terrains and slopes can help (i) identify areas prone to accidents or hazardous conditions, (ii) improve accessibility and comfort for all visitors, (iii) identify high-traffic areas and implement measures to minimize erosion, protect sensitive ecosystems, conserve biodiversity and manage resources such as maintenance, signage and infrastructure upgrades, and (iv) create pleasant outdoor spaces experiences for visitors while reducing congestion and conflicts.
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 4 Implemented Statistical and AI Methods
### Support Vector Regression (SVR) Model with a Radial Basis Function (RBF) Kernel  
\[f(x)=\sum_{i=1}^{n}\alpha_{i}K(x,x_{i})+b \tag{3}\]  
where \(f(x)\) is the predicted output, \(n\) is the number of support vectors, \(\alpha_{i}\) is the coefficients associated with the support vectors, \(K(x,x_{i})\) is the kernel function, which measures the similarity between \(x\) and the support vectors \(x_{i}\), and \(b\) is a bias term. The RBF kernel is defined as follows:  
\[K(x,x_{i})=\exp\biggl{(}-\frac{\|x-x_{i}\|^{2}}{2\sigma^{2}}\biggr{)} \tag{4}\]  
where \(\sigma\) is a parameter that controls the width of the Gaussian distribution.  
This equation captures the non-linear relationship between input features and output in the SVR model. The specific form of the kernel function, in this case, the RBF kernel, contributes to the model's ability to capture complex patterns in the data.  
It should be noted that the SVR model is designed to handle non-linear relationships and may not have a simple linear equation like traditional linear regression.
# Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions
## 5 Conclusions  
In this paper, a discrete-time position controller that is suitable for AUVs is developed. The proposed method considers noise and uncertain hydrodynamic parameters and, additionally, all the system parameters to be unknown, except from the rigid body inertia matrix, which is the norm for practical applications. The suggested technique modifies the Ensemble Kalman Filter accordingly to afford accurate positional estimation of an AUV under noisy conditions and major system uncertainties. The contribution of this paper is essential, as, compared to the traditional Ensemble Kalman Filter algorithm, the proposed one is more robust and effective for AUVs.  
The effectiveness of the proposed modified Ensemble Kalman Filter method on quite a few simulated scenarios involving various nuisances and parameter setups is demonstrated. Indeed, the results of simulations highlight that the proposed discrete-time controller achieves the precise positional control for an AUV in the presence of both noise and uncertain hydrodynamics. By comparing the root-mean-square errors of the proposed controller against the conventional linear controller and time-delay controller, it can be found that the proposed discrete-time controller affords the most accurate positional tracking. In particular, the proposed controller outperforms the conventional time-delay controller in root-mean-square error by a percentage range of approximately 72.1-97.4%. Moreover, compared to the model predictive control, the proposed controller takes at least 89.5% less average calculation time. Thus, the proposed controller has the advantages of high tracking accuracy and high timeliness. Future research shall investigate the effectiveness of the proposed controller under rapidly changing system disturbances.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy constraints.[PERSON]  
The authors declare no conflict of interest[PERSON]
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 2 Materials and Methods
### Sample Preparation  
According to the experimental requirements, ethylene glycol was used as the matrix, and the required amount of water was precisely weighed using an analytical balance and added to the ethylene glycol. The prepared solution was stirred on a magnetic stirrer at 500 rpm for 3 min, resulting in 36 samples of ethylene glycol with varying water concentration (called theoretical value). The moisture concentration range was set between 0.002% and 1%, ensuring a gradient distribution of water content across the samples. Among these, 28 sets of data were designated as the training set for model development, and 8 sets of data were reserved as the validation set for model verification. A detailed overview is provided in Table 2.
## 3 Target Data Processing and Computing
### Three Data Processing Paradigms
#### 3.1.2 Stream Processing  
Stream processing is becoming increasingly common as businesses nowadays need to process large amounts of data in real time. It helps to develop responsive applications and accelerate decision making. The most common and popular use cases for data stream processing include real-time data analytics, fraud detection, cyber security, stock market monitoring, healthcare monitoring systems, and sensor data processing. Big data stream processing platforms such as Apache Spark [27], Storm [54], Flink [28], and Kafka [55] are available for stream processing, and each of them is designed to efficiently handle the continuous flow of data from diverse sources. Apache Spark is not only proficient in batch processing but also possesses robust stream processing capabilities, making it a comprehensive choice for hybrid scenarios. Apache Storm specializes in real-time data processing, providing fault-tolerant and scalable solutions for streaming applications. Apache Flink stands out with its sophisticated event-time processing and state management features, which are ideal for scenarios requiring the precise handling of event sequences. Kafka, while primarily recognized as a distributed event streaming platform, complements these frameworks by serving as a distributed messaging system, facilitating the seamless integration and transport of streaming data. In addition, all the commercial and public cloud providers provide services for stream processing application development, such as Amazon Kinesis, Azure Stream Analytics, and Google Cloud Dataflow. Streaming systems process data as soon as they arrive and produce the processing results shortly afterward. In other words, streaming processing provides data management tasks to consume, process, and produce data securely and reliably. Stream processing starts by consuming data from an integrated data source, performing single or multiple stream processing actions on them, then publishing the results back to the data sink.  
Among stream processing platforms, we selected Apache Flink to deploy in edge, fog, and cloud layers. Apache Flink is a powerful, open-source stream processing framework designed to efficiently process large-scale data with low latency and high throughput. Its technological underpinnings revolve around a distributed data stream processing model, providing fault tolerance and stateful processing capabilities. In the context of edge and fog computing, where resource constraints and network challenges are significant, Apache Flink offers support for event-time processing, which enables accurate handling of events across distributed and potentially unreliable environments [28]. This is important for applications at the edge, where time-sensitive data processing is essential. Furthermore, in the case of scenarios where intermittent connectivity or device failures may occur, Flink'sstate management ensures resilience and consistency, overcoming challenges related to network variability and device reliability [56].
## 1 Introduction  
Engineers are familiar with formulations written down in codes and technical handbooks that are used to solve geotechnical issues, however, they do not often have the opportunity to contrast their predictions, therefore the risk of using that formulation is not properly assessed.  
Safety factor design methods are based in evaluating the uncertainty of any input introduces on the calculation. One of these inputs is the verification equation. It is usually named model uncertainty.  
This paper is a practical case where a particular geotechnical formulation is addressed in order to obtain an insight on its uncertainty and to know how to reduce it properly using the outputs of the site investigation. The formulation case is the laterally loaded piles in sand deposits. The piles were installed and tested recently in a new industrial facility in the Persian Gulf.  
Land reclamations are frequent constructions at Persian Gulf, close to the shores of the Arabic Peninsula. For this study case, after building a 4-Ha land reclamation on shallow sea waters, the new platform was tested with an extensive soil investigation. On the platform, four (4) metallic tubular piles were driven and tested by static lateral loads.  
In this paper, the load test records are compared to the predicted displacement from the site investigation using well-known formulation based mainly on SPT test [1, 2]. The analytical methods used are the p-y curve method developed by the API (American Petroleum Institute) [3] and the horizontal ballast modules methods obtained from the deformation modulus, as proposed on several codes.  
The uncertainty of the API and horizontal ballast method is obtained by comparing predicted and measured deflections.
# Global Time-Varying Path Planning Method Based on Tunable Bezier Curves
## 6 Simulation
### Comparison of Different Methods
(**a**) \(t=90\) s. (**b**) \(t=92\) s. (**c**) \(t=94\) s. (**d**) \(t=96\) s. (**e**) \(t=98\) s. (**f**) \(t=100\) s.  
(2) the path is not smooth; and (3) there is a big difference between the paths planned at each time. All in all, in the movement process of the manipulator, other methods are difficult to ensure real-time, global and obstacle avoidance at the same time. As can be seen from Figure 13, feature points change with the change of obstacle position, representing the key information of the obstacle position. It can be seen from the figure that the path planned by the GTVP method is not the shortest path, but the path has smooth characteristics. The GTVP method is used to plan the path in the middle of the obstacles, so that the path is as far away from all obstacles as possible. For real-time path planning, the path planning of each moment with the GTVP method is related to the path planning result of the previous moment, thus reducing the overall simulation time and realizing real-time path planning. In a word, the GTVP method adjusts the path corresponding to each moment through the feature points of obstacles, so as to achieve the requirements of ensuring real-time, global and obstacle avoidance at the same time. Each method is used to plan the path in the environment of \(t=100\) s and the boxplots shown in Figure 14 shows the simulation time statistics for 50 simulations. As shown in the figure, the average times required for simulation using RRT, Q-RRT\({}^{*}\), MDA+RRT and GTVP are 0.008 s, 0.5992 s, 1.6136 s and 0.0934 s, respectively. As shown in the figure, path planning using the RRT algorithm takes the shortest time, but the planned path is not smooth, unfeasible and the path changes greatly at adjacent moments. The path planning time of the GTVP method is much less than the other two methods and the path obtained is smooth and continuous in real time.
## 2 System Architecture
### Information Management System  
The IMS is the control authority of the A-HMI, as it coordinates the information flow between vehicle and driver and manages visual, auditory, and haptic channels shared among various applications, according to driving safety criteria [43]. The generic application request, encoded as a vector that specifies the action and the priority class, is processed by the IMS. Specifically, taking into account the DVE state, the IMS performs the following adaptive decision-management functions [60]:  
* change of output modality and channel (displays, audio devices, etc.);
* change of physical layout (color, font, format, etc.);
* I/O action coordination based on prioritization, i.e., termination, interruption, retardation, resumption, or suppression of output messages.  
The IMS reply, formulated as a vector as well, sets out the outcome of the request (accepted or delayed), the selected channel(s), and the provision modality for the submitted request, suitably adapted for duration, enhancement, and simplification of DVE actual state, as indicated by the IMS rules. From the management of global information flow perspective, the IMS avoids conflicts and overload of information by queuing application messages appropriately on the basis of DVE state information. The control cycle of the IMS consists of a two-stage process, whose computational logic is implemented in two modules, respectively: (1) Filter and Queue; (2) Modality and Channel. Each module exploits a rules-based logic, developed according to its specific target, by using the requested action vector, the DVE state, and the channels status as inputs of the logic.
## 5 Case Study
### Dynamic Modelling  
Considering the building architecture, Autocad software was used to build the geometric model of the buildings. Then, the physical properties of the envelope and the users' profile were introduced through the TmBuild interface of the transient simulation tool TRNSYS v17 [36]. The weather data of Vitoria-Gasteiz was also included in the simulation by the Typical Meteorological Year provided by the software. Accordingly, the heating demand was calculated hourly. That consumption was compared to the monthly bills, which were at the same time related with specific meteorological conditions, in order to adjust the building-model parameters. In addition, the DHW demand was obtained following the recommendations given by the Spanish Institute for Diversification and Energy Saving (IDAE) [37; 38] which considers the use of the building, the distribution of the dwelling and the number of tenants.  
Figure 4: Schematic of the installation with numbering of flows, labelling of components and legend.  
The heating and DHW demands and the outside temperature during a winter week (from 1 January to 7 January) and a summer week (from 1 August to 7 August) appear in Figure 5. As mentioned, the electric demand is not included since only the thermal demand, covered by boilers and cogeneration engines, was analyzed.  
Afterwards, a Simulation Studio interface was used to model the heating and DHW thermal facility. This tool enables the dynamic representation of the HVAC system by linking different types from the library or from user-made models. Each type corresponds to a specific component of the system and is based on a grey-box modeling. For space reasons, the method to validate the model does not appear along this work, but is the one developed in reference [13], and appears also in reference [5]; if the reader wants to deepen on model validation with Trnsys reference [39], can also be read. Figure 6 depicts the schematic diagram to be followed.  
Throughout the 8760 h of the yearly simulation (starting from 1 January until 31 December), the required data for the mass, energy and exergy balances were obtained from the software, being basically the flow rates and temperatures of each flow. As already mentioned in Figure 2, this is the previous step required for applying thermoeconomic in building thermal systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Investment [€]** & \\ \hline
**C1** & 1 Micro-cogeneration & 20,148 \\
**C2** & 2 Micro-cogeneration & 20,148 \\
**M1** & Mixer of cogeneration & 7679 \\
**D1** & Diverter of cog. \& pump & 8184 \\
**HX1** & Cog. HX\& tank pump & 1720 \\
**T1** & Cog. tank \& pump & 6216 \\
**B1** & Cog. tank \& pump & 21,713 \\
**B2** & 1 Boiler & 21,713 \\
**HC** & 2 Boiler & 1640 \\
**H** & Hydraulic compensator & 6502 \\
**HX2** & Heating dist. \& pump & 2531 \\
**T2** & DHW HX \& primary pump & 6084 \\
**DHW** & Tank \& secondary pump & 21,280 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equipment acquisition costs.  
Figure 5: Heating and DHW demands and outside temperature during (**a**) a winter week and (**b**) a summer week.