esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
## 3 Results
### Nasal Obstruction Assessment
#### 3.1.2 Acoustic Rhinometry  
Acoustic rhinometry (AR) is a noninvasive method for measuring the nasal cavity geometry by detecting sound wave reflections, which aids in the diagnosis and monitoring of nasal obstruction [33]. It allows for objective measurements of minimal cross-sectional area (MCA), minimal cross-sectional volume (MCV), and total nasal volume (TNV).  
Minimal cross-sectional area (MCA): MCA is the narrowest section of the nasal airway, often located at the nasal valve or anterior inferior turbinate, and is used to evaluate airway patency [34]. Two studies in this review assessed MCA using acoustic rhinometry [22; 24]. In the first [22], individuals with a deviated septum showed significant increases in MCA at one and three months postoperatively compared to baseline (\(p<0.001\)). However, there was no difference between the one- and three-month values (\(p>0.05\)). In the second [24], both right and left MCA1 and MCA2 values were significantly different between the nasal obstruction and control groups (\(p<0.01\)).  
Minimal cross-sectional volume (MCV): MCV measures the volume of the nasal passage near the MCA, allowing for volumetric assessment of constricted areas. In one study [24], MCV1 and MCV2 assessments for both nasal sides revealed significant differences between the nasal obstruction and control groups (\(p<0.01\)).  
Total nasal volume (TNV): TNV is estimated by integrating the area-distance curve from the nasal entrance to a predetermined posterior point in the cavity [35]. TNV values are reported to improve after surgeries for nasal obstruction [22].
## 2 Materials and Methods
### Methodology
#### 2.3.2 Analysis of Carbon Sequestration Driving Contribution Using GWR-RF  
GWR-RF combines the advantages of Geographically Weighted Regression (GWR) and Random Forest (RF). GWR addresses spatial non-stationarity in geographical phenomena by creating local regression models, thus considering the local effects of spatial objects [68]. RF, known for its robustness against overfitting and ability to gauge the significance of variables, constructs multiple decision trees, with the final prediction determined through a voting process among these trees [69].  
GWR-RF integrates a spatial weight matrix (SWM, a matrix measuring spatial proximity) and RF into a local regression analysis framework, enabling the handling of high-dimensional variables with nonlinear relationships and multicollinearity. The process involves [34; 35]: (1) establishing SWM for each spatial unit in the study area,  
\[w_{ij}=\{\begin{matrix}[1-(\frac{d_{ij}}{b})^{2}]^{2},d_{ij}\leq b\\ 0,d_{ij}>b\end{matrix} \tag{2}\]  
where \(d_{ij}\) is the distance between county \(i\) and county \(j\), and \(b\) refers to the bandwidth, (2) selecting adjacent elements of each spatial unit based on SWM, (3) constructing local RF models using these units and their neighbors, and (4) repeating these steps to build local RF models for each spatial unit, estimating the importance of local variables. We employed a 5-fold cross-validation approach for hyperparameter optimization of the model and selected the median value of 50 random seed results as the final model to ensure its robustness.  
In the analysis of driving forces within the GWR-RF framework, the absolute change in CS is the sum of the absolute changes in various influencing factors, and the contribution of human activities, climate change, increase in CO\({}_{2}\) concentration, nitrogen deposition, photosynthesis, and soil nitrogen and phosphorus to the change in CS in each county is the proportion of the absolute value sum, in%. To validate the superiority of the GWR-RF attribution analysis method, meanwhile, we also selected ordinary least squares (OLS), GWR, and RF as a comparative study of GWR-RF to evaluate the performance of these models in terms of indicators such as residual sum of squares (RSS), Akaike information criterion (AIC), AIC with correction (AICc), coefficient of determination (\(R^{2}\)), and adjusted \(R^{2}\) (_Adj._\(R^{2}\)).
## 4 Analysis of Experimental Results
### Influence of Operating Posture on Separation Effect  
Large particles are more obvious in the separation by gravity. In order to study the impact of large sand particles on the separation effect of the separator, in the actual experimental process in addition to the addition of quartz sand, but also in the circulating water tank, a small amount of large sand particles, with a particle size of about 1 mm, was added. As shown in Figure 13, when the separator was placed vertically, the experimental results and the simulation cloud showed that the sand was separated and settled to the bottom of the separator. When the separator was placed parallel to the ground, a unilateral accumulation of sediment was formed due to the influence of gravity, but the location of the sediment accumulation was also at the bottom of the separator. Therefore, the above  
Figure 11: Comparison curve of pressure drop test and simulation of separator.  
Figure 12: Changes in separator phenomena with increasing inlet flow rate.  
two cases show that the separator exhibits good performance in separating mud and sand in different operating postures.
# Assessment of the Structural State of Dissimilar Welded Joints by the Acoustic Emission Method
## 4 Discussion
There are at least three reasons why these AE signals can be associated with the presence of diffusion layers in the dissimilar welded joint. Firstly, these AE signals waveforms are specific for specimens with diffusion interlayers; when testing defect-free welded joints, signals of this waveform were not recorded. Secondly, signals of a specific waveform appear at a stress of 250-300 MPa. This level of stress equals the ultimate tensile stress of the ferrite phase. Moreover, an additional maximum of the AE hit rate appeared in the specimen with interlayers under static tension at such loads. In addition, such an AE waveform was detected during tension of dissimilar welded joints with diffusion interlayers in [22]. Based on highly mentioned reasons, it can be concluded that AE signals with a repeated waveform with different amplitudes and low AE rise time (up to 10 \(\upmu\)s) are markers of diffusion layers presence in a dissimilar welded joint.
## 3 System Design and Implementation  
Medical resources are very precious. Normally hospitalized all-day-long care is only applied to patients who are in critical condition and need urgent medical treatment. Out-patients with chronic diseases that need only intensive care are not advised to occupy a hospital bed, but instead they need intensive home care to ensure their diseases are under control. Therefore, self-monitoring, self-management, mobility and flexibility are the key concepts for success of the implementation of a ubiquitous healthcare system for out-patients with chronic diseases. A ubiquitous healthcare system must be a robust, reliable and convenient application, so that patients can do around the clock monitoring and go around without any restrictions.  
The proposed system is mainly for ECG monitoring and heart rate estimation, which is useful in the detection of the underlying heart conditions of individuals and the rehabilitation of patients recovering from recent heart attacks. Figure 1 presents the system design of the proposed real time ECG monitoring and analyzing on an Android(tm) mobile device with hardware examples, communication protocol, and software implementation. There are three architecture layers in the proposed idea: the BSL(body sensor layer), PNL (personal network layer) and GNL (global network layer). The first layer, BSL, consists of ECG sensor nodes worn on the patient's body. The ECG sensor node in the BSL communicates with the PNL which consists of a base station node and an AndroidTM smart phone exploiting the IEEE 802.15.4 Zigbee based communication protocol, to measure and transmit the real-time quality ECG signal wirelessly from ECG devices on the patient's body to the mobile device for display and analysis. A smart phone with a base station node in PNL acts as a higher level data and communication coordinator, and allows for user-interaction with the body sensor network. An AndroidTM mobile device with wireless networking capabilities has the ability to communicate with a higher application services layer, the web server at GNL, with any mobile network provided by the telecommunications service provider, such as CDMA or GSM, 3G networks and Wi-Fi services. This enables the data transmission and communication between patients and doctors who are at a distant location.  
In short, the proposed ubiquitous healthcare system is divided into two modes as shown in Figure 1, the real-time mode and the store-and-forward mode. In real time, the ECG signals are available to be viewed and monitored on the smart phone device immediately after the vital sign acquisition from the sensor node and the base station node. A patient is able to capture his heart condition through the real time signal presentation and analyze the performance immediately. In the store-and-forward mode, the mobile phone transfers data such as the medical history and the summary reports to the web server side for further comprehensive analysis. The data is stored and it can be accessed and retrieved at a later time as well.  
Figure 1: System design of real time ECG monitoring and analyzing on an AndroidTM mobile device.
## 5 The Evolution towards a Global Smart Grid Deployment: Challenges and Perspectives
### Data Communication and WSANs
#### 5.1.1 Harsh Environmental Operation Scenario  
Electrical grids constitute a hostile environment for any installation of communication technologies, especially when the transmission is realized without wires. The electromagnetic pollution generated by high currents and voltages, as well as high power commutation devices (present in DC-DC converters and inverters) are sources of problems for the correct operation of WSANs: sensors and actuators will be impacted not only from the point of view of radiofrequency interferences, but also in terms of high humidity levels, dust, caustic or corrosive ambiances and vibrations. Concerning the network communication topology, the varying channel characteristics and eventual node failures can modify the network topology, disturbing the information exchange between sensors. The reliability of the communication network is also perturbed in terms on latency and packet errors. In MGs, the impact caused by islanding must be also considered, in terms of network topology variation and electrical disturbances that will be generated.  
Therefore, it is crucial to assure the robustness of WSANs in order to achieve a correct operation and communication between nodes. Traditionally, wired communications such as Power Line Carrier (PLC) have been employed to transmit information through the electrical grid [85]. Nevertheless, PLC is also affected by the power lines characteristics [86; 87]. Thus, wireless communication protocols can be utilized, assuring their immunity face to disturbances. A possibility is to implement fault diagnosis methods to detect failures that can occur in any component of the node (sensor, actuator, transceiver, memory, or power system) [88].
# Mesoporous Carbon as Galvanic-Corrosion Activator Accelerates Fe Degradation
## 1 Introduction  
Biodegradable metal materials, including iron (Fe), magnesium and zinc, have currently drawn much attention for their potential bone repair application [1; 2; 3; 4]. Among them, Fe is recognized as the preferable choice to provide structure support for the defect bone in load-bearing area due to its excellent mechanical properties [5; 6]. Meanwhile, Fe is one of the essential nutritional elements, and involves a variety of oxidases and metabolic enzymes [7; 8]. Its favorable biocompatibility has also been confirmed by in vitro and in vivo research [9; 10]. Unfortunately, Fe degrades too slowly in the physiological environment, which significantly restricts its further clinical orthopedic application [11; 12].  
To improve the corrosion behavior, previous researchers incorporated the noble phase into Fe matrix as a cathode to enhance micro-galvanic corrosion [13; 14; 15]. For instance, [PERSON] et al. [16] added Pd into Fe, which improved the degradation rate from 0.04 to 0.12 mm/year. [PERSON] et al. [17] incorporated graphene oxide into Fe as a galvanic corrosion couple, which enhanced the degradation rate from 0.09 to 0.41 mm/year. Mesoporous carbon (MC) possesses a holder electrode potential (0.207 V) than Fe (\(-0.440\) V) [18]. More significantly, it possesses a high specific surface area, which can increasearea ratio between the cathode and anode [19]. Previous studies confirmed that an increased area ratio of cathode and anode could enhance the galvanic corrosion effect [20; 21; 22]. Moreover, MC has excellent electrical conductivity, which can decrease the electron transfer resistance and increase corrosion current density [23; 24]. Thus, MC is expected to act as an effective galvanic corrosion activator in the Fe matrix. Besides, MC was reported to be of favorable biocompatibility, and has been used as drug delivery for biomedical application [25].  
Based on the above considerations, herein, MC was introduced into Fe matrix aiming to obtain Fe-based composite with tailored degradation rate. Fe/MC composite was manufactured using laser additive manufacturing. Compared with other technologies, laser additive manufacturing can produce bone implants with a complex structure and personalized shape [26; 27; 28; 29]. The microstructure characteristic, mechanical properties, as well as the corrosion behavior were studied systematically. Meanwhile, the corresponding corrosion mechanism was elaborated. Moreover, the cytocompatibility of Fe/MC composite was also evaluated.
## 4 Discussion
### Effect of Drainage Trough Depth on Run-Over Risk  
Figure 10 shows that contact scores for jumping impact scenarios are heavily influenced by drainage trough depth. The limb contact score falls from 44% to 18% as the trough depth increases from 0 to 1 m. Contact scores from standing and lying show less dependency on trough depth. The secondary contact phase for a jumping pedestrian occurs later than for standing and lying. This yields two benefits: there is additional time for the train to decelerate before run-over can occur and there is more time for the pedestrian to fall into the trough before roll-over.  
The greatest change in contact scores can be seen between as trough depth increases from 0-0.5 m, while additional trough depths yield little further benefit. This is likely due to the secondary contact position of the pedestrian. Cases which yielded no benefit from a 0.5 m trough due to their secondary contact positioning are unlikely to benefit from a 1 m trough. Conversely, pedestrians who avoided the train due to a 0.5 m trough will not further benefit from a 1 m trough. Third rail contact does not significantly depend on trough depth, with a 2% reduction between 0 and 0.5 m, and the contact score for third rail contacts remains between 20 and 25% for the full simulation test sample. Due to the nature of rigid multibody modelling, this is likely an overestimation as the pedestrian's limbs or other body regions cannot be severed. This can cause the pedestrian in the model to be dragged along the rails, increasing the likelihood of third rail contact. Overall, a drainage trough with a depth of 0.5 m may reduce the risk of wheel-pedestrian contact but will offer no benefit for third rail contact prevention. Future studies optimising the geometry of the trough through both width, depth, and curvature may offer further insights into the safety benefit. This research could be used to inform future track design policy.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 3 Proposed Scheme
### Image Encryption  
After vacating room, the image owner randomly selects an integer \(r_{i,j}\), \(i=1,2,\cdots,M\), \(j=1,2,\cdots,N\) for each pixel \(p_{i,j}\), and encrypts the image with the public key \(pk\) to obtain the ciphertexts.  
\[M(C_{p_{i,j}^{\prime}})=(C_{1,i_{j}},C_{2,i_{j}})=(r_{i,j}G,M(P_{i,j}^{\prime} )+r_{i,j}pk) \tag{18}\]
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## Data Bus Blood Oxygen Sensor  
The main modules of the fixed measurement station are: general purpose input/output (GPIO), Serial and radio frequency identification (RFID). These modules are part of the patient identification and data collection module. After identifying the patient, the sampled values are automatically assigned to a specific user. The data captured during sampling include blood oxygen, respiration rate, and galvanic resistance.  
The fixed measurement station consists of two principal devices: the sensor unit and a Raspberry Pi chip. The sensor unit receives the sample information from the sensors and transmits the data via a serial link. The Raspberry Pi, programmed in Linux, acts as an intermediary between the sensors and the cloud. Finally, Raspberry Pi stores the sensor readings, user data, and replicates the information in the PlaMoS database, located in Cloud, as a redundant safety feature. Figure 3 shows a block diagram of the PlaMoS fixed measurement station with the implemented programming languages, along with all of the processes.  
The blood oxygen value is obtained using a blood oxygen sensor (MAX30100). This sensor implements two LEDs (infrared and red) that emit light at different wavelengths. The light passes through the user's finger to be measured by a photodiode. Oxyhemoglobin and deoxyhemoglobin absorbs infrared and red light differently. The different light absorption level is then calculated by the MAX 30100 and applied in the algorithm illustrated in Figure 4.  
Figure 2: PlaMoS fixed measurement station hardware.  
The respiration rate is calculated measuring the duration of a single breath and extrapolating the average number of breaths per minute. The respiration sensor obtains the threshold of every breath. A timer is then used to measure the time between any two thresholds and to calculate the breaths per minute. Figure 5 represents the algorithm employed to calculate the respiration rate.  
Figure 4: Blood oxygen monitoring algorithm.  
Figure 3: Block diagram of the PlaIMoS fixed measurement station.  
The galvanic skin response monitoring is used to detect the physiological state of users by measuring their sweat gland activity. The galvanic sensor measures the skin response voltage and calculates the value by using an analog to digital converter (ADC) converter. Figure 6 presents the algorithm implemented for this measurement.  
Figure 5: Respiration rate monitoring algorithm.  
Figure 6: galvanic skin response monitoring algorithm.  
The final version of the PlaIMoS fixed measurement station is shown opened in Figure 7 and closed in Figure 8.  
The package information generated by the fixed measurement station sent to the Web service has the following structure (Table 2).
## 1 Introduction
### Research Contribution  
The main contributions of this research are:  
* Deployment of trust in VANET utilizing VFC and hybrid integration of OAs, which include Cuckoo/CSA (ABC) and Firefly (GA) with authentication/KDE. VFC provides a search space for information processing and achieves efficiency in computational overhead due to advantages in rapidly stored vehicular information processing using the V2V and V2 RSU, and RSU2 FS communication behavior in this research.
* Real-time detection of all forms of attacks, including HDSA attack detection, such as DoS JSA, PD, and RCRCO in VANET, to provide trustworthiness in the network.
* Provision of IEEE 802.11p benefits information processing. which utilize hybrid multicast and unicast broadcast data transmission in VANET for efficient and real-time transmission of safety information.
* Provision for single next hop vehicle (SNHV) probability analysis for efficient data processing within the elliptical segment area transmission range (ESATR) in VANET.
* Provision for regression model prediction based upon reduced delay and jitter in VANET for secure road safety provision.  
The rest of the paper is organized as follows. Section 2 presents the related work. Section 3 presents the secure real-time detection of DoS attack models (DAM) and jamming signal attack models (JAM). Both attack models provide Hybrid DoS attack model (HDAM) prevention mechanisms in VANET. Section 4 presents the preventive mechanisms and the system models, including the system architecture model (SAM) and elliptical segment area transmission range model (ESATRM), OA deployment, and trustworthiness of nodes of the proposed scheme. Section 5 presents the resulting analysis and discussion. Section 6 is a miscellaneous section for background study comparison of VANET protocols, and Section 7 presents the conclusions.
## 4 Discussion of the Results Analysis: First Findings on Trends and Best Practices
### Main Findings for the Grey Literature Sample  
The statistics related to the Grey literature shown in Section 3.1.3 can help to achieve an understanding of the current state of the use of RS and geometric technologies across Europe if a careful reading of the indexed texts is also undertaken. While the numbers found reassure us that \"Photography\" and \"Satellite Remote Sensing\" are increasingly established technologies--and in particular the use of \"Aerial\" and \"Optic and/or multi-spectral\" data, respectively--there are, however, some considerations to make to highlight the commonalities and differences across the analyzed documents and thus across the European countries.
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 2 VoIP Application and Scenarios
### VoIP Implementation Scenarios
Some of the common VoIP codecs are G.711, G.729, G.722, G.723, and G.726. VoIP codecs performance has been studied in, e.g., [11], which uses G.711, G.729, and G.723 in an indoor environment; the results show that G.711 outperforms the other two due to A-law/u-law algorithmic properties related to compression and high bandwidth usage features. In our experiments, we have investigated VoIP applications in various scenarios and identified the challenges that VoIP applications face and the possible ways for improving VoIP quality of service. The G.711 and G.729 codecs are used in our experiments since they are commonly used for providing better call quality and better bandwidth usage, respectively. In [24; 39] VoIP codecs have been used in various scenarios and G.711 and G.729 are considered as the most widely used.