esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Controlled Expiratory Pressures on Cerebrovascular Changes During Valsalva Maneuver
## 4 Discussion
### Limitations  
In this study, we observed no increase in the RI at 30 mmHg VM; however, because the RI was measured in the large-diameter CCA, the RI in the peripheral blood vessels of the brain was unknown. Considering the small diameter of the peripheral blood vessels in the brain, vascular resistance may be higher; therefore, it will be necessary to observe hemodynamic changes at pressures lower than 30 mmHg. In addition, considering the research results showing that the increased blood velocity in POST2 can increase cerebrovascular risk, it is necessary to confirm the expiratory pressure that can stimulate only vascular stiffness while minimizing the increase in PSV and RI. In this study, cerebral blood velocity, including in the MCA, was not directly measured. Future studies should further investigate changes in cerebral blood velocity.  
The cerebral hemodynamics (rSO2 and HbO) and vascular system were depended on strain intensity, body position, and VM phases. Thus, future studies should include time-specific dynamic responses corresponding to the phases of VM and the factors influencing hemodynamics and vascular system. Finally, because this study was conducted in healthy young adults, the results cannot be extrapolated to patients with vascular diseases or the elderly. Furthermore, in this study, vascular stiffness was measured only once immediately after the VM, so only temporary changes in vascular stiffness were confirmed. To achieve clinical implications, a future study investigating the changes in PWV after VM to determine the duration of its effect is necessary. In addition, customized VM pressures that take into account individual characteristics, including age, disease, and vascular health status, should be considered. Therefore, additional research for VM is needed on appropriate and/or personalized pressures in patients with vascular diseases.
# A New Data Processing Approach for the SHPB Test Based on PSO-TWER
## 2 Related Work
### The Stress Equilibrium Factor  
The assumption of stress uniformity is a precondition for proving the validity of test data in the SHPB test [12]. Due to the difference in wave impedance between elastic bars and rock materials, the transmission and reflection are relatively complicated in rock materials. It takes a certain amount of time for a rock specimen to reach the stress equilibrium; the equilibrium time can be calculated with the length of the specimen and the p-wave velocity, as shown in Equation (3):  
\[\tau_{\mathrm{s}}=\frac{L_{\mathrm{s}}}{C_{\mathrm{s}}} \tag{3}\]  
This phenomenon of stress difference at the two ends of specimens during dynamic loading determines the stress response and equilibrium effects in rock specimens. The first method is the relative stress difference (\(\alpha_{k}\)) between the two ends of the specimen, where a smaller value represents a higher state of stress equilibrium inside the specimen, and \(\alpha_{k}\) is calculated as shown in Equation (4):  
\[\alpha_{k}=\frac{\Delta\sigma_{k}}{\overline{\sigma_{k}}}\times 100\%=\frac{T_{BS}[ \sigma_{\mathrm{i}}(t_{k})-\sigma_{\mathrm{i}}(t_{k-1})]-F_{SB}\Delta\sigma_{ k-1}}{T_{BS}\frac{\sigma_{\mathrm{i}}(t_{k})+\sigma_{\mathrm{i}}(t_{k-1})}{T_{ BS}\sigma_{\mathrm{i}}(t_{k-1})}+F_{SB}\overline{\sigma}_{k-1}}\times 100\% \tag{4}\]  
Figure 2: Waveform signals and denoising effects of different filters: (**a**) original signal and smoothed signal; (**b**) fractal dimension calculation at incident wave; (**c**) fractal dimension calculation at transmitted wave; (**d**) fractal dimension at incident wave; (**e**) fractal dimension at transmitted wave.  
where \(\Delta\sigma_{k}\) and \(\overline{\sigma_{k}}\) are the stress difference and the stress average at the two ends of the rock specimen, respectively. Furthermore, \(T_{BS}\) and \(F_{SB}\) are the transmission and reflection coefficients, respectively, while \(t_{k}\) and \(\sigma_{i}(t_{k})\) are the time of the \(k\)th propagation of the stress wave in the granite specimen and the corresponding stress, respectively. Another calculation index used to judge the stress equilibrium state of the specimen is the stress equilibrium factor. When the stress equilibrium factor is close to 1, this means that the rock specimen has reached the stress equilibrium state, which can be calculated by Equation (5):  
\[\sigma_{\text{eq}}(t)=\frac{\sigma_{T}(t)}{\sigma_{I}(t)+\sigma_{R}(t)} \tag{5}\]  
where \(\sigma_{\text{eq}}(t)\) is the stress equilibrium factor of the specimen at time t, and \(\sigma_{I}(t)\), \(\sigma_{R}(t)\), and \(\sigma_{T}(t)\) are the stress of the incident, reflected, and transmitted waves at time t, respectively, in MPa.  
Figure 3: The waveform signals and denoising effects of different filters: (**a**) original signal and smoothed signal; (**b**) fractal dimension calculation at incident wave; (**c**) fractal dimension calculation at transmitted wave; (**d**) fractal dimension at incident wave; (**e**) fractal dimension at transmitted wave.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 5 Performance Evaluation
### Compared Algorithms
#### 5.2.2 Ogp  
In order to reduce the expected packet latency in duty-cycled WSNs, [PERSON] et al. [19] proposed a geographic routing algorithm, called optimal geographical progress (OGP). Each node maintains a set of possible forwarders that make geographical progress of more than a threshold and forwards the packets to the first wake-up node from among possible forwarding candidates. [PERSON] et al. showed that the threshold value has a great impact on the packet E2E delay, and a low latency can be obtained by using an appropriate threshold value. Note that OGP does not have a mechanism to determine the duty cycle interval, so we used the duty cycle interval computed by the proposed DDS scheme for the OGP algorithm.
## 2 Related Work and Motivation
In [15], sink mobility is used to improve network lifetime and energy efficiency in three dimensional UWSNs. An optimal mobile sink strategy is proposed to maximize network lifetime by converting each mobility pattern into a combination of circular motions via mapping. MobiRoute defined by authors in [16] supports sink mobility. Sink's controlled and predictable mobility and pause time are favorable features of MobiRoute. Autonomous Underwater Vehicle (AUV) aided Underwater Routing Protocol (AURP) in [17] minimizes total number of data transmissions by using multiple MSs or AUVs as relay nodes, which results in high packet delivery ratio and low energy consumption. In AUV aided Energy Efficient Routing Protocol (AEERP) for UWSNs [18], MS collects data from gateway nodes. Among all the nodes, gateway nodes communicate with MS. Gateway nodes are selected on the basis of their residual energy. Localization-free, non-cooperative and cooperative, depth-based routing protocols for UWSNs are stated here. In [3], a novel routing protocol known as Depth-Based Routing (DBR) is proposed that uses depth of sensor nodes as a routing metric and forwards data towards sink by applying greedy algorithm. In [4], an energy efficient routing protocol, named as Energy Efficient Depth Based Routing (EEDBR) is presented. EEDBR utilizes depth as well as residual energy of sensor nodes as routing metrics so that network lifetime can be improved. Authors in Improved Adaptive Mobility of Courier Nodes in Threshold-Optimized DBR (iAMCTD) [5] propose a forwarding function based routing scheme. It is a localization-free and flooding-based routing protocol for underwater applications. iAMCTD achieves energy conservation by incorporating threshold optimized data routing. CoDBR [6] is cooperative depth based routing protocol in which cooperation is employed at network layer. Cooperative routing increases reliability and throughput of the network. Potential relays are selected on the basis of depth information. Data from source node is cooperatively forwarded to the destination by relay nodes. In UWSNs, high throughput along with reliability of the received information, reduced delay, low BER and energy conservation are of great concern. Novel routing solutions are therefore required for efficient data routing. Cooperative communication achieves high throughput along with reliable data transfer by reducing BER, however, it results in high propagation delay. Sink mobility reduces propagation delay and achieves energy conservation, however, it does not cater for reliability of received information. In this regard, many cooperative communication protocols [12] are proposed that either investigate the physical layer or the MAC layer, however, the network layer is still unexplored. More specifically, cooperative routing has not yet been jointly considered with sink mobility. Therefore, this work focuses on an efficient mathematical model for cooperative routing along with sink mobility in UWSNs because we are interested in taking advantage of positive aspects of both the techniques along with minimizing their drawbacks.
# Oral Microbiome and Host Health: Review on Current Advances in Genome-Wide Analysis
## 7 Host-Oral Microbiome Interactions in Health
\begin{table}
\begin{tabular}{p{42.7 pt} p{341.4 pt} p{341.4 pt}} \hline \hline
**Factor** & & **Reference** \\ \hline \multirow{4}{*}{Genetics} & - & Genetic polymorphism in miRNA202 is involved in hBD1 salivary level as well as caries experience [64] & - & Genes expressed in dental enamel development are associated with molar–incisor hypomineralization [65] \\  & - & GLUT2 and TAS1R2 genotypes individually and in combination are associated with caries risk [66] & - & Host genetic control of the oral microbiome in health and disease [67] \\  & - & Microbial abundance and some aspects of the microbial population structure are influenced by heritable traits in saliva [68] & \\ \hline \multirow{4}{*}{Immunity} & - & Immune cell network mediating immune surveillance at oral mucosa and gingiva [69; 70] & \\  & - & The innate host response in caries and periodontitis [71] & \\  & - & Secretory immunity with special reference to the oral cavity [72] & \\ \hline \multirow{4}{*}{Attachment surface} & - & Surface properties influence oral biofilm formation [73] & \\  & - & Differences in relation to the microbial diversity of modified resins during the initial phase of biofilm maturation [74] & \\  & - & Biomaterial-associated infection of implants and devices [46] & \\ \hline \multirow{2}{*}{Diet} & - & Vegan diet influences on the human salivary microbiota [75] & \\  & - & Short- and medium-chain fatty acids exhibit antimicrobial activity for oral microorganisms [76] & \\ \hline \multirow{4}{*}{Cigarette smoking} & - & Smoking decreases structural and functional resilience in the subgingival ecosystem [77] & \\  & - & Firmicutes were statistically elevated in smokers at the expense of Proteobacteria and Fusobacteria in non-smokers [78] & \\  & - & Tobacco smoking affects the salivary gram-positive bacterial population [79] & \\ \hline Alcohol & - & Alcohol affects to the oral microbiome composition [80; 81; 82] & \\ \hline Oral hygiene & - & Toothbrushing frequency is related to the incidence and increment of dental caries [83] & \\ \hline \multirow{4}{*}{Socioeconomic status} & - & Socioeconomic factors, such as education and income, are associated with disparities in the prevalence and severity of periodontal disease [84] & \\  & - & A strong association between carigenic bacteria and socioeconomic status was found [85] & \\ \cline{1-1}  & - & Differences in socioeconomic status were reflected in the bacterial profile of saliva [86] & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Host Factors to Modulate the Oral Microbiome.
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## Experiments
### Experimental Results and Analysis
Notably, it achieves a superior balance between simplification rate (91.89%) and processing speed (0.01289 s), highlighting its efficiency and practicality for real-time applications. The algorithm significantly reduces the data volume to 15,906 downsampled points from the original 196,133, maintaining essential details without the extensive manual parameter tuning required by other methods. This adaptability not only streamlines the downsampling process but also enhances usability,  
Figure 11: Comparative experimental evaluation of various downsampling methods. Voxel downsampling: (**a**) leafsize = 0.1, (**b**) leafsize = 0.01, and (**c**) leafsize = 0.05. Uniform downsampling: (**d**) every k points = 10, (**e**) every k points = 25, and (**f**) every k points = 100. Random downsampling: (**g**) leafsize = 0.1, (**h**) leafsize = 0.01, and (**i**) leafsize = 0.05. Grid downsampling: (**j**) target number of triangles = 5000, (**k**) target number of triangles = 10,000, and (**l**) target number of triangles = 15,000. Cluster downsampling (**m**) leafsize = 0.1, (**n**) leafsize = 0.01, and (**o**) leafsize = 0.05. FPS: (**p**) target number of triangles = 5000, (**q**) target number of triangles = 10,000, and (**r**) target number of triangles = 15,000.  
setting a new benchmark in 3D point cloud processing. The comparison underscores the algorithm's potential to revolutionize point cloud processing tasks, offering a user-friendly, efficient, and effective solution for various applications.
## 4 Hardware Description
### Sensor Node Design  
The diagram of the sensor node can be seen in Figure 3.  
The sensor node is powered by a 3.7 V 3500 mAh PoLi battery, which directly supplies the WiPy module (its power range is 3.5 V to 5.5 V) by connecting it to the Vin pin. The WiPy has a voltage regulator that provides a stable voltage of 3.3 V and 550 mA, externally connected to pin 3V3 of the module. This voltage is used to provide power to the sensors of the node through a PNP transistor that allows, controlling its state (cut off/saturation), for the power supply activation of the sensors. The activation of this transistor is controlled by pin P9 of the module, configured as a digital output. As the 3V3 connection to the sensor power supply can be controlled by software, maximum energy optimization is achieved as the sensors are only powered when measurements are needed.  
Figure 3: Sensor node architecture diagram.  
Figure 2: WiPy 3.0 development board based on ESP32 chip.  
The sensor nodes are able to measure temperature, relative humidity and light conditions through the use of these components:  
LDR NSL06553 (Advanced Photonics, Camarillo, CA, USA). This sensor is used for light detection in undesired truck door opening events. LDR is connected to a fixed 113 k\(\Omega\) resistor, making a resistive divider that is powered by 3.3 V sourced by the DC/DC converter of the WiPy. This resistive divider provides a voltage output proportional to the LDR value and connected to P19 (ADC1_4) of the WiPy module.  
DHT22 module (Aosong Electronics Co., Ltd. Guangzhou, China). This device is based on AM2302 that includes one NTC temperature and a humidity-sensing component formed by two electrodes with a moisture-holding substrate between them. The temperature range is \(-40\) to 125 \({}^{\circ}\)C (accuracy \(\pm 0.5\)\({}^{\circ}\)C and resolution 0.1 \({}^{\circ}\)C) and the relative humidity range is 0% to 100% (accuracy \(\pm 2\)% and resolution 0.1%). A one-wire proprietary bus is used for communication between digital I/O P11 of WiPy module and DHT22. The sensor data frames are composed of 40 bits [57] (see Equation (1)).  
\[\text{DATA}=16\text{ bits }_{\text{RH\_data}}+16\text{ bits }_{\text{Temperature\_data}}+8\text{ bits }_{\text{check-sum}} \tag{1}\]  
In addition, the device can measure the battery voltage using another resistive divider connected to analogic P16 (ADC1_3 of WiPy module).  
The size of the Electronic PCB is 73 \(\times\) 28 mm. A 3D view of the PCB is depicted in Figure 4, where the battery and programming connectors can be observed, and the plugs for LDR and DHT22 (JP4) are presented. Two push-buttons are added for reset and programming sequence purposes.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 2 System Model
### Random Beamforming at Receiver  
We adopt random beamforming at a desired receiver for decoding the signal from the selected \(S\) users [28]. For each time slot, the base station constructs beamforming vectors represented by an \(M\times S\) matrix,  
\[\mathbf{U}=\Big{[}\mathbf{u}^{[1]},\cdots,\mathbf{u}^{[S]}\Big{]}, \tag{1}\]  
where \(\mathbf{u}^{[l]}\in\mathbb{C}^{M\times 1}\) is the \(l\)-th orthonormal random vector and generated according to the isotropic distribution for \(l\in\{1,\cdots,S\}\). The information of generated beamforming vectors is broadcasted to all transmitters for scheduling process. The detailed scheduling procedure will be explained in Section 3.
## 5 Discussion  
The proposed model demonstrated technical efficiency through a case study of a restaurant in a tourist city. The results showed that SA of SM can be successfully incorporated into PE, is relevant for generating a BSC, and offers support for making better strategic decisions. In addition, the system is versatile and can be applied to various companies regardless of the size and type of business. To reinforce the implementation, we suggest that the planning team commit to the importance of SP and value potentially useful information that can be obtained from the opinions of SM customers. Natural language processing (NPL) continues to challenge researchers, so the method used here aimed to correct spelling errors, abbreviations, spams, etc. The application of various approaches, such as LBA and MLA, allowed for the comparison of polarity results and contributed to the algorithms employed. Python programming language and the support of its libraries enabled the process to be made more efficient, quicker, and automatic in the model. This work allows for the incorporation of technological tools and innovation into an enterprise's processes. As a medium-term goal, it is intended to completely digitize processes and develop marketable software to assist the SP task in this sector. The main advantages of the proposed model concerning related work are as follows:  
* Incorporating two lines of research that seem opposed but work together, that is, the SA of SM in SP for the generation of a BSC.
* The system offers the opportunity to integrate and recognize expert knowledge and continuously validate the results.
* The application of opinion mining algorithms enables the detection of customer sentiments and their satisfactory grouping towards the KPIs of interest.
* The information can be treated anonymously without violating the rights of the SM users or discriminating against the opinions of the planning team.
* The automatic processing of this information reduces the time required for this type of exercise by approximately 70%.  
The proposed model demonstrates that SP can be addressed and improved using several methods and approaches. Despite the benefits highlighted by this study, more effort is required to address some drawbacks. For instance, on the one hand, within the existing limitations in the model, we understand that, at the moment, there is no friendly and easy-to-use interface. In the same way, programming knowledge is required so that the system can be powered. On the other hand, the databases used should be expanded so that the results have greater consistency.  
In future work, it is proposed that the system can detect the bots that may exist in the comments issued, the influence behind the post, and the sarcasm within the opinions. Moreover, the opportunity to integrate more robust and consistent databases, possibly from new SM, is becoming increasingly important for companies. The recent announcement of Meta by Facebook has evolved the digital connection of SM. Therefore, we consider that more powerful methods, such as ours, will contribute to the evolution and benefits of what this implies.
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
## 2 State-of-the-Art Literature Review  
As mentioned above, existing works are framed in the context of available individual (i.e., disaggregated) mobility data. In [1], to develop a communication infrastructure for connected and automated vehicles (CAVs), a clustering algorithm is proposed, whose evaluation is carried out via simulations that make use of a Gauss-Markov mobility (GMM) model [12; 13] as a complementary tool for estimating future locations of single vehicles. In [2], the mobility prediction for individual vehicles is addressed within a grid partition of the city's geographical space, and a second-order Markov model is assumed to characterize each driver's mobility preferences within the grid. The employed hybrid architecture that combines a convolutional neural network (CNN) and a recurrent neural network (RNN) makes use of disaggregated historical grid occupation values, which are available for each individual vehicle; the resulting scheme improves the quality of vehicle mobility prediction, especially for those vehicles that have a strong individual mobility preference. In [3], the same authors consider a similar scenario to address individual mobility prediction to support intelligent vehicle applications; they propose a deep RNN-based algorithm whose results match the theoretical analysis and improve state-of-the-art previous results. In [5], a deep RNN making use of a long short-term memory (LSTM) architecture is proposed to predict individual vehicle mobility on a grid, based on individual sensor data; the prediction step is followed by a vehicle recruiting algorithm to optimize crowdsensing; the results improve the quantity of collected sensing data versus existing algorithms. In [4], a model based on a general-purpose sequence prediction model, named the compact prediction tree (CPT), is proposed for driver route prediction; the results show an efficient noise tolerance of the proposed algorithm. The prediction of individual vehicle trajectories, based on consecutive previous GPS locations combined with a statistical inference module for online mobility prediction, is proposed in [6]. This inference module is based on a hidden Markov model (HMM), and the prediction stage employs an improved version of the Viterbi algorithm. Such improvements reduce computational costs. In [14], the authors present a hierarchical trajectory prediction structure using a capsule neural network (CapsNet) that embeds local temporal and global spatial correlations through hierarchical capsules represented on a grid map. This procedure outperforms state-of-the-art methods.  
So far, all the existing proposals address individual vehicle prediction making use of disaggregated available data. The restriction of only having access to aggregate data is one of the novel contributions of the work presented here.