esa-sceva/satcom-chunk-collection huggingface dataset:
_Mar. Drugs_**2015**, _13_, 2785-2812; doi:10.3390/md13052785  
_marine drugs_  
**ISSN 1660-3397**  
www.mdpi.com/journal/marinedrugs  
_Article_  
Natural Marine and Synthetic Xenobiotics Get on Nematode's Nerves: Neuro-Stimulating and Neurotoxic Findings in _Caenorhabditis elegans_  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1,2,+ and [PERSON] 1**  
1 Department of Biology, Freshwater and Stress Ecology, Humboldt-Universitat zu Berlin, Spathstr. 80/81, 12437 Berlin, Germany; E-Mails: [PERSON].S.); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (N.S.)  
2 Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China  
\({}^{\dagger}\) Present address: Wenzhou Medical School, Whenzhou 325035, China.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-176-7826-7341.  
Academic Editor: [PERSON]  
_Received: 9 February 2015 / Accepted: 23 April 2015 / Published: 6 May 2015_  
**Abstract:** Marine algae release a plethora of organic halogenated compounds, many of them with unknown ecological impact if environmentally realistic concentrations are applied. One major compound is dibromoacetic acid (DBAA) which was tested for neurotoxicity in the invertebrate model organism _Caenorhabditis elegans_ (_C. elegans_). This natural compound was compared with the widespread synthetic xenobiotic tetrabromobisphenol-A (TBBP-A) found in marine sediments and mussels. We found a neuro-stimulating effect for DBAA; this is contradictory to existing toxicological reports of mammals that applied comparatively high dosages. For TBBP-A, we found a hormetic concentration-effect relationship. As chemicals rarely occur isolated in the environment, a combination of both organobromines was also examined. Surprisingly, the presence of DBAA increased the toxicity of TBBP-A. Our results demonstrated that organohalogens have the potential to affect single organisms especially by altering the neurological processes, even with promoting effects on exposed organisms.  
keywords: tetrahydrocomobisphenol-A; dibromoacetic acid; acute environmental toxicology; neurobehavior; gene expression; GFP; ontogeny; _C. elegans_
## 4 Discussion
For newborns who have shorter ear canal lengths than adults (13-23 mm vs. 25-30 mm), the standing wave effects influence higher frequencies [79]. The frequency region where FPL calibration trended less variable results between trials was only seen at the higher frequencies tested in newborns. A larger sample size is needed to definitively determine if FPL versus SPL calibration is more beneficial for repeated measures at higher frequencies. Overall, it is difficult to get the same exact probe placement in the ear canal for serial measurements and FPL can assist in combatting added variability due to inconsistent probe placements [24]. In order to monitor cochlear health, baseline measurements have to be recorded, thus the fit of the probe tip is crucial. When the yellow eartip that provided a deeper insertion into the ear canal was used, a greater number of newborns (92%) and DPOAE responses were recorded compared to the red eartip (71%). The difference in the recorded DPOAE levels across frequency for the FPL calibration method for the red versus the yellow eartip is negligible (Figure 4), consistent with the literature that FPL is not as heavily influenced by probe placement and insertion depth [24; 25; 26]. However, for SPL calibrations, the yellow eartip generally yielded greater DPOAE levels at most frequencies tested. Monitoring a patient's half wave resonance to ensure a deep insertion in the ear canal and similar placements for subsequent measures will help reduce variability in DPOAE measures. In addition, monitoring SOAEs is beneficial as these responses also can influence DPOAE levels. [PERSON] et al. [75] reported that 85% of newborns have at least one SOAE, with an average of four to five SOAEs per emitting ear. Similarly, in the current study, SOAEs were identified in 86% of newborns with an average of five SOAEs per newborn. Others report that a greater number of SOAEs and higher levels are recorded in females than males [55; 61; 62; 65; 66; 71; 72; 73]. The number of male and female newborns with present SOAEs was similar and the average number of present SOAEs was higher in males (6.75/ear) compared to females (4.7/ear). The SOAE average levels for females and males were only separated by 1 dB, with the females displaying the greater levels. [PERSON] et al. [16] reported that those with SOAEs had significantly greater DPOAE levels. Given the high percentage of newborns with present SOAEs, 32% recorded at frequencies >4 kHz, the recording parameters to evoke HF DPOAEs needs to be maximized in newborns so the influence of SOAE presence on DPOAE characteristics can be examined.
## 1 Introduction
In relation to steering gear applications, fluid pulsations at the pipe's terminus represent a primary source of radiated noise. Therefore, this study simulates realistic boundary conditions pertinent to ship hydraulic pipeline systems and completes an experimental investigation into the frequency domain vibration response of the target pipeline under fluid excitation combined with a fluid noise generator. This work substantiates the engineering applicability of our proposed method. The structure of this paper is organized as follows: In the Section 2, an FSI 14-equation model for a hydraulic pipeline, incorporating Friction coupling, is established. Additionally, the theory of the classical TMM is summarized and its instability mechanisms are analyzed. After that, the fundamental principles of the MVTMM, including the recursive algorithm and matrix representation for complex boundaries, culminating in a comprehensive frequency-domain characteristic analysis method tailored for FSI in pipelines is described in detail. Section 3 validates the performance of the proposed method using data from the Dubee pipeline and the FEM. In Section 4, an experimental investigation into the FSI vibration characteristics of a U-shaped hydraulic pipeline under fluid excitation is conducted to demonstrate the engineering applicability of this method. Section 5 concludes with final remarks.
## 4 Experimental Evaluation
### Primary-Base station nodes radio link experiment  
A similar test is carried out with the Primary-Base Station radio link. One primary node and one base station node are placed in an obstacle-free environment from 0 m to 700 m in 50 m intervals. At each distance, the primary node transmits 30 messages which are received by the base station. The Primary-Base station radio link implements a communication protocol which is in charge of collisions. Therefore, all the frames transmitted by the primary node are received by the base station if it is within the communication range. However, because of the distance, some messages get lost and the primary node must retransmit them until it receives an acknowledgement from the base station. The test messages to be transmitted are made up of 6,000 bits. In a perfect link transmission a frame rate of 9,000 bps is achieved, because of the header and CRC load. However, we observe in Figure 9 this frame rate gets reduced because of the retransmission handled by the protocol. A maximum transmission range of 720 m has been achieved. We have repeated this test for 15 different positions and four different nodes.  
Figure 7: Physical arrangement of nodes for the Primary-Secondary radio link modelling experiment. Figure not to scale.  
Figure 8: Average number of frames received and erroneous messages in a Primary-Secondary radio link experiment for all the distances.  
Figure 9: Average frame rate achieve in the Primary-Base station radio link experiment for different transmission distances.
## 6 Conclusions  
This study investigated the hydrodynamic and morphological effects caused by non-powered floating objects moving over flat seabeds using computational fluid dynamics (CFD) simulations across 96 different scenarios. These scenarios systematically varied towing speed, squat ratio, towing duration, and bow shapes to elucidate their collective impact on sediment resuspension.  
The findings indicate that the towing speed (with Froude number) and squat ratio are the most critical parameters influencing sediment resuspension. Higher towing speeds significantly increased sediment resuspension due to increased flow velocities and shear stresses, while greater clearance heights reduced these effects. The geometry of the floating object also played a significant role; increasing the width amplified sediment resuspension, whereas increasing the length mitigated it. The bow shape affected the amount of sediment resuspension, with blunt bows causing higher sediment disturbance than raked bows.  
The multivariate polynomial regression model developed provides a reliable tool for predicting sediment resuspension based on key parameters, with an R-squared value of 0.9647. The model's effectiveness in capturing the complex interactions between variables emphasizes its potential utility in aiding the design and operational planning of floating objects.  
These findings bear significant implications for the design and operational planning of floating structures in environmentally sensitive marine ecosystems. By optimizing towing speeds, adjusting squat ratios, and selecting appropriate bow geometries, engineers and designers can effectively mitigate sediment resuspension, thereby minimizing the ecological footprint of maritime activities.  
Future research endeavors should aim to expand the dataset to encompass a broader array of operational scenarios and environmental conditions, thereby enhancing the model's generalizability and robustness. Additionally, incorporating supplementary environmental variables and validating the model against empirical field data will bolster its predictive accuracy and applicability. Such advancements will facilitate the development of sustainable maritime practices that harmoniously balance operational efficiency with environmental stewardship, ensuring the preservation of ecological integrity in marine environments.  
Conceptualization, N.G., O.B., M.S.K. and E.I.; methodology, N.G. and O.B.; software, O.B.; investigation, N.G. and O.B.; resources, N.G., O.B., M.S.K. and E.I.; writing--original draft preparation, N.G. and O.B.; writing--review and editing, N.G., O.B., M.S.K. and E.I.; visualization, O.B.; supervision, N.G., M.S.K. and E.I. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Balikesir University Research Grant No. 2019/021.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction
However, the transmission performance of higher-order QAM with soft-decision Viterbi decoding in multipath fading channels has not been sufficiently investigated. On the basis of this background, in this study we investigate the transmission performance of OFDM-based 1024- and 4096-QAM with soft-decision Viterbi decoding in multipath fading channels using link-level simulations. Specifically, we clarify the bit error rate (BER) performance of OFDM-based 1024- and 4096-QAM as a function of coding rate under two multipath fading channel models: extended pedestrian A (EPA) and extended vehicular A (EVA). We also demonstrate the influence of phase error on OFDM-based 1024- and 4096-QAM and clarify the relationship between phase error and the SNR penalty required to achieve a BER of \(1\times 10^{-2}\). This paper is organized as follows. In Section 2, we introduce an OFDM-based 1024- and 4096-QAM transmission model and its associated computer simulation conditions. In Section 3, we discuss the BER performance of OFDM-based 1024- and 4096-QAM in multipath fading channels and the SNR penalty in the presence of phase error. Finally, we conclude our work in Section 4.
## 2 Materials and Methods
### Segmentation Options  
Hide Lumen (first checkbox in the top left side of the panel): this box, which becomes active only after the AAA lumen segmentation, places a \"patch\" (a piece of texture) on the already processed lumen and activates a specific image contrast to make it easier for the user the selection of AAA contours for Total AAA segmentation. It helps also to identify in the image the EPV of the aneurysm.  
Show Contour Lumen (second checkbox from the top in the left side of the panel): activates/deactivates the red outline of the AAA lumen after it is segmented. It acts on all the views.  
Show Contour Total Vessel (third checkbox from the top in the left side of the panel): activates/deactivates the blue outline of the thrombotic part of the total AAA after it is segmented. It acts on all the views.  
Double Pass (last checkbox in the bottom left side of the panel): activates/deactivates a double pass (i.e., head-foot and foot-head) analysis, which repeats the segmentation process for both directions and then merges the results, in order to avoid underestimation of the segmented volume. It is recommended to leave it on and, in case of errors, modify the contour smoothness.  
Contour Smoothness (pop-up menu, in the right side of the panel): five step menu-none, low, medium, high, max-which modifies the regularity of the contour during the segmentation. Changing the contour smoothness step from \"none\" to \"max\" the obtained effect is a gradual increasing in the smoothing of the segmented region profile. Higher values are safer in order to not include unwanted structures (e.g., the collateral small vessels that should be excluded), while lower values are prone to guarantee more adherence to the real silhouette of the segmented anatomical structure. The default value is set to \"medium\" which is considered and tested as a fair compromise; however, the user can try with different smoothness levels if during the visual checking of results, he observes under- or over-estimated segmented regions.
###### Abstract  
A two-stage precoder is widely considered in frequency division duplex massive multiple-input and multiple-output (MIMO) systems to resolve the channel feedback overhead problem. In massive MIMO systems, users on a network can be divided into several user groups of similar spatial antenna correlations. Using the two-stage precoder, the outer precoder reduces the channel dimensions mitigating inter-group interferences at the first stage, while the inner precoder eliminates the smaller dimensions of intra-group interferences at the second stage. In this case, the dimension of effective channel reduced by outer precoder is important as it leverages the inter-group interference, the intra-group interference, and the performance loss from the quantized channel feedback. In this paper, we propose the machine learning framework to find the optimal dimensions reduced by the outer precoder that maximizes the average sum rate, where the original problem is an NP-hard problem. Our machine learning framework considers the deep neural network, where the inputs are channel statistics, and the outputs are the effective channel dimensions after outer precoding. The numerical result shows that our proposed machine learning-based dimension optimization achieves the average sum rate comparable to the optimal performance using brute-forcing searching, which is not feasible in practice.  
m _Applied_  
_sciences_
## 6 Simulation and Evaluation of Results
### Remaining Energy  
Figure 8 represents the remaining energy of IoT nodes in different methods. Based on this figure, CTSRD consumed the least energy in comparison with other schemes. On average, it stores more energy by 16.65% and 35.35% compared to EEMSR and E-BEENISH, respectively. The reason for this issue was stated when examining the network lifetime. Note that E-BEENISH has the worst performance because it focuses only on the energy parameter when choosing CHs and ignores security. Moreover, it does not consider multi-hop paths between CHs. This increases the energy consumption in some IoT nodes. EEMSR has resolved the problems mentioned in E-BEENISH, however, it uses the genetic algorithm in the routing process and may fall into the local optimum. Furthermore, the designed fitness function only focuses on the distance between CHs. That is not enough. In CTSRD, the routing algorithm between IoT nodes is performed using a dragonfly algorithm-based routing tree. When designing this fitness function, intra-cluster traffic and energy are considered to balance the energy consumption between IoT nodes.  
Another experiment expresses the balanced distribution of energy consumption between IoT nodes by evaluating the standard deviation of their consumed energy in Figure 9. If the energy consumed in the IoT network is uniformly distributed between the IoT nodes, the standard deviation is close to zero. However, if the energy consumed by IoT nodes is unbalanced, the standard deviation increases and approaches one. As shown in this figure, CTSRD has a more successful performance than EEMSR in terms of energy consumption (approximately 16.11%) and has a very good performance (almost three times that of E-BEENISH).  
Figure 8: Comparison of different methods with regard to residual energy.  
Figure 7: Comparison of different methods with regard to the network lifetime, and the coordinates of the base station are equal to \((50,50)\).
## 2 CO Device Implementation
### Electronic Circuit
#### 2.4.2 Detection Circuit (Signal Adaptation + Signal Amplification)  
In the detection stage, the following five steps are carried out: (a) The sensor terminals are connected to the Wheatstone bridge according to Figure 3, (b) The chemical sensor is exposed to the carbon monoxide atmosphere placing the electronic circuit in a secure area, (c) When the chemical sensor detects the presence of CO, the Wheatstone bridge has an imbalance and the following conditions are met:  
\[V_{A}\
eq V_{B}\;\textit{and}\;V_{A}<V_{B} \tag{6}\]  
(d) Both voltages \(V_{A}\) and \(V_{B}\) have been compared applying a differential amplifier based on the operational amplifier, see Figure 3.  
From Figure 3, the differential amplifier satisfies  
\[V_{o}=V_{B}\Bigg{(}\frac{R_{6}}{R_{6}+R_{5}}\Bigg{)}\Bigg{(}1+\frac{R_{4}}{R_{ 3}}\Bigg{)}-V_{A}\frac{R_{4}}{R_{3}} \tag{7}\]  
Figure 3: Electronic circuit proposed for the CO detection.  
If \(R_{3}=R_{5}\) and \(R_{4}=R_{6}\), Equation (7) takes the form  
\[V_{o}=A(V_{B}-V_{A}) \tag{8}\]  
where the amplification, \(A=\frac{R_{4}}{R_{3}}=\frac{R_{6}}{R_{5}}\) is a factor and \(V_{o}\) is an output voltage of our differential amplifier. Immediately, we impose the conditions \(R_{3}=R_{4}\) and \(R_{5}=R_{6}\). Then, the output voltage is the difference  
\[V_{o}=V_{B}-V_{A} \tag{9}\]  
(e) The voltage \(V_{o}\) is applied as an input signal to the comparator circuit verifying that:  
\[V_{Alarm}=A_{ol}V_{o} \tag{10}\]  
where \(V_{Alarm}\) is the alarm signal produced by the detection device and \(A_{ol}\) is the gain of the operational amplifier in an open loop. Thus, the signal is amplified by the operational amplifier. According to Equation (10), the device will generate an alarm signal when the chemical sensor is exposed to carbon monoxide, CO.
## 5 Integrating 5G and Tactile Internet to Industrial Case Studies
### New Developments in Wi-Fi for IIoT  
The modern factory is a complex environment. Advanced machines and robots are equipped with a wide of sensors that connect to cloud-based analytics engines that evaluate  
Figure 19: Hierarchical architecture of a smart factory (adapted from [71]).  
Figure 18: A generalized architecture of a wireless tactile internet system in a teaching factory [69].  
performance, manage production schedules, maintain supplies, and orchestrate all factory activities. 5G will give the high-speed manufacturing environment far more flexibility by eliminating the need for wired connectivity. The 5G's technology specifications far surpass those of other wireless protocols, summarized in Table 6.
## 4 Discussion
### Clinically Acceptable Limits
Figure 4b depicted that during the initial 18 min, the Zephyr CT measurement was lower than the ICTS measurement. However, after 18 min, both devices recorded similar measurements. Table 4 showed that Zephyr has a higher accuracy in measuring CT when the maximum temperature over the course of a physical task is considered compared to when the average temperature is considered. This result is consistent with the outcome of the studies conducted by [PERSON] et al. [34] and [PERSON] et al. [23] that Zephyr CT consistently underestimated ICTS. However, [PERSON] et al. recorded \(-\)0.192 \({}^{\circ}\)C as the bias value. The difference in the bias value could be because [PERSON] et al. 's study was conducted in an outdoor environment and the participants were football athletes. Athletes and average humans may generate different CT during physical exertions [35; 36] and testing the efficacy of core temperature devices in different environmental conditions could affect their measurement [37]. Similarly, [PERSON] et al. [23] evaluated the accuracy of the Zephyr sensor in four distinct conditions and reported bias ranging from \(-\)0.2 to 0.3 \({}^{\circ}\)C, indicating that Zephyr underestimated and overestimated CT across the four conditions explored in the study. While the bias reported in this study fell within the range reported by [PERSON] et al. [23], it is important to note that [PERSON] et al. validated the accuracy of ZephyrCT against rectal probe temperature data while this study used a telemetry pill. Based on available evidence, Zephyr is a practical tool for tracking an individuals' CT in real time and can serve as a substitute for ICTS under certain circumstances that allows for \(\leq\pm\)0.5 \({}^{\circ}\)C bias in temperature measurement. The proportional bias found in the SS and Zephyr CT monitoring devices implied that the reported temperature values consistently deviate from the true values. This introduces the possibility of inaccurate readings, which can misinform supervisors about the actual thermal condition of the workers. The practical implications of the proportional bias are two-fold. Firstly, if the device consistently overestimates the CT, it may lead to unnecessary interventions or work interruptions, resulting in disruptions to productivity and potentially affecting worker morale. Conversely, if the devices consistently underestimate the CT, they may fail to detect hazardous thermal conditions and delay necessary interventions, thereby putting workers at risk of heat-related illnesses. Therefore, to ensure the practical effectiveness of the monitoring devices, addressing and rectifying the issue of proportional bias is of utmost importance. This may involve examining the sensor technology, calibration methods, or other environmental and physiological factors that could contribute to the observed bias. Understanding the sources of bias can guide future improvements in wearable sensor technology and measurement techniques.