esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Testing of an Active Noise Control System for Urban Road Traffic Noise
## 4 Single- and Dual-Frequency Noise Reduction Tests
### Dual-Frequency Test Result Analysis  
To evaluate the performance of our active noise control system under more complex acoustic signals, we tested it using two specific dual-frequency noise sources, 100 Hz + 1000 Hz and 200 Hz + 1000 Hz, with reference to the frequency range where traffic noise spikes occur. Following the experimental steps outlined earlier, we identified the distances that yielded the best noise reduction results and represented them in Figure 11.  
Figure 11: Maximum noise reduction and relative positions under different dual-frequency noise.  
Figure 10: Single-frequency test results.  
As per the results shown in Figure 12, notable noise reduction was observed at both frequencies when the noise source was 200 Hz + 1000 Hz, with an approximate reduction of 15 dB(A). However, when the noise source was 100 Hz + 1000 Hz, we noticed a significant noise reduction at 1000 Hz, but the noise reduction at 100 Hz was less pronounced.  
Before the system was turned on, the sound pressure level at 100 Hz was distinctly lower than 70 dB(A). This result further confirmed the crucial impact of the sound system configuration on the effectiveness of active noise control. Factors such as the frequency response range of the sound system and gain settings could influence the performance of noise reduction. Hence, we suggest optimizing the relevant configurations of the sound system--for instance, expanding the frequency response range and adjusting gain settings--to achieve superior noise reduction outcomes.
# Comparative Transcriptomics for Pepper (_Capsicum annuum_ L.) under Cold Stress and after Rewarming
## 4 Discussion
### Network Analysis Revealed the Key Genes of Cold Resistance of Pepper  
In this study, WGCNA analysis revealed eight hub genes in modules after the four key modules related to cold resistance and different stages of treatment of pepper were identified. These genes were divided into three groups. The first group was encoding and regulating genes, related to protein synthesis and modification, including the bzip transcription factor, CAF1, EIF3F, CAMKMT, and APRR9. This kind of research is relatively clear and is also the most direct response to stress. The second is coding effector proteins, such as zinc finger protein and HSP, which can directly protect plant cells from stress. Overexpression of SICZFP1 from tomato in transgenic Arabidopsis and rice induced constitutive expression of _COR_ genes and conferred enhanced tolerance to cold treatments for transgenic plants [78]. HSPs, as a molecular chaperone, can regulate the correct folding of proteins, which is a key protein to maintain cell homeostasis under stress. Cold stress resulted in upregulation of Hsp70 and downregulation of Hsp90 in winter wheat, which contributed to plant phenotypic variation in plant response to stress [79]. The third encodes enzymes, such as CYP, which are involved in catalysis of variety of reactions that include growth, development, and secondary metabolite biosynthetic pathways. CBF/DREB1 transcription factor regulates the expression of _SLCYP72A184_, _SLCYP85A1_, and _SLCYP96A48_ genes, which can be used as candidate genes for cold tolerance in tomato [80].
# Gateway-Assisted Retransmission for Lightweight and Reliable IoT Communications
## 2 Related Works
where \(\alpha=1/4\) and \(\beta=1/8\). 4. The RTO \(\Omega\) is calculated by SRTT and RTTVAR as follows: \[\Omega=\gamma^{*}+4V_{\gamma}.\] (5)
5. When retransmission is triggered, exponential back-off is performed. Note that the specification suggests that RTO value is bounded within 1 and 60 s.  
Note that RFC 6298 ignores the RTT samples of the retransmitted data, which follows [PERSON]'s algorithm [PERSON] (1999). CoCoA, which is introduced as an Internet-Draft Draft (2008), enhances the congestion control mechanism for CoAP. Based on the calculation in RFC 6298, it defines two extra RTOs. One is called strong RTO (denoted as \(\Omega_{strong}\)) and \(\Omega_{strong}=\gamma^{*}+4V_{\gamma}\). The other is weak RTO (denoted as \(\Omega_{weak}\)) and \(\Omega_{weak}=\gamma^{*}+V_{\gamma}\). When there is no RTT sample, the RTO is set to 2 (i.e., \(\Omega=2\)). The subsequent update of RTO occurs when an RTT is sampled. If the RTT is sampled from the first transmission of a message, the RTO is updated as \[\Omega=0.5\Omega+0.5\Omega_{strong}.\] (6)  
On the other hand, if the RTT is sampled from the retransmission of a message, the RTO is updated as  
\[\Omega=0.75\Omega+0.25\Omega_{weak}. \tag{7}\]  
If the RTO is not updated over 30 s, the RTO is updated by  
\[\Omega=(2+\Omega)/2. \tag{8}\]  
Another improvement is the back-off mechanism. In CoCoA, variable back-off factor (VBF, denoted as \(\Delta\)) is decided based on the initial RTO as follows:  
\[\Delta=3,\quad if\quad\Omega<1\;\mathrm{s},\] \[\Delta=2,\quad if\quad 1\;\mathrm{s}\leq\Omega\leq 3\;\mathrm{s},\] \[\Delta=1,\quad if\quad\Omega>3\;\mathrm{s}. \tag{9}\]  
Note that \(\Delta\) is always equal to 2 in exponential back-off used by CoAP and RFC 6298. When a message is sent, one of the two policies, persistent policy or Replacing-Retransmitted-Publication (RRP) policy, is chosen. In persistent policy, when a message is sent, the device drops following messages until it receives the corresponding ACK. While the number of retransmissions matches the maximum retransmissions number, the device does not wait anymore. RRP policy is like persistent policy except that if the message is retransmitted due to RTO timeout, the message can be replaced by a newly generated message. In previously described protocols, MQTT-SN uses persistent policy while CoAP, RFC 6298, and CoCoA use RRP. We observe that neighboring devices would observe similar RTT (with the same server), and thus most dynamic RTO computation could be reduced by sharing the computed RTO values between devices. Based on those previous works, we propose a gateway-assisted mechanism to help devices get RTO earlier. Consequently, the number of retransmissions is reduced, which results in considerable power savings.
## 2 Methods
### Dynamic Selection of Regularization Factor  
The regularization factor \(\lambda\) determines the relative weight relationship between the data space and the model space in the objective function. When \(\lambda\) trends toward zero, the fitting degree of the data is better, but the model is rougher. When \(\lambda\) trends toward infinity, the fitting degree of the data is worse, but the model is smoother.  
The method of selecting regularization factors is usually L-curve method, as shown in Figure 8.  
This curve represents the relationship between data fitness and model fitness with different regularization factors. It requires many experiments to select \(\lambda\)[14]. Generally, the regularization factor at the maximum curvature of the L-shaped curve is the optimal regularization factor [37]. As shown in Figure 8, \(\lambda=6\) is the optimum solution.  
Figure 8: Using the L-curve method to determine the optimal value diagram.  
Because the process of selecting the most regularization factors needs a large number of calculation experiments, this paper proposes an improved method for selecting regularization factors.  
The value of the regularization factor represents the weight relationship between the degree of data fitting and the degree of model fitting. In order to improve the degree of data fitting and model fitting, we need to adjust the value of the regularization factor in the iterative process. According to the above reasons, this paper designs a method to select dynamic regularization factors.  
In the iterative process, the large-scale shape of the model is outlined by using a larger regularization factor, and the inversion result is smoother at this time. Then the regularization factor is continuously reduced to accurately describe the disturbance of the small-scale slowness model, and the inversion result will be closer to the real model. This method not only ensures the convergence and stability of the inversion results, but also improves the fitting degree of the data.  
Assuming that there is a simple linear relationship in the fitting difference between the data and the model and regularization factor \(\lambda\), we can select the next \(\lambda\) according to the variation of the fitting difference of each iteration inversion. \(\lambda\) decreases with the decrease of data fitting difference. So, regularization factor \(\lambda\) can be selected dynamically.
# Optimal Design and Hybrid Control for the Electro-Hydraulic Dual-Shaking Table System
## 2 System Design and Optimization  
### System Description  
The dual-shaking table system has two identical electro-hydraulic shaking tables, as shown in Figure 1. Each shaking table is composed of an upper platform (table), two reaction walls, 16 spherical hinges, a base platform and eight hydraulic actuators: two for \(X\) direction, two for \(Y\) direction and four for \(Z\) direction, as shown in Figure 1b.  
The actuators are controlled by three-stage electro-hydraulic servo valves. Each actuator is equipped with a linear variable differential transformer (LVDT) displacement sensor to measure the displacement of the piston rod, an acceleration sensor fixed on the platform as close as to the piston rod as possible and two pressure sensors, which are used to measure the pressures of the upper and down cavity of hydraulic cylinder. Each shaking table can realize six-DOF motions: three translations along \(X\), \(Y\) and \(Z\) axes and three rotations around \(X\), \(Y\) and \(Z\) axes.  
The characteristics of the load are very important to the shaking table system performance. They not only affect the control performance of the individual shaking table, but also affect the control performance of the other shaking table due to the coupling action of the load. The stiffness of the load affects the coupling action and the eccentricity of the load mass affects the synchronization control precision. If the stiffness of the load and the eccentricity of the load mass are relatively large, the coupling action between the tables is strong and the synchronization error is large, which will make the control of the shaking table more difficult and vice versa. However, because the main research scope of this paper is the control of the tables and is not the research of the specimen's response performance and an experiment specimen is very expensive to build, so this paper uses a simple specimen with relatively high stiffness to verify the proposed control strategy in the later experiment verification section.  
Figure 1: The electro-hydraulic dual-shaking table system. (**a**) Dual-shaking table; (**b**) Top view of dual-shaking table.  
The large-span test specimen is fixed on two tables, which provide synchronous motions to test the anti-seismic performance of the specimen. The specimen is a relatively stiff specimen and will be used in the following experimental validation tests in Section 4 of this paper. The size of the specimen is 7.5 m \(\times\) 2.5 m \(\times\) 0.3 m and the mass of the specimen is 6 t. The specimen is composed of two floors of steel plates through welding and there is a cylinder about 0.2 m height connected with it through welding. The mass of the cylinder is about 300 kg and the diameter is about 2 m. The main specific system parameters are listed in Table 1. Note that the two shaking tables will share the total mass of the load, but the mass will not surely be halved as this is due to the possible eccentricity of the load mass, which is one of the factors causing the differing dynamic performance of the two shaking tables.
## 2 Materials and Methods
### Network Architecture  
Several state-of-the-art CNN architectures were previously introduced to realize efficient image-to-image transformation. The proposed approach employs a network architecture based on the 2D U-net, a detailed description of which was previously reported [25; 26]. This architecture was selected owing to its known superior performance in the medical imaging field.  
Figure 2 depicts a schematic of the 2D U-net architecture comprising independent encoding (left) and decoding (right) workflows. The encoding workflow corresponds to that of typical CNN architectures, which are constructed by repeatedly using two \(3\times 3\) 2D convolutional layers. The processing within each such layer is followed by batch normalization (BN), the use of a leaky rectified linear unit (LReLU), and \(2\times 2\) max pooling for downsampling. Additionally, the number of feature channels is doubled during each downsampling step. In contrast, the decoding path comprises a \(3\times 3\) 2D deconvolutional layer. The processing within this layer is followed by BN, an LReLU, upsampling, and skip connection with the corresponding linked-feature maps in the encoding path, and two \(3\times 3\) 2D convolutional layers. The processing within each of these two layers is--once again--followed by BN and an LReLU. A linear function was employed to activate the output layer.  
During each iteration, the above-described U-net architecture was trained using the loss function of the mean squared error, expressed by the following:  
\[\theta^{i}=\begin{cases}\underset{\theta^{i}}{\operatorname{argmin}}\|x_{ true}-f_{\theta^{i}}\Big{(}\big{|}\mathcal{F}^{-1}(y_{0})\big{|}\Big{)}\|&i=0\\ \underset{\theta^{i}}{\operatorname{argmin}}\|x_{true}-f_{\theta^{i}}\Big{(}x ^{i}\Big{)}\|&\text{otherwise.}\end{cases} \tag{3}\]  
Here, \(x_{true}\) denotes the fully sampled image corresponding to each zero-filling image. The loss function was minimized using the Adam optimizer [27] at a learning rate of 0.0001, and the number of epochs was 100. A small batch of 32 images was used for training. These hyperparameters were empirically selected. All U-net processing was performed on a computer running the Ubuntu 16.04 operating system, an NVIDIA Quadro P6000 (Sant Clara, CA, USA) graphics processing unit with 24 GB memory, TensorFlow 1.9 [28], and Keras 2.2.4 [29].  
Figure 1: Sequential schematic of the proposed iterative method with the blue and red regions representing the k-space and image space, respectively.
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 4 Discussion and Results  
We have evaluated the performance of the integrating 5G with TSN in terms of the time synchronization accuracy, overall end-to-end latency and jitter behavior. All the measurements have been conducted in an industrial shopfloor with over-the-air transmissions. For validation, the data traffic with the highest requirements has been tested with 50 bytes  
Figure 10: Time shaping carried out in various parts of the setup of 5G integrated with TSN.  
Figure 9: Arrival time window in the context of jitters and time synchronization.  
message sizes and a cycle time of 2 ms, simulating the control data traffic in the mobile robotics use case described in Section 2.
## 2 Results
### SLC10A1/NTCP Expression and Hepatic Metabolism  
Based on the results of our gene set enrichment analyses and the association with metabolic pathways, we further investigated whether the observed inter-individual variability of _SLC10A1_/NTCP expression was linked to altered endogenous metabolite levels in the liver tissues. Therefore, untargeted metabolomics analysis was performed as previously described [66]. Correlation analyses of metabolite levels and either mRNA expression or protein levels indicated significant associations with specific metabolites (Figure 7 and Supplementary Table S5) quantified in corresponding liver tissue. The highest correlation was observed for betaine even after correction for multiple testing. However, no NTCP-dependent transport of betaine was detected in HEK cells recombinantly expressing NTCP (Supplementary Figure S7). Moreover, certain acylcarnitines, amino acids as well as cyclic AMP (cAMP) were significantly correlated with both mRNA expression and protein levels of _SLC10A1_/NTCP (Figure 7).  
In addition, the association of genetic variants within the _SLC10A1_ gene region and metabolite levels was investigated. As shown in Supplementary Table S6, significant associations of certain variants were detected in univariate and multivariate analyses. However, only the associations with 4-hydroxy-L-proline showed a trend of significance (\(p=0.05\)) after adjustment for multiple testing.
## 1 Introduction
### Biros-Vilts \"Suomi NPP\" Data Comparison for the Devastating \"Paradise Fire\" in California/US
If the regular VIIRS M-band active fire product data (see [[https://viarsland.gsfc.nasa.gov](https://viarsland.gsfc.nasa.gov)]([https://viarsland.gsfc.nasa.gov](https://viarsland.gsfc.nasa.gov)) (accessed on 1 December 2021)), with a 750 m \(\times\) 750 m pixel size in the swath +/ - 31.5\({}^{\circ}\) around the nadir, were used for the estimation of the FRP in the \"Paradise\" fire scene from 14 November 2018, the derived FRP values would probably not be disturbed by saturation errors. New generations of geostationary meteorological satellites now make it possible to record the diurnal cycle of fires, beginning from medium fires with FRP figures of \(\sim\)50-100 MW upward [13]. The combination of these frequently provided coarser-spatial resolution data with data from the above-described operational polar orbiting IR sensors and higher-resolution FireBIRD-type data will offer a new quality in climate change-related carbon emission investigations.
## 2 Materials and Methods
### Limits  
Occupational exposure limit values (OELVs) and short-term OELVs are crucial in ensuring worker safety. OELVs represent the average exposure during a work schedule of 8 h per day and 40 h per week, while short-term OELVs refer to the 15 min exposure that should not be exceeded at any time during a workday, even if the 8 h OELV is within limits.  
According to Spanish and European legislation until August 2023, the recommended OELVs for NO and NO\({}_{2}\) in mining and tunnelling activities are 25 ppm and 2 ppm, respectively, with corresponding short-term OELVs of 30 ppm and 5 ppm. However, Directive 2017/164/EU proposed significant reductions in NOx limits, with the proposed OELVs for NO being 2 ppm and NO\({}_{2}\) being 0.5 ppm. The short-term OELV for NO\({}_{2}\) was proposed at 1 ppm. Table 1 provides a comparison of the current limits and the proposed limits.  
Directive 2017/164/EU proposed a drastic reduction in NOx limits. It is essential to note that the proposed reduction in NOx limits in Directive 2017/164/EU is already being implemented in other underground activities, such as works in underground car parks. However, the proposed levels will be subject to review before the transitional period ends. The directive acknowledges that there are currently difficulties with the availability of measurement methodologies that could be used to demonstrate compliance with the proposed limit value for nitrogen dioxide in underground mining and tunnelling environments [25; 26].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Gas**} & **Current** & **Current Short-Term** & **Proposed** & **Proposed Short-Term** \\  & **OELV (ppm)** & **OELV (ppm)** & **OELV (ppm)** & **OELV (ppm)** \\ \hline CO & 25 & 100 & 20 & 100 \\ CO\({}_{2}\) & 5 & 12.5 & 5 & - \\ SO\({}_{2}\) & 0.5 & 1 & 0.5 & 1 \\ H\({}_{2}\)S & 5 & 10 & 5 & 10 \\ NO & 25 & 30 & 2 & - \\ NO\({}_{2}\) & 3 & 5 & 0.5 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: OELVs in mining: current limits and proposed limits.
## 2 Materials and Methods
### Assessment of the Physiological Responses
#### 2.3.2 Hematological and Biochemical Responses  
Blood samples were collected via jugular venipuncture using a BD Vacutainer(r) system (Plymouth, UK). The blood samples were collected in the following order: K2-EDTA tubes first (BD Vacutainer(r), Plymouth, UK) for hematological analysis, followed by dry tubes (BD Vacutainer(r), Plymouth, UK) for biochemical analysis. A total of 5 mL of blood was collected per tube. Blood samples were collected once at each time point: before the treadmill session, immediately after the treadmill session, and at 30 min and 24 h post-treadmill session.  
Samples in EDTA and dry tubes were stored at +4 \({}^{\circ}\)C and sent to a reference laboratory (Vetlab sp. z o.o., Warsaw, Poland) within no more than 2 h of blood collection for hematological and biochemical analyses, respectively. From the hematological analysis, red blood cell count (RBC, \(\times 10^{12}\)/L) and HGB (g/dL) were measured using an ABC Vet analyzer (Horiba Medical, Minami-ku, Japan). For the biochemical analysis, CK (U/L), AST (U/L), and LDH (U/L) activities were evaluated using a Miura One analyzer (ISE. S.r.l., Albuccione, Italy). The normal reference ranges were considered as follows: RBC, 2 to 12 \(\times\) 10\({}^{12}\)/L; HGB, 8 to 17 g/dL; CK, 90 to 565 U/L; AST, 1 to 450 U/L; and LDH, 0.1 to 639 U/L. Lactate concentration (LAC, mmol/L) was analyzed in whole blood immediately after sample collection using an Accusport(r) device (Roche Diagnostics, Basel, Switzerland). If the device returned a LAC value below the detectable level, annotated as low, the value 0.5 mmol/L was assigned.
# SDR-Enabled Multichannel Real-Time Measurement System for In Situ EMF Exposure Evaluation
## 3 Results
### LTE-A and IEEE 802.11 ax Signals Measurements  
In Figure 13 we have represented the temporal evolution of the field strengths for the LTE-A speed test and streaming scenarios presented in Table 1. For the reported results, the averaging was performed over 10,000 signal samples, corresponding to 0.5 ms at a signal sampling rate of 20 MHz. Good synchronization was obtained between the values measured by the three enabled SDR channels marked by red, blue, and yellow traces, mostly visible at the beginning of a transmission session for both the speed test and streaming. The absolute field strengths measured by the E-field probes differ on the three considered channels, with probes measuring on the Oy and Ox direction being positioned closer to the DUT as compared to the probe measuring on the Oz direction.  
In Figure 14, we have represented the temporal evolution of the field strengths for the 802.11 ax file download (a) and file upload--20 MHz ChBw (b) cases. We observe that the measured field values are below 1 V/m. For this communication standard, the measured field strengths are higher on the Ox axis followed by the Oz and Oy axis. As compared to the LTE-A scenario, this must be analyzed by taking into consideration the different antennas used by the DUT. The E-field values obtained for file upload are higher than for file download because during file upload, the DUT device is the emitting device, while for file download, it only sends acknowledgement frames and the Wi-Fi router is the emitting device.  
In Figure 15, we have represented the temporal evolution of the field strengths for the LTE-A (a) and IEEE 802.11 ax (b) experimental scenarios. For the LTE-A measured data, the speed test forces the LTE network resources in both the uplink and downlink direction and this resulted in the highest E-field strength values (29.8 V/m). The field values measured for streaming were the lowest even though streaming is a bandwidth-consuming application. Moreover, streaming resulted in the highest E-field variability as this type of service has low requirements in terms of network latency.  
Figure 13: Time evolution of Ex, Ey, Ez, and Etotal for LTE-A speed test (a) and streaming (b).  
For the IEEE 802.11 ax network, we have investigated the enabled SDR measurement system capabilities in continuous time retrieval of CP data for larger frequency bandwidths (40/80 MHz). As can be observed from Figure 15b the increase in channel bandwidth is not associated with a substantial modification of the measured field level as a result of the antenna parameter variation with the operating bandwidth.