esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurements
### Upper UWB Channel 7.25 GHz  
The performance for the upper UWB band is also investigated. The centre frequency is 7.25 GHz with a 500 MHz bandwidth. In Figure 11a the measured S\({}_{21}\) for the walking subject as a function of time are shown. As observed for previous cases, the S\({}_{21}\) is almost constant for the first 10 s, with a S\({}_{21}\) value equal to \(\sim\)\(-53.5\) dB. When the subject is walking, the S\({}_{21}\) range remains between \(-54.22\) dB and \(-51.56\) dB. Finally when the subject is stationary at the end of the room the S\({}_{21}\) is almost constant and  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Minimum path loss [dB]** & **Maximum path loss [dB]** & **K-factor [dB]** \\ \hline Meas 1 & 52 & 52 & 20.5 \\ Meas 2 & 53 & 57 & 18.0 \\ Meas 3 & 53 & 57 & 16.2 \\ Meas 4 & 55 & 56 & 17.2 \\ Meas 5 & 52 & 57 & 17.3 \\ \hline \end{tabular}
\end{table}
Table 4: K-factor estimation for the different measurements for the 3.95 GHz channel.  
Figure 10: **(a)** Measured S\({}_{21}\) for a walking subject; **(b)** CDF of the path loss for the five cases.  
equal to \(-\)53.75 dB. The CDFs of the five measurements are shown in Figure 11b. In this case the measurements are much closer to each other. The measured path loss values and K-factors are summarized in Table 5. The K-factor is greater than the previous case and always greater than 20.9 dB. The difference between the maximum and the minimum K-factor is equal to 5.1 dB.  
As expected, the K-factor is greater than the previous cases. Although the maximum realized gain is less than the 3.95 GHz case, the beamwidth of the antenna is smaller, resulting in a decreased signal scattering in the surrounding environment. The minimum path loss values are similar to the 3.95 GHz because of the greater realized gain for the footwear antenna.
# Dynamic Deflection Measurement on Stiff Bridges with High Piers by Preloaded Spring Method
## 3 Parameter Analysis and Optimization
### Optimization Design for SWSS  
Based on the influence of the above parameters on the DLAs, empirical formulas for optimizing the SWSS are proposed in Equation (10). It considers factors such as the stretched-wire section stiffness, spring stiffness, and stretched-wire length. In Equation (10), the integrated stiffness \(K_{e}\) is the product of the spring stiffness and stretched-wire length, and the \(QI\) is defined as an integrated index. When utilizing the PSM to measure the dynamic deflection of a bridge, the SWSS needs to meet the inequalities, and the measurement error will be controlled within 10%.  
Before measurement, the spring stiffness is selected based on the stretched-wire length, which is equivalent to the pier height of a bridge. The selected spring stiffness and stretched-wire length are substituted into the integrated index \(QI\) to determine the stretched-wire section stiffness. The greater the \(QI\), the smaller the errors.  
\[\left\{\begin{array}{l}K_{e}=k_{e}L\leq 1250\\ QI=\frac{E_{w}A_{w}-120k_{e}L}{1000}\geq 1\\ m_{p}\geq 0.1+0.076(L-0.5)\end{array}\right.\quad(0<k_{s}\leq 300,\ 55135\leq E _{w}A_{w},\ 1<L\leq 30) \tag{10}\]  
where \(K_{e}\) is the integrated stiffness of the SWSS; \(QI\) is an integrated index to evaluate the selected parameters; \(k_{s}\) is the stiffness of the compression spring; \(L\) is the length of the stretched wire (m); \(E_{w}\) is the elasticity modulus of the stretched wire (MPa); \(A_{w}\) is the cross-section area of the stretched wire (mm\({}^{2}\)); \(m_{p}\) is the mass of the pull bar (kg).  
Determining the wind load is a stochastic process and the field test environment is complex and variable, so it is difficult to accurately predict its effects by numerical analysis. However, However, it is not difficult to infer that the greater the initial preload force, the less the PSM measurements are influenced by wind. Therefore, it is recommended to use the PSM to measure the dynamic deflection of stiff bridges in a windless environment. The initial preload force of the spring can be determined according to the wind speed at the test site, and a greater value is warranted. It should not exceed the capacity of the compression spring and stretched-wire strength, and should be less than the weights of heavy objects on the bracket. The PSM generally allows large measuring ranges from 1 to 300 mm due to the compression characteristics of the compression spring. The PSM can be employed only if there is a platform underneath the measurement point on which the bracket can be placed, and the platform is a fixed point (no displacement).
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
## 2 Materials and Methods
### Probability Distributions for the Statistical Characterization of Supraharmonics
#### 2.3.5. Mixture of Normal Distributions  
The mixture of normal distributions (MixN) is considered in this paper for the statistical characterization of supraharmonics due to its ability to model a multimodal behavior of the random variable. This may occur at low-voltage nodes that are subject to primary and secondary emission and/or to different operation regimes.  
The \(\mathcal{C}\)-component MixN PDF is:  
\[\begin{array}{c}f_{MixN}(x|\mu_{1},\sigma_{1},\mu_{2},\sigma_{2},\dots,\mu_ {C},\sigma_{C},w_{1},w_{2},\dots,w_{C})=\\ =w_{1}\frac{1}{\sigma_{1}\sqrt{2\pi}}e^{-\frac{(x-\mu_{1})^{2}}{2\sigma_{1}^{2} }}+w_{2}\frac{1}{\sigma_{2}\sqrt{2\pi}}e^{-\frac{(x-\mu_{2})^{2}}{2\sigma_{2} ^{2}}}+\dots+w_{C}\frac{1}{\sigma_{C}\sqrt{2\pi}}e^{-\frac{(x-\mu_{C})^{2}}{2 \sigma_{C}^{2}}},\end{array} \tag{13}\]where \(x\in\mathbb{R}\), \(\mu_{1},\ldots,\mu_{C}\) are the mean of the mixture components, \(\sigma_{1},\ldots,\sigma_{C}\) are the standard deviations of the mixture components, and \(w_{1},\ldots,w_{C}\) are the weights assigned to each component that satisfy:  
\[\sum_{i=1}^{C}w_{i}=1,\ w_{i}\geq 0\ \forall i=1,\ldots,C. \tag{14}\]  
The MLE of the parameters on a dataset \(\mathbf{x}=\{x_{1},\ldots,x_{N}\}\) of observed samples has no explicit formulation; thus, the implicit optimization problem (to be solved numerically) is reported:  
\[\begin{split}\hat{\mu}_{1},\sigma_{1},\hat{\mu}_{2},\hat{\
u}_{2 },\ldots,\hat{\
u}_{C},\sigma_{C},\hat{w}_{1},\hat{w}_{2},\ldots,\hat{w}_{C}= \\ =\operatorname*{argmax}_{\mu_{1},\sigma_{1},\mu_{2},\sigma_{2}, \ldots,\mu_{C},\sigma_{C},\hat{w}_{1},\hat{w}_{2},\ldots,\hat{w}_{C}}\prod \limits_{n=1}^{N}f_{MixN}(x_{n}|\mu_{1},\sigma_{1},\mu_{2},\sigma_{2},\ldots, \mu_{C},\sigma_{C},w_{1},w_{2},\ldots,w_{C})\\ \text{s.t. }\sigma_{1},\ldots,\sigma_{C}>0\\ w_{1},\ldots,w_{C}>0\\ \sum_{i=1}^{C}w_{i}=1.\end{split} \tag{15}\]  
Note that the MLE of the MixN parameters is complicated by the fact that the transition to the log-likelihood has no positive impact since the PDF is a weighted sum of exponentials in \(x\). Therefore, in this paper, we apply the expectation-maximization algorithm [27; 28] to obtain the estimations of the parameters. Note eventually that \(C\) is a hyper-parameter of the distribution; for the supraharmonic data considered in this paper, good characterization performance is achieved by picking between \(C=2\) and \(C=3\).
# The Complex Relationship between Virulence and Antibiotic Resistance
## 3 Virulence Mechanisms
### Virulence Plasmid  
Plasmids are an adaption bacteria utilize to transfer genetic material between bacteria of the same species, as well as between different bacterial species. Many plasmids in pathogenic bacteria only encode virulence genes, antibiotic resistance genes, or genes required to activate virulence genes on the chromosome. Shiga-toxin producing _E. coli_ (STEC) are associated with food borne illness. One of the most important serotypes, O157:H7, possesses a large virulence plasmid--pO157. This highly studied plasmid is believed to encode the virulence genes _elxA_ (enterohemolysin) [137], _etpC_ to _etpO_ (Type II Secretion System) [138], _espP_ (serine protease) [139], _katP_ (catalase-peroxidase) [139], _toxB_ (a potential adhesion) [140], _stcE_ (zinc metalloprotease) [141], and _ecf_ (_ae_ conserved fragment) [142] (For a review, see [143]). One study investigated 22 STEC and 4 non STEC strains, isolated from humans, pigs, and a variety of other environmental samples, comparing whole genome sequences to identify virulence and novel antibiotic resistance plasmids. A total of 39 novel plasmids were identified, many of them contained virulence genes from pathogenic _E. coli_ groups, including STEC. In addition, 8 of these plasmids contained 29 antibiotic resistance genes encoding resistance to these major antibiotic groups: aminoglycos, carbapenem, penicillin, cephalosporyrins, chloramphenicol, dihydrofolate reductase inhibitors, sulfonamides, tetracyclines and resistance to heavy metals. Broad-spectrum antibiotic resistance could be conferred because many of the plasmids contained more than one antibiotic resistance gene. This study showed that virulence plasmids can be found in many _E. coli_ strains or serotypes, which were isolated from diverse locations, demonstrating that plasmids with multiple virulence and antibiotic resistance genes can be easily spread amongst the _E. coli_ species [144].
## 4 Results and Discussion
### Comparisons with the State of the Art Literature  
We evaluated the effectiveness of the best deep neural network, DeepBreastcancerNet, with other techniques for classifying BC tumors into benign, malignant, and normal. In further detail, we contrasted the proposed work with cutting-edge DL methods [33; 35; 41]. [PERSON] et al. [34] proposed a novel framework for the classification and detection of BC. The authors used TL techniques, whereas they used GoogLeNet, VGG and ResNet for features extraction. After that, the combined extracted features are given into a fully connected layer for classification. The proposed novel framework reached 97.52% classification accuracy. [PERSON] et al. provided a detailed overview of recent advanced development in computer-aided detection or diagnosis of BC detection. [PERSON] et al. [41] the proposed DL model for automated BC identification makes use of six alternative performance elevation matrices and the TL technique. They used five pretrain DL models, for feature extraction and classification. A 98.96% accuracy rate for BC detection was demonstrated for the VGG16 model. A thorough comparison of several strategies based on accuracy. Although accuracy is the most often used parameter in all pertinent research, it is listed as a performance parameter in Table 7. As far as we currently know, suggested DL method outperforms all recently published state-of-the-art methods. The capability of the proposed methodology to extract more stable and distinctive deep features for classification helps it get the best outcomes. Additionally, we employed a balanced combine dataset. In study [33] they  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Accu** & **Prec** & **Recall** & **F1-Score** \\ \hline Proposed Model & 99.35\% & 99.60\% & 99.66\% & 99.60\% \\ AlexNet & 89\% & 90.3\% & 90.3\% & 90.3\% \\ GoogLeNet & 98.71\% & 99\% & 97.66\% & 98\% \\ Shufflenet & 99.03\% & 99.33\% & 98\% & 99\% \\ ResNet-50 & 98.06\% & 98.33\% & 98.66\% & 97.33\% \\ SqueezeNet & 70.81\% & 73\% & 72\% & 62\% \\ ResNet-18 & 99\% & 99\% & 98.33\% & 99\% \\ Resnet101 & 99\% & 99\% & 98.33\% & 98.33\% \\ ExceptionNet & 99.03\% & 98.33\% & 98\% & 99\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Presents pre-trained model experimental results.  
also used combine dataset, however our method outperformed them in terms of accuracy. The comparison findings showed that the proposed approach was more effective than these techniques. Additionally, categorization, which requires more complicated computing, required the usage of hand-crafted engineering. Additionally, the suggested model outperformed the stated techniques in terms of accuracy, as shown in Table 7.
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 4 Optical Transmission: Key Challenges and Proposed Directions
### Capacity Scaling: What to Expect in the F6G
Finally, OFDMA-PON enables the convergence between wireless and fixed networks, allowing one to manufacture universal components for both uses. Further, Nyquist-WDM-PON [82] is a capable solution for PON, which aims to minimize the overall number of transceivers by aggregating multiple transceivers onto a single hub transceiver. The hub transceiver uses coherent technology and multiple combined subcarriers based on the Nyquist criterion into a single optical wavelength. In this way, a 100G or a 400G wavelength can be divided into 4 \(\times\) 25 Gb/s or 16 \(\times\) 25 Gb/s subcarriers, respectively, or even into a combination of different subcarriers and line rates. Using Nyquist-WDM-PON, the network migrates from point-to-point architecture towards point-to-multipoint, decreasing the overall number of optical interfaces from 2N to N + 1, where N is the number of access nodes. This solution provides significant advantages compared with legacy PON systems. The most important are (a) a decreased number of transceivers due to the use of hub transceivers, (b) better routing efficiency, density, and simplicity, as the overall number of ports is significantly decreased, (c) better alignment of CapEx with actual bandwidth requirements and quick adaptation on changing bandwidth demands and traffic patterns, and (d) lower OpEx in terms of power consumption, footprint, number of aggregation sites, and support. The concept of Nyquist-WDM-PON has been already commercialized in a version with the name \"XR-Optics\" [83]. Regarding the physical layer performance of Nyquist-WDM-PON and WDM-OFDM-PON, a comparative analysis is illustrated in [84] and shown in Figure 20. This analysis shows that the physical layer performance is highly dependent on the linewidth, resulting in considerable phase noise. In [84], two phase noise compensation methods were considered, namely, common phase error (CPE) and orthogonal basis expansion (OBE). Nyquist-WDM-PON showed a greater performance compared with WDM-OFDM-PON. This happened as the inter-carrier-interference (ICI) in WDM-OFDM-PON cannot be mitigated by CPE. Next, when OBE was used, WDM-OFDM-PON attained a greater performance than Nyquist-WDM-PON under the two cases, especially at small laser-linewidth. WDM-OFDM-PON is vulnerable to phase noise due to the long OFDM symbol period, but with the aid of an effective phase noise suppression method, it can attain a lower BER compared with Nyquist-WDM-PON and eventually qualify as a cost-effective solution for access networks.
# Terahertz-Wave Absorption Gas Sensing for Dimethyl Sulfoxide
###### Abstract  
Gas sensing for dimethyl sulfoxide (DMSO) based on rotational absorption spectroscopy is demonstrated in the 220-330 GHz frequency range using a robust electronic THz-wave spectrometer. DMSO is a flammable liquid commonly used as a solvent in the food and pharmaceutical industries, materials synthesis, and manufacturing. DMSO is a hazard to human health and the work environment; hence, remote gas sensing for DMSO environmental and process monitoring is desired. Absorption measurements were carried out for pure DMSO at 297 K and 0.4 Torr (53 Pa). DMSO was shown to have a unique rotational fingerprint with a series of repeating absorption bands. The frequencies of transitions observed in the present study were found to be in good agreement with spectral simulations carried out based on rotational parameters derived in prior work. Newly, intensities of the rotational absorption lines were experimentally observed and reported for DMSO in this study. Measured intensities for major absorption lines were found in very good agreement with relative line intensities estimated by quantum mechanical calculations. The sensor developed here exhibited a detection limit of 1.3 \(\times\) 10\({}^{15}\)-2.6 \(\times\) 10\({}^{15}\) DMSO molecules/cm\({}^{3}\) per meter of absorption path length, with the potential for greater sensitivity with signal-to-noise improvements. The study illustrates the potential of all electronic THz-wave systems for miniaturized remote gas sensors.  
gas sensing; terahertz (THz); dimethyl sulfoxide; absorption; spectroscopy Article  
Terahertz-Wave Absorption Gas Sensing for Dimethyl Sulfoxide  
[PERSON], [PERSON], Terahertz-Wave Spectroscopy Spectroscopy
## 2 Materials and Methods
### Microdsimetric Measurements Using TEPC
#### 2.2.1 Data Acquisition and Processing  
The charge induced by radiation in the sensitive volume (SV) and collected by the anode was fed to a low-noise charge-sensitive preamplifier (PA) specifically designed for this detector and constructed at LNL. The signal was then sent in parallel to two spectroscopy linear amplifiers, each with a different gain setting and a shaping time of 2 \(\upmu\)s. The amplified signals were further processed using a dual-channel peak-detecting analog-to-digital converter (ADC), specifically the ORTEC Model ASPEC-MCA-927. To verify the linearity of the electronic circuitry, a high-precision pulse generator was employed.
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 5 Communication Systems of Integrated ZigBee and RFID Technology
### Performance Testing of Communication System  
The reliability of a communication system consisting of a coordinator, router node (RFID reader/router), a ZigBee sensor node and three electronic tags was tested. The test was carried out under the environment that ambient temperature is 28 \({}^{\circ}\)C and ZigBee node emission power is 4.5 dBm.  
The test is conducted in two cases: (1) outdoor test: open field without obstacles (2) indoor test: router nodes are placed in the laboratory, and coordinators are placed in corridor outside the laboratory. The shape of the corridor is rectangle with right corner. The corridor locates on the second floor of a building. The thickness of the concrete wall of the corridor is 26 cm. The host computer sends instructions of reading (or writing) tag to the reader, then the corresponding router performs command of reading (or writing) to operate the electronic tag. The packet loss rate can be calculated after counting the times of router fail to execute the instructions of coordinator correctly or the times of information error after executing the instructions. During the test, the distance between the coordinator and the router nodes changes constantly to verify the impact of different test distances on the reading (or writing) results during the test. The maximum distance of the tag reading is 10 cm.  
The test results are shown in Table 1. In outdoor test, when the transmission distance between the coordinator and router (RFID reader/router) is in the range of 80-100 m the maximum packet loss  
Figure 11: Hardware for integrated RFID and wireless sensor networks.  
rate is 3%; when the test distance is 80 m or less, the packet loss rate is 0. In indoor test, when the transmission distance is 30-50 m and the maximum packet loss rate is 3%. When the test distance is 30 m or less, the packet loss rate is 0. The test results show that the system can achieve stable data transmission. I also prove that the wireless communication between the ZigBee coordinator and the ZigBee router increases the transmission distance of the reader system.
## 2 Experimental Setup  
### Raw Test Materials  
The cement used in this specific study was \"Tianshan brand PO 42.5 ordinary Portland cement\", with an apparent density of 3100 kg/m\({}^{3}\) and specific surface area of 355 m\({}^{2}\)/kg. The steel slag (SS) was produced by Zhengzhou Keruize Environmental Protection Technology Co., Ltd., Zhengzhou, China. The SS demonstrated an apparent density of 3350 kg/m\({}^{3}\) and bulk density of 1600 kg/m\({}^{3}\). The stockpiled SS at the facility and the SS powder used in experiments are shown in Figure 3. The SS particle size was 74 \(\upmu\)m. Additionally, chemical compositions of cement and SS, determined by X-ray fluorescence (XRF), are presented in Table 1. As shown in Table 1, these materials share similar chemical compositions, particularly CaO, SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\), which play crucial roles in the hardening process of the paste [37]. The ceramsite was manufactured by Jiangmen Xinhui Weiye Environmental Ceramsite Co., Ltd., Jiangmen, China with its fundamental properties being listed in Table 2.  
The foaming agent refers to a category of surface-active substances or compound formulations that generate abundant, uniform, and stable bubbles in a solution through physical or chemical mechanisms. The experiments employed a plant-based protein foaming agent manufactured by Weihai Zhongsheng New Building Materials Co., Ltd., Weihai, China. This foaming agent appears as a pale yellow translucent oily liquid, which requires 25-fold water dilution and achieves a 30-fold expansion ratio. Foam properties  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Oxide** & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **SO\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **K\({}_{2}\)O** & **TiO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** & **Na\({}_{2}\)O** \\ \hline Cement & 63.00 & 20.95 & 5.39 & 4.52 & 3.21 & 1.34 & 0.77 & 0.39 & 0.19 & 0.07 \\ Steel slag & 8.18 & 27.38 & 5.71 & 4.02 & 34.97 & 2.17 & 2.51 & 0.34 & 0.35 & 7.15 \\ \hline \hline \end{tabular} Note: Numerical data is given by the suppliers of materials.  
\end{table}
Table 1: Chemical compositions of cement and steel slag.  
Figure 3: Steel slag used in this study: (**a**) stacking site; (**b**) experimentation.  
were tested according to Appendix A of CJJ/T177-2012 \"Technical code for bubble-mixed lightweight soil filling engineering\" [38] and JC/T2199-2013 (2017) \"Foaming agent for foam concrete\" [39]. The foam characteristics are presented in Table 3.