esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Diet on Mercury Bioaccumulation in Farmed Gilhead Seabream (_Sparus aurata_)
## 2 Materials and Methods
### Statistical Analysis  
All statistical analyses were implemented using statistical software R 4.3.1 (R Core Team, 2023). Prior to analysis, data normality was assessed by the Shapiro-Wilk test. Since all variables violated the normality assumption, non-parametric analyses were considered more robust and appropriate. Specifically, the non-parametric Kruskal-Wallis test was applied to assess significant differences in mercury concentration among diets and sampling periods. Statistical significance was assessed using the library \"stats\" in R choosing a significance alpha level of 0.05.  
The distribution of mercury concentrations in each of analyzed organs was visualized using boxplots, stratified by diet, exposure and sampling period. The LOESS curves (Local Polynomial Regression Fitting) were fitted on the boxplots to capture non-linear trends, describing the pattern of mercury accumulation over the exposure period and highlighting potential differences across the groups with several diets. The optimal LOESS curves were selected by analyzing the span parameter, which controls how the fit at a specific point in the series weights the data nearest to it. A too large span produces an over-smoothed regression, while a too small span causes a large variance and less reliable fits due to insufficient data points being considered. The optimal span values were selected minimizing the Generalized Cross-Validation (GCV) criterion, ensuring the selected span provided the best balance between fit and smoothness.
# Common-Mode Voltage Analysis and Reduction for the Quasi-Z-Source Inverter with a Split Inductor
## 2 CMV Analysis in Different Operational States for the qZSI  
As shown in Figure 1, the qZSI consists of two identical inductors (\(L_{1}\),\(L_{2}\)), two identical capacitors (\(C_{1}\),\(C_{2}\)), a diode (\(D\)) and a conventional VSI. There are three different operation states in the qZSI system: the active state, the null state and the ST state. Figure 2 illustrates the equivalent circuits of the qZSI in the three states [31; 32]. As a transformerless inverter, leakage currents may appear, as previously mentioned. The leakage current is related to the CMV, which is defined as the average value of the voltages between the outputs (as \(u\), \(v\), \(w\)) and the common reference in a three-phase inverter. In the qZSI, the negative terminal (marked as N) of the input source \(v_{\text{in}}\) is the common reference. Thus, the CMV for the qZSI is obtained as [33]:  
\[v_{\text{cmv}}=\frac{v_{uN}+v_{vN}+v_{wN}}{3} \tag{1}\]  
where \(v_{\text{cmv}}\) is the CMV and \(v_{iN}\) (\(i=u,v,w\)) is the voltage from point \"\(i\)\" to the negative terminal N.  
The space-vector PWM (SVPWM) has eight possible combinations of the switch vectors in the conventional VSI: six active vectors (\(V_{1}\), \(V_{2}\), \(V_{3}\), \(V_{4}\), \(V_{5}\), \(V_{6}\)) and two zero vectors (\(V_{0}\), \(V_{7}\)), as shown in Figure 3. \(\theta\) in Figure 3 is the angle of the output reference \(\mathbf{V}_{\text{ref}}\). In addition to the eight traditional switching states, the qZSI has seven ST zero states (\(V_{st}^{u}\), \(V_{st}^{v}\), \(V_{st}^{up}\), \(V_{st}^{uu}\), \(V_{st}^{ww}\), \(V_{st}^{ww}\), \(V_{st}^{www}\)). The CMV can be calculated according to the equivalent circuits in Figure 2 as follows with the corresponding switching states.
## 4 Results and Discussion
### Quality of BDS-3/BDS-2 Real-Time Precise Products
For the BDS-3/BDS-2 MEO satellites, the errors of real-time satellite clocks fall between \(-0.5\) and \(0.5\) ns, whereas the corresponding variation range of BDS-2 GEO and IGSO satellites is expanded four times and twice, respectively. Figure 12 provides the statistics of errors of BDS-3/BDS-2 precise satellite clock and orbit corrections in the real-time scenario. The satellite-dependent statistical values are at a similar level for the satellites belonging to the same orbit types. It can be seen that the average values of the root mean squares (RMSs) of cross-track/along-track/radial orbit errors over the analysis period (70 days) for the satellites within the same satellite constellations are \(1.666/3.144/0.430\) m for BDS-2 GEO satellites, and \(0.168/0.171/0.153\) m for BDS-2 IGSO satellites, which are significantly larger than the corresponding RMSs of MEO satellites of BDS-2 and BDS-3 at the centimeter level except for the along-track direction. For BDS-2 GEO and IGSO satellites, the average RMS clock errors are respectively \(0.87\) and \(0.55\) ns, which are nearly two to three times larger than the corresponding RMSs of MEO satellites of BDS-2 and BDS-3. The comprehensive contributions of satellite clock and orbit errors to range measurement are usually expressed by the SISRE [35]. The accuracies of real-time BDS-2 GEO and IGSO precise products are \(0.477\) and \(0.109\) m in terms of the mean SISRE, respectively, and those of MEO satellites are similar, i.e., \(0.080\) m for BDS-3  
Figure 8: Radial errors of BDS-3/BDS-2 precise satellite orbits in the real-time situation. Different colors in the figure refer to different satellites. MEO satellites and 0.071 m for BDS-2 MEO satellites, respectively. Figure 13 shows the statistics of errors of GPS precise satellite products in the real-time situation with respect to IGS final products. The mean cross-track/along-track/radial orbit accuracies of GPS are 0.028/0.035/0.028 m and the mean clock accuracies are 0.14 ns, and the corresponding statistics of MEO satellites of BDS-2 and BDS-3 are 1.2-3.7 times larger than the GPS ones (see Figure 12). It is clear that better stability can be achieved for the GPS real-time precise products, and the mean SISRE is 0.039 m, which is about half of that of MEO satellites of BDS-2 and BDS-3. Figure 10: Cross-track errors of BDS-3/BDS-2 precise satellite orbits in the real-time situation. Different colors in the figure refer to different satellites. Figure 9: Along-track errors of BDS-3/BDS-2 precise satellite orbits in the real-time situation. Different colors in the figure refer to different satellites. Figure 11: Errors of BDS-3/BDS-2 precise satellite clocks in the real-time situation. Different colors in the figure refer to different satellites. Figure 12: Statistics of errors of BDS-3/BDS-2 real-time precise satellite orbits and clocks.
## 4 Experiments and Results  
This section presents a comprehensive evaluation of the proposed model, including the experimental setup, performance metrics, and analysis of results. The experiments focus on assessing the predictive capacity of the model in protein secondary structure prediction (PSSP) under varying configurations of subsequence lengths and feature dimensionalities. We provide both quantitative metrics and training diagnostics to examine model behavior in detail.  
This section is organized as follows: In Section 4.1, we describe the evaluation metrics used to assess model performance, including accuracy, precision, recall, and F1 score. Section 4.2 presents and analyzes the model's performance across different configurations, with a focus on Q3/Q8 classification schemes.
## 2 Materials and Methods
### Electron Temperature Observations by Incoherent Scatter Radars  
ISRs exploit the Thomson backscatter from ionospheric electrons to retrieve the value of several ionospheric parameters including \(T_{\rm e}\)[8]. Specifically, \(T_{\rm e}\) profiles are recorded by ISRs in the ionospheric altitude range from about 100 to 1000 km.  
\(T_{\rm e}\) observations collected by Jicamarca, Arecibo, and Millstone Hill ISRs were used in this work for validation purposes. Jicamarca ISR is located right above the magnetic equator at a Quasi-Dipole (QD, [29]) latitude of 0.2\({}^{\circ}\)N, Arecibo (QD latitude 27.0\({}^{\circ}\) N) is a low-latitude station, while Millstone Hill (QD latitude 51.8\({}^{\circ}\)N) is a mid-latitude/sub-auroral station. Jicamarca data are from 1996 to 2020, Arecibo ones from 1974 to 2015, and Millstone Hill ones from 1976 to 2020. ISRs data are not continuous in time but the length of the datasets here used allow for a quite uniform representation of the different diurnal and seasonal trends.  
The ISRs \(T_{\rm e}\) data (including corresponding \(T_{\rm e}\) error) were downloaded from the [[http://cedar.openmadrigal.org](http://cedar.openmadrigal.org)]([http://cedar.openmadrigal.org](http://cedar.openmadrigal.org)) website (accessed on 24 September 2021). The altitudes of Swarm satellites fall into standard remote sensing altitude ranges of all three ISRs, which provide reliable data for decades. A similar dataset referring to \(T_{\rm i}\), for Millstone Hill was recently employed in [PERSON] et al. [30].  
To perform a comparison with Swarm B-measured and IRI-modeled values, we selected \(T_{\rm e}\) values recorded by ISRs in the range 510 \(\pm\) 20 km. Only the most reliable ISRs' data were selected, i.e., those with a percentage error lower than 10%. This filtering mostly affected data recorded during the night and early-morning, which are those characterized by the largest dispersion.
## 7 Emergence of the BSC in Scattering
### Symmetry Protected BSCs  
First the effect of enhancement of scattering function in the near zone of the infinite periodic array of dielectric spheres was shown in Ref. [56] in the vicinity to the symmetry protected BSCs with \(m=0\). As it follows from Equations(20) and (21) only one diffraction channel \(n=0\) is open for low frequencies \(k_{0}\) where the majority of the BSCs occur as shown in Figure 5. Although the BSCs can not be probed directly by an incident wave they are seen as collapses of Fano resonance when the BSC point is approached in the parametric space. That phenomenon was observed for scattering of EM waves by arrays of rods [30; 33; 37; 40; 44; 45; 51; 52; 81]. In this subsection we report a similar Fano resonance collapse in the differential and total cross-sections vs. frequency when the wave number \(k_{z}\) tends to zero or the radius of the spheres approaches the BSC radius. The Fano resonance for the present system can be interpreted as an interference of the optical paths through and between the spheres.  
Let us consider an incident plane wave with the wave vector in the \(x,z\) plane and polarizations: (a) TE polarized with electric field along the \(y\)-axis and (b) TM polarized with magnetic field along the \(y\)-axis.  
For \(m=0,k_{z}\
eq 0\) Equations (13) and (14) gives that \(p_{l0}^{TE}=0,q_{l0}^{TM}=0\). Then taking into account that \(\mathcal{B}_{vl}^{00}=0\) (see Appendix A) we have from Equations (16) for the TE incident plane wave  
\[Z_{TE,l}^{-1}a_{l}^{0}-\sum_{v}a_{v}^{0}\mathcal{A}_{vl}^{00}=q_{ l0}^{TE},\] \[Z_{TM,l}^{-1}b_{l}^{0}-\sum_{v}b_{v}^{0}\mathcal{A}_{vl}^{00}=0. \tag{48}\]  
The second equation gives \(b_{l}^{0}=0\), and scattering of plane wave with TE polarization is given by only \(a_{k}\). Then the type I BSCs is quasi BSC weakly coupled with the TE continuum for small \(k_{z}\). That results in sharp resonant contribution in the cross-section \(\sigma_{TE,TE}\) as shown in Figure 10a. The cross-sections \(\sigma_{TE\to TM},\sigma_{TM\to TM}\) and \(\sigma_{TM\to TE}\) have no features related to these BSCs and are not shown in Figure 10a. If the plane wave falls onto the array normally \(\alpha=-\pi/2\) (\(k_{z}=0\)) we have fully invisible type I BSC that is shown by dash line in Figure 10a. Alternatively, the symmetry protected type II of the symmetry protected BSCs with the only amplitudes \(b_{k}\) can be observed via the cross-section \(\sigma_{TM\to TM}\) as shown in Figure 10b. Thus, although the BSCs have no effect for the normal incidence they are detected by collapse of Fano resonances in total cross-sections for \(k_{z}\to 0\).
## 3 Experiment, Results, and Discussion
### Evaluation Scenario  
A scenario for evaluation is proposed, consisting of real operator data from a GSM network deployment, corresponding to an urban area of 2422 km\({}^{2}\) with around 570,000 inhabitants. Figure 13 represents the reference scenario with the existing deployment. This deployment congregates 23 sites with a total of 70 cells. The city center is intensely covered with cells, while for the surrounding rural areas cells are only present along streets. The BCCHs used by the operator are in the 900 MHz band, with channels between 63 and 88, while for the 1800 MHz band, channels range between 733 and 769. The TCH channels for 900 MHz range between 89 and 124, while for 1800 MHz they are between 770 and 824.  
This scenario was split into two reference scenarios. First, a single site scenario of three cells was manually planned. The site is colored blue in Figure 13. Second was the whole scenario of 70 cells operating in specific channels.  
Figure 12: Frequency allocation request in Metric SaaS.  
Figure 13: Evaluation scenario, extracted from a real Global System for Mobile communications protocol (GSM) cellular deployment.
# Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO
## 3 Methods
### Clustering Anchors  
In the prediction process of the original YOLOv4 network, each YOLO Head prediction feature layer corresponds to 3 priori boxes preset in the yolo_anchors file. Among them, the 9 default anchors in the yolo_anchors file are obtained by the author through clustering all the ground truth boxes in the VOC dataset. These 9 anchors are universal to most object detection situations and will not change in the process of training and prediction.  
However, for this dataset, because the extremely tiny targets account for about 20% of all data, the accurate detection of the extremely tiny targets is an important indicator to test the improved network prediction effect. Because this paper adds a new feature layer to the original 3 prediction feature layers, the original yolo_anchors can no longer meet the needs of this experiment.  
Therefore, this paper uses the K-means clustering algorithm to cluster the ground truth boxes in the augmented dataset, randomly select 12 points as the cluster center of the algorithm, calculates the similarity between each ground truth boxes and the cluster center, assigns these ground truth boxes to the cluster center with the closest similarity, calculates the mean of all samples in each cluster, and then updates the center of the cluster. Repeat the first two steps until the centers of the 12 clusters no longer change, or the  
Figure 5: Improved YOLOv4 network structure.  
maximum number of iterations set is reached, then the clustering ends, and the values of the 12 anchors we need are obtained.  
The twelve Anchor values obtained in this paper using the K-means algorithm at multiple different feature layer scales are shown in Table 1.
## 4 System Development
### Frontend of the System
#### 4.1.1 Search Function of Spots and Facilities  
Figure 4 shows the screen for the search function of spots and facilities. When searching for a destination using the function, users select a category which the destination belongs to from among the pictograms list that is displayed. Then, a list of sightseeing spots, tourism-related facilities, and disaster support facilities under the selected category will be displayed on the screen. Users can select a destination from the above list and a station from among the station list. Next, the selected destination and station will be displayed on the digital map of Web-GIS. While using the GPS installed into the users' mobile information terminals, the shortest route from the station to the destination will be displayed on the digital map. Additionally, using the function, users can search the alternative routes and display them on the digital maps of Web-GIS.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 5 Simulation Results
### Empty Slots Utilization  
One of the key criteria used to assess the effectiveness of the TDMA-enabled scheme in the live IoT networks is the slot usage ratio. Usually, a TDMA-enabled method with the highest feasible slot utilization ratio is seen as a great option for the conventional networks in general and particularly in the IoT if and only if other performance metrics are not sacrificed. In terms of slot usage ratio, the suggested neighborhood-enabled TDMA technique was compared to several field-proven algorithms. Figure 2 unequivocally demonstrates the proposed TDMA-enabled systems' superior performance over that of their competitors. Even though the suggested technique facilitates device mobility, it still outperformed the current strategy in terms of slot usage ratio. By allocating several vacant slots to a respective member device as long as these were accessible, the suggested technique attained the highest possible slots utilization ratio over existing schemes. In addition, IoT networks are used in a variety of event-driven application fields (ideally automatically) to help people manage different crucial occurrences. Moreover, the suggested neighborhood-enabled TDMA system was created for the event detection application area, but it is equally applicable in other application areas as well. Figures 3 and 4 demonstrates that the suggested strategy successfully attained the highest feasible usage ratio of open slots. These findings were gathered under various conditions, such as when all member devices were active and when only a specific percentage were. The suggested strategy performed better than the benchmark approaches used in both cases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Deployment Area of the IoT & 1200 m \(\times\) 1200 m \\ Member Devices \(C_{i}\) & 50–1000 \\ Server Modules \(S_{j}\) & 20 \\ Preamble and Physical Headers \(S_{j}\) & 15 us \\ Length of Beacon \(S_{j}\) & 75 to 110 bytes \\ Back-off Time \(S_{j}\) & random \\ IFS \& Gaurd Time \(S_{j}\) & 40 us \\ Slot \(S_{j}\) & 70 us \\ SNR \(p\) & 10 dB \\ Feedback Bits & 8 Bits \\ Energy (\(E_{i}\)) & 52,000 mAh \\ Residual Energy (\(E_{r}\)) & \(E_{i}\)=\(E_{c}\) \\ Transmission Power (\(P_{T_{s}}\)) & 91.4 mW \\ Channel Delay (\(Ch_{delay}\)) & 15 ms \\ Receiving Power (\(P_{R_{s}}\)) & 59.1 mW \\ Power Consumption (IM) & 1.27 mW \\ Power Consumption (SM) & 15.4 \(\mu\)W \\ Transceiver Energy (\(T_{i}\)) & 1 mW \\ Transmission Range (\(T_{r}\)) & 500 m \\ Receiving Power Threshold (\(RTS_{n}\)) & 1024 bits \\ Packet Size (\(P_{size}\)) & 128 bytes \\ Distance Between Devices & 300 m \\ Sampling Interval & 10 s \\ Topologies & Static and Random \\ \hline \hline \end{tabular}
\end{table}
Table 1: IoT network parameters.  
Figure 3: Empty slots utilization in the IoT network. Devices: 20.  
Figure 2: Empty slots utilization in the IoT network. Devices: 10.
# Electromagnetic Assessment of UHF-RFID Devices in Healthcare Environment
## 3 Results
### Hospital Universitario de Canarias (HUC)--Tenerife (Measurement-Based Assessment)
#### 3.1.1 EMF Exposure near MO-RFID Reader  
Radiation pattern, (to include maximum values from measurements for horizontal and vertical positions of measuring antenna and adjusted RFID antenna position) was sampled out around antenna starting in front of UHF RFID antenna with angle step of 45\({}^{\circ}\), to cover major, lateral, and back radiation.  
The E-field distribution was evaluated along three axes passing through the center of the antenna of the MO-RFID reader (gun): in the front and back of the reader plane--perpendicular to it and at the side of the reader--perpendicular to the reader's edge at some distances of the reader center. The results were normalized to an output power equal to the case considered in the numerical simulations (1 W). Measurements obtained in Torre Norte 4 Floor may be observed in Table 5.  
Most of E-field intensity levels were in the range from 0.040 V/m to 0.090 V/m. Nevertheless, a peak value of 0.210 V/m was registered at the angle 0\({}^{\circ}\), at the height of 1.0 m and at a distance of 1.5 m. Lower values of 0.137 V/m and 0.116 V/m were registered at the 180\({}^{\circ}\) angle, at 1.5 and 2.0 m, respectively. The peak value of 0.210 V/m corresponds to front position: MO-RFID reader and antenna are at the height of 1 m and between them, there are 1.5 m and the lowest at the 180\({}^{\circ}\) angle.  
In addition, a peak of 0.190 V/m was registered at the height of 1.7 m, at the 0\({}^{\circ}\) angle and at a distance of 1 m, obtaining lower values, 0.155 V/m and 0.127 V/m, at the 180\({}^{\circ}\) angle, at 1.0 and 1.5 m, respectively. As for the previous measurement results, peak value was obtained in a front position and the lowest at the 180\({}^{\circ}\) angle. The following is a concise and precise description of the results of the experiment. The complete and detailed information is available in [13].
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
###### Abstract  
Polymer stabilized liquid crystal (PSLC) devices modulating the light that goes through them have broad applications. In this study, to improve the electro-optical properties of a PSLC device, Ag nanowires with diameter about 20 nm were doped into PSLC active layer with different concentrations. The influence of Ag nanowires concentration on the driving voltage, on-state response time and frequency modulation characteristics of a PSLC device were studied. The results indicate that the doping of Ag nanowires can reduce the driving voltage of PSLC cell up to 42%. The response time of the PSLC cell can decrease by about 41%. Meanwhile, frequency modulation does not show significant improvement upon Ag nanowire doping. Overall, Ag nanowire doping can improve the electro-optical properties of PSLC device effectively.  
PSLC device; Silver nanowires doping; Electro-optical property; Smart glass; Privacy window
# A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE
###### Abstract  
5G Communications will support millimeter waves (mm-Wave), alongside the conventional centimeter waves, which will enable much higher throughputs and facilitate the employment of hundreds or thousands of antenna elements, commonly referred to as massive Multiple Input-Multiple Output (MIMO) systems. This article proposes and studies an efficient low complexity receiver that jointly performs channel estimation based on superimposed pilots, and data detection, optimized for massive MIMO (m-MIMO). Superimposed pilots suppress the overheads associated with channel estimation based on conventional pilot symbols, which tends to be more demanding in the case of m-MIMO, leading to a reduction in spectral efficiency. On the other hand, MIMO systems tend to be associated with an increase of complexity and increase of signal processing, with an exponential increase with the number of transmit and receive antennas. A reduction of complexity is obtained with the use of the two proposed algorithms. These algorithms reduce the complexity but present the disadvantage that they generate a certain level of interference. In this article, we consider an iterative receiver that performs the channel estimation using superimposed pilots and data detection, while mitigating the interference associated with the proposed algorithms, leading to a performance very close to that obtained with conventional pilots, but without the corresponding loss in the spectral efficiency.  
massive MIMO; post-processing; superimposed pilots; SC-FDE; mm-Wave; 5G 2020