esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Recent Applications

Table 5: Recent applications of ion-imprinted polymers (2017-2022). Recently, the chemical imprinting technique with ions has been combined with Restricted Access Materials (RAM) to obtain absorbents with high selectivity and satisfactory ability to exclude macromolecules. [PERSON] and coworkers [99] synthesized an IIP-RAM for the adsorption of Cu\({}^{2+}\) ions in human urine and blood serum samples. The adsorbent had a maximum adsorption capacity of 15.9 mg g\({}^{-1}\) with a pre-concentration factor of 30.0 and satisfactory exclusion of macromolecules. From the textural parameters, the IIP-RAM exhibited a surface area of 86.3 m\({}^{2}\) g\({}^{-1}\), with a total pore volume of 0.17 cm\({}^{3}\) g\({}^{-1}\) and an average pore diameter of e 6.29 nm. The authors report that IIP-RAM showed selective capacity for Cu\({}^{2+}\) ions in the presence of different ions (Zn\({}^{2+}\), Co\({}^{2+}\), Ni\({}^{2+}\), Pb\({}^{2+}\) and Cd\({}^{2+}\)) at different concentrations (0.2, 0.5, 1.0, 2.0 and 5.0 mg L\({}^{-1}\)). [PERSON] and [PERSON] [100] synthesized an IIP with RAM based on poly(allythtihourea) and modified it with 2-hydroxyethyl methacrylate (HEMA) and bovine serum (BSA) to preconcentrate Cu\({}^{2+}\) ions and simultaneous exclude of protein from milk samples using FIA-FAAS. The proposed method was effectively applied to the copper determination in different milk samples, needful only pH adjustment followed by the preconcentration step as sample pretreatment. The content of copper in the sample of bovine milk was 0.635 \(\pm\) 0.042 mg kg\({}^{-1}\), whereas soybean milk samples were between 0.048 \(\pm\) 0.008 and 0.094 \(\pm\) 0.005 mg kg\({}^{-1}\). The authors define the proposed extraction method can be considered as a simple, fast, and low-cost analytical strategy when compared to conventional microwave-assisted acid digestion. In the same way, [PERSON] and coworkers [101] developed an IIP with RAM for online preconcentration of Cd\({}^{2+}\) and simultaneous clean-up of humic acid from natural water samples. An addition/recovery test was employed to attest to the proposed method's accuracy at two-level of Cd\({}^{2+}\) with the presence of humic acid in a real water sample. The recovery percentages obtained were between 93.8 and 108.7%. Likewise, the cadmium content was analyzed employing TORT-2 (lobster hepatopancreas) as certified reference material. The proposed method was applied to samples of water, food (linseed, 301.8 \(\pm\) 15.3 \(\mu\)g kg\({}^{-1}\)), herbal medicine (_Ginkgo biloba_ L., 221.2 \(\pm\) 5.8 \(\mu\)g kg\({}^{-1}\)), and cigarette (77.9 \(\pm\) 6.3 \(\mu\)g kg\({}^{-1}\)) after a spiking procedure with a known concentration of Cd\({}^{2+}\). The authors consider that the proposed method was satisfactory when compared with other works reported in the literature for cadmium determination.
# Household Energy Management
## 4 Simulation Studies of the EEM Algorithm for a Single Household
### Analysis of Test Results
If only the ToU tariff is used, the RES application reduces the daily energy consumption by 6.8 kWh (\(-27.6\%\)) and its costs by PLN 5.84 (\(-43.5\%\)). On the other hand, the simultaneous use of ToU and EEM and the use of RES reduce the daily energy consumption by 9.6 kWh (\(-37.8\%\)) and its costs by PLN 6.11 (\(-48.2\%\)). In the performed evaluation tests of the EEM algorithm, there was a lack of choice of intermediate values of power levels in the appliances. For example, when operating EEM2 in the time period requested by DSO, the boiler was turned off and the option of switching on other appliances with a total power greater than \(P_{h\_DSO}\) was blocked. If the boiler, as a smart appliance, had 2 additional power levels defined (as in Section 3.3), then for the comfort of using hot water at that time, EEM2 would reduce its power to 1 kW. Thus, a power reduction of 0.5 kW would be achieved in the given time period.
## 3 Antenna Performance Enhancement Techniques
### Effect of Substrate Choice on Performance Enhancement  
The appropriate selection of a substrate is the main requirement of an antenna implementation for valuable and fruitful results, as in [37]. For antenna fabrication, various substrates with different permittivity and loss tangents are available to be used based on choice and requirements of a researcher. For enhancement of gain and reduction in power loss, the selection of a substrate could play a vital role. Substrates with less relative permittivity and low loss tangents must be selected to achieve the goal of enhanced value of gain and a decreased value of power loss.
# Low-Cost 400 Gbps DR4 Silicon Photonics Transmitter for Short-Reach Datacenter Application
## 5 Summary and Prospect  
We have presented the MACOM silicon photonics transceiver architecture and tested the modulator and the silicon photonics engine as a DR4 transmitter. With all the MACOM components integrated in the transceiver, we were able to demonstrate a considerably cost-effective short reach link inside the datacenter. Thanks to the >40 GHz high bandwidth of the MZM, 4 \(\times\) 100 Gb/s PAM-4 signals were transmitted and recovered with a low TDECQ of \(\sim\)3 dB.  
Our future study will be focusing on characterizing the receiver part of the silicon photonics engine, putting the transceiver on an evaluation board in a short reach link and packaging all the components in a QSFP-DD defined module as a reference prototype design for datacenter applications.  
**Author Contributions:** Investigation, data curation and writing--original draft preparation, H.Z.; methodology, H.Z., S.A., L.J., Y.L., R.B., M.W. and H.Y.; software and validation, N.K.; writing--review and editing, R.B., L.J., R.K. and R.G.; supervision and project administration, R.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank [PERSON] from MACOM connectivity BU for important discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: 53.125 Gbaud PAM-4 eye pattern from each lane of the transmitter.
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
## 5 Power optimal Allocation Algorithm  
Based on NOMA two-hop hybrid communication, the optimal power distribution problem of combined system normalized throughput and packet loss probability can be described by the following mathematical model:  
\[\left\  
that physical layer solution is met; C8 ensures that the number of competing stations and transmitting power meet a certain relationship.  
Figure 7 shows the three-dimensional curve of optimization target \(G(P_{S},P_{R},a_{1},a_{2})\) changing with transmitting power \(P_{S}\) and power distribution factor d of user \(D_{1}\). It can be seen from the figure that under different combinations of \(P_{S}\) and \(a_{1}\) parameters, the system optimization objective \(G(P_{S},P_{R},a_{1},a_{2})\) has a unique extreme point in the interval, indicating that the optimization objective function with \(P_{S}\) and \(a_{1}\) as variables is continuously derivable and there is a global optimal solution.  
The above optimization problem is a mixed-integer nonlinear solving problem due to the corresponding relationship between the transmitting power and the number of competing stations in the listening range, and the nonlinear equations need to be solved during the solving process. In order to simplify the solution complexity and ensure the accuracy, this paper uses genetic algorithm and other methods to determine the optimal transmission power \(P_{S}\) and power distribution factor \(a_{1}\) with accuracy of 0.01 and verifies it using an exhaustive method.  
Figures 8 and 9, respectively, show the comparison of optimization objective and delay of different algorithms with the change of SNR. It can be seen from the figure that under different SNRs and channel fading coefficient conditions, the optimal power allocation algorithm has better performance than the average power allocation system. This is because the optimal power allocation algorithm in this paper establishes the dynamic relationship between the transmitting power and the number of competing stations, comprehensively considers the influence of non-ideal channel transmission at the physical layer and competitive access at the MAC layer on system performance, and optimizes the total transmit power at the source end and the power allocated to different users, so that the system has better performance in terms of normalized throughput, packet loss rate, and delay. At the same time, it can adapt to the change of channel conditions.  
Figure 7: Three-dimensional curve of optimization target with transmission power and user power allocation factor.
## 4 Development
### Solution
#### 4.2.2 Definition of the Building  
The objective was to model a generic building that can contain a facade based on the origami elements described above. To do so, a simple building design was sought, focusing on only two aspects: dimensions and orientation.  
According to studies of actual applications of this type of facade, they tend to be more common in skyscrapers. Therefore, a building with an octahedral shape of 21 m on each side of the base and 48 m in height was proposed. This is equivalent to 16 floors in height with a distance between floors of three metres. It is important to note that the structure supporting the origami element must be three metres away from the internal face of the building. This is done to ease the circulation of air currents and to avoid heat accumulation, as well as to improve maintenance tasks.  
Figure 4: Geometric properties dynamic element.  
One of the final objectives of the project was the development of an automated system that acts according to the solar path, whereby orientation is a key factor. This orientation varies according to the project location. As indicated above, the building had four facades, two of which will be shielded and two of which will be unshielded. If the building was located in the northern hemisphere of the earth, the best position of the edge between the two shielded facades was facing to the south. If the building was in the southern hemisphere, the edge should face to the north.  
At the same time, the season of the year must be considered. As can be seen in Figure 5, in the summer, the sun rises over the horizon between east-northeast (ENE) and northeast (NE) and sets between west-northwest (WNW) and northwest (NW), which varies depending on the summer day. In spring and autumn, it rises between the near east-northeast (ENE) and east-southeast (ESE) directions and sets between west-northwest (WNW) and west-southwest (SW). Finally, the sun in winter rises between east-southeast (ESE) and southeast (SE) and sets between west-southwest (SW) and southwest (SW). It should be noted that the sun only rises in the east (E) and sets in the west (W) twice a year, called the vernal and autumnal equinoxes. The day of the year when the sun rises and sets closest to the north is called the summer solstice. The winter solstice occurs on the day of the year when the sun rises and sets closest to the south.  
In this context, it was decided to place the building in the city of Madrid. Madrid is in the northern hemisphere with coordinates of 40\({}^{\circ}\)25\({}^{\prime}\)00\({}^{\prime\prime}\). By means of the logic previously described, it is established that the edge between the two shielded facades must be oriented to the South. Therefore, these two facades will face Southwest and Southeast.
## 2 Background
### Cosine Similarity  
To distinguish between truthful and deceitful gradient vectors, we employ cosine similarity--a widely used metric for quantifying the alignment between two vectors--to assess the directional consistency between a local model gradient (denoted as \(\mathbf{g}_{i}^{(t)}\)) and the aggregated sum gradient vector \(\mathbf{G}_{s}^{(t)}=\sum_{i=1}^{N}\mathbf{g}_{i}^{(t)}\). Namely,  
\[cs_{i}^{(t)}=cos\ \theta_{i}^{(t)}=\frac{<\mathbf{g}_{i}^{(t)},\mathbf{G}_{s}^{(t )}>}{||\mathbf{g}_{i}^{(t)}||\cdot||\mathbf{G}_{s}^{(t)}||}=<\frac{\mathbf{g }_{i}^{(t)}}{||\mathbf{g}_{i}^{(t)}||},\frac{\mathbf{G}_{s}^{(t)}}{||\mathbf{ G}_{s}^{(t)}||}>, \tag{2}\]  
where \(\mathbf{G}_{s}^{(t)}\) guides the global decent to an appropriate direction, \(||\cdot||\) denotes the \(l_{2}\)-norm, and \(<,>\) signifies the dot product between vectors. The cosine similarity metric for \(\mathbf{G}_{s}^{(t)}\) and the gradient \(\mathbf{g}_{i}^{(t)}\) calculates the ratio of their inner product to the product of their respective magnitudes, mathematically expressed as \(cos\ \theta_{i}^{(t)}=\frac{<\mathbf{g}_{i}^{(t)},\mathbf{G}_{s}^{(t)}>}{|| \mathbf{g}_{i}^{(t)}||\cdot||\mathbf{G}_{s}^{(t)}||}\).
## 3 Satellite Precipitation Uncertainty Distribution Approximation  
In this section, the three above-described satellite-based precipitation uncertainty distribution types, i.e., (i) constellation change-induced uncertainties; (ii) sampling uncertainty and (iii) comparison with rain-gauges network, were explored using the aforementioned GMM model. The performances of the GMM model were also contrasted with that of a simple Gaussian model as a function of the precipitation accumulation.
# Study on Temporal and Spatial Distribution of Landslides in the Upper Reaches of the Yellow River
## 6 Conclusions  
Based on the study of the spatial plane morphology, distribution characteristics, and spatio-temporal distribution of landslides in the upper reaches of the Yellow River, we have the following three understandings:  
(1) Landslide remote sensing images in the upper reaches of the Yellow River have obvious features. The spatial distribution patterns of landslides mainly include dumb-bell shape, rectangle, saddle type, long-arc shape, triangle, and side-by-side shape.  
(2) The length and width of the landslide deposit body are mainly concentrated in 550-1500 m and 600-1500 m, and the length and width extend in the direction of polarization. The average elevation of the sliding body is mainly concentrated in the range of 2000-2800 m, and the relative elevation difference between the front and rear edges is concentrated in the vicinity of 150-400 m and 750 m. The average slope of the slide body is mainly distributed in the range of 15-20\({}^{\circ}\).  
(3) Giant landslides are mainly distributed in the Jianzha basin area, bounded by the Jishishian fault and the Lashishian fault in the West Qinling Mountains. The giant ancient landslides are mostly multi-stage and distributed in the form of landslide groups.  
Conceptualization, Y.S.; methodology, X.Z.; software, Z.L. and D.L.; validation, R.X.; formal analysis, K.W.; data curation, S.W.; writing--original draft preparation, Z.L.; writing--review and editing, S.W. and P.G.; supervision, K.W.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Qinghai Provincial Bureau of Geology and Mineral Exploration and Development Plan Project (2024-24-4) and the Qinghai Key R & D and Transformation Plan Project (2019-SF-130).  
Not applicable.  
Data are contained within the article.  
Author [PERSON] was employed by the company Hubei Shenlong Geological Engineering Investigation Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract  
Mode pairing is a crucial step for the stability of any model-updating strategy based on experimental modal parameters. Automatically establishing a stable and assertive correspondence between numerical and experimental modes, in many cases, proves to be a very challenging task, especially in situations where complex mode shapes are present. This article presents a novel formulation for the automatic mode pairing between experimental and numerical complex modes based on an Energy-based Modal Assurance Criterion (EMAC). The efficiency of the proposed criterion was demonstrated on the basis of a case study involving the pairing between numerical and experimental modes of a passenger railway vehicle. A highly complex detailed FE numerical model of the vehicle was developed involving the modeling of the carbody, bogies and axles. A numerical damped modal analysis allowed obtaining the main global rigid-body and flexural modes of the vehicle's carbody, as well as several local modes associated to the vibration of specific components of the carbody. Due to the localized damping provided by the suspensions, these modes presented complex modal ordinates, especially for the rigid-body modes. The comparison between the results obtained from the application of the EMAC and the classical MAC criteria, on the pairing of five global mode shapes, proved that the EMAC criterion is much more assertive, avoiding mismatches between the experimental global modes and some of the local numerical modes with similar configurations, and, consequently, establishing the correct correspondences between experimental and numerical modes.  
model updating: automatic mode pairing: complex modal parameters; energy-based MAC 20231350. [[https://doi.org/10.3390/app13010350](https://doi.org/10.3390/app13010350)]([https://doi.org/10.3390/app13010350](https://doi.org/10.3390/app13010350))
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
### Related Works on SAR  
In the literature, there is a considerable amount of research on RF-EMF absorption and SAR values for different tissues and organs. The summary of these studies is provided in Table 1. The distribution of the electric field in a human head is presented in [9], while SAR and temperature distribution in a child head model was investigated through simulations and is reported in [10]. Studies [11; 12; 13; 14; 15] investigated age and tissue-dependent exposure and reported a higher SAR in children's head and brain compared with adults, while [16] found no significant SAR differences between the child and the adult head. In [17], SAR calculations were performed, and showed that the averaged SAR of adults vary, and that for child models it was over the fundamental safety limits. In [18], adult male head region SAR values caused by base stations were investigated. In [19], SAR evaluations were undertaken for a pregnant women model and found showed that SAR values were higher than the specified limits. In [20], whole-body average SAR values were evaluated using flat phantoms. SAR values of anatomical human models and box-shaped phantoms were compared. In [14; 21], it is stated that absorption level differences may be because of device usage patterns. The role of the position of the radiation source has been determined for both children [22], and for a computational human body model [23]. SAR values in different human tissues as a result of varying source-to-antenna distances and radiated powers were evaluated using ANSYS in [24], and a positive correlation between operation frequency, radiated power and SAR values was shown. The SAR values of 2G, 4G, and 5G mobile phone antennas were simulated in [25] using a SAM model and an anatomical head model, and the usage of a new head model for the SAR evaluation of 5G mobile was suggested. Ref. [26] investigated the effects of different commercial cell phone RF wave exposures on brain, eye, and skin tissues under laboratory conditions. Localized SAR values were then calculated based on the temperature increase in the selected tissue and were found to be below ICNIRP limits. The SAR levels from 900 MHz electromagnetic waves in different departments of several hospitals were evaluated in [27], the highest electric field strength and the corresponding SAR values were measured in a radiology department. In [28], SAR and heat transfer in human head tissues (skin, bone, brain) were assessed for different frequencies, antenna types, and antenna separation distances. Their results reveal that SAR values due to mobile phones was higher than those due to Bluetooth and to Wi-Fi devices.
# A Signaling-Free Underwater Code Division Multiple Access Scheme
## 3 Estimating the Number of Nodes in Transmission  
As shown in Figure 1, suppose that node \(X\), located at a distance \(x\) from the sink, is examining the channel availability for data transmission while node \(Y\) is transmitting. For mathematical description simplicity, using polar coordinates, we locate the sink at the origin (0, 0), node \(X\) at \(\left(x,-\frac{\pi}{2}\right)\), and node \(Y\) at \(\left(r,\phi\right)\). Let \(\theta\) and \(l\) denote the angle and distance between node \(X\) and \(Y\), respectively. Let \(P_{Y}^{t}\) denote the transmission power at node \(Y\) and \(P_{S}^{rec}\) denote the common received power at sink then,  
\[P_{Y}^{t}\cdot\int_{B}A^{-1}(r,f)df=P_{S}^{rec}=S(f)\cdot B \tag{4}\]  
where \(S(f)\) represent the common received power spectral density at the sink. We assume that \(S(f)\) is flat for \(f\in B\) where \(B\) is a signal bandwidth. Therefore, the transmission power at node \(Y\) is given by  
\[P_{Y}^{t}=\frac{S(f)\cdot B}{\int_{B}A^{-1}(r,f)df} \tag{5}\]  
In a similar manner, the received power at node \(X\) when node \(Y\) is transmitting is  
\[P_{X\gets Y}^{rec}=P_{Y}^{t}\cdot\int_{B}A^{-1}(l,f)df \tag{6}\]  
Because \(P_{X\gets Y}^{rec}\) is obtained from the specific location of node \(Y\) with \(r\) and \(\theta\), over the whole region with radius \(R\), we can obtain the expected value of received power at node \(X\) from a node \(Y\):  
\[E\Big{[}P_{X\gets Y}^{rec}\Big{]} =\int_{0}^{R}\int_{0}^{2\pi}P_{X\gets Y}^{rec}\cdot f_{r, \Theta}(r,\theta)d\theta dr \tag{7}\] \[=\int_{0}^{R}\int_{0}^{2\pi}P_{X\gets Y}^{rec}\cdot f_{r}(r) \cdot f_{\Theta}(\theta)d\theta dr\] \[=\int_{0}^{R}\int_{0}^{2\pi}P_{X\gets Y}^{rec}\cdot\frac{2r} {R^{2}}\cdot\frac{1}{2\pi}d\theta dr\]  
where \(f_{r,\Theta}(r,\theta)\), \(f_{r}(r)\), and \(f_{\Theta}(\theta)\) represent the joint probability density functions of \(r\) and \(\theta\), the probability density function of \(r\), and the probability density function of \(\theta\), respectively. We assume that \(f_{r,\Theta}(r,\theta)\) is uniformly distributed over the cell with radius R. Because \(E\Big{[}P_{X\gets Y}^{rec}\Big{]}\) corresponds to the value for one node \(Y\), the value is multiplied when multiple nodes are transmitting and accordingly, the number of transmitting nodes is estimated in a way that the measured receiving power at node \(X\) becomes closest to the certain multiple times of \(E\Big{[}P_{X\gets Y}^{rec}\Big{]}\) like  
\[N_{e}={{}_{n}^{\arg}}\Big{(}\min\Big{|}P_{X}^{rec}-nE\Big{[}P_{X\gets Y}^{ rec}\Big{]}\Big{)} \tag{8}\]  
where \(P_{X}^{rec}\) denotes the measured receiving power at node \(X\).