esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LoRa and Localization Algorithms
### Overview of LoRa
#### 3.1.4 Sensitivity (S)  
Sensitivity is defined as the ability of the receiver to amplify the weak signals that are obtained by the receiver. In LoRa, the spreading factor, noise figure, and bandwidth  
Figure 1: LoRa Physical, Application, and MAC layers of protocol architecture.  
are considered to be inputs for providing the sensitivity as output. Sensitivity (\(S\)) is calculated as:  
\[S=-174+10 log_{10}\ BW+NF+SNR \tag{3}\]  
where \(BW\) is the band width of the channel, \(NF\) is the noise figure gain in dB, and \(SNR\) is the signal to noise ratio power in dB.  
Bit Rate/data rate is defined as the rate at which bits are transferred from one location to another. The bit rate (\(R_{bit}\)) of LoRa is expressed as:  
\[Rbit=SF*\frac{BW}{2^{SF}}*CR \tag{4}\]
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 5 Experimental Results
### Performance Comparison with Existing Methods  
This section compares the proposed approach to many existing ones, such as the stacked intelligent metasurface-aided downlink multiuser transmission (SIM-ADMT) [38], FB-TCP (fuzzy based-transmission control protocol) [32], MP-TCP (multi-path-transmission control protocol) [33], Deep IA (deep learning initial access) [34], and UL-SCH (uplink shared channel) + PUSCH (physical uplink shared channel) [31], and it evaluates its efficacy using performance metrics such as \"throughput (Mbps), round-trip times, end-to-end delays, packet loss, SNR, and beamforming accuracy\".  
A theoretical comparison with BBR, CUBIC, and NewReno reveals the following:  
BBR is bandwidth-probing centered but is not congestion-responsive. CUBIC is less aggressive in its recovery rate post-congestion incidents. The overly aggressive reduction of NewReno results in wasteful recovery on high-BDP networks. Our approach provides better link utilization with stability and is most suitable for use in mmWave networks.
# Dual-Polarized Multi-Beam Fixed-Frequency Beam Scanning Leaky-Wave Antenna
## 2 Methods
Figure 6a,b show the 3-D radiation patterns of the antenna. Figure 6c,d depict the radiation patterns of the X-polarized antenna with small and large capacitances, respectively. Where the large capacitance state is the capacitance of the varactor diode is 3.1 pF, the small capacitance state is the capacitance of the varactor diode is 0.5 pF. It is observed that the X-polarized antenna has a gain range of 9 to 11.2 dBi and a 3 dB beamwidth range of 8\({}^{\circ}\) to 11\({}^{\circ}\) with small capacitance, while the X-polarized antenna has a gain range of 11.3 to 13 dBi and a 3 dB beamwidth range of 8\({}^{\circ}\) to 10.5\({}^{\circ}\) with large capacitance. The maximum gain change during scanning is 2.3 dB. Similarly, Figure 6e,f illustrate the radiation patterns of Y-polarized antennas with small and large capacitances,  
Figure 3: Period and electric field distribution of the dual-polarized antenna. X-polarized antenna (**a**) period, (**b**) electric field distribution; Y-polarized antenna (**c**) period, (**d**) electric field distribution. (\(h_{1}=5.14\) mm, \(h_{2}=12.7\) mm, \(h_{3}=17.5\)mm, \(h_{4}=18\) mm, \(h_{5}=9\) mm, \(h_{6}=11\) mm, \(p=43.8\) mm, \(C_{2}=1.5\) pF). respectively. Where the large capacitance state is the capacitance of the varactor diode is 1.1 pF, the small capacitance state is the capacitance of the varactor diode is 0.2 pF. The gain range of Y-polarized antennas under small capacitance is 11.2 to 11.6 dBi, and the 3 dB beamwidth range is 11\({}^{\circ}\) to 14\({}^{\circ}\). Meanwhile, the gain range of X-polarized antennas under large capacitance is 10.3 to 12.4 dBi, and the 3 dB beamwidth range is 13\({}^{\circ}\) to 18\({}^{\circ}\). The maximum gain change during scanning is 1.3 dB. Figure 4: Dispersion curves of SPPs unit and modulation period. (**a**) X-polarized unit, (**b**) Y-polarized unit, (**c**) X-polarized modulation period, (**d**) Y-polarized modulation period. \[Z_{s}(x)=jX_{s}[1+Mcos\bigg{(}\frac{2\pi x}{p}\bigg{)}] \tag{1}\]0 Simulated S-parameters of the LWA array. (**a**) X-polarized antenna, (**b**) Y-polarized antenna.
## 5 Comparative Analysis: Mechanical vs. Petrographic Properties
### Case Studies and Experimental Results
[108] (2023) revealed that the microstructural characteristics of diabases, especially low alteration, few soft minerals, and preserved igneous textures, directly influence the mechanical performance and durability of concrete. Diabases from Veria-Naousa and Guevyugiel showed superior results compared to those from Edessa and Cyprus. Correspondingly, norite with its coarse-grained igneous texture and typically low alteration may be used as a suitable aggregate in concrete applications since it offers comparable strength and durability. [PERSON] et al. [109] (2025) developed an automated technique for identifying and quantifying pore types by examining pore-matrix contact relationships. Their method enhances pore identification accuracy by precisely aligning high-resolution mineral distribution maps with secondary electron images, overcoming challenges posed by low-resolution data. While intergranular pores are often misidentified, the approach performs better with coarse-grained rocks like norite compared to finer-grained ones. Meanwhile, [PERSON] et al. [110] (2024) tackled strong ground pressure in hard-rock mining by applying graded ground fracturing to weaken multiple rock layers. This technique lowers rock strength and breakage severity, enhancing mine safety and stability. A case study at Tashan Coal Mine demonstrated notable crack formation and decreased support resistance and damage. This method shows promise for managing ground pressure in norite formations as well.
As an earlier pioneering work, the Pockels effect by the Langmuir-Blodgett film coated on Ag was sensitively detected as the SPP resonance shift, and the nonlinear susceptibility \(\chi^{(2)}\) was evaluated [13; 14]. Then, an electro-optic (EO) polymer in contact with Ag was demonstrated as a light modulator, because the shift of the resonance dip due to the \(\Delta n\) is proportional to the \(E\) field applied to the EO polymer [15]. In all these studies, Kretschmann geometry was used. Since then, high-speed and high-modulation-depth plasmon light modulators with more sophisticated structures have been reported [16; 17; 18; 19], where EO materials, such as EO polymers exhibiting the Pockels effect, have been used as dielectrics in contact with the surface. More interestingly, a deep optical modulation was recently demonstrated using the shift of interference fringes on ITO due to the Pockels effect of water within a nanometer-order layer. The interference effect appeared as a sharp spectral dip in the total reflection spectrum at the ITO-water interface, and evanescent light penetrating the water side picked up the \(\Delta n\) there, causing the dip to shift spectrally. Note that the principle of this modulator is the same as Reference [15], because the mechanism of generating the sharp dip is both \"coherent perfect absorption\" [20; 21]. Therefore, if the Pockels coefficient of water at the interface of a metal showing sharp SPP resonance is large, it would provide a unique method of light modulation because the SPP and the Pockels effect are both the interfacial effect. In this paper, with the above motivation, we used Ag, which has long been studied for its sharp plasmon resonance, to observe the spectral shift of the plasmon resonance caused by the Pockels effect of water in the EDL of its interface. The magnitude of the Pockels coefficient was evaluated as \(|r_{33}|=5\pm 1\) pm/V. Here, the lower index \"3\" designates the electric-field direction normal to the electrode surface. Although it is difficult to estimate the thickness of the EDL under an alternating current (AC) \(E\) field, we have shown that a reliable value for the coefficient can be estimated by considering the penetration depth of SPPs without knowing the exact thickness. This value is consistent with the order of magnitude estimated for Pt, indicating that the Pockels coefficient of water is smaller on the noble metal surface than on the oxide or nitride electrode surface.
# A Molecularly Imprinted Polymer for Selective Extraction of Phenolic Acids from Human Urine
###### Abstract  
Studies for monitoring the bioavailability of dietary flavonoid compounds generate great interest. Among them, low-molecular-weight phenolic acids, secondary metabolites present in colonic catabolism and urinary excretion, have been proposed as biomarkers of polyphenol intake. Using 4-hydroxyphenylacetic acid as a template, a molecularly imprinted polymer (MIP) was synthesized for selective extraction of these hydroxylated metabolites from human urine samples and posterior analysis in an HPLC-DAD-MS system. Polymers were characterized by Scanning electron microscopy (SEM), Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), Brunauer-Emmett-Teller (BET) method, and binding experiments. MIP presents specific recognition ability for template and analogues molecules. This capacity of recognition and the pH dependence of the binding strength was also studied. The method was validated over a concentration range of 0.25-40 mg/L, \(r^{2}\) > 0.995. In the optimized conditions, the recovery value was 94% with RSD 1.2%. The Limit of Detection (LOD) and Limit of Quantification (LOQ) were 1.22 and 3.69 mg/L, respectively. In our knowledge, it is the first time that this methodology is applied to analyze urinary catabolites of the polyphenol compound and to provide a specific method and simple analysis alternative. The selective extraction of these metabolites improves the application and results obtained by other less sensitive analysis methods than the validation method. It also facilitates the development of new screening methods.  
molecularly imprinted polymer; polyphenol metabolites; solid phase extraction; human urine; phenolic acids; HPLC-DAD-MS +
Footnote †: journal: Applied Sciences  
1
# Electromagnetic Testing of Rod Cluster Control Assemblies in Pressured-Water Reactor Power Plants
## 2 Principle  
Figure 1 shows the basic principle of the EHaS probe inserted outside of the control rod with a defect. The EHaS probe consists of a bobbin coil wound around an EHaS. The bobbin coil is an electromagnetic source which is supplied by an AC. An induced current in the circumferential direction of the control rod appears due to the first magnetic field from the coil, and is perturbed by the defect. The intensity of the induced current penetrates the control rod according to the skin effect, with a skin depth calculated by Equation (1) [9; 10]. The skin effect limits the penetration depth of the induced current, thus the EHaS probe is sensitive to surface defects. A secondary magnetic field appears due to the induced current, and the radial component (Br) is then measured by the EHaS. By measuring the radial magnetic field Br, the defect can be detected and evaluated quantitatively.  
The output voltages Vr of the EHaS, which have a linear relationship with the magnetic field Br, are processed through high-pass filters (HPFs), amplifiers, root-mean-square (RMS) circuits, and transferred (V\({}_{\text{RMS}}\)) to a computer by a National Instruments data acquisition (NI-6255 USB type, National Instruments, Austin, Texas, USA). The processing of the signal is simplified as the NI device is also used to control the power supplies for all of the signal processing units and EHaS probe, and to control the scanning of the control rods.  
\[\delta=\frac{1}{\sqrt{\pi f\mu\sigma}} \tag{1}\]  
\[V_{r}(t)=K\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\  
\[V_{RMS}=\sqrt{\frac{1}{T}\int\limits_{0}^{T}[V_{r}(t)]^{2}dt}=\frac{K\cdot I \cdot B_{r0}}{\sqrt{2}} \tag{3}\]  
Here, \(f\), \(\mu\), and \(\sigma\) are the exciting frequency, absolute permeability, and electrical conductivity, respectively, of the control rod material, and \(K\), \(I\), \(t\), \(B_{r0}\), \(T\) and \(\varphi\) are the Hall constant, the input current of the Hall sensor, and the time, amplitude, period, and phase of the radial magnetic field Br, respectively.
# Design and Development of a Wideband Planar Yagi Antenna Using Tightly Coupled Directive Element
## 5 Antenna Fabrication and Measurements
Using (1), the pulse fidelity factor was calculated to be 0.97. It can be concluded that the proposed printed Yagi antenna is capable of radiating broadband signals with low dispersion.
## 1 Introduction
After each decomposition of original signal, the low frequency part can be repeatedly decomposed again. Each layer of decomposition decomposes the input signal of this layer into a low frequency approximate component part and a high frequency detailed part. In addition, the out sampling frequency of each stage can be halved. The significance of halving the sampling frequency is that it will not cause information loss. This paper designs an improved harmonic detection method based on DFT and DWT using the Bartlett-Hann window function. According to reference [13], the Bartlett-Hann window is a linear combination of the Bartlett and Hanning windows. Taking into account its main lobe width and side lobe attenuation, it is more effective for detecting steady-state harmonics. For the high frequency part, based on the Hanning window DFT and DWT hybrid harmonic detection method, only the attenuation harmonic component is analyzed, and no consideration is given to whether the high frequency part containing the steady-state signal component and the attenuation harmonic component coexist. The method designed in this article can be divided into three cases for processing by analyzing the high frequency part. The steady-state harmonics in the high frequency part are distinguished from the transient harmonics, and the error of the steady-state harmonic component extraction is less than 5%. The amplitude of the attenuated harmonic component at the beginning is basically consistent with the theoretical amplitude. It can improve the detection accuracy of the hybrid detection strategy in the low frequency part, and separate and extract the doped steady-state harmonics from the non-steady-state harmonics in the high frequency part. Simulation experiments show that the algorithm designed in this paper has good harmonic detection accuracy for harmonic detection, and can achieve separation and extraction of high frequency parts. The rest of this article is organized as follows. Section 2 introduces the principle of the hybrid harmonic detection method based on the Bartlett window DFT and DWT. Section 3 introduces the design of an improved method based on the Bartlett-Hann window DFT and DWT hybrid harmonic detection. Section 4 provides simulation results of test signals and test data. Section 5 summarizes this study.
## 3 Results and Discussion
the concrete was peeled off the surface of concrete confined with the PET FRP after the second impact with a strain rate of 272 s\({}^{-1}\). Under the third impact at a strain rate of 280 s\({}^{-1}\), partial concrete was crushed with no rupture of the external PET FRP. Figure 8 shows the dynamic stress-strain curves of specimens confined with the CFRP and the PET FRP at a strain rate of around 220 s\({}^{-1}\). The PET and CFRPs were designed for a comparison based on ultimate jacket strengths (i.e., the product of tensile strength and thickness of FRP jacket). One layer of the CFRP provides an ultimate jacket strength of 729.8 N/mm that falls between the values provided by one layer and two layers of the PET FRPs (631.6 N/mm and 1263.2 N/mm, respectively). The dynamic compressive strength of the specimen confined with one layer of the CFRP was 89.4 MPa, whereas the corresponding value for one layer of the PET FRP-confined concrete was 82.9 MPa with a larger compressive strain of 4.58%. In addition, the dynamic compressive strength and the corresponding strain of two layers of the PET FRP confined-concrete were remarkably higher than those of the CFRP-confined counterpart, indicating the superior impact performance of the PET FRP-confined concrete due to the large rupture strain characteristic. Figure 9 shows the dynamic compressive stress-strain curves of the specimen confined with two layers of the PET FRP under multiple impacts. The compressive strength of two layers of the PET FRP-confined concrete decreased after the second impact due to the progressive concrete damage, which reveals superior impact-resistant behavior as far as deformability is concerned due to the large rupture strain characteristic. Figure 8: Comparison of the dynamic compressive stress–strain curves of the specimen at a strain rate of around 220 s\({}^{-1}\). Figure 7: Failure modes of the concrete confined with the CFRP (**a**) and the PET FRP (**b**). Figure 10a shows the time histories of specimens with three different confining conditions (i.e., no FRP, one layer of AFRP, and PET FRP) under the same drop height of 2 m from the drop-weight tests. It can be seen that, compared to the unconfined specimen, the application of AFRP led to an increase in the peak impact force. It can also be seen in Figure 10a that the application of PET FRP not only improved the peak impact force, but also prolonged the impact process, leading to a superior impact resistance. Figure 10b shows the time histories of the identical PET FRP-confined specimens tested at different drop heights. As can be seen in Figure 10b, the peak impact force of the specimen slightly increased, and the impact duration gradually decreased with the increasing drop height from 2 to 5 m. It is worth noting that the duration of the specimen impacted under a 4 m drop height was longer than that of the counterpart under a 3 m drop height, which can be considered a test scatter in this study.
## 4 Applications of \({}^{31}\)P qNMR for Phosphorus-Containing Compounds  
Quantitative nuclear magnetic resonance is a widely recognized method for analyzing phosphorus compounds, with applications ranging from food analysis [44; 45; 46; 47], pharmaceuticals [9; 48; 49; 50; 51; 52], and biological research [53; 54; 55] to energy materials and polymers. Compared to \({}^{1}\)H and \({}^{13}\)C qNMR techniques, \({}^{31}\)P qNMR offers notable advantages, including rapid and precise identification of phosphorus-containing compounds, enhanced sensitivity, and broad signal dispersion.
# Logitwise Distillation Network: Improving Knowledge Distillation via Introducing Sample Confidence
## 1 Introduction
By incorporating this confidence as weights into the method of feature-based distillation, the impact of teacher mispredictions on the student model will be greatly reduced. Generally, KD networks usually conduct knowledge distillation from layer to layer, allowing student features or logits to mimic the features or logits of the teacher at the same layer as closely as possible. However, in LDN, the credibility of teacher features is extracted from the logit information in different samples and incorporated as weight information into the feature-based distillation process. We also propose a simple and effective loss term called FD loss. It applies LDN to the layer before the logit layer, the embedding layer. Unlike conventional feature-based distillation methods, LDN incorporates the ranking information of ground truth in logits as weights in different steps of feature distillation. LDN is reflected in three areas: weighted class means, feature selection, and positive and negative sample adjustment (PNSA). It effectively utilizes the sample confidence information from logits to guide the distillation of feature layers, thereby minimizing the impact of incorrect supervision caused by teacher prediction errors. Our principal contributions are threefold:  
1. **Dynamic Sample Confidence Mechanism**: Unlike prior works that crudely filter samples via binary correctness labels (e.g., [PERSON] et al. [23]), LDN quantifies continuous confidence through ground truth rankings in teacher logits. This allows for the fine-grained weighting of samples, effectively suppressing error propagation while retaining nuanced supervision from partially correct predictions. 2. **Multi-Stage Error Correction Framework**: We propose the first synergistic integration of (i) weighted class means to amplify high-confidence prototypes, (ii) adaptive feature selection to toggle between raw features and class means, and (iii) PNSA to invert supervision for error-prone samples. This tripartite design systematically addresses error accumulation at feature, sample, and distribution levels. 3. **Plug-and-Play Generalizability**: FD loss requires no architectural modifications or additional training phases. When integrated into SOTA methods (e.g., DKD, ReviewKD), it consistently boosts accuracy by 0.3-2.4% across both homogeneous (ResNet families) and heterogeneous (ResNet\(\rightarrow\)MobileNet) architectures, demonstrating unprecedented compatibility. In the following sections, we present a comprehensive analysis and evaluation of our proposed method. In Section 2, we analyze relevant work, compare existing methods, and highlight the unique features of LDN. In Section 3, we describe the theoretical foundations and key concepts underlying our method. Section 4 presents the results and analysis of our experiments, demonstrating the effectiveness and performance of our method. Furthermore, finally, we conclude the paper in Section 5, summarizing the contributions of our work and outlining potential directions for future research.