esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Discussion
### Evaluation Metrics  
In this paper, we select some universal evaluation metrics, such as Hamming loss, one-error, coverage, ranking loss, and average precision, to verify the classification effect of different models. These metrics are shown as the following formulas. In these formulas, \(S=\{(x_{i},y_{i})\mid 0<i\leqslant n\}\) is the dataset, \(h(.)\) is the multi-label classification model, \(n\) is the number of input instances, and \(q\) is the output dimension and the number of labels for each instance. \(x_{i}\) is the set of input samples, \(y_{i}\) is the set of output values. \(Y_{i}\) is the set of labels, \(l\) is the subset of \(Y_{i}\). \(f(.)\) is the objective function. \(rank_{f}(.)\) is the ranking function.  
Hamming loss indicates the gap between outputs of classifier and the true label and can be defined as:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Train** & **Test** & **Dim** & **Label** \\ \hline Art [40] & 2000 & 3000 & 462 & 26 \\ Business [40] & 2000 & 3000 & 438 & 30 \\ Computer [40] & 2000 & 3000 & 681 & 33 \\ Yeast [40] & 1500 & 917 & 103 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details about the multi-label classification datasets.  
Figure 14: The comparative diagram of evolution before and after. (**a**) the initial distribution of evolutionary population; (**b**) the distribution of evolutionary population after 100 generations.  
\[HammingLoss_{S}(h)=\frac{1}{n}\sum_{i=1}^{n}\frac{1}{q}|h(x_{i})\Delta Y_{i}| \tag{19}\]  
where \(h(x_{i})\Delta Y_{i}\) means the symmetric difference between outputs and ground-truth.  
One-error is used for evaluating the times that top-ranked label is not in the set of relevant labels. In addition, the computational process is expressed as:  
\[One-Error_{S}(h)=\frac{1}{n}\sum_{i=1}^{n}\frac{1}{q}[\left[\arg\max_{p\in y_{ i}}f(x_{i},p)\right]\
ot\in Y_{i}] \tag{20}\]  
Coverage, as shown in Equation (21), reveals the minimum query times of finding all the relevant labels in a sequence of output set:  
\[Coverage_{S}(h)=\frac{1}{n}\sum_{i=1}^{n}\max_{l\in Y_{i}}rank_{f}(x_{i},l)-1 \tag{21}\]  
Ranking loss measures the average fraction that the irrelevant labels are in front of the relevant labels in a rank of output values associated with an input instance. The computational process is described as follows:  
\[RankingLoss_{S}(h)=\frac{1}{n}\sum_{i=1}^{n}\frac{1}{|Y_{i}|\overline{Y_{i}}| }|\{(l_{1},l_{2})\mid f(x_{i},l_{1})\leqslant f(x_{i},l_{2}),(l_{1},l_{2}) \in Y_{i}\times\overline{Y_{i}}\}| \tag{22}\]  
where \(\overline{Y_{i}}\) denotes the complementary set of \(Y_{i}\) in the label space \(Y\).  
Average precision evaluates the average fraction of relevant labels ranked above a particular label. This indicator can be formulated by the following equation:  
\[AveragePrecision_{S}(h)=\frac{1}{n}\sum_{i=1}^{n}\frac{1}{|Y_{i}|}\sum_{l\in Y_{ i}}\frac{|\{\{^{\prime}\mid rank_{f}(x_{i},^{\prime})\leqslant rank_{f}(x_{i},l), \textit{l}^{{}^{\prime}}\in Y_{i}\}|}}{rank_{f}(x_{i},l)} \tag{23}\]
## 2 Materials and Methods  
### Patient and Study Criteria  
A retrospective analysis of patients diagnosed with OPSCC and treated with concurrent chemoradiation therapy (CRT) was conducted at COH main campus and selected satellite sites. Main eligibility criteria included a diagnosis of OPSCC, age 18 years or older, definitive/adjuvant treatment including concurrent CRT at a COH site between February 2009 to February 2017, available demographic, treatment, and survival data, functional outcomes, and recent follow-up at a COH site within a year. We utilized our institutional electronic medical record (EMR) to analyze 400 consecutive head and neck cancer patients from various COH sites to enroll in our study. Of the initial 400 patients reviewed, only 94 met the inclusion criteria including OPSCC diagnosis, recent follow-up, and history of CRT treatment at a COH site. Confounding factors considered were age, gender, and T stage. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the COH institutional review board (#19010).  
The primary endpoint was overall survival (OS) of patients treated at the main COH campus versus patients treated at selected COH satellite sites, including South Pasadena, Rancho Cucamonga, Antelope Valley, and Santa Clarita. Secondary endpoints included treatment responses, feeding tube dependency, weight loss, and narcotic use.
## 2 Materials and Methods
### Characterization Techniques  
Fourier transform infrared spectroscopy (FT-IR) spectra were recorded on a Perkin-Elmer spectrum GXA model in a range of 4000-550 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\). Atomic force microscopy (AFM) images were recorded using a NanoWizard 3 NanoScience atomic force microscope (JPK Instruments AG, Berlin, Germany) in tapping mode to identify the thickness of the GO sheets. Transmission electron microscopy (TEM) experiments were performed on a Tecnai G220 transmission electron microscope (FET) with an accelerating voltage of 200 kV. GO sheets were dispersed in ethanol by sonication for 30 min, and some pieces were collected on micro-film for TEM observation. Differential scanning calorimetry (DSC) was carried out using a TA instruments DSC Q200. The samples were heated from room temperature to 350 \({}^{\circ}\)C at four different heating rates: 20 \({}^{\circ}\)C/min, 15 \({}^{\circ}\)C/min, 10 \({}^{\circ}\)C/min, and 5 \({}^{\circ}\)C/min in N\({}_{2}\) atmosphere. Thermogravimetric analysis (TGA) was carried out using a TA instruments TGA Q500 from room temperature to 900 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min in N\({}_{2}\). Dynamic mechanical properties were determined using a TA instruments dynamic thermomechanical analysis (DMA) Q800 in the three-point bending mode at a frequency of 1 Hz and a heating rate of 10 \({}^{\circ}\)C/min. The water absorption of the resin was determined by the ISO62:2008. This test was done by putting the cured resin at a temperature of 23.0 \(\pm\) 1 \({}^{\circ}\)C, and weighting the resin once every 24 h. The dielectric constant was carried out by the Agilent 4294A at a frequency from 400 Hz to 100 MHz at room temperature. Before the test, resin spline should be copper plated. The flexural strength and tensile strength were determined by the Universal Testing Machine (UTM) at room temperature by the national standard they followed.
# DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information
## 4 Methodology
### Double Attention over Target Model  
DAT is a variant of DAC, and word vectors are also encoded by the DAWE model and trained by the Skip-gram model. In contrast to DAC, DAT takes context embedding as attention to guide the generation of the word vector of the target word. The model structure of DAT is shown in Figure 7.  
For each target word \(w_{t}\in W\), we have:  
\[w_{t}=\sum_{i=1}^{S^{(w_{t})}}\mathrm{att}\Big{(}s_{i}^{(w_{t})},C(w_{t}) \Big{)}s_{i}^{(w_{t})}, \tag{12}\]  
where \(\mathrm{att}\Big{(}s_{i}^{(w_{t})},w_{\mathrm{context}}\Big{)}\) denotes that the context word set \(C(w_{t})\) is used as attention to calculating the weight of \(i\)th sense of \(w_{t}\), as follows:  
\[\mathrm{att}\Big{(}s_{i}^{(w_{t})},w_{\mathrm{context}}\Big{)}=\frac{\exp \biggl{(}\hat{s}_{i}^{(w_{t})}\cdot C(w_{t})\biggr{)}}{\sum_{j=1}^{S^{(w_{t})} }\exp\biggl{(}\hat{s}_{i}^{(w_{t})}\cdot C(w_{t})\biggr{)}}, \tag{13}\]  
The calculation of \(\hat{s}_{i}^{(w_{t})}\) is similar to DAC (Equation. (10) and Equation (11)), which is the weighted sum of all sememe embeddings of sememe set \(X_{i}^{(w_{t})}\) corresponding to sense \(s_{i}^{(w_{t})}\), where \(C(w_{t})\) denotes the context, and its corresponding word vector is obtained by the average of all context word vectors in the context window. It is formalized by the following:  
\[C(w_{t})=\frac{1}{2K}\sum_{j=t-k}^{j=t+k}w_{j},\,j\
eq t, \tag{14}\]  
where k is the size of the context window.  
DAT uses context as attention and is richer in contextual semantics than DAC, hence it should be more conducive to the choice of sememes and senses.
# Distributed Bragg Reflectors for GaN-Based Vertical-Cavity Surface-Emitting Lasers
###### Abstract  
A distributed Bragg reflector (DBR) is a key building block in the formation of semiconductor microcavities and vertical cavity surface emitting lasers (VCSELs). The success in epitaxial GaAs DBR mirrors paved the way for the ubiquitous deployment of III-V VCSELs in communication and mobile applications. However, a similar development of GaN-based blue VCSELs has been hindered by challenges in preparing DBRs that are mass producible. In this article, we provide a review of the history and current status of forming DBRs for GaN VCSELs. In general, the preparation of DBRs requires an optimization of epitaxy/fabrication processes, together with trading off parameters in optical, electrical, and thermal properties. The effort of epitaxial DBRs commenced in the 1990s and has evolved from using AlGaN, AlN, to using lattice-matched AlInN with GaN for DBRs. In parallel, dielectric DBRs have been studied since 2000 and have gone through a few design variations including epitaxial lateral overgrowth (ELO) and vertical external cavity surface emitting lasers (VECSEL). A recent trend is the use of selective etching to incorporate airgap or nanoporous GaN as low-index media in an epitaxial GaN DBR structure. The nanoporous GaN DBR represents an offshoot from the traditional epitaxial approach and may provide the needed flexibility in forming manufacturable GaN VCSELs. The trade-offs and limitations of each approach are also presented.  
distributed Bragg reflector; VCSEL; GaN +
Footnote †: journal: Applied Physics Letters
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology
### Hardware Configuration  
Our study's hardware infrastructure comprises a selection of Raspberry Pi devices and a variety of sensors, each chosen for its specific role within our IoT framework. The following outlines the key components of our hardware setup:  
* **Raspberry Pi Devices:*
* _10 \(\times\) Raspberry Pi 4 Model B_ : These are the workhorses of our setup, deployed for edge computing and intensive data processing tasks.
* _5 \(\times\) Raspberry Pi Zero W_: These smaller units are used for less demanding tasks, primarily for collecting sensor data.
* **Sensor Array:*
* _15 \(\times\) DHT22 Temperature and Humidity Sensors_: Key for monitoring environmental conditions, providing accurate temperature and humidity readings.
* _10 \(\times\) MPU605 Gyroscope and Accelerometer Sensors_: Employed to track motion and orientation, crucial for applications requiring movement analysis.
* _8 \(\times\) LDR Light Sensors_: These sensors are tasked with detecting changes in light intensity, useful in both indoor and outdoor settings.
* _7 \(\times\) HC-SR04 Ultrasonic Distance Sensors_: Utilized primarily for distance measurement and object detection, they play a pivotal role in spatial analysis.
* _5 \(\times\) Soil Moisture Sensors_: Specifically selected for agricultural applications, these sensors provide valuable data for smart farming solutions.  
Figure 1: Proposed system architecture.  
This hardware ensemble, consisting of Raspberry Pi devices and a diverse set of sensors, constitutes the core of our IoT network. It is adeptly designed to handle a wide spectrum of data collection and processing operations. The Raspberry Pi 4 models, with their advanced capabilities, are integral for more demanding computational tasks. In contrast, the Raspberry Pi Zero W units offer a compact energy-efficient solution for simpler activities. The assortment of sensors capture a broad range of environmental and physical parameters, which are vital for the thorough deployment and effectiveness of the TinyML algorithms central to our research.
# Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System
## 5 Conclusions  
For a multi-area hybrid power system, including WTG and PVG, the incorporation of wind power and solar power generations introduces increased variability and unpredictability to the power system. It is a challenge to work on the LFC task. In this paper, a novel optimal ADRC strategy is proposed to solve this problem. The contribution is summarized as follows: The three areas that interconnect hybrid power systems, including 2 WTGs and 3 PVGs, are established. The mathematical model is established and implemented on the Matlab/Simulink platform. The novel optimal ADRC is then proposed. Since the error feedback of ACEs is introduced into LQR, smaller frequency fluctuation and tie-line power deviation can be obtained. Thus, a combination of LQR and ADRC is proposed. Various experiments demonstrated the effectiveness of the proposed method. However, the tuning of parameters for the ADRC controller currently relies on manual experience. It is anticipated that an intelligent optimization algorithm will be incorporated in future developments. Additionally, it is worth exploring the implementation of more sophisticated control methods, such as those based on state space representation, as a means of enhancing the effectiveness of load frequency control (LFC) strategies in hybrid power systems. The objective of this study is to enhance the stability and performance of the load frequency control system, ensuring its robustness.  
Figure 11: Frequency deviation \(\Delta f\) in each control area.  
Figure 12: Tie-line power exchange between control areas.  
Author Contributions:Conceptualization, methodology, validation, and writing--review and editing, K.Z.; software, formal analysis, writing--original draft preparation, and resources, Y.W.; data curation, visualization, and investigation, B.L.; supervision and project administration, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The public data that support the findings of this study are openly available in [Github] at [[[https://github.com/sergio-dorado/Decentralized-LFC-ADRC](https://github.com/sergio-dorado/Decentralized-LFC-ADRC)]([https://github.com/sergio-dorado/Decentralized-LFC-ADRC](https://github.com/sergio-dorado/Decentralized-LFC-ADRC))] (accessed on 31 July 2024).  
The authors declare no conflicts of interest. The funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
# Surface-Modified Compounds Improve the Detection Sensitivity of Terahertz Metasurface Biosensors
## 3 Results  
### Metasurface Sensors Detect Cancer Markers  
Before the experiment, PBS was used as buffer to prepare cancer marker of different concentrations.  
As a control, the cancer markers were detected using an unmodified metasurface sensor. The detection process was as follows, after the metasurface sensor was washed and dried with purified water, 10 uL of cancer markers was added to sensor, and then incubated for 30 min. After that, the sensor was then fixed in THz-TDS and scanned to obtain the THz time-domain spectrum. Then, change the concentration and class of cancer marker, and repeat the above steps. THz frequency-domain spectrum can be obtained by Fast Fourier  
Figure 6: Microscope image of THz metasurface sensor sample (20\(\times\)).  
Figure 7: (**a**) THz-TDS used in the experiment, (**b**) Schematic diagram of sample holder.  
transform (FFT) of THz time-domain spectrum. Analyze the experimental results through frequency-domain spectrum. The experimental results are shown in Figure 8.  
Among them, Figure 8a is the frequency-domain spectrum of SCC and Figure 8b is the frequency shift of the unmodified sensor caused by changing the SCC concentration. After linear fitting, the relationship between SCC concentration and frequency shift can be obtained as follows: F = 0.5429 \(\times\) C\({}_{\text{SCC}}\) + 4.1, R\({}^{2}\) = 0.8660, where F is the frequency shift, C is the concentration of SCC after taking the logarithm. The sensitivity of the sensor to the concentration can be defined as S = \(\Delta\)F/\(\Delta\)C, which is the change in frequency shift caused by each unit of concentration. The sensitivity of the unmodified sensor to SCC was S\({}_{\text{SCC}}\) = 0.5429 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)).  
For CEA, Figure 8c,d show the experiment results of the unmodified sensor to CEA. The relationship between the concentration of CEA and the frequency shift is  
Figure 8: Results of three cancer markers detected by the unmodified THz metasurface sensor. (**a**,**b**) detection SCC, (**c**,**d**) detection CEA, (**e**,**f**)detection NSE.  
F = 0.5714 \(\times\) C\({}_{\rm CEA}\) + 3.6714, R\({}^{2}\) = 0.8342. Therefore, the sensitivity of the sensor to CEA was S\({}_{\rm CEA}\) = 0.5714 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)).  
For NSE, Figure 8e,f show the experiment results of the unmodified sensor to NSE. The relationship between the concentration of NSE and the frequency shift is F = 0.5321 \(\times\) C\({}_{\rm NSE}\) + 3.8214, R\({}^{2}\) = 0.8244. Therefore, the sensitivity of the sensor to NSE is S\({}_{\rm NSE}\) = 0.5321 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)).  
The experimental results indicate that when the THz metasurface sensor is not modified, its detection sensitivity for the above three biomarkers is not high, all of which are less than 0.6 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)). Moreover, the linear relationship between the concentration and the frequency shift was not obvious, and the R\({}^{2}\) was less than 0.9. Therefore, it is necessary to modify the surface of the sensor to improve detection sensitivity.
## 2 Comprehensive Evaluation Method for AC/DC Hybrid Power Network Planning
### Evaluation Index System for AC/DC Hybrid Microgrid Planning
#### 2.1.2 The Economic Indicators  
The economic indicators include three parts: initial investment cost, operation and maintenance cost, and network loss cost [20].  
\[f_{\text{DGI}}=\sum_{t=1}^{T}\frac{1}{(1+r)^{t}}\sum_{t=1}^{N}\omega_{\text{ DGI}}f_{\text{dgI}}\eta_{\text{DG}}S_{\text{DGI}}+\sum f_{Li}\cdot L_{i}+\sum f_{T_{ i}}, \tag{3}\]  
\[f_{\text{DG2}}=t_{\text{DGmax}}\sum_{t=1}^{T}\frac{1}{(1+r)^{t+0.5}}\sum_{i=1} ^{N}\omega_{\text{DGI}}f_{\text{dg2}}P_{\text{DGI}}. \tag{4}\]  
In Equations (3) and (4), DG represents Distributed Generation. \(f_{\text{DG1}}\) is the initial investment cost. \(f_{\text{DG2}}\) is the operation and maintenance cost. \(T\) is the planning life. \(r\) is the discount rate. \(N\) is the total number of load nodes. \(\omega_{\text{DG1}}\) indicates whether distributed power is installed at node \(i\). \(0\) is not installed and \(1\) is installed. \(f_{\text{dg1}}\) is the initial investment cost per unit capacity of distributed generation. \(\eta_{\text{DG}}\) is the power factor. \(S_{\text{DGI}}\) is the installed capacity of distributed power supply. \(t_{\text{DGmax}}\) is the maximum annual working time of distributed power supply. \(f_{Li}\) is the unit length cost of transmission line \(i\). \(L_{i}\) is the length of line \(i\). \(f_{\text{TI}}\) is the cost of transformer or converter \(i\). \(f_{\text{dg2}}\) is the maintenance cost of unit capacity of distributed power supply. \(P_{\text{DGI}}\) is the unit output of distributed power supply at node \(i\).  
\[f_{\text{LOSS}}=\sum_{t=1}^{T}\frac{C}{\left(1+r\right)^{t+1.5}}\sum_{k=1}^{l} R_{k}\frac{P_{Lk}^{2}+Q_{Lk}^{2}}{V_{k}^{2}}t_{\text{max}}. \tag{5}\]  
In Equation (5), \(f_{\text{LOSS}}\) is the network loss cost. \(C\) is the electricity price. \(l\) is the total number of branches. \(R_{k}\) is the branch resistance. \(P_{Lk}\) and \(Q_{Lk}\) are the active and reactive power transmitted by branch k. \(V_{k}\) is the branch \(k\) terminal voltage. \(t_{\text{max}}\) is the maximum annual load loss hours of branch \(k\).
# High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
## 2 Materials and Methods
### Materials and Experiments
The GACOS is an emerging source of ZPD data; it is quasi-real-time and globally acquirable with a high resolution of 90 m \(\times\) 90 m. In this section, it proves to perform as well as ERA5. In Section 3.3, we calculate the upper limit and variation ranges of the ionospheric delay for the whole year. Section 3.4 analyzes the impact of the DEM elevation error on the geolocation accuracy. The SRTM and the WorldDEM are used in this section. The SRTM has a resolution of 30 m \(\times\) 30 m; its vertical datum reference is the EGM96 geoid model, and raw data were collected between 11 and 22 February 2000. The WorldDEM has a higher resolution of 12 m [45]; its vertical datum reference is the EGM2008 geoid model, and raw data were acquired in January 2011 and ceased around mid-2015.
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
## 5 Conclusions
### Contribution to Scientific Research  
The research advances seabed classification techniques by demonstrating the feasibility of using spectral signatures for sediment differentiation. Unlike conventional methods, which rely heavily on backscatter intensity and empirical ground-truthing, the proposed approach provides a quantitative and reproducible means of classifying marine soils. This work lays the foundation for further exploration into spectral-based acoustic methods and their integration with advanced signal processing and AI-driven classification frameworks.
## 3 Strategy and Performance Analysis of Spread Spectrum Code Acquisition  
There is delay and rain attenuation in signal propagation, and satellite communication is exposed to various electromagnetic interferences in space. The satellite channel model can be designed as an ideal Gaussian channel; the SNR reflects the influence of rain decline, the anti-interference ability is enhanced by spread spectrum technology, and the signal synchronization is carried out by acquisition technology.