esa-sceva/satcom-chunk-collection huggingface dataset:
# Searching via Nonlinear Quantum Walk on the 2D-Grid
## 3 Numerical Results  
Numerical simulations show in Figure 2 the searching time, \(T^{\prime}\) as a function of \(c\) at fixed \(N\) and, in Figure 3, the corresponding success probability for several values of \(c\). In all cases, we have chosen \(g=\log N/\pi\), which ensures that \(g\delta\sim 1\). Notice that, for \(c=0\), the searching time \(T^{\prime}\) is the same as for the linear algorithm, \(T\), but the success probability is lower, because the initial eigenstate \(|s\rangle\) is not rotated to the state \(|\Gamma\rangle\), as in the linear case. In Figure 2, we can also remark that the probability peak becomes narrower, the more \(c\) increases, which would require more attempts to estimate the maximum of the probability curve, making the algorithm sub-optimal [22], as we discuss later. These considerations suggest to choose a value for \(c\) which, while ensuring the good oscillatory behaviour of the system, leaves the peaks wide enough. Looking at Figure 3, a reasonable choice is  
\[c=1/E=\sqrt{\frac{N\log N}{16\pi}}, \tag{38}\]  
(whose value is 5.52 for \(N=900\) in Figure 3). For the following, we will keep the above choice for our analysis.  
Now, we run both algorithms, the linear and the nonlinear ones, and we derive from both, a numerical characterization for the searching times \(T(N)\) (linear), \(T^{\prime}(N)\) (nonlinear), and the success probabilities \(\bar{p}(N)\). The first peak probability in the linear and in the nonlinear case behaves as \(1/\log(N)\), as is shown in Figure 4, approximately with the same pre-factor \(\sim\)4.3.  
Figure 5 shows the searching times, \(T\), and \(T^{\prime}\). As expected, in the linear model, we recover \(T=O(\sqrt{N\log N})\). The nonlinear case, instead, shows a significant advantage respect to the linear case, with  
\[T^{\prime}=O(N^{1/4}\log^{3/4}N), \tag{39}\]  
Figure 4: Success probability as a function of \(N\) for the nonlinear and linear algorithm (inset).  
Figure 2: Searching time of the algorithm as a function of \(c\), \(N=900\).  
which yields an overall complexity algorithm of \(O(N^{1/4}\log^{5/4}N)\), using amplitude amplification.
## 2 Materials and Methods  
AAO film (Figure 2) with an average thickness of 55 \(\upmu\)m, interpore diameter of 70 nm, and interpore center distance of 125 nm was used in this study. AAO film was fixed onto ITO conductive glass with insulating sealant and insulating adhesive tape. Firstly, the AAO film and ITO glass were fixed together by insulating sealant, to ensure the sealing between AAO film and ITO glass. Subsequently, the other areas of the ITO glass were covered with adhesive tape to prevent the unwanted Cu deposition. The AAO film was set as a cathode and copper plate was employed as an anode. The anode copper plate was prepared with 1 mol/L H\({}_{2}\)SO\({}_{4}\) solution to remove the surface oxides. An electrolyte mixture of 1.5 mol/L CuSO\({}_{4}\)-5H\({}_{2}\)O and 1.5 mol/L H\({}_{2}\)SO\({}_{4}\) aqueous was used.  
Electrodeposition experiments were carried out by a direct current (DC) power supplier (ZHAO XIN, Shenzhen, China). SEM images were collected using Merlin Compact (ZEISS, Oberkochen, Germany) with an acceleration voltage of 20 kV. Crystalline structure was characterized by X-ray diffractometer (BRUKER AXS GMBH, Rheinstetten, Germany) using a Rigaku D/max-yB diffractometer with a Cu K\(\alpha\) emission source and wavelength of \(\lambda\) = 1.5418 A, a scanning speed of 5\({}^{\circ}\)/min and a step of 0.02\({}^{\circ}\). The electrochemical testing was accomplished by electrochemical workstation (CHENHUA, Shanghai, China). Hg(s)/Hg\({}_{2}\)SO\({}_{4}\)(aq)/SO\({}_{4}\)\({}^{2-}\)(aq) was set as reference electrode. The density of the Cu-AAO interposer was measured by drainage method. Heat capacity  
Figure 1: The schematic diagram of electrodeposition (**a**) with seed layer and (**b**) with indium tin oxide (ITO) glass.  
was measured by a differential scanning calorimeter (NETZSCH, Bavaria, Germany), and the thermal diffusion coefficient was collected by a laser thermal conducting instrument (NETZSCH, Bavaria, Germany). The pore-filling percentage was counted by the number of filled pores divided by the number of all pores. For the convenience of the pore-filling percentage calculation, the samples were polished to remove the excess copper bulks on top to reveal the filled Cu pillars. Under each parameter, three samples were selected for the pore-filling percentage calculation.  
Figure 2: (**a**) Optical image, (**b**) top view, (**c**) cross-sectional, (**d**) magnified images of AAO and (**e**) the installation for electrodeposition.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 5 Experimental Evaluation
### Cosine Similarity Method
#### 5.1.2 Bucket Category  
In order to improve the algorithm, a new strategy was designed, that was named the bucket category, as explained in Section 4.5.2.  
Table 3 shows a summary of the main results with the Cosine similarity method using the multiple runs algorithm and the bucket category concept, obtained using the three different approaches too. The first, second and third approaches consider the bucket category concept for five, seven, and \(11\) runs, respectively.  
Table 3 shows improvements on the previous results. Precision had a 5-fold improvement, while there are 2-3 fold improvements in recall and 4-fold improvements in F1, using the concept of bucket category. Therefore, this combination of multiple runs and bucket category search was adopted in the remainder of the project and is called ICS method.
## 5 Simulations Results
### Tuning Parameters and Operating Conditions  
The performance of the plant strongly depends on the selected controller set points due to the plant nonlinearities. The set point selected for the performance evaluation correspond to the economically optimal steady state condition found considering the average values of the inputs in one operating period. The variable \(DO\) (\(S_{O2}\)) in the second tank is controlled at a set point \(2.09\,\mathrm{g/m^{3}}\) and the variable \(S_{NO}\) in the first compartment is controlled at a set point of \(1.66\,\mathrm{g/m^{3}}\). The influent considered has been described in Figures 3-5. The plant responses and the corresponding performance indices for 168 (One week) operating hours are compared.  
The NECLGPC weights, as well as the prediction and control horizons, affect the closed loop behavior of the plant, so a proper tuning is required. In this work, the tuning has been performed evaluating the plant behavior by means of simulations. The selected tuning parameters for the controllers described in cases 1, 2, 3 and 4 are: control horizon \(n_{u}=2\); prediction horizon \(n_{y}=4\); output weight \(w_{1}=diag(0.155,0.01)\); input weight \(w_{2}=diag(0.01,0.01)\), the weight of the economic term \(w_{3}=0.01\) and sampling period of 15 minutes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control** & **Economic Function** & **Constraints** & **Type of Predictions** & **Predictions Model** \\ \hline Case 1: & None & None & Open loop & State dependent coefficient \\ Case 2: & Quadratic Gradient based & None & Open loop & State dependent coefficient \\ Case 3: & None & Yes & Closed loop & State dependent coefficient \\ Case 4: & Quadratic Gradient based & Yes & Closed loop & State dependent coefficient \\ \hline \hline \end{tabular}
\end{table}
Table 5: Controllers characteristics.  
The control variables and its rates are bounded as shown in Equations (6) and (9) and therefore, the optimization problem (3) is a nonlinear and constrained. The bounds for the input variables and its rate are \(Q_{a}<3850m^{3}/d\), \(Kla<200\)d\({}^{-1}\), \(-100<\Delta Q_{a}<100\) and \(-24<\Delta K_{La}<24\).
## 1 Introduction
### Motivation  
One of the most developed and deployed air interface cellular mobile communication technology is 3 GPP LTE, while its successor, 5G NR, has been for some time and still is a major focus of many researchers regarding various use-cases in network resource management, Internet of Things (IoT) devices, intelligent transport systems, and other topics [6; 14]. With the gradual deployment of 3 GPP 5G NR commercial cells, both non-standalone (NSA) and standalone (SA), SDR-based transceivers can be used to implement a flexible wideband radio solutions, which can be deployed in any region, have common  
Figure 1: General structure diagram of a direct conversion architecture transceiver.  
radio management features, support various channel bandwidths, and can be used as O-RAN compliant white-box hardware [15].  
To comply with the 3 GPP 5G NR channel bandwidth requirements, transceivers used in eNodeB/gNodeB cells have to work over an extremely wide range of channel bandwidth options, ranging from 1.4 MHz up to 400 MHz, when both frequency range \(1\) (FR1) and frequency range 2 (FR2) is taken into account (excluding ultra-wideband carrier aggregation scenarios) [16; 17]. Since 3 GPP LTE/5G NR uses orthogonal frequency-division multiplexing (OFDM) scheme, the entire signal chain of the transceiver has to be designed with heavy focus on the linearity of the blocks to handle the high peak-to-average power ratio (PAPR) and avoid intermodulation distortion. These requirements dictate that channel selection filters must have variable bandwidth close to over two decades while maintaining good linearity and dynamic range parameters across the entire frequency range. Integrated LPF designed for SDR applications should also have configurable order of the filter to achieve best performance in respect to power, linearity, and channel selectivity [18]. Therefore, development of integrated transceiver blocks that meet the aforementioned requirements is very important for SDR technology.
# An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks
## 3 System Model and Problem Formulation  
In this section, we present the system model and formally define the maximum flow problem within LEO satellite networks. We first introduce the general formulation of the maximum flow problem, which serves as the foundation for modeling data throughput in satellite networks. Then, considering the dynamic and resource-constrained nature of these networks, several practical limitations are accounted including visibility conditions that govern communication links, processing capacity limits of satellites, and bandwidth availability for data transmission. Additionally, we explore how the dynamic evolution of the network topology and potential network failures impact flow optimization. Together, these factors shape the problem of maximizing flow in real-world LEO networks, forming the basis for our proposed solution.
## 2 Literature Review
### Communication Technologies for IoT Healthcare
#### 2.3.2 Energy Constraints and the Need for Low-Power IoT Communication  
With the use of wearable and implanted technology, remote health monitoring systems are essential to contemporary healthcare since they allow for continuous patient surveillance. Managing energy restrictions to increase device longevity and guarantee dependable performance is an essential challenge in these systems.  
Power-Efficient Sensing and Transmission: Wireless Body Area Networks, which are made up of sensor nodes that run on batteries and gather and send physiological data, are frequently utilized in health monitoring. Since regular battery replacements are impractical, particularly for implantable devices, energy efficiency in these networks is essential. Research indicates that the transmission and processing of data uses a lot of energy. Low-power communication protocols such as BLE and LoRaWAN offer extended battery life while ensuring reliable data transmission.  
Techniques that include eliminating redundant data at the sensor level, lowering the amount of transmitted data, and conserving energy have been suggested as ways for reducing this [40].
## 3 Experiments
### Data Preprocessing
#### 3.2.3. Data Slicing  
In order to make full use of the data, this paper adopts a slicing approach to enhance the data information. For example, one original time series contained vibration responses recorded by eight sensors, and each vibration response contained 4096 data points. The slice length \(Ns\) and the sliding step \(s\) were chosen to divide the sample into multiple slices, and each slice had the same class label as the original time series. Thus, by varying \(Ns\) and \(s\), different numbers of training samples can be obtained, and this method is particularly effective in the case of insufficient data samples. In this work, we set \(\text{Ns}=1024\), \(\text{s}=100\), and the data was expanded from 100 to 3000 using data slicing. Figure 5 shows the detailed method of data slicing.  
Figure 5: The illustration of data slicing.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set  
As described in Figure 5, various detection methods are used to identify a fake image. The two most prominent methods are based on forensic features and deep learning. This section will discuss methods using feature sets extracted from images. Under each method, a brief comparison is also shown among different research works. It is to be noticed that performance comparison is only made among various techniques that use the same evaluation parameter and dataset.
## 4 Modeling
### Biased Diversity Combination
This results into a multi-variable optimization problem, where the following six SNR difference functions are defined as,  
\[D_{1} = SNR^{+}_{\overline{\text{TO}}}-SNR^{in}_{\text{TO}}=\frac{((1- \lambda)(\Delta+\beta_{T}))^{2}}{\lambda^{2}\sigma_{L}^{2}+(1-\lambda)^{2} \sigma_{T}^{2}}-SNR^{in}_{\text{TO}}, \tag{20}\] \[D_{2} = SNR^{+}_{\overline{\text{LO}}}-SNR^{in}_{\text{LO}}=\frac{( \lambda(\Delta+\beta_{L}))^{2}}{\lambda^{2}\sigma_{L}^{2}+(1-\lambda)^{2} \sigma_{T}^{2}}-SNR^{in}_{\text{LO}},\] (21) \[D_{3} = SNR^{-}_{\overline{\text{TO}}}-SNR^{out}_{\text{TO}}=\frac{((1- \lambda)(\Delta-\beta_{T}))^{2}}{\lambda^{2}\sigma_{L}^{2}+(1-\lambda)^{2} \sigma_{T}^{2}}-SNR^{out}_{\text{TO}},\] (22) \[D_{4} = SNR^{-}_{\overline{\text{LO}}}-SNR^{out}_{\text{LO}}=\frac{( \lambda(\Delta-\beta_{L}))^{2}}{\lambda^{2}\sigma_{L}^{2}+(1-\lambda)^{2} \sigma_{T}^{2}}-SNR^{out}_{\text{LO}},\] (23) \[D_{5} = SNR^{+}_{\overline{\text{TL}}}-SNR^{in}_{\text{TL}}=\frac{( \Delta+\lambda\beta_{L}+(1-\lambda)\beta_{T})^{2}}{\lambda^{2}\sigma_{L}^{2}+ (1-\lambda)^{2}\sigma_{T}^{2}}-SNR^{in}_{\text{TL}},\] (24) \[D_{6} = SNR^{-}_{\overline{\text{TL}}}-SNR^{out}_{\text{TL}}=\frac{( \Delta-\lambda\beta_{L}-(1-\lambda)\beta_{T})^{2}}{\lambda^{2}\sigma_{L}^{2}+ (1-\lambda)^{2}\sigma_{T}^{2}}-SNR^{out}_{\text{TL}}, \tag{25}\]  
which are jointly minimized for \(\lambda\), \(\beta_{T}\), \(\beta_{L}\), \(\sigma_{T}\) and \(\sigma_{L}\) when the experimental lateralization change is set as \(\Delta=10\). Note that a set solution for the parameters can be extracted for each absolute azimuth values, \(\theta=0^{\circ},10^{\circ},20^{\circ},30^{\circ}\). Let us define the parameter vector for the optimization problem as \(\mathbf{x}=[\lambda,\beta_{T},\beta_{L},\sigma_{T},\sigma_{L}]\) and the mean square SNR error over the six SNR differences as \(F(\mathbf{x})=\frac{1}{6}\mathbf{\sum}_{k=1}^{6}D_{k}^{2}\). Then the optimization problem is defined to minimize the mean square error,  
\[\mathbf{x}^{*}=argmin_{\text{s.t.}\ \mathbf{x}_{0}=0,\ \mathbf{l}_{\mathbf{b}} \leq\mathbf{x}\leq\mathbf{u}_{\mathbf{b}}}F(\mathbf{x}), \tag{26}\]  
where \(\mathbf{x}_{0}\) is the initial parameter vector and \(\mathbf{l}_{\mathbf{b}}\) and \(\mathbf{u}_{\mathbf{b}}\) are set to [0, 0, 0, 0, 0] and [1, 10, 100, 100, 100] as the lower and upper bounds for the \(\mathbf{x}\) vector, respectively. The above optimization problem was solved by the nonlinear solver (fmincon) based on the Rosenbrock function [36] using the MATLAB Optimization Toolbox [37].
# Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
## 2 Materials and Methods
### Data
#### 2.2.2 Sentinel-2 Image Time Series  
All available Sentinel-2A and Sentinel-2B images collected from June 2017 to June 2023 were used in this study. The timeframe allowed us to conduct an in-depth analysis of crop changes from a pre-pandemic and a post-pandemic perspective. The two-satellite constellation has a short revisit interval of five days, which mitigates the negative impact of heavy cloud cover on tropical crop mapping. Here, we selected the pre-processed spectral reflectance product from four image bands at the 10 m resolution, including NIR, R, G, and B, which are more suitable than the coarser resolutions to map heterogeneous, smallholder farmlands. Utilizing all accessible Sentinel-2 data enabled us to gather an ample supply of cloud-free, high-quality images for crop mapping through harmonic regression (see Section 2.3 for details). This dense image time series has the capability to accurately depict crop phenological stages ([PERSON] et al., 2019 [16]), proving particularly effective for our study in delineating various types of crops. Notably, some of these crops exhibited comparable plant growth patterns across seasons.
## 6 IoT Encapsulation Network Layer Standards
### 6 LoWPAN  
A fixed IPv6 has 320 bits (320 octets). The 16-bit payload section defines the number of bits attached to the header. This makes it go from 1 byte up to one less 4 GB (jumbogram). IPv6 over a low-power wireless personal area network (6 LoWPAN) is a widely used standard. The underlying protocol is IEEE 802.15.4, which can convert large data streams into small packets, thus converting IPv6 headers into small chunks that do not exceed 128 bytes. Fragmentation header (11) of 6 LoWPAN handles this compression and breakdown. Various topologies are being supported by 6 LoWPAN that include mesh and star in abundance. These topologies are also supported by different 6 LoWPAN headers that include mesh header (10) and dispatch header (01). No header (00) is used for single-hop data delivery. Here, any frame that does not follow 6 LoWPAN specifications is automatically discarded by this receiver [35].  
Data frames, once defined by a unique 6 LoWPAN header, are multi-casted in the neighborhood where each connected node receives and analyzes the frame and does processing according to the header type.  
Characteristics of 6 LoWPAN  
* 6 LoWPAN [35] has a long sleep mode, enabling smart battery consumption and network sustainability;
* Different length addressing enables network scalability and variable packet length for a constrained environment;
* Its relatively lower bandwidth consumption makes it scalable and supports reliable packet delivery because of low SNR, and the reliability is not managed by any other algorithm, so low bandwidth facilitated it;
* Mobility is inherently supported in 6 LoWPAN for its dynamic channel assignment and local addressing [6];
* The deployment costs are relatively low and there is no massive processing over sensor nodes.
## 2 Materials and Methods
### GOSIF Data  
The spatial coverage and temporal resolution of OCO-2 SIF products depend on the footprint of the satellite and the observation mode. Moreover, the sparse footprint of OCO-2 results in a poor match with the flux towers. The GOSIF dataset was developed to address this limitation. The GOSIF dataset is a global product of SIF, with a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of 8 days spanning from 2000 to 2017 ([[http://globalecology.unh.edu/](http://globalecology.unh.edu/)]([http://globalecology.unh.edu/](http://globalecology.unh.edu/))). The GOSIF dataset was developed by [PERSON] and [PERSON] [18]. For each grid cell, the enhanced vegetation index (EVI), land cover type (MCD12C1), air temperature, vapor pressure deficit (VPD), and photosynthetically available radiation (PAR) were used as the input for a cubist regression tree model. Among them, EVI was derived from the MODIS bidirectional reflectance distribution function (BRDF)-corrected reflectance product MCD43C4. Daily PAR, VPD, and air temperature were obtained from MERRA-2 [18]. The cubist regression tree model is an extension of [PERSON]'s M5 model tree. Cubist creates rule-based predictive models from training samples, and each rule has an associated multivariate linear regression model [14]. Their modeled SIF showed a good correlation with the GPP of EC flux sites (R\({}^{2}\) = 0.73, \(p\) < 0.001). We extracted GOSIF data for 0.1 degree surrounding each flux tower during the period from 2014 to 2017. Further, we wanted to explore the effects of the radius on estimating GPP using GOSIF. We thus extracted GOSIF data within 25 km of each flux tower. Finally, we used linear interpolation to interpolate eight days to one day.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **OCO-2 SIF** & **MODIS** & **GOSIF** \\ \hline Temporal coverage & 09/2014 to present & 02/2000 to present & 2000 to 2017 \\ Spatial resolution & \(1.3\times 2.25\) km\({}^{2}\) & 500 m \(\times\) 500 m & 0.05\({}^{\circ}\times\) 0.05\({}^{\circ}\) \\ Spatial sampling method & intermittent & continuous & continuous \\ Revisit period & 16 days & 8 days & 8 days (and monthly, annual) \\ \hline \end{tabular}
\end{table}
Table 2: Basic information of the OCO-2 sun-induced chlorophyll fluorescence (SIF), MODIS, and GOSIF datasets.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 3 Results
### Comparison with Other Classifiers
#### 3.3.1 Classification Accuracy
As can be seen from the classification accuracy of the proposed method and M3, the new features extracted from dual-frequency Pol-SAR data are effective and can improve the classification accuracy of lithology. SSAE is more suitable for lithology classification than SVM, because SSAE can extract deep features of input data and classify the lithology with these features. Therefore, the classification result of the proposed method is better than SVM. By comparing the performance of the proposed method and [PERSON]'s method, we can tell that the algorithm in this paper is more effective for lithology classification. Generally, the proposed method outperforms the reference methods because new features extracted from dual-frequency Pol-SAR data can increase the class separability of the input data, and SSAE is suitable for lithology classification.
## 3 System Concept
### RF Concept and Design  
The radar system operates at a single frequency of 24 GHz. As shown in Figure 3, a voltage-controlled oscillator (VCO) from Infineon Technologies, Neubiberg, Germany (BGT24 MTR11) is used to generate the RF signal to be sent via the transmit (Tx) antenna. In order to ensure a low phase noise and a stable output frequency, a phase-locked loop (PLL) from Analog Devices, Norwood, MA, USA (ADF4159) is used in integer mode. The electromagnetic wave transmitted by the system is reflected from the PUT's chest and then captured by the receive (Rx) antenna. After amplification by a low noise amplifier (LNA) from SiliconRadar, Frankfurt/Oder, Germany (LNA_24_004) with variable gain, the signal enters the six-port receiver structure, where it is superimposed with the reference signal. This is provided by an additional output of the VCO and passes a digitally adjustable attenuator (Analog Devices HMC1018) to control its power level. The four outputs of the six-port structure are connected to active power detectors (Analog Devices ADL6010) and the resulting low frequency (LF) signals are filtered, amplified and digitized by the interface board.