esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background and Problem Statement  
### Background  
The most popular anonymization algorithm, \(k\)-anonymity, was formulated in [1]. The \(k\)-anonymity algorithm guarantees that, for each record, there are at least \(k-1\) other records included in the released data that have the same values for a set of quasi-identifier attributes (which are defined as special attributes that can be linked with external data to uniquely identify individual records in the released data), thereby ensuring that every record in the released data is indistinguishable from at least \((k-1)\) others, despite a linkage attack [1; 2]. Each record in a dataset is generalized into an indistinguishable group, called the equivalent class, by replacing the specific values of the quasi-identifier attributes with more general values. For instance, let us consider the example table in Figure 2a, in which the attributes, _Age_ and _Zip_, are quasi-identifier attributes, and the attribute, _Disease_, is a sensitive attribute. Let us further assume that the domain generalization hierarchies of \(Age\) and \(Zip\) are defined as in Figure 3. Then, the \(k\)-anonymous table in Figure 2b is obtained by replacing the values of the quasi-identifier attributes, \(Age\) and \(Zip\), of each record with more general values defined in the domain generalization hierarchies. For example, the first record, \(\langle 14,3068\), \(Pneumonia\rangle\), in Figure 2a is generalized as \(\langle 10-20,3060-3070\), \(Pneumonia\rangle\) in Figure 2b, and thus is indistinguishable from the next three records (i.e., RID = 2, 3, 4).  
Many \(k\)-anonymity algorithms employ the concept of a generalization lattice to compute an anonymous table. A generalization lattice over attribute domain generalization hierarchies is constructed using a set of all possible combinations of the generalization levels of each attribute (Figure 3). Then, an optimal \(k\)-anonymized table is computed by traversing the generalization lattice in a bottom-up manner until the \(k\)-anonymity property is satisfied. See [2] for a more detailed description of the \(k\)-anonymity algorithm.
# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 4 Proposed Method
### Improved Dynamic Task Planning Algorithm Based on Market Auction Mechanism
#### 4.4.1 Insertion Mechanism for Task Areas
The detailed steps of the new task area insertion process are presented as follows:  
Step 1: For all UAVs, check whether the number of unexecuted areas of each UAV is less than 1 or not. If the number of unexecuted areas is less than 1, \(a_{new}\) is directly taken as the next task area of the UAV, and the increased distance of the UAV is recorded as \(cost_{i}\) and then Step 4 is executed. Otherwise, the following steps 2-4 are executed;  
Step 2: Insert \(a_{new}\) between unexecuted areas \(w_{1}\) and \(w_{2}\). Calculate the additional distance (additional cost) that the UAV\({}_{i}\) needs to travel in this insertion as \(cost_{i,1}\), then calculate \(cost_{i,2},cost_{i,3}...cost_{i,num_{i}}\) in turn;  
Step 3: Record the minimum cost of UAV\({}_{i}\) competition \(a_{new}\) as \(cost_{i}\) and consider this insertion point as the best one for UAV\({}_{i}\), denoted as \(pos_{i}\);  
Step 4: Calculate the feasibility FD of the UAV\({}_{i}\) using Formula (15), and the UAVs with \(FD\geq 0\) join the bidding for \(a_{new}\); The remaining UAVs with \(FD<0\) will give up competing for the task;  
Step 5: For all UAVs participating in the bidding, the UAV with the lowest cost and \(FD\geq 0\) is selected as the winner of \(a_{new}\), and \(a_{new}\) is inserted into the position of \(pos\) for the UAV reconnaissance sequence. Figure 7 shows the insertion process for a new task area. Figure 7: The flowchart of the insertion process for a new task area. After all new task areas \(q_{new}\) have been inserted, the reconnaissance time reallocation method described in Section 4.4.3 is employed to determine the optimal reconnaissance time \(t\) corresponding for the new reconnaissance sequence \(q\).
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 5 Modelling Framework  
In this section, the underlying framework for building the decision-support tool is explained. Figure 3 shows a block diagram of the proposed decision-making tool. The tool is intended to be used by experts in the domain, indicating that it is designed to complement the expertise of the user rather than to be used as a standalone tool. The expert provides key details on the availability of resources (number of icebreakers, ports operational on any given day) and other potential scenarios not captured through available data. The inputs to the model include vessel information, such as dimensions and hv-curve coefficients, traffic, and ice data. These data are processed to obtain expressions for speed variation for vessels and icebreakers, trip details (origin, destination, time of departure), ice conditions for each day, and location of dirways. The traffic data mentioned in the earlier sections are obtained by processing (Automatic Identification System) AIS data [18]. This information is a necessary input to the simulation model. A simulation engine is at the core. It consists of agent-based models to capture the entity behaviors, discrete-event models to simulate periodic as well as randomly occurring events, and process flow models to represent vessel and icebreaker movements both at sea and in ports. Visualization is implemented, including a color-scheme to indicate varying ice thicknesses. The primary output of the simulation is a visualization of traffic flows that shows how the vessels move across changing dirways to destinations. The speeds of the vessels vary as per the ice conditions of the paths along which they are moving. The icebreakers can also be seen to assist vessels that cannot maintain a speed above the minimum specified threshold. Key Performance Index (KPI) of average waiting time is reported at the end of the simulation. The outcomes are presented to the domain expert (decision maker), who uses them for future configurations and provides feedback to the simulation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Hv\_Ship** & **Hvcode** & **w** & \(\mathbf{a_{I}}\) & \(\mathbf{b_{I}}\) & \(\mathbf{c_{I}}\) & \(\mathbf{a_{d}}\) & \(\mathbf{b_{d}}\) & \(\mathbf{c_{d}}\) \\ \hline Example\_ship & 0 & 15 & \(-\)199.84 & 210.24 & 87.997 & \(-\)0.1658 & 1.5976 & 7.4582 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example of hv curve coefficients.
## 2 Vein Pattern Identification System
### Image Acquisition Unit  
The image acquisition unit is the frontend part of the system, used for capturing a person's palm image in a contactless way. This is a crucial step, as it affects the complexity of the image preprocessing unit. Capturing high-quality images results in less complexity in the image processing requirements and expectedly yields fewer errors in human recognition. In contrast, low-quality images typically result in more complex image processing and higher error rates. Obtaining high-quality images allows the relevant key points and features to be extracted more effectively. The design of palm imaging systems for human recognition has been an active research topic over the last decade. These systems exploit the concept of the infrared light absorption of deoxyhemoglobin reduced hemoglobin (HbR) in the blood. Most of the systems presented in the literature use NIR [23; 41; 42; 43; 44; 45] with wavelengths of 750-1500 nm or far infrared (FIR) [23; 46] light sources. Figure 2 shows our proposed image acquisition system using an 850-nm wavelength light source. The system can be designed to operate in two modes, i.e., a transmission mode [28; 47; 41] where the infrared light source and the infrared sensitive camera are installed on opposite sides of the hand, and a reflection mode, [17; 19; 24; 48; 47] where the infrared light source and infrared-sensitive camera are aligned on the same side of the hand. We opted to implement a transmission mode configuration, because it is considered to be more resistant to noise [41]. As shown  
Figure 1: Proposed contactless human recognition using the dorsal vein pattern.  
in Figure 2, an infrared LED is installed in the scatter-light protection cylindrical case with a 5 cm diameter and 5 cm height. A concave lens is also installed at the orifice of the cylindrical case to focus the light onto the subject's hand. On the opposite side of the infrared LED, an 850-nm wavelength IR-filtered camera is installed. For specific usage scenarios, the FUJIKO CCTV is modified with the removal of the IR cut filter to reduce the infrared transmission. Equipped with the replacement of IR filter, it selectively transmits light at a 850 nm wavelength, with an adjustable arm for focal length control. To prevent the scattering of ambient light, the system is also covered with protective plastic, leaving only the front side exposed for hand imaging, as shown in Figure 2b. Samples of IR-radiated vein images are shown in Figure 2c.
# Xanthone Derivatives and Their Potential Usage in the Treatment of Telangiectasia and Rosacea
## 2 Xanthone Derivatives and Their Vascular Activity
### Sealing Blood Vessel Activity
#### 2.1.2 Endothelial and Mitochondrial Dysfunction
Vasodilator responses to acetylcholine in rings of the isolated thoracic aorta have been shown to be impaired in the presence of lysophosphatidylcholine (LPC), a major component of ox-LDL. Daviditin A (Figure 5) significantly attenuates the inhibition of endothelium-dependent relaxation by LPC. Moreover, in cultured endothelial cells, xanthone derivatives inhibited the increase in the release of LDH, the upregulation of MCP-1 expression, and the enhancement of monocyte adhesion concomitantly with a reduction in ADMA levels. These findings suggest that xanthone derivatives protect against endothelial damage induced by high lipid levels, and their protective effect on the endothelium is related to a reduction in the ADMA concentration [34]. Figure 4: Structure of mangiferin: 1,3,6,7-tetrahydro-2-((2S,3R,4R,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl))tetrahydro-2H-pyran-2-yl)-xanthone. 2.1.3. Inhibition of Hyaluronidase  
The inhibition of hyaluronidase by xanthone derivatives is a novel concept, being related to permeability through the blood vessels. Only one study was found in which all tested compounds were inactive against hyaluronidase. The structures tested were xanthone derivatives with only methoxy, hydroxy, or methylbromo moieties [35]. On the other hand, it was found that \(\gamma\)-magnostin (Figure 6) showed potent values of inhibition of hyaluronidase (IC\({}_{50}\) 23.85 \(\mu\)g mL\({}^{-1}\)) [36].
# Cost Analysis and Optimization of Modern Power System Operations
## 7 Key Insights  
Unit commitment optimization significantly reduces operating costs when properly modelled with realistic system constraints.  
Incorporating spinning reserves improves system reliability but requires additional generation margin and slightly increases startup/shutdown costs.  
Renewables and ESS not only reduce fuel dependence but also enhance system flexibility and lower costs.  
Demand-side management, even with modest flexibility, presents a powerful tool for achieving both economic and operational benefits.  
Overall, the study concludes that a flexible, intelligently managed power system--leveraging modern technologies and renewable resources--offers a more economical, reliable, and sustainable energy future.
## 3 Formulation of Mathematical Models
### Model 1: Single Population Model  
In the following model, tumor cell volume is denoted by \(x\) (mm\({}^{3}\)), and we assume that the total volume is a combination of CS and CR cells. Intracellular androgen cell levels are denoted by \(Q\) (nM), and PSA levels by \(P\) (\(\frac{\mu Q}{\Gamma}\)). Droop's equations govern the growth rate of cancer cells [27], where \(\mu\) represents the maximum cell growth rate and \(q\) the minimum concentration of androgen to sustain the tumor. Similar to [28], we assume an androgen-dependent death rate, where \(R\) denotes the half saturation level. However, we also assume a time dependent maximum baseline death rate \(v\), which decreases exponentially at rate \(d\) to reflect the cell castration-resistance development due to the decreasing death rate. We also include a density-independent death rate \(\delta\) that constrains the total volume of cancer cells to be within realistic ranges [31]:  
\[\frac{dx}{dt} = \underbrace{\mu(1-\frac{q}{Q})x}_{\text{growth}}-(\underbrace{v \frac{R}{Q+R}+\delta x)x}_{\text{death}}, \tag{2}\] \[\frac{dv}{dt} = -dv,\] (3) \[\frac{dQ}{dt} = \underbrace{Y}_{\text{production}}\underbrace{(Q_{m}-Q)}_{\text{ diffusion}}-\underbrace{\mu(Q-q)}_{\text{uptake}},\] (4) \[\frac{dP}{dt} = \underbrace{bQ}_{\text{baseline}}+\underbrace{\sigma xQ}_{\text{ tumor production}}-\underbrace{\epsilon P}_{\text{clearance}},\] (5) \[\gamma = \gamma_{1}u(t)+\gamma_{2},\ \ u(t)=\left\{\begin{array}{ll}1, \text{on treatment},\\ 0,\text{off treatment}.\end{array}\right. \tag{6}\]  
In this model, androgen is assumed to be the most limiting nutrient. We assume that the androgen concentration in cancer cells is approximately the same as the androgen concentration in serum [29]. Parameter \(\gamma_{1}\) denotes the constant production of androgen by the testes, and \(\gamma_{2}\) denotes the production of androgen by the adrenal gland and kidneys. As over 95% of androgen is produced in the testes, we have that \(\gamma_{1}>>\gamma_{2}\). Parameter \(u(t)\) is a switch between on and off treatment cycles. Luteinizing-hormone releasing hormone agonists only stop testes production of androgen during treatment. During treatment, \(\eta_{2}\) will be the only production of androgen. \(Q_{m}>q\) denotes the maximum androgen level in serum. The androgen uptake by prostate cells is assumed to be proportional to the difference of the maximum possible and the current androgen levels in serum. Androgen in cells is depleted for growth at a rate of \(\mu(Q-q)\). PSA is produced by both the regular cells in the prostate at the rate \(bQ\) and by the cancer cells at the rate \(\sigma xQ\). Notice that we have assumed that cell production of PSA is assumed to be dependent on levels of androgen. Finally, PSA is cleared from serum at rate \(\epsilon\).
## 4 Use Cases for Multimedia Analysis in Archiving Community Memories
### Measuring the Temporal Pulse of Social Multimedia  
The detection of trends within streams of communication in a social network is often necessary for deciding the important topics or turning points within an archive. Although most analysis is performed in an offline, batch manner, it is possible to perform trend analysis on a real-time stream during the crawling process. Traditional approaches to trend and topic detection are based on the analysis of textual content within the stream; however, it is possible to use the near-duplicate detection techniques described above in Section 4.1 for finding bursts of sharing activity for a single non-text media item, thereby finding trending multimedia. Using the SIFT-LSH near-duplicate detection approach, described above, on a temporally shifting window of images, it is possible to find those images that are being shared within a particular time-period (say the last hour). These can be marked in the archival process as being important to crawl and archive, and the times at which they were deemed important can also be stored as metadata. Our work on finding trending images on Twitter in real-time [36] (a video demo is available at [41]) implements this approach and also has subsequently been adapted to work on archived Twitter data. A screenshot of the visualisation of the trending images is shown in Figure 4.  
Figure 3: Process for efficiently finding near-duplicates of images (or video frames) by constructing a duplicate graph from the sketches of local features. LSH, locality sensitive hashing.
## 3 Basic Simulation Model and Settings  
Taking a specific SBG pair as an example, the basic parameters are shown in Table 1. Since the final machining parameters are determined by optimization, there is uncertainty here.  
The working conditions and material properties are defined as follows: elastic modulus \(E\) = 2.06 \(\times\) 10\({}^{5}\) MPa, Poisson's ratio \(\mu\) = 0.29; applied torque on gear \(T\) = 300 Nm; input rotational speed \(\omega_{p}\) = 1000 rpm; initial temperature \(T_{0}\) = 80 \({}^{\circ}\)F; initial lubricating oil viscosity \(\eta_{0}\) = 0.05 Pa s; pressure viscosity exponent \(\alpha\) = 12.5 GPa\({}^{-1}\); RMS roughness  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Pinion** & **Gear** \\ \hline Teeth number & 27 & 79 \\ Modulus (mm) & 3.15 & 3.15 \\ Pressure angle (\({}^{\circ}\)) & 20 & 20 \\ Mean spiral angle (\({}^{\circ}\)) & 30 & 30 \\ Face width (mm) & 30 & 30 \\ Shaft angle (\({}^{\circ}\)) & 90 & 90 \\ Mean cone distance (mm) & 116.49 & 116.49 \\ Hand of spiral & Right & Left \\ Pitch angle (\({}^{\circ}\)) & 18.87 & 71.13 \\ Root angle (\({}^{\circ}\)) & 17.9 & 72.1 \\ Addendum (mm) & 3.34 & 1.32 \\ Dedendum (mm) & 1.91 & 3.94 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters.  
Figure 3: Flow chart for SBG efficiency optimization based on hybrid PSOGSA algorithm.  
\(\sigma=0.30\mu\)m; Reynolds equation solution domain \(5\leq X\leq 5\), \(5\leq Y\leq 5\); thermal conductivity of lubricating oil \(K_{f}=0.25\).  
There are two different optimal design examples of SBG transmission efficiency. One takes second-order design parameters as optimization variables, and the other takes machining tool parameters as optimization variables. Therefore, the numbers of the optimization variables are different in these two simulation examples. For the two different simulations, the control parameters could be defined as the same before optimization of SBG efficiency: \(c_{1}=1.2\) and \(c_{2}=1.5\) are determined to balance global optimization and convergence rate. The dimension of generation is defined as \(D=10\), the maximum number of iterations \(NI=8\), and convergence criteria \(\xi<0.00005\). The same optimal parameter settings would help to compare the differences between these two different settings, which would be in favor of the design and optimization of SBG.  
It should be pointed out that the present method could also be used in different design methods or modeling processes, such as higher-order design, high-contact ratio design, etc. [42; 43].
## 5 Automatic Signal Recognition  
Given that prior works [27; 29; 30; 31] were dedicated to recognizing ISM band (non-streaming) technologies using time domain features, the remainder of this work aims to automate technology recognition using a different feature space established from the RSSI distribution, to identify a different set of technologies, including both non-streaming technologies (represented by Wi-Fi) and streaming technologies beyond the ISM bands (i.e., DVB-T, LTE). The usage of the feature space is illustrated via a sample algorithm in Section 5.1 and evaluated in Section 5.2; then, the algorithm and feature space are extended for recognition of the mixed LTE-U and Wi-Fi signal in Section 5.3.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
###### Abstract  
Crop yield prediction before the harvest is crucial for food security, grain trade, and policy making. Previously, several machine learning methods have been applied to predict crop yield using different types of variables. In this study, we propose using the Geographically Weighted Random Forest Regression (GWRFR) approach to improve crop yield prediction at the county level in the US Corn Belt. We trained the GWFR and five other popular machine learning algorithms (Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)) with the following different sets of features: (1) full length features; (2) vegetation indices; (3) gross primary production (GPP); (4) climate data; and (5) soil data. We compared the results of the GWFR with those of the other five models. The results show that the GWFR with full length features (R\({}^{2}\) = 0.90 and RMSE = 0.764 MT/ha) outperforms other machine learning algorithms. For individual categories of features such as GPP, vegetation indices, climate, and soil features, the GWFR also outperforms other models. The Moran's I value of the residuals generated by GWFR is smaller than that of other models, which shows that GWFR can better address the spatial non-stationarity issue. The proposed method in this article can also be potentially used to improve yield prediction for other types of crops in other regions.  
c 202214 2022  
A 2022
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 6 Numerical Results and Performance Analysis  
To demonstrate the effect of the studied techniques, numerical results are presented in this section to illustrate the core network performance of the platform. First, we evaluated the channel estimation scheme, interpolation methods, and equalization algorithms. Then, with the scheme that has the best performance, we evaluated the capacity of the Platform core network. Simulations were carried out using LTE signals generated using the Matlab LTE Toolbox. The downlink reference measurement channels specified by 3 GPP in TS 36.101 Appendix A.3[6] were used. The main parameters involved in the measurements are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Signal bandwidth, \(F_{s}\) & 5 MHz \\ Carrier Frequency,\(f_{s}\) & 2.105 GHz \\ Tx/Rx schemes & SISO and 2 \(\times\) 2 MIMO \\ LTE Duplex Scheme & LTE-FDD \\ RMC number & R.6 and R.11 \\ Modulation & 16-QAM and 64-QAM \\ Target Code Rate & 1/2 and 3/4 \\ Scenarios & LOS and NLOS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main Parameters Used in the Experiments.  
Two typical indoor-to-indoor scenarios were considered, one with LOS and the other with NLOS between the eNB and the Relay Node. The measurements were carried out with the developed SDR Platform described in Section 2, at the E.T.S.I de Telecomunicacion of the Universidad Politecnica de Madrid (UPM) on the fourth floor of building C. The first scenario that we considered is shown in Figure 5, where the eNB and RN equipment had LOS. The eNB downlink transmitter was placed on the left side of the building and the D&F Relay Node was positioned about 50 m from the eNB. This link is presented in Figure 5 with a yellow beam. We considered another indoor-to-indoor scenario, as shown in Figure 6. In this sense, the eNB and Relay Nodes were placed on the left of the building and in the middle of the hall perpendicular to that of the eNB, respectively. In this scenario, the eNB and RN had NLOS and were separated by 55 m.