esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 3 Results and Analysis
### Application in Strong Pollution Environment  
In the simulation of background clutter, a strong target existed in the range cell near the CUT. The echo obtained by simulation is shown in Figure 10. We can see the echoes of the moving target that was to be detected and the jamming target after range compression, the moving target to be detected at a 336 range gate, and the strong jamming target added at a 567 range gate. The Capon spectrum is shown in Figure 10. In the Capon spectrum of the space-time two-dimensional plane, we can see that the background clutter forms the clutter ridge, while the moving target and pollution target are far from the clutter ridge.  
After leaving 20 range gates on the left and right of the moving target at 336 as protection range gates, 300 cell echoes were taken to estimate CCM, and the results are shown in Figure 11. Figure 11 shows that there was strong pollution in the training sample used to estimate the CCM. If we use the traditional method to estimate the CCM, i.e., using the training sample containing strong pollution, no null is formed at the clutter ridge and the clutter is not well suppressed. Figure 11 shows the result after passing the detector, which could not detect the moving target with certainty.  
Using the echo obtained in Figure 3, the 320-350 range gates were used to estimate CCM. The results obtained by STAP are shown in Figure 12. Comparing the results of the two processing methods, it can be seen that the filter formed by the traditional STAP method does not form a notch at the ridge of the clutter, the clutter is not suppressed, and the moving target cannot be detected. However, the method proposed in this paper shows a clear notch at the clutter ridge and a clear directional pattern for the moving target. Therefore, the moving target can be detected clearly and accurately after the detector output, whi  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Platform frequency & 5e9 \\ Bandwidth & 2006e \\ PRF & 654 \\ Number of pulses in one CPI & 32 \\ Elements & 20 \\ Interval between elements & 0.12 \\ Scene & \(800\times 800\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 10: Simulated echo after adding a strong jamming target: (**a**) echo after adding a strong jamming target, (**b**) the Capon after adding a strong jamming target.  
proposed in this paper can have an obvious clutter suppression effect in an heterogeneous environment where the training samples contain strong pollution, and the moving-target detection effect is quite good.
## 5 Evaluation of the Security Aspects of the Designed Protocol
### Secure against the DOS Attack  
Among the prevalent and widely encountered network intrusions, Distributed Denial of Service (DDoS) attack stands out, which are utilized by the attacker for the purpose of repeatedly transmitting messages. The primary objective of this attack is to disrupt network operations, introduce deficiencies in optimal performance, and, most significantly, cause delays in network services. To safeguard the security and operational integrity of a network, it is imperative to establish security mechanisms designed to counteract DoS and DDoS attacks. To safeguard against this vulnerable attack, we implemented security measures during the execution of our protocol process.  
**Random Numbers**: As mentioned earlier, the use of random numbers has different advantages, which can face serious challenges in the implementation of attacks against all types of network attacks. To prevent the reuse of expired messages and their repetition in other communication sessions, random numbers guarantee the condition of message uniqueness in each session, which challenges the condition of message reuse for adversarial purposes. The attribute of uniqueness enables communication parties to detect unauthorized communication requests by examining consistency in generated values.  
**Timeframe Verification (\(\boldsymbol{\upalpha}\)T):** Utilizing a method to calculate the time differential of received messages serves as an additional security measure, aiding in the detection of unauthorized messages and communication requests. The receiving entity evaluates the value \"\(\Delta\)T\" to detect the allowed time interval of the message as well as the oldness of the message. Therefore, to maintain the security and performance of the network, if the received message is outside the allowed time frame, it will disconnect the connection.
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 7 Preprocessing Methodology
### Speed Profiles  
The speed profiles in these experiments exhibit non-synchronous behavior, particularly during the back-and-forth motion, where rapid speed changes occur because the cart decelerates as it stops before reversing direction and then accelerates as it changes direction until reaching the nominal speed again. This phenomenon becomes even more significant as the nominal speed increases. Figure 10 illustrates the speed profiles observed in various experimental configurations. Figure 10a shows the nominal speed profile of 1000 mm/s along the curved module of the system, representative of Experiment Type 2. In particular, this speed profile is consistent with other experiments involving back-and-forth movements, such as Experiment Types 1, 3, and 5. The primary distinction is that Experiment Type 5 features three movers, while Experiment Types 1, 2, and 3 involve a single mover. Figure 10b, on the other hand, represents the characteristic speed profiles of Experiment Types 4, 6, 7, and 8. In Experiment Type 4, a single mover continuously circulates along the track, whereas Experiment Types 6, 7, and 8 involve three movers executing similar continuous movements. The comparison across these profiles highlightsthe impact of varying the number of movers on the system's dynamic behavior while maintaining consistency in movement patterns.  
Figure 10: Speed vs. Position profiles of the independent cart system with highlighted extraction windows, illustrating variations across different experimental configurations. (**a**) Single-cart experiment showing the velocity-position relationship for one cart. (**b**) Multi-cart experiment involving three carts, demonstrating the combined effect of multiple carts on the motion profile. Position is measured in millimeters (mm), and speed in millimeters per second (mm/s).
# Balanced Parallel Exploration of Orthogonal Regions
## 5 Analysis
### Room Scan
#### 5.2.1 First Room Exploration
With agents splitting their work with another agent, according to Algorithm 4 one agent had work \(Area_{1}\), \(Area_{2},\ldots,Area_{i}\) and reached the threshold. Upon returning, it shares half the work, some area \(Area_{j}\), such that the first agent contains \(Area_{1}\), \(Area_{2},\ldots,Area_{j}\) and the second agent contains \(Area_{j+1},\ldots,Area_{i}\). With this addition, agents can perform work in parallel, gaining the ability to share work but requiring another \(D\) each time sharing happens to account for the new agent to traverse to its given area. Therefore, total work becomes at most \(4n+(n/2D)3D=4n+3n/2\leq 6n\). At the end of the room scan of a room with \(n\) points, given the initial work of all agents is \(0\), one of the two holds:  
1. If \(n<2 Dk\), the difference in work between any two agents is \(4D\), or otherwise,
2. the ratio of the work between any two agents is bounded by \(8\). Proof. : For case i, as agents return to meet after \(2D\) work, where \(n<2 Dk\), then there are not enough traversable points for all agents to get work. This will lead to some out of \(k\) agents completing \(4D\) work and other agents not completing any work. For case ii, \(n\geq 2 Dk\), such that all agents were given a chance to be Active. The Leader maintains a ratio 4 of work between all agents and a ratio 3 between all other agents and the Leader. As two agents may approach the ratio 4 and the lower one could complete work just after the one with greater work has completed, then the agent with greater work will double their current work before returning being at most ratio 8 more work than this agent. After returning this agent will be set to Inactive and swapped with the lower total work agent. As the Leader has global knowledge of the work of all agents, after they share information, the Leader can maintain the ratio 8 of work between all agents. At the end of the room scan with \(n\) points, each agent performs \(O(n/k)\) work when \(n\geq 2 Dk\). Proof. : From Lemma 9, let the total work be \(W=c_{1}n\), for some constant \(c_{1}>0\). From case (ii) of Lemma 10, the ratio of the work between any two agents is at most 8. Therefore, since there is an agent that has work at most \(c_{1}n/k\), each agent's work does not exceed \(8c_{1}n/k=O(n/k)\).
# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
###### Abstract  
Address matching is a crucial step in geocoding; however, this step forms a bottleneck for geocoding accuracy, as precise input is the biggest challenge for establishing perfect matches. Matches still have to be established despite the inevitability of incorrect address inputs such as misspellings, abbreviations, informal and non-standard names, slangs, or coded terms. Thus, this study suggests an address geocoding system using machine learning to enhance the address matching implemented on street-based addresses. Three different kinds of machine learning methods are tested to find the best method showing the highest accuracy. The performance of address matching using machine learning models is compared to multiple text similarity metrics, which are generally used for the word matching. It was proved that extreme gradient boosting with the optimal hyper-parameters was the best machine learning method with the highest accuracy in the address matching process, and the accuracy of extreme gradient boosting outperformed similarity metrics when using training data or input data. The address matching process using machine learning achieved high accuracy and can be applied to any geocoding systems to precisely convert addresses into geographic coordinates for various research and applications, including car navigation.  
geocoding; machine learning; address; alias +
Footnote †: journal: Applied Sciences
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
## 2 Design, Materials, and Methods  
ECG signals comprise low-frequency (P and T waves and ST segments) and medium-frequency (QRS complexes) components [30]. This study aimed to reduce the storage space required for low-frequency segmentation using the fixed and dynamic-block modes. In this study, \"block\" refers to a quantity of ECG records, and it is the basic unit of data to be compressed. Both modes are used for real-time and lossless ECG data compression for wearable devices, particularly for PEMs. Herein, fixed-block mode is discussed in three main sections: Section 2.1 discusses delta coding [31], Section 2.2 discusses zero separation, and Section 2.3 discusses zero run length encoding (ZRLE). In Section 2.4, the format output provides a detailed description of the compressed result. Since the dynamic-block mode is similar to fixed-block mode, Section 2.5 explains only the major differences between the two modes.  
Figure 2 shows a block diagram of the process used in the proposed encoding scheme. This encoding scheme processes only the voltage values. First, delta coding is used to generate delta values by reducing the dynamic range of the ECG volume. Next, the delta value is rotated one bit to the left in zero separation. This rotation can displace the sign flag of the highest bit, which produces the lowest bit so that the remaining bits shift to the left. The rotated delta value is then split into low bits and high bits with the prefix parameter \"split resolution\". Finally, one of the listed coding methods, ZRLE, which is based on run length encoding (RLE), is used to encode the zero value of high-bit data only. This scheme bypasses low-bit data and can be subsequently enhanced.  
Figure 1: Easy chip integration.
## 3 Mobility-Aware Duty Cycling
### Simulation Parameters  
In reference to the simulation parameters in Table 1, the simulation time is calculated to ensure an exact number of circuits of the network by the MSN. As such, with the speed at 2 mps (meters per second) the sink shall complete exactly 2 circuits of the network, for 10 mps, 10 circuits and so on.  
Interference distance is calculated as thus [38]:  
\[interferenceDistance=\frac{(\frac{SoL}{Freq})^{2}*Power\quad\frac{1.0}{Alpha}}{(16* PI^{2}*10^{\frac{SAT}{10}})}\quad, \tag{1}\]  
where \(SoL\) denotes the speed of light (i.e., 30,000,000 mps); _Freq_ stands for the carrier frequency; _Power_ indicates the transmitter power; _SAT_ is the signal attenuation threshold; and _Alpha_ represents the path loss alpha.  
Received signals with power below the sensitivity value are ignored. In this case the value was adjusted from \(-85\) dBm to \(-75\) dBm in order to reduce the number of signals received and thus lessen the risk of network failure due to node overload.  
Figure 3: Media access control (MAC) implementation.  
All parameters are consistent across all simulation runs apart from the speed of the sink node and the interference distance of the nodes. The path loss alpha is adjusted across four different values, as detailed in the test parameters; this in order to alter the size of the interference distance, which decreases as the alpha value increases.
## 4 Simulation Results  
In this section, we design an exemplary MIMO video SAR system based on BFD FMCW. The performance of the proposed system is evaluated in terms of peak sidelobe ratio (PSLR), resolution, image size, and frame rate, using numerical simulations. As well as antenna parameters, other system  
Figure 9: Signal processing block diagram for MIMO video SAR with a beat frequency division FMCW waveform. FMCW: frequency-modulated continuous wave; BFD: beat frequency division.  
and geometric parameters are chosen according to traditional rules, such as the radar equation, or to mirror parameters of existing systems.
# Antiproliferative Activity of Aminobenzylnaphthols Deriving from the Betti Reaction
## 3 Results and Discussion
### Mitochondrial Function  
Mitochondrial functions were evaluated using MTT assays after a 24 and 72 h treatment with **1** and **2** at decreasing concentrations (200, 100, 50, and 25 \(\upmu\)M). They are reported as the percentage of the viability of each untreated cell. The results are represented in Figures 2 and 3.  
After 24 h of exposure, **1** and **2** (200, 100, 50, and 25 \(\upmu\)M) of Caco-2 and SH-SY5Y cells showed comparable linear tendencies of dose-dependent effects of cytotoxicity.  
Concentrations of **1** from 25 to 200 \(\upmu\)M were related with a reduction of about 80-70% and 79-62% in cell viability for Caco-2 and SH-SY5Y tumor cell lines, respectively.  
On the other hand, the treatment of **2** with similar concentrations showed a reduction of about 89-65% and 73-26% in cell viability for Caco-2 and SH-SYSY cells, respectively, thus showing a dependence on the type of employed tumor cell lines.  
After a 72h treatment, the trend of cytotoxicity was analogous to the figures reported after 24 h for **1** and SH-SYSY cells. Concentrations of molecule **1** from 25 to 200 \(\upmu\)M were related with a reduction of about 76-15% in cell viability for Caco-2 and 80-64% for SH-SYSY cells, respectively.  
In the case of molecule **2**, treatments were related with a reduction of about 84-75% and 91-37% in cell viability for Caco-2 and SH-SYSY cells, respectively (Figure 3; \(p\) < 0.05 Dunnett's test).  
MTT assays were used to calculate cytotoxic effects of cisplatin, as a \"positive control\" to evaluate its antiproliferative activity on Caco-2 and on SH-SYSY cells and to evaluate the efficacy of the compounds **1** and **2**. As illustrated in Figures 2 and 3, after 24 h of exposure, the cisplatin treatment was associated with a reduction of about 90-55% and 51-50%, whereas, after 72 h of exposure, a reduction of about 62-23% and 77-44% in cell viability was observed for Caco-2 and SH-SYSY cells, respectively.  
Moreover, MTT data were employed to determine EC\({}_{50}\) (50% effective concentration) values and to evaluate the toxicity of the compounds **1** and **2** for Caco-2 and SH-SYSY cell lines.  
In summary, the results collected in Table 1 show that the antiproliferative activity towards Caco-2 cell lines at 24 h or 72 h of exposure to compound **2** is slightly better than the action of cisplatin. On the other hand, at 72 h, compound **1** increases its performance. As far as the antiproliferative activity towards SH-SYSY cell lines is concerned, compound **1** has an antiproliferative activity at 24 h or 72 h, similar to the action of cisplatin.  
Figure 3: Human Caco-2 adenocarcinoma and SH-SYSY neuroblastoma cells after 72 h exposure to cisplatin, **1**, and **2** (200, 100, 50, and 25 \(\upmu\)M).  
Figure 2: Human Caco-2 adenocarcinoma and SH-SYSY neuroblastoma cells after 24 h exposure to cisplatin, **1**, and **2** (200, 100, 50, and 25 \(\upmu\)M).
## 3 Results and Discussion
### Validation of Hazard Assessment Models  
The largest rain-fed maize-producing region in China is NEC [59], with maize production accounting for 38.9% of the total national grain production. The study area has the largest area under maize cultivation, accounting for 49.2% of the total area under all crops (National Bureau of Statistics of China, 2020). Therefore, we selected maize as a representative crop in the study area to analyze the linear relationship between climatic yield anomalies and drought hazards during the growing season (Figure 12). The results showed that increasing drought risk reduced climatic yield anomalies. The drought hazard and index was highly significantly (\(p<0.001\)) negatively correlated with climatic yield anomalies in all months, except May. The seedling emergence of maize occurred in May, when the occurrence of drought had a weaker effect on the final maize yield. The effect of drought hazard on climatic yield anomalies was much higher in July and August than in the other months when maize was in the flowering and filling stage, a critical period for grain and yield formation [12]. The drought hazard in August explained 28% (R\({}^{2}\) = 0.28) of the variation in maize yield. NEC is also the main soybean-producing area in China, and soybean cultivation is mainly concentrated in Heilongjiang Province. Soybeans (Figure S1) in Heilongjiang Province showed the same results as maize (Figure 12) in NEC. [PERSON] et al. [31] showed that moisture anomalies (drought and flooding) explain 34% of the variation in maize yield, which is similar to the results of this study. The first step in risk analysis is hazard assessment, and the hazard findings in this study reflect the actual situation of drought impact on crop yield. One of the primary barriers to high and stable maize yield in NEC is drought risk [60]. Studies have shown that combined heat and drought stress is a major factor limiting maize yield [34], which means that drought risk may further increase as the climate warms and extreme weather events increase. Dynamic drought risk  
Figure 11: Temporal variation of drought hazard during the growing season in NEC, 1982–2020 (**a–e**).  
assessments are necessary for emergency drought mitigation, in addition to static drought risk assessments that serve as a roadmap for planning drought prevention and response strategies [61]. The knowledge gained from this study can serve as a technical support and foundation for dynamic risk assessment and management.
## 3 Experimental Results
### Experimental Conditions
Figure 9: Experimental optical observations reconstructed from each hologram in the case of CAO encoding (**a**), MAO encoding (**b**), [PERSON]’s encoding (**c**), and [PERSON]’s encoding (**d**) where the eye of the observer is focused on the fore-positioned object. Each arrow indicates the focused object between a pair of 3D objects in a camera-captured image from optically reconstructed H3D scenes (716 \(\times\) 450 pixels). Here, each scanned area (27 \(\times\) 38 pixels) appears in the shape of multiple lines in white color near the front-positioned 3D cube [see also the Supplementary video (a)–(d)]. For a quantitative evaluation of the image quality of the holographic 3D reconstruction, we select a scanned measurement area with the same region on photographic images to capture the optically reconstructed H3D scenes with respect to four different encodings. The scanned test area that we chose for each encoding consists of 27 \(\times\) 38 pixels, as shown in Figure 9. By a statistical analysis within the scanned areas, we calculate the corresponding holographic 3D image's brightness (HIB) and holographic contrast ratio (HCR). Here, we define the HIB as the peak value of a grey-level curve along each selected line of the scanned areas. The HCR is defined as  
\[\mathrm{HCR}_{ij}=\left(G_{Wi}-G_{Oi}\right)/\left(G_{Bj}-G_{Oj}\right), \tag{7}\]  
where \(G_{Wi}\) and \(G_{Oi}\) represent the grey level and the dark-noise level (compensation term of \(G_{Wi}\)), respectively, measured at a target pixel point \((x_{i},\ y_{i})\) in the bright state condition. \(G_{Bj}\) and \(G_{Oj}\) represent the grey level and the dark-noise level (compensation term of \(G_{Bj}\)), respectively, measured at a target pixel point \(\left(x_{j},\ y_{j}\right)\) in the black state condition. In general, the HCR value is equal to the result of dividing the representative grey-value of the bright object area by that of the dark background area on the given holographic 3D reconstruction. In order to calculate the HCR, each representative value of the bright area and dark area is required. In the study, we take a size of 27 \(\times\) 38 pixels (row \(\times\) column) as a rectangular sample area within the bright/dark region for the HCR. The sampling areas in Figure 9 are marked with white lines, but all pixels' values of the rectangular area are taken for the statistical analysis. We set the mean value of grey levels within the scanning area as a representative value because the measuring unit of the brightness (HIB) in the holographic 3D experiment is 8-bit greyscale. Figure 12a,b show  
Figure 11: Surface intensity map of the bright area to be scanned on the optically reconstructed H3D object according to each encoding: (**a**) CAO encoding, (**b**) MAO encoding, (**c**) [PERSON]’s encoding (BUR), and (**d**) [PERSON]’s encoding (LEE). the brightness distribution of the bright and dark regions by using the boxplot method. Here, the blue asterisk represents the mean (average) value of the samples and the red cross represents the median value of the samples.