esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2012**, _12_, 1336-1351; doi:10.3390/s120201336  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 3**  
1 Center for Large-Scale Integrated Optimization & Networks (CLION), FedEx Institute of Technology, University of Memphis, 365 Innovation Drive, Memphis, TN 38152, USA; E-Mails: [EMAIL_ADDRESS] (L.W.); [EMAIL_ADDRESS] (E.M.); [EMAIL_ADDRESS] (M.K.); [EMAIL_ADDRESS] (K.I.); [EMAIL_ADDRESS] (S.R.B.)  
2 Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA; E-Mail: [EMAIL_ADDRESS]  
3 Computer Engineering Department, Istanbul Kultur University, E5 Atakoy Campus E5 Otoyolu Londra AsfaltA, BakA+, Tokyo, 34156 Istanbul, Turkey; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-901-678-2497; Fax: +1-901-678-2480.  
_Received: 30 October 2011; in revised form: 10 January 2012 / Accepted: 10 January 2012 / Published: 31 January 2012_  
**Abstract:** The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios.  
**Keywords:** Doppler radar; wireless sensor mote; autonomous sensor network; surveillance; tracking
# Correlation Study of Auroral Currents with External Parameters During 10-12 October 2024 Superstorm
## 4 Discussion
### Correlation Analysis
During the May 2024 magnetic storm, the PEJ intensity was primarily correlated with Dst, whereas during the October 2024 storm, it was mainly correlated with Em and Pd. However, the latitude of PEJs is predominantly correlated with Pd and Dst during most local time sectors. This finding provides valuable reference for future magnetic storm research. During this October storm, both Em and Pd played crucial roles in the variations in FAC and PEJ densities and latitudes across different local time sectors. Em reflects the combined influence of IMF By, Bz, and solar wind velocity, while Pd is determined by the product of Nsw and the square of Vsw. Both Em and Pd incorporate the effect of Vsw, but Em does not account for Nsw, and Pd does not include IMF By or Bz. [PERSON] and [PERSON] (2001) [26] identified a positive correlation between Nsw and auroral current intensities, as  
Figure 6: The Spearman correlation analysis between peak PEJ densitiy and seven external parameters. (**a,c**) Northern Hemisphere and (**b,d**) Southern Hemisphere. Different colors denote different local times. Brown and yellow are for duskside eastward (J+) and dawnside westward PEJs (\(-\), (**a,b**)) and for daytime and nighttime westward PEJs (**c**), (**d**), respectively. The highest correlation coefficients are further denoted by half gray bars. indicated by the AL and AU indices, with a stronger relationship under southward IMF conditions. [PERSON] et al. (2011) [27] found that around noon, R1 currents are predominantly influenced by Vsw, while R2 currents are more strongly affected by Nsw. Therefore, it is suggested that future solar wind electric field coupling parameters incorporate solar wind mass density, as this could further optimize the correlation coefficients with FACs and PEJs. One should note that in this paper, [PERSON]'s rank correlation analysis was primarily employed to examine the relationship between FACs, PEJs, and external parameters. However, this method only identifies monotonic relationships between the variables and does not provide evidence of causality. Therefore, causality cannot be directly inferred from correlation alone. The reliability of the conclusions could be strengthened by complementing the analysis with additional methods or by incorporating relevant physical mechanisms in future work.
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 4 Experiment result and Analysis
### Testing by Simulated Data
#### 4.1.3 Computation Analysis  
When the uncooperative illuminator and the receiver of the PBR are located at fix sites, the non-uniform polar grid is fixed according to the acquisition geometry. The calculation of the grid is done in preprocess only once. So, the computation analysis of the grid construction is not involved in this section. In frame processing, assume the grid size is \(\text{m}_{1}*\text{m}_{2}\). The calculation of A for one processing point concludes \(\text{m}_{1}+\text{m}_{2}\) additions. The calculation of \(\Psi A\) concludes  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Detection Accuracy Rate** &
\begin{tabular}{c} **False Alarm Decline** \\ **Rate** \\ \end{tabular} & **Miss Detection Rate** \\ \hline
97.45\% & 10.24\% & 2.55\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance indexes of the suppression algorithm.  
Figure 9: Contrast of before and after the suppression: (**a**) simulated data before suppression; (**b**) clutter suppression result.  
additions. In interframe processing, assume the structural element size is \(\mathrm{s_{1}*s_{2}}\). The calculation of \(\Gamma\) concludes \(2\mathrm{m_{1}*m_{2}}\) additions. The calculation of M needs \((\mathrm{s_{1}*s_{2}+1})\cdot(\mathrm{m_{1}*m_{2}})\) multiplications and \((\mathrm{s_{1}*s_{2}-1})\cdot(\mathrm{m_{1}*m_{2}})\) additions.  
Suppose the frame number is k and the points number in each frame is n. So the whole process needs \(\mathrm{k}[\mathrm{n}(\mathrm{m_{1}+m_{2}})+(2+\mathrm{s_{1}*s_{2}})\cdot( \mathrm{m_{1}*m_{2}})]\) additions and \(\mathrm{k}(\mathrm{s_{1}*s_{2}-1})\cdot(\mathrm{m_{1}*m_{2}})\) multiplications. That is \(\mathrm{o}(\mathrm{k*n})\) additions and \(\mathrm{o}(\mathrm{k})\) multiplications. Therefore, the addition times depends on the total number of points. In addition, the multiplication times only depends on the number of frames.  
During tracing, the calculation amount relating to the number of clutter points is mostly caused by the Euclidean distance calculation between points. Assume M is the total number of points for tracing in each frame. In two consecutive frames, there are M\({}^{2}\) point pairs for processing. For each point pair, the calculation of the Euclidean distance needs 3 additions and 3 multiplications. Thus, the calculation amount of the Euclidean distance is \(\mathrm{o}(\mathrm{k*M^{2}})\) additions and \(\mathrm{o}(\mathrm{k*M^{2}})\) multiplications. Where k is the total number of frames.  
Suppose our algorithm can suppress 90% clutter points, and this suppression process only consumes \(\mathrm{o}(\mathrm{k*M})\) additions and \(\mathrm{o}(\mathrm{k})\) multiplications. After suppression, the calculation amount of the Euclidean distance will descend to the 1% of the original. Thus, we pay low calculation amounts for reducing much more computation amounts of tracing.
## 3 Performance Evaluation
### Average Number of Transmission Times (\(T_{avg}\))
Therefore, the proposed Mod-Scm scheme not only obtains a better OP and SOP performance, but also achieves a lower average number of transmissions. As shown in Figure 9, the average number of transmissions of Mod-Scm-2 is lower than that of Mod-Scm-1. However, at high \(\Delta\) regimes, the performance of Mod-Scm-1 and Mod-Scm-2 is almost the same. Finally, as observed, increasing the number of reflective elements in the RIS also reduces the average number of transmissions significantly. Figure 8: OP and SOP as a function of \(x_{\text{SB}}\) when \(\Delta=\)11 dB, \(\alpha=0.375\), \(K=4\), and \(N_{\text{max}}=6\). Figure 9: The average number of transmissions for the successful data exchange as a function of \(\Delta\) (dB) when \(x_{\text{SB}}=0.4\), \(\alpha=0.2\) and \(N_{\text{max}}=7\). Figure 10 presents the impact of \(\alpha\) on the average number of transmissions of FC packets for the successful data exchange between two sources in the Mod-Scm scheme when \(\Delta=8.5\) dB, \(K=5\), and \(N_{\text{max}}=7\). As seen in Figure 10, the average number of transmissions in Mod-Scm-1 and Mod-Scm-2 achieves the minimum value as \(\alpha=0.5\). Moreover, the positions of the SB station also impacts the average number of transmissions significantly. In this figure, the average number of transmissions in Mod-Scm-1 and Mod-Scm-2 with \(x_{\text{SB}}=0.5\) is the same, and is lowest as compared with \(x_{\text{SB}}=0.1\) and \(x_{\text{SB}}=0.2\).
## 2 Climbing Scaffold Status Monitoring Method
### Climbing Scaffold Monitoring and Processing Workflow  
The overall process of monitoring the status of the external climbing framework in super-high-rise buildings based on BeiDou/GNSS technology consists of four key stages: data acquisition, preprocessing, filtering optimization, and state recognition with warning mechanisms.  
1. Data Acquisition and Transmission: BeiDou/GNSS-integrated receivers are deployed at critical locations on the climbing framework (e.g., four corners), while a reference station is established around the building. Dual-frequency observations from the four satellite constellations are collected to ensure comprehensive coverage. The monitoring data are transmitted to the data processing center in real time at a 1 s epoch interval, ensuring data continuity and timeliness.
2. Data Preprocessing: Given that GNSS signals are susceptible to multipath effects and non-line-of-sight errors in urban canyon environments, an improved Rainer criterion is first applied to remove gross errors. The confidence interval is dynamically adjusted to prevent deformation data from being misidentified as gross errors. Additionally, missing data are interpolated using a piecewise cubic Hermite interpolation method to ensure the completeness of the data sequence.
3. Filtering Optimization and State Modeling: The preprocessed data are fed into a robust Kalman filter model. By adjusting the observation noise covariance matrix (using the IGG3 equivalent weight function), the impact of continuous gross errors and short-term multipath effects is mitigated. Furthermore, height constraint conditions are introduced--setting threshold limitations during the vertical climbing phase--to further optimize dynamic positioning accuracy. The filtered data effectively separate high-frequency noise from low-frequency displacement trends, providing smooth and reliable time-series data for further analysis.
4. State Recognition and Warning Mechanism: Based on the filtered data, a sliding-window CUSUM algorithm is employed to detect the climbing status in real time. The feature quantity is calculated by analyzing the mean difference between a reference window and a detection window, while predefined thresholds are used to determine the start and end times of the climbing motion. The climbing height and duration are also recorded. In both horizontal and vertical directions, a multi-level CUSUM warning model is established to accumulate minor displacement deviations and dynamically trigger different warning levels. If monitoring values exceed the predefined safety thresholds, the system automatically generates warning notifications and transmits them to the construction management platform, facilitating timely intervention by decision-makers.  
The overall monitoring framework based on BeiDou/GNSS technology for the external climbing framework in super-high-rise buildings is illustrated in the following diagram:
# Recent Advances of Integrative Bio-Omics Technologies to Improve Type 1 Diabetes (T1D) Care
## 6 Device and Personal Health Records  
Pharmacological treatment of T1D is necessary but is not the only therapeutic strategy used in clinics to empower individuals in managing their blood glucose variation and prevent associated complications [57]. Wearable therapeutic devices are common in the treatment of T1D and from continuous glucose monitors (CGM) to insulin pumps, broad data is available to analyze and establish patterns in the control and management of T1D. Investigators can evaluate behavioral factors to assess how variations in personal management affect glycemic outcomes. Advanced Hybrid Closed Loop (AHCL) systems have been shown to be effective for HbA1c reduction [3, 4, 5]. These devices allow individuals to learn about their personal differences, which can be further analyzed for improved disease control. In the [PERSON] et al. study, investigators explored associations between meal size, insulin dose, glycemic variability, and sleep disturbances [58]. They found that smaller meals and smaller insulin dosages were associated with decreased glycemic variability and better outcomes. They also found that there was no association between interrupted sleep and glycemic outcomes. The authors conclude that even if using an insulin pump or CGM is not the individual's treatment of choice, the insight gained from analyzing device data can still be applied to other methods of management. Furthermore, analysis of device data can be used to predict adverse glycemic events and can prevent poor outcomes [59].  
Daily fluctuations in blood glucose levels differ among individuals with T1D. Longitudinal blood glucose control can be assessed by HbA1c measurements every 3-4 months or via calculating the CGM Time in Range (70-180 mg/dL). Personal health records involvingdiet, exercise, insulin compliance as well as other factors, can also be used to study whether to what degree individual behavior affects glycemic control [60]. Furthermore, levels of endogenous residual insulin production, as measured by stimulated c-peptide must be considered by care provides, since it is a contributing factor to glycemic control in new-onset individuals with T1D [61]. In fact, [PERSON] et al. reported that beta-cell responsiveness to hyperglycemia and alpha-cell responsiveness to hypoglycemia are observed only at high levels of residual c-peptide that likely contribute to glycemic control [62]. [PERSON] et al. revealed that a glucometer-connected mobile app may increase an individual's engagement with other aspects of care, such as self-monitoring of blood glucose (SMBG) frequency. While data from personal health records can help outline trends and predict future adverse events for the individual, how to transition to algorithm creation and machine learning is still unclear. The quality of data used should be reliable and representative of the target population to ensure validity [63; 64; 65; 66]. More research needs to be done to ascertain how and to what degree mobile devices can improve glycemic control in individuals with T1D [67].
# Generative Music with Partitioned Quantum Cellular Automata
## 4 Music Mapping
### One-Dimensional PQCA Mapping  
A bar of 18 cells forms the one-dimensional PQCA. The mapping scheme generates clusters of musical notes. Each cycle of the PQCA produces an eighteen-bit long bitstring, which is converted into either a cluster or a rest (i.e., silence).  
A cluster is a group of notes that are played simultaneously. It can have up to 12 notes. Conversely, a rest is when a cycle does not generate any notes at all.  
To encode musical information, the bitstring is split into four sections (Figure 18). Each section forms a code representing a property of the cluster, as follows (the bit order is from the left to the right of the bitstring):  
* Code A (bits 1 and 2) defines the source of the notes. There are four different sources to choose from (Figure 19).
* Code B (bits 3, 4 and 5) defines the duration of the cluster. There are eight different durations to choose from (Figure 20).
* Code C (bit 6) = rest switch.
* Code D (bits from 7 to 18) defines the notes of the cluster; these notes are picked from the source defined by code A  
The pitches for a cluster are picked from one of four sources, according to code A. Hence, this code requires two bits to encode four options. The sources can be customised. The composition _Qubism_ used two sets of sources, one of which is shown in Figure 19.  
There are eight options for the duration of a cluster, which are defined by code B (Figure 20). It requires three bits to encode eight options. Moreover, code C establishes if the respective cluster is active (code \(C=1\)) or constitutes a rest (code \(C=0\)).  
Figure 19: A set of four sources of pitches to build clusters.  
Figure 20: One-dimensional PQCA codes for note durations.  
Note that in Figure 19, the sources are already displayed as clusters of twelve pitches. Formed by the last twelve bits of the bitstring (from 7 to 18), code D defines which notes in the selected source will constitute the resulting cluster.  
The positions of digits \"1\" in the string (from the left to the right) correspond to the positions of the notes to be picked from the source (from the bottom to the top). For instance, let us consider code \(\mathrm{D}=\{011001001001\}\). This instructs the system to pick the second, third, sixth, ninth and twelfth notes to form the cluster, as illustrated in Figure 21. However, if code \(\mathrm{C}=0\), then the system would output a rest, respective to the duration of the cluster instead. A real example is discussed in Section 5.
# Resource Allocation for Network Slicing in RAN Using Case-Based Reasoning
## 4 Numerical Results
### Determination of \(k\)  
For choosing the best value of \(k\), we compared three different \(k\) value determination methods, fixed value \(k\) (1,3,5,7), the K value determined by cross-validation as CV-KNN [13], and the proposed O-KNN. The prediction error for these different approaches over different numbers of query cases is shown in Figure 5. Obviously, the O-KNN and CV-KNN outperform the fixed \(k\) value with the O-KNN being slightly better.  
Figure 4: Prediction error with different weight functions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Subcarrier spacing & 15 kHz \\ \hline Transmit power & 23 dBm \\ \hline Shadow fading & log-normal \\ \hline Path loss & COST231-Hata \\ \hline Required bitrate of S1 users & 1 Mbps \\ \hline Required bitrate of S2 users & 2 Mbps \\ \hline User distribution & Uniform \\ \hline No. of cases in library & Up to 10,000 \\ \hline No. of test (query) cases & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._The other factor to consider is the search time to find the best \(k\) value. This is illustrated in Figure 6. The search time is averaged over 300 query cases. The result show that O-KNN has a search time that is approximately one-tenth of that of CV-KNN because the search space is reduced by spare learning.
## 3 Methodology
### Object Re-Identification
#### Spatial Distance Estimation  
Although, the 2-D Euclidean distance measure works well between frames with similar angles across similar LSUs, there are cases where the angle and zoom changes across similar LSUs. The topological information contained within the frame is also lost, making it impossible to obtain a realistic distance estimation. To compensate for the topological information, we propose to use depth maps, in combination with the location graph, to estimate a more realistic spatial distance between the objects in a frame. To obtain depth information, we use Dense Depth [24], pre-trained on NYU Depth V2 dataset [25]. The  
Figure 8: Example of multiple instance object class. This example is taken from the Season 4 Episode 16 of _New Girl_ TV SOAP show. In the left side image (frame 26070) there are three different objects of the class (_vase_) detected, while in the right image (frame 27604), there are two objects of this class detected.  
estimated depth is used as a third dimension, and thereby the Euclidean measure is re-calculated as shown in Figure 10.  
Let \(x\) and \(y\) be the centre points of the objects \(O_{x}\) and \(O_{y}\), respectively, in a frame. Then the distance between them is given by:  
\[distance=|x-y| \tag{7}\]  
The estimated depth has a range of values that are clipped between 10 and 1000, where 10 is the closest and 1000 is the farthest. If the depth values at points \(x\) and \(y\) can be represented as \(\delta(x)\) and \(\delta(y)\), the depth between the objects can be estimated by:  
\[depth=|\delta(x)-\delta(y)| \tag{8}\]  
Finally, from Equations (7) and (8), the actual distance between the objects can be calculated as follows:  
\[D_{x}^{y}=\sqrt{(distance)^{2}+(depth)^{2}} \tag{9}\]
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 4 Results and Discussion
### Transaction Time Distribution  
We analyzed transaction completion times to assess the responsiveness of the system under real-world blockchain conditions. A total of 1000 transactions were executed on the Sepolia testnet, with the resulting distribution visualized in Figure 7. The X-axis represents 10-s transaction time intervals, while the Y-axis reflects the relative frequency (percentage) of transactions that fall into each range.  
As shown in Figure 7, this distribution illustrates a prominent peak in the 90-100 s range, which accounts for 29.0% of all recorded transactions, making it the most dominant interval by a significant margin. Several other bins, such as 0-10, 20-30, 40-50, and 80-90 s, each hold 9.7% of the transactions, suggesting moderate activity, with none approaching the peak observed for the 90-100 s bin. Only a small fraction of transactions occur in the extremities, with a minimum confirmation time of 12 s and a maximum of 108 s.  
Figure 6: This figure shows the relative frequency and cumulative distribution of transaction fees from 1000 blockchain interactions. Most transactions (over 95%) incur fees below ETH 0.001, as shown by the bars and CDF curve, indicating cost efficiency. Higher fees are limited to rare events such as contract deployment.  
These results indicate that while the system is capable of handling confirmations under 60 s in many cases, network conditions and transaction queueing likely contribute to longer confirmation delays. The variability in transaction completion times points to external factors such as blockchain network congestion or wallet latency rather than to inconsistencies in the contract itself [11]. Although this variability may not critically impact batch-based supply chain operations, it could still present a limitation for IoT-integrated use cases requiring real-time responsiveness, which we plan to resolve in our future work.