esa-sceva/satcom-chunk-collection huggingface dataset:
# Verification of a Probabilistic Model and Optimization in Long-Range Networks
## 2 Materials and Methods
To mitigate the probability of concurrent transmissions, a controlled transmission interval of 1 s (\(\lambda=1\) pkt/s) per node was implemented in the experimental setup. This spacing between transmissionsreduced, but did not eliminate, packet collisions. Beyond the challenges posed by packet collisions and intra-SF interference, another important aspect to consider in this setup is the payload characteristics. The size of the transmitted packets was fixed at 20 bytes for all experiments. This payload length was selected to balance transmission time and energy consumption, while ensuring that the experimental conditions remained realistic for practical IoT applications. The corresponding _Time on Air_ (ToA) was calculated based on the selected transmission parameters, which included a spreading factor of SF = 7, a bandwidth of 125 kHz, and a coding rate of 4/5. Using the standard formula for LoRa packet airtime:  
\[\text{SIR}_{n}=\frac{P_{\text{signal}}}{I_{\text{average}}} \tag{1}\]0  
the computed ToA was approximately 50 ms per packet. Regarding the duty cycle, LoRaWAN and ISM band regulations enforce duty cycle restrictions depending on the region. In our setup, the transmissions adhered to the European ISM band regulations, which impose a maximum duty cycle of 1% in most sub-bands. This means that each transmitter was limited to transmitting for a maximum of 1% of the total time, ensuring compliance with spectrum usage constraints. Given the calculated ToA of 50 ms per transmission and a transmission interval of 1 s, the effective duty cycle for each node was as follows:  
\[\text{SIR}_{n}=\frac{P_{\text{signal}}}{I_{\text{average}}} \tag{1}\]1  
This exceeds the 1% limit, indicating that either the transmission interval must be increased or the payload size reduced to comply with regulations. However, this experimental setup was conducted in a controlled environment where regulatory constraints were not strictly enforced. In a real-world deployment, either a longer transmission interval or a reduction in transmission power would be necessary to comply with regulatory requirements. Future improvements to this experimental framework could involve using multi-channel gateways to allow for simultaneous packet reception across different frequencies, thereby reducing the probability of collisions. Additionally, implementing an adaptive SF allocation mechanism, where nodes dynamically adjust their SF based on network conditions, could further mitigate intra-SF interference and improve overall network performance. Another approach would be to introduce a scheduling mechanism or duty-cycle-aware transmission coordination to better distribute packet transmissions over time, reducing congestion and increasing successful packet delivery rates. Overall, the results demonstrate that while a single-channel gateway is sufficient for controlled experiments, real-world deployments with highnode densities would benefit from multi-channel reception, adaptive SF selection, and collision avoidance strategies to enhance the network.
## 3 Theoretical Framework
### K-Means Algorithm  
The K-means algorithm is a clustering method in the field of data mining and machine learning [18]. The K-means algorithm was introduced by [PERSON] in 1957 as a technique for pulse-code modulation (PCM) quantization in signal processing [18; 19]. It was later rediscovered by [PERSON], who, in 1967, introduced the algorithm as a clustering technique in the context of pattern recognition. The K-means algorithm was initially applied in signal processing and image compression, which have proven to be very effective. It is also known for its simplicity, efficiency, and effectiveness in partitioning data into clusters based on similarity [20].  
Integrating the K-means algorithm into the Bee Colony Optimization (BCO)) helped to solve complex optimization problems that required clustering [21; 22]. The K-means clustering method used an iterative procedure to assign data points to clusters based on centroid proximity and update centroids until convergence. Although the K-mean is effective for many applications, it has some limitations relating to initialization, cluster shape assumptions, and scalability [2].  
The K-Means clustering algorithm or scheme is a well-known partitioning technique that aims to divide \(n\) data points into \(K\) clusters. The scheme repeatedly assigns data points to the nearest cluster centroid (cluster heads) and updates the centroids based on the mean of the data points in each cluster.  
The following steps constitute the process involved in K-Means clustering:  
**Step 1: Initialization**  
* Select the number of clusters, \(K\).
* Randomly initialize \(K\) cluster centroids, \(C=\{c_{1},c_{2},\ldots,c_{K}\}\).  
**Step 2: Assignment**  
* For every data point \(x_{i}\), calculate the distance to each centroid \(c_{j}\) using a distance metric (e.g., Euclidean distance).
* Assign the data point \(x_{i}\) to the cluster with the nearest centroid: \(argmin_{j}=\left\{\left\|x_{i}-c_{j}\right\|^{2}\right\}\).
## 3 Research Methodology
### Data Description and Preprocessing  
The present study analyzed a retailing mart database consisting of 5870 members. The dataset used for analysis was \"online_retail_II.csv\", which contains 1,044,848 transactions spanning from December 2009 to December 2011. The dataset includes eight variables: invoice, stockcode, description, quantity, invoice date, price, customer ID, and country. Four variables were selected for this study, including customer ID, which represents the customer identity number associated with each transaction.  
Modified RFMCEIP (recency-frequency-monetary-customer engagement index-periodicity) variables. Firstly, the recency value was calculated as the number of days since each customer's last purchase. Secondly, the frequency value was determined by counting the number of invoices for each customer, indicating the number of items purchased in each transaction. The monetary value was obtained by summing up the total spending of each customer. Periodicity is the average duration deviation between transaction times and was calculated using the formula (5).  
\[Periodicity=stdev(\Delta t_{i},\Delta t_{i+1}\ldots\Delta t_{n}) \tag{5}\]  
where \(t_{i}\) denotes the date corresponding to the \(i\)th visit of the customer; last but not least, the \(CEI\) was calculated as Formulas (1)-(4) to show the activeness of each customer to visit the shop. We used the RFMCEIP calculation results to assess the correlation levels between customer- or product-oriented variables. The selection of variable combinations aimed to minimize the risk of collinearity, wherein variables with strong correlations (above 0.8) may have similar patterns, and one variable can represent the others.
## 2 Materials and Methods
### Samples Collection  
A sample set including 5433 individual points was defined to fulfil research objectives. All points were classified as \"forest\" (class 1) or \"not forest\" (class 0). The \"forest\" class included all forest varieties, whereas the \"not forest\" class included all non-forest geospatial elements (meadows, fields, rivers, lakes, communications, facilities, and others).  
A total of 5433 points were defined in the sampling process, of which 2509 points were classified in class 1--\"forest\", and 2924 points were classified in class 0--\"not forest\" (Figure 5). Detailed VHR (very high resolution) aerial photos and satellite images were used to define the samples as precisely as possible.  
A comprehensive analysis was conducted on the samples to guarantee the exactness of the classifications. A substantial fraction, as much as 10%, underwent rigorous on-site re-evaluation, enhancing the validation procedure and confirming the precision of the results. In certain instances, when the sample class could not be determined from the accessible satellite and aerial imagery, instant field-based identification and verification were performed to obtain the most precise dataset.
## 3 Reliability Sensitivity Calculation
 \tag{15}\]  
Reliability sensitivities in the original space \(X\) are thus written as [28]:  
\[\frac{\partial\hat{p}_{f}}{\partial\mu_{X_{j}}}=\frac{\partial\hat{p}_{f}}{ \partial\mu_{v_{j}}}\frac{\partial\mu_{v_{j}}}{\partial\mu_{X_{j}}}=\frac{1} {\sigma_{j}}\frac{1}{N\sqrt{2\pi}}\sum\limits_{i=1}^{N}e^{-b_{i}^{2}/2}a_{j} \tag{16}\]  
\[\frac{\partial\hat{p}_{f}}{\partial\sigma_{X_{j}}}=\frac{\partial\hat{p}_{f}}{ \partial\sigma_{v_{j}}}\frac{\partial\sigma_{v_{j}}}{\partial\sigma_{X_{j}}}= \frac{1}{\sigma_{j}}\frac{1}{N\sqrt{2\pi}}\sum\limits_{i=1}^{N}e^{-b_{i}^{2}/2 }a_{j}^{2}b_{i} \tag{17}\]  
as well as the standard deviation with the formulas [28]:  
\[\sigma\bigg{(}\frac{\partial\hat{p}_{f}}{\partial\mu_{X_{j}}}\bigg{)}=\frac{1 }{\sigma_{j}}\sqrt{\frac{1}{N-1}\left\{\frac{1}{N}\underset{i=1}{\overset{N}{ \sum}}\bigg{[}\frac{a_{1j}}{\sqrt{2\pi}}e^{-b_{i}^{2}/2}\bigg{]}^{2}-\bigg{(} \frac{\partial\hat{p}_{f}}{\partial\mu_{v_{j}}}\bigg{)}^{2}\right\}} \tag{18}\]  
\[\sigma\bigg{(}\frac{\partial\hat{p}_{f}}{\partial\sigma_{X_{j}}}\bigg{)}=\frac{ 1}{\sigma_{j}}\sqrt{\frac{1}{N-1}\left\{\frac{1}{N}\underset{i=1}{\overset{N}{ \sum}}\bigg{[}\frac{a_{1j}}{\sqrt{2\pi}}b_{i}e^{-b_{i}^{2}/2}\bigg{]}^{2}- \bigg{(}\frac{\partial\hat{p}_{f}}{\partial\sigma_{v_{j}}}\bigg{)}^{2}\right\}} \tag{19}\]  
For those problems involving non-normal random variables with the nonlinear transformation between the original space and standard normal space, \(\partial\mu_{v_{j}}/\partial\mu_{X_{j}}\) is not easy to compute. The region that has the greatest influence on the calculation accuracy of reliability sensitivity often does not overlap with the region that has the greatest contribution to the failure probability. It means we have to choose different sampling PDF to calculate the failure probability and reliability sensitivity for the accuracy. To get around this, the reliability sensitivity can be calculated by the finite difference method, that is [28]  
\[\frac{\partial\hat{p}_{f}}{\partial\theta}\approx\frac{\hat{p}_{f}\Big{|}_{ \theta+\Delta\theta}-\hat{p}_{f}\Big{|}_{\theta}}{\Delta\theta}=\frac{1}{N( \Delta\theta)}\underset{i=1}{\overset{N}{\sum}}\Big{[}\Phi_{(0,1)}\left(-b_{i }|_{\theta+\Delta\theta}\right)-\Phi_{(0,1)}\left(-b_{i}|_{\theta}\right)\Big{]} \tag{20}\]  
where \(\theta\) is the mean or standard deviation of a random variable, and the corresponding standard deviation is obtained as [28]:\[ \begin{split}&\sigma\Big{(}\frac{\partial\hat{p}_{f}}{\partial\theta} \Big{)}\approx\frac{\sigma(\hat{p}_{f}\big{|}_{\theta+\Delta\theta}-\hat{p}_{f }\big{|}_{\theta})}{(\Delta\theta)}\\ &=\sqrt{\frac{E\big{[}\big{(}\theta_{f|_{\theta+\Delta\theta}}- \hat{p}_{f|\theta}\big{)}^{2}\big{]}-E\big{[}\big{(}\theta_{f|_{\theta+\Delta \theta}}-\hat{p}_{f|\theta}\big{)}\big{]}^{2}}{(N-1)(\Delta\theta)^{2}}}\\ &=\frac{1}{\Delta\theta}\sqrt{\frac{1}{(N-1)N}\sum_{i=1}^{N} \Bigl{(}\Phi_{(0,1)}\bigl{(}-b_{i}\big{|}_{\theta+\Delta\theta}\bigr{)}-\Phi_{ (0,1)}\bigl{(}-b_{i|_{\theta}}\bigr{)}\Bigr{)}^{2}-\Bigl{(}\hat{p}_{f|\theta+ \Delta\theta}-\hat{p}_{f|\theta}\Bigr{)}^{2}}\end{split}  \tag{21}\]
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 5 Numerical Simulation
### Verification of the Bottom Non-Singular Sliding Mode Guidance Law
#### 5.2.4 Salvo Attack on Maneuvering Target with the NSMG Law  
The above simulation examples show that the non-singular sliding mode guidance law NSMG can be applied to strike missions under different initial conditions and different designated impact time. Therefore, the NSMG law can be applied to cooperative combat scenarios. At the same time, in order to verify the effectiveness of the extended guidance law for a maneuvering target, it is assumed that four UAVs with different initial conditions attack the same uniformly moving target. The proportional guidance coefficients are all 3, and the initial launch time is consistent. The other simulation parameters are shown in Table 1.  
When all four UAVs adopt the extended form of guidance law under maneuvering target in Section 3.2, the simulation results are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**UAVs/Target**} & **Initial Position** & \multirow{2}{*}{**Velocity (m/s)**} & **Initial Flight Path** & **Designated** \\  & **(km)** & & **Angle (deg.)** & **Time (s)** \\ \hline M1 & (0,0) & 330 & 0 & \\ M2 & (5,8) & 320 & 30 & \\ M3 & (15,5) & 310 & 5 & 45 \\ M4 & (5,—8) & 300 & 45 & \\ Target & (10,0) & 50 & 30 & \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of multiple UAVs’ cooperative strike against maneuvering target.  
Figure 6: Simulation results under different the initial leading angles.  
Figure 7(a)-d show the variation curves of the flight trajectory, leading angle, impact time error and acceleration command of four UAVs over time when the NSMG law is applied to a cooperative attack scenario. It can be seen from the figures that the four UAVs under different initial conditions can strike the target at the same designated time, meeting the demand of time coordination. The terminal leading angle of each UAV is 0, and the corresponding acceleration command is also 0. At the initial moment, the estimated value of the time-to-go of each UAV is less than the desired time-to-go. Under the action of the acceleration command, the amplitude of the leading angle increases to extend the flight time, and finally the impact time error gradually converges to 0.  
In conclusion, the impact time control cooperative guidance law based on sliding mode control theory has been fully verified, and the guidance law is suitable for strike missions under different initial conditions and different designated impact times. For maneuvering targets, the predictive collision point strategy is used to extend the form of the guidance law, which can realize an accurate attack.
# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
Motivated by the need to enhance CNN performance in RSSI-based indoor positioning, this study introduces a novel local batch normalization (LBN) technique designed to address the challenges of uneven data distributions and localized feature variability. RSSI data often exhibit significant non-independent and identically distributed (non-IID) characteristics due to spatial dependencies, temporal fluctuations caused by dynamic environmental changes, and localized variations introduced by obstacles and noise. These properties lead to uneven data distributions, increasing the complexity of training models and exposing the limitations of traditional BN techniques. The proposed LBN approach normalizes inputs at a more localized level, enabling the model to better handle signal fluctuations and enhancing the generalization ability of the CNN model in dynamic environments. By specifically targeting location-dependent feature variations, LBN aims to improve the robustness of RSSI-based indoor positioning systems under irregular distributions and environmental noise. This paper is organized as follows: Section 2 provides background information, including an overview of BN and LN, and their limitations. Section 3 presents the proposed LBN-aided positioning scheme, detailing the methodology and CNN architecture. Section 4 introduces the simulation setup, experimental design, and data processing and analyzes the results by comparing the performance of the baseline CNN model with those incorporating BN, LN, and LBN. Finally, Section 5 concludes the study by summarizing key findings and suggesting future research directions.
## 4 Experimental Results
### Dataset Selection and Experiment Preprocessing  
To evaluate the proposed system, a noisy ECG signal and a clean ECG signal were acquired from the nstdb [31] and the mitdb [30], respectively. The mitdb contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, which were recorded at a 360 Hz sampling rate with 11-bit resolution using an analog/digital converter [33]. The mitdb includes several types of arrhythmia, and it has been widely used as the dataset for heartbeat classification [48]. The nstdb provides twelve 30 min ECG recordings (two leads per recording) and three 30 min recordings of noise typical in ambulatory ECGs [49]. The noisy ECG was created by mixing six levels (~6 dB, 0 dB, 6 dB, 12 dB, 18 dB, and 24 dB) of EM noise into the clean ECG recordings from mitdb_118 and mitdb_119 [50]. First, the noise was added to the recordings after the first 5 min. Subsequently, 2 min noise segments with 2 min noise-free segments were alternated until the end.  
In this study, the noisy ECG signal was acquired from the noise segment of 24 ECG leads in the nstdb, which can be found in 5-7, 9-11, 13-15, 17-19, 21-23, 25-27, and 29-30 min in every ECG lead. Subsequently, each segment was further separated into several nonoverlap fragments with a length of 1024 samples. Finally, 6888 noisy ECGs were used to evaluate the denoising performance. For the clean ECG signal, the corresponding indices of the clean ECG in mitdb-118 and mitdb-119 were used. The 6888 fragments were split into a training set and testing set, with 80% and 20% of the fragments, respectively. The dc offset was removed from all fragments in the preprocessing phase in consideration of the effect of the dc offset on PRD. Moreover, the 11-bit digitized ECG signal was divided by 2048 to normalize the input data within 0 and 1.
# Advanced Microbubbles as a Multifunctional Platform Combining Imaging and Therapy
## 2 Advanced Microbubbles for Imaging and Therapy
### Inflammation
They concluded with US imaging inflammation in a murine acute colitis model could be quantitatively detected and the natural pathway of leukocyte recruitment in inflammatory tissues has been revealed. [PERSON] et al. assessed acute inflammation with dual P- and E-selectin-targeted US imaging [62]. Mice at different stages of inflammation were imaged following intravenous injection of P- and E-selectin-targeted MBs. It showed the US signals increased due to induction of acute inflammation at days 0, 1, and 3, and the percentage of P- and E-selectin-positive vessels increased in immunofluorescence. This method has been consolidated to accurately measure acute inflammation in a clinically relevant murine model of chronic IBD. The molecular US imaging also can monitor the early inflammatory effects following acute kidney injury with P-selectin-targeted and VCAM-1-targeted MBs, which has been developed by [PERSON] et al. [63]. Non-invasive imaging for detecting acute cardiac transplant rejection was considered impossible in the past. [PERSON] conjugated anti-rat ICAM-1 to lipid-based MBs (MBICAM) to detect acute rejection of cardiac transplantation. MBICAM was found to adhere to inflammatory endothelial cells in vitro and in vivo; moreover, myocardial video intensity from adhered MBICAM was found significantly higher, thereby implying that ICAM-1-targeted MBs may offer a non-invasive US imaging methodology for the successful detection of acute cardiac transplant rejection [64].
###### Abstract  
Research related to thermal cameras, which are major control measures, is increasing to overcome the limitations of closed-circuit television (CCTV) images. Thermal cameras have the advantage of easily detecting objects at night and of being able to identify initial signs of dangerous situations owing to changes in temperature. However, research on thermal cameras from a comprehensive perspective for practical urban control is insufficient. Accordingly, this study presents a thermal camera-based abnormal-situation detection service that can supplement/replace CCTV image analysis and evaluate service preferences. We suggested an integrated Fuzzy AHP/TOPSIS model, which induces a more reasonable selection to support the decision-making of the demand for introducing thermography cameras. We found that developers highly evaluated services that can identify early signs of dangerous situations by detecting temperature changes in heat, which is the core principle of thermography cameras (e.g., pre-fire phenomenon), while local governments highly evaluated control services related to citizen safety (e.g., pedestrian detection at night). Clearly, while selecting an effective service model, the opinions of experts with a high understanding of the technology itself and operators who actually manage services should be appropriately reflected. This study contributes to the literature and provides the basic foundation for the development of services utilizing thermography cameras by presenting a thermography camera-based abnormal situation detection service and selection methods and joint decision-making engagement between developers and operators.  
abnormal situation detection service; thermal camera; integrated fuzzy AHP/TOPSIS model; preference evaluation; AHP; TOPSIS
## 2 Materials and Methods
### Methodology
#### 2.6.1 The General Framework of the MSAMT Method
**Input:**\(D\), problem dimension; \(N\), initial population size; \(ND\), population size of dynamic ensemble of surrogates; \(NP\), evolutionary population size; DB, database; \(NFE\), the number of exact evaluations; \(NFE_{max}\), the maximum number of exact evaluations; \(f\), the real objective function; \(n\), the number of clusters; \(rmp\), matching probability; \(k_{max}\), the maximum number for each running of multitasking optimization;  
**Output:** the position of the best individual and its fitness value;  
1: Generate \(N\) initial samples by LHS;  
2: Evaluate exactly \(N\) initial samples by the real objective function and save them to the DB;  
3: **while**\(NFE<NFE_{max}\), **do**  
4: \(//\)Decision space partition based global search  
5: Randomly select \(n\) samples from the DB as clustering centers;  
6: Calculate the distance between all samples in the DB and each clustering center and assign each sample to the clustering center nearest to it;  
7: Calculate the mean of each cluster as the new clustering center;  
8: Repeat rows 6 and 7 until the new center is unchanged from the original center and output \(n\) central position;  
9: **for**\(i=1\) to \(n\), **do**  
10: Calculate the \(r_{i}\) according to Equation (14);  
11: **end for**  
12: Determine in which region each of the samples in the database is located;  
13: **for**\(i=1\) to \(n\), **do**  
14: **if** the number of exactly evaluated samples distributed in the \(i\)th region is larger than \(5\times D\), **then**  
15: Select all exactly evaluated samples in the \(i\)th region to construct the RBF surrogate model;  
16: **else**  
17: Select the \(5\times D\) neighboring exactly evaluated samples to the \(i\)th region to construct the RBF surrogate model;  
18: **end if**  
19: **end for**  
20: \(//\)Dynamic ensemble of surrogates based local search  
21: Apply Algorithm 2 to construct a dynamic ensemble of surrogates;  
22: \(//\)Perform global and local searches using RBF models and the dynamic ensemble of the surrogate-assisted G-MFEA, respectively;  
23: Apply Algorithm 3 to generate an evolutionary population and let \(k=0\);  
24: Apply Algorithm 4 to perform multi-tasking optimization;  
25: Locate the \(n+1\) individuals having better predicted fitness;  
26: Evaluate the real fitness value of the \(n+1\) best individuals and save them to the DB;  
27: \(NFE=NFE+n+1\);  
28: **end while**  
**Algorithm 1** Pseudocode of the MSAMT algorithm