esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Model and Methodology
### Key Generation Protocol  
The following will be used in the subsequent parts of this paper:  
* \(k\) The private key of the system, which is composed by a set of cryptographic primitives from elliptic curves subspaces.
* \(P\) The public part of the generated combined key.
* \(G\) The curve generator point is a fixed point on the elliptic curve used to generate public keys. It is defined for and known to all devices on the network.  
* \(K\) The shared session key, calculated by multiplying one device's private key by another device's public key. This is used to encrypt communication sessions.
* \(\lambda\) It is used to calculate the sum or doubling of points on an elliptic curve. The formula for it depends on the nature of the points (identical or different).
* \(F_{p}\) and \(F_{a}\), \(F_{p}\) is the finite field defined by the prime number \(p\), and \(F_{a}\) is a subsp. ace derived using a contraction parameter \(a\).
* Parameter contraction \((x|\alpha,\beta|\) and \(y|\alpha,\beta|)\). The parameters \(\alpha\) and \(\beta\) define the limits of contraction in the calculation of points on the elliptic curve, reducing computational complexity.
* \(k^{-1}\) Represents the modular inverse of a number k in the finite field \(F_{p}\) and it is calculated as \(k^{-1}\equiv k^{p-2}\mod p\).  
In this proposed model, the key exchange is based on an optimized version of the Elliptic Curve Diffie-Hellman (ECDH) algorithm. This method offers advantages for IoT networks because it consumes fewer resources than other forms of asymmetric encryption.  
1. Private Key Generation (\(k\)): Each IoT device generates a private key \(k\) using a deterministic algorithm to minimize computational requirements. In this context, a pseudo-random number generator based on a linear congruential algorithm is sufficient, ensuring a compromise between complexity and security.
2. Key Exchange: The public key is generated by multiplying the private key \(k\) with a generator point of the elliptic curve, according to the formula: \[P=k\cdot G\] This operation allows the efficient exchange of public keys between nodes and gateways.
3. Calculation of the Common Key (\(K\)): Once the public keys are exchanged between nodes, each device calculates the common key \(K\): \[K=k_{private}\cdot P_{public}\] This result is identical for both participating nodes, being used as a session key for message encryption.
## 3 Results and Discussion
### Verification of Classification Accuracy
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Date** & **Algorithm** & **KMA** & **Verification** & \
\begin{tabular}{c} **Mode 1** \\ **(\(\mu\)g\(\cdot\)m\({}^{-3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Mode** \\ **(\(\mu\)g\(\cdot\)m\({}^{-3}\))** \\ \end{tabular} \\ \hline
17 March 2009 & AD & AD & O & 0.191 & 1.540 \\
12 April 2009 & LH & HZ & O & 0.195 & 0.237 \\
2 April 2011 & UH & HZ & O & 0.068 & 0.172 \\
12 March 2010 & MI & MI & O & 0.246 & 0.087 \\
13 April 2010 & MI & MI & O & 0.072 & 0.313 \\
19 May 2011 & MI & MI & O & 0.047 & 0.094 \\
16 April 2009 & PR & PR & O & 0.034 & 0.127 \\
15 April 2010 & PR & PR & O & 0.037 & 0.200 \\
23 May 2011 & PR & PR & O & 0.029 & 0.007 \\ \hline \end{tabular} Here, O indicates that both results match. The aerosol mass concentrations of Modes 1 and 2 are the daily median values. AD: Asian dust, LH: long-range transported haze, UH: urban haze, MI: mist, PR: precipitation, and HZ haze.  
\end{table} 
Table 5: Meteorological phenomena observed by the KMA, and the corresponding results derived from the classification algorithm. The third case was recorded on 13 April 2010 during a simultaneous MI event, as consistently identified by both KMA observation values and the classification algorithm. The backward trajectory analysis (Figure 11) showed a different pattern from AD or HZ, which was generally transported over long distances from the northwest. The analysis did not record a time lag in the aerosol mass concentration time series from Annyeondo  
Figure 10: (**a**) Backward trajectory of air masses at 0000 UTC 12 April 2009 in Busan. Time series of aerosol mass concentrations in (**b**) Mode 1 and (**c**) Mode 2 at Busan (black line) are presented with those of Annyeondo (red line) for 9–12 January 2009. The dashed lines are the median aerosol mass concentration for the CD cases in Modes 1 and 2, respectively. Figure 9: (**a**) Backward trajectory of air masses at 0400 UTC 17 March 2009 in Busan. Time series of aerosol mass concentrations in (**b**) Mode 1 and (**c**) Mode 2 at Busan (black line) are presented with those of Annyeondo (red line) for 25–26 December 2009. The dashed lines are the median aerosol mass concentration for the CD cases in Modes 1 and 2, respectively. to Busan. Based on the mass concentration in Mode 1, the observation results at Busan produced a value close to the reference value for CD. Moreover, a high RH was recorded (up to 87%, 06 LST), and at that time, a high concentration of 4.573 \(\upmu\)g\(\cdot\)m\({}^{-3}\) was recorded in the first channel. No significant increase in the concentration was observed in Mode 1, but a relatively high concentration was recorded in Mode 2 after 12 LST when the RH decreased to \(\leq\)50%.
## 5 Performance Evaluation
### Implementation and Evaluation  
For implementing the algorithms (except LSTM), we use the Scikit-learn built-in Machine Learning classifiers (e.g., DecisionTreeClassifier for DT algorithm). For ensemble  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**KNN** & \multicolumn{3}{c}{**Decision Tree**} & \multicolumn{2}{c}{**Random Forest**} \\ \hline Parameter & Value & Parameter & Value & Parameter & Value \\ \hline n\_neighbors & 12 & max\_depth & 7 & max\_depth & 29 \\ \hline metric & manhattan & criterion & gini & criterion & entropy \\ \hline weights & distance & min\_samples\_leaf & 1 & min\_samples\_leaf & 2 \\ \hline \multicolumn{5}{c}{n\_estimators} & 160 \\ \hline \hline \end{tabular}
\end{table}
Table 1: hyper-parameter values- Part I.  
Figure 1: The walk-forward technique for 6 units.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**SLP**} & \multicolumn{2}{c}{**MLP**} & \multicolumn{2}{c}{**LSTM**} \\ \hline Par. & Val. & Par. & Val. & Par. & Val. & Par. & Val. \\ \hline kernel & rbf & loss & perceptron & hidden\_size & 60 & input\_size & 3 \\ \hline C & 1 & alpha & 0.1 & solver & adam & hidden\_size & 20 \\ \hline gamma & 0.01 & penalty & l2 & activation & tanh & layer\_dim & 1 \\ \hline - & - & - & - & solver & SLP & - & - \\ \hline - & - & - & - & learning\_rate & invescaling & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: hyper-parameter values- Part II.  
learning (i.e., combination of algorithms), we implement the hard voting technique considering the same weight for inputs (i.e., algorithms). As a result, we combine odd numbers of algorithms (i.e., 3 and 5 algorithms). To this end, we utilized the Scikit-learn voting classifier (i.e., VotingClassifier). We also use the random search function available in Scikit-learn to find appropriate values for hyper-parameters.  
For calculating training/testing execution time of an algorithm, we consider the following approach. As already mentioned, we use the walk-forward technique where we need to execute every algorithm several times using different size of the dataset. We calculate an execution time by measuring the starting time and ending time of execution, and then consider the duration. To do this, we utilize the available \"time\" function. Finally, the execution time of an algorithm for training/testing is considered as the average of all execution times for training/testing of that algorithm.  
In order to evaluate MLNN algorithms, we consider three parts. In Part.I, we implement and evaluate performance of the MLNN algorithms described in Section 4. Notice that we use the one-vs-one method for using binary classification algorithms such as SLP for our three-class classification. Part II evaluates the performance of different combinations of MLNN algorithms, known as ensemble learning algorithms, and finally Part III implements and analyzes the proposed recommendation system introduced in Section 3.
## 4 Results
### Non-Invasive Investigations
#### 4.1.2 Portable Raman Analysis  
Based on the ED-XRF results, Raman analyses were carried out by portable instruments on representative areas of the painting surface in order to identify the presence of inorganic compounds.  
Raman spectroscopy allowed us to recognise two forms of oxalates: weddellite and whewellite, identified by the presence of characteristic signals at 1467 and 1490 cm\({}^{-1}\) due to the \(\
u\)(C-O) stretching vibration, respectively, and the \(\
u\)(C-C) stretching mode [31] in black (points M19 and M23) (Figure 3a) and whitish areas (M20). In all samples, calcium carbonate was revealed by the bands at 1085 and 713 cm\({}^{-1}\)[32]. Occasional traces of sulphates (points M23 and M19) revealed gypsum presence, thanks to the \(\
u_{1}\) (SO\({}_{4}\)) Raman vibration mode at 1005 cm\({}^{-1}\)[33] (Figure 3a). Sulphates are probably related to restoration interventions.  
The Raman spectrum of a brownish area (point M24.1) shows the band at 350 cm\({}^{-1}\) (Figure 3b) ascribable to As-S stretching vibrational mode [34] of the orpiment pigment together with quartz signal at 466 cm\({}^{-1}\)[32], which one evidenced also in M19 and M20. The signals at 510 and 370 cm\({}^{-1}\) (Figure 3c) identified in a reddish area (point M43) has ascribed to feldspar minerals [35].  
Figure 3: Portable Raman spectra of points: (**a**) M23; (**b**) MY24.1; (**c**) M43.
## 5 The Recharging Scheme for the Nodes in the RCCH  
Although the efficiency of data transmission can be enhanced with the help of the nodes in the RCCH, the average energy consumption of CHs and CCHs are still higher than that of the CNs in the same annulus-sector. Due to this unbalanced energy consumption, an \"energy hole\" may appear.  
In recent years, using one or more Wireless Charging Vehicles (WCVs) to recharge sensor nodes periodically or responsively has become a research hotspot. However, the application bottlenecks (e.g., slow wireless recharging speed, high recharging cost, etc.) have made it unrealistic to recharge every node in the network.  
Figure 7: Data forwarding strategy with the help of the candidate cluster heads.  
For this purpose, it is assumed that in each annulus, there is a WCV that is responsible for recharging nodes in the RCCH of the same annulus. By minimizing the recharging cost, the energy consumption of the entire network may be balanced and the energy hole problem may be alleviated. The definitions of wireless recharging parameters are given in Table 3.
# Fast Target Recognition Based on Improved ORB Feature
## 3 Feature Extraction and Matching Based on an Improved ORB Algorithm
### Adaptive Histogram Equalization  
Due to shooting or environmental problems, there may be uneven image brightness, low contrast, high noise, and so on. The features after matching with the original image are concentrated in the areas with high contrast, while the features extracted from other areas are relatively few, so the obtained feature points cannot describe the whole image. The histogram distribution of the original image is shown in the upper-left picture and the bottom-left picture in Figure 4. Although ORB feature matching has a good matching effect under good illumination, the matching effect will be greatly reduced under insufficient illumination or overexposure.  
Adaptive histogram equalization (AHE) is a computer image processing technology used to improve image contrast. Different from the ordinary histogram equalization  
Figure 4: In the upper left is a histogram of overexposed images; in the upper right is a histogram of an overexposed image after adaptive histogram equalization; in the bottom left is a histogram of an underexposed image; in the bottom right is a histogram of an underexposed image after adaptive histogram equalization.  
algorithm, the AHE algorithm changes the image contrast by calculating the local histogram of the image and then redistributing the brightness. Therefore, the algorithm can improve the local contrast of the image and obtain more image details.  
After adaptive histogram equalization, the details of the dark part of the original image become clearer and the candidate points with a higher Harris response value change during feature point detection, so high-quality matching point pairs in other areas of the same image can be obtained. After adaptive histogram equalization, the histogram distribution is shown in the upper-right picture and the bottom-right picture in Figure 4.
## 5 Result and Discussion
These findings show that the suggested system successfully adjusts to various fading conditions and that performance-complexity trade-offs are significantly impacted by the detection method selection. In the context of Nakagami-\(m\) fading channels, higher \(m\) values, which indicate less severe fading, are expected to further enhance BER performance due to improved signal reliability. Similarly, introducing the transmit antenna correlation (\(\rho>0\)) is likely to affect the antenna index bits and reduce spatial diversity, thereby increasing detection ambiguity in GQSM and deteriorating system performance. Although the current simulations do not explicitly alter these parameters, their influence mechanisms align with established MIMO communication theory and highlight insightful areas for further research. In terms of search times, Table 4 shows the intricacy of the system under examination using the three detection methods in addition to ML detection scheme 3 is more complex  
Figure 2: Bit Error Rate (BER) vs. Average Signal to Noise Ratio (SNR) of GQSM\(-\)QQSTBC using three distinct low detection techniques, each with a transmit antenna correlation of \(\rho=0.9\) and full channel state information available to the receiver \(\eta=1\) under various m fading coefficients of the Nakagami channel (one-sided Gaussian fading channel [\(m=0.5\)], Rayleigh fading channel [\(m=1\)] and Rician fading channel [\(m=1.5\)]). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Partial CSIR (\(\eta\))** & **ML** & **Technique I** & **Technique II** & **Technique III** \\ \hline
0.98 (SNR = 9 dB) & 0.0112 & 0.01399125 & 0.01107 & 0.0092475 \\
0.98 (SNR = 18 dB) & 0.0037 & 0.000399 & 0.0002837 & 0.000246 \\
0.99 (SNR = 9 dB) & 0.008016 & 0.01386125 & 0.01161 & 0.0096875 \\
0.99 (SNR = 18 dB) & 0.001515 & 0.0004 & 0.000275 & 0.00027 \\
1 (SNR = 9 dB) & 0.005304 & 0.01464375 & 0.01139 & 0.0096425 \\
1 (SNR = 18 dB) & 0.0001875 & 0.00032875 & 0.0003275 & 0.0002625 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bit Error Rate (BER) for a particular Average Signal to Noise Ratio (SNR) of GQSM\(-\)QQSTBC when three distinct low detection techniques are employed, each with partial channel state information accessible to the receiver (\(\eta=0.98\), \(0.99\), and \(1\)) and a transmit antenna correlation of \(\rho=0.9\). than detection schemes 1 and 2, according to [14]. Compared to all three detection methods, machine learning is much more difficult and grows exponentially.
# Evaluating and Ranking SCPMS Enablers Using ISM and SWARA
###### Abstract  
The supply chain performance measurement system (SCPMS) is considered an integral and important part of supply chain management (SCM) for an effective and efficient supply chain (SC). The healthier and more flexible SCPMS is based on the enablers from which the SC metrics are taken. The identification of such enablers must be aligned with the strategic objectives of the organization and mapped to the SC measurement objectives. Hence, this study identifies a set of enablers and ranks them. Interpretive structural modeling (ISM)-based methodology is used in the current study to model the SCPMS implementation enablers. MICMAC analysis is further employed to categorize and comprehend the importance of each SCPMS enabler. ISM offers relationship modeling of SCPMS enablers, whereas MICMAC helps with classifying them into four categories. The Delphi method is then used to validate the ISM model. The SCPMS variable is also used in prioritization using stepwise weight assessment ratio analysis (SWARA). Practicing managers may benefit when developing and installing the SCPMS to satisfy the requirements of supply chain 4.0 for Industry 4.0 ([4.0]). They will also be able to recognize and allocate resources while implementing SCPMS. The prioritization using SWARA provides an important ranking according to its importance. The SCPMS enablers of 'top management support', 'SC 4.0 performance measurement awareness', and'managerial readiness' are found to be significant.  
Delphi technique; ISM; MICMAC; SCPMS; SCM; SWARA Article Evaluating and Ranking SCPMS Enablers Using and SWARA  
1
# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 5 Experimental Results
### YOLOv8 Model Performance
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Country** & **JP** & **SE** & **RU** & **SA** & **TR** & **UK** & **DE** & **ES** & **IT** & **NOR** & **Universal** \\ \hline Accuracy of pre-trained & 95.5 & 99.4 & 99.3 & 99.1 & 98.8 & 98.7 & 95.8 & 96.9 & 94.5 & 94.2 & 98.2 \\ Accuracy of non-pre-trained & 93.8 & 97.6 & 95.7 & 98.0 & 94.4 & 97.2 & 95.6 & 95.8 & 90.3 & 89.5 & 98.0 \\ Precision of pre-trained & 99.1 & 100 & 97.1 & 97.0 & 97.3 & 93.2 & 97.8 & 93.3 & 99.0 & 98.8 & 97.6 \\ Precision of non-pre-trained & 95.8 & 97.3 & 94.8 & 94.8 & 95.6 & 93.5 & 94.8 & 93.8 & 89.8 & 97.7 & 96.3 \\ Recall of pre-trained & 99.0 & 97.9 & 98.3 & 98.1 & 99.0 & 98.5 & 91.1 & 98.2 & 91.0 & 87.0 & 95.8 \\ Recall of non-pre-trained & 89.4 & 95.2 & 87.9 & 92.3 & 89.2 & 90.1 & 87.5 & 93.4 & 85.6 & 78.7 & 95.1 \\ F1-score of pre-trained & 99.0 & 98.9 & 97.7 & 97.5 & 98.1 & 95.8 & 94.3 & 95.7 & 94.8 & 92.5 & 96.7 \\ F1-score of non-pre-trained & 92.5 & 96.2 & 91.2 & 93.5 & 92.3 & 91.8 & 91.0 & 93.6 & 87.6 & 87.2 & 95.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance Results Among Countries and the Universal Model  
Figure 8: Universal YOLOv8 performance. (**a**): Accuracy universal performance of YOLOv8. (**b**): Precision universal performance of YOLOv8. (**c**): Recall universal performance of YOLOv8. (**d**): F1-score universal performance of YOLOv8. when the model predicts there is no ambulance but there is. Based on the recorded true positives, true negatives, false positives, and false negatives, we can easily calculate the accuracy, precision, recall, and F1-score (i.e., more details on how to calculate the accuracy, precision, recall, and F1-score can be found in [35]).
## 4 Results
### The Evaluation of the Performance of the Optimal ANN Model  
Figure 7 shows the predicting results and error of the ANN model with optimal structure of 5-10-1. As shown in Figure 7a, there is an exceptionally good agreement between the predicted and experimental UCS values, with the exception of few points where the error is large. The maximum error is 3.5 MPa and the minimum error is only 0.001 MPa. Figure 7b reveals that the regression results between the experimental and predicted UCS values are better, with the data points more evenly distributed on both sides of the ideal regression curve. The bell-shaped curve in Figure 7c intuitively shows that the random error between the experimental and predicted UCS approximately obey a normal distribution, with the error mainly concentrated around 0. This shows that the model not only has excellent predictive power, but is also very robust.  
Although some achievements were obtained in this research, some limitations have also been identified. The omission of other influencing variables of the UCS of wet-mix shotcrete, such as aggregate type and size distribution, curing temperature and physiological characteristics of the binder, is a clear limitation of the current research. Therefore, more influential variables should be used as the ANN inputs to improve the generalization capability in the future investigation.  
The above results indicate that the optimal ANN model is well trained and can learn the complex nonlinear relationship successfully between the inputs and outputs. Consequently, this ANN model is reliable for constructing the objective function of PSO optimization.  
Figure 6: Optimal ANN architecture.
# An Architecture for Reliable Transportation of Delicate Goods
## 5 Workflow
### Merchandise Transmission
#### 5.2.1 Merchandise Deliver Protocol  
The merchandise deliver protocol is a specific part of the iteration protocol (Figure 5) between mobile app and device, executed by the operator who wants to deliver the merchandise to the next operator (Figure 7). The operator, using the mobile app, chooses the merchandise and requests the delivery, sending the request to the cloud server and passing as parameter the merchandise temporary identifier (Figure 7--message 1). Notice that the option to request the delivery is only available if the merchandise is stated as RECEIVED.  
The cloud server knows the identification of who is requesting the delivery and knows who the current operator is. If both match, the merchandise is marked as DEPOSITED. Besides that, the service generates an Authentication token and encrypts symmetrically the code of the request, the elapsed time since the beginning of the transport (registered on the cloud) and the merchandise identification. The token is passed to the mobile app (Figure 7--message 1.3), that uses it to gain access to the Closed Access BLE Services (characteristic eleven of the Open Access BLE Service) (Figure 7--message 2). The device will decrypt the Authentication token (Figure 7--message 2.1), compare the merchandise temporary identifiers, and also the elapsed time with the Real Time Clock of the device  
Figure 7: Merchandise deliver protocol.  
(the comparison accepts a margin of error). If they match, the authentication request is accepted, and the required Close Access BLE Service is turned on (Figure 7--message 2.2).  
After that, the mobile app requests the Data token to the cloud server (Figure 7--message 3), passing once more the merchandise temporary identifier. The Data token produced is encrypted symmetrically the merchandise temporary identifier and new state (DEPOSITED). This token is used by the mobile app to update the state of the device (Figure 7--message 4). Once done, the Closed Access BLE service is disabled (Figure 7--message 4.1) and, since the state changed and this is a notification characteristic, the mobile app will be notified to update the state (Figure 7--message 4.2), disabling the option to deliver the merchandise and activate the option to receive it.  
Meanwhile, the cloud server also sends a push notification to the mobile phone of the next operator (the merchandise receiver) (Figure 7--message 1.4 and 1.5). The notification will contain the maximum time for the next operator to collect the merchandise, status of the merchandise and other relevant information.  
The next operator does not represent a single person, but all the collaborators of the company registered as being able to receive merchandises. Thus, the push notification is sent to all these collaborators (Figure 7--message 1.4 and 1.5).