esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Simulation Environments and Scenario  
To validate the SFPCR scheme's performance, an NS-3-based simulation environment was used to model multihop communications in the LoRa network. The performance of the FSRC scheme, the cluster-based scheme, and the mesh scheme were compared to the SFPCR scheme based on the relay installation and selection procedure. The NS-3 simulation tool is suitable for large networks anchored on discrete event simulation models of a system that changes to its state occurrence at discrete points in the simulation time [53].  
Except for the GW, all the devices had the same communication needs. Each LoRa device sent out data packets on a regular basis, and transmissions only happened during certain time slots to avoid collisions. LoRa devices always kept their duty cycle below 1%, which is the minimum amount allowed by regulations [4]. Different spreading factors were spread out evenly based on how far away the devices were from the GW.  
Table 2 lists the simulation parameters used in our model. We considered a circular network region with a cell radius of \(R=6\) km and a GW in its centre to handle communication. The LoRa devices' distance from the GW was based on a Gaussian distribution. The parameters bandwidth, spreading factor, transmission power, and code rate were assigned to each device. In addition, each device transmitted an average of 20 bytes every packet. The estimated transmit power could be set from 2 dBm to 14 dBm, the channel bandwidth was \(125\leq BW\leq 250\) kHz, and the channel code rate was 4/5. It is hard to say how many data the GW can handle, but it is safe to presume that more data provided more often will limit how many devices it can interface with. We compared the performance of two existing schemes to see which one was better. We also investigated the differences between them to see if our SFPCR scheme would be better based on the performance metrics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Cell radius (R) & 6 km \\ Number of LDs (N) & 100–1000 \\ Spreading factor (SF) & 7–12 \\ Bandwidth & 125,250 (kHz) \\ Payload length & 10–250 (Bytes) \\ Transmission power & 14 dBm \\ Data rate & 0.25–5.47 (kbps) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 5 Experimental Results and Analysis
### Experimental Results and Analysis
#### 5.4.2 Evaluation of LSTM-BO-SVM Model Efficiency
For benign software, the F1-score of the LSTM-BO-SVM model is 98%, which is higher than other models, ranging from 0.1% to 10.7%. As shown in Figure 14, on the CICMalDroid-2020 dataset, the LSTM-BO-SVM model achieves an F1-score of 97.9% for malware, which is higher than the other models, ranging from 0.3% to 7.2%. For benign software, the F1-score of the LSTM-BO-SVM model is 99%, which is higher than other models by 0.1% to 3.2%. According to the experimental findings, the LSTM-BO-SVM model has a high F1-score when detecting both benign software and malware. Although the BiLSTM-BO-SVM model performs slightly worse, it still outperforms the other models. In contrast, the LSTM-SVM model performs poorly in terms of the F1-score. In addition, the F1-score of benign software is higher than that of malware, which may be due to the fact that malware uses techniques such as code obfuscation in order to avoid detection, which makes it more difficult to detect.
## 2 Results and Discussion
### PCA, PLS-DA, and OPLS-DA
The Y-variables for WR were set to 0 and those for pigmented rice to 1. The score plots of OPLS-DA model showed good separation. The projection model of WR and pigmented rice group showed \(R^{2}\)X of 0.311, \(R^{2}\)Y of 0.823, and \(Q^{2}\) of 0.753. The \(Q^{2}\)-value was higher than 0.50, indicating a good prediction power of the model. Two OPLS in the score plots explained 31.1% of the total variance (OPLS 1, 8.29%; OPLS 2, 22.8%). OPLS 1 explained the separation of WR and pigmented rice, whereas OPLS 2 elucidated the separation of RR and BR. Variable importance in projection (VIP) plots were used to explain the contribution of metabolites to the OPLS models. VIP values greater than 1.00 indicate a significant influence on the model. Overall, 46 metabolites in the VIP plot had VIP values greater than 1.00 (Table S6). In the analysis, C18:0, C20:0, sinapinic acid, and C18:3 were top-ranked metabolites. These results were in agreement with PLS-DA data. However, OPLS 2 had a greater explanatory power in this OPLS-DA model than OPLS 1. Next, OPLS-DA was performed for BR and RR groups to clarify the metabolic differences between them (Figure 2B). The validation parameters of the projection model were \(R^{2}\)X of 0.374, \(R^{2}\)Y of 0.986, and \(Q^{2}\) of 0.935. The \(Q^{2}\)-value was above 0.90, indicating excellent prediction ability of this model. In addition, the score plot showed good separation by color. Overall, 50 metabolites in VIP plots had cut-off values above 1.00 (Table S7). Lutein, stigmasterol, vanillic acid, protocatechuic acid, and \(\beta\)-carotene were top-ranked metabolites in VIP plots. These results were consistent with those of PLS-DA. Finally, the two OPLS-DA models were tested by a permutation test and analysis of variance of the cross-validated residuals (CV-ANOVA) to determine the risk of over-fitting the OPLS model (Figure S2). The permutation test was performed with 200 permuted models generated by using randomized Y-variables. When the \(Q^{2}\)-value of the permutation test is smaller than that of the actual (unpermuted) OPLS model, the model is considered to be predictive. Both OPLS models had \(Q^{2}\)-values smaller than those of the permuted models. Hence, the two OPLS-DA models were predictive. Furthermore, \(p\)-value in the CV-ANOVA test of the two OPLS models was lower than 0.05 (pigmented rice vs. non-pigmented rice (WR), 1.48 \(\times\) 10\({}^{-15}\); BR vs. RR, 1.20 \(\times\) 10\({}^{-21}\)). A \(p\)-value below 0.05 indicates that the model is validated.
# Efficient Key Exchange Using Identity-Based Encryption in Multipath TCP Environment
## 5 Experimental Evaluation of Proposed Model
### Security Complexity of ECC  
In order to offer confidentiality of communication between PKG and the communicating node, the messages are required to be encrypted using a public key encryption scheme such as RSA, Elgamal, ECC, etc. ECC offers the same level of security as compared to RSA even after using the small size of the key. The security of the public key cryptosystem is higher if the attacker requires an exponential amount of time with respect to key size to initiate an attack.  
The well-known methods to solve ECC are naive exhaustive search, Baby Step Giant Step (BSGS), the square root, and Silver-Pohling-Hellman (SPH) [48]. The computation time required to solve ECC using the naive exhaustive search method is \(n=\) order of \(P\), as it adds point \(P\) to itself until it obtains \(Q=kP\), which is infeasible if the number of steps is larger. The BSGS is an extension of the naive exhaustive search, but its space complexity and time complexity is O(\(n\)), which is too high, as it requires volatile memory for \(n\)_points_ and additional \(n\) steps. Hence, the time required to solve ECC using the square root method varies exponentially in terms of key length, and public key cryptosystems are secure against the attack, which require an exponentially proportional time to key size, and the ECC can be considered secured against the square root method. Moreover, the SPH is only useful when the order of the curve is defined by selecting the product of small prime numbers. As for products of large prime numbers, the computation time varies exponentially for SPH, which makes ECC secure against the SPH method. One of the other general-purpose methods to solve ECC is the Pollard-p algorithm, whose time complexity is similar to BSGS, but the space complexity is O(1). The computational complexity of the Pollard method is too high, as the expenses to attack ECC-163 within 1 year are approximately USD 200 million. Moreover, the current status of the security breach of ECC [48] states that \"a 112-bit key for the prime field and a 109-bit key for the binary field are the extreme level security breach till date\". Thus, ECC is the best-suited scheme for encrypting the packets between nodes and PKG. The HMAC can be used to produce the session key, which uses the hash function to offer the higher level of security. The security complexity of HMAC is much higher than others.
## 6 Numerical Experiments
### Sensitivity Analyses  
This section investigates the impacts of three factors, namely (1) the sample size \(N\), (2) the number of selected candidate locations \(P\), and (3) the minimum distance between any two selected candidates (denoted as \(R\)), on key dependent variables. Specifically, Section 6.5.1 examines the effects of \(N\) and \(P\) on the average minimum passenger detour distance, and Section 6.5.2 explores the impacts of \(R\) on the rankings of the candidate locations obtained by the proposed optimization framework.
# Emerging Biomedical Applications of Carbon Dot and Polymer Composite Materials
## 2 General Aspects of Carbon Dot/Polymer Composites
### Pharmacology of the Carbon Dot--Fate--In the Body  
As previously indicated, the majority of CD research has been focused on synthesis, toxicity, and effects. However, pharmacological studies regarding CDs in terms of pharmacokinetics and pharmacodynamics (PKPD) are scarce. The journey of CDs metabolism begins with administration and ends with elimination. The average dose administered differ by studies. [PERSON] and colleagues found that CDs, prepared from citrate by a hydrothermal method, at a concentration of 25-100 \(\upmu\)g/mL, inhibited uveal melanoma growth [39]. Furthermore, Phellodendi Cortex Carbonisatus CDs, fabricated by the solvothermal method, showed excellent hemostatic effects in mice at a dose of 1 mg/kg [40; 41]. CDs are biomassformed by enzymatic degradation, notably myeloperoxidase, neutrophil, and eosinophil peroxidase enzymes (in the presence of H\({}_{2}\)O\({}_{2}\)), which are mainly involved in the metabolism of CDs, as investigated by [PERSON] et al. [42]. In summary, the smaller the size, the lesser the toxicity and more efficient the processing, which includes metabolism, target interaction, and elimination. Ideal CDs have a high specificity to the target, minimal off-site interaction, high cell uptake, are readily excreted or biodegradable, hydrophilic, and have a hydrodynamic size of 6 nm or less. CDs can be excreted by the kidney depending on their size and charge [43].  
[PERSON] et al. investigated the acute toxicity of the CDs prepared from nitric acid [44]. This study concluded that CDs of 5.1 to 51 mg/kg body weight in mice were safe, and no toxic signs or mortality were recorded even after 2 weeks in acute toxicity evaluations. However, subacute doses (0.2 to 20 mg/kg) caused mild changes in liver enzymes and blood lipoprotein profile, indicating a possibility of mild toxic effects; thus, species variation should be considered. In addition, the bioavailability of graphene quantum dots (GQD) in lung tissues has been detected by Fourier-transform infrared spectroscopy (FT-IR) [45]. The intravenously injected GQDs prepared from graphite flakes into the rats showed no significant acute toxicity with 5 and 15 mg/kg body weight, but mild foreign body reactions to the GQD were found. Future studies regarding CDs absorption, distribution, biotransformation, and excretion are essential for understanding the effects of the body on CDs and to enable researchers to design CDs with optimum effects according to the PK characteristics indicated above.
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 4 System Design
### LoRa Ranging and Localization
#### 4.3.2 Ranging Algorithm
Output: Estimated distance between anchor point and target node \(\hat{d}\)
1 The anchor selects the \(t\) channel between itself and the target node, \(t=1\); while\(t\leq N\)do
2 The results of TOF ranging \(d_{t}\) are calculated by the Formula (3), measure the RSSI value \(r_{t}\) to form the measurement data set \(p_{t}=(d_{t},r_{t})\);
3 The anchor selects the next channel, \(t=t+1\);
4 end while
5\(\mathbf{\Gamma}\) is filtered according to the Formula (4), obtain \(\mathbf{\Gamma^{\prime}}\);
6 Cluster \(\mathbf{\Gamma^{\prime}}\), and find out all kinds of central point \(\mathbf{C}_{k}\) according to Formula (5);
7\(\hat{d}\) is calculated by the Formula (6);
8\(\hat{d}\) is calculated by the Formula (7);
9 return\(\hat{d}\);
```  
**Algorithm 1**Distance estimation algorithm based on TOF-RSSI fusion clustering
# Enhancing Multimodal Emotion Recognition through Attention Mechanisms in BERT and CNN Architectures
## 4 Results and Discussion
### Discussion
Compared to existing works, which may rely on simpler or unimodal approaches, our system achieves superior accuracy and contextual sensitivity but at the cost of increased computational overhead and data dependency. For example, traditional systems might use simpler statistical techniques or shallow learning models that, while less computationally intensive, fail to capture the depth of emotional context provided by our CNN and BERT-based architecture. As we aim to enhance the capabilities of our multimodal emotion-recognition system, addressing its current limitations is essential. Future research should focus on optimizing the model to reduce computational demands, enabling real-time processing and making the system suitable for deployment on devices with limited computational capabilities. Techniques such as model distillation or the development of lightweight versions of BERT could maintain high accuracy while reducing resource requirements. Additionally, integrating additional modalities such as physiological signals [45] and facial expressions could enrich the emotion-recognition process. This expansion would involve developing new fusion techniques capable of effectively handling the complexity and diversity of data from these sources. Another critical area of focus is improving data efficiency by reducing dependency on large, annotated datasets. Exploring methods like transfer learning, few-shot learning, and synthetic data generation could enhance the system's robustness and facilitate its application in data-scarce environments. Finally, adapting the system for specific emotional recognition tasks tailored to different cultural contexts or specialized domains requires flexible and scalable model configurations. Investigating modular approaches and domain-specific adaptations will enable more targeted and effective emotion recognition, enhancing the system's utility across various global and professional settings. By pursuing these strategic areas, our research will not only overcome existing challenges, but also expand the practical utility and reach of our emotion-recognition system, ensuring it continues to lead in the field of affective computing.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 4 Verification of the Proposed Method
### Establishment of WNN  
WNN parameters were selected tentatively by experience for verification purposes. The number of nodes in the network's input layer is four, and the number of nodes in the output layer is nine. According to the empirical formula, the number of nodes in the network's hidden layer was selected as 11. Meanwhile, the learning rate \(\eta\) of the network was selected as 0.01, and the momentum factor \(a\) is 0.5. In addition, the excitation function of the hidden layer adopts the Morlet wavelet function, and the excitation function of the output layer adopts the tansig function.  
When establishing the network, the WDO algorithm and the normalized test sample data are used to optimize the connection coefficient matrix of the WNN initially. Secondly, the WNN learning and training are carried out, and the MSE function is used as the network performance function. The learning and training process of the WNN is shown in Figure 6.  
It can be seen from the figure that the WNN obtains a small network error in the process of 100 times of learning and training, and the MSE value is only \(4.7\times 10^{-6}\). The small network error indicates that the WNN as a surrogate model of the finite element model has good fitting accuracy.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions  
This section evaluates the performance of each swarm model in carrying out mining tasks such as ore detection, transportation, and quality assessment. The ant model's task specialization effectively reduces congestion and increases the efficiency in ore transportation. The firefly model's advanced communication system improves the accuracy and speed of ore detection and transportation. The honeybee model's centralized control ensures precise ore quality assessment, focusing on prioritizing high-grade ores during extraction. All models were tested in a standard scenario within a 15 m by 15 m environment, where 20-ore blocks are concentrated in a single pile. This arrangement mimics the geological structure of the Pilbara iron ore mine, where hematite is typically found in extensive, flat sedimentary layers known as banded iron formations (BIF). These formations consist of alternating layers of iron oxides, such as hematite and magnetite, and silica-based minerals like quartz or chert. By flattening the ore pile across the area in the simulation, the layered nature of the ore deposits in Pilbara has been replicated, allowing for a more realistic  
Figure 9: Arena setup in ROS for four different swarm robotics models: (**a**) baseline model, where all robots perform the same tasks without role specialization or communication; (**b**) ant model, which introduces role specialization between “picker” and “explorer” robots and features a collection zone that mimics the task division seen in ant colonies; (**c**) firefly model, utilizing a light-guiding robot to coordinate other “picker” robots through bioluminescent-inspired communication, improving task coordination and resource detection; (**d**) honeybee model, which mimics bee colony behaviour with a “master robot” coordinating tasks through centralized communication from the “dance stages” to identify high-quality ore (hematite).  
assessment of the considered models' performance for mining activities. The strengths and limitations of each considered swarm model have been summarized in a spider graph analysis, allowing for identifying the ones best suited for real-world mining applications.
###### Abstract  
Islanding phenomenon is one of the consequences of the emergence and development of microgrids in the power system. Injected signal cancellation is a common problem in a multi-distributed generation that has a significant influence on active islanding detection methods. In this study, this issue was analyzed by injecting a perturbation signal in the multi-photovoltaic system. Furthermore, the promising solution to eliminate injected signal cancellation was proposed in this paper. The solution was validated through mathematical explanations and simulation results.  
Keywords:islanding detection; DC grid; active islanding detection method; injected signal cancellation; multi-distributed generations; perturbation signal; photovoltaic system +
Footnote †: journal: Applied Sciences
# Development of a Simplified Human Body Model for Movement Simulations
## 3 Results
Of all the lower limb joints, the hips were subject to the greatest loads, and the moment of force for F/E assumed initial values of about 50 Nm. A similar regularity was observed for F/E in the other lower limb joints (Figure 11). This is due to the fact that forces were applied to the feet (each about 350 N), imitating the effect resulting from body weight. For the hips, the F/E moments of the force reached min. and max. values of 30.90 Nm and 144.85 Nm for the left limb and 28.21 Nm and 136.20 Nm for the right limb. The extremes for the A/A movement were 4.18 Nm and 37.44 Nm for the left joint (Figure 10a), 2.96 Nm, and 58.90 Nm for the right joint (Figure 10b). For the knee joints, the moment min. and max. extremes were: 31.90 Nm and 60.36 Nm for F/E of the left knee (Figure 11a); 31.22 Nm and 62.36 for F/E of right knee; \(-\)30.34 Nm and \(-\)4.88 Nm for A/A of left; \(-\)31.93 Nm and \(-\)10.08 Nm for A/A of right. For the ankle joints, the torque values for A/A and Rot were small, while for F/E, these values were quite stable and constantly around \(-\)50 Nm (Figure 11b). The moments of force for the left (Figure 12a) and right shoulders had similar absolute values and waveforms. However, the values were not as large as those for the previous joints. The torques for Rot were close to 0, while both for F/E and A/A had a very changing  
Figure 12: Moment of force for (**a**) left shoulder joint and (**b**) left elbow joint. character with values ranging from around -18.0 Nm to 17.5 Nm (F/E) and from about -13.5 Nm to 12.0 Nm (A/A). In the case of elbows, the graphs of the left (Figure 12b) and right connections are also very similar and generally do not exceed 5 Nm. The moments of force for the wrists were the lowest in the model, and did not even reach 1 Nm. This is due to the fact that the hand is the lightest in relation to the other segments. Table 1 shows a summary of the obtained minimum and maximum values (in the simulation) for the angular displacements and moments of force. The sign for the moment of force is maintained to evaluate the motion's direction and if it alters. To summarize, according to the measured movement, very high results of F/E are obtained for the hip (nearly 94\({}^{\circ}\)), knee, and thorax (above 50\({}^{\circ}\)). Significant values of A/A are only in the shoulder (around 90\({}^{\circ}\), resulting from arm movement from the T pose to the body front), and noteworthy numbers of Rot are obtained only for the elbow (as much as \(-\)80\({}^{\circ}\)). The moment of the force values also agreed with the theoretical loads that could appear in the human body during the simulated movement. Omitting the connection of pelvis with ground, the highest value of torque was obtained for the F/E of the Thorax in connection with the pelvis (around 137 Nm). The next highest was for F/E of the hip, exceeding 144 Nm. The highest for A/A was also in the hip (nearly 59 Nm). Torques for Rot