esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Evaluation and Validation  
#### 4.3.1 Stratified K-Fold Cross-Validation  
To address the imbalance in the dataset, stratified K-fold cross-validation was utilized to evaluate the performance of the hate speech detection and classification models. This approach enhances traditional K-fold cross-validation by ensuring that each fold maintains the same class proportions as the original dataset, thereby preventing distortions caused by uneven distributions. The dataset is divided into K-folds, with K\(-\)1 folds used for training and one for testing in each iteration. This process was repeated K times, ensuring every fold serves as the test set once. For this study, K = 5 was selected, providing a balanced and robust assessment of the models. Figure 4 illustrates the class distribution across folds in the first (a) and second (b) layers of the hate speech classification system.  
The method ensures that the class distributions are preserved using the following equations:  
* Class proportion in the dataset  
Figure 4: In panel (**a**), the distribution of “Hate” and “Not Hate” classes is shown for each of the 5 folds used during stratified K-fold cross-validation. Panel (**b**) depicts the distribution of hate speech categories—”Social-HS”, “Political-HS”, and “Religious-HS”—in the second layer across the same folds.  
\[i=\frac{n_{i}}{N} \tag{1}\]  
where:  
* \(n_{i}\) is the number of samples of class i in the dataset.
* \(n\) is the total number of samples in the dataset.
* Class proportion in each fold: \[\frac{n_{i,j}}{n_{j}}\approx\frac{n_{i}}{N}\] (2)  
where:  
* n\({}_{\text{i,j}}\) is the number of samples of class i in fold j.
* n\({}_{\text{j}}\) is the total number of samples in fold j.  
By applying stratified K-fold cross-validation with K = 5, the models are evaluated on folds that reflect the original dataset's class distribution, resulting in a more accurate and comprehensive evaluation, particularly for imbalanced datasets.
# Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
## 2 Construction of Battery Model  
### Selection of Equivalent Circuit Model  
The equivalent circuit model is the most widely used battery model in recent years. The more common battery equivalent circuit models are the first-order Thevenin model, the second-order Thevenin model, and the PNGV model, among which the PNGV model is relatively accurate, but its computational complexity is very high [19]. The second-order Thevenin model adds an RC parallel loop to the first-order Thevenin model [20]. Compared with the first-order Thevenin model, the second-order Thevenin model describes the polarization characteristics of the battery more accurately [21], which ensures the accuracy of the model, while the computational complexity is not too high. Therefore, the second-order Thevenin model is selected in this paper, and the model is shown in Figure 1.  
Based on the definition of SoC, the SoC variation of the battery can be expressed in Equation (1) [22]:  
\[SoC_{t}=SoC_{0}+\int_{t_{0}}^{t}\frac{\eta}{Q_{n}}I_{L}(t)\mathrm{d}t \tag{1}\]  
Figure 1: Equivalent circuit diagram of the second-order Thevenin model.  
where \(SoC_{t}\) represents the SoC value of the battery at the moment \(t\), \(SoC_{0}\) represents the value of SoC in the initial state, \(\eta\) is the coulombic efficiency, \(Q_{n}\) represents the single rated capacity of the battery [23], \(I_{L}(t)\) represents the current value at the moment \(t\), and defines the discharge current as negative and the charge current as positive.  
Based on [PERSON]'s law, and incorporating the change in battery SoC versus time, the dynamic equation of the equivalent circuit can be expressed as follows [24]:  
\[U_{t}=\text{OCV}(SOC)-U_{c1}-U_{c2}-I_{L}\cdot R_{0} \tag{2}\]  
\[\begin{cases}\frac{\text{d}SoC}{\text{d}t}=-\frac{y}{Q_{N}}\cdot I_{L}\\ \frac{\text{d}U_{c1}}{\text{d}t}=-\frac{U_{c1}}{R_{1}C_{1}}+\frac{I_{L}}{C_{1 }}\\ \frac{\text{d}U_{c2}}{\text{d}t}=-\frac{U_{c2}}{R_{2}C_{2}}+\frac{I_{L}}{C_{2 }}\end{cases} \tag{3}\]  
where \(U_{t}\) represents the terminal voltage of the lithium-ion battery at moment \(t\); OCV (\(SoC\)) represents the open-circuit voltage value of the battery in the current state; \(R_{0}\) represents the Ohmic internal resistance of the battery; \(R_{1}\) and \(C_{1}\) represent the polarization internal resistance and capacitance during battery discharge, respectively; \(R_{2}\) and \(C_{2}\) represent the concentration polarization resistance and capacitance, respectively [25]; \(U_{c1}\) and \(U_{c2}\) represent the voltage across the two RC loops, respectively; and \(I_{L}\) is the operating current.
# Spectrum Slicing for Multiple Access Channels with Heterogeneous Services
## 5 Non-Orthogonal Multiple Access  
We now examine the performance of NOMA schemes, in which the intermittent users' packets can collide with the broadband user's packets, and among themselves. If the broadband user frame (i.e., \(N\) packets contained in it) has been recovered, the receiver performs SIC to remove the broadband user's packets from the slots. In the next step, the receiver attempts decoding intermittent users' packets which may be contained in the slots affected by SIC. According to the channel model, the decoding succeed only if there was a single intermittent user transmission (i.e., packet) in a slot, and it was not affected by a channel erasure. As for the OMA case, we consider the grouping case. In this case, each intermittent user can transmit once every \(G\) slots, along with the other users in the same group.
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 2 Materials and Methods
### Mp  
Multipath is a type of interference to GNSS receivers which is caused by reflected signals. In this paper, a 30\({}^{\circ}\) satellite elevation mask is applied to all the parameters, which removes most of themultipath effects. An assumption is then made that in general, MP values that are higher than average for signals above 30\({}^{\circ}\) during scintillation events are likely to be affected by scintillation and not multipath. MP1 and MP2 can be calculated using Equations (9) and (10) defined in [13]:  
\[MP1\equiv P_{1}-\big{(}1+\frac{2}{a-1}\big{)}L_{1}+\big{(}\frac{2}{a-1}\big{)}L_ {2} \tag{9}\] \[=M_{PR_{1}}+B_{1}-\big{(}1+\frac{2}{a-1}\big{)}M_{CP_{1}}+\big{(} \frac{2}{a-1}\big{)}M_{CP_{2}}\] \[MP2\equiv P_{2}-\big{(}\frac{2a}{a-1}\big{)}L_{1}+\big{(}\frac{2a }{a-1}-1\big{)}L_{2}\] \[=M_{PR_{2}}+B_{2}-\big{(}\frac{2a}{a-1}\big{)}M_{CP_{1}}+\big{(} \frac{2a}{a-1}-1\big{)}M_{CP_{2}} \tag{10}\]  
where \(P_{i}\) is the pseudorange observable for frequency \(i\); \(L_{i}\) is the phase observable for frequency \(i\); \(M_{CP_{i}}\) is the carrier phase multipath for frequency \(i\); \(a=\frac{f_{i}^{2}}{f_{i}^{2}}\), where \(f_{i}\) is the frequency of signal \(i\); \(B_{i}\) is a bias terms, which is defined as:  
\[B_{1}\equiv-\bigg{(}1+\frac{2}{a-1}\bigg{)}n_{1}\lambda_{1}+\bigg{(}\frac{2}{ a-1}\bigg{)}n_{2}\lambda_{2} \tag{11}\]  
\[B_{2}\equiv-\bigg{(}\frac{2a}{a-1}\bigg{)}n_{1}\lambda_{1}+\bigg{(}\frac{2a}{ a-1}-1\bigg{)}n_{2}\lambda_{2} \tag{12}\]  
where \(n_{i}\lambda_{i}\) is the integer wavelength phase ambiguity for frequency \(i\). In this paper, MP1 and MP2 are generated using the quality control (QC) command of the TEQC software on the receiver independent exchange format (RINEX) version 2.11 files from the three stations, which include both observation and navigation files [14].
## 4 Experimental Results
### Performance Analysis
#### 4.3.1 Qualitative Evaluation  
Figure 7 illustrates the instance segmentation results. Figure 7a,b are the input images and the ground truth, respectively. As shown in Figure 7(a1-a4), the input images contained some psoriasis areas with different sizes, arbitrary boundaries, and different severity levels. For example, the psoriasis region in Figure 7(a4) is larger than that in Figure 7(a1,a4) and has more blur effect compared with Figure 7(a1). In addition, the input images also have some background regions with different sizes. For example, the images in Figure 7(a1,a2) have a bigger background, and those in Figure 7(a4) only include a small part of background. The color information of some background areas is a little similar than the skin region in Figure 7(a3), and a little background region (the seal) is similar to the psoriasis area in Figure 7(a1).  
The instance segmentation results of the proposed scheme are shown in the third column of Figure 7. In Figure 7c,d, the background remains unchanged, different colors are used to represent each detected individual psoriasis region and the skin area, and each bounding box represents an instance. Compared with Figure 7b,c, the psoriasis regions  
Figure 7: Instance segmentation results: (**a1–a4**) Input, (**b1–b4**) ground truth, (**c1–c4**) proposed, and (**d1–d4**) Mask R-CNN.  
and normal skin areas were localized and classified well using the proposed scheme. The results show that the proposed scheme located the psoriasis regions well despite their different sizes, severity levels, and arbitrary boundaries. Furthermore, although the input images contained different background regions, the proposed scheme not only located psoriasis regions but also distinguished the psoriasis regions from the normal skin and the background well. These experimental results show that the instance segmentation scheme can deal with unconstrained psoriasis images well.  
To evaluate the robustness of the proposed scheme, two common operators (blurring and noise insertion) were selected as postprocessing for testing. Figure 8 illustrates the instance segmentation results of the proposed scheme for the blurred and noisy images. The first and second columns of Figure 8 are the input images and the ground truth, respectively. In the third column of Figure 8, the first two images are blurred versions, and the others are noisy versions. As we can observe in Figure 8(c2), a part of the background was similar to the skin and the blur effect was obvious. The last column of Figure 8 shows the instance segmentation results. As we can see in Figure 8d, the proposed scheme detected these psoriasis regions and normal skin areas in blurred and noisy images. The results show that the proposed scheme was robust to blurring and noise insertion. Furthermore, according to Figures 7 and 8, these experimental results show that the proposed scheme processed unconstrained psoriasis images effectively even when blurring and noise insertion occurred.
## 3 System Design
### Background and Motivation
Due to the aforementioned advantages of ICN technology, incrementally deploying ICN networks on top of the existing network infrastructure can greatly empower the implementation of network functions, thus possessing potential application value. This proposed approach aims to reduce the deployment verification cost for ICN service providers and improve the utilization of underlying network resources. In the ICN overlay network, different ICN routers are interconnected through IP logical links. The underlying network resources possessed by ICN routers are limited, while regular internet users have a large amount of idle bandwidth resources. Therefore, deploying ICN routers at the internet user side can expand the underlying network resources of the ICN overlay network.
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 5 Conclusions  
As WSNs based on solar power can harvest energy periodically, they aim to optimize the energy consumption rather than minimize it. In this study, we control the reliability of the data transmission using the FEC scheme in order to optimize the energy consumption given that there are frequent errors in general WSN environments. In the FEC scheme, when a relatively longer parity is employed, broken data are more likely to be fixed, which leads to a decrease in the data loss rate, thereby increasing the reliability. However, the energy consumption can be larger, leading to increased blackout times. When using a shorter parity, the opposite results occur.  
In this paper, we proposed EA-RS, which is a localized method for increasing the reliability of WSNs by using surplus energy without increasing blackout times. More specifically, EA-RS computes the amount of surplus energy that should be discarded owing to battery capacity limitations by predicting the energy collection and consumption of the next cycle, using only information about itself and its neighbors. Then, it adjusts the data recovery rate by fine-tuning the optimal parity length best suited to the energy status of itself and the target node. Through various experiments, we verified the effectiveness of the proposed method, which can enhance the reliability without adversely affecting the blackout time.  
[PERSON] and [PERSON] conceived of this scheme. [PERSON] and [PERSON] designed and performed the experiments. [PERSON] and [PERSON] analyzed the data. The manuscript was written by [PERSON] and commented on by all authors.  
This research was supported partially by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2016R1C1B1015937), and by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2018R1D1A1B07045972).  
The authors declare no conflict of interest.
# Cost-Aware IoT Extension of DISSECT-CF
## 3 Cost Modeling in DISSECT-CF
### IoT Pricing
#### 3.1.3 Amazon's IoT platform  
Amazon's IoT platform can also be classified as a \"pay as you go\" service. Prices have two components: publishing cost (the number of messages published to AWS IoT) and delivery cost (the number of messages delivered by AWS IoT to devices or applications). A message is a 512-byte block of data and the pricing in EU and US regions denotes 5 US dollars per million messages. In addition, there is no charge for deliveries to some other AWS Services.
## 3 Proposed DC-GCDRAM Architecture
### Logic-Arithmetic Mode
Figure 11(b) shows the simulated characteristics of the V\({}_{\text{REF\_NOR}}\) and V\({}_{\text{REF\_NAND}}\) generators for temperature variations (\(-40\)\({}^{\circ}\)C, \(25\)\({}^{\circ}\)C and \(85\)\({}^{\circ}\)C) as well as all possible process corners including skewed FS and SF. At the 1.0 V supply, the relative errors between the reference targets (\(0.86\)V\({}_{\text{DD}}\) and \(0.38\)V\({}_{\text{DD}}\)) and generated voltages are less than 0.35% for V\({}_{\text{REF\_NOR}}\) and 0.53% for V\({}_{\text{REF\_NAND}}\). In Figure 13, the 2500-point Monte Carlo simulations for the NOR/OR and NAND/AND outputs are shown for the three possible cases (AB = 00, 01/10, and 11). The functional results are all correct with the proposed reference scheme. The other thing to be considered with care when designing the DRAM-based CIM unit would be the data retention of the dynamic memory. In the DC-GCDRAM, the data stored on the gain cells are destroyed by the cell leakages. This storage degradation not only affects the retention time of the GCDRAM but may also induce a computational error in the logical operation. To prevent this leakage-induced computing error [55; 56], we set a constraint on retention time, as shown in Figure 14. During standby, storage in the DN is left floating, and thus the gain cell leakages cause the stored information to change with time. Note that the retention time of all-NMOS cells with unbalanced leakages is predominantly determined by the bit cells with data '1' [49; 52]. Meanwhile, in the logic operation, the unit cell current \(\rm I_{CELL}\) drawn by a single bit with data '1' for a time interval of \(\tau\) develops an RDL voltage difference, i.e., \(\Delta V~{}=~{}\tau\times I_{CELL}/(C_{RBL}+C_{RDL})\), where \(\rm C_{RBL}\) and \(\rm C_{RDL}\) are the RBL and RDL capacitance. If two bits discharge the RDL, the corresponding voltage drop will be \(2\times\Delta V\). Here, we define a minimum high-level storage \(\rm DN(1)_{MIN}\) to ensure that the RDL voltage drop by two bits is larger than \((V_{DD}-V_{REF,NAND}+V_{OS})\). This  
\[C_{OUT}\ =\ \overline{\overline{AB}*\overline{(A+B)*C_{IN}}} \tag{2}\]0 Reference scheme: (**a**) circuit schematic and (**b**) reference voltages at \(\rm V_{DD}\) = 1.0 V across process and temperature variation. FS: fast \(\rm N/slow~{}P\); SF: slow \(\rm N/fast~{}P\); FF: fast \(\rm N/fast~{}P\); SS: slow \(\rm N/slow~{}P\); TT: typical \(\rm N/typical~{}P\). \[C_{OUT}\ =\ \overline{\overline{AB}*\overline{(A+B)*C_{IN}}} \tag{2}\]1 Monte Carlo simulations for \(\rm NOR/OR\) and \(\rm NAND/AND\) outputs. \(\rm V_{DD}\) = 1.0 V; \(\rm T\) = 25 \({}^{\circ}\)C; \(\rm 2500\) iterations. specific DN level might guarantee the correct NOR/OR and NAND/AND operations during the logic-arithmetic mode. From extensive simulations, DN(1)MIN in this work has been observed to be 0.64 V at 1.0 V and 85 \({}^{\circ}\)C with VOS of 80 mV. The corresponding retention time at this condition was found to be 120 \(\upmu\)s.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 2 Related Works
### Task/Volume-Balanced MTSPs  
The traveling salesman problem (TSP) is a typical NP-hard combinatorial optimization problem, which comprises finding the best traversal route at the lowest cost (time, distance, etc.) through a given number of cities, in which all cities are visited only once by a single traveler, except for the starting city [31]. The MTSP, on the other hand, comprises \(M\) travelers visiting a portion of cities separately and each city (except the starting city) is only visited by any traveler once and, eventually, finding the minimum cost to finish traversing all of the cities [32]. When \(M=1\), MTSP is transformed into classical TSP, so TSP is a special case of MTSP [33].  
The genetic algorithm (GA) has definite advantages for solving the task-balanced MTSP problem. [PERSON] et al. [34] proposed a two-stage chromosome encoding method based on classical GA and designed corresponding genetic operators to solve the MTSP both in terms of the shortest total distance and the shortest \"longest distance\", which could effectively reduce the solution space and eliminate redundant solutions. [PERSON] et al. [35] successfully proved the advantages of the improved uniparent GA for solving the MTSP, as well as proposing three algorithms to solve the MTSP with multiple starting points and closed loops. [PERSON] et al. [36] combined the K-means clustering algorithm and the GA to solve the multi-objective MTSP, which avoided travelers crossing paths and also reduced computing time. However, the correctness of the results and the convergence performance of the above algorithms still need to be improved as retaining good individuals while maintaining the diversity of the population within the GA for the MTSP remains a challenging problem.
# Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment
## 3 Research Model and Hypotheses Development  
According to the literature, Technology Acceptance Model (TAM) is a reliable model for exploring the acceptability of new technologies [20; 39] and describing user behavior and technology usage [42]. However, because TAM was commonly explored in the context of other technologies that are different to in-vehicle applications, we aim to investigate other constructs related to driver acceptance by extending the basic TAM model. The basic independent variables, i.e., perceived usefulness (PU) and perceived ease of use (PE), were first selected from the basic TAM model to represent users' intention to use the in-vehicle applications. We assumed that if user-perceived in-vehicle applications are useful, the user would also perceive that the applications are easy to use and eventually will intend to use the applications. AT and PU are the most important factors influencing the user acceptance [21]. Moreover, if a user perceives that the in-vehicle applications are easy to be used, i.e., users can easily activate the application when they are in the driver's seat, and if the interaction process through the human-machine interface is simple and useful for a driving experience, the user will indicate a positive attitude (AT) and a high probability of accepting the applications. However, we removed the actual use variable because the in-vehicle applications are momentarily not essential components of all vehicle models, and it is beyond the scope of this study. The intention to use in-vehicle applications (BI) is set as the target variable of the model to represent users' or drivers' actual system use. Three other variables relevant to the context of drivers were included, including trust (T), system characteristics (SCs) and social influence (SI). SC refers to the preferable design or features of the in-vehicle applications, whereas SI represents social factors that influence a user's opinion on aspects of the in-vehicle applications. A study among licensed drivers
# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 4 Discussion  
In the proposed ST-YOLOv8 model, the introduction of key design choices such as the ASPP module and Wise-IoU loss function has led to significant improvements in detection performance, particularly evident in the enhancement of precision, recall, and F1 score for small target ship detection in SAR images. The ASPP module, by capturing multi-scale contextual information through atrous convolutions with varying dilation rates, enables the model to more accurately identify ship targets, thereby reducing false positives and contributing to a noticeable increase in precision. Additionally, by expanding the receptive field, the ASPP module aids the model in capturing smaller targets, leading to an improvement in recall. The combined effect of these enhancements results in a higher F1 score, indicating a better balance between precision and recall.  
Furthermore, the adoption of the Wise-IoU loss function further refines the model's detection capabilities. By dynamically adjusting sample weights, the Wise-IoU loss function allows the model to focus more on challenging samples during training, such as small targets or occluded objects. This dynamic focusing mechanism leads to a further increase in precision by reducing the impact of low-quality bounding boxes. Although the improvement in recall is relatively modest, the overall effect is still positive, contributing to a slight increase in recall and a subsequent elevation in the F1 score, demonstrating an optimized balance between precision and recall. Additionally, the model's generalization capabilities are expected to improve further in addressing complex sea conditions and weather variations in SAR images, adapting to diverse application scenarios, particularly in defense monitoring, maritime security, and ship search and rescue. By incorporating domain-specific prior knowledge or adaptive multimodal data fusion techniques, ST-YOLOv8's detection performance can be further enhanced, enabling it to play a key role in more specialized tasks in the future.  
Synthetic Aperture Radar (SAR) ship detection has garnered significant attention due to its military applications, such as maritime surveillance and strategic target identification. However, the potential misuse of SAR technology in military contexts poses considerable risks. For instance, unauthorized detection and tracking of civilian vessels could lead to unwarranted harassment or even escalation of conflicts. Additionally, the high-resolution imaging capabilities of SAR systems may inadvertently capture sensitive information, raising privacy concerns.  
To mitigate these risks, it is imperative to implement robust data encryption and anonymization techniques to safeguard sensitive information. Additionally, clear regulatory frameworks and ethical guidelines should be established to govern the use of SAR technology and vessel monitoring systems, ensuring that privacy is respected while maintaining the benefits of advanced maritime surveillance.