esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### Model Comparative Analysis  
This study uses UNet as the baseline, and its evaluation metrics are slightly inferior to the subsequent improved network models, as shown in Table 2. DeepLabV3+ [14] employs atrous convolution and depthwise separable convolution in the encoder and decoder to encode the multi-scale context, achieving a better performance while reducing computational complexity. UNet++ [15] replaces the skip connections with dense connections based on UNet, integrating features at different levels, but needs to perform better on the rice leaf cell dataset, and is generally inferior to UNet. ResUNet [16] introduces a residual structure in each convolutional block, deepening the network depth and outperforming UNet. SwinUNet [37] replaces all convolutions in the UNet architecture with SwinTransformer modules, but weakens position information, performing far worse than other methods on the rice leaf cell dataset, with indistinct textures and boundaries. UTNet [45] fuses convolutions and Transformers, but its performance is still inferior to CellGAN.  
In the rice leaf cell dataset, as shown in Figure 9, compared to UNet, this method's IoU is improved by 3.52%, Dice by 2.06%, and Acc by 1.55%; compared to the pure Transformer architecture SwinUNet, IoU is improved by 13.93%, Dice by 8.54%, and Acc by 7.68%; and compared to the convolutional-Transformer hybrid architecture UTNet, IoU is improved  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **IoU\%** \(\uparrow\) & **Dice\%**\(\uparrow\) & **Acc\%**\(\uparrow\) \\ \hline UNet [13] & 89.69 & 94.43 & 95.77 \\ DeepLabV3+ [14] & 90.06 & 94.67 & 95.98 \\ UNet++ [15] & 88.32 & 93.52 & 94.32 \\ ResUNet [16] & 90.24 & 94.82 & 96.23 \\ SwinUnet [37] & 79.28 & 87.95 & 89.46 \\ UTNet [45] & 91.00 & 95.29 & 96.13 \\ CellGAN & 93.21 & 96.49 & 97.32 \\ \hline \hline \end{tabular} Note: “\(\uparrow\)” indicates that CellGAN demonstrates improved performance compared to other models in the comparative results.  
\end{table}
Table 2: Comparison Experiments of Different Models.  
Figure 8: Comparison of Four Different Datasets.  
by 2.21%, Dice by 1.20%, and Acc by 1.19%. Overall, the CellGAN method in this paper performs the best. Figure 10 visually demonstrates the differences in the segmentation performance of various models on the rice leaf cell dataset. Although UNet has limitations in modeling long-range relationships, it performs well in fine-grained feature recovery related to using simple \(3\times 3\) convolutional feature extraction. Although the other networks have slightly higher metrics than UNet on the validation set, UNet's feature extraction for cell boundaries is the most accurate. Compared to other methods, this method achieves the highest Dice coefficient, and the boundary distinction effect is closest to the label, verifying its effectiveness.
## 3 Communication Protocols in IoMT
### Protocol Range and Data Transmission Rate in IoMT  
Based on the technical information presented in [23], here we will make a comparison between the different protocols used in the healthcare systems, based on the range and data transmission rate.  
* **Short-Range protocols:** ZigBee provides a mesh network structure. When it comes to setting and planning device energy use, ZigBee is easier than 6 LowPAN. ZigBee also outperforms alternative protocols such as Z-Wave in terms of device hopping and energy usage [23]. In contrast to ZigBee networks, 6 LoWPAN networks appear to have lower latency and packet loss rates, which make them suitable for medical services. When comparing 6 LowPAN implementations in medical contexts to BLE implementations, a relevant research work [24] reveals that 6 LowPAN is more efficient when employing IP-based applications, albeit there are connection concerns when barriers are present. When it comes to network communication, 6 LoWPAN devices interact directly with one another, whereas LoRaWAN data is sent through gateways and routers. On the other hand, Z-Wave has a longer optimal range than ZigBee and 6 LowPAN due to its sub-1 GHz band--this communication band also allows Z-Wave to have less interference. The disadvantage is its lower data-transmission rates.
* **Long-Range protocols:** For long-range protocols with low power consumption such as LoRaWAN and LTE-M (Long Term Evolution Machine Type Communication) networks, the main LPWAN (Low Power Wide Area Networks) technologies can also provide long-range connectivity of 10 km distances using subgigahertz (GHz) radio frequencies, even on a global scale as LTE-M networks provide a robust infrastructure and built-in security mechanism that can support most applications [25]. These technologies offer the manufacturers of connected objects to communicate data over long distances with low power consumption. Despite the need for specific hardware, LTE-M offers the best compromise between throughput and autonomy, which makes it ideal for multiple domains, including healthcare.
## 2 Control Algorithm
### Parameter Tuning
#### 2.5.2 Effect of \(\eta\)  
It can be expected that the larger the value of \(\eta\), the more decreasing the rate of the Lyapunov function from \(\dot{V}\leq-\eta|s|\). Suppose \(|s|>\Phi\) at beginning. The upper bound for \(\dot{V}\) is given in (33). Since \(V=s^{2}/2=|s|^{2}/2\), we conclude the upper bound of \(V\) satisfies  
\[\dot{V} = -\eta\frac{|s|^{2}}{\Phi} \tag{35}\] \[= -\frac{2\eta}{\Phi}V\]  
(35) implies that \(V\) converges exponentially with time constant \(\tau_{\eta}=\Phi/2\eta\).  
Figure 3 demonstrates the comparison. \(\eta=100\) in Figure 3a, whereas \(\eta=0.01\) in Figure 3b. Other parameters are set identically. It is obvious that the responses with large \(\eta\) are faster, regardless of the type of controls.  
Figure 3: Comparison of parameter effectiveness. (**a**) \(\eta=100\); (**b**) \(\eta=0.01\).
## 3 Results and Discussion
### Resistance to Fatigue Cracking  
The results of the fatigue tests are shown in Figure 8. Below the graph, the representative fatigue models of the mixtures are presented alongside the respective coefficients of determination (R\({}^{2}\)) and the specific deformations for 10\({}^{6}\) cycles (\(\varepsilon_{\theta}\)). The models relate the tensile strain (\(\mu\varepsilon\)) applied on the specimens to the number of cycles (\(N\), loading applications) until asphalt concrete reaches 50% of initial stiffness.  
Figure 7: Results for rutting depth (%) after 30,000 cycles.  
For the conditions adopted in the tests carried out in this research, from the curves shown in Figure 8, it can be observed the higher fatigue strength of the SBS 60/85 mixture, followed by the 3% NC + 2% SBS, 3% NC and conventional mixtures.  
Concerning the fatigue behavior prediction based on the rheological study, it can be noted that it was partially accomplished. In this sense, the superior behavior of the SBS 60/85 and 3% NC + 2% SBS mixtures was expected, due to the greater predominance of the elastic behavior (lower phase angles). However, according to the respective results, it was expected that the best performance would be of the 3% NC + 2% SBS mixture, in comparison to the SBS 60/85, which did not occur. Probably, in terms of fatigue, the mixture modified by nanoclay and SBS was impaired due to the considerable increase in the stiffness of the material, which was verified in the empirical characterization of the binder (low penetration) and also in the rheological study of the mixtures.
# Farm Monitoring System with Drones and Optical Camera Communication
## 5 Experimental Results
### Results
#### 5.2.2 Light Source Detection  
We employed YOLOv5, which is a famous machine learning model using a CNN, to detect the LED panels from the optical signal images. To ensure the recognition accuracy of the LED panels mounted on the sensor nodes, we collected 5178 images as training data and marked the positions of the LED panels. The resolution of the images was changed to \(1920\times 1080\). The results of the machine learning experiments with different parameters are presented in Table 4. YOLOv5l was selected as the model weight due to its superior detection accuracy and relatively short training time. We evaluated the performance of  
Figure 11: Sensor node placement (Red point: sensor node, Orange arrow: drone trajectory).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\theta\) **[Deg]** & \(a\) & \(b\) & \(c\) \\ \hline
0 & 0.070 & 0.070 & 0.012 \\
30 & 0.080 & 0.044 & 0.015 \\
45 & 0.009 & 0.010 & 0.018 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Difference between measured and theoretical values [m].  
each parameter configuration by comparing the final loss values and the mean Average Precision (mAP). After comparing all configurations, we adopted the parameter set with a batch size of 16, an image size of 640, 300 epochs, and a final loss of 0.0030712, achieving an mAP of 0.71541, as indicated by the underlined values in Table 4. The PC environment was Ubuntu 20.04 and the GPU environment was NVIDIA GeForce RTX 3090. The software environments were CUDA 11.3, CUDNN 8.8, and Python 3.8. The datasets were obtained by changing the location and time at which the images were taken to improve recognition accuracy under different conditions. This is because noise from ambient light changes the visibility of LED panels.  
The results for the detection accuracy of the LED panels using YOLOv5 are shown in Figure 12. The accuracy of the object detection model was evaluated using loss and mAP. The object loss function is a measure of the probability that the detection target is within the region of interest. The lower the value of the loss function, the higher the accuracy. Figure 12a shows the evolution of the loss function per epoch. Before the training batch reached 50, both loss function values decreased rapidly. When the training batch reached 50, the decrease in both loss function values gradually slowed down. The mAP is the average of the average accuracy per class. In this paper, mAP and AP are equal because the number of classes is one. The higher the mAP value, the more accurate the network. Figure 12b shows the evolution of mAP per epoch. The value of mAP_0.5 reached 0.98. The mAP_0.5:0.95 reached 0.71. We confirmed that the object detection model successfully detected the LED panels.
## 3 Results
### The Influence of Directional Wave Spectra on NRCS Simulation at Different Azimuth Angles
When the wind speed is greater than 6 m/s, the differences between the upwind and downwind NRCSs estimated with the A + Cosine reach the same magnitude as those in CMOD5.n. The A + Sech and A + Gaussian have better agreement with the ASCAT data. The upwind-downwind differences from the ASCAT data first increase and then decrease with the increase in wind speed, and this trend can be well simulated by the A + Sech and A + Gaussian. Overall, compared with the ASCAT data, the AH18 + Sech performs better at low and moderate incidence angles, while the A + Sech and A + Gaussian spectra work better at high incidence angles. Compared with the CMOD5.n, the A + Gaussian generally has overall satisfactory performance in predicting the upwind-downwind differences at different conditions, except for the high wind condition, where the A + Sech has better performance.
## 4 Distance-Based Energy Aware Routing (DEAR) Algorithm
### DEAR Algorithm
#### 4.2.2 Flow Chart of DEAR  
From Figure 5 we can see that the flow chart of DEAR algorithm consists of two phases. On the left side is the route setup phase and on the right side is the route maintenance phase.  
Figure 5: DEAR flow chart.  
If the ACK message is not received within a certain time, a link failure is detected. Then a local repair process will be initiated first. If the node can find an alternative next hop node, it will determine its next hop in a similar way like above. Or else, it will notify all the involved nodes about the failure of this link by sending a route error (RERR) message. This broken link will be deleted by all involved nodes from their routing table and neighboring table and they will avoid using this link later on. Finally, the source node will restart the route setup phase.
## 3 Results and Discussion
### Biological Sampling  
Most studies demonstrate that only specific single species of zooplankton were analyzed using the acoustic method and acoustic backscattering model [7; 47; 48; 49]. However, this research found that the observed area had diverse types of zooplankton. Table 2 shows the zooplankton composition of the biological samples taken by plankton-net and all zooplankton categorized as mesoplankton  
Figure 6: Vertical section of MVBS from mobile (upper) and moored (down) ADCP during (**a**) daylight, (**b**) twilight, (**c**) night time. The dotted red line indicates the position of the zooplankton with red ‘x’ marks visualized the location of moored ADCP. The red square box in both echograms was in the same location and time and are compared further in Figure 8.  
with sizes ranging between 0.2-1.1 mm. Zooplankton was then classified into eight groups: protozoa, crustaceans, urochordate, chaetognath, nematode, gastropods, pelecypods, and polychaete. However, by comparing all samples data, the nauplius (stadia) zooplankton was excluded from the analysis because their size is too small (\(<\)0.01 mm). ADCP is not able to quantify small-size zooplankton because they cannot be detected by the 750 kHz ADCP used in this study [50]. The MVBS and the biological samples found in the observed area needed to be verified through specific target strength (TS) measurements at the laboratory for the further research, especially by measuring TS for each zooplankton found (Table 2) to determine zooplankton abundance. Regarding the species differentiation in the acoustic scattering layer, detection of the movement speed of the target echo was effective in addition to the evaluation of the MVBS value and the spatial distribution [51]. MVBS value depends not only on the size but also on the abundance of zooplankton in one volume. The spatial variation of zooplankton was varied by the morphological and environment characteristic changes in the strait. The SSL in the observed area comprises various mixed zooplankton species. Thus, it is complicated to differentiate among the species in the SSL. However, in this study, we only focused on the MVBS observation and vertical velocity of the dominant zooplankton because the numbers of other species were too few and their sizes were too small to observe.
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## Security Issues and Challenges in WSN-IoT
### Identification, Authentication, and Authorization  
Nodes are the IoT building blocks that need to be defined in the network or physically. IoT networks cover a large area to track the transmission between devices and acquire access to the entire network. The total naming layout of nodes is unsafe without data consistency [69]. DNS cache positioning assaults may wreak havoc on the network's overall performance. For each target to be uniquely identified, node identification is necessary. The false node should be detected efficiently since each mark indicates a potential attack location. The network must be defended against physical or logical attacks on devices and their data. Authentication requires checking the identity of the nodes [70]. Undeniably, if contact with the correct node is not ensured, the secrecy and fairness of the messages exchanged cannot be guaranteed. An attacker can access the network and insert erroneous statements if the authentication is poorly handled. It is challenging to ensure authentication because of the wireless media's existence and the nature of sensor networks. Authentication involves confirming that you are who you claim to be. This is commonly achieved using an authentication method based on a username and password [71]. This scheme, though, is not safe enough. Passwords typically need to be updated regularly, and unattended computers should not be used. Authentication also requires the authentication method for both the sender and the recipient to validate the messages' origin [72].
## 3 Results and Discussion
### Analysis of Response Output Index Results
#### 3.1.1 Density
The smaller the distance between the point and the diagonal, the higher the accuracy of the prediction; the greater the degree of deviation from the top to the bottom, the greater the error. It can be seen that there are seven sets of measured values that coincide with the predicted values, and there are six sets of measured values that are distributed on the diagonal line but not far away. There are no outliers in the whole distribution, which indicates that the method of obtaining the predicted value via the fitting equation is reliable. Three-dimensional coordinates can only represent the effects of two impact factors at the same time, so when analyzing the interaction of the two factors, the third impact factor is taken as the median value within the range and is fixed. The contour plot and response surface plot between the density and each influencing factor are shown in Figure 4.  
It can be seen from Figure 4 that the location with the highest density is in the middle dark parts of the contour plot and the response surface plot. There are five measured points in each surface graph, indicating how well the predicted surface fits the actual values. It can be found from the surface graph that the predicted values fit the surface better. Increasing the amount of NTC and BF makes the mixture density increase first and then decrease, however the two factors influence each other. Under the premise that the asphalt-aggregate ratio is 5.5%, the effect of the fiber content on density gradually increases during the increase of the NTC content from 1% to 7%. In the process of increasing the fiber content from 3.5% to 9%, the effect of NTC on the density decreases slightly. In addition, as the asphalt-aggregate ratio increases, the density of the mixture increases first and then decreases. Figure 3: The measured and predicted values for density.
# Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage
## 2 Materials and Methods
### Damages Identification  
The work of the belt conveyor was imitated using the abovementioned test rig. The test campaign first included the work of the belt without loads and damages, and then the loaded work, and, finally, the work with intentionally made damages. The tests were performed at three different rotary speeds of the tail pulley, namely, \(n_{1}=159\) rpm, \(n_{2}=318\) rpm, and \(n_{3}=520\) rpm. Finally, the machine learning (ML) algorithms were tested for the rotary speed of \(n_{1}=159\) rpm, which corresponded with a linear belt speed of \(v_{1}=0.5\) m/s.  
Since it is widely recognized that longitudinal and transverse tears belong to the main types of catastrophic failures, and the longitudinal ones appear more commonly than transverse ones [31], the damages were made in two stages as follows:  
* First, three cuts were made, namely, two longitudinal cuts through UW I and UW II, which were 50 mm and 70 mm long, respectively, and one partial longitudinal cut UW III, which was 45 mm long and 1 mm deep.
* Then, two more cuts were added, namely, a longitudinal partial cut through UW IV which was 50 mm long and 1.5 mm deep, and a transverse cut through UP I, which was 10 mm long in the middle of the belt. Thus, three different states of the belt were analyzed: the undamaged one, the one with three cuts, and the one with five cuts.  
In order to avoid additional uncertainty propagation due to the calculations, rounding, and approximations, the data were analyzed in analog-to-digital units (ADUs) obtained from the analog-to-digital converter. It should be noted that a higher value in ADUs corresponded with smaller pressure on the strain gauge.  
To train a machine learning (ML) model, usually a batch of data are used [32]. The respective data collected from the electronic system were analyzed from the perspective of belt state monitoring. Statistical parameters were calculated, including standard deviation and kurtosis, and the data were imported to MatLab (R2024b). From the _Classification Lerner_ application, 10 algorithms were used, and a typical number of folds for the cross-validation was chosen, namely, \(k_{\mathrm{p}}=5\)[33].  
Then, the analysis of 18 features was performed with the _Diagnostic Feature Designer_ application. The purpose of the test was to determine which features are the most significant for machine learning, and a one-way analysis of variance (ANOVA) model [34] was found to be useful for this analysis.
# CWSXLNet: A Sentiment Analysis Model Based on Chinese Word Segmentation Information Enhancement
## 3 CWSXLNet  
### Deficiencies of XLNet Model in Handling Chinese  
When the XLNet model is used to process Chinese, because it uses SentencePiece for text segmentation, and SentencePiece tends to split longer sub-words when using the BPE method for word segmentation, these longer words, when separated from the original corpus, tend to be used less frequently in other text, causing the waste of the limited positional space of the word table. Furthermore, because SentencePiece tends to segment longer words rather than individual Chinese character, the required word list is unusually large. With a limited word list length of 32,000, there are still many commonly used Chinese words not included in the word list, which can only be represented by <UNK> during word embedding, affecting the model's understanding of semantics and sentiment analysis.  
Figure 6 shows the sub-words of the word list in [31], which proposed Chinese pre-trained XLNet model. The numbers in Figure 6 indicate the weights. It can be seen that SentencePiece prefers to split out longer sub-words, which are less frequently used in the detached pre-trained corpus and waste space in the word list.  
The basic unit of the Chinese language is the Chinese character. Several Chinese characters make up words. Several Chinese words form sentences. Unlike English, Chinese words are not separated by spaces, and there are approximately 55,000 commonly used Chinese words. For comparison, the length of XLNet's word list is 32,000. To avoid problems caused by long word lists, we reduce the granularity to the Chinese character instead of the Chinese word. However, this approach creates another problem: the relationship between characters from the same word is lost. To avoid this, we discovered a way to improve the connection between characters from the same word.  
First, we want to use a tokenized tool to separate Chinese words from sentences, and use spaces to separate Chinese words like in English. Secondly, we reduce the granularity to the Chinese character. Finally, we need to improve the relationship between characters that form the same word, and make sure that the improvement fits perfectly to the XLNet model. This is a brief introduction to our CWSXLNet model; we explain the model in more detail below.
## 3 An overview of AVs
### Challenges for AVs in Adverse Weather
#### 3.5.1 Performance of Sensors  
Adverse weather conditions on roads provide serious difficulties for sensors in AVs. The current sensing technologies, including LiDAR and cameras, perform well in clear weather but have trouble when the roadway is covered with snow and may contain erroneous reflections caused by rain or snow, making it difficult to discern between different objects [13]. Even though forward radar is good at spotting large objects and piercing through weather, it cannot improve safety applications on its own. In snowy circumstances, ultrasonic detectors may set off false alarms, and glare and surface reflection are just two examples of weather-related variables that might affect sensor accuracy.