esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Imaging Processing
#### 2.3.1 Image Preprocessing and Image Enhancement
The expansion operation can fill small holes inside the object, connect the neighboring object, and also smooth the boundary of the object. By using the open operation, the object outline of the image can be smoothed, long and thin curves will be broken, and small protrusions can be eliminated, which can help eliminate noise and isolate pixels in the image. In contrast to the open operation, the closed operation first performs the expansion operation, followed by the corrosion operation, again using the same structural elements. The closing operation can make up for the narrow break curve in the image, eliminate small holes, and fill the break in the contour line, which can also help connect broken objects and recover details that may have been lost due to filming or processing. Following the application of open and close operations, it became evident that the isolated minor points surrounding the stage were eradicated, and the initially divided maize particles were reunified. This processing effect was very useful in image analysis, which could obviously improve the quality of the image and the accuracy of subsequent processing (Figure 5). As illustrated in Figure 4, the images were grouped together following the completion of the open and close operations. Despite the Otsu algorithm dividing the  
Figure 4: Otsu method image binarization. maize seeds into disparate sections, the open and close operations were still able to reunite them, effectively removing noise pixels while simultaneously rectifying image defects. When processing maize images for accurate cutting, the minimum external rectangle method was adopted in this study. This method is a classical computational geometry technique that aims to determine the smallest rectangle that can tightly surround a specific area in an image, such as a maize seed. By applying the minimum external rectangle method, we were able to accurately locate the central point of the maize and obtain the coordinates of its four vertices, thus achieving an efficient division of the maize seeds (Figure 6). In order to ensure the integrity of each kernel image and adapt to the processing requirements of the subsequent network model, a certain number of pixels were expanded from the initial minimum external rectangle. This step was conducted to avoid losing any important features or details of the maize seed during cropping, thus ensuring that the resulting single-case maize seed image was complete and suitable for further analysis. By this method, the image of a single maize seed could be effectively extracted from the original image to provide a high-quality data set for the subsequent processing and recognition.
## 4 Implementation and Experiments
### Experimental Results  
#### 4.4.1 NutritionCLIP Experimental Results  
This section compares the results of our fine-tuned NutritionCLIP model with other zero-shot learning-based image classification models. As shown in Tables 9-11, we evaluate the performance of different models across three food image datasets--Food 101, CNFOOD-241, and Taiwanese Food 101. Each table reports key performance metrics, including top 1 accuracy, top 5 accuracy, precision, recall, and F1 score, comprehensively assessing model effectiveness in zero-shot scenarios.  
The tables above show that NutritionCLIP significantly outperforms other models across all three datasets in terms of top 1 accuracy, Top 5 accuracy, precision, recall, and F1 score. This indicates that our fine-tuned NutritionCLIP model is highly accurate and reliable in food image classification.  
Specifically, on the CNFOOD-241 dataset, NutritionCLIP achieved a top 1 accuracy of 0.91, far surpassing the performance of other models, demonstrating its superior adaptability to different food image datasets. The precision and recall also reached 0.88 and 0.90, respectively, indicating the model's precision and comprehensiveness in the prediction process.  
Additionally, on the Food 101 dataset, NutritionCLIP achieved a top 1 accuracy of 0.80, significantly outperforming other models. Even without specific dataset adjustments, NutritionCLIP can still provide reliable results.  
On the Taiwanese Food 101 dataset, NutritionCLIP continued to perform well, with a top 1 accuracy of 0.73, showing its stable performance on diverse datasets. These results indicate that with proper fine tuning, NutritionCLIP can significantly enhance model performance in food image classification tasks.  
Overall, these experimental results demonstrate the excellent performance of our proposed NutritionCLIP model in food image classification and its strong adaptability across different food datasets.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 3 Background
### RPL Overview
This interplay of DIO, DIS, and DAO messages orchestrates the intricate dance of network construction, ensuring a robust and dynamic infrastructure within RPL. The Routing Protocol for Low-Power and Lossy Networks (RPL) is very complicated. The DODAG building phase is the most important part. This is when the sink-node guides the (re)construction of the DODAG. This process proceeds through the strategic use of routing control messages, namely DIO, DAO, DAOACK, and DIS. The sink-node initiates the DODAG construction by multicasting the initial DIO message, sparking a cascade of activity. As nodes join, they multicast numerous DIO messages, shaping the network structure as they integrate. Meanwhile, nodes, excluding the sink, leverage DAO messages to disseminate reverse route information. Detached or unconnected nodes, seeking integration, send DIS messages to potentially connected neighbors, enhancing the network's cohesiveness. DIO messages play a crucial role by encapsulating routing metrics, constraints, and the Objective Function (OF) pivotal for the formation of routing paths. Simultaneously, the foundational aspect of DODAG maintenance ensures the network's sustained health and stability. This critical functionality relies on the synchronized propagation of DIO messages, establishing the bedrock for the network's convergence time. Regulated by the Trickle timer algorithm, this process is finely tuned to adapt to network dynamics. Any modification to the DODAG, be it selecting a new parent or encountering an unreachable parent, triggers a reset of the Trickle timer. The algorithm, foreseeing the energy implications, predicts a more frequent transmission of DIO messages in unstable networks and a less frequent transmission in stable networks, effectively conserving energy. This harmonious interplay of DODAG building and maintenance encapsulates the resilience and adaptability in RFL, ensuring a robust foundation for LLNs [4].
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
## 5 Seismic Fragility Analysis of the TTLS with Foundation Deformation
### Foundation Settlement Analysis  
Figure 15 shows the IDA curves of the transmission tower when support A settles 0.07 m in the Z-direction. The maximum tower top displacement range for RM is 0.79 m-1.97 m, whereas for the JM, the range is 1.5 m-3.3 m. The corresponding displacement range for the JM is approximately 1.68-1.90 times larger than the corresponding range for the RM. Then, the simulation results are fitted by regression analysis, and the calculation model of the seismic demand index for the tower-line system is determined according to Equation (2), as shown in Figure 16.  
Figure 17 shows the seismic fragility curves of the RM and the JM under the foundation settlement. The seismic failure probability of the TTLS with foundation settlement is greater than that of the TTLS with a fixed foundation. The reason for this result is that foundation settlement can cause additional stress on some members of the transmission tower, leading to earlier failure of some members under the seismic loads, thereby increasing the seismic failure probability of the tower-line system. In addition, the seismic fragility curve of the model, considering the bolt slippage effect, shifts to the right, which indicates that the failure probability of the JM subjected to earthquakes decreases after foundation settlement. When the PGA is 2 g, the exceedance probabilities of the four limit states for the RM are 95.87%, 80.79%, 55.52%, and 14.82%, respectively. However, the exceedance probabilities of the corresponding four limit states for the JM are 90.00%, 73.65%, 34.48%, and 6.38%, respectively. Compared to the failure probability of the JM subjected to foundation settle  
Figure 16: Calculation models of seismic demand index: (**a**) RM; (**b**) JM.  
Figure 15: IDA curves of TTLS: (**a**) RM; (**b**) JM.  
ment, the failure probability of the RM increases by 1.07 times, 1.1 times, 1.61 times, and 2.32 times, respectively. Therefore, ignoring the bolt slippage effect would overestimate the seismic failure probability of the TTLS with foundation settlement.  
Figure 18 shows the intersegment displacement ratios of each layer of the RM and the JM. Under 15 seismic waves, the maximum _ISDR_ values for the two TTLS models with foundation settlement mainly appear in section B1, which indicates that this layer is the weak position of the transmission tower.
# The Optimization of Covert Communication in Asymmetric Jammer - Assist Systems
## 2 System Model
### Hypothesis Test  
[PERSON] conducts a binary hypothesis test [8] based on \(n\) consecutive observations \(Z_{w}^{n}=[Zw,1,...,Zw,n]\in\mathbb{C}^{n}\) to ascertain whether [PERSON] is communicating to [PERSON]. The total power of the jammer remains constant \(P_{I}\), but [PERSON] can adjust the power distribution ratio \(\alpha\) of the jammer's output signals along the real and imaginary axes. Specifically, the jammer's power on the \(x\)-axis is \(\alpha P_{j}\), and on the \(y\)-axis, it is \((1-\alpha)P_{j}\). Due to the differing noise power along the two axes, this configuration results in an asymmetric jammer. Let \(\sigma_{w,y,0}^{2}\) and \(\sigma_{w,y,0}^{2}\) denote the variance of the complex Gaussian noise at [PERSON] on the \(x\)-axis and \(y\)-axis. Specifically, the null hypothesis (\(\mathcal{H}_{0}\)) posits the absence of communication, where each sample \(Zw,i=n_{w,i}\) is an independent and identically distributed (i.i.d.) complex-Gaussian random variable following the distribution \(\mathcal{CN}(0,\sigma_{a}^{2})\) with \(\sigma_{a}^{2}=\sigma_{w,x,0}^{2}+\sigma_{w,y,0}^{2}+P_{I}\). On the other hand, the alternative hypothesis (\(\mathcal{H}_{1}\)) suggests communication is occurring, and each sample \(Zw,i=h_{w}D_{i}+n_{w,i}\). [PERSON]'s objective is to discriminate between these two hypotheses:  
\[\mathcal{H}_{0}: Zw=n_{w}, \tag{9}\] \[\mathcal{H}_{1}: Zw=h_{w}D+n_{w}, \tag{10}\]  
where \(h_{w}\) is the channel coefficient from [PERSON] to [PERSON]. Let \(\mathbb{P}_{1}^{n}\) (resp. \(\mathbb{P}_{0}^{n}\)) represent the input distribution corresponding to [PERSON]'s \(n\) observations under the conditions of \(\mathcal{H}_{1}\) (resp. \(\mathcal{H}_{0}\)), respectively. The probability of _false alarm_, rejecting \(\mathcal{H}_{0}\) when it is true, is denoted by \(\mathcal{P}_{FA}\), while the probability of _missed detection_, accepting \(\mathcal{H}_{0}\) when it is false, is denoted by \(\mathcal{P}_{MD}\). [PERSON] has knowledge of the distributions \(\mathbb{P}_{1}^{n}\) and \(\mathbb{P}_{0}^{n}\), and can conduct an optimal statistical hypothesis test such that \(\mathcal{P}_{FA}+\mathcal{P}_{MD}\geq 1-\sqrt{\mathcal{D}(\mathbb{P}_{1}^{n} \|\mathbb{P}_{0}^{n})}\), where \(\mathcal{D}(\mathbb{P}_{1}^{n}\|\mathbb{P}_{0}^{n})\) represents the KL divergence between \(\mathbb{P}_{1}^{n}\) and \(\mathbb{P}_{0}^{n}\). The objective is to achieve covert communication by ensuring that the sum of error probabilities is one, i.e., \(\mathcal{P}_{FA}+\mathcal{P}_{MD}=1\). This implies making \(\mathcal{D}(\mathbb{P}_{1}^{n}\|\mathbb{P}_{0}^{n})\) negligible [32; 33; 34], specifically by guaranteeing that the KL divergence satisfies the condition  
\[\mathcal{D}(\mathbb{P}_{1}^{n}\|\mathbb{P}_{0}^{n})\leq\epsilon, \tag{11}\]  
where \(\epsilon\) is an arbitrarily small value within the range (0,1). [PERSON] possesses knowledge regarding the construction of the codebook and jammer noise, both the set of all potential discrete constellation sets and input distribution, and he receives the symbols corrupted by AWGN.
# Treatment of Water Contaminated with Diesel Using Carbon Nanotubes
## 3 Results
### NMR Characterization  
Figure 5a-c report the \({}^{13}\)C-NMR spectra carried out on the filtrate of the systems containing different quantities of carbon nanotubes. The samples obtained after a treatment time of 5 min were taken into consideration, since the previously reported data show that these times represent a fair compromise between short times and high diesel abatement percentages.  
Figure 4: TG, DTG (**a**) and DTA (**b**) curves of a sample of CNTs after the diesel adsorption process.  
The analyses were carried out on the samples containing the quantities of nanotubes of 0.3, 0.5 and 1.0 g, respectively, because these are representative of the entire range of the quantities of nanotubes used, these being the lowest, medium and highest values, respectively.  
After the established treatment period, the nanotubes were removed from the vacuum filtration system, and the resulting filtrate was characterized. The filtrate appeared as a single phase, with all visible diesel adsorbed.  
The reported spectra show weak signals in a range of 10-50 ppm, referable to the presence of hydrocarbons [68], which denote only traces of diesel, thus confirming the high efficiency of carbon nanotubes in diesel adsorption.  
Furthermore, the \({}^{13}\)C-NMR spectra show that by increasing the quantity of nanotubes in the adsorption treatment, the signals associated with the presence of diesel become increasingly weak, confirming a greater purification of the treated water.
## 3 Results and Discussion
### Odour-Active Compounds
Nonanal imparts a citrus fragrance (OAV 8.795) and contributes to the overall flavour of many wines [23]. DL also showed increased levels of five aroma components, i.e., \(\beta\)-damascenone (1-(2,6,6-trimethyl-1,3-cyclohexation-1-yl)-2-butten-1-one, OAV 1132,666.667), benzene-propanoic acid ethyl ester (OAV 488.889), decanoic acid ethyl ester (OAV 61.650), 3-methyl-1-butanol acetate (OAV 39.333) and dodecanoic acid ethyl ester (OAV 3.364); these were also characteristic VOCs of DL. It is worth noting that the contributions of 5-methyl-2-furancarboxaldehyde (OAV 0.300) and heptanal were significantly reduced in DL, while heptanal was not detected. \(\beta\)-Damascenone, which imparts berry and rose scents, is commonly detected in grape [37], acai berry and other fruits [38]. The \(\beta\)-damascenone in DL is derived from sea buckthorn. Another four esters that are also widely present in brandy and Chinese liquor [39] were the main flavour compounds of DL, and contributed greatly to the overall flavour. It has been reported that benzenepropanoic acid ethyl ester (OAV 18.69), decanoic acid ethyl ester (OAV 231.71), 3-methyl-1-butanol acetate (OAV 43.68) and dodecanoic acid ethyl ester (OAV 460.36) contribute to the aroma of jujube brandy [23]. Figure 4: Odour–active compounds and differential metabolites in SJ, SW and DL by GC–MS (\(n=3\)). (**A**) Venn diagram of odour–active compounds. Volcano plot of metabolites after fermentation (**B**) and distillation (**C**).
## 6 Simulation Results and Discussion
### Scenario (2)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**33-Bus**} & \multicolumn{2}{c}{**69-Bus**} & \multicolumn{2}{c}{**85-Bus**} \\ \cline{2-7}
**Outputs** & **Without** & **With** & **Without** & **With** & **Without** & **With** \\  & **DSTATCOMs** & **DSTATCOMs** & **DSTATCOMs** & **DSTATCOMs** & **DSTATCOMs** & **DSTATCOMs** \\ \hline Optimal size (kVAr) and & & 696.15 (11) & & 653.78 (17) & & 1133.36 (9) \\ location & & 1309.73 (30) & & 1459.28 (61) & & 1073.75 (32) \\ Ploss (kW) & 135.84 & 55.17 & 124.08 & 48.74 & 171.75 & 64.66 \\ \% Reduction in Ploss & & 59.39 & & 58.18 & & 62.35 \\ Vmin (p.u.) & 0.91109 & 0.95211 & 0.92155 & 0.95195 & 0.89245 & 0.95098 \\ VSImin (p.u.) & 0.68904 & 0.81178 & 0.72124 & 0.81493 & 0.63436 & 0.79202 \\ Cost of loss (USD/yr) & 71,396.33 & 48,123.80 & 65,214.82 & 32,567.67 & 90,271.13 & 37,289.27 \\ Cost of DSTATCOMs & & 10,639.09 & & 11,207.57 & & 14,189.55 \\ (USD/yr) & & 12,633.44 & & 17,472.80 & & 38,792.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effects of DSTATCOMs in the presence of renewable generation and load uncertainty. Figure 14: (**a**) Voltage profile for the IEEE 33-bus network without DSTATCOMs; (**b**) voltage profile for the IEEE 33-bus network with DSTATCOMs.
# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
## 2 Related Work
[30,31] proposed a distributed method where each node runs multi-armed bandits to find the best channel and transmit power. The authors showed that each node acting in a selfish way can still result in better spatial reuse as a system. [PERSON] et al. [32] proposed an approach where a single user dynamically selects the best channel to transmit its packet among many available channels. The user uses a DQN (Deep Q Network) to learn the optimal policy of selecting the channel without knowing the system statistics. [PERSON] et al. [33] extended this work to address the environment where multiple nodes are running DQN to select the best channel, while there are other nodes running different protocols like TDMA and ALOHA. [PERSON] et al. [34] proposed a method called DLMA (Deep reinforcement Learning Multiple Access), which replaces CSMA for channel access in a single wireless LAN. Instead of measuring channel status as in CSMA, DLMA uses ACK feedbacks to determine when to transmit its packet. [PERSON] et al. [35] proposed a two-scale method for controlling CST using MLP (Multi-Layer Perceptron). While the local scale control is used to adjust CST to reduce interference and increase parallel transmissions, the global scale control is used to improve fairness among nodes. [PERSON] et al. [36] proposed a scheme where interferers are recognized based on reinforcement learning and decisions to transmit concurrently are made accordingly. Reinforcement learning techniques are used for topics other than spatial reuse, such as channel bonding, network monitoring, and link configuration. These works are summarized in the survey paper by [PERSON] et al. [37]. Finally, note that rate control is an important factor that needs to be considered when nodes dynamically select parameters like CST and transmit power. If a node finds out that the SNR at the receiver is not high enough for a certain MCS level, it can choose to wait or send the packet using a lower MCS level. [PERSON] et al. [38] proposed a rate adaptation scheme where nodes transmitting in parallel adjust their MCS levels so that the colliding packets are safely received at their receivers. [PERSON] et al. [39] proposed a rate control algorithm that can be used with IEEE 802.11 ax spatial reuse features. In our paper, we do not consider rate control and assume nodes use a fixed MCS level. Further improvements may be possible by applying rate control with the proposed method, and we leave this issue as a future work.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 4 Experiments and Evaluation
### Analysis and Comparison of the Test Results
#### 4.3.4 Comparison of the Spectrogram  
Figure 5 shows the spectrogram comparison for the original speech, the enhanced speech obtained by the ISCLP with filter order \(L=6\), the enhanced speech obtained by the WPE, and the enhanced speech obtained by the proposed algorithm. The input SNR is set to 10 dB and \(T_{60}=800\) ms. Compared with the original speech, these three algorithms effectively reduce the reverberation and exhibit different characteristics at the same time. As shown in Figure 5b, much of the reverberation and noise are suppressed by the ISCLP, but the distortion of the desired signal is also serious. Figure 5c shows that in the WPE, most of the reverberation is suppressed and the desired speech is retained well, whereas the noise is hardly suppressed. Figure 5d shows that the proposed algorithm can reduce the noise and reverberation effectively, and can retain the high frequency components of the desired speech. Compared with the WPE algorithm, the proposed algorithm has better ability for suppressing the noise.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 4 Results and Discussion
### Winter Wheat Mapping
#### 4.2.3 Classification Results Using SAR and Optical Images  
As mentioned above, winter wheat has the highest separability when the optical image corresponds to the jointing period. Thus, in the present study, we selected optical images on 28 March and SAR data to combine. As shown in Table 4, the classification accuracy was improved when multi-temporal SAR and optical images were combined. The application of RF allowed us to increase the overall accuracy from 98.92% to 99.35% (F1 measure = 98.06%). For winter wheat, the addition of SAR images to the optical images increased the F1 measure by 3.23% for RF, and 1.48% for SVM, respectively; SVM was better than RF in terms of the overall accuracy and kappa coefficient, but the F1 measure was lower than RF. For other areas, the classification accuracies were also improved. Some earlier studies have also shown that the classification accuracy could be improved when SAR and optical images were combined [26; 33; 57; 65]. The reason might be that the optical image contains rich spectral information, SAR data has more space texture information, and the combination of SAR and optical images can make up the defects of two kinds of remote sensing images in crop identification to improve the classification accuracy. The results indicated that SAR data is not only a suitable substitution to optical images of crop classification in urban agriculture regions, but also as a supplemental data source, especially in cloud-prone areas.  
In this paper, satisfactory classification accuracy was achieved in urban agricultural regions with fragmented planting parcels, and higher classification could be achieved in areas with less-fragmented parcels. Therefore, the results of this study have good application potential for winter wheat classification in other areas.