esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis and Optimization  
Considering the aggregation threshold, the aggregation timer, the data aggregation ratio, the probability and the service requirement as the dominant parameters that determine the performance of the data aggregation during the transmission process, simulation experiments implemented by C++ programing language in different scenarios are conducted in this section.  
The density of nodes and the number of layers do not affect the performance of networks. Some experiments are designed, and the service guarantee rate of each network are compared. Table 4 proves that the structure of network does not affect the performance. Thus, the structure parameters of the network in simulations are constant, the radius of the network is 100 m, the radius of transmission of all nodes is 20 m and the number of nodes in the network is 1000.  
In a scenario of the simulation, the number of the type of service requirements and the value of each requirement are predetermined, requirements are randomly assigned to the nodes. The averages are calculated based on the value of predetermined requirements. The average gap is the average difference between the value of requirements in a scenario. To present the scalability of DDAR, the environment parameters are shown in Table 5. A set of service requirements are described as [Q, average \(v(Q)\), average gap of each Q].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(L\) = 4 & \(L\) = 5 & \(L\) = 6 & \(L\) = 7 \\ \hline \(\mathcal{N}/L=10\) & 97.61 & 97.66 & 97.73 & 97.71 \\ \(\mathcal{N}/L=30\) & 97.53 & 97.66 & 97.81 & 97.74 \\ \(\mathcal{N}/L=50\) & 97.65 & 97.73 & 98.02 & 98.42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Service guarantee rate (%).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(R\) & 100 (m) \\ \(r\) & 20 (m) \\ \(\mathcal{N}\) & 1000 \\ \(P_{a}\) & \{0.1, 0.5, 0.9\} \\ \(\lambda\) & \{0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9\} \\ \(\eta\) & \{0.02, 0.05, 0.1, 0.2, 0.25\} \\ \(Q\) & \{3, 5, 7\} \\ Q & \{3, 50, 30\},[5, 50, 15],[7, 50, 10] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.  
In a simulation, 200,000 packets are generated in the whole network. 200 simulations are conducted for the same setting and get the average value of each indicator. To demonstrate the optimization effect, the performance of DDAR is compared to the CDAR network with the same environment. The Common Data Aggregation Routing (CDAR) scheme is that: in CDAR scheme, all the aggregators are homogenous in aggregation threshold and aggregation timer, which means that there is no differentiated aggregation routing, data packets with different service requirements are handled as the homogenous data packets.  
We first study the performance of the optimization in various scenarios regarding service guarantee rate in Section 5.1. In Section 5.2, we study the optimization performance regarding network lifetime. The optimization on energy consumption is shown in Section 5.3. Finally, we compare the performance of the common DDAR scheme and the improved DDAR scheme in Section 5.4.
# Failure Analysis of Gantry Crane Slewing Bearing Based on Gear Position Accuracy Error
## 3 Analysis of Results  
### Mechanical Simulation Results  
#### 3.1.1 Analysis of Deformation Results  
After the contact parameters and boundary conditions are set, the assembly model is analyzed and calculated. The overall deformation of the assembly model is shown in Figure 12. As can be seen from the figure, under the actual working conditions of loading, the rotary table structure, the slewing support, and the cylinder structure all produce horizontal displacement in the \(x\)-axis direction relative to the initial position, which is consistent with the deformation trend during the actual operation.  
From Figure 13, it can be seen that the inner and outer rings of the slewing bearing and the rollers are displaced in the direction of the \(x\)-axis (where the arm is located), and the displacement produced by each part is not of the same size. A larger displacement is produced among the slewing bearing inner ring, with the upper row of the roller and radial roller reaching 15.0017 mm. The displacement of the outer ring of the slewing bearing and the lower row of the roller produced is relatively small. The slewing bearing inner and  
Figure 11: The actual location and method of laying the strain gauges.  
Figure 12: Overall deformation of the assembled body model after loading.  
outer rings between the gap change, the upper gap becomes smaller, and the lower gap becomes larger. From this result, it can be obtained that the initial contact relationship of the internal structure of the slewing ring has changed, and the inner and outer rings have produced different deflection situations, which are consistent with the trend of load action. In order to determine whether this situation has an effect on the axis parallelism of the gear, the meshing relationship between the pinion and the large ring of the slewing ring needs to be further studied.
# Explainable Artificial Intelligence: Advancements and Limitations
## 5 Conceptual Explanations
By doing so, post hoc CBMs maintain the model intervention property of traditional CBMs while avoiding the high costs of concept annotation for every image thanks to CAVs. This approach efficiently brings together the flexibility of CAVs and the structured decision-making process of CBMs, allowing for more scalable and transparent model analysis. Even though post hoc CBMs were shown to be effective as a global explicator via model editing experiments, their concept prediction and localisation abilities are under-explored. This is due to post hoc CBMs not being trained to explicitly predict the concepts unlike traditional CBMs, which hinders the possibility to do any direct evaluation on individual concept predictions. Another methodology that aims to reduce the concept annotation cost, the CounTEX framework, connects image classifiers with textual concepts, leveraging a multi-modal embedding space, such as that provided by CLIP [111], to generate counterfactual explanations. It aims to explain classifier decisions by identifying and quantifying the contribution of specific, human-interpretable concepts derived from text. By mapping between the latent spaces of the target classifier and the CLIP model, CounTEX creates a projection mechanism to explain both correct and incorrect classifications in terms of these textual concepts. This approach aims to address the challenge of concept-annotated datasets requirement by utilising text-driven concepts [112]. An essential aspect of concept-based interpretability is the accurate prediction and localisation of highly important concepts. For example, if an animal classifier identifies an image as an _antelope_ and indicates the _horn_ as a critical concept for this prediction, the _horn_ should not only be present in the input image but also activate the network in a way that aligns with the image region containing the _horn_. While several methodologies discussed earlier provide relatively efficient solutions for concept-based explanations, their outputs are typically limited to a single level of abstraction. This means they lack the visual components necessary to highlight the precise regions corresponding to concepts in a given image. Furthermore, there is a noticeable absence of standardised evaluation metrics and benchmark datasets, making it challenging to compare these methodologies and comprehensively assess their strengths and limitations. These gaps in concept-based explanation methodologies are addressed in [75], where the authors introduced their novel approach, _Multilevel XAI_, that generates both concepts and their corresponding heatmaps within the input image. Additionally, to address the lack of evaluation tools, [PERSON] et al. [113] proposed novel metrics for concept prediction and localisation while advocating for the use of an existing dataset, Caltech-UCSD Birds (CUB) [114], as a benchmark to evaluate concept-based explanation methodologies. These contributions enable more robust comparisons and comprehensive assessments of concept-based XAI methodologies.
## 3 Algorithm Description
### Sending and Reading phase  
Following the backoff period, the reader proceeds with frequency control. If CSMA is indicated, it awaits beacon messages on the control channel for a specific period (Tmin). In the absence of beacons, the reader designates a free frequency in its table as \"FDMA\". Subsequently, the reader initiates communication with neighboring readers through REQUEST1 frames, with the aim of frequency reuse. In the event of no response, the reader broadcasts a REQUEST2 message to identify a reader using a new frequency.  
Upon completing the sending phase, the reader can utilize the data channel for tag queries. If the reader has an assigned frequency (FDMA), it accesses the concurrent access channel directly. Otherwise, if no frequency is assigned, the reader resorts to CSMA for accessing the concurrent access channel.  
By following these systematic steps, the reader efficiently establishes frequency allocation, engages in communication with neighboring readers, and accesses the data channel.
## 4 Video Compression Sub-Application Layer Protocol for WVSN
### M-Frame Transmission in Dynamic Periods  
MPEG-2 transfers I-Frames at regular periods. Thus, it requires extra bandwidth and has high energy consumption. M-MPEG model uses the adapted period to transmit M-Frame because in the WVSNs applications camera and background are often stationary and no fast moving object exists. This idea leads to many constraints of VSNs to be observed on the part of the M-MPEG. These adapted periods are selected in accordance with variations of background that is described in the following.  
The proposed adapted M-Frame transmission is based on the number of pixels that should be transmitted in M-Frame and D-frame packets. Therefore, two functions were defined to determine when an M-Frame should be transmitted instead of a D-Frame. These functions are called SPMF (Sending Pixels in M-Frame method) and SPDF (Sending Pixels in D-Frame method).
# CCFont: Component-Based Chinese Font Generation Model Using Generative Adversarial Networks (GANs)
## 7 Evaluation
### Quantitative Evaluation  
For quantitative comparison, we chose L1 and L2, Structural Similarity Index (SSIM) as our pixel-by-pixel difference metrics and FID (Frechet Inception Distance) scores [30] for distribution difference metrics. The results are presented in Table 2. Unseen TC 300 Chinese characters and fonts were used in each model to generate these results, and the values were calculated by comparing them with the ground truth. The zi2 zi model requires fine-tuning, whereas the MX-Font and CCFont models do not. We also evaluated the CCFont model for both TC and SC characters.  
As shown in Table 2, the L1/L2 loss of the CCFont was the smallest. In contrast, the structural similarity of the zi2 zi model was the highest, mainly because it requires extra fine-tuning, which is computationally expensive and time-consuming. The CCFont is slightly lower than the zi2 zi model, and it shows that it is still a very high-quality result compared to the MX-Font. CCFont showed the best FID score, indicating that the lower the score, the closer it was to the original. Figure 23 shows a bar graph of the values in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**INDEX** & **L1-Loss \(\downarrow\)** & **L2-Loss \(\downarrow\)** & **SSIM \(\uparrow\)** & **FID \(\downarrow\)** & **Ref** \\ \hline
**zi2 zi** & 0.5104 & 0.4224 & **1.1619** & 104.8 & w/ finetuning \\
**MX-Font** & 0.5388 & 0.5360 & 0.3276 & 132.5 & w/o finetuning \\
** CCFont** & **0.2728** & **0.2689** & **0.8808** & **29.2** & w/o finetuning \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparative evaluation of the proposed method and the baselines.  
Figure 21: Failure examples (font styles 3 and 5).  
Figure 22: Failure examples of a character. Out, src, and tgt represent output, source, and target images, respectively.  
As shown in Figure 23, the L1/L2 loss value of CCFont was the best, and there was a significant difference between the models in terms of the degree of similarity. The result of zi2 zi was the best, and the result of CCFont can be considered very good, as no additional training for unseen fonts was performed. MX-Font, which requires a large number of font styles, had a very low SSIM score because it has fewer font styles.  
Figure 24 compares the FID scores for each model and shows that CCFont had the lowest scores, closest to the original image.
## 3 Methodology
### Soft Pool  
Pooling operations are applied to capture the most important characteristics and reduce feature map dimensionality after convolutional operations [33]. Specifically, the pooling operation splits the input feature map into many regions that are not overlapping, with each region taking a representative value (such as maximum, average, etc.) as the output feature value. The pooling operation can decrease the model's computational complexity while retaining important information and improving its robustness. Max pooling and average pooling are the main pooling operations in convolutional networks [34].  
The max pooling method takes the maximum pixel value of the pooling region as the output feature value, preserving the texture features of the input images but may lose some useful background information [34]. The average pooling method computes the mean value of pixels in each pooling region as the output value, preserving the overall information of the image, but is more sensitive to noise than other pooling methods [34]. Thus, [PERSON] et al. designed the soft pooling method, which calculates the weight of each pixel in the pooling region, then multiplies each pixel by its corresponding weight, and sums them up [35]. Soft pooling does not simply calculate the maximum or average value of pixels in the pooling region as the representative feature but calculates the representative feature based on the softmax weighting method [35]. Soft pooling balances the effects of average pooling and max pooling while utilizing the beneficial characteristics of both. In this study, to reduce the loss of important texture features in the downsampling step, the soft pooling methods are a substitute for the max pooling methods in the downsampling of 3D UNet.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 4 PEAR Solution: Model Design and System Implementation
### PEAR Energy Solution  
In real use case scenarios, to ensure the economic and operational efficiency, battery-powered IWSN field devices require scheduled maintenance procedures, such as battery replacement for a subset of field devices having the same lifetime expectancy. IWSN maintenance procedures involve variable costs directly related to field device service and fixed costs associated with factory logistics and downtime. To minimize the fixed costs, the IWSN maintenance cycles should be predictable on a field device cluster basis.  
To match IWNNs requirements (see section on problem definition and prerequisites), energy profiles are defined as the maximum energy consumption rate required to meet the lifetime expectation of field devices. From the PEAR algorithm perspective, the energy consumption rate is equivalated to the number of datalink protocol data units (DPDUs) transmission (Tx) and reception (Rx) opportunities per minute. Each DPDU Tx/Rx opportunity takes place in a ISA100.11a timeslot of 10 ms. A set of field devices having the same energy profile defines a profile cluster.  
The PEAR algorithm aims to balance the energy consumption between and within profile clusters in order to meet the lifespan demands of all devices in a cluster. Moreover, if the energy consumption is below the Rx and Tx threshold for all devices associated with a profile, the energy consumption is not further balanced.  
Depending on the communication needs and the battery capacity, the field devices have different unallocated energy margins than can be used to route data packets from other devices while respecting their expected lifetime. These margins create an energy potential in the network that is discovered and leveraged by PEAR. To simplify the scenario premises, the battery capacity is considered the same for all field devices belonging to an energy profile.  
In this paper, the network lifetime is evaluated on a per energy profile basis, and it represents the time until the first field device within a profile depletes its energy capacity.
## Materials and Methods
### Extraction and Isolation  
The dried leaves of PFCA (300 g) were pulverized and then extracted by SC-CO\({}_{2}\) extraction method using a laboratory-scale supercritical fluid extraction system (Ilshin Autoclave Co., Daejeon, Korea). The powdered sample was placed into the extraction column of SC-CO\({}_{2}\) extractor. The predetermined conditions are as follows: pressure, 400 bar; temperature, 40 \({}^{\circ}\)C. The flow rate of CO\({}_{2}\) (99.9%) was constant at 60 mL/min, for an extraction time of 3 h. The oil was collected 13.81 g (4.6% \(w/w\)) and stored in a refrigerator at 4 \({}^{\circ}\)C. A part (2 g) of the extract was subjected to RP-C\({}_{18}\) CC (MeOH-water, 1:1 to 9:1, \(v/v\)) to yield 11 fractions (F01-F11). Fraction F03 (25 mg) was chromatographed on sephadex LH-20 using 100% MeOH to give five sub-fractions (F0301-F0305). The third fraction (F0303, 11 mg) was purified by preparative HPLC (YMC Triart C18,MeOH-water = 2:3, 4 mL/min, UV 280 nm) to yield **1** (\(t_{\rm R}\) 21.03 min, 6 mg). The dried leaves of PFC (300 g) were extracted by the SC-CO\({}_{2}\) extraction method under the same conditions as mentioned above, affording 14.84 g of oil extract (4.9% \(w\)/\(w\)) was obtained. A part (1 g) of the extract was subjected to RP-C\({}_{18}\) CC (MeOH-water, 2:1 to 4:1, \(v\)/\(v\)) to yield twenty fractions (F01-F20) and pure compounds **2** (2 mg), **3** (5 mg), and **4** (9 mg).  
9-Hydroxy-isoegomaketone ((\(ZE\))-1-(3-furanyl)-4-hydroxy-4-methyl-2-penten-1-one; **1**). Colorless oil. UV (MeOH) \(\lambda_{\rm max}\) (log \(\varepsilon\)) 230 (3.69), 254 (3.44) nm; \({}^{1}\)H-NMR (CDCl\({}_{3}\), 500 MHz) \(\delta\) 8.81 (1H, br s, H-5), 7.46 (1H, s, H-2), 7.08 (1H, d, \(J\) = 15.3 Hz, H-8), 6.84 (1H, br s, H-4), 6.77 (1H, d, \(J\) = 15.3 Hz, H-7), 1.40 (6H, s, H-10 and H-11); \({}^{13}\)C-NMR (CDCl\({}_{3}\), 125 MHz) \(\delta\) 184.6 (C-6), 153.4 (C-8), 147.6 (C-5), 144.4 (C-2), 128.4 (C-3), 122.7 (C-7), 109.1 (C-4), 71.3 (C-9), 29.6 (C-10 and C-11); HREIMS \(m\)/\(z\) 180.0788 [M]\({}^{-}\) (calcd. for C\({}_{10}\)H\({}_{12}\)O\({}_{3}\), 180.0786).
# Experimental and Numerical Study on Dynamics of Two Footbridges with Different Shapes of Girders
## 3 Results and Discussion
### Comparison between the Experimental and Numerical Results
#### 3.1.2 Example 2  
A comparison between the experimentally and numerically obtained frequencies, together with the corresponding damping ratios and MACs of the arch bridge above the University Route in Bydgoszcz, are shown in Table 5. The mode shapes are also displayed in Figures 17a and 18a depending on the type of excitation. Additionally, the representations of MAC matrix between the FEM model and filed test mode shapes are shown in Figures 17b and 18b. The identified vibrations correspond to the first and the fifth bending modes of the deck marked as B_Vd1 and B_Vd5 of the FEM model. Torsional mode shapes B_Td1 and B_Td2 were also determined with high accuracy. It was also possible to accurately identify the La4 mode shape, which was characterized by a relatively high coefficient of vertical vibration of the deck. It can be concluded on the basis of created MAC matrices (Figures 17b and 18b) that the first four mode shapes are characterized by a high similarity. Almost identical shapes of vibrations occurred for pairs of vibrations B_Vd1 and B_La4 as well as B_Td1 and B_Td2 with close frequencies. Despite the sinusoidal signal with variable frequency, the third mode shape could not be identified using a vibration exciter. The other four mode shapes were identified with high compliance and MAC value greater than 0.97. With the impulse excitation, five mode shapes were obtained with high accuracy and MAC value close to 1. However, the vertical mode shapes of platform vibration B_Vd2-B_Vd4 could not be identified. Despite various methods of excitation of the structure, including excitation with a sinusoidal signal with variable frequency, it was not possible to determine the frequencies corresponding to these mode shapes. The frequencies of the FEM model provided a fairly good approximation of the actual footbridge frequencies (Table 3). The largest discrepancies (but still close to 5%) were obtained for the fifth mode.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Frequency (Hz)** & **Relative Difference (\%)** & **Damping Ratio (\%)** & **MAC (-)** \\ \cline{2-6}
**Mode** & **Field Test** & **FEM Model** & **\(\Delta\)f** & **\(\xi\)** & **Impulse** \\ \hline Vd1 & 1.76 & 1.70 & 3.5 & 4.2 & 0.99 \\ Vd2 & 3.23 & 3.27 & \(-\)1.2 & 0.4 & 0.99 \\ Vd3 & 4.99 & 4.94 & 1.0 & 0.7 & 0.99 \\ Vd4 & 6.86 & 6.81 & 0.7 & 1.2 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation between experimental and FEM model modal parameters of the arch bridge above expressway S8.  
Figure 16: (**a**) Comparison of experimental vertical mode shapes (field test—blue; FEM model—red) (**b**) Representation of MAC matrix between the FEM model and filed test mode shapes of the arch bridge above expressway S8.
## 4 Results and Discussion
### Design of the Multi-Agent System Architecture (MAS)
#### 4.2.1 Intelligent Entities: Function, Required Data, and Human Interaction  
The design of the multi-agent system (MAS) is based on the integration of various intelligent entities, each of them responsible for managing a specific aspect of the port operation (Table 1). These entities work in a coordinated manner to optimize efficiency and decision-making within the port environment, ensuring more dynamic and automated management. The main intelligent entities of the system are presented below, along with their respective functions, the data they require for their operation, and the level of interaction they maintain with users.