esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 5 Barcode Data Extraction
### Barcode Reconstruction and Recognition  
In the edge image, we determine the colors of modules of the Finder Pattern and the Timing Pattern based on the knowledge of the DM scheme, as shown in Figure 13a. Colors of modules of the data region can be recognized by the topological relationship in the edge image. The reconstruction result is shown in Figure 13b. In addition, Figure 13c shows some incorrect modules in the reconstructed barcode image. The black modules are correct, and the red modules are incorrect.  
The required Reed-Solomon error correction built into Data Matrix ECC200 is able to reconstruct and verify the data scanned for improved accuracy. In addition, the decoding software can correctly decode these symbols with the incorrect modules. Table 2 shows the size, capacity, and error correction features of each Data Matrix format. If the number of error character codes of a barcode reconstruction symbol is not more than the max correctable error of the format, the barcode symbol can be correctly identified.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 4 Experiment
### Experimental Result
#### 4.3.3 Visual Analysis and Validation of Attention Mechanism  
To understand the correlation between visual features and semantic features intuitively, we explored the relationship between the video description and the video content and attempted to analyze their inner links visually. Finally, we proposed a visualization model based on the video description.  
We selected a video in the MSVD and used the method introduced in the previous sections to obtain the salient region of each word in the sentence description model. In Figure 5, it was divided into  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **BLEU-4** & **METEOR** & **CIDEr** & **ROUGE-L** \\ \hline TDDF(VGG+3D) & 37.3 & 27.8 & 43.8 & 59.2 \\ LSTM-GAN & 36.0 & 26.1 & - & - \\ STAT\_GlobFeat & 37.1 & 25.9 & 41.0 & - \\ STAT & **37.9** & 26.8 & **44.0** & - \\ Our S-C-T & **37.9** & **28.4** & 40.6 & **59.3** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different models on Microsoft Research-Video to Text (MSR-VTT).  
Figure 4: Examples of video description result of our model.  
three parts: the beginning of the video, the body of the video, and the end of the video. Additionally, each part displayed four images. The descriptive sentence was \"a cat chases a dog.\" Every picture had the same five video frames with a descriptive word representing the salience response of the descriptive word in the image over time in the top left corner. Furthermore, the luminosity of the salient region had a positive relationship with the intensity of salience response in that region.  
In Figure 5, our model responded effectively to nouns such as \"cat\" and \"dog\". Therefore, our model can differentiate nouns well. Additionally, the strongest salience responses to word \"cat\" were concentrated in the cat. Likely, the salience response to word \"dog\" is accurate, even with the video running. For articles like \"a\", because it is not very relevant to the vision, all regions in the video are equally treated, so there would be no salient regions. Similarly, verbs like \"'chases\", there was no region of salience response initially. However, with the movement of the cat and the dog  
Figure 5: The visual salience diagram based on video description. (**a**) The beginning of video; (**b**) the middle of video; and (**c**) the end of video.  
Meanwhile, in Figure 7, the image contains examples of verbs. For example, there is a verb \"play\" in Figure 7, Row 1. We can see the response to \"play\" is mainly concentrated around the guitar, indicating that the model has a certain response ability to the verb.  
The salience visualization analysis of the video description illustrates the correlation between visual features and the sentence description. Furthermore, it validated the consistency of visual features and sentence descriptions, and established a corresponding effective model.  
Figure 6: Salience response to some images.  
Figure 7: Salience response to the verb.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 5 Results
### Reflection Gain Impact on PNFR Level
#### 5.3.1 Reflection Gain Impact on Function of SNR Level  
In Figures 12 and 13, the PNFR level depending on the reflection gain and the SNR level is presented for configurations with one and two PRS resources, respectively. For this purpose, the observation time of ten 5G frames (100 ms) was chosen. It can be seen that configurations with the same reflection gain increase their PNFR at the same rate, exactly as in the simulation before. They also achieve a maximum PNFR approximately at the same level; in the case of configurations with one PRS resource, the maximum PNFR is 80 dB and the lowest one is 75 dB. It is worth mentioning that PRS-12-1-1-RES is underperforming compared to other configurations with the same reflection gain. It requires a higher SNR to start to increase the PNFR, and it achieves a maximum PNFR at a level of 70 dB. The same situation occurs in the case of configurations with two PRS resources. All configurations, except for PRS-12-1-2-RES, gain in their PNFR at the same rate (considering configurations with the same reflection gain) and achieve a maximum PNFR at approximately the same level of 82 dB. PRS-12-1-2-RES underperforms and requires a higher SNR level to start increasing in PNFR for all reflection gains and achieves a maximum PNFR at a level of 74 dB. In general, lower gain values require higher SNR values to start increasing in PNFR, yet at the end, they achieve a maximum PNFR at a level similar to the others. This suggests that detecting targets with very low reflection gains is feasible under excellent radio conditions. Furthermore, optimizing the PRS configuration according to radio conditions can enhance this capability.  
Figure 11: Root mean square error of range estimation for different PRS configurations (solid lines with asterisk markers) together with PNFR as a reference (dashed lines).
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 7 Introducing the System of Systems Lifecycle Management Model
### Forming the SoSLM Methodology  
The Arrowhead--presented in Section 6--is an ever-expanding framework that continuously introduces new systems or updates existing ones in line with the latest industry trends. As the expectations and requirements associated with Industry 4.0 frequently change [101], a methodology to support the formation and introduction of new systems needs to be developed. As previously emphasized, Arrowhead is SOA-based, built by different levels of systems (Section 6.1). These systems offer services that are used by other systems, internally (local cloud) or even externally (inter-cloud). These are the initial conditions on which the SoSLM approach are developed.  
The primary function of the systems is thus servicing; therefore, the approach of the ITIL service cycle can be the basis and core part of the new model, and it can be considered the main SoSLM cycle. Based on the process engineering frameworks presented by Section 3, an IIoT-specific PLM model is introduced in Section 5. Arrowhead was built by taking into account the main elements of the mentioned PLM approach. In the design of SoSLM, the elements defined in the PLM model can be associated with the SoSLM main cycles and they can be integrated as the SoSLM model's subcycles.  
However, the systems to be developed can be diverse and different in functionality, so it is essential that the main processes--along with the defined main and subcycles--are correctly identified. For a system to be integrated, it is a must that the developers, operators, users, or even other systems use the same tool sets for provision of the previously identified system-specific processes. To this end, a fourth part should also be designed, which brings participants to the same understanding of the tools to be used. The fourth ring can also be considered a kind of toolchain, which is a topic continuously occupying the research community [102; 103]. In line with this trend, the SoSLM also supports the description of toolchains.  
Figure 9 demonstrates the results of the utilized lifecycle management approach. The model is a concentric circle of four circles, where the rings are constant in terms of their functionality.
# GNSS Based Low-Cost Magnetometer Calibration
## 4 Achieved Results  
In total, we performed 20 measurements and calibrated the data at 1-10 min intervals. Then, we determined the direction vector of the calibrated magnetometer. The RMSE error for individual calibration methods was determined and statistically processed. The results of the measurements were comparable to each other. In the case of shorter calibration (1-3 min), the most successful calibration was through the arithmetic mean or linear regression. For a longer time (more than 3 min), the algorithm was more accurate through the search of the calibration matrix. The results can be seen in Figure 7. The boxplot was created using the seaborn 0.11.2 library. The average error in determining the direction based on the magnetometer was around 10\({}^{\circ}\) after a calibration time of at least 5 min. The result was strongly dependent on the track during calibration. The calibration result was more accurate if the vehicle traveled in several directions during the calibration. The path during the calibration in Figure 6 was zigzag, and the result of the calibration after 5 min was 4.33\({}^{\circ}\) when using the search of the calibration data through the matrix.  
Figure 6: Map calibration and subsequent position prediction.  
Figure 7: Boxplot of calibration results.
## 3 Results  
### The Inactivity of Larvae with Coagulation  
To evaluate the effectiveness of the coagulation process using different doses of coagulant on inactive larvae of varying turbidity, jar coagulation tests were conducted with chironomid larvae in synthetic turbid water. The behavior of looping larvae was investigated in relation to turbidity levels, with a particular focus on their activity over time. The results show that the looping activity is significantly reduced after the completion of the sedimentation process, regardless of the turbidity level. When examining the looping activity of different instars, it was observed that only instars 3 and 4 remained active looping until day 4, regardless of turbidity levels. In contrast, instar 1 only demonstrated active looping behavior at low turbidity levels and only for the first hour. At high turbidity levels, only instar 2 demonstrated looping behavior for the first hour (Figure 2).  
The behavior of the inactive larvae was observed for up to 60 min, during which they were seen crawling at the bottom of the beaker and attempting to catch the flocs that formed. However, counting the number of crawling larvae proved difficult as the first instar larvae covered their bodies with floc after 30 min, while the second to fourth instars continued to do so for up to an hour. By the second to the fourth day, almost all the larvae had covered their bodies with floc, and by the fifth day, all the inactive larvae were completely covered with floc, and there was no crawling activity (Figure 3).  
Figure 3: Chironomid larvae looping activity since (**a**) sedimentation process (after slow mixing), (**b**) 0 min after sedimentation (**c**) after 4 days (**d**) after 5 days of the coagulation process. Arrow symbol indicates individual larvae with looping activity.  
Figure 2: Chironomid larvae looping activity after 5 days of the coagulation process. (**a**) Low turbidity, (**b**) high turbidity.  
The results in Figure 4 revealed that exposure to the coagulant caused larvae mortality after 2 days. In low turbidity, one instar 3 larvae was found dead, while in high turbidity, two larvae (instar 3 and 4) were dead. After 5 days, a total of 2 larvae were found dead in low turbidity and 3 larvae in high turbidity. Visual observations of the dead larvae revealed that their body was not covered with floc, and their color had turned pale red. When compared to larvae without coagulant exposure at both turbidity levels, only two larvae died by visual observation from day 3 to day 5.
# A Comparison of the Fuel Consumption and Truck Models in Different Production Scenarios
## 2 Materials and Methods
### Nested Pits Definition  
Optimizing long-term production scheduling is one of the most important aspects of a mining plan. The objective of long-term production scheduling is to determine the timing and sequence of mining [32]. Additionally, it is necessary to determine the movement of materials, ore, and waste, to maximize profit within existing economic, technical, and environmental constraints [33; 34]. The economical block model was used as the input for the optimization process, considering the production for each scenario. The ultimate pit defines what is economically mineable in a deposit, and it was obtained from the Lerchs and Grossmann algorithm [35], which is used in the _Maptek's Pit Optimiser_ module. Table 3 presents the economic parameters used in the optimization process; in addition to the costs already described, the discount rate is also included, which is one of the parameters of greatest interest when analyzing the feasibility of a project. For this study, a value of 8% was used, which is common in feasibility-level projects [36].  
For more information about this algorithm, authors such as [PERSON] [7] have made a review of models and algorithms in large-scale optimization. Additionally, [PERSON] et al. [33] analyzed some strategic mining optimization options in open pit mining and underground mining, maximizing NPV and comparing results in terms of tons mined and Stripping Ratio (SR). The algorithm evaluates the economic value of each sub-block and parent block, considering the costs required for its extraction and processing, the revenue from its subsequent sale, and the release costs of the associated waste [31]. If this value is positive, it is considered a suitable ore for extraction and processing, and otherwise it will be waste [10].  
Within the ultimate pit, the deposit is divided into nested pits ranging from the smallest pit, with the highest value per ton of ore, to the largest pit, with the lowest value per ton of ore [7]. Nested pits have very close boundaries, with minimal tonnage differences from one boundary to another. However, if the orebody grade increases sharply with depth, or the orebody is discontinuous, pushbacks are designed because a significant tonnage difference occurs between the nested pits.
# Honey Therapy in Diabetic Foot Ulcers: A Promising Strategy for Effective Wound Healing
## 3 Honey as an Antimicrobial and Anti-Inflammatory Agent
The in vitro evidence suggests that honey might initiate healing in recalcitrant wounds by accelerating several mechanisms such as the desquamation of devitalized tissue, the mobilization of macrophages to the wound bed, increasing cellular energy sources, enhancing granulation and epithelization, and preventing infection [83]. Moreover, honey might improve wound healing by preventing the biofilm formation of _S. aureus_, _Streptococcus pyogenes_, and _P. aeruginosa_ by impairing bacterial binding at wound sites and on the surfaces of keratinocytes [86]. In mature wound biofilms, manuka and honeydew honey were alsoable to decrease the viability of _S. aureus_, _Streptococcus agalacticiae_, and _P. aeruginosa_ within 48 h [39]. This evidence suggests an advantage for honey in chronic wound treatment. In vivo, honey is widely reported as a safe, cost-effective, and beneficial dressing biomaterial for wound healing [18]. C57 BL/6] mice treated with 50 uL of undiluted indigenous New Zealand rewarewa honey were able to inhibit edema and leukocyte infiltration after four hours of application, supporting honey's anti-inflammatory activity, which targets the neutrophil respiratory burst, recruitment, and swelling [87]. In humans, treating acute wounds such as burns with honey significantly reduced the healing and infection time due to honey's antimicrobial, cell proliferation, and anti-inflammatory effects [88]. Similarly, chronic wounds, such as venous leg ulcers, treated with honey demonstrated increased rates of healing with lower rates of infection than with hydrogel [89]. In patients with pressure ulcers at stages II and III, five weeks of treatment with gauze dressings embedded with monofloral unprocessed honey decreased the ulcer size when compared to patients treated with ethoxy-diamino-acidine and nitrofurazone dressings [90], showing honey's effectiveness in wound healing as an advanced treatment.
## 2 Data and Method
### Methods
#### 2.2.2 The DEM Generation  
Point cloud filtering is the process of separating ground points and non-ground points in the airborne LiDAR point cloud data. The obtained ground points can generate a DEM. Triangulated Irregular Network progressive adaptive filtering algorithm is a universal algorithm which was first proposed by [PERSON] [59]. As the Figure 3 shows, the algorithm first selects the initial seed point to construct an initial TIN, and then gradually judges and adds other points to the ground points. The criteria are the angle and vertical distance from this point to the original triangular plane. When the angle and vertical distance are both less than the set thresholds, the point is classified as a ground point. The specific steps of the algorithm are as follows:  
\begin{table}
\begin{tabular}{l c} \hline
**Properties of the Data** & **Contents** \\ \hline Attitude of points (m) & 1000 \\ \hline Points density (pts/m\({}^{2}\)) & 20 \\ \hline LiDAR scanner type & riegl-vq-1560i \\ \hline Overlap of flight lines & 20\% \\ \hline Horizontal accuracy(cm) & 15–25 \\ \hline Vertical accuracy & 15 \\ \hline Flight platform & Cessna 208b aircraft \\ \hline \end{tabular}
\end{table}
Table 1: The details of the test data in Hupao National Park.  
Figure 1: The information of the test site and the ten sample plots. (**a**) The location of the ten test plots. (**b**) The plots with green circles are selected for the experiment. (**c**) 3D overview map of LiDAR points data for the 10 test sample sites.  
1. Selection of initial seed points. The ten plots in this study are all forest plots without buildings, so the default value of 20 m is used as the grid length to divide, and the lowest elevation point in each grid is taken as the starting seed point for the algorithm.
2. Construction of a triangulation network. Use of the starting seed point to construct a sparse TIN as the initial triangulation.
3. Iterative processing. In a certain iteration, judge the points one by one based on the initial triangulation. Take point P as an example: calculate the distance from P to the TIN and the maximum angle between the three vertices of the triangle; if both are within the set threshold ranges, mark P as a ground point and add it to the TIN (otherwise P is classified as a non-ground point). Repeat this process until all points that meet the threshold conditions are classified as ground points. The next iteration obtains new ground points until no more points meet the threshold conditions and the iteration stops.  
The experimental results show that the filtering algorithm based on the irregular triangle network has fewer iterations and performs well for most terrain data.
# Joint Pricing, Server Orchestration and Network Slice Deployment in Mobile Edge Computing Networks
## 4 The Three-Stage Stackelberg Game
### Construction of the Three-Stage Stackelberg Game  
In Figure 2, we present the three-stage Stackelberg game, which can be considered as containing two two-stage Stackelberg games. In the first two-stage Stackelberg game, the IPL is the leader, while the NPL and the RAL are the followers. In the second two-stage Stackelberg game, the NPL is the leader, while the RAL is the follower. Next, we will demonstrate the process of the three-stage Stackelberg game along with Figure 2. In the beginning of each game, the IPL first speculates on the reactions of the NPL and the RAL to decide the prices and thus maximize the objective function of \(P1\). Then, the IPL sends the pricing scheme to the NPL and RAL (arrow A in Figure 2). Once the NPL receives the pricing scheme, it first speculates on the reactions of the RAL to determine the server activation and slice deployment scheme with the goal of \(P2\). Then, the NPL sends its scheme to the RAL and feeds the number of activated servers back to the IPL (arrow B). Once the RAL receives the IPL's pricing scheme and the NPL's scheme, it decides the resource allocation scheme with the goal of \(P3\). Then, the RAL feeds the resource allocation scheme back to the IPL and the NPL, who calculate their own utilities based on the feedback (arrow C). Note that it has been pointed out in [37] that the utility of the leader and follower can be the same in the Stackelberg game. Therefore, using the same utility function for the NPL and RAL is reasonable.