esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experimental Results and Comparisons  
#### 3.2.1 SSR Luminance Decomposition Results Based on Adaptive Bilateral Filtering  
The RGB decomposition of the obtained background segmentation image was performed first, as shown in Figure 8. It could be seen that the defects had the greatest contrast with the normal surface in the R-component map, so the R-component map was enhanced as the original image for SSR image enhancement.  
The brightness decomposition of SSR (filter size 40 \(\times\) 40), MSR (filter size 20 \(\times\) 20, 30 \(\times\) 30, 40 \(\times\) 40) based on bilateral filtering, and SSR (filter size base size 40 \(\times\) 40) based on adaptive bilateral filtering were carried out for Figure 8a, respectively, and the results for the reflection layer obtained are shown in Figure 9. As could be seen from the figure, compared with the SSR algorithm, the decomposition results of the improved SSR algorithm in the normal surface region were basically the same as the SSR decomposition results, and the smoothing region did not cause much change in the filter strength. Compared to MSR, the decomposition results of the improved SSR algorithm in the scar region were equally good in preserving the inner edges of the scars with a clearly visible texture. Meanwhile, the scar information at the edge was also recovered by appropriately reducing the intensity of bilateral filtering close to the image edge. As could be seen from Figure 9c, when the scar appeared at the image edge, a slight interference region appeared at the image edge scar.  
Figure 8: RGB color space decomposition results. (**a**) Original picture; (**b**) R-component picture; (**c**) G-component picture; (**d**) B-component picture.  
This may be caused by the fact that the bilateral filter adaptive adjustment method made both spatial and gray domain weight coefficients appear to be adaptively reduced, and the adjustment effect of both was superimposed. Further discussion on this issue is required in the follow-up.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 4 Common Preparation Methods
### Gas Phase Method
#### 4.3.3 Molecular Beam Epitaxy (MBE)  
Molecular beam epitaxy (MBE) is a newly developed method of film production and is also a special vacuum coating process. MBE is a new manufacturing technology for single crystal films [189; 190; 191; 192; 193]. MBE means to spray a hot atomic beam or molecular beam onto the surface of a heated substrate under vacuum conditions and react with the surface of the substrate to deposit the film.  
The advantages of MBE include the facts that the beam intensity is easy to control precisely, the surface morphology of the epitaxial material can be made into a multi-layer structure of different compositions, and the composition and doping of the epitaxial layer can be changed at will. MBE can accurately control the thickness of the epitaxial layer, the doping profile of the heterojunction interface, and the flatness of the heterojunction interface, which is beneficial for improving the purity and integrity of the epitaxial layer. MBE has made it possible to control the precise growth of atomic layers and subsequent atomic layers. MBE can produce thin monocrystalline thin films on the order of tens of atomic layers and can also produce thin films with different doping and different compositions.
## Validation Methodologies of the Proposed Method
The impact of the proposed method on positioning accuracy improvement is demonstrated by down-weighting the detected multipath measurements with a simple linear method based on the output of random forest:  
\[\mathrm{w}=\frac{\mathrm{n}_{\mathrm{tree}}}{4\mathrm{n}_{\mathrm{m}}- \mathrm{n}_{\mathrm{tree}}}\ (\mathrm{n}_{\mathrm{m}}\ >\ \mathrm{n}_{\mathrm{tree}}/2) \tag{10}\]  
where \(\mathrm{w}\) is the weight multiplier of a measurement in the weight matrix, \(\mathrm{n}_{\mathrm{tree}}\) is the total number of trees in random forest, and \(\mathrm{n}_{\mathrm{m}}\) is the number of trees in random forest that votes for multipath class. The work described in this paper is multidisciplinary research. In order to further help readers who may not be familiar with both multipath errors in GPS measurements and machine learning theory, a summary of key objectives and features of the paper and the proposed method is given below:  
* The proposed method aims to detect multipath errors in carrier phase measurements (not signals). The multipath errors in measurements are the output of processed signals from receiver hardware. Therefore, the multipath errors to be detected in carrier-phase measurements are the results of the combination of antenna architecture and receiver signal processing architecture (including correlator design). Characteristics of multipath errors in carrier phase measurements of combinations of antennas and receivers are very similar. Factors affecting the phases and magnitudes of carrier phase multipath errors can be found in [3]. * The proposed method aims to detect multipath errors. The definition of multipath errors is described in Section 1.1 and [3]. Non-line-of-sight only measurements are not multipath contaminated measurements; therefore, the non-line-of-sight only problem is outside the scope of this investigation. * The proposed method doesn't rely on the repeated multipath characteristics in sidereal days, and doesn't need multipath pattern/signature to build up in the previous consecutive measurement epochs in real-time implementation of the multipath detection. A machine learning classifier can learn the characteristics of multipath errors in carrier phase measurements from CNR and MP data in the training datasets as described in Section 2.
## 5 Proposed Solution
### Proposed Mechanism  
Our proposed solution is done in eight steps. Some premises are assumed, such as a neighbor coefficients table is created in each sensor node when it starts; a coefficients table is created in the sink; every sensor node remains in promiscuous mode and it stores neighbor coefficients; sampling window must be suitable to maximum size of the packet and defined early by the developer. Figure 4 shows the mechanism according to the steps detailed below:  
1. Step #1: the sensor node stores a fixed number of samples of gathered readings from all the variables in each cycle.
2. Step #2: each sensor node calculates coefficients \(\beta\) and \(\alpha\) of the multiple linear regression function when the sampling window reaches the maximum storage threshold previously defined.
3. Step #3: before sending its \(\beta\) and \(\alpha\) coefficients to the sink, the sensor node looks for duplicated entry in its neighbor coefficients table. These coefficients are received from its neighbor sensor nodes by broadcast.
4. Step #4: if the values generated by the sensor node have already been sent to a neighbor sensor node, the sensor node drops its \(\beta\) and \(\alpha\) coefficients. Then, it sends a special packet of reduced size, named correlation packet. This packet advertises that the sensor node is correlated to another neighbor sensor node.
5. Step #5: if coefficients \(\beta\) and \(\alpha\) have not been sent yet by another neighbor sensor node, the sensor node sends them to its parent node until the sink is reached.  
* Step #6: the sensor node also sends the sequence of variable readings which is used as independent variable. It is worth mentioning that this variable is calculated by using [PERSON]'s coefficient [Equation (2)]. In our experiment the independent variable is the temperature.
* Step #7: when coefficients \(\beta\) and \(\alpha\) reach the sink, they are used in the multiple linear regression function to predict the readings which have not been sent. Moreover, these coefficients are stored for later use by the correlation packets (Step #4).
* Step #8: if a correlation packet reaches the sink instead of the coefficients, the sink looks for entries from the correlated node in its coefficients table (Step #7). Then \(\beta\) and \(\alpha\) coefficients previously stored, are used to predict the readings.
# Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection
## 6 Conclusions  
Edge detection of images can be used for routine maintenance for bridges. When combined with drone photography technology, it not only especially saves a lot of manpower, but also allows people to focus on the areas that are not easily observed. However, there are many images with complex background in the inspection process. Conventional inspection algorithms do not perform satisfactorily.  
The main contributions of the authors of this paper are:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Information Entropy** & **SSIM** \\ \hline Wavelet transform modulus maxima method & 2.5796 & 0.0030 \\ [PERSON] & 2.4989 & 0.0030 \\ [PERSON] & 2.1128 & 0.0027 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information entropy and SSIM for the three algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Information Entropy** & **SSIM** \\ \hline Background single image (Figure 2) & 2.5691 & 0.0023 \\ Background complex image (Figure 11) & 2.5796 & 0.0030 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information entropy and SSIM for the two types of images.  
1. Facing images with more complex backgrounds, a combination of wavelet transform mode maxima and OTSU is used to perform edge detection on them, which improves the overall edge detection effect while replacing the traditional practice of manually debugging thresholds and improving the efficiency of edge detection;
2. Information entropy and SSIM are used as evaluation metrics to quantitatively evaluate the detection effect when evaluating the merits of the detection effect. According to these two metrics, the difference between different processing results can be reflected more objectively, which is more convincing compared to the artificial subjective evaluation.  
In summary, compared with the traditional method, the combination of wavelet transform mode maxima and OTSU has advantages in both efficiency and effectiveness in the process of image edge detection, and good prospects for engineering applications.  
Conceptualization, N.Y. and L.H.; data processing, N.Y. and L.H.; writing--original draft preparation, N.Y.; writing--review and editing, N.Y., L.H. and Y.L.; supervision and project administration, D.Z., X.Z. and W.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Supported by Major scientific and technological projects of Yunnan Province; Research on Key Technologies of ecological environment monitoring and intelligent management of natural resources in Yunnan (202202 AD080010).  
Not applicable.  
The data are not publicly available as they involve the subsequent application of other studies.  
The authors would like to thank the editors and the anonymous reviewers for their valuable suggestions.  
The authors declare no conflict of interest.
## 2 Routing Protocol for LLNs
### Upward Topology Construction and Maintenance  
A node wanting to join a DODAG has INFINITE_RANK. To become a parent, it must (re-)attach to the DODAG and obtain a new rank by selecting a preferred parent. A node is attached when its parent set is not empty. During (preferred) parent selection, a node computes the rank it will itself advertise in DIOs. This process is governed by an Objective Function (OF). The OF used in an RPL instance is uniquely identified by the Objective Code Point (OCP) field of the DODAG Configuration option. According to RFC 6550 [4] (p. 67), \"the candidate neighbor set is a subset of the nodes that can be reached via link-local multicast. The selection of this set is implementation and OF dependent.\" From this set, a node filters candidate parents, as prescribed by the OF. Nonetheless, universal rules apply:  
* All candidate parents must belong to the same DODAG version as the node itself.
* Within a DODAG version, a node's advertised rank must be greater than the rank advertised by any of its parent set members.  
The OF's main objectives are preferred parent selection and rank computation. Typically, these operations require a routing metric [16]. It is useful to distinguish between: (1) a metric that a node can infer from the link towards a neighbor (a link metric) or based on a property it possesses (a node metric); and (2) a metric reported by a neighbor, based on the upward path it provides. For example, Objective Function 0 (OF0) [17] is commonly used. With OF0, forming a parent set and, subsequently, selecting a preferred parent requires a node to know what rank it would obtain if it were to choose a given candidate neighbor as its preferred parent. That is, while observing universal rules concerning the DODAG version and advertised rank, the parent set contains two nodes for which the resulting rank would be lowest, i.e., one preferred parent and a backup feasible successor. The generic equation for rank computation (that is, with OF0) is given by (1).  
\[R(n)=R(c)+\Delta R_{c}\, \tag{1}\]  
where:  
Figure 3: Format of a DODAG Configuration option. Note that a DODAG Configuration option is an RPL Control Message option with option Type \(=\) 0x04. In practice, it is only ever used to append DIO messages.  
A common OF0 alternative is the Minimum Rank with Hysteresis Objective Function (MRHOF) [11]. With MRHOF, parent set formation/preferred parent selection involves calculating the \"path cost\" through every neighbor, keeping a number of neighbors through which the path cost is lowest (whilst observing universal/MRHOF-specific rules) as parents and selecting the lowest-cost parent as preferred if the cost improvement over the current preferred parent exceeds a threshold (=hysteresis component). Then, after rank (\(\
eq\)path cost) computation for every parent, a node determines the rank (and possibly metric) it will advertise in future DIOs. RFC 6719 [11] provides a more in-depth explanation.
_Appl. Sci._**2014**, \(4\), 331-350; doi:10.3390/app4030331  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Selection of a Visible-Light _vs._ Thermal Infrared Sensor in Dynamic Environments Based on Confidence Measures**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{1,2,*}\) and [PERSON] \({}^{1,2}\)**  
\({}^{1}\) Instituto de Investigacion en Informatica de Albacete, Albacete 02071, Spain;  
E-Mails: [EMAIL_ADDRESS] (J.S.-C.); [PERSON]SS] (M.T.L.)  
\({}^{2}\) Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, Albacete 02071, Spain  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]SS];  
Tel.: +34-967-599-200; Fax: +34-967-599-224.  
_Received: 16 June 2014; in revised form: 26 July 2014 / Accepted: 4 August 2014 / Published: 8 August 2014_  
**Abstract:** This paper introduces a confidence measure scheme in a bimodal camera setup for automatically selecting visible-light or a thermal infrared in response to natural environmental changes. The purpose of the setup is to robustly detect people in dynamic outdoor scenarios under very different conditions. For this purpose, two efficient segmentation algorithms, one dedicated to the visible-light spectrum and another one to the thermal infrared spectrum, are implemented. The segmentation algorithms are applied to five different video sequences recorded under very different environmental conditions. The results of the segmentation in both spectra allow one to establish the best-suited confidence interval thresholds and to validate the overall approach. Indeed, the confidence measures take linguistic values \(LOW\), \(MEDIUM\) and \(HIGH\), depending on the reliability of the results obtained in visible-light, as well as in thermal infrared video.  
**Keywords:** visible-light sensor; thermal infrared sensor; people detection; confidence measures
## 2 Related Work
### Shortcomings of Existing Methods  
Existing methods effectively solve the problem of task shaping in single-server high-concurrency scenarios and can cope with bursty tasks of a certain scale. However, the existing method is a static parameter setting method [30], i.e., the token bucket fill rate \(r\) needs to be preset. The complexity of the task is constantly changing during the actual task request, and the fixed token fill rate makes it possible to have continuous bursts or a large number of discards in the overall service under high-concurrency conditions. Specifically, the existing methodology still suffers from the following two shortcomings, as shown in Figure 2.  
When the requested task is simpler (i.e., occupies fewer system resources) and the current token bucket rate is less than the actual system processing capacity, the utilization of system resources is lower, increasing the average processing delay of the task volume and the service rate is lower.
* When the requested task is more complex (i.e., occupies more system resources) and the current token bucket rate is greater than the system's actual processing capacity, the system responds to more tasks and there is task blocking, which likewise increases the average processing delay and lowers the service rate.  
These limitations highlight a fundamental gap in existing approaches: static parameter tuning cannot reconcile the conflicting demands of simple and complex tasks within the same system. Even advanced methods like HTB and PQTBA rely on predefined rules or priorities, which are ill-suited to the fluid task hierarchies of GIS platforms. For example, HTB's hierarchical queueing fails to dynamically reallocate resources between map rendering (simple) and spatial analysis (complex), while PQTBA's priority rules cannot adapt to emerging task urgencies in disaster response scenarios.  
The classical token bucket algorithm is incapable of adapting to dynamic workloads and exhibits limitations in GIS service task processing. The adaptive task traffic-shaping method proposed in this paper is capable of dynamically adjusting the task processing rate, thereby optimizing resource utilization, reducing processing delays, and accommodating the complex task requirements of GIS platforms in high-concurrency scenarios.  
This paper bridges these gaps by introducing a novel adaptive framework that integrates multi-dimensional load metrics, queueing theory, and predictive analytics. The proposed algorithm directly addresses these gaps by introducing the following: (1) a fully dynamic token rate adjustment mechanism driven by real-time multi-dimensional load assessment (CPU, memory, and resource links); (2) a hybrid M/M/1-HMM framework that theoretically links task arrival-process dynamics to adaptive shaping. This approach avoids single-metric bias, eliminates manual rate tuning, and exhibits strong self-adaptability and robustness. Customizing parameters according to the task characteristics of GIS service systems enhances efficiency in specific scenarios.
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
###### Abstract  
The globalization of integrated circuit (IC) design and fabrication has given rise to severe concerns with respect to modeling strategic interaction between malicious attackers and Hardware Trojan (HT) defenders using game theory. The quantitative assessment of attacker actions has made the game very challenging. In this paper, a novel rough set theory framework is proposed to analyze HT threat. The problem is formulated as an attribute weight calculation and element assessment in an information system without decision attributes. The proposed method introduces information content in the rough set that allows calculation of the weight of both core attributes and non-core attributes. For quantitative assessment, the HT threat is characterized by the closeness coefficient. In order to allow HT defenders to use fast and effective countermeasures, a threat classification method based on the k-means algorithm is proposed, and the Best Workspace Prediction (BWP) index is used to determine the number of clusters. Statistical tests were performed on the benchmark circuits in Trust-hub in order to demonstrate the effectiveness of the proposed technique for assessing HT threat. Compared with k-means, equidistant division-based k-means, and k-means++, our method shows a significant improvement in both cluster accuracy and running time.  
Hardware Trojan; rough set theory; quantitative assessment; k-means
###### Abstract
Several experiments were carried out; the results compared with the simulations showed very good agreement [30]. In [31], [PERSON] et al. assessed to what extent the less investigated InfraRed-B band (1.5-3 \(\upmu\)m) can combine the information obtained from InfraRed-A (0.7-1.4 \(\upmu\)m) and InfraRed-C (3-5 \(\upmu\)m) bands and the related experimental setups. In fact, the application of the IR-B systems is relatively rare as there are only a limited amount of commercial systems available due to the technical complexity of the lens coating. In particular, four objects were studied in both reflectographic and thermographic mode in the IR-B spectral range and their results benchmarked with IR-A and IR-C images. For multispectral application, a single benchmark was made with macroscopic reflection mode Fourier transform infrared (MA-rFTIR) results. IR-B proved valuable for the visualization of several features; finally, potential pathways for additional applications such as pigment identification in multispectral mode or characterization of the support (panels, canvases) are indicated. [PERSON] et al. presented an efficient numerical approach for field infrared smoke transmittance based on grayscale images. The results proved to be of sufficient precision for engineering applications; in particular, the method greatly simplified the field trial process, thus improving efficiency [32]. The article written by [PERSON] et al. described an experiment applying infrared thermographic measurements during long-term monitoring and fault detection in uninterruptible power supplies (UPS). The assumption that the battery overheats implied its damaged state was the leading factor of the experiments. They were based on real measured data on various UPS battery sets and several statistical examinations confirming the great relevance of thermal features with mostly over 90% detection accuracy [33]. Lastly, in [34], [PERSON] et al. presented a saliency histogram and geometrical invariability-based method able to detect both bright and dark small moving targets effectively in infrared (IR) video sequences. Quantitative analyses demonstrated the detecting precision of the proposed approach; it can be up to 97%. In addition, the receiver operating characteristic (ROC) curves further verified that the method outperforms other state-of-the-art methods in both detection rate and false alarm rate.