esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Confounding Effect Model for Software Defect Prediction
## 4 Experiments and Results
### Experiments for the Proposed Prediction Model
#### 4.3.2 Parameters Selection  
When controlling the confounding variables, we need to make appropriate changes to the effect values. The values of parameters d and n will affect the transformation, and different parameter values will lead to different model prediction performance results. In order to choose the value of d and n reasonably, we conducted experiments. On each dataset, we perform gridded experiments with d and n ranging from 0 to 1 with a step size of 0.1, respectively. The experimental results for project JM1 are presented in Figure 5, which is a heatmap, where the darker color means the larger F1 score. The abscissa represents the value of n, and the ordinate represents the value of d. By observation, we conclude the general rule that when n is larger than d, the models generally achieve better predictive ability. Without loss of generality, parameter d is set to equal the defect rate, and parameter n equals the non-defect rate in the following experiment.
## 8 Conclusions  
The experimental and analytical studies showed that the hybrid beam column connection performed well under seismic loads. The following main conclusions can be drawn from the present study:  
1. The hybrid frames have self-centering capabilities, provided the amount of mild steel is optimized. With an increase in mild steel percentage, energy dissipation increases.
2. If the post-tensioning force is not sufficient to yield the mild steel in compression, the frame will not return to its initial state.
3. The strength of precast hybrid connection is almost equal to that of its monolithic counterpart. Both the mild steel bar and post-tensioning steel contribute to the strength of connection.
4. Hybrid frames can attain more drift due to the gap opening phenomenon at the beam-column interface.
5. The energy dissipation of the hybrid frame connection is less than that of monolithic frames.
6. The failure of the precast hybrid connection is mainly due to mild steel bar fracture or bond failure. Crushing of concrete in the compression zones at the beam-column interface was noticed. The experiments showed minor damage in the frame member even at high drift ratios because the damage was concentrated only in the connection region.
7. The gap opening at the interface increases the span between the columns to accommodate the gap. This is called beam growth. Even if the connection satisfies code requirements, it is important to study the performance of the complete frame using the connection.
8. Joint shear force is transferred by friction generated due to post-tensioning force.  
Writing of initial draft was done by [PERSON] Conceptualization and funding acquisition was contributed by [PERSON] Supervision of research activity planning and execution provided by [PERSON] Critical review of work, drafting and edition was done by [PERSON]. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the deanship of scientific research at King Khalid University for funding this work through the research groups program under grant number (R.G.P. 2/77/41).  
The authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper.
## 3 Results and Discussion
### Ionospheric Disturbance during Earthquake
Figure 8: Distribution of VTEC near the earthquake epicenter on DOY 105, 106, and 110, 2013 (the day earthquake happened); **(a,b)** indicate distribution of VTEC at 8:00 and 10:00 of DOY 105; **(c,d)** stand for distribution of VTEC at 8:00 and 10:00 of DOY 106; **(e,f)** denote distribution of VTEC at 8:00 and 10:00 of DOY 110; the time here indicates UTC time. Figure 9: VTEC time series of the earthquake epicenter on DOY 105, 106, and 110, 2013 (the day earthquake happened): **(a)** is the VTEC time series; **(b)** is the difference between VTEC on DOY 106 and VTEC on DOY 105 and 110, 2013; the red line stands for the time of earthquake occurred; the time here indicates Beijing time. According to the results in Figure 11, further research on the ionospheric anomaly on DOY 109 and 110 is presented. Here, we mainly focus on the variation of VTEC during the periods of several hours before and after the earthquake. The IDW method mentioned in the SF-PPP-based regional VTEC model part is adopted to estimate the distribution of VTEC near the epicenter before and after the earthquake. The VTEC data on DOY 109 and 110 were applied for spatial interpolation. The corresponding solutions are portrayed in Figure 11, from which we can know that the VTEC decreased firstly and then increased gradually. Moreover, the feature of the VTEC distribution was similar to the results in Figure 8. Thus, the location showed a higher VTEC distributed in the south area of the epicenter. Figure 11d also shows the VTEC distribution near the earthquake rupture. After the earthquake occurred, the VTEC started to raise obviously. Figure 11: Distribution of VTEC near the earthquake epicenter from 10:00 UTC on DOY 109 to 6:00 UTC on DOY 110, 2013 (the day earthquake happened); subfigures (**a–f**) are the variation process in an interval of four hours. Figure 10: Distribution of VTEC of the world on DOY 105, 106, and 110, 2013 (the day earthquake happened) from GIM data; (**a,b**) indicate distribution of VTEC at 8:00 and 10:00 of DOY 105; (**c,d**) stand for distribution of VTEC at 8:00 and 10:00 of DOY 106; (**e,f**) denote distribution of VTEC at 8:00 and 10:00 of DOY 110; the time here indicates UTC time. Shown in Figure 12 are coseismic ionospheric distributions in the Tohoku-Oki earthquake, from which we can find that the VTEC increased visibly at 4:00 UTC by comparing the result in Figure 12d (the subfigure of Figure 12a) with those in Figure 12e,f (the subfigure of Figure 12b,c). A similar phenomenon can also be discovered in other researches (e.g., [PERSON] et al. [59]).
# Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks
###### Abstract  
Cognitive radio is an intelligent wireless solution that aims to enhance the access to the radio spectrum. In this technology, secondary users sense the radio spectrum, select the best channel among a pool of free channels, and determine the optimal transmission parameters to meet their quality-of-service requirements while maximizing the spectral efficiency. Over the past decade, several channel-ranking mechanisms have been proposed. However, these mechanisms consider only the remaining idle time of the channel and exclude some crucial parameters. This convincingly demonstrates a strong need for a new channel quality-ranking model that jointly considers several parameters to select the best communication channel for transmission. This paper proposes a utility model that integrates several important parameters for ranking channels. First, we underline the importance of the process of the channel quality ranking. Then, we describe a multi-attributes, utility-based, channel quality-ranking model. Finally, we describe a series of experiments and their results, which show that our model effectively ranks the best communication channels first.  
cognitive radio; spectrum decision; spectrum sensing; channel ranking; multi-attributes utility function; gradient descent; non-linear regression Article
## 4 Results and Discussion
### Creation of Design Alternatives Based on Variations in the Building Coverage Ratio  
Second, an algorithm for calculating the building coverage ratio is added. When alternatives with a low coverage ratio and alternatives with a high coverage ratio are listed, they appear as shown in Figure 10. The coverage ratio is the area occupied by a building compared to the area of the site. By including this value in the algorithm, the difference in design as a function of building density can be recognized. In other words, it can be seen that the alternatives with a low building-to-land ratio have a relatively unvaried shape. In contrast, it can be seen that alternatives with a high coverage ratio are created with diversified forms such as \"L\"-, \"U\"-, and \"T\"-shaped alternatives. This approach allows architects and designers to create a spectrum of design alternatives, from more minimal, open layouts to denser, complex alternatives. By recognizing and understanding these differences, architects and designers can make more informed decisions when it comes to selecting the most appropriate design solution for a particular site or project.  
Figure 9: Multiple alternative outputs.
## 2 Background
### Wireless Sensor Network Constraints  
Even though WSNs are used in many applications, there are several constraints and challenges that face WSN deployment, coverage, and connectivity. Some of these factors are [22]:  
* Limited energy resources: Due to the small size of a sensor node, the battery supported will be small, with a limited lifetime, and this leads to limited processing power and limited storage capacity, resulting in increasing the energy consumption problem.
* Low data rate: There is a higher latency in WSN communication. WSN works in short communication ranges, and the transmission data rate depends on the frequency used.
* Communication failures: The failed nodes result in communication failures, so there should be a fault tolerance to overcome the interruptions when this occurs.
* Security issues: The wireless communication channels of WSN are vulnerable to passive and active attacks, thus resulting in serious problems.
### The Information-Driven Approach
#### The Relation-Based Approach  
A relation-based approach views the problem of sensing, tracking from a new angle. Instead of sensing the environment through extensive and detailed low-level data and solving the problem based on low-level signal processing, [PERSON] proposes to sense _relation_[6]. Relation-based approaches sense the environment based on high-level description of the task and then _command_ selective sensor nodes to sense and communicate. The selection process results in minimizing the computational, communication, and sensing resources.  
Relation-based approach provides us a new angle to solve collaborative information processing problems. If the information-driven and the mobile-agent approaches use a bottom-up approach for developing the tracking, then relation-based approach uses top-down solution, which might be more attractive since it is closer to how human thinks and behaves.
# Multi-View Visual Relationship Detection with Estimated Depth Map
## 4 Experimental Procedure
### Visual Relationship Detection
#### 4.1.3 Recall Performance Evaluation and Comparison  
In this section, we compare the multi-view VRD framework with existing strong baselines to demonstrate the effectiveness of multi-view information in the visual relationship detection framework. Two evaluation conditions were formulated for recall performance testing: different normalization strategies (ImageNorm in Equation (3) and RegionNorm in Equation (4)) for depth multi-view spatial features. The recall performance of the different VRD frameworks is shown in Table 1.  
First, we analyzed the performance of the multi-view VRD framework with an ImageNorm normalization strategy (Multi-view[Img]). Compared with RDBN [7], which the multi-view framework is based on, the performance of the proposed framework with multi-view information was better in most of the recall evaluation tasks. Particularly for the zero-shot learning task (\"Unseen\" column) of predicate detection, the multi-view framework achieved state-of-the-art performance compared with existing VRD methods, especially in the specific depth condition \(|\Delta\text{Depth}|>100\) (from 42.6% in RDBN to 44.3% in multi-view). This showed that the multi-view information generated from the original RGB images and estimated corresponding depth maps was able to enhance the generalization ability of the visual relationship detection framework. Intuitively, in the depth condition \(|\Delta\text{Depth}|>100\), the subject is far from the object; thus, the estimated depth maps can provide relatively accurate depth information. Furthermore, with multi-view visual features generated from RGB-D images, the dimensions of the visual feature vectors were extendedcompared with single-view feature vectors. The performance comparison showed that feature vectors with more abundant visual information can improve the detection accuracy and generalization ability of the VRD model. Then, we analyzed the performance of the multi-view VRD framework with a RegionNorm normalization strategy (Multi-view[Reg]). We found that the multi-view VRD framework with RegionNorm was good at visual relationship detection in \(10<|\Delta\text{Depth}|<30\), compared with the framework based on ImageNetNorm. For example, compared with baseline RDBN in zero-shot learning of a predicate detection task, the multi-view model improved performance from 21.6% to 22.4%. In all depth ranges, compared with RDBN, which the multi-view model is based on, the multi-view framework enhanced the performance of zero-shot learning in a predicate detection task from 22.5% to 22.9%. Intuitively, in a depth range of \(10<|\Delta\text{Depth}|<30\), the subject is closed to the object. Thus, in this depth condition, the RegionNorm strategy was more adapted to the spatial relationship between the object pairs. In conclusion, the multi-view visual relationship detection framework with more abundant visual information was able to improve the detection accuracy and generalization ability of the VRD model.
## 3 Results and Discussion
### Applicability on Actual Wastewater  
To evaluate the validity of the use of MWCNTs as adsorbents, real wastewater mixed with simulated dye samples was collected to examine the removal of IV2R by the adsorbent under optimization conditions. The results confirmed that the highest removal was significantly altered by changing the kind of water, for which deionized water exhibited the lowest influence on the adsorption method, with color removal of 98.4% at an acidic pH after 120 min. By contrast, real wastewater comprises very high concentrations of interfering ions from numerous pollutants, which had a significant influence on the elimination efficiency of IV2R dye, with color removal of 88% after 120 min. Regardless, the results revealed that MWCNTs adsorbents can be successfully used as a low-cost adsorbent to remove IV2R dye from aqueous mixtures and wastewater. The process showed a typical feature of effluents after the treatment of dye, observed to be 110 mg L\({}^{-1}\) of TSS and 652 mgL\({}^{-1}\) of TDS, as presented in Table 6. Moreover, these results show that wastewater containing dye is well treatable through this method, with percentages of color removal of 98.4% and 88.2%; additionally, TSS and TDS removal decreased after the treatment process.
## 1 Introduction
However, it was reported that controlling temperature was not suggested because diffusivity of foaming gas could not be the same and controlled for foam plastic insulation material from each different manufacturer. In addition, closed cells in cellular structure, which mainly play an insulating role in foam material, can be readily damaged as temperature increases [8]. Meanwhile, thickness control can be relatively accurately conducted to modulate the degradation speed as follows in the below equation by avoiding microstructure corruption [9]. \[\text{time}_{2} =\text{time}_{1}\left(\frac{\text{thickness}_{2}}{\text{thickness} _{1}}\right)^{2} \tag{2}\]  
The change over time can be divided into primary and secondary stages. Considering duration time, it is quite difficult to analyze the secondary stage experimentally at least for over ten years. To address this practically, International Organization for Standardization (ISO) suggested an accelerated laboratory test method to speed up the foaming gas release intentionally by slicing thick specimens, which makes it possible to predict the thermal properties after 25 years. In addition to that, ISO 11561 [6], which specifies the accelerated laboratory test method by slicing, deals with both the thermal resistance in the 25-yearaverage and the value after 25 years. It indicates that differences below 10% may occur for the measurement depending on the sample thickness. Therefore, there has been an intense discussion, but not much research has been performed empirically to figure out how this calculative difference could be induced. The foaming gas type was examined to investigate the possibility of influence on thermal resistance change [10]. The thermal resistance of one or two organic insulation materials was observed, but influencing parameters were not systemically explored in previous cases [11]. In this study, we analyzed changes in the thermal performance of cellular plastic insulation materials over the actual elapsed time and based on the slice accelerated method described in ISO 11561, respectively. Measured thermal conductivity values of each method were compared and estimated by the scaling method suggested in ISO 11561 to elucidate the practical approach conditions in tests and scientific meanings.
## 2 Materials and Methods
### Aminopeptidase Activities Assay  
In order to obtain the soluble fraction, the tissue samples were homogenized with 0.5 mL of 10 mM Tris-HCl buffer (pH 7.4) and ultracentrifuged at 100,000\(\times\)\(g\) for 30 min at 4 \({}^{\circ}\)C. The resulting supernatants were used to measure the enzymatic activities corresponding to the soluble fraction, and the protein content. To solubilize the membrane proteins, the pellets were rehomogenized in 10 mM of Tris-HCl buffer (pH 7.4) with 1% Triton-X-100, and ultracentrifuged at 100,000\(\times\)\(g\) for 30 min at 4 \({}^{\circ}\)C. The supernatants were kept for at least 4 h at 4 \({}^{\circ}\)C and shaken with SM-2 biobeads, in order to remove the detergent used to solubilize the membrane proteins. The resulting samples were used to measure the enzymatic activities of the membrane-bound fraction, and the protein content [67; 68; 69].  
The enzymatic activities of the soluble and membrane-bound fractions of APA, ASAP, APN and APB were measured by a fluorimetry assay using, as substrates, the aminoacyl-\(\beta\)-Naphthylamides (aa-\(\beta\)-NA) L-Asp-\(\beta\)-NA, L-Glu-\(\beta\)-NA, L-Ala-\(\beta\)-NA and L-Arg-\(\beta\)-NA, respectively, according to the methods previously described by us. Then, 96-well black plates were used, and 10 uL of the supernatant sample was pipetted into each well and incubated for 30 min at 37 \({}^{\circ}\)C in 100 uL of the substrate solutions. After 30 min of incubation, the enzymatic reactions were stopped by adding 100 uL of 0.1 M acetate buffer (pH 4.2). The \(\beta\)-NA released as a result of the enzymatic activity was quantified fluorometrically at a 412 nm emission with an excitation of 345 nm. Each determination was made in triplicate. The soluble and membrane-bound activities were expressed as pmoles of the corresponding aa-\(\beta\)-NA hydrolyzed per minute and per mg of protein (pmol aa-\(\beta\)-NA/min/mg protein). Figure 1 summarizes the experimental design and procedures.  
Figure 1: Experimental design scheme and specific experimental procedures.
# Innovative Approaches for Minimizing Disinfection
## 3 DBPs
### DBPs Precursors--Natural Organic Matter (NOM)
A standard method of separating NOM into hydrophobic and hydrophilic fractions involves identifying hydrophobic acids, mainly known as humic substances, as the predominant component of DOC in water. On the other hand, the hydrophilic fraction is usually found in lower relative concentrations and comprises molecular groups like carbohydrates and proteins [20]. NOM, commonly present in surface waters, is the primary source of DBP precursors. Effective control of DBP formation often involves the removal of NOM. Techniques such as coagulation, activated carbon adsorption, and membrane filtration are standard methods  
Figure 4: GAC filter adsorber. Figure 3: Sand filtration beds. employed in drinking water treatment plants (DWTPs) to eliminate NOM. Combining conventional and advanced treatment processes is a promising approach to improve drinking water quality. However, a systematic assessment of the efficiency of full-scale conventional and advanced treatment processes in removing DBP precursors is crucial. NOM, a complex mixture with diverse components of different sizes, structures, and properties complicates this process. These NOM components exhibit varying reactivities in DBP formation, directly impacting the types and amounts of DBPs formed. Furthermore, removal efficiency depends on the specific types of NOM components present. Therefore, a thorough characterization of NOM is crucial for accurately evaluating the removal of DBP precursors in drinking water treatment [13]. Typically, drinking water sources contain NOM, a complex mixture of organic compounds from decaying plant and animal material, and microbial activity. This includes primarily humic and fulvic acids that react with chlorine to generate various chlorinated DBPs. In addition to THMs, the most common DBP group, HAAs, are the second most prevalent. Furthermore, treated water has been found to contain various other groups of DBPs, such as HANs, haloketones (HK), chloral hydrate (CH), chloropicrin, chlorophenols, N-chloramines, halofuranones, bromohydrins, chloramino acids, and cyanogen bromide [6]. Over the past two decades, elevated levels of NOM have been observed due to global warming, acid rain, soil erosion, and water pollution [15; 27]. This increase has compromised the efficacy of water treatment processes, leading to a shortened lifespan of activated carbon resources [10].
## 3 Proposed Methods
### Edge Video Detection Acceleration Based on Motion Inference
T denotes the minimum pixel change threshold where motion has occurred, and \(F_{i}(x,y)\) denotes the pixel value at frame i in (\(x,y\)) position. The three-frame difference method does not perform well for objects with similar internal pixels, and the effect is very dependent on the parameter \(T_{pixel}\), which represents the minimum pixel change of the object movement. Thus, we proposed T frames dynamic and static separation method to make up for its deficiencies. The static layers as the dynamic background of these T frames are obtained according to Algorithm 2. In Algorithm 2, we recommend \(T_{pixel}\) to be in the range of 10 to 20, and the motion layer can be obtained by \(f_{0}\) subtracting the dynamic background, which is expressed as Equation (5). ```
Input: frame list \(F=\{f_{0},f_{1},\ldots,f_{k}\}\); minimum pixel change of object movement \(T_{pixel}\); Output: dynamic background mask;  convert frame \(f_{0}\) to gray frame \(f_{0}\)gray\(\cdot\)bgmask = (\(f_{0}\)gray \(>0\)); for\(f_{i}\) in \(F\):  convert frame \(f_{i}\) to gray frame \(f_{i}\)gray; \(bgmask=bgmask\)(\(f_{i}\)gray \(-f_{0}\)gray \(<T_{pixel}\)) return\(bgmask\)
```  
**Algorithm 2** Get dynamic background mask. The final motion information is inferred from the motion layer and the _IOU_ matching mechanism. As shown in Figure 8, if the value of the motion mask is greater than a certain threshold, the objects involved to this motion are seen as the same. Thus, the detection results of the key frame can be passed to the non-key frames.