esa-sceva/satcom-chunk-collection huggingface dataset:
# Operationalisation of Building Inspections and Repair: Systematisation-Based Approach
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline
**Code** & **Category** & **Code** & **Diagnosis Method** \\ \hline \multirow{8}{*}{D-A} & & D-A1 & Endoscopy (NDT) \\  & & D-A2 & Analysis by touch (NDT) \\  & & D-A3 & Assessment of the slope, flatness, orthogonality, and alignments (NDT) \\  & & D-A4 & Tell-tale gauge and gypsum testimonies (NDT) \\  & & D-A5 & Crack width ruler and crack-measuring microscope (NDT) \\  & & D-A6 & Mechanical strain gauge (NDT) \\  & & D-A7 & Assessment of the cracking and blistering level (standards EN ISO 4628-4:2016 [15] and EN ISO 4628-2:2016 [16]) (NDT) \\  & & D-A8 & Polyethylene film (NDT) \\  & & D-A9 & Thickness measurement by depth gauging and difference in thickness (standard EN ISO 2808:2019 [17]) (DT) \\ \hline \multirow{8}{*}{D-B} & & D-B1 & Eddy currents (standard ASTM E2884-17 [18]) (NDT) \\  & & D-B2 & Leakage electrical detection (standards ASTM D8231-19 [19] and ASTM D7877-14 [20]) (NDT) \\  & & D-B3 & Electrical capacitance test (NDT) \\  & & D-B4 & Magnetometry (NDT) \\  & & D-B5 & Conductivity meter (NDT) \\  & & D-B6 & Galvanic half-cell (standard ASTM C876-15 [21]) (DT) \\ \hline \multirow{2}{*}{D-C} & Thermo-hygrometric & D-C1 & Measurement of the ambient and/or surface temperature and humidity (NDT) \\  & methods & D-C2 & Measurement of the in-depth humidity (DT) \\  & & D-C3 & Infrared thermography (standard ISO 18251-1:2017 [22]) (NDT) \\ \hline \multirow{2}{*}{D-D} & Sound and acoustic & D-D1 & Ultrasound (NDT) \\  & methods & D-D2 & Percussion (NDT) \\  & & D-D3 & Assessment of acoustic insulation (NDT) \\ \hline \multirow{2}{*}{D-E} & Nuclear methods & D-E1 & Nuclear method (NDT) \\  & & D-F1 & Watertightness test (NDT) \\  & & D-F2 & Water jet (NDT) \\  & & D-F3 & Submersion of the base of window frames (NDT) \\  & & D-F4 & Initial surface absorption test (ISAT) and Karsten-tube (NDT) \\ \hline \multirow{4}{*}{D-G} & & D-G1 & Sphere impact, grid cutting, scratching and abrasion tests (Martinet Baronnie) (DT) \\  & & D-G2 & Pencil method (standard EN ISO 15184:2020 [23]) (DT) \\  & & D-G3 & Surface hardness and pendulum rebound hammer (NDT) \\ \cline{1-1}  & & D-G4 & Metal blade (NDT) \\ \cline{1-1}  & & D-G5 & Micro-perforation (DT) \\ \cline{1-1}  & & D-G6 & Controlled penetration and perforation test (DT) \\ \hline \end{tabular}
\end{table} 
Table 1: Classification list of diagnosis methods within a global inspection system for the non-structural building envelope [14].
## 4 Three-Phase Inverters
### Droop Control  
As an introduction to the topic of grid supporting inverters, this section reviews basic principles of the droop control method. Droop controllers are implemented locally in each generator, and form a distributed control system that stabilizes the grid. The communication between the controllers is minimal, and information is shared by means of the grid frequency and voltage. Droop controllers are used in power systems in order to:  
* Regulate the frequency and active power.  
Figure 26: Grid feeding inverter, connected to a photovoltaic (PV) source.  
Figure 25: A basic implementation of a Phase Locked Loop (PLL).  
* Regulate the voltage and reactive power.
* Promote fair sharing of active power among generators.
* Promote fair sharing of reactive power among generators.
* Allow generators of different sizes to operate in parallel.  
In essence, this is achieved by implementing an inverse relationship between active power and frequency, and between reactive power and voltage, as explained next.
## 3 Experimental Area and Data Preparation  
We used the typical high-density urban area of Fuzhou, the capital city of Fujian Province in China, located on the southeastern coast of China (covering the core areas of Gulou District, Taijiang District, Cangshan District, and Jin'an District), as the experimental region. The geographical range was from 119\({}^{\circ}\)15\({}^{\prime}\)E to 119\({}^{\circ}\)25\({}^{\prime}\)E and from 26\({}^{\circ}\)00\({}^{\prime}\)N to 26\({}^{\circ}\)12\({}^{\prime}\)N (Figure 3a). The area was situated on the lower alluvial plain of the Min River, characterized primarily by plains (with an average elevation of approximately 10 m) interspersed with hills, such as Gaogai Hill and Qingliang Hill, which correspond to the two small red triangular regions marked in Figure 3b. The area had a dense water network, where water bodies accounted for 4.8% of the region [38], and the main stream of the Min River runs through the city (Figure 3c), forming a natural geographical boundary. The region contained diverse building types, including high-rise residential clusters, commercial complexes, transportation hubs, and urban villages, which are dense human-made features. The geological disaster risk induced by surface deformation was high, making it an ideal test area for validating the PSInSAR monitoring method in high-density urban areas.  
This experiment used 25 scenes of TerraSAR-X ascending strip-mode data (range resolution: 0.9 m, azimuth resolution: 1.8 m, incidence angle: 32.6\({}^{\circ}\)) obtained from 20 May 2023, to 29 October 2024, as the primary dataset. The image from 17 January 2024, was selected as the reference image for registration, with the spatiotemporal baseline parameters shown in Figure 4a. To verify the reliability of the results, 45 scenes of Sentinel-1 ascending IW-mode data (range resolution: 2.3 m, azimuth resolution: 13.9 m, incidence angle: 44.0\({}^{\circ}\)) were also obtained, with the image from 20 January 2024, used as the reference image (spatiotemporal baseline shown in Figure 4b). The external DEM used was the Copernicus  
Figure 3: Experimental area and data: (**a**) optical images and usage data of the study area (red rectangle represents TerraSAR-X image coverage, blue rectangle represents Sentinel-1 image coverage); (**b**) topographic shading map (the two small red triangles from left to right indicate the locations of Gaogai Hill and Qingliang Hill); (**c**) river network map with Min River and tributaries annotated.  
DEM GLO-30m to remove terrain effects. Data preprocessing was carried out using the GAMMA 2023 software platform, and the main steps included (1) spatial clipping of the study area (Figure 3); (2) multi-temporal data precise registration based on the reference image; and (3) differential interferometry processing and geocoding to generate differential interferograms after terrain removal, coherence maps, and range geometry parameter files, which provided standardized input for the PSInSAR processing.
## 2 Materials and Methods  
### Study Area and Sample Collection  
In December 2019 and December 2020, a total of 140 dustfall samples were collected from 14 main administrative districts in Shanghai, including: Yangpu (YP), Hongkou (HK), Huangpu (HP), Jing'an (JA), Xuhui (XH), Pudong (PD), Jiading (JD), Jinshan (JS), Minhang (MH), Songjiang (SJ), Putuo (PT), Fengxian (FX), Changing (CN), Baoshan (BS), Qingpu (QP), and Chongming (CM). These districts cover 5 different functional areas, namely, industrial, transportation, commercial, residential, and clean areas. During the sampling period, it was necessary to meet the basic condition of sunny weather for more than one week. The sampling procedure followed the following three steps [19]. First, plastic brushes, dust buckets and other appliances were used to collect about 10 g of dust from the window sills of buildings exposed to the air at a height of about 1.5 m from the ground. The samples were kept in airtight bags and quickly brought back to the laboratory for later experiments. Second, the samples were put into sealed bags for preservation. Finally, a global positioning system (GPS) was used to record the latitude and longitude of each sample point, and the environmental characteristics of the sampling points was also recorded. The spatial distribution and sampling positions of all sampling points are shown in Figure 1.
## 6 Probability of Bit Error
Probability ratio \(\rho\) between the probability of bit error with rain and the probability of bit error with no rain (ideal case): \(P(\varepsilon)=10^{-2}\ (blue)\), \(10^{-3}(green)\), \(10^{-4}(magenta)\), \(10^{-5}(cyan)\), \(10^{-6}(black)\). Direct case: continuous lines; with interference: dashed lines. **Left**: negative interfering impulse; **Right**: positive interfering impulse.
# A Review on Distribution System State Estimation Algorithms
## 4 Auxiliary Algorithms
### Bad Data Detection
Although they are extensively used, in most publications, SVMs play the role of the baseline algorithm for identifying whether the proposed ML method of the publication has a better performance. In [100], an ANN with one hidden layer that can automatically infer underlying physical relationships exploiting sensor data is shown. The authors of [104] train an ANN for forecasting current and voltage measurements and identifying whether FDIA occurs in the system. In [82], the RNNs' ability to exhibit temporal dynamic behavior, finding patterns in sequential data, is examined. Here the RNNs are utilized as sequence classifiers to detect potential measurement manipulation. The authors of [103] extend this work for detecting FDIAs in a subset of the system's nodes. More recently, the authors of [105] exploit the Convolutional Neural Networks' (CNNs) excellence in pattern recognition in FDIA detection. Similar to CNNs, another ML algorithm commonly used in image processing is employed in [102] for detecting FDIAs, called the Margin Setting Algorithm (MSA). MSA is compared with the SVM and ANN algorithms. Other supervised learning algorithms found in literature are the k-nearest neighbors (k-NN) and the extended nearest neighbor (ENN) algorithms [106]. Unsupervised learning, in which unlabeled data are provided for classification, is another ML technique used in bad data detection. In [107], a k-means clustering (KMC) algorithm is used. KMC's objective is to separate the unlabeled data into k different clusters. Another method similar to KMC is proposed in [108; 109] and called the Fuzzy Clustering (FC) algorithm. The difference between FC and KMC is that in FC the boundaries of the clusters are overlapping, so a single sample could belong in multiple clusters. Overall, data-driven algorithms for bad data detection are proven to be more accurate than their model-based counterparts. Although the model-based algorithms are simpler and more robust in most of the cases, their accuracy is a considerable tradeoff.
## 1 Introduction
### Objectives  
The primary objective of this paper is to present a set of techniques that seek to optimize the accuracy of object-based change detection results so that they can be integrated with other data sets with minimal error inflation and, thus, aid applications such as effectiveness monitoring and informing land management issues [15,33, 34, 35]. Those techniques include: (i) a procedure for substantially eliminating commission error in mapped change locations through efficient error checking; (ii) a process that supports lowering change prediction thresholds to increase the mapped change area while decreasing omission errors; and (iii) a software system to facilitate the presented techniques. These techniques are applied after typical accuracy assessment procedures [36, 37] such that they could be used to increase the accuracy of existing studies employing object-based change detection.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 3 Transmission Schemes Based on Virtual Cell Merging
### Virtual Cell Merging with ZF and MVC-MMSE Transmission
#### 3.1.2 Virtual Cell Merging Based on the Interference Graph  
With \(g_{th}\) appropriately selected, it is reasonable to consider that the initial virtual cells corresponding to the connected vertexes in \(\mathcal{G}_{b}\) have strong interference with each other. Therefore, for the sake of ICI mitigation, it is intuitive to group the initial virtual cells that connect to each other into one coordinating cluster. This directly leads to our virtual cell merging algorithm, where initial virtual cells represented by the vertexes within the same MCC of \(\mathcal{G}_{b}\) merge into one virtual cell cluster. Details of the virtual cell merging algorithm are presented in Algorithm 1. Figure 2 is an illustration of the interference graph-based virtual cell merging process. In this illustration, there are nine initial virtual cells, denoted by V1, \(\cdots\),V9 in the circles in Figure 2. Herein, \(g_{th}\) is set to two, hence the edges with weights larger than two are considered to be connected, denoted by the solid lines. The dashed edges or the edges that have not been drawn all have a weight of less than two and are considered tobe disconnected. It easy to get the MCCs of the graph according to Algorithm 1. We have four MCCs in this illustration, indicating that four virtual cell clusters are finally formed, where all of the RBAs of each cluster jointly serve the users therein.  
```
Initialization:  Each user \(k\in\mathcal{K}\) selects \(N_{0}\) closest RBAs to form his or her virtual cell \(\mathcal{V}_{k}\); interference graph \(\mathcal{G}\) is constructed according to (7). Given \(g_{th}\), the binary graph \(\mathcal{G}_{b}\) is generated according to (8), as well as its describing matrix \(\mathbf{T}\). Set the initial merged virtual cell cluster index as \(m=1\). while\(\mathcal{K}\
eq\emptyset\)do \(\forall\) user \(k\in\mathcal{K}\), initialize the user set as \(\mathcal{K}=k\) and the RBA set as \(\mathcal{B}_{m}=\mathcal{V}_{k}\).  Remove user \(k\) from the unmerged user set: \(\mathcal{K}=\mathcal{K}\setminus\mathcal{K}_{m}\).  Generate the connected user set with \(\mathcal{K}_{m}\) as \(\mathcal{C}_{m}=\{j\in\mathcal{K}|\sum T_{\mathcal{K}_{m},j}\
eq 0\}\). while\(\mathcal{C}_{m}\
eq\emptyset\)do  Update the current user set \(\mathcal{K}_{m}=\mathcal{K}_{m}\cup\mathcal{C}_{m}\).  Update the current RBA set \(\mathcal{B}_{m}=\mathcal{B}_{m}\cup\mathcal{V}_{\mathcal{C}_{m}}\).  Update the unmerged user set \(\mathcal{K}=\mathcal{K}\setminus\mathcal{C}_{m}\).  Recalculate the connected user set with \(\mathcal{K}_{m}\) as \(\mathcal{C}_{m}=\{j\in\mathcal{K}|\sum T_{\mathcal{K}_{m},j}\
eq 0\}\). endwhile \(m=m+1\). endwhile return\(\mathcal{K}_{j}\) and \(\mathcal{B}_{j}\), (\(1\leq j\leq m-1\))
```  
**Algorithm 1** Interference graph-based virtual cell merging algorithm.
## 2 Materials and Methods
### Proposed Method
#### 2.3.2 cGAN-TV Loss  
In this study, the discriminator produced a final result with dimensions of 30 \(\times\) 30. Each grid had a value between 0 and 1, which indicated the fake and the real image, respectively, as seen in the final output of Figure 2. Each grid represented the original image patch with a size of 70 \(\times\) 70. The classification results were then be compiled and used to calculate the discriminator loss.  
The generator obtains its input in the form of a sketch, and therefore we used the loss equation from the cGAN (cGAN loss). This equation was modified by adding the term \(\mathrm{L_{1}(G)}\) to the equation, as follows:  
\[\min_{G}\max_{D}\mathrm{L_{cGAN}(G,\ D)}+\ \lambda\mathrm{L_{1}(G)} \tag{1}\]where L\({}_{\rm cGAN}\)(G, D) is the loss obtained from the sigmoid cross-entropy and L\({}_{1}\)(G) is the loss obtained from the Mean Absolute Error (MAE) between the image generated by the generator and the ground truth of the image, and \(\lambda\) is the weight that controls the contribution of L\({}_{1}\) loss. The use of L\({}_{1}\) loss aims to preserve the low-frequency features so as to improve the quality of the generated images [22].  
[PERSON] et al. [8] compared several generator loss experiments, i.e., L\({}_{\rm cGAN}\)(G, D) only, L\({}_{1}\)(G), and both. The best results were obtained by using both, with \(\lambda=100\). The term L\({}_{1}\)(G) was imposed so that the image generated by the generator would be more similar to the ground truth. In the case of translation from sketch to image, this similarity is required.  
However, the image results contained artifacts in the form of unsmooth borders that clearly separated colors among local regions of the face. To smooth the image and minimize noise that the generator may produce, this study used an additional term, i.e., Total Variation Loss (TVL). Images that have a high Total Variation (TV) also have high noise, and by regularizing the TV, the image can have minimized noise while maintaining the edges and promoting the connectivity of pixels in the images [23; 24; 25; 26]. By using TVL, the generator can produce a smoother image while maintaining the edges of the image. The final formula for total loss with the addition of TVL is as follows:  
\[\min_{\rm G}\max_{\rm D}\rm{L}_{\rm cGAN}(G,D)+\lambda_{1}L_{1}(G)+\lambda_{2 }TV(G) \tag{2}\]  
TVL is achieved by calculating TV from the image generated by the generator and weighted by the value of \(\lambda_{2}\). TV is defined as the sum of the absolute differences between adjacent pixels in the horizontal and vertical directions, as follows:  
\[TV(G)=\sum_{i,j}\big{|}g_{i+1,\;j}-g_{i,\;j}\big{|}+\big{|}g_{i,\;j+1}-g_{i,\;j} \big{|} \tag{3}\]
## 3 Experimental Results and Discussion
### Experimental Results  
#### 3.3.1 Comparison Experiments  
Compare the performance of SCSA enhancement through comparison experiments, in which CBAM, CoordAtt, ECA, GAM, and SCSA modules were added to the YOLOv8 model in turn to compute P-precision, Recall, map@50, Params, and GFLOPs.  
Based on the data in Table 2, we compared and analyzed the performance of different detection methods. Five detection methods are listed in the table: CBAM, CoordAtt, ECA, GAM, and SCSA. In terms of precision (P), the SCSA method performs the best at 97.2%, followed by ECA and CBAM. In terms of recall (Recall), the ECA method is the highest at 95.2%, while SCSA follows closely. In terms of average precision (mAP@50), SCSA also leads with 97.5%, followed by ECA and CoordAtt. In terms of model complexity, the number of parameters (Params) and the amount of computation (GFLOPs) are the two key metrics.  
The GAM method has the highest number of parameters at 4.65 M, while the number of parameters for CoordAtt and ECA is the lowest, both at 3.01 M. In terms of computation volume, GAM is also the highest, 9.4 G. Taken together, the SCSA method excels in  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Classes** & **P (\%)** & **Recall (\%)** & **mAP@50 (\%)** & **Params (M)** & **GFLOPs** \\ \hline CBAM & 96.8 & 94 & 96.8 & 3.07 & 8.1 \\ CoordAtt & 96.4 & 94.6 & 97.2 & 3.01 & 8.1 \\ ECA & 96.6 & 95.2 & 97.4 & 3.01 & 8.1 \\ GAM & 96.3 & 94.1 & 96.7 & 4.65 & 9.4 \\ SCSA & 97.2 & 94.7 & 97.5 & 3.04 & 8.2 \\ \hline \end{tabular}
\end{table}
Table 2: Comparative test result.  
precision, recall, and average precision, and strikes a better balance between performance and efficiency, although its number of parameters and computation volume are slightly higher than that of CoordAtt and ECA, but considering its performance advantages, these additional resource consumptions are reasonable.
# Balance of Antioxidants vs. Oxidants in Perinatal Asphyxia
## 5 Free Radical Biomarkers in NE
### Enzymes Against Free Radicals  
XO is an enzyme containing molybdenum that produces free radicals in the presence of purines hypoxanthine and xanthine. XO increases with hypoxia/ischemia, leading to the breakdown of adenine monophosphate into these purines [10]. The main source of XO is likely from endothelial cells rather than the brain tissue itself, and the term XO refers to two forms, xanthine oxidoreductase, which is present in healthy cells, and xanthine dehydrogenase, which is converted to an oxygen radical-producing form during ischemia. Although XO is barely detectable in the adult human brain [74], studies have shown that newborns have low levels of both total xanthine dehydrogenase and XO [75].  
SOD, GPx, and catalase are the initial line of defense against oxidative stress. After experiencing hypoxia/ischemia, their activity sharply increases to counteract the rise in ROS and their detrimental effects. Research has shown that asphyxiated newborns have significantly higher levels of SOD, catalase, and GPx in their cord blood compared to non-asphyxiated newborns [76]. However, by 24 h, only catalase and SOD levels remain elevated in the blood of infants who have experienced NE compared to those who have not [21]. Studies have also found a correlation between the levels of catalase and SOD in the cord blood and the severity of neonatal asphyxia [76]. Therefore, the measurement of these antioxidant enzymes in the cord blood could provide valuable insights into the timing and severity of neonatal asphyxia [76]. Of note, these findings are based on small groups of participants, so further research is needed to confirm these results in larger studies. Regarding the levels of these antioxidant enzymes in other body fluids, a previous study has shown that the activity of SOD was significantly higher in the cerebrospinal fluid up to 72 h after birth in term newborns who have experienced neonatal asphyxia, whereas the activities of GPx and catalase increased only in the most severely affected cases [77]. Most of the research on these topics was conducted before the standard use of therapeutic hypothermia, which raises questions about how hypothermia might affect the levels of these antioxidant enzymes.
## 7 Performance Evaluation
### Simulation Configurations
#### 7.2.1 Gps, DRR, and DRRF  
We illustrate that DRRF outperforms GPS and DRR by showing the fairness and the overhead reduction _versus_ system utilization. To exercise the effect of the scheduler, we used Constant Bit Rate (CBR) traffic flows. We did not use TCP (Transmission Control Protocol) flows because the slow-start feedback mechanism of TCP has a significant impact on the resource usage, and it is difficult to isolate  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Scheme** & **Number of MSs** & **Bit Rates** & **MCS** & **Note** \\ \hline
7.2.1(a) GPS,DRR, and DRRF & 5 MSs & CBR; 3 Mbps each & QPSK3/4, 16 QAM3/4, 64 QAM2/3, 64 QAM3/4 & \\ \hline
7.2.1(b) GPS,DRR, and DRRF & 15 MSs & CBR: 500 Kbps & QPSK3/4 & \\ \hline
7.2.2 DRRF and GWF & 4 MSs & CBR: 5 Mbps & QPSK3/4 and 1.0. & Weight = 0.0, 0.5, and 1.0. \\ \hline
7.2.3(a) DRRF extension & 5 MSs & CBR: 3 Mbps & QPSK3/4 & One MS is 2 Mbps min rate \\ \hline
7.2.3(b) DRRF extension & 5 MSs & CBR: 3 Mbps & QPSK3/4 & One MS is 500 kbps max rate \\ \hline
7.2.3(c) DRRF extension & 5 MSs & CBR: 1.5 Mbps & QPSK3/4 & 2 MSs with priority 1 and the other 2 MSs with priority 2 \\ \hline
7.2.4(a) EDF, DRRF, EDF-DRRF & 3 MSs + 1 MS & 3 Videos: 1.35 Mbps average rate 1 CBR: 3 Mbps & QPSK3/4 & Underload \\ \hline
7.2.4(b) EDF, DRRF, EDF-DRRF & 5 MSs & 5 Videos: 1.35 Mbps average rate & QPSK3/4 & Overload \\ \hline
7.2.4(c) EDF, DRRF, EDF-DRRF & 3 MSs + 1 Ms & 2 Videos: 1.35 Mbps average rate 1 Video: 3.3 Mbps average rate 1 CBR: 3 Mbps & QPSK3/4 & One ill-behaved flow \\ \hline \end{tabular}
\end{table}
Table 5: System Configurations.  
the effect of TCP from that of the scheduling algorithm. In stead, we use CBR traffic so that the results indicate effects on non-TCP applications like mobile video.  
First (7.2.1a), to show the effect of throughput fairness, there are 5 MSs in this configuration competing at the same bottleneck link. Each MS has 3 Mbps CBR traffic rate. Each MS has only one flow, and is mapped to one of the MCSs shown in Table 4 (from QPSK3/4 to 64 QAM3/4).  
Second (7.2.1b), to show the effect of the overhead reduction _versus_ the system utilization, we simulated 15 MSs. Again, each MS has only one flow with 500 kbps CBR traffic. The modulation and coding scheme was fixed to QPSK3/4.  
Note that five flows (15 Mbps) and fifteen flows (7.5 Mbps) can saturate the link capacity so that the fairness is exercised; there are dropped packets due to buffer overflow at the base station.