esa-sceva/satcom-chunk-collection huggingface dataset:
# Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data
## 1 Introduction
The integration of data analytics with the Lynchian approach for exploring the image of the city has drawn interest from scholars belonging to diverse fields, including spatial cognition, urban studies, urban planning, information and communication technologies, and artificial intelligence [14; 15; 16]. Research studies have been investigating city images based on different techniques, such as using small-size data analytics--i.e., surveys and interviews--geographical information system analysis, sketch maps, and so on [16; 17]. Now, with the help of the huge amount of existing social media data (i.e., social media big data) generated per minute, city images can be examined from a broader perspective and cover a much larger scale and scope [18]. However, despite its abundant and easy availability, the use of big data, specifically social media data and its analysis as an approach has found a limited application in the field of urban studies. Moreover, studies that have specifically used social media data to comprehensively test the image of the city using [PERSON]'s theory base are even rarer [17]. For instance, references [19; 20] have tried to understand one psychogeographical aspect of cities using crowdsourced data, which they are limited to one or two aspects such as place identity. Moreover, reference [21] have tried to understand the streets of London through [PERSON]'s theory using Flickr and Open Street Map (OSM), which lacks discussions on how community perceptions on social media influence the development of the city image. This paper, therefore, aims to contribute to this understudied area of research. This study, hence, attempts to address the question of 'How can the image of a city be examined by using geo-coded social media data?' To address the research question, the study adopted the Capture-Understand-Present (CUP) framework introduced by [22].
## 3 Results
### Therapy and Analysis of Predictors of Relapse  
Twenty out of forty-four (46%) patients were treated by means of local steroids, whereas 24/44 (54%) patients were treated with a combination of local and systemic therapy (prednisone + dapsone). Chi-square analysis showed a non-significantly different distribution between patients treated with local therapy with respect to patients treated with local and systemic therapy (\(p\) = ns).  
Thirty-nine out of forty-four (88%) patients achieved clinical remission (defined as composite endpoint PRmT or better) and were included in the analysis of relapse. In particular, 16/39 (41%) patients achieved PRmT (nine with local therapy and seven with local and systemic), 21/39 (53%) achieved CRMT (6 treated with local therapy and 15 with local and systemic therapy), 2/39 (5%) achieved PRoT (2 treated with local therapy), and 0/39 (0%) patients achieved CRoT. Specifically, in all cases of CRmT where the withdrawal of therapy was attempted, it was soon (within less than 2 months) followed by the onset of new lesions that required the re-introduction of minimal therapy (including local therapy) to be resolved. The mean time to achieve clinical remission (composite outcome) was 11.20 \(\pm\) 11.10 months (see Supplementary Table S1 for details).  
Seventeen out of thirty-nine (43%) patients experienced relapse in a median time of 11.23 \(\pm\) 9.14 months.  
Cox proportional hazard model analysis showed that BP180 ELISA > 20 U/mL was the only variable associated with relapse in the multivariate analysis (HR 3.4; \(p\) = 0.01). In particular, 9/17 patients with BP180 ELISA > 20 U/mL experienced relapse in a mean time of 9.66 \(\pm\) 9.20 months compared to 8/17 patients below the cut-off (mean time to relapse: 13.0 \(\pm\) 9.09 months) (Figure 3). See Table 2 for details.  
Figure 3: Kaplan–Meier curve showing relapses after clinical remission (composite outcome partial clinical remission or better, PRoM or better) in patients with levels of BP180 NC16A ELISA below and above the cut-off of 20 U/mL.  
Cox proportional hazard model analysis was also performed in the subgroup of patients treated with local therapy and in the subgroup of patients with local and systemic therapy. The results disclosed an association between BP180 \(>\) 20 and relapse only in the group treated with local and systemic therapy (HR 5.2 \(p\) = 0.01). In particular, of the 11 relapses that occurred in the group treated with local and systemic therapy, 4 had BP180 \(<\) 20, whereas 7 had BP180 \(>\) 20.
## 1 Introduction  
Airport delay serves as a key indicator of an airport's overall operational performance, reflecting its efficiency and the effectiveness of flight scheduling. Various factors, such as air traffic control, weather conditions, aircraft maintenance, flight scheduling, airport facilities, and human factors, work together to cause airport delays. Serious delays can lead to flight congestion, a higher risk of conflicts or accidents, and major inconvenience for airlines, passengers, and airport operations. To reduce these negative effects, intelligent airport delay prediction systems have emerged as a key part of Intelligent Air Traffic Management (IATM). These systems aim to improve operational efficiency by using Artificial Intelligence (AI) for real-time monitoring and early warnings of flight delays.  
The impact of weather on airport delays is particularly pronounced, especially when convective weather in terminal areas, such as high winds, hailstroms, or thunderstorms, disrupts aircraft operations and ground equipment. Therefore, an effective airport delay prediction system must take weather conditions into account. The primary objective of this paper is to characterize the influence of terminal area convective weather on flight operations by employing a multi-channel pictorial method that accurately depicts the relative positions of convective weather and inbound/outbound flight flows, thereby improving existing delay prediction models in terms of both accuracy and practicality.  
Compared to previous research, this study introduces a novel data representation method by rasterizing terminal area convective weather and flight data into Multi-Channel Traffic and Weather Scene Images. A Convolutional Neural Network (CNN) is then used to extract complex delay-related features from these images, allowing the development of a high-precision airport delay prediction model. The structure of the paper is as follows: Section 2 reviews related work on the weather delay index and deep prediction models. Section 3 explains the two main methods. Section 4 introduces the dataset and evaluates the proposed model through comparative experiments. Section 5 summarizes the study and outlines future research directions.
## 4 Test Results and Discussion
### The Test Results for Part Two  
The research emphasis of this part is about the influence of the recycling speed, \(\mathbf{v_{\mathrm{R}}}\), on the recycling process of a single-plate helical pile. For the influence of the recycling speed, \(\mathbf{v_{\mathrm{R}}}\), on the recycling process, the test results of the recycling torque (\(T_{\mathrm{R}}\)); the embedment depth (\(z\)) responses of the single-plate helical pile; the vertical force on central shaft (\(F_{\mathrm{push}}\)) of the single-plate helical pile; and the embedment depth (\(z\)) responses of single-plate helical pile with same pitch of helix, \(p=0.03\) m, same outer diameters of helix, \(D_{\mathrm{H-out}}=0.20\) m, and different recycling speeds, \(\mathbf{v_{\mathrm{R}}}=0.8\)\(p/r\) and \(1\)\(p/r\), are compared in Figures 8 and 9. Observations from Figures 8 and 9 led to the following comments:  
1. The change-trends of the recycling torque, \(T_{\mathrm{R}}\), of the single-plate helical pile and the vertical force on central shaft, \(F_{\mathrm{push}}\), of the single-plate helical piles during the recycling process are same under different recycling speeds, \(\mathbf{v_{\mathrm{R}}}\); they both increase significantly at the beginning of the recycling process of the single-plate helical piles until they reach some peak value. Then they drop stably until the helix is close to the soil surface.
2. Besides the recycling torque, \(T_{\mathrm{R}}\), of the single-plate helical pile and the vertical force on central shaft, \(F_{\mathrm{push}}\), of the single-plate helical pile both increase with the increase of the recycling speed, \(\mathbf{v_{\mathrm{R}}}\).  
Figure 8: The test results of TN-3. (**a**) Recycling torque. (**b**) Vertical force on shaft.
## 3 Results  
### Time to Accomplish the Tasks  
Figure 8 shows the descriptive statistics for the time to accomplish the task. The ANOVA results testing the effects of device, order information display, and gender on the time to accomplish the task showed that the effects of device (\(F\) = 19.19, \(p\) < 0.0001) and order information display (\(F\) = 108.16, \(p\) < 0.0001) were both significant, while the effects of gender were insignificant (\(F\) = 1.35, \(p\) = 0.246). The interaction effects of the device and order information display were significant (\(F\) = 21.43, \(p\) < 0.0001) (see Figure 9). All other interaction effects were not statistically significant. The _SNK_ test results showed that the time to accomplish the task for the HoloLens 2 (7.68 \(\pm\) 2.53 min) condition was statistically significantly (\(p\) < 0.05) higher than that of paper (6.03 \(\pm\) 1.24 min) and M400 (5.65 \(\pm\) 1.73 min) conditions. However, the difference in the time to accomplish the task between the paper list and M400 AR glasses was not statistically significant.  
Considering the order information display, the _SNK_ test results showed that the time to accomplish the task using the QR code information (10.16 \(\pm\) 4.30 min) was significantly (\(p\) < 0.05) higher than that of the paper list (6.03 \(\pm\) 1.28 min), order map (4.97 \(\pm\) 1.57 min), and 3D graph (4.87 \(\pm\) 1.50 min) conditions. Additionally, the time to accomplish the task for the paper list condition was significantly (\(p\) < 0.05) higher than both the order map and 3D graph conditions. The difference between the latter two conditions was insignificant.
## 3 The Proposed IoMT Model
### Registration of Patients and Guest Healthcare Centres  
To benefit from the facility of the proposed IoMT model, users have to be registered by the HC. Patients and guest healthcare centres that benefit will be given identification codes. Each time a patient or healthcare centre registers, the shared secret key is updated before adding the client to the network.  
\begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Description** \\ \hline \(HC_{id}\) & Healthcare identification number \\ \(P_{id}\) & Patient identification number \\ \(GHC_{id}\) & Guest healthcare identification number \\ \(y^{2}=x^{3}+ax+b\ mod\ p\) & EC equation \\ \(\mathbf{p}\) & The large prime number obtained from chosen EC \\ a,b & Constants of EC such that \(4a^{2}+27b^{2}\ mod\ p\
eq 0\) \\ G & The base point which produces the subgroup of the elliptic curve that has a large prime as its order \\ n & Order (number of a point) of the subgroup. nG = 0, prime n is large \\ \(\mathbf{h}\) & The cofactor of the subgroup, which is the ratio \(\frac{|E|}{|E_{r}|}=\) \\ \(\mathbf{\underline{\textit{order of elliptic curve E}}}\) & \(\frac{\textit{\textit{order of elliptic curve E}}}{\textit{\textit{ order of EC}}}\). \(\mathbf{h}\) should be small \(h\leq 4\), preferably, \(\mathbf{h}=1\) \\ \(\textit{\textit{pr}}_{HC}\) & Healthcare private key \\ \(\textit{\textit{pr}}_{patient}\) & Patient private key \\ \(\textit{\textit{pr}}_{GHC}\) & Guest healthcare private key \\ \(\textit{\textit{pub}}_{BC}\) & Healthcare public key (\(\textit{\textit{pr}}_{HC}\times\mathbf{G}\)) \\ \(\textit{\textit{pub}}_{patient}\) & Patient public key (\(\textit{\textit{pr}}_{patient}\times\mathbf{G}\)) \\ \(\textit{\textit{pub}}_{GHC}\) & Guest healthcare public key (\(\textit{\textit{pub}}_{GHC}\times\mathbf{G}\)) \\ \(H_{HC}\) & The hash function used by healthcare \\ nList & A list containing reference IDs (\(HC_{id}\), \(P_{id}\), \(GHC_{id}\)) \\ HASH & Signing cipher message Cm hash function \\ ssk & Shared secret key \\ SNP & Share node point = (ssk \(\times\) G) \\ PRK & Private random key (bit size is dependent on the type of EC) \\ K & A random integer is chosen from (\(1\), \(\mathbf{p}-1\)) \\ CM & The ciphertext (all encrypted points) \\ \(\oplus\) & Exclusive OR operation \\ \(\star\) & Addition operation used for the ECC encryption process \\ T & Timestamp \\ \hline \end{tabular}
\end{table}
Table 2: Description of the notations used in the proposed scheme.
## 3 Results and Discussion
### Sem/xrd  
To investigate whether the addition of HPMC changed the type of CaCO\({}_{3}\) crystals produced by the MICP treatment, the MICP reaction was first carried out in sterile glassware A with the addition of bacterial solution and cementation solution. Then, the same amounts of bacterial solution, cementation solution, and HPMC (viscosity of 25 mpa.s) were added in sterile glassware B for the reaction. The products in glassware A and glassware B were washed, filtered, and dried to obtain calcium carbonate. The calcium carbonate in Glass Dish A and Glass Dish B were also analyzed by XRD, as shown in Figure 13. Three types of calcium carbonate crystals were identified in these two samples, including calcite, sphalerite, and aragonite. In sample A, the CaCO\({}_{3}\) precipitate consisted of 91.6% calcite, 6.3% aragonite, and 2.1% vaterite (Figure 13). Sample B was identified as consisting of 89% calcite, 9.6% aragonite, and 1.4% vaterite (Figure 13). This indicates that the primary CaCO\({}_{3}\) crystal type of both samples was calcite. HPMC did not change the predominant crystal type of CaCO\({}_{3}\) generated by the MICP treatment. In this study, CaCl\({}_{2}\) was used as the calcium source, and the primary crystal type of CaCO\({}_{3}\) obtained from MICP treatment was calcite, which is also consistent with the conclusion obtained from the study by [PERSON] et al. [33]. Moreover, the addition of HPMC slightly increased the aragonite content, and it is interesting to investigate if, in time, the addition of HMPC can increase the formation of aragonite and evaluate aragonite's behavior in long-term conditions in future MICP studies.  
SEM tests were performed on the MICP-treated specimens in G3 (reactant III) and G6 (reactant III), and the SEM images clearly showed the structural differences between the two specimens with and without the addition of HPMC for the MICP technique. Compared with the specimen without HPMC (Figure 14), the surface layer of the specimen treated with HPMC was more compact, and the soil particles and the pores between the soil particles were filled with more induced calcium carbonate crystals (Figure 15). HPMC increased the viscosity of the reactants and helped to induce more calcium carbonate precipitation in the surface layer of the specimens, thus improving the surface strength of the specimens.  
Figure 13: X-ray diffraction results.
# A 2D-View Depth Image- and CNN-Based 3D Model Identification Method
## 3 Experimental Results
### 3D Model Identification
From the results, we can conclude that the proposed identification method works well on the 400 test 3D models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Threshold** & \begin{tabular}{c} **True** \\ **Positive** \\ \end{tabular} & \begin{tabular}{c} **True** \\ **Negative** \\ \end{tabular} & \
\begin{tabular}{c} **False** \\ **Positive** \\ \end{tabular} &
\begin{tabular}{c} **False** \\ **Negative** \\ \end{tabular} & **Accuracy** \\ \hline
0.3 & 199 & 75 & 125 & 1 & 68.50\% \\
0.4 & 199 & 126 & 74 & 1 & 81.25\% \\
0.5 & 198 & 161 & 39 & 2 & 89.75\% \\
0.6 & 193 & 178 & 22 & 7 & 92.75\% \\
0.7 & 183 & 191 & 9 & 17 & 93.50\% \\
0.8 & 172 & 194 & 6 & 28 & 91.50\% \\
0.9 & 131 & 197 & 3 & 69 & 82.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of 3D model identification results with default settings and different thresholds. Figure 5: Graph of 3D model identification results with default settings and different thresholds. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**1:** & **Input** :\(Q_{c}\) & _\(p=avg(GoogLeNet(Q_{c}))\) /* get prediction values*/ \\
**2:** & _\(p=avg(GoogLeNet(Q_{c}))\) /* get prediction values*/ \\
**3:** & _\(p=avg(GoogLeNet(Q_{c}))\) /* get prediction values*/ \\
**4:** & _\(p=avg(GoogLeNet(Q_{c}))\) /* get prediction values*/ \\
**5:** & _\(p=avg(GoogLeNet(Q_{c}))\) /* get prediction values*/ \\
**6:** & _\(p=avg(GoogLe  
The Figure 6 shows how the number of depth images influence the identification accuracy. As described in Section 2.1, we took 6, 18, 42, and 66 depth images from vertices of regular convex polyhedrons, and the same numbers of depth images were used in both the training and test parts to yield identification accuracy. From the graph, we can see that the accuracy is improved by the increasing number of depth images. The improvement also slows down as the number of depth images increases and there is nearly no gap between 42 and 66 images when they are at the highest accuracies. The Figure 7 shows the influence of the number of 3D models used for training on identification accuracy. In this experiment, the 400 test 3D models remained unchanged, but we reduced the training 3D models in each category by 5, 10, and 15, representing 250, 500, and 750 training 3D models in total, respectively. From the graph, it can be observed that the identification accuracy is very low when using five 3D models. However, there is a great improvement when the number of 3D models for training jumps from 5 to 10. The highest accuracy improves about 3% from 10 to 15 models, but no improvement is produced from 15 to 20. This experiment indicates that, for a given original 3D model, 15 properly pose-deformed 3D models may be enough for our method to learn its other pose deformations, and this number can be used for 3D model identification. Figure 6: The influence of number of depth images on the identification accuracy. Figure 7: The influence of the number of 3D models used for training on the identification accuracy.
# Novel Boundary Edge Detection for Accurate 3D Surface Profilometry Using Digital Image Correlation
###### Abstract  
Digital image correlation (DIC) has emerged as a popular full-field surface profiling technique for analyzing both in-plane and out-of-plane dynamic structures. However, conventional DIC-based surface 3D profilometry often yields erroneous contours along surface edges. Boundary edge detection remains one of the key issues in DIC because a discontinuous surface edge cannot be detected due to optical diffraction and height ambiguity. To resolve the ambiguity of edge measurement in optical surface profilometry, this study develops a novel edge detection approach that incorporates a new algorithm using both the boundary subset and corner subset for accurate edge reconstruction. A pre-calibrated gauge block and a circle target were reconstructed to prove the feasibility of the proposed approach. Experiments on industrial objects with various surface reflective characteristics were also conducted. The results showed that the developed method achieved a 15-fold improvement in detection accuracy, with measurement error controlled within 1%.  
Keywords:digital image correlation (DIC); edge detection; random speckle images; surface profilometry; automated optical inspection (AOI) +
Footnote †: journal: Applied Physics Letters
# Greenhouse Ventilation Equipment Monitoring for Edge Computing
## 3 Experiments
### Training of Object Detection and Embedding Model
#### 3.2.2 Influence of Various Image Enhancement Techniques  
This study demonstrates that without applying enhancement techniques to the original images, achieving accurate representations of targets in reconstructed images remains a challenge. This discrepancy primarily arises due to significant variations in the appearance of images under diverse lighting conditions, even for identical target states. To address this issue, we evaluated three distinct image enhancement strategies: normalization, gamma transformation, and histogram equalization.  
As shown in Figure 10, histogram equalization markedly surpasses alternative methods in enhancing the visibility of critical details in reconstructed images, such as the blades of fans. Comparing the enhanced images, the reason may be that it is difficult to effectively distinguish details in images processed by other means except for histogram equalization. Inadequate representations of these blades in reconstructed images could significantly impede the accurate analysis of their rotation.
# A Multi-Period Optimal Reactive Power Dispatch Approach Considering Multiple Operative Goals
## 3 Tests and Results
### Test Systems
#### 3.2.1 IEEE 57-Bus Test System  
The IEEE 57-bus test system consists of 62 branches and 26 control variables; these include 7 generation units, 16 transformers, and 3 capacitors. A single-line diagram of the system is depicted in Figure 2, whereby each color represents a different voltage control area with a single pilot node. The total active and reactive load demand are 1250 MW and 336 MVAr for period 11 on the 100 MVA base. The initial output of active and reactive power from the generators come from the solution of the unit commitment (UC) problem reported in [41]. Additionally, the initial settings of the tap transformers are in the nominal ratio and all shunt elements are connected.  
The minimum and maximum limits of control variables for the IEEE 57-bus test system are as follows. Each transformer ratio varies from 0.9 to 1.1 per-unit (p.u) in equal steps of 0.01. Shunt elements are between 0 and its MVAr parameter, which have no steps. Voltage set points of generators vary in the range of [0.85, 1.1] p.u (except for lower voltage limits of the pilot nodes, which are reported in Table 1). Table 1 shows the shunt MVAr parameter as well as the lower voltage limits for pilot nodes taken from [44]. In this case, the maximum number of maneuvers allowed in a day (\(\overline{M}\)) is set to 150.  
Figure 1: The 24 h load curve performed on the IEEE 118-bus test system.  
Figure 2: IEEE-57 single-line diagram, annotated with pilot nodes and voltage control areas.
# Effect of Particle Size on Mechanical Property of Bio-Treated Sand Foundation
###### Abstract  
In the field of geotechnical engineering, microbially induced calcium precipitation technology is feasible and sustainable alternative to improve the engineering characteristics of sand foundation under different geological conditions for a long time. However, it is unclear how the effects of different sand particle sizes on the engineering characteristics of bio-treated sand column. The method of intermittent injection in batches was used to develop a series of bio-treated sand columns. The results showed that the mechanical properties of the bio-treated column improved by increasing the particle size. Low concentration of bacterial suspension and cementation reagent leads to the increase of calcium carbonate and unconfined compressive strength. Additionally, the total injection times increased, thus risking time cost. Furthermore, the increase of sand particle size was beneficial to the uniformity of the spatial distribution of calcium carbonate in the bio-treated column. The coefficient of variation was reduced by up to 52.0%. Scanning electron microscopy results confirmed that the size and uniformity of calcite crystals on the surface of sand particles were related to the concentration of cementation solution.  
bio-treatment; sand foundation; sand particle size; calcium carbonate contents; unconfined compressive strength Article