esa-sceva/satcom-chunk-collection huggingface dataset:
# Success of Implementing Cloud Computing for Smart Development in Small Construction Projects
###### Abstract  
The objective of this study is to ascertain the essential elements that contribute to the successful implementation of cloud computing in small-scale construction projects, with the ultimate goal of promoting intelligent development in Malaysia. The construction sector is undergoing rapid transformation, and the integration of cloud computing technology can make a substantial contribution to the achievement of project objectives and the promotion of sustainable development. Nonetheless, there exists a dearth of comprehension regarding the function of cloud computing in minor construction undertakings within the Malaysian context. In order to bridge this gap, a mixed-methods approach was implemented, which encompassed a comprehensive review of the literature, interviews with experts, and a preliminary survey that involved 160 participants. Utilizing the findings of the pilot survey, the process of Exploratory Factor Analysis (EFA) was employed to discern and eliminate nonessential determinants of success. A survey utilizing primary questionnaires was conducted with a sample size of 230 participants. The subsequent analysis of 16 critical success factors was carried out through the application of Structural Equation Modeling (SEM). The findings indicate that there are four fundamental constructs that play a crucial role in the effective execution of a project. These include cost, quality, and time management (\(\beta\) = 0.352); planning success (\(\beta\) = 0.360); organizational success (\(\beta\) = 0.351); and communication and coordination (\(\beta\) = 0.299). The research results have favorable ramifications for the construction sector in Malaysia. The integration of cloud computing technology in minor construction endeavors has the potential to augment project efficacy and foster sustainable development. This study offers a roadmap for stakeholders in the construction industry to effectively utilize cloud computing technology for smart development by identifying critical success factors.  
cloud computing; critical success factors; small construction projects; Malaysia; smart development Article
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 3 Weak String Erasure  
In order to better demonstrate the application of joint measurement and error estimation technology in two-party cryptographic protocols, we first discuss its enhancement to the security performance of weak string erasure (WSE), which was originally proposed by [PERSON] [2], and studied as the basic protocol of other two-party cryptographic protocols.
# A 77 GHz Transmit Array for In-Package Automotive Radar Applications
## 3 Analytical Model  
A transmit array analytical model based on the evaluation of power transfers [10] is used in this paper. Four parameters have been considered to compute the TA's efficiency (Figure 7): \(P_{1}\) is the power at the input of the focal source, while \(P_{2}\) is the power radiated by the same source. \(P_{3}\) is the incident power collected by the receiving side of the transmit array. Finally, \(P_{4}\) is the power re-radiated from the transmission side of the array. The antenna efficiency is defined as follows:  
\[\eta=\frac{P_{4}}{P_{1}}=\eta_{FS}\eta_{SO}\eta_{IL}\]  
where \(\eta_{FS}\) is the realized efficiency of the focal array (\(P_{2}/P_{1}\)), \(\eta_{SO}\) is the spillover efficiency (\(P_{3}/P_{2}\)) and \(\eta_{IL}\) is the insertion loss of the unit cell (\(P_{4}/P_{3}\)). To enhance directivity and spillover efficiency, it is essential to examine the behaviour of the transmit array versus variations in its design parameters such as focal distance (\(f\)) and \(D\), as well as the number and spacing of its elements. For simplicity, the feed's radiation pattern is assumed to be equal to \(\cos^{\#}\Theta\) with \(q=0.5\). Figure 8 illustrates the TA's performance in terms of spillover efficiency and gain for different \(f/D\) ratios, based on the previously discussed analytical model. The optimal performance is achieved when \(f/D\) equals 0.5. Additionally, the cell size is a crucial factor for a given \(f/D\) value. Figure 9 depicts how element spacing affects the transmit array's directivity. As shown, the directivity increases as the spacing between the elements decreases, leading to a chosen spacing of \(\lambda/3\) to maximize directivity.  
Figure 8: x8 array: calculated gain (dashed line), directivity (continuous line) and spillover efficiency (dotted line) for different values of \(f/D\) (spacing \(\lambda/3\)).  
Figure 7: Transmit array geometry.
## 3 Training Data Collection
### Command Structured Parsing Algorithm and Details
#### 3.2.2 Structured Processing of Natural Language Commands Based on Dependency Parsing  
This section describes the use of dependent syntactic analysis to generate a dependent syntactic tree from natural language instructions, combine it with a semantic parsing algorithm to complete the structured representation of natural language instructions, and automatically synthesize AOM statements. In this paper, we use the HanLP Chinese language processing tool to analyze the dependency syntax of unstructured natural language commands. The dependency syntax analysis module in HanLP uses the maximum entropy model to find the most probable dependency relationship between any two words and its probability based on the information of the words themselves, lexical properties, suffixes, and the distance between the two words, and then determines the node position of the word in the dependency tree and its relationship with the final position; the minimum spanning tree algorithm is used to obtain the entire dependency tree. We explain the common dependency relations next, as shown in Table 1.  
Next, we introduce and analyze the dependencies of natural language commands of types c, d, and e, respectively. Object juxtaposition natural language commands (c): For example, the semantic dependencies of the command \"  
```
Input: Dependency syntactic analysis tree generated by each keyword node, each node of the tree contains the original word:

word, lexical property: postag, dependency: deprel, node position dependency: head

Output: Structured AOM Statements

1: Initialize AOM = \(\O\)

2: FOR relation IN deprel/*Iterative dependencies*/

3: num = num + 1/*Automatic loop + 1 for list subscript positioning*/

4: IF relation == VOB/*Traversing the verb-object relationship*/

5: Action\(\leftarrow\)word[head[num] \(-\) 1]/*Find the word prototypes on which the VOB relationship depends as

action attributes*/

6: ELSE IF relation == 'COO': /Traversing the COO relationship/

7: Object\(\leftarrow\)word[head[num] \(-\) 1]/*Use the COO-dependent word prototype as the first object*/

8: Object\(\leftarrow\)word[head[num] \(-\) 1]/*Use the COO-dependent word prototype as the first object*/

9: Object\(\leftarrow\)word[num]/*The COO itself corresponds to the word prototype for the second object*/

10: ELSE IF relation == 'ATT'/*Traversing the ATT relationship*/

11: Member\(\leftarrow\)word[head[num] \(-\) 1]/*ATT-dependent word prototypes as manipulated member attributes*/

12: END IF

13: AOM\(\leftarrow\){Action_1 : \"word_1\", Object: {Object_1: \"word_2\", Object_2: \"word_3\"}, Member1_1: \"word_4\"}/*Update AOM*/

14: END FOR

15: RETURN AOM
```  
**Algorithm 2** Semantic parsing process for the juxtaposition of natural language commands for objects.
## 4 Data-Driven CIM Construction
### Data-Driven CIM Framework  
CIM is a city-level information management system that integrates advanced technologies such as BIM, GISs, and the IoT. Traditional CIM primarily relies on manual modeling and static data, whereas data-driven CIM achieves dynamic, real-time, and intelligent urban management through comprehensive processes of data collection, fusion, analysis,and application. CIM encompasses multiple critical aspects, including data acquisition, data processing, data modeling, visualization, and application. To enhance the efficiency and intelligence of CIM development, a data-driven CIM Development Framework (as illustrated in Figure 3) is designed, aiming to address the key challenges such as data governance, data standardization, and data integration in CIM implementation [31; 36; 40]. This framework comprises three core components: data collection, data processing, and data application. It incorporates the successful practices of BIM and GISs while addressing the unique requirements of CIM, ensuring data security, consistency, and efficient utilization, thereby advancing the construction of smart cities [52].  
Data-driven CIM offers several core advantages: By integrating BIM, GISs, and big data analytics, CIM provides accurate, real-time urban operational data, serving as a scientific foundation for urban decision making. Leveraging IoT sensors and real-time data analytics, CIM can rapidly respond to the changes in urban operations and autonomously adjust the management strategies. Furthermore, data analytics facilitates the optimization of resource allocation by identifying the patterns in resource utilization, improving the efficiency of transportation, energy, and environmental management, and enhancing the overall urban operational efficiency. As a key enabler of smart cities, CIM supports applications in intelligent transportation, smart grids, urban safety management, and more, thereby boosting cities' competitiveness and sustainability.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 4 Propulsion Systems in Nanosatellites
### Pulse Plasma Thruster
#### 4.2.3 Nanosatellite Propulsion Systems in Use and Under Development  
Many nanosatellites use cold gas thrusters, simple and reliable systems for attitude control and small maneuvering tasks. For example, the SNAP-1 propulsion system by Surrey Satellite Technology Ltd. (SSTL) uses butane as a propellant and has been widely used in small satellite missions. Other cold gas systems, such as those developed by VACCO, are also commercially used [67; 66].  
Field Emission Electric Propulsion (FEEP) systems, such as ENPULSION's NANO thrusters, provide ultra-precise thrust for orbital corrections, enabling prolonged mission durations for nanosatellites. Additionally, cold gas propulsion systems, like VACCO's butane-based thrusters, offer simplicity and reliability for small satellite operations. These systems have been demonstrated in missions like MarCO and SNAP-1, highlighting their effectiveness in various operational scenarios.  
Grey literature sources provide critical insights into emerging propulsion technologies. For example, NASA's CubeSat Propulsion Roadmap outlines the development of advanced FEEP and electrospray systems designed for CubeSats with power budgets under 20 W. ESA's ATHENA project, documented in its project overview, focuses on scalable electrospray thrusters capable of supporting multi-satellite constellations [73; 74].  
Additionally, [PERSON]'s micro-resistojet system is another propulsion technology used in CubeSats, integrating both primary and secondary propulsion within a compact volume. This system has been applied to several missions, providing thrust at specific impulses up to 150 s [58].  
Furthermore, [PERSON] and [PERSON] [75] analyzed power, propulsion, and thermal subsystems for a 3U CubeSat designed to monitor Earth's radiation imbalance. Their findings highlight critical challenges in optimizing thermal control and propulsion for compact systems, particularly for missions demanding high precision.
# Modified Pseudo-Dynamic Bearing Capacity of Shallow Strip Foundation near Slope on Non-Uniform Soil
###### Abstract  
In engineering practice, many foundations are forced to be placed on slopes, whose stability is deeply affected by seismic force. Therefore, an accurate assessment of the seismic bearing capacity of strip foundations close to slopes is a crucial guide for engineering. Herein, an analytical procedure considering pseudo-dynamic influence is proposed, which offers a new assessment framework for seismic bearing capacity of strip foundations close to slopes on non-uniform soils. Considering the temporal and spatial characteristics of earthquake action, the seismic bearing capacity of foundations at different normalized frequencies is obtained, and depth profiles of seismic acceleration coefficients at different normalized frequencies are presented. The consistency between this paper's computational results and the published literature substantiates the dependability of the proposed analytical procedure. The sensitivity analyses are carried out for horizontal seismic coefficients, normalized frequencies, damping ratios, internal friction angles, slope angles, cohesion, and non-uniform coefficients, and the results are presented for practice reference in engineering.  
Keywords:slope; bearing capacity; modified pseudo-dynamic; non-uniform soil; upper analysis +
Footnote †: journal: Applied Sciences
## 2 Experimental Setup
There are, in total, 23 pressure sensors which are uniformly installed on the surface of structure A and B. Two LVDTs (Linear Variable Differential Transformer) are installed horizontally and vertically on the top of structure B, as shown in Figure 2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensor No.** & **W1** & **W2** & **W3** & **W4** & **W5** & **W6** & **W7** & **W8** \\ \hline Location (m) & 26.95 & 29.95 & 32.95 & 35.95 & 38.95 & 41.95 & 44.67 & 47.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Location of the wave profile sensors (distance to the wave maker). Figure 1: Schematic map of the physical model constructed in the wave flume (unit: m) (the revetment is labelled as A, and the vertical caisson is labelled as B). In order to relatively precisely control the accuracy of the dry density of the reclaimed coral sand foundation, the physical model is built layer by layer, with the help of a plate-type vibrator, making sure the dry density of each layer reaches its expected value. In this process, these pore pressure sensors are installed to their preset positions. The completed physical model with and without armor blocks is shown in Figures 3 and 4. It is worthy of notice that the wave flume is divided into two parts in its width direction at the section where the physical model is constructed. As a result, the water in front of, and behind, the physical model is naturally connected together in wave flume. Therefore, the overtopped water can return back to the water domain quickly in the wave flume without any loss. Figure 4: A real view of the completed physical model with accropodes and rubble in front of revetment breakwater. Figure 3: A real view of the completed physical model without accropodes or rubble in front of the revetment breakwater. Figure 2: Schematic map of the location of the 14 pore pressure sensors, 23 pressure sensors and 2 displacement sensors (LVDT (Linear Variable Differential Transformer)) in the physical model (unit: m). The reclaimed coral sand foundation of the artificial islands in the South China Sea may be in a loose state due to the adverse influence of the construction technology or the limited time for the construction. In addition, it is widely recognized that the breakwater will be at great risk if its foundation is loose. Therefore, the main purpose of this study is to experimentally evaluate the stability of the revetment breakwater built on reclaimed loose coral sand foundation in the South China Sea under the long-term impact of extreme regular wave. In addition, it is also hoped to explore the effect of some factors, such as dry densities of the reclaimed coral sand foundation, armor blocks, tidal level and the wave period on the stability of the revetment breakwater. Accordingly, two dry densities of the reclaimed coral sand foundation, two water depths, two wave periods are set in the tests, and armor blocks are used in Test 4, Test5 and Test6, as illustrated in Table 3.
###### Abstract
This approach builds upon the LVI-SAM framework and significantly improves system performance by utilizing panoptic segmentation to enhance the effectiveness of multi-sensor fusion SLAM. The primary contributions of this paper encompass the following:  
Figure 1: Schematic diagram of the dynamic scene. The triangle in the figure is the camera, the cars represent dynamic objects, the red dots represent dynamic feature points, and the black dots are static feature points. The illustration emphasises significant disparities in the motion of dynamic and static entities as they change positions between two frames, ultimately reflecting the characteristic features of a dynamic scenes. 1. Introducing a novel SLAM approach, PSMD-SLAM, which leverages the benefits of multi-sensor fusion and panoptic segmentation for dynamic environments. 2. Implementing dynamic object detection and removal based on panoptic segmentation, utilizing probability propagation-based and principal component analysis-based (PCA-based) clustering methods in visual and LiDAR data, respectively. 3. Proposing a fast and robust dynamic object six-degrees-of-freedom (6D) pose estimation module that fuses panoptic segmentation information with multi-sensor fusion data for accurate estimation. The remainder of this manuscript is structured as follows: Section 2 delineates the pertinent literature, Section 3 elucidates the proposed framework, Section 4 showcases the experimental outcomes, and the Section 5 offers a synthesis.
## 4 Results and Discussion
### Numerical Results--SBD2D Communication Using Data Integration  
The results of our proposed algorithm are displayed in Figure 9 below. According to the findings, combining SBD2D communication with a data integration technique indicates a considerable improvement in the energy efficiency throughout the process of data exchange. This is illustrated by the fact that this improvement was observed throughout the entire process. The graph indicates that, even as the nodes exchange data, the overall energy consumption does not increase significantly. This is true up until the stage where the final node integrates all of the data and sends the packet to the base station. In addition,  
Figure 8: Energy efficient algorithm results in smart beekeeping D2D communication.  
it was discovered that the amount of energy used during the transmission of the packet to the base station does not exceed 1 joule.  
It can be seen from the graphs in Figure 9 that the utilization of D2D communication and data integration is a significant contributor to the consistent low energy consumption that occurs during the exchange of data between the nodes. Employing this strategy brings about an immense decrease in the system's total energy consumption. D2D communication eliminates the energy consumption that is involved with sending data through numerous intermediate nodes by making it possible for direct communication to take place between these nodes, without the requirement for centralized routing. Data integration techniques combine and aggregate many data points into a single packet, which is then transmitted from each node to the base station, as opposed to delivering individual data packets individually. This aggregation reduces the number of duplicated transmissions, resulting in energy savings. The low energy consistency usage during the data exchange provides strong evidence that the integration process successfully cuts down on the transmission overhead and maximizes the use of network resources.  
The energy required to transmit the integrated data packet from the last node to the base station is less than 1 joule. This demonstrates that the final transmission stage also benefits from the energy efficiency gained by the D2D communication and data integration approaches. Maintaining such a low energy usage during packet transmission is a significant achievement, as it helps to reduce energy waste and promotes beekeeping as a sustainable practice. The benefits of a lower latency in D2D communication are clearly illustrated by the findings. Since data may be shared in real time across surrounding nodes, the hive can react quickly to changing conditions and make better decisions. In smart beekeeping, this aspect of D2D communication is vital, since it enables beekeepers to quickly handle any problems, improve hive management, and increase bee productivity. Since data are exchanged quickly within a short time between the nodes, the energy usage is reduced.
# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 2 Related Works and Concepts
### Rolling Shutter Impact on ENF Extraction  
When using the rolling shutter, the pixels of each frame are captured by scanning over a rectangular CMOS sensor row-by-row rather than recording all of the pixels in a frame at one time instance, as is done with a global shutter. Consequently, the ENF is embedded by successively recording the intensity of each frame row. The sequential capturing of rows of each frame enables a considerably faster sensing of the ENF signal as a result of the upscaling of the effective sampling rate by multiplying it with the number of rows of the sensed frame. The process of acquiring video using a camera with a rolling shutter mechanism is illustrated in Figure 4. In every frame period \(T_{c}=\frac{1}{f_{c}}\), where \(f_{c}\) is the frame rate, every frame's row is successively sampled and captured for \(T_{ro}\) seconds, accompanied by \(T_{idle}\) seconds of inactivity before moving on to the next frame row [23; 48].  
Figure 3: Framework of the proposed MUSIC approach.  
The \(T_{ro}\) is referred to as the read-out time of the camera, which is the length of time it takes to obtain the rows of a frame. Every camera has a unique \(T_{ro}\) value [21,31]. Furthermore, the work in [31] demonstrates that inside a camera device, the \(T_{ro}\) might change based on the resolution or frame rate of a video. Due to the exposure of pixels in different rows at different times, and their simultaneous display during playback, the rolling shutter can generate skew, blur, and other visual distortions, particularly with objects moving rapidly and in quick flashes of light [49]. As a result of the associated distortions, the rolling shutter's sequential read-out process has long been seen as unfavorable to image/video quality. Conversely, recent research has demonstrated that techniques such as computational photography and computer vision can be employed to exploit the rolling shutter mechanism [50,51].