esa-sceva/satcom-chunk-collection huggingface dataset:
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 4 Qualitative Analysis
### Construction Phase
#### 4.3.4 Safety Monitoring  
Safety management during construction, particularly in critical tasks like lifting and assembling, is paramount for ensuring on-site safety. The integration of the IoT, BIM, digital twins, and other advanced technologies facilitates the perception, analysis, and prediction of potential safety hazards. This integration enables the timely detection of and response to safety threats. The IoT acts as the \"keen eye\" of safety monitoring. Radio frequency identification (RFID) combined with GPS can collect real-time data on the type, production attributes, arrival time, location, and installation status of precast elements. The data is uploaded to the cloud, providing decision support for site management personnel and workers [81]. However, RFID may encounter issues such as limited coverage and unstable information transmission on construction sites. In contrast, low power consumption data transmission (LoRa) and a narrowband Internet of Things (NB-IoT) can offer better coverage and reliable data transmission [82].  
The analysis of on-site data is important for safety risk assessment and dynamic safety control. Nevertheless, many risk analysis methods overlook the interaction of risk factors. To address this gap, [PERSON] et al. [83] introduced a digital twin-based lifting risk management framework that incorporates a risk-coupling model. This model enables realtime sensing and the virtual-real interaction of multi-source data, such as safety protection wearing status, sling inclination, the quality level of precast elements, the actual service life of hoisting machinery, and the layout of the storage yard. Furthermore, [PERSON] et al. [84] proposed a safety risk prediction framework leveraging a digital twin-support vector machine algorithm to forecast lifting risks in advance.
## 2 Mesh Stiffness Calculation of Gears
### Mesh Stiffness Calculation of Gears with Healthy Teeth  
Mesh stiffness is a time-varying parameter that can reflect the meshing conditions of gears. In this paper, the meshing stiffness of one tooth is calculated from defections caused by bending, axial compressive, shear, and contact deflection. According to Reference [47], in order to calculate the deflections of a tooth, the tooth is considered as a non-uniform cantilever beam with an effective length \(L_{e}\) and under applied force \(F\). Meanwhile, the tooth is divided into \(n\) segments, as shown in Figure 2.  
On the basis of the [PERSON]'s beam theory [48], the stiffness of the tooth bending, shear, and axial compressive are given as follows.  
\[\frac{1}{k_{bj}}=\sum\
olimits_{i=1}^{n}\frac{1}{E_{e}}\left(\begin{array}{ c}\frac{L_{i}^{3}+3L_{i}^{2}S_{ij}+3L_{i}S_{ij}{}^{2}}{3l_{j}}\cos^{2}a_{j}-\\ \frac{L_{i}^{2}Y_{j}+2L_{i}Y_{j}S_{ij}}{2j}\cos a_{j}\sin a_{j}+\\ \frac{12(1+v)L_{i}}{5A_{j}}\cos^{2}a_{j}+\frac{L_{i}}{A_{j}}\sin^{2}a_{j}\\ \end{array}\right) \tag{1}\]  
where \(L_{i}\) and \(a_{j}\) represent the thickness of a microelement and the pressure angle of the contact point \(j\), respectively. \(S_{ij}\) is the distance between a micro-element and the contact point in the X direction. \(Y_{j}\) is the corresponding half tooth thickness. \(A_{j}\) and \(I_{j}\) denote the cross-sectional areas and area moments of inertia, respectively. The \(A_{j}\) and \(I_{j}\) could be expressed by Equations (2) and (3).  
\[A_{j}=2 BY_{j} \tag{2}\]  
Figure 1: Schematic diagram of gear meshing.  
Figure 2: Schematic diagram of a gear tooth cantilever beam.  
\[I_{j}=\frac{1}{12}B(2Y_{j})^{3} \tag{3}\]  
\(Ee\) represents the [PERSON]'s modulus, which can be expressed as:  
\[E_{e}=\left\{\begin{array}{ll}E&\frac{B}{H_{p}}<5\\ \frac{E}{1-v^{2}}&\frac{B}{H_{p}}\geq 5\end{array}\right. \tag{4}\]  
in which \(E\) and \(v\) are the [PERSON]'s modulus and the [PERSON]'s ratio, respectively. \(B\) is the teeth width, and \(H_{p}\) is the thickness on the pitch circle.  
The Hertzian contact stiffness is calculated by Equation (5).  
\[\frac{1}{k_{hj}}=\frac{4(1-v^{2})}{\pi BE} \tag{5}\]  
For a single-tooth-pair meshing duration, the total effective stiffness is defined by the equation below [28].  
\[k_{pg}=\frac{1}{\frac{1}{k_{h}}+\frac{1}{k_{hp}}+\frac{1}{k_{hg}}} \tag{6}\]  
where \(p\) and \(g\) are pinion and gear, respectively.  
For a double-tooth-pair meshing duration, there are two pairs of gears meshing at the same time. Therefore, the total effective stiffness is defined as [28]:  
\[k_{t}=k_{pg1}+k_{pg2}=\sum_{f=1}^{2}\frac{1}{\frac{1}{k_{h,f}}+\frac{1}{k_{hp, f}}+\frac{1}{k_{bg,f}}} \tag{7}\]  
where \(f=1\) for the first pair and \(f=2\) for the second pair of meshing teeth.
# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
###### Abstract  
Optical camera communication (OCC) is one of the most promising optical wireless technology communication systems. This technology has a number of benefits compared to radio frequency, including unlimited spectrum, no congestion due to high usage, and low operating costs. OCC operates in order to transmit an optical signal from a light-emitting diode (LED) and receive the signal with a camera. However, identifying, detecting, and extracting data in a complex area with very high mobility is the main challenge in operating the OCC. In this paper, we design and implement a real-time OCC system that can communicate in high mobility conditions, based on You Only Look Once version 8 (YOLOv8). We utilized an LED array that can be identified accurately and has an enhanced data transmission rate due to a greater number of source lights. Our system is validated in a highly mobile environment with camera movement speeds of up to 10 m/s at 2 m, achieving a bit error rate of \(10^{-2}\). In addition, this system achieves high accuracy of the LED detection algorithm with mAP0.5 and mAP0.5:0.95 values of 0.995 and 0.8604, respectively. The proposed method has been tested in real time and achieves processing speeds up to 1.25 ms.  
Keywords:optical camera communication (OCC); object detection; LED segmentation; YOLOv8 +
Footnote †: journal: Sensors
# A Metamaterial Solution for Soundproofing on Board Ship
## 2 Benchmark Solution for Sound Insulation on Board  
A standard solution for noise insulation on board ship is considered to set a benchmark in terms of acoustic performance, weight, and thickness. The panel is composed of a couple of aluminum honeycomb panels and a mineral wool layer (Figure 1).  
Figure 1: Traditional solution components: (**a**) honeycomb aluminum panels; (**b**) mineral wool layer.  
Figure 2 shows the prototype mounted in the acoustically coupled chambers to measure its STL according to ISO 15186 1-2 [23; 24]. The panel, with dimensions of 800 \(\times\) 800 mm, was installed on the test window between the reverberant and the hemi-anechoic chamber, with mastic used for sealing. The reverberant room had a volume of 252 m\({}^{3}\) and was equipped with a tetrahedral source of the Genelec 8351A type and six B&K 1/4\" type 4135 microphones. The sound power in the receiving room (hemi-anechoic chamber) was measured using a B&K sound intensity PP type 2681 probe. The weighted sound reduction index (Rw) was evaluated according to ISO 717-1 [25], leading to the value of Rw (Figure 3) used as a benchmark. Since the results cannot be explicitly reported for confidentiality reasons, only the 10 dB increase is reported on the ordinate axis.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### The Mamdani-Type Fuzzy Inference Process
#### 4.4.1 Fuzzify Input Variables  
The initial step involves converting the crisp numerical values of the input variables, solar irradiance, and battery charge level, into corresponding membership values within the relevant fuzzy sets using predefined membership functions. This fuzzification process assigns each input a degree of membership in the associated fuzzy linguistic sets, ranging from 0 to 1, regardless of the specific input values.  
In the given example, three If-Then rules establish four fuzzy linguistic sets: \"solar irradiance is high\"; \"solar irradiance is low\", \"battery charge level is full\", and \"battery charge level is low\". To apply fuzzy reasoning, the input variables--solar irradiance and battery charge level--are fuzzified based on their respective membership functions. For instance, as illustrated in Figure 5, a solar irradiance value of 600 W/m\({}^{2}\) is mapped onto the membership function curve for the linguistic set \"solar irradiance is high\", resulting in a membership value of \(\mu=0.66\) for this fuzzy set.  
Figure 4: Logic flow of solar power system efficiency.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 3 Routing Protocols  
In the last section, we present four current UAV swarm communication architectures, and find that the multi-layer swarm Ad hoc network architecture has better comprehensive performance through comparison. Considering the U-T-U communication, routing protocol plays a crucial role in reliable end-to-end data transmission. This makes routing protocol an attractive research topic in the field of UAV swarm communication. However, due to the rapid mobility of UAVs, the instability of air links, the limited resources, and the variability of Quality of Service (QoS) requirements, routing in UAV swarm communication network has become a vital task [44; 45]. Moreover, the traditional Ad hoc routing protocols cannot be used in UAV swarm communication without improvement [46; 47]. Therefore, designing routing protocols that fit most mission scenarios and UAV characteristics remains a challenge for researchers.  
In this section, we first introduce the common UAV swarm communication routing technology, which is the basis of the routing protocol design and implementation. Then, we give the routing protocol a classification. The classification is based on the technologies followed by the routing protocols. Further, we provide a detailed overview of each class of routing protocols. At the end of this section, we remark the advantages and disadvantages of routing protocols.
## 4 Discussion
### Coherence Factor Matrix  
As stated in 2.5, the coherence factor (CF) matrix is an effective method to reduce artifacts and interferences in the image. The CF matrix is obtained in the delayed signal. Since the CF matrix is used as a weight to amplify the reflection wave and suppress the artifacts for each shot, it could be theoretically applied into other prestack time domain migration methods. Figure 27 here presents the results when the CF matrix was applied into Kirchhoff migration and DAS beamforming. The results shows that the CF matrix can obviously reduce the artifact caused by surface wave and interference on two sides of the images. However, there was also a contrast reduction at the lower floor in Kirchhoff. This might be a result of different travel time (delay time) calculation algorithm between MVSS beamforming and Kirchhoff migration.
## 2 Materials and Experimental Methods
### Preparation of Selected Materials and Specimens  
Since the ASME code-certified materials were extensively used for nuclear applications and are expected to be applied in novel lead- and LBE-cooled fast reactors, they were utilized for corrosion tests performed in this study, as exemplified by ferritic/martensitic steels such as T91 and HT9, and austenitic stainless steels such as the type 316L stainless steel (SS316L). The elemental compositions of these three alloys are summarized in Table 1.  
Corrosion test specimens were processed using the electric discharge machining method, with each specimen machined into a 20 mm-long and 10 mm-wide rectangular plate with 2 mm thickness, as shown in Figure 2. Two holes were cut in the plate to fix its position in liquid LBE, and all surfaces and edges were mirror-polished using diamond suspensions with particle sizes as small as 0.05 \(\upmu\)m.  
To obtain a detailed microstructure, proper heat treatment procedures and etching techniques were applied, as summarized in Table 2. T91 and HT9 samples were initially normalized at 1050 \({}^{\circ}\)C for 1 h and were then air-cooled, tempered at 750 \({}^{\circ}\)C for 2 h, and again air-cooled. For SS316L, 1 h solid solution heat treatment at 1050 \({}^{\circ}\)C and water quenching were employed to obtain an austenitic structure. The selected alloys were treated with suitable etchants, i.e., [PERSON]'s reagent (5 mL HCl + 2 g picric acid + 100 mL ethanol) for T91 and HT9 and 5% Nital (5 mL HNO\({}_{3}\) + 95 mL ethanol with electrolytic 3 V) for SS316L. Grain sizes were determined in compliance with ASTM E112 through the intercept method. As shown in Figure 3, the prior austenite grain (PAG) sizes of tempered martensitic T91 and HT9 steels were measured as \(\sim\)30 and \(\sim\)35 \(\upmu\)m, respectively, with that of SS316L being \(\sim\)25 \(\upmu\)m.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Main Elements** & **Fe** & **Cr** & **Ni** & **Mo** & **Mn** & **Si** & **W** & **Nb** & **V** & **C** \\ \hline \multirow{2}{*}{T91} & Nominal & Bal. & 8.5 & 0.2 & 1 & 0.5 & 0.3 & - & 0.06 & 0.2 & 0.1 \\  & Measured & Bal. & 8.61 & 0.18 & 0.96 & 0.45 & 0.229 & - & 0.07 & 0.22 & 0.09 \\ \multirow{2}{*}{HT9} & Nominal & Bal. & 11.5 & 0.5 & 1 & 0.6 & 0.4 & 0.5 & - & 0.3 & 0.1 \\  & Measured & Bal. & 11.63 & 0.50 & 1.03 & 0.55 & 0.36 & 0.52 & - & 0.28 & 0.11 \\ \multirow{2}{*}{SS316L} & Nominal & Bal. & 17.0 & 10.0 & 2.2 & 0.9 & 0.4 & - & - & - & 0.02 \\  & Measured & Bal. & 16.87 & 10.31 & 2.07 & 0.92 & 0.44 & - & - & - & 0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elemental compositions of tested materials (in wt.%).  
Figure 1: (a) Schematic diagram and (b) picture of a static lead–bismuth eutectic (LBE) corrosion cell.
# Optimal Crew Scheduling in an Intensive Care Unit: A Case Study in a University Hospital
## 2 Material and Method  
### Problem Description and Limitations  
Effective workforce scheduling in hospitals is a complex and critical task that directly impacts healthcare service quality, staff well-being, and operational costs. In Pediatric Intensive Care Units (PICUs), ensuring continuous and adequate staffing levels is particularly challenging due to the high variability of patient demands, staff availability constraints, and institutional regulations. Traditionally, shift scheduling is manually handled by senior nurses and hospital administrators, which is not only time consuming but also prone to inefficiencies and unfair shift distributions.  
The problem addressed in this study involves the optimization of hospital staff scheduling, specifically focusing on nurses, junior doctors, senior doctors, and caregivers. The goal is to develop a mathematical model that minimizes operational costs while ensuring fair workload distribution and compliance with hospital policies. The proposed model considers multiple constraints, such as shift requirements for different personnel groups, legal and institutional regulations regarding work hours, the fair allocation of night shifts to prevent staff burnout, ensuring that required skill levels are met for each shift, and minimizing sudden staff shortages and overtime costs.  
This study formulates the scheduling problem as a Mixed-Integer Programming (MIP) model, designed to generate optimal shift assignments that satisfy all predefined constraints while improving workforce efficiency. The model is implemented and solved using the GAMS 23.5 software, providing a fast and scalable approach for hospital administrators.  
Although our proposed model offers several advantages, it has certain limitations. First, the model was tested using data from a single hospital (Cukurova University Hospital), which may limit its generalizability to other settings. Second, the model assumes static shift durations and predefined constraints, meaning it does not yet account for real-time changes such as emergency patient influx or last-minute staff shortages. Third, our approach does not include regulatory and contractual constraints, which vary across different countries and healthcare systems. Future work should focus on enhancing the model's flexibility by incorporating real-time data integration and adaptive scheduling algorithms.
## 1 Introduction
A set of proportional fairness constraints is imposed to ensure that each user can obtain the required data rate in the RA optimization problem. In [21], a suboptimal resource allocation algorithm called root-finding was proposed in which subcarrier and power allocation was carried out separately. The method is suitable for high channel-to-noise ratio case. In [22], a linear method is proposed to solve the proportional resource allocation in OFDMA systems in which the proportion of subcarriers distributed to each user is the same as their desired data rate ratio. Therefore, the proportional fairness for the linear method is relaxed to a certain level. In this paper, the research content focuses on resource allocation in OFDMA systems for wireless sensor networks. In addition, a new efficient resource allocation algorithm ACO-SPA based on ant colony optimization (ACO) and suboptimal power allocation (SPA) is proposed, in order to solve the RA optimization problem with proportional fairness constraints in OFDMA systems for wireless sensor networks. First, the ant colony optimization algorithm is utilized to solve the subcarrier allocation. In view of the system sum rate and proportional fairness among users, the Jain's fairness index is utilized to describe the system proportional fairness, and is introduced into the heuristic information in ant's transition probability. Then a simple suboptimal power allocation algorithm is proposed, with strict proportional fairness. Based on the power allocation principle that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal, the system achieves the strict proportional fairness. The rest of this paper is organized as follows. In Section 2, the system model of OFDMA downlink resource allocation problem is introduced. The ant colony optimization algorithm is utilized to solve the subcarrier allocation algorithm and a simple suboptimal power allocation method with strict proportional fairness is developed in Section 3. In Section 4, the performance of the proposed ACO-SPA algorithm is shown in contrast with root-finding and linear algorithms. The conclusions are drawn in Section 5.
# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 4 Case Study Results
### Image Processing  
Image frames were first retrieved out of the videotape. Image cropping was applied to exclude miscellaneous regions, such as the sky, which assembles the color feature of pavement markings due to high levels of illumination (Figure 7a). All original image frames were sorted in sequence and numbered accordingly. To remove perspective effects, two feature pairs, which are assumed to be located on two parallel lane lines in the bird's eye view images, were manually selected. For curved roads, since only a 20 m segment in the near field was targeted, this assumption still holds true. The transform matrix was computed in a way that the resulting images roughly cover the pavement at the widest location along the route and a length of 20 m. Road-side facilities and plants can, to a large extent, be eliminated.  
Based on road-plane images, features were then extracted. The hybrid feature extraction method tested in Section 3.2 was modified, by customizing the color feature detector for different line markings. A three-channel filter was used in the HSL color space for yellow markings and a one-dimensional filter was used in the gray-scaled image for white markings. Thresholds were determined to cope with intra-color variations and successfully distinguish inter-color differences. Based on the information for both color and shape (extracted using Sobel operation), a feature map of the pavement markings can be obtained for analysis. The result is shown in Figure 7c.  
To further isolate individual line marking for classification and analysis, a combination of Canny edge detection and Hough line transform was utilized, as detailed in Section 3.3. Key parameters in this approach, i.e., the minimum length of lines and the maximum gap between two feature points that belong to the same line, were pre-defined so as to leave out noise from the surroundings. Outlier lines were further removed with regard to their slopes and spatial placement. Figure 7a-f illustrates each step of the image processing method. It shows that a binary feature map of line markings can be obtained and the region of interest (i.e., individual markings) can be successfully segmented.  
Figure 7: Illustrations of image processing steps, including: (**a**) original image; (**b**) bird’s eye view image; (**c**) output of hybrid descriptor; (**d**) output of Canny detector; (**e**) result of Hough transform; (**f**) result of segmentation.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 4 Proposed Methodology  
Throughout this section, we explain the proposed approach and the algorithms employed. Justifications for the methodology are also described.  
The proposed MIMO-HC protocol comprises three stages: (1) Cluster Head selection, (2) Hybrid cluster creation, and (3) data gathering and propagation. These are discussed below. Figure 1 is the proposed architecture. From the figure the circles of variable diameter reflect the unequal clusters architecture with two types of communication topologies: single-hop and multi-hop (chain). In addition, routes that are linking the cluster heads represent the sub-optimal multi-hop routing model. As compared to the existing ones, the proposed method has several unique advantages including:  
* a much better load balancing capability,
* lower transmission overhead, and
* supports a fault tolerance feature which is not present in the existing literature.  
Justifications of the proposed methodology are provided in Section 5.