esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Cooling Mechanism of Injection Mold
### Heat Transfer Analysis of the Cooling Channel of the Injection Molding
#### 3.3.2 Calculation of Cooling Process  
Calculating the cooling process in plastic injection molding comprises three essential aspects: determination of the cooling time for the plastic part within the mold, optimization of the flow rate of the coolant, and principled arrangement of the cooling channels. The cooling time of the plastic part refers to the time taken for the melt to fill the cavity and meet the requirements for demolding, which includes achieving full cure, required strength and stiffness, and preventing deformation or cracking during ejection. Among these factors, achieving a complete cure of the plastic part is a key criterion for successful molding.  
(1) The temperature at the center of the maximum wall thickness of the plastic part is lower than the heat deflection temperature of the injection material;  
(2) The average temperature in a cross-section of the plastic part is lower than the required ejection temperature of the plastic part;  
(3) The temperature at the center layer of the thickest part of the plastic component must reach the solid melting point or the required percentage value of the crystallinity for crystalline plastics.  
Assuming that the temperature at the center of the maximum wall thickness of the plastic part is lower than the heat deflection temperature of the injection material required for the cooling time, then there is [28]:  
\[t_{c}=\frac{\delta^{2}}{\pi^{2}\alpha}\text{ln}\bigg{[}\frac{4}{\pi}\bigg{(} \frac{T_{M}-T_{W}}{T_{E}-T_{W}}\bigg{)}\bigg{]} \tag{15}\]  
where \(\theta\) is the thickness of the plastic part, \(T_{M}\) is the melting temperature of the plastic part, \(W\) is the temperature of the mold cavity surface, \(T_{E}\) is the ejection temperature of the  
Figure 4: Schematic diagram of heat balance of the cooling channel.  
plastic part, and \(\alpha\) is the heat diffusion rate of the plastic, defined as \(\alpha=\lambda\). The cooling time of the plastic part is [20]:  
\[t=\frac{[c(T_{M}-T_{E})]\rho l\frac{\theta}{\beta}}{T_{W}-T_{C}}\{\frac{1}{2\pi \lambda_{m}}\text{ln}\left[\frac{2l\text{sin}(2\pi\frac{\dot{\pi}}{\tau})}{\pi d }\right]\frac{1}{0.032 Re^{0.8}}\} \tag{16}\]  
This is only suitable for finding the cooling time at a certain point on the plastic part.
## 5 High-Rise Building Optimal Evacuation Efficiency
### Optimal Evacuation Efficiency on Intermediate Floors  
Because the recommended evacuation method for the fourth and fifth floors with a stair-determined scheme is 4W5L, under the 2L4W5L evacuation scheme, the third floor's recommended method was studied. A comparison of evacuation times for different third-floor evacuation methods is shown in Figure 10. According to Figure 10, using stairs for evacuation on the third floor provides the highest efficiency, and the optimal evacuation method is 2L3L4W5L, which is consistent with the best evacuation method for an assistance ratio of less than 40% in the simulation results.  
Figure 9: Comparison of evacuation times for different evacuation methods on the fourth floor.  
Similarly, for elevator-determined evacuation on the fourth and fifth floors, the recommended strategy is 4D5L; therefore, under evacuation plan 2L4D5L, a comparison of evacuation times for different methods on the third floor is shown in Figure 11. From Figure 11, it can be observed that, on the third floor, the unlimited evacuation method results in the highest efficiency, and the optimal evacuation strategy is 2L3W4D5L, which aligns with the simulation result in which assistance ratio is greater than or equal to 40%, matching the conclusions drawn. This consistency supports the idea that stairwell-determined evacuation is suitable for low-assistance ratio evacuations, while elevator-determined evacuation is more effective for high-assistance ratio evacuations. Upon analysis, the reasons for this are as follows:  
1. When the assistance ratio is low, evacuating all middle floors using elevators leads to excessive elevator time consumption, which is considered a waste of resources for those middle floors; however, relying solely on stairwells is less effective due to limited assistance for the middle floors, making it more challenging for differently-abled people to evacuate, resulting in longer evacuation times. Therefore, an unrestricted evacuation method is more efficient in this scenario. In this case, differently-abled people without assistance can use elevators for descent, while those with assistance, both differently-abled and non-differently-abled individuals, can effectively utilize stairwells for evacuation.  
Figure 11: Comparison of evacuation times for different evacuation methods on the third floor under elevator-determined evacuation.  
Figure 10: Comparison of evacuation times for different evacuation methods on the third floor of the stairwell.  
2. When the assistance ratio is high, most differently-abled people have assistance from non-differently-abled individuals. In the case of mid-level floors, the use of elevators is less effective compared to upper-level floors, and mid-level evacuees can efficiently and in an orderly fashion descend via the stairwells, reducing congestion.  
To more concisely display the analysis process of the optimal evacuation plan for each floor, one stair-based evacuation is used as an example to create the flowchart, as shown in Figure 12.
## 3 Methodology
### Data Analysis
#### 3.3.1 Spatial Agreement between the Fluxes Tower Footprint and the Image Region  
The footprint may be interpreted in analogy to the \"field of view\" of the instrument. The footprint was influenced by the atmospheric stability (e.g., wind speed and direction), observation height, and the underlying surface roughness. It was calculated by analytical models of C flux contributions, based on approximation to the actual physical process [43, 44]. The CBS flux tower footprint was defined using methods developed from a previous study [44]. This study [44] reported that the footprint (distance from flux tower) of CBS Flux station ranged between 181 m to 3070 m. The footprint was delimited as a 1 km\({}^{2}\) square region, which included the eddy covariance tower at its center. The reflectance spectra were then extracted and averaged from the region to approximate spatial coverage agreement between the flux tower footprint and the image region.
# Machine-Learning-Based Path Loss Prediction
## 3 Materials and Methods  
### Measurement Scenarios and Setup  
All measurement studies were conducted in different highway environments in two cities, Trabzon and Gumushane in Turkiye. A highway environment can typically be defined as a road with fast-flowing traffic, multiple lanes, and few or no surrounding structures, such as buildings and trees. The highways Trabzon-Giresun and Gumushane-Erzincan, where the data on V2V communication were collected in this study, are shown in Figure 1a and Figure 1b, respectively. These roads were selected as suitable testing environments because of their different traffic and geographical structures, which capture different climatic conditions and road features to provide an extensive dataset for path loss prediction. The Trabzon-Giresun highway has coastal areas with high humidity, while the Gumushane-Erzincan highway is characterized by significant altitude changes and mountainous terrain with frequent snowfall in winter. Thus, the varied geography and design of these highways offer beneficial data on how environmental factors impact V2V communication, facilitating the development of accurate machine-learning models for path loss prediction.  
Figure 2 shows a block diagram of the measurement system used to gather the data. The transmitter (Tx) and receiver (Rx) were represented by two different automobiles fitted with DSRC + GPS antennas. Data processing and collection were performed using theDSRC on-board unit (DSRC-OBU) devices with the technical specifications given in Table 2 that link each car to a laptop.  
For the experimental measurements in this study, two passenger cars, one Tx (2022 Skoda Fabia) and one Rx (2020 Volkswagen Golf), were used. The measurement setup was identical in both vehicles and included a laptop computer, a Cohda Wireless MK5 OBU kit [49], a 12V/220V inverter, an in-car camera, and a cigarette lighter multiplexer, as shown in Figure 3. The laptops were connected to the DSRC OBU device via an Ethernet port, and the sending and receiving of data packets were controlled through these computers.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 2 Main Methods of Underwater Wireless Communication
### Comparison Between Commercial Systems and Research Prototypes  
To better understand the evolution of underwater wireless communication technologies, it is important to compare commercially available systems with recent academic research prototypes. Table 7 summarizes representative systems across acoustic, RF, and optical modalities, highlighting their key performance metrics and typical use cases.  
Figure 3: Experimental design of underwater electric field communication system.  
As shown in table, commercial systems emphasize field robustness and energy efficiency, while research systems focus on pushing data rates, intelligent control, or experimental mechanisms. Bridging the gap between innovation and deployment remains a key challenge and direction for future work.
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 3 Proposed Method
### Dataset Pre-Processing
#### 3.2.3 Normalization Processing  
Normalization is a very important pre-processing step in deep learning models. Since different evaluation metrics have different units and ranges, the range of values of the data may also vary greatly. If normalization is not undertaken, the analysis results of the data may be biased. In addition, the neural network algorithm has very strict requirements on the range of input data. If the range of data is too large, convergence of the algorithm may be poor or fail, thus making the model fail to achieve the expected effect. Therefore, normalization can make the data have a fixed range of values, generally in the range of [0,1], which can be convenient for subsequent deep learning model training. This paper used Equation (3) for normalization.  
\[\mathrm{F}(x_{i})=\frac{x_{i}-\mathrm{Min}(\mathrm{X})}{\mathrm{Max}(\mathrm{ X})-\mathrm{Min}(\mathrm{X})} \tag{3}\]  
where \(\mathrm{Min}(\mathrm{X})\) is the minimum value in X and \(\mathrm{Max}(\mathrm{X})\) is the maximum value in X.
## 2 Distribution System Testbed  
### Circuit and Hardware  
The circuit and hardware of the testbed are shown in Figure 1; the diagram represents an electrical grid/substation with metering devices. This testbed is focused on simulating different electrical fault test use case scenarios. The one-line diagram in Figure 1 represents an electrical substation that has a sectionalized bus system [38] with power transformers and feeders connected to an IB-PV array farm as shown in Figure 1a. The meters and relays in the testbed rack (Figure 1b) are indicated with numerical values, and their locations are shown in the circuit (Figure 1a). The red highlighted relays and meters in Figure 1 are used in this research. This circuit is based on a previous publication in [22]. In Figure 1, the relays and meters are selected based on their protection, measurement or control functions, and locations. The devices numbered 1 and 2 (SEL451) are overcurrent protection relays located at 12.47 kV side of utility \(A\) substation feeders; devices 3 and 4 (SEL735) and 5 and 6 (SEL734) are power meters located on fuse feeders to measure customer loads in utility \(A\); device 7 (SEL421) is a distance protection relay located at 34.5 kV side of the utility \(A\) substation; device 8 (SEL351S) is an overcurrent protection relay located at the 12.47 kV side of utility \(C\) substation; and device 9 (SEL700 GT) is a protective relay with a synchrony check control function located in the point of common coupling between the grid side and IB-PV array side.  
Utility \(A\) has a substation with a distribution grid. Utility \(A\)'s substation has two transformers with a primary and secondary voltage of 34.5 and 12.47 kV, respectively. In this study, the 12.47 kV power grid with the load feeders is commonly set as a radial system, but it could also be connected to the IB-PV array farm (utility \(C\)). Utility \(C\) is an IB-PV array farm, and utility \(B\) is the fossil fuel source. In the feeder, loads are configured two SEL735 [39] meters and two SEL734 [40] meters. In one power transformer side, a SEL421 [41] relay is set. The SEL451 [11] relays are set at substation feeders. The SEL351S [42] relay is installed at the IB-PV array farm side, and the SEL700 GT [43] relay is set at the grid common point of connection. In the real-time simulator (RTS), relays and meters are connected. The relays and meters are synchronized with a time clock system and connected to a trigger recorded event system.
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 6 Conclusions  
Rapid and accurate identification of road damage can tremendously benefit the road maintenance industry and significantly contribute to the economy. Applying deep learning techniques becomes crucial as it considerably facilitates road pavement inspection and provides a comprehensive view of the pavement's condition. The present study introduces an enhanced approach for detecting pavement damage utilizing the YOLOv5s model. Initially, our task involved the identification of an optimal backbone for our model that would exhibit excellent accuracy and low computational complexity. After the model's backbone was identified, experiments were conducted using diverse attention mechanisms at various locations within the YOLOv5s architecture. Several alterations have been proposed that lead to a notable enhancement in the results, following the adoption of the ECA attention mechanism. The Focal Loss function is utilized instead of the binary cross-entropy (BCE) loss function, while the K-means\({}^{++}\) algorithm is employed to determine the optimal anchor box for our model. Subsequently, a label smoothing algorithm is used. Ultimately, a novel detection head was integrated into a new prediction module at the neck of the YOLOv5s framework. Our research emphasizes the essential significance of deep learning in revolutionizing road pavement inspection, thereby providing a more efficient and effective road infrastructure management solution. While our model is specialized for detecting common types of pavement damage, it contributes to developing safer and more efficient transportation networks in line with the evolving needs of modern civilizations.  
Conceptualization, K.D.; methodology, A.A.S. and K.D.; software, A.A.S.; validation, A.A.S.; formal analysis, A.A.S.; investigation, A.A.S.; resources, A.A.S., S.S. and K.D.; writing--original draft preparation, A.A.S.; writing--review and editing, S.S., K.D. and I.H.S.; visualization, A.A.S.; supervision, S.S., K.D. and I.H.S.; project administration, S.S., K.D. and I.H.S.; and funding acquisition, I.H.S. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Comparison of performance between YOLOv5s-baseline and our improved YOLOv5s: (**a**) mAP0.5 comparison; (**b**) precision comparison; (**c**) recall comparison.
# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 3 Methodology
### Data Collection  
We collected literature from the Web of Science (WoS) website on February 11, 2023, to prepare data for scientometric analysis. To begin with, we used (TP = (event * prediction) OR (event * forecasting)) as topics in the WoS to perform a broad search, where \"OR\" means at least one topic appearing in the document, and \"*\" means any characters. In order to investigate how event prediction research has evolved over the last decade, which is usually selected as the time interval for conducting scientometric surveys in multiple disciplines, the time range was set from 1 January 2012 to 31 December 2022. As a result, a total of 9191 documents were returned. Since the documents with the selected topic anywhere in the full paper can be returned, it is likely that a large number of documents are not closely related to event prediction in the original search results. We added further filters by taking three types of constraints into consideration. First, the literature types were set as articles, review papers, editorial materials, and conference papers. Second, the research category was set as Meteorology Atmospheric Sciences, Computer Science Information Systems, Environmental Sciences, Geosciences Multidisciplinary, Computer Science Artificial Intelligence, Computer Science Interdisciplinary Applications, Imaging Science Photographic Technology, and Transportation Science Technology, which covers almost all event types summarized in Section 2 as well as retains effective sources for analyzing interdisciplinary research patterns. Third, the language was set as English. As a result, a total of 4473 documents were finally obtained and exported.
## 6 Conclusions  
The use of THz band provides a prime alternative for establishing high-speed wireless communication links. In this context, the proposed LOS multi-hop DF relaying THz system has been proved to significantly extend the total THz coverage area by achieving encouraging results in terms of the ABER metric. According to our findings, the most detrimental effect which mitigates the ABER performance is humid air by means of molecular attenuation due to water vapor along the propagation path, especially along with rain conditions. Contrary to FSOs, which are highly vulnerable to fog and atmospheric turbulence and to a lesser extent to rain and humid air, our findings validate that fog, along with atmospheric turbulence, are not the most important ABER degrading factors for THz links. The latter therefore gives rise to the development of hybrid FSO/THz links which will be able to efficiently co-operate in both rain and fog conditions along with humid air turbulent channels. Another important outcome of this paper is that the boresight component should be considered on the estimation of stochastic misalignment-induced fading which can unavoidably but significantly further degrade the ABER performance of THz links. Finally, the feasibility of utilizing higher order \(L\)-PAM modulation formats in the THz band has been verified, which enhances the total spectral efficiency but at a reasonable and acceptable expense of ABER performance degradation.  
Conceptualization, G.K.V., K.A. and H.E.N.; Methodology, G.K.V., K.A. and H.E.N.; Software, G.K.V. and K.A.; Validation, G.K.V., H.E.N. and K.A.; Investigation, G.K.V., H.E.N. and K.A.; Resources, G.K.V., K.A. and H.E.N.; Writing--original draft preparation, G.K.V.; Writing--review and editing, G.K.V. and H.E.N.; Supervision, G.K.V., K.A. and H.E.N.; Funding acquisition, G.K.V., K.A. and H.E.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ajman University, grant number 2021-IRG-ENIT-10.  
**Acknowledgments:** The authors acknowledge funding from Ajman University under grant agreement 2021-IRG-ENIT-10.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Determining the Safety Level of State Roads: An Italian Case Study
## 6 Results and Discussion  
The present study applied the HSM method to an Italian case study to compare its safety indicator with alternative indicators. The study comprised mainly the preliminary activities, followed by the calibration process and the evaluation of the safety indicators. The preliminary activities were crucial for contextual characterization and data identification essential for applying the US HSM predictive model. Calibration was necessary to adapt the predictive model to the specific Italian study context, ensuring accurate results. Once calibrated, the model was used to evaluate safety indicators. Moreover, alternative safety indicators are computed for the same elementary sections, and the results are compared. Thus, the adopted procedure allowed for the following main results:  
Figure 6: Safety performance indicators distribution for 1 km elementary sections according to: (**a**) Excess of expected crashes over predicted crashes; (**b**) SAPO. The color gray represents a neutral condition, warm colors (yellow to red) represent a condition of increasing severity, and cool colors (light green to dark green) represent a gradually improving condition.  
* Determination of the calibration coefficient for the HSM predictive model in the Italian context.
* Safety ranking of 1 km of elementary road sections within the managed network according to six safety performance indicators.
* Comparative analysis of safety rankings based on different indicators.
* Proposal of a method to prioritize road sections for field inspection, integrating SAPO and excess crash metrics.
# A Review of Blast Loading in the Urban Environment
## 4 Experimental Methods and Instrumentation
### Imaging Techniques
#### 4.1.3 Retroreflective Shadowgraphy  
[PERSON] and [PERSON] [127] explored improvements to retroreflective shadowgraph techniques. Retroreflective shadowgraphy requires three core elements: a light source, a screen and a camera. Note that [PERSON] originally advanced this technique by modernising the work of [PERSON] (1672) and applying the method to produce imaging of gunshots and explosions [128, 129]. The basic setup from [PERSON] is shown in Figure 10. The light cast onto the screen refracts through disturbances in the air caused by an explosion, casting a shadow onto the back screen. This setup is simple, and many tolerances exist that achieve a good-quality image. A retroreflective screen is preferred over a simple white screen as the reflected light is largely directed back to the source, increasing the light intensity at the camera lens--essential for high-speed imaging. One factor that must be accounted for is the parallax, which will make shocks appear slightly smaller.  
[PERSON] and [PERSON] [127] proposed a number of improvements to the retroreflective shadowgraphy technique. The principal improvement involves coinciding with the camera lens and the light source to eliminate double imaging and better show _\"thir, sharp refractive disturbances\"_. The most viable optical configuration utilises a rod mirror that can fit over a camera lens; a diagram is shown in Figure 11. The rod mirror setup was found to minimise light losses and removes the possibility of double imaging; thus, it was the preferred approach. The study showcases some experimental imaging captured using the rod mirror technique that reinforces the high-quality imaging possible from this approach.  
[PERSON] et al. [33] also utilised a variation of this imaging to capture shock wave interaction with finite obstacles. The \"pure in-line shadowscopy\" (PILS) method was formerly used by [PERSON] et al. [130]; it is well-equipped for ultra-high-speed imaging and shares many aspects with the previously discussed retroreflective shadowgraph technique. The optical configuration used is shown in Figure 12. Here, a transparent blast table is used to allow for a view of the retroreflective panel underneath. Some distance between the study area and the retroreflective panels is required to improve the optical sensitivity of the shadowscopy system. Generally, sensitivity is maximised when the distance between the  
Figure 11: Diagram of the retroreflective shadowgraphy setup using a rod mirror, adapted from Hargather and Settles [127].  
Figure 10: Basic retro-reflective shadowgraph set-up, adapted from [PERSON] and Settles [127].  
panel and the study area is 30-70% of the distance between the study area and the camera. A smaller distance allows for better-focused imaging, and a larger distance results in bolder shadows cast onto the screen.