esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal Surface Defect Detection Using Modified YOLO
## 3 The Proposed Method
### K-Means++  
YOLOv3 uses K-Means cluster analysis to obtain anchor boxes, but it has certain limitations. K-Means is sensitive to the selection of initial clustering centers, and the clustering results of different initial clustering centers are very different. Since the K value is not easy to determine in the clustering process, resulting in inaccurate positioning, it is extremely important to select the appropriate K cluster centers.  
Aiming at the problems of K-Means selecting initial clustering centers, K-Means++ is used to solve the shortcomings. Randomly select a sample as the current first cluster center. Then calculate the shortest distance between each sample and the existing cluster center, and classify the sample into the category corresponding to the cluster center with the smallest distance. At the same time, the probability of each sample being identified as the next cluster center is calculated, and the sample with the highest probability is selected as the next center. The formula for calculating the probability is:  
\[p=\frac{D(x)^{2}}{\sum_{i=1}^{n}D(x_{i})^{2}} \tag{5}\]  
\(D(x)\) is the shortest distance from each sample point to the current center. Each time an object is allocated, the cluster center will be recalculated based on the objects in the existing cluster, and this process will be repeated until no objects are reassigned to other clusters. Finally, K cluster centers are screened out. As a benefit from the difference between the acquired 12 anchor boxes, the effect of detecting the target is significantly improved.  
The process of selecting cluster centers by K-Means++ greatly reduces the dependence of the clustering results on the K value and makes the distance between the initial cluster centers as far as possible, which effectively solves the defects of K-Means.
## 8 Discussion
It can make decisions with a high Accuracy rate in less time and works with many systems, such as Bring Your Own Device (BYOD), the Internet of Things (IoT) and distributed systems. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Ref** & **Brief Description** & **ML Approach** & **Dataset** & **Performance** & **Time (s)** \\ \hline ([PERSON], 2022) [18] & Propose Deep Learning Based Access Control (DLBAC) that uses a black-box neural network to decide the access decision. The method performed well in determining whether to allow or reject access requests, but it does not take the time factor into account. & Neural Network Algorithm, Inter-pretation Methods & Amazon Kaggle, Synthetic & 88.5\% & NA \\ \hline ([PERSON], 2021) [15] & Proposed an adaptive ABAC policy learning approach to automate decision-making tasks. The method contributed to policy mining, but its performance is less than ideal. & Epsilon Greedy, Reinforcement Algorithm & Amazon Kaggle & 0.59\% & 0.588 \\ \hline ([PERSON], 2021) [19] & Suggested a decision-making algorithm for building an access control knowledge graph using user and resource attributes. The model was presented as a case study for determining university resource usage based on user characteristics. & Boosting Window (BW) Algorithm, One-hot Encoding, & [PERSON] & 89.64\% & NA \\ \hline \end{tabular}
\end{table} 
Table 6: Comparing the effectiveness of the proposed solution’s results to those of existing solutions.
# To Cache or Not to Cache
## 3 Design
### Overheads  
**FOMO**, augmenting the underlying caching algorithm, adds its own overheads on top of the caching algorithm. As such, **FOMO** is designed to keep its own overheads low while achieving its goals. All of **FOMO**'s operations are achievable in \(O(1)\) time complexity. In terms of space overhead, **FOMO**'s only large requirement is an array of around _cache size_ entries large enough to serve as a hash table. Each entry of the array consists of an integer to track the block address and two pointers (next and prev) for its place in the Miss-History. The rest are a few integers and pointers whose space requirements do not scale with the cache size. This includes the period countdown, both hit counters for the cache and the Miss-History, a bit for the current state of FOMO, and the list sentinel for the Miss-History, which itself is composed of two pointers (first and last).  
Should **FOMO** become more integrated with any algorithm, the hash tables may be merged, along with the entries, to possibly reduce the space overhead significantly by eliminating redundancy. Furthermore, some space and time overhead may be removed by altering **FOMO** to use a CLOCK or FIFO structure for its Miss-History instead of an LRU list.
## 2 Related Work and Our Motivation
### Motivation for This Work  
The earlier [10; 11] dynamic adaptive frame-aggregation selection schemes could maximize the system throughput performance of a WLAN in terms of the maximum system throughput and channel utilization and the minimum space channel time. However, this approach does not consider the issue of delay. The motivation of this work was an intention to extend the previous work [11] to contribute an adaptive aggregation algorithm aiming to achieve the optimal trade-off between maximizing system throughput and minimizing system delay.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 4 Top-Performing Models on Benchmark Datasets
### Discussion  
The comparative results presented above reveal several key trends in state-of-the-art 2D human pose estimation. First, transformer-based models such as ViTPose and PCT achieve superior accuracy on large-scale datasets (e.g., COCO and MPII), largely due to their capacity for global context modeling and the use of pretraining strategies. However, their high computational cost and large parameter size present major challenges for real-time or resource-constrained deployment. Second, lightweight architectures, such as Lite-HRNet and MobilePose, deliver competitive performance with significantly fewer FLOPs and parameters, making them well-suited for embedded and mobile applications. This highlights the ongoing trade-off between model accuracy and efficiency and underscores the need for task-specific model selection. Third, generalization across datasets remains a critical challenge. While some models perform well on specific benchmarks, their robustness often degrades in occlusion-heavy or crowded scenarios (e.g., OCHuman, CrowdPose). Hybrid methods--such as BUCTD and PoseFix--show promise in bridging this gap by combining bottom-up and top-down strategies or adding refinement stages. Overall, the current landscape indicates that no single model offers universal superiority. Instead, designing pose estimation systems for real-world applications requires balancing multiple objectives, including accuracy, efficiency, interpretability, and generalization.
## 3 Challenges in China's Weather Routing Services
### Deficiencies in Maritime Communication Capabilities  
Under current maritime communication infrastructure, real-time data exchange between vessels and shore-based systems heavily relies on commercial satellite networks dominated by international providers [36]. While a global challenge, China's reliance on potentially foreign-controlled networks presents specific strategic considerations. Furthermore, bandwidth constraints of these traditional GEO/MEO maritime satellites severely limit data transmission capacity and service scope for weather routing [46]. In operational workflows, shipborne weather routing terminals transmit route-specific weather forecasts and onboard observational data to shore-based platforms via satellite links. Due to limited bandwidth, these datasets must undergo compression or thinning prior to transmission. For instance, acquiring 7-day, 3-hourly weather forecasts for a 10\({}^{\circ}\)\(\times\) 10\({}^{\circ}\) maritime area at a 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) spatial resolution generates approximately 40 MB of data, with single-transmission costs using traditional systems potentially exceeding hundreds of USD. While emerging Low Earth Orbit (LEO) satellite constellations (e.g., Starlink Maritime) promise higher bandwidth and potentially lower costs, their widespread adoption on commercial vessels, consistent global coverage, and proven reliability for critical services are still evolving [47]. Consequently, most ocean-going vessels currently restrict data transmission volumes and frequencies, typically by degrading the spatiotemporal resolution of meteorological products. Such compromises inevitably reduce data granularity, undermining the accuracy and real-time responsiveness of weather routing services. Moreover,vessel-collected observational data hold significant potential for addressing marine observational gaps, enriching oceanic datasets, and enhancing numerical forecasting accuracy. However, the transmission of these data remains constrained by the bandwidth limitations and prohibitive costs of prevalent maritime satellite networks, severely hindering their full utilization in operational and research contexts.
# Analysis and Implementation of a Hybrid DC Converter with Wide Voltage Variation
## 3 Operation Principle
### Backward Power Flow  
If the converter is operated at backward power operation, the power flow is transferred from \(V_{o}\) to \(V_{in}\), as shown in Figure 1e. Since \(V_{in}\) is controlled at 400 V, the half-wave diode rectifier is used on the left-hand side. Therefore, \(S_{ac1}\) is on. The full-bridge resonant circuit is operated on the right-hand side so that \(S_{ac2}\) is off and \(S_{5}\)-\(S_{8}\) are operated with the PFM scheme. Figure 3a provides the switching signals of the converter under backward power flow in every switching cycle. Figure 3b-i show these state equivalent circuits.  
State 1 [\(t_{0}\leq t<t_{1}\)]: \(C_{55}\) and \(C_{58}\) are discharged to zero voltage at \(t_{0}\). \(i_{L,2}(t_{0})\) is negative so that \(D_{55}\) anf \(D_{58}\) are forward biased. Active switches \(S_{5}\) and \(S_{8}\) can be turned on after \(t_{0}\) to have soft switching turn-on operation. Since \(i_{L,1}>0\), \(D_{51}\) is forward biased and \(C_{1}\) is charged.  
State 2 [\(t_{1}\leq t<t_{2}\)]: At \(t_{1}\), \(i_{L,2}>0\). Thus, \(i_{L,2}\) flows through \(S_{5}\) and \(S_{8}\). Backward power is transferred from \(V_{o}\) to \(V_{in}\) in this state. One can obtain \(V_{ub}\) = \(V_{C1}\) = \(V_{in}/2\) and \(V_{de}\) = \(V_{o}\). The resonant frequency is \(f_{r,2}=1/(2\pi\sqrt{L_{r,2}C_{r,2}})=1/(2\pi\sqrt{L_{r,1}C_{r,1}})=f_{r,1}\).  
State 3 [\(t_{2}\leq t<t_{3}\)]: If \(f_{sw}\) <\(f_{r,1}\), \(i_{L,1}\) = 0 at \(t_{2}\) and \(D_{51}\) is reverse biased. The resonant frequency on right-hand side circuit is \(f_{p,2}=1/(2\pi\sqrt{C_{r,2}(L_{m,2}+L_{r,2})})\) and \(f_{p,2}\) < \(f_{p,1}\).  
State 4 [\(t_{3}\leq t<t_{4}\)]: \(S_{5}\) and \(S_{8}\) are off at \(t_{3}\). Due to \(i_{L,2}(t_{3})>0\), \(C_{56}\) and \(C_{57}\) are discharged by \(i_{L,2}\).  
State 5 [\(t_{4}\leq t<t_{5}\)]: \(C_{56}\) and \(C_{57}\) are discharged to zero voltage at \(t_{4}\). Since \(i_{L,2}(t_{4})>0\), \(i_{L,2}\) flows through \(D_{56}\) anf \(D_{57}\). Then, \(S_{6}\) and \(S_{7}\) turn on after \(t_{4}\) to achieve soft switching turn-on operation. Owing to \(i_{L,1}<0\), \(D_{52}\) becomes forward biased and \(C_{2}\) is charged.  
State 6 [\(t_{5}\leq t<t_{6}\)]: After \(t_{5}\), \(i_{L,2}<0\) and \(i_{L,2}\) flows through \(S_{6}\) and \(S_{7}\). Backward power flow is from \(V_{o}\) to \(V_{in}\) in this state operation.  
State 7 [\(t_{6}\leq t<t_{7}\)]: \(i_{L,1}=0\) at \(t_{6}\). Then, \(D_{52}\) is off. The resonant frequency on the right-hand side circuit is \(f_{p,2}=1/(2\pi\sqrt{C_{r,2}(L_{m,2}+L_{r,2})})\).  
State 8 [\(t_{7}\leq t<T_{sw}+t_{0}\)]: \(S_{6}\) and \(S_{7}\) turn off at \(t_{7}\). Since \(i_{L,2}(t_{7})<0\), \(i_{L,2}\) discharges \(C_{55}\) and \(C_{58}\). At time \(T_{sw}+t_{0}\), \(C_{55}\) and \(C_{58}\) are discharged to zero voltage.
###### Abstract  
The effect of the scanning strategy during selective laser melting (SLM) of Ti-6 Al-4V was investigated. An optimized cellular scan strategy (island scan modeled) was compared to a simple cellular scan strategy (island scan stripes) and a simple antiparallel line scanning strategy (line scan). Surface texture was investigated by optical three-dimensional (3D) surface measurements, which when combined with light optical microscopy (LOM), revealed deflections caused by the thermal stresses during the build process. Elevated edges caused by the edge-effect dominate the surface texture of all investigated specimens. The scanning strategy determines the surface texture, and the lowest surface roughness was obtained by the line scan strategy. Porosity was investigated with X-ray computed tomography-imaging. Mainly spherical porosity was observed for the line scan and island scan modeled specimens, while the island scan stripes strategy showed more lack-of-fusion defects and a higher total porosity amount. Microstructure was investigated with LOM and scanning electron microscopy (SEM). The microstructure in Ti-6 Al-4V was largely martensitic \(\alpha^{\prime}\) and prior \(\beta\) grains. The morphology is different for the various scan strategies, and decomposition of \(\alpha^{\prime}\) into lamellar \(\alpha/\beta\) was observed in the bottom part of the island scan specimen. Accordingly, the hardness decreased in the decomposed part of the specimen.  
SLM; selective laser melting; powder bed fusion; Ti-6 Al-4V; scanning strategy; porosity; microstructure; surface topography +
Footnote †: journal: Applied Sciences
## 3 Numerical Examples  
The mass, stiffness, and damping coefficients of an eight-story shear building [25] are given in Table 3. In both cases, the stiffness and the angle of the tendons are 36\({}^{\circ}\) and 372,100 N/m, respectively [8]. HS and FPA are used to determine the parameters of PID controllers for both cases. The maximum top displacement of uncontrolled structures under the seismic excitations defined in Tables 1 and 2 are between 5.16 (Loma Prieta Saratoga--Aloha second component) and 97.53 cm (Chi-Chi, Taiwan, TCU084 first component). Therefore, the critical excitation is used as the first component of the TCU084 record of the Chi-Chi--Taiwan earthquake--in the optimization process to determine the parameters of PID and the objective function of both cases shown in Table 4. The input of this excitation to the structure is given in Figure 5. The objective functions such as the first-floor displacement (m), velocity (m/s), acceleration (m/s\({}^{2}\)) or total acceleration (m/s\({}^{2}\)) are very close for both metaheuristic algorithms. For example, the objective function; x\({}_{\text{r}}\) (the first-floor displacement) for case 1 is calculated as 0.1562 cm according to both algorithms if the displacement feedback strategy is considered. These values for case 2 are calculated according to FPA and HS as 0.1165 and 0.1161 cm, respectively. However, the harmony search algorithm has better results for the structural responses because the objective functions of HS which are determined by any numerical combination of design variables are revealed to be more successful little differences than those of FPA for different feedback strategies generally. In both cases, the structural reactions under all ground motions (total 56 records) are reduced except one record (Gazli, USSR Karakyr first Component) using the active control system. In Case 1, the reductions in displacements vary between 0.63% (Imperial Valley-06 El Centro Array#6-s component) and 13.35% (Chi-Chi, Taiwan, TCU065 first component), while these values in Case 2 change between 2.50% (Imperial Valley-06 Bonds Corner first component) and 45.46% (Chi-Chi, Taiwan TCU084 s component). The reductions in structural responses in Case 2 are more noticeable than these values in Case 1. The main reason is the distribution of the control force which is limited to up to 10% of the total mass of the structure. In the first three ground motions with high structural reactions (earthquake number 25 in Table 1, and 19, and 26 in Table 2), the results of the different feedback strategies are close to each other. However, the acceleration feedback under 56 ground motions is the most suitable feedback for both cases. The maximum structural responses of all excitations and the time history responses of acceleration feedback control for the critical excitation for cases according to harmony search are shown in
# Purely Gain-Coupled Distributed-Feedback Bragg Semiconductor Laser Diode Emitting at 770 nm
## 4 Conclusions  
In this paper, we demonstrated a new type of purely gain-coupled DFB laser based on periodic injection windows working at 770 nm. The periodically injected current maintained the periodic gain of our device and managed to pick out an FP mode to realize single longitudinal mode lasing under gain coupled mechanism in the unetched FP cavity. Our devices have the following excellent characteristics: maximum output power 116.3 mW, maximum SMSR 33.25 dB, and 3 dB linewidth 1.78 pm. Meanwhile, the fabrication process required only general i-line-lithography. No secondary epitaxy or nanoscale grating fabrication technique was needed. In addition, our DFB laser is compact and highly integrated as compared with external cavities and has better output power as compared with commercial products, resulting in a cost-effective way of making DFB lasers to satisfy the booming market of atomic clocks and laser gyroscopes in robots, and thus having great commercial potential.  
Conceptualization, Y.C. and L.Q.; methodology, C.R. and Y.C.; software, Z.Z. and Y.S.; validation, C.R. and Y.C.; formal analysis, C.R., P.J., Z.L., and Y.Z.; investigation, P.J., N.Z., Y.L., and Y.Z.; resources, L.Q. and Y.Z.; data curation, C.R. and Y.C.; writing--original draft preparation, C.R.; writing--review and editing, Y.C.; visualization, C.R.; supervision, Y.C. and L.Q.; project administration, L.Q.; funding acquisition, L.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Major Project of China (2017 YFB0503100, 2018 YFB0504600, 2018 YFB2200300); the Frontier Science Key Program of the President of the Chinese Academy of Sciences (QYZDY-SSW-JSC006); the National Natural Science Foundation of China (NSFC) (62090051, 62090052, 62090054, 11874353, 61935009, 61934003, 61904179, 61727822, 61805236,62004194); the Science and Technology Development Project of Jilin Province (20200401069 GX, 20200401062 GX, 20200501007 GX, 20200501008 GX, 20200501009 GX); the Special Scientific Research Project of Academicin Innovation Platform in Hainan Province (YSPTZX202034), and the Dawn Talent Training Program of CIOMP. Independent Innovation Project of State Key Laboratory of Luminescence and Applications (SKL1-Z-2020-02).  
Not applicable.  
The authors thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], for helping with this article.  
The authors declare no conflict of interest.
# Methodological Framework to Collect, Process, Analyze and Visualize Cyber Threat Intelligence Data
## 5 Proposed Solution and Methodology
### Solution Overview  
According to the proposed eight-step model, Figure 4 describes technologies and products behind our tool that supports all eight steps.  
Figure 4: Tool architecture and technologies.  
Management step can be considered the main hub that connects all the other steps together. It uses MySQL as the main database to store data in a structured man and can be compared to Cold Storage.  
Indexer step is where we save our feeds and any other frequently accessed data. We chose Elasticsearch because of its efficiency in indexing and fast search. Analogously to the MySQL comparison, Elasticsearch can be compared to Hot Storage.  
Collect step is where we collect feeds from internet and any external source that are manually input on the tool by our user.  
Generate step uses Logstash to control data ingestion and data destination inside our tool. With that, we created a pipeline to standardize feed ingestion and its correct storage inside our databases.  
Search step is where the user can search for indexed data. It uses all technology embedded under it in order to allow fast and efficient search from the user's perspective.  
Share step uses the TAXII protocol to achieve information sharing. We used a TAXII Server to allow standardized STIX sharing, making our tool integrable with any other market tool.  
Step 7 (Visualization) and 8 (Analysis) are web based and were built to run on any modern system. The visualization step was developed using Python and Flask, a microframework for web developing that allows efficient and robust applications. Analysis step was developed using Java Script language because of the D3.js library that allow us to work with large datasets of feeds and create dynamic visualizations of IOCs.
## Materials and Methods
### Foamed Concrete Preparation  
A foam generator--SG S9 provided by Sika Germany--was used to produce the foam. The dosage of the foaming agent was 2 wt% of the water, with an applied water pressure of 3 bar. As recommended by the provider, the foam should be continuously produced without pulses in order to achieve a high foam stability. Accordingly, compressed air pressure should be adopted until foam is produced uniformly and steadily. The foam density was measured at about 35 kg/m\({}^{3}\). In the mixing process, cement, water, fly ash, and fine lightweight aggregates were mixed in an Eirich mixer with a high shear intensity, to homogeneously distribute the cement and fine materials and to prevent the agglomeration of the particles. Concurrently, foam was produced with the required volume being measured out. Fifty liters of Zyklos concrete mixer was used to mix the foam and the paste. One portion of the paste per volume was mixed with two portions of the foam produced per volume. The fresh properties, including a flow table test according to EN 206-1, were measured, but without dropping or raising (jolting) the flow table, as this may affect the stability of the foam bubbles. Thereafter, 100 mm cubic molds were filled without compaction. After two days, the concrete specimens were demolded and cured in a climate chamber with controlled humidity and temperature of 99% and 21 \({}^{\circ}\)C, respectively. A compressive strength test, using 100 mm cubes, was carried out according to EN 12390-3 at 28 days. Three cubes of each type of concrete were tested, with the mean value considered in this evaluation. The drying shrinkage of the foamed concrete was measured as specified in DIN 52450, using the Graf-Kaufmann method, in which prisms with sample sizes of 40 \(\times\) 40 \(\times\) 160 mm\({}^{3}\) are tested at appropriate time intervals. For thermal conductivity measurements, a Hot Disk device, according to ISO 22007-2, was used. Measurement was carried out by positioning a sensor between two samples; in this case the sensor was concurrently used as both a temperature monitor and a current supplier [11].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & \multirow{2}{*}{**Designation**} & **Cement (C)** & **Fly Ash (FA)** & **Fine Perlite** & **Fine Liaver** & **Paste:Foam** & **Fresh Density** \\  & & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(FP) (L)** & **(FL)** & **Paste:Foam** & **(kg/m\({}^{3}\))** \\ \hline
1 & C & 400 & - & - & - & 1:2 & 596 \\
2 & C/FA & 300 & 100 & - & - & 1:2 & 610 \\
3 & C/FA/FP50 & 300 & 100 & 50 & - & 1:2 & 595 \\
4 & C/FA/FP100 & 300 & 100 & 100 & - & 1:2 & 584 \\
5 & C/FA/FL50 & 300 & 100 & - & 50 & 1:2 & 565 \\
6 & C/FA/FL100 & 300 & 100 & - & 100 & 1:2 & 580 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix compositions of foamed concrete.  
Figure 1: Particle size distributions of the fine materials used.