esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed PMFAN-GDSO Approach  
### Overview of PMFAN-GDSO  
In response to the high dynamism exhibited by FlexE client services, a traffic-driven proactive slot orchestration algorithm, denoted as PMFAN-GDSO, has been proposed. This algorithm comprises two distinct components: the proactive multi-flow assisted attention-based neural network (PMFAN) for traffic prediction, and the genetic-based deterministic slot orchestration algorithm (GDSO) for slot orchestration. These two algorithms operate in accordance with the logic depicted in Figure 2. Initially, PMFAN leverages the historical data pertaining to service bandwidth fluctuations provided in the database. This encompasses both the historical data of the target business and that of background businesses. Through this, it derives a predictive value for the current business bandwidth at the subsequent time instance. This value signifies the potential bandwidth fluctuations that may manifest in the ensuing time step, effectively furnishing the timeslot allocation module with proactive bandwidth information. Subsequently, GDSO utilizes this information to compute the requisite number of timeslots for the impending time step. Upon the arrival of the next time step, an assessment is made to ascertain whether the traffic aligns with the predicted scenario. Should it do so, the slot allocation scheme is promptly executed, obviating the need for computing this plan. Conversely, if it deviates, the process reverts to the prior step, recalculating the slot allocation scheme based on the actual bandwidth. The former results in a reduction in the computational time required for a timeslot scheme, while the latter is consistent with the delay incurred in re-computation when alterations in the business bandwidth occur.
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Case Study 1: Coupling of Continuous Substructures
#### 3.1.6 Validation and Reliability  
The frequency-based substructuring (FBS) method aligns more closely with the finite element method (FEM) results for higher modes, indicating its superior accuracy in representing a structure's dynamic behavior, particularly where local effects and substructure interactions are significant. While the modal method effectively captures the primary modes, it often oversimplifies the response in higher modes, where detailed local effects and interface interactions become critical. FBS addresses these interactions by incorporating the dynamic stiffness and receptance matrices at interfaces, leading to results that more accurately reflect the FEM, a concept referred to as interface dynamics. This makes FBS particularly reliable for applications requiring precise predictions of higher modes, such as in noise, vibration, and harshness (NVH) or high-frequency vibration control studies in vehicle applications.  
In the comparison of receptance components--H11, H14, H22, and H33--up to 100 Hz, all three methods show strong agreement. Notably, H22, which represents the rotational receptance at node 1 due to a couple excitation at the same node, exhibits the most significant error. Figures 4 and 5 illustrate that the translational receptance components H11 and H33, due to symmetry, match exactly across methods. The modal method performs slightly better for these translational components, but it shows larger discrepancies for rotational receptance components H14 and H22, which involve complex interactions of the rotational displacement and moments.
# Post-Disaster Recovery Monitoring with Google Earth Engine
###### Abstract
The study showed that the proposed approach significantly decreased the manual work of training area collection, while maintaining the accuracy of the detected damaged, reconstructed, and newly constructed buildings at a high level. However, extensive computational power is needed to execute deep learning methods. Most of the developed RS-based approaches in post-disaster damage and recovery assessments focus on the use of costly very high-resolution data that require extensive digital storage and computing capacity to make use of them. In recent years, freely available RS images made available through platforms such as Google Earth have attracted the attention of researchers to extract useful information [27; 28]. In addition, cloud-based platforms such as Google Earth Engine (GEE) provide free RS data and computing power with a coding environment to develop and implement user-defined methods and process the data [29; 30]. Freely available, low- to medium-resolution images by data providers (e.g., Copernicus) are mainly collected in these platforms. The potential of such systems has been studied for vegetation cover change analysis [31], land cover and land use classification [32; 33], change detection/analysis [34; 35], wetland map generation [36; 37], rangeland and crop monitoring [38; 39], and in the DRM domain for flood prevention and emergency response [40], drought assessment [41], and wildfire progress mapping [42]. However, they have not yet been studied to monitor the post-disaster recovery process. Furthermore, there is a need for higher spatial resolution images for detailed analysis (e.g., functional assessments), especially in urban areas. The aim of this study was to test the suitability of GEE for a large-scale post-disaster recovery assessment. The big time series Landsat 7 and 8 data available on GEE were used to first generate image composites for the before, event/just after, and post-disaster times (i.e., three time-steps after the disaster) to obtain cloud-free images, by adapting the method developed by [PERSON] et al. [43]. Then, the Random Forest classifier available on the cloud computing platform was employed to classify the generated images and monitor the land cover changes during the recovery process. The entire Leyte island (with an area of ca. 7300 km\({}^{2}\)) in the Philippines was selected to test the developed approach, and municipality-based damage and recovery maps for the entire island were generated for the selected time epochs. Leyte region was hit by Super Typhoon Haiyan on 8 November 2013. As one of the strongest typhoons on record worldwide, it resulted in more than 6000 fatalities, and total damages were estimated at ca. USD 2.2 billion [44].
## 6 Conclusions  
In this study, a new stream clustering algorithm, S-CF Tree, which uses a tree data structure, was proposed. The proposed model was tested using Turkish data in different job categories from Kariyer.net, the company with the richest job posting content in Turkey. Our model was compared with the BIRCH algorithm, which has similar characteristics, and other approaches previously used in the literature (SOM, \(k\)-Means++, and DQ). The S-CF Tree model presented the most satisfactory results, both in terms of complexity and accuracy. Consequently, unlike the existing methods, the proposed algorithm uses stream logic. In this way, a live system in which new data can be constantly adapted was created. Moreover, although a similar approach to clustering algorithms was proposed in the existing methods, a new and efficient clustering pattern could be implemented since a tree structure was used. Additionally, it was found that the matching performance for similar job postings was faster and had higher accuracy than that of the existing methods. It is believed that the approach described in this study will encourage the use of new libraries and algorithms in the field of artificial intelligence, considering clustering and association analysis algorithms that have not been used before in the considered field. We believe that these approaches, which are expected to be very important in text mining, will provide fast and effective guides for producing solutions in future studies. In addition, to date, the technical skills (i.e., hard skills) that are effective for recruitment have been studied, and the inference of these qualities from advertisement texts has been studied. In the future, besides technical skills, we can foresee that new studies will be initiated on this subject with new algorithms to be focused on, including studies on soft skills, which will demonstrate the continuity and success of the recruitment process, which have not yet been implemented in Turkish job posting texts.  
Conceptualization, Y.D. and F.D.; methodology, Y.D.; software, Y.D.; validation, Y.D., F.D. and A.K.; formal analysis, Y.D. and K.C.K.; investigation, Y.D. and U.T.; data curation, K.C.K. and U.T.; writing--original draft preparation, Y.D.; writing--review and editing, Y.D., F.D., A.K., K.C.K. and U.T.; visualization, Y.D. and F.D.; supervision, Y.D.; project administration, Y.D. and A.K.; funding acquisition, K.C.K. and U.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} Acc & Accounting \\ \(B\) & Branching number \\ BIRCH & Balanced Iterative Reducing and Clustering Using Hierarchies \\ CF-tree & Cluster Features \\ Coo & Cooking \\ CSE & Computer Science and Engineering \\ \(D\) & Diameter \\ DIANA & Divisive Analysis Clustering Algorithm \\ DQ & Database Queries \\ FP & Frequent Pattern \\ \(k\) & Number of clusters \\ \end{tabular}
## 1 Introduction
### Purpose and Scope of the Review  
This review aimed to analyze the hydrological and meteorological conditions in the southern part of the Baltic Sea with respect to LNG handling. The authors have examined the hydrodynamic and meteorological characteristics of the region, their impact on the safety and logistics of LNG transportation, and possible hazards posed by the specific characteristics of the area. Special focus has been placed on aspects that may affect the development and operation of the LNG infrastructure in the region, such as temperature, sea currents, waves, salinity, ice, barometric pressure, precipitation, sunshine duration, fogginess, humidity, water level, tides, and extreme weather conditions. The study has been restricted to an analysis of a Baltic Sea area marked on the map (Figure 1). The analysis covers the southern part of the Baltic Sea based on the data available on sea-going vessels and the hydrological and meteorological conditions. Sections of traffic streams in the main shipping routes in the southern part of the Baltic Sea have been examined. Based on the analysis of shipping traffic streams, eleven research sections were identified along the main maritime routes. These sections can be analyzed in terms of vessel traffic intensity and technical parameters, which makes it possible to determine both the movement trends of different types of ships during the analyzed period and the length and width of the studied shipping lanes.  
A detailed analysis covers the port areas of Leba, Ustka, Hel, Swinoujscie, Szczecin, Kolobrzeg, and Gdynia based on data and information on the main shipping routes in the Baltic Sea obtained using the IWRAP system.  
The primary objective of this study is to evaluate the impact of meteorological and hydrological conditions on the feasibility and safety of LNG bunkering operations at key ports in the southern Baltic Sea. While LNG infrastructure, maritime transport, and fuel bunkering are interconnected elements of the LNG supply chain, this research focuses specifically on the operational constraints imposed by environmental factors during the bunkering phase. By combining long-term observational data, regulatory thresholds, and climate projections, the study identifies risk conditions that may disrupt the continuity of LNG supply. The results are intended to inform planning decisions related to port  
Figure 1: The area under analysis.  
adaptation, risk mitigation, and investment prioritization in the context of increasing reliance on LNG as a transitional marine fuel.
# Effects of Growth Parameters on the Analysis of _Aspergillus flavus_ Volatile Metabolites
## Materials and Methods
### Fungal Isolates and Growth  
The aflatoxinic isolate K73 was collected from corn sampled in Sunflower County, MS. This isolate was used for an SPME fiber comparison study. Two aflatoxinic isolates 5-3B and 4-3A were isolated from pig feed in Maben, MS. These isolates were used for the growth parameters' (growth substrate, concentration of spore suspension and temperature) effects study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Growth Substrate** & **Abbreviation** & **pH** &
\begin{tabular}{c} **Media** \\ **Concentration** \\ \end{tabular} & **Ingredients** \\ \hline Corn meal agar & CMA & 6.0 \(\pm\) 0.2 & 17 g/L & 2 g corn meal, infusion from solid, 15 g agar \\  & & & 30 g saccharose, 2.0 g NaNO\({}_{3}\), 1.0 g K\({}_{2}\)HPO\({}_{4}\), 0.5 g \\ Czapek solution agar & CSA & 7.3 \(\pm\) 0.2 & 49 g/L & MgSO\({}_{4}\), 0.5 g KCl, 0.01g FeSO\({}_{4}\), and 15 g agar \\  & & & 30 g sucrose, 10 g asparagine, 3.5 g (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 1 g \\ Chemical defined agar & CDA & - & 60.28 g/L & K\({}_{2}\)HPO\({}_{4}\), 500 mg MgSO\({}_{4}\), 200 mg CaCl\({}_{2}\), 10 mg \\  & & & ZnSO\({}_{4}\cdot\)7H\({}_{2}\)O, 50 mg MgCl\({}_{2}\), 2 mg FS\({}_{4}\), and 15 g agar \\  & & & 30 g sucrose, 10 g asparagine, 3.5 g (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 1 g \\ Chemical defined liquid & CDL & - & 45.28 g/L & KH\({}_{2}\)HPO\({}_{4}\), 500 mg MgSO\({}_{4}\), 200 mg CaCl\({}_{2}\), 10 mg \\  & & & & ZnSO\({}_{4}\cdot\)7H\({}_{2}\)O, 5 mg MnCl\({}_{2}\), 2 mg FeSO\({}_{4}\) \\ Malt extract agar & MEA & 4.7 \(\pm\) 0.2 & 33.6 g/L & 12.75 g maltose, 2.75 g dextrin, 2.35 g glycerol, 0.78 g \\  & & & & \multicolumn{1}{c}{} & \\  & & & & \multicolumn{1}{c}{} & \\  & & & & \multicolumn{1}{c}{} & \\  & & & & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table}
Table 1: Growth substrate and their ingredients used in the study.  
In a typical experiment, the fungal isolate was cultured in a Petri dish containing MEA medium at 30 \({}^{\circ}\)C in the incubator for 7 days and was subcultured every two weeks to maintain fresh spores. Fresh spores were extracted using 0.02% Tween 20 solution and diluted to the desired concentration using sterile distilled water while the number of spores was counted using the hemocytometer (C. A. Hausser and Sons, Philadelphia, PA, USA). A spore suspension (10 \(\mu\)L) was injected into a 50-mL Erlenmeyer flask containing 30 mL of the sterile growth medium. The Erlenmeyer flasks (Fisher Scientific Inc., Pittsburgh, PA, USA) were then covered with aluminum foil and sealed with parafilm.
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 3 System Model Design
### The First-Factor Fuzzy Input: Computation of the Remaining Energy Level
To this end, \(L^{c}_{(node_{i})}\) from \(node_{j}\) can be expressed as:  
\[L^{c}_{(node_{i})}\frac{P(j,v)}{\sum\limits_{k\in R}p(k,v)} \tag{3}\]  
in which \(R\) is the se of neighbor nodes of \(node_{j}\). To maintain the stability of data transmission environment in the networks, we set a lower bound on the remaining energy level of mobile nodes in the network. When the remaining energy level of mobile node is equal to or below this lower bound, the node needs to replenish energy and cannot participate in the routing and transmission of messages. Therefore, the remaining energy level \(L^{re}_{(node_{j},\Delta t)}\) form \(node_{j}\) (as shown in Equation (1)) can be updated as:  
\[L^{re}_{(node_{j},\Delta t)}=\frac{\max\left\{L^{c}_{ini}-L^{c}_{(node_{j})},L^ {c}_{\min}\right\}}{L^{c}_{ini}} \tag{4}\]  
where \(L^{c}_{\min}\) is the lower bound of the remaining energy level from mobile nodes in networks and \(L^{c}_{ini}\) is the initial energy level of mobile nodes. Since the nodes move around randomly in the same communication area and constantly communicate with each other, they update their remaining energy level \(L^{re}_{(node_{j},\Delta t)}\) at different time stamps \(\Delta t\). Because of \(L^{c}_{ini}>L^{c}_{\min}\), the above Equation (5) holds that \(L^{re}_{(node_{j},\Delta t)}\in(0,1]\).
## 3 Materials and Methods
### Materials  
The host polymer used in this study was polyester (Vylon200, abbreviated as PES, Toyobo, M\({}_{\mathrm{n}}\) = 1.7 \(\times\) 10\({}^{4}\)). For titanium dioxide nanoparticles, rutile- (RUT, STR-100N; 16 nm; Sakai Chemical Industry: Osaka, Japan), anatase- (ANA, grade name SSP-M; average particle diameter 15 nm; Sakai Chemical Industry, Osaka, Japan), anatase-rutile mixed crystal- (P25; 21 nm; Nippon Aerosil, Tokyo, Japan), and amorphous-dominant (AMO, Idemitsu titania IT-S; 17 nm; Idemitsu Kosan, Tokyo, Japan [31])-type structures were used. Transmission electron microscopy (TEM) images and X-ray diffraction (XRD) patterns of the titanium dioxide nanoparticles are shown in Figures S1 and S2, respectively, in the Supplementary Materials. The AMO used in this study contained 10 wt% ANA, as determined using the differential scanning calorimetry (DSC) method [32; 33; 34].  
Films with a thickness of 10 um were prepared on aluminum foil (25 mm \(\times\) 50 mm \(\times\) 50 um) by the commonly used spin-coating method (1400 rpm, 30 s). The coating solution for the PES film was prepared by dissolving 5.4 g of PES in a mixed solvent consisting of 9.0 mL toluene and 7.2 mL tetrahydrofuran (THF). For the preparation of the titanium-oxide-doped films, the coating solutions were prepared using the above-described method, except that the total amount of TiO\({}_{2}\) and PES was adjusted to 5.4 g. The titanium dioxide nanoparticles were dispersed in the solutions using a mix rotor (VMR-5, AS ONE) for 3 h, followed by stirring using a magnetic stirrer for at least 12 h. The employed TiO\({}_{2}\) doping levels were  
Figure 1: Charged-water penetration (CWP) model for the triboelectric charging of polymer films.  
1, 5, 7.5, and 10 wt%. After spin coating of the coating solutions, the resulting films were dried at 20 \({}^{\circ}\)C for 1 h, followed by drying in a dryer (DN64, Yamato) at 60 \({}^{\circ}\)C for 3 h. The aluminum foil samples were decreased by sonication in acetone for 30 min and then dried in the dryer at 60 \({}^{\circ}\)C.  
Spherical iron particle powder (T80, Powdertech, Kashiwa, Japan, average particle size: 80 \(\upmu\)m), which was not polymer-coated and reduced with hydrogen gas, was used as the counterpart to rub the films. The morphology of the iron particles was determined using scanning electron micrography as shown in the Supplementary Materials (Figure S3).  
Figure 2: Apparatus (**a**) and measuring system (**b**) for the triboelectric charging of polymer films.
## 1 Introduction
### Background
TX power and the current received signal strength indicator (RSSI), are used to determine the distance from the iBeacons to the mobile devices. The TX power is the calibrated strength of the signal measured at 1 m from the source device (iBeacons), whereas RSSI is a measure of the strength of the iBeacon's signal from the receiving mobile devices. Extensive research has been done on the methods in locating a mobile device with the iBeacon, given the potential interference from environmental factors on radio signals and the accuracy of RSSI, as well as the variations of radio frequency (RF) in indoor environments.
# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 5 Performance Analysis
### Numerical Results
#### 5.2.2 Throughput Analysis  
Figure 9 illustrates the throughput (in bps) achieved both in cases of BPSK and QPSK modulations across various gain values, corresponding to different categories of fish species.  
For rare species, low gain values combined with small packet lengths result in comparatively modest throughput levels, ranging from approximately 3700 bps to 5000 bps. Although these conditions represent a challenging scenario, the observed performance underscores BPSK robustness and ability to work even under limited power amplification. In the case of intermediate fish species, both BPSK and QPSK operate within gain ranges of approximately 5 to 9 and 6 to 9, respectively. Due to the increased packet size and slightly improved gain, BPSK throughput rises sharply in the initial portion of this range and then stabilizes. By contrast, QPSK reaches a throughput of nearly 12,000 bps at the lower end of its gain range and stabilizes around 13,000 bps as gain increases further. For common fish species, the highest gain values are employed to achieve substantially improved throughput.  
BPSK stabilizes at approximately 14,250 bps, although occasional declines occur due to increased bit losses at very high gains. Meanwhile, QPSK initiates this segment around 16,000 bps and continues to show an upward trend, demonstrating its ability to leverage higher gain values and larger packet sizes for further throughput enhancement.  
Figure 8: BER comparison for BPSK and QPSK for each class.  
Figure 9: Throughput comparison for BPSK and QPSK at different gain values.
###### Abstract  
This paper is concerned with Field Programmable Gate Arrays (FPGA)-based systems for energy-efficient high-throughput string comparison. Modern applications which involve comparisons across large data sets--such as large sequence sets in molecular biology--are by their nature computationally intensive. In this work, we present a scalable FPGA-based system architecture to accelerate the comparison of binary strings. The current architecture supports arbitrary lengths in the range 16 to 2048-bit, covering a wide range of possible applications. In our example application, we consider DNA sequences embedded in a binary vector space through Locality Sensitive Hashing (LSH) one of several possible encodings that enable us to avoid more costly character-based operations. Here the resulting encoding is a 512-bit binary signature with comparisons based on the Hamming distance. In this approach, most of the load arises from the calculation of the \(\mathcal{O}(m*n)\) Hamming distances between the signatures, where m is the number of queries and n is the number of signatures contained in the database. Signature generation only needs to be performed once, and we do not consider it further, focusing instead on accelerating the signature comparisons. The proposed FPGA-based architecture is optimized for high-throughput using hundreds of computing elements, arranged in a systolic array. These core computing elements can be adapted to support other string comparison algorithms with little effort, while the other infrastructure stays the same. On a Xilinx Virtex UltraScale+ FPGA (XCVU9P-2), a peak throughput of 75.4 billion comparisons per second--of 512-bit signatures--was achieved, using a design with 384 parallel processing elements and a clock frequency of 200 MHz. This makes our FPGA design 86 times faster than a highly optimized CPU implementation. Compared to a GPU design, executed on an NVIDIA GTX1060, it performs nearly five times faster.  
FPGA; bioinformatics; multiple string matching; parallel computing; Hamming distance; locality sensitive hashing; hardware acceleration; parallel system architecture +
Footnote †: journal:  
_arxiv.org/abs/1209.00000_