esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Ambulance Allocation in Dynamic Urban Environments: A Historic Data-Driven Approach
## 4 Numerical Experiments
### Case Study  
In Figure 3, the number of zero zones in Seongdong-gu varies by season. We observed that the number of zero zones in each region changed with the change in the number of available ambulances (\(P_{t}\)), and the seasonal variation in Seongdong-gu decreased. Table 3 shows the average number of zero zones in all regions and the number of zero zones in each region as \(P_{t}\) changes. The number of ambulances \(P_{t}\) before applying the mathematical model was 17.  
Through the mathematical model, we observed that, when the number of ambulances was 15, the number of zero zones reduced by 32.75%. However, this reduction corresponded to an increase to 51.46% when the number of ambulances reached 19. In the case of Seongdong-gu, there were initially 52 zero zones. The mathematical model suggested a reduction in the number of zero zones by 11.05% with 15 and 16 ambulances, 24.04% with 17 ambulances, and a diminished reduction of 13.94% with 18 ambulances. The pattern reversed and increased to 26.95% with 19 ambulances.  
From an overall regional perspective, we see that the number of ambulances was less than the 17 currently in operation, but they were efficiently utilized. We also see that the average reduction in zero zones in Seongdong-gu when there were 19 ambulances was twice as high as when there were 15 ambulances.  
Table 4 shows the change in the number of seasonal zero zones in Seongdong-gu as \(P_{t}\) changes. From 15 ambulances to 18 ambulances, there were deviations in the number of zero zones by season. When the number of ambulances reached 19, the number of zero zones became the same.  
From an ambulance operations perspective, minimizing the number of zero zones is important. However, when the number of zero zones changes over time, it becomes a cumbersome task to keep track of when and where the zero zones are for operational policy purposes. From a robust policy perspective, we found that 19 ambulances were needed to cover the area analyzed. However, since the introduction of additional ambulances is expensive, the ambulance operation manager should make decisions based on the presented mathematical model and economic considerations.  
Figure 4: (**a**) shows spring; (**b**) shows summer; (**c**) shows fall; (**d**) shows winter. Red cells represent ambulances deployed to existing fire stations. Black cells represent ambulances deployed to new potential centers. Black-bordered cells with no color are base centers, meaning that no ambulances are deployed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(P_{t}\) & **Before** & **Total Average** & **Seongbuk-gu** & **Dongdaemun-gu** & **Seongdong-gu** \\ \hline
15 & 171 & 115 & 51 & 17.75 & 46.25 \\
16 & 171 & 112.25 & 51 & 15 & 46.25 \\
17 & 171 & 90 & 38 & 12.5 & 39.5 \\
18 & 171 & 90.75 & 36 & 10 & 44.75 \\
19 & 171 & 83 & 35 & 10 & 38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variation in the number of zero zones with the number of ambulances.
# Role of Exposure on the Microbial Consortium on Historical Rural Granite Buildings
## 2 Materials and Methods
### Bacteria and Fungi Next Generation Sequencing Analysis
#### 2.2.3 Bioinformatic Analysis  
The 16S and ITS rRNA raw sequence data were first demultiplexed and then processed using PEAR V.0.9.1 ([[http://www.exelixis-lab.org/web/software/pear](http://www.exelixis-lab.org/web/software/pear)]([http://www.exelixis-lab.org/web/software/pear](http://www.exelixis-lab.org/web/software/pear)); Accessed: 6 October 2020) with default parameters, except for the overlap between the sequences of each end, which was fixed at 70 nts. This finally produced a single file with all overlapping sequences. Trimming steps were applied to remove adaptors and low-quality and short sequences. Primers for the target overlapping sequences were trimmed using CUTADAPT v.1.8.1 software. Sequences with quality values lower than Phred Q20 were excluded using BBMap version 3.38 ([[https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/](https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/)]([https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/](https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/)); Accessed: 6 October 2020). Finally, only sequences longer than 200 nts were retained, as shorter-length sequences are more likely to be incorrectly assigned to certain taxonomic groups.  
For 16S rRNA only, chimeric reads were identified and excluded using Chimera UCHIME53 [43]. High-quality reads were taxonomically identified using operational taxonomic units (OTUs), and samples were assigned to the same group when reads showed 97% similarity (clustering at 97% homology was carried out using CD-HIT; [PERSON] et al., 2012).  
Based on the OTU approach, the alpha-diversity of the biofilm samples was estimated in terms of community richness (Chao-1) and diversity (Shannon). The longest read of each cluster was used as a reference for taxonomic classification, which was conducted using a local BLAST search with the blast-2.2.26+ algorithm ([[http://blast.ncbi.nlm.nih.gov/](http://blast.ncbi.nlm.nih.gov/)]([http://blast.ncbi.nlm.nih.gov/](http://blast.ncbi.nlm.nih.gov/)) Blast.cgi; Accessed: 6 October 2020), default settings and the NCBI database for 16s rRNA. In the final step, the taxonomic path of the reference sequence from each cluster was mapped to the additional reads within the corresponding cluster plus the corresponding replicates (as identified in the previous analysis step) to finally produce (semi-) quantitative information (number of individual reads representing a taxonomic path). Alpha and Beta diversity were measured using the VEGAN package in R. This analysis was performed by Lifesequencing-ADM.  
The diversity of bacteria, fungi and Chlorophyta (green algae) on the N- and W-facing walls of the churches studied was evaluated using the Shannon index [44].
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 1 Introduction  
The wireless sensor network presented was developed to facilitate the continual real-time monitoring of the movement of bell towers due to bell ringing. Such movements could lead to cracks in the tower [1] and in extreme cases the destruction of the tower [2]. The tower considered is part of Durham Cathedral which is a UNESCO world heritage site. In Durham Cathedral, English full circle ringing style is practised where the bells are rung in patterns and rotate through an angle of slightly greater than 360\({}^{\circ}\). Bell ringing causes the tower to sway with such magnitude that it is felt by the ringers in the bell chamber making it extremely difficult to keep the correct ringing times. In cases, tower movements can be so excessive that bells had to be hung dead meaning that they are no longer allowed to swing [3]. A typical bell, such as the one shown in Figure 1, is mounted in a robust steel frame and has a large wooden wheel attached to it. It is rung by pulling a rope that is attached to the circumference of the wheel and hangs down into a chamber underneath where the ringers are.  
Investigating the interaction between the forces produced by ringing bells and their support is of paramount importance to ensure that regular ringing does not cause significant degradation to the tower jeopardising its structural integrity and safety. Once the mechanisms of tower movement are properly understood and characterised, steps can be taken to alleviate the effects, for example, by avoiding potentially damaging ringing patterns or reconfiguring the bell arrangement. A logical approach to investigate this relationship is to measure the forces produced by each bell and then correlate them with the back and forth, and the left and right sway of the tower. The forces exerted by a particular rotating bell on the bell frame can be shown to depend on only its angle of rotation as described in Section 1.1. However, it should be noted that the forces from each bell can couple or add together to produce a greater effect.  
The chosen nine-axis sensor system can measure the angular rotation of each bell in the tower within \(\pm 4^{\circ}\) during an hour-long sample time, and due to its 4.4 \(\times\) 4.4 \(\times\) 8.0 cm size, it could be conveniently installed on the wooden bell wheel. The large and complex motion and the close spacing of the bell structures called for a self-powered wireless solution that also enabled real-time data monitoring. Further details of the sensor system are described in Section 2.
## 6 Conclusions  
In this paper, we propose a robust Conformer-based MEC model called Uni2 Mul, which focuses on optimizing unimodal representation and multimodal fusion. We divide the implementation of Uni2 Mul into two stages. In stage one, we construct individual unimodal neural networks for each modality and train them using IUAs to optimize the unimodal representations. This results in pre-trained unimodal models with superior performance. In stage two, we concatenate the hidden representations of these pre-trained unimodal models and feed the concatenated feature into the Conformer-based fusion network. This fusion network includes a sub-task that predicts IUAs for MEC. We also perform ablation experiments for the two stages separately. For stage one, we construct three different neural network structures for each modality and train these networks using IUAs and IMAs, respectively. For stage two, we try four fusion methods: concatenate, multi-head attention, Transformer, and Conformer. We summarize our overall findings as follows:  
1. Unimodal models trained using IUAs can learn more differentiated information and improve the complementarity between modalities compared to those trained using IMAs.
2. The hidden representations of the pre-trained unimodal models serve as effective inputs for the fusion network. This ensures that the differentiated information learned using the unimodal models is passed unchanged to the fusion network.
3. The Conformer module, with its multi-head attention mechanism and convolutional kernel, excels in paying attention to important intra-modal information and capturing inter-modal relationships. It is the best among the four fusion strategies mentioned above.  
Conceptualization, L.Z.; Funding Acquisition, N.J.; Methodology, L.Z.; Resources, N.J.; Software, L.Z.; Validation, C.L.; Visualization, C.L.; Writing--Original Draft, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Doctoral Research Innovation Program of China People's Police University, grant number BSKY202201; 2022 Humanities and Social Science Research Youth Foundation Project of Ministry of Education, grant number 22 YJC860014; and Hebei Province Science and Technology Support Program, grant number 18215601. The APC was funded by the 2022 Humanities and Social Science Research Youth Foundation Project of the Ministry of Education.  
Not applicable.  
The authors declare no conflict of interest.
# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 3 Large Language Models for Networking
### Network Design and Planning
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Process** & **Work** & **Year** & **Baseline Model(s)** & **Network Application(s)** \\ \hline \multirow{8}{*}{Network Analytics} & BERTOps [153] & 2023 & BERT & Log Analysis \\  & LogGPT [154] & 2023 & GPT-2 & Log Analysis, Anomaly Detection \\  & [PERSON] et al. [155] & 2023 & GPT-3.5, GPT-4 & Cybersecurity, Vulnerability Detection \\  & [PERSON] et al. [156] & 2024 & Phi-2 & Telecommunication \\  & LILAC [157] & 2024 & GPT-3.5 & Log Parsing, Log Analysis \\  & Mobile-LLaMA [158] & 2024 & LLaMA 2 & Network Data Analytic Function, \\  & & & Telecommunications, 5G \\ \hline \multirow{8}{*}{CyBERT} & [PERSON] et al. [159] & 2020 & DistilBERT & Cybersecurity, Man-in-the-Middle \\  & & & Attack, Internet of Things \\  & & & & \\ CyBERT [160] & 2021 & BERT & Cybersecurity \\  & MalBERT [161] & 2021 & BERT & Cybersecurity, Malware Detection \\  & SecureBERT [162] & 2022 & BERT & Cybersecurity, Cyber \\  & & & Threat Intelligence \\ [PERSON] et al. [163] & 2022 & GPT-2 & Cybersecurity, Malware Detection \\  & NorBERT [164] & 2022 & BERT & Network Monitoring, Fully Qualified \\  & & & Domain Name \\  & PAC-GPT [165] & 2023 & GPT-3 & Cybersecurity, Network Traffic \\  & [PERSON] et al. [166] & 2023 & GPT-4 & Network Incident Management \\  & & & & Information Security, Log Parsing, \\  & & & Anomaly Detection \\  & [PERSON] et al. [168] & 2023 & GPT-4, GPT-3, & Network Lifecycle Management, \\  & & & Text-davinci-003, & Network Traffic, Program Synthesis \\  & & & Google Bard \\ \multirow{4}{*}{Network Management} & [PERSON] et al. [169] & 2023 & GPT-2, BERT, & QoS, Telecommunication \\  & & DistilBERT, RoBERTa & & \\  & [PERSON] et al. [170] & 2023 & GPT-3.5, PaLM 2, & Cybersecurity \\  & & Prometheus & & \\  & Cyber Sentinel [171] & 2023 & GPT-4 & Cybersecurity \\  & [PERSON] et al. [172] & 2023 & GPT-3.5 & Cybersecurity \\  & & & Net-GPT [173] & 2023 & LLaMA 2, DistilGPT-2 & Cybersecurity, Network Protocol, \\  & & & Man-in-the-Middle Attack \\  & & & & \\ [PERSON] et al. [174] & 2023 & RoBERTa & Network Measurement, Internet \\  & & & of Things \\ \multirow{2}{*}{HuntGPT [175]} & \multirow{2}{*}{2023} & \multirow{2}{*}{GPT-3.5} & \multirow{2}{*}{Cybersecurity, Anomaly Detection, } \\  & & & Intrusion Detection \\  & & & & \\ [PERSON] et al. [176] & 2023 & GPT-2 & Cybersecurity \\ \multirow{2}{*}{ShieldGPT [177]} & \multirow{2}{*}{2024} & \multirow{2}{*}{GPT-4} & Cybersecurity, Network Traffic, \\  & & & Distributed Denial of Service Attack \\ SecurityBERT [178] & 2024 & BERT & Cybersecurity, Cyber Threat Detection, \\  & & & Internet of Things \\ [PERSON] et al. [179] & 2024 & ALBERT & Network Optimization, \\  & & & Intent-Based Networking \\ DoLLM [180] & 2024 & LLaMA 2 & Cybersecurity, Distributed Denial of Service Attack \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# Full-Scale Experimental Investigation of the Interaction between Trains and Tunnels
## 3 On-Board Tests
### External Pressure
#### 3.2.1 Single Train Case  
Figure 11 reports the results obtained during a crossing of a generic tunnel with a constant speed of 250 km/h. Figure 11a shows the values acquired from all the sensors on one side in correspondence of different carriages. The major differences can be noted during the entry of the train in the tunnel. The sensors along the train are affected by the rise due to the friction, while this does not happen for the sensor closest to the tail. The differences acquired in this first part remain constant throughout the crossing of the tunnel and represent the steady pressure field identified by the TNS.  
Another useful and interesting comparison is the one presented by Figure 11b using sensors in the same longitudinal position but placed on opposite sides of the train. As it can be seen, the pressure along the whole train is equal on both sides and there are no wall effects on the sensor closer to the tunnel wall, as it was found in [25]. This result is useful for the simulation because it demonstrates the one-dimensionality of the phenomenon.  
With regard to the repeatability of the results, six different runs, all at the nominal speed of 250 km/h and with the same direction, are superimposed in Figure 12. The differences between all the runs are limited to about 100 Pa. This is an excellent result if account is taken on the fact that on board is not possible to correct the pressure values with the speed of the train or of the air flow, as it can be done with the measurements at trackside for the pressure signature. The temporal evolution of the pressure is greatly affected by the speed of the train not only for the amplitude of the pressure waves generated but also for the chronology of the train-waves crossings.  
Figure 11: Comparison between the pressure acquired (**a**) in different positions and (**b**) on different sides of the sealed train.
###### Abstract  
S Salinity serves as a critical environmental factor influencing the physiological and morphological characteristics of _Spirulina_, a filamentous cyanobacterium used for food production and commercial purposes. This study examined a _Spirulina_ strain's responses to different salinity levels (10-45 ppt) through three independent laboratory experiments that determined growth, productivity, and size structure. Growth across salinity treatments was assessed by monitoring optical density in 24-well microplates over 20 days and estimating specific growth rates using a logistic growth model. Primary productivity under different salinity and light conditions was measured using light and dark bottle experiments to calculate gross primary productivity (GPP) and to estimate photosynthetic efficiency through linear regression of GPP against light intensity. The size structure was assessed through tube-based experiments and image analysis, with organism sizes categorized and analyzed to identify salinity-induced patterns in filament structure. The study demonstrated that the _Spirulina_ strain achieved its greatest growth at 10 ppt yet produced the highest photosynthetic efficiency between 27 and 45 ppt because it reallocated energy during salinity stress. The morphological analysis revealed that the _Spirulina_ strain produced medium-sized filaments between 400 and 799 \(\upmu\)m at elevated salinity levels, and our analysis confirmed substantial variations in size structure. The _Spirulina_ strain demonstrates both physiological and morphological plasticity when exposed to salinity changes. The cultivation of the _Spirulina_ strain at 27 ppt provides conditions that support moderate growth, enhanced productivity, and manageable morphological shifts while using its natural salinity tolerance to improve the efficiency and scalability of production for diverse biotechnological applications.  
Department of Biology and Environmental Science, College of Science, University of Philippines Cebu, Lahug, Cebu 6000, Philippines; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]  
cyanobacteria; salt stress; maximum specific growth rate; morphological plasticity; biomass productivity; cultivation
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 3 Methodology
### Accuracy Test  
In landslide susceptibility modeling, imbalanced datasets are common, with significantly fewer landslide (positive class) samples than non-landslide (negative class) samples. The ROC curve evaluates model performance across thresholds by plotting the True Positive Rate (TPR) against the False Positive Rate (FPR). The TPR and FPR are defined as:  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{1}\]  
\[\text{FPR}=\frac{\text{FP}}{\text{TN}+\text{FP}} \tag{2}\]  
where TP, FN, TN, and FP represent the number of true positives, false negatives, true negatives, and false positives, respectively. The Area Under the Curve (AUC), ranging from 0 to 1, quantifies model performance. Higher AUC values indicate better performance, while values near 0.5 suggest random guessing, and values below 0.5 indicate predictions worse than random, requiring adjustments. The ROC curve and AUC provide an intuitive method for comparing models, making them accessible to non-experts. They are particularly useful in multi-model comparisons and cross-study evaluations, offering clear benchmarks for decision making. By analyzing the ROC curve, researchers can identify the optimal balance between sensitivity and specificity, selecting a threshold that maximizes landslide detection, while minimizing false positives. This approach ensures robust model performance in real-world applications.
###### Abstract  
Free space optical communication (FSOC) systems operating in the space-atmosphere channel are susceptible to severe turbulence-induced scintillation, particularly in up-link configurations where the adaptive optics (AO) pre-correction becomes ineffective due to anisoplanatic constraints. This study presents a novel scintillation suppression strategy utilizing self-focusing optical pin-like beams (OPBs) with tailored phase modulation, combining theoretical derivation and numerical simulation. It is found that increasing the shape factor \(\gamma\) and modulation depth C elevates the average received power and reduces the scintillation index at the focal point. Meanwhile, quantitative evaluation of the five OPB configurations shows that the parameter set \(\gamma=1.4\) and C = \(7\times 10^{-5}\) gives a peak scintillation suppression efficiency. It shows that turbulence induced scintillation is suppressed by 44% with the turbulence intensity D/r\({}_{0}=10\), demonstrating exceptional effectiveness in up-link transmission. The findings demonstrate that OPB with optimized \(\gamma\) and C establish an approach for uplink FSOC, which is achieved through suppressed scintillation and stabilized power reception.  
free space optical communication; optical pin-like beams; scintillation 28 May 2025  
Received: 10 July 2025  
Accepted: 14 July 2025  
Published: 20 July 2025  
Citation: [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Method for Suppressing Scintillation in Up-Link Optical Communication Using Optical Pin-like Beams Propagating Through Atmospheric Turbulence
# Influence of Linear Springing on the Fatigue Damage of Ultra Large Ore Carriers
###### Abstract  
Springing occurs more frequently to ultra large ships, as their natural frequencies are relatively low and approaches the spectral peak of the wave energy spectrum in certain sea states. Springing is a resonance phenomenon between the waves and the ship hull, and this high frequency vibration has a significant influence on the fatigue damage of hull structures. This paper deals with the influence of springing on the fatigue damage of ultra large ore carriers. The springing responses of ultra large ore carriers are calculated by a three-dimensional linear hydroelasticity method in the frequency domain. A fatigue damage calculation method accounting for the springing effect is applied that is based on the fatigue spectrum analysis method and the approximation method of joint narrowband. The fatigue damage of the deck and bottom longitudinals in the deck and bottom of ultra large ore carriers of various sizes has been calculated by this method. The influences of ship size, structural damping, sea state, wave heading, navigational speed, and loading condition on the structure fatigue damage ratio of ultra large ore carrier are analyzed.  
Keywords:hydroelasticity; springing response; fatigue damage; ultra large ore carrier +
Footnote †: journal: Applied Sciences
## 4 Experiments and Results
### Settings  
The Python language used in our experiment was based on the PyTorch framework. PyTorch is a free, open-source machine learning framework built using Python and Torch libraries. It is one of the most recognized research platforms for deep learning. The framework is intended to minimize the length between the research prototype and deployment. This study used Ubuntu 18.04.2 as the operating system with the graphics card of Nvidia RTX 2080 Ti. The Turing GPU architecture and the all-new RTX platform power GeForce RTXTM graphics cards. It delivers up to six times the speed of the previous generation of graphics cards and introduces real-time ray tracing and AI in games. Because the weights of YOLOv4 are obtained by training the COCO dataset, in order to obtain the weight values of SSDD, this study trained the SAR image dataset from scratch. The pulse was set to 0.9 and the learning rate to 0.001. The maximum number of iterations was 20,000, and the input image size was 608 \(\times\) 608.