esa-sceva/satcom-chunk-collection huggingface dataset:
# GPU-Enabled Serverless Workflows for Efficient Multimedia Processing
## 6 Conclusions and Future Work  
This paper has described the extension of the SCAR framework to support GPU-enabled serverless workflows for efficient data processing across diverse computing infrastructures. By combining the use of both AWS Lambda, for the execution of a large number of short jobs, and AWS Batch, for the execution of resource-intensive GPU-enabled applications, an open-source event-driven managed platform has been developed to create scale-to-zero serverless workflows. To test its performance, a case study has been defined and deployed on AWS. The behaviour of the platform, along with an analysis of deep learning inference applications running on GPUs and CPUs in the cloud has been exposed, highlighting the contributions of this study. The developments have been released as an open-source contribution to the SCAR tool, publicly available to reproduce the results described in this paper.  
Future works involve the integration of the developed platform with on-premises serverless providers, as well as further extending the semantics of the Functions Definition Language (FDL) to accommodate additional workflow operators, thus allowing the definition of enhanced hybrid serverless workflows. In order to avoid failed executions of functions in AWS Lambda when applications reach the timeout and to find the most suitable allocation of memory for the _lambda_ and _lambda-batch_ execution modes, we consider integrating SCAR with a preprofiling tool such as _AWS Lambda Power Tuning_[37]. In addition, we plan to incorporate external data sources for long-term persistence outside AWS, such as the EGI DataHub [38].  
Conceptualisation, G.M.; methodology, S.R. and G.M.; software, S.R.; validation, S.R.; investigation, S.R.; resources, G.M.; data curation, S.R.; writing--original draft preparation, S.R. and G.M.; writing--review and editing, S.R. and G.M.; visualisation, S.R.; supervision, G.M.; project administration, G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish \"Ministerio de Economia, Industria y Competitividad\" for the project \"BigCLOE\" with reference number TIN2016-79951-R.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AWS Lambda** & **AWS Batch/EC2** & **Total** \\ \hline _FFmpeg_ & \$0.00255510 & - & \$0.00255510 \\ \hline _audio2 str_ & \$0.00758180 & - & \$0.00758180 \\ \hline _YOLOv3_ & \$0.00000258 & \$0.04687500 & \$0.04687758 \\ \hline Workflow & \$0.01013948 & \$0.04687500 & \$0.05701448 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cost analysis of the first workflow execution distinguishing between the two AWS services used for the processing.
## 2 Materials and Methods
### Study Zones
#### 2.1.3 Start Bay: Gravel Beach Backed by Lagoon and Steep, Rocky Cliffs  
Start Bay is a 12 km long embayment aligned SSW-NNE and located on the southern coast of Devon, South West England (UK), where the effects of erosion, accretion, and beach rotation have been well studied and documented [19; 20]. Meso- to macro-tidal with near and spring tidal ranges of 1.8 m and 4.3 m, respectively, the embayment comprises five sub-embayment gravel barrier beaches, named, from south to north, Hallsands, Beesands, Slapton Sands, Forest Cove, and Blackpool Sands. Between each pair of sub-embayments lie short headlands/rocky outcrops. Within Start Bay, gravel is finer to the east due to the lateral grading of material [21], with coarser grains being transported southwest with larger, easterly waves and finer grains being well sorted and transported northeast with smaller but more frequent southerly swells. The tidal range can reach up to 6 m, and the wave climate in Start Bay is modulated by Start Point, a rocky headland offering shelter from longer-period southerly waves, and Skerries Bank, an offshore banner bank that sits east of the main beaches in the southern half of the embayment and is \(-\)5 m Ordnance Datum Newlyn at its shallowest. Furthermore, the entirety of Start Bay is bound by large headlands, and the system is considered a closed sediment cell, with no sediment sources except for some confined areas of cliff erosion [19]. Total sediment budgets (supra- to sub-tidal), with spatially varying uncertainty levels, indicate that two-thirds of total sediment flux occurs in the supra-tidal area; that beach rotation first occurs within sub-embayments; and that additional sustained and extreme energy levels are then required for full embayment rotation to occur, with significant headland bypassing [19]. The occurrence of these beach rotation events is attributed [22] to a higher frequency of southerly storms and/or a lower frequency of easterly storms over a given time period. Thus, the development and stability of the beach on annual timescales depend on the relative contributions of the two storm types and their sequencing.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Rapid-DEM
#### 2.1.1 Data

Table 1: Rapid-DEM uses four data sources in order to detect, plan, fuse, and update DEM in areas experiencing topographic change.
## 3 Results
### Production Media Optimization for Lacase Production
#### 3.4.5 Effects of Surfactant on Laccase Production  
In our study, the effect of surfactants, _viz._, Tween-20 and Tween-80, on the production of laccase by _Pestalotiopsis_ CDBT-F-G1 isolate were studied at concentrations ranging from 0.25 to 1.0 mM. Supplementing the culture media with Tween-80 (0.75 mM) stimulated production of laccase (54 \(\pm\) 14 U/mL) (Figure 5e). At concentrations below or above 0.75 mM Tween-80, the increments were modest. On the other hand, 0.75 mM Tween-20 resulted in higher levels laccase in the culture media but it was less pronounced as compared to the effect of Tween-80.
# Large-Language-Model-Enabled Text Semantic Communication Systems
###### Abstract  
Large language models (LLMs) have recently demonstrated state-of-the-art performance in various natural language processing (NLP) tasks, achieving near-human levels in multiple language understanding challenges and aligning closely with the core principles of semantic communication Inspired by LLMs' advancements in semantic processing, we propose LLM-SC, an innovative LLM-enabled semantic communication system framework which applies LLMs directly to the physical layer coding and decoding for the first time. By analyzing the relationship between the training process of LLMs and the optimization objectives of semantic communication, we propose training a semantic encoder through LLMs' tokenizer training and establishing a semantic knowledge base via the LLMs' unsupervised pre-training process. This knowledge base facilitates the creation of optimal decoder by providing the prior probability of the transmitted language sequence. Based on this, we derive the optimal decoding criteria for the receiver and introduce beam search algorithm to further reduce complexity. Furthermore, we assert that existing LLMs can be employed directly for LLM-SC without extra re-training or fine-tuning. Simulation results reveal that LLM-SC outperforms conventional DeepSC at signal-to-noise ratios (SNRs) exceeding 3 dB, as it enables error-free transmissions of semantic information under high SNRs while DeepSC fails to do so. In addition to semantic-level performance, LLM-SC demonstrates compatibility with technical-level performance, achieving approximately an 8 dB coding gain for a bit error ratio (BER) of \(10^{-3}\) without any channel coding while maintaining the same joint source-channel coding rate as traditional communication systems.  
large language model; joint source-channel coding; joint source-channel decoding; semantic communication +
Footnote †: journal: Applied sciences
# Reconstruction of Ancient Lake after Peat Excavation--A Case Study about Water Quality
## 3 Results  
### Aquatic Plants and Plant Communities Identified in Reservoir  
During studies, macrophytes were in their initial state of development, however, actual species composition and the first identified plant communities were the answer sought for water trophy in the reconstructed lake. Until the year 2014 in the post-beat lake Nienawiszcz, 30 plant species were recorded in the water and on the bankside. The most frequent taxa were seeds, sedges, horsetails, mints and in some places pond-liiles. Only 16 sites of small areas (a few square meters) with initially developed phytocenosis of aquatic plants were found in the reservoir with three poorly developed plant communities, from one class _Phragmiteeta_. Dominant communities were _Phragmitetum_ (11 areas), _Caricetum ripariae_ (four areas) and _Caricetum actiiformis_ (in one site), all characteristic of eutrophic waters. The most frequently identified plants in the reservoir were reeds (_Phragmites australis_), _Carex riparia_, _Carex actiiformis_, _Typha latifolia_, _Polygonum amphibium_ and _Equisetum palustre_. They are tolerant both for low and high concentrations of trophic substances, but generally prefer eutrophic waters. Most of aquatic taxa covered narrow belts of water line dispersed around water body.
# Demonstration of Wastewater Recycling in a Slaughterhouse
## 2 Materials and Methods
### Utilization of Artificial Intelligence  
AI tools, specifically DeepL (Version: 24.8.2.13437+065a4 aef7f5622c450f562a6e3 dd06d 996 cf56f4) and OpenAI's language models, were used to assist with language refinement and grammatical corrections. These tools were applied solely to enhance clarity, readability, and grammatical accuracy, with no changes made to the scientific content of the manuscript.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Parameter** & **Unit** & \begin{tabular}{c} **Process Water Reuse** [26] \\ **(Drinking Water Quality)** \\ \end{tabular} & \begin{tabular}{c} **Reuse Without** \\ **Product Contact** [27; 30] \\ \end{tabular} & \
\begin{tabular}{c} **Agricultural** \\ **Irrigation** [28; 29] \\ \end{tabular} &
\begin{tabular}{c} **Wastewater** \\ **Discharge** [25; 27] \\ \end{tabular} \\ \hline BOD\({}_{5}\) & mg: L\({}^{-1}\) & \(<\)1 & 10 & 10 & 25 \\ COD & mg: L\({}^{-1}\) & 5 & - & 90 & 110 \\ Nitrate & mg: L\({}^{-1}\) & 50 & - & - & - \\ TOC & mg: L\({}^{-1}\) & No abnormal change & - & - & - \\ _E. coli_ & cfu: L\({}^{-1}\) & 0 & 0 & 10 & - \\ _Legionella_ spp. & cfu: L\({}^{-1}\) & - & \(<\)1000 & 1000 & - \\ Turbidity & NTU & 10 & 5 & 5 & - \\ TSS & mg: L\({}^{-1}\) & - & 10 & 30 & 35 \\ Conductivity & \(\upmu\)S: cm\({}^{-1}\) & 2500 & - & - & - \\ Na\({}^{+}\) & mg: L\({}^{-1}\) & 200 & - & - & - \\ NH\({}_{4}\)\({}^{+}\) & mg: L\({}^{-1}\) & 0.5 & - & - & - \\ F\({}^{-}\) & mg: L\({}^{-1}\) & 1.5 & - & - & - \\ Cl\({}^{-}\) & mg: L\({}^{-1}\) & 250 & - & - & - \\ NO\({}_{3}\)\({}^{-}\) & mg: L\({}^{-1}\) & 50 & - & - & - \\ SO\({}_{4}\)\({}^{2-}\) & mg: L\({}^{-1}\) & 250 & - & - & - \\ pH & - & 6.5–9.5 & 6–9 & 6.5–8.4 & 6–9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Limits for wastewater reuse.
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 2 System Model
### Channel Model  
In WBAN, multiple transmission links can exist under channel models \(CM1\) to \(CM4\) based on the location of sensor nodes [26]. The instances considered in this work are as follows:  
1. In-body communication,
2. On-body line of sight (LOS) communication,
3. On-body non line of sight (NLOS) communication.  
In these models, the channel conditions are greatly affected by distant dependent propagation path loss, shadowing, and additive white Gaussian noise (AWGN). The path loss and shadowing are incorporated in channel as follows:  
\[P_{L}=PL(d)+X, \tag{1}\]  
where \(PL(d)\) is the path loss at a given distance \(d\) from node and can be defined by Friis formula for free space. \(X\) is the normally distributed shadowing factor with zero mean and standard deviation sigma. The channel model parameters are defined in Table 1 for in-body communication [24] and on-body communication [27; 28]. The parameters include frequency (\(f\)), reference distance (\(d_{0}\)), path loss exponent (\(n\)), shadowing factor (\(X\)), and reference path loss (\(PL(d_{0})\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **In-Body** & **On-Body line of sight (LOS)** & **On-Body non line of sight (NLOS)** \\ \hline \(f\) (_frequency_) (Hz) & 402-405 MHz & 2.45 GHz & 3.1 GHz \\ \(d_{0}\) (_reference distance_) (cm) & 5 & 10 & 10 \\ \(n\) (_path loss exponent_) & 4.22 & 3.11 & 5.9 \\ \(X\) (_shadowing factor_) (dB) & 6.81 & 6.1 & 5 \\ \(PL(d_{0})\) (_reference path loss_) (dB) & 49.81 & 35.2 & 48.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Channel model parameters.  
Figure 1: Different communication scenarios using single relay cooperation. (**a**) one-way relay communication; (**b**) two-way relay communication.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Solvent Extraction  
Extraction is one of the most used methods for separating gallium and indium from solutions. The basis of the process is the selective solubility of the ingredients in an extractant, i.e., an organic solvent. It can occur both in a liquid-solid system and a liquid-liquid system, but in the case of extraction of gallium and indium, the separation of these metals from solutions is used. There are many reagents available on the market suitable for this purpose. The decisive factor in the selection of the extractant is its insolubility or very poor solubility in the solution used for the separation. Moreover, temperature, concentration of a diffusing component, and mixing are of great importance for the extraction efficiency, as they increase the contact surface between the phases and accelerate the diffusion process of the chosen component [23; 59].  
The efficiency of the gallium and indium extraction process is also influenced by other factors that should be considered when selecting the extractant and setting the parameters under which the process will be carried out. For example, the presence of other metals in the solution is of great importance. It is essential to use an extractant to which the accompanying metals have the lowest possible affinity so that the minimum amount can pass to the extractant, and that they are relatively easy to remove. Moreover, the choice of reagents also depends on the acid used in the earlier step (or acids, if a mixture of them was used) [32] and on the pH [21], because they affect the form in which gallium and indium are present in the solution, and not every form has an equally high affinity for the given extracting solvent [37]. The contact time between the phases is also important, and in some cases the level of pressure also plays a role [60].
# Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function
## 2 Definition of Reliability of the Air Traffic Network  
Currently, there is no general definition of air traffic reliability [35; 36; 37; 38]. Based on the characteristic analysis of air traffic, we propose a definition of air traffic reliability, that is, the ability of the air traffic network to continuously satisfy the air traffic demand under the influence of random factors on air traffic operation within a certain period of time [37]. This ability is derived from structural reliability and functional reliability. The structural reliability can be measured by the network connectivity. The functional reliability can be characterized by the balance of air traffic capacity and demand and flight punctuality. Therefore, the reliability of the air traffic network includes connectivity reliability, travel-time reliability, and capacity reliability.  
The connectivity reliability of the air traffic network (CORN) reflects the interconnection of airports from the perspective of complex networks. In complex network theory, the distance between two points is the number of edges passing from one point to the other point. A short distance between two airports means less flight transfer times and strong connectivity reliability. If airport A is isolated in the airport network, the distance between airport A and any other airport is infinite. Thus, the connectivity reliability of airport A is low.  
The travel-time reliability of the air traffic network (TTRN) is used to describe the time-varying characteristics of flight time. The larger the number of punctual flights, the more reliable the air traffic network. The TTRN is useful for formulating flight schedules.  
The capacity reliability of the air traffic network (CARN) describes the ability of the air traffic network to accommodate a certain traffic demand at a certain service level. Under different service levels, the capacity reliability of the network is different. The CARN is the basis of forming air traffic congestion management strategies.
# Siamese Tracking with Adaptive Template-Updating Strategy
## 4 Experiments and Results
### OTB100 Results  
We created a group label for these vehicle videos and modified the initialization part of our Siamese tracking network for tests because our requirement for the task was different from that in the OTB100 challenge. In detail, we used the center point of each bounding box according to the ground truth instead of our initialization point.  
According to the requirement of our system, we only tested the OPE metric. Figure 10 shows the precision plots and the success plots of OPE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **C1** & **C2** & **C3** & **C4** & **C5** \\ \hline Kernel size & \(11\times 11\times 3\) & \(5\times 5\times 48\) & \(3\times 3\times 256\) & \(3\times 3\times 192\) & \(3\times 3\times 192\) \\ Stride & 4 & 8 & 16 & 16 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kernel sizes and strides in our Siamese network.  
Figure 9: Architecture of our Siamese tracking network.  
As can be seen from Figure 10, our shape-adaptive template method achieved the best performance between the 10 trackers. The distance accuracy and overlap accuracy in OPE are shown in Figure 10a,b, respectively. It is noteworthy that we compared the central accuracy scores of the success rate under the 20-pixel threshold because, in our system center, accuracy is the most important, and most target sizes in our scenarios were about 20 to 40 pixels. In the experiment, our method achieved a success rate of 0.892 on distance accuracy of the 20-pixel threshold, and 0.738 on overlap accuracy. Our method achieved the best performance for both methods: in terms of distance accuracy, our method was 9.9% better than SiamFC, which ranked second, and in terms of overlap accuracy, our method was better by 10.0% as well.
## 5 Security and Privacy Threat Modelling
### Threat Modelling of Multiuser Single AMRSys with IoT  
In a care home or care setting, to optimise the service utilisation and minimize the cost of robotic operation, a single assistive robot can be deployed to manage and provide services to multiple users. In this scenario, as shown in Figure 5, a smart and personalized  
Figure 4: Threat Modelling of Single User Single AMRSys with IoT.  
wellbeing-monitoring IoT system could be integrated, coordinated, and managed by the robot for every user's living space, e.g., bedroom, shared living room, etc. However, the key challenges in this multi-user service system are the vulnerability and high chance of leakage of personal information. Secure privacy-aware communication, maintaining the secure storage of each user's information, without errors of mixing or crossing with other user's information; seamless continuous identification, authentication, and authorisation of each user; dynamic key management; and pre-emptive measures, etc. are important. In this situation, apart from leaking information to three types of people, i.e., to inappropriate children, unintended visitors, and intruders (inside or remote), now the inmates or care mates living together are potential candidates through which user's privacy could be compromised. Moreover, the robot needs to have a priority-based scheduling service with pre-emptive mechanisms, so that it maintains fairness while attending users, based on emergency and urgency. It should be designed in such a way that it does not have conflicts or become mixed up with the services it aims to provide regarding the user's needs and requests to preserve users' privacy and to protect important user data from leaking, collecting, or storing in an unintended location or with unintended users.
## 4 Discussion  
### Carbon Sequestration Capacity Across Different Ecosystems in Semi-Arid Regions  
The NEE of the ENF (_Pinus sylvestris_ var. _mongolica_) from August to December was \(-\)100.875 gC\(\cdot\)m\({}^{-2}\), indicating that the ecosystem functioned as a carbon sink. Previous studies have reported relatively high carbon sink capacities in comparable arid ecosystems. For example, the Mojave Desert in the United States exhibited a total NEE ranging from \(-\)127 to \(-\)102 gC\(\cdot\)m\({}^{-2}\)[34; 35], which is higher than that reported for the Tengger Desert in China (\(-\)23.4 to \(-\)13.9 gC\(\cdot\)m\({}^{-2}\)) [36] and the Fukang Desert ecosystem (\(-\)49 gC\(\cdot\)m\({}^{-2}\)) [37]. Other studies have also reported significant variability in the carbon source-sink status, even within the same desert ecosystem. For instance observed that the NEE in the Baja California region of Mexico ranged from \(-\)52 to \(-\)9 gC\(\cdot\)m\({}^{-2}\)[38].  
In comparison, the total NEE of the ENF (_Pinus sylvestris_ var. _mongolica_) was higher than that of other semi\(-\)arid ecosystems in northern China, such as the desert steppe in Inner Mongolia (\(-\)69 to \(-\)7.2 gC\(\cdot\)m\({}^{-2}\)) [39] and the semi-arid degraded grassland in Tongyu, Jilin (\(-\)73 to 0.32 gC\(\cdot\)m\({}^{-2}\)) [40]. Overall, these results suggest that the ENF, as one of the key afforestation species in China's \"The Great Green Wall\" Shelterbelt Project, possesses a strong carbon sequestration capacity, highlighting its important role in sand fixation and ecological protection.  
Figure 6: The relationship between SIF, GPP and ET, VPD, T\({}_{\rm air}\), R\({}_{\rm g}\), PAR, and SWC.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 2 Related Work
### Combating Data Imbalance
#### 2.1.1 Resampling  
Various techniques have been proposed to address the imbalance in alert data, such as random undersampling [8], which involves selecting and deleting samples from the majority class, typically the negative class. Another common technique is overweighting the minority (positive) class instances [19; 20] to increase the amount of data related to attack events by replication or duplication. The synthetic minority oversampling technique (SMOTE), Cluster-SMOTE, adaptive synthetic (ADASYN) algorithm, and generative adversarial network were adopted in [8; 21; 22; 23; 24; 25; 26; 27; 28] to present new information or variational data samples to the ML model. In addition, related works [8; 29] explored a combination of undersampling and oversampling techniques to attain a balanced data distribution and improve the accuracy of classifiers.