esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Granularity User Anomalous Behavior Detection
## 4 Methodology
### Fine-Grained Anomaly Detection (FAD)  
Fine-grained anomaly detection (FAD) is used to construct a random forest classification model for targeted user detection of coarse-grained detected users. This section constructs a random forest classification model for each anomalous user detected by the coarse-grained anomaly detection module. The processing flow for each anomalous user includes data oversampling, model construction, and user-level anomalous behavior detection.  
First, for the anomalous users detected in the coarse-grained anomaly detection stage, all of their behavior data are extracted. Then, the training and testing sets of the user's behavior data are divided based on the time range. Data oversampling is conducted on the training set after partitioning. If there are no anomalous behaviors labeled in the user's behavior data, no model construction will be carried out. This is considering that the coarse-grained anomaly detection model may provide false positives for anomalous users. Because there is no anomalous behavior in the behavior data of normal users who are falsely detected as anomalous users, so there is no need to construct an anomaly detection model. Then, using the historical behavior data of each user as the training set, an anomaly detection and classification model is trained for the behavior pattern of each anomalous user. Finally, the remaining behavior data of the user are used as the test set to test the performance of the fine-grained anomaly detection model.  
The fine-grained anomaly detection module algorithm based on random forest is similar to Algorithm 1. The only difference is that the input of the model is the behavior data training set of a certain user, and the output is the normal or anomalous state of that user's behavior. Building an independent user behavior model for each user helps the model learn the behavior patterns of each user, improve anomaly detection efficiency for specific users, and reduce false alarms of normal behavior.
### Main Contribution  
To the best of the authors' knowledge, this work is the first VNF-SCF deployment optimization model for the particular case of Live-Streaming vCDN. We propose a vCDN model where we take into account, at the same time:  
* VNF-instantiation times,
* Content-delivery and content-ingestion resource usage,
* Utilization-dependant processing times,
* Fine-grained cache-status tracking,
* Operational costs composed of data-transportation costs and hosting costs,* Multi-cloud deployment characteristics,
* No _a-priory_ knowledge of session duration.  
We seek to jointly optimize QoS and operational costs with an Online DRL-based approach. To achieve this objective, we propose a _dense-reward_ model and an _enhanced-exploration_ mechanism for over a dueling-DDQN agent, which combination leads to the convergence to sub-optimal SFC deployment policies.  
Further, in this work, we model bounded network resource availability to simulate network overload scenarios. Our aim is to create and validate a _safe-exploration_ framework that facilitates the assessment of market-entry conditions for new cloud-hosted Live-Streaming vCDN operators.  
Our experiments show that our proposed algorithm is the only one to adapt to the model conditions, maintaining an acceptance ratio above the state-of-art techniques for SFC deployment optimization while keeping a satisfactory balance between network throughput and operational costs. This paper can be seen as an upgrade proposal for the framework presented in [14]. The optimization objective of SFC deployment that we pursue is the same: maximizing the QoS and minimizing the operational costs. We enhance the algorithm used in [14] to find a suitable DRL technique for the particular case of Live-Streaming in v-CDN scenarios.
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
###### Abstract  
Smart cities work with large volumes of data from sensor networks and other sources. To prevent data from being compromised by attacks or errors, smart city IT administrators need to apply attack detection techniques to evaluate possible incidents as quickly as possible. Machine learning has proven to be effective in many fields and, in the context of wireless sensor networks (WSNs), it has proven adequate to detect attacks. However, a smart city poses a much more complex scenario than a WSN, and it has to be evaluated whether these techniques are equally valid and effective. In this work, we evaluate two machine learning algorithms (support vector machines (SVM) and isolation forests) to detect anomalies in a laboratory that reproduces a real smart city use case with heterogeneous devices, algorithms, protocols, and network configurations. The experience has allowed us to show that, although these techniques are of great value for smart cities, additional considerations must be taken into account to effectively detect attacks. Thus, through this empiric analysis, we point out broader challenges and difficulties of using machine learning in this context, both for the technical complexity of the systems, and for the technical difficulty of configuring and implementing them in such environments.  
anomaly detection; information security; outlier detection; smart cities; support vector machines; isolation forest; wireless sensor networks; testbed +
Footnote †: journal: Sensors
# Academic Proposal for Heritage Intervention in a BIM Environment for a 19 th Century Flour Factory
## 4 Project Proposal: Heritage Intervention Results & Discussion
### Phase 2. Second Step: Current Condition  
Exhaustive fieldwork was carried out by performing topographic measurements to obtain real measurements of the building, and dimensions of the surrounding terrain, to perform BIM of the current condition of the industrial building.  
Currently, more than half a century since its closure in 1961, the old flour mill \"La Julita\" is in a state of ruin and abandonment. Comparing the current and original conditions, many elements of its composition have been modified or destroyed by the passage of time. First, the terrain around the structure has undergone severe transformations that have practically covered the three canals, leaving only the one closest to the river open; however, water does not pass through them (Figure 6).  
Figure 5: BIM model of the body of the factory: first phase, original condition. (**a**): Front view; (**b**) Rear view.  
Likewise, the first-floor windows are bricked, and the rest of the floors are empty openings in which, in some cases, part of the old wooden frame persists; furthermore, many of the brick Sardinia-style intels have disappeared (Figure 3).  
Compared with the brick, the exterior stone walls have not deteriorated much, due to the inherent strength of the material itself. The interior wood structure is practically destroyed, the floor slabs no longer exist, and only some wood structures remain connected to the exterior enclosures, interior partitions, and the structure of the roof, which has nearly collapsed entirely. Due to the extreme deterioration of the structure, there has been some attempt at reinforcement, due to the presence of steel beams that protrude at the height of the second floor.  
In this phase, the condition of the building was modeled as it currently exists (Figure 6). According to the phase modeling, a warehouse addition was built after the initial construction, which allows a comparison of the difference in styles between the two parts of the industrial complex. This addition is probably a reflection of the probable changes experienced by the company, assuming that there was an increase in production that required an increase in the storage area.
# III-Nitride Short Period Superlattices for Deep UV Light Emitters
## 2 Growth and Structural Characterization
Simulations based on the experimentally determined SPSL parameters yield an excellent fit to the experimental data, as shown in Figure 3. The deviation of the well and barrier thicknesses from their integer lattice parameters (integer ML multiples) can be attributed to many factors, including residual strain in the SPSL, formation of interfacial layers, interface roughness, composition fluctuations in the well and barrier, stacking faults (SFs), and inversion domain boundaries (IDBs). The detailed analysis of significance of all these factors was conducted in reference [61]. Crystalline microstructure of any semiconductor is a very important factor influencing the performance of all light emitters. Cross-sectional structure of SPSLs should be investigated by transmission electron microscopy (TEM), in order to get a nanoscale resolution. Two TEM cross-sections of AlN/AlGaN SPSL, grown by GSMBE, are shown in Figure 4. Although the growth conditions (substrate temperature, flux ratio, growth time) were the same, the crystalline quality of these SPSLs were very different. It is clearly seen that SPSL grown directly on sapphire substrate contains a very high density of inversion domain boundaries (IDBs). These domains start to grow from substrate/layer interface, mostly due to incomplete nitridation of sapphire at the onset of epitaxial growth [45; 70]. It was shown [71] that IDBs dominate the light emission process in GaN containing these defects. A similar result was reported for MBE grown AlGaN/GaN SLs [72]. However, SPSLs with high density of IDBs have inferior electrical properties [45] and cannot be used in the preparation of light-emitting devices. The most detailed impact of sapphire nitridation on the formation of inverse domains in AlN layers grown by MOCVD was discussed in a recent paper [70]. Figure 3: A long range 20-\(\omega\) scan of (0002) reflection for AlN/Al\({}_{\mathrm{x}}\)Ga\({}_{1-\mathrm{x}}\)N (0.07 \(<\) x \(<\) 0.09) obtained using a hybrid X-ray mirror. Black line—data [61], red line—simulations (courtesy of Dr. [PERSON]).
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 1 Introduction
This paper presents the design of a monitoring and notification system that leverages smartwatches to track the status and activities of factory operators, ensuring immediate communication of critical alerts. By utilizing public cloud services, the system enables scalable data storage, processing, and analysis, facilitating rapid responses to potential issues  
Figure 1: Manufacturing interactions. in the factory environment. The proposed solution aims to improve worker productivity, safety, and response time to operational events, while also reducing the need for manual interventions. The integration of smartwatches and cloud computing technologies provides a cost-effective, flexible, and easily deployable system, offering significant advantages over traditional monitoring methods. This paper outlines the architecture, development process, and key functionalities of the system, and discusses its potential implications for the future of industrial monitoring and automation. The paper is structured as follows: Section 2 examines some related work, while Section 3 delves into the methodology, detailing key components such as system architecture, the implementation of the proposed solution, and some advantages of our proposed system over other studies. This includes configuring AWS Cognito Service, setting up tables in AWS DynamoDB, implementing functions in AWS Lambda, creating APIs in AWS API Gateway, and developing the server, web, and mobile applications. Section 4 presents the results, outlining the system's primary and detailed use cases. In Section 5, we discuss potential applications and implications of our findings, as well as future research directions. Finally, Section 6 draws conclusions.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 2 Materials and Methods
### Testing Process  
This study utilized a sector scanning strategy and a longitudinal vertical scanning method with ordinary machine oil as the coupling agent. During the testing process, it was necessary to establish focusing rules, complete instrument function calibration, and encoder calibration. For different test pieces, adjustments to the relevant parameters of the focusing rules needed to be made based on information such as the dimensions of the test piece, weld seam width, and weld seam depth.
## 3 Optimization and Analysis
### Verify Printing Effect
#### 3.2.1 Printing Fit Test  
In order to reflect the laying of continuous fibers from different angles, the printing fit was evaluated. Representative print specimens were designed, as shown in Figure 9. The g-code generated by corner optimized slicing software was printed, and the printing results were counted and analyzed. Printing was terminated after completing one CFRF print for observation and analysis.  
Table 3 shows the measurement data for CFRP print fit degree at all angles. It can be seen that, with the increase in printing angle, the fitting degree of the actual trajectory and the theoretical trajectory increases gradually. In comparison with the most prominent 30\({}^{\circ}\) printing test, the optimized fit was improved by more than 90%, which proved that the optimized scheme greatly improved the fit of fibers when printing sharp angles.  
\begin{table}
\begin{tabular}{c c} \hline
**Name** & **Parameter** \\ \hline Printing temperature of matrix material & 215 \({}^{\circ}\)C \\ Printing temperature of CFRF & 215 \({}^{\circ}\)C \\ Matrix material & PLA \\ CFRF material & CGFRF-PLA [27] \\ Fiber type & Glass fiber \\ Temperature of hot bed & 50 \({}^{\circ}\)C \\ Environmental temperature & 25 \({}^{\circ}\)C \\ Environmental humidity & 30\% \\ Printing speed of matrix material & 3600 mm/min \\ Printing speed of CFRP & 300 mm/min \\ Printing layer height & 0.2 mm \\ \hline \end{tabular}
\end{table}
Table 2: The printing parameters.  
Figure 8: Definition diagram of fit degree.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Serial Number**} & **1** & **2** & **3** & **4** & **5** & **Mean** \\ \hline \multirow{3}{*}{30\({}^{\circ}\)} & Before optimization & 3.5 & 3.4 & 3.4 & 3.4 & 3.3 & 3.4 \\  & Optimized & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 \\ \hline \multirow{3}{*}{60\({}^{\circ}\)} & Before optimization & 0.9 & 0.9 & 0.8 & 0.9 & 0.8 & 0.86 \\  & Optimized & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 \\ \hline \multirow{3}{*}{90\({}^{\circ}\)} & Before optimization & 0.5 & 0.2 & 0.5 & 0.3 & 0.5 & 0.4 \\  & Optimized & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 \\ \hline \multirow{3}{*}{120\({}^{\circ}\)} & Before optimization & 0.2 & \textless{}0.1 & 0.1 & 0.1 & 0.2 & \textless{}0.15 \\  & Optimized & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 \\ \hline \end{tabular}
\end{table}
Table 3: Printing fit measurement data of CFRF (mm).
## 7 Conclusions--Future Work  
This work presented a pipelined dynamic task scheduling approach with linear complexity, which was designed to handle system changes (in terms of the number of nodes or tasks) for applications that require heavy communication between nodes and tasks. Our approach is organized in a set of communication steps, where there is a one-to-one communication between the system's nodes. The basic procedures (transferring, processing and packing) required by a big data processing system are pipelined using three different stages.  
Figure 8: Load balancing comparisons with a random topology for both scenarios.  
Figure 9: Load balancing comparisons with a random topology for both scenarios.  
As a result of our organization in steps, the buffering space required is reduced, resulting in higher throughput, as a percentage of streams are processed immediately as they arrive to the target node. A second advantage of our strategy is the almost perfect load balancing, as a result of the way that communication is organized. The experimental results obtained have shown that our scheme (as well as the competitive schemes we used for comparisons) generally performs better for linear topologies and when the transfer times are generally larger compared to the processing times (first scenario). When processing requires more time, we observed a reduction of performance.  
In the future, we plan to extend this work in order to produce faster processing and communication time. One concept under investigation is the use of more transformations for the schedule matrix (\(SM\)) in order to break down the most expensive communications into fewer separated steps. Then, the less expensive steps would also be gathered into separate steps; this may result in reduced communication times. Furthermore, we plan to study certain applications and try to improve their performance using some of the ideas presented in this work; for example, pipelining the operations required by a certain application could reduce the overall processing time.  
Conceptualization, S.S., methodology, S.S.; software, S.S. and S.A.; validation, S.S. and S.A.; formal analysis, S.S., and S.S.; investigation, S.A., resources, S.A., writing--original draft preparation, S.S.; writing--review and editing, S.S., S.A.; funding acquisition, S.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Western Macedonia, Faculty of Education, Department of Early Childhood Education.  
The authors declare no conflict of interest.
# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 3 Large Language Models for Networking
### Network Analytics  
Network analytics is crucial for monitoring, analyzing, and understanding network performance, behavior, and security. It ensures optimal operation and timely detection and resolution of issues. By integrating LLMs into network analytics, we can leverage their powerful language understanding and inference capabilities to offer advanced solutions for real-time data analysis and predictive maintenance. Various approaches have been proposed for using LLMs in log analysis. For instance, GPT-2C in [148] is a run-time system that uses a fine-tuned GPT-2 model to parse dynamic logs for intrusion detection systems. LILAC in [157] leverages LLMs with an adaptive parsing cache to improve accuracy and efficiency in parsing complex log data for network analysis tasks. LogGPT [150], on the other hand, utilizes language interpretation capabilities of ChatGPT to tackle high-dimensional and noisy log data for anomaly detection in network management. LAnoBERT in [151] uses the BERT model for unsupervised log anomaly detection, enhancing performance in network log data analysis. Another variant of LogGPT in [154] employs GPT for predicting the next log entry based on preceding sequences and uses a reinforcement learning strategy to improve anomaly detection. BERTOps in [153] is an LLM-based framework designed for automating network tasks like log format detection, classification, and parsing to enhance operational workflows.  
Beyond log analysis, other notable contributions include a GPT-based framework for detecting vulnerabilities in source code by analyzing sensitive code segments [155]. The Network Traffic Transformer (NTT) in [149] is adapted to learn and generalize network traffic dynamics from packet traces, showing potential for prediction tasks in networking. NetLM in [152] utilizes a transformer-based LLM for understanding and managing network traffic dynamics through multi-modal representation learning and incremental control policy generation. NetBERT in [147] is a domain-specific LLM pre-trained on networking corpora, improving tasks like classification and information retrieval within the networking domain. The study in [156] enhances Phi-2 with RAG to improve operational efficiency in telecom-related queries while addressing resource constraints. Mobile-LLaMA in [158] is a variant of the LLaMA 2 model tailored for 5G network management, enhancing capabilities in packet analysis, IP routing analysis, and performance analysis.
## 3 Results and Discussion
### The First Assay (127 Compounds) Results and Obtained TRPA1 Agonists  
From the first assay, evaluating 127 physicochemically diverse compounds in Library707, we obtained 64 TRPA1 agonists (22 P1s and 42 P2s) (Table S4a). The 64 compounds induced concentration-dependent Ca\({}^{2+}\) responses in TRPA1-expressing cells but not in the parent cells, indicating that they are TRPA1 agonists (Figure S1a,b). As shown in Figure 3b, a diverse space of Library707 compounds could be assayed. The UMAP plot indicates that there was no \"clustered region of compounds,\" which resulted in finding agonists. In other words, these data indicate that the TRPA1 agonist molecular rule is complex and has multiple agonist forms.  
From the first assay, we discovered compounds bearing electron-withdrawing groups (clusters 20, 21, 32, 100, and 120), which had been suggested to be the agonist structural rule previously [8, 9]. These findings indicate that our assay platform can effectively evaluate TRPA1 agonists with electron-withdrawing groups. However, interestingly, compound 45 (cluster 22) and compound 285 (cluster 85), compounds with similar electron-withdrawing groups, did not show any activity. These findings imply that the conventionally known \"structural rule of having an electron-withdrawing group to be TRPA1 agonist\" is not a  
Figure 3: Library707 profiled by 27 molecular descriptors. **(a)** Hierarchical clustering of Library707 by 27 molecular descriptors. Compounds are classified objectively by their complete physicochemical compound properties. **(b,c)** UMAP visualization of chemical data space of Library707 for the first assay **(b)** and the second assay **(c)**. Each dot indicates a chemical compound labeled with assay results from the first assay. Categories of their assay results are indicated as follows: P1 (high activity), dark red; P2 (medium activity), light red; N1 (not measurable), light blue; N2 (no activity), dark blue.  
universal rule. Furthermore, we discovered that compound 73 (cluster 33) does not resemble the previously reported TRPA1 agonists, indicating that our variety assay succeeded in finding novel candidate structures to be a potential TRPA1 agonist.