esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Impact of AI-Based Cyber Security Financial Sector Management
## 3 Cyber Security in Financial Sector Management (CS-FSM)
### Enhanced Encryption Standard Technique  
EES is not a Feistel cipher but an iterative one. 'Substitution-permutation network' is the underlying concept. Many different processes are linked together in this way. Some are simple substitutions of one input for another, while others entail bit shuffles (permutations). Figure 4 shows the transformation of information into the EES model. Clients or authorized persons want to store their data in a safer way so that they cannot be found or stolen; their financial information is encrypted using a security key, which is then changed into a cipher text. The data are stored in the database. Again, the same person can use those data by decrypting the information with the same security key they use to encrypt; in this way, their information is safer from cyberattacks. The financial statements are designed to offer details on an organization's strategy, financial condition, and profitability, among other things. Readers of financial accounts use this data to understand better how to allocate resources. Network security keys are just another name for the enabled device's password. To access the communication network, one must enter a network security key, a type of network passcode signature which safeguards a system and its devices from unauthorized intrusions.  
The financial sector is being held more and more liable for the security, confidentiality, and integrity of private consumer data (NPI). Businesses in the banking and financial services sector can safeguard private information such as client financial records, social security numbers, income, and account numbers by safeguarding nonpublic personal information and personally identifiable information (PII). If unencrypted data are lost after a data breach, an organization may be subject to fines that can reach millions of dollars, in addition to indirect expenses such as diminished brand value, legal fees, and lost business. Nowadays, almost all commercial databases offer some kind of database-level encryption. A clear benefit of database layer encryption is the integration with other database access control services, resulting in fewer security gaps and it is tailored for database speed.  
The encryption method may incorporate encryption key management more effectively. Keeping the encryption keys distinct from the data they protect and managing them  
Figure 4: Working of Enhanced Encryption Standard Technique.  
throughout their entire lifecycle are two of the key duties of a key management solution. In this study, the researcher used advanced encryption standard (AES) algorithms. The researcher used Python 3.8.10 for this. Python AES offers three distinct key lengths: 128, 192, or 256 bits long, with a fixed block size of 128 bits (16 bytes). Using pycryptodome, we can use AES-128 to encrypt some data, store the data in a file, read the same data again, and then decrypt the data.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 5 Discussion
### Analysis of Model Performance  
The comparative evaluation of LR, SVM, and LSTM models provided critical insights into their performance. LR and SVM demonstrated high accuracy and precision, making them well-suited for scenarios where computational efficiency and reliability are essential. However, their limitations in capturing temporal dependencies in environmental data were evident.  
The LSTM model, which is designed to leverage sequential data, showed moderate performance relative to LR and SVM. While it excelled in identifying temporal trends and fluctuations, its overall accuracy and F1 score were comparatively lower. This discrepancy may stem from the limited size of the training dataset or suboptimal hyperparameter configurations. Nevertheless, the LSTM's capacity to forecast stress-inducing conditions highlights its potential for real-time applications requiring predictive capabilities.
## 3 Data and Methods
### Unsupervised Machine Learning Models
#### 3.3.3 Hierarchical Clustering  
Hierarchical clustering is another unsupervised algorithm that is based on hierarchical methods. When using hierarchical clustering, each object is regarded as a cluster, and then, the clusters are merged step by step according to some rules so that the number of cluster classes is reached. The advantages are: the similarity of distance and rule is easy to define and is limited, and the hierarchical relationship of classes can be found and can be clustered into other shapes. Meanwhile, the disadvantages are: the computational complexity is too high, a singular value can also have a great influence, and the algorithm is likely to cluster into chains. To build a hierarchical clustering model:  
1. Each object is regarded as a class, and the minimum distance between two objects is calculated;
2. The two classes with the smallest distance are combined into a new class;
3. Recalculate the distance between the new class and all classes;
4. Repeat (a) and (b) until all classes are finally merged into one class.  
The effective parameters are cluster number, linkage, and affinity, of which linkage contains Ward, average, and complete; and affinity contains Euclidean, Manhattan, and cosine. Ward can only be combined with Euclidean when averaged, which provides the best performance but a large computation, and can be combined with Manhattan and cosine. We should study the parameters and validate the result before building the final hierarchical clustering model.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 6 Case Study: Topic-Grounded Consultant Agents vs. LLM
### Quantitative Performance Evaluation
#### 6.7.1 Fair Comparison Methodology  
We implemented equivalent financial news processing pipelines using both TB-CSPN and LangGraph frameworks, processing a dataset of 30 financial news items covering diverse market scenarios. Critically, both systems utilize LLMs for semantic understanding:  
* _TB-CSPN Pipeline_: Single LLM call for topic extraction followed by deterministic rule-based coordination
* _LangGraph Pipeline_: Multiple LLM calls throughout the workflow (consultant \(\rightarrow\) supervisor \(\rightarrow\) worker nodes)  
This design isolates architectural differences while ensuring both systems leverage modern language models for semantic processing, avoiding the unfair comparison of LLM-based versus non-LLM systems.  
Complete evaluation methodology, benchmark implementations, and reproduction instructions are available in the public repository at [[https://github.com/Ariberts/tb-cspn-poc/tree/main/evaluation](https://github.com/Ariberts/tb-cspn-poc/tree/main/evaluation)]([https://github.com/Ariberts/tb-cspn-poc/tree/main/evaluation](https://github.com/Ariberts/tb-cspn-poc/tree/main/evaluation)) (accessed on 3 August 2025).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Step** & **TB-CSPN Behavior** & **LangGraph-Style Pipeline** \\ \hline
4. Coordination & A Supervisor agent interprets topics using rules and thresholds to issue a directive. & Next LLM node attempts to determine next action based on prior response and memory context. \\ \hline
5. Delegation & A Worker agent executes directive based on topic-weighted logic. & Another LLM node or code function simulates the action; logic often embedded in prompt. \\ \hline
6. Traceability & All steps are logged: input, topics, thresholds, and directives. & Logging may capture prompt input/output but lacks structural interpretation. \\ \hline
7. Adaptation & Change the rule set or threshold for a Supervisor agent or topic categories for a Consultant agent. & Must modify multiple prompts or retrain orchestration logic; changes are brittle. \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Baseline Optimization:Our LangGraph implementation was systematically optimized using state batching, memory persistence, and efficient node routing to ensure fair comparison. The 3 LLM calls per item reflect architectural requirements rather than implementation inefficiency: LangGraph requires separate LLM inference for consultant analysis, supervisor coordination, and worker execution, while TB-CSPN achieves coordination through deterministic rule processing after initial topic extraction.
# Home Automation System Based on Intelligent Transducer Enablers
## 4 Home Automation System Based on Intelligent Transducer Enablers  
As was described in Section 3, the main plug-and-play mechanisms for transducer networks are aimed at making the transducers accessible without having to configure the network to which they are connected. This approach, despite being useful in some scenarios, has two main problems. The most obvious one is that it requires implementing the behavior of several hardware elements and their communication protocols. This increases the total cost and the complexity of the overall system. Currently, the ISO/IEC/IEEE 21451 standard has been available for years, but, as far as we are aware, it has not been implemented in any successful commercial project that required a large deployment. The second problem is that the standard is not oriented towards IoT. Even though it supports HTTP communications, the whole idea of TEDS and how transducers and their data are defined, was not conceived of with the Semantic Web in mind. This would require an additional layer on the standard, increasing the current overhead that it already imposes.  
HASITE (Home Automation System based on Intelligent Transducer Enablers) has been designed for tackling the two issues mentioned. On the one hand, it has been optimized for easing intelligent transducer node identification and configuration within a smart home deployment. On the other hand, HASITE provides easy, understandable and parseable access to the transducers and their data. Thus, HASITE is characterized by its ability for self-configuring and auto-describing transducers nodes and for using wireless communications.
## 3 Materials and Methods
### Reference Dataset Design
#### 3.2.1 Classification Legend  
A multi-level legend is proposed in Table 2. The first step was to gather the information on the presence of tree cover, single trees or the absence of woody vegetation (Level 0 in Table 2). Any woody element with a height of over 3 m was considered to be a tree. The second step was to categorize the canopy cover into four categories (Level 1 in Table 2): 80-100% canopy cover, corresponding to a closed evergreen forest or bushland; 40-80%  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite Constellation** & **Index** & **Temporal Interval** &
\begin{tabular}{c} **Compositing** \\ **Technique** \\ \end{tabular} \\ \hline
**Sentinel-2** & NDVI & Bi-monthly (6 layers) & Maximum \\ \hline
**Sentinel-2** & Shadow Index & Annual &
\begin{tabular}{c} Maximum of monthly \\ 90 th percentile \\ \end{tabular} \\ \hline
**Sentinel-1** & VV & Annual &
\begin{tabular}{c} Minimum of monthly \\ 10 th percentile \\ \end{tabular} \\ \hline
**Sentinel-1** & VH & Annual &
\begin{tabular}{c} Minimum of monthly \\ 10 th percentile \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Sentinel-1 and Sentinel-2 layers selected for the classifications.  
Figure 4: Example of temporal profiles (in Db) for the year 2018 in VV and VH polarization for different land cover classes.  
canopy cover, corresponding to a closed woodland or bushland; 10-40% canopy cover, corresponding to an open woodland or bushland and 1-10% canopy cover, representing a landscape that is covered by grassland or agriculture and single isolated trees. The third step was to document some of the characteristics that are specific to the type of woody vegetation that was present (Level 2 in Table 2). For the 80-100% class, the distinction between evergreen or semi-evergreen and deciduous trees was made.
## 5 Results
### Comparison of Confusion Matrices Between AlexNet and SwinT  
This section provides an analysis of the confusion matrices obtained from two representative classifiers: AlexNet (CNN-based) and the SwinT (Transformer-based). The test set contained 75 events per class. The rows in the matrices represent the true labels, while the columns represent the predicted labels.  
The confusion matrix for AlexNet is presented in Figure 4.  
The following can be observed:  
* AlexNet performed well on the 'Cloudy', 'Cooling or Lavaflow', and 'Lava Fountain' classes, with minimal misclassifications. Notably, it correctly identified 71/75 events for 'Cloudy', 72/75 events for 'Cooling or Lavaflow', and 69/75 for 'Lava Fountain'.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Class** & **ViT** & **SwinT** & **DeiT** & **PvT** & **CvT** & **TNT** & **LeViT** & **Twins-SVT** \\ \hline Cloudy & 0.88 & 0.94 & 0.86 & 0.91 & 0.85 & 0.75 & 0.87 & 0.90 \\ \hline Cooling or lavaflow & 0.90 & 0.95 & 0.87 & 0.94 & 0.87 & 0.89 & 0.85 & 0.97 \\ \hline Degassing & 0.90 & 0.97 & 0.86 & 0.93 & 0.90 & 0.86 & 0.85 & 0.95 \\ \hline Lava fountain & 0.87 & 0.93 & 0.84 & 0.95 & 0.95 & 0.83 & 0.88 & 0.95 \\ \hline No activity & 0.89 & 0.97 & 0.89 & 0.95 & 0.91 & 0.86 & 0.82 & 0.94 \\ \hline Strombolian & 0.89 & 0.95 & 0.87 & 0.92 & 0.91 & 0.84 & 0.70 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: f1-score for each class using Transformer classifiers.  
Figure 4: Confusion matrix for AlexNet. Rows represent true classes; columns represent predicted classes.  
* The 'Degassing' and 'Strombolian' classes showed higher rates of misclassification. For instance, nine events in the 'Degassing' class were misclassified as 'No Activity', and six events in the 'Strombolian' class were misclassified as 'Cooling or Lavaflow' and 'No Activity'.
* Misclassification often occurred between semantically similar classes, such as 'Degassing' and 'No Activity' or 'Strombolian' and 'Lava Fountain'. The confusion matrix for the Swin Transformer (SwinT) is shown in Figure 5. From the confusion matrix, we note the following:
* A strong performance: the SwinT accurately classified 'Cooling or Lavaflow' in 73 out of 75 cases and performed well on 'Lava Fountain' and 'Cloudy'.
* Misclassifications: the 'Strombolian' class was often misclassified, primarily as 'No Activity' or 'Lava Fountain'.
* Similar errors to AlexNet: like AlexNet, the SwinT struggled with distinguishing 'Degassing' from 'No Activity' and 'Lava Fountain' from 'Strombolian'.
## 3 System Architecture
### Computing Power Allocation System
Second, after receiving the task parameters, the edge server converts it with the server information into state space data. Third, the agent makes the current optimal edge server selection strategy based on the current state. Fourth, the system returns the optimal strategy to the vehicle and the vehicle sends the batch of image data to the edge server determined by the computing power allocation strategy for image segmentation. Fifth, the edge server executes the image segmentation task and returns the results to the vehicle. If the edge server is at capacity, the task may be redirected to a cloud server for processing, ensuring that the task is completed without further delay. Finally, the system rewards or punishes the PER-iSAC agent based on the reward mechanism.
## 1 Introduction
The test results showed that the implemented algorithms produced good results compared to other algorithms such as Genetic Algorithm, Non-dominated Sorting Genetic Algorithm, iterative search, and biogeography-based optimization algorithm, [PERSON] and [PERSON] (2018) carried out the optimization of grinding process parameters to improve the SR and reduce production cost and time. A multi-objective dragonfly algorithm was employed for optimizing the process parameters. Results revealed that the proposed algorithm outperformed the Non-dominated Sorting Genetic Algorithm-II. [PERSON] and [PERSON] (2018) proposed a novel hybrid algorithm, Sine-Cosine Whale Optimization Algorithm, to optimize the process parameters of the multi-pass milling process by minimizing the total production time. [PERSON] et al. (2018) optimized the variable-angle composite cylinders via filament winding manufacturing process using GA. Similarly, [PERSON] et al. (2018) proposed a reliability-based design optimization technique to improve the buckling load of winding cylinders subjected to radial compression. The moving search windows in the Kriging metamodel are used to accelerate its convergence and reduce the number of training iterations. The results of this study demonstrated the advantages of adopting a variable stiffness design for achieving a maximum load capacity. [PERSON] et al. (2018) proposed a genetic algorithm (GA) to enhance the strength of a cylindrical shell under internal pressure by optimizing the stacking sequence. The results offered asymmetric and non-conventional angles for internally pressureal composite tubes, as opposed to the well-known \(\pm\) 55\({}^{\circ}\) winding angle advice (for first ply failure approach). In this research work, turning experiments are conducted on the Hastelloy X material using the PVD TiAlN carbide insert tool under dry, wet, and LN\({}_{2}\) environments. The \(v_{c},f\), a\({}_{\text{p}}\), and machining environment are considered input turning process parameters, and CF, SR, and CT are considered machinability indices. The evolutionary algorithms namely grasshopper optimization (GHO) (2018, 2019, 2020), genetic algorithm (GA) (2019, 2020), particle swarm optimization (PSO) (2019, 2020), moth flame (MFO) (2019, 2020), grey wolf optimization (GWO) (2019, 2020) algorithms are used to identify the optimal set of turning process parameters (MATLAB R2020b version). A clear picture of the experimentation and subsequent processes are detailed in Figure 1.  
Figure 1: Flow chart of experimental part and metaheuristic algorithm.
## 1 Introduction
[22] analyzed the attenuation mechanism of the sandstone strength under different drying\(-\)wetting cycles and chemical solutions according to the basic principles of chemical thermodynamics and chemical kinetics. The stability of the main minerals in the neutral, alkaline, and acidic solutions was confirmed. As an advanced non-contact full-field deformation measurement, the digital image correlation (DIC) method is increasingly used in rock mechanics [23; 24]. [PERSON] et al. [25] investigated the strain field and strain localization of the rock surface using the 3D-DIC method. They found that the strain localization occurred gradually and developed at a low rate before the peak stress but became visible after the peak stress. [PERSON] et al. [26] observed that the displacement evolution in the deformation localization zone of rock specimens remained linear before the peak stress and showed an accelerating characteristic in the post-peak stage. They further carried out a series of uniaxial compression tests at different loading rates on the rock and proposed that a high loading rate resulted in the more rapid evolution of deformation fields and the greater stress value corresponding to the onset of deformation localization [27]. In essence, the evolution of strain fields during rock deformation and failure reflected the generation and expansion patterns of internal fractures [28]. [PERSON] et al. [29] proposed an extended DIC method for discontinuous displacement measurements of soft rock. This method could identify the imperceptible fractures and quantitatively analyze the fracture patterns. [PERSON] et al. [30] carried out a series of mechanical experiments with the DIC method on 3D-printed rock-like material, and they investigated the fracture initiation and propagation mechanism as well as fracture coalescence. The above investigations have mainly focused on the variation in the mechanical parameters of rocks subjected to drying\(-\)wetting cycles in a neutral environment, and some scholars have realized that rock engineering is mostly exposed to a complex hydrochemical environment. However, there remains a lack of data on the failure mode and deterioration mechanism of rocks under the coupling effect of drying\(-\)wetting cycles and acidic solutions. This study used the red sandstone collected from the Panjiatuo landslide as the experimental object and investigated the coupling effect of drying\(-\)wetting cycles and acidic solutions on the physical and mechanical properties. The strain field evolution and failure mode of the red sandstone before and after cyclic drying\(-\)wetting treatment in different solutions were highlighted using the DIC method. In addition, the micro-mechanism of mechanical property deterioration was analyzed by combining scanning electron microscopy and X-ray diffraction.
# A Versatile 100 Hz Laser System with Few-Cycle and TeraWatt Pulses for Applications
## 1 Introduction  
Ultrashort pulse laser systems operating in large-scale facilities like APOLLON, BELLA, CLF, ELI, etc., [1] are heavily booked for secondary source developments and user experiments. The need for scientific and technological developments related to high average power laser-matter interactions, e.g., target systems [2; 3], diagnostics, etc., calls for frequent experimental testing with appropriate laser sources.  
The choice of laser type and material is hence dictated by the near-infrared wavelength regime, and the broad bandwidth with which we aim to work. Potential applications in a wide range of R&D projects in a university setting pose two further, major criteria. First, the system must be tailored easily for various demands, and second, its basic design and assembly must ensure stable operation for long hours. Thus, an obvious choice is the Ti:Sa laser technology [4], mature enough even for the industrial production of lasers with a wide range of fixed parameters, from multi-kHz repetition rate high average power devices [5; 6; 7] through few Hz repetition rate several TW peak power lasers [8; 9] to shot-on demand, multi-PW peak power ones [10; 11; 12; 1].  
Our versatile laser system was designed utilizing the recent achievements in Ti:Sa-based short pulse amplification. The three-stage, 100 Hz repetition rate laser system is based on the configuration of negatively and positively chirped pulse amplification [13], and utilizes extraction during pump [14]. The few-cycle output provides pulses with 7.1 fs and 0.25 mJ, while the power output supports 26 fs pulses with almost 50 mJ. The output energy stability is better than 0.9%. Such a system has been successfully used for the precise cutting of porous target materials, and the development of a high repetition rate liquid sheet target system.