esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Limitations and Future Directions  
Despite its contributions, this study has several limitations that warrant consideration and offer directions for future research. These limitations can be grouped into four main categories.  
First, the type of Industry 4.0 technologies may act as a potential moderator in the examined relationships. However, due to the relatively recent emergence of many Industry 4.0 technologies and the limited number of related empirical studies, this paper was unable to explore the moderating effects of specific technology types. When more studies become available, future research can investigate these effects to provide sector-specific insights for Industry 4.0 implementation.  
Second, additional TAM-related constructs (e.g., facilitating conditions) [166] may also play a significant role in technology adoption. However, due to the scarcity of empirical studies examining these constructs within Industry 4.0 contexts, they could not be included in this meta-analysis. Future studies are encouraged to incorporate these variables to provide more comprehensive and tailored guidance.  
Third, some scholars have emphasized that TAM and its extensions should be applied cautiously across different sectors and organizational contexts [167]. Given that the current study is based primarily on TAM-related literature, the generalizability of its conclusions may be limited. Future research could adopt alternative frameworks to validate and extend the current findings.  
Fourth, while this study focused on the independent effects of PU, PEOU, and SI on behavioral intention, it did not explore the interrelations among these constructs. For instance, PEOU may indirectly influence BI through PU. Future research should examine these pathways to understand the dynamic mechanisms behind technology adoption better.
# A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication
## 5 Conclusions  
To address the dynamic construction demands of task-oriented knowledge bases in semantic communication systems, a novel LLM-driven knowledge graph framework is proposed in this study. By fusing prompt engineering and few-shot learning, the accuracy of entity extraction and relationship analysis is significantly improved. Experiments show that LLMs combined with few-shot learning and prompt engineering (such as DeepSeek) perform well in knowledge reasoning and dynamic updating, and can quickly generate task-related entities and association scores, supporting real-time reasoning for new tasks. This scheme not only reduces manual intervention costs but also provides a technical basis for knowledge-driven optimization of semantic communication systems. Future work can further explore the generalization ability of LLMs in complex task scenarios. This can optimize model parameters to reduce the \"illusion\" phenomenon, and enhance the reliability and practical application value of the knowledge base.  
Conceptualization, C.G. and J.Y.; Data curation, C.G.; Formal analysis, Y.L.; Funding acquisition, C.G. and J.Y.; Investigation, J.L.; Methodology, C.G. and C.W.; Project administration, C.G. and J.Y.; Resources, Z.L.; Software, C.G.; Supervision, C.W. and J.Y.; Validation, C.G. and J.L.; Visualization, C.G. and W.G.; Writing\(-\)original draft, C.G.; Writing\(-\)review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Grant No. 62402505, the Independent Innovation Science Fund of National University of Defense Technology under Grant No. 22-ZZCX-055 and the Graduate Research Innovation Project of National University of Defense Technology under Grant No. XJZH2024006.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
## 3 System Design and Architecture
### Core Components
#### 3.2.2. Laravel Back-End (MVC Architecture)  
The back end of AccessiDashboard is implemented in Laravel (PHP), following a traditional Model-View-Controller pattern. This separation of concerns allows the system to manage data and business logic independently from presentation, which is important for serving different front-end modalities (standard vs. accessible views).  
Key data models include, for example:  
* Sensor--representing an IoT sensor or data source. A Sensor record holds metadata such as a unique ID, a human-readable name or label, the type of measurement (e.g., temperature, humidity, air quality index, etc.).
* SensorReading--representing a single data point from a sensor. Each reading has a timestamp, a value (or multiple values if the sensor outputs a complex object), and a foreign key linking it to a Sensor. This model might also include derived fields (like whether this reading triggered an alert, or a categorization of the value such as \"moderate\" or \"high\").
* LongDescription--representing an AI-generated descriptive text for a visual element or set of readings. It stores the text of the description, the time it was generated, the context (which sensor it corresponds to), and metadata like which model generated it or who (if an admin edited it).  
Controllers in Laravel handle requests from the client side. For instance, a \"DashboardController\" has methods to handle loading the main dashboard page, which pulls recent \"SensorReading\" data for each sensor and passes it to the view. The view is a Blade template [38] that loops through sensors and displays each either as a chart or text depending on the mode. We implemented two sets of Blade templates: one for the visual layout (with charts and minimal text, arranged in a grid layout), and one for the accessible layout (with extensive text, data tables, and so on). The appropriate view is rendered based on the user's selection or a query parameter. The back end also integrates with external services. The most notable is the AI service for generating descriptions (discussed in the next subsection).  
Another aspect is user management and security. Although our demonstrator is focused on the accessibility feature, it also includes user accounts, login sessions, and role-based access control (administators have the rights to configure and test sensors, while other users are limited to viewing them). Laravel provides middleware for authentication which we utilize. All communication is over HTTPS [39] to secure data in transit. In cases where IoT data might be sensitive (e.g., health data), we ensure that personal identifiers are not included with sensor data, or we provide anonymization if needed.  
Overall, the Laravel back-end provides a robust foundation to support the accessible front-end. It manages real-time data updates, orchestrates AI calls, and ensures that content delivered to the user is appropriate for their chosen mode while enforcing data privacy and security standards.
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Fungal Aegerolysins from Ascomycata--Sordarionycetes  
#### 2.3.1 Aegerolysins from _Beauveria bassiana_  
The fungus _Beauveria bassiana_ (Bals.-Criv.) Vuill., 1912 (_Hypocreales_) is considered both an entomopathogen and an endophyte. Entomopathogenic fungi can also perform other functions in nature, including endophystism, antagonism to plant diseases, promotion of plant growth, and root colonization [30; 31]. The fungus _B. bassiana_ is already known as a biopesticide for biological control of insect pests and as an asymptomatic companion of plants.  
Structural modeling of beauveriolysin A (**BlyA**) suggests that it has a similar three-dimensional fold to other aegerolysins from fungi or bacteria [52]. **BlyA** showed the ability to bind specifically to lipid vesicles containing Chol in combination with both mammalian and invertebrate major membrane sphingolipids, SM, or CPE, respectively [52]. However, **BlyA** itself is neither hemolytic to bovine erythrocytes nor toxic to Sf9 insect cells [52]. Given its similarity to other aegerolysinin pore-forming partnering proteins, **BlyB** was considered a candidate, but the possible formation of pores together with **BlyA** remains to be confirmed [52]. Similar protein pairs may be encoded by several entomopathogenic fungi from the class _Sordarionycetes_[52]. **BlyB** has been shown to best align the structures of some bacterial and mammalian MACPF proteins, rather than **PlyB**[52]. Two structures have been solved from _Proteobacteria_, for the Gram-negative insecticidal protein GNIP1 Aa (PDB ID: 6 FBM) from _Chromobacterium picscinae_, which has been shown to be specifically toxic to western corn rootworm larvae during feeding [121], and for Plu-MACPF (PDB ID: 2 QP2) from a lethal pathogen of insects _Photorhabus luminescens_[122]; no data are available on their partner proteins. The macrophage-expressed gene 1 protein (MPEG-1) or perforin-2 (PDB ID: 6U23 and analogs), which plays a role in the human immune system, was recently shown to function alone [123; 124; 125]. The murine complement C9 (PDB ID: 6 CXO) and the human complement C8\(\alpha\) chain are also involved in the immune system (PDB ID: 2 RD7); in contrast to MPEG-1, proteins C8\(\alpha\) and C9 are involved in the formation of a heterooligomeric protein-membrane attack complex (MAC) [123; 124; 126; 127]. In conclusion, several proteins that served as templates for construction of a protein model of **BlyA**, several possible binary toxin partner proteins, and also organisms from which these proteins are derived, also have insecticidal (or immunological) properties [52]. **BlyA** could belong to a set of insecticidal proteins and metabolites that enable entomopathogenic fungi to have their specific lifestyle [52].  
The sequences of **BlyA** are identical in all strains ARSEF 2860, D15, GCA_001682635.1, and JEF-007.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### Data Management Requirements
#### 2.2.1 Data Representation  
At present, there are mainly three mechanisms for data transmission on resource constrained devices: CBOR, FlatBuffers, and protocol buffers [25; 26; 27; 28]. CBOR (Concise Binary Object Representation) is a data format with good scalability that minimizes the size of the code, has a relatively small message size and no version negotiation. The current draft is RFC 7049. TinyCBOR is a C/C++ based CBOR implementation from Intel that meets industrial standards. FlatBuffers, originally developed by Google for games and other performance-critical applications, is an efficient cross-platform serialization library that supports languages such as C++, C#, C, Go, Java, JavaScript, PHP, and Python.  
Figure 1: LoRA architecture.  
Protocol buffers is a language- and platform-neutral, extensible XML structured data serialization method developed by Google, but it is smaller, faster, and simpler. It is easy to use, and can define customized structured data and automatically generate the read/write method for the structured data. Open source implementations of this mechanism support a variety of platforms and languages.  
From the above three data description mechanisms, it can be seen that the goals of these methods are to reduce the number of data transmissions, reduce the resource consumption of data processing, and simplify the data processing process to meet the requirements of IoT applications.
# Impact of Ferment Processing Parameters on the Quality of White Pan Bread
## 1 Introduction
The main purpose of preparing a ferment was to pre-condition the yeast to optimize its function during bread making, thus reducing fermentation time of the dough. The amount of flour in a liquid ferment varies from 0% to 50%, with a higher percentage of flour resulting in improved bread quality [12]. Other advantages include reduced floor space in the bakery, lower labour requirements, increased production flexibility, improved sanitation, increased hydration, reduced time for dough mixing, improved dough machineability, stronger and more uniform dough, and bread with a softer crumb, better keeping quality, and improved flavour [13]. Limited research has been conducted to evaluate the impact of ingredients and processing variables that are used to prepare ferment. Flour quality used for ferment preparation should be consistent and of good protein quality [14]. Adequate fermentation is required for the ferment to reach maturation by monitoring the level of acid development by measuring the pH or total titratable acidity (TTA) [15]. A range of pH in liquid ferments between 4.5 and 5.2 appeared to be the most desirable for optimal conditioning of the gluten, enzymatic reactions, and preventing mould growth in bread [16]. Inorganic salts appeared to be effective in preventing the pH from dropping below 4.5 [16]. The amount of flour used affects the pH of the ferment, with ferments containing higher flour levels resulting in a more gradual decline in pH probably due to the buffering effect of flour [17]. Fermentation temperature (26-30 \({}^{\circ}\)C) has also been found to affect pH and TTA levels of ferment [17]. Longer fermentation times were required to reach optimum fermentation when ferments were prepared using a lower flour to water ratio at a fermentation temperature of 26 \({}^{\circ}\)C [17]. Longer fermentation times are also recommended for ferments containing higher amounts of flour to improve flour hydration and flavour development to produce bread with improved crumb softness and flavour and slower staling [18]. Consumer preference for foods that are free from artificial ingredients and are \"clean label\" is growing. Food processors are looking to develop products that are made from fewer ingredients, but also made from naturally derived ingredients [19], suggesting that the use of liquid ferment in the production of bread will gain more attention. Limited research has been undertaken to examine the factors that influence the quality of liquid ferments and their effects on bread quality. Therefore, it was the objective of this study to examine the use of liquid ferment in white pan bread using a no-time dough-baking process. Specifically, the effects of ferment storage time, fermentation time, and fermentation temperature on the quality of ferment and bread quality were investigated.
## 3 System Model  
This section provides a solution to the identified problem in which beacon-based broadcasting of node localization with scheduled underwater nodes is addressed, although many schemes already exist for beacon-based broadcasting for node localization underwater without a scheduling mechanism [21; 22; 23; 24; 25; 26; 27]. The scenario of beacon broadcasting is when a beacon node finds another volunteer node in the communication zone for localization to include the action of data packet forwarding. There are multiple objectives, including the minimization of nodes broadcasting, accommodating a set of minimum neighboring nodes with the maximum set of localized nodes, and priority-based scheduled beacon broadcasting, for beacon node localization An effective method is provided to manage beacon nodes for optimal broadcasting, with its objective function framed as a multi-objective scheduling problem, where beacon node broadcasting is considered for two instances: (i) adaptive beacon broadcasting and (ii) opportunistic beacon broadcasting modes. The mechanism of Beacon Node Localization (BNL) [33] is also incorporated because it influences more power scheduling with the help of metaheuristic Bio-Inspired Algorithms (BIAs) during critical condition of unlocalized nodes. The BNL and scheduled BNL also help in reducing the cost of nodes and energy during the broadcasting of nodes in a large scale of area such as UASNs.  
The sonobuoys are assumed to locate and satisfy the initial node position that helps to determine the other node locations through beacon nodes. These beacon nodes are deployed usually on seabed (middle of the ocean) and set accordingly with respect to area of interest. Beacon nodes include two-way motion which is diagonal (3D space) and vertical to accommodate the number of nodes for its location [21]. Many responsibilities such as running the beacon chain, synchronizing the beacon chain with peer nodes, and keeping track of the nodes chain state are in line with beacon node localization [33]. The underwater network is connected to the base station, which is further linked with sonobuoys. The sonobuoys send classified messages to beacon nodes. Beacon nodes are either adaptive or opportunistic for further broadcasting for scheduling (according to the defined fitness function) which is taken from [34; 35]. Under the concept of opportunistic broadcasting and routing underwater, the proposed technique first locates the nearby node. The nodes that are located first as the result of beacon-based broadcasting are called a 'primary node'. The primary nodes then locate further nodes associated as'secondary node'. Figure 1 presents a quick description of the system model.
## 4 UV-VIS-IR Radiation Detectors
### Infrared Detectors for Fourier-Transform Infrared (FT-IR) Spectroscopy
FT-IR imaging systems have been largely employed in CH by using FPA detectors that collect typically 64 \(\times\) 64 infrared spectra simultaneously, significantly reducing the acquisition times [236; 237]. Despite the very appealing features of FPA detectors, the obtained spectra are affected by thermal noise and show lower signal/noise ratio, especially in the reflection mode and in the presence of poorly reflective sample surfaces. Moreover, the 900 cm\({}^{-1}\) cut-off of FPA detectors does not allow for the identification of substances that show characteristic absorption bands below this wavenumber. Future development of IR detector arrays regards the improvement of pixel sensitivity, the increase in pixel density, the cost reduction in IR imaging array systems through the \begin{table}
\begin{tabular}{c c c} \hline
**Detector Type** & **Spectral Range (cm\({}^{-1}\))** & **D\({}^{*}\) (cm Hz\({}^{1}\)/ W\({}^{-1}\))** \\ \hline DTGS & 10,000–370 & \(>\)2 \(\times\) 10\({}^{8}\) \\ \hline MCT Narrow-Band & 10,000–750 & \(>\)4 \(\times\) 10\({}^{10}\) \\ \hline MCT Mid-Band & 10,000–600 & \(>\)2.5 \(\times\) 10\({}^{10}\) \\ \hline MCT Wide-Band & 10,000–420 & \(>\)5 \(\times\) 10\({}^{9}\) \\ \hline MCT Photovoltaic & 4800–850 & \(>\)3 \(\times\) 10\({}^{10}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of detectors of different areas basing on their D\({}^{*}\) values. Figure 13: FT-IR principle of image formation using single-element detectors, 1-D (LA) or 2-D (FPA) systems. As an example, the image obtained on a cross section of a pictorial layer sample is shown. use of less cooling sensor technology combined with integration of detectors and signal-processing functions. Finally, the improvement in the functionality of IR imaging systems passes through the development of multispectral and hyperspectral sensors. In the framework of the INFN-CHNet network, the DAPNE-Light synchrotron radiation laboratory is a facility open to external users, able to provide chemical analyses on cultural heritage materials, namely pigments, binders, and degradation products. A Vertex70 V FT-IR interferometer is coupled with an Hyperion3000 IR microscope equipped with a 64 \(\times\) 64 pixel Focal Plane Array MCT detector, giving access to micro and macro analyses, mapping, and imaging on different materials. A portable FT-IR spectrometer, equipped with a room-temperature DTGS detector, is as well available for in situ analyses.
## 3 Results and Discussion  
### Morphology of Escherichia coli Nissle 1917 and Bacteriophage K5  
EcN is a non-pathogenic probiotic strain with the serotype O6:K5:H1. It is flagelated and, thus, is mobile and can interact with mucus and adhere to the human intestinal tract [54]. Its K5 polysaccharide capsule, comprised of \(\alpha\)-N-acetylglucosamine and \(\delta\)-glucuronic acids, represents the specific binding site for bacteriophage K5. Bacteriophage K5 encodes for the depolymerase endo-\(\alpha\)-N-acetylglucosaminidase, which enables the hydrolysis of polysaccharides in the EcN capsule [55; 56].  
Bacteriophage K5 is assigned to a group of bacteriophages that are similar to T7 bacteriophages (belonging to the so-called \"T7 supergroup\" or are \"T7-like\"). Typically, they are composed of an icosahedral capsid, a short tail, and fibers [57]. To the best of our knowledge, there is not much information about bacteriophage K5 regarding its dimensions or other characteristics available in the literature.  
EcN and bacteriophage K5 were visualized by atomic force microscopy (Figure 2). Well recognizable are the EcN cells with their flagella, as well as the bacteriophage K5 capsids. The capsid diameter was determined to be 68-88 nm on average. This is slightly larger than the capsid of the bacteriophage T7 (61-63 nm) and other T7-like bacteriophages (e.g., SP6 and K1-5: 65-68 nm) [57; 58]. However, the larger appearance could also be associated with the applied methodology; the measured values should be verified in the future using a scanning electron microscopy image.  
Figure 2: Atomic force microscopy image of **(a)**_Escherichia coli_ Nissle 1917 and **(b)** bacteriophage K5. Bacteriophages (selection) are indicated by yellow arrows in both pictures.  
As mentioned before, ECN and bacteriophage K5 were particularly well suited for the following study as a test model as they enabled laboratory work without strict precautionary safety measures and work in food-grade areas. Furthermore, they would be beneficial for future feasibility studies involving animals or humans.
## 2 Related Study
### Paderborn University Bearing Dataset  
A condition-monitoring experimental bearing dataset based on vibration and motor current signals was used from Germany's Paderborn University, as shown in Figure 4[23]. Experimental datasets were generated by installing different types of damaged ball bearings in the bearing test module. The setup consisted of healthy bearings with 1920 and 1600 entries for outer and inner ring damage, respectively. The most common bearing damage analysis [24] involves using motor current signals (MCSs) to convert time-domain signals into frequency-domain signals to observe the spectrum differences between normal and abnormal bearings as shown in Figure 5.  
Feature engineering was applied to extract features from raw data, including common statistical, signal factor-related, fast Fourier transform (FFT), power spectral density (PSD)-related, and wavelet packet decomposition (WPD)-related features, as listed in Table 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature Types** & **Feature** \\ \hline Time Domain (22 Features) & Clearance factor, max, mean, median, min, coefficient of variation, crest factor, local max, local min, max in range, frequency, impulse factor, kurtosis, percentile, peak to peak, RMS, shape factor, skewness, standard deviation, variance, X of max, X of min \\ \hline Frequency Domain (22 Features) & Clearance factor, coefficient of variation, crest factor, frequency, impulse factor, kurtosis, local max, local min, max in range, max, mean, median, min, percentile, peak to peak, RMS, shape factor, skewness, standard deviation, variance, X of max, X of min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key time- and frequency-domain physical and statistical feature values of vibration signals transformed by MCM.  
Figure 4: Germany’s Paderborn University Bearing Dataset, consisting of: (1) motor, (2) torque measurement shaft, (3) rolling bearing test module, (4) flywheel, and (5) load motor.  
Figure 3: Vibration Severity defined in ISO10816 [22].