esa-sceva/satcom-chunk-collection huggingface dataset:
# Synchrotron X-ray Microprobes: An Application on Ancient Ceramics
## 4 Discussion  
From experimental studies it is known that in order to obtain a good quality black gloss ceramic a multi-phase firing process it is required, with a precise control of temperature and firing duration during each ORO (oxidizing-reducing-oxidizing) phase. These parameters also affect the final appearance of the black gloss, turning from bluish to brownish red [31, 45]. Generally speaking, if the slip is <<well-made>>, no hematite or maghemite (Fe\({}^{3+}\)) is formed and only Fe\({}^{2+}\) should be present. Otherwise, if Fe\({}^{3+}\) phases are present, the location of the different Fe-phases inside the gloss is of interest. Fe\({}^{3+}\) phases usually form a thin slip under/on the top of the black-gloss. When other phases are present--likely the Zn-spinel phases observed in south-Italian products--it is relevant to investigate their mineralogical nature and their location inside the gloss thickness.  
The results of this investigation evidenced that in Attic vessels the Fe-based black gloss is mainly due to magnetite and hercynite, indicating an ideal and well-made production routine. On the other hand, in Sicilian colonial vessels (Geloan) prevalently hematite has been found, indicating a different technological signature for the black-gloss, which in fact appears quite different in its aesthetical features (matte and brownish red). As per South-Italian workshops and Laconian products, in some cases only hercynite has been found, indicating well-made pieces. In other cases, Fe\({}^{3+}\)--diffusely distributed on the gloss thickness--would indicate not perfectly assessed production routines or unstable conditions in ancient kilns.  
Overall, South-Italian workshops (Chalcidian and probably Locrian) seem to be discriminated for their Zn signature, indicating a shared fabrication method in these colonies. In fact, as suggested by the literature, Zn could be considered not only a provenance tracer but a technological marker, related to the clay refinement methods [43]. Other studies would correlate the Zn occurrence as typical of specific Greek clays used for the black gloss [46], however, from our characterization studies, such higher Zn traces have to be considered as specific of South-Italian products. XANES spectra and maps collected on these samples revealed the nature of Zn-spinel as ZnAl\({}_{2}\)O\({}_{4}\) (Gahnite) which is localized over the entire gloss thickness. Experimental studies would be highly useful to better understand the technological choices behind these specific colonial products.
# MMNET: A Multi-Modal Network Architecture for Underwater Networking
## 3 Intelligent Network Management Based on Application Requirements
### MMNET Network Management  
At present, although there are many kinds of underwater communication technologies, there are still some limitations, which cannot be used as the information forwarding control of network management. First of all, acoustic communication has the characteristics of long propagation delay, high bit error rate and low transmission bandwidth. If it is used to manage the network, it will not be able to timely transfer the status of the network and occupy valuable communication bandwidth. Although underwater optical communication and underwater magnetic communication overcome the characteristics of high delay and small bandwidth of acoustic communication, their short communication distance limits the coverage of the network. The limitations of various underwater communication technologies affect the centralized network management mode in an underwater network. In view of this feature, MMNET chooses the distributed network management mode. Each node manages its own network and reduces the transmission of control information. It can not only enhance the sensitivity of network control, but also reduce the occupation of communication bandwidth and expand the coverage of the network. Two major functions of MMNET network management are as follows.
## 3 Method
### AM-HDGAT for Text
#### 3.4.4 Adversarial Training Framework  
To ensure robustness in security-critical document processing applications within AttenFlow, we incorporate adversarial training into our framework. This component is particularly crucial for applications such as spam detection and content moderation, where adversarial attacks are common.  
**Adversarial Perturbation Generation:** We generate adversarial perturbations \(\delta\) in the embedding space using the projected gradient descent (PGD) method. The perturbation is computed to maximize the classification loss:  
\[\delta^{(t+1)}=\Pi_{\mathcal{B}_{c}}\left(\delta^{(t)}+\eta\cdot\text{sign}( \
abla_{\delta}L(Z+\delta^{(t)},y))\right) \tag{18}\]  
where \(\Pi_{\mathcal{B}_{c}}\) denotes projection onto an \(\ell_{\infty}\) ball of radius \(\epsilon\), \(\eta\) is the step size, and \(L\) is the classification loss.  
**Robust Training Objective:** The model is trained to maintain correct classifications on both original samples \(Z\) and adversarially perturbed samples \(Z+\delta\). The adversarial loss component is  
\[L_{adv}=\mathbb{E}_{(Z,y)}\bigg{[}\max_{\|\delta\|_{\infty}\leq\epsilon}L(Z+ \delta,y)\bigg{]} \tag{19}\]  
**Total Loss Function:** The complete training objective combines the standard classification loss, adversarial loss, and graph regularization terms:  
\[L_{total}=L_{cls}+\lambda_{adv}L_{adv}+\lambda_{graph}L_{graph} \tag{20}\]  
where \(L_{cls}\) is the standard cross-entropy loss, \(L_{graph}\) encourages smooth predictions over the graph structure, and \(\lambda_{adv}\), \(\lambda_{graph}\) are hyperparameters controlling the relative importance of each component.  
The graph regularization loss is defined as follows:  
\[L_{graph}=\frac{1}{2}\sum_{i,j}A_{ij}\|\mathbf{h}_{i}-\mathbf{h}_{j}\|_{2}^{2} \tag{21}\]  
This term encourages similar documents (connected in the graph) to have similar representations, promoting consistency in the learned embeddings and supporting the overall document management objectives of AttenFlow.  
Through this comprehensive framework, AM-HDGAT for text effectively combines document-level relationship modeling, multi-modal feature fusion, and adversarial robustness training to achieve superior performance in text classification tasks within document management systems. The approach particularly excels in semi-supervised scenarios where labeled data are limited, as the graph structure enables effective propagation of label information throughout the corpus, thereby complementing the retrieval capabilities of RCCF to provide a complete solution for automated document processing in the AttenFlow architecture.
## 4 Hierarchical Structuring of CSFs
### Obtaining the Hierarchy Graph  
The hierarchy graph is generated by identifying the interrelationships between each pair of CSFs according to \(S\). The element in row 1 of the skeleton matrix (\(S\)) appears at the first level of ISM. Hence, CSFs (7, 11 and 15) appear at the top of the ISM. Thereafter, CSFs (4, 6, 10 and 12) located in the 4 th to the 7 th row of the skeleton matrix (\(S\)) appear at the second level of ISM. The process continues in a similar form. The interrelationship between CSFs can be drawn from a skeleton matrix (\(S\)) (Table 5). For instance, for CSF12 (policy support on the protection of local ecological environment), the entry corresponding to CSF11 (planning and implementation of sustainable strategies) is 1 and entries for all other CSFs are 0. Therefore, CSF12 influences CSF11, whereas there is no relationship between CSF12 and the other remaining CSFs. In a similar way, the interrelationships between different CSFs are identified and denoted as directional links in the ISM (Figure 4).  
Figure 4: Interpretative structural model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**CSF (i/j)**} & **7** & **11** & **15** & **4** & **6** & **10** & **12** & **2** & **5** & **8** & **16** & **1** & **3** & **9** & **13** & **14** & **17** & **18** \\ \hline \multirow{3}{*}{L\({}_{1}\)} & 7 & & & & & & & & & & & & & & & & & & & & \\  & 11 & & & & & & & & & & & & & & & & & & & \\  & 15 & & & & & & & & & & & & & & & & & & & & \\ \hline \multirow{3}{*}{L\({}_{2}\)} & 4 & 1 & & & & & & & & & & & & & & & & & & & & \\  & 6 & 1 & & & & & & & & & & & & & & & & & & \\  & 10 & & & 1 & & & & & & & & & & & & & & & & \\  & 12 & & & 1 & & & & & & & & & & & & & & & & \\ \hline \multirow{3}{*}{L\({}_{3}\)} & 2 & & & & & & 1 & & & & & & & & & & & & & & \\  & 5 & & & & 1 & & & & & & & & & & & & & & & \\  & 8 & & & & & & & 1 & & & & & & & & & & & & \\  & 16 & & & 1 & & & 1 & & & & & & & & & & & & \\ \hline \multirow{3}{*}{L\({}_{4}\)} & 1 & & & & & 1 & & & & & & & & & & & & & & & \\  & 3 & & & & & & & & & & 1 & & & & & & & & \\ \cline{1-1}  & 9 & & & & & & & & & & & & & & & & & & & \\ \cline{1-1}  & 13 & & & & & & & & & & & & & & & & & & & \\ \cline{1-1}  & 14 & & & & 1 & & & & & & & & & & & & & & & \\ \hline \multirow{3}{*}{L\({}_{5}\)} & 17 & & & & & & & & & & & & & & & & & & & & 1 \\  & 18 & & & & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Skeleton matrix (\(S\)) of CSFs.
# How Biological and Environmental Factors Affect the Quality of Lavender Essential Oils
## 3 Biological and Environmental Factors Affecting EO Production
### Environmental Factors  
EO quality is variable based on geographical location and climatic fluctuations [36]. Among the environmental factors, temperature fluctuations and precipitation can contribute to differences in quantity and quality in aromatic plants [33]. Specifically, rain precipitation affected the ratio between linalool and linalyl acetate in lavender [37]. Differences observed in spike lavender EO yield have clear correspondence with the variation in rainfall between years [38]; as a result, higher EO yields may arise from an increased number of glandular trichomes or from the ability of trichomes to store more EO. The wettest  
Figure 1: Monoterpenes biosynthesis of Lavender essential oil.  
years result in a higher proportion of linalool, 34%, and a significantly lower proportion of camphor.  
In contrast, the 1,8-cineole percentage increases dramatically in dryer years. Furthermore, drought stress results in the accumulation of 1,8-cineole [39]. On the contrary, drought before harvest results in an increased amount of linalool [40]. Temperature drops followed by rainfall decrease the linalool content in flowers; however, linalyl acetate was found to remain unaffected [41]. In contrast, rainfall and temperature drops lead to an accumulation of teriphen-4-ol [41]. For _Lavandula tafilia_, the Thermo-Mediterranean climate favours essential oil more than the Meso- and Supra-Mediterranean climates [14].  
Altitude and slope orientation influence lavender essential oil production and compound contribution. _L. angustifolia_ var. etherio grew from 330 to 710 m, resulting in variable amounts of essential oil. Cultivars with a southern orientation, characterised by lower rainfalls and higher temperatures, stimulate essential oil production, reaching 1.89%, compared to that of north orientation cultivars, which accumulate at 0.81% in dry weight [13]. In the same work, a southern orientation and low altitude have led to much higher linalool and linalyl acetate contents, albeit the lower 1.8 cineole. Generally, high production of lavender essential oil can be achieved in the Mediterranean climate and at medium altitudes [42].  
Populations grown at lower latitudes yield higher EO with an increased ratio of 1,8-cineole to linalool in the EO composition. Meanwhile, populations grown at higher latitudes produce lower EO, and the ratio of 1,8-cineole to linalool content is also low [43]. On the contrary, lavender plants growing at higher altitudes have double the content of essential oil [44].  
Furthermore, optimal essential oil production correlates positively with the presence of pollinators [45]. Specifically, linalool and linalyl acetate attract pollinators [17].
## 5 Experiments and Discussions
At this stage, the first joint undergoes three phases: deceleration, stop and acceleration, which will cause a large error due to inertial force and reducer backlash. Figure 9c,d are the enlarged views of portion A and portion B separately. Because of the continuous disturbance of hanging plate weight, the classical PID controller had a large error, and the maximum error was 0.216, while the tracking error curves of pre-filter AFE PID controller were relatively stable in Figure 9c. The maximum tracking error of the classical PID controller was 0.582, while that of pre-filter AFE PID controller was 0.426 in Figure 9d. Figure 9b shows the trajectory tracking error. The pre-filter AFE PID controller only had a large error when the first joint changed direction, and the error in other phases was relatively small. However, the classical PID controller was seriously affected by continuous disturbance. Besides the large error at the stage of the robot changing the direction, there were also large errors in the other stages, such as portion A. These results further show that the pre-filter AFE PID controller has a smaller tracking error and higher robustness than the classical PID controller. Figure 9: _Cont._  
Figure 9: Cont. A half sinusoidal wave excitation with amplitude of 25 deg and frequency of 0.4 Hz was employed to drive the robot when the number of middle part modules was 1. The tracking response and error are plotted in Figure 10. Portion C in Figure 10a represents the initial operation phase of the robot. At this phase, the joint suddenly accelerated from the static state, which caused a large tracking error due to the inertial force and reducer backlash. Portion D is the error fluctuation caused by the continuous disturbance of the hanging plate weight. Figure 10c,d are the enlarged views of portion C and portion D, respectively, and it can be seen that the pre-filter AFE PID controller had a faster stability speed and a smaller tracking error than the classical PID controller. Figure 10b shows the trajectory tracking error, the maximum error of pre-filter AFE PID controller was 0.547, while that of classical PID controller was 0.625. In addition, the classical PID controller was seriously affected by the continuous disturbance of the hanging plate weight, and obvious error fluctuations occurred many times in the whole operation process, while the pre-filter AFE PID controller was more robust. The results were the same as the previous experiment, and showed that the Pre-Filter AFE PID controller had a better ability to suppress the disturbance than the classical PID controller. Figure 9: The trajectory tracking experiments under a half sinusoidal wave excitation of 0.2 Hz with amplitude 25\({}^{\circ}\). (**a**) Tracking performance; (**b**) the trajectory tracking error; (**c**) enlarged view for portion A in (**a**); (**d**) enlarged view for portion B in (**a**). Figure 10: _Cont._
## 2 Physical and Mechanical Properties of Rock Mass Under THMC Coupling
### Theoretical Study of Multi-Field Coupling Mechanics in Rocks
#### 2.3.2 Advancements in Fracture Mechanics  
In the 1950s, [PERSON] made significant contributions to fracture mechanics by deriving theoretical solutions for the stress and displacement fields near the tip of a crack based on the stress function [150]. His work built upon [PERSON]'s energy criterion, which relates fracture propagation to the energy state of the material and provided a foundation for analyzing the direction and evolution of cracks in brittle rocks. The Griffith energy criterion explains that during the fracture process in brittle materials, such as rock, the relationship between crack evolution and propagation direction is influenced by the initial crack angle. When compressive shear stress acts on a crack, the tensile stress at the crack tip concentrates, leading to crack propagation perpendicular to the direction of the applied shear stress. This understanding led to the development of compressive-shear fracture strength theory.  
[PERSON] et al. [147; 148; 149] further contributed to the field by determining the initiation angle of compression-shear cracks using the minimum stress strength criterion. They employed the Mohr-Coulomb and Drucker-Prager strength criteria to establish criteria for compression-shear failure of brittle rocks. These criteria are essential for predicting and analyzing failure in rocks subjected to complex stress states, such as those found in deep underground engineering or slope stability analysis. [PERSON] et al. [151] expanded upon these foundational concepts through experimental studies on multi-crack materials. Their work explored the mechanisms of crack propagation in rocks with pre-existing flaws, utilizing the Rock Failure Process Analysis (RFPA2D V1.0) software to simulate and validate the observed crack behavior. This combination of experimental and numerical methods has advanced the understanding of how cracks propagate and interact in fractured rock masses, contributing significantly to the development of models capable of predicting rock mass failure under various field conditions.
# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
## 3 System Architecture and Design
### Estimation of Data Quality, Participant's Reputation, and Filtering Outliers  
In the previous sections, we described our models for benchmarking the sensors and to select participants based on the quality of the sensors on the smartphones that run our CrowdPower client application. However, we cannot neglect the possibility of malicious or faulty data being sent either intentionally or carelessly. Thus, to reduce the unnecessary processing requirements of the MSaaS platform and ensure proper management of resources, it is essential for the data to be filtered and its quality estimated once received at the server. We propose the following approach to estimate the actual data quality.  
We use an unsupervised learning technique integrated with user reputation to realize data quality. We ensure that we detect and remove data anomalies. We also provide a model simulation to evaluate our method and reveal its benefits. Based on a sensing campaign request from data consumers, the MSaaS server selects participants (data providers) to provide sensory data of an incident. The data received are expected to vary between participants and a single user if the data are provided more than once. Each campaign is for a duration of time, and for the sake of illustration, here, we consider that the data  
Figure 8: Flowchart of participant selection algorithm.  
producers (participants with smartphones) send in data as numerical values multiple times for a given campaign duration.  
We assume that a task such as monitoring temperature at an accident scene has K time slots for the duration T of the campaign. The selected participants within the area of interest (AoI) is denoted by N. In each time slot \(k\), \(1\leq k\leq K\), each participant i \(\in\) N provides sensing data \(d\_(ik)\) to CrowdPower. The Data Analysis component uses an unsupervised learning technique to estimate the data quality of the participants, which is a set \(Q\_k\) = {_q_(1,k),... q_(n,k)_} for the data received from participants in a slot k. To enhance the quality estimation, we also integrate participant's reputation R_(i,k), for k slots. The reputation score is relative to the quality of the data sent by the user in previous time slots of the campaign duration.  
In addition to quality evaluation and reputation analysis, we also assimilate the data based on user classification where participants are either malicious users N_k'M or trustworthy N_k'T. This is determined by checking if the distance of the sensor data from the consensus (determined by the centroid of the cluster) crosses a predefined threshold. Finally, based on these evaluations, we determine d_k, which is the estimated data for the time slot k. The pseudocode and evaluation of these models are provided in the Evaluation section of this paper.
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 4 Emerging Trends and Future Directions
### Federated Learning for Privacy-Preserving Analysis  
Federated learning enables model training across multiple decentralized devices or servers while keeping data localized, addressing privacy concerns in sensitive domains. This approach is particularly relevant for healthcare applications where patient data privacy is paramount but collaborative model improvement is beneficial.
## 2 Material and Methods
### Experimental Setup and PA Module
#### Wedge  
An ultrasonic inspection typically requires the use of free-flowing water, a film of water, local immersion, or ultrasonic gel as a coupling medium between the transducer and surface under inspection. The restriction of existing methods of couplant usage by VR platform requirements was addressed by developing a new wedge of elastomer material that is similar to a wheel probe roller material [4], which has low attenuation, low acoustic impedance, and lower couplant requirements. The wedge thickness was optimized for a 10 MHz PA transducer that is based on ultrasonic beam characteristics, near-field length, and attenuation in the elastomer material. The elastomer wedge and transducer are enclosed in a light-weight nylon holder for the PA transducer-wedge assembly, as shown in Figure 3. A miniature encoder for capturing positional data is also clamped to the nylon holder.
# Table Recognition for Sensitive Data Perception in an IoT Vision Environment
###### Abstract  
Internet of Things (IoT) technology allows us to measure, compute, and decide about the physical world around us in a quantitative and intelligent way. It makes all kinds of intelligent IoT devices popular. We are continually perceived and recorded by intelligent IoT devices, especially vision devices such as cameras and mobile phones. However, a series of security issues have arisen in recent years. Sensitive data leakage is the most typical and harmful one. Whether we are just browsing files unintentionally in sight of high-definition (HD) security cameras, or internal ghosts are using mobile phones to photograph secret files, it causes sensitive data to be captured by intelligent IoT vision devices, resulting in irreparable damage. Although the risk of sensitive data diffusion can be reduced by optical character recognition (OCR)-based packet filtering, it is difficult to use it with sensitive data presented in table form. This is because table images captured by the intelligent IoT vision device face issues of perspective transformation, and interferences of circular stamps and irregular handwritten signatures. Therefore, a table-recognition algorithm based on a directional connected chain is proposed in this paper to solve the problem of identifying sensitive table data captured by intelligent IoT vision devices. First, a Directional Connected Chain (DCC) search algorithm is proposed for line detection. Then, valid line mergence and invalid line removal is performed for the searched DCCs to detect the table frame, to filter the irregular interferences. Finally, an inverse perspective transformation algorithm is used to restore the table after perspective transformation. Experiments show that our proposed algorithm can achieve accuracy of at least 92%, and filter stamp interference completely.  
IoT photographing leakage; sensitive data security; directional connected chain; table recognition; table recognition metrics +
Footnote †: journal: Applied Sciences