esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Future Work  
Building on these findings, several promising directions for future research and system development could be considered:  
* Cross-lingual and transfer learning: Future studies should investigate cross-lingual transfer learning approaches that leverage annotated data from multiple languages or domains to improve model generalizability and robustness, especially in languages or platforms with limited labeled data. Multilingual transformers and transfer learning can help bridge resource gaps and facilitate rapid adaptation to new domains.
* Semi-supervised and active learning: To address annotation scarcity and improve coverage of rare or subtle phenomena, employing semi-supervised learning (leveraging large amounts of unlabeled data) and active learning (prioritizing the most informative or uncertain samples for human annotation) could significantly improve model performance and annotation efficiency.
* Adaptive context modeling: Exploring architectures that dynamically select or weight relevant turns, rather than relying on fixed context windows, may yield better contextual understanding, especially for implicit bias and sarcasm. Techniques such as hierarchical attention or memory networks could be considered.
* Rich pragmatic and multimodal signals: Incorporating pragmatic cues (e.g., speaker intent, conversation roles, or thread structure) and multimodal information (e.g., accompanying images and metadata) could improve detection of implicit and nuanced forms of bias.
* Bias mitigation and fairness evaluation: Systematic analysis of model and annotation biases, including the cultural perceptions and subjectivities of annotators, should be incorporated, with transparent reporting and fairness audits.
* Multiple expert annotators: Engaging a higher number of annotators with varying expertise coming not only from computer science, but also from social science (to understand systemic sexism/racism and their social dynamics), language (to analyze nuanced language use), and community representatives with life experience from affected groups, among others.  
These directions will not only address the limitations of the present study but also propel future research in automated, fair, and contextually grounded detection of toxicity and bias in online discourse across languages and platforms.
## 2 Materials and Methods
### Background Information
#### 2.1.2 Previous Environmental Investigations  
In compliance with the current legislations about protection and safeguard of soil, subsoil, and ground waters [24; 25], from the year 2000, ENI started to present a site characterization plan, after which remediation activities were needed. The environmental characterization (EC) included 1100 soil coring samples and 110 piezometers for the groundwater monitoring, and it allowed refinement of the conceptual model of a contaminated site (i.e., sources, types, and concentrations of contaminants, potentially contaminated media, potential exposure pathways, and final receptors).  
As stated by [PERSON] et al. [26] and as confirmed by the EC, from a geological perspective, the area is characterized by a middle-upper Pleistocene \"terraced marine deposits\". These terraces consist of an incised substrate composed of marine clay (Argille Subappennine) overlain by Holocene sandy, gravelly sandy and alluvial sandy silty and coastal deposits outcrops. This area is also characterized by great variability, moving from calcareous and calcarenitic outcrops to flat alluvial deposits near the seacoast (Figure 2). The local hydrography appears to be characterized by the tributaries of the Bradano and the Lato rivers in the hinterland of the Gulf of Taranto. The groundwater is divided into two aquifer systems, one more deeply located in the Mesozoic carbonate basement (about \(-55\) m from sea level) and a surface one in the terraced marine deposits (about \(-2\) m from sea level) reflecting the seasonality of the rainfall regime [27].  
The results of the EC, shown spatially in Figure 3, demonstrated contamination of top and deep soil. Chemical analysis showed how most samples had heavy hydrocarbon-related contamination, some with short-chain hydrocarbons and some with benzene, ethylbenzene, and xylene.  
After the site characterization was completed, an environmental-health risk analysis (EHRA) was performed. The main pollutants investigated were BTEX (Benzene, Toluene, Ethylbenzene and Xylenes), classified as priority pollutants regulated by many environmental organizations around the world. Since the industrial use of the area, the human target considered in the analysis was \"worker\". The pathways considered in the analysis included: (i) outdoor dust inhalation, (ii) outdoor vapors from  
Figure 1: Area of ENI refinery in Taranto, South of Italy. The red line shows the perimeter of the SIN area and the yellow line indicates the localization of factories.  
top soil, (iii) outdoor vapors from deep soil, and (iv) soil runoff towards groundwater. The remediation targets (RTs) considered for this specific case in deep soil were obtained using GIUDITTA software and are described below: (1) RT for C\(<\)12 = 891 mg/kg; (2) RT for C\(>\)12 = 365 mg/kg.
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### Discussion
#### 4.5.5 Data Consistency and Synchronization  
Maintaining data consistency and synchronization across IoT and Distributed Computing Systems presents a significant challenge. In DCS, information is often distributed over varying nodes, each of which may process and update data independently [98]. Ensuring all nodes have a consistent view of the data and that changes are accurately reflected across the entire system is complex. For IoT networks, where devices generate data continuously and in real-time, achieving data consistency becomes even more challenging [99; 100]. Information gathered from varied IoT devices has to be integrated and synchronized to provide a unified view. Discrepancies or delays in data synchronization can lead to inconsistent or outdated information, affecting both decision-making and operational efficiency with regard to management [101; 102]. The challenge is further compounded by issues such as network latency, device failures, and data transmission errors that might impact business in varying ways.
## 3 Pressure Sensor Concept and Design
### Membrane Sensor (MS)
#### 3.2.2 Loading and Boundary Conditions  
During each test, the sensor could be subjected to two kinds of solicitations. The first one represents the measured interstitial pore pressure, which acted on the sensor's upper and lateral surfaces up to the sealing joint. This pressure was supposed to be in equilibrium with the pressurized free water inside the concrete sample. Since the real contact conditions between the sensor and the cap plug were not well known, it was assumed that the sensor's upper and lower surfaces were constrained in displacement in the vertical direction and free in the horizontal one. Then, to simulate the behavior of the sensor, two types of boundary conditions were applied: the first one consisted in blocking the vertical displacement of all the upper and lower nodes of the sensor (Figure 5a), while the second boundary condition type, more smooth, consisted in blocking only half of the nodes, as shown in Figure 5b. The second load was more difficult to characterize and corresponded to a potential mechanical effect (ME) due to the confining pressure. Indeed, when the confining pressure acted on the cap outer surface, it generated some strain and resulting displacement \(u_{pc}\) on its inner surface (Figure 5c). Since the radial tolerance between the cap and sensor diameters was equal to 0.015 mm for larger values of \(u_{pc}\), the confining pressure may generate some stress in the pressure sensor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor** & \(I_{sup}\) & \(I_{mid}\) (Thickness) & \(I_{inf}\) & \(I_{total}\) & **d** \\ \hline \(s_{1}\) & 7 & 2.5 & 4.5 & 14 & 35 \\ \(s_{2}\) & 5 & 4.5 & 4.5 & 14 & 35 \\ \(s_{3}\) & 2.5 & 7 & 4.5 & 14 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dimensions of the different proposed sensors in (mm).  
Figure 4: Steel sensor of membrane type: (**a**) cross section of the collecting cap with the membrane sensor; (**b**) picture of the cap and pressure sensor; and (**c**) proposed design of the membrane sensor with the gauge and sealing joint position.
## 2 Literature Review and Theoretical Foundation
### Antecedents to Information Disclosure
#### 2.2.4 Privacy Calculus  
The privacy calculus originates from the Theory of Reasoned Action [71] and the Theory of Planned Behavior [72] and is defined as the risk-benefit dilemma users face when engaging in online transactions [73; 74]. In general, in a transaction, incentives are offered by the company in exchange for a certain degree of privacy of the user [73; 74]. Because humans are rational beings, this theory explains that users will always try to limit the risk required to maximize their benefit.  
This phenomenon also applies to information disclosure in chatbot exchanges. Specifically, the sensitivity of the query increases the risk for the user to disclose, while the benefit is often the promise of a better experience. In other words, users trade information of varying sensitivity (e.g., habits, preferences, personal identification) in exchange for better products and service recommendations that are deemed tailored to their profile [75]. Thus, according to the privacy calculus, users will perceive the value of sensitive information as higher than more general information. When it comes to chatbot interactions, it could be argued that users will be inclined to gatekeep more information classified as high in terms of sensitivity compared to those classified as lower in sensitivity.  
Taking the above into consideration, we propose a relationship between question sensitivity and information disclosure as follows:  
**H1.**_Question sensitivity negatively influences users' information disclosure to chatbots._
## 3 Types of Supplementary Dampers and Their Characteristics
### Base Isolation Systems
#### 3.6.6 Applications  
Base isolation systems find applications in a wide range of structures, particularly those where seismic protection is critical. They are commonly used in critical facilities such as hospitals and emergency response centers, where continuous operation during and after an earthquake is essential. Historic buildings often benefit from base isolation as a seismic retrofit solution, preserving the structure while significantly improving its seismic performance. In bridge engineering, base isolation is particularly valuable in high seismic zones, reducing the forces transmitted to the substructure. Additionally, base isolation techniques are employed for the protection of sensitive equipment, ensuring operational continuity in facilities such as data centers and manufacturing plants.
# Software Defined Wireless Mesh Network Flat Distribution Control Plane
###### Abstract  
Wireless Mesh Networks (WMNs), have a potential offering relatively stable Internet broadband access. The rapid development and growth of WMNs attract ISPs to support users' coverage anywhere anytime. To achieve this goal network architecture must be addressed carefully. Software Defined Networking (SDN) proposes new network architecture for wired and wireless networks. Software Defined Wireless Networking (SDWN) has a great potential to increase efficiency, ease the complexity of control and management, and accelerate technology innovation rate of wireless networking. An SDN controller is the core component of an SDN network. It needs to have updated reports of the network status change, as in network topology and quality of service (QoS) in order to effectively configure and manage the network it controls. In this paper, we propose Flat Distributed Software Defined Wireless Mesh Network architecture where the controller aggregates entire topology discovery and monitors QoS properties of extended WMN nodes using Link Layer Discovery Protocol (LLDP) protocol, which is not possible in multi-hop ordinary architectures. The proposed architecture has been implemented on top of POX controller and Advanced Message Queuing Protocol (AMQP) protocol. The experiments were conducted in a Mininet-wifi emulator, the results present the architecture control plane consistency and two application cases: topology discovery and QoS monitoring. The current results push us to study QoS-routing for video streaming over WMN.  
SDWN; SDWMN; SDN distributed control plane; topology discovery; QoS monitoring +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 166; doi:10.3390/f11080166
## 3 Results and Discussion
### Sensory Analysis  
Figure 3 shows the results of sensory analysis of the cavas after 18 months of aging in the bottle on lees. The Chardonnay variety showed the highest values of the fresh fruit descriptor when the Y2 and Y5 yeasts were used. This behavior may be directly related to the high concentrations of esters and acetates that were observed in the analytical analysis. Furthermore, it was observed how cava obtained with Y2 and Y4 presented the highest scores for bakery and floral descriptors, in addition to Y5 for the latter. These aromatic descriptors are related to the high concentrations of fatty acids and alcohols, which are related to lactic and rose aromas, respectively. In comparison, it was shown that the cava obtained with the yeast Y3 was the most evolved sensorial and also showed the highest scores of candidied fruit and evolution descriptors, which could be due to its high content of ethyl acetate. In the Xarel.lo variety, it was observed that the cavas with the highest fresh fruit descriptors were those made with Y4 and Y5 yeasts, followed by those obtained with Y2. This can be directly related to the high concentrations of esters and acetates found in gas chromatography analysis. As happened with the Chardonnay variety, Xarel.lo cava obtained with Y3 was the most sensorially evolved. Therefore, it can be emphasized that non-_Saccharomyces_ yeasts provided more fruity and fresh aromatic profiles, especially in the case of _T. delbrueckii_, and much less evolved than most _Saccharomyces_ yeasts.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 3 Background
### Routing Protocol for Low-Power and Lossy Networks (RPL)
#### 3.3.2 Minimum Rank with Hysteresis Objective Function (MRHOF)  
Another RPL protocol, Minimum Rank with Hysteresis Objective Function (MRHOF), looks for the best path between source and destination nodes in resource-constrained wireless networks with changeable conditions. MRHOF aims to minimize routing overhead while balancing path quality and network stability. MRHOF evaluates each node's rank value to find the shortest path using the minimum rank algorithm. MRHOF uses hysteresis to prevent frequent changes and preserve path stability in the face of varying network conditions. Additive metrics are compatible with this protocol because they use metrics to minimize path costs [33].
## 2 Extraction and Purification Methods of Ginger Polysaccharides
### Extraction Methods of Ginger Polysaccharides
#### 2.1.3 Enzyme-Assisted Extraction Method  
Plant polysaccharides, one of the essential substances for maintaining normal life activities, are present in plant cell walls, as are ginger polysaccharides [39; 40]. The suitable extraction method is to achieve high-efficiency extraction by enhancing the decomposition of cell walls without destroying the basic characteristics of ginger polysaccharides. EAE is the use of the enzyme specificity to destroy the plant cell wall under certain conditions, so that polysaccharides can be dissolved from the cell. The main influencing factor of this extraction method is enzymes. Enzymes react with accurate specificity and region selectivity and can maintain the biological activity of compounds [41; 42]. EAE technology has been proved to have an edge over conventional techniques in sustainable polysaccharide extraction. The findings of [PERSON] et al. (2020) showed that the extraction conditions of ginger polysaccharides obtained by EAE were 40 \({}^{\circ}\)C and 2 h, while the extraction conditions of ginger polysaccharides extracted by hot water were 100 \({}^{\circ}\)C and 4 h. Obviously, EAE has an advantage in economizing time [43]. In another study conducted by [PERSON] et al., two polysaccharides (EGP1 and EGP2) were extracted from the ginger using the EAE method under certain reaction conditions. Compared with the hot water extraction method, enzyme-assisted extraction not only shortened the extraction time but also increased the yield of ginger polysaccharide from 6.83% to 8.13% [32]. Therefore, compared with the traditional hot water extraction method, this method has the advantages of short time, low energy consumption, good repeatability, and high extraction rate [44].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Polysaccharide Fraction** & **Extraction Methods** & **Time (min)** & **Temperature (\({}^{\circ}\)C)** & **Solid–Liquid Ratio (g/mL)** & **Total Yield (\%)** & **Ref.** \\ \hline Ginger polysaccharide & Complex-enzyme hydrolysis & 60 min & 55 \({}^{\circ}\)C & 1:25 & 22.18\% & [45] \\ (GPS) & & extraction & & & \\ \hline Ginger pomace & & & & & \\ polysaccharides extracted & Hot water extraction & 120 min & 70 \({}^{\circ}\)C & 1:40 & 12.13 \(\pm\) 1.15\% & [46] \\ by hot water (HW-GPPs) & & & & & \\ \hline Ginger pomace & & & & & \\ polysaccharides extracted & Ultrasonic assisted & & & & \\ by ultrasonic–assisted & extraction & & & & \\ (UA-GPPs) & & & & & \\ \hline A neutral ginger & & & & & \\ polysaccharide fraction & Hot water extraction & 180 min & 90 \({}^{\circ}\)C & 1:20 & N/A & [47] \\ (NGP) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of ginger polysaccharides extraction methods.
## 2 Materials and Methods  
### Study Sites and Sampling  
Locations of rivers and sampling sites are shown in Figure 2. Color-coded \({}^{137}\)Cs deposition (\(\mathrm{Bq}\cdot\mathrm{m}^{-2}\)) were obtained by aircraft monitoring [7] normalized to 2 July 2011.  
There are four major rivers in Minami-Soma City: the Odaka, the Ohta, the Niida, and the Mano Rivers. The Mizunashi River is a tributary stream that joins to the Niida River. These rivers are 12-16 km long from the upstream to the river mouth and flow into the Pacific Ocean except the Niida River, which starts from the Iitate Village, passing through about 40 km long, and it finally flows into the Pacific Ocean. It is noted that rivers in Ukraine are more than 400 km and about 160-200 km in Europe, which are longer than present rivers in Fukushima.  
Sediment and river water were sampled from five or four sampling sites at the river side beneath the bridges during 2012 to 2016. Sampling sites at the N4 site of the Niida River and the P4 site of the Mizunashi River were added in 2013, and at the P1 site of the Mizunashi River in 2014. The N3 sampling site on the Niida River was changed to the N3\({}^{\prime}\) site in 2013 for easier access. These river systems include three dams; the Yokogawa Dam on the Ohta River, the Takanokura Dam on the Mizunashi River, and the Mano Dam on the Mano River, which were constructed for irrigation and flood prevention.  
Since the Niida River starts from the Iitate Village where high radioactive contamination was observed, sediments and water samples were once performed in September 2012 in the Iitate Village at sampling sites Q\({}_{1}\)-Q\({}_{4}\) along the Niida River and R\({}_{1}\)-R\({}_{4}\) along the tributary stream of the Itioi River as shown in Figure 2.  
River sediments (\(\sim\)300 g) were collected from 20 cm \(\times\) 20 cm areas to a depth of 3 cm with a small plastic shovel covering the sand surface by hand so as not to disturb the sediment layer. The sampling was performed at the central region of the river channel or near the edge of the river mouth. Surface river water was also collected directly in a polypropylene bottle (\(\sim\)1 L). Sampling was conducted on normal flow days between October and December to avoid heavy rainfall and the typhoon season in Japan. It is well known that radiocesium transport occurs after heavy rainfall.  
Figure 1: Locations of the Minami-Soma City and the Iitate Village from the FDNPP.  
[PERSON] et al. [26] performed water sampling at the Natsui River and the Same River (sampling sites were about 40 km and 58 km to the south of the FDNPP, respectively) in September 2011. They reported that heavy rainfall after typhoon increased \({}^{134}\)Cs and \({}^{137}\)Cs concentrations in river water to 0.85 Bq\(\cdot\)L\({}^{-1}\) in high flow conditions, whereas they were 0.009-0.098 Bq\(\cdot\)L\({}^{-1}\) under normal flow conditions.
## 3 TMDP-Based Resource Allocation Algorithm and Problem Linearization
### TMDP-Based Resource Allocation Algorithm  
The optimization problem \(\mathbf{P}_{1}\) is classified as a MINLP problem, which includes logarithmic terms in its objective function, complicating the solution process. Although Karush-Kuhn-Tucker (KKT) conditions and other methods can be applied to determine optimality in nonlinear programming, the presence of integer constraints limits the feasible solution space, making conventional nonlinear solving methods potentially unsuitable. Therefore, we consider linearizing the optimization problem \(\mathbf{P}_{1}\), transforming it into a MILP problem. This transformation not only allows us to leverage existing efficient MILP solvers, but also significantly reduces the computational complexity, thereby improving solution efficiency.  
The most commonly used exact algorithm for solving MILP problems is the Branch-and-Bound (B&B) algorithm. This algorithm iteratively narrows down the variable ranges through branching and bounding, progressively approaching the optimal solution. It primarily relies on the concept of a search tree, where the feasible solution space is viewed as the root node. Branches are created according to certain rules, and the lower bounds of the objective values (for minimization problems) are computed in each branch to prune branches that cannot yield better solutions. The key to the B&B algorithm lies in determining the branching strategy.  
In recent years, the design of branching strategies based on machine learning has gained considerable attention. These strategies, formulated by learning the target problem, can potentially outperform traditional, problem-agnostic branching strategies. Based on the work of [PERSON] et al. (2019), we aim to apply a machine learning-based MILP algorithm known as the Tree Markov Decision Process (TMDP) method. The TMDP framework generalizes the traditional MDP by incorporating a tree structure that better captures the branching process in MILP solvers. This method has demonstrated superior performance over traditional solvers like SCIP in several MILP problems, such as the Multiple Knapsack problem.  
To utilize the TMDP method for solving MILP problems, it is essential to first linearize the original MINLP problem. Thus, we consider transforming the optimization problem into a MILP problem in the next subsection.