esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The relationship between hBDs and cancer is complex and likely depends on the specific cancer type and the context in which hBDs are expressed. Head and neck cancer occurs in the tissues in certain parts of the head, throat, and nasal cavity. It accounts for approximately 4% of all cancer types in the United States. It is more common in men than in women, and the risk of developing head and neck cancer increases with age. The risk factors for head and neck cancer include tobacco use (smoking and smokeless tobacco), alcohol consumption, and certain infections, such as human papillomavirus (HPV). The main types of this cancer are mouth, oral cavity, lip, esophagus, nasal cavity, and laryngeal cancers [22]. Previous studies have suggested a potential link between periodontal disease and an increased risk of head and neck cancers, specifically oral cancer. The inflammation and infection associated with periodontal disease may play a role in the development and progression of these types of cancers [23]. Radiotherapy is a standard treatment option for patients with head and neck cancer. Radiotherapy involves the use of high-energy radiation to kill cancer cells and shrink tumors. It can be used alone or in combination with other treatments, such as chemotherapy, to eliminate tumoral tissue and improve patient outcomes. Radiotherapy can be administered in different ways depending on the location and stage of the cancer. External beam radiotherapy is the most common type of radiotherapy for head and neck cancer and involves the use of a machine to deliver high-energy X-rays to the tumor from outside the body. While it is an effective treatment for various cancer types, including head and neck cancer, it can also have side effects, including radiotherapy-induced oral mucositis, changes in taste, dry mouth, difficulty swallowing, and changes in saliva production [24; 25; 26]. According to the literature, the expression of hBDs may have different characteristics in the presence of tumor tissue, and its secretory regulation may also vary depending on the type and anatomical location of the tumor [18; 20; 27; 28; 29; 30; 31; 32; 33; 34]. To our knowledge, the effect of radiotherapy on oral hBD 1-3 levels have not been described previously. In the present study, we hypothesized that there are differences in the oral hBDs response against radiotherapy, which are based on their distinct expression characteristics and anti-tumorigenic properties. Therefore, we aimed to investigate oral hBD-1, hBD-2, and hBD-3 levels in individuals with head and neck cancer and the changes in their levels in relation to radiotherapy.
## 5 Performance Evaluation  
In order to evaluate the performance of the proposed algorithm, we conducted simulations through ns-2.34 with the parameter values shown in Table 2. We adopted the IEEE 802.15.4 specification model as a reference to carry out efforts in the battery module. We conducted extensive simulation scenarios of the proposed algorithm represented as fuzzy in the graphs. We compared the results for guaranteed pre-configured lifetime \(\alpha\) and real-time communication with FML [12] and our previous scheme EPGLT [5].
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 3 Proposed Methodology
### System Model  
Smartphones, desktop computers, and additional smart electronic gadgets are examples of aggregation nodes. An SN is a collection of small, battery-powered biosensors. Each node is responsible for collecting one or more types of biological data. The collected data are transmitted wirelessly to the aggregate node, which is transmitted to the health data centre for comprehensive analysis. Transferring biological data involves a few hops and a maximum of three hops [24].  
The adaptive binary bird swarm optimization algorithm, (ABBSOA), chooses the best pathways for data transmission using binary decision-making. By repeatedly probing the network, it finds possible paths and ranks them according to how well they save energy and how stable the network is. The ABBSOA algorithm finds the best data pathways by balancing exploration and exploitation, guaranteeing reliable communication while minimizing energy usage.  
A hybrid framework combining MSSOA (modified snake swarm optimization algorithm) with ABBSOA prioritizes optimum routes and sensor states, whereas MSSOA optimizes clustering and pathfinding judgments. MSSOA employs behaviours inspired by snakes to modify network clusters dynamically to guarantee energy-efficient routing. By optimizing the routing solutions given by MSSOA and managing binary choices like sensor activation and route selection, ABBSOA fine-tunes the optimization process.  
ABBSOA chooses cluster heads (CHs) based on the energy levels and sensor node positions. Nodes at advantageous locations (such as those near the network's core or important connection hubs) and with higher residual energies are given precedence. In this way, we may improve the network's energy management by picking CHs that are both energy efficient and adequate for data aggregation.  
The ABBSOA fitness function considers energy consumption, node distance, and network stability metrics to assess the network's energy efficiency. The variables representing the solutions are a mix of continuous and binary types. Clustering factors like distance and radius are represented by continuous variables, yet decisions about sensor activation (i.e., whether sensors are active or not) and route selection are made using binary variables. The fitness function is useful when optimizing energy usage and network stability.  
Figure 2: The flow of the proposed algorithms.  
System Assumptions
* All SNs are arranged in the appropriate positions on the human body based on their various purposes. After placement, all SNs receive their IDs, and their postures remain constant.
* Initially, energy at each node is the same, and all nodes can perform biological data integration. Simultaneously, the transmission energy can be modified continually, and R is the optimum wireless communication distance.
* The wireless module is turned off when the node is dormant, but the low-power identification module continues functioning.
# Increasing Resilience of Production Systems by Integrated Design
## 1 Introduction
### Technological Aspects and Requirements for Risk Mitigation  
The established way of developing a production system was to design it according to a specified task and to increase productivity to a certain or required level. This led and still leads to static solutions in relevant domains such as process, structure, drive systems, and controller engineering [1]. Furthermore, data flows and energy supply were designed once and have remained more or less the same over the entire production system life cycle. A secondary interest lies in energy and resource efficiency, but this also leads to monolithic rather than modular solutions.  
To effectively integrate resilience into the design of modern production systems, various changes must be considered, especially in the mentioned domains. As the most important aspect, the production system shall react to procurement, production, and personnel risks and therefore adapt to dynamic changes in order sequence and processing. Typical reasons are priority orders, changes in demands for products, shortages of raw materials, or shortages of personnel resources. Hence, the new generation of production systems must quickly adapt to changing processes and variations in throughput by using techniques for fast reconfiguration, commissioning, and fault tolerance. For example, [2] identified product and process modularity as key aspects to increase flexibility and hence improve the tolerance towards supply disruption. In order to realize such adaptivity for the whole production system, we propose leveraging modularity on all layers of hardware design, automation, and shop-floor software.  
Furthermore, hardware and software design need to be intertwined by applying concepts such as decentralized controllers, self-description of modules, and the encapsulation of controller code into the modules [3].  
Regarding connectivity and data exchange, standardized interfaces and data models should be established to support transparency and to complete traceability within a single production system as well as along the supply network. To accomplish this, the standard \"Open Platform Communications Unified Architecture\" (OPC UA) can be combined with linked data approaches [4] for modeling system structures, components, processes, and products as well as for linking them to related operating data.  
Furthermore, manufacturing systems require a digital counterpart to enable fast commissioning, optimization, and controller code review in parallel to the production process. All of these aspects diffuse into a \"framework for resilient manufacturing systems\", which will be explained in detail in the following sections.
## 3 Results
### The E-Field in the Driver's Body  
The induced electric field in the human tissues were averaged over a small continuous tissue volume with a volume of 2 mm \(\times\) 2 mm \(\times\) 2 mm. Figure 8a shows the E-field in the driver's body. The larger area of the electric field was predominately located near the legs; the other parts of the body were significantly weaker than the leg area. The maximum E-field was about 1.04 mV/m, which is well below the BR of 400 mV/m (the smaller of the 100 Hz and 5.4 kHz exposure limits) defined by the ICNIRP for general public exposure in the trunk [10]. Figure 8b and c shows the E-field in the frontal and sagittal sections of the driver's body, respectively. The E-field on the right arm was stronger than that of other parts, and the maximum value was 0.25 mV/m. The E-field was also related to the distance to the inverter; the closer the distance to the inverter, the stronger the E-field.  
Figure 9a shows the E-field in the driver's head. There was no significant difference in E-field in the scalp, skull, and brain of the human head. The E-field was significantly below the BR of 40 mV/m (the smaller of the 100 Hz and 5.4 kHz exposure limits) defined by the ICNIRP for general public exposure in the central nervous system (CNS) [10]. Figure 9b and c show the E-field inside the brain tissues and in different sections of the brain tissues, respectively. As shown, the large area of the E-field in the brain is in the cerebellum. The distribution was also affected by the spatial distance of the cerebellum to the inverter and the dielectric parameters of the cerebellum.  
Figure 8: Distributions to the E-field: (**a**) the E-field in the driver’s body; (**b**) the E-field in the frontal section of the body; and (**c**) the E-field in the sagittal section of the body.  
Figure 9: Distributions of the E-field: (**a**) the E-field in the head; (**b**) the E-field in the brain tissues; and (**c**) the E-field in different sections of the brain tissues.  
Figure 10a,b show the E-field in the main organs and a comparison of the maximum E-field in different organs, respectively. As shown, the E-field in the liver was significantly larger, with the maximum being approximately 0.1 mV/m. The maximum E-field values in the kidney and heart were approximately 0.03 and 0.04 mV/m, respectively. The distribution characteristic was not only affected by the distance from the inverter but also related to the dielectric parameters of different organs.
## 5 Discussion
### Strategy for Coming Years in Poland  
In response to the extensive challenges and the pursuit of newfound opportunities, researchers have formulated a comprehensive plan for the years ahead [56]. In \"Strategia Centrum e-Zdrowia na lata 2023-2027\" (\"Strategy for the E-Health Centre in Poland for the period 2023-2027\") outlined the primary challenges anticipated in the coming years, categorized into four dimensions:  
* Financial challenges: developing and sustaining systems entail significant costs that demand careful budgetary considerations.
* Human resources: the availability of specialized personnel is crucial for effectively executing projects.
* Technological evolution: the ever-changing landscape of new technologies necessitates constant adaptation to ensure systems remain aligned with the latest advancements.
* Legal framework: the regulatory framework within which systems operate can limit the ability to implement certain solutions.  
To address these challenges, an all-encompassing strategy plan was devised, focusing on five strategic areas pivotal for the development of a modern, technology-driven healthcare sector centered around the patient:  
* Services. Facilitating patients to manage their health-related events in one centralized location, covering diverse aspects of healthcare.
* Stakeholders. Strengthening collaboration among all relevant parties, including patients, healthcare workers, and governing bodies, to ensure engagement and the achievement of common shared goals.
* Development. Emphasizing steady progress, enhancing security, and fostering innovation as critical components leading to success.
* Resources. Acknowledging the importance of recruiting and retaining skilled healthcare professionals possessing the necessary expertise to fulfill defined goals.
* Processes. Implementing agile methodologies and a responsive approach to system management to ensure the timely and budget-conscious delivery of projects.  
Similar initiatives are underway globally, and the HACID (Hybrid Artificial Collective Intelligence for Decision Making) project stands out on the European scene. Financed primarily by the EU, the HACID aims to create a decision support system that combines collective human intelligence and AI [57]. The project addresses two main goals: overcoming knowledge fragmentation in medicine and supporting policymakers. There is a clear commitment to collaborating with the healthcare and AI sectors to implement technology-driven solutions that enhance interoperability across healthcare fields. Access to knowledge and resource sharing between researchers is a key component of this project, making its impact even more pronounced. Knowledge structurization is envisioned by creating a decision support system (HACID-DSS) that would organize domain knowledge in graphs (Domain Knowledge Graph--DKG). Subsequently, experts would engage in discussions for cases within each domain and create a Case Knowledge Graph (CDG) as a subset of the DKG. The HACID aims to aggregate solutions provided by experts, expanding them through AI suggestions.
## 4 Experiments and Results
### Explainable Artificial Intelligence (XAI)  
Experimental results indicate that XGBoost delivered the best results with the selected features after SMOTE oversampling. The nature of the XGBoost model is black box. Therefore, XAI was applied to make the high-performance model more transparent. In the ML model, some models like DT, Logistic Regression (LR), K-Nearest Neighbor (KNN) are simple and transparent models. While the complex models like SVM, ANN and ensemble models are opaque. In the current study post-hoc approach using LIME and SHAP was  
Figure 2: Confusion matrix for XGBoost.  
applied. Figure 3 provides the global explanation of the XGBoost model using selected features and an oversampled dataset. Figure 4 shows a local explanation of the XGBoost model using a selected and oversampled dataset. In addition, the rules are extracted from the XGBoost model using the global surrogate model. Extracted rules are mentioned below.  
* IF (C_Uz4 > 0.218) && (AA_Tz1 <= 22.0) && (C_Uz4 <= 0.4) THEN response: 8309.0 | based on 19,759 samples
* IF (C_Uz4 <= 0.218) && (BBB_z3 > -0.058) && (C_Uz4 <= 0.145) THEN response: 12172.0 | based on 13,325 samples
* IF (C_Uz4 <= 0.218) && (BBB_z3 <= -0.058) && (B_Uz4 <= 0.18) THEN response: 7681.0 | based on 13,047 samples
* IF (C_Uz4 > 0.218) && (AA_Tz1 <= 22.0) && (C_Uz4 > 0.4) THEN response: 303.0 | based on 4387 samples
* IF (C_Uz4 > 0.218) && (AA_Tz1 > 22.0) && (BBB_z3 > -0.423) THEN response: 2.0 | based on 3721 samples
* IF (C_Uz4 <= 0.218) && (BBB_z3 <= -0.058) && (B_Uz4 > 0.18) THEN response: 5.0 | based on 2210 samples
* IF (C_Uz4 <= 0.218) && (BBB_z3 > -0.058) && (C_Uz4 > 0.145) THEN response: 2.0 | based on 132 samples
* IF (C_Uz4 > 0.218) && (AA_Tz1 > 22.0) && (BBB_z3 <= -0.423) THEN response: 1.0 | based on 2 samples  
Figure 3: Global Explanation of XGBoost using SHAP.
# Estimation of Rock Load of Multi-Arch Tunnel with Cracks Using Stress Variable Method
## 4 Crack Analysis by Stress Transition Effect  
As mentioned in the previous section, for a three-arch tunnel, the adjacent tunnels on both sides of the central tunnel are excavated after ensuring that the displacement converges. However, the excavation of an adjacent tunnel induced an increase in the rock load and the stress transfer to the corners, resulting in cracks at the corners of the concrete lining, where the stress concentration may occur. Therefore, structural analyses were conducted on the concrete lining of the central tunnel using the beam-spring model. The crack-induced rock load was back-calculated by applying the rock load to the corner of the lining.  
As presented in Figure 17, the concrete lining is modeled as beam and springs attached in the radial direction to the beam, to consider the interaction between the concrete lining and the surrounding ground. Note that the springs were only attached to the crown and inverted, and both sides were free boundary conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Rock Mass Class**} & \multicolumn{2}{c}{**Rock Load Height (m) and Extent of Load Distribution1 (\%)**} \\ \cline{2-4}  & **Step 6** & **Step 13** & **Step 19** \\ \hline III & 3.8 (29.5\(\%\)) & 8.7 (67.4\(\%\)) & 12.9 (100\(\%\)) \\ IV & 9.5 (25.1\(\%\)) & 15.5 (39.7\(\%\)) & 39 (100\(\%\)) \\ V & 9.6 (24.6\(\%\)) & 15.7 (40.3\(\%\)) & 39 (100\(\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rock load obtained in different rock classifications with respect to the construction steps.  
Figure 16: Rock load development during various construction steps.  
The concrete lining was modeled as beam elements with an elastic modulus of 27.6 GPa and a thickness of 600 mm. The excavation of the left tunnel may impose the rock load on the central tunnel, resulting in shear forces and bending moment at the shoulder where the stresses were concentrated, as shown in Figure 18. Through back analysis, the rock load of the central tunnel was computed as 209 kN/m\({}^{2}\), resulting in a rock load height of 10 m. This indicated that the cracks occurred at both shoulders of the central tunnel when the adjacent tunnels were excavated under the rock load height of more than 10 m.  
These results indicate whether the rock load causes the cracks on the central tunnel during the excavation of adjacent tunnels. Figure 19 presents the rock load heights at various deformation moduli of the rock at the stage of completion of excavation of the left tunnel. As shown in the figure, a rock load height of 18.5 m was computed after the excavation of the left tunnel. This height is greater than the crack-induced rock load height of 10 m. In addition, when the modulus of rock was less than 4000 MPa, the rock load height was computed as more than 10 m.  
Figure 19: Rock load heights at various deformation moduli after the completion of excavation of the left tunnel.  
Figure 17: Structural analysis of the concrete lining of the central tunnel using the beam-spring model.
# Efficient Detection of Pre-Proinsulin by Double Antibody Sandwich ELISA
## 3 Results
### Construction of Quantitative Curves  
With P5 as the solid-phase antibody, PPI standard solutions with a series of concentrations were added, and biotin-labeled P2 was used as the detection antibody to determine PPI by double-antibody sandwich ELISA. Using OD 450 nm and PPI concentration as vertical and horizontal coordinates, respectively, four-parameter fitting was carried out, and the standard curve was drawn (Figure 4). We obtained the fitting curve equation as follows: \(y=7.92579+[(0.130957-7.92579)/(1+(x/96.6272)~1.51852)]\) (R\({}^{2}\) = 1). The detection method had a linear range of 0.645-82.5 pg/mL.
# A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
## 2 The MVB Voltage Waveform Digitalization Algorithm
### MVB Voltage Waveform Digitalization Algorithm Extraction Algorithm
#### 2.2.1 Decoding  
The electrical characteristics of MVB on-board devices vary, and the working environment is complex, so the voltage waveform of the MVB physical layer is not an ideal square-wave signal, which leads to unnecessary interference. In order to facilitate the decoding of the MVB voltage waveform, preprocessing of the waveform data is first carried out. The transmission rate,\(f_{mvb}\), of the MVB bus is 1.5 Mbit/s, and the sampling rate of the physical-layer waveform is assumed to be \(f_{s}\). In other words, a single bit in Figure 4 has \(s_{bit}\) sampling points in the actual sampling waveform. Here is the equation.  
\[s_{bit}=f_{s}/f_{mvb} \tag{1}\]  
After determining the width of the bit, the waveform signal will be adjusted to a square wave that is easy to handle. IEC61375-3-1 mentions that the steady-state amplitude of the high-level voltage of the MVB bus under light load conditions does not exceed \(\pm\)5.5 V; the steady-state amplitude of the high-level voltage under heavy load conditions is not less than \(\pm\)1.5 V; and the minimum receiving threshold of MVB equipment is (\(-\)0.2 V, +0.2 V). Therefore, three intervals can be set: (\(-\infty\), \(-\)0.2 V), (\(-\)0.2 V, +0.2 V), (+0.2 V, +\(\infty\)), and the waveform signal is converted into a unified amplitude of \(-\)1/0/1, similar to a digital quantity. The 0 value represents that the MVB bus is in a silent state; when the MVB bus is active, 1 represents a high level and \(-\)1 represents a low level.  
The preprocessed data become a string of arrays containing only \(-\)1/0/1, and the mutual transformation of these three numbers represents that there is an edge in the MVB physical-layer voltage waveform. The time index of all the edges can be obtained by using the algorithm for detecting numerical abrupt changes. In addition, at the physical layer, the signal of data transmission starts from 0 V to a high level, lasts for 0.5 bits, and ends with the non-data symbols \"NL\" and \"NH\". This indicates that each frame starts with a change from 0 to 1 and ends with a change from 1 to 0, so the parity order in which the array mutates can indicate the direction of a waveform edge.  
As the bit time of the MVB frames is about 666.7 ns, it can be used as a basic unit of time intervals. At the same time, MVB frames consist of four kinds of data or symbols, leading to different intervals between edges. For example, the different intervals of the start delimiter are shown in Figure 6. Thus, we can decode waveforms with the information of edges.  
Here is the whole process of decoding:  
* Data preprocessing, and waveforms are turned into digital quantities;
* Detection of the position of edges;
* Decoding of the bits according to Table 1.  
Figure 5: Flow chart of the digitalization algorithm.