esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 T2G Communication Security, Vulnerabilities, and Threats  
With the widespread adoption of wireless communication technologies in high-speed railway systems, the T2G communication system has become a vital component for real-time data exchange and fault monitoring. Cyberattacks targeting the T2G system can disrupt train operations, endanger passenger safety, cause property damage, and result in significant economic losses. Therefore, understanding the types of attacks that threaten the T2G system and assessing their potential impacts are essential for designing effective, intelligent defense strategies. To establish a practical foundation for threat modeling, the following five assumptions are made:  
* The attacker possesses in-depth knowledge of the T2G system and may be capable of physically following the train;
* The attacker can impersonate legitimate communication entities or replay previously captured T2G data packets;
* The attacker is able to access communication devices and modify or relay data packets at different times and locations;
* The attacker may exploit radio equipment deployed along the railway to eavesdrop on, forge, or jam T2G communications;
* The attacker cannot physically dismantle, damage, or illegally relocate T2G devices without being detected.
# A Review of the Use of Native and Engineered Probiotics for Colorectal Cancer Therapy
## 3 Genetically Modified-Bacteria for Colorectal Cancer (CRC) Treatment
### Engineered Bacteria Treat CRC via Therapeutic Protein Delivery
#### 3.1.5 Immunoregulatory Factors
In this system, a synchronized lysis circuit (SLC) was employed to control the lysis of the chassis by sensing the EcN density within the tumor, which ensured that the bacterial population was enriched at the tumor site. Compared to conventional clinical therapy, this strategy induced an enhanced therapeutic response, including relative increases in activated T cells and systemic memory T cell populations, and eventually led to tumor regression in syngeneic CT26 murine models [97]. Another study reported that engineered attenuated _S. typhimurium_ with a shRNA plasmid was used for the downregulation of the expression of the immune checkpoint protein IDO. This strategy activated innate immunity and inhibited CRC growth in mice and was superior to IDO inhibitor epacadostat and anti-PD1 antibody [94]. Figure 6: Engineered bacteria harboring immunoregulatory can modulate innate and adaptive immune responses and promote the infiltration of immune cells into tumors.
## 6 Results and Discussion
### Effect of Fog Conditions on OAM/OCDMA-FSO System  
The performance of our proposed system is evaluated under different fog conditions: LF, MF, and HF. Figure 5(a)-c show the _log_(BER) values against the propagation range. When the water droplets suspended in the air increase, the level of fog increases and the attenuation increases, resulting in shortening of the FSO link.  
It is noticed that all channels under LF can propagate longer distances than under MF and HF. Channels 1 and 4 that propagate using \(LG_{0,0}\) and \(LG_{0,10}\), respectively, give the best _log_(BER) values, which are \(-25.41\) and \(-25.08\) under LF at \(600\) m, \(-25.22\) and \(-25.5\) under MF at \(425\) m, and \(-24.9\) and \(-24.75\) under HF at \(340\) m. The lowest performance is for channel \(\#3\) that propagates using \(LG_{0,0}\) and channel \(\#6\) that propagates using \(LG_{0,10}\) OAM beam but is still accepted as it is less than the threshold value of \(-2.56\).  
Figure 7 displays the eye diagrams for the six channels under LF at \(600\) m FSO range. All channels have wide eye openings, and this reveals good receiving for the transmitted information.  
Figure 5: Spatial intensity profiles: (**a**) \(LG_{0,0}\); (**b**) \(LG_{0,10}\); (**c**) multiplexed mode.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sequence length & \(1024\) \\ Samples & \(32\) \\ Transmitter aperture diameter (\(D_{TX}\)) & \(10\) cm \\ Receiver aperture diameter (\(D_{RX}\)) & \(20\) cm \\ Beam divergence angle (\(\theta\)) & \(0.25\) mrad \\ Photodiode responsivity (\(\Re\)) & \(1\) A/W \\ Thermal noise power density & \(10^{-22}\) W/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._Figure 6: _Log_(BER) against FSO link for OAM/OCDMA-FSO system under (**a**) LF, (**b**) MF, and (**c**) HF.  
Figure 7: Eye diagrams for (**a**) channel #1, (**b**) channel #2, (**c**) channel #3, (**d**) channel #4, (**e**) channel #5, and (**f**) channel #6 under LF at 600 m FSO range.  
Table 6 tabulates the _log_(BER) values at maximum achieved propagation range for our proposed model under different fog conditions (600 m under LF, 425 m under MF, and 340 m under HF).
# Using IoT for Cistern and Water Tank Level Monitoring
## 3 Monitoring of Water Architecture
### Water Tank and Cistern System Description
#### 3.5.2 Tank Sensor Node Electronic Circuit Diagram  
The following diagram (Figure 10) shows the connections of the tank sensor node, which are as follows:  
1. Wemos D1 mini: This device acts as the main controller of the tank node. This component is responsible for receiving and processing the data from the connected sensors, sending this information to a Raspberry Pi4 server through the MQTT protocol. The programming environment used to configure the microcontroller was Arduino IDE, where the necessary input and output pins were assigned for each component.  
2. Ultrasonic waterproof sensor (JSN-SR04T-2.0): This ultrasonic sensor measures the water level in the tank. It sends ultrasonic waves that bounce off the water's surface, allowing it to calculate the distance between the sensor and the water. The sensor connections to the microcontroller include the following: * 5V pin connected to the 5V pin of the microcontroller. * Ground pin (GND) connected to the microcontroller's GND. * Trigger pin (Trig) connected to the D7 pin of the microcontroller. * Echo pin (Echo) connected to the D8 pin of the microcontroller.
## 2 Project Overview
### Foundation Pit Support and Drainage Design  
The proposed site is located in the urban area of Haikou, close to the main road of the city, residential buildings and university apartment buildings. The nearest building is located only 3-5 m from the edge of the foundation pit. The west side of the foundation pit is the main road of the city, and the surrounding environment has strict requirements for formation deformation. Moreover, the soil layer at the bottom of the project is rich in water and has high permeability and a high groundwater level, necessitating the use of reasonable support and dewatering measures to ensure the stability of the foundation pit.  
The depth of the foundation pit of this project is 9.20-10.75 m. Based on the geotechnical engineering conditions of the foundation pit, the surrounding environmental conditions and the requirements of the site, the design of the foundation pit support structure is as follows: the foundation pit adopts the SMW construction method pile + internal support method, and the SMW construction method pile is a three-axis mixing pile with a diameter of 850 mm and an interval of 600 mm to form a bottom-down water-resisting curtain. According to the different stratum conditions, the lengths of the three-axis mixing piles are 24 m and 26 m, respectively. The construction method for pile steel adopts the close insertion type and the insert jump type. The length of the H-shaped steel is 24 m, and its dimensions are 700 mm \(\times\) 300 mm \(\times\) 13 mm \(\times\) 24 mm. The cross-section of the main supporting structure of the foundation pit is shown in Figure 4a. Two elevator shaft (pit-in-pit) structures are placed at the bottom of the foundation pit of this project, with a depth of approximately 2.65 m. A IV-type Larsen steel sheet pile with dimensions of 400 mm \(\times\) 170 mm \(\times\) 15.5 mm is used for support, and its length is 6.00 m. The cross-section of the supporting structure of the foundation pit elevator shaft (pit-in-pit) is shown in Figure 4b.  
Figure 3: Geological and hydrological conditions and related design parameters of a typical section of a foundation pit (\(\gamma\) is the weight of the soil; \(E_{\text{s1-2}}\) is the compression modulus of the soil; \(c\) is the soil cohesion; and \(\varphi\) is the internal friction angle of the soil.).  
Within the depth range of foundation pit excavation, the groundwater that has an impact on foundation pit construction is mainly the pore phreatic water stored in layers 1, 2, and 3 and the confined water stored in layers 3, 6, and 7. The foundation pit adopts the scheme of cutting off the water outside the pit and dewatering and drainage in the pit. The external water source is cut off by the supporting structure outside the pit, and the drainage of the drainage well is reasonably set in the pit to ensure the effective operation of the foundation pit project.
## 4 Development of Machine Learning Models
### Regression-Based Machine Learning Techniques
#### 4.1.4 Decision Tree (DT)  
The LR, SLR, and RR models fit the data by developing a linear relationship, but the relationship between the predictor and response variables might be nonlinear. In such cases, non-parametric approaches, such as Decision Tree and Random Forest, can be used to develop the nonlinear relationship. These models can provide more accurate predictions but are challenging to interpret. In the DT model, a tree-like structure is formed by splitting the regression region into a hierarchy of simple decisions involving one or several input features until a stopping criterion is met. The root node, or the first decision node, is the best predictor [50; 51]. The best split is identified through the minimization of impurity or node error. In the present study, 563 splits were used to identify the response variable.
## 3 Results and Discussions
### Performance Characterization of Microcapsules
#### 3.2.5 Self-Healing Performance Verification  
Epoxy coatings have excellent adhesion to steel, possess outstanding shielding performance, and can effectively block the penetration of moisture and corrosive substances. In this study, the prepared microcapsules were added to the epoxy coating and evenly applied to the carbon steel substrate, resulting in an epoxy coating with self-healing functionality. To verify the self-healing performance of the microcapsules, the coating was first scratched, and then the epoxy coatings containing 8 wt% microcapsules and those without microcapsules, respectively, were placed in a 3.5 wt% NaCl solution to simulate a marine environment, soaked for 24 h, and the changes in the scratches were observed.  
The coating containing 8 wt% microcapsules showed a significant reduction in scratches after being soaked in NaCl solution for 12 h, and after 24 h, the scratches had become very faint, as shown in Figure 10a. In contrast, the coating without microcapsules showed almost no change in scratches after soaking for 12 h and 24 h, as shown in Figure 10b. This indicates that the addition of self-repairing microcapsules significantly enhances the self-repairing performance of the coating.  
Figure 10: (**a**) Scratch repair effect of self-healing coating containing 8 wt% microcapsules, (**b**) scratch image of coating without microcapsules.  
By comparing the surface morphology of the coating containing 8 wt% microcapsules after friction damage, as shown in Figure 11a, with the surface morphology of the coating without microcapsules after abrasion, as shown in Figure 11b, it can be observed that after the microcapsules are damaged and ruptured, the core material flows out and fills the wear marks. This indicates that the core material inside the microcapsules has good flowability and can promptly repair the damage to the coating.
## 1 Introduction  
The tumor suppressor p53 prevents tumorigenesis, blocks metastasis, stops cancer cell proliferation, and induce apoptosis, resulting in mutations in cancer cells [1]. p53 is important to various biological functions include numerous target genes, such as p21, is involved in p53-dependent cell cycle arrest, while the Puma play important roles in p53 mediated apoptosis [2]. Furthermore, p53 is activated by regulator genes as well [1, 3, 4]. p53 is the primary regulator of cell cycle arrest or apoptosis in mammalian cell reactions to cellular stress, including nuclear or ribosomal stress [5, 6, 7]. Furthermore, p53 induce the anti-oxidant genes, such as GLS2. These genes remove the reactive oxygen species (ROS) and protect against oxidative insults [8]. Although many anti-cancer drugs involved in p53-dependent apoptosis have been studied, the p53-regulatory pathways are not completely understood.  
Ribosomal biogenesis is essential for growth of cancer cell. Many ribosomal proteins, such as L5, L11, and L23, were able to inhibit growth of tumor cells by activating p53 in response to ribosomal stress. It appears that L5 and L11 work together to activate p53. Furthermore, L5, L11, and L23 are also bind to MDM2 [9, 10, 11, 12].  
The colon cancer is known as the third common cancer in the world [13]. Despite advances in modern medicine, about 50% of colon cancer patients have experienced cancer recurrence and mortality rate is about 40% [14]. Surgery is the main treatment for colon cancer and treatments, such as 5-FU and Oxaliplatin, are used. However, these treatments are accompanied by a variety of side effects such as anemia, loss of appetite, feeling sick, headache, etc. [15, 16, 17]. This is why the discovery and scientific proof of new colorectal cancer treatments is needed.  
Brassinin is a phytoalexin found in crucible vegetables, which is anti-cancer effects [18, 19]. Brassinin inhibits human liver cancer cell proliferation via mitochondrial dysfunction and inhibits invasive potential of lung cancer cells via PI3K/ Akt/mTOR signaling cascade [18, 20]. Recently, Brassinin enhances the anti-cancer actions of paclitaxel by targeting JAKs/STAT3 pathways in colon cancers [19]. However, the detail mechanism of Brassinin in colon cancer cells are not fully understand so far. Thus, in this study, we hypothesized that Brassinin may suppress the proliferation of colon cancer cells and induces apoptosis through activating p53.
# Highly Reliable Fuzzy-Logic-Assisted AODV Routing Algorithm for Mobile Ad Hoc Networks
## 2 System Model and AODV Protocol
### AODV Protocol  
The AODV protocol is the most commonly used on-demand routing protocol [7]. The protocol performs the routing discovery process only when data need to be transmitted, which can relatively reduce the overhead of control messages [19], and can adapt to rapid changes in the topology [20]. The key technologies are the \"request-response\" mechanism and the sequence number mechanism, which can avoid routing loops. If the source node needs to transmit a message with a node in the network, first determine whether there is an available link to the destination node. If there is, use the available link for communication; otherwise, the node performs the path finding process. The source node starts to send routing requests to all neighboring nodes to find a path through RREQ. After receiving the RREQ, the intermediate node creates a reverse path to the source node. Then, continue to send the RREQ message. When the message reaches the destination node or the node containing the destination node information, the reverse route is created successfully. At the same time, the destination node generates a RREP message and transmits this message through the reverse path. After the source node receives the RREP message for the first time, the forward route is created, and the source node communicates with the destination node through this route.  
However, in the process of path discovery, flooding and sending RREQ messages to surrounding nodes will cause serious control message overhead. In addition, the creation of a reverse route will cause a network storm and cause serious packet loss. In route selection process, the destination node only receives the first arrived RREQ message and  
Figure 1: System model of establishing and evaluating the fuzzy-logic-assisted AODV routing protocol.  
establishes a forward route by responding to the source node with RREP packets. The process of establishing the route does not take the energy and stability of the nodes into consideration, which may result in the interruption of the link and in data loss and affects the routing reliability. In the following section, fuzzy logic is used to compromise multiple parameters to select the optimal relay node to establish the optimal route and enhance routing reliability.
# Combining Supervised and Unsupervised Fuzzy Learning Algorithms for Robust Diabetes Diagnosis
## 2 Materials and Methods  
The proposed method for combining fuzzy unsupervised and fuzzy supervised learning algorithm can be demonstrated as Figure 1.  
The core input (diabetes patient information) is given to the unsupervised learner FCM. For \(n\) data and \(c\) fuzzy clusters (\(c<n\)), FCM allows each value to belong to one or more clusters depending on the degree of membership to each cluster. After FCM process, only non-zero membership clusters act as the input of fuzzy supervised learner (FMM) thus the middle layer and the output layer may not be fully connected as shown in cluster 1 of Figure 1.  
The FCM algorithm used in this paper is as Algorithm 1 below. ```
Step 1. Initialize the number of cluster \(c\) (\(2\leq c<n\)), exponential weight \(m=2\) (\(1\leq m<\infty\)), and stopping condition \(\epsilon=0.0001\) Step 2. Compute the distance between the \(i\)-th cluster centroid \(\mathbf{v}_{i}\) and \(k\)-th data \(\mathbf{x}_{k}\) as Equation (1). \[\mathbf{d}_{ik}=\sqrt{(\mathbf{x}_{k}-\mathbf{v}_{i})^{\frac{m}{k-1}}}\] (1) Step 3. Compute the membership degree of \(k\)-th data and \(i\)-th cluster as Equation (2). \[\mathbf{u}_{ik}=\frac{1}{\sum_{i=1}\left(\frac{\mathbf{d}_{ik}}{\mathbf{x}_{k}}\right)^{ \frac{m}{k-1}}}\] (2) Step 4. Compute centroid of each cluster by Equation (3). \[\mathbf{v}_{i}=\frac{\sum_{i=1}(\mathbf{u}_{ik})^{2}\mathbf{x}_{k}}{\sum_{i=1}(\mathbf{u}_{ik} )^{2}}\] (3) Step 5. Stop the process if the difference between the centroid position by Equation (3) and previous centroid is less than \(\epsilon\), otherwise, go to Step 2 and repeat the process. ```  
**Algorithm 1**Fuzzy c-means process. The FMM learning process shown in Algorithm 2 consists of Initialization, Expansion, Overlap Test, and Contraction [21]. It selects an input pattern and find the closest hyperbox to that pattern and expand. If a hyperbox cannot satisfy the expansion criteria, a new hyperbox is formed and added to the system. Hyperbox overlap may cause undesirable ambiguity; therefore, a contraction process is used to eliminate any undesired hyperbox  
Figure 1: Structure of Proposed Method. overlaps. In classification learning, the overlap is eliminated only for hyperboxes that represent different classes.
# Buffer Allocation via Bottleneck-Based Variable Neighborhood Search
## 3 Solution Methodology
### Variable Neighborhood Search (VNS)
#### 3.4.2 Neighborhood Change Criterion  
The neighborhood change criterion is used when no satisfactory throughput is generated. Compared with the throughput \(T_{hig}^{j-1}\) before one buffer update, if the current throughput \(T_{hig}^{j}\) is improved by less than a fixed percentage, the neighborhood structure will be changed and additional neighborhood structure will be tested. The equation is described as  
\[\frac{T_{hig}^{j}-T_{hig}^{j-1}}{T_{hig}^{j-1}}<\varepsilon \tag{10}\]  
According to our experiment results and error control, \(\varepsilon\) is set to 0.1%.
# MedChain: Efficient Healthcare Data Sharing via Blockchain
## 3 MedChain Model
### System Architecture  
MedChain is constructed on a decentralized network, which connects all healthcare providers, including hospitals, medical centers, clinics, and healthcare corporates. The MedChain network contains two types of peer nodes: super peers and edge peers. A simple but reasonable peer selection approach is adopted. Super peers consist of the servers from large healthcare providers, such as national hospitals, which are more capable in computing and storage, providing the main infrastructure of data sharing. The edge nodes are the servers from small providers such as community clinics, which only store the actual patient data. The overall network model is shown in Figure 2, in which a trusted Certificate Authority (CA) is employed for certificating and validating public keys. It contains two sub-networks: a blockchain service network and a directory service, which store different types of information derived from healthcare data.  
The resources of a super peer is divided into three modules: blockchain service, directory service, and healthcare database (HDB), as shown in Figure 3. The blockchain server maintains a complete blockchain for verifying data integrity and auditing activities. The directory server maintains the inventory of user healthcare data, maps them to the actual location of storage, and manages sessions for data sharing. The servers of the two types on all super-peers form two sub-networks. The last component, HDB, stores the actual healthcare data of patients. Note that MedChain does not require the healthcare provider to migrate the actual data to the new system. Instead, it provides the reference to the data in the legacy system for access. Thus, it is an integrated solution, which can reduce the difficulty of adoption, since most healthcare providers are reluctant to migrate their data to a new platform.
# Feasibility Study on Grouting Compactness Detection in Sleeves Using Piezoelectric Transducers
###### Abstract  
Steel sleeve grouting connections are widely used in prefabricated concrete structures. It is well known that insufficient grouting increases the chance of structural failure. As such, it is critical to monitor the density and compactness of grouting sleeve during the construction process, which however remains significant challenges as it is deeply buried in the beam and column. In this study, a lead zirconate titanate (PZT)-based sleeve grouting compactness detection method was systematically investigated. Five grouting sleeves samples with different degrees of compactness were prepared and four PZT transducers were surface-bonded on opposite sides of the outer wall of each sleeve. Two acts as actuators to generate stress wave signals, and the other two operate as sensors to receive the signals. The wavelet packet energy and Hilbert-Huang transform methods were applied to process the stress wave signals, and with the chosen characteristic parameters, the correction model of the grouting compactness was established. Experimental results show that the wavelet packet total energy values and the Hilbert energy peak values are related to the grouting compactness, indicating the feasibility of using PZT sensors to detect the compactness of grouting sleeves.  
grouting sleeve; lead zirconate titanate (PZT); grouting compactness; wavelet packet energy (WPE); Hilbert-Huang transform (HHT) +
Footnote †: journal: Applied sciences