esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Implantable medical devices are widely used for therapeutic [5] or life-saving purposes such as cardiac arrhythmia, diabetes, and Parkinson's disease [20]. Applications include drug delivery systems, pacemakers, implantable cardiac defibrillators (ICDs) and Neurostimulators [1]. Some real-time monitoring applications include physiological parameters like blood pressure, glucose levels and collecting data for further analysis [5]. These devices often contain electronic components that perform increasingly sophisticated sensing, computation, and actuation, in many cases without any patient interaction [1] as in the applications mentioned above, performing complex analyses with sophisticated decision-making capabilities. They are capable of storing detailed personal medical information, and communicate automatically, remotely, and wirelessly [2]. Implanted biosensors form a wireless network that can be used for data aggregation and data dissemination applications [5]. The system introduced in this paper is conceived to be implanted under the human skin. The powering and communication between this device and an external primary transmitter are based on an inductive link [21]. The design presents two different approaches: defining a true/false alarm system based on either amperometrics or impedance into a grid of nano-biosensors that could permit the monitoring of several diseases by _in vivo_ analysis of the corresponding biomarkers.
## 4 Materials and Methods
### Photosynthetic Efficiency Analysis  
Photosynthetic efficiency in WT and _OsVTC1-1_ RNAi line after rice blast inoculation was determined by measurement of chlorophyll fluorescence FluorImager software version 2.2 (CF Imager, Technologica Ltd., Colchester, UK). Detached leaves were placed in the CF Imager and dark adapted for 20 min before measurement of basal fluorescence (F\({}_{0}\)). Maximal fluorescence (F\({}_{\mathrm{m}}\)) was induced by a saturating flash of blue light (0.5 s at 6000 umol m\({}^{-2}\) s\({}^{-1}\)). Variable fluorescence (F\({}_{\mathrm{v}}\)) was calculated as F\({}_{\mathrm{m}}\)-F\({}_{0}\) to provide the dark-adapted quantum efficiency of photosystem II (F\({}_{\mathrm{v}}\)/F\({}_{\mathrm{m}}\)) [53]. Three replicates were performed for each rice line.
Therefore, the single martensite phase of the Ni-Mn-Ga alloy was chosen in this study to achieve (1) shape changes via introducing different fields (e.g., compressive and magnetic fields) and (2) fundamental investigations of single-crystal (SC) Ni-Mn-Ga alloys and their composites. Among the martensite phases, the modulations that are most frequently found are five-layer modulated (5M) martensite, seven-layer modulated (7M) martensite, and non-modulated martensite (NM or 2M). Among the martensite phases, the applied fields (e.g., stress or magnetic fields) for the twin motions of the martensite variants are the lowest for the 5M martensite [19; 20]. Therefore, the 5M martensite was targeted in this work. In addition, it has been reported that the shape deformation of Ni-Mn-Ga alloys is as low as about 0.12% due to the constraints of the twin motion at grain boundaries while the alloy is a polycrystal [21]. Thus, in this work, Ni-Mn-Ga alloys were prepared in the form of SC, which can achieve a large MFIS, as previously reported [22; 23; 24]. The use of composites is an efficient strategy to balance properties among different materials [25]; hence, some efforts have been made to achieve shape deformation under a stress field or a magnetic field by using different Ni-Mn-Ga-based materials [26; 27]. [PERSON] et al. worked on polycrystalline Ni-Mn-Ga foams, where the grain boundaries can be replaced by the pores. As a result, a very large MFIS of \(\sim\)2.0-8.7% was achieved [28; 29]. However, deteriorated stability and poor performance in terms of cyclic motions were also brought about due to the instability of the complex structure. Furthermore, the intrinsic brittleness of Ni-Mn-Ga alloys still remains a problem [30; 31]. To solve the embrittlement issue, some studies working on composites, such as SC Ni-Mn-Ga alloys/polymer composites, have also been carried out [32; 33; 34; 35; 36]. It was found that an MFIS of \(\sim\)4%, which is slightly smaller than that of SC 5M martensite, can be achieved by taking advantage of SC Ni-Mn-Ga particles/polymer composites [35]. The trade-off between the MFIS and the problems, such as brittleness and stability, was properly balanced by using these composites. Nevertheless, their crystallographic direction was not controlled and evidence for MVR was not revealed. Hence, in this study, field-induced MVR was focused on to reveal the origin of the shape deformation. Hence, based on the aforementioned merits of SC and single 5M martensite Ni-Mn-Ga alloys, as well as the advantages of their polymer composites, we conducted fundamental investigations of SC 5M martensite Ni-Mn-Ga alloy/polymer composites in this study. The crystallographic direction of the SC single 5M martensite Ni-Mn-Ga alloys was identified and evidence of MVR after the introduction of the externally applied fields (e.g., compressive and magnetic fields) was also revealed.
# Experimental and Numerical Predictions of Cryogenic Leakages in Welded Steel Plates
## 1 Introduction
The lateral expansion is higher in the base metal than in the welded joints. The Sandia laboratory [21; 22] performed experimental and computational analysis of the thermally induced fracture of steel plate structures with and without a welded joint. Liquid nitrogen (LN\({}_{2}\)) was applied onto the sections of each steel plate to achieve thermal stress. They investigated the cooling of steel plates subjected to cryogenic liquids and studied the development and propagation of cracks in the plates due to the influenced thermal stresses and lowering fracture toughness. [PERSON] et al. [17] fabricated carbon steel through a thermomechanically controlled process (TMCP). It can be applied to hull or module structure construction to manage possible brittle fracture. The hull construction and stress condition of floating-liquefied natural gas (FLNG) were presented through a series of real large-scale structure tests. The performances of the material expected under realistic cases were evaluated. However, few studies have experimentally investigated the mechanical behavior and crack propagation of welding structures under cryogenic temperatures. In contrast, tension tests, Charpy impact tests, numerical methods, and other methods have been used to investigate this behavior. Thus, this relatively unknown risk of cryogenic spills on a welded structure is yet to be fully studied or quantified at the level of a realistic design. This study aimed to investigate abrupt cryogenic leakages and their impact on a structure having welded joints by carrying out a comparative analysis of structural deformation, with or without the appearance of cracks, from cryogenic spill experiments. LN\({}_{2}\) was supplied to the center region of the steel plate, which contained the welding. The temperature history and strain were measured by thermocouples and strain gauges, respectively, attached to the steel plate during the experiment [22; 23; 24]. The appearance of cracks and their spread were observed, and they are analyzed herein. In addition, numerical analyses were conducted using the commercial software package ABAQUS/Standard [25] to predict the residual stress and distortion in weldments fabricated using flux-cored arc welding (FCAW), and to predict the temperature history and thermal stress of the welding structure during cryogenic liquid leakage. The appropriateness of the FEA model was validated by the experiments.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 4 Proposed Methodology
### Quality of Service Measures
#### 4.2.4 QoS Score  
The final QoS score is computed using a weighted sum of three components: latency, throughput, and jitter. The QoS score is computed as follows:  
\[QoS_{i}=\omega_{1}\times Latency+\omega_{2}\times Throughput+\omega_{3}\times Jitter \tag{7}\]  
where \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) are the weights for latency, throughput, and jitter, respectively, representing the importance of each measure. The sum of weights \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) is always 1. The weights reflect the relative importance of each metric in ensuring reliable communication. If \(QoS\geq 0.5\), the nodes are considered for further trustworthiness evaluation. The values assigned to these weights are given below.  
* \(\omega_{1}=0.5\): Given the highest weight since low latency is critical for real-time communication and network responsiveness.
* \(\omega_{2}=0.3\): Moderately weighted because higher throughput improves performance but is not as critical as latency in determining reliability.
* \(\omega_{3}=0.2\): Given the lowest weight since minor fluctuations in delay do not significantly impact trust unless excessive. While jitter reflects variation in packet delivery time, it is less critical in some applications of IoT. Accordingly, the weight \(\omega_{3}\) assigned to jitter is the smallest value.  
These values prioritize low latency for real-time responsiveness, followed by high throughput, and then minimal jitter. These weights ensure a balanced and practical trust evaluation mechanism for IoT contexts.
## 2 Materials and Methods
### Soil Village Detection
#### 2.3.2 Machine Learning Approaches  
In the machine learning approaches, we utilized the XGBoost (XGB) method [36], a gradient-boosted decision tree method that has been successfully used in the classification of imbalanced datasets, as is the case in this study [37]. XGB operates by constructing a series of decision trees in a sequential manner, using gradient boosting to optimize performance. Each tree is built to correct the errors made by the previous ones, resulting in a model that progressively improves by learning from prior mistakes. This method incorporates advanced techniques such as regularization to prevent overfitting, the efficient handling of sparse data, and parallelization to enhance the computational speed. Introduced in 2016, XGB provides significant advancements over traditional boosting methods, offering superior predictive accuracy and training efficiency [38]. The final model output was a combination of predictions from all individual trees, weighted to minimize errors and maximize the overall performance.  
Figure 2: Graphical representation of the process used in this work.  
To use XGB for detecting soil tillage operations in the data, we transformed the time series of each variable into a tabular form. In this form, each column represented one variable (3 columns in total: the VH, NDVI, and CR), and each row corresponded to a specific date per parcel. The total number of rows was obtained by multiplying the number of rows by the 24 time intervals of 15 days. Additionally, for each row, we added the corresponding values from the previous time period and calculated the difference in VH (similarly to the rules-based approach). Thus, each row now contained 8 columns: the VH, NDVI, CR, \(\Delta\)VH, VH\({}_{\text{prev}}\), NDVI\({}_{\text{prev}}\), CR\({}_{\text{prev}}\), and \(\Delta\)VH\({}_{\text{prev}}\) (where the variables with \"prev\" refer to the data in the previous time period).  
Since the number of dates with soil tillage was lower than the number of dates without operations, we tested two approaches: (1) using the raw imbalanced dataset and (2) applying the Synthetic Minority Oversampling Technique (SMOTE), an advanced oversampling method that generates synthetic examples for the minority class by creating new data points across the feature space through interpolation between existing minority class samples. This process helps to balance the dataset by amplifying the representation of the minority class without merely duplicating the existing data.  
To find the best hyper-parameters of the XGB boost model, we performed a Bayesian optimization approach with 100 iterations, considering the following hyper-parameters: the number of estimators (between 1 and 100 with an interval of 1), the learning rate (between 0.01 and 1 with intervals of 0.015), the maximum tree depth (between 1 and 30), and L1 and L2 regularization (between 0 and 10 with a 0.1 interval).
## 1 Introduction
The contribution is quite unique compared with previous mobile sink based problems because there is only a tiny time window for the data collector to meet with each UGV, for which the exact appearance time is also inaccurate. If all the UGVs have the same cycle, the problem can be recognized as a Travelling Salesman Problem (TSP). If each UGV has a different cycle, it can be converted into a layered TSP problem. However, the speed of the mobile sink depends on how these sub-cycles are formed. These approaches focus on the shortest path while ignoring the precedence relationship of the UGVs. Deadline driven methods deal with the situation that each UGV must be visited according to some kind of sequence (energy depletion, buffer overflow, etc.). However, meeting the current deadline of a UGV may result in missing that of another two in the future since the sequence is also affected by the distance between two UGVs. There is also related  
Figure 1: Sensor network that monitors a certain area with multiple events. work on TSP with a time window [12; 13; 14]. However, although our work looks similar to these papers, it is distinguished from these special cases of the TSP problem. First, the mobile sink is not required to return the start point. Second and most important, the network graph keeps changing along the time scale. The problem is not as hard as the TSP problem due to timing and precedence constraints. The search space is reduced to a limited area. Third, the object is to visit as many UGVs as possible in a period. The k-hop based search algorithm and bipartite matching based minimal mobile sinks finding algorithm are the main originalities of our work. Our contribution is four-fold:  
1. We consider the trajectory planning problem with time window and precedence constraints which evolves throughout time. 2. We develop optimal algorithms to solve the trajectory planning of the mobile sink in the ideal case. 3. We consider the situation cycles are changing dynamically and develop a greedy algorithm to solve it. 4. We develop a method to calculate the optimal number of mobile sinks when the area is too large for a single mobile sink to serve. The rest of the paper is organized as follows: Section 2 discusses the issues in the existing work and our research incentive. Section 3 presents the problem formulation. Then, we give details of the trajectory planning implementation in Section 4. The algorithm for calculating an optimal number of mobile sinks is presented in Section 5. The effectiveness and improvement of our method are shown with the experimental results in Section 6. Section 7 concludes this paper.
# Research on SWIM Services Dynamic Migration Method +
## 4 SWIM Service Dynamic Migration Method
### Implementation of Migration Mechanism  
In the process of SWIM providing services to the SC, the implementation process of the SWIM service dynamic migration method is shown in Algorithm 1. The SWIM service dynamic migration method obtains the number of SWIM service providers providing the service topic in the SWIM network, and the quality of service according to the service subject of the SC subscription. Determine the range of the time interval, the parameters of the shift register, and the output period of the pseudo-random sequence. The timer is started in the SWIM network, and the pseudo-random sequence is output bit by bit. According to the output sequence of the shift register and the timing value, it is determined whether the dynamic migration mechanism of the SWIM service is trigged. According to different triggering reasons, the service migration target node is selected by using different strategies, and the SWIM service dynamic migration module sends the service request to the migration destination. The node, or the transfer destination node transfers data to the service message queue.  
```
Input:\(T\), \(T_{l}\), \(T_{h}\), \(T\), \(Q\) Output:
1. Begin
2. If\(t>T_{h}\)
3. Time interval trigger migration mechanism;
4. \(t=0\);
5. Else if\(T_{l}<t<T_{h}\&Q(t)=1\)
6. The migration mechanism is trigged by a pseudo-random sequence;
7. \(t=0\);
8. End if
9. End if
10. If Trigger by time interval
11. Obtains QoS records of network-wide service nodes;
12. Select the optimal QoS node;
13. Else if Trigger trigged by pseudo-random
14. Randomly selecting migration nodes;
15. End if
16. End if
17. Sends service status information to the destination node;
18. The message queue establishes a connection with the migration destination node;
19. End
```  
**Algorithm 1**The implementation process of the system-wide information management (SWIM) service dynamic migration method.
## 3 Experimental Investigation
### Measuring the Flexural Rigidity of the Rod  
An experimental investigation of the rod flexural rigidity is necessary due to the following reasons:  
* steel class, its heat, and mechanical treatment;
* influence of the ductility of the rigid connections at the ends on the rod's lateral stiffness.  
The modulus of elasticity of the steel strip was measured using a standard procedure [22] with a confidence level of 90% and a mean value depreciation area corresponding to 204 \(\pm\) 2 GPa.  
The ductility of rigid connections is caused by contact deformations at the contact area of the rod and the elastic compliance of the attachment screws. It consists in a decrease in the rod's lateral stiffness [23; 24; 25]. In the present research, this decrease was taken into account considering the stiffness reduction coefficient \(\lambda\), which was determined experimentally by comparing the experimental and calculated values of the transverse stiffness at \(N=0\)  
\[\lambda=K_{0}/K_{0}^{*}, \tag{27}\]  
Figure 9: Linear approximation of \(M_{b}{R_{b}}^{2}\) vs. \(1/f^{2}\) for experimental values of the wheel’s rotating mass moment of inertia.  
where \(K_{0}\) is the experimental value of the rod flexural rigidity at \(N=0\), and \(K_{0}^{*}\) is the calculated value of the rod flexural rigidity. Following Equations (18)-(20) at \(N\to 0\), for the rigid-rigid connection:  
\[K_{0}^{*}=12[1-L/R+\left(L/R\right)^{2}/3]EI/L^{3}, \tag{28}\]  
for the pinned-rigid connection:  
\[K_{0}^{*}=3(1-L/R)^{2}EI/L^{3}, \tag{29}\]  
and the for rigid-pinned connection:  
\[K_{0}^{*}=3 EI/L^{3}. \tag{30}\]  
The experimental values of the rod flexural rigidity were obtained by static testing according to the scheme shown in Figure 8 by adding a weight at one of the cables ends and measuring the corresponding displacement of the weight. The tests were carried out for all end connection types and specimens' lengths planned for obtaining the buckling load. The dependence of displacement X on the load at the end of the rod for the rigid-rigid connection and rod length of 610 mm (\(L/R=2\)) is shown in Figure 10. The experimental values of the rod flexural rigidity were obtained by the least-squares method [26]. It was found that, for the investigated length range of 443-915 mm, just the end connection combination has an essential effect on the end factors. Independent of the length, the following values can be used: 0.89 for rigid-rigid, 0.96 for pinned-rigid, and 0.94 for rigid-pinned connections.  
Following the determined values of the stiffness reduction coefficient, from Equations (28)-(30), the effective rod's length should be increased by 1.039, 1.013, and 1.02 times, relative to the measured values. The value of \(N_{cr}\) was calculated using Equation (21), considering the influence of ductility on increasing the rod's effective length. Accordingly, the values of the critical load, calculated correspondingly to the measured rod's length, should be decreased by 8%, 3%, and 4%, respectively.
###### Abstract  
The mechanical properties of fully-grouted bolt support are critical for the safety of support engineering works. To study the influences of factors including the bolt length and diameter, strength of the rock, and fracture angle on the mechanical properties of fully-grouted bolt support, compression tests were conducted on an anchored rock mass, considering the shortcomings of pullout tests on bolts. The discrete element software PFC2D (4.0) was adopted for numerical simulation and analysis from two aspects, namely, the stress distribution and anchorage force supplied by such bolts. The research found that by increasing the bolt diameter and length as well as the strength of the rock, the maximum anchorage force of bolts increases. Whereas the stress distribution of all bolts increases at first and then decreases along the bolts, and there is only one peak on the stress distribution curves, which also gradually shifts to a greater depth. In a fractured rock mass, the maximum anchorage force of bolts decreases, then increases (and is minimized at a fracture angle of 45\({}^{\circ}\)) with the decrease in fracture angle. The influence of fractures with different angles on the stress distribution of bolts is mainly reflected in the fracture zone. The bolt stress decreases abruptly in the zone with a fracture angle of 90\({}^{\circ}\), forming a valley. The bolt stress increases suddenly in the zones with fracture angles of 60\({}^{\circ}\) and 45\({}^{\circ}\), thus forming peaks. The bolt stress does not increase or decrease suddenly in the zone with a fracture angle of 30\({}^{\circ}\). Therefore, it necessitates consideration of the influences of fractures on the anchorage force and the selection of bolts of appropriate size during anchorage design. After installation, the bolt stress should be monitored for stability and early warning of anchored rock mass according to changes in the stress provided.  
Keywords:fully-grouted bolts; compression tests on anchored rock mass; mechanical properties of bolts support; numerical simulation; fractured rock mass +
Footnote †: journal: Applied sciences
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Controlled Dummy Adaptive Distribution (CAD)  
_CAD_[55; 56] is based on _DAD_ and _DUD_. It is developed to guarantee the maximum delay of a real event. It forces a node to transmit its real packet if the node failed to transmit using probability within a predefined consecutive number of intervals. Adjusting this number is critical because, if it is large, this provides higher anonymity, whereas, if it is small, it decreases the delay. The relationship in _CAD_ is considered as a trade-off between privacy and latency [55; 56].