esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Biosensors in the Field of Healthcare
### Future Direction--State-of-the-Art for Cardiovascular Smart Stents/Grafts
Alternatively, electrical impedance spectroscopy (EIS), depicted in Figure 10, is a unique technique for an in vitro real-time monitoring of cell behaviour. The concept of studying cell proliferation and growth using the EIS technique was first utilised by the pioneers [PERSON] and [PERSON] in the 1980s. When cells spread and settle over the sensing electrodes, they impinge the flow of alternating current, changing the impedance value. The change in impedance delivers essential information about the cell type, growth, and movement [167; 168]. Figure 10: Schematic illustration of the concept of electrical impedance spectroscopy. (**A**) shows the current passing through a conductive substance such as cultural media. (**B**) shows cells proliferation and settlement over gold electrodes, deposited on the surface of a glass slide, where the current is impinged by cellular membrane. Figure 9: Illustrative diagram showing CAD evolvment over years. It is noted that the 2 nd DES lowered the incidence rate of restenosis, but still pose threat to blood vessel. Innovative smart stents/grafts are thought to be able to address these challenges and lower their the risk of adverse reaction through wireless proactive treatment. This figure created by Biorender. [PERSON] et al. developed a smart stent specifically designed for ISR detection, following EIS technology. In their design, the stent functions as one electrode and a silver-silver chloride wire as another electrode [169]. Currently, [PERSON] et al. and his team at the University of Glasgow have exploited the EIS approach to develop the first self-reporting smart graft/stent, illustrated in Figure 11, for the early detection and prevention of vascular disease, such as ISR and thrombosis. For the smart graft, they integrated a 16-electrode polyimide sensor into the wall of PTFE and connected it to micro-implantable electronics. In a porcine model (in vivo), one end of the graft with electrodes was attached to the venous system, while the other end was linked to the arterial system. A small pocket was created in the neck to accommodate the electronics. Their translational work showed that it is possible to wirelessly detect and treat vascular disease. In addition to the smart graft project, Mercer's group developed a smart stent, where the entire stent's structure functions as a sensor. This is achieved by converting the stent's strut into two distinct positive and negative electrodes, separated by non-conductive segments. This design allows the detection of restenosis over a larger area, rather than being limited to areas that are covered by discrete electrodes [81,170].
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
###### Abstract  
Fifth Generation mobile networks (5G) promise to make network services provided by various Service Providers (SP) such as Mobile Network Operators (MNOs) and third-party SPs accessible from anywhere by the end-users through their User Equipment (UE). These services will be pushed closer to the edge for quick, seamless, and secure access. After being granted access to a service, the end-user will be able to cache and share data with other users. However, security measures should be in place for SP not only to secure the provisioning and access of those services but also, should be able to restrict what the end-users can do with the accessed data in or out of coverage. This can be facilitated by federated service authorization and access control mechanisms that restrict the caching and sharing of data accessed by the UE in different security domains. In this paper, we propose a Data Caching and Sharing Security (DCSS) protocol that leverages federated authorization to provide secure caching and sharing of data from multiple SPs in multiple security domains. We formally verify the proposed DCSS protocol using ProVerif and applied pi-calculus. Furthermore, a comprehensive security analysis of the security properties of the proposed DCSS protocol is conducted.  
5G; security protocol; network services; federated identity; data caching; data sharing; authorization; formal methods; ProVerif; applied pi calculus +
Footnote †: journal:  
0021  
DOSS Protocol for Data Caching and Sharing Security in a 5G Network  
1
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 5 Results
### Sharing Confidential Information
#### 5.3.2 Scenario 2: W-9 Tax Form with a Certified Public Accountant  
In this scenario, we first asked the participants to review the W-9 form, state whether they considered it to include sensitive information, and rate this form's information sensitivity. All participants stated that they considered this employee information form to include sensitive information, such as SSNs and account numbers. We found that 94.7% of the participants rated this form's information as \"very sensitive\".  
Following the hypothetical scenario, the participants were asked to email this form to the CPA's email address. After that, the participants rated their satisfaction with sharing their SSNs in the W-9 form with the CPA using their regular or standard email and secured email. Also, they were asked to rate their concerns about sharing their SSNs in the W-9 form with the CPA using their regular or standard email and secured email.  
We used the Wilcoxon signed-ranks test to compare participants' satisfaction with sharing sensitive information via standard email and secure email. The test indicated that the participants' satisfaction with sharing SSNs in the W-9 form via secured email was rated higher than sharing SSNs in this form via standard email (Z = \(-\)3.9, \(p\) < 0.001 (Table 5)). Therefore, we found that all the participants rated their satisfaction as moderately or very satisfied when sharing their SSNs with the CPA using secure email compared with standard email. We also conducted another Wilcoxon signed-ranks test and found that the participants were more concerned if they shared their SSNs with the CPA via regular or standard email compared with secured email (Z = \(-\)3.7, \(p\) < 0.001 (Table 5)). Therefore, we found that 68.4% of the participants were \"not at all concerned\" if they shared their SSNs with the CPA using secure email compared with standard email.  
\begin{table}
\begin{tabular}{l l l l l} \hline  & **Sharing via Standard** & **Sharing via Email Security** & **Z Score** & \(p\) **Value** \\  & **Email (Mean)** & **Tool (Mean)** & **Z Score** & \(p\) **Value** \\ \hline Users’ Satisfaction: Employee form & Mean = 1.8 & Mean = 3.7 & Z = \(-\)3.6 & \(p\) < 0.001 \\ Users’ Satisfaction: W-9 Form & Mean = 1.4 & Mean = 3.7 & Z = \(-\)3.9 & \(p\) < 0.001 \\ Users’ Satisfaction: Medical Form & Mean = 1.6 & Mean = 3.7 & Z = \(-\)3.8 & \(p\) < 0.001 \\ \hline Users’ Concerns: Employee Form & Mean = 3.2 & Mean = 1.4 & Z = \(-\)3.8 & \(p\) < 0.001 \\ Users’ Concerns: W-9 Form & Mean = 3.2 & Mean = 1.3 & Z = \(-\)3.7 & \(p\) < 0.001 \\ Users’ Concerns: Medical Form & Mean = 3.4 & Mean = 1.3 & Z = \(-\)3.9 & \(p\) < 0.001 \\ \hline \end{tabular}
\end{table}
Table 5: Participants’ ratings of satisfaction and concerns about sharing confidential information via standard email and end-to-end encrypted email.
## 5 Conclusions  
1. The interface-layer of viscoelasticity and the actual shape of large-particle inclusion were firstly considered and a new interface-layer method was proposed to predict viscoelastic parameters of multi-layered CGS based on homogenization method and elastic-viscoelastic corresponding principle. The tested creep curves of the multi-layered CGS agreed well with the predicted ones and can prove the existence of the interface-layer and verify the validity of this new method.
2. For the one-layered CGS, the creep deformation decreased as the shape factor (\(\rho\)) of pebble inclusion increased and \(\rho\) was in the range of 1-1.8.
3. The viscoelastic parameters of the interface-layer were smaller than the average values of one-layered CGS which consisted of the interface-layer.
4. For the two-layered CGS, the creep deformation decreased with the increase of the interface-layer height (\(h\)) and the suitable interface-layer height was 20-30% as much as the height of one-layered CGS.
5. For the three-layered CGS, the creep deformation decreased with the increase of the interface-layer number (\(N\)) and at least one interface-layer must be taken into account.
6. For the safety point of view, it is better to use a high degree of irregular pebble and a large difference in gradation of CGS and more layer numbers to reduce the creep deformation of multi-layered coarse-grained soil. Construction costs should be taken into account in design of pavement or embankment.  
Conceptualization, J.Z. and Q.R.; data curation, J.Z.; funding acquisition, Q.R.; methodology, J.Z. and W.Y.; project administration, Q.R.; supervision, Q.R. and W.Y.; validation, J.Z. and Q.R.; writing--original draft, J.Z.; writing--review and editing, Q.R., and W.Y. All authors have read and agreed to the published version of the manuscript. National Natural Science Foundation of China under Grant number 51274251.  
The authors sincerely thank the support of the above fund project and the help of leaders and operators of the test site. Without these supports, the data and field test required by this research would not be able to completed. The authors declare no conflict of interest.
## 6 Conclusions  
In this paper, various ML algorithms, namely, ANN, RF, SVM, and DT are used to classify soils based on [PERSON]'s soil behavioral types. To optimize the performance of these models, hyperparameter tuning was performed using Bayesian optimization. Additionally, cross-validation was employed to ensure the models' generalization ability and optimal performance. The study used 232 CPTs from ISSMGE's database and randomly divided the dataset into training and testing datasets. The performance of each ML model was evaluated using performance metrics such as sensitivity, precision, F1_score, and overall accuracy.  
Based on the findings of this study, the following conclusions can be made:  
* The ANN model achieved an overall accuracy of 98.39%. It achieved high scores in multiple performance metrics for majority soil classes. However, the model achieved lower sensitivity and F1_score values of 0.67 and 0.77, respectively, for minority soil class 7.
* The DT model achieved an overall accuracy of 95.67%, indicating high performance in classifying soils. Additionally, the model demonstrated excellent sensitivity, precision, and F1_score across all soil types, with scores ranging from 0.88-0.98.
* The SVM model outperformed the other models with the highest overall accuracy of 99.84%. It achieved almost perfect scores for all performance metrics across all soil types.
* The RF model achieved an overall accuracy of 99.23% and demonstrated high performance across all soil types. Similar to the SVM model, the RF model also achieved almost perfect scores for all performance metrics across all soil types.
* In general, the SVM and RF models achieved a high level of overall accuracy (almost 100%) in classifying soils, even when trained with imbalanced CPT datasets. These models exhibited outstanding performance on both majority and minority soil classes, indicating their potential as valuable tools in geotechnical engineering. Integrating these ML models into software programs for rapid and accurate soil classification in real-world projects can aid in making informed decisions.
* Future research could focus on improving the performance of the ANN and DT models in classifying soils based on CPT data. This could involve exploring other approaches such as training the models using balanced datasets.  
Conceptualization, A.T.C.; methodology, A.T.C.; software, A.T.C.; formal analysis, A.T.C.; writing--original draft preparation, A.T.C.; writing--review and editing, A.T.C.; supervision, R.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets can be downloaded at the following link: [[http://140.112.121/issmge/tc304.htm?=6](http://140.112.121/issmge/tc304.htm?=6)]([http://140.112.121/issmge/tc304.htm?=6](http://140.112.121/issmge/tc304.htm?=6)) (Accessed on 20 January 2023).  
This publication was financially supported by Szechenyi Istvan University.  
The authors declare no conflict of interest.
## 3 Model Inversion and Application Case
### Application Case
Figure 11b illustrates the fit between the measured resistivity data \(R_{a}\) and the simulated apparent resistivity data \(R_{s}\). Ideally, all data points should align along the diagonal. However, as illustrated in the figure, the majority of the data points cluster in the central region, exhibiting significant deviations from the diagonal. This discrepancy may be attributed to the fact that, in practical measurements, the electrode configuration does not strictly conform to a regular \"\(\square\)\"-shaped electrode array, compounded by the relative complexity of positioning between the power and measurement electrodes. Consequently, these factors likely degrade the quality of the measured data, resulting in notable differences between the measured and simulated data. Figure 12 shows the 3D resistivity inversion results from this survey. It can be observed that the horizontal resistivity slices at different depths generally show a pattern of higher  
Figure 11: Error convergence curve and data crossplot. (**a**) RMS iterative convergence curve. (**b**) Illustration of the fitting relationship between measured and simulated resistivity data. Figure 10: Line graph of measured resistivity variations before and after calibration. resistivity in the center and lower resistivity around the edges, reflecting the resistivity variation in the pagoda's foundation and its surroundings from shallow to deep. This reveals the structural and material properties of the pagoda foundation and indicates that the foundation structure remains in a relatively stable condition. From the figure, it can be seen that the central region of the pagoda's foundation exhibits high-resistivity characteristics, and as depth increases, the high-resistivity area expands radially from the center. This centrally symmetric high-resistivity distribution aligns with the core stability principle commonly found in early architectural engineering. By using dense materials with high density and low moisture content, a barrier with high erosion resistance and low permeability was created, preventing moisture from the surrounding low-resistivity areas from seeping into the core region, thus enhancing the load-bearing capacity and stability of the pagoda foundation. To maintain the structural stability, we recommend establishing an effective drainage system around the tower foundation to reduce the risk of erosion caused by water accumulation in shallow low-resistivity zones. Additionally, conducting long-term resistivity imaging surveying on the foundation is advised to enable early identification of potential degradation trends and stress concentration points within the tower structure. This will facilitate targeted conservation and restoration efforts for the heritage site.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Deep Keypoint-Based Loop Closing
#### 3.3.2 Loop Node Detection  
As shown in Figure 3, the loop closure thread receives keyframes filtered from the local mapping thread, storing them in the inverted database to construct a Bag-of-Words (BoW) model. The BoW model utilizes the feature descriptors of the current frame \(K_{c}\) to match descriptors stored in the database. Each keyframe is converted into a unique BoW vector for representation. We compute the similarity score between the current keyframe \(K_{c}\) and the BoW vectors of inverted keyframes to identify the most similar candidate keyframe \(K_{h}\). However, the candidate keyframe \(K_{h}\) only considers differences in the numerical values of descriptors and neglects differences in descriptor positions, which may lead to mismatches.  
Thus, we first employ the Grid-Based Motion Statistics (GMS) [50]-based brute force matching method to match deep keypoints between candidate closed-loop nodes. If the number of matches is below a threshold (i.e., 20 in our system), the candidate loop keyframe is classified as a mismatch. GMS operates on the principle that the number of keypoints near correctly matched feature points should surpass the number near incorrectly matched keypoints. Following a procedure similar to ORB-SLAM2 [5], we compute the similarity transformation matrix \(\mathbf{T}_{\text{sim}}\) between candidate closed-loop nodes through matched keypoints. Subsequently, \(\mathbf{T}_{\text{sim}}\) is applied to match map points between the current keyframe \(K_{c}\) and the candidate keyframe \(K_{h}\). Further optimization of the similarity transformation matrix \(\mathbf{T}_{sim}\) is conducted using the matched map points. If the number of inliers in the optimization function exceeds the threshold, the candidate keyframe \(K_{h}\) is deemed a matched keyframe \(K_{m}\).
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 4 Research Methodology  
In this section, we explain in detail about our methodology. We use Figure 3 to illustrate the components of our proposed method and provide an overview of our system. The detail of each component is discussed in the subsections below. The algorithm's detail can be viewed in Algorithms 1 and 2.  
```
Initialize (a)  Airsim UAV and set target object's segmentation ID (b)  Environment (c)  Network \(Q\) with random weights \(\theta\) (d)  Target network \(Q^{\prime}\) with weights \(\theta^{\prime}=\theta\) (e)  Experience Replay \(\mathcal{D}=[]\) (f)  DQN agent Initialize policy \(\pi\) for data collection for\(i=1\), \(num\_steps\)do  Choose action \(a\) according to current state \(s\) and policy \(\pi\): \(a=\pi(s)\)  Perform action \(a\), observe next state \(s^{\prime}\), reward \(r\), and \(done\) signal  Store the transition: \(\mathcal{D}.append(a,s,s^{\prime},r,done)\)  Initialize policy \(\pi\) for training for\(i=1\), \(num\_epochs\)do  Choose action \(a\) according to current state \(s\) and policy \(\pi\): \(a=\pi(s)\)  Perform action \(a\), observe next state \(s^{\prime}\), reward \(r\), and \(done\) signal  Store the transition: \(\mathcal{D}.append(a,s,s^{\prime},r,done)\)  Sample a batch from \(\mathcal{D}\) for\(j=0\), \(length(batch)-1\)do if\(not\ done_{j}\)then \(y_{j}=r_{j}+\gamma\cdot\max_{a^{\prime}_{j}}Q^{\prime}(s^{\prime}_{j},a^{ \prime}_{j})\) else \(y_{j}=r_{j}\)  Compute loss \(L=\frac{1}{length(batch)}\sum_{k=0}^{length(batch)-1}(Q(s_{k},a_{k})-y_{k})^{2}\)  Update weights \(\theta\) of network \(Q\) by minimizing the above loss  Every \(M\) steps, update weights \(\theta^{\prime}=\theta\)
```  
**Algorithm 1** Deep Q-Network
# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 5 Experiments and Results
### Discerning the Inner Workings of the Proposed SAM2-UNet Architecture
#### 5.1.4 Mean Absolute Error  
The mean absolute error (MAE) is easy to understand and calculate, as it measures the average difference between predicted and actual values. It is interpretable because it is in the same units as the data, making it practical for real-world applications. The MAE is less sensitive to outliers than other metrics like MSE, which helps in scenarios with extreme values. Thus, the MAE offers a straightforward way to assess model accuracy without the complications of squared error penalties [24].  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y_{i}}|\]
## 2 Materials and Methods
### Classification
#### 2.6.2 Fuzzy C-Means  
Since fuzzy sets were introduced, the concept of data having a membership grade to different membership functions has been used frequently. At the FCM algorithm, clusters are represented as the membership functions, and data points are assigned to several clusters. The membership degree to an input belonging to a particular cluster is evaluated. In the end, a data point consists of multiple membership functions, and the objective function is computed by the Euclidian distance [54; 55]. Fuzzy C-means is a well-known clustering algorithm that is widely applied for segmenting and classifying medical images [56]. MATLAB commands were used to create the FCM classifier. Commands \"fcm\" and \"genfs\" were used to create the clusters [37]. The command \"evalfis\" was used for the test data [37]. Due to the nature of the method, the test data are labeled from a range (in Figure 7, the labels are extracted as \"Y\"). The ROC curve is extracted, and the optimum threshold is found (in Figure 7, T is the threshold). If a label of data is greater than the threshold, Y = 1; otherwise, Y = 0. As conducted at the K-means method, two groups are created (\"G1\" and \"G2\" in Figure 7) with labels \"0\" and \"1\", without knowing which is the malignant or benign. Two truth tables are created, the first using the labeled data with \"0\" as malignant and the second truth table with \"1\" as malignant. Two accuracies are extracted (\"Acc1\" and \"Acc2\" in Figure 7). We use the truth table of the accuracy that is greater than the other.  
Figure 7: Classification using decision tree based on FCM.
## 3 Methodology
### Data Investigation & Pre-Processing
#### 3.2.1 Data-Type Correcting  
In order to ensure precise calculations, inconsistent data types (e.g., numeric columns erroneously saved as strings) were transformed into their correct formats.  
* Data standardizing: Standardizing the data is a transformation process that increases the integrity and quality of the data that is used in future calculations. For this study, we used Z-index standardization shown in Equation 1. This method normalizes the data to have zero mean and unit variance. \[Z=\frac{X-\mu}{\sigma}\] (1)  
Here:  
* \(Z\) denotes the standardized merit,
* \(X\) signifies the initial data instance,  
Figure 2: Detailed steps of data preparation for the proposed experiment.  
\(\mu\) represents the mean value of the data, \(\bullet\)\(\sigma\) indicates the standard deviation of the results.
2. Categorical encoding: Any machine learning algorithm requires numerical input to perform mathematical operations, so categorical encoding converts categorical data, which contains arbitrary labels or discrete components, into a numerical format. For the encoding technique, we used label encoding in this study to convert the attack classes labels to numeric values, such as converting label DDoS to class \"0\". Unlike one-hot encoding, label encoding preserves the ordinal nature of categorical variables, allowing for interaction between dummy variables while providing a meaningful numeric representation of the synonyms. It treats categorical features as a variable to keep the categorical variables' interpretability and thus helps in efficient data processing, such as the given in Equation 2.  
\[f(x_{i})=y_{i} \tag{2}\]  
Here,  
* \(f(x_{i})\) denotes the function for encoding categorical values to numeric values;
* \(x_{i}\) represents the set of categorical data;
* \(y_{i}\) represents the set of converted numerical data.