esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 4 Experimental Results and Discussion
### Feature Visualization  
In order to illustrate the feature extraction ability of the proposed method more intuitively, we use the Gradient-weighted Class Activation Mapping (Grad-CAM) [57] method for feature visualization. The method shows the important regions of the image predicted by generating a rough attention map from the last layer of the model. The brighter the color of the attention map, the higher the importance of the corresponding region of the image. Some images are selected from the ASGC dataset for testing, and the results are shown in Figure 13. It can be seen that the proposed method can highlight the real class of the object and has strong localization and recognition abilities.
# Ultrasound in Telemedicine: A Brief Overview
## 3 Results
### Safety of Data  
As the value of personalized data increases and cheap data storage is abundantly available, telemedicine companies can record and analyze a complete set of parameters longitudinally in real-time, which is an unprecedented feat in the history of medicine. However, patients' access to and control over these stored data are not always guaranteed. In Western societies, where people are wary of privacy violations, these developments should spark an intense public discourse, which seems to lag behind the staggering pace of the technological advancement.  
In a tele-guided setting, there are several ways to document the obtained images and results. There are various ultrasound documentation programs on portable devices and guidelines on how to appropriately document an ultrasound examination [29]. Moreover, there are cloud-based monitoring systems that do not require bedside supervision and documentation, which may enhance the ability of physicians to study and document ultrasound images [30]. Data protection is an important issue related to these cloud-based documentation applications. Although recent cloud-based products comply with EU regulations regarding data privacy and protection, this remains a hot topic.
## 2 Materials and Methods
### Data Processing
#### 2.3.4 Model Construction and Accuracy Test  
The regression analysis method was used to construct the model for estimating chlorophyll content in the peanut leaves. Root mean square error (RMSE), coefficient of determination (R2) and slope values were used to evaluate goodness of fit between the predicted and observed values. Statistical analysis and contour mapping of R2 and standard error (SE) values were processed using a self-programming software (Mathworks, 2000) script. The smaller the RMSE, the higher the model accuracy. The RMSE calculation formula used is as follows:  
\[\text{RMSE }=\sqrt{\frac{1}{M}\sum_{j=1}^{M}\left(Y_{j}-X_{j}\right)^{2}}\]  
where \(Y_{j}\) and \(X_{j}\) are the predicted and observed values, respectively and \(M\) is the number of samples.
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 6 Multiple Robots
### Presence of Chaos in Generated Trajectories for Multiple Mobile Robots
#### 6.4.1 Coverage Percentage of the Test Area  
In Table 1 we show the average coverage percentage obtained for \(1\leq n\leq 10\) mobile robots. The trajectories were calculated using 100 different initial conditions for each robot. We used 25 iterations of the algorithm for each trajectory so the full coverage (100%) was not reached.  
It can be observed that the increment in coverage does not follow linear behavior. This is partially due the size of the robot taking up space in the work area, and could also imply that there is an optimum number of robots for a given work area. We plan to further investigate this result as part of our future work.
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
## 3 Analysis of Long-Term Clock Bias, Frequency, Drift Rate, and Noise  
The parameters of clock bias, frequency, and drift rate are key indexes in describing an onboard clock's physical characteristics [21; 22]. The clock bias data analyzed in this paper are clock offsets after preprocessing. The clock frequency is obtained by Equation (1). The quadratic polynomial model listed in Section 2.3 is used to fit the daily data of the clock offset and obtain the drift rates and fitting residuals.
# A Survey of Internet of Things (IoT) Authentication Schemes +
## 3 Security Issues in IoT  
### Security Services  
As previously mentioned, the use of connecting objects in everyday people's lives can make security issues life-threatening. The smartness integrated into homes, cars, and electric grids can be diverted into harmful scenarios when exploited by hackers. Different hacking scenarios presented in the past years [23; 24] illustrate the level of harm that could result from a security breach, especially with the development and large adoption of IoT applications dealing with sensitive information (personal, industrial, governmental, etc.).  
The main IoT security concerns are: authentication, authorization, integrity, confidentiality, non-repudiation, Availability, and privacy [25; 26; 27].  
1. _Authentication_: The process of confirming and insuring the identity of objects. In IoT context, each object should have the ability to identify and authenticate all other objects in the system (or in a given part of the system with which it interacts).
2. _The authorization_: The process of giving permission to an entity to do or have something [28].
3. _Integrity_: The way toward keeping up the consistency, precision and dependability of information over its whole life cycle. In IoT, the alteration of basic information or even the infusion of invalid information could prompt major issues, e.g., in smart health systems use cases it could lead to the death of the patient [29].
4. _Confidentiality_: The process of ensuring that the information is only accessed by authorized people. Two main issues should be considered [27] regarding confidentiality in IoT: firstly,  
Figure 1: IoT architecture models.  
to ensure that the object receiving the data is not going to move/transfer these data to other objects and, secondly, to consider the data management.
5. _Non-repudiation_: The way toward guaranteeing the ability to demonstrate that a task or event has occurred (and by whom), with the goal that this cannot be denied later. In other words, the object cannot deny the authenticity of a specific data transferred.
6. _Availability_: The process of ensuring that the service needed is available anywhere and anytime for the intended users. This includes in IoT, the availability of the objects themselves.
7. _Privacy_: The process of ensuring non-accessibility to private information by public or malicious objects [30].
## 2 Theoretical Background
Most often, these are fairly simple combinatorial arrangements. There are a number of voter designs. These are summarised in the work [36]. Some of the more important constructions include the classical voter [37], the Kshirsagar voter [38], the [PERSON] and Naviner voter [39], the Balasubramanian voter [40] or the Mux-based voter [36]. The structures of the individual voters are shown in Figure 3.  
The classic voter is a design based on an AND-OR structure. This type of solution fit well into older PLD (Programmable Logic Device) circuit architectures such as CPLDs (Complex Programmable Logic Devices), where the core of the circuits were PAL (Programmable Array Logic) cells [41; 42]. Of course, there is nothing to prevent these voters from also being used in FPGAs. In the case of the Kshirsagar voter or Naviner voter, the  
Figure 3: Examples of voter constructions: classical voter (**a**), Kshirsagar voter (**b**), Ban and Naviner voter (**c**), Balasubramanian voter (**d**) or Mux-based voter (**e**). Figure 2: The idea of TMR operation. designs are based on XOR gates, leading to more reliable fault tolerance. A certain modification of the classical voter is the Balasubramanian voter, where the AND-OR structure is implemented in the form of a logic cell. It is also possible to implement the voter using only multiplexers (the Mux-based voter). The work [36] is limited to a power and area analysis of transistor structures corresponding to individual voter designs. In the case of FPGA devices, voters are implemented either as IP (Intellectual Property) cores or in the form of HDL (Hardware Description Language) descriptions. Of course, the HDL description itself can take different forms: an array description, a structural description or a description in the form of equations. Often, FPGA device chip manufacturers provide their voter solutions [43; 44]. Independent companies offering synthesis tools also support high-reliability designs [45]. In many cases, the inputs as well as the outputs from the voter are complex interfaces, e.g., AXIS (Advanced eXtensible Interface; Stream protocol) [43]. Taking all this into account, it is crucial to efficiently map the voter along with redundant modules (mitigation circuits) into LUT (Look-up Table) blocks, which form the basis of the FPGA chip architecture. LUT blocks are simple memories in which logic functions with a limited number of inputs are implemented. Of course, at the appropriate stages of logic synthesis, complex designs are split (function decomposition) and mapped into these resources [8]. Let us therefore consider the concepts of a test environment implemented in a real FPGA chip to test the correctness of error correction by TMR mitigation circuits.
## 3 Vertex Disjoint Path Construction Algorithm
### Algorithm Description and Implementation  
There are two vertex disjoint path algorithms, including BuildPathSet and Convert. BuildPathSet is the master algorithm. The Convert algorithm can change one bit of the \(u\) node number to the corresponding bit of the \(v\) node. The flow of the vertex disjoint path algorithm is as follows (refer to Algorithm 1). First, a series of \(v\) nodes located at the vertices of the same type A switch \(v_{2},v_{1},\ldots,v_{n-1}\) are selected. Then, a vertex \(v_{n}\) located on the same type B switch as \(v\) is determined. The mode of the first path is as follows. First, we reach node \(x\). The value of the \(u_{0}\) bit of \(x\) is 0. In addition, \(x\) is on the same type A switch as the source node. The path is selected from node \(x\) to node \(v_{1}\). The second path pattern is as follows. First, some data arrive at the \(y\) node. The value of the \(u_{0}\) bit of \(y\) is 1. In addition, node \(y\) and the source node reside on the same type \(A\) switch. The path is from node \(y\) to node \(v_{2}\). The other paths follow the same rules. The Convert algorithm is invoked \(n-1\) times during the process from the source node to the target node. One digit is changed from the highest digit of the number to the lowest digit each time.  
Figure 2: Structure of \(BCCC^{1}_{3,2}\).  
```
1:Two different vertices \(u=u_{k+1}u_{k}\ldots u_{0}\) and \(v=v_{k+1}v_{k}\ldots v_{0}\) in \(BCCC(n,k)\). Let the neighbor node of the same type \(A\) switch with \(v\) be \(v_{1},v_{2},\ldots,v_{n-1}\), and select any a neighbor node in the same type \(B\) switch as \(v_{n}\).
2:\(n\) vertex disjoint paths from \(u\) to \(v\).
3:functionBuildPathSet(\(BCCC(n,k),u,v\))
4:\(z=v\);
5:for\(i=0\); \(i=n-1\); \(i++\)do
6:\(v=v_{i+1}\), \(Path=\emptyset\);
7: add \(u\) to \(Path\);
8:if\(u_{u0}\
eq i\)then
9:\(u=u_{k+1}u_{k}\ldots u_{u0}\ldots u_{0}\), \(u_{u0}=i\), \(i\in\{1,2,\ldots,k+1\}\);
10: add \(u\) to \(Path\);
11:for\(j=k+1\); \(j>0\); \(j--\)do
12:if\(u==z\)then
13: break;
14:if\(u_{j}\
eq v_{j}\)then
15:\(u\), \(path\) = CONVERT(\(u,v,u_{j},v_{j}\), \(Path\));
16:if\(u_{0}\
eq v_{0}\)then
17:\(u=u_{k+1}u_{k}\ldots v_{0}\);
18: add \(u\) to \(Path\);
19:if\(u\
eq z\)then
20: add \(v,z\) to \(Path\);
21:print(\(Path\));
22:functionCONVERT(\(u\), \(v\), \(u_{x}\), \(v_{y}\), \(Path\))
23:\(u=u_{k+1}u_{k}\ldots u_{x}\ldots u_{0}\), \(x\in\{1,2,\ldots,k+1\}\);
24:\(v=v_{k+1}v_{k}\ldots v_{y}\ldots v_{0}\), \(y\in\{1,2,\ldots,k+1\}\);
25:if\(u_{0}==v_{0}\)then
26:\(w=u\);
27:else
28:\(u_{0}=v_{y}\);
29:\(w=u_{k+1}u_{k}\ldots v_{y}\);
30: add \(w\) to \(Path\);
31:\(u_{x}=u_{y}\);
32:\(u=u_{k+1}u_{k}\ldots v_{y}\ldots u_{0}\);
33: add \(u\) to \(Path\);
34:return(\(u\), \(Path\));
```  
**Algorithm 1** Vertex Disjoint Path Algorithm of BCCC.
# Non-Ionizing Radiation Measurements for Trajectory Radars
## 5 Conclusions  
This paper presents an NIR measurement campaign using a specific method for trajectorying radars. We perform and analyze the power density measurements from a C-band trajectography radar considering the radar's high gain and locations with a high probability of both exposure and presence of people.  
The main contributions of this work are summarized below.  
1. A specific method for measuring non-ionizing radiation power density levels due to trajectography radars is proposed. To the best of our knowledge, there are no studies about it;
2. Unpublished NIR measurement results are obtained;
3. The need to adopt control and alert measures was verified due to the high NIR level (above the limit for the general population) at a point close to the radar antenna.  
The measurement setup is able to characterize the NIR exclusively from the radar emissions, by using the spectrum analyzer channel power function and a directional receiving antenna. Four measurement points were selected, three at the far field region and one at the near-field region of the radar's antenna.  
All measured and predicted values were presented and discussed, considering each measurement scenario. As the main qualitative results, we concluded that all measured power density values are below the occupational population limit established by ICNIRP and Anatel. However, the general population limit is exceeded at one point located near the radar antenna. It shows the need for restricting general population access to the area very close to the Bearn's antenna as well as the need for preventive actions to avoid health injuries to the occupational population, by giving orientation about the risks and controlling the permanence time at the area around the radar's antenna (actions already performed by CLII regarding its radars).  
Conceptualization and methodology: J.M.L.B.F. and M.E.C.R.; software and data curation: J.M.L.B.F., M.M.d.M.C., D.L.F. and W.S.A.; validation: M.E.C.R. and V.A.d.S.J.; formal analysis and investigation: [PERSON], M.E.C.R. and V.A.d.S.J.; resources: J.M.L.B.F. and A.G.D.; writing--original draft preparation: [PERSON] and M.M.d.M.C.; writing--review, editing and visualization: [PERSON], M.E.C.R. and V.A.d.S.J.; supervision: [PERSON] and V.A.d.S.J. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001.  
Not applicable.  
The authors would like to thank the Centro de Lancamento da Barreira do Inferno of Brazilian Air Force for the partnership. Special thanks to the Bearn radar's team and to the members of the research and innovation sector of CLII.  
The authors declare no conflict of interest.
## 3 Task-Driven Transfer Approach
### Partial Task Construction
#### 3.1.1 DFS (Depth-First Search) Model Split Technique  
As shown in Figure 4, the DFS Model Split technique parses the layer-level model structure graph of the target task to derive the partial task model structure of each guest party and the merging task model structure of the host party. The partial task model structure and merging task model structure of TT-VFDL-SIM are determined only by the DFS Model Split technique. Each guest party uses the input layer for its own data as the starting node and parses the layer-level model structure graph of the target task based on Depth-First Search (DFS). When parsing the FM part of the target task model, the number of units \(d_{l}\) in each dense layer \(l\) is kept the same. The FM part parsed from the target task model in this way is called \"Split FM\". As a result, each partial task model structure of TT-VFDL-SIM consists of an FE part for each input and a split FM, as shown in Figure 2.  
The host party's merging task model structure is also derived through the DFS Model Split technique. The input to the merging task model is task-driven features sent from each guest party. Therefore, the starting node of DFS-based graph parsing in the host party is the layer that concatenates the last outputs of each FE part of the target task model. As a result, the merging task model structure is identical to the FM part structure of the target task model. Table 2 summarizes all formulations used in this paper. Table 3 summarizes the partial task model structure of each guest party and the merging task model structure of the host party for TT-VFDL-SIM using the formulations in Table 2. The model structures trained by each party in P-VFDL-SL and DT-VFDL-AE, the comparison methods summarized in Section 2.4, are also summarized in Table 3.  
Figure 4: DFS (Depth-First Search) Model Split technique to derive a partial task model structure of each guest party and merging task model structure of the host party: this is the result of applying the DFS Model Split technique to three target tasks for each type of FM part. According to Depth-First Search graph parsing, starting from the input layer for each guest party’s data, it reaches the output layer through the corresponding separate FE part and FM part layers.
## 5 Explanation of the Prediction Results
### Analysis of a Group of Patients at High Risk  
To identify the group of high-risk patients, we applied K-means clustering [46] to the data set of 92 gastric cancer patients. Using the elbow method [47], the optimal K was 3, and the number of patients in groups 1, 2, and 3 was 31, 36, and 26, respectively. Figure 4(a) shows the distribution of each group's probability of progression to gastric cancer. Group 1 showed a markedly high probability for progression to gastric cancer; therefore, we defined these patients as the high-risk group 3. Figure 4(b) shows 3D scatterplots for each group.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Chi-Squared** & \(p\)**-Value** \\ \hline Sex & 12.9966 & 0.0003 \\ Alcohol habit & 13.1282 & 0.0690 \\ Smoking status & 18.5011 & 0.0001 \\ Smoking duration & 20.9237 & 0.0003 \\ Frequency of smoking & 2.1212 & 0.5476 \\ Family history of cancer & 0.9882 & 0.3202 \\ Income & 0.4009 & 0.8184 \\ Exercise & 0.0 & 1.0 \\ Number of endoscopic & 2.0452 & 0.5631 \\ screenings & BMI & 122.9448 & 0.4844 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chi-squared analysis of demographic and lifestyle data.  
Figure 6 shows boxplots of age at diagnosis for each group. The average age at diagnosis was 57.0, 69.0, and 45.9 years, respectively. In addition, we identified that income, BMI, regular exercise, and the number of endoscopic screenings did not show any significant difference between groups.
## 4 Discussion
The limitations and the field of application of the method are identical to those present in the well-known and used RC (Recovery Coefficient) method [22], but, unlike the latter, it has the great advantage that the final formula has been obtained theoretically; therefore, the final formula intrinsically contains the dependencies of the various parameters that influence the PVE: size of the lesion, lesion/background concentration ratio, and FWHM of the PSF of the system. For this reason, it is not necessary, as is the case for the RC method, to measure experimentally these parameters every time they change. The method was experimentally validated by acquiring an IEC NEMA PHANTOM, which allowed to evaluate the recovery of the quantitative data by varying:  
1. the size of the lesions, filling spheres with a diameter of 10, 13, 17, 22, 28, and 37 mm. 2. the signal-to-background activity concentration ratio (SBR); in particular we have acquired the values of around 3:1 and 10:1, which covers the standard range of clinical variation in terms of the lesion/background concentration ratio. 3. the statistical noise, acquiring the same activity for different acquisition times (1, 2, 4, and 8 min)
4. the tomographic reconstruction algorithm, with and without TOF. The method guarantees an excellent recovery of the quantitative data in the analyzed conditions. Considering the sigma that characterizes the acquisition system, which is around 2.5 mm (value measured experimentally), the PVE effect determines a loss of counts greater than 50% and the corrective method allows to obtain accuracies of less than 17%, for lesion dimensions equal to or greater than 13 mm in diameter and for acquisition times equal to or greater than 2 min. By increasing the acquisition time, substantial improvements are not appreciated and this is an important and comfortable result, given that an acquisition time of 2 min per bed is the standard clinical PET acquisition time. Additionally, the application of TOF during the reconstruction algorithm inherently reduces the effect of PVE and the application of the corrective method at this data allows obtaining results that are even more accurate. Finally, increasing the SBR, in general makes the effect of PVE to be more marked and the accuracy of the corrected data is lower.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 6 Discussion  
The results indicate that the proposed individual vessel trajectory prediction model using SGPR is superior to conventional models that use FGPR or SVR. The FGPR method's drawbacks of increased storage expense and time complexity have been mitigated. Moreover, compared to SVR, an algorithm for supervised learning that utilizes sparse kernel techniques like SGPR, the proposed method demonstrated superior predictive accuracy. The results of applying GA and BO to the hyperparameter optimization of SGPR were compared, and GA-based optimization was found to achieve high accuracy. The superior performance of SGPR can be attributed to the thorough optimization of hyperparameters. And, the trajectory prediction model was developed using only the vessel's continuous position data, without information about the vessel's course or speed, by applying the motion model.  
To ensure the robustness of the proposed algorithm, the test dataset remained unseen during training and was used solely for the final evaluation. Long-term predictions and changes in prediction time were introduced to assess whether the model's predictions remained consistent under slight variations in the input data. Additionally, the performance of the developed model was evaluated using multiple metrics, including RMSE, MAE, ADE, FDE, and elapsed time.  
This study was limited to a data-driven environment, and the developed model may be sensitive to noise and unexpected situations. This issue can often be addressed by training with more data. However, to prevent overfitting to specific data and performance degradation in unfamiliar scenarios, it is important to consider a robust control methodology, as demonstrated in [45; 46]. Secondly, SGPR relies on a subset of the data and may still struggle in cold start scenarios, as it requires some initial data points to build a reliable model. To address this challenge, the exploration of combining SGPR with other machine learning methods, such as neural networks, will be undertaken to develop a more robust initial prediction framework. Finally, since the proposed method is limited to ships with available AIS information, extending this method to ships without the AIS is necessary. This could involve incorporating other tracking technologies or predictive models to handle missing or incomplete data.