esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 3 Results
### HADA Development Dataset  
The HADA was integrated using the C# programming language with the ML.NET tool. It was built for a single elderly person who wore a continuous monitoring bracelet for two years (between 1 January 2023 and 31 December 2024). The bracelet was worn almost constantly during this period, except when the user had to charge it or when it was not in use, such as during baths. Thus, monitoring was performed continuously for two years, resulting in 676 recordings.  
The elderly individual was born on 24 October 1954 and was 68 years old at the start of the monitoring. From a medical history perspective, the individual underwent surgery in 2000 for a left frontoparietal meningioma, and the postoperative recovery was complete without sequelae. This information is important to understand the user's health context throughout the monitoring period.  
The SQL Server database contains three tables: the table for monitoring the parameters AR, mR, and MR, the table for monitoring the parameters DST and SST, and the table for monitoring the parameter S. Figure 8 presents the frequency measurements: heart rate, DST, SST; and parameter S per day of the monitored patient.  
For calculating the statistics used in building the model, the values for each day were summarized into a single value by calculating the minimum, maximum, and average for heartbeats, summing the number of steps, and directly taking the unprocessed values of DST and SST.  
Figure 7: IoT and Azure cloud architecture with HADA for elderly health monitoring.
# A Public Infrastructure for a Trusted Wireless World
## 2 Background
### Nonrepudiation  
Nonrepudiation is the assurance that the sender of information is provided with proof of delivery and the recipient is provided with proof of the sender's identity, so neither can later deny having processed the information. Public key cryptography provides nonrepudiation via a digital signature. A digital signature is a means of identifying the sender of a digital message, similar to the way a written signature on a paper or document proves authorship. Using public key cryptography, a sender, [PERSON], uses his private key to digitally sign a message. The receiver, [PERSON], uses [PERSON]'s public key to confirm the message's signature so that she can trust it came from [PERSON]. If [PERSON] wants to send a private message to [PERSON], [PERSON] encrypts the message with [PERSON]'s public key. Only [PERSON] can decrypt the message with her private key ensuring that only [PERSON] reads the message. [PERSON] digitally signs the message with his private key so that [PERSON] can assure herself the message came from [PERSON]. This is how public key cryptography allows a means of authenticating the identity of one another [11].
# A Novel Digitized Method for the Design and Additive Manufacturing of Orthodontic Space Maintainers
###### Abstract  
Primary definition is crucial in influencing the emergence of permanent teeth. Premature primary tooth loss can result in undesired tooth motions and space loss in the permanent dentition. Typically, fixed or removable dental appliances are adopted to maintain edentulous space until the eruption of permanent teeth. However, traditional space maintainers have limitations in terms of variability in tooth anatomy, potential allergic reactions in some individuals (i.e., nickel sensitivity), difficulties in maintaining oral hygiene, and patient acceptance. The present study introduces a fully digital framework for the design and manufacturing of customized pediatric unilateral space maintainers using generative algorithms. The proposed approach overcomes the current challenges by using a biocompatible resin material and optimizing the device's size, design, and color. The methodology involves intraoral scanning, surface selection, and trim, generative 3D modeling, finite element analysis (FEA), and additive manufacturing (AM) through vast photopolymerization. FEA results demonstrate the device's mechanical performance and reliability, while additive manufacturing ensures design freedom, high resolution, surface finishing, dimensional accuracy, and proper fit. The mechanical interlocking system facilitates easy and effective positioning of the device. This digital approach offers the potential for wider usage of space maintainers and can be further validated through experimental assessments and clinical studies.  
pediatric dentistry; digital space maintainer; generative design; FEA analysis; 3D printing
## 2 Materials and Methods
### System's Software Programming and Workflow
#### 2.2.3 Plant Finding
Additionally, a real-time video is displayed below the sliders to provide users with a convenient visual representation (Figure 8b). 8. Having available the upper and lower thresholds for the color values in the HSV space from the previous step, the algorithm proceeds by tracking a particular color (i.e., green) in the image by defining a mask using the _inRange()_ method in OpenCV. After this, a series of erosion and dilation operations are performed to enhance the solidity and distinguishability of the plants, which may result in the appearance of small blobs in the images. For the erosion morphological operation that is performed twice, a \(~{}3\times 3\) (8-neighborhood) structuring element is applied. The dilation operation is performed once with a similar structuring element. The objects present in the image are determined using the _findContours()_ function in OpenCV. In case no objects are found, the process is repeated from the beginning, with a slight shift of the carriage to the left (equal to the number of steps set by the user as Jump Steps). * If multiple contours are detected, the _contourArea()_ function in OpenCV is applied to discard the smaller ones, retaining only the largest contour. If the identified contour is deemed too small to represent a plant, the process is repeated, with a slight shift of the carriage to the left (empirically set to 2000 steps). * Once a contour that potentially represents a plant is identified, the _minEnclosingCircle()_ function is utilized to obtain the coordinates of the circle's center and its radius. This information is then used to draw a circle around the plant. * If the center of the plant does not align with the center of the image, the carriage is shifted either to the left or right, aligning the camera precisely with the plant (by means of bringing the camera exactly on top of the plant). Subsequently, the entire process is repeated from the beginning. Otherwise, the position of the carriage (and consequently, the plant's position) is recorded in a file, and the plant counter is increased by one. * If there are no more plants to be searched for, the procedure concludes. Figure 10: Flowchart of plant finding procedure.
# Wheeler Method for Evaluation of Antennas Submerged in Lossy Media
## 3 Analysis of the Wheeler Cap Method of Antenna Surrounded by the Lossy Media
### Measurement  
#### 3.2.1 Measurement Setup  
A UHF band antenna was devised and the radiation efficiency was measured when the antenna was submerged within an artificial lossy material. The detailed measurement setup is introduced in Figures 7 and 8. The AUT was made of NMHA type and was blocked from the medium by a capsule-shaped polyimide (\(\varepsilon_{r}\) = 3.5, tan\(\delta\) = 0.008) insulation layer (Figure 7a). Antenna in the UHF band within the lossy medium are described in Table 1. Radiation shields with three radii (20, 30, 70. Unit: mm) were designed for demonstration. It was fabricated by covering a copper sheet on a hemisphere model using a 3D printing methodology (Figure 7b).  
The overall measurement system, including a lossy medium and container, is described in Figure 8. The material with the electrical properties (\(\varepsilon_{r}\) = 58, \(\sigma\) = 0.82 S/m) at 400 MHz was composed of deionized water (51.16%), sodium chloride (1.49%), sugar (46.78%), bactericidal (0.05%), and hydroxyethyl cellulose (0.52%) [28]. The lossy medium  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & \(\mathbf{d_{a}}\) & \(\mathbf{p_{a}}\) & \(\mathbf{h_{a}}\) & \(\mathbf{t_{a}}\) & \(\mathbf{r_{i}}\) \\ \hline Value (mm) & 9 & 3 & 2 & 0.8 & 5 \\ \hline
**Parameter** & \(\mathbf{h_{i}}\) & \(\mathbf{t_{i}}\) & \(\mathbf{r_{g}}\) & \(\mathbf{t_{g}}\) & \(\mathbf{t_{w}}\) \\ \hline Value (mm) & 13 & 1 & 100 & 0.07 & 0.07 \\ \hline \end{tabular}
\end{table}
Table 1: Fabricated Antenna Parameter.  
Figure 8: Measurement system for evaluating the radiation efficiency of a submerged antenna.  
Figure 7: Illustration of (**a**) normal-mode helical antenna and capsule-shaped insulation layer, (**b**) hemisphere-shaped radiation shield.  
filled a 200 mm \(\times\) 200 mm \(\times\) 100 mm container. The antenna was placed in the center of the ground.
## Appendix A Numerical Methods  
Let us consider a GP equation of the form  
\[i\partial_{\tau}\varphi(r,\tau)=H\varphi(r,\tau), \tag{10}\]  
with  
\[H=-\frac{1}{2}\
abla_{r}^{2}+V(r)+g|\varphi(r,\tau)|^{2}, \tag{11}\]  
and  
\[\
abla^{2}=\frac{\partial^{2}}{\partial r^{2}}+\frac{2}{r}\frac{\partial}{ \partial r}. \tag{12}\]  
The Crank-Nicholson algorithm (on a discrete space-time grid) consists in solving the following system of linear equations with the components of the vector \(\varphi^{n+1}\equiv(\varphi_{1},\ldots,\varphi_{j},\ldots\varphi_{N})^{n+1}\) being the unknown variables [32]  
\[\left(1+\frac{i\Delta\tau}{2}H\right)\varphi^{n+1}=\left(1-\frac{i\Delta\tau} {2}H\right)\varphi^{n}. \tag{13}\]  
Notice that this algorithm preserves the unitarity for real-time evolutions.  
Derivatives can be written in terms of central differences [37],  
\[\frac{\partial^{2}\varphi}{\partial r^{2}} \rightarrow \frac{\varphi_{j+1}-2\varphi_{j}+\varphi_{j-1}}{\Delta^{2}} \tag{14}\] \[\frac{2}{r}\frac{\partial\varphi}{\partial r} \rightarrow \frac{2}{r_{j}}\frac{\varphi_{j+1}-\varphi_{j-1}}{2\Delta} \tag{15}\]  
that allow to write the linear system Equation (13) in the following tridiagonal form,  
\[a_{j}\varphi_{j-1}^{n+1}+b_{j}\varphi_{j}^{n+1}+c_{j}\varphi_{j+1}^{n+1}=d_{j}. \tag{16}\]  
The explicit expression for the coefficients \(a_{j}\), \(b_{j}\), \(c_{j}\), and \(d_{j}\) (\(j=1,\cdots N\)) are  
\[a_{j} = \frac{i\Delta\tau}{2}\Bigg{[}\frac{1}{\Delta^{2}}-\frac{1}{r_{j} \Delta}\Bigg{]}, \tag{17}\] \[b_{j} = 1+\frac{i\Delta\tau}{2}\bigg{[}-\frac{2}{\Delta^{2}}+V(r_{j})+g| \varphi_{j}^{n}|^{2}\bigg{]},\] (18) \[c_{j} = \frac{i\Delta\tau}{2}\Bigg{[}\frac{1}{\Delta^{2}}+\frac{1}{r_{j} \Delta}\Bigg{]},\] (19) \[d_{j} = -a_{j}\varphi_{j-1}^{n}+(2-b_{j})\varphi_{j}^{n}-c_{j}\varphi_{j +1}^{n}, \tag{20}\]  
which have to be accompanied by suitable boundary conditions in order to preserve the tridiagonal form.
# Uncovering Vegetation Changes in the Urban-Rural Interface through Semi-Automatic Methods
## 2 Materials and Methods
### Study Area  
We selected five municipalities of Portugal (Figure 1) to test the predictive capacity of the model. [PERSON] et al. [25] highlight the difficulty in validating maps using time series due to the lack of information available for past periods. Because of this same difficulty, the availability of a database capable of being used as validation, the choice of the test municipalities and the period of analysis were conditioned by the existence (or not) of those data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Code** & \begin{tabular}{c} **Class** \\ **Name** \\ \end{tabular} & \begin{tabular}{c} **Study Area** \\ **Municipalities (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Study Area** \\ **100 m Buffer (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Mainland** \\ **Portugal (\%)** \\ \end{tabular} & **Fuel** \\ \hline
100 & Artificial land & 5.24 & 2.32 & 3.10 & No \\
200 & Agriculture & 19.64 & 26.90 & 35.19 & No \\
420 & Spontaneous grassland & 3.78 & 3.63 & 8.28 & No \\
311 & Cork oak and holm oak & - & - & 9.42 & No \\
312 & Eucalyptus & 29.02 & 33.92 & 8.57 & Yes \\
313 & Other broadleaved forests & 8.66 & 7.81 & 4.07 & Yes \\
321 & Maritime pine & 8.27 & 4.32 & 4.74 & Yes \\
322 & Stone pine & 0.05 & 0.02 & 1.59 & Yes \\
323 & Other conifers & 0.11 & 0.11 & 0.22 & Yes \\
410 & Bush & 16.91 & 16.99 & 17.60 & Yes \\
500 & Bare soil & 6.80 & 3.49 & 3.52 & No \\
610 & Wetlands & 0.49 & 0.13 & 0.25 & No \\
620 & Water & 1.04 & 0.37 & 3.45 & No \\ \hline \end{tabular}
\end{table}
Table 1: COSsim 2018 land cover classes.  
The delimitation of the modelling area was made from a buffer of 100 m around the built areas, corresponding to the footage of the largest range of fuel management band established in the Decree-of-Law 124/2006. The total area with fuel analysed corresponds to 7311.88 hectares and the area without fuel corresponds to 9250.88 hectares, according to COSsim 2018.
## 3 Model Description
### Overview of Research  
The overall research method of this study is sown in Figure 8, which includes experimental, data collection, model training and optimization, prediction, and evaluate. First, the outdoor meteorological data from the nearest weather station and the historical indoor weather data from two solar greenhouses were collected, the training set of each experiment was divided by four sliding windows (T-3h, T-6h, T-12h, and T-24h), and the test set of each best experiment was also divided by four sliding windows (T+3h, T+6h, T+12h, and T+24h), a total of 16 training sets and the corresponding test sets were generated, the sequence number and division methods of each combination of training set and the corresponding test set are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Factor** & **A\({}_{\mathbf{c}}\)** & **B\({}_{\mathbf{c}}\)** & **C\({}_{\mathbf{c}}\)** & **D\({}_{\mathbf{c}}\)** & **E\({}_{\mathbf{c}}\)** & **F\({}_{\mathbf{c}}\)** & **G\({}_{\mathbf{c}}\)** \\ \hline Levers & \(8,16,32\) & \(1,2,3\) & \(4,8,16\) & \(1,2,3\) & \(10,20,30\) & \(10,20,30\) & \(10,20,30\) \\ \hline CNN-LSTM1 & 8 & 1 & 4 & 1 & 10 & 10 & 10 \\ CNN-LSTM 2 & 8 & 2 & 8 & 2 & 20 & 20 & 20 \\ CNN-LSTM 3 & 8 & 3 & 16 & 3 & 30 & 30 & 30 \\ CNN-LSTM 4 & 16 & 1 & 4 & 2 & 20 & 30 & 30 \\ CNN-LSTM 5 & 16 & 2 & 8 & 3 & 30 & 10 & 10 \\ CNN-LSTM 6 & 16 & 3 & 16 & 1 & 10 & 20 & 20 \\ CNN-LSTM 7 & 32 & 1 & 8 & 1 & 30 & 20 & 30 \\ CNN-LSTM 8 & 32 & 2 & 16 & 2 & 10 & 30 & 10 \\ CNN-LSTM 9 & 32 & 3 & 4 & 3 & 20 & 10 & 20 \\ CNN-LSTM 10 & 8 & 1 & 16 & 3 & 20 & 20 & 10 \\ CNN-LSTM 11 & 8 & 2 & 4 & 1 & 30 & 30 & 20 \\ CNN-LSTM 12 & 8 & 3 & 8 & 2 & 10 & 10 & 30 \\ CNN-LSTM 13 & 16 & 1 & 8 & 3 & 10 & 30 & 20 \\ CNN-LSTM 14 & 16 & 2 & 16 & 1 & 20 & 10 & 30 \\ CNN-LSTM 15 & 16 & 3 & 4 & 2 & 30 & 20 & 10 \\ CNN-LSTM 16 & 32 & 1 & 16 & 2 & 30 & 10 & 20 \\ CNN-LSTM 17 & 32 & 2 & 4 & 3 & 10 & 20 & 30 \\ CNN-LSTM 18 & 32 & 3 & 8 & 1 & 20 & 30 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of an orthogonal design experiment for the hyperparameter of CNN-LSTM model.  
Figure 8: The overview of the research in this paper.  
Secondly, we developed three deep models of DNNR, LSTM and CNN-LSTM, in order to improve the efficiency of model training, the combination of hyperparameters for three models were generated using the orthogonal design, respectively. For three different types of models, each experimental model designed by orthogonal experiments was trained and tested 16 times in 16 datasets. For the DNNR, LSTM and CNN-LSTM models, we obtained the training results for 144, 144, and 288 experimental models, respectively.
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 3 RARZ Protocol
### Protocol Description  
The detailed description of the algorithms is depicted from the following algorithms,  
```
1. Begin
2. Initialization
3. Set NodeRingID \(\leftarrow-1\) //initially in simulation all nodes ID's set to \(-1\)
4. Set MaxRings \(\leftarrow\) 10
5. Set MsgSeqNo \(\leftarrow\) 1
6. Set RingID \(\leftarrow\) 0
7. Set RecvPCKT \(\leftarrow\) 0
8. Set NODE_RADIO_RANGE \(\leftarrow\) 100
9. While RingID \(<\) = MaxRings
10. If Node = BS then, BroadCast Message with following parameters,
11. Set MsgKind \(\leftarrow\) 0
12. Set RingID \(\leftarrow\) RingID + 1
13. Set NODE_RADIO_RANGE \(\leftarrow\) NODE_RADIO_RANGE + 50
14. Else
15. If NodeRingID \(\leftarrow-1\) then
16. Set NodeRingID \(\leftarrow\) RingID
17. End If
18. End If
19. End of While
20. Set NODE_RADIO_RANGE \(\leftarrow\) 100 //After ring formation, set the node max rangeEnd
21. End
```  
**Algorithm 1**Pseudo code for Network Configuration/setup phase  
```
1. Begin
2. For each node Schedule MSG
3. Set MsgKind \(\leftarrow\) 0
4. If Msg = SelfMsg then //SelfMsg is node own schedule message
5. Set MsgKind \(\leftarrow\) 1
6. BroadCast Msg
7. End if
8. If MsgKind \(\leftarrow\) 1 and Node \(\leftarrow\) BS then
9. Set RecvPCKT \(\leftarrow\) RecvPCKT + 1
10. Delete Msg
11. Else
12. If MsgScheduled = RecvMsgSeqNo then
13. CANCEL Event
14. Delete Msg
15. Else
16. If RecvPCKT_ringID < = RingID then
17. Delete Msg
18. Else
19. Schedule MSg for relaying by adding its own RingID
20. End if
21. End if
22. End while
23. End
```  
**Algorithm 2**Pseudo code for Data Communication phaseWhenever a node acts as an intermediate node and sends the data further, there are certain nodes that would not see the data for which they have already started their timers. This will result in duplicate packets because their timers will also expire for the same data. We handled this situation in [31].
## 4 Dynamic Placement of SFC Based on Meta Reinforcement Learning and Fuzzy Logic
### Overview  
As IoT-MEC scenarios are rapidly changing and often affected by many factors and traditional intelligent schemes cannot adapt to changes in the environment and need to retrain the neural network, we utilize meta reinforcement learning to tackle the above challenges. It comprises an outer model and a inner model. The former is used to learn the initial parameters of the inner model. When the environment undergoes changes, such as variations in cloudlet performance or link bandwidth, the parameters of the neural network in the inner model can be adjusted by the outer model. The latter uses DDQN and fuzzy logic to realize an SFC placement decision model by receiving the SFCRs and initial parameters.  
Figure 2 shows an illustration of the MRLF-SFCP. _outer model\({}_{1}\)_ is used to adjust the parameters of the VNF placement neural network. _outer model\({}_{2}\)_ is used to adjust the parameters of the traffic routing neural network. To achieve optimal SFC placement, first, we use _outer model\({}_{1}\)_ to acquire the initial parameters of neural network. Then, the optimal combination of VNF instances is acquired using _inner model_ (DDQN1). Subsequently, a verification is conducted to ascertain the placement status of the necessary VNF instances on the designated cloudlets. If the required VNF instances do not exist, they are dynamically placed onto the corresponding cloudlets. Then, we utilize _outer model\({}_{2}\)_ to acquire the initial parameters of _inner model_ (DDQN2). Additionally, to establish connectivity between the source and destination node, as well as the selected cloudlets generated by _inner model_ (DDQN1), MRLF-SFCP employs fuzzy logic for preliminary link evaluation and utilizes the DDQN algorithm to determine the optimal route. For instance, in Figure 1, there are four paths that need to be produced in a predefined order, namely, (IoT terminal, \(Cloudlet_{3}\)), (\(Cloudlet_{4}\)), (\(Cloudlet_{4}\), \(Cloudlet_{5}\)), and (\(Cloudlet_{5}\), src). We need to perform fuzzy logic and DDQN four times for traffic routing. In particular, the evaluation of link quality, considering factors such as available bandwidth, link and node delays, and packet loss rate, is performed using fuzzy logic. The evaluation result is input to _inner model_ (DDQN2) for routing selection.
# SDN Anomalous Traffic Detection Based on Temporal Convolutional Network
## 4 Experiments and Analysis
### Dataset
The InSDN dataset differs from other standard intrusion detection datasets in several key aspects, particularly in its relevance to SDN-specific security threats. It includes control-plane-specific attacks, such as Flow Table Flooding and Controller Resource Exhaustion, which are not present in traditional datasets. Additionally, InSDN's traffic patterns reflect the dynamic nature of SDN architectures, where network flows are centrally managed by controllers rather than distributed routing mechanisms. To ensure a balanced evaluation, our study includes these datasets to validate the model's performance under more generalized network conditions.
## 3 Description of the Carols Campaigns
### Measurement Sites  
We selected three different test sites for the CAROLS data acquisitions, over land and ocean surfaces. Simultaneously to the CAROLS measurements, different _in situ_ measurements were acquired in order to qualify the CAROLS data, the direct emissivity models, and finally the inversion algorithms.  
The ocean measurements were made over the Bay of Biscay [Figure 7(a)]. In order to support the Airborne CAROLS experiment, ship campaigns were organized by the LOCEAN team for each CAROLS campaign, along the same track as that used by the aircraft. Measurements of SSS, sea surface temperature (SST), wind speed, foam, wave spectrum, heat and momentum flux were collected on the ship. Different drifters measuring SSS and SST were deployed. All flights passed over or close to the Gascogne Buoy (45.2\({}^{\circ}\)N, 5\({}^{\circ}\)W), which is an operational buoy operated by the French Meteorological Office providing SST, pressure, wind, wave spectrum.  
This first land site, characterized by a temperate climate, is situated in the South West of France [Figure 7(b)]. The Soil Moisture Observing System--Meteorological Automatic Network Integrated Application (SMOSMANIA) determines soil moisture values, based on a portion of automatic ground station network (the RADOME network) [15].  
\begin{table}
\begin{tabular}{l|c|c|c} \hline  & **CAROLS’2007** & **CAROLS’2008** & **CAROLS’2009** \\ \hline
**Period** & 24/09/2007–28/09/2007 & 12/11/2008–24/11/2008 & 23/04/2009–28/05/2009 \\ \hline
**Number of flights** & 4 flights & 4 flights & 13 flights \\ \hline
**Studied sites** & Gulf of Biscay, South & Gulf of Biscay, South & Gulf of Biscay, South West \\  & West of France & West of France & of France, Valencia site \\ \hline
**Departure** & Toulouse (France) & Toulouse (France) & Toulouse (France) \\ \hline \end{tabular}
\end{table}
Table 2: Description of the flights undertaken during the CAROLS campaigns.  
The second land Site, with semi-arid climate, is located near Valencia in South East Spain, about 80 km inland, to the West of Valencia [Figure 7(c)]. Within the Valencia validation site, an area of 30 km \(\times\) 50 km was selected for the experiment [16].  
Figure 7: Illustration of flight transects, **a** (top) illustration of one ocean flight transect over the Gulf of Biscay, **b** (middle) Illustration of a SMOSMONIA flight transect. Markers are pointing to the location of the 12 SMOSMANIA measurement sites, **c** (bottom) Illustration of a Valencia transect flight.