esa-sceva/satcom-chunk-collection huggingface dataset:
# Protection of Personal Data in the Context of E-Commerce
## 3 Protection of Personal Data on the Internet
### The Importance of Protecting Personal Data on the Internet  
The increasing value of personal data can be attributed to its crucial role in marketing and the potential risks associated with its misuse and involvement in unlawful activities. Companies like Google and Facebook have revolutionized the Internet advertising industry by harnessing extensive top-notch personal data. As a result, they have outperformed traditional advertising channels like television and radio. Personal data protection within information systems has emerged as a pressing social concern and a vital aspect of contemporary business operations.  
Severe consequences can arise from mishandling personal data, such as privacy violations and infringing fundamental human rights. Individuals face significant risks when fraudulent activities, such as unauthorized contracts and bank withdrawals, result from data breaches. An economic model called the Personal Information Management System (PIMS) has emerged in response to the growing demand for strong data protection. PIMS enables users to securely manage their data instead of relying on companies that may potentially sell it. Data confidentiality is prioritized in these systems, contrasting with service providers monetizing client data.  
Contractual obligations ensure that PIMS providers prioritize the confidentiality of personal data, providing a secure option for users who may not have the time or technical know-how to handle their data. Implementing this approach can make data transfers between companies with similar activities safer while protecting personal information from exploitation.
# Dementia Classification Using Deep Reinforcement Learning for Early Diagnosis
## 2 Related Work
### Literature Review and Observation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Year** & **Aim** & **Methodology** & **Feature** & **Dataset** & **Limitation** \\ \hline \hline \multirow{8}{*}{[20]} & \multirow{8}{*}{(2021)} & \multicolumn{5}{c}{ADNI Dataset} \\ \cline{3-6}  & & & DTI processing in three dimensions using CNNs and RF Rank modulated decision fusion for mapping the outputs of CNNs and RF. & Fractional Anisotropy (FA) & \multirow{8}{*}{ADNI database (ED) Dementia} \\  & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \hline \multirow{8}{*}{[21]} & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} \\  & & & & & (FM) & \\ \cline{3-6}  & & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \hline \hline \multirow{8}{*}{[21]} & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} \\  & & & & & (FM) & \\ \cline{3-6}  & & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \hline \hline \multirow{8}{*}{[25]} & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} & Using DL technology, Alzheimer’s illness can be recognized automatically using MRI data. & \multirow{8}{*}{3D brain MRI with DL Training} & CNN Network Training & ADNI and OASIS database \\ \cline{3-6}  & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \hline \multirow{8}{*}{[34]} & \multirow{8}{*}{(2022)} & Alzheimer’s Disease Diagnosis and Monitoring Using Classical Machine Learning Techniques & A unique method for diagnosing and monitoring AD using ML & A correlation-based feature selection (CFS) & OASIS and ADNI dataset & A large dataset is a significant challenge. \\ \cline{3-6}  & & & & (FM) & \\ \hline \multicolumn{6}{c}{Other Dataset} \\ \cline{3-6}  & \multirow{8}{*}{(2021)} & \multirow{8}{*}{(2021)} & The diagnosis of AD in (Mild Cognitive Impairment) MCI patients using DNN & Volumetric Features Extraction and DNN & Structural MRI features & GUARD & Individual Modalities \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & (FM) & \\ \hline \multirow{8}{*}{[29]} & \multirow{8}{*}{(2020)} & Automatic detection and classification of dementia using DL & Transfer learning and Deep neural networks & Clockdrawing test\({}^{\prime}\) (CDT) & From July 2018, a neuropsychiatric clinic will be located in Nürmberg. & \multirow{8}{*}{Overfitting} \\ \cline{3-6}  & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \cline{3-6}  & & & & (FM) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Study of literature for Deep learning-based Dementia.
## 4 Results
### Using the Real Database  
The potential of all developed neural networks (NN1, NN1_dry, NN1_wet, NN2, NN2_dry, NN2_wet, NN3, NN3_dry, and NN3_wet) to estimate soil moisture was evaluated using the real database collected over the French study area. This evaluation was performed using only the database elements with NDVI values lower than 0.75. Indeed, in this study, the results showed that the radar signal in the VV and VH polarizations is not sensitive to soil moisture for an NDVI higher than 0.75. The rain information registered over the study area was used to determine the appropriate neural networks with a priori information. The neural networks with a priori information of dry to slightly wet (NN1_dry, NN2_dry, NN3_dry) were applied if the SAR image was acquired far from a rainy episode or in the context of the fast drying out of soil (high temperatures). The neural networks with a priori information of very wet (NN1_wet, NN2_wet, NN3_wet) were applied if the SAR image was acquired after an intense rainy episode.  
Similar results are obtained with the VV alone, the VH alone or using the VV and VH together in the case where no a priori information on the \(mv\) is considered with a bias of approximately 2.6 vol % (overestimation of \(mv\)) and an RMSE of approximately 7.2 vol %. For all neural networks, the results show that the use of a priori information considerably improves the precision of the estimated soil moisture by approximately 2 vol % for both the bias and RMSE. As an example, the NN1 (no a priori information, VV) overestimates the in situ soil moisture by 2.6 vol % and allows for soil moisture estimates with an accuracy of 7.2 vol % (RMSE), whereas the NN1_dry and NN1_wet (with a priori information, VV) provide a higher accuracy on the estimated \(mv\) (bias = 0.2 vol % and RMSE = 5.0 vol %) (Figure 9a,d). Moreover, with the consideration of a priori information on the \(mv\), the results show that the use of the VV alone allows for better soil moisture estimates than the use of the VH alone and a similar precision when both the VV and VH polarization are used. The RMSE on the \(mv\) estimates using a priori information on the \(mv\) is 5.0 vol % with VV (bias = 0.2 vol %), 5.9 vol % with the VH alone (bias = \(-1.1\) vol %), and 5.1 vol % with the VV and VH together (bias = 0.4 vol %).  
For very rough plots with an \(Hrms\) higher than 3 cm (red ellipses in Figure 9), the NNs highly overestimate the in situ \(mv\). For example, with the VV alone using a priori information on the \(mv\) (NN1_dry and NN1_wet), the estimated \(mv\) values are higher than the reference \(mv\) by approximately 9 vol %. For very smooth plots with an \(Hrms\) lower than 1 cm (blue ellipses in Figure 9), the NNs highly underestimate the in situ \(mv\). For example, using the VV alone with a priori information on the \(mv\) (NN1_dry, and NN1_wet), the estimated \(mv\) values are lower than the in situ \(mv\) by approximately 10 vol %.  
Figure 9: \(Cont.\)
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 2 Limitations on Existing IIoT Solutions
### Long-Range Connectivity  
The first idea that comes to mind in order to solve the issues described above is cellular networks: they are based on public infrastructure, they are widely deployed and cover large areas, and they are operated employing well-known standards such as GSM, GPRS, or 3G/4G. Following this strategy, the edge-sensors collect the data of interest and, afterwards, send it to the cloud via a cellular data link, e.g., GPRS, 3G, _etc._ However, the main problem with these systems is that they were designed to fulfill different requirements than those of IIoT services. While in cellular networks the trend has been increasing the available bandwidth, aiming to accomplish the increasing demand of multimedia traffic by human users [13], in IIoT services the strategy should be optimizing bandwidth usage and decreasing energy consumption and costs [14]. Current cellular base stations are capable of hosting a small number of connected users (in comparison with the needs of sensorization services), with a relatively high bandwidth assured for each of them. In turn, what a machine-only network demands is a solution for supporting a huge number of low-throughput connected devices that send short messages only once in a while. Therefore, the current cellular solutions are clearly inefficient in terms of scalability and energy consumption. Regarding the former, one possible strategy for organizing and providing connectivity to independent systems is using femtocells [15] or picocells [16]. However, this solution notably increases the system cost as new equipment and connection infrastructure are required. Focusing on energy efficiency, cellular networks need a quasi-constant communication between edge-nodes and the base station for management tasks (protocol overhead), which is completely devastating for battery lifetime. Moreover, existing cellular networks work on scarce and expensive (licensed) frequency bands.  
Another solution with even more drawbacks is satellite communications. Although they provide a good coverage worldwide, the energy consumed in each transmission is too much for IIoT applications. In addition, the high latency of these transmissions could be inadmissible for certain applications with strict temporal constraints. Finally, with respect to network costs, subscribing a satellite connection plan is still excessively expensive. Although cheaper, current cellular network operators have not substantially reduced their subscription fees. For all these reasons, Low-Power Wide Area Networks appear as an alternative long-range solution to give response to the IIoT services' demands.
## 2 Materials and Methods
### Theory Basics
#### 2.2.2 Self-Triggered Model Predictive Control
\tag{16f}\]  
where (16c) and (16d) represent the constraints on the control input and state variables, respectively, while (16e) represents the terminal constraint. By minimizing the aforementioned cost function under the given constraints, we can obtain the optimal control sequence \(U^{*}(\vartheta_{\tau})\). Similarly, at time \(\vartheta_{\tau}\), only the first element of the control sequence is applied to the control system. The algorithm running structure is shown in Algorithm 2. ```
1. Build a control model: \(\chi_{ra}(\vartheta+1)=A_{r}\chi_{ra}(\vartheta)+B_{r}\Delta\mu_{ra}(\vartheta)\),
2. Build a prediction model: \(\chi_{a}(l;\vartheta_{\tau})=A\chi_{a}(l-1;\vartheta_{\tau})+B\mu_{a}(l-1; \vartheta_{\tau})\),
3. Define \(\tau_{a}\) and \(\{\tau_{a},\vartheta\},\vartheta\in\{1,2,...,N\}\),
4. Get \(\tau_{a+1}\), from \(\tau_{a+1}=\tau_{a+1}+\sigma T,\sigma\in\{1,2,...,N_{p}\}\),
5. Define the cost function: \[J_{a}(\chi_{a}(\tau_{a,\theta+1}),\mu_{a}(\tau_{a,\theta+1}))=\sum_{l=0}^{N_{ p}}\left[\parallel\chi_{a}(l;\tau_{a,\theta+1})-R_{a,l}(\tau_{a,\theta+1}) \parallel_{Q_{a}}^{2}+\parallel\mu_{a}(l;\tau_{a,\theta+1})\parallel_{R_{a}}^ {2}\right]\] \[+\parallel\chi_{a}(N_{p};\tau_{a,\theta+1})-R_{a,l}(\tau_{a,\theta +1})\parallel_{P_{a}}^{2};\]
6. Introduce constraints: (16c,d,e),
7. Solve 5 and get the optimal control sequence: \(U^{*}(\vartheta_{\tau})\),
8. Repeat steps 3-7. ```  
**Algorithm 2** Self-Triggered Model Predictive Control
## 4 Case Study--Analysis of Disruptions
### Analysis Objectives  
* Identification of lost/delayed containers;
* Identification of temperature exceedances during transportation: \(\bigcirc\)  At the beginning;
* During transportation;  
Figure 2: Diagram of the methodology.  
At the end of the cycle.
* Identification of container utilization.  
Data collection devices (Gateways) are positioned in the area where containers are received, and goods are dispatched in containers. The arrangement of Gateways is presented below.  
In a typical DC, there are three types of stored goods, and the storage areas are colloquially named accordingly:  
* Dry: Products stored at room temperature, dry, without the need for temperature control, mainly products with a long shelf life;
* Fresh: Products stored at controlled temperatures, i.e., from \(2\)\({}^{\circ}\)C to \(6\)\({}^{\circ}\)C, referred to as fresh products;
* Frozen: Products stored at controlled temperatures, i.e., below \(-12\)\({}^{\circ}\)C (frozen) or \(-20\)\({}^{\circ}\)C (deep-frozen).
###### Abstract  
We aimed to compare the spatiotemporal parameters and joint kinematics during unperturbed and perturbed gait between individuals with osteoarthritis (OA) who did or did not undergo total knee replacement (TKR) one year post a baseline evaluation. OA subjects scheduled for TKR (TKR group; \(n=14\)) and not scheduled for TKR (NTKR group; \(n=17\)) were age-matched. Outcome measures included: joint range of motion, timed up and go, joint pain levels, Oxford score, and the Activities-specific Balance Confidence Scale. In addition, spatiotemporal gait parameters and joint kinematics were recorded during perturbed and unperturbed gait. After one year, most of the TKR group (71%), but only 41% of the NTKR group, increased their gait velocity by more than 0.1m/sec, which is the meaningful clinical important difference for gait velocity. After perturbation of the contralateral limb, the TKR group showed a greater decrease in the maximal extension of the OA hip compared to the NTKR group (\(p=0.031\)). After perturbation of the OA limb, more subjects decreased their OA knee flexion-extension range in the NTKR group compared to the TKR group (\(p=0.011\)) and more subjects decreased their maximal ankle plantar flexion in the TKR group (\(p=0.049\)). Although the surgery was successful in terms of pain reduction and increased functionality, individuals following TKR exhibited unique compensatory strategies in response to the perturbation of both limbs. These findings might suggest that balance deficits remain in individuals following TKR and therefore are associated with a risk of falls.  
gait analysis; arthroplasty; fall; knee surgery; joint pain; gait perturbation +
Footnote †: journal: Applied Sciences
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 2 Materials and Methods
### Precipitation Data  
In the study carried out by [PERSON] et al. [27], it was shown that precipitation can hinder the detection of moving events when using a similar approach to that employed in this research. In order to investigate the effect precipitation has on temporal coherence, precipitation amounts before the images in the first interferometric pair after the moving events were estimated using a DualPol weather radar located in Surgavere, 50 km to the north-west of the study area. The weather radar was produced by Vaisala Group, and it operates in the C-band (wavelength of 5.33 cm).  
The Estonian Weather Service provided 15-minute scan files which were processed with the _wradlib_ Python package ([PERSON] et al. [37]) to produce pseudo constant altitude plan position indicator (PseudoCAPPI) estimates in the height of 500 m. Using the 15 min interval PseudoCAPPI estimates, accumulated rainfall maps at 300 m \(\times\) 300 m spatial resolution were computed for 1 h, 3 h, 6 h, 12 h, and 24 h before the Sentinel-1 acquisitions. Precipitation estimates for each of the fields were created by averaging those 300 m \(\times\) 300 m pixels whose centre intersected with the spatial extent of the moving event. The accuracy of precipitation estimates was assessed by comparing 1-h estimates larger than 0.10 mm with hourly measurements (259 measurements with an average of 1.1 mm and a maximum of 9.5 mm) in Toravere meteorological station (15 km to the east of the study area) over the period from May to September 2015. The coefficient of determination R\({}^{2}\) was 0.67 and Root Mean Squared Error (RMSE) was 0.95. Comparing the accuracy of precipitation estimates to the results from [PERSON] et al. [38], it was concluded that our estimates are accurate enough to be used in this study.  
Precipitation amounts can show great spatial variability. For example, Toravere meteorological station did not register any precipitation between 13:00 to 16:00 UTC on 31 July, while precipitation estimates indicated 1.4 mm precipitation over the measurement areas. By using the estimates from the weather radar, we aimed to take this spatial variability into account.  
Precipitation estimates for three image pairs before and three after a moving event were used to analyse the effect precipitation has on coherence values. The _matplotlib_ Python package [39] was used for visualisation.
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 3 Proposed Algorithm
### Main Loop  
In Algorithm 1 the main loop of the proposed method is given. When the initial object is selected, the KCF tracking model \(M\) is initialized. KCF tracker runs in all frames of the sequence. When tracking result is none or with large error, the MTCNN detector is started in the next frame. Discrimination fuses the tracking result \(Tr\) and detecting result \(Dr\) into a final result \(R\).  
The main works of discrimination as follows: (1) When \(Tr=0\) and \(Dr\
eq 0\), we set \(R=Dr\); (2) when \(Tr\
eq 0\) and \(Dr\
eq 0\), \(R\) equals to the maximum one of two similarities (\(t\_S^{c}\) and \(d\_S^{c}\)), then \(R\) is used to update \(M\); (3) when \(Tr=0\) and \(Dr=0\), the tracker is failure, we set \(R(i)=R(i-1)\).  
At this stage, we get the result in frame \(i\). After printing the result, the next image is processed.  
In frame \(i+1\), whether the detector is performed is decided by the parameter of _EnableDetect_ in frame \(i\). If the _EnableDetect_ is set as True in frame \(i\), we start to detect. In two cases the _EnableDetect_ is True: (1) no tracking result; (2) \(\text{RNet.score}(R,\text{I}(i))<0.99\). To avoid tracking other objects that are not faces, we test the result every \(k\) frames (we used three in the experiment). The result is sent to R-Net to calculate the score of face classification. If the score is less than 0.99, we set _EnableDetect_ to True. Compared to the complex O-Net, we chose R-Net which can achieve good accuracy and faster speed.  
Then, we proceed to the next frame until the last one. In a word, the whole tracking process is completed.  
Figure 5: Face detection strategy.  
```
Input: image I, EnableTrack = false, EnableDetect = True, the frequency of judge whether tracker's result is correct \(k\). Output: the final result \(R\).
1 for\(i=1\):\(n\)do
2 if EnableDetect \(\parallel\) EnableTrack then
3 Detecting;
4 EnableDetect = False;
5 endif
6 if\(i=1\)then
7 given the initial object: initobj;
8 initialize tracking filter model \(M\);
9 else
10 if(\(enableTrack\parallel Dr(i)!=0\))then
11 Tracking;
12 if\(Tr(i)!=0\)then
13 EnableTrack = True;
14\(R(i)=Tr(i)\);
15 if\(Dr(i)!=0\)then
16\(R(i)=\max(\text{matchTemplate}(initobj,Tr(i)),\text{ matchTemplate}(initobj,Dr(i)))\);
17\(M=\text{update}(\text{I}(i),R(i))\);
18 endif
19 elseif\(Dr(i)!=0\)then
20\(R(i)=Dr(i)\);
21\(M=\text{update}(\text{I}(i),R(i))\);
22\( EnableDetect=\text{True}\);
23 else
24\(enableTrack=\text{False}\); \(enableDetect=\text{True}\);
25\(R(i)=R(i-1)\);
26 endif
27 if\(k>3\)&& RNet.score(\(R\), I(\(i\))) \(<0.99\)then
28\(enableDetect=\text{True}\);
29\(k=1\);
30 endif
31\(k\)++;
32 endif
33 endif
34 show(\(R(i)\));
35 endfor
```  
**Algorithm 1** Overall Process of the Proposed Algorithm.
## 2 Experiment
### Experimental Setup  
#### 2.2.1. Aquathermolysis Experiments  
The setup of the aquathermolysis experiments is detailed in Figure 1. The aquathermolysis experiments were performed in a closed autoclave reactor (Anhui Kemi Machinery Technology Co., Ltd., Hefei, China). The 300 mL reactor was made of Hastelloy C276, and its maximum operating pressure and temperature can achieve 34.5 MPa and 500 \({}^{\circ}\)C, respectively. A thermal control system was used to control the temperature of the autoclave reactor by an electric furnace and displayed the temperature data transmitted by an internal temperature probe in real time. A pressure gauge (Jasco 25-1009-SW-02L-400 BR-X6W) with a range of 0-40 MPa was used to monitor the pressure during each experiment. The top of the reactor was equipped with a magnetic stirrer with stirring fluid, and a circulating water bath was provided to prevent the magnetic stirrer from being damaged at high temperatures. A vacuum pump was applied to empty the air in the system in advance to improve the accuracy of the experiments. Before each experiment, a certain amount of heavy oil and water were weighed by an electronic balance, and nitrogen was used as an eluent gas. After each experiment, 500 mL gas bags were used to collect the gas products. A gas chromatograph (7890A, Agilent CO., Ltd., Santa Clara, CA, USA) equipped with a flame ionization detector (FID) and sulfur chemiluminescence detector (SCD) was used to analyze the composition of gas products.
## 3 Results and Discussion
Figure 3: (**a**) The top and bottom layers of the fabricated proposed antenna, (**b**) simulated and measured antenna reflection coefficient, (**c**) proposed antenna design steps, and (**d**) reflection coefficient comparison for the design steps. Figure 4: _Cont_. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Notch No.** & **10\(-\)dB BW (GHz)** & \(f_{e}\) & **PBW \%** & **Gain at/\(f_{e}\)** \\ \hline
1 & 2.88-3.93 & 3.1 & 33.87 & \(-\)6.35 \\ \hline
2 & 4.15-4.88 & 4.64 & 15.73 & \(-\)0.69 \\ \hline
3 & 5.23-5.88 & 5.55 & 11.71 & \(-\)1.20 \\ \hline
4 & 6.44-6.97 & 6.95 & 7.63 & 0.70 \\ \hline
5 & 7.29-7.82 & 7.58 & 6.99 & 1.07 \\ \hline
6 & 8-9.08 & 8.84 & 12.22 & 0.98 \\ \hline \multicolumn{5}{c}{Measured Characteristics of the Proposed Antenna at the Resonances} \\ \hline
**Band No.** & **6\(-\)dB BW (GHz)** & \(f_{e}\) & **PBW \%** & **Gain at/\(f_{e}\)** \\ \hline
1 & 2.43-3.03 & 2.7 & 22.22 & 1.02 \\ \hline
2 & 3.71-4.23 & 4.05 & 12.84 & 3.52 \\ \hline
3 & 4.76-5.38 & 5.05 & 12.28 & 3.77 \\ \hline
4 & 5.83-6.54 & 6.04 & 11.75 & 3.82 \\ \hline
5 & 6.85-7.44 & 7.15 & 8.25 & 4.06 \\ \hline
6 & 7.56-8.01 & 7.90 & 5.78 & 3.5 \\ \hline
7 & 9.27–13.88 & 11.55 & 39.83 & 4.46 \\ \hline \end{tabular}
\end{table} 
Table 2: Measured characteristics of the proposed antenna at the notches. Figure 4: _Cont._
## 3 Bottom up Meets Top down-The Emerging Use of Google Earth by Grass Root Initiatives
### Bottom-up Example: Vancouver Views
#### 3.2.4 Discussion  
The Vancouver Views example demonstrates the potential of Google Earth to merge user-generated content visualizing a citizen group's point of view with existing Open Data, provided by the City, and Google's own data, in this case a high quality 3D model of the downtown buildings [47]. The use of Google Earth facilitated access to visual information, as well as data display from multiple perspectives. However, despite the accessibility of Google Earth itself, the actual implementation still requires considerable expert knowledge to prepare and transform the geodata, model the 3D buildings and calculate the view cones. While there is anecdotal evidence that the visualization might have impacted the policy making process, it is not possible without more data to directly link the amended City Report to the grassroots campaign or to distinguish the role of the Google Earth visualizations from WEN's other efforts.