esa-sceva/satcom-chunk-collection huggingface dataset:
# Twitter-Based Safety Confirmation System for Disaster Situations
## 3 Proposed Application T-@npi
### Operational Test for Registration of Safety Information  
To verify the submission of safety information by users, an operational test was conducted from October 30 to November 20 of 2018. The experimental participants (42 in total) were 17 students from the School of Information and Telecommunication Engineering (IT) and 25 students from the School of Business Administration (BA). All students were recruited from Tokai University (Tokyo, Japan). A single account created for this test was shared among all participants. Only one participant failed to submit their safety information, seemingly because they did not correctly enable the location function of his smartphone.  
After completion, the operational test was reviewed in a group discussion. None of the IT participants reported difficulty with operating the system. Some participants opened that more detailed information could be provided by offering more functions and answer options. In contrast, some of the BA participants considered that the system should be simplified. The differences in opinions appeared to depend on the skills of the smartphone operations. The skills problem could be solved by providing two types of registration pages: easy mode and advanced mode.  
Moreover, the opinions and contentious aspects of the system were obtained from a questionnaire allocated to each participant. Representative comments from the participants are shown below.  
* It would be useful if I could check people who have found my information. (IT)
* The web application (accessed by web browsers) is time-consuming compared with a native application. This should be improved. (IT)
* To popularize the system, the application should collaborate with other popular applications used in ordinary times. (BA)
* The system should additionally collaborate with SNSs other than Twitter. (BA)
* The application may be abused because we can post anonymously. (BA)  
The above comments will guide the improvements of the proposed system.
Sensors_**2014**, _14_, 2199-2224; doi:10.3390/s140202199  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Multimetric, Map-Aware Routing Protocol for VANETs in Urban Areas**  
**[PERSON] \({}^{1,2,\ast}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\),**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\)**  
**and [PERSON] \({}^{2}\)**  
\({}^{1}\) Department of Telematic Engineering, Universitat Politecnica de Catalunya (UPC),  
C[PERSON] 1-3, Barcelona 08034, Spain; E-Mails: [EMAIL_ADDRESS] (L.U.-A.);  
[EMAIL_ADDRESS] (M.A.I.); [EMAIL_ADDRESS] (D.R.-M.);  
[EMAIL_ADDRESS] (L.J.C.L.); [EMAIL_ADDRESS] (A.M.M.)  
\({}^{2}\) Faculty of Informatics, Autonomic University of Sinalo (UAS), De los Deportes Avenue and  
Leenismo Internacional s/n, Mazatlan 82107, Mexico; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +52-1-6691-20-23-83.  
_Received: 3 December 2013; in revised form: 4 January 2014 / Accepted: 8 January 2014 / Published: 28 January 2014_  
**Abstract:** In recent years, the general interest in routing for vehicular _ad hoc_ networks (VANETs) has increased notably. Many proposals have been presented to improve the behavior of the routing decisions in these very changeable networks. In this paper, we propose a new routing protocol for VANETs that uses four different metrics. which are the distance to destination, the vehicles' density, the vehicles' trajectory and the available bandwidth, making use of the information retrieved by the sensors of the vehicle, in order to make forwarding decisions, minimizing packet losses and packet delay. Through simulation, we compare our proposal to other protocols, such as AODV (Ad hoc On-Demand Distance Vector), GPSR (Greedy Perimeter Stateless Routing), I-GPSR (Improvement GPSR) and to our previous proposal, GBSR-B (Greedy Buffer Stateless Routing Building-aware). Besides, we present a performance evaluation of the individual importance of each metric to make forwarding decisions. Experimental results show that our proposed forwarding decision outperforms existing solutions in terms of packet delivery.  
keywords:vehicular _ad hoc_ networks; multi-metric forwarding decisions; geographic routing protocol
## 3 Results  
Table 1 presents the wet chemistry results of dry matter, protein, and starch content in fresh tuber samples of white Guinea yarn (_D. rotundata_). There was a significant effect (\(p<0.05\)) of method and location on the starch content of D. rotundata, but only location effect (\(p>0.05\)) on dry matter and protein. The average content of dry matter, protein, and starch (%) from the Ibadan location were \(36.5\pm 4.24\), \(1.58\pm 0.36\), and \(28.75\pm 3.96\) in chopped samples; \(36.60\pm 4.18\), \(1.62\pm 0.34\), and \(27.47\pm 3.84\) in grated samples; and \(37.14\pm 4.87\), \(1.60\pm 0.33\), and \(26.97\pm 3.88\) in blended samples, respectively. For tubers collected from the Ubiaja location, the average dry matter content was found as \(37.98\pm 6.06\), protein \(1.69\pm 0.45\), and starch \(30.97\pm 1.90\) in chopped samples. Likewise, the average dry matter content was \(38.17\pm 5.19\) (grated) and \(38.75\pm 5.49\)% (blended), protein \(1.69\pm 0.53\) (grated) and \(1.84\pm 0.37\) (blended), and starch \(31.35\pm 1.57\) (grated) and \(31.49\pm 1.57\) (blended).  
Table 2 describes the dry matter, protein and starch contents of _D. alata_. There was a significant effect (\(p<0.05\)) of method and location on the starch content of D. alata, but only location effect (\(p>0.05\)) on dry matter and protein. The average dry matter, protein, and starch for the Ibadan location had a range (mean \(\pm\)_SD)_ for an average dry matter of \(20.61\)%-\(44.86\)% (\(29.99\pm 4.52\)%); for the protein of \(0.68\)%-\(3.11\)% (\(1.52\pm 0.33\)%), and starch of \(19.25\)%-\(36.93\)% (\(21.59\pm 1.68\)%) for chopped samples. Blended samples had an average of \(21.38\pm 1.70\)% for starch, \(1.51\pm 0.31\)% for protein, and \(31.14\pm 5.17\)% for dry matter. The estimation of starch was, on average, \(21.38\pm 1.70\)% with blended tubers and \(21.69\pm 1.79\)% with grated yam. The protein content of samples was estimated with an average of \(1.51\pm 0.31\)% when blended and \(1.58\pm 0.27\)% when grated. Likewise, the dry matter (%) was estimated as \(31.14\pm 5.17\) in blended samples and as \(30.86\pm 4.25\) in grated fresh tuber samples. Also, in the Ubiaja location, the average content of dry matter (%) in chopped tubers was \(32.45\pm 4.95\), starch \(28.41\pm 2.31\), and protein \(1.64\pm 0.51\). Grated samples contained \(27.93\pm 2.78\)% starch, \(32.04\pm 4.35\)% dry matter, and \(1.74\pm 43\)% protein. Blended tubers had values that ranged from \(20.18\)% to \(35.48\)% starch, \(22.30\)% to \(47.69\)% dry matter, and \(0.83\)% to \(4.84\)% protein.  
than that in grated samples with a mean value of \(27.47\pm 3.84\%\) (\(75.05\%\) dw) from Ibadan (Table 1). This could be because of variations in the particle size of the samples from different sampling methods. Starch content for _D. rotundata_ (Table 1) reported in this study is consistent with average values reported in the literature [29; 30; 31]. Similarly, the average starch content of chopped D.alata, \(28.41\pm 2.31\) from Ubiaja (Table 2), was higher when compared with values obtained for samples that were grated (\(27.93\pm 2.78\)) and blended (\(23.32\pm 2.32\)).
## 2 Cost Model of Predictive Maintenance
### Optimization Process
#### 2.4.2 Decision Variables  
The decision variables to calculate through the optimization process are:  
* \(RUL_{lim}\): the limit of the RUL that indicates that a PdM should be performed on the system. It means:
* If \(RUL_{system}\leq RUL_{lim}\) then the system should be maintained.
* If \(RUL_{system}>RUL_{lim}\) then the system operates normally and does not need to be maintained.
* \(\theta\): the inspection step, i.e., the interval between two consecutive inspections that can be deduced easily from the optimal \(N_{in}\) (see Equation (3)).
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 2 Materials and Methods
### Multispectral Stacking Module  
The BigEarthNet remote sensing image repository is sourced from the Sentinel-2 satellite, which is equipped with 13 multispectral bands and is a product of the European Space Agency (ESA), headquartered in Paris, France. It provides diverse typologies of Earth's surface information. These bands span the spectral regions of visible light, near-infrared, and shortwave infrared, a design that confers an advantage in applications such as monitoring vegetation health, agricultural management, forest surveillance, and land cover change detection. In contrast, other common satellites like Landsat or MODIS may be more suitable for long-term time series analysis, atmospheric and oceanic studies, as well as broad-scale global dynamic monitoring. The primary uses of each band and the types of information they typically represent are shown in Table 1.  
The multispectral stacking module is positioned before the input module of the model, leveraging information from 12 distinct spectral bands while excluding the 10 th band, which does not reflect surface information. In contrast to conventional RGB images, which comprise only three bands--where the R (red) channel corresponds to the B04 band, the G (green) channel corresponds to the B03 band, and the B (blue) channel corresponds to the B02 band--this module aims to effectively integrate information from a larger number of bands. To accommodate the varying resolutions of satellite image data, the 12 bands are divided into three groups.  
The four bands with a resolution of 10 m (B02, B03, B04, B08), each with dimensions of 120 \(\times\) 120, are stacked along the third dimension to create a new tensor, thereby generating an image with four channels. Similarly, the six bands with a resolution of 20 m (B05, B06, B07, B8A, B11, B12), each sized at 60 \(\times\) 60, are stacked along the third dimension to form a new tensor, resulting in an image with six channels. The remaining two bands with a resolution of 60 m (B01, B09), each sized at 20 \(\times\) 20, are also stacked along the third dimension to produce an image with two channels. Subsequently, images of bands with 20 m and 60 m resolutions are resized to match the 120 \(\times\) 120 dimensions of the 10-m bands using bicubic interpolation. Finally, the module concatenates these three tensors along  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Band Number**} & **Central Wavelength** & **Primary Use** \\  & **(nm)** & **Red edge–**V  
the third dimension, generating a new tensor that yields a multi-channel image of size 120 \(\times\) 120. The structure of the multispectral stacking module is illustrated in Figure 1.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
###### Abstract
The bias between the FLS and anemometer measurements depends on the sea conditions and the dynamic response of the buoy to the waves and can be up to several times the anemometer measurement. The FLS measurements can be corrected by associating the wind LIDAR with an Inertial Measurement Unit (IMU) measuring the movements of the buoy at high frequency. One correction method involves processing each of the radial wind speed measurements according to the IMU to cancel the effect of the buoy's motion. A detailed review of this method is presented in [16]. This method is based on a strong interaction between the two devices for a real-time correction, or on a storage of all the time series from the LIDAR and the IMU for a subsequently applied correction. In this study, we focus on the pulsed profiler WindCube v2\({}^{\text{\textregistered}}\) LIDAR from Leosphere\({}^{\text{\textregistered}}\). The contribution of this paper consists in a less computationally intensive model-based correction estimating the atmospheric turbulence from the time series of an IMU and the 10-min statistics of a pulsed floating LIDAR. By grouping the corrections of each radial measurement of the LIDAR in global matrices, this method reduces the need for time synchronization between the two devices and reduces the amount of data to be stored. The use of a simple model also allows to study and understand the influence of the buoy's movements on the floating wind LIDAR measurement. In Section 2, we explain the theory from the WindCube v2\({}^{\text{\textregistered}}\) LIDAR as well as the contribution of an IMU for buoy motion measurement. In Section 3, we present the model and its implementation for estimating the atmospheric turbulence intensity from 10-min floating LIDAR statistics. Then, we present the experimental conditions from the campaign conducted off the shores of Fecamp (Normandie, France), and its measurements were used to evaluate the correction method. In Section 4, we apply the model to understand buoyancy motions of increasing complexity; from the effect of a sinusoidal motion with one degree of freedom on a single measurement radius to the motions of the experimental validation campaign.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 2 Internet of Healthcare Things (IoHT)
### IoHT Components
#### 2.2.3 Intermediate Layer  
Devices in this layer have the responsibility to transmit data to potent computing resources like cloud servers. These devices act like gateways that enable data flow from the sensor devices to the cloud or central servers for storage and further analysis. These devices can run multiple communication protocols such as WiFi, Bluetooth, GSM, etc., [53] and forward the collected data to the server. Some intermediate devices can store data as well for pre-processing algorithms to evaluate whether the data is clinically relevant or not [45]. Some of the intermediate devices are discussed here briefly.  
Smart Hub is used to make communication easy with smart IoT devices but has many vulnerabilities that draw the attention of attackers into sniffing the traffic of the smart hub. If they can access networks, they will be able to spot IoT healthcare devices in the network [54].  
Access Points facilitate a wireless connection between different healthcare devices and connect them with remote servers.  
IoT Gateways primarily act as the bridge to connect sensor networks with conventional communication networks, enabling protocol conversion and device management.  
The System on Chip (SoC) is a device that integrates all components of a computer system, helps to normalize data collected from different sensors, and controls actuators based on applications. In this way, it reduces the load on a central server and minimizes the communication [55].  
Personal Digital Assistants (PDAs) are extensively used in the healthcare domain to support healthcare providers [56]. Such systems receive data from different wearable and implanted devices and process them through a variety of software applications [57].
###### Abstract  
In the treatment of brain diseases, most potent drugs that have been developed exhibit poor therapeutic outcomes resulting from the inability of a therapeutic amount of the drug to reach the brain. These drugs do not exhibit targeted drug delivery mechanisms, resulting in a high concentration of the drugs in vital organs leading to drug toxicity. Chitosan (CS) is a natural-based polymer. It has unique properties such as good biodegradability, biocompatibility, uncoadhesive properties, and it has been approved for biomedical applications. It has been used to develop nanocarriers for brain targeting via intranasal administration. Nanocarriers such as nanoparticles, in situ gels, nanoemulsions, and liposomes have been developed. In vitro and in vivo studies revealed that these nanocarriers exhibited enhanced drug uptake to the brain with reduced side effects resulting from the prolonged contact time of the nanocarriers with the nasal mucosa, the surface charge of the nanocarriers, the nano size of the nanocarriers, and their capability to stretch the tight junctions within the nasal mucosa. The aforementioned unique properties make chitosan a potential material for the development of nanocarriers for targeted drug delivery to the brain. This review will focus on chitosan-based carriers for brain targeting.  
Keywords:chitosan; drug delivery; brain targeting; nanoparticles; in situ gels; hydrogels; Alzheimer disease; epilepsy; Parkinson syndrome +
Footnote †: journal: Applied Soft. 2019, 9, 2219; doi:10.3390/app9112219
# Electromagnetic Assessment of UHF-RFID Devices in Healthcare Environment
## 2 Materials and Methods
### EMF Spatial Distribution and Level Measurements  
The comprehensive exposure assessment, source identification, and configuration at a maximum transmission power or actual maximum power was accurately performed for the purpose of this paper. The characteristic features of the RF EMF exposure were studied with respect to its spatial distribution to be denoted in E-field strength (E, in V/m), based on results of measurements or calculations.  
2.2.1 Hospital Universitario de Canarias (HUC)--Tenerife (Assessment of MO-RFID and AS-RFID Readers)  
The measurements were performed, with the use of frequency selective equipment, i.e., a R&S FSH6 spectrum analyzer (measurement uncertainty <1.5 dB, typical 0.5 dB) and an antenna ETS-LINDGREN, Mod.3181 (500-9000 MHz), that are specified in Figure 3.  
Figure 3: Measurement set up used for in situ measurements of EMF emitted by studied RFID readers: (**a**) Rhode & Schwartz FSH6 spectrum analyzer, (**b**) Antenna ETS-LINDGREN, Mod.3181 on tripod.  
The measurements were made at an RFID core frequency (866 MHz), in a \"demo\" mode of MO-RFID reader operation, whereby undisturbed emission was guaranteed, without a need for human involvement in the procedure.  
The measurements were carried out in a wide-open area, a tower located on the 4 th floor of the hospital. Measurements were recorded at distances of from 0.25 and 4 m, and at angles between 0\({}^{\circ}\) and 180\({}^{\circ}\), rotating in 45\({}^{\circ}\) angle steps.  
The E-field was assessed near the reader with an antenna located vertically (compliant with a regular position when the MO-RFID reader is used at a \"gun-like\" position). The exposure scenarios (ESs) include:  
* Exposure caused by activation of MO-RFID reader placed on a table, at a 1 m height, with reading tags located at a 1 m height from the floor
* Five angles (degrees): 0, 45, 90, 135, and 180
* Five distances (m): 0.25, 0.5, 1, 2, 4.  
An AS-RFID reader was placed inside a room at an approximate height of 3.5 m, without windows and with a door near the wall. Measurements were carried out at 866 MHz and at 1.0 m, 1.3 m and 1.7 m heights from the floor, representing the zone of genitalia, breast and abdomen, and head respectively to the floor, as they are considered sensitive parts of a human body. The levels were also assessed inside the Smartshelf 0.075 m to the rear part, which was a minimum distance of proximity. The fact that there were no doors in the wardrobe can be used to examine the worst-case scenario. When it is located at the patient examination area, they would be exposed rather sporadically and of short duration.
## 4 Experiments and Results  
### Experimental Setup  
In this section, we executed our experiments on a high-performance computing cluster equipped with Intel(r) Core(tm) i5-1035G1 processors and 16 GB of RAM, running over 512 GB SSD storage. These machines are networked through a 10 Gbps Ethernet network for fast node communication. For the software environment, Google Colab, Python 3.10, supported libraries such as Scikit-learn and TensorFlow Federated MPI and were used for distributed processing. This configuration considerably supported the tasks of machine learning, federated learning, and data pre-processing. Each component was well prepared with the required computation power so that hardware limitations did not affect our results. Python was chosen as our main programming language due to its extensive library for both machine learning and data analysis. Concretely, we have used Scikit-learn to implement models like Decision Trees, Random Forests, Gradient Boosting, and KNeighborsClassifier, while for efficient classification tasks, XGBoost has been used. For Federated Learning, which allows training neural networks in a decentralized way while preserving privacy, we used TensorFlow Federated. Finally, we have also employed Google Colab to continue some of the development and testing. To comprehensively assess model performance, we used several metrics:  
* Accuracy: Ratio of the number of correctly classified instances to the number of instances in total.  
Figure 5: Federated learning and machine learning process flow.  
Precision: Proportion of true positive predictions out of the total number of positive predictions.
* Recall: Proportion of true positive predictions out of the total number of positive instances.
* F1-score: A number that considers both precision and recall as if they were orthogonal.
* Classification Report: Helps derive model evaluation metrics such as precision, recall, F1-score, exhorting all have support (the number of occurrences of actual class) for each model.  
Scalability tests simulated increasing numbers of vehicles from 100 to 10,000, with minimal performance degradation. Our approach also addresses real-world constraints like latency and bandwidth limitations, and the feature reduction minimizes energy consumption, making this system viable for large-scale IoV deployments.