esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 3 System Design
### SpO2 Sensor Operation  
In this work, a wrist-worn reflectance-type pulse oximeter is proposed as an alternative to traditional transmittance-based pulse oximeter. A wrist-worn configuration is extremely desirable as it eliminates the requirement and discomfort associated with finger-mounted probes and allows for possibility of integration into a watch-type device. The additional SpO2 data allows blood oxygen parameters to be monitored during various physical activities that enables implementation of a wide range of new applications. These applications include monitoring of respiratory function, true rest and recovery times from strenuous sport activities, sleep apnea detection modes along with a wide range of medical conditions.  
The PPG sensor monitors change in the light intensity via reflection from the tissue as illustrated in Figure 5. The SpO2 measurement is performed by illuminating the body surface (wrist in this case) with two wavelengths of light using Red and Infra-Red LEDs as shown. Reflected light from the skin, tissue and blood vessels is then captured using a photodetector [64]. The absorptive properties at each wavelength determine the level of oxygen saturation that is calculated from post-processing of measured signals. The signal amplitude is proportional to the quantity of blood flowing through theblood vessels. Each individual heartbeat is measured via the PPG sensor, and the signal is passed through digital filters to obtain the individual Systolic and diastolic points for each wavelength. The PPG waveform has an alternating current (AC) component and a direct current (DC) component. The AC component corresponds to variations in blood volume synchronizations with the heartbeat. The DC component arises from the optical signals reflected by the tissues and is determined by the local optical scattering properties as well as venous and arterial blood volumes. Since multiple wavelengths are used, variations in skin tissue types and blood volumes are mostly canceled out due to Beer Lambert Modulation Ratios. A final calibration table relating to typical SpO2 Ratio between 660 nm and 940 nm allows a simple lookup table to be created showing the percentage of saturated blood oxygen with the measured patient.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 4 BAN-Logic Analysis  
### Introduction to BAN-Logic  
BAN-logic is used to establish session key security between the MU and the FA to prove that the session key is safeguarded in the authentication phase of our scheme. The main process has four proofs:  
* MU believes that FA believed the session key: MU\(\leftarrow\)SK\(\rightarrow\)FA
* A believes the session key: MU\(\leftarrow\)-SK\(\rightarrow\)FA
* FA believes that MU believed the session key: MU\(\leftarrow\)-SK-\(\rightarrow\)FA  
According to the BAN-logic characteristics of the security analysis, the following basic symbolic representation rules are used [29; 30; 31]:  
1. (X, Y): X or Y is one part of the parameter (X, Y).  
Figure 3: Roaming phase.  
Figure 2: Authentication phase.  
2. <X>Y: X can be obtained through the secret parameter Y.
3. [X]K: X is encrypted under the key K.
4. P\(\leftarrow\)-K-\(\rightarrow\)Q: [PERSON] and [PERSON] may use the shared secret key K to communicate. The third party does not know the secret key K.
5. P\(<\)=S=>Q: S is only known between [PERSON] and [PERSON]. [PERSON] and [PERSON] must use S to prove the identity of each other.
# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 3 Area of Study
### FSC Algorithm Accuracy Assessment  
The accuracy of the algorithm was calculated using the root-mean-squared-error (RMSE), which compared the estimated FSC values with FSC values determined by two specialists in image interpretation [42]. The RMSE was calculated with the following formula:  
\[\text{RMSE}=\sqrt{\frac{1}{\text{N}}\sum(\text{FSC estimated}-\text{FSC observed})^{2}} \tag{2}\]  
The RMSE was calculated for two periods: 2017-2018 and 2018-2019 in both cases from 1 October to 30 April. The first period has a total of 177 images, and the second period 92 images. All the classified images were acquired at 11:30 LST except for two images with fog, which were changed to images acquired at 12:00 and 12:30 LST of the same day after the fog had lifted.  
Gathering observed FSC values required help from visual image interpretation and PhenoCam experts outside the study. Both experts visually estimated FSC over the entire ground surface imaged, and not just within the ROI, for the winters of 2017-2018 and 2018-2019. The visual estimates of FSC were provided as values between 0% and 100%. It is important to mention the experts were instructed to estimate FSC at 10% intervals (0%, 10%, 20%...100%).  
In addition, we selected two images for each of the five winters we studied (10 images total) to validate the classification. The selected images had an FSC estimated by our algorithm at between 40% and 60%. On each selected image, we randomly selected 50 points per class (two classes, \"snow\" vs. \"no snow\"; 100 points per image), resulting in 1,000 validation points. For each validation point, we visually compared the snow/no-snow result with the corresponding pixel from each PhenoCam source image, noting when our algorithm correctly and incorrectly identified \"snow\" or \"no snow\" pixels [40].
## 4 MASLD-HCC Treatment  
The EASL and AASLD guidelines do not recommend a treatment pathway based solely on the etiology of the underlying liver disease. These guidelines have traditionally focused on patients with viral hepatitis. Notably, the BCLC staging system does not account for non-cirrhotic HCC. Patients with MASLD-HCC (metabolic-associated steatotic liver disease-related HCC) are less likely to have established cirrhosis or clinically significant portal hypertension [114]. As a result, these patients are less frequently included in surveillance programs and tend to present with more advanced disease at diagnosis, limiting opportunities for curative treatment [115].  
Furthermore, patients with MASLD-HCC are often affected by additional comorbidities related to metabolic syndrome, complicating treatment decisions.  
Increasing evidence suggests that the etiology of the liver disease may influence treatment responses [116]. However, data specific to MASLD-HCC are limited. For instance, the pivotal SHARP study included only patients with viral hepatitis and alcohol-related liver disease. A recent international cohort study of patients treated with sorafenib did not show significant differences in outcomes between MASLD-HCC and other etiologies, but this study had a small MASLD-HCC subgroup [117].  
Pre-clinical and clinical data suggest that MASLD-HCC may have a reduced response to ICI therapy. A retrospective cohort study on the therapeutic efficacy of lervatinib showed similar OS and PFS between MASLD-HCC and other etiology subgroups [118]. A meta-analysis of eight studies assessing systemic therapies concluded that ICIs offer more benefit in viral HCC than in non-viral HCC. However, the efficacy of tyrosine kinase inhibitors (TKIs) and anti-VEGF agents appeared unaffected by the underlying liver disease etiology [119]. Notably, none of these studies made further distinctions within the \"non-viral HCC\" group in terms of MASLD, alcohol, or other chronic liver disease etiologies.
# Formal Modeling of IoT-Based Distribution Management System for Smart Grids
## 3 Problem Statement and System Model
### Intelligent Electronic Device (IED)  
Intelligent electronic devices in the distribution network of smart grids play a crucial role. Whenever a fault is encountered because of the failure of the transformers or the line current surpasses the threshold value, the substation's overcurrent relay trips the circuit breaker and the IED associated with that circuit breaker forwards an alarm message to other load-switch IEDs that are associated and operated by the IEDs of the substation's circuit breaker. The relay will determine the fault as temporary or permanent if after one or two consecutive trippings of the circuit breaker, the system comes back to its previous state; that is, if the power supply is restored, the fault is considered temporary.  
In the second scenario, after some consecutive tripping, if the system is unable to recover itself, the IEDs of the circuit breaker interact with the load switches of the IED to identify the actual fault location. After the fault identification, the fault localization will be finished when the load switch of the feeder terminal unit raises the fault flag; then, the next task of the IED is to isolate that area by tripping off the specific load switch. The load switch cuts off the power supply to the rest of the network within a short time span and transfers a message to each IED of the system components, including relays, circuit breakers, and tie switches, for the purpose of power supply restoration in the substation's faulty area. If the non-active switch is unable to restore the substation's power supply through the primary source, then it will choose another fault-free energy side. Figure 5 shows the possible states of the IED in the substation.
# An Intelligent Real-Time Object Detection System on Drones
## 4 Results and Discussion
### NN Model Speed  
The NN engine is an accelerator for NN models and it can leverage various NN models for object classification and detection. We validate three models in our experiments, i.e., ResNet-50, Yolov3, and Yolov3-tiny models. Table 4 summarizes the NN models and their corresponding processing time. In general, NN models require a large number of weights that are trained using numerous labeled images. For ResNet-50 and Yolov3, the weight sizes are 147 MB and 384 MB, respectively. The weights need to be transferred from the host to the FPGA and, thus, it takes a long time--16.01 s for ResNet-50 and 39.31 s for Yolov3--to load the weights.  
Though it is time-consuming to load the weights, the weight loading happens only once and it does not cost extra time after loading. On average, the processing time of each image is only 1.17 s and 0.8 s for Yolov3 and ResNet-50, respectively.  
It is worth mentioning that Yolov3-tiny is a widely used NN model for object detection, which dramatically improves the processing speed. Compared to Yolov3, the weight size is reduced from 384 MB to 51 MB and the processing time decreases from 1.17 s to 0.13 s, i.e., 8 FPS. Therefore, we adopt the Yolov3-tiny NN model for real-time object detection.  
Figure 7: The power consumption of the FPGA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System Component** & **Battery Duration** \\ \hline
**Drone** & 11 min \\
**FPGA** & 2 h \\ \hline \hline \end{tabular}
\end{table}
Table 3: Battery duration for drone and the FPGA.
## 3 Results and Discussion
### Multivariate Analysis
#### 3.2.1 Principal Component Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pre-Processing Methods** & **RMSEC** & **RMSECV** & \begin{tabular}{c} **Principal Component** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Eigenvalue of** \\ **Cov (V)** \\ \end{tabular} & \
\begin{tabular}{c} **\% Variance Captured** \\ **This PC** \\ \end{tabular} &
\begin{tabular}{c} **\% Variance** \\ **Captured Total** \\ \end{tabular} \\ \hline \multirow{4}{*}{**Mean centering**} & \multirow{4}{*}{180.47} & \multirow{4}{*}{254.18} & 1 & \(1.29\times 10^{2}\) & **39.29** & 39.29 \\ \cline{2-5}  & & & 2 & \(\mathbf{6.36\times 10^{6}}\) & 19.34 & 58.64 \\ \cline{2-5}  & & & 3 & \(5.72\times 10^{6}\) & 17.42 & **76.05** \\ \hline \multirow{4}{*}{**First derivative of Savitzky–Golay**} & \multirow{4}{*}{31.30} & \multirow{4}{*}{44.06} & 1 & \(2.17\times 10^{6}\) & **68.52** & 68.52 \\ \cline{2-5}  & & & 2 & \(\mathbf{3.81\times 10^{6}}\) & 12.02 & 80.54 \\ \cline{2-5}  & & & 3 & \(2.75\times 10^{6}\) & 8.66 & 89.20 \\ \cline{2-5}  & & & 4 & \(1.08\times 10^{6}\) & 3.39 & **92.59** \\ \hline \multirow{4}{*}{**Secondderivative of Savitzky–Golay**} & \multirow{4}{*}{19.20} & \multirow{4}{*}{25.86} & 1 & \(5.47\times 10^{5}\) & **54.96** & 54.96 \\ \cline{2-5}  & & & 2 & \(1.90\times 10^{6}\) & 19.06 & 74.01 \\ \cline{1-1} \cline{2-5}  & & & 3 & \(\mathbf{8.61\times 10^{6}}\) & 8.65 & 82.67 \\ \cline{1-1} \cline{2-5}  & & & 4 & \(4.62\times 10^{4}\) & 4.65 & 87.31 \\ \cline{1-1} \cline{2-5}  & & & 5 & \(3.77\times 10^{4}\) & 3.79 & **91.10** \\ \cline{1-1} \cline{2-5}  & & & 1 & \(5.12\times 10^{5}\) & **47.57** & 47.57 \\ \cline{1-1} \cline{2-5}  & & & 2 & \(1.72\times 10^{6}\) & 15.96 & 63.53 \\ \cline{1-1} \cline{2-5}  & & & 3 & \(\mathbf{8.88\times 10^{6}}\) & 8.24 & 71.77 \\ \cline{1-1} \cline{2-5}  & & & 4 & \(6.25\times 10^{4}\) & 5.80 & 77.57 \\ \cline{1-1} \cline{2-5}  & & & 5 & \(3.75\times 10^{4}\) & 3.48 & **81.06** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the PCA model with different pre-processing methods. Figure 3: Spectrum pre-processed with (a) mean centering, (b) the first derivative of the Savitzky–Golay method, (c) the second derivative of the Savitzky–Golay method, and (d) the third derivative of the Savitzky–Golay method. Figure 4: Q vs Hotelling’s T\({}^{2}\) in the PCA model. Both parameters are below 95%. Pre-processing methods: (**a**) mean centering method, (**b**) first derivative of the Switzky–Golay method, (**c**) second derivative of the Switzky–Golay method, and (**d**) third derivative of the Switzky–Golay method. Figure 5: Conf. Figure 5: PCA scores and loadings with different pre-processing methods. (**a,b**) mean centering, (**c,d**) first derivative of Savitzky–Golay, (**e,f**) second derivative of Savitzky–Golay, and (**g,h**) third derivative of Savitzky–Golay method.
###### Abstract  
The main purpose of the current study was to propose innovative composite films based on a corn starch/polyvinyl alcohol (PVA) blend (starch-PVA 40:60) and loaded with 3 different levels of chitosan nanoparticles (CNPs) (I, 3, and 5% \(w/v\)) to strengthen its physical, mechanical, structural, thermal and antimicrobial attributes. The synthesized CNPs were spherical with a particle size of ca. 100 nm as demonstrated by scanning electron microscopy (SEM) micrographs and dynamic light scattering tests. The results showed that the CNPs incorporation within the starch-PVA 40:60 films promoted a uniform surface without any considerable pores. These films were characterized by a homogeneous CNP distribution within the polymer matrix, causing a significant decrease in water vapor permeability (WVP) (e.g., from 0.41 for the control film to 0.28 g\(\cdot\)mm/kPa\(\cdot\)h\(\cdot\)m\({}^{2}\) for the composite film loaded with 5% CNPs). The film solubility, transparency, glass transition and melting temperatures, and elongation at break were also reduced by increasing the CNP content from 1% to 5%, while total color and tensile strength parameters increased. The antibacterial effects of CNPs were more effective against Gram-positive bacteria (_Staphylococcus aureus_) than Gram-negative bacteria (_Escherichia coli_ and _Salmonella typhimurium_). It can be concluded that the addition of CNPs to the starch-PVA matrix could improve its functional and technological attributes for food packaging applications.  
polyvinyl alcohol; starch; chitosan nanoparticles; antibacterial effects; packaging film Article
## 6 Design Considerations for LPWAN Networks
### Location Identification  
Determining the location of the devices is an important requirement for typical IoT applications. Devices in cellular network-based infrastructure have a built-in location method based on a cell ID. This capability is cost- and battery-effective, but is usually only as accurate as the cell site's footprint. Time differences of arrival (TDoA) designs locate devices using the available infrastructure, including gateways. Because gateways and cells are able to collect and transmit timing information, devicescan be located by comparing the time associated with the arrival of signals at multiple gateways or cells, sometimes called the triangulation scheme. It is one of the lowest cost solutions. Satellite-based location methods also work nicely for use cases that are not particularly battery sensitive.
# How May Building Morphology Influence Pedestrians' Exposure to PM2.5?
## 2 Materials and Methods
### Modeling
When the wind blew into the inlet, this angle was 180\({}^{\circ}\), whereas when it blew away from the inlet, it was 0\({}^{\circ}\). In other cases, the angle varied between 0\({}^{\circ}\) to 180\({}^{\circ}\). Consequently, PM2.5 maxima coincided with the angle at 180\({}^{\circ}\) and minima at 0\({}^{\circ}\), confirming the cosine of this angle as the appropriate measure to include in the model. Considering all the variables explained above, the fixed-effects model in Equation (1) was specified as Equation (3) to fit the panel data model using the R package 'plm' (version 2.6.4) [26]. Further, this study also tested spatial autocorrelation in the errors for each data collection run. The distance used to determine neighbors was set to 51.5 m, ensuring each data point had at least one neighbor. The weight matrix for computing spatial autocorrelation was derived using the'spdep' package in R (version 1.3.4) [27], and spatial autocorrelation (Moran's I) using the 'ape' package (version 5.8) [28]. \[ \begin{array}{ll}PM2.5_{it}=\alpha_{i}&+Wind\;Speed_{it}+Cos\;Wind\;Dir_{it}+Sin \;Wind\;Dir_{it}+Temperature_{it}+Relative\;Humidity_{it}\\ &+Cosine\;of\;the\;angle\;between\;trex\;direction+wind\;direction_{it}\\ &+Directional\;Viewhed\;_{it}\end{array}  \tag{3}\]  
The fixed effects in the model represented location-specific characteristics that were not included in the model. The study related these fixed effects with building morphological characteristics: building coverage ratio, mean building area, number of buildings, average distance between nearest-neighbor buildings, and mean building height. The study assumed that the key time-invariant factor was building morphology and correlated the estimated fixed effects with time-invariant building morphological parameters. In order to assess the contribution of building morphological characteristics, the study developed a regression model relating building morphological characteristics with the fixed effects.
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 5 Fiber-Optic Hydrogen Sensors
### Interference-Modulated Fiber-Optic Sensors
The reference spectra used as sensing indicators showed variations in wavelengths of 0.34, 1.51, 2.38, and 2.48 nm for hydrogen gas volume fractions of 1-4%. The response time (rise or fall time) was 10-12.5 s. The sensitivity of the sensor was improved at low hydrogen volume fraction levels due to the higher birefringence strain sensitivity of the polarization-maintaining optical fiber (PMF). The sensitivity of the sensor was improved at low hydrogen volume fraction levels. Figure 8: Interference stripes as a function of hydrogen concentration. Figure 7: Butler fiber-optic-type sensor schematic.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 4 Datasets for Dense Video Captioning
### Data Preprocessing and Augmentation Techniques  
Effective data preprocessing and augmentation play a pivotal role in enhancing the performance of dense video captioning models. When dealing with the datasets mentioned, several common techniques are employed to optimize the data for training these models.  
Firstly, temporal segmentation is applied to videos. This involves dividing them into meaningful clips based on temporal annotations [62; 63; 64]. By focusing on relevant parts of the video, this technique helps in improving the accuracy and relevance of the captions generated by the model [65; 66; 67].  
Next, frame sampling is utilized to extract keyframes from each video segment. This step is crucial for reducing computational load while capturing essential visual information [4; 7; 68]. By selecting representative frames, the model can process the video more efficiently without losing important details [66; 69; 70; 71].  
In addition to visual data preprocessing, text preprocessing is also essential. Captions are cleaned by removing punctuation and stop words, and undergoing tokenization and lemmatization [64; 67]. This process ensures that the captions are in a standardized format, making it easier for the model to understand and generate accurate descriptions [64; 67].  
To further enhance the diversity and robustness of the training data, data augmentation techniques are employed. One such technique is temporal shifting, where the boundaries of temporal segments are slightly shifted [72]. This helps create more robust models by exposing them to variations in the temporal boundaries of events.  
Another augmentation technique is caption paraphrasing. By generating paraphrased versions of captions, the diversity of the training data is increased [73; 72]. This encourages the model to learn different ways of describing the same event, improving its ability to generate varied and accurate captions [74].  
Lastly, synthetic noise is added to video frames or captions. This technique enhances the model's robustness by exposing it to noisy data during training [71]. By learning to handle synthetic noise, the model becomes better equipped to deal with real-world noise and variations, leading to improved performance in dense video captioning tasks.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 3 Experimental Results  
Based on the proposed embedding and detecting schemes, we built a watermarking system aiming to enhance the security of AM. The major components of this system include an encoder and a decoder. The former is responsible for embedding watermarks, while the latter is utilized to verify contents. Besides these two programs, the slicer and simulator, developed in the researches of [26; 27], are also employed to translate voxel models into G-code programs and to convert G-code programs into voxel models, respectively. Many experiments had been designed and conducted to test the system. Some of the test results are presented and analyzed in this section.