esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 5 Performance Evaluation  
### Evaluation of Logger Threads  
Table 5 presents the performance evaluation results of threads constituting the logger. The processing performance is compared by calculating the average execution time and standard deviation of the execution time for each individual thread. Among the total of eight threads, the object detection thread has the longest execution time at 76 ms. This thread is configured to operate using a GPU and includes the encoding time of sensitive objects. Figure 15 presents the elapsed time and standard deviation for each thread within the logger. As shown in Figure 15, the image save thread exhibits a higher standard deviation in execution time compared to other threads, indicating high variability in its execution time. This variability arises from the write performance limitations inherent in storage media utilizing a microSD card. To reduce the observed standard deviation, a high-throughput storage medium should be considered. With an average execution time of 31.64 ms, the capture thread meets the 30 frames per second (FPS) performance specification.
## 4 Methodology and Algorithm Explanation
### Feature Selection Phase
#### 4.3.1 Select Features Based on Mutual Information (MI)  
Mutual information is calculated between two variables to determine their mutual dependency. Mutual information always equals or exceeds zero. The higher the degree of mutual information, the stronger the association between the two variables. When mutual information equals 0, it indicates that the two variables are independent [35]. It is therefore connected to the entropy of a random feature, which is established by the quantity of data the feature includes. The mutual information between two discrete random variables, \(X\) and \(Y\), is defined by Equation (2).  
\[I(X;Y)=\sum\
olimits_{x\in X}\sum\
olimits_{y\in Y}P(x\cdot y)log_{2}\bigg{(} \frac{P(x,y)}{p(x)p(y)}\bigg{)} \tag{2}\]  
where \(H(X)\) is the entropy of \(X\), \(H(X\mid Y)\) is the conditional entropy of \(X\) given \(Y\), \(p(x)\) and \(p(y)\) are the marginal distributions of \(x\) and \(y\), and \(P\) (\(x,y\)) is the joint distribution of \(x\) and \(y\)[36].
## 3 Results
### Benefits, Challenges, and Limitations
#### 3.4.3 Limitations  
Despite its benefits, ChatGPT and AI tools face several limitations in educational and programming contexts. Issues with validity and bias limit their generalizability, particularly in complex coding tasks like Java and SQL [7; 14; 23; 25]. Usability challenges also arise for learners with minimal technical skills, restricting AI's accessibility [25].  
AI-generated assessments, including multiple-choice questions, often lack completeness and alignment with learning objectives, raising concerns about rater bias and reliability [8]. Additionally, reliance on preexisting datasets affects assessment originality and academic integrity [24]. Research findings are further constrained by small sample sizes and short study durations, limiting insights into long-term AI impact [9; 10; 21]. Studies often face methodological challenges, such as low participation, which may skew results, as students predisposed to AI technology are more likely to engage. Furthermore, student familiarity with course content influences the quality of chatbot queries, affecting AI-generated responses. Manual feedback evaluation by a single teaching assistant can also introduce accuracy concerns. Another limitation is the exclusive use of GPT-4 in some studies, without comparison to alternative LLMs [27].  
Ethical concerns include plagiarism risks, student over-reliance, and AI's role in academic dishonesty, particularly among first-year learners [16; 22]. Computational demands, adaptation challenges, and inconsistent AI-generated responses affect reliability and scalability [18; 19; 20]. Moreover, AI's limited application in database learning and computational thinking remains a research gap [23].  
To enhance AI's effectiveness in computer science education, improvements are needed in personalization, adaptability, and ethical safeguards [14; 23]. Addressing these challenges and limitations is crucial for sustainable and responsible AI adoption in education.  
Is it inevitable that these limitations, such as difficulties in recognizing correct answers, understanding context, and processing complex queries, contribute to reduced trust in AI tools. These issues intensify concerns around the reliability of AI in supporting educational and programming tasks, especially in database design courses. The frequent errors and inconsistencies in responses limit the overall effectiveness of AI tools, making it challenging for them to meet the demands of both learners and educators in complex learning environments [28].
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
###### Abstract  
We propose two new ocean wind retrieval models for right circular-vertical (RV) and right circular-horizontal (RH) polarizations respectively from the compact-polarimetry (CP) mode of the RADARSAT Constellation Mission (RCM), which is scheduled to be launched in 2019. For compact RV-polarization (right circular transmit and vertical receive), we build the wind retrieval model (denoted CoVe-Pol model) by employing the geophysical model function (GMF) framework and a sensitivity analysis. For compact RH polarization (right circular transmit and horizontal receive), we build the wind retrieval model (denoted the CoHo-Pol model) by using a quadratic function to describe the relationship between wind speed and RH-polarized normalized radar cross-sections (NRCSs) along with radar incidence angles. The parameters of the two retrieval models are derived from a database including wind vectors measured by in situ National Data Buoy Center (NDBC) buoys and simulated RV- and RH-polarized NRCSs and incidence angles. The RV- and RH-polarized NRCSs are generated by a RCM simulator using C-band RADARSAT-2 quad-polarized synthetic aperture radar (SAR) images. Our results show that the two new RCM CP models, CoVe-Pol and CoHo-POL, can provide efficient methodologies for wind retrieval.  
compact polarization (CP); RADARSAT Constellation Mission (RCM); geophysical model function (GMF); wind retrieval; CoVe-Pol and CoHo-Pol models; right circular horizontal polarization model; right circular vertical polarization model +
Footnote †: journal: Remote Sensing, 10, 1938
# A Novel Benthic Phage Infecting _Shewanella_ with Strong Replication Ability
## 3 Results and Discussion  
Plage Basic Characteristics: Infection Mode, Morphology, Host Ranges, and Existence or Absence of Lipids in Plage Capsid  
The phage S0112 is a lytic phage infecting _S. indica_ KJW27, which can form small clear round plaques on plates with an average diameter of 0.5-1 mm after two days of incubation with bacterial hosts (Figure 1a). Morphological analysis via TEM showed that phage S0112 had a polyhedral capsid (\(62\pm 3\) nm in diameter) and a long, flexible, non-contractile tail (\(97\pm 1\) nm in length and \(10\pm 1\) nm in width) (Figure 1b).  
Host range analysis revealed that phage S0112 could not infect any other bacterium except _S. indica_ KJW27 among the eighteen tested strains, which include four different _Shewanella_ spp. from marine sediments and seawater that have similar evolutionary status with _S. indica_ KJW27 (Table 1). After being treated with chloroform, the phage can still survive on a bacterial lawn, indicating the absence of lipids in phage capsid. As introduced above, only two phages (i.e., VpKK5 and AB1) isolated from coastal sediments have been identified comprehensively in the past [18; 19]. Based on the few reports on coastal sediment phages, the three known benthic phages including phage S0112 in this study were found to affect only very limited species within a specific genus. However, we believe that the benthic phages in coastal sediments must have much more complex and diverse biological characteristics than what we know now and remain to be explored. Isolating more phages from coastal sediments is of great significance to discover their largely unknown characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Genus and Species** & **Strain** & **Source** &
\begin{tabular}{c} **Strains Lysed by** \\ **Phase S0112** \\ \end{tabular} \\ \hline _Shewanella indica_ & KJW27  & sediment & + \\ _Shewanella basallis_ & CJW-54 & sediment & - \\ _Shewanella allikensis_ & JC5 & sediment & - \\ _Shewanella japonica_ & KCTC22435 & sediment & - \\ _Shewanella algae_ & JCM 21037 & seawater & - \\ _Alginthatherium selulinitis_ & ALS 81 & sediment & - \\ _Wesesai ocami_ & SDUM189001 & sediment & - \\ _Sedilintic luteus_ & SDUM701001 & sediment & - \\ _Kodimonas selulinitis_ & N39 & sediment & - \\ _Rosenobacter denittificans_ & OCH114 & seawater & - \\ _Marinobtacher virinfum_ & D7035 & wastewater & - \\ _Halomonas denittificans_ & D7027 & saline water & - \\ _Vibrio alginolyticus_ & CIP 82.01 & seawater & - \\ _Vibrio nexcalooticus_ & NC 470 & seawater & - \\ _Vibrio azureus_ & NBRC 104857 & seawater & - \\ _Vibrio harregui_ & NBRC 15634 & seawater & - \\ _Vibrio parahametolytica_ & NBRC 12711 & seawater & - \\ _Vibrio campelliti_ & CAIM 519 & seawater & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Infection specificities of the phage S0112 against eighteen bacterial strains.  
Figure 1: (**a**) Phage plaques formed in double-layer agar plates and (**b**) transmission electron microscopy image of the phage S0112.
## 2 Materials and Methods
### Data Collection and Statistical Analysis  
Tomato plant growth parameters (plant height, leaf area, and stem diameter) and leaf chlorophyll measurements were collected at days 25, 50, 75, and 110 after transplanting (DAT) and the data were summed for the entire season. The use of non-destructive methods provides a cost-efficient means for frequent measurements of leaf chlorophyll over a large area. Previous studies have found that spectral indices derived from light absorption or reflection in the visible and near-infrared (NIR) regions have good correlations with leaf chlorophyll. A portable chlorophyll meter (SPAD-502/501, Soil Plant Analysis Development, Konica-Minolta, Inc., Osaka, Japan) was used to measure the chlorophyll content of the plants by taking measurements with eight readings between the midrib and the leaf margin. Since the SPAD meter readings were relative quantities, they were converted to actual leaf chlorophyll content using the generalized transformation equations developed by [PERSON] et al. [29].  
Tissue analysis was performed to determine the Na\({}^{+}\), K\({}^{+}\), and Ca\({}^{2+}\) concentrations in leaf petioles. Tomatoes were harvested when at least 80% of the fruits attained the red ripeness stage. The harvested fruits were weighed and graded. The unmarketable yield represented fruits exhibiting cracking, catface, blossom-end rot, blotchy ripening, and fruits that fell into the category of extra-small-sized (<40 mm). The collected data were analyzed using the SAS software program (Version 9.4, SAS Institute Inc. Cary, NC, USA). Significant differences among the individual treatments were evaluated using an analysis of variance (ANOVA) and the least significant difference (LSD) test. The collected data from the experiments were subjected to a tri-factorial ANOVA (salinity \(\times\) nutrition \(\times\) block) with nutrient and salt treatments as the main factors. In addition, a principal component analysis (PCA) was performed to determine the biophysical parameters that were responsive to salinity stress.
## 2 Numerical Analysis Model
### Vehicle-Bridge Coupled Model
#### 2.2.1 Train Spatial Vibration Equation  
A CRH (China Railway High Speed Train) carriage can be simplified to four wheels, two bogies and one body which means seven rigid bodies. The topology diagram of the carriage is shown in Figure 1. The vehicle at its center point will have five DOFs, i.e., lateral, rolling, yawing, pitching and vertical displacements, described as \(\left\{{{v_{c}}}\right\}=\left\{{{X_{c}}\;{Y_{c}}\;{\psi_{c}}\;{\theta_{c}}\;{ \theta_{c}}}\right\}^{\rm{T}}\)[22]. The train's bogie has four DOFs at the center of gravity, i.e., lateral, roll, yaw and vertical movement. \(\left\{{{v_{bq}}}\right\}=\left\{{{X_{bq}}\;{Y_{bq}}\;{\psi_{bq}}\;{\theta_{bq} }}\right\}^{\rm{T}}\) were designated as four DOF of front bogie, and another one denoted as \(\left\{{{v_{bh}}}\right\}=\left\{{{X_{bh}}\;{Y_{bh}}\;{\psi_{bh}}\;{\theta_{ bh}}}\right\}^{\rm{T}}\). Only two DOFs are included for the \(i\)th wheelest on the bogie: lateral and rolling displacements, which are denoted as \(\left\{{{v_{si}}}\right\}=\left\{{{X_{si}}\;{y_{bh}}}\right\}^{\rm{T}}\). A simplified model of the train is shown in Figure 1, and their definitions are concluded in Table 1.  
The relative position of the train to the bridge is constantly changing as the train travels over the bridge. Based on the two fundamental theories above, it is possible to derive the stiffness, mass, damping matrix ([KV], [MV], [CV]) and load vector quantity {P} of the train [19; 20]. Based on the set-in-right-position rule of matrix formation, the overall stiffness matrix [K], mass matrix [M], damping matrix [C] and load array {P} of the train-bridge system at any moment t can be calculated by combining the train and bridge dynamic matrices.  
Figure 1: The simplified model of the train: (a) elevation view; (b) side view; (c) plan view.  
It can be deduced from D'Alembert's principle for the total potential energy of the \(j\)th carriage.  
\[\Pi_{vj}=V_{mj}+V_{Fj}+U_{Ej}+U_{Ewj}+V_{gj} \tag{2}\]  
The total vibration potential energy of \(j\) train on the bridge at \(t\) time as:  
\[\begin{split}&\Pi_{v}=\sum\limits_{j=1}^{N}\Pi_{vj}=\sum\limits_{j= 1}^{N}\left[V_{m\!sj}+V_{m\!sj}+V_{m\!cj}+V_{Fu1j}+V_{Fu2j}+V_{Fu3j}+V_{Fu1j}+V_ {F42j}+\\ & V_{F43j}+V_{F4j}+U_{Et1j}+U_{Et2j}+U_{Et3j}+U_{Et1j}+U_{Et2j}+U_ {Et2j}+U_{Et3j}+U_{Egj}+V_{gj}\right]\end{split} \tag{3}\]  
where \(V_{m\!j}\) is the work of inertial forces on car body, bogie and wheelset. \(V_{Fj}\) is the work of damping suspension systems. \(U_{Ej}\) is the energy of deformation at damping suspension systems. \(U_{ej}\) is the strain energy at damping suspension systems. \(U_{Ewj}\) is the gravitational potential energy at wheelsets. \(V_{gj}\) is the gravitational potential energy at car body.
# Multiband and Lossless Compression of Hyperspectral Images
## 3 Lossless Multiband Compression for Hyperspectral Images (LMBHI)
### Modeling and Coding of Prediction Errors  
Starting from the consideration that a prediction error can assume positive or negative values. Similarly to [23], we use an invertible mapping function (highlighted in the Equation (7)), in order to have only non-negative values. It is important to note that the mapping function does not alter the redundancy among the errors. For the coding of the mapped prediction errors we use the Arithmetic Coder (AC) scheme.  
\[M\left(error\right)=\left\{\begin{array}{rl}&2\cdot\left|error\right|\quad if \ error\geq 0\\ &2\cdot\left|error\right|-1\quad otherwise\end{array}\right. \tag{7}\]
## 1 Introduction
physiological signals, such as electrocardiograms (ECGs), electroencephalograms, and PPGs, belong to one-dimensional signals, several studies have used a one-dimensional deep convolution neural network (1D DCNN) to classify the different arrhythmic types and the signal quality [14; 15; 16; 17]. Some studies have transferred the 1D signal to a two-dimensional (2D) signal by short time frequency transform [18], wavelet transform [19], and power spectral density [20]. Then, the 2D DCNN employed these images as the input to do the classifications. However, in these studies, a segment signal of about 2 to 5 s was transferred to an image. Thus, the methods only were suitable for processing consistent, continuous signals. When the heart is in the systolic phase, an amount of blood, which is called the stroke volume (SV), is pumped into systematic circulation. In this period, the volume of the thoracic cavity will change. Thus, impedance cardiography (ICG) was proposed by [PERSON] et al., which is a noninvasive measurement technique to measure cardiac hemodynamic parameters [21]. In the SV calculation with ICG technology, LVET is one of the most important parameters, which is measured by the ICG signal. As the signal-to-noise ratio of the ICG signal is very low, the accuracy of LVET is not high enough. [PERSON] et al. used a reflective PPG sensor placed on the neck to measure the LVET [5]. The LVETs measured by the PPG pulses were more accurate than those by the ICG pulses. Therefore, the accuracy of SV measured by their proposed technique was higher than the traditional ICG technique. According to previous mentions, the higher the SQI of the PPG signal, the more accurate the physiological parameters measured by the PPG signal. Unlike previous rule-based methods, our proposed approach does not require any predetermined features in the PPG signal. In this study, we used the raw morphology of PPG pulses to determine their quality levels. Thus, the aim of this study is to develop a novel SQI approach to access the quality of PPG pulses for improvement of SV measurement with a 2D DCNN. The PPG pulse and its differential pulse were segmented from the continuous PPG signal, and then they were merged into an image. Both the 2D deep residual neural network (DRNN) and 2D DCNN were used to determine the quality level of each PPG pulse. Fourteen healthy adult males participated in this experiment. The beat-to-beat SVs were measured by our ICG device [5] and medis(r) CS2000 for about three minutes, simultaneously. We utilized the error percentage of measured SVs to define the levels of SQI for each PPG pulse. The error percentage represents the accuracy of the SV measurements. The results showed that the 2D DRNN could assess the levels of SQI for the PPG pulses more easily than the rule-base method and the general DCNN.
## 3 Results
### Analysis of Image Quality Metrics  
The image quality metrics were obtained to compare with the dose information. The mean of image quality indices of CT number, noise, SNR, and CNR were summarized in Table 4. The reduced tube voltage and tube current lead to an increase in image noise. Noting that the highest mean of noise was 13.1 \(\pm\) 0.6 HU presented in P6. There is no significant difference of CNR in P4 and P6 after reducing the tube voltage from 100 to 80 kVp. However, reducing tube voltage in P4, P5, and P6 resulting in significant differences (\(p<0.05\)) of SNR value. The highest mean of SNR and CNR in P9 (disabling TCM) was 6.66 \(\pm\) 0.5 HU and 1.34 \(\pm\) 0.2 HU, respectively, and the organ dose in P9 protocol were among the highest. Thus, the highest FOM value was obtained in P9, followed by P7 and P2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Image Quality**} & \multicolumn{8}{c}{**CT CAP Protocol**} \\ \cline{2-9}  & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** & **P9** \\ \hline CT Number & 22.5 \(\pm\) 0.9 & 22.3 \(\pm\) 0.9 & 19.9 \(\pm\) 1.9 & 19.5 \(\pm\) 0.8 & 20.3 \(\pm\) 1.9 & 10.1 \(\pm\) 1.3 & 22.6 \(\pm\) 0.5 & 22.0 \(\pm\) 0.9 & 21.4 \(\pm\) 0.9 \\ (Mean) & 8.9 \(\pm\) 1.1 & 9.3 \(\pm\) 0.9 & 10.5 \(\pm\) 1.6 & 10.8 \(\pm\) 2.3 & 11.5 \(\pm\) 0.6 & 13.1 \(\pm\) 0.6 & 7.4 \(\pm\) 1.0 & 8.8 \(\pm\) 0.7 & 3.2 \(\pm\) 0.2 \\ SNR & 2.54 \(\pm\) 0.4 & 2.41 \(\pm\) 0.2 & 1.93 \(\pm\) 0.4 & 1.87\(\pm\)0.4 \({}^{*}\) & 1.77 \(\pm\)0.1\({}^{*}\) & 0.77 \(\pm\)0.1\({}^{*}\) & 3.10 \(\pm\) 0.4 & 2.51 \(\pm\) 0.2 & 6.66 \(\pm\) 0.5\({}^{*}\) \\ CNR & 0.71 \(\pm\) 0.1 & 0.69 \(\pm\) 0.1 & 0.47 \(\pm\) 0.1 & 0.59 \(\pm\) 0.1 & 0.45 \(\pm\) 0.1 & 0.54 \(\pm\) 0.1 & 0.23 \(\pm\) 0.1\({}^{*}\) & 1.34 \(\pm\) 0.2 \({}^{*}\) \\ FOM & 3.65 & 3.90 & 2.97 & 3.59 & 3.54 & 0.75 & 5.73 & 3.73 & 6.47 \\ Dunnett \(t\)-tests (\(p\) value) & Control & 1.000 & 0.058 & 0.611 & 0.028 & 0.779 & 0.255 & 0.000 & 0.000 \\ \hline \hline \end{tabular}  
* \(p\)-value < 0.05.  
\end{table}
Table 4: A comparison of all objective image quality parameter values acquired in different CT CAP protocols.  
Figure 3: The mean organ-absorbed doses of standard pediatric CT CAP examinations with varying tube potential and tube current in: (**a**) Thyroid, (**b**) lungs, (**c**) liver, (**d**) stomach, (**e**) gonads, and (**f**) skin.
## 4 Machine-Learning-Aided Positioning in 5G Networks  
In this section, we present machine-learning-based methods for indoor and outdoor positioning. The experiments used machine learning approaches to enhance the positioning accuracy in 5G networks and can be divided into groups based on the used localization techniques discussed earlier (Table 2).
## 1 Introduction
### Relevant Background  
DR applications have been extensively studied in the literature; a detailed survey on practical evidence for DR concepts can be found in [7]. While the early studies have generally considered the technical aspects such as effectiveness and applicability of these programs, the recent studies have started to focus on the active participation of consumers by providing them various options so that their comfort level is not affected so much, which aims to increase their willingness to participate in DR programs. With this objective, developing user interfaces has come into prominence as these tools considerably increase the ease of applicability of DR programs from the perspective of end-users. As one of these tools, a HEM graphical user interface (GUI) was proposed by [PERSON] et al. [8] in order for consumers to monitor their detailed consumption values, comfort preferences and the defined demand limit. [PERSON] et al. [9] introduced a mobile phone-based management system which controls the appliances in a smart home considering time-of-use (ToU) electricity rates taken from a utility company. [PERSON] et al. [10] presented a user interface module that is capable of communicating with modern operating systems such as Windows and Android. For smart campuses consisting of controllable loads, local renewable energy generation units, and a storage system, an energy management approach allowing DR interactions with utilities was presented in [11], considering feedback on user comfort collected from consumers via a mobile app compatible with iPhone operating system (iOS) and Android. [PERSON] et al. [12] developed an operating system with a mobile interface which monitors and controls different loads for reducing the energy consumption of commercial buildings in DR applications. The role and potential benefits of smart phone apps that can be used in smart homes were evaluated in detail in [13].  
There are also several DR oriented web- and mobile-based platforms developed by different companies. Intelen, as a service provider to system operators, developed a DiG module for enhancing the DR penetration ratio among the service area of its clients both in web and mobile versions [14]. Silver Spring Networks [15], also a service provider, provided a mobile thermostat app and updated a demand optimizer demand response management system application. Transpower in New Zealand [16], South California Edison in USA [17], etc., as system operators also developed mobile apps for DR services to their customers to enable easier use and higher penetration of DR solutions.