esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Zinc Sulfide Films
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Similarity Results:**} \\ \hline \(\bullet\) & Well-adherent films on the substrate. \\ \(\bullet\) & Uniform substrate coverage based on the SEM analysis. \\ \(\bullet\) & Polycrystalline in nature with a cubic structure based on the XRD analysis. \\ \(\bullet\) & Similar findings in optical absorption behavior. \\ \(\bullet\) & Stoichiometric composition based on the EDX spectrum. \\ \(\bullet\) & The band gap was almost the same (3.5 eV); however, the band gap reduced when the film thickness increased. \\ \hline \multicolumn{3}{c}{**Differences:**} \\ \hline Properties & Thermal evaporated films & SILAR-deposited films \\ \hline Color of the thin films & Violet in color. & Bluish white in color. \\ \hline Structure of the films & Strong peak corresponding to the (111) plane. & Strong peak corresponding to (111), (220), and (311) planes. \\ \hline \multicolumn{3}{c}{Stress increased when the film thickness increased.} \\ \multicolumn{3}{c}{This technique needs high thermal energy and the vapor atoms have high thermal energy.} \\ \multicolumn{3}{c}{The film thickness increased during the experiment and was exposed to} \\ \multicolumn{3}{c}{already-synthesized layers (to the heat} \\ \multicolumn{3}{c}{radiated from the evaporation boat).} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Similarity and differences in zinc sulfide thin films prepared by using the SILAR method and thermal evaporation technique.
## 1 Introduction
Furthermore, their method provides perfect black pixel reconstruction, which makes the restored image more recognizable by human vision. In 2021, [PERSON] and [PERSON] proposed three (\(n\), \(n\)) XOR-based RGVCS [8] to improve [PERSON] et al. 's method. The shares created in the scheme of [PERSON] et al. must have the same camouflage. Using [PERSON] and [PERSON]'s scheme allows shares to have various camouflage objects. They are both methods of encrypting one secret image at a time. In comparison to the VCS mentioned above, the visual multi-secret sharing scheme (VMSSS) has the ability to simultaneously encrypt numerous secrets into shares. As a result, it can reduce certain extra expenses while improving the encryption's performance. In recent years, various study projects on VMSSS have been made, including ([9; 10; 11; 12; 13]). In 2008, [PERSON] et al. proposed a four-secrets sharing scheme [11]. This method is encrypting four secret images into the shares, and one can rotate one share by 0, 90, 180, and 270 degrees and stack it on the other share to restore the four secret images, respectively. However, this approach can only encrypt four square secrets at the same time. To break through the constraints on the number and shape of secret images, [PERSON] et al. in 2018 proposed a new VMSSS via random grids [9]. One secret image is first divided into numerous fragments, each of which is then independently encrypted to the associated share. By moving one share 0, \(w/p\), \(2w/p\), \(3w/p\),..., (\(N\)-\(1\))\(w/p\) pixels and stacking it on another share, the first, second, third,..., \(N\)th secret images are recovered, where \(w\) is the width of the image. In this paper, we propose four (\(n\), \(n\)) XOR-based VMSSSs (or XOR-based RGVCSs) that can simultaneously encrypt more than one secret image and separately make each share disguised as a meaningful image. The rest of this paper is organized as follows. Section 2 shows the related work. Section 3 gives the proposed scheme and some experimental results. Some analyses are presented in Section 4. The conclusion and future work are given in Section 5.
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
In contrast, our pulse signal screening approach assesses the reliability of each pulse signal by leveraging noise assessment indicators. This method can be applied not only to algorithm-based remote photoplethysmography (rPPG) techniques such as CHROM [6], POS [7], and OMIT [8], but also to deep learning approaches like PULSEGAN [9] and Physnet [10]. Through experimental evaluations, we have verified that the proposed method demonstrates improvements even when applied to rPPG based on RGB cameras, in addition to its applicability to NIR measurements, yielding 100% accuracy. Therefore, we propose a pulse signal screening method based on noise assessment in the NIR environment, which effectively enhances the accuracy of rPPG-based heart rate measurements. Therefore, we can summarize the contribution points of our research as follows. First, this is the first study to determine and screen the confidence interval (time sequence) of the extracted rPPG signal to obtain higher accuracy. The method proposed in this study quantifies the target person's movement or excessive light changes, which can be easily acquired from the video itself. Therefore, it can be widely and easily applied to various studies to extract rPPG. Second, we used infrared cameras, which are more robust to sunlight changes than RGB cameras. In addition, the self-collected data used in our experiments were obtained from equipment that is reasonably priced compared to existing studies. We have shown in our experiments that the data have an acceptable level of accuracy. Finally, we have shown through discussion that high accuracy was indeed obtained in the selected intervals. Figure 2: Overall flow of proposed method.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 6 Adapting the Cloud Detector to the Target Domain  
In this section, we provide details of the steps involved in the post-deployment domain adaptation stage (Section 3.3). More specifically, we provide details of our proposed bandwidth-efficient SDA algorithm for offline adaptation (Section 6.1) and our solution for TTA on satellite hardware to achieve online adaptation (Section 6.2).
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 6 Perspectives
### Organic Combination with Other Data Sources  
This paper focuses on optical and thermal remote sensing, whereas many other data sources have also been utilized in agricultural drought monitoring [288]. These include microwave remote sensing, LiDAR, gravity remote sensing (e.g., GRACE), and other non-remote-sensing data sources. The most significant obstacle lies in that the mechanisms behind each data source can be dramatically different. For example, optical and thermal remote sensing mainly captures the information of the \"skin\" of the land surface [155; 156], while microwaves can have a penetrating depth ranging from several centimeters to several decimeters. The gap is even more prominent when comparing remote sensing data with station-based or reanalyzed meteorological or hydrological data. It is acceptable to combine different data sources through some machine learning processes [289; 290; 291], but it would be better to have a better depiction of the underlying physical mechanisms and find out how to coalesce their information while minimizing obfuscation errors. That is why models with better comprehensibility and interpretability are preferred.
# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
## 2 Experiments and Results
### Clustering  
After automatic detection and feature extraction, the potential vocal signals detected by our algorithm are combined together and transformed into the MFCC feature for classification. The datasets are built with these MFCC features of different cetacean species. To verify the feature vector, unsupervised classification methods are performed on the datasets listed in Table 2. Various vocal signals are randomly selected and combined for clustering. The two classification datasets contain two class combinations such as the Rissos' dolphin and right whale (RDRW) combination, right whale and no whale (RWNW) combination, Rissos' dolphin and no whale (RDNW) combination. Similarly, the three classification datasets are made of three class combinations, the same as the four and five classification datasets. Afterward, four clustering algorithms (K-means, PCA, GMM, SSC) are evaluated on these datasets using two metrics (ACC and F-score) mentioned above. The clustering accuracy of each dataset is ranged from 0 to 100%. We run each clustering algorithm ten times and record the mean square error according to the ten-time results. The clustering results via ACC and F-score metric on different datasets are listed in Tables 4 and 5.
## 2 Overview of IoV
### VANETs to IoV Transition  
Vehicular Ad hoc Networks (VANETs) consist of self-organized sets of vehicles in a mobile network [33]. VANET is a core component of an intelligent transportation system [33]. It was introduced to enhance vehicular communication using Dedicated Short-Range Communication (DSRC) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication [33; 51]. This communication allows users to make timely, safe, and efficient smart decisions with regards to road safety, traffic flow, and overall improved travel experience. In the VANET system, vehicles are considered as a mobile node that connects to other vehicles to create a network [33; 49]. During operations, vehicles are connected when they fall within the coverage and are disconnected as they fall outside of the coverage range [33; 49]. These constraints, coupled with other shortfalls such as the increasing number of vehicles connected to the network, and several factors such as traffic jams, tall buildings, and poor driver behaviors, ultimately affect its performance and use [49]. Also, the VANETs' framework failed to support the global interconnectedness required of the smart vehicular system [27], thereby necessitating the transition of the VANET into IoV system [27; 51]. The IoV system leverages on technological advancements such as ubiquitous connectivity, 5G and 6G wireless technologies, sensor devices, smart vehicles, and cloud computing to provide a more powerful vehicular network [27; 37]. In IoV, vehicles are constantly connected to the internet, to form a network of vehicles to facilitate the provision of several smart vehicular services [45]. It is made of majorly three basic parts: (1) the intra-vehicular network, (2) the inter-vehicular network, and (3) the vehicular mobile Internet [27]. The intra-vehicular network is the communication of vehicles within the same network; inter-vehicular network enables communication between vehicles of different networks while vehicular mobile network is the connection that links vehicles and mobiles devices in the network. Vehicles in the IoV system are usually equipped with smart functionalities and devices that enable communication and data collection. Some of these devices include On Board Units (OBUs), sensors, event data records, cameras, electronic control units, GPS modules, and other wireless communication technologies (i.e., Bluetooth). The IoV system ensures that diverse communication dynamics (vehicle-to-vehicle (V2V), vehicle-to-roadside unit (V2R), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-sensor (V2S), are achieved [27; 37]. Table 3 summarizes the different communication dynamics in the IoV system. Although IoV is often confused to mean something that can be used interchangeably with ITS, it differs in the sense that IoV focusses on information exchange among humans, vehicles, and the road infrastructures in place [45]. A detailed review on the different communication networks is highlighted by [37].
# Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks
## 4 Results
### Effect of Interference Sources in Network PDR
This case is not shown in the figure as 0% of packets were destroyed. However, when the interference sources were activated, packet losses increased among all nodes in the network. These losses were higher when nodes were near the interference sources. In the case of the saturated scenario, almost all packets were affected by interference and losses were very high for every configuration. Thus, they are not shown in the figure. Regarding the real scenario, it is relevant that for the DR3 configuration, which is supposed to be more robust to noise than DR5 (the SNR margin is greater and the on-air time of packets is longer, so the receiver has more time to recover the packet), the percentage of destroyed packets was higher. This is due to the longer time on-air it takes for a packet to be received in DR3. This usually means that DR3 is more favourable for other LoRaWAN intra-network interference sources, as the other nodes' packets would take a shorter or the same time to be transmitted and the power received by the interferer is within the SNR margin. When external sources that transmit during much longer periods and with higher power were included, spending more time on-air only served to give the interferer a greater chance to cause a packet loss. This value of packets destroyed refers only to downlink traffic, which, as described, is scheduled so in theory the only sources that can trigger interference are the external ones introduced in this scenario. Additionally, DROB achieved values between DR3 and DR5. These results were expected after 24 h, as the nodes spent time configured among DR3, DR4, and DR5. Figure 10: Destroyed packets due to interference during 24 h for all nodes. Figure 9: Power of interference received by each node, for noiseless, saturated, and real scenarios.
### Related Work  
Several studies were relied upon for handcrafted features related to the structure of character image and/or associated statics. The main advantage of handcrafted features lies in their capability of customization. One can select a number of such features as per  
Figure 1: Consonant set of the Hindi script.  
the problem of pattern recognition. For example, [PERSON] [26] selected the features belonging to dots, lines, curves, and loops presented in the character image; [PERSON], [PERSON], and [PERSON] [22] focused on gradient and curvature-based features due to richness of the curves in the Hindi script; [PERSON], [PERSON], and [PERSON] [27] took advantage of the time-frequency localization ability of DWT for salient feature extraction; [PERSON], [PERSON], and [PERSON] [15] developed a curve-fitting algorithm to receive curved-like features and combined it with the number of loops, intersection points, end points presented in the character image; [PERSON] and [PERSON] [28] worked on the features based on chain-code, zone-based-centroid, background-directional-distribution, and distance-profile; [PERSON] and [PERSON] [17] addressed the problem of poor recognition of similar-shaped characters by focusing on critical regions of such characters and applied the Fisher discriminant function to extract features from these regions; [PERSON], [PERSON], [PERSON], and [PERSON] [29] implemented opposition-based multi-objective Harmony Search algorithm to select features from local regions from character images. The handcrafted features need continuous human supervision and typical computations are associated with it.  
CNN-based features are auto generative. The associated convolution and pooling operations make these features computationally-efficient. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [30] introduced multi-column multi-scale CNN architecture for feature extraction from character images; [PERSON] et al. [31] examined the effect of increasing convolutional layers on recognition accuracy. They also developed a hybrid model of CNN and Bi-directional Long Short-Term Memory (BLSTM) for the task; [PERSON] and [PERSON] [32] adopted a layer-wise training scheme of CNN architecture; [PERSON] and [PERSON] [33] implemented the transfer learning approach on a pre-trained DCNN model: Alexnet for character recognition. It can be observed from existing state-of-the-art methods related to CNN that the feature-vector sizes are large and might increase the classification cost.  
The literature review reveals further research scope in the direction of exploring pre-trained DCNN models for feature extraction, classification-cost reduction, and experimentation with a combination of handcrafted and CNN-based features.
###### Abstract  
Accurate temperature and thermal dose prediction are crucial to high-intensity focused ultrasound (HIFU) hyperthermia, which has been used successfully for the non-invasive treatment of solid tumors. For the conventional method of prediction, the tissue properties are usually set as constants. However, the temperature rise induced by HIFU irradiation in tissues will cause changes in the tissue properties that in turn affect the acoustic and temperature field. Herein, an acoustic-thermal coupling model is presented to predict the temperature and thermal damage zone in tissue in terms of the Westervelt equation and Pennes bioheat transfer equation, and the individual influence of each dynamic tissue property and the joint effect of all of the dynamic tissue properties are studied. The simulation results show that the dynamic acoustic absorption coefficient has the greatest influence on the temperature and thermal damage zone among all of the individual dynamic tissue properties. In addition, compared with the conventional method, the dynamic acoustic absorption coefficient leads to a higher focal temperature and a larger thermal damage zone; on the contrary, the dynamic blood perfusion leads to a lower focal temperature and a smaller thermal damage zone. Moreover, the conventional method underestimates the focal temperature and the thermal damage zone, compared with the simulation that was performed using all of the dynamic tissue properties. The results of this study will be helpful to guide the doctors to develop more accurate clinical protocols for HIFU treatment planning.  
HIFU; dynamic tissue property; Westervelt equation; thermal damage zone
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 5 Numerical Results
### Network Lifetime  
In this section, the numerical results of the proposed SASC architecture with other solutions perform in terms of network lifetime. Figure 2 illustrates that the experimental results of SASC improve network lifetime by an average of 13% in comparison with the existing works under a varying number of nodes. Similarly, on the other hand, the experimental results in Figure 3 demonstrate the improvement of SASC by an average of 12% in the comparison of existing solutions under varying constant bit rate (CBR) data traffic. Such improvements are due to that SASC generates clusters based on nodes locality and balances the energy consumption among sensor nodes. Unlike other solutions that divide the sensor nodes into non-optimized clusters without considering the condition of the nodes and randomly selecting the cluster head, SASC presents an optimal way for cluster formation based on centroid vectors. Moreover, the existing solutions offer data security without considering the constraints of low powered sensor nodes. While the proposed SASC architecture is more simplified in terms of communication overheads and leads to improved network lifetime. Under heavy network load, the existing solutions increase frequent re-transmissions of data packets and route breakages thereby result in a compromised network lifetime.  
Figure 2: Network lifetime in a varying number of nodes.
## 3 Results
### Spatial Pattern and Temporal Trend of Key Ecosystem Services
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ecosystem Services** & **Subregions** & **Mean** & **Slope** & **R\({}^{2}\)** & **P** \\ \hline \multirow{7}{*}{WR\({}_{\mathrm{pua}}\)} & GGP & 108.76 & \(-\)0.2283 & 0.0176 & 0.6978 \\ \cline{2-6}  & MSRNC & 125.35 & 0.4587 & 0.0629 & 0.4570 \\ \cline{2-6}  & ASRNC & 30.08 & 0.0473 & 0.0044 & 0.8457 \\ \cline{2-6}  & ADRX & 27.65 & \(-\)0.3970 & 0.2472 & 0.1197 \\ \cline{2-6}  & ASMRYY & 63.31 & 0.2047 & 0.0744 & 0.4172 \\ \cline{2-6}  & HGRLP & 41.70 & 0.2916 & 0.0593 & 0.4706 \\ \cline{2-6}  & AGRSC & 147.61 & \(-\)1.0921 & 0.0964 & 0.3527 \\ \cline{2-6}  & AHRSCHH & 141.33 & 0.7153 & 0.0393 & 0.5592 \\ \cline{2-6}  & LMHRMLYP & 214.32 & \(-\)0.3466 & 0.0016 & 0.9066 \\ \cline{2-6}  & YGPR & 187.11 & \(-\)1.8224 & 0.1084 & 0.3228 \\ \cline{2-6}  & AHRHG & 246.66 & \(-\)0.0342 & 0.0000 & 0.9931 \\ \hline \multirow{7}{*}{SWAL\({}_{\mathrm{pua}}\)} & GGP & 15.62 & \(-\)0.0841 & 0.0251 & 0.6420 \\ \cline{2-6}  & MSRNC & 2.37 & 0.1407 & 0.2696 & 0.1017 \\ \cline{2-6}  & ASRNC & 5.25 & \(-\)0.0611 & 0.0685 & 0.4367 \\ \cline{2-6}  & ADRX & 11.80 & 0.6213 & 0.2194 & 0.1462 \\ \cline{2-6}  & ASMRYY & 20.70 & 0.0420 & 0.0010 & 0.9253 \\ \cline{2-6}  & HRGLP & 18.78 & \(-\)0.7505 & 0.6079 & 0.0047 \({}^{**}\) \\ \cline{2-6}  & AGRSC & 56.93 & \(-\)0.3829 & 0.0635 & 0.4546 \\ \cline{2-6}  & AHRSCHH & 9.96 & \(-\)0.3343 & 0.3447 & 0.0575 \\ \cline{2-6}  & LMHRMLYP & 7.51 & \(-\)0.1435 & 0.0333 & 0.5914 \\ \cline{2-6}  & YGPR & 21.76 & \(-\)0.9375 & 0.3746 & 0.0453 \({}^{*}\) \\ \cline{2-6}  & AHRHG & 10.44 & \(-\)0.1267 & 0.0165 & 0.7069 \\ \hline \multirow{7}{*}{SWIL\({}_{\mathrm{pua}}\)} & GGP & 16.58 & \(-\)1.0071 & 0.4570 & 0.0224 \({}^{*}\) \\ \cline{2-6}  & MSRNC & 3.24 & \(-\)0.3714 & 0.6288 & 0.0036 \({}^{**}\) \\ \cline{2-6}  & ASRNC & 41.51 & \(-\)1.5977 & 0.2511 & 0.1163 \\ \cline{2-6}  & ADRX & 36.08 & \(-\)3.4267 & 0.5883 & 0.0059 \({}^{**}\) \\ \cline{2-6}  & ASMRYY & 24.06 & 0.0445 & 0.0005 & 0.9497 \\ \cline{2-6}  & HRGLP & 12.28 & 0.1235 & 0.0134 & 0.7350 \\ \cline{2-6}  & AGRSC & 0.61 & \(-\)0.0583 & 0.6016 & 0.0050 \({}^{**}\) \\ \cline{2-6}  & AHRSCHH & 0.01 & \(-\)0.0008 & 0.2652 & 0.1050 \\ \cline{2-6}  & LMHRMLYP & 0.03 & \(-\)0.0029 & 0.5356 & 0.0105 \({}^{*}\) \\ \cline{2-6}  & YGPR & 0.02 & \(-\)0.0005 & 0.0086 & 0.7868 \\ \cline{2-6}  & AHRHG & 0.39 & \(-\)0.0021 & 0.0027 & 0.8796 \\ \hline \end{tabular} Note: the * symbol on the top-right of the P value means the tendency is significant (P < 0.05), and the ** symbol means the tendency is very significant (P < 0.01).  
\end{table} 
Table 6: Statistical parameters of ecosystem services indicators in the Grain for Green Program (GGP) area from 2000 to 2010. The units of the mean and slope of \(\mathrm{WR_{pua}}\) are \(10^{4}\) t \(\mathrm{km^{-2}}\) and \(10^{4}\) t \(\mathrm{km^{-2}}\) yr\({}^{-1}\), respectively, while for both \(\mathrm{SWAL_{pua}}\) and \(\mathrm{SWIL_{pua}}\), units are t ha and t ha yr\({}^{-1}\), respectively.
## 2 Issues in Topology Design
### Networking Related Considerations  
Connectivity, robustness, and reliability are the most important issues regarding networking related considerations. In sensor networks, one of the main concerns is that sensor nodes can die anytime, and because of wirelessness, the probability of missing a message is high. The topology should take care of the communication model whenever a sensor node dies or is not responding.  
Connectivity, robustness, and reliability are directly related to the distance between two nodes that communicate wirelessly. For a pair of nodes with a short distance between them, higher values of connectivity, robustness, and reliability are achievable than in a pair of nodes having a longer distance. Therefore, it is advantageous to select the closest node as a neighbouring node along the chain. However, adopting the greedy method of choosing the nearest neighbour results in fewer longer links at the end of the chain formation phase. On the other hand, applying a brute-force search for searching neighbour nodes is not suitable for WSNs due to the scarcity of processing power and memory. Thus, while designing the chains for the proposed topology, we put emphasis on keeping the chains shorter in order to reduce time delays and memory complexities.