esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Model and Hypothesis Development
### Open Reachability  
Open reachability--defined as the extent to which other users related to an SNS user can connect to said user \"anytime and anywhere\" to share information and communicate--is not SNS-user-centered, but is related to other users surrounding the SNS user. Certain SNS users have too many relationships on SNSs, meaning that others can always share information with or inform them of news [35]. Social relationships are one of the important elements embedded in SNSs. Unlike existing communication services such as email and telephones, SNSs feature endless and open connectivity to others whom users know and do not know. In other words, SNS users can easily reach other users on SNSs. Open reachability on SNSs thus enables users to expand their social relations [36].  
Most previous research has shown that SNS users connect with existing friends and family members on SNS platforms. In addition, studies have shown that new relationships can be established while using SNSs [37; 31]. However, SNSs do sometimes connect users with unwanted people. Many SNS users have difficulty managing their SNS accounts because of incoming communication from those they do not know or other unwanted users. [PERSON] and [PERSON] [38] claimed that SNS users spend considerable time managing their accounts because of incoming connections, messages, and notifications. Such characteristics of SNSs can cause fatigue among users. [PERSON] et al. [32] also argued that engaging and communicating with unwanted people can cause SNS users to feel fatigue. They suggested that burdens resulting from undesired connections and negative feelings about simple relationships can lead to psychological disharmony. Finally, [PERSON] et al. [21] suggested that the burden of social relationships on SNSs may lead to declines in SNS use. Based on these claims, we proposed the following hypothesis.  
**Hypothesis 2.**_Open reachability is positively associated with SNS fatigue._
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 2 Proposed Chaos-Based Cryptosystem
### Circular Substitution Process Based on the S-Box and Their Inverse Processes
#### 2.2.2 Circular Substitution Equation Based on the Constructed S-Box  
Once the S-box is constructed, then it is used to perform the substitution operation. Unlike the circular substitution used by [PERSON] et al. [28], which was performed in ECB mode on the entire plain image, we achieved here the circular substitution in CBC mode on blocks of size 1024 pixels each. The advantages of proceeding in CBC mode are, on the one hand, because it is a secure mode (ECB mode is not secure), and on the other hand, the encryption/decryption processes are carried out on blocks that can be stored and computed on IoT devices which are constrained resource in terms of computing capabilities, and also energy and memory capacities. In addition, in ECB mode, the decryption process requires receiving the entire ciphered image before decrypting it.  
To start the substitution process, the input plain image is split into \(b_{l}\) blocks \(p_{l}\), each of size \(|p_{l}|=1024\) pixels (32 \(\times\) 32 bytes). Notice that the elements of the S-box are considered to be circular (the last element followed the first element) with a head pointer \(h\) initialized to a constant (or a random) value in the range of 0 to 255. Each pixel \(p_{l}(k)\) of a given block \(l\) is substituted by an element of the S-box, \(s_{l}(k)\), according to the pixel value \(p_{l}(k)\), the previous substituted pixel \(q_{l-1}(k)\), and the pointer \(h\) (see Equations (14)-(16)). After a pixel is substituted, the value of the pointer \(h\) is adapted to a new value using the second row of Equation (14), where \(m_{h}\in[0,\ 255]\) is a random value.  
\[\left\{\begin{array}{l}s_{l}(k)=S[y_{l}(k)+h]\ mod\ 256\\ h=s_{l}(k)\oplus m_{h}\end{array}\right. \tag{14}\]  
where  
\[y_{l}(k)=p_{l}(k)+q_{l-1}(k) \tag{15}\]  
\[q_{l-1}(k)=\left\{\begin{array}{ll}ivb&if\ l=0\\ c_{l-1}(k)&if\ l>0\end{array}\right. \tag{16}\]  
where \(c_{l-1}(k)\) is the previous ciphered pixel obtained after the horizontal and vertical addition diffusion of the block \(sp_{l-1}\), which is the permuted of the substituted block \(s_{l-1}\), \(k=1,2,\ldots,|p_{l}|\ l=1,2,\ldots,b_{l}\)  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline  & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** &  
\[b_{l}\ =\ (image\ size/block\ size)\ =\ (R\times C\times P/|p_{l}|)\ (R\text{: number of rows, }C\text{: number of columns, and }P\text{: number of planes }(P=1\text{ for a grayscale image and }P=3\text{ for an RGB image).}\]  
From Equations (14)-(16), we can observe that all the same pixel values of the plain image are substituted by different values, and then the security is strengthened against cryptographic attacks. In addition, there is an intrinsic diffusion effect in each substituted block.
## 3 Results and Discussions  
### Thermal Determination  
The mass change in AC-RGL and Raw-RGL adsorbents was monitored and recorded between 30 and 900 \({}^{\circ}\)C as indicated in Figure 1. The thermogram of the Raw-RGL shows several mass losses up to 850 \({}^{\circ}\)C. The first weight loss of approximately 8% due to water loss occurs from 31 \({}^{\circ}\)C to 107 \({}^{\circ}\)C. The decomposition of hemicellulose, cellulose and lignin accounts for the second weight loss of Raw-RGL of approximately 61% between 107 and 688 \({}^{\circ}\)C. The third and final step on Raw-RGL after degradation of lignocellulosic material is a tail obtained between 688 and 865 \({}^{\circ}\)C due to pyrolysis of the residues. For AC-RGL, the first mass loss of approximately 5% occurs between 31 and 167 \({}^{\circ}\)C due to the loss of water molecules, and the second mass loss is in the range of 167-693 \({}^{\circ}\)C. In the third step, the weight loss beyond 693 \({}^{\circ}\)C indicates the complete decomposition of organic groups. A slight weight loss at 383 and 572 \({}^{\circ}\)C observed on Raw-RGL could be due to some bigger unstable molecules [38]. This loss was not observed on AC-RGL.
# Near-Real-Time Detection of Insect Outbreaks in Urban Trees Using a PlanetScope Time Series
## 1 Introduction
Field surveys are also subject to human error and may not provide real-time data, potentially delaying the interventions necessary to counteract infestations. For this reason, in this study, field data were integrated with remote-sensing data, where ground data facilitate satellite analysis and enhance its specificity, while the remote-sensing data augment and complement the field data. The aims of this study were (i) to evaluate the effectiveness of PlanetScope satellite constellation images to monitor damage caused by the infestation of _T. parvcini_s on _Pinus pinea_; and (ii) to identify the advantages and limitations of combining ground surveys and remote-sensing data for insect-outbreak detection, monitoring, and management in urban areas.
## 2 Material and Methods
### Methodology of the Proposed Prediction Model
#### 2.2.3 Classifier  
In this study, we employed an SVM algorithm [45], a standard classifier which has been widely used in many neuroimaging studies [46]. The 10-fold class-validation strategy was used during the classification stage. To this end, the entire dataset was divided into ten equal subsets, of which one fold was used as a test set and the other nine folds as a training set. The prediction accuracy was calculated in each iteration, and the average accuracy was computed at the end of 10 iterations. The sensitivity (SEN), specificity (SPE), and accuracy (ACC) were calculated as follows:  
\[Sensitivity=TPR=\frac{TP}{TP+FN} \tag{13}\]  
\[Specificity=TNR=\frac{TN}{TN+FP} \tag{14}\]  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{15}\]  
where TP, FN, TN, and FP denote true positive, false negative, true negative, and false-positive results, respectively. In addition, we reported the area under the receiver operating characteristic curve (AUC) for each experiment.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No. Feature** & \(f_{1}\) & \(f_{2}\) & \(f_{3}\) & \(f_{4}\) & \(f_{5}\) & \(\cdots\) & \(f_{n-1}\) & \(f_{n}\) \\ \hline Particle value & 1 & 0 & 1 & 1 & 0 & \(\cdots\) & 1 & 0 \\ \hline \end{tabular}
\end{table}
Table 2: A sample particle representation of selected features.
## 2 Background
### Named Entity Recognition  
Named Entity Recognition (NER) is the task of identifying named entities such as specific location, treatment plan, medicines/drug, and critical health information from the clinical text. NER was first introduced in 1995 [18] where the three categories (Entity, Name, and Number) were defined. The original design idea for NER was to parse the text, to identify proper nouns from the text, and to categorize them.  
NER is an extremely popular machine learning method and is also considered a base technique for many of the NLP tasks. Prior to 2011, all work on NERs was domain-specific and was designed to performing specific tasks based on ontologies. [PERSON] et al. [19] introduced a neural network-based NER, which for the first time, made it domain independent. This approach is now quite common, and there are many variations proposed over the last decade that leverage Recurrent Neural Networks (RNN) and word embeddings among others.
## 5 Conclusions  
In this paper, we have proposed a novel CS M-ary SVM NLE scheme to mitigate the nonlinearity of the optical communication system. This scheme firstly divided the PS-64 QAM constellation into two subsets by a SVM. Then the inner and outer sets were detected by M-ary SVM NLE scheme, respectively. This scheme improves the Q factor of the PS-64 QAM signal with lower complexity. Experimental verification of the proposed CS M-ary NLE scheme was demonstrated in a 120 Gb/s PS-64 QAM coherent optical communication system with a transmission distance of 375 km. The experimental results show that for the proposed scheme, the LOPDR of PS-64 QAM is 0.6 dBm and 1.6 dBm higher than that of the M-ary SVM scheme and without the NLE scheme, respectively. In addition, for the proposed scheme, the Q factor of PS-64 QAM is improved by about 0.50 dB and 0.96 dB compared with employing M-ary SVM NLE and without employing NLE, respectively. In terms of complexity, we used the number of SVs to measure the complexity of the two NLE schemes. The results show that the complexity of the proposed scheme is lower than that of the M-ary SVM scheme under the entire measured launched optical power range from \(-5\) dBm to \(+5\) dBm.  
Conceptualization, H.X. and Y.W.; methodology, H.X. and Y.W.; software, H.X. and X.W.; validation, H.X., X.W. and C.L.; formal analysis, H.X. and X.H.; investigation, C.L.; resources, Y.W.; data curation, H.X. and X.H.; writing--original draft preparation, H.X.; writing--review and editing, H.X.; visualization, X.W.; supervision, Y.W. and Q.Z.; project administration, Q.Z.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (62075014); National Key Research and Development Program of China (2021 YFB2900703).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.  
Figure 11: (**a**) The number of support vectors (**b**) CPU running time.
## 3 Numerical Implementation
### Radar Reconstruction  
At first, a reference simulation with no enclosed tumor was performed as described above. Each transmission antenna sends a pulse signal through the medium without inclusion. The signals were captured by the receivers on the top. The reference signals are denoted as \(\vec{s}_{a,b}^{0}\,=\,[s_{1}^{0},s_{2}^{0},\ldots,s_{T}^{0}]\), where \(a\) indicates which transmitter was used for sending the signal, \(b\) denotes the transmitter receiving the signal, and \(T\) denotes the length of the signal.  
After that, the simulation was carried out with the inclusions. In our case, we investigated one inclusion. The inclusion signals are denoted as \(\vec{s}_{a,b}\,=\,[s_{1},s_{2},\ldots,s_{T}]\), using the same notation as in the first case.  
The reconstruction process was carried out as follows. At first, the root-mean-square deviation (RMSD) was computed for each transmitter-receiver pair \((a,b)\) by  
\[RMSD_{a,b}\,=\,\sqrt{\frac{1}{T}\sum_{t=1}^{T}(s_{a,b}^{0}(t)\,-\,s_{a,b}(t))^ {2}}. \tag{21}\]  
Figure 5: Structure of the used FEM model. On the bottom and top are the dual-use EIT electrodes/radar antennas. On the right and left are pure EIT electrodes. The skin is colored in purple. The rest of the breast is modeled with fat, which is represented by the pink-colored areas. The size of the model is \(100\,\mathrm{mm}\,\times\,100\,\mathrm{mm}\). This is the same as in [PERSON] et al. [24].  
The vector \(RMSD_{a}\) is defined as \(RMSD_{a}=[RMSD_{a,1},\ldots,RMSD_{a,B}]\), where \(B\) is equal to the number of receivers. The next step for the reconstruction is to compute the mean \(\overline{RMSD}_{a}=\frac{1}{B}\sum_{b=1}^{B}RMSD_{a,b}\) of each \(RMSD_{a}\) for each transmitter, where \(B\) is the number of receivers. Only values above the mean are used for the final reconstruction, resulting in  
\[RMSD_{a,b}^{final} = \begin{cases}RMSD_{a,b}&RMSD_{a,b}>\overline{RMSD}_{a}\\ 0&\text{ \ \ \ \ \ \ \ \ \ \ \ \ \  
After that, the calculations follow the procedure described in Section 3. This mean that we use Equation (10) to calculate \(\mathbf{t}^{exp}(k)\). Thus, the calculation of the prior is performed the same way as for the EIT data. The new scattering transformation with EIT and radar information can be stated as  
\[\mathbf{t}^{fusion}(k)\;=\;\begin{cases}\mathbf{t}_{R_{1}}^{EIT}(k),&|k|\leq R_{1}\\ \mathbf{t}_{R_{2}}^{radar}(k),&R_{1}<|k|\leq R_{2}\\ 0,&R_{2}<|k|\end{cases}. \tag{23}\]  
\(\mathbf{t}^{fusion}(k)\) combines the advantages of both the EIT and radar. The concept is also visualized in Figure 7. Note that this is equivalent to [PERSON] et al.'s Equation 3.6 in [21]. However, the actual calculation of \(\mathbf{t}_{R_{2}}^{radar}(k)\) is different from their calculations. In addition, we have a one-dimensional prior instead of a two-dimensional one, compared to their setup. Later, we analyze the behavior of under- and overestimation of the conductivity, by scaling the added conductivity with a factor. The reconstruction with the help of \(t^{fusion}\) follows the same procedure as the pure EIT reconstruction (step 3 of the D-Bar algorithm).
## 1 Introduction
In the absence of biomolecules, the CoF was quite similar for both hydrogels, with values between 0.25 and 0.3. The authors observed a significant increase in friction for HEMA/PVP when the lubricant included cholesterol and for TRIS/NVP/HEMA when it contained albumin [7]. [PERSON] et al. (2011) studied the influence of hyaluronic acid as a lubricant in CL systems. Two types of commercially available CL were tested (senofilcon A and balafilcon A) with different lubricant concentrations, loads, and speeds. Using a borosilicate glass pin, normal loads between 2 and 20 mN, and sliding speeds of 20 to 3600 um/s, they verified values of CoF of the order of magnitude of 0.6 in saline solution and a significant reduction in this value in hyaluronic acid [8]. [PERSON] et al. (2015) studied the ability of proteoglycan 4 (PRG4) to lubricate and adhere to CLs and demonstrated that PRG4 significantly reduces friction against corneal and eyelid tissues. As PRG4 in solution was able to effectively lubricate CLs based on senofilcon A and narafilcon A, they suggested that this protein, when used as a lubricant or in the constitution of CL, may have the clinical capacity to reduce friction and improve comfort in alive [9]. With the aim of investigating and associating the deposition of lysozyme, the most abundant tear protein, and friction between the contact lens and the eye with discomfort and ocular changes, [PERSON] et al. (2018) developed a preservative-free CL care solution to investigate whether it could effectively remove lysozyme and provide lubrication. Two CL materials were studied, etafilcon A and polymacon, using a rotational tribometer based on the measurement of friction force. A polyethylene (PE) support was used for the CL, and a quartz glass was used as a counterbody, with normal loads of 60 mN and rotation speeds of 1 rpm and testing times of 900 s [10]. The results suggested that the deposition of lysozyme on the surface of the material increases the friction between the contact lens and the glass, and when adding the lubricating solution, the friction coefficient decreases significantly for non-ionic CL. In other words, they showed that the preservative-free solution can effectively reduce the friction caused by lysozyme for non-ionic CL under certain test conditions. However, the effect of the solution on CL behaviour will be different when all tear components are present [10]. The main objective of this work is to study the influence of BSA (Bovine Serum Albumin) protein content on the friction of soft contact lenses. For this purpose, a new methodology was used, which consists of using a tribometer with the operating principle of a pendulum with horizontal movement, and the study of friction is based on the evaluation of the dissipated energy along the free vibration response of the system after the application of a mechanical impulse.
# Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations
## 4 Discussion
### Blockchain Contributions in IoV-Assisted Smart Grid
Blockchain can enable a decentralized platform, allowing cars to share resources with others in order to enhance efficiency and performance [65]. Blockchain can handle resource-sharing issues, by promoting trust while also maintaining entity security and privacy. Furthermore, with the support of blockchain, non-moving vehicles may safely and effectively share their idle computing and networking resources when parked [93; 94]. **Contribution 7--Demand Response Management and Coordination:** Since blockchain technologies have the ability to offer cost-effective smart grid solutions, they represent better alternatives for tackling challenges such as traffic congestion and road safety within the IoV. They also efficiently lower the cost of communication between multiple participants (e.g., drivers, vehicles, RSUs, etc.). It is underlined that blockchain might enhance end-consumer, EV and renewable energy engagement in IoV-assisted smart grids. As a result, these users are exposed to the true cost of energy, which leads to more efficient energy usage and makes the demand response price signals more appropriate [92]. This procedure may be achieved by utilizing various aspects of blockchain technology, such as smart contracts. The use of smart contracts in demand response might improve their efficiency and lower their operational costs. Furthermore, they can enhance the confidentiality of demand response communication [95]. Furthermore, one of the recent applications of blockchain is EV charging management and optimization, which is starting to grow quickly [96]. EV drivers may share their private chargers with others thanks to blockchain-based applications. Private owners can make their chargers available to the public while they are not in use by using P2P EV charging systems. In exchange, customers may make some profit from their idle charger by expanding its use [97].
# A Systematic Review of Cyber Range Taxonomies: Trends, Gaps, and a Proposed Taxonomy
## 5 Discussion
### Emerging Trends  
Our analysis identified several notable shifts influencing cyber range development and utilization. Firstly, virtualization technologies continue to diversify, moving away from traditional monolithic architectures toward flexible, containerized micro-ranges and hybrid-cloud deployments. This evolution reflects a broader trend toward scalability and adaptability, particularly for large-scale, national-level exercises.  
Secondly, the role of AI in cyber range environments is growing from exploratory proof-of-concept stages to integrated functionalities. Although recent studies [35; 38] illustrate promising AI-driven adaptations in scenario generation and Red team automation, critical challenges remain unresolved. Limited high-quality training data, opaque decision-making processes hindering post-exercise analyses, and the potential for overly autonomous systems to surpass learners' comprehension highlight the necessity for further research into explainable and pedagogically aligned AI systems.  
Finally, federation is increasingly recognized as a preferred architecture for multi-organizational training. Recent implementations emphasize balancing resource sharing with organizational autonomy, employing peer-to-peer VPN or message-bus infrastructures. Despite clear benefits in scenario expansion and collaborative training, federation introduces substantial complexity in orchestration interoperability, latency management, and maintaining the trustworthiness of shared telemetry data. The proposed taxonomy's newly articulated _Federation_ sub-characteristic explicitly addresses these complexities, assisting stakeholders in aligning architectural choices with specific training objectives and constraints.
# Effects of Precipitation and Fire on Land Surface Phenology in the Brazilian Savannas (Cerrado)
###### Abstract
More specifically, the study aims to: (i) identify the driest and wettest seasonal cycles at the ENP during the MODIS data analysis period (2002-2023), using a 40-year rainfall time series from a meteorological station to determine rainfall patterns; (ii) examine the relationships between precipitation and each of the 13 TIMESAT phenological metrics derived from the 250-m MODIS MOD13Q1 product in both grassland and woodland areas of the park; and (iii) investigate the influence of fire and vegetation recovery in burned areas on the determination of LSP metrics in pre- and post-disturbance seasonal cycles, using the 30-m spatial resolution HLS product, which combines data from the Landsat and Sentinel-2 satellites.
## 2 Results  
### Uplc-Ms  
The extracts of the leaves, stems, and roots of _A. paniculata_ were scanned in the positive mode using UPLC-MS (Table 1). The results reveal that the primary chemical constituents were diterpene lactones and flavonoids. The identified diterpene lactones included andrographolide, 14-deoxyandrographolide, dehydroandrographolide, 14-deoxyandrographiside, paniculide B, 125-hydroxyandrographolide, and morpholactone, bisandrographolide A, and androrograpanin. The detected flavonoids consisted of 5, 4'-dihydroxy-7, 2', 6'-trimethoxy flavone, andrographidin A, 5-hydroxy-7, dihydrocall-capflavone I, 8-dimethoxy flavanone, apigenin-7, 5-hydroxy-7, 4'-dimethylether, and 8-dimethoxy flavone.