esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 4 Existing Similar Algorithms  
### [PERSON]'s Algorithm  
[PERSON] et al. designed an image encryption scheme with AES (short for [PERSON]'s algorithm) [15]. [PERSON] performs the following steps to encrypt the plain image \(I_{\text{m}\times\text{n}}\).  
Step 1: divide \(I_{\text{m}\times\text{n}}\) into \(t\) pixel blocks with the size \(4\times 4\) to form 128 bits;  
Step 2: Tent map is used to generate the initial vector \(IV\) of AES;  
Step 3: for the first block \(I_{1}\) of \(I_{\text{m}\times\text{n}}\), encrypt it by:  
\[C_{1}=\text{AES}_{\text{e}}\Big{(}K,IV\bigoplus I_{1}\Big{)} \tag{11}\]  
where \(K\) is the secret key, and \(C_{1}\) is the first encrypted block;  
Step 4: for the \(i\)th block \(I_{\text{i}}\) of \(I_{\text{m}\times\text{n}}\), encrypt it by:  
\[C_{\text{i}}=\text{AES}_{\text{e}}\Big{(}K,C_{\text{i}-1}\bigoplus I_{\text {i}}\Big{)},\quad i=2,\ 3,\ \cdots,\ \text{t} \tag{12}\]  
where \(C_{\text{i}}\) is the \(i\)th encrypted block.  
### [PERSON]'s Algorithm  
[PERSON] et al. designed an image encryption scheme using AES and visual cryptography (short for [PERSON]'s algorithm) [19]. [PERSON] performs the following steps to encrypt the plain image \(I_{\text{m}\times\text{n}}\).  
Step 1: encode \(I_{\text{m}\times\text{n}}\) with the Base64 standard;  
Step 2: initiate the AES 256-bit key;  
Step 3: encrypt the Base64 encoded text with AES;  
Step 4: convert the cipher text of AES into the encryption image.  
### [PERSON]'s Algorithm  
[PERSON] et al. designed an image encryption scheme with AES (short for [PERSON]'s algorithm) [20]. [PERSON] performs the following steps to encrypt the plain image.  
Step 1: segment \(I_{\text{m}\times\text{n}}\) into \(4\times 4\) pixel blocks;  
Step 2: convert these blocks into 128-bit sequences;  
Step 3: encrypt these 128-bit sequences with AES;  
Step 4: convert the cipher text of AES into \(4\times 4\) encrypted pixel blocks;Step 5: combine these encrypted pixel blocks into the encrypted image.
## 4 Discussion
### SDM-Based Validation of Management Zones  
As mentioned before, once a crop field has been subdivided into individual MZ, it is necessary to corroborate the existence of differences between them in terms of the agricultural phenomenon to be managed. This process, known as validation of management zones, can be developed through different statistical approaches such as clustering validation indexes. CVI represent the most cited approach to the validation of MZ in PA; nevertheless, there are numerous indexes available for such purposes today and no clear agreement in terms of which one offers the best results [10]. Moreover, they are incapable of assessing agronomically meaningful differences between MZ. ANOVA tests, on the other hand, are a straightforward means to corroborate the existence of statistically significant differences between MZ. However, they are limited to the evaluation of individual variables that could or could not reflect the multidimensionality of complex environments. Additionally, they assume independence in the input dataset, a condition that is not met when environmental values are spatially referenced [11]. Finally, although MLM do account for spatial correlation in the data and consider the conjunct effect of different variables over the modeled phenomenon, they demand meticulous parameterization and their implementation tends to be more computationally intensive than other methods [11].  
The validation of MZ based on SDM background tests offers different advantages. Since SDM tools (e.g., background tests) were developed to facilitate the study of species geographic distributions, they are spatially explicit in design. This is relevant because they offer _ad hoc_ methodologies to minimize the effect of spatial biases usually present in SDM-related processes such as modeling species distributions and comparing environmental preferences between species/populations (e.g., differences in sampling effort between populations, spatially uneven samplings, differences in the habitat available to populations in geographic regions where they do not overlap) [58]. Moreover, SDM tools perform between-zone comparisons in terms of complex multivariate environments and determine environmental similarity based on relative rather than absolute measures (i.e., Schoener's \(D\)) [59]. These features make SDM background tests a more realistic and flexible approach to validating MZ than the rest of methodologies discussed above (at least in SSPM). Finally, the use of ecological networks to explain inter-zonal environmental differences is not only easy to set up (only two parameters are needed; i.e, a \(D\) threshold and a significance level for such a threshold) but also improves considerably the interpretability of SDM background tests when used to validate site-specific management zones with pest control purposes.
## 4 Experiments
### Push-Plate Kiln
#### 4.1.2 CFD Simulation  
1. Mathematical model  
CFD is the sub-branch of CAE dealing with fluid flow and thermal processes, wherein the transport of mass, momentum, and energy is modelled via the following equations [19]:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u_{j})}{\partial x_{j}}=0 \tag{12}\]  
\[\frac{\partial(\rho u_{i})}{\partial t}+\frac{\partial(\rho u_{i}u_{j})}{ \partial x_{j}}=-\frac{\partial p}{\partial x}+\frac{\partial}{\partial x_{j} }\left(\mu\frac{\partial u_{i}}{\partial x_{j}}\right)+S_{U} \tag{13}\]  
\[\frac{\partial(\rho\Phi)}{\partial t}+\frac{\partial(\rho\Phi u_{j})}{ \partial x_{j}}=\frac{\partial}{\partial x_{j}}\left(\Gamma\frac{\partial( \rho\Phi)}{\partial x_{j}}\right)+S_{\Phi} \tag{14}\]  
where \(u_{i}\) and \(P\) are the velocity and the pressure, and \(\rho\) and \(\mu\) are the fluid density and viscosity, respectively. In the scalar equation, \(\phi\) denotes a generic passive scalar (temperature or concentration), and \(\Gamma\) is the diffusivity coefficient. The last terms in Equations (13) and (14) account for sources of momentum and any other quantity, e.g., energy.  
2. Grid generation and solution calculation  
The geometry of the pusher kiln is divided into unstructured meshes. Figure 8a shows the overall view of the mesh, and Figure 8b-d shows the enlarged images of the three parts of the mesh, respectively. The grid consists of approximately 147 k cells.  
The airflow adopts the standard k-e model, and the radiation model adopts the discrete coordinate (DO) method.  
Figure 8: CFD grid of push-plate kiln. (**a**) is the overall view of the grid of the push-plate kiln. (**b**) is the enlarged view of the kiln head (Part1), (**c**) is the enlarged view of the outlet (Part2), and (**d**) is the enlarged view of the kiln tail (Part3).
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 3 Convolutional Neural Network Architecture  
Convolution Neural Networks (CNN) are advanced networks that, like the recommended NN-based DPD model, require a large amount of training data. The RoF link is then cascaded with this model, but the outcome is unknown. However, because the output of an RoF connection is known, we create an RoF NN model and train it to mimic the original RoF link. We can now backpropagate through this RoF CNN and adjust the parameters in the recommended CNN DPD model after we have formulated it.  
The convolutional layer is the first layer of a CNN. With the help of a kernel, it extracts features from input data and outputs a feature map (convoluted data).  
The kernel or filter (kernel matrix), input data (input matrix), and feature map are all components of a convolutional operation.  
Assuming the examined RoF link has \(H(n)\) function and \(y(n)\) as an output signal, and that a baseband signal \(x(n)\) must be delivered over it where \(H(n)=\frac{y(n)}{x(n)}\), DPD tries to calculate the inverse transfer function of this RoF link, represented by \(\hat{I}^{-1}\), whose output is then indicated by \(\hat{x}(n)\).  
This can be written as:  
\[\hat{x}(n)=\hat{I}^{-1}(x(n)) \tag{1}\]  
while,  
\[y(n)=Gx(n)=I(\hat{x}(n)) \tag{2}\]  
as \(G\) represents the gain. The CNN here finds out the \(\hat{I}^{-1}\) utilized for predistortion. A direct training cannot be done for establishing the CNN for DPD as the ideal \(\hat{x}(n)\) is not known as illustrated in Figure 2.  
Initially, the second CNN simulates the RoF link. Here, \(\hat{x}(n)\) is an input and \(\frac{y(n)}{G}\) as output for a generic RoF link. This leads to the CNN learning, and it can now identify an approximate transfer function \(\hat{I}\). The model weights are fixed once the RoF CNN model is formed, and then it is connected to the CNN DPD model. To calculate error using a loss function, we now use the original input, \(x(n)\) and output as training data. We then backpropagate it through \(\hat{I}\) to train \(\hat{I}^{-1}\).
# A Novel Connected-Components Algorithm for 2D Binarized Images
## 1 Introduction
### Run-Length-Based Methods  
Run-Length Encoding (RLE) methods address the inefficiencies of pixel-wise algorithms by grouping consecutive black pixels into runs and processing them at a higher abstraction level. These approaches were first explored by [PERSON] and [PERSON] [16] and have been further developed in recent years.  
RLE is a technique where contiguous sequences of identical values are stored as runs instead of individual pixel values. For a binary image, this means that black pixels in each row are stored as (start, end) coordinate pairs. For example, in a row with pixels \([0\ 0\ 1\ 1\ 1\ 0\ 0\ 1\ 1\ 0\ 0\ 0]\), the run-length representation is \([(2,4),\ (7,8)]\).  
Instead of assigning labels to individual pixels, run-length-based algorithms operate on runs, significantly reducing the number of comparisons needed. The process follows these steps:  
* Extract all black runs per row: Each sequence of consecutive black pixels is stored as a run.
* Create run-length structures: Each run is initially treated as an independent component.
* Merge connected runs across rows: Runs are linked based on 4-way or 8-way connectivity rules.  
Let \(\text{R}(\text{x},\ \text{y},\ \text{w})\) be a run starting at pixel \((\text{x},\ \text{y})\) with width w. Runs \(\text{R}_{1}\) and \(\text{R}_{2}\) from successive rows are merged if \(\text{x}_{1}\ \leq\ \text{x}_{2}+\text{w}_{2}\) and \(\text{x}_{1}+\text{w}_{1}\ \geq\text{x}_{2}\) (for 4-way connectivity), or additionally \(|\text{x}_{1}-\ \text{x}_{2}|\ \leq\ 1\) (for 8-way connectivity).  
This method significantly reduces the number of label updates compared to pixel-wise methods and is well-suited for parallelization on modern architectures [17].
# Hardware Model Checking Algorithms and Techniques
## 1 Introduction
#### Outline  
The rest of the paper is organized as follows:  
* In Section 2, we provide some preliminary notions with the purpose of presenting the adopted notation and terminology and make this work self-contained. In particular, we first provide some general concepts from the field of logic and computer science and then we discuss in more depth the necessary concept to contextualize model checking in detail.
* In Sections 3-7, we present a complete description of the most relevant bit-level SAT-based hardware model checking algorithms.
* In Section 8, we present a practical evaluation of model checking algorithms over industrial-level benchmarks.
* In Section 9, we summarize the current work and provide some insights concerning future developments in the field.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 3 Proposed Algorithm
### Computational Efficient Calculation  
In this paper the computation of WHT is done using a customised algorithm developed for calculating 8 \(\times\) 8 WHT cells. The algorithm has been implemented in MATLAB (2013) using the idea of fast computation of Discrete Fourier Transform (DFT), without using any loops and using only matrix additions and subtractions. With this customized technique, computation of WHT of an 8 \(\times\) 8 image takes only 0.15 milliseconds which is considerably less than the time for execution using existing MATLAB implementation which consumed 1 milliseconds for the same applied image. The cyclomatic complexity of the proposed algorithm is found to be equal to one which means that there is a single path from input to output. On the other hand cyclomatic complexity of MATLAB implemented WHT and DCT is 12. A new, computationally efficient CDMA based watermarking algorithm using above mentioned WHT is formulated with a specific selection of frequency coefficients which has better robustness and performance compared to the existing similar algorithms using other transforms like DCT [3]. Variation of robustness with change in spreading code length, number of spreading codes and type of spreading code is studied in detail.
## 5 Experiments and Results  
### Human and Joint Recognition  
For all video clips, the human detection algorithm is applied. As shown in Figure 5, the red box represents the target box, which marks the human target. In the red box, the human joints are identified and connected with different lines, representing the skeletal curves of the human body and displaying their specific behavioral characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Normal** & **TOG \({}^{*}\)** & **TOC** & **TOM** & **TOH** \\ \hline Training & 49 & 19 & 29 & 26 & 27 \\ Validation & 6 & 3 & 5 & 6 & 7 \\ Testing & 18 & 5 & 12 & 13 & 16 \\ \hline \hline \end{tabular} \({}^{*}\) For the abbreviations mentioned above: TOG: take off gloves; TOC: take off coat; TOM: take off mask; TOH: take off hats.  
\end{table}
Table 1: The overall quantity of videos in each category for determining the wearing of protective equipment.
## Materials and Methods
### Analysis of Nucleotide Sequences and Phylogenetic Relationships  
The nrDNA-ITS amplicons (approximately 800 bp long) were extracted from the agarose gels using a Gel Extraction Kit (SolGent, Daejeon, Korea) and subcloned into the pGEM-T Easy vector (Promega, Madison, WI, USA) for sequencing. The cloning of inserts was confirmed by colony PCR using the vector primer set, T7 and SP6, and the sequences of cloned amplicons were determined on both strands by Sanger sequencing using ITS1 and ITS4 primers and an automatic DNA sequence analyzer (ABI 3730, Applied Biosystems Inc., Foster City, CA, USA). To check the sequence quality of the amplified products for each sample, such as PCR errors, misreading, and geographical sequence variation, we obtained nucleotide sequences from five inserted DNA fragments per sample and compared their nucleotide sequences after alignment of the entire sequences using the multiple alignment tool (version 7.2.5, BioEdit, North Carolina State University, Raleigh, NC, USA) [29]. In addition, all sequences were subjected to a similarity search against the GenBank database to confirm the origins of the individual nrDNA-ITS sequences using a basic local alignment search tool (BLAST, [[http://blast.ncbi.nlm.nih.gov/Blast](http://blast.ncbi.nlm.nih.gov/Blast)]([http://blast.ncbi.nlm.nih.gov/Blast](http://blast.ncbi.nlm.nih.gov/Blast))). The resulting representative nrDNA-ITS sequences of each sample were registered in the NCBI GenBank (accession numbers: _A. stricta_, KY829514-KY829517; _A. triphyllla_, KY829518-KY829521; _A. triphyllla_ var. _japonica_, KY829522-KY829525; _C. lanccolata_, KY829526-KY829528; _C. pilosula_, KY829529-KY829533; and _G. littoralis_, KY829534-KY829537).  
The final sequences of the \(\sim\)800 bp nrDNA-ITS amplicons were analyzed and manually curated using the BioEdit program, version 7.2.5 [29]. The inter- and intra-species variability of all sequences was analyzed using the MEGA6 program, version 6.06, with the Kimura-2-parameter (K2P) model [30]. To analyze the phylogenetic relationships of the species targeted in this study, we inferred a phylogenetic tree using the neighbor joining (NJ) method with the K2P model, pairwise deletion for gaps/missing data treatment, and 1000 replications for bootstrapping with _Cisium japonicum_ (KM051436) as an outgroup reference.
# Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management
## 5 Combining IoT with Blockchain Technology: Emerging Research Areas
### Security
An integrated system incorporating barcodes, RFID tags, sensors, and Blockchain could facilitate consumers to reject the purchase of counterfeits even with an authentic tag if the seller does not possess the (digital) ownership of products in the Blockchain system [106]. Consequently, Blockchain-based solutions can help to crosscheck and verify the identification and the authenticity of IoT devices. For example, Chronicled, a San Francisco-based technology startup, combines IoT sensors, NFC embedded adhesive seals, and QR codes in an Ethereum Blockchain platform [107]. IoT devices are protected against forging and counterfeiting thanks to the tamper-resistant nature of the Blockchain. Hence, we posit:  
RP2: Blockchain technology positively impacts the security of IoT solutions.
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 4 Discussion
However, many GRs are not related to taste receptors but function in the detection of sugars, bitter compounds, and non-volatile pheromones [53]. Interestingly, GR28B represents a new class of thermosensor and is required for thermotaxis [54]. We found GR28B differentially expressed in C and M (Figure 4C, Table S2). This might be evidence of its new role in the detection of different scents. IR genes are expressed in coeloconic sensilla of the antenna and respond, among others, to water and amines [55]. IRs are not related to insect ORs but rather have evolved from ionotropic glutamate receptors (iGluRs), a conserved family of synaptic ligand-gated ion channels [56]. In this study, we identified 26 IR transcripts, and 18 of them encode IR25A (Table 2, Table S2). It is conceivable that the IR25A ancestor initially evolved as a sensory detector for external glutamate, analogous to the synaptic function of iGluRs, and that it only later acquired a co-receptor function after duplication and diversification of the IR repertoire [56]. We also identified 14 SNMP transcripts in the parasitoid wasps, including 11 encoding SNMP1 and 3 encoding SNMP2 (Table 2, Table S2). The SNMP1 has been shown to be antenna specific and play an important role in pheromone detection [57]. While SNMP2, which acts as a second lepidoperan and also associates with pheromone-sensitive sensilla, has been shown to be expressed in sensilla support cells rather than neurons [58; 59]. The identification and differential expression of olfactory-related transcripts revealed the complex chemosensory system of _D. longicaudata_ and supported a diverse function of olfactory genes in discriminating different chemical cues. The limitations of this project may include the use of the tissue mixture of insects. It is said that some OBPs are expressed in insects with a sex preference. For example, MsepOBP5 exhibited female-biased expression in 0- and 5-day-old adults; MsepOBP22 displayed female-biased expression in 0- and 5-day-old adults but was male-biased in 3-day-old adults [60]. Due to the difficulty of the sample preparation, it is hard to get enough material for sequencing with the same sex. Additionally, it is difficult to determine the tissue-specific olfactory genes. However, our findings provide a basis of future studies about the olfactory system in _D. longicaudata_.
## 3 Classification of Optical Waveguide Sensors  
Integrated optical waveguides can be divided into passive devices (grating couplers, couplers, wavelength division/mode division multiplexers, filters, and isolators) and active devices (lasers, optical switches, modulators, and photodetectors). Passive device material systems include SiO\({}_{2}\)/SiN/SOI/SOS/chalcogenide glass (SOI is silicon on insulator and SOS is silicon on sapphire), whereas active device material systems include lithium niobate (LiNbO\({}_{3}\))/polymer/germanium/gallium arsenide (GaAs)/indium phosphide (InP).  
In terms of waveguide types, most devices are composed of strip and ridge waveguides. In addition, there are slot (slit), photonic crystal (PhC), subwavelength grating (groove), and surface plasmon trench waveguides. For the detection of biochemical substances in the form of gases or liquids, the concentration or density of the substance under test is the primary factor affecting the refractive index. The refractive index does not change with the volume of the substance under test; hence, it is suitable for the detection of trace substances. However, the refractive index change is not large; hence, it is necessary to improve the sensitivity of the sensor and expand the detection limit range.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 3 Results and Discussion  
### Adsorption Experiment  
#### 3.1.1 Effect of SDS Loading on the Adsorption of Cr(VI)  
The maximum removal of Cr(VI) by modified chitosan beads can be obtained by controlling the SDS concentration. In order to research the optimum initial concentrations of SDS loading on chitosan beads for Cr(VI) removal, the chitosan beads were modified with SDS solutions with varying initial SDS concentrations (10-6000 mg/L). The experiment was conducted under the following conditions: adsorbent dose of 0.05g, initial concentration of Cr(VI) of 1 mg/L, pH of 4, and contact time of three days. The effect of surfactant loading on chitosan beads towards Cr(VI) adsorption is shown in Figure 2. The low SDS concentrations (0-1000 mg/L) range in Figure 2a was extended and shown in Figure 2b. From this figure, it was found that the adsorption capacity increased with an increase in the SDS concentration from 10 to 40 mg/L, and that it attained the maximum capacity at the initial SDS concentration of 40 mg/L. With a higher concentration of SDS, a higher adsorption capacity could not be obtained compared to the case of a low concentration range (10 to 100 mg/L). It is considered that the positively charged amino group was occupied by SDS, and thereby the adsorption reached saturation. Then, 40 mg/L was considered as the optimum SDS loading for further experiments.  
Figure 2: Adsorption of Cr(VI) for different initial SDS concentrations (10–6000 mg/L). (**a**) Effect of initial SDS concentrations (10–6000 mg/L) on the adsorption of chromium (Cr) (VI). (**b**) Effect of initial SDS concentrations (10–1000 mg/L) on the adsorption of Cr(VI).