esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT-Based Fog Computing Model
## 6 Experiment and Discussion of Results
### Simulating the Terminal Layer
#### 6.2.1 Energy Consumption  
From the perspective of the IoT terminal layer, conservation of the nodes' energy consumption is the goal that the routing protocol needs to achieve. We modified the original LIBP [4], with the use of multiple Sink nodes. The simulation results have been summarized in Table 4. The table shows energy consumption levels when the modified LIBP (with multiple Sink Nodes) are used as proposed by IoT-FCM. It can be seen that the highest energy consumed by the sink node in each network are respectively 5.84%, 5.71%, 6.00%, 5.86%. These are lower than the energy consumption of 6.60% recorded when the original LIBP (with a single Sink Node) was used. Similarly, the average energy consumed when using multiple sink node in each network are respectively 4.23%, 3.92%, 4.38%, 5.07%. While the original LIBP shows that on the average, using the single sink node, energy consumption was at 6.60%. Comparing the results, the LIMP protocol used by IoT-FCM shows lower energy consumption versus the original LIBP. This in turn implies better battery life of sink nodes.  
Figure 10: The sum of the energy consumption of all fog nodes.
# [PERSON]'s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm
###### Abstract  
Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi's entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.  
image segmentation; multi-level thresholding; Renyi's entropy; meta-heuristics algorithm +
Footnote †: journal: Applied Sciences
## 2 Material and Methods
### Virtual Screening  
For the virtual screening of ginsenosides against ADGRG3, ADGRG3 was selected as the target, and its relevance in cancer was confirmed through gene expression analysis (Section 2.1). Ginsenosides, chosen for their structural similarity to steroid drugs and their known pharmacological benefits in cancer and immune modulation, were screened against ADGRG3. The 7D/77 [56] crystal structure of ADGRG3 (Protein Data Bank, (PDB), 2.90 A resolution) was used for docking to ensure reliable interaction modeling. The ADGRG3 structure was prepared for docking by removing water molecules and heteroatoms, adding hydrogens, and assigning appropriate charges. As the 3D structures of most ginsenosides are not readily available in public databases, 3D conformations of 128 ginsenosides [57] were constructed using ChemDraw professional 20.0. Four control ligands (beclometha-sone dipropionate, dexamethasone, HCY, and prednisolone) were screened for their ability to bind to ADGRG3 through MD, Beclomethasone dipropionate (PubChemID: 21700), an approved small-molecule drug reported as an agonist against ADGRG3 in gene cards ([[https://www.genecards.org/cgi-bin/carddisp.pl?gene=ADGRG3#drugs_compounds](https://www.genecards.org/cgi-bin/carddisp.pl?gene=ADGRG3#drugs_compounds)]([https://www.genecards.org/cgi-bin/carddisp.pl?gene=ADGRG3#drugs_compounds](https://www.genecards.org/cgi-bin/carddisp.pl?gene=ADGRG3#drugs_compounds)), accessed on 25 April 2023 and 20 February 2025), and the glucocorticoid prednisolone (PubChemID: 5755) were used as the controls. Dexamethasone (PubChemID: 5743), an anticancer and anti-inflammatory drug that is structurally similar to ginsenosides, and hydrocortisone (HCY), the co-crystallized ligand in the 7D/77 PDB structure, served as the references for ADGRG3 binding [58; 59]. The grid box coordinates and size parameters used for MD are presented in Supplementary Table S1.  
DogSiteScorer [60] (accessed on 25 April 2023) was used to predict potential binding sites within ADGRG3, ensuring accurate binding pocket identification. The active site prediction and further details for ADGRG3 using DoGSiteScorer are listed in Supplementary Table S2. Virtual screening of the prepared ADGRG3 structure and ginsenoside ligands was performed with AutoDock Vina 1.2.5 [61] on the Linux platform, using script-based automation for all 128 ginsenosides.
## 3 Dataset and Experimental Setup
### Feature Selection  
To get a better understanding of how important the different features are and to reduce the high feature space, we train two Decision Tree (DT) models on the tasks of drowsiness detection and distraction detection, respectively, and we evaluate the overall feature contribution in terms of information gain.  
More specifically, we train each model on all 73 features plus the four raw signals and we compute the increase in information gain caused by each feature, after every split, for both tasks. Final scores are assigned by averaging the scores for each feature over the two tasks. Equations (1)-(3) describe the mathematical formulation of our analysis with respect to information entropy and gain. We use Python's scikit learn library for this purpose. Figure A1 in the Appendix A illustrates all 73 features and their final importance scores. The top five performing features are listed and described in Table 2.  
\[E(x)=-\sum_{i}^{n}p(x_{i})log_{2}p(x_{i}) \tag{1}\]  
where \(E(x)\) is the entropy of feature \(x\), \(x_{i}\) is a specific feature value, \(p(x_{i})\) is the probability of \(x_{i}\) and \(n\) is the total number of possible values that variable \(x\) can take.  
\[IG_{x,t}=E(y)-E(y|x) \tag{2}\]  
where \(IG_{x},t\) is the information gain with respect to feature \(x\) for task \(t\), \(E(y)\) is the entropy of the dependent variable \(y\) and \(E(y|x)\) is the entropy of \(y\) given feature \(x\). \(E(y|x)\) is calculated as shown in Equation (1) but the probabilities for the values of variable \(y\) are calculated under the condition of feature \(x\).  
\[Total\_IG_{x}=\sum_{i}IG_{x,t_{i}} \tag{3}\]  
where \(Total\_IG_{x}\) is the information gain with respect to feature x across both tasks and \(t_{i}\) is the task id.
# Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks
## 2 Data Acquisition and Representation
### Embedded System  
The hardware system designed, assembled and implemented for this work is based on a low-power consumption microcontroller, some FSRs and a low-energy bluetooth module (see Figure 3), using the connections shown in Figure 4. The elements are described below:  
* Microcontroller: a STMicroelectronics F031K6 board with a Cortex-M0 CPU, 32 KB of flash memory and 4 KB of RAM memory.
* FSRs: seven force sensor resistances connected to the analog inputs of the microcontroller using a resistive divider with a 10 K\(\Omega\) fixed resistance.
* Bluetooth: HM-10 BLE (Bluetooth Low-energy) module connected as a serial port to the microcontroller.  
The firmware implemented inside the microcontroller consists on a freeRTOS implementation that contemplates a bi-directional serial communication with the bluetooth module (process 1 for reception and process 3 for transmission) and a periodic readings of the sensors with data transformation (process 2), with a binary semaphore and a queue to communicate these process. In order to understand the full operation of the system, a state machine diagram is presented (see Figure 5) with these states:  
Figure 4: Connections schematic.  
Figure 3: **(up-left)** Below of the instrumented insole, **(up-right)** Embedded system with connections, **(down)** Top view of the final system.  
* Idle (process 1): the system is waiting for the first byte of a command from the bluetooth module.
* Command check (process 1): once the starting byte is received, the system reads the full command byte by byte (if there is an error, the command is discarded). It can receive two types of commands: a stop command (\"[S]\") or a frequency command (\"[N]\", where 'N' is a number that represents the frequency of the timeout used for reading the sensors). When the command is checked, the semaphore that controls the reading process is activated (or deactivated depending on the type of command).
* Sensors reading (process 2): activated by the process 1 semaphore. This process reads continuously sensors data, transforms the information and sends it to the process 3 using a queue. The reading frequency depends on the configuration given by the user.
* Data transmission (process 3): the information from the seven sensors is packed and sent to the bluetooth module.  
Several works have designed and implemented an instrumented footwear insole to measure some aspects from the way of walking of the user (like [9; 10; 19; 20]), but the novelty of this work is based on the Deep-Learning system running on the computer. Next, the user application is described before starting with the Neural Network architecture section.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 3 Proposed 2-Stage Pipelined Architecture for SIMECK32/64
### Control Unit  
The objective of the control block is to generate the control signals that determine the operation of the SIMECK circuit. A finite-state-machine (FSM) based controller is utilized to provide the control functionalities. The FSM incorporates a total of four states, i.e., \(IDLE\), \(ENCRYPT\), \(DECRYPT\), and \(FINISH\). The corresponding details for these states are given as follows:  
* The \(IDLE\) states denotes that the processor is not performing any computation. It always stays in this state until a 1-bit start signal (not shown in Figure 1) becomes 1. As the processor receives a start signal 1, the next FSM state will be either \(ENCRYPT\) or \(DECRYPT\), depending on the value of the 1-bit mode signal.
* The \(ENCRYPT\) specifies the encryption computation. It operates on the value of a 1-bit mode signal. Whenever a 1-bit mode signal becomes 0, the SIMECK CryptoCore is responsible for an encryption operation. The processor will remain in the \(ENCRYPT\) state until it finishes the encryption computation. As shown in Figures 2 and 3, the data path of our SIMECK circuit contains registers (RA, RB, RC & RD for key expansion and Rdata1 & Rdata2 for data expansion). These registers operate as a pipeline in our SIMECK design. Therefore, our circuit takes 16 clock cycles to execute 32 SIMECK rounds for the encryption process. After finalizing the encryption operation, a done signal is required to set. Once the done signal is generated, a processor will change the state from \(ENCRYPT\) to \(FINISH\).  
* Alike the \(ENCRYPT\) operation, \(DECRYPT\) defines the decryption operation to be computed when a 1-bit mode signal is 1. The processor will remain in the \(DECRYPT\) state until the decryption operation is finished. Similar to the \(ENCRYPT\), the data path of our SIMECK circuit contains registers (RA, RB, RC & RD for key expansion and Rdata1 & Rdata2 for data expansion). These registers operate as a pipeline in our SIMECK design. Then, our circuit takes 16 clock cycles to execute 32 SIMECK rounds for the decryption computation. After the execution of the decryption operation, a done signal is needed to generate as 1. After generating the done signal, a processor will transform the state from \(DECRYPT\) to \(FINISH\).
* The \(FINISH\) state indicates the completion of either \(ENCRYPT\) or \(DECRYPT\) operations. The processor will remain for only one cycle in the \(FINISH\) state after the done signal becomes 1. In the next cycle, the processor will change its state to \(IDLE\).
## 3 Performance Evaluations
### Fiber Channel
#### 3.2.2 Radar System  
In this phase of our analysis, we revisit the radar parameters outlined in Section 3.1 and concisely summarized in Table 1. Our attention now shifts to Figure 13, which presents the correlator output at the radar receiver, specifically when transmitting the text image and subsequently receiving its reflected version via a fiber channel. The length of this fiber channel varies across 10, 25, 50, and 75 km. Evidently, as depicted in the figures, there is a discernible trend: the PSL ratio declines whenever a fiber null exists close to the carrier frequency. This observation underscores the influence of fiber nulls on signal fidelity.  
Figure 12: Frequency response of fiber for lengths 10, 25, 50, and 75 km.  
Figure 13: The PSL ratio of radar’s correlator for fiber lengths 10, 25, 50, and 75 km.  
Moreover, Figure 14 provides insight into another critical aspect: the impact of fiber length on the detectability of two targets separated by a distance of 43 cm, aligning with the theoretical resolution. It is noteworthy that as the carrier frequency becomes closer to fiber nulls, discerning the presence of two closely positioned targets becomes progressively more challenging. This phenomenon highlights the practical implications of fiber nulls not only on signal fidelity but also on the radar system's ability to resolve closely spaced targets, crucial for accurate detection and tracking.
# Integrating Text Classification into Topic Discovery Using Semantic Embedding Models
## 3 Proposed Approach
### Text Classification  
The text was preprocessed via text cleaning, removing stop words, and converting to lowercase. The text classification process was carried out as proposed in [3]. The method, which includes CNN, was used to assess the performance of three embedding models of semantic relations and to produce a set of classes for each corpus used. The classification task generated the corresponding classes in each corpus used. For the _20-Newsgroups_ corpus, 20 classes were obtained, and for the _Reuters_ Corpus, 90 classes were obtained. The classes are the basis for topic discovery since they are the input data set to each topic algorithm. In addition, classification was carried out to have an ordered corpus with semantic relationships between the texts. The hypothesis focuses on how finding topics in a classified corpus will improve the coherence of the retrieved topics. Therefore, integrating the classification of two corpora with semantic embedding models for topic discovery is the main contribution of this paper.  
Figure 1: Proposed approach to incorporate text classification into the topic discovery process.  
In this paper, the semantic embedding model used in text classification was developed in [3], which involves semantic relationships of synonymy, hyponymy, and hyperonymy.  
In [3], the authors presented a novel approach based on relationships extracted from Wikipedia to create embedding models. The creation of embedding models is conditional on the available semantic relations in the text. The process focuses on extracting semantic relationships from an English corpus from Wikipedia. Synonymy, hyponymy, and hyperonymy relationships are extracted with a set of lexical-syntactic patterns from the literature. The relationships are embedded using the procedure proposed by [11] based on matrix factorization. A text classification using CNN was carried out to compare the performance of the relationship-based embeddings and the word-based models, such as _fastText_, _GloVe_, and the WordNet-based model presented in [11]. Therefore, the performance of the semantic embedding model based on three semantic relationships was the best result obtained by [3]. For that reason, in this paper, the semantic embedding model incorporated in text classification for topic discovery is shaped by these three semantic relations.
## 3 Results and Discussions
### Permanent Deformation
#### 3.1.3. Different Temperature and Moisture Content Corresponding Regions  
Figure 9 demonstrates the interrelationship between the vertical cumulative strain rates in regions A, B, and C, and the varying number of loading cycles, subject to different temperature conditions. Figure 9a depicts the initial cumulative strain rate as falling within a span from 0.0086 to 0.084. As the cyclic loading cycles accumulate, the cumulative strain rate undergoes a swift decrease, reaching a range of approximately 2.5 \(\times\) 10\({}^{-4}\) to 4.11 \(\times\) 10\({}^{-3}\) at 1000 cycles. Further incrementing the number of cycles to 10,000 results in a further decrease in the cumulative strain rate, now falling within a range of roughly 4.63 \(\times\) 10\({}^{-5}\) to 4.75 \(\times\) 10\({}^{-4}\), before gradually converging towards zero. Figure 9b illustrates the initial cumulative strain rate fluctuating within a range of 0.037 to 0.125. As the loading cycles proliferate, the cumulative strain rate diminishes swiftly. Specifically, when the loading cycles reach 1000, the cumulative strain rate falls within the range of approximately 9.56 \(\times\) 10\({}^{-4}\) to 5.5 \(\times\) 10\({}^{-3}\). Furthermore, upon completing 10,000 cycles, the cumulative strain rate diminishes to approximately 1.56 \(\times\) 10\({}^{-4}\) to 7.55 \(\times\) 10\({}^{-4}\). Figure 9c portrays a general decline in the vertical cumulative strain rate as the number of loading cycles increases. However, certain curves demonstrate a rebound tendency, with the cumulative strain rate at approximately 100 loading cycles ranging from 0.03 to 0.085.  
As shown in Figure 10a, the initial cumulative strain rate falls within a range of 0.014 to 0.03. Thereafter, the cumulative strain rate diminishes rapidly with the augmentation of loading cycles. At 1000 cycles, the cumulative strain rate is approximately 3.78 \(\times\) 10\({}^{-4}\) to 1.1 \(\times\) 10\({}^{-3}\), and at 10,000 cycles, it further decreases to around 4.42 \(\times\) 10\({}^{-5}\) to 1.57 \(\times\) 10\({}^{-4}\), gradually approaching zero thereafter. As illustrated in Figure 10b, the initial cumulative strain rate ranges from 0.033 to 0.128. Afterward, the cumulative strain rate experiences a pronounced drop as the count of cyclic loading cycles rises. At 1000 cycles, the cumulative strain rate is approximately 1.8 \(\times\) 10\({}^{-3}\) to 3.2 \(\times\) 10\({}^{-3}\), and at 10,000 cycles, it further diminishes to about 2.61 \(\times\) 10\({}^{-4}\) to 4.3 \(\times\) 10\({}^{-4}\). As shown in Figure 10c, the vertical  
Figure 10: Correlation between the respective vertical cumulative strain rates and the loading cycle count for regions A, B, and C, influenced by varying water content levels.  
cumulative strain rate predominantly demonstrates a declining pattern as the number of cyclic loading cycles increases, yet some curves exhibit a tendency to rebound, with the cumulative strain rate settling within the range of 0.08 to 0.182 when the loading cycle count attains 100.
## 3 Results and Discussion
### Esters  
Esters were the most-abundant volatiles (Figure 1). This result disagreed with the findings by [PERSON] et al. [6], where esters were the second-most abundant following lactones. This result may support the findings of a previous study, which suggested that if a headspace sampling method is used instead of a vacuum steam distillation, there is the possibility for more esters to be identified [18]. In peach fruit, it has been reported that lactones and esters are known for \"fruity\" aromas [19]. A total of ten esters were identified  
Figure 3: Relative concentration per cultivar (ng:g\({}^{-1}\)) of alcohols in 42 peach cultivars. Mean values of 3 fruit pure samples per cultivar. Standard error (SE) bars are reported.  
(Figure 4). Hexyl acetate (E3), 2-hexen-1-ol, acetate, (Z)- (E4), and 3-hexen-1-ol, acetate, (Z)- (E2) were the most-abundant esters, and they contributed over 95% of the total esters (Figure 4). For the individual cultivars, \"Fiesta Gem\" had the most-abundant content of hexyl acetate (E3), 1129.8 ng\(\cdot\)g\({}^{-1}\), followed by \"Blazeprince\", 1023.7 ng\(\cdot\)g\({}^{-1}\), and \"Fairtime\", 819.8 ng\(\cdot\)g\({}^{-1}\). \"Summerflame\" had the lowest relative concentration with only traces of hexyl acetate (Tables 4 and S1). Hexyl acetate is characterized by \"fruit\" and \"herb\" odors (Flavornet database) and \"fruity\" flavor (Good Scents Company database). The overall high content of hexyl acetate in this study may influence the \"sweet\" and \"fruity\" attributes of peaches [20].  
The level of 3-hexen-1-ol, acetate, (Z)- (E2), was abundant in \"Fairtime\" with 410.8 ng\(\cdot\)g\({}^{-1}\) followed by \"Summerflame\" and \"Blazeprince\" with 307.1 ng\(\cdot\)g\({}^{-1}\) and 193.9 ng\(\cdot\)g\({}^{-1}\), respectively (Tables 4 and S1). The relative concentration of compounds 3-hexen-1-ol, acetate, (Z)- (E2) and hexyl acetate (E3) were above their odor threshold (13 ng\(\cdot\)g\({}^{-1}\) and 2 ng\(\cdot\)g\({}^{-1}\), respectively), which suggests that they may have an important effect on the overall peach aroma. This result was consistent with previous research, where hexyl acetate and 3-hexen-1-ol, acetate, (Z)- were identified as among the key odorants for the flavor quality of peach fruit [8]. Late-season cultivars had higher 3-hexen-1-ol, acetate, (Z)- (E2) and 2(E)-hexenyl butanoate (E8) relative concentrations than early- and mid-season cultivars (Table 3). Methyl octanoate (E6) relative concentrations were the highest for mid-season cultivars in comparison to late- and early-season cultivars (Table 3). The relative total concentration of esters per cultivar was higher in early-season cultivars in comparison with the mid- and late-season cultivars (Figure 1).  
Figure 4: Relative concentration per cultivar (ng\(\cdot\)g\({}^{-1}\)) of esters in 42 peach cultivars. Mean values of 3 fruit pure\(\cdot\)se samples per cultivar. Standard error (SE) bars are reported.
# Signal Detection for Ambient Backscatter Communication with OFDM Carriers
## 2 System Model and Problem Formulation
### Tag Operation  
To ensure the orthogonality of received subcarriers over the useful symbol period as well as efficient joint allocation of subcarriers and powers among legacy users, we need to design a waveform to convey information bit in tag symbol, where the CP is longer than the delay spread of the channel. In fact, the tag uses the waveform construction \(x(n)\) in [12] to convey the bit \(B\) in each tag symbol as  
\[x(n)=\begin{cases}\Pi(n)+\Pi\left(n-\frac{N+N_{cp}}{2}\right),&\text{if $B(n)=0$ (bit 0),}\\ \Pi(n)-\Pi\left(n-\frac{N-N_{cp}}{2}\right),&\text{if $B(n)=1$ (bit 1),}\end{cases} \tag{2}\]  
where the square function \(\Pi(n)\) is defined as  
\[\Pi(n)=\begin{cases}1&\text{for $n=0,1,\cdots,\frac{N+N_{cp}}{2}-1$},\\ 0\,&\text{otherwise}\end{cases}. \tag{3}\]