esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Influence of Different Processing Methods on the Phloroglucinol of L. japonica  
Phlorotannins are a group of major polyphenol secondary metabolites found only in brown algae and are known for their bioactivities and multiple health benefits [25; 36]. At the same time, the phlorotannins are a complex molecular combination formed by the polymerization of phloroglucinol [37]. Therefore, phloroglucinol was used as the standard, and phloroglucinol content in kelp treated by different processing methods was compared. The determination results were shown in Table 2. The content of phloroglucinol in raw _L. japonica_ was 3.42 mg/g, and the content of phloroglucinol in the _L. japonica_ was significantly decreased after the four cooking methods (_p_ < 0.05). Among them, the most significant decrease in the phloroglucinol content was observed in blanched kelp (1.98 mg/g), followed by steamed kelp (2.28 mg/g), baked kelp (2.33 mg/g), and boiled kelp (2.83 mg/g). [PERSON] et al. reported changes in the content of bioactive compounds in seaweed after cooking. The results showed that the content of phloroglucinol in cooked wakame decreased from 3986 mg/kg to 2600 mg/kg, but the content of phloroglucinol in fresh kelp was 1175 mg/kg and increased by 1409 mg/kg after cooking [38]. In addition, [PERSON] et al. studied the changes of bioactive compounds in dried seaweed _Himanthalia elongata_ under different cooking conditions and processes. They found that rehydrated seaweed had the highest content of phloroglucinds (3963.0 mg/kg), followed by boiling (2528.1 mg/kg) and steaming (1234.9 mg/kg) [39]. Overall, the different processing methods had a significant effect on the phloroglucinol content of seaweeds. All of these cooking methods are thermal processing, so the heating mode, heating temperature, and heating time affect the formation of phenolic compounds in seaweeds [40]. Some studies have reported that under the conditions in thermal processing, polyphenols might undergo isomerization and decarboxylation, thus converting to smaller molecules or more complex compounds [41; 42]. In addition, heat could cause cellular damage and induce enzymatic activity and oxidative stress, leading to thermal degradation of phenolic compounds [41]. In this experiment, there was no significant difference in the phloroglucinol content of steamed kelp, boiled kelp, and baked kelp. (_p_ > 0.05). Therefore, relatively speaking, boiling might be a better method to retain the content of phloroglucinol in kelp. However, to date, there has been limited research on the effects of cooking on the levels of phloroglucinol from brown algae.  
Figure 2: Distribution of T\({}_{2}\) relaxation times in _L. japonica_ cooked with four different cooking methods.
## 5 Data Analysis and Main Findings
### Assessment of Standard Deviation (SD) and Normal Distribution  
The standard deviation (SD) and the normal distribution are calculated to determine the level of data dispersion and the distance between the mean curve and the direction of the deviation [61]. The SD must not exceed 1.0. A low SD indicates that the values are spread out over a small area of the mean curve and that the sample size is a good representation of the target audience. Skewness and kurtosis tests are commonly used to determine normal distribution [60]. These values should range from 0 to 2.50 [59]. The skewness and kurtosis values of this present study ranged from \(-\)0.235 to 1.429 and \(-\)1.170 to 2.058, respectively, which were within the recommended range.
# Faster and Better Nested Dissection Orders for Customizable Contraction Hierarchies
## 2 Materials and Methods
### Customizable Contraction Hierarchies  
A Customizable Contraction Hierarchy (CCH) is an index data structure which allows fast shortest path queries and fast adaptation to new metrics (arc weights) in road networks. It consists of three phases: a _preprocessing_ phase which only uses the network topology but not the arcs weights, a faster _customization_ phase which adapts the index to new arc weights, and a _query_ phase which quickly answers shortest path queries.  
The preprocessing phase computes a _contraction order_ of the nodes, e.g., via nested dissection, and then simulates contracting all nodes in that order, inserting shortcut arcs between all neighbors of a contracted node. Shortcuts represent two-arc paths via the contracted node.  
The customization phase assigns correct weights to shortcuts by processing all arcs \((u,v)\) in the order ascending by rank of \(u\), i.e., the position of \(u\) in the order. To process an arc \((u,v)\), it enumerates all triangles \(\langle u,w,v\rangle\) where \(w\) has lower rank than \(u\) and \(v\), and updates the weight of \((u,v)\) if the path \((u,w,v)\) is shorter.  
There are two different algorithms for \(s\)-\(t\) queries. For every shortest \(s\)-\(t\) path in the original graph, there is a shortest \(s\)-\(t\) path in the CCH that consists of two paths \((s,\ldots,x)\) and \((x,\ldots,t)\) such that the arcs in the first path are from lower-ranked to higher-ranked nodes, whereas the arcs in the second path are from higher-ranked to lower-ranked nodes [10]. The first, basic query algorithm performs bidirectional Dijkstra search from \(s\) and \(t\) and relaxes only arcs to higher-ranked nodes. The second query algorithm uses the _elimination tree_ of a CCH to avoid priority queues, which are typically a bottleneck. In the elimination tree, the parent of a node is its lowest-ranked upward neighbor. The ancestors of a node \(v\) are exactly the nodes in the upward search space of \(v\) in the basic query [21]. For the \(s\)-\(t\) query, the outgoing arcs of all nodes on the path from \(s\) to the root and all incoming arcs of all nodes on the path from \(t\) to the root are relaxed. The node \(z\) minimizing the distance from \(s\) to \(z\) plus the distance from \(z\) to \(t\) determines the distance between \(s\) and \(t\).  
The query complexity is linear in the number of arcs incident to nodes on the paths from \(s\) and \(t\) to the root. Similarly, the customization running time depends on the number of triangles in the CCH. Fewer shortcuts result in less memory consumption and faster queries. We aim to minimize these metrics by computing high quality contraction orders.
# Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing
## 6 Conclusions  
In this article, the Predict Optimistic Time and Cost (POTC) algorithm has been proposed for scheduling scientific workflows onto heterogeneous systems more effectively. The proposed algorithm is tested with some of the real-world scientific workflows, namely the Epigenomics and Montage workflows. The proposed POTC algorithm has significantly outperformed the other existing algorithms, namely ELSH, MOPT, PEFT, and BDHEFT, in terms of performance metrics and cost metrics, such as schedule length ratio, speedup, efficiency, and normalized schedule cost. The main contribution of this proposed algorithm is the introduction of ranking before scheduling and performing the execution of tasks based on ranks which are based on cost and time. In addition to ranking the scheme and processor selection scheme, the deadline and budget are also considered in this work to improve the speed of the overall execution of tasks. From the experiments conducted in this work, it is proven that the proposed ranking scheme minimizes the processing time and cost and assigns the best processors for each of the tasks. Instead of considering only the earliest finish time, the selection phase also considered the processor with minimum cost for executing the task. All processors are tested, and the processor which takes minimum time and minimum cost is selected in this algorithm. Moreover, the forecast feature has been proposed in the algorithm while maintaining the same quadratic complexity. The proposed POTC algorithm outclassed all other scheduling algorithms with respect to the scheduling length ratio and cost. Statistically, POTC has the lowest average schedule length ratio, the lowest cost, and better speedup and efficacy. Thus, considering the results, it is concluded that for the DAGs in a heterogeneous environment, the proposed static scheduling algorithm named POTC exhibits an improved performance.  
In the future, more experiments based on the various real workflows with many characteristics can be conducted. Energy consumption is also one of the crucial user requirement in clouds. As a future study, we plan to integrate this objective in the proposed algorithm. We are also interested in implanting this algorithm onto hybrid cloud platforms in addition to edge-cloud computing systems.  
Conceptualization, J.H.; Methodology, J.H. and S.C.; Supervision, S.C.; Writing-original draft, J.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** No conflict of interest.
## 5 Performance Evaluation
### Dedg 3d-Uwsn Highlights  
The designed DEDG 3D-UWSN was developed to effectively collect data from the network nodes with the aim of improving network lifetime and reducing delay in transmission. The idea of this work was to present support for both normal data gathering, as well as emergency event transmission. The major process handled in this DEDG 3D-UWSN is depicted in Table 7. This shows that the process, along with the resulting improvement that is achieved by our simulation, is theoretically justified.  
As discussed in earlier sections, the proposed DEDG 3D-UWSN performs data gathering with the assistance of two AUVs in the network and achieves prolonged network lifetime and minimizes delay in data collection. Here, we provide the major research highlights of this DEDG 3D-UWSN:  
* The 3D-UWSN with two AUVs incorporated was designed with the aim of achieving delay-aware data gathering by optimal positioning of AUVs and by using inter-cluster routing in case of exceeded waiting time. To assist a large-scale network and faster data gathering, two AUVs are employed. Incorporation of sleep time for sensor nodes reduces energy consumption;
* The optimal selection of CH using MO-SHO ensures prolonged sustainment of CH, and the reduction of redundant data improves delivery time. Then, data gathering of the AUV from for CHs at optimal position reduces delay. Here, fuzzy-LeNet was used, which performs faster and results in an appropriate solution;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**\# of Sensors**} & \multicolumn{5}{c}{**Tour Length (m)**} \\ \cline{2-6}  & **AEC** & **ALP** & **Bipartite K-Means** & **ELMPP** & **DEDG 3D-UWSN** \\ \hline
20 & 350 & 450 & 390 & 420 & 320 \\
40 & 400 & 490 & 420 & 450 & 350 \\
60 & 420 & 530 & 470 & 490 & 370 \\
80 & 470 & 570 & 520 & 540 & 420 \\
100 & 500 & 630 & 580 & 600 & 450 \\ \hline \hline \end{tabular}
\end{table}
Table 6: AUV tour length.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Process**} & \multicolumn{2}{c}{**Resulting Improvement**} \\ \hline Clustering & Reduces energy consumption in data gathering from the CHs \\ Optimal CH selection & Reduces energy consumption by mitigating unnecessary selection of CH which \\  & needs to exchange node information \\ Sleep–wakeup scheduling & Saves energy of the sensor, and also all the data are sensed \\ Data gathering & Reduces energy consumption since the AUV is positioned one hop from the CH \\  & Reduced collection delay by collecting data from four CHs at a time \\ \hline \hline \end{tabular}
\end{table}
Table 7: Process involved in DEDG 3D-UWSN.  
* In conditions with delayed arrival of the AUV, the gathered data are transmitted to the AUV via an inter-cluster route. This is performed in order to make free space in CHs to gather upcoming sensed information from CHs.  
The sensors not only sense normal data; they also sense critical emergency data which are sent immediately by selecting a forwarder which sequentially reaches the AUV and then the surface sink
## Congestion Prediction Modeling for Quality of Service Improvement in Wireless Sensor Networks  
**[PERSON], [PERSON] and [PERSON] *  
Department of Computer Engineering, Kyung Hee University, 1732 Deogyeong-daero, Seochon-dong,  
Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea; E-Mails: [EMAIL_ADDRESS] (G.-W.L.);  
[EMAIL_ADDRESS] (S.-Y.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-31-201-2454; Fax: +82-31-206-2958.  
_Received: 10 March 2014; in revised form: 23 April 2014 / Accepted: 23 April 2014 / Published: 30 April 2014_  
**Abstract:** Information technology (IT) is pushing ahead with drastic reforms of modern life for improvement of human welfare. Objects constitute \"Information Networks\" through smart, self-regulated information gathering that also recognizes and controls current information states in Wireless Sensor Networks (WSNs). Information observed from sensor networks in real-time is used to increase quality of life (QoL) in various industries and daily life. One of the key challenges of the WSNs is how to achieve lossless data transmission. Although nowadays sensor nodes have enhanced capacities, it is hard to assure lossless and reliable end-to-end data transmission in WSNs due to the unstable wireless links and low hard ware resources to satisfy high quality of service (QoS) requirements. We propose a node and path traffic prediction model to predict and minimize the congestion. This solution includes prediction of packet generation due to network congestion from both periodic and event data generation. Simulation using NS-2 and Matlab is used to demonstrate the effectiveness of the proposed solution.  
**Keywords:** Wireless Sensor Networks (WSNs); congestion prediction; traffic demands analysis; traffic modeling
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 4 Simulation Setup and Performance Evaluation
### Performance Evaluation
#### 4.2.5 Throughput Analysis of PUTPOSH as a Function of Primary User Arrivals  
Figure 10 shows the throughput analysis of the PUTPOSH strategy. The throughput of primary, secondary and overall system is calculated in terms of the arrival rate of PU. The CRU has limited or opportunistic access to the channels and the lowest throughput is achieved against every PU arrival. At the channel, PU has priority access and yields greater throughput. The overall system throughput represents the combined throughput of a CRN system that can be achieved through PUTPOSH. With the PUTPOSH approach, the growing number of PU interarrivals due to the condensed total service period and the effective channel exploitation.  
Table 3 summarizes the quantitative analysis of the existing and PUTPOSH schemes in a comparative manner. The comparison is based on the overall service time, the number of handoffs and the channel utilization, which are already discussed in the above subsections.  
Figure 9: Comparison of PUTPOSH with stationary (reactive and proactive) spectrum access, and hybrid schemes w.r.t. the channel utilization.
# Methodological Framework to Collect, Process, Analyze and Visualize Cyber Threat Intelligence Data
## 2 Threat Intelligence Background
### CTI Standards
#### 2.3.4. Taxii  
MITRE also developed TAXII--an application layer protocol that specifies a collection of services--for businesses to exchange threat intelligence information data. It was designed specifically for the transmission of data in the STIX format, but it is not limited to that as the transport protocol used by it is Hyper Text Transfer Protocol Secure (HTTPS), offering a variety of sharing models such as peer-to-peer, spoke-hub distribution and source subscriber [21].
# A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Set** & \(MAE_{te}\) & \(RMSE_{te}\) & \(K\) \\ \hline (I)ex & 0.649 & \(4.6297\times 10^{-5}\) & 97 \\ (I)rec & 1.665 & \(1.029\times 10^{-3}\) & 66 \\ (II)ex & 0.875 & \(7.441\times 10^{-5}\) & 93 \\ (II)rec & 1.354 & \(4.927\times 10^{-4}\) & 74 \\ (III)ex & 0.899 & \(5.055\times 10^{-5}\) & 94 \\ (III)rec & 1.369 & \(5.269\times 10^{-4}\) & 79 \\ (IV)ex & 0.936 & \(4.855\times 10^{-5}\) & 86 \\ (IV)rec & 1.758 & \(8.998\times 10^{-4}\) & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(MAE_{te}\), \(RMSE_{te}\), and \(K\) for each exercise set. The superiority of the proposed NN model over the mathematical model [9,10] may be verified from Table 4, where the testing mean relative absolute error (_MRAE_) is given for all datasets. The predicted _MRAE_, displayed in the second column of Table 4, is defined as follows:  
\[MRAE^{pr}=\frac{1}{K}\sum_{k=1}^{K}\frac{\left|hr_{k}^{pr}-hr_{k}^{me}\right|}{hr _{k}^{me}} \tag{6}\]  
where \(hr_{k}^{pr}\) stands for the \(k\)-th predicted value of the HR by the NN and \(hr_{k}^{me}\) is the corresponding \(k\)-th measured value. The simulated _MRAE_, denoted as \(MRAE^{si}\) hereafter and depicted in the third column of Table 4, may be defined by Equation (6) provided that \(hr_{k}^{pr}\) is replaced by \(hr_{k}^{si}\); the latter represents the \(k\)-th simulated value of the HR, i.e.,  
Figure 5: Measured, \(hr_{k}^{te}\), and predicted, \(hr_{k}^{pr}\), HR values for sets (I) and (II). Figure 6: Measured, predicted, and simulated HR values for sets (III) and (IV). the output of the mathematical model [9,10]. Table 4 suggests that the \(MRAE^{pr}\) is less than 1%, regarding the exercise period, whereas the \(MRAE^{si}\) exceeds 1% for the same dataset. The \(MRAE\) is greater for both models as regards the recovery period. However, the \(MRAE^{pr}\) does not exceed 2.5% for any dataset, while the \(MRAE^{si}\) is much greater and may reach the value of 5.87%. The last column of Table 4 offers a direct comparison between the results obtained by the NN and the mathematical model by displaying the \(MRAE^{pr-si}\), defined as: \(MRAE^{pr-si}=\frac{1}{K}\sum\limits_{k=1}^{K}\left(\left|hr_{k}^{pr}-hr_{k}^{ si}\right|/hr_{k}^{pr}\right)\). The difference between the two models, regarding the \(MRAE^{pr-si}\), varies in the range 0.83-1.7% for the exercise period, whereas it increases up to 5.75% for the recovery period.
## 2 Various PPF Strategies
### PPF of Chloro Monoliths  
Sulfonic acid functionalized poly(3-chloro-2-hydroxypropyl methacrylate (HPMA-Cl)-_co_-EDMA) was synthesized as new a monolith for CEC applications [32]. The template monolith was first obtained via in situ polymerization of HPMA-Cl as monomer and EDMA as crosslinker in the presence of AIBN. The porogen mixture used was isopropanol and _n_-decanol. After that, poly(HPMA-Cl-_co_-EDMA) was subjected to PPF with NaHSO\({}_{3}\) to achieve strong sulfonated monolith, see Scheme 22. The template monolith can be easily modified with different ligands such as, anionic, cationic or neutral groupsto have different CEC applications. The proposed monolithic capillary column could successfully separate phenol derivatives, alkylbenzenes and benzoic acids.  
A new mixed-mode monolithic capillary column was successfully prepared and evaluated in nano-LC [33]. First, the poly(HPMA-Cl-co-EDMA) was synthesized by in situ polymerization. Next, the resulting monolith was modified with \(N\),\(N\)-dimethyl-\(N\)-dodecylamine to achieve a mixed-mode monolith (i.e., anion-exchange and hydrophobic), see Scheme 23. The separation of alkylbenzenes, phenolic compounds and inorganic anions was successfully resolved due to the multiple interactions including hydrophobic, electrostatic and anion exchange. The column efficiency was found to be up to 23,000 plates/m for the separation of inorganic anions.  
[PERSON] et al. [34] reported the preparation of a monolithic capillary column and its surface modification for HILIC mode in nano-LC. The poly(HPMA-Cl-co-EDMA) was prepared in capillary by in situ radical polymerization. After the polymerization was completed, a triethanolamine solution (TEA-OH) was passed through the monolithic capillary column in order to increase the hydrophilicity of the monolith, see Scheme 24. The separation of nucleotides, nucleosides and benzoic acid derivatives was successfully resolved with estimated plate heights up to 20 \(\upmu\)m.  
In another study, the poly(HPMA-Cl-co-EDMA) monolith was subjected to the post-polymerization modification with polyethyleneimine obtaining cationic stationary phase for reversed phase and hydrophilic interaction in CEC [35]. The obtained monolithic column was successfully used for the separation of alkylbenzenes, phenols, PAHs and nucleosides.  
**Scheme 24.** Schematic of the derivatization of poly(HPMA-Cl-co-EDMA) monolith with triethanolamine solution (TEA-OH). Taken with permission from [34].
# A Secure IoT-Based Irrigation System for Precision Agriculture Using the Expeditious Cipher
## 2 Materials and Methods
### Proposed IoT System Architecture
#### 2.2.2 Lightweight Cryptography Layer  
In this layer, a lightweight protocol must be deployed, because the proposed IoT-based irrigation system is made up of constrained devices in terms of memory and energy. Characteristics of lightweight cryptography were highlighted in ISO/IEC 29192 and ISO/IEC  
Figure 3: The proposed model. (**a**) proposed model architecture. (**b**) proposed model hardware.  
JTC 1/SC 27. Lightweight properties are evaluated based on chip size and energy consumption, and small code and/or RAM size in case of software implementation [21]. So, the Expeditious Cipher (X-cipher) was selected [31]. The X-cipher was proposed in 2011 and is considered a lightweight high-throughput encryption protocol. The cipher sub-keys are generated using the well-studied SHA-512 and Whirlpool 512 hash functions. The two hash functions are cascaded in a pseudo-random manner that depends on the user key to enhance the cipher security. The cipher utilizes a variable-size user key. The encryption rate can attain 512 bits per cycle in the case of hardware implementation with parallelization encryption paths. Eight rounds are recommended for the proper operation of the Expeditious Cipher (X-cipher). The circuitry of the encryptor and decryptor are identical, as such, a high code density and small implementation area are achieved, as required by lightweight cryptography. An additional important feature of the X-cipher is the separation of key scheduling process from the encryption process, which allows the change of cipher design, key, and block size by simply changing the hash function. The Expeditious Cipher (X-cipher) complete encryption algorithm is presented in [31]. NodeMCU has a cryptography module called crypto that contains various functions for working with cryptographic algorithms. The AES 128-bit is supported in ECB and CBC modes, in addition to several hash functions, namely, MD5, SHA-1, SHA-256, SHA-384, and SHA-512. The MQTT module implemented in C language is available on the NodeMCU ESP8266 board to run the MQTT client to publish the temperature and humidity readings to the MQTT broker running on Raspberry Pi. So, because the proposed Expeditious Cipher (X-cipher) depends on the SHA-512 hash function that is already implemented in the crypto module and the modulo 2 addition between the hashed key and the plain text, the selected lightweight algorithm can be compiled easily on the NodeMCU board. Table 2 compares the operational features of AES (traditional encryption protocol), PRESENT (lightweight standard protocol), X-cipher (selected lightweight protocol), and the lightweight encryption protocol proposed recently in the literature.