esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
### Product Configuration System  
The product configuration system offers tailored products with short lead time to market in a mass customization environment. It bridges the gap between CRs and product ECs as well as assists in order acquisition and fulfillment [28; 34]. Generally, it is based on an MTO strategy that configures predefined engineering characteristics upon the placement of orders, to satisfy diverse CRs [35]. The product configuration system utilizes CRs as inputs and derives the optimized recommended ECs as outputs to automatically generate a bill of material (BOM) for manufacturing [30; 35; 36].  
Many pieces of research have been carried out in the field of the product configuration model under Industry 4.0. [PERSON] et al. [37] refined the product configuration process based on the similarities between product variance to increase transparency in order processing and better control the quotation process. [PERSON] et al. [28] presented a product requirement modelling and optimization method based on product configuration design. The characterization of the properties of products (e.g., parts, components) was utilized to express the product requirements in the requirement modelling process and implemented in the product function and structure model. [PERSON] et al. [31] proposed a personalized product configuration process to determine design attributes in a cloud-based adaptable product configuration. The adaptable interfaces enable the efficient changing of optional and personalized modules among different service providers with respect to the dynamic changes of CRs. [PERSON] et al. [38] developed a new bi-objective optimization model with the consideration of a customer satisfaction index (CSI) and greenhouse gas (GHG) emission of products in product configuration. [PERSON] and [PERSON] [9] proposed a hybrid prototyping digital product configuration model with virtual reality, virtual prototyping, and rapid prototyping to generate design specification of orders to link to the process of control of production flow. Up to this point, however, research about product configuration system facing the challenges of mass customization with high-complexity, low-volume products, and production was still lacking. Related studies about high-complexity, low-volume products, and production are discussed in the next section.
## 3 System Development
### Implementation Phase  
The specific hardware and software technologies selected to realize the smart environment are listed below. A summary of the overall development process, from analysis to implementation, is shown in Table 2.  
* **A RaspberryPi4** microcomputer acts as a central node for collecting data from a group of sensors and a camera. In particular, it collects data from the temperature, humidity, and ozone sensors, while the camera captures images or videos for detecting the presence of people. This is possible by combining **TensorFlow Lite** and **OpenCV**, so as to capture videos from the camera and apply machine learning models to process each frame in real time.
* **Sensors** such as DHT11 (for humidity and temperature) and KH-NPOD-100 DGOzone (for ozone).
* **CRJ O3-UV-500 Ozonizer** is a sanitizing machine equipped with UV-C lamps that generate ozone and a fan device; see Figure 3.  
It is automatically activated based on the results of analyses performed by AWS services or by the RaspberryPi.
* **Node-Red**, an open-source framework, commonly used for industrial automation and IoT projects, aimed at connecting hardware devices, APIs, and AWS online services through a flow-based, block-oriented interface. In particular, we configured the environment by using Node-RED to collect, store, and display data locally with a database, as shown in Figure 4.  
Figure 3: The ozonizer machine CRJ O3-UV-500.  
* **PostgreSQL to locally store (in a relational way) the data collected from the sensors for analysis and reporting; see Figure 5.**
* **MQTT (Message Queuing Telemetry Transport) as the messaging protocol owing to its efficient management of large-scale IoT deployments, low-bandwidth requirement, energy efficiency, and robustness in unreliable networks. These features make MQTT an ideal candidate to interconnect the smart environments' components deployed at edges and on the cloud. In particular, we opted for MQTT version 5, a keep-alive duration of 30 s, and a maximum packet size of 149,504 bytes.**
* **MQTT (Message Queuing Telemetry Transport) as the messaging protocol owing to its efficient management of large-scale IoT deployments, low-bandwidth requirement, energy efficiency, and robustness in unreliable networks. These features make MQTT an ideal candidate to interconnect the smart environments' components deployed at edges and on the cloud. In particular, we opted for MQTT version 5, a keep-alive duration of 30 s, and a maximum packet size of 149,504 bytes.
## 3 Results
### Plasmid Construction Variations and Influence on clI Expression  
The WT \(pR\) mRNA can form a stem-loop structure ahead of the amino-terminal region of _clI_ (Figure 2) [36; 53]. It was proposed [36] that this stem-loop structure reduces the rate of translation initiation, possibly by restricting the access of ribosomes to the initiation region and that the binding of IHF to this region was essential for the translation of _clI_ mRNA [47]. Several plasmid variants were generated (Figure S1, plasmids 3), including ones with upstream WT bases 38,339 through the _clI_ mRNA start codon. These encoded the potential for mRNA stem-loop formation occluding the ribosomal binding site (RBS bases 38,343-38,348) ahead of AUG for _clI_ (Figure 2) but not including the IHF binding site, nor most of the region of dyad symmetry present in the WT _clI_ mRNA [47]. These plasmids encoded the \(-10\) and \(-35\) regions of \(pE\) (respectively, bases 38,350-38,355 outside of _clI_ and the dual CCTT sequences encoded by bases 38,369-38372 and 38,379-38,382 within the amino terminal end of _clI_). Some of them included the pO94 sequence, or four point mutations believed to inactivate \(pE\), i.e., mutations cy3048 or cy2001 within \(-10\) and cy42 or cy3001 within each of the CCTT sequences in \(-35\). Several differences were apparent when comparing data for plasmids with/without the 38,339-38,360 sequence that could form a stem-loop structure. Plasmid [clI] was 21-fold more toxic in cells raised to 39 \({}^{\circ}\)C than [sR-38339-pE-clI] (Tables 2 and 5), suggesting the formation of a stem-loop can reduce _clI_ expression. In contrast, the ability of _oop_ expression from plasmid [clI-oop-pO94] to reduce CII toxicity at 42 \({}^{\circ}\)C (Table 2) decreased by 8-fold for [sR-38339-pE-clI-oop-pO94] (Table 5) and suppression of plasmid loss at 42 \({}^{\circ}\)C no longer occurred. This suggests stem-loop formation ahead of the _clI_ message can influence activities involving pO94. Adding \(-10\) region mutations cy3048 or cy2001 (Figure S1) to [sR-38,339-pE-clI] reduced CII toxicity by 10 to 80-fold, and, even without _oop_, they completely suppressed CII-dependent plasmid loss at 42 \({}^{\circ}\)C (Table 5). The \(-35\) region mutation cy3001 reduced plasmid loss and cy42 eliminated
## 5 Results and Discussion
### Evaluation of Landslide Susceptibility
The research area's landslide geological hazards were subjected to accuracy testing using the receiver operating characteristic curve (ROC) (Figure 14). The cumulative percentage of the prone area from high to low is used as the horizontal axis, and the cumulative percentage of the number of disaster points is used as the vertical axis to create the ROC curve. The evaluation accuracy of the susceptibility results is determined by measuring the area beneath the curve. The accuracy increases with the area. The AUC of the area under the ROC curve is a good indicator to test the quality of the geohazard susceptibility zoning map. With an area underneath the research curve (AUC) of 0.8736, the evaluation accuracy is 87.36%. This suggests the weighted information model for landslide susceptibility evaluation in Tekes County has strong applicability, as does using the information model as the foundation and the entropy approach to calculate weights. The outcomes should serve as a guide for Tekes County's efforts to prevent and manage landslide disasters.
# A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
## 4 Results Analysis
### SNR Effects on the Proposed GGS-MAF-1 Algorithm  
In this experiment, different \(SNR\) (from 0 dB to 40 dB) are used to analyze the NM of the proposed GGS-MAF-1 algorithm for CN input. \(B=4\) is chosen for GGS-MAF-1 algorithm, and other parameters are same as previous experiments. Simulation performance of the GGS-MAF-1 algorithm with different SNRs is shown in Figure 4. Figure 4 demonstrates that the NM decreases when SNR increases from 0 to 40 dB. We can see that the NM of the proposed GGS-MAF-1 algorithm is lower than other algorithms.  
Figure 3: Performance of the proposed GGS-MAF-1 algorithm with different \(B\). White Gaussian noise (WGN), colored noise (CN), speech signal (SS), least mean square (LMS), zero-attraction LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS), least mean fourth (LMF), normalized LMS (NLMS), mixed error criterion (MEC), proportionate normalized LMS (PNLMS), improved PNLMS (IPNLMS).
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 4 Proposed Solution
### dp-Based Pruned Local Search  
Effective search algorithms often combine global and local techniques. The local search algorithm that we propose builds a _search beam_ around every new path \(p_{AB}\) obtained from the generator. This process utilises the structure of the solution (design) space, which is a multi-stage graph (i.e., directed, acyclic, and layered); in this domain, the search beam is just a thin (_\"tube-shaped\"_) subgraph of \(G_{3}\) that contains \(p_{AB}\).  
Having built this local search graph, we can apply dynamic programming to find--in a very effective way--all locally best new paths. This process can be additionally boosted by pruning because the evaluation of a single route segment can be aborted as soon as we know that it is not \"promising\" (this process can be based on any effective _admissible heuristic_; knowing the current approximation of the Pareto set helps as well). Since the cost of each route segment has to be obtained from simulation (it is unknown upfront), this improvement can be significant [50].  
The proposed dp-based local search--mainly due to its exploitation capabilities--is the key element of the algorithm: for every path obtained from the generator, it evaluates \(3\times 9^{n_{r}-2}-1\) new paths (for instance, for \(n_{r}=10\), it corresponds to 129,140,162 new paths).
## 3 Preliminaries
### Beacon Message Types  
Beacon message contains identifiers to establish connections among the network nodes. Each node uses beacon identifiers for performing transmission of data packet, sensing along with reception of data packet [7]. From the beacon identifiers, each node maintains a neighbor table which consists of cube number, unique ID and \(X\), \(Y\), \(Z\) coordinates. The ultimate objective to broadcast beacon messages among the network nodes is to acquire information of neighbor nodes and closest sonobuoy [6]. To acquire coordinates of each node in the acoustic environment, we have used the same mechanism as discussed in [34]. The periodic broadcast of the beacon message increase the overhead resulting in low network performance. Therefore, we only transmit the changed identifier in the beacon message to keep neighbor table fresh and avoid the data packet loss. Due to the transmission of updated identifier, unnecessary flooding of broadcast message is avoided and the purpose of neighbor information also fulfilled. Similarly, any sonobuoy belonging to the set \(S_{s}\) has quintuple of information that includes ID of the sonobuoy, \(X\), \(Y\), \(Z\) coordinates, sequence number of the beacon message and \(\Lambda\) as a flag to indicate that latest neighbor information is propagated among the neighbor nodes. With the help of neighbor information, every node transmits its sensed data to reach its nearest sonobuoy through its neighbor nodes.
## 2 Numerical Methods
### Grid System and the Measurement Locations  
Figure 3 shows a schematic of the three-dimensional computational grid. The +X-axis runs east and the +Y-axis runs north. The FLACS code uses Cartesian coordinates; the pipe must be rectangular. If a 1-inch-diameter leakage source is rendered square, the length of a side is 2.25 cm. During FLACS computation, the grid size should be set so that the leakage source area does not exceed 1.25-fold of the grid area. Because the pipe diameter is very small compared with the computational domain, grid sides of 2.25 cm in all directions were used to divide a 1 \(\times\) 1 \(\times\) 1-m\({}^{3}\) region near the source of leakage, but the size was extended to 10 m in more remote regions. The stretched grid system is shown in Figure 3.  
Parallel computation was conducted using a message-passing interface with 40 cores running on a PC with an Intel Xeon Silver 4214 CPU @ 2.20 GHz. The computation time, depending on the number of grids, was approximately 48-76 h of wall clock time for each case based on the optimized grid system.  
Figure 4 shows the locations where hydrogen concentrations were measured numerically based on the radius (R) from the leakage point. The angle (\(\theta\)) represents the counterclockwise rotation angle in reference to the eastward direction. The wind is blowing from the southwest, corresponding to 259 and 246\({}^{\circ}\) counterclockwise from the east, respectively, as shown in Table 1. Specifically, hydrogen concentrations were measured at radii of 30, 50, and 100 m, and at heights (H) of 0.1, 1.0, and 1.8 m above the ground. The sensing locations were categorized based on radius, height, and angle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wind Speed** & \begin{tabular}{c} **Day,** \\ **Strong Sun** \\ \end{tabular} & \begin{tabular}{c} **Day,** \\ **Moderate Sun** \\ \end{tabular} & \
\begin{tabular}{c} **Night,** \\ **Clouds (>50\%)** \\ \end{tabular} &
\begin{tabular}{c} **Night,** \\ **Clouds (<50\%)** \\ \end{tabular} \\ \hline \(<\)2 m/s & A & B & E & F \\ \hline
2–3 m/s & A–B & B–C & E & F \\ \hline
3–5 m/s & B & B–C & D & E \\ \hline
5–6 m/s & C & C–D & D & D \\ \hline \(>\)6 m/s & C & D & D & D \\ \hline \end{tabular}
\end{table}
Table 2: The Pasquill stability classes.  
Figure 3: The computational domain and the grid system.
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 1 Introduction
### Metamaterials and Metasurface Structures
Furthermore, the RMS antenna elements can be packed into a relatively small space and be operated over a wide range of frequencies, making them more flexible in size than conventional antennas [14].
# EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer's Disease
## 2 Materials and Methods
### Feature Extraction
#### 2.2.2 Double Digital Filter Construction  
The second step includes the construction of two Finite Impulse Response (FIR) digital filters, i.e., a second-order Butterworth filter for high-pass (FIR-H) and low-pass (FIR-L) frequencies by using the cutoff frequencies (\(f_{cut}\)) previously identified [27]. Thus, each EEG signals provided by an electrode is double-filtered, and two signals are generated in the time domain, called \(EEG^{(L)}\) and \(EEG^{(H)}\), where the first value is the EEG filtered with FIR-L, while the second value is the EEG filtered with FIR-H. Subsequently, we compute the power (see Appendix A) of these two signals, \(P_{xx}^{(L)}\) and \(P_{xx}^{(H)}\), with the aim to calculate the square of their absolute difference:  
\[P_{(L-H)}^{2}=\
ormalsize P_{xx}^{(L)}-P_{xx}^{(H)}\
ormalsize^{2} \tag{1}\]  
This value is the extracted feature corresponding to our biomarker. Figure 1 shows a schematic representation of the double digital filter construction.  
In this way, we are able to represent each initial EEG signal as an array of values sampled in a single feature, \(P_{(L-H)^{\prime}}^{2}\), reducing the input size. This procedure is iterated for all subjects, as shown in Table 1. To ensure that the cutoff frequencies identified in the first stage of the procedure are really the best for class separation, we vary \(f_{cut}\) from 1 Hz to 18 Hz with a step size of 1 Hz, in order to achieve the best class separation.
## 4 Materials and Methods
### Electrophoretic Mobility Shift Assay (EMSA)  
To analyze the physical protein-DNA interaction between the different CrtR proteins and their putative native target DNA, bandshift assays were performed [77]. The His-tagged CrtR proteins were mixed in varying molar excess with 30-90 ng of PCR amplified and purified promoter fragments of the target genes in bandshift (BS) buffer (50 mM Tris-HCl, 10% (_v/v_) glycerol, 50 mM KCl, 10 mM MgCl\({}_{2}\), 0.5 mM EDTA, pH 7.5) in a total volume of 20 \(\mu\)L. The 5' UTR of _crtR_ genes were PCR-amplified and purified with NucleoSpin kit (MACHEREY-NAGEL GmbH & Co. KG, Duren, Germany). Promoter fragments were amplified using the respective oligonucleotide pairs (Table 2). A 78 bp-fragment of the upstream region of cg2228 was added in every sample as a negative control using oligonucleotides cg2228_fw and cg2228_rv. After 30 min of incubation at room temperature, gel shift samples were separated on a native 6% (_w/v_) polyacrylamide. Additionally, the binding affinity in the presence of 100-650 \(\mu\)M GGPP as effector was analyzed by incubation of the protein with the effector under buffered conditions for 15 min at room temperature prior to the addition of the promoter. Subsequently, the gel shift samples were separated on a 6% DNA retardation gel (Life Technologies GmbH, Darmstadt, Germany) at 100 V buffered in 44.5 mM Tris, 44.5 mM boric acid and 1 mM EDTA at pH 8.3. Staining of the DNA was achieved with ethidium bromide.
## 4 Discussion
### Questionnaire Results  
Finally, we considered the free responses to the questionnaire. Some students found referring to problems with similar ideas helpful, suggesting that WOJR worked as designed. However, other students commented that the problem statement for a similar problem was difficult to understand. The reason may be because AOJ was adopted as the search destination for similar problems, and AOJ contains many past problems such as the ACM ICPC, which have problem statements written in a complicated manner. Therefore, expanding the problem collection during the search for similar problems may overcome this issue.  
There are two possible factors influencing the low similarity to the assignments. The first factor is the problem set. As mentioned above, AOJ contains many problems from past programming contests, some of which are complex. Since this course deals with more straightforward problems than those in programming contests, no similar appropriate problems may exist. Therefore, extending problem repositories containing less complex problems may improve the results. The second factor is the similarity calculation method. WOJR calculates the similarity of the problem using the similarity of the source code via TF-IDF. The experiment revealed very few similar problems related to tree structures and graphs because their code tends to have diverse tokens, and TF-IDF does not work well in such a situation. Because TF-IDF mainly reflects the token similarity, calculating the similarity utilizing existing code clone detection techniques [18; 19; 20], which consider the structural viewpoint, may improve the performance. In the second experiment, there are helpful similar problems that only TF-IDF can find, and ones that only JPlag can find. Therefore, we think we need to combine multiple similarity metrics to recommend similar problems.  
A two-step narrowing may be necessary since the calculation cost of similarity considering the structural viewpoint is generally high. The similar problem candidates should be initially narrowed using the similarity, which has a small calculation cost. Then, the final candidates should be determined from the narrowed candidates, which have a higher calculation cost.  
Finally, some students indicated that they would like an explanation of the algorithm. Existing online judges only contain problems and answer code. Consequently, such an explanation is beyond the scope of WOJR.
## 4 Case Study and Discussion
### Simulation Environment
#### 4.1.2 Generation Profile of the Coordinated Renewables  
Considering the settlement interval eligible in the current market system, hourly generation histories from the PV and the WPGs were used for the simulation. Table 5 presents the peak periods in effect by the SO in South Korea.  
The BESS coordinated with the WPG has also not been in operation considering profit maximization thus far. As illustrated in Figure 3, the BESS discharged only during the interval specified by the SO eligible for the multipliers, without considering any other chances for profit maximization. Moreover, it can be observed that the charging operation of the BESS was scheduled without consideration for the opportunities for the greater revenue, charging when the SMP is greater, as illustrated in Figure 3. In Figure 4, it can be observed that the DER did not respond to the system as intended, with virtually no coordinated control of the renewables.  
Figure 5 shows the DA scheduling windows considering the multipliers. The multiplier is only eligible for energy generated the PV during the specified window, from 10:00 a.m. to 16:00 p.m., and which is discharged during the other windows, indicated in red and blue, regardless of the seasonal peak periods.  
Figure 4: Intraday control status of the output compared with patterns of the SMP.  
Figure 3: Intraday operation of the BESS charging and discharging without profit maximization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Season** & **Month** & \
\begin{tabular}{c} **Period Eligible for** \\ **REC Multiplier** \\ \end{tabular} &
\begin{tabular}{c} **Operation for REC** \\ **Multiplier** \\ \end{tabular} \\ \hline Winter & Nov.15–Mar.16 & 09:00–12:00 \\ Spring & Mar. 17–Jun.6 & 09:00–12:00 \\ Summer & Jun.7–Sep.20 & 13:00–17:00 \\ Fall & Sep.21–Nov.14 & 18:00–21:00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Peak periods defined by the local SO for WPG–BESS operation in the Main Land.  
Figure 6 and 7 are illustrations of the participation methods currently in effect in the subject site. The BESS with the PV device has been programmed to charge and discharge only during the windows determined at the operator's discretion, without the reliability constraints on the SoC. In Figures 6 and 7, it can be observed that the only parameters influencing profit maximization were SMP and REC. However, since the price of the REC for one day remained the same during the scheduling intervals, the SMP and the cost of the operations were the volatile variables influencing profit maximization. However, despite the SMP ahead of 2300 h was greater, the BESS was scheduled to discharge in the hours with lower1a SMP with greater operating costs, discharging multiple times unnecessarily as illustrated in Figures 6 and 7.