esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Integer Programming  
In order to solve the proposed task sequencing and task allocation problems to balance the Type 2 case of U-shaped conveyor assembly lines, a mixed-integer programming model is developed. The main purpose of the proposed model is to minimize the cycle time. The sequence of all tasks must follow the precedence relationships of assembly. Therefore, a corresponding sequence number is assigned to each task. For each workstation, a single operator is assigned to execute all tasks that are allocated in it. The walking times between assembly tasks are taken into consideration. For two consecutive tasks in the same workstation, the second task starts where the first task ends. In this case, the walking times are zero and can be ignored. A mathematical programming model is proposed, for which the following notation is used:  
**Indices**  
\(i\), \(r\), \(s\): task index,  
\(j\), \(p\), \(q\): sequence number index,  
\(w\): workstation index, and  
\(e\): segment index. **Parameters**  
\(l\): number of tasks. \(l\): sequence number of last task in the task sequence. \(W_{max}\): number of workstations (equal to number of operators available). \(PR\): a precedence relationship between tasks; if \((r,s)\in PR\) means assembly task \(r\) must be completed before task \(s\) starts. \(CT\): cycle time. \(WT_{pq}\): walking time from assembly task with sequence number \(p\) to \(q\). \(VW\): normal velocity of working. \(VC\): velocity of conveyor (smaller than \(VW\)). \(a\): the length of the U-shaped conveyor assembly cell. \(b\): the width of the U-shaped conveyor assembly cell. \(T_{i}\): assembly time of task \(i\). \(l_{j}^{x}\): X coordinate of completing point of assembly task with sequence number \(j\). \(l_{j}^{y}\): Y coordinate of completing point of assembly task with sequence number \(j\). \(d_{j}^{c}\): moving distance of an item from the entrance point of the U-shaped conveyor to the completing point of assembly task with sequence number \(j\). **Decision variable**  
\[X_{ij}=\left\{ \begin{array}{ll}1,&\text{sequence number $j$ assigned to task $i$}\\ 0,&\text{otherwise}\end{array} \right.\]  
\[Y_{jw}=\left\{ \begin{array}{ll}1,&\text{task with sequence number $j$ assigned to workstation $w$}\\ 0,&\text{otherwise}\end{array} \right.\]  
\[U_{pqw}=\left\{ \begin{array}{ll}1,&\text{operator walks from task with sequence number $p$ to $q$ in workstation $w$}\\ 0,&\text{otherwise}\end{array} \right.\]  
\[Z_{je}=\left\{ \begin{array}{ll}1,&\text{the completing point of task with sequence number $j$ located in segment $e$}\\ 0,&\text{otherwise}\end{array}
## 2 Background and Methods
### User Experience  
The term \"user experience\" (UX) has a wide range of meanings [43]. According to [PERSON] [44], 'experience' refers to the way interactive products are used: the sense of ownership, the feeling of use, how well the tools serve their purpose, whether they are understood to work and how well the tool fits into the environment in which it is used.  
The UX is (1) the consequence of the user's internal state (expectations, needs, motivations, moods, etc.), (2) the characteristics of the intended system (including all products, services and infrastructure that are involved in the interaction when the product under consideration is used) and (3) the context (or environment) within which the interaction takes place.  
UX is subjective: the user's state influences the perception of the system, which in turn influences the experience and the user's state. Built-in navigation systems have the potential to influence the relationship between users and their environment in deep and complex ways. Empirical and theoretical analyses show that the use of GPS units changes people's understanding of the world around them, their learning habits, their navigation techniques and their knowledge of spaces and places. GPS navigation is based on abstract representations of these spaces and places [45].
# Effects of Irrigation with Desalinated Water on Lettuce Grown under Greenhouse in South Korea
## 3 Results and Discussion
### Soil
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Experiment** & **Treatment** & **pH** & **EC\({}_{\bullet}\)** & **CEC** & **O.M.** & **T-N** & **T-P** & **P\({}_{2}\)O\({}_{5}\)** & **Ca\({}^{2+}\)** & **Mg\({}^{2+}\)** & **Na\({}^{+}\)** & **K\({}^{+}\)** & **SAR** \\ \hline \multicolumn{12}{c}{**(mg kg-1)**} \\ \hline \multicolumn{12}{c}{**Initial soil**} & \multicolumn{1}{c}{5.6} & 8.5 & 73.20 & 66.5 & 0.60 & 1676.04 & 1521.47 & 4534.83 & 1935.54 & 1042.14 & 5478.84 & 3.26 \\ \hline \multirow{12}{*}{Saline} & \multirow{3}{*}{1 st} & BS@01 & 5.4 & 15.4 & 72.02 & 38.6 & 0.52 & 1288.19 & 1275.72 & 4632.07 & 1482.61 & 1001.26 & 4402.62 & 3.28 \\  & & BS@02 & 5.3 & 19.1 & 76.49 & 51.9 & 0.66 & 1539.14 & 1239.64 & 5045.08 & 1478.67 & 1307.24 & 4309.38 & 4.16 \\  & & BS@03 & 5.4 & 20.6 & 72.21 & 45.6 & 0.60 & 1548.49 & 1158.85 & 4230.28 & 1255.74 & 1861.18 & 5691.48 & 6.45 \\  & & BS@04 & 5.4 & 25.0 & 69.46 & 53.0 & 0.68 & 1542.49 & 3111.66 & 4562.05 & 1382.54 & 2112.87 & 4503.81 & 7.03 \\ \cline{2-12}  & & BS@01 & 5.0 & 14.3 & 62.98 & 58.6 & 0.58 & 1269.38 & 873.23 & 5712.24 & 1562.14 & 783.95 & 2910.64 & 2.37 \\  & & BS@02 & 5.2 & 15.1 & 72.31 & 64.4 & 0.63 & 1032.37 & 593.57 & 4749.03 & 1245.23 & 1434.42 & 3227.31 & 4.79 \\  & & BS@03 & 5.1 & 30.8 & 74.13 & 65.2 & 0.60 & 934.71 & 768.36 & 6078.54 & 1594.38 & 5482.92 & 3033.99 & 16.17 \\  & & BS@04 & 5.0 & 34.8 & 75.31 & 66.2 & 0.55 & 9552.22 & 809.55 & 5652.04 & 1400.86 & 8225.07 & 2785.87 & 25.52 \\ \hline \multirow{12}{*}{Desalinated} & \multirow{3}{*}{1 st} & AS@01 & 5.7 & 13.8 & 65.78 & 62.3 & 0.60 & 1317.75 & 958.96 & 6475.56 & 1739.61 & 1254.90 & 3465.75 & 3.57 \\  & & AS@02 & 5.6 & 18.3 & 67.10 & 62.3 & 0.77 & 1621.03 & 1380.52 & 6830.55 & 1945.71 & 1666.51 & 4717.29 & 4.58 \\  & & AS@03 & 5.8 & 13.7 & 77.71 & 56.9 & 0.64 & 1119.45 & 8445.55 & 6737.94 & 1750.41 & 11643.33 & 3548.91 & 3.27 \\  & & AS@04 & 5.6 & 11.4 & 61.36 & 58.8 & 0.65 & 8832.00 & 4498.29 & 6996.66 & 1788.36 & 1736.33 & 3111.33 & 32.52 \\ \cline{2-12}  & & AS@01 & 6.0 & 9.7 & 65.83 & 65.4 & 0.71 & 907.93 & 497.39 & 8161.89 & 2163.45 & 1107.33 & 2676.24 & 2.81 \\  & & AS@02 & 5.8 & 10.8 & 79.78 & 65.1 & 0.67 & 1022.57 & 668.31 & 6551.91 & 1696.20 & 869.55 & 2490.48 & 2.48 \\  & & AS@03 & 5.9 & 8.5 & 75.90 & 66.7 & 0.63 & 762.93 & 471.04 & 6363.93 & 1584.57 & 781.50 & 2528.16 & 2.27 \\  & & AS@04 & 5.8 & 11.2 & 95.99 & 65.4 & 0.69 & 891.57 & 388.81 & 6587.28 & 1899.66 & 1206.78 & 2830.80 & 3.37 \\ \hline \hline \multicolumn{12}{c}{EC\({}_{\bullet}\): electric conductivity of soil; EC; cation exchange capacity; O.M: organic matter; T-N; total nitrogen; F.-P; total phosphorus; SAR: sodium absorption ratio; BS: before water treatment using the BWD system, i.e., saline water irrigation; AS: after water treatment using the BWD system, i.e., desalinated water irrigation.} \\ \end{tabular}
\end{table} 
Table 3: Chemical properties of the sampled soils from each experiment.
## 2 Related Work
### Underwater Delay-Tolerant Networks  
The use of Delay-Tolerant Networks (DTNs) [32] is becoming very common in underwater scenarios. DTNs consist in a type of networks especially designed to support long communication delays and intermittent connection between nodes in extreme environments. This concept implementation is generally based on a technique known as store and forward message switching. This method consists in moving entire data packets (or fragments) from one storage place in one node to other storage place in another node, similar to what happens in the classical postal mail systems. For this purpose, nodes that are part of a DTN implementation, such as router devices, should always have persistent storage since a link to a next hop may be unreachable for a considerable amount of time. The protocol used in this kind of network, which implements the store-and-forward technique mentioned above, is called the Bundle Protocol (BP), defined in [33]. In the protocol stack, BP runs below the application layer. The interface defined between the BP and the protocol suite is called convergence layer adapter. As the name suggests, the data units defined in BP are called Bundles and include the following: (1) a header consisting of one or more DTN blocks inserted by the BP agent; (2) a source-application's user data, including control information sent from source node to destination node defining how to process, store, dispose of, and otherwise, handle the user data; and (3) an optional bundle trailer, composed of zero or more DTN blocks.  
Although the first applications of the DTN concept were related to interplanetary communications, several works have already explored the idea of using DTNs in almost every type of environment where a permanent end-to-end path between the communications nodes is not possible to maintain, as in underwater environments. In [34; 35], experimental tests using different bundle protocol implementations were conducted, where acoustic modems were used to perform the communications between the underwater nodes in both cases. The use of the DTN concept in these environments was proven to improve the communications performance with any of the two tested implementations when compared to a typical point-to-point network architecture. The same conclusion was found in [10; 36; 37].
## 4 Experiment
At the same time, the output voltage of the system at different departure angles from 0\({}^{\circ}\) to 180\({}^{\circ}\) is tested as shown by the orange line in Figure 7. The inconsistent performance of each receiving sub module in Figure 7 is caused by manual errors in circuit welding, packaging, etc. Although there are errors in the experiment, when the departure angle changes continuously, the prototype can continuously output the signal. It shows that although loss exists in the switching process, it does not have a serious impact on the signal reception and output of the whole model. Compared with the directional receiver in Reference [27], the system can realize omni-directional reception. The system can be miniaturized without the acquisition-tracking-pointing, the erbium-doped fiber amplifier or the beaconing light system, and the system has stronger anti-turbulence capability than the directional receiver. However, due to the limited performance of large area detector, its communication rate is far lower than that of the directional receiver. Therefore, the system is suitable for some occasions where there is no need for high communication rate, but it needs to be miniaturized and omnidirectional.
## 4 Methodology
### Deep Neural Network Model  
To achieve the stated goal, we suggested the following methodology to develop the speaker identification system using the DNN model:  
1. Preparation of the training data: The speech signal will be segmented into frames, and the frames with no pitch estimation value, either unvoiced or silent, will be ignored.
2. Training the classifier: DNN will evaluate the speaker identification system and estimate feature vectors. As shown in Figure 7, the system model is illustrated.
3. Testing the classification: We will use the verification files on the dataset to test the model.  
Subsequently, the DNN result will be documented and analyzed. The critical point for any speaker identification system is the input features that provide a robust model with great accuracy. The system's output will be classes of a 2D matrix that the train system will predict which class the speaker belongs to in terms of identifying the speakers.  
Performance Evaluation  
To evaluate the system's accuracy and robustness, we will use an accuracy rate equation that refers to the probability of correctly identifying the speaker. The total number of successfully recognized speakers' classes is divided by the total number of tested speakers' classes, and it can be formulated as Equation (8):  
\[\text{Accuracy rate}=\frac{\text{correctly predicted class}}{\text{total testing class}}\times 100. \tag{8}\]  
Additionally, other standard evaluation matrices, such as precision, recall, and F1 score, are described below in detail.  
The precision, recall, and F1 score measurements depend on the system's confusion matrix. The confusion matrix summarizes the prediction results of a classification problem. It shows how the classification model becomes confused when it makes predictions. It provides insight into the errors made by the classifier and the types of mistakes made. Table 4 illustrates the confusion matrix, and from it, we calculate Equations (9)-(11).  
\[\text{Precision}=\frac{\text{True Positive}}{\text{True Positive}+\text{ False Positive}}, \tag{9}\]  
Figure 7: Proposed speaker identification model.  
\[\text{Recall}=\frac{\text{True Positive}}{\text{True Positive}+\text{False Negative}}, \tag{10}\]  
\[\text{F1}=2\bigg{(}\frac{\text{Precision }*\text{ Recall}}{\text{Precision }+\text{ Recall}}\bigg{)}. \tag{11}\]
# SDS: Scrumptious Dataflow Strategy for IoT Devices in Heterogeneous Network Environment
###### Abstract  
Communication technologies have drastically increased the number of wireless networks. Heterogeneous networks have now become an indispensable fact while designing the new networks and the way the data packet moves from device to device opens new challenges for transmitting the packet speedily, with maximum throughput and by consuming only confined energy. Therefore, the present study intends to provide a shrewd communication link among all IoT devices that becomes part of numerous heterogeneous networks. The scrumptous dataflow strategy (SDS) for IoT devices in the heterogeneous network environment is proposed and it would deal with all link selection and dataflow challenges. The SDS would accomplish the targeted output in five steps: Step 1 determines the utility rate of each heterogeneous link. Step 2 develops a link selection attribute (LSA) that gauges the loads of network features used for the link selection process. Step 3 calculates the scores of all heterogeneous networks. Step 4 takes the LSA table and computes the network preference for different scenarios, such as round trip time (RTTP), network throughput, and energy consumption. Step 5 sets the priority of heterogeneous networks based on the scores of network attributes. Performance of the proposed SDS mechanism with state of the art network protocols, such as high-speed packet access (HSPA), content-centric networking (CCN), and dynamic source routing (DSR), was determined by conducting a simulation with NS2 and, consequently, the SDS exhibited its shrewd performance. During comparative analysis, in terms of round trip time, the SDS proved that it utilized only 16.4 milliseconds to reach IoT device 50 and was first among all other protocols. Similarly, for network throughput, at IoT device 50, the throughputs of the SDS are recorded at 40% while the rest of other protocols were dead. Finally, while computing the energy consumption used to reach IoT device 50, the SDS was functional and possessed more than half of its energy compared to the other protocols. The SDS only utilized 302 joules while the rest of the protocols were about to die as they had consumed all of their energy.  
wireless communication; scrumptous; heterogeneous network; IoT device; routing 2022
# Study on Classification of Fishing Vessel Operation Types Based on Dilated CNN-IndRNN
###### Abstract  
At present, fishery resources are becoming increasingly depleted, and a reliable assessment of fishing activity is a key step in protecting marine resources. Correctly identifying the type of fishing operation can help identify illegal fishing vessels, strengthen fishing regulations, and prevent overfishing. Aiming to address these problems, this study first collects and preprocesses fishing vessel AIS data. Improvements are proposed on the basis of the convolutional neural network (CNN), long short-term memory (LSTM), and other models, changing the CNN to dilated CNN and LSTM to independently recurrent neural network (IndRNN). The results of the experiment show that the accuracy, precision, recall, and F-1 score of the model are finally obtained as 93.12%, 93.10%, 93.14%, and 93.10%, respectively. Overall, the new model proposed in this study offers a significant improvement in performance compared to the models of other scholars in the past.  
fishing vessel regulation; fishing vessel operation type; deep learning; dilated CNN; IndRNN +
Footnote †: journal:  
a high accuracy rate, but there is still room for optimization in the model structure. [PERSON] et al. [16] identified six fishing gear types based on vessel AIS data and environmental data using a deep learning-based approach for gear type recognition. To capture the complex dynamic patterns in the trajectories of gear types, a sliding window-based data-slicing method was used to generate the training dataset. In order to compare the performance of the proposed models, a support vector machine (SVM) model was also developed. The CNN model showed an average of 0.963 dots per inch (DPI), while the SVM model showed an average of 0.814 DPI. The results show that the model can effectively identify the gear types of the fishing vessels. [PERSON] et al. [17] proposed a vessel geospatial trajectory analysis visualization system (VA-TRAC), which accomplished the monitoring, identification, and verification of fishing vessel trajectories, aiming to solve the problem of illegal fishing. This work provides an effective identification of illegal fishing behavior by fishing vessels, which is of great significance in the regulation of fishing vessels. [PERSON] [18] grouped speed and heading time series and constructed six offshore fishing vessel category identification models for the Korean Peninsula using the image recognition method. Finally, [PERSON] et al. [19] used different models for different types of fishing vessels, including a hidden Markov model for travlers, with the observed variable being vessel speed, which ultimately achieved 83% accuracy.  
The above studies generally used the model of a convolutional neural network, which is slightly simpler in model structure and lacks the ability to extract features from complex fishing vessel AIS sequence data. In this paper, a deep learning model combining the dilated CNN and IndRNN was used to improve the feature extraction ability of the model and obtain better recognition results.
## 2 The Constrained Multi-Objective Component Deployment Problem
### Graph Representation  
The allocation of software components to hardware units connected with network links can be represented as bipartite matching (with complicating constraints) [10]. A bipartite graph consistsof two sets of vertices, where edges exist between vertices of the two different sets. For component deployment, one set of vertices are the software components and the second set of vertices are hardware units. A solution to the problem is to select a single edge for every software component vertex that maps it to a hardware unit vertex. In addition to the usual constraints (e.g., selecting edges between software components and hardware units) there are relationships within some of the software components themselves and also the hardware units.  
Figure 2 shows a small example problem visualised as a bipartite graph. The software components are \(c_{1}\), \(c_{2}\) and \(c_{3}\) and hardware units are \(h_{1}\), \(h_{2}\) and \(h_{3}\). An edge between a software component and a hardware unit indicates that the software component may be assigned to the corresponding hardware unit (for example, \(c_{1}\) may be assigned to \(h_{1}\) or \(h_{2}\)). Furthermore, two software components may be linked (indicated by the edge between \(c_{1}\) and \(c_{2}\)), in which case, they should both be assigned to the same hardware unit or if not, both hardware units they are assigned to must also be linked. For example, if \(c_{1}\) is assigned to \(h_{1}\), then \(c_{2}\) can be assigned to \(h_{1}\) or \(h_{2}\) since both these hardware units are connected to the link \(n_{1}\).  
While the problem considered in this study has obvious similarities to bipartite matching, extending or adapting existing matching algorithms is not possible in a straightforward manner, since the problem studied here is multi-objective and consists of several complicating constraints.
## 2 Materials and Methods
### DNA Extraction and PCR Amplification  
Total microbial genomic DNA was extracted from new corn stalk tests and silage tests utilizing the E.Z.N.A.(r) DNA Unit (Omega Bio-tek, Norcross, GA, USA), according to the manufacturer's instructions. The quality and concentration of the DNA were determined by 1.0% agarose gel electrophoresis and a NanoDrop2000 spectrophotometer (Thermo Scientific, United States) and kept at \(-80\)\({}^{\circ}\)C further use. The hypervariable region V3-V4 of the bacterial 16S rRNA quality was intensified with preliminary sets 338F (5\({}^{\prime}\)-ACTCCTACGGAGGGCAGC-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GGACTACHVGGGTWTCTAAT-3\({}^{\prime}\)) [22] by a T100 Warm Cyler PCR thermocycler (BIO-RAD, Hercules, CA, USA). The PCR reaction mixture including 4 \(\upmu\)L of 5 \(\times\) Quick Pfu buffer, 2 \(\upmu\)L of 2.5 mM dNTPs, 0.8 \(\upmu\)L each primer (5 \(\upmu\)M), 0.4 \(\upmu\)L Quick Pfu polymerase, 10 ng of template DNA, and ddH\({}_{2}\)O to a final volume of 20 \(\upmu\)L. The PCR enhancement cycling conditions were as follows: initial denaturation at 95 \({}^{\circ}\)C for 3 min, followed by 27 cycles of denaturing at 95 \({}^{\circ}\)C for 30 s, strengthening at 55 \({}^{\circ}\)C for 30 s, and expansion at 72 \({}^{\circ}\)C for 45 s, and a single expansion at 72 \({}^{\circ}\)C for 10 min, and finishing at 4 \({}^{\circ}\)C. The PCR product was extracted from 2% agarose gel and cleaned utilizing the PCR Clean-Up Pack (YuHua, Shanghai, China) according to the manufacturer's instructions, and measured utilizing Qubit 4.0 (Thermo Fisher Logical, Waltham, MA, USA). The cleaned amplicons were pooled in equimolar sums and paired-end sequenced on an Illumina PE300 stage (Illumina, San Diego, CA, USA), conforming to the standard conventions from Majorbio Bio-Pharm Innovation Co. Ltd. (Shanghai, China). The crude sequencing reads were stored in the NCBI Sequence Read Archive (SRA) database (Promotion Number: PRJNA1182493).
# Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment
## 4 System Data Life Cycle and Analysis
### Processing Module  
The Processing Module is responsible for data enrichment when new data occurs. Using the system architecture designed to operate in the cloud computing network, an algorithm was implemented to process large amounts of data that depends on processing module configuration. Firstly, the algorithm downloads the number of vulnerabilities which have not been fixed or have been marked as removed. Then, the algorithm retrieves information stored in the system to assess the amount of available resources which can be used for calculations. The task division subject to available resources is assigned according to Equation (1).  
\[d=\begin{cases}v//t&\text{if }v//t\leq t\\ t&\text{other}\end{cases} \tag{1}\]  
where:  
\(d\): the number of data processed by one processing module  
\(t\): the number of available processing modules  
\(v\): the number of vulnerabilities that are not fixed or removed  
Each time before calculations begin, the verification of the Equation (1) allows for vertical scaling without restarting the system. In order to accelerate the target distribution [33] value calculations, every CVE query is stored in the cache that is shared by all counting modules. As a result, only one counting module sends the query to the database at a time and the rest of the modules retrieve this information from cache. The vulnerability scores that have already been calculated are saved in a separate thread by bulk method [63]. As a result, the calculating loop does not require waiting for the result of the save operation.