esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Laser Joining Quality by Visual Inspection, Computer Simulation, and Deep Learning
## 3 Results
### Deep Learning Algorithm and Process  
Welding quality was evaluated under each condition using the DL algorithm. Figure 8(a)-c show cases with defects and those with a good end-product along with the experimental schedule described in Table 1. The welding quality was assessed based on the stochastic results, however the general trend of weld quality was identical to that of visual inspection. The detailed analysis is described in the following.  
Under the low-energy condition where the heat input was set to 3 J/mm (experiments #1-1-5), the welding quality was not identical between the laser powers of 3 and 5.5 W. To obtain quantitative results, DL was performed on photographs of the weld surface. The results in each case (3, 5.5, 8, 10.5, and 13 W) were classified as fail or good, with the accuracy of \(>\)97% and \(>\)99%, respectively.  
Under the mid-energy condition where the heat input was set to 4 J/mm (experiments #2-1-2-5), the welding quality was more acceptable than at lower energies regardless of the power levels. The laser power was increased from 5 to 15 W in increments of 2.5 W, and the quality of the laser welding was evaluated based on the images, which were classified as good or fail using our DL algorithm with an accuracy \(>\)99% or \(>\)94%, respectively.  
Figure 8: Temperature distribution for different heat inputs.  
Figure 9: _Cont._  
Under the high-energy condition where the heat input was set to 5 J/mm (experiments #3-1-3-5), the laser power was increased from 7 to 14.5 W in increments of 2.5 W. The welding quality was acceptable except for experiments #3-4 and #3-5, where extremely high peak powers generated bubbles on the welded parts. Good and fail corresponded to accuracies of over 99% and 91%, respectively.  
As a summary of the DL results, the weld quality was statistically assessed, as shown in the bubble chart in Figure 10. While deterministically concluded in Figure 6, the results can be statistically estimated with a varying reliability. By providing statistical data to operators, one can select the best region for the welding where a high level of success can be achieved.  
Figure 10: Stochastic prediction of weld quality by the developed Deep Learning  
Figure 9: Quality evaluation by deep learning.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 2 The Proposed Method of Spoofing Detections' Fusion
### Performance of Detections' Fusion
\begin{array}{rcl}P_{fa}&=&P\bigg{(}m_{1}(C)+m_{2}(C)>1\Big{|}H_{0}\bigg{)} \\ &=&P_{0}^{(1)}\bigg{(}m_{1}(C)<0.5\bigg{)}\cdot P_{0}^{(2)}\bigg{(}m_{2}(C)>1-m_ {1}(C)\Big{|}m_{1}(C)<0.5\bigg{)}\\ &&+\ P_{0}^{(2)}\bigg{(}m_{2}(C)<0.5\bigg{)}\cdot P_{0}^{(1)}\bigg{(}m_{1}(C)>1- m_{2}(C)\Big{|}m_{2}(C)<0.5\bigg{)}\\ &&+\ P_{0}^{(1)}\bigg{(}m_{1}(C)>0.5\bigg{)}\cdot P_{0}^{(2)}\bigg{(}m_{2}(C)>0.5\bigg{)}\\ &&>\ P_{fa}^{(1)}\cdot P_{fa}^{(2)}\end{array}  \tag{15}\]  
Based on Equations (13) and (14), the range of \(P_{fa}\) can be given as:  
\[P_{fa}^{(1)}\cdot P_{fa}^{(2)}<P_{fa}<P_{fa}^{(1)}+P_{fa}^{(2)}-P_{fa}^{(1)} \cdot P_{fa}^{(2)} \tag{16}\]  
Meanwhile, \(P_{md}\) of fused detections can be given as follows:  
\[ \begin{array}{rcl}P_{md}&=&P\bigg{(}m(A)\geq 0.5\Big{|}H_{1}\bigg{)} \\ &=&P_{1}^{(1)}\bigg{(}m_{1}(A)\geq 0.5\bigg{)}\cdot P_{1}^{(2)}\bigg{(}m_{2}(A) \geq 1-m_{1}(A)\Big{|}m_{1}(A)\geq 0.5\bigg{)}\\ &&+\ P_{1}^{(2)}\bigg{(}m_{2}(A)\geq 0.5\bigg{)}\cdot P_{1}^{(1)}\bigg{(}m_{1}(A) \geq 1-m_{2}(A)\Big{|}m_{2}(A)\geq 0.5\bigg{)}\\ &&-\ P_{1}^{(1)}\bigg{(}m_{1}(A)\geq 0.5\bigg{)}\cdot P_{1}^{(2)}\bigg{(}m_{2}(A) \geq 0.5\bigg{)}\\ &&<&P_{md}^{(1)}+P_{md}^{(2)}-P_{md}^{(1)}\cdot P_{md}^{(2)}\end{array}  \tag{17}\]  
where \(P_{md}^{(1)}+P_{md}^{(2)}-P_{md}^{(1)}\cdot P_{md}^{(2)}\) is the missed detection probability of two detectors if their results are not fused together. Each missed detection cannot be re-examined in the case that two detectors are not fused. Similar to Equation (14), there is:  
\[P_{md}>P_{md}^{(1)}\cdot P_{md}^{(2)} \tag{18}\]  
Based on Equations (17) and (18), the range of \(P_{md}\) can be given as:  
\[P_{md}^{(1)}\cdot P_{md}^{(2)}<P_{md}<P_{md}^{(1)}+P_{md}^{(2)}-P_{md}^{(1)} \cdot P_{md}^{(2)} \tag{19}\]  
The range of \(P_{fa}\) and \(P_{md}\), Equations (16) and (19), just provides rough boundaries of \(P_{fa}\) and \(P_{md}\). They clearly show that both \(P_{fa}\) and \(P_{md}\) in the case of fusion are less than the corresponding values in the case that detections are not fused. The actual ranges depend on the performance of the detectors, which are fused. Monte Carlo simulations based on two detectors are given in the following section to demonstrate the performance of fusion. The above discussions are based on the fusion of two detectors. Actually, the combination rule of Equation (4) is associative, that is:  
\[m_{1}\oplus m_{2}\oplus m_{3}=m_{1}\oplus(m_{2}\oplus m_{3})=m_{1}\oplus m_{2}^{\prime} \tag{20}\]  
where \(\oplus\) represents the operation of the combination and \(m_{2}^{\prime}\) represents the combination of \(m_{2}\) and \(m_{3}\). The derivation of the associative law is provided in Appendix B. Equation (20) means that the combination of multiple detections can be equivalent to the combination of two detections after some of them are fused together. Thus, discussions about two detections are sufficient to extend to the cases of multiple detections.
## 4 Network Performance Analyses  
### Network Architecture for Confined Space Healthcare Applications  
Figure 9 presents a proposed generic network architecture that integrates different types of nodes. It should be noted that the approach includes a plan that provides increased integration between the different architecture levels of the application. This is convenient because the architecture can make the optimal decisions necessary to reconfigure the system to provide improved QoS. The general goals of the architecture are to:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Processor** & **Derivation** & **Squaring** & **Integrating Window** & **Total Computing Time** & **Period [μs]** & **Utilization Rate (U)** \\ \hline LPC2124–ARM & 70.2 μs & 142 μs & 280.5 μs & 492.7 μs & 3,000 & 16.4\% \\ MSP430F1611 & 191.9 μs & 162.5 μs & 697.8 μs & 1,052.2 μs & 3,000 & 35\% \\ PIC18F458 & 406.2 μs & 209 μs & 1,083.7 μs & 1,698.9 μs & 3,000 & 56.6\% \\ MC9S08 GB60 & 497.2 μs & 332 μs & 707.35 μs & 1,536.55 μs & 3,000 & 51.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computation time for the Pan and Tompkins algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Processor** & **Utilization Rate (U)** & **Active Power (P\({}_{\lambda}\)) [mVA]** & **P\({}_{\lambda}\) x U** \\ \hline LPC2124–ARM & 16.4\% & 180 & 29.52 \\ MSP430F1611 & 35\% & 19.2 & 6.72 \\ PIC18F458 & 56.6\% & 220 & 124.52 \\ MC9S08 GB60 & 51.3\% & 51.6 & 26.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PA \(\times\) U parameters.  
* Minimize latencies.
* Optimize power consumption.  
The main coordinator node is responsible for coordinating the complete application. It has a fixed location and communication with local coordinators is supported by means of wireless links. It sends synchronization signals to the local coordinators of the sub-networks.  
The Local Coordinator controls the activity inside the sub-network and carries out the following functions:  
* Transmits information from a single mobile node located in each patient.
* Sends synchronization signals to nodes in its sub-network which sample variables for patient's state analysis
* Develops routing packets between sub-networks using multi-hop techniques.
* Distributes QoS indexes of nodes, which belong to its sub-network.
* Calculates its sub-network QoS index, and distributes this value and its neighboring sub-networks indexes (those reached in a single communication hop) between nodes in its sub-network, where it can either:  
* Accept linking new nodes to sub-network.
* Update the best routes in the routing tables of data (which will be function of hops and the utilization percentage -information transmitting- of each router node).
###### Abstract  
Wireless sensor networks (WSNs) are often deployed in hostile environments, where an adversary can physically capture some of the sensor nodes. The adversary collects all the nodes' important credentials and subsequently replicate the nodes, which may expose the network to a number of other security attacks, and eventually compromise the entire network. This harmful attack where a single or more nodes illegitimately claims an identity as replicas is known as the node replication attack. The problem of node replication attack can be further aggravated due to the mobile nature in WSN. In this paper, we propose an extended version of multi-level replica detection technique built on Danger Theory (DT), which utilizes a hybrid approach (centralized and distributed) to shield the mobile wireless sensor networks (MWSNs) from clone attacks. The danger theory concept depends on a multi-level of detections; first stage (highlights the danger zone (DZ) by checking the abnormal behavior of mobile nodes), second stage (battery check and random number) and third stage (inform about replica to other networks). The DT method performance is highlighted through security parameters such as false negative, energy, detection time, communication overhead and delay in detection. The proposed approach also demonstrates that the hybrid DT method is capable and successful in detecting and mitigating any malicious activities initiated by the replica. Nowadays, crimes are vastly increasing and it is crucial to modify the systems accordingly. Indeed, it is understood that the communication needs to be secured by keen observation at each level of detection. The simulation results show that the proposed approach overcomes the weaknesses of the previous and existing centralized and distributed approaches and enhances the performance of MWSN in terms of communication and memory overhead.  
mobile wireless sensor network; hybrid; node replication attack; danger theory; danger zone; wireless sensor network +
Footnote †: journal: Sensors
## 5 Experiment Setup and Results
### Experiment Setup  
The experiments were conducted on a desktop computer with an AMD Ryzen 7 PRO 4750 U processor (1.70 GHz), Radeon Graphics, and 16 GB RAM. All analyses were performed using Python v3.10. The proposed framework was evaluated on two benchmark datasets: DUC and CNN/DailyMail. While DUC contains more formal and academic content, CNN/DailyMail includes contemporary news articles from various domains. The texts were preprocessed with care to preserve their structural and contextual integrity, then converted into graph representations for summarization.
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 3 Experiments and Results
### Fusion-Positioning Experiment
The fusion-positioning method based on PF also shows the instability after the first turn, which suggests that particle degradation affects the positioning stability and accuracy as the time grows. Contrary to the instability and the navigation path drift, the navigation trajectory indicated by our proposed method has no large drift error and is closer to the actual experimental path. There is no doubt that the mutation and reconstruction of the particle set methods improve the performance of the particle filter and ensure the stability and high precision of the fusion-positioning algorithm.
## 5 Experimental Results
### Imbalance Learning Performance Comparative Analysis
#### 5.4.4 Statistical Analysis of Results  
The above analysis of the behaviour and performance of classifiers was based on the groupings formed by considering _average accuracy_, _F-measure_ and _minimum accuracy per class_ on the datasets. In order to extend the analysis provided above, a non-parametric statistical test [74; 75] is conducted for validating the effectiveness of margin based bagging method. The Friedman test is recognised as one of the best tests when multiple different datasets are used. Therefore, in our experiment, the Friedman test [74] is leveraged to verify whether there is a significant difference among the mean ranks of different alternatives when different algorithms provide varying performances on different data sets. Tables 3-5 have provided a summary of mean ranks of comparative algorithms on all datasets. The null hypothesis \(H_{0}\) that we used was that the ranks of _average accuracy_, _F-measure_ and _minimum accuracy per class_ across the three reference classifiers and the proposed method with four margin definitions was the same. When the significant level is selected as 0.05, the null hypotheses \(H_{0}\) in terms of all three metrics can be rejected. To verify whether our method performs better than other algorithms, we compute the critical difference (CD) chosen by the Bonferroni-Dunn post-hoc test.  
Figure 4 presents the results of post-hoc tests on _average accuracy_, _F-measure_ and _minimum accuracy per class_ for comparative algorithms over all the datasets. If the difference between the mean ranks of two algorithms in terms of an evaluation metric is greater or equal to CD, then we can state that there is a statistical difference between the two algorithms. As CD = 1.900, the Tables 3 and 4 performances of margin based method are significantly better than that of bagging, UnderBagging and SMOTEBagging. The _minimum accuracy per class_ performance of the proposed method with first three margin definitions is significantly better than that of bagging and other state-of-the-art methods. From the above analysis, we can state that the proposed method obtains a good tradeoff between the majority class and minority class performances when tested on multi-class imbalanced data sets. Furthermore, unsupervised max margin statistically outperforms other margins especially for the improvement of the classification of the smallest class instances.
## 2 Review of Existing Work
### Problem Identification  
Recently, ML- and meta-heuristic-based approaches have found extensively use in resolving the issues related to intrusion detection. Various issues have been identified through the review of conventional works, and they are discussed in this section, as follows:  
* Existing models have concentrated on determining the impacts of the network dataset. They have suggested using the ML model to improve prediction performance. They have also suggested the development of schemes related to feature engineering to obtain a higher accuracy [9];
* Though Ref. [10] sought to perform intrusion detection based on ML, the system they suggested did not employ any feature selection methodologies;
* Conventional models have used the NSL-KDD dataset. Accordingly, Ref. [12] employed a cost-sensitive classifier, and achieved 81.1% accuracy. Similarly, Ref. [18] used Modified GWO, with an accuracy rate of 81%. Moreover, Ref. [19] employed EBGWO and achieved a classification accuracy of 87.46%. In addition, Ref. [20] utilized GSO-PCA and reached 94.08% accuracy. In addition, other research works have also considered the CICIDS-2017 dataset. In accordance with this, Ref. [21] used an ensemble method that achieved a 96.25% accuracy rate. Further, Ref. [25] used TLBO-GA, and achieved a 97% accuracy rate. Furthermore, Ref. [26] employed Rao-SVM and achieved 97% accuracy. Though better performances have been achieved, there is scope for further improvement;* The article [13] suggested that future works could consider employing various semi-supervised and meta-heuristic approaches in intrusion detection so as to accomplish a high accuracy rate and low computational complexity when detecting extensive attacks in CNs (computer networks).
## 4 Results
### EASY Concept Design Details: System Functionality for Different Users
#### 4.4.2.8 Contributed Observational and Monitoring Information  
Landholders, extension officers, and agricultural consultants would be able to contribute to EASY their own local observations and activity records to EASY. EASY would allow these users to create new datasets for capturing primary observational and monitoring information. Landholders could then contribute their own observations to these datasets using EASY, thereby supplementing existing published datasets with more local and specific environmental information.
## 3 Effects of Arsenic Contamination
### Effects on Agriculture  
Groundwater is the main source of water used for agriculture in Bangladesh. Consequently, when contaminated with arsenic, it can result in the introduction of arsenic into agricultural soils and crops, such as rice and vegetables [51; 67; 68]. For the same reason, it has also been found in many areas in Bangladesh that high arsenic concentrations are present in the agricultural soils and the usage of groundwater contaminated with arsenic for irrigation has been identified as the main cause [50; 52].  
A main consequence is the dual arsenic accumulation in vegetables and rice grains from both soils and irrigation water [30; 50; 51; 69; 70; 71]. More seriously, the phytotoxic effects of arsenic can result in a significant reduction of crop yields [52]. The variation of arsenic contents of selected foods from different countries is demonstrated by the data in Table 2. In general, arsenic concentrations in these foods are lowest when obtained in countries with little or no exposure to arsenic contamination of water source.  
Rice, which is a staple part of the diet in Bangladesh, has been extensively studied in the context of mitigating and reducing the uptake of arsenic during its cultivation. An excellent review on this topic has been recently published [72]. The authors have indicated that by employing water management, physico-chemical and biological strategies, either alone or combined, the uptake of inorganic and methylated arsenic species in rice cultivation can be successfully decreased. These approaches may also be adapted for other crops. The adoption of these strategies is a major challenge for countries like Bangladesh, where socio-economic factors can be a hindrance.
# Performance of Linear and Spiral Hashing Algorithms
## 2 Background  
Hashing schemes implement key-value stores and provide the key-based operations of inserts, updates, deletions, and reads. A hash record consists of a key and a value. Hashing places the record in a container at a location calculated from the key. Typically, the location can contain more than a single record, in which case the container is called a bucket. If a hashing scheme has a fixed number of buckets, then the number of records in a bucket would depend on the total number of records, as would the complexity of accessing a record in the bucket. Dynamic hashing schemes, therefore, adjust the number of buckets to the number of records. Typically, the number of buckets is increased by _splitting_ a bucket and decreased by _merging_ two buckets. In this manner, the average number of records in a bucket is bound by a constant. A key-based operation (such as a lookup, modification of the value, or an insert) first finds the bucket of the affected record. An insert adds the record to the bucket, e.g., by pushing on a stack. A lookup, delete, or update finds the record in the bucket and performs the operation. By splitting, the data structure guarantees that the expected number of records in a bucket is bounded by a constant. The bucket access happens in constant time. The expected time for lookups, deletions, or updates is linear in the average number of records visited, which is bound by a constant. As a result, the key-based operations are performed in the expected time complexity \(O(1)\). For this to work, we need to have good hash functions. A horrible hash function, for example, would map every key to 0, so that all records are stored in Bucket 0. A lookup for a non-existent record would then scan through all the records, resulting in a time complexity of \(O(n)\).  
Originally, classic dynamic hashing schemes like extensible hashing [2], LH [3], and SH [4; 5] stored buckets in the blocks of Hard Disk Drives. Nowadays, the buckets can also be containers in different nodes of a distributed system, containers in different storage systems such as NVRAM or flash memory, or containers in the main memory. Containers in the main memory can store their records in a linked list or an array. In fact, main memory data structures have gained enormous importance, and we focus on them.  
If the buckets of a dynamic hashing scheme are pages in a Solid State Drive (SSD) or a Hard Disk Drive (HDD), then a bucket can only contain a fixed number of records. If the number of records assigned to a bucket exceeds the capacity of the bucket, then some records are stored in an overflow bucket. The number of overflow buckets is usually small. This is not an issue if buckets reside in the main memory.
## 2 Experimental Section
### Used Materials: VEMCO  
Manual tracking was conducted from a boat equipped with a VH165 omnidirectional hydrophone (VEMCO Ltd., Bedford, NS, Canada) and a VR100 receiver (VEMCO Ltd.) to locate and track ultrasonic transmitters.  
At sea, the accuracy of the manual-tracking method depends on several factors. Firstly, the depth of the transmitter and the presence/absence of a thermocline act as an acoustic barrier. Secondly, vegetation, rocks, waves (in severe weather conditions), and others obstacles and interferences can reduce the power of the signal. At the same study site, it was estimated that the accuracy of the fixes ranged from 10 m to 50 m.  
Each position, manually taken, was recorded with date, time, crab ID and signal power. Those coordinates were transformed to geodetic coordinates (WGS84) and interpreted as a theme in the QGIS environment (www.qgis.org).  
Tags used in this study are V13 TP-1L coded transmitters. These transmitters send acoustic pings at 69 kHz that are infrequent and random about an average delay (60 s). This ping train includes an ID number which allows identification of the specific tag along with the sensor telemetry data and it is equipped with temperature and depth sensors.  
The physical specifications for this transmitter are: 13 mm diameter, 45 mm in length, 12 g weight in air, 6 g weight in water and a power output of 150 dB re 1\(\upmu\) Pa @ 1 m. The acoustic positioning transmitters did not exceed 1.1% of the body weight of the crab (Table 1).
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 4 Preliminaries  
Human skeletal data, in terms of skeleton-based action classification, are usually represented in the form of a graph structure \(G=(V,E)\). Here, \(V\in\mathbb{R}^{N\times C}\) denotes a set containing \(N\) joint nodes. Each node has \(C\) features (for example, spatial coordinates or velocities of joints). The edge set \(E\) represents connections between the nodes and can be expressed through an adjacency matrix \(A\in\mathbb{R}^{N\times N}\). Graph Convolutional Networks (GCNs) have become popular in recent papers because they perform well in modeling problems classifying actions based on skeletons [26, 29, 30, 31]. Usually, a 3D time skeleton sequence used for classification is shown as a tensor \(f_{in}\in\mathbb{R}^{C\times T\times N}\), where \(C\) denotes the channel dimension, \(T\) the number of frames, and \(N\) the number of nodes of joints. The fundamental relationship for classic GCNs is defined by Equation (3):  
\[f_{out}=\sigma\Big{(}\Lambda^{\frac{1}{2}}(A+I)\Lambda^{\frac{1}{2}}f_{in}W \Big{)} \tag{3}\]  
Here, \(\Lambda^{\frac{1}{2}}(A+I)\Lambda^{\frac{1}{2}}\) denotes an \(A\) matrix using a diagonal \(\Lambda\) matrix for normalization. \(\sigma\) is any nonlinear activation function like \(ReLU\) or sigmoid, and \(W\) is a learnable weight matrix that combines features across channels [31, 32].