esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Study models allow clinicians to carry out space analysis and to evaluate the discrepancy in tooth size and arch length by using [PERSON]'s ratio and other parameters such as Little's Irregularity Index (LII). LII is a method introduced in 1975 by [PERSON] [1], used to calculate the misalignment of mandibular anterior teeth, from canine to canine, through the sum of the displacements of the interproximal contact points of the incisors from contiguous teeth [2]. Once summed, the value represents the degree of anterior crowding. LII has been used mostly as a valid measurement of anterior arch length discrepancy [3; 4; 5; 6] and to analyze the normal maturational changes of the dentition.  
Nowadays, thanks to the introduction of new technologies, such as three-dimensional (3D) intra- and extra-oral scanners, the use of digital models is increasingly widespread and offers important advantages: less storage space than plaster models; instantaneous accessibility to patients' records on the computer screen; accuracy and efficiency of tooth measurements, arch sizes and dental crowding; and online sharing systems of diagnostic records [7]. However, measurements made on the plaster models are still considered as the \"gold standard\" despite requiring physical storage space [8; 9], and can undergo breakage and degradation issues in the long term [10; 11]. It is still questionable if digital measurements can substitute those obtained from dental casts and controversial if any definitive conclusions have been reached [11]. Although some authors [2; 10; 12; 13; 14] have compared LII measurements between plaster casts and digital models, they have not focused the correlation with different degrees of crowding, according to the American Board of Orthodontics (ABO) Discrepancy Index.  
Accordingly, the objective of this investigation was to evaluate the accuracy and reproducibility of the LII digital measurements, on the upper and lower arches, comparing the values taken from the plaster casts with the ones taken from digital models and to evaluate if the different degrees of dental crowding could influence these measurements.
## 3 Results  
The optimization setup was already established and surveyed in recent papers. The b-pillar has two main load cases: Nastran calculates the linear load case and RADIOSS a non-linear load case. Accordingly, for the linear and the non-linear load case, the b-pillar offers multiple objective functions for evaluation. The potential objective functions are the mass \(f_{m}(x)\), the static deflection (torsion) \(f_{s}(x)\), the intrusion in the lateral direction \(f_{i}(x)\), and the absorbed energy during the impact \(f_{ac}(x)\). The optimization scenario is always multi-objective and can be a multidisciplinary optimization scenario in the case of the deflection and crash case. Figure 3 shows the two load cases. The Nastran load case is torsional and evaluates the stiffness of the b-pillar. The RADIOSS load case is an impact load case, where an impactor with 15.56 m/s and one ton of mass hits the b-pillar in the lateral direction. Twenty-four DVs can change the b-pillar. Eight DVs each control, the outer sheet metal, the inner sheet metal, and the stiffener. Again, the stiffener is positioned in-between the upper and the lower shell. The following first two examples compare the performance of the two algorithms NSGA-II and GOMORS for the linear and non-linear load case for a simple optimization scenario without infeasibility. The sampling size is 100 members, while the number of additional evaluations is 100 members. The variation of the b-pillar takes place in the eight DVs of the stiffener.  
The stiffener varies by implicit parametrization between the upper and the lower shell, as shown in Figure 4. Eight sections control the shape and create many possible designs. By setting the DVs implicitly, no infeasible designs are created. Still, the effort it takes to develop such an implicit design is very high.
# Error Detection for Arabic Text Using Neural Sequence Labeling
###### Abstract  
The English language has, thus far, received the most attention in research concerning automatic grammar error correction and detection. However, these tasks have been less investigated for other languages. In this paper, we present the first experiments using neural network models for the task of error detection for Modern Standard Arabic (MSA) text. We investigate several neural network architectures and report the evaluation results acquired by applying cross-validation on the data. All experiments involve a corpus we created and augmented. The corpus has 494 sentences and 620 sentences after augmentation. Our models achieved a maximum precision of 78.09%, recall of 83.95%, and F\({}_{0.5}\) score of 79.62% in the error detection task using SimpleRNN. Using an LSTM, we achieved a maximum precision of 79.21%, recall of 93.8%, and F\({}_{0.5}\) score of 79.16%. Finally, the best results were achieved using a BiLSTM with a maximum precision of 80.74%, recall of 85.73%, and F\({}_{0.5}\) score of 81.55%. We compared the results of the three models to a baseline, which is a commercially available Arabic grammar checker (Microsoft Word 2007). LSTM, BiLSTM, and SimpleRNN all outperformed the baseline in precision and F\({}_{0.5}\). Our work shows preliminary results, demonstrating that neural network architectures for error detection through sequence labeling can successfully be applied to Arabic text.  
A Article  
Error Detection for Arabic Text Using Neural Sequence Labeling  
1
## 4 Discussion of Results
### Regional Scale Analysis
#### 4.2.2 Oil Spill Impacts in Rivers State Region  
Figure 8 indicates that at the state scale, the present remediation and clean-up operations are ineffective in slowing the expansion rate of bare land and land subsidence in the region. The measured area in 2023 exceeds the 95% confidence interval of the linear fit, likely as a result of the unprecedented quantity spilled in the region in 2022, as shown in Figure 9. The largest of these spills involved 26,000 barrels of oil over a two-month period, during which only 810 barrels were reportedly recovered. As the spill occurred in a tidal zone over a long period, the spill had most likely spread throughout the Delta before more could be recovered and contributed to extensive damage that was apparent over an extended area within this two-year period. Further detailed in the corresponding spill report are the affected properties, which include surface water and vegetation impacted by oil staining, and observations of withering vegetation [14].  
For a more localized environmental impact assessment, we analysed oil spill data from the Nigerian Oil Spill Monitor database [14]. Though it has been noted that many of the occurrences in the province are under-reported, these data nevertheless provide critical context for understanding the accelerated rate of mangrove decline in specific areas. As illustrated in Figure 10, there is a high frequency of low-quantity spills along the pipelines in the Port Harcourt area. While these spills have resulted in the degradation of the mangroves to the Southern end of the pipeline, the damage to the surrounding  
Figure 8: Linear regression of the annual classification of bare land measured in hectares across the full study region, where the blue region is the 95% confidence interval of the linear fit.  
Figure 9: Total quantity spilt annually in the region as reported by NOSDRA [14].  
agricultural land would be of more relevance to this area. However, quantifying the impact on agricultural land was not feasible in this study due to the unavailability of open-access, high cadence, and high-resolution multi-spectral observations, required to overcome cloud cover challenges and the small parcel size.  
The central peninsula surrounding Bille, west of Bonny Island, has experienced some of the largest oil spills in recent years. The impact of these spills is evident in the concentration of land degradation shown in Figure 7. We examine this area in more detail in the next section through the lens of our Pipeline Impact Indicator.
## 4 Design and Optimization of Hardware Prototype System
### Hardware Prototype Optimization
#### 4.2.2 Synchronization of Data Sampling  
In the GNSS/INS applications, unknown timing errors between IMU and GNSS measurements have a significant influence on the data fusion performance of the Kalman filter. Additionally, the most effective method is to sample IMU data under the GNSS PPS (pulse per second) trigger, which could realize the time synchronization essentially. It needs two conditions: (i) the GNSS receiver can provide PPS; or (ii) the IMU sampling time could be controlled by an external signal. In this hardware prototype; the GNSS receiver subsystem could provide PPS, and the IMU module consists of gyroscopes and accelerometers with analogue interfaces, sampled by a multiplexing analog to digital converter (ADC) with controllable sampling time.  
The IMU data sampling control module is designed in the FPGA, without additional cost of hardware [26]. Six signals from the sensors (three-axis gyroscopes and accelerometers) are sent to the input of the ADC, and the ADC is triggered by a 200-Hz pulse train to sample the IMU data. The PPS signal is generated in the time base module of the receiver to initialize a time-stamp counter. Then, the counter could generate the 200-Hz pulse train to trigger the ADC, and the time stamp is added to the IMU data. Figure 12 shows the sequence diagram of the IMU data sampling. The starting time of the first ADC channel conversion is delayed by two clock cycles compared to the PPS signal, with the first cycle using PPS rising edge detection and generating 200-Hz sampling pulses and the second cycle for triggering the conversion by the 200-Hz pulse train. Since the system clock is 39 MHz in the logic analyzer, the signal delay of Channel No. 0 is 0.05 \(\upmu\)s. Other channels' signal synchronization errors are caused by their previous channel sampling time, and the longest delay is five-times the ADC sampling time. Since the shortest delay is 0.05 \(\upmu\)s and the longest one is 22 \(\upmu\)s, the effect of sampling time delay could be ignored for the deeply-coupled system, even in a highly dynamic environment.
## 2 Materials and Methods
### Methods
#### 2.3.4 Geographical Detector Model  
The geographical detector model, proposed by [PERSON] et al. [25], was first applied to explore the driving mechanism of environmental risks and human health in the Heshun Region of China. GDM is based on spatial variance analysis with no linear assumptions. It is a novel spatial statistical method used to explore the driving forces behind spatial differentiation [26]. GDM not only can quantify the influences of different driving factors on the spatial differentiation of the CI, but it can also ascertain the interactions between different driving forces [57]. Furthermore, when using GDM, the explanatory variable can be numerical data or qualitative data. The core concept behind GDM is that the spatial distribution of a response variable will be similar to its driving factors [58]. In other words, if some driving factors affect the spatial differentiation of the CI, then the spatial distribution of the CI and the driving factors may show greater consistency. The power of determinant (\(q\)) is used to measure the spatial correspondence or similarity between CI and driving forces, which can be expressed as  
\[q =1-\frac{\sum_{i=1}^{L}N_{i}\sigma_{i}^{2}}{N\sigma^{2}} \tag{7}\]where \(L\) denotes the total strata number of the driving factors; \(N_{i}\) and \(N\) denote the sample number in stratum \(i\) and the total sample number, respectively; and \({\sigma_{i}}^{2}\) and \({\sigma^{2}}\) refer to the local variance of CI within stratum \(i\) and the global variance of CI, respectively. The range of \(q\) is \([0,1]\); thus, \(q=0\) indicates that the spatial distribution of CI is not affected by the driving forces, and \(q=1\) indicates that the driving forces completely determine the spatial characteristics of CI. The larger the value of \(q\), the higher the spatial correlation between CI and driving factors.  
GDM, which can mitigate the inherent limitations of the statistical methods [59], primarily involves four detectors: factor, risk, ecological, and interaction. Therefore, GDM has been increasingly used to explore the driving mechanism behind spatial differentiation. [[http://www.geodetector.cn/](http://www.geodetector.cn/)]([http://www.geodetector.cn/](http://www.geodetector.cn/)) accessed on 11 May 2021 is the Geodetector website, where more details regarding GDM can be found, along with the free software.  
The factor detector assesses the explanatory power of each driving factor for the spatial differentiation of CI based on calculating the \(q\) value. A risk detector can determine the favourable ranges or characteristics of the driving factors to the foliage clumping effects with a confidence of 95%. The difference in the influences of two driving forces on the spatial differentiation of CI was ascertained using the ecological detector. The interactions between two driving forces were determined by the interaction detector. The \(q\) value of the two interactive factors was compared with that of a single factor; thus, the types of interactions can be divided into five classes (Figure 3).
## 4 Numerical Results
When M increases to 500, the BER performance increases much more further with nearly 5 dB at BER = \(10^{-4}\), which also shows the advantages of massive MIMO with very large number of BS antennas. Noticeably, the performance of the proposed scheme is improved significantly when the length of the estimated signal vector \(L\) increases, regardless of the value of \(M\). The gain is nearly 1.5 dB at BER = \(10^{-5}\) when the length \(L\) increases from 2 to 3. When \(M=500\), the scheme of [13] only performs better than [12] at low BER (\(\leq 10^{-4}\)) while the performance of the proposed scheme is remarkably better than both [12; 13]; the gain is approximately 3 dB at BER = \(10^{-5}\) in comparison with [13]. Figure 3 shows the simulation results for the aforementioned schemes under Rician fading with \(T=7\), \(L=2\), \(M=128\) and different values of the Rician factor \(K_{r}=0,1\), and \(10\). Notice that when \(K_{r}=0\), the Rician channel becomes a Rayleigh channel. The error floor still happens when \(K_{r}=1\) in the case of [12]. However, when the LOS component of the Rician channel becomes stronger with \(K_{r}=10\), the error floor seems to disappear and the performance of [12] is improved much more than [13]; the performance gap is nearly 1 dB at BER = \(10^{-5}\). The performances of scheme of [13] are nearly the same with different values of \(K_{r}\), this is because the detector in [13] cancels out the channel coefficient between two consecutively received symbols. The performance of the proposed scheme is the best among three schemes. Even with \(K_{r}=0\) (i.e., Rayleigh fading), the performance of the proposed scheme remains better than the other schemes with \(K_{r}=1\) or \(10\). When \(K_{r}\) is increased from 0 to 1 and 10, the performance of the proposed scheme is improved significantly with gains 5.5 dB and 9.5 dB, respectively. In summary, we conclude that the proposed scheme shows much better performance in Rician channels than in Rayleigh channels.
## 5 Technical Tips for Performing Effective Superselective cTACE
### Usefulness of CT/CBCT and TACE Guidance Software  
A unified CT and angiography system that has been developed in Japan can improve the technical success rate of superselective cTACE [84]. However, it is relatively expensive and is not widely distributed. CBCT is a new technology to obtain CT images using a flat panel detector of an angiography unit. It has been reported that CBCT can improve not only local tumor control but also the prognosis of HCC patients [71; 85]. TACE guidance software, including automated tumor feeder detection (AFD), is also a novel software using CBCTHA data that can reduce the physician's workload and improve the treatment accuracy [26; 72; 86; 87; 88; 89]. This software can provide useful information regarding the order of embolization and division of chemotherapeutics for each tumor feeder. The curability and invasiveness can also be controlled by changing the virtual target size including the safety margin. If curative TACE is expected, a wider safety margin should be created to embolize all potential tumor feeders. If palliative TACE is recommended, the safety margin should be narrower to reduce the adverse effects [90]. In our previous analysis of 155 HCCs \(\leq\)5 cm (mean diameter, \(17.4\pm 7.4\) mm [range, 10-48]) including 18 angiographically occult tumors, AFD software using CBCTHA data could detect 85.4% of tumor-feeding subsubsegmental hepatic arteries, 90.3% in new tumors and 75.3% in recurrent tumors. Regarding the technical success, complete ultraselective cTACE could be performed in 82.6% of tumors [72].  
AFD software can also be useful to identify a tumor feeder arising from extrahepatic arteries and expand the indication and safety of TACE through extrahepatic collaterals. However, if the tumors are fed by the cystic, gastric, or colonic artery, the safety margin should not be included in the target to avoid non-target embolization [90; 91].  
In our recent analysis, the 5-year survival rate of 102 patients with tumors \(\leq\)7 cm and \(\leq\)5 lesions (mean largest tumor diameter, \(25.3\pm 12.3\) mm [range, 6-69]; mean tumor number, \(1.9\pm 1.1\) [range, 1-5]) treated with superselective cTACE using TACE guidance software was 60%, and about half (13/27) of patients died of HCC-unrelated causes [26]. This indicates that TACE guidance software can prolong a patient's lifespan.
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 6 Discussion and Analysis
### Analysis of Algorithm Performance with Different Graph Type  
We evaluated the DNTS algorithm's iterations per second under different graph types. We chose 32 instances in Section 5.2.1, excluding 500 groups. Based on the size of these instances, we categorized them into sparse and dense graphs.  
In randomized instances, the main factor affecting the iterative performance of the algorithm is the size of the instance since the graph is not very structured. From Figure 7, we find that both plots have distinct peaks and troughs and that the numbers of peaks and troughs are more numerous and of greater magnitude in Figure 6(a) and relatively fewer in Figure 6(b). The higher the density of the graph is, the lower is the iterative performance of the algorithm. In addition, we analyzed the instances at indexes 1 and 2 in Figure 6(a) and indexes 1 and 3 in Figure 6(b) to test the number of stable extensions.  
Analyzing these four instances, we find that the number of arguments in the stable extension from instance 1 is higher than in instance 2 in Figure 6(a) and instance 3 in Figure 6(b), potentially affecting the algorithm's iteration speed. Additionally, the graph's ring structure may impact performance, with the algorithm's search process becoming extremely difficult when there are too many rings or nested rings in the graph.  
By analyzing the iterative performance of sparse and dense graphs, it can be seen that the DNTS algorithm has the most stable and superior performance in dense graphs, especially in medium and large instances. The DNTS-NT algorithm, on the other hand, has outstanding performance in sparse graphs in some instances, but its overall stability is not as good as that of the DNTS algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **DNTS** & \multicolumn{5}{c}{**DINTS-NP**} \\ \hline
**Group** & **Best** & **Worst** & **Average** & **Best** & **Worst** & **Average** \\ \hline
1000 & 50 & 50 & 50 & 50 & 50 & 50 \\
2000 & 50 & 50 & 50 & 48 & 47 & 47.67 \\
3000 & 49 & 49 & 49 & 49 & 49 & 49 \\
4000 & 45 & 45 & 45 & 45 & 44 & 44.33 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Experimental results on the number of instances correctly solved.  
Figure 7: The evaluation of iterations per second with the growth of the problem size. The x-axis represents the increasing size of instances from left to right. (**a**) Experimental results for sparse graphs; (**b**) experimental results for dense graphs.
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
## 2 Hybrid TOA/RSS Localization: Formulation and Resolution Approaches
### RSS Model  
Let \(r_{i,b}\), with \(i=1,\ldots,N\), denotes the average power (in dB) that the blind node measures based on the signal transmitted by the \(i\)-th anchor. According to the widely-used path loss model [45], each \(r_{i,b}\) can be explicitly expressed as:  
\[r_{i,b}=P_{0}-10\alpha\log_{10}\frac{\|\mathbf{x}_{i}-\mathbf{x}_{b}\|}{d_{0}}+n_{i,b}, \tag{1}\]  
where \(\|\cdot\|\) is the Euclidean distance operator, \(\alpha>0\) denotes the path loss exponent regulating the slope of the decaying power law, and \(P_{0}\) is the power at reference distance \(d_{0}\); in the following, we assume \(d_{0}=1\) m without loss of generality. As to \(n_{i,b}\), it represents the normally-distributed (in dB) shadowing term having zero-mean and variance \(\sigma_{r}^{2}\). It is worth noticing that fast signal variations related to small-scale fading effects can be mitigated by averaging \(r_{i,b}\) over time ([11, 46, 47], see also [48] for details concerning the computational aspects); hence, Equation (1) is a sufficiently general model to represent the average received power as only affected by the shadowing component (\(n_{i,b}\)), as also demonstrated by the extensive experimental campaigns conducted in [49, 50, 51, 45]. Following the above model, we have that:  
\[r_{i,b}\sim\mathcal{N}(P_{0}-10\alpha\log_{10}\|\mathbf{x}_{i}-\mathbf{x}_{b}\|, \sigma_{r}^{2}), \tag{2}\]  
where \(\mathcal{N}(\mu,\sigma^{2})\) denotes the Gaussian probability density function (pdf) with mean \(\mu\) and variance \(\sigma^{2}\), and the shadowing variance \(\sigma^{2}\) (expressed in dB) does not depend on the range.  
The parameters \(P_{0}\), \(\alpha\) and \(\sigma_{r}^{2}\) describing the statistical characterization of the RSS measurements \(r_{i,b}\) typically depend on the specific environment at hand. A value of \(\alpha=2\) is commonly used to represent free space propagation, while \(\alpha>2\) is typical for most realistic outdoor and indoor scenarios. As we have seen in Section 1, most of the existing hybrid TOA/RSS estimators assume that such values are perfectly known a priori. Although this assumption is reasonable to carry out theoretical analyses aimed at understanding the fundamental behaviors and limits of the proposed techniques, in practical WSN deployments all the model parameters are unknown and must be estimated \"on the field\".