esa-sceva/satcom-chunk-collection huggingface dataset:
# Gain Equalization for Few-Mode Erbium-Doped Fiber Amplifiers via Strong Mode Coupling
###### Abstract  
Few-mode erbium-doped fiber amplifiers (FM-EDFAs) are one of the most important optical subsystems for successful space division multiplexed transmission systems. In this paper, we propose a new FM-EDFA designed to achieve significantly reduced differential modal gain (DMG) via strong mode coupling. Using a new numerical model based on a fiber transfer matrix, the DMGs of FM-EDFAs are systematically investigated and two different types of six-mode fiber amplifiers are analyzed, as exemplar demonstrations. In a uniformly doped step-index fiber, the DMG can be reduced from 9.3 to 1.1 dB (i.e., 8.2 dB reduction) and further reduced to 0.5 dB in a dual-layer doping structure.  
space division multiplexing; few-mode fibers; erbium-doped fiber amplifiers; strong mode coupling +
Footnote †: journal:  
0000-0002-4971-1999][PERSON]
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 4 The Proposed Framework for Travel Time Prediction
### Spatiotemporal Data Refinement and Categorization  
Data refinement is crucial in ensuring accurate predictions in ML. It involves techniques such as cleaning, normalization, and data transformation to make the data consistent and suitable for analysis. By eliminating irrelevant, missing, or noisy data, these techniques contribute to improving model performance and mitigating the risk of biased predictions. Additionally, data refinement involves handling outliers and skewness, encoding categorical variables, and scaling numerical variables, all of which play a crucial role in enhancing model accuracy [38].  
To illustrate our comprehensive methodology for data refinement, we have developed a flowchart, as depicted in Figure 3. This graphical representation outlines the sequential stages in the spatiotemporal data refinement process, from raw data to the final dataset, highlighting the key steps undertaken to achieve optimal data quality and suitability for subsequent analysis.  
In this paper, we have utilized ArcGIS [39] as the main tool for the Geographic Information System (GIS) analysis, visualization, and data management. With its extensive suite of tools and functionalities, ArcGIS Pro has enabled us to efficiently work with spatial data in various formats, including shapefiles, geo-databases, and raster data. Furthermore, its seamless integration capabilities with external sources such as web services, databases, and cloud-based services have significantly enhanced our data processing workflows.
# Assessing the Sustainability of Alternative Shaft Construction Methods
## 3 Numerical Model
### Validation of the Numerical Model against Intrumentation Data  
The lateral deflections caused by the WA2 shaft excavation were measured by 10 inclinometers, 9 of which were installed in the diaphragm wall panels, while 1 was installed in the soil behind the shaft. [PERSON] et al. [34] presented the records of the  
Figure 3: Experimental and interpreted (a) \(\mathrm{G_{max}}\) and (b) \(\mathrm{S_{u}}\) profiles.  
horizontal displacements at four depths of the excavation (5.0, 11.5, 18.0, and 25.0 m), as can be seen in Figure 4. Unfortunately, the data available are not directly associated with the location of the inclinometer and, consequently, only a general characterization of the shaft behavior can be performed. As expected, the movements caused by the shaft excavation increase with the depth of excavation. However, it should be noted that the registered displacements can be considered as very small, not surpassing 8 mm in any inclinometer. Prior to the construction of the ring beam (Figure 4a,b), the shaft deflected, almost like a cantilever, with its rotating point below the base of the excavation, suggesting that the panels might have deformed, at least to some extent, independently. After the construction of the ring beam (Figure 4c,d), it is possible to observe some bulging in the middle and lower portions of the diaphragm wall, while the displacements in the upper part remained approximately constant, which is typical of this type of structure [53].  
Superimposed on the figure are the horizontal displacements predicted by the numerical model for the reference analysis (SBE-Ref). The results obtained for all excavation depths are in reasonable agreement with the average movement observed for the shaft. For a depth of excavation of 5.0 m, the model captures the cantilever movement, while for larger depths, bulging for the lower portion of the shaft, particularly below the ring beam, is also predicted adequately. Near the surface, the model predicts smaller displacements than those recorded, although still inside the envelope of horizontal displacements recorded. Based on the results, it can be concluded that the numerical model is able to reasonably predict the behavior of the shaft excavation.
## 4 Implementation
### Evaluation Tools and Metrics  
Different evaluation metrics were used in this work to evaluate the performance of the proposed model including accuracy, FPR, TPR, precision, recall, and F-Score. Accuracy represents the ratio of correctly classified threats to the total number of classified threats, so it demonstrates how accurate an model in classifying threats [52]. The FPR represents the ratio of misclassified data as a different type of threat, and the TPR represents a model's ability to correctly classify threats. A low FPR and a high TPR demonstrate the ability of a model to correctly classify cyber threats [53]. Precision, recall, and F-Score were used to evaluate the overall performance of the proposed model; a high value of precision indicates a low FPR, and recall represents a model's ability to correctly classify threats. Equations (1)-(6) represent the evaluation metrics, where _FP_ is false positive, _TP_ is true positive, _TN_ is true negative, and _FN_ is false negative.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[\text{FPR}=\frac{FP}{FP+TN} \tag{2}\]  
\[\text{TPR}=\frac{TP}{TP+FN} \tag{3}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{4}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{5}\]  
\[\text{F}-\text{Score}=\frac{2(\text{Precision}\;\times\;\text{Recall})}{ \text{Precision}\;+\;\text{Recall}} \tag{6}\]
## 4 Health Prognostics
### HS Division
#### 4.2.2 Time-to-Start and Failure Threshold  
The analysis of the degradation stage can guide the selection of appropriate methods or models. When starting RUL prediction, it is necessary to set the time-to-start as the prediction starting point while according to a failure threshold to set the prediction ending point to ensure the implementation of the RUL prediction process. Therefore, the prediction start time and end thresholds are also an urgent problem to be solved in failure prognostics.  
(1) Time-to-start (TTS)  
The starting point of prediction is to determine the beginning of the prediction process. The data before this time is usually used as training samples to predict the health condition after this time. Therefore, selecting an appropriate prediction starting point can avoid the interference of health signals to the model or the loss of key information and improve the RUL prediction efficiency. However, due to the influence of noise and random occurrence of early faults, a more accurate method is needed to determine the appropriate starting point of prediction. [PERSON] et al. used the system degradation state model to detect the deviation between the baseline and corresponding current distribution and identified the fault starting point with the specified confidence and false alarm rate [204]. [PERSON] et al. used the first CNN model and the proposed 3/5 principle to identify the initial fault point [205]. [PERSON] et al. proposed an adaptive prediction starting point selection method based on 3\(\sigma\) interval [206].  
The failure threshold is the endpoint of RUL prediction. It is considered that the equipment cannot meet functional requirements when the degradation curve exceeds the fault threshold. The determination of fault threshold plays a key role in the accuracy of RUL prediction. It is usually defined as degradation reaching the predetermined limits set by experts. However, due to the impact of uncertainty, it is difficult to determine the failure threshold. [PERSON] et al. divided the health state into two stages and added a false alarm trigger mechanism [207]. [PERSON] et al. used the probability trigger mechanism to determine the alarm threshold [208]. [PERSON] et al. combined Chebyshev inequality and Mahalanobis distance to determine failure threshold [209]. [PERSON] et al. applied the PCA method based on the variance-sensitive adaptive threshold to overcome the problems of false alarms caused by using a fixed threshold [210]. Moreover, some studies had shown that the failure threshold was not clearly defined and may be probabilistic, and the possibility of using a probabilistic failure threshold was beneficial [152; 211].
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 6 Field Data Analysis
### Pedestrian Kinematic Test  
Figure 14 shows the data collection setup, trajectory and multipath environment for a pedestrian kinematic test. The same receiver was mounted on a cart moving at a velocity of 0.5 to 2 m/s (to simulate a pedestrian motion) through a suburban area. A reference trajectory was obtained using a NovAtel SPAN (NovAtel Inc., Calgary, AB, Canada) system consisting of a tactical grade Inertial Measurement Unit (IMU) and a GPS receiver. Similar to the static test, the phase velocity trend method [36] was used for cycle slip detection with 2 cycles as the detection threshold. Compared to the static scenario, the length of the moving average was empirically reduced to 60 s to account for the dynamic characteristics of the data that result in faster multipath variations. The \(N\) and \(M\) were chosen as 10 and 4 to satisfy the overall false alarm probability equal to 1.1 \(\times\) 10\({}^{-8}\). Only GPS L1 was investigated as the mean number of measurements on L2C, and L5 was lower than four limiting positioning outputs to a few epochs.  
Figure 13: Positioning RMS errors and 3D improvement percentages for GPS (**a**) L1, (**b**) L2C and (**c**) L5 frequencies—Static test scenario.  
Exclusion of affected measurements showed slightly higher performance compared to the iterative change of measurement weights (de-weighting approach). For exclusion, the consecutive exclusion method was used due to its relatively lower complexity. While more or less similar monitoring results (but with faster multipath variations as a function of time) were observed as those for the static scenario, numerical results are presented here for performance evaluation. Table 2 and Figure 15 show the corresponding results for different positioning approaches. Similar to the static test scenario, a C/N\({}_{0}\)-based LS solution was considered to evaluate the performance of the detection/exclusion technique. The Consecutive exclusion was used and the detected measurements were consecutively excluded based on their impact on measurement geometry. The exclusion continued until the tolerable PDOP threshold of 8 was exceeded. The corresponding mean and RMS values have been presented in the first two rows of Table 2. A graphical presentation of the RMS values has also been provided in Figure 15. It is observed that detection and exclusion of affected measurements improves positioning performance by 29% in 3D positioning RMS values.  
In the next step, the positioning performance was investigated for the CMC-based multipath correction and the combined approach, presented in Sections 3.1 and 5, respectively. Due to the limited number of simultaneous measurements on all three frequencies (
## 4 Results
The top plot in Figure 12 indicates that orbiters in low-altitude trajectories (100 km) exhibit the highest contact frequency, with 20 to 24 contacts per day, suggesting more frequent communication interruptions. These orbits also show the shortest aggregated contact durations, ranging from 20 to 27 h daily. Medium- and high-altitude orbits (500 to 1300 km) demonstrate better performance, with daily contact times stretching from 24 to 40 h and fewer contact interruptions, between 14 and 16 per day. The polar orbits are marked by significant spikes, reflecting lengthy contact durations due to their alignment. This prevents the satellite from being occluded by Mars, thereby ensuring prolonged communication windows (see snapshot in Figure 13b). Figure 14 shows the trade-off between daily contact frequency and duration for the 16 Mars orbiter scenarios. It effectively illustrates the inverse relationship between altitude and communication disruption: lower-altitude orbits experience increased frequency of contact but suffer from shorter durations due to the relative motion of Mars and Earth, causing more frequent occlusions. Figure 12: Time plots for daily contact frequency (**top**) and daily contact duration (**bottom**) for the 16 Mars orbits. Figure 13: Scenario snapshots with two simultaneous DSNs in sight in (**a**) and 90-degree inclination orbit with permanent sight in (**b**). Conversely, higher altitude orbits benefit from longer, more stable communication windows. This is attributed to their wider field of view above the Martian horizon, which remains unobstructed for extended periods even as Mars rotates. The plot emphasizes the superior duration of contacts for higher altitudes, implying that satellites closer to Mars, despite being in a favorable orbital plane, still succumb to the planet's horizon, interrupting the line of sight necessary for continuous communication.
# Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER
## 4 Experimental Setup  
### Datasets  
The experiment begins by exploring the NER datasets. The CoNLL 2003 dataset (English language) [39], was taken from Reuters news corpus between August 1996 and 1997. This dataset consists of four types of named entities (i.e., person, location, organization, and miscellaneous). The number of occurrences of each type of named entity is shown in Table 1. The dataset consisted of three parts: a training set, a development set, and a test set. To be specific, the training and development datasets were collected from the news at the end of August of 1996, while the test dataset was obtained from the news in December of 1996.  
Figure 5: The delayed combination of feature encodings.  
Figure 6: The comparison between delayed and early combinations  
OntoNotes 5.0 is made up of 300 K Arabic, 900 K Chinese, and 1745 K English text data instances and covers six types of documents such as newswire, websites, broadcasting news, broadcasting conversation, magazine and telephone conversation [40]. It consists of eleven types of named entity and seven types of values and we use the English data and follow the train-validate-test split by [40]. We excluded the telephone conversation section when evaluating our model because it has quite noisy annotations. The detailed statistics of this dataset are given in Table 2.  
We selected these two datasets for benchmarking our model because they have been most actively used for evaluating and comparing NER models until now even though they are a little old. Especially, CoNLL 2003 is a little small and so takes less time to train a model. So, it is suitable for testing and tuning model feasibility at the early stage.  
On the contrary, OntoNotes 5.0 is quite large (about five times the size of CoNLL 2003 in the number of sentences in the training set) and has manymore types of named entities. So, it is suitable for testing model extensibility at the latter stage.
## 2 Results
### Identification of 14 Tepene Syntase Genes  
Terpene synthases form a mid-size family that is highly diversified throughout the plant kingdom [36]. We identified 14 terpene synthases, which form a small TPS gene family (Figure 3A, Table S6). This family is even smaller than the family in _Arabidopsis thaliana_, which contains 32 TPS genes [36]. Phylogenetic analysis of the translated coding sequences placed _Sb_TPS1 to _Sb_TPS5 in the TPS-a clade (Figure 4), members of which have high similarity with gamma-cadinene synthase and germacrene D synthase (Table S6), showing that they may be responsible for sesquiterpene biosynthesis in _S. barbata_. Compared with the 22 genes in _A. thaliana_, _S. barbata_ had an obvious reduction in the TPS-a subfamily. _Sb_TPS4 form a cluster with _Af_TPS21, while the other four genes form a species-specific group, indicating they had different origins and different functions compared with the genes in _A. thaliana_.  
_Sb_TPS6 was resolved in the TPS-b clade, which usually contains monoterpene synthases [36]. _Sb_TPS6 did not cluster with any of the TPS-b subfamily genes in _A. thaliana_. _Sb_TPS7 clustered with S-linalool synthase (_Af_TPS14) in _Arabidopsis_, which is in the TPS-g subfamily. _Sb_TPS8 to _Sb_TPS11 clustered with _ent_-copalyl diphosphate synthase (AtTPS31) in _A. thaliana_, forming the TPS-c clade. Three TPS, _Sb_TPS12 to _Sb_TPS14, clustered with _ent_-kaurene synthase (_Af_TPS32), which were part of TPSe/f clade (Figure 4; [36]). Despite the small gene families of TPS-a and b, _S. barbata_ has a relatively large gene family in TPS-c and the TPS-e/f subfamily, which will generate structural backbones of the abundant diterpenoids in _S. barbata_.  
_Sb_TPS8 and _Sb_TPS12 had similar expression patterns and were exclusively expressed in the root and flower, which was placed in group II. _Sb_TPS11 had a different expression pattern and had the highest expression in aerial parts rather than in root and flower. _Sb_TPS14 had relatively high expression in all three organs. These two genes were clustered in group III. _Neo_-clerodane diterpenoids are always isolated from the aerial parts of _S. barbata_[9; 10; 37] and thus _Sb_TPS11 was predicted to be involved in _neo_-clerodane diterpenoid biosynthesis in _S. barbata_.
# PSO Optimized Active Disturbance Rejection Control for Aircraft Anti-Skid Braking System
## 2 System Description
### Aircraft Tire Dynamics  
The friction characteristic between the tire and the ground is the main factor affecting the process of aircraft landing and braking. In fact, the bonding force between the tire and the road surface is the most important force that makes the aircraft decelerate. Moreover, the tires transfer the vertical load \(F_{z}\) into longitudinal friction \(F_{x}\), as illustrated in Figure 2. In fact, \(F_{x}\) depends on a large number of features of the road, tire, and suspension. Most often, they can be described as follows:  
\[F_{x}=f(F_{zm},\alpha,\gamma,\lambda) \tag{5}\]  
The Pacejka model, represented in Equation (3), is perhaps the most well-known tire friction model [15]. This is because the model matches the experimental data well and is easy to implement. The expression of the tires has the following form:  
\[f(F_{z}m;\alpha;\gamma;\lambda)=cos(C_{xt}arctan(B_{xt}\lambda))F_{x0} \tag{6}\]  
where the expressions of the different terms appearing in Equation (6) depend on \(F_{z},\alpha,\gamma,\lambda\), and several constant parameters, which are determined by the structural characteristics of the tire and can be easily identified from experimental data [15]. In this work, the aircraft is assumed to be symmetric during the braking process, and in the rest of this work, the design of the control algorithm is performed under the assumptions of small side slip and camber angles (i.e., \(t\ =0\) and \(\gamma\ =0\)), which is to say that only longitudinal forces of the tire are of concern. Thus, Equation (4) can be simplified into the following:  
\[F_{x}=F_{z}\mu(\lambda) \tag{7}\]  
Based on the Pacejka model, the longitudinal coefficient between the tire and the ground has the following form [16; 17]:  
\[\mu(\lambda)=Dsin(Carctan(B\lambda)) \tag{8}\]  
The parameters of the three most commonly used runway models are depicted in Table 2, while \(\mu-\lambda\) curves of the friction coefficient and wheel slip ratio are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Road Condition** & **B** & **C** & **D** \\ \hline Dry & 14.0326 & 1.5344 & 0.8 \\ Wet & 8.2098 & 2.0192 & 0.4 \\ Ice & 7.201788 & 2.0875 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the three most commonly used runway models.  
Figure 2: Aircraft landing roll force.
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 5 Simulations and Parameter Effects
### Summary  
The impact of each of the five parameters (energy, next-node distance, sink distance, node density, and message priority) utilised in the proposed MFWF was comparatively assessed to determine their effect on end-to-end delay, rebroadcast nodes, saved rebroadcasts, and network lifetime. That is to support the construction of application-specific MFWFs in Section 6. The end-to-end delay was observed to be the lowest for the sink distance parameter (\(f3\)), followed by residual energy (\(f1\)) and message priority (\(f5\)) (see Figure 2). The sink distance parameter (\(f3\)) expectantly produced the best percentage of rebroadcast nodes and saved rebroadcasts (see Figures 3 and 4). Residual energy (\(f1\)) and message priority (\(f5\)) generated the next best rebroadcast nodes and saved rebroadcasts results. Network lifetime was affected by the next-node distance (\(f2\)) with the best longevity of the five parameters (see Figure 5). These findings should inform the construction of the MFWF for various application in the following section (see Section 6). Nonetheless, the parameters were examined in isolation, and therefore other considerations were also taken into account and discussed.