esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Further Evidence of the Functional Distinctions of _Otc
#### 5.2.1 Clause-Initial _Otc_  
Conjunctive adjuncts, as suggested by [PERSON] and [PERSON] [2], are characteristically thematic, which typically operate in the clause as part of the theme. In the case of _OTC_, 50 out of 77 instances of the expression are found to occur in the initial position in BAWE-E. The common initial position can be understood as the best way to emphasize the contrast of the clause complex introduced by the conjunctive. This is because _OTC_ in the initial position functions as the textual theme to signal to the reader the forthcoming contrast. However, as discussed in the previous sections, the contrast signaled by _OTC_ can fall into two semantic categories, i.e., replacive and adversative. Replacive and adversative _OTC_ in the initial position are exemplified by (4) and (5), respectively.  
(4) _As will be shown later in this essay, more recent critics of [PERSON] do not argue in favour of the association of London with prosperity. on the contrary, they underline the connection of the city with death or hell._ (BAWE-E)  
(5) _Off-site pollen data is obtained from bogs and lakes at distance from archaeological sites, particularly at upland, northern, and western locations. on the contrary, on-site pollen data is obtained within archaeological soil contexts, although data has the difficulty in interpretation compared to off-site pollen data_ ([PERSON] 1998:69-88). (BAWE-E)  
In (4), the clause-initial _OTC_ functions to signal a contrast between the clause complex it introduces and the preceding clause complex. The topical theme in the second clause complex, i.e., 'they', by being a pronoun, refers back to the topical theme of the preceding clause complex, i.e.,'more recent critics of [PERSON]'. It is thus obvious that the corresponding themes of the two clause complexes carry the content of the replacement, that is, what more recent critics of [PERSON] believe to be false about London is replaced by what they believe to be true about London. The focus of information is conveyed by the rheme of the second clause complex, i.e., 'underline the connection of the city with death or hell'. In this manner, the contrast is established between the negative and positive aspects of the same topical theme. Overall, the second clause complex introduced by _OTC_ intensifies a denial explicitly expressed by the negation 'not' in the preceding clause complex.  
In contrast, unlike (4), the second clause complex in (5) does not involve a denial of the proposition made in the first clause complex. Instead, while still having _OTC_ functioning as textual theme, the second clause complex focuses on a contrast effected by a different topical theme, i.e., 'on-site pollen data', which is not recoverable from any part of the preceding clause complex.
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 5 Model Fitting Results
### Example 2  
In Example 2, the PL parameters for the single-slope log-distance PL model were taken from a measurement campaign [10]. In [10], the MLE parameter estimates are \(\hat{\alpha}=2.2\), \(\hat{\beta}=51\), and \(\hat{\sigma}=7.6\), and we used these parameters as the true values. The noise threshold is \(PL^{*}=95\) dB. The OLS fitting without the censored data gives \(\hat{\alpha}=1.3\), \(\hat{\beta}=63\), and \(\hat{\sigma}=5.6\)[10]. The fitted PL models are illustrated in Figure 3; the training data were created with a uniform distribution of distances between 10 and 200 m. The parameter estimates are summarized in Table 1.  
The true PL distribution and the MLE fitted model predict a significant probability of censored PL between 21 and 470 m (\(\overline{PL}(d)\pm 1.96\sigma\)). The OLS fitted model overestimates this range to 430-2200 m link distance range. This overestimation is clearly larger than in Example 1 due to smaller path loss exponent \(\alpha\).
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 4 Result and Analysis
### Learning Curve  
Figure 8 shows the two kinds of model evaluation, the Receiver Operating Characteristic (ROC) and the Precision-Recall (PR) curve. The ROC curve shown in Figure 8a, evaluates the performance of the video anomaly detection model by illustrating the trade-off between the true positive rate (TPR) and the false positive rate (FPR) at different classification thresholds. The red curve represents the actual model's performance, while the blue dashed line serves as a baseline, indicating random guessing. A well-performing model should have its ROC curve significantly above this baseline, which is the case here. The steep initial rise of the curve suggests that the model can detect a substantial portion of anomalies with a low false positive rate, which is crucial for anomaly detection. However, as the curve flattens, increasing sensitivity results in a higher number of false positives, potentially leading to unnecessary alerts. The Precision-Recall (PR) curve shown in Figure 8b for video anomaly detection provides insight into the model's ability to distinguish anomalies from normal events. Initially, at very low recall values, the model achieves extremely high precision, indicating that the first few detected anomalies are highly accurate with minimal false positives. As recall increases, meaning the model detects more anomalies, precision remains relatively stable before gradually declining. This suggests that while the model can correctly identify many anomalies with high precision, its performance degrades as it attempts to detect a larger number of anomalies. The sharp decline in precision at higher recall values indicates that an increasing number of normal events are being misclassified as anomalies, leading to a rise in false positives. This trade-off is common in anomaly detection systems, where optimizing high recall often results in reduced precision.  
Figure 8: The model evaluation: (**a**) ROC curve for model evaluation; (**b**) PR curve for model evaluation.  
To improve the model's effectiveness, techniques such as better feature extraction, threshold tuning, or post-processing methods like spatial smoothing can help reduce false positives while maintaining a balance between precision and recall. The optimal operating point for the model depends on the application--whether prioritizing precision to minimize false alarms or maximizing recall to ensure all potential anomalies are detected.
## 3 FEM Model
### Geometry, Element Assumption and Analysis  
The numerical model is shown in Figure 4a. The considered reference system is superimposed in the same figure. Concrete elements are modeled with the eight-node structural solid element. Through a mesh generator, a desired element size of 50 mm for the column concrete core and the concrete cover is considered, while a size of 100 mm is set for the foundation concrete. The longitudinal reinforcement is modeled as truss bond-slip reinforcement, while the columns transverse reinforcement and the foundation reinforcement are modeled as embedded reinforcement. The \"element by element\" discretization method is used for reinforcement. In Figure 4b the casting position of the corroded reinforcements, together with the load direction \(x\), is shown.  
Figure 4: (**a**) Numerical model, (**b**) Column base cross-section and corroded bars casting position, (**c**) Displacement history adopted in the non-linear analyses.  
Figure 3: Experimental test results; (**a**) un-corroded A25-corroded B25 specimens; (**b**) un-corroded A30-corroded B30 specimens.  
Regarding the boundary conditions, translational restraints in the orthogonal direction to the base and side faces of the foundation are applied, to simulate the foundation clamping provided during the experimental test. A translational restraint at the column top and along the load direction is modeled to perform a displacement control non-linear cyclic analysis. Therefore, a tying constraint for the nodes of the cross-section at the load height is applied to impose the equality of the \(x\)-component of the displacement.  
The non-linear structural analyses are performed by applying in a first phase the axial load of 300 kN at the top of the column, then the displacement histories shown in Figure 4c is applied in accordance with experimental tests (up to 3.5% Drift for un-corroded specimens and up to 1.5% drift for corroded specimens). The structural non-linear analyses are performed using a regular Newton-Raphson iterative method for the solution of the non-linear equation set (imposing 20 maximum number of iterations for step) with a convergence norm based on displacement and force (convergence tolerance is assumed equal to 0.01).
## 2 Existing Leak Detection and Localization Methods  
Existing pipeline leak detection and localization methods can be divided into three categories based on signal processing, model estimation and knowledge.
## 4 The Ordering of the Stages
### The Round-Off Output Noise Variance
Using Equations (7) and (25) in Equation (24) yields:  
* In case of absolute bound scaling the equivalent of Equation (25) is given by: \[\prod\limits_{m=k+1}^{p-1}S_{m}=\prod\limits_{m=0}^{p-1}S_{m}=\frac{\sum\limits_{ n=0}^{p_{k}}\left|f_{k}\left(n\right)\right|}{\sum\limits_{n=0}^{p}\left|h \left(n\right)\right|}.\] (29) Here \(h(n)\) are the impulse response (having a length \(p\)) samples of the filter given by the transfer function \(H\left(z\right).\) Applying Equation (29) on Equation (22) and using Equation (7) yields: \[\sigma_{e}^{2}=\frac{1}{2\pi}\int\limits_{-\pi}^{+\pi}P_{\infty}\left(\omega_ {n}\right)d\omega_{n}-\left|m_{e}\right|^{2} \tag{18}\]0 (30) and by applying Equation (11): \[\sigma_{e}^{2}=\frac{1}{2\pi}\int\limits_{-\pi}^{+\pi}P_{\infty}\left(\omega_ {n}\right)d\omega_{n}-\left|m_{e}\right|^{2} \tag{18}\]1 (31) As for infinity bound scaling, the optimal ordering to obtain a minimal output round-off noise variance is determined by the ratio: \[\sigma_{e}^{2}=\frac{1}{2\pi}\int\limits_{-\pi}^{+\pi}P_{\infty}\left(\omega_ {n}\right)d\omega_{n}-\left|m_{e}\right|^{2} \tag{18}\]2 (32) for every \(k\in\left\{0,1,...,P-2\right\}.\) In case this ratio is significantly larger than 1 for every \(k\) value, it is best to order the stages in increasing coefficient magnitude, _i.e._, the stage with the largest coefficient(s) at the end. In case the ratio is not significantly larger than 1, the optimal ordering should be determined exhaustively.
###### Abstract  
Variational modal decomposition (VMD) is frequently employed for both signal decomposition and extracting features; however, the decomposition outcome is influenced by the quantity of intrinsic modal functions (IMFs) and the specific parameter values of penalty factors. To tackle this issue, we propose an algorithm based on the Halton sequence and the Laplace crossover operator for the sparrow search algorithm-VMD (HLSSA-VMD) to fine-tune the parameters of VMD. First, the population initialization by the Halton sequence yields higher-quality initial solutions, which effectively addresses the issue of the algorithm's sluggish convergence due to overlapping and the lack of diversity of the initial solutions. Second, the introduction of the Laplace crossover operator (LX) to perturb the position of the best individual in each iteration helps to prevent the algorithm from becoming ensnared in a local optimum and improves the convergence speed of the algorithm. Finally, from the simulation of 17 benchmark test functions, we found that the HLSSA exhibited superior convergence accuracy and accelerated convergence pace, as well as better robustness than the particle swarm optimization (PSO) algorithm, the whale optimization algorithm (WOA), the multiverse optimization (MVO) algorithm, and the traditional sparrow search algorithm (SSA). In addition, we verified the effectiveness of the HLSSA-VMD algorithm on two simulated signals and compared it with PSO-VMD, WOA-VMD, MVO-VMD, and SSA-VMD. The experimental findings indicate that the HLSSA-VMD obtains better parameters, confirming the superiority of the algorithm.  
SSA; Halton sequence; Laplace crossover operator; VMD +
Footnote †: journal:  
0000-0001-9991-4052-740][PERSON], [PERSON], [PERSON], [PERSON], X. An Improved Sparrow Search Algorithm for the Optimization of Variational Modal Decomposition Parameters. _Appl. Sci._**2024**, _14_, 2174. [[https://doi.org/10.3390/app14052174](https://doi.org/10.3390/app14052174)]([https://doi.org/10.3390/app14052174](https://doi.org/10.3390/app14052174))
## 3 Results and Discussion
The technological scheme primarily shows the sieving of the raw materials, the crushing of oversized particles (larger than 2 mm), and their distribution into containers. Before entering the mixer, the raw materials are weighed and then fed into another larger mixer,  
Figure 5: Technological scheme to produce granular organic biofertilisers: 1—mill; 2—sieves; 3—elevator; 4—distribution device; 5—container; 6—dispenser-scale; 7—transport; 8—container with mixer; 9—dispenser–distributor; 10—drum granulator; 11—drum dryer; 12—cyclone; 13—cooling drum. where the raw materials are mixed with the binder. Wetted raw materials are supplied to the drum granulator through a distribution dispenser, and then they move to the dryer. If necessary, steam can be supplied to the granulator, and warm air is supplied to the dryer. After drying, the product must be sieved, because, according to the results of the experiment, a certain number of granules smaller than 2 mm and larger than 4 mm is always formed; such granules are recycled back into the technological line. The appropriate-sized commercial fraction (2-4 mm) is cooled and stored. Because dusty air is produced during the crushing, sieving, and drying processes, it will be cleaned using a cyclone. Analysis of the results of this study makes it obvious that the quality of the developed product should be improved, and this should primarily be performed by maximising the amount of commercial fraction and by minimizing the moisture content of this fraction to ensure the desired granule size and strength. Also, is very important that the raw material mixture should be mixed well before granulation to achieve the desired granule shape and size; this is especially the case when using BE, which is responsible for an irregular shape of granules. The correct shape, size, and density of the granules ensures that they are evenly distributed over the soil surface. Better drying is likely to ensure the long-term stability of the product, which is an important factor for fertilisers. The influence of the recycled fraction (the formation of which is inevitable and provided for in the technological scheme) on the granulation process should also be analysed and taken into account. Solving all these problems would further improve the quality of the created organic bio-fertilisers and their competitiveness; granular organic fertilisers are often designed for slow-release applications [70], providing a steady supply of nutrients over an extended period of time. This slow-release feature helps prevent nutrient leaching and reduces the risk of nutrient runoff. Like other organic fertilisers, these would have a dual effect--improving both plant growth and soil properties by increasing the amount of organic matter and reducing acidity.
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 3 Methodology
### Data Preprocess: Label Transform  
Owing to the atmospheric distortion and the long exposure time, the received photons from the target object are smeared over a few pixels. On the other hand, the brightness of space targets is unstable due to the movement. Hence, we take the advantage of intensity correlations of neighboring pixels and transform the label information of point coordinates. Then we generate the according bounding box labels as depicted in Figure 5. The conversion process is regarding each target point as the central position of the according bounding box. The distances between the center point and the top, bottom, left and right edges are all set to 2 to generate the ground truth of bounding box. Then, coordinates of the upper left corner and coordinate values of the lower right corner are calculated according to the offset value. Thus, we obtain new annotation files ready for training the single-frame object detector.
# Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
## 3 Microgrid Multi-Objective Optimization Model
### Uncertainty of Wind and Solar Power  
Wind power and solar power are affected by various factors such as weather conditions and equipment maintenance, making wind speed and solar irradiance inherently random and uncertain. This results in the highly random nature of the output power from both WT and PV systems, thereby compromising the reliability of MG optimal scheduling.
# A New Coding Paradigm for the Primitive Relay Channel +
## 4 Numerical Results  
Let us consider the special case of the erasure relay channel. In Figure 4, we compare the achievable rate (36) of our scheme with the existing upper and lower bounds, i.e., the cut-set upper bound (3) (which coincides with the improved bound (5)), the decode-and-forward lower bound (16) and the compress-and-forward lower bound (27). We consider two pairs of choices for \(\varepsilon_{\textsc{sd}}\) and \(\varepsilon_{\textsc{ss}}\): \((\varepsilon_{\textsc{sd}},\varepsilon_{\textsc{ss}})=(0.85,0.5)\) for the plot on the left (see Figure 4), and \((\varepsilon_{\textsc{sd}},\varepsilon_{\textsc{ss}})=(0.4,0.2)\) for the plot on the right. We plot the various bounds as functions of \(\mathcal{C}_{\textsc{sd}}\). Our scheme outperforms both decode-and-forward and compress-and-forward for an interval of values of \(C_{\textsc{sd}}\) in both settings. As \(C_{\textsc{sd}}\) increases, the improvement guaranteed by our strategy decreases, until eventually the performance of our scheme is matched by compress-and-forward.  
For a general primitive relay channel, it is not immediate how to compare the partial decode-compress-and-forward rate given in (28) with our new rate given in (33)--the partial decode-compress-and-forward scheme involves three auxiliary random variables \((U,V,\hat{Y}_{\textsc{ss}})\) and the complex joint distribution expressed in (29) to maximize over. Thus, one immediate advantage of our new rate is that it is easier to compute. In fact, the proposed lower bound involves only one auxiliary random variable (\(\hat{Y}_{\textsc{ss}}\)). Even if we simplify the partial decode-compress-and-forward rate as in (31), the formula remains harder to evaluate (two auxiliary random variables: \(U,\hat{Y}_{\textsc{ss}}\)). Although a full optimization over all parameters is very challenging, we have specialized (31) to the setting of the erasure relay channel and, for fairness of comparison with the other schemes, we have considered the case in which \(\hat{Y}_{\textsc{ss}}\) is obtained from \(Y_{\textsc{ss}}\) via an \(\text{EEC}(\hat{\varepsilon}_{\textsc{bs}})\). Then, by performing the maximization numerically over \(\hat{\varepsilon}_{\textsc{bs}}\) and over all the auxiliary random variables \(U\) s.t. \(|U|\leq 2\), the achievable rate of partial decode-compress-and-forward does not improve upon decode-and-forward and compress-and-forward. Therefore, in this setting, partial decode-compress-and-forward is strictly worse than our proposed scheme.  
In [25], for \(\varepsilon_{\textsc{sd}}=0.85\), \(\varepsilon_{\textsc{ss}}=0.5\) and \(C_{\textsc{sd}}=0.99125\), the proposed soft decode-and-forward strategy based on LDPC codes achieves a rate of 0.507, while both decode-and-forward and compress-and-forward achieve a rate of 0.5. Our new coding strategy is reliable for rates up to 0.545, hence it outperforms all existing lower bounds. As a reference, note that in this setting the cut-set bound is 0.575.
## 4 Time-Series Analysis
### Extraction of Natural Frequency  
The natural frequencies of different segments were mainly calculated using the FFT method [19]. The calculated results of all segments are shown in Table 2. The results of the time-series analysis under a 10-mm amplitude and the 0.5-9-Hz frequency band are shown in Figure 9. The growth of small-peak amplitudes in Figure 9 reflects the reduction of the signal-to-noise ratio in the observations. Table 2 shows that the principal frequencies of the data segments within 0.5-8 Hz can be acquired accurately. The maximum deviation was less than 2.5%. The principal frequency could still be acquired at 9 Hz, but the signal had already become unstable. When the frequency was 10 Hz, the main frequency value was unobtainable, and the measurement data were lost. The same results were obtained at two amplitudes (3 and 5 mm). The maximum frequency that could be obtained was 6 Hz at a 20-mm amplitude and 4 Hz at 30-mm amplitude. Therefore, the maximum frequency of the wMPS under the amplitude range of 3-10 mm is 9 Hz, which can meet the requirements for measuring the natural frequencies of rigid structures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{5}{c}{**Amplitude (mm)**} \\ \cline{2-6}  & **3** & **5** & **10** & **20** & **30** \\ \hline
0.5 & 0.506 & 0.506 & 0.504 & 0.506 & 0.511 \\
1 & 1.010 & 1.007 & 1.025 & 1.019 & 1.014 \\
2 & 2.039 & 2.038 & 2.040 & 2.051 & 2.048 \\
3 & 3.054 & 3.053 & 3.076 & 3.003 & 3.081 \\
4 & 4.081 & 4.087 & 4.049 & 4.069 & 4.155 \\
5 & 5.086 & 5.059 & 5.076 & 5.123 & - \\
6 & 6.066 & 6.036 & 6.040 & 6.102 & - \\
7 & 7.095 & 7.036 & 7.033 & - & - \\
8 & 8.123 & 8.086 & 8.035 & - & - \\
9 & 9.110 & 9.100 & 9.132 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measured frequencies of oscillations of different segments.  
Figure 8: Illustrative diagrams of absolute amplitude errors of different segments.  
Figure 9: Principal frequency diagram of spectral analysis of different segments. (**a**) Frequency diagram of 0.5 Hz; (**b**) frequency diagram of 1 Hz; (**c**) frequency diagram of 2 Hz; (**d**) frequency diagram of 3 Hz. As showed in Figures (**a–d**), the frequency curve is very smooth, there are no obvious small-peak amplitudes; (**e**) frequency diagram of 4 Hz; (**f**) frequency diagram of 5 Hz; (**g**) frequency diagram of 6 Hz; (**h**) frequency diagram of 7 Hz; (**i**) frequency diagram of 8 Hz. In (**e–i**), the growth of small-peak amplitudes more and more with the increase of sampling rate. That reflects the reduction of the signal-to-noise ratio in the observations; (**j**) frequency diagram of 9 Hz. Though the principal frequency could be acquired, but the signal had become unstable.
## 4 The System Model
### Security and Performance Model
The parameter \(\lambda\) indicates the arrival rate, and \(\mu\) presents the service rate of the gateway node, including the data transmission time. When random delays are interposed in the gateway node, the attacker needs more samples to average and successfully guess the cluster key. Therefore, it takes more time for him or her to conduct the timing attack. As a result, the attacking rate \(\lambda_{3}\) decreases as this mitigation method is taken, which is represented by the dashed arc in the figure.