esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characterization of Bio-Coagulant  
Fourier-transform infrared spectroscopy was used to identify the functional groups in the powdered form of the PS bio-coagulant. Figure 2 shows the obtained results. Several peaks are present, reflecting the presence of various functional groups.  
At 2856.57 and 2926.01 cm\({}^{-1}\), peaks indicate the presence of the C-H group (a single bond between carbon and hydrogen), ester groups (the C=O function) attributed to the band at 1745.57 cm\({}^{-1}\), and amide groups associated with proteins assigned to the peak centered at 1643.35 cm\({}^{-1}\)[28; 29]. The presence of the C=C double bonds and polysaccharides (COOH groups) was inferred from the bands at 1550.76 cm\({}^{-1}\) and 1043.48 cm\({}^{-1}\), respectively [30].  
The presence of certain functional groups, such as NH- at 1643.35 cm\({}^{-1}\), C=O at 1745.57 cm\({}^{-1}\), and COOH at 1043.48 cm\({}^{-1}\), confirms the presence of various coagulating agents, including polysaccharides, total phenolics, and protein in _Pumpkin seeds_. These  
\begin{table}
\begin{tabular}{c c c c} \hline
**Standard Run** & **pH** & **PSE Dosage (mL/L)** & **FeCl\({}_{3}\) Dosage (mL/L)** \\  & **(X\({}_{\textbf{3}}\))** & **(X\({}_{\textbf{2}}\))** & **(X\({}_{\textbf{3}}\))** \\ \hline
4 & 10 & 20 & 5.5 \\
1 & 4 & 2 & 5.5 \\
6 & 10 & 11 & 1 \\
14 & 7 & 11 & 5.5 \\
5 & 4 & 11 & 1 \\
15 & 7 & 11 & 5.5 \\
10 & 7 & 20 & 1 \\
12 & 7 & 20 & 10 \\
2 & 10 & 2 & 5.5 \\
13 & 7 & 11 & 5.5 \\
8 & 10 & 11 & 10 \\
11 & 7 & 2 & 10 \\
7 & 4 & 11 & 10 \\
9 & 7 & 2 & 1 \\
3 & 4 & 20 & 5.5 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental plan with real values.  
Figure 2: FTIR results for _Pumpkin seed_ waste powder form.  
functional groups contribute to the efficiency of the coagulation process by providing adsorption sites for suspended and colloidal matter, thereby facilitating the removal of turbidity, COD, phosphate, and UV 254 absorbance [28].  
Figure 3 shows the surface morphology of the powdered coagulant, which confirms its heterogeneous nature. This structure facilitates the process of coagulation-flocculation due to the presence of the functional groups in PS, such as amine, amide, protein, and carboxylic acids [31]. Conversely, the presence of fibrous networks may assist in the adsorption and bridging of various pollutants present in the wastewater such as COD, turbidity, aromatic compounds, and phosphate [32].
# Spectrum Based Power Management for Congested IoT Networks
## 5 Test Results and Analysis
### Communication States  
The two major states considered in our work are: (1) Connected state where the IoT device access the channel to transmit data packet to its coordinator we called it TX state. Includes synchronization, transmission/reception(TX/RX), listening and release. Synchronization (SYNC) is performed by the IoT device to re-synchronize with the network whenever it exits from the Idle state. TX/RX is a period where IoT device receive and transmit packet data. For a short period of time the IoT device listen after every TX/RX and if not received any notification it will enter to idle state this whole process is called listening and release.
###### Abstract  
In cold climate regions, ships navigate through diverse ice conditions, making the varied interaction scenarios between hulls and ice critically important. It is crucial to consider the safety and integrity of the hull during an ice-hull interaction, especially in the presence of lightweight structures. Proper design and material selection can help improve the structure's ability to withstand ice forces. Within the scope, understanding the behavior of ice and its interaction with the structure can inform the development of appropriate measures to minimize possible damage or failure. The current study focuses on the interactions occurring during the impact loading phases, which are characteristic of thin first-year ice. A sandwich structure made with carbon fiber-reinforced epoxy prepreg and PVC core was investigated. Low-velocity ice impact was modelled using the Ansys Workbench 2023 R2 and LS-DYNA R11 explicit solver. As the material model, the *MAT055 was chosen based on the literature, while ice was represented with its equation of state. The Tsai Wu criterion was adopted to identify tensile and compressive failure in the matrix and fibers. This simulation allowed us to evaluate how the composite material responds to ice impacts, considering factors such as the speed of the impact, the shape and thickness of the ice, and the properties of the composite material itself.  
ice impact; reinforced composite hulls; ice-structure interaction; SPH-FEM coupling; LS-DYNA explicit solver; *MAT055
# Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification
## 3 Proposed Method
### Total Loss Function Design  
In this paper, we combine the proposed MMFSD loss function with two commonly used loss functions, i.e., the hard-mining triplet (HMT) loss function [38] and the cross-entropy (CE) loss function [39], to supervise the training process. The total loss function is formulated as follows:  
\[\mathcal{L}_{Total}=\lambda\mathcal{L}_{MMFSD}+\mathcal{L}_{HMT}+\mathcal{L}_{CE}, \tag{9}\]  
Figure 2: The overall framework of visible-infrared person re-identification.  
where \(\lambda>0\) is a hyper-parameter used to control the contribution of MMFSD loss function; \(\mathcal{L}_{HMT}\) and \(\mathcal{L}_{CE}\) denotes HMT and CE loss functions, which are formulated as follows:  
\[\mathcal{L}_{HMT}=\frac{1}{M}\sum\limits_{m=1}^{M}log[1+exp(\max\limits_{f_{i }\in\mathcal{F}_{in}^{+}}\|f_{m}-f_{i}\|_{2}-\min\limits_{f_{j}\in\mathcal{F}_ {in}^{-}}\|f_{m}-f_{j}\|_{2})], \tag{10}\]  
\[\mathcal{L}_{CE}=-\frac{1}{M}\sum\limits_{m=1}^{M}\sum\limits_{k=1}^{K} \epsilon_{m,k}log(p_{m,k}), \tag{11}\]  
where \(M=2\times N\times K\) represents the total number of samples in a mini-batch; \(K\) denotes the number of classes; \(N\) denotes the number of visible (or infrared) samples of each class; \(f_{m}\) represents the feature of \(m\)-th sample; \(\mathcal{F}_{in}^{+}\) and \(\mathcal{F}_{in}^{-}\) denotes the positive set and the negative set of the \(m\)-th sample \(f_{m}\); \(p_{m,k}\) represents the posterior probability of the \(m\)-th sample belongs to the \(k\)-th class (i.e., \(Class_{k}\)), which is calculated with the Softmax function; \(\epsilon_{m,k}\) is the indicator function of smoothed class labels, which is formulated as follows.  
\[\epsilon_{m,k}=\left\{\begin{array}{ll}1-\frac{(K-1)\zeta}{K},&x_{m}\in Class _{k},\\ \frac{\zeta}{K},&x_{m}\
otin Class_{k},\end{array}\right. \tag{12}\]  
where \(\zeta\) is the regularized parameter for label smoothing, and it is set to 0.1 as a common setting. Furthermore, as shown in Figure 2, the MMFSD loss function and the HMT loss function are assigned to the GeM layer, while the CE loss function is arranged on the BN layer, which is so-called batch normalization neck (BNNeck) [29].
# GPR Data Interpretation Approaches in Archaeological Prospection
## 4 Multi-Disciplinary Approach
### GIS-Based Integration  
The multi-disciplinary approach is gaining increasing popularity in archaeological prospection as it helps the understanding of the complex buried past. Several studies exist in the recent literature showing improvements in GPR data interpretability by identifying some targets that are difficult to detect solely when following the conventional approach described in the introduction of this article. An example is the identification of ancient roads at the archeological site of Naxos in Sicily that were imprinted as linear fragments in the resulted GPR C-scans [18]. Their interpretation was only possible when the corresponding C-scans were superimposed in the ancient city's reconstructed plan and aerial photo. Another similar example is related to the identification of anomalies as part of a Roman villa when superimposed to an older aerial photo [19]. Additionally, several anomalies observed with data collected from a GPR survey at the necroolis in Lecce were easier to interpret when the C-scans were georeferenced and superimposed in the ancient city map [8]. GPR interpretation of a structural complex was also enhanced by superimposing the data collected at the Ancient Mantineia to an old excavation map [20]. In another example, GPR and Terrestrial Light Detection and Ranging (T-LiDAR) techniques were employed, combining in this way information \"visible\" from the surface and the \"invisible\" one, in order to visualize the buried Roman structures detected from GPR [21].  
In the studies mentioned above, the GPR data were combined with spatial information on the surface as well as archaeological information obtained from surface investigations, ancient sources, and excavations. Other studies employed manifold geophysics, a prospection approach that uses multiple geophysical methods to survey the area under investigation [22]. The effectiveness of using several geophysical methods is shown in Reference [23], where magnetics, EM, and GPR were employed to survey a Neolithic settlement. GPR could map better houses but it could not spot ditches or burned areas revealed by magnetics, while EM gave information that helped understand settlement limits. This information was extracted by superimposing and comparing georeferenced images of each method that were imported into GIS. More examples of this approach showing the contribution and limitations of each sensor involved in similar Neolithic settlements are presented and discussed in references [24; 25]. Other studies included geochemical methods to understand better and characterize the archaeological remains [26; 27]. Further, the integration of multiple sensors and hence their information can lead to creating a holistic 3D model, being more immersive in reconstructing the past [28; 29].
# Image Noise Reduction by Means of Bootstrapping-Based Fuzzy Numbers
## 4 Bootstrap Mean in Fuzzy Metric Space  
[PERSON] introduced the bootstrap resampling method in [23], which is used to estimate the statistical distribution based on independent observations. The bootstrap is a method that, regardless of many hypotheses, brings the sample conditions closer to the community conditions by creating many samples and obtains a more reliable estimate by considering all the cases of sample formation. The bootstrap will take a sample by substituting the original sample so that each sample taken by this method is independent, but has an equal distribution. In other words, the samples taken by the bootstrap method have an equal population distribution; however, each sample shall be independent of other samples.  
[PERSON] in [12] presented the strong law for the bootstrap, then [PERSON] et al. [13] presented the law of large numbers for bootstrapped U-statistics. In addition, [PERSON] [24] introduced the weak law of large numbers (WLLN) and the strong law of large numbers (SLLN) for bootstrap sample means under minimal moment conditions. In this section, we present an algorithm to remove or reduce noise in color images using the bootstrap in the fuzzy metric space.  
Let \(\{X_{k}|k\geq 1\}\) be an infinite sequence of independent and identically distributed fuzzy random variables defined on a probability space \((\Omega,\mathcal{A},P)\) and \(X_{1}\) be integrable. For each \(n=1,2,\dots\), let \(Y_{n,1},Y_{n,2},\dots,Y_{n,m(n)}\) be the ordinary Efron bootstrap sample of size \(m(n)\) where \(\{m(n)\}\) is a sequence of positive integers. The variables \(Y_{n,1},Y_{n,2},\dots,Y_{n,m(n)}\) result from sampling \(m\) times the sequence \(\{X_{1},X_{2},\dots,X_{n}\}\) with replacement such that, at each stage, any one element has probability \(\frac{1}{n}\) to be picked [24].  
Suppose that \(\bar{X}_{n}^{*}\) is the bootstrap sample mean where  
\[\bar{X}_{n}^{*}=\frac{1}{m(n)}\sum_{i=1}^{m(n)}Y_{n,i}.\]  
[PERSON] et al. [25] provided limit theorems for fuzzy random variables. In fact, they showed  
\[d_{1}(\frac{1}{n}\sum_{i=1}^{n}X_{i},E(co\,X_{1}))\to 0,\,\,\text{a.e.}\]where \(E(\infty\,X_{1})\) is the expectation of a convex fuzzy random variable. Now, if we apply this theorem for the bootstrap mean, then we have  
\[d_{1}(\frac{1}{m(n)}\sum_{i=1}^{m(n)}Y_{nj,i},E(\infty\,X_{1}))\to 0,\text{ a.e.}\]  
[PERSON] et al. [14] showed with an example the use of the SLLN for the bootstrap mean in the fuzzy metric space. They believed that, sometimes, the expert's opinion may be important in determining the magnitude or smallness of the distance. In this case,  
\[M_{d_{1}}^{{}^{\prime}}(\frac{1}{m(n)}\sum_{i=1}^{m(n)}Y_{nj,i},E(\infty\,X_{1 }),t)\to 1,\text{ a.e.}\]  
Due to this feature, in the next section, we provide an algorithm for reducing image noise using the bootstrap mean for pixels with a fuzzy value.
# Approaching Electroencephalographic Pathological Spikes in Terms of Solitons
## 4 The Remarkable Physical Features of Solitons
### Different Shapes for Different Solitary Waves  
One of the most distinguished features of solitons is the shape, so different from the usual sine waves (Figure 1). Solitary waves include W-shaped, bright, dark, kink-dark, singular, kink antikink solitons [34; 40], cnoidal waves (Figure 1A), abnormally large waves (also termed \"freak\" or \"rogue\" waves), \"thick\" or \"top-table\" solitons, algebraic solitons, solitons of different polarities [46], multi-solitons with doubly-localized peaks [1], etc. Unlike \"bright\" solitons, which locally amplify the surrounding medium, \"dark\" solitons stand for a local decrease in the wave amplitude able to cause a depression [44]. Dark solitons on the surface of water might influence extreme events like tsunamis, such that an almost flat ocean surface may evolve into a singular, destructive rogue wave with high amplitude, as energy is conveyed in the central zone from the surrounding areas [44] (Figure 1B). Contrary to the usual solitons that keep their profile unchanged during propagation, the Peregrine soliton displays a double spatio-temporal localization characterized by progressive, standardized and cyclic changes in shape [47] (Figure 1D). Apart from the ones described above, other parameters may influence the large variety of solitons' shapes. These parameters include hyperbolic function solutions, elliptic parameters for cnoidal waves, variable-coefficient functions, modulation instability gain [34], the Sine-Gordon expansion method [40], etc. Furthermore, periodic traveling-wave solutions may give rise to \"trains\" of many solitons (Figure 1F). This occurs in the solar wind; whereas weak shock waves produce only single soliton pulses, strong shocks produce trains of solitons [36] (Figure 1G).  
Therefore, when looking for the possible occurrence of solitary waves in EEG traces, it is mandatory to carefully evaluate whether the shape of the electric spikes matches the features of solitons.
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 3 Methodology
### Overview of the Improved Algorithm  
As with the spatial contextual algorithm, the forest fire detection algorithm proposed in this study includes four major steps: data preprocessing, cloud masking, potential fire pixel judgment, and relative fire pixel judgment based on STM. A STM method workflow is illustrated in Figure 6.  
Figure 6: Workflow of the spatio-temporal model (STM) based forest fire detection method.
# Defect Detection in Striped Images Using a One-Dimensional Median Filter
###### Abstract  
Defect detection is a key element of quality assurance in many modern manufacturing processes. Defect detection methods, however, often involve a great deal of time and manual work. Image processing has become widely used as a means of reducing the required detection time and effort in manufacturing. To this end, this study proposes an image-processing algorithm for detecting defects in images with striped backgrounds--defect types include scratches and stains. In order to detect defects, the proposed method first pre-processes images and rotates them to align the stripes horizontally. Then, the images are divided into two parts: blocks and intervals. For the blocks, a one-dimensional median filter is used to generate defect-free images, and the difference between the original images and the defect-free images is calculated to find defects. For the intervals, defects are identified using image binarization. Finally, the method superposes the results found in the blocks and intervals to obtain final images with all defects marked. This study evaluated the performance of the proposed algorithm using 65 synthesized images and 20 actual images. The method achieved an accuracy of 97.2% based on the correctness of the defect locations. The defects that could not be identified were those whose greyscales were very close to those of the background.  
defect detection; stripe; image processing; median filter +
Footnote †: journal: Medical Engineering  
18 November 201925 January 2020; Published: 4 February 2020
## 6 Simulation Study
### The Verification of Tradeoff  
In this experiment, we will show the trading off between utilization rate of PMs and delay for task offloading by using our proposed TLRL algorithm. We evaluate these two indicators by simulating offloading tasks that are selected from Table 1 randomly. We record these two indicators after each decision of TLRL for offloading tasks. We adjust the weight factor \(\beta\) from 0.1 to 0.9 to do experiments separately and observe the change of the two indicators.  
From these results, we can conclude that our proposed TLRL algorithm can trade off the utilization rate of PMs and delay by adjusting the weight \(\beta\) in the reward effectively. According to Formula (10), the weights for the utilization rate of PMs and delay are \(\beta\) and \(1-\beta\) respectively. The experimental results in Figure 10 show the process of online learning optimal policy when \(\beta\) takes different values and track the changes of the utilization rate of PMs and delay during the learning process. We assume that the number of needed offloaded tasks is more than 800 in a real MCC environment and our proposed algorithm could converge. Therefore, our proposed algorithm will lean an optimal policy, which make the utilization rate of PM and delay keep stable in an interval. In Figure 10a-e, we can see that the utilization rate of PMs converges to a larger value when the weight factor \(\beta\) is set to a larger value. Correspondingly, the weight of delay for current offloaded task \(1-\beta\) becomes smaller, which lead to a larger delay. Therefore, if the utilization rate of PM is preferred, then a larger \(\beta\) is chosen and vice versa. Moreover, the TLRL algorithm can learn the optimal policy for task offloading with the increase of offloading tasks. This is because the TLRL comprises with DRL in layer one and \(Q\) learning in layer two, both of the two methods can converge according to Bellman Equation. We define the global \(Q\)-function for TLRL algorithm given by Formula (18), which indicates the convergence of TLRL algorithm. We can see that \(Q^{g}\) is composed of the \(Q\) values from above two algorithms. Therefore, TLRL algorithm can also converge which indicates that it can learn the optimal policy. Therefore, the two indicators will become stable in a certain interval that is related to the value of \(\beta\).  
Figure 10: _Cont._
# Building Wireless Control Applications with XBee and LabVIEW
## 5 Case Study
### Discussion
In the presented tests, a sampling period (100 ms) which was much bigger than the expected communication delay (46 ms) could, in part, mask the communication delay. In other words, if the delay was steady and considerably lower than the sampling period this became the most relevant parameter for the control scheme. In such a case, there should not have been any problem with the performance of the wirelesscontrol scheme, from the communications perspective. On the contrary, if the delay were much higher than the sampling period, the stability of the system could be affected. Some authors [33; 34] have analyzed this issue and have proposed using different techniques such as the Smith predictor or Kalman filters, in order to compensate the communication delay in the control scheme.
# Full-Abstract Biomedical Relation Extraction with Keyword-Attentive Domain Knowledge Infusion
## 4 Evaluation
### Overhead of the Keyword Attention Mechanism  
Table 7 reports an overhead comparison between Kw-BioBERT and BioBERT in terms of the training and inference speed, both in examples per second (ex/s). BioBERT posted an average speed of 11 ex/s during training, and Kw-BioBERT was almost two times faster, with a speed of 20.5 ex/s. During inference, the speeds of BioBERT and Kw-BioBERT were 66.5 and 72.6 ex/s, respectively. The increase of speed brought by Kw-BioBERT is mainly due to the attention layer added into BERT and replacing the \(N\)th standard transformer encoder. In other words, the original design of self-attention performs pair-wise attention between every pair of tokens, and the proposed keyword masked self-attention only concerns the attention (1) from tokens in \(seq_{A}\) to the two entity mentions in \(seq_{B}\) and (2) from the two mentions in \(seq_{B}\) to the keywords in \(seq_{A}\), greatly reducing the attention calculations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Year** & **Pre** & **Rec** & **F1** \\ \hline ME+CNN [10] & 2017 & 55.7 & 68.1 & 61.3 \\ Bran [40] & 2018 & 55.6 & 70.8 & 62.1 \\ GCNN [5] & 2019 & 52.8 & 66 & 58.6 \\ EoG [7] & 2019 & 62.1 & 65.2 & 63.6 \\ LSR w/o MDP Nodes [39] & 2020 & - & - & 64.8 \\ BiOnt [12] & 2020 & 57.7 & 71.7 & 64 \\ GLRE [6] & 2020 & 65.1 & 72.2 & 68.5 \\ SIRE [25] (SOTA) & 2021 & - & - & 70.8 \\ Kw-BioBERT (ours) & 2021 & **75.5** & **77.5** & **76.4** \\ \hline Bran + data & 2018 & 64.0 & 69.2 & 66.2 \\ Kw-BioBERT + data & 2021 & **82.9** & **79.2** & **80.8** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance of the benchmarks and our method on the CDR test set. The highest value of each metric is marked in bold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Pre** & **Rec** & **F1** \\ \hline BERT & 65.4 & 67.9 & 66.3 \\ Kw-BERT & 68 & 68.9 & 68.4 \\ BioBERT & 73.6 & 75.6 & 74.5 \\ Kw-BioBERT & **75.5** & **77.5** & **76.4** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study. The highest value of each metric is marked in bold.