esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Discussion
### Accuracy Analysis
#### 4.2.3 Results on the Houston 2013 Dataset  
Figure 10 provides a comparison of the visual performance for the Houston data. The visual comparisons are similar due to the dispersed categories and the large number of data. However, the observation from the same dense data point section shows that SFFNet has fewer classification errors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**CLASSES** & **CLASSES-NAME** & **3D-CNN** & **WFCG** & **MDGCN** & **AMGCFN** & **SFFNet** \\ \hline
1 & Asphalt & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & 98.74 \(\pm\) 0.56 & 95.38 \(\pm\) 0.02 & **100.00 \(\pm\) 0.00** \\
2 & Meadows & 88.54 \(\pm\) 2.11 & 99.21 \(\pm\) 2.03 & **100.00 \(\pm\) 0.00** & 99.56 \(\pm\) 0.01 & 99.32 \(\pm\) 0.55 \\
3 & Gravel & 98.99 \(\pm\) 2.06 & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & 79.16 \(\pm\) 0.20 & **100.00 \(\pm\) 0.00** \\
4 & Trees & 96.94 \(\pm\) 2.25 & **100.00 \(\pm\) 0.00** & 99.83 \(\pm\) 1.69 & 86.40 \(\pm\) 0.06 & **100.00 \(\pm\) 0.00** \\
5 & Painted metal sheets & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & 98.35 \(\pm\) 0.42 & **100.00 \(\pm\) 0.00** \\
6 & Bare Soil & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & 98.36 \(\pm\) 0.03 & **100.00 \(\pm\) 0.00** \\
7 & Bitumen & 53.08 \(\pm\) 0.86 & 62.31 \(\pm\) 2.68 & 24.62 \(\pm\) 1.24 & 91.27 \(\pm\) 0.07 & **100.00 \(\pm\) 0.00** \\
8 & Self-Blocking Bricks & 67.72 \(\pm\) 1.88 & 61.38 \(\pm\) 2.89 & 96.28 \(\pm\) 1.42 & 96.02 \(\pm\) 0.03 & **98.48 \(\pm\) 0.14** \\
9 & Shadows & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & **100.00 \(\pm\) 0.00** & 70.96 \(\pm\) 0.13 & **100.00 \(\pm\) 0.00** \\ \hline OA & & 89.96 \(\pm\) 2.56 & 94.54 \(\pm\) 1.22 & 96.55 \(\pm\) 0.33 & 95.62 \(\pm\) 0.01 & **99.04 \(\pm\) 0.43** \\ AA & & 89.47 \(\pm\) 1.38 & 91.43 \(\pm\) 1.23 & 91.05 \(\pm\) 0.49 & 90.66 \(\pm\) 0.02 & **99.76 \(\pm\) 0.20** \\ kappa & & 87.60 \(\pm\) 2.87 & 93.47 \(\pm\) 1.32 & 96.12 \(\pm\) 0.32 & 94.18 \(\pm\) 0.02 & **99.44 \(\pm\) 0.53** \\ \hline \end{tabular}
\end{table}
Table 6: Accuracy (%) on the Pavia dataset of the different methods.  
Figure 9: Classification results on the Pavia dataset.  
Figure 10: Classification results on the Houston dataset.  
The Houston dataset is larger and more complex compared to the other datasets. It is more fragmented in terms of category distribution. Table 7 provides the classification results of the five frameworks on the Houston dataset. Regardless of the classification accuracy of each class or the overall classification accuracy, our framework is still able to achieve the best results, with up to 100.00%. The sub-optimal framework (WFCG) has about 4% lower classification accuracy than SFFNet, indicating that the construction of the refined graph data structure is effective in improving classification accuracy.
## 4 Technologies for Nanocellulose Production
### Production of Nanocellulose from Cellulose
#### 4.2.5 Cryo-Crushing  
Cryo-crushing is a different mechanical method in which the cellulose is released from its frozen state to form NC. Briefly, in this procedure cellulose is suspended in water and then subjected to liquid nitrogen, which results in the freezing of cellulose. This frozen cellulose is then pulverized by using mortar and pestle. The pressure exerted by crushing frozen cellulose fibrils generates high-impact forces, which results in the rupturing of the cell wall and the formation of nanocellulose [41; 106]. The high cost, low recovery, inconsistency in NC uniformity and high energy requirement are few of the shortcomings of this process [35]. Nanocellulose preparation using chemical-assisted cryo-crushing was executed within wheat straw and soy hulls. The study revealed that the crystallinity of the nanofibers was enhanced 35% and 16% for the wheat straw nanofibers and soy hull nanofibers, respectively. Moreover, the nanofibers obtained from the wheat straw had a diameter in the range of 10-80 nm and lengths at a few thousand nanometers [107].
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Sample Value Shifting Method  
Voice data are composed of a series of samples, where the bit depth of samples is set by the microphone. Common sample bit depths include 8 and 16 bits, with values ranging from \(-128\) to \(127\) and \(-32\),\(768\) to \(32\),\(767\), respectively. Due to the signed operations involved in the additive mixing of individual samples, the resulting mixed sample values can be negative. However, in the next step, the voice samples are concatenated into a big integer format of the big integer library. If any sample is negative, the big integer format cannot represent the negative values of the internal samples. Furthermore, the additive homomorphic property of the Paillier encryption algorithm does not apply to negative numbers. Therefore, a value-shifting method is necessary to address the negative number issue. The process is described as follows.  
* Shift the sample value according to its n-bit length by adding \(2^{n-1}\) (where n is the bit depth of a sample), making the range between 0 and a positive integer. For example, if the sample is 8 bits, add \(2^{8-1}=128\) to shift the sample value range from \(-128\) to \(127\) upwards to 0 to 255.
* After completing step 1 for each sample, each sample is concatenated into a big integer format to perform the big integer format mixing, as detailed in the next section.
* After the mixing of each audio track using the big integer format mixing method, the result of each sample is shifted down by subtracting \(2^{n-1}\times k\) (where \(k\) is the number of mixed audio tracks) to restore each sample to its original range.  
For example, suppose that there are two audio tracks, Track A and B, each containing one sample (8 bits) (i.e., sample1 from Track A and sample 2 from Track B). After shifting each sample upward in step 1, the values are transformed into sample1 + \(2^{8-1}\), sample2 + \(2^{8-1}\), respectively. These are then additively mixed to obtain the mixed result sample3\({}^{\prime}\) = sample1 + \(2^{8-1}\) + sample2 + \(2^{8-1}\). Finally, according to step 3, sample3\({}^{\prime}\) is shifted downward to yield the final mixed sample3 = sample3\({}^{\prime}-2^{8-1}\times 2\).
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 3 Preliminaries and Methods
### Task Description  
In the predictive business monitoring task, given an event log \(L=\{\sigma_{1},\sigma_{2},\cdots,\sigma_{m}\}\), the goal is to build a predictive model to predict the likely subsequent execution state of a business process prefix (i.e., a business process case that has not yet completed execution). Specifically, given a business process case \(\sigma=\langle e_{1},e_{2},\ldots,e_{n}\rangle\) and its one prefix \(prefix(\sigma)=\langle e_{1},e_{2}\,,\ \ldots,\ e_{n^{\prime}}\rangle\), the prediction goals for the next activity prediction task and the remaining execution time prediction task are as follows, respectively,  
\[y^{(act)}(prefix(\sigma))=\mathbf{e}_{n^{\prime}+1}, \tag{1}\]  
\[y^{(time)}(prefix(\sigma))=\mathbf{e}_{n}.time-\mathbf{e}_{n^{\prime}}.time, \tag{2}\]  
The predictive business monitoring task is carried out in two main segments: firstly, the business process prefix set \(prefix(L)\) is generated based on the given event log \(prefix(L)\) in the following Equation (3).  
\[prefix(L)=\{prefix(\sigma)|\sigma\in L,\ len(prefix(\sigma))>n_{0}\}, \tag{3}\]  
where \(len(\cdot)\) denotes the number of events contained in an input business process case or prefix, and \(n_{0}\) is a pre-assigned length threshold. In short, for each business process case in the event log \(L\), its prefix set is generated, and the prefix set of each case is combined. Since prefixes containing a small number of events have less information and are of less value for subsequent model training, a threshold \(n_{0}\) is specified to filter the shorter prefixes.  
Then, according to the prediction target of the predictive business monitoring task (such as the next activity or the remaining execution time), the prediction target value is calculated for each business process prefix in \(prefix(L)\) according to Equation (1) or Equation (2), respectively, and the training dataset is then built as follows:  
\[D(L)=\Big{(}\Big{(}prefix(\sigma),y^{(act/time)}(prefix(\sigma))\Big{)}\Big{|}prefix(\sigma)\in prefix(L)\Big{)} \tag{4}\]
## 2 Preprocessing
### Features Selection
#### 2.2.2 Calculated Power of Signals  
The power of a signal is a crucial concept in signal processing that quantifies the average energy contained in the signal over time. It provides insight into the strength and variability of the signal, which can be particularly important for applications in various fields such as telecommunications, audio processing, and biomedical engineering, including EEG analysis. Below is a description of signal power, including its definitions, types, calculations, and implications: Definition of signal power signal power: The power of a signal is defined as the average energy per unit time. It indicates how much energy a signal carries and is often measured in watts (W). Mathematically, for a continuous-time signal \(x(t)\), the average power \(P\) can be expressed as  
\[P=\lim_{T\to\infty}\frac{1}{T}\int_{-T/2}^{T/2}|x(t)|^{2}\,dt.\]  
Instantaneous power: The instantaneous power of a signal at a given time \(t\) is defined as  
\[P(t)=|x(t)|^{2},\]  
where \(T\) is the period of the signal. Aperiodic Signals: For aperiodic signals (non-repeating), the average power is calculated over an infinite duration, as shown in the earlier definitions. Power Spectral Density (PSD) The power spectral density (PSD) is a measure that describes how the power of a signal is distributed across different frequency components. It is often used in the context of analyzing signals in the frequency domain. The PSD can be calculated using techniques such as the Fourier transform or the Welch method and provides insight into how signal power varies with frequency.
## 3 Results and Discussion
### Influencing Factors of Relatively Steady Seepage Field  
#### 3.3.1 Influence of Rainfall Patterns  
In order to analyze the influence of different rainfall patterns on the same tailings pond under the same annual rainfall conditions, the final distribution forms of saturation lines of different rainfall patterns under the same annual rainfall amount are calculated. The results are shown in Figure 10.  
Figure 10: _Cont._Figure 10: Effects of rainfall patterns: (**a**) Taiyuan 2018, Taiyuan 2019, and Taiyuan 2020 (\(i=0.05\)); (**b**) Taiyuan 2018, Taiyuan 2019, and Taiyuan 2020 (\(i=0.1\)); (**c**) Taiyuan 2018, Taiyuan 2019, and Taiyuan 2020 (\(i=0.15\)); (**d**) Zhengzhou 2018, Zhengzhou 2019, and Zhengzhou 2020 (\(i=0.05\)); (**e**) Zhengzhou 2018, Zhengzhou 2019, and Zhengzhou 2020 (\(i=0.1\)); (**f**) Zhengzhou 2018, Zhengzhou 2019, and Zhengzhou 2020 (\(i=0.15\)); (**g**) Guiyang 2018, Guiyang 2019, and Guiyang 2020 (\(i=0.05\)); (**h**) Guiyang 2018, Guiyang 2019, and Guiyang 2020 (\(i=0.1\)); (**i**) Guiyang 2018, Guiyang 2019, and Guiyang 2020 (\(i=0.15\)).  
From the calculation results in Figure 10, when the annual rainfall and the bottom slope of the tailings pond are certain, different rainfall patterns have little effect on the saturation line distribution of the RSSF.  
Therefore, when analyzing the long-term distribution of the saturation line in the dry-stack tailings pond for certain conditions of annual rainfall, the selection of rainfall patterns has little influence on the final saturation line results of the RSSF. In contrast to the response of the seepage field in a general slope under short-term rainfall [21; 22], the relatively stable saturation line in the dry-stack tailings pond under long-term rainfall is not sensitive to the selection of rainfall patterns. The finding brings convenience to the calculation of RSSF by a numerical method.
## 4 Discussion
### Effect of Storage Temperature  
The SCFA fermentation products were mainly detected after 20 \({}^{\circ}\)C storage and were not or to a low extent detected after 4 \({}^{\circ}\)C and 8 \({}^{\circ}\)C storage. This has a negative impact on the quality of the algae suspensions as most of these metabolites have a strong odor. Acetic acid was the main organic acid detected, followed by propionic acid and butyric acid. The last two clearly have an unpleasant odor and their levels in 20 \({}^{\circ}\)C stored concentrates (Figure 4) were far above their odor threshold values in water (20 mg/L and 0.24 mg/L for propionic and butyric acid, respectively) [39]. In addition, the branched SCFA isobutyric and isovaleric acid were formed (Figure 4 and Supplementary Materials Figure S3) and their levels exceeded their odor threshold values (8.1 mg/L and 0.12-0.7 mg/L, respectively) [39]. Unbranched SCFA, and to a lesser extent branched SCFA, are also found in foot sweat [40] and in human stool [41] as bacterial fermentation products of carbohydrates (only unbranched SCFA) and proteins (both unbranched and branched SCFA) [41]. In particular, isovaleric acid, the main cause of feet odor [40], was higher than its odor threshold (25 times higher after 14 days storage in closed tubes at 20 \({}^{\circ}\)C).  
For algae stored during 14 days in closed bottles, H\({}_{2}\)S was clearly present in the headspace (Supplementary Materials Figure S1) and a sulfur odor was perceived (authors personal observation) during 20 \({}^{\circ}\)C storage but not during 4 \({}^{\circ}\)C or 8 \({}^{\circ}\)C storage. This is possibly caused by sulfate-reducing bacteria as seen in the anoxic zones of lakes [35] and during the anaerobic digestion of marine microalgae for biofuel production [42]. Indeed, sulfate was present in the initial cultivation medium, mainly in the form of MgSO\({}_{4}\) (\(\sim\)2.6 g/L). Yet, other compounds like (homo-)cysteine can also be H\({}_{2}\)S precursors [43]. Next to H\({}_{2}\)S, methanethiol was detected after 20 \({}^{\circ}\)C storage in closed recipients and not after 4 \({}^{\circ}\)C and 8 \({}^{\circ}\)C storage. Methanethiol is involved in the foul odor of cyanobacterial mats [44] and its presence in fresh water systems was mainly ascribed to anoxic, biogenic degradation processes [45].  
In summary, it is clear that several metabolites with an unpleasant odor were released during 20 \({}^{\circ}\)C storage and not released or released much less during 4 \({}^{\circ}\)C and 8 \({}^{\circ}\)C storage. The tested 20 \({}^{\circ}\)C storage conditions are hence unsuitable for the storage of algae for food applications. Storage at 4 \({}^{\circ}\)C had an additional advantage over 8 \({}^{\circ}\)C and 20 \({}^{\circ}\)C storage in that it resulted in lower microbial counts.
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 3 System Design
### Source Domain Refinement (SDR)
#### 3.3.3 Stable AP's Weight Enhancement  
APs showing less signal fluctuations are more stable and hence are more reliable in the online signal matching process [18]. It has been observed that if we add some bias to the  
Figure 4: RPs used to train algorithm for regression.  
stable AP's RSSI measurements of each area independently, it will increase the difference among the fingerprints of different areas. Consequently, the classification accuracy will be increased. However, the values of the bias terms are arbitrary, and large values can change the originality of the RSS vectors significantly, which decreases the accuracy. The stability of an AP is directly proportional to its frequency of occurrence. The procedure is explained below. Assuming that total \"L\" APs are detected in \(D_{R1}\) dataset, a total number of sub-areas are \"A\", and \(N_{a}\) represents the total number of training samples present in the \(a_{th}\) sub-area, where \(a\in A\):  
1. We define a detection vector \(d_{a}^{l}=[d_{a}^{l,1},d_{a}^{l,2},\dots,d_{a}^{l,N_{a}}]\), where \(d_{a}^{l,n}\in\{0,1\}\), is the detection indicator for the \(l^{th}\) AP of \(n^{th}\) sample in the sub-area \"a\". When the value of a particular RSSI feature is above a threshold, \(Th_{a}\), the corresponding AP is detected, and the value of \(d_{a}^{l,n}\) would be considered as 1 or otherwise, 0. The detection vector \(d_{a}^{l}\) is calculated for each AP in each sub-area.
2. For the current sub-area \"a\", the sum \(S_{a}^{l}\) for the \(l^{th}\) AP's detection indicators \(d_{a}^{l,n}\) can be calculated as: \[S_{a}^{l}=\sum_{n=1}^{N_{a}}d_{a}^{l,n}\] (3) And the distinction vector \(K_{a}\in R^{(L\times 1)}\) can be written as, \(K_{a}=[S_{a}^{1},S_{a}^{2},\dots,S_{a}^{L}]\), which we can normalize by dividing the whole vector \(K_{a}\) by the maximum entry in the vector \(K_{a}\): \[G_{a}=\frac{K_{a}}{max(K_{a})}\] (4)
3. Sort \(G_{a}\) in descending order, where each entry is the stability indicator of the corresponding AP in sub-area \"a\". Now, select those APs whose stability indicator is greater than a threshold \(Th_{a}^{\prime}\) and add a small bias \(b_{a}\) into the RSSI measurements of the selected APs. Remember that the values of \(Th_{a}^{\prime}\) and \(b_{a}\) are arbitrary.
# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
## 2 Acquisition of Partial Discharge Datasets
### Partial Discharge Detection Platform and Pressurization Method  
This experiment is conducted in the partial discharge shielded room, and the partial discharge experimental platform is shown in Figure 2.  
The platform mainly includes three parts: power supply system, test chamber, and measurement system. Among them, the power supply system consists of a test transformer and capacitor divider, which mainly provides the test cavity with industrial frequency AC voltage. The test chamber is a 550 kV true GIS section. The defect model is set in the test chamber, and the partial discharge signal generated by the defect model is transmitted to the partial discharge analyzer by the UHF sensor connected to the chamber for signal acquisition.  
Set the test pressurization procedure as follows: raise the system voltage value to 318 kV and hold for 5 min; continue to raise the voltage to 550 kV and hold for 3 min; then raise the voltage to 740 kV and hold for 1 min; lower the voltage to 381 kV for partial discharge measurement, and lower the voltage to 0 after the partial discharge measurement.  
Partial discharge defect data of GIS is collected by simulating partial discharge defects through experiments. Corona discharge is collected by gluing different lengths of copper pins perpendicular to the high-voltage conductor on the GIS bus. Copper pins of 1 mm, 2 mm, and 3 mm were set up to obtain PRPD patterns of corona discharge respectively. A total of 500 profiles were collected for each type of defect. Finally, 1217 valid PRPD profiles were  
Figure 1: PD defect model of GIS. (**a**) metal tip defects, (**b**) floating electrode defects, (**c**) insulating void defects, (**d**) free metal particle defects.  
Figure 2: PD detection platform of GIS.  
selected by manual screening. In simulating the type of floating electrode discharge defect, we use insulating tape to suspend different sizes of epoxy between the high voltage electrode and the metal housing. The width of the epoxy resin was set to 2 mm, 3 mm, and 4 mm, respectively. About 400 maps were collected for each type of defect by experiments. Finally, 1023 valid PRPD maps were obtained by manual screening. In the insulation discharge defect type simulation, insulators with simulated bubble diameters of 1 mm and 2 mm are installed on the experimental platform. A total of 500 partial discharge PRPD maps were collected for each type of defect through experiments, and finally, 948 valid PRPD maps were obtained through manual screening. In the simulation of free particle discharge defect types, 2 and 5 g of metal particles were placed in our experimental platform, and 600 maps were collected for each type of defect. Finally, 1053 valid PRPD patterns were obtained by manual screening.
# Detection of Welding Defects Using the YOLOv8-ELA Algorithm
###### Abstract  
To address the issue of the low precision in detecting defects in aluminum alloy weld seam digital radiography (DR) images using the current target detection algorithms, a modified algorithm named YOLOv8-ELA based on YOLOv8 is proposed. The model integrates a novel HS-FPN feature fusion module, which optimizes the parameter efficiency and enhances the detection performance. For better identification of small defect features, the CA attention mechanism within HS-FPN is substituted with the ELA attention mechanism. Additionally, the first output layer is enhanced with a SimAM attention mechanism to improve the small target recognition. The experimental findings indicate that, at a 0.5 threshold, the YOLOv8-ELA model achieves mean average precision ([EMAIL_ADDRESS]) values of 93.3%, 96.4%, and 96.5% for detecting pores, inclusions, and incomplete welds, respectively. These values surpass those of the original YOLOv8 model by 1.4, 2.3, and 0.1 percentage points. Overall, the model attains an average mAP of 95.4%, marking a 1.3% improvement over its predecessor, confirming its superior defect detection capabilities.  
weld defects; deep learning; target detection; YOLOv8 2025155204
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
## 3 Materials and Methods
### Thermal Desorption Gas Chromatography Mass Spectrometry (TD-GC-MS)  
The TFME sheets with adsorbed samples were placed inside a 3.5\({}^{\prime\prime}\) long and 1/4\({}^{\prime\prime}\) O.D. glass tube (Supelco, Bellefonte, PA, USA) for thermal desorption in a TD-30R autosampler (Shimadzu, Kyoto, Japan). Meshes composed of DVB, HLB and PDMS were thermally desorbed at 250 \({}^{\circ}\)C, with Carboxen meshes at 275 \({}^{\circ}\)C, for 10 min. As a carrier gas for thermal desorption, Helium 6.0 purified on an HP-2 Heated Helium Purifier (ViCi Valco Instruments, Houston, TX, USA) and a double set of Carrier Gas Purifiers (Agilent, Santa Clara, CA, USA) was used with a flow of 60 mL/min. The desorbed gases were focused on a cold trap at \(-\)20 \({}^{\circ}\)C filled with Carboxen. Injection into a Nexis 2030 Gas Chromatograph (Shimadzu, Kyoto, Japan) took place at 350 \({}^{\circ}\)C over 2 min in split-less mode with a carrier gas flow of 0.82 mL/min (linear velocity 33.0 cm/s). Chromatographic separation was done on an Rt-Q-Bond capillary column 30 m \(\times\) 0.25 mm \(\times\) 8 \(\upmu\)m (Restek, Bellefonte, PA, USA) using the following temperature program: initial 60 \({}^{\circ}\)C held for 2 min, then ramped 8 \({}^{\circ}\)C/min to 110 \({}^{\circ}\)C @1 min, ramp 3 \({}^{\circ}\)C/min to 120 \({}^{\circ}\)C @7 min, ramp 3 \({}^{\circ}\)C/min to 155 \({}^{\circ}\)C @7 min, ramp 3 \({}^{\circ}\)C/min to 225 \({}^{\circ}\)C @4 min, ramp 10 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C @7 min. Data acquisition was done with a QP-2020 Mass Spectrometer (Shimadzu, Kyoto, Japan), working in SCAN mode (within 33-235 \(m/z\) range).
## 1 Introduction
* We propose a novel TC-PCA model by integrating a trimming set into the two-group \(k\)-means clustering model, which makes the proposed method not sensitive to outliers. * We mathematically prove that the estimator of TC-PCA is insensitive to outliers, which shows the robustness of the proposed method. In addition, we mathematically prove the convergence of the proposed algorithm. This paper is organized as follows. In the next section, we briefly review related work. Section 3 presents the proposed TC-PCA model and the overall implementation. In addition, we discuss the mathematical properties and performance of the proposed method. Experimental results are shown in Sections 4 and 5 concludes the paper. Figure 2: Illustration of the clustering property of the principal component analysis (\(l_{1}\)-PCA).