esa-sceva/satcom-chunk-collection huggingface dataset:
# A Graph Convolutional Network-Based Method for Congested Link Identification
## 4 Performance Evaluation
### Choosing Model Parameters  
We implemented our GCN-based algorithm using the deep learning framework PyTorch. In this part, we reveal the relevant parameters of our GCN model.  
(1) Hyperparameters  
The hyperparameters of the GCN-based congested link identification algorithm we propose mainly include the learning rate, batch size, training epochs, the number of hidden layers, and the number of hidden units. In the simulations, we manually adjusted and set the learning rate to 0.01, the batch size to 200, the number of training epochs to 50, and the number of hidden layers to 3. The number of hidden units significantly influences the performance of the GIC algorithm. The selection of the optimal number of hidden units is described below.
2. Training  
In the training phase, we used the training data set (80% of the total data) as input, and the remainder was used as input during the testing process. We trained the GCN model using the Adam optimizer.
# Mitigating Electrochemical Effects in Resistive Frost-Detection Sensors
Techniques such as electrical heating, hot gas bypass, and reverse-cycle defrosting melt and dislodge the frost, thereby restoring effective heat transfer and returning the system to normal function [3]. Traditional defrosting methods typically operate on preset time intervals without considering the actual frost buildup in the system [4]. This approach assumes a uniform rate of frost formation; however, variations in ambient temperature, humidity, and system load often lead to differences in frost accumulation [5]. As a result, defrost cycles are often triggered either too early, when frost levels are too low to impact performance, or too late, after excessive frost buildup has already degraded efficiency for an extended period. This leads to overall system inefficiency, unnecessary energy expenditure, and thermal disturbances within the refrigerated chamber. In contrast, demand defrost strategies employ real-time frost detection to trigger defrost cycles only when needed [6]. This minimizes wasted energy by defrosting solely in response to actual frost accumulation. Moreover, by synchronizing the defrost cycle with the true condition of the heat exchanger, demand defrost contributes to maintaining a more stable temperature environment within the refrigeration system. Traditional methods often cause unnecessary periodic warming, which exposes refrigerated products to cyclic temperature fluctuations. These fluctuations can accelerate quality degradation and reduce shelf life, whereas demand defrost, by reducing the amount of defrost cycles to the strictly necessary, helps preserve the freshness and quality of perishable items [4]. Demand defrost technologies can detect, predict, or measure frost accumulation. Prediction methods are based on computational models and machine learning algorithms to forecast frost formation based on environmental and operational data [7]. Detection methods are categorized into indirect approaches, which monitor ambient conditions such as temperature, humidity, refrigerant temperature, airflow, or pressure drops [8], and direct approaches, which use sensors to directly measure frost buildup [9]. There are several types of frost-detection sensors used in direct measurement approaches. Capacitive sensors detect frost by measuring variations in dielectric properties between water, air, and ice [10]; optical sensors use infrared- or laser-based techniques to detect frost accumulation by analyzing light reflection or absorption [11]; piezoelectric sensors determine frost thickness by measuring changes in resonance frequency, which directly correlates with frost thickness [12]. Image-processing techniques use cameras and machine vision algorithms to detect frost patterns on heat exchanger surfaces [13; 14; 15], and resistive sensors measure changes in electrical resistance as frost accumulates on the sensor surface [16; 17; 18]. These resistive sensors are simple, cost-effective, and sufficiently reliable for frost detection.
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 6 Results and Discussion  
### Feature Comparison Results  
In this article, a comparison of the accuracy of GTCC and MFCC was undertaken for spoken LID in the proposed system. The accuracy of GTCC compared with MFCC was evaluated in Experiment 1 of Table 4 for all files using a considered MCV corpus with the execution of five runs. Table 5 shows the experimental results for GTCC and MFCC, as well as combined features.  
The highest average testing accuracy was achieved when concatenating GTCC and MFCC as system features. The average precision of GTCC for spoken LID in Arabic was 94.88%, Chinese 94.20%, English 91.30%, French 90.58%, German 74.84%, Russian 92.98%, and Spanish 89.86%. In contrast, the average precision of MFCC for spoken LID in Arabic was 94.82%, Chinese 94.88%, English 89.76%, French 92.14%, German 76.78%, Russian 95.32%, and Spanish 91.72%. The results indicate that GTCC outperformed MFCC in Arabic and English spoken LID. However, the most effective results were obtained when GTCC was combined with MFCC. These results are illustrated in Figure 4. However, Table 5 shows that consumption time is a major issue in spoken LID systems when using more than one feature, especially in the real-time setting.  
The average precision when combining GTCC with MFCC in Experiment 1 for spoken LID in Arabic is 95.78%, Chinese 94.47%, English 92.35%, French 92.49%, German 80.95%, Russian 95.25%, and Spanish 93.49%. Depending on the testing accuracies and experiment consumption time, the systems in Experiments 1 and 11 achieved the highest accuracy and the lowest execution time, as shown in Table 4. The proposed architecture's accuracy in  
Figure 4: The average precision of each feature.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features:** & \multicolumn{2}{c}{**GTCC**} & \multicolumn{2}{c}{**MFCC**} & \multicolumn{2}{c}{**GTCC-MFCC**} \\ \hline
**Per Files (\%)** & **Testing** & \multirow{2}{*}{**Time Taken**} & \multirow{2}{*}{**Testing** **Accuracy (\%)**} & \multirow{2}{*}{**Time Taken**} & **Testing** **Accuracy (\%)** & **Time Taken** \\
**Experiment 1** & & & & & & **Accuracy (\%)** & **Time Taken** \\ \hline Run \# 1 & 89.07 & 142 min 49 s & 89.21 & 137 min 48 s & 92.21 & 162 min 40 s \\ Run \# 2 & 90.00 & 145 min 31 s & 89.93 & 139 min 2 s & 91.79 & 161 min 36 s \\ Run \# 3 & 88.68 & 145 min 43 s & 90.11 & 139 min 52 s & 91.25 & 163 min 11 s \\ Run \# 4 & 89.21 & 145 min 54 s & 90.82 & 140 min 52 s & 91.86 & 164 min 57 s \\ Run \# 5 & 88.64 & 147 min 3 s & 90.57 & 141 min 15 s & 91.68 & 153 min 46 s \\ Average & 89.12 & 145 min 36 s & 90.13 & 138 min 34 s & 91.76 & 160 min 38 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of GTCC and MFCC for spoken LID in the proposed system.  
Experiment 1 reached 92.21% in around 163 min, while in Experiment 11 it reached 93.00% in 221 min. Each experiment was performed for five runs per sequence and per whole file.
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 4 Application Research and Method Comparison
### Monitoring Data Processing
#### 4.1.1 Decomposition of Landslide Displacement Data  
The displacement deformation of a landslide is influenced by multiple factors. Therefore, in order to obtain an accurate prediction result of the landslide displacement, it is necessary to decompose the acquired landslide displacement into several subcomponents, corresponding to the influences of different factors [11; 23; 44; 45]. The landslide undergoes displacement deformation under the influence of its own geological conditions. This displacement deformation component caused by the landslide itself is defined as the trend displacement component. Many studies have shown that seasonal precipitation and changes in the water level of the reservoir area in the Three Gorges region indirectly affect the displacement deformation of the landslide [14; 32; 46]. Therefore, the displacement deformation components caused by the influencing factors with periodic changes are defined as the periodic displacement components. In addition, the landslide displacement is deformed by other external factors, such as artificial activities and earthquakes. These kinds of displacement deformation components are defined as random displacement components.  
The parameters need to be set before VMD can be used for the cumulative 120 weeks of displacement data. Considering that the displacement component contains at least three terms (trend, period, and random), the modal number K = 3. During the experiment, the penalty parameter and the rising step have a great influence on the decomposition results of the data, especially in the periodic term and random term. The parameters were set to \(\alpha=2000\), \(\tau=0\), DC = 0, and init = 1 after several trials.  
It was determined that, when K = 3, the decomposed periodic term was not obvious. Therefore, K = 4 was adopted for the decomposition, and the results of the decomposition were processed analytically. The more significant IMF1 and IMF2 were used in the trend term displacement. IMF3, which had good periodicity and low-frequency characteristics, was used as the periodic term. The higher-frequency IMF4 was used as the random term. The decomposition results are shown in Figure 6.
# 3L-YOLO: A Lightweight Low-Light Object Detection Algorithm
###### Abstract  
Object detection in low-light conditions presents significant challenges due to issues such as weak contrast, high noise, and blurred boundaries. Existing methods often use image enhancement to improve detection, which results in a large amount of computational resource consumption. To address these challenges, this paper proposes a detection method, 3L-YOLO, based on YOLOv8n, which eliminates the need for image enhancement modules. First, we introduce switchable atrous convolution (SAConv) into the C2f module of YOLOv8n, improving the model's ability to efficiently capture global contextual information. Second, we present a multi-scale neck module that aggregates shallow features and incorporates a channel attention mechanism to prioritize the most relevant features. Third, we introduce a dynamic detection head, which employs a cascade of spatial, scale, and channel attention mechanisms to enhance detection accuracy and robustness. Finally, we replace the original loss function with MPDIoU loss, improving bounding box regression and overall reliability. Additionally, we create a synthetic low-light dataset to evaluate the performance of the proposed method. Extensive experiments on the ExDark, ExDark+, and DARK FACE datasets demonstrate that 3L-YOLO outperforms YOLOv8n in low-light object detection, with improvements in [EMAIL_ADDRESS] of 2.7%, 4.3%, and 1.4%, respectively, across the three datasets. In comparison to the LOL-YOLO low-light object detection algorithm, 3L-YOLO requires 16.9 GFLOPs, representing a reduction of 4 GFLOPs.  
Keywords:low-light object detection; YOLO; atrous convolution; attention mechanism +
Footnote †: journal: Applied sciences
## 2 Results and Discussion
### Linearity, Precision, LOD, LOQ and Accuracy  
The proposed method showed a good linearity with determination coefficients (r\({}^{2}\)) higher than 0.9970 for all investigated compounds, confirming the suitability of CAR/PDMS/DVB fiber to monitor VOCs in wrapped cheese samples with excellent linearity. Accuracy was estimated by recovery assay. The recoveries calculated using the matrix-matched calibration curves were in the range of 82% and 95% for all selected VOCs. The detection and quantification limits of VOCs (LOD and LOQ) as well as all validation results were presented in Table S1 (Supplementary Materials).  
Figure 1: Total VOCs peak area (arbitrary unit; area/10\({}^{5}\)) for CAR/PDMS/DVB, CAR/PDMS, PDMS/DVB, PDMS and PA fibers at different extraction times (15, 45, 60, 90, 120, 180, 240 min).  
Precision of the method expressed as the relative standard deviations (R.D.S.) obtained from the determination of six SPME cheese extracts (each prepared separately from a representative pool of cheese samples) showed values less than 5.5% for all selected compounds. These results confirm the efficacy of SPME method for the determination of VOCs compounds migrated from cling-films to PDO cheees.
# Use of Mineral Waste for the Production of Artificial Aggregates
## 3 Slags from Iron and Steel Production  
### Origin of Waste  
Iron and steel production has been and remains one of Poland's important industries for more than two hundred years. This activity involves the generation of a large amount of mineral waste, mainly slag. This waste is generated both during the production of pig iron (blast furnace slags) and during steelmaking (steelmaking converter slags) [20; 21; 22; 23; 24; 25; 26].  
The feedstock for the blast furnace process consists of iron ores, coke, and carbonate rock. The main iron ores used today are magnetite and hematite; in the past, siderite was also important. These minerals provide a source of Fe in the blast furnace process. Iron ores may be contaminated with other minerals that will affect the chemical composition of the product (pig iron) and waste (blast furnace slag).  
Coke is the fuel used in the blast furnace and a temperature above 1500 \({}^{\circ}\)C is needed to melt the charge material. The coke combustion process also creates reducing conditions in the blast furnace. The reducing agents in this process are carbon and carbon monoxide (CO) and hydrogen (H\({}_{2}\)). Under reducing conditions, the oxide iron ores (magnetite and hematite) are converted first to wustite (FeO) and then to metallic iron.  
Fluxes are also used in the blast furnace process to lower the melting point of the iron ore and to separate the liquid metal from the other components. The most commonly used fluxes are limestone and dolomite.  
The blast furnace process leads to the formation of an iron-carbon alloy (so-called pig iron) and blast furnace slag, which is a waste product. Pig iron is an intermediate product for the production of steel and various alloys. The steelmaking process involves removing impurities such as silicon, manganese, carbon, phosphorus, sulphur, and others from the pig iron. These impurities are oxidized and transferred to the steelmaking slag, which is also a waste product (Figure 3).  
In the 19 th and 20 th centuries, smelter slags were subject to storage in heaps located near the smelters. It was only at the turn of the 20 th and 21 st centuries that attempts to reduce the deposition of slags in the environment became more important, as well as efforts to put them to practical use.  
Blast furnace slags and steelmaking slags differ due to the smelting technology used, as well as the type of feedstock, the fluxes, and additives used [21; 22; 23]. These have an effect on their chemical composition, phase composition, and structural characteristics.  
Slags are formed as a result of melt differentiation. The liquid metal, being heavier, collects at the bottom, while the lower-density melt components gather above. This enables them to be separated from the molten metal.  
Figure 3: Samples of blast furnace slags.
## 2 Comparative Method
### Data and Application of the Scaling Approaches
#### 2.4.2 Application for Ground-Related OIs  
According to Section 2.3.2, the climate mitigation potential of the ground-related OIs is estimated based on the fuel, energy, and emission savings per unit source as part of the reference airport study. In the case of SETX, the unit source is an aircraft type. In the case of ELEC, it is a vehicle class, and in the case of INFR, it is a conceptual office building. The resulting changes in KPIs per OI are then assessed in detail at the selected reference airport Milan Malpensa Airport (MXP), for which detailed data were available to the project consortium. For this purpose, the airport-specific flight schedule with its exact taxi times leads to emission decreases due to the implementation of SETX. The effects from ELEC at MXP are modeled based on the detailed ground support equipment (GSE)fleet composition and the respective mileage to obtain changes in GHG emissions. Under consideration of the climate conditions at MXP, energy savings for the conceptual office building are scaled to the airport level. Required investments and possible investment amortization times can also be assessed for MXP as a reference airport in this context.  
Finally, changes in fuel and energy consumption as well as emission savings are generalized from MXP airport to all airports in the ECAC area (target airports) based on the number of airport movements. Thereby, the following assumptions are considered:  
* SETX: We estimate the taxi times at the different airports based on average taxi times at Europe's most frequented airports for one representative aircraft type.
* ELEC: We assume an equal distribution of ground fleet vehicle categories regarding vehicle sizes and mileages, while the fleet size itself is expected to scale with the annual air traffic at the considered airports.
* INFR: We incorporate the climate zones corresponding to the airport location as this is expected to significantly influence the efficacy of the selected energy-efficiency measures.  
Conclusively, the generalized emissions are converted to ATR100 following the approach presented by [PERSON] & Schumann [69]. Further details on the individual studies and the applied approach can be found in Appendix A.3.
## 4 Discrete Time Markov Chain Models and Analysis
### Multi-Link Access with the Longest Backoff (MLA-L)
\begin{split} P\Big{(}B_{N,i}^{MLA-L}=k\Big{)}&=P \Big{(}B_{N,i}^{MLA-L}\leq k\Big{)}-P\Big{(}B_{N,i}^{MLA-L}\leq(k-1)\Big{)}\\ &=\bigg{(}\frac{k+1}{W_{i}}\bigg{)}^{N}-\bigg{(}\frac{k}{W_{i}} \bigg{)}^{N}.\end{split}  \tag{31}\]  
As shown in Figure 8, we can derive PMF when there are two stations attached to an MLD:  
\[ \begin{split} P\Big{(}B_{2,i}^{MLA-L}=k\Big{)}&= \bigg{(}\frac{k+1}{W_{i}}\bigg{)}^{2}-\bigg{(}\frac{k}{W_{i}}\bigg{)}^{2}\\ &=\frac{2k+1}{W_{i}^{2}}\end{split}  \tag{32}\]  
and therefore one-step transition probabilities shown in Equation (28) can be expressed. In the same way as MLA-S, we can solve \(\tau_{MLA-L}\) by means of solving non-linear equation with the followings. \[P(s_{MLA-L}=i|TX)=\frac{(1-p_{MLA-L})p_{MLA-L}{}^{i}}{1-p_{MLA-L}{}^{R+1}}, \tag{33}\]  
and  
\[ \begin{split} P(TX|s_{MLA-L}=i)&=\frac{1}{1+E[b_{ i,MLA-L}]}\\ &=\frac{1}{1+\sum\limits_{k=0}^{W_{i}-1}k\cdot P\Big{(}B_{N,i}^{ MLA-L}=k\Big{)}}\\ &=\frac{1}{1+\sum\limits_{k=0}^{W_{i}-1}k\bigg{\{}\bigg{(}\frac{k +1}{W_{i}}\bigg{)}^{N}-\bigg{(}\frac{k}{W_{i}}\bigg{)}^{N}\bigg{\}}},\quad \text{for}\quad i\in[0,m].\end{split}  \tag{34}\]  
where \(E[b_{i,MLA-L}]\) is the average value of backoff counter for stage \(i\) in MLA-L case. Then we can get \(\tau_{MLA-L}\) by means of solving non-linear equations with Equations (3), (4), (33) and (34). Finally, throughput equation for MLA-L can be expressed in the same way as MLA-S, as seen in Equation (23) with substituting \(P_{s,MLA-S}\) and \(P_{tr,MLA-S}\) for \(P_{s,MLA-L}\) and \(P_{tr,MLA-L}\), respectively as follows. \[S_{MLA-L}=\frac{P_{s,MLA-L}P_{tr,MLA-L}E[P]\bigg{\{}1+\Big{(}1-P_{tr,single} \Big{)}^{N_{th}}\bigg{\}}}{(1-P_{tr,MLA-L})\sigma+P_{tr,MLA-L}P_{s,MLA-L}T_{s}+ P_{tr,MLA-L}(1-P_{s})T_{c}} \tag{35}\]
## 2 Related Work  
### Aspect-Based Sentiment Analysis  
Aspect-based sentiment analysis has attracted extensive attention due to its more fine-grained information extraction compared with traditional sentiment analysis. In an ABSA task, the single-element prediction tasks include Aspect Term Extraction (ATE) [9; 10], Opinion Term Extraction (OTE) [11; 12], Aspect Sentiment Classification (ASC) [13], and Aspect Category Detection (ACD) [14] tasks. In order to perform fine-grained sentiment analysis more comprehensively, researchers have carried out composite tasks to predict multiple elements such as Aspect-Opinion Pair Extraction (AOPE) [15; 16], Aspect-Category Sentiment Analysis (ACSA) [17], Aspect-Sentiment Triple Extraction (ASTE) [18; 19; 20], Aspect-Sentiment Quadruple Extraction (ASQE) [2; 21; 22; 23], etc. Among them, the most challenging task is the ASQE task because it can predict four sentiment elements at once, providing the most complete sentiment analysis results.  
For the ASQE task, [PERSON] et al. [2] transformed the original four-tuple prediction task into a text generation task and obtained the final target sequence by setting the output template of the target. This end-to-end generative modeling forms a new paradigm. Unlike the traditional autoregressive generation mode, [PERSON] et al. [6] proposed an efficient hybrid generation framework to generate emotional tuples in parallel and locate the corresponding emotional entities simultaneously. [PERSON] et al. [24] reformulated the ABSA task as a tree sequence-to-path generation problem. [PERSON] et al. [25] addressed the problem of data scarcity in ASQE tasks by realizing data augmentation through the inverse process of text generation quadruples. [PERSON] et al. [21] modeled ASQE as a multi-round machine reading comprehension task and established the association between different subtasks through a multi-turn hierarchical query strategy.  
The prompt template of the above generation method is single, and the disorder of elements is not taken into account. [PERSON] et al. [7] believe that the quadruple attribute itself is disordered and found that the template order affects the performance of sequence-to-sequence learning through experiments, and the changes at different positions had different trends. Specifically, they used minimum entropy to select the appropriate template order and used multiple template orders to enhance the data in ASQE tasks to achieve good results. On this basis, [PERSON] et al. [8] combined training and inference with multi-view prompts based on the disorder of quad-element attributes to guide the model to generate multiple sentiment tuples and then vote to select the final result. Their work selects the element order based on the element order using a pre-trained model, and there is no interactive learning for multiple template cue generation processes.  
Unlike existing methods, we design a dual-template prompt for the element order of aspect and opinion terms and add a mutual learning module to enhance the interactive training of knowledge information generated through multiple paths.