esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Supervised Approaches  
Supervised approaches rely on labeled stance data for each target and optimize classification through task-specific learning. The early methods predominantly leveraged feature engineering with traditional machine learning algorithms such as support vector machines (SVMs) [17] and logistic regression [18], utilizing handcrafted lexical, syntactic, and sentiment-based features. However, deep learning techniques have significantly improved stance detection by enabling automatic feature extraction from raw text [14; 19].  
One of the earliest deep learning approaches, BiCond [20], employs a bi-directional conditional encoding framework that separately encodes text and target representations before merging them for final stance prediction. With the advent of transformer-based models, particularly BERT [6], stance detection has benefited from capturing richer contextual dependencies between text and target.  
Building upon BERT-based architectures, researchers have explored various methods to enhance stance detection. CrossNet [21] adopts a self-attention layer to extract important contextual words toward the target in learning target-specific stance features, facilitating generalization. ASGCN [22] integrates aspect-based sentiment analysis (ABSA) with stance detection by employing graph convolutional networks (GCNs) to model syntactic dependencies in text. Similarly, TPDG [23] introduces a target-adaptive pragmatic dependency graph coupled with an interactive graph neural network, effectively capturing both intra-target and cross-target dependencies. While TPDG includes zero-shot experiments, its primary contribution lies in the supervised paradigm due to its reliance on labeled data for training.  
In addition to these methods, other works have explored multi-task learning [24] and contrastive learning [25] to enhance model robustness and generalization. Despite achieving high accuracy with sufficient labeled data, supervised approaches remain constrained by their dependence on extensive annotations, limiting their applicability to emerging topics, new targets, and low-resource domains.
# Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks
## 1 Introduction
Another contribution of this work is the study of the transferability of our summarization system to the domain of talk shows in Spanish. In this case, the documents are fragments of audio transcriptions of TV programs and the summaries consist of sentences written manually. Since we do not have a sufficiently large corpus of TV programs to train our summarization system for the talk shows, we use the ES-NEWS corpus. It should be noted that the characteristics of the corpus used for training are different from those of the TV programs. In the case of newspaper articles, there are some sentences that usually appear at the beginning of the article, that contain the main ideas or the relevant information underlying the article. However, the TV programs are conversations where the ideas and information are more scattered in the speaker turns. Even so, both of them expose content of similar topics. In a previous work [15], we proposed a supervised approach to text summarization which is based on Siamese Hierarchical Attention Neural Networks using distributed vector representation of words, the SHA-NN system. Siamese Neural Networks are capable of learning from positive and negative samples. During the training phase, we provide the network with positive and negative document-summary pairs; a positive pair is a document and its summary and a negative pair is a document and a summary of other different document randomly extracted from the training set. Furthermore, this model is enriched with an attention mechanism that provides the final score associated to each sentence of the input document, which allows us to establish a ranking and to select the most salient sentences to build the summary. We performed some experiments on the CNN/DailyMail corpus that confirmed the good behaviour of our approach to the summarization problem. In this work, we address the application of the SHA-NN system to summarize TV programs, in particular, Spanish TV talk shows. First, in order to train our summarization system, the ES-NEWS corpus was built. Second, the SHA-NN system was evaluated on this text corpus. Third, a test corpus has been built with talk shows, the LN24-SUMM corpus. Finally, the summarization system trained with the ES-NEWS corpus has been applied to the LN24-SUMM test corpus. We present a preliminary evaluation of our summarization system on the transcribed speech of the LN24-SUMM test corpus. Despite the different characteristics between the two corpora, the results of transferability between domains are promising.
# Medical Applications of the GEMPix
## 6 Microdosimetry
Any particle track in the AmBe data set with an energy below this limit was assigned as 'photon-like', the remaining tracks were assigned as 'neutron-like'. The dose distributions of the 'photon-like' component of the AmBe data set and the pure Am-241 data set match well. In conclusion, this analysis makes use of the fact that the GEMPix provides multi-channel results (i.e., 2D images of particle tracks), while this is not possible in a TEPC as it features only a single channel. Figure 13: The GEMPix detector (foreground) set up for measurements with an AmBe source (the metal cylinder housing the source is visible in the background). Two-dimensional particle track images were obtained from the measurements using the same data set as for the dose spectra (Figure 15). This is impossible with conventional TEPCs and opens the door for many new applications in microdosimetry. These include: particle tracks can be studied in detail (track structure microdosimetry), particle identification becomes possible (helping to disentangle contributions from different types of radiation in a mixed field) and effects of the size of sensitive volumes can be studied offline on a single data set. An outline of the results recently obtained can be found in [56], and the full results are given in [57]. Figure 14: Dose spectra obtained with the GEMPix and an AmBe source (mixed photon/neutron field) in the standard representation for microdosimetry (lineal energy, y, on the \(x\)-axis and the dose distribution d(y) multiplied by y on the \(y\)-axis [51]). The complete data set (black line), the ‘neutron-like’ component (dash-dotted black line) and the ‘photon-like’ component (dotted black line) are shown. As a comparison, a measurement with the GEMPix and an \({}^{241}\)Am source (photons only) is presented (red line). Figure 15: Examples of particle tracks measured with the GEMPix: a proton track (from neutron irradiation) with a visible increase in the energy deposition towards the end of the track (**left**) and an electron track (from \({}^{137}\)Cs irradiation, **right**).
## Materials and Methods
### FT-NIR Spectroscopy Acquisition  
To investigate the feasibility of distinguishing the cultivars of sweet corn seeds at a single-kernel level, the FT-NIR spectra of all 380 sweet corn seeds in each cultivar were collected using a Fourier transform infrared spectrometer (Antaris II FT-NIR Analyzer; Thermo Scientific Co., Waltham, MA, USA). The supporting control software of the spectrometer (RESULT, Thermo Scientific Co., Waltham, MA, USA) was used to set the instrument parameters and record the spectra. Figure 1 shows the schematic of the FT-NIR spectroscopy acquisition for sweet corn seed kernels. The spectrometer used an InGaAs detector with a high sensitivity and stability to NIR signals and a high-intensity halogen light as the light source. It also used an integrating sphere in a diffuse reflectance mode to improve the spectral quality of heterogeneous samples. The seed kernel was placed in a cylindrical sample cup with a diameter of 10 mm. The cup had a quartz window on the bottom, through which the light source irradiated the seed sample. Because of the difference of the structure and composition between the two sides of the corn seed and the limited penetration depth of the light source, the pre-experiment showed that the spectra collected from the embryo side and endosperm side were significantly different. While using the Euclidean distances and spectral anglesas the evaluation index to evaluate the difference between spectrums, the differences between the mean spectrums from two sides of kernels within the same cultivar were in the same order of magnitude as those of two cultivars from the same embryo side. Hence, only the spectra that were collected while the light source irradiated the embryo side of the seeds were used in this study. The spectra were collected at a resolution of 4 cm\({}^{-1}\) intervals (0.6 nm at 1250 nm), crossing the range of 10,000 to 4,000 cm\({}^{-1}\) (1000-2500 nm), containing 1557 wavelength points per spectrum. The mean spectrum from 32 successive scans of each individual seed was used as the seed spectrum. Finally, 760 spectra in total were obtained and stored as \(\log(1/R)\), where \(R\) is the relative reflectance signal value corrected by the supporting control software with the white and dark reference using the following equation:  
\[R=\ \frac{R_{0}\ -\ D}{W\ -\ D} \tag{1}\]  
where \(R_{0}\) was the original reflectance intensity in each wavelength from the seed samples, \(W\) was the reflectance intensity from the internal gold reference flag when the light source was turning on, and \(D\) was the reflectance intensity from the internal gold reference flag when the light source was turning off.  
To minimize the effects of temperature and moisture fluctuations during spectral acquisition [31], the spectrometer instrument was fully preheated and placed in an environment with a temperature of \(25\pm 1\ ^{\circ}\)C and a relative humidity of \(50\pm 3\%\). The seed samples were also placed in the same environment for more than 30 days before the collection of the spectra.
# Cationic and Anionic Dye Adsorption on a Natural Clayey Composite
## 3 Results and Discussion
### Adsorbent Amount Effect  
While the absorbent amount increases from 0.2 to 1.2 g/50 mL and the absorbate concentration is 50 mg/L, the CNB and ADY dye removal increases from 78% to 97% and from 87% to 97%, respectively (Figure 7). Several authors reported that dye removal is proportional to the adsorbent amount [16; 49; 52]. The ADY adsorption quickly progresses closer to the complete adsorption. The CNB adsorption slowly progresses closer to the complete adsorption. Thus, ADY removal demands a smaller amount of RC particles.  
Figure 6: (**a**) Effect of initial Basic Navy Blue 2 RN (CNB) dye concentration on dye removal. (**b**) Effect of the initial Drimaren Yellow Cl-2R (ADY) dye concentration on dye removal. Adsorbent dose 0.5 g/50 mL. pH (cationic) = 6, pH (anionic) = 2.  
Figure 7: (**a**) Effect of adsorbent dosage on Basic Navy Blue 2 RN dye removal. (**b**) Effect of adsorbent dosage on Drimaren Yellow CL-2R dye removal. Adsorbate concentration 50 mg/L. pH (cationic) = 6, pH (anionic) = 2.  
In contrast, the CNB dye's emotion demands more RC particles. However, following the above-proposed model, the differences in RC particle amounts strongly depend on the IPs' and KPs' proportions in the RC's total amount. The results in Figure 7 support the proposed adsorption models. With an increment in the adsorbent dose in the CNB adsorption system, the number of active absorption sites increases for more molecules' chemisorption on the IPs' surfaces [18].  
Figure 8 shows that the amount of dye adsorbed at equilibrium (adsorption capacity) decreases from 9.6 mg/g to 2.0 mg/g for the CNB dye. From 10.9 mg/g to 2.0 mg/g for ADY dye as the adsorbent amount increases. The initial adsorbate concentration is 50 mg/g. If the adsorbent amount increases, there are more available sites for adsorption. As the adsorbate amount remains constant, then each RC particle gets fewer adsorbed dye molecules. The authors of [16; 53; 54] reported a similar trend.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 4 Results
### Accuracy Test in Static Experiment  
The estimation errors are shown in Figure 9. The curves were calculated based on the results of performing detection 50 times. Ex, Ey, Ez, and Eagle represent the errors of \({}^{W}\)x, \({}^{W}\)y, \({}^{W}\)z, and \(\theta\), respectively. The error was larger with a longer detection distance. When the distance was less than 2000 mm, the algorithm showed better and more stable performance. The error was larger when the pallet angle and distance increased. When the angle was larger than 20\({}^{\circ}\), the standard deviation (STD) of each error increased. The maximum values of Ex, Ey, Ez, and Eagle were 94.64 mm, 44.74 mm, 63.11 mm, and 6.07 mm, respectively. The correlation coefficient (COF) between the error and angle of the pallet is shown in Table 1. The COF represents the correlation between two variables. The error had a positive correlation to the angle of the pallet, except for Ex, Ey, Ez, and Eagle at 3000 mm and for Ey and Eagle at 4000 mm.  
Figure 8: Multiple pallet recognition results contrast: The red box represents the results of the recognition algorithm. (**a,c**) is the recognition result of the proposed method, and (**b,d**) is the recognition result of yolov5.  
The time consumptions of the proposed algorithm were 72.44, 85.45, 117.63, and 182.84 ms and the STDs were 0.81, 0.59, 14.45, and 13.02 at 1000, 2000, 3000, and 4000 mm, respectively. Less time was consumed when the distance was smaller. Moreover, the computation time had no relation to the angle of the pallet.
# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 3 Cluster-Based Control Message Management for FANET
### Cluster-Based Operations
Fuzzy C-means algorithm runs towards the minimization of the objective function Equation (8). Its input is the number of clusters returned by the Gap statistics. Then, assigns coefficients randomly to each point for being in the clusters. And finally, it computes the centroid of each cluster and the new membership index of each point until the centroid stops change. Algorithm 1 describes the whole process, which is computed at each time interval at the controller node \(CN\). In this way, the controller node \(CN\) is responsible for creating the cluster, electing the CH, assign the cluster members, and disseminating the defined clusters and CH. Each cluster member sends contextual information to its CH, which performs data aggregation to avoid unnecessary data transmission. Afterward, each CH sends the aggregate data packets to the controller \(CN\). In this way, each CH works as a local controller to perform more complex tasks, such as the controlling of each group members, collecting data from non-CHs for data aggregation, take the local decision, sending aggregated data to the controller, forward control messages to/from the controller node \(CN\). ```
Input: Data vectors \(\{x_{n}\}_{n=1}^{N}\), number of clusters \(K\)
1\(M_{k,i}=\text{RandomFloat}()\)\(\forall k\in[1,\ldots,K]\,,\forall i\in[1,\ldots,N]\,.\) Initialize fuzzy partition matrix \(M\). repeat\(q_{j}=\frac{\sum_{k=1}^{N}u_{ij}^{m}\,\cdot\,x_{i}}{\sum_{i=1}^{N}u_{ij}^{m}}\)\(\triangleright\)Compute the centroid for each cluster \(j\) with current partition. \(M_{ij}=\frac{1}{\sum_{k=1}^{c}\left(\frac{\left\|\frac{\cdot\cdot\cdot q_{j}}{ \left\|x_{i}-q_{k}\right\|}}{\left\|x_{i}-q_{k}\right\|}\right)^{\frac{1}{m-1}}}\)\(\triangleright\)Update the fuzzy partition matrix. untilCentroid stops change
```  
**Algorithm 1**Fuzzy C-means
## 3 Experimental Results and Discussion
### Experimental Results
#### 3.3.5 Comparison with Other Models  
To verify the validity of the improved model, we base the YOLO-SSW against a wide range of object detection models' Quasi-tests to extend our comparison to include Faster R-CNN [35], SSD [16], RTEDTR [36], Centernet [37], YOLOv3 [12], YOLOv5, YOLOv6 [14], YOLOv7 [15], YOLOv8n, YOLOv9 [38], YOLOv10n [39], and YOLO11 [40]; corresponding test results are shown in Table 6.  
Figure 13: **(a)** Precision-confidence curve of Experiment 1; **(b)** Precision-confidence curve of Experiment 2; **(c)** Precision-confidence curve of Experiment 3; **(d)** Precision-confidence curve of Experiment 4; **(e)** Precision-confidence curve of Experiment 5; **(f)** Precision-confidence curve of Experiment 6; **(g)** Precision-confidence curve of Experiment 7.  
In terms of parameter scale, YOLOV10n has the smallest parameter (2.27 M), YOLOV3 has the largest parameter (61.55 M), and YOLO-SUMAS has a parameter of 3.33 M, which is at a low level. In terms of detection performance, YOLO-SUMAS's mAP@50 reaches 99.1%, which is significantly higher than that of YOLOV9 (97.8%) and YOLOV5 (97.5%). Its precision (98.8%) is close to that of YOLOV11 (98.9%), but its recall rate (99.2%) is significantly higher than that of the latter (94.6%). In terms of the F1 score, YOLO-SUMAS leads with 99.0%, which is better than the highest value of 96.7% of other models. Although the F1 score of RTEDTR is 95.69%, the number of parameters (19.88 M) is about 6 times that of YOLO-SUMAS. Overall, YOLO-SUMAS has outstanding performance in the balance between parameter quantity and detection performance, especially in the recall rate and comprehensive indicators. The radar plot of the above performance comparison is shown in Figure 16.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classes** & **P (\%)** & **R (\%)** & **mAP@50 (\%)** & **F1** & **Params (M)** \\ \hline Faster R-CNN [35] & 61.4 & 75.6 & 71.1 & 67.8 & - \\ SSD [16] & - & - & 72.3 & - & 25.1 \\ RTEDTR [36] & 96.8 & 94.6 & 96.5 & 95.69 & 19.88 \\ Centernet [37] & - & - & 94.69 & - & 32.7 \\ YOLOv3 [12] & 96.4 & 93.3 & 95.5 & 94.82 & 61.55 \\ YOLOv5 & 98.6 & 94.0 & 97.5 & 96.2 & 9.1 \\ YOLOv6 [14] & 93.4 & 89.10 & 94.00 & 91.2 & 4.23 \\ YOLOv7 [15] & 96.0 & 89.9 & 93.6 & 92.8 & 6.0 \\ YOLOv8n & 97.6 & 90 & 94.4 & 93.0 & 3.01 \\ YOLOv9 [38] & 98.1 & 95.4 & 97.8 & 96.7 & 6.2 \\ YOLOv10n [39] & 96.8 & 88.6 & 94.3 & 92.52 & 2.27 \\ YOLOv11 [40] & 98.9 & 94.6 & 97.6 & 96.7 & 9.4 \\ YOLO-SUMAS (Ours) & 98.8 & 99.2 & 99.1 & 99.0 & 3.33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of different models.  
Figure 14: **(a)** Recall-confidence curve of Experiment 1; **(b)** Recall-confidence curve of Experiment 2; **(c)** Recall-confidence curve of Experiment 3; **(d)** Recall-confidence curve of Experiment 4; **(e)** Recall-confidence curve of Experiment 5; **(f)** Recall-confidence curve of Experiment 6; **(g)** Recall-confidence curve of Experiment 7.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 2 Experimental
### Physiochemical Characterization of CAA-CS Hydrogels
#### 2.3.4 The Stability Test of CAA-CS Composite Hydrogel  
The swelling degree belongs to an important index of stability tests. The stability of hydrogels in physiological environments directly influences their drug sustained-release performance. Excessive swelling would result in structural collapse [28; 29]. The swelling ratio of CAA-CS hydrogels was determined using a gravimetric method. Hydrogel samples were immersed in phosphate-buffered saline (PBS) and incubated in a constant-temperature water bath at 37 \({}^{\circ}\)C. At specific time intervals, the hydrogel samples were removed, excess surface water was blotted with filter paper, and the samples were weighed to observe the swelling behavior of the polysaccharide gels under physiological conditions. The swelling degree of the gel is described by Equation (3) [30]  
\[\text{Swelling degree }(\%)=\frac{w_{s}-w_{i}}{w_{i}}\times 100\% \tag{3}\]  
where \(w_{\text{s}}\) is the mass of the hydrogel after reaching swelling equilibrium, and \(w_{\text{i}}\) is the mass of the dry gel before swelling.  
There exists a close relationship between the degradation rate and stability of hydrogels. The degradation rate of the hydrogel was determined using a weight loss method. Dried hydrogel samples were immersed in phosphate-buffered saline (PBS, pH = 7.34) for 48 h to achieve swelling equilibrium. The swollen hydrogel samples were then transferred to a 37 \({}^{\circ}\)C PBS shaker operating at 60 rpm. At specific time intervals, the hydrogel samples were freeze-dried and weighed to calculate the degradation rate of the gel. The degradation rate (%) is described by Equation (4): [31]  
\[\text{Degradation degree }(\%)=\frac{W_{t}-W_{d}}{W_{d}}\times 100\% \tag{4}\]  
where \(w_{d}\) is the dry weight of the degraded gel and \(w_{t}\) is the initial dry weight of the gel.  
The polysaccharide hydrogel was immersed in a phosphate-buffered saline (PBS, pH = 7.34) solution to ensure complete submersion. The hydrogel was then incubated at 37 \({}^{\circ}\)C, and its swelling behavior was monitored and documented through photography at immersion intervals of 24, 48, and 72 h. Additionally, a separate set of hydrogels was subjected to thermal treatment by placing them in an oven at 100 \({}^{\circ}\)C for 2 h. The morphological changes were subsequently examined to evaluate the thermal stability of the hydrogels.
# Multi-Objective Optimisation of Welding Parameters for AZ91D/AA6082 Rotary Friction Welded Joints
###### Abstract  
Recently, rotary friction welding has been used to join magnesium alloys. FRW uses friction heat to bond magnesium alloys with aluminium alloys. Combining these light alloys can provide many promising applications in the industry. The welding parameters such as friction and upsetting force, rotational speed, and welding time play a significant role in determining the joint strength. The paper presents a new approach to multi-objective optimisation of friction welding process parameters for AZ91D/AA6082 alloy joints. Multi-objective optimisation is based on artificial neural networks and genetic algorithms as non-conventional AI techniques. The methods were used to determine the following optimal welding process parameters: friction force, upsetting force and friction time for simultaneously maximised tensile strength and minimised metal loss (shortening) during welding. The ultimate tensile strength and metal loss of the friction welding joints were studied numerically and experimentally. Moreover, the influence of welding parameters on the ultimate tensile strength and shortening of friction joints was also studied. A genetic algorithm successfully found a set of welding parameters for which the joint strength increases from 24 to 81 MPA and the joint shortening decreases from 8.25 to 0.23 mm. The results show that a low friction force and upsetting force give a high value of tensile strength and the lowest shortening of the bimetal joints.  
rotary friction welding; AZ91D magnesium alloy; tensile strength; MOGA Article
## 5 Data Communication Phase
### Inter-Cluster Transmission
#### 5.2.1 Multi-Hop by CHs
In [135], the authors propose an advance multi-hop routing protocol called LEACH-L as a contribution to LEACH protocol, which substitutes one-hop transmission with multi-hop wise energy-saving selection of relay nodes towards the BS. The paper addresses the issue that although multi-hop routing cuts down the energy consumption in data transmission for every individual node, it increases the circuit energy consumption. Therefore, two thresholds are defined to decide between single-hop and multi-hop transmission from CHs to the BS: the shortest efficient distance for data transmission and Max-distance as the longest distance of direct transmission.