esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Paleoproductivity  
The level of paleoproductivity directly determines the material basis for the formation of organic matter, and high paleoproductivity is conducive to the formation of shale with high TOC content [32]. Ancient climate has a significant impact on the level of  
Figure 10: Binary diagram of geochemical indicators U/Th and V/V + Ni in different sections of the Qiongzhusi formation in the southern Sichuan Basin.  
Figure 9: Binary diagram of geochemical indicators V/Cr and V/V + Ni in different sections of the Qiongzhusi formation in the southern Sichuan Basin.  
paleoproductivity. In arid to semi-humid climates, an increase in climate humidity can improve productivity levels, while excessively humid and hot climates are not conducive to the survival of organisms and can reduce paleoproductivity levels.  
Previous studies have shown that Ba and P are indicators used to infer paleoproductivity [33]. In practical applications, the priority for inferring paleoproductivity using Ba is higher than that of P. Among them, the content of biogenic barium (Ba\({}_{\text{bio}}\)) is an important indicator of primary paleoproductivity [34]. Typically, the content of barium associated with biological activities is calculated, excluding the input of terrigenous barium, to indicate paleoproductivity. The biogenic barium content is calculated using Formula (1) [35]:  
\[\text{Ba}_{\text{bio}}=\text{Ba}_{\text{sample}}-\text{Al}_{\text{sample}} \times(\text{Ba}/\text{Al})_{\text{PAAS}} \tag{1}\]  
Here, Ba\({}_{\text{bio}}\) is the content of biogenic barium, in \(\mu\)g/g; Ba\({}_{\text{sample}}\) indicates the content of Ba measured in shale samples, in \(\mu\)g/g; Al\({}_{\text{sample}}\) is the content of Al measured in shale samples, in \(\mu\)g/g; (Ba/Al)\({}_{\text{PAAS}}\) is the standard value of Australian shale.  
By plotting the barium content of shale in the Qiongzhusi formation, it is clear that the paleoproductivity of the Qiongzhusi formation in well Z201 is extremely high, with an average Ba\({}_{\text{bio}}\) value of 1411.56 \(\mu\)g/g. Within the Qiongzhusi formation, the paleoproductivity of the Qiong 1 member is slightly higher than that of the Qiong 2 member (Figure 11), indicating that the bottom shale of the Qiongzhusi formation in well Z201 has a higher paleoproductivity level than the top shale. This inference is highly consistent with the TOC content of the intervals, with the TOC content of the Qiong 1 member higher than that of the top Qiong 2 member. Additionally, the calculated Ba\({}_{\text{bio}}\) content is close to the measured barium content of the shale samples, suggesting minimal terrigenous input of barium. The barium in the shale of this interval is primarily of biogenic origin. Overall, well Z201 in the rift basin within the deep water continental shelf area exhibits a very high paleoproductivity level.
# Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation
## 2 Related Works
### Research on Speech Emotion Recognition
#### 2.2.2 Convolutional Neural Network  
Convolutional neural networks (CNNs) are models that can be trained while retaining the spatial information of an image and are used in various fields such as image classification, object detection, and image processing, and are also widely used in the field of emotion recognition, as shown in the work of [PERSON] et al. [28]. They proposed a CNN architecture based on Log-Mel-spectrograms. The datasets used for the experiment were Emo-DB [13] and IEMOCAP [15], and the experiments were conducted in two settings: speaker-dependent and speaker-independent. In the speaker-dependent experiment, 80% of the data were used for training and 20% for testing, while in the speaker-independent experiment, data from eight speakers were used for training and two for testing. Additionally, data augmentation techniques such as speed, pitch, and shift adjustments were applied to Emo-DB to increase the data volume. The results showed that, in the speaker-dependent experiment, Emo-DB achieved WA of 84.61% and UAR of 82.27%, while IEMOCAP achieved WA of 58.99% and UAR of 59.32%. In the speaker-independent experiment, Emo-DB achieved WA of 72.02% and UAR of 75.35%, while IEMOCAP recorded WA of 59.33% and UAR of 55.22%.
# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 5 Results and Discussions
### Case Study  
For a comprehensive understanding of our proposed method, we allowed its performance to be visualized through a case study, which was selected from the EmoryNLP test dataset. As illustrated in Figure 6, our model and the baseline model both made mistakes when predicting the emotions of utterance (2) and utterance (3). By checking the video corresponding to this conversation, we found that [PERSON] had already started checking in for boarding. [PERSON] was afraid that [PERSON] could not see him coming to find her, so [PERSON] cried out to [PERSON] anxiously. In this dialogue, if we only consider the text features, we will lack some emotional information, so we cannot accurately predict the corresponding  
Figure 5: Heatmap of the confusion matrix of ERMC-MHGNN on the EmoryNLP dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**MELD**} & \multicolumn{2}{c}{**EmoryNLP**} \\ \cline{2-5} \# & **Shift** & **w/o Shift** & **Shift** & **w/o Shift** \\  & **(1003)** & (861) & **(673)** & **(361)** \\ \hline DialogXL & 57.33 & 71.43 & 33.88 & 43.77 \\ DAG-ERC & 59.02 & 69.45 & 37.29 & 42.10 \\ ERMC-MHGNN & 62.01 & 72.36 & 38.93 & 41.83 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test accuracy of ERMC-MHGNN and the partial baseline models on samples with an emotional shift and without one. ’(0’ indicates the number of samples.  
emotions. In our future work, we will use multimodal features to make up for the lack of emotional information in the text features.  
When the conversation went to utterance (8) and utterance (9), we found that [PERSON]'s communication with [PERSON] was interrupted, and [PERSON] was urged to board. In addition, in combination with the semantics of utterance (9), [PERSON] could not carry the big candy bar that [PERSON] gave her. So, [PERSON]'s mood changed. We found that the baseline model directly predicted the emotion of utterance (9) within the emotion of utterance (8). The baseline model did not combine the context of this conversation well and did not fully consider multiple historical contexts. Our model processed the utterances of three speakers through hypergraphs and captured [PERSON]'s emotional change from 'neutral' to 'angry' in the current context by using two types of speaker dependence.
## 3 TCM Protocol
### The Topology Maintenance Phase
#### 3.3.3 New Nodes Join in or Node Death  
When the node \(S_{i}\) joins in a cluster, it will broadcast the join request information, the pre-stored identity \(ID_{Si}\), the public key \(P_{Si}\), and the digital signature (\(W_{Si}\),\(w_{Si}\)): \(S_{i}\)\(\rightarrow\)*_Join_req\(||D_{Si}||P_{S_{i}}||(W_{Si}\),\(w_{Si}\)). After the current cluster head \(C_{i}\) verifies its legitimacy by (\(W_{Si}\),\(w_{Si}\)), it will give the response answer \(C_{i}\)\(\rightarrow\)\(S_{i}\):\(ACK||ID_{C_{i}}||P_{C_{i}}||(W_{C_{i}}\),\(w_{C_{i}}\)). On the other hand, after the node \(S_{i}\) verifies the legitimacy of the cluster head by (\(W_{Ci}\),\(w_{Ci}\)), \(S_{i}\) will join in cluster \(C_{i}\), and establish the session key with \(C_{i}\). \(C_{i}\) assigns a time plot to \(S_{i}\), and broadcasts this within the cluster. The topology control protocol is a dynamic clustering algorithm, the energy factor of the nodes has been considered in the clustering process. When there are dead nodes in network, they are not allowed to participate in the clustering and they will be excluded by the network.
## 3 Background and Overview of LEACH
### LEACH Overview
#### 3.3.2 Steady State Phase  
At the end of the setup phase operation, every CM can start the data transmission towards its CH during its own assigned time slot. We assume that each CM has always data to transmit. Regardless of the communication in each cluster, the CH must keep its radio receiver on for receiving all data from its CMs. In addition, when TDMA time slot is over or a round time has ended the CH performs data compression over the received data from its CMs and sends the resulting data to the BS. As the distance from the BS to the CH is significantly larger than the distance separating this latter and its CMs, this transmission requires high-energy consumption compared to other data transmission operations. This whole process represents the steady state phase operation of LEACH. Subsequently, the sensor nodes which have not been elected as CH in previous rounds will participate to become CH in next rounds.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Metrics  
For estimation of the quality of the predicted solar irradiance, several metrics are typically used, such as mean squared error, root mean squared error, mean absolute error, mean absolute percentage error, and R squared. The emphasis should be put on the last predicted timestep, which represents the future solar irradiance, one day ahead. This practice is effective for selecting a model that best predicts the future. Mean absolute error expresses the error of solar irradiance directly in kWh/m\({}^{2}\)/day. During metrics calculation, timesteps before the last (future) timestep are not included, but, during training, all timesteps are used to calculate the gradients and provide important information to the model.
## 3 Results
### Physical Properties  
#### 3.2.1 Dry Matter (DM)  
Water is a very important factor affecting food storage. It is believed that water activity in food is a more important physicochemical property than water content because, in heterogeneous systems, it determines the distribution of water between different components [38]. Table 4 shows the results of dry matter content, water activity, and bulk density. No significant effect of the drying method (\(p\)-value = 0.0443) on dry matter values was observed in the study. Higher DM values were recorded in the material after vacuum drying than after freeze drying. The opposite observations were noted by [PERSON] et al. (2021) when subjecting courgette to vacuum drying (45 \({}^{\circ}\)C) [8]. This is likely related to the higher vacuum-drying temperature (60 \({}^{\circ}\)C) used in the study reported here [8]. Samples during vacuum drying reach much higher temperatures than during freeze drying. The higher the sample temperature during vacuum drying, the higher the dry matter values. [PERSON] et al. (2021) reported a higher dry weight value for materials vacuum-dried at a higher temperature. Moreover, using inulin and inulin-trehalose, they observed higher water activity and lower moisture content for chokeberry dried in a vacuum at 60 degrees than in the case of freeze-drying [39]. This is related to the different nature of the drying process and the different final temperatures of the material. In the case of freeze drying,  
Figure 3: Changes in the VOCs profile of processed broccoli. the final temperature is 20 \({}^{\circ}\)C, and in the case of VD, up to 60 \({}^{\circ}\)C. Due to the changes occurring during drying, the products obtained after FD and VD are not the same materials. After vacuum drying, the material reaches an equilibrium moisture level lower than in FD at a much higher temperature, which may also be related to high drying shrinkage, color change, and degradation of volatile compounds, which are related to changes in the physical, chemical, and structural properties that may affect the water activity. The DM of vacuum-dried broccoli (60 \({}^{\circ}\)C; 24 h) was 0.973, while [PERSON] et al. (2023) observed lower DM values (0.903) for broccoli dried by the same method at 60 \({}^{\circ}\)C for 10.5 h. Increasing the drying time was found to result in higher dry matter values [3]. An increase in dry matter content after pretreatment (VI) was observed in all samples tested. These results are in line with studies by other authors, who applied the vacuum
# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 3 Collection Methodology  
From 16 November 2017 until 5 February 2018, three LPWAN datasets were collected in two different environments. Firstly, a Sigfox dataset was recorded in a large area between Antwerp and Ghent (\(\pm\)1068 km\({}^{2}\)), which was delineated because of its overall rural characteristics. Secondly, another Sigfox dataset was recorded in an urban area in and around the city center of Antwerp (52.97 km\({}^{2}\)). Lastly, a LoRaWAN dataset was collected in the same urban area. The messages in these datasets contain GPS coordinates, which allows the user to correlate accurate location information with RSSI measurements on that location. For the Sigfox datasets, a proprietary, nation-wide Sigfox network which was deployed by EngeiM2M was used. The LoRaWAN dataset was collected over a proprietary, nation-wide network that was rolled out by Proximus. In this section, the collection and structure of each dataset is discussed.
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 3 Material: WLS200S Lidar
### Lidar Specifications  
#### 3.1.1 Instrument Description  
A DWL is an active remote sensing instrument that emits a laser pulse into the atmosphere and records the radiation backscattered by particles. The WLS200S operates at a wavelength of \(\lambda=1.54\) um, which means that the laser beam is mainly scattered by atmospheric aerosol particles having diameters above about 100 nm. Because of their movements relative to the instrument, the backscattered signal is subject to the Doppler effect and its frequency is shifted compared to the emitted laser pulse frequency [40]. Since the particle movements are assumed to follow the wind, the frequency shift is proportional to the wind velocity in the Line-Of-Sight (LOS) direction. This leads to the relation \(\Delta f=-\frac{2V_{los}}{c}\), where \(c\) corresponds to the speed of light and \(V_{los}\) to the LOS velocity, counted positive when the wind is blowing away from the lidar.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 3 Results
### SHAP-Based Interpretation of XGBoost Feature Contributions
\(GRVI\), \(SBI\), \(MSAVI\), and \(WI\) show a less consistently negative correlation with the predicted crop yield. Therefore, while high values of these VIs tend to be associated with lower predicted yields, this negative association is not strong and can vary across different data points. The rest of the VIs show a more concentrated distribution of SHAP values around zero, which suggests a minimal impact on the model's predictions. Besides,  
Figure 4: Feature importance for crop yield prediction based on mean absolute SHAP values from XGBoost. the color gradients cannot provide clear understandings into the direction of their influence, since blue and red are seen across both negative and positive SHAP values.
## 3 Results
### Growth Rates  
Mean weight, mean body length, survival, specific growth rate, and feed conversion ratio were used as metrics to evaluate the effect of colored light. According to the measured data, the R group showed the highest final weight among the fish, and it also had the lowest initial weight. Therefore, the R group had the highest performance, with the best growth rates and DWG (see Table 4). In addition, the data obtained show that the R and G groups presented the best gains in weight and length. However, the G group had the worst survival. The feed conversion ratio was better in the R and Natural groups compared with the other groups. The SGR value was significantly higher in the groups R and G compared with the other groups. The quantitative distribution of the final weight of the fish for each group is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**Treatments**} \\ \cline{2-5}  & **R** & **G** & **B** & **Natural** \\ \hline Initial biomass (g) & 30 & 30 & 30 & 30 \\ Initial weight (g/fish) & 0.2 & 0.2 & 0.3 & 0.3 \\ Initial number of fish & 133 & 132 & 111 & 113 \\ Final Biomass (g) & 2193 & 1330 & 1461 & 1362 \\ Total biomass gained (g) & 2163 & 1300 & 1431 & 1332 \\ Final number of fish & 127 & 79 & 94 & 84 \\ Final individual weight (g/fish) & \(17.3\pm 3.3\) a  & \(16.8\pm 3.1\) a  & \(15.5\pm 2.8\) b  & \(16.2\pm 3.0\) ab  \\ Final individual body length (cm/fish) & \(8.2\pm 1.0\) a  & \(8.0\pm 0.9\) a  & \(7.7\pm 0.9\) b  & \(7.9\pm 0.8\) ab  \\ Survival (\%) & 95.5 & 59.9 & 84.7 & 74.3 \\ SGR (\%/day) & 6.0 & 5.9 & 5.6 & 5.7 \\ DWG (g/day) & 0.2 & 0.2 & 0.2 & 0.2 \\ FCR & 0.8 & 1.3 & 1.2 & 1.0 \\ \hline \hline \end{tabular}  
* SGR = specific growth rate; DWR = daily weight gain; FCR = feed conversion rate. Data are presented as the mean \(\pm\) standard deviation. Values in the same row with different superscript letters indicate significant differences based on the one-way ANOVA and Tukey’s test (\(p<0.05\)).  
\end{table}
Table 4: Growth rates of Nile tilapia.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Treatments**} \\ \cline{2-5}  & **R** & **G** & **B** & **Natural** \\ \hline Temperature (\({}^{\circ}\)C) & 28.03 \(\pm\) 1.03 & 28.38 \(\pm\) 0.96 & 28.94 \(\pm\) 1.22 & 28.65 \(\pm\) 0.93 \\ Dissolved Oxygen (mg/L) & 6.65 \(\pm\) 0.58 & 6.67 \(\pm\) 0.43 & 6.71 \(\pm\) 0.47 & 6.72 \(\pm\) 0.47 \\ pH & 7.91 \(\pm\) 0.17 & 7.91 \(\pm\) 0.16 & 7.92 \(\pm\) 0.16 & 8.01 \(\pm\) 0.20 \\ \(NH_{3}\)-N (mg/L) & 0.07 \(\pm\) 0.01 & 0.07 \(\pm\) 0.01 & 0.08 \(\pm\) 0.01 & 0.10 \(\pm\) 0.01 \\ TAN (mg/L) & 0.79 \(\pm\) 0.18 & 0.76 \(\pm\) 0.21 & 0.86 \(\pm\) 0.14 & 0.93 \(\pm\) 0.22 \\ \hline \hline \end{tabular}  
* Data are presented as the mean \(\pm\) standard deviation.  
\end{table}
Table 3: Water quality parameters.
# Target Speaker Extraction by Fusing Voiceprint Features
###### Abstract  
It is a critical problem to accurately separate clean speech in the multispeaker scenario for different speakers. However, in most cases, smart devices such as smart phones interact with only one specific user. As a consequence, the speech separation models adopted by these devices only have to extract the target speaker's speech. A voiceprint, which reflects the speaker's voice characteristics, provides prior knowledge for the target speech separation. Therefore, how to efficiently integrate voiceprint features into the existing speech separation models to improve their performance for the target speech separation is an interesting problem not fully explored. This paper attempts to solve this issue to some extent and our contributions are as follows. First, two different voiceprint features (i.e., MFCCs and d-vector) are explored in the performance enhancement for three speech separation models. Second, three different feature fusion methods are proposed to efficiently fuse the voiceprint features with the magnitude spectrograms originally used in the speech separation models. Third, a target speech extraction method which utilizes the fused features is proposed for two speaker-independent models. Experiments demonstrate that the speech separation models integrated with voiceprint features using three feature fusion methods can effectively extract the target speaker's speech.  
target speech separation; target speaker extraction; voiceprint; feature fusion Article
# Silver Nanoparticles-Loaded Exfoliated Graphite and Its Anti-Bacterial Performance
## 3 Results and Discussion
### Morphology Observation by SEM and TEM  
Figure 2 shows SEM images of Ag nanoparticles-loaded EG. EG has a worm-like shape (Figure 2c). Due to its loose and porous character, the EG volume is hundreds times that of the GICs, which leads to the very low density of EG [36; 37]. Only the spacing of the graphite layers (C axis) was expanded; thus the conductive characteristics of each EG sheet layer remains the same as graphite flakes by nature.  
One GIC had been split into thousands of smaller pieces without separating completely, therefore there is plenty of pores in EG, shown in Figure 2d. In addition, some functional groups such  
Figure 1: (**a**) X-ray diffraction (XRD) patterns of flake graphite, graphite intercalation compounds (GICs), and Ag-GICs, insert shows XRD patterns of Ag-GICs, the 2 Theta from 35\({}^{\circ}\) to 80\({}^{\circ}\); (**b**) FT-IR spectra of GICs, Ag-GICs, and Ag-EG.  
as hydroxyl, carboxyl, carbonyl groups existed on the surface and pores of the EG after acid and high temperature treatments, and they could promote the adsorption of polar molecule.  
Figure 2e shows the distribution of Ag particles. Most particles have a diameter less than 10 nm, but there are some particles that appear larger size, which could be the result of aggregation of two or more particles together. The Ag particles size and quantity are influenced by the concentration of the AgNO\({}_{3}\) solution. With the increase of concentration, the Ag particles appeared in larger size, and there are more Ag particles decorated on the EG sheets [25].  
Figure 3 shows transmission electron microscopy (TEM) images of Ag nanoparticles-decorated exfoliated graphite. A low-magnification TEM micrograph (Figure 3a) presents plenty of wrinkles owing to the thin structure of the sheet. Uniform single-entity Ag nanoparticles embedded in the sheets after ultrasonication, revealing that there is a good inherent interfacial bonding in Ag and expansible graphite. The distribution of particles ranges in the diameter less than 10 nm (the average diameter of about 100 particles), the smaller particles are spherical in shape, whereas the larger nanoparticles are in an anomalous form. The presence of the bigger particles can be due to agglomeration of the two or more particles together. Ag nanoparticles loaded on EG comes near to the size of those loaded on GO sheets [23], but smaller than those loaded on ACFs [38; 39]. Figure 3c clearly shows the interface between the sheet and Ag, indicating that Ag nanoparticles are well attached between the surfaces of the sheet. The measured lattice fringe spacing of 0.23 nm in these Ag nanoparticles corresponds to the (111) crystal plane.  
Figure 2: Scanning electron microscopy (SEM) images of Ag nanoparticles-loaded EG. (**a**) Ag nanoparticles synthesized on EG sheets; (**b**) the corresponding selected area energy dispersive spectrometer (EDS); (**c**) the worm-like shape of EG; (**d**) pores in EG; (**e**) defects on EG sheets where Ag nanoparticles grew on.