esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Latency Evaluation
For this to be successful, routes are manually added to each device's IP table to indicate the UE sessions that must be routed through the Core Network. Each packet is thereby routed from the GOOSE Publisher to the Core Network. The Core Network then further encapsulates the TCP packet into the GTP packet for tunneling it to the gNB. The gNB base station handles all scheduling of packets to the UE over the wireless interface. As the final step in this network scenario, the UE needs to extract all received TCP packets from GTP, then further extract the encapsulated GOOSE packet, and finally publish it over the wired Ethernet interface so that it can reach the DUT. Compared to the TCP 2-Hop configuration, the GTP encapsulation and gNB scheduling result in additional latencies that are combined and labeled as 5G-specific latency, as shown in Figure 4.  
Figure 5: Network configurations GOOSE and TCP. 5G-TLS--This configuration utilizes the same topology and routing as the previous 5G scenario but adds TLS encryption through the OpenSSL library. This scenario is used to measure the latency effect of adding security features to the GOOSE communication rather than sending unencrypted data over the network. Similar to the 5G TCP configuration, the session is opened before GOOSE traffic is sent, so the TLS handshake is not re-occurring for each individual packet transmission. However, since encryption can be a computationally costly operation, this step aims to quantify the latency impact of this processing. Latency Isolation--For further isolation of the variance within the packet latency, separate network taps were placed on the Core Network, gNB, and OT device to compare transmission times. Each tap was connected between the workstation and switch, with traffic captured in a single Wireshark [33] session. This method allows us to compare the recorded epoch time of each packet reception for timing measurements. For the timing diagram shown in Figure 4, the original GOOSE message identified as message generation, the GTP packet from the Core Network to gNB, and UE to OT device GOOSE packet are all captured. We then compared the original GOOSE packet, the GTP packet, and the forwarded GOOSE packets for timing comparison, as shown in Figure 7.  
Figure 6: Latency breakdown tests timing diagram.
## 5 Discussion
Moreover, the measurements show that the electron temperature is higher when the discharge current is lower [40; 41], so that the effect of a decrease in the discharge current is to cause a decrease in the \(\beta\) parameter value and a decrease in the \(\Lambda\) parameter (Figure 4). We have performed a series of novel measurements at constant background pressure, changing the discharge current, which show that the predicted trend is correct. This does not allow us to distinguish between the effect of the ion to electron temperature ratio \(\beta\) and the effect of the ion mean free path to Debye length ratio \(\Lambda\) in the experiments. However, if the discharge current is kept constant, the plasma density remains constant and so does the electron density [40]. Moreover, if the background pressure is decreased, the measurements show that the electron temperature, \(T_{e}\), increases [40; 41], causing a decrease in \(\beta\). Accordingly, regarding the \(\Lambda\) parameter, we predict two opposite influences: (a) the Debye length increases with \(T_{e}\), and thus the \(\Lambda\) parameter decreases; (b) the collisions are less frequent, increasing \(\lambda_{+}\), and so the \(\Lambda\) parameter increases. Therefore, the trend that the \(\Lambda\) parameter will follow with changing background pressure alone cannot be predicted for a constant discharge current. We have also performed a series of novel measurements at constant discharge current to verify the influence of the background pressure on both the \(\beta\) and \(\Lambda\) parameters. Figure 5 shows that a decrease in the \(\beta\) parameter and an increase in the \(\Lambda\) parameter are experimentally found when the gas pressure decreases, according to argument (b) above. As the transition is found for decreasing background pressure, this proves that the most relevant parameter in the transition between radial and orbital behavior of the ions in the helium plasma is the positive ion to electron temperature ratio, \(\beta\). Figure 4: Increasing figure between \(\beta\) and \(\Lambda\) for constant background helium pressure \(p=20.2\) Pa.  
Figure 5: Decreasing figure between \(\beta\) and \(\Lambda\) for constant discharge current \(I_{d}=5.0\) mA in a helium discharge.
# Saturable Absorption Dynamics of Highly Stacked 2D Materials for Ultrafast Pulsed Laser Production
## 5 Graphene and Graphite SAs
### Graphite SAs  
To investigate the potential of highly stacked graphene in saturable absorption applications, graphite in a pencil was incorporated into a 1550-nm fiber laser system by simple sketching onto ferrule connectors in the fiber ring cavity [80], which would be a stringent case of highly stacked graphene. Despite its rawness, this system produced well-defined Q-switched pulsed lasers. The details of the laser system are shown in Figure 20. A 3-m long EDF, which exhibited almost complete absorption (30 dB) of 980-nm pump beam, was used as a gain medium, and a LD with a maximum power of 273 mW was used as a pump source. The LD was coupled with the gain fiber through a 980/1550 nm WDM and for unidirectional light, an isolator was placed between a WDM and a graphite SA.  
When the pump power reached 169 mW, Q-switched pulsed lasers were produced in the ring cavity. The Q-switched pulses maintained up to the maximum pump power of 273 mW with good stability. The oscilloscope traces of the output pulses showed the typical pump power-dependent features of Q-switching (Figure 21a). The repetition rate increased from 39.86 to 46.08 kHz as the pump power increased, whereas the pulse width decreased from 2.71 to 1.98 us. The optical spectrum measured at the pump power of 273 mW indicated that the center wavelength and 3 dB bandwidth of laser pulses were 1559.3 nm and 2.5 nm, respectively (Figure 21b). The average energy and peak energy of  
Figure 19: (**a**) Measured optical spectrum and (**b**) autocorrelation trace of the laser system. Reprinted with permission from [79].  
Figure 18: Tm-doped fiber laser system incorporated with a sandwiched graphene SA module. Reprinted with permission from [79].  
the output laser pulses monotonically increased as the pump power increased. The system used a very thick raw state of graphite and further improvement of performance would be possible through structure refinement and system optimization, which was actually achieved by continued studies [81].
# Speech Sentiment Analysis Using Hierarchical Conformer Networks
## 4 Experimental Assessment
### Datasets  
We evaluate our proposed method on different benchmarks such as weighted and unweighted accuracy, precision, recall and F1_score using two commonly used datasets, IEMOCAP [38] and RAVDESS [39] speech corpora. The proposed sentiment recognition framework's capabilities are compared with most advanced models, and the model performance, training, validation accuracy and loss plots for both datasets are shown in Figure 6.  
**IEMOCAP**: Interactive Emotion Binary Motion Capture dataset was proposed in 2008 and widely used in speech sentiment analysis tasks. It is collected by professional actors through hours of dialogue and conversation in different scenes. It consists of five parts and lasts up to 12 h. A total of nine emotions are included in the dataset: anger, happiness, excitement, sadness, depression, fear, surprise, other and neutral states. Table 1 shows the percentage of unused sentiment in the dataset. Each conversation is evaluated by three or more evaluators and when more than half of the evaluators agree, the conversation is marked with the corresponding label. When the evaluators disagree, the conversation is marked \"Other\". It is more natural to acquire data this way.  
**RAVDESS**: The RAVDESS dataset consists of two parts: the emotional speech part and the song data part. In the speech sentiment analysis task, we mainly focus on the emotional speech part. The emotional voice part is collected by 12 professional male actors and 12 professional actresses performing dialogue performances, with a total of 1440 dialogues. There are eight emotions: calm, happy, sad, angry, fearful, surprised, disgusted and neutral. Table 2 shows the percentage of unused sentiment in the dataset. During data collection, actors were asked to speak two fixed sentences with different sentiment categories; therefore, the balance of the RAVDESS dataset is good, but the naturalness is relatively poor. In this paper, the RAVDESS dataset is used as an auxiliary dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Utterances** & **Participation (\%)** \\ \hline Anger & 1103 & 19.9 \\ Happiness & 1636 & 29.6 \\ Sad & 1084 & 19.6 \\ Neutral & 1708 & 30.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The distribution of different emotions in the dataset IEMOCAP.  
Figure 6: Experimental results on two datasets.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 2 Related Work  
### Single-Image Deblurring  
Image deblurring research has evolved from traditional methods to deep learning approaches. Early studies primarily focused on image prior-based methods [17; 18; 19], where researchers assumed uniform blur kernels and designed various natural image priors to compensate for the ill-posed deblurring process. However, these methods showed limited effectiveness when handling non-uniform blur in complex dynamic scenes. To address this limitation, methods were proposed [20; 21] that introduced additional segmentation to estimate blur kernels separately for different image patches, while other approaches [22; 23] derived pixel-level blur kernels through motion field estimation.  
With the advancement of deep learning, numerous CNN-based methods have been proposed to address dynamic scene deblurring. [PERSON] et al. [24] first applied CNNs to non-uniform motion blur kernel estimation, achieving deblurring through local linear blur kernel prediction. Following the emergence of large-scale datasets for single-image deblurring, several works [25; 26; 27] began using CNNs to generate sharp images directly from blurry inputs in an end-to-end manner. [PERSON] et al. [28] proposed a multi-scale CNN architecture that could restore sharp images without explicit blur kernel estimation. [PERSON] et al. [29] introduced a scale-recurrent network, employing a coarse-to-fine strategy through recursive learning. By processing images at multiple scales, this approach effectively handled varying degrees of blur. Building on this concept, DMPHN [30] decomposed images into hierarchical patches for more efficient processing and better detail preservation. The emergence of the transformer architecture brought new possibilities to image deblurring. [PERSON] et al. [31] proposed the image processing transformer, adapting the vision transformer architecture for image restoration tasks. Restormer [32] efficiently combined multi-head self-attention mechanisms with progressive feature extraction, achieving state-of-the-art performance while maintaining computational efficiency.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 3 Results and Discussion
### Result Analysis
#### 3.1.5 Throughput  
BALIA paired with Reno at the path level exhibited the highest average throughput, followed closely by OLIA with BBR. This observation suggests that such algorithm combinations are more efficient in utilizing the available bandwidth than other examined configurations. As a result, they may offer a superior quality streaming service, such as delivering a higher-resolution video. Conversely, wVegas with Reno registered the lowest average throughput, indicating less efficient bandwidth usage, which could result in a lower-resolution stream.  
A detailed view of the throughput performance is provided in Figure 4, specifically plots (b), (d), and (f). All three SPTCP CC algorithms showed aggravated fluctuations, with a tendency to improve over time. BALIA outperforms the other algorithms in all three combinations, providing the highest throughput. LIA exhibits a stable rise in throughput over time, wVegas oscillates around its mean value, resulting in an almost flat average throughput, while OLIA emerges as the most unstable algorithm. OLIA's throughput shows the most significant variations, and in combination with Cubic at the path level, it often approaches zero.  
These results emphasize the importance of effective buffer management. While the observed throughput variations are evident, they do not pose an inherent threat to stream consistency as long as there are no significant drops in the transmission speed. With proper buffer management, even with these fluctuations taking place, the amount of data transferred will be sufficient to achieve a decent streaming quality.
## 3 Tabu Search Approach
### Special features of Tabu Search-based strategies for Sensor Network Design
#### 3.2.7 Path relinking within the framework of Tabu Search  
After a predefined number _frecpr_ of consecutive moves, the basic TS procedure switches to a PR phase that stops when the reference set \(R\) has a cardinality \(\leq\) 1. Then, either stopping conditions are verified or the procedure is repeated to form a new \(R\). The procedure devoted to build the set \(R\) is essential to generate new high quality solutions. In this work, \(R\) is created during the TS phase and then improved during the PR phase. To build \(R\) the following steps, which aim at ensuring quality and diversity of solutions, are executed:  
1. A set \(P\) is made up of each solution that, at some stage of the TS phase, improves the best current solution and becomes the best one
2. The first half of \(R\) is loaded with the best solution vectors \(\mathbf{p}\) from set \(P\)
3. For each solution vector \(\mathbf{p}\) that belongs to \(P\) but is not included in \(R\), that is \(\mathbf{p}\in\{P/R\}\), the Hamming distance, \(d\), between \(\mathbf{p}\) and \(R\) is computed \[d(\mathbf{p},R)=Min\quad\sum_{j}^{n}\left|q_{j}^{i}-p_{j}\right|\quad\forall\ i\in R\] (6)d) The second half of \(R\) is loaded with **p**\(\in\)\(\{\)_P/R_\(\}\) that maximizes the Hamming distance. The _IS_ and _GS_ are defined as the worst and best solutions in \(R\), respectively. Regarding the rules to identify the neighbour structure and the guiding attributes, two types of moves are proposed:  
1. Let us consider **q\({}_{i}\)** and **q\({}_{g}\)** represent the current _IS_ and _GS_, respectively. Starting from **q\({}_{i}\)**, the idea is to generate neighbours that tend to **q\({}_{g}\)** at last. To generate the first neighbour, **n\({}_{1}\)**, it is set equal to **q\({}_{i}\)**. Then the elements of both vectors are compared from left to right until a difference is found. For this position, for example the _k_-th position, the element of **q\({}_{i}\)** is replaced by the corresponding element of **q\({}_{g}\)**. The comparison finishes and the first neighbour is obtained. The same procedure is repeated between **n\({}_{1}\)**and **q\({}_{g}\)** to obtain **n\({}_{2}\)** but in this case the comparison starts at the (k+1) position. The rest of the neighbours are obtained in the same way until the last neighbour has only one element that differs with respect to **q\({}_{g}\)**. 2. The rationale behind this move is to build the neighbourhood by changing the position of a measurement. Consequently the total number of measurements remains unchanged. At first, the arithmetic difference between vectors **q\({}_{g}\)** and **q\({}_{i}\)** is calculated for each element. Positive (+1), negative (-1) and zero differences are obtained. The positions of positive and negative differences are registered in position vectors **pp** and **np**. Then all combinations between each element of **pp** and all the elements of **np** form a set of interchanges between the corresponding elements of **q\({}_{i}\)**. Each interchange generates a neighbour. _3.
# Lacase in Biorefinery of Lignocellulosic Biomass
## 2 Pretreatment of Lignocellulosic Biomasses
### Organosolv Pretreatment  
Since the 1970s, organosolv pulping is known as an eco-friendly pulping method to extract lignin from lignocellulosic feedstock. One of the key advantages of organosolv pulping is that it enables the isolation of lignin as a solid material and carbohydrates as a syrup, both of which can be utilized as chemical feedstocks [25]. Furthermore, the solvents used in the process can be easily recovered by distillation and recycled for pretreatment purposes. Organosolv pretreatment is similar to organosolv pulping, but does not achieve the same degree of delignification as the latter. This approach allows for the utilization of all biomass components, but it comes with certain inherent drawbacks, such as the need for elaborate washing arrangements to prevent the re-precipitation of dissolved lignin [26]. Additionally, the use of organic solvents in organosolv pretreatment can be expensive and must be recovered as much as possible. This process must also be performed under tight and efficient control due to the hazardous nature of organic solvents, which are volatile. Organosolv pretreatment can be carried out in various solvents, with or without added catalysts, and at temperatures ranging from 100 to 250 \({}^{\circ}\)C [27]. Acid catalysts, such as mineral acids and some organic acids, can accelerate delignification and xylan degradation, leading to higher yields of xylose [15]. The pretreatment yields three separate fractions: dry lignin, an aqueous hemicellulose stream, and a relatively pure cellulose fraction. However, solvent removal is necessary to avoid inhibiting organism growth, enzymatic hydrolysis, and fermentation [28].
## 3 Materials and Methods
### Description of Distributed Systems
#### 3.2.1 Data Parallelism  
DP is a technique that allows distributed training of neural networks and consists of partitioning the dataset into multiple DS devices like CPUs or GPUs [34]. Data parallelization can be performed in two ways: asynchronous and synchronous. The choice of technique depends on how the processing is performed, as each GPU learns from the overall training to aggregate what it has learned into the gradients. Once each GPU calculates its gradients, they are summed or averaged in an aggregation operation that allows all the model parameters to be updated [9].  
Figure 2 represents a parameter server (PS) infrastructure that consists of multiple worker nodes (which, in this case, can either be GPUs or CPUs) running parallel training processes, where each node computes gradients based on a local copy of the neural network model and then communicates those gradients to a central unit that averages and updates the global model parameters.  
Figure 2: Data parallelism is applied in the DS.  
In a PS architecture, the training workload is distributed across multiple worker nodes, each of which performs forward and backward propagation on a subset of the data. These workers independently compute the gradients of the loss function with respect to the model parameters [9]. The computed gradients are then sent to one or more PSs, which are responsible for aggregating the gradients (e.g., by averaging) and updating the global parameters accordingly. Once updated, the new parameters are broadcast back to the workers, allowing them to synchronize their local models. This architecture enables scalable and efficient distributed training, especially in scenarios involving large datasets and deep neural networks (DNNs), such as those shown in Figure 2: 38-Cloud for remote sensing, MSLD for lesion detection, and CIFAR-100 for general image classification.
## 4 Results and Discussion
### Deflection Angle Combinations for Zero Pitch Moment  
Figure 23 illustrates a combination of the thrust control method where the pitch moment was maintained at zero during the deflection of the upper and lower cascades. In this thrust control configuration, as the upper cascades\({}^{\prime}\) angle of deflection increased, the lower cascades\({}^{\prime}\) angle of deflection initially increased and then decreased to maintain the pitch moment induced by the thrust control configuration at zero.  
Figure 23: Deflection angle combinations for zero pitch moment (\(C_{M}=0\)) in both cascades.  
Figure 21: \(C_{M}\) variation with deflection angles in lower cascade deflection.  
Figure 22: \(T\) variation with deflection angles in lower cascade deflection.  
Figure 24 illustrates the experimental outcomes regarding the influences of deflection angles on the thrust and lifting coefficients under a condition where the pitch moment remained constant. The thrust coefficient, depicted in black, decreased linearly with an increasing deflection angle, demonstrating a reduction to 20% of its initial value at higher angles. In contrast, the lifting coefficient, shown in red, initially remained stable and then exhibited a significant increase, peaking around the middle of the angle range before declining. This behavior indicates that the deflection scheme not only preserved the initial enhancement of lift, but also augmented it further at certain deflection angles. Importantly, these adjustments in the deflection angles reduced the thrust significantly while maintaining a nearly constant pitch moment, thereby optimizing the aerodynamic performance by enhancing lift without compromising stability.