esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Automatic Berthing Support Program for Autonomous Ships
## 3 Results
### Verification of the Automatic Berthing Support Program
#### 3.2.2 Experimental Results  
After passing under Mokpo Bridge, the training ship SEGERO turned to port, approached the pier from behind (Figure 7a), and used a tug and bow thruster to achieve a parallel berth alongside the pier (Figure 7b). The automatic berthing support program not only displays the required thruster (or tug) power for the bow and stern based on external forces but also shows the designated berthing location if pier information is pre-entered, along with the real-time distance to that location.  
During the berthing experiment, SEGERO used a bow thruster and a tugboat at the stern to berth. The horsepower of the bow thruster and tugboat, used for approximately 8 min while parallel to the dock, was compared with the horsepower calculated in the ship's automatic berthing program (indicated as Tugboat Operation, Power in the program).  
\begin{table}
\begin{tabular}{c c} \hline
**Time** & **Order** \\ \hline
12 September 13:11:23 & Dead slow/perpendicular push \\
13:11:44 & Stop engine \\
13:12:20 & Dead slow/perpendicular push \\
13:14:05 & Slow/perpendicular push \\
13:14:46 & Dead slow/perpendicular push \\
13:15:25 & Stop engine \\
13:15:54 & Dead slow/perpendicular push \\
13:16:43 & Stop engine \\
13:17:25 & Slow/perpendicular push \\
13:18:00 & Stop engine \\
13:21:00 & Dead slow/perpendicular push \\
13:22:20 & Stop engine \\
13:26:00 & Tug line let go \\ \hline \end{tabular}
\end{table}
Table 8: Tug order records.  
Figure 7: Berthing procedure of SEGERO: (**a**) approaching and (**b**) berthing.  
A time series comparison between the power of the bow thruster of SEGERO (Thruster(F)) and the bow output calculated by the program (Program(F)) is shown in Figure 8. The maximum value of Thruster(F) was 1105 hp, with an average of 544 hp. The maximum value of Program(F) was 183 hp, with an average of 105 hp. Similarly, a time series comparison between the stern tug power of SEGERO (Tug(A)) and the stern output calculated by the program (Program(A)) is shown in Figure 9. The maximum value of Tug(A) was 595 hp, with an average of 300 hp, whereas the maximum value of Program(A) was 263 hp, with an average of 131 hp.
## 3 Results and Discussion
### Optimization of the Triangular Deflector  
Considering the particle accumulation in Figures 11 and 12, the triangular deflector is installed on the right-side wall of the vertical duct. With a fixed installing position, the optimal design of the triangular deflector is carried out for the angle against the wind and the height. Lower RSD of velocity and total pressure loss are preferred while determining the optimal design.  
Figure 14 shows the RSDs of velocity at plane 1 and the total pressure losses for deflectors with different angle against the wind. As shown in Figure 14, when the height is maintained to be 300 mm, no obvious impact is observed for the RSDs with different angle against the wind. However, when the angle increases, the total pressure loss increases as well. There is a deviation of 9.70 Pa between the pressure losses with the angles of \(10^{\circ}\) and \(60^{\circ}\). Meanwhile, for the deflectors with the angles of \(10^{\circ}\) and \(15^{\circ}\), the total pressure losses are close and the corresponding distributions of particles larger than \(8.8\times 10^{-2}\) mm are nearly the same, as shown in Figure 15. Considering the machining difficulty and the material consumption, the angle against the wind of \(15^{\circ}\) is adopted.  
Figure 13: The distributions of the four classes of particles at plane 1 with no deflector installed.  
Figure 16 shows the RSDs of velocity at plane 1 and the total pressure losses for different heights of the deflector. It is indicated that with a fixed angle against the wind of \(15^{\circ}\), the RSDs increase slightly for the deflectors of 500 mm and 700 mm, whereas the corresponding pressure losses increase noticeably. Figure 17 shows the distributions of particles larger than \(8.8\times 10^{-2}\) mm for the deflectors of 100 mm, 200 mm, 300 mm. The corresponding RSDs of the particles are 86.61%, 81.49%, 67.49%, respectively. It is revealed that the deflector with a height of 300 mm provides the best dispersal effect among the three deflectors.  
Figure 14: Statistical indices for deflectors with different angles against the wind: (**a**) the relative standard deviation (RSDs) of velocity, (**b**) the total pressure loss.  
Figure 15: The distributions of the particles larger than \(8.8\times 10^{-2}\) mm for the deflectors of different angles.  
Consequently, the angle against the wind of 15\({}^{\circ}\) and the height of 300 mm are adopted for the triangular deflector.
## 4 Results and Discussion
### Model Performance  
The mathematical formulation, explained in Section 3, has been compared with its MINLP version to observe the huge time differences between nonlinear and linear versions. Thus, Table 1 shows the times of execution for both models and their respective objective functions (OF). When the model is not multi-period, both formulations reach the same OF. However, when the model is extended to multi-periods, the computation burden for the MINLP increases faster than the MILP to the point that at hour 12 the time for the MINLP is around 5.5 h, which is much longer than the alternative version that takes only 3.79 s, keeping an error close to the 7%. Due to the exponential time execution increase for the MINLP version, Table 1 has been completed until hour 12 and not beyond. Nevertheless, the MILP running time for 72 h is close to a minute that puts it within the operational range of DSO.
## 4 Technologies for Nanocellulose Production
### Microwave Pretreatment
#### Chemical Pretreatment of Lignocellulosic Biomass  
LBL pretreatment with acid has extensively been studied by different researchers over the years. Acid pretreatment usually solubilizes hemicellulose and a little amount of lignin by breaking glycosidic bonds and increasing the accessibility of enzymes to cellulose. The pretreatment with acid can be carried out in two ways, either concentrated acid (30-70%) at a low temperature (<50 \({}^{\circ}\)C) or dilute acid (0.1%) at a high temperature (>200 \({}^{\circ}\)C). However, concentrated acids are not frequently used for pretreatment owing to their limitation of corroding the vessel, toxicity and production of inhibitors. Various types of acids are used for the pretreatment, including acetic acid, ascorbic acid, oxalic acid, phosphoric acid and maleic acid. However, hydrochloric acid and sulfuric acid are most commonly used [66; 67; 68]. A study reported the ascorbic acid (AsA)-assisted dilute acid pretreatment of wheat straw, corn stover and corncob. The pretreatment resulted in enhanced cellulose recovery of 74.87%, 75.02% and 89.47% for wheat straw, corn stover and corncob, respectively [69]. Pretreatment of loblolly pine using dilute acid (H\({}_{2}\)SO\({}_{4}\)) effectively removed 80% (\(w/w\)) hemicellulose and improved the delignification rate by 17%, which resulted in the production of 97% pure cellulose [70].
# Intelligent Traffic Management in Next-Generation Networks
## 3 DL/ML Applications in Software-Defined Networking
### Traffic Classification  
The explosion in the number of smart devices has resulted in significant growth of network traffic as well as new traffic types. For example, mobile data traffic will be 77 exabytes per month, which is 7 times that in 2017 [1]. In this context, the purpose of traffic classification is to understand the type of traffic carried on the Internet [76]. It aims to identify the application's name (YouTube, Netflix, Twitter, etc.) or type of Internet traffic (streaming, web browsing, etc.). Therefore, traffic classification has significance in a variety of network-related activities, from security monitoring (e.g., detecting malicious attacks) and QoS provisioning to providing operators with useful forecasts for long-term and traffic management. For example, identifying the application from the traffic can help us to manage bandwidth resources [24]. Additionally,  
Figure 4: General federated learning architecture [73], local model training on the client-side and global aggregation of updated parameters in the FL server.  
network operators need to know what is flowing over their networks promptly so they can react quickly in support of their various business goals [77]. Thereby, traffic classification has evolved significantly over time. It tries to separate several applications based on their feature \"profile\". Because of its importance, several approaches have been developed over the years to accommodate the diverse and changing needs of different application scenarios. There are three major approaches to achieve application identification and classification: (i) port-based, (ii) payload-based (deep packet inspection), and (iii) ML-based [50]. Since this paper focuses on the application of ML/DL in softwarized networks, we will present only the approaches based on ML and DL models.
# Hg\({}^{2+}\) Optical Fiber Sensor Based on LSPR with PDDA-Templated AuNPs and CS/PAA Bilayers
## Materials and Methods
### Functionalization of the Optical Fiber  
To fabricate the LSPR sensor, eight steps are required to functionalize the optical fiber, as shown in Figure 1. The bare middle section of the multimode fiber was hydroxylated by immersion in piranha solution (sulfuric acid and hydrogen peroxide) in a 7:3 volume ratio at 80 \({}^{\circ}\)C for half an hour, followed by thorough rinsing with deionized water and drying in a nitrogen flow. The fiber was placed in a vacuum drying oven at 110 \({}^{\circ}\)C for one hour, making the hydroxyl group more active. The prepared fiber was first immersed in PDDA solution (30 wt%) for eight hours and then in aqueous AuNP solution for 90 min. The AuNPs were successfully adsorbed onto the PDDA nanosheet with electrostatic interaction. After that, the surface of the fiber was rinsed with ultrapure water to remove the excess and unbounded AuNPs and was then baked in an oven at 60 \({}^{\circ}\)C for 2 h. The flame brushing technique was used to implement the high-temperature annealing process. Only the sensing section of the fiber was moved quickly over the flame of the alcohol lamp; thus, the AuNPs upon optical fiber substrate was clean. Then, we used 3-MPA as a crosslinker, which included both the terminated thiol and -COOH groups. The removal of the citrate in the flame brushing provides enough free space for the sulfhydryl group, and hence the sulfhydryl linking between AuNPs and 3-MPA becomes easier and stronger. We then used EDC/NHS to activate the other terminated -COOH groups of the 3-MPA. Finally, we loaded the chitosan (CS)/PAA bilayers on optical fiber by using the layer-by-layer self-assembly method. First, the treated fiber was immersed in CS solution for 7 min at PH = 2.0. Second, poly acrylic acid (PH = 5) was linked onto the chitosan through electrostatic adsorption between them. After each step, the fiber was rinsed with ultrapure water and dried in the vacuum drying oven to remove the CS or PAA that failed to absorb on the substrate. The first and second processes were repeated 4 times to form four CS/PAA bilayers, and to finish the final sensor device.  
Figure 1: Schematic of the processes involved in the optical localized surface plasmon resonance (LSPR) sensor preparation.
## 4 Experiments and Results
### Attention Deficit Hyperactivity Disorder Diagnosis  
Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is one of the most common mental health disorders, affecting around 5-10% of school aged children. In order to diagnose this disorder automatically, MR images, including structural MRI (sMRI) and functional MRI (fMRI) have been investigated in many studies. The MRI data analysed in this paper were from the ADHD200 consortium [26; 29]. Initially, they posted a large training dataset including 776 samples comprised of 491 typically developing individuals and 285 patients with ADHD. For the ADHD-200 global competition, the ADHD-200 consortium also released a hold-out dataset from 171 subjects, including 94 Typically Developing Children (TDC) and 77 ADHD patients [8]. For each sample, both fMRI scans and associated T1 weighted structural scans were provided. With the R-fMRI Maps Project, [PERSON] et al. processed the MRI dataset and provided three kinds of voxel based morphometric features, including Grey Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF), and three kinds of features from fMRI scans, including Regional Homogeneity (ReHo), fractional Amplitude of Low Frequency Fluctuations (IALFF) and Voxel Mirrored Homotopic Connectivity (VMHC) in [30]. In our experiment, these features were regarded as three individual input channels of network.
## 4 Experiments and Discussion
### Qualitative Evaluation
#### Image Blurring  
Due to the movement or exposure issues when collecting the concrete images, there may be image blurring or degradation, which may bring about difficulty in recognizing the true crack damage areas. Simply put, the boundary line of the crack region may be unclear, due to the image blurring problem. With this condition, the crack damage detecting models using edge analysis (e.g., Otsu and Canny) failed to detect the whole crack damage regions using the blurry concrete image, as depicted in Figure 7(7-9). Compared with the SVM based crack detector, DL and our presented MECD method performed better for coping with the image blur problem. However, the curved parts of blurry images were not well recognized by the DL-based method (see Figure 7(9)). By comparison, the presented method exploited the multi-layer ELM-based sparse feature representation, which could extract the compact and sparse hidden information of input image patches. With the resulting informative image region features, and the followed ELM based crack classification process, the developed crack damage detecting model achieved more accurate detecting results.
# Design of a Dual-Path Speech Enhancement Model
## 2 Backgrounds  
The Glance and Gaze Network (GaGNet) [17] introduces a collaborative two-branch framework, where the Glance branch focuses on noise suppression in the magnitude domain, while the Gaze branch restores fine spectral details in the complex domain. This division of tasks allows the model to effectively suppress background noise while preserving important speech characteristics. Similarly, U-Former [18], a U-Net-based model, employs a two-branch architecture that integrates dilated complex-valued and real-valued processing within a Conformer network. By incorporating time-frequency attention mechanisms and a hybrid encoder-decoder structure, U-Former captures long-range dependencies while maintaining real-time processing efficiency, making it well-suited for practical SE applications. Furthermore, Complex Nested U-Net with Two-Branch (CNUNet-TB) [19] builds upon the two-branch framework by incorporating multiscale feature extraction blocks, allowing the model to capture both local and global spectral information. Additionally, it utilizes complex masking and mapping techniques with a shared encoder structure, enhancing its ability to handle intricate speech signals and improve SE performance.  
While these models demonstrate strong capabilities in both noise suppression and speech restoration, they often struggle to consistently balance these two objectives. Inparticular, aggressive noise suppression may come at the cost of speech distortion, while overly fine-grained reconstruction may retain residual noise. This trade-off can degrade speech intelligibility and naturalness, especially in highly non-stationary noise environments. Furthermore, not all of these studies clearly analyze the specific role of each branch in noise suppression or speech restoration, making it difficult to understand the source of performance trade-offs and optimize branch interactions effectively. These limitations--and the lack of a clear understanding of each branch's role in previous models--underscore the need for a more flexible and interpretable architecture that can adaptively enhance speech quality while preserving linguistic clarity.
# Flexible Convolver for Convolutional Neural Networks Deployment onto Hardware-Oriented Applications
## 3 Results
The memory used in the AWB and PWB is equivalent to \(Mem_{WB}=Mem_{AWB}+Mem_{PWB}=(w^{(j)})^{2}\times n_{fo}\times 2\); in the case that \(w^{(j)}=5\), 800 FFDs would be used. Finally, since the number of weight buffers equals 7 (\(nv=7\)), the number of FFDs can be calculated as \(Mem_{MB}=((w^{(j)})^{2}\times nv)+nv\). It should be noted that additional memory is used for the convolver input and output memories. Nevertheless, only the memory directly consumed by the convolver architecture is taken into account for its quantification. Hence, the total memory (\(Mem\)) is given by:  
\[Mem=Mem_{PLB}+Mem_{MB}+Mem_{WB}+Mem_{SA}, \tag{8}\]  
Table 3 summarizes the proposed convolver's performance results, taking into account seven different CNNs. Likewise, information about the obtained results by other authors in related works is provided. This table shows the design strategy (method) for each work, the complexity of the CNN model, the throughput (quantified in millions of operations per second), and the time required to process an image. In the case of complexity, for the works [22; 53] and ours, the total number of multiplications required by the network is considered. For the remaining works, it is assumed that complexity is expressed in multiplications-accumulations (GMACs or GOps) since no information is provided on how this metric was quantified. The processing time shown is obtained through hardware processing. The processing time per image for our architecture is 245.6, 161.83, and 261.75 us for the one-, two-, and three-convolution layer CNNs, respectively. In the case of the custom CNN with three convolution layers, this time is less than that obtained by our previous works [22; 53] and that reported in [54] that uses a CNN of similar complexity. The throughput for our architecture is in the range of [0.95, 2.71] GOp/s, which is superior to the works that are based on a similar CNN model. Throughput depends on the convolutional layer hyperparameters and hardware resources intended for operations. As shown in Table 4, our design uses only 112 multipliers, which restricts maximum throughput; only Ref. [40] reports a usage of 64 multipliers (DSPs) which is less than ours; nonetheless, details of the rest of the hardware resources utilization is not provided. Regarding logic elements usage (Slices in the case of Xilinx-FPGAs-based architectures and Adaptive Logic Modules (ALMs) when Intel FPGAs are used), our architecture is the one with the least hardware resources usage, excluding work [56] where a multiplierless processing is proposed. To estimate power consumption, the Intel _Early Power Estimator_ (EPE) tool was used. The result shows that the proposed convolver consumes only 0.289 W, being the best-reported value. The memory usage for our case only considers the flexible convolver. Hence, the amount of memory required to store input images is excluded. It is important to highlight that the results shown in Table 4 for our
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 3 Related Work
### Word Representation  
Learning word embeddings for frequent words is sufficiently handled by word representation models such as word2 vec [24] and Glove [54]. However, these models are deficient in representing rare words because they rely on context in the corpus, hence the emergence of compositional models where word representation vectors are generated from sub-word units such as characters, character n-gram, morphemes and syllable-like units. Table 2 provides a summary of these compositional models. The character-aware language models, although effective in handling the rare and new words, inadequately capture semantic meaning of words because characters carry no semantic meaning. The morpheme based models and models using syllable-like units take care of semantic meaning in the word representation but the external segmentation algorithms introduce errors in the models [29]. Our proposed model uses a finite syllabic alphabet to resolve the problem of segmentation errors.
## 4 The Algorithms
### The DSR-Fix Variant Algorithm  
In DSR-Fix, each order \(r_{j}\) has the fixed drop-off window, i.e., \(t_{j}^{e}=t_{j}^{l}=t_{j}\), i.e., a courier must deliver \(r_{j}\) to the drop-off region \(v_{j}^{d}\) before \(t_{j}\) and wait until \(t_{j}\) to drop off it. Moreover, orders cannot be shared and must be served in sequence. For any two orders \(r_{j}\) and \(r_{k}\), they can be served consecutively if one courier can pick up the order \(r_{k}\) immediately after finishing the order \(r_{j}\). There must be a long enough period of time between drop-off periods of \(r_{j}\) and \(r_{k}\) such that \(t_{j}+\tau_{jk}\leq t_{k}\), where \(\tau_{jk}=d_{v_{j}^{d}v_{k}^{p}}+d_{v_{k}^{p}v_{k}^{d}}\) denotes the time distance of traveling from the drop-off region of \(r_{j}\) to the pickup region of \(r_{k}\) and delivering \(r_{k}\) to its drop-off region \(v_{k}^{d}\). For such a DSR-Fix problem, an efficient courier-shareability network (**CSN**)-based algorithm is proposed in [6].  
**Definition 1**: _Courier-shareability network (**CSN**). The CSN is a directed network \(CSN{=}(V,E)\), where each node \(v_{j}\in V\) corresponds to an order \(r_{j}\). The directed edge \((v_{j},v_{k})\in E\) if and only if \(r_{j}\) and \(r_{k}\) can be served consecutively, i.e., \(t_{j}+\tau_{jk}\leq t_{k}\)._  
The existence of an edge \((v_{j},v_{j})\) in the CSN indicates that the two orders \(r_{j}\) and \(r_{k}\) can be served consecutively, and a delivery plan in CSN corresponds to a sequence of orders that can be served by a courier. Solving the DSR-Fix problem is equivalent to finding \(n\) node-disjoint paths in such a way to maximize the cover of \(V\) in the constructed CSN [18]. The problem of finding the maximum \(n\) node-disjoint paths cover on directed acyclic graphs can be solved efficiently by using the Hopcroft-Karp maximum-matching algorithm [26]. Algorithm 1 describes the CSN-based DSR-Fix algorithm, in which the Hopcroft-Karp maximum matching algorithm is shown in Algorithm 2. In Algorithm 2, Steps 1-2, for each order \(r_{j}\), we create an in-order \(r_{j1}\) and an out-order \(r_{j2}\) in the bipartite graph \(G^{\beta}\). In Steps 3-5, for each directed edge \((r_{j},r_{k})\) in the CSN, we create an edge between the in-order \(r_{k1}\) and out-order \(r_{j2}\) in the bipartite graph \(G^{\beta}\). Given the bipartite graph \(G^{\beta}\), the Hopcroft-Karp algorithm is utilized to return the maximum matchings \(\mathcal{M}\). In Algorithm 1, top \(n\) node-disjoint delivery plans correspond to top \(n\) longest consecutive matchings. Figure 2 shows a toy example on how to construct the CSN and how the optimal delivery path can be found by the Hopcroft-Karp maximum-matching algorithm.  
Figure 1: The algorithm framework for DSS-Fle. Derived from DSR-Fix, we first propose DSR-Fle and DSS-Fix algorithms, and derived from DSR-Fle and DSS-Fix together, we propose the DSS-Fle algorithm.