esa-sceva/satcom-chunk-collection huggingface dataset:
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Requirements on Tools in MLOps
#### 5.1.1 Data Preprocessing Phase (DPR)
In visualizing diverse aspects of gathered information (for example, the distribution of specific attributes values or the alteration of a variable over time), the respective user is supported with a quick overview of potential problems or errors in the data set. DPR-5: Data Versioning  
Data versioning is an essential requirement for implementing MLOps, similar to the importance of code versioning in DevOps. Without data versioning, reproducibility cannot be achieved in ML. The approaches for data versioning can be divided into two sections, i.e., git-style alike and time-difference-based. The git-style approaches track immutable files but cannot store the differences between them. DPR-6: Data Labeling  
The labeling of data is a mandatory task in supervised learning scenarios. When manually preprocessing such data sets, several tools can support the respective actor (e.g., data scientist and domain expert) in extending the various samples with a target label (e.g., the attribute to be predicted when executing the model).
## 4 Results and Discussion
### CO\({}_{2}\) Capture Performance  
Figure 5 shows the rich and lean loading outcomes for IAP at various solvent circulating flow rates. For the IAP, CO\({}_{2}\)-rich loading was measured from 84 to as high as 133 g- CO\({}_{2}\)/kg-solvent at a 150 Nm\({}^{3}\)/h due gas flow rate. The CO\({}_{2}\) working capacity at L/G = 1.7 L/Nm\({}^{3}\), the lowest energy operating condition, was analyzed to be 126 g- CO\({}_{2}\)/kg-solvent. The working capacity of the IAP is calculated to be 268% greater than that of MEA 30 mass% at optimum operating conditions of each solvent. The IAP was measured to have a significantly lower CO\({}_{2}\) lean loading value of 4 g-CO\({}_{2}\)/kg-solvent under optimum operating conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **MEA 30 mass\%** & **IAP** \\ \hline Flue gas flow rate & 150 Nm\({}^{3}\)/h \\ Flue gas temperature & 36–42 \({}^{\circ}\)C \\ CO\({}_{2}\) in the flue gas & 14 vol\% \\ O\({}_{2}\) in the flue gas & 16.5 vol\% \\ H\({}_{2}\)O in the flue gas & 7 vol\% \\ Solvent flow rate & 400–720 L/h & 200–800 L/h \\ Lean solvent temperature entering the absorber column & 40 \({}^{\circ}\)C \\ Stripper pressure & 100 kPa \\ Target CO\({}_{2}\) capture rate & 88–92\% & 88–92\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Operating conditions for the PCCP tests.  
Figure 4: Feed gas flow rate and CO\({}_{2}\) capture rate from IAP operation over 1000 h.
# A Smartphone-Based Mobility Assistant Using Depth Imaging for Visually Impaired and Blind
## 2 Related Studies
### Spatial Awareness  
Assistive devices are continuously evolving and expanding with the rapid development of technologies and the extensive research to improve the QoL of VIB people. Earlier researches on assistive technologies focused mainly on mobility and orientation, cognitive and context awareness, obstacle detection, etc. Navigation is a huge concern for the VIB as safety concerns arise with the lack of visual information. Obstacles, terrain, and overall environmental inconsistencies have to be taken into account when travelling from one destination to another [9]. Sighted people perceive spatial awareness through the means of direction, acquiring one's specific location and relative position [10]. However, VIB people lack the capability to do so, hence, assistive navigating technologies for spatial awareness have been developed. The following studies were conducted to alleviate these challenges.  
A previous study by [PERSON] et al. developed a personal wearable assistive device with a modular architecture that was based on a robot operating system that detects and warns about obstacles using the Intel RealSense camera for outdoor navigating scenarios [11]. A similar study by [PERSON] et al. developed a multi-module navigational assistant for blind people that generates landmarks through the implementation of various points of interest, which adjusts based on the environment. Orientation and location are provided to the user by means of audio feedback and vibration actuators, allowing a non-intrusive and reliable navigation. However, the research is still in progress and actual testing is yet to be delivered [12]. Another approach utilized an eBox 2300 TM equipped with a USB camera, ultrasonic sensor, and a headphone for detecting obstacles of up to 300 cm through the implementation of ultrasound-based distance measurement. It also includes an additional human presence algorithm that is programmed to detect face, skin, and cloth within 120 cm [13]. The developed module has proven to have promising result as it detects obstacles with 95.45% accuracy, but it is mounted on a helmet as a sensor unit while carrying the eBox 2300 TM weighing at about 500 g, which compromises the portability and flexibility aspect. Research by [PERSON] et al. developed an application called ISANA on the Google Tango tablet by taking advantage of the embedded RGB-D camera that provides depth information, enabling navigation and a novel way-point path finding. It also has a smart cane that handles interaction from the tablet to the user, which outputs vibration feedback and tactile input whenever an obstacle is detected [14]. The developed application is a robust system that also implements semantic map construction and a multi-modal user interface; however, the support for the Google Tango tablet has already shut down back in 2018 because of a newer augmented reality system [15].
## 5 Results and Discussion  
### MFCC Feature Extraction  
The images obtained after FFT and MFCC extraction are shown in Figure 11.  
Figure 11: MFCC spectrograms under different operating conditions: (**a**) normal; (**b**) potential fault 1; (**c**) flooding; (**d**) potential fault 2; (**e**) dry tray; (**f**) potential fault 3; (**g**) weeping.  
It can be seen that after the two-step transformation, the differences between the passive acoustic signals under different categories become more apparent, especially reflected in certain specific MFCC values, showing stable and clearly defined spectral lines. By identifying these features, it is possible to monitor the operating status of the distillation column.
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 3 Problem Description
### General Dependency Graph  
The generic dependency graph includes all possible VNFs, as depicted in Figure 3, where NM = network monitoring, ENC = encryption, DEC = decryption, FW = firewall, and DPI = deep packet inspection. The similar VNFs distinguished by \"source\" and \"destination\" represent the placement of the same functions in different domains/locations according to the traffic direction. The transformations add these security functions only for cross-domain SFCs [17]. The arrows show the dependencies between VNFs, which is a transitive relation. For instance, the arrow from the source firewall to the encryption function means that the firewall VNF depends on the encryption VNF and must, therefore, be executed after it. Note here that the dependencies are in the reversed direction from the assumed traffic direction. Furthermore, the network monitoring VNF is assumed to have no dependencies to create a scenario in which several FGs are possible. The Time-aware Shaper (TAS) VNF is a latency reduction function (see Section 4.1.4) without dependency and it shall be allocated on each server hosting the FG. Therefore, the FG composition method only adds it to the beginning of the FG. Figure 4 presents the FGs that are possible from the general dependency graph.  
Figure 3: General dependency graph.
## 3 Results
### Beam Quality and Power Stability
**(a)** Temperature dependence of the output powers for different incident pump powers of 2.95, 4.98 and 7.03 W. **(b)** Output powers with respect to the pump powers at corresponding optimal temperatures (annotated alongside the data points in the graph).
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 2 Definitions and Basic Concepts for Cooperative Drone-Based Counter-UAS Systems
### Multi-Drone Missions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{\begin{tabular}{c} **Weight** \\ **(in kg)** \\ \end{tabular} } & \begin{tabular}{c} **Normal** \\ **Operating** \\ **Altitude (in m)** \\ \end{tabular} & \begin{tabular}{c} **Mission** \\ **Radius, Range** \\ **(in km)** \\ \end{tabular} & \begin{tabular}{c} **Typical** \\ **Endurance (in h)** \\ \end{tabular} & \begin{tabular}{c} **Payload** \\ **(in kg)** \\ \end{tabular} & \begin{tabular}{c} **Available UAV** \\ **Models in Market** \\ \end{tabular} \\ \hline Micro & \textless{}2 & \textless{}140 & 5 & \textless{}1 & \textless{}1 & \begin{tabular}{c} DJI Spark, DJI Mavic, \\ Parrot Bebop2 \\ \end{tabular} \\ \hline Mini & 2–25 & \textless{}1000 & 25 & 2–8 & \textless{}10 & \
\begin{tabular}{c} DJI Matrice600, DJI \\ Inspire2, Airborne \\ Vanguard \\ \end{tabular} \\ \hline Small & 25–150 & \textless{}1700 & 50 & 4–12 & \textless{}50 &
\begin{tabular}{c} AAI Shadow 200, \\ Scorpion 3 Hoverbike \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Micro-, mini and small UAV classifications based on weight, altitude, range and payload. Other definitions and classifications are possible for the collaborative sets of drones. Indeed, such sets may be also managed as an interoperable system of systems (SoS) in order to apply the interoperability concepts, as in reference [8]. In any case, the joint performance of a networked set of drones (teams or swarms) for a common mission is expected to exceed the sum of the performances achievable in the equivalent single-drone mission. In more detail, the following general advantages are expected for a typical multi-drone mission with teams or swarms of drones:  
* Multiple simultaneous interventions--the system may simultaneously collect data from multiple locations. * Efficiency--the system may split up in order to efficiently cover a large area, optimizing available resources. * Complementarity--the system may perform different tasks with growing accuracy. Clearly, this feature holds for drone teams. * Reliability--the system assures fault-tolerant missions by providing redundancy and capability of reconfiguration in the case of a failure of individual vehicles. * Safety--the team or swarm may usually apply the smallest vehicles for a mission with respect to the equivalent single-drone mission. For a permit to fly, the usage of smaller drones is safer than a single great and heavy drone. * Cost efficiency--a single vehicle to execute some tasks may be an expensive solution when compared with several low-cost vehicles.
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
## 2 Materials and Methods
### Preparation of Coated AuNRs (AuNRs/LA, AuNRs/LA,GG, and AuNRs/GG-LA)  
In a typical AuNRs/LA preparation, 20 mL of CTAB-AuNRs were washed 3 times with Milli-Q H\({}_{2}\)O and recovered by centrifugation (10,000 rpm, 5 min, and 24 \({}^{\circ}\)C). Subsequently, lipoic acid (LA) and glucose were added under stirring to the dispersion of washed AuNRs, following the Au:LA:glucose molar ratios equal to 1:2:2. The dispersion was thus neutralized adjusting the pH to 7 with NaOH. The mixture was kept incubating for 18 h at room temperature, washed one last time with ultrapure water, and the initial volume was restored by adding the same solvent.  
AuNRs/LA,GG were produced from AuNRs/LA by incubation with gellan gum (GG). In particular, after the last washing of the AuNRs/LA, the pellet was slowly dripped under stirring into a polymeric dispersion of GG in Milli-Q water (1 mg\(\cdot\)mL\({}^{-1}\); pH 7) and maintained at room temperature under stirring for 18 h. To formulate AuNRs/LA,GG, an Au: GG weight ratio of 1:10 was set.  
Finally, for the preparation of AuNRs/GG-LA, 20 mL of CTAB-AuNRs was washed 4 times and recovered by centrifugation (10,000 rpm, 5 min, 24 \({}^{\circ}\)C). The pellet was then added dropwise into a dispersion of GG-LA in Milli-Q water (1 mg\(\cdot\)mL\({}^{-1}\)) and glucose (mol/mol ratio Au/glucose equal to 1:2), neutralized with NaOH, and incubated 18 h at room temperature under stirring. The coating with GG-LA was accomplished setting an Au:GG-LA weight ratio of 1:10.  
The Au concentration of CTAB-AuNRs was calculated by UV-VIS analysis by measuring the absorbance of the sample at the fixed wavelength of 400 nm [33].
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 1 Introduction
Driving primitives are extracted through a Sparse Inverse Covariance Clustering (SICSC) method, clustering trajectory data to reveal distinct behaviors without predefined labels, achieving robust adaptability. Temporal consistency is maintained via a dynamic programming-based Expectation-Maximization (EM) framework, while a Toeplitz graphical Lasso with ADMM refines model sparsity, enabling precise and interpretable risk analysis and demonstrating effectiveness in complex, multi-vehicle scenarios. Figure 1: Discretization of the road network space into a 3 \(\times\) 13 grid. Real-World Model Validation: Extensive data collection experiments were conducted across diverse road conditions in Wuhan, China, to assess the model's generalization and effectiveness. Data from 20 drivers were collected over 12 km of urban roads, 34 km of expressways, and 45 km of highways. After preprocessing, this dataset validated the HTSA-LSTM model's performance under real-world driving conditions, demonstrating its robustness and applicability in varied traffic environments. In summary, this study introduces the HTSA-LSTM model, which integrates dynamic spatiotemporal attention mechanisms and variations in driving behavior to improve the prediction of future vehicle trajectories. The following sections will discuss the model's architecture, the principles of the spatiotemporal attention mechanism, the training and evaluation using the NGSIM dataset, and the validation of its effectiveness with real-world driving data. This paper is structured into three principal components. Section 2 delineates the model architecture development, emphasizing the integration of temporal-spatial attention mechanisms and a driving style analysis module for performance enhancement. Section 3 presents a rigorous experimental validation protocol, wherein individual module contributions were systematically assessed through comprehensive evaluations of the NGSIM dataset. Finally, Section 4 conducts comparative real-world driving data on an authentic driving dataset to corroborate the model's practical applicability and operational robustness. We then summarize and analyze the entire text in Section 5.
## 3 Spectral Features for Seismic Elephant Rumble Detection
### Mel-Frequency Cepstral Coefficients  
The MFCCs of a signal provide a characteristic representation of the signal. When extracting MFCCs, the signal is divided into overlapping frames of fixed 25 ms. The frames are shifted by 50% to create an overlap to ensure continuity. On each frame, a Hamming window is applied to taper the signal towards the frame boundaries. This is carried out to enhance the harmonics, smooth the edges, and reduce the edge effect while taking the discrete Fourier transform (DFT) [18] of the signal. Each windowed frame is converted into a magnitude spectrum by applying a DFT as follows:  
\[X(k)=\sum_{n=0}^{N-1}x(n)\cdot e^{-j\frac{2\pi}{N}nk},\quad k=0,1,\ldots,N-1, \tag{8}\]  
where \(X(k)\) is the DFT of the input sequence \(x(n)\), \(N\) is the number of samples in the sequence, and \(k\) is the frequency index. The Mel spectrum is obtained by applying a Mel filterbank to the Fourier transformed signal [19]. The approximation of the Mel-frequency scale from the actual frequency is expressed as follows.  
\[m=2595\log_{10}\biggl{(}1+\frac{f}{700}\biggr{)}, \tag{9}\]  
where \(m\) is Mel-frequency scale of the actual frequency \(f\) in Hz. The Mel spectrum of the magnitude spectrum \(X(k)\) is computed by multiplying the magnitude spectrum by each of the triangular Mel weighting filter as follows:  
\[s(m)=\sum_{k=0}^{N-1}|X(k)|^{2}H_{m}(k);\quad 0\leq m\leq M-1, \tag{10}\]  
where \(s(m)\) is the filterbank output at index \(m\), \(X(k)\) is the DFT of the signal, \(H_{m}(k)\) is the Mel filterbank coefficient at \(k\) and \(m\), \(N\) is the number of DFT points, and \(M\) is the number of Mel frequency bins. The triangular Mel weighting filters are defined as  
\[H_{m}(k)=\begin{cases}0&\text{if }k<f(m-1)\\ \frac{2(k-f(m-1))}{f(m)-f(m-1)}&\text{if }f(m-1)\leq k\leq f(m)\\ \frac{2(f(m+1)-k)}{f(m+1)-f(m)}&\text{if }f(m)<k\leq f(m+1)\\ 0&\text{if }k>f(m+1)\end{cases}, \tag{11}\]  
where \(H_{m}(k)\) is the Mel filterbank coefficient at \(k\) and \(m\), \(m\) is the Mel index, and \(f(m)\) is the Mel frequency corresponding to index \(m\).  
The discrete cosine transform is applied to the log-compressed filterbank coefficients. Since most of the signal information is represented by the first few MFCC coefficientsand the dataset is small, only the first few coefficients are extracted. Finally, the MFCC is calculated as  
\[c(n)=\sum_{m=0}^{M-1}\log_{10}(s(m))\cos\left(\frac{\pi n(m-0.5)}{M}\right), \tag{12}\]  
where \(c(n)\) is cepstral coefficient at index \(n\), \(s(m)\) is filterbank output at index \(m\), \(M\) is the number of Mel frequency bins, and \(C\) is the number of cepstral coefficients.
# Environmental Attention-Guided Branchy Neural Network for Speech Enhancement
###### Abstract  
The performance of speech enhancement algorithms can be further improved by considering the application scenarios of speech products. In this paper, we propose an attention-based branchy neural network framework by incorporating the prior environmental information for noise reduction. In the whole denoising framework, first, an environment classification network is trained to distinguish the noise type of each noisy speech frame. Guided by this classification network, the denoising network gradually learns respective noise reduction abilities in different branches. Unlike most deep neural network (DNN)-based methods, which learn speech reconstruction capabilities with a common neural structure from all training noises, the proposed branchy model obtains greater performance benefits from the specially trained branches of prior known noise interference types. Experimental results show that the proposed branchy DNN model not only preserved better enhanced speech quality and intelligibility in seen noisy environments, but also obtained good generalization in unseen noisy environments.  
Keywords:speech enhancement; attention mechanism; noise classification; branchy deep neural network +
Footnote †: journal: Applied Sciences
## 2 Theoretical Section
### Measurement of Weight Fraction of Glass Fiber (Gc) of Laminate Structure
#### 2.2.3 Matrix Burn-Off  
To accurately measure Gc and the volume of inner defects, such as porosity, voids, and delamination, ISO 12215 and the classification society rules recommend the use of combustion methods whenever possible. However, this method is not widely used because the process is inconvenient and requires special equipment, and it is considered optional. ASTM D3171-15 [22] proposes two methods for determining the percentages of the raw materials contained in composite materials by removing the matrix. The first method is digestion, which involves chemical removal of the matrix phase, while the other involves the burn-off of the matrix in a furnace. The fibers used in GFRP ship structures can be deformed by the acid solvent used for digestion; therefore, the burn-off method is more appropriate.  
After calcining the matrix of the laminate structure, Gc can be determined by comparing the weight of the specimen before and after combustion. This is calculated as follows:  
\[\text{Gc}=M_{f}/M_{i}\times 100, \tag{5}\]  
where \(M_{i}\) and \(M_{f}\) are the weights (g) of the specimen before and after combustion, respectively.  
The burn-off method has the advantage of allowing the matrix resin to be calcined slowly, which provides an accurate characterization of the quality of the laminate structure. Moreover, by dividing Equation (5) with the weight of the specimen before and after combustion, the volume of the voids can be obtained [22]:  
\[\text{V}_{\text{void}}=(1-M_{f}/M_{i}\times g_{c}/g_{f}-(M_{i}-M_{f})/M_{i} \times g_{c}/g_{r})\times 100, \tag{6}\]  
where \(g_{f}\) and \(g_{r}\) are the densities (g/cm\({}^{3}\)) of the glass fiber and resin, respectively.
###### Abstract  
Terahertz quantum cascade laser sources based on intra-cavity difference frequency generation from mid-IR devices are an important asset for applications in rotational molecular spectroscopy and sensing, being the only electrically pumped device able to operate in the 0.6-6 THz range without the need of bulky and expensive liquid helium cooling. Here we present comb operation obtained by intra-cavity mixing of a distributed feedback laser at \(\lambda\) = 6.5 \(\upmu\)m and a Fabry-Perot device at around \(\lambda\) = 6.9 \(\upmu\)m. The resulting ultra-broadband THz emission extends from 1.8 to 3.3 THz, with a total output power of 8 \(\upmu\)W at 78 K. The THz emission has been characterized by multi-heterodyne detection with a primary frequency standard referenced THz comb, obtained by optical rectification of near infrared pulses. The down-converted beatnotes, simultaneously acquired, confirm an equally spaced THz emission down to 1 MHz accuracy. In the future, this setup can be used for Fourier transform based evaluation of the phase relation among the emitted THz modes, paving the way to room-temperature, compact, and field-deployable metrological grade THz frequency combs.  
terahertz lasers; quantum cascade lasers; intracavity difference frequency generation +
Footnote †: journal:  
_Applied Sciences_
# Global Power Dynamics in the Contemporary Space System
## 5 System Dynamics--Global Power Dynamics
### Transitional Dynamics from Cooperation to Competition  
The conclusion of the Cold War marked the advent of a novel international scenario in which the United States, as the sole superpower, assumed a leadership role in international space cooperation, uniting the majority of space powers under a collective initiative: the International Space Station. However, since the advent of the 21 st century, there has been a gradual yet persistent shift in this global dynamic, marked by a transition from cooperation to competition among space powers.  
This transition can be characterized as a process of \"securitization\", which refers to the incorporation of space activities into the broader international security agenda. Many space powers regard outer space as a substantial geopolitical asset, a perspective attributable to its strategic and economic value. According to [PERSON] (2017), the prevailing sentiment is that the domination of terrestrial orbit directly correlates with the control of outer space, ultimately leading to the attainment of global preeminence. In the contemporary geopolitical landscape of the 21 st century, classical geopolitics and the realist perspective are gaining prominence ([PERSON] et al., 2018).  
In light of the assumptions outlined above, certain experts posit that the present decade will be characterized by a period of revision in all spheres of interaction between the predominant space powers, marking a shift towards a more competitive and potentially conflictual space system. Within this emerging global context, there exists a risk that the inherent competitive dynamics could escalate into a confrontation, thereby leading to an expansion of the space dimension of the international security agenda ([PERSON], 2018).