esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Reactor Setup and Operation  
#### 2.2.1 Biofilm Growth  
Biofilm was cultured in fed batch mode in a 3.0-L large glass vessel (R1) with 6 side ports and a multiport glass top cover. Raw reject wastewater (W) was obtained from Knarrdalstrand wastewater treatment plant (WWTP), Porsgrunn, Norway. The plant treats municipal wastewater collected from the counties of Porsgrunn and Skien serving a total population of approximately 80,000. Reject water was centrifuged at 10,000 rpm for 15 min and was analysed for total solids (TS), volatile solids (VS), chemical oxygen demand (COD) and acetic acid concentration. W was then spiked with 1 mL/L acetic acid (17.5 mM) to provide easily digestible COD for biofilm growth (W\({}_{\mathrm{a}}\)). Seven g/L of sodium bicarbonate (83.3 mM) was then added to provide a carbonate source that can be reduced to methane (W\({}_{\text{a}}\)). Reject water pH changed from 7.8 (W) to 7.0 (W\({}_{\text{a}}\)) and was used for carrying out the biofilm growth and biomethane production experiments.  
The carbon felt cathode pieces were arranged as an elliptical chain by stringing them on a titanium wire. They were separated from each other by 1 cm Tygon tubes of 0.5 mm inner diameter and were strung on the titanium wire after each electrode piece. The graphite rod electrode was used as the anode and an Ag/AgCl electrode (+0.209 V vs. SHE; 3 M NaCl, QVMF2052, ProSense, [PERSON], The Netherlands) was used as the reference electrode. All the potentials mentioned in this article are presented as vs Standard Hydrogen Electrode (SHE).  
R1 was fitted with a carbon felt cathode chain, graphite rod anode and the reference electrode through the multiport top cover. The reactor was then filled with 2.5 L of the prepared reject water (W\({}_{\text{a}}\)) leaving 500 mL of headspace. The reactor was inoculated with a total 5 mL digested anaerobic sludge obtained from various treatment plants. The reactor was bubbled with nitrogen gas for 20 min while the magnetic stirrer mixed the prepared reject water. The electrodes were then connected to their respective terminals and the reactor was operated at potentials of \(-\)1.00, \(-\)0.95, \(-\)0.90 and \(-\)0.85 V vs. SHE each for 1 week during the 1 st month of operation. The reactor was then operated for another 1-month period at a constant potential of -0.80 V. During the 2-month biofilm growth period, 500 mL of wastewater was removed every week and was replaced with freshly prepared W\({}_{\text{a}}\) of the same volume.
# Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient
## 2 Statistical Speech Recognition Using a DNN-Based Acoustic Model
### BLSTM-Based Acoustic Model  
Various probabilistic models have been used for the acoustic model. In this study, we use bidirectional long short-term memory (BLSTM) for the acoustic model. For a given input feature sequence \(\mathbf{X}=\mathbf{x}_{1},\cdots,\mathbf{x}_{T}\), LSTM computes hidden vector at time, \(\mathbf{h}_{t}\), using the function \(\mathcal{H}(\mathbf{x}_{t},\mathbf{h}_{t-1},\mathbf{c}_{t-1})\) as follows [19; 20; 21]:  
\[\mathbf{i}_{t} =\sigma(\mathbf{W}_{xi}\mathbf{x}_{t}+\mathbf{W}_{hi}\mathbf{h}_ {t-1}+\mathbf{b}_{i}) \tag{4}\] \[\mathbf{f}_{t} =\sigma(\mathbf{W}_{xf}\mathbf{x}_{t}+\mathbf{W}_{hf}\mathbf{h}_ {t-1}+\mathbf{b}_{f})\] (5) \[\mathbf{a}_{t} =\tanh(\mathbf{W}_{xc}\mathbf{x}_{t}+\mathbf{W}_{hc}\mathbf{h}_ {t-1}+\mathbf{b}_{c})\] (6) \[\mathbf{c}_{t} =\mathbf{f}_{t}\odot\mathbf{c}_{t-1}+\mathbf{i}_{t}\odot \mathbf{a}_{t}\] (7) \[\mathbf{o}_{t} =\sigma(\mathbf{W}_{xo}\mathbf{x}_{t}+\mathbf{W}_{ho}\mathbf{h}_ {t-1}+\mathbf{b}_{o})\] (8) \[\mathbf{h}_{t} =\mathbf{o}_{t}\odot\tanh(\mathbf{c}_{t}) \tag{9}\]  
where the \(\mathbf{W}\) means weight matrices, the \(\mathbf{b}\) means bias vectors, \(\sigma\) is the sigmoid function, \(\mathbf{i}\), \(\mathbf{f}\), \(\mathbf{o}\) and \(\mathbf{c}\) are respectively the input gate's activation vector, forget gate's activation vector, output gate's activation vector and cell activation vector. BLSTM computes the forward hidden vector \(\overrightarrow{\mathbf{h}}_{t}\), and the backward hidden vector \(\overleftarrow{\mathbf{h}}_{t}\) at time \(t\) as follows [20]:  
\[\overrightarrow{\mathbf{h}}_{t} =\mathcal{H}(\mathbf{x}_{t},\overrightarrow{\mathbf{h}}_{t-1}, \overrightarrow{\mathbf{c}}_{t-1}) \tag{10}\] \[\overleftarrow{\mathbf{h}}_{t} =\mathcal{H}(\mathbf{x}_{t},\overleftarrow{\mathbf{h}}_{t+1}, \overleftarrow{\mathbf{c}}_{t+1}) \tag{11}\]  
Then, the output is computed as follows [20]:  
\[\mathbf{y}_{t}=\mathbf{W}_{\overrightarrow{h}y}\overrightarrow{\mathbf{h}}_{t }+\mathbf{W}_{\overleftarrow{h}y}\overleftarrow{\mathbf{h}}_{t}+\mathbf{b}_ {y} \tag{12}\]  
On top of the last layer, \(softmax\) function is used to obtain the probability of the \(k\)th class for an input feature vector \(\mathbf{x}_{t}\). For given input feature and target output dataset, \((\mathbf{X},\mathbf{Y})\), the BLSTM model parameter training is a general optimization problem to find parameters \(\hat{\theta}\) that minimizes a loss function, \(\mathcal{J}(\mathbf{X},\mathbf{Y};\theta)\), as follows:  
\[\hat{\theta}=\underset{\theta}{\text{argmin}}\mathcal{J}(\mathbf{X},\mathbf{Y };\theta) \tag{13}\]  
Figure 1: Probabilistic components for automatic speech recognition.  
where the argmin operation is carried out through gradient descent algorithm as follows:  
\[\theta_{t+1}=\theta_{t}-\alpha_{t}\
abla\mathcal{J}(\mathbf{X},\mathbf{Y};\theta_ {t}) \tag{14}\]  
where \(\alpha_{t}\) is a learning rate.
# Remote Sensing and Machine Learning for Safer Railways: A Review
## 3 Descriptive Analysis
### Methods Reported  
When broadly categorized, ML models are either supervised or unsupervised, with the former requiring labeled data to train the models. The types of supervised ML models include naive Bayes (NB), decision tree (DT), logistic regression (LR), artificial neural network (ANN), and others [16]. There are also several types of unsupervised ML models that seek to identify data clusters that have similar characteristics [17]. The authors meticulously counted the type of methods used in each of the selected publications to gain insights into their approaches. Table 2 summarizes the dominant methods reported in the final batch of relevant articles reviewed, considering that some studies employed multiple methods. Figure 5 shows how the methods were distributed based on their frequency of appearance  
Figure 3: Proportion of publications by journal. and the year of the publication. Figure 6 shows how the publications were distributed by year and split by the methods used. Figure 4: The number of publications by country. Figure 5: Distribution of methods by publication year. Based on the studies that disclosed details of the datasets used, they varied widely in size, source, and quality, reflecting the diversity of RS and ML applications in railway inspection. The majority of studies employed publicly available datasets, which included high-resolution images and sensor data from real-world railway environments, while others generated synthetic datasets to train and validate their models. A further assessment of the details of these datasets can influence the generalizability and reliability of the research outcomes. This diversity in data sources highlights the adaptability of RS and ML techniques across different contexts and conditions, enhancing the understanding of their potential limitations and strengths. Most of the models used are a variant of ANNs as indicated Table 2. The entry \"Y\" and \"N\" in the \"ANN\" column indicates yes and no, respectively, if the model is a variant of ANN. A notable 20% of the methods used are outside the ANN domain. CNN methods emerged as the most frequently employed, featured in 11 different studies. Eleven articles utilized non-ANN methods, encompassing memory-based anomaly detection, big data analytics (BDA), and others. Six publications applied an ANN variant called You Only Look Once (YOLO). This method differs from the traditional two-step object detection techniques that first propose regions of an image and then classify their contents. Instead,
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
###### Abstract  
Vehicular Ad Hoc Networks (VANETs) have attracted a lot of attention during the last decade. VANETs can not only improve driving safety, but also convenience, and support most future Intelligent Transportation System (ITS). Due to the highly dynamic network topology of VANETs, many geographic routing protocols have been proposed and use real-time traffic information as an important metric to select a reliable forwarding path. However, most of the existing works do not describe how to gather real-time traffic. They either assume this information is already available, or can query an existing traffic center. Few studies have noticed this issue but the proposed solutions only consider a small region. In this paper, we propose a Comprehensive Real-Time Traffic Map (CRT Map) to collect wide-ranging real-time traffic information with low overhead. In the design of a CRT Map, the concept of Crowdsensing is adopted. Vehicles cooperatively gather traffic information and share it with each other to construct an overview of the whole road network traffic. In addition, we design a CRT Map Based Routing (CBR), which takes into account the connectivity of consecutive roads in routing decisions. Simulation results show that the CBR can achieve a lower end-to-end delay and a higher packet delivery ratio.  
VANETs; geographic routing protocol; real-time traffic information; crowdsensing +
Footnote †: journal: Applied Sciences
## 2 Related Works
### Ensemble Selection Strategies  
The second part is oriented towards reporting briefly some of the most important points related with Ensemble Selection concept [32; 33]. To be more specific, some usual keywords in this field are Multiple Classification Systems (MCSs), Static Ensemble Classifier (SEC), and Dynamic Ensemble Classifier (DEC), as well as classifiers' competence and diversification. The way that all these terms are connected is the fact that when a new ensemble learner is designed, the main ambitions are the employment of complementary and diverse participants, following the main asset of MCSs regarding the continuous increase of the predictive rate. The main difference between the remaining two terms is the fact that SEC strategies examine a global solution regarding the total set of unknown instances, while the DES approaches provide a separate solution per test instance using mainly local restrictions. Despite their distinct roles, they can be combined under hybrid mechanisms sharing similar measurement metrics or ML techniques for converging to their decisions [34; 35].  
Ensemble Selection has been inserted as a new stage into the original chain of constructing an ensemble learner, taking into consideration both the importance of computational needs that arise when we trust ensembles with too many participants and the fact of discarding less accurate models or models that reduce the internal diversity. This tactic is usually referred to as ensemble pruning or selective ensemble. A taxonomy of these techniques has been proposed in [36], assigning them to four different categories: (i) ranking-based, (ii) clustering-based, (iii) optimization-based, and (iv) others, including the remaining techniques that cannot be strictly categorized to any of the previous three subsets. Another taxonomy was demonstrated in 2014, concerning mainly the actual need of DES in practice and the relation between the inherent complexity of classification problem, measured by appropriate metrics, and the contribution of the examined Dynamic Selection approaches [16]. Prototype selection techniques have also been examined in the abovementioned framework, acting beneficially towards both reducing computational resources and boosting the classification accuracy [37]. Furthermore, one related work on the field of SSL has been proposed using the competence of selected classifiers that stems from an affinity graph, achieving smoothness of the decisions for neighboring data [17].
## 3 Results
### Error Analysis from the TSEB Algorithms
#### 3.1.1 TSEB Data Analysis Combined (LIRF and IIC 2020-2021)
[68] indicated that the smallest errors observed when predicting LE and crop \(\mathrm{ET_{a}}\) occurred at spatial resolutions less than 15 m.  
The comparative analysis of multiple RS sensors presented a relevant understanding of their data quality as input for predicting maize \(\mathrm{ET_{a}}\) using the \(\mathrm{TSEB}\) RS of the \(\mathrm{ET_{a}}\) algorithms evaluated in the study. Despite the overall agreement in \(\mathrm{ET_{a}}\) accuracy observed in the existing literature, it is important to consider factors such as spectral resolution, revisit frequency, and sensor-specific limitations when deciding which RS sensor (data) is better, given the site-specific conditions and sustainable irrigation needs. Since the MSR5 (proximal RS sensor) has limitations regarding the spatial coverage of large fields in a timely manner, operational costs, and data processing, its use could be hindered throughout different stages of the growing season. Given that the high-spatial-resolution spaceborne platforms (e.g., Sentinel-2 and Planet CubeSat) had similar accuracy performances (NMBE and NRMSE) compared to the MSR5 (Figures 10 and 11), their use could be justified when proximal RS devices are not ideal for collecting data over large agriculture fields with significant areas of canopy heterogeneity that make on-site data acquisition challenging and unrealistic.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 5 Software Simulation  
A software simulation platform was developed for the performance tests and analysis of the adaptive tracking method. The simulation system block diagram is shown in Figure 6. It includes four parts: GPS intermediate frequency (IF) signal simulation module, INS data generating module, GPS software receiver module and integration filter module.  
To assess the performance advantages of the proposed adaptive tracking method, several tests are conducted. First, we verify the theoretical dynamic stress error analysis results by comparing them to simulated ones. Then we compare the actual phase tracking error of the discriminator output with the theoretically calculated one. Finally, through comparison with the traditional tracking method, the performance improvements of the adaptive tracking method can be verified.  
Figure 4: The minimum phase tracking error for different dynamics and \(C/N_{0}\).  
Figure 5: Optimal choices of \(B_{\pi}\) (**left**) and \(T\) (**right**) for different dynamic and \(C/N_{0}\).
## 2 Materials and Methods
### XRD, FTIR, and SEM-EDS Microscopic Analyses  
To further investigate the mechanism of heavy metal immobilization by the EICP method, the precipitates from liquid batch tests were analyzed using X-ray diffraction (XRD, Bruker D8 Advance, Karlsruhe, Germany), FTIR (PerkinElmer Spectrum Two FTIR Spectrometer, Waltham, MA, USA) and scanning electron microscopy (SEM, Hitachi SU-8010, Tokyo, Japan) with energy dispersive X-ray (EDS, Oxford X-max80, Oxford, UK).
###### Abstract  
Laser ablation of a bulk graphite target in water using femtosecond laser pulses (pulse width \(\leq\) 190 fs) was performed to investigate the synthesis of polyynes and carbon-based nanomaterials and compare them with the well-studied cases of longer pulse (picosecond or nanosecond) ablations. The laser ablation products were characterized using UV-vis absorption spectroscopy, Raman spectroscopy, X-ray diffraction, and transmission electron microscopy, whereas the induced plasma plumes were characterized using laser-induced breakdown spectroscopy. Carbon-based nanomaterials and short-chain polyynes (C\({}_{6}\)H\({}_{2}\) and C\({}_{8}\)H\({}_{2}\)) are formed in the solutions as proven by their characteristic absorption band at -262 nm and peaks in the region of 190-240 nm as well as at around 2100 cm\({}^{-1}\) in the Raman spectra, respectively. Polyynes (C\({}_{8}\)H\({}_{2}\)) are present in the solutions that are produced under an ablation that is carried out in two intervals with a short pause between them, which is contrary to a continuous ablation that is performed for the same total time duration. The ablation products have a hexagonal graphite crystal structure. The carbon-based nanomaterials consist of large non-spherical and small spherical nanoparticles as well as sheet-like structures. The results of the study were compared with previous studies and discussed based on those studies.  
laser ablation; nanoparticles; colloids; polyynes; graphite +
Footnote †: journal: Applied Sciences  
Acticle
# Analysis of the New Thomson Scattering Diagnostic System on WEST [PERSON]
## 2 Materials and Methods
### Normal Operation Plasma Scenario
#### 2.3.2 Local Analysis  
A local study of the mirror and its bracket (Figure 5) has been carried out to evaluate the local displacement of the mirror and the temperature profile inside these two components, independently from the rest of the structure.  
Figure 4: Complete geometry of Thomson scattering diagnostic system.  
This study is divided into two steps:  
1. A thermal-hydraulic analysis by means of CFD (computational fluid dynamics) method, in order to derive temperature and pressure gradients;
2. A structural steady-state analysis using FEM (finite element method) in order to check optical and mechanical criterions.  
The CFD results (temperature profile of structural bodies, pressure in fluid-structure interfaces) obtained in the first step have were as input data for the second step of FEM calculation.  
The plasma heat flux value is calculated from plasma radiation data [10] considering appropriate form factor and surface reflectivity (reflectivity of 0.925 for mirror face and 0.65 for all others faces). Figure 6 illustrates the opening for plasma radiation (a) as well as the plasma heat flux distribution for the worst-case scenario (b).  
The incident heat flux on mirror and bracket faces can be calculated from the plasma radiation passing through the endoscope opening (set-up of Figure 7).  
Figure 5: Mirror and its bracket geometry.  
Figure 6: (a) Head of endoscope: opened shutter configuration; (b) plasma heat flux.  
Form factors of each face toward all others faces are defined prior to calculate the heat flux on these surfaces.  
The incident heat flux on the mirror face is calculated considering the plasma emitted heat flux in addition to the reflected heat flux on all bracket surfaces.  
In the same way, the incident heat flux on each bracket face is calculated considering the plasma emitted heat flux in addition to the reflected heat flux on mirror surface.
## 3 Research Design and Instruments
### Description of Study  
In 2023, a pilot study was conducted at the University of Dubrovnik and at the Faculty of Electrical Engineering and Computing at the University of Zagreb, in which the participants participated individually under the supervision of an examiner. Before the study began, each subject was verbally informed about the context, the research procedure, and the purpose and objectives of the study. Each subject gave informed consent before participating in the user tests.  
The hardware on which the test was carried out consisted of an Acer laptop, model Nitro AN515-46, AMD Ryzen 7 6800H 3.20 GHz processor, NVIDIA GeForce RTX 3050 graphics card, 16 GB RAM, and a first-generation Leap Motion device.  
The Leap Motion Controller was chosen as the primary input device for this study as it offers a combination of precision, accessibility, and suitability for natural 3D interaction. Its tracking accuracy allows for precise detection of hand movements, which is important in experiments evaluating the speed of task execution and fine motor control. The controller requires no additional external cameras and allows for natural interaction with the bare hand. In terms of practical application, it is available, easy to set up, compact, and portable, which was important for us as the research took place in various remote locations. The device easily connects to the most common XR development environments via its official SDK, making it easy to create applications and develop interactions [102].  
Due to its characteristics, the Leap Motion controller sensor has attracted a lot of attention in the field of hand gesture recognition and has captivated researchers from various fields. All these features make the device particularly suitable for use in NUI research in 3D environments [104].  
Each test subject has 15 min to complete the first phase of the study. Due to the nature of the study, the specifics of the used hardware, the duration of the study per test subject, and the desire for each participant to use the application under identical conditions, the number of test participants was limited, so that ultimately 53 people took part in the study.  
Since the time required to complete the tasks is an important aspect in evaluating the usability and efficiency of the UI, a statistical analysis of the time required to complete the tasks in the first part of the study was performed. The measures used in the analysis were the mean (M), median (C), standard deviation (\(\sigma\)), minimum (min) and maximum (max) values, range (R), lower (Q1) and upper (Q3) quartiles, interquartile range (IQR), symmetry index (S), and flatness index (K). The unit of measurement for all results was the second.  
In the second part of the research, participants completed a survey created in Google Forms. This part of the research was not time-limited. The survey statements evaluate the participants' subjective experience of interacting with 3D objects and focus on the experience of ease, precision, intuitiveness, and frustration when handling the objects.
## 3 Model Construction
### Non-Time Series Prediction Model
#### 3.1.1 LightGBM  
For processing non-time series data, GBDT is often harnessed by researchers because of the distributed training feasibility and low memory consumption. So far, there have been various extended models, including the categorical boosting (CatBoosting) and the extreme gradient boosting (XGBoost) [36], but their efficiency and scalability are not ideal when the amount of data is large. LightGBM is more suitable for seismic prediction that are based on large amounts of data in terms of decision making and scalability by using gradient-based one-side sampling (GOSS), exclusive feature bundling (EFB) [37].