esa-sceva/satcom-chunk-collection huggingface dataset:
# Operation Margin of the ITER Central Solenoid During the Plasma Scenario
## 3 Model Description
### Coupling Between Thea and Heater Models  
As previously discussed, several 1D lines are defined in _Heater_. These lines allow one to model the heat convection between the jacket (modelled in _Heater_) and the helium flowing in the pancakes (modelled in _Thea_). The coupling between the two models is the following [28].  
1. The wall temperature \(T_{wall}\) of the bundle in _Thea_ is set as equal to that of the 1D line at the corresponding position (see Equation (4)) in _Heater_: \[T_{Thea}^{wall}(x)=T_{Heater}(x\prime)\] (4) where \(x\) is the longitudinal coordinate of the pancake in _Thea_ and \(x^{\prime}\) is the corresponding position of the 1D lines in _Heater_.
2. The heat flux between the bundle and the 1D line in _Heater_ is computed as follows: \[\dot{q}(x)=h(x)\Big{(}T_{Thea}^{wall}-T_{Thea}\Big{)}\] (5) where the Dittus-Boelter-Giarratano correlation for turbulent conditions [51] is adopted to determine the heat transfer coefficient \(h(x)\).
3. The same heat flux is assumed in the 1D line in _Heater_.
# Graphene-to-Phoneme Conversion with Convolutional Neural Networks
###### Abstract  
Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2 seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections and, furthermore, a model that utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network-based architecture was also evaluated on the NetFalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate.  
graphene-to-phoneme (G2P); encoder-decoder; LSTM; 1D convolution; Bi-LSTM; residual architecture Article
## 2 Materials and Methods  
Here, we will briefly describe the followed procedure for studying the effectiveness use of TEM for adipose, fat, lung, cortical bone, and muscle human tissues. Hence, we started by proposing those materials and their physical properties. Then, the energy absorption and the exposure buildup computations will be carried out. Finally, we will define some other radiological parameters used along the TEMs searching procedure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Elemental Composition** \\ \hline Adipose & H(12.001), C(64.005), N(0.800), O(22.902), Na(0.050), Mg(0.002) \\  & P(0.016), S(0.070), Cl(0.120), K(0.030), Ca(0.002), Fe(0.002) \\ AD1 & H(8.359), C(69.133), N(2.360), O(16.938), F(3.070), Cl(0.140) \\ AD2 & H(11.180), C(53.310), O(35.510) \\ AD3 & H(14.370), C(85.630) \\ \hline Bone & H(3.392), C(15.509), N(3.972), O(44.128), Na(0.060), Mg(0.210) \\  & P(10.206), S(0.310), Ca(22.213) \\ CB1 & Al(100.000) \\ CB2 & H(4.840), C(38.436), Cl(56.724) \\ CB3 & H(3.100), C(31.260), N(0.990), O(37.570), Cl(0.050), Ca(27.030) \\ CB4 & C(24.020), F(75.980) \\ CB5 & H(4.686), O(56.630), P(10.867), K(27.817) \\ \hline Fat & H(12.210), C(76.080), O(11.710) \\ FA1 & H(14.369), C(85.607), O(0.004), Sb(0.020) \\ FA2 & H(11.701), Li(3.48)), C(72.748), O(12.071) \\ FA3 & H(11.840), C(77.320), O(10.840) \\ \hline Lung & H(10.134), C(10.237), N(2.866), O(75.752), Na(0.184), Mg(0.007) \\  & Al(0.001), P(0.082), S(0.225), Cl(0.266), K(0.194), Ca(0.010) \\  & Fe(0.041), Zn(0.001) \\ LU1 & H(5.741), C(73.947), N(2.010), O(18.142), Sb(0.160) \\ LU2 & H(6.000), C(51.440), N(4.290), O(30.720), Al(7.550) \\ LU3 & H(10.100), B(8)), C(79.200), O(0.120), S(1.910), Zn(0.670) \\ \hline Muscle & H(10.200), C(12.300), N(3.500), O(72.893), Na(0.080) \\  & Mg(0.020), P(0.200), S(0.500), K(0.300), Ca(0.007) \\ MU1 & H(8.830), C(64.450), N(4.050), O(20.350), Cl(2.240), Sb(0.080) \\ MU2 & H(8.871), C(66.817), N(3.100), O(21.132), Sb(0.080) \\ MU3 & H(5.740), C(77.460), O(16.800) \\ MU4 & H(10.200), C(12.010), N(3.540), O(74.250) \\ MU5 & H(5.549), C(75.573), O(18.878) \\ MU6 & H(11.430), C(65.580), O(9.220), Mg(13.480), Ca(0.290) \\ MU7 & H(13.401), C(77.799), O(3.500), Mg(3.860), Ti(1.440) \\ MU8 & H(9.750), C(63.160), N(0.940), O(16.020), Al(9.600), Cl(0.530) \\ MU9 & H(8.119), C(58.344), N(1.780), O(18.638), Mg(13.029), Cl(0.090) \\ MU10 & H(9.799), C(63.683), N(12.379), O(14.139) \\ MU11 & H(14.860), C(85.140) \\ MU12 & H(8.051), C(59.986), O(31.963) \\ MU13 & H(7.74), C(92.26) \\ MU14 & H(10.2), C(76.8), N(3.6), O(5.9), F(1.7), Ca(1.8) \\ MU15 & H(9.654), C(87.489), N(0.06), O(0.473), S(1.539) \\ Ti(0.332), Zn(0.453) \\ HU16 & H(11.19), O(88.81) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage elemental composition of studied tissues and their equivalent material candidates.
# Exploring Data Model Relations in OpenStreetMap
## 2 Related Work and Applications
They argue that, while they have conceptualized the notions of rural and urban as binary at a broad level these categories are more naturally conceived along a continuum. [PERSON] [18] argues that user-generated maps are complex social systems and the quality of a contribution is difficult to assess and subsequently he argues that the structure of groups is more important than the intelligence of group members in VGI projects. **Software and web-based applications:** Despite the absence of extensive academic inquiry into relations, the OSM community itself has developed a number of software applications for error checking and quality assurance on relations. The Relation Analyzer (RA) [19] analyses OSM relations for several quality-assurance-related aspects (amongst others): relations with gaps accompanied by a list of the segments if there are gaps, a height profile, type of ways included, tags. The RA tries to build a linked series of ways using some general rules and then creates a rating based on the type of relation this object corresponds to. Not all relation types are supported yet. The Keepright tool [20] flags the existence of relations which have no type tag and relations which have no tags at all. The Keepright tool can also perform some quality analysis (QA) checking on relations which represent turn restrictions. The JSON (Java For OpenStreetMap) tool is one of the most frequently used tools in OSM by contributors to edit the contents of the OSM database. JOSM is usually used by more experienced contributors to OSM. JOSM provides some support for QA of relations [21] as they are being created or edited. Two of the most popular software tools for the import of raw OSM data into PostgreSQL PostGIS enabled databases [22] are osmconvert[23] and osmosis[24]. Both tools take OSM data and perform extract, transform and load processing into a database with their predefined data model structure. Both osmconvert and osmosis import relations including all members of each relation, all tags and all attribute information. It is then possible, although not always possible or simple, to recreate the geometry of the relation by computing the geometry of all members of the relation and reconstructing the relation structure.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 4 Double-Scale Channel State Prediction  
In this section, the proposed prediction algorithms for the large-scale and small-scale channel states are presented, respectively.
## 3 Methodology: Requirements Analysis, Selection of Buildings, and Technologies
### Choosing the Set of Buildings for Retrofitting Sensor and Control Networks
#### 3.2.2 Selection of Buildings  
In our project, we focus on buildings at the campus of FZ Julich. A graphical overview of the buildings at the campus, including the construction years of the buildings, is given in [62]. There are about 150 buildings on the property of FZ Julich in total, most of which could be considered for the project. For the building stock, a representative and statistically relevant subset of buildings was selected. Those to be equipped in a first phase are shown  
Figure 1: Visualization of the physical inter-connections on the district level, the sensor and control networks on the room level (e.g., floor heating and lighting, left), building-wide metering (e.g., heating and electricity), and existing building automation (e.g., heating and air-conditioning); connection of BACS components to an overarching ICT architecture (dotted lines). in Table 2. As the table shows, the selected buildings cover a large time span in the year of construction, and have different typical types of use and different characteristics in terms of the building envelope, heating, and ventilation systems. Another group of buildings listed and described in Table 3 will be further equipped in the future. In total, about 18 existing buildings are earmarked to be included in the project. Many of these buildings are connected to the _low-temperature district heating_ (LTDH) network installed on the campus. In the following discussion, we highlight two buildings due to their role, prerequisites, and special suitability for the installation of sensor and control networks. The first is the existing building with index two in Table 2, which is the _Jailich School Laboratory_ (JSL). The second is the proposed _climate-neutral administrative building_ (CAB, index 17 in Table 3), for which planning is already well advanced. The JSL was chosen to become a special showcase for the installation of innovative BACS because the building is visited by both students and employees, as on-campus science courses and internal meetings are conducted there. The application of advanced sensor and control networks in newly erected edifices, as well as innovative ventilation concepts shall be studied in offices and shared areas within the planned CAB. Erected in the year 2006, the JSL comes with modern insulation and a preexisting wiring infrastructure, enabling the easy retrofitting of wired devices (via cable trays). Additionally, lighting and blinds-control wiring is implemented centrally, allowing for the easy integration of actuation into advanced BACS setups. With regard to heating, two different systems are used at the JSL. On the ground floor, radiators are used together with floor heating in larger rooms. Offices and all remaining rooms on other stories are
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BER & Bit Error Rate \\ BCH & Bose-Chaudhuri-Hocquenghem \\ CDMA & Code-Division Multiple Access \\ ECC & Error Correction Coding \\ ISI & Inter-Symbol Interference \\ PDP & Power Delay Profile \\ PRBS & Pseudo-Random Binary Sequence \\ SF & Scattering Function \\ SCFF & Space-Frequency Correlation Function \\ SNR & Signal-to-Noise Ratio \\ STFCF & Space-Time-Frequency Correlation Function \\ TVIR & Time-Varying Impulse Responses \\ TVTF & Time-Varying Transfer Function \\ UAC & Underwater Acoustic Communications \\ \end{tabular}
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### Feature Extraction--MFCC  
Voiceprint recognition relies on extracting meaningful features from speech signals to identify individuals. In this process, feature extraction is crucial to the model's performance. These features include attributes like fundamental frequency, intensity, timbre, and tempo of speech. Such features help the model capture the unique voice characteristics, enhancing the accuracy of recognition. Effective feature extraction not only improves the model's ability to identify learned cases but also helps it generalize to new, unseen data, making the model more robust.
## 3 Results
### Evaluation Metrics  
Three common evaluating metrics, Precision (P), Recall (R), and F1 value, are used to measure the accuracy of the proposed method. The number of image frames processed per second (FPS) is the measurement of the method speed performance. The Precision metric represents the ratio of detection that are true positives, and the Recall metric means the ratio of positives that are detected accurately. The Precision and Recall metrics can be formulated as follows:  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{4}\]  
\[\text{Recall}=\frac{TP}{TP+FN}, \tag{5}\]  
where, \(TP\), \(FP\), and \(FN\) indicate the number of true positive, false positive, and false negative, respectively.  
The F1 value is calculated as (6):  
\[F1=\frac{2*(\text{Precision}*\text{Recall})}{\text{Precision}+\text{Recall}}, \tag{6}\]  
Figure 3: Horizontal bounding box and rotation bounding box.
# An Earthquake Forecast Model Based on Multi-Station PCA Algorithm
## 3 AETA System
### Data Preprocessing  
Figure 3 shows the data preprocessing work flow of AETA. In the cloud server, the raw data will be reduced to one value per minute according to Equation (1). The constants in Equation (1) are results of the device metadata of the Analog-to-Digital Converter (ADC). As Figure 4 showed, each point represents a minute, and the raw time-series data whose frequency is 500 Hz is reduced to a series data whose frequency is 1/60 Hz. These data are used to be the input of machine learning, and we call it the minute data.  
Figure 1: AETA system contains probe, station, server and UI of client and webpage.  
Figure 2: Distribution of AETA stations in Sichuan province in China. The stations used in next section are marked by blue stars, and positions of other stations marked by red triangles.  
\[MEAN=\frac{1}{30000}\sum_{i=1}^{30000}12.288\times\frac{|VALUE_{i}|}{32768} \tag{1}\]  
The sliding window uses a constant length window to slide on the input series with a constant step to cut a series of sub-series and deal with the sub-series. We use the sliding window method to process the minute data; the window length is 60, and the step is also 60, so we average per hour to generate one number. In this way, one day will contain 24 values, and they will use as a vector of length 24 according to the chronological order, and the data become a series of vectors, in which each vector represents one day. The sliding window method is used to organize these vectors. Since the sun's activity influences the local geomagnetic, a period window of 27 is used to reduce this influence, and the final input is a series of matrices \(A_{24\times 27}\), in which 27 means the sun's activity period and 24 means hours in one day.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Monostable Multivibrator Timing Inaccuracies  
The monostable multivibrator exhibits timing inaccuracies characterized by \(\pm 2.0\) ms standard deviation for a nominal 10.0 ms pulse duration. This represents 20% relative timing accuracy, significantly affecting PPM pulse positioning precision. These variations introduce systematic errors in the pulse position modulation process, degrading demodulation accuracy. Temperature effects, component aging, and supply voltage variations contribute additional timing uncertainties not captured in the current simulation model. These factors would further degrade system performance in practical implementations.
## 3 Proposed Methodology
### Classification  
As shown in Figure 1, the classification phase contains a hierarchical design, including two levels. The first level is a global search using one classifier to detect the type of received signals (SoI, SoI + CWI, SoI + MCWI, and SoI + CI). Furthermore, a localized search is deployed for each type of signal to recognize the modulation type. To this end, we used the same classifier design proposed in our previous work based on MLP [21] trained using 10-fold cross-validation technique.  
Cross-validation is a statistical approach used to evaluate the ability of ML-based models on unseen data. Generally, it is deployed in applied ML to compare and select a model for a given predictive modeling problem [52]. As the benefits of this generalization technique can refer to easy to understand, simple implementation, and results in skill estimates that generally have a lower bias than other methods such as a simple train/test split [52]. The procedure has a key parameter called \(k\) that refers to the number of groups that a given data sample is to be split into such that the procedure is often called k-fold cross-validation [52]. The implementation steps of k-fold cross-validation are as follows:  
Step 1: Shuffle the dataset randomly and split it into \(k\) groups.  
For each unique group:  
Step 2: Take the group as a holdout or test dataset.  
Step 3: Take the remaining groups as a training dataset.  
Step 4: Fit a model on the training set and evaluate it on the test set.  
Step 5: Retain the classification accuracy and discard the model.  
Finally, the model's performance is evaluated by the average of all the obtained classification accuracy values.  
Therefore, each observation in the dataset is assigned to an individual group and stays in that group during the training procedure. Therefore, each sample has the opportunity to be used in the holdout set once and used to train the model \(k-1\) times [52]. Choosing an optimal value for \(k\) is very crucial since a poorly chosen value for \(k\) may result in a misrepresentative of the model's skill, such as a score with a high variance or bias [52]. This study considers \(k=10\) due to achieved experiments results with low bias in estimated model skill. The value of \(k=10\) is widespread in the field of applied ML tasks.  
Figure 4: Variation of the best fitness values per generations number for RFI classification application using two features.
### Clinical Motivation for Ambulatory Monitoring of Subglottal Pressure  
There is strong evidence that laboratory measures of Ps can discriminate patients with vocal hyperfunction from vocally typical control speakers, with effect sizes that appear to be even higher than other aerodynamic measures related to glottal airflow characteristics [70]. In addition, patients with phonotraumatic vocal fold lesions (nodules or polyps) have been reported to exhibit Ps values over two standard deviations greater than normative Ps values [28]. Changes in Ps have also been associated with the post-surgical outcomes in patients with UVFP [71] and laryngeal cancer [24]. However, the literature has relied solely upon estimating Ps during non-natural syllable strings when studying the effects of voice disorders on Ps. Furthermore, the studies have assessed vocal behavior in controlled laboratory or clinical settings that provide only brief snapshots of vocal function [23; 72; 73]. The current study builds upon ongoing work that is advancing ACC-based technology to enable effective strategies for ambulatory voice monitoring and biofeedback [51; 53; 65; 74; 75; 76; 77; 78; 79; 80; 81; 82]. Previous studies of Ps for clinical voice assessment have documented the importance of evaluating Ps in the context of the vocal SPL produced [83; 84; 85; 86; 82; 83; 86]. The current study focuses on the validity and feasibility of ambulatory Ps estimation that could then be augmented in the future with ambulatory measures of vocal SPL, as well as with perceptual ratings of vocal symptoms such as vocal effort, discomfort, and fatigue [6; 87; 88; 89; 90].
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
###### Abstract
identified the wasteful inconsistency-correction effort of D* Lite and proposed D* Lite with Reset for path planning in complex environments [26]. To find a route by which each vehicle can reach all depots to fulfil the demands of all the customers with the shortest travel distance, [PERSON], [PERSON] et al. developed a 2-opt guided discrete antilon optimization algorithm [27]. Comparison with heuristic approaches such as genetic algorithm and ant colony optimization showed that it has better performance. This paper improves the traditional D* Lite algorithm in several ways and applies it to achieve multi-goal path planning and dynamic obstacle avoidance for USVs in unknown and complex environments. We expand the search neighbor and divide a sharp turn into several turning steps to solve the issue of limited steering maneuverability in USVs. To optimize the planned path, we compare the estimated distance with the straight distance from the current waypoint to the goal waypoint during autonomous navigation. A minimum binary heap is used to optimize the priority queue of the D* Lite, thereby reducing the path search time and improving the path search efficiency. Section 2 introduces the concepts of D* Lite. We improve the D* Lite in Section 3 and analyse the simulation results in Section 4. In Section 5, we apply the improved D* Lite to a real USV for further tests. Finally, we conclude and discuss future research issues in Section 6.
## 2 Materials and Methods
### Experimental Set-Up  
The experiments were conducted in 250 mL serum bottles with 100 mL biomass and 100 mL synthetic wastewater (without VFA). The composition of synthetic wastewater can be found in [PERSON] et al. (2013). In brief, 0.024 g L\({}^{-1}\) of (NH4)\({}_{2}\)HPO\({}_{4}\), 0.34 g L\({}^{-1}\) of NH4 HCO\({}_{3}\), 0.002 g L\({}^{-1}\) of KCl, 0.166 g L\({}^{-1}\) of MgCl\({}_{2}\)6H\({}_{2}\)O, 0.166 g L\({}^{-1}\) of CaCl\({}_{2}\)\(\cdot\)2H\({}_{2}\)O, 0.006 g L\({}^{-1}\) of FeCl\({}_{2}\)\(\cdot\)4H\({}_{2}\)O and 0.5 g L\({}^{-1}\) of NaHCO\({}_{3}\) were added, followed by 0.2 mL L\({}^{-1}\) trace elements. The composition of trace elements was: 1.25 g L\({}^{-1}\) of CoCl\({}_{2}\)\(\cdot\)6H\({}_{2}\)O, 1.25 g L\({}^{-1}\) of H\({}_{3}\)BO\({}_{3}\), 3.06 g L\({}^{-1}\) of MnCl\({}_{2}\)\(\cdot\)4H\({}_{2}\)O, 0.10 g L\({}^{-1}\) of Na\({}_{2}\)MoO\({}_{4}\)\(\cdot\)4H\({}_{2}\)O, 1.25 g L\({}^{-1}\) of NiCl\({}_{2}\)\(\cdot\)6H\({}_{2}\)O, 1.25 g L\({}^{-1}\) of ZnCl\({}_{2}\) and 1.95 g L\({}^{-1}\) of thiamine. After adding the inocula and medium, the serum bottles were closed with aluminum caps and silicone rubber septa and then sparged with nitrogen gas for 3 min.  
Following the first day's feeding, serum bottles with only synthetic wastewater and 100 mL inocula were incubated overnight on the shaker at 35 \({}^{\circ}\)C and 200 rpm (Infors AG CH-4103, Bottmingen, Switzerland) for activity recovery and further removal of residual VFA. The pH was adjusted to 7.00 by 1 mol L\({}^{-1}\) NaOH or 1 mol L\({}^{-1}\) HCl, which was within the non-interfering pH range for sodium effect on methanogens [7]. In the second day's feeding, a combined solution of sodium acetate and sodium chloride (NaCl) (pH adjusted to 7) was injected into the serum bottles. The amount of sodium acetate added to each serum bottle was 2318 \(\pm\) 65 mg L\({}^{-1}\) acetic acid (HAc).  
The buffer capacity was maintained using 1.5 g sodium bicarbonate (NaHCO\({}_{3}\)) per gram of chemical oxygen demand (COD) [5]. The final measured total sodium concentrations were 1.3, 2.4, 3.2, 5.1, 7.3 and 9.1 g Na\({}^{+}\) L\({}^{-1}\). The incubation period for all batch tests lasted 48 h at 35 \({}^{\circ}\)C and 200 rpm (Infors AG CH-4103, Switzerland). The HAc degradation rates were calculated using data from the 6 th to 48 th h, during which a linear decrease in HAc concentration was observed. The rates were normalised against volatile suspended solids (VSS) concentrations. At the 6 th and 48 th h, samples were drawn from the serum bottles for tests of VFA, biogas and EPS. At the end of incubation (48 th h), samples were drawn for the ATP test.