esa-sceva/satcom-chunk-collection huggingface dataset:
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 2 Preliminaries
### Incremental Learning Algorithm Based on 'Clone-and-Branch'
#### 2.1.1 Step 1: Empirical Searching  
In order to select optimal sharing layer number for learning new tasks, they generate an 'accuracy vs. sharing' trade-off curve. Through this trade-off curve, we can observe the fewer earlier layers are used to share, the better the inference performance of the branch network, since the layers in a large DCNN can extract more detailed features of specific tasks as going up to the upper level. On the other hands, if they share more  
Figure 1: The incremental network structure called ‘clone and branch’. The shaded gray rectangle is the base network. Each time we update the network for the new task, some initial parts of convolutional layers are shared and frozen, so only the branch part of layers is retrained with the new task data, while the last layers of the base network remain disconnected.  
than a certain ratio of the network parameters, a drastic accuracy drop can be observed. Thus, this curve allows to determine the optimal sharing configurations without severe accuracy degradation on new task. However, there is a large overhead for determining the optimal sharing configuration that meets quality specifications from this curve. Because for generating an 'accuracy vs. sharing' trade-off curve, they need to utilize only a brute force manner such as searching for all possible sharing layer cases. This is because the problem of estimating the optimal sharing layer number is a non-convex and non-differential problem, so they cannot be solved by using powerful optimization techniques such as the gradient descent algorithm or other convex optimization strategies, and it leads to high computational complexity.
## 3 Methodology
### Tests of AR Placement Methods  
In this research, the study of the performance of AR placement methods includes both indoor and outdoor environments since some methods are suitable only for the outdoor, such as the inertial sensor-based method (GNSS Smartphone) and Trimble SiteVision (for manual and automatic placement by GNSS). The methods assessed in our study are reported in Figure 7.  
Among marker-based methods, we tested two free solutions; the first one is a framework API called AR Foundation which brings together two software development kits (SDKs) (ARcore for Android and ARKit for IOS) and the second is Vuforia SDK. We chose these two applications because they are most popular for the development of AR applications. We note that Vuforia SDK is the most used SDK by the AR community thanks to its simplicity in developing AR applications (does not require programming knowledge) and its free trials.  
Within marker-less methods, we consider two sub-classes of methods, namely sensor-based methods and vision-based methods. We used the edge-based method available on Vuforia (object recognition) for model-based methods, and the VSLAM (visual SLAM) technology of the ARcore SDK for the no-model-based approach, particularly for the approach based on plane detection.  
To properly carry out a comparative study between the different methods of placement of 3D models by AR, we used optimal test conditions for each method, especially the marker-based methods. High-quality markers (rating) with a good distribution on site were used to correctly detect the markers and to avoid the influence of the distance from them.
## 3 A Review of Evaluation Criteria
### Temporal Evaluation Criteria  
The primary temporal evaluation criteria used in the existing literature are the latency and the reaction time.  
* _Latency_. This is measured by the average delay between transmitting an event message and receiving it at the sink. There have also been other measures used by many researchers to calculate the latency. One way is to calculate the total time elapsed to perform an action by the sensor nodes, for example, disseminate information to a set of nodes, or to complete a number of data collection rounds. Another way to calculate the latency is to calculate the time duration for identifying an event or to reach a consensus for a measured value.
* _Reaction Time_. The definition of this term varies among researchers, but essentially captures the average time it takes for the sink to receive data or particular messages after some change occurs in the network.
## 1 Introduction
The main contribution of this paper is the application, tuning and extension of the techniques described in [15] and [16] for the purpose of real-time pedestrian trajectory prediction at large-scale events using data gathered by means of a crowd monitoring system. In contrast to previous works, the movement behavior of the traced individuals differs severely (festival visitors instead of commuters), the GPS traces are less detailed, the granularity of the segmented network is far finer than in the original method (block size instead of neighborhoods) and the forecast is adapted to be time dependent. Parameter tuning ensured that the two techniques can now also cope with the first three differences in the characteristics in the input. The extension of the time-dependent cell sequences facilitates the forecasting of pedestrians movements for 15 min or more ahead in real-time while no historic information on those movements is available. This paper continues as follows. First an overview of the current literature on model- and data-driven state forecasting techniques is provided in Section 1. Section 2 will accordingly describe the crowd movement forecasting procedure. This section elaborates on the data-driven segmentation of space, cell sequence derivation methods, data division strategies, implemented forecasting models and the comparison methodology. The case-study, including site and data description, are presented in Section 3. The following Section 4 provides an overview of the results, which comprises of a benchmark of the RNN-GRU against several shallow neural networks and Markov chain models which incorporate varying degrees of memory. Finally Section 5 discusses the findings, provides preliminary conclusions regarding the applicability of the new crowd movement forecasting procedure and identifies several avenues for future research.
# Free Space Optical Communication Networking Technology Based on a Laser Relay Station
###### Abstract  
Optical communication modulation technology and networking technology are two important technologies for constructing free-space optical (FSO) communication. In this paper, pulse width modulation (PWM) is used to realize free-space optical communication. The process of signal modulation and demodulation is implemented by means of a field programmable gate array (FPGA). An optical communication relay system is constructed to realize communication networking. The binary data bits in the communication process are converted into pulse signals of different widths, the data demodulation process is realized by sampling with a high-speed analog-to-digital converter (ADC), the data level is determined by counting the proportion of high and low voltages sampled in a pulse period. The relay system analyzes the routing target after receiving the pulse signal from the transmitter, and then sends the data to the target receiver. The experimental results show that the constructed system can achieve point-to-multipoint free-space optical communication. Additionally, using ADC to demodulate the received signal increases the stability of the free-space optical communication system. This system provides the design prototype system of FSO communication networking technology.  
free-space optical (FSO) communication; pulse width modulation (PWM); field programmable gate array (FPGA); relay station; communication networking +
Footnote †: journal: Applied  
Article
# Software Project Management Using Machine Learning Technique--A Review
## 2 Preliminary Study
### Software Project Management Estimation Based on ML  
Figure 1 illustrates the procedure of software project management estimation, which can be summarized, as follows.  
Step 1. Data Collection: token extraction, word tokens are essential characteristics for calculating effort in the project. A key part of the project material is defined by Tokens. As a core component of the estimation model, Unigram language modelling concerning tokens was used.  
Step 2. Feature Extraction: after the extraction of tokens, the project features were chosen for further analysis: Term Frequency-Inverse Document Frequency (TF-IDF) of each token 'Term Frequency' is a tool for calculating the sense of a phrase that considers word frequency and the reverse counts of records, including this word. This is a method of measuring a word's meaning.  
Step 3. Model Construction: for different classification algorithms, the derived features are used as inputs to Weka. The following is an overview of how we chose the learning algorithms: Naive Bayes: a classification is a probabilistic classification that is focused on the theorem of Bayes that takes independent features from the classroom. Naive Bayes, with its simplicity, fights the high dimensionality of the data with the subjective assertion of freedom, which can also exceed more complex classification approaches. J48 Decision tree: Java's open source C4.5. It is a decision tree-generating algorithm where the tanning collection is not linearly separable, decision trees suit the training results well. Random Forest: it is a category classifier that consists of several decision trees and the class outputs, which are the statistical model of the individual trees output groups.  
Step 4. Analysis: the study found the best ML models to outperform and showed that the project risk calculation using machine learning is more effective in minimizing the fault of the project, which improves the likelihood of the project answer, providing an alternative way to efficiently reduce the probabilities and increase the output ratio for growth.  
Figure 1: Example of Methodology for Developing Software Project Management Estimation.
# Investigation of High-Q Lithium Niobate-Based Double Ring Resonator Used in RF Signal Modulation
In this paper, a high-sensitivity electro-optical modulator for 35 GHz satellite communication is designed, and the structural parameters and ideal performance of the double ring modulator are determined by using FDTD. The structural coefficients that fundamentally affect the performance of the double ring structure, such as transmission coefficient and transmission loss, are exhaustively studied by the transmission matrix method. The optimized double ring structure can achieve a Q-factor that is two orders of magnitude higher than that of the single ring structure, and its application in 35 GHz communication can achieve higher modulation sensitivity. Compared with other devices used in the field of high frequency RF modulation, it has the characteristics of higher sensitivity and integration. Ultimately, the optimized double ring structure with excellent performance has a very broad application potential in the field of communication and biochemical sensing.
## 2 Materials and Methods
### Physicochemical Properties Analysis  
Before measuring physicochemical properties, beers were degassed in aluminum foil-wrapped glass bottles with caps slightly unscrewed using an Elmasonic S 30H ultrasonicator (Elma Schmidbauer GmbH, Singen, Germany) at 37 kHz for 30 min.  
An Accumet AE 150 digital pH meter (Fisher Scientific, Waltham, MA, USA), calibrated at pH 4, 7, and 10, was used to measure the pH of control and treated samples within an hour after treatment. Approximately 15 mL of sample was used per reading and discarded. The pH was measured in triplicate.  
Control and treated samples were analyzed for color using the standard reference method (SRM) adopted from ASBC [25], expressed in dimensionless SRM values. Samples were prepared in 3 mL two-sided plastic cuvettes with a 10 mm light path. BA samples were prepared by backward-pipetting 3 mL of sample into the plastic cuvettes without dilution. CA samples were diluted at a 5:1 distilled water-to-beer ratio to obtain an absorbance reading below 1. Thus, the CA samples were prepared by backward-pipetting 2.5 mL of distilled water into plastic cuvettes followed by 0.5 mL of beer sample. Each sample's absorbance was read at 430 nm using a UV-vis spectrophotometer (Shimadzu UV-1800 UV-Vis Spectrophotometer). Samples were prepared and measured in triplicate. The SRM value was obtained using Equation (2). The dilution factors used for BA and CA were 1 and 6, respectively.  
\[SRM\ value=abs_{430}\times dilution\ factor\times 12.7 \tag{2}\]  
The bitterness of control and treated samples for both beers was determined using a modified version of the manual isooctane extraction method adopted from ASBC [25]. For this purpose, 10 mL of each sample was transferred into an aluminum-foil-wrapped 50 mL clear polypropylene centrifuge tube (Corning, Corning, NY, USA) and acidified with hydrochloric acid (HCl, 1 mL, 3M). Afterward, 20 mL of isooctane was added to extract the bitterness compounds in the fume hood. The mixture was homogenized horizontally at 25 \({}^{\circ}\)C in a mechanical orbital shaker at 300 rpm for 15 min using the C76 Water Bath Shaker (New Brunswick Scientific, Enfield, CT, USA). The organic layer was separated from the aqueous layer by centrifuging at 400\(\times\)\(g\) for 5 min at 20 \({}^{\circ}\)C using the Sorvall Legend X1R Centrifuge (Thermo Scientific, Waltham, MA, USA). The absorbance of the organic layer (upper) was measured spectrophotometrically at 275 nm by pipetting into 3 mL quartz cuvettes. The aluminum foil wrapping around the centrifuge tube was removed to provide visual assistance in the transferring of the organic layer. The absorbance was measured using the UV-1800 UV-Vis Spectrophotometer (Shimadzu, Kyoto, Japan) calibrated with acidified isooctane (20 mL isooctane with 1 mL 3M HCl added). The IBU was obtained by using Equation (3). Readings were performed in duplicate.  
\[IBU=abs_{275}\times 50 \tag{3}\]  
TSS in control and treated samples were measured using a handheld 0-32% Brix refractometer (Sper Scientific, Scottsdale, AZ, USA), and expressed in \({}^{\circ}\)Brix. TSS were measured in triplicate.
## 4 Conclusions  
In this work SETs have been fabricated using thermal ALD and plasma enhanced-ALD to deposit tunnel barriers of Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), combinations of the two, and SiN\({}_{x}\). Table 1 provides a summary of the different metals, dielectrics, and treatments and the most important observations relating to each. This demonstrates the ability of ALD to controllably deposit a variety of films on metal substrates including Ni and Pt. The two major interrelated issues with this technique have been: (1) oxidation of  
Figure 17: (**a**) Typical Coulomb diamond plot for a Ni-SiN\({}_{x}\)-Ni SET with 21 cycles of SiN\({}_{x}\) (\(\sim\)1 nm) along with a \(G(V_{ds})\) cross section at \(V_{g}\) = 47 mV (a peak of CBOs); and (**b**) CBOs from the same device exhibiting RTS noise.  
the metal leads during ALD; and (2) the formation of traps near the island, most likely in the barrier dielectric or at the metal-dielectric interfaces that lead to strong RTS-type electrical noise. In the case of Ni-SiO\({}_{2}\)-Ni SETs, it has been shown that both issues can be mitigated by a combination of forming gas annealing during fabrication and hydrogen plasma treatment after fabrication. Here post-fabrication RHP treatment appeared the most important factor in noise mitigation, potentially due to annealing of charged defects in SiO\({}_{2}\) dielectric. Likewise, due to the instability of platinum oxide, ridding platinum-based devices of their native oxide has proven possible using argon anneal, however these devices also exhibited time instabilities caused potentially by reoxidation of Pt at Pt/Al\({}_{2}\)O\({}_{3}\) interfaces. In addition, the choice of SiN\({}_{x}\) as the tunnel barrier seems to avoid parasitic oxide formation in the tunnel junctions so that no in-fabrication or post-fabrication is needed to restore the MIM behavior in the SETs, but lowering the electrical noise has proved to be more elusive. Further research is needed to determine the source of the traps and to investigate how post-fabrication hydrogen plasma is effective in passivating these traps, and what species cause the oxidation of the metal layer deposited on top of the ALD oxide. Future work will also explore amorphous metal thin films [52] (unlike the polycrystalline films used in this work) and the effects of surface morphology on barrier uniformity.  
**Acknowledgments:** The authors would like to acknowledge the National Science Foundation for funding this research (NSF DMR-1207394, CHE-1124762, DGE-1313583, and ECCS-1509087). The authors are grateful to Professor [PERSON] for multiple useful discussions.  
**Author Contributions:** G.K. fabricated and measured all the Ni-based devices. L.C.S., H.G. and M.S.M. fabricated and measured Pt-based devices. M.J.F. ran simulations of the fabricated devices. A.O.O. and G.L.S. designed experiments and oversaw cryogenic testing. A.N.N. contributed to theoretical explanation of plasma annealing. The manuscript was written by [PERSON], [PERSON] and [PERSON] and edited by all authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 High-Voltage Battery and Air Cooling System Modeling
### Single Temperature Lumped-Parameter Model and Battery Cooling System  
Temperature has an extremely major impact on battery lifetime, which is especially holds when it falls beyond the ideal thermal thresholds that are currently estimated within 15 \({}^{\circ}\)C and 35 \({}^{\circ}\)C [12]. The high-voltage battery pack is an extended body characterized by a spatial distribution of temperature values, which is rather complex to model [35; 36]. A wide range of battery thermal modeling options is available in the literature. Among these, lumped-parameter thermal models can be suggested as a good trade-off between model accuracy and ease of implementation in Matlab/Simulink\({}^{\circledR}\) software. A single temperature lumped-parameter model of the battery has been implemented here by taking inspiration from the works by [PERSON] et al. [37] and [PERSON] et al. [38].  
The battery pack considered in this paper is constituted by a total of 1440 cylindrical cells of type A123 26650. As reported by the manufacturer [26], each cell is shaped in a cylinder of \(\mathcal{O}26\times 65\) mm and weights of 76 g. Reasonable values for battery and air thermal parameters were obtained by combining cell manufacturer data with data extracted from the work presented by [PERSON] et al. in 2013 [39]. These values have been reported in Table 2. In particular, they refer to the battery pack mass \(m_{bat}\), to its specific heat \(c_{bat}\) and to its sizes: length \(l_{bat}\), height \(H_{bat}\), and width \(w_{bat}\). \(S_{bat}\) represent the exchange surface with the surrounding air, which characterizes for a specific heat \(c_{p,air}\). Despite being modeled using a thermal lumped approach, the battery pack was not assumed to be lumped from a spatial point of view, as displayed in Figure 3. Notably, 40, 3, and 12 cells were particularly aligned along \(l_{bat}\), \(H_{bat}\), and \(w_{bat}\), respectively. The assembly of the battery pack was conceived to leave sufficient space for the channels in which the air flows when the cooling system is in operation. These channels were assumed to be 15 mm wide, and they constitute the overall cooling exchange surface between air and cells \(S_{cooling}\) whose value is reported in Table 2. \(h_{side,air}\) and \(h_{cooling,air}\) are convective coefficients for the air surrounding the battery and the air forced to circulate in the cooling channels, respectively.  
The rest of this sub-section is structured as follows: the air cooling system for cabin and battery is described first. The numerical thermal modeling approach adopted for the battery pack is then illustrated, and the implemented HEV battery thermal management strategy is finally described.
# A Novel Method for Modeling the Electromagnetic Characteristics of Switched Reluctance Motors
## 3 Modeling the Entire Flux-Linkage Characteristics
### Five-Order Fourier Series Flux-Linkage Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **a1** & **a2** & **a3** & **a4** & **a5** & **a6** & **a7** \\ \hline \(\phi_{0}\) & \(2.0721\times 10^{-2}\) & \(-9.3272\times 10^{-4}\) & \(5.8819\times 10^{-4}\) & \(-1.4294\times 10^{-4}\) & \(1.8268\times 10^{-5}\) & \(-1.1749\times 10^{-6}\) & \(3.0049\times 10^{-8}\) \\ \(\psi_{75}\) & \(3.3840\times 10^{-2}\) & \(-1.6889\times 10^{-4}\) & \(4.0050\times 10^{-4}\) & \(-1.5927\times 10^{-4}\) & \(2.4348\times 10^{-5}\) & \(-1.6901\times 10^{-6}\) & \(4.4497\times 10^{-8}\) \\ \(\psi_{1125}\) & \(7.5204\times 10^{-2}\) & \(-1.1415\times 10^{-2}\) & \(7.7113\times 10^{-3}\) & \(-2.2192\times 10^{-3}\) & \(2.8990\times 10^{-4}\) & \(-1.8233\times 10^{-5}\) & \(4.5204\times 10^{-7}\) \\ \(\psi_{15}\) & \(1.0796\times 10^{-1}\) & \(-1.1565\times 10^{-2}\) & \(1.0335\times 10^{-2}\) & \(-3.2401\times 10^{-3}\) & \(4.3977\times 10^{-4}\) & \(-2.7691\times 10^{-5}\) & \(6.7627\times 10^{-7}\) \\ \(\psi_{22.5}\) & \(1.7879\times 10^{-1}\) & \(-3.1390\times 10^{-2}\) & \(2.1949\times 10^{-2}\) & \(-6.9017\times 10^{-3}\) & \(9.7977\times 10^{-4}\) & \(-6.5724\times 10^{-5}\) & \(1.7067\times 10^{-6}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Polynomial coefficients of the flux-linkage characteristic curves on five special positions. To quantify the accuracy of the proposed analytical calculation method, the performance index is presented in Figure 7c,d and defied as follows:  
\[ \begin{cases}e=|\psi_{calculated}-\psi_{measured}|\\ \delta_{RMSE}=\sqrt{\frac{1}{N}\sum\limits_{j=1}^{N}e^{2}(j)}\end{cases}  \tag{13}\]  
where \(N\) is the number of samples of the current. It can be observed from Figure 7b,c that the errors in \([15^{\circ},22.5^{\circ}]\) between the calculated and measured method are relatively large. However, the errors for all of the rotor positions are less than 0.01. The results show that the calculated and measured flux-linkages agree well, which verifies the accuracy and effectiveness of the proposed analytical method.