esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Sequential Control Scheme to Stabilize Squeezed Vacuum States
## 3 FPGA Implementation of Sequential Control Scheme  
To realize steady squeezed states, at least three servo-control loops are necessary: OPO cavity length, relative phase between the pump and seed beams, and relative phase between the LO and seed beam. The seed beam, namely the locking beam in our setup, served as a mediator for phase stabilization between the pump beam and LO. The block diagram of the sequential control scheme is shown in Figure 2. During initialization, the operation process was similar to that of bright squeezed state generation [25]. The locking beam (seed beam) had a coherent amplitude that could be used to extract the error signals for cavity- and phase-locking. With the initialization complete, the system was switched to carry mode. In carry mode, the voltage hold circuits started working, and the locking beam was blocked. Owing to the design of the quasi-monolithic OPO and short optical path, the setup was stable at the timescale of several seconds without the servo-control loop. Thus, the OPO cavity and two relative phases maintained the same working status as the ready mode within a certain period of time. In the meantime, squeezed vacuum states were stably generated. After the period of work, the locking beam was switched on, the voltage hold circuit stopped working, and the system was switched to ready mode. In ready mode, the system was again recovered to the optimal status with the aid of the locking beam, which was better prepared for the next carry mode.  
The time sequence of the locking scheme is shown in Figure 3. We used an FPGA-based hardware platform to generate the time sequence and control the output signal. The on-off action of the locking beam was actuated by two AOMs. The voltage holder confirmed that the driving voltage imposed on the PZT remained unchanged during the time of no locking beam, until the next locking pulse arrived. After the arrival of the next locking pulse, three servo-control loops were activated, whereas the  
Figure 2: Block diagram of the FPGA-based algorithm. LO, local oscillator; HV, high voltage.  
voltage hold circuits were disabled. The spectrum analyzer operated in gate mode to synchronize the measurement process. To perform the complete measurement process, the LO phase operated in three modes: one was locked to zero with respect to the locking beam to measure quadrature amplitude component; the second was locked to \(\pi\)/2 with respect to the locking beam to measure quadrature phase component; the third was scanning mode.  
The time sequence described above was realized by virtue of the self-designed operation control program, which was compiled on an FPGA hardware platform. The sequential control scheme, based on the FPGA platform, can be extended to the generation of multipartite entangled states, and the number of its control loops presented a linear increase with the increase of the dimension of the state [47, 48].
## 7 Conclusions  
This survey has presented a comprehensive review of the current state of mobile biometric authentication, highlighting the most significant algorithms and popular dynamics. Within behavioral biometric authentication schemes, touch and motion dynamics were popular choices for mobile authentication models, with the greatest number of studies present out of all sources. Touch dynamics schemes performed well with a variety of DL and ML algorithms, mainly featuring CNNs, RNNs, and SVMs. Motion dynamics schemes were also effective with CNNs and SVMs, especially when paired together to form a hybrid CNN + SVM architecture. Within motion schemes, accelerometer and gyroscope were prevalent, as demonstrated in Figure 4. In keystroke dynamics models, LSTM-RNNs were outstandingly common and proficient. Schemes that utilized gait dynamics worked well with CNNs and RNNs, specifically when combined in a hybrid CNN + LSTM architecture. In physiological authentication models, CNN completely dominated. As demonstrated in Figure 8, CNN architectures were used in the majority of facial authentication models, providing sufficient performance both as a standalone classifier and a feature extractor. This trend persisted in other physiological models using ocular, fingerprint, vein, and voice characteristics.  
To summarize, deep learning has been found to be an effective method of classification in a variety of different contexts within behavioral and physiological authentication. CNNs and RNNs were overwhelmingly the most effective and popular algorithms for mobile biometric authentication schemes presented in studies from the past five or so years. CNN has proven to be exceptional not only in authentication contexts across both behavioral and physiological dynamics, but also as a feature extractor capable of enhancing the classification performance of many other algorithms. Most notably, hybrid authentication schemes have been observed to be a compelling and advancing architecture for classification models, resulting in higher accuracies than standalone models in many studies such as [35, 67, 69, 70, 71].  
Overall, the usage of DL algorithms and hybrid authentication models is worthy of and in need of further investigation within the field of mobile biometric authentication. The rise in the prevalence of DL algorithms across both behavioral and physiological dynamics indicates great change in the field of mobile biometric authentication, as studies investigate more complex architectures for their models. With many studies starting to take advantage of hybrid architectures, it is reasonable to assume that hybrid DL architectures have the potential to advance biometric authentication past previous boundaries. While behavioral biometric authentication requires more research and experimentation to become usable in real-world scenarios, experimentation in hybrid architectures shows that growth in this field is possible and that investment into the study of biometric mobile authentication is worthwhile.
## 1 Introduction
[PERSON] in [14] presents a combination of fuzzy logic rules with a genetic algorithm to help save energy in the engine. [PERSON] in [15] demonstrates an adaptive stochastic model predictive control strategy for a plug-in hybrid electric bus. A more detailed representation of energy management strategies has been introduced in [2; 16]. [PERSON]'s Minimum Principle was extensively investigated and has proven its effectiveness in providing optimal fuel consumption for plug-in hybrid vehicles for the current decade. In [17; 18], the map-based ECMS factor is adapted using a tan function  
Figure 1: Variation of the state of charge of the battery during the three modes of operation for a PHEV. for the required state of charge of the battery to be tracked according to the appropriate active mode, which manages the Chevrolet-volt engine on/off frequency. Each mode's optimal control and speed are stored offline in tables and then controlled online using underestimated distance online. In [19], the authors provide a method to get lambda plots which present the LaGrange multiplier thresholds using dynamic programming to be applied after estimating its initial value on ECMS or PMP method on both series and parallel architectures. A comparison between EMS control using the PMP method with available drive pattern information or predictive driving information is introduced in [20] and applied to series-production vehicles where controllers have been developed to deal with uncertainty. One of the most critical points of this method is the accurate calculation of the \(\lambda\) multiplier. Offline, when the driving cycle is entirely known, \(\lambda\) could be calculated precisely to achieve a targeted Soc at the end of the trip. However, online this represents a real challenge as future use is never precisely known. This paper presents a derived method from optimization via the PMP algorithm commonly known as Lambda-control (\(\lambda\)-control). It aims to extend an \(\lambda\)-control strategy used for HEV in [21; 22] and then developed here for a PHEV. Thus, the objective is to focus on the development of an efficient calculation of the initial value of the multiplier of the \(\lambda\)-control strategy, which is directly related to the evolution of the state of charge of the battery and the fuel flow. This is done by determining an estimated initial value calculated offline at the beginning of the trip yielding benchmark results, and subsequently correcting it online to ensure optimal control in real-time cases. The article is structured as follows: Section 2 gives an overview of the modeling of the plug-in hybrid architecture: representation methods, driving cycle, simulation environments, and vehicle dynamics modeling. Then, in Section 3, the proposed \(\lambda\)-control algorithm is described, including both the offline and online calculation methods. Finally, strategy performances and robustness are tested using the appropriate tools implemented on MATLAB/Simulink with standardized driving cycles as inputs.
# Bent and Branched Microstrip-Line Antennas for Circular Polarization
## 4 Conclusions  
We have analyzed three line-antennas with loops. Analyses reveal that segment length \(L_{\text{s}}\) is one of the critical parameters of bent and branched line antennas for CP radiation. For the bent line antenna, \(L_{\text{s}}\) is influential in both the beam direction and axial ratio, while \(L_{\text{s}}\) is influential in only the axial ratio for the branched line antenna. Subsequently, we designed another branched line antenna modified with additional loops. It is demonstrated numerically and experimentally that loop addition results in a gain enhancement of 2.0 dB without any change in design parameters and CP wave bandwidth of the original antenna.  
A comparison with other CP antennas such as helixes has yet to be conducted.  
Conceptualization, K.H.; software, K.H.; validation, K.N.; investigation, K.N.; resources, K.H.; data curation, K.N.; writing--original draft preparation, K.H.; writing--review and editing, K.H.; visualization, K.H.; supervision, K.H. and H.N.; project administration, K.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The numerical and experimental data used to support the findings of this study are included in this article.  
The authors would like to thank [PERSON] for his invaluable assistance in preparing this manuscript.  
The authors declare that there are no conflict of interest regarding the publication of this article.
## 1 Introduction
The catalytic nature of Zn\({}^{2+}\) has been recently applied in the fields of CO\({}_{2}\) valorization and absorption [40; 41], while the application in membrane-based gas separation remains limited [42; 43; 44]. However, the reversible nature of Zn\({}^{2+}\) catalyzed reactions may be further exploited in CO\({}_{2}\) selective membranes through the expected improvement of the affinity of CO\({}_{2}\) molecules to Zn\({}^{2+}\) binding sites. While high catalytic activity of Zn\({}^{2+}\) often requires the usage of halogen salts [45; 46],their strength might be excessive, resulting in saturation of the membrane with the reaction products or too strong a coordination of CO\({}_{2}\). Thus, anions with lower catalytic activity would be more suitable for membrane gas separation: CO\({}_{3}\)\({}^{2-}\), NO\({}_{3}\)\({}^{-}\), OAc\({}^{-}\), OTf\({}^{-}\), [Tf\({}_{2}\)N]\({}^{-}\), etc. Drawing on the existing literature, [Tf\({}_{2}\)N]\({}^{-}\) has already received wide approval for its high CO\({}_{2}\) affinity, arising from the presence of fluorine atoms [47; 48; 49; 50]. Additionally, it has much higher chemical stability in comparison to other fluorine-containing anions, such as [BF\({}_{4}\)]\({}^{-}\) and [BF\({}_{6}\)]\({}^{-}\), which hydrolyze in ambient conditions forming hydrofluoric acid [51]. This work implements Zn[Tf\({}_{2}\)N]\({}_{2}\) salt in PIL/IL composites with the expectation to intensify the CO\({}_{2}\) sorption selectivity in PIL-based materials (Figure 1). The sorption performance of developed materials is assessed both in direct and indirect measurements by gravimetric sorption and time-lag experiments, respectively. Furthermore, this paper highlights the possibility to deploy the PIL/IL/Zn\({}^{2+}\) composite materials as competitive TFC membranes for robust CO\({}_{2}\) capture from feeds similar to flue gas with substantial humidity content.
# Mobile Broadband Performance Evaluation: Analysis of National Reports
## 3 Case Studies
### Annual Report, 20 Years of Responsibility for Networks: Germany  
Regulator\(\rightarrow\) The Bundesnetzagentur organization, starting in the early 2000s, is responsible for measuring and assessing the quality of mobile broadband service in Germany. Bundesnetzagentur is also responsible for assisting energy, transportation, and postal service qualities. The latest report was released in 2019 [17]. However, the evaluation results are up to the year 2018.  
Operators\(\rightarrow\) Although Deutsche Telekom AG (DTAG) is the oldest MNO in Germany, there are also two more major MNOs: Vodafone and Telefonica Germany.  
Method\(\rightarrow\) Data gathered from MNOs and surveys by Bundesnetzagentur are used to compile the data in the report. Although there is no section in the report that specifically discusses the method, the results and the explanation implied where the data for the report are collected from.  
Metrics\(\rightarrow\) There are simple technical metrics and other non-technical metrics used in this report.  
* Coverage \(\rightarrow\) from at least one MNO and each MNO;
* Speed \(\rightarrow\) download;
* Applications \(\rightarrow\) Messages sent, call minutes, and data volumes;
* Revenue per sim card;
* Number of subscribers.  
_Evaluated services_\(\rightarrow\) Although applications provided via 2G, 3G, and 4G are evaluated, the specific services used for each application are not mentioned.  
_Result_\(\rightarrow\)  
* In 2018, the 4G data service is available for 97.5% and 95.5% of the population with 2 Mbps and 6 Mbps download speed from at least one MNO. When considering individual MNOs, DTAG, Vodafone and Telefonika Germany provide 98%, 93%, and 88% coverage, respectively, which means the service from all MNOs is 95%.
* More than 1993 m GB of data were sent through mobile broadband communication using 6.78 million UMTS- and LTE-enabled devices.
* On the other hand, while the calls made via mobile broadband services are on the rise and surpassing fixed network calls, the number of SMS is on the decline. In the year 2018, around 119 billion minutes and \(>\)8.9 billion messages were sent.
* While the revenue from data service is on the rise, the revenue generated from voice and SMS remains constant when compared to the past data.  
_Policies_\(\rightarrow\) When allocating a new spectrum, a new coverage regulation that obliges MNOs to provide access to data rates of \(>\)10 Mbps for each household was issued. To close the coverage gap, MNOs are required to install new base stations at roads and railways as well as not-spots.
## 4 Results and Discussion
### Time Domain and Frequency Domain Analysis
In this plot, magnitude and phase-angle loci at log scale versus phase diagram are in the imaginary and real axes. The location of poles and zeros of the system is presented in the pole-zero plot, and the eigenvalue of each converter in the form of a graph is depicted in Figures 7-9 for the input, isolation, and output stages, respectively. Through the use of these methods, the SST's design can be optimized for modern power distribution system applications, where stability is the main concern. Figure 8: Isolation stage (**a**) Bode plot, (**b**) Nyquist plot, (**c**) Nichols plot, (**d**) Root locus, (**e**) Pole–zero plot, (**f**) Eigen plot. Figure 7: Input stage: (**a**) Bode plot, (**b**) Nyquist plot, (**c**) Nichols plot, (**d**) Root locus, (**e**) Pole–zero plot, (**f**) Eigen plot.
# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 4 Discussion and Conclusions  
It is still a challenge to compromise between computational cost and accuracy in small targets detecting for self-driving vehicles in actual scenarios. An improved YOLOv3 object detection and tracking method is proposed in this paper, and it has the following advantages: (1) The modified model is accurate in small object detection but can be done at real-time, which comes at the expense of slower performance. (2) This paper applies data augmentation techniques to make up for the lack of proper representation of the imbalance in the dataset. Experimental results show that equalization of extremely low-frequency traffic signs improves the detection accuracy. (3) To improve video detection accuracy, Deep-Sort is added in the process of video detection, which effectively reduces the false detection and missed detection caused by external factors, and improves the robustness of the proposed model. Consequently, compared with the comparative methods [16; 23; 26; 53; 54], the proposed method has more advantages in real-time traffic sign video detection, including detection accuracy, speed, and robustness.  
Figure 7: The left column (**a**,**c**,**e**) show the original video detection results, and the right column (**b**,**d**,**f**) show the video detection results after adding Deep-Sort.  
Traffic sign detection is an important part of driving in the autonomous environment. However, even in the Real World, pedestrian and vehicle movements must be accounted for in self-driving systems. We will discuss this matter and put it to the test in the coming researches.  
Conceptualization, Y.L. and S.S.; methodology, Y.L.; software, S.S.; validation, S.S., Q.H. and G.L.; investigation, Y.L., S.S., Q.H., and G.L.; resources, Y.L.; writing--original draft preparation, Y.L. and S.S.; writing--review and editing, S.S.; visualization, Q.H., and G.L.; supervision, Y.L.; project administration, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61863003 and Natural Science Foundation of Guangxi Province, grant number 2016 GXNSF AA380327.  
Data Availability Statement: Data sharing not applicable.  
This work was supported in part by the National Natural Science Foundation of China, and in part by the Natural Science Foundation of Guangxi Province.  
The authors declare no conflict of interest.
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 4 Results and Discussion
### Damage Detection
#### 4.4.5 Overall Performance of the Damage Detection Models  
It can be seen from the results of the inference for each type of damage that the most accurate damage detector is the model trained on the MobileNet architecture. The MobileNet model most consistently achieved the highest confidence scores when predicting each type of damage with the highest overall confidence score of 97.58% when predicting the damaged roof in Figure 6b. This is likely a result of the unique structure of the MobileNet architecture; the depthwise separable convolutions allow for these detections to have an improved computation and overall model scale in comparison to the EfficientNet and ResNet models. Additionally, the width and resolution multipliers that are incorporated into the model architecture likely give the MobileNet model a significant advantage in scaling the layers for a more-tailored fit to each object class. Thus, each classifier corresponding to the four object classes predicts the type of damage after it is located in the image with high accuracy.  
However, the damage detection model with the most consistent classifier was actually EfficientNet. As can be seen in Tables 4-7, EfficientNet was the most consistent model with classifying the predicted damage as the correct object/label. Out of the top three predictions among each of the four types of damage, EfficientNet incorrectly classified the damage a total of three times, while MobileNet and ResNet incorrectly classified the damage six times and eight times, respectively. This result is likely due to the primary advantage of the EfficientNet architecture being its efficiency of predictions due to the use of compound scaling. In turn, this efficiency is optimized by the BiFPN mentioned previously; increased efficiency of accurate predictions leads to the EfficientNet model producing more consistently accurate classifications of the damage--but at the cost of  
Figure 9: Inference carried out on the image of _structural damage_ for each of the three models. The tan colored bounding boxes correspond to the correct predictions of _structural damage_ from the first row of Table 7.  
losing a certain degree of accuracy in predicting the precise location of the damage, thus leading to a lower confidence score for EfficientNet in general.  
Since our in-house dataset for damage detection was not included in the original dataset used to develop all three AI models (ResNet, MobileNet, and EfficientNet), then our models are subject to developing a certain level of negative transfer [36,37]. The effect of negative transfer on model performance leads to less accurate predictions made by each model. Lower confidence scores can be attributed to the negative impact each model endured from transferring learned features of each pre-trained model to the target domain of our dataset.
# Design and Analysis of Systematic Batched Network Codes
## 3 Systematic Outer Codes
### Computation Cost  
At first, it seems that the systematic outer code increases the encoding and decoding computation cost because an additional decoding step is employed in the systematic encoding, and an additional ordinary encoding step is employed in the systematic decoding. However, after careful evaluation, we see that the encoding computation cost of the systematic outer code is lower than that of the ordinary outer code. The decoding computation cost of the systematic outer code depends on the number of message packets received at the destination node. In the worst case, where no message packets are received, the decoding computation cost is doubled.  
To assist our discussion, we denote by \(b\) the average computation cost of encoding a packet using the ordinary outer code, and we denote by \(c\) the computation cost of decoding the ordinary outer code using \(K\) coded packets. Here, we assume that the decoding is successful with zero coding overhead. Suppose that the packet length \(T\) is much larger than \(M\), which means that the coefficient vector length is much less than \(T\). According to the analysis in [23], \(b=O(M)\) and \(c=O(KM)\) linear combination operations (LCOs). (A linear combination operation (LCO) refers to the computation of a linear combination \(\mathbf{x}+\mathbf{a}\mathbf{y}\), where \(\mathbf{x}\) and \(\mathbf{y}\) are two packets of \(T\) field elements and \(\alpha\) is an element from the base field.) Moreover, for the two-step decoding and the joint decoding, \(Kb\approx c\). For the inactivation decoding, if the number of inactive packets is bounded by a constant, \(Kb\approx c\).