esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent TCP Congestion Control Method Based on Deep Q Network
## 1 Introduction
Compared with NewReno, QTCP improves the throughput by 59.5%. The results may look good; however, it has been 20 years since the NewReno method was proposed in 1999. The NewReno method has been deprecated in practice. The cubic algorithm, which is used as the default congestion control method in Linux OS, improves the throughput by 200% compared with NewReno. Compared with the cubic method, QTCP has little advantage. To build a more efficient congestion control method by leveraging reinforcement learning, we propose TCP-DQN, which is a congestion control method based on the DQN algorithm, to implement efficient and reliable data migration in virtual data space. DQN [9] is the state-of-the-art method of the Q-learning family. DQN uses experience replay and target network to reduce the correlation between training data, solving the oscillation problem in the traditional Q-learning method. The main contributions of this paper are as follows  
1. We propose TCP-DQN. By leveraging the state-of-the-art DQN algorithm, the training of the congestion control is more stable and faster than the conventional Q-learning method. 2. We implemented TCP-DQN and compared it with the cubic method, which is the state-of-the-art congestion control algorithm and is used as the default congestion control method in Linux kernel. The results show that TCP-DQN can improve the throughput by 200% compared with cubic.
## 3 Calculation of Charging Station Demand
### Energy Demand per City  
As the first step of modeling, it is necessary to estimate the number of charging stations for the main cities--the main consumers of electric vehicles. This is a basic step in performing the estimation of charging station positioning at specific destinations.  
The demand analysis was performed based on the current state of the electric vehicle market and charging infrastructure in key cities of Kazakhstan: Almaty, Astana, Karaganda, Shymkent, and Aktau. The information regarding the energy demand in Kazakhstan was analyzed according to research in the field [16; 17]. These studies show that Kazakhstan has experienced significant growth in electric power, which is very significant for EV market growth. Based on this research, a model was created to estimate possible growth.  
\[N_{t}=N_{n}+N_{e} \tag{1}\]where \(N_{t}\) is the total number of needed charging stations per city. The information is based on the current state \(N_{n}\) and the estimated number of required charging stations \(N_{e}\). It is determined as the number of EV charging stations for which the cost function \(\mu(N_{e})\) is minimized. The cost function is determined as shown below:  
\[\mu=N_{e}{}^{2}+max(\frac{C_{d}*P}{P_{c}}-(N_{e}+x)*100,0){}^{2} \tag{2}\]  
where \(\mu\) is a cost function used for the minimization function. It is determined to be a mono-extremal function based on \(N_{e}\) with additional data per city, such as \(C_{d}\)--the current city demand for electric power--and \(P\) and \(P_{c}\)--the city and country populations, respectively.  
The data included such aspects as the city population, existing charging stations, and current electricity demand. Firstly, population data were obtained from reports from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. Information on the number of existing charging stations in each city was sourced from official reports and statistics [18; 19]. Finally, current electricity demand (kWh), reflecting the current load on charging stations, is crucial for determining future infrastructure needs. This approach makes it possible to take into account real conditions and create a reasonable forecast that can be used for the long-term planning of charging station infrastructure development in Kazakhstan.  
As the calculation result, the forecast shown in Figure 1 was created.  
As can be observed in Figure 1, the current charging station number is relatively low in cities such as Karaganda, Shymkent, Aktau, and Atryau. Based on this, it can be assumed that a prediction based only on power supply demand can provide inaccurate data. To enhance the data accuracy, the forecast of EV market growth has to be taken into account, too.  
Figure 1: Charging station requirement forecast for main Kazakhstan cities.
# Angular Circle Array Multiple Input Multiple Output Underwater Optical Wireless Communications +
## 2 System Model
### Derivation of the Transceiver Structure
Through Figure 7a, the remaining transmitter x and y coordinates are obtained as follows:  
\[ \begin{cases}x_{t}^{i}=r_{t}\cos\left(\varphi_{t}^{i}\right)\\ y_{t}^{i}=r_{t}\sin\left(\varphi_{t}^{i}\right)\end{cases} . \tag{12}\]  
After obtaining the transmitter \(x\) and \(y\) coordinates, we then derive the transmitter \(z\) coordinates. Through Figure 8a, the \(z\)-coordinate of transmitter \(t_{1}\) is obtained as \(z_{t}^{1}=A_{t}\sin(\theta_{lin})\). Due to the symmetric structure of the transmitter, in addition to transmitter \(t_{1}\), a circle of transmitters around the symmetric arrangement of the \(z\)-coordinate and the plane inclination angle \(\theta\) and elevation angle \(\beta\) parameter settings are the same. Thus, the \(z\)-coordinate is unified as follows:  
\[z_{t}^{i}=r_{t}\tan\beta_{t}, \tag{13}\]  
where \(r_{t}=A_{t}\sin\theta_{lin}/2\tan\beta_{t}\). Figure 7: Transceiver _xoy_ plan view of angular circle array MIMO communication system: (**a**) transmitters and (**b**) receivers. Receiver coordinates are basically the same as the transmitter coordinates, only the corresponding parameters need to be modified for the receiver parameters, and the \(z\)-coordinate is adjusted to \(z_{r}^{i}=L-A_{r}\sin\theta_{rin}\), where \(L\) is the communication distance.
## 4 Conclusions  
As HSP47 is an attractive therapeutic target for the treatment of fibrotic diseases. Understanding of its physiological binding interactions would provide valuable information for the selection and design of small HSP47 inhibitors. Here, we identified the HSP47 binding sites on native collagens II and III using the collagen toolkits and showed that collagen binding involves two neighboring subpockets on the HSP47 surface. Subsequent structure-based drug screening through targeting of these surface areas identified two novel small-molecule HSP47 inhibitors, which might serve as lead compounds for the development of HSP47 inhibitors through chemical structure modification.  
The following are available online at [[https://www.mdpi.com/article/10_3390/biom11070983/s1](https://www.mdpi.com/article/10_3390/biom11070983/s1)]([https://www.mdpi.com/article/10_3390/biom11070983/s1](https://www.mdpi.com/article/10_3390/biom11070983/s1)).  
Conceptualization, A.Z., J.M.G., and R.W.F.; data curation, H.C.; funding acquisition, R.W.F. and A.Z.; investigation, H.C. and P.S.; methodology, H.C., P.S., G.L., D.B., and S.W.H.; resources, D.B.; supervision, S.W.H., A.Z., J.M.G., and R.W.F.; writing--original draft, H.C.; writing--review and editing, P.S., A.Z., J.M.G., and R.W.F. All authors have read and agreed to the published version of the manuscript.  
This work was funded by National Natural Science Foundation of China (81502981); the National Key Research and Development Program of China (2016 YFA0500601); British Heart Foundation, grant numbers FS/17/31/32848, FS/11/86/29137, and RG/15/4/31268, and Welcome Trust, Biomedical Resource Grant, 094470/Z/10/Z.  
Figure 5: Predicted binding mode of HSP47 inhibitors. (**a**) HSP47 is shown as surface presentations, while the collagen mode peptide (blue) is shown as a cartoon. The Arg0 binding area on HSP47 is colored yellow (subpocket 1). while the binding area for collagen residues at positions -4 and -3 is colored slate (subpocket 2). The potential binding interactions between HSP47 and the compounds Col003 (**a**) COM II (**b**), Hs1 (**c**), and Hs55 (**d**) are shown in the compounds as sticks and labeled. Compounds Col003, Com II, Hs1, and Hs55 are shown as magenta, cyan, green, and pink sticks, respectively. The black dashed lines represent the hydrogen bonds.
## 3 Measuring Process and Results
### Measuring the System Construction
The measurement method and the corresponding data processing algorithm made the movement of the probe and the machine tool independent of each other and avoided the possibility of damaging the control accuracy of the machine tool by connecting the probe hardware with the machine tool. Compared with the single-point real-time corresponding measurement method, this measurement method greatly improved the measurement efficiency. Figure 11: Method of locating the center position of a calibration sphere. The experimental measured data were restored to the design model, and then a two-dimensional contour was extracted to obtain the tooth height and pitch of the DOE. Figure 13 records the difference between the measured and designed values of the tooth height and pitch of the diffraction structure in three groups of measurement results. The results show that the measurement method can effectively realize the complete measurement of the feature information of the curve-based DOE. The average pitch error was 0.5 \(\upmu\)m, and the average tooth height error was 0.0084 \(\upmu\)m. The experiment found that the relative error of the second set of data was smaller, indicating that the higher the calibration accuracy of the optical axis of the probe, the higher the accuracy of the DOE feature measurement. Therefore, the second set of data was used to reconstruct the three-dimensional shape of the curve-based DOE, and then the curved base was subtracted to obtain the DOE feature information. The final processing results of the measured data are shown in Figure 14.  
The experimental verification shows that the DOE measurement method proposed in this paper is feasible, and the DOE is measured at a constant contour scanning speed based on the principle of confocal measurement. The non-point positioning measurement form eliminates the need to acquire system coordinates in real time and reduces the dependence on the positioning coordinate points in the  
Figure 14: Measured data processing results. Figure 13: Error of (**a**) DOE pitch measurement; (**b**) tooth height measurement. measurement process. It can realize high-precision and fast measurements of the complete curve-based DOE feature. This method is also applicable to the measurement of DOE feature information of an arbitrary base.
## 4 Simulation Verification and Discussion
### Sensitivity Analysis  
Reference [42] established the proportionality between the time-delay margin and the actuator's time constant for the INDI controller, as demonstrated by rigorous mathematical proofs. Consequently, the actuator's natural frequency has a significant impact on the stability and robustness of the INDI-based control algorithm. In the case of lateral-directional control with the ESO-based INDI controller, several parameters are involved, including the model and actuator parameters. Thus, it is essential to clarify the effect of perturbing different parameters on the proposed controller's robustness. Specifically, the uncertainty parameters, including the control derivative parameters (\(L_{\delta_{x}}\), \(L_{\delta_{y}}\), \(N_{\delta_{x}}\), and \(N_{\delta_{y}}\)), the static aerodynamic parameters (\(L_{\beta}\), \(N_{\beta}\), \(Y_{\delta_{x}}\), and\(Y_{\delta_{y}}\)), and the damping aerodynamic parameters (\(L_{p}\), \(L_{r}\), \(N_{p}\), and \(N_{r}\)), are set to 20%, 30%, and 50% of their nominal values, respectively, as per the linearized model given by Equation (3). The actuator uncertainty is assumed to be the same as that specified in Table 5.  
The robust control toolbox in MATLAB [43] is utilized to determine the sensitivity of each uncertainty element through parameter perturbation. Figure 14 displays the percentage contribution of each parameter to the deterioration of robustness, where the sum of the percentage is equal to 1. The larger the percentage of the parameter, the greater the impact on the deterioration of robustness. Notably, the natural frequency of the actuator is identified as the parameter with the greatest impact on robustness, contributing to 60% of the deterioration. This conclusion aligns with the findings in reference [42]. The uncertainty of the control derivative parameters is identified as the second most influential parameter, as has been suggested in the previous literature [32; 42]. However, the directional static and damping aerodynamic parameters are found to have little influence on the robustness. Figure 14 also emphasizes the significance of the lateral static and damping aerodynamic parameters for the LSR aircraft during supersonic flight.  
Figure 14: Different parameters’ percentage contributions to the deterioration of robustness.  
Figure 13: RMS comparison between NDI and the proposed control law: (**a**) Roll angular rate tracking error RMS and (**b**) Yaw angular rate tracking error RMS.
## 4 The Proposed Algorithm
### Markov Decision Process Modeling
#### 4.1.4 MATD3-TORA-Based Task Offloading and Resource Allocation Algorithm
```
Input: The total number of training iterations (\(I_{max}\)), the learning rate for the actor network (\(\alpha_{actor}\)), the discount factor (\(\gamma\)), the experience replay buffer size (\(N_{p}\)), the mini-batch size (\(N_{b}\)), and the Gaussian action noise (\(n_{t}\)); Output: The actor networks \(\mu(s_{t}^{1}|\theta_{1}),\dots,\mu(s_{t}^{1}|\theta_{M}^{u})\);
1 Initialize the actor networks \(\mu(s_{t}^{1}|\theta_{1}),\dots,\mu(s_{t}^{1}|\theta_{M}^{u})\), the critic networks \(Q_{1}(s_{t}\theta_{1}^{Q1}),\dots,\theta_{M}^{Q1}\) and \(Q_{2}(s_{t}\theta_{1}^{Q2}),\dots,\theta_{M}^{Q2}\);
2 Initialize the experience replay buffer \(D\);
3 for\(k=1,2,\dots,I_{max}\)do
4 Reset: \(s_{0}\leftarrow\) env.reset() ;
5 Initialize the simulation parameters for the multi-UAV MEC system environment ;
6 for\(u=1,2,\dots,I_{max}\)do
7 UAV \(u\) executes an action \(a_{t}^{u}\) perturbed by Gaussian noise \(n_{t}\) according to Equation (41);
8 Obtain the next state \(s_{t}^{u}\) and reward \(r_{t}^{u}\);
9 Calculate the normalization state \(s_{t}^{int}\);
10\(D\leftarrow\) Store the transition tuple \((s,a,r,s^{\prime})\) in the experience replay buffer;
11 Sample \(N_{b}\) from the experience replay buffer \(D\);
12 for\(u=1,2,\dots,I_{max}\)do
13 Add truncated Gaussian noise \(\epsilon\) to the action \(a_{t}^{u}\);
14 Update the critic network \(u\) according to the Minimize loss function \(L(\theta_{u}^{Q_{i}})\) of Equations (43) and (44);
15 ifUpdate the actor networkthen
16 Update the actor network \(u\) according to Equations (45) and (46);
17
18 Soft update on the target network parameters according to the Equations (47) and (48);
```  
**Algorithm 1**The MATD3 algorithm for task offloading and resource allocation.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 2 Related Work  
In this section, the paper introduces the related works from three aspects: network slicing, satellite navigation and resource allocation methods. In previous studies, these three topics were usually studied separately.
# Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
## 1 Introduction
To study this question in a systematic manner, we adopt a dataset commonly used to study audio synchronization, modify it to construct benchmarks in which the average global time warping conditions are carefully controlled, and then use our controlled benchmarks to study the effectiveness of several strategies for handling global time warping. Our goal is to understand the effect of global time warping on the alignment accuracy of DTW and to identify a set of best practices for handling varying global time warping conditions. This paper has three main contributions. First, we introduce a framework for systematically studying the effect of global time warping conditions in an audio-audio alignment task. Second, we explore several ways to improve the robustness of DTW to varying levels of global time warping, and we characterize their effectiveness in our controlled benchmarks. Third, we provide a clear recommendation for best practice in handling global time warping conditions: sequence length normalization with downsampling. This method achieves the best alignment accuracy across a wide range of global time warping conditions, while maintaining or reducing runtime compared to standard usages of DTW. Code for reproducing our experiments can be found at [[https://github.com/HMC-MIR/ExtremeTimeWarping](https://github.com/HMC-MIR/ExtremeTimeWarping)]([https://github.com/HMC-MIR/ExtremeTimeWarping](https://github.com/HMC-MIR/ExtremeTimeWarping)) (accessed on 5 February 2024). The rest of the paper is organized as follows. Section 2 introduces four different methods for handling or mitigating the effect of global time warping. Section 3 describes our experimental setup to study the effect of global time warping under controlled conditions and presents our empirical results and findings. Section 4 conducts two additional analyses to gain a deeper insight into the results. Section 5 concludes the work.
## 1 Introduction
### Literature  
Of particular relevance to this research work is the large-scale MIMO designs presented in [20; 21]. [PERSON] [20] presented a compressive sensing-based estimation scheme for a phased-array antenna using the sounding reference signal of LTE. [PERSON] [21] developed a blind estimation mechanism that works with a single RF chain without any reference signal. However, they [20; 21] do not consider the new advances in 802.11\(ay\) networks.  
Looking ahead, several more 6G goals and the progression of the data speeds of wireless networks generally target _Tbps_ from 2030 [22]. The primary goal of multi-connectivity with IEEE 802.11\(ay\) networks lies in enhancing the performance and quality of experience (QoE) at the end device while promoting load balancing and bonding across several links. Within the envisaged environment, this objective of sustainable convergence will undoubtedly lead to many concerns for the ongoing wireless research as to whether and how this convergence and the target data rate are feasible. Since different experts from several fields also pose an identical problem, we look from a cross-layer perspective to the upcoming IEEE 802.11_ay_ obstacles and opportunities before us [4].  
Perhaps two best-known examples of a TRA-Layer multipath transport that guarantees reliable in-order delivery are MPTCP LIA [23] and DALIA [11] (as reported in [24]). Multipath schedulers have been developed based on a round-robin approach, a lowest RTT first strategy, retransmission and penalization, and delay awareness (using an indirect measurement of HoL blocking). More recently, schedulers have been proposed based on bufferbloat mitigation. Earliest Delivery Path First (EDPF) has served as the baseline scheduler for much work. While it is optimal for loss-free links with a constant known delay, EDPF cannot take into account the time-varying nature of link delays (due to bonding, beamforming, MIMO, etc.) and also packet loss. Mitigating the HoL blocking in multipath connections remains a challenging and largely unsolved research problem [9], especially for short control information transfers and/or mmWave links with asymmetric and fluctuating characteristics [6; 9].  
Fortunately, federated learning (FL) proposed by Google [25] (and enhanced by Deakin [19]) can potentially perform the required distributed learning, where each device uploads its local learning model at fixed slots to the server for (ensemble) averaging of the learning and develops a global model, which in turn is downloaded by the device for their local learning updates [25; 26]. However, to fully utilize the potential of the federated learning for intelligent scheduling over IEEE 802.11_ay_, its slot duration needs to be highly adaptive, and the multi-armed bandit approach [27; 28] can efficiently moderate such a distributed dynamism [29].
## 4 Discussion
### Novel Approach  
Although the results have shown inconsistencies in the classification of certain classes, uncertainties may be associated with the heterogeneity of the landscape within the 100 m \(\times\) 100 m cell of the sensor used, making it difficult to map small environmental changes that may not be detected due to the spatial resolution. On the other hand, the better temporal resolution increased the probability of finding pixels without the influence of clouds, making it possible to extract values at least once a year, which might not occur in sensors with lower temporal resolution.  
Thus, from the point of view of quickly assessing the main LULC classes with the low computational cost of cloud computing and human resources, the proposed approach has the advantage of presenting a higher availability of cloud-free pixels. For example, Landsat-like sensors can have a maximum of 23 cloud-free pixels in a year and up to 60 in the Sentinel-2A and Sentinel-2B satellites (only Sentinel-2A scenes are available in Brazil, so far), while PROBA-V can obtain up to 73 synthetic 5-day composites (64). In fact, these are hypothetical values that depend on the frequency of cloud cover, especially during the rainy season (October-April), which reduces data availability. Figure 9 shows the frequency of the cloud-free and cloud shadow-free pixels over Mato Grosso state in 2015 and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **This study (km\({}^{2}\))** & **MapBiomas (km\({}^{2}\))** & **Difference** \\ \hline Cropland & 39,464 & 52,219 & \(-\)32\% \\ Forestland & 421,954 & 432,923 & \(-\)3\% \\ Water Body & 3429 & 17,711 & \(-\)416\% \\ Dry Savanna & 131,875 & 97,944 & 26\% \\ Pastureland & 228,208 & 231,189 & \(-\)1\% \\ Humid Savanna & 77,336 & 68,962 & 11\% \\ \hline Total & 902,266 & 900,947 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the areas mapped by this study and by the MapBiomas project.  
their comparison with the moderate spatial resolution. The average number of clear observations was 29 pixels without cloud and cloud shadows over the year (Figure 9B). In addition, there was a clear gradient of the spatial distribution of data availability from east to west (Figure 9A). In the east, a drier region with a predominance of Cerrado physiognonies, we found clearer observations than those in the west, which is characterized by dense Amazon forest (Figure 9A). Data availability is a limiting factor in reconstructing the phenological behavior of the main LULC classes in a time series classification [57]. Nevertheless, PROBA-V imagery has an excellent trade-off between data availability and spatial resolution, with a minimum of 20 \"clear\" pixels per year (Figure 9A).