esa-sceva/satcom-chunk-collection huggingface dataset:
# Compressible Diagnosis of Membrane Fouling Based on Transfer Entropy
## 3 Methodology
### Membrane Fouling Detection Model  
Autoencoder (AE) is a multi-layer neural network of unsupervised learning in the deep learning technique, which includes an encoder and a decoder (as shown in Figure 2). The encoder compresses the input data \(\mathbf{X}=[x_{1},x_{2},\ldots,x_{m}]\) to obtain the outputs of the hidden layer \(\mathbf{H}=[h_{1},h_{2},\ldots,h_{s}]\). And the decoder takes the outputs of the hidden layer as the input and obtains the output \(\hat{\mathbf{X}}=[\hat{x_{1}},\hat{x_{2}},\ldots,\hat{x_{m}}]\). \(\mathbf{H}\) can be expressed as  
\[\mathbf{H}=f(\mathbf{W}_{x}\mathbf{X}), \tag{6}\]  
where \(f(x)=1/(1+\mathrm{e}^{-x})\) is the activation function. \(\mathbf{W}_{x}\) represents the weight between the input layer and the hidden layer, and \(s<m\). The output value \(\hat{\mathbf{X}}\) can be expressed as  
\[\hat{\mathbf{X}}=f(\mathbf{W}_{\mathbf{h}}\mathbf{H}), \tag{7}\]  
where \(\mathbf{W_{h}}\) is the weight between the hidden layer and the output layer.  
The back-propagation algorithm is used to adjust the parameters of AE in the training process. For the input variable \(\mathbf{X}=[x_{1},x_{2},\ldots,x_{m}]\), the objective function is defined as  
\[J=\frac{1}{2m}\sum_{i=1}^{m}\|x_{i}-\hat{x}_{i}\|^{2}. \tag{8}\]  
To judge the existence of membrane fouling, the collected data samples for the autoencoder are tested after training. Assuming that the AE captures the discipline of the input-output accurately, the maximum root mean square error is considered as the threshold that judges the incidents of membrane fouling. When the reconstruction error is greater than the threshold of the maximum root mean square error, it means membrane fouling exists in the wastewater treatment process.  
Figure 2: Autoencoder structure.
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 4 5G Mobile Services
### Immersive 5G Services  
#### 4.1.1 Virtual Reality and Augmented Reality  
VR and AR are the essential contents of future communications [14] as shown in Figure 4. The availability of low-cost head-mounted display (HMD) demands real-time and bandwidth-intensive services. However, long time usage of VR/AR results in dizziness and motion sickness. A possible solution to such a problem is to increase the refresh rate to at least 100 Hz and decrease motion-to-photon latency (the time required to fully reflect the motion of a user on the display screen) to 20 ms or less [15]. Furthermore, the service technology needs to cater to problems regarding insufficient image quality, viewing angle, input interface and information display methods. Future VR/AR requires offering a replica of the real site to the user. Hence, future service technology should handle a wide variety of entertainment content. Moreover, it should provide telepresence services and give the user the experience that he/she is in the same place as a remote user. Telepresence services need to be more realistic by offering the user the capability to use the five senses. Thus, VR and 3D holograms can be used together to make this a reality. Future 5G services will have to handle 4-K or 8-K resolutions to solve the image quality problem. The current VR technology provides a 110 degree viewing angle, and it needs to be extended to 200 degrees to meet the minimum level of user satisfaction. The user's equipment or HMD is limited in resources, such as memory, storage capacity, heat management and battery. This is a major obstacle in providing high definition video and 3D graphics in mobile environments. Cloud processing is one of the solutions to overcome this problem, and thus, 5G networks require supporting high bandwidth with low latency. Real-time AR/VR services cannot be achieved without higher transmission rate and low latency. In terms of a core network, network slicing can be one of the key technologies to realize low latency high throughput VR/AR services with other wireless technologies [16]. To alleviate the burden of fronthaul, backhaul and backbone networks and reduce end-to-end latency of networks, local computing with fog radio access networks (FRAN) is also required to provide real-time VR/AR services [17]. Accurate localization is also required for mobile AR services In summary, 5G requires low latency processing and telecommunication technologies with an extremely high data rate to support real-time VR/AR and telepresence services.
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 5 Experimental Results
### Comparison  
In order to provide a better understanding of how well our method works, we provide a fair comparison between some related techniques:  
* Recurrent neural network [26]: a RNN-based mobility model analyzes the user's past locations sequentially.
* Long short-term memory [27]: LSTM-based approaches deploy state units and a forget gate to learn the mobility pattern.
* Gated recurrent unit [28]: GRU-based techniques control the impact of the latest observations using update and reset gates.
## 2 The UKF Detection of PQDs
### Analysis of Anti-Noise Detection Performance  
Referring to the data in [21], the PQD data set was obtained from matlab 2020. The total sampling time is set to 0.2 s, the sampling frequency is 10 kHz, the sampling interval is 0.1 ms, the frequency of the voltage signal is 50 Hz, and the period is 0.02 s. The voltage amplitude is normalized. UKF is used to track the amplitude change in voltage swell under different signal-noise ratios (SNRs). Figure 2a,b are 40 db and 20 db, respectively. The red curve represents the theoretical value, the black curve is the UKF tracking result, and the pink rectangular area is the rise or fall adjustment time span of the filter waveform curve. It can be seen from the diagram that as the signal-to-noise ratio decreases, the interference of the waveform tracking curve becomes larger and larger, and the waveform rise or fall span gradually becomes larger. Figure 1: The flowchart of UKF algorithm. Figure 2: Tracking voltage surge disturbance by UKF. (**a**) In total, 40 db amplitude variation; (**b**) 20 db amplitude variation. In order to further compare the performance of signal detection methods, the quantitative analysis and comparison of each index of KF and UKF in tracking and detecting voltage swell are carried out. The root mean square error of the amplitude is defined as follows:  
\[e_{\text{RMSE}}=\sqrt{\frac{1}{m}\sum_{\text{k=1}}^{m}\left(x_{\text{k}}-\overline {x}_{\text{k}}\right)^{2}} \tag{17}\]  
The mean absolute error of the amplitude is defined as follows:  
\[e_{\text{MAE}}=\frac{1}{m}\sum_{\text{k=1}}^{m}\left|x_{\text{k}}-\overline{x} _{\text{k}}\right| \tag{18}\]  
where \(m\) is the total sampling points, \(x_{\text{k}}\) is the state estimation value, and \(\overline{x}_{\text{k}}\) is actual value of state. The performance comparison of KF and UKF for PQDs is shown in Table 1.  
It can be seen that the root mean square error and the mean absolute error of UKF for voltage swell tracking curve estimation are less than KF under the same SNR environment in Table 1. As the SNR increases, the corresponding root mean square error and mean absolute error gradually decrease, which is consistent with the theoretical analysis. Therefore, UKF has better robustness and can better handle interference and noise. The mathematical expression and parameter constraints of PQD in this paper are shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **SNR** & \(\epsilon_{\text{RMSE}}\) & \(\epsilon_{\text{MAE}}\) \\ \hline \multirow{3}{*}{KF} & 15 & 0.0206 & 0.0809 \\  & 25 & 0.0100 & 0.0404 \\  & 35 & 0.0087 & 0.0280 \\ \hline \multirow{3}{*}{UKF} & 15 & 0.0149 & 0.0765 \\  & 25 & 0.0092 & 0.0387 \\ \cline{1-1}  & 35 & 0.0085 & 0.0274 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of KF and UKF performance.
## 5 Result and Discussion
### Results from the Customer Survey
#### 5.2.2 Part 2: RESOLVD Value Propositions
An interesting observation is that from the DSOs perspective, cybersecurity was the first most rated proposition within a five-year timeline, which slightly differed from the technology developers' views with a low CFI. Furthermore, 67% of the customers would have liked to get a combination of few value propositions, rather than a complete package (see Figure 8). This shows that different customers had different needs, and thus, the solution should be modular enough to be customizable for different customers. It is also worth noting, from Figure 9, that many of the H2020 RESOLVD value propositions, such as improve the continuity of supply to customers, improve the capability to prevent congestion and over/under voltage, improve the voltage control ability through local reactive power injection and improve power quality, were all tightly related to the possibility of managing energy storage systems in grids. Thus, in contrast to the previous analysis depicted in Figure 8 on the DSO needs, mostly stressing grid observability, new software for decision support and interoperability as the most relevant needs in the short term, the analysis in Figure 7 suggests that to actually solve grid operational aspects, energy storage technologies and associated power electronics are key assets to be exploited, following the software tools and observability capabilities previously mentioned.
## 3 Experimental Setup and Result Analysis
### Experimental Results
#### 3.4.3 ROC Curve  
The ROC curve is a critical tool to assess a classifier's performance, illustrating its generalization capability in classification tasks. It plots the true positive rate (TPR) on the vertical axis against the false positive rate (FPR) on the horizontal axis. The classifier generates probability values ranging from 0 to 1, indicating the likelihood that a sample belongs to a specific category. By adjusting a threshold value, samples are categorized as positive or negative; probabilities above the threshold are positive, while those below are negative. The ROC curve is constructed by gradually lowering the threshold from 1 to 0, calculating the corresponding (FPR, TPR) points, and connecting these points. The area under the ROC curve (AUC) ranges from 0 to 1, with higher values indicating better model performance.  
Figure 6a shows the ROC curves for breast cancer histopathological images at 40\(\times\) magnification. The AUC values for each type of breast cancer image ranged from a minimum of 0.93 to an average of 0.98. The ROC curves for adenopathy, mucinous carcinoma, and papillary carcinoma exhibited an AUC of 1. However, the model's performance was weakest for lobular carcinoma, which had the lowest AUC, indicating poorer recognition capability. At 100\(\times\) magnification, as shown in Figure 6b, the minimum AUC for different breast cancer pathology types was 0.95, with an average AUC of 0.97, demonstrating balanced recognition across various pathology types. Notably, lobular carcinoma's AUC reached 0.99, indicating strong recognition at this magnification. Figure 6c displays the ROC curves at 200\(\times\) magnification. Here, the AUC values for each type of breast cancer pathology image were at least 0.93, with an average AUC of 0.97. The model excelled in recognizing adenopathy and mucinous carcinoma, both achieving an AUC of 1. However, tubular adenoma had the lowest AUC, indicating poorer recognition performance at this magnification. Finally, Figure 6d presents the ROC curves at 400\(\times\) magnification. The AUC values for each type of breast cancer pathology image reached a minimum of 0.95, with an average AUC of 0.96. While the improved model proposed in this chapter performed well overall, its performance slightly declined compared to other magnifications. This decline is attributed to the increased detail at 400\(\times\) magnification, which may cause a loss of contextual information and consequently affect the model's recognition accuracy.
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 1 Introduction
Note that an X gate is applied to the second register (q3) to put the second register into an eigenstate of the phase gate. For validation of this work, a 2-qubit version of this circuit was run on the IBM Perth quantum computer. The process of doing QPE is akin to sampling from \(P(x|\theta)\) with \(x\in\{0,1,\ldots,2^{n}-1\}\) and using the results to estimate the exact value of \(\theta\). The main difficulty with performing QPE on today's quantum devices is that present-day devices are plagued by noise in spite of mitigation attempts [5; 6; 7], which adds an unknown term to the distribution. The process of performing QPE on today's devices is similar to sampling from another distribution \(\widetilde{P}(x|\theta)=\left|2^{-n}\sum_{k=0}^{2^{n}-1}e^{-2\pi i|k2^{-n}(x-2 ^{n}\theta)}\right|^{2}+\epsilon(x)\), where \(\widetilde{P}(x|\theta)\) is a proper probability distribution, but the form and effect of \(\epsilon(x)\) is not known with certainty. It is possible to recreate some types of noise in simulations of quantum circuits, and one type of noise, depolarizing noise, is added in this paper. Examples of \(\widetilde{P}(x|\theta)\) are found in Section 2.2. The datasets were created with 21 levels of depolarizing noise so that the model performance on a variety of noise could be evaluated. The researchers sought to have a single, tunable noise parameter for circuit generation, and depolarizing noise was straightforward to implement. Levels of noise from 0 to 0.2 in steps of 0.01 were chosen to represent the range of noise realistically possible, from ideal at 0 to extremely high at 0.2. It is worth noting that this choice was mostly for practical purposes, since it is computationally expensive  
Figure 1: A circuit diagram for a 3-qubit QPE circuit where the unitary operator is a phase gate with phase \(2/3\)—this corresponds to a \(\theta=1/3\) in the probability distribution. to perform these simulations. Someone could, however, produce more data with finer than 0.01 steps in noise and go beyond a noise level of 0.2. In the training of the models, the level of noise will be uncertain since the quantum phase is estimated by a model, and it is desirable for a model to have consistent predictive performance regardless of the noise level. An example of this is shown in Figure 2, where the left panel shows a model whose performance varies with noise, and the right panel shows mostly level performance.
## 2 Related Work
### Data Collection with Mobile Sinks
When collecting data from these cluster heads within the direct communication range, the mobile sink can adjust its speed to accommodate the network conditions in different regions, and thus improve the network performance. Nevertheless, in some situations, the motion is strictly uncontrollable. Considering path-fixed mobile sinks with a constant speed, [PERSON] et al. [33] suggest that optimizing the assignment of sensor nodes to subsinks (i.e., rendezvous nodes) can improve the network throughput as well as conserve the routing cost. This Maximum Amount Shortest Path (MASP) problem is solved with a genetic algorithm. However, it adopts the rendezvous-based routing mode, and does not take the delay performance into account. This paper complements the design of delay-aware energy-efficient data collection on a fixed path with a strictly uncontrollable mobile sink.
## 4 System Architecture
### Hardware  
The hardware components used by the system include the VR headset (HTC, VIVE Pro, Taipei, Taiwan), the steering wheel (Guillemot Co., Thrustmeter T300 RS GT, Carentoir, France), and bio-sensors (Thought Technology Ltd., ProComp Infiniti, Montreal, Canada). The HTC VIVE Pro is a popular virtual reality immersive headset. The kit headset includes VR HMD, two base stations for movement tracking, and two motion controllers for virtual world interaction. The two base stations are able to connect the immersive environment with the subject's reactions to scene objects via motion controllers. The Thrustmeter T300 RS GT Edition is a high-end racing steering wheel equipped with a feedback mechanism, a specialized T3 PA acceleration pedal that is integrated with steering wheel motion to provide realistic driving feedback to the subjects. The ProComp Infiniti is an eight-channel biofeedback system for real-time data acquisition that can be used with combinations of sensors including EEG, EKG, RMS EMG, skin conductance, heart rate, blood volume, pulse, respiration, temperature, force, and voltage.
## 2 Study Area and Design Concept  
### Study Tour Project Area  
China's Yellow Sea (Bohai) Sea migratory bird habitat is an important node in the field of global ecological protection, located in the center of the East Asia-Australasian Migratory Bird Flyway [26]. This area is famous for its unique natural ecological environment and rich biodiversity, providing a key place for many endangered species to live, reproduce and migrate [27]. As an important part of the world's natural heritage, the ecological protection of the Yellow Sea (Bohai) Sea migratory bird habitat is not only of global significance, but also an important support for regional economic and social development [28].  
As shown in Figure 1, China's Yellow (Bohai) Sea migratory bird habitat covers coastal areas such as Yancheng, Jiangsu, which are famous for their rich tidal wetland resources. It has large-scale intertidal mudflats and is one of the largest wetlands in East Asia. Every year, more than 2 million migratory birds stop, forage, or breed on this migration route, including rare species such as red-crowned cranes and black-headed gulls. These migratory birds not only rely on wetland resources here, but also play a vital role in global biodiversity conservation. Wetlands are a core component of migratory bird habitats and an important part of the Earth's ecosystem. However, due to human activities such as reclamation, industrial development, and pollution, the area and ecological functions of the Yellow (Bohai) Sea wetlands have declined significantly in the past few decades. In recent years, through international cooperation and policy promotion, the protection of China's Yellow (Bohai) Sea migratory bird habitats has achieved remarkable results, which provides students with a realistic and vivid case to understand the relationship between humans and land.  
The ecological protection of the Yellow (Bohai) Sea migratory bird habitats provides rich teaching resources and practical opportunities for geography research. During the research activities, students can observe the habitat behavior of migratory birds through field visits to the wetland environment and understand the dynamic changes in the wetland  
Figure 1: Location and overview of the Yellow Sea (Bohai Sea) migratory bird habitat in Yancheng, China.  
ecosystem. In addition, the conflict between wetland protection and development is also an important geographical issue, which can guide students to think about how to balance the relationship between economic development and ecological protection, thereby deepening their understanding of the \"coordination of humans and land\".
## 6 Actor-Critic Methods
### Deep Deterministic Policy Gradient
#### 6.1.4 Goal-Oriented Obstacle Avoidance in Continuous Action Space  
The proposed method in [30] extends the DDPG framework by integrating depth-wise separable convolutional layers to handle depth image inputs effectively. This enhancement allows for efficient processing of large-scale sequential depth images, combined with goal-oriented navigation capabilities.  
The actor network takes a stack of sequential depth images along with positional information in polar coordinates. The stack of depth images from \(t-n\) to \(t\) provides a short-term memory, enabling the robot to detect peripheral obstacles that may not be visible in the current input. The depth-wise separable convolutional operation is defined as follows:  
\[\mathbf{z}_{i}^{\text{depth}}=\sigma\left(\sum_{j=1}^{n}k_{i,j}*x_{j}\right), \tag{73}\]  
where \(k_{i,j}\) represents the filter applied to channel \(j\), \(x_{j}\) is the input feature map, and \(\sigma\) is the activation function.  
The polar coordinates of the robot's position relative to the goal are concatenated with the processed depth image features. The output of the actor network consists of continuous control commands:  
\[[\omega,v]=\pi(s;\theta^{\pi}), \tag{74}\]  
where \(\omega\) is the angular velocity, \(v\) is the linear velocity, and \(\pi(s;\theta^{\pi})\) represents the actor policy parameterized by \(\theta^{\pi}\).  
The critic network evaluates the Q-value using the current state and action:  
\[Q(s,a;\theta^{Q})=\mathbb{E}\left[r_{t}+\gamma\max_{a}Q(s_{t+1},a;\theta^{Q}) \middle|s,a\ \right], \tag{75}\]  
where \(r_{t}\) is the immediate reward and \(\gamma\) is the discount factor.  
The reward function is designed to encourage linear motion and penalize unnecessary angular adjustments:  
\[r(s_{t},a_{t})=\begin{cases}r_{\text{goal},}&\text{if }d_{t}<d_{\text{threshold }},\\ r_{\text{collision}},&\text{if a collision occurs},\\ c_{v}\cdot v-c_{\omega}\cdot|\omega|,&\text{otherwise},\end{cases} \tag{76}\]  
where \(d_{t}\) is the distance to the goal, and \(c_{v}\) and \(c_{\omega}\) are coefficients for linear and angular velocities.  
The target networks for the actor and critic are updated using a soft update mechanism:  
\[\theta^{\prime}\leftarrow\tau\theta+(1-\tau)\theta^{\prime}, \tag{77}\]  
where \(\tau\) is a small constant controlling the update rate.  
This method enables end-to-end learning of a goal-oriented obstacle avoidance policy, trained entirely in simulation and transferable to real-world scenarios without additional fine-tuning. Algorithm 4 shows a step-by-step pseudocode of the method.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 2 Literature Review
### Theoretical Framework Linking Macroeconomic Variables to Treasury Yields  
The selection of macroeconomic variables in this study is grounded in well-established economic theories that explain the behavior of interest rates and bond yields in response to changes in monetary and fiscal policy, inflation, and economic growth.  
1. Federal Funds Rate (DFF):  
According to the Expectations Theory of the Term Structure of Interest Rates, long-term interest rates (such as the 10-year Treasury yield) reflect expectations of future short-term rates. Therefore, changes in the effective federal funds rate, a primary policy tool of the Federal Reserve, signal anticipated shifts in future monetary policy and directly influence the yield curve [3].  
2. Core Consumer Price Index (CPILFESL):  
Inflation expectations are central to interest rate determination under the Fisher Effect, which posits that nominal yields adjust in response to expected inflation to preserve real returns. Core CPI, by excluding volatile food and energy prices, provides a stable measure of underlying inflation and is closely monitored by central banks in policy decisions [15].  
3. Real GDP Growth (GDP_growth):  
Strong economic growth typically increases the demand for credit and raises expectations of inflationary pressure, both of which contribute to higher bond yields. This relationship is supported by IS-LM and Taylor Rule models, which show that higher output levels lead to upward pressure on interest rates as central banks react to overheating economies [2].  
4. Federal Government Debt Growth (GFDEBTN_growth):  
The impact of fiscal policy on interest rates can be interpreted through the Ricardian Equivalence and Crowding-Out Hypothesis. While Ricardian theory suggests debt issuance has neutral long-term effects if consumers anticipate future taxation, the crowding-out effect argues that higher government borrowing increases competition for loanable funds, pushing yields higher [4; 16].  
Together, these variables capture the core channels through which monetary, fiscal, and macroeconomic conditions influence bond markets. Including them in a machine learning framework allows for empirical testing of these theoretical linkages in a high-dimensional, nonlinear setting.