esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
###### Abstract  
Dynamic path planning enables vehicles to autonomously navigate in unknown or continuously changing environments, thereby reducing reliance on fixed maps. Deep reinforcement learning (DRL), with its superior performance in handling high-dimensional state spaces and complex dynamic environments, has been widely applied to dynamic path planning. Traditional DRL methods are prone to capturing unnecessary noise information and irrelevant features during the training process, leading to instability and decreased adaptability of models in complex dynamic environments. To address this challenge, we propose a dynamic path-planning method based on our Causal State-Masking Twin-delayed Deep Deterministic Policy Gradient (CSM-TD3) algorithm. CSM-TD3 integrates a causal inference mechanism by introducing dynamic state masks and intervention mechanisms, allowing the policy network to focus on genuine causal features for decision optimization and thereby enhancing the convergence speed and generalization capabilities of the agent. Furthermore, causal state-masking DRL allows the system to learn the optimal mask configurations through backpropagation, enabling the model to adaptively adjust the causal features of interest. Extensive experimental results demonstrate that this method significantly enhances the convergence of the TD3 algorithm and effectively improves its performance in dynamic path planning.  
dynamic path planning; vehicles; deep reinforcement learning; causal inference mechanism; state masks +
Footnote †: journal:
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 2 Methodology
### Codec Network Based on Attention Mechanism  
As shown in Figure 7, the final result of the ACB is fused with the internal and external networks:  
\[\bar{x}_{l}=\left[W_{l1}\cdot\bar{x}_{l}^{sp}+b_{l1};W_{l2}\cdot\bar{x}_{l}^{out }+b_{l2}\right] \tag{12}\]  
where \(W_{l1}\) and \(W_{l2}\) are trainable weight matrices and \(b_{l1}\) and \(b_{l2}\) are biases corresponding to the matrices.  
In spite of the deep spatial dependency between nodes in the result of the ACB, temporal dependency must be further mined to improve prediction accuracy. In traffic flow prediction, extensive experimental studies have proved that LSTM and GRU models have the advantage of effectively processing time series by collecting historical information and referring to previous scenarios. Based on this conclusion, in order to ensure our model obtains this advantage, we construct the codec network with the GRU. Due to the limited storage space, the length of the context vectors is fixed, which restricts the expression ability of the codec network.  
For the benefit of finding long-term dependencies in sequences, we combine the codec network with a temporal attention mechanism. The temporal attention mechanism allocates reasonable weights to each period efficiently via self-learning and parallel computing. Temporal attention weights are calculated as follows:  
\[k^{p}=V_{l}^{en}\cdot\sigma\big{(}W_{l1}^{en}\cdot[d_{l-1}^{en};s_{l-1}^{en}]+ W_{l2}^{en}\cdot\bar{x}_{l}+b_{l}^{en}\big{)} \tag{13}\]  
\[\ell^{p}=\frac{\exp(k^{p})}{\sum_{i=1}^{T}\exp(k^{i})} \tag{14}\]  
where \(W_{l1}^{en}\), \(W_{l2}^{en}\), and \(V_{l}^{en}\) are trainable weight matrices, \([d_{l-1}^{en};s_{l-1}^{en}]\) is the result of connecting hidden layers with cell state layers in the previous GRU units of the decoder, \(\bar{x}_{l}\) is the output of the ACB at moment \(l\), and \(b_{l}^{en}\) is the bias. Then, the context vectors are calculated as:  
\[h_{l}^{en}=G^{en}\big{(}h_{l-1}^{en},\bar{x}_{l}\big{)} \tag{15}\]  
\[C=\sum_{l=1}^{T}\ell^{l}\cdot h_{l}^{en} \tag{16}\]  
where Equation (15) derives the output \(h_{l}^{en}\) of the GRU units at moment \(l\), \(G^{en}\) denotes the GRU units of the encoder, \(h_{l-1}^{en}\) is the output of the previous GRU unit, and \(\bar{x}_{l}\) is the output of the ACB at moment \(l\). Equation (16) derives the context vector \(C\), and \(\ell^{l}\) is the temporal weight.  
Then, the context vectors are decoded as follows:  
\[d_{l}^{de}=G^{de}\big{(}d_{l-1}^{de},[\hat{y}_{l-1};C]\big{)} \tag{17}\]  
\[\hat{y}_{l}=V_{l}^{de}\cdot\sigma\Big{(}W_{l}^{de}\cdot[d_{l}^{de};C]+b_{l}^{ de}\Big{)} \tag{18}\]  
Figure 7: Fusion of internal network and external network.  
where \(G^{de}\) denotes the GRU unit of the encoder, \(d_{l-1}^{de}\) is the previous hidden layer of the decoder, \(d_{l}^{de}\) is the current hidden layer of the decoder, \(W_{l}^{de}\) and \(V_{l}^{de}\) are trainable matrices, \(b_{l}^{en}\) is the bias, and \(\hat{y}_{l}\) is the final prediction result.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 2 Materials and Methods  
### Architecture for Logical Range  
As introduced in Section 1, the Logical Range comprises a variety of networked task-processing devices, including theodolites, cameras, radar systems, and Time-Space-Position Information (TSPI) units. These devices are interconnected through a high-speednetwork to support real-time data processing, task coordination, and multimedia information flow (e.g., image/video display).  
Figure 1 illustrates the overall structure of the Logical Range, where heterogeneous devices collaborate to execute computationally intensive tasks. These devices are responsible for data computation, command distribution, real-time acquisition, and visualization, resulting in dynamic and unpredictable workloads.  
The resource usage of these devices is highly complex and variable, typically involving metrics such as CPU usage, memory consumption, network I/O (uplink/downlink), and disk activity. Among them, CPU usage is particularly critical, as most operations in Logical Range scenarios are computationally intensive and latency-sensitive. Therefore, this study focuses on CPU usage as the primary time-series signal for load forecasting and task scheduling optimization.  
As illustrated in Figure 2, the CPU usage data from all task-processing devices are recorded over time to form a multivariate time series, denoted as  
\[\mathbf{x}=\{x_{i}:i=1,2,\ldots,T\}, \tag{1}\]  
where \(x_{i}\) represents the aggregated CPU usage at time step \(i\). The sequence \(\mathbf{x}\) exhibits strong temporal dependencies, and accurate forecasting of its future values is essential for reducing latency, avoiding resource bottlenecks, and improving experimental throughput in Logical Range systems.  
To construct an effective forecasting dataset, we normalize \(\mathbf{x}\) and apply a sliding time window strategy. At each time step \(i\), a subsequence of length \(w\),  
\[\mathbf{x}_{i}^{w}=[x_{i-w},x_{i-w+1},\ldots,x_{i}] \tag{2}\]  
is extracted as the input sample, and the forecasting target is set as the value \(x_{i+d}\) at a future time step \(d\) ahead. The forecasting function is defined as  
\[x_{i+d}=f(\mathbf{x}_{i}^{w}:d), \tag{3}\]  
Figure 1: Architecture of Logical Range Environment.  
where \(f\) is the deep learning model proposed in this study and \(d\) is the forecasting step, with \(d\geq 1\). The complete dataset can be constructed as  
\[S=\{\ldots,(\mathbf{x}_{i}^{w},\mathbf{x}_{i+d}),(\mathbf{x}_{i+1}^{w},\mathbf{x}_{i+d+1}),(\mathbf{x }_{i+2}^{w},\mathbf{x}_{i+d+2}),\ldots\} \tag{4}\]  
and we denote each sample as \(s_{i}^{w}=(\mathbf{x}_{i}^{w},\mathbf{x}_{i+d})\). This sliding window mechanism enables continuous load forecasting and dynamic resource allocation. The processing of \(S\) is depicted in Figure 2.
# Directional Paging for 5G Communications Based on Partitioned User ID
## 2 Related Work
### Paging Monitoring by the UE  
In each IDRX cycle, the UE gets to stay in sleep mode ('OFF' duration) to conserve energy. However, the UE is expected to wake up for a specific sub-frame called the Paging Occasion (PO) to monitor the Physical Downlink common Control Channel (PDCCH) for paging. If the PDCCH for paging is received in the PO, then the UE decodes the Physical Downlink Shared Channel (PDSCH) to receive the paging message. If the page is not intended for the UE, it sleeps again till the next PO. Every IDRX cycle, the UE monitors only one PO in a specified Paging Frame (PF). A PF is one radio frame that includes one or multiple POs (up to four in LTE). Figure 2 shows the PFs and POs that can be monitored by the UE with identifier UE ID\({}_{x}\). The UE determines the exact timing when it should wake up to receive the paging message that is being sent to it by utilizing PF and PO information [12]. In LTE/LTE(A), the PO and PF that the UE is expected to monitor are elaborated in 3 GPP specifications [12]. PF is the radio frame that satisfies:  
\[SFN\mod T =(T/N)\times(UEID\mod N) \tag{1}\] \[N =min(T,nB) \tag{2}\]where SFN is the System Frame Number and T is the DRX cycle of the UE. The term 'nB' gives the total number of POs in one DRX cycle and can be any one of T/32, T/16, T/8, T/4, T/2, T, 2T or 4T, the knowledge of which comes from the system information.  
Subsequently, the PO is determined as:  
\[i_{\mathrm{s}} = (UEID/N)\mod N_{\mathrm{s}} \tag{3}\] \[N_{\mathrm{s}} = max(1,nB/T) \tag{4}\]  
where \(N_{\mathrm{s}}\) is the number of POs in a PF and \(i_{\mathrm{s}}\) indicates the sub=frame number (i.e., PO) in the PF, the value of which is pre-defined for each value of \(N_{\mathrm{s}}\).  
In the proposed PIDP mechanism, we propose to redesign the aforesaid procedure to accommodate a shorter UE ID size in the paging message. A shorter UE ID size would result in the reduction of paging resources in directional 5G communications.
# IW-NeRF: Using Implicit Watermarks to Protect the Copyright of Neural Radiation Fields
## 4 Proposed Method
### Data Representation and Transformation  
In the initial phase, we depict the watermark information data using implicit neural representations. The structure of the image noise-resilient (INR) model is a multilayer perceptron (MLP) network comprising n hidden layers of size D, employing the activation function \(\sigma(\cdot)\). The output of the INR model can be formulated as shown in Equation (3):  
\[y =\mathrm{W}^{(n)}(g_{n-1}0\cdots og_{1}og_{0})(h_{0})+\mathrm{b}^{ (n)}, \tag{3}\] \[\text{where }h_{i+1}=g_{i}(h_{i})=\sigma(\mathrm{W}^{(i)}h_{i}+ \mathrm{b}^{(i)})\]  
Figure 3: We present an implicit representation watermarking algorithm tailored for NeRFs. This approach encapsulates the watermark information implicitly within the NeRF model through a specified key, facilitating subsequent watermark extraction using the same key.  
Here, \(h_{i},i\in\{0,1,2,\ldots,n\}\) represents the input of the \(i\)-th layer, and \(y\) denotes the corresponding output value of \(h_{0}\). Additionally, \(g_{i}\) signifies the hidden layer, \(\text{W}^{(i)}\) represents the weight matrix of the \(i\)-th layer, and \(\text{b}^{(i)}\) denotes the bias parameter of the \(i\)-th layer. The INR network architecture utilizes a 10-layer MLP network structure with the ReLU activation function. In the context of a given input H, the output set M corresponding to Y is associated with a loss function as in Equation (4):  
\[L_{w}=\text{min}\sum\limits_{(h,y)\in M}\|F_{\sigma}(h)-y\|_{2}^{2} \tag{4}\]  
Here, \(F_{\sigma}(\cdot)\) denotes the neural representation of the data. For instance, in the context of the NeRF model and a given set of images from diverse perspectives \(H_{i}\), the set M comprises three-dimensional coordinates, camera pose \(h=(x,y,z,\theta,\varphi)\), corresponding RGB values, and opacity \(y=(r,g,b,\sigma)\).
# Dysprosium Absorption of Aluminum Tolerant- and Absorbing-Yeast
## 3 Results
### Dy Absorption in Yeast Strains  
Two Al-tolerant strains, Alt-OF2 and Alt-OF5, were tested for Dy absorption. The brewing yeast _S. cerevisiae_ was used as the standard yeast for Dy absorption, and the change profiles for the Dy contents in yeast cells are shown in Figure 2. Alt-OF2 and Alt-OF5, which showed higher Al absorption, rapidly absorbed Dy for approximately two hours and remained at the stationary level for up to four hours; after this, they exhibited stationary absorption (approximately 2.0 pg/cells). On the other hand, interestingly, the _S. cerevisiae_ strain, BY4741, interestingly appeared to be able to absorb Dy less than Alt-OF2 and Alt-OF5; however, the Dy absorption in _S. cerevisiae_ is rarely reported. These results suggest that yeast strains have the ability to absorb Dy and that Al-absorbing strains also have a greater ability to absorb Dy than the popular yeast _S. cerevisiae_. Moreover, these results also suggest that the mechanisms involved in Al tolerance or absorption have an effect on the Dy absorption because Alt-OF2 and Alt-OF5, isolated as Al-tolerant and -absorbing yeast strains, exhibit more Dy absorption than BY4741.  
Figure 1: Growth profiles of yeast strains in Dy-containing media. Yeast strains were cultured in medium containing Dy at 0 mM (circles), 0.1 mM (triangles), 1.0 mM (squares), and 10 mM (diamonds) at 30 \({}^{\circ}\)C with shaking. (**A**) _S. cerevisiae_ BY4741; (**B**) Alt-OF2; (**C**) Alt-OF5.
## 6 Limitations  
The purpose of this research was to study the literature related to serious games for learning history using AR, to design and develop a serious game for teaching ancient history to students from the 4 th grade and up, and to evaluate students' experience playing  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Question** & **Not at All** & **Slightly** & **Moderately** & **Very Much** & **Extremely** & **Mean** \\ \hline The game was enjoyable/fun & - & - & 2 & 7 & 4 & 4.15 \\ The game was beautifully designed & - & - & 2 & 7 & 4 & 4.15 \\ The game helped me gain knowledge & - & - & 2 & 6 & 5 & 4.23 \\ about the history of Ancient Greece & - & - & - & - & 13 & 5 \\ I would like to use games of this type & - & - & - & - & 13 & 5 \\ in education & - & - & - & - & 13 & 5 \\ I would like to use AR in education & - & - & 1 & - & 13 & 5 \\ I enjoyed the tour of archaeological & - & - & 1 & - & 13 & 5 \\ sites using AR & - & - & 1 & 4 & 8 & 4.53 \\ I enjoyed the escape room & - & - & - & 2 & 11 & 4.84 \\ I liked the use of AR in the quiz game & - & - & - & 2 & 11 & 4.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results on game experience.  
the game as well as to determine whether their knowledge improved. However, there are several important limitations and constraints that must be considered when interpreting the results.  
Firstly, one limitation of this study is that it primarily assesses short-term knowledge gains without investigating the long-term retention of historical knowledge and skills. While the immediate increase in test scores suggests that the game effectively enhances short-term learning, this study does not provide insights into how well students retain this knowledge over an extended period. This limitation is significant because long-term retention is crucial for a comprehensive understanding of the effectiveness of educational tools. Future studies should include follow-up assessments to evaluate the durability of the knowledge and skills gained through the game. By conducting follow-up evaluations weeks or months after the initial game session, researchers can gain valuable insights into the lasting impact of AR-based learning.  
In addition, the small sample size of 13 students may not be sufficient or representative, which could affect the generalization of the results and the drawing of conclusions for a broader population. Second, the creation of graphics and audiovisual material poses a significant challenge, as using only free resources may lead to limited creative and aesthetic possibilities. This can impact the quality and attractiveness of the game, as well as the participants' experience.  
Finally, the implementation time is a significant limitation, as the development of a 3D game with AR requires a substantial amount of time for design, development, and testing. The limited time available may impact the quality of the game and the evaluation of the research results.
## 4 Numerical Results
### 5G Antenna Design
Figure 9 depicts the \(S_{11}\) magnitude of the aperture-coupled E-shaped patch antenna versus frequency. From the presented graph, we can derive that the obtained antenna operates satisfactorily in the n258 and n260 5G NR frequency bands. The antenna has a dual-frequency tuning operation (\(-44.71\) dB at 26.54 GHz and \(-29.02\) dB at 38.61 GHz) within the frequency bands of 5G NR (n258 and n260). It is worth mentioning that the \(-10\) dB bandwidth of the obtained antenna practically covers the whole previously mentioned 5G frequency bands of interest. Moreover, Figure 10 portrays the 3D pattern of the antenna gain (realized gain) achieved by the WOA at 26.4 GHz (Figure 10a) and 38.5 GHz (Figure 10b). It is worth noting that the maximum gain is 7.13 dB at 26.4 GHz and 4.95 dB at 38.5 GHz. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(W_{antenna}\) & 4.14 & \(L_{antenna}\) & 2.86 & \(W_{slot}\) & 1.40 \\ \hline \(L_{slot1}\) & 0.77 & \(L_{slot2}\) & 0.91 & \(D_{slot}\) & 1.04 \\ \hline \(W_{ground}\) & 4.90 & \(L_{ground}\) & 5.86 & \(W_{ap}\) & 4.14 \\ \hline \(L_{ap}\) & 1.09 & \(W_{slit}\) & 0.14 & \(L_{slit}\) & 2.46 \\ \hline \(O_{ap}\) & 0.88 & \(W_{feed}\) & 1.09 & \(L_{feed}\) & 3.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Best values of the design parameters obtained by the WOA (all values are given in mm). Figure 9: Reflection coefficient (\(S_{11}\)) versus frequency of the best-obtained antenna design case (blue solid line: \(S_{11}\) parameter, red dash line: \(-10\) dB limit, and gray shaded areas: n258 and n260 5G NR frequency bands). Finally, Figure 11 illustrates the surface current distribution of the optimized antenna design. From the presented results, we can obtain that the surface current is maximized mostly at the center area of the E-shaped patch and minimized at the edges of the antenna.
###### Abstract  
Carbon capture and storage is key for sustainable economic growth. CO\({}_{2}\)-enhanced oil recovery (EOR) methods are efficient practices to reduce emissions while increasing oil production. Although it has been successfully implemented in carbonate reservoirs, its effect on wettability and multiphase flow is still a matter of research. This work investigates the wettability alteration by carbonated water injection (CWI) on a coquina carbonate rock analogue of a Pre-salt reservoir, and its consequences in the flow of oil. The rock was characterized by routine petrophysical analysis and nuclear magnetic resonance. Moreover, micro-computed tomography was used to reconstruct the pore volume, capturing the dominant flow structure. Furthermore, wettability was assessed by contact angle measurement (before and after CWI) at reservoir conditions. Finally, pore-scale simulations were performed using the pore network modelling technique. The results showed that CWI altered the wettability of the carbonate rock from neutral to water-wet. In addition, the simulated relative permeability curves presented a shift in the crossover and imbibition endpoint values, indicating an increased flow capacity of oil after CWI. These results suggest that the wettability alteration mechanism contributes to enhancing the production of oil by CWI in this system.  
CCS; carbonated water injection; CO\({}_{2}\)-EOR; pore network modelling; relative permeability
# Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
## 2 Related Work
### Dialogue Corpora  
To develop an affect-aware dialogue system, relevant conversational datasets are required. Most of the existing dialogue corpora are either domain-specific and task-oriented [31; 32] or collected without full control over the content, e.g., from social media [33; 34], and, therefore, are generally inappropriate in a therapeutic setting. There are, however, examples of emotionally-grounded conversational datasets for English, such as EmpatheticDialogues [35] and DailyDialog [36], which are labeled with emotions at the dialogue and utterance level, respectively.  
The DailyDialog dataset [36] consists of daily conversations obtained through crawling websites for English learners. In total, it contains 13k dialogues, manually labeled with dialogue acts and emotions at the utterance level. The emotion-labeling scheme distinguishes six basic emotions: anger, disgust, fear, happiness, sadness, and surprise.  
The emotion distribution in the dataset is highly imbalanced, with \"happiness\" being more than 10 times more frequent than the other emotions. There are also a large number of utterances (83% of the entire dataset) marked as representing no emotion.  
The authors of Reference [35] propose a new benchmark for empathetic dialogue generation and EmpatheticDialogues (ED) itself--a novel dataset with about 25k personal dialogues. Each dialogue is grounded in a specific situation where the speaker was feeling a given emotion, with the listener responding actively. The resource consists of crowdsourced one-on-one conversations, and covers a large set of emotions in a balanced way. This dataset is larger and contains a more extensive set of emotions than many similar emotion-prediction datasets from other text domains. The authors' experiments show that models built on this dataset are considered more empathetic by human evaluators, compared to models merely trained on large-scale Internet-crawled opinion-oriented data.  
To the best of our knowledge, no similar dialogue corpora exist for Polish.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Powering and Navigation
#### 2.3.6 Flexible Filaments  
Sperm cells move via the propagation of bending waves, which are at least ten times larger than bacterial flagella [109]. The viscoelastic parameters of the fluid determine the waveform of the moving eukaryotic flagella [110]. Microbots that have a versatile tail will travel in a manner that resembles flagellated eukaryotic propulsion. Consequently, passive filaments operated at one end can act effectively as propulsive appendages for microrobots. Therefore, to replicate the movement of sperm cells, the active propagation of bending waves is required, as seen in the flagella of spermatozoa.