esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Results Discussion
#### 5.2.3 Robustness Analysis  
The robustness of the proposed model was verified by the comparison with different price scenarios. Five typical MCP scenarios were selected for the comparison. Scenario 1 embodied the situation for good prediction, and actual electricity price was higher than the prediction value. Scenario 2 embodied the situation for good prediction and the actual electricity price was lower than the prediction value. Scenario 3 embodied the situation for perfect prediction and the actual electricity price was exactly the same as the prediction value. Scenario 4 embodied the situation for poor prediction and the actual electricity price was higher than the prediction value. Scenario 5 embodied the situation for poor prediction and the actual electricity price was lower than the prediction value. For different scenarios, the indicators for evaluating the prediction result, including the mean absolute percent error (MAPE), the mean average error (MAE) and the relative correlation coefficient (R), are listed in Table 6.  
Figure 7: Comparisons of supply energy for two markets. (**a**) The supply energy for two markets obtained by the proposed model; (**b**) The supply energy for two markets obtained by the conventional model.  
The comparison results are shown in Table 7. It was found that the benefits of other MCP scenarios were in the range of Scenario 1 and Scenario 5 for both models. Compared with the conventional model, the proposed model achieved better power generation benefits in different MCP scenarios. Even in the adverse scenario with a serious deviation in the price prediction, the proposed model still had relatively higher benefits. For instance, in Scenarios 4 and 5, the benefits were 7.135 billion yuan and 6.961 billion yuan for the proposed model. However, they were 6.959 billion yuan and 6.827 billion yuan for the conventional model, decreased by 176.022 million yuan and 132.952 million yuan, respectively. This indicated that the proposed model could better adapt to different price scenarios in the future, and could enhance the profitability and robustness for the generation scheduling.
# Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling
## 3 DS-FedIDS
### Dynamic Sampling-FedIDS Approach  
As illustrated in Figure 1, the DS-FedIDS architecture employs federated learning to enable collaborative intrusion detection while preserving data privacy by keeping local traffic data on each client. The framework consists of the following key components. Each Client maintains local Traffic Data, processed in its entirety to capture both normal and attack patterns, with dynamic sampling applied to address class imbalance. A Global Base Model, shared across all clients, extracts common features from client traffic data, updated through parameter aggregation to ensure privacy-preserving collaboration. Adaptive Personal Layers, unique to each client, refine classification for client-specific traffic patterns, leveraging dynamic sampling to enhance the detection of minority attack classes.  
The training process, as shown in Figure 2, comprises the following key phases:  
* **Forward Attack Data**: The client updates global parameters, promoting a consistent malicious-signature repository across all clients.
* **Dynamic Up/Down Sampling**: The client inspects local class distributions (normal sub-classes or minority attacks) and rebalances data.
* **Local Training**: The now-balanced normal data are used to refine each client's personal layers. Attack patterns remain in the global portion.
* **Model Updates**: Gradients or parameter deltas are sent to the federated server for aggregation, culminating in a shared global model distributed back to the clients.  
Figure 1: Overview of the DS-FedIDS architecture for federated intrusion detection. The framework leverages a global model for shared feature extraction and personalized layers for client-specific traffic pattern adaptation, with dynamic sampling enhancing minority class detection, all while preserving data privacy through federated learning.
## 2 Preliminaries  
### Kalman Filter  
Generally, the Kalman filter can be described in two stages: firstly, one-step prediction; and secondly, measurement correction. Considering the discrete-time dynamic model of pushing distance estimation, the state vector is \(x_{k}=\left[p_{k},v_{k}\right]^{T}\), which indicates the current pushing distance and pushing velocity. The observation \(y_{k}\) is the measured displacement. The details of mathematics are described as following steps.  
(1) Prediction  
The new best estimation is predicted using the previous best estimation (priori estimation), the process equation is:  
\[\hat{x}_{k}^{-}=A_{k}\hat{x}_{k-1}^{+}+B_{k}u_{k}+w_{k} \tag{1}\]  
where the state vector \(\hat{x}_{k}^{-}\) is the prior estimation at time \(k\) and \(\hat{x}_{k-1}^{+}\) is the posterior estimation at time \(k-1\); \(A_{k}\) is the transition matrix; \(B_{k}\) is control matrix; \(u_{k}\) is the known control input; and \(w_{k}\) is the processing noise, a zero-mean Gaussian processing noise with covariance \(Q=E(w_{k}\cdot w_{k}^{T})\).  
Since the processing equation contains processing noise \(w_{k}\). The error covariance matrix of priori estimation is added by processing the noisy covariance matrix, as shown in following equation:  
\[P_{k}^{-}=A_{k}P_{k-1}^{+}A_{k}^{T}+Q \tag{2}\]  
Similarly, \(P_{k}^{-}\) is the prior error covariance at time \(k\) and \(P_{k-1}^{+}\) is the posteriori error covariance at time \(k-1\). The processing noise covariance \(Q\) is the processing noise covariance and needs to be tuned.  
(2) Correction  
The correction stage is essential for refining estimation with measurements. The measurement equation is shown as follows:  
\[y_{k}=H_{k}x_{k}^{-}+v_{k} \tag{3}\]  
where \(H_{k}\) is the observation matrix. \(v_{k}\) is the zero-mean Gaussian measurement noise with covariance \(R=E(v_{k}\cdot v_{k}^{T})\). The observation noise covariance \(R\) is unknown and waiting for tuning.  
To correct estimation with measurement, the Kalman gain is very important to propagate the recursive procedure. The Kalman gain is:  
\[K_{k}=P_{k}^{-}H_{k}^{\mathcal{T}}(H_{k}P_{k}^{-}H_{k}^{\mathcal{T}}+R)^{-1} \tag{4}\]  
Then, based on the Kalman gain and measurements, the posterior estimation can be calculated as follows:  
\[\hat{x}_{k}^{+}=\hat{x}_{k}^{-}+K_{k}(y-H_{k}\hat{x}_{k}^{-}) \tag{5}\]  
Finally, the posterior error covariance is reassigned as follows:  
\[P_{k}^{+}=(I-K_{k}H_{k})P_{k}^{-} \tag{6}\]  
The processing noise covariance and observation noise covariance are set through experience, which sometimes leads to large estimation error. To tackle this problem, both \(Q\) and \(R\) should be optimized via varying estimation algorithms.
## 5 Conclusions  
Regarding the problem of relation overlapping, we analyzed the current mainstream methods and proposed the Relation extraction method based on the Entity Attention network and Cascade binary Tagging framework (REACT). To demonstrate the effectiveness of REACT, we constructed a high-quality Chinese dataset. Experiments on this dataset showed that compared to the baselines, REACT achieved higher F1-scores, even when faced with complex situations, maintaining a certain advantage. In future work, we plan to integrate REACT with recently popular large language models. After identifying the head entity, we will use the large model to provide additional prior knowledge about the head entity. By introducing prior knowledge, the model can better understand the specific meaning and characteristics of the head entity, allocate attention weights more accurately, and improve the performance of identifying tail entities and relations.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **N = 1** & **N = 2** & **N = 3** & **N \(\geq\) 4** \\ \hline RSAN & 64.5 & 61.3 & 58.7 & 51.2 \\ \hline CasRel & 62.9 & 65.0 & 68.6 & 63.4 \\ \hline TPLinker & 65.7 & 67.6 & 68.4 & 61.5 \\ \hline RANGE & 66.0 & 68.3 & 71.4 & 65.8 \\ \hline \end{tabular}
\end{table}
Table 6: F1-score for extracting relational triples from sentences with different numbers (denoted as N) of triples.  
Figure 12: F1-score for extracting relational triples from sentences with different overlapping patterns.  
**Author Contributions:** Conceptualization, L.K.; methodology, L.K.; software, L.K.; validation, L.K. and S.L.; formal analysis, L.K.; investigation, L.K.; resources, L.K.; data curation, L.K.; writing--original draft preparation, L.K.; writing--review and editing, L.K.; visualization, L.K.; supervision, L.K.; project administration, L.K.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Major Science and Technology Projects in Xinjiang Uygur Autonomous Region (No. 2022A02012-1); National Natural Science Foundation of China (61966034).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the first author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Goal and Scope
#### 2.1.1 Product Characterisation: the NMC111 Battery  
The electric vehicle analysed in this study is equipped with an NMC111 battery made up of 192 cells. Each battery cell has a cathode composed of LiNi\({}_{0.33}\)Co\({}_{0.33}\)Mn\({}_{0.33}\)O\({}_{2}\), with the same molar ratios as Nickel, Manganese and Cobalt, and a graphite anode. The weight and the energy capacities of a single battery pack are 226 kg and 35 kWh, respectively. The main characteristics of the NMC111 battery pack are reported in Table 1. The primary data for this battery (i.e., numbers of cells and units, as well as the nominal voltage and available energy of the cell) were provided by the vehicle manufacturer.  
Each ZEBRA battery pack is characterised by 266 kg and 32 kWh of storage capacity and contains 144 cells. The ZEBRA battery and its model have already been described in [35]. Therefore, only the NMC111 model created by the authors of this study is analysed in the following sections.  
In the manufacturing and End-of-Life (EoL) stages, the materials and components required for the NMC battery are grouped into cell materials and non-cell materials. The cell components are made up of the electrodes, the separator, the electrolyte, and the cell container, while the non-cell components refer to the Battery Management System (BMS), cooling system, and battery packaging.
## 5 Comparison of MAC Protocols for IBSN  
We considered more than 30 MAC protocols designed for generic sensor networks and BSN in order to broadly evaluate their use in IBSN. Table 11 is a qualitative comparison of different protocols based on network features discussed in Section 3. We used the results of each protocol as mentioned in the respective article to qualitatively analyze the trade-off made between each requirement, such as energy-efficiency, reliability, software overheads, throughput, latency, and hardware complexity. This comparison will present the commonly made trade-offs in the MAC protocols designed for BSN and WSN. Energy efficiency is the amount of energy consumed to successfully transmit a data packet including the energy consumed by the network management overheads. (\(+\)) for energy efficiency indicates that the MAC protocol is specifically designed to operate at low power application, such as BSN. It also indicates that the main aim of the protocol was to improve energy-efficiency and longer network life-time over the performance metrics such as throughput, reliability and QoS. (\(-\)) indicates that the MAC protocol consumes an enormous amount of energy for successful communication, which can be caused due to large overheads, elongated active duty-cycle, or lack of collision-prevention mechanism. (\(O\)) indicates that the MAC protocol was not designed to be energy-efficient, but rather to improve the performance metrics such as throughput, reliability and other QoS.
## 6 Future Directions
### Standardization of Metadata and Data Structures  
Efforts by organizations, such as IXAS and the Japan Society for Synchrotron Radiation Research, are driving the standardization of metadata and data structures. Aligning with international standards (e.g., the NeXus format) and cooperating with these initiatives will enhance data interoperability and reusability, thereby increasing the efficiency and impact of PEFC research. Harmonized data frameworks ensure that the FC-BENTEN database can support an evolving global ecosystem of synchrotron-based studies.
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 5 Performance Evaluation
### Simulation Environment  
The node density in tactical MANETs changes continuously as the mission evolves. However, the number of nodes in tactical MANETs can research up to several hundred (say, 300) nodes. Typically, the largest traffic volumes flow to small combat units in the front-line including squads, platoon or company formations. The network scalability issue involving as much as 300 nodes is resolved by assuming a 3-level hierarchy network clustering at battalion and company, and then a platoon is formed over the whole tactical MANET. The battalion tactical operations center (TOC) is normally located at rear of tactical operations area where the command post is able to monitor events and assist commanders and subordinate units in mission planning. A unique hierarchical architecture of a tactical MANET comprising different communication links with different maximum ranges is shown in Figure 5.  
Figure 4: Route discovery with RREQ and RREP messages in the proposed LQA-AODV protocol.  
Figure 3: The first 4 bytes of RREQ packet in proposed LQA-AODV protocol containing new Link Quality field.  
In tactical MANETs, units and troops often move in tactical formations. The specific (high resolution) position of a unit may have limited impact on connectivity, however, group mobility must be considered. In addition, nodes in tactical MANETs are usually diverging from their initially clustered position. The nodes may move partly in the direction of the leader node, and also partly in their own independent direction when fulfilling the mission objectives. A reference point group mobility (RPGM) model is suitable for such scenarios where a group's individual units and their commander form natural clusters [10].  
The parameters and other setting of a tactical MANET assumed in our simulations is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Object** & **Parameter** & **Value** \\ \hline \multirow{8}{*}{Network node} & Medium & Wireless channel \\  & The traffic model & Constant bit rate \\  & Network interface & WirelessPhy \\  & MAC & 802.11 TDMA \\  & Antenna & Omni-directional \\  & Routing protocol & AODV, AOMDV, H-AODV, \\  & & LQA-AODV \\  & Number of nodes & 300 (100 vehicle, 200 soldiers) \\  & Packet size & 512 bytes \\  & Number of nodes & 300 nodes (100 vehicles, 200 soldiers) \\ \hline \multirow{8}{*}{Network scenario} & Simulation time & 1 h \\  & Simulation area size & 15,000 \(\times\) 15,000 m\({}^{2}\) \\  & Pause time & 60 s \\  & & 25 m/s \\ \cline{1-1}  & Maximum Speed & soldiers: 5 km/h \\ \cline{1-1}  & & vehicles: 90 km/h \\ \cline{1-1}  & Transmit power & 46 dBm (vehicles) \\ \cline{1-1}  & Receiver sensitivity (RSS threshold \(X_{\text{th}}\)) & \(-\)97 dBm \\ \cline{1-1}  & SINR threshold values (\(\delta_{\text{th}}\)) & 20 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters and other settings.  
Figure 5: Hierarchical architecture of tactical MANETs.
# Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks
## 5 Numerical Results
### Simulation 3: Performance Comparison  
Finally, we examined the speed distribution of BSs of the three methods. Due to the fixed number of UE, only the data rate of each BS was compared. We set \(I\) to 60 and \(N_{i}\) to 1200. Figure 7 shows the cumulative distribution function (CDF) of the data rate of the three methods. To quantify the fairness of the performance distribution, a well-known fairness metric called [PERSON]'s index was used here. Given a set of numbers \([x_{1},x_{2},\ldots,x_{n}]\), [PERSON]'s index \(\frac{\left(\sum_{i=1}^{n}x_{i}\right)^{2}}{n\cdot\sum_{i=1}^{n}\left(\frac{x_ {i}^{2}}{x_{i}^{2}}\right)}\) ranges from \(\frac{1}{n}\) (worst case) to 1 (best case). When each member of the set has equal value, the index becomes one. As shown in Table 2, PSR had the highest Jain's index. About 80% of the three methods were concentrated in a range of 0-100 Mbps. The third area was large and had many low-speed BSs. Although SPT and CFC had very high inner BSs, their outer BSs had extremely low data rate, and about 60% were concentrated below 20 Mbps. This caused many BSs to work in a low-utility condition, making the overall system less effective. Due to PSR's utility-oriented behavior, it provided a higher outer data rate, concentrated near 50 Mbps. Because of the strong marginal effect of the utility, even when the speed was doubled at low speed, it could get several times more utility improvement.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **PSR** & **SPT** & **CFC** \\ \hline Jain’s index & 0.5243 & 0.1499 & 0.2107 \\ \hline \end{tabular}
\end{table}
Table 2: Value of Jain’s index for each mechanism.  
Figure 7: Distribution of different methods.
# Temporal Convolution Network Based Joint Optimization of Acoustic-to-Articulatory Inversion
## 6 Conclusions  
In this study, we proposed a lightweight acoustic-to-articulatory inversion system. We built and evaluated the temporal convolution network-based AAI system with a joint optimization method on the corpus of USC-TIMIT and IEEE-EMA. We predicted a 16-dim output of the articulator movements in the midsagittal plane with the real-time sensor recordings. The performance of our system outperforms the Bi-LSTM baseline with a relative 18.1% decline in RMSE and 9.7% advance in PCC. This result shows that the novel TCN model can extract proper convolutional features for the articulatory movement reconstruction from the normalized speech features cross speakers. Changing the weight parameter \(\lambda\) will affect the final performance of the AAI system. Training the model evenly on the two optimization aspects yields the best results. We leave for the future work the dynamic regulation of the target function weight and other hierarchical feature modeling methods.  
Conceptualization, G.S., Z.H., L.W. and P.Z.; methodology, G.S.; implementation G.S.; validation, G.S., Z.H., L.W. and P.Z.; formal analysis, G.S.; writing--original draft preparation, G.S.; supervision, Z.H., L.W. and P.Z. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Key Research and Development Program (No. 2020 YFC2004100).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AAI Acoustic-to-Articulatory Inversion  
Bi-LSTM Bi-directional Long Short Term Memory  
TCN Temporal Convolution Network  
RMSE Root Mean Square Error  
PCC Pearson Correlation Coefficient  
ASR Automatic Speech Recognition  
CALL Computer-Aided Language Learning  
EMA Electro-Magnetic Articulography  
rtMRI real-time Magnetic Resonance Imaging  
GMM Gaussian Mixture Model  
HMM Hidden Markov Model  
DNN Deep Neural Networks  
MDN Mixture Density Networks  
RNN Recurrent Neural Networks  
CNN Convolution Neural Networks  
MFCC Mel-Frequency Cepstral Coefficients
# Influence of Preconditioning and Tunnel Support on Strain Burst Potential
## 4 Results  
### Models' Validation  
Figure 7 presents the comparison between the depths of failure for the spalling and damage initiation limits of the 50 preliminary models compared with the published graphs of [PERSON] et al. (2011) (Figure 7a) and [PERSON] (2003) (for a K = 1.5) (Figure 7b). Moreover, the published ([PERSON] et al., 2014) depths of failure of the outer excavation damage zone (EDZ\({}_{o}\)) and highly damaged zone (HDZ) of mudstone for a stress ratio, K, of 1.5 are presented. Figure 6a shows a good correlation between the depths of failure of the empirical data and the depth of failure of the spalling limit. It can be noted that, when presenting a ratio between the maximum wall stress, \(\sigma_{max}\), and the uniaxial compressive strength (UCS) of intact rock higher than 1, there are no empirical data, and in this case the depths of failure of the numerical models are lower than the ones expected with the empirical relation. From Figure 7b, a consistency can be observed between the damage initiation and spalling limit of the preliminary models and the mudstone. Moreover, the preliminary models' depths of failure better fit the model proposed by [PERSON] (2003) for a \(\sigma_{max}\)/UCS ratio higher than 1.  
Figure 6: Influence of a support system and preconditioning on assessed limits.  
Figure 7: Numerical modeling depth of failure of spalling and damage initiation limits (**a**) compared with the depth of failure of [PERSON] (2011) and (**b**) compared with the depth of excavation damage zones of mudstone (2003, 2004).  
To go further, a nonlinear regression analysis was conducted to determine the best fit equation to the damage zone of the preliminary models and compare them with the ones assessed for three rock types studied by [PERSON] & [PERSON] (2014): mudstone, granite and limestone. The mean uniaxial compressive strength (UCS) of each type of rock is also presented. The results are shown Table 4. The form of the equation was first proposed by [PERSON] et al. (2016), where the multiplier and the exponent in the general form of the Equation (1) are B and D, respectively:  
\[\mathrm{Rf}/\mathrm{a}=1+\mathrm{B}(\sigma_{max}/\mathrm{CI}-1)^{\mathrm{D}} \tag{1}\]  
The table shows higher \(\mathrm{R}^{2}\) values for the EDZ\({}_{0}\) and damage initiation limit than for the HDZ and spalling limit. While the mudstone presents the higher difference in terms of UCS with the andesite studied in this paper, it presents the best correlation in terms of multiplier and exponent.
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 2 Materials and Methods  
### System Architecture  
This study has developed a wearable device for monitoring the activity levels of horses. The sensors in the hardware devices are connected, as shown in Figure 1. The STM32 is the core control unit, integrating various sensors to achieve motion detection, gait perception, and data transmission functionalities. The MPU6050 sensor is connected to the STM32 via the inter-integrated circuit (I2C) interface and is responsible for accelerometer data collection. The display module is connected to the central control chip via the service provider interface (SPI) for real-time data display. The power management module ensures a stable device power supply, including the lithium battery charging management chip TP4054, linear regulator XC6206P302 MR, and over-current protection chip SY6280. The 4G transmission module connects to the STM32 via the universal asynchronous receiver transmitter (UART) serial port interface, enabling data transmission to remote servers. The appearance of the wearable device is shown in Figure 11, the system flowchart is presented in Figure 12, and the comprehensive workflow of the entire system is detailed in Figure 2.