esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military
## 1 Introduction  
For the warring parties, how to extract a large amount of military information accurately in the shortest time will be the key link to win the initiative of war. Military named entity recognition (MNER) is a fundamental and important link of military information extraction used to detect military named entities (MNEs) from military text and classify them into predefined categories, such as troops, weapons, locations, missions, and organizations. It can extract valuable information from raw data to improve the efficiency of intelligence reconnaissance, command decision-making, organization, and implementation.  
At present, military named entity recognition has a wide range of applications in the construction of a knowledge graph, intelligent question-answering systems, and in information retrieval. But the MNER task is still full of challenges due to the following reasons: (1) The limited number of supervised training data; (2) compared with English, Chinese lacks the explicit vocabulary boundary and the inherent definite article. At the same time, the proprietary vocabulary has no hint information such as spelling changes [1] (3). In addition to military instruments, other military texts do not have a uniform language expression format. The military corpus always has a large number of complex expressions, such as combination, nesting and abbreviation. Currently, most research on MNER has employed statistical methods of machine learning, including conditional random fields (CRF) [2], the hidden Markov model (HMM) [3], and maximum entropy (ME) [4], and so forth. These methods depend on handcrafted features and domain-specific knowledge resources excessively. However, the process of building feature templates is inefficient and expensive. And these traditional models have poor generalization ability.  
To avoid cumbersome feature engineering and reduce the dependence on linguistic knowledge, our objective is to provide a neural network architecture that combines a bidirectional long short-term memory (BiLSTM) network with a self-attention mechanism to learn contextual features automatically. The experiment results show that our proposed model can improve the performance of MNER tasks.  
The main contents of the paper are arranged as follows. Section 2 introduces the related work in the field of MNER briefly. Section 3 describes the model we proposed for MNER task in detail. Section 4 presents the experimental settings and process. Section 5 discusses the experimental results from multiple perspectives. Section 6 summarizes the research work of this paper and clarifies the direction of the next research.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs for High-Performance Satellite Communication Systems
#### 3.6.6 Future of Antenna Designs for Space Applications  
Upcoming trends in antenna design are certain to transform satellite communication systems driven by state-of-the-art technologies such as Reconfigurable Intelligent Surfaces (RIS) and integrated artificial intelligence (AI). RIS technologies are promising to enhance satellite networks, offering dynamic beam reconfiguration, adapting the operational requirements in real time. Converging with AI and machine learning (ML), these system designs can optimize radiation patterns, minimize interference, and direct resources more efficiently. This technology convergence is crucial for addressing and solving the growing demand for high-speed data transfer and reliable satellite communications along with their challenges, especially for advanced applications such as Direct-to-Device connection and IoT systems. Such innovations are leading the way toward next-generation systems capable of seamlessly supporting 5G-Beyond and 6G networks.  
In addition, antenna advancements play a vital role in supporting and advancing satellite constellations like Starlink, OneWeb, and Amazon Kuiper. These kinds of systems need antenna designs with high scalability, low cost, and high efficiency. At the same time, these designs must be sustainable, with reduced energy consumption and recycled materials. Lightweight, compact, and low-profile antennas minimize material usage without compromising efficiency, and their technical characteristics become more and more environmentally friendly, reducing the environmental impact of large-scale satellite constellations. Following these steps, antenna technologies not only meet the current demand for innovation and novelty but also are shaping a more sustainable future for space-based communications.
## 4 Results
### Curtailment Profiles  
This research distinguishes between different curtailment variations: option 1, option 2a and option 2b (see Section 3.5.1). The day-ahead flexibility requests set a fixed curtailment level at 578 W. This value represents 75% of the maximum forecasted PV power. In case of curtailment option 1, all expected PV production larger than this limit is curtailed.  
Curtailment option 2 (a and b) represents the DSO explicitly requesting flexibility at specific timeslots. In the context of this paper, this is done by manually setting curtailment limits at these timeslots. The limit is again 578 W. For option 2 (a and b) the timeslots at which curtailment is requested are the following:  
* 2 June from 11:00 until 15:00
* 7 June from 10:00 until 14:00
* 4 July from 12:00 until 15:00
* 5 July from 12:00 until 16:00
* 5 August from 12:00 until 14:00  
In case of curtailment option 2a, no flexibility has been enabled in the last week of May, June and July. The historical data used with methods III and IV is therefore not influenced  
Figure 10: Example of the reference profile with which the baselining methods are benchmarked.  
Figure 9: Comparison of forecasted and measured irradiance data during the first week of August 2019.  
by flexibility activation. The user dilemma (Section 2.1.5), part of the criterion proneness to manipulation, does therefore not play a significant role.  
In case of option 2b, flexibility has been utilised in some of the days in the last weeks of May, June and July. This is expected to reflect back in the results of methods III and IV, as the historical data used to get the baselines is now influenced by previously activated flexibility. The additional timeslots at which flexibility is activated for curtailment option 2b are the following:  
* 27 May from 12:00 until 14:00
* 29 May from 12:00 until 16:00
* 31 May from 12:00 until 14:00
* 29 June from 12:00 until 16:00
* 30 June from 12:00 until 16:00
* 1 July from 12:00 until 16:00
* 30 July from 12:00 until 14:00  
Figures 11 and 12 show the curtailment profiles for curtailment options 1 and 2 (a and b). These profiles correspond with the results of steps 2 (before correction) and 4 (after correction), described in Section 3.5. It can be observed that in some cases the curtailment limit is exceeded because irradiance was higher than predicted day-ahead. Depending on how conservative a DSO sets its flexibility needs in advance, higher than expected irradiance might cause overloading as this was not foreseen when determining the (day-ahead) curtailment limit. On the other hand, it can also be observed that in some cases the day-ahead predictions overestimate the reality. This can lead to a curtailment profile, where the DSO requests market parties to provide flexibility, which, however, turns out to be no longer necessary, as in reality solar irradiance is lower than predicted.
## 3 Results  
This section presents a numerical simulation example demonstrating the effectiveness of the proposed method. In this example, different source-to-site distances (10, 25, 50, and 75 km) were selected to estimate the PGA probability distributions, while obtaining corresponding time histories and response spectra. At each distance, 10,000 samples of source parameters were used in the MCS to generate artificial earthquakes with a magnitude of \(M_{w}\) 5.4, identical to the 2017 Pohang Earthquake.  
The proposed methodology highlights the importance of detailed seismic analysis to ensure the safety and preparedness of critical infrastructure in proximity to the focus. For example, the Hangsa Dam, currently under construction and set to reach a height of 53 m, is located approximately 25 km from the epicenter. Additionally, the Wolseong Nuclear Power Plant is situated about 45 km from the focus. Another critical facility potentially at risk is the Andong Dam, which stands 83 m tall and is located 75 km from the epicenter.  
Artificial earthquakes were generated as outlined in Section 2.3.2, resulting in the derivation of 10,000 PGAs for source-to-site distances of 10, 25, 50, and 75 km. A Kolmogorov-Smirnov test was then performed on the artificially generated PGAs at each distance. The test yielded \(p\)-values of 0.87, 0.30, 0.15, and 0.30, respectively, all of which exceed the 5% significance level, supporting the null hypothesis that the log-transformed PGAs follow a normal distribution. This confirms the assumption of normality for further statistical analysis. The probability distribution of PGAs at each distance was determined by fitting the data to a lognormal distribution, as in Equation (20), where it is also used for the probability distribution of \(f_{c}\). The parameters of this lognormal distribution for PGA, \(\mu\) (mean) and \(\sigma\) (standard deviation) are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **10 km** & **25 km** & **50 km** & **75 km** \\ \hline \(\mu\) & \(-1.437\) & \(-3.233\) & \(-4.452\) & \(-5.236\) \\ \(\sigma\) & 0.934 & 0.852 & 0.827 & 0.823 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of PGA probability distributions for different source-to-site distances.  
Figure 5: Response spectra for 100 out of 10,000 generated artificial earthquakes at source-to-site distances of **(a)** 10 km, **(b)** 25 km, **(c)** 50 km, and **(d)** 75 km. The black solid line represents the response spectrum for the median PGA of artificial earthquakes. The 100 spectra were chosen based on the smallest deviation between their individual PGAs and the median PGA.  
Figure 6: Distribution of PGAs from artificial earthquakes at four different source-to-site distances, along with the PGAs from actual earthquakes at 20 stations. Circles represent individual PGAs from stations, while x marks indicate the median PGAs of artificial earthquakes. It is noted that generated PGAs for all source-to-site distances are truncated at 0.5 g for data visibility.  
Figure 7: _Cont._
## 4 Illustration of the Architecture Using a Simple Scenario  
Let us imagine the simple scenario depicted in Figure 3, where a user would like to contact his bank. Currently, this would entail searching for the bank URL, navigating the site, browsing the website for the correct contact information and then starting a new application to contact either via email or phone. With our proposed architecture, the following steps would be taken after the user declares his intentions and communication partner:  
1. The identity of the bank is discovered using a search engine or through prior knowledge of a domain name or URL. Thanks to the link between the user's identity and the context of the communication, the bank identity might actually be retrieved from the user's profile. In this case, the user would only have to reference \"bank\" or any other generic identifier for the endpoint.
2. The bank domain is contacted by the discovery process to resolve the contact endpoint based on the context and intention: e.g., if the intention is to perform a voice call, the identity of the closest branch level 1 support would be returned.
3. The VIS is created and the identities of the endpoints, together with a set of local domain identifiers, are provided at the domain level of the communicating entities.
4. At the domain level, routes or paths are configured to connect to the next domain until the identifier corresponding to the other endpoint is reached. Each step involves the VIS APIs to ensure the data is accessible by all levels and domains (depending on authorization).
5. The endpoints establish a secure channel based on the keys corresponding to the identities and, if necessary, using the IDM system as a trust bridge.
6. Now the service is ready to be provisioned and the VIS can be adjusted to support any number of future applications or identities related to the intent.  
In a similar way, this mechanism can be used to contact any other number of entities simultaneously focusing on the endpoints and on the channel for the communication as a part of the identity of the participants. Also, the approach is not restricted to the entities being human. Objects or things may want to set up a communication completely without human intervention, with \"intentions\" that have been programmed into them. The same applies for autonomous software entities and services.
## 3 Method and Model
### Dataset Composition
#### 3.1.1 IEMOCAP Dataset Text, Audio, and Synthesized Video  
The IEMOCAP dataset is a multimodal emotional dataset that provides emotionally labeled dialogue resources recorded by 10 speakers. The research team selected 3106 audio samples from the IEMOCAP dataset for the study. The audios were categorized by 3-4 evaluators into 6 categories of emotion, happy, sad, angry, excited, frustrated, and neutral, resulting in 3-4 emotion labels for each audio. In order to determine the main emotion labels of the selected texts, this paper uses a membership function calculation with a normal distribution. With this method, we calculated the membership for each statement on the six emotions in the IEMOCAP dataset [26].  
To ensure that the generated audio data have corresponding video information, a diffusion network-based model, Dream-talk, is used in this study to generate videos of the upper frontal part of the speaker corresponding to the audio data. Diffusion models have shown great capabilities in data generation tasks. This process involves the gradual recovery of clear facial movements from noisy data, thereby generating clear, high-quality facial expressions.  
In the actual evaluation of students' emotional expressions, the required recorded videos are of the upper frontal half of the person's body. Since the video data in the IEMOCAP dataset are side videos of the speaker's whole body, which does not meet the practical needs of the actual student emotion evaluation task, this study utilizes the 3106 audio data files in the IEMOCAP dataset to generate the speaker video. This enhances the applicability of the dataset to the task of this study.
## 3 Methodology
### Preliminaries  
We use the graph \(G=(V,E,A)\) to describe the spatial topological network. \(V\) denotes the set of nodes which represent the spatial-temporal data collectors, such as traffic sensors, meteorological observation stations. \(|V|\) is the quantity of nodes, \(E\) denotes the set of edges in the spatial networks, and \(|E|\) is the quantity of edges. \(A\in R^{N\times N}\) is the adjacent matrix of the spatial networks. \(\mathcal{X}_{t}=(\mathcal{X}_{t,1},\mathcal{X}_{t,2},\ldots,\mathcal{X}_{t,N}) \in R^{N\times C}\) represents the features of every node in the spatial graph at time \(t\), where \(\mathcal{X}_{t,v}\in R^{C}\) is the feature vector of node \(v\) at time \(t\), and \(C\) is the quantity of features.  
The spatial-temporal prediction is defined as follows: given the historical spatial-temporal matrices \(\mathcal{X}=\big{(}\mathcal{X}_{t-T_{k}+1},\ \mathcal{X}_{t-T_{k}+2},\ldots, \mathcal{X}_{t}\big{)}\in R^{N\times T_{k}\times C}\) from the past \(T_{h}\) time slices, the task is to predict the sequence of future spatial-temporal matrices \(\mathcal{Y}=(\mathcal{X}_{t+1},\mathcal{X}_{t+2},\ldots,\ \mathcal{X}_{t+T_{p}})\in R^{N\times T_{p} \times C}\) over the next \(T_{p}\) time slices.
## 4 Evaluation of the Platform and Discussion
### Energy Augmented Reality System
#### 4.2.2 Energy Augmented Reality: Real Environment  
To evaluate the correct performance of the whole energy AR system, an Android app was designed and implemented. The energy AR app was tested in a smartphone with the following hardware features: Huawei P20 Little, CPU Kirin 659, 4 GB RAM, Android v.9.  
As can be observed in Figure 9, whenever the user focusses its camera on a fume hood or a microwave, they are recognized by the AR app and a pop-up window is shown with all the energy information generated by them.  
It is worth mentioning that the AR app was designed to show the value of energy parameters using different colors depending on how close they are to high or low levels; this way, users will be able to interpret the information faster and in a more intuitive way. For example, when the active power value is below 25 W, the color is white, while it is blue if it is above 100 W, green if it is above 1000 W, and eventually red if it is above 3000 W.  
Finally, it is also important to mention that, once the camera is focused on a device, for example, the microwave, the energy information is updated in real-time every 15 s on its own. This way, a user does not have to focus the camera on another point and go back to the microwave to have the information updated.  
Figure 8: The relation between detected features and light conditions.
# A Review of Training and Guidance Systems in Medical Surgery
## 3 Methods
### Synthesis of the Selected Works
#### 3.8.14. Colorectal Procedures  
Colorectal cancer is a type of cancer that starts in the colon or the rectum. These cancers can also be named colon cancer or rectal cancer, depending on where they start. Most colorectal cancers begin as a growth on the inner lining of the colon or rectum. To improve the treatment of colorectal cancer, a rectal examination should be performed. In this area, [PERSON] et al. developed an augmented reality system for digital rectal visualization [110]. The application uses sensors to track the finger's maneuvers and applied pressure while using a benchtop model and Microsoft HoloLens. The system can display the performance and essential metrics on the real benchtop model using an AR device. HoloLens is used to visualize the benchtop model's internal components and overlay the relevant virtual anatomy.  
For colorectal surgery, market counts with the simulator sold by CAE Healthcare called PROMIS. It is a virtual simulator that provides accurate and comprehensive performance feedback. PROMIS's hardware model is a replication of the peritoneal cavity. It can be modified with disposable and reusable components that are placed inside the model. It has remote access features so that collaborative training can be done in PROMIS. [PERSON] et al. used the PROMIS station to evaluate hand-assisted laparoscopy (HAL) versus the typical approach (TA) [111]. Conventional laparoscopic surgery was performed using the PROMIS in its usual configuration; however, for HAL, it was adapted with cameras mounted inside the peritoneal cavity. Results show that HAL tests were made faster without differences in intraoperative errors than TA tests.
## 1 Introduction
Since only one RF port is required for multibeam operation, the proposed LWA can be made compact compared to multiple-input-multiple-output (MIMO) or phased array antenna solutions. This makes the LWA an ideal candidate for 5G hot-spots, which are proposed by the ITU-R as part of the enhanced mobile broadband (eMBB) family of 5G, where high user densities are foreseen [1,4]. Furthermore, installations with a large number of small 5G cells with high spectrum reuse synergize with the proposed low-cost PCB LWA. In this context, the developed LWA supports network function centralization, as beam steering is achieved via optical frequency scanning. This approach does not require additional control signals. In addition, by using dense wavelength division multiplexing (DWDM) optical channels in the RoF feeding network and remote optical heterodyning, multiple steerable beams are generated by the LWA. This allows supporting multiple mobile users in a hot-spot with individual beams carrying individual user data. The paper is organized as follows: First, the 60 GHz substrate integrated waveguide leaky-wave antenna design is outlined and its fundamental properties explained. The in-depth antenna characterization of the SIW LWA is then presented, beginning with the measurement setup and showing its performance based on scattering parameters, gain, radiation patterns, beam steering behavior, and polarization. After that, the application of the SIW LWA for providing beam steering capabilities to centralized 5G hot-spots based on fiber-wireless RoF transport is described. Finally, single link and point-to-multipoint fiber-wireless transmission experiments are demonstrated using the SIW LWA.
# A Numerical Study of Blast Resistance of Carbon Fiber Reinforced Aluminum Alloy Laminates
## 5 Conclusions  
In this paper, the dynamic mechanical responses of carbon fiber reinforced aluminum alloy laminates with different radii of curvature, numbers of layers, and layering methods under external explosion loading were investigated. The simulation results have guiding significance for the optimization design of fiber reinforced composite structures under external explosion loading. The conclusions can be summarized as follows:  
Figure 23: Energy absorption vs. fiber layering direction and radius of curvature.  
Figure 22: Maximum deformation vs. fiber layering direction and radius of curvature.  
1. The results show that the curvature radius has the most direct effect on the structural deformation failure. With the decrease in the curvature radius, the displacement of the center point of the backplane decreases, but the energy absorption of the shock wave also decreases. With the increase in the radius of curvature, the absorption energy of the aluminum alloy layer increases significantly and plays a major role.
2. Parametric study shows that the layered structure has little effect on the deformation of the structure. In contrast, the increase in the number of layered structures will significantly improve the energy absorption of the shock wave. With the increase in the radius of curvature, the influence will be more significant. In the multilayer fiber energy absorption, the outer fiber has more energy absorption, and the difference increases with the increase in curvature radius.
3. The fiber layering mode determines the failure mode and impact resistance of the fiber. Under the same curvature radius, the \(0^{\circ}\) and orthogonal arrangements of the fiber layer have the minimum deformation, and the \(45^{\circ}\) arrangement has the maximum deformation. With the increase in curvature radius, the difference in the deformation of different arrangement modes increases. The energy absorption of the shock wave shows the opposite rule.  
Conceptualization, B.Z. and S.F.; methodology, S.F.; software, B.Z.; validation, B.Z.; formal analysis, B.Z.; investigation, B.Z.; resources, S.F.; data curation, B.Z.; writing--original draft preparation, B.Z.; writing--review and editing, B.Z.; visualization, B.Z.; supervision, S.F.; project administration, S.F.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.