esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Empirical Results
### Analysis of Network Characteristics of Charging Stations in Five Districts of Jinan City
#### 5.2.1 Network Index Analysis Based on Node Local Structure
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ranking** & **Station Number** & **Site Name** & **Area** \\ \hline
1 & 12 & Special call car charging station (Jiayi Harmony Yidian, Ginza, Jinan) & Huaiyin District \\
2 & 13 & Epp Public charging station & Huaiyin District \\
3 & 11 & Eway Energy car charging station & Huaiyin District \\
4 & 42 & Special call public charging stations & Lixia District \\
5 & 43 & Special call public charging stations & Lixia District \\
6 & 44 & Special call car charging station (Shandong News Building Electric Vehicle) & Lixia District \\
7 & 51 & Eway Energy Car charging station & Lixia District \\
8 & 52 & Eway Energy Vehicle public charging station & Lixia District \\
9 & 53 & Eway Energy Vehicle public charging station & Lixia District \\
10 & 39 & Unitech Car Charging Station (Watt Road) & Lixia District \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Top 10 sites with node degree. the entire network, indicating that the nearby charging stations are more numerous, have closer contact, their charging is more convenient, and users benefit from rich charging resources. It further shows that road traffic is relatively developed. It can be seen from Table 2 that the stations with high degree centrality are mainly distributed in Huaiyin District and Lixia District, indicating that the road conditions in these two areas are superior, the population density is larger, and the economy is better. Among them, the stations with the highest degree of centrality are the special electric car charging station (Jinan Ginza Jiayi Harmony Yidian) and the Aipu public charging station, indicating that it has a large number of charging stations connected within itself, covering a wide range, playing a key role in all charging station stations, and that its robustness is high. When other stations are damaged or affected, they can still maintain a high degree of independence.
# Synthetic Jet Actuators with the Same Cross-Sectional Area Orifices-Flow and Acoustic Aspects
## 4 Conclusions  
In the paper, SJAs with three different orifices were investigated. The circular, square, and slot orifices had the same cross-section area, and the orifice height was equal to the characteristic size for a given type of orifice, respectively the diameter, the side, and the width.  
Although the orifices do not meet the basic criteria of similarity as the same hydraulic diameter or the aspect ratio, the basic characteristics of the SJ were similar for each type of orifice. Among the most important should be mentioned: the Reynolds number and the centerline momentum velocity at the characteristic velocity, and the energetic efficiency in a function of the real power. The similarity concerns both the course of the graphs and the obtained values. The differences caused by considering the equivalent diameter to calculate the Reynolds number were also discussed.  
The SPL was measured for each type of orifice at different real power. In the entire studied range, the lowest noise was generated by the actuator with the square orifice, and then by the actuator with the circular orifice. The greatest noise was generated by the actuator with the slot orifice, even though the slot orifice had the smallest height and SPL is positively correlated with height [51]. We have thus shown that the shape of the nozzle is more important in terms of the generated noise than the orifice height.  
In the end, the differences in the selection method of the orifice parameters in this paper and others were discussed. Due to the similarity of the results this method of the parameters selection seems good for initial orifice selection. It can be the basis for further differentiation and determining how changing geometric parameters affects the specified values when comparing orifics with different shapes.  
Conceptualization, E.S. and J.W.; Methodology, E.S., M.M. and J.W.; Software, Validation, Formal Analysis, Investigation, M.M. and J.W.; Resources, Data Curation, Writing--Original Draft Preparation, E.S.; Writing--Review & Editing, E.S., M.M. and J.W.; Visualization, E.S.; Supervision, E.S. and J.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Center for Research and Development, Poland. Grant No.: LIDER/6/0024/L-10/18/NCBR/2019.  
Not applicable.  
Some or all data generated or used during the study are available from the corresponding author by request.  
The authors declare no conflict of interest.
# Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data
## 2 Methods
### Forward Models
The AMSFMM method is very fast and presents an efficient approach to modelling the thousands of data pairs required to train the neural network (the time to compute a single travel time field in Matlab on a standard desktop computer is 2.18 s, therefore to compute the 32 travel time fields required to construct the ToF matrix requires approximately 70 s per model--and this could be significantly sped up using a compiled language). To avoid the inverse crime and test the limitations of the model based training approach, an alternative forward modelling approach is implemented to generate the test data with which we can assess the trained DNN's performance. For this Onscale, a commercial finite element (FE) package [38], is used to model the ultrasonic wave propagation through a given weld geometry (with dimensions and material properties as defined in Section 2.1). This FE model incorporates more of the physics that describes the wave propagation, such as reflections, scattering and frequency dependence, and so better represents the data we would expect to observe experimentally. However this model incurs significant computational expense which is why its use is restricted to generating test data only. The FE method takes approximately 3 min to simulate wave propagation for a single element (96 min for 32 elements). The FE test data used in this paper is modelled in Onscale using a Ricker wavelet with a central frequency of 2 MHz as the source-time function. Pressure loads are applied at the top of the model (simulating the transmitters) and the displacement is recorded at points determined by the receiving array configurations shown in Figure 3. Free boundary conditions are applied on all sides of the domain to model the finite extent of the steel plates. Note however, that any reflections from the vertical boundaries occur at a time much later than the first time of arrival and so do not interfere with the construction of the ToF matrices. The values for the finite element node spacing (\(\Delta x,\Delta y\)) are selected to ensure spatial stability conditions (\(\Delta x,\Delta y=\lambda/15\), where \(\lambda=2.54\) mm is the shortest wavelength present in the problem). Following the simulation for each transmitting array element, the ToF to each receiving transducer is automatically picked by selecting the time for the arriving energy to meet some threshold. In this work, the threshold is taken to be 1% of the peak displacement in the recorded signal.
# Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
## 1 Introduction
We can computationally model three scenario-related views, including chatbot users who make a query, system developers who design the Q&A workflow, and medical experts who provide medical expertise. This proposed model is expected to operate upstream of chatbot-based response generation. For queries within known intents, the original query is passed to the chatbot for answer generation. For detected unknown intents, the system may invoke a curated non-chatbot response or trigger human expert intervention protocols. This study uses a multi-view decision boundary learning approach to fuse multiple pieces of knowledge and detect queries with unknown intents. Our approach assumes a high-dimensional space where queries with the same intent can be mapped and limited within a sphere. It aims to learn the centroid and radius of each sphere to outline the decision boundary. Specifically, we first obtained a medical knowledge base whose data were crawled from an authoritative medical website, Xunyiwenyao. A knowledge graph em bedding technique was used to transfer the entities and relations to unique embeddings, identify related entities from a given query, and match them with the transferred entity embeddings. Then, the query itself was embedded, incorporating an attention mechanism guided by the intent label design. The query embedding and matched entity embeddings were further integrated to train an intent classification model. After training, the final classification layer was removed, allowing the model to generate transformed vector representations for new queries. These representations reside in a high-dimensional space. Finally, an unknown intent detection model was trained to learn the centroid and radius within this space. Queries with representations falling outside all known intent boundaries are identified as having unknown intents. To evaluate the effectiveness of our method, we conducted a series of laboratory-level experiments using a real-world user query dataset from the Alibaba Quake Search Engine, made available through the Tianchi platform [16]. Each query in the dataset has been labeled with a known intent class. We assessed the model performance under varying conditions, with the proportion of unknown intents set to 25%, 50%, and 75%, respectively. Comparative evaluations against several benchmark methods showed that our approach outperformed existing baselines across all settings, confirming the robustness and effectiveness of the proposed design.
# Dual-Circularly Polarized 60 GHz Beam-Steerable Antenna Array with 8 \(\times\) 8 Butler Matrix
## 1 Introduction  
Recently, various studies on millimeter-wave communication technologies have been performed to provide higher data rates and increased system capacities. 60 GHz-based millimeter-wave communication is attractive for a number of reasons, including a large amount of spectrum availability, miniaturization capability, superior frequency reuse, and a multi-gigabit data rate. In the millimeter-wave frequency bands, due to atmospheric absorption, the free-space path loss is more significant than that in conventional cellular bands below 6 GHz. To compensate for the high path loss over millimeter-wave frequencies, array antennas with high gain are commonly used. Since array antennas have a directional beam pattern with limited beam coverage, it should be designed to have beam-steering capability with the proper beam pattern to obtain a wide communication coverage [1; 2]. Among the various beam-steering techniques, switched beam networks have been highlighted because of the advantages of low cost, simple implementation, and low power consumption. The Butler matrix is one of the switched beam networks and is especially popular due to its simpler structure and wider bandwidth [3; 4; 5; 6; 7; 8; 9; 10; 11; 12].  
Meanwhile, polarization diversity and the use of circular polarization are important to improve the spectral efficiency and compensate for the polarization mismatch in wireless communication systems. In particular, the use of circular polarization can effectively suppress multi-path fading in line-of-sight (LOS) environments and give a higher probability of a successful link [13; 14; 15; 16; 17; 18]. This is due to the fact that circularly polarized signals are much better at penetrating and bending around obstacles compared with linearly polarized signals.  
Several research works have been reported in designing the phased array antenna using the Butler matrix for millimeter-wave applications. However, most of previous research only dealt with improvement of the Butler matrix performances, such as an isolation between the ports and an amplitude imbalance at the output ports. On the other hand, only a few studies focused on not only those performances but also on their polarization properties. In [19], circular polarization was achieved by using a \(1\times 6\) magneto-electric dipole antenna array with a \(5\times 6\) Butler matrix. In [20], the polarization diversity was considered together with the dual-polarization characteristic using a waveguide-type Butler matrix.  
In this paper, we propose a dual-circularly polarized 60 GHz beam-steerable patch antenna array with an \(8\times 8\) Butler matrix to simultaneously resolve the limited beam coverage and polarization mismatch in millimeter-wave wireless communication systems.
# Magneto-Optical Traps for Cold Atomic Gravimetry: Research Status and Development Trends
## 1 Introduction
Building on the previous methods, in 1986, [PERSON] et al. proposed three ways to use external fields to break the limits of the optical Earnshaw theorem [19]: (1) Using an external field, such as a magnetic field, to make the transition frequency of the atom vary with space; (2) Using an external field to make the atomic orientation vary with space; (3) Using optical pumping to change the atomic state. They found that the atomic potential trap obtained by this method would have a much greater trap depth than the dipole force trap, and it can trap more atoms and at a lower temperature. Inspired by this idea, MOT was created. [PERSON] first proposed the construction of atomic traps by combining an inhomogeneous static magnetic field and optical pressure. This idea was soon taken up and implemented jointly by [PERSON]'s group at Bell Labs and [PERSON]'s group at MIT in 1987 [20]. They modified the device that produces the optical molasses. Six quarter-wave plates were installed to produce circularly polarized laser beams. A pair of coils with opposite currents was used to generate the desired quadrupole magnetic field. A magnetic field gradient was used to tune the atomic resonance so that the radiation pressure provided both cooling and damping forces. In this way, \(10^{7}\) atoms were trapped in two minutes, and the atomic density reached \(2\times 10^{11}/\)cm\({}^{3}\) at a temperature of 600 \(\upmu\)k. They called the atomic trap a MOT. In 1988, [PERSON] et al. [21] first proposed the use of six orthogonal standing wave laser beams to cool trapped atoms in their overlapping region; they achieved the trapping of atoms in the standing wave laser field. They found that in the standing wave field, cold atoms could be stored for a long time in the cross-sectional region of laser beam under high laser intensity and low-pressure conditions. Since then, MOTs have been widely studied and applied worldwide. The other sections of this paper are organized as follows: the second section gives an introduction to the working principles of a MOT; the third section presents the research history on and structure of MOTs. This section contains a review of applications for different types of MOTs, the fourth section gives a summary of and discussion on the prospective development of MOTs for cold atomic gravimetry.
## 2 Related Work  
### Human Activity Recognition  
A lot of research has been conducted on human activity recognition (HAR) by sensors, and several models have been proposed to map the sensor data to specific activity categories. However, most of these methods require subjects to wear special equipment on specific parts for data collection and performing required behavior. There are two shortcomings of the existing data. One is the need to use special equipment, which is not convenient to collect the data. Second, it required the participants to do specific activities which have a great impact on daily life.  
Human activity recognition requires time-series sensor data in most cases, such as accelerometers, gyroscopes, etc. Some of the models recognize the state of users by accelerometers and rotation sensor data on the mobile phone. The user's behavior can be identified by artificially designed features after collecting the data from mobile phones. However, noise is unavoidable when we use mobile phone sensors for activity prediction [10], which make it difficult to achieve high performance. Moreover, feature extraction based on artificially designed features performs steadily in some cases but is not good at identifying the vital features, as well as the fact that it is strongly affected by noise. The complexity of human activity improves the difficulty of recognition.  
In 2017, [PERSON] et al. proposed the DeepSense model [10] to solve noise and feature extraction problems in a unified way. DeepSense combines the features of Convolution and Recurrent neural networks to improve the recognition capability of correlations and features between sensors. The model has better performance in integrating the features of different types of sensors by transforming the features into the form of the model on the temporal dimension. DeepSense mainly uses three functional modules, which are the Exclusion Convolution module, Fusion Convolution module, and Recurrent Neural Network, which can have better performance on HAR. Compared to DeepSense, the AttnSense proposed by [PERSON] et al. in 2019 optimized the model by using the Attention mechanism [11]. In addition to using a Conventional Convolution network, AttnSense used Gated Recurrent Unit (GRU) in the Recurrent Neural Network, which simplifies the calculation and used the Attention mechanism to calculate weights in data fusion to better fuse the features of all types of sensors. In addition, in the output process of GRU, all states are fused through the Self-Attention mechanism, which can extract the correlation of data in the temporal dimension better and improve the accuracy of HAR.  
In recent years, activity recognition is highly applied by many smartphone makers, such as Huawei and Xiaomi. Compared with existing applications such as the Huawei recognition system and the Xiaomi recognition system, we utilize more sensors than them and our system can recognize more complex activities, as theirs can only recognize some simple activities.
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 5 D2D Authentication Process for Cellular IoT Network
### Key Generation and Authentication Process in the LTE Framework  
In an LTE network, the key generation process, authentication, ciphering, and integrity flow process in a core EPS network are shown in Figure 2. The network consists of one root key (K) and several intermediate keys (CK, IK), which are derived from root key K. The third leaf keys are generated by the intermediate key [25; 26]. The different functionalities of the used keys and their authentication level in the authentication and key agreement (AKS) protocol for LTE-based radio networks are listed below:  
* K includes a random number of bits and is used for certain users master keys, which are stored in USIM and AuC.
* The intermediate key (CK, IK) is a 128-bit key which is derived from the root key, K.
* The access security management entity key ( \(K_{ASME}\)) is generated from the intermediate key (CK, IK) with the use of two additional parameters: the serving network ID and bitwise sum of two additional parameters. The \(K_{ASME}\) is distributed as the local master key.
* The key for evolved node B (\(K_{eNB}\)) is derived from \(K_{ASME}\) and the additional input counter. This additional input is required to ensure that each new key \(K_{eNB}\) differs from the previous key.
* Keys \(K_{RRCenc}\), \(K_{RRCint}\) and \(K_{UPenc}\) are used to authenticate the integrity of RRC and subscribers. The key generation attains the key separation and prevents associated key attacks. Once any key is changed, only the dependent keys are unaffected.  
Figure 2: Key generation and authentication procedure in LTE network.  
Figure 2 describes the key generation, confidentiality, integrity, ciphering and authentication process in an LTE framework for RRC communication, non-access stratum communication, and user equipment authentication. The session key generated from the root key is K, and this is used to store the address of the user service identity module (USIM) and home subscriber server (HSS), which produces a cipher key (CK), a integrity (IK), and a new session key (\(K_{ASME}\)). This \(K_{ASME}\) then acquires another cipher and IK for every communication channel [18; 27].
# A Practical Resource Management Scheme for Cellular Underlaid D2D Networks
###### Abstract  
In this paper, we investigate a resource management scheme for cellular underlaid device-to-device (D2D) communications, which are an integral part of mobile caching networks. D2D communications are allowed to share radio resources with cellular communications as long as the generating interference of D2D communications satisfies an interference constraint to secure cellular communications. Contrary to most of the other studies, we propose a distributed resource management scheme for cellular underlaid D2D communications focusing on a practical feasibility. In the proposed scheme, the feedback of channel information is not required because all D2D transmitters use a fixed transmit power and every D2D transmitter determines when to transmit data on its own without centralized control. We analyze the average sum-rates to evaluate the proposed scheme and compare them with optimal values, which can be achieved when a central controller has the perfect entire channel information and the full control of all D2D communications. Our numerical results show that the average sum-rates of the proposed scheme approach the optimal values in low or high signal-to-noise power ratio (SNR) regions. In particular, the proposed scheme achieves almost optimal average sum-rates in the entire SNR values in practical environments.  
D2D; cellular-aided D2D; underlay; mobile caching +
Footnote †: journal:  
_Future Internet_**2017**, 9, 62; doi:10.3390/f9040062