esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Performance of High-Voltage Vertical GaN LEDs via Modification of Micro-Cell Geometry
## 1 Introduction  
Recently, high-efficiency GaN-based light-emitting diodes (LEDs) have been used in a wide range of applications, such as full-color displays, traffic signals, automobiles, solid-state lighting, and backlights of liquid-crystal displays [1; 2; 3]. To achieve the high output power, the chip size of a LED device is typically enlarged for sustaining the high-injection-current operation. However, when a high injection current is applied in the device operation, the problem of LED efficiency droop occurs frequently because carrier overflow forms in the quantum wells [4; 5].  
To solve this problem, the high-voltage LEDs (HV-LEDs) were proposed. The HV-LED is defined as a GaN-based LED with series-connection micro-cells, which can be operated at a voltage higher than 20 V and an injection current lower than 100 mA [6; 7; 8; 9; 10; 11; 12]. The design and fabrication of HV-LEDs caneliminate the problem of efficiency droop and maintain the high output power. In addition to reducing efficiency droop, applying HV-LEDs can improve the current spreading. When the HV-LEDs are used, the injection current spreads uniformly to all micro-cell LEDs and turns on all chips. This phenomenon can enhance the output power of LEDs. For example, the total output power of HV-LEDs is double that of alternating current LEDs (AC-LEDs) that have a configuration similar to that of HV-LEDs and the same injection current. This doubled output power can be attributed that only half of the active area is driven when an AC-LED is operated.  
In recent studies, HV-LEDs have exhibited superior performance in reducing efficiency droop and an improved output power compared with conventional LEDs and AC-LEDs. However, these studies focused on the lateral LEDs fabricated on sapphire substrates. For lateral LEDs, the current-crowding phenomenon and low heat-conducting problem caused by the poor electrical and thermal conductivities of sapphire substrates inhibit the device performance. Additionally, the waveguide effect is unavoidable in the sapphire-based LED structure and reduces light intensity because of the limitation of the light path.  
At present, several techniques consisting of flip-chip packaging [12] and vertical-type structure [13] were proposed to overcome the disadvantage of low heat dissipation occurred in the lateral LEDs. In this study, vertical HV-LEDs were proposed to further enhance the device performance. The metal bonding process and laser lift-off technique were both employed to transfer HV-LEDs from sapphire to Cu metal substrates featuring a high thermal conductivity, resulting in the improved heat dissipation in the LEDs. In addition, the drawback of the waveguide effect in lateral LED structures can be solved efficiently by using vertical LEDs, thus improving the light emission efficiency. Moreover, the micro-cell geometry of the HV-LEDs was also modified to enhance the optoelectronic performance.
# IoT-Enabled Smart Cities: Evolution and Outlook
## 4 Open Challenges for IoT-Enabled Smart Cities
### Integration on Future Networks and Novel Computing Paradigms  
As more smart city services are implemented, the number of devices connected to and operated on the network is growing exponentially. In addition, with the introduction of 5G network technology, various services such as smart city, smart factory, and smart car are being developed. Such diverse IoT services must support requirements such as high bandwidth and ultra-low latency [99].  
Cloud computing methods provided by conventional service providers, however, must go through a central IoT platform to process various data generated by the devices. This has led to a rapid increase in the backhaul data traffic, inevitably slowing down service speeds. To solve this problem, extensive research is being conducted focusing on application of edge computing technology to reduce network load caused by traffic increase and to support IoT services from a relatively short distance, in turn, enabling the provisioning of ultra-low latency services [100].  
For example, communication standardization organizations such as 3 GPP and ETSI, as well as IoT standardization organizations such as oneM2M acknowledge the importance of edge computing standardization, cooperating with other institutions to develop standards and enhance compatibility. The Multi-access Edge Computing Industry Specification Group (MEC-ISG), a group in ETSI, is working on utilizing multi-access in edge computing and enabling compatibility with standards for network slicing provided by the 3 GPP core network [100]. In addition to the virtualization of the network, virtualization and edge computing of IoT platform features are being conducted: One edge node will be able to host a 3 GPP network, ETSI MEC edge computing, and ultra-low latency IoT service platform features dynamically to support various services.
## 3 Methodology
### Neural Network Models  
#### 3.9.1 MLP-NN  
This study proposed the network architectures RNN and MLP-NN. Figure 6 depicts the MLP-NN model architecture, which has two hidden layers of 50 hidden units. Ten features were taken as inputs in the model to detect different types of objects, such as landslide, cut slope, bare soil, and vegetation. The MLP-NN model was trained through a back-propagation technique with the Adam optimizer and a batch size of 64. The hyper-parameters used in this NN were carefully selected through grid search and a 10-fold cross validation process.
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 4 System Model
### Traffic Model and Metrics  
Each node will then request to the UAB to transmit one uplink packet of size \(B\). We assume NB-IoT nodes are already synchronized to the cellular network; they start their operations from the RA, exchanging the required NB-IoT signalling messages, and, if it is completed correctly, the required uplink resources are scheduled in NPUSCH. As one can imagine, the procedure is robust but its completion is not guaranteed. In fact, the main obstacles may be found in the UAB movement, channel fluctuations and collisions. The last two are application and environment dependent, while the first may be subject to a proper trade-off of the different latency, energy consumption, throughput and success rate metrics.  
To later assess this trade-off, let us formulate here the performance metrics which are node-dependent; network-dependent metrics are formalized in the following. In the following, pedex \(u\) will indicate a generic node in the network.  
Latency, that is the interval elapsing between the instant when the node has a packet to transmit to the service completion, is computed as:  
\[\Delta\tau_{u}=\tau_{u,\text{tx}}-\tau_{u,\text{start}} \tag{5}\]  
where \(\tau_{u,\text{tx}}\) is the time instant in which the node transmits its packet and \(\tau_{u,\text{start}}\) when this data is first available. For ease of evaluation, we assume \(\tau_{u,\text{start}}\) equals the time in which the UAB starts its trajectory for all nodes \(u\). Then, the throughput, \(t_{u}\), achieved by a single node \(u\) whose transmission is considered successful depends on \(\Delta\tau_{u}\) as:  
\[t_{u}=\frac{B}{\Delta\tau_{u}} \tag{6}\]  
If a node \(u_{n}\) is not able to transmit its packet demand, it would be \(t_{u_{n}}=0\) b/s. Finally, the energy consumption has to account for the signalling related to the RA procedure. It holds:  
\[E_{u}= E_{\text{tx},u}+E_{\text{rx},u}+E_{\text{idle},u}+E_{\text{sleep},u}=\] \[= V~{}I_{\text{tx}}~{}T_{\text{tx},u}+V~{}I_{\text{rx}}~{}T_{\text{ tx},u}+V~{}I_{\text{idle}}~{}T_{\text{idle},u}+V~{}I_{\text{sleep}}~{}T_{\text{sleep},u} \tag{7}\]  
where \(V\) indicates the voltage with which the IoT node is powered, \(I_{\text{tx}}\) and \(I_{\text{rx}}\) the current needed in transmission and reception mode, respectively, \(I_{\text{idle}}\) the current present when the node stays in idle, and \(I_{\text{sleep}}\) the current during PSM. Similarly, we indicate with \(T_{\text{tx},u}\), \(T_{\text{tx},u}\), \(T_{\text{idle},u}\), \(T_{\text{speed},u}\) the times spent by each node \(u\) for being in the corresponding operation mode. Of course, this depends on the message exchange described in Section 3 (thatincludes an alternating of transmission and reception modes). Some of these parameters are fixed and shown in Table 3[34].
# Characteristics of the Deformation and Fracture of Overlaying Slopes in Roof Cutting
## 2 Engineering Background  
The industrial site of Shanxi Dongtai Xinyuan Coal Industry Co., Ltd. (located in Linfen City, China) was formed by large-scale excavation and filling. The company set up a platform of about 18 m-50 m wide at the top by excavating the entire 10 m creep slope, which formed the current southern slope topography.  
The burial depth of the 10101 working face was about 160 m; the thickness of the coal seam was 8.34 m, including 0.24 m mudstone gangue; and the overlay limestone formation of the coal seam was 12.30 m. The length of the 10101 working face was 155 m, and the back stopping was carried out through receding full-collapse integrated mechanization with a large mining height of 8 m at one time. The mining was proceeded along the bottom plate and supported by bolts, nets, steel ladder beams and anchor cables. This blasting roof-cutting test was located in the 10101 transportation channel. Figure 1 is a histogram of a borehole near the transportation channel. The south side of the working surface was the 10102 working surface, and the north side of the work surface was the industrial square. The specific location of the work surface is shown in Figure 2.  
The slope deformation caused by the mining at the 10101 working face was mainly manifested as surface deformation and damage to buildings on the slope.  
According to the measurement results at the owner's monitoring points above 10101 working face, the cumulative maximum surface subsidence after mining was up to 4.4 m, and dense crack zones were developed. Due to the mining, the brick-and-concrete houses in the original slope-top village above the transportation channel of the working surface suffered a local collapse and a large number of vertical cracks and were no longer suitable for living.  
Given the serious slope deformation, urgent slope control measures were needed. At the site, a combination of ground and underground measures was mostly used to maintain the slope stability. Ground measures included slope cutting, anti-slip piles, etc. Most ground measures were costly and required long construction periods; the underground  
Figure 1: Composite stratigraphic column.  
measures were mostly the increase in slope protection coal pillars, which also caused a lot of waste of coal.  
At the same time, roof cutting for pressure relief is used, where the movement of the overburden structure is artificially regulated to control the overlay rock formation and surrounding rocks. However, there is a lack of theoretical research on its impact on slope movement and ground subsidence. Therefore, in the case study of the 10101 working face, particle flow code numerical simulation was carried out to study the influence of different roof-cutting heights on slope deformation and damage.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 5 Simulation Results and Analyses
### Validation of LSHADE-SPACMA Technique to Solve the TNEP Problem
#### 5.1.2 93-Bus Colombian System  
The Colombian test system had 93 buses and 155 circuits. It was designed to meet a 9750 MW load system, and 155 control variables were optimized to minimize the total investment cost. The initial configuration for the system can be seen in [PERSON] et al. [17].  
The best topology for the Colombian system using LSHADE-SPACMA is depicted in Table 3, which produced the same results obtained by [PERSON] et al. [13]. Both methods added 6 circuits at an investment cost of 316.44 million USD, while LSHADE-SPACMA got the global optimum solution in 475.19 s. The results demonstrate that LSHADE-SPACMA is sufficient to solve large-scale systems and complex problems in one run.
## 1 Introduction  
In the recent past, increasing population density and urbanization in large cities have led to the concentration of business centers in certain areas, putting considerable pressure on transportation infrastructure. This strain adversely impacts the efficiency of public transportation systems, adds to increased traffic congestion, and causes extended travel times. Delays in public transport, in particular, encourage individuals to use private vehicles, thereby aggravating carbon emissions and contributing to environmental pollution [1]. The increase in traffic density directly affects the performance of public transport modes such as bus services [2]. Delays and irregular passenger volumes undermine public confidence in transportation services and, therefore, necessitate developing strategies aimed at enhancing service quality for a more sustainable and environmentally friendly system. Since bus and subway systems, compared to private vehicles, generate significantly markedly diminished greenhouse gas output, both short- and long-term passenger flow forecasts are crucial for the sustainability of public transportation. While long-term forecasts generally demonstrate consistent trends, short-term predictions are prone to sudden fluctuations caused by factors such as weather conditions, cultural events, or sporting activities that can lead to congestion and increased travel times. In light of the recent concept of \"SmartMobility\", all transportation systems aim to integrate with each other via information and communication technologies. As such, intelligent transportation systems (ITSs) are becoming increasingly important. For public transportation services--a key component of intelligent transportation systems--to operate more consistently, it is crucial to design them accurately, with the projection of future passenger demand forming a fundamental part of this process. Moreover, long-term forecasts are valuable for urban planning and policy-making; however, short-term predictions require real-time and up-to-date data for operational decisions [3].  
Given the possibility of unexpected passenger surges at particular times of the day, it becomes imperative to adjust transit schedules accordingly. Collecting relevant data was once a significant challenge, but the emergence of Advanced Public Transportation Systems (APTSs) has greatly simplified the process. Technologies such as Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC), which are integral to APTSs, now enable the real-time collection of operational data [4]. The integration of these data sources notably enhances the accuracy of short-term forecasts, offering significant benefits to local authorities and urban planners in managing passenger demand, improving travel comfort, and decreasing operational costs [5]. Therefore, passenger flow forecasting has a critical role in intelligent transportation systems [6].
# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 5 Conclusions and Future Work  
This paper proposes a multi-source consistency domain adaptation model MCDANN that can adapt the fault diagnosis model to other unknown working conditions. MCDANN is based on the domain adversarial network, using the MK-MMD distance to further reduce the difference between the source domain and the target domain in the feature space, and using sub-domain alignment and multi-source domain adaptation methods to perform more fine-grained matching in the domain to solve the domain Mismatch problem. On the CWRU data set, experiments are carried out using the noisy fault data set with a signal-to-noise ratio of \(-4\). The experiments show that the MCDANN can achieve an accuracy rate of more than 96% under various unknown working conditions, which is better than other adaptation methods.  
The current experiments are only carried out on the bearing dataset. If more fault datasets containing domains can be obtained, the optimization of the existing model can undoubtedly go further. In addition, the current method has many ideal preconditions, such as the same number of labels in the source and target domains, and the source domain has many and abundant data. Compared with the experimental conditions, the actual situation is undoubtedly much more complicated, and this is also a direction that can be further expanded, such as small sample learning, local domain adaptation, etc. On the other hand, domain adaptation methods usually need to access the target domain data. Although the target domain data has no labels, the information contained in the target domain data itself is enough for the model to learn enough knowledge to complete the domain transfer. However, if one or more source domain data can be used for training to obtain a model with excellent performance without accessing the target domain data in the unknown domain, it is undoubtedly more practical work, which is also an extension of the domain adaptation problem and domain generalization problem.  
Funding acquisition, H.C.; methodology, H.C.; software, L.S. and S.Z.; validation, L.S. and S.Z.; formal analysis, H.C., Y.Y., and N.A.; investigation, L.S., S.Z., and Y.Y.; resources, H.C.; data curation, L.S. and Y.Y.; writing--original draft, L.S. and S.Z.; supervision, H.C.; project administration, H.C. and N.A.; writing--review and editing, Y.Y. and N.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China National Key R&D Program during the 13 th Five-year Plan Period (Grant No. 2018 YFB1700405).
## 2 Materials and Methods  
### Non-Thermal Plasma Reactor and Processed Gas  
The reactor is made of an Al\({}_{2}\)O\({}_{3}\) ceramic cylinder (length of 150 mm, inner and outer diameter of 16 mm and 20 mm, respectively) which is wrapped with copper foil connected electrically to the ground (Figure 1). Inside the cylinder, a brass tube operating as a high-voltage electrode and gas provider is placed coaxially. This electrode is covered first with a tube made of porous reticulated vitreous carbon (RVC) and then with a porous Al\({}_{2}\)O\({}_{3}\) ceramic tube so that the gas gap between two ceramic tubes is 2 mm. The task of the RVC tube is to work as a high-voltage electrode and to transmit gas towards the porous ceramics. The whole reactor, including gas inlet and outlet, is placed in a furnace heated to 300 \({}^{\circ}\)C.  
As a source of nanosecond pulses, we used an NPG-15/2000 pulse generator by Megaimpulse Ltd., producing negative voltage pulses of 29 kV with a repetition rate in the range of 50 Hz-3.0 kHz. This power source implies the use of voltage and current measurement typical for pulsed corona discharge. The voltage pulses were measured using a Tektronix P6015A high-voltage probe, whereas the current pulses were measured using a Pearson 2877 coil. The single discharge pulse energy (\(E_{p}\)), was obtained  
Figure 1: DBD reactor with RVC electrode and porous ceramic tube as the second dielectric barrier.  
after the experiment by the integration of the pulse voltage (\(U\)) times the current (\(I\)) over the pulse duration (\(t\)):  
\[E_{p}=\int_{pulse}U(t)\cdot I(t)d(t) \tag{1}\]  
The specific energy input (\(SEI\)) is usually used as a variable in experiments with non-thermal plasma reactors. In this case, it was calculated from a formula:  
\[SEI=\frac{E_{p}\cdot f}{Q} \tag{2}\]  
where \(f\) is the pulse repetition rate in Hz, and \(Q\) is the gas flow rate in L/s at standard conditions.  
All experiments were performed with a constant flow rate of 0.2 L/min (residence time 3.9 s) of the operating gas, which was a dry biomass producer gas simulator CH\({}_{4}\)(2.5%)CO\({}_{2}\)(11%): H\({}_{2}\)(14%):CO(21.5%):N\({}_{2}\)(51%) to which water vapor and the main compounds of wood tar, i.e., benzene, toluene, and naphthalene [9], were added. Water vapor was added by injecting distilled water from a syringe pump into the hot gas line inside the furnace. The aromatic hydrocarbons dissolved in isopropanol were then added in the same way. The resulting composition of the inlet gas was as follows: CH\({}_{4}\)(2%)CO\({}_{2}\)(9%):H\({}_{2}\)(12%):CO(18%):N\({}_{2}\)(42%):H\({}_{2}\)O(17%) with 10 g/m\({}^{3}\) of tar (58% benzene, 3% toluene, 39% naphthalene).
## 2 Design Approach
Considering length of the pitch as \(p\) and 2\(\pi\) mode of operation, one may have  
\[\beta_{e}\ p=2\pi \tag{3}\]  
Length of the gap (\(G\)) may be determined using the relation  
\[\mathrm{GAP}=\frac{\theta}{\beta_{e}} \tag{4}\]  
and considering a gap transit angle (\(\theta\)) of 1.5 radian. Initial dimensions of the RF section have been determined using the following conventional analytical formulae. When electrons are accelerated by DC voltage \(V_{0}\) before entering the input cavity is called DC electrons velocity (\(U_{0}\)) in m/s  
\[U_{0}=\sqrt{\frac{2 eV_{0}}{m}}=0.593\times 10^{6}\sqrt{V_{0}} \tag{5}\]  
where \(e\) = charge of electron = 1.602 \(\times\) 10\({}^{-19}\) C and \(m\) = mass of electron = 9.109 \(\times\) 10\({}^{-31}\) kg. Angular frequency (\(\omega\)) may be determined using the relation  
\[\omega=2\pi f \tag{6}\]  
where \(f\) is the linear frequency. Electron wave number (\(k\)) and drift tube radius (\(a\)) may be determined using the relation  
\[k=\frac{\omega}{U_{0}} \tag{7}\]  
\[a=\frac{1}{k} \tag{8}\]  
The beam radius (\(b\)) can be determined using a beam fill factor of 70%, which is conventionally of the drift tube radius (\(a\)) for proper beam transmission through the drift section without interception. Therefore,  
\[b=0.7\times a \tag{9}\]  
and charge density  
Figure 1:0  
If the electron beam continues beyond the axial position (\(\lambda_{p}\)), the electrons would continue to oscillate. The frequency at which they would oscillate is the electron plasma frequency (\(\omega_{p}\)). Figure 1:1where \(\epsilon_{0}\) = 8.854 \(\times\) 10\({}^{-12}\) farad/m and (e/m) = 1.758 \(\times\) 10\({}^{11}\) C/kg. The equation shows that the plasma frequency is a function of the density of electron beam and DC velocity of the electrons. The reduced plasma frequency \(\omega_{q}\) is given by the expression  
Figure 1:2  
where \(R\) = 0.36784 is the plasma reduction factor. \(R\) can be found from standard curves or table. The reduced plasma wavelength (\(\lambda q\)) and drift tube length (\(L\)) can be calculated as  
Figure 1:3  
Figure 1:4  
The magnetic field, which exactly balances the space-charge force of the electrons, is called the Brillouin field. The resulting electron motion immersed in Brillouin field in form of a smooth rotating beam is called Brillouin flow [2]. The magnetic flux density to be provided by the focusing system in the case of Brillouin flow is  
Figure 1:5  
where \(B_{B}\) is the Brillouin field value in Gauss, \(I_{b}\) is beam current in Amperes, \(V_{b}\) is the beam voltage in volts, and \(r_{b}\)_is_ the beam radius in meters.