esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Tunnel Face Support Pressure in Layered Soft Ground
###### Abstract  
In shield construction, the limit support pressure of the tunnel face has an important influence on ground settlement and construction safety. In this study, MIDAS GTS NX software was used to conduct a series of three-dimensional finite element simulations to investigate variations in excavation face support pressure under different layered ground conditions. By changing the shear strength parameters of the top or bottom layers, the influence of composite layers with different formation boundaries on the support pressure of the excavation face was studied. It was observed that when the formation boundary is placed above the axis, the support pressure of the excavation face is more sensitive to a change in the parameters in the lower part of the formation than in the upper part. Conversely, when the formation boundary lies below the axis, this sensitivity becomes reversed. Additionally, we derived a robust and accurate equation to estimate the limiting face support pressure in layered soft ground based on numerical modeling.  
face support pressure; layered soft ground; numerical simulation +
Footnote †: journal: Applied sciences
## 5 Comparison with Other Known Methods
### Comparison with Tuning Methods Using the Sopdt Model  
We shall assume that the plant \(G_{7}(s)=\frac{3\,e^{-2s}}{(5s+1)^{2}}\) is controlled by a PID regulator tuned using the described method and [PERSON]'s method [8] when \(K=1\), \(\xi=1\), \(T=5\), \(\theta=2\). The PID controller settings calculated using the proposed method from the recommended relationships (57), (59), and (60) should have values of  
\[K_{P}=0.6131\;K_{P}=0.7664\;T_{i}=10\;T_{d}=2.5\]  
The resulting response of the system is shown in Figure 11. The achieved settling time was 13.3 s. Since the proportional gain of the PID controller recommended by [PERSON] depends on the assumed value of the parameter corresponding to the time constant of the desired response \(\tau_{c}\), in order for it to allow the system so tuned to achieve a similar settling time, that is, 13.3 s, it is necessary to determine the parameter \(\tau_{c}\) from the following relationships:  
\[t_{s}\cong 5.83\cdot\tau_{c}+\theta\qquad\qquad\tau_{c}=\frac{t_{s}-\theta}{5.83 }=1.9383\]  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Tuning Method** & **IAE** & **ITAE** & **ISE** & **ITSE** \\ \hline CHR & 6.795 & 36.45 & 4.332 & 12.054 \\ Chen-Seborg & 4.233 & 11.64 & 3.231 & 5.727 \\ Abbas & 4.071 & 12.588 & 3.021 & 5.005 \\ \multirow{2}{*}{proposed} & critical & 5.436 & 18.654 & 3.963 & 9.15 \\  & damped & & & & \\  & min settling & & & & \\  & time & 4.518 & 12.198 & 3.507 & 6.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of integral indicators.  
Then, the desired closed-loop transfer function will be equal to \(G_{dc}(s)=\frac{e^{-2s}}{(1.9838s+1)^{2}}\).  
Really, such Laplace transfer function \(G_{c}(s)\) will provide the response of the system at a settling time of 13.3 s. Then, the required regulator settings determined by the Chien methods should be equal to  
\[K_{P}=0.8464\qquad T_{i}=10\qquad T_{d}=2.5\]  
Figure 11 compares the desired and actual responses obtained when tuning the PID controller using both methods.  
Also, the controller tuned using [PERSON]'s method does not allow for achieving the expected response that ensures the settling time, which is achieved in this control system but using the proposed method. If one were to take into account the recommendations given by Chien for the parameter \(\tau_{c}\), one would have to assume an even smaller time constant:  
\[\tau_{c}=\max(0.5,1)=1\]  
which would lead to even greater overshooting (\(\chi=20\%\)) and a longer settling time (\(t_{\text{s}}=15\) s).
# SingMonitor: E-bike Charging Health Monitoring Using Sound from Power Supplies
## 3 Background
### E-bike Overcharging and Its Causes  
**E-bike overcharging:** E-bikes are mainly powered by lead-acid and lithium batteries, which have current market shares of 30.83% and 46.41%, respectively [40]. Overcharging can cause e-bike battery capacity loss or even fire. Overcharging for lead-acid batteries raises the internal temperature, producing harmful gases and drying out the electrolyte, ultimately leading to positive grid corrosion. Grid corrosion can significantly shorten battery life, but lead-acid batteries are generally less susceptible to fire [41]. Fires caused by overcharging typically occur on lithium batteries. Lithium batteries usually consist of a positive and negative pole material and a separator that divides the two. Overcharging can cause severe side reactions within the lithium battery, which can cause the internal separator to rupture, resulting in severe thermal runaway and, eventually, a fire [42].  
**Causes of e-bike overcharging:** E-bike chargers mainly use three-stage charging: constant current, constant voltage, and float charging. Figure 2 shows that the charger indicator is red during the constant current and constant voltage stages. In the experiments, we use an ammeter to measure the change in current to distinguish between these two stages. When the battery is charged to 80\(\sim\)95%, the charger enters the float stage, and the indicator light turns green. The charger monitors the charging current and the battery voltage via an internal feedback circuit, which decides when to switch charging stages. When the charger charges the battery with a high current or voltage for a long time due to quality problems, it cannot switch to the float stage in time and the battery will be overcharged, resulting in a loss of capacity or even a fire. Also, when the battery is faulty and thus cannot reach its rated voltage during charging, the charger will continue to supply power at a high current, thus causing the battery to overcharge. The duration of each stage varies depending on the charger and the battery. _SingMonitor_ calculates the duration of each charging stage during initialization (see Section 5) to build the normal charging pattern for the corresponding e-bike. Long-term float charging is generally harmless, and _SingMonitor_ is more concerned with the duration of the constant-current and constant-voltage stages.
# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 5 Experiments
### Experimental Setup  
#### 5.1.1 Data Description and Evaluation Indicators  
To evaluate the performance of different models on tag-aware recommendations, we utilize a common benchmark dataset: Movielens ([[https://grouplens.org/datasets/movielens/](https://grouplens.org/datasets/movielens/)]([https://grouplens.org/datasets/movielens/](https://grouplens.org/datasets/movielens/)), accessed on 1 August 2022), where only those users who have both rating and tag information can retain. More importantly, the dataset also provides additional item category information. Following a similar preprocessing strategy [3; 13], we eliminate the infrequent tags that are used less than \(5\times\) in the Movielens 10M dataset and less than \(10\times\) in the Movielens 20M dataset. The statistics of the experimental datasets are summarized in Table 1. In addition, we split the data to generate training, validation, and testing set according to a ratio of 7:1:2.  
In order to comprehensively evaluate the performance of tag-aware recommendations from multiple perspectives, we adopt the following different types of evaluation indicators.  
* Accuracy. We take two commonly used metrics to estimate the recommendation accuracy: Hit Ratio (HR) and Recall [58]. HR considers whether the user interacted with its recommended items at least one time, while Recall calculates the ratio between the number of correctly predicted items and the size of the user's actual interaction item set. A higher value of HR or Recall indicates more accurate recommendations;
* Diversity. We take the intra-list distance (ILD) and category coverage (CC) [59] to estimate the recommendation diversity. ILD calculates the average distance between  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\#Users** & **\#Items** & **\#Tags** & **\#Categories** & **\#Interactions** \\ \hline Movielens 10M & 1651 & 5381 & 1586 & 19 & 36,728 \\ Movielens 20M & 6961 & 17,441 & 5006 & 19 & 394,745 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical information of experimental datasets.  
all pairs of items in the top-\(N\) recommendation list, while CC measures the diversity by computing the number of recommended categories. A higher value of ILD or CC means more diverse recommendations;
* Trade-off. We take the F-score (F1) indicator [24] to measure the trade-off between accuracy and diversity. Formally, F-score can be defined as: \[\text{F1}=\frac{2\times\text{accuracy}\times\text{diversity}}{\text{ accuracy}+\text{diversity}},\] (16) where accuracy can be HR or Recall and diversity can be ILD or CC. In our experiments, we use HR and CC to calculate F-score.
# Key Technologies in the Context of Future Networks: Operational and Management Requirements
## 2 Key-Enabled Technologies for Future Networks
### Cloud Computing  
With the rapid evolution of the services, processes and enterprise business models, traditional provisioning of resources (computing, storage and network) has evolved to a new concept known as Cloud Computing [12]. In cloud computing, a particular resource can be leased by users to a third party, according to their needs. This innovative service-oriented model provides an elastic infrastructure, while decreasing the costs of hardware acquisition. Cloud computing offers different service models depending on the available resources to be leased. For instance, Software as a Service (SaaS) enables the sharing of software programs running on a cloud infrastructure. For its part, Platform as a Service (PaaS) architectures allow users to execute customized programs created with different libraries, tools, or programming languages supported by the cloud environment. In the Infrastructure as a Service (IaaS) model, the user is able to customize the different computing resources (processing, storage, and network) and deploy arbitrary software (e.g., different Operating Systems). In particular, Openstack [19] appears as a promising open source project to manage cloud platforms through its set of services (nova, neutron, keystone, telemetry, etc.), which could be integrated not only into traditional networks, but also SDN and NFV approaches. In the context of Future Networks, cloud computing would facilitate on-demand network access to available resources by means of its elastic capacity. This modality benefits the final users as well as the service provider.
## 4 Simulation Results
### Simulation Results of the Smart Inverter Control
#### 4.1.1 The Per-Unit Value of the Mains Voltage Equals 1 p.u.  
When the per-unit value of mains voltage detected was 1 p.u., the PF of the photovoltaic smart inverter stayed at 1. The simulation result is shown in Figure 11. Figure 11 also displayed the output power of the smart inverter when the per-unit value of mains voltage equaled 1 p.u. From Figure 11, it could be observed that the PV system only provided real power to the mains. The reactive power was neither provided nor absorbed. Figure 12, by contrast, displayed the output voltage and current waveforms of the mains and smart inverter. From Figure 12, it could be observed that the output voltage and current were in a three-phase balance and the same phase.  
\begin{table}
\begin{tabular}{c c} \hline
**Electrical Parameter** & **Specifications** \\ \hline Range of input voltage (\(V_{in}\)) & 90–108 V \\ Inductance (\(L\)) & 0.5 mH \\ Output capacitor (\(C_{out}\)) & 470 μF/450 V\({}_{\mathrm{DC}}\) \\ Output voltage (\(V_{out}\)) & 400 V \\ Switch frequency of boost converter (\(f\)) & 20 kHz \\ \hline \end{tabular}
\end{table}
Table 1: The electrical parameters of the boost converter.  
\begin{table}
\begin{tabular}{c c} \hline
**Electrical Parameter** & **Specifications** \\ \hline DC-link capacitor (\(C_{dcbus}\)) & 1000 μF/450 V\({}_{\mathrm{DC}}\) \\ DC-link voltage (\(V_{dcbus}\)) & 400 V \\ Filter inductance (\(L_{1\,a,b,c}\)) & 12.86 mH \\ Filter inductance (\(L_{2\,a,b,c}\)) & 2.57 mH \\ Filter capacitor (\(C_{\,a,b,c}\)) & 4 μF/400 V\({}_{\mathrm{AC}}\) \\ AC voltage (\(V_{grid\,a,b,c}\)) & 220 V\({}_{rms}\) \\ Switch frequency of SPWM (\(f_{in}\)) & 20 kHz \\ \hline \end{tabular}
\end{table}
Table 2: Electrical parameter of inverter.  
\begin{table}
\begin{tabular}{c c} \hline
**Electrical Parameter** & **Specifications** \\ \hline Open circuit voltage (\(V_{oc}\)) & 131.6 V \\ Short circuit current (\(I_{sc}\)) & 16.42 A \\ Maximum output power point voltage (\(V_{pm}\)) & 105.2 V \\ Maximum output power point current (\(I_{pm}\)) & 15.22 A \\ Rated maximum output power (\(P_{max}\)) & 1600 W \\ \hline \end{tabular}
\end{table}
Table 3: Electrical specifications of KYOCERA KC200 GT PVMA.
## 2 Materials and Methods  
### Materials  
Platonin was provided from Gwo Chyang Pharmaceuticals (Tainan, Taiwan). It was then dissolved in phosphate-buffered saline (PBS) and stored at 4 \({}^{\circ}\)C until used. Penicillin/streptomycin/L-glutamine, Dulbecco's modified Eagle medium (DMEM), fetal bovine serum (FBS), and trypsin (0.25%) were from Invitrogen (Waltham, MA, USA). LPS was obtained from Sigma-Aldrich (St. Louis, MO, USA), human high oxidized low density lipoprotein (770,252-4) was purchased from Kalen Biomedical LLC (Montgomery Village, MD, USA), okadaic acid (>98% purity) from Merck Millipore (Billerica, MA, USA), and recombinant murine interferon gamma (IFN-\(\gamma\)) from Peprotech (Rocky Hill, NJ, USA). Moreover, we procured anti-iNOS polyclonal antibody (pAb) from Santa Cruz Biotechnology (Dallas, TX, USA); anti-IL-18 pAb from BioVision (Milpitas, CA, USA); anti-phospho-p65 (Ser536), anti-phospho-c-JNK (Thr183/Tyr185), and anti-lxBa monoclonal antibodies (mAbs) from Cell Signaling (St. Louis, MO, USA); and anti-\(\alpha\)-tubulin mAb from Thermo Fisher Scientific (Waltham, MA, USA). The secondary antibodies including horseradish peroxidase-conjugated Immunoglobulin G (IgG) of donkey anti-rabbit and sheep anti mouse were purchased from Amersham (Buckinghamshire, UK). The reporter plasmid nuclear factor-\(\kappa\)B (NF-\(\kappa\)B)-Luc and activator protein 1 (AP-1)-Luc were purchased from Signosis (Santa Clara, CA, USA). A Dual-Luciferase Reporter (DLR) Assay System was procured from Promega (Madison, WI, USA). The enhanced chemiluminescent (ECL) detection kit and hypond-P polyvinylidene dilluoride (PVDF) membranes were obtained from GE Healthcare Life Sciences (Waukesha, WI, USA).  
Figure 1: Effects of platonin on Lipopolysaccharides/Interferon gamma (LPS/IFN-\(\gamma\))-induced inducible nitric oxide synthase (iNOS) expression, and production of mature Interleukin 1B(IL-1ß) (mIL-1ß) and monocyte chemoattractant protein-1 (MCP-1) production in vascular smooth muscle cells (VSMCs). (**A**) The chemical structure of platonin. (**B**,**C**) VSMCs were pretreated without (**B**) or with (**C**) platonin (0.5–2 M) for 30 min and then stimulated with LPS, IFN-\(\gamma\), or the combination of LPS (50 \(\upmu\)g/mL) and IFN-\(\gamma\) (100 units/mL, LPS/IFN-\(\gamma\)) for 24 h. Western blotting assay was used to determine the cytosolic expression of iNOS and mIL-1ß content in the medium. MCP-1 production was determined using the ELISA kit described in “Materials and Methods.” Data are presented as mean \(\pm\) structural equation modeling (S.E.M.) (\(n=3\)); **+** \(p<0.01\), *** \(p<0.001\) compared to the resting (R) group; # \(p<0.05\) compared with the positive control group.
## 2 Materials and Methods
### Quality Assessment  
Two methods were used to evaluate the quality of the UAV SfM results. The first method evaluates the accuracy of the DSM and DOM generated by different projects. The root mean square error (_RMSE_) was further calculated by comparing the CP coordinates estimated in the calculation results with the reference CP coordinates measured with the GNSS RTK receiver. Specifically, horizontal accuracy was verified on DOM, while vertical accuracy was verified by extracting the elevation of the corresponding DSM on a larger range of 120 vertical checkpoints.  
\[RMSE_{X} =\sqrt{\frac{\sum_{i=1}^{n}\Delta x_{i}^{2}}{n}}, \tag{1}\] \[RMSE_{Y} =\sqrt{\frac{\sum_{i=1}^{n}\Delta y_{i}^{2}}{n}},\] (2) \[RMSE_{Z} =\sqrt{\frac{\sum_{i=1}^{n}\Delta z_{i}^{2}}{n}}, \tag{3}\]  
where \(\Delta x\), \(\Delta y\), and \(\Delta z\) are the differences between RTK checkpoint coordinates and model-extracted coordinates, and \(n\) is the number of checkpoints. Calculation of the horizontal \(RMSE\) (\(RMSE_{XY}\)) is as follows:  
\[RMSE_{XY}=\sqrt{RMSE_{X}{}^{2}+RMSE_{Y}{}^{2}}, \tag{4}\]  
The second method uses the Multiscale Model to Model Cloud Comparison (M3C2) tool of the CloudCompare software version 2.10. [37], which calculates the distance of the reference cloud and comparison point cloud relative to the local surface normal direction through two parameters (user-defined normal proportion and projection proportion). The process runs directly on the point cloud and does not require grid partitioning, avoiding the uncertainties involved in the interpolation process.  
To compare point clouds, the M3C2 distance between the reference point cloud generated by the 16 GCPs participating in BA and other project point clouds was calculated. Then, the mean and standard deviation of the M3C2 distance calculation were used to evaluate the accuracy and precision, respectively, of each point cloud. By plotting the error and its distribution curve, we determined the influence of the GCP distribution on the spatial distribution of the M3C2 distance difference and determined the possible pattern of the spatial distribution of error.  
Figure 4: Schemes with different distributions of GCPs. **(a)** Five distributions of one GCP; **(b)** two distributions of four GCPs. The distributions are shown by different colours.
# Research on a Support System for Automatic Ship Navigation in Fairway
## 3 Mathematical Model of Ship Motion  
### Ship Kinematics  
To determine the state of dynamic ship movement (position, heading, and velocity), kinematics equations are applied. These equations show the relationship of ship parameters in two referenceframes: The Earth-fixed reference frame O-\(\eta\xi\) and the body-fixed reference frame G-xy. The origin of the body-fixed reference frame is placed at the center of gravity of the ship, as described in Figure 2. By neglecting ship motions in directions, such as heave, pitch, and roll, the transformation between these reference frames is expressed by Equation (1) as follows:  
\[\dot{\eta} =u\cos\psi-v\sin\psi\] \[\dot{\zeta} =u\sin\psi+v\cos\psi \tag{1}\] \[\dot{\psi} =r\]
## 3 Results
### Study Characteristics
#### 3.2.2 Settings of Cogeneration Application  
The selected studies included cogeneration systems applied to various contexts (i.e., residential, commercial, and industrial). Five studies reported evaluation details of the system in the residential sector [15; 22; 26; 28; 32], eight studies in the commercial sector [14; 21; 25; 27; 29; 30; 35; 36], four studies in the industrial sector [20; 23; 24; 34] and one study in the hotel sector [33]. One study did not clearly mention the specific sector in which the data were obtained [31].
## 4 High Capacity 2 \(\times\) 2 OPS Node with Contention Resolution
### Switching and Buffering Experimental Setup  
In this experiment, we used the same optical packet transmitter described in Section 3.1. After the transmitter, the eight-packet train was split in a 1 \(\times\) 2 splitter to simulate two different transmitters named TX1 and TX2, and both TX1 and TX2 were input to the optical switching node, as shown in Figure 12. We reconfigured our 31-FDL buffering system, which consisted of nine 1 \(\times\) 8 PLZT switches,  
Figure 11: (a) architecture of the 2 \(\times\) 2 optical switching node with contention resolution by packet dropping and optical packet buffering; (b) picture of the 2 \(\times\) 2 OPS node prototype.  
Figure 10: Improved BER measurements with respect to Figure 9. (a) Back-to-back payload BER for optical packets P1-P8 as a function of the wavelength channel measured at point A in the setup of Figure 8; (b) payload BER after optical switching and label swapping measured at point B in Figure 8; (c) payload BER after packet addition measured at point C in Figure 8.  
fiber sheet delay lines (FSDLs) and an FPGA-based buffer manager, and made a 2 \(\times\) 2 OPS node system where two PLZT switches were followed by three FSDLs. The unit delay time of each FSDL was 100 ns. One of three different delays {0, 100, 200} ns was given to the packet by changing an output port of the switch. In this demonstration, only packets with label A were processed and characterized. When the multiple optical decoders [42; 43] recognized an input label as label A, gate signals output the optical packet to the buffer manager. The buffer manager can resolve a maximum contention of 4 \(\times\) 1 packets.  
The aggregated output packets after the OPS node were input into a polarization-diverse coherent receiver, shown in Figure 8, using a free-running tunable laser as local oscillator (LO) with linewidth of approximately 100 kHz (the combined linewidth of TX plus RX was approximately \(\sim\)200 kHz). The four-channel electrical signal was then digitized at 50 GS/s and the offline processing was performed with MATLAB with a DCBMRX as described in Sections 2.2 and 3.1. Figure 12b shows a diagram of the optical packets used in the experiment, consisting of an optical header of 64 symbols followed by a variable-length PDM-16 QAM payload. The format of the received optical packet train and the methodology for processing them was similar to the one described on Section 3.1.
# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
## 3 Test and Result Analysis  
### Performance Test of Filtering Algorithm for Invalid Targets  
The installation of the ARS 408-21 XX millimeter-wave radar should not only reduce the invalid targets caused by the low installation position that is susceptible to orbital interference but also consider the adjustment of the radar's pitch angle during the debugging phase. Therefore, the three-dimensional universal joint is first installed in front of the vehicle, the acrylic plate is then installed on it, and finally, the millimeter-wave radar is fixed on the acrylic plate through bolts. The installation position is about 520 mm from the ground, which meets the installation requirements. The actual vehicle installation is shown in Figure 5.  
Figure 4: Filtering process.  
In order to verify the effectiveness of the invalid target filtering algorithm during the driving process of the radar in the alleyway and the invalid target filtering effect caused by the vibration of the vehicle itself, the test was divided into three parts: the car was at a standstill, in a smooth driving state and in the vibration state of the speed bump driving. The test environment is shown in Figure 6, where the obstacles are experimenter A, trolleys and experimenter B. The one in black is Experimenter A, the one in gray is Experimenter B, and the blue car is the cart.