esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatial Compounding Method for Non-Delayed Sequential Beamforming
## 2 Materials and Methods
### Experiment Setup  
The Verasonics Vantage 256 scanner (Redmond, WA, USA) and a L11-4v linear array were used to collect experimental data. The array has a 6.25-MHz central frequency,  
Figure 3: Wave propagation path for (**a**) left end of the active aperture and (**b**) right end of the active aperture. \(\overrightarrow{r_{VS}}\), \(\overrightarrow{r_{r}}\), \(\overrightarrow{r_{e}}\), and \(\overrightarrow{r_{ip}}\) denote the positions of the virtual source, the receive element, the transmit origin, and the imaging point, respectively. \(\overrightarrow{r_{ip}}\) denotes the position for the end of the active aperture. The blue arrows indicate the transmit path, and the red arrows indicate the receive path. The active elements are indicated as black.  
128 elements, 67% bandwidth, and 0.3-mm pitch size. The excitation signal is a 1-cycle sinusoid tone burst at the central frequency. Transmit F-number (F #, defined as the ratio between focal length and the aperture diameter) is set to 0.75 for all experiments. During transmission, the number of the active elements is 16, boxcar apodization is used for every transmit event. The focused beam is translated by an element pitch in the lateral direction of 0.3 mm. The transmit focus is always on the centerline of the active aperture. During reception, all 128 elements are active to collect radio-frequency data. The raw data are sampled at 25 Msamples/s, resulting in a 0.078 mm-axial sampling distance. The Verasonics system stores 4096 samples of each receiver signal which covers data ranging from 3 mm to 34 mm in depth. Off-line processing is performed to produce the final images. B-mode images were obtained through Hilbert transform and log-compression. A phantom made of nylon threads of 0.2-mm diameter immersed in water was used for the quantification of the lateral resolution, and a lab-made cyst phantom is used to quantify realistic image contrast. In vivo imaging is performed to image the long-section and cross-section of the carotid artery.
###### Abstract  
This study aimed to develop and validate an automated segmentation method for surface muscles using a three-dimensional (3D) U-Net based on selective voxel patches from whole-body computed tomography (CT) images. Our method defined a voxel patch (VP) as the input images, which consisted of 56 slices selected at equal intervals from the whole slices. In training, one VP was used for each case. In the test, multiple VPs were created according to the number of slices in the test case. Segmentation was then performed for each VP and the results of each VP merged. The proposed method achieved a segmentation accuracy mean dice coefficient of 0.900 for 8 cases. Although challenges remain in muscles adjacent to visceral organs and in small muscle areas, VP is useful for surface muscle segmentation using whole-body CT images with limited annotation data. The limitation of our study is that it is limited to cases of muscular disease with atrophy. Future studies should address whether the proposed method is effective for other modalities or using data with different imaging ranges.  
surface muscle; segmentation; voxel patch; whole-body CT; 3D U-Net +
Footnote †: journal: Applied Sciences
###### Abstract  
As one of the typical cyber physical systems (CPS), the user energy management system (UEMS) plays an increasingly significant role in the smart grid, such as participating in automated demand response (ADR). Traditional analyses related to the UEMS in ADR programming mainly focus on energy management strategies or algorithms, where the interdependence and interplay between the cyber system and the physical system is neglected. This paper firstly presents an ADR control strategy of the UEMS with the objective of minimizing electricity bills and meeting users' comfort constraints. Then, a hybrid scheme including Message Queuing Telemetry Transport (MQTT) and Message Queuing Telemetry Transport for Sensor Network (MQTT-SN), which are publish-subscribe communication protocols, is developed to establish the cyber system of the UEMS. To evaluate the cyber-physical performance of the UEMS in ADR programs, the hybrid dynamic models of major behaviors of the UEMS are proposed and a UPPAAL ([[http://www.uppaal.org/](http://www.uppaal.org/)]([http://www.uppaal.org/](http://www.uppaal.org/)))-based methodology of the formal specification and verification is also proposed. In case studies, the impact of communication reliability on the proposed ADR control strategy is studied and the quality of service (QoS) mechanism provided by MQTT/MQTT-SN is demonstrated as a cost-effective solution for the ADR control strategy under unreliable communication.  
user energy management system (UEMS); automated demand response (ADR); cyber physical system (CPS); formal specification and verification; UPPAAL; MQTT/MQTT-SN +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Characterization of Hydrolysates  
The sugar composition of the raw hydrolysates and the treated BSG hydrolysates was determined by HPLC, as described above.  
The hydrolysis efficiency, i.e., the conversion of cellulose, xylan, and arabian into glucose, xylose, and arabin (represented as GluR, XylR and AraR, respectively), were obtained using the following equations (adapted from [37]):  
\[\text{GluR (\%)}=0.90\times\text{((C}_{\text{sugar }}\times\text{V)/(cellulose content }\times\text{M))}\times 100 \tag{7}\]  
\[\text{XylR or AraR (\%)}=0.88\times\text{((C}_{\text{sugar }}\times\text{V)/(xylan or arabian content }\times\text{M))}\times 100 \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Raw Material** & \begin{tabular}{c} **Concentration of H\({}_{2}\)SO\({}_{4}\)** \\ **(\% \(w/w\))** \\ \end{tabular} & \
\begin{tabular}{c} **Liquid to Solid Ratio** \\ **(\% \(w/w\))** \\ \end{tabular} &
\begin{tabular}{c} **Time of Hydrolysis** \\ **(min)** \\ \end{tabular} & **Reference** \\ \hline \hline Banana peels & 1 & 10 & 40 & [36] \\ BSG & 3 & 8 & 20 & [37] \\ Corncobs & 6 & 10 & 40 & [17] \\ Grape pomace & 3.3 & 8 & 30 & [38; 39] \\ Grape stalks & 3 & 8 & 20 & [40] \\ Sawdust & 6 & 8 & 20 & [41] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hydrolysis conditions applied to the different lignocellulosic materials.  
where 0.90 and 0.88 are the stoichiometric factors for the conversion of glucose to equivalent cellulose, and xylose/arabinose to equivalent xylan/arabian, respectively.  
The furfural, 5-hydroxymethylfurfurural (HMF), and acetic acid contents in the hydrolysates were also determined by HPLC, using an Aminex HPX-87H (Bio-Rad, Hercules, CA, USA) ion exclusion column, operated at 30 \({}^{\circ}\)C, equipped with a UV-Vis detector. The mobile phase was 10 mM H\({}_{2}\)SO\({}_{4}\) and the flow rate 0.6 mL/min. Acetic acid detection was performed at 210 nm, and furfural and HMF were detected at 280 nm.  
The total phenolics present in the raw hydrolysates was determined as described by [PERSON] et al. [43]. Briefly, the pH of the hydrolysates was raised to pH 12 by an addition of NaOH (6 mol/L), and the optical density was measured at 280 nm in a UV-Vis spectrophotometer. The total phenolics concentration (g/L), TP, was given by the Equations (9) and (10):  
\[\mathrm{TP}=4.187\times 10^{-2}\times\left(\left(\mathrm{abs}_{\mathrm{LIG280}}- \mathrm{abs}_{\mathrm{PD280}}\right)-3.279\times 10^{-4}\right. \tag{9}\]  
\[\mathrm{AbsPD280}=\left(\mathrm{C}_{\mathrm{F}}\times\varepsilon_{\mathrm{F}} \right)+\left(\mathrm{C}_{\mathrm{HMF}}\times\varepsilon_{\mathrm{HMF}}\right) \tag{10}\]  
where \(\mathrm{Abs}_{\mathrm{LIG280}}\) is the absorbance at 280 nm; \(\mathrm{C}_{\mathrm{F}}\) and \(\mathrm{C}_{\mathrm{HMF}}\) are the concentrations (g/L) of furfural and HMF, respectively, determined by HPLC; and \(\varepsilon_{\mathrm{F}}\) and \(\varepsilon_{\mathrm{HMF}}\) are the extinction coefficients (L/g cm) of furfural (146.85) and HMF (114.00). All measurements were performed in duplicate.
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 1 Introduction
### Backgrounds  
#### 1.1.1 Volumetric Computed Tomography (CT) Data  
Volumetric computed tomography (CT) data features a voxel structure, with each voxel having an intensity value. The standard data format for this volume data is the Digital Imaging and Communications in Medicine (DICOM) format, which contains more than 90 valuable information fields such as intensity values, patient details, modality, and manufacturer, acquisition data, and soon [1; 2; 3]. The volume data is obtained by X-ray computed tomography (CT) scanning. In practice, the volume data is divided into three parallel planes, the sagittal, axial, and coronal planes, and is used in the analysis of many operations. For dental applications, cone beam computed tomography (CBCT) is used [4; 5; 6]. While a 'fan-shaped' X-ray beam is used in medical CT, a 'cone' X-ray beam is used in cone beam CT. Because medical CT features higher X-ray exposure than CBCT, the resolution of the volume data provided by medical CT is higher than that provided by CBCT [7]. However, CBCT is used in many fields of dentistry due to the low X-ray exposure associated with it. Also, CBCT data is much easier to use with 3D interpolation since, due to its X-ray geometry, it forms isotropic voxels, whereas medical CT forms anisotropic voxels.
# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
## 4 Proposed IRS Selection and Access Link Connection Approach with 3D Simulation Environment  
In this section, we describe the simulation environment modeled after an actual city and present an optimal method for IRS selection and how to establish access links in UAV networks.  
Figure 2: System model of IRS-assisted UAV networks.  
Figure 3 shows the map data around the target area, while Figure 4 depicts the replicated simulation area. We sourced building locations and heights from PLATEAU [41], an open-data project led by Japan's Ministry of Land, Infrastructure, Transport, and Tourism that is focused on 3D city modeling nationwide. The sequence of replication is shown in Figure 5. After reading the polygon data of a building and detecting endpoints based on the maximum and minimum values of latitude and longitude, we then simplified and replicated buildings as a quadrangle within MATLAB R2022a. In this way, an arbitrary city can be reproduced in MATLAB, and the necessary UAVs and IRSs can be retrofitted to achieve numerical analysis with a low processing load.  
A LoS connection between the UAV and UE is highly likely in rural areas, where tall structures are sparse. In such cases, direct radio wave provision from the UAV is generally sufficient. Conversely, NLoS conditions between the UAV and UE become more probable in densely built urban environments with many tall buildings. This scenario increases the potential for improving UE reception power via IRS-assisted path modifications. The use of mmWaves allows for antenna miniaturization and the placement of multiple arrays. UAVs and IRSs can direct radio waves in any desired direction through beamforming (BF). mmWaves are characterized by their linearity and low diffraction, and they experience significant attenuation when encountering obstacles. So, it is not sufficient to make LoS judgments probabilistically as in the past; it is effective to make LoS judgments based on whether there is no obstruction between the sender and receiver. The high density of buildings also expands the number of potential sites for IRS installation. For this reason, we selected the Shinjuku Ward in Tokyo as our model environment. Shinjuku is renowned worldwide for its high-density urban layout. The specific area spans approximately 1.2 km\({}^{2}\)  
Figure 4: A 3D simulation environment around Tokyo Metropolitan Government Office.  
Figure 3: Actual environment map data around Tokyo Metropolitan Government Office in Google Earth.  
with longitudes ranging from 139.6864\({}^{\circ}\) E to 139.7004\({}^{\circ}\) E and latitudes from 35.6829\({}^{\circ}\) N to 35.6943\({}^{\circ}\) N; it has a large park in the west and a cluster of high-rise buildings in the east.
## 2 Methodology
### Validation of the Model  
As mentioned in Section 2.1, the models used for this parametric analysis were previously validated by the research group at the Instituto Superior Tecnico (University of Lisbon) [16; 18; 19]. The validation was based on experimental tests conducted at the Laboratory for Earthquake Engineering of the National Technical University of Athens (NTUA), Greece, within the framework of the European FP7 SAFECLADDING project [15]. Specifically, the model was validated using the specimen RIS1D25C reported in [15]. The RIS1D25C specimen consists of a full-scale precast reinforced concrete wall with dimensions of 2.67 m in height, 1.5 m in width, and 20 cm in thickness, connected by two 25 mm diameter steel rebars (Figure 2a). The experimental tests carried out by these authors involved the application of monotonic or cyclic horizontal loads at the top of the wall using a hydraulic actuator with displacement control. The connections were composed of steel reinforcing bars (rebars) that primarily resisted tensile stresses during loading. These rebars extended from the panels and were inserted into preformed ducts within the beams. The gap between the rebars and the ducts was filled with high-strength, non-shrink grout to ensure a secure connection.  
Three numerical models were developed in [16; 18; 19], all based on the geometric characteristics of the experimental setup, with a monotonic load applied at the top of the wall under displacement control. The authors concluded that these models aligned well with the experimental response. For this parametric study, the SS_1 modelling strategy was adopted, with modifications to the analysed variables. Figure 2b presents the calibration results for the SS_1 model used in this study (for further details, see [16]). The geometric non-linearity was considered using the default approach in SeismoStruct [17], ensuring proper capture of second-order effects.
## 2 State of the Art
### Simulations--Related Works  
The concept of simulations is a convenient solution for predicting the behavior of complex systems based on their input values. By simply adjusting input values, the behavior and its results can be easily observed. Simulations benefit from software-based platforms, which usually do not require any additional costs (except for hardware and licenses). Results can be obtained in reasonable time, but sometimes missing knowledge on certain parameters leads to the risk of getting incorrect or  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**No.** & **Authors** & **Year** & **Method Used** & **Number of Devices** \\ \hline
[56] & [PERSON] et al. & 2007 & analytical evaluation & 1 HE, 6 REPs, 15 CPEs \\
[57] & [PERSON] and [PERSON] & 2017 & simulation & 1 HE, 3 REPs, 140 CPEs \\
[58] & [PERSON] et al. & 2005 & simulation & 1 HE, 2 REPs, 3 CPEs \\
[59] & [PERSON] et al. & 2011 & real measurements & 1 HE, 10 REPs, 25 CPEs \\
[60] & [PERSON] et al. & 2014 & real measurements & 1 HE, 20 CPEs \\
[61] & [PERSON] & 2011 & project presentation & 550 HEs, 10,000 REPs, 120,000 CPEs \\
[62] & [PERSON] et al. & 2008 & real measurements & 1 HE, 8 REPs \\ \hline \hline \end{tabular}
\end{table}
Table 1: State of the art.  
Figure 2: Impact of time division multiplex for PLC and Broadband over Power Lines (BPL) repeaters.  
misleading outcomes. For simplifying the process of simulation, some input factors are skipped on purpose due to their predicted minor impact on the final results. Sometimes, those seemingly non-significant values can also result in a synergistic effect, which could have a much stronger influence on some values or behaviors in a particular area of simulation.  
Simulations investigating the influence of repeaters on network performance have been introduced in some research papers. Research presented in [56] evaluates a PLC network with tree-based topology with use of repeaters in regards to OPERA (Open PLC European Research Alliance) specifications. Authors implemented TDMA (Time-division multiple access) with spatial reuse, and they observed its impact on the overall network performance. Authors in [57] introduce a simulation of Adaptive Layer Switching (ALS) for a PLC network. The authors describe ALS and its influence on performance while switching between CSMA/CA and TDMA with possible benefits resulting from resilient switching between both methods.  
The topic of noise influence on network performance is partially covered by research in [58]. This simulation of a PLC network in the 1-20 MHz band is based on a measured communication channel model of a real power grid in the suburbs of Paris. Results of the simulations are provided based on a simplified noise model on combined bus and linear topology.
## 4 Properties of Lignosulfonate-Improved Loess
### Static Strength Properties  
#### 4.2.1 Tensile Strength  
Through experiments, [PERSON] et al. [43] found that the tensile strength of improved soil increases first and then decreases with the increase in the calcium lignosulfonate dosage. The inflection point is at 1%, which is 26.1% higher than that of plain soil. It decreases slightly in the range of 1-2%; the tensile strength of the improved soil decreases sharply after more than 3%. The results show that calcium lignosulfonate can effectively cement the soil particles and increase the cohesion of the soil under the condition of low dosage, thereby improving the tensile strength of the improved soil. When the dosage of lignosulfonate exceeds 2%, the tensile strength decreases. In summary, the recommended dosage range of lignosulfonate is 1-2%.
## 3 The Scheme for Simulating Fully Mechanized Mining with a Large Mining Height
### Hydraulic Support Simulation Method  
Due to the limitations of the DEM numerical simulation, the top hydraulic bracket cannot be guaranteed to be smooth. Therefore, it is difficult for the newly built support unit to keep good contact with the roof and bottom plates. In order to prevent stress concentration in the bonding between the support and the roof and bottom plates, the hydraulic support was built with rigid support units in the numerical simulation. Specifically, the information of the particles at the hydraulic support was recorded during model building. A hydraulic support with full contact with the roof and bottom plate was constructed with rigid cluster units in situ, as shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Layer** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{**Rock Type**} & \multirow{2}{*}{**Thickness**} & \multirow{2}{*}{**Density**} & \multicolumn{2}{c}{**Elastic Modulus**} & \multirow{2}{*}{**IF Angle**} & \multirow{2}{*}{
\begin{tabular}{c} **Tensile** \\ **Strength** \\ **(Mean \(\pm\) SD)** \\ \end{tabular} } & \multirow{2}{*}{**Compressive Strength**} \\  & & & & & **(Mean \(\pm\) SD)** & & **(Mean \(\pm\) SD)** \\ \cline{3-8}  & & **m** & **kg/m\({}^{3}\)** & **GPa** & \({}^{\circ}\) & **MPa** & **MPa** \\ \hline
14 &
\begin{tabular}{c} Sandy \\ mudstone \\ \end{tabular} & 15.39 & 2660 & 17.9 \(\pm\) 2.43 & 32.5 & 2.70 \(\pm\) 0.18 & 23.4 \(\pm\) 2.12 \\
13 & Coal & 0.63 & 1440 & 2.6 \(\pm\) 0.22 & 29.8 & 1.28 \(\pm\) 0.08 & 12.4 \(\pm\) 0.94 \\
12 & Mudstone & 0.38 & 2620 & 12.6 \(\pm\) 1.03 & 27.4 & 1.73 \(\pm\) 0.12 & 15.9 \(\pm\) 1.29 \\
11 & Limestone & 0.95 & 2715 & 21.8 \(\pm\) 1.66 & 33.4 & 2.13 \(\pm\) 0.17 & 29.6 \(\pm\) 2.68 \\
10 &
\begin{tabular}{c} Medium \\ sandstone \\ \end{tabular} & 4.35 & 2696 & 23.7 \(\pm\) 2.12 & 27.8 & 2.40 \(\pm\) 0.13 & 26.4 \(\pm\) 1.64 \\
9 & Mudstone & 12.25 & 2620 & 15.0 \(\pm\) 1.64 & 27.6 & 1.25 \(\pm\) 0.09 & 18.2 \(\pm\) 2.56 \\
8 & Limestone & 7.25 & 2715 & 22.4 \(\pm\) 1.90 & 36.6 & 2.25 \(\pm\) 0.17 & 31.8 \(\pm\) 2.44 \\
7 & Mudstone & 2.80 & 2620 & 15.8 \(\pm\) 0.98 & 27.2 & 1.84 \(\pm\) 0.14 & 21.0 \(\pm\) 1.72 \\
6 & Limestone & 12.30 & 2715 & 21.1 \(\pm\) 2.45 & 36.8 & 1.99 \(\pm\) 0.16 & 22.5 \(\pm\) 2.41 \\
5 & Mudstone & 0.35 & 2620 & 12.8 \(\pm\) 1.70 & 34.7 & 1.72 \(\pm\) 0.12 & 17.6 \(\pm\) 1.94 \\
4 & 9\# Coal & 1.13 & 1440 & 2.7 \(\pm\) 0.24 & 29.5 & 1.33 \(\pm\) 0.09 & 13.6 \(\pm\) 1.19 \\
3 & Mudstone & 0.50 & 2630 & 12.6 \(\pm\) 0.76 & 38.8 & 1.78 \(\pm\) 0.12 & 17.7 \(\pm\) 1.40 \\
2 & 10\# Coal & 6.71 & 1440 & 2.8 \(\pm\) 0.19 & 29.4 & 1.34 \(\pm\) 0.10 & 13.8 \(\pm\) 1.08 \\
1 & Mudstone & 4.41 & 2630 & 12.5 \(\pm\) 1.02 & 38.7 & 1.83 \(\pm\) 0.16 & 16.4 \(\pm\) 1.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Macroscopic parameters of rock formation.
## 3 Results
### N-Ammonia Concentration  
Although the whole Activated Sludge Process is measured by the Operation Cost (\(OC\)), first, we will look at the ammonia (\(S_{NH}\)) signal in order to deeply understand the \(OC\) measure (showed in the last Section 3.3). Notice first that \(OC\) (see Equation (1)) is a trade off between the energy used (mostly the energy for blowing oxygen into the aerobic tanks) and the Effluent Fines (mostly obtained because of high values of \(S_{NH}\) at the effluent). Thus, the greater the N-Ammonia concentration \(S_{NH}\) at the effluent, the higher the payments in fines at the WWTP.  
In Figure 11, we show the \(S_{NH}\) initial phase for each weather condition: second column of Table 2. If we look at the dry weather condition in Figure 11, we see that the RL agent is the worst in fines (too high ammonia) for each top of the graph. Clearly, the higher the \(S_{NH}\) concentration, the worse the fine. On the contrary, the B2, C and D groups are the PIDs with the best behavior in N-ammonia. In the same Figure 11, for rainy weather, the RL is a little better: it is the worst for some tops, but not all. In addition, for rainy weather, some bottoms are better for RL. The results are similar for stormy weather.  
Now, let us focus on the final phase, Figure 12, the third column of Table 2. Let us start with dry weather. Comparing it with Figure 11, we see lower tops and lower bottoms in the RL agent's behavior; thus, it behaves better than the rest of the controls (except for some tops that the agent cannot improve more). However, in rainy weather, the agent is one of the worst (at least concerning the \(S_{NH}\) behavior). Finally, we see an improvement in the stormy weather, in which we can see lower tops and lower bottoms as well. In summation, we see a light improvement for dry and stormy weather conditions in \(S_{NH}\) behavior in the final phase.  
As a summary, let us say that N-ammonia concentration is only a part of the whole picture, because saving energy is also important. In this section, we wanted to show in detail that the RL agent can learn to sacrifice the effluent water cleaning in order to save energy. In fact, the agent's decision depends on the context, because its final objective is to reduce the global operation cost in the WWTP.  
Let us focus on this measure \(OC\) in the next subsection.  
Figure 11: \(S_{NH}\) concentration for the **initial** phase.