esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Setup
### Discretization of the Computational Domain  
The computational domain was discretized by hexahedral cells. The number of cells greatly influences the results of numerical simulations, necessitating a balance between the time required to conduct numerical simulations and solution accuracy [21; 22].  
Volumetric controls in specific regions with abrupt changes in flow or where the flow needs to be described in more detail are generated. The mesh was additionally refined at the anticipated position of the free surface to accurately capture the Kelvin wake. Mesh at the symmetry plane is given in Figure 3, and at the position of the free surface in Figure 4. Considering that the computational domain represents the water of limited depth and width, boundary layers develop both at the bottom and the side boundaries. Therefore, the areas around the hull, as well as along the side and bottom boundaries, were refined with prism cells. Figure 5 depicts the discretization of the boundary layer around the hull in the stern region, while Figure 6 shows the discretized boundary layer along the bottom and side boundaries. The value of the \(y^{+}\) parameter around the hull, at the bottom and side boundaries, was kept below 1 [23] within numerical simulations performed using both turbulence models. That enabled the comparison of the numerical results obtained using RKE and SSTKO turbulence models. Within the software package STAR-CCM+ both SSTKO and RKE can be used for the all \(y^{+}\) wall treatment. RKE is available with the option of using a two-layer approach, which enables it to be used with fine mesh that resolves the viscous sublayer. The two-layer all \(y^{+}\) wall treatment uses an approach that is identical to the all \(y^{+}\) wall treatment. Figure 7 illustrates the distribution of the dimensionless parameter \(y^{+}\) along the hull. Table 2 displays the number of cells of the mesh utilized in the numerical simulations conducted for shallow and confined water.  
Figure 3: Mesh cross-section.  
Figure 2: Boundary conditions of the computational domain for confined water.
## 2 Methodology and Data Collection
### Kriging-Based Channel Model Development
[CrossRef]
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* (17) [PERSON]; [PERSON]; [PERSON] On the Performance of Neural Network Residual Kriging in Radio Environment Mapping. _IEEE Access_**2019**, \(7\), 94557-94568. [CrossRef]
* (18) [PERSON]; [PERSON]; [PERSON] Modeling the Kriging-Aided Spatial Spectrum Sharing over Log-Normal Channels. _IEEE Wirel. Commun. Lett._**2019**, \(8\), 749-752. [CrossRef]
* (19) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Fast Radio Map Construction by Using Adaptive Path Loss Model Interpolation in Large-Scale Building. _Sensors_**2019**, _19_, 712. [CrossRef] [PubMed]
* (20) [PERSON]; [PERSON] Kriging-Based Interference Power Constraint: Integrated Design of the Radio Environment Map and Transmission Power. _Trans. Cogn. Commun. Netw._**2017**, \(3\), 13-25. [CrossRef]
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* (22) [PERSON] _Indoor Wireless Communications: From Theory to Implementation_, 1 st ed. ; Wiley: Chichester, UK, 2017; ISBN 978047074160. * (23) ITU. _Recommendation UIT-R P.525-4_; Ginebra: Sharm El-Sheikh, Egypt, 2019. * (24) [PERSON]; [PERSON] A Spatial Estimation-Based Handover Management for Challenging Femtocell Deployments. In Proceedings of the 2014 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2014, Odessa, Ukraine, 27-30 May 2014; IEEE Computer Society: Piscataway, NJ, USA, 2014; pp. 144-148. * (25) [PERSON] A Kriging Approach to Predicting Coverage in Wireless Networks. _Int. J. Mob. Netw. Des. Innov._**2009**, \(3\), 65-71. [CrossRef]
* (26) [PERSON] Spatial Data. In _MATLAB Recipes for Earth Sciences_, 4 th ed. ; Springer: Berlin/Heidelberg, Germany, 2015; pp. 1-427. ISBN 9783662462447.
## 4 Future Work and Discussion
### Discussion  
In our experiments, the 5G connection proved reliable when sending data to the cloud device. For future development, adopting MQTT (Message Queuing Telemetry Transport) instead of BLE (Bluetooth Low Energy) could be investigated. MQTT is lightweight and is designed for high-latency, low-bandwidth networks. These characteristics make it ideal for IoT environments, where devices must communicate over long distances. In contrast to BLE's short range and point-to-point connections, MQTT allows multiple devices to communicate over a broader network. Finally, MQTT's publish-subscribe model would simplify the data exchange procedure, enabling effective data handling and lower power consumption.  
Regarding the results of our experiments, it can be concluded that the power characteristics of the presented architecture would be significantly improved if the use of the Raspberry Pi could be avoided, since it is the dominant factor in the power consumption of the system. In order to reach this goal, the system would need to be updated at the very least in the following two ways:  
1. To use an integrated 5G module directly on the ESP32 for long-range communication. These modules are starting to become available and we plan on procuring one for future research.  
2. To implement the needed application-layer protocols on the ESP32 software. This is challenging, because, unlike the Raspberry Pi that runs Linux, the ESP32 needs to be programmed in C/C++ directly, without the ability to use most of the Linux libraries. The current 5G IoT sensor monitoring system could face several limitations. For instance:
* Physical risks: outdoor devices could be exposed to weather conditions such as extreme heat or cold conditions, as well as theft risks.
* Power constraints: the reliance on batteries or solar panels is challenging.
* Environmental interference: the BLE protocol is sensitive to electromagnetic interference, which may affect the reliability, especially in industrial environments.
* Sensor vulnerability: sensor accuracy could vary based on extreme weather conditions
* Network instability: this could lead to high energy consumption.
## 3 Methodology  
### Preliminaries  
In this work, we concentrate on the tasks of data imputation and prediction based on transfer passenger flow derived from all metro lines and their corresponding bus stops in the Shenzhen public transportation system. Firstly, we define our study objects below.  
Definition 1: Transfer Passenger Flow: Unlike the typical modeling studies on traffic data obtained from single types of vehicles, our study involves transfer passenger flow across metro and bus systems. Specifically, we collected smartcard data recorded on the automatic fare collection system of public transport when passengers transfer between the metro and bus systems, and then organized them as a 2D matrix. Of particular interest is that a metro station might have multiple bus stops; the flows refer to the total number of people from the metro to all its corresponding bus stops, so the flows cannot exceed the number of total exits from the metro. Therefore, taking the metro stations as the horizontal axis, we assume the transfer flow is \(\mathbf{Y}\in\mathbb{R}^{M\times N}\), where \(\boldsymbol{\psi}_{t}\in\mathbb{R}^{N}\) is an observed vector of \(N\) stations at \(t\) time steps; \(M\) is the total number of time slices and \(\boldsymbol{\psi}_{i,t}\) is the transfer flow of the \(i\)th station on the \(t\)th time point.  
Definition 2: Transfer Flow Imputation: Data loss is difficult to avoid due to various failures, manifested as a certain rate of zeros in matrix \(\mathbf{Y}=\{\boldsymbol{\psi}_{1},\boldsymbol{\psi}_{2},\cdots,\boldsymbol{ \psi}_{t}\}\). Before starting the prediction task, our repair process works by fitting a high-precision mapping function \(\mathcal{F}_{1}(\theta_{1})\) with a partially observed flow, then reconstructing a complete traffic matrix \(\mathbf{Y_{I}}\).  
Definition 3: Transfer Flow Prediction: Traffic prediction is a classic time series modeling issue, which takes a series of historical data as input and optimizes a desired model by continuously evaluating and reducing the errors between the true and predicted values in the hope of eventually accurately predicting future traffic data [10]. In this study, given the imputed transfer passenger flow \(\mathbf{Y_{I}}\) of all metro-bus stations at the previous \(H\) time slots of its traffic network, our goal is to learn the complex function of \(\mathcal{F}_{F}(\theta_{F})\) in Equation. (1) and then calculate future transfer flow through this mapping relationship:  
\[(\overset{\boldsymbol{\wedge}}{\boldsymbol{\psi}_{t}},\cdots,\overset{ \boldsymbol{\wedge}}{\boldsymbol{\psi}_{H+2}},\overset{\boldsymbol{\wedge}}{ \boldsymbol{\psi}_{H+1}})=\mathcal{F}_{F}((\boldsymbol{\psi}_{t},\cdots, \boldsymbol{\psi}_{H+2}\boldsymbol{\psi}_{H+1})|(\boldsymbol{\psi}_{H},\cdots, \boldsymbol{\psi}_{2},\boldsymbol{\psi}_{1})\boldsymbol{\psi}_{F}) \tag{1}\]  
where \(\mathcal{F}(\cdot)\) denotes the desired model and \(\theta=\theta_{I}\cup\theta_{F}\) stands for the learnable parameters.
# Building Penetration Losses at 3.5 GHz: Dependence on Polarization and Incidence Angle
## 2 Theoretical Background
### Uniform Plane Wave Incident on A Slab  
The case studied in Section 2.1. was quite simple, as just one discontinuity was considered in the propagating medium. If we intend to study the attenuation produced by building facades, a more realistic approach would be to consider a slab of thickness d; thus, with two parallel flat discontinuities in the propagating medium.  
This will be the case depicted in Figure 3, where the incident wave will suffer multiple reflections at both discontinuities.  
For this case, a solution using [PERSON]'s equations can also been found [21]. The results for 30 cm thick slabs made of concrete or brick and a 15 cm slab made of brick were calculated and are given in Figure 4, both for perpendicular and parallel polarized incident fields. As can be seen for this case, there is no monotonic variation of the attenuation with the incidence angle.  
Figure 3: Plane wave incident on a slab of thickness d.  
For an actual facade, where multiple slabs of different materials may be present, the variation of the transmission losses may exhibit a much more complex behaviour. Furthermore, modeling that reality becomes increasingly difficult when each of those slabs is non-homogeneous and some of the discontinuities are rough instead of flat, as happens in the case of a slab with a small thickness.  
Figure 4: Transmission losses, in dB: (**a**) for perpendicular polarized electric field; (**b**) for parallel polarized electric field.  
many construction structures. To assess the penetration losses in such complex structures, an experimental approach is needed.
# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
## 3 Methods
### Development of Vibration Measurement Module  
This study used a low-cost accelerometer (ADXL335) to measure vibration values. The ADXL335 is a micro-electromechanical system (MEMS) accelerometer, and when  
Figure 2: (a) XBee 3 Pro modules and (b) XCTU application for XBee settings.  
an acceleration force is applied to a specific axis, the microstructure supported by the polysilicon spring is deflected to change the capacitance. These variations are designed to be proportional to the acceleration of the corresponding axis. The static acceleration due to gravity and dynamic acceleration due to impact or vibration can be measured in the range of \(\pm\)3 g for the X-, Y-, and Z-axes, respectively. The sensor operates from a power supply of 1.8 to 3.6 V and typically consumes only 350 uA of current, making it suitable for this study, which requires low power. The ADXL35 sensor sends out voltage through three analog output pins according to the acceleration values of the X-, Y-, and Z-axes. The sensor was powered by the VCC pin and grounded through the GND pin (Figure 3a). The relationship between the output analog value and the acceleration is the same as that in Equation (1). Here, the ADC value is an analog value, Vref is the reference voltage, 1.65 V is the voltage at 0 g, and 0.330 V is the sensitivity scale factor.  
\[\text{Acceleration value}=((\text{ADC value}\times\text{Vref})/1024)-1.65)/0.330, \tag{1}\]  
Figure 3b is a schematic figure, and Figure 3c shows the actual product of the vibration measurement module developed in this study. Two AA-size batteries were built to supply a voltage of approximately 3 V, and a 3.3 V regulator was connected to supply a constant voltage. Because both the Xbee module and ADXL35 were optimized for an input voltage of 3.3 V, a constant voltage output from the regulator was supplied to each. Xbee's analog pin measures the voltage from 0 to 3.3 as a digital number of 0 to 1023 (10 bit); therefore, when a voltage of 1.65 V corresponding to 0 acceleration is input, a value of approximately 511 is measured. The value data obtained are sent to another Xbee module that acts as a router and are finally transmitted to the coordinator.
# Highly Linear Loaded-Line Phase Shifter Utilizing Impedance Transformer and PIN Diode
## 6 Conclusions  
This paper presented the design and implementation of a highly linear loaded-line digital phase shifter. The proposed architecture combines the use of PIN diodes in a parallel-to-ground configuration with impedance transformers to enhance linearity performance. This approach enabled the development of a one-bit phase shifter achieving an IIP3 exceeding 100 dBm. A prototype was designed, fabricated, and experimentally validated, with both simulation and measurement results confirming the intended 45-degree phase shift. The findings demonstrate that the integration of parallel PIN diodes with optimized impedance matching and layout design yields a compact, low-loss, and highly linear phase shifter--well-suited for modern RF and full-duplex communication systems. Future work  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & **Number of** & **Total IL** & **IL** & **IIP3** & **Topology** \\  & **Bits** & **(dB)** & **(dB/bit)** & **(dBm)** & **Topology** \\ \hline
[8] (2023) & 3-bit & 6 & 2 & 46 & Switched line \\ \hline
[9] (2024) & 1-bit & 1.2 & 1.2 & 95 & Switched line \\ \hline
[3] (2025) & 3-bit & 4.2 & 1.2 & 90 & Switched line \\ \hline This work & 1-bit & 0.8 & 0.8 & 100 & Loaded line \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results.  
may involve translating these high-linearity phase shifter designs to mm-wave frequencies to support advanced 5G and radar phased array applications.  
Conceptualization, F.G.; methodology, F.G. and A.D.; software, F.G.; validation, F.G. and A.D.; formal analysis, F.G. and J.Z.; investigation, F.G.; resources, F.G. and A.D.; data curation, F.G.; writing--original draft preparation, F.G.; writing--review and editing, J.Z. and Y.H.; visualization, F.G.; supervision, J.Z. and Y.H.; project administration, J.Z.; funding acquisition, J.Z. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article.  
The authors declare no conflict of interest.
## 5 Conclusions  
As a result of the increase in power electronics applications, such as the development of semiconductor technology, studies on WECSs have increased recently. The energy produced in wind turbines at variable speeds is not smooth and does not reach the desired levels. For this reason, electrical conversion methods are used to make the energy obtained more smooth and more useful. In this study, a novel MLI topology in a PMSG-based WECS is investigated. This inverter topology is called the PUC MLI. Compared to other inverters of the same level, the PUC MLI has advantages in terms of reducing the switching number, switching losses, harmonics, and control complexity. For this reason, it is preferred. The PMSG-based WECS is designed, simulated, and experimentally verified in the laboratory at different speeds and loads. In conclusion, according to the results obtained, some changes are observed as the inverter level changes depending on the level of the generated output voltage and the load values. In the experimental study, the output voltage of the PMSG varies approximately between 25 V and 35 V, while the input voltage of the PUC MLI is determined as 45 V. This value is obtained from the output of the DC-DC boost converter  
Figure 30: PUC5 MLI for RL\({}_{2}\).  
Figure 29: PUC5 MLI for RL\({}_{1}\).  
When the inverter level is increased from 5 to 7, it is observed that the THD of the voltage of the PUC7 MLI decreases from 28.8% to 14.24% and the THD of the current decreases from 5.49% to 4.62% for the RL\({}_{1}\) load, while the THD of the voltage decreases from 24.96% to 11.65% and the THD of the current decreases from 5.09% to 4.10% for the RL\({}_{2}\) load. When the inverter changes from five to seven levels, it is observed that the THD value decreases, more power is transferred to the load, and it is transformed into a smoothed sine waveform. In future studies, it is planned to adopt advanced controller approaches based on deep learning. It is also planned to design a grid-connected wind energy conversion system with higher power output.  
Methodology, H.Y.H.; Software, H.Y.H.; Validation, H.Y.H.; Investigation, H.Y.H.; Resources, H.Y.H.; Writing--original draft, H.Y.H.; Writing--review & editing, A.M.V.; Supervision, A.M.V.; Project administration, A.M.V. All authors have read and agreed to the published version of the manuscript.  
This research was made possible by a grant from the Scientific Research Projects Unit of Gaziantep University under project number MF.DT21.09.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors would like to thank the Scientific Research Projects Unit of Gaziantep University for its financial support.  
The authors declare no conflict of interest.
# Development of a Low-Cost Automated Demand Response Controller for Home Energy Management
## 3 Results
### OpenADR Event Messages  
The demand response controller is used as a VEN, through DR Manager, in the OpenADR network to actively inquire about VTN event messages in PULL mode. The VEN sends an OpenADR Request with an XML message including requestID and VEN ID generated with random numbers to the VTN, as shown in Figure 8. Without a demand response event, the Response content replied by VTN contains the corresponding requestID and a responseCode: 200 for success and 4 XX for error. Figure 9 shows the ADR event information received by the developed VEN. The event started at 03:00:00 on 2 August, and the duration is 180 min (PT180M). Figures 10 and 11 show that the VEN receives different states of demand response events replied to by the VTN. The major transmitted information is the event's starting time, date-time, and duration. When the <createdDateTime> in the <eventDescriptor> is before (less than) the <dtstart> in the <eiActivePeriod>, the VEN is in a far state, as shown in Figure 10 where responseCode is 200 and <createdDateTime> is 19:15:08 on 1 August. When the <createdDateTime> is equal to the <dtstart> (in this case, it is 03:00:00, 2 August), the event is induced, and the response state changes into active (Figure 11a). When the event ran for 180 min, at 06:00:00, 2 August, the state was changed into the completed state (Figure 11b).  
Figure 8: The VEN requesting event information from the VTN.  
Figure 6: Architecture diagram of the smart socket.  
Figure 7: Architecture diagram of the infrared module.
# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 1 Introduction
In addition, MedSAM [8], a deep learning model designed specifically for medical image segmentation, is inspired by the Segment Anything Model (SAM) [9]. Unlike conventional segmentation approaches that require large volumes of labeled data, MedSAM utilizes powerful pre-trained capabilities to produce accurate segmentations with minimal annotations. Its interactive segmentation feature allows users to refine results through prompts such as points, bounding boxes, or masks, thereby enhancing workflow efficiency, especially in organ segmentation tasks like lung delineation. Furthermore, MedSAM demonstrates strong performance in challenging scenarios involving irregular organ shapes, low contrast, and blurred anatomical boundaries, making it a valuable tool for complex internal organ segmentation [10]. Our preliminary experiments, along with other related studies, evaluating the performance of nnUNet for pneumothorax segmentation on chest radiographs, indicate that nnUNet alone struggles to consistently produce accurate results, particularly in challenging cases characterized by subtle intensity variations and anatomical noise. These variations can obscure the boundaries between pneumothorax and adjacent tissues, increasing the likelihood of missegmentation. Moreover, anatomical noise, resulting from overlapping structures or tissues with similar radiographic intensities, further complicates the differentiation of pneumothorax from surrounding anatomy [11]. Consequently, there is a critical need for more robust techniques that can enhance image contrast, suppress background noise, and improve the precision of pneumothorax delineation in complex imaging environments. In summary, high-precision segmentation of pneumothorax is critical for the development of reliable automated diagnosis systems. Our proposed method introduces a novel framework, MedSAM SPCA, designed to enhance segmentation performance through the integration of anatomical priors and contrast adjustment techniques. The key contributions of our approach are as follows:  
* We propose SPCA, a method that incorporates spatial priors of the lung region, enhances contrast in chest X-ray images, and generates a probabilistic atlas from ground truth masks in the CANDID-PTX dataset. * We construct an attention map by combining the lung segmentation mask of each pneumothorax case with the corresponding probabilistic atlas. * We fuse these spatial priors into an attention-enhanced nnUNet architecture, which integrates the CBAM into the baseline U-Net. This design directs the model's focus toward the lung region, thereby improving the network's ability to identify relevant patterns and structures associated with pneumothorax in X-ray images.
# Spatiotemporal Analysis of Evapotranspiration and Effects of Water and Heat on Water Use Efficiency
## 1 Introduction
Therefore, based on previous research, this study analyzed the multi-year spatial-temporal changes of Xinjiang evapotranspiration and WUE, and it quantitatively evaluated WUE's sensitivity to temperature and precipitation, providing data support for ecological restoration and water resource utilization and management in the region. In summary, the objectives of this study are as follows: (1) monthly and annual scale changes and spatial changes of Xinjiang actual evapotranspiration and potential evapotranspiration (\(Ep\)) were analyzed; then, \(E\) and \(Ep\) trend changes can be predicted according to the Hurst index; (2) The spatiotemporal variation trend of based on different hydrological indicators of water use efficiency; (3) The response and threshold of different hydrological processes of WUE indexes to climate change were analyzed.