esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Temperature Characteristics of Lubrication Film of Valve Plate Pair in Axial Piston Pumps
## 2 Modeling
### Modeling of Film Temperature
Figure 7: Schematic diagram of viscous friction between two parallel panels. where \(A_{\rm cli}\) is the heat transfer area between the cylinder block and the oil film, \(k_{2}\) is the thermal conductivity of the cylinder block material, and \(L_{1}\) is the thickness of the cylinder block. The heat interaction rate \(Q_{\rm oil}\) between the oil film and the inlet oil after cooling is as follows:  
\[Q_{\rm oil}=k_{3}2\pi rh(T_{1}-T_{0}) \tag{21}\]  
The heat flow diagram of the oil film temperature of the valve plate pair is shown in Figure 5:0 The oil film temperature considering the above factors can be expressed as follows:  
Figure 5:1  
where \(k_{3}\) is the thermal conductivity of the hydraulic oil. Considering the influence of oil temperature on oil viscosity, the viscosity-temperature characteristic model is as follows:  
Figure 5:2  
where \(T_{\rm vis}\) is the hydraulic oil temperature. Due to a temperature shift in the circumferential direction of the valve plate, heat conduction occurs from the high-temperature area to the low-temperature area in the interior of the valve plate. The change term of the temperature inside the valve plate with time is introduced and expressed in the Cartesian coordinate system:  
Figure 5:3  
where \(\rho_{\rm vlv}\) is the density of the plate, \(c_{\rm p}\) is the specific heat capacity, \(\lambda_{\rm vlv}\) is the heat transfer coefficient of the plate, \(T\) is the temperature, \(q\) is the heat source term, and \(t\) is the time. The heat conduction equation in cylindrical coordinates is as follows:  
Figure 5:4 \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \hline
**Viscosity** & & & & & & & & & & \\
**(10\({}^{-2}\) Pa-s)** & 11.19 & 6.19 & 3.77 & 2.49 & 1.75 & 1.29 & 1.00 & 0.81 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relationship between viscosity and temperature. Figure 9: Distribution of oil film heat flow diagram. Using the finite difference method to solve the heat conduction Equation (25), the heat conduction equation after discretization is as follows:  
\[ \begin{split}& T_{i,j,k}^{p+1}=T_{i,j,k}^{p}-(2A_{1}+B_{1}+2C_{1}+2D_ {1})T_{i,j,k}^{p}+(A_{1}-B_{1})T_{i+1,j,k}^{p}+A_{1}T_{i-1,j,k}^{p}\\ &+C_{1}(T_{i,j-1,k}^{p}+T_{i,j+1,k}^{p})+D_{1}(T_{i,j,k+1}^{p}+T_ {i,j,k-1}^{p})+E_{1}\end{split}  \tag{26}\]  
where \(A_{1}\), \(B_{1}\), \(C_{1}\) and \(D_{1}\) are, respectively, represented as follows:  
\[A_{1}=\frac{\lambda_{\text{vlv}}\Delta t}{\rho_{\text{vlv}}c_{p}\Delta r^{2}} \tag{27}\]  
\[B_{1}=\frac{\lambda_{\text{vlv}}\Delta t}{\rho_{\text{vlv}}c_{p}r\Delta r} \tag{28}\]  
\[C_{1}=\frac{\lambda_{\text{vlv}}\Delta t}{\rho_{\text{vlv}}c_{p}r^{2}\Delta \theta^{2}} \tag{29}\]  
\[D_{1}=\frac{\lambda_{\text{vlv}}\Delta t}{\rho_{\text{vlv}}c_{p}\Delta z^{2}} \tag{30}\]  
\[E_{1}=\frac{q\Delta t}{\rho_{\text{vlv}}c_{p}} \tag{31}\]
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 5 Implementation Issue
### Power Allocation  
The achievable throughput is affected by the transmit power allocation. If it is assumed that two symbols are superposed in one signal, we should consider how much power should be allocated for each symbol. Basically, allocating more power to strong symbols can reduce the error propagation. However, allocating more power to strong symbols increases the error probability of weak symbols since the power of a weak symbol is too low. As a result, power allocation to each symbol should be considered according to the number of the symbols at the superposed signal subject to total power constraint.
## 2 Materials and Methods
### Datasets and Preprocessing
#### 2.2.3 Land Cover Data  
The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid product (MCD12C1) of IGBP (International Geosphere-Biosphere Programme) classification ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)) (accessed on 3 May 2022)) for 2001-2020, with a spatial resolution of 0.05\({}^{\circ}\), was used. Moreover, the land cover data were reclassified into seven classes (evergreen forests, deciduous forests, mixed forests, woody savannas, savannas, grasslands, and croplands). In addition, the vegetation cover data and GOSIF-GPP data were resampled to a spatial resolution of 0.1\({}^{\circ}\) using a nearest neighbor interpolation method.
# Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains
## 5 Conclusions  
In this paper, we focused on intermediate domains to solve the problems of noisy labels in other UDA methods for ReID. We first added a domainness factor \(\lambda\) in the loss function of SPGAN that can control the process of image translation. A high \(\lambda\) can generate images close to the target domain. The target domain of UDA is the source domain of GAN. Images generated by GAN will be regarded as intermediate domains. Pseudo labels produced by clustering algorithms present a high reliability given that intermediate domains own part of the style of the source domain.  
We then conducted a clustering-based UDA baseline along a bridge consisting of a series of intermediate domains. Strategies that included mean teacher, joint fine-tuning with source data and memory bank were utilized in the baseline. The model pre-trained on the source domain was fine-tuned on intermediate domains firstly. The UDA process was conducted repeatedly given that intermediate domains contain several datasets. We adapted the model to the target domain after fine-tuning on the last intermediate domain.  
Moreover, the proposed method can be easily applied to any other SOTA methods to further improve the performance of these algorithms. The results of this study can provide a reference for both the academia and industry.  
Conceptualization and methodology, H.X. and H.L.; software, validation, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, visualization and project administration, H.X.; formal analysis and supervision, H.X. and J.G.; funding acquisition, W.J. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Duke\(\rightarrow\)Market**} & \multicolumn{2}{c}{**Market\(\rightarrow\)Duke**} \\ \cline{2-5}  & **mAP** & **Rank-1** & **mAP** & **Rank-1** \\ \hline cycleGAN & 77.8 & 90.4 & 68.2 & 81.5 \\ SPGAN & **80.2** & **92.4** & **69.1** & **82.3** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The comparison of UDA performance between cycleGAN and SPGAN.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 5 Data Transferring in LS-WSNs
### Data-Collection Steps in LS-WSNs  
The flowchart given in Figure 18 summarizes the major steps of data collection in LS-WSNs. According to application areas of sensor networks, the issue of deploying large-scale sensor networks for coverage purposes can be categorized into area-based coverage deployment that requires sensors to be deployed in the whole field, and location-based coverage deployment that requires sensors to be deployed in specific locations [111]. The diffusion of control messages is commonly done by flooding (and controlled-flooding) methods and gossiping methods [36]. Finally, the data-transferring step relies on knowledge recorded by the control message dissemination step to achieve data collection by adopting either flat-routing, hierarchical-routing, or location-based-routing schemes.
## 3 Simulation Framework
### Messages  
The configuration and data flow between modules are implemented using Messages. Although the presented framework architecture is flexible and enables the definition of different message types, for the evaluation purpose, three message types are defined: Data, Frame and Control.  
Data Messages are generated by the HUT and are used to transfer information to and from the T/R module. These messages are transmitted over the UDP protocol in a binary format, as illustrated in Figure 2. The message format consists of two main parts: a header and a payload. The header includes five 4-byte fields. The first field, Req/Res, indicates whether the message is sent from the HUT to the Channel module (uplink) or a response sent by the Channel module to the HUT module (downlink). Each Data Message has a unique Data ID, which helps packet loss detection, since this mechanism is not provided by  
Figure 1: Architecture overview of distributed simulation framework.  
the UDP protocol. Additionally, the Slot ID field identifies the timeslot to which the Data Message belongs. The HUT module makes predictions, and the timeslot represents a time period for which one prediction is considered valid. The Metadata field is a bit-accessible field that contains other miscellaneous parameters and control signals needed for data processing across the modules. The MODCOD (modulation and coding) field contains an index representing the modulation and code rate used for the current transmission. The payload section consists of \(n\) bits of user data.  
Frame Messages are generated within the T/R module, where they encapsulate the content of Data Messages in the appropriate terrestrial or satellite physical layer format. Once created, these messages are transmitted from the T/R module to the Channel module via a UDP link, where they undergo further processing within the corresponding channel model.  
In addition to the two messages that are in raw binary format, Control Messages are implemented using ASCII string format. These messages are generated on the HUT side and transmitted to the device via the TCP protocol. For every Control Message received and processed by the device, a corresponding response is generated to indicate the execution status. For example, the device supports the \"hello\" control message, which serves to verify the device's presence at a specific IP address. The command for this check is \"device hello\", and if the device software is running on the platform, the expected response is \"OK\".
## 2 Methods
### Establishment of the FE Model  
The deformation of in vivo human skin is primarily influenced by muscle movement, contraction, and relaxation. This section focuses on simulating the mechanical response of skin layers under muscle-generated compression using Abaqus software (ver. 6.14, Dassault Systemes, Paris, French). A two-dimensional (2D) finite element (FE) model is developed, incorporating both skin and muscle layers. Firstly, the skin structure with three distinct layers consisting of the epidermis, dermis, and hypodermis is established, as illustrated in Figure 2. The model spans a length of \(L_{\mathrm{x}}=10\) mm. Secondly, the mechanical behavior of each layer is endowed with material properties of a linear elastic model to ensure computational efficiency. The thicknesses and mechanical properties of each layer in the human skin and muscle models are summarized in Table 1[33]. Thirdly, skin and muscle structure are discretized using four-node reduced integration elements (CPS4R). A mesh convergence study is conducted to verify that the grid resolution meets the convergence requirements. Each layer is then tied to the next layer through well-defined interfaces. Finally, critical buckling modes are determined via eigenvalue analysis of the Buckle step, followed by wrinkle morphology evolution tracking through the Static General step. The eigensolver employing the subspace iteration method is configured to compute 8 eigenvalues.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Young’s Modulus (MPa)** & **Poisson’s Ratio** & **Thickness (mm)** \\ \hline Epidermis & \(100\times 10^{-3}\) & 0.485 & 0.1 \\ Dermis & \(5\times 10^{-3}\) & 0.495 & 1.0 \\ Hypodermis & \(3\times 10^{-3}\) & 0.495 & 0.2 \\ Muscle & \(800\times 10^{-3}\) & 0.48 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters and thicknesses for each layer of soft tissue.  
Figure 2: A FE model of soft tissue that includes skin and muscle layers. The bottom of the muscle is fixed in the y-direction displacement and constrained against rotation about the \(z\)-axis. Symmetry constraints are applied to the right sides of both the skin and muscle. Muscle contraction is simulated by applying a horizontal displacement load to the left side of the muscle.
## 8 Conclusions  
Full encryption of video streams with real-time requirements was conducted for a remote-control marine-based application with live video feedback to a human pilot. Network and video latencies were measured for different video encoding algorithms and encryption string size settings. In terms of video stream, H.264 and MJPEG encoding algorithms were tested, while the video stream was encrypted with string sizes of 128, 256 and 512 bytes.  
In order to perform full encryption of the video in real-time, two FPGA-based devices were added to the system. Network traffic (containing a video stream) was routed through these nodes and encrypted/decrypted. It is visible from the presented results that enabling encryption using the BITW technique with AES counter mode (AES-CTR) does not greatly influence performance of the video stream in terms of latency. Although in most tests the latencies were marginally higher with encryption, there were a few cases where it was not the case, which indicates that there are other factors (related to the camera and its output stream) which potentially have greater influence on the latency than the encryption itself. The benefits gained by having an encrypted video stream are numerous (privacy and data protection) and outweigh the costs (latency) of the encryption itself.  
To avoid or reduce this latency, one of the targets for future work is to integrate the security directly into the camera (i.e., at the video source) and also into the equipment on the receiving end of the stream. However, this approach nullifies the major advantage of realising provision of end-to-end security using a bump-in-the-wire technique where the BITW device can be retrofitted to legacy infrastructure that does not have security capabilities. This BITW retrofitting provides a cost-effective solution for security upgrading of expensive equipment.  
Conceptualization, D.T. and T.N.; funding acquisition, D.T.; investigation, A.K. and M.R.; methodology, A.K. and M.R.; project administration, D.T. and T.N.; resources, A.K. and M.R.; software, A.K. and M.R.; supervision, D.T. and E.O.; writing--original draft, A.K.; writing--review and editing, D.T., T.N. and M.R.  
This work was supported, in part, by Science Foundation Ireland grant 13/RC/2094 and co-funded under the European Regional Development Fund through the Southern and Eastern Regional Operational Programme to Lero--the Irish Software Research Centre (www.lero.ie) and also co-funded by IRC--Irish Research Council grant EPSPG/2011/257.  
The authors declare no conflict of interest.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 8 Limitation, Challenges, and Opportunities of Existing Solutions
### Practical Challenges in Applying ML for Building Energy Efficiency
These issues have different root causes and different effects on how ML is applied to improve building energy efficiency. Strategies like data collecting, preprocessing, model regularization, explainable ML approaches, distributed computing, system integration techniques, resource optimization, and privacy-preserving techniques are possible responses to these problems. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Challenge/Opportunity**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Impact on Energy Prediction and Management**} & \multicolumn{1}{c}{**Proposed Solutions/Directions for Future Research**} \\ \hline \multirow{4}{*}{Data Quality and Availability} & Inconsistent or incomplete & & \\  & data can limit the & & consumption forecasts and \\  & effectiveness of ML models. & optimization strategies. & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Integration with \\ Existing Systems \\ \end{tabular} } & Challenges in integrating ML & Can hinder the deployment of & \begin{tabular}{c} Create adaptable ML models \\ and middleware solutions \\ that can interface with various \\ systems and protocols. \\ \end{tabular} \\ \hline \multirow{4}{*}{Scalability} & The need for ML models that & \begin{tabular}{c} Affects the ability to maintain \\ efficiency as buildings and \\ systems evolve. \\ \end{tabular} & \begin{tabular}{c} Focus on scalable algorithms \\ and cloud-based solutions \\ that can grow with \\ system demands. \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} Energy Efficiency vs. \\ User Comfort \\ \end{tabular} } & Balancing energy savings with & \begin{tabular}{c} Optimal energy management \\ strategies may conflict with \\ user comfort preferences. \\ \end{tabular} & \begin{tabular}{c} Investigate multi-objective \\ optimization techniques; \\ incorporate user feedback and \\ adaptive learning algorithms. \\ \end{tabular} \\ \hline \multirow{4}{*}{Data Quality and Availability} & Inconsistent or incomplete & \
\begin{tabular}{c} Affects the accuracy of energy \\ data can limit the \\ effectiveness of ML models. \\ \end{tabular} &
\begin{tabular}{c} Develop robust data collection \\ and preprocessing methods; \\ exploring techniques to handle \\ missing or noisy data. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Challenges and opportunities in ML for smart buildings.
## 7 Near Real Time Weather Prediction Applications, User Connectivity Initiatives
### PGRR and NRT Applications and User Connectivity Initiatives  
The PGRR science program initiated at the beginning of the JPSS program has been providing vital program functions on (i) addressing data needs across NOAA line offices (e.g., NWS, NMFS, OAR, etc.) and other agencies, (ii) identification of requirements and prioritization via the LORWG, (iii) fostering algorithm development for high-quality data products, Cal/Val, publications, reprocessing and enterprise solutions, and (iv) overseeing deliveries of software packages to operations and CSPP DB services. A wide number of PGI and near-real-time applications are ongoing in Atmospheric Chemistry, River Ice and Flooding, Fire and Smoke, Soundings, Data Assimilation, Imagery/Now casting, Ocean/Coastal applications, Hydrology, Arctic, and Land data assimilations, and details on these activities are published through JPSS Science Seminar Annual Digest publications [113].
## 3 Results and Discussion  
### Airflow Speed Drop Along the Spouts  
The airflow speeds measured along the longitudinal axle of cannon and hand spouts are graphed separately in Figure 4. Specifically, Figure 4a shows an important decrease in the air speed from the inner to the outermost position for the cannon-type nozzle (about 55% in every case). Nevertheless, this decrease was not constant along the axle, registering a slight slope between 5 and 10 cm from the inner position and very important slopes from 16 cm on (Figure 4a). Thus, the speed decreased at a variable rate ranging from 0 to 3.67 m s\({}^{-1}\) cm\({}^{-1}\). The effect of the nozzle border on the speed decrease was noticeable: the air speed decrease was less extreme near of the spout border, becoming more important in the 12 cm after the first 6 cm interval. This effect could be due to a border effect of air enclosure before spreading out of the main current direction in the following sections. Concurrently, Figure 4b shows the response of the air speed in the hand-type nozzle. The two first sections, between the OP and the AP, kept the air speed relatively constant, with less than 10% decrease in total in the most extreme case (677 rev min\({}^{-1}\) of the test bench, Figure 4b). After the air spout border, there was an abrupt decrease in the air speed, with a maximum mean value of 7.56 m s\({}^{-1}\) cm\({}^{-1}\) in the case of the maximum test bench fan rotary speed.  
Figure 4: Airflow speed (m s\({}^{-1}\)) measured along the longitudinal axle of the spout at different distances (cm) from the conventional position of liquid hose (CP) at four test bench fan rotary speeds: (**a**) cannon-type and (**b**) hand-type pneumatic nozzles. Error bars show the \(\pm\) Standard Error of the Mean.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Morphology and Size of MXene Flakes
#### 3.1.1 Size of MXene Flakes  
The size of MXene flakes also significantly influences the sticking behavior. Smaller flakes have a higher specific surface area compared to larger flakes, promoting stronger inter-particle interactions. As a result, smaller flakes have a tendency to clump together more easily, causing sticking. On the other hand, larger flakes, with a lower surface area/volume ratio, are less likely to stick to each other, reducing the tendency for adhesion between flakes and decreasing sticking.  
So far, it has been found that the average particle sizes and conductivity of the sonication-derived layered MXene nanosheets decreased gradually from approximately 600 to 300 nm and from 667 to 2500 S cm\({}^{-1}\), respectively, as the sonication time increased. In contrast, the MXene nanosheets fabricated using the power-focused delamination (PFD) technique showed a much larger average diameter of approximately 4900 nm and an electrical conductivity of 8260 \(\pm\) 130 S cm\({}^{-1}\) (film thickness: 11 \(\upmu\)m), as can be seen on Figure 2[44]. When comparing the electrical conductivity of the MXene materials with that of reported pure MXene films, it becomes apparent that the electrical conductivity of the PFD-synthesized MXene nanosheets was higher than that of the sonication-synthesized MXene nanosheets, highlighting the superiority of the PFD strategy. The LPFD-Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) MXene solution with large flakes (L) showed a 6.1 times higher proportion of the nanosheets compared to the small flakes (S) S-Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\), demonstrating the PFD strategy's capability to produce exfoliated MXenes with high yields and larger sheet sizes. Furthermore, the surface area of LPFD-Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) (12.5 m\({}^{2}\)g\({}^{-1}\)) was slightly higher than that of S-Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) (10.8 m\({}^{2}\)g\({}^{-1}\)), potentially attributed to the compact stacking of MXene materials with smaller lateral size during the sonication process.
## 2 System Design
### Base Station Module  
The base station module is the core of the RWMS. Comprised of the hardware and the software, it integrates the RTU (Remote Terminal Unit), MCU, and GPRS/GSM functions of the unit.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 4 Challenges in Indoor Localization Systems
### Power Consumption and Battery Life  
Battery life is a critical concern for wearable and mobile localization devices. BLE and ZigBee are favored for their low power consumption, making them suitable for long-term tracking in healthcare and industrial settings [62; 65]. However, more precise technologies like UWB require higher power, limiting their use in battery-operated devices unless optimized power management techniques are employed [26]. In military operations, where devices need to function for extended periods without recharging, balancing power efficiency with accuracy is essential [36].