esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
### Explanatory Variables
#### 3.2.4 Variable Description and Classification  
In summary, this study incorporates ten dependent variables and three categories of explanatory variables. Table 1 summarizes the definitions and categories of all variables considered in the empirical analysis.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Categories** & **Variable** & **Description** \\ \hline \multirow{7}{*}{Socioeconomic and Transportation Accessibility Variables} & GDP & GDP Density \\  & Population & Population Density \\  & Housing Price & Average housing price of the community \\  & City Center & Distance from the center point to the city center \\  & Road Mileage & Total mileage of arterial, major, minor roads, and urban branch roads \\  & Bus Stops & Number of bus stops \\  & Healthcare & \(EF\) value for general hospitals, specialty hospitals, clinics, etc. \\  & Companies & \(EF\) value for enterprises, companies, factories, etc. \\  & Education & \(EF\) value for universities, high schools, libraries, etc. \\  & Shopping & \(EF\) value for shopping centers, department stores, etc. \\  & Catering & \(EF\) value for restaurants, snack bars, coffee houses, etc. \\  & Living & \(EF\) value for post offices, courier logistics, laundromats, etc. \\  & Sports & \(EF\) value for fitness centers, stadiums, swimming pools, etc. \\  & Auto Repair & \(EF\) value for car sales, maintenance, car rentals, etc. \\  & Hotels & \(EF\) value for star-rated hotels, chain hotels, aparnottels, etc. \\  & Tourism & \(EF\) value for parks, zoos, botanical gardens, museums, etc. \\  & Entertainment & \(EF\) value for cinemas, game centers, dance halls, KTV, etc. \\  & Finance & \(EF\) value for banks, ATMs, financial companies, etc. \\  & Residential & \(EF\) value for office buildings, residential areas, communities, etc. \\  & Transportation & \(EF\) value for airports, train stations, bus stops, parking lots, etc. \\  & Buildings & Proportion of buildings in streetscape images \\  & Greenery & Proportion of vegetation/green coverage in streetscape images \\  & Sky & Proportion of sky area in streetscape images \\ Streetscape Feature Variables & Roads & Proportion of visible roads in streetscape images \\  & Traffic Control & Proportion of traffic lights, traffic signs, guardrails, etc. \\  & Sidewalks & Proportion of sidewalks in streetscape images \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definitions and categories of study variables.
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 5 Simulation and Results Analysis
### Simulation Metrics
#### 5.2.1 End-to-End Delay (EED)  
It is the time elapsed since a packet is sent by a node and until the packet is received by the Sink/BS node, taking into account the latencies experienced in all its path, including the latency of the CH node [54]. It is calculated as follows:  
\[EED=T_{rec}-T_{sent}, \tag{5}\]  
where \(T_{rec}\) is the time when the Sink/BS node receives a data packet, and \(T_{sent}\) is the time when a non-CH node sends that data packet.
## 2 Theoretical Background
### Field Confinement Factor  
To describe the interaction of light with the surrounding material, the field confinement factor \(\Gamma\) is used as a figure of merit to characterize devices like slot-waveguides and their ability to confine an optical mode within a certain region [19]. The field confinement factor is defined as the ratio of the time averaged energy flow through the area of interest \(A_{\text{int}}\) to the time averaged energy flow through the total area \(A_{\text{tot}}\)[20]  
\[\Gamma=\frac{\int\int_{A_{\text{int}}}\text{Re}\left\{\left[\mathbf{E}\times \mathbf{H}^{*}\right]\cdot\mathbf{e_{z}}\right\}\ \mathrm{d}x\mathrm{d}y}{\int\int_{A_{\text{tot}}}\text{Re}\left\{\left[ \mathbf{E}\times\mathbf{H}^{*}\right]\cdot\mathbf{e_{z}}\right\}\ \mathrm{d}x\mathrm{d}y}, \tag{1}\]  
where \(\mathbf{E}\) and \(\mathbf{H}^{*}\) are the electric and magnetic field vectors, respectively, and \(\mathbf{e_{z}}\) is the unit vector in \(z\) (propagation) direction. Depending on the application, the area of interest is regarded differently. In EO modulators, it is the area of the slot region \(A_{\text{int}}=A_{\text{slot}}\), where \(\Gamma\) is most significant. Among others, the Pockels-effect enables the operation of EO modulators by inducing a refractive index change trough an applied electric field \(E\):  
\[n(E)=n_{0}-\frac{1}{2}\cdot n_{0}^{3}\cdot r_{33}\cdot E\cdot\Gamma, \tag{2}\]  
where \(n_{0}\) is the initial refractive index and \(r_{33}\) the coefficient of the electro-optical tensor in the organic material. It becomes evident, that the factor \(\Gamma\), which ranges from \(0\dots 1\), largely influences the strength of the Pockels-effect and thereby the modulation. In this work, the unperturbed refractive index \(n_{0}\) is the refractive index of the polymer (Figure 2).
# Investigation on the Mass Flow Rate of a Refrigerator Compressor Based on the p-V Diagram
## 1 Introduction  
The refrigerator compressor determines the mass flow rate of the refrigerator system, which has a significant impact on the refrigeration capacity of the refrigerator [1; 2]. Moreover, the flow rate of refrigerant is an important performance parameter that reflects the transport capacity of the compressor [3; 4].  
Since the flow rate of the reciprocating compressor is unsteady, it is very difficult to measure the instantaneous flow rate directly. The refrigerant flow rate of the compressor is usually measured by a mass flow meter in the refrigeration system or calculated by a mathematical model with many empirical coefficients. To predict the refrigerant mass flow rate in a domestic refrigerator, [PERSON] et al. [5] developed a theoretical model of adiabatic capillary tubes. [PERSON] et al. [6; 7; 8] proposed a simple technique to measure refrigerant mass flow rate by quick-closing valves and a tank. Based on the technique, the author studied the refrigerant mass charge distribution in both steady-state and transient conditions of the refrigerator.  
Researchers have analyzed the influence factors of refrigerant mass flow rate to improve the efficiency of the compressor. [PERSON] et al. [9] stated that flow rate loss was linked to the backflow rate through the suction valve. [PERSON] et al. [10] found that in refrigerator compressors, the vapor superheating in the suction process accounts for 49% of the overall mass flow rate loss. [PERSON] et al. [11] revealed that the compressor mass flow rate was strongly linked to the valve motion.  
A p-V diagram of the reciprocating compressor is traditionally used to analyze the thermodynamic process in the cylinder [12]. [PERSON] et al. [13] showed that the experimental p-V diagram of a CO\({}_{2}\) compressor and presented the flow losses related to the suction and discharge valves in the diagram. Their research clearly showed that the suction power loss was lower than the discharge flow loss since the amplitude of the pressure loss in the discharge process was much larger than that in the suction process.  
Based on the literature mentioned above, few studies have focused on the direct measurement for the flow rate of the refrigerator compressor, which is helpful to reveal the law of mass flow rate variation and to optimize the compressor. In this paper, a refrigerator compressor is modified to facilitate the installation of sensors to record the pressure variation in the compressor cylinder. Based on the measurements and investigation of the expansion and compression phases of the refrigerator compressor, a calculation method for refrigerant mass flow rate is presented. Furthermore, the calculation method is verified by experiments and used to study the performance of compressor.
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## Principles of Coil Guns  
### Physical Concept  
Magnetic field in a looped circuit composed of magnetic material and air gap tends to be maximized when a current is applied. [PERSON] law used in magnetic circuits tells that: \(NI=R\Phi\), with:  
* \(I\): current in the coil (A)
* \(N\): number of turns of the coil
* \(\Phi\): flux (Wb)
* \(R\): reluctance (H\({}^{-1}\))  
Increasing the magnetic field is similar to an increase of \(\Phi\). For a given current and number of coil turns, this increase can be done by reducing the reluctance \(R\) of the magnetic circuit. Its expression is \(R=\frac{1}{\mu_{0}h_{r}}\frac{l}{S}\), with:* \(\mu_{0}=4\pi 10^{-7}\)\(H\cdot m^{-1}\): permeability of vacuum
* \(\mu_{r}\): relative magnetic permeability
* \(S\): cross-sectional area of the circuit (m\({}^{2}\))
* \(l\): length of the magnetic circuit (m)  
The way to reduce reluctance is to minimize the air gap in the magnetic circuit, replacing it by a portion of the iron plunger (having a relative magnetic permeability equal to 5000 or more). Thus, under a strong coil current, the plunger will be propelled in order to reduce the air gap, with an important force dependent on the coil current and the number of coil turns. Figure 8 shows a three-stage coil gun. In this case, coil 1 is powered first, then coil 2, then coil 3. This is the principle of a multi-stage variable reluctance actuator.  
Our case study focuses on coil gun implementations having one, two, three or four coils, with a fixed overall size and quantity of copper, as shown in Figure 9. It is important to note that between each coil, an iron plate has been placed in order to close the magnetic circuit around each coil.  
Figure 8: Three Coils Electromagnetic Launcher Principle [14].  
Figure 9: Coil gun configurations with 1, 2, 3 and 4 coils sharing the same quantity of copper.  
In our case study, each coil is powered by an identical capacitor. These capacitors have an global overall capacitance equal to (4700 \(\mu\)F). This capacitance is split into \(n\) smaller equal ones, where \(n\) is the number of coils. Each capacitor can be discharged, one at a time, in its corresponding coil producing a strong current which generates a magnetic force. The iron rod, mobile part of the magnetic circuit slides in a stainless steel tube in order to reduce the air gap of the magnetic circuit. This iron rod is attracted and accelerated as long as the air gap can be minimized. It is slowed down if the plunger goes to far and the air gap increases again. To avoid that, the duration of the current pulse in each coil has to be limited in time.  
Discharge from the capacitor to the coil inductor can be described by a second order \(RLC\) differential equation. This equation has non-constant coefficients because the value of the inductor highly depends on the value of the current in the coil and on the plunger position in the sliding tube. This mixed non-linear model combining electrical and mechanical inputs will be presented in the following sections.
## 5 Conclusions  
This study addressed the necessity for high accuracy and sensitivity in the measurement of inter-satellite long-range beams. A theoretical model of heterodyne coherent angle measurement was established, and the principles of spatial coherent mixing and DWS angular phase conversion were analyzed. Based on the Monte Carlo method, a simulation system of heterodyne coherent angle measurement was constructed, and a phase-locked loop was combined to measure the given angular deflection analog quantity with high accuracy. An experimental system was further constructed to test the accuracy and detection sensitivity of the heterodyne coherent angle measurement and compare them with direct detection. When the local oscillation laser beam power was \(-7\) dBm, the loop bandwidth was \(10\) Hz, and the signal laser beam power as \(-16\) dBm. The accuracy of the heterodyne coherent angle measurement was \(2.54\) urad and \(2.85\) urad, which reached the urad order of magnitude and was improved by about \(5\) times compared with direct detection. The detection sensitivity of the heterodyne coherent method was \(-50\) dBm, which was in the order of nW, using \(20\) urad as the angle measurement accuracy threshold. This enhancement over the direct detection methods exhibited an impressive magnitude of \(1000\)-fold improvement. In comparison with studies by other scholars, this study analyzed and compared the performance of heterodyne coherent and direct detection angle measurement techniques in terms of accuracy and sensitivity. In terms of system performance, the heterodyne coherent angle measurement system constructed in this study showed a certain improvement compared with the coherent off-target detection system described in the literature [17], and the angle measurement accuracy improved from \(5\) urad in the literature to \(2.85\) urad at a high optical power level. This research provides support for long-distance beam measurements between satellites and has reference significance in the measurement of gravitational waves and the design of laser communication terminals between satellites.  
Conceptualization, Z.Z. and X.Y.; Data curation, Z.Z.; Formal analysis, Z.Z.; Funding acquisition, X.Y.; Investigation, Z.Z. and D.Z.; Methodology, Z.Z., P.L., and X.Y.; Project administration, X.Y.; Resources, P.L. and X.Y.; Software, Z.Z.; Supervision, S.T. and X.Y.; Validation, Z.Z.; Visualization, Z.Z. and D.Z.; Writing--original draft, Z.Z.; Writing--review and editing, S.T. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2022 YFB2903402).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Conclusions
Beyond addressing the current gaps, the review identified a set of precise research questions and unresolved engineering problems that can guide further investigations:  
* What storage combinations deliver the best trade-off between short-term response time and long-duration capacity under constrained capital budgets? * How can distributed control agents resolve local conflicts without requiring central coordination or frequent communication in resource-limited networks? * Which methods allow real-time AI models to provide interpretable feedback for operational decisions in dynamic grid conditions? * What is the minimum communication infrastructure required to maintain secure system operation when integrating blockchain or AI-based monitoring? * How can financial planning tools incorporate risk-adjusted cost models that respond to regulatory change and resilience expectations? While the reviewed studies partially address the research questions, they also reveal several technical and methodological limitations. Closing these gaps will require targeted experimentation, model validation in operational settings, and interdisciplinary collaboration across control engineering, computer science, and regulatory planning. Conceptualization, P.A. and D.O.-C.; methodology, P.A. and D.O.-C.; writing--original draft preparation, P.A., D.B. and D.O.-C.; writing--review and editing, A.R., D.T. and C.W.V.-M.; supervision, D.B., A.R., C.W.V.-M. and D.O.-C.; project administration, D.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors thank the Direccion de Investigacion y Desarrollo (DIDE) of the Universidad Tecnica de Ambato, Ecuador, for supporting this work through the research project PFISEI36, titled \"Development of Computational Tools for the Management and Optimization of Smart Microgrids.\" The authors also thank the Universidad de Cuenca, Ecuador, for providing access to the facilities of the Microgrid Laboratory of the Faculty of Engineering, allowing the use of its equipment, and authorizing technical staff to support the experiments described in this article. Finally, the results presented in this research document the partial findings of the project titled \"Implicaciones energeticas de la transformationen urbana en ciudades intermedias: Caso de estudio Cuenca-Ecuador,\" winner of the Convocatoria Fondo I+D+i XIX, Project Code IDI No. 007, funded by the Corporacion Ecuatoriana para el Desarrollo de la Investigacion y la Academia (CEDIA) and co-financed by the Vicerrectorado de Investigacion e Innovacion of the Universidad de Cuenca, Ecuador. The authors declare no conflicts of interest.
## 4 Discussion  
In this study, an innovative multi-stage overlapping-fog-droplet segmentation method is proposed, which significantly improves the segmentation accuracy by fusing an optimised XGBoost classification model with an improved concave-point-matching strategy. Specifically, for different types of overlapping droplets, the method in this paper adopts a customised segmentation technique, which effectively solves the problem of the lack of accuracy faced by existing segmentation methods when dealing with complex overlapping droplets. Experimental results show that the method in this paper performs well in terms of the accurate segmentation of overlapping fog droplet images. Compared with the advanced watershed algorithm, the method in this paper not only significantly improves the segmentation accuracy in handling overlapping or large overlapping droplets, but also avoids the destruction of critical detail information in the image by morphological operations (e.g., dilation and erosion) by optimising the feature classification and enhancing the concave-point-matching strategy. This innovative design maximises the preservation of image integrity while significantly improving the prevalence of unseparation in existing concave-point-segmentation algorithms when dealing with single concave points of strongly overlapping fog droplets. In addition, the method in this paper further improves the robustness and adaptability of segmentation through a multi-stage segmentation process, which provides reliable technical support for high-precision segmentation of complex overlapping fog droplets. The results validate the comprehensive advantages of this paper's method in preserving image details, improving segmentation accuracy, and optimising processing efficiency. However, the segmentation method proposed in this paper still has certain limitations. (1) For the overlapping droplet without concave features, the segmentation and extraction accuracy of this method still needs to be improved. (2) When dealing with extreme cases such as a small number of unconventional shaped droplets, due to the complexity of their boundaries and contours, this method is prone to false concave detection, which may lead to incorrect segmentation and affect the overall effect. (3) This method is optimised in an indoor experimental environment (uniform lighting and simple background), so its segmentation effect may be affected under uncontrolled conditions.
## 3 Results
### Effect of the Time Sampling Interval on the Angular Combination
Due to its special position of the Sun-Earth L1 point, the solar direction and viewing direction of the observed point, however, almost coincide. Consequently, the ACNI maximum is 67, and the ACNI minimum is 9. For a Moon-based platform, its maximum ACNI is 9063, which accounts for 77.7%. The minimum ACNI is 596, accounting for 5.11%. The angular sampling results benefit from its variable orbital inclination and altitude. In this sense, lunar observations can provide unique and continuous angular sampling coincident with other satellite observations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Statistic** & **GEO Satellite** & **L1 Satellite** & **Moon-Based Platform** \\ \hline \multirow{3}{*}{ACNI} & Maximum & 372 & 67 & 9063 \\  & Minimum & 0 & 9 & 596 \\ \cline{1-1}  & Maximum & 3.19\% & 0.57\% & 77.70\% \\ \cline{1-1}  & Minimum & 0.00\% & 0.00\% & 5.11\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison table of the angular characteristics of each observation platform. Figure 9: Variation in maximum and minimum differences of the ANCI ratio with the time sampling interval difference. In general, the ACNI can be used to effectively characterize variations in the angular combination. Through experiments, we can determine the magnitude of the difference of the angular combination and the extent of the impact of the time sampling interval with respect to different observed points. We found that the two highest ACNI lines correspond to two latitude lines, which indicate the positions of the observed points with the most abundant angular combination. The angular coverage ratio of observed points at the equator is 15% lower than the case of these two latitudes lines. The time sampling interval has different effects on different observed points, with a greater effect on the observed points in the mid-low latitude zone. When the time sampling interval increases from 10 min to 2 h, the maximum loss of angular combinations reaches 50% for the observed points in the mid-low latitude zone, whereas the minimum loss in the high latitude zone is less than 0.02%. These results provide guidance for selecting time sampling intervals for Moon-based Earth observations with respect to the angular combination performance.
## 5 Laboratory Validation  
Nonlinearity is considered in the experimental studies and validation. A 7-story plan frame considering geometric nonlinearity was studied with rigid boundary condition and the impact force on the top story identified. The vibration of a planar flexible seven-story steel frame was investigated considering the geometric nonlinearity. This frame was fabricated in the laboratory of the Hong Kong Polytechnic University [11] and the impact force identified based on the method proposed in Section 3. The height of each story is 300 mm and the length of the beam on each story is 500 mm. In this study, the displacement-based fiber element was used to model the beam and column components while geometric nonlinearity was factored in due to flexibility of the structure. During the test, the frame retained elastic performance because the impact force was not that large. Two lumped masses were placed on each story of the frame on the 1/4 length and 3/4 length of the beam to simulate the mass effect of the floor slab, and the weight of each lumped mass was around 3.95 kg. The ends of the columns in the first story were welded to a thick steel base plate which was connected rigidly to the ground. The planar frame was made of Q235 steel. The areas of the beam and column section are 49.98 mm \(\times\) 8.92 mm and 49.89 mm \(\times\) 4.85 mm, respectively. The primary parameters of the model consist of modulus of elasticity (E = 206 GPa), Poisson ratio (\(\upsilon\) = 0.31), initial yield strength (\(f_{y}\) = 235 MPa), post yield stiffness ratio (\(b\) = 0.02), and density (\(\rho\) = 7.85 g/cm\({}^{3}\)). All the parameters above are taken as known parameters in the force identification process and were obtained with material test data.  
Figure 7: Identification of multi-frequency external force considering geometric nonlinearity. **(a)** Force identification result without measurement noise. **(b)** Force identification result with 5% measurement noise.