esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Experimental Parameter Settings
#### 4.2.5 Parameter Settings for E3W  
The parameters of the E3W model were the same as those described above, with the weighting parameters set to 1 for X and 2 for Y.  
We obtained the best values for the weighted parameters X and Y in the GreedySoup Fine-tune stage. As E3W consists of a combination of four sub-models, parameters 1-4 were chosen for the experiments. When the output of a model resulted in the best classification area for that model, the value was assigned to Y, or was assigned to X otherwise.  
For example, as the best classification area of Word2 Vec + TextCNN is Class 4, when the output of Word2 Vec + TextCNN is Class 4, the weight of this output is Y; otherwise, it is X.  
From the experiments, when Y was greater than or equal to X, the results were the best. However, in theory, Y greater than X has the best effect, because there are four sub-models in the E3W, and when two sub-models incorrectly choose a category, the weighting ensures that there is still a chance of obtaining the correct result if the other two sub-models choose the correct category. Additionally, X equal to 1 and Y equal to 2 is also in the range where Y is greater than or equal to X. Therefore, the final parameters used in this paper were X = 1, Y = 2. The experimental results are detailed in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Category** & **Translation of the Description** \\ Animal husbandry & Stabilizing poultry numbers Haikou City promotes livestock Transformation \\  & Bavaria adds new standardized farms for livestock development \\ \hline Side business & Fur Deep Processing Expansion Project of Changli County Fur Co. \\  & Feed processing plant external idle full set of feed processing equipment scrap \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._In addition, in GreedySoup Fine-tune, there may be a situation where multiple categories are weighted equally, making it impossible to select the best output. In this case, two solutions are provided in this paper:  
1. The smaller category label was selected as a result (if class 0 and class 4 had the same weight, then class 0 was selected as the final result). This operation achieved good results in the experiment. This is mainly because E3W has a lower classification accuracy for class 4, while classes 0, 1, 2, and 3 have more text, more obvious features, and a higher probability of correct classification.
2. When the final weights of two categories were the same, if one of the outputs came from the best classification area of the model, then the weights of the other three sub-models were ignored. This operation ensured that the accuracy of E3W was not lower than that of either sub-model. The above two solutions significantly improved the classification accuracy on the data set, where the improvement with both approaches was almost identical.
## 2 Finite Element Model  
### Experimental Overview  
A quasi-static test on the seismic behavior of two SC composite cantilever beams subjected to out-of-plane cyclic loading was carried out by the authors to verify a developed finite element model of the SC composite beam [20]. As shown in Figures 1 and 2, the section of the SC composite beam is 630 mm \(\times\) 460 mm, the length is 3 m, and the shear span versus depth ratio is 6.67. The space of tie-bars is 210 mm at the longitudinal and transversal directions, and the number of tie-bars is 42. The spaces of studs at the longitudinal and transversal directions are 105 mm and 210 mm, respectively. The number of studs in SCB2 is 112. The materials in two SC composite beams are shown in Table 1. Groove fillet welding was used to connect shear connectors and steel plate. The test setup is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Specimens** & \(f_{cu}\) & \(t_{s}\) & \(f_{y,s}\) & \(f_{u,s}\) & \(d_{stud}\) & \(f_{u,stud}\) & \(d_{tie}\) & \(f_{y,tie}\) & \(f_{u,tie}\) \\  & **(MPa)** & **(mm)** & **(MPa)** & **(MPa)** & **(mm)** & **(MPa)** & **(mm)** & **(MPa)** & **(MPa)** \\ \hline SCB1 & 56.14 & 9.6 & 347.6 & 475.7 & - & - & 10.0 & 371.4 & 540.0 \\ SCB2 & 50.73 & 9.6 & 347.6 & 475.7 & 10 & 523 & 9.2 & 532.0 & 763.0 \\ \hline \hline \end{tabular} Note: \(f_{cu}\) is the concrete cube compressive strength, \(t_{s}\) is the thickness of steel plate, \(f_{yz,s}\) is the yield tensile strength of steel plate, \(f_{u,s}\) is the ultimate tensile strength of steel plate, \(d_{stat}\) is the diameter of studs, \(f_{u,stat}\) is the ultimate tensile strength of studs, \(d_{ijk}\) is the diameter of tie-bars, \(f_{y,iti}\) is the yield tensile strength of tie-bar, and \(f_{u,tie}\) is the ultimate tensile strength of tie-bar.  
\end{table}
Table 1: Properties of materials.  
Figure 1: Specimen details: (a) SCB1; (b) SCB2.
## 4 Results and Analysis of the Flight Test
### Flight Test Process
During the flight, the aircraft tilts to a certain extent in the pitch and roll directions, with a tilt angle of no more than 10\({}^{\circ}\), to test the ability of the stabilization platform to maintain vertical shooting of the ground during maneuvering. During the testing process, the airborne communication system located in Chongqing sent the ground data of Nanpingba Island captured by the industrial camera loaded on the image stabilization platform to the processing center near the Beijing ground station via satellite. The processing center stored the received images and performed image stitching and other tasks.
## 3 Aeroacoustic Model
### Propagation Model
#### 3.2.2 Scattering Model  
The scattering problem through the BIE (14) is solved by assuming the coefficient C = 1/2, and solving the problem on the surface body, \(S\). According to the collocation approach, the Equation (14) reads  
\[\begin{split}\frac{1}{2}h(x_{s},x_{q})=h_{inc}(x_{s},x_{q})+\oint_{ S}B_{1}(x_{s},x_{s})h(x_{s},x_{q})dS(x_{s})\\ +\oint_{S}B_{2}(x_{s},x_{s})\frac{\partial h}{\partial n}(x_{s},x _{q})dS(x_{s})\end{split} \tag{16}\]  
where the unknowns terms are the function \(h(x_{s})\) and its derivative, while the incident noise field, \(h_{inc}\), is the known term computed with Equation (15).  
The discretization of the above equation leads to a system of N algebraic equations with 2N unknowns, where N is the number of elements that can be solved introducing N boundary condition equations expressed as a linear relation between \(h\) and \(\frac{\partial h}{\partial n}\). A generalized boundary condition reads  
\[\alpha(x)\frac{\partial h}{\partial n}(x,x_{q})+\beta(x)h(x,x_{q})=\gamma(x) \tag{17}\]  
In the case of a soft and vibrating wall in absence of mean flow, the boundary condition of Equation (17) assume the pressure-based formulation  
\[\frac{\partial p}{\partial n}=-ik\frac{\rho c}{Z}p+ik\rho cV_{n} \tag{18}\]  
where \(Z\) is the acoustic impedance and \(V_{n}\) is the vibrating wall normal velocity. Once the inverse problem is solved, the total acoustic field is computed with the BIE (14), assuming C = 1. The collocation approach here discussed has been implemented in CIRA code [22] where the stability of the algebraic system is ensured with the CHIEF algorithm [25], consisting in adding a set of M points internal to the scattering bodies, in which the sought function is null. It leads to a system of 2N + M equations with 2N unknowns that needs to be solved with a least-square technique. The internal points are collocated with a stochastic procedure within the scattering body [26] and a robust Oct-tree algorithm [24]. In order to reduce the computational and memory cost of the dense-matrix related to the BEM approach, an Adaptive black-box Fast Multipole Method with Chebyshev polynomial interpolation (AbbFMM) has been implemented [24].
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 4 Modeling of the Rain and Fog Effect in the Virtual LiDAR Sensor
### Interaction Between the Electromagnetic Waves and Hydrometeors  
There are three scattering theories (Rayleigh, Mie, and geometric/ray optics) that can explain the interaction between the electromagnetic wave and hydrometeors depending on the size parameter \(x=\frac{\pi D}{\lambda}\). When the particle size \(x\) is very small compared to the incident wavelength (\(x<<1\)), the Rayleigh scattering theory is used. Mie scattering theory is used when the incident wavelength and particle size are comparable or equal (\(x\approx 1\)).  
Figure 4: Exemplary visualization of a rain field generated by sprinklers resembling a real-world rain simulator.  
Geometric/ray optics is used when the incident wavelength is very small compared to particles (\(x>>1\)). The average raindrop diameter \(D\) is 1 mm, while the average fog droplet diameter \(D\) is 10 um. The size parameter of the LiDAR sensor operating at a wavelength of 905 nm is \(x=3471\) for raindrops and \(x=35\) for fog droplets [29].  
In conclusion, the interaction between LiDAR waves and raindrops can be explained using geometric/ray optics and the Mie scattering theory in the case of fog droplets. In Ref. [30], the authors have found that the results from the Mie scattering theory and geometric/ray optics agree well with each other for \(x\geq 400\). We, therefore, use the Mie scattering theory to model the rain and fog effect on the LiDAR sensor performance.
## 2 Experimental Procedure
### Acoustic Emission Monitoring  
To detect acoustic emission, two MICRO-80 sensors (MISTRAS Group, Sucy en Brie, France) were put on the specimen inside the grips with vacuum grease as a coupling agent. Acquisition parameters were set as follows: preamplification 40 dB, threshold 32 dB, peak definition time 50 us, hit definition time 100 us, hit lockout time 1000 us. AE signal descriptors such as amplitude, energy, duration, counts, average frequency, rise time and location as well as time, load and strain were recorded in real time by a MISTRAS 2001 data acquisition system.  
For AE sources localization, AE wave velocity \(Ce_{0}\) was determined before tests using a pencil lead break procedure on as received composites. This velocity was measured equal to 10,000 m/s. Since the elastic modulus decreases as damage occurs in the material, it is important to take into account the evolution of \(Ce\) during the mechanical test in order to better evaluate the location of the AE sources. As proposed by [PERSON] [25], the elastic secant modulus during unloading \(E_{i}(\varepsilon)\) was measured during a cycled tensile test, where hysteresis loops were obtained at different strains. The velocity \(Ce(\varepsilon)\) was then determined by using the Equation (1):  
\[\frac{Ce(\varepsilon)}{Ce_{0}}=\sqrt{\frac{E_{i}(\varepsilon)}{E_{0}}} \tag{1}\]  
where \(Ce_{0}\) and \(E_{0}\) are respectively the velocity and the elastic modulus in the undamaged state and \(Ce_{e}(\varepsilon)\), \(E_{i}(\varepsilon)\) respectively the velocity and the elastic modulus under a maximum strain \(\varepsilon\). At the end of the tensile test, the velocity on SiC/SiC composite was found to be equal to 6480 m/s, instead of 10,000 m/s on the undamaged state. This decrease of wave velocity is thus not negligible. The location of sources has been calculated using the difference in times of arrival on each sensor. Only the signals coming from the working length of the specimens are then analyzed.
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 2 Related Works
### Bio-Inspired Networking  
Bio-inspired methods exhibit inherent adaptability in nature and exploit inherent features to design a scalable, robust, and adaptive system that is capable of simple local interactions between the entities comprising the system. Biological design approaches have been applied to various fields in the communication and networking domains, including MAC design, resource allocation, routing, and security [27; 28; 29]. These bio-inspired methods demonstrated that the system reaches a globally optimal state, in which a system-wide optimization objective is accomplished even though each node comprising the large complex system repeatedly performs a simple operation with limited local information.  
Among the bio-inspired models, we adopted the Delta-Notch inter-cell signaling model to design a MECS sleep control scheme. The Delta-Notch lateral inhibition process determines the fate of endothelial cells during sprouting angiogenesis using the levels of Delta and Notch proteins in a cell. The Delta and Notch levels of a cell are regulated in response to the average Delta level of neighboring cells. This lateral inhibition process is modeled using the following coupled ordinary differential equations [30; 31]:  
\[\frac{\partial n_{i}}{\partial t}=f(\bar{d}_{i})-n_{i} \tag{1}\] \[\frac{\partial d_{i}}{\partial t}=v(g(n_{i})-d_{i}), \tag{2}\]  
where \(n_{i}\) is the Notch level in cell \(i\) and \(d_{i}\) is the Delta level. Additionally, \(\bar{d}_{i}\) is the average Delta level surrounding cell \(i\) and \(v\) is a positive constant. Functions \(f\) and \(g\) represent the production rates of the Notch and Delta levels, respectively. These are expressed as \[f(x)=\frac{x^{k}}{a+x^{k}}\] \[g(x)=\frac{1}{1+bx^{k}},\]  
where \(a,b>0\) and \(k,h\geq 1\).
## 4 Overview of the Proactive Transmission of Replicated Frames (PTRF) Mechanism
### Basic Architecture of a PTRF-Enabled Device  
When comparing the reliability achievable by different fault-tolerance mechanisms in TSN, it is fundamental to take into account the unreliability of the extra hardware that is needed to implement those mechanisms. In this sense let us refer to talkers, listeners and bridges that include extra hardware components that implement PTRF mechanisms as PTRF-enabled devices.  
Figure 4 depicts the architecture of a PTRF-enabled bridge, highlighting the extra components it includes when compared with a COTS TSN bridge. This figure also differentiates between the extra components depending on whether they are needed for transmission or reception. In this sense note that PTRF-enabled talkers and listeners also include the extra components of Figure 4 that correspond to the transmission and the reception respectively.  
As we can see on the left side of the figure, PTRF relies on two new components to manage replicas upon reception. Specifically, we can see the PTRF Replica Identification Table, in which the bridge or the listener stores information related to the last replica received through each stream. This information is used to eliminate surplus replicas upon reception. The second component is a Replica Reception Counter, which is responsible for monitoring how many correct replicas reach each device for each message edition. In any case, this counter is devised for future extensions of PTRF; e.g., to provide it with the capacity of assessing if the number of generated replicas is enough, or it has to be reconfigured to adapt to an increasingly harsh environment. Therefore, from hereon we will exclude this component from the reliability analysis carried out in this work.  
If we now focus on the right side of Figure 4, we see that PTRF includes two additional components to transmit replicated frames. First, the device includes the PTRF Stream Identification Table. This table is needed for knowing which frames must be replicated and how many replicas must be created for each one of them. Furthermore, the device includes  
Figure 4: Basic architecture of a TSN bridge with the PTRF mechanism. The additional components PTRF includes are highlighted in blue. Solid lines represent the path frames follow inside the bridge, whereas discontinuous lines represent management or configuration interactions. Reproduced as in [11].  
the Replica Creation Counter to track the number of replicas it generates for each frame, so that it queues the appropriate number of such replicas in the output queue.  
For the sake of succinctness, we do not describe in more detail how these components are used. For a thorough description of the role that each component plays in the operation of PTRF the reader can refer to [11].
## 1 Introduction
Thus, more adaptive methods such as machine learning can improve positioning accuracy by compensating for positioning errors in individual cases [41; 42; 43; 44; 45]. The purpose of this research is to create a methodology for increasing the positioning accuracy of an industrial robot and minimizing the vibration of the robot gripper by applying machine learning based on the developed mathematical model for estimating the positioning error. The mathematical model of positioning errors makes it possible to compensate for the positioning error by changing the speed of movement of the reference point of the gripperbefore determining the direct kinematic task. The method is focused on the accuracy of approaching the destination point in given arbitrary positions of the robot.
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 3 Results
### Planar Rectangle Spiral Antenna
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Code** & **Value** & **1 GHz** & **1.5 GHz** & **2 GHz** \\ \hline Wire-Grid & Maximum gain & 3.07 & 3.72 & 4.09 \\ \hline Triangle-Grid & Maximum gain & 2.88 & 3.6 & 3.98 \\ \hline EMPro & Maximum gain & 2.89 & 3.66 & 4.03 \\ \hline Wire-Grid & RD & \({}^{*}-6\%\) & \(-2\%\) & \(-1\%\) \\ \hline Triangle-Grid & RD & 0\% & 2\% & \(1\%\) \\ \hline \hline \end{tabular} \({}^{*}\)(\(-\)) indicates by what percent the value is bigger than the reference one (here EMPro).  
\end{table} 
Table 4: The maximum gain values of the planar spiral antenna and the percentage of their relative difference from EMPro. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **Frequency (GHz)** & **Impedance (Ohm)** & **Memory (GB)** & **Time (ms)** & **N** & **Cond (Z)** \\ \hline \multirow{3}{*}{Wire-Grid} & 1 & 156.0 + j15.4 & \({}^{*}\approx\)3.15 & 28,372 & 8015 & \(3.02\times 10^{12}\) \\  & 1.5 & \(194.3-\mathrm{j8.6}\) & \(\approx\)3.15 & 26,972 & 8015 & \(3.03\times 10^{12}\) \\  & 2 & \(203.3-\mathrm{j63.4}\) & \(\approx\)3.15 & 34,313 & 8015 & \(3.06\times 10^{12}\) \\ \hline \multirow{3}{*}{Triangle-Grid} & 1 & \(207.1-\mathrm{j1.4}\) & \(\approx\)0.15 & 1920 & 1279 & \(1.79\times 10^{4}\) \\  & 1.5 & \(214.4+\mathrm{j6.6}\) & \(\approx\)0.27 & 10,502 & 3145 & \(9.36\times 10^{3}\) \\ \cline{1-1}  & 2 & \(201.6-\mathrm{j6.4}\) & \(\approx\)0.54 & 28,401 & 5171 & \(1.85\times 10^{4}\) \\ \hline \multirow{3}{*}{EMPro} & 1 & \(183.3+\mathrm{j6.2}\) & \(\approx\)1.21 & 71,000 & 325,584 & - ** \\ \cline{1-1}  & 1.5 & \(182.7+\mathrm{j21.8}\) & \(\approx\)1.21 & 96,000 & 543,864 & - \\ \cline{1-1}  & 2 & \(184.4+\mathrm{j24.7}\) & \(\approx\)1.21 & 112,000 & 825,440 & - \\ \hline \hline \end{tabular} \({}^{*}\)(\(\approx\)) denotes that the value is approximately equal to these numbers. \({}^{**}\)(\(-\)) denotes that the data are not available.  
\end{table} 
Table 5: The input impedance values of the planar spiral antenna, obtained using different codes with the corresponding computational costs.
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
###### Abstract  
The growing number of daylighting metrics--often overlapping in scope or terminology--combined with the need for prior familiarization to interpret and apply them effectively, has created a barrier to their adoption beyond academic settings. Consequently, this study analyzes a representative set of established and emerging daylighting metrics to evaluate applicability, synergies, and limitations. Particular attention is given to their implications for occupant health, well-being, performance, and energy use, especially within the context of sensorless smart control systems. A virtual room model was simulated using DaySim 3.1 in two contrasting climates--Seville and London--with varying window-to-wall ratios, orientations, and occupancy schedules. The results show that no single metric provides a comprehensive daylighting assessment, highlighting the need for combined approaches. Daylighting Autonomy (DA) proved useful for task illumination, while Useful Daylight Illuminance (UDI) helped identify areas prone to excessive solar exposure. Spatial metrics such as Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE) offer an overview but lack necessary granularity. Circadian Stimulus Autonomy (CSA) appears promising for evaluating circadian entrainment, though its underlying models remain under refinement. Continuous Overcast Daylight Autonomy (DAo.con) shows the potential for sensorless lighting control when adjusted for orientation. A nuanced, multi-metric approach is therefore recommended.  
daylight metric; sustainable building design; lighting control systems; Circadian Stimulus Autonomy; healthy architecture
## 4 Conclusions  
The smart wireless CO\({}_{2}\) sensor-node prototype was implemented in a compact size on a single board with a dimension of 25 mm x 60 mm. The node was built using low-cost and low-power components available in the market. The functionality and performance test shows that the developed sensor node consumes quick time and low energy for one cycle of CO\({}_{2}\) measurement (initialization, data acquired, data transmitted, sleep mode). It consumes only 11.351 s and 0.4436 mWh, respectively, to provide real-time and continuous indoor CO\({}_{2}\) concentration data with a time interval of 10 s. With this energy consumption, the main power storage (supercapacitor) can power the sensor node in 108 s. However, the capacity of the supercapacitor can be increased to extend the sensor-node life. Another potential method is integrating the sensor node with a micro energy harvester to harvest energy from indoor ambient energy sources (artificial light, ambient temperature, airflow rate) in further development. It considers building an autonomous sensor node for long periods of indoor CO\({}_{2}\) monitoring applications with an energy generator to extend the sensor node life.  
The validation and field experiment results show that the developed sensor node has high accuracy with a measurement error of 0.5% and provides real-time, continuous, and remote measurement of CO\({}_{2}\) concentrations in indoor and outdoor environments. This proved that the measurements were easily accessed using an IoT system on a graphical user interface.  
The measurement results and features of the developed sensor node are very promising and suitable for indoor air-quality monitoring applications, indicating that the sensor node could be applied for long periods of monitoring and recording indoor CO\({}_{2}\) concentration data. Thus, the indoor CO\({}_{2}\) concentration measurement data can be utilized as an indicator to minimize the risk of the indoor SARS-CoV-2 airborne transmission.  
Conceptualization, C.B.D.K.; methodology, C.B.D.K. and A.A.; formal analysis, A.A. and A.F.P.; investigation, A.A. and A.F.P.; supervision, C.B.D.K.; resources, C.B.D.K.; writing--original draft preparation, A.A. and A.F.P.; writing--review and editing, C.B.D.K. All authors have read and agreed to the published version of the manuscript.  
This research funding was by the Ministry of Science and Technology of Taiwan (MOST: 110-2222-E-167-003-MY3).  
The data presented in this study are available at the request of the corresponding author.  
The authors would like to thank the Ministry of Science and Technology of Taiwan for funding support and the Refrigeration, Air Conditioning, and Energy Engineering Department of the National Chin-Yi University of Technology, Taiwan, for support in the system design.  
The authors declare no conflict of interest.