esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In the current industry 4.0 era, almost all fields of industry are characterized by sophisticated manufacturing approaches associated with various services that are fully dependent on rapid technological changes. Such a situation necessitates that warehouse and logistic distribution centers (DCs) accomplish their critical roles for the productive success of any organization. Such a productive role implies the use of well-established measurement approaches for effectiveness and efficiency while considering the differentiation among these approaches both conceptually and practically [1]. DCs are expected to perform effectively, thus enabling future evaluation of the extent to which they attain the strategic and/or operational objectives of the firm. DCs should also be operated efficiently, allowing firms to monitor to what extent DCs are capable of converting the different kinds of available resources into tangible, measurable forms of outputs. However, within the context of the relevant MCDM-based research applications [2, 3, 4, 5, 6, 7, 8, 9, 10], neither the original version of DCs, i.e., warehouses [11], nor the extended version of DCs (or what is currently known as logistic centers) [12] have provided robust evidence for handling the issue of the indistinguishability between effectiveness and efficiency (i.e., the two wings of productivity). In other words, none of the previous MCDM applications have attempted to research interweaving MCDM approaches in order to evaluate and/or select DCs, whether as warehouses [2, 4, 13] or as logistic centers [3, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21], with regard to effectiveness and efficiency. Hence, this paper aims to develop a productivity evaluation model for a set of DCs belonging to an international automotive vehicles and spare parts firm located in the Middle East, North Africa, and Turkey (MENAT). The developed model was established based on the employment of two MCDM techniques: the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) and data envelopment analysis (DEA). PROMETHEE II was employed to evaluate the effectiveness of nine DCs based on seven criteria. The same criteria were then utilized to develop a DEA model in order to measure the efficiency of the investigated DCs. Finally, a strategic roadmap was created based on the developed productivity evaluation model to formulate six different strategies through which DC locations can be assessed. The rest of the paper is organized as follows: Section 2 presents the relevant literature and the utilized methods, PROMETHEE II and DEA, are then introduced in Section 3. The application, results and discussion, implications, and conclusions are presented in Sections 4-7, respectively.
# Wind Load Distribution in Float Photovoltaic System
## 4 Modeling Description  
### Design Variables for Wind Load Distribution for CFD Analysis  
PV power plants transmit external loads based on independent foundations. The design standards suggest that only the horizontal projected area should be considered, but for the optimal design of the structural system, it is necessary to examine the wind load impact due to the geometry of the PV power plant, so the wind load impact on the PV modules was examined through flow analysis [13; 14; 15; 16; 17].  
The wind load evaluation of solar panels was performed by a previous researcher. As a result, it was confirmed that the CFD analysis and the experimental results were consistent [18]. The wind tunnel test results showed that the average pressure coefficient and CFD simulation and experimental results were similar.  
The PV power generation is most efficient when sunlight is incident perpendicular to the PV module. Considering the angle of incidence of sunlight, it is best to install PV modules at an angle of about 30, as addressed in Figure 4[14]. However, the power production efficiency of solar modules has recently increased, and the power generation efficiency is not significantly different depending on the installation angle. When installing a PV power structure on the water, it is affected by various water levels and has the same effect when installed at 10 to 15 degrees [19; 20]. Therefore, solar power plants installed  
Figure 3: Load transfer mechanism for floating PV plants.  
in saltwater reclamation areas, which are being developed on a large scale, are mostly designed with a solar module angle of about 10 to 15 for economic reasons.  
Considering the characteristics of PV power plants developed and constructed on a large scale in recent years, the installation angle of PV modules was assumed to be 12\({}^{\circ}\) in the flow analysis. In addition, since the wind load, which is the dominant load in the structural design of PV power plants, varies in each region, the design wind speed was divided into 30, 45, and 70 m/s to consider various regional characteristics, and the wind was divided into forward and backward directions relative to the PV module in consideration of the inclination angle of the PV module. These load design parameters are shown in Table 2. Two wind directions were considered forward (\(\beta\) = 0\({}^{\circ}\)) and backward (\(\beta\) = 180\({}^{\circ}\)), as shown in Figure 5.
## Nomenclature  
\(N_{G}\)Number of generation units  
\(N_{J}\)Set of loads number  
\(N_{s}\)Number of scenarios  
\(N_{W}\)Number of wind turbine units  
\(N_{P}\)Number of photovoltaic units  
\(N_{K}\)Number of EVs  
\(T\)Scheduling time (24 h a day)  
\(i(j)\)Index of generating units (loads), running from 1 to \(N_{G}\) (\(N_{J}\))  
\(b\),\(n\),\(r\)Indices of system buses  
\(t\)(\(s\))Index of time (scenario), running from 1 to \(T\) (\(N_{s}\))  
\(w\)(\(p\))Index of WT (PV) units, running from 1 to \(N_{W}\) (\(N_{P}\))  
\(k\)Index of EVs, running from 1 to \(N_{K}\)  
\(C_{m,t}\)(\(C_{p,t}\))Energy bid submitted by WT \(w\) (PV \(p\)) in period \(t\) (\(\epsilon\)/kWh)  
\(C_{L,t}^{R\mu}\)(\(C_{l}^{R\mu}\))Bid of the up (down)-spinning reserve submitted by unit \(i\) in period \(t\) (\(\epsilon\)/kWh)  
\(C_{L,t}^{R\mu}\)(\(C_{l}^{R\mu}\))Bid of the up (down)-spinning reserve submitted by load \(j\) in period \(t\) (\(\epsilon\)/kWh)  
\(C_{L,t}^{R\mu}\)Bid of the non-spinning reserve submitted by unit \(i\) in period \(t\) (\(\epsilon\)/kWh)  
\(SUC_{j,t}\)(\(SDC_{j,t}\))Start-up (shut-down) cost of unit \(i\) in period \(t\) (\(\epsilon\))  
\(\rho(t)\)Electricity price in period \(t\) (\(\epsilon\))  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **F\({}_{1}\) (\(\epsilon\))** & **F\({}_{2}\) (kg)** & **F\({}_{3}\) (\(\epsilon\))** & **Objective Function (\(\epsilon\))** \\ \hline Without EVPC & 293.6 & 3408.691 & 11.7 & 237.6 \\ With EVPC & 324.2 & 3202.842 & 0.0 & 282.1 \\ \hline \hline \end{tabular} EVPC: Electric vehicle power controller.  
\end{table}
Table 1: The optimization results of microgrid (MG) in scheduling time horizon.
## 3 Experimental Research and Analysis
### Experiments and Analysis
#### 3.3.2 Comparison of Object Detection Inference Images  
HSS-YOLO and YOLO11 perform object detection comparisons using test datasets from different scenarios. The specific experimental comparison images are shown below:  
Figure 8 shows the operating scenario required by the inspection robot. Under dim lighting and interference light, YOLO11 exhibited missed detections, while HSS-YOLO perfectly solved this issue and achieved higher confidence.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Parameters** & **GFlops** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **FPS** \\ \hline YOLO11 & 2.59 M & 6.4 & 0.932 & 0.902 & 0.949 & 100 \\ \hline YOLO11 + HetConv & 2.29 M & 5.8 & 0.930 & 0.908 & 0.947 & 90 \\ \hline YOLO11 + ShuffleAttention & 2.59 M & 6.4 & 0.935 & 0.903 & 0.948 & 100 \\ \hline YOLO11 + Inner-SIoU & 2.59 M & 6.4 & 0.944 & 0.937 & 0.964 & 105 \\ \hline HSS-YOLO & 2.29 M & 5.8 & 0.945 & 0.931 & 0.962 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments on public datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Parameters** & **GFlops** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **FPS** \\ \hline YOLO11 & 2.59 M & 6.4 & 0.859 & 0.787 & 0.836 & 100 \\ \hline HSS-YOLO & 2.29 M & 5.8 & 0.905 & 0.815 & 0.876 & 92 \\ \hline YOLOv8 & 3.0 M & 8.2 & 0.857 & 0.781 & 0.831 & 90 \\ \hline YOLOv5 & 2.51 M & 7.2 & 0.843 & 0.756 & 0.82 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison across different versions.  
Figure 12 also shows a significant improvement in door handle recognition in the same indoor environment.
## 3 Theoretical Part
### XR Technologies in Education
#### 3.2.2 Augmented Reality (AR)  
AR (Augmented Reality) enhances the real world by overlaying digital information, images, sounds, and other multimedia content onto physical environments. Unlike VR, which requires full immersion in a digital world, AR integrates these virtual elements with the real world, allowing for a more accessible and user-friendly experience. This characteristic makes AR a powerful tool in education, where it is used to bridge the gap between theoretical knowledge and practical experience, without requiring complete immersion into a virtual environment [19].  
AR is especially effective in fields where hands-on learning is essential. In biology, for example, AR can project models of the human body into the classroom, allowing students to explore the internal organs interactively, without the need for costly equipment or a laboratory setup. Similarly, in architecture and design, AR can be used to visualize building structures or room layouts within real-world environments before the physical construction begins. This gives students a clearer understanding of scale and spatial relationships, enhancing their learning experience without the need for physical models or prototypes [20].  
The benefits of AR in education are numerous. It has been shown to significantly increase student engagement by creating interactive and immersive learning experiences. Additionally, AR allows for personalized learning, where students can engage with the material at their own pace and according to their own learning style. Furthermore, AR encourages a more dynamic interaction between students and the content, fostering greater retention of information. Research supports these findings, showing that AR improves both motivation and knowledge retention, particularly when used in conjunction with traditional teaching methods [21].  
However, similar to VR, the successful integration of AR into educational settings requires adequate technical infrastructure and teacher training. Teachers need to be equipped with the skills to use AR effectively in their lessons, and schools must provide the necessary technical support to ensure smooth implementation. With these factors in place, AR has the potential to revolutionize education, offering new ways for students to interact with and explore the curriculum [22].
## 2 Fundamentals of Frequency-Selective Surfaces
### From FSS Design to Practical Implementation  
The transition from theoretical design to practical implementation highlights the complexity of tailoring FSSs for specific purposes, such as adapting to varying electromagnetic environments, enabling smart skin functionality, or blending with architectural landscapes. Practical applications often demand solutions beyond simple scenarios, as they typically involve multiple services operating across different frequency bands and varying angles of incidence. Consequently, canonical designs are rarely sufficient, necessitating the combination of FSS structures to address real-world challenges. For instance, pairing two square loops can create dual frequency nulls, corresponding to the resonances of each loop. However, the achievable band ratio may be constrained by manufacturing limitations [33]. Meandered structures can also help reduce this band ratio.  
To build FSSs with multiple resonance frequencies, two primary approaches are employed:  
* Single-layer unit cells that integrate the different resonant elements for each frequency. The feasibility of this approach depends on the desired frequencies and design constraints. In some cases, different designs are implemented on either side of the single-layer FSS substrate.
* Multi-layered FSSs, where each layer contains a unit cell tuned to a different resonant frequency. This approach, or, alternatively, single-layer FSSs with different designs on the top and bottom of the substrate, is sometimes used with non-symmetric unit cells to enable polarization independence. The integration of active devices, such as diodes, into FSSs introduces several advanced functionalities.
* Dynamic passband and shielding capabilities: The FSS can allow signal transmission at low power levels while providing shielding at high power levels. This feature is useful for controlling signal strength without fully blocking communication (e.g., signals from a nearby mobile base station).  
* Switchable passband/shielding: By adjusting the bias voltage of the diodes, the FSS can switch between passband and shielding within its operating bandwidth. In complex designs, performance can be controlled for each polarization, or the attenuation in the rejection band can be tuned. This capability is beneficial in situations where signal blocking is only needed at specific times, such as at night in residential areas or during working hours in offices.
* Switchable absorption bandwidth: Changing the ON/OFF state of the diodes allows the FSS to switch between absorption in different frequency bands, providing additional flexibility in controlling electromagnetic wave transmission.
# Online Foot Location Planning for Gait Transitioning Using Model Predictive Control
## 3 Control Architecture  
The control method used in this paper is MIT's Convex MPC [4]. The overall control block diagram [4] is shown in Figure 2.  
Based on Convex MPC, the UPMPC for selecting the foot location is added. Operator Input is the user input for commands such as gait, speed and direction. Reference Trajectory generates a reference state for the robot based on user input, such as the robot's pose and speed. State Estimator estimates the robot's state based on the sensor data. Swing Trajectory is used in three main purposes: selection of swing legs, determination of swing time and generation of swing trajectory. Swing Trajectory generates the desired swing trajectory for the swing leg to track based on the reference foot location input by the UPMPC. MPC outputs the desired GRFs based on the output of Reference Trajectory. The UPMPC selects the foot location of the swing leg.  
Figure 1: The black block indicates that the single leg of the quadruped robot is in the support phase. FL: Front Left Leg. FR: Front Right Leg. HL: Hind Left Leg. HR: Hind Right Leg.  
Figure 2: The red block indicates the control frequency at 0.5 kHz, the blue block indicates the control frequency at 30 Hz, and the green block indicates the control frequency at 4.5 kHz [4]. \(x_{ref}\)is the reference state of the robot. \(x_{est}\) is the estimated state of the robot. \(\mathbf{p}_{desW}\) is the desired foot location in the world coordinate system. \(\mathbf{f}^{(i)}\) is the GRFs in the world coordinate system and \(\mathbf{f}^{(i)}_{B}\) is the GRFs in the ontology coordinate system. \(K_{p},K_{d},\tau_{ff},\mathbf{p}_{desB},\mathbf{v}_{desB}\) denotes the PD control parameters of the joint, the feedforward force, the desired foot location under the body coordinate system, and the desired foot velocity in the body coordinate system, respectively. The swing leg is position controlled and the control effect of the joint is adjusted according to the PD control gain \(K_{p},K_{d}\).  
The UPMPC input variables are the desired state, the current estimated state and the GRFs planned by MPC when switching from the swing phase to the support phase. The output variables are the desired foot location. Using the LIP model and dynamics model of the robot, the selection of the foot location is simplified to a QP problem. The desired result is obtained from the solution of the QP problem. The detailed mathematical derivation and introduction are in shown in Section 4.
## 3 Results
### Fault Diagnosis
#### 3.2.12 Diagnostic Accuracy  
In this section, the accuracy of the fault diagnosis techniques previously considered is evaluated quantitatively. To this end, we selected the statistical parameter Kurtosis as the diagnostic accuracy index of the specific signal processing technique adopted, which usually denotes the impulsivity of the vibration signal in the time domain. In the present case, Kurtosis is computed in the frequency domain, indicating the impulsivity of the harmonics of the shaft rotation and fault frequencies, i.e., the emerging of the related peaks with respect to noise, and therefore the accuracy of the signal processing method considered.  
In Table 5, the values of Kurtosis computed for the different fault diagnosis techniques previously considered are reported. Signals from both sensor 6 on bearing 3 (last 49 s) and sensor 7 on bearing 4 (last 163 s) are processed within the frequency bandwidth 0-1000 Hz.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Bearing** & **Kurtosis** \\ \hline \multirow{2}{*}{PSD} & 3 & 46.3 (Figure 20) \\ \cline{2-3}  & 4 & 41.1 (Figure 22) \\ \hline \multirow{2}{*}{SES} & 3 & 22.7 (Figure 24) \\ \cline{2-3}  & 4 & 17.5 (Figure 25) \\ \hline \multirow{2}{*}{ TSA + SES} & 3 & 23.0 (Figure 26) \\ \cline{2-3}  & 4 & 17.8 (Figure 27) \\ \hline \multirow{2}{*}{ALP + SES} & 3 & 24.2 (Figure 28) \\ \cline{2-3}  & 4 & 18.2 (Figure 29) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Kurtosis of the fault diagnosis techniques considered.  
These analyses make it explicit that all the employed techniques have produced rather impulsive trends. While PSD shows higher values of Kurtosis than SES, all the combinations of the latter with other preliminary methods lead to a trivial increase in the numbers in the third column. Indeed, the maximum values of Kurtosis are found for the combination of CPW and SES, and for IES methods, and therefore it is confirmed that these techniques are the most effective in the fault diagnosis of the IMS first bearing dataset. It must be stressed that the FIR Filter also exhibits significant values but detects dynamics not directly related to the phenomenon of faults, as mentioned above.
## 3 Test Data and Evaluation Methodology  
### Test Data  
The developed procedure is tested on the Pleiades panchromatic images. The level of processing of all images is primary (sensor-corrected) with spatial resolutions from 0.7 m to 0.8 m, depending on the off-nadir viewing angle and with small deviations throughout the images. For the research, 14 images were acquired that cover two areas of interest (AOIs):  
* The first is named Ljubljana (LJ) and it comprises a dataset of six images of the Slovenian capital acquired during 2013 and 2014; area of the images is around 68 km\({}^{2}\); and, four images were acquired during the same pass.
* The second is named Kranj (KR) and the dataset has eight images of the city of Kranj that were taken in 2013; the images area is approximately 38 km\({}^{2}\); and, the set is constituted by four stereopairs.  
The images present mainly urban areas, but they also encompass agricultural land and hilly landscape with forests. Additional properties of the images can be seen in Table 1; the first six images are from Ljubljana AOI, while the rest are from Kranj AOI.
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 5 Discussion
### Model Performance and Prediction Accuracy  
The LSTM model demonstrates exceptional prediction accuracy across all GNSS parameters. The remarkably small prediction errors observed in MP1, MP2, CNR1, and CNR2 validate the effectiveness of our architecture and training approach. While the elevation angle (EL) shows minimal temporal variation, its inclusion proves valuable for interpreting signal anomalies, highlighting that feature importance cannot be judged solely by temporal variability.  
Figure 7: Comparison of LRP relevance scores over time between normal and anomalous conditions for different GNSS features. L1 frequency multipath error variation (MP1) under (**a**) normal and (**b**) anomalous conditions; L2 frequency multipath error variation (MP2) under (**c**) normal and (**d**) anomalous conditions; L1 frequency carrier-to-noise ratio (CNR1) under (**e**) normal and (**f**) anomalous conditions; L2 frequency carrier-to-noise ratio (CNR2) under (**g**) normal and (**h**) anomalous conditions; and satellite elevation angle (EL) under (**i**) normal and (**j**) anomalous conditions. Anomalies were injected from sample 1400 to 2000, showing consistently elevated LRP scores during the anomalous period across all features.
## Appendix C ANOVA Analysis for Each Response in the Second Application of RSM  
The ANOVA was performed on each of the temperatures obtained from the CFD-FVM model, reported in Tables 5 and 8 (for a total of 61 \(n\)-tuples): \(T_{1}\) (Table A4), \(T_{2}\) (Table A5), \(T_{3}\) (Table A6). For the significance of all reported statistical quantities, please see [75].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F Value** &
\begin{tabular}{c} _p-Value_ \\ **Prob > F** \\ \end{tabular} \\ \hline Model & 80,413.35 & 8 & 10,051.67 & 478.27 & \textless{}0.0001 \\ A—\(T\) & 37,650.37 & 1 & 37,650.37 & 1791.44 & \textless{}0.0001 \\ B—\(\textit{in}_{xx}\) & 12,515.28 & 1 & 12,515.28 & 595.49 & \textless{}0.0001 \\ C—\(H_{h}\) & 1456.62 & 1 & 1456.62 & 69.31 & \textless{}0.0001 \\ D—\(H_{xx}\) & 40,253.34 & 1 & 40,253.34 & 1915.29 & \textless{}0.0001 \\ E—\(e_{ex}\) & 5770.08 & 1 & 5770.08 & 274.55 & \textless{}0.0001 \\ F—\(C_{r}\) & 0.90 & 1 & 0.90 & 0.043 & 0.8372 \\ G—\(C_{h}\) & 3.70 & 1 & 3.70 & 0.18 & 0.6763 \\ H—\(e_{h}\) & 9.63 & 1 & 9.63 & 0.46 & 0.5014 \\ Residual & 1092.87 & 52 & 21.02 & & \\ Cor Total & 81,506.22 & 60 & & & \\ \hline Model & 80,395.26 & 5 & 16,079.05 & 796.02 & \textless{}0.0001 \\ A—\(T\) & 38,763.81 & 1 & 38,763.81 & 1919.07 & \textless{}0.0001 \\ B—\(\textit{in}_{ex}\) & 13,605.73 & 1 & 13,605.73 & 673.58 & \textless{}0.0001 \\ C—\(H_{h}\) & 1583.19 & 1 & 1583.19 & 78.38 & \textless{}0.0001 \\ D—\(H_{xx}\) & 42,108.25 & 1 & 42,108.25 & 2084.64 & \textless{}0.0001 \\ E—\(e_{ex}\) & 6165.34 & 1 & 6165.34 & 305.23 & \textless{}0.0001 \\ Residual & 1110.96 & 55 & 20.20 & & \\ Cor Total & 81,506.22 & 60 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANOVA for the response surface reduced linear model concerning \(T_{2}\) (2 nd dataset) before and after the backward selection process.