esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 5 Conclusions and Future Work  
This work addresses the interaction of essential underground communication functions and presents a distributed cross-layer framework called DECMN. This framework is designed to effectively utilize bandwidth-limited MI channels. By leveraging the DS-CDMA technique and MPNMRA, reliable transmission links can be established with minimal network information. Furthermore, fulfilling the statistical delay constraints, an analytical cross-layer design is employed to achieve optimal link performance for a given code length. The result outcomes highlight the effectiveness of the proposed approach in improving the performance of MI-based wireless underground sensor networks. Simulation results demonstrate the advantages of DECMN, including lower latency, higher throughput, and reduced energy usage.  
The effect of the presence of obstacles and the orientation of the antenna on radio transmission is an important aspect that needs to be covered as a future prospect in the cross-layer protocol design. It is important to consider optimization problems for wireless underground sensor networks beyond single-objective problems. Multi-objective and many-objective optimization, which involve optimizing multiple conflicting objectives simultaneously, is an important research area to explore in the future.  
P.S.: conceptualization, writing--original draft, N.M.: writing--review and editing, supervision, V.M.: writing--review and editing, T.T.: methodology, investigation, A.S.: writing--review and editing, S.L.: writing--review and editing, and R.S.: visualization, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
Authors declare that they have no conflicts of interest to report regarding the present study.
# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
## 3 Ultrasonic Image Data Processing and Feature Extraction
### Intensity Compensation of Flip-Chip Ultrasonic Images  
In acoustic microscopy imaging of electronic packaging samples, continuous scanning and thermal cycling tests often induce minor positional deviations, leading to inevitable defocusing. Consequently, calibration and intensity compensation must be implemented to correct these defocus errors. Assuming the transducer is ideally focused, although solder joint degradation may alter the intensity of the image, the intensity within the central region of the flip chip's C-scan images, indicated by the blue area in Figure 4 (where the coupling fluid is filled and the acoustic impedance remains constant), should remain stable. Therefore, the average intensity of this region was selected as a reference for intensity compensation across all flip-chip ultrasonic images.  
First, calculate the mean intensity \(a_{i}\) for the central region of each flip-chip image \(I_{(x_{i},y_{i})}\). This mean intensity measurement is essential, as it represents the typical intensity of the image's central zone, which is less prone to peripheral distortions. Next, use the mean intensity \(a_{0}\) from the central region of the flip-chip image obtained at cycle 0 of the accelerated thermal cycling experiment as the reference point. This reference intensity \(a_{0}\) serves as the benchmark for assessing variations in subsequent images. Then, for each image, compute an intensity compensation value \(G_{i}\). This value is determined by subtracting the reference mean intensity \(a_{0}\) from the mean intensity \(a_{i}\) of each subsequent image. The computation is expressed as \(G_{i}=a_{i}-a_{0}\). Finally, apply this compensation value \(G_{i}\) to the corresponding image \(I_{(x_{i},y_{i})}\), resulting in an image \(I_{(x_{i},y_{i})}\) that has been adjusted for intensity. This adjustment ensures uniformity in intensity across different imaging cycles.
## 2 Experiments
### Experimental System  
In the present study, the icing tests were carried out in an icing wind tunnel system, which was designed and built as a combination of a conventional wind tunnel and a cold natural environment. The computer model and a picture of the experimental system are shown in Figure 3.  
Figure 2: Simplified model of a VAWT.  
As shown in Figure 3, the icing wind tunnel system is different from the conventional icing wind tunnels referred to in the literature. It is composed of a wind tunnel and a test section segment. The test section segment includes a spray section, a mix section, a rotor section and a water vapor exhaust section. The cooling system is omitted in this experimental system because the laboratory was located in the north-east of China. The average temperature is in the range of \(-21{\sim}-11\)\({}^{\circ}\)C in winter. Outside low-temperature air is pulled into the wind tunnel and the test section. A low-speed, open-loop conventional tunnel, was modified to form the icing wind tunnel. The outlet size was \(1\times 1\) m\({}^{2}\). An array of five spraying nozzles, which were movable, was located in the spray section in front of the outlet of the wind tunnel, providing the icing conditions for the test. As the experimental temperature was stable after precooling, the array of spraying nozzles was inserted into the mix section. The cross sections of the spray section, the mixing section and the test section were all \(1\times 1\) m\({}^{2}\). When the icing wind tunnel system is operating, cold air is pulled into the wind tunnel and ejected into the test section. In the spray section and the mix section, the cold air is fully mixed with water droplets ejected from the spraying nozzles. Then, the water droplets are cooled and transformed into super-cooled water droplets, which flow with the cold air into the rotor section. In the rotor section, the super-cooled water droplets impinge on the rotating cylinder surface, and the ice accretion occurs. The icing shape occurring on the model sample surface was captured by a Phantom v5.1 high-speed camera. The resolution ratio was \(1024\times 1024\) pixels.
## Article  
Analysis of the Effect of Antenna Pointing Error Caused by Satellite Perturbation on Space Terahertz Communication  
[PERSON] and [PERSON]  
Academy of Military Sciences, Institute of Systems Engineering, Beijing 100141, China  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]  
The terahertz frequency band has the advantages of having a large bandwidth, narrow beam, strong penetration, and high security, and is an important direction for the frequency expansion of next-generation satellite communications. Due to the narrow terahertz beam, the pointing error caused by satellite perturbation will affect the attenuation of the terahertz wave power, resulting in a decrease in the received power of the system and affecting the communication performance of the system. In order to accurately analyze the influence of pointing error caused by satellite perturbation on the performance of space terahertz communication system, based on satellite perturbation model and Gaussian channel model, this paper deduces the accurate analysis of signal power attenuation model without pointing error and with pointing error untie. The antenna pointing error in the real environment is simulated by using the TLE data of the actual satellite, and the influence of the pointing error on the space terahertz communication is analyzed. The results show that the antenna pointing error caused by satellite perturbation changes periodically with the operation of the satellite, which will cause great signal power attenuation, reduce the signal-to-noise ratio of the space terahertz communication system, and seriously affect the communication quality. In addition, the higher the frequency, the greater the impact on space terahertz communication, which needs to be solved through attitude control and antenna compensation.  
satellite perturbation; pointing error; Gaussian channel model; terahertz communication
# Reference Mapping Considering Swaps of Adjacent Bases
## 4 Discussion  
The two main strengths of SOAP3-swap are its speed and its alignment ratio. As shown in Tables 1 and 2, except for SOAP3-dp, SOAP3-swap was significantly faster than the other tools in our experiments. With respect to alignment ratio, SOAP3-swap aligned more read pairs than any of the other reference mapping tools. For example, when \(S_{SW}\) was set to \(-1\), \(-2\), and \(-3\), for the human genome, SOAP3-swap aligned 4667 more read pairs, 2318 more read pairs, and 972 more read pairs, respectively, than SOAP3-dp, and for the drosophila genome, SOAP3-swap aligned 1253 more read pairs, 454 more read pairs, and 129 more read pairs, respectively, than SOAP3-dp. Since the alignment results can be the basis for reconstructing genomes and identifying mutations of genomes, the higher alignment ratio can be more helpful to analyze genomes.  
When \(S_{SW}\) was set to \(-1\), \(-2\), and \(-3\), for the human genome, SOAP3-swap aligned 273, 379, 202, 998, and 145, 117 read pairs, respectively, and for the drosophila genome, SOAP3-swap aligned 318, 343, 213, 178, and 159, 104 read pairs, respectively, as shown in Table 3. Since we performed the paired-end alignment in our experiment, we considered it as a swap occurrence when a swap occurred in at least one read. This result shows that some of the read pairs aligned by SOAP3-dp may actually have been aligned by swaps rather than insertion/deletion. Therefore SOAP3-swap can provide a different alignment result compared to SOAP3-dp because it performs alignment by considering all of the mismatches, gaps, and swaps.
## 5 Results and Discussion
### Overall Comparative Performance Results  
Figure 3 shows the accuracy, F1-macro, F1-micro, F1-weighted, and ROC-AUC results of kNN, RF, XGBoost, DNN, and the hybrid model (DNN+RF) using the OvR approach evaluated under an 80% train and 20% test data split. The comparative analysis of the performance metrics across the algorithms shows the superiority of the proposed hybrid model (DNN+RF) over traditional and standalone methods. The hybrid model achieved the highest accuracy (0.92) and ROC-AUC (0.97), proving its ability to correctly classify multi-class driver injury severity. In terms of F1-macro (0.89) and F1-weighted (0.92), the hybrid model consistently outperformed standalone DNN (0.85 and 0.90, respectively) and RF (0.85 and 0.86, respectively), showcasing its balanced performance across all classes, including minority ones. While XGBoost and kNN both exhibited a reasonable performance, with accuracies of 0.89 and 0.85, respectively, their lower F1-macro scores (0.84 and 0.77) reveal their limitations in handling imbalanced data. These findings emphasize the benefits of the hybrid approach, which combines DNN's ability to extract features and RF's strong classification performance. This makes it a powerful and dependable solution for complex, imbalanced, multi-class problems.  
Table 2 summarizes the performance results of the previous studies on driver injury severity that use ML and DL models. The proposed hybrid model (DNN+RF) significantly outperforms the methods reported in prior studies on driver injury severity prediction. Compared to [PERSON] et al. [12], where CNN and RNN achieved 70.30% and 73.76% accuracy, respectively, the hybrid model demonstrated a higher accuracy of 92%, alongside robust F1-macro (0.89) and ROC-AUC (0.97) values. Similarly, the hybrid model exceeds the results of [PERSON] et al. [13] and [PERSON] et al. [14], which achieved maximum accuracy values of 79.19% and 72.71% MAE, respectively. Furthermore, compared to [PERSON] and [PERSON] [20], whose RF model achieved a 91.72% F1-Score, the hybrid model achieved a higher F1-weighted score of 0.92, emphasizing its effectiveness in handling multi-class prob  
lems. These findings prove that combining the strengths of DL and ensemble techniques enhances predictive performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  
Figure 3: OvR results of accuracy, F1-macro, F1-micro, F1-weighted, and ROC–AUC values (80–20% train–test split).
# Cartesian Aerial Manipulator with Compliant Arm
## 1 Introduction  
### Aerial Manipulation  
Aerial manipulation robots (drones equipped with robotic arms) are extending the potential applications of multirotor platforms, allowing the realization of tasks involving the manipulation of objects and tools in flight, as well as interaction with the environment with a certain level of dexterity and control capability. The development of this technology is motivated by interest in reducing the time, cost and risk for human workers associated with the realization of certain tasks in high altitude or difficult access workspaces such as power lines [1, 2], chemical plants [3], oil and gas refineries [4], and other infrastructures [5, 6]. Recent works in this field have demonstrated the possibility to conduct operations such as object grasping [7, 8, 9], valve turning [10], sensor installation and retrieval [2, 11], contact-based inspection [3, 12, 13], insulation of cracks and leaks [14], or the realization of other tasks with grippers and other tools [15, 16]. Several prototypes and morphologies of manipulators have been specifically developed for their integration in multi-tors, including multi-joint arms [17, 18], dual arm systems [7, 10], linear actuators [11], delta manipulators [14], compliant joint arms [2, 19, 20], long reach aerial manipulators [2, 21], or three-arm manipulators used for object grasping and as reconfigurable landing gear [22].  
Compliance, either mechanical [7, 19, 20, 21, 22, 23] or at control level [24, 25, 26, 11], is a highly desirable feature for an aerial manipulation robot operating in contact with the environment since the stability of the aerial platform may be compromised due to the interaction wrenches exerted on flight [24, 25, 27]. The design and development of compliant manipulators aim to increase safety by exploiting the energy storage capacity and passivity properties of springs [7, 19, 26] and elastomers [23] while protecting the aerial robot againstimpacts and overloads [23]. Unlike most industrial manipulators that use expensive force-torque sensors in the joints or in the end effector, the servo actuators (Herkulex, Dynamixel) typically employed for building lightweight robotic arms for aerial manipulation do not provide a direct measurement of the torque; or this is based on the current, so its accuracy is low due to the friction of the gearbox. This has motivated the development of methods for estimating and controlling the forces and torques from the measurement of the deflection in the elastic element, either at joint level [7; 19] or in the Cartesian space [26]. Despite the evident benefits of mechanical compliance during the physical interactions of the aerial robot while flying, the main drawback of these mechanisms is the reduction in position accuracy due to the deflection of the joints [19] or links [2], a consequence of the second order dynamics associated with the mass-spring-damper system [26]. As a result, the realization of tasks requiring accurate positioning tends to become more difficult.
## 4 Case Study
The detection results of MITM, FDI, DoS, and simultaneous FDI-DoS attacks through the use of the real-time CECD-AS algorithm are presented in Table 1 in terms of accuracy, precision, recall, and F1-score values. In the paper [6] where CECD-AS is presented, its cyber threat detection performance is shown to outperform the state-of-the-art physics-based and machine learning-based techniques. The results in this paper are based on the data and experiments discussed [6]. Table 1 shows that the real-time CECD-AS algorithm performs extremely well for a variety of cyber threats discussed in this paper. The enhancement in the  
Figure 11: Throughput benchmark of controllers. Figure 12: Prediction accuracy of KNN for attacks in power grid line parameter. detection is due to the integration (combined) data from communication and power grid that added values to the ECD-AS algorithm. Hence, data from the two layers, i.e., power grid and communication grid, complement each other.
## 5 Simulation Results
### Case 1: Addition of RES  
In this mode of operation, a solar and wind-based RES is installed in the SH with the following considerations:  
* In supplying the electrical loads, the wind and solar power resources must be given priority.
* Heating is still provided by the auxiliary boiler.
* Bidirectional power flow is considered. In this way, the consumers can sell surplus electric energy to the utility.
* The RES curve in the subsequent figures is the summation of both wind and PV output powers in each time interval.  
Figure 7 shows the optimal allocation of HES where the negative utility powers indicate the utility power purchase from the SH. In the intervals 01:00-05:00, 11:00-16:00 and 23:00-24:00 the electrical load is less than the total RES generation, and the SH is selling excessive energy to the utility. The peak loading in the system occurs in 06:00-10:00 and 17:00-22:00 when the RES cannot fulfill the demand, therefore, the power is purchased from the utility. The daily operational expenses of the SH are $2.6272 in this case.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Symbol** & **Value** & **Unit** & **Symbol** & **Value** & **Unit** \\ \hline \multicolumn{6}{c}{**EV**} \\ \hline \(d\) & 40 & mi & \(SOC_{EV_{max}}\) & 100 & \% \\ \(\eta_{EV}\) & 6.2 & - & \(SOC_{EV_{min}}\) & 100 & \% \\ \(C_{EV}\) & 16 & (kWh) & \(T_{i}\) & 17:00 & hour \\ \(P_{EV_{linear}}\) & 3.3 & (kW) & \(T_{o}\) & 07:00 & hour \\ \(SOC_{EV_{min}}\) & 20 & \% & & & \\ \hline \multicolumn{6}{c}{**FC**} \\ \hline \(P_{FC_{max}}\) & 1.2 & (kW) & \(\Delta P_{FC_{obs}}\) & 0.9 & (kW) \\ \(P_{FC_{min}}\) & 0.05 & (kW) & \(\alpha\) & 0.15 & \$ \\ \(\Delta P_{FC_{exp}}\) & 0.75 & (kW) & \(\beta\) & 0 & \$ \\ \hline \multicolumn{6}{c}{**BESS**} \\ \hline \(W_{B_{max}}\) & 3 & (kWh) & \(P_{B_{delay}}\) & 2.25 & (kW) \\ \(W_{B_{min}}\) & 0 & (kWh) & \(\eta_{B,ch},\eta_{B,ch}\) & 1 & - \\ \(P_{B_{delay}}\) & \(-0.75\) & (kW) & \(C_{B_{max}}\) & 0.001 & \((\$/\)kW) \\ \hline \multicolumn{6}{c}{**General**} \\ \hline \(n\) & 24 & hour & \(P_{c}\) & 0.5 & - \\ \(T\) & 1 & hour & \(P_{m}\) & 0.1 & - \\ \(C_{gas}\) & 0.05 & \((\$/\)kW) & & **Renewables** & \\ \(C_{L_{b}}\) & 0.13 & \((\$/\)kW) & \(P_{w}\) & 2 & (kW) \\ \(C_{L_{t}}\) & 0.07 & \((\$/\)kW) & \(P_{pv}\) & 1.3 & (kW) \\ \hline \hline \end{tabular}
\end{table}
Table 3: System Parameters.
# Performance Analysis of Existing ITS Technologies: Evaluation and Coexistence
## 3 Performance Evaluation and Comparision
### Performance Evaluation and Comparision  
The main goal of the first part is to define the optimal configurations for each ITS connectivity according to several parameters. These include parameters that constantly vary in environments, such as density and the quantity of data transmitted, and other parameters related to adjusting the channel capacity to the flow, such as the MCS coding. The results obtained allow us to observe the effect of these parameters on the system's performance, showing the impact of the density on the range and delays.
## 3 Results and Discussion
### Micro-Raman Spectroscopy
#### 3.4.1 Iron Samples  
For the iron samples, Raman confirmed the presence of iron oxides (hematite and magnetite), iron hydroxides (goethite) and ferrous carbonate (siderite).  
In detail, sample 1 was investigated in three different areas: the first in the most fractured and dark area, the second on the surface with a less altered appearance and the third with the attempt to identify the restored and repaired area with the resin (Figure 15a). The spectra show that the majority part of the sample is composed of hematite (Fe\({}_{2}\)O\({}_{3}\)) [46].  
Figure 15b shows the spectra acquired on sample 5. The two analyses are made on spherules and unaltered surfaces, respectively. The first spectrum suggests the occurrence of a red pigment similar to hematite, while the second is the presence of siderite (FeCO\({}_{3}\)) [47].  
The center of sample 8 is characterized by light spots than the rest of the sample. Spectrum 1 shows goethite (Fe\({}^{+3}\)O(OH)) [48], spectrum 2 leptodrocite [49], while spectrum 3 corresponded to ilmenite (Figure 15c).  
Starting from the outside, different areas were investigated in sample 10 (as shown in Figure 15d): a grey-green part (point 1), a lighter one tending to beige (point 2) and a rust-colored area (point 3). Spectrum 1 was represented by magnetite (FeSO\({}_{4}\)) [46], while spectrum 2 goethite and finally spectrum 3 siderite.  
Figure 15e shows the micro-Raman results related to the last sample 32. The first area selected (point 1) had been identified in the center of the fragment in a beige area, while the second was located in an area with similar color but more homogeneous. Finally, the third point was chosen close to the previous one but in a grey area. The last sample did not show new results; also, in this case, spectrum 1 was characterized by hematite, while all the other investigated areas contain siderite (spectrum 2) and goethite (spectrum 3).
## 3 Method
### Dynamo Script Overview and Implementation
#### 3.2.6 Extracting the General Performance Output  
In this method the values of the shared outputs are extracted during the design automation process, as shown in Figure 7. This method can be used to determine parameters such as the number of buildings, building area, site area, total floor area, building-to-land ratio, and floor area ratio. These general output values were derived using the calculation formulas fundamental to the architecture.  
* Number of buildings: the total number of distinct structures within a designated area or plot.
* Building area: This represents the footprint of the building on the land, typically measured in square meters or square feet. This provides information on how much of the land is directly under the buildings.
* Site area: The total area of the land or plot on which the building or buildings are situated. This includes building footprints, open spaces, gardens, driveways, and other elements within the boundaries.
* Total floor area: The sum of the floor areas of the building across all levels. This typically includes corridors, stairwells, and possible areas such as balconies, depending on the specific definition used.
* Building-to-land ratio (BLR or site coverage): This is calculated by dividing the building area by the site area. This ratio provides information on the extent to which a site is occupied by its building footprint. For instance, a BLR of 0.5 or 50% means that half of the site is covered by buildings.
* Floor area ratio (FAR) or floor space index (FSI): This is calculated by dividing the total floor area by the site area. The FAR provides a measure of site density. A higher FAR  
Figure 7: Visual programming of the general performance output.  
indicates more built-up space relative to the plot size. Urban planners often use these values to control and limit the amount of construction in a given area.  
For an automated design process, these values can be automatically extracted using computer-aided design tools and building information modeling (BIM) software. These tools can be programmed to calculate metrics based on the design data input, enabling architects and designers to instantly evaluate different design alternatives based on these quantitative measures. These metrics are crucial in the field of generative design, where numerous design alternatives are generated. They facilitate the rapid filtering and ranking of the generated designs based on the desired criteria, ensuring that the final chosen design aligns with the project's goals and constraints.