esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 3 Proposed Algorithm for Mobile 3D Body Circumference Measurement
### Experimental Environment and Dataset  
As shown in Figures 5 and 6, uniform 3D data such as cylinder and mannequin were used to ensure the consistency and reproducibility of the experiments. In Figures 5 and 6, (b) in each figure is a depth map visualizing the distance between the camera and the object. Lower depth values are represented by darker colors, while higher depth values are represented by brighter colors, visually depicting the three-dimensional structure of the object. The actual circumference of the cylinder is 17.27 cm, and the estimated circumference measured by LiDAR is 17.68 cm, demonstrating an accuracy within a 3% error margin. The results for the cylinder and mannequin are shown in Table 3, respectively. The regression equation is tailored to humans, and the Mannequin has a different shape compared to an actual person, resulting in slightly larger errors. The experimental environment involved using a model equipped with an iPhone's LiDAR sensor to simultaneously collect RGB images and depth maps, maintaining data consistency by photographing objects under the same lighting conditions and fixed distances. Through such standardized datasets, the performance of the algorithm could be systematically evaluated, and errors that could occur in measuring various body parts could be analyzed.  
Figure 7a,c visualize the distance measurements before correction for the cylinder and mannequin, respectively. Distance values exceeding 12 cm between adjacent pixels were observed, which can be interpreted as the distance between the object and the background rather than within the object itself. Points where the values suddenly increase indicate locations beyond the object's boundary. In contrast, Figure 7b,d show the distance measurements after correction for the cylinder and mannequin, respectively, with the maximum value not exceeding 1 cm. This suggests that by detecting edges using depth values and correcting the keypoints, the lengths to the object's boundary surfaces were accurately measured.  
Figure 5: The (**a**) shows the mannequin’s RGB photo, (**b**) is depth map, (**c**) is edge detection using the Canny algorithm, and (**d**) is keypoint detection in the depth map photo.  
Figure 6: The (**a**) shows Cylinder’s RGB photo, (**b**) is depth map, (**c**) is edge detection using the Canny algorithm, and (**d**) is keypoint detection in the depth map photo.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Actual (cm)**} & **Estimated (cm)** & **Error (\%)** \\ \hline \multirow{4}{*}{Mannequin} & Waist & 55 & 46.7 & 18 \\  & Chest & 60 & 61.2 & 2 \\ \cline{1-1}  & Hip & 62 & 57.19 & 5 \\ \cline{1-1}  & Buttock & 63 & 67.18 & 1.08 \\ \hline \multicolumn{2}{c}{Cylinder} & 17.27 & 17.68 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Body measurements of the mannequin by body part. The sizes obtained from the LiDAR sensor are compared with the actual sizes.
## 3 The Proposed Algorithm  
In this section, we describe the flow of the proposed IHSAO algorithm in detail. Our proposed algorithm is mainly based on AO and is enhanced with the leadership mechanism of SSA, tent chaotic map, and PIOBL strategy. In the initial stage, we use the tent chaotic map to generate the initial population, thus promoting the population diversity. After that, the following process is iteratively executed. Firstly, before the AO algorithm is executed, we update the population position by the leader mechanism of SSA, i.e., Equation (2). The introduction of the SSA leader mechanism in the proposed algorithm effectively enhances the exploitation capability of the original AO. Secondly, the position update formulas of the different four stages of AO are executed using Equation (6), Equation (8), Equation (15), or Equation (16), depending on the specific situation. Finally, the optimal position \(X_{best}\) and the fitness value are filtered out using Equation (24) from the PIOBL strategy. The PIOBL strategy enhances the ability to escape from the local optimum. After completing the last iteration, the optimal value \(X_{best}\) is output. The flow chart and pseudo-code of the proposed IHSAO are shown in Figure 4 and Algorithm 1, respectively.  
```
1. Input the population \(N\), and the generation number \(T\)
2. Initialize the positions of each individual by tent chaotic map \(X_{i}(i=1,2,\cdots,N)\) // tent chaotic map
3. While\(t\leq T\)
4. Check if the position goes beyond the search space boundary and then adjust it
5. Evaluate the fitness values of all search agents
6. Update the global optimum \(X_{best}\)
7. Calculate \(X_{i}\) using Equation (3) // SSA
8. For\(i=1\) to \(N\)
9. If\(t\leq\dfrac{2}{3}T\)then//AO
10. If\(t\leq 0.5\)then
11. Update the position using Equation (6)
12. Else
13. Update the position using Equation (8)
14. End if
15. Else
16. If\(r\leq 0.5\)then
17. Update the position using Equation (15)
18. Else
19. Update the position using Equation (16)
20. End if
21. End if
22. End For
23. Generate the opposite position of \(X_{best}\) using Equation (24) and save the one with better fitness //PIOBL
24. \(t=t+1\)
25. End While
26. Return\(X_{best}\)
```  
**Algorithm 1** Pseudo-code of the proposed IHSSAO algorithm  
Figure 4: Flow chart of the proposed IHSSAO.
## 8 Future Directions
### Machine Learning Integration  
Machine learning algorithms, such as Reinforcement Learning (RL), can optimize adaptive beamforming and dynamic channel estimation. These algorithms learn from the environment, adjusting the UAV's position and orientation for optimal signal transmission and reception. A neural network-based channel estimator can predict and compensate for channel variations in real time, improving the communication link's robustness against environmental changes and pointing errors.
# Influence of the Quality of Consumer Headphones in the Perception of Spatial Audio
## 2 Headphones Measurements and Virtual Headphone Simulation
### Binaural Room Impulse Responses Measurements  
In order to generate the spatiality of sound sources, some Binaural Room Impulse Responses (BRIR) [14] were measured with a HATS B & K Type 4100.  
Reverberation is an influential factor for spatial localization [3; 15], and because of this, we decided to record our own BRIR with natural reverberation instead of using dry responses from a library. The impulse responses were recorded in a rectangular room with a volume of 132 m\({}^{3}\) and a reverberation time of about 0.7 s. Nine different azimuth angles were recorded (0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), 300\({}^{\circ}\), 330\({}^{\circ}\)) in the horizontal plane at 1.5 m of distance.  
These measures were used to simulate binaural sound source positions in Test 4 (Section 6).
# Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
## 4 Conclusions  
In this paper, the implementation of a Wideband \(\Delta\)DOR core for the tracking of interplanetary spacecraft is presented. It guarantees 1 nrad accuracy in ranging measurements. The algorithm was simulated in floating arithmetic to check the performances. A FLP to FXP optimization phase was performed to reduce the hardware complexity maintaining the specification of 50 dB of Dynamic Range on the generated RRC signal. The core was implemented in an ALTERA Stratix II EP2S180 FPGA. An experimental set up of the entire system based on an ALTERA Stratix II Development Board was implemented and measured. The proposed W-\(\Delta\)DOR core will be used in the compatibility tests in the European Space Operations Centre (ESOC)--Darmstadt in Germany, to verify the accuracy performance of this technique. The next step will be the implementation of the W-\(\Delta\)DOR core in radiation-hard FPGA technology to be featured in future interplanetary missions.  
Conceptualization, L.I. and G.C.C. and L.S.; methodology, D.G. and L.D.N. and R.F.; software, M.M. and D.G.; validation, M.M., and G.D.A. and F.C. and A.P.P.; formal analysis, L.I. and G.C.C. and L.S.; resources, L.S.; data curation, M.M.; writing-original draft preparation, M.R. and D.G. and M.M. and L.D.N. and R.F.; writing-review and editing, M.R. and M.M.; visualization, D.G. and M.M. and L.D.N. and R.F.; supervision, G.C.C. and L.S.; project administration, L.S.; funding acquisition, L.S.  
This work was carried out in the frame of an ESA contract on \"Flexible and Autonomous TT&C Transponders for Multi-Mission Applications\" (Contract No. 4000114372/15/NL/FE).  
Figure 8: Test case 4 measured.  
Figure 9: Test case 4 simulated.
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 6 Experiments and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Stroke** & **Mean** & **Max** & **Min** & \(\pm\)**SD** \\ \hline \multirow{4}{*}{THETIS} & Forehand & 89.68\% & 96.18\% & 77.57\% & 4.28\% \\ \cline{2-6}  & Backhand & 91.73\% & 94.93\% & 86.56\% & 3.33\% \\ \cline{2-6}  & Volley forehand & 91.64\% & 97.16\% & 80.81\% & 3.36\% \\ \cline{2-6}  & Volley backhand & 93.46\% & 96.52\% & 85.43\% & 3.71\% \\ \hline \multirow{4}{*}{Tennis-Mocap} & Forehand & 92.15\% & 95.74\% & 86.42\% & 3.80\% \\ \cline{2-6}  & Backhand & 92.37\% & 96.28\% & 88.98\% & 2.59\% \\ \cline{2-6}  & Volley forehand & 91.62\% & 95.39\% & 83.65\% & 3.63\% \\ \cline{2-6}  & Volley backhand & 92.02\% & 95.15\% & 89.91\% & 4.15\% \\ \hline \multirow{4}{*}{3 DTennisDS} & Forehand & 93.36\% & 96.01\% & 89.19\% & 5.13\% \\ \cline{2-6}  & Backhand & 90.82\% & 93.82\% & 86.59\% & 3.63\% \\ \cline{2-6}  & Volley forehand & 95.62\% & 97.44\% & 83.73\% & 2.68\% \\ \cline{2-6}  & Volley backhand & 93.53\% & 97.67\% & 88.13\% & 3.13\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Obtained recall results for individual strokes based on the proposed model.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 2 Background
### Data Rescaling Techniques  
Prior to applying the statistical methods cited above, it is vital to proceed to data rescaling because of the fact that input variables are measured at different scales. This might create biased data analysis. Thus, input data must be transformed or rescaled such that the effect of one attribute cannot dominate the others [11]. This implies transforming the original data to fall within a smaller or standard range such as [\(-\)1, 1] or [0, 1].  
The most common normalization techniques available in literature are Z-Score Normalization, Min-Max Normalization, and Normalization by Decimal Scaling.  
Let V be a numeric variable with \(n\) observed values \(v_{1}\), \(v_{2}\),..., \(v_{n}\).  
* **Z-Score Normalization** The mean and standard deviation measures are used to rescale the data such that resultant features have zero mean and a unit variance [11]. Each instance of the data \(v_{i}\) is transformed into \(v^{\prime}_{i}\) as follows: \[v^{\prime}_{i}=\frac{v_{i}-\mu}{\sigma}\] (7) where \(\mu\) and \(\sigma\) denote the mean and standard deviation of the \(i\)-th original value of the variable V, \(i=1,\ldots\ldots\ldots\ldots,n\), respectively.
* **Min-Max Normalization** The min-max rescaling technique maps each value \(v_{i}\) of V to \(v^{\prime}_{i}\) in the range [0, 1] by computing the new value as follows: \[v^{\prime}_{i}=\frac{v_{i}-\min V}{\max V-\min V}\] (8) where \(v_{i}\) is the \(i\)-th original value of the variable V, \(i=1,\ldots\ldots\ldots\ldots,n\), minV and maxV are respectively the minimum and the maximum computed over all the values of the variable V. For every variable, the minimum value of that variable gets transformed into 0 and the maximum value gets transformed into 1. If = minV; then \(v^{\prime}_{i}=0\) If = maxV; then \(v^{\prime}_{i}=1\)* **Normalization by Decimal Scaling** This technique normalizes each variable by determining its maximum value, which is equal to moving the decimal points of the instance values. The approach is appropriate for data with logarithmic variations in variables [11].  
Each instance \(v_{i}\) of the given data is rescaled into \(v^{\prime}_{i}\) as follows:  
\[v^{\prime}_{i}=\frac{v_{i}}{10^{j}} \tag{9}\]  
where \(j=log_{10}(\max(v_{i}))\).  
In our study, we applied these three techniques to the original data generated in our study and their outcomes are compared [11].
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 4 Discussion
In addition, due to the different gradients of water and fertilizer stress treatments, which may have an impact on the growth and development rate of the crop, this poses a significant challenge to our data collection process. The analysis of the regression coefficients combining the four base learners showed that the highest weight was given to the grain filling stage with or without LAI, which is consistent with the highest accuracy of the previous prediction of the grain filling stage. Therefore, the use of the grain filling stage as a key learner in the grain yield prediction has a very positive effect on the improvement of the prediction effectiveness of the ensemble model. This is rational since grain filling is the stage where wheat transfers organic matter such as starch and protein produced by photosynthesis from the vegetative organs to the grains, and the vegetation indexes in this period are closely related to the final quality of thousand-grain weight [29]. However, the regression coefficients for the jointing period showed negative values in some cases, which is obviously detrimental to improving the model accuracy. The reason for this phenomenon is that the crop cover is small and the ground surface is exposed at the jointing stage, so it is easy to be disturbed by the soil background and other factors during the spectral data collection [78]. Moreover, the LAI at this stage is relatively small and photosynthesis is weak, resulting in the accumulation of little dry matter yet, and its contribution in the integrated model is poor compared to other growth stages. The \(t\)-test results (Table 5) show that the GY prediction model with stacked four growth stages performed with good accuracy and robustness than any single growth stage, which has important implications for the practical application of the prediction model.
# Enhanced Spectral Amplitude Coding OCDMA System Utilizing a Single Photodiode Detection
## 2 SPD Detection Technique  
The SAC-OCDMA receiver in this technique is shown in Figure 1, which is drawn by Optisystem ver. 7 (Optiwave, Ottawa, ON, Canada). The received signal is decoded by a decoder that has the same spectral response of the intended encoder. The detected output from the decoder is either w for active user or \(\lambda\) for interferers, where the weight w represents the number of occupied frequency bins in the user encoder, and \(\lambda\) is the maximum number of common frequency bins occupied by any two codes of the family [20].  
The remainder of the signal from the decoder is then transmitted to the subtractive decoder (s-Decoder) to cancel out signals with mismatched signatures, i.e., interferers. After optical subtraction, the output from the s-Decoder is either zero for active user or \(\lambda\) for interferers.  
To decode the received signal, inexpensive uniform fiber Bragg gratings (FBGs) are used. This implies that the interference signals are cancelled in the optical domain before the conversion of the signals to the electrical domain. As a consequence, the new SPD scheme alleviates both PIN and MAI in the optical domain. The logical representation of interferences cancellation when using EDW codes is illustrated in Table 1.
# Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images
## 2 Related Work
### Absolute Moment Block Truncation Coding  
To compress both grayscale and color images, in 1984, absolute moment block truncation coding (AMBTC) was proposed by [PERSON] and [PERSON] [21]. An image is first divided into \(n\)\(\times\)\(n\) blocks. For example, to compress the image block \(X\), the AMBTC algorithm computes mean value \(M\) to classify all pixels of block \(X\) into two different groups. If the value of pixels is smaller than \(M\), these pixels belong to the first group. Otherwise, these pixels are considered members of the second group. Concurrently, the corresponding bit value, 0 or 1, is recorded in the bitmap, \(B\), if the pixel belongs to the first group or second group, respectively.  
After obtaining the two groups, two mean values, \(H_{k}\) and \(L_{k}\), of the block \(X\) are calculated by Equations (1) and (2), respectively:  
\[L_{k}=\frac{1}{n\times n-Z}\times\sum_{X_{i}<M}X_{i} \tag{1}\]  
\[H_{k}=\frac{1}{Z}\times\sum_{X_{i}\geq M}X_{i} \tag{2}\]  
where \(Z\) denotes the number of pixels with the value larger than or equal to the value of \(M\). If \(Z=n\)\(\times\)\(n\), set \(H_{k}=L_{k}=M\).  
After encoding by AMBTC, each block is represented by a compressed trio (\(H_{k},L_{k},B\)), where \(H_{k}\) and \(L_{k}\) are the two mean values and \(B\) is the bitmap. Figure 1 shows an example of the AMBTC algorithm.  
Assume that the original image block size of \(4\)\(\times\)\(4\) pixels is shown in Figure 0(a). The mean value \(M\) of the pixel values at each block \(X\) is calculated as \(M=134\). Then, each pixel is compared to the mean value \(M\) to generate the bitmap table \(B\) of this block, as presented in Figure 0(b). According to Equations (1) and (2), two mean values, \(H_{k}\) and \(L_{k}\), are calculated as \(H_{k}=156\) and \(L_{k}=106\), respectively. The compressed trio of the block is determined as (156, 106, 0100110011101110), which is sent to a receiver. After obtaining the compressed trio, the receiver can decode it to reconstruct the image block shown in Figure 0(c). In general, if an image is sized as \(512\)\(\times\)\(512\) pixels, then its code stream after encoding is sized as \((4+4+16)\times\left(\frac{512\times 512}{4\times 4}\right)=524,288\) bits, which is one-quarter the size of the original image.
## 3 Distance-Based Opportunistic Push/Subscribe Content Dissemination Model  
### Hybrid Wireless Communication  
We assume the smartphones in our study can communicate through cellular networks and WiFi networks, respectively. First of all, a smartphone is able to contact the cellular network base-station to access the Internet. Secondly, the smartphone is able to contact a WiFi router as well. In addition, the smartphone is supposed to be able to make direct WiFi contacts. WiFi direct contact is a new capability that builds a peer-to-peer connection between two WiFi devices. Two smartphones with WiFi direct communication ability can transmit and receive data without a WiFi router [16].
# Large-Scale Experimental Static Testing on 50-Year-Old Prestressed Concrete Bridge Girders
## 3 Loading Test Setup  
### Reaction Steel Frame  
A large steel reaction frame was designed to investigate the structural behavior and load-carrying capacity of the girders with different loading configurations (Figure 7). The test bench was provided by the Center SISCON of Politecnico di Torino for testing structures with a span of up to 30 m.  
The PC beams under testing were positioned on the supporting frame (blue color in Figure 7), where specifically designed bridge bearings replicate the simple support static scheme. The hinge and roller have a support plate of 400 \(\times\) 400 mm and allow for \(\pm\)4\({}^{\circ}\)of rotation; the roller allows a longitudinal displacement of \(\pm\)150 mm. The base plate of the bearings is bolted to the supporting frames, which in turn are bolted to the main frames (black color in Figure 7). The latter consist of two main longitudinal steel beams, each composed by 5 I-shaped welded steel segments. These segments were built by welding a 2 m high web to 1 m wide flanges with plates stiffeners spaced 1 m. The longitudinal beams are transversally connected by 5 I-shaped elements 1.5 m long. The loading system (red color in Figure 7) is constituted by two couples of hydraulic jacks hosted into steel member hinged to the steel frame with single capacity of 1200 kN and a maximum stroke of 750 mm. The hydraulic jacks, contrasting on the steel member, act on the pushing system, which transfer the load to a steel transverse beam through six 32 mm steel bars. The transverse steel beams were used to transfer the load from each couple of hydraulic load cylinders to the specimen under testing. Both the supporting frame and the loading system can be installed through a bolted connection along the longitudinal beams with a step of 10 cm in order to obtain different loading schemes. The hydraulic testing system allows for the performance of force-controlled loading tests at a controlled loading rate.  
The span length of the flexural tests reported in the present work was 19 m and the two couple of hydraulic jacks were located 0.6 m away from the midspan to conduct the loading tests in a three-point bending type configuration (Figure 8), aimed at investigating the bending resistance of the girders.  
Figure 7: Technical drawing of the reaction steel frame in black, supporting frames in blue and loading frames in red (in cm).  
Other test configurations will be used in the future to study different failure mechanisms, such as combined bending/shear and pure shear rupture mechanisms.
## 2 Design of the Minimum Variance Control Law
Therefore, the adaptive feature of the minimum variance controller could be enhanced by implementation of an additional self-tuning mechanism, which allows a real-time adjustment of this control penalty factor (\(\rho\)). In other words, instead of a constant \(\rho\), the system will operate with a variable \(\rho\). The proposed control strategy is based on the observations that these extreme values of \(\rho\) (very small or very big) each have their own advantages:  
A high value of \(\rho\) ensures a better penalty of the excessive control (and a better system stability), to the detriment of qualitative output performance;  
A small value of \(\rho\) can ensure better qualitative performance of the controlled output, but with the risk of destabilizing the control system (since the control is much less penalized). By considering both situations, the main goal of this paper is to find a compromise solution that can solve the problem, requiring the design of an adequate controller tuning algorithm, which will be presented in the next paper section. Regarding the stability of the minimum-variance control system, a complete analysis was carried out by authors in other papers [10; 11; 16; 17].
###### Abstract  
In this work, a numerical study on the loop-terminated Mach-Zehnder interferometer (LT-MZI) structure for CO\({}_{2}\) gas sensing applications is carried out via the finite element method. The sensing arm is covered with a polyhexamethylene biguanide (PHMB) polymer which is highly receptive to CO\({}_{2}\) gas. The refractive index of the host material decreases due to the absorption of the CO\({}_{2}\) gas resulting in a shift in the interference pattern of the LT-MZI structure. As a result, a redshift in the wavelength is observed in the transmission spectrum of the device. The sensitivity of the device is estimated at 7.63 pm/ppm, 34.46 pm/ppm, and 74.78 pm/ppm for the sensing arm lengths of 5 um, 10 um, and 15 um, respectively. The sensitivity can be further enhanced, however, at the cost of the bigger footprint of the device. Utilizing the innovative sensor design, a comprehensive range of CO\({}_{2}\) gas concentrations spanning from 0 to 524 ppm is effectively detected. This compact and highly sensitive device serves as a vital tool for monitoring indoor CO\({}_{2}\) levels, fostering a healthier breathing environment for all occupants.  
loop-terminated Mach-Zehnder interferometer; gas sensor; integrated photonic; silicon-on-insulator; polyhexamethylene biguanide +
Footnote †: journal: Applied Physics Letters