esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dual-Band 8-Antenna Array Design for 5G/WiFi 5 Metal-Frame Smartphone Applications
## 2 Proposed MIMO Antenna
### Antenna Element
In general, to enhance isolation and achieve low correlation, external decoupling structures are introduced between antenna elements. Designs with eight MIMO antennas often utilize an additional slot in the ground plane to improve isolation. As the equivalent circuit of this decoupling structure resembles a parallel RLC configuration, resonance will occur at specific frequency, consequently influencing the surface current distribution within the ground layer and altering the transmission line characteristics of the antenna unit. This study demonstrates that the slot not only enhances mutual isolation between antenna elements but also serves as the ground clearance area to expand bandwidth. Figure 3a presents simulation results of reflection coefficients with/without DS. The simulated S11 and S22 continue to cover desired bands even without DS. Figure 3b illustrates simulated isolation curves S12 and S23 with/without DS1 and DS2. The utilization of decoupling structures effectively attenuates mutual coupling at 3.5 GHz, 4.9 GHz, and 5.5 GHz. In the desired frequency bands, the low isolation (11 dB) at 3.5 GHz improves distinctly with the introduction of DS1 between Ant. 1 and Ant. 2. Furthermore, with the incorporation of DS2, the simulated S23 complies with the 15 dB requirement in the desired frequency bands. Figure 3: Comparison of simulated (**a**) reflection coefficients and (**b**) transmission coefficients between the antenna without decoupling structures and the proposed antenna. Figure 2: (**a**) Simulated reflection coefficients and (**b**) transmission coefficients of the proposed antenna.
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## 4 Experiment Results
In this experiment, we had SCATTER PHY operating in full-duplex mode, where one PHY transmitted to the other and vice versa. Each one of the PHYs transmitted 20 subframes in a row with a 1 ms gap between transmissions. As can be noticed, as the selected PHY Tx BW increased, both CPU and memory utilization also increased; however, there was no memory nor CPU starvation for any of the used MCS and PHY Tx BW values. As can be also seen, for a given PHY Tx BW, as the MCS value increased, the CPU usage also increased. This was because the turbo encoding, turbo decoding, and synchronization processing tasks demanded much more CPU power for data processing as the MCS value increased, i.e., as data rates increased. For an MCS equal to 31 and a PHY Tx BW of 9 MHz, the CPU utilization of SCATTER PHY was approximately equal to 280%. That means that less than three CPU cores were being used, consequently leaving the other cores underused for long intervals. In the case of memory usage, it can be noticed that for a given PHY Tx BW, the memory utilization was almost constant for all MCS values, and therefore, it was practically independent of the selected MCS value. This result was expected once all the RAM memory used by SCATTER PHY was pre-allocated during its initialization. Therefore, based on the results presented in Figure 12, it can be concluded that SCATTER PHY did not exhaust memory or CPU resources as the configured MCS or PHY Tx BW values increased. Figure 11: Comparison of the variation of the maximum number of turbo decoding iterations for a PHY Tx BW of 1.27 MHz and three different MCS values. Figure 13 presents the results of the CPU consumption for the main SCATTER PHY's functions for three different MCS values and a PHY Tx BW of 4.5 MHz. We used valgrind with its callgrind tool for the assessment of the CPU consumption [27]. This experiment used the same setup used for the profiling of memory and CPU. The figure highlights the Rx and Tx functions with highest CPU processing load. By analyzing the figure, we see that the bit interleaving processing used more CPU as the MCS value increased. As can be seen, the CPU time-usage was approximately 11% when the MCS value was set to zero; however, it increased to approximately 51% when the MCS value was set to 31. The synchronization processing task required a considerable amount of CPU time for all considered MCS values. The CPU time consumption for the synchronization ranged from 9% to more than 23%. The CPU time for the synchronization was independent of the selected MCS due to the fact that this processing did not involve data coding/decoding. It can be also noticed that the turbo decoding iteration task consumed more CPU time as the MCS increased, going from 2% (MCS 0) to more than 18% (MCS 31). Additionally, we see that memory copy and setting functions consumed less CPU time as the MCS value increased. This was due to the fact that other functions started consuming more CPU when the MCS increased. Figure 12: CPU and memory utilization of SCATTER PHY.
## 3 Result
### Effects of Functional Rehabilitation Alone in Control Group  
At T1 in the control group we observed statistically significant improvements in FIQ (49.55 \(\pm\) 6.44 vs. 43.03 \(\pm\) 5.86; \(p\) < 0.05) and SF-12 (25.07 \(\pm\) 3.2 vs. 27.29 \(\pm\) 3.2; \(p\) < 0.05) scale scores. No statistically significant improvement, in contrast, was found in NRS (7.22 \(\pm\) 0.8 vs. 6.8 \(\pm\) 0.9; \(p\) = 0.08), NPI (83.88 \(\pm\) 5.1 vs. 86.11 \(\pm\) 4.9; \(p\) = 0.10) and PHQ-9 (13.74 \(\pm\) 3.08 vs. 12.34 \(\pm\) 2.89; \(p\) = 0.09) scale scores (Table 3).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{1}{c}{**Total (_n_ = 60_)**} & \multicolumn{1}{c}{**Treatment**} & \multicolumn{1}{c}{**Control**} & \multicolumn{1}{c}{**\(p\)-Value**} \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Group (_n_ = 33_)**} & \multicolumn{1}{c}{**Group (_n_ = 27_)**} & \multicolumn{1}{c}{**\(p\)-Value**} \\ \hline
**Age, mean \(\pm\) SD** & 58.7 \(\pm\) 5.4 & 61.3 \(\pm\) 4.8 & 59.8 \(\pm\) 4.5 & 0.22 \\ \hline
**Gender, \(n\) (\%) & & & & \\
**Male** & 26 (43.3) & 15 (45.4) & 11 (40.7) & \\
**Female** & 34 (56.7) & 18 (54.6) & 16 (59.3) & 0.67 \\ \hline
**BMI, mean \(\pm\) SD (Kg/m\({}^{2}\))** & 28.8 \(\pm\) 3.2 & 28.4 \(\pm\) 2.9 & 27.9 \(\pm\) 3.3 & 0.62 \\ \hline
**Occupation, \(n\) (\%) & & & & \\
**Working** & 42 (70) & 24 (72.7) & 18 (66.6) & \\
**Unemployed** & 18 (30) & 9 (27.3) & 9 (33.4) & 0.34 \\ \hline
**Oxygen therapy \(n\) (\%) & & & & \\
**Yes** & 39 (65) & 22 (66.7) & 17 (59.3) & \\
**No** & 21 (35) & 11 (33.3) & 10 (40.7) & 0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: General characteristics at baseline.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **T0** & **T1** & _p_**-Value** \\ \hline
**NRS** & 7.18 \(\pm\) 0.9 & 4.9 \(\pm\) 0.75 & \textless{}0.05 \\
**NPI** & 83.33 \(\pm\) 6.47 & 85.30 \(\pm\) 6.02 & 0.21 \\
**FIQ** & 50.66 \(\pm\) 10.63 & 41.96 \(\pm\) 8.68 & \textless{}0.05 \\
**SF-12** & 26.18 \(\pm\) 2.88 & 30.45 \(\pm\) 2.49 & \textless{}0.05 \\
**PHQ-9** & 13.03 \(\pm\) 3.51 & 9.84 \(\pm\) 3.02 & \textless{}0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of combining functional rehabilitation with ALC supplementation in treatment group at T1.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 4 Deep-Learning Techniques
### Long Short-Term Memory (LSTM) [44]  
Long short-term memory is a type of RNN; specifically, it overcomes the problem of the RNN's inability to remember very long chains of dependencies. It adds a memory cell, which helps to remember on top of the input gate, output gate, and forget gate. However, LSTM may resolve the issue of gradient vanishing, but still suffers from gradient exploding. The reason for this is that gradients can exponentially increase with respect to the number of layers. Moreover, LSTMs are not parallelizable; therefore, they can take a long time to train, and they still cannot remember very long chains of dependencies, even though they are better than RNNs in that sense.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 5 Results
### Semantic Segmentation Speed Results  
We measured the processing speed of each frame for both models. Recall that we chose MobileNetV2 as the feature extractor for DTS-Net-Lite to achieve real-time processing. In general, depth-wise separable convolution-based architecture has considerably higher processing speed than normal convolutional architectures, as discussed in detail  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Img Size** & **Dataset** & **mIOU\%** & **Pix acc.\%** & **Speed (FPS)** \\ \hline DTS-Net & 480 \(\times\) 480 & VOC2012 & 27.66 & 90.07 & 22 \\ DTS-Net & 640 \(\times\) 480 & NYUV2 & 33.92 & 79.4 & 18.1 \\ DTS-Net & 1024 \(\times\) 512 & CITYSCAPES & 47.7 & 85.1 & 9.5 \\ DTS-Net-Lite & 256 \(\times\) 256 & VOC2012 & 14.72 & 87.0 & **24** \\ DTS-Net-Lite & 320 \(\times\) 256 & NYUV2 & 24.5 & 66.76 & 21.3 \\ DTS-Net-Lite & 512 \(\times\) 256 & CITYSCAPES & 35.3 & 82.8 & 14.7 \\ \hline DTS-Net+ NLF & 480 \(\times\) 480 & VOC2012 & **91.1** & **98.8** & 15.5 \\ DTS-Net+NLF & 640 \(\times\) 480 & NYUV2 & 62.77 & 84.73 & 12.8 \\ DTS-Net+NLF & 1024 \(\times\) 512 & CITYSCAPES & 80.72 & 93.1 & 6.6 \\ DTS-Net-Lite+NLF & 256 \(\times\) 256 & VOC2012 & 83.8 & 96.1 & 19.2 \\ DTS-Net-Lite+NLF & 320 \(\times\) 256 & NYUV2 & 48.46 & 73.3 & 14.2 \\ DTS-Net-Lite+NLF & 512 \(\times\) 256 & CITYSCAPES & 61.5 & 82.06 & 11.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results that were obtained from DTS-Net and DTS-Net-Lite with and without NLF for each dataset VOC2012, NYUV2, and CITYSCAPES with the mIOU%, Pixel accuracy(Pix.acc.)% and the speed in FPS.  
Figure 8: Sample results of predicted and NLF-filtered semantic segmentation (NLF Seg) results that were obtained using DTS-Net-Lite on PASCAL VOC2012 (rows 1 and 2), NYUV2 (rows 3 and 4), and CITYSCAPES (rows 5 and 6) with input images and ground-truth segmentation masks (Seg).  
in Section 3.1. DTS-Net-Lite is faster than DTS-Net because inverted residual and linear bottle-necks are used; however, DTS-Net can achieve an average processing speed. In the experiments, DTS-Net-Lite attained a speed of 19.2 frames per second (fps) with NLF filtration on PASCAL VOC2012 as the image size was 256 \(\times\) 256. Without the NLF, it attained 24 fps, as the patch-by-patch filtration required more computations, which consumed more time. DTS-Net with NLF achieved only 17.5 fps on PASCAL VOC2012 and 22 fps without NLF. DTS-Net-Lite achieved speeds of 14.1 and 21.3 fps with and without NLF, respectively, on the NYUV2 dataset. On the CITYSCAPES dataset, DTS-Net-Lite achieved a low speed of 11.7 fps with NLF, as the image size was 512 \(\times\) 256, and 14.7 fps without NLF. Table 2 lists the results achieved with DTS-Net and DTS-Net-Lite, that is, the mIOU, the Pix.acc, and the speed.
## 6 Security Solutions
### Summary and Insight  
In this section, we have discussed various solutions that can protect IoT devices from physical attacks, ensure the authenticity and confidentiality of communications, and protect security-critical services from unauthorized access. We have found that the TPM technologies, secure boot routines, one-time programmable memories, and secure debugging interfaces can be integrated with IoT hardware and software to prevent host-based attacks. We have also found that the lightweight HIP-based schemes, such as HIP-PSK and Slimfit, can be suitable for managing the identities of billions of IoT devices, including mobile and stationary nodes, and preventing identity impersonation-type attacks. Moreover, we have identified that the delegation-based DTLS schemes can be adopted by IoT systems to ensure confidentiality, authenticity, and integrity of the sensor information as they unburden IoT nodes from computation- and communication-intensive cryptographic operations. Finally, have found from our analysis that the distributed CapBAC models can be utilized, ensuring protected access to security-critical services and enabling IoT devices to provide real-time information at the same time.  
Although most of the research we have reviewed modifies the conventional security schemes to make them applicable to IoT systems, only a few of them are tested on the real IoT devices and considered the real-world smart application scenarios. As such, further research is required to validate and confirm the usability of HIP, DTLS, and CapBAC by IoT systems consisting of a large number of smart nodes.
## 6 Experimental Results
Thus, it is obvious that many thresholds between these distributions will yield both low false negative and false positive errors. To check the presence of a watermark sequence, we compare the test statistic \(q\) with the acceptance threshold \(T\), computed as a function of the mean \(m\) of the distribution of \(q\) under the hypothesis \(H_{I}\). The ideal acceptance threshold for which the detection errors (i.e., the false negative error \(P_{n}\) and the false positive error \(P_{\!f}\)) are virtually zero is \(T_{{}_{ideal}}=\dfrac{m_{{}_{H_{1}}}}{2}\). Nevertheless, as can be observed from Figure 3, we can choose an acceptance threshold much smaller (e.g., \(T_{{}_{practical}}=5\)), maintaining at the same time _extremely low_ values for the probability of false positive and false negative errors. The false positive detection error for different values of the acceptance threshold \(T\) is presented in Table 1.  
Note that, for all values of \(T\) in Table 1, the probability of false negative error is virtually null, being \(T\leq m_{{}_{H_{1}}}\big{/}2\). On the contrary, for \(T>m_{{}_{H_{1}}}\big{/}2\), \(P_{n}\) tends to increase, whilst \(P_{f}\) remains virtually zero. The robustness of the proposed scheme was tested by applying several signal processing operations and common attacks, which can also be found in the standard benchmarking tools (e.g., Stirmark, Checkmark, Optimark), to a suite of 100 watermarked images taken from common image databases [58, 59]. In Table 2, the average of the output statistic \(q\), obtained detecting the watermark from each attacked image, is reported. Figure 3: Distributions of the variable \(q\) under the hypothesis \(H_{0}\) (left) and \(H_{I}\) (right) for the reference image “Lena” with the ideal (dashed green line) and practical (dashed blue line) acceptance thresholds. We applied the sampling operation (i.e., up-sampling and down-sampling) on the watermarked images using different sampling factors (namely 0.8, 1.3 and 1.75). Nevertheless, the watermark can be detected with high confidence from the attacked images i.e., the averages of the output statistic \(q\) are 14.56, 20.16 and 18.78. Normally, the detector can easily distinguish the watermark from a down-sampled image. However, to obtain even better performance from the detector, one can firstly up-sample the attacked image by estimating the applied distortion, in case the original image is available. \begin{table}
\begin{tabular}{|c|c|c|} \hline \multicolumn{2}{|c|}{**Threshold \(T\)**} & \multicolumn{2}{c|}{**Probability of False positive error \(P_{f}\left|P_{f}\left(q\geq T\right)\right|\)**} \\ \hline
3 & \multicolumn{2}{c|}{3\(\times 10^{-4}\)} \\ \hline
4 & \multicolumn{2}{c|}{1.23\(\times 10^{-7}\)} \\ \hline
5 & \multicolumn{2}{c|}{2.54\(\times 10^{-12}\)} \\ \hline \(\frac{m_{H_{1}}}{2}\) & \multicolumn{2}{c|}{1.36\(\times 10^{-20}\)} \\ \hline \end{tabular}
\end{table} 
Table 1: The probability of false positive error \(P_{f_{j}}\) associated to the acceptance threshold \(T\).
# An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
## 2 Proposed Internet of Things (IoT) Glucose Monitoring System
### Methods  
The Beer-Lambert law [38] is an optic measure considering the relationship between the absorbance and the amount of a material. It permits to compute the amount of a material in a sample by using its absorbance rate, so the light absorption of a material is proportional to the quantity of the same material. The Beer-Lambert law expresses how the light is absorbed by matter: (a) the intensity of transmitted light decreases exponentially as concentration of the substance in the solution increases, and (b) the intensity of transmitted light decreases exponentially as the distance travelled through the substance increases [38; 39]. This formulation was used as the main principle for measuring the glucose concentration in a blood sample by passing a laser-beam through the fingertip. A simplified model of this law is shown in Equation (1):  
\[\text{absorption}=\frac{\text{intensity of incident light}}{\text{intensity of transmitted light}} \tag{1}\]  
The artificial neural network (ANN) used for estimating the blood glucose concentration was constructed and trained using Tensorflow [40], which is an open-source platform for deep learning and machine learning developed and maintained by Google. It allows the creation of a variety of neural network models applied to multidimensional data arrays, referred to as tensors. Some of its main features include that can run on multiple central processing units (CPUs) but also on GPUs (General purpose Graphical Processing Unit), and can be implemented with C++, Python and Java.  
The ANN was implemented on a _Flask server_[41], a microframework for Python which allows the creation of web APIs (Application Programming Interfaces) for a variety of applications. Here, the Flask server is used to provide Python microservices to be consumed by the end device.
## 2 Background
### IPMC Technology  
IPMCs have received a significant amount of attention since they offer new opportunities for the development of flexible underwater robotic systems due to their intrinsic properties: large bending deformation under a low applied voltage (4-6 V), low density, and potential biocompatibility and biodegradability. They are usually manufactured by Nafion as the foundation polymer matrix, a thin ion-exchange layer based on a perfluorinated ionomer membrane in a hydrated state with water as a solvent, plated with platinum and attaching a couple of electrodes to both surfaces. The ion-exchange layer is neutralized with Na\({}^{+}\) counterions to balance the electric charge of the anions attached to the ionomer. Likewise, IPMC units can be also manufactured using different types of both ionomer membranes (such as Flemion) and counterions (such as Li\({}^{+}\), Rb\({}^{+}\) or K\({}^{+}\)) with water, ethylene glycol, or glycerol as solvent.  
If a voltage is applied to one of the electrodes, an ion migration is produced, moving the hydrated cations and water molecules inside the polymer forward to the opposite surface. This motion will increase the gradient concentration, producing electrostatic and osmotic forces inside the polymer and forcing the bending as a result. Therefore, IPMCs are electrically driven materials that undergo bending deformations in the presence of external voltages.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 2 Related Work
Collectively, these AI-driven advancements represent a paradigm shift in MIMO detection methodologies. From probabilistic generative models to deep unfolding networks, DL approaches effectively address the limitations of traditional detectors. Nevertheless, challenges persist in balancing detection accuracy, computational complexity, and practical deployment feasibility. These challenges motivate the design of novel architectures like MIMONet, which aim to unify efficient FFNN design with application-aware optimization for 6G ma-MIMO systems.
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 3 System Model, Problem Formulation and Proposed Scheme  
In this section, we present a system model, formulate the optimization problem, and describe the proposed on-off power control scheme for FBS which uses cooperative transmission.
# Numerical Study of the Toughness of Complex Metal Matrix Composite Topologies
## 4 Results and Discussion
Figure 10: Load (**a**), broken surface in the CT specimen (**b**) and total absorbed energy (**c**), versus displacement. However, for the CT specimens with complex structures, the crack tends to follow the contour of the structure while propagating in both materials (Figures 12 and 13). The crack changes its path when it reaches the lattice/matrix interface or when it has a contact with a truss (Figure 12). The change in trajectory may be due to the struts themselves or to the change in hardness of the materials. Figure 11: Crack propagation in the full MS CT specimen (**a**) and in the full MS-TiC CT specimen (**b**). Figure 12: Crack paths in the MS-TiC inserts according to the failure stress of the DEM bonds. The blue color of the bonds corresponds to the MS-TiC failure stress. Gyroid_01 (**a**), Gyroid_02 (**b**), octet-truss (**c**), FBCZZ_01 (**d**), FBCCZ_02 (**e**) and FBCCZ_03 (**f**). A test was therefore carried out with a simple cylindrical reinforcement to verify the hypothesis of the influence of the interface or the struts on the crack deviation. The crack spreads in a rather straight line in the CT specimen despite a slight deviation due to the propagation of the crack along the interface MS-TiC cylinder/MS interface (Figure 14). The difference in hardness of the two materials does not therefore seem to be the reason for the deviation in the crack path. There is no abrupt change in the trajectory of the crack for gyroids because they have a zero-average curvature. Their shape is smoother than octet-truss or FBCCZs which have struts oriented at 35\({}^{\circ}\) and 45\({}^{\circ}\) (Figure 13). The angle of the truss thus seems to be determinant in the crack propagation. The crack spreads more chaotically in the octet-truss structure because it has more oriented struts than FBCCZs. The number of struts and their orientation are therefore predominant in  
Figure 14: Crack path in the MS of a CT specimen containing a MS-TiC cylindrical reinforcement (**a**) and in the MS-TiC cylindrical insert (**b**). Figure 13: Crack paths in the metal which surrounds the MS-TiC topologies (MS) according to the failure stress of the DEM bonds. The red color of the bonds corresponds to the MS failure stress. Gyroid_01 (**a**), Gyroid_02 (**b**), octet-truss (**c**), FBCCZZ_01 (**d**), FBCCZ_02 (**e**) and FBCCZ_03 (**f**). the crack path in the CT specimen. In all cases, this seems to create a decohesion between the MS-TiC topological structure and the MS matrix (Figure 15). Table 3 is a summary of the results obtained for each specimen configuration. From this table, a ranking of the topological structures according the absorbed energy is proposed: 1. FBCCZ_01, 2. FBCCZ_02, 3. Gyroid_01, 4. FBCCZ_03, 5. Octet-truss and 6. Gyroid_02.
## 4 Impact of Network Latency on Teleoperation
### CAV Teleoperation Requirements
In that case, it will be feasible for the current networks to handle a vehicle density of about 10 vehicles per kilometre square (veh/km\({}^{2}\)) [91]. Most studies focus on driving at low speeds, such as less than 50 km/h [32]. However, a few studies suggest the maximum speed teleoperated vehicles can tolerate is 100 to 250 km/h, above which performance will degrade significantly [41]. Therefore, to avoid such degradation, it is recommended to perform remote driving at low-to-medium speeds rather than at high speeds. With regards to distance, CAV teleoperations can be conducted over long distances, for example, 5200 km in [65] and 19,000 km in [90].