esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Simulation Results
Figure 10a shows the channel utilization of the Slotted ALOHA algorithm, while Figure 10b presents the channel utilization of the STCA algorithm without SIC. The channel utilization of the STCA algorithm with SIC is shown in Figure 9, which is consistent with the results of the STCA algorithm without SIC and, therefore, is not repeated here. It is evident that the relatively simple Slotted ALOHA algorithm produces relatively stable experimental results. Similarly, the STCA algorithm proposed in this paper also exhibits stable results, with its channel utilization significantly outperforming that of the Slotted ALOHA algorithm. Figure 10c illustrates the throughput of the IRSA algorithm, while Figure 10d shows the throughput of the STCA algorithm without SIC. The results indicate that the proposed STCA algorithm demonstrates superior stability compared with IRSA, with throughput remaining consistently high without any decline. Channel utilization (Figure 10a,b) reflects the efficiency of TTS slot usage, while throughput (Figure 10c,d) measures the rate of successful data access. For STCA algorithms, high throughput directly translates into sustained high channel utilization. Compared with IRSA (Figure 10c), which shows fluctuating throughput under varying loads, STCA without SIC (Figure 10d) maintains consistently high throughput, validating its superior stability. As shown in Figure 10, the channel utilization of the proposed STCA with SIC (Figure 10b) fluctuates slightly during system operation, while SA (Figure 10a) exhibits milder fluctuations. This is because the higher complexity of STCA with SIC increases the  
Figure 9: (**a**) Channel utilization comparison between STCA without SIC and Slotted ALOHA. (**b**) Channel utilization comparison between STCA with SIC and IRSA. (**c**) Channel utilization comparison between STCA with SIC and Slotted ALOHA. (**d**) Channel utilization comparison between STCA with VFL and Slotted ALOHA. difficulty of prediction and estimation. However, its higher channel utilization makes such minor stability issues negligible.
## 2 Principles
### Geometrically Shaping of Odd-Bit QAM Constellations for Compatibility with the PCPE Algorithm  
In terms of improving the adaptability of the signal structure to the receiving-end CPR algorithm through the GS technology, optimization schemes for the BPS algorithm [22], the differential BPS algorithm [23], and the pilot-based phase recovery algorithm [24] have been reported in the current literature. However, the GS scheme costumed for the PCPE algorithm has not yet been reported. To solve the incompatibility between the PCPE algorithm and odd-bit QAM signals, we adopted the PO algorithm, and set the objective function in the PO process to the MI performance of odd-bit QAM signals after PCPE recovery under specified SNR and line width. Moreover, to further simplify the geometrical designing process, we adopted the same approach as in [27], that is, only the constellation points in the first quadrant were subjected to the PO process, and then flipped along the in-phase axis and quadrature axis, respectively, to obtain the overall constellation.  
During the PO process, the MI performance corresponding to each constellation is the average after 100 Monte Carlo simulations with a symbol rate of 32 GBaud, a combined linewidth of 200 kHz, a total symbol sequence length of 16,384, and a block length of PCPE phase recovery of 64. As for the specified SNR, it is 13 dB for 32 QAM and 18 dB for 128 QAM, these parameter settings refer to the SNR required for both signals to reach the 20% SD-FEC threshold when only affected by AWGN. In the GS process, the structure of the constellations is adjusted to maximize the MI values that can be achieved by PCPE under these conditions, which is conducive to reducing the SNR cost required for reliable communication as much as possible in practical applications.  
The MI trace curves corresponding to GS-32 QAM and GS-128 QAM signals are shown in Figure 4a,b. Notably, post the PO process, effective resolution of the incompatibility issue between PCPE and odd-bit QAM signals is observed. Substantial enhancements in MI performance are noted under the evaluated SNRs for both 32 QAM and 128 QAM, approximately 0.33 bits/symbol and 0.72 bits/symbol, respectively. The optimal constellation diagrams for these two modulation formats are depicted in Figure 5a,b, while their corresponding squared constellation diagrams are presented in Figure 5c,d. A comparative analysis of the constellation structures in Figures 2 and 5 reveals that the GS process facilitates the transformation of the odd-bit QAM constellation diagram from a regular cross shape to a square shape. This transformation imparts a constellation structure akin to that of even-bit QAM, playing a pivotal role in elevating the estimation accuracy of the PCPE algorithm.  
Figure 4: MI trace curves for (**a**) GS-32 QAM and (**b**) GS-128 QAM.
## 2 Measurement Setup and Technique
### Equipment  
During the data collection process, a Tannoy System 600 loudspeaker (Tannoy Ltd.: Coatbridge, Scotland) served as the sound source. It has a frequency response with \(\pm\)3 dB tolerance bandlimited to 52-20 kHz with cross-over frequency at 1.8 kHz [23].  
The RIRs were measured using two microphone arrays: the Rode NT-SF1 microphone (Rode: Sydney, Australia) and the mh acoustics em32 Eigenmike (mh acoustics: Summit, NJ, USA). The NT-SF1 comprises four matched condenser cardioid microphone capsules arranged in a tetrahedral array. It is capable of capturing spatial sound in first-order ambisonics with a frequency response of \(\pm\)3 dB tolerance in the range of 30-20 kHz [24]. The em32 consists of thirty-two omnidirectional electret microphones mounted on a rigid sphere of 8.4 cm diameter. It can capture spatial sound up to fourth-order ambisonics with a frequency response bandlimited to 30-20 kHz, offering a nominally flat response up to 8 kHz [25; 26].  
The source and the receivers are shown in Figure 2. The Eigenmike and NT-SF1 were connected to a computer using the Eigenmike Interface Box (EMIB) and the Zoom F6 [27], respectively. Note that the Zoom F6 interface was configured to record in Ambisonic B-format with a Furse-Malham (FuMa) normalization scheme.
# mIoT Slice for 5G Systems: Design and Performance Evaluation
## 5 Performance Evaluations
### Simulation Results and Discussion
When the device density increases, the CP signaling goes close to zero as a very large portion of devices is affected by RA collisions and it is not thus able to send a connection request towards the CN. It is worth noting that, when reaching the maximum load the radio can support (-7 \(\times\) 10\({}^{6}\) UE/km\({}^{2}\) and -10\({}^{6}\) UE/km\({}^{2}\) for AP = 10 min and AP = 2 min, respectively), 5 GS mIoT slice reduces the signaling towards the CN of about 97% and 83% for AP = 10 min and AP = 2 min, respectively. This is because most of CP traffic is bearer re-setup which is a heavy load for 4 GS while 5 GS mIoT slice drastically reduces this signaling. Consequently, 5 GS mIoT slice can also avoid CP overload in scenarios where the radio is overloaded. It is worth underlining that the analysis in Figure 9 considers only one RAN node, and that practical scenarios with thousands of RAN nodes connected to the same CN would generate an overall CP signaling from three to four orders of magnitude higher, thus collapsing the CN. The analysis represented in Figure 9 shows that 5 GS mIoT is successfully designed to drastically reduce the CP signaling even in scenarios with massive load on the RAN, meaning that 5 GS mIoT can effectively be adopted in 5G deployments with higher RAN  
Figure 8: UP Resource Utilization among different classes of devices. For 5 GS mIoT, all classes of devices have U = 100 and T = 1 min. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Traffic Type** & **Number of EPS Bearers (4G)** & **Number of ACN Bearers (5 GS mIoT)** \\ \hline Water meters & \textasci{-8.7}\times 10^{6}\) & \textasci{-9}\times 10^{4}\) \\ Electricity meters & \textasci{-4.3}\times 10^{6}\) & \textasci{-5}\times 10^{4}\) \\ Gas meters & \textasci{-1}\times 10^{8}\) & \textasci{-1}\times 10^{6}\) \\ Bike fleet management & \textasci{-2}\times 10^{6}\) & \textasci{-3}\times 10^{4}\) \\ Pay-as-you-drive & \textasci{-2.3}\times 10^{7}\) & \textasci{-2.4}\times 10^{5}\) \\ \hline \hline \end{tabular} EPS: Evolved Packet System; ACN: Aggregate Core Network.  
\end{table} 
Table 3: Number of enabled bearers for different classes of devices. capacity compared to 4G systems (thus generating more traffic towards the CN) without overloading the CN. This means that 5 GS mIoT slice could allow RAN nodes to work close to their maximum congestion levels without affecting the CP entities in the CN.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 5 Numerical Results
### DSP and BER
Figure 8: DSP versus the overhead (\(\epsilon\)) for different coding schemes at \(k=128\) and erasure probabilities of \(\varepsilon_{1}=0.01\), \(\varepsilon_{2}=0.01\), and \(\varepsilon_{3}=0.1\). Figure 7: Bit error rate (BER) versus the overhead (\(\epsilon\)) for different coding schemes at \(k=128\) and erasure probabilities of \(\varepsilon_{1}=0,\varepsilon_{2}=0\), and \(\varepsilon_{3}=0\). Moreover, Figures 10-13 show the BER of the same schemes at a larger length of source symbols, \(k=256\), under many channels' environments. For example, Figure 10 shows the BER in perfect channels between all nodes. We can see that the best algorithm is our proposed algorithm when it applies to the DRSD (i.e., MBDRSD). In addition, Figure 11 is depicted in a different scenario, where a perfect channel is between the relay and the destination (\(\varepsilon_{3}=0\)), whereas homogeneous channels are between each sender and the relay (\(\varepsilon_{1}=0.01\) and \(\varepsilon_{2}=0.01\)). Moreover, Figure 12 depicts a scenario in which all three channels have the same erasure probability (\(\varepsilon_{1}=\varepsilon_{2}=\varepsilon_{3}=0.01\)). Finally, Figure 13 is obtained when the erasure channel between the relay and the destination is \(\varepsilon_{3}=0\), and the erasure channels between each sender and the relay are \(\varepsilon_{1}=\varepsilon_{2}=0.01\). As can be seen from the figures, the MBLT codes are superior to the regular LT codes. Moreover, similar improvements are obtained once the proposed scheme is applied to the DRSD. Figure 10: BER versus the overhead (\(\epsilon\)) for different coding schemes at \(k=256\) and erasure probabilities of \(\varepsilon_{1}=0\), \(\varepsilon_{2}=0\), and \(\varepsilon_{3}=0\). Figure 9: BER versus the overhead (\(\epsilon\)) for different coding schemes at \(k=128\) and erasure probabilities of \(\varepsilon_{1}=0.01\), \(\varepsilon_{2}=0.01\), and \(\varepsilon_{3}=0.1\). Figure 11: BER versus the overhead (\(\epsilon\)) for different coding schemes at \(k=256\) and erasure probabilities of \(\varepsilon_{1}=0.01\), \(\varepsilon_{2}=0.01\), and \(\varepsilon_{3}=0\). Figure 12: BER versus the overhead (\(\epsilon\)) for different coding schemes at \(k=256\) and erasure probabilities of \(\varepsilon_{1}=0.01\), \(\varepsilon_{2}=0.01\), and \(\varepsilon_{3}=0.01\). Figure 13: BER versus the overhead (\(\epsilon\)) for different coding schemes at \(k=256\) and erasure probabilities of \(\varepsilon_{1}=0.01\), \(\varepsilon_{2}=0.01\), and \(\varepsilon_{3}=0.1\).
## 4 Results and Discussion
### Comparison of Helmets
#### 4.3.1 Positioning  
Most commercial helmets have rigid structures and come in standard sizes such as small, medium, and large. Although these sizes are designed to approximate electrode  
Figure 35: (a) Power band, (b) focus widget of Subject 9.  
positioning according to international standards, they do not ensure precise alignment due to the variability in the shapes and sizes of the cranial joints.  
This significant diversity in head shapes and sizes makes it difficult for a fixed-size design to fit properly across all anatomical configurations. Consequently, standard commercial helmets often do not position the electrodes accurately, which can affect the quality and precision of the measurements.  
This study compares the actual positions of the F3 and F4 points, as defined by the international 10-20 system, with the electrode placements of the Ultracortex Mark IV and Emotiv EPOC headsets. The results indicate that these headsets do not always align precisely with the intended electrode positions, as shown in Figure 36 for the Ultracortex headset and Figure 37 for the Emotiv EPOC headset.  
Figures 36 and 37 illustrate how fixed-structure helmets can alter electrode positioning, while the Chameleon headset eliminates these discrepancies thanks to its adjustable electrodes, ensuring precise alignment. This is due to its ability to adapt to head width and allow electrode placement at different positions as needed, as shown in Figure 38.  
The positioning error of the F3 and F4 points was evaluated based on 30 measurements for each headset, comparing the actual positions with those registered by the devices. For the Emotiv headset, the average error was 2.55 cm with a standard deviation of 0.97 cm. Meanwhile, the Ultracortex headset showed an average positioning error of 2.85 cm with a standard deviation of 0.784 cm.  
It is crucial to note that the proposed helmet offers considerable versatility. Its electrodes can be easily placed at various locations within international reference sys  
Figure 37: Comparison of the F3 and F4 electrode positioning according to the international 10–20 system and their placement in the Emotiv helmet.  
Figure 36: Comparison of the F3 and F4 electrode positioning according to the international 10–20 system and their placement in the Ultracortex helmet.  
tems, providing a significant advantage over other commercially available helmets with fixed configurations.  
In addition, more tests with women are presented in Appendix A to demonstrate the versatility, effectiveness, efficacy, and great performance of the Chameleon-1 helmet.
## 4 Taxonomy of MAC Protocols Based on Their Use of WuR Technology
### MAC Protocols with WuR  
A TDMA-based scheme combined with WuR can be used to design a power efficient MAC. Authors of [28; 60; 61], have proposed TDMA-based MAC protocols for IBSN with WuR. Use of WuR can minimize the extra power consumption by the RTS-CTS packet exchange which is done by the main radio. The packet inter-arrival parameters are re-configurable values for each node. For example, in the case of a patient, a doctor/nurse or in-charge person can set the packet inter-arrival time for temperature monitor to be 6 h or 21,600 s. This will cause the central node controller (CNC) to send a WuR signal to the particular node after the specified intervals and complete the data communication. The node, between two consecutive wake-up periods, can switch off its main radio and go to the sleep state to save power. It does not have to contend for data communication.  
An ultra-low-power and traffic adaptive protocol designed for BAN is discussed in [71]. They used a traffic adaptive mechanism to accommodate on-demand and emergency traffic through wake-up radio. Authors of [60] have proposed a MAC protocol which supports dependability and QoS guarantee for the most important life-critical message and majority of real-time traffic. The protocol can be used with different physical layers: UWB, MICS, WMTS, HBC. A dynamic network size greater than six nodes to less than 100 nodes per network can be achieved. An improved quality-of-service addresses throughput, access latency, and priority. High scalability is realized. Star, cluster-tree and peer-to-peer, are supported in the MAC protocol proposed in [61]. Table 10 comprehends the MAC protocols with WuR based on the special feature and potential drawback with respect to the IBSN scenario.
## 4 Experiments and Discussions  
### Experimental Configuration Framework  
In this study, we utilized the YOLOv5-3.1 code base as our starting point, and the network was trained using the AdamW optimizer with an incipient learning incidence of 0.01. To enhance training, we used a cyclic learning rate of 0.2 and a weight decay of 0.0005. Throughout the experiment, we ran 200 epochs with batch sizes of 8. During the warm-up stages, the momentum was set to 0.8 and then changed to 0.937. All picture input sizes were changed to 640 \(\times\) 640 by web preprocessing. For the sake of equitable comparison, we employed the default data augmentation method and did not use any pre-training models.  
The computer setup utilized in this experiment consisted of a PC platform with an Intel Core i5-9300H CPU running at 2.4 GHz, -an NVIDIA GeForce GTX 1660 Ti GPU, 16 GB of memory, and the Windows 11 operating system. The development framework employed was PyTorch, specifically Python version 3.7.11 with Torch version 1.7.0 and Torchvision version 0.8.1.
# An FPGA Design with High Memory Efficiency and Decoding Performance for 5G LDPC Decoder
###### Abstract  
A hardware-efficient implementation of a Low-Density Parity-Check (LDPC) decoder is presented in this paper. The proposed decoder design is based on the Hybrid Offset Min-Sum (HOMS) algorithm. In the check node processing of this decoder, only the first minimum is computed instead of the first two minimum values among all the variable-to-check message inputs as in the conventional approach. Additionally, taking advantage of the unique structure of 5G LDPC codes, layered scheduling and partially parallel structures are employed to minimize hardware costs. Implementation results on the Xilinx Kintex UltraScale+ FPGA platform show that the proposed decoder can achieve a throughput of 2.82 Gbps for 10 decoding iterations with a 5G LDPC codelength of 8832 bits and a code rate of 1/2. Moreover, it yields a check node memory reduction of 10% with respect to the baseline and provides a hardware usage efficiency of 4.96 hardware resources/layer/Mbps, while providing a decoding performance of 0.15 dB better than some of the existing decoders.  
5G new radio; decoder architectures; Field Programmable Gate Array (FPGA); Low-Density Parity-Check (LDPC) codes; Min-Sum algorithm Article  
An FPGA Design with High Memory Efficiency Performance for 5G LDPC Decoder  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 2 Principles of Light Polarization and PS-OCT
### Technical Approaches to PS-OCT  
During the past 25 years, a great variety of PS-OCT layouts has been devised. PS-OCT schemes differ in terms of optical technology (fiber optics vs. bulk optics), number of input states, number of detected variables, and reconstruction algorithm. The use of free-space beams in bulk optics permits defined polarization states at any location within the interferometer. Fiber optics provide easier system alignment, but the polarization of light will in general be influenced by birefringence and polarization mode dispersion in optical fibers. PS-OCT has been performed with as little as one input state and one detected intensity signal. Such settings correspond to regular OCT, however with altered reference polarization for cross-polarization imaging [32,33] or for imaging with variable reference polarization [34]. In contrast, the most comprehensive PS-OCT approaches detected up to 16 elements of the sample's Muller matrix--in every single image pixel [35, 36, 37, 38]. In the following, we are going to describe two major categories of PS-OCT schemes: PS-OCT with a single circular input stage and PS-OCT based on sample illumination by multiple polarization states.
## 2 Related Work
### Synchronization  
Some features are not yet included in Version 1.0 of the DCP standard, such as clock synchronization. In the Ref. [7], the authors discussed different clock synchronization mechanisms. These might be required if DCP participant clocks differ in the ratio of speed and offset, resulting in the oversampling or undersampling of variables. A synchronization approach is implemented in the present work, see Section 3.3.
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 3 Massive MIMO Performance Gains
### Spatial Diversity Gain  
In wireless communication system, the received signal suffers from the effects of propagation environment. To achieve high link quality, special diversity techniques are used to improve the received signal quality through multipath propagation like MIMO system. The idea of diversity includes providing each terminal multiple copies of the transmitted signal. Therefore, the probability that one of the copies is not facing a deep fade effect rises. By growing the number of separated paths, the link probability also rises. Spatial diversity gain can be defined as the measurement of link reliability and the number of independent fading links as the diversity order. Massive MIMO system provides a large degree of freedom compared with the number of terminals, which means improving the channel reliability and the quality of received data. The link reliability is measured by the outage probability or the bit error rate. For Rayleigh fading channel the measurement of diversity depends on the degree of correlation between the fading coefficient random variables which they construct the massive MIMO channel matrix. Therefore, fewer correlations between channel coefficients lead to less fluctuation in the received signal, then link reliability can be expected. The diversity gain can limit the amount of variation in the received signal which can be computed by diversity measurement (\(D_{div}\)) as [27]  
\[D_{div}(C)=\frac{(trC)^{2}}{tr(C^{2})}. \tag{12}\]  
From expression (12), it can be noted that a high value of diversity referring to lower fluctuation in the received signal, where \(C\) is the correlation matrix for i.i.d Rayleigh fading channel matrix of massive MIMO system. The diversity gain is calculated according to the diversity measurement, which is the product of the diversity measurement at the downlink side and uplink side in terms of the correlation matrix as shown below  
\[D_{div}(C)=D_{div}(C)\times D_{div}(C_{UL}). \tag{13}\]  
The benefit of diversity measurement at both channels is to decide which of the massive MIMO channel propagation matrix has stronger fading correlation or more diversity, the correlation matrix at the downlink side for massive MIMO channel can be written as  
\[C_{DL}=E[G^{H}G]. \tag{14}\]  
While the correlation matrix for massive MIMO at the uplink side is  
\[C_{UL}=E[GG^{H}]. \tag{15}\]