esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The positive airway pressure (PAP) is a gold standard in therapy for obstructive sleep apnea (OSA) patients, though weight loss is among the most effective supportive therapeutic methods. The aim of the study is to conduct a systematic review of randomized controlled trials (RCTs) of diet therapy interventions for OSA patients treated with PAP. The systematic review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42023398374). The 1436 records were screened and five records were included identified as RCTs available within PubMed and Web of Science databases until 1 February 2023. Two researchers independently conducted identification, screening, inclusion, and evaluation of RCTs, using the revised Cochrane risk-of-bias tool for randomized trials. The studies were conducted in groups with patients treated or intended to be treated with CPAP, while patients with moderate-to-severe or severe OSA were included. Within all included studies, an experimental diet was compared with a control group; however, various interventions were applied, including dietary intervention, which was compared with no intervention, and dietary and physical activity intervention, which was only compared with general advice. The applied intervention included various dietary approaches, such as various energy deficits or dietary health-promoting behaviors. The monitored variables included apnea-hypopnea index (AHI), sleep time, sleep efficiency, sleep latency, oxygen desaturation episodes, oxygen saturation, OSA type, Epworth Sleepiness Scale score, Pittsburgh Sleep Quality Index, and quality of life. For the majority of studies, some concerns were defined for the risk of bias; however, for one study the risk was high. Three studies supported the positive influence of diet therapy, one study somewhat supported it and one study did not support it. It may be stated that for excessive body mass individuals, even if PAP is applied, the diet therapy should be included in order to reduce body mass, reduce the symptoms of OSA, and improve the quality of life. This therapeutic option should be applied, even if no effect on OSA is noted, as body mass reduction have multiple positive effects, which may also influence the quality of life.  
obstructive sleep apnea (OSA); diet; diet therapy; positive airway pressure (PAP) therapy; dietary education; body mass reduction
## 5 Conclusions  
This work introduces a refined WDM-PON-FSO framework incorporating OFDM and LDPC decoding to address fronthaul requirements in 5G and beyond C-RAN deployments. The adoption of LDPC decoding significantly strengthens resistance to channel impairments by mitigating noise and turbulence-induced errors. Through iterative decoding, LDPC codes offer additional robustness, ensuring reliable performance even in challenging atmospheric conditions. Meanwhile, the use of OFDM boosts spectral efficiency and miti  
Figure 4: PAPR versus OSNR plots.  
gates multipath fading effects, enabling high data rates and effective bandwidth allocation in dynamic FSO channels.  
Simulation and theoretical investigations underscore LDPC's pivotal contribution to sustaining low BER, preserving signal quality, and integrating seamlessly with WDM-PON and FSO setups. Notably, the LDPC-coded system extends the maximum reliable range to roughly 2000 m, whereas the original system experiences sharp BER deterioration past 1250-1625 m. This improvement underlines LDPC's effectiveness in enhancing link stability over longer distances. Combined with OFDM's adaptability and spectral efficiency, the proposed design presents a robust and scalable approach to high-capacity fronthaul networks. These findings highlight the potential of the proposed system to meet the demanding performance targets of 5G and forthcoming network generations.  
Conceptualization, D.A. and F.E.-N.; Methodology, D.A. and F.E.-N.; Software, D.A. and F.E.-N.; Validation, D.A. and F.E.-N.; Formal analysis, D.A. and F.E.-N.; Investigation, D.A. and F.E.-N.; Resources, D.A. and F.E.-N.; Data curation, F.E.-N.; Writing--original draft, D.A. and F.E.-N.; Writing--review & editing, F.E.-N.; Visualization, D.A. and F.E.-N.; Supervision, F.E.-N.; Project administration, F.E.-N.; Funding acquisition, D.A. and F.E.-N. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2023/01/26720).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 3 Optimization
### Antennas and GA  
When designing antennas, there may be a need for a particular desired shape or desired multiband or even multifrequency operation. Optimization methods find application in this important case as well. In 1994, the authors of [172] proposed the use of GA in antenna design. Later in 1996, a deductive antenna design method was proposed to specify the desired electromagnetic properties, resulting in the so-called genetic wire antennas [173]. The authors of [174; 175] then developed this approach and applied it to synthesize wire antennas loaded with concentrated components. In 1999, this combination was used to simultaneously control both achievable gain and grating lobe reduction in the optimal synthesis of Luneberg lens antennas in [176; 177; 178]. In [179], GA was also used to optimize the maximum level of the side lobes of a planar array. Under the same work title, researchers used GA for amplitude and phase adaptive zeroing in [180], and the same has been undertaken later in [181]. The same optimization method was used to design automobile antennas in [182], patch antennas with circular polarization in [183], and uniformly excited broadband low sidelobe linear and planar antenna arrays in [184]. A new application of GAs to design rectangular microstrip antennas was presented in [185; 186; 187; 188; 189]. Using FDM and GA, the authors of [190] designed a compact broadband low-profile antenna. In [191], GA was used to synthesize a directional circular arc array pattern to minimize the bit error rate in an indoor wireless communication system. In [192], optimized shapes of multiband microstrip antennas using GA were investigated. For two-, three-, and four-band microstrip shapes, the obtained antennas showed good performance at the design frequencies.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 6 Open Issues and Research Challenges/ Future Challenges and Research Opportunities
### Consumer's Satisfaction  
_What is the challenge?_  
EVs can be charged at a public site, i.e., a mall, park, office, etc., or at a private location, i.e., home charging. However, the charging time is a serious concern that needs to be tackled efficiently. Home charging requires a longer charging time which can affect the overall efficiency of EV charging. However, EV charging at a public location tends to take less time, but it requires a complex infrastructure that can be quite costly for EVs.  
_Possible solution: Charging time_  
The feasible solution to resolve issues with charging time is to design an efficient EV charging infrastructure that also has a low cost so as to make it affordable for EVs.
# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 3 The Proposed Method  
An improved density peaks clustering algorithm based on divergence distance and the tissue--like P system is presented in this section. In the beginning, the divergence distance is introduced into the DPC algorithm, and clustering centers are automatically selected by score values. The whole algorithm is carried out within the structure of the tissue--like P system. Then, distinct rules and operations are respectively presented. Finally, algorithm complexity analysis is performed.
## 7 Discussion  
The proposed technique ECKF has been introduced to estimate the sequence components of biased, unbalanced, and distorted grid voltages caused by the existence of DC offsets generated from the measurement and A/D conversion stages.  
The simulation results show that the conventional ECKF provides a poor response compared with the modified ECKF in the case of biased grid voltages. In addition, an experimental setup is introduced where the FCS model predictive current controller is used and the reference currents are generated based on the positive and negative fundamental components estimated by using the conventional and modified ECKF. Experimental results have been introduced to validate the priority of the proposed ECKF over the conventional one in the case of unbalanced and distorted conditions. As illustrated in Table 3, the current THD in the case of modified ECKF is reduced by 80% compared with the conventional one.  
In general, the modified ECKF estimator achieves less total harmonic distortion and corrects current magnitudes and phase angles as compared with the conventional ECKF filter presented in the literature.  
It is worth noting that this proposed ECKF is unable to deal with even harmonics due to possible nonlinearities and asymmetries, which are postponed for future work. This point can be useful for current tracking in grid-tie uncontrolled rectifier applications.
## 4 Proposed Learning-Based Optimization Scheme  
We propose a supervised learning-based deep neural network that learns the non-linear mapping of channel parameter \(\left|h_{k}\right|\) to an optimal solution for transmit power and uplink/downlink allocation time \((P^{*},\tau^{*})\) where \(\tau^{*}=[\tau^{*}_{0},\tau^{*}_{1},\tau^{*}_{2}\dots\tau^{*}_{k}]\).
## 3 Experiments
### Visualization of Attention Mechanism  
We picked a comment sentence with changing sentiment features as a case study and visualized the attention results. To make the visualized results comprehensible, we removed the deep neural network module to make the sentiment attention mechanism directly work on the word embedding, thus we can check whether the effect of the attention mechanism conforms with human's attention performance. The visualization results are shown in Figure 5.  
In Figure 5a, we can find that the distribution of attention weights is relatively uniform throughout the sentence when the attention mechanism is initialized. This is because the parameters of the attention mechanism are uniformly distributed during the initialization stage, which makes the attention score of each word similar. This phenomenon is similar to the performance of human's attention, when a human is asked to read a sentence, he will always give an overview of all the words. As the training process progresses, the attention mechanism gradually focuses on words that contribute a lot to the overall sentiment polarity of the sentence. This is related to the supervised learning process, in which the attention mechanism interactively learns the relationship between the context and the sentiment words to optimize the performance of text sentiment classification. In the same way, after reading a sentence, humans only pay attention to several key parts of this sentence, which helps to understand the general meaning of this sentence. As shown in Figure 5b, the ultimate result of the attention mechanism mainly highlights three words (i.e., \"fails\", \"but\", and \"adaptation\"), which are in line with the sentiment polarity of this sentence. Finally, with the purpose of improving the quality of word embedding, the sentiment attention mechanism is combined with the conventional word embedding, which serves as the input layer of the deep neural network to extract text structure information. From the perspective of the overall training process of the model, the sentiment attention mechanism will highlight distinguishable words contributing to the orientation of sentiment polarity, so that the model can avoid blindly learning all the context words in the next step.
## 2 Methods
### Spatial-Spectral Cooperative Attention Module (SSCA)
#### 2.2.1 Spectral Attention Branches  
In the spectral attention branch, this paper adopts a dual-channel processing structure. The first channel constructs the spectral weight matrix \(M_{r}(X)\), through global average pooling followed by a fully connected layer, focusing on modeling global dependencies across spectral bands. The second channel introduces a spectral enhancement module that nonlinearly transforms the original spectral features to generate the enhanced spectral features \(Y_{1}\):  
\[Y_{1}=Avg3d(X)+f^{3\times 3\times 3}\ (X-(Avg3d(X)), \tag{2}\]  
\[M_{r}(X)=\sigma(F_{c}(Avg3d(f^{3\times 3\times 3}(X))), \tag{3}\]  
where \(f^{3\times 3\times 3}\) represents a 3D convolution operation used to extract spectral-spatial features from the input spectral features. \(Avg3d\) performs global average pooling on the extracted spectral-spatial feature map. \(F_{c}\) is then passed through a fully connected layer for further processing. The Sigmoid activation function is applied to the final feature map to generate the spectral attention weight \(M_{r}(X)\) in the \([0,\ 1]\) range.  
In the spectral weight learning branch, the input feature map first undergoes two consecutive 3D convolution layers. After global average pooling (GAP), the spectral weight map \(M_{r}(X)\) is generated by a convolution layer followed by the Sigmoid activation function.  
In the parallel spectral information extraction branch, the input feature map first undergoes 3D average pooling (\(Avg3d\)) to extract low-frequency information \(X_{1}\). The high-frequency information \(X_{2}\) is obtained through residual computation, which is the difference between the input feature map and \(X_{1}\); \(X_{2}\) is then processed through a 3D convolution and added to the low-frequency information \(X_{1}\) to obtain the spectral-enhanced feature map \(X_{3}\).  
After the spectral weight extraction is completed, the learned spectral attention weight map \(M_{r}(X)\) is element-wise multiplied with the feature map \(X_{3}\) output from the spectral enhancement branch, achieving adaptive weighting across different spectral bands.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 4 Graph-Based RA in Cellular Homogeneous and Het-Nets
### Miscellaneous Methods
The channel allocations which provide the highest utility (maximum weight) for the users in each independent set, are then found.
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Received Power vs. Distance Across Frequencies in the FSPL Model  
Figure 7 illustrates the impact of frequency on received power. The analysis compares three frequencies: 24 GHz, 28 GHz, and 32 GHz, which represent typical 5G mmWave frequencies. The results clearly show that higher frequencies are associated with increased path loss, particularly over longer distances, due to the relationship between frequency and free-space attenuation.  
Figure 5: Effect of satellite height on received power (short range).  
Figure 6: Heatmap of received power (dBm) vs. distance and satellite height.  
Figure 7: Effect of frequency on path loss in free-space model.  
At 1000 km, the received power at 24 GHz is approximately \(-125\) dBm, whereas it drops to around \(-128\) dBm at 28 GHz and \(-130\) dBm at 32 GHz. This 5 dB difference between 24 GHz and 32 GHz highlights the increasing sensitivity of higher frequencies to path loss over longer distances. By 3000 km, the received power at 24 GHz falls to around \(-145\) dBm, while at 28 GHz and 32 GHz, it reaches approximately \(-148\) dBm and \(-150\) dBm, respectively. The cumulative increase in path loss with frequency becomes evident over long-range communication, as higher frequencies experience greater attenuation.
## 4 Bringing CRKG into the Field: Securing Practical IoT Communications
### Short-Range IoT Communication Solutions
#### 4.1.3 IEEE 802.11/WiFi  
IEEE 802.11/WiFi is the dominant wireless technique for the local area networks, which is incorporated in almost all smartphones, laptops, tablets, etc. IEEE 802.11 defines the physical layer and MAC layer primitives, and many of these features are beneficial for key generation.  
IEEE 802.11 adopts different physical layer modulation techniques, including OFDM, DSSS, and MIMO. For all the physical layer modulations, RSSI at the receiver represents the channel quality and is available at all commercial WiFi transceivers, which makes it the most popular channel parameter for CRKG. In addition, OFDM is used in the IEEE 802.11a/g/n/ac, which provides high data rate with high spectral efficiency. The frequency domain channel estimation can be obtained by transmitting training symbols. This results in finer-grained channel information and allows capturing more channel randomness. However, channel estimation is not made public in many transceivers and specialized software defined radio platforms are required for access. The prototypes of IEEE 802.11 have been developed under both TDD and IBFD mode.  
Distributed coordination function (DCF) is the key component of the IEEE 802.11 MAC protocol. The receiver sends an acknowledgment (ACK) packet for each received data packet to confirm the successful reception. The time interval between these two transmissions is very low, in the order of microseconds. On the other hand, WiFi usually operates in the indoor environments when the channel experiences slow fading. The channel variation is usually introduced by the movement of humans, e.g., with speeds up to two meters per second. Therefore, the data and ACK packets can be leveraged to obtain highly correlated channel measurements. It is worth noting that key generation does not require extra and dedicated transmissions but can be achieved based on existing packets, therefore, the overhead can be kept at minimum.  
Some experimental results are shown in Figure 8, with IEEE 802.11g (OFDM) tested. The testbed consists of two WARP boards [53] and the experiments are carried out in an indoor office environment. The received power of [PERSON] and [PERSON] is shown as an example, where high correlation between the channel measurements of [PERSON] and Bob can be observed.  
Figure 7: Relative frequency of the single events with different processing approaches at 16 bit with preprocessing according to [52] and RBA. The red line shows the “perfectly” uniform distribution achieving maximum entropy.
# Terahertz Emission Enhanced by a Laser Irradiating on a T-Type Target
###### Abstract  
The generation of high field terahertz emission based on the interaction between an ultra-intense laser and solid targets has been widely studied in recent years because of its wide potential applications in biological imaging and material science. Here, a novel scheme is proposed to enhance the terahertz emission, in which a linearly polarized laser pulse irradiates a T-type target including a longitudinal target followed by a transverse target. By using two-dimensional particle-in-cell simulations, we find that the electron beam, modulated by the direct laser acceleration via the interaction of the laser with the longitudinal solid target, plays a crucial role in enhancing the intensity of terahertz emission and controlling its spatial distribution. Compared with the single-layer target, the maximum radiated electromagnetic field's intensity passing through the spatial probe point is enhanced by about one order of magnitude, corresponding to the terahertz emission power increasing by two orders of magnitude or so. In addition, the proposed scheme is robust with respect to the thickness and length of the target. Such a scheme may provide important theoretical and data support for the enhancement of terahertz emission efficiency based on the ultra-intense laser irradiation of solid targets.  
terahertz emission; T-type target; laser-plasma interaction; PIC simulation; electron acceleration +
Footnote †: journal:  
Article
# A Vision-Based Approach for Autonomous Motion in Cluttered Environments
## 3 Methods
### Perception System
#### 3.2.3 Raycast Model  
The occupancy grid map used for motion planning includes two states: occupied and free. In Section 3.2.2, we judged that some cells observed by the camera are in the occupied state. The Raycast operation is to further judge the occupied state of other related cells based on the current state information of the robot and the calculated obstacle information.  
The principle of the Raycast Model is shown in Figure 4. The location information of the vehicle can be estimated by the T265 camera, while the location information of the obstacles can be obtained by performing a calculation using the depth map. If there are other obstacles in the middle cell (the blue cells in the figure) where the line between the vehicle position and the obstacle position passes, the obstacles will be blocked and cannot be observed by the camera. Therefore, the blue cells in the figure should all be free states. Then we use the Bresenham algorithm to speed up the calculation of the passing nodes.  
In our system, the inputs are the robot pose and depth map. When we obtain the pose and depth map, we can traverse the depth map to obtain the position of each obstacle observed by the camera. The corresponding cells of the obstacle in the occupation grid map are judged to be occupied, and the cells passed by the line segment connecting the robot position to the obstacle position are judged to be free.