esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mixed Method with Effective Color Reduction
## 4 Results and Discussion
### Results of BS+ITATCQ Applied Independently  
The results of BS+ITATCQ3 were compared to those of BS (Table A3) and ITATCQ (Table A4) applied independently. ITATCQ was executed during 25 iterations and the same \(\alpha\) values used to test BS+ITATCQ were considered.  
It can be clearly observed that the quantized image is of a better quality for the combined method in all cases. Obviously, the combined method consumes more time than each method applied independently.  
The amount of error is better for BS+ITATCQ3 than for BS from iteration 5 for all images and palette sizes. The percentage of error reduction obtained by the new method ranges in \([9.9\%,30.1\%]\) at iteration 5 and it ranges in \([10.9\%,32.3\%]\) at iteration 25. The largest differences obtained when comparing both methods correspond to the Caps image.  
The average MSE obtained by ITATCQ at iteration 25 is also worse than the result obtained using BS+ITATCQ3 at iteration 5 for all images and palette sizes. In addition, when comparing the results of iteration 25, the percentage of error reduction obtained by the second method varies in between 17.8% and 93.9%. In this case, the most significant differences correspond to the Mandrill image. Table A4 shows that the average error and the standard deviation of ITATCQ are very large for several images (especially for the Mandrill image). This is because the smallest \(\alpha\) values considered do not generate palettes with \(Q_{max}\) colors. The most extreme case occurs for the Mandrill image when \(\alpha=0.25\), in which the quantized image only includes one color and the associated error is very large.  
The study proposing the ITATCQ method reported that better results are obtained if the range of \(\alpha\) values considered for the tests is adjusted according to the size of the quantized palette [40]. Since this was not applied here, a second comparison was performed that considers the minimum MSE obtained by ITATCQ. In this case, the results of BS+ITATCQ3 at iteration 5 are better than those of ITATCQ at iteration 25 in all cases except for \(1\), corresponding to the Snowman image with 64 colors. In this case the difference is less than 0.3%. Therefore, ITATCQ errors are worse than those of the new method, even if the comparison considers the minimum error obtained by ITATCQ for each image and palette size.  
The execution time of BS+ITATCQ3 is not the sum of the execution time of BS and ITATCQ applied independently, as might be expected. This is because the results reported for ITATCQ are the averages for the set of \(\alpha\) values tested. The smallest \(\alpha\) values generate palettes with less than \(Q_{max}\) colors in some cases, so the execution time is reduced. Conversely, when ITATCQ is applied to the result of BS, the initial palette always includes the maximum number of colors allowed.
# Gold Nanoparticles for Drug Delivery and Cancer Therapy
## 6 Molecular Nanoprobes  
NPs interact with light and have strong tunable surface plasmon resonance, which can be detected using multiple imaging modalities [104]. Au NPs have the ability to passively accumulate on tumor cells. Au NPs are an attractive contrast agent for X-ray-based computed tomography imaging. Their capabilities of producing photoacoustic signal and photothermal effect are also very valuable in medical imaging and diagnostics [105]. Au NPs have optical properties that are useful in biosensors in living cells. Surface plasmon resonance scattering image for Au NPs conjugated to monoclonal anti-epidermal growth factor (anti-EGFR) receptor antibodies in non-malignant epithelial cell line (HaCAT), and two malignant oral epithelial cell lines (HOC 313 clone 8 and HSC 3) were studied. The antibody conjugated NPs specifically and homogeneously bind to the surface of the cancer type cell with 600% greater  
Figure 4: Relationship between the dose enhancement ratio and Au NP concentration with variation of the skin target thickness using the 105 (**A**) and 220 kVp (**B**) photon beams [103].  
affinity than to the noncancerous cells. This yields a sharper SPR absorption band with a red shifted maximum compared to that of the noncancerous cell. SPR scattering image generated from this antibody conjugated Au NPs are useful for diagnosis [106]. Ultra-small Au NPs less than 10 nm in diameter have proven to be promising in biomedical field. Their potential applications in cancer treatment and medical imaging have not yet been reviewed. There are several systems under development based on ultra-small Au NPs for use in diagnosis and cancer therapy. Some applications being developed include using ultra-small Au NPs for tumor visualization and bio-imaging in different fields such as magnetic resonance imaging, fluorescence imaging, photoacoustic imaging and x-ray scatter imaging. They are also being studied in tumor chemotherapy, radiotherapy and gene therapy [107].
## 5 Performance Analysis and Simulation
### Analysis of Communication Overhead  
Because the energy of WSNs is limited and lower energy consumption means a higher lifetime of the WSNs, so energy consumption is another key factor in WSNs. In this paper, the energy consumption is represented by communication overhead.  
Suppose that the communication overhead for each node to forward a data packet is the same. The communication overhead in this paper is represented by the number of nodes through which the data packets pass. Since the SN determines the PNCS in this paper, the SN is usually wired and has powerful hardware resources, the communication overhead of the SN is usually not considered in related researches. At the same time, there are initialization phases in all of the related researches and the costs are similar. Hence, we take the same approach as Reference [18], and do not analyze the communication overhead of initialization phase. Therefore, we mainly analyze the communication overhead of the phases of determining PN and the routing.
## 2 Materials and Methods
### Determination of Gravitational Potential along the Target Satellite Orbit
Figure 2: The satellite frequency signal transmission (SFST) technique for determining the gravitational potential difference (GP) between two satellites. Analogous to the satellite-to-ground link, we postulate that the GP difference between the GS and TS is measured using the SFST method, with the absolute GP values of the GS provided a priori. Consequently, this allows the GP values of the TS to be derived. The altitude of a typical GS is roughly 35790 km above the equator. Referring to Figure 3, in the scenario where the TS is a LEO satellite in a polar orbit, positioned approximately 500 km above the geoid, the distance between the target satellite and Earth's center is about 6870 km. The distance between a GEO satellite and Earth's center is about 42,170 km. When the TS is placed at the poles (N or S), it can connect the GS without being blocked by the Earth. The TS is visible until reaching the P point (if block threshold OH = 6400 km, the angle of PON = 12.8\({}^{\circ}\)). Thus, only two uniformly distributed GSs are necessary to maintain continuous SFST links between a GS and the TS, enabling the determination of the GP values of the TS's orbital sphere. However, in practical scenarios, it is advisable to employ three uniformly distributed GSs to ensure stable and reliable frequency signal connections (see Section 3.1). The GP distribution across the TS-sphere allows the gravitational field external to the TS sphere to be derived. Furthermore, employing the spherical harmonic expansion expression, the identified gravitational field can be extended downward to the Earth's surface [38]. Therefore, the GP values over the TS sphere can be used directly to construct an Earth Gravitational Model.
# Fully Metallic Additively Manufactured Monopulse Horn Array Antenna in Ka-Band
###### Abstract  
The Laser Powder-Bed Fusion Additive Manufacturing (LPBF AM) technique is evaluated for the manufacturing of fully metallic monolithic microwave components. To validate the manufacturing technique, a difference pattern array of \(4\times 4\) horn antennas is designed to operate at mm-Wave frequencies. The antenna is based on \(H\)-plane power dividers and a complex structure to obtain a difference radiation pattern by rotating twisted sections in two different orientations. The prototype is manufactured with a monolithic piece of aluminum alloy AlSi10 Mg, providing a lightweight single structure that includes both radiating elements and a feeding network consisting of twisters and power dividers in a waveguide. The prototype was experimentally evaluated in an anechoic chamber and the near-field planar acquisition range, obtaining good agreement with full-wave simulations within an operational bandwidth from 34 to 36 GHz. The results demonstrate that the LPBF AM technique is a suitable candidate to produce challenging monolithic metal-only microwave components in the K\({}_{a}\)-band, such as monopulse antennas.  
additive manufacturing: horn array antenna; LPBF; monopulse antenna; difference pattern +
Footnote †: journal: Applied sciences  
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# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### Benchmark Models
#### 3.6.4 CNN 3D-2 Layers [22]  
The model is built on the Keras framework and utilizes a two-layer 3D Convolutional Neural Network. It takes a tensor input with size [p \(\times\) p \(\times\) channels] and produces a reshaped output of size [p \(\times\) p \(\times\) channels \(\times\) 1]. The first layer has 64 2 \(\times\) 2 \(\times\) 1 pixel kernel filters, and the second layer has 32 2 \(\times\) 2 \(\times\) 1 pixel kernel filters; both layers use ReLU activation. In this structure, the hierarchical feature extraction process is enabled, and at the same time, the channel dimension is preserved. The feature maps from the second layer are flattened to form a 1D vector, which is fed into a fully connected layer of 16 hidden units with ReLU activation. The network is topped off with a dense output layer having [output_shape] nodes with Softmax activation, used to generate probabilities across all the target classes.
## 3 Results
### Kinematics and Kinetics  
#### 3.1.1 Angular Displacement and Torque  
At the ankle joint, there were no differences between older and younger adults in ankle dorsiflexion and plantarflexion angular displacement and torque during the SLS phase (Figure 2A,B). However, at the hip joint, older adults exhibited greater hip extension torque (F = 7.51, \(p\) = 0.01, \(n^{2}\) = 0.23), while younger adults showed greater hip abduction torque (F = 5.65, \(p\) = 0.03, \(n^{2}\) = 0.19, Figure 2D).  
Figure 2: Kinematics and kinetics during the single-limb support (SLS) phase (**A**). Ankle joint angular displacement (\({}^{\circ}\)) (**B**). Ankle joint torque (Nm/kg) (**C**). Hip joint angular displacement (\({}^{\circ}\)) (**D**). Hip joint torque (Nm/kg) (**E**). Trunk angular displacement (\({}^{\circ}\)) (**F**). Trunk Torque (Nm/kg). (**G**) Latency to the start of L m CoM displacement during the SLS phase. In the figure, asterisks (\({}^{\circ}\)) indicate statistically significant differences between older and younger adults (\(p\) < 0.05). The error bars represent the standard deviation. FL (flexion), EX (extension), Sec (second), L m (Lateral-to-Medial), CoM (center of mass).  
Greater trunk flexion (F = 8.68, \(p\) = 0.01, \(\eta^{2}\) = 0.26) and extension (F = 7.22, \(p\) = 0.01, \(\eta^{2}\) = 0.23, Figure 2E) angular displacement were observed in older adults. Additionally, older adults demonstrated greater trunk extension torque compared to younger adults (F = 5.15, \(p\) = 0.03, \(\eta^{2}\) = 0.17, Figure 2F). Older adults exhibited a delayed latency (time in seconds) to the start of L-to m displacement of the CoM from the perturbation onset compared to younger adults (F = 4.65, \(p\) = 0.04, \(\eta^{2}\) = 0.16, Figure 2G).  
1.2 Relationship between Trunk Flexion and the Latency to the Start of Lateral to Medial CoM Displacement  
Across all participants, there was a positive correlation between trunk flexion angular displacement (\(\lx@math@degree\)) and latency to the start of L-to m displacement of CoM from the onset of the surface drop (r = 0.41, \(p\) = 0.04, Figure 3B).
## 4 Results and Discussion
### Divergence of Attributes
#### 4.3.4 Cost  
Two studies agreed that the cost of the equipment and overall implementation cost of AR in the construction industry pose a challenge [36, 37]; however, [PERSON] et al. (2013) reported an opposite finding [44]. Upon comparing these three papers, we found that the type of devices used by [PERSON] et al. (2013) was a head-mounted AR device (example, HoloLens-2), whereas the other papers used an AR module embedded in a smartphone. It is not surprising that the cost of using an existing smartphone is relatively not as much of a challenge compared to other types of devices, also considering that a smartphone would already be available with most personnel working on site. This indicates that \"cost\" is another attribute that is directly related to the type of device in use.
## 6 Focused Challenges in 5G HetNet
Load balancing is another critical issue in 5G HetNet that arises due to the exponential increase in the number of devices and data volume. This issue arises because network traffic is not evenly distributed throughout, and some BSs may become overloaded while others remain underutilized. This can result in reduced network efficiency, increased latency, and dropped connections. The 5G HetNet uses advanced load-balancing techniques to address this challenge by dynamically distributing network traffic across BSs based on network conditions and resource availability. This ensures optimal network resource utilization, prevents any single BS's overload, and provides users with a seamless and reliable network experience. Signaling overhead in 5G HetNet is another significant issue that can impact network efficiency and performance. This issue arises because the HO process between BSs involves multiple signaling messages between the device and the network, which can result in excessive signaling overhead. This can lead to increased HO latency, reduced network efficiency, and dropped connections. To address this challenge, 5G HetNet uses advanced signaling mechanisms that minimize the number of signaling messages required and reduce the burden on the network. This includes using intelligent HO mechanisms, such as predictive and proactive HO, that can anticipate the device's movement and optimize the HO process. These advanced signaling mechanisms ensure a seamless HO process, improve network efficiency, and provide users with a reliable network experience. ICI in 5G HetNet refers to signal interference caused by signals from adjacent cells. The challenge arises because the radio frequencies used by different cells overlap, resulting in interference that can degrade signal quality and impact network performance. The ICI issue can lead to dropped connections, reduced network efficiency, and increased latency. To address this challenge, 5G HetNet uses advanced signal processing techniques such as interference coordination and suppression, which can reduce the impact of ICI in 5G HetNet. Finally, ensuring security and privacy protection for user data from unauthorized access is another critical challenge in 5G HetNet. This issue arises because 5G HetNet supports a wide range of applications, devices, and technologies that require varying levels of security and privacy protection. To tackle this issue while transmitting large volumes of data across 5G networks, 5G HetNet utilizes several advanced end-to-end encryption techniques, user authentication protocols, advanced technologies, and the use of secure virtual private networks (VPNs) to protect user data, ensure network security, and enhance network reliability. These measures ensure that user privacy is protected and 5G networks can be trusted to handle massive data securely. The main causes and consequences of these challenges are summarized in Table 10.
## 4 Energy Conserving Solutions in Wireless Networking-Based IoT
### Energy Conserving Solutions for WWAN-Based IoT
#### 4.1.3 Energy Conserving Solution for 3 GPP MTC Devices  
3 GPP has specified diverse power saving schemes for MTC devices. In a 3 GPP standard [46], UE Power Saving Mode (PSM) is introduced in which a UE may remain registered to the core network with no need to re-attach or re-establish connection with the core network. The UE will be active only during the exchange of signaling messages such as Tracking Area Update (TAU) and then the TAU procedure will take place between M2M gateways and the core network. However, we think that device triggering for a UE in a deep sleep mode needs further studies. In a 3 GPP standard [51], it is suggested that an MTC device can transfer data only after it has performed a TAU procedure. At that time, the device has transitioned to an active state from a previous sleep state, and is ready to receive or transmit data for a particular allowed time. 3 GPP also discussed an optimized TAU signaling mechanism for a case where a current configured TAU timer causes some congestion problems. The timer may be reconfigured by Mobility Management Entity (MME), and then, a detach procedure and a re-attach procedure are followed to allocate the new timer to the MTC devices.  
Another energy consumption issue is related to a connection establishment procedure. [PERSON] _et al._[53] suggested suppressing explicit network registration or attachment procedures for certain M2M devices to save their power. An M2M device has to select a radio access network (RAN) from available networks and then it will capture and transmit sensing data to a corresponding M2M server without any need for explicit registration. After transmission, it will go to a low power state for a particular amount of time. Later, when the device wakes up, it selects a RAN and carries out the same procedure. A power saving mechanism in which a device can transfer data during an initial connection establishment procedure is suggested in [54]. After the device can access a network, it will append collected sensing data to a bandwidth allocation message called a network entry message. If data are successfully transmitted, then the procedure is completed. Otherwise, it will retry the procedure for predetermined times or has to establish a connection to transmit data.
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.1 Comparative Experimental Results and Analysis
(6) Both CTRN and MTQADC use time-sensitive embedding operations and multi-granularity time aggregation, but in terms of overall Hits@1 and Hits@10 evaluation metrics, MTQADC is 0.049 higher and 0.002 lower, respectively. In terms of time granularity, for the Before/After question type, MTQADC outperforms CTRN by 0.261, 0.159, and 0.198 in daily, monthly, and yearly granularities, respectively. Compared to MultiQA, MTQADC benefits from data augmentation and convolutional feature learning modules. (7) Both MultiQA and MTQADC use time-sensitive embedding operations and multi-granularity time aggregation, but in terms of overall Hits@1 and Hits@10 evaluation metrics, MTQADC is 0.049 higher and 0.002 lower, respectively. In terms of time granularity, for the Before/After question type, MTQADC outperforms MultiQA by 0.261, 0.159, and 0.198 in daily, monthly, and yearly granularities, respectively. Compared to MultiQA, MTQADC benefits from data augmentation and convolutional feature learning modules. The data augmentation module enriches the problem representation by paraphrasing the questions, thereby expanding the limited question set and enhancing the problem representation. Furthermore, by applying convolutional processing to the questions, MTQADC gradually strengthens the understanding of complex relationships, thereby improving the accuracy and efficiency of the question-answering system. Overall, MTQADC outperforms other models, but there is still room for improvement in certain problem types.
## 4 Experiments
### Quantitative Results  
#### 4.2.1 Comparison with Baselines  
We compared our proposed model with four baseline models using the ADE and FED metrics on the ETH and UCY datasets, and the results are listed in Table 1. Our model achieved the best predictive performance with the lowest average ADE and FED errors across all five scenarios, indicating its superiority over the other models.  
The linear model demonstrated poor predictive performance, except in the Hotel scenario, where pedestrian movement was predominantly linear with minimal interactions. In contrast, S-LSTM tended to predict the average value of future trajectories and performed poorly in multimodal trajectory prediction. In the Eth scenario, where pedestrians often entered sidewalks with high degrees of uncertainty, S-LSTM's prediction error was considerably larger, as it could not generate diverse predictions. In contrast, generative adversarial networks could generate more plausible paths, leading to a greater diversity of predictions and better results.  
While S-GAN and SoPhie considered the effects of neighboring pedestrians and the relative position of pedestrians in social interactions, our model considered all pedestrians in the scene and extracted not only the relative position but also the movement speed and  
Figure 4: Views of the two simulated scenarios, namely Crossroad (**a**) and Plaza (**b**). The red line in each scenario represents the trajectory of a pedestrian (enclosed in a red box) generated by the CARLA simulator. The green lines indicate the manually labeled possible paths for the last 12 time steps, covering all the potential paths the pedestrian could take. The blue line represents one of the generated predictions.  
velocity angle as motion features. Therefore, our model demonstrated better performance compared to S-GAN and SoPhie on the UCY datasets, where pedestrians have complex social interactions. For example, in the Zara2 scenario, our model demonstrated a 5.3% and 14.3% decrease in the ADE and FDE, respectively, compared to S-GAN and SoPhie.