esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performances of the Predictive Equations  
The equations investigated in this section are listed in Tables 1 and 2. For each one of these equations, the predicted \(\rm f_{cc}\) and \(\rm\varepsilon_{cu}\) values are plotted against the actual experimental measurements. The coefficient of determination (R\({}^{2}\)), mean absolute error (MAE), and root mean squared error (RMSE) metrics are utilized in order to quantify the accuracies of these predictions. The deviations of the predicted values from the measurements are shown with dotted lines in Figures 4 and 5. The relationships between the equation predictions are also visualized using Taylor diagrams and the Pearson correlation coefficient.  
The performances of the equations in predicting the confined ultimate strength of concrete are listed in Table 4. According to the error metrics in Table 4, the equations were far from accurate. In particular, the negative values of the R\({}^{2}\) score are an indicator of low prediction accuracy. Also, the plotted predictions in Figure 4 show that the predicted values can be more than 120% lower or more than 60% greater than the actual values. The equations proposed by [PERSON] et al. [21], [PERSON] and [PERSON] [22] and [PERSON] et al. [24] tend to overestimate the ultimate strength and, for some of the specimens, this overestimation is nearly 60%. On the other hand, the equations proposed by [PERSON] and [PERSON] [23] tend to underestimate the ultimate strength, and this underestimation can exceed 120% for some of the data samples. A similarly low accuracy of prediction was also observed in the equations for the prediction of the ultimate strain. Figure 5 displays the plot of the experimental ultimate strain values against the predicted values. The equations developed by both [PERSON] et al. [21] and [PERSON] et al. [24] tend to underestimate the ultimate strain, although, for some of the data samples, these equations can also overestimate the ultimate strain by more than 60%. The accuracy of these two models have been determined by error metrics and listed in Table 5. Again, the negative values of the R\({}^{2}\) scores in Table 5 indicate the poor performance of these predictive equations.  
Figure 4: Equation predictions for the confined compressive strength, according to (**a**) [PERSON] et al. [21], (**b**) [PERSON] and [PERSON] [22], (**c**) [PERSON] and [PERSON] [23] and (**d**) [PERSON] et al. [24].  
Figure 5: Equation predictions for the confined compressive strength, according to (**a**) [PERSON] et al. [21] and (**b**) [PERSON] et al. [24].
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 3 Problem Statement  
Network congestion occurs owing to traffic-based bandwidth consumption. Congestion is caused by malicious attackers or flash crowds [10] of innocent users. Under network congestion, that is, when the performance of the Internet, including the latency or loss rate, is highly affected by a hazard or when a link failure occurs, it can take several minutes or more until failover [3]. Fault recovery of BGP at times takes several minutes before the routes converge to a consistent form [4]. Path outages even lead to significant disruptions in communication lasting tens of minutes or longer [5; 6; 7].
# Coupling Denoising to Detection for SAR Imagery
## 3 Results
### Quantitative Evaluation  
To quantitatively evaluate the detection performance, we calculate mean average precision (mAP). The mAP metric is widely used as a standard metric to measure the performance of object detection and estimated as the average value of AP over all categories. Here, AP computes the average value of precision over the interval from recall = 0 to recall = 1. The precision weighs the fraction of detections that are true positives, while the recall measures the fraction of positives that are correctly identified. Hence, the higher the mAP, the better the performance.  
As shown in Table 2, we compare the proposed network with the traditional two-stage detection model under two different backbones such as ResNet-50-FPN and ResNet-101-FPN [43; 44]. By varying despeckling approaches, we set several baseline models as previous work processes: (1) inputting non-preprocessed real SAR images, (2) feeding denoised SAR images into the traditional two-stage detection model after denoising via representative techniques called Lee filter [22] or PPB filter [25]. We observe that the despeckling effect of applying Lee filter is more minor than PPB filter. PPB filter enables us to reduce more speckle noises; but, much detailed information visually gets concealed. This validates our experimental results that the baseline model with PPB filter slightly performs inferior compared to the baseline model with Lee filter. On the other hand, our detection network provides significant advances in performance under all backbone architectures. Through observation of the test results, this is attributed to the suppression of many false positive detections resulting from speckle noise problems of real SAR images.  
Figure 8: Two paired examples of noisy SAR (left) and despeckled SAR (right) images. Red bounding boxes for each image enlarge corresponding sub-regions. As shown in the enlarged windows, scattering waves and speckle noises are relatively less observed in denoised examples.  
Figure 9: Image triples are shown in which the left image is groundtruth, while the middle image is for baseline models (traditional two-stage detection models with real SAR images), and the right image is for our models. The groundtruth and predicted bounding boxes are plotted in blue color for A class, yellow color for E class, and pink color for S class. The numbers on the bounding boxes in the middle and right images denote the confidence score for each corresponding category. We visualize all detected bounding boxes after NMS and thresholding detector confidence at 0.05.
## 4 Methodology and Modeling
### Selection of Representative Cities  
According to ABNT NBR 15220-3 [17], Brazil is divided into eight bioclimatic zones (BZ). ASHRAE Standard 90.1 [34] divides Brazil into four climatic regions: 0A (extremely hot humid), 1A (very hot humid), 2A (hot humid), and 3A (warm humid). However, the division of the entities is not reciprocal; that is, national climate classifications are not equivalent: the city or Rio de Janeiro, for example, according to ABNT, is located in bioclimatic zone 8 (most extreme), while the American Association classifies it in climatic region 1A. Most cities in climatic region 0A are also in bioclimatic zone 8. As a result, it was decided to prioritize the classification according to ABNT since it presents a more significant variability of climates and their characterization for defining construction guidelines.  
To evaluate the impact of the envelope in different climatic contexts, cities representing extreme bioclimatic zones 1 and 8, with \"antagonistic\" climates, are selected based on their population [35]. Table 1 condenses this information.  
It is worth stressing that, as a limitation of the present research, a direct extrapolation of the results for each of the cities to the other locations in their respective bioclimatic zones is not entirely feasible since, in zone 8, for example, the vast majority of cities are located in the north and northeast regions of Brazil; thus they present a different solar chart than the city of Rio de Janeiro.
## Materials and Methods
### Image Pre-Processing  
Satellite image processing was conducted in ENVI 5.4 (Harris Corporation, Melbourne, FL, USA) and classification was done in ArcGIS Pro 2.0 (ESRI Inc., Redlands, CA, USA) (Figure 2). The images were masked to the RCR, which sped up processing and made NDVI and water thresholds more relevant for our study site. RE and WV imagery come georeferenced but had to be radiometrically calibrated to top of atmosphere reflectance using parameters provided by DigitalGlobe, Inc., (Westminster, CO, USA) and Planet, Inc., (San Francisco, CA, USA). Atmospheric correction was not applied because this study classified a single image in time ([PERSON], 2015) and previous work has demonstrated only minimal improvement after atmospheric correction for WorldView imagery and other similar multispectral imagery ([PERSON], 2015). Pan-sharpening was not applied in the study due to the lack of a panchromatic band on the RapidEye satellite. For each image, a composite was created in addition to the standard RE and WV bands in order to test the impact of including additional indices derived from the original bands on classification accuracy. These composite images included normalized difference vegetation index (NDVI) to emphasize vegetation ([PERSON], 2015), as suggested by [PERSON] et al. (2015) and a homogeneity texture filter using a 3 \(\times\) 3 kernel (run on NIR1 for WV; NIR for RE) following [PERSON] et al. (2015). Texture layers have been found to help distinguish wetland edges. We eliminated water habitats in our image processing workflow by using a normalized ratio of blue to NIR and eliminating all pixels below a threshold. This threshold was determined by incrementing the value until it included all water pixels in sample polygons drawn to exemplify the multiple Subtidal Haline types (deep, silty, brackish, sandy). Where R\(\lambda\) is the spectral reflectance of the band centered at \(\lambda\) wavelength, indices and thresholds were:  
\[\text{RE Index: (R475 $-$ R805)/(R475 $+$ R805) threshold $=0.40$}, \tag{1}\]  
\[\text{WV Index: (R427 $-$ R950)/(R427 $+$ R950) threshold $=0.93$}. \tag{2}\]  
These threshold values differ due to the increased wavelength of WV's NIR 2 band (R950) over RE's NIR 1 band (R805). This process successfully eliminated most water pixels below \(-0.5\) m (NAD83) in the images. This threshold was applied after composite creation to prevent distorting the texture filter with additional edge effects. The final outputs from this image processing include four image products: WV with the standard 8 bands, WV 8 bands + NDVI + texture, RE with the standard 5 bands, and RE 5 band + NDVI + texture.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 3 Results and Discussion
### In Vitro Release of Hydrogel Loaded with IL-2
#### 3.5.2 Release Test After Gel Loading with IL-2  
To further study the release characteristics of different proportions of IL-2 in CAA-CS hydrogels, we immersed the drug-loaded gels in PS solution. The release behavior of IL-2 in CAA-CS hydrogels presented a distinct two-stage characteristic, as depicted in Figure 7. Within the initial 24 h, the release of 5% IL-2 exhibited an approximately linear relationship (Figure 7b), attaining 23.4%, indicating a relatively rapid initial release. As time elapsed, the cumulative release rate gradually decreased. Within 72 h, the cumulative release rate of IL-2 ranged from 18.4% to 34.7%, suggesting a gradually decelerating release rate over time, as illustrated in Figure 7a. Furthermore, with the increase in the CAA content, the cumulative release rate of IL-2 correspondingly decreased. This kinetic feature of transition from rapid release to sustained release reflects the controlled-release effect of the hydrogel network on the drug: initially, it is mainly the rapid release of the drugs adsorbed on the  
Figure 5: (**a–d**) The gelation time of CAA-CS series hydrogels, (**e**) storage modulus and loss modulus of CAA-CS series hydrogels, (**f**) viscosity of CAA-CS Series Hydrogels.  
surface; later, it relies on the slow diffusion of the drugs due to the swelling and relaxation of the gel network.  
Firstly, IL-2 loaded on the surface of CAA-CS hydrogels is rapidly released. This rapid release in the initial stage is mainly attributed to the high concentration of IL-2 on the surface of the hydrogels and the short contact time with the PBS solution, causing it to rapidly diffuse into the release medium in the early stage. As time progresses, the interaction between CAA-CS hydrogels and the PBS solution intensifies, and the hydrogels undergo significant swelling after absorbing water. This swelling process enables the release of IL-2 embedded within the CAA-CS hydrogels. The release of IL-2 is influenced by the interaction between CS and CAA, with the hydrogen bonding between hydroxyl and amino groups and the dynamic enamine bond between amino and acetoacetyl groups in the molecules jointly affecting the release rate of IL-2. PBS, as the release medium, gradually disrupts these interactions, allowing IL-2 to be gradually released from within the gel.  
Figure 6: (**a**) Maximum absorption wavelength of IL-2 in PBS solution, (**b**) the maximum absorption wavelength of CAA-IL-2 drug-containing solution), (**c**) IL-2 phosphate buffer solution (pH = 7.2–7.4, PBS) standard curve, (**d**) the standard curve of IL-2 in CAA solution), (**e**) the drug loading rate of CAA-CS hydrogels loaded with the model drug IL-2, (**f**) the encapsulation efficiency of CAA-CS hydrogels loaded with the model drug IL-2.  
Figure 7: (**a**,**b**) Release profile of IL-2 from CAA-CS hydrogels in PBS.
## 3 Simulation Results  
A simulation study was performed to analyze the proposed pole head design for an effective active zone. The results showed that the output torque was proportional to the pole head design, which enhanced the flux density and also increased the overall braking torque. At most positions of the MRB structure, the magnetic flux lines are more evenly distributed when the ideal current flows through the coil that also enhances magnetic field strength. The entire working surface area is determined by the pole head structure, the number of poles, the pole head gap and the specific pole radius. It was discovered that a bigger pole area results in a higher output torque and that the magnetic core influences magnetic efficiency under varied input currents.
# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 2 System Framework Design
### CNN + LSTM Classification Network  
Human action is a continuous process that contains both spatial and temporal features. CNNs have achieved remarkable success in image recognition, target detection, and face recognition and have subsequently been widely used in gesture, action, and gait recognition in millimeter-wave point clouds. The LSTM network, through the designed structure of memory cells, input, forgetting, and output gates, effectively solves the traditional problem of long-term dependency in RNNs [26]. The memory unit allows for the network to store and maintain information in long sequences. In contrast, the gating mechanism allows for the network to selectively read, write, and forget information to capture long-term contextual relationships better. The traditional RNNs are prone to the problem of gradient vanishing or gradient explosion when dealing with long sequences, making it challenging to learn long-term dependencies efficiently [27]. LSTMs help alleviate these problems through gating mechanisms, especially forgetting gates, making the network more stable during training; the CNN + LSTM framework is used in this paper. Firstly, spatial feature extraction is performed separately for each frame after aggregation by the CNN, and then the extracted features are fed into the LSTM network in frame order (chronological order), as shown in Figure 5. The LSTM network captures the patterns and relationships in the time series. The LSTM network outputs are connected to form a complete feature representation. Finally, the classification of actions is outputted through a fully connected layer. The advantage of this structure is that each CNN branch focuses on a one-time frame, which helps to extract the spatial information of that time frame. The LSTM network integrates the information from these time frames and models the temporal relationships [28]. This approach allows for the better handling of time series data and reduces the model's over-reliance on the entire sequence.  
Figure 4: Point cloud of human body after rounding off \(y-\)axis component.  
Figure 5: CNN + LSTM model.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 5 Results
### Mobility and Handover Analysis  
The handover analysis was carried out for a UAV traveling at a constant altitude of 500 m. The UAV communication link has to be handed over between various ground stations and satellites along its path to maintain constant connectivity. This is shown in Figure 17.  
Figure 16: Calculation of range of 6G satellites.  
Figure 17: Visualization of use case considered for analysis.  
The assumptions for the ground base stations are as follows (Table 2). The coordinates (\(-1000\), \(0\), \(0\)), (\(2000\), \(800\), \(0\)), and (\(9000\), \(-3000\), \(0\)) represent a deliberate placement of base stations across a broad geographic area. This distribution allows for the assessment of UAV communication effectiveness over varying distances and directional paths, which is crucial for testing the network's handover protocols and signal continuity. By setting the base stations at these points, there is an overlap of coverage areas, which is essential for evaluating the handover algorithms.  
The 6G terrestrial base station is assumed to have 38 dBm transmitter power [34]. The handover analysis was carried out along the UAV path, and the active base station is shown in Figure 18.  
Figure 18 shows that there are four handovers taking place along the UAV path. Serving BS = 0 means the signal is lost, as the RSS is below the signal threshold. For the considered use case, since the serving base station is never zero, constant connectivity is maintained throughout the flight path. The received signal strength (RSS) of the received signal from each of the stations can be observed in Figure 19.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline BS1 coordinates & \(-1000,0,0\) \\ BS2 coordinates & \(2000,800,0\) \\ BS3 coordinates & \(9000\)\(-3000,0\) \\ Frequency (f) & \(3.5\) GHz \\ UAV initial position (m) & \(0,0,500\) \\ UAV final position (m) & \(9000,0,500\) \\ UAV velocity & \(45\) mV/s \\ Attenuation constants & Same as Table 1 \\ Power BS1 & \(8\) dB (6G ground station) [34; 35] \\ Power BS2 & \(8\) dB (6G ground station) \\ Power BS3 & \(8\) dB (6G ground station) \\ Signal threshold & \(-120\) dB (assumed for 6G) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial values for handover analysis: Case-1.  
Figure 18: Serving base station along the UAV path.  
From Figure 19, it can be seen why there are four handovers. The received signal strength of the new signal is stronger than the sum of the old signal strength and its entropy at that position. The maximum signal strength received via the drone is observed to be higher than the signal threshold of \(-120\) dB, considered for 6G connectivity. Another interesting inference from the graph is that, even though the satellite comes into the visible range at x = 4000 m, the signal strength is not enough to sustain 6G connectivity.
## 2 Materials and Methods
### LLM Selection  
Several LLM-based chatbots exist and are publicly accessible. Google's Bard is a free-to-use chatbot running on the PaLM 2 LLM, available through the web browser and the API. While the original PaLM LLM, which became publicly available in March 2023, was trained on 540 billion parameters [75], PaLM 2 is purportedly trained on a more compact 340 billion parameters, making it more efficient, more performant, and more cost-effective [76]. PaLM 2's training data include \"hundreds of human and programming languages, mathematical equations, scientific papers, and web pages\" [76]. Bard was previously powered by Google's LaMDA LLM, a conversational AI model capable of fluid, multi-turn dialogue that was fine-tuned using transformer-based neural language models containing up to 137 billion parameters [77].  
Meta's LLaMA was created to advance researchers' work in the subfields of AI [78]. LLaMA's initial release consisted of a 65-billion-parameter model which has been expanded to 70 billion parameters with the release of LLama 2 [79]. Currently, researchers and other users must apply for access to LLama 2, which can be run locally only.  
OpenAI's Generative Pre-trained Transformer (GPT) models are _pre-trained_ with large quantities of source material and use a _transformer_ architecture to efficiently _generate_ output that is highly dependent on the input [80]. OpenAI offers several different models and two means of access. The ChatGPT web interface with the GPT-3.5 model is free to use, while GPT-4 is available to paying subscribers at a fixed monthly rate. The use of the API is also restricted to paying users but access is prepaid, and funds are debited as a function of prompt and response tokens: OpenAI's text completion models use stochastic sampling of a set of tokens, which can be words, characters, punctuation, etc., to select the next token in the completion [42]. Similarly, input prompts are deconstructed into a set of tokens, a method of natural language processing (NLP) known as tokenization [81].  
The base GPT-3.5 model can utilize a maximum of 4097 tokens, and the base GPT-4 model roughly doubles that token number. Both GPT models are available in large-context form, with about 16k and 32k token maximums [42]. OpenAI trained GPT-3 with 175 billion neural network parameters [82] but parameter information is not available for the newer models. During training, parameters are adjusted to minimize the loss function value which computes the error in predicting the next token in the completion given the context of the preceding tokens [83]. With respect to these identified features and comparisons between LLM platforms, and our prior familiarity with the web portal and API usage, we opted to use OpenAI's ChatGPT-3.5 model to support this research study. Future work may expand to ChatGPT-4, as well as Google's and Meta's LLMs.
## 3 Results
### Characterization of Levan Polymer
#### 3.3.2 Nuclear Magnetic Resonance (NMR) Spectroscopy  
The \({}^{1}\)H NMR spectrum of the partially purified levan (Figure 3) showed six unexchangeable proton signals related to fructose as the forming monomer of levan. \({}^{1}\)H NMR (400 MHz, D\({}_{2}\)O) \(\delta\) 4.11 ppm (d, 1H, H-3), 4.02 ppm (t, 1H, H-4), 3.87-3.81 ppm (m, 2H, H-5 & H-6a), 3.69 ppm (d, 1H, H-1a), 3.59 ppm (d, 1H, H-1b), and 3.48 ppm (t, 1H, H-6b). The spectrum of \({}^{13}\)C NMR for the partially purified levan demonstrated six carbon signals at \(\delta\) d 104.17, 80.26, 76.26, 75.17, 63.36, and 59.87 ppm; Figure 4. The spectrum of \({}^{13}\)C NMR for the partially purified levan demonstrated six carbon signals at \(\delta\) 104.18, 80.26, 76.27, 75.17, 63.37, and 59.88 ppm; Figure 4. The chemical changes in the carbon atoms are illustrated in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Chemical Shift (ppm)**} \\ \cline{2-7}
**Levan of Bacterial Strain** & **CI** & **C2** & **C3** & **C4** & **C5** & **C6** \\ \hline Standard Levan of & & & & & & \\ _Z. mobilis_[26] & 60.76 & 104.64 & 77.68 & 75.75 & 80.78 & 63.95 \\ Standard Levan of & & & & & & \\ _Bacillus_ sp. [27]. & 59.85 & 104.19 & 76.27 & 75.73 & 80.28 & 63.33 \\ Standard Levan of & & & & & & \\ _Pseudomonas fluorescens_[10] & 59.87 & 104.17 & 76.26 & 75.17 & 80.26 & 63.36 \\ Levan of _Bacillus subtilis_ strain MZ292983.1 & 59.88 & 104.18 & 76.27 & 75.17 & 80.26 & 63.37 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between \({}^{13}\)C NMR chemical shift between the characterized levan from _Bacillus subtilis_ strain MZ292983.1 and the previously reported and extracted levan.  
Figure 3: \({}^{1}\)H NMR spectrum of levan from _Bacillus subtilis_ strain MZ292983.1. The recorded spectrum was recorded at 400 MHz for the sample in D\({}_{2}\)O.