esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
## 2 Materials and Methods
### Base Algorithm  
In this section, the base algorithm proposed by [33] will be presented, being an algorithm in 5 stages. The first stage involves an iterative search of the scenes. To reduce potential issues with targets that span across multiple scenes, the current images are extended by 2 km from the spatially adjacent scenes. Then, \(352\times 352\) pixel cutouts are made from the images with 75% overlap between successive cutouts. There are two reasons for this overlap. The first is to reduce the likelihood of missing any cutout that does not fully cover a target in the image. The second is that the previously generated datasets have centered targets, so a greater overlap increases the probability of having a cutout with the target centered. However, there is a trade-off as increasing the overlap also increases the computational cost of the algorithm. Thus, 75% was the empirically determined value that balances these factors.  
The second stage involves an image classification algorithm that determines whether the current cutout contains the target or not. This step acts as a filter for the subsequent steps, forwarding only the cutouts most likely to contain the target. After this, the segmentation of the cutout is performed using a pre-trained segmentation algorithm to generate the specific shape of the target.  
In the fourth stage, post-processing of the generated segment occurs. Then, the minimum bounding box of the segment is generated using the algorithm presented by [38], and the smallest dimension of this rectangle is checked. If it is less than 250 m in spatial dimensions, this value was chosen because we verified that the smallest landing strip in the training set was 434.23 m. Thus, 250 m becomes a reasonable threshold for discarding detections, and the segment is discarded. This step is important because small scars in the forest, despite their size, can closely resemble landing strips.  
Still, in the fourth stage, the centroid of the segment is calculated and related to a pixel coordinate. Thus, knowing the position of the centroid relative to the cutout and the position of the cutout relative to the entire scene, it is possible to determine the centroid's position in the complete scene. With this position, and using the georeferenced coordinate of the original image, the geographic location of the landing strip can be obtained.  
In the fifth and final stage, clustering of the points generated by the algorithm is performed. This stage uses a modified version of hierarchical clustering [39], which merges points based on a distance threshold. If two points are within this threshold distance, they are merged into a cluster. The reason for this is that there is significant overlap between cutouts, so a valid target is likely detected by more than one cutout. Therefore, clustering merges these points to avoid multiple predictions of the same target. Due to the required size of the target, a merging distance of 1250 m was used. The Algorithm 1 presents the pseudo-code of the algorithm.
# Vibroacoustic Optimization Study for the Volute Casing of a Centrifugal Fan
## 4 **Volute Vibroacoustic Model, FEM Validation, and Simulation**
### **Vibroacoustic Mathematical Model**  
For a continuous system of an actual structure, which was dispersed by FEM, the dynamic balance equation is as follows:  
\[\mathbf{M}\ddot{x}(t)+\mathbf{C}\dot{x}(t)+\mathbf{k}x(t)=\mathbf{F}(t) \tag{3}\]  
As the structure is subjected to external harmonic force, the external force can be expressed as follows:  
\[\mathbf{F}\Big{(}t\Big{)  
Introducing the frequency ratio \(\lambda=\omega_{n}/\omega\) and the dimensionless vibration mode amplification factor \(\beta_{i}\) results in the following:  
\[\beta_{i}=\frac{1}{\sqrt{\left(\lambda^{2}-1\right)^{2}+\left(2\zeta_{i}\lambda \right)^{2}}} \tag{12}\]  
Ordering:  
\[\psi_{i}=\arctan\frac{2\zeta_{i}\lambda}{\lambda^{2}-1} \tag{13}\]  
Substituting equations (12) and (13) into Equation (11) results in the following:  
\[y_{i}(t)=\frac{F_{i}}{\omega^{2}}\beta_{i}e^{j(\omega t-\psi)} \tag{14}\]  
At this point, the vibrational displacement is as follows:  
\[x(t)=\sum_{i=1}^{n}\varphi_{i}y_{i}(t)=\sum_{i=1}^{n}\varphi_{i}\frac{F_{i}}{ \omega^{2}}\beta_{i}e^{j(\omega t-\psi)} \tag{15}\]  
The vibrational velocity is as follows:  
\[\dot{x}(t)=\sum_{i=1}^{n}\varphi_{i}y_{i}(t)=\sum_{i=1}^{n}\varphi_{i}\frac{F_{ i}}{\omega}\beta_{i}e^{j(\omega t-\psi+\frac{\pi}{2})} \tag{16}\]  
The active output power is as follows:  
\[W_{o,active}=\int\limits_{S}Re(I_{n})ds=\frac{1}{2}\int\limits_{S}Re(pv_{n}^{ *})ds \tag{17}\]  
The relationship between the plane wave sound pressure p and the surface velocity \(v_{n}^{*}\) is as follows:  
\[p=\rho_{0}c_{0}v_{n}^{*} \tag{18}\]  
Substituting Equation (18) into Equation (17) results in the following:  
\[W_{o,active}=\int\limits_{S}Re(I_{n})ds=\frac{1}{2}\rho_{0}c_{0}\int\limits_{S }Re(v_{n}^{*2})ds \tag{19}\]  
Substituting Equation (16) into Equation (19) results in the following:  
\[W_{o,active}=\int\limits_{S}Re(I_{n})ds=\frac{1}{2}\rho_{0}c_{0}\int\limits_{S }Re(\sum_{i=1}^{n}\varphi_{i}\frac{F_{i}}{\omega}\beta_{i}e^{j(\omega t-\psi+ \frac{\pi}{2})})^{2}ds \tag{20}\]  
According to Equation (20), it can be concluded that the structural acoustic radiation power is mainly determined by the modal shape \(\varphi_{i}\), the applied exciting force F\({}_{i}\), and the frequency amplification factor \(\beta_{i}\). Therefore, the following methods can be used to control vibrational noise:  
(1) With the structural model and material determined, the vibrational sound radiation can be weakened by attenuating the amplitude of the applied exciting force;  
(2) With the determination of the exciting force, the geometric parameters of the structural model are modified to reduce the modal shape;  
(3) To reduce the amplitude amplification factor, the natural frequency and the external excitation force frequency should be avoided.  
Regarding the studied fan volute structure, the structural mode can be changed by controlling the thickness distribution of the structure if the geometry, the stiffness, and the constraint position are all fixed.
# Development of Injectable Thermosensitive Chitosan-Based Hydrogels for Cell Encapsulation
## 3 Results
### Swelling Ratio and Stability Tests  
For each type of sample, three freeze-dried samples were weighted before and after hydration at different time points in order to investigate their swelling properties. The swelling tests showed that all hydrogels absorbed most of the PBS solution in the first 30 min (SR equal to \(920\pm 302\), \(880\pm 92\), 714 \(\pm\) 92 for CH+SHC, CH+SHC+BGP, CH+SHC+BGP+ARG, respectively) and were able to retain the absorbed water without any release up to 48 h (Figure 2). The three hydrogel formulations have all very high SR values, although CH+SHC+BGP+ARG present a SR significantly lower suggesting a stronger interaction among the chitosan chains due to the presence of three GAs. Moreover, the higher standard deviation of CH+SHC samples might suggest a high inter-specimen variability.  
Figure 1: Inversion tube test: pictures of the three hydrogel formulations taken immediately after the mixing at RT and after 5 min of incubation at 37 \({}^{\circ}\)C.  
The samples were weighted at different time points in order to determine their stability at the operating conditions. Hydrogels showed weight loss rapidly increasing in the first 6 h, which reached after 24 h a maximum value of \(21.2\%\pm 1.5\%\) and \(23.2\%\pm 0.7\%\) for CH+SHC and CH+SHC+BGP+ARG that remained almost constant up to 96 h in PBS at 37 \({}^{\circ}\)C. Samples with SHC+BGP without ARG presented weight loss after 24 h of \(23.7\%\pm 1.7\%\), similar to the other two hydrogel formulations that however showed a small but significantly different increase in the weight loss (\(28.5\pm 1.2\%\)) at 48 h, approximately constant throughout the rest of the test (\(p<0.05\)) (Figure 3).
# A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
## 3 Materials and Methods
### Data Gathering and Annotation
Aspects like \"Packaging\" and \"Payment\" had the fewest mentions, indicating they were less frequently discussed in the reviews. The overall distribution suggests that certain aspects like shipping, item quality, and customer service are critical points of feedback for customers. To find the correlation between the trust aspect and other aspects, we transformed our annotated dataset by creating new columns for each aspect with \"_positive\" and \"_negative\" suffixes. Subsequently, we filled those columns with 1 or 0 based on whether the original column had a positive (2) or negative (1) value. This created new columns for each aspect in the format 'aspect_positive' and 'aspect_negative', marking the occurrence with '1' if the value is positive or negative, respectively, and '0' otherwise. As presented in Tables 2 and 3 and Figure 3, positive trust is strongly influenced by positive experiences in shipping, item quality, and pricing. Negative trust is most affected by negative experiences in customer service, shipping, and refund processes. This insight can help prioritize improvements in these areas to enhance or protect trust levels. The quantitative analysis of the dataset reveals several key observations that emerge regarding the relationship between various aspects and the sentiment of trust. Positive experiences with shipping, item quality, and customer service are highly correlated with positive trust sentiments. For instance, efficient and timely shipping (327 occurrences), high-quality items (214 occurrences), and good customer service (104 occurrences) significantly bolster trust. Conversely, negative experiences in these areas substantially diminish trust. Negative shipping experiences (192 occurrences), poor customer service (162 occurrences), \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Aspect** & **Definition** \\ \hline
1 & Shipping & Quality, cost, and timeline of delivery \\
2 & Trust & Customers’ overall sentiment about the store \\
3 & Item quality & Quality of the products \\
4 & Customer service & Quality of customers’ interactions with the \\  & & shop’s representatives \\
5 & Pricing & Price level, including discounts and campaigns \\
6 & Product features & Quality of product images, descriptions, and size guides \\  & & provided on the website \\
7 & Refund process & Speed and quality of handling refund issues \\
8 & Return process & Speed, convenience, and cost of the return process \\
9 & App experience & User experience with the store’s website \\
10 & Delivered product status & Condition of delivered products, e.g., broken \\  & & Availability and accuracy of information, including \\
11 & Information & misleading ads \\
12 & Packaging & Attractiveness and quality of packaging \\
13 & Payment & Quality and diversity of the financial transaction process \\
14 & Product availability & Variety of products or brands offered \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of aspects categories and definitions.
## 2 Materials and Methods
### Evaluation and Statistical Analysis  
The segmentation accuracies for the DLS model were evaluated using the DSC [34]. All DSCs were calculated from the 3D volumes predicted by the DLS model. The DSC indicates the similarity between predicted contours and reference contours delineated by a radiation oncologist. DSC could be calculated by the following equation:  
\[DSC=\frac{2n(\mathbf{T}\cap\mathbf{D})}{\mathbf{n}(\mathbf{T})+\mathbf{n}(\mathbf{D})},\]  
where \(\mathbf{T}\) is the reference contour delineated by a radiation oncologist and \(\mathbf{D}\) is the contour estimated using the DLS method. \(\mathbf{n}(\mathbf{T})\) is the number of voxels in contour \(\mathbf{T}\), \(\mathbf{n}(\mathbf{D})\) is the number of voxels in contour \(\mathbf{D}\) and \(\mathbf{n}(\mathbf{T}\cap\mathbf{D})\) is number of overlapping voxels between reference contour \(\mathbf{T}\) and estimated contour \(\mathbf{D}\).  
The accuracy for the automated prediction approach of CTRs was assessed using the PCC, the ICC and a Bland-Altman analysis [35]. The PCC evaluates the correlation between the reference and predicted CTRs for both the internal (B) and external test datasets. The ICC of ICC (2,1) tests the reliability between the reference and predicted CTRs. ICC (2,1) is the intraclass correlation coefficient where the model is two-way random effects, the type is single rater/measurement, and the definition of relationship considered to be important is absolute agreement [26].  
The degree of agreement between reference and predicted CTRs was assessed using the Bland-Altman analysis. The limits of agreement in the analysis were defined as the mean difference \(\pm\) 1.96 \(\times\) standard deviation of the differences, and systematic biases in the differences between predicted and measured values were evaluated.  
Prognostic predictability of predicted CTRs was assessed by using \(p\)-values in a KM analysis [36], which has been widely used in time survival analyses in the medical field [37]. The \(p\)-values were evaluated using a log-rank test in KM curves for time to progression between low-risk and high-risk patients, who were stratified with the medians of predicted CTRs for internal test dataset B and the external test dataset. If the CTR of a patient was higher than the median, the patient was considered a high-risk patient. Otherwise, the patient was regarded as a low-risk patient. The \(p\)-values less than 0.05 were considered statistically significant differences.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 2 System Model  
The considered uplink C-RAN system is depicted in Figure 1, which consists of two users, two RRHs, and the BBU pool. Both users and both RRHs are equipped with a single antenna. For the wireless link from user \(i,\ i=1,2\), to RRH \(j,\ j=1,2\), the channel gain is denoted as \(h_{ji}\).  
We assume that the wireless links experience block fading. The channel gain of each link remains constant during one round of transmission, but independently and randomly changes round by round, satisfying some probability distribution (e.g., Rayleigh distribution). As for the receiver side, the BBU pool has the knowledge of the instant channel gain between each user and RRH. On the other hand, the user is not required to be aware of the instant channel information. During each transmission round, the two users continuously transmit to the BBU pool until the latter recovers the messages and feeds back an ACK. The detailed procedure of each transmission round is as follows.  
Firstly, user \(i,\ i=1,2\), encodes its message \(m_{i}\) of length \(K\) using Raptor code to obtain the coded bits \(c_{i}\) and then modulates the bits into symbols \(x_{i}\), which are sent to the RRHs. Note that the degree profiles used for Raptor encoding are fixed over all transmission rounds. At each RRH, the received signals are preprocessed into the baseband signals, which can be expressed as  
\[\mathbf{y}=\mathbf{H}\sqrt{p}\mathbf{x}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{y}\triangleq[y_{1},y_{2}]^{T}\), \(\mathbf{H}\triangleq\left[h_{ji}\right]_{2\times 2}\), \(\mathbf{x}\triangleq[x_{1},x_{2}]^{T}\), \(\mathbf{n}\triangleq[n_{1},n_{2}]^{T}\), \(y_{j},\ j=1,2\), is the base band signal at RRH \(j\), \(P\) is the transmission power of each user, \(n_{j}\) is the independent additive white Gaussian noise at RRH \(j\) with a mean of zero and variance of \(\sigma_{0}^{2}\).  
Figure 1: Rateless coded transmission for two-user uplink in cloud radio access network (C-RAN) with two remote radio heads (RRHs).  
Then, RRH \(j\) quantizes the baseband signals to meet the fronthaul link capacity restriction. The quantized signals \(\hat{y}_{j}\) are uploaded to the BBU pool through the fronthaul link. At the BBU pool, an iterative receiver structure with a multi-user detector (MU detector) and a channel decoder based on the belief propagation (BP) algorithm is applied. When the messages from both users are successfully recovered, the BBU pool feeds back the ACK via RRHs to inform the users to stop transmitting.
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Evaluation  
In this subsection, we compare the introduced methods based on their radar setup, i.e. between automotive radar and spinning radar setups, for the odometry and ego-localization task. We try to compare these methods both in terms of how well they perform, but also try to highlight the importance of utilized hardware by analyzing the inference speed and device used to test the model. We also present relevant classical approaches for comparison to see how the deep learning models perform in comparison to established methods. The data come directly from the papers which are cited.  
Finding a common ground for a fair comparison among the mentioned methods poses considerable challenges. To ensure a fair evaluation, methods should ideally be tested on the same dataset and under identical conditions. However, achieving this is complicated due to the inherent differences in datasets and conditions across various tasks, particularly when comparing methods designed for spinning radar sensors with those tailored for automotive radar datasets and vice versa.  
Moreover, assessing inference speed is crucial for both odometry and localization tasks, especially in real-time applications like autonomous driving. The computation time of the model should ideally not be the primary bottleneck; instead, the focus should be on the data gathering speed of the perception sensor, such as radar data generation. This implies that fair comparisons require testing odometry or localization models on identical hardware.  
However, practical challenges hinder the attainment of this ideal scenario. Data generation speeds can vary not only between different types of sensors (e.g., automotive vs. spinning) but even among sensors of the same type. For automotive sensors, additional pre-processing steps may introduce significant differences in data generation speeds between differently configured sensors or models. Standardizing to a single sensor is also impractical, as different applications demand varying sensor specifications, leading to differences in resolution, accuracy, and price.  
Furthermore, the computational hardware of a system can constrain model choices. Implementing a large model may not be feasible if the system lacks the capability to support modern GPUs due to space or price limitations. Choosing an appropriate model that aligns with the environment of the system adds another layer of complexity, making the comparison of similar methods across different environments challenging. Despite these challenges, efforts to establish standardized evaluation metrics and practices will contribute to more meaningful comparisons in the field.
# Comparison of Two Intergranular Corrosion Tests on EN AW-6016 Sheet Material
## 3 Results  
Representative micrographs for each sample are shown in Figure 1. For all ISO-11846:1995-based samples, a corrosive attack along the grain boundaries was seen. Some grains appeared to be etched out most likely because the surrounding grain boundary was completely solved. The amount of \"solved-out\" grains appears to be increasing with the amount of acid in the test solution. The samples tested with a solvent amount of 5 mL/cm\({}^{2}\) appeared to be showing more solved-out grains (Figure 1a,c,e) compared to their 6 mL/cm\({}^{2}\) counterparts (Figure 1b,d,f).  
PV1113-tested samples showed a reduced IGC attack. However, cavities similar to a pitting corrosion attack were seen as well (Figure 1g,h).  
The average corrosion depths are shown in Table 4. They vary between 173.74 \(\upmu\)m for the 3.2 sample and 272.43 \(\upmu\)m for the 2.3 sample. In general, PV1113 samples show a reduced IGC depth compared to ISO 11846:1995-based test samples. The minimum deviance is \(\sim\)9 \(\upmu\)m for the samples 3.1 and 1.3. The corrosion speed was determined by calculating the average corrosion depth by the respective testing time. It was found to be up to 11 times higher for the PV1113. The same holds true for the HCl/NaCl ratio. Solution amount was more than 3 to almost 3.5 higher for PV1113.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \(\varnothing\) **Corrosion** & \(\varnothing\) **Corrosion** & \(\varnothing\) **Corrosion** & \(\varnothing\) **Account (mL/cm\({}^{2}\))** & **Test Method** & **vol.\% HCl** & **vol.\% NaCl** & **HCl/NaCl** & **Corrosion Type** \\ \hline
**1.1** & 227.63 & 9.49 & 5 & ISO 11846:1995 & 0.33 & 1.36 & 0.24 & IGC \\
**1.2** & 249.71 & 10.40 & 5 & ISO 11846:1995 & 0.36 & 1.36 & 0.27 & IGC \\
**1.3** & 199.58 & 8.32 & 5 & ISO 11846:1995 & 0.40 & 1.36 & 0.294 & IGC \\
**2.1** & 229.44 & 9.56 & 6 & ISO 11846:1995 & 0.33 & 1.36 & 0.24 & IGC \\
**2.2** & 230.58 & 9.61 & 6 & ISO 11846:1995 & 0.36 & 1.36 & 0.27 & IGC \\
**2.3** & 272.43 & 11.35 & 6 & ISO 11846:1995 & 0.40 & 1.36 & 0.294 & IGC \\
**3.1** & 190.98 & 95.49 & 18.5 & PV1113 & 2.25 & 0.83 & 2.7 & IGC + cavities \\
**3.2** & 173.74 & 86.87 & 18.5 & PV1113 & 2.25 & 0.83 & 2.7 & IGC + cavities \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average corrosion depth and speed of the tested samples. Fractions of HCl and NaCl are also shown.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample Name** & 1.1 & 1.2 & 1.3 & 2.1 & 2.2 & 2.3 \\ \hline
**Solution Amount (mL/cm\({}^{2}\))** & 5 & 5 & 5 & 6 & 6 & 6 \\ \hline
**37\% HCl Concentration (mL/L)** & 9 & 10 & 11 & 9 & 10 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Varying testing parameters of the ISO 11846:1995-based tests.
## 3 Results and Discussion
### Analysis of Stress Characteristics  
At the azimuth angle shown in Figure 3, the blades in various yaw states were selected to analyze the influence of yaw on blade stress. The equivalent stress characteristics of the three blades at the rated wind speed of 8 m/s were analyzed at different blade tip speed ratios (\(\lambda\) = 4, 5, 6).