esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Cellulose Fibers
#### 3.1.2 Morphological Analysis
Following alkaline treatment, surface  
Figure 4: Optical images of samples of untreated grass clippings (**a**), alkali-treated samples (**b**), and bleached samples (**c**). Figure 5: SEM images and elemental analysis of untreated grass clippings, alkali-treated samples, and bleached samples. modifications and the removal of various impurities are observed, accompanied by the formation of fibrous structures. These changes are attributed to the alkali-induced partial breakdown of cell walls. However, after bleaching, a reduction in the fiber diameter is observed, accompanied by the emergence of belt-like structures, likely resulting from mechanical stress and fiber deformation during the bleaching process. Additionally, a cleaner and more textured surface of the fibers is apparent, possibly due to the elimination of organic contaminants and pigment residues. EDS analysis, coupled with elemental mapping of a cellulose sample derived from the grass clippings after alkaline treatment and bleaching, reveals a predominant presence of C (44.17 wt.%) and O (50.80 wt.%), consistent with the composition observed after alkaline treatment alone. Minor amounts of Mg (1.90), Si (1.54), and Ca (1.59 wt.%) were also detected in the sample. The presence of Mg is attributed to using a magnesium-containing reagent during bleaching, which may have persisted in small quantities post-processing and purification. Conversely, the increased presence of Si and Ca in the sample after bleaching suggests their preservation from the original raw material and a potential increase due to changes in C and O content, likely resulting from chemical reactions during the bleaching process.
## 3 Methodology  
### Data Representation and Learning Depth Shape of Brushstroke Using Adversarial Networks  
Painting forgery detection using GAN models is the task of discriminating forged paintings based on learning the parameters from original data distribution during the training. All the images, both natural-light images and RTI images (46 images), were captured by a Sony a7R III camera. Natural light was used for shooting natural-light pictures, while 80 LED lights were combined for RTI images. Our dataset consisted of \(N\) painting images (23 images, 7952 \(\times\) 5304 pixels) that display the original artist's style (natural-light images), with \(n\) = 1, 2,..., \(N\), where \(I_{n}\in R^{a\times b}\) is a resolution image of size \(a\times b\). We extracted \(m\) patches \(x_{m,n}\), of size \(c\times c\) (8904 image patches at the scale of 256 \(\times\) 256) from painting images \(I_{n}\) outcoming in patch-sized brushstroke data \(x\) = \(x_{m,n}\in X\), where \(m\) = 1, 2,..., \(M\). In a similar fashion, (23 images, 7952 \(\times\) 5304 pixels), using RTI images \(I\_RTIn\), the corresponding RTI patch-sized brushstroke data \(y\) = \(y_{m,n}\in Y\) was prepared with the same location and size as the patch-sized brushstroke data \(x\). To learn a 3D representation of brushstroke shape based on RTI data, we trained a cGAN model using a paired set of \(I_{o}\) = \(\{x_{m,n},\ y_{m,n}\}_{n=1,\ m=1}^{N,M}\), where \(I_{n}\), \(I\_RTIn\in I_{o}\).  
Our testing dataset was composed of \(p\) unseen painting images (9 images from original painting A ([PERSON]), forged painting B and C ([PERSON]), at the scale of 7952 \(\times\) 5304), which were segmented into a resolution of \(a\times b\) for both the painting images \(U_{p}\in R^{a\times b}\) and their corresponding RTI images \(U\_RTIp\in R^{a\times b}\) where \(p\) = 1, 2,..., \(P\). From the query painting images, we extracted \(m\) image patches of both painting image \(qx_{m,p}\) and RTI image \(qy_{m,p}\), with the size of \(c\times c\). The collection of the query image patches is denoted as \(q\) = \(\{qx_{m,p},\ qy_{m,p}\}_{p=1,\ m=1}^{P,M}\), where \(m\) = 1, 2,..., \(M\), and the ground truth (GT) labels of \(qy_{m,p}\in\{0,1\}\). Consequently, the testing data are \(qx_{m,p}\in Up\) and \(qy_{m,p}\in U\_RTIp\), where \(Up\), \(U\_RTIp\in Qp\), and \(Qp\) is an unseen painting where \(p\) = 1, 2,..., \(P\).  
For all the datasets including training, validation, and testing, by considering no overlap between patches and that partial patches are excluded, each image (7952 \(\times\) 5304 pixels) was divided into 620 patches at resolution of a 256 \(\times\) 256, while in data processing, the patches without clear brushstroke were removed. To be more precise, 8904 image patches for training, 1472 image patches for validation, and 4602 of the image patches for testing were used.
## 4 Results and Discussion
### F1-Score Variants
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**GoEmotions Distilled Lexicon Applied to the**} & \multicolumn{3}{c}{**GoEmotions Aggregated Distilled Lexicon**} \\  & \multicolumn{3}{c}{**XED Dataset**} & \multicolumn{3}{c}{**Applied to the XED Aggregated Dataset**} \\ \hline
**Threshold** & **Macro F1** & **Micro F1** & **Weighted F1** & **Macro F1** & **Micro F1** & **Weighted F1** \\ \hline
0.000 & 0.328 & 0.330 & 0.337 & 0.478 & 0.520 & 0.626 \\ \hline
0.100 & 0.307 & 0.339 & 0.319 & 0.481 & 0.526 & 0.629 \\ \hline
0.200 & 0.289 & 0.341 & 0.305 & 0.484 & 0.549 & 0.632 \\ \hline
0.300 & 0.257 & 0.311 & 0.272 & 0.474 & 0.560 & 0.624 \\ \hline
0.400 & 0.210 & 0.279 & 0.226 & 0.404 & 0.505 & 0.548 \\ \hline
0.500 & 0.185 & 0.262 & 0.198 & 0.363 & 0.453 & 0.478 \\ \hline
0.600 & 0.159 & 0.245 & 0.171 & 0.319 & 0.401 & 0.388 \\ \hline
0.700 & 0.145 & 0.219 & 0.154 & 0.286 & 0.344 & 0.308 \\ \hline
0.800 & 0.121 & 0.177 & 0.125 & 0.249 & 0.278 & 0.244 \\ \hline
0.900 & 0.081 & 0.109 & 0.085 & 0.167 & 0.170 & 0.151 \\ \hline
1.000 & 0.044 & 0.051 & 0.046 & 0.093 & 0.107 & 0.106 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation metrics for the GoEmotions distilled lexicon applied to the XED dataset and for the GoEmotions Aggregated distilled lexicon applied to the XED Aggregated dataset. Figure 17: Performance of the GoEmotions distilled lexicon applied to the XED dataset. (**a**) Chart that depicts the micro, macro, and weighted F1-scores for the GoEmotions distilled lexicon, applied to the XED dataset across various thresholds; (**b**) chart that depicts the micro, macro, and Weighted F1-scores for the GoEmotions Aggregated distilled lexicon, applied to the XED Aggregated dataset across various thresholds. The weighted F1-score demonstrates that it is sensitive to the presence of more labeled tokens in the lexicons, which can be seen in the lower thresholds, and this might explain the higher values that it shows at lower thresholds. However, when applied to the XED dataset, the weighted F1 performs comparably to the macro F1-score. As can be seen in Table 2, the XED dataset is balanced. This trend in the weighted F1-score changes with the aggregated XED dataset, where negative emotions are combined into a single class, thus skewing the class balance. Under these conditions, at lower thresholds, the weighted F1 shows better performance. It is also a metric that is responsive to changes in class balance and, in this case, word availability.
# Assessing the Security and Privacy of Baby Monitor Apps
## 6 Discussion & Conclusions  
Previous work has shown that mobile applications and various video surveillance solutions show deficiencies in the security measures and privacy protections implemented. Analyzing and reporting these weaknesses improves the security and privacy levels of these solutions, e.g., by encouraging manufacturers to fix security weaknesses or informing users about their use of insecure systems. Unfortunately, little work has been performed investigating the security and privacy implications of applications built for video surveillance utilizing mobile phones. Therefore, this study extends previous work in the field of mobile app video surveillance by analyzing the security and privacy implications of the usage of baby monitor apps.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**App** & **Confidentiality** & **Security issues** \\ \hline Dormi & \(\blacklozenge\) & Clear text transmission of sensitive information (CWE-319). \\  & & Channel accessible by non-endpoint (CWE-300). \\ BabyCam & \(\blacklozenge\) & Clear text transmission of sensitive information (CWE-319). \\  & & Insufficiently protected credentials (CWE-522). \\  & & Channel accessible by non-endpoint (CWE-300). \\  & & Weak password requirements (CWE-521). \\ Saby & \(\blacklozenge\) & - \\ Babyphone Mobile & \(\blacklozenge\) & Weak password requirements (CWE-521). \\ \hline \hline \end{tabular} \(\blacklozenge\): Appropriate security measures ensure confidentiality. \(\blacklozenge\): Confidentiality may be compromised under some circumstances. \(\blacklozenge\): Confidentiality of the video footage is not guaranteed.  
\end{table}
Table 11: Confidentiality of Monitoring Streams.  
Figure 7: BabyCam — unencrypted communication and weak password encoding. The asterisk indicates that the app is allowed to accept cross-origin HTTP requests from all origins.
## 2 Materials and Methods
### Spectrum Used for Non-Destructive Estimation of Starch and TSS Properties  
Spectral graphs of the Fuji apple samples have several peaks, including peaks at wavelengths of 496, 549, 683, 849, 875, and 962 nm. In this study, spectral data around these peaks were analyzed, and starch and TSS properties were estimated. For non-destructively estimation of starch content and TSS, the spectral peaks with the best results were thoroughly analyzed. Therefore, spectral data of 800 to 900 nm could predict the amount of starch content, and spectral data of 920 to 980 nm to estimate TSS.  
Predicting starch and TSS requires 3 to 9 wavelengths. Limiting factors to develop a portable device are cost and size. Therefore, the number of wavelengths used for prediction should be as low as possible. Thus, it is crucial to select the most effective wavelengths. In this study, a hybrid ANN-simulated annealing algorithm was used to select the most effective wavelengths. This algorithm is like the metal annealing operation process performed repetitively to achieve a stable material state with lowest energy use [33].  
Table 1 gives the hidden layer structure of the neural network used to select the most effective wavelengths. Each neural network has an input and an output vector. The output vector contains data on fruit properties and the input vector contains spectral data. The simulated annealing algorithm has the task of selecting adequate inputs to the ANN. Following this procedure, the simulated annealing algorithm introduces vectors of different sizes as inputs to the ANN and records the optimal vector. Different wavelengths within the vector are chosen as the most effective wavelengths.  
\begin{table}
\begin{tabular}{c c} \hline
**Number of Layers** & **First Layer: 19** \\ \hline Number of Neurons & \(1\) \\ Transfer Function & tansig \\ Backpropagation Network Training Function & traincgb \\ Back-propagation Weight/Bias Learning Function & learnwh \\ \hline \end{tabular}
\end{table}
Table 1: The hidden layer structure of artificial neural network (ANN) used to select the most effective wavelengths (tansig is transfer function; traincgb is training function; learnwh is learning function).
# Transparent, Pliable, Antimicrobial Hydrogels for Ocular Wound Dressings
## 2 Materials and Methods
### Release of Antimicrobials  
Hydrogel-forming solutions (100 \(\upmu\)L) loaded with antimicrobials (1 mg/mL) were pipetted into each well (\(n\) = 5) of a 96-well glass plate and cooled at 4 \({}^{\circ}\)C to form the hydrogel samples. Once the gelation was complete, 100 \(\upmu\)L of PBS solution was pipetted into each well to allow for the diffusion of antimicrobials out of the hydrogels. The PBS solutions were replaced after 5 min of release and transferred into empty wells. The replacement of PBS solution was then repeated at 10 min, 15 min, 30 min, 1 h, 2 h, 4 h, and 12 h. The PBS solutions were analyzed with an AD200C Plate Reader (Beckman Coulter, Australia) to determine the concentration of antimicrobials via measurement of absorbance. Calibration curves to link absorbance to the concentration were performed prior to this experiment (Figure S2). For both cefazolin and doxycycline, the absorbance was measured at \(\uplambda\) = 271 nm. The antibiotic release was taken as the total accumulated release.
## 3 Results and Discussion
### Fatigue Damage Constitutive Model Based on Internal Variables  
Internal variable theory was used to describe the degenerative behavior of fatigue deformation modulus [60]. Since the fatigue damage variables defined by irreversible strain seem to be ideal for reflecting the fatigue degradation of rock materials, we have:  
\[E_{0i}=P(\sigma_{mi})^{Q}\times\varepsilon_{ri}^{C_{2}} \tag{33}\]  
where \(E_{0i}\) represents the fatigue deformation modulus; \(\sigma_{mi}\) represents the maximum cyclic stress; \(\varepsilon_{ri}\) represents the irreversible axial plastic strain; footnote \(i\) denotes the \(i\)-th cycle; and \(P\), \(Q\), and \(C_{2}\) are the undetermined coefficients of the model.  
Introducing Equation (28) into Equation (27), we can derive a freeze-thaw damage fatigue constitutive model of fractured granite under uniaxial compression based on internal variables:  
\[\sigma_{1i}=P(\sigma_{mi})^{Q}\times\epsilon_{ri}^{C_{2}}\epsilon_{1i}\big{(}1-D_ {gi}\big{)} \tag{34}\]  
The undetermined coefficients \(P\), \(Q\), and \(C_{2}\) of the model are determined using a multi-parameter fitting method. The Weibull distribution parameters \(m\) and \(F_{0}\) are calculated using Equations (32) and (33). The initial value \(\epsilon_{1i}\) of the axial strain is defined as the ratio of the minimum cyclic stress to the initial tangent modulus in each cycle, while the loading modulus \(E_{l}\) and unloading modulus \(E_{u}\) are adopted during loading and unloading, respectively.  
Additionally, to account for the crack closure stage of the rock sample during the initial loading process, a compaction coefficient \(R_{0}\) is proposed to modify the fatigue model. This coefficient is defined as the ratio of the secant modulus to Young's modulus. Thus, the constitutive model can be expressed as:  
\[\sigma_{1i}=R_{0}P(\sigma_{mi})^{Q}\times\epsilon_{ri}^{C_{2}}\epsilon_{1i} \big{(}1-D_{gi}\big{)} \tag{35}\]
# Future Perspectives for Gamma-ray Burst Detection from Space
## 3 eXTP
### Instrument Design  
The eXTP scientific payload consists of four main instruments: the spectroscopic-focusing array (SFA), the large-area detector (LAD), the polarimetry-focusing array (PFA), and the wide-field monitor (WFM). The SFA comprises nine identical Wolter-I grazing-incidence X-ray telescopes and is mainly used for spectral and timing observations of celestial sources in the energy range of 0.5-10 keV. In its current design, the SFA total effective area is 7400 cm\({}^{2}\) (at 2 keV) and the instrument FoV is circular with a diameter of 12 arcmin. The instrument achieves an energy resolution of 180 eV at 6 keV and a timing resolution of 10 \(\upmu\)s in the whole operational energy band. The LAD is designed to perform photon-by-photon observations of X-ray sources in the 2-30 keV energy range. The LAD is exploiting the technology of innovative large-area Silicon Drift Detectors [22] (SDDs), to provide an unprecedented large effective area, reaching 3.4 m\({}^{2}\) at 8 keV. The instrument is not designed for imaging purposes, and the FoV is limited to 1 deg using micro-channel collimator plates [23] to simultaneously reduce source confusion and background. The LAD achieves an energy resolution of 260 eV at 6 keV and a timing resolution of 10 \(\upmu\)s in the whole operating energy range. A similar instrument is also planned on-board the STROBE-X mission (see Section 10). The WFM exploits the same SDD technology coupled with coded masks to provide a coverage of about 5.5 sr of the sky at any time in the energy range of 2-50 keV. The instrument is capable of localizing X-ray sources within an accuracy of \(<\)1 arcmin and performing timing (spectral) investigations on these objects with a time (energy) resolution of 10 \(\upmu\)s (\(<\)300 eV at 6 keV). The WFM is equipped with an automatic alert system to promptly broadcast to the ground (within a few tens of seconds at the most) the onset time and position of bright impulsive events detected on-board, including GRBs. A similar instrument is also planned on-board the STROBE-X mission (see Section 10). The PFA provides polarimetric capabilities in the energy range of 2-8 keV, exploiting X-ray optics optimized for polarimetric observations and a similar detector technology compared to that currently flying on the IXPE mission (the so-called gas pixel detectors (GPDs); see, e.g., [24] and the references therein).
## 5 Impact of Color Conflicts Caused by Late Arrivals, Node Mobility or Topology Changes
### Resolution of Notified Color Conflicts  
Upon detection of a color conflict, the MAC layer locally notifies SERENA indicating the addresses of the two conflicting nodes [29]. When a node \(N\) receives such an indication, two cases are possible:  
* either \(N\) is a conflicting node. If \(N\) has the smallest priority or the conflicting node is not a 1, 2 or 3-hop neighbor, then \(N\) selects another color. Otherwise \(N\) sends a \(Conflict\) message to the other conflicting node inviting it to change its color.
* or \(N\) is not a conflicting node. \(N\) sends a \(Conflict\) message to the conflicting node that is either the only 1, 2 or 3-hop neighbor, or has the smallest priority.  
The \(Conflict\) message can be routed to reach its final destination. When a node must change its color, it selects the smallest color different from the previous one and not used in \(\mathcal{N}(N)\).
## 4 Performance Evaluation
### Friendly-Sharing Diffusion Evaluation
Similarly to the previous buffer occupancy analysis, it is interesting to point out that even making small movement pauses with our Friendly-Sharing improves the delivery probability and latency without much transmission overhead. In our previous analysis, a full cooperation is assumed, since, as indicated before, we suppose that installing the application shows that the users are willing to follow its indications. As reasonable as this argument could be, there are cases where users may not want to stop or simply cannot stop. Furthermore, users will stop longer when contacting other related users or depending on past interactions; that is, stopping probability and duration has a strong social dependence. However, no social data can be easily extracted from the movement traces. We reflect this situation through the _probability of stopping_ (\(p_{s}\)). This probability ranges from 1, which reflects full cooperation of both users (as considered in the previous evaluations) to 0, indicating no cooperation (and no waiting time, which is the basic epidemic protocol). In order to evaluate the impact of this probability, we set the buffer size to 200 MB considering 16 s maximum stop time for both TTL (which is one of the experiments represented in Figure 9). We evaluated the delivery success ratio and latency for different values of the stop probability. This probability is modelled as a uniform distributed random variable independent for each contact. The results are presented in Figure 11, showing a linear correlation  
Figure 10: Overhead results: Buffer occupancy and forwarded bytes. (**a**) maximum of the average buffer occupancy from each node; and (**b**) average bytes daily forwarded per node (\(y\)-axis in log scale). between stop probability and delivery success and latency. Moreover, we can see that stopping 16 s with a stopping probability of 0.5, we obtain a similar delivery and latency performance as with a 8 s unconditional stop, as displayed in Figure 9. A similar result can be observed for a probability of 0.25, which corresponds to 4 s, and for a probability of 0.75, that corresponds to 16 s. We can, therefore, conclude that the effect of the stop probability is equivalent to reducing the waiting time by a factor \(p_{s}\).
## 3 Results and Discussion
### Consumer Evaluation
#### 3.1.1 Irish Consumers  
There was no significant (\(p\) > 0.05) difference among Irish consumers liking of the sensory attributes (overall appearance, color, flavor, saltiness, texture) or overall liking of the three butters (Hedonics Table 1). These results contradict a previous study by [PERSON] et al. 2016 [22], who found that Irish consumers preferred the appearance and flavor of butters produced from grass and clover diets, compared to butter produced from TMR. However, in this study, Irish consumers did perceive the color intensity of FS-GRSS and FS-CLVR butters significantly (\(p\) < 0.017) higher than FS-TMR butter (Intensity Scale Evaluation Table 1), presumably due to higher \(\beta\)-carotene levels, with 41.6% of panelists grouping the FS-TMR butter as 'not yellow enough' (JAR Evaluation Table 1). However, this did not negatively influence their liking of the FS-TMR butter.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 4 Localization Algorithm
### Reliable Response Detection  
Due to the limitations of the UWB chip, the CIR we obtained was sampled in nanoseconds [46]. We find that nanosecond time resolution can bring distance errors of more than ten centimeters. In addition, the chip has an embedded TOA estimation algorithm [46], which can estimate the TOA of the first response before the nanosecond sampling, so the TOA estimation of the first path given by the chip is more accurate, i.e., the time resolution is 15.65 ps. Thus, we directly estimate the TOA with the algorithm embedded in the devices [46]. Then, we use the interpolation technique to improve the time resolution of the observed CIR, and get more accurate TOA estimates for other responses. In practice, we use the sinc function for interpolation.  
We calculate the amplitude of the complex CIR, and denote it as \(r(n\Delta_{d})\), where \(\Delta_{d}\) is the original time resolution and \(\Delta_{d}=1.0016\) ns. Then we use the sinc function to interpolate the original sequence \(r(n\Delta_{d})\). The interpolation result \(r(t)\) is given by  
\[r(t)=\sum_{n=-\infty}^{+\infty}r(n\Delta_{d})\frac{\sin((t-n\Delta_{d})\frac{ \pi}{\Delta_{d}})}{(t-n\Delta_{d})\frac{\pi}{\Delta_{d}}}. \tag{8}\]  
Figure 3: An illustration of the received channel impulse response (CIR) at the anchor from two agents’ Resp messages. We introduce an individual delay \(\delta_{i}\) so that the pulses can avoid overlap and avoid strong multipaths. \(\Delta t_{12}\) indicates the time-difference-of-arrival (TDOA) of two responses.  
In general, we extend the the time resolution by 64 times using interpolation technique so that the time resolution of \(r(t)\) is similar to 15.65 ps. Next, we extract the timestamp corresponding to the largest amplitude as the improved TOA estimation. TOA estimation after interpolation is more accurate than TOA estimation directly on the raw CIR.  
Note that the largest \(N_{\text{a}}\) peaks in the CIR are not necessarily the line-of-sight (LOS) MPCs of \(N_{\text{a}}\) agents, since the NLOS MPCs can be stronger than the LOS MPC. However, the direct-path of each agent Resp message is generally stronger than its multipaths, so we can search for the largest amplitude of the \(i\)-th (\(i>1\)) agent's first path near \(\delta_{i}-\delta_{1}\) after the first response in a CIR.  
After we obtain the TOA estimation of the \(N_{\text{a}}\) responses from the agents, we can calculate the TDOA of a certain response and the first arrival response. Then the distance estimation can be calculated using (7).