esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 2 Mechanisms of Spoilage
### Lipid Oxidation
However, lipid hydroperoxides can decompose further into volatile secondary oxidation products. Also, the presence of transition metals, particularly iron derived from hemoglobin, myoglobin, and cytochrome, can catalyze and expedite lipid oxidation in fish [35]. In addition to non-enzymatic pathways, lipid oxidation in fish can also occur through enzymatic mechanisms involving lipases, a process referred to as lipolysis. Lipases catalyze the hydrolysis of triglycerides into glycerol and free fatty acids, thereby contributing to  
Figure 3: Mechanistic flowchart of lipid oxidation in fish tissue. The upward arrows (\(\uparrow\)) indicate an increase in the corresponding environmental or processing fac-tors—such as higher temperature, stronger light exposure, increased concentration of metal ions, or elevated salinity. rancidity and a decline in oil quality [36]. The principal lipolytic enzymes found in fish include triacyl lipase, phospholipase A2, and phospholipase B, which are located in the skin, blood, and tissues [37; 38]. Furthermore, several environmental and processing factors profoundly affect lipid oxidation rates in fish. Elevated temperatures increase molecular mobility, accelerating radical initiation, hydroperoxide decomposition, and formation of secondary oxidation products. Exposure to UV and visible light activates endogenous photosensizers (e.g., chlorophyll, riboflavin, heme proteins), generating singlet oxygen that reacts directly with unsaturated lipids. Transition metal ions (Fe\({}^{2+}\), Cu\({}^{2+}\)) catalyze lipid hydroperoxide breakdown, producing additional free radicals and propagating the chain reaction. Finally, high salinity and ionic strength promote lipid-protein interactions and membrane destabilization, further enhancing oxidation susceptibility. Controlling these parameters, through strict temperature management, light-blocking packaging, metal chelators, and optimized salinity, can significantly mitigate oxidative spoilage and help preserve fish quality [39; 40; 41]. The oxidative degradation of lipids in fish is closely associated with protein denaturation, the functional breakdown of endogenous antioxidant mechanisms, and a reduction in nutritional value due to the depletion of fat-soluble vitamins. Lipid oxidation generates reactive aldehydes and radicals that interact with proteins, causing cross-linking, aggregation, and irreversible denaturation. Such interactions impair muscle protein functionality, texture, and water-holding capacity. Concurrently, oxidative stress consumes endogenous antioxidants (e.g., tocopherols, carotenoids), reducing nutritional quality by depleting essential nutrients, vitamins, and essential fatty acids, notably omega-3 polyunsaturated fatty acids [42; 43].
## 3 Result and Discussion
### Learning Convergence of the Model  
This section presents the study of the learning convergence of the models in terms of loss and accuracy, achieved by the LSTM models utilized in the proposed system for activity and localization prediction. The objective is to achieve improved convergence with a minimal number of training epochs, thereby reducing computational overhead.  
The learning convergence of the LSTM model for indoor localization can be seen in Figure 5, which illustrates both loss and accuracy metrics over 300 epochs. It can be seen that the training loss continues to decline steadily, settling around 0.030, while the validation loss stabilizes near 0.035. The learning curve indicates effective learning and good generalization to unseen data. Simultaneously, the training and validation accuracy graphs show a consistent upward trend, both approaching 90%. Therefore, the convergence patterns in both loss and accuracy confirm that the model has successfully learned the indoor localization task and achieved high accuracy.  
The convergence of the LSTM model for activity recognition is depicted in Figure 6, which shows both loss and accuracy over 300 epochs. Initially, the training and validation  
Figure 4: Visualization of the acquired location and activity data which is used to analyze movement patterns and behavioral trends over a specified period. (**a**) indicates RSSI value, (**b**) indicates 3-axis accelerometer data, and (**c**) indicates 3-axis gyroscope data.  
losses start above 0.05, decrease steadily, and stabilize around 0.035 towards the end. The accuracy graphs show a significant rise from around 20% at the beginning to nearly 80% by the 300 th epoch for both training and validation accuracies. Therefore, the consistent improvement in both loss and accuracy metrics affirms that our model effectively learned on the activity recognition dataset.
# STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
## 2 Dataset
### Data Acquisition  
Unlike publicly available datasets for VOS tasks, medaka larvae are only about 3 mm in size, and acquiring their videos involves a series of complex and rigorous operations. We need to prepare a plate on a microscope equipped with a white LED array for bright-field imaging, a LED fluorescence excitation light source, a fixed plate bracket, movable optical devices, and a temperature-controlled incubation cover. One hour before imaging, medaka embryos at 32-36 h post-fertilization (hpf) were transferred from a methylene blue-containing medium (maintained at a constant 28 \({}^{\circ}\)C) into plain embryo medium (ERM). After equilibrating for 15 min in the microscope's incubation chamber under bright-field illumination, use the Nikos CMOS camera (2048 \(\times\) 2048 pixels) to capture video images of medaka embryos at 32-36 hpf.  
A total of 63 AVI format medaka heartbeat videos were collected, each with a duration of approximately 11 to 13 s. The videos were recorded at a frame rate of 15 frames per second (fps) and a resolution of 640 \(\times\) 480. Medaka hearts normally beat at a frequency of around 2 beats per second [14], which is lower than the video's frame rate of 15 fps. The frame rate of the video is deemed sufficient for accurate heart rate estimation. Despite the video's resolution being limited to 640 \(\times\) 480, it was focused on the cardiac region, with the ventricle occupying approximately 160 \(\times\) 128. Therefore, the video resolution is adequate for precise ventricular contour segmentation. Undoubtedly, higher fps and resolution contribute to the precision of the dataset, further enhancing the improvement of the model. We hope that more scholars are willing to openly share their data. We are very grateful to Dr. [PERSON] [15] from the German Centre for Organismal Studies at Heidelberg University for providing this original dataset. The video data are freely available and can be Obtained from the Open Science Framework (OSF), a platform that supports scientific research in fields such as social science, medicine, and engineering. The download address is [[https://osf.io/6](https://osf.io/6) svkf]([https://osf.io/6](https://osf.io/6) svkf) (accessed on 24 March 2019).  
Due to different angles during video capture, there is a significant difference in the direction of the heart presentation. Therefore, we divide videos into two categories:  
1. The video captured on the ventral side of a medaka is named with the prefix \"R\". This perspective allows for simultaneous observation of the atrium and ventricle of the medaka, as shown in Figure 1a-d.
2. The video captured on the lateral-right side of a medaka is named with the prefix \"N\". The atrium from this perspective is obstructed by the ventricles, and only the ventricles can be observed, as shown in Figure 1e-h.
# Deterioration Process of Cementitious Material Properties under Internal Sulphate Attack
## 3 Results and Discussion  
### XRD Analysis  
#### 3.1.1 Samples with Different Contents of Gypsum  
Figure 5 shows the XRD patterns of mortar specimens with different SO\({}_{3}\) contents in the aggregate at 28 d and 360 d under 20 \({}^{\circ}\)C maintenance. Figure 5a shows that all mortar specimens have calcium alumina and calcium hydroxide diffraction peaks at 28 d, and the intensity of calcium alumina and calcium hydroxide diffraction peaks is weaker for 0% SO\({}_{3}\) content in the aggregate. When the SO\({}_{3}\) content in the aggregate was 0%, 0.6% and 1.2%, no gypsum diffraction peaks were detected in the mortar specimens at 28 d, indicating that all the gypsum was dissolved. When the SO\({}_{3}\) content in the aggregate was greater than or equal to 2.4%, there were gypsum diffraction peaks in the specimens, and the higher the SO\({}_{3}\) content in the aggregate, the stronger the intensity of the gypsum diffraction peaks. From Figure 5b, it can be seen that the intensity of the calcium alumina peak of the mortar specimen with SO\({}_{3}\) content greater than or equal to 0.6% in the aggregate at 360 d is stronger than that of the specimen with 0% SO\({}_{3}\) content in the aggregate, indicating that the mortar specimen mixed with gypsum sand generates more calcium alumina than that of the mortar specimen without gypsum sand. The gypsum diffraction peak of the mortar specimen with 2.4% SO\({}_{3}\) content in the aggregate has disappeared, indicating that all the gypsum has dissolved. The gypsum diffraction peaks of the mortar specimens with SO\({}_{3}\) content of 4.8% and 7.2% SO\({}_{3}\) in the aggregate still existed, indicating that the gypsum was not completely dissolved, and the gypsum of the two groups of mortar specimens with SO\({}_{3}\) content of 4.8% and 7.2% SO\({}_{3}\) in the aggregate was sufficient within 360 d.  
Figure 5: XRD patterns of mortars with different SO\({}_{3}\) content in aggregate.
## 2 Materials and Methods
### Transmitter and Receiver Hardware  
A PPM transmitter and receiver are implemented to demonstrate the capability of the system as a testbed for Lunar FSO communications systems. A field-programmable gate array (FPGA) development board (STEMlab 125-14, Red Pitaya, Solkan, Slovenia) generates a pseudo-random binary sequence (PRBS), mapped to a 4- or 16-order PPM waveform output. A high-skew-rate amplifier (THS3491, Texas Instruments, Dallas, TX USA) matches the digital pin on the development board to the modulation port of a high-extinction-ratio optical amplitude modulator (MXER-LN-10, iXblue, Saint-Germain en Laye, France). A fibre-coupled seed laser (Koheras BASIK X15, NKT Photonics, Birkerod, Denmark) in the 1550 nm optical C-band is used, as it conveniently has polarisation-maintaining output fibre, avoiding polarisation-dependent losses in the modulator due to birefringence.  
The transmitter PPM waveforms were measured over a 2 km fibre link in a lab environment to determine the minimum possible slot width due to bandwidth limitations of components. At 16 ns and 8 ns slot widths, the transmitted pulses exhibit well-defined edges shorter than 1 ns. At 8 ns width, the raw bitrates with 4- and 16-PPM mappings (and guard slots) are 50 Mb/s and 25 Mb/s, respectively. PPM symbols with 4 ns slot widths were tested but appeared distorted, most likely by the electrical transmission properties of the physical interconnect between the FPGA pin and the driver amplifier, limiting the achievable slot clock speed and, therefore, data rate. For the demonstration, the PRBS was framed into 15120 bit codewords, using the 4-PPM format with an 8 ns slot width. A faster slot clock could be achieved with a purpose-built electro-optical interface, to achieve the O2O-specified 260 Mb/s.  
The receiver hardware comprises a single-mode fibre (SMF)-coupled InGaAs photodetector (Menlo Systems FPD510-FC-NIR, typical to the coherent detection systems more often used by the group) and digitiser (ATS9360, AlazarTech, Pointe-Claire, Canada), with sufficient bandwidth to observe the PPM waveforms. An SMF-coupled detector was used as it was available, but a multi-mode fibre-coupled or free-space detector would be better suited, as PPM communication is not mode-selective. Slot and symbol synchronisation and demodulation are conducted offline in MATLAB. The incoming PRBS is used to measure the bit-error rate (BER) of a sequence of PPM symbols, provided the first eight PPM symbols in a record are received without error. As the incoming light is coupled to SMF, the PPM signal experiences fading from angle-of-arrival variations caused by turbulence, wind-buffeting of the drone, and mechanical vibrations of the optical payload. A variable optical attenuator was also added ahead of the photodetector to manually control the link margin.
## 2 Data and Methodology  
### Datasets Employed  
The IBCs for the model were obtained from the operational analysis/forecast cycle at 12:00 a.m. UTC 17 September 2020 from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) at 0.25\({}^{\circ}\) horizontal resolution, and from the US National Weather Service (NWS) Global Forecast System (GFS) forecasts at 0.5\({}^{\circ}\) horizontal resolution.  
For the examination of the medicane experiments, we used the following spaceborne observational platforms: (1) the 3D corrected reflectivity data at Ku-band from the GPMCO DPR, available over a 245 km wide swath, with an approximate horizontal resolution of 5 km (at nadir) and 250 m range resolution [34]. In particular, the overpass at 10:31 p.m. UTC 17 September 2020 over the Ionian Sea was used to evaluate the simulated reflectivity factor, integrated rainwater and solid hydrometeor (snow and graupel) contents, and instantaneous surface precipitation rate as reported in the 2A-DPR product [41]; (2) the coastal ocean surface wind product from the Advanced Scatterometer (ASCAT), with sampling resolution of 12.5 km and effective resolution of 25 km [42]. The MetOp-C satellite overpass that captured TLC Ianos at 7:11 p.m. UTC was used to verify the simulated near-surface winds.
## 2 Theoretical Foundations and Development of Hypotheses
### Measurement Model  
As to the statistical techniques used, we opted for the variance-based approach or Partial Least Squares (PLS), utilizing the SmartPLS software version 3.2.7. All the constructs were modeled in mode A. This involves an evaluation of the measurement model based on the application of the traditional measurements of internal consistency, reliability, and validity [46]. In this line, in a first phase the factor loadings of the indicators were analyzed. These must attain the value of 0.6 for an exploratory \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**GENDER**} & **Female** & **Male** & & \\  & 44.30\% & 55.70\% & & \\ \hline \multirow{2}{*}{**AGE**} & **Under 31** & **31–50** & **51–70** & **Over 70** \\  & 14.70\% & 57.90\% & 34.60\% & 1.02\% \\ \multirow{2}{*}{**JOB**} & **Student** & **Self-employed** & **Employee** & **Unemployed** & **Retired** \\  & 4.30\% & 26.10\% & 61.10\% & 7.70\% & 0.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Profile of the respondents. research work, which implies a refining process of the scale aimed at eliminating the indicators that are not very reliable. The final scale must assure a high internal consistency, measured both through the Cronbach alpha and the composite reliability index. The results for the reliability, both individual and composite, are offered in Table 2.  
The average variance extracted (AVE) is applied to attest the convergent validity of the Mode A constructs. In our model, all the constructs and dimensions satisfy this criterion as their AVEs surpass the level of 0.5 (Table 2). To accept the discriminant validity, which indicates that each construct differs from the other constructs, two means are used: The squared correlation of the construct must not exceed the value of the AVE [47] and the HTMT index (Heterotrait-Monotrait Ratio) [48]. All the values of the matrix are below 0.85 [49] ([PERSON], 2010), and also the confidence intervals for the HTMT do not contain the value 1 [48], so the discriminant validity is accepted (Table 3).
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 4 Experiments for LoRa PHY
### Combined Indoor and Outdoor Environment
(**a**) In the second experiment, the gateway stays at the location (Building #7), and nodes ( marks) are placed both on the inside and outside of the buildings; (**b**) A node is protected by a plastic enclosure and placed inside a building. It is noted that 2.03 km and 0.95 km are averaged values. This implies that a node located at the maximum communication distance may fail to transmit data to the gateway with a probability of 50% when assuming that the RSSI values simply follow Gaussian distribution, as shown in Figure 12. Therefore, the maximum range may be shortened if an application requires higher communication reliability. To compute the accurate maximum ranges in a conservative way, this section reports the worst-case measurement data. The standard deviation of the received power in the cafeteria with many obstacles and people moving around is 5.5 dB. It is computed that to increase the communication reliability from 50% to 90% at the maximum distance, the average receiving sensitivity shifts by 7.1 dB,  
Figure 11: Histogram of RSSI values at 4 different locations. (**a**,**b**) are recorded at the library and the cafeteria, respectively, where there are few people during experiments. (**c**,**d**) are from the crowded cafeteria; the results show that people moving around influence the signal strength. The average/standard deviation values in the histograms are (**a**) \(-\)79.616/3.0862, (**b**) \(-\)97.99/3.0068, (**c**) \(-\)100.51/4.9566, and (**d**) \(-\)93.319/5.4463 for the four locations. Figure 10: RSSI (received signal strength) values in the combined indoor and outdoor environment. The \(\gamma\) value is computed as 3.956 based on the measurement from which the path loss equation is drawn (the red line). The maximum communication ranges are then estimated as 2 km and 0.95 km. from \(-137\) dBm to \(-129.9\) dBm. This reduces the maximum communication range from 2.03 km to 1.34 km. Similarly, 2.9 dB must be secured to provide 70% reliability. Table 5 summarizes the maximum communication ranges to achieve communication reliability of 50\(\sim\)90% with all available SF values in the combined environment.
## 2 Background and Literature Review
### IPv6 Routing Protocol for Low Power and Lossy Networks (RPL)  
Routing attacks such as blackhole, sinkhole, rank, version number, selective forwarding, DIS flooding, and DAO flooding attacks are extensively studied in [3; 9; 13; 14]. Below, we briefly explain the RPL protocol and the existing security attacks.
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 2 Dataset Introduction
Figure 1 shows the time domain waveforms of each modulation mode in the dataset, where each image is a randomly selected sample of the corresponding modulation mode. In Figure 1, each type of signal is divided into IQ channels. The orange line is the I signal, and the blue line is the Q signal. The horizontal axis of each type of signal graph is the time axis, and the vertical axis is the amplitude of the signal. As can be seen from Figure 1, the time domain waveform of the signal varies with time. Therefore, such a signal is also called a time signal and has time sequence features. The so-called time sequence feature means that with time changing, the amplitude of each type of signal performs different regular and different high and low jumps. Thus, each type of signal has different characteristics. Using neural networks can automatically distinguish these signals after training. However, most of the current networks extract only the time sequence features of IQ signals. The relevant features are ignored. The so-called relevant features refer to the connection between the two paths of IQ signals. The connection features between I and Q are different for different signal types. By adding relevant features, more training features are added. Although there are many similarities and differences among the above modulation waveforms, it is not easy to identify them visually due to the influence of noise, pulse shaping, distortion, etc. In particular, the time domain waveforms of the two signals, AM-DSB and WBFM, are very similar, so the time sequence features and input to the neural network are similar and not easily distinguishable.
## 3 Results and Discussion
### Theoretical Assay
#### 3.7.4 Molecular Docking Analysis  
We used molecular docking to identify the possible binding mode between the \"Keap1-kelch\" domain and AO and HERA and compared with quercetin, which acts as reference inhibitor and thus a better indirect antioxidant. A 3D interaction map was created (Figure 12)  
Figure 11: Side view of molecular simulations for the most favorable modes of adsorption for the inhibitors on Fe (1 0 0) surface.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Inhibitor Energy** & \begin{tabular}{c} **Myristic Acid** \\ **(C14:0)** \\ \end{tabular} & \begin{tabular}{c} **Palmitoleic Acid** \\ **(C16:1)** \\ \end{tabular} & \begin{tabular}{c} **Palmitic Acid** \\ **(C16:0)** \\ \end{tabular} & \
\begin{tabular}{c} **Linoleic Acid** \\ **(C18:2 n-6)** \\ \end{tabular} &
\begin{tabular}{c} **Stearic Acid** \\ **(C18:0)** \\ \end{tabular} \\ \hline Total energy & \(-25.02\) & \(-19.70\) & \(-25.02\) & \(-18.40\) & \(-33.12\) \\ \hline Adsorption energy & \(-3.50\) & \(-4.178\) & \(-4.50\) & \(-5.17\) & \(-4.99\) \\ \hline Rigid adsorption energy & \(-37.70\) & \(-35.64\) & \(-71.01\) & \(-40.14\) & \(-49.68\) \\ \hline \(\mathrm{d_{Ead}/d_{Ni}}\) & \(-4.50\) & \(-5.17\) & \(-6.17\) & \(-6.17\) & \(-5.99\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: The calculated descriptors (\(\mathrm{kcal/mol}\)) of AO and HERA on Fe (1 0 0).  
which depicts the coupling manner of quercetin at the hKeap1 binding location, (PDB: 4L7B) [61; 63] which concluded \"Gly364, Leu365, Ala366, Gly367, Arg415, Ile416, Gly417, Val418, Gly462, Gly464, Val465, Gly509, Gly509, Ala510, Gly511, Val512, Ala556, Ala556, Leu557, Gly558, Ile559, Gly603, Gly605, and Val606 residues\".  
The myristic acid has strong hydrogen-bonding interactions with Tyr334. Palmitoleic acid interacted with Pro348 through formation of an H-bond with C=O of carboxylic acid. Besides, OH of the carboxy group for palmitic acid interacted with Leu557 by H-bond donor. Linoleic acid localized in a binding pocket by formation of H-bonds between their C=O with Thr609 and Tyr329. Furthermore, stearic acid interacted with TyrSer55 and Tyr334 (Figure 12).