esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Analysis  
### Comparison of Haze-Free Detection Algorithms  
#### 3.1.1 Comparison with Labeled Images  
The experiments utilized the Indian Driving Dataset (IDD) (dataset website: [[https://idd.insaan.iiit.ac.in/dataset/download](https://idd.insaan.iiit.ac.in/dataset/download)]([https://idd.insaan.iiit.ac.in/dataset/download](https://idd.insaan.iiit.ac.in/dataset/download)) (accessed on 26 August 2024)) comprising 6732 images, partitioned into training, validation, and test sets in an 8:1:1 ratio. The training and validation sets were employed for training DeepLabv3+, PSP-Net, U-Net, and the proposed algorithm until convergence. Hyperparameters were configured as follows: batch size = 8, learning rate = 0.0001. The Figures 9 and 10 illustrates a comparison of structured and unstructured road images, with gray denoting passable areas and black representing impassable areas. The similarity between red and gray reflects the algorithm's effectiveness.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Plan** & **First Training** & **Second Training** \\ \hline
1 & Mixed training with dehazed and haze-free datasets & — \\
2 & Dehazed dataset & Haze-free dataset \\
3 & Haze-free dataset & Dehazed dataset \\
4 & Dehazed dataset & — \\
5 & Haze-free dataset & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training plan.  
Figure 9: _Cont._In straightforward scenarios, all four algorithms exhibit satisfactory detection outcomes. However, in complex scenarios, the proposed algorithm excels in accurately segmenting the edges and contours of objects in the images. For instance, the segmentation of car outlines and road edges demonstrates results where the contours closely resemble those in the labeled images, showcasing its superiority.
## 2 Materials and Methods
#### Electromagnetic Waves Propagation and Phase-Matching Condition  
The propagation of electromagnetic waves through a crystal is described by the Pockels effect [17]. The polarization of the nonlinear crystal is receipted through the electromagnetic field that is applied to the crystal [18].  
\[P=\chi(E)E \tag{1}\]  
where \(P\) is the electric polarization, and is proportional to the applied electric field \(E\), \(\chi(E)\) is the electric susceptibility. By expanding \(\chi(E)\) in powers of the field \(E\) the nonlinear properties of the crystal can be described.  
\[P=(\chi_{1}+\chi_{2}E+\chi_{3}E^{2}+\ldots)E \tag{2}\]  
where \(\chi_{1}\) is the linear susceptibility. The quantities \(\chi_{2}\) and \(\chi_{3}\) are the second and third-order nonlinear optical susceptibilities, respectively. The second-order nonlinear optical effects are dominant in the nonlinear crystals.  
\[P=E(\chi_{1}+\chi_{2}E) \tag{3}\]  
The term \((\chi_{1}+\chi_{2}E)\) is proportional to the electric field. Therefore, a linear dependence is obtained between the electromagnetic field and the refractive index of the crystal. Due to the optimization of nonlinear crystals, the electro-optical (EO) process is a popular method for THz detection. This process requires a phase-matching condition. This condition is satisfied when the phase of the THz wave travels at the group velocity of the optical pulse [19]. To obtain the phase matching condition, it is desirable that the coherent length Lc is as long as possible [20].  
\[Lc=\frac{c}{2}f\mid n_{\text{THz}}-n_{g}\mid \tag{4}\]  
where \(c\) is the speed of light, \(n_{\text{THz}}\) is the refractive index in THz pulse, \(f\) is THz frequency, and \(n_{g}\) is the group index of the optical pulse as defined by [21]  
\[n_{g}=n_{o}-\lambda_{0}\frac{dn_{o}}{d\lambda_{o}} \tag{5}\]  
Figure 2: Schismatic diagram for the fiber coupling electro-optic detection system.  
\(\lambda_{o}\) is the optical pulse wavelength and the refractive index \(n_{o}\) is defined by  
\[n_{o}^{2}=A+\frac{B\lambda_{o}^{2}}{\lambda_{o}^{2}-c^{2}} \tag{6}\]  
The parameters A,B,C is constants, where \(A=8.95\)\(\upmu\)m, \(B=2.054\)\(\upmu\)m, and \(C=0.39\)\(\upmu\)m [22].
## 4 Model Validation
### Analysis of Fire Recognition by the DCN_Fire Model  
Figure 7 shows the visual characteristics after the input images had passed through the Conv 2 and Conv 3 layers. It could be observed that the lower layer captured low  
Figure 6: Training and validation loss curve of (**a**) the DCN_Fire model, (**b**) the 13-layer DCNN model, and (**c**) the 13-layer DCNN model.  
level texture, edges, and color features while the higher layer captured sparse higher-level features by eliminating irrelevant content.  
The DCN_Fire model was used to identify forest fire images and verify the effectiveness and rationality of the classification of the forest fire risk from aerial images (Figure 8). Since the smoke features in the smoke (Figure 8c) and mist (Figure 8d) images were highly similar, DCN_Fire mistakenly classified forest mist images as fire risk images. This was not surprising as humans can also mistakenly identify mist as smoke.  
The test set consists of 1200 random images with and without flames from the database. From the results in Table 5, it could be seen see that the false negative rate with the DCN_Fire model (0.13%) was very low.  
The same training and test data sets were used to verify and compare the recognition accuracy of the DCN_Fire model and other mainstream ML models (Kim's CNN, AlexNet, 8-layer CNN + Fisher vector, HOG + SVM, and deep belief net + neural net) for fire risk recognition. The training results showed that DCN_Fire was more accurate than the other models and performed well (Figure 9, Table 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Classifier** & **Accuracy** \\ \hline DCN\_Fire & Softmax & 98.3\% \\ [PERSON]’s CNN model & Softmax & 93.2\% \\ AlexNet & Softmax & 97.3\% \\ Eight-layer CNN + Fisher vector & SVM & 95.1\% \\ HOG + SVM & SVM & 43.3\% \\ Deep belief net + neural net & BPNN & 87.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: DCN_Fire compared with the accuracy of other models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**TP** & **FN** & **TN** & **FP** & **TPR** & **TNR** & **FNR** & **General Accuracy** \\ \hline
592 & 8 & 588 & 12 & 98.7\% & 98.0\% & 0.13\% & 98.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of the DCN_Fire model on the test data set.  
Figure 7: The feature mapping process after the Conv 2 and Conv 3 layers in the DCN_Fire model. (**a**) The original input image, (**b**) the Conv 2 feature map, and (**c**) the Conv 3 feature map.  
Figure 8: Fire risk image classification using the DCN_Fire model. (**a**) Images without fire characteristics; (**b**) images with flames; (**c**) images with smoke; and (**d**) misclassified images with mist.  
Figure 9: Accuracy comparison of the DCN_Fire model and other mainstream ML models.
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## Abbreviations  
The following abbreviations are used in this manuscript:  
ANDi  All-normal dispersion ChG  Chalcogenide  FIR  Far-infrared IR Infrared  MIR  Mid-infrared  MOF  Microstructured optical fiber  MOPA  Master oscillator power amplifier  NA  Numerical aperture  OPA  Optical parametric amplifier  OPO  Optical parametric oscillator  OWB  Optical wave break  PCF  Photonic crystal fiber  SC  Supercontinuum  SCF  Suspended-core fiber  SPM  Self phase modulation  SRS  Stimulated Raman scattering  ZBLAN  ZrF\({}_{4}-\)BaF\({}_{2}-\)LaF\({}_{3}-\)AlF\({}_{3}-\)NaF  ZDW  Zero dispersion wavelength
## 3 Dynamic Characteristics and Test Cases  
### Dynamic Characteristics of the Test CSB  
White noise excitation with bandwidth of 0.1-50 Hz was used to excite the dynamic characteristics of test CSB. The stochastic subspace identification method (SSI) [21] is used to identify the stochastic state space model from the recorded accelerations. Figure 8 shows the estimated in-plane modal shapes and frequencies of first six modes. The first mode is dominated by longitudinal sway of the  
Figure 6: Arrangement of steel blocks for (**a**) tower; (**b**) pier and (**c**) girder.  
Figure 7: Bridge model assembled on shake tables.  
girder. The 2\({}^{\rm nd}\) mode shows vertical antisymmetric vibration of girder combining longitudinal bending vibration of tower. The 2\({}^{\rm nd}\), 4\({}^{\rm th}\) and 6\({}^{\rm th}\) modes are antisymmetric around the vertical axis, while the 3\({}^{\rm rd}\) and 5\({}^{\rm th}\) modes are symmetric.
# Nosemosis in Honeybees: A Review Guide on Biology and Diagnostic Methods
## Course of Infection  
### Type A Nosemosis  
During _N. apis_ infections (called nosemosis type \"A\") there is a strong seasonality. Forager bees bring the parasite to the hive (with infected pollen, nectar, or robbed food stores). During the summer, the disease develops slowly and there are usually no symptoms. Young bees and brood remain uninfected as _Nosema_ spp. does not infect larvae [11]. The slow progress of the disease in the summer is also due to the sensitivity of _N. apis_ spores to high temperature [26]. During the autumn, there are more older bees in the colony compared to bees of younger generations, so more bees are infected (in comparison to the summer). In addition, autumn bees live longer than summer bees, so spores have more time to multiply to greater numbers. Then, in the winter cluster, they infect the rest of the bees. If the colony survives the winter, there is usually an outbreak of the disease in early spring [11; 29].  
At the individual level, infection with _N. apis_ influences mostly workers. Epithelial gut cells lyse and the secretion of digestion enzymes decreases [30]. Atrophy occurs in the hypopharyngeal glands of nurse bees [31; 32], which leads to a decrease in royal jelly production, thereby reducing brood rearing. Because of epithelial cell degeneration, the digestion and absorption of nutrients become severely impaired, the bees become malnourished, and often \"die of starvation\" while eating considerably more. As a consequence of almost no absorption of nutrients, there are not enough proteins to build a fat body [30].  
Because food is not ingested properly, diarrhea can be observed (see Table 1). The front wall of the hive, frames, and the entrance are often contaminated with brown feces [11]. Adult bees have extended (swollen) abdomens, and they crawl around the hives and hive entrances as they are unable to fly. Infection with _N. apis_ causes a faster transition of nurse bees into guardian bees and foragers (which means that those insects age faster) [33] but surprisingly does not reduce bee lifespans significantly [34]. However, the number of bees decreases, spring development of the colony is inhibited, and honey production is reduced [30]. In extreme cases, the colony dies during the winter or early spring [35]. Significant damage to yolk granules and the degeneration of the ovaries can be observed in infected queens [36]. Additionally, studies show that _N. apis_ infection has a negative influence on the effectiveness of sperm transfer to the queen's spermatheca [37]. In addition, during the spring, when the infected queen lays a smaller number of eggs or does not lay at all, she is usually replaced by supersedure [36].
# The DREAM Endstation at the Linac Coherent Light Source
## 2 Hardware
### The Gas Jet and Skimmer Section  
The main sample delivery scheme of the DREAM endstation is a CW supersonic gas jet. The key performance parameters for the standard configuration gas jet are given in Table 1. One of the main jet features is a two-nozzle setup. The basic idea is to have one line for the main sample, and the second gas line is for a calibration gas. This way, there is one line for a well-understood gas, which is used to align, calibrate, and find timing for any pump and probe experiments. This means that one has only to translate the sample gas manipulator in one direction from one line to the other to check the calibration and timing for experiments. In case of a clogged line, the second line also can be used for sample gas, without having the whole jet removed to fix the clogged line. However, the removal of the jet source is necessary to separate the first skimmer section from the rest of the gas delivery line and the main chamber with one gate valve (see (6) in Figure 4). To avoid pumping the volume between the first skimmer and the gate valve, a bypass with a second gate valve is in place. Another major feature of the sample line is the capability to tip and tilt the whole gas jet assembly (see (4) in Figure 2). This can be used to align the jet to the X-ray beam if necessary. The catcher is a two-stage setup with a residual gas analyzer (RGA) in the second section for analysis and alignment of the sample jet. The first section is equipped with a leak valve in case the main chamber needs to be backfilled. For this reason, all pumps in the catcher section can be slowed down. The supersonic gas jet with two nozzles is only one possibility of a sample source. Future capabilities will include but are not limited to sample sources for aerosols [21], pyrolysis [22], and effusive oven sources [23; 24]. Different sample delivery schemes are feasible upon reasonable request.  
\begin{table}
\begin{tabular}{l r} \hline Operation Temperature [K] & 5 to 700 \\ Gas Nozzles & 2 \\ Gas Nozzle Aperture [\(\mu\)m] & 10 to 500 \\ Operation Mode & CW \\ Operating Pressure [hPa] & 100–\(6\times 10^{4}\) \\ Distance to First Skimmer [mm] & 2 to 100 \\ \hline \end{tabular}
\end{table}
Table 1: Main gas jet parameters.  
Figure 3: The DREAM spectrometer; indicated are the optical laser out-coupling (1), the detector HV feedthroughs (2), the optical laser in-coupling (3), the 120 mm delay line detector (4), the spectrometer blades with an inner diameter opening of 120 mm (5), the opening for the X-rays and optical laser (6), the spectrometer rail and support frame (7), the second 120 mm delay line detector (8), the second spectrometer flange (9), and the installation support rail guiding (10).
## 5 Simulation Results  
Time-domain simulations were carried out using MATLAB/Simulink, with a positive-sequence phasor model implementation, that is sufficient to capture the frequency control dynamics of the studied system. The ED problem, providing dispatch orders and participation factors (\(P_{ED}\) and \(pf_{ED}\)) to the AGC, is executed using the Gurobi optimization solver in Python. Its timeframe is chosen equal to 15 min.  
System response is simulated over an interval of one hour, using the wind and solar radiation timeseries depicted in Figure 7a,b. The resulting residual load (load-RES), supplied by the dispatchable units (DU and BESS), is shown in Figure 7c and corresponds to a constant load of 3.5 MW. No RES curtailments take place under the simulation conditions.  
Figure 6: Study case autonomous system.  
Four (4) diesel units (of 1 MW each) are on-line in the beginning of the simulation interval. One diesel unit (G4) trips at \(t\) = 150 s, followed by the synchronization of another unit (G5) 12.5 min after the contingency. A 30% initial charging level is assumed for the BESS, to study the response of the system when the battery SOC reaches the activation zone of the proposed method.
## 2 Materials and Methods
### Shelf Life Determination of Untreated and Treated Tomato Samples  
Untreated, OD-treated (180 min at 60% glycerol), PEF-OD-treated (100 pulses at 1.8 kV/cm, OD for 90 min at 60% glycerol) and HP-OD-treated (600 MPa for 5 min, OD for 90 min at 60% glycerol) halved tomatoes at conditions selected based on the experiments described in previous sections were subjected to shelf life determination. Approximately 100 g of each product were packed into polyethylene-polyppropylene sachets (15 \(\times\) 15 cm) and stored in the dark in incubators at constant temperatures between 0 \({}^{\circ}\)C and 25 \({}^{\circ}\)C. At regular time intervals samples were withdrawn and analyzed in terms of total aerobic viable counts (_TVC_) according to [PERSON] et al. (2018), and sensory attributes based on the characteristics described in Section 2.4.6. Microbial growth versus storage time for each storage temperature was mathematically modelled using the Baranyi growth model ([PERSON], 2014). The evolution of the sensory total acceptance versus storage time for each storage temperature was mathematically modelled using a simple zero order equation of the form \(S=S_{0}-k_{s}\)\(t\), where \(S\) is the sensory attribute at time \(t\), \(S_{0}\) is the sensory attribute at time zero and \(k_{s}\) the deterioration rate constant. The dependence of the rate constant and lag phase parameters of the Baranyi equation, as well as the dependence of the sensory deterioration rate constant from storage temperature, was mathematically modelled using the Arrhenius equation. Combining the mathematical models used, the following expressions for the shelf life were derived, similar to [PERSON] et al. (2018):  
\[t_{\text{SL}_{S}}=\frac{S_{0}-S_{L}}{k_{ref,\text{s}}\text{exp}\left(-\frac{E_ {\text{s}E}}{R}\left(\frac{1}{T}-\frac{1}{T_{ref}}\right)\right)} \tag{7}\]  
\[t_{\text{SL}_{MG}}=L(T)+\frac{\text{log}N_{L}-\text{log}N_{0}}{k_{ref,\text{s }}\text{exp}\left(-\frac{E_{\text{s}E}}{R}\left(\frac{1}{T}-\frac{1}{T_{ref}} \right)\right)} \tag{8}\]  
where \(t_{\text{SL}_{S}}\) is the shelf life determined in terms of sensory deterioration, \(t_{\text{SL}_{MG}}\) is the shelf life determined in terms of microbial growth, \(S_{0}\) is the initial sensory score, \(S_{L}\) is the acceptance limit of the sensory score, \(\text{log}N_{L}\) is the limiting microbial population (set at 6.5 \(\text{log}(\text{CFU}/\text{g})\), \(\text{log}N_{0}\) the initial microbial population, \(R\) is the universal gas constant and \(E_{a}\) and \(k_{ref}\) the activation energy and rate constants at the reference temperature, respectively, for each measured parameter. In Equation (8), the parameter \(L(T)\) represents the dependence of the microbial growth lag phase from storage temperature via the Arrhenius equation. Since different processing conditions were expected to lead to different shelf life estimations based on the two parameters, both values were calculated. The parameter leading to the lowest shelf life estimate at each storage temperature was considered as the limiting parameter.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 4 Discussion
This approach emphasizes that not only the overall image but also the details can be crucial for classification. This methodology is important in the context of weed detection, as often, the differences lie in subtle details. Our approach inherently separates color, texture, and shape factors. In the future, we aim to further expand the features by observing leaf orientation, direction, vein texture, and edge patterns, thus enhancing the feature set. In other work, [PERSON] [20] utilized drone or remote sensing data, which is similar to our dataset in the sense that the images were captured from above. They employed a combination of deep learning techniques for localization and classification, utilizing 70 categories with a vast range of 700-13,405 images per category. The training process took approximately 2 days. Speed is crucial in weed control, especially after rainfall when weeds can proliferate rapidly. While there are numerous weed types, only a few species typically appear at any given time. This means that when new weeds emerge, we can capture and label some images. However, it is impractical to generate thousands of images per weed and label them manually. Additionally, the agricultural field cannot afford to wait for days to label images and train a network. In future research endeavors, we aim to enhance detection results by incorporating additional shape-based features into the analysis. Potential methods could include Fourier descriptors, Zernike moments, or deep learning-based feature extraction approaches. These techniques offer greater flexibility and adaptability to accommodate the complex shape variations inherent in plant imagery. Additionally, we intend to explore the integration of more texture-based features to further enrich the feature representation and improve the classifier's ability to discriminate between different weed species. By leveraging a broaderrange of feature descriptors, we anticipate advancing the accuracy and robustness of our weed detection and classification algorithms, ultimately facilitating more effective weed management strategies in agricultural settings.
## 3 MPTCP Congestion Control Background
### Weighted Vegas (wVegas)  
wVegas [8] is designed to provide a more accurate representation of network conditions. It estimates the RTT for each subflow individually and uses packet marking to detect congestion, instead of relying solely on loss events like traditional TCP algorithms. To adjust the sending rate, it uses a weighted average method, resulting in a more stable and gradual response to changes in network conditions.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 2 Background and Related Work
### Two ECG Leads or More  
Multiple Leads ECG provides a better view of the heart condition for long-term cardiac diagnoses. Further, additional ECG leads are required to diagnose sinus rhythm or arrhythmias. In [17], the authors developed a 3-lead ECG wearable device that transfers the acquired ECG data to a mobile device through BLE communications, then relays the data to a backend system through mobile connectivity. Their hardware is based on the RPI development board as the main MCU, and a breakout Printed Circuit Board (PCB) was designed with an analog frontend for acquiring the ECG signals. The system featured a backend component to store collected ECG data. Despite the RPI's compact size, multiple communication protocols, and computation capabilities, it is not optimized for wearable technologies since it is not designed for low-power computing. [PERSON] et al. [18] developed a 3-lead ECG acquisition device to acquire ECG signals during different pregnancy stages. The signals are transmitted to an Android application using BLE. Then, the application performs real-time filtering to remove baseline drift and high-frequency interference. The sample entropy algorithm is then used for fetal ECG extraction performed offline. To that extent, [PERSON] et al. [19] designed an intelligent vest using non-adhesive electrodes to collect 3-lead ECG signals transmitting data to a mobile device to display the received signals. The system acts as a Holter monitor, where no data analytics or classification is done on the collected ECG data. This component is needed in critical heart conditions.  
Physicians recommend that increasing the number of active ECG leads helps understand heart conditions. To that extent, [PERSON] et al. [20] developed a 5-lead ECG acquisition hardware using ADAS1000 chip as an Analog Frontend (AFE) on a custom-designed PCB interfaced to an RPI module using the serial SPI. The system featured a backend component to store the collected ECG data. However, the hardware is not optimized for wearable technologies since the RPI is not designed for low-power computing. Moreover, the device doesn't offer offline ECG data recording in disconnected scenarios when Bluetooth connectivity is unavailable. Lastly, the authors [21] introduced an early detection module to notify patients about abnormal heart conditions. However, the module relies on getting the classification results (e.g., Mild and Severe over a certain period) using a script running on a host desktop.