esa-sceva/satcom-chunk-collection huggingface dataset:
# Dispersive FSO Performance Estimation with Gaussian Pulses and Laplace Modeled Time Jitter
## 2 System Model
### TJ Influence Estimation  
To estimation the influence of the TJ effect on each receiving pulse, the Laplace distribution model was used due to its symmetrical behavior that corresponds to the same probability to detect the information carrier, i.e., the longitudinal Gaussian pulse, before or after the correct moment. Its probability density function (pdf) with respect to \(T\) is given as [11; 25; 26; 27]:  
\[f_{T}(T)=\frac{1}{2\sigma_{T}}\exp\biggl{(}-\frac{|T-\upmu_{T}|}{\sigma_{T}} \biggr{)}, \tag{5}\]  
where \(\sigma_{T}^{2}\) indicates the distribution variance, which models the strength of the TJ, and \(\upmu_{T}\) denotes its mean value, which can be equal to zero since the TJ effect is modelled here by symmetrical distribution.  
Combining all the studied effects, their joint instantaneous irradiance, \(I\), is given as:  
\[I(z,T)=L(z)B(z)I_{T}(z,T)=LB\exp\Bigl{[}-\bigl{(}T/T_{pw}\bigr{)}^{2}\Bigr{]}. \tag{6}\]  
Thus, it turns out that \(T=\pm T_{pw}\sqrt{\ln(LB/I)}\), which means (5) can be rewritten as:  
\[f_{I}(I)=\frac{f_{T}\Bigl{[}\sqrt{\ln(LB/I)}\Bigr{]}}{|dI/dT|_{T=+T_{pw}\sqrt{ \ln(LB/I)}}}+\frac{f_{T}\Bigl{[}-\sqrt{\ln(LB/I)}\Bigr{]}}{|dI/dT|_{T=-T_{pw} \sqrt{\ln(LB/I)}}}, \tag{7}\]  
Resulting in the following expression:  
\[f_{I}(I)=\frac{g_{T}\Bigl{\{}\exp\Bigl{[}\sqrt{\ln(LB/I)}\Bigr{]}\Bigr{\}}^{- g_{T}}}{2\sqrt{\ln(LB/I)}I}, \tag{8}\]where \(g_{T}=T_{pw}/\sigma_{T}\).  
Next, the expected irradiance of \(I\) is computed by [39]:  
\[E[I]=\int\limits_{0}^{LB}{{If_{I}(I)dI}}. \tag{9}\]  
By applying Equation (8) into Equation (9) and using an RV transformation of the form: \(y_{I}=\sqrt{\ln(LB/I)}\), Equation (9) gives the following outcome:  
\[E[I]=\sqrt{\pi}g_{T}LB\exp\left({g_{T}^{2}/4}\right)\textit{erfc}(g_{T}/2)/2, \tag{10}\]  
where _erfc_(.) stands for the complementary error function [40].
## 2 Chemical Synthesis of Nucleoside Di- and Triphosphates
### Synthesis of Nucleoside Di- and Triphosphates via Mixed P(III)-P(V) Anhydrides  
One of the more recently proposed method for the synthesis of nucleoside di- and triphosphates was described by [PERSON] et al., which uses the high reactivity of P(III)-P(V) mixed anhydrides generated in situ (Scheme 13). In the first stage, an unprotected nucleoside phosphate (NMP) is allowed to react with a slight excess (1.1-1.4 equiv.) of bis(fluoreylmethyl) phosphoramidite **20** in the presence of a tetrazole-type of activator, leading to the formation of mixed anhydride **21**, which was then oxidized in situ. After the removal of the phosphate-protecting groups (\(\beta\)-elimination under very mild conditions), nucleoside 5\({}^{\prime}\)-diphosphates could be obtained in 75-93% yields as piperidinium salts. If NDP is used as a substrate, the method permits the synthesis of the corresponding nucleoside 5\({}^{\prime}\)-triphosphates. Interestingly, despite the known high reactivity of phosphoramidites, no phosphitylation of the 3\({}^{\prime}\)-OH group of nucleosides with reagent **20** was observed, probably due to the steric hindrance imparted by two fluoreylmethyl groups [82].  
This approach has many advantages, including, among others, the use of unprotected NMPs, short reaction times, the ease of purification of the products, and good yields. However, as in other methods based on P(III)'s chemistry, strictly anhydrous conditions are crucial for the successful reactions, as traces of water lead to the formation of various by-products.
## 2 GFZ-Wireless Sensing Unit (GFZ-WSU): Hardware And Communications
### Hardware
Tests carried out at the GFZ-Potsdam and observations from the test-bed seismic early-warning network in Istanbul [14], where some wireless stations are equipped with a buffer battery and connected to a solar panel through a solar controller, indicate that a 60 W solar panel and a 40 Ah battery can deliver the necessary energy for nearly continuous operating status. In these cases, the one of the channels of the ADC board can be used for the battery voltage surveillance.
## 4 Distributed Hotspot Network Architecture
### Internetwork Backhaul  
Internetworking for incoming messages from IoT devices via various hotspots and the blockchain are transmitted to target application server/remote users. This internetwork backhaul can be optional, if the LoraWAN-aware design is considered where LoraWAN-based platforms can act as the message delivery system. For example, helium channels can be deployed to serve the backhaul connectivity to remote network servers/operators. A standard the things network (TTN) is also capable of performing similar tasks. Otherwise, a regular cellular-based internetwork backhaul may be considered.
# Slot Optimization Based on Coupled Airspace Capacity of Multi-Airport System
## 3 Multi-Airport System for Cooperative Optimization of Flight Schedules
### Multi-Airport System for Cooperative Optimization of Flight Schedules
#### 3.3.1 Model Notation  
To facilitate clarity and comprehension, definitions for the sets, parameters, and variables that feature in the mathematical model were provided in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline
**Sets** & \\ \hline \(M\) & Set of flights in the multi-airport system \\ \(U\) & Set of airports in the multi-airport system \\ \(I\) & Set of airline companies \\ \(M_{u}\) & Set of flights in airport \(u\) \\ \(Mu\),\(a\) & Set of arrival flights in airport \(u\),\(u\in U\) \\ \(Mu\),\(d\) & Set of departure flights in airport \(u\),\(u\in U\) \\ \(M_{u}^{2}\) & Set of transit flights in airport \(u\),\(u\in U\) \\ \(M_{t}\) & Set of flights in the time interval \([1,t]\) \\ \(M_{t}^{k}\) & Set of flights passing fix \(k\) in the time interval \([1,t]\) \\ \(M_{t}^{k\prime}\) & Set of flights passing fix \(k^{\prime}\) in the time interval \([1,t]\) \\ \(M_{t}^{s}\) & Set of flights of the \(s\) aircraft type in the time interval \([1,t]\) \\ \(Q\) & Set of aircraft types \\ \(S\) & Set of parking position types \\ \(T\) & Set of time slices \\ \(K\) & Set of corridors in airport terminal areas \\ \(H\) & Set of slots in the day, \(h\in H\) \\ \(T_{m}\) & Set of possible actual flight time slices for flight \(m\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model notation.
# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 2 Methodology
We applied JPEG compression levels of 100% for the unimpaired image and JPEG compression at 75%, 50%, 25%, 15% and 5% for the impairment levels \(l\) from 1-5, respectively. The selected spherical source images were used in prior works [2], enabling a direct comparison between results described for an augmented reality setting (SAR) and the virtual reality setting (SVR) we consider in this contribution. We illustrate the employed spherical images for additional reference in Figure 3.  
Similar to our prior works, we captured EEG data in addition to the user QoE feedback. The MUSE headband directly provides 10-Hz measurements for TP9, Fp1, Fp2 and TP10 positions (denoted as positions \(p\), \(p\in\{1,2,3,4\}\), respectively) for these EEG bands:  
* Low \(\imath_{p}\) at 2.5-6.1 Hz,
* Delta \(\delta_{p}\) at 1-4 Hz,
* Theta \(\theta_{p}\) at 4-8 Hz,
* Alpha \(\alpha_{p}\) at 7.5-13 Hz,
* Beta \(\beta_{p}\) at 13-30 Hz and
* Gamma \(\gamma_{p}\) at 30-44 Hz. Figure 2: Schematic view of the placement for the EEG headband (top) and the VR viewer (bottom). The headband’s electrodes connect at four positions on the forehead (TP9, Fp1, Fp2 and TP10 positions) of the subject and behind the ear for a reference. The generated data have been made publicly available as part of the Visual Interface Evaluation for Wearables (VIEW) datasets; see [2].
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 3 Underwater Acoustic Target Recognition Model with Delay-Doppler Joint Feature  
### Time-Frequency and Delay-Doppler Features of Ship-Radiated Noise  
This section mainly introduces the Time-Frequency and Delay-Doppler features of ship-radiated noise. This paper examines the ship-radiated noise signal using FFT and extracts the frequency characteristics of ship-radiated noise. The primary sources of ship-radiated noise are vibration noise, propeller noise, and cavitation noise from bubbles. Vibration noise and propeller noise are the most significant, and ship-radiated noise is  
Figure 4: The principle of the convolutional neural network.  
which can more intuitively reflect the Delay-Doppler feature information of the signal. The speed feature of the target can be obtained by using the propagation speed and Doppler frequency shift of acoustic waves in a seawater medium. The signal's Delay-Doppler characteristic information can be reflected more directly.  
The signal's Time-Frequency domain feature depicts the target's intrinsic characteristics, and the signal's properties remain relatively constant throughout time. The target's velocity characteristics, or its dynamic characteristics, may be reflected in the signal's Delay-Doppler feature. The speed range of different targets is different. For example, torpedoes can reach more than 60 knots per hour, speedboats can reach about 40-60 knots per hour, ordinary fishing boats can reach about 10-15 knots per hour, and large oil tankers can reach less than 10 knots per hour in ports. The targets of different sizes and functions have corresponding speed ranges; so, speed can be used to roughly divide target types.  
Figure 10 shows the idea of using the above two signal features as the basis for classification in this paper. Since different targets have different shapes and sizes, their speed ranges are also different; so, they have different features in the Delay-Doppler domain. Targets may be categorized into several speed ranges based on the target signal's Delay-Doppler properties, and the target size may be determined by analyzing the speed properties of the signal. The radiated noise generated by different targets may have frequency overlap; so, only depending on the signal's Time-Frequency features to identify the target is challenging. The properties of the target from another feature domain of the signal may be described by the Delay-Doppler feature, and the foundation of the classification is the joint features that combine the Time-Frequency and Delay-Doppler features. The dimension of the description signal is increased, which can reflect the target information more comprehensively.  
Figure 8: Delay-Doppler feature of an Oceanliner.  
Figure 9: Three-dimensional distribution of the Delay-Doppler feature.
## 5 Laboratory Test Description
### Antenna Noise Tests  
These tests, as the ones described in previous Section 6.1, are part of the synthetic noise injection tests, so the same setup in used (Figure 24). The signal injected in this case is shown in Figure 8. This signal has peaks in frequencies of 41.5 and 83 kHz, reaching 95 and 100 dBuV respectively.  
The main goal of these tests is twofold: (1) evaluate the increase of the noise when the gain of the amplifier reaches its maximum value; and (2) evaluate the effectiveness of a simple and cheap filter configuration such as filter F2 to mitigate the noise in this situation.  
This set of tests comprises from test A1 to A6. From A1 to A3, the gain of the amplifier is set to 25% and, from A4 to A6, it is set to the maximum.  
Figure 36 shows the spectral signal measured for test A1. It can be seen that the  
Like test A1, A4 consists of the injection of the community antenna noise without filtering, but in this case the gain of the amplifier is set to the maximum. Figure 39 shows that, as a result, the peaks of the noise are increased up to 100 and 115 dBuv respectively.  
Tests A5 and A6 are analogous to tests A2 and A3 but with the gain of the amplifier configured to its maximum value. Based on Figures 40 and 41, the filters do not seem to remarkably reduce the peaks of the noise in this case.  
Figure 38: Spectral signal without PRIME communications in test A3.  
Figure 39: Spectral signal without PRIME communications in test A4.
## 4 Future Trends
### Parallelization of Neural Networks  
The review of deep learning NNs presented in Section 2 showed that the accuracy of models could be improved by increasing the number of parameters and the scale (i.e., nodes, layers, etc.). However, these actions also derive slower training times, which is inefficient for adaptive autonomous driving scenarios. Therefore, it has been acknowledged that deep learning models need to be parallelized to accelerate training and deployment. Indeed, deep learning models can be trained and executed in multiple GPUs. The key consideration to take advantage of their parallelization is to know how to divide the GPUs' tasks. Hence, three approaches should be considered if anyone wants to train parallelized models: data parallelism, model parallelism, and data-model parallelism [73].  
Data parallelism: Data parallelism is a different kind of parallelism that, instead of relying on the process or task concurrency, is related to both the flow and the information structure. Each GPU uses the same model to trains on different data subsets. If there are too many computational nodes, it is necessary to reduce the learning rate to keep a smooth training process.
* Model parallelism: In model parallelism, each computational node is responsible for parts of the model by training the same data samples. The computational nodes communicate between them when a neuron's input is from another computational node's output. However, the performance is worse than data parallelism, and the performance of the network will be decreased if it has too many nodes.
* Data-model parallelism: Both previous models have disadvantages, but they also have positive characteristics. Model parallelism could get good performance with many neuron activities, and data parallelism is efficient with many weights.  
In summary, data-model parallelism combines the best of the first two approaches and is more suited for AV tasks as long as there is a clear idea of where to apply each step. For instance, the convolutional layer of a CNN could use data parallelism (since it contains about 90% of the computation and 10% of the parameters). In contrast, the fully connected layer could only rely on model parallelism because it contains 90% of the parameters and 10% of the computation.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 6 Effect on Food Quality Characteristics
### Effects on Allergens, Toxins and Anti-nutritional Factors  
In several studies PL treatment is proposed to mitigate the allergenicity and toxicity of foods, which extends the applicability of PL treatment in food industry. For the first time, [PERSON] et al. [142] showed the use of PL in mitigation of allergenicity of peanuts. They employed PL treatment for the prepare a peanut based product with hypoallergenic properties by reducing peanut allergens Ara h 1 and Ara h 3 to significant level. The major reason for changes in allergenicity of the proteins could be attributed to changes in structure, conformation and solubility of the protein subunits [143]. Pulsed UV treatment has ever since extended to other protein sources. PL treatment for 6 min led to a decrease in the major soy extracts allergen levels (i.e., glycinin, Gly m6 and \(\beta\)-congylcinin, Gly m5) probably through aggregation [144]. They also observed 50% lowering in Immunoglobulin E (IgE) binding after treatment of 6 min. In another study, [PERSON] et al. [143] showed reduction in immunoreactivity of Gly m6 and Gly m5 in soy protein isolate by 91% after 4 min treatment at 0.98 J/cm\({}^{2}\)-s fluence. Also, PL treatment of 4 min was found to decrease the activity of the tropomyosin, a major allergen in shrimp, and to decrease the IgE-binding capacity of shrimp extract [145]. [PERSON] et al. [146] showed that PL exposure for 6-7 min reduced allergens of almond protein extracts and their IgE binding. [PERSON] [99] showed that PL was also effective in reducing antigenicity as well as the binding of IgE by isolated milk and egg proteins, in even shorter times [146]. Gluten immunoreactivity has been reduced by 50% using PL application at 26.26 J/cm\({}^{2}\)[147]. [PERSON] et al. [148] treated \(\beta\)-lactoglobulin solution and reduced immunoreactivity of IgE and IgG by 45% and 85% after 16 J/cm\({}^{2}\) PL treatment.  
Mycotoxins also have been inactivated by PL treatment in several studies. Mycotoxins like deoxynivalenol (DON), zearalenone, ochratoxin and aflatoxin (AF) B1 in solution were reduced by \(72.5\pm 1.1\), \(84.5\pm 1.9\), \(98.1\pm 0.2\) and \(92.7\pm 0.8\)%, respectively [149]. In another study, [PERSON] et al. [150] demonstrated that PL dose of up to 35.8 J/cm\({}^{2}\) reduced patulin level up to 22% in apple juice, whereas a 12 J/cm\({}^{2}\) treatment led to 51% lowering in patulin from apple puree. Furthermore, [PERSON] et al. [151] showed that PL treatment completely eliminated the mutagenic activity of AF B1 and AF B2 in rice products like rough rice and rice bran. PL dose of 0.52 J/cm\({}^{2}\) per pulse for 80 s caused reduction by 75.0% and 39.2% of AF B1 and AF B2 in rough rice, respectively, while a 15 s treatment caused reduction in rice bran by 90.3% and 86.7% of AF B1 and AF B2, respectively. In a very recent study, concentration of DON in germinating barley was reduced by 35.5% using intense PL after 180 pulses for 1 min [152].
## 3 Genetic Approaches  
### Epigenetic and Molecular Pathway for DUX4  
The expression of DUX4 can be inhibited by enhancing the epigenetic suppression of D4Z4 repeats or by inhibiting upstream signals. Overexpression of SMCHD1 in myotubes of FSHD1 and FSHD2 can inhibit DUX4 expression [51], indicating that specific delivery of SMCHD1 in muscle tissue may be a treatment scheme, and small molecule drugs for enhancing the activity of SMCHD1 are being developed. In recent years, through the screening of chemical libraries and genomes, many cascade reactions have been revealed, and compounds that can inhibit DUX4 expression have been identified, such as bromine domain and terminal external (BET) inhibitors, \(\beta\)2 adrenergic receptor agonists, phosphodiesterase (PDE) inhibitors, p38 inhibitors, and Wnt agonists [52; 53; 54; 55; 56] (Figure 3). As for molecule drugs, only one small molecule drug has entered clinical trials in Europe and America: losmapimod, an inhibitor of p38 signal, which is also the first drug targeting DUX4. At present, phase I clinical trials [57] (Table 1) have been carried out. Through oral administration of losmapimod to healthy volunteers and FSHD patients, the results show that losmapimod can reduce the expression of DUX4 gene in the myotubes of FSHD patients in a dose-dependent manner and inhibit the death of skeletal muscle cells. Losmapimod is well tolerated in patients. The latest phase IIb clinical trial (Table 1) of losmapimod (ReDUX4) has been completed [58], which is one of the largest clinical trials of FSHD intervention so far. The study is a 48-week randomized, double-blind, placebo trial, and the subjects were recruited from 17 neurological centers across the United States, France, Canada, and Spain, comprising a total of 80 FSHD1 patients. The preclinical experiment evaluated the safety and effectiveness of losmapimod in FSHD patients. The results showed that repeat dosing of losmapimod was safe and well tolerated in adults. This preclinical trial was helpful in designing the phase III study of losmapimod in the treatment of FSHD, which may make losmapimod a candidate drug for the treatment of FSHD. After the next round of clinical trials, this may make losmapimod a candidate drug for the treatment of FSHD.  
Figure 3: Summary of therapeutic approaches targeting DUX4 in facioscapulohumeral muscular dystrophy. Targeting DUX4 therapy mainly includes targeting the epigenetics of D4Z4 fragments and directly targeting DUX4 mRNA.
# Pile Base and Shaft Capacity under Various Types of Loading
## 2 Pile Bearing Capacity
### Base Bearing Capacity  
The load on the pile base is transferred to the soil by pressure. The value of unit resistance generated under the base depends on the type and condition of the soil underneath the pile base, the level of stresses at the base level, installation effects, and many other factors [4; 12; 13]. During the installation of a pile, additional residual stresses may appear as a result of soil displacement [14] (deformations created during the installation of displacement piles). Soil setup (increasing capacity) may also be observed due to pore pressure dissipation during soil reconsolidation. According to [14], residual stresses can cause the overestimation of the shaft bearing capacity and the underestimation of the base bearing capacity.  
Determining the pile base bearing capacity in a traditional static load test requires the use of specially instrumented piles [15]. Separate behaviour of the pile base under a load can be tested during the bidirectional test [9], although different conditions of the test can influence the bearing capacity of the pile base.
## 5 Smart Grid Characteristics
### Integration of Smart Home Appliances  
Smart appliances are devices that can connect to the power grid, shut off power during peak hours, and intelligently change their power consumption. Smart devices are typically installed on the user side, which includes smart buildings, and help decouple household electricity demand from peak hours. In a study from the UK, it is found that with smart device penetration of 20 percent, demand-side management can provide up to 54 percent of the operating reserve demand, depending on the time of day [43].
## 5 Results
### Synthetic Augmentation Results  
Synthetic data were created to improve the dataset and address class imbalance. Figure 5 presents a sample of the synthetic images generated by DCGANs with size 64 \(\times\) 64, which are very close to authentic images in terms of crispness, naturalness, and realism. A total of 2000 augmented and synthetic images per class were combined with real images to improve the performance of the helmet recognition system. The results in Table 4 demonstrated that the DCGANs + YOLOv8 + TTA model was more robust to variations in the input data, resulting in more accurate results.  
These variations include changes in lighting conditions, camera angles, image quality, and occlusion. Additionally, the DCGANs + YOLOv8 + TTA model is more effective at detecting minority classes. Since small datasets can limit the ability of neural networks to locate objects and can result in overfitting, greater improvements can be expected  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **mAP50** & **mAP50-95** & **Precision** & **Recall** & **mAP** & **F1** & **fps** \\ \hline YOLOv5 & 0.842 & 0.459 & 0.890 & 0.804 & 0.621 & 0.80 & 150 \\ DCGANs + YOLOv5 & 0.823 & 0.465 & 0.754 & 0.799 & 0.601 & 0.78 & 150 \\ YOLOv7 & 0.851 & 0.521 & 0.911 & 0.830 & 0.643 & 0.82 & 155 \\ DCGANs + YOLOv7 & 0.862 & 0.526 & 0.915 & 0.836 & 0.654 & 0.82 & 155 \\ YOLOv8 & 0.866 & 0.603 & 0.928 & 0.891 & 0.680 & 0.91 & 155 \\ DCGANs + YOLOv8 & 0.913 & 0.721 & 0.945 & 0.893 & 0.782 & 0.93 & 143 \\ DCGANs + YOLOv8 + TTA & 0.949 & 0.749 & 0.972 & 0.919 & 0.810 & 0.96 & 92 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results on the validation and test datasets.  
Figure 5: Sample output from DCGANs for “DriverNoHelmet” class.  
in these minority classes as a result of synthetic minority sampling. As expected, the model's performance on the classes P1 Helmet, P1 NoHelmet, and P2 NoHelmet improved as shown in Figure 6. Figure 6 compares the F1-confidence and precision-recall curves for the YOLOv8 vs. DCGANs + YOLOv8 + TTA models. Therefore, by saturating the original dataset with augmented and synthetic images generated by DCGANs, the model's detection and recognition performance can be greatly enhanced, as presented in Figure 7, which shows inference on a sampled test data.