esa-sceva/satcom-chunk-collection huggingface dataset:
# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 2 Related Work
### Feature Fusion Network  
Enhancing the performance of image target segmentation necessitates the fusion of more discerning multiscale features. Masterfully handling and depicting multi-scale information is a primary challenge in this field. The Feature Pyramid Network (FPN) approach by [PERSON] et al. [31] was among the initial studies to construct a feature pyramid, alternately combining adjacent feature levels using lateral connections and top-down pathways to enhance feature portrayal. Furthering the overall feature hierarchy, [PERSON] et al. [32] added a second bottom-up path aggregation network (PANet) atop the FPN to boost information flow and minimize the gap between the most elementary and highest feature levels. To better combine semantic and geographical information, DLA [33] created hierarchical depth aggregation structures and iterative depth aggregation. Aiming to achieve multiscale feature rebalancing, [PERSON] et al. [34] utilized Balanced IOU sampling, a Balanced Feature Pyramid, and a Balanced L1 loss in the Libra R-CNN framework. These strategies targeted imbalances at the sample level, feature level, and object level, respectively. [PERSON] et al. [35] introduced a Weighted Bidirectional Feature Pyramid Network (BiFPN) to facilitate efficient multiscale feature integration. For leveraging cross-scale features, STDL [36] proposed a scale-shifting module, M2 det [37] offered a U-shaped design for fusing multiscale features, and G-FRNet [38] incorporated gate units to manage the exchange of cross-feature information. A neural structure search approach is also used by NAS-FPN [39] to identify a more reliable fusion structure that offers the best single-shot detector. [PERSON] et al. [40] developed an Adaptive Multiscale Feature Fusion Network capable of dynamically learning or suppressing the scale-specific features at the moment of fusion. By deriving a spatial fusion coefficient for each feature at every scale location, this approach enhances the scalability of the features and realizes their adaptive integration. Potential conflicting information (i.e., inconsistency) on the fusion time space is automatically learned or suppressed. In this research, we create the intermediate fusion approach that can better collect coarse-grained and fine-grained information by using an attention mechanism.
## 7 Mitigating Jamming Interference  
To ensure the safe operation of vehicle networks while dealing with jamming transmitters, a number of strategies can be used to mitigate the negative effects of jamming equipment. Slot-based V2X systems are a potential side-link blocking approach [37] that can be used to lessen several negative effects of organized jamming. The use of deliberate side-link blocking and initiatives to jam V2X exchanges can be reduced by altering the rate at which information is exchanged. A probabilistic-channel browsing technique [38] that aims to react to the blocking disruption may also be employed to lessen the disruption brought on by the clustering process of jammers. The channel surfing technique mostly works by changing the control channel to a distinct channel. Also, an abrupt variation in channels can aid in minimizing the impact of jamming by broadcasting the messages and transmitting the contents on an un-jammed channel, as described in [39]. This strategy helps in reducing the negative effects of jamming. The work in [40] examines a convoy system that uses a behavior-based structure whereby VNs cooperate and exchange addresses to counter jammer assaults in an effort to lessen the interference triggered by jamming devices. Furthermore, in a jamming-disturbed environment, the multiple-input multiple-output-based method suggested in [41] may be applied to analyze the data obtained. In real-world scenarios, the rate-adaptation and power-management techniques outlined in [42; 43] can possibly be applied to mitigate the disruptive effects of jamming devices.
## 3 Results and Discussions
### Temperature  
Figure 2 shows the temperature distribution of the flow field when Gupta 5-, 7-, and 11-species chemical reaction models are used at the three freestream conditions. It can be found that the peak temperatures of the flow field exceed 10,000 K in the shock layer for all three cases, and the translational-rotational and vibrational-electronic temperatures are highly unequal within a distance behind the shock, due to the existence of a significant thermochemical nonequilibrium effect. However, the differences between different  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(H\) (km) & \(V_{\infty}\) (km/s) & \(Ma\) & \(\rho_{\infty}\) (kg/m\({}^{3}\)) & \(p_{\infty}\) (Pa) & \(T_{\infty}\) (K) & \(T_{\infty}\) (K) \\ \hline
71.02 & 11.31 & 38.94 & \(8.57\times 10^{-5}\) & 5.20 & 210 & 810 \\
59.62 & 10.97 & 34.34 & \(3.86\times 10^{-4}\) & 28.20 & 254 & 1560 \\
48.37 & 9.83 & 29.05 & \(1.32\times 10^{-3}\) & 108.00 & 285 & 1520 \\ \hline \end{tabular}
\end{table}
Table 2: Freestream conditions for the present study.  
Figure 1: Grid for Cylindrical Flow Field.  
chemical kinetic models rise gradually as the altitude and Mach number increase. To show more details, the temperature distribution along the stagnation line given by the 5-, 7-, and 11-species models of Dunn-Kang, [PERSON], and Park combined with the two temperature models are shown in Figure 3, respectively. The comparison of different chemical kinetic models reveals that the total number of gas species has a remarkable effect on both the peak and equilibrium temperatures inside the shock layer. Meanwhile, the thermodynamic nonequilibrium states in the flow field are also inconsistent between different model combinations, mainly affected by the total number of gas species. For 11-species model, the extremely high temperature behind the shock promotes the rapid exchange of energy among various modes, including the excitation of the vibrational energy modes of molecules, and the thermodynamic equilibrium quickly reaches in a very short distance. For 7-species model, thermodynamic nonequilibrium exists in most area of the shock layer, and the thermodynamic equilibrium just appears near the wall; For 5-species model, thermodynamically frozen flow occurs in most regions of the shock layer.  
Figure 2: _Cont._  
Figure 3: Temperature distribution along the stagnation line under different conditions.  
Figure 2: Temperature distribution around the flow field considering Gupta model under different conditions.  
The temperature distribution along the stagnation line shows the following trends: (i) the temperature \(T\) of the one-temperature model is slightly lower than the translational-rotational temperature of the two-temperature model; (ii) the differences between the results of the different chemical kinetic models with the same number of gas species are small, and the differences between the temperature results are greatly affected by the number of gas species. According to the magnitude of translational-rotational temperature, the descending order for the three species models is 5-species > 7-species > 11-species.
## 4 Advanced Modeling Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multirow{2}{*}{**Temperature (K)**} & \multicolumn{6}{c}{**Parameters**} \\ \cline{2-7}  & & **n** & \(\mathbf{D_{m}}\) & \(\mathbf{N_{c}}\) & \(\mathbf{Q_{sat}}\) & \(\mathbf{(-\varepsilon_{1})}\) & \(\mathbf{(-\varepsilon_{2})}\) \\ \hline \multirow{3}{*}{**Na-ZSM-5**} & 0 \({}^{\circ}\)C & 3.23 & 11.83 & 1.75 & 28.65 & \(-\)15.265 & \(-\)10.702 \\  & 30 \({}^{\circ}\)C & 2.55 & 10.72 & 2.02 & 27.88 & \(-\)17.022 & \(-\)11.87 \\  & 60 \({}^{\circ}\)C & 2.39 & 9.27 & 2.18 & 26.14 & \(-\)18.772 & \(-\)13.244 \\ \hline \multirow{3}{*}{**Mg-ZSM-5**} & 0 \({}^{\circ}\)C & 1.44 & 16.75 & 2.16 & 27.97 & \(-\)16.696 & \(-\)10.491 \\  & 30 \({}^{\circ}\)C & 1.02 & 13.92 & 2.89 & 26.83 & \(-\)17.543 & \(-\)10.561 \\  & 60 \({}^{\circ}\)C & 0.99 & 13.31 & 2.94 & 25.82 & \(-\)19.27 & \(-\)11.989 \\ \hline \multirow{3}{*}{**La-ZSM-5**} & 0 \({}^{\circ}\)C & 0.85 & 42.95 & 2.88 & 68.63 & \(-\)17.426 & \(-\)9.15 \\  & 30 \({}^{\circ}\)C & 0.81 & 33.32 & 3.23 & 60.18 & \(-\)18.271 & \(-\)10.441 \\  & 60 \({}^{\circ}\)C & 0.79 & 30.56 & 3.43 & 54.82 & \(-\)19.154 & \(-\)11.833 \\ \hline \multirow{3}{*}{**Zn-ZSM-5**} & 0 \({}^{\circ}\)C & 1.51 & 40.9 & 2.21 & 74.72 & \(-\)16.061 & \(-\)10.4 \\  & 30 \({}^{\circ}\)C & 1.05 & 34.2 & 2.7 & 61.74 & \(-\)16.748 & \(-\)10.761 \\ \cline{1-1}  & 60 \({}^{\circ}\)C & 1.01 & 25.31 & 3.19 & 56.53
## 7 Conclusions  
To obtain in situ monitoring data in the most remote areas of the TP, we developed an IoT system enabled by IoT satellites launched under the Xingyun project to collect field data. A ground IoT terminal was established to operate in conjunction with an environmental monitoring device configured with a LEOBIT satellite transceiver to enable its remote IoT functionality and allow the device to transmit monitoring data to a data center via satellites.  
We installed five of these newly developed ground IoT terminals across the TP to monitor the temperature and wind speed in the Muggangqiong glacier area, precipitation (rain) and relative humidity in the Jialabailei glacier area, and snow depth, land surface temperature, tree stemflow rate, photosynthetically activated radiation, and stem diameters of ten trees in the Qilian Mountains since 2020. We evaluate the performance of the satellite-enabled IoT system in terms of the time delay, packet loss rate, and throughput. The results indicated that the performance of transmitting cached data as packets smaller than 200 B in size was basically consistent with that of transmitting cached data as packets exceeding 200 B in size (200 B is the maximum satellite bandwidth), and the packet creation mode demonstrated its advantages in transmitting cached data over 200 B in size within the satellite-enabled IoT network. The successful application of the satellite-enabled IoT network represents an important step toward the ubiquitous application of environmental monitoring across the TP, which could play an undeniably important role in advancing the state of scientific research on the TP.  
Writing-original draft, Y.C. and M.Z.; Methodology, R.J. and J.G.; Acquisition of data, W.Y. and X.N.; Writing--review and editing, X.L., B.A. and T.C.; Conceptualization, X.L.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (2019 QZKK0206), the National Key Research & Development Program of China (2016 YFC0500106), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20100104), and the Science and Technology Program of Gansu Province (21 JR7 RA248).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We wish to thank [PERSON], [PERSON] and [PERSON] for their contributions to the experimental test and data retrieval.  
The authors declare no conflict of interest.
# Removal of Organic UV Filters Using Enzymes in Spent Mushroom Composts from Fungicultures
## 1 Introduction
Spent mushroom compost (SMC), a waste product of the mushroom industry, contains white-rot fungus extracellular enzymes that can be used to degrade many organic pollutants such as acetaminophen, sulfonamides and tetrabromobisphenol-A [25; 26; 27]. SMC is mainly composed of 95% natural wood chips and other ingredients such as rice bran, corn flour and wheat flour. Therefore, in addition to the use of extracellular enzymes to decompose organic pollutants, SMCs can also be used to remove organic pollutants by adsorption [25]. The application of SMCs for organic UV filter removal is a mycoremmediation-based green and sustainable technology. The aim of this study is to test the removal efficiency of three organic UV filters (EHS, HMS and EHMC) using SMCs of four different mushrooms (_Pleurotus ergvgii_, _Pleurotus digmor_, _Pleurotus ostreatus_ and _Auricularia polytricha_). The effects of enzyme extracts of the four SMCs were investigated by batch experiments. bioreactor experiments were conducted to simulate the efficiency of UV filter removal in reverse osmosis (RO) filtration water and wastewater (wastewater treatment plant effluent) by direct use of SMCs.
# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 3 Results of the Systematic Review
### Relevant Solutions Based on Speech Recognition
highlight the measuring of accuracy of ASR solutions. The word error rates were as follows: 22.4% for Google, 12.4% for IBM, 10.8% for Deepgram, 10.5% for SpeechText.AI, 5.6% for Rev AI, 5.3% for Amazon and Microsoft, 4.7% for Tencent, 4.3% for AssemblyAI, 3.4% for Speechmatics, and 2.9% for Whisper [7]. The advancements in SR algorithms have reached a mature level, demonstrating significant improvements in accuracy and functionality across various applications. Leading solutions have achieved impressively low word error rates, indicating a high degree of reliability in real-world usage. In Figures 2 and 3 are the trends of the keywords \"voice recognition\" and \"speech recognition\" over the last 15 years. From these two figures, we can conclude that voice and speech recognition technologies have matured and become widely integrated into a multitude of devices (e.g., smartphones, smart speakers, cars, etc. ), such that people may view them as a standard feature rather than a novel technology. This normalization can lead to fewer searches since consumers are no longer actively learning about or exploring the technology; they simply expect it to be there.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 2 Probability Distributions
### Generalized Exponential Distribution (EG2-Gupta)  
The Generalized Exponential Distribution is an alternative to the two-parameter Gamma and Weibull distributions. It was introduced by [PERSON] and [PERSON] in 1999 [22].  
\[\mathrm{f(x)}=\alpha\cdot\beta\cdot\exp(-\beta\cdot x)\cdot\left(1-\exp(-\beta \cdot x)\right)^{\alpha-1} \tag{39}\]  
\[\mathrm{F(x)}=1-\left(1-\exp(-\beta\cdot x)\right)^{\alpha} \tag{40}\]  
\[\mathrm{x(p)}=-\frac{\ln\left(1-\left(1-\mathrm{p}\right)^{1/\alpha}\right)}{\beta} \tag{41}\]  
where \(\alpha,\beta>0\) are the shape and the scale parameters; \(\mathrm{x}>0\).
## 3 GA Hybrid-Fog Model
### IoD Technologies  
The rapid evolution of UAV technology allows for its integration with diverse networks and systems to complete complex missions. To improve the availability of IoD services, IoD systems must effectively leverage current technologies [23]. Below, we explore the most significant and recent technologies employed in IoD networks:  
* IoD Cloud Computing Technology  
Given the limitations in wireless communication and the computing capabilities of drones, cloud computing is well suited for IoD systems. Specifically, cloud computing offers data processing and storage services for UAVs. As a result, UAV devices do not need to be highly powerful in terms of CPU speed and memory capacity, since complex computations can be handled by the cloud [24].  
Cloud computing offers numerous advantages, including extended battery life, increased storage, scalability, and reliability. However, it also faces challenges, such as security and privacy concerns, bandwidth and data transfer issues, data management and synchronization difficulties, and energy efficiency problems [25].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
* IoD Fog Computing Technology  
Due to the limited computing resources on drones, managing computationally intensive tasks locally is challenging, which often leads to the use of cloud-based offloading. However, cloud computing solutions may be inadequate for applications requiring low latency and high reliability. This is mostly due to the slow response times resulting from long-distance data transmission. To address these challenges, fog computing has been proposed [26]. It offers an intermediate layer positioned at the edge of the network between UAVs and the cloud, which comprises numerous fog nodes. This fog layer connects to the cloud layer via the Internet or communicates with UAVs through a wireless connection.
* IoD Edge Computing Technology  
IoD edge computing enhances fog and cloud computing capabilities for real-time IoT applications. By handling some UAV data locally at edge IoD devices, the edge layer reduces the computing load without relying on fog or cloud interventions. This shift to edge-based data processing and storage significantly improves latency. Recent research on the IoD has increasingly focused on edge computing to support real-time applications, such as smart transportation, video streaming, surveillance, augmented reality, emergency response, etc. [27]. In the proposed system, tasks are offloaded to edge UAVs using a GA. The UAVs are selected based on their proximity and lower processing frequency to ensure both low latency and high reliability.
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Functional Overview of Transceiver Operation  
Figure 4 shows the schematic cross-section and optical interfaces of the IOCore micro-transceiver.  
Figure 4b shows a top view of the complete IOCore transceiver chip, which is split into a transmitter section on the left-hand side and a receiver section on the right-hand side.  
In the transmitter section (Figure 4d), an edge emitting a Fabry-Perot Quantum Dot laser diode is passively aligned within a compliant recess in the silicon substrate such that it couples 1310-nanometer continuous wave (CW) light into four single-mode silicon waveguides in the substrate. Each waveguide conveys the CW light to a Mach-Zehnder interferometer (MZI). High-speed differential electronic signals passed to the chip through electrical signal pads drive the MZI modulator circuit generating the corresponding modulated 1310-nanometer optical signal, which is then coupled out of the chip through a vertical grating coupler.  
The vertical grating coupler couples the optical signal out of the silicon photonic substrate at an angle of 82\({}^{\circ}\) from the plane of the silicon substrate into a short untapered optical waveguide called an \"optical pin\". Figure 4a shows a photo of a proprietary array of twelve optical pins on the transmitter section, of which only four are used in this design.  
Figure 3: IOCore micro-transceiver footprint of 5 \(\times\) 5 mm [3].  
In the receiver section (Figure 4e), optical signals are coupled from multimode fibers into optical pins on the same array datum as the transmitter optical pins. The receiver optical pins, however, are tapered to both confine and guide the light directly to integrated silicon germanium photodetectors arranged under the optical coupling interface area. This eliminates the requirement for intermediary fiber-to-waveguide coupling structures, such as lossy polarization splitting grating couplers, and moves the light straight to the photodetector, which is directly connected to transimpedance amplifier (TIA) and limiting amplifier (LA) ICs, thus converting the received optical signals into differential voltages.  
The optical pin arrays in the transmitter (Figure 4a) and receiver (Figure 4c) sections are embedded in a glass plate mounted onto the silicon substrate. These form the multimode optical interface, which allows bidirectional communication between a multimode fiber array, such as an MT ferrule, and the micro-transceiver, thus permitting a lower cost and more robust assembly solution with respect to temperature and vibration. This differentiates IOCore from most other commercial silicon photonic transceivers, which have single-mode interfaces.  
Simulations by AIO Core predict the insertion loss of the transmit optical pin--i.e., the insertion loss between the vertical grating coupler and the output facet of the optical pin at the point of contact with the MMF--to be 0.54 dB. Furthermore, simulations predict the insertion loss on the receiver optical pin--i.e., the insertion loss between the MMF and the receiving photodetector--to be 0.68 dB.
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### PPPM Analysis and the Derivation of Answers to Questions
#### 4.5.11 PPPM Analysis of TraVaS (Q2.2.3)  
In this section, we present the quantitative results of applying the _TraVaS_ PPPM model on our real-life event logs under two different aspects as result and time utility qualities. Here, the parameters \(\epsilon\) and \(\delta\) regulate the privacy risk with respect to the model's approximate differential privacy mechanism. All the other hyperparameters are drawn from the authors original publication. In particular, for the BPIC13 and Sepsis event logs, the values were set due to their similar high trace variant count nature. For each of the \(\epsilon\) and \(\delta\) values, the experiments were run 10 times and averaged to reduce the nondeterministic behavior of DP.  
Figure 20: Discovered SaCoFa anonymization model quality results.  
Result Utility Quality  
The Figure 22 heatmap shows the applied _TraVaS_ model for the PM quality metrics with respect to five different \(\epsilon\) and \(\delta\) parameter values behavior on our real-life event logs. For all the results, the baseline metric values without any anonymization are included in Table 3 and employed for the comparative interpretations. This model had successful results for all the event logs at all configurations.  
All the _TraVaS_ anonymized event logs showed alignment with their baseline fitness and generalization values. However, for all the event logs, there was an increase in precision for a weak privacy budget (when \(\epsilon\) and \(\delta\) goes high) in comparison with their baselines. The CoSeLog event log showed a significant increase in precision beside generalization for the strongest privacy protection. Specifically, the precision and generalization applied to the Sepsis event log exhibited deviations for different \(\epsilon\) and \(\delta\) values. Simultaneously, for the CoSeLog and Road Traffic Fines event logs, the generalization values were reduced  
Figure 21: Discovered SaCoFa anonymization runtime (s) metric results.  
Figure 22: Discovered TraVaS anonymization model quality results.  
drastically. On the other hand, for the BPIC13 event log, the metric results showed well-defined trends for each parameter configuration.  
Time Utility Quality  
Figure 23 includes the runtime(s) performance metric results of the applied _TraVaS_ PPPM model on our real-life event logs. During the experimentation with the _TraVaS_ algorithm, a nearly steady trend in model runtime was observed with increasing privacy levels as a consequence of the specific \(\epsilon\) and \(\delta\) privacy parameter modifications for all the event logs. On the Road Traffic Fines event log, the runtime for the anonymization values fluctuated with the \(\epsilon\) and \(\delta\) parameter values to a negligible degree. Overall, this model had a manageable runtime performance, especially with the event logs that had the highest instance count sizes.  
Figure 23: Discovered TraVaS anonymization runtime(s) metric results.