esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, we derived exact closed-form OP expressions for AF relaying. To verify our proposed analysis, the theoretical results obtained were compared with Monte-Carlo simulation results.  
Figure 11: Training state of the BP neural network.  
Figure 12: Regression of the BP neural network.  
The effect of \(K\) and \(N_{\mathrm{t}}\) on OP performance was also investigated. To predict OP performance, a BP neural network-based OP performance prediction algorithm was proposed. When compared with the LWLR, SVM, and ELM methods, the BP neural network-based method was found to consistently have higher OP performance prediction results. The MSE of the BP neural network was 0.0018862, which is lower than the MSE of the LWLR, SVM, and ELM methods. The proposed algorithm can be used to predict the OP performance of vehicular communication systems employed in inter-vehicular communications, intelligent highway applications, and mobile ad-hoc applications.  
In the future, the impact of correlated \(N\)-Nakagami channels on OP performance will be evaluated. The long-short term memory (LSTM) model will be considered to predict OP performance. Compared to the BP algorithm, the LSTM could offer more details on time-series and capture short- and long-term memory, adaptively reflecting environmental categories.  
[PERSON] proposed the algorithm; [PERSON] conceived and designed the simulations and provided comments on the paper's organization; [PERSON] contributed towards the performance results and analytic evaluations.  
This research was funded by the National Natural Science Foundation of China (Nos. 61901409, 61961013, 61701144), Opening Foundation of Key Laboratory of Opto-technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of Education (Grant No. KFKT2018-2), Shandong Province Colleges and Universities Young Talents Initiation Program(No.2019 KJN047),Shandong Province Natural Science Foundation (No.ZR2017 BF023), Shandong Province Postdoctoral Innovation Project (No. 201703032), and Doctoral Found of QUST(Grant No.010029029)  
The authors would like to thank the editor and the anonymous reviewers for their valuable comments.  
The authors declare no conflict of interest.
## 2 Image Pre-Processing
### Greyscale Processing for Pipeline Images  
Grayscale is an important feature to characterize the brightness and darkness of an image. In recent years, on the basis of image grayscale differences and discontinuous changes, it has been used in target recognition, image segmentation, and machine vision technique [23]. Grayscale occupies less memory and enables faster computer operations compared to color images. The mean value method, maximum value method, and weighted average method are the three commonly used techniques for transforming color photos into grayscale.  
Where the maximum value method is to directly take the value of the component with the largest value among the three components of a \(R\),\(G\),\(B\) Equation (1). [PERSON] (_R_), green (_G_) and blue (_B_) are the three color channels of color images.  
\[R=G=B=\text{max}(R,G,B) \tag{1}\]  
The mean method is to take the mean of the values in the three components of \(R\),\(G\),\(B\) Equation (2).  
\[R=G=B=(R+G+B)/3 \tag{2}\]  
The weighted average method is based on the sensitivity of the human eye for the \(R\),\(G\),_B_'s three colors, according to a certain weighted average, obtained in Equation (3).  
\[I(u,v)=0.3\times I_{\text{R}}(u,v)+0.59\times I_{\text{G}}(u,v)+0.11\times I_{ \text{B}}(u,v) \tag{3}\]  
Figure 1: Image pre-processing process.  
where: \(I(u,v)\) denotes the gray value at coordinate, \(I_{R}(u,v)\), \(I_{B}(u,v)\) and \(I_{G}(u,v)\) denote the luminance value of the pixel's three color components, respectively.  
The maximum value method (Figure 2B1-B3), the average value method (Figure 2C1-C3), and the weighted average method (Figure 2D1-D3) were used to grayscale process the three original defect images (Figure 2A1-A3) of cracks, fractures and holes, respectively. From Figure 2, it can be seen that the weighted average method produces the best results for the grayscale image, and the grayscale image has moderate brightness and does not cover the characteristics of the pipe defects. As a result, the image grayscale uses the weighted averaging method.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 5 Implementation
### Output Response
#### 5.2.2 mBart Model Response  
The mBart model's performance was assessed using Swahili queries under both Fine-Tune and Retrieval-Augmented Generation (RAG) conditions. Table 13 demonstrates the differences in response quality between these two approaches. For the Fine-Tune method, the model provided brief and partially accurate answers, often lacking context and depth. In contrast, the RAG approach significantly improved the responses by incorporating detailed external information. For instance, while the Fine-Tune response mentioned only [PERSON]'s name, the RAG output elaborated on [PERSON]'s experiments with electromagnetism, describing his work on the movement of magnetism near a wire. This added context highlights the enhanced capabilities of RAG in providing comprehensive answers. Figure 20 further illustrates this comparison, showing that the RAG-generated outputs consistently outperform Fine-Tune responses in quality and relevance. These results underscore the effectiveness of the RAG framework in enhancing the mBart model's ability to handle complex queries, especially for low-resource languages like Swahili.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Language** & **Question** & **Fine-Tune Answer** & **RAG Answer** \\ \hline _Swahili_ & Historia ya nishati ya umeme ilianzia wapi? & Nishati ya umeme ilianzia na uvumbuzi wa [PERSON]. & [PERSON] alihusiana na sumakuumeme kwa sababu alifanya majaribio kuhusu mwendo wa sumaku karibu na waya. \\ \hline _English_ & _Where did the history of electricity energy begin?_ & _The history of electricity began with the invention of [PERSON]_ & _[PERSON] is associated with electromagnetism because he conducted experiments on the movement of magnetism near a wire._ \\ \hline \hline \end{tabular}
\end{table}
Table 13: Swahili question and answer with English translation for Figure 20.  
Figure 19: Response created by mT5 model in both cases for Fine-Tune and RAG, with RAG showing a good response compared to Fine-Tune.
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.10 Virtual Capacitor/Inductor Method  
By using frequency amplitude that is lower or greater than the nominal frequency, the grid-connected inverter in this manner acts as a virtual capacitor or inductor. Therefore, even though there is a balance between generation and consumption, this virtualization causes the load voltage or frequency to change when the main grid is unplugged. This method has a fast response to detect the islanding, but it can degrade the power quality [27; 28].
## 2 Design of the State Vector
### Coupling Process  
The coupling process of the linear velocities of the IMU-camera system requires a weighted summation as follows:  
\[\dot{\mathbf{v}}_{w}^{ic}=\mu_{v}\mathbf{v}_{w}^{i}+\lambda(1-\mu_{v})\mathbf{R}_{c}^{i}\mathbf{v }_{w}^{c}, \tag{2}\]  
The angular velocity, which covaries with the rotation of the coordinate frames, cannot be directly summed. According to Appendix A and the proposed lemmas in Appendix B, with \(\mu_{\omega}\in[0,1]\), the following equations hold:  
\[\frac{d}{dt}\left(\overline{\mathbf{q}}_{w}^{i}\right)^{\mu_{\omega}}=\frac{1}{2} \left(\overline{\mathbf{q}}_{w}^{i}\right)^{\mu_{\omega}}\otimes\left(\mu_{\omega }\overline{\mathbf{\omega}}_{i}^{i}\right), \tag{3}\]  
\[\frac{d}{dt}\left(\overline{\mathbf{q}}_{w}^{\omega^{\prime}}\right)^{1-\mu_{ \omega}}=\frac{1}{2}\left(\overline{\mathbf{q}}_{w}^{\omega^{\prime}}\right)^{1- \mu_{\omega}}\otimes\left((1-\mu_{\omega})\overline{\mathbf{\omega}}_{c}^{c}\right), \tag{4}\]  
where \(\overline{\mathbf{q}}_{w}^{\omega^{\prime}}\) is the equivalent rotation of the visual measurement in the IMU frame.  
After further deduction according to Appendix A, the derivative of \(\overline{\mathbf{q}}_{w}^{ic}\) is written as follows:  
\[\begin{array}{l}\dot{\overline{\mathbf{q}}}_{w}^{ic}=\frac{d}{dt}\left( \overline{\mathbf{q}}_{w}^{i}\right)^{\mu_{\omega}}\otimes\left(\overline{\mathbf{q}} _{w}^{\omega^{\prime}}\right)^{1-\mu_{\omega}}+\left(\overline{\mathbf{q}}_{w}^{i }\right)^{\mu_{\omega}}\otimes\frac{d}{dt}\left(\overline{\mathbf{q}}_{w}^{\omega }\right)^{1-\mu_{\omega}}\\ =\frac{\mu_{w}}{2}\left(\overline{\mathbf{q}}_{w}^{i}\right)^{\mu_{\omega}}\otimes \overline{\mathbf{\omega}}_{i}^{i}\otimes\left(\overline{\mathbf{q}}_{w}^{i}\right)^{ 1-\mu_{\omega}}+\frac{1-\mu_{w}}{2}\overline{\mathbf{q}}_{w}^{i}\otimes\overline{ \mathbf{\omega}}_{c}^{c}\end{array}, \tag{5}\]  
where \(\overline{\mathbf{\omega}}=[0,\mathbf{\omega}]\) is the supplementary vector of the body angular vector \(\mathbf{\omega}\).  
Angular velocity fusion according to Equation (5) is performed while assuming that \(\overline{\mathbf{q}}_{w}^{ic}=\overline{\mathbf{q}}_{w}^{\omega^{\prime}}=\overline{ \mathbf{q}}_{w}^{i}\), which are all attached to the IMU frame. Thus, potential errors can be caused by the assembly and measurement noise for a real IMU-camera integrated rig. However, after careful calibration and the filter process, the influence of the possible inconsistency is negligible.
## 3 Proposed System Model
### Integration of DQN-Base Approach
#### 3.3.3 Reward Function R  
The reward \(R\) for the receiver-initiated MAC Protocol in (UWSN), we incorporated several aspects of network performance, including energy efficiency, latency, reliability, collision avoidance, and packet delivery success. The function should reflect trade-offs between these objectives, balancing the receiver's decisions based on real-time network conditions as depicted in Equation (15).  
\[R_{t}=w_{1}\cdot(-P_{\text{energy}})+w_{2}\cdot(-C_{\text{collisions}})+w_{3} \cdot Q_{\text{delivery}}-w_{4}\cdot T_{\text{delay}}+w_{5}\cdot E_{\text{ load}}+w_{6}\cdot D_{\text{critical}}. \tag{15}\]where:  
* \(P_{\text{energy}}\): Total energy consumed by the courier node as the receiver node. Minimizing this leads to energy-efficient behavior.
* \(C_{\text{collisions}}\): Number of transmission collisions. Minimizing this improves channel utilization and reduces congestion.
* \(Q_{\text{delivery}}\): Quality of delivery, specifically for critical data packets (e.g., emergency data). Maximizing this ensures reliable communication for time-sensitive data.
* \(T_{\text{delay}}\): Transmission delay. Minimizing this ensures low latency, particularly for critical data.
* \(E_{\text{load}}\): Energy load (a measure of how much energy each node has left). It helps balance load across the network, preventing overuse of high-energy nodes.
* \(D_{\text{critical}}\): A factor representing the importance of critical data. Higher values should prioritize these packets over non-critical data.  
The sum of all weights equals one, which ensures a harmonious balance in optimizing the diverse goals of the system. With the power of continuous learning, the courier node as the receiver node (Rx) adapts seamlessly to the dynamic conditions of the underwater environment. Intelligently adjusts wake-up timing, transmission power, and prioritization of data, allowing real-time fine-tuning to maximize energy efficiency, minimize delays, and ensure robust communication reliability. This advanced framework, driven by the reward function, enables the DQN agent to make the best decisions for efficient allocation of TDMA slots, optimal wake-up scheduling, and prioritizing critical data. Adapting continuously to the environment, the system strikes the perfect balance between energy conservation, transmission reliability, and delay minimization, providing an efficient solution in complex and dynamic network conditions.
# Ultraviolet Transient Absorption Spectrometer with Sub-20-fs Time Resolution
## 1 Introduction
In proteins, aromatic amino acids have been proposed as built-in structural markers [22; 23] whose TA signal is able to deliver information on their secondary structure and on the misfolding processes that lead to aggregation, fibril formation and are the precursors of several neurodegenerative diseases. Despite the strong scientific motivation and the great potential interest, pushing the time resolution of UV TA spectroscopy to the 10-20 fs domain, which is nowadays customary in the visible range, requires addressing several technological challenges:  
1. the generation of few-optical-cycle pulses in the UV is difficult, due to the lack of suitable broadband gain media, either linear or nonlinear, in this frequency range;
2. standard methods for the temporal characterization of ultrashort pulses, based on nonlinear frequency up-conversion processes such as second harmonic generation (SHG), cannot be applied to the UV, and more complex techniques are required;
3. due to the proximity with the onset of electronic transitions, all transparent materials in the UV display a strong dispersion, causing a frequency chirp; this calls for a careful design of the spectroscopic system to minimize the number and the thickness of refractive optical elements and prevent the loss of temporal resolution. This paper describes our recent efforts at developing an ultrafast TA spectroscopy system in the UV with high temporal resolution for the study of the ultrafast optical response of biomolecules. The system combines sub-20-fs pump pulses tunable in the 3.35-4.7 eV range (corresponding to 260-370 nm) with broadband probe pulses obtained by white-light-continuum (WLC) generation in different materials. The paper is organized as follows: Section 2 reviews the methods used for the generation and characterization of ultrashort UV pulses; Section 3 describes the experimental setup of the UV TA spectrometer; Section 4 characterizes the performance of the system and presents representative experimental results; finally, Section 5 draws conclusions and discusses prospects for applications and improvements of the system.
# Detecting Multiple Mixed Bacteria Using Dual-Mode Hyperspectral Imaging and Deep Neural Networks
###### Abstract  
Identifying and analyzing mixed pathogenic bacteria is important for clinical diagnosis and antibiotic therapy of multiple bacterial infection. In this paper, a dual-mode hyperspectral microscopic detection technology with hybrid deep neural networks (DNNs) was proposed for simultaneous quantitative analysis of four kinds of pathogenic bacteria in mixed samples. To acquire both transmission and fluorescence spectra regarding the mixed pathogens, we developed a dual-mode hyperspectral detection system with fine spectral resolution and wide wavelength range, which can also generate spatial images that can be used to calculate the total amount of mixed bacteria. The dual-mode spectra were regarded as mixed proportion characteristics and the input of the neural network for predicting the proportion of each bacterium present in the mixture. To better analyze the dual-mode spectral data, we customized a mixed bacteria measurement network (MB-Net) with hybrid DNNs architectures based on spectral feature fusion. Using the fusion strategy, two DNNs frameworks applied for transmission/fluorescence spectral feature processing were stacked to form the MB-Net that processes these features simultaneously, and the achieved average coefficient of determination (\(R^{2}\)) and RMSE of validation set are 0.96 and 0.03, respectively. To the best of our knowledge, it is the first time of simultaneously detecting four types of mixed pathogenic bacteria using spectral detection technology, showing excellent potential in clinical practice.  
hyperspectral detection; deep learning; mixed bacteria detection +
Footnote †: journal: Applied Sciences
# Optimal Prefix Free Codes with Partial Sorting +
## 2 Solution
### General Intuition
: _Consider the text \(T=\)\"aaaaaaabcc_ _\" from Figure 2 (composed of one occurrence of \"b\", two occurrences of \"c\", four occurrences of \" -\", and eight occurrences of \"a\"), such that the frequencies of its messages follow an exponential distribution, so that an optimal prefix free code assigns different codelengths to almost all messages (see the third column of the array in Figure 2). Any optimal prefix free code for this instance yields all the information required to sort the messages by frequencies. Accordingly, the \(\mathrm{EI}\) signature \(\mathcal{S}(T)=\)\"\(E\boxplus\mathbf{EI}\boxplus\mathbf{EI}^{\prime}\) of this instance has three blocks of \(\mathit{Es}\) (out of three possible ones) for this value of the alphabet size \(n=4\), indicating a more difficult instance. The same holds with more general distributions, as long as no two pairs of message frequencies are within a factor of two of each other._  
Figure 1: Illustrations for the instance based on the text \(T=\)“ba_bb_caca_ba_cc”, minimizing the number of occurrences of “EI” in its \(\mathrm{EI}\) signature \(\mathcal{S}(T)=\)”\(\mathbf{EEE}\boxplus\mathbf{II}\)”. The columns of the array respectively list the messages, their numbers of occurrences and the code lengths assigned to each. Those various examples should give an intuition of the features of the instance that our techniques aim to take advantage of. We describe those techniques more formally in the two following sections, starting with the deferred data structure allowing to partially sort the weights in Section 2.2, and following with the algorithm itself in Section 2.3.
## 4 Darts vs. Ship Detection  
Figure 2 provides an overview of the target detection components (for ships). There are three parts: a backbone, a detection head and post-processing. A backbone is a neural network taking charge of the feature extraction. In this section, the backbone is designed by Darts techniques [7; 8] that will be specified later. By considering computation efficiency, we introduce a simple detection head that is composed of Relu6 and a \(1\times 1\) convolution. Once the detection head process finishes, target identification occurs next. Note that at this moment, there exists more than one candidate rectangular box (each candidate has a confidence level) for each target. To output one final rectangular box for each target detected depends on post-processing, where non-maximum suppression is performed.  
In the following, we first apply techniques and settings directly from Darts [7] and PC-Darts [8] to construct the backbone.
## 3 System Model
### Scenario Description  
Figure 1a illustrates a typical scenario in Lebanon. In this scenario, we consider a single broadcaster MVDS head-end. The head-end co-exists with four Earth stations communicating with three satellites located in the GEO orbit. All the communications between these systems occur in the same Ku-band.  
The four earth stations (ES1 to ES4) communicate with three GEO satellites: satellite 1 (S1), the victim satellite (S2), and satellite 3 (S3). Adjacent satellites are spaced 2 degrees apart, as seen from any corresponding ES, as set by the International Telecommunication Union (ITU) [16, 17]. In this scenario, the desired signal is transmitted from ES2 towards S2. Satellite S2 retransmits the signal back to ES4, as shown in Figure 1a. This signal suffers from interference. Satellite S2 considered in this study is the IS-1002 located at 35\({}^{\circ}\) west. This satellite provides coverage to the area under study (Lebanon) [4].  
In practical situations, S2 receives undesired signals from ES1 and ES3 (the dashed red line in Figure 1a). We refer to this type of interference as adjacent satellite interference (ASI). To study its effect, we will evaluate the carrier-to-ASI (C/ASI) value at S2.  
Another interference arises from the MVDS broadcaster. This interference comes from the nature of the transmitting antennas used in these systems. These antennas have non-negligible side lobe levels. If a side lobe aligns perfectly with S2, it potentially disrupts the desired signal on S2 (straight red line in Figure 1a). The introduced interference I by the MVDS system is not negligible, as we will show later. We will consider the worst-case scenario where the first side lobe of the MVDS transmitting antenna aligns perfectly with S2. In our case, the satellite most susceptible to interference is the one located at 35 degrees west or 105 degrees east from the observation point in Beirut [4]. Thus, we will consider that S2 is located at 35 degrees west.  
In addition, we will consider that ASI, self-interference (including intermodulation, adjacent channel interference, and cross-polarization interference), and MVDS interference all combine to destructively affect the received signal at ES4. This worst-case scenario aims to evaluate the desired signal in a possible realistic setting and will help identify the most reliable recommendations and solutions to mitigate the interference (as shown in the planner block diagram Figure 1c of Figure 1a, which shows all the important parameters).
# Temperature Condition Analysis for Freezing on Tunnel Lining Back Side According to Tunnel Length
## 3 Air Temperature Change inside the Tunnel in Winter
### Characteristics of Ground Temperature and outside Air Temperature in Winter
#### 3.1.2 Outside Air Temperature Variation with DTR  
The daily variation in the outside air temperature is controlled by incoming energy from the sun and outgoing energy from the Earth's surface [17]. The minimum and maximum temperatures are reached depending on the energy rate change, as shown in Figure 4a. The air temperature has a diurnal temperature range (DTR) and changes over time in a day. The daily minimum temperature (\(T_{min}\)) and maximum temperature (\(T_{max}\)) are measured primarily at 7:00, before sunrise, and around 15:00, just before sunset in the winter. Therefore, the air temperature in the winter shows that peak values occur at the time of the maximum and minimum temperatures and change periodically, as shown in Figure 4b.  
Meteorological data for the last 30 years (1991-2020) was surveyed to analyze a change in the DTR during the winter, as shown in Table 1. The highest diurnal temperature range (DTR) was 14.8 \({}^{\circ}\)C in Uiseong, located in the southeast, and the lowest DTR was 7.3 \({}^{\circ}\)C in Incheon, located in the northwest. The average DTR was 10.5 \({}^{\circ}\)C in the winter.  
Figure 3: Ground temperature changes at a depth of 5 m in winter according to the Korea Meteorological Administration (KMA) [16]. (**a**) Ground temperature changes by depth from the surface for the last 30 years; (**b**) Temperature distribution at a depth of 5 m in Korea in January.  
The air temperature inside the tunnel is affected by the outside air temperature variation, and the DTR inside a tunnel also changes according to the tunnel length. This study analyzed the DTR correlation between the outside and inside temperatures of the tunnel using the measurements. The change pattern of the DTR was simplified and applied to the heat transfer analysis.