esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Odor-Active Compounds Detected Using GC-O
2,3-butanedione was formed by decomposition of the \(\alpha\)-acetolactic acid synthesized by yeast, and 2,3-pentanedione from \(\alpha\)-aceto-\(\alpha\)-hydroxybutyric acid [30]. 1-octen-3-one, belonging to the lipid-derived compound, was formed via the autoxidation of UFAs [31]. The formation of both methylcyclopentone and malt0 were associated with the Maillard reaction. Methylcyclopentone has been identified in volatile compounds of the glucose-tyrosine model system and the glucose-histidine model system [32], and maltol has been formed directly from the Amadori product which was the intermediate of the Maillard reaction [33]. Both DMHF and HEMF could be produced not only by the Maillard reaction but also could be biosynthesized by yeasts [2]. There were five alcohols (4, 13, 30, 38 and 46) identified as odor-active compounds. Ethanol, 3-methylbutanol and phenethyl alcohol were the metabolites of yeast; ethanol was formed by the EMP pathway and both 3-methylbutanol and phenethyl alcohol were derived from amino acid catabolism via the Ehrlich pathway [20]. 2-furanmethanol was a known thermal degradation product of ribose during the Maillard reaction. Linalool was identified in Japanese CSS, though not in Chinese CSS. It might come from the kombu, which is only used in Japanese CSS, because some kombu contains linalool [34]. Four phenols (45, 48, 51 and 52) were detected as aroma-active compounds; they could be formed by two pathways. Firstly, they were synthesized by different yeasts from some phenolic acids present in materials used for manufacturing SS, for example, 4-ethylphenol from \(p\)-coumaric acid and 4-ethylguaiacol from ferulic acid [35]. Secondly, they were produced by lignin pyrolysis; for instance, guaiacol and 2,6-dimethoxyophenol could be obtained from coconut shell pyrolysis [36]. Before wheat and soybeans were used for manufacturing SS, they were roasted. Lignin underwent pyrolysis, and some phenols were produced. Four sulfur-containing compounds (1, 19, 24 and 39) were identified as odor-active compounds, and they were the common odorants in six CSS samples. Methanethiol was only detected in the isolate obtained via SPME; because its boiling point was about 6 \({}^{\circ}\)C, it was removed easily when the isolate obtained by solvent extraction was concentrated to recover the solvent. It arose from the degradation of methionine or cysteine derivatives. Dimethyl trisulfide came from the oxidation of methanethiol. Methional was a Strecker aldehyde, and it could originate from Strecker or microbiological degradation of methionine. Methionol was formed through the decarboxlation of 4-methylthio-2-oxobutyric acid, which was transamination product of methionine [20; 37]. These four sulfur-containing compounds have been found in GSS.
## 4 Results
### Security Analysis
#### 4.1.3 Replay Attack  
In this study, the challenge response mechanism is implemented to ensure communication integrity. During authentication, a random challenge is generated by the smart contract and sent to the user. The user must generate the corresponding response using their registered security key or device. Since the challenge is random and used for one-time verification only, attackers cannot intercept and replay the communication, thus enhancing system security.  
Similarly, public key encryption is utilized for secure communication and identity verification. The user's security key or device stores a private key, while the server holds the corresponding public key. During verification, the security key or device encrypts the challenge using the private key and sends the encrypted result to the server. Since the private key is only stored in the security key or device, attackers cannot reuse the same encrypted result in a replay attack, further enhancing security.  
Additionally, a one-time verification mechanism is supported, where each identity verification utilizes a different authentication credential that becomes invalid after successful use. Even if attackers intercept previous communications, they cannot replay the same authentication credential in subsequent verifications.  
Lastly, to safeguard private keys and sensitive data, secure elements within the device are employed. These secure elements, such as hardware security modules or trusted execution environments, offer robust protection mechanisms to prevent attackers from directly accessing or copying the private keys stored within the security key or device, thereby reducing the risk of replay attacks.
## 4 Results  
Computer simulations of the proposed second order notch IIR filter with time-varying pole radius have been made using the Mathworks Matlab 2018b. The considered notch filter with reduced transient response has been tested using ECG signals uploaded from the MIT-BIH database (Massachusetts Institute of Technology-Beth Israel Hospital). The proposed notch filter with time-varying pole radius has been used to remove the 50 Hz powerline interference from above mentioned ECG signals. The sampling rate of the processed signal was equal to 500 Hz.
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 4 Experiment
### Setting
#### 4.1.2 Metrics  
Performance evaluations were conducted using existing metrics, including MOTA, IDs, IDF1, HOTA, and FPS [19; 20; 21]. MOTA primarily assesses detection performance by integrating FP, FN, and IDs, providing a holistic measure of tracking reliability. IDF1 focuses on the consistency of identity matching between consecutive frames. HOTA fuses DetA and AssA into a single metric, distinguishing itself by evaluating performance across a spectrum of detection similarity thresholds rather than a fixed criterion. This design inherently incorporates localization precision [1]. Notably, direct comparisons of FPS for computational efficiency are often problematic due to inherent variability in hardware configurations and software optimizations across experimental setups. Additionally, for trackers following the TBD paradigm, the computational overhead of object detection is typically excluded.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 2 Related Works  
### Named Data Networking Advantages over IP-Based Network in Disaster Response  
The limitation of IP-based networks compared with NDN in disaster response has been discussed in several studies [2; 3; 4]. There are three main advantages of NDN over IP architecture. Firstly, location-independent routing capability allows NDN to perform more timely information dissemination than IP-based networks. Group communication between responders can be easily created by using group name prefixes in NDN. Meanwhile, IP addresses need to be distributed first before messages can be exchanged. Moreover, the NDN in-network caching feature allows it to provide lower latency data delivery than the IP network. Secondly, NDN can handle the responder mobility challenge better than IP architecture. NDN provides mobility built-in support whereas IP need to use patch like mobile-IP. Lastly, since bandwidth is limited inside the network in disaster scenarios due to damaged network infrastructure, NDN is more convenient. By using the name as the main network entity, NDN packet is smaller than IP counterpart which includes the address inside. Moreover, the interest aggregation feature of NDN also helps it to utilize network bandwidth.
_Appl. Sci._**2013**, \(3\), 694-724; doi:10.3390/app3040694  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**A Review of Cavity Design for Kerr Lens Mode-Locked Solid-State Lasers**  
**[PERSON] and [PERSON] ***  
Department of Physics and BINA Center of Nano-Technology, Bar-Ilan University,  
Ramat-Gan 5290002, Israel; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +972-3-531-7482; Fax: +972-3-531-7697.  
_Received: 12 September 2013; in revised form: 28 November 2013 / Accepted: 29 November 2013 / Published: 10 December 2013_  
**Abstract:** We provide a critical review of the fundamental concepts of Kerr lens mode-locking (KLM), along with a detailed description of the experimental considerations involved in the realization of a mode-locked oscillator. In addition, we review recent developments that overcome inherent limitations and disadvantages in standard KLM lasers. Our review is aimed mainly at readers who wish to realize/maintain such an oscillator or for those who wish to better understand this major experimental tool.  
**Keywords:** ultrafast lasers; pulsed lasers; solid-state lasers; mode-locked lasers
## 2 Background and Key Features
### UAV-Assisted SAGINs
#### 2.1.1 Architecture of UAV-Assisted SAGINs
The core of NFV lies in abstracting and managing computing, storage, and network resources through Network Virtualization and Virtual Network Function (VNF) technologies. VNF detaches network functions from underlying physical networks, enabling on-demand deployment and dynamic management. Multiple VNFs can be combined into Service Function Chains (SFCs), which are dynamically orchestrated and deployed by SDN controllers. This approach has been applied to address resource orchestration and management challenges in SAGINs [20]. Furthermore, NFV leverages virtual network platforms to enable computing-capable network nodes to perform data processing tasks, thereby advancing edge computing and cloud computing.
## 3 Results and Discussion
### The Trend in Phenol Concentration during Aging
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Total Polyphenols** & **Final Total** & **Total** & **Anthoyanins** & **Final Total** & **Decolourable** & **Final** & **Anthoyanins** & **Final** \\  & **at Backing** & **Polyphenols** & **Anthoyanins** & **Anthoyanins** & **Anthoyanins** & **Anthoyanins** & **Ratio at** & **Anthoyanins** \\  & **(g/L Catechins)** & **(g/L Catechins)** & **(g/L Malvin)** & **(g/L Malvin)** & **at Backing (g/L Malvin)** & **(g/L Malvin)** & **(g/L Malvin)** & **Ratio (g/L Malvin)** & **Ratio (g/L Malvin)** \\ \hline C & 4.42 \(\pm\) 0.03 a & 3.32 \(\pm\) 0.03 c & 0.55 \(\pm\) 0.02 b & 0.25 \(\pm\) 0.01 b & 0.42 \(\pm\) 0.02 b & 0.14 \(\pm\) 0.02 ab & 76.4 \(\pm\) 0.1 b & 56.0 \(\pm\) 0.2 a \\ \hline CP & 4.29 \(\pm\) 0.04 b & 4.06 \(\pm\) 0.03 a & 0.48 \(\pm\) 0.01 c & 0.25 \(\pm\) 0.01 b & 0.34 \(\pm\) 0.01 c & 0.11 \(\pm\) 0.01 b & 70.8 \(\pm\) 0.1 e & 44.0 \(\pm\) 0.1 e \\ \hline T & 3.75 \(\pm\) 0.04 cd & 2.98 \(\pm\) 0.02 e & 0.38 \(\pm\) 0.02 d & 0.22 \(\pm\) 0.01 c & 0.30 \(\pm\) 0.01 d & 0.11 \(\pm\) 0.01 b & 78.9 \(\pm\) 0.1 a & 50.1 \(\pm\) 0.1 c \\ \hline TP & 3.55 \(\pm\) 0.04 e & 3.13 \(\pm\) 0.04 d & 0.34 \(\pm\) 0.01 e & 0.21 \(\pm\) 0.02 c & 0.25 \(\pm\) 0.01 e & 0.10 \(\pm\) 0.01 b & 73.5 \(\pm\) 0.1 c & 45.5 \(\pm\) 0.2 d \\ \hline G & 3.85 \(\pm\) 0.03 c & 3.33 \(\pm\) 0.03 c & 0.66 \(\pm\) 0.02 a & 0.33 \(\pm\) 0.01 a & 0.48 \(\pm\) 0.02 a & 0.18 \(\pm\) 0.01 a & 72.7 \(\pm\) 0.1 d & 54.5 \(\pm\) 0.2 b \\ \hline GP & 3.65 \(\pm\) 0.03 d & 3.42 \(\pm\) 0.02 b & 0.58 \(\pm\) 0.03 b & 0.31 \(\pm\) 0.02 a & 0.41 \(\pm\) 0.01 b & 0.14 \(\pm\) 0.02 a & 70.6 \(\pm\) 0.1 e & 45.2 \(\pm\) 0.1 d \\ \hline \hline \end{tabular} Different letters indicate statistically significant differences at \(p\leq 0.05\) according to the results of one-way ANOVA and [PERSON]’s HSD test. Values are the mean of three technical replicates \(\pm\) SD.  
\end{table} 
Table 8: Chemical parameters at racling. C = Castellina, G = Gaiole, T = Tavarnelle, CP = Castellina plantarum, GP = Gaiole plantarum, and TP = Tavarnelle plantarum. differences were found due to the activity of these microorganisms [41], because there was a different decrease in the colour product, despite the fact that at the end of the aging phase they had very similar concentrations of total anthocyanins. This observation seems to indicate a different stability of the chromatic component present in the different wines.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 6 EKF, UKF, and CKF
### Unified Algorithm of UKF and CKF  
Unlike the EKF, the UKF and CKF can operate without the need for derivative calculations. In this study, we use the UT [14] in UKF. The scaled UT (SUT) can also be used in the UKF [15]. In order to present the UKF and CKF in a unified way, we define  
\[i_{0}=\left\{\begin{matrix}0&\text{for}\ \text{UKF},\\ 1&\text{for}\ \text{CKF},\end{matrix}\right. \tag{61}\]  
\[\gamma=\left\{\begin{matrix}n+\kappa&\text{for}\ \text{UKF},\\ n&\text{for}\ \text{CKF},\end{matrix}\right. \tag{62}\]  
where \(\kappa\) is a parameter used in the UT and a value of \(3-n\) is suggested [14]. Following [18], we summarize the prediction and measurement update steps in Sections 6.2.1 and 6.2.2,respectively. The UKF utilizes \(2n+1\) sigma points, whereas the CKF employs \(2n\) cubature points in its computation. Let \(m=2n\). The weights used in the UKF and CKF are given by  
\[\text{UKF}:\;\;w_{0}=\frac{\kappa}{\gamma},\quad w_{i}=\frac{1}{2\gamma},\quad \text{for}\;\;i=1,\,2,\,...,m, \tag{63}\]  
\[\text{CKF}:\;\;w_{i}=\frac{1}{2\gamma},\;\;i=1,2,...,m. \tag{64}\]
## 2 Materials and Methods
### Crop Type Classification: BiLSTM Model  
At the time of image classification, the extracted feature vectors are fed into the BiLSTM model. The BiLSTM approach receives the feature vector as input and executes the detection method. The LSTM signifies a different RNN method, which solves the problem of gradient vanishing of RNN by offering a threshold method and memory unit [21]. However, \(x\) denotes the network input at different times, \(y\) refers to the network outcome, \(h\) stands for the hidden layer (HL), \(u\) refers to the weighted input to HLs, \(w\) demonstratesthe weighted input of the previous node HL to the existing node HL, and \(v\) signifies the weighted input in HL to the output layer.  
During the actual implementation of the LSTM technique, the LSTM unit was upgraded at time \(t\) as:  
\[i_{t}=\sigma(W_{i}h_{t-1}+U_{i}x_{t}+b_{t}) \tag{7}\]  
\[f_{t}=\sigma\Big{(}W_{j}h_{t-1}+U_{f}x_{t}+b_{f}\Big{)} \tag{8}\]  
\[\tilde{c}=tanh(W_{c}h_{t-1}+U_{c}x_{t}+b_{c}) \tag{9}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\tilde{c}_{t} \tag{10}\]  
\[o_{t}=\sigma(W_{o}h_{t-1}+U_{o}x_{t}+b_{o}) \tag{11}\]  
\[h_{t}=o_{t-1}\odot tanh(c_{t}) \tag{12}\]  
At this point, \(\odot\) stands for the equal product of elements, and \(\sigma\) denotes the sigmoid function. \(x_{t}\) signifies the input vector at time \(t\). \(h_{t}\) refers to the HL vector named as the output vector and the storage of all the data at time \(t\) and the preceding time. \(b_{t}\), \(b_{f}\), \(b_{c}\), \(b_{o}\) demonstrates the offset vector. \(W_{i}\), \(W_{f}\), \(W_{c}\), \(W_{o}\) implies the weight of various gates to the HL vector \(h_{t}\). \(U_{i}\), \(U_{f}\), \(U_{c}\), \(U_{o}\) stands for the weighted input vector. \(x_{t}\) stands for the input, forgotten, unit, and output gates, correspondingly. Utilizing the 3-gates infrastructure, the LSTM permits the recurrent network to maintain the useful data of the task from the memory units at the time of the trained method, therefore evading the problem of the RNN disappearing but reaching an extensive range of data.  
In addition to processing the series data, the BLSTM presents more backward estimate procedures, for instance, different normal LSTM cases. This process employs the subsequent data of sequences. At last, the forward and reverse estimations are executed. The values were resultant of the output layer simultaneously; thus, as the outcome, all of the sequence data are reached in 2 \(\times\) 2 directions, which is utilized to complete a variety of natural language processing tasks.
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 6 Simulation and Analysis
### Simulation Parameter  
In the simulation scenario, the access model of TSIN is composed of a LEO satellite and 50 base stations. The user's position follows an independent homogeneous Poisson process. The BS communication channel adopts a Rayleigh fading model, and the satellite communication channel adopts an LES model. As shown in Table 2, the altitude of each BS is set at 10 m, and the altitude of the satellite is 1000 km. Define \(d_{B}\) is the distance between the user and base station, then the path loss model expression is \(52.87+37.6 log10*d_{B}\). The path loss model of satellite channel adopts free space model, and the expression is \(57.88+20 log10*d_{S}\), where \(d_{S}\) is the distance between the user and satellite.
## 4 Experimental Study
### Tested Benchmarks  
Forty-four benchmarks were used to evaluate the relative performances of the proposed method upon the two scenarios. The twenty-one DMOPs test beds are as follows: five FDA [6], three dMOP [12], seven UDF [81] and six F(ZJZ) [14] functions. The twenty-three MaOP problems are composed of seven MaF test suite MaF1-7, seven DTLZ1-7 functions and nine WFG1-9 problems. The test configurations are detailed in Table 5 according to the number of variables (D) and objectives (M). For dynamic multi-objective optimization, [PERSON] et al. [6] presented three types of DMOPs, which are classified into three categories according to the time-varying POF and POS. The DMOPs in type I have a dynamic change in the POS and the POF remains the stable. Both POS and POF are changed for the DMOPs in type II. However, type III of DMOP presents a time-varying POF, where the POS is unchanged. The main properties of all tested problems are reported in Table 5, presenting a variation in both POS and POF.  
papers [11; 15; 54; 55]. The experimental study was conducted using a personal computer with 8 Go of Ram and an i7 Intel processor. A Java implementation of the proposed method was performed on the jMetal framework [82]. Furthermore, the Taguchi method was used to select the meta-parameters of the beta-behaved profiles.
# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 3 BLIP-2 Optimization Efficiently Using LoRA
### Data Generation  
The proposed method develops a new dataset using the BDD-100K dataset to enhance the accuracy of understanding and predicting vehicle behavior in autonomous driving situations [4]. Due to the scarcity of suitable scene-caption pair datasets for autonomous driving, we select BDD-100K, which includes approximately 60,000 samples providing diverse road traffic information.  
From annotations provided in the BDD-100K dataset, essential road traffic information such as scene \(s_{i}\), time of day \(t_{i}\), weather \(w_{i}\), detected object \(O\), and traffic light color \(co_{i}\) are extracted to initially create raw natural language captions \(p_{i}\). This information is formatted through a predefined template as follows:  
\[p_{i}=\text{Template}(s_{i},t_{i},w_{i},O_{i},co_{i}). \tag{1}\]  
These sentences are then refined by utilizing a language model such as GPT-4o, resulting in the final caption dataset c used for fine-tuning:  
\[c_{i}=\text{LM}(p_{i}). \tag{2}\]  
During the data generation process, questions such as \"Describe the current road conditions including weather, time of day, and visible objects (road, vehicles, traffic lights) along with their counts\" are used to generate complete natural language sentences, effectively addressing data scarcity and supporting the creation of captions suitable for autonomous driving scenarios. The structured input information used for raw caption generation, including the scene type, weather, and object details, is summarized in Table 1, while Figure 2 presents an example of how these raw captions are refined into final, human-like descriptions.  
Figure 2: This figure illustrates an example of a raw caption and its corresponding final caption.  
This transformation ensures that the generated captions are more contextually rich and readable to humans, allowing vision-language models to better understand and describe complex road environments.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
### Related Work
To assess the effectiveness of EH and FSO based communication, the joint energy and the signal-to-noise ratio (SNR) coverage probability are derived, i.e., the joint probability of the UAV receiving enough energy to ensure successful operation (hovering and communication) and having a received SNR higher than a given threshold. To finish this brief review, we must cite the work presented in [33] where a mixed FSO/RF network with EH was proposed. In this scheme, ground stations transmit backhaul traffic to the UAVs, which act as moving BSs. Subsequently, the UAVs transmit RF signals to the ground users. Accordingly, the UAVs harvest and store the received energy coming from the ground stations through their FSO links. The proposed framework is considered as an optimization problem to maximize the energy efficiency.
# Localization of Radio Sources Using High Altitude Platform Station (HAPS)
## 1 Introduction  
### Background and Objectives  
Japan has over 300 million radio stations, and the number continues to increase each year. As of 2022, land mobile stations, including mobile phone terminals, accounted for 98.9% of all radio stations, indicating the widespread use of mobile communication devices [1]. However, unauthorized radio stations that exceed power limits or use illegal frequencies have been confirmed to exist [2], necessitating appropriate countermeasures.  
Currently, the DEURAS radio monitoring system is in operation to monitor and crack down on illegal radio stations. This system uses fixed sensor stations installed across various locations and vehicle-mounted sensor stations, which are remotely controlled by central stations located in regional communication bureaus. These sensors monitor radio waves, measure the direction of radio sources, and identify the locations of illegal radio stations [3]. However, in dense urban environments with numerous high-rise buildings, direct line of sight between sensor stations and illegal radio stations may be obstructed, and the presence of multiple reflected waves may hinder accurate location estimation. Additionally, the monitoring range of a single sensor station is limited, and as of 2018, approximately 350 sensor stations were deployed [4].  
In recent years, high-precision localization methods using UAVs (unmanned aerial vehicles), such as drones, in non-line-of-sight environments have been proposed but have not yet been implemented [5]. Furthermore, accurate localization of radio transmission sources over a wide area could contribute to rapid rescue operations in the event of a disaster when ground base stations become inoperative.  
This paper aims to address these challenges by utilizing a HAPS (high-altitude-platform station) [6; 7; 8; 9]. A HAPS refers to a system that operates unmanned aerial vehicles, such as aircraft flying at approximately 20 km in the stratosphere, as communication base stations to provide communication services over wide areas. By leveraging a HAPSs for radio source localization, it is expected that the impact of reflected waves can be reduced due to its high-altitude operation, allowing for wider radio monitoring and localization compared to conventional monitoring systems. This paper explores the use of HAPSs for monitoring illegal radio transmissions and estimating the location of mobile terminals during disasters. The effectiveness of this approach is demonstrated primarily through numerical simulations performed using MATLAB R2024a [10]. Additionally, instead of aiming for pinpoint localization of illegal radio transmissions, this study focuses on narrowing down the general transmission area. This approach assumes that after the localization by a HAPS, an illegal radio detection vehicle will be used for more precise identification.