esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Low-Frequency Passive Acoustic Source Localization
## 3 Test Case Setups
### Case 3: Three-Dimensional ASL  
This case is an extension of case 2. Similar to case 2, we used the four-microphone array and the two sources (cases 3 (i) for S1 and 3 (ii) for S2) with a classification approach for this case. The ranges of the azimuth angle, \(\theta\), and the elevation angle, \(\alpha\), were discretized into \(N_{1}\) and \(N_{2}\) classes, respectively. The setup is shown in Figure 10. We placed the source (S1 and S2 in separate sub-cases) at real, random values of \(\theta\) and \(\alpha\) and fixed \(r\) (=10 m ) and used the real part of the resulting CSM to train the CNN. However, instead of using a conventional CNN, we used a multi-task CNN in which the separate tasks of determining \(\theta\) and \(\alpha\) were performed simultaneously by a single model. This is made possible because both tasks rely on the same input feature (CSM for S1 and GCC for S2). Separate networks for \(\theta\) and \(\alpha\) are an option (like case 2); however, it is not very efficient as individual datasets have to be generated and each network has to be trained separately. A multi-task network improves data efficiency and reduces training time and the chances of overfitting significantly as the model has to generalize over multiple tasks. Figure 11 shows the block diagram of the multi-task CNN used. This is an example of _hard-parameter sharing_, wherein the tasks share hidden layers while having task-specific output layers [36]. For both output branches (\(\theta\) and \(\alpha\)), the activation function in the last layer was _Softmax_, and _ReLU_ activation was used everywhere else. The optimizer and the loss function were _ADAM_ and _cross-entropy_ respectively, for both branches.  
Figure 9: GCC pattern for a pair of microphone signals polluted with uncorrelated noise. The GCC feature map is symmetric about the origin. Therefore the useful correlation coefficients can be picked from either side of the origin.  
All CNN models in this paper were built using the deep learning frameworks _Keras_ and _Tensorflow_, the details of which are shown in Table 1. The training for all the cases was carried out on Google Colaboratory.
## 5 Case Study
### Terrorist Attack Damage Estimation
#### 5.2.2 Estimating Damage  
In Kingery-Bulmash equations, the measuring criterion for damage is known as level incident overpressure under explosion, which can be calculated based on the weight of TNT explosives and the distance to the explosion's center [44]. According to the explosive blast study conducted by the U.S. Federal Emergency Management Agency [45], the level of overpressure that causes severe damage to metal structures, and which therefore affects the functionality of the substation components, is 7 psi. Given the case-study projectile size, the threshold radius for explosive damage affecting component functionality is approximately 12 m. Calculated from the data above, Table 2 lists the number of functioning units and transmission lines that are predicted to fail after each attack. The threshold distance is subject to uncertainty in the charge weight and may be reduced up to 10.5 and 8.8 m in case of larger explosive warhead of 30 and 50 kg TNT, respectively. Larger scenarios will require increased protection of the facility at the direct vicinity of the substation.
# Twitter-Based Safety Confirmation System for Disaster Situations
## 2 Related Studies and Services  
### Safety Confirmation Services in Disaster Situations  
The safety of victims must be confirmed immediately after a large-scale natural disaster. Various services are provided for confirming individual safeties. Google Person Finder [24] is a web service that helps people to reconnect after a disaster. The site was launched in 2010 and was used after the Great East Japan Earthquake of 2011, the 2011 Christchurch earthquake, the 2010 Haiti earthquake, the 2013 Boston Marathon Bombing, and other disasters. In the aftermath of the Great East Japan Earthquake, 670,000 people were recorded on the service. Restoring Family Links [25] and Safe & Well [26] also provide online systems for sharing safeties and missing-persons information.  
Safety confirmation services in Japan include the Disaster Emergency Message Dial (171) [27] and the Disaster Emergency Message Board (web171) [28]. Communication carriers provide these services as alternative channels when the network is congested by a large number of safety confirmation phone calls. After the Great East Japan Earthquake, both 171 and web171 were not used by many people [13]. [PERSON] and [PERSON] analyzed the reasons why few people use the systems for confirming safeties in disaster situations [29]. Examples of such reasons include that the systems are not used at ordinary times, and these systems are difficult to use. To sum up, to gain the maximum advantage from these services, users checking for safety information must use them actively, and victims who have registered their information must use them frequently. [PERSON] reviewed the safety confirmation systems including 171 and web171 [30]. This article noted an importance of immediate confirmations for people needed to be rescued and such systems cannot contribute to the rescue activities for survivors.  
Considering that point, the above systems can be useful only to deliver the information about safe people from the disaster affected areas to the outside of the area.  
The utilization of social media services is an effective solution for confirming safety information because many people use them at ordinary times and are familiar to them. Some social networking services (SNSs) can provide their own safety confirmation services. An example is Facebook, the most popular SNS worldwide [31]. In such a system, users in a disaster zone are expected to confirm their safety by reply, and the response is sent to the users' friends on their respective timelines. A popular communication tool in Asia is LINE ([[https://line.me/en/](https://line.me/en/)]([https://line.me/en/](https://line.me/en/))), which is more suitable than Facebook for confirming the safety of users to close friends. Users of the LINE application are sent requests for their safety, and their responses are displayed on their timelines [32]. On Twitter, many tweets were posted during the recent disasters in Japan. However, Twitter itself does not provide any functions to facilitate sharing the safety information.
## 4 Experiments and Results
### Scenario 1: Estimating Path Loss in Terrestrial Indoor Environments  
In order to estimate the Path Loss in an Indoor Environment, the very first experiment was setup in Seminar Hall of the Department of Telecommunication Engineering at Mehran University of Engineering & Technology (MUET), Jamshoro, Pakistan. Following the experimental setup as shown in Figure 2, four IRIS motes (M1, M2, M3 and M4) are used. M1 placed at 0\({}^{\circ}\), M2 at 90\({}^{\circ}\), M3 at 180\({}^{\circ}\) and M4 placed at 270\({}^{\circ}\) with respect to the base station. The antenna orientations of the IRIS transmitters were at 0\({}^{\circ}\) vertical in line with each other and also with the base station's antenna.  
The reference distance is taken as 0.3 m. As per the experimental setup proposed in Section 4, distance is increased with a difference of 0.3 m up to 1.5 m distinctive points.  
These results are obtained from RSS at different distances in an indoor environment. Figure 3 illustrates the overall path loss plotted from the RSS data collected in terrestrial indoor environments. With the help of a curve fitting tool in MATLAB, the best possible approximation of linear regression is calculated. Mathematically, this linear approximation is given as:  
\[y=18\times x+37 \tag{9}\]  
The blue graph depicts the averaged RSSI from 0.3 m to 1.5 m. From the above Equation (9), we get the value for slope of the line \(m=18\). Therefore, from the relation stated in Section 3.1, we calculate the value for path loss exponent \(n=1.8\). This verifies the path loss exponent for a terrestrial indoor environment as mentioned in Table 2. Figure 3 is the bar graph illustrating the RSS measured at each distance.
# Evaluation of PID-Based Algorithms for UGVs
## 2 Related Work
### Comparative Approaches to the Use of PID Controller Algorithms in UGVs
The controller was designed to handle the non-linearity of reference trajectory tracking and adjust the speeds of the robot's DC motors. Additionally, [PERSON] et al. [26] adopted an algorithmic optimization approach for autonomous systems by introducing a simulation-based optimization technique for autonomous transport systems. They implemented a Real-Coded Genetic Algorithm with Scalable Non-Uniform Mutation (RCGA-SNUM). This method was designed to minimize traffic accidents through the simulation of autonomous vehicles on an Artificial Multi-Connected Road Network (AMCRN). Despite extensive study on PID control for UGVs, issues persist in its adaptation to dynamic and unstructured situations. These issues encompass the management of nonlinear dynamics resulting from fluctuating terrain conditions, the assurance of dependable trajectory tracking amongst external disturbances, and the achieving of real-time sensor fusion for accurate control. The design is further complicated by the need for scalability across various workloads and the minimization of latency in resource-constrained systems.
# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 3 AI-Driven Optimization of MIMO Isolation Using SRRs
### AI Model 2 for SRR Dimension Prediction
These geometric parameters directly impact the electromagnetic characteristics of the SRR, particularly its resonant frequency and the effectiveness of the stop-band it introduces. As such, fine-tuning these dimensions is essential for optimizing isolation (S12) and return loss (S11) performance. To identify the most influential parameters affecting S-parameters, a random forest regression model was applied. The feature importance analysis (See Figure 19) reveals the following insights:  
* For S11, the most dominant parameter is gx, followed by e1, indicating that the overall size and trace width of the resonator have the strongest influence on the input matching behavior;
* For S12, both gx and r1 are the most impactful. This suggests that the spatial placement and overall resonator size critically govern the coupling between antenna elements. The gap length xl has a minor, but non-negligible, effect on both S11 and S12, while e1 remains important across both metrics, likely due to its role in controlling the current distribution and inductive properties of the SRR. This analysis highlights that optimizing gx, r1, and e1 should be prioritized during the design of SRRs to effectively control and improve the isolation in MIMO systems.
## 3 Industrial Applications
### Building and Construction  
The scale of use of metal AM technologies for construction applications is currently low due to long printing times, small print volumes, high initial cost, and environmental issues; however, the number of applications in this field is increasing. For instance, Arup Group, an international engineering consultancy, developed a topology optimised node (Figure 7a,b). This component is made of stainless steel and it is 75% lighter than the original node, which is manufactured by traditional methods [88]. Figure 7c shows the printed Nematox facade node, which is developed at the University of Applied Sciences in Detmould. This part is made of Aluminum and it is printed with the Concept Laser system [89]. Another example of metal AM application is the MX3D bridge (Figure 7d). The Joris Laarman Lab and Arup use a WAAM system, which is attached to a robotic arm to print stainless steel components of the Amsterdam's bridge [9].  
The above literature shows that metal AM is enabling the construction industry to create complex structural shapes with minimised weight and a reduced production lead time. Currently, metal AM is used for production of customized components on demand. Furthermore, metal AM is beneficial for the circumstances where construction components are lost or damaged, and on-site production using AM can be of significant benefit as waiting for replacements may cause construction delays and costs [88].
## 1 Introduction
For example, JonInbi\({}^{\text{TM}}\) (Shanghai Jianglai Biotechnology Co., Ltd., Shanghai, China) and Thermo Invitrogen\({}^{\text{TM}}\) ELISA kits (Thermo Fisher Scientific, Waltham, MA, USA) have detection ranges of 1.56-100 ng/mL and 1.23-300 ng/mL, respectively, in which both LOD values are higher than some reported retail milk with \(<\)0.5 ng/mL IGF-1 content [21]. With the growing prevalence of bovine colostrum products in the market, the quality of these products has become a matter of great concern [10; 29]. In particular, the false advertising of IGF-1 content has become a problem in the dairy market; the actual content is usually lower than the broadcast one. Therefore, ultrasensitive IGF-1 detection is necessary for the supervision of the quality of bovine colostrum products. The fluorescent sandwich ELISA method was developed as a good substitute for the traditional ELISA method because of its excellent sensitivity and stable signal [30]. 10-acetyl-3,7-dihydroxyphenoxyoxazine (ADHP) is the most widely used fluorogenic substrate for horseradish peroxidase (HRP) [31]. ADHP is non-fluorescent, but when it reacts with H\({}_{2}\)O\({}_{2}\) under enzyme catalysis, a new product of resorufin with red fluorescence is produced; therefore, the fluorescence intensity is used to indicate the content of HRP or other HRP-linked substances. Research on the use of ADHP for the detection of other targets has been documented. [PERSON] et al. compared the effects of different substrates on the sensitivity and accuracy of ELISA [32], showing that ADHP enhanced the detection sensitivity by approximately five folds in comparison to TMB. Herein, a new kind of ultrasensitive, rapid, and stable fluorescent sandwich ELISA method was developed in comparison with traditional ELISA, to detect trace amounts of IGF-1 in bovine colostrum powders, and the scheme of the fluorescent sandwich ELISA using a fluorogenic peroxidase substrate of ADHP is shown in Figure 1. The immobilized capture antibody and detection antibody are matched antibody pairs, which specifically bind with IGF-1, forming a \"sandwich\". The biotinylated detection antibody is further connected with streptavidin-HRP based on the streptavidin-biotin interaction. In the presence of HRP, transparent and non-fluorescent ADHP reacts with H\({}_{2}\)O\({}_{2}\) to produce resorufin with stable fluorescent signals, and thus, the amount of IGF-1 is related to the fluorescence intensity. The fluorescence working solution is optimized according to the contents of the enzyme and substrate and environmental factors. The standard curve, linear range, and LOD of IGF-1 are obtained using the established fluorescent sandwich ELISA method. To effectively remove the interfering impurities and retain as much IGF-1 in bovine colostrum powders, three different multi-step pretreatments are compared, and furthermore, IGF-1 recovery assays are performed on treated colostrum samples to validate the accuracy of the fluorescent sandwich ELISA and quantify the IGF-1 content in colostrum powders. Finally, the specificity of the fluorescent sandwich ELISA is verified by low cross-reactivity rates.
## 4 Results and Discussion
### Optimization of Model Parameters
The network structure was composed of convolution layer, pooling layer, and full connection layer; 1D-CNN included two convolution layers, two pooling layers, and three full connection layers. The cross-entropy function was selected as the objective function. The convolution network included two convolution layers (C1 and C3) and two pool layers (S2 and S4), and F5 was used to flatten the output of S4 into a one-dimensional sequence to facilitate the connection between the pool layer (S4) and the fully connected layer (F5). The machine learning algorithm development environment proposed in this article was trained and tested on Windows 10, NVIDIA GTX 1060 (4G) GPU, and CUDA v10.0 using the Intel Core i76700 2.6 GHz six-core processor. The programming language uses OpenCV 3.0, Tensorflow 2.0, and Python 3.6.4.
# Vitamin C in Plants: From Functions to Biofortification
## 2 Vitamin C as an Antioxidant
MDHA is reduced to ASC by MDHA reductase (MDHAR), which is a flavin enzyme that utilizes NAD(P)H as electron donors [43]. Many cell compartments possess MDHAR activity. _Arabidopsis_ has five genes coding for MDHAR2 and 3, localized in the cytosol; MDHAR1 and 4, in peroxisomes and membranes; and MDHAR6 in chloroplasts and mitochondria [48]. DHA can be reduced to ASC by DHA reductase (DHAR), which utilizes GSH as an electron donor, leading to the formation of glutathione disulphide (GSSG). DHAR has an important role in maintaining the reduced ASC in order to avoid DHA degradation [49]. DHAR activity has been identified in cytosol, chloroplasts, mitochondria, and peroxisomes [48]. _Arabidopsis_ has three genes coding for DHAR localized differently in the cells: _DHAR1_ localized in cytosol and peroxisomes, _DHAR2_ localized only in the cytosol, and _DHAR3_ targeted to chloroplasts [50]. In the ASC-GSH cycle, GSSG is reduced to GSH by the NADPH-dependent glutathione reductase (GR). GR plays a pivotal role in maintaining the correct balance between reduced GSH and ASC pools [51]. GR activity has been detected in chloroplasts, cytosol, mitochondria, and peroxisomes [52]. In _Arabidopsis_, two genes encode for GR in plants: GR1 is predicted to code a cytosolic isoenzyme and GR2 encodes for a dual-targeted plastidic/mitochondrial protein [53]. Owing to its high antioxidant properties and to the presence of an effective system for redox regeneration, in plants, vitamin C plays a significant role in the defense against oxidative stress, which arises in response to biotic or abiotic stresses [54]. The important role of vitamin C in the tolerance to several stresses is underlined by the increase in the enzymes involved in biosynthesis and recycling, observed in the presence of adverse environmental conditions [49; 55]. Interestingly, feeding with \(\upiota\)-galactono-1,4-lactone, which enhances the vitamin C content, can increase resistance to various kinds of stress [56; 57; 58; 59].
## 2 Modeling and Analysis of Carrier-Phase Differential Positioning
### Modeling of the Integer Ambiguity Parameter Identification Based on GWO  
Gray wolf optimization (GWO) is a new kind of swarm intelligence optimization algorithm, which simulates the social hierarchy mechanism and hunting behavior of gray wolf groups in nature [17]. In the gray wolf population, there is a strict social hierarchy. The \(\alpha\) wolf is the highest-level gray wolf. The \(\beta\) and \(\delta\) wolves are the two suboptimal gray wolves, and the lowest-grade gray wolf is called the \(\tau\) wolf. Gray wolf hunting is mainly divided into three stages: tracking, encirclement, and attack. The gray wolf population is \(N\) and the search space is \(D\) dimension. Since the position of the gray wolf is related to the potential solution of the integer ambiguity, the position of the \(i\)th gray wolf needs to be rounded after the update, which can be denoted as \(X_{i}=\text{round}(x_{i1},x_{i2},\ldots,x_{iD})\). The iteration process of GWO can be described as follows:  
\[\left\{\begin{array}{c}D=\left|CX_{p}(t)-X(t)\right|\\ X(t+1)=X_{p}(t)-AD\end{array}\right. \tag{22}\]  
where \(t\) is the number of iterations; \(A\) and \(C\) are coefficient vectors; \(X_{p}\) and \(X\) are the position vectors of prey and gray wolves. \(A\) and \(C\) are calculated as follows:  
\[\left\{\begin{array}{c}A=2 ar_{1}-a\\ C=2r_{2}\end{array}\right. \tag{23}\]  
where \(a\) is the convergence factor which decreases linearly from 2 to 0 as the number of iterations increases; \(r_{1}\) and \(r_{2}\) are random numbers in the interval \([0,1]\).  
Among the wolves, \(\alpha\), \(\beta\) and \(\delta\) are closest to the prey. And the position of these three can be used to determine the location of the prey, which can be described mathematically as follows:  
\[\left\{\begin{array}{c}D_{\alpha}=\left|C_{1}X_{\alpha}-X\right|\\ D_{\beta}=\left|C_{2}X_{\beta}-X\right|\\ D_{\delta}=\left|C_{3}X_{\delta}-X\right|\end{array}\right. \tag{24}\]  
where \(D_{\alpha}\), \(D_{\beta}\) and \(D_{\delta}\) denote the distance between \(\alpha\), \(\beta\) and \(\delta\) and other individuals, respectively; \(X_{\alpha}\), \(X_{\beta}\) and \(X_{\delta}\) denote the current positions of \(\alpha\), \(\beta\) and \(\delta\); \(C_{1}\), \(C_{2}\) and \(C_{3}\) are random vectors; \(X\) is the current position of the gray wolf. \(X_{1}\), \(X_{2}\) and \(X_{3}\) indicate the position of \(\tau\) wolf need to be adjusted by the influence of \(\alpha\), \(\beta\) and \(\delta\) wolves. The direction in which the individual is moving towards the prey can be written as:  
\[\left\{\begin{array}{c}X_{1}=X_{\alpha}-A_{1}D_{\alpha}\\ X_{2}=X_{\beta}-A_{2}D_{\beta}\\ X_{3}=X_{\delta}-A_{3}D_{\delta}\end{array}\right. \tag{25}\]  
Then, the next position of the gray wolf is:  
\[X(t+1)=\frac{X_{1}+X_{2}+X_{3}}{3} \tag{26}\]
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 2 Related Work  
### Deep Learning-Based Multimodal Models  
With the popularity of deep learning, many multimodal recommendation approaches based on convolutional neural networks (CNNs) have been developed. For instance, DeepCoNN [27] introduces a novel deep learning architecture that leverages user and item reviews to improve recommendation accuracy. It utilizes two parallel CNNs, one for users and one for items, to extract features from review text, which are then combined through a shared layer to model user-item interactions. This approach addresses the data sparsity issue common in CF by incorporating rich semantic information from reviews. DVBPR [28] utilizes a Siamese CNN framework to extract visual features of items from its images, and these features serve as item representations for user preference prediction. The item representation generated by the CNN architecture can be trained end-to-end with the recommendation system. MRLM [29] addresses the challenges of heterogeneous and multimodal item descriptions in the Internet of Things (IoT) environment. It integrates global feature representation learning (GFRL) and multimodal feature representation learning (MFRL) to jointly capture user-item interactions and multimodal features. Additionally, to capture users' different preferences for multimodal features, some multimodal recommendation models also use attention mechanisms. For example, VECF [30] addresses the problem that different users may have different focus points on the same item by using the VGG model to pre-segment the item image and employing attention mechanisms to capture the users' attention on different image regions, which are used to enhance item representation in the image modality. ACF [31] incorporates component-level and item-level attention mechanisms into traditional collaborative filtering models to allocate attentive weights for deducing the underlying user preferences reflected in implicit feedback. Deep learning-based models have excellent recommendation performance, but these models mostly tend to focus more on the modal information of items and neglect the rich interaction information between users and items.