esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 3 LIMS System: fs-Laser Ablation Ion Source and Time-of-Flight Mass Analyser
### Selected Femtosecond-LIMS Systems
#### 3.3.1 Laboratory: Li-O-TOFMS  
A buffer-gas-assisted high-irradiance laser ionisation orthogonal time-of-flight mass spectrometer, LI-O-TOFMS, used in the Huang group, was successfully coupled with a femtosecond laser ablation ion source (Figure 4). The LI-O-TOFMS demonstrated high sensitivity, high mass resolution and advanced quantitative capabilities for element analysis [20,129]. High laser irradiance (>10\({}^{10}\) W/cm\({}^{2}\)) applied for the sample ablation produces high ion concentrations at reduced abundances of polyatomic species. Furthermore, the ion fractionation effects are reduced yielding improved quantitative chemical analysis of the samples. For conducting chemical imaging studies, the sample is remotely manipulated by an _x,y,z_-translation stage with micrometre positioning accuracy at the focal plane of the laser beam. Fast ions produced in ablation pass through an inert buffer gas cell (e.g., He at 800 Pa). This allows for reducing isobaric interferences due to multiple-charged species (charge exchange with the buffer gas) and polyatomic ions (delayed with respect to atomic ion bunch due to frictional forces with a buffer gas). However, only a small fraction of the ablated ions present in the plasma plume is extracted and transported via a lens system in the ion extraction region. Thus, laser ablation and ionisation in a low-pressure source with ambient gas and the subsequent orthogonal extraction and ion transport into the ion extraction region of the mass analyser can introduce additional fractionation processes. The ions are selected for the mass spectrometric analysis by pulse train data acquisition mode, applying a pulse width of 3 \(\upmu\)s and pulse frequency of 40 kHz. This method allows one to select a front ion package and not delayed polyatomic ions for mass spectrometric analysis. Polyatomic ions arrive at the ion extraction region with a delay compared to atomic ions. The maximum kinetic energy distribution of polyatomic ions is reduced by the collision cell and only a small fraction of these species is mixed with the atomic ions selected by the extraction pulse. An fs-laser (S-Pulse HP, Amplitude System, France) with a wavelength of 1030 nm and pulse duration of 500 fs is employed in the laser ablation ion source. The femtosecond laser radiation was focused either by the lens or telescope system. A digital storage oscilloscope is employed with a recording length of 500 \(\upmu\)s to match the flight time of ion packets of all the elements. With the orthogonal ion extraction from the laser ablation ion source, ion cooling combined with orthogonal ion extraction method, the mass resolution of 2200 was demonstrated [20,133].
## 1 Introduction  
### Background  
The number of marine accidents arising from technical issues has continued to decrease, as the quality, regulations, and systems related to the safe operation of ships have been stipulated and enforced by international maritime conventions. Despite such efforts, marine accidents are caused not only by technical factors, but also by human and organizational factors, which are complexly interlinked owing to the large-scale high-speed automation of ships. Thus, it is necessary to systematically analyze the causes to prevent the recurrence of marine accidents [1]. Marine accidents are characterized by large-scale damage to property and the environment, as well as threats to human life. Because marine accidents occur mainly because of human error, lack of technical skills, and organizational factors, it is necessary to systematically analyze the causes of marine accidents and prepare practical countermeasures [2].  
Ships constitute the core of maritime transportation, and the active introduction of smart information technology according to the 4 th industrial revolution into the navigation, cargo, and engine systems of ships has improved the level of informatization, automation, and intelligence, while increasing the complexity of the design and operation of ship systems. Consequently, even if all internal factors of a ship automation system appear to operate normally from the outside, an unpredictable accident can occur internally at any time owing to the interaction among these factors [3]. Because large ship accidents occur as a result of multiple causes as opposed to a particular cause in a complex manner, the conventional accident analysis method that views marine accidents from the perspective of causality of one-to-one correspondence is limited to systematically analyzing complex marine accidents to identify the cause and prevent the occurrence of similar accidents [4].  
The level of technology for several types of information, automation, intelligence, and digitization that support ship automation and unmanned systems has continually been upgraded. Nevertheless, analysis of the systematic causality of marine accidents caused by complex factors, including human error, is still in its preliminary stages [5]. This study analyzed the systematic causality of human error factors in various marine accidents that could occur during the arrival and departure of mega container ships, which are at the peak of speed increase and ship enlargement. As an example, this study reviews the accident case of the Motor Vessel (MV) Milano Bridge at Busan New Port.
## 4 Spatial Knowledge
### Qualitative Spatial Reasoning  
After segmentation usually comes the step of filtering the data. In our case, the filtering is performed by the tracking algorithm and is less interesting at this stage (see one of our previous papers [29]). The next challenge lies in the spatial representation of the data. It is important to limit the number of possible basic directions, since a qualitative representation is desirable. The QSR framework by [PERSON] et al. [32] provides a scalable and simple way to represent distance and orientation relationships between entities. Scalability is important in order to obtain a model that does not depend on the localization method and technology used. Moreover, the model enables various granularity levels, which suits our situation well. While the framework could easily evolve in the future, the number of basic directions was fixed to eight in this project. These eight basic qualitative directions are \(D=\{E,NE,N,NW,W,SW,S,SE\}\) that stand, respectively, for the following: East, Northeast, North, Northwest, West, Southwest, South, Southeast.  
To adequately exploit the framework of [PERSON] et al. [32], a qualitative unit of distance has to be defined. This part was trickier than defining the qualitative orientation (our basic directions) since it would bound the granularity of the recognizable gestures. The distance is, accordingly, defined as a function of the average localization error \(\varepsilon=15\) cm. The qualitative distances are therefore expressed in terms of \(\varepsilon\) number. Figure 5 shows a sample atomic gesture described in the QSR framework. The atomic gesture is the pair \((E,\,\varepsilon_{3})\). The benefit of this unit is that it would gracefully allow the gestures to evolve if, for example, the localization method would become more accurate in the future.  
Figure 4: Same dataset, three granularity values, and three different resulting sequences of atomic gestures.
## 2 Materials and Methods
### Statistical Analysis  
The Shapiro-Wilk test was used to assess the data normality. The mean and one standard deviation (M \(\pm\) 1 SD) were computed as descriptive statistics. The difference between pre and post-tests was analyzed with a dependent sample \(t\)-test. Cohen d was selected as an effect size (d) and interpreted as trivial if d \(<\) 0.2, medium if 0.2 \(>\) d \(<\) 0.5, and large if d \(\geq\) 0.5 ([PERSON] et al., 2017). The percentage of variation (\(\Delta\)) between M1 and M2 was also calculated (e.g., 100 \(-\) [M1\(\cdot\)100/M2]). Pearson (r) Correlation Coefficients were computed to establish the associations between performance (T25) and symmetry; those values were interpreted as high if r \(\geq\) 0.60, moderate if 0.30 \(\geq\) r \(<\) 0.60 and low if r \(<\) 0.30 ([PERSON] et al., 2017). All statistical analyses were performed using the SPSS software (v.27, IBM, SPSS Inc., Chicago, IL, USA) and GraphPad Prism (v.9, GraphPad Software, San Diego, CA, USA). The statistical significance was set at \(p\)\(\leq\) 0.05.
###### Abstract  
The variations in the atmospheric refractivity in the lower atmosphere create a natural phenomenon known as atmospheric ducts. The atmospheric ducts allow radio signals to travel long distances. This can adversely affect telecommunication systems, as cells with similar frequencies can interfere with each other due to frequency reuse, which is intended to optimize resource allocation. Thus, the downlink signals of one base station will travel a long distance via the atmospheric duct and interfere with the uplink signals of another base station. This scenario is known as atmospheric duct interference (ADI). ADI could be mitigated using digital signal processing, machine learning, and hybrid approaches. To address this challenge, we explore machine learning and deep learning techniques for ADI prediction and mitigation in Time-Division Long-Term Evolution (TD-LTE) networks. Our results show that the Random Forest algorithm achieves the highest prediction accuracy, while a convolutional neural network demonstrates the best mitigation performance with accuracy. Additionally, we propose optimizing special subframe configurations in TD-LTE networks using machine learning-based methods to effectively reduce ADI.  
TD-LTE; ADI; machine learning; SVM; random forest; LSTM; CNN Article
## 3 Preliminaries  
### Two-Dimensional Mesh and 2D REmesh Architecture  
Figure 1 shows a \(4\times 4\) 2D mesh topology with and without one column of redundant cores. As shown in Figure 1, the 2D mesh architecture is among the simplest and most direct topologies [36]. In this configuration, each processor core is linked to the network through a router, and each router is connected to four neighboring routers. The 2D mesh topology is highly symmetrical and scalable, making it straightforward to implement in on-chip networks. However, its boundary nodes are somewhat constrained due to the limited number of connected routers, which may affect efficient network performance.  
Figure 2 shows a \(4\times 5\) 2D REmesh topology with one column of redundant cores, consisting of processor cores, on-chip routers, links, network interfaces, and multiplexers. Except for the components necessary for maintaining the normal operation of the system, all other redundant components remain in a dormant state. The 2D REmesh structure exhibits several distinctive characteristics compared to the traditional 2D mesh architecture:  
1. Constant Number of Redundant Cores: The 2D REmesh maintains the same number of redundant cores as the conventional 2D mesh, ensuring that the total area occupied by processor cores remains consistent between both structures;
2. Addition of Routers: To enhance system scalability, the REmesh integrates an additional row and an extra column of routers atop the traditional 2D mesh. The area occupied by a single router is significantly smaller than that of a processor core, and as the network size increases, the proportion of the total area attributed to routers diminishes, rendering the area overhead associated with these additional routers acceptable;
3. Multiplexer Connectivity: Unlike the fixed connections in a traditional 2D mesh, routers in the 2D REmesh are connected through multiplexers. This arrangement features three router types: corner routers, which connect solely to adjacent cores; edge routers, which can connect to one of two neighboring cores via multiplexers; and interior routers, which can connect to any of the four surrounding cores. By default, each router connects to the lower-right core, facilitating greater flexibility and enabling dynamic restoration of the physical topology;
4. Enhanced Ports and Connections: The 2D REmesh structure increases overall bandwidth and mitigates data transmission bottlenecks by augmenting the number of ports and connections. This design fosters enhanced scalability, optimized load balancing,  
Figure 1: Conventional NoC architectures with and without redundancy: (**a**) 2D mesh architecture without redundancy and (**b**) 2D mesh architecture with redundancy.  
and improved flexibility, while providing robust fault tolerance and the capability for topology reconfiguration in the event of faulty cores.  
Consequently, the 2D REmesh architecture can fully leverage the redundant resources of the on-chip network, thereby providing more efficient network performance.
## 2 Related Work
### Collaborative Caching in IoV  
Several studies have explored collaborative caching as a means of optimizing storage in IoV. [PERSON] et al. [17] proposed a content popularity-based collaborative caching method, utilizing LSTM to predict vehicle user requests and applying reinforcement learning to maximize cache hit rates. [PERSON] et al. [18] designed both cooperative and non-cooperative RSU proactive caching schemes, where vehicle mobility patterns were predicted to optimize content allocation, thereby reducing communication delays in vehicular ad hoc networks. [PERSON] et al. [19] considered the limited capacity of RSUs and developed an RSU caching scheme based on a multi-object auction mechanism, aiming to maximize the volume of data downloaded by vehicle users.  
While existing studies have proposed effective solutions for collaborative caching in RSU-based IoV scenarios [17; 18; 19], they primarily consider RSUs as service providers that distribute general-purpose content from third-party providers to improve the user experience of vehicles. These works often focus on enhancing content allocation efficiency through vehicle mobility prediction and content popularity analysis, thereby laying a foundation for storage optimization in IoV. However, they give limited attention to the challenges of collaborative storage in blockchain-based environments. In contrast, our work targets the blockchain-enabled SIoV scenario, where RSUs not only provide services to vehicles but also serve as blockchain network nodes. To preserve the integrity of the distributed ledger, our proposed scheme requires each RSU cluster to collaboratively maintain at least one complete copy of the blockchain, and we optimize the storage strategy accordingly under these stricter network constraints to improve both system efficiency and service quality.
# Edge Computing for IoT-Enabled Smart Grid: The Future of Energy
## 3 Survey of EC-IoT Smart Grids
### Advanced Metering Systems  
SGs are emerging as the next generation to replace the traditional operations of the existing grids. Advanced metering systems are one of the important components of a smart grid. This system is responsible for collecting and analyzing data from smart meters. Therefore, it plays an important role for the smart grid to operate safely, stably and efficiently. In this subsection, we surveyed the recent proposed solutions and techniques for advanced electrical measuring systems.  
Distribution grids often have to change to match the development of local electricity loads. Consequently, connection verification is one of the significant challenges of the smart grid. To solve this problem, [PERSON] et al. (2021) [46] have introduced a cloud-based collaboration mechanism to determine outlier users and valid connections. Specifically, they use the relationship propagation clustering-based local outlier factor algorithm to analyze the metrics provided by smart meters in order to identify and verify edge transformer voltage outliers. Moreover, they also proposed a mechanism for information exchange between edge transformers to improve accuracy in identifying outliers. The results have indicated that the proposed solution improves 66% of the service response time compared to other algorithms.  
The green energy trend leads to the installation of solar photovoltaic panels becoming more and more popular in households. As a result, households will use combined solar and grid power sources. In some cases, households with excess electricity can even sell it to electricity suppliers. This presents a significant challenge in separating indigenous demand and solar production from net demand. To solve this issue, [PERSON] et al. (2021) [47] have proposed using smart meters that have low-resolution but are popular to measure data. Specifically, they construct a joint probability density function (PDF) of monthly diurnal and nocturnal native demands for consumers based on the Gaussian model. Then, they use a maximum likelihood prediction scheme to determine hourly solar power generation. The results have demonstrated that the proposed solution improves the volatility of load and enhanced accurate disaggregation compared to other solutions.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### SMS Alert  
An SMS-based alert system is an important component of the SFSS. This method is precisely designed to send critical warnings to the farm owner's mobile device, thereby alerting them to the presence of intruders on their agricultural grounds. The primary goal is to provide farmers with timely information, allowing them to take quick action to protect their crops from potential injury or theft. The purposes of the SMS alerts are as follows:  
* Ensure Prompt Intervention: The capacity to receive timely notifications is crucial in the context of contemporary farming, where distant monitoring is required due to the enormous expanses of farmland one farmer can cover. The SMS-based alert system meets this demand directly, guaranteeing that the farm owner is kept up to date in real time, regardless of their actual location. The benefit of this fast alarm system is that it allows for quick responses and mitigates any possible crop damage.
* Integration of AI Model with Cloud System: The SFSS's intricacy holds the key to launching SMS-based alerts. The AI model installed in the cloud by this system is critical in intrusion detection. When the AI model detects an irregularity or encronachment on agricultural land, it immediately initiates the SMS alert system. To commence message transmission, this method connects to the cloud system.  
* Farmer Empowerment: Beyond the technological elements, the actual value of the SMS-based alarm system rests in the farmer empowerment it provides. The system acts as a watchful and proactive guardian, ensuring that farm owners have the knowledge they need to take action to safeguard their agricultural interests. This method instills a feeling of security and trust in the agricultural community by bridging the physical divide and providing real-time information.
* Security and prevention: The SMS-based alert system serves a dual purpose in terms of prevention. For starters, it is an important instrument for preventing possible risks from invaders, both animal and human, thus safeguarding crops. Second, it generates a climate of heightened security inside the farmlands, dissuading trespassers who may be discouraged by the knowledge that their presence is being observed and instantly reported.  
As an important component of the SFSS, the SMS-based alert system highlights the connection between sophisticated technology and practical agricultural demands. It allows farmers to safeguard their crops and creates a more secure and efficient agricultural environment by providing them with rapid alerts.
# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 3 Materials and Methods
### Evaluation  
To evaluate the performance of our method, we calculate the precision, recall, and accuracy. These metrics are based on the correctly/incorrectly classified signs which are defined with the true positives (\(TP\)), false positives (\(FP\)), true negatives (\(TN\)), and false negatives (\(FN\)) described below [38]:  
* True Positive (\(TP\)) refers to the number of predictions where the classifier correctly predicts the positive class as positive.
* True Negative (\(TN\)) indicates to the number of predictions where the classifier correctly predicts the negative class as negative.
* False Positive (\(FP\)) denotes to the number of predictions where the classifier incorrectly predicts the negative class as positive.
* False Negative (\(FN\)) refers to the number of predictions where the classifier incorrectly predicts the positive class as negative.  
The precision indicates the proportion of positive identifications that were actually correct. The precision is calculated with Equation (3).  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
The recall represents the proportion of actual positives correctly identified. The recall is calculated with Equation (4).  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
The accuracy measures how often the predictions match the labels, that is, the percentage of predicted values that correspond with actual values with Equation (5).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
The next section describe the results obtained.  
Figure 5: Training curves for the models with 32 neurons. Best seen in color.
## 3 Results and Discussion
### Quantitative Analysis of Biospeckle Activity
Due to the specificity of this phenomenon, the inertia moment is not a good indicator of infestation. As an alternative to extraction of a single line or column from consecutive frames, THSP may be created by a pixel collection randomly selected from a window of fixed size. In our study we adopted the strategy of selection of pixels with values following a Gaussian distribution. Further, the same quantitative measures used previously were calculated. Table 3 presents means, medians and effects of mean ranks comparison for BA indices calculated from THSP built in such a way. Indicators AVD and IM had lower values in comparison to those from Table 1, whereas NAD, RVD and BAI were slightly different. As observed in earlier analyses, the RVD indicator did not differ significantly between groups, so this index is not appropriate for identification of infested fruits. As might be expected, indices IM, AVD and NAD did not distinguish fruits in early stage of infestation from healthy ones. The only indicator which differed significantly in all groups was BAI. Slight differences between BAI means and medians in groups prove its robustness to outliers and extremes. A similar indicator of BA employing the Pearson correlation coefficient, was proposed by other researchers as a reliable measure suitable for monitoring the quality of fresh and stored fruits [36; 41; 42; 43; 44]. Although results obtained in this and other analyses are similar, random selection of pixels seems to be a better solution if analysing regions heterogeneous in terms of BA; hence, THSP created in this way should be more representative for such regions.