esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Manager for Hyper-Connected RPAS Environments
## 3 Communication Infrastructure Manager: Design and Deployment
### Network Layer Connectivity  
The first step is to enable connectivity at the network level (IP level) between the RPA and RPS domains. This procedure may be straightforward in some cases; however, it may require additional configuration in others that may be too technology dependant. Each  
Figure 3: Reference scenario.  
device may have different interfaces (e.g., 5G, proprietary radio, Wi-Fi, satellite) and must be appropriately configured.  
For example, to enable multi-hop communication in Wi-Fi (ad hoc), the use of a routing protocol is required. This way, when two devices are not in the Wi-Fi range, but have a path using other intermediate nodes as a relay, communication at the network level will be automatically enabled.Optimized Link State Routing (OLSR) [24] protocol has been used in our case. OLSR is a well-known legacy MANET protocol that enables multi-hop connectivity, exemplified in our previous work [25], performing acceptably for the proposed scenarios.  
Another representative example is cellular communication, the end-to-end communication between two devices using the public Internet. Typically, devices connect to the Internet using a private IP address (configured by the Internet provider) and a Network Address Translation (NAT). Therefore, these devices can browse the Internet but are not accessible from external machines. In order to solve this mentioned problem, a Virtual Private Network (VPN) server has been configured. Therefore, all devices connect to the VPN server, which acts as a relay to enable device-to-device connectivity. The performance does not significantly drop when using the VPN server as a relay (this effect has previously been studied in [7]). The VPN server has been deployed at the 5G Telefonica Open Network Innovation Centre (5 TONIC) (5 TONIC: [https://www.5 TONIC.org/](https://www.5 TONIC.org/) (accessed on 14 January 2023)) laboratory. This server (seen in Figure 3) is considered an external service. This fact means that they are services that are not in the RPS, the RPA, or the UTM domain but are required for everything to work as expected. Another example of these external services in Labyrinth is the one dedicated to the path calculation algorithm responsible for enabling a feasible 3D path for the RPA based on UTM constraints. It has not been included in the picture since this algorithm is out of the scope of this article, but more details can be obtained in this reference [26].
## 7 Discussion
### Fully- vs. Semi-Automatic Results  
Fully automatic methods report a wide range of Dice coefficients for EAT segmentation ranging from 0.94 to 0.69. In terms of Dice coefficients, the reported results that are near or better than inter-observer results are [20; 25; 36; 42; 46; 47]. However, in [PERSON] and [PERSON] [47] and [PERSON] et al. [42], the authors validate their method on only 30 and 15 patients, respectively, and [20; 36; 46] all use the same dataset of only 20 patients from the same geographic area. Given the small study size these works were validated on,it is hard to tell whether their results would generalize well to a wider population. [PERSON] et al. [25] report a Dice coefficient of 0.89, which is around the lower end of reported inter-observer variability. Given that three out of 98 scans failed to be segmented and were removed from the analysis, we may conclude that their Dice coefficient would be slightly lower in real-world scenarios. After taking all of this into account, we conclude that the current state-of-the-art fully automatic EAT segmentation methods are not yet robust enough for use in studies or clinical settings, and are not better than manual segmentation. For fully automatic PAT segmentation, [PERSON] et al. [54] evaluate their PAT segmentation method on 422 patients and report Dice coefficients 0.88 or greater, which is slightly lower than reported inter-observer variability.  
Semi-automatic methods, on the other hand, seem to achieve better results. [PERSON] et al. [9] report a Dice coefficient of 0.93, slightly better than reported inter-observer Dice coefficients, and validates the result on 145 patients with the largest number of slices of any work to our knowledge. The patients were also located in Sicily, a notably diverse region, which could also mean their results are more general than in other works. Still, the automatic part of their method not only saves time for the physicians but also offers fewer avenues for inter-observer variability errors and simple human errors. [PERSON] et al. [46] report a Dice coefficient of 0.94 with another semi-automatic method, albeit validated on a much smaller cohort of 20 patients. We conclude that, currently, semi-automatic methods perform better than fully automatic methods for EAT segmentation. Wealso conclude that semi-automatic methods may be better than fully manual segmentation, both in terms of saving time and improving the accuracy of the segmentation.
## 3 Noise Measurement and Reduction in Mode-Locked Lasers
### Carrier-Envelope Phase Noise
#### 3.2.1 Carrier-Envelope Phase Noise Measurement Methods
In this setup, the 1040 nm spectral part in the _f-2f_ interferometer is amplified by a Yb-fiber amplifier, as shown in Figure 5. The motivation of this setup aims to scale the frequency doubling efficiency of the 1040 nm part in the SHG crystal. This method is capable of improving the signal-to-noise ratio of \(f_{\text{ceo}}\) signal by 20 dB compared to purely passive schemes. _Linear method:_ Date back to 2007, research on carrier-envelope phase detection without nonlinear process was demonstrated [101]. In this linear technique, interference between subsequent pulses from a pulse train may frustrate the interference between identical pulses, leading to a modification of interference contrast which is related to the carrier-envelope phase. Carrier-envelope phase information could be retrieved through detection of the fringe visibility. This technique allows characterizing the carrier-envelope phase with virtually arbitrarily low bandwidth and power levels. _Electrical mixing:_ Electrical mixing can also be applied to detect \(f_{\text{ceo}}\) signals. In 2015, [PERSON] et al. beat a continuous wave laser with an erbium-doped fiber femtosecond laser. Through mixing the frequency-divided beat signal with the mode-locked laser's 60 th harmonic of the repetition frequency, \(f_{\text{ceo}}\) signal could be obtained [102]. Using this technique, the phase noise measurement is limited to the noise floor of the instrument when the offset frequency is higher than 100 kHz. This approach has been applied in \(f_{\text{ceo}}\) frequency noise measurement of Er:Yb:glass lasers [103] and quantum cascade lasers [104]. _Time-domain detection:_ In addition to frequency domain detection, [PERSON] et al. also explored a method to detect \(f_{\text{ceo}}\) signals in the time domain [105]. This method utilized the BOC technique to phase lock the repetition frequency of the laser. Interferometric cross-correlation detects the \(f_{\text{ceo}}\) signal of an erbium-doped fiber femtosecond laser in the time domain. After the error signal from the interference, cross-correlation is Fourier transformed, the power spectrum measurement of the \(f_{\text{ceo}}\) signal with an offset frequency of 2 MHz can be realized. _Optical heterodyne:_\(f_{\text{ceo}}\) PSDs could also be analyzed with the participation of two mode-locked lasers. [PERSON] et al. proposed a method to detect the relative \(f_{\text{ceo}}\) noise of two fiber lasers using the optical heterodyne method [106]. The method was based on spectral density analysis of the relative \(f_{\text{ceo}}\) beat-note obtained by direct optical heterodyning between a pair of tightly repetition-rate phase-locked OFCs. An ultra-broadband (5 mHz to 8 MHz) \(f_{\text{ceo}}\) noise spectrum with above 270 dB dynamic range was recorded and validated in passively mode-locked Yb-fiber lasers.
## 2 Methods
### Stability Conditions and Absorbing Boundary Conditions  
The stability condition and absorbing boundary condition of the finite-difference scheme are two important aspects. The stability condition is a restrictive condition given for the numerical dispersion problem in the simulation process, and the absorbing boundary condition is used to absorb and suppress the reflected wave generated by the artificial boundary.  
[PERSON] et al. [23] derivated the 3D elastic wave equation in the cylindrical coordinate system and pointed out that the finite-difference stability conditions in the cylindrical coordinate system are more strict than that in the Cartesian coordinate system. This is because the meshing method is still following that under the Cartesian coordinate system. When the number of meshes is fixed, frequency dispersion will occur until the length of the mesh element in the direction will gradually increase to the limitation of stability condition with the increases of radius. Therefore, to maintain the stability of the calculation and avoid numerical dispersion, the only way is to sacrifice the amount of calculation. To solve the above problems, we can possibly adopt a variable grid method instead, gradually increasing the number of grid divisions as the radius increases, so that the length of the division element in the direction is always under the stability condition in the Cartesian coordinate system as shown in the following formula, which can save the amount of calculation as much as possible while avoiding the numerical dispersion.  
\[\left\{\begin{array}{l}\Delta r,\dfrac{2\pi i\Delta r}{N},\Delta z\leq \dfrac{\lambda_{\text{min}}}{6}\\ \Delta t\leq\dfrac{\min\left(\Delta r,\dfrac{2\pi i\Delta r}{N},\Delta z \right)}{\sqrt{3}v_{\text{max}}}\end{array}\right.\left(i=1,2,3\cdots\right) \tag{5}\]  
Among them, \(\Delta r,\Delta z\) are the minimum grid length in space, \(i\) is the number of divisions in the \(r\) direction, \(N\) denotes the number of meshing elements in the \(\theta\) direction, \(\Delta t\) is the sampling time interval, and \(v_{max}\) represents the maximum wave velocity.  
Because forward modeling is only performed in a limited area, artificial boundaries will be formed around the area. When seismic waves propagate to the boundary, reflections will occur, which will interfere with the original wavefield and cause boundary problems. Aiming at this problem, we adopt the elastic wave PML absorption boundary in the cylindrical coordinate system proposed by [PERSON] [23].
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## Mining Closed Frequent Itemsets with Map-Reduce
### Reduce Step
#### 3.2.7 Duplication Elimination  
As we saw in the run example in Section 3.2.4, a closed itemset can have more than one generator, meaning that two different reduce tasks can produce the same closed itemset. Furthermore, these two reduce tasks can be in two different iterations. We have to identify duplicate closed itemsets and eliminate them. The naive way to eliminate duplications is by submitting another Map-Reduce job that sends all identical closed itemsets to the same reducer. However, this means that we need another Map-Reduce job for that, which greatly damages performance. Line 7 in Algorithm 4 takes care of that without the need for another Map-Reduce round. In the run example, we have already seen how it works when the duplication happens on the same round.  
What we would like to show is that the duplication elimination step does not \"lose\" any closed itemsets. We now explain the method.  
Consider that itemset \(c=\{i_{1},i_{2},\ldots,i_{n}\}\) is a closed, frequent itemset, and its generator \(g=\{i_{g1},i_{g2},\ldots,i_{gn}\}\), \(m<n\), such that \(h(g)=c\). According to our algorithm, \(g\) was created by adding an item to a previously found closed itemset. We denote that itemset \(f\), and the added item \(i_{gj}\) such that \(g=f\cup\{i_{gj}\}\). Suppose that \(i_{gj}>c\backslash g\). Our algorithm will eliminate \(c\). We should show that \(c\) can be produced by a different generator. Consider \(i_{k}\) to be the smallest item in \(c\backslash g\). Since \(i_{k}\in c\) is frequent, and since \(i_{k}\
otin g\), then surely \(i_{k}\
otin f\), meaning that the algorithm will add it to \(f\), creating \(g^{\prime}=f\cup\{i_{k}\}\). It is possible that \(h(g^{\prime})\subset c\), however if we keep growing \(g^{\prime}\) with the smallest items, we will eventually get \(c\).
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 3 Proposed Approach
### Elaboration and Flow of Proposed Approach  
This section uses a flow diagram to demonstrate the intricacies of the QoS violation detection process using ANFIS fuzzy-if-then rules. The evaluation focuses on erroneous system behavior against threshold values and in the end, optimal scalable resource allocation.  
Details of the ReSQoV are explained in reference [28]. There are three primary processes, as indicated in Figure 3 (sequenced from left to right from numbers 1.1, 1.2, and so on): (1) \"violation detection and prediction;\" (2) \"resource evaluation;\" and (3) \"resource allocation.\" Sub-processes exist under each major process; for example, process (1) has sub-processes 1.1 to 1.6. Process (2) has sub-processes 2.1 to 2.7, and process (3) has sub-processes 3.1 to 3.6. These steps are summarized below.  
1. Violation Detection and Prediction: the initialization of the environment, which includes keeping a log of the present status of the available resources. QoS monitored values of each process node are then compared to the fuzzy-if-then rules. If there is definitely no violation (DNV) or if the state is normal (N), then normally perform execute; otherwise, if there is a probably violation (PV) or a definitely violation (DV), then perform resource evaluation.
2. Resource Evaluation: to find the corresponding defects, a resource evaluation is conducted. It searches for newly added cloud resource nodes and any other changes. A resource check is necessary to avoid performance deterioration, maximize resource use, and eliminate defects. To find flaws in the application, it runs software checks. It looks through the logs for any application-related events. The type of event (error, critical), the event id, and the time stamp are all stored. Check the following settings for resource thresholds: The program tracks memory and CPU consumption. The system generates notifications when the memory and CPU consumption values exceed the threshold.  
Resource Allocation: the resource requirement is determined by the value of the contention and coherence parameters. As more workload is assigned, it fluctuates (serialization or queuing). The serialization and queuing occur when more workloads are executed on certain resources and the required resources are less than available. During the resource scheduling process, resource contention is measured using USL. Increasing the number of resources (CPU cores, RAM) boosts throughput while lowering response time. The base for the additional resource allocation is the ideal contention value. The USL model and its prediction outputs will calculate resource requirements, and when the required resources are available, resources are assigned to VM. The following Algorithm 1 presents the intents of the proposed mechanism for resource allocation.  
Figure 3: Flow diagram of the ReSQoV model.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 7 Challenges and Limitations of AI Chatbots in Infectious Disease Outbreaks
### Ineffectiveness with Diverse Populations  
AI chatbots often fail to meet the needs of diverse populations due to linguistic, cultural, and socioeconomic limitations. These systems struggle to interpret input from non-native speakers and lack cultural context, leading to miscommunication and misinformation, particularly among marginalized communities [145]. This shortfall exacerbates existing health disparities, as these groups face barriers in accessing reliable health information, increasing their risk during epidemics.  
The biases in AI models, stemming from non-representative training data, limit their effectiveness in underrepresented languages and cultural contexts [146]. While multilingual models and culturally adaptive systems have been developed, significant gaps remain in scaling these solutions and ensuring they meet the needs of marginalized communities [147]. To address this, expanding training datasets to include more diverse languages and developing speech recognition systems that accommodate different dialects are critical steps [148]. In addition to technical innovations, socio-technical measures are essential. Community-centered design approaches, which involve local populations in chatbot development, ensure that the technology is culturally relevant and accessible [149]. Collaborations with health agencies and local organizations can help tailor information dissemination strategies to meet the specific needs of different communities [150]. Furthermore, digital literacy programs are vital for enabling marginalized populations to effectively engage with AI chatbots, improving access to critical health information. Key research areas include enhancing AI chatbots for low-resource languages, reducing bias, and assessing the public health impacts of communication barriers created by ineffective AI systems. Participatory design methods, which involve end-users in chatbot development, are essential for increasing engagement and trust among marginalized communities [151].
# SpaceSheep: Satellite Communications for Ovine Smart Grazing
## 3 Results  
The monitoring system was tested and functionally validated, and its performance was assessed in terms of latency and generated traffic volume. Additionally, tests were conducted using different numbers of events to determine the scalability of the system depending on the size of the herd being monitored.  
To perform the tests on the system, a Raspberry PI 3 Model B+ single board computer was used to implement the gateway, and the cloud application component was hosted in a virtual machine enabled with 8 GB RAM and 2 cores, using the Ubuntu 20.04.3 LTS server Linux operating system (Ubuntu 20.04.3 LTS), which was hosted at the datacenter in the Instituto de Telecomunicacoes.  
The performance of the signaling encoding APIs translates, especially in terms of the volume of information being transported and the latency of the communications.
## 4 Future Research  
This study proposed the integration of two optimization methods with a routing mechanism to enhance its efficiency in VANETs. Optimization helps the routing protocol adapt to changes in VANET scenarios. The methods in this research considered the scenario from both geographical and topological perspectives. However, network scenarios can also be influenced by various factors, including network traffic, application requirements, reliability, and security [36]. Hence, integrating this method while considering multi-dimensional evaluation metrics--such as latency, network stability, and energy efficiency--can provide a more comprehensive analysis of protocol performance based on traffic, application requirements, reliability, scalability, and security, offering new directions for future research. Moreover, the proposed method can also be adapted to different network protocol stacks, including transport layer and physical layer protocols, making it suitable for deployment in more challenging real-world applications, such as suburban areas, tunnels, or multi-level structures [37].  
Figure 17: Throughput comparison between (**a**) AODV, GPSR, CM-AODV, and (**b**) CM-GPSR for different packet sizes.
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
###### Abstract  
In recent years, there has been significant progress in object detection within the domain of natural images. However, the field of satellite remote sensing images has consistently presented challenges due to its significant scale variations and complex background interference. Achieving satisfactory results by directly applying conventional image object detection models has proven to be difficult. To address these challenges, this paper introduces BA-YOLO, an improved version of the YOLOv8 object detection model. It incorporates several notable enhancements. Firstly, to fuse an increased number of features more effectively, we introduce the design concept of a higher-performing Bi-directional Feature Pyramid Network (BiFPN). Secondly, to retain sufficient global contextual information, we integrated a module in BA-YOLO that combines multi-head self-attention and convolutional networks. Finally, we employed various data augmentation techniques such as Mixup, Cutout, Mosaic, and multi-scale training to enhance the model's accuracy and robustness. Experimental results demonstrate that BA-YOLO outperforms state-of-the-art detectors and has been evaluated on the DOTA dataset. BA-YOLO achieves a mean average precision (mAP) of 0.722 on the DOTA dataset.  
satellite images; object detection; self-attention mechanism; BiFPN; deep learning +
Footnote †: journal: _Sci_  
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