esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Wireless Control Applications with XBee and LabVIEW
## 4 Experimental Tests  
In order to evaluate whether XBee technology was suitable for control applications, several experiments at different sampling periods and distances were designed to compare wireless and wired communications. First, the Analog-to-Digital Converter (ADC) included in the XBee device was compared with a National Instruments myDAQ USB-6008 board and an Arduino Uno board. Second, the offset and message dropout were analyzed for different distances and sampling periods. Third, the message delay was analyzed at different distances and sample periods. Finally, the influence of Wi-Fi interferences in the XBee transmission was checked. This evaluation aimed to shed some light into the application of this technology in control applications.  
The test set up was aimed at analyzing the performance of wireless communications in control applications. The experimental layout for a Single Input Single Output (SISO) Control System with one sensor actuator and controller has been reproduced in Figure 1. The NI VISA device was used to establish the communication between the XBee and the LabVIEW application. Just for evaluation purposes, one potentiometer was used as the generic analog sensor and one small DC motor was used a generic actuator. The sensor module, implemented by means of an Arduino board, sampled the values of the sensor and sent them to the XBee antenna. For the actuator, an Arduino was also connected to a XBee module (by means of an Arduino XBee shield). This module received the control signal generated by the LabVIEW control algorithm. The DC motor was connected to one of the PWM Arduino board pins. The XBee frames followed the format defined in the previous section. In parallel to the wireless communication, a NI myDAQ USB-6008 acquisition board was used to compare wireless and wired communications. This was a low cost board designed by National Instruments and, consequently, had very good compatibility with LabVIEW, by means of ad hoc libraries and drivers. The NI myDAQ USB-6008 also provided higher resolution (12 bits), when compared to the XBee and Arduino ADCs (10 bits). Due to its higher resolution and lower bit error rate, it was considered to be the reference value for the tests carried out. The following subsections describe these experiments and briefly discusses the results.
## 2 Materials and Methods
### Image Segmentation
#### 2.3.2 Multiresolution Segmentation Algorithm  
Of the many segmentation algorithms available, multiresolution segmentation (MRS, see [69]) is the most successful segmentation algorithm capable of producing meaningful image objects in many remote sensing application domains [41], though it is relatively complex and image- and user-dependent [37; 51]. The MRS algorithm uses the Fractal Net Evolution Approach (FNEA), which iteratively merges pixels based on homogeneity criteria driven by three parameters: scale, shape, and compactness [39; 46; 47]. The scale parameter is considered the most important, as it controls the relative size of the image objects and has a direct impact on the subsequent classification steps [64; 66; 70].
## Appendix A ML and Optimization Algorithms  
### Radial Basis Function Neural Network (RBFNN)  
RBFNNs are a type of artificial neural network designed for approximating scattered multi-dimensional input/output data. Introduced in the 1980s [47], they effectively solve non-linear problems with a single hidden layer, offering computational efficiency over networks with multiple hidden layers. The hidden layer comprises RBF units, which map inputs to a higher dimensional feature space. Advantages include faster training compared to MLPs and strong generalization due to a clear hidden layer [48]. However, limitations include slower control parameter tuning versus MLPs and challenges with large datasets [49]. RBFNN suitability depends on the dataset size and inputs, but their single hidden layer is often beneficial for medium-sized datasets [50]. RBFNNs and k-Nearest Neighbor (k-NN) both employ distance-based approximations but differ in implementation. RBFNNs calculate Euclidean distance between new cases and hidden neuron centers, applying weights to each neuron [48]. Two key parameters must be determined in RBFNNs; in this study, the growth optimizer (GO) was used to find their values, rather than relying on trial-and-error [48].
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Ground Segment Modeling
#### 3.2.1 Acquisition of Traceroute Data  
Network measurement is a conventional method for evaluating network performance. Existing research primarily focuses on assessing satellite network performance through the analysis of latency, throughput, and jitter between several controlled nodes. Traditional network measurement approaches, which require a large number of controlled  
Figure 5: Visualization of the orbital plane of shell 6. To improve the visualization effect, the real\(-\)time altitude of the satellites is increased by a factor of one thousand.  
nodes, are prohibitively expensive. However, conducting large-scale asynchronous reverse measurements using a single server node can significantly reduce costs.  
The technique of large-scale asynchronous measurement originated with ZMap [44], which uses asynchronous I/O and efficient data structures to perform high-speed network scans, thus supporting extensive cybersecurity studies and analyses. Building upon our previous work [42], we conducted a cost-effective, large-scale asynchronous Traceroute from a server node outside of Starlink's AS14593 to investigate the network layer structure of the satellite network within this autonomous system. By limiting the packet sending rate, we completed an autonomous system measurement of AS14593 within 18 min, collecting over 200,000 Traceroute records.
## 6 Conclusions  
As IoT quickly becomes a reality, there is an increased need for the MEC paradigm. The MEC is a network architecture that supports computing, analysis, and storage capacity at the edge of the network. In particular, it can provide performance benefits for applications that require IoT environments. However, providing short-latency connections and ensuring the appropriate execution of latency-sensitive tasks without incurring deadline violations have remained as challenges to be addressed. In order to overcome these challenges, we propose a latency-classification-based deadline-aware task offloading algorithm based on the urgency of tasks while avoiding deadline violations. We establish a simulation environment to evaluate the performance of the LCDA approach and compare the proposed algorithm to the existing offloading algorithms. Based on the simulation output, we can prove that the LCDA achieves significant processing time reduction with a reasonable number of task failures for latency-sensitive applications. As future work, we intend to apply machine learning techniques to calculate efficiently the deadlines of tasks as well as the edge cloud server's resources. In addition, we intend to continue expanding the algorithm to reduce task failure rates of the LCDA.  
All the authors contributed equally to work. All authors read and approved the final manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1A2C1006754).  
The authors declare no conflict of interest.
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 3 Design of Smart Collision Avoidance Control System for USVs
### Information Acquisition of the Controlled Vessels and Surrounding Ships  
The controlled USV can acquire real-time information of two ships using AIS and ARPA. Acquired information includes the heading angle of the controlled USV (\(C_{o}\)), target ship (\(C_{T}\)), and the velocity of the controlled USV and target ship, \(V_{o}\), and \(V_{T}\), respectively, as shown in Figure 5.  
In Figure 5, \(V_{Ox}\) = \(V_{O}\times\) sin (\(C_{O}\)) and \(V_{Oy}\) = \(V_{O}\times\) cos (\(C_{O}\)) are components of \(V_{O}\), and, similarly, \(V_{Tx}\) = \(V_{T}\times\) sin (\(C_{T}\)) and \(V_{Ty}\) = \(V_{T}\times\) cos (\(C_{T}\)) are components of \(V_{T}\). The relative velocity (\(V_{TO}\)) between these two encountered ships can be expressed as:  
\[V_{TO}=\sqrt{\left(V_{Tx}-V_{Ox}\right)^{2}+\left(V_{Ty}-V_{Oy}\right)^{2}} \tag{1}\]  
The relative heading angle (\(C_{TO}\)) can be expressed as follows:  
\[C_{TO}=\tan^{-1}\left(\frac{V_{Tx}-V_{Ox}}{V_{Ty}-V_{Oy}}\right)+\vartheta \tag{2}\]  
\[\text{where }\vartheta=\left\{\begin{array}{ccc}0^{\circ}&,&V_{Tx}-V_{Ox} \geq 0&,&V_{Ty}-V_{Oy}\geq 0\\ 180^{\circ}&,&V_{Ty}-V_{Oy}<0\\ 360^{\circ}&,&V_{Tx}-V_{Ox}<0&,&V_{Ty}-V_{Oy}\geq 0\end{array}\right.\]  
Figure 3: At same periods, (**a**) maintain speed and (**b**) decrease speed.  
The relative distance between the two encountered ships is expressed as:  
\[D=\sqrt{\left(x_{\mathrm{T}}-x_{\mathrm{O}}\right)^{2}+\left(y_{\mathrm{T}}-y_{ \mathrm{O}}\right)^{2}} \tag{3}\]  
The true bearing (\(\phi\)) of other ship relative to the controlled USV is formulated as (4).  
\[\phi=\tan^{-1}\biggl{(}\frac{x_{\mathrm{T}}-x_{\mathrm{O}}}{y_{\mathrm{T}}-y_{ \mathrm{O}}}\biggr{)}+\varphi \tag{4}\]  
\[\text{where }\varphi=\left\{\begin{array}{ccc}0^{\circ}&,&x_{\mathrm{T}}-x_{ \mathrm{O}}\geq 0&,&y_{\mathrm{T}}-y_{\mathrm{O}}\geq 0\\ 180^{\circ}&,&y_{\mathrm{T}}-y_{\mathrm{O}}<0\\ 360^{\circ}&,&x_{\mathrm{T}}-x_{\mathrm{O}}<0&,&y_{\mathrm{T}}-y_{\mathrm{O}} \geq 0\end{array}\right.\]  
The relative bearing (RB) of the controlled USV to target ship is described as (5).  
\[\text{RB}=\phi-C_{\mathrm{O}} \tag{5}\]  
Figure 4: Flowchart of the smart collision avoidance system for USVs.  
Distance to closest point of approach (DCPA) and time to closest point of approach (TCPA) are:  
\[\text{DCPA}=D\sin(C_{TO}-\phi-180^{\lx@math@degree}) \tag{6}\]  
\[\text{TCPA}= \frac{D}{V_{TO}}\cos\left(C_{TO}-\phi-180^{\lx@math@degree}\right) \tag{7}\]
## 1 Introduction
The contributions of this article can be summarised as: (1) development of a low complexity algorithm to measure time-frequency behaviour of interference to be used in low data rate links, (2) validation of the method through theoretical analysis, simulations, hardware-in-the-loop testing and in-orbit measurements on-board a small satellite, and (3) analysis of the in-orbit interference environment measured in the UHF radio amateur band. The structure of the paper is as follows. In Section 2, the LUME-1 satellite and its constraints are introduced. The software architecture and measurement algorithm are explained in Section 3, followed by the description of the setup for simulations, laboratory (flatast), and satellite measurements in Section 4. The theoretical, simulation, lab, and in-orbit measurement results are presented in Section 5 and discussed in Section 6. Finally, conclusions are presented in Section 7.
# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 5 Experiments  
### Experimental Setting  
In this section, we describe the experimental setup used to evaluate the proposed Uncertainty-Aware Active Meta-Learning (UA-AML) methodology. The experimentswere designed to assess the effectiveness of UA-AML in handling few-shot learning tasks, including relation classification, text classification, and out-of-scope intent detection. We conducted experiments using multiple benchmark datasets that are commonly used for evaluating meta-learning-based natural language understanding (NLU) models:  
* FewRel [39]: A few-shot relation classification dataset containing 100 relation classes, where 64 classes are used for meta-training, 16 classes for meta-validation, and 20 classes for meta-testing.
* Amazon Review Sentiment Classification (ARSC) [40]: A sentiment classification dataset containing 13 domains. For evaluation, four domains (Books, DVDs, Electronics, and Kitchen) were used for meta-testing, while the remaining were used for meta-training.
* CLINC150 [37]: The CLINC150 dataset is a large-scale benchmark for intent classification in natural language understanding (NLU). It contains 22,500 utterances across 150 intent classes spanning 10 diverse domains, along with out-of-scope (OOS) examples for open-world evaluation. This dataset is widely used for benchmarking deep learning models, zero-shot and few-shot learning, and out-of-domain detection in conversational AI systems.  
For all experiments, a pre-trained BERT base model was employed as the text encoder. The model was fine-tuned using a meta-learning framework, with task augmentation and Uncertainty-Aware Active Meta-Learning strategies applied to improve generalization.  
To evaluate the effectiveness of UA-AML, we compared its performance against existing few-shot learning models, including BERT-PAIR, a BERT-based model used as a baseline for few-shot relation classification, and meta-learning baseline models such as Matching Networks, Prototypical Networks, Graph Networks, Relation Networks, and SNAIL for few-shot text classification tasks.
# A Convex Optimization Algorithm for Electricity Pricing of Charging Stations
## 3 Convex Optimization Algorithm
### Scaling Algorithm  
DSO and CSs are cooperative game relations, but both sides have their own benefits. The problem of power planning for charging stations is a multi-objective programming, and the Pareto front can balance the benefits of DSO and CSs well. According to the Pareto front, all the cooperation results can be obtained, and then the appropriate cooperation decision can be selected [24]. Therefore, the key to the problem is how to get the Pareto front of the model. According to the scalarization basic theorem [21], the Pareto front can be obtained by transforming the objective function into a set of constraints and solving the model many times. In this paper, the objective function of DSO is transformed into a constraint like Equation (28). By changing \(D_{L}\), the model is solved many times to obtain the Pareto front.  
\[\|P_{G,t}-\overline{P}_{G}\|_{2}\leq D_{L}. \tag{28}\]  
In order to simplify the model, standard deviation as Equation (29) is used to simplify Equation (28), which can be rewritten as a set of linear constraints.  
\[\frac{1}{T}\sum_{t\in T}|P_{G,t}-\overline{P}_{G}|\leq D_{L}. \tag{29}\]  
The details of scaling method are described in Algorithm 3.  
```
1\(D_{L}\): = +\(\infty\), \(P_{G,t}\): obtain by Algorithm 1,\(f_{DSO}\): obtain by Algorithm 1
2\(D_{L}\): = \(\frac{1}{T}\sum\limits_{l\in T}|P_{G,t}-\overline{P}_{G}|\), \(t\) = 0, obj\({}_{1}\)[t]: = \(f_{DSO}\), obj\({}_{2}\)[t]: = \(f_{CSs}\)
3 repeat
4 solve MILP by algorithm 1,\(f_{DSO}\): obtain by Algorithm 1,\(f_{CSs}\): obtain by Algorithm 1
5 if\(f_{CSs}\) == obj\({}_{2}\)[t] then
6 break
7 else
8 t: t++
9 obj\({}_{1}\)[t]: = \(f_{DSO}\), obj\({}_{2}\)[t]: = \(f_{CSs}\)
10 endif
11 until the problem is infeasible
12 return obj\({}_{1}\) and obj\({}_{2}\)
```  
**Algorithm 3**Scaling algorithm
## 2 Materials and Methods
### Data
#### 2.2.3 Precipitation (P)  
The Global Precipitation Measurement (GPM) precipitation dataset, which is organized as an international project led by the National Aeronautics and Space Administration (NASA) and the Japanese Space Agency (JAXA), is an international network of satellites that provide next-generation global observations of rain and snow [47]. Building upon the success of the Tropical Rainfall Measuring Mission (TRMM), the GPM concept centers on the deployment of a \"Core\" satellite carrying an advanced radar/radiometer system to measure precipitation from space and serve as a reference standard to unify precipitation measurements from a constellation of research and operational satellites. The Integrated Multi-satellitE Retrievals for GPM (IMERG) Level3 V06 monthly \"Final\" precipitation product with a 0.1\({}^{\circ}\) spatial resolution was used as a precipitation variable for climatic factors.
## 3 Results
### Induced Stresses  
During tunnel excavation, the field stress condition is disturbed. This disturbance in the field stress is a governing factor influencing the rock-bolt capacity. A stress decrease that is perpendicular to the longitudinal axis of the bolt leads to unstable splitting of the grout, whereas an increase reduces the potential for unstable splitting. A change in stress parallel to the longitudinal axis of the bolt has an opposite effect. In high-stress environments, the consideration of the stress-change effect is important during bolt design and installation [50]. Along the tunnel periphery, the stress condition around each rock bolt is not hydrostatic. The major and minor principal stress values and directions have a direct effect on the crack propagation in the rock surrounding the bore hole during the pull-out test, as shown in Figure 8. Laboratory studies confirm that, by decreasing the confining stresses, the bolt capacity decreases and the radial fracturing increases [43].  
Figure 8: Effect of confining pressure on borehole surrounding rock mass during pull-out test: (**a**) low confining stress (\(\sigma_{2}=\sigma_{3}\)), (**b**) high confining stress (\(\sigma_{2}=\sigma_{3}\)), and (**c**) confining stress, where \(\sigma_{2}\)\(\
eq\)\(\sigma_{3}\).  
Empirical classification systems do not consider dynamic loading, although the conventional tunnel practices involve blast-induced vibrations in close proximity to the installed grouted rock bolts. Field test results show that no decrease was observed in the strength of rock bolts in the field pull-out test, where the bolts were installed 3.4 and 22 m away from the blasting face, irrespective of the grout's settling time [51]. The effect of blast induced vibration from the adjacent tunnel shows that it affects the rock-bolt performance [52]. A numerical study of fully grouted rock bolts under dynamic loading showed that, if the vibration wave propagates from the opposite side, perpendicular to the rock bolt longitudinal axis, multiple wave reflections from the free surface along the excavation periphery affects the bolt performance [41].
## 1 Introduction  
Identification and differential diagnosis of testicular masses (TM) in daily practice is often challenging. According to EAU guidelines, ultrasound (US) still represents the mainstay of scrotal imaging [1]. However, magnetic resonance imaging (MRI) of the scroth provides higher sensitivity and specificity than US in the diagnosis of TM [2, 3]. Nonetheless, the cost of MRI is still too high to justify its routine use [4]. Hence, there is an urgent need for new, minimally invasive, cost-effective, and highly reproducible imaging techniques for real-time evaluation of TM.  
Since the introduction of US contrast agents, contrast-enhanced ultrasound (CEUS) has been applied for morphological characterization and local staging of different malignancies [5]. CEUS is always performed after conventional B-mode US, therefore it must be regarded as a combination of both methods. Over the past decades, some studies described the role of CEUS, as a promising approach in differential diagnosis of testicular masses [6, 7]. Here, the use of qualitative and quantitative parameters, such as evaluatingwash-in, washout, and the time-intensity curves, may help distinguish between malignant and benign neoplastic TM [8]. Meanwhile, fewer studies evaluated the role of CEUS in non-neoplastic testicular lesions, such as segmental ischemia, abscess, and orchiepididymitis, with overlapping results [7; 9; 10].  
In consequence, to assess the real value of this promising imaging technique and its potential applications in clinical practice, we performed a systematic review and meta-analysis of the literature. Notably, our aim was to evaluate the diagnostic accuracy of CEUS in differentiating both neoplastic vs. non-neoplastic and malignant vs. benign TM.
## 4 Discussion
### Strengths and Limitations  
This study brings new insights into the late effects of CT on salivary gland functioning as no firm conclusion has been reached in the literature. No data are available for Italy to our knowledge.  
This study has some limitations. First, due to the cross-sectional design it was not possible to establish baseline levels for salivary parameters with which to compare subsequent measurements. It has been reported that patients having low salivary secretion before cancer treatment are more likely to develop hyposulivation following CT. Furthermore, we did not collect information on the subjective feeling of dry mouth. This may be a relevant issue, because CCSs who experience hyposulivation at a young age may not appreciate their oral mouth as abnormal and thus they may not complain of xerostomia [22]. We could not assess the drug influence on salivary parameters due to the multiagent CT regimens administered to the enrolled children. Finally, the results of this study cannot be generalized to other hematological malignancies. Further studies with larger sample sizes should be encouraged to tease out the confounding effects of tumor stage, pre-existing dental status, and salivary functioning, and other comorbidities; the long-term effect of CT on saliva production needs further investigation.
# Volatiles in Berries: Biosynthesis, Composition, Bioavailability, and Health Benefits
## 3 The Chemical Composition of Volatile Compounds in Berries
### Raspberry  
In the Rosaceae family, raspberry (_Rubus spp._) is a fruit with an attractive appearance and unique flavor [102; 103]. There are red raspberry (_Rubus ideaus_) and black raspberry (_Rubus occidentalis_). Different cultivars and varieties of raspberries are grown worldwide,in Europe, North America, and Asia [88]. Raspberries have been reported to contain an aroma impact compound, which is a single compound that has an odor characteristic of raspberry [67]. This compound has been identified as 1-(phydroyphenyl)-3-butanone and is referred to as raspberry ketone. Approximately 200 volatile compounds were detected in raspberries [76]. Raspberry ketone, \(\alpha\)-ionone, \(\beta\)-ionone, linalool, (Z)-3-hexenol, geraniol, nerol, \(\alpha\)-terpineol, franeol, hexanal, \(\beta\)-ocime, 1-octanol, \(\beta\)-pinene, \(\beta\)-damascenone, ethyl 2-methylpropanoate, (E)-2-hexenol, heptanal, and benzaldehyde have been identified as the raspberry aroma. Among them, \(\alpha\)-ionone, \(\beta\)-ionone, geraniol, nerol, linalool, and raspberry ketone especially contributed to the red raspberry aroma. Monoterpene is an important class of fruit volatile organic compounds (VOCs) [104]. This class contains some of the most aroma-active compounds, such as citronellol, nerol, geraniol, \(\alpha\)-terpineol, and linalool. The volatile composition of raspberries was identified with 30 compounds, including (Z)-hexenol, hexanal, (E)-2-hexenol, 2-heptanone, \(\delta\)-octalactone, \(\delta\)-decalactone, geraniol, \(\alpha\)-ionone, \(\beta\)-ionone, and terpinen-4-ol [79]. The main volatile compounds in raspberries include monoterpenes (20%), acids (14%), alcohols (12%), esters (12%), aldehydes (8%), ketones (7%), C-13 norisogenroids (6%), hydrocarbons (6%), lactones (4%), sesquiterpenes (4%), furans (3%), sulfur (3%), and phenols (1%) [105]. [PERSON] et al. identified 78 and 73 volatiles from black and red raspberry extracts, respectively [69]. The major chemical class was monoterpene (61% in black raspberries, 47% in red raspberries) in both raspberries. In black raspberries, (\(-\))-myrtenol, linalool, \(\alpha\)-terpineol, 2-ethylhexanol, cuminaldehyde, hexanoic acid, ethyl acetate, and (\(+\))-myrtenol were the major compounds. In red raspberries, myrtenol, butanoic acid, linalool, eugenol, 3-methylbutanoic acid, \(\alpha\)-ionone, and vanillin were found as major compounds.