esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Worst-Case Integrity Risk and Sensitivity Determination
\begin{array}{ll}\frac{\partial(WIR)}{\partial URA}=&P_{\text{sat}}(1-P_{ \text{sat}})^{n-1}\sum\limits_{i=1}^{n}\big{\{}c_{1}P(T,n-m,\lambda_{i})+c_{2} \big{[}Q\big{(}z_{i}^{+}\big{)}+Q\big{(}z_{i}^{-}\big{)}\big{]}\big{\}}\\ &+2c_{3}(1-P_{\text{sat}})^{n}P(T,n-m)\\ \frac{\partial(WIR)}{\partial P_{\text{sat}}}=&\Big{[}(1-P_{\text{sat}})^{n-1 }-(n-1)P_{\text{sat}}(1-P_{\text{sat}})^{n-2}\Big{]}\\ &\times\sum\limits_{i=1}^{n}\big{\{}\big{[}Q\big{(}z_{i}^{+}\big{)}+Q\big{(}z _{i}^{-}\big{)}\big{]}P(T,n-m,\lambda_{i})\big{\}}-2n(1-P_{\text{sat}})^{n-1} Q(z_{0})P(T,n-m)\\ \\ &\text{where $c_{1}$, $c_{2}$ and $c_{3}$ are defined as}\\ &c_{1}=\frac{\partial\big{[}Q\big{(}z_{i}^{+}\big{)}+Q\big{(}z_{i}^{-}\big{)} \big{]}}{\partial URA}=\frac{URA}{\sqrt{2n\sigma^{2}}}\Bigg{[}z_{i}^{+}e^{- \frac{(z_{i}^{+})^{2}}{2}}+z_{i}^{-}e^{-\frac{(z_{i}^{-})^{2}}{2}}\Bigg{]}\\ &c_{2}=\frac{\partial\big{[}P(T,n-m,\lambda_{i})\big{]}}{\partial URA}=\frac{ b_{2}^{2}\times S_{ij}\times URA}{2\sigma^{4}}\int_{0}^{T}e^{-\frac{(z+\lambda_{i})}{2} }\Big{(}\frac{\lambda_{i}}{\lambda_{i}}\Big{)}^{\frac{n-m}{2}-\frac{1}{2}}h(x, n-m,\lambda_{i})dx\\ &c_{3}=\frac{\partial\big{[}Q\big{(}z_{0}\big{)}\big{]}}{\partial URA}=\frac{ z_{0}\times URA}{\sqrt{2n\sigma^{2}}}e^{-\frac{\alpha}{2}}\end{array}  \tag{10}\]  
\(h(x,n-m,\lambda_{i})\) is defined as  
\[h(x,n-m,\lambda_{i})=\frac{n-m-2+\lambda_{i}}{\lambda_{i}}I_{(n-m)/2-1}\Big{(} \sqrt{\lambda_{i}x}\Big{)}-\sqrt{\frac{x}{\lambda_{i}}}I_{(n-m)/2-2}\Big{(} \sqrt{\lambda_{i}x}\Big{)} \tag{11}\]  
where \(I_{(n-m)/2-1}\big{(}\sqrt{\lambda_{i}x}\big{)}\) and \(I_{(n-m)/2-2}\big{(}\sqrt{\lambda_{i}x}\big{)}\) are the modified Bessel function of the first kind of \((n-m)/2-1\) and \((n-m)/2-2\) order, respectively. The detailed derivation is shown in Appendix A. It can be found that the worst-case integrity risk error can be expressed as a linear combination of the _URA_ and \(P_{\text{sat}}\) errors. Based on (8), the sensitivity of _URA_ and \(P_{\text{sat}}\) on the worst-case integrity risk error can be determined. To characterize the RAIM integrity loss resulting from the improved integrity parameters, a conservative integrity risk estimation method was proposed based on worst-case protection. The integrity-related parameters were categorized into three groups. The worst-case integrity risk was calculated by constraining the first group of parameters. By deriving the worst-case integrity risk error introduced by the improved \(P_{\text{sat}}\) and _URA_, the sensitivity of \(P_{\text{sat}}\) and _URA_ on the worst-case integrity risk was determined. The acceptable \(P_{\text{sat}}\) and _URA_ were obtained by comparing the worst-case integrity risk and the required integrity risk.
## 3 System Framework Overview
### Middleware Layer
#### 3.2.1 Context Acquisition
According to this modeling structure the context is described by four main ontologies, which may have attributes to describe their basic properties. Additionally, some entities are part of the parent ontologies while they may also have some more children to describe them:  
* Plant ontology is used to identify the plants which are being cultivated in the PF facility as well as their profile, health conditions, possible diseases, etc. * Place ontology is used to describe the precise position of the plant cultivations. * Environment ontology is used to describe the environmental conditions of the surroundings. * Device ontology is used to describe the details of the system's sensing and acting devices. Figure 7: Indicative OWL diagram for agriculture context-aware service modeling structure ontology. The aforementioned modeling structure is suitable for deriving knowledge from the current contextual data as well as for detecting any possible inconsistencies in them. This kind of structure is also convenient for the definition of rules whereas it can effortlessly be modified or extended according to the requirements of the system for one or multiple distinct PF facilities. The modeling structure that is represented by the proposed ontology in Figure 7 can be converted to Extensible Markup Language (XML) in the level of implementation. XML is a platform-independent and resilient tool, suitable for usage in various phases of information representation as well as for communication based on the SOAP. A characteristic example of XML code listing concerning the generated context-aware modeling structure is presented in Figure 8.
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 4 Architecture Improvements for Multi-Target Detection
### Network Model Update  
The GAM attention mechanism and small-object detection layer are integrated into the model to obtain the improved YOLOv8 model as shown in Figure 12.  
The added small-target detection layer can retain the position information of small targets and improve the detection effect of small targets. The GAM attention mechanism can adaptively adjust the channel weight and feature weight so that the network can better capture image features. This section adds the GAM module to the end of the backbone network.
# Emotion Recognition from Speech in a Subject-Independent Approach
###### Abstract  
The aim of this article is to critically and reliably assess the potential of current emotion recognition technologies for practical applications in human-computer interaction (HCI) systems. The study made use of two databases: one in English (RAVDESS) and another in Polish (EMO-BAJKA), both containing speech recordings expressing various emotions. The effectiveness of recognizing seven and eight different emotions was analyzed. A range of acoustic features, including energy features, mel-cepstral features, zero-crossing rate, fundamental frequency, and spectral features, were utilized to analyze the emotions in speech. Machine learning techniques such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and support vector machines with a cubic kernel (cubic SVMs) were employed in the emotion classification task. The research findings indicated that the effective recognition of a broad spectrum of emotions in a subject-independent approach is limited. However, significantly better results were obtained in the classification of paired emotions, suggesting that emotion recognition technologies could be effectively used in specific applications where distinguishing between two particular emotional states is essential. To ensure a reliable and accurate assessment of the emotion recognition system, care was taken to divide the dataset in such a way that the training and testing data contained recordings of completely different individuals. The highest classification accuracies for pairs of emotions were achieved for Angry-Fearful (0.8), Angry-Happy (0.86), Angry-Neutral (1.0), Angry-Sad (1.0), Angry-Surprise (0.89), Disgust-Neutral (0.91), and Disgust-Sad (0.96) in the RAVDESS. In the EMO-BAJKA database, the highest classification accuracies for pairs of emotions were for Joy-Neutral (0.91), Surprise-Neutral (0.80), Surprise-Fear (0.91), and Neutral-Fear (0.91).  
speech; emotion recognition; human-computer interaction; classification 2025202556958
## 7 Conclusions and Open Issues  
The miniaturization of electronic devices coupled with the advances in internet technology and wireless communication has established a plethora of pervasive and ubiquitous applications. The Internet of Medical Things, IoMT, is a paradigm shift in the health industry that has been put in place to improve medical services, reduce the cost, increase life expectancy, and so forth. IoMT is composed of low-end, middle, and high-end nodes. Wearables are low-end nodes that are composed of multiple medical sensors, energy scavenging units, wireless communication circuitry, embedded system, storage unit, and power management unit. To increase the operating hours of wearables, several techniques can be used at the algorithm level, including all the design abstraction layers of the VLSI design, and energy harvesting techniques. Additionally, a significant power reduction can be achieved by reducing the transmission rate from wearables to the fog layer. This work systematically reviewed the approaches taken to design energy-efficient IoMT edge nodes. In particular, the following topics have been discussed: technology trends, sub-Nyquist sampling, approximate computing, multi-core technology, energy harvesting techniques, and the implementation of deep learning on edge devices. Among these topics, approximate computing for IoMT is under-researched and needs further exploration as it is a very promising technique to reduce energy consumption. Energy harvesting technologies provide promising possibilities, even if they are not frequently used. Multi-source energy harvesting can be coupled with algorithmic techniques, such as computational offloading, which is a topic that needs further investigation. At the technology level, RISC-V-based multicore architecture is a promising solution for wearables. However, the security and trustability of RISC-V need further exploration. Last but not least, the involvements of different stakeholders and end-users in determining the functional and non-functional requirements for the development of the IoMT are often overlooked and need to be accounted for before, during, and after the development of an IoMT platform.  
Conceptualization: All authors. Writing: I.B.D. (Introduction, Related works, Technologies, Conclusion and open issues); M.E. (Implementation: Deep-learning in IoMT Edge Devices, Conclusion and open issues); M.B.A., G.B. and O.K. (Techniques: Energy Harvesting for IoMT Devices, Conclusion and open issues). Methodology: All authors. Review and editing: I.B.D. and M.B.A. All authors have read and agreed to the published version of the manuscript  
This research was funded by the \"Deutscher Akademischer Austauschdienst\" (DAAD), within the Bismon-57477606 project.  
The authors declare no conflict of interest.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 3 Prototyping Methodology
### Prototype Development
#### 3.2.5 Portable Case  
An external shell was made by stacking two rectangular trays and fixing them with screws to protect the electronic circuits. The Arduino MCU, the battery, the data logger, the Wi-Fi module, and the wires are enclosed within this plastic-box. The GPS, RGB (red, green, blue), illuminance, wind speed, and globe temperature sensors are elevated from the box by hollow metallic rods (approx. 50 cm from the box). There is a gap (approx. 8 cm) between the rods to avoid shadows of one sensor on the other. The air temperature plus humidity probe is fastened at the top edge of the casing for data procurement at human shoulder level. The particulate matter sensor and the sound detector are engraved on the top-right and top-left side of the box, respectively. Metallic plates are used to fix the rods on the exterior body. This rectangular package can be placed inside a bag to be carried conveniently by the user. The globe temperature sensor (DS18B20) is enclosed in a blackened table tennis ball. The positioning of the components and the dimensions of the prototype is further explained in Figure 4.
## 3 Results and Discussion
### Effect of Axial Loading on Elastic Supports  
The natural frequencies of the elastically restrained tapered beams under axial loading were first investigated, and some of the results computed for **Example 1** were compared with those analyzed in a previous study [4]. The results of the comparison between the proposed method and those of previous studies are presented in Table 8. The beam model had elastic supports (\(K_{W}\) = 9.745 \(\times\) 10\({}^{9}\) N/m and \(K_{\Phi}\) = 1.319 \(\times\) 10\({}^{9}\) Nm/rad) at \(\overline{x}\) = 0, and a free-end condition at \(\overline{x}\) = 1. The axial loading at the free end was assumed. The comparison results agreed well with each other, as illustrated in Table 8; however, the first natural frequency under compressive loading could not be found at a value similar to that of the buckling load (\(\overline{F}_{cr}=\pi^{2}/4\)) [50]. It is a well-known fact that a compressive load decreases the natural frequency and a tensile load increases the natural frequency [51]. To investigate the effect of axial loading on the natural frequencies of elastically restrained tapered beams, the values of the translational and rotational springs at \(\overline{x}\) = 0 are considered as \(K_{w}\) = 22,026.5 N/m and \(K_{\Phi}\) = 22,026.5 Nm/rad, and the computed results are tabulated in Table 9. The compressive loading value at which the first natural frequency vanished differed because of the effect of the taper ratio. As illustrated in Figure 14, these values exhibited significant differences when comparing uniform beams and tapered beams with \(c\) = 0.5 and were also different for single- and double-tapered beams with \(c\) = 0.5. Therefore, the effect of elastic supports on the first natural frequency under compressive loading was analyzed. Figure 13: Comparison of \(n\) = 1 and \(n\) = 2 for effects of the concentrated mass on the first three natural frequencies when \(c\) = 0.5: **(a)** Cases A3 and A4, **(b)** Cases A7 and A8, and **(c)** Cases A11 and A12. The compressive load at which the first natural frequency vanished was investigated as a function of the elastic spring value. It was assumed that the first natural frequency vanished when it was less than \(\omega=1\) rad/s; the results are presented in Figure 15. Because  
Figure 14: Effects of the axial loading on the first three natural frequencies of elastically restrained tapered beam.
## 4 Materials and Methods
### Reflection Mechanism
#### 4.4.3 Integrated Control and Adaptation  
The full reflection signal is obtained by aggregating numerical and semantic feedback:  
\[R(t)=R_{\text{quant}}(t)+R_{\text{LLM}}(t)\]  
This composite signal modulates several aspects of the optimization process:  
* Adjusting variation operator probabilities (e.g., promoting M2 when diversity collapses);
* Retiring stale few-shot prompts and injecting newly inferred ones;
* Replacing redundant individuals or reviving historically diverse heuristics;
* Altering prompt selection distributions to promote semantic novelty.  
By combining runtime-derived measurements with LLM-guided interpretation, the reflection module enacts informed and adaptive strategy updates, enabling the system to respond fluidly to stagnation, convergence, or missed opportunities in the search space.  
To illustrate how reflective evolution operates in practice, the following example is provided based on the TSP optimization task. When the system detects that recent high-performing heuristics rely on entropy-based node selection and that population diversity is declining, it responds by: (1) increasing the sampling probability of exploratory mutation (M2), (2) pruning low-yield prompt templates, and (3) augmenting new prompts with example heuristics that penalize frequently visited nodes or encourage recency-based edge selection. These modified prompts are then used by the LLM to generate the next population, effectively injecting learned structural patterns into the search loop.
## 7 Rationale for ka-band selection
### Technical aspects
#### Rain issues
More recently, methods of determination of oceanic rain cell characteristics from the analysis of altimeter waveforms have been proposed and tested. These studies have mainly investigated Ku and C band altimeter measurements for which the signal attenuation by rain is one or two orders of magnitude smaller than that at Ka band. Because the method developed in the past to compute the altimeter waveforms in presence of rain are based on some approximations that are no longer valid for the Ka band, [PERSON] [33] developed a new approach that has been shown to perform better for strong attenuation. Rain distortion/detection algorithms as well as flagging algorithms in the presence of rain are presently under development based on [PERSON] [33] preliminary studies. Note that, symmetrically, the sensitivity of Ka band measurements to rain may also lead to a way of estimating very light rainfall over the oceans, for which we dramatically lack of information and which could lead to a great improvement of our knowledge of the oceanic rain climatology [33].
# Development and Application of MAGIC-f Gel in Cancer Research and Medical Imaging
## 4 Irradiation of MAGIC-f Gel  
The dosimetry analysis using MAGIC-f gel is based on the chemical reaction induced by irradiation. The study of three-dimensional dose distribution in MAGIC-f gel is performed by using different radiation sources. As polymer gel dosimetry has been studied and used in various clinical applications since the 1950s; most studies use radiation of gamma rays and high-energy X-ray photons from different clinical linear accelerator sources [3]. Another form of irradiation is protons and heavy ions, but due to the saturation effect, the dosimetric reaction is lower than that from using gamma and X-rays [37; 38; 39; 40; 41; 42; 43]. Most of the studies on MAGIC-f gel also used gamma rays and high-energy X-ray photons from different clinical linear accelerator sources; however, only one study by [PERSON] et al. used an electron beam [44].  
Figure 4 summarizes the basic chemical reaction on the MAGIC-f gel dosimeter after irradiation. In this reaction, several different free radicals are produced as a result of radiolysis of water molecule. The free radicals then react with the vinyl group (double bond between carbon atoms C = C) of the monomers, i.e., methacrylic acid. Moreover, there will be successive propagation and crosslinking reaction until all the radicals are consumed by the chain transfer reaction to form the polymer. This reaction is directly proportionate to the amount of the irradiation on the MAGIC-f gel dosimeter.  
Figure 4: Chemical reaction after irradiation on MAGIC-f gel.  
Figure 3: Atomic (**left side**) and chemical (**right side**) composition for MAGIC-f gel.
## 5 Conclusions  
We analyzed statistical relationships between backscatter coefficients and rice phenology using multi-temporal quad-polarized RADARSAT-2 SAR datasets and rice biophysical data measured in the Meishan study area, Sichuan Province, China. Although the degree of the correlation is affected by uncertain factors such as diverse patterns of rice cultivation, there was a strong correlation between the RADARSAT-2 backscattering coefficients and rice phenology.  
VH, VV, and HH backscattering coefficients were used to distinguish transplanting, vegetative, reproductive, and maturity phases. In particular, the VV/VH threshold was used to separate plants in the transplanting phase from plants in the vegetative, reproductive, and maturity phases, the HH/VH  
Figure 12: Gradual temporal change of HH/VH data on subdivisions of vegetative and reproductive phase.  
threshold to delineate plants in the vegetative phase from plants in the reproductive and maturity phases, and the HH/VV threshold to distinguish plants in the reproductive phase from plants in the maturity phase. Then, a decision tree classifier was developed. The accuracy level was 86.2% in the delineation of rice phenology. The analysis procedure was relatively simple, ensuring the reliability of the method. Therefore, there should be a great potential to use RADARSAT-2 SAR data to monitor rice phenology during its growth period and in large spatial extent. Finally, the concise decision logic should make this study easy understood and implemented in other studies related to local rice production.  
This work was supported by the National Natural Science Foundation of China (41471294), and Fundamental Research Funds for the Central Universities (ZYGX2015)112).  
[PERSON] and [PERSON] conceived and designed the study, and developed the proposed methods; [PERSON] collated and analyzed the data; [PERSON], [PERSON], and [PERSON] wrote the paper; [PERSON] and [PERSON] participated in the fieldwork. All authors have read and approved the final version of the manuscript.  
The authors declare no conflict of interest.
## 4 STGPT2 UGAN Design
### System Overview  
To solve the above problem, we develop STGPT2 UGAN, as depicted in Figure 1. STGPT2 UGAN is composed of a generator and a discriminator, which are denoted as \(Gen\) and \(Dis\), respectively. The core idea of STGPT2 UGAN is employing a GAN to develop an adversarial training strategy, which includes supervised training and unsupervised training. In particular, \(Gen\) strives to produce the predicted wind speed series in the future \(Q\) timesteps through minimizing the MSE loss. Furthermore, we fed the true and predicted future wind speed series into \(Dis\) for unsupervised training. \(Dis\) minimizes the adversarial loss, which intends to discriminate whether the predicted wind speed series is the true one, i.e., originating from the training sample, or created one, i.e., originating from \(Gen\). \(Gen\) and \(Dis\) are iteratively trained using an adversarial strategy. The model completes the training when the produced wind speed series closely resembles the true ones, making it difficult for \(Dis\) to distinguish between them.  
In \(Gen\), we first adopt a Fully Connected (FC) layer to reshape the wind speed representations in the historical \(P\) timesteps. The transformed wind speed representations are then fed into STGPT-2, containing an STB and TGPT-2. The STB is employed to extract spatio-temporal correlations. We then use TGPT-2 to produce wind speed series for the future \(Q\) timesteps. Specifically, an STB is composed of \(L\) stacked Spatio-Temporal Attention (STA) modules. In each STA module, since spatial attention and temporal attention can dynamically control the extent of the model's attention in both spatial and temporal dimensions, effectively handling the sophisticated relationships in these two dimensions, we leverage the two kinds of attention mechanisms to mine spatio-temporal correlations. After obtaining the spatio-temporal characteristics from the STB, TGPT-2, an improved version of GPT-2 generates the wind speed series in the future \(Q\) timesteps. The underlying explanation is that the pre-trained GPT-2 with billions of corpora offers abundant inherent knowledge to estimate the predicted series.  
\(Dis\) is established through a GCN layer, an LSTM layer, and a classification layer. In detail, this is because GCNs utilize filters in the Fourier domain to explore the spatial associations between graph nodes and their first-order neighbors. The \(Dis\)'s output and the true future wind speed series are inserted into the GCN layer to mine spatial dependencies. Furthermore, given that LSTM with a recursive structure, long-term memory unit, and gating mechanism memorizes long- and short-term time information, we leverage it to capture temporal relationships. Finally, the LSTM layer's output is inserted into a classificationlayer to determine whether the input of \(Dis\) is the \(Gen\)'s output or the true future wind speed series.