esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Heuristic pathfinding algorithms, such as GA, ACO, and PSO, can break through the search direction limitations in close-range scenarios, finding shorter paths than classic pathfinding algorithms. However, when the distance between the start and end points is too large, the efficiency of heuristic pathfinding algorithms significantly decreases, and their solution quality may even fall short of that of classic pathfinding algorithms. Although [16] has shown that the ratio of SGPs to TSPs is limited within a certain range, as the map expands and the distance between the start and end points increases, there can still be a significant gap between SGPs and TSPs. Previous research has also attempted to overcome the search angle limitations to shorten path lengths. For example, [PERSON]. [18] proposed a variant of A* algorithm, aimed at finding paths shorter than the SGP. [PERSON]. [19] introduced an Any-angle pathfinding (Anya) algorithm that focuses on the shortest path between two points in the grid, but it is limited to solving problems involving grid vertices and cannot search for points within the grid. [PERSON]. [20] applied the Block A* algorithm to game maps to find paths at any angle. This paper proposes an SGP vertex extraction and filtering algorithm (SGPVEFA), which is an accurate algorithm specially used to solve the true shortest path in two-dimensional obstacle environment. The innovation points of this paper are as follows. 1. A method is proposed to optimize SGPs into TSPs. 2. An obstacle grid vertex extraction algorithm based on an equivalent path is proposed. 3. An obstacle vertex set screening method based on an improved dynamic optimization algorithm is proposed. Subsequent experiments have confirmed that SGPVEFA can efficiently optimize SGPs to TSPs, regardless of the map size or the distance between the starting and ending points. Compared to other algorithms, the paths found by SGPVEFA are shorter and contain fewer nodes. In long-distance scenarios, the optimization effect of SGPVEFA is particularly significant. The rest of this paper is organized as follows: Section 2 details the definition and association of SGPs and TSPs in the obstacle grid map; Section 3 introduces the obstacle vertex extraction algorithm for SGPs; Section 4 introduces the TSP points screening method for the obstacle vertex set; Section 5 conducts simulation experiments on the proposed algorithm; and Section 6 is a summary.
# Monitoring a Pre-Normative Multi-Family Housing Case-Study in a Mediterranean Climate
## 4 Results
### Relative Humidity  
Regarding relative humidity, a similar tendency is shown during the winter and mid-season months, when indoor relative humidity values can stray outside the comfort limit values (40%-60%), with slightly high daily fluctuations of 10%, but show a growing tendency in line with that of the outdoor values. In contrast, outdoor measured relative humidity shows values close to 100% and much sharper daily oscillations of 30%-40 % (Figure 11).  
Figure 11: Monthly record of indoor relative humidity in the monitored rooms of Dwelling A.  
The evolution of indoor relative humidity during mid-season months (spring: March, April and May; autumn: September, October and November) indicates that the windows of the dwelling have been opened. In the beginning of April, when more rainfall periods with low temperatures and high relative humidity values close to 100% take place, recorded values indoors are equally elevated, reaching from 50%-70% (Figure 11a). This illustrates that opening the windows during mid-season months is fairly important, with the aim of fighting against large fluctuations in hygothermal values in the B4 climate zone.  
In contrast, the summer results for relative humidity show evidence of two facts (Figure 11b). On the one hand, there is the use of night ventilation by opening the windows during night hours, thereby leading relative humidity to tend towards outdoor values. On the other hand and in fact due to high outdoor temperatures, the use of cooling devices during daylight hours is also demonstrated in the decrease of indoor relative humidity values, since windows remain closed and cooling devices reduce the level of air humidity. Hence, the importance of night energy dissipation and that of ventilation is demonstrated yet again.
###### Abstract  
The immense representation power of deep learning frameworks has kept them in the spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks (GCNs) can be used to compensate for the lack of spatial information in Convolutional Neural Networks (CNNs). However, most GCNs construct graph data structures based on pixel points, which requires the construction of neighborhood matrices on all data. Meanwhile, the setting of GCNs to construct similarity relations based on spatial structure is not fully applicable to HSIs. To make the network more compatible with HSIs, we propose a staged feature fusion model called SFFNet, a neural network framework connecting CNN and GCN models. The CNN performs the first stage of feature extraction, assisted by adding neighboring features and overcoming the defects of local convolution; then, the GCN performs the second stage for classification, and the graph data structure is constructed based on spectral similarity, optimizing the original connectivity relationships. In addition, the framework enables the batch training of the GCN by using the extracted spectral features as nodes, which greatly reduces the hardware requirements. The experimental results on three publicly available benchmark hyperspectral datasets show that our proposed framework outperforms other relevant deep learning models, with an overall classification accuracy of over 97%.  
hyperspectral image classification; convolutional neural network; graph convolutional neural network +
Footnote †: journal: Applied sciences
## 1 Introduction
A plethora of studies with varied research methodologies has led to a broad range of results, depending on the datasets, recording protocol, emotion elicitation technique, detected features, temporal window, classifiers, involved modality, number of participants, and emotion models. While BCI research encompasses a wide spectrum of applied domains, our research interest is specifically focused on electroencephalography (EEG)-based emotion detection, although objective measures of emotion can also be acquired from physiological cues derived from the physiology theories of emotion. Instruments that measure blood pressure responses, skin responses, pupillary responses, brain waves, heart responses, facial recognition, speech, and posture are often used as objective measures in affective computing [7]. This review will seek to understand EEG-based emotion recognition trends. The review will examine published literature with the aim of providing insights for future emotion recognition systems to practitioners and researchers. According to the Gartner's 2016 Hype Cycle report on trending research topics, both Brain-Computer Interface and Affective Computing are at the Innovation Trigger stage. These researchers predict that mainstream adoption will occur in 5-10 years for Affective Computing, and in more than 10 years for BCI research. This phenomenon is captured in Figure 1.  
During our literature review, we observed that articles on emotion recognition cut across various disciplines, including clinical neurology, rehabilitation, neurobiology, engineering, psychology, computer science, medical physics, and biomedical engineering. Hence, conducting a comparative analysis of articles is difficult since different journals and different scientific domains have different research focuses and methodologies. Accordingly, the main objective of this review is to classify and summarize research that is relevant to emotion recognition systems and to provide conceptual frameworks for integrating and classifying emotion recognition articles. This system of classification will be useful for literature reviews on emotion recognition research. The following sections illustrate our proposed classification framework for emotion recognition literature reviews based on research articles. In Section 2, we outline our research methodology. Section 3 describes our proposed classification framework for emotion-recognition-based literature reviews. Section 4 presents our discussion. In Section 5, we provide insights for future research and discuss the challenges and trends in EEG-based emotion recognition. Finally, in Section 6, we present the study's conclusions.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 7 Conclusions  
This paper investigated the fusion localization of land-based stations and satellites. And, the corresponding ICWLS algorithm is proposed for DOA-based localization and TDOA-based localization, respectively. Firstly, the pseudo-linear equations are established by using auxiliary variables, and then the localization problem is modeled as an optimization problem with two quadratic equation constraints. To ensure that the algorithm can converge to the global optimal solution, the results of the previous iteration are substituted  
Figure 8: Distribution of RMSE and CCRLB with systematic error.  
into the equation constraints so that the nonconvex quadratic constraints can be converted into linear constraints. Simulations show that the localization accuracy of the proposed method can reach the corresponding CCRLB. In addition, this paper proposed to fuse the DOA-based localization and TDOA-based localization results using Kalman filtering. The simulation results show that the accuracy of fusion result is higher than the DOA-based localization and TDOA-based localization.  
Although the proposed method can improve the positioning accuracy, the method requires the ability to receive signals from different frequencies. In addition, the method does not take into account the errors caused by the ionospheric multi-path effect and other effects. These will be studied in depth in the next step. In addition, we will further consider the practical application of the proposed method.  
Software, P.Z.; writing--original draft, P.Z.; supervision, P.Z.; writing--review and editing, L.J.; data curation, T.T.; methodology, T.T.; resources, Z.W.; supervision, Z.W.; validation, D.W.; formal analysis, D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62171469.  
Not applicable.  
Dataset available on request from the authors.  
We gratefully acknowledge the valuable comments and suggestions from the reviewers and the handling editor.  
The authors declare no conflicts of interest.
## 3 Materials and Methods
### Analyses of Volatile Compounds  
Volatile compounds from citrus fiber/blackberry gels were extracted using solid-phase microextraction (SPME). For the extraction, 5 g of sample and 1 g of NaCl were weighed in a 10 mL glass vial. SPME fiber coated with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) sorbent (50/30 \(\upmu\)m, StableFlexTM, Supelco, Bellefonte, PA, USA) was used for the extraction of volatiles. The sample vials were conditioned in a temperature-controlled heating module at 40 \({}^{\circ}\)C for 45 min and agitated at 350 rpm. After extraction, fiber was removed from samples and volatiles were thermally desorbed in the injector port of the GC. Analysis was conducted on a GC 7890B gas chromatograph (Agilent Technologies, Santa Clara, CA, USA) which was equipped with a 5977A mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Volatiles were desorbed into a GC injector port at 250 \({}^{\circ}\)C for 7 min, in splitless mode. The gas chromatograph was fitted with a HP5 capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) (Agilent Technologies, Santa Clara, CA, USA). Helium was used as the carrier gas at a flow rate of 1 mL/min at 40 \({}^{\circ}\)C. Owen temperature was programmed as follows: the initial temperature of 40 \({}^{\circ}\)C was held for 2 min, then 6 \({}^{\circ}\)C/min up to 230 \({}^{\circ}\)C. Volatiles were identified using a mass selective detector. The detector operated in the m/z range between 45 and 450, and ion source and quadrupole temperature were maintained at 230 and 150 \({}^{\circ}\)C, respectively. Compounds were confirmed by matching their mass spectra with the NIST (National Institute of Standards and Technology mass spectral database, Gaithersburg, MD, USA) and through retention time (RT) and retention index (RI) (Table 1). Two repetitions were conducted for each sample. Quantification was conducted by myrtenol as an internal standard and results were expressed as \(\upmu\)g/kg.
## 3 Results and Discussion
### Chromatographic Separation  
The main factors that affect chromatographic behavior were investigated aiming for a sufficient chromatographic separation, including UPLC columns, mobile phase, additives and gradient program. [PERSON] CORTECSTM C18 UPLC column provided the optimal resolution and stability for all compounds  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Analyte** & **Precursor** & \
\begin{tabular}{c} **Quantification** \\ **ion** \\ \end{tabular} & **CV/CE1** &
\begin{tabular}{c} **Confirmation** \\ **ion** \\ \end{tabular} & **CV/CE1** & **RT2** \\ \hline ZEN & 317.1 & 175.0 & 20/24 & 130.9 & 20/32 & 3.24 \\ \(\alpha\)-ZOL & 319.1 & 159.8 & 6/30 & 174.1 & 6/20 & 2.78 \\ \(\beta\)-ZOL & 319.1 & 159.8 & 36/28 & 174.1 & 36/26 & 2.11 \\ \(\alpha\)-ZAL & 321.1 & 277.0 & 6/20 & 303.1 & 6/22 & 2.60 \\ \(\beta\)-ZAL & 321.1 & 303.1 & 26/20 & 277.3 & 26/28 & 1.92 \\ ZAN & 319.1 & 275.0 & 48/22 & 205.1 & 48/22 & 3.10 \\ \({}^{13}\)C-ZEN & 335.2 & 139.9 & 20/32 & 185.0 & 20/24 & 3.24 \\ \hline \hline \end{tabular}  
* CV, cone voltage (V); CE, collision energy (eV). 2 RT, retention time (min).  
\end{table}
Table 1: MS/MS transitions of ZEN and its metabolites.  
and was chosen for further studies. Several combinations of mobile phase were separately evaluated, that is, the additives (formic acid, acetic acid, ammonium formate or ammonium acetate) in water, the organic modifier (methanol or acetonitrile) and the gradient elution program. Common additives like formic acid, ammonium formate, acetic acid and ammonium acetate gave slight influence on intensity and no contribution for separation. However, the organic modifier considerably helped the separation. ZEN, ZAN and \(\alpha\)-ZOL were hardly separated completely while using methanol or acetonitrile alone as the organic modifier, which was also discussed previously [33]. Eventually, a mixture of acetonitrile and methanol (20/80, \(v/v\)) was employed and provided a complete baseline separation of all compounds within an 8-min run time. A chromatogram of standards mixture of all analytes was presented in Figure 1.
# Empowering a Broadband Communications Course with a Unified Module on 5G and Fixed 5G Networks +
## 6 Conclusions  
In this paper, we have designed and analyzed a course module that presents 5G and F5G in a unified manner, following the common timeline of evolution for wireless and fixed networks. Upon the module's completion, 32 students of those that have attended the course in the previous academic year, and 16 of those that have attended the course in this academic year filled in questionnaires. Based on their responses, more than half of the students improved their knowledge regarding 5G/F5G topics. Moreover, the number of students who designated the module of high importance significantly increased after the module (from 45% before the module to 92% after the module, in the previous academic year). Further, the number of students who voted as likely to engage more with the topic after the module's completion was up to about 70%. In addition, based on the 20-question quiz that was offered to the students of the current academic year, the students answered correctly about 14 out of 20 questions, finding difficulties mainly in four questions primarily related to the differences between the 5G/F5G and 6G/F6G, in terms of number of service categories, offered bit rate, and FTTx type.  
Based on this quiz, we can deduce that the students were more familiar with service categories and their requirements than with network capabilities, which requires devising a more focused plan toward this direction for the next academic year. In accordance, our plans for the next academic year include (a) the offering of short notes for a deeper understanding of the related topics, (b) the generation of more multiple-choice questions, which will be analyzed to reveal more students' difficulties and misunderstandings, (c) the assignment of small tasks/projects which will enrich the students' presentation skills, and allow them to engage with a wider variety of topics presented by their classmates, and (d) the encouragement of students to actively participate in webinars and/or workshops to become more familiar with the topic.  
Conceptualization, D.U., G.P., I.M., M.L.; methodology, D.U., G.P., I.M., M.L.; validation, D.U., G.P., I.M., M.L.; formal analysis, D.U., G.P., I.M., M.L.; investigation, D.U., G.P., I.M., M.L.; data curation, D.U., G.P., I.M., M.L.; writing--original draft preparation, D.U., G.P., I.M., M.L.; writing--review and editing, D.U., G.P., I.M., M.L.; visualization, D.U., G.P., I.M., M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The questionnaires were anonymized from one student who was selected to gather them.  
The data discussed in this article have been referenced at the corresponding points.  
This paper is an extended version of our paper published at the 17 th International Conference on Telecommunications (ConTEL), held in Graz, Austria, from 11 to 13 July 2023.  
The authors declare no conflicts of interest.
## 3 Numerical Examples
When \(M=1\), the MGSM array beam will reduce into a GSM array beam, which will have the smallest effective beam size. While, in the influence of \(N=D\), we can find that as \(z\) increases, the effects of \(N=D\) on the effective beam size of the Gaussian beam will disappear. These results mean that the MGSM array beams with different \(N=D\) will have almost the same effective beam size. Thus, the larger the \(N=D\), the higher the power the Gaussian beam will have. This phenomenon is beneficial for free space optical communications using MGSM array beams. Figure 5: The cross sections of \(N\times D\) MGSM array beam with \(N=D=3\), propagating through non-Kolmogorov turbulent atmosphere for \(\sigma\). (**a**) \(z=600\) m and (**b**) \(z=5000\) m.  
Figure 6: The \(W_{x}\) of \(N\times D\) MGSM array beams with \(N=D=3\) propagating through non- Kolmogorov turbulent atmosphere. (**a**) different \(C_{n}^{2}\) and (**b**) different \(\sigma\). The influences of non-Kolmogorov turbulent atmosphere parameters \(\alpha\), \(L_{0}\), and \(l_{0}\) on \(W_{x}\) are shown in Figure 8. When \(z\) increases to \(z=1000\) m, the MGSM array beams will become the Gaussian beam, the effective beam size, \(W_{x}\), of the Gaussian-like beam translated by the MGSM array beams will increase with \(\alpha\), and a decrease of \(l_{0}\) or an increase of \(L_{0}\). The phenomenon can be explained as follows: The strength of non-Kolmogorov turbulent atmosphere depends on \(\alpha\), \(L_{0}\), and \(l_{0}\).
## 5 Results and Discussion
### Overall Analysis of McCann Retinex Algorithm  
Figure 9 illustrates the overall performance of the McCann Retinex algorithm at iteration 20 on the HRF and Oasis brain MRI databases. Regarding the PSNR and contrast enhancement, the McCann Retinex algorithm obtained good contrast and PSNR enhancement on both databases. Figure 9a shows that the average PSNR on the axial plane images was 23.46 dB with an average contrast of 69.79, the average PSNR on the coronal plane images was 23.73 dB with an average contrast of 63.39, and the average PSNR on the sagittal plane images was 24.55 dB with an average contrast of 41.02. Figure 9b shows that the average PSNR on low quality images from the HRF database was 39.58 dB, with an average contrast of 26.46, and the average PSNR on high quality images from the HRF database was of 41.69 dB, with an average contrast of 44.61. The McCann Retinex algorithm performed much better on the Oasis brain MRI database than on the HRF images database. The contrast enhancement played an important role in improving the performance of the segmentation process, and the validation is described Section 5.4.
## 4 CLAP-PRE Scheme
### System Flow  
First, every user in the cryptosystem needs to send his own identity to KGC and request KGC to generate a partial private key. The KGC runs the algorithm \(PPKE\) and sends the partial private key to the user in a secure way. After receiving the partial private key, the user runs the algorithm \(UKG\) to generate his own private key and public key. Then the user generates the proof of his public key using the algorithm \(Proof\). Everyone can validate the proof using the algorithm \(Verify\) without interacting with others.  
Second, when a data owner wants to share his data with others, the data owner runs the algorithm \(CreatePath\) to create a delegation path in his preferred order. The data owner generates the ciphertext and re-encryption keys using the algorithm \(Enc\) and the algorithm \(RKGen\). After that, the data owner sends the delegation path, the ciphertext and the re-encryption keys to the proxy.  
Third, the proxy recovers the identities from the delegation path and the re-encryption keys from set \(RK_{ID_{i}}\), then runs the algorithm \(ReEnc\) to re-encrypt the ciphertext. The proxy needs to query the delegatee if he wants to deal with the encrypted data. If so, the proxy sends the ciphertext to the delegatee; otherwise, the proxy finds the next delegate along with the corresponding re-encryption key and repeats until a delegatee wants to decrypt.
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 3 Analytical Method
### Seismic Ground Response Analysis  
Seismic ground response analysis is used to predict the seismic behavior on the ground surface influenced by the properties of soil deposits [3]. The one-dimensional ground response analysis, which is a simulation of shear waves propagating vertically through shallow soil layers on the assumption that all boundaries are horizontal, is an approach to capture site effects on ground shaking [3,4]. A series of one-dimensional ground response analyses (linear and equivalent linear methods) are introduced.  
In the linear method, the material properties are assumed to remain constant during shaking [20]. The material properties are assumed to be unchanged in dynamic processes during the earthquake. The equivalent linear method, initially proposed by [PERSON] and [PERSON] (1970), accounts for material yielding and damping by iteratively matching the shear modulus and damping ratio to a characteristic strain level [20]. Both are in the frequency domain with linear visco-elastic material behavior. A ground motion time history of the bedrock (input) is converted to the frequency domain by the fast Fourier transform (FFT) algorithm and then multiplied by the transfer function to generate the ground surface motion (output) Fourier series. Thereafter, the ground surface (output) can be transformed back into the time domain by the inverse fast Fourier transform (IFFT), as shown in Equation (3):  
\[a_{output}(t)=IFFT\big{[}f(w)*FFT\big{[}a_{input}(t)\big{]}\big{]} \tag{3}\]  
\[f(w)=\frac{u_{output}(\omega)}{u_{input}(\omega)} \tag{4}\]  
where \(a_{output}(t)\) is ground surface acceleration, \(a_{input}(t)\) is bedrock acceleration, and \(f(w)\) is a bedrock-to-surface transfer function, as shown in Equation (4). \(u_{output}\) is ground surface displacement, and \(u_{input}\) is bedrock displacement.