esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As a result, packets will have different priorities for the applications depending on the carried information, and such priorities may be exploited by cross-layer optimization solutions [10]. In such contexts, we propose a DWT-based selective retransmission mechanism where reliable transmission is only assured for the most relevant data to the application, while low relevant packets are not retransmitted if they are discarded due to corruption during transmission. When the packet error rate is low, most data will reach the sink, resulting in high-quality images. On the other hand, increasing in the packet error rate will decrease the image quality since lost low-relevant packets will not be retransmitted. We believe that, for many WISN applications, reduction in the image quality is acceptable since energy is preserved. The proposed approach assumes that the images gathered by the source nodes are encoded using a DWT-based technique, where the information carried by each packet through a multi-hop network has its own relevance for the application. Thus, the source nodes establish a priority level to every packet transmitted to the sink. Based on such priorities, the network can provide three different transmission services: reliable, semi-reliable and unreliable. The adopted transmission service is reflected on the way intermediate nodes process corrupted packets, which can be retransmitted or silently discarded. It is assumed that source nodes transmit only still images or scalar data, but the operation of the transmission services could be easily adapted if video streaming is required, depending on the application requirements and the adopted video coding technique. In order to assess the expected benefits, we designed a comprehensive energy consumption model to be applied in extensive mathematic simulations, showing the average results for different network configurations. Additionally, the expected image quality when some packets are lost and not retransmitted is analyzed. The rest of this paper is organized as follows. Section 2 brings some related works that contributed to our investigation. The problem formulation is presented in Section 3. Section 4 describes the proposed selective retransmission solution, followed by mathematical verifications in Section 5. Finally, the conclusions and references are presented.
# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 4 Results and Discussion
### Defining Spatiotemporally Enhanced MD Data in QB4 MobOLAP
Representation of a complete mobility fact cube at the schema level in RDF is given by a set of triples \(t_{\text{fs}}\in T\) and encoded as (fs a qb:DataStructureDefinition; qb: component [qb: measure ms, sdmx-measure:obsValue; qb4 mob:aggregate-functionMAF]). Qb4 MobOLAP extends the built-in functions of QB4 OLAP with mobility aggregation functions, which are added with the class qb4 mob: MobilityTemporalAggrFunc or qb4 mob:MobilitySpatialAggrFunc. An example of a mobility fact instance \(f_{\text{m}}\) with its relation to measuring values and dimension levels is a truck route delivery of some fruits (ffb:Route), which are created routes with coordinate points of the location that change by time. The cardinality of the dimension level truck and fact member is one-to-many, where a truck could have some delivery routes. Specification of the mobility aggregate function for route (coordinate points by time) can be specified as the \"Time Centroid/TCentroid\" route of locations.
# A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization
## 2. Accident Description and Prediction Method
### Prediction Methods
#### 2.2.1 Kernel ELM
The SVM algorithm maps features to high-dimensional spaces through kernel functions to improve its learning performance, and the kELM model takes the above methods into consideration to improve its own performance. \[\hat{y}(x)=\left[ \begin{array}{c}K(x,x_{1})\\ \vdots\\ K(x,x_{n})\end{array} \right]\left(\frac{I}{C}+K(X,X)\right)^{-1}F \tag{9}\]  
where \(K(\cdot)\) is a kernel function. From Equation (9), it can be seen that the weight and offset of the input layer to the hidden layer of kELM are obtained through the mapping operation of the kernel function, eliminating the influence of random weights on ELM performance. The number of neurons and bias in the hidden layer need not be set and optimized. The normal kernel functions are Gaussian, polynomial, linear, and sigmoid. They can be written as follows:  
\[K(x,y)=\exp\Big{(}-\|x-y\|^{2}/k_{1}\Big{)} \tag{10}\]  
\[K(x,y)=\left(x^{T}\cdot y+k_{1}\right)^{k_{2}} \tag{11}\]  
\[K(x,y)=x^{T}\cdot y \tag{12}\]  
\[K(x,y)=\tanh\Big{(}k_{1}x^{T}\cdot y+k_{2}\Big{)} \tag{13}\]  
where \(k\) is the parameter of the kernel function, which is set by the user. Whether its value is reasonable or not closely affects the performance of kELM. In order to realize the optimal prediction of cellular traffic data by kELM, all four kernel functions were used in this study for feature mapping of kELM. Then, the optimal mapping method was selected from them. In addition, the optimization algorithm was used to search for optimal parameter values of the kernel function.
## 3 Materials and Methods
### Method IFPC (Isolation Forest--Predicates' Conjunction)  
An IFPC method has been developed for identifying complex anomalies in multidimensional time series (Figure 6), and can be outlined in the following steps:  
1. Data preparation: - Data download; - Data concatenation; - Preprocessing (normalization) of data.
2. For all sensor channels: - Identifying anomalous sample instances by the basic method; - The output of time series with marked anomalies; - Detection of complex anomalies by the logical operation of conjunction.
3. Extracting and displaying dates where complex anomalies were detected.  
To verify the IFPC method, a source code was written in the integrated development environment Visual Studio Code using Python 3.12.4 with the following libraries:  
* pandas (data processing);
* matplotlib (data visualization);
* numpy (linear algebra problems);
* scipy.stats (statistics);
* statsmodels (test for stationarity of the series);
* sklearn (machine learning methods);
* pmdarima (ARIMA model);
* keras (deep learning methods).  
Figure 6: IFPC-method flowchart.  
The program has several data sources: downloaded.csv and.json files and user-supplied parameters.  
The 12.csv files contain unlabeled data for 12 months; in other words, the dataset does not contain any markings that classify a particular indicator value as normal or abnormal. The JSON file contains unlabeled data for June-July 2022. The data structure in different file types is identical to shown in Figure 7.  
For one time stamp, readings from 7 sensor channels are presented.  
In the case of system disruption, the indicators' values have the NAN (Not-A-Number) value. The files provided do not contain NAN values, so this article does not consider the case of system malfunctions (hardware failure).  
The computational experiment aims to verify the hypothesis that the wastewater excess contamination MPC in energy facilities has occurred if anomalies are detected by all sensor channels at the same time.  
Input data:  
1. Unlabeled dataset for 2020-2021 consisting of 12.csv files;
2. Unlabeled dataset for June-July 2022 in.json format;
3. Three dates actually laboratory recorded facts of exceeding contamination MPC--7, 8, and 9 June in 2022.  
Output data: time series plots with anomalies marked and a list of dates where the contamination MPC may have been exceeded.
# The 2014 Effusive Eruption at Stromboli: New Insights from In Situ and Remote-Sensing Measurements
## 2 Materials and Methods
### Seismic Network (Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano - INGV-OV)  
During the 2014 eruption the seismic broadband network of Stromboli [29] was operating with 10 broadband seismic stations. In this work, we used data from 3 seismic stations (Figure 1b). STR1 is located on the flank of the volcano at an elevation of about 560 m and has the longest and most complete record of seismological parameters. STRA is located at about 840 m, it is the closest to the summit craters, therefore is particularly suitable to study the dynamics of explosive activity. Lastly, STR8, located at about 570 m, is the closest to the SdF, hence it is the best suited to study seismic signals related to landslides in this area.  
The seismological parameters considered here are: the amplitude of volcanic tremor, the amplitude of explosion quakes and the inclination of the seismic polarization in the Very-Long-Period (VLP) band (0.05-0.5 Hz). We also performed the analysis of seismic signals, using neural networks, which allows detecting signals related to landslides occurring along the SdF [30]. Landslides signals have shown to be a reliable precursor of effusive activity at Stromboli. Their occurrence rate increased markedly during the onset of the previous effusive eruption in 2007 [12]. To quantify the intensity of landslide  
Figure 2: (**a**) Gravel flow associated with the 7 July 2014 overflow. The landslide is composed by mixed breccia from the overflow front and the (mainly fine-grained) debris eroded along the Sciara del Fuoco (photo taken by the Universita degli Studi di Firenze personnel during field survey). (**b**) Landslides associated with the collapse of the NEC-hornito, occurred on 6 August 2014 (photo taken by UNIFI personnel during boat survey).  
activity, [PERSON], et al. [31] defined a landslide index following a fuzzy logic scheme. This parameter varies smoothly between 0 and 1, with 0 indicating a low probability of ongoing landslide activity and, conversely, 1 indicating a high probability of occurrence.
# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
## 2 System Model
### MIMO Transmitter  
The transmitter converts the binary bit stream into electromagnetic waves that can be in the wireless channel. The basic composition of a transmitter in the MIMO system is shown in Figure 1 below. First, the binary bit data is converted to a symbol stream by a modulator, often using modulation methods such as MPSK, MQAM, etc., where M indicates the number of binary bits. Then, channel coding is performed to improve the reliability of the communication system, followed by space-time coding, which is the key technology of MIMO systems. Space-time coding decomposes the symbol stream to be transmitted into multiple symbol streams, thus, improving the efficiency of the communication system and further ensuring the reliability of the system. According to different encoding methods, space-time codes can be divided into three categories: a space time block code (STBC), space time trellis code (STTC) and layered space time code (LSTC). In this paper, we chose the most representative Alamouti space-time coding scheme in the STBC coding scheme. Alamouti coding is the best scheme to obtain a spatial diversity gain through transmission diversity. The idea of the Alamouti space-time coding is shown in Figure 2 below. When the number of transmit antennas is two, the modulated and channel-coded symbols \([s_{1}\,s_{2}]\) are sent to the Alamouti encoder, and a matrix \(S\) with a dimension of \(2\times 2\) will be generated. Its structure is shown below. In the first time slot, \([s_{1},s_{2}]\) is sent through both antennas and in the next time slot, \([-s_{2}^{*},s_{1}^{*}]\) is sent:  
\[S=\left[\begin{array}{c}s_{1},-s_{2}^{*}\\ s_{2},s_{1}^{*}\end{array}\right] \tag{1}\]
# CommuSpotter: Scene Text Spotting with Multi-Task Communication
## 3 Text Spotter with Communication
### Architecture  
The whole framework of CommuSpotter is shown in Figure 2. It contains text detection and text recognition modules. Given an image, \(I\), a backbone of ResNet-50 is used to extract feature maps, \(P\), denoted as \(\{P_{2},P_{3},P_{4},P_{5},P_{6}\}\), with a Feature Pyramid Network (FPN) following [31]. The Region Proposal Network (RPN) generates some Region of Interest (RoI) proposals, \(R\), in five scales of \(\{32^{2},\,64^{2},\,128^{2},\,256^{2},\,512^{2}\}\) on the pyramid features. The redundant candidates are filtered out by Non-Maximum Suppression (NMS). The pyramid features, \(P\), and proposals, \(R\), are processed by the RoIAlign [32] layer to generate the RoI features, \(F_{RoI}\) and \(F^{\prime}_{RoI}\), which are then fed into the text detection module and text recognition modules. Moreover, the semantic expertise, \(S\) and \(S^{\prime}\), from the feature maps are extracted via softmax operations and attention-based refinements.  
In the text detection module, the RoI features, \(F_{RoI}\), interact with the expertise from the text recognition module through the specially designed Conversation Mechanism (CM) (in Section 3.2) to generate meaningful text detection through the classification layer. In the text recognition module, we adopt a segmentation-based method for text instance segmentation as the word expertise, as described in Section 2.2). Additionally, we introduce text semantic segmentation expertise \(S\) and \(S^{\prime}\) as character details for text recognition. The representation is communicated by our designed Conversation Mechanism (CM) (in Section 3.3). Finally, text sequences are recognized by the RNN mechanism [33]. Note that the contents in this Section 3.1 are existing pipelines of scene text spotting, Sections 3.2 and 3.3 below are our proposed approaches.
## 4 Discussion
### Temperature Played a Vital Role in Regulating the Global NEP IAV  
Temperature, precipitation, and solar radiation have been reported as the most important climate factors in regulating the IAV of the carbon cycle in the different ecosystems [11; 38]. Water availability and temperature are strong drivers of photosynthesis and ecosystem respiration of global terrestrial ecosystems [39; 40]. In this study, the climate attribution result indicated that temperature contributed more than precipitation and solar radiation, consistent with temporal NEE variability being mainly driven by temperature fluctuations [11].  
In this study, we used the partial correlation method to analyze the correlations between NEP and one of the three climate variables (TAVG, PRCP, and RAD) while avoiding the influence of the other two variables [28]. The partial correlation showed that temperature played a negative role in the global IAV of NEP, indicating that increased temperature has a negative influence on NEP increasing. This finding is also consistent with the sensitivities of NEP to temperature being \(-0.9\)\(\pm\)\(0.4\) Pg C \({}^{\circ}\)C\({}^{-1}\) and a greater stimulation of Re than photosynthesis by higher temperatures [10]. This is mainly because increasing temperatures can also induce heatwaves and drier conditions, which may decrease GPP and increase Re [41]. The observed positive correlation between mean tropical land temperature and CO\({}_{2}\) growth rate implies smaller land carbon uptake and enhanced atmospheric CO\({}_{2}\) growth during warmer years [37]. In the tropical region, the increasing temperature may result in GPP decreases, mainly due to the photosynthesis thermal optimum exceed ing, whereas Re increases with temperature. Thus, increasing temperatures in the tropics decrease NEP by reducing GPP and increasing Re.  
Our analysis shows that the contributions of the precipitation were smaller than temperature but played significant positive roles in the carbon sink in some areas of the globe. These findings are also consistent with previous studies, which have shown that the IAV of NPP or NEP exhibits a positive correlation with temporal variability in precipitation in monsoon areas such as China [42], India [43], and Australia [44]. This finding agrees with the understanding that carbon uptake could be mostly regulated by water availability, since the temperature is not a limiting factor [45]. This finding is also consistent with studies that emphasize the role of water-limited ecosystems on the global IAV of NEP [46].
# A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping
## 2 Background
### Singular Value Decomposition  
Singular value decomposition (SVD) is a powerful linear algebraic tool [24] for decomposing matrices into three key parts: two orthogonal matrices and a diagonal matrix. In the field of image processing, SVD is widely used in dimensionality reduction, denoising and compression scenarios. Its core idea is to identify and extract the dominant features in the matrix, which are measured by singular values, so that the matrix can be decomposed into multiple feature subspaces. Matrix \(A\in R^{m\times n}\) can represent an image, and the size of the matrix is \(m\times n\); then,  
\[U=[U_{1},U_{2},U_{3},\cdots,U_{m}] \tag{2}\]  
\[V=[V_{1},V_{2},V_{3},\cdots,V_{m}] \tag{3}\]  
which enable the following:  
\[U^{T}AV=\begin{bmatrix}\sigma_{1}&&&\\ &\sigma_{2}&&\\ &&\ddots&\\ &&&\sigma_{m}\end{bmatrix}=\Sigma \tag{4}\]  
where \(V\in R^{m\times n}\) and \(U\in R^{m\times n}\), both of which are orthogonal matrices, and \(\Sigma\in R^{m\times n}\) denotes a diagonal matrix whose values on the off-diagonal are all 0, and whose values on the diagonal are satisfied:  
\[\sigma_{1}\geq\sigma_{2}\cdots\geq\sigma_{r}\geq\sigma_{r+1}=\cdots=\sigma_{m} \ =\ 0 \tag{5}\]  
where \(r\) denotes the rank of \(A\) whose size is the number of non-zero real numbers on the diagonal, and \(\sigma_{i}\) (\(i=1,2,\cdots,m\)) is the singular value of the matrix.  
From the perspective of image processing, the singular values of the image matrix are closely related to the key features of the image itself, which makes the singular values have excellent stability. Even if the image is slightly disturbed or attacked, its singular value can remain relatively stable and will not change significantly. It is worth mentioning that the singular values of images are invariant to geometric transformations (such as rotation, scaling and translation), which makes them resistant to various image processing operations. Therefore, the introduction of singular value decomposition in digital-imagewatermarking technology can greatly enhance the robustness of the algorithm and make the watermark more difficult to be destroyed or tampered with. This method not only enhances the security of the watermark, but also ensures the stable extraction of the watermark in the image processing process.
## 3 Artificial Simulation Signal Denoising
Since the frequency-domain \(SNR\) and time-domain \(SNR\) are computed in different representation domains, the frequency-domain \(SNR\) may be affected by factors such as the selection of sampling frequency windows during the FFT process, leading to certain discrepancies between the two. However, overall, both exhibit the same evaluation trend. In summary, the proposed method achieves the highest \(SNR\) and the lowest MSR for denoising artificial simulated signals. It allows for an adaptive optimization of VMD decomposition parameters to avoid the influence of manually selected parameters on the results, thereby enhancing both denoising accuracy and efficiency.
# A Hybrid RF/FSO Transmission System Based on a Shared Transmitter
###### Abstract  
In this work, we propose a novel design of a hybrid transmission integrated system for radio frequency (RF) and free-space optical (FSO) communications, in which the RF and FSO links are able to share the transmitter and the transmission link. In this system, the RF link can usually be considered as a complement to the FSO link, and the hybridization of the two transmissions allows the system to adapt to a variety of complex environments while maintaining a high rate of transmission, improves system stability, and greatly reduces system complexity through the design of a shared transmitter. An experimental demonstration of the system has been carried out, and the results show that the hybrid RF/FSO system supports 50 Gbit/s transmission, satisfying the 20% soft-decision forward error correction (SD-FEC) threshold of 2.4 \(\times\) 10\({}^{-2}\). Additionally, for the FSO link, the system supports 100 GBaud QPSK and 32 GBaud 16 QAM transmissions individually. The proposed structure combines the advantages of RF and FSO, providing a foundation for future high-speed, broadband, all-environment communication.  
hybrid transmission; shared transmitter; radio frequency; millimeter wave communication; free space optical; digital signal processing +
Footnote †: journal: Sensors  
0000-0001-5002-4800][PERSON]
# Integrated Photodetectors Based on Group IV and Colloidal Semiconductors: Current State of Affairs
## 2 Figure of Merits for Characterizing Photodetectors
Also, light reflection effect on the photodetector surface produces a leakage into the transduction, thus the PD, or the array of PD, is typically covered by an illumination window with an anti-reflection coating, reducing this effect. Furthermore, in an actual photodiode shot noise, thermal noise, and dark-current noise generates a signal-to-noise ratio (SNR) degradation in the photodetection process, which incisively affects the performance of an optical communication system. By increasing the load resistance, thermal noise can be decreased. Also, improvement of material quality and junction structure optimization can reduce the reverse saturation current and, then, dark-current noise. Nevertheless, shot noise is a white noise generated by the photo detection process itself and cannot be reduced. It is called the quantum limit of the optical system and it represents the fundamental limit of the photodiode performance. Often, photodetectors are classified on the basis of the mechanism involved in the detection. As well know, the interaction between light and matter strongly depends by the photon energy and the band structure of the material constituent the photodetector. Then, effects of the light-matter interaction exploited for the photon-carrier transduction in PDs are summarized as following [25; 26; 27]:  
* _Photoemission or photoelectric effect_: Energy of photons supplies exactly the energy gap from the conduction band to free electrons, increasing the mobility of electrons. * _Thermal effect_: Energy of photons supplies to mid-gap transition states then an electron decay back to lower bands, generating phonon and thus heat. * _Photochemical effect_: In some materials, photons can induce a chemical change as crosslinking or the destruction of a chemical bond. * _Polarization effect_: In some materials, photons can cause changes in polarization states, which can change the refractive index or induces birefringence effects. * _Weak interaction effects_: Photons can lead to secondary effects such as gas pressure changes or photon drag [28; 29].