esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 2 Study Area and Dataset
### Meteorological Data and EC Measurements  
A measurement tower 62 m high was set up with seven levels of routine meteorological profile measurement systems and an open path eddy covariance measurement system. Meteorological data were continuously measured at the forest flux tower from 2003 to 2007. These data included air temperature and relative humidity (Model HMP45C, Campbell Scientific Inc., Logan, UT, USA), precipitation (Model 52203, Rm Young, Traverse City, MI, USA), wind speed and direction. Photosynthesis active radiation (PAR) was measured with a quantum sensor (Model LI190 SB, LICOR Inc., Lincoln, NE, USA). Other meteorological data, including vapor pressure deficit (VPD), incoming shortwave radiation (Srad) and day-length from sunrise to sunset were calculated with the MTCLIM 43 model based on the measured daily maximum and minimum temperature and precipitation [23; 24]. For large scale simulations, DAYMET was used to lengthen the period of recording, and to interpolate and extrapolate from daily meteorological data to grid estimates over large regions [25]. DAYMET data contained daily maximum and minimum temperature, precipitation, VPD, Srad, and day-length and were calculated on a 1-km grid over the Changbai Mountains.  
The open path eddy covariance system is composed of a three-dimensional sonic anemometer (CAST3, Campbell, KY, USA) and a fast responding open path infrared gas analyzer (LI-7500, LI-COR Inc., Lincoln, NE, USA). The collection frequency for raw flux data was 10 Hz, and for climate data 0.5 Hz. The 30-min averaged values of every variable were calculated. Also a series of preprocessing was conducted including outlier removal, coordinate rotation, time lag analysis, frequency response calibration, and WPL correction. As the half-hour net CO\({}_{2}\) exchange was calculated using EdiRe software, the net ecosystem exchange (NEE) could be obtained. To estimate the night-time net CO\({}_{2}\) exchange, the net CO\({}_{2}\) exchange was regressed with the air or soil temperature using an exponential function. The built model was then used to calculate the ecosystem respiration (ER). Next, NEE and ER were summed to estimate the ecosystem gross primary productivity (GPP).  
Five-year (from 2003 to 2007) carbon flux data and meteorological data were thus collected at the forest flux site. Additionally, the latitude, topography, soil texture, and other background information were collected at the forest site.
## 2 Simplified Trilinear Non-Dimensional Pushover Curve
### Validation  
A wide comparative analysis has been performed with reference to G-, S-, and O-Moment Resisting Frames. The analysis encompassed 140 distinct geometric configurations of low-rise frames. These variations involved adjusting the number of days, \(n_{b}\) from 2 to 6, the number of storeys, \(n_{s}\) from 2 to 8, and the bay span at 3.00 m, 4.50 m, 6.00 m, and 7.50 m. Additionally, three different design approaches were considered for a total of 420 designed structures. All configurations were evaluated with dead loads (\(G_{k}\)) set at 3.50 \(kN/m^{2}\), live loads (\(Q_{k}\)) at 3.00 \(kN/m^{2}\) and an inter-storey height of 3.50 m [27].  
Pushovers have been carried out using the SAP2000 computer program (v.24) [33] with a load pattern distribution compliant with the first vibration mode.  
Beams (Figure 3) and columns (Figure 4) have been modelled as beam-column elements with plastic hinges (\"P-hinge\" elements) located at their ends. Plastic hinges accounting for the interaction between axial force and bending moment have been defined for columns, while for beams the axial force interaction has been neglected. The pushover analysis has been led under displacement control considering both geometrical and mechanical non-linearities.  
Figure 4: Moment/rotation model for columns.  
Figure 3: Moment/rotation model for beams.  
The accuracy of the proposed trilinear model is depicted in Figures 5 and 6. In particular, a comparison between the results of the pushover analysis and those derived by the simplified approach is provided with reference to the prediction of displacements \(\delta_{c}\) and \(\delta_{D}\). The \(x\)-axis denotes the theoretical values obtained by Equations (8) and (9), while the \(y\)-axis represents the values derived by SAP 2000.  
By observing Figures 5 and 6, it is immediately evident that the accuracy of the simplified approach is very high. In fact, in the case of \(\delta_{c}\), the mean value of the ratio \(\delta_{C.SAP}/\delta_{C.th}\) is 0.96 with a standard deviation equal to 0.14, while, in the prediction of \(\delta_{D}\), the mean value is about 0.93 with a standard deviation of 0.16.
## 3 Trajectory Optimization Design
### Adaptive Genetic Algorithm  
#### 3.3.1 Encoding and Decoding  
The encoding of the genetic algorithm [21; 22; 23] is a process of converting the parameters from solution space into genetic spatial. The time target of trajectory planning is encoded by binary coding. The variation range of the optimization variables is relatively small; therefore, using a certain length of binary coding can ensure the accuracy of the solution. The time range of the pickup manipulator in the working state is set to [\(t_{0}.t_{f}\)]. If the time variable is represented by a binary coding of length \(l\), [\(t_{0}.t_{f}\)] can be equally divided into \(2^{l}-1\) segments. Thus, the accuracy of the time parameter can be adjusted by adjusting the value of the coding length \(l\). The longer the length of the binary coding, the better the performance of the time division, and the higher the accuracy of the time variable, as shown in formula (14). In this work, we take the binary code length \(l=9\), \(t_{f}=5.0\), \(t_{0}=0\). An encoding example of the time target is 110001010.  
\[\delta=\frac{t_{f}-t_{0}}{2^{l}-1}, \tag{16}\]  
Decoding is the reverse operation of the encoding, that is, the process of converting the binary coding of the time-optimal target into a real number. If the binary coding corresponding to the time-optimal target is \(x:x_{1}x_{2}\ldots x_{l-1}x_{l}\), the decoding expression corresponding to the time variable of the pickup manipulator is:  
\[t=t_{0}+\frac{t_{f}-t_{0}}{2^{l}-1}\sum_{i=1}^{l}x_{i}2^{l-i}, \tag{17}\]
# Ultra-Low-Loss Silicon Waveguides for Heterogeneously Integrated Silicon/III-V Photonics
## 2 Ultra-Low Loss Waveguides for Si/III-V Heterogeneous Integration
### Waveguide Loss Consideration  
Due to the line-edge roughness associated with optical lithography, which is further aggravated in dry-etching processes, the propagation loss of the planar waveguides is typically dominated by the radiative loss induced by the interaction between the optical mode and the roughness on the waveguide sidewalls. Here, we estimate the waveguide radiative loss using the \(n_{w}\) model approximation [30]. This approximation is particularly useful if one only needs to quantify the relative comparisons of the propagation losses among different waveguide geometries under the same fabrication conditions. According to this model, waveguide loss can be approximated as:  
\[\alpha_{r}=A\Big{(}\sigma,L_{c},n_{eff}\Big{)}\frac{\partial n_{eff}}{ \partial w} \tag{1}\]  
where \(A\) is the proportionality factor determined by the sidewall roughness rms (\(\sigma\)) and the roughness correlation length (\(L_{c}\)), \(n_{eff}\) is the effective index of the waveguide optical mode, and \(w\) is the width of the waveguide. A full expression of \(A\) can be found in [31]. Given a waveguide geometry that is far from mode cut-off condition, the magnitude of the factor \(A\) in general does not depend on the waveguide width.  
Figure 3a shows the measured propagation loss of the two \"more conventional\" silicon waveguide geometries for the heterogeneous silicon platform used in previous works [10; 32; 33]. One is a fully 500 nm etched strip waveguide, and the other is 231 nm etched rib waveguide. The widths of both waveguides are 1 \(\upmu\)m. Empirical values of the roughness parameters were obtained (line edge roughness rms \(\sigma\sim\)5 nm and correlation length \(L_{c}\sim\)60 nm) and then Equation (1) is used to generate theoretical curve corresponding to the radiative losses at varying etch depth. The theoretical waveguide loss curves with varying etching depths (500 nm, 231 nm and 56 nm) are then calculated and plotted in Figure 3b, showing a reasonable agreement with experimental data for the 500 nm and 231 nm etch depths. According to these model results, low propagation loss in the range of 3-10 dB/m can be expected for the 56 nm rib silicon waveguides with larger than 1.5 \(\upmu\)m width.
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 3 Experimental Results
### Datasets
#### 3.1.2 IBSR18  
The Internet Brain Segmentation Repository (IBSR18) (See the \"Internet Brain Segmentation Repository\" (IBSR) project's web site at [[https://www.nitrc.org/projects/ibsr/](https://www.nitrc.org/projects/ibsr/)]([https://www.nitrc.org/projects/ibsr/](https://www.nitrc.org/projects/ibsr/)) (accessed on 19 April 2022)) was created by the Center for Morphometric Analysis at the Massachusetts General Hospital. IBSR18 contains 18 T1 weighted MR brain images and their corresponding segmentation ground truth images. The images have 1.55 mm thickness with a resolution of \(256\times 256\times 128\). All the images are bias field corrected using the Autoseg method developed by the University of North Carolina at Chapel Hill (See the \"AutoSeg\" repository [[https://www.nitrc.org/projects/autoseg/](https://www.nitrc.org/projects/autoseg/)]([https://www.nitrc.org/projects/autoseg/](https://www.nitrc.org/projects/autoseg/)) (accessed on 19 April 2022)).
# An Oracle Bone Inscriptions Detection Algorithm Based on Improved YOLOv8
## 2 Materials and Methods
### Input Data Assumptions  
This study uses an improved YOLOv8 model to detect oracle bone inscriptions in the OBI detection dataset. To ensure effective training and accurate evaluation, the assumptions for the input data are as follows:  
* Image Format: All input images are in standard JPEG image format;
* Color Space and Channels: The color space of the input images is RGB, and each image has three color channels;
* Bounding Box Coordinate Representation: In the data preprocessing stage, all bounding box labels in the original JSON file are converted using Formulas (1)-(4);
* Class Label Representation: The class label is represented by an integer, starting with 0. Since all oracle bone inscriptions in the dataset are treated as a single category in this study, it is assumed that 0 represents this unique class label;
* Label Format and Transformation: The label file is converted into a txt file in the preprocessing stage; irrelevant label information from the original JSON file in the oracle bone inscription detection dataset is removed during data preprocessing, specifically parity bits; each oracle bone inscription's label information is on a separate line in the label file, consisting of the class label and the bounding box information; the data are separated by spaces;
* Data Preprocessing: None of the input images have undergone any form of data preprocessing and are maintained in their unaltered state. The dataset has been cleansed to ensure a one-to-onecorrespondence between images and label files. The label information has been preprocessed to meet the model's requirements;
* Data Distribution and Diversity:  
Considering the characteristics of the OBI detection dataset, it is assumed that the oracle bone characters in the dataset have diverse forms and varied sizes and a random distribution of noise and cracks in the images.
# Antarctic Surface Ice Velocity Retrieval from MODIS-Based Mosaic of Antarctica (MOA)
## 3 Methods  
### Feature Tracking  
Coregistration accuracy of two MOA products was checked by selecting well-dispersed runataks or outcrops from Landsat-8 Rock Outcrop. Considering the offsets of all of them are within half of a pixel (\(\sim\)20 m, see Section 4.3 for detail), it can be safely accepted that the whole image's mis-registration is within 1 pixel as well. Feature tracking based on a normalized cross-correlation (NCC) algorithm has already been implemented with dozens of software applications, in which images from two different times are compared using correlation techniques to derive glacier displacement over the time period [56]. Here we employ COSI-Corr [57] to acquire the raw displacement field because it outperforms other similar operators in most cases [58]. This tool is originally written in IDL as an extension of ENVI software, and it embeds modified phase correlation, Frobenius norm, and singular value decomposition techniques [59] to achieve a more robust field. Based on the ICESat-1 Antarctic drainage system published by the Goddard Space Flight Center [60] and their respective flow characteristics, the whole continent was divided into seven drainages accordingly as shown in Figure 1, namely Amundsen, Dronning Maud, Filchner-Ronne, Lambert, Peninsula, Ross and Wilkes Drainage. Each one of seven drainages was tested iteratively to obtain optimal but slightly different tracking parameters. By adjusting reference and search window size, step, iteration times, and mask threshold, the output binary file comprises the Easting displacement component in the X dimension, the Northing displacement component in the Y dimension, and the signal-to-noise ratio (SNR) as the primitive displacement field.  
Figure 1: Flowchart of processing and analysis steps.
## 3 Proposed Method
### Database Description  
This study utilized patient data sourced from the publicly available MIMIC-III (Medical Information Mart for Intensive Care) database [29], including both PPG and ABP signals.  
MIMIC-III is a comprehensive database containing diverse information on patients in intensive care units, comprising vital signs, medications, laboratory measurements, clinical notes, fluid balance, procedural and diagnostic codes, imaging reports, hospital stay durations, and survival data. It encompasses records from 53,423 patient admissions, representing data from 38,597 distinct patients, with a signal frequency set at 125 Hz.  
Data extraction was performed using two clinical devices: the Philips CareVue Clinical Information System (models M2331A and M1215A; Philips Healthcare, Andover, MA, USA) and iMDsoft MetaVision ICU (iMDsoft, Needham, MA, USA). These devices facilitated continuous collection of physiological measurements such as heart rate, Arterial Blood Pressure, respiratory rate, and SpO2.  
Due to the extensive size of the database and its computational demands, we selected a subset of 1629 users for analysis. Within this subset, sample windows of ABP data containing systolic and diastolic values within the intervals of 75 to 165 mmHg and 40 to 80 mmHg were excluded to mitigate outlier effects on the signals and facilitate correlation with hypertension classes.  
Figure 1: Overview of the NeuBP Method Generation Process, which is divided into four steps: (**I**) data acquisition, (**II**) preprocessing, (**III**) application of neural model, and (**IV**) classification of ABP after prediction of systole and diastole labels.
## 2 Related Work
### Encoder-Decoder Structure  
An encoder-decoder architecture comprises not just two layers, but a conceptual framework that typically includes two main components: an encoder and a decoder. These two components work in conjunction. The encoder processes the input sequence to create an encoded representation that is passed onto the next component, the decoder. The decoder interprets the compressed representation delivered by the encoder to make predictions and generates the output sequence [39]. This architecture is versatile, can be applied to a wide range of applications, and is particularly effective in sequence-to-sequence tasks, where both the input and output are in the form of sequences. Sequence-to-sequence operations are used in a variety of fields, including natural language processing, machine translation, speech recognition, music generation, and more [39; 40; 41; 42].  
The encoder performs three key functions within these architecture:  
* Processes the input sequence to create a compressed representation.
* Extracts important features and patterns from the input data and represents them in a compressed form.
* The compressed representation is used by the decoder to generate the output sequence. The decoder is responsible for the following crucial steps:* Interprets the compressed representation generated by the encoder to produce an output sequence.
* Predicts and generates an output sequence in the context of an input sequence.
* The output sequence is based on the content of the input sequence; however, it can also add new content based on the information extracted from the encoder.
## 6 Complexity and Performance Analysis  
In this section, we analyse the complexity of the Strike cipher. The cipher is composed of three main stages: security parameters setup, lightning strike generation and data encryption. The complexity analysis revealed efficient results, as tabulated in Table 6.  
The performance of the Strike cipher is also examined and analysed. The cipher is implemented in a Python environment installed on four computing devices with different computing capabilities to measure the algorithm's efficiency. The specifications of these machines are listed in Table 7.  
The performance of the Strike stream cipher is tested and compared against well-known stream ciphers, including AES-128, Snow 2.0-128, Salsa-20, HC-128 [34; 37], Trivium [8], Mickey-128 [9], Grain [10] and the cipher proposed by [28]. The testing results show that the Strike cipher achieved a throughput of 22,796.4 Mbit/s on the M1 machine. The results are tabulated in Table 8, which shows that our stream cipher outperforms other ciphers. The last column of Table 8 shows the ratio of efficiency of our cipher compared to others. The efficiency ranges from 2.5% (against [28]) to 99.9% (against Grain). Figure 9 visualises the thro  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Security Parameter** & **Lightning Strike** & \\  & **Setup** & **Generator** & **Data Encryption** \\ \hline Complexity & O(logn) & O(\(n\)) & O(\(n\)log\(n\)) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Complexity Analysis.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Processor** & **RAM Memory** & **Storage Capacity** & **Operating System** \\ \hline Machine 1 (M1) & Intel Core i7\({}^{\text{\textregistered}}\) & 6 GB & 500 GB HDD, 128 GB SSD & Windows 10\({}^{\text{\textregistered}}\) \\ Machine 2 (M2) & Intel Core i5\({}^{\text{\textregistered}}\) & 4 GB & 500 GB HDD, 128 GB SSD & Windows 10\({}^{\text{\textregistered}}\) \\ Machine 3 (M3) & Intel Core i3\({}^{\text{\textregistered}}\) & 2 GB & 500 GB HDD & Windows 10\({}^{\text{\textregistered}}\) \\ Machine 4 (M4) & Intel Celeron\({}^{\text{\textregistered}}\) & 2 GB & 500 GB HDD & Windows 10\({}^{\text{\textregistered}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Specification of testing environments.  
In addition, the Strike cipher is tested on different machines (M1-M4). The results presented in Table 9 show that the algorithm's structure was not affected by the computing power capabilities available on different machines. For instance, the major difference in performance is found between M1 and M4, where the performance is degraded by 7.25%; this is mainly due to the slower CPU processing speed and lower RAM capacity.
# Effects of Physical Exercise on Substance Use Disorder: A Comprehensive Review
## 1 Introduction
The neurobiological mechanisms underlying the potential efficacy of physical exercise in SUD relate to its ability to modulate neurotransmission systems disrupted by chronic substance use. Regular physical exercise influences the release of neurotransmitters, such as dopamine, serotonin, and noradrenaline [20], which are intimately linked to reward circuits and addiction processes [21]. Moreover, positive effects on neuroplasticity and neurogenesis have been demonstrated, processes that are essential for the recovery of brain alterations induced by substance abuse [21]. From a psychosocial perspective, physical exercise can help reduce anxiety and depression symptoms, which are frequently comorbid in individuals with SUD [22]. Likewise, it can foster the development of adaptive coping strategies, enhance self-efficacy, and facilitate the establishment of healthy habits, aspects that are crucial for the recovery process [23]. Despite these promising findings, the current scientific literature presents certain limitations and heterogeneity regarding intervention protocols, studied populations, and outcome variables. Understanding the available evidence on the effects of physical exercise in individuals with SUD is essential to guide clinical practice and future research in this field. This comprehensive review aims to synthesize and analyze the available scientific evidence of the effects of physical exercise in individuals with SUD, considering both the underlying neurobiological mechanisms and the observed psychosocial benefits. Unlike previous studies, this review incorporates a unique analysis of exercise program variables--such as type, intensity, frequency, and duration--and their specific impact on various psychological and physical outcomes in SUD. By exploring these dimensions, the study aims to provide a more detailed understanding of the role of structured exercise programs in the rehabilitation process. Additionally, the main gaps in current knowledge will be identified, and future lines of research will be proposed.
# Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies
## 6 Stack4 Things as the CPS Framework  
Stack4 Things (S4T) is a platform that extends the OpenStack framework with IoT capabilities. The design of S4T is split into two subsystems: the first is hosted in a datacenter where IoTronic is deployed; the second subsystem is represented by a number of geo-distributed IoT devices that host the S4T device-side agents, named Lightning-Rod (LR).  
The communications between the Cloud-side, IoTronic, and its device-side counterpart, LR, are built exploiting a mechanism based on WebSockets with a reverse tunneling approach that is able to bypass firewall and NAT systems (see Figure 4).  
As S4T is compatible with OpenStack, the interaction with other (OpenStack) services (e.g., Keystone for access management and Neutron for networks) is easily provided, and advanced user-facing features, such as containerized applications at the network edge and virtual networking are granted. In a nutshell, S4T provides the support of (among others):  
1. **Authorization/ Authentication:** S4T can manage users' authentication exploiting the OpenStack identity service called Keystone; it is also able to provide authorization to access and manage remote IoT devices.
2. **Remote access and management:** Exploiting the service forwarding facilities through the Cloud offered by S4T, a user can access (e.g., through vnc or ssh) their IoT devices without having to consider the device localization or networking configurations. This is possible thanks to a reverse-tunneling mechanism, based on Websockets.
3. **Remote customization/contextualization:** Using S4T, the application logic to be executed on the devices can be defined by an user and then distributed in form of functions and deployed on IoT devices, according to authorization and privacy policies, even at runtime. Python and Node.js are available as runtime environments in S4T.  
Figure 4: S4T architecture overview.  
S4T was developed keeping in mind the holistic approach introduced in the previous section. S4T strongly exploits the concepts of interoperability and network and device management through the adoption of virtualization. S4T also simplifies the development and management of applications by adopting a serverless paradigm for the network edge using the OpenStack FaaS subsystem Qinling, as described as follows.