esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Distance Analysis
#### 5.6.2 Efficiency  
The efficiency of our model was assessed through the FRR and FAR measurements, both of which had zero values indicating efficient classification. The study analyzed various factors such as trust ranking, success rate, completion rate, average success ratio, overall completion rate, and average success ratio. The overall values are shown in Table 4.  
Although the ratios were acceptable, they were not high, which showed that our model's efficiency needed to be raised. Other mechanisms that could be used to measure it include resource efficiency, risk vs. trust balance, model interpretability, scalability, performance, and the security-usability trade-off, cross-validation, and accuracy-related metrics that we utilized.
## 4 Experiment, Results and Analysis
### Ablation Studies
Conversely, when the convolutional kernel size decreases, the model can capture finer details of features but may lose some important information. Excessively small convolutional kernels may result in inadequate feature extraction, affecting model accuracy. For our constrained information extraction branch cgMLP module, as it is introduced from other tasks into language identification, we investigated the impact of different convolutional kernel sizes on model performance. From Table 4, it can be seen that the model performs optimally when the convolutional kernel size is 3. Therefore, we set the convolutional kernel size of our cgMLP module to 3. In addition, we also investigated the impact of different branch weights on model performance for the dual-branch structure, and the experimental results are shown in Table 5. From the table, we can see that when the weight is set to 0 or 1, either using only convolution or transformer, the performance is not as good as using the dual-branch. Moreover, the best performance is achieved when the weights of both branches are set to 0.5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Kernel Size**} & \multicolumn{2}{c}{**3 s**} & \multicolumn{2}{c}{**10 s**} & \multicolumn{2}{c}{**30 s**} \\ \cline{2-7}  & _EER_ & \(\mathbf{C_{avg}}\) & _EER_ & \(\mathbf{C_{avg}}\) & _EER_ & \(\mathbf{C_{avg}}\) \\ \hline
1 & 7.77 & 7.15 & 3.97 & 3.37 & 3.33 & 3.04 \\
3 & 7.40 & 6.34 & 3.67 & 3.16 & 2.55 & 2.32 \\
5 & 7.39 & 6.49 & 3.98 & 3.50 & 3.16 & 2.82 \\
15 & 7.31 & 6.67 & 3.71 & 3.35 & 2.96 & 2.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: In the local information extraction branch cgMLP, the equal error rate (EER) and the average cost (Cavg) is compared based on different kernel sizes of deep convolution. In this ablation study, except for the different kernel sizes of cgMLP, everything else remains consistent with the B-transformer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fusion Method**} & \multicolumn{2}{c}{**3 s**} & \multicolumn{2}{c}{**10 s**} & \multicolumn{2}{c}{**30 s**} \\ \cline{2-7}  & _EER_ & \(\mathbf{C_{avg}}\) & _EER_ & \(\mathbf{C_{avg}}\) & _EER_ & \(\mathbf{C_{avg}}\) \\ \hline Baseline & 7.89 & 7.10 & 3.89 & 3.46 & 2.91 & 2.62 \\ Add & 7.58 & 6.50 & 4.15 & 3.60 & 2.96 & 2.71 \\ Concat & 7.41 & 6.52 & 3.77 & 3.27 & 2.83 & 2.49 \\ Deep-wise convolution & 7.40 & 6.34 & 3.67 & 3.16 & 2.55 & 2.32 \\ Gated & 7.51 & 6.75 & 3.97 & 3.53 & 2.92 & 2.72 \\ Attention & 7.62 & 6.84 & 3.84 & 3.41 & 2.77 & 2.41 \\ Inter-Attention & 7.81 & 6.76 & 3.87 & 3.37 & 2.85 & 2.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Disentangling Branchformer block. Starting from the Branchformer block, we gradually remove the additional modules we added to revert it to its original state: (1) Removal of the Macaron-style FFN; (2) Removal of the deep convolution fusion module; (3) Removal of the local information extraction branch. All ablation study results are evaluated while keeping other parameters unchanged.
## 1 Introduction  
Light emitting diodes (LEDs) have been widely used for various applications such as illumination, display, traffic lights, head and tail lights of automobiles. Unlike other light sources, the LED can switch at relatively high speed. The wide spreading application of LEDs allows us to open up a new era of spatial optical communications [1-3].  
The image sensor communication (ISC) technology is essential for the spatial optical communication because to find the light source and to intensify the light energy density at the receiver, the optical receiver has to have a photo detector array and signal light source finding and tracking functions. A few approaches for ISC have been reported. CMOS ISC chips have been used for ID beacon detection [4,5], where very low data rates are required. In an ISC chip for an optical wireless LAN application [6], the data rate of several hundreds of MHz has been demonstrated. In this approach, however, photo-diode current of a 2-D detector array directly flows into external receiver circuits, and because of this, extremely large optical power using laser lights is required.  
In this paper, a CMOS optical light pulse receiver (LPR) cell array with an imager on the same plane is presented. The main target application is car-to-car and road-to-car spatial optical communications. The purpose of this development is to achieve long-range spatial optical communication up to 100 m using cheap LED light sources, moderate data rate up to 1 MHz, and a light-source finding and tracking function. Toward this goal, the proposed architecture uses a mixed highly-sensitive CMOS imager and wide-dynamic-range light pulse receiver array for the received optical pulses based on a pinned photodiode technology [7,8]. A successful spatial optical communication is demonstrated using a developed VGA-size prototype chip. This paper focuses on the design and evaluation of the CMOS LPR cell array with the theory for the transient response, device simulations, and chip implementation. The ISP chip is designed for moving target light source finding and tracking. The image signal output and 25-channel parallel LPR cell signal outputs can be used for this purpose. In the experiment of this paper, however, the image signal is used for finding non-moving light source and one-channel LPR cell signal output only is used for the demonstration of the spatial optical communication.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 3 Results  
### Spatial Distributions of GPP, ET, and WUE  
WUE is calculated by the ratio of GPP to ET; therefore, the spatial distribution of GPP and ET may have great influence on WUE. Figure 3 shows the spatial distributions of the annual average GPP, ET, and WUE from 2001 to 2017 over the Three-North region of China. As shown in Figure 3a, the GPP of most research areas was low (less than 200 g-C\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\)), suggesting that the vegetation conditions in northwest China are very poor with very limited organic matter production. ET mainly showed a decreasing trend from approximately 900 mm\(\cdot\)y\({}^{-1}\) in the southwest to less than 50 mm\(\cdot\)y\({}^{-1}\) in the desert region in the northwest, as shown in Figure 3b. The distributions of GPP and ET demonstrated extreme spatial diversity, as the southwest of this region had both high GPP and high ET, while the northwest of this region had both low GPP and low ET. For WUE, as shown in Figure 3c, it had a similar spatial pattern as GPP, suggesting that GPP may be the major influencing factor. The spatial distribution of WUE suggested that, in those regions with extremely low water supply (desert/Gobi), vegetation primary production is limited.
## 1 Introduction
In another study, [PERSON] and [PERSON] 2023 [37] introduced a simple technique for estimating the propagation direction of TIDs using in situ electron density data from the Swarm A-C satellites, which fly in close proximity. This method was validated by comparing TID cases observed on detrended GNSS d-TEC maps over different continents. The Ionospheric Plasma IRregularities (IPIRs) product of Swarm [38] offers global irregularity characterization across satellite tracks. [PERSON] et al. 2020 [39] correlated Swarm plasma plumes with Jicamarca Radio Observatory (JRO) observations, reporting that Swarm density fluctuations indicate plasma plumes and spread F near JRO. This paper focuses on ionospheric irregularities through the study of the possible connection between SF and MSTIDs at middle latitudes recorded by Digisonde and Swarm satellites, respectively. Specifically, we assess whether fluctuations observed in the topside ionosphere by Swarm satellites are linked to irregularities in the bottomside ionosphere detected by Digisonde as SF, with MSTID signatures in GNSS-derived d-TEC maps. To explore this link, we have selected ten potential case studies from 2015 to 2017 over European midlatitude Digisonde sites, covering various geomagnetic conditions.
###### Abstract  
This study presents a novel green synthesis method for aluminum phosphate (AIPO\({}_{4}\)) nanoplates using extracts from _Quercus suber_ somatic embryos. Traditionally, metallic nanoparticles are synthesized using harmful chemical reagents and solvents, but our approach leverages bioactive compounds, such as phytic acid (IP6) from plant extracts, offering a more sustainable alternative. We observed a high phosphate content of 23.6 uMol PO\({}_{3}\)\({}^{-}\)/mg dry weight (DW) in the extracts, which is significantly higher than in other _Quercus_ species. Characterization through UV-VIS spectroscopy and XRD confirmed the formation of complex crystal nanoplates, where anisotropic aluminum phosphate with a triclinic crystal structure coexists with monoclinic structures, both forms of aluminum phosphate tridymite. Electron microscopy (TEM and SEM) revealed the hierarchical organization of these nanoplates, distinguishing them from the more commonly observed spherical nanoparticles. Fourier-transform infrared (FTIR) spectroscopy further validated the incorporation of phytic acid into the nanoplate structure. This innovative method not only advances sustainable nanomaterial synthesis but also demonstrates the potential for unique optical and structural properties in aluminum phosphate nanoplates, paving the way for future applications in specialized fields.  
green synthesis; aluminum phosphate nanoplates; sustainable methods; natural reducing agents
## 4 Discussion
\begin{tabular}{l l} \(B_{cal}(SOC)\) & Pre-exponential factor depending on SOC (Ah/s\({}^{\text{Zcal}}\)) \\ \(C_{N}\) & Nominal capacity of the battery (Ah) \\ \(C_{c}\) & Normalized capacity of the battery during charge (per unit) \\ \(C_{d}(0)\) & Normalized initial battery capacity (Ah) \\ \(C_{d}(t)\) & Normalized capacity of the battery during discharge (per unit) \\ \(Crate\) & Current through the battery divided by its nominal rated capacity (h\({}^{-1}\)) \\ \(C_{rem}(t)\) & Remaining battery capacity (Ah) \\ \(DOD\) & Depth of discharge (\%) \\ \(E_{d_{cal}}(SOC)\) & Activation energy depending on SOC (Jmol\({}^{-1}\)) \\ \(f_{PV\_loss}\) & PV loss factor \\ \(g\) & Electrolyte proportionality constant (V) \\ \(\text{G}(t)\) & Irradiance over the module surface at time \(t\) (kW/m\({}^{2}\)) \\ \(I_{b}(t)\) & Current that effectively affects the battery charge (A) \\ \(I_{bat}(t)\) & Battery current (positive charging, negative discharging) \\ \(I_{limit}\) & Limit current above which only cycling degradation is considered (A) \\ \(I_{PV}(t)\) & PV output current during time \(t\) (A) \\ \(I_{SC}\) & Shortcut current of the PV module (A) \\ \(N\) & Number of cycles \\ \(N_{PV\_p}\) & Number of PV strings in parallel \\ \(P_{PV}(t)\) & Output power of the PV generator (W) \\ \(\text{Q}(t)\) & Capacity loss (\%) \\ \(Q_{cal}(t)\) & Calendar capacity fade (\%) \\ \(Q_{cyc}(t)\) & Cycle capacity fade (\%) \\ \(R\) & Gas constant (8.314 J/mol-K) \\ \(SOC\) (\(t\)) & State of Charge of the battery (per unit) \\ \(t\) & Time (hour) \\ \(T\) & Ambient temperature (K) \\ \(V_{DC}(t)\) & DC bus voltage (V) \\ \(V_{0}\) & Open-circuit equilibrium cell voltage at the fully charged state (V) \\ \(z_{cal}(SOC)\) & Dimensionless constant (assumed a value of 0.5). \\ \(Z_{IEC}\) & Number of full charge-discharge cycles defined by the IEC standard \\ \(Z_{N}\) & Equivalent number of full cycles since the beginning \\ \(\Delta C^{c}(t)\) & Capacity loss by corrosion (Ah) \\ \(\Delta C^{d}(t)\) & Capacity loss by cycling (degradation) (Ah) \\ \end{tabular}
## 5 Connected Vehicles  
### CV Technologies  
Integrated with three networks: intra-vehicle network, inter-vehicle network, and onboard mobile Internet, the CV, also known as the \"Internet of Vehicles\" (IoV), is intrinsically a large distributed communication system for V2X interaction, where X involves other CVs, individuals, and networks [82]. As a profound scenario for the application of the Internet of Things (IoT) in the intelligent transportation system (ITS), the CVs enable a multifunctional network that supports intelligent transportation management, intelligent dynamical information services, and intelligent driving control [83]. The information exchange process follows specific communication protocol standards such as the IEEE 802.11p WAVE standard or cellular data protocols [84]. Typical CV systems are equipped with interfaces to WiFi or cellular networks (GSM, HSDPA, LTE, or 5G expected to be fitted on a large scale) and other short-range communication technologies [85]. When the vehicle is connected to the network, it has access to numerous cloud information services, such as instant emergency calls, real-time navigation, and media amusement, as exhibited in Figure 7. As the communication gateway, the global vehicular ID (GID) terminal is undoubtedly the technological key of the CV [86]. With integrated information sensors, the network communication module, and global online identification (an \"online vehicle plate\"), it allows the vehicle to have worldwide network connectivity and global positioning and tracking capabilities [87].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Challenges** & **Opportunities** \\ \hline Battery degradation & Integration of battery degradation costs with the change in the charging/discharging strategies \\ \hline Cyber attacks & New technologies that enhance the resiliency of the V2G and V2H systems against cyber–physical attacks and new security standards \\ \hline Time delay & Large bandwidth communication channels such as LAN, and WAN networks \\ \hline Stability issues & Development of robust controllers \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of challenges and opportunities of V2G and V2H for EVs [81].
# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 3 Results  
### Experimental Setup and Dataset  
The experimental configuration is as follows: a Windows 11 system with a 32-core Intel(R) Xeon(R) Platinum 8350C processor made by the Intel Corporation, Santa Clara, CA, USA and an RTX A5000 (24 GB) GPU graphics card made by the Nvidia, Santa Clara, CA, USA; the program runs on a 64-bit ubuntu 20.04 environment with Cuda 11.3, Python 3.8, and Pytorch 1.11.0.  
The public dataset of brain tumor segmentation challenge (BraTS Challenge) 2021 [25] is utilized to evaluate the segmentation accuracy of the proposed approach. It has a total of 2040 cases; 1251 of them are included in the training set cases, 219 of them are included in the validation set, and the remaining 570 cases are in the test set. Each case contains four modalities of MRI, as shown in Figure 6, where, T1 (T1-weighted images) modality can be used to show the anatomical structure of the tumor; T2 (T2-weighted images) modality can be used to detect edema, inflammation, etc.; the T1 CE (Contrast-Enhanced Imaging) modality can show the internal condition of tumors and hemangiomas, and FLAIR (Fluid Attenuated Inversion Recovery) modality can better show the condition around the tumor as well as the edema area. The dimensionality of each modality is 240 \(\times\) 240 \(\times\) 155. Since the BraTS 2021 training set has labels, while the validation set and test set have no labels, the dataset is divided into a training set, a validation set, and a test set in a ratio of 8:1:1 to evaluate the segmentation accuracy of the proposed approach.  
Figure 6: Examples of the four modalities of a case: (**a–d**) are the FLAIR, T1, T1 CE, and T2 modalities for the case.  
In this experiment, the network is trained for 120 iterations due to the large number of 3D image parameters, as well as the amount of computation needed for training, and due to the limited hardware available for the experiment, the batch size of this experiment is set to be 1. The weights of the network are updated using the SGD optimizer, while the warm-up learning strategy is used to warm up the network by firstly preheating for 10 epochs, with a minimum learning rate of 0.002 and a maximum learning rate of 0.004.
## 3 Problem Statement
### Edge Normal Vector in Polygon Mesh Topology  
Figure 4 shows the relationship between face normal, edge normal, and vertex normal.  
Given that vectors \(\bar{V}_{A}\), \(\bar{V}_{B}\), and \(\bar{V}_{C}\) each represent the position vector of vertices on the face \(F\), the face normal vector \(\widetilde{N}_{F}\), which is perpendicular to both \(\bar{V}_{B-A}\) and \(\bar{V}_{C-A}\), is obtained using the cross product of these two vectors.  
\[\widetilde{N}_{F}=(\bar{V}_{B}-\bar{V}_{A})\times(\bar{V}_{C}-\bar{V}_{A}) \tag{1}\]  
In general, 'edge normal' is not defined in computational mesh geometry. However, in this research, the edge normal \(\widetilde{N}_{E_{i}}\) of edge \(E_{i}\) is defined as a vector dissecting the folding angle between the two adjacent faces into half. Given that the face normal of each face \(F_{i}\) and \(F_{i+1}\) are \(\widetilde{N}_{F_{i}}\) and \(\widetilde{N}_{F_{i+1}}\) respectively, the edge normal \(\widetilde{N}_{E_{i}}\) is:  
\[\widetilde{N}_{E_{i}}=\widetilde{N}_{F_{i}}+\widetilde{N}_{F_{i+1}} \tag{2}\]  
Vertex normal is an average of the face normal of the faces fanning around a vertex [30,31]. In computer graphics, the average can be weighted by the surface area for rendering purposes [32], but in this research, the average is unweighted.
## 1 Introduction  
As today's markets are being reshaped by groundbreaking technologies, there is more pressure on the industry to become more flexible and adaptable in order to meet the changing needs of markets. With more competition in efficiency, productivity, and quality in the global market, companies need to make big changes to their production plans, technologies, and management [1]. Thanks to technological advancements, many different industries have been able to boost their performance and productivity through the use of automation, digitalization, and artificial intelligence, as well as the unprecedented availability and affordability of computing power, smart sensors, data acquisition systems, intelligent robotics, information and communication technology (ICT), the introduction of the Internet of Things (IoT), Big Data, and cloud computing [2].  
The transition from classical manufacturing to intelligent manufacturing, which is the natural consequence of digitalization and new advanced technologies, has given rise to a fourth industrial revolution, or Industry 4.0. The term was first coined in Germany in 2011 [3], and it comprehends new manufacturing techniques, ones that incorporate digitalization into the industrial world through the use of IoT. The success of Industry 4.0 relies heavily on the smart deployment of key enabling technologies (KETs) [4], being the pillars of this revolution. Figure 1 depicts the most common Industry 4.0 pillars identified in the literature.  
The following subsections will highlight the major pillars and enablers of Industry 4.0; cyber-physical systems, Internet of Things, Big Data, cloud computing, artificial intelligence, robotics, and Smart Factory.
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
## 3 Communication Protocol
### Reception Decoding  
The signal received, \(RF_{rx}(t)\), is band-pass filtered, amplified, and digitally converted at rate \(T_{RC}=T_{TC}\). Then, it is correlated with the original chirp signal (2) according to the pulse compression technique. The resulting compressed signal \(R(n)\) presents a 'peak' for every '1' bit of the original bitstream and no peak for every '0' bit. A simple amplitude threshold can be used to recover the bitstream. Figure 4 shows, for example, the received and the compressed signals for the arbitrary bit sequence \"1111010111010011100100\" that corresponds to the 24-bit data packet with the 16-bit payload \"0101110100111001\". The bitstream is recovered with a threshold at half-amplitude.  
For every new bit that is decoded, the last 24-bit are considered a prospective packet. The preamble and the CRC are checked. If the result is negative, the process repeats with the next bit. If it is positive, the payload is extracted, and the process jumps to the next 24 bits. More details are given in the FPGA implementation.  
Figure 3: Format of the 24-bit data packet P\({}_{23\cdot 0}\). It is composed by the CRC C\({}_{3\cdot 0}\), the payload W\({}_{15\cdot 0}\), and the fixed preamble “1111”.
# Biodegradable Contrast Agents for Photoacoustic Imaging
## 5 Polymeric Materials as Biodegradable PA Agents  
Benefiting from the development of polymer science and nanotechnology, polymeric materials have attracted great attention as a new class of PA agents that offers considerable structural diversity and designed material properties via chemical functionalization [57; 58; 59; 60; 61; 62; 63; 64]. Notably, polymer nanoparticles have been successfully demonstrated as molecular imaging agents [65]. Such nanoparticles principally consist of \(\pi\)-conjugated polymer cores and hydrophilic polymer shells, which are generally formed via nanoprecipitation or nanoemulsion techniques. Therefore, the photophysical properties of a semi-conducting polymer can be translated into detectable ultrasonic signals in response to laser  
Figure 4: Biodegradable gold vesicles (BGVs). (**A**) Scanning electron microscopy image of BGVs (inset: 3D electron density map of a broken BGV). (**B**) PA signal intensities in tumor tissue after intratumoral injection of gold vesicles (GVs) and BGVs. (**C**) In vivo depth-resolved two-dimensional ultrasound, PAI and volumetric PAI and ultrasound images of tumor tissue before and after injection of BGVs. (**D**) Tumor growth in each group of MDA-MB-435 tumor-bearing mice. Reproduced with permission from [34]. Copyright John Wiley and Sons, 2013.  
pulses, while a peripheral surfactant polymer stabilizes the overall structure under physiological conditions. Seminal review papers have covered semi-conducting-polymer-based PA agents [66; 67]. In contrast, this section focuses on polymeric materials for biodegradable contrast agents. For this purpose, the agents are expected to be biocompatible, or at least amphiphilic, possess cleavable bonds, or exhibit the desired optical properties [68; 69; 70]. As an example, owing to their intrinsic properties, natural polymeric materials such as polysaccharides, phospholipids and deoxyribonucleic acid (DNA) can be used as biodegradable agents (Figure 5). In addition, conventional hydrophilic polymers play a key role as an essential component for the biocompatibility of organic nanostructures including particles, hydrogels, micelles, vesicles and polymersomes [68; 71; 72; 73; 74; 75]. Theses polymers have been incorporated into contrast agents through controlled polymerization, copolymerization, or postpolymerization modification (Figure 6). More interestingly, functional polymers that have chemically removable groups (e.g., labile groups, stimuli-responsive groups, or self-immolative groups) have shown biodegradability when exposed to bio-related signals such as pH, enzymes, temperature and biogenic chemicals.