esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Subcarrier Index Modulation is an OFDM variant that provides superior power and bandwidth efficiency. In this paper, we present a novel, double-sided pulse interval modulation (DS-PIM)-based SIM OFDM technique. The proposed technique exploits the variable symbol size of DPIM to provide a variable sub-block size and enable dynamic assignment of subcarriers rather than the fixed size of conventional SIM OFDM. In comparison with conventional Subcarrier Index-Modulated OFDM (SIM-OFDM), the proposed approach shows a 12.5% reduction in bandwidth usage for a 2-bit index word. On average, 3.5 subcarriers are employed by the proposed technique per sub-block, in comparison with 4 subcarriers for the conventional technique. The proposed technique provides a superior spectral efficiency compared with conventional SIM-OFDM, even for higher-order modulation.  
Subcarrier Index Modulation; Orthogonal Frequency Division Multiplexing; LiFi; visible light communication +
Footnote †: journal: Electronics Letters  
Aided SIM-OFDM for 6G Light Fidelity
###### Abstract  
In this study, _Arthrospira platensis_ was grown in the presence of different glycerol concentrations (0.5-9 g/L) under three light intensities (5, 10 and 15 Klux) in semi-continuous mode and under non-axenic conditions. The aim of this study was to investigate the growth performance, the biomass biochemical composition and any interactions between _A. platensis_ and bacteria that would potentially grow as well on glycerol. The results here show that glycerol did not have any positive effect on biomass production of _A. platensis_. In contrast, it was observed that by increasing glycerol concentration the growth performance of _A. platensis_ was restricted, while a gradual increase of bacteria population was observed, which apparently outcompeted and repressed _A. platensis_ growth. Chlorophyll fluorescence measurements (Quantum Yields) revealed that glycerol was not an inhibiting factor _per se_ of photosynthesis. On the other hand, cyanobacterial biomass grown on glycerol displayed a higher content in proteins and lipids. Especially, protein productivity was enhanced around 15-35% with the addition of glycerol compared to the control. In distinction, carbohydrate and photosynthetic pigments (phycocyanin and chlorophyll-\(\alpha\)) content decreased with the increase of glycerol concentration. The results here suggest that _A. platensis_ did not utilize glycerol for biomass production but most probably as metabolic energy carrier towards synthesis of proteins and lipids, which are more energy consuming metabolites compared to carbohydrates. The study revealed that the addition of glycerol at amounts of 0.5-1.5 g/L could be a strategy to improve protein productivity by _A. platensis_.  
**Keywords:**_Arthrospira platensis_ (Spirulina); mixotrophy; glycerol; protein production
# CommuSpotter: Scene Text Spotting with Multi-Task Communication
###### Abstract  
Scene text spotting is a challenging multi-task modulation for locating and recognizing texts in complex scenes. Existing end-to-end text spotters generally adopt sequentially decoupled multi-tasks, consisting of text detection and text recognition modules. Although customized modules are designed to connect the tasks closely, there is no interaction among multiple tasks, resulting in compatible information loss for the overall text spotting. Moreover, the independent and sequential modulation is unidirectional, accumulating errors from early to later tasks. In this paper, we propose CommuSpotter, which enhances multi-task communication by explicitly and concurrently sharing compatible information in overall scene text spotting. To address task-specific inconsistencies, we propose a Conversation Mechanism (CM) to extract and exchange expertise in each specific task with others. Specifically, the detection task is rectified by the text recognition task to filter out duplicated results and false positives, while the text recognition task is corrected by the rectified text detection task to replenish missing characters and decrease non-text interruptions. Consequently, the communication compensates for interaction information and breaks the sequential pipeline of error propagation. In addition, we adopt text semantic segmentation in the text recognition task, which reduces the complex design of customized modules and corresponding extra annotations. Compared with state-of-the-art methods, experimental results show that our method achieves competitive results with computation efficiency.  
scene text spotter; end-to-end text spotting; text detection; text recognition Article
###### Abstract  
Physical exercise and adequate energy intake are the most important needs for optimum health, and they are also strongly connected to the amount of energy used when participating in any kind of physical activity. Total energy expenditure (TEE) may be calculated by combining resting energy expenditure (REE) with the physical activity level (PAL). Validated PAL guidelines are generally accessible and used by the general public, however less is known about PAL in competitive athletes. This research aimed at analyzing the physical activity level and the energy expenditure of athletes who participate in various sports on a daily basis. The research included 53 female athletes (43.39% elite and 56.61% non-elite), and 47 male athletes (40.42% elite and 59.57% non-elite) who competed in athletics, basketball, badminton, cricket, hockey, kabaddi, volleyball, and wrestling. The PAL, the BMR--basal metabolic rate, and the TEE were used to measure energy expenditure throughout a regular training phase. The PAL was determined using the athletes' 24 h activity profiles, which consisted of their practice, study, leisure, and sleep hours. The Harris-Benedict equation was used to calculate the BMR. The TEE was computed by multiplying the BMR by the PAL. The time spent on work/practice, rest, and leisure activities was used to calculate the athletes' daily activity load. The PAL was 2.33 \(\pm\) 0.47, the TEE was 3532.18 \(\pm\) 827.75 kcal/day, and the BMR was 1515.06 \(\pm\) 203.52 kcal/day. Field hockey players had the greatest physical activity load (3.0 \(\pm\) 0.00), followed by wrestling (2.6 \(\pm\) 0.20), boxes (2.6 \(\pm\) 0.30), basketball players (2.3 \(\pm\) 0.40), athletics athletes (2.2 \(\pm\) 0.43), volleyball players (2.0 \(\pm\) 0.20), kabaddi players (1.8 \(\pm\) 0.20), cricketers (1.8 \(\pm\) 0.10), and badminton players (1.7 \(\pm\) 0.10). The PAL values were higher in the elite than in the non-elite female athletes (2.81 \(\pm\) 0.23, and 2.12 \(\pm\) 0.34, respectively), as well as in the male athletes (2.60 \(\pm\) 0.28, and 1.94 \(\pm\) 0.30, respectively). These data showed that the PAL values of both the elite and the non-elite athletes were at the WHO-recommended threshold yet were characterized as intensely active.  
athletes' activity pattern; basal metabolic rate; physical fitness; total energy expenditure 2023
## 4 Experimental Results and Discussion
### ARM Experimentation  
In order to evaluate our TSR system, we used a video sequence describing a road scene that lasted approximately 2 min. Figure 14 shows examples of recognised road sign images (bumpy road, general caution, speed limit (50 km/h)). The achieved processing time was calculated from the detection moment of the sign to its classification. During this short span, the system spends an average of 0.08 s to correctly identify each detected traffic sign.  
Obtained recognition processing time is not as efficient as expected, and this is due to hardware performance when loading the video sequence in the programming console. For this reason, we test our TSR system in real time using the laptop webcam.  
Figure 15 shows that our TSR system can correctly recognise each sign (32 signs) in an average of 0.06 s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**GTSRB Dataset Accuracy Results (\%)**} & \multicolumn{3}{c}{**BTSCB Dataset Accuracy Results (\%)**} \\ \hline
**Epoch** & **Training** & **Validation** & **Testing** & **Training** & **Validation** & **Testing** \\ \hline
70 & 99.99 & 99.97 & 99.91 & 99.35 & 99.31 & 99.27 \\ \hline \hline \end{tabular}
\end{table}
Table 11: TSC_MA accuracy rates results trained for 70 epochs on GTSRB and BTSCB.  
Figure 14: Examples of road sign recognition in a video sequence.
# MR Images, Brain Lesions, and Deep Learning
## 2 The Literature Review: Selection Criteria
### Computer-Aided Diagnosis in Medical Imaging (CADx System)
#### 3.2.3 Image Segmentation
These are defined as:  
\[Specificity=\ \frac{TN}{FP+TN} \tag{11}\]  
\[Accuracy=\ \frac{TP+TN}{TP+FP+FN+TN} \tag{12}\]
* _Average symmetric surface distance (ASSD, mm):_ Represents the average surface distance between two segmentations (computed and reference and vice versa) and is an indicator of how well the boundaries of the two segmentations align. The ASSD is measured in millimeters, and a smaller value indicates higher accuracy [75; 177; 180]. The average surface distance (ASD) is given as: \[ASD(X,Y)=\sum_{x\in X}min_{y\in Y}d(x,y)/|X|\] (13) where \(d(x,y)\) is a 3D matrix consisting of the Euclidean distances between the two image volumes X and Y, and the ASSD is defined as [177]: \[ASSD(X,Y)=\ \{ASD(X,Y)+ASD(Y,X)\}/2\] (14)
* _Hausdorff's distance (HD, mm)_: It is more sensitive to segmentation errors appearing away from segmentation frontiers than the ASSD [180]. The Hausdorff measure is an indicator of the maximal distance between the surfaces of two image volumes (the computed and reference segmentations) [26; 180]. The HD is measured in millimeters, and like the ASSD, a smaller value indicates higher accuracy [177]. \[d_{H}(X,Y)=max\{max_{x\in X}min_{y\in Y}d(x,y),\ max_{y\in Y}min_{x\in X}d(y,x)\}\] (15) where \(x\) and \(y\) are points of lesion segmentations \(X\) and \(Y\), respectively, and \(d(x,y)\) is a 3D matrix consisting of all Euclidean distances between these points [177]. * _Intra-class correlation (ICC):_ Is a measure of correlation between volumes segmented and ground-truth lesion volume [180]. * _Correlation with Fazekas score:_ A Fazekas score is a clinical measure of the WMH, comprising two integers in the range [0, 3] reflecting the degree of a periventricular WMH and a deep WMH, respectively [180]. * _Relative volume difference (VD, %):_ It measures the agreement between the lesion volume and the ground-truth lesion volume. A low VD means more agreement [182]. \[VD=\frac{\left(v_{s}-v_{g}\right)}{v_{g}}\] (16) where \(v_{s}\) and \(v_{g}\) are segmented and ground-truth lesion volumes, respectively. Lastly, we define [178] reproducibility, which is a measure of the degree of agreement between several identical experiments. Reproducibility guarantees that differences in segmentations as a function of time result from changes in the pathology and not from the variability of the automatic method [178]. Tables 2 and 5 tabulate databases, modalities, and the evaluation measures reported in the literature.
## 2 Research Methodology
### Magnetron Structure Analysis
#### 2.1.3 Hull Cut-off and Hartree Conditions  
The electron motion in the magnetic field within the cylindrical radius of the cathode (\(\mathrm{R}_{\mathrm{C}}\)) and the radius of the anode (\(\mathrm{R}_{\mathrm{A}}\)) are excited by applying a high voltage (\(\mathrm{V}_{\mathrm{0C}}\)) and a permanent magnetic field (\(\mathrm{B}_{\mathrm{0C}}\)), with the magnetic field directed towards the page. The appropriate voltage and magnetic field are applied in order to cause the electron to be emitted from the cathode to the surface of the anode and then backwards to the interaction space area, as shown in Figure 3.  
Figure 2: Structural dimensions measured by using coordinate measuring machine. (**a**) CMM Mitutoyo Beyond 707; (**b**) Internal structure measurement; (**c**) 3D diagram.  
The boundary of the magnetron operation is ensured by using the Hull cut-off and Hartree conditions; these represent the range of the oscillation effect of the magnetron. The Hull cut-off condition is described as the maximum range of the magnetron's operation that can occur if an electron moves to the adjacent anode block with zero velocity in the radius of the anode and assuming that the electron motion moves towards the cathode, referring to the laws of energy conservation. The Hartree condition, which represents the minimal range of the magnetron's operation, determines the anode voltage and magnetic field that are necessary to obtain an anode current with a non-zero value. The electron's motion in the interaction space around the cathode is determined by the magnetic field, which adheres to the laws of energy conservation and the condition of electron motion in a cylindrical coordinate that moves around the radius of the cathode with the same radius. Therefore, the equations to express anode voltage using the Hull cut-off and Hartree conditions are expressed as follows:  
\[\mathrm{V_{0C}}=\frac{\mathrm{e}}{8\mathrm{m}}(\mathrm{B_{0C}})^{2}\left( \frac{\mathrm{R_{A}}^{2}-\mathrm{R_{C}}^{2}}{\mathrm{R_{A}}}\right)^{2}, \tag{4}\]  
\[\mathrm{V_{H}}=\frac{\omega_{0}\mathrm{B}}{2}\left(\mathrm{R_{A}}^{2}- \mathrm{R_{C}}^{2}\right)-\frac{\mathrm{m}}{2\mathrm{e}}\mathrm{R_{A}}^{2} \omega_{0}{}^{2}, \tag{5}\]  
where \(\omega_{0}=\frac{\omega}{\mathrm{n}}\), \(\omega\) is resonant frequency, \(\mathrm{n}=\frac{\mathrm{N}}{2}\), is mode operation, N is number of cavities, e = electron charge (1.6 \(\times\) 10\({}^{-19}\) coulombs), m = electron mass (9.11 \(\times\) 10\({}^{-31}\) kg).
## 3 Results and Discussion
### Hourly Daylighting Quantity
#### 3.2.1 Men's Prayer Hall Analysis
The Ui value of 0.65 is commendable, suggesting a relatively uniform light distribution across the space. Skylights (Scenario 3) introduce a different dimension to daylighting. Harnessing zenithal light, they bathe the interior in a soft glow, especially effective in spaces with high ceilings like mosques. However, skylights can sometimes lead to non-uniform light distribution, with the central areas receiving disproportionate brightness, as also observed by [PERSON], [PERSON] et al. in their study \"Design Optimization of the Skylight for Daylighting and Energy Performance Using NSGA-II\", where they found similar challenges in achieving uniform lighting across the space due to skylight configurations [58]. The introduction of vertical shading in Scenario 6 seeks to mitigate this, achieving a Ui of 0.77, a marked improvement. The hybrid approach (Scenario 7) is a bold endeavor, amalgamating various daylighting strategies in a bid to harness their collective strengths. At 2 m, the illuminance peaks at 21,402.8 lux. However, integrating multiple strategies is not without its challenges. The Ui value of 0.47 suggests pockets of uneven brightness, a testament to the complexity of harmonizing diverse daylighting techniques. In summation, each scenario, with its unique design approach, offers insights into the nuanced balance between brightness and uniformity. The data underscores the importance of tailored design decisions, ensuring that sacred spaces like mosques are not only aesthetically pleasing but also functional and comfortable for their users.
## 3 Results and Discussion
### Characterization of the Raw Hydrolysates
#### 3.2.3 Phenolic Compounds Content  
Phenolic compounds are also present in all raw hydrolysates at concentrations between 0.19 and 1.85 g/L. Due to grape composition [52], grape pomace and grape stalk hydrolysates had the highest contents of total phenolics (1.85 and 1.62 g/L, respectively). Similarly, banana peel hydrolysate also revealed a total phenolics concentration of 1.23 g/L, which is in accordance with the reported values (1.35 g/L) [36]. Additionally to their natural origin, phenolic compounds can be obtained as a result of the lignin degradation [16]. In fact, the highest lignin fraction was found in the grape pomace and grape stalk materials, which is concomitant with the highest contents of total phenolics.
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
## 5 Design of the Offner Spectrometer  
The Offner spectrometer is a concentric three-reflection optical system, which was developed from a reflective Offner relay optical system. The Offner spectrometer with convex spherical grating consists of three mirrors, and the second mirror is a convex spherical grating. It has a small volume and negligible spectral \"smile\" and \"keystone\" distortions. Therefore, this design adopts the Offner configuration with convex spherical grating. The specific parameters of the Offner spectrometer are shown in Table 5.  
Figure 8: The relative illumination of the objective lens.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Working wavelength (nm) & 400\(-\)1000 \\ Slit length (mm) & 13.5 \\ Slit image dispersion width (mm) & 2.6 \\ Object space NA & 0.11 \\ \hline \end{tabular}
\end{table}
Table 5: Offner spectrometer parameter index.  
Figure 7: The field curvature and distortion of the objective lens.  
The designed Offner spectrometer is shown in Figure 9. The first mirror has the same radius of curvature as the third mirror, and the second mirror is a convex spherical grating, whose frequency is 102 LP/mm. Since the grating is used as the dispersive element, there is interference with the second-order diffracted light, and a cut-off filter should be used to avoid interference.  
The MTFs of the Offner spectrometer at different wavelengths (400 nm, 600 nm, 800 nm, and 1000 nm) are shown in Figure 10. It can be seen that the MTFs in all spectral bands are greater than 0.45, which is close to the diffraction limit.  
For spectrometers, the dispersion image of the slit will cause spectral \"smile\" and \"keystone\" distortions. The \"smile\" distortion (sometimes called spectral line curvature or slit curvature), which refers to a curvature of the monochromatic image of the slit in the spectral dimension, leads to the misregistration of the sensor pixels across the \(FOV\) of the spectrometer in the spectral dimension. On the other hand, the \"keystone\" distortion, which refers to variations in the slit magnification with the wavelength, leads to interband spatial misregistration in the spectral dimension [1]. After  
Figure 10: MTF of the Offner spectrometer (**a**) 400 nm; (**b**) 600 nm; (**c**) 800 nm; (**d**) 1000 nm.  
Figure 9: Configuration of the Offner spectrometer.  
optimization, the maximum \"keystone\" distortion and spectral line curvature are 8 and 19 \(\upmu\)m, respectively, which could be improved by spectral calibration.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### SADRL Applied to Distributed Agents in a Multi-Agent Environment
#### 6.1.7. [PERSON]'s SADRL Approach in a Multi-Agent Environment  
In [54], [PERSON] et al. embedded SADRL in user equipment (UEs) to select and associate itself with one of the base stations (A.1) that provides the best possible transmission opportunities (i.e., operating channels and time slots) for data transmission based on channel quality (i.e., the signal-to-interference-plus-noise ratio (SINR) levels). The UEs measure and report the SINR level of their selected operating channels to centralized entities (X.3.1), which are the associated base stations. The associated base station gathers data from neighboring distributed agents and distributes it to UEs. This enables UEs to learn and select actions based on network-wide data. The UEs maximize their individual rewards in a competitive (X.2.1) manner. The proposed approach addresses the challenges of high dynamicity (C.2) due to the dynamic condition of the operating channels, and large state spaces (C.3) with the large number of UEs in a heterogeneous environment.  
For each UE \(i\), the state \(s_{t}^{i}\) represents whether or not the QoS requirements are met. The action \(a_{t}^{i}\) represents a transmission opportunity, which includes the base station, operating channel, and time slots. The reward \(r_{t+1}^{i}(s_{t+1}^{i})\) includes: (a) utility, which is a positive value covering the selected base station and the transmission power; and (b) an action selection cost, which is a negative value. The reward is applicable whenever the SINR of agent \(i\) meets the minimum QoS requirements; otherwise, the reward is a negative value. The traditional SADRL approach has been shown to increase throughput (P.3), and to reduce computational complexity (O.3) and the communication overhead (O.4).
## 1 Introduction
While the importance of homogeneity is well-established, most studies have primarily concentrated on either the injection or mixing method, with the immersion method potentially presenting limitations for field applications. Moreover, the impact of SA additive on the homogeneous distribution of MICP has not been thoroughly investigated or fully understood, although SA gel's remarkable microorganism immobilization capability is demonstrated by the uniform distribution observed in microscope images [26]. Hence, this study aimed to examine the impact of SA as a natural biopolymer on the MICP process under varying SA concentrations (0.5%, 1.0%, 1.5%, 2.0%, 2.5%), particle sizes of soils (three particle sizes), and treatment procedures (injection and mixing), with a specific focus on achieving a uniform distribution of CaCO\({}_{3}\). The effect of SA on MICP was assessed by analyzing the calcium carbonate content, unconfined compressive strength, and hydraulic conductivity. Furthermore, SEM and EDS techniques were used to analyze and compare the microstructure and mineral composition of the soils after and before treatment, offering insights into how homogeneity affects the MICP bio-cemented samples with SA additive. This insight would enhance the understanding of homogeneity and the role of SA in the MICP technique.