esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Analysis and Results  
This section presents the MU-FTN-DCSK system's simulation results over AWGN and multi-path Rayleigh fading channels. For the Rayleigh channel, we consider an \(L=3\) multi-path channel model with power levels of \(\{P_{1}=\frac{7}{10},P_{2}=\frac{2}{10},P_{3}=\frac{1}{10}\}\), with \(P_{i},i=1,\ldots 3\) being the variance of the channel gains \(\alpha_{i,l}\). Due to the high computation time dedicated for the sinc filter at transmission, since a continuous-time signal is simulated with many points for processing, the transmitter is implemented in C++ to use the multi-thread and multi-processors features. For this purpose, the \(Intel\) MKL library is used to implement the filter and other vector manipulations. Moreover, a comparison with state-of-the-art modulations is carried out to establish the proposed system's relative performances.
# Cell-Free Massive MIMO Power Consumption with Serial Front-Hauls
## 4 Power Consumption Comparison  
The evaluation scenario consists of an indoor scenario of 50 m \(\times\) 50 m, where 100 APs are uniformly deployed with an inter-site distance of 5 m. Additionally, 20 UEs are uniformly distributed in the scenario. This configuration is similar to the one proposed in [25] for industrial indoor scenarios.  
In order to measure the impact of using dedicated or serial front-haul models, the front-haul power consumption has been estimated taking into account numerical values from [9, 12] summarized in Table 1. Regarding spectral efficiency in the downlink, the value has been calculated considering an average spectral efficiency per user equal to 4.3 bits/s/Hz as obtained in [25] using max-min fairness power allocation schemes. Notice that a given value for the spectral efficiency is assumed since optimizing this metric is out of the scope of this work. In the calculations, all the users are considered to be served simultaneously, with the total spectral efficiency being 86 Mbits/s/Hz due to multiplying the average spectral efficiency per user by the number of UEs in the scenario.
## 4 Conclusions  
Three approaches for enhancing modified Au NP system stability were developed and their performances were evaluated. These approaches involved different strategies to enhance the stability of Au NPs for the colorimetric detection of DPP-IV in a high-ionic-strength medium.  
The first strategy used the incorporation of a PEGylated sequence with a dithiol anchor because the surface properties of these systems can be engineered through the introduction of functional ligands to create biorecognition ligands [9; 32].  
The other two strategies used a peptide sequence (CALNN) designed by [8]. This peptide was selected from 58 peptide sequences tested for their high stability and resistance to electrolyte-induced Au NP aggregation. In one strategy, the CALNN peptide was introduced separately as a stabilizer with the previously tested functional ligands, while the third approach involved the decoration of the CALNN peptide with the GPDC ligand.  
The stability of these three systems was evaluated against high-ionic-strength solution and exhibited high resistance to aggregation in solutions with up to 0.5 M NaCl, with the highest stability achieved by C/G-capped Au NPs. Further stability studies were conducted by zeta potential measurements. C/G-capped Au NPs displayed the highest zeta potential value, which coincides with its highest stability in saline solution, whilst the PEGylated ligand expressed a low zeta potential value, which might be attributed to the low surface charge of the ligand.  
DPP-IV/CD26 activity using the three approaches was detected under optimal conditions and only C/G-capped Au NPs showed a response with a color change when incubated with DPP-IV/CD26. C/G-capped Au NPs were less sensitive than the monofunctionalized systems, with a dynamic range of 10-30 U/L but with very high stability in ionic solutions. This could be attributed to the presence of compact dense layers of CALNN, which hampered effective Au NP aggregation and hence reduced the analytical sensitivity.  
In spite of these limitations, the developed bifunctionalized assay was successfully evaluated by gel electrophoresis, zeta potential, and hydrodynamic radius measurements and tested for the assay of DPP-IV activity. These Au NP systems provide insight into different stabilization systems and their effect on the aggregation process. There are a number of parameters to be considered when incorporating a stabilizer, such as the length of the stabilizer residue, the compact density of the stabilizer, and the stabilizer-to-functional ligand ratio [33].  
Conceptualization, P.G. and H.A.; Methodology, H.A.; Validation, P.G., H.A. and N.W.; Formal Analysis, H.A.; Investigation, H.A.; Data Curation, H.A.; Writing--Original Draft Preparation, H.A.; Writing--Review and Editing, H.A.; Visualization, P.G. and N.W.; Supervision, P.G.; Project Administration, P.G. and N.W. This research received no external funding. The authors declare no conflicts of interest.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 2 Damage Detection Using the Quasi-static Displacement Influence Lines of Bridges
### Damage Detection Using the Null Space Method Based on Quasi-static Displacement Influence Lines  
Unlike quasi-static strain ILs based on the BOTDA technique, only a few quasi-static displacement ILs are usually obtained for actual bridges. Based on the method presented in Reference [25], an improved method is proposed to determine if bridges are damaged by using a low number of quasi-static displacement ILs. In this section, the main damage detection process based on quasi-static displacement ILs consists of three parts: the determination of the damage feature, the generation of a metric for evaluating the damage feature and the discussion of the quasi-static load conditions before and after damage, each of which is described in detail as follows.
## 4 Numerical Results and Analysis
### GSM Beam Probability Density Distribution of Receiving Optical Intensity  
Figure 4 shows the probability density distribution of the normalized central receiving optical intensity in the downlink at a transmission distance of 10 km under various source coherence parameters. At a fixed zenith angle of \(\theta=60^{\circ}\) and a transmission distance at \(z=10\) km, along with other simulation parameters from Table 1, the curves are normalized using the average receiving optical intensity of fully coherent light.  
As shown in Figure 4, increasing the source coherence parameter causes the probability density distribution of the central receiving optical intensity of the GSM beam to shift to the left, the peak value to increase, and the curve to become sharper. This observation suggests that the partially coherent GSM beam demonstrates an improved resistance to atmospheric turbulence in the downlink. These results indicate that the partially coherent  
Figure 4: Probability density function (PDF) curve of the normalized central receiving optical intensity at different \(\zeta_{s}\).  
Figure 3: Variation in the scintillation index in the downlink with the transmission distance at different coherence parameter and zenith angle \(\theta\) of (**A**) \(0^{\circ}\), (**B**) \(30^{\circ}\), (**C**) \(45^{\circ}\), (**D**) \(60^{\circ}\), (**E**) \(80^{\circ}\), and (**F**) \(88^{\circ}\).  
beam is more resistant to turbulence: Its larger coherence radius causes turbulence-induced phase distortions to be averaged out, reducing scintillation. However, if coherence is too low, increased beam spread can reduce SNR. Therefore, an intermediate coherence yields optimal performance.
# A Scalable Deep Network for Graph Clustering
## 4 Experiments  
### Datasets  
To evaluate the performance of our model, we conduct extensive experiments on five public benchmark datasets, the specific details of them are shown in Table 1.  
* **USPS**[29]: The USPS is a digit dataset automatically scanned from envelopes by the U.S. Postal Service containing a total of 9298 16 \(\times\) 16-pixel grayscale samples; the images are centered, normalized, and show a broad range of font styles.
* **HHAR**[30]: The Heterogeneous Human Activity Recognition (HHAR) dataset contains 10,299 sensor records from different smart terminals. All samples are divided into 6 types of human activities, including riding, lying, sitting, standing, walking, and climbing stairs and down the stairs.
* **Reuters**[31]: Reuters is a simple, widely used dataset for text classification. It includes 46 different subjects: some subjects have more samples, but each subject in the training set has at least 10 samples.
* **ACM**: The ACM dataset is a paper network from ACM digital library. It contains papers published in KDD, SIGMOD, SIGCOMM, MobiCOMM, and VLDB, which can be divided into three categories (databases, wireless communication, data mining).
* **CiteSeer**: The CiteSeer is a citation network. Papers in this dataset are divided into Agents, AI (Artificial Intelligence), DB (Database), IR (Information Retrieval), ML(Machine Learning), and HCI, containing a total of 3312 papers. It records information between cited papers or citations.
* **DBLP**: The DBLP dataset is an author network. If two authors are collaborators, then there is an edge connection between them. We label their research fields according to their papers published in international journals and conferences.
* **Flickr**: The Flickr is an image network which is constructed by forming links between shared Flickr public images. Edges are formed between pictures from the same location, pictures submitted to the same gallery, group, or collection, pictures that share a common tag, pictures taken by friends, etc.
# Ultrasound-Assisted Extraction of \(\beta\)-Asarone from Sweet Flag (_Acorus calamus_) [PERSON]
###### Abstract  
In this study, the extraction efficiency of \(\beta\)-asarone from Malaysian _Acorus calamus_ from Acoraceae family using conventional solvent extraction and ultrasound-assisted extraction techniques was compared. The results showed that the ultrasound-assisted extraction technique significantly improves the extraction yields and process feasibility without changing the structure of the active compound, i.e., \(\beta\)-asarone. The extraction yield increment was found to be \(\sim\)2.5-fold and \(\sim\)1.6-fold at 1:100 and 1:50 solid-to-solvent ratio, at 30% applied sonication power. The positive impact of sonication can also be observed for both mechanistic stages of extraction, i.e., the washing and diffusion stages, due to the favorable physical effect of acoustic cavitations. The observation was supported by the SEM images of the plant residue. The characterization of the extract was carried out using HPLC, NMR, UV and IR techniques. In conclusion, ultrasound assistance increases the extraction efficiency by \(\sim\)2.5-fold even at only 30% applied ultrasonic power at a 1:100 solid-to-solvent ratio. The present study also provides an efficient and simple method for accurate direct dosing of _Acorus calamus_ extracts to an application.  
ultrasound assistance; asarone; HPLC; NMR; UV; IR; _Acorus calamus_
## Materials and Methods
### Chemical Composition Determination
#### 2.4.4 Extractable Pectins  
Extractable pectins from the crude olive pomace, liquid and pulp fraction samples were fractionated into water-soluble pectin, chelator-soluble pectin and hydroxide-soluble pectin and quantified, according to [PERSON] et al. [25]. First, 1.0 g of the sample was homogenised in 20 g of deionised water for 10 min. The homogenate was filtrated (Whatman No. 1 filter paper) and the retentate and filtrate were collected. Water-soluble pectin was determined as the precipitate that resulted from the addition of 95% ethanol to the filtrate (1:5) and then allowing it to stand overnight in a refrigerator. Chelator-soluble pectin was determined from the water-extracted residue, by boiling the residue with 95% ethanol for 10 min, followed by 3 successive extractions of the resulting residue with 50 mL, 20 mM Na\({}_{2}\)-EDTA and pH 8.0. Following each extraction, the suspension was filtered, and the filtrates were combined. The residue obtained from the Na\({}_{2}\)-EDTA extractions was subsequently extracted with 50 mM NaOH (50 mL) for 15 min at room temperature, the suspension was filtered, and the filtrate was collected for measurement of hydroxide-soluble pectin. The referred quantification was based on a colorimetric assay (AOAC 994.13, 2007) using galacturonic acid for determination of the calibration curve. Briefly, 250 \(\upmu\)L boric acid-sodium chloride solution (content) and 250 \(\upmu\)L of the sample (or standard) were mixed with 4 mL of 96% H\({}_{2}\)SO\({}_{4}\) and incubated at 70 \({}^{\circ}\)C for 40 min. Afterwards, 200 \(\upmu\)L of dimethylphenol reagent (100 mg of 3, 5-dimethylphenol in 100 mL of glacial acetic acid) was added and mixed, and the absorbance of the resulting solution was measured at 400 and 450 nm, respectively [25]. Water-soluble, chelator-soluble and hydroxide-soluble pectins were quantified as galacturonic acid equivalents (GUAE).
## 5 Experiments
### Ablation Studies and Parameter Studies
#### 5.4.2 Parameter Studies  
Subsequent to the ablation studies, a series of parameter sensitivity analyses are undertaken to assess the influence of key hyperparameters, including the factor number and embedded size of user and group representations, on the framework performance. The variations in performance metrics across different configurations of factor numbers and embedding dimensions are depicted in Figure 3.  
The analysis illustrated in Figure 3 demonstrates that increasing the embedded size generally enhances the framework performance, which is attributable to the enriched representational capacity of the neural networks. However, the performance gains tend to plateau at higher dimensions, suggesting diminishing returns with excessively large embedding sizes.  
Interestingly, the optimal number of factors varies with the embedding size. Larger embedding dimensions benefit from a higher number of factors, leveraging the finer granularity afforded by the disentangled representations. Conversely, smaller embedding sizes exhibit optimal performance with fewer factors, indicating that overly granular disentanglement may be counterproductive when representational capacity is limited.  
The comprehensive ablation and parameter studies elucidate the effectiveness of our design and the hyperparameter configurations in optimizing the performance of DAGA for group recommendations. These insights not only affirm the framework robustness but also pave the way for future enhancements in disentangled representation learning for recommendation methods.
## 6 Results and Discussion  
The sensitivity and performance of the communication links were analyzed as a function of various parameters. We experimented with different values of pulse width, amplitudes, and bias sensitivity in our experimental setup, evaluating their impact on the bit error rate.  
For the tested parameters, which included transmitter frequency [Hz], the maximum number of pulses (\(n_{\text{max}}\)), and pulse width for n-pulse modulation, as well as receiver bias sensitivity value, the system achieved error-free reception, at least for a group of pixels. These promising results can be largely attributed to the utilization of a controlled, noise-free environment.
# Flocculation Harvesting Techniques for Microalgae: A Review
## 4 Chemical Flocculation  
Chemical flocculation of algae occurs due to charge neutralization and electrostatic bridging between the suspended algal cells and the applied flocculant(s), resulting in floc formation and subsequent sedimentation (Figure 4). Multivalent inorganic chemicals, biopolymers, or inorganic-organic hybrid polymers have been extensively used as algae-harvesting flocculants. Aluminum sulfate and ferric chloride are of the most popular inorganic flocculants for wastewater clarification and algal biomass recovery [60]. Chitosan, cationic starches, modified tannins, and polyacrylamide are examples of organic polymers that are widely used [32; 61]. The harvesting efficiency of both organic and inorganic flocculants depends largely on their physicochemical properties such as solubility and electronegativity, as well as the operating conditions, such as dosage and algal solution characteristics (e.g., cell density, pH, and ionic strength) [62; 63]. It should be noted that the sizes of the flocs formed through charge neutralization with conventional inorganic chemicals are generally small, requiring high dosage for algae flocculation. On the other hand, the bridging and sweeping reactions between polymeric flocculants and algal cells can lead to the formation of larger sized flocs, thereby promoting efficient biomass recovery at a relatively low dosage [24]. Table 3 briefly compares the different inorganic and organic chemical flocculants for algae harvesting.  
Figure 4: A schematic diagram of chemical flocculation harvesting of microalgae using a cationic polyelectrolyte. Reprinted from Reference [29], distributed under the terms of the Creative Commons Attribution License.
## 3 Results  
### ML Prediction Model  
The initial ePRO dataset included 992 filled symptom questionnaires from the 31 ICI-treated cancer patients in outpatient settings comprising 18 monitored symptoms collected weekly using the Kaiku Health ePRO tool (Table 1). The irAE data included physician-confirmed prospectively collected irAE (\(n=26\)) data in the eCRFs of the KISS trial from those 31 patients, containing initiation and end dates, CTCAE class and severity, and nature (colitis, diarrhea, arthritis, rash, hyperglycemia, neutropenia, pneumonitis, itching, cholangitis, mucositis, hypothyroidism, and hepatitis). Prospectively assessed treatment responses (\(n=63\)) by the study physicians were also retrieved from the eCRF. The patients with partial (PR) or complete (CR) responses (\(n=19\)) were characterized as responders, while stable (SD) and progressive disease (PD) (\(n=44\)) were categorized as a non-response.  
Figure 1: Complete modeling framework for ORR prediction.  
The complete modeling framework for ORR prediction is illustrated in Figure 1. We also tested several other commonly used ML models, such as logistic regression, elastic-net regression, support vector machines, LightGBM, and random forests, but XGBoost had the best performance with the LOOCV evaluation, and, thus, it was chosen as the model for the study.
# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
## 2 Related Work
### Pedestrian Re-Identification for UAVs  
Meta-learning and transfer learning approaches have been utilized to enhance model's adaptability to UAV-captured images [29; 30]. The subspace pooling convolutional feature map technique has demonstrated potential in improving the model's identification ability in complex environments by extracting rich and diverse features [31]. Additionally, unsupervised learning methods have opened new avenues for enhancing model adaptability to UAV imagery. These methods facilitate the deep exploration of the inherent structure and patterns within the data [32; 33]. The integration of covariance information has further strengthened model robustness. This is achieved by combining traditional hand-crafted features with deep learning representations, thereby improving the model's ability to handle viewpoint variations [34]. [PERSON] et al. [35] proposed a multi-resolution feature-aware network that significantly enhances model performance. This is accomplished by incorporating self-attention and cross-attention modules, which effectively address resolution variations and complex backgrounds. Moreover, independent approaches have been proposed to enhance model robustness and accuracy in complex UAV environments [36]. These approaches integrate ternary group loss, large-margin Gaussian mixture loss, multi-branch architectures, and channel group learning strategies. Reference [37] proposes an innovative Masked Relation Guided Transformer (MRG-T) framework to address the information redundancy problem in pedestrian attribute identification through three modules. The Mask Region Relationship Module (MRRM) focuses on key regions to extract robust features. The Mask Attribute Relationship Module (MARM) mines semantic associations among attributes through attribute label masks. The Region and Attribute Mapping Module (RAMM) aligns spatial regions and attributes based on a cross-attention mechanism. [PERSON] et al. [38] proposed a Transformer-based ReID algorithm that significantly improves fine-grained image retrieval performance. Additionally, reference [39] proposes a Vision Transformer (ViT)-based parametric instance learning method for pedestrian re-identification. Feature alignment and similarity are optimized through self-supervised learning and instance differentiation. The model's robustness to occlusion and alignment problems is enhanced by zero-padding and displacement techniques. To address scale variation and occlusion problems in pedestrian re-identification for Unmanned Aerial Vehicle (UAV) filming, a Multi-Granularity Intra-Attention (MGAiA) network is proposed in reference [40]. A multi-granularity attention (MGA) module is designed to enhance the global perception of the feature extraction model and to explore discriminative features under scale variation.  
Additionally, an intra-attention mechanism (AiA) is proposed to generate attention weights with different granularities, reducing the negative impact of occlusion.