esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Thermal Treatment  
To compare the effects of the PEF and heat treatments, two thermal treatments were performed. Thermal-1 was heat-treated at 95 \({}^{\circ}\)C for 30 s, and we have chosen this processing condition as a typical pasteurization condition for orange juice from the cited References [15,16]. Sterilization at 121 \({}^{\circ}\)C for 20 min (Thermal-2) is commonly used for autoclave sterilization and was used for representing excessive heat treatment in this study.  
Analysis of Volatile Compounds by Headspace Solid Phase Microextraction (HS-SPME) and Gas Chromatography-Mass Spectrometry (GC-MS)  
The volatile compounds in the headspace of orange juice samples were analyzed via HS-SPME GC/MS [17,18] following the analytical method of [PERSON], [PERSON], and [PERSON] [19]. A total of 1 mL orange juice was transferred to a 6 mL vial that contained a magnetic stirring bar (3 \(\times\) 10 mm). The sample bottle was sealed with a Teflon septum and aluminum cap. The SPME fiber coated with 100 \(\upmu\)m polydimethylsiloxane (Supelco, St. Louis, MO, USA) was inserted into the headspace of a sample bottle, which was magnetically stirred and heated at 60 \({}^{\circ}\)C for 20 min to attain an equilibrium of volatile compounds between the headspace and the SPME coating. The SPME fiber was removed from the bottle and inserted into the 0.75 mm inner diameter splitless glass liner of the GC injection port and held for 2 min at 220 \({}^{\circ}\)C to desorb the volatile compounds adsorbed on the SPME  
Figure 1: The inactivation of (**A**) _Escherichia coli_ and (**B**) _Saccharomyces cerevisiae_ inoculated in sterilized orange juice by pulsed electric field (PEF) treatments. PEF treatments were conducted at a field strength of 10, 15, and 20 kV/cm with different pulse frequencies, which resulted in PEF total specific energy inputs of 50 kJ/L (PEF-50), 100 kJ/L (PEF-100), and 150 kJ/L (PEF-150). ND, not detected.  
coating. The desorbed volatile compounds were separated using an Agilent 6890 gas chromatograph (Agilent Technologies, Palo Alto, CA, USA) equipped with a HP-5 30 m capillary column (0.53 mm internal diameter) coated with 2.65 \(\upmu\)m 5% phenyl substituted methylpolysiloxane (Agilent Technologies, Palo Alto, CA, USA) and a 5973 MSD (Agilent Technologies, Palo Alto, CA, USA). The nitrogen gas flow rate was 2.5 mL/min. The GC oven temperature was programmed from 60 to 120 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and then to 200 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and held for 10 min at the final temperature. Each peak was identified based on the mass fragmentation pattern, which was compared to spectral data in the NIST and WILEY library. Odor descriptions of the identified flavor compounds were obtained from the commercially available Leffngwell and Associates Flavor-Base and Acree's Flavornet [20].
## 3 Proposed Method
### Analysis of Practical Features  
The aforementioned phase transition property of the proposed measurement matrix ensures its effective ability to capture information. Nevertheless, in practical applications, certain practical features of measurement matrices must be taken into consideration, including computational complexity in the measurement process and memory cost. The practical features of the aforementioned measurement matrices are analyzed in Table 2. For fairness, we assumed that the size of the measurement matrix was \(M\times N\), and each floating-point element required \(B\) bits of storage space. Evidently, the proposed measurement matrix possesses the following advantages:  
1. A flexible number of measurements: The proposed measurement matrix provides a flexible number of measurements.
2. Low computational complexity: As a sparse measurement matrix, the proposed matrix requires only addition operations during the measurement process, without involving multiplication. Compared to multiplication, addition operations are computationally less intensive, especially in binary matrices, where bit-wise addition is much simpler and less computationally expensive than multiplication. Therefore, this approach significantly reduces computational load.
3. Low memory cost: The Gaussian random matrix, characterized by its floating-point entries, requires the largest storage space, specifically \(M\times N\times B\) bits. In contrast, for the symmetric measurement matrix and the random binary measurement matrix, since the two possible values can be encoded with just 1 bit, their storage space is significantly reduced to \(M\times N\) bits. The bipartite graph measurement matrix requires \(N\times d\times log_{2}M\) bits, as it only stores the positions of the non-zero entries. The measurement matrix proposed in this paper only requires the storage of a single Legendre sequence, as all other columns can be generated by cyclically shifting this Legendre sequence, which requires merely \(N\) bits. This storage requirement is significantly lower compared to the other measurement matrices discussed.  
The aforementioned advantages make our proposed measurement matrix more suitable for practical applications, especially in scenarios involving resource-constrained sensors. In the following section, we compare the performance of our proposed measurement matrix with the above measurement matrices using random signals and images.
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 2 Cross-Domain Fusion Constellation Design Method
### Basic Constellation Design  
#### 2.2.1 Communication Constellation Generation Method  
In ultra-dense low earth orbit (LEO) satellite ground communications, users are more concerned about the communication backhaul capacity, which reflects the service quality obtained by users. Therefore, the main indicators of the communication constellation consider the communication backhaul capacity of the user terminal. Assuming there are \(k\) user terminals, \(u\) in the satellite coverage area, the communication capacity \(R\) of each user terminal is [15].  
\[R_{i,u}=\frac{B}{k_{i}}\text{log}_{2}(1+\frac{P_{g,u}}{\sigma^{2}+I_{i,u}}). \tag{1}\]  
\(B\), \(P\), \(\sigma\), \(I\) are signal bandwidth, power signal, white Gaussian noise and interference, respectively. For each user terminal, if there are \(m\) satellites covering the area, the service capacity obtained by the user terminal is  
\[CA_{u}=\sum_{m\in S_{u}}R_{m,u}. \tag{2}\]  
We use the typical LEO polar orbit constellation as the initial constellation, and the paper [15] demonstrates the coverage advantage of the polar orbit constellation in communication through verification. The design goal of the communication domain is to use as few satellites as possible and cover as many ground users as possible on the premise of meeting the requirements of the communication backhaul capacity CArequest.  
\[R_{\text{cov}}^{com}\geq 95\%. \tag{3}\]  
The above constraints indicate that the communication backhaul capacity is larger than the constraint requirements. Refer to the paper [16], and the coverage area should exceed 95%.
## 3 Results
For the indoor thermal-hyrometric conditions and air quality parameters of the OR using the conventional HVAC system, the indoor air temperature was negatively correlated with RH level (\(\rm r_{s}=-0.624\), \(p<0.01\)), RH was negatively correlated with CO\({}_{2}\) (\(\rm r_{s}=-0.769\), \(p<0.01\)), and PM\({}_{\geq 0.5}\) was positively correlated with PM\({}_{10}\), PM\({}_{2.5}\), and PM\({}_{1}\) (\(\rm r_{s}=0.793\)-0.981, \(p<0.01\)) (Table 2). For the OR using the LDAC system, air temperature was negatively correlated with RH (\(\rm r_{s}=-0.732\), \(p<0.01\)) and CO\({}_{2}\) (\(\rm r_{s}=-0.521\), \(p<0.01\)), while PM\({}_{\geq 0.5}\) was positively correlated with PM\({}_{10}\), PM\({}_{2.5}\), and PM\({}_{1}\) (\(\rm r_{s}=0.678\)-0.953, \(p<0.01\)). For the OR using the RDAC system, temperature and RH were negatively correlated (\(\rm r_{s}=-0.629\), \(p<0.01\)), while CO\({}_{2}\) was positively correlated with PM\({}_{\geq 0.5}\) (\(\rm r_{s}=0.422\), \(p<0.01\)), PM\({}_{10}\) (\(\rm r_{s}=0.321\), \(p=0.043\)), PM\({}_{2.5}\) (\(\rm r_{s}=0.395\), \(p=0.012\)), and PM\({}_{1}\) (\(\rm r_{s}=0.345\), \(p=0.029\)); PM\({}_{10}\) was also positively correlated with both PM\({}_{2.5}\) and PM\({}_{1}\) (\(p<0.01\)). The airborne bacterial concentration in the OR using the RDAC system was positively correlated with PM\({}_{10}\) (\(\rm r_{s}=0.779\), \(p<0.01\)) and PM\({}_{2.5}\) (\(\rm r_{s}=0.681\), \(p=0.03\)), but not for the other systems. The annual total energy consumption of the conventional HVAC system (176,007.8 kWh) was higher than both the LDAC system (146,781.1 kWh) and the RDAC system (140,782.8 kWh). The median daily total energy consumption of the HVAC system (14.1 kWh/m\({}^{2}\)) was significantly higher than that of the LDAC system (11.8 kWh/m\({}^{2}\), \(p<0.01\)) and the RDAC system (10.1 kWh/m\({}^{2}\), \(p<0.01\)) (Figure 2). The median daily energy consumption of the full water unit in the LDAC system (2.1 kWh/m\({}^{2}\)) was significantly lower than that in the HVAC system (3.7 kWh/m\({}^{2}\), \(p<0.01\)) and that in the RDAC system (3.7 kWh/m\({}^{2}\), \(p<0.01\)) (Figure 2). The median daily energy consumption of the hot water unit in the conventional HVAC system (4.4 kWh/m\({}^{2}\)) was slightly higher than that in the RDAC system (3.1 kWh/m\({}^{2}\), \(p=0.326\)). Furthermore, the median daily energy consumption of the fan, heat pump, and other equipment in the LDAC system (9.8 kWh/m\({}^{2}\)) was significantly higher than that in the HVAC system (5.7 kWh/m\({}^{2}\), \(p<0.01\)) and that in the RDAC system (2.9 kWh/m\({}^{2}\), \(p<0.01\)). Figure 2: Daily energy consumption in HVAC, LDAC, and RDAC systems. \(\dagger\): compared to HVAC system, \(p<0.01\); \(\ddagger\): compared to LDAC system, \(p<0.01\). \begin{table}
# Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements
## 4 Simulation Results
\[G\Delta_{BER=10^{-2}}=1.87\times\ln\left(R/P\right)-0.59; \tag{27}\]  
\[G\Delta_{BER=10^{-3}}=2.08\times\ln\left(R/P\right)+0.20; \tag{28}\]  
\[G\Delta_{BER=10^{-4}}=2.06\times\ln\left(R/P\right)+0.98. \tag{29}\]  
In the third iteration, Equations (30)-(32) represent the fitted curves for the BER values of \(10^{-2}\), \(10^{-3}\) and \(10^{-4}\), respectively. \[G\Delta_{BER=10^{-2}}=0.67\times\ln\left(R/P\right)-0.12; \tag{30}\]  
\[G\Delta_{BER=10^{-3}}=0.30\times\ln\left(R/P\right)+0.43; \tag{31}\]  
\[G\Delta_{BER=10^{-4}}=0.25\times\ln\left(R/P\right)+0.68. \tag{32}\]  
Using the previous equations, it is now possible, for a given BER performance, to know the impact on the \(E_{b}/N_{0}\) ratio of changing from a SISO system to a MIMO system. Note that this relationship is a function of the \(R/P\) ratio, so the higher this ratio the greater the impact. As an example, and according to Equation (27), for a BER of \(10^{-2}\) and the first iteration of IB-DFE, the value of \(E_{b}/N_{0}\), improves by approximately 2.4 dB (\(G\Delta=2.4\) dB), when moving from a SISO system to a MIMO system with 2 transmitting antennas and 10 receiving antennas (\(R/P=5\)). When changing from a SISO system to a MIMO system, the associated complexity will also be increased, as the number of transmitting and receiving antennas will increase. For the specific case of IB-DFE, this complexity is associated with the need to invert matrices, in order to calculate the \(\mathbf{F}\) factor, whose dimensions will always be at least \(R\times P\). The complexity of this receiver is therefore greater than for the linear case, since one more IDFT/DFT pair exits per iteration [25; 26]. Figure 6: Difference between theoretical BER performance for SISO and MIMO systems for the third iteration when using IB-DFE.
# Applications of Machine Learning Algorithms in Geriatrics
## 1 Introduction  
Modernization and technological advancements over the past decades have led to an improvement in the quality of life, increasing the aging population [1]. For these reasons, life expectancy is much higher than it was a few hundred years ago, when humans were exposed to a series of diseases for which there was no treatment at that time. As life expectancy increased, people became more concerned with the quality of their own lives, improving it through healthy eating, sports, alleviating daily activities, and consuming medications that lead to longevity [2, 3]. In this context, the issue arises of studying moderntechniques associated with artificial intelligence (AI) to explore the possibility of increasing longevity in a context where the population is becoming increasingly healthier. Thus, the emphasis is placed on longevity coupled with both mental and physical health. To achieve this goal, prevention and early diagnosis are key elements for the elderly.  
In the scientific literature, geriatrics is a medical branch, while gerontology is a socio-biological branch. The two concepts are often confused. Geriatrics is a medical discipline focused on prevention, diagnosis, personalized treatment, and pain relief caused by diseases specific to the elderly. Gerontology is an interdisciplinary science of aging. In this paper, only works that fall within the field of geriatrics will be analyzed.  
This study is motivated by the growing interest in the use of AI. Geriatrics involves managing patients with multiple comorbidities and personalized needs. This approach is assisted by the integration of modern technologies for optimizing medical practice. This study provides a synthesis of recent literature (1 January 2020-31 May 2025) regarding the exploration of ML algorithms in geriatrics.  
The paper addresses several categories of the public, including researchers and PhD students in the field of AI and medical sciences, university faculty, medical staff interested in integrating modern technologies into practice, and the industrial sector in the field of medical technology.  
The study aims to answer the following research questions (RQs):  
* RQ1: In which subfields of geriatrics is ML applied?
* RQ2: What are the purposes of using algorithms in the context of geriatrics?
* RQ3: What metrics are used to evaluate the performance of these models, and how do they relate to the practical integration of applications?  
The structure of the paper is based on an introduction that contextualizes the issue of aging and the implications of AI in geriatrics. Section 2 presents the methodology, which describes the process of selecting articles from the specialized literature. Section 3 is dedicated to the ML models used in the research articles extracted from the Web of Science (WOS) database. Section 4 comprised the bibliometric analysis of the papers from the literature. Subsequently, a discussion is conducted to interpret the results in Section 5. Section 6 presents conclusions that synthesize the main ideas and outline future research directions.
## 4 Experimental Results
### Results with Different Numbers of Used GCPs and RPC Refinement Methods  
The geometric corrections of satellite data with RFMs can be done with various methods. The most common is the model refinement (correction) with affine parameters (as presented in Section 2.2.3.). Another popular method is the bias correction with offsets. This method is very simple and it only translates the pixels by the same value on each of the axes. Although the method is simple and fast, it can perform even better than the affine with some satellites. Figure 13 presents the difference between the results that were obtained with both refinement methods. The comparison is only performed between the RMSEs at ICPs. The results indicate that both of the methods perform with similar accuracies. It can be concluded that, when high number of GCPs are used, both methods can be utilized with Pleiades imagery.  
Different numbers of GCPs are used in the correction to test the behaviour and robustness of the proposed algorithm. For each image, decimating the initial set when considering the point quality (from image matching) and even the distribution of the points in the image generates various sets of GCPs. Every set has a different number of points ranging from over 200 to 9. For comparison purposes, all of the obtained point sets are used in the geometric model with both the affine and offset refinement methods.  
Table 4 lists the test results for two selected images. The first image is from the Ljubljana AOI, while the second is from Kranj AOI. RMSEs at GCPs are mostly in the same range, while there is a small decrease in accuracy at ICPs when sets with less than 30 points are used. This is only valid for the affine refinement as the offset methods present mostly unchanged RMSEs for all the sets.  
Figure 12: Comparison of the final number of GCPs without and with the reduction to the city perimeter area for the previous and proposed method.
# GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
## 4 Experiment
### The Comparison on ModelNet40 Dataset
For example, our framework gives F1 scores higher than PointNet++ in 23 categories and lower than PointNet++ in only 11 categories. In detail, F1 scores from GSV-NET are at least 10% higher than PointNet++ in five classes and at least 10% lower than PointNet++ in only one. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Data Format** & **ACC** \\ \hline VoxNet [18] & Voxelization & 83\% \\
3D ShapeNets [17] & Voxelization & 77.32\% \\
3D-A-Nets [53] & Voxelization & 90.5\% \\ EEM [54] & Point Cloud & 90.5\% \\ PointGCN [36] & Point Cloud & 89.5 \\ PointNet [25] & Point Cloud & 89.2\% \\ PointNet++ [26] & Point Cloud & 90.7\% \\
3 DmFV+VoxNet [20] & Point Cloud & 88.5\% \\
3 DmFV-Net [20] & Point Cloud & 91.6\% \\ MVCNN, metric,12\(\times\)[24] & 12 views & 89.5\% \\ MVCNN, 12\(\times\)[24] & 12 views & 89.9\% \\ MVCNN, metric,80\(\times\)[24] & 80 views & 90.1\% \\ MVCNN, 80\(\times\)[24] & 80 views & 90.1\% \\ TCN-MVCNN [55] & 12 views & 90.5\% \\ Reeb graph convolution [16] & graph & 89.9\% \\ MIFN, PC+MV [15] & Point Cloud + 12 views & 90.83\% \\ MIFN, PC+MV+PV [15] & Point Cloud + 12 views + panorama-views & 91.86\% \\ \hline GSV-NET & Point Cloud + 3 views & 92.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with various approaches on the point cloud dataset: ModelNet40. Figure 11: The confusion matrix for point cloud dataset: ModelNet40 (in percentage).
# A One-Phase Tree-Structure Method to Mine High Temporal Fuzzy Utility Itemsets
## 2 Related Works
To carry out association broadcasting, realize visualization, and determine valuable information for a human resource management system, [PERSON] and [PERSON] used some techniques about fuzzy data mining to implement data partition [30]. They used an incremental mining approach to handle newly inserted data and a clustering approach to cluster larger amounts of data, respectively. Furthermore, [PERSON] et al. applied the patient profile (including age, gender, and medical condition) to generate fuzzy partitions and thenpresented a fuzzy pattern mining approach with three phases to find the combinations between risk factors and diseases [31]. The mining results derived from the traditional UIM [8; 9] lack quantitative information because they only find high utility itemsets without containing item amount information. The quantitative relationships of the items are not mined and displayed. As mentioned above, fuzzy FIM could find the semantic meaning for item quantity relationships. Thus, [PERSON] et al. solved the fuzzy utility mining (FUM) problem to fit real applications [17]. They considered the item quantities, item unit profits, and semantic meaning to mine high fuzzy utility itemsets. The same as the linguistic conversion step in fuzzy FIM, their approach first converts the quantitative values into fuzzy terms and then mines high fuzzy utility linguistic patterns. Since UIM does not keep the downward-closure property, so does FUM. Therefore, they designed an upper-bound measure to hold the property in FUM. [PERSON] et al. then adopted tree structures to mine fuzzy utility itemsets [32]. After that, [PERSON] et al. also used a fuzzy list structure embedded in trees to improve the efficiency of [PERSON] et al. 's approach [33]. Since the mining process in FUM consumed too much runtime in the mining process, [PERSON] et al. proposed an evolutionary algorithm to reduce the computational cost [34]. Besides, to consider the on-shelf time of an item, [PERSON] et al. presented a research issue to consider the time factor for an item in FUM [16]. They introduced the designed upper-bounds for reducing the search space in mining and presented the TP-TFU (two-phased temporal fuzzy utility mining) method to find high fuzzy temporal-based itemsets. [PERSON] et al. then designed a two-phase tree-structure approach to improve the efficiency of TP-TFU [18]. In this paper, a one-phase approach will be proposed.
# Biosynthesis of Polyhydroxyalkanoates from Defatted _Chlorella_ Biomass as an Inexpensive Substrate
###### Abstract  
Microalgae biomass has been recently used as an inexpensive substrate for the industrial production of polyhydroxyalkanoates (PHAs). In this work, a dilute acid pretreatment using 0.3 N of hydrochloric acid (HCl) was performed to extract reducing sugars from 10% (\(w/v\)) of defatted _Chlorella_ biomass (DCB). The resulting HCl DCB hydrolysate was used as a renewable substrate to assess the ability of three bacterial strains, namely _Bacillus megaterium_ ALA2, _Cupriavidus reactor_ KCTC 2649, and _Halofrax meditterna_ DSM 1411, to produce PHA in shake flasks. The results show that under 20 g/L of DCB hydrolysate derived sugar supplementation, the cultivated strains successfully accumulated PHA up to 29.7-75.4% of their dry cell weight (DCW). Among the cultivated strains, _C. nocator_ KCTC 2649 exhibited the highest PHA production (7.51 \(\pm\) 0.20 g/L, 75.4% of DCW) followed by _H. meditterna_ DSM 1411 and _B. megaterium_ ALA2, for which a PHA content of 3.79 \(\pm\) 0.03 g/L (55.5% of DCW) and 0.84 \(\pm\) 0.06 g/L (29.7% of DCW) was recorded, respectively. Along with PHA, a maximum carotenoid content of 1.80 \(\pm\) 0.16 mg/L was produced by _H. meditterna_ DSM 1411 at 120 h of cultivation in shake flasks. PHA and carotenoid production increased by 1.45- and 1.37-fold, respectively, when HCl DCB hydrolysate biotransformation was upscaled to a 1 L of working volume fermenter. Based on FTIR and \({}^{1}\)H NMR analysis, PHA polymers accumulated by _B. megaterium_ ALA2 and _C. nocator_ KCTC 2649 were identified as homopolymers of poly(3-hydroxybutyrate). However, a copolymer of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) with a 3-hydroxyvalerate fraction of 10.5 mol% was accumulated by _H. meditterna_ DSM 1411.  
Acid pretreatment; _Bacillus megaterium_; Carotenoids; _Cupriavidus reactor_; Defatted _Chlorella_ biomass; _Halofrax meditterna_; Polyhydroxyalkanoates